diff --git a/config.json b/config.json index 913b327da8fb6215d2ba568c99dff2c42020fb92..1858594a7c68b917782f6acc61213e3cbd2cd090 100644 --- a/config.json +++ b/config.json @@ -1,11 +1,13 @@ { "architectures": [ - "StructRoberta" + "StructRoberta", + "StructRobertaForSequenceClassification" ], "attention_probs_dropout_prob": 0.1, "auto_map": { "AutoConfig": "modeling_structroberta.StructRobertaConfig", - "AutoModelForMaskedLM": "modeling_structroberta.StructRoberta" + "AutoModelForMaskedLM": "modeling_structroberta.StructRoberta", + "AutoModelForSequenceClassification": "modeling_structroberta.StructRobertaForSequenceClassification" }, "bos_token_id": 0, "classifier_dropout": null, diff --git a/finetune/boolq/all_results.json b/finetune/boolq/all_results.json new file mode 100644 index 0000000000000000000000000000000000000000..9f2382cb845900801022efe2a8ed3239aa7a9966 --- /dev/null +++ b/finetune/boolq/all_results.json @@ -0,0 +1,16 @@ +{ + "epoch": 10.0, + "eval_accuracy": 0.6556016802787781, + "eval_f1": 0.7272727272727272, + "eval_loss": 1.2519346475601196, + "eval_mcc": 0.2660328187905566, + "eval_runtime": 1.4662, + "eval_samples": 723, + "eval_samples_per_second": 493.101, + "eval_steps_per_second": 62.064, + "train_loss": 0.3020157072279188, + "train_runtime": 97.3268, + "train_samples": 2072, + "train_samples_per_second": 212.891, + "train_steps_per_second": 1.849 +} \ No newline at end of file diff --git a/finetune/boolq/config.json b/finetune/boolq/config.json new file mode 100644 index 0000000000000000000000000000000000000000..4a05363b2cdf362715477e08a3dc5678f8da6992 --- /dev/null +++ b/finetune/boolq/config.json @@ -0,0 +1,50 @@ +{ + "_name_or_path": "final_models/glue_models/structroberta_s1_final/", + "architectures": [ + "StructRobertaForSequenceClassification" + ], + "attention_probs_dropout_prob": 0.1, + "auto_map": { + "AutoConfig": "modeling_structroberta.StructRobertaConfig", + "AutoModelForMaskedLM": "modeling_structroberta.StructRoberta", + "AutoModelForSequenceClassification": "modeling_structroberta.StructRobertaForSequenceClassification" + }, + "bos_token_id": 0, + "classifier_dropout": null, + "conv_size": 9, + "eos_token_id": 2, + "hidden_act": "gelu", + "hidden_dropout_prob": 0.1, + "hidden_size": 768, + "id2label": { + "0": 0, + "1": 1 + }, + "initializer_range": 0.02, + "intermediate_size": 3072, + "label2id": { + "0": 0, + "1": 1 + }, + "layer_norm_eps": 1e-05, + "max_position_embeddings": 514, + "model_type": "roberta", + "n_cntxt_layers": 0, + "n_cntxt_layers_2": 0, + "n_parser_layers": 4, + "num_attention_heads": 12, + "num_hidden_layers": 12, + "pad_token_id": 1, + "position_embedding_type": "absolute", + "problem_type": "single_label_classification", + "relations": [ + "head", + "child" + ], + "torch_dtype": "float32", + "transformers_version": "4.26.1", + "type_vocab_size": 1, + "use_cache": true, + "vocab_size": 32000, + "weight_act": "softmax" +} diff --git a/finetune/boolq/eval_results.json b/finetune/boolq/eval_results.json new file mode 100644 index 0000000000000000000000000000000000000000..7f403d0e01879b15f6a6f7baa4f3a14daa4c11a2 --- /dev/null +++ b/finetune/boolq/eval_results.json @@ -0,0 +1,11 @@ +{ + "epoch": 10.0, + "eval_accuracy": 0.6556016802787781, + "eval_f1": 0.7272727272727272, + "eval_loss": 1.2519346475601196, + "eval_mcc": 0.2660328187905566, + "eval_runtime": 1.4662, + "eval_samples": 723, + "eval_samples_per_second": 493.101, + "eval_steps_per_second": 62.064 +} \ No newline at end of file diff --git a/finetune/boolq/merges.txt b/finetune/boolq/merges.txt new file mode 100644 index 0000000000000000000000000000000000000000..94b4f252cb566a4dac62a08c78d85169ff4e91be --- /dev/null +++ b/finetune/boolq/merges.txt @@ -0,0 +1,31794 @@ +#version: 0.2 +Ġ t +h e +Ġ a +i n +Ġt he +r e +Ġ w +o u +Ġ s +o n +e r +h a +n d +i s +i t +Ġ b +Ġ o +Ġ c +Ġ m +Ġ f +in g +o r +a n +e d +l l +Ġ d +e n +Ġt o +e s +Ġ p +Ġo f +Ġ y +Ġ in +a r +a t +Ġa nd +Ġ l +ha t +Ġy ou +a s +Ġ g +o m +Ġ n +Ġ I +i c +o w +l e +v e +o t +Ġ h +Ġ he +a l +e t +Ġ ha +' s +u s +Ġt h +a y +Ġ it +Ġ is +Ġb e +Ġt hat +Ġ on +i on +i d +Ġ e +g h +u t +l d +en t +i m +Ġ re +Ġw as +a c +Ġw e +v er +a m +T he +. . +Ġf or +l y +Ġ S +s t +r i +r o +i r +a d +s e +k e +Ġ . +Ġs t +gh t +o o +' t +Ġ A +ou t +it h +Ġm e +Ġ C +a ll +Ġ k +Ġ u +Ġa n +Ġ M +Ġw ith +u r +Ġa s +Ġd o +c t +he r +c e +i ll +t er +i f +c h +Ġw h +Ġs e +om e +Ġa re +Ġ H +i l +Ġn ot +Ġha ve +Ġ B +Ġ 1 +a nd +Ġa t +Ġth is +ou ld +an t +Ġg o +us t +at ion +Ġl i +o p +a in +Ġk n +Y ou +e a +Ġ P +Ġ T +Ġ , +u n +g e +o l +t h +Ġh is +u l +Ġ " +r om +.. . +o re +Ġ W +e ll +Ġn e +Ġ r +Ġkn ow +Ġ j +ou r +Ġ D +Ġd e +p p +Ġc an +Ġ G +Ġthe y +u m +Ġ The +Ġs o +es t +Ġ ? 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See all RoBERTa models at https://huggingface.co/models?filter=roberta +] + + +class StructRobertaConfig(RobertaConfig): + model_type = "roberta" + + def __init__( + self, + n_parser_layers=4, + conv_size=9, + relations=("head", "child"), + weight_act="softmax", + n_cntxt_layers=3, + n_cntxt_layers_2=0, + **kwargs,): + + super().__init__(**kwargs) + self.n_cntxt_layers = n_cntxt_layers + self.n_parser_layers = n_parser_layers + self.n_cntxt_layers_2 = n_cntxt_layers_2 + self.conv_size = conv_size + self.relations = relations + self.weight_act = weight_act + +class Conv1d(nn.Module): + """1D convolution layer.""" + + def __init__(self, hidden_size, kernel_size, dilation=1): + """Initialization. + + Args: + hidden_size: dimension of input embeddings + kernel_size: convolution kernel size + dilation: the spacing between the kernel points + """ + super(Conv1d, self).__init__() + + if kernel_size % 2 == 0: + padding = (kernel_size // 2) * dilation + self.shift = True + else: + padding = ((kernel_size - 1) // 2) * dilation + self.shift = False + self.conv = nn.Conv1d( + hidden_size, hidden_size, kernel_size, padding=padding, dilation=dilation + ) + + def forward(self, x): + """Compute convolution. + + Args: + x: input embeddings + Returns: + conv_output: convolution results + """ + + if self.shift: + return self.conv(x.transpose(1, 2)).transpose(1, 2)[:, 1:] + else: + return self.conv(x.transpose(1, 2)).transpose(1, 2) + + +class RobertaEmbeddings(nn.Module): + """ + Same as BertEmbeddings with a tiny tweak for positional embeddings indexing. + """ + + # Copied from transformers.models.bert.modeling_bert.BertEmbeddings.__init__ + def __init__(self, config): + super().__init__() + self.word_embeddings = nn.Embedding( + config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id + ) + self.position_embeddings = nn.Embedding( + config.max_position_embeddings, config.hidden_size + ) + self.token_type_embeddings = nn.Embedding( + config.type_vocab_size, config.hidden_size + ) + + # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load + # any TensorFlow checkpoint file + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + # position_ids (1, len position emb) is contiguous in memory and exported when serialized + self.position_embedding_type = getattr( + config, "position_embedding_type", "absolute" + ) + self.register_buffer( + "position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)) + ) + if version.parse(torch.__version__) > version.parse("1.6.0"): + self.register_buffer( + "token_type_ids", + torch.zeros(self.position_ids.size(), dtype=torch.long), + persistent=False, + ) + + # End copy + self.padding_idx = config.pad_token_id + self.position_embeddings = nn.Embedding( + config.max_position_embeddings, + config.hidden_size, + padding_idx=self.padding_idx, + ) + + def forward( + self, + input_ids=None, + token_type_ids=None, + position_ids=None, + inputs_embeds=None, + past_key_values_length=0, + ): + if position_ids is None: + if input_ids is not None: + # Create the position ids from the input token ids. Any padded tokens remain padded. + position_ids = create_position_ids_from_input_ids( + input_ids, self.padding_idx, past_key_values_length + ) + else: + position_ids = self.create_position_ids_from_inputs_embeds( + inputs_embeds + ) + + if input_ids is not None: + input_shape = input_ids.size() + else: + input_shape = inputs_embeds.size()[:-1] + + seq_length = input_shape[1] + + # Setting the token_type_ids to the registered buffer in constructor where it is all zeros, which usually occurs + # when its auto-generated, registered buffer helps users when tracing the model without passing token_type_ids, solves + # issue #5664 + if token_type_ids is None: + if hasattr(self, "token_type_ids"): + buffered_token_type_ids = self.token_type_ids[:, :seq_length] + buffered_token_type_ids_expanded = buffered_token_type_ids.expand( + input_shape[0], seq_length + ) + token_type_ids = buffered_token_type_ids_expanded + else: + token_type_ids = torch.zeros( + input_shape, dtype=torch.long, device=self.position_ids.device + ) + + if inputs_embeds is None: + inputs_embeds = self.word_embeddings(input_ids) + token_type_embeddings = self.token_type_embeddings(token_type_ids) + + embeddings = inputs_embeds + token_type_embeddings + if self.position_embedding_type == "absolute": + position_embeddings = self.position_embeddings(position_ids) + embeddings += position_embeddings + embeddings = self.LayerNorm(embeddings) + embeddings = self.dropout(embeddings) + return embeddings + + def create_position_ids_from_inputs_embeds(self, inputs_embeds): + """ + We are provided embeddings directly. We cannot infer which are padded so just generate sequential position ids. + + Args: + inputs_embeds: torch.Tensor + + Returns: torch.Tensor + """ + input_shape = inputs_embeds.size()[:-1] + sequence_length = input_shape[1] + + position_ids = torch.arange( + self.padding_idx + 1, + sequence_length + self.padding_idx + 1, + dtype=torch.long, + device=inputs_embeds.device, + ) + return position_ids.unsqueeze(0).expand(input_shape) + + +# Copied from transformers.models.bert.modeling_bert.BertSelfAttention with Bert->Roberta +class RobertaSelfAttention(nn.Module): + def __init__(self, config, position_embedding_type=None): + super().__init__() + if config.hidden_size % config.num_attention_heads != 0 and not hasattr( + config, "embedding_size" + ): + raise ValueError( + f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention " + f"heads ({config.num_attention_heads})" + ) + + self.num_attention_heads = config.num_attention_heads + self.attention_head_size = int(config.hidden_size / config.num_attention_heads) + self.all_head_size = self.num_attention_heads * self.attention_head_size + + self.query = nn.Linear(config.hidden_size, self.all_head_size) + self.key = nn.Linear(config.hidden_size, self.all_head_size) + self.value = nn.Linear(config.hidden_size, self.all_head_size) + + self.dropout = nn.Dropout(config.attention_probs_dropout_prob) + self.position_embedding_type = position_embedding_type or getattr( + config, "position_embedding_type", "absolute" + ) + if ( + self.position_embedding_type == "relative_key" + or self.position_embedding_type == "relative_key_query" + ): + self.max_position_embeddings = config.max_position_embeddings + self.distance_embedding = nn.Embedding( + 2 * config.max_position_embeddings - 1, self.attention_head_size + ) + + self.is_decoder = config.is_decoder + + def transpose_for_scores(self, x): + new_x_shape = x.size()[:-1] + ( + self.num_attention_heads, + self.attention_head_size, + ) + x = x.view(new_x_shape) + return x.permute(0, 2, 1, 3) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.FloatTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, + output_attentions: Optional[bool] = False, + parser_att_mask=None, + ) -> Tuple[torch.Tensor]: + mixed_query_layer = self.query(hidden_states) + + # If this is instantiated as a cross-attention module, the keys + # and values come from an encoder; the attention mask needs to be + # such that the encoder's padding tokens are not attended to. + is_cross_attention = encoder_hidden_states is not None + + if is_cross_attention and past_key_value is not None: + # reuse k,v, cross_attentions + key_layer = past_key_value[0] + value_layer = past_key_value[1] + attention_mask = encoder_attention_mask + elif is_cross_attention: + key_layer = self.transpose_for_scores(self.key(encoder_hidden_states)) + value_layer = self.transpose_for_scores(self.value(encoder_hidden_states)) + attention_mask = encoder_attention_mask + elif past_key_value is not None: + key_layer = self.transpose_for_scores(self.key(hidden_states)) + value_layer = self.transpose_for_scores(self.value(hidden_states)) + key_layer = torch.cat([past_key_value[0], key_layer], dim=2) + value_layer = torch.cat([past_key_value[1], value_layer], dim=2) + else: + key_layer = self.transpose_for_scores(self.key(hidden_states)) + value_layer = self.transpose_for_scores(self.value(hidden_states)) + + query_layer = self.transpose_for_scores(mixed_query_layer) + + if self.is_decoder: + # if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states. + # Further calls to cross_attention layer can then reuse all cross-attention + # key/value_states (first "if" case) + # if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of + # all previous decoder key/value_states. Further calls to uni-directional self-attention + # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case) + # if encoder bi-directional self-attention `past_key_value` is always `None` + past_key_value = (key_layer, value_layer) + + # Take the dot product between "query" and "key" to get the raw attention scores. + attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) + + if ( + self.position_embedding_type == "relative_key" + or self.position_embedding_type == "relative_key_query" + ): + seq_length = hidden_states.size()[1] + position_ids_l = torch.arange( + seq_length, dtype=torch.long, device=hidden_states.device + ).view(-1, 1) + position_ids_r = torch.arange( + seq_length, dtype=torch.long, device=hidden_states.device + ).view(1, -1) + distance = position_ids_l - position_ids_r + positional_embedding = self.distance_embedding( + distance + self.max_position_embeddings - 1 + ) + positional_embedding = positional_embedding.to( + dtype=query_layer.dtype + ) # fp16 compatibility + + if self.position_embedding_type == "relative_key": + relative_position_scores = torch.einsum( + "bhld,lrd->bhlr", query_layer, positional_embedding + ) + attention_scores = attention_scores + relative_position_scores + elif self.position_embedding_type == "relative_key_query": + relative_position_scores_query = torch.einsum( + "bhld,lrd->bhlr", query_layer, positional_embedding + ) + relative_position_scores_key = torch.einsum( + "bhrd,lrd->bhlr", key_layer, positional_embedding + ) + attention_scores = ( + attention_scores + + relative_position_scores_query + + relative_position_scores_key + ) + + attention_scores = attention_scores / math.sqrt(self.attention_head_size) + if attention_mask is not None: + # Apply the attention mask is (precomputed for all layers in RobertaModel forward() function) + attention_scores = attention_scores + attention_mask + + if parser_att_mask is None: + # Normalize the attention scores to probabilities. + attention_probs = nn.functional.softmax(attention_scores, dim=-1) + else: + attention_probs = torch.sigmoid(attention_scores) * parser_att_mask + + # This is actually dropping out entire tokens to attend to, which might + # seem a bit unusual, but is taken from the original Transformer paper. + attention_probs = self.dropout(attention_probs) + + # Mask heads if we want to + if head_mask is not None: + attention_probs = attention_probs * head_mask + + context_layer = torch.matmul(attention_probs, value_layer) + + context_layer = context_layer.permute(0, 2, 1, 3).contiguous() + new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) + context_layer = context_layer.view(new_context_layer_shape) + + outputs = ( + (context_layer, attention_probs) if output_attentions else (context_layer,) + ) + + if self.is_decoder: + outputs = outputs + (past_key_value,) + return outputs + + +# Copied from transformers.models.bert.modeling_bert.BertSelfOutput +class RobertaSelfOutput(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward( + self, hidden_states: torch.Tensor, input_tensor: torch.Tensor + ) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + hidden_states = self.LayerNorm(hidden_states + input_tensor) + return hidden_states + + +# Copied from transformers.models.bert.modeling_bert.BertAttention with Bert->Roberta +class RobertaAttention(nn.Module): + def __init__(self, config, position_embedding_type=None): + super().__init__() + self.self = RobertaSelfAttention( + config, position_embedding_type=position_embedding_type + ) + self.output = RobertaSelfOutput(config) + self.pruned_heads = set() + + def prune_heads(self, heads): + if len(heads) == 0: + return + heads, index = find_pruneable_heads_and_indices( + heads, + self.self.num_attention_heads, + self.self.attention_head_size, + self.pruned_heads, + ) + + # Prune linear layers + self.self.query = prune_linear_layer(self.self.query, index) + self.self.key = prune_linear_layer(self.self.key, index) + self.self.value = prune_linear_layer(self.self.value, index) + self.output.dense = prune_linear_layer(self.output.dense, index, dim=1) + + # Update hyper params and store pruned heads + self.self.num_attention_heads = self.self.num_attention_heads - len(heads) + self.self.all_head_size = ( + self.self.attention_head_size * self.self.num_attention_heads + ) + self.pruned_heads = self.pruned_heads.union(heads) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.FloatTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, + output_attentions: Optional[bool] = False, + parser_att_mask=None, + ) -> Tuple[torch.Tensor]: + self_outputs = self.self( + hidden_states, + attention_mask, + head_mask, + encoder_hidden_states, + encoder_attention_mask, + past_key_value, + output_attentions, + parser_att_mask=parser_att_mask, + ) + attention_output = self.output(self_outputs[0], hidden_states) + outputs = (attention_output,) + self_outputs[ + 1: + ] # add attentions if we output them + return outputs + + +# Copied from transformers.models.bert.modeling_bert.BertIntermediate +class RobertaIntermediate(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.intermediate_size) + if isinstance(config.hidden_act, str): + self.intermediate_act_fn = ACT2FN[config.hidden_act] + else: + self.intermediate_act_fn = config.hidden_act + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.intermediate_act_fn(hidden_states) + return hidden_states + + +# Copied from transformers.models.bert.modeling_bert.BertOutput +class RobertaOutput(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.intermediate_size, config.hidden_size) + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward( + self, hidden_states: torch.Tensor, input_tensor: torch.Tensor + ) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + hidden_states = self.LayerNorm(hidden_states + input_tensor) + return hidden_states + + +# Copied from transformers.models.bert.modeling_bert.BertLayer with Bert->Roberta +class RobertaLayer(nn.Module): + def __init__(self, config): + super().__init__() + self.chunk_size_feed_forward = config.chunk_size_feed_forward + self.seq_len_dim = 1 + self.attention = RobertaAttention(config) + self.is_decoder = config.is_decoder + self.add_cross_attention = config.add_cross_attention + if self.add_cross_attention: + if not self.is_decoder: + raise ValueError( + f"{self} should be used as a decoder model if cross attention is added" + ) + self.crossattention = RobertaAttention( + config, position_embedding_type="absolute" + ) + self.intermediate = RobertaIntermediate(config) + self.output = RobertaOutput(config) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.FloatTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, + output_attentions: Optional[bool] = False, + parser_att_mask=None, + ) -> Tuple[torch.Tensor]: + # decoder uni-directional self-attention cached key/values tuple is at positions 1,2 + self_attn_past_key_value = ( + past_key_value[:2] if past_key_value is not None else None + ) + self_attention_outputs = self.attention( + hidden_states, + attention_mask, + head_mask, + output_attentions=output_attentions, + past_key_value=self_attn_past_key_value, + parser_att_mask=parser_att_mask, + ) + attention_output = self_attention_outputs[0] + + # if decoder, the last output is tuple of self-attn cache + if self.is_decoder: + outputs = self_attention_outputs[1:-1] + present_key_value = self_attention_outputs[-1] + else: + outputs = self_attention_outputs[ + 1: + ] # add self attentions if we output attention weights + + cross_attn_present_key_value = None + if self.is_decoder and encoder_hidden_states is not None: + if not hasattr(self, "crossattention"): + raise ValueError( + f"If `encoder_hidden_states` are passed, {self} has to be instantiated with cross-attention layers by setting `config.add_cross_attention=True`" + ) + + # cross_attn cached key/values tuple is at positions 3,4 of past_key_value tuple + cross_attn_past_key_value = ( + past_key_value[-2:] if past_key_value is not None else None + ) + cross_attention_outputs = self.crossattention( + attention_output, + attention_mask, + head_mask, + encoder_hidden_states, + encoder_attention_mask, + cross_attn_past_key_value, + output_attentions, + ) + attention_output = cross_attention_outputs[0] + outputs = ( + outputs + cross_attention_outputs[1:-1] + ) # add cross attentions if we output attention weights + + # add cross-attn cache to positions 3,4 of present_key_value tuple + cross_attn_present_key_value = cross_attention_outputs[-1] + present_key_value = present_key_value + cross_attn_present_key_value + + layer_output = apply_chunking_to_forward( + self.feed_forward_chunk, + self.chunk_size_feed_forward, + self.seq_len_dim, + attention_output, + ) + outputs = (layer_output,) + outputs + + # if decoder, return the attn key/values as the last output + if self.is_decoder: + outputs = outputs + (present_key_value,) + + return outputs + + def feed_forward_chunk(self, attention_output): + intermediate_output = self.intermediate(attention_output) + layer_output = self.output(intermediate_output, attention_output) + return layer_output + + +# Copied from transformers.models.bert.modeling_bert.BertEncoder with Bert->Roberta +class RobertaEncoder(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + self.layer = nn.ModuleList( + [RobertaLayer(config) for _ in range(config.num_hidden_layers)] + ) + self.gradient_checkpointing = False + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.FloatTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = False, + output_hidden_states: Optional[bool] = False, + return_dict: Optional[bool] = True, + parser_att_mask=None, + ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPastAndCrossAttentions]: + all_hidden_states = () if output_hidden_states else None + all_self_attentions = () if output_attentions else None + all_cross_attentions = ( + () if output_attentions and self.config.add_cross_attention else None + ) + + next_decoder_cache = () if use_cache else None + for i, layer_module in enumerate(self.layer): + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + layer_head_mask = head_mask[i] if head_mask is not None else None + past_key_value = past_key_values[i] if past_key_values is not None else None + + if self.gradient_checkpointing and self.training: + + if use_cache: + logger.warning( + "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." + ) + use_cache = False + + def create_custom_forward(module): + def custom_forward(*inputs): + return module(*inputs, past_key_value, output_attentions) + + return custom_forward + + layer_outputs = torch.utils.checkpoint.checkpoint( + create_custom_forward(layer_module), + hidden_states, + attention_mask, + layer_head_mask, + encoder_hidden_states, + encoder_attention_mask, + ) + else: + if parser_att_mask is not None: + layer_outputs = layer_module( + hidden_states, + attention_mask, + layer_head_mask, + encoder_hidden_states, + encoder_attention_mask, + past_key_value, + output_attentions, + parser_att_mask=parser_att_mask[i]) + else: + layer_outputs = layer_module( + hidden_states, + attention_mask, + layer_head_mask, + encoder_hidden_states, + encoder_attention_mask, + past_key_value, + output_attentions, + parser_att_mask=None) + + + hidden_states = layer_outputs[0] + if use_cache: + next_decoder_cache += (layer_outputs[-1],) + if output_attentions: + all_self_attentions = all_self_attentions + (layer_outputs[1],) + if self.config.add_cross_attention: + all_cross_attentions = all_cross_attentions + (layer_outputs[2],) + + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + if not return_dict: + return tuple( + v + for v in [ + hidden_states, + next_decoder_cache, + all_hidden_states, + all_self_attentions, + all_cross_attentions, + ] + if v is not None + ) + return BaseModelOutputWithPastAndCrossAttentions( + last_hidden_state=hidden_states, + past_key_values=next_decoder_cache, + hidden_states=all_hidden_states, + attentions=all_self_attentions, + cross_attentions=all_cross_attentions, + ) + + +# Copied from transformers.models.bert.modeling_bert.BertPooler +class RobertaPooler(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.activation = nn.Tanh() + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + # We "pool" the model by simply taking the hidden state corresponding + # to the first token. + first_token_tensor = hidden_states[:, 0] + pooled_output = self.dense(first_token_tensor) + pooled_output = self.activation(pooled_output) + return pooled_output + + +class RobertaPreTrainedModel(PreTrainedModel): + """ + An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained + models. + """ + + config_class = RobertaConfig + base_model_prefix = "roberta" + supports_gradient_checkpointing = True + + # Copied from transformers.models.bert.modeling_bert.BertPreTrainedModel._init_weights + def _init_weights(self, module): + """Initialize the weights""" + if isinstance(module, nn.Linear): + # Slightly different from the TF version which uses truncated_normal for initialization + # cf https://github.com/pytorch/pytorch/pull/5617 + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.Embedding): + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.padding_idx is not None: + module.weight.data[module.padding_idx].zero_() + elif isinstance(module, nn.LayerNorm): + if module.bias is not None: + module.bias.data.zero_() + module.weight.data.fill_(1.0) + + def _set_gradient_checkpointing(self, module, value=False): + if isinstance(module, RobertaEncoder): + module.gradient_checkpointing = value + + def update_keys_to_ignore(self, config, del_keys_to_ignore): + """Remove some keys from ignore list""" + if not config.tie_word_embeddings: + # must make a new list, or the class variable gets modified! + self._keys_to_ignore_on_save = [ + k for k in self._keys_to_ignore_on_save if k not in del_keys_to_ignore + ] + self._keys_to_ignore_on_load_missing = [ + k + for k in self._keys_to_ignore_on_load_missing + if k not in del_keys_to_ignore + ] + + +ROBERTA_START_DOCSTRING = r""" + + This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the + library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads + etc.) + + This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. + Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage + and behavior. + + Parameters: + config ([`RobertaConfig`]): Model configuration class with all the parameters of the + model. Initializing with a config file does not load the weights associated with the model, only the + configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. +""" + + +ROBERTA_INPUTS_DOCSTRING = r""" + Args: + input_ids (`torch.LongTensor` of shape `({0})`): + Indices of input sequence tokens in the vocabulary. + + Indices can be obtained using [`RobertaTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + [What are input IDs?](../glossary#input-ids) + attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*): + Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + + [What are attention masks?](../glossary#attention-mask) + token_type_ids (`torch.LongTensor` of shape `({0})`, *optional*): + Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, + 1]`: + + - 0 corresponds to a *sentence A* token, + - 1 corresponds to a *sentence B* token. + + [What are token type IDs?](../glossary#token-type-ids) + position_ids (`torch.LongTensor` of shape `({0})`, *optional*): + Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, + config.max_position_embeddings - 1]`. + + [What are position IDs?](../glossary#position-ids) + head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): + Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: + + - 1 indicates the head is **not masked**, + - 0 indicates the head is **masked**. + + inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_size)`, *optional*): + Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This + is useful if you want more control over how to convert `input_ids` indices into associated vectors than the + model's internal embedding lookup matrix. + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned + tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for + more detail. + return_dict (`bool`, *optional*): + Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. +""" + + +class RobertaModel(RobertaPreTrainedModel): + """ + + The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of + cross-attention is added between the self-attention layers, following the architecture described in *Attention is + all you need*_ by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz + Kaiser and Illia Polosukhin. + + To behave as an decoder the model needs to be initialized with the `is_decoder` argument of the configuration set + to `True`. To be used in a Seq2Seq model, the model needs to initialized with both `is_decoder` argument and + `add_cross_attention` set to `True`; an `encoder_hidden_states` is then expected as an input to the forward pass. + + .. _*Attention is all you need*: https://arxiv.org/abs/1706.03762 + + """ + + _keys_to_ignore_on_load_missing = [r"position_ids"] + + # Copied from transformers.models.bert.modeling_bert.BertModel.__init__ with Bert->Roberta + def __init__(self, config, add_pooling_layer=True): + super().__init__(config) + self.config = config + + self.embeddings = RobertaEmbeddings(config) + self.encoder = RobertaEncoder(config) + + self.pooler = RobertaPooler(config) if add_pooling_layer else None + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.embeddings.word_embeddings + + def set_input_embeddings(self, value): + self.embeddings.word_embeddings = value + + def _prune_heads(self, heads_to_prune): + """ + Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base + class PreTrainedModel + """ + for layer, heads in heads_to_prune.items(): + self.encoder.layer[layer].attention.prune_heads(heads) + + # Copied from transformers.models.bert.modeling_bert.BertModel.forward + def forward( + self, + input_ids: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + token_type_ids: Optional[torch.Tensor] = None, + position_ids: Optional[torch.Tensor] = None, + head_mask: Optional[torch.Tensor] = None, + inputs_embeds: Optional[torch.Tensor] = None, + encoder_hidden_states: Optional[torch.Tensor] = None, + encoder_attention_mask: Optional[torch.Tensor] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + parser_att_mask=None, + ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPoolingAndCrossAttentions]: + r""" + encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): + Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if + the model is configured as a decoder. + encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*): + Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in + the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): + Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. + + If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that + don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all + `decoder_input_ids` of shape `(batch_size, sequence_length)`. + use_cache (`bool`, *optional*): + If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see + `past_key_values`). + """ + output_attentions = ( + output_attentions + if output_attentions is not None + else self.config.output_attentions + ) + output_hidden_states = ( + output_hidden_states + if output_hidden_states is not None + else self.config.output_hidden_states + ) + return_dict = ( + return_dict if return_dict is not None else self.config.use_return_dict + ) + + if self.config.is_decoder: + use_cache = use_cache if use_cache is not None else self.config.use_cache + else: + use_cache = False + + if input_ids is not None and inputs_embeds is not None: + raise ValueError( + "You cannot specify both input_ids and inputs_embeds at the same time" + ) + elif input_ids is not None: + input_shape = input_ids.size() + elif inputs_embeds is not None: + input_shape = inputs_embeds.size()[:-1] + else: + raise ValueError("You have to specify either input_ids or inputs_embeds") + + batch_size, seq_length = input_shape + device = input_ids.device if input_ids is not None else inputs_embeds.device + + # past_key_values_length + past_key_values_length = ( + past_key_values[0][0].shape[2] if past_key_values is not None else 0 + ) + + if attention_mask is None: + attention_mask = torch.ones( + ((batch_size, seq_length + past_key_values_length)), device=device + ) + + if token_type_ids is None: + if hasattr(self.embeddings, "token_type_ids"): + buffered_token_type_ids = self.embeddings.token_type_ids[:, :seq_length] + buffered_token_type_ids_expanded = buffered_token_type_ids.expand( + batch_size, seq_length + ) + token_type_ids = buffered_token_type_ids_expanded + else: + token_type_ids = torch.zeros( + input_shape, dtype=torch.long, device=device + ) + + # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length] + # ourselves in which case we just need to make it broadcastable to all heads. + extended_attention_mask: torch.Tensor = self.get_extended_attention_mask( + attention_mask, input_shape, device + ) + + # If a 2D or 3D attention mask is provided for the cross-attention + # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length] + if self.config.is_decoder and encoder_hidden_states is not None: + ( + encoder_batch_size, + encoder_sequence_length, + _, + ) = encoder_hidden_states.size() + encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length) + if encoder_attention_mask is None: + encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device) + encoder_extended_attention_mask = self.invert_attention_mask( + encoder_attention_mask + ) + else: + encoder_extended_attention_mask = None + + # Prepare head mask if needed + # 1.0 in head_mask indicate we keep the head + # attention_probs has shape bsz x n_heads x N x N + # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] + # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] + head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) + + embedding_output = self.embeddings( + input_ids=input_ids, + position_ids=position_ids, + token_type_ids=token_type_ids, + inputs_embeds=inputs_embeds, + past_key_values_length=past_key_values_length, + ) + encoder_outputs = self.encoder( + embedding_output, + attention_mask=extended_attention_mask, + head_mask=head_mask, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_extended_attention_mask, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + parser_att_mask=parser_att_mask, + ) + sequence_output = encoder_outputs[0] + pooled_output = ( + self.pooler(sequence_output) if self.pooler is not None else None + ) + + if not return_dict: + return (sequence_output, pooled_output) + encoder_outputs[1:] + + return BaseModelOutputWithPoolingAndCrossAttentions( + last_hidden_state=sequence_output, + pooler_output=pooled_output, + past_key_values=encoder_outputs.past_key_values, + hidden_states=encoder_outputs.hidden_states, + attentions=encoder_outputs.attentions, + cross_attentions=encoder_outputs.cross_attentions, + ) + + +class StructRoberta(RobertaPreTrainedModel): + _keys_to_ignore_on_save = [r"lm_head.decoder.weight", r"lm_head.decoder.bias"] + _keys_to_ignore_on_load_missing = [ + r"position_ids", + r"lm_head.decoder.weight", + r"lm_head.decoder.bias", + ] + _keys_to_ignore_on_load_unexpected = [r"pooler"] + + def __init__(self, config): + super().__init__(config) + + if config.is_decoder: + logger.warning( + "If you want to use `RobertaForMaskedLM` make sure `config.is_decoder=False` for " + "bi-directional self-attention." + ) + + + if config.n_cntxt_layers > 0: + config_cntxt = copy.deepcopy(config) + config_cntxt.num_hidden_layers = config.n_cntxt_layers + + self.cntxt_layers = RobertaModel(config_cntxt, add_pooling_layer=False) + + if config.n_cntxt_layers_2 > 0: + self.parser_layers_1 = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(int(config.n_parser_layers/2)) + ] + ) + + self.distance_ff_1 = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff_1 = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight_1 = nn.Parameter( + torch.zeros((config.n_cntxt_layers_2, config.num_attention_heads, n_rel)) + ) + self._rel_weight_1.data.normal_(0, 0.1) + + self._scaler_1 = nn.Parameter(torch.zeros(2)) + + config_cntxt_2 = copy.deepcopy(config) + config_cntxt_2.num_hidden_layers = config.n_cntxt_layers_2 + + self.cntxt_layers_2 = RobertaModel(config_cntxt_2, add_pooling_layer=False) + + + self.parser_layers_2 = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(int(config.n_parser_layers/2)) + ] + ) + + self.distance_ff_2 = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff_2 = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight_2 = nn.Parameter( + torch.zeros((config.num_hidden_layers, config.num_attention_heads, n_rel)) + ) + self._rel_weight_2.data.normal_(0, 0.1) + + self._scaler_2 = nn.Parameter(torch.zeros(2)) + + else: + self.parser_layers = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(config.n_parser_layers) + ] + ) + + self.distance_ff = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight = nn.Parameter( + torch.zeros((config.num_hidden_layers, config.num_attention_heads, n_rel)) + ) + self._rel_weight.data.normal_(0, 0.1) + + self._scaler = nn.Parameter(torch.zeros(2)) + + self.roberta = RobertaModel(config, add_pooling_layer=False) + + if config.n_cntxt_layers > 0: + self.cntxt_layers.embeddings = self.roberta.embeddings + if config.n_cntxt_layers_2 > 0: + self.cntxt_layers_2.embeddings = self.roberta.embeddings + + self.lm_head = RobertaLMHead(config) + + self.pad = config.pad_token_id + + # The LM head weights require special treatment only when they are tied with the word embeddings + self.update_keys_to_ignore(config, ["lm_head.decoder.weight"]) + + # Initialize weights and apply final processing + self.post_init() + + def get_output_embeddings(self): + return self.lm_head.decoder + + def set_output_embeddings(self, new_embeddings): + self.lm_head.decoder = new_embeddings + + @property + def scaler(self): + return self._scaler.exp() + + @property + def scaler_1(self): + return self._scaler_1.exp() + + @property + def scaler_2(self): + return self._scaler_2.exp() + + @property + def rel_weight(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight, dim=-1) + + @property + def rel_weight_1(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight_1) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight_1, dim=-1) + + + @property + def rel_weight_2(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight_2) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight_2, dim=-1) + + + def compute_block(self, distance, height, n_cntxt_layers=0): + """Compute constituents from distance and height.""" + + if n_cntxt_layers>0: + if n_cntxt_layers == 1: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler_1[0] + elif n_cntxt_layers == 2: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler_2[0] + else: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0] + + gamma = torch.sigmoid(-beta_logits) + ones = torch.ones_like(gamma) + + block_mask_left = cummin( + gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1 + ) + block_mask_left = block_mask_left - F.pad( + block_mask_left[:, :, :-1], (1, 0), value=0 + ) + block_mask_left.tril_(0) + + block_mask_right = cummin( + gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1 + ) + block_mask_right = block_mask_right - F.pad( + block_mask_right[:, :, 1:], (0, 1), value=0 + ) + block_mask_right.triu_(0) + + block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :] + block = cumsum(block_mask_left).tril(0) + cumsum( + block_mask_right, reverse=True + ).triu(1) + + return block_p, block + + def compute_head(self, height, n_cntxt_layers=0): + """Estimate head for each constituent.""" + + _, length = height.size() + if n_cntxt_layers>0: + if n_cntxt_layers == 1: + head_logits = height * self.scaler_1[1] + elif n_cntxt_layers == 2: + head_logits = height * self.scaler_2[1] + else: + head_logits = height * self.scaler[1] + index = torch.arange(length, device=height.device) + + mask = (index[:, None, None] <= index[None, None, :]) * ( + index[None, None, :] <= index[None, :, None] + ) + head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1) + head_logits.masked_fill_(~mask[None, :, :, :], -1e9) + + head_p = torch.softmax(head_logits, dim=-1) + + return head_p + + def parse(self, x, embs=None, n_cntxt_layers=0): + """Parse input sentence. + + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + distance: syntactic distance + height: syntactic height + """ + + mask = x != self.pad + mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0) + + if embs is None: + h = self.roberta.embeddings(x) + else: + h = embs + + if n_cntxt_layers > 0: + if n_cntxt_layers == 1: + parser_layers = self.parser_layers_1 + height_ff = self.height_ff_1 + distance_ff = self.distance_ff_1 + elif n_cntxt_layers == 2: + parser_layers = self.parser_layers_2 + height_ff = self.height_ff_2 + distance_ff = self.distance_ff_2 + for i in range(int(self.config.n_parser_layers/2)): + h = h.masked_fill(~mask[:, :, None], 0) + h = parser_layers[i](h) + + height = height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, dim=-1 + )[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max + ) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where( + margin_left > margin_right, margin_right, margin_left + ).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + else: + for i in range(self.config.n_parser_layers): + h = h.masked_fill(~mask[:, :, None], 0) + h = self.parser_layers[i](h) + + height = self.height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = self.distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, dim=-1 + )[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max + ) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where( + margin_left > margin_right, margin_right, margin_left + ).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + + return distance, height + + def generate_mask(self, x, distance, height, n_cntxt_layers=0): + """Compute head and cibling distribution for each token.""" + + bsz, length = x.size() + + eye = torch.eye(length, device=x.device, dtype=torch.bool) + eye = eye[None, :, :].expand((bsz, -1, -1)) + + block_p, block = self.compute_block(distance, height, n_cntxt_layers=n_cntxt_layers) + head_p = self.compute_head(height, n_cntxt_layers=n_cntxt_layers) + head = torch.einsum("blij,bijh->blh", block_p, head_p) + head = head.masked_fill(eye, 0) + child = head.transpose(1, 2) + cibling = torch.bmm(head, child).masked_fill(eye, 0) + + rel_list = [] + if "head" in self.config.relations: + rel_list.append(head) + if "child" in self.config.relations: + rel_list.append(child) + if "cibling" in self.config.relations: + rel_list.append(cibling) + + rel = torch.stack(rel_list, dim=1) + + if n_cntxt_layers > 0: + if n_cntxt_layers == 1: + rel_weight = self.rel_weight_1 + elif n_cntxt_layers == 2: + rel_weight = self.rel_weight_2 + else: + rel_weight = self.rel_weight + + dep = torch.einsum("lhr,brij->lbhij", rel_weight, rel) + + if n_cntxt_layers == 1: + num_layers = self.cntxt_layers_2.config.num_hidden_layers + else: + num_layers = self.roberta.config.num_hidden_layers + + att_mask = dep.reshape( + num_layers, + bsz, + self.config.num_attention_heads, + length, + length, + ) + + return att_mask, cibling, head, block + + def forward( + self, + input_ids: Optional[torch.LongTensor] = None, + attention_mask: Optional[torch.FloatTensor] = None, + token_type_ids: Optional[torch.LongTensor] = None, + position_ids: Optional[torch.LongTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, MaskedLMOutput]: + r""" + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ..., + config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the + loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]` + kwargs (`Dict[str, any]`, optional, defaults to *{}*): + Used to hide legacy arguments that have been deprecated. + """ + return_dict = ( + return_dict if return_dict is not None else self.config.use_return_dict + ) + + + if self.config.n_cntxt_layers > 0: + cntxt_outputs = self.cntxt_layers( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict) + + + if self.config.n_cntxt_layers_2 > 0: + distance_1, height_1 = self.parse(input_ids, cntxt_outputs[0], n_cntxt_layers=1) + att_mask_1, _, _, _ = self.generate_mask(input_ids, distance_1, height_1, n_cntxt_layers=1) + + cntxt_outputs_2 = self.cntxt_layers_2( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + parser_att_mask=att_mask_1) + + sequence_output = cntxt_outputs_2[0] + + distance_2, height_2 = self.parse(input_ids, sequence_output[0], n_cntxt_layers=2) + att_mask, _, _, _ = self.generate_mask(input_ids, distance_2, height_2, n_cntxt_layers=2) + + elif self.config.n_cntxt_layers > 0: + distance, height = self.parse(input_ids, cntxt_outputs[0]) + att_mask, _, _, _ = self.generate_mask(input_ids, distance, height) + else: + distance, height = self.parse(input_ids) + att_mask, _, _, _ = self.generate_mask(input_ids, distance, height) + + outputs = self.roberta( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + parser_att_mask=att_mask, + ) + sequence_output = outputs[0] + prediction_scores = self.lm_head(sequence_output) + + masked_lm_loss = None + if labels is not None: + loss_fct = CrossEntropyLoss() + masked_lm_loss = loss_fct( + prediction_scores.view(-1, self.config.vocab_size), labels.view(-1) + ) + + if not return_dict: + output = (prediction_scores,) + outputs[2:] + return ( + ((masked_lm_loss,) + output) if masked_lm_loss is not None else output + ) + + return MaskedLMOutput( + loss=masked_lm_loss, + logits=prediction_scores, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +class RobertaLMHead(nn.Module): + """Roberta Head for masked language modeling.""" + + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + + self.decoder = nn.Linear(config.hidden_size, config.vocab_size) + self.bias = nn.Parameter(torch.zeros(config.vocab_size)) + self.decoder.bias = self.bias + + def forward(self, features, **kwargs): + x = self.dense(features) + x = gelu(x) + x = self.layer_norm(x) + + # project back to size of vocabulary with bias + x = self.decoder(x) + + return x + + def _tie_weights(self): + # To tie those two weights if they get disconnected (on TPU or when the bias is resized) + self.bias = self.decoder.bias + + +class StructRobertaForSequenceClassification(RobertaPreTrainedModel): + _keys_to_ignore_on_load_missing = [r"position_ids"] + + def __init__(self, config): + super().__init__(config) + self.num_labels = config.num_labels + self.config = config + + if config.n_cntxt_layers > 0: + config_cntxt = copy.deepcopy(config) + config_cntxt.num_hidden_layers = config.n_cntxt_layers + + self.cntxt_layers = RobertaModel(config_cntxt, add_pooling_layer=False) + + if config.n_cntxt_layers_2 > 0: + self.parser_layers_1 = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(int(config.n_parser_layers/2)) + ] + ) + + self.distance_ff_1 = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff_1 = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight_1 = nn.Parameter( + torch.zeros((config.n_cntxt_layers_2, config.num_attention_heads, n_rel)) + ) + self._rel_weight_1.data.normal_(0, 0.1) + + self._scaler_1 = nn.Parameter(torch.zeros(2)) + + config_cntxt_2 = copy.deepcopy(config) + config_cntxt_2.num_hidden_layers = config.n_cntxt_layers_2 + + self.cntxt_layers_2 = RobertaModel(config_cntxt_2, add_pooling_layer=False) + + + self.parser_layers_2 = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(int(config.n_parser_layers/2)) + ] + ) + + self.distance_ff_2 = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff_2 = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight_2 = nn.Parameter( + torch.zeros((config.num_hidden_layers, config.num_attention_heads, n_rel)) + ) + self._rel_weight_2.data.normal_(0, 0.1) + + self._scaler_2 = nn.Parameter(torch.zeros(2)) + + else: + self.parser_layers = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(config.n_parser_layers) + ] + ) + + self.distance_ff = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight = nn.Parameter( + torch.zeros((config.num_hidden_layers, config.num_attention_heads, n_rel)) + ) + self._rel_weight.data.normal_(0, 0.1) + + self._scaler = nn.Parameter(torch.zeros(2)) + + self.roberta = RobertaModel(config, add_pooling_layer=False) + + if config.n_cntxt_layers > 0: + self.cntxt_layers.embeddings = self.roberta.embeddings + if config.n_cntxt_layers_2 > 0: + self.cntxt_layers_2.embeddings = self.roberta.embeddings + + + self.pad = config.pad_token_id + self.classifier = RobertaClassificationHead(config) + + # Initialize weights and apply final processing + self.post_init() + + + @property + def scaler(self): + return self._scaler.exp() + + @property + def scaler_1(self): + return self._scaler_1.exp() + + @property + def scaler_2(self): + return self._scaler_2.exp() + + @property + def rel_weight(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight, dim=-1) + + @property + def rel_weight_1(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight_1) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight_1, dim=-1) + + + @property + def rel_weight_2(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight_2) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight_2, dim=-1) + + + def compute_block(self, distance, height, n_cntxt_layers=0): + """Compute constituents from distance and height.""" + + if n_cntxt_layers>0: + if n_cntxt_layers == 1: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler_1[0] + elif n_cntxt_layers == 2: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler_2[0] + else: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0] + + gamma = torch.sigmoid(-beta_logits) + ones = torch.ones_like(gamma) + + block_mask_left = cummin( + gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1 + ) + block_mask_left = block_mask_left - F.pad( + block_mask_left[:, :, :-1], (1, 0), value=0 + ) + block_mask_left.tril_(0) + + block_mask_right = cummin( + gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1 + ) + block_mask_right = block_mask_right - F.pad( + block_mask_right[:, :, 1:], (0, 1), value=0 + ) + block_mask_right.triu_(0) + + block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :] + block = cumsum(block_mask_left).tril(0) + cumsum( + block_mask_right, reverse=True + ).triu(1) + + return block_p, block + + def compute_head(self, height, n_cntxt_layers=0): + """Estimate head for each constituent.""" + + _, length = height.size() + if n_cntxt_layers>0: + if n_cntxt_layers == 1: + head_logits = height * self.scaler_1[1] + elif n_cntxt_layers == 2: + head_logits = height * self.scaler_2[1] + else: + head_logits = height * self.scaler[1] + index = torch.arange(length, device=height.device) + + mask = (index[:, None, None] <= index[None, None, :]) * ( + index[None, None, :] <= index[None, :, None] + ) + head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1) + head_logits.masked_fill_(~mask[None, :, :, :], -1e9) + + head_p = torch.softmax(head_logits, dim=-1) + + return head_p + + def parse(self, x, embs=None, n_cntxt_layers=0): + """Parse input sentence. + + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + distance: syntactic distance + height: syntactic height + """ + + mask = x != self.pad + mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0) + + if embs is None: + h = self.roberta.embeddings(x) + else: + h = embs + + if n_cntxt_layers > 0: + if n_cntxt_layers == 1: + parser_layers = self.parser_layers_1 + height_ff = self.height_ff_1 + distance_ff = self.distance_ff_1 + elif n_cntxt_layers == 2: + parser_layers = self.parser_layers_2 + height_ff = self.height_ff_2 + distance_ff = self.distance_ff_2 + for i in range(int(self.config.n_parser_layers/2)): + h = h.masked_fill(~mask[:, :, None], 0) + h = parser_layers[i](h) + + height = height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, dim=-1 + )[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max + ) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where( + margin_left > margin_right, margin_right, margin_left + ).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + else: + for i in range(self.config.n_parser_layers): + h = h.masked_fill(~mask[:, :, None], 0) + h = self.parser_layers[i](h) + + height = self.height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = self.distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, dim=-1 + )[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max + ) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where( + margin_left > margin_right, margin_right, margin_left + ).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + + return distance, height + + def generate_mask(self, x, distance, height, n_cntxt_layers=0): + """Compute head and cibling distribution for each token.""" + + bsz, length = x.size() + + eye = torch.eye(length, device=x.device, dtype=torch.bool) + eye = eye[None, :, :].expand((bsz, -1, -1)) + + block_p, block = self.compute_block(distance, height, n_cntxt_layers=n_cntxt_layers) + head_p = self.compute_head(height, n_cntxt_layers=n_cntxt_layers) + head = torch.einsum("blij,bijh->blh", block_p, head_p) + head = head.masked_fill(eye, 0) + child = head.transpose(1, 2) + cibling = torch.bmm(head, child).masked_fill(eye, 0) + + rel_list = [] + if "head" in self.config.relations: + rel_list.append(head) + if "child" in self.config.relations: + rel_list.append(child) + if "cibling" in self.config.relations: + rel_list.append(cibling) + + rel = torch.stack(rel_list, dim=1) + + if n_cntxt_layers > 0: + if n_cntxt_layers == 1: + rel_weight = self.rel_weight_1 + elif n_cntxt_layers == 2: + rel_weight = self.rel_weight_2 + else: + rel_weight = self.rel_weight + + dep = torch.einsum("lhr,brij->lbhij", rel_weight, rel) + + if n_cntxt_layers == 1: + num_layers = self.cntxt_layers_2.config.num_hidden_layers + else: + num_layers = self.roberta.config.num_hidden_layers + + att_mask = dep.reshape( + num_layers, + bsz, + self.config.num_attention_heads, + length, + length, + ) + + return att_mask, cibling, head, block + + def forward( + self, + input_ids: Optional[torch.LongTensor] = None, + attention_mask: Optional[torch.FloatTensor] = None, + token_type_ids: Optional[torch.LongTensor] = None, + position_ids: Optional[torch.LongTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, SequenceClassifierOutput]: + r""" + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., + config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If + `config.num_labels > 1` a classification loss is computed (Cross-Entropy). + """ + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + if self.config.n_cntxt_layers > 0: + cntxt_outputs = self.cntxt_layers( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict) + + + if self.config.n_cntxt_layers_2 > 0: + distance_1, height_1 = self.parse(input_ids, cntxt_outputs[0], n_cntxt_layers=1) + att_mask_1, _, _, _ = self.generate_mask(input_ids, distance_1, height_1, n_cntxt_layers=1) + + cntxt_outputs_2 = self.cntxt_layers_2( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + parser_att_mask=att_mask_1) + + sequence_output = cntxt_outputs_2[0] + + distance_2, height_2 = self.parse(input_ids, sequence_output[0], n_cntxt_layers=2) + att_mask, _, _, _ = self.generate_mask(input_ids, distance_2, height_2, n_cntxt_layers=2) + + elif self.config.n_cntxt_layers > 0: + distance, height = self.parse(input_ids, cntxt_outputs[0]) + att_mask, _, _, _ = self.generate_mask(input_ids, distance, height) + else: + distance, height = self.parse(input_ids) + att_mask, _, _, _ = self.generate_mask(input_ids, distance, height) + + outputs = self.roberta( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + parser_att_mask=att_mask, + ) + sequence_output = outputs[0] + logits = self.classifier(sequence_output) + + loss = None + if labels is not None: + if self.config.problem_type is None: + if self.num_labels == 1: + self.config.problem_type = "regression" + elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): + self.config.problem_type = "single_label_classification" + else: + self.config.problem_type = "multi_label_classification" + + if self.config.problem_type == "regression": + loss_fct = MSELoss() + if self.num_labels == 1: + loss = loss_fct(logits.squeeze(), labels.squeeze()) + else: + loss = loss_fct(logits, labels) + elif self.config.problem_type == "single_label_classification": + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) + elif self.config.problem_type == "multi_label_classification": + loss_fct = BCEWithLogitsLoss() + loss = loss_fct(logits, labels) + + if not return_dict: + output = (logits,) + outputs[2:] + return ((loss,) + output) if loss is not None else output + + return SequenceClassifierOutput( + loss=loss, + logits=logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +class RobertaClassificationHead(nn.Module): + """Head for sentence-level classification tasks.""" + + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + classifier_dropout = ( + config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob + ) + self.dropout = nn.Dropout(classifier_dropout) + self.out_proj = nn.Linear(config.hidden_size, config.num_labels) + + def forward(self, features, **kwargs): + x = features[:, 0, :] # take token (equiv. to [CLS]) + x = self.dropout(x) + x = self.dense(x) + x = torch.tanh(x) + x = self.dropout(x) + x = self.out_proj(x) + return x + + +def create_position_ids_from_input_ids( + input_ids, padding_idx, past_key_values_length=0 +): + """ + Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols + are ignored. This is modified from fairseq's `utils.make_positions`. + + Args: + x: torch.Tensor x: + + Returns: torch.Tensor + """ + # The series of casts and type-conversions here are carefully balanced to both work with ONNX export and XLA. + mask = input_ids.ne(padding_idx).int() + incremental_indices = ( + torch.cumsum(mask, dim=1).type_as(mask) + past_key_values_length + ) * mask + return incremental_indices.long() + padding_idx + + +def cumprod(x, reverse=False, exclusive=False): + """cumulative product.""" + if reverse: + x = x.flip([-1]) + + if exclusive: + x = F.pad(x[:, :, :-1], (1, 0), value=1) + + cx = x.cumprod(-1) + + if reverse: + cx = cx.flip([-1]) + return cx + + +def cumsum(x, reverse=False, exclusive=False): + """cumulative sum.""" + bsz, _, length = x.size() + device = x.device + if reverse: + if exclusive: + w = torch.ones([bsz, length, length], device=device).tril(-1) + else: + w = torch.ones([bsz, length, length], device=device).tril(0) + cx = torch.bmm(x, w) + else: + if exclusive: + w = torch.ones([bsz, length, length], device=device).triu(1) + else: + w = torch.ones([bsz, length, length], device=device).triu(0) + cx = torch.bmm(x, w) + return cx + + +def cummin(x, reverse=False, exclusive=False, max_value=1e9): + """cumulative min.""" + if reverse: + if exclusive: + x = F.pad(x[:, :, 1:], (0, 1), value=max_value) + x = x.flip([-1]).cummin(-1)[0].flip([-1]) + else: + if exclusive: + x = F.pad(x[:, :, :-1], (1, 0), value=max_value) + x = x.cummin(-1)[0] + return x diff --git a/finetune/boolq/predict_results.txt b/finetune/boolq/predict_results.txt new file mode 100644 index 0000000000000000000000000000000000000000..426c0d5fa7e0ea55efe511072bb262e9f9d13a83 --- /dev/null +++ b/finetune/boolq/predict_results.txt @@ -0,0 +1,724 @@ +index prediction +0 1 +1 0 +2 1 +3 0 +4 1 +5 1 +6 1 +7 0 +8 1 +9 0 +10 1 +11 0 +12 0 +13 0 +14 1 +15 1 +16 1 +17 1 +18 1 +19 1 +20 0 +21 1 +22 1 +23 1 +24 0 +25 0 +26 0 +27 1 +28 0 +29 0 +30 1 +31 0 +32 1 +33 1 +34 1 +35 1 +36 1 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". +A gh +A ld +A unty +B erm +B ah +B OY +C arr +C ulture +C rying +C atholic +D ang +F le +F ew +F eed +F ront +G ab +G ray +G ibbs +H ong +H erm +I ra +I aw +J or +J ana +K y +K night +K askaskia +K ELLY +L ED +L osing +L anguage +M onday +M ATT +N S +N urse +N othin +P ha +P ap +P ont +P rem +P EOPLE +R i +R uss +S ou +S ty +S alt +T ie +T unes +V i +W el +W ARD +Y awang +b able +b ahn +c ress +c ole +c rit +c ÃŃa +d oc +d ling +e ff +f ool +f ired +f urther +f amous +f emale +g is +g all +g um +g irlfriend +h oun +h wa +i ations +j ord +j ah +k il +k ai +k ids +m and +m ap +m oving +m ummy +o form +s and +t ha +t hang +u aries +v id +v ine +v able +v ati +w ine +w ag +z le + ¡ +à ¬ +Å Ħ +Ġt umble +he x +he id +in ine +in stead +Ġw izard +ou ching +on tain +ha ha +is cher +Ġb irl +Ġb ert +Ġb raid +Ġo t +Ġo mitted +Ġc his +Ġm asks +Ġm uttering +Ġf oll +an ley +Ġd azed +Ġd ouche +es on +Ġp he +Ġp au +ar ina +ar oni +ar von +at io +Ġl icked +Ġl icking +Ġg it +Ġn il +ĠI ld +le ments +Ġh uts +Ġhe ur +al ming +Ġit ching +Ġit chy +Ġe up +ut sc +Ġwe ights +Ġfor fe +Ġfor ci +ĠS ter +ĠS ino +ĠS ales +ĠS hib +ĠS iri +st ed +st ack +st range +st icks +ri y +ad itionally +Ġst if +Ġst rew +Ġst ains +Ġst ables +Ġst unning +oo ges +ĠA part +ĠA men +ĠA cha +ĠC ertain +ĠC ey +ĠC LA +ĠC ARR +Ġu gh +ĠM ist +ĠM OM +ĠM IGHT +Ġas semble +ill ors +Ġse ag +ĠH ollow +ĠH ades +ĠH ym +il ization +ĠB old +Ġli ability +op ping +ĠP K +ĠP ill +ĠP ew +ĠP ied +ĠP oe +ĠP OWER +ĠP iazza +ĠT il +ĠT ia +ĠT ick +un er +ĠW ap +Ġr ushes +Ġj est +Ġj ets +ĠD M +ĠD ort +ĠD OU +ĠD IS +ĠD uart +ĠD izzy +ĠG ina +um en +Ġso ils +Ġso aring +est us +Ġal igned +Ġall erg +ist ered +ĠR T +ĠR im +ĠR ac +ĠR iding +ĠR umble +ie ving +art y +ab lan +ĠL ac +ĠL ust +ĠL um +ĠL em +ĠL ions +ĠL ars +Ġcon gest +ĠN ights +ig l +han na +han sa +Ġup hold +Ġcom ical +Ġher ring +ĠJ ude +Ġout look +Ġout lined +ra ult +Ġpl umber +Ġpl aus +od i +ink meaner +Ġpro state +Ġpro stitutes +Ġex clude +Ġv ener +Ġv ines +Ġv ipers +ard y +ity a +ĠO D +ies selin +Ġman nik +ud ers +ud eau +ap o +ĠK iri +ven lock +Ġtr acing +ure en +ĠHe w +ĠHe gel +ove l +ick i +ci us +ĠU CLA +reat ic +ĠSt ras +ĠSt empniak +No ll +other wise +ne ither +Ġback grounds +Ġover d +Ġgr inding +That s +Ġen act +Ġen cl +Ġen vious +og g +og un +pl anet +ord ered +Ġdown stream +Ġun real +Ġun wanted +Ġun ravel +iz oph +Ġam iable +Ġcomp osing +Ġbl ush +ĠV on +ĠV ig +ĠIn v +urn al +Ġplay doh +ress ions +av or +av ius +Ġdis ks +Ġgu iding +ĠY ek +be es +ip ani +ip hon +Ġfl aws +ect ing +vers ation +ks het +Ġrem nants +ix by +Ġsha ving +Ġsec urities +Ġspe ars +Ġlast ly +li k +Ġend ocr +Ġlea ked +Ġfam ine +Ġnight gown +ny a +de gree +Ġ18 10 +Ġ18 28 +Ġ18 32 +gg les +ĠShe ldon +ĠAr um +Ġmar ries diff --git a/finetune/cola/modeling_structroberta.py b/finetune/cola/modeling_structroberta.py new file mode 100644 index 0000000000000000000000000000000000000000..9195a79dc7ae5e8c606bb77a05b8a1d5672bfdc0 --- /dev/null +++ b/finetune/cola/modeling_structroberta.py @@ -0,0 +1,2146 @@ +import copy +import math +from typing import List, Optional, Tuple, Union +import torch +import torch.utils.checkpoint +from packaging import version +from torch import nn +import torch.nn.functional as F +from torch.nn import CrossEntropyLoss, MSELoss, BCEWithLogitsLoss +from transformers.activations import ACT2FN, gelu +from transformers.modeling_outputs import ( + BaseModelOutputWithPastAndCrossAttentions, + BaseModelOutputWithPoolingAndCrossAttentions, + MaskedLMOutput, + SequenceClassifierOutput +) +from transformers.modeling_utils import ( + PreTrainedModel, + apply_chunking_to_forward, + find_pruneable_heads_and_indices, + prune_linear_layer, +) +from transformers.utils import logging +from transformers import RobertaConfig + + +logger = logging.get_logger(__name__) +ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST = [ + "roberta-base", + "roberta-large", + "roberta-large-mnli", + "distilroberta-base", + "roberta-base-openai-detector", + "roberta-large-openai-detector", + # See all RoBERTa models at https://huggingface.co/models?filter=roberta +] + + +class StructRobertaConfig(RobertaConfig): + model_type = "roberta" + + def __init__( + self, + n_parser_layers=4, + conv_size=9, + relations=("head", "child"), + weight_act="softmax", + n_cntxt_layers=3, + n_cntxt_layers_2=0, + **kwargs,): + + super().__init__(**kwargs) + self.n_cntxt_layers = n_cntxt_layers + self.n_parser_layers = n_parser_layers + self.n_cntxt_layers_2 = n_cntxt_layers_2 + self.conv_size = conv_size + self.relations = relations + self.weight_act = weight_act + +class Conv1d(nn.Module): + """1D convolution layer.""" + + def __init__(self, hidden_size, kernel_size, dilation=1): + """Initialization. + + Args: + hidden_size: dimension of input embeddings + kernel_size: convolution kernel size + dilation: the spacing between the kernel points + """ + super(Conv1d, self).__init__() + + if kernel_size % 2 == 0: + padding = (kernel_size // 2) * dilation + self.shift = True + else: + padding = ((kernel_size - 1) // 2) * dilation + self.shift = False + self.conv = nn.Conv1d( + hidden_size, hidden_size, kernel_size, padding=padding, dilation=dilation + ) + + def forward(self, x): + """Compute convolution. + + Args: + x: input embeddings + Returns: + conv_output: convolution results + """ + + if self.shift: + return self.conv(x.transpose(1, 2)).transpose(1, 2)[:, 1:] + else: + return self.conv(x.transpose(1, 2)).transpose(1, 2) + + +class RobertaEmbeddings(nn.Module): + """ + Same as BertEmbeddings with a tiny tweak for positional embeddings indexing. + """ + + # Copied from transformers.models.bert.modeling_bert.BertEmbeddings.__init__ + def __init__(self, config): + super().__init__() + self.word_embeddings = nn.Embedding( + config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id + ) + self.position_embeddings = nn.Embedding( + config.max_position_embeddings, config.hidden_size + ) + self.token_type_embeddings = nn.Embedding( + config.type_vocab_size, config.hidden_size + ) + + # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load + # any TensorFlow checkpoint file + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + # position_ids (1, len position emb) is contiguous in memory and exported when serialized + self.position_embedding_type = getattr( + config, "position_embedding_type", "absolute" + ) + self.register_buffer( + "position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)) + ) + if version.parse(torch.__version__) > version.parse("1.6.0"): + self.register_buffer( + "token_type_ids", + torch.zeros(self.position_ids.size(), dtype=torch.long), + persistent=False, + ) + + # End copy + self.padding_idx = config.pad_token_id + self.position_embeddings = nn.Embedding( + config.max_position_embeddings, + config.hidden_size, + padding_idx=self.padding_idx, + ) + + def forward( + self, + input_ids=None, + token_type_ids=None, + position_ids=None, + inputs_embeds=None, + past_key_values_length=0, + ): + if position_ids is None: + if input_ids is not None: + # Create the position ids from the input token ids. Any padded tokens remain padded. + position_ids = create_position_ids_from_input_ids( + input_ids, self.padding_idx, past_key_values_length + ) + else: + position_ids = self.create_position_ids_from_inputs_embeds( + inputs_embeds + ) + + if input_ids is not None: + input_shape = input_ids.size() + else: + input_shape = inputs_embeds.size()[:-1] + + seq_length = input_shape[1] + + # Setting the token_type_ids to the registered buffer in constructor where it is all zeros, which usually occurs + # when its auto-generated, registered buffer helps users when tracing the model without passing token_type_ids, solves + # issue #5664 + if token_type_ids is None: + if hasattr(self, "token_type_ids"): + buffered_token_type_ids = self.token_type_ids[:, :seq_length] + buffered_token_type_ids_expanded = buffered_token_type_ids.expand( + input_shape[0], seq_length + ) + token_type_ids = buffered_token_type_ids_expanded + else: + token_type_ids = torch.zeros( + input_shape, dtype=torch.long, device=self.position_ids.device + ) + + if inputs_embeds is None: + inputs_embeds = self.word_embeddings(input_ids) + token_type_embeddings = self.token_type_embeddings(token_type_ids) + + embeddings = inputs_embeds + token_type_embeddings + if self.position_embedding_type == "absolute": + position_embeddings = self.position_embeddings(position_ids) + embeddings += position_embeddings + embeddings = self.LayerNorm(embeddings) + embeddings = self.dropout(embeddings) + return embeddings + + def create_position_ids_from_inputs_embeds(self, inputs_embeds): + """ + We are provided embeddings directly. We cannot infer which are padded so just generate sequential position ids. + + Args: + inputs_embeds: torch.Tensor + + Returns: torch.Tensor + """ + input_shape = inputs_embeds.size()[:-1] + sequence_length = input_shape[1] + + position_ids = torch.arange( + self.padding_idx + 1, + sequence_length + self.padding_idx + 1, + dtype=torch.long, + device=inputs_embeds.device, + ) + return position_ids.unsqueeze(0).expand(input_shape) + + +# Copied from transformers.models.bert.modeling_bert.BertSelfAttention with Bert->Roberta +class RobertaSelfAttention(nn.Module): + def __init__(self, config, position_embedding_type=None): + super().__init__() + if config.hidden_size % config.num_attention_heads != 0 and not hasattr( + config, "embedding_size" + ): + raise ValueError( + f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention " + f"heads ({config.num_attention_heads})" + ) + + self.num_attention_heads = config.num_attention_heads + self.attention_head_size = int(config.hidden_size / config.num_attention_heads) + self.all_head_size = self.num_attention_heads * self.attention_head_size + + self.query = nn.Linear(config.hidden_size, self.all_head_size) + self.key = nn.Linear(config.hidden_size, self.all_head_size) + self.value = nn.Linear(config.hidden_size, self.all_head_size) + + self.dropout = nn.Dropout(config.attention_probs_dropout_prob) + self.position_embedding_type = position_embedding_type or getattr( + config, "position_embedding_type", "absolute" + ) + if ( + self.position_embedding_type == "relative_key" + or self.position_embedding_type == "relative_key_query" + ): + self.max_position_embeddings = config.max_position_embeddings + self.distance_embedding = nn.Embedding( + 2 * config.max_position_embeddings - 1, self.attention_head_size + ) + + self.is_decoder = config.is_decoder + + def transpose_for_scores(self, x): + new_x_shape = x.size()[:-1] + ( + self.num_attention_heads, + self.attention_head_size, + ) + x = x.view(new_x_shape) + return x.permute(0, 2, 1, 3) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.FloatTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, + output_attentions: Optional[bool] = False, + parser_att_mask=None, + ) -> Tuple[torch.Tensor]: + mixed_query_layer = self.query(hidden_states) + + # If this is instantiated as a cross-attention module, the keys + # and values come from an encoder; the attention mask needs to be + # such that the encoder's padding tokens are not attended to. + is_cross_attention = encoder_hidden_states is not None + + if is_cross_attention and past_key_value is not None: + # reuse k,v, cross_attentions + key_layer = past_key_value[0] + value_layer = past_key_value[1] + attention_mask = encoder_attention_mask + elif is_cross_attention: + key_layer = self.transpose_for_scores(self.key(encoder_hidden_states)) + value_layer = self.transpose_for_scores(self.value(encoder_hidden_states)) + attention_mask = encoder_attention_mask + elif past_key_value is not None: + key_layer = self.transpose_for_scores(self.key(hidden_states)) + value_layer = self.transpose_for_scores(self.value(hidden_states)) + key_layer = torch.cat([past_key_value[0], key_layer], dim=2) + value_layer = torch.cat([past_key_value[1], value_layer], dim=2) + else: + key_layer = self.transpose_for_scores(self.key(hidden_states)) + value_layer = self.transpose_for_scores(self.value(hidden_states)) + + query_layer = self.transpose_for_scores(mixed_query_layer) + + if self.is_decoder: + # if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states. + # Further calls to cross_attention layer can then reuse all cross-attention + # key/value_states (first "if" case) + # if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of + # all previous decoder key/value_states. Further calls to uni-directional self-attention + # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case) + # if encoder bi-directional self-attention `past_key_value` is always `None` + past_key_value = (key_layer, value_layer) + + # Take the dot product between "query" and "key" to get the raw attention scores. + attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) + + if ( + self.position_embedding_type == "relative_key" + or self.position_embedding_type == "relative_key_query" + ): + seq_length = hidden_states.size()[1] + position_ids_l = torch.arange( + seq_length, dtype=torch.long, device=hidden_states.device + ).view(-1, 1) + position_ids_r = torch.arange( + seq_length, dtype=torch.long, device=hidden_states.device + ).view(1, -1) + distance = position_ids_l - position_ids_r + positional_embedding = self.distance_embedding( + distance + self.max_position_embeddings - 1 + ) + positional_embedding = positional_embedding.to( + dtype=query_layer.dtype + ) # fp16 compatibility + + if self.position_embedding_type == "relative_key": + relative_position_scores = torch.einsum( + "bhld,lrd->bhlr", query_layer, positional_embedding + ) + attention_scores = attention_scores + relative_position_scores + elif self.position_embedding_type == "relative_key_query": + relative_position_scores_query = torch.einsum( + "bhld,lrd->bhlr", query_layer, positional_embedding + ) + relative_position_scores_key = torch.einsum( + "bhrd,lrd->bhlr", key_layer, positional_embedding + ) + attention_scores = ( + attention_scores + + relative_position_scores_query + + relative_position_scores_key + ) + + attention_scores = attention_scores / math.sqrt(self.attention_head_size) + if attention_mask is not None: + # Apply the attention mask is (precomputed for all layers in RobertaModel forward() function) + attention_scores = attention_scores + attention_mask + + if parser_att_mask is None: + # Normalize the attention scores to probabilities. + attention_probs = nn.functional.softmax(attention_scores, dim=-1) + else: + attention_probs = torch.sigmoid(attention_scores) * parser_att_mask + + # This is actually dropping out entire tokens to attend to, which might + # seem a bit unusual, but is taken from the original Transformer paper. + attention_probs = self.dropout(attention_probs) + + # Mask heads if we want to + if head_mask is not None: + attention_probs = attention_probs * head_mask + + context_layer = torch.matmul(attention_probs, value_layer) + + context_layer = context_layer.permute(0, 2, 1, 3).contiguous() + new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) + context_layer = context_layer.view(new_context_layer_shape) + + outputs = ( + (context_layer, attention_probs) if output_attentions else (context_layer,) + ) + + if self.is_decoder: + outputs = outputs + (past_key_value,) + return outputs + + +# Copied from transformers.models.bert.modeling_bert.BertSelfOutput +class RobertaSelfOutput(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward( + self, hidden_states: torch.Tensor, input_tensor: torch.Tensor + ) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + hidden_states = self.LayerNorm(hidden_states + input_tensor) + return hidden_states + + +# Copied from transformers.models.bert.modeling_bert.BertAttention with Bert->Roberta +class RobertaAttention(nn.Module): + def __init__(self, config, position_embedding_type=None): + super().__init__() + self.self = RobertaSelfAttention( + config, position_embedding_type=position_embedding_type + ) + self.output = RobertaSelfOutput(config) + self.pruned_heads = set() + + def prune_heads(self, heads): + if len(heads) == 0: + return + heads, index = find_pruneable_heads_and_indices( + heads, + self.self.num_attention_heads, + self.self.attention_head_size, + self.pruned_heads, + ) + + # Prune linear layers + self.self.query = prune_linear_layer(self.self.query, index) + self.self.key = prune_linear_layer(self.self.key, index) + self.self.value = prune_linear_layer(self.self.value, index) + self.output.dense = prune_linear_layer(self.output.dense, index, dim=1) + + # Update hyper params and store pruned heads + self.self.num_attention_heads = self.self.num_attention_heads - len(heads) + self.self.all_head_size = ( + self.self.attention_head_size * self.self.num_attention_heads + ) + self.pruned_heads = self.pruned_heads.union(heads) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.FloatTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, + output_attentions: Optional[bool] = False, + parser_att_mask=None, + ) -> Tuple[torch.Tensor]: + self_outputs = self.self( + hidden_states, + attention_mask, + head_mask, + encoder_hidden_states, + encoder_attention_mask, + past_key_value, + output_attentions, + parser_att_mask=parser_att_mask, + ) + attention_output = self.output(self_outputs[0], hidden_states) + outputs = (attention_output,) + self_outputs[ + 1: + ] # add attentions if we output them + return outputs + + +# Copied from transformers.models.bert.modeling_bert.BertIntermediate +class RobertaIntermediate(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.intermediate_size) + if isinstance(config.hidden_act, str): + self.intermediate_act_fn = ACT2FN[config.hidden_act] + else: + self.intermediate_act_fn = config.hidden_act + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.intermediate_act_fn(hidden_states) + return hidden_states + + +# Copied from transformers.models.bert.modeling_bert.BertOutput +class RobertaOutput(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.intermediate_size, config.hidden_size) + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward( + self, hidden_states: torch.Tensor, input_tensor: torch.Tensor + ) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + hidden_states = self.LayerNorm(hidden_states + input_tensor) + return hidden_states + + +# Copied from transformers.models.bert.modeling_bert.BertLayer with Bert->Roberta +class RobertaLayer(nn.Module): + def __init__(self, config): + super().__init__() + self.chunk_size_feed_forward = config.chunk_size_feed_forward + self.seq_len_dim = 1 + self.attention = RobertaAttention(config) + self.is_decoder = config.is_decoder + self.add_cross_attention = config.add_cross_attention + if self.add_cross_attention: + if not self.is_decoder: + raise ValueError( + f"{self} should be used as a decoder model if cross attention is added" + ) + self.crossattention = RobertaAttention( + config, position_embedding_type="absolute" + ) + self.intermediate = RobertaIntermediate(config) + self.output = RobertaOutput(config) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.FloatTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, + output_attentions: Optional[bool] = False, + parser_att_mask=None, + ) -> Tuple[torch.Tensor]: + # decoder uni-directional self-attention cached key/values tuple is at positions 1,2 + self_attn_past_key_value = ( + past_key_value[:2] if past_key_value is not None else None + ) + self_attention_outputs = self.attention( + hidden_states, + attention_mask, + head_mask, + output_attentions=output_attentions, + past_key_value=self_attn_past_key_value, + parser_att_mask=parser_att_mask, + ) + attention_output = self_attention_outputs[0] + + # if decoder, the last output is tuple of self-attn cache + if self.is_decoder: + outputs = self_attention_outputs[1:-1] + present_key_value = self_attention_outputs[-1] + else: + outputs = self_attention_outputs[ + 1: + ] # add self attentions if we output attention weights + + cross_attn_present_key_value = None + if self.is_decoder and encoder_hidden_states is not None: + if not hasattr(self, "crossattention"): + raise ValueError( + f"If `encoder_hidden_states` are passed, {self} has to be instantiated with cross-attention layers by setting `config.add_cross_attention=True`" + ) + + # cross_attn cached key/values tuple is at positions 3,4 of past_key_value tuple + cross_attn_past_key_value = ( + past_key_value[-2:] if past_key_value is not None else None + ) + cross_attention_outputs = self.crossattention( + attention_output, + attention_mask, + head_mask, + encoder_hidden_states, + encoder_attention_mask, + cross_attn_past_key_value, + output_attentions, + ) + attention_output = cross_attention_outputs[0] + outputs = ( + outputs + cross_attention_outputs[1:-1] + ) # add cross attentions if we output attention weights + + # add cross-attn cache to positions 3,4 of present_key_value tuple + cross_attn_present_key_value = cross_attention_outputs[-1] + present_key_value = present_key_value + cross_attn_present_key_value + + layer_output = apply_chunking_to_forward( + self.feed_forward_chunk, + self.chunk_size_feed_forward, + self.seq_len_dim, + attention_output, + ) + outputs = (layer_output,) + outputs + + # if decoder, return the attn key/values as the last output + if self.is_decoder: + outputs = outputs + (present_key_value,) + + return outputs + + def feed_forward_chunk(self, attention_output): + intermediate_output = self.intermediate(attention_output) + layer_output = self.output(intermediate_output, attention_output) + return layer_output + + +# Copied from transformers.models.bert.modeling_bert.BertEncoder with Bert->Roberta +class RobertaEncoder(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + self.layer = nn.ModuleList( + [RobertaLayer(config) for _ in range(config.num_hidden_layers)] + ) + self.gradient_checkpointing = False + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.FloatTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = False, + output_hidden_states: Optional[bool] = False, + return_dict: Optional[bool] = True, + parser_att_mask=None, + ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPastAndCrossAttentions]: + all_hidden_states = () if output_hidden_states else None + all_self_attentions = () if output_attentions else None + all_cross_attentions = ( + () if output_attentions and self.config.add_cross_attention else None + ) + + next_decoder_cache = () if use_cache else None + for i, layer_module in enumerate(self.layer): + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + layer_head_mask = head_mask[i] if head_mask is not None else None + past_key_value = past_key_values[i] if past_key_values is not None else None + + if self.gradient_checkpointing and self.training: + + if use_cache: + logger.warning( + "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." + ) + use_cache = False + + def create_custom_forward(module): + def custom_forward(*inputs): + return module(*inputs, past_key_value, output_attentions) + + return custom_forward + + layer_outputs = torch.utils.checkpoint.checkpoint( + create_custom_forward(layer_module), + hidden_states, + attention_mask, + layer_head_mask, + encoder_hidden_states, + encoder_attention_mask, + ) + else: + if parser_att_mask is not None: + layer_outputs = layer_module( + hidden_states, + attention_mask, + layer_head_mask, + encoder_hidden_states, + encoder_attention_mask, + past_key_value, + output_attentions, + parser_att_mask=parser_att_mask[i]) + else: + layer_outputs = layer_module( + hidden_states, + attention_mask, + layer_head_mask, + encoder_hidden_states, + encoder_attention_mask, + past_key_value, + output_attentions, + parser_att_mask=None) + + + hidden_states = layer_outputs[0] + if use_cache: + next_decoder_cache += (layer_outputs[-1],) + if output_attentions: + all_self_attentions = all_self_attentions + (layer_outputs[1],) + if self.config.add_cross_attention: + all_cross_attentions = all_cross_attentions + (layer_outputs[2],) + + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + if not return_dict: + return tuple( + v + for v in [ + hidden_states, + next_decoder_cache, + all_hidden_states, + all_self_attentions, + all_cross_attentions, + ] + if v is not None + ) + return BaseModelOutputWithPastAndCrossAttentions( + last_hidden_state=hidden_states, + past_key_values=next_decoder_cache, + hidden_states=all_hidden_states, + attentions=all_self_attentions, + cross_attentions=all_cross_attentions, + ) + + +# Copied from transformers.models.bert.modeling_bert.BertPooler +class RobertaPooler(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.activation = nn.Tanh() + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + # We "pool" the model by simply taking the hidden state corresponding + # to the first token. + first_token_tensor = hidden_states[:, 0] + pooled_output = self.dense(first_token_tensor) + pooled_output = self.activation(pooled_output) + return pooled_output + + +class RobertaPreTrainedModel(PreTrainedModel): + """ + An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained + models. + """ + + config_class = RobertaConfig + base_model_prefix = "roberta" + supports_gradient_checkpointing = True + + # Copied from transformers.models.bert.modeling_bert.BertPreTrainedModel._init_weights + def _init_weights(self, module): + """Initialize the weights""" + if isinstance(module, nn.Linear): + # Slightly different from the TF version which uses truncated_normal for initialization + # cf https://github.com/pytorch/pytorch/pull/5617 + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.Embedding): + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.padding_idx is not None: + module.weight.data[module.padding_idx].zero_() + elif isinstance(module, nn.LayerNorm): + if module.bias is not None: + module.bias.data.zero_() + module.weight.data.fill_(1.0) + + def _set_gradient_checkpointing(self, module, value=False): + if isinstance(module, RobertaEncoder): + module.gradient_checkpointing = value + + def update_keys_to_ignore(self, config, del_keys_to_ignore): + """Remove some keys from ignore list""" + if not config.tie_word_embeddings: + # must make a new list, or the class variable gets modified! + self._keys_to_ignore_on_save = [ + k for k in self._keys_to_ignore_on_save if k not in del_keys_to_ignore + ] + self._keys_to_ignore_on_load_missing = [ + k + for k in self._keys_to_ignore_on_load_missing + if k not in del_keys_to_ignore + ] + + +ROBERTA_START_DOCSTRING = r""" + + This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the + library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads + etc.) + + This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. + Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage + and behavior. + + Parameters: + config ([`RobertaConfig`]): Model configuration class with all the parameters of the + model. Initializing with a config file does not load the weights associated with the model, only the + configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. +""" + + +ROBERTA_INPUTS_DOCSTRING = r""" + Args: + input_ids (`torch.LongTensor` of shape `({0})`): + Indices of input sequence tokens in the vocabulary. + + Indices can be obtained using [`RobertaTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + [What are input IDs?](../glossary#input-ids) + attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*): + Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + + [What are attention masks?](../glossary#attention-mask) + token_type_ids (`torch.LongTensor` of shape `({0})`, *optional*): + Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, + 1]`: + + - 0 corresponds to a *sentence A* token, + - 1 corresponds to a *sentence B* token. + + [What are token type IDs?](../glossary#token-type-ids) + position_ids (`torch.LongTensor` of shape `({0})`, *optional*): + Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, + config.max_position_embeddings - 1]`. + + [What are position IDs?](../glossary#position-ids) + head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): + Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: + + - 1 indicates the head is **not masked**, + - 0 indicates the head is **masked**. + + inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_size)`, *optional*): + Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This + is useful if you want more control over how to convert `input_ids` indices into associated vectors than the + model's internal embedding lookup matrix. + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned + tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for + more detail. + return_dict (`bool`, *optional*): + Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. +""" + + +class RobertaModel(RobertaPreTrainedModel): + """ + + The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of + cross-attention is added between the self-attention layers, following the architecture described in *Attention is + all you need*_ by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz + Kaiser and Illia Polosukhin. + + To behave as an decoder the model needs to be initialized with the `is_decoder` argument of the configuration set + to `True`. To be used in a Seq2Seq model, the model needs to initialized with both `is_decoder` argument and + `add_cross_attention` set to `True`; an `encoder_hidden_states` is then expected as an input to the forward pass. + + .. _*Attention is all you need*: https://arxiv.org/abs/1706.03762 + + """ + + _keys_to_ignore_on_load_missing = [r"position_ids"] + + # Copied from transformers.models.bert.modeling_bert.BertModel.__init__ with Bert->Roberta + def __init__(self, config, add_pooling_layer=True): + super().__init__(config) + self.config = config + + self.embeddings = RobertaEmbeddings(config) + self.encoder = RobertaEncoder(config) + + self.pooler = RobertaPooler(config) if add_pooling_layer else None + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.embeddings.word_embeddings + + def set_input_embeddings(self, value): + self.embeddings.word_embeddings = value + + def _prune_heads(self, heads_to_prune): + """ + Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base + class PreTrainedModel + """ + for layer, heads in heads_to_prune.items(): + self.encoder.layer[layer].attention.prune_heads(heads) + + # Copied from transformers.models.bert.modeling_bert.BertModel.forward + def forward( + self, + input_ids: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + token_type_ids: Optional[torch.Tensor] = None, + position_ids: Optional[torch.Tensor] = None, + head_mask: Optional[torch.Tensor] = None, + inputs_embeds: Optional[torch.Tensor] = None, + encoder_hidden_states: Optional[torch.Tensor] = None, + encoder_attention_mask: Optional[torch.Tensor] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + parser_att_mask=None, + ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPoolingAndCrossAttentions]: + r""" + encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): + Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if + the model is configured as a decoder. + encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*): + Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in + the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): + Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. + + If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that + don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all + `decoder_input_ids` of shape `(batch_size, sequence_length)`. + use_cache (`bool`, *optional*): + If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see + `past_key_values`). + """ + output_attentions = ( + output_attentions + if output_attentions is not None + else self.config.output_attentions + ) + output_hidden_states = ( + output_hidden_states + if output_hidden_states is not None + else self.config.output_hidden_states + ) + return_dict = ( + return_dict if return_dict is not None else self.config.use_return_dict + ) + + if self.config.is_decoder: + use_cache = use_cache if use_cache is not None else self.config.use_cache + else: + use_cache = False + + if input_ids is not None and inputs_embeds is not None: + raise ValueError( + "You cannot specify both input_ids and inputs_embeds at the same time" + ) + elif input_ids is not None: + input_shape = input_ids.size() + elif inputs_embeds is not None: + input_shape = inputs_embeds.size()[:-1] + else: + raise ValueError("You have to specify either input_ids or inputs_embeds") + + batch_size, seq_length = input_shape + device = input_ids.device if input_ids is not None else inputs_embeds.device + + # past_key_values_length + past_key_values_length = ( + past_key_values[0][0].shape[2] if past_key_values is not None else 0 + ) + + if attention_mask is None: + attention_mask = torch.ones( + ((batch_size, seq_length + past_key_values_length)), device=device + ) + + if token_type_ids is None: + if hasattr(self.embeddings, "token_type_ids"): + buffered_token_type_ids = self.embeddings.token_type_ids[:, :seq_length] + buffered_token_type_ids_expanded = buffered_token_type_ids.expand( + batch_size, seq_length + ) + token_type_ids = buffered_token_type_ids_expanded + else: + token_type_ids = torch.zeros( + input_shape, dtype=torch.long, device=device + ) + + # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length] + # ourselves in which case we just need to make it broadcastable to all heads. + extended_attention_mask: torch.Tensor = self.get_extended_attention_mask( + attention_mask, input_shape, device + ) + + # If a 2D or 3D attention mask is provided for the cross-attention + # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length] + if self.config.is_decoder and encoder_hidden_states is not None: + ( + encoder_batch_size, + encoder_sequence_length, + _, + ) = encoder_hidden_states.size() + encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length) + if encoder_attention_mask is None: + encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device) + encoder_extended_attention_mask = self.invert_attention_mask( + encoder_attention_mask + ) + else: + encoder_extended_attention_mask = None + + # Prepare head mask if needed + # 1.0 in head_mask indicate we keep the head + # attention_probs has shape bsz x n_heads x N x N + # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] + # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] + head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) + + embedding_output = self.embeddings( + input_ids=input_ids, + position_ids=position_ids, + token_type_ids=token_type_ids, + inputs_embeds=inputs_embeds, + past_key_values_length=past_key_values_length, + ) + encoder_outputs = self.encoder( + embedding_output, + attention_mask=extended_attention_mask, + head_mask=head_mask, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_extended_attention_mask, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + parser_att_mask=parser_att_mask, + ) + sequence_output = encoder_outputs[0] + pooled_output = ( + self.pooler(sequence_output) if self.pooler is not None else None + ) + + if not return_dict: + return (sequence_output, pooled_output) + encoder_outputs[1:] + + return BaseModelOutputWithPoolingAndCrossAttentions( + last_hidden_state=sequence_output, + pooler_output=pooled_output, + past_key_values=encoder_outputs.past_key_values, + hidden_states=encoder_outputs.hidden_states, + attentions=encoder_outputs.attentions, + cross_attentions=encoder_outputs.cross_attentions, + ) + + +class StructRoberta(RobertaPreTrainedModel): + _keys_to_ignore_on_save = [r"lm_head.decoder.weight", r"lm_head.decoder.bias"] + _keys_to_ignore_on_load_missing = [ + r"position_ids", + r"lm_head.decoder.weight", + r"lm_head.decoder.bias", + ] + _keys_to_ignore_on_load_unexpected = [r"pooler"] + + def __init__(self, config): + super().__init__(config) + + if config.is_decoder: + logger.warning( + "If you want to use `RobertaForMaskedLM` make sure `config.is_decoder=False` for " + "bi-directional self-attention." + ) + + + if config.n_cntxt_layers > 0: + config_cntxt = copy.deepcopy(config) + config_cntxt.num_hidden_layers = config.n_cntxt_layers + + self.cntxt_layers = RobertaModel(config_cntxt, add_pooling_layer=False) + + if config.n_cntxt_layers_2 > 0: + self.parser_layers_1 = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(int(config.n_parser_layers/2)) + ] + ) + + self.distance_ff_1 = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff_1 = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight_1 = nn.Parameter( + torch.zeros((config.n_cntxt_layers_2, config.num_attention_heads, n_rel)) + ) + self._rel_weight_1.data.normal_(0, 0.1) + + self._scaler_1 = nn.Parameter(torch.zeros(2)) + + config_cntxt_2 = copy.deepcopy(config) + config_cntxt_2.num_hidden_layers = config.n_cntxt_layers_2 + + self.cntxt_layers_2 = RobertaModel(config_cntxt_2, add_pooling_layer=False) + + + self.parser_layers_2 = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(int(config.n_parser_layers/2)) + ] + ) + + self.distance_ff_2 = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff_2 = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight_2 = nn.Parameter( + torch.zeros((config.num_hidden_layers, config.num_attention_heads, n_rel)) + ) + self._rel_weight_2.data.normal_(0, 0.1) + + self._scaler_2 = nn.Parameter(torch.zeros(2)) + + else: + self.parser_layers = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(config.n_parser_layers) + ] + ) + + self.distance_ff = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight = nn.Parameter( + torch.zeros((config.num_hidden_layers, config.num_attention_heads, n_rel)) + ) + self._rel_weight.data.normal_(0, 0.1) + + self._scaler = nn.Parameter(torch.zeros(2)) + + self.roberta = RobertaModel(config, add_pooling_layer=False) + + if config.n_cntxt_layers > 0: + self.cntxt_layers.embeddings = self.roberta.embeddings + if config.n_cntxt_layers_2 > 0: + self.cntxt_layers_2.embeddings = self.roberta.embeddings + + self.lm_head = RobertaLMHead(config) + + self.pad = config.pad_token_id + + # The LM head weights require special treatment only when they are tied with the word embeddings + self.update_keys_to_ignore(config, ["lm_head.decoder.weight"]) + + # Initialize weights and apply final processing + self.post_init() + + def get_output_embeddings(self): + return self.lm_head.decoder + + def set_output_embeddings(self, new_embeddings): + self.lm_head.decoder = new_embeddings + + @property + def scaler(self): + return self._scaler.exp() + + @property + def scaler_1(self): + return self._scaler_1.exp() + + @property + def scaler_2(self): + return self._scaler_2.exp() + + @property + def rel_weight(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight, dim=-1) + + @property + def rel_weight_1(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight_1) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight_1, dim=-1) + + + @property + def rel_weight_2(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight_2) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight_2, dim=-1) + + + def compute_block(self, distance, height, n_cntxt_layers=0): + """Compute constituents from distance and height.""" + + if n_cntxt_layers>0: + if n_cntxt_layers == 1: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler_1[0] + elif n_cntxt_layers == 2: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler_2[0] + else: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0] + + gamma = torch.sigmoid(-beta_logits) + ones = torch.ones_like(gamma) + + block_mask_left = cummin( + gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1 + ) + block_mask_left = block_mask_left - F.pad( + block_mask_left[:, :, :-1], (1, 0), value=0 + ) + block_mask_left.tril_(0) + + block_mask_right = cummin( + gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1 + ) + block_mask_right = block_mask_right - F.pad( + block_mask_right[:, :, 1:], (0, 1), value=0 + ) + block_mask_right.triu_(0) + + block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :] + block = cumsum(block_mask_left).tril(0) + cumsum( + block_mask_right, reverse=True + ).triu(1) + + return block_p, block + + def compute_head(self, height, n_cntxt_layers=0): + """Estimate head for each constituent.""" + + _, length = height.size() + if n_cntxt_layers>0: + if n_cntxt_layers == 1: + head_logits = height * self.scaler_1[1] + elif n_cntxt_layers == 2: + head_logits = height * self.scaler_2[1] + else: + head_logits = height * self.scaler[1] + index = torch.arange(length, device=height.device) + + mask = (index[:, None, None] <= index[None, None, :]) * ( + index[None, None, :] <= index[None, :, None] + ) + head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1) + head_logits.masked_fill_(~mask[None, :, :, :], -1e9) + + head_p = torch.softmax(head_logits, dim=-1) + + return head_p + + def parse(self, x, embs=None, n_cntxt_layers=0): + """Parse input sentence. + + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + distance: syntactic distance + height: syntactic height + """ + + mask = x != self.pad + mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0) + + if embs is None: + h = self.roberta.embeddings(x) + else: + h = embs + + if n_cntxt_layers > 0: + if n_cntxt_layers == 1: + parser_layers = self.parser_layers_1 + height_ff = self.height_ff_1 + distance_ff = self.distance_ff_1 + elif n_cntxt_layers == 2: + parser_layers = self.parser_layers_2 + height_ff = self.height_ff_2 + distance_ff = self.distance_ff_2 + for i in range(int(self.config.n_parser_layers/2)): + h = h.masked_fill(~mask[:, :, None], 0) + h = parser_layers[i](h) + + height = height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, dim=-1 + )[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max + ) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where( + margin_left > margin_right, margin_right, margin_left + ).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + else: + for i in range(self.config.n_parser_layers): + h = h.masked_fill(~mask[:, :, None], 0) + h = self.parser_layers[i](h) + + height = self.height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = self.distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, dim=-1 + )[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max + ) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where( + margin_left > margin_right, margin_right, margin_left + ).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + + return distance, height + + def generate_mask(self, x, distance, height, n_cntxt_layers=0): + """Compute head and cibling distribution for each token.""" + + bsz, length = x.size() + + eye = torch.eye(length, device=x.device, dtype=torch.bool) + eye = eye[None, :, :].expand((bsz, -1, -1)) + + block_p, block = self.compute_block(distance, height, n_cntxt_layers=n_cntxt_layers) + head_p = self.compute_head(height, n_cntxt_layers=n_cntxt_layers) + head = torch.einsum("blij,bijh->blh", block_p, head_p) + head = head.masked_fill(eye, 0) + child = head.transpose(1, 2) + cibling = torch.bmm(head, child).masked_fill(eye, 0) + + rel_list = [] + if "head" in self.config.relations: + rel_list.append(head) + if "child" in self.config.relations: + rel_list.append(child) + if "cibling" in self.config.relations: + rel_list.append(cibling) + + rel = torch.stack(rel_list, dim=1) + + if n_cntxt_layers > 0: + if n_cntxt_layers == 1: + rel_weight = self.rel_weight_1 + elif n_cntxt_layers == 2: + rel_weight = self.rel_weight_2 + else: + rel_weight = self.rel_weight + + dep = torch.einsum("lhr,brij->lbhij", rel_weight, rel) + + if n_cntxt_layers == 1: + num_layers = self.cntxt_layers_2.config.num_hidden_layers + else: + num_layers = self.roberta.config.num_hidden_layers + + att_mask = dep.reshape( + num_layers, + bsz, + self.config.num_attention_heads, + length, + length, + ) + + return att_mask, cibling, head, block + + def forward( + self, + input_ids: Optional[torch.LongTensor] = None, + attention_mask: Optional[torch.FloatTensor] = None, + token_type_ids: Optional[torch.LongTensor] = None, + position_ids: Optional[torch.LongTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, MaskedLMOutput]: + r""" + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ..., + config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the + loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]` + kwargs (`Dict[str, any]`, optional, defaults to *{}*): + Used to hide legacy arguments that have been deprecated. + """ + return_dict = ( + return_dict if return_dict is not None else self.config.use_return_dict + ) + + + if self.config.n_cntxt_layers > 0: + cntxt_outputs = self.cntxt_layers( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict) + + + if self.config.n_cntxt_layers_2 > 0: + distance_1, height_1 = self.parse(input_ids, cntxt_outputs[0], n_cntxt_layers=1) + att_mask_1, _, _, _ = self.generate_mask(input_ids, distance_1, height_1, n_cntxt_layers=1) + + cntxt_outputs_2 = self.cntxt_layers_2( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + parser_att_mask=att_mask_1) + + sequence_output = cntxt_outputs_2[0] + + distance_2, height_2 = self.parse(input_ids, sequence_output[0], n_cntxt_layers=2) + att_mask, _, _, _ = self.generate_mask(input_ids, distance_2, height_2, n_cntxt_layers=2) + + elif self.config.n_cntxt_layers > 0: + distance, height = self.parse(input_ids, cntxt_outputs[0]) + att_mask, _, _, _ = self.generate_mask(input_ids, distance, height) + else: + distance, height = self.parse(input_ids) + att_mask, _, _, _ = self.generate_mask(input_ids, distance, height) + + outputs = self.roberta( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + parser_att_mask=att_mask, + ) + sequence_output = outputs[0] + prediction_scores = self.lm_head(sequence_output) + + masked_lm_loss = None + if labels is not None: + loss_fct = CrossEntropyLoss() + masked_lm_loss = loss_fct( + prediction_scores.view(-1, self.config.vocab_size), labels.view(-1) + ) + + if not return_dict: + output = (prediction_scores,) + outputs[2:] + return ( + ((masked_lm_loss,) + output) if masked_lm_loss is not None else output + ) + + return MaskedLMOutput( + loss=masked_lm_loss, + logits=prediction_scores, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +class RobertaLMHead(nn.Module): + """Roberta Head for masked language modeling.""" + + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + + self.decoder = nn.Linear(config.hidden_size, config.vocab_size) + self.bias = nn.Parameter(torch.zeros(config.vocab_size)) + self.decoder.bias = self.bias + + def forward(self, features, **kwargs): + x = self.dense(features) + x = gelu(x) + x = self.layer_norm(x) + + # project back to size of vocabulary with bias + x = self.decoder(x) + + return x + + def _tie_weights(self): + # To tie those two weights if they get disconnected (on TPU or when the bias is resized) + self.bias = self.decoder.bias + + +class StructRobertaForSequenceClassification(RobertaPreTrainedModel): + _keys_to_ignore_on_load_missing = [r"position_ids"] + + def __init__(self, config): + super().__init__(config) + self.num_labels = config.num_labels + self.config = config + + if config.n_cntxt_layers > 0: + config_cntxt = copy.deepcopy(config) + config_cntxt.num_hidden_layers = config.n_cntxt_layers + + self.cntxt_layers = RobertaModel(config_cntxt, add_pooling_layer=False) + + if config.n_cntxt_layers_2 > 0: + self.parser_layers_1 = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(int(config.n_parser_layers/2)) + ] + ) + + self.distance_ff_1 = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff_1 = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight_1 = nn.Parameter( + torch.zeros((config.n_cntxt_layers_2, config.num_attention_heads, n_rel)) + ) + self._rel_weight_1.data.normal_(0, 0.1) + + self._scaler_1 = nn.Parameter(torch.zeros(2)) + + config_cntxt_2 = copy.deepcopy(config) + config_cntxt_2.num_hidden_layers = config.n_cntxt_layers_2 + + self.cntxt_layers_2 = RobertaModel(config_cntxt_2, add_pooling_layer=False) + + + self.parser_layers_2 = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(int(config.n_parser_layers/2)) + ] + ) + + self.distance_ff_2 = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff_2 = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight_2 = nn.Parameter( + torch.zeros((config.num_hidden_layers, config.num_attention_heads, n_rel)) + ) + self._rel_weight_2.data.normal_(0, 0.1) + + self._scaler_2 = nn.Parameter(torch.zeros(2)) + + else: + self.parser_layers = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(config.n_parser_layers) + ] + ) + + self.distance_ff = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight = nn.Parameter( + torch.zeros((config.num_hidden_layers, config.num_attention_heads, n_rel)) + ) + self._rel_weight.data.normal_(0, 0.1) + + self._scaler = nn.Parameter(torch.zeros(2)) + + self.roberta = RobertaModel(config, add_pooling_layer=False) + + if config.n_cntxt_layers > 0: + self.cntxt_layers.embeddings = self.roberta.embeddings + if config.n_cntxt_layers_2 > 0: + self.cntxt_layers_2.embeddings = self.roberta.embeddings + + + self.pad = config.pad_token_id + self.classifier = RobertaClassificationHead(config) + + # Initialize weights and apply final processing + self.post_init() + + + @property + def scaler(self): + return self._scaler.exp() + + @property + def scaler_1(self): + return self._scaler_1.exp() + + @property + def scaler_2(self): + return self._scaler_2.exp() + + @property + def rel_weight(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight, dim=-1) + + @property + def rel_weight_1(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight_1) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight_1, dim=-1) + + + @property + def rel_weight_2(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight_2) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight_2, dim=-1) + + + def compute_block(self, distance, height, n_cntxt_layers=0): + """Compute constituents from distance and height.""" + + if n_cntxt_layers>0: + if n_cntxt_layers == 1: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler_1[0] + elif n_cntxt_layers == 2: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler_2[0] + else: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0] + + gamma = torch.sigmoid(-beta_logits) + ones = torch.ones_like(gamma) + + block_mask_left = cummin( + gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1 + ) + block_mask_left = block_mask_left - F.pad( + block_mask_left[:, :, :-1], (1, 0), value=0 + ) + block_mask_left.tril_(0) + + block_mask_right = cummin( + gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1 + ) + block_mask_right = block_mask_right - F.pad( + block_mask_right[:, :, 1:], (0, 1), value=0 + ) + block_mask_right.triu_(0) + + block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :] + block = cumsum(block_mask_left).tril(0) + cumsum( + block_mask_right, reverse=True + ).triu(1) + + return block_p, block + + def compute_head(self, height, n_cntxt_layers=0): + """Estimate head for each constituent.""" + + _, length = height.size() + if n_cntxt_layers>0: + if n_cntxt_layers == 1: + head_logits = height * self.scaler_1[1] + elif n_cntxt_layers == 2: + head_logits = height * self.scaler_2[1] + else: + head_logits = height * self.scaler[1] + index = torch.arange(length, device=height.device) + + mask = (index[:, None, None] <= index[None, None, :]) * ( + index[None, None, :] <= index[None, :, None] + ) + head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1) + head_logits.masked_fill_(~mask[None, :, :, :], -1e9) + + head_p = torch.softmax(head_logits, dim=-1) + + return head_p + + def parse(self, x, embs=None, n_cntxt_layers=0): + """Parse input sentence. + + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + distance: syntactic distance + height: syntactic height + """ + + mask = x != self.pad + mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0) + + if embs is None: + h = self.roberta.embeddings(x) + else: + h = embs + + if n_cntxt_layers > 0: + if n_cntxt_layers == 1: + parser_layers = self.parser_layers_1 + height_ff = self.height_ff_1 + distance_ff = self.distance_ff_1 + elif n_cntxt_layers == 2: + parser_layers = self.parser_layers_2 + height_ff = self.height_ff_2 + distance_ff = self.distance_ff_2 + for i in range(int(self.config.n_parser_layers/2)): + h = h.masked_fill(~mask[:, :, None], 0) + h = parser_layers[i](h) + + height = height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, dim=-1 + )[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max + ) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where( + margin_left > margin_right, margin_right, margin_left + ).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + else: + for i in range(self.config.n_parser_layers): + h = h.masked_fill(~mask[:, :, None], 0) + h = self.parser_layers[i](h) + + height = self.height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = self.distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, dim=-1 + )[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max + ) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where( + margin_left > margin_right, margin_right, margin_left + ).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + + return distance, height + + def generate_mask(self, x, distance, height, n_cntxt_layers=0): + """Compute head and cibling distribution for each token.""" + + bsz, length = x.size() + + eye = torch.eye(length, device=x.device, dtype=torch.bool) + eye = eye[None, :, :].expand((bsz, -1, -1)) + + block_p, block = self.compute_block(distance, height, n_cntxt_layers=n_cntxt_layers) + head_p = self.compute_head(height, n_cntxt_layers=n_cntxt_layers) + head = torch.einsum("blij,bijh->blh", block_p, head_p) + head = head.masked_fill(eye, 0) + child = head.transpose(1, 2) + cibling = torch.bmm(head, child).masked_fill(eye, 0) + + rel_list = [] + if "head" in self.config.relations: + rel_list.append(head) + if "child" in self.config.relations: + rel_list.append(child) + if "cibling" in self.config.relations: + rel_list.append(cibling) + + rel = torch.stack(rel_list, dim=1) + + if n_cntxt_layers > 0: + if n_cntxt_layers == 1: + rel_weight = self.rel_weight_1 + elif n_cntxt_layers == 2: + rel_weight = self.rel_weight_2 + else: + rel_weight = self.rel_weight + + dep = torch.einsum("lhr,brij->lbhij", rel_weight, rel) + + if n_cntxt_layers == 1: + num_layers = self.cntxt_layers_2.config.num_hidden_layers + else: + num_layers = self.roberta.config.num_hidden_layers + + att_mask = dep.reshape( + num_layers, + bsz, + self.config.num_attention_heads, + length, + length, + ) + + return att_mask, cibling, head, block + + def forward( + self, + input_ids: Optional[torch.LongTensor] = None, + attention_mask: Optional[torch.FloatTensor] = None, + token_type_ids: Optional[torch.LongTensor] = None, + position_ids: Optional[torch.LongTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, SequenceClassifierOutput]: + r""" + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., + config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If + `config.num_labels > 1` a classification loss is computed (Cross-Entropy). + """ + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + if self.config.n_cntxt_layers > 0: + cntxt_outputs = self.cntxt_layers( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict) + + + if self.config.n_cntxt_layers_2 > 0: + distance_1, height_1 = self.parse(input_ids, cntxt_outputs[0], n_cntxt_layers=1) + att_mask_1, _, _, _ = self.generate_mask(input_ids, distance_1, height_1, n_cntxt_layers=1) + + cntxt_outputs_2 = self.cntxt_layers_2( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + parser_att_mask=att_mask_1) + + sequence_output = cntxt_outputs_2[0] + + distance_2, height_2 = self.parse(input_ids, sequence_output[0], n_cntxt_layers=2) + att_mask, _, _, _ = self.generate_mask(input_ids, distance_2, height_2, n_cntxt_layers=2) + + elif self.config.n_cntxt_layers > 0: + distance, height = self.parse(input_ids, cntxt_outputs[0]) + att_mask, _, _, _ = self.generate_mask(input_ids, distance, height) + else: + distance, height = self.parse(input_ids) + att_mask, _, _, _ = self.generate_mask(input_ids, distance, height) + + outputs = self.roberta( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + parser_att_mask=att_mask, + ) + sequence_output = outputs[0] + logits = self.classifier(sequence_output) + + loss = None + if labels is not None: + if self.config.problem_type is None: + if self.num_labels == 1: + self.config.problem_type = "regression" + elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): + self.config.problem_type = "single_label_classification" + else: + self.config.problem_type = "multi_label_classification" + + if self.config.problem_type == "regression": + loss_fct = MSELoss() + if self.num_labels == 1: + loss = loss_fct(logits.squeeze(), labels.squeeze()) + else: + loss = loss_fct(logits, labels) + elif self.config.problem_type == "single_label_classification": + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) + elif self.config.problem_type == "multi_label_classification": + loss_fct = BCEWithLogitsLoss() + loss = loss_fct(logits, labels) + + if not return_dict: + output = (logits,) + outputs[2:] + return ((loss,) + output) if loss is not None else output + + return SequenceClassifierOutput( + loss=loss, + logits=logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +class RobertaClassificationHead(nn.Module): + """Head for sentence-level classification tasks.""" + + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + classifier_dropout = ( + config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob + ) + self.dropout = nn.Dropout(classifier_dropout) + self.out_proj = nn.Linear(config.hidden_size, config.num_labels) + + def forward(self, features, **kwargs): + x = features[:, 0, :] # take token (equiv. to [CLS]) + x = self.dropout(x) + x = self.dense(x) + x = torch.tanh(x) + x = self.dropout(x) + x = self.out_proj(x) + return x + + +def create_position_ids_from_input_ids( + input_ids, padding_idx, past_key_values_length=0 +): + """ + Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols + are ignored. This is modified from fairseq's `utils.make_positions`. + + Args: + x: torch.Tensor x: + + Returns: torch.Tensor + """ + # The series of casts and type-conversions here are carefully balanced to both work with ONNX export and XLA. + mask = input_ids.ne(padding_idx).int() + incremental_indices = ( + torch.cumsum(mask, dim=1).type_as(mask) + past_key_values_length + ) * mask + return incremental_indices.long() + padding_idx + + +def cumprod(x, reverse=False, exclusive=False): + """cumulative product.""" + if reverse: + x = x.flip([-1]) + + if exclusive: + x = F.pad(x[:, :, :-1], (1, 0), value=1) + + cx = x.cumprod(-1) + + if reverse: + cx = cx.flip([-1]) + return cx + + +def cumsum(x, reverse=False, exclusive=False): + """cumulative sum.""" + bsz, _, length = x.size() + device = x.device + if reverse: + if exclusive: + w = torch.ones([bsz, length, length], device=device).tril(-1) + else: + w = torch.ones([bsz, length, length], device=device).tril(0) + cx = torch.bmm(x, w) + else: + if exclusive: + w = torch.ones([bsz, length, length], device=device).triu(1) + else: + w = torch.ones([bsz, length, length], device=device).triu(0) + cx = torch.bmm(x, w) + return cx + + +def cummin(x, reverse=False, exclusive=False, max_value=1e9): + """cumulative min.""" + if reverse: + if exclusive: + x = F.pad(x[:, :, 1:], (0, 1), value=max_value) + x = x.flip([-1]).cummin(-1)[0].flip([-1]) + else: + if exclusive: + x = F.pad(x[:, :, :-1], (1, 0), value=max_value) + x = x.cummin(-1)[0] + return x diff --git a/finetune/cola/predict_results.txt b/finetune/cola/predict_results.txt new file mode 100644 index 0000000000000000000000000000000000000000..6f4092ac88776cb70377cf0188a52632e124191f --- /dev/null +++ b/finetune/cola/predict_results.txt @@ -0,0 +1,1020 @@ +index prediction +0 1 +1 1 +2 0 +3 1 +4 1 +5 1 +6 0 +7 1 +8 1 +9 1 +10 1 +11 1 +12 1 +13 1 +14 1 +15 1 +16 1 +17 1 +18 1 +19 1 +20 1 +21 1 +22 1 +23 1 +24 1 +25 1 +26 1 +27 1 +28 0 +29 1 +30 1 +31 0 +32 1 +33 1 +34 0 +35 1 +36 1 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a/finetune/control_raising_control/eval_results.json b/finetune/control_raising_control/eval_results.json new file mode 100644 index 0000000000000000000000000000000000000000..078bdc39a9e824c1a27ce9b64dd572fe8e189cad --- /dev/null +++ b/finetune/control_raising_control/eval_results.json @@ -0,0 +1,11 @@ +{ + "epoch": 10.0, + "eval_accuracy": 0.8782693147659302, + "eval_f1": 0.8888282263017813, + "eval_loss": 0.8318932056427002, + "eval_mcc": 0.7745042173172888, + "eval_runtime": 26.2408, + "eval_samples": 13382, + "eval_samples_per_second": 509.969, + "eval_steps_per_second": 63.756 +} \ No newline at end of file diff --git a/finetune/control_raising_control/merges.txt b/finetune/control_raising_control/merges.txt new file mode 100644 index 0000000000000000000000000000000000000000..94b4f252cb566a4dac62a08c78d85169ff4e91be --- /dev/null +++ b/finetune/control_raising_control/merges.txt @@ -0,0 +1,31794 @@ +#version: 0.2 +Ġ t +h e +Ġ a +i n +Ġt he +r e +Ġ w +o u +Ġ s +o n +e r +h a +n d 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See all RoBERTa models at https://huggingface.co/models?filter=roberta +] + + +class StructRobertaConfig(RobertaConfig): + model_type = "roberta" + + def __init__( + self, + n_parser_layers=4, + conv_size=9, + relations=("head", "child"), + weight_act="softmax", + n_cntxt_layers=3, + n_cntxt_layers_2=0, + **kwargs,): + + super().__init__(**kwargs) + self.n_cntxt_layers = n_cntxt_layers + self.n_parser_layers = n_parser_layers + self.n_cntxt_layers_2 = n_cntxt_layers_2 + self.conv_size = conv_size + self.relations = relations + self.weight_act = weight_act + +class Conv1d(nn.Module): + """1D convolution layer.""" + + def __init__(self, hidden_size, kernel_size, dilation=1): + """Initialization. + + Args: + hidden_size: dimension of input embeddings + kernel_size: convolution kernel size + dilation: the spacing between the kernel points + """ + super(Conv1d, self).__init__() + + if kernel_size % 2 == 0: + padding = (kernel_size // 2) * dilation + self.shift = True + else: + padding = ((kernel_size - 1) // 2) * dilation + self.shift = False + self.conv = nn.Conv1d( + hidden_size, hidden_size, kernel_size, padding=padding, dilation=dilation + ) + + def forward(self, x): + """Compute convolution. + + Args: + x: input embeddings + Returns: + conv_output: convolution results + """ + + if self.shift: + return self.conv(x.transpose(1, 2)).transpose(1, 2)[:, 1:] + else: + return self.conv(x.transpose(1, 2)).transpose(1, 2) + + +class RobertaEmbeddings(nn.Module): + """ + Same as BertEmbeddings with a tiny tweak for positional embeddings indexing. + """ + + # Copied from transformers.models.bert.modeling_bert.BertEmbeddings.__init__ + def __init__(self, config): + super().__init__() + self.word_embeddings = nn.Embedding( + config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id + ) + self.position_embeddings = nn.Embedding( + config.max_position_embeddings, config.hidden_size + ) + self.token_type_embeddings = nn.Embedding( + config.type_vocab_size, config.hidden_size + ) + + # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load + # any TensorFlow checkpoint file + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + # position_ids (1, len position emb) is contiguous in memory and exported when serialized + self.position_embedding_type = getattr( + config, "position_embedding_type", "absolute" + ) + self.register_buffer( + "position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)) + ) + if version.parse(torch.__version__) > version.parse("1.6.0"): + self.register_buffer( + "token_type_ids", + torch.zeros(self.position_ids.size(), dtype=torch.long), + persistent=False, + ) + + # End copy + self.padding_idx = config.pad_token_id + self.position_embeddings = nn.Embedding( + config.max_position_embeddings, + config.hidden_size, + padding_idx=self.padding_idx, + ) + + def forward( + self, + input_ids=None, + token_type_ids=None, + position_ids=None, + inputs_embeds=None, + past_key_values_length=0, + ): + if position_ids is None: + if input_ids is not None: + # Create the position ids from the input token ids. Any padded tokens remain padded. + position_ids = create_position_ids_from_input_ids( + input_ids, self.padding_idx, past_key_values_length + ) + else: + position_ids = self.create_position_ids_from_inputs_embeds( + inputs_embeds + ) + + if input_ids is not None: + input_shape = input_ids.size() + else: + input_shape = inputs_embeds.size()[:-1] + + seq_length = input_shape[1] + + # Setting the token_type_ids to the registered buffer in constructor where it is all zeros, which usually occurs + # when its auto-generated, registered buffer helps users when tracing the model without passing token_type_ids, solves + # issue #5664 + if token_type_ids is None: + if hasattr(self, "token_type_ids"): + buffered_token_type_ids = self.token_type_ids[:, :seq_length] + buffered_token_type_ids_expanded = buffered_token_type_ids.expand( + input_shape[0], seq_length + ) + token_type_ids = buffered_token_type_ids_expanded + else: + token_type_ids = torch.zeros( + input_shape, dtype=torch.long, device=self.position_ids.device + ) + + if inputs_embeds is None: + inputs_embeds = self.word_embeddings(input_ids) + token_type_embeddings = self.token_type_embeddings(token_type_ids) + + embeddings = inputs_embeds + token_type_embeddings + if self.position_embedding_type == "absolute": + position_embeddings = self.position_embeddings(position_ids) + embeddings += position_embeddings + embeddings = self.LayerNorm(embeddings) + embeddings = self.dropout(embeddings) + return embeddings + + def create_position_ids_from_inputs_embeds(self, inputs_embeds): + """ + We are provided embeddings directly. We cannot infer which are padded so just generate sequential position ids. + + Args: + inputs_embeds: torch.Tensor + + Returns: torch.Tensor + """ + input_shape = inputs_embeds.size()[:-1] + sequence_length = input_shape[1] + + position_ids = torch.arange( + self.padding_idx + 1, + sequence_length + self.padding_idx + 1, + dtype=torch.long, + device=inputs_embeds.device, + ) + return position_ids.unsqueeze(0).expand(input_shape) + + +# Copied from transformers.models.bert.modeling_bert.BertSelfAttention with Bert->Roberta +class RobertaSelfAttention(nn.Module): + def __init__(self, config, position_embedding_type=None): + super().__init__() + if config.hidden_size % config.num_attention_heads != 0 and not hasattr( + config, "embedding_size" + ): + raise ValueError( + f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention " + f"heads ({config.num_attention_heads})" + ) + + self.num_attention_heads = config.num_attention_heads + self.attention_head_size = int(config.hidden_size / config.num_attention_heads) + self.all_head_size = self.num_attention_heads * self.attention_head_size + + self.query = nn.Linear(config.hidden_size, self.all_head_size) + self.key = nn.Linear(config.hidden_size, self.all_head_size) + self.value = nn.Linear(config.hidden_size, self.all_head_size) + + self.dropout = nn.Dropout(config.attention_probs_dropout_prob) + self.position_embedding_type = position_embedding_type or getattr( + config, "position_embedding_type", "absolute" + ) + if ( + self.position_embedding_type == "relative_key" + or self.position_embedding_type == "relative_key_query" + ): + self.max_position_embeddings = config.max_position_embeddings + self.distance_embedding = nn.Embedding( + 2 * config.max_position_embeddings - 1, self.attention_head_size + ) + + self.is_decoder = config.is_decoder + + def transpose_for_scores(self, x): + new_x_shape = x.size()[:-1] + ( + self.num_attention_heads, + self.attention_head_size, + ) + x = x.view(new_x_shape) + return x.permute(0, 2, 1, 3) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.FloatTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, + output_attentions: Optional[bool] = False, + parser_att_mask=None, + ) -> Tuple[torch.Tensor]: + mixed_query_layer = self.query(hidden_states) + + # If this is instantiated as a cross-attention module, the keys + # and values come from an encoder; the attention mask needs to be + # such that the encoder's padding tokens are not attended to. + is_cross_attention = encoder_hidden_states is not None + + if is_cross_attention and past_key_value is not None: + # reuse k,v, cross_attentions + key_layer = past_key_value[0] + value_layer = past_key_value[1] + attention_mask = encoder_attention_mask + elif is_cross_attention: + key_layer = self.transpose_for_scores(self.key(encoder_hidden_states)) + value_layer = self.transpose_for_scores(self.value(encoder_hidden_states)) + attention_mask = encoder_attention_mask + elif past_key_value is not None: + key_layer = self.transpose_for_scores(self.key(hidden_states)) + value_layer = self.transpose_for_scores(self.value(hidden_states)) + key_layer = torch.cat([past_key_value[0], key_layer], dim=2) + value_layer = torch.cat([past_key_value[1], value_layer], dim=2) + else: + key_layer = self.transpose_for_scores(self.key(hidden_states)) + value_layer = self.transpose_for_scores(self.value(hidden_states)) + + query_layer = self.transpose_for_scores(mixed_query_layer) + + if self.is_decoder: + # if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states. + # Further calls to cross_attention layer can then reuse all cross-attention + # key/value_states (first "if" case) + # if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of + # all previous decoder key/value_states. Further calls to uni-directional self-attention + # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case) + # if encoder bi-directional self-attention `past_key_value` is always `None` + past_key_value = (key_layer, value_layer) + + # Take the dot product between "query" and "key" to get the raw attention scores. + attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) + + if ( + self.position_embedding_type == "relative_key" + or self.position_embedding_type == "relative_key_query" + ): + seq_length = hidden_states.size()[1] + position_ids_l = torch.arange( + seq_length, dtype=torch.long, device=hidden_states.device + ).view(-1, 1) + position_ids_r = torch.arange( + seq_length, dtype=torch.long, device=hidden_states.device + ).view(1, -1) + distance = position_ids_l - position_ids_r + positional_embedding = self.distance_embedding( + distance + self.max_position_embeddings - 1 + ) + positional_embedding = positional_embedding.to( + dtype=query_layer.dtype + ) # fp16 compatibility + + if self.position_embedding_type == "relative_key": + relative_position_scores = torch.einsum( + "bhld,lrd->bhlr", query_layer, positional_embedding + ) + attention_scores = attention_scores + relative_position_scores + elif self.position_embedding_type == "relative_key_query": + relative_position_scores_query = torch.einsum( + "bhld,lrd->bhlr", query_layer, positional_embedding + ) + relative_position_scores_key = torch.einsum( + "bhrd,lrd->bhlr", key_layer, positional_embedding + ) + attention_scores = ( + attention_scores + + relative_position_scores_query + + relative_position_scores_key + ) + + attention_scores = attention_scores / math.sqrt(self.attention_head_size) + if attention_mask is not None: + # Apply the attention mask is (precomputed for all layers in RobertaModel forward() function) + attention_scores = attention_scores + attention_mask + + if parser_att_mask is None: + # Normalize the attention scores to probabilities. + attention_probs = nn.functional.softmax(attention_scores, dim=-1) + else: + attention_probs = torch.sigmoid(attention_scores) * parser_att_mask + + # This is actually dropping out entire tokens to attend to, which might + # seem a bit unusual, but is taken from the original Transformer paper. + attention_probs = self.dropout(attention_probs) + + # Mask heads if we want to + if head_mask is not None: + attention_probs = attention_probs * head_mask + + context_layer = torch.matmul(attention_probs, value_layer) + + context_layer = context_layer.permute(0, 2, 1, 3).contiguous() + new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) + context_layer = context_layer.view(new_context_layer_shape) + + outputs = ( + (context_layer, attention_probs) if output_attentions else (context_layer,) + ) + + if self.is_decoder: + outputs = outputs + (past_key_value,) + return outputs + + +# Copied from transformers.models.bert.modeling_bert.BertSelfOutput +class RobertaSelfOutput(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward( + self, hidden_states: torch.Tensor, input_tensor: torch.Tensor + ) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + hidden_states = self.LayerNorm(hidden_states + input_tensor) + return hidden_states + + +# Copied from transformers.models.bert.modeling_bert.BertAttention with Bert->Roberta +class RobertaAttention(nn.Module): + def __init__(self, config, position_embedding_type=None): + super().__init__() + self.self = RobertaSelfAttention( + config, position_embedding_type=position_embedding_type + ) + self.output = RobertaSelfOutput(config) + self.pruned_heads = set() + + def prune_heads(self, heads): + if len(heads) == 0: + return + heads, index = find_pruneable_heads_and_indices( + heads, + self.self.num_attention_heads, + self.self.attention_head_size, + self.pruned_heads, + ) + + # Prune linear layers + self.self.query = prune_linear_layer(self.self.query, index) + self.self.key = prune_linear_layer(self.self.key, index) + self.self.value = prune_linear_layer(self.self.value, index) + self.output.dense = prune_linear_layer(self.output.dense, index, dim=1) + + # Update hyper params and store pruned heads + self.self.num_attention_heads = self.self.num_attention_heads - len(heads) + self.self.all_head_size = ( + self.self.attention_head_size * self.self.num_attention_heads + ) + self.pruned_heads = self.pruned_heads.union(heads) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.FloatTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, + output_attentions: Optional[bool] = False, + parser_att_mask=None, + ) -> Tuple[torch.Tensor]: + self_outputs = self.self( + hidden_states, + attention_mask, + head_mask, + encoder_hidden_states, + encoder_attention_mask, + past_key_value, + output_attentions, + parser_att_mask=parser_att_mask, + ) + attention_output = self.output(self_outputs[0], hidden_states) + outputs = (attention_output,) + self_outputs[ + 1: + ] # add attentions if we output them + return outputs + + +# Copied from transformers.models.bert.modeling_bert.BertIntermediate +class RobertaIntermediate(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.intermediate_size) + if isinstance(config.hidden_act, str): + self.intermediate_act_fn = ACT2FN[config.hidden_act] + else: + self.intermediate_act_fn = config.hidden_act + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.intermediate_act_fn(hidden_states) + return hidden_states + + +# Copied from transformers.models.bert.modeling_bert.BertOutput +class RobertaOutput(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.intermediate_size, config.hidden_size) + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward( + self, hidden_states: torch.Tensor, input_tensor: torch.Tensor + ) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + hidden_states = self.LayerNorm(hidden_states + input_tensor) + return hidden_states + + +# Copied from transformers.models.bert.modeling_bert.BertLayer with Bert->Roberta +class RobertaLayer(nn.Module): + def __init__(self, config): + super().__init__() + self.chunk_size_feed_forward = config.chunk_size_feed_forward + self.seq_len_dim = 1 + self.attention = RobertaAttention(config) + self.is_decoder = config.is_decoder + self.add_cross_attention = config.add_cross_attention + if self.add_cross_attention: + if not self.is_decoder: + raise ValueError( + f"{self} should be used as a decoder model if cross attention is added" + ) + self.crossattention = RobertaAttention( + config, position_embedding_type="absolute" + ) + self.intermediate = RobertaIntermediate(config) + self.output = RobertaOutput(config) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.FloatTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, + output_attentions: Optional[bool] = False, + parser_att_mask=None, + ) -> Tuple[torch.Tensor]: + # decoder uni-directional self-attention cached key/values tuple is at positions 1,2 + self_attn_past_key_value = ( + past_key_value[:2] if past_key_value is not None else None + ) + self_attention_outputs = self.attention( + hidden_states, + attention_mask, + head_mask, + output_attentions=output_attentions, + past_key_value=self_attn_past_key_value, + parser_att_mask=parser_att_mask, + ) + attention_output = self_attention_outputs[0] + + # if decoder, the last output is tuple of self-attn cache + if self.is_decoder: + outputs = self_attention_outputs[1:-1] + present_key_value = self_attention_outputs[-1] + else: + outputs = self_attention_outputs[ + 1: + ] # add self attentions if we output attention weights + + cross_attn_present_key_value = None + if self.is_decoder and encoder_hidden_states is not None: + if not hasattr(self, "crossattention"): + raise ValueError( + f"If `encoder_hidden_states` are passed, {self} has to be instantiated with cross-attention layers by setting `config.add_cross_attention=True`" + ) + + # cross_attn cached key/values tuple is at positions 3,4 of past_key_value tuple + cross_attn_past_key_value = ( + past_key_value[-2:] if past_key_value is not None else None + ) + cross_attention_outputs = self.crossattention( + attention_output, + attention_mask, + head_mask, + encoder_hidden_states, + encoder_attention_mask, + cross_attn_past_key_value, + output_attentions, + ) + attention_output = cross_attention_outputs[0] + outputs = ( + outputs + cross_attention_outputs[1:-1] + ) # add cross attentions if we output attention weights + + # add cross-attn cache to positions 3,4 of present_key_value tuple + cross_attn_present_key_value = cross_attention_outputs[-1] + present_key_value = present_key_value + cross_attn_present_key_value + + layer_output = apply_chunking_to_forward( + self.feed_forward_chunk, + self.chunk_size_feed_forward, + self.seq_len_dim, + attention_output, + ) + outputs = (layer_output,) + outputs + + # if decoder, return the attn key/values as the last output + if self.is_decoder: + outputs = outputs + (present_key_value,) + + return outputs + + def feed_forward_chunk(self, attention_output): + intermediate_output = self.intermediate(attention_output) + layer_output = self.output(intermediate_output, attention_output) + return layer_output + + +# Copied from transformers.models.bert.modeling_bert.BertEncoder with Bert->Roberta +class RobertaEncoder(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + self.layer = nn.ModuleList( + [RobertaLayer(config) for _ in range(config.num_hidden_layers)] + ) + self.gradient_checkpointing = False + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.FloatTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = False, + output_hidden_states: Optional[bool] = False, + return_dict: Optional[bool] = True, + parser_att_mask=None, + ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPastAndCrossAttentions]: + all_hidden_states = () if output_hidden_states else None + all_self_attentions = () if output_attentions else None + all_cross_attentions = ( + () if output_attentions and self.config.add_cross_attention else None + ) + + next_decoder_cache = () if use_cache else None + for i, layer_module in enumerate(self.layer): + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + layer_head_mask = head_mask[i] if head_mask is not None else None + past_key_value = past_key_values[i] if past_key_values is not None else None + + if self.gradient_checkpointing and self.training: + + if use_cache: + logger.warning( + "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." + ) + use_cache = False + + def create_custom_forward(module): + def custom_forward(*inputs): + return module(*inputs, past_key_value, output_attentions) + + return custom_forward + + layer_outputs = torch.utils.checkpoint.checkpoint( + create_custom_forward(layer_module), + hidden_states, + attention_mask, + layer_head_mask, + encoder_hidden_states, + encoder_attention_mask, + ) + else: + if parser_att_mask is not None: + layer_outputs = layer_module( + hidden_states, + attention_mask, + layer_head_mask, + encoder_hidden_states, + encoder_attention_mask, + past_key_value, + output_attentions, + parser_att_mask=parser_att_mask[i]) + else: + layer_outputs = layer_module( + hidden_states, + attention_mask, + layer_head_mask, + encoder_hidden_states, + encoder_attention_mask, + past_key_value, + output_attentions, + parser_att_mask=None) + + + hidden_states = layer_outputs[0] + if use_cache: + next_decoder_cache += (layer_outputs[-1],) + if output_attentions: + all_self_attentions = all_self_attentions + (layer_outputs[1],) + if self.config.add_cross_attention: + all_cross_attentions = all_cross_attentions + (layer_outputs[2],) + + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + if not return_dict: + return tuple( + v + for v in [ + hidden_states, + next_decoder_cache, + all_hidden_states, + all_self_attentions, + all_cross_attentions, + ] + if v is not None + ) + return BaseModelOutputWithPastAndCrossAttentions( + last_hidden_state=hidden_states, + past_key_values=next_decoder_cache, + hidden_states=all_hidden_states, + attentions=all_self_attentions, + cross_attentions=all_cross_attentions, + ) + + +# Copied from transformers.models.bert.modeling_bert.BertPooler +class RobertaPooler(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.activation = nn.Tanh() + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + # We "pool" the model by simply taking the hidden state corresponding + # to the first token. + first_token_tensor = hidden_states[:, 0] + pooled_output = self.dense(first_token_tensor) + pooled_output = self.activation(pooled_output) + return pooled_output + + +class RobertaPreTrainedModel(PreTrainedModel): + """ + An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained + models. + """ + + config_class = RobertaConfig + base_model_prefix = "roberta" + supports_gradient_checkpointing = True + + # Copied from transformers.models.bert.modeling_bert.BertPreTrainedModel._init_weights + def _init_weights(self, module): + """Initialize the weights""" + if isinstance(module, nn.Linear): + # Slightly different from the TF version which uses truncated_normal for initialization + # cf https://github.com/pytorch/pytorch/pull/5617 + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.Embedding): + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.padding_idx is not None: + module.weight.data[module.padding_idx].zero_() + elif isinstance(module, nn.LayerNorm): + if module.bias is not None: + module.bias.data.zero_() + module.weight.data.fill_(1.0) + + def _set_gradient_checkpointing(self, module, value=False): + if isinstance(module, RobertaEncoder): + module.gradient_checkpointing = value + + def update_keys_to_ignore(self, config, del_keys_to_ignore): + """Remove some keys from ignore list""" + if not config.tie_word_embeddings: + # must make a new list, or the class variable gets modified! + self._keys_to_ignore_on_save = [ + k for k in self._keys_to_ignore_on_save if k not in del_keys_to_ignore + ] + self._keys_to_ignore_on_load_missing = [ + k + for k in self._keys_to_ignore_on_load_missing + if k not in del_keys_to_ignore + ] + + +ROBERTA_START_DOCSTRING = r""" + + This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the + library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads + etc.) + + This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. + Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage + and behavior. + + Parameters: + config ([`RobertaConfig`]): Model configuration class with all the parameters of the + model. Initializing with a config file does not load the weights associated with the model, only the + configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. +""" + + +ROBERTA_INPUTS_DOCSTRING = r""" + Args: + input_ids (`torch.LongTensor` of shape `({0})`): + Indices of input sequence tokens in the vocabulary. + + Indices can be obtained using [`RobertaTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + [What are input IDs?](../glossary#input-ids) + attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*): + Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + + [What are attention masks?](../glossary#attention-mask) + token_type_ids (`torch.LongTensor` of shape `({0})`, *optional*): + Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, + 1]`: + + - 0 corresponds to a *sentence A* token, + - 1 corresponds to a *sentence B* token. + + [What are token type IDs?](../glossary#token-type-ids) + position_ids (`torch.LongTensor` of shape `({0})`, *optional*): + Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, + config.max_position_embeddings - 1]`. + + [What are position IDs?](../glossary#position-ids) + head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): + Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: + + - 1 indicates the head is **not masked**, + - 0 indicates the head is **masked**. + + inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_size)`, *optional*): + Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This + is useful if you want more control over how to convert `input_ids` indices into associated vectors than the + model's internal embedding lookup matrix. + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned + tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for + more detail. + return_dict (`bool`, *optional*): + Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. +""" + + +class RobertaModel(RobertaPreTrainedModel): + """ + + The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of + cross-attention is added between the self-attention layers, following the architecture described in *Attention is + all you need*_ by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz + Kaiser and Illia Polosukhin. + + To behave as an decoder the model needs to be initialized with the `is_decoder` argument of the configuration set + to `True`. To be used in a Seq2Seq model, the model needs to initialized with both `is_decoder` argument and + `add_cross_attention` set to `True`; an `encoder_hidden_states` is then expected as an input to the forward pass. + + .. _*Attention is all you need*: https://arxiv.org/abs/1706.03762 + + """ + + _keys_to_ignore_on_load_missing = [r"position_ids"] + + # Copied from transformers.models.bert.modeling_bert.BertModel.__init__ with Bert->Roberta + def __init__(self, config, add_pooling_layer=True): + super().__init__(config) + self.config = config + + self.embeddings = RobertaEmbeddings(config) + self.encoder = RobertaEncoder(config) + + self.pooler = RobertaPooler(config) if add_pooling_layer else None + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.embeddings.word_embeddings + + def set_input_embeddings(self, value): + self.embeddings.word_embeddings = value + + def _prune_heads(self, heads_to_prune): + """ + Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base + class PreTrainedModel + """ + for layer, heads in heads_to_prune.items(): + self.encoder.layer[layer].attention.prune_heads(heads) + + # Copied from transformers.models.bert.modeling_bert.BertModel.forward + def forward( + self, + input_ids: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + token_type_ids: Optional[torch.Tensor] = None, + position_ids: Optional[torch.Tensor] = None, + head_mask: Optional[torch.Tensor] = None, + inputs_embeds: Optional[torch.Tensor] = None, + encoder_hidden_states: Optional[torch.Tensor] = None, + encoder_attention_mask: Optional[torch.Tensor] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + parser_att_mask=None, + ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPoolingAndCrossAttentions]: + r""" + encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): + Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if + the model is configured as a decoder. + encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*): + Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in + the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): + Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. + + If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that + don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all + `decoder_input_ids` of shape `(batch_size, sequence_length)`. + use_cache (`bool`, *optional*): + If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see + `past_key_values`). + """ + output_attentions = ( + output_attentions + if output_attentions is not None + else self.config.output_attentions + ) + output_hidden_states = ( + output_hidden_states + if output_hidden_states is not None + else self.config.output_hidden_states + ) + return_dict = ( + return_dict if return_dict is not None else self.config.use_return_dict + ) + + if self.config.is_decoder: + use_cache = use_cache if use_cache is not None else self.config.use_cache + else: + use_cache = False + + if input_ids is not None and inputs_embeds is not None: + raise ValueError( + "You cannot specify both input_ids and inputs_embeds at the same time" + ) + elif input_ids is not None: + input_shape = input_ids.size() + elif inputs_embeds is not None: + input_shape = inputs_embeds.size()[:-1] + else: + raise ValueError("You have to specify either input_ids or inputs_embeds") + + batch_size, seq_length = input_shape + device = input_ids.device if input_ids is not None else inputs_embeds.device + + # past_key_values_length + past_key_values_length = ( + past_key_values[0][0].shape[2] if past_key_values is not None else 0 + ) + + if attention_mask is None: + attention_mask = torch.ones( + ((batch_size, seq_length + past_key_values_length)), device=device + ) + + if token_type_ids is None: + if hasattr(self.embeddings, "token_type_ids"): + buffered_token_type_ids = self.embeddings.token_type_ids[:, :seq_length] + buffered_token_type_ids_expanded = buffered_token_type_ids.expand( + batch_size, seq_length + ) + token_type_ids = buffered_token_type_ids_expanded + else: + token_type_ids = torch.zeros( + input_shape, dtype=torch.long, device=device + ) + + # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length] + # ourselves in which case we just need to make it broadcastable to all heads. + extended_attention_mask: torch.Tensor = self.get_extended_attention_mask( + attention_mask, input_shape, device + ) + + # If a 2D or 3D attention mask is provided for the cross-attention + # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length] + if self.config.is_decoder and encoder_hidden_states is not None: + ( + encoder_batch_size, + encoder_sequence_length, + _, + ) = encoder_hidden_states.size() + encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length) + if encoder_attention_mask is None: + encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device) + encoder_extended_attention_mask = self.invert_attention_mask( + encoder_attention_mask + ) + else: + encoder_extended_attention_mask = None + + # Prepare head mask if needed + # 1.0 in head_mask indicate we keep the head + # attention_probs has shape bsz x n_heads x N x N + # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] + # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] + head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) + + embedding_output = self.embeddings( + input_ids=input_ids, + position_ids=position_ids, + token_type_ids=token_type_ids, + inputs_embeds=inputs_embeds, + past_key_values_length=past_key_values_length, + ) + encoder_outputs = self.encoder( + embedding_output, + attention_mask=extended_attention_mask, + head_mask=head_mask, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_extended_attention_mask, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + parser_att_mask=parser_att_mask, + ) + sequence_output = encoder_outputs[0] + pooled_output = ( + self.pooler(sequence_output) if self.pooler is not None else None + ) + + if not return_dict: + return (sequence_output, pooled_output) + encoder_outputs[1:] + + return BaseModelOutputWithPoolingAndCrossAttentions( + last_hidden_state=sequence_output, + pooler_output=pooled_output, + past_key_values=encoder_outputs.past_key_values, + hidden_states=encoder_outputs.hidden_states, + attentions=encoder_outputs.attentions, + cross_attentions=encoder_outputs.cross_attentions, + ) + + +class StructRoberta(RobertaPreTrainedModel): + _keys_to_ignore_on_save = [r"lm_head.decoder.weight", r"lm_head.decoder.bias"] + _keys_to_ignore_on_load_missing = [ + r"position_ids", + r"lm_head.decoder.weight", + r"lm_head.decoder.bias", + ] + _keys_to_ignore_on_load_unexpected = [r"pooler"] + + def __init__(self, config): + super().__init__(config) + + if config.is_decoder: + logger.warning( + "If you want to use `RobertaForMaskedLM` make sure `config.is_decoder=False` for " + "bi-directional self-attention." + ) + + + if config.n_cntxt_layers > 0: + config_cntxt = copy.deepcopy(config) + config_cntxt.num_hidden_layers = config.n_cntxt_layers + + self.cntxt_layers = RobertaModel(config_cntxt, add_pooling_layer=False) + + if config.n_cntxt_layers_2 > 0: + self.parser_layers_1 = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(int(config.n_parser_layers/2)) + ] + ) + + self.distance_ff_1 = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff_1 = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight_1 = nn.Parameter( + torch.zeros((config.n_cntxt_layers_2, config.num_attention_heads, n_rel)) + ) + self._rel_weight_1.data.normal_(0, 0.1) + + self._scaler_1 = nn.Parameter(torch.zeros(2)) + + config_cntxt_2 = copy.deepcopy(config) + config_cntxt_2.num_hidden_layers = config.n_cntxt_layers_2 + + self.cntxt_layers_2 = RobertaModel(config_cntxt_2, add_pooling_layer=False) + + + self.parser_layers_2 = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(int(config.n_parser_layers/2)) + ] + ) + + self.distance_ff_2 = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff_2 = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight_2 = nn.Parameter( + torch.zeros((config.num_hidden_layers, config.num_attention_heads, n_rel)) + ) + self._rel_weight_2.data.normal_(0, 0.1) + + self._scaler_2 = nn.Parameter(torch.zeros(2)) + + else: + self.parser_layers = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(config.n_parser_layers) + ] + ) + + self.distance_ff = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight = nn.Parameter( + torch.zeros((config.num_hidden_layers, config.num_attention_heads, n_rel)) + ) + self._rel_weight.data.normal_(0, 0.1) + + self._scaler = nn.Parameter(torch.zeros(2)) + + self.roberta = RobertaModel(config, add_pooling_layer=False) + + if config.n_cntxt_layers > 0: + self.cntxt_layers.embeddings = self.roberta.embeddings + if config.n_cntxt_layers_2 > 0: + self.cntxt_layers_2.embeddings = self.roberta.embeddings + + self.lm_head = RobertaLMHead(config) + + self.pad = config.pad_token_id + + # The LM head weights require special treatment only when they are tied with the word embeddings + self.update_keys_to_ignore(config, ["lm_head.decoder.weight"]) + + # Initialize weights and apply final processing + self.post_init() + + def get_output_embeddings(self): + return self.lm_head.decoder + + def set_output_embeddings(self, new_embeddings): + self.lm_head.decoder = new_embeddings + + @property + def scaler(self): + return self._scaler.exp() + + @property + def scaler_1(self): + return self._scaler_1.exp() + + @property + def scaler_2(self): + return self._scaler_2.exp() + + @property + def rel_weight(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight, dim=-1) + + @property + def rel_weight_1(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight_1) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight_1, dim=-1) + + + @property + def rel_weight_2(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight_2) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight_2, dim=-1) + + + def compute_block(self, distance, height, n_cntxt_layers=0): + """Compute constituents from distance and height.""" + + if n_cntxt_layers>0: + if n_cntxt_layers == 1: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler_1[0] + elif n_cntxt_layers == 2: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler_2[0] + else: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0] + + gamma = torch.sigmoid(-beta_logits) + ones = torch.ones_like(gamma) + + block_mask_left = cummin( + gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1 + ) + block_mask_left = block_mask_left - F.pad( + block_mask_left[:, :, :-1], (1, 0), value=0 + ) + block_mask_left.tril_(0) + + block_mask_right = cummin( + gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1 + ) + block_mask_right = block_mask_right - F.pad( + block_mask_right[:, :, 1:], (0, 1), value=0 + ) + block_mask_right.triu_(0) + + block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :] + block = cumsum(block_mask_left).tril(0) + cumsum( + block_mask_right, reverse=True + ).triu(1) + + return block_p, block + + def compute_head(self, height, n_cntxt_layers=0): + """Estimate head for each constituent.""" + + _, length = height.size() + if n_cntxt_layers>0: + if n_cntxt_layers == 1: + head_logits = height * self.scaler_1[1] + elif n_cntxt_layers == 2: + head_logits = height * self.scaler_2[1] + else: + head_logits = height * self.scaler[1] + index = torch.arange(length, device=height.device) + + mask = (index[:, None, None] <= index[None, None, :]) * ( + index[None, None, :] <= index[None, :, None] + ) + head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1) + head_logits.masked_fill_(~mask[None, :, :, :], -1e9) + + head_p = torch.softmax(head_logits, dim=-1) + + return head_p + + def parse(self, x, embs=None, n_cntxt_layers=0): + """Parse input sentence. + + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + distance: syntactic distance + height: syntactic height + """ + + mask = x != self.pad + mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0) + + if embs is None: + h = self.roberta.embeddings(x) + else: + h = embs + + if n_cntxt_layers > 0: + if n_cntxt_layers == 1: + parser_layers = self.parser_layers_1 + height_ff = self.height_ff_1 + distance_ff = self.distance_ff_1 + elif n_cntxt_layers == 2: + parser_layers = self.parser_layers_2 + height_ff = self.height_ff_2 + distance_ff = self.distance_ff_2 + for i in range(int(self.config.n_parser_layers/2)): + h = h.masked_fill(~mask[:, :, None], 0) + h = parser_layers[i](h) + + height = height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, dim=-1 + )[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max + ) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where( + margin_left > margin_right, margin_right, margin_left + ).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + else: + for i in range(self.config.n_parser_layers): + h = h.masked_fill(~mask[:, :, None], 0) + h = self.parser_layers[i](h) + + height = self.height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = self.distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, dim=-1 + )[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max + ) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where( + margin_left > margin_right, margin_right, margin_left + ).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + + return distance, height + + def generate_mask(self, x, distance, height, n_cntxt_layers=0): + """Compute head and cibling distribution for each token.""" + + bsz, length = x.size() + + eye = torch.eye(length, device=x.device, dtype=torch.bool) + eye = eye[None, :, :].expand((bsz, -1, -1)) + + block_p, block = self.compute_block(distance, height, n_cntxt_layers=n_cntxt_layers) + head_p = self.compute_head(height, n_cntxt_layers=n_cntxt_layers) + head = torch.einsum("blij,bijh->blh", block_p, head_p) + head = head.masked_fill(eye, 0) + child = head.transpose(1, 2) + cibling = torch.bmm(head, child).masked_fill(eye, 0) + + rel_list = [] + if "head" in self.config.relations: + rel_list.append(head) + if "child" in self.config.relations: + rel_list.append(child) + if "cibling" in self.config.relations: + rel_list.append(cibling) + + rel = torch.stack(rel_list, dim=1) + + if n_cntxt_layers > 0: + if n_cntxt_layers == 1: + rel_weight = self.rel_weight_1 + elif n_cntxt_layers == 2: + rel_weight = self.rel_weight_2 + else: + rel_weight = self.rel_weight + + dep = torch.einsum("lhr,brij->lbhij", rel_weight, rel) + + if n_cntxt_layers == 1: + num_layers = self.cntxt_layers_2.config.num_hidden_layers + else: + num_layers = self.roberta.config.num_hidden_layers + + att_mask = dep.reshape( + num_layers, + bsz, + self.config.num_attention_heads, + length, + length, + ) + + return att_mask, cibling, head, block + + def forward( + self, + input_ids: Optional[torch.LongTensor] = None, + attention_mask: Optional[torch.FloatTensor] = None, + token_type_ids: Optional[torch.LongTensor] = None, + position_ids: Optional[torch.LongTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, MaskedLMOutput]: + r""" + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ..., + config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the + loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]` + kwargs (`Dict[str, any]`, optional, defaults to *{}*): + Used to hide legacy arguments that have been deprecated. + """ + return_dict = ( + return_dict if return_dict is not None else self.config.use_return_dict + ) + + + if self.config.n_cntxt_layers > 0: + cntxt_outputs = self.cntxt_layers( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict) + + + if self.config.n_cntxt_layers_2 > 0: + distance_1, height_1 = self.parse(input_ids, cntxt_outputs[0], n_cntxt_layers=1) + att_mask_1, _, _, _ = self.generate_mask(input_ids, distance_1, height_1, n_cntxt_layers=1) + + cntxt_outputs_2 = self.cntxt_layers_2( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + parser_att_mask=att_mask_1) + + sequence_output = cntxt_outputs_2[0] + + distance_2, height_2 = self.parse(input_ids, sequence_output[0], n_cntxt_layers=2) + att_mask, _, _, _ = self.generate_mask(input_ids, distance_2, height_2, n_cntxt_layers=2) + + elif self.config.n_cntxt_layers > 0: + distance, height = self.parse(input_ids, cntxt_outputs[0]) + att_mask, _, _, _ = self.generate_mask(input_ids, distance, height) + else: + distance, height = self.parse(input_ids) + att_mask, _, _, _ = self.generate_mask(input_ids, distance, height) + + outputs = self.roberta( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + parser_att_mask=att_mask, + ) + sequence_output = outputs[0] + prediction_scores = self.lm_head(sequence_output) + + masked_lm_loss = None + if labels is not None: + loss_fct = CrossEntropyLoss() + masked_lm_loss = loss_fct( + prediction_scores.view(-1, self.config.vocab_size), labels.view(-1) + ) + + if not return_dict: + output = (prediction_scores,) + outputs[2:] + return ( + ((masked_lm_loss,) + output) if masked_lm_loss is not None else output + ) + + return MaskedLMOutput( + loss=masked_lm_loss, + logits=prediction_scores, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +class RobertaLMHead(nn.Module): + """Roberta Head for masked language modeling.""" + + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + + self.decoder = nn.Linear(config.hidden_size, config.vocab_size) + self.bias = nn.Parameter(torch.zeros(config.vocab_size)) + self.decoder.bias = self.bias + + def forward(self, features, **kwargs): + x = self.dense(features) + x = gelu(x) + x = self.layer_norm(x) + + # project back to size of vocabulary with bias + x = self.decoder(x) + + return x + + def _tie_weights(self): + # To tie those two weights if they get disconnected (on TPU or when the bias is resized) + self.bias = self.decoder.bias + + +class StructRobertaForSequenceClassification(RobertaPreTrainedModel): + _keys_to_ignore_on_load_missing = [r"position_ids"] + + def __init__(self, config): + super().__init__(config) + self.num_labels = config.num_labels + self.config = config + + if config.n_cntxt_layers > 0: + config_cntxt = copy.deepcopy(config) + config_cntxt.num_hidden_layers = config.n_cntxt_layers + + self.cntxt_layers = RobertaModel(config_cntxt, add_pooling_layer=False) + + if config.n_cntxt_layers_2 > 0: + self.parser_layers_1 = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(int(config.n_parser_layers/2)) + ] + ) + + self.distance_ff_1 = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff_1 = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight_1 = nn.Parameter( + torch.zeros((config.n_cntxt_layers_2, config.num_attention_heads, n_rel)) + ) + self._rel_weight_1.data.normal_(0, 0.1) + + self._scaler_1 = nn.Parameter(torch.zeros(2)) + + config_cntxt_2 = copy.deepcopy(config) + config_cntxt_2.num_hidden_layers = config.n_cntxt_layers_2 + + self.cntxt_layers_2 = RobertaModel(config_cntxt_2, add_pooling_layer=False) + + + self.parser_layers_2 = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(int(config.n_parser_layers/2)) + ] + ) + + self.distance_ff_2 = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff_2 = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight_2 = nn.Parameter( + torch.zeros((config.num_hidden_layers, config.num_attention_heads, n_rel)) + ) + self._rel_weight_2.data.normal_(0, 0.1) + + self._scaler_2 = nn.Parameter(torch.zeros(2)) + + else: + self.parser_layers = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(config.n_parser_layers) + ] + ) + + self.distance_ff = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight = nn.Parameter( + torch.zeros((config.num_hidden_layers, config.num_attention_heads, n_rel)) + ) + self._rel_weight.data.normal_(0, 0.1) + + self._scaler = nn.Parameter(torch.zeros(2)) + + self.roberta = RobertaModel(config, add_pooling_layer=False) + + if config.n_cntxt_layers > 0: + self.cntxt_layers.embeddings = self.roberta.embeddings + if config.n_cntxt_layers_2 > 0: + self.cntxt_layers_2.embeddings = self.roberta.embeddings + + + self.pad = config.pad_token_id + self.classifier = RobertaClassificationHead(config) + + # Initialize weights and apply final processing + self.post_init() + + + @property + def scaler(self): + return self._scaler.exp() + + @property + def scaler_1(self): + return self._scaler_1.exp() + + @property + def scaler_2(self): + return self._scaler_2.exp() + + @property + def rel_weight(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight, dim=-1) + + @property + def rel_weight_1(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight_1) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight_1, dim=-1) + + + @property + def rel_weight_2(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight_2) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight_2, dim=-1) + + + def compute_block(self, distance, height, n_cntxt_layers=0): + """Compute constituents from distance and height.""" + + if n_cntxt_layers>0: + if n_cntxt_layers == 1: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler_1[0] + elif n_cntxt_layers == 2: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler_2[0] + else: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0] + + gamma = torch.sigmoid(-beta_logits) + ones = torch.ones_like(gamma) + + block_mask_left = cummin( + gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1 + ) + block_mask_left = block_mask_left - F.pad( + block_mask_left[:, :, :-1], (1, 0), value=0 + ) + block_mask_left.tril_(0) + + block_mask_right = cummin( + gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1 + ) + block_mask_right = block_mask_right - F.pad( + block_mask_right[:, :, 1:], (0, 1), value=0 + ) + block_mask_right.triu_(0) + + block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :] + block = cumsum(block_mask_left).tril(0) + cumsum( + block_mask_right, reverse=True + ).triu(1) + + return block_p, block + + def compute_head(self, height, n_cntxt_layers=0): + """Estimate head for each constituent.""" + + _, length = height.size() + if n_cntxt_layers>0: + if n_cntxt_layers == 1: + head_logits = height * self.scaler_1[1] + elif n_cntxt_layers == 2: + head_logits = height * self.scaler_2[1] + else: + head_logits = height * self.scaler[1] + index = torch.arange(length, device=height.device) + + mask = (index[:, None, None] <= index[None, None, :]) * ( + index[None, None, :] <= index[None, :, None] + ) + head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1) + head_logits.masked_fill_(~mask[None, :, :, :], -1e9) + + head_p = torch.softmax(head_logits, dim=-1) + + return head_p + + def parse(self, x, embs=None, n_cntxt_layers=0): + """Parse input sentence. + + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + distance: syntactic distance + height: syntactic height + """ + + mask = x != self.pad + mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0) + + if embs is None: + h = self.roberta.embeddings(x) + else: + h = embs + + if n_cntxt_layers > 0: + if n_cntxt_layers == 1: + parser_layers = self.parser_layers_1 + height_ff = self.height_ff_1 + distance_ff = self.distance_ff_1 + elif n_cntxt_layers == 2: + parser_layers = self.parser_layers_2 + height_ff = self.height_ff_2 + distance_ff = self.distance_ff_2 + for i in range(int(self.config.n_parser_layers/2)): + h = h.masked_fill(~mask[:, :, None], 0) + h = parser_layers[i](h) + + height = height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, dim=-1 + )[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max + ) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where( + margin_left > margin_right, margin_right, margin_left + ).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + else: + for i in range(self.config.n_parser_layers): + h = h.masked_fill(~mask[:, :, None], 0) + h = self.parser_layers[i](h) + + height = self.height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = self.distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, dim=-1 + )[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max + ) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where( + margin_left > margin_right, margin_right, margin_left + ).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + + return distance, height + + def generate_mask(self, x, distance, height, n_cntxt_layers=0): + """Compute head and cibling distribution for each token.""" + + bsz, length = x.size() + + eye = torch.eye(length, device=x.device, dtype=torch.bool) + eye = eye[None, :, :].expand((bsz, -1, -1)) + + block_p, block = self.compute_block(distance, height, n_cntxt_layers=n_cntxt_layers) + head_p = self.compute_head(height, n_cntxt_layers=n_cntxt_layers) + head = torch.einsum("blij,bijh->blh", block_p, head_p) + head = head.masked_fill(eye, 0) + child = head.transpose(1, 2) + cibling = torch.bmm(head, child).masked_fill(eye, 0) + + rel_list = [] + if "head" in self.config.relations: + rel_list.append(head) + if "child" in self.config.relations: + rel_list.append(child) + if "cibling" in self.config.relations: + rel_list.append(cibling) + + rel = torch.stack(rel_list, dim=1) + + if n_cntxt_layers > 0: + if n_cntxt_layers == 1: + rel_weight = self.rel_weight_1 + elif n_cntxt_layers == 2: + rel_weight = self.rel_weight_2 + else: + rel_weight = self.rel_weight + + dep = torch.einsum("lhr,brij->lbhij", rel_weight, rel) + + if n_cntxt_layers == 1: + num_layers = self.cntxt_layers_2.config.num_hidden_layers + else: + num_layers = self.roberta.config.num_hidden_layers + + att_mask = dep.reshape( + num_layers, + bsz, + self.config.num_attention_heads, + length, + length, + ) + + return att_mask, cibling, head, block + + def forward( + self, + input_ids: Optional[torch.LongTensor] = None, + attention_mask: Optional[torch.FloatTensor] = None, + token_type_ids: Optional[torch.LongTensor] = None, + position_ids: Optional[torch.LongTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, SequenceClassifierOutput]: + r""" + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., + config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If + `config.num_labels > 1` a classification loss is computed (Cross-Entropy). + """ + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + if self.config.n_cntxt_layers > 0: + cntxt_outputs = self.cntxt_layers( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict) + + + if self.config.n_cntxt_layers_2 > 0: + distance_1, height_1 = self.parse(input_ids, cntxt_outputs[0], n_cntxt_layers=1) + att_mask_1, _, _, _ = self.generate_mask(input_ids, distance_1, height_1, n_cntxt_layers=1) + + cntxt_outputs_2 = self.cntxt_layers_2( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + parser_att_mask=att_mask_1) + + sequence_output = cntxt_outputs_2[0] + + distance_2, height_2 = self.parse(input_ids, sequence_output[0], n_cntxt_layers=2) + att_mask, _, _, _ = self.generate_mask(input_ids, distance_2, height_2, n_cntxt_layers=2) + + elif self.config.n_cntxt_layers > 0: + distance, height = self.parse(input_ids, cntxt_outputs[0]) + att_mask, _, _, _ = self.generate_mask(input_ids, distance, height) + else: + distance, height = self.parse(input_ids) + att_mask, _, _, _ = self.generate_mask(input_ids, distance, height) + + outputs = self.roberta( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + parser_att_mask=att_mask, + ) + sequence_output = outputs[0] + logits = self.classifier(sequence_output) + + loss = None + if labels is not None: + if self.config.problem_type is None: + if self.num_labels == 1: + self.config.problem_type = "regression" + elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): + self.config.problem_type = "single_label_classification" + else: + self.config.problem_type = "multi_label_classification" + + if self.config.problem_type == "regression": + loss_fct = MSELoss() + if self.num_labels == 1: + loss = loss_fct(logits.squeeze(), labels.squeeze()) + else: + loss = loss_fct(logits, labels) + elif self.config.problem_type == "single_label_classification": + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) + elif self.config.problem_type == "multi_label_classification": + loss_fct = BCEWithLogitsLoss() + loss = loss_fct(logits, labels) + + if not return_dict: + output = (logits,) + outputs[2:] + return ((loss,) + output) if loss is not None else output + + return SequenceClassifierOutput( + loss=loss, + logits=logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +class RobertaClassificationHead(nn.Module): + """Head for sentence-level classification tasks.""" + + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + classifier_dropout = ( + config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob + ) + self.dropout = nn.Dropout(classifier_dropout) + self.out_proj = nn.Linear(config.hidden_size, config.num_labels) + + def forward(self, features, **kwargs): + x = features[:, 0, :] # take token (equiv. to [CLS]) + x = self.dropout(x) + x = self.dense(x) + x = torch.tanh(x) + x = self.dropout(x) + x = self.out_proj(x) + return x + + +def create_position_ids_from_input_ids( + input_ids, padding_idx, past_key_values_length=0 +): + """ + Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols + are ignored. This is modified from fairseq's `utils.make_positions`. + + Args: + x: torch.Tensor x: + + Returns: torch.Tensor + """ + # The series of casts and type-conversions here are carefully balanced to both work with ONNX export and XLA. + mask = input_ids.ne(padding_idx).int() + incremental_indices = ( + torch.cumsum(mask, dim=1).type_as(mask) + past_key_values_length + ) * mask + return incremental_indices.long() + padding_idx + + +def cumprod(x, reverse=False, exclusive=False): + """cumulative product.""" + if reverse: + x = x.flip([-1]) + + if exclusive: + x = F.pad(x[:, :, :-1], (1, 0), value=1) + + cx = x.cumprod(-1) + + if reverse: + cx = cx.flip([-1]) + return cx + + +def cumsum(x, reverse=False, exclusive=False): + """cumulative sum.""" + bsz, _, length = x.size() + device = x.device + if reverse: + if exclusive: + w = torch.ones([bsz, length, length], device=device).tril(-1) + else: + w = torch.ones([bsz, length, length], device=device).tril(0) + cx = torch.bmm(x, w) + else: + if exclusive: + w = torch.ones([bsz, length, length], device=device).triu(1) + else: + w = torch.ones([bsz, length, length], device=device).triu(0) + cx = torch.bmm(x, w) + return cx + + +def cummin(x, reverse=False, exclusive=False, max_value=1e9): + """cumulative min.""" + if reverse: + if exclusive: + x = F.pad(x[:, :, 1:], (0, 1), value=max_value) + x = x.flip([-1]).cummin(-1)[0].flip([-1]) + else: + if exclusive: + x = F.pad(x[:, :, :-1], (1, 0), value=max_value) + x = x.cummin(-1)[0] + return x diff --git a/finetune/control_raising_control/predict_results.txt b/finetune/control_raising_control/predict_results.txt new file mode 100644 index 0000000000000000000000000000000000000000..e70196260292e63647a94afae2ede05ceb3c0db3 --- /dev/null +++ b/finetune/control_raising_control/predict_results.txt @@ -0,0 +1,13383 @@ +index prediction +0 1 +1 0 +2 1 +3 0 +4 1 +5 0 +6 1 +7 0 +8 1 +9 0 +10 1 +11 0 +12 1 +13 0 +14 1 +15 1 +16 1 +17 0 +18 1 +19 1 +20 1 +21 0 +22 1 +23 0 +24 1 +25 0 +26 1 +27 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0.6650972962379456, + "eval_f1": 0.4907749077490775, + "eval_loss": 3.0905210971832275, + "eval_mcc": 0.4409609972700157, + "eval_runtime": 40.4529, + "eval_samples": 20603, + "eval_samples_per_second": 509.308, + "eval_steps_per_second": 63.679 +} \ No newline at end of file diff --git a/finetune/control_raising_lexical_content_the/merges.txt b/finetune/control_raising_lexical_content_the/merges.txt new file mode 100644 index 0000000000000000000000000000000000000000..94b4f252cb566a4dac62a08c78d85169ff4e91be --- /dev/null +++ b/finetune/control_raising_lexical_content_the/merges.txt @@ -0,0 +1,31794 @@ +#version: 0.2 +Ġ t +h e +Ġ a +i n +Ġt he +r e +Ġ w +o u +Ġ s +o n +e r +h a +n d +i s +i t +Ġ b +Ġ o +Ġ c +Ġ m +Ġ f +in g +o r +a n +e d +l l +Ġ d +e n +Ġt o +e s +Ġ p +Ġo f +Ġ y +Ġ in +a r +a t +Ġa nd +Ġ l +ha t +Ġy ou +a s +Ġ g +o m +Ġ n +Ġ I +i c +o w +l e +v e +o t +Ġ h +Ġ he +a l +e t +Ġ ha +' s +u s +Ġt h +a y +Ġ it +Ġ is +Ġb e +Ġt hat +Ġ on +i on +i d +Ġ e +g h +u t +l d +en t +i m +Ġ re +Ġw as +a c +Ġw e +v er +a m +T he +. . +Ġf or +l y +Ġ S +s t +r i +r o +i r +a d +s e +k e +Ġ . +Ġs t +gh t +o o +' t +Ġ A +ou t +it h +Ġm e +Ġ C +a ll +Ġ k +Ġ u +Ġa n +Ġ M +Ġw ith +u r +Ġa s +Ġd o +c t +he r +c e +i ll +t er +i f +c h +Ġw h +Ġs e +om e +Ġa re +Ġ H +i l +Ġn ot +Ġha ve +Ġ B +Ġ 1 +a nd +Ġa t +Ġth is +ou ld +an t +Ġg o +us t +at ion +Ġl i +o p +a in +Ġk n +Y ou +e a +Ġ P +Ġ T +Ġ , +u n +g e +o l +t h +Ġh is +u l +Ġ " +r om +.. . +o re +Ġ W +e ll +Ġn e +Ġ r +Ġkn ow +Ġ j +ou r +Ġ D +Ġd e +p p +Ġc an +Ġ G +Ġthe y +u m +Ġ The +Ġs o +es t +Ġ ? +Ġa l +Ġa ll +Ġa b +Ġm y +is t +er s +Ġ R +i e +Ġyou r +ar t +Ġb y +Ġf rom +es s +Ġs h +Ġo r +Ġ 2 +Ġ F +Ġw hat +n t +a b +o s +Ġ L +e l +Ġon e +Ġc on +Ġ N +Ġw he +Ġthe re +i g +i ght +ha n +all y +Ġu p +q u +e m +Ġc om +on e +Ġha d +Ġb ut +Ġhe r +Ġs u +re d +r y +Ġ J +' re +Ġh im +Ġ out +r a +ac k +at e +oo d +ver y +oo k +Ġli ke +Ġw or +Ġs a +re s +am e +i ve +Ġp l +Ġ us +Ġ ( +on g +' m +Ġwe re +â Ģ +a ke +o d +in k +c k +Ġs he 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See all RoBERTa models at https://huggingface.co/models?filter=roberta +] + + +class StructRobertaConfig(RobertaConfig): + model_type = "roberta" + + def __init__( + self, + n_parser_layers=4, + conv_size=9, + relations=("head", "child"), + weight_act="softmax", + n_cntxt_layers=3, + n_cntxt_layers_2=0, + **kwargs,): + + super().__init__(**kwargs) + self.n_cntxt_layers = n_cntxt_layers + self.n_parser_layers = n_parser_layers + self.n_cntxt_layers_2 = n_cntxt_layers_2 + self.conv_size = conv_size + self.relations = relations + self.weight_act = weight_act + +class Conv1d(nn.Module): + """1D convolution layer.""" + + def __init__(self, hidden_size, kernel_size, dilation=1): + """Initialization. + + Args: + hidden_size: dimension of input embeddings + kernel_size: convolution kernel size + dilation: the spacing between the kernel points + """ + super(Conv1d, self).__init__() + + if kernel_size % 2 == 0: + padding = (kernel_size // 2) * dilation + self.shift = True + else: + padding = ((kernel_size - 1) // 2) * dilation + self.shift = False + self.conv = nn.Conv1d( + hidden_size, hidden_size, kernel_size, padding=padding, dilation=dilation + ) + + def forward(self, x): + """Compute convolution. + + Args: + x: input embeddings + Returns: + conv_output: convolution results + """ + + if self.shift: + return self.conv(x.transpose(1, 2)).transpose(1, 2)[:, 1:] + else: + return self.conv(x.transpose(1, 2)).transpose(1, 2) + + +class RobertaEmbeddings(nn.Module): + """ + Same as BertEmbeddings with a tiny tweak for positional embeddings indexing. + """ + + # Copied from transformers.models.bert.modeling_bert.BertEmbeddings.__init__ + def __init__(self, config): + super().__init__() + self.word_embeddings = nn.Embedding( + config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id + ) + self.position_embeddings = nn.Embedding( + config.max_position_embeddings, config.hidden_size + ) + self.token_type_embeddings = nn.Embedding( + config.type_vocab_size, config.hidden_size + ) + + # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load + # any TensorFlow checkpoint file + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + # position_ids (1, len position emb) is contiguous in memory and exported when serialized + self.position_embedding_type = getattr( + config, "position_embedding_type", "absolute" + ) + self.register_buffer( + "position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)) + ) + if version.parse(torch.__version__) > version.parse("1.6.0"): + self.register_buffer( + "token_type_ids", + torch.zeros(self.position_ids.size(), dtype=torch.long), + persistent=False, + ) + + # End copy + self.padding_idx = config.pad_token_id + self.position_embeddings = nn.Embedding( + config.max_position_embeddings, + config.hidden_size, + padding_idx=self.padding_idx, + ) + + def forward( + self, + input_ids=None, + token_type_ids=None, + position_ids=None, + inputs_embeds=None, + past_key_values_length=0, + ): + if position_ids is None: + if input_ids is not None: + # Create the position ids from the input token ids. Any padded tokens remain padded. + position_ids = create_position_ids_from_input_ids( + input_ids, self.padding_idx, past_key_values_length + ) + else: + position_ids = self.create_position_ids_from_inputs_embeds( + inputs_embeds + ) + + if input_ids is not None: + input_shape = input_ids.size() + else: + input_shape = inputs_embeds.size()[:-1] + + seq_length = input_shape[1] + + # Setting the token_type_ids to the registered buffer in constructor where it is all zeros, which usually occurs + # when its auto-generated, registered buffer helps users when tracing the model without passing token_type_ids, solves + # issue #5664 + if token_type_ids is None: + if hasattr(self, "token_type_ids"): + buffered_token_type_ids = self.token_type_ids[:, :seq_length] + buffered_token_type_ids_expanded = buffered_token_type_ids.expand( + input_shape[0], seq_length + ) + token_type_ids = buffered_token_type_ids_expanded + else: + token_type_ids = torch.zeros( + input_shape, dtype=torch.long, device=self.position_ids.device + ) + + if inputs_embeds is None: + inputs_embeds = self.word_embeddings(input_ids) + token_type_embeddings = self.token_type_embeddings(token_type_ids) + + embeddings = inputs_embeds + token_type_embeddings + if self.position_embedding_type == "absolute": + position_embeddings = self.position_embeddings(position_ids) + embeddings += position_embeddings + embeddings = self.LayerNorm(embeddings) + embeddings = self.dropout(embeddings) + return embeddings + + def create_position_ids_from_inputs_embeds(self, inputs_embeds): + """ + We are provided embeddings directly. We cannot infer which are padded so just generate sequential position ids. + + Args: + inputs_embeds: torch.Tensor + + Returns: torch.Tensor + """ + input_shape = inputs_embeds.size()[:-1] + sequence_length = input_shape[1] + + position_ids = torch.arange( + self.padding_idx + 1, + sequence_length + self.padding_idx + 1, + dtype=torch.long, + device=inputs_embeds.device, + ) + return position_ids.unsqueeze(0).expand(input_shape) + + +# Copied from transformers.models.bert.modeling_bert.BertSelfAttention with Bert->Roberta +class RobertaSelfAttention(nn.Module): + def __init__(self, config, position_embedding_type=None): + super().__init__() + if config.hidden_size % config.num_attention_heads != 0 and not hasattr( + config, "embedding_size" + ): + raise ValueError( + f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention " + f"heads ({config.num_attention_heads})" + ) + + self.num_attention_heads = config.num_attention_heads + self.attention_head_size = int(config.hidden_size / config.num_attention_heads) + self.all_head_size = self.num_attention_heads * self.attention_head_size + + self.query = nn.Linear(config.hidden_size, self.all_head_size) + self.key = nn.Linear(config.hidden_size, self.all_head_size) + self.value = nn.Linear(config.hidden_size, self.all_head_size) + + self.dropout = nn.Dropout(config.attention_probs_dropout_prob) + self.position_embedding_type = position_embedding_type or getattr( + config, "position_embedding_type", "absolute" + ) + if ( + self.position_embedding_type == "relative_key" + or self.position_embedding_type == "relative_key_query" + ): + self.max_position_embeddings = config.max_position_embeddings + self.distance_embedding = nn.Embedding( + 2 * config.max_position_embeddings - 1, self.attention_head_size + ) + + self.is_decoder = config.is_decoder + + def transpose_for_scores(self, x): + new_x_shape = x.size()[:-1] + ( + self.num_attention_heads, + self.attention_head_size, + ) + x = x.view(new_x_shape) + return x.permute(0, 2, 1, 3) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.FloatTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, + output_attentions: Optional[bool] = False, + parser_att_mask=None, + ) -> Tuple[torch.Tensor]: + mixed_query_layer = self.query(hidden_states) + + # If this is instantiated as a cross-attention module, the keys + # and values come from an encoder; the attention mask needs to be + # such that the encoder's padding tokens are not attended to. + is_cross_attention = encoder_hidden_states is not None + + if is_cross_attention and past_key_value is not None: + # reuse k,v, cross_attentions + key_layer = past_key_value[0] + value_layer = past_key_value[1] + attention_mask = encoder_attention_mask + elif is_cross_attention: + key_layer = self.transpose_for_scores(self.key(encoder_hidden_states)) + value_layer = self.transpose_for_scores(self.value(encoder_hidden_states)) + attention_mask = encoder_attention_mask + elif past_key_value is not None: + key_layer = self.transpose_for_scores(self.key(hidden_states)) + value_layer = self.transpose_for_scores(self.value(hidden_states)) + key_layer = torch.cat([past_key_value[0], key_layer], dim=2) + value_layer = torch.cat([past_key_value[1], value_layer], dim=2) + else: + key_layer = self.transpose_for_scores(self.key(hidden_states)) + value_layer = self.transpose_for_scores(self.value(hidden_states)) + + query_layer = self.transpose_for_scores(mixed_query_layer) + + if self.is_decoder: + # if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states. + # Further calls to cross_attention layer can then reuse all cross-attention + # key/value_states (first "if" case) + # if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of + # all previous decoder key/value_states. Further calls to uni-directional self-attention + # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case) + # if encoder bi-directional self-attention `past_key_value` is always `None` + past_key_value = (key_layer, value_layer) + + # Take the dot product between "query" and "key" to get the raw attention scores. + attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) + + if ( + self.position_embedding_type == "relative_key" + or self.position_embedding_type == "relative_key_query" + ): + seq_length = hidden_states.size()[1] + position_ids_l = torch.arange( + seq_length, dtype=torch.long, device=hidden_states.device + ).view(-1, 1) + position_ids_r = torch.arange( + seq_length, dtype=torch.long, device=hidden_states.device + ).view(1, -1) + distance = position_ids_l - position_ids_r + positional_embedding = self.distance_embedding( + distance + self.max_position_embeddings - 1 + ) + positional_embedding = positional_embedding.to( + dtype=query_layer.dtype + ) # fp16 compatibility + + if self.position_embedding_type == "relative_key": + relative_position_scores = torch.einsum( + "bhld,lrd->bhlr", query_layer, positional_embedding + ) + attention_scores = attention_scores + relative_position_scores + elif self.position_embedding_type == "relative_key_query": + relative_position_scores_query = torch.einsum( + "bhld,lrd->bhlr", query_layer, positional_embedding + ) + relative_position_scores_key = torch.einsum( + "bhrd,lrd->bhlr", key_layer, positional_embedding + ) + attention_scores = ( + attention_scores + + relative_position_scores_query + + relative_position_scores_key + ) + + attention_scores = attention_scores / math.sqrt(self.attention_head_size) + if attention_mask is not None: + # Apply the attention mask is (precomputed for all layers in RobertaModel forward() function) + attention_scores = attention_scores + attention_mask + + if parser_att_mask is None: + # Normalize the attention scores to probabilities. + attention_probs = nn.functional.softmax(attention_scores, dim=-1) + else: + attention_probs = torch.sigmoid(attention_scores) * parser_att_mask + + # This is actually dropping out entire tokens to attend to, which might + # seem a bit unusual, but is taken from the original Transformer paper. + attention_probs = self.dropout(attention_probs) + + # Mask heads if we want to + if head_mask is not None: + attention_probs = attention_probs * head_mask + + context_layer = torch.matmul(attention_probs, value_layer) + + context_layer = context_layer.permute(0, 2, 1, 3).contiguous() + new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) + context_layer = context_layer.view(new_context_layer_shape) + + outputs = ( + (context_layer, attention_probs) if output_attentions else (context_layer,) + ) + + if self.is_decoder: + outputs = outputs + (past_key_value,) + return outputs + + +# Copied from transformers.models.bert.modeling_bert.BertSelfOutput +class RobertaSelfOutput(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward( + self, hidden_states: torch.Tensor, input_tensor: torch.Tensor + ) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + hidden_states = self.LayerNorm(hidden_states + input_tensor) + return hidden_states + + +# Copied from transformers.models.bert.modeling_bert.BertAttention with Bert->Roberta +class RobertaAttention(nn.Module): + def __init__(self, config, position_embedding_type=None): + super().__init__() + self.self = RobertaSelfAttention( + config, position_embedding_type=position_embedding_type + ) + self.output = RobertaSelfOutput(config) + self.pruned_heads = set() + + def prune_heads(self, heads): + if len(heads) == 0: + return + heads, index = find_pruneable_heads_and_indices( + heads, + self.self.num_attention_heads, + self.self.attention_head_size, + self.pruned_heads, + ) + + # Prune linear layers + self.self.query = prune_linear_layer(self.self.query, index) + self.self.key = prune_linear_layer(self.self.key, index) + self.self.value = prune_linear_layer(self.self.value, index) + self.output.dense = prune_linear_layer(self.output.dense, index, dim=1) + + # Update hyper params and store pruned heads + self.self.num_attention_heads = self.self.num_attention_heads - len(heads) + self.self.all_head_size = ( + self.self.attention_head_size * self.self.num_attention_heads + ) + self.pruned_heads = self.pruned_heads.union(heads) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.FloatTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, + output_attentions: Optional[bool] = False, + parser_att_mask=None, + ) -> Tuple[torch.Tensor]: + self_outputs = self.self( + hidden_states, + attention_mask, + head_mask, + encoder_hidden_states, + encoder_attention_mask, + past_key_value, + output_attentions, + parser_att_mask=parser_att_mask, + ) + attention_output = self.output(self_outputs[0], hidden_states) + outputs = (attention_output,) + self_outputs[ + 1: + ] # add attentions if we output them + return outputs + + +# Copied from transformers.models.bert.modeling_bert.BertIntermediate +class RobertaIntermediate(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.intermediate_size) + if isinstance(config.hidden_act, str): + self.intermediate_act_fn = ACT2FN[config.hidden_act] + else: + self.intermediate_act_fn = config.hidden_act + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.intermediate_act_fn(hidden_states) + return hidden_states + + +# Copied from transformers.models.bert.modeling_bert.BertOutput +class RobertaOutput(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.intermediate_size, config.hidden_size) + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward( + self, hidden_states: torch.Tensor, input_tensor: torch.Tensor + ) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + hidden_states = self.LayerNorm(hidden_states + input_tensor) + return hidden_states + + +# Copied from transformers.models.bert.modeling_bert.BertLayer with Bert->Roberta +class RobertaLayer(nn.Module): + def __init__(self, config): + super().__init__() + self.chunk_size_feed_forward = config.chunk_size_feed_forward + self.seq_len_dim = 1 + self.attention = RobertaAttention(config) + self.is_decoder = config.is_decoder + self.add_cross_attention = config.add_cross_attention + if self.add_cross_attention: + if not self.is_decoder: + raise ValueError( + f"{self} should be used as a decoder model if cross attention is added" + ) + self.crossattention = RobertaAttention( + config, position_embedding_type="absolute" + ) + self.intermediate = RobertaIntermediate(config) + self.output = RobertaOutput(config) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.FloatTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, + output_attentions: Optional[bool] = False, + parser_att_mask=None, + ) -> Tuple[torch.Tensor]: + # decoder uni-directional self-attention cached key/values tuple is at positions 1,2 + self_attn_past_key_value = ( + past_key_value[:2] if past_key_value is not None else None + ) + self_attention_outputs = self.attention( + hidden_states, + attention_mask, + head_mask, + output_attentions=output_attentions, + past_key_value=self_attn_past_key_value, + parser_att_mask=parser_att_mask, + ) + attention_output = self_attention_outputs[0] + + # if decoder, the last output is tuple of self-attn cache + if self.is_decoder: + outputs = self_attention_outputs[1:-1] + present_key_value = self_attention_outputs[-1] + else: + outputs = self_attention_outputs[ + 1: + ] # add self attentions if we output attention weights + + cross_attn_present_key_value = None + if self.is_decoder and encoder_hidden_states is not None: + if not hasattr(self, "crossattention"): + raise ValueError( + f"If `encoder_hidden_states` are passed, {self} has to be instantiated with cross-attention layers by setting `config.add_cross_attention=True`" + ) + + # cross_attn cached key/values tuple is at positions 3,4 of past_key_value tuple + cross_attn_past_key_value = ( + past_key_value[-2:] if past_key_value is not None else None + ) + cross_attention_outputs = self.crossattention( + attention_output, + attention_mask, + head_mask, + encoder_hidden_states, + encoder_attention_mask, + cross_attn_past_key_value, + output_attentions, + ) + attention_output = cross_attention_outputs[0] + outputs = ( + outputs + cross_attention_outputs[1:-1] + ) # add cross attentions if we output attention weights + + # add cross-attn cache to positions 3,4 of present_key_value tuple + cross_attn_present_key_value = cross_attention_outputs[-1] + present_key_value = present_key_value + cross_attn_present_key_value + + layer_output = apply_chunking_to_forward( + self.feed_forward_chunk, + self.chunk_size_feed_forward, + self.seq_len_dim, + attention_output, + ) + outputs = (layer_output,) + outputs + + # if decoder, return the attn key/values as the last output + if self.is_decoder: + outputs = outputs + (present_key_value,) + + return outputs + + def feed_forward_chunk(self, attention_output): + intermediate_output = self.intermediate(attention_output) + layer_output = self.output(intermediate_output, attention_output) + return layer_output + + +# Copied from transformers.models.bert.modeling_bert.BertEncoder with Bert->Roberta +class RobertaEncoder(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + self.layer = nn.ModuleList( + [RobertaLayer(config) for _ in range(config.num_hidden_layers)] + ) + self.gradient_checkpointing = False + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.FloatTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = False, + output_hidden_states: Optional[bool] = False, + return_dict: Optional[bool] = True, + parser_att_mask=None, + ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPastAndCrossAttentions]: + all_hidden_states = () if output_hidden_states else None + all_self_attentions = () if output_attentions else None + all_cross_attentions = ( + () if output_attentions and self.config.add_cross_attention else None + ) + + next_decoder_cache = () if use_cache else None + for i, layer_module in enumerate(self.layer): + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + layer_head_mask = head_mask[i] if head_mask is not None else None + past_key_value = past_key_values[i] if past_key_values is not None else None + + if self.gradient_checkpointing and self.training: + + if use_cache: + logger.warning( + "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." + ) + use_cache = False + + def create_custom_forward(module): + def custom_forward(*inputs): + return module(*inputs, past_key_value, output_attentions) + + return custom_forward + + layer_outputs = torch.utils.checkpoint.checkpoint( + create_custom_forward(layer_module), + hidden_states, + attention_mask, + layer_head_mask, + encoder_hidden_states, + encoder_attention_mask, + ) + else: + if parser_att_mask is not None: + layer_outputs = layer_module( + hidden_states, + attention_mask, + layer_head_mask, + encoder_hidden_states, + encoder_attention_mask, + past_key_value, + output_attentions, + parser_att_mask=parser_att_mask[i]) + else: + layer_outputs = layer_module( + hidden_states, + attention_mask, + layer_head_mask, + encoder_hidden_states, + encoder_attention_mask, + past_key_value, + output_attentions, + parser_att_mask=None) + + + hidden_states = layer_outputs[0] + if use_cache: + next_decoder_cache += (layer_outputs[-1],) + if output_attentions: + all_self_attentions = all_self_attentions + (layer_outputs[1],) + if self.config.add_cross_attention: + all_cross_attentions = all_cross_attentions + (layer_outputs[2],) + + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + if not return_dict: + return tuple( + v + for v in [ + hidden_states, + next_decoder_cache, + all_hidden_states, + all_self_attentions, + all_cross_attentions, + ] + if v is not None + ) + return BaseModelOutputWithPastAndCrossAttentions( + last_hidden_state=hidden_states, + past_key_values=next_decoder_cache, + hidden_states=all_hidden_states, + attentions=all_self_attentions, + cross_attentions=all_cross_attentions, + ) + + +# Copied from transformers.models.bert.modeling_bert.BertPooler +class RobertaPooler(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.activation = nn.Tanh() + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + # We "pool" the model by simply taking the hidden state corresponding + # to the first token. + first_token_tensor = hidden_states[:, 0] + pooled_output = self.dense(first_token_tensor) + pooled_output = self.activation(pooled_output) + return pooled_output + + +class RobertaPreTrainedModel(PreTrainedModel): + """ + An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained + models. + """ + + config_class = RobertaConfig + base_model_prefix = "roberta" + supports_gradient_checkpointing = True + + # Copied from transformers.models.bert.modeling_bert.BertPreTrainedModel._init_weights + def _init_weights(self, module): + """Initialize the weights""" + if isinstance(module, nn.Linear): + # Slightly different from the TF version which uses truncated_normal for initialization + # cf https://github.com/pytorch/pytorch/pull/5617 + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.Embedding): + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.padding_idx is not None: + module.weight.data[module.padding_idx].zero_() + elif isinstance(module, nn.LayerNorm): + if module.bias is not None: + module.bias.data.zero_() + module.weight.data.fill_(1.0) + + def _set_gradient_checkpointing(self, module, value=False): + if isinstance(module, RobertaEncoder): + module.gradient_checkpointing = value + + def update_keys_to_ignore(self, config, del_keys_to_ignore): + """Remove some keys from ignore list""" + if not config.tie_word_embeddings: + # must make a new list, or the class variable gets modified! + self._keys_to_ignore_on_save = [ + k for k in self._keys_to_ignore_on_save if k not in del_keys_to_ignore + ] + self._keys_to_ignore_on_load_missing = [ + k + for k in self._keys_to_ignore_on_load_missing + if k not in del_keys_to_ignore + ] + + +ROBERTA_START_DOCSTRING = r""" + + This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the + library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads + etc.) + + This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. + Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage + and behavior. + + Parameters: + config ([`RobertaConfig`]): Model configuration class with all the parameters of the + model. Initializing with a config file does not load the weights associated with the model, only the + configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. +""" + + +ROBERTA_INPUTS_DOCSTRING = r""" + Args: + input_ids (`torch.LongTensor` of shape `({0})`): + Indices of input sequence tokens in the vocabulary. + + Indices can be obtained using [`RobertaTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + [What are input IDs?](../glossary#input-ids) + attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*): + Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + + [What are attention masks?](../glossary#attention-mask) + token_type_ids (`torch.LongTensor` of shape `({0})`, *optional*): + Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, + 1]`: + + - 0 corresponds to a *sentence A* token, + - 1 corresponds to a *sentence B* token. + + [What are token type IDs?](../glossary#token-type-ids) + position_ids (`torch.LongTensor` of shape `({0})`, *optional*): + Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, + config.max_position_embeddings - 1]`. + + [What are position IDs?](../glossary#position-ids) + head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): + Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: + + - 1 indicates the head is **not masked**, + - 0 indicates the head is **masked**. + + inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_size)`, *optional*): + Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This + is useful if you want more control over how to convert `input_ids` indices into associated vectors than the + model's internal embedding lookup matrix. + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned + tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for + more detail. + return_dict (`bool`, *optional*): + Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. +""" + + +class RobertaModel(RobertaPreTrainedModel): + """ + + The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of + cross-attention is added between the self-attention layers, following the architecture described in *Attention is + all you need*_ by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz + Kaiser and Illia Polosukhin. + + To behave as an decoder the model needs to be initialized with the `is_decoder` argument of the configuration set + to `True`. To be used in a Seq2Seq model, the model needs to initialized with both `is_decoder` argument and + `add_cross_attention` set to `True`; an `encoder_hidden_states` is then expected as an input to the forward pass. + + .. _*Attention is all you need*: https://arxiv.org/abs/1706.03762 + + """ + + _keys_to_ignore_on_load_missing = [r"position_ids"] + + # Copied from transformers.models.bert.modeling_bert.BertModel.__init__ with Bert->Roberta + def __init__(self, config, add_pooling_layer=True): + super().__init__(config) + self.config = config + + self.embeddings = RobertaEmbeddings(config) + self.encoder = RobertaEncoder(config) + + self.pooler = RobertaPooler(config) if add_pooling_layer else None + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.embeddings.word_embeddings + + def set_input_embeddings(self, value): + self.embeddings.word_embeddings = value + + def _prune_heads(self, heads_to_prune): + """ + Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base + class PreTrainedModel + """ + for layer, heads in heads_to_prune.items(): + self.encoder.layer[layer].attention.prune_heads(heads) + + # Copied from transformers.models.bert.modeling_bert.BertModel.forward + def forward( + self, + input_ids: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + token_type_ids: Optional[torch.Tensor] = None, + position_ids: Optional[torch.Tensor] = None, + head_mask: Optional[torch.Tensor] = None, + inputs_embeds: Optional[torch.Tensor] = None, + encoder_hidden_states: Optional[torch.Tensor] = None, + encoder_attention_mask: Optional[torch.Tensor] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + parser_att_mask=None, + ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPoolingAndCrossAttentions]: + r""" + encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): + Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if + the model is configured as a decoder. + encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*): + Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in + the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): + Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. + + If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that + don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all + `decoder_input_ids` of shape `(batch_size, sequence_length)`. + use_cache (`bool`, *optional*): + If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see + `past_key_values`). + """ + output_attentions = ( + output_attentions + if output_attentions is not None + else self.config.output_attentions + ) + output_hidden_states = ( + output_hidden_states + if output_hidden_states is not None + else self.config.output_hidden_states + ) + return_dict = ( + return_dict if return_dict is not None else self.config.use_return_dict + ) + + if self.config.is_decoder: + use_cache = use_cache if use_cache is not None else self.config.use_cache + else: + use_cache = False + + if input_ids is not None and inputs_embeds is not None: + raise ValueError( + "You cannot specify both input_ids and inputs_embeds at the same time" + ) + elif input_ids is not None: + input_shape = input_ids.size() + elif inputs_embeds is not None: + input_shape = inputs_embeds.size()[:-1] + else: + raise ValueError("You have to specify either input_ids or inputs_embeds") + + batch_size, seq_length = input_shape + device = input_ids.device if input_ids is not None else inputs_embeds.device + + # past_key_values_length + past_key_values_length = ( + past_key_values[0][0].shape[2] if past_key_values is not None else 0 + ) + + if attention_mask is None: + attention_mask = torch.ones( + ((batch_size, seq_length + past_key_values_length)), device=device + ) + + if token_type_ids is None: + if hasattr(self.embeddings, "token_type_ids"): + buffered_token_type_ids = self.embeddings.token_type_ids[:, :seq_length] + buffered_token_type_ids_expanded = buffered_token_type_ids.expand( + batch_size, seq_length + ) + token_type_ids = buffered_token_type_ids_expanded + else: + token_type_ids = torch.zeros( + input_shape, dtype=torch.long, device=device + ) + + # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length] + # ourselves in which case we just need to make it broadcastable to all heads. + extended_attention_mask: torch.Tensor = self.get_extended_attention_mask( + attention_mask, input_shape, device + ) + + # If a 2D or 3D attention mask is provided for the cross-attention + # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length] + if self.config.is_decoder and encoder_hidden_states is not None: + ( + encoder_batch_size, + encoder_sequence_length, + _, + ) = encoder_hidden_states.size() + encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length) + if encoder_attention_mask is None: + encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device) + encoder_extended_attention_mask = self.invert_attention_mask( + encoder_attention_mask + ) + else: + encoder_extended_attention_mask = None + + # Prepare head mask if needed + # 1.0 in head_mask indicate we keep the head + # attention_probs has shape bsz x n_heads x N x N + # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] + # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] + head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) + + embedding_output = self.embeddings( + input_ids=input_ids, + position_ids=position_ids, + token_type_ids=token_type_ids, + inputs_embeds=inputs_embeds, + past_key_values_length=past_key_values_length, + ) + encoder_outputs = self.encoder( + embedding_output, + attention_mask=extended_attention_mask, + head_mask=head_mask, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_extended_attention_mask, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + parser_att_mask=parser_att_mask, + ) + sequence_output = encoder_outputs[0] + pooled_output = ( + self.pooler(sequence_output) if self.pooler is not None else None + ) + + if not return_dict: + return (sequence_output, pooled_output) + encoder_outputs[1:] + + return BaseModelOutputWithPoolingAndCrossAttentions( + last_hidden_state=sequence_output, + pooler_output=pooled_output, + past_key_values=encoder_outputs.past_key_values, + hidden_states=encoder_outputs.hidden_states, + attentions=encoder_outputs.attentions, + cross_attentions=encoder_outputs.cross_attentions, + ) + + +class StructRoberta(RobertaPreTrainedModel): + _keys_to_ignore_on_save = [r"lm_head.decoder.weight", r"lm_head.decoder.bias"] + _keys_to_ignore_on_load_missing = [ + r"position_ids", + r"lm_head.decoder.weight", + r"lm_head.decoder.bias", + ] + _keys_to_ignore_on_load_unexpected = [r"pooler"] + + def __init__(self, config): + super().__init__(config) + + if config.is_decoder: + logger.warning( + "If you want to use `RobertaForMaskedLM` make sure `config.is_decoder=False` for " + "bi-directional self-attention." + ) + + + if config.n_cntxt_layers > 0: + config_cntxt = copy.deepcopy(config) + config_cntxt.num_hidden_layers = config.n_cntxt_layers + + self.cntxt_layers = RobertaModel(config_cntxt, add_pooling_layer=False) + + if config.n_cntxt_layers_2 > 0: + self.parser_layers_1 = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(int(config.n_parser_layers/2)) + ] + ) + + self.distance_ff_1 = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff_1 = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight_1 = nn.Parameter( + torch.zeros((config.n_cntxt_layers_2, config.num_attention_heads, n_rel)) + ) + self._rel_weight_1.data.normal_(0, 0.1) + + self._scaler_1 = nn.Parameter(torch.zeros(2)) + + config_cntxt_2 = copy.deepcopy(config) + config_cntxt_2.num_hidden_layers = config.n_cntxt_layers_2 + + self.cntxt_layers_2 = RobertaModel(config_cntxt_2, add_pooling_layer=False) + + + self.parser_layers_2 = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(int(config.n_parser_layers/2)) + ] + ) + + self.distance_ff_2 = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff_2 = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight_2 = nn.Parameter( + torch.zeros((config.num_hidden_layers, config.num_attention_heads, n_rel)) + ) + self._rel_weight_2.data.normal_(0, 0.1) + + self._scaler_2 = nn.Parameter(torch.zeros(2)) + + else: + self.parser_layers = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(config.n_parser_layers) + ] + ) + + self.distance_ff = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight = nn.Parameter( + torch.zeros((config.num_hidden_layers, config.num_attention_heads, n_rel)) + ) + self._rel_weight.data.normal_(0, 0.1) + + self._scaler = nn.Parameter(torch.zeros(2)) + + self.roberta = RobertaModel(config, add_pooling_layer=False) + + if config.n_cntxt_layers > 0: + self.cntxt_layers.embeddings = self.roberta.embeddings + if config.n_cntxt_layers_2 > 0: + self.cntxt_layers_2.embeddings = self.roberta.embeddings + + self.lm_head = RobertaLMHead(config) + + self.pad = config.pad_token_id + + # The LM head weights require special treatment only when they are tied with the word embeddings + self.update_keys_to_ignore(config, ["lm_head.decoder.weight"]) + + # Initialize weights and apply final processing + self.post_init() + + def get_output_embeddings(self): + return self.lm_head.decoder + + def set_output_embeddings(self, new_embeddings): + self.lm_head.decoder = new_embeddings + + @property + def scaler(self): + return self._scaler.exp() + + @property + def scaler_1(self): + return self._scaler_1.exp() + + @property + def scaler_2(self): + return self._scaler_2.exp() + + @property + def rel_weight(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight, dim=-1) + + @property + def rel_weight_1(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight_1) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight_1, dim=-1) + + + @property + def rel_weight_2(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight_2) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight_2, dim=-1) + + + def compute_block(self, distance, height, n_cntxt_layers=0): + """Compute constituents from distance and height.""" + + if n_cntxt_layers>0: + if n_cntxt_layers == 1: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler_1[0] + elif n_cntxt_layers == 2: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler_2[0] + else: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0] + + gamma = torch.sigmoid(-beta_logits) + ones = torch.ones_like(gamma) + + block_mask_left = cummin( + gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1 + ) + block_mask_left = block_mask_left - F.pad( + block_mask_left[:, :, :-1], (1, 0), value=0 + ) + block_mask_left.tril_(0) + + block_mask_right = cummin( + gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1 + ) + block_mask_right = block_mask_right - F.pad( + block_mask_right[:, :, 1:], (0, 1), value=0 + ) + block_mask_right.triu_(0) + + block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :] + block = cumsum(block_mask_left).tril(0) + cumsum( + block_mask_right, reverse=True + ).triu(1) + + return block_p, block + + def compute_head(self, height, n_cntxt_layers=0): + """Estimate head for each constituent.""" + + _, length = height.size() + if n_cntxt_layers>0: + if n_cntxt_layers == 1: + head_logits = height * self.scaler_1[1] + elif n_cntxt_layers == 2: + head_logits = height * self.scaler_2[1] + else: + head_logits = height * self.scaler[1] + index = torch.arange(length, device=height.device) + + mask = (index[:, None, None] <= index[None, None, :]) * ( + index[None, None, :] <= index[None, :, None] + ) + head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1) + head_logits.masked_fill_(~mask[None, :, :, :], -1e9) + + head_p = torch.softmax(head_logits, dim=-1) + + return head_p + + def parse(self, x, embs=None, n_cntxt_layers=0): + """Parse input sentence. + + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + distance: syntactic distance + height: syntactic height + """ + + mask = x != self.pad + mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0) + + if embs is None: + h = self.roberta.embeddings(x) + else: + h = embs + + if n_cntxt_layers > 0: + if n_cntxt_layers == 1: + parser_layers = self.parser_layers_1 + height_ff = self.height_ff_1 + distance_ff = self.distance_ff_1 + elif n_cntxt_layers == 2: + parser_layers = self.parser_layers_2 + height_ff = self.height_ff_2 + distance_ff = self.distance_ff_2 + for i in range(int(self.config.n_parser_layers/2)): + h = h.masked_fill(~mask[:, :, None], 0) + h = parser_layers[i](h) + + height = height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, dim=-1 + )[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max + ) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where( + margin_left > margin_right, margin_right, margin_left + ).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + else: + for i in range(self.config.n_parser_layers): + h = h.masked_fill(~mask[:, :, None], 0) + h = self.parser_layers[i](h) + + height = self.height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = self.distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, dim=-1 + )[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max + ) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where( + margin_left > margin_right, margin_right, margin_left + ).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + + return distance, height + + def generate_mask(self, x, distance, height, n_cntxt_layers=0): + """Compute head and cibling distribution for each token.""" + + bsz, length = x.size() + + eye = torch.eye(length, device=x.device, dtype=torch.bool) + eye = eye[None, :, :].expand((bsz, -1, -1)) + + block_p, block = self.compute_block(distance, height, n_cntxt_layers=n_cntxt_layers) + head_p = self.compute_head(height, n_cntxt_layers=n_cntxt_layers) + head = torch.einsum("blij,bijh->blh", block_p, head_p) + head = head.masked_fill(eye, 0) + child = head.transpose(1, 2) + cibling = torch.bmm(head, child).masked_fill(eye, 0) + + rel_list = [] + if "head" in self.config.relations: + rel_list.append(head) + if "child" in self.config.relations: + rel_list.append(child) + if "cibling" in self.config.relations: + rel_list.append(cibling) + + rel = torch.stack(rel_list, dim=1) + + if n_cntxt_layers > 0: + if n_cntxt_layers == 1: + rel_weight = self.rel_weight_1 + elif n_cntxt_layers == 2: + rel_weight = self.rel_weight_2 + else: + rel_weight = self.rel_weight + + dep = torch.einsum("lhr,brij->lbhij", rel_weight, rel) + + if n_cntxt_layers == 1: + num_layers = self.cntxt_layers_2.config.num_hidden_layers + else: + num_layers = self.roberta.config.num_hidden_layers + + att_mask = dep.reshape( + num_layers, + bsz, + self.config.num_attention_heads, + length, + length, + ) + + return att_mask, cibling, head, block + + def forward( + self, + input_ids: Optional[torch.LongTensor] = None, + attention_mask: Optional[torch.FloatTensor] = None, + token_type_ids: Optional[torch.LongTensor] = None, + position_ids: Optional[torch.LongTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, MaskedLMOutput]: + r""" + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ..., + config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the + loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]` + kwargs (`Dict[str, any]`, optional, defaults to *{}*): + Used to hide legacy arguments that have been deprecated. + """ + return_dict = ( + return_dict if return_dict is not None else self.config.use_return_dict + ) + + + if self.config.n_cntxt_layers > 0: + cntxt_outputs = self.cntxt_layers( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict) + + + if self.config.n_cntxt_layers_2 > 0: + distance_1, height_1 = self.parse(input_ids, cntxt_outputs[0], n_cntxt_layers=1) + att_mask_1, _, _, _ = self.generate_mask(input_ids, distance_1, height_1, n_cntxt_layers=1) + + cntxt_outputs_2 = self.cntxt_layers_2( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + parser_att_mask=att_mask_1) + + sequence_output = cntxt_outputs_2[0] + + distance_2, height_2 = self.parse(input_ids, sequence_output[0], n_cntxt_layers=2) + att_mask, _, _, _ = self.generate_mask(input_ids, distance_2, height_2, n_cntxt_layers=2) + + elif self.config.n_cntxt_layers > 0: + distance, height = self.parse(input_ids, cntxt_outputs[0]) + att_mask, _, _, _ = self.generate_mask(input_ids, distance, height) + else: + distance, height = self.parse(input_ids) + att_mask, _, _, _ = self.generate_mask(input_ids, distance, height) + + outputs = self.roberta( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + parser_att_mask=att_mask, + ) + sequence_output = outputs[0] + prediction_scores = self.lm_head(sequence_output) + + masked_lm_loss = None + if labels is not None: + loss_fct = CrossEntropyLoss() + masked_lm_loss = loss_fct( + prediction_scores.view(-1, self.config.vocab_size), labels.view(-1) + ) + + if not return_dict: + output = (prediction_scores,) + outputs[2:] + return ( + ((masked_lm_loss,) + output) if masked_lm_loss is not None else output + ) + + return MaskedLMOutput( + loss=masked_lm_loss, + logits=prediction_scores, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +class RobertaLMHead(nn.Module): + """Roberta Head for masked language modeling.""" + + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + + self.decoder = nn.Linear(config.hidden_size, config.vocab_size) + self.bias = nn.Parameter(torch.zeros(config.vocab_size)) + self.decoder.bias = self.bias + + def forward(self, features, **kwargs): + x = self.dense(features) + x = gelu(x) + x = self.layer_norm(x) + + # project back to size of vocabulary with bias + x = self.decoder(x) + + return x + + def _tie_weights(self): + # To tie those two weights if they get disconnected (on TPU or when the bias is resized) + self.bias = self.decoder.bias + + +class StructRobertaForSequenceClassification(RobertaPreTrainedModel): + _keys_to_ignore_on_load_missing = [r"position_ids"] + + def __init__(self, config): + super().__init__(config) + self.num_labels = config.num_labels + self.config = config + + if config.n_cntxt_layers > 0: + config_cntxt = copy.deepcopy(config) + config_cntxt.num_hidden_layers = config.n_cntxt_layers + + self.cntxt_layers = RobertaModel(config_cntxt, add_pooling_layer=False) + + if config.n_cntxt_layers_2 > 0: + self.parser_layers_1 = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(int(config.n_parser_layers/2)) + ] + ) + + self.distance_ff_1 = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff_1 = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight_1 = nn.Parameter( + torch.zeros((config.n_cntxt_layers_2, config.num_attention_heads, n_rel)) + ) + self._rel_weight_1.data.normal_(0, 0.1) + + self._scaler_1 = nn.Parameter(torch.zeros(2)) + + config_cntxt_2 = copy.deepcopy(config) + config_cntxt_2.num_hidden_layers = config.n_cntxt_layers_2 + + self.cntxt_layers_2 = RobertaModel(config_cntxt_2, add_pooling_layer=False) + + + self.parser_layers_2 = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(int(config.n_parser_layers/2)) + ] + ) + + self.distance_ff_2 = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff_2 = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight_2 = nn.Parameter( + torch.zeros((config.num_hidden_layers, config.num_attention_heads, n_rel)) + ) + self._rel_weight_2.data.normal_(0, 0.1) + + self._scaler_2 = nn.Parameter(torch.zeros(2)) + + else: + self.parser_layers = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(config.n_parser_layers) + ] + ) + + self.distance_ff = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight = nn.Parameter( + torch.zeros((config.num_hidden_layers, config.num_attention_heads, n_rel)) + ) + self._rel_weight.data.normal_(0, 0.1) + + self._scaler = nn.Parameter(torch.zeros(2)) + + self.roberta = RobertaModel(config, add_pooling_layer=False) + + if config.n_cntxt_layers > 0: + self.cntxt_layers.embeddings = self.roberta.embeddings + if config.n_cntxt_layers_2 > 0: + self.cntxt_layers_2.embeddings = self.roberta.embeddings + + + self.pad = config.pad_token_id + self.classifier = RobertaClassificationHead(config) + + # Initialize weights and apply final processing + self.post_init() + + + @property + def scaler(self): + return self._scaler.exp() + + @property + def scaler_1(self): + return self._scaler_1.exp() + + @property + def scaler_2(self): + return self._scaler_2.exp() + + @property + def rel_weight(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight, dim=-1) + + @property + def rel_weight_1(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight_1) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight_1, dim=-1) + + + @property + def rel_weight_2(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight_2) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight_2, dim=-1) + + + def compute_block(self, distance, height, n_cntxt_layers=0): + """Compute constituents from distance and height.""" + + if n_cntxt_layers>0: + if n_cntxt_layers == 1: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler_1[0] + elif n_cntxt_layers == 2: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler_2[0] + else: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0] + + gamma = torch.sigmoid(-beta_logits) + ones = torch.ones_like(gamma) + + block_mask_left = cummin( + gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1 + ) + block_mask_left = block_mask_left - F.pad( + block_mask_left[:, :, :-1], (1, 0), value=0 + ) + block_mask_left.tril_(0) + + block_mask_right = cummin( + gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1 + ) + block_mask_right = block_mask_right - F.pad( + block_mask_right[:, :, 1:], (0, 1), value=0 + ) + block_mask_right.triu_(0) + + block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :] + block = cumsum(block_mask_left).tril(0) + cumsum( + block_mask_right, reverse=True + ).triu(1) + + return block_p, block + + def compute_head(self, height, n_cntxt_layers=0): + """Estimate head for each constituent.""" + + _, length = height.size() + if n_cntxt_layers>0: + if n_cntxt_layers == 1: + head_logits = height * self.scaler_1[1] + elif n_cntxt_layers == 2: + head_logits = height * self.scaler_2[1] + else: + head_logits = height * self.scaler[1] + index = torch.arange(length, device=height.device) + + mask = (index[:, None, None] <= index[None, None, :]) * ( + index[None, None, :] <= index[None, :, None] + ) + head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1) + head_logits.masked_fill_(~mask[None, :, :, :], -1e9) + + head_p = torch.softmax(head_logits, dim=-1) + + return head_p + + def parse(self, x, embs=None, n_cntxt_layers=0): + """Parse input sentence. + + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + distance: syntactic distance + height: syntactic height + """ + + mask = x != self.pad + mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0) + + if embs is None: + h = self.roberta.embeddings(x) + else: + h = embs + + if n_cntxt_layers > 0: + if n_cntxt_layers == 1: + parser_layers = self.parser_layers_1 + height_ff = self.height_ff_1 + distance_ff = self.distance_ff_1 + elif n_cntxt_layers == 2: + parser_layers = self.parser_layers_2 + height_ff = self.height_ff_2 + distance_ff = self.distance_ff_2 + for i in range(int(self.config.n_parser_layers/2)): + h = h.masked_fill(~mask[:, :, None], 0) + h = parser_layers[i](h) + + height = height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, dim=-1 + )[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max + ) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where( + margin_left > margin_right, margin_right, margin_left + ).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + else: + for i in range(self.config.n_parser_layers): + h = h.masked_fill(~mask[:, :, None], 0) + h = self.parser_layers[i](h) + + height = self.height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = self.distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, dim=-1 + )[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max + ) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where( + margin_left > margin_right, margin_right, margin_left + ).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + + return distance, height + + def generate_mask(self, x, distance, height, n_cntxt_layers=0): + """Compute head and cibling distribution for each token.""" + + bsz, length = x.size() + + eye = torch.eye(length, device=x.device, dtype=torch.bool) + eye = eye[None, :, :].expand((bsz, -1, -1)) + + block_p, block = self.compute_block(distance, height, n_cntxt_layers=n_cntxt_layers) + head_p = self.compute_head(height, n_cntxt_layers=n_cntxt_layers) + head = torch.einsum("blij,bijh->blh", block_p, head_p) + head = head.masked_fill(eye, 0) + child = head.transpose(1, 2) + cibling = torch.bmm(head, child).masked_fill(eye, 0) + + rel_list = [] + if "head" in self.config.relations: + rel_list.append(head) + if "child" in self.config.relations: + rel_list.append(child) + if "cibling" in self.config.relations: + rel_list.append(cibling) + + rel = torch.stack(rel_list, dim=1) + + if n_cntxt_layers > 0: + if n_cntxt_layers == 1: + rel_weight = self.rel_weight_1 + elif n_cntxt_layers == 2: + rel_weight = self.rel_weight_2 + else: + rel_weight = self.rel_weight + + dep = torch.einsum("lhr,brij->lbhij", rel_weight, rel) + + if n_cntxt_layers == 1: + num_layers = self.cntxt_layers_2.config.num_hidden_layers + else: + num_layers = self.roberta.config.num_hidden_layers + + att_mask = dep.reshape( + num_layers, + bsz, + self.config.num_attention_heads, + length, + length, + ) + + return att_mask, cibling, head, block + + def forward( + self, + input_ids: Optional[torch.LongTensor] = None, + attention_mask: Optional[torch.FloatTensor] = None, + token_type_ids: Optional[torch.LongTensor] = None, + position_ids: Optional[torch.LongTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, SequenceClassifierOutput]: + r""" + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., + config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If + `config.num_labels > 1` a classification loss is computed (Cross-Entropy). + """ + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + if self.config.n_cntxt_layers > 0: + cntxt_outputs = self.cntxt_layers( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict) + + + if self.config.n_cntxt_layers_2 > 0: + distance_1, height_1 = self.parse(input_ids, cntxt_outputs[0], n_cntxt_layers=1) + att_mask_1, _, _, _ = self.generate_mask(input_ids, distance_1, height_1, n_cntxt_layers=1) + + cntxt_outputs_2 = self.cntxt_layers_2( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + parser_att_mask=att_mask_1) + + sequence_output = cntxt_outputs_2[0] + + distance_2, height_2 = self.parse(input_ids, sequence_output[0], n_cntxt_layers=2) + att_mask, _, _, _ = self.generate_mask(input_ids, distance_2, height_2, n_cntxt_layers=2) + + elif self.config.n_cntxt_layers > 0: + distance, height = self.parse(input_ids, cntxt_outputs[0]) + att_mask, _, _, _ = self.generate_mask(input_ids, distance, height) + else: + distance, height = self.parse(input_ids) + att_mask, _, _, _ = self.generate_mask(input_ids, distance, height) + + outputs = self.roberta( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + parser_att_mask=att_mask, + ) + sequence_output = outputs[0] + logits = self.classifier(sequence_output) + + loss = None + if labels is not None: + if self.config.problem_type is None: + if self.num_labels == 1: + self.config.problem_type = "regression" + elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): + self.config.problem_type = "single_label_classification" + else: + self.config.problem_type = "multi_label_classification" + + if self.config.problem_type == "regression": + loss_fct = MSELoss() + if self.num_labels == 1: + loss = loss_fct(logits.squeeze(), labels.squeeze()) + else: + loss = loss_fct(logits, labels) + elif self.config.problem_type == "single_label_classification": + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) + elif self.config.problem_type == "multi_label_classification": + loss_fct = BCEWithLogitsLoss() + loss = loss_fct(logits, labels) + + if not return_dict: + output = (logits,) + outputs[2:] + return ((loss,) + output) if loss is not None else output + + return SequenceClassifierOutput( + loss=loss, + logits=logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +class RobertaClassificationHead(nn.Module): + """Head for sentence-level classification tasks.""" + + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + classifier_dropout = ( + config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob + ) + self.dropout = nn.Dropout(classifier_dropout) + self.out_proj = nn.Linear(config.hidden_size, config.num_labels) + + def forward(self, features, **kwargs): + x = features[:, 0, :] # take token (equiv. to [CLS]) + x = self.dropout(x) + x = self.dense(x) + x = torch.tanh(x) + x = self.dropout(x) + x = self.out_proj(x) + return x + + +def create_position_ids_from_input_ids( + input_ids, padding_idx, past_key_values_length=0 +): + """ + Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols + are ignored. This is modified from fairseq's `utils.make_positions`. + + Args: + x: torch.Tensor x: + + Returns: torch.Tensor + """ + # The series of casts and type-conversions here are carefully balanced to both work with ONNX export and XLA. + mask = input_ids.ne(padding_idx).int() + incremental_indices = ( + torch.cumsum(mask, dim=1).type_as(mask) + past_key_values_length + ) * mask + return incremental_indices.long() + padding_idx + + +def cumprod(x, reverse=False, exclusive=False): + """cumulative product.""" + if reverse: + x = x.flip([-1]) + + if exclusive: + x = F.pad(x[:, :, :-1], (1, 0), value=1) + + cx = x.cumprod(-1) + + if reverse: + cx = cx.flip([-1]) + return cx + + +def cumsum(x, reverse=False, exclusive=False): + """cumulative sum.""" + bsz, _, length = x.size() + device = x.device + if reverse: + if exclusive: + w = torch.ones([bsz, length, length], device=device).tril(-1) + else: + w = torch.ones([bsz, length, length], device=device).tril(0) + cx = torch.bmm(x, w) + else: + if exclusive: + w = torch.ones([bsz, length, length], device=device).triu(1) + else: + w = torch.ones([bsz, length, length], device=device).triu(0) + cx = torch.bmm(x, w) + return cx + + +def cummin(x, reverse=False, exclusive=False, max_value=1e9): + """cumulative min.""" + if reverse: + if exclusive: + x = F.pad(x[:, :, 1:], (0, 1), value=max_value) + x = x.flip([-1]).cummin(-1)[0].flip([-1]) + else: + if exclusive: + x = F.pad(x[:, :, :-1], (1, 0), value=max_value) + x = x.cummin(-1)[0] + return x diff --git a/finetune/control_raising_lexical_content_the/predict_results.txt b/finetune/control_raising_lexical_content_the/predict_results.txt new file mode 100644 index 0000000000000000000000000000000000000000..cbb2c3bf8359f2053bf854f6c815833d638777aa --- /dev/null +++ b/finetune/control_raising_lexical_content_the/predict_results.txt @@ -0,0 +1,20604 @@ +index prediction +0 1 +1 0 +2 0 +3 0 +4 0 +5 0 +6 1 +7 0 +8 0 +9 0 +10 0 +11 0 +12 1 +13 0 +14 0 +15 0 +16 0 +17 0 +18 1 +19 0 +20 0 +21 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See all RoBERTa models at https://huggingface.co/models?filter=roberta +] + + +class StructRobertaConfig(RobertaConfig): + model_type = "roberta" + + def __init__( + self, + n_parser_layers=4, + conv_size=9, + relations=("head", "child"), + weight_act="softmax", + n_cntxt_layers=3, + n_cntxt_layers_2=0, + **kwargs,): + + super().__init__(**kwargs) + self.n_cntxt_layers = n_cntxt_layers + self.n_parser_layers = n_parser_layers + self.n_cntxt_layers_2 = n_cntxt_layers_2 + self.conv_size = conv_size + self.relations = relations + self.weight_act = weight_act + +class Conv1d(nn.Module): + """1D convolution layer.""" + + def __init__(self, hidden_size, kernel_size, dilation=1): + """Initialization. + + Args: + hidden_size: dimension of input embeddings + kernel_size: convolution kernel size + dilation: the spacing between the kernel points + """ + super(Conv1d, self).__init__() + + if kernel_size % 2 == 0: + padding = (kernel_size // 2) * dilation + self.shift = True + else: + padding = ((kernel_size - 1) // 2) * dilation + self.shift = False + self.conv = nn.Conv1d( + hidden_size, hidden_size, kernel_size, padding=padding, dilation=dilation + ) + + def forward(self, x): + """Compute convolution. + + Args: + x: input embeddings + Returns: + conv_output: convolution results + """ + + if self.shift: + return self.conv(x.transpose(1, 2)).transpose(1, 2)[:, 1:] + else: + return self.conv(x.transpose(1, 2)).transpose(1, 2) + + +class RobertaEmbeddings(nn.Module): + """ + Same as BertEmbeddings with a tiny tweak for positional embeddings indexing. + """ + + # Copied from transformers.models.bert.modeling_bert.BertEmbeddings.__init__ + def __init__(self, config): + super().__init__() + self.word_embeddings = nn.Embedding( + config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id + ) + self.position_embeddings = nn.Embedding( + config.max_position_embeddings, config.hidden_size + ) + self.token_type_embeddings = nn.Embedding( + config.type_vocab_size, config.hidden_size + ) + + # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load + # any TensorFlow checkpoint file + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + # position_ids (1, len position emb) is contiguous in memory and exported when serialized + self.position_embedding_type = getattr( + config, "position_embedding_type", "absolute" + ) + self.register_buffer( + "position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)) + ) + if version.parse(torch.__version__) > version.parse("1.6.0"): + self.register_buffer( + "token_type_ids", + torch.zeros(self.position_ids.size(), dtype=torch.long), + persistent=False, + ) + + # End copy + self.padding_idx = config.pad_token_id + self.position_embeddings = nn.Embedding( + config.max_position_embeddings, + config.hidden_size, + padding_idx=self.padding_idx, + ) + + def forward( + self, + input_ids=None, + token_type_ids=None, + position_ids=None, + inputs_embeds=None, + past_key_values_length=0, + ): + if position_ids is None: + if input_ids is not None: + # Create the position ids from the input token ids. Any padded tokens remain padded. + position_ids = create_position_ids_from_input_ids( + input_ids, self.padding_idx, past_key_values_length + ) + else: + position_ids = self.create_position_ids_from_inputs_embeds( + inputs_embeds + ) + + if input_ids is not None: + input_shape = input_ids.size() + else: + input_shape = inputs_embeds.size()[:-1] + + seq_length = input_shape[1] + + # Setting the token_type_ids to the registered buffer in constructor where it is all zeros, which usually occurs + # when its auto-generated, registered buffer helps users when tracing the model without passing token_type_ids, solves + # issue #5664 + if token_type_ids is None: + if hasattr(self, "token_type_ids"): + buffered_token_type_ids = self.token_type_ids[:, :seq_length] + buffered_token_type_ids_expanded = buffered_token_type_ids.expand( + input_shape[0], seq_length + ) + token_type_ids = buffered_token_type_ids_expanded + else: + token_type_ids = torch.zeros( + input_shape, dtype=torch.long, device=self.position_ids.device + ) + + if inputs_embeds is None: + inputs_embeds = self.word_embeddings(input_ids) + token_type_embeddings = self.token_type_embeddings(token_type_ids) + + embeddings = inputs_embeds + token_type_embeddings + if self.position_embedding_type == "absolute": + position_embeddings = self.position_embeddings(position_ids) + embeddings += position_embeddings + embeddings = self.LayerNorm(embeddings) + embeddings = self.dropout(embeddings) + return embeddings + + def create_position_ids_from_inputs_embeds(self, inputs_embeds): + """ + We are provided embeddings directly. We cannot infer which are padded so just generate sequential position ids. + + Args: + inputs_embeds: torch.Tensor + + Returns: torch.Tensor + """ + input_shape = inputs_embeds.size()[:-1] + sequence_length = input_shape[1] + + position_ids = torch.arange( + self.padding_idx + 1, + sequence_length + self.padding_idx + 1, + dtype=torch.long, + device=inputs_embeds.device, + ) + return position_ids.unsqueeze(0).expand(input_shape) + + +# Copied from transformers.models.bert.modeling_bert.BertSelfAttention with Bert->Roberta +class RobertaSelfAttention(nn.Module): + def __init__(self, config, position_embedding_type=None): + super().__init__() + if config.hidden_size % config.num_attention_heads != 0 and not hasattr( + config, "embedding_size" + ): + raise ValueError( + f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention " + f"heads ({config.num_attention_heads})" + ) + + self.num_attention_heads = config.num_attention_heads + self.attention_head_size = int(config.hidden_size / config.num_attention_heads) + self.all_head_size = self.num_attention_heads * self.attention_head_size + + self.query = nn.Linear(config.hidden_size, self.all_head_size) + self.key = nn.Linear(config.hidden_size, self.all_head_size) + self.value = nn.Linear(config.hidden_size, self.all_head_size) + + self.dropout = nn.Dropout(config.attention_probs_dropout_prob) + self.position_embedding_type = position_embedding_type or getattr( + config, "position_embedding_type", "absolute" + ) + if ( + self.position_embedding_type == "relative_key" + or self.position_embedding_type == "relative_key_query" + ): + self.max_position_embeddings = config.max_position_embeddings + self.distance_embedding = nn.Embedding( + 2 * config.max_position_embeddings - 1, self.attention_head_size + ) + + self.is_decoder = config.is_decoder + + def transpose_for_scores(self, x): + new_x_shape = x.size()[:-1] + ( + self.num_attention_heads, + self.attention_head_size, + ) + x = x.view(new_x_shape) + return x.permute(0, 2, 1, 3) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.FloatTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, + output_attentions: Optional[bool] = False, + parser_att_mask=None, + ) -> Tuple[torch.Tensor]: + mixed_query_layer = self.query(hidden_states) + + # If this is instantiated as a cross-attention module, the keys + # and values come from an encoder; the attention mask needs to be + # such that the encoder's padding tokens are not attended to. + is_cross_attention = encoder_hidden_states is not None + + if is_cross_attention and past_key_value is not None: + # reuse k,v, cross_attentions + key_layer = past_key_value[0] + value_layer = past_key_value[1] + attention_mask = encoder_attention_mask + elif is_cross_attention: + key_layer = self.transpose_for_scores(self.key(encoder_hidden_states)) + value_layer = self.transpose_for_scores(self.value(encoder_hidden_states)) + attention_mask = encoder_attention_mask + elif past_key_value is not None: + key_layer = self.transpose_for_scores(self.key(hidden_states)) + value_layer = self.transpose_for_scores(self.value(hidden_states)) + key_layer = torch.cat([past_key_value[0], key_layer], dim=2) + value_layer = torch.cat([past_key_value[1], value_layer], dim=2) + else: + key_layer = self.transpose_for_scores(self.key(hidden_states)) + value_layer = self.transpose_for_scores(self.value(hidden_states)) + + query_layer = self.transpose_for_scores(mixed_query_layer) + + if self.is_decoder: + # if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states. + # Further calls to cross_attention layer can then reuse all cross-attention + # key/value_states (first "if" case) + # if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of + # all previous decoder key/value_states. Further calls to uni-directional self-attention + # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case) + # if encoder bi-directional self-attention `past_key_value` is always `None` + past_key_value = (key_layer, value_layer) + + # Take the dot product between "query" and "key" to get the raw attention scores. + attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) + + if ( + self.position_embedding_type == "relative_key" + or self.position_embedding_type == "relative_key_query" + ): + seq_length = hidden_states.size()[1] + position_ids_l = torch.arange( + seq_length, dtype=torch.long, device=hidden_states.device + ).view(-1, 1) + position_ids_r = torch.arange( + seq_length, dtype=torch.long, device=hidden_states.device + ).view(1, -1) + distance = position_ids_l - position_ids_r + positional_embedding = self.distance_embedding( + distance + self.max_position_embeddings - 1 + ) + positional_embedding = positional_embedding.to( + dtype=query_layer.dtype + ) # fp16 compatibility + + if self.position_embedding_type == "relative_key": + relative_position_scores = torch.einsum( + "bhld,lrd->bhlr", query_layer, positional_embedding + ) + attention_scores = attention_scores + relative_position_scores + elif self.position_embedding_type == "relative_key_query": + relative_position_scores_query = torch.einsum( + "bhld,lrd->bhlr", query_layer, positional_embedding + ) + relative_position_scores_key = torch.einsum( + "bhrd,lrd->bhlr", key_layer, positional_embedding + ) + attention_scores = ( + attention_scores + + relative_position_scores_query + + relative_position_scores_key + ) + + attention_scores = attention_scores / math.sqrt(self.attention_head_size) + if attention_mask is not None: + # Apply the attention mask is (precomputed for all layers in RobertaModel forward() function) + attention_scores = attention_scores + attention_mask + + if parser_att_mask is None: + # Normalize the attention scores to probabilities. + attention_probs = nn.functional.softmax(attention_scores, dim=-1) + else: + attention_probs = torch.sigmoid(attention_scores) * parser_att_mask + + # This is actually dropping out entire tokens to attend to, which might + # seem a bit unusual, but is taken from the original Transformer paper. + attention_probs = self.dropout(attention_probs) + + # Mask heads if we want to + if head_mask is not None: + attention_probs = attention_probs * head_mask + + context_layer = torch.matmul(attention_probs, value_layer) + + context_layer = context_layer.permute(0, 2, 1, 3).contiguous() + new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) + context_layer = context_layer.view(new_context_layer_shape) + + outputs = ( + (context_layer, attention_probs) if output_attentions else (context_layer,) + ) + + if self.is_decoder: + outputs = outputs + (past_key_value,) + return outputs + + +# Copied from transformers.models.bert.modeling_bert.BertSelfOutput +class RobertaSelfOutput(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward( + self, hidden_states: torch.Tensor, input_tensor: torch.Tensor + ) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + hidden_states = self.LayerNorm(hidden_states + input_tensor) + return hidden_states + + +# Copied from transformers.models.bert.modeling_bert.BertAttention with Bert->Roberta +class RobertaAttention(nn.Module): + def __init__(self, config, position_embedding_type=None): + super().__init__() + self.self = RobertaSelfAttention( + config, position_embedding_type=position_embedding_type + ) + self.output = RobertaSelfOutput(config) + self.pruned_heads = set() + + def prune_heads(self, heads): + if len(heads) == 0: + return + heads, index = find_pruneable_heads_and_indices( + heads, + self.self.num_attention_heads, + self.self.attention_head_size, + self.pruned_heads, + ) + + # Prune linear layers + self.self.query = prune_linear_layer(self.self.query, index) + self.self.key = prune_linear_layer(self.self.key, index) + self.self.value = prune_linear_layer(self.self.value, index) + self.output.dense = prune_linear_layer(self.output.dense, index, dim=1) + + # Update hyper params and store pruned heads + self.self.num_attention_heads = self.self.num_attention_heads - len(heads) + self.self.all_head_size = ( + self.self.attention_head_size * self.self.num_attention_heads + ) + self.pruned_heads = self.pruned_heads.union(heads) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.FloatTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, + output_attentions: Optional[bool] = False, + parser_att_mask=None, + ) -> Tuple[torch.Tensor]: + self_outputs = self.self( + hidden_states, + attention_mask, + head_mask, + encoder_hidden_states, + encoder_attention_mask, + past_key_value, + output_attentions, + parser_att_mask=parser_att_mask, + ) + attention_output = self.output(self_outputs[0], hidden_states) + outputs = (attention_output,) + self_outputs[ + 1: + ] # add attentions if we output them + return outputs + + +# Copied from transformers.models.bert.modeling_bert.BertIntermediate +class RobertaIntermediate(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.intermediate_size) + if isinstance(config.hidden_act, str): + self.intermediate_act_fn = ACT2FN[config.hidden_act] + else: + self.intermediate_act_fn = config.hidden_act + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.intermediate_act_fn(hidden_states) + return hidden_states + + +# Copied from transformers.models.bert.modeling_bert.BertOutput +class RobertaOutput(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.intermediate_size, config.hidden_size) + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward( + self, hidden_states: torch.Tensor, input_tensor: torch.Tensor + ) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + hidden_states = self.LayerNorm(hidden_states + input_tensor) + return hidden_states + + +# Copied from transformers.models.bert.modeling_bert.BertLayer with Bert->Roberta +class RobertaLayer(nn.Module): + def __init__(self, config): + super().__init__() + self.chunk_size_feed_forward = config.chunk_size_feed_forward + self.seq_len_dim = 1 + self.attention = RobertaAttention(config) + self.is_decoder = config.is_decoder + self.add_cross_attention = config.add_cross_attention + if self.add_cross_attention: + if not self.is_decoder: + raise ValueError( + f"{self} should be used as a decoder model if cross attention is added" + ) + self.crossattention = RobertaAttention( + config, position_embedding_type="absolute" + ) + self.intermediate = RobertaIntermediate(config) + self.output = RobertaOutput(config) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.FloatTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, + output_attentions: Optional[bool] = False, + parser_att_mask=None, + ) -> Tuple[torch.Tensor]: + # decoder uni-directional self-attention cached key/values tuple is at positions 1,2 + self_attn_past_key_value = ( + past_key_value[:2] if past_key_value is not None else None + ) + self_attention_outputs = self.attention( + hidden_states, + attention_mask, + head_mask, + output_attentions=output_attentions, + past_key_value=self_attn_past_key_value, + parser_att_mask=parser_att_mask, + ) + attention_output = self_attention_outputs[0] + + # if decoder, the last output is tuple of self-attn cache + if self.is_decoder: + outputs = self_attention_outputs[1:-1] + present_key_value = self_attention_outputs[-1] + else: + outputs = self_attention_outputs[ + 1: + ] # add self attentions if we output attention weights + + cross_attn_present_key_value = None + if self.is_decoder and encoder_hidden_states is not None: + if not hasattr(self, "crossattention"): + raise ValueError( + f"If `encoder_hidden_states` are passed, {self} has to be instantiated with cross-attention layers by setting `config.add_cross_attention=True`" + ) + + # cross_attn cached key/values tuple is at positions 3,4 of past_key_value tuple + cross_attn_past_key_value = ( + past_key_value[-2:] if past_key_value is not None else None + ) + cross_attention_outputs = self.crossattention( + attention_output, + attention_mask, + head_mask, + encoder_hidden_states, + encoder_attention_mask, + cross_attn_past_key_value, + output_attentions, + ) + attention_output = cross_attention_outputs[0] + outputs = ( + outputs + cross_attention_outputs[1:-1] + ) # add cross attentions if we output attention weights + + # add cross-attn cache to positions 3,4 of present_key_value tuple + cross_attn_present_key_value = cross_attention_outputs[-1] + present_key_value = present_key_value + cross_attn_present_key_value + + layer_output = apply_chunking_to_forward( + self.feed_forward_chunk, + self.chunk_size_feed_forward, + self.seq_len_dim, + attention_output, + ) + outputs = (layer_output,) + outputs + + # if decoder, return the attn key/values as the last output + if self.is_decoder: + outputs = outputs + (present_key_value,) + + return outputs + + def feed_forward_chunk(self, attention_output): + intermediate_output = self.intermediate(attention_output) + layer_output = self.output(intermediate_output, attention_output) + return layer_output + + +# Copied from transformers.models.bert.modeling_bert.BertEncoder with Bert->Roberta +class RobertaEncoder(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + self.layer = nn.ModuleList( + [RobertaLayer(config) for _ in range(config.num_hidden_layers)] + ) + self.gradient_checkpointing = False + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.FloatTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = False, + output_hidden_states: Optional[bool] = False, + return_dict: Optional[bool] = True, + parser_att_mask=None, + ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPastAndCrossAttentions]: + all_hidden_states = () if output_hidden_states else None + all_self_attentions = () if output_attentions else None + all_cross_attentions = ( + () if output_attentions and self.config.add_cross_attention else None + ) + + next_decoder_cache = () if use_cache else None + for i, layer_module in enumerate(self.layer): + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + layer_head_mask = head_mask[i] if head_mask is not None else None + past_key_value = past_key_values[i] if past_key_values is not None else None + + if self.gradient_checkpointing and self.training: + + if use_cache: + logger.warning( + "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." + ) + use_cache = False + + def create_custom_forward(module): + def custom_forward(*inputs): + return module(*inputs, past_key_value, output_attentions) + + return custom_forward + + layer_outputs = torch.utils.checkpoint.checkpoint( + create_custom_forward(layer_module), + hidden_states, + attention_mask, + layer_head_mask, + encoder_hidden_states, + encoder_attention_mask, + ) + else: + if parser_att_mask is not None: + layer_outputs = layer_module( + hidden_states, + attention_mask, + layer_head_mask, + encoder_hidden_states, + encoder_attention_mask, + past_key_value, + output_attentions, + parser_att_mask=parser_att_mask[i]) + else: + layer_outputs = layer_module( + hidden_states, + attention_mask, + layer_head_mask, + encoder_hidden_states, + encoder_attention_mask, + past_key_value, + output_attentions, + parser_att_mask=None) + + + hidden_states = layer_outputs[0] + if use_cache: + next_decoder_cache += (layer_outputs[-1],) + if output_attentions: + all_self_attentions = all_self_attentions + (layer_outputs[1],) + if self.config.add_cross_attention: + all_cross_attentions = all_cross_attentions + (layer_outputs[2],) + + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + if not return_dict: + return tuple( + v + for v in [ + hidden_states, + next_decoder_cache, + all_hidden_states, + all_self_attentions, + all_cross_attentions, + ] + if v is not None + ) + return BaseModelOutputWithPastAndCrossAttentions( + last_hidden_state=hidden_states, + past_key_values=next_decoder_cache, + hidden_states=all_hidden_states, + attentions=all_self_attentions, + cross_attentions=all_cross_attentions, + ) + + +# Copied from transformers.models.bert.modeling_bert.BertPooler +class RobertaPooler(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.activation = nn.Tanh() + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + # We "pool" the model by simply taking the hidden state corresponding + # to the first token. + first_token_tensor = hidden_states[:, 0] + pooled_output = self.dense(first_token_tensor) + pooled_output = self.activation(pooled_output) + return pooled_output + + +class RobertaPreTrainedModel(PreTrainedModel): + """ + An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained + models. + """ + + config_class = RobertaConfig + base_model_prefix = "roberta" + supports_gradient_checkpointing = True + + # Copied from transformers.models.bert.modeling_bert.BertPreTrainedModel._init_weights + def _init_weights(self, module): + """Initialize the weights""" + if isinstance(module, nn.Linear): + # Slightly different from the TF version which uses truncated_normal for initialization + # cf https://github.com/pytorch/pytorch/pull/5617 + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.Embedding): + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.padding_idx is not None: + module.weight.data[module.padding_idx].zero_() + elif isinstance(module, nn.LayerNorm): + if module.bias is not None: + module.bias.data.zero_() + module.weight.data.fill_(1.0) + + def _set_gradient_checkpointing(self, module, value=False): + if isinstance(module, RobertaEncoder): + module.gradient_checkpointing = value + + def update_keys_to_ignore(self, config, del_keys_to_ignore): + """Remove some keys from ignore list""" + if not config.tie_word_embeddings: + # must make a new list, or the class variable gets modified! + self._keys_to_ignore_on_save = [ + k for k in self._keys_to_ignore_on_save if k not in del_keys_to_ignore + ] + self._keys_to_ignore_on_load_missing = [ + k + for k in self._keys_to_ignore_on_load_missing + if k not in del_keys_to_ignore + ] + + +ROBERTA_START_DOCSTRING = r""" + + This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the + library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads + etc.) + + This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. + Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage + and behavior. + + Parameters: + config ([`RobertaConfig`]): Model configuration class with all the parameters of the + model. Initializing with a config file does not load the weights associated with the model, only the + configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. +""" + + +ROBERTA_INPUTS_DOCSTRING = r""" + Args: + input_ids (`torch.LongTensor` of shape `({0})`): + Indices of input sequence tokens in the vocabulary. + + Indices can be obtained using [`RobertaTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + [What are input IDs?](../glossary#input-ids) + attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*): + Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + + [What are attention masks?](../glossary#attention-mask) + token_type_ids (`torch.LongTensor` of shape `({0})`, *optional*): + Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, + 1]`: + + - 0 corresponds to a *sentence A* token, + - 1 corresponds to a *sentence B* token. + + [What are token type IDs?](../glossary#token-type-ids) + position_ids (`torch.LongTensor` of shape `({0})`, *optional*): + Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, + config.max_position_embeddings - 1]`. + + [What are position IDs?](../glossary#position-ids) + head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): + Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: + + - 1 indicates the head is **not masked**, + - 0 indicates the head is **masked**. + + inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_size)`, *optional*): + Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This + is useful if you want more control over how to convert `input_ids` indices into associated vectors than the + model's internal embedding lookup matrix. + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned + tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for + more detail. + return_dict (`bool`, *optional*): + Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. +""" + + +class RobertaModel(RobertaPreTrainedModel): + """ + + The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of + cross-attention is added between the self-attention layers, following the architecture described in *Attention is + all you need*_ by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz + Kaiser and Illia Polosukhin. + + To behave as an decoder the model needs to be initialized with the `is_decoder` argument of the configuration set + to `True`. To be used in a Seq2Seq model, the model needs to initialized with both `is_decoder` argument and + `add_cross_attention` set to `True`; an `encoder_hidden_states` is then expected as an input to the forward pass. + + .. _*Attention is all you need*: https://arxiv.org/abs/1706.03762 + + """ + + _keys_to_ignore_on_load_missing = [r"position_ids"] + + # Copied from transformers.models.bert.modeling_bert.BertModel.__init__ with Bert->Roberta + def __init__(self, config, add_pooling_layer=True): + super().__init__(config) + self.config = config + + self.embeddings = RobertaEmbeddings(config) + self.encoder = RobertaEncoder(config) + + self.pooler = RobertaPooler(config) if add_pooling_layer else None + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.embeddings.word_embeddings + + def set_input_embeddings(self, value): + self.embeddings.word_embeddings = value + + def _prune_heads(self, heads_to_prune): + """ + Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base + class PreTrainedModel + """ + for layer, heads in heads_to_prune.items(): + self.encoder.layer[layer].attention.prune_heads(heads) + + # Copied from transformers.models.bert.modeling_bert.BertModel.forward + def forward( + self, + input_ids: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + token_type_ids: Optional[torch.Tensor] = None, + position_ids: Optional[torch.Tensor] = None, + head_mask: Optional[torch.Tensor] = None, + inputs_embeds: Optional[torch.Tensor] = None, + encoder_hidden_states: Optional[torch.Tensor] = None, + encoder_attention_mask: Optional[torch.Tensor] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + parser_att_mask=None, + ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPoolingAndCrossAttentions]: + r""" + encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): + Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if + the model is configured as a decoder. + encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*): + Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in + the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): + Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. + + If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that + don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all + `decoder_input_ids` of shape `(batch_size, sequence_length)`. + use_cache (`bool`, *optional*): + If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see + `past_key_values`). + """ + output_attentions = ( + output_attentions + if output_attentions is not None + else self.config.output_attentions + ) + output_hidden_states = ( + output_hidden_states + if output_hidden_states is not None + else self.config.output_hidden_states + ) + return_dict = ( + return_dict if return_dict is not None else self.config.use_return_dict + ) + + if self.config.is_decoder: + use_cache = use_cache if use_cache is not None else self.config.use_cache + else: + use_cache = False + + if input_ids is not None and inputs_embeds is not None: + raise ValueError( + "You cannot specify both input_ids and inputs_embeds at the same time" + ) + elif input_ids is not None: + input_shape = input_ids.size() + elif inputs_embeds is not None: + input_shape = inputs_embeds.size()[:-1] + else: + raise ValueError("You have to specify either input_ids or inputs_embeds") + + batch_size, seq_length = input_shape + device = input_ids.device if input_ids is not None else inputs_embeds.device + + # past_key_values_length + past_key_values_length = ( + past_key_values[0][0].shape[2] if past_key_values is not None else 0 + ) + + if attention_mask is None: + attention_mask = torch.ones( + ((batch_size, seq_length + past_key_values_length)), device=device + ) + + if token_type_ids is None: + if hasattr(self.embeddings, "token_type_ids"): + buffered_token_type_ids = self.embeddings.token_type_ids[:, :seq_length] + buffered_token_type_ids_expanded = buffered_token_type_ids.expand( + batch_size, seq_length + ) + token_type_ids = buffered_token_type_ids_expanded + else: + token_type_ids = torch.zeros( + input_shape, dtype=torch.long, device=device + ) + + # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length] + # ourselves in which case we just need to make it broadcastable to all heads. + extended_attention_mask: torch.Tensor = self.get_extended_attention_mask( + attention_mask, input_shape, device + ) + + # If a 2D or 3D attention mask is provided for the cross-attention + # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length] + if self.config.is_decoder and encoder_hidden_states is not None: + ( + encoder_batch_size, + encoder_sequence_length, + _, + ) = encoder_hidden_states.size() + encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length) + if encoder_attention_mask is None: + encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device) + encoder_extended_attention_mask = self.invert_attention_mask( + encoder_attention_mask + ) + else: + encoder_extended_attention_mask = None + + # Prepare head mask if needed + # 1.0 in head_mask indicate we keep the head + # attention_probs has shape bsz x n_heads x N x N + # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] + # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] + head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) + + embedding_output = self.embeddings( + input_ids=input_ids, + position_ids=position_ids, + token_type_ids=token_type_ids, + inputs_embeds=inputs_embeds, + past_key_values_length=past_key_values_length, + ) + encoder_outputs = self.encoder( + embedding_output, + attention_mask=extended_attention_mask, + head_mask=head_mask, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_extended_attention_mask, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + parser_att_mask=parser_att_mask, + ) + sequence_output = encoder_outputs[0] + pooled_output = ( + self.pooler(sequence_output) if self.pooler is not None else None + ) + + if not return_dict: + return (sequence_output, pooled_output) + encoder_outputs[1:] + + return BaseModelOutputWithPoolingAndCrossAttentions( + last_hidden_state=sequence_output, + pooler_output=pooled_output, + past_key_values=encoder_outputs.past_key_values, + hidden_states=encoder_outputs.hidden_states, + attentions=encoder_outputs.attentions, + cross_attentions=encoder_outputs.cross_attentions, + ) + + +class StructRoberta(RobertaPreTrainedModel): + _keys_to_ignore_on_save = [r"lm_head.decoder.weight", r"lm_head.decoder.bias"] + _keys_to_ignore_on_load_missing = [ + r"position_ids", + r"lm_head.decoder.weight", + r"lm_head.decoder.bias", + ] + _keys_to_ignore_on_load_unexpected = [r"pooler"] + + def __init__(self, config): + super().__init__(config) + + if config.is_decoder: + logger.warning( + "If you want to use `RobertaForMaskedLM` make sure `config.is_decoder=False` for " + "bi-directional self-attention." + ) + + + if config.n_cntxt_layers > 0: + config_cntxt = copy.deepcopy(config) + config_cntxt.num_hidden_layers = config.n_cntxt_layers + + self.cntxt_layers = RobertaModel(config_cntxt, add_pooling_layer=False) + + if config.n_cntxt_layers_2 > 0: + self.parser_layers_1 = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(int(config.n_parser_layers/2)) + ] + ) + + self.distance_ff_1 = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff_1 = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight_1 = nn.Parameter( + torch.zeros((config.n_cntxt_layers_2, config.num_attention_heads, n_rel)) + ) + self._rel_weight_1.data.normal_(0, 0.1) + + self._scaler_1 = nn.Parameter(torch.zeros(2)) + + config_cntxt_2 = copy.deepcopy(config) + config_cntxt_2.num_hidden_layers = config.n_cntxt_layers_2 + + self.cntxt_layers_2 = RobertaModel(config_cntxt_2, add_pooling_layer=False) + + + self.parser_layers_2 = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(int(config.n_parser_layers/2)) + ] + ) + + self.distance_ff_2 = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff_2 = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight_2 = nn.Parameter( + torch.zeros((config.num_hidden_layers, config.num_attention_heads, n_rel)) + ) + self._rel_weight_2.data.normal_(0, 0.1) + + self._scaler_2 = nn.Parameter(torch.zeros(2)) + + else: + self.parser_layers = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(config.n_parser_layers) + ] + ) + + self.distance_ff = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight = nn.Parameter( + torch.zeros((config.num_hidden_layers, config.num_attention_heads, n_rel)) + ) + self._rel_weight.data.normal_(0, 0.1) + + self._scaler = nn.Parameter(torch.zeros(2)) + + self.roberta = RobertaModel(config, add_pooling_layer=False) + + if config.n_cntxt_layers > 0: + self.cntxt_layers.embeddings = self.roberta.embeddings + if config.n_cntxt_layers_2 > 0: + self.cntxt_layers_2.embeddings = self.roberta.embeddings + + self.lm_head = RobertaLMHead(config) + + self.pad = config.pad_token_id + + # The LM head weights require special treatment only when they are tied with the word embeddings + self.update_keys_to_ignore(config, ["lm_head.decoder.weight"]) + + # Initialize weights and apply final processing + self.post_init() + + def get_output_embeddings(self): + return self.lm_head.decoder + + def set_output_embeddings(self, new_embeddings): + self.lm_head.decoder = new_embeddings + + @property + def scaler(self): + return self._scaler.exp() + + @property + def scaler_1(self): + return self._scaler_1.exp() + + @property + def scaler_2(self): + return self._scaler_2.exp() + + @property + def rel_weight(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight, dim=-1) + + @property + def rel_weight_1(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight_1) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight_1, dim=-1) + + + @property + def rel_weight_2(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight_2) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight_2, dim=-1) + + + def compute_block(self, distance, height, n_cntxt_layers=0): + """Compute constituents from distance and height.""" + + if n_cntxt_layers>0: + if n_cntxt_layers == 1: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler_1[0] + elif n_cntxt_layers == 2: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler_2[0] + else: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0] + + gamma = torch.sigmoid(-beta_logits) + ones = torch.ones_like(gamma) + + block_mask_left = cummin( + gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1 + ) + block_mask_left = block_mask_left - F.pad( + block_mask_left[:, :, :-1], (1, 0), value=0 + ) + block_mask_left.tril_(0) + + block_mask_right = cummin( + gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1 + ) + block_mask_right = block_mask_right - F.pad( + block_mask_right[:, :, 1:], (0, 1), value=0 + ) + block_mask_right.triu_(0) + + block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :] + block = cumsum(block_mask_left).tril(0) + cumsum( + block_mask_right, reverse=True + ).triu(1) + + return block_p, block + + def compute_head(self, height, n_cntxt_layers=0): + """Estimate head for each constituent.""" + + _, length = height.size() + if n_cntxt_layers>0: + if n_cntxt_layers == 1: + head_logits = height * self.scaler_1[1] + elif n_cntxt_layers == 2: + head_logits = height * self.scaler_2[1] + else: + head_logits = height * self.scaler[1] + index = torch.arange(length, device=height.device) + + mask = (index[:, None, None] <= index[None, None, :]) * ( + index[None, None, :] <= index[None, :, None] + ) + head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1) + head_logits.masked_fill_(~mask[None, :, :, :], -1e9) + + head_p = torch.softmax(head_logits, dim=-1) + + return head_p + + def parse(self, x, embs=None, n_cntxt_layers=0): + """Parse input sentence. + + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + distance: syntactic distance + height: syntactic height + """ + + mask = x != self.pad + mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0) + + if embs is None: + h = self.roberta.embeddings(x) + else: + h = embs + + if n_cntxt_layers > 0: + if n_cntxt_layers == 1: + parser_layers = self.parser_layers_1 + height_ff = self.height_ff_1 + distance_ff = self.distance_ff_1 + elif n_cntxt_layers == 2: + parser_layers = self.parser_layers_2 + height_ff = self.height_ff_2 + distance_ff = self.distance_ff_2 + for i in range(int(self.config.n_parser_layers/2)): + h = h.masked_fill(~mask[:, :, None], 0) + h = parser_layers[i](h) + + height = height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, dim=-1 + )[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max + ) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where( + margin_left > margin_right, margin_right, margin_left + ).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + else: + for i in range(self.config.n_parser_layers): + h = h.masked_fill(~mask[:, :, None], 0) + h = self.parser_layers[i](h) + + height = self.height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = self.distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, dim=-1 + )[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max + ) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where( + margin_left > margin_right, margin_right, margin_left + ).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + + return distance, height + + def generate_mask(self, x, distance, height, n_cntxt_layers=0): + """Compute head and cibling distribution for each token.""" + + bsz, length = x.size() + + eye = torch.eye(length, device=x.device, dtype=torch.bool) + eye = eye[None, :, :].expand((bsz, -1, -1)) + + block_p, block = self.compute_block(distance, height, n_cntxt_layers=n_cntxt_layers) + head_p = self.compute_head(height, n_cntxt_layers=n_cntxt_layers) + head = torch.einsum("blij,bijh->blh", block_p, head_p) + head = head.masked_fill(eye, 0) + child = head.transpose(1, 2) + cibling = torch.bmm(head, child).masked_fill(eye, 0) + + rel_list = [] + if "head" in self.config.relations: + rel_list.append(head) + if "child" in self.config.relations: + rel_list.append(child) + if "cibling" in self.config.relations: + rel_list.append(cibling) + + rel = torch.stack(rel_list, dim=1) + + if n_cntxt_layers > 0: + if n_cntxt_layers == 1: + rel_weight = self.rel_weight_1 + elif n_cntxt_layers == 2: + rel_weight = self.rel_weight_2 + else: + rel_weight = self.rel_weight + + dep = torch.einsum("lhr,brij->lbhij", rel_weight, rel) + + if n_cntxt_layers == 1: + num_layers = self.cntxt_layers_2.config.num_hidden_layers + else: + num_layers = self.roberta.config.num_hidden_layers + + att_mask = dep.reshape( + num_layers, + bsz, + self.config.num_attention_heads, + length, + length, + ) + + return att_mask, cibling, head, block + + def forward( + self, + input_ids: Optional[torch.LongTensor] = None, + attention_mask: Optional[torch.FloatTensor] = None, + token_type_ids: Optional[torch.LongTensor] = None, + position_ids: Optional[torch.LongTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, MaskedLMOutput]: + r""" + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ..., + config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the + loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]` + kwargs (`Dict[str, any]`, optional, defaults to *{}*): + Used to hide legacy arguments that have been deprecated. + """ + return_dict = ( + return_dict if return_dict is not None else self.config.use_return_dict + ) + + + if self.config.n_cntxt_layers > 0: + cntxt_outputs = self.cntxt_layers( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict) + + + if self.config.n_cntxt_layers_2 > 0: + distance_1, height_1 = self.parse(input_ids, cntxt_outputs[0], n_cntxt_layers=1) + att_mask_1, _, _, _ = self.generate_mask(input_ids, distance_1, height_1, n_cntxt_layers=1) + + cntxt_outputs_2 = self.cntxt_layers_2( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + parser_att_mask=att_mask_1) + + sequence_output = cntxt_outputs_2[0] + + distance_2, height_2 = self.parse(input_ids, sequence_output[0], n_cntxt_layers=2) + att_mask, _, _, _ = self.generate_mask(input_ids, distance_2, height_2, n_cntxt_layers=2) + + elif self.config.n_cntxt_layers > 0: + distance, height = self.parse(input_ids, cntxt_outputs[0]) + att_mask, _, _, _ = self.generate_mask(input_ids, distance, height) + else: + distance, height = self.parse(input_ids) + att_mask, _, _, _ = self.generate_mask(input_ids, distance, height) + + outputs = self.roberta( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + parser_att_mask=att_mask, + ) + sequence_output = outputs[0] + prediction_scores = self.lm_head(sequence_output) + + masked_lm_loss = None + if labels is not None: + loss_fct = CrossEntropyLoss() + masked_lm_loss = loss_fct( + prediction_scores.view(-1, self.config.vocab_size), labels.view(-1) + ) + + if not return_dict: + output = (prediction_scores,) + outputs[2:] + return ( + ((masked_lm_loss,) + output) if masked_lm_loss is not None else output + ) + + return MaskedLMOutput( + loss=masked_lm_loss, + logits=prediction_scores, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +class RobertaLMHead(nn.Module): + """Roberta Head for masked language modeling.""" + + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + + self.decoder = nn.Linear(config.hidden_size, config.vocab_size) + self.bias = nn.Parameter(torch.zeros(config.vocab_size)) + self.decoder.bias = self.bias + + def forward(self, features, **kwargs): + x = self.dense(features) + x = gelu(x) + x = self.layer_norm(x) + + # project back to size of vocabulary with bias + x = self.decoder(x) + + return x + + def _tie_weights(self): + # To tie those two weights if they get disconnected (on TPU or when the bias is resized) + self.bias = self.decoder.bias + + +class StructRobertaForSequenceClassification(RobertaPreTrainedModel): + _keys_to_ignore_on_load_missing = [r"position_ids"] + + def __init__(self, config): + super().__init__(config) + self.num_labels = config.num_labels + self.config = config + + if config.n_cntxt_layers > 0: + config_cntxt = copy.deepcopy(config) + config_cntxt.num_hidden_layers = config.n_cntxt_layers + + self.cntxt_layers = RobertaModel(config_cntxt, add_pooling_layer=False) + + if config.n_cntxt_layers_2 > 0: + self.parser_layers_1 = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(int(config.n_parser_layers/2)) + ] + ) + + self.distance_ff_1 = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff_1 = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight_1 = nn.Parameter( + torch.zeros((config.n_cntxt_layers_2, config.num_attention_heads, n_rel)) + ) + self._rel_weight_1.data.normal_(0, 0.1) + + self._scaler_1 = nn.Parameter(torch.zeros(2)) + + config_cntxt_2 = copy.deepcopy(config) + config_cntxt_2.num_hidden_layers = config.n_cntxt_layers_2 + + self.cntxt_layers_2 = RobertaModel(config_cntxt_2, add_pooling_layer=False) + + + self.parser_layers_2 = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(int(config.n_parser_layers/2)) + ] + ) + + self.distance_ff_2 = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff_2 = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight_2 = nn.Parameter( + torch.zeros((config.num_hidden_layers, config.num_attention_heads, n_rel)) + ) + self._rel_weight_2.data.normal_(0, 0.1) + + self._scaler_2 = nn.Parameter(torch.zeros(2)) + + else: + self.parser_layers = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(config.n_parser_layers) + ] + ) + + self.distance_ff = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight = nn.Parameter( + torch.zeros((config.num_hidden_layers, config.num_attention_heads, n_rel)) + ) + self._rel_weight.data.normal_(0, 0.1) + + self._scaler = nn.Parameter(torch.zeros(2)) + + self.roberta = RobertaModel(config, add_pooling_layer=False) + + if config.n_cntxt_layers > 0: + self.cntxt_layers.embeddings = self.roberta.embeddings + if config.n_cntxt_layers_2 > 0: + self.cntxt_layers_2.embeddings = self.roberta.embeddings + + + self.pad = config.pad_token_id + self.classifier = RobertaClassificationHead(config) + + # Initialize weights and apply final processing + self.post_init() + + + @property + def scaler(self): + return self._scaler.exp() + + @property + def scaler_1(self): + return self._scaler_1.exp() + + @property + def scaler_2(self): + return self._scaler_2.exp() + + @property + def rel_weight(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight, dim=-1) + + @property + def rel_weight_1(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight_1) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight_1, dim=-1) + + + @property + def rel_weight_2(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight_2) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight_2, dim=-1) + + + def compute_block(self, distance, height, n_cntxt_layers=0): + """Compute constituents from distance and height.""" + + if n_cntxt_layers>0: + if n_cntxt_layers == 1: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler_1[0] + elif n_cntxt_layers == 2: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler_2[0] + else: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0] + + gamma = torch.sigmoid(-beta_logits) + ones = torch.ones_like(gamma) + + block_mask_left = cummin( + gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1 + ) + block_mask_left = block_mask_left - F.pad( + block_mask_left[:, :, :-1], (1, 0), value=0 + ) + block_mask_left.tril_(0) + + block_mask_right = cummin( + gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1 + ) + block_mask_right = block_mask_right - F.pad( + block_mask_right[:, :, 1:], (0, 1), value=0 + ) + block_mask_right.triu_(0) + + block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :] + block = cumsum(block_mask_left).tril(0) + cumsum( + block_mask_right, reverse=True + ).triu(1) + + return block_p, block + + def compute_head(self, height, n_cntxt_layers=0): + """Estimate head for each constituent.""" + + _, length = height.size() + if n_cntxt_layers>0: + if n_cntxt_layers == 1: + head_logits = height * self.scaler_1[1] + elif n_cntxt_layers == 2: + head_logits = height * self.scaler_2[1] + else: + head_logits = height * self.scaler[1] + index = torch.arange(length, device=height.device) + + mask = (index[:, None, None] <= index[None, None, :]) * ( + index[None, None, :] <= index[None, :, None] + ) + head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1) + head_logits.masked_fill_(~mask[None, :, :, :], -1e9) + + head_p = torch.softmax(head_logits, dim=-1) + + return head_p + + def parse(self, x, embs=None, n_cntxt_layers=0): + """Parse input sentence. + + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + distance: syntactic distance + height: syntactic height + """ + + mask = x != self.pad + mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0) + + if embs is None: + h = self.roberta.embeddings(x) + else: + h = embs + + if n_cntxt_layers > 0: + if n_cntxt_layers == 1: + parser_layers = self.parser_layers_1 + height_ff = self.height_ff_1 + distance_ff = self.distance_ff_1 + elif n_cntxt_layers == 2: + parser_layers = self.parser_layers_2 + height_ff = self.height_ff_2 + distance_ff = self.distance_ff_2 + for i in range(int(self.config.n_parser_layers/2)): + h = h.masked_fill(~mask[:, :, None], 0) + h = parser_layers[i](h) + + height = height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, dim=-1 + )[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max + ) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where( + margin_left > margin_right, margin_right, margin_left + ).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + else: + for i in range(self.config.n_parser_layers): + h = h.masked_fill(~mask[:, :, None], 0) + h = self.parser_layers[i](h) + + height = self.height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = self.distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, dim=-1 + )[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max + ) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where( + margin_left > margin_right, margin_right, margin_left + ).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + + return distance, height + + def generate_mask(self, x, distance, height, n_cntxt_layers=0): + """Compute head and cibling distribution for each token.""" + + bsz, length = x.size() + + eye = torch.eye(length, device=x.device, dtype=torch.bool) + eye = eye[None, :, :].expand((bsz, -1, -1)) + + block_p, block = self.compute_block(distance, height, n_cntxt_layers=n_cntxt_layers) + head_p = self.compute_head(height, n_cntxt_layers=n_cntxt_layers) + head = torch.einsum("blij,bijh->blh", block_p, head_p) + head = head.masked_fill(eye, 0) + child = head.transpose(1, 2) + cibling = torch.bmm(head, child).masked_fill(eye, 0) + + rel_list = [] + if "head" in self.config.relations: + rel_list.append(head) + if "child" in self.config.relations: + rel_list.append(child) + if "cibling" in self.config.relations: + rel_list.append(cibling) + + rel = torch.stack(rel_list, dim=1) + + if n_cntxt_layers > 0: + if n_cntxt_layers == 1: + rel_weight = self.rel_weight_1 + elif n_cntxt_layers == 2: + rel_weight = self.rel_weight_2 + else: + rel_weight = self.rel_weight + + dep = torch.einsum("lhr,brij->lbhij", rel_weight, rel) + + if n_cntxt_layers == 1: + num_layers = self.cntxt_layers_2.config.num_hidden_layers + else: + num_layers = self.roberta.config.num_hidden_layers + + att_mask = dep.reshape( + num_layers, + bsz, + self.config.num_attention_heads, + length, + length, + ) + + return att_mask, cibling, head, block + + def forward( + self, + input_ids: Optional[torch.LongTensor] = None, + attention_mask: Optional[torch.FloatTensor] = None, + token_type_ids: Optional[torch.LongTensor] = None, + position_ids: Optional[torch.LongTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, SequenceClassifierOutput]: + r""" + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., + config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If + `config.num_labels > 1` a classification loss is computed (Cross-Entropy). + """ + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + if self.config.n_cntxt_layers > 0: + cntxt_outputs = self.cntxt_layers( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict) + + + if self.config.n_cntxt_layers_2 > 0: + distance_1, height_1 = self.parse(input_ids, cntxt_outputs[0], n_cntxt_layers=1) + att_mask_1, _, _, _ = self.generate_mask(input_ids, distance_1, height_1, n_cntxt_layers=1) + + cntxt_outputs_2 = self.cntxt_layers_2( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + parser_att_mask=att_mask_1) + + sequence_output = cntxt_outputs_2[0] + + distance_2, height_2 = self.parse(input_ids, sequence_output[0], n_cntxt_layers=2) + att_mask, _, _, _ = self.generate_mask(input_ids, distance_2, height_2, n_cntxt_layers=2) + + elif self.config.n_cntxt_layers > 0: + distance, height = self.parse(input_ids, cntxt_outputs[0]) + att_mask, _, _, _ = self.generate_mask(input_ids, distance, height) + else: + distance, height = self.parse(input_ids) + att_mask, _, _, _ = self.generate_mask(input_ids, distance, height) + + outputs = self.roberta( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + parser_att_mask=att_mask, + ) + sequence_output = outputs[0] + logits = self.classifier(sequence_output) + + loss = None + if labels is not None: + if self.config.problem_type is None: + if self.num_labels == 1: + self.config.problem_type = "regression" + elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): + self.config.problem_type = "single_label_classification" + else: + self.config.problem_type = "multi_label_classification" + + if self.config.problem_type == "regression": + loss_fct = MSELoss() + if self.num_labels == 1: + loss = loss_fct(logits.squeeze(), labels.squeeze()) + else: + loss = loss_fct(logits, labels) + elif self.config.problem_type == "single_label_classification": + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) + elif self.config.problem_type == "multi_label_classification": + loss_fct = BCEWithLogitsLoss() + loss = loss_fct(logits, labels) + + if not return_dict: + output = (logits,) + outputs[2:] + return ((loss,) + output) if loss is not None else output + + return SequenceClassifierOutput( + loss=loss, + logits=logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +class RobertaClassificationHead(nn.Module): + """Head for sentence-level classification tasks.""" + + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + classifier_dropout = ( + config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob + ) + self.dropout = nn.Dropout(classifier_dropout) + self.out_proj = nn.Linear(config.hidden_size, config.num_labels) + + def forward(self, features, **kwargs): + x = features[:, 0, :] # take token (equiv. to [CLS]) + x = self.dropout(x) + x = self.dense(x) + x = torch.tanh(x) + x = self.dropout(x) + x = self.out_proj(x) + return x + + +def create_position_ids_from_input_ids( + input_ids, padding_idx, past_key_values_length=0 +): + """ + Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols + are ignored. This is modified from fairseq's `utils.make_positions`. + + Args: + x: torch.Tensor x: + + Returns: torch.Tensor + """ + # The series of casts and type-conversions here are carefully balanced to both work with ONNX export and XLA. + mask = input_ids.ne(padding_idx).int() + incremental_indices = ( + torch.cumsum(mask, dim=1).type_as(mask) + past_key_values_length + ) * mask + return incremental_indices.long() + padding_idx + + +def cumprod(x, reverse=False, exclusive=False): + """cumulative product.""" + if reverse: + x = x.flip([-1]) + + if exclusive: + x = F.pad(x[:, :, :-1], (1, 0), value=1) + + cx = x.cumprod(-1) + + if reverse: + cx = cx.flip([-1]) + return cx + + +def cumsum(x, reverse=False, exclusive=False): + """cumulative sum.""" + bsz, _, length = x.size() + device = x.device + if reverse: + if exclusive: + w = torch.ones([bsz, length, length], device=device).tril(-1) + else: + w = torch.ones([bsz, length, length], device=device).tril(0) + cx = torch.bmm(x, w) + else: + if exclusive: + w = torch.ones([bsz, length, length], device=device).triu(1) + else: + w = torch.ones([bsz, length, length], device=device).triu(0) + cx = torch.bmm(x, w) + return cx + + +def cummin(x, reverse=False, exclusive=False, max_value=1e9): + """cumulative min.""" + if reverse: + if exclusive: + x = F.pad(x[:, :, 1:], (0, 1), value=max_value) + x = x.flip([-1]).cummin(-1)[0].flip([-1]) + else: + if exclusive: + x = F.pad(x[:, :, :-1], (1, 0), value=max_value) + x = x.cummin(-1)[0] + return x diff --git a/finetune/control_raising_relative_token_position/predict_results.txt b/finetune/control_raising_relative_token_position/predict_results.txt new file mode 100644 index 0000000000000000000000000000000000000000..05acf004aa690d6d3b744d17319e6cff1064455a --- /dev/null +++ b/finetune/control_raising_relative_token_position/predict_results.txt @@ -0,0 +1,24532 @@ +index prediction +0 1 +1 0 +2 0 +3 1 +4 1 +5 0 +6 1 +7 0 +8 0 +9 1 +10 1 +11 0 +12 1 +13 0 +14 0 +15 1 +16 1 +17 0 +18 1 +19 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mode 100644 index 0000000000000000000000000000000000000000..4a05363b2cdf362715477e08a3dc5678f8da6992 --- /dev/null +++ b/finetune/lexical_content_the_control/config.json @@ -0,0 +1,50 @@ +{ + "_name_or_path": "final_models/glue_models/structroberta_s1_final/", + "architectures": [ + "StructRobertaForSequenceClassification" + ], + "attention_probs_dropout_prob": 0.1, + "auto_map": { + "AutoConfig": "modeling_structroberta.StructRobertaConfig", + "AutoModelForMaskedLM": "modeling_structroberta.StructRoberta", + "AutoModelForSequenceClassification": "modeling_structroberta.StructRobertaForSequenceClassification" + }, + "bos_token_id": 0, + "classifier_dropout": null, + "conv_size": 9, + "eos_token_id": 2, + "hidden_act": "gelu", + "hidden_dropout_prob": 0.1, + "hidden_size": 768, + "id2label": { + "0": 0, + "1": 1 + }, + "initializer_range": 0.02, + "intermediate_size": 3072, + "label2id": { + "0": 0, + "1": 1 + }, + "layer_norm_eps": 1e-05, + "max_position_embeddings": 514, + 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18200, + "eval_samples_per_second": 509.48, + "eval_steps_per_second": 63.685 +} \ No newline at end of file diff --git a/finetune/lexical_content_the_control/merges.txt b/finetune/lexical_content_the_control/merges.txt new file mode 100644 index 0000000000000000000000000000000000000000..94b4f252cb566a4dac62a08c78d85169ff4e91be --- /dev/null +++ b/finetune/lexical_content_the_control/merges.txt @@ -0,0 +1,31794 @@ +#version: 0.2 +Ġ t +h e +Ġ a +i n +Ġt he +r e +Ġ w +o u +Ġ s +o n +e r +h a +n d +i s +i t +Ġ b +Ġ o +Ġ c +Ġ m +Ġ f +in g +o r +a n +e d +l l +Ġ d +e n +Ġt o +e s +Ġ p +Ġo f +Ġ y +Ġ in +a r +a t +Ġa nd +Ġ l +ha t +Ġy ou +a s +Ġ g +o m +Ġ n +Ġ I +i c +o w +l e +v e +o t +Ġ h +Ġ he +a l +e t +Ġ ha +' s +u s +Ġt h +a y +Ġ it +Ġ is +Ġb e +Ġt hat +Ġ on +i on +i d +Ġ e +g h +u t +l d +en t +i m +Ġ re +Ġw as +a c +Ġw e +v er +a m +T he +. . +Ġf or +l y +Ġ S +s t +r i +r o +i r +a d +s e +k e +Ġ . +Ġs t +gh t +o o +' t +Ġ A +ou t +it h +Ġm e +Ġ C +a ll +Ġ k +Ġ u +Ġa n +Ġ M +Ġw ith +u r +Ġa s +Ġd o +c t +he r +c e +i ll +t er +i f +c h +Ġw h +Ġs e +om e +Ġa re +Ġ H +i l +Ġn ot +Ġha ve +Ġ B +Ġ 1 +a nd +Ġa t +Ġth is +ou ld +an t +Ġg o +us t +at ion +Ġl i +o p +a in +Ġk n +Y ou +e a +Ġ P +Ġ T +Ġ , +u n +g e +o l +t h +Ġh is +u l +Ġ " +r om +.. . +o re +Ġ W +e ll +Ġn e +Ġ r +Ġkn ow +Ġ j +ou r +Ġ D +Ġd e +p p +Ġc an +Ġ G +Ġthe y +u m +Ġ The +Ġs o +es t +Ġ ? +Ġa l +Ġa ll +Ġa b +Ġm y +is t +er s +Ġ R +i e +Ġyou r +ar t +Ġb y +Ġf rom +es s +Ġs h +Ġo r +Ġ 2 +Ġ F +Ġw hat +n t +a b +o s +Ġ L +e l +Ġon e +Ġc on +Ġ N +Ġw he +Ġthe re +i g +i ght +ha n +all y +Ġu p +q u +e m +Ġc om +on e +Ġha d +Ġb ut +Ġhe r +Ġs u +re d +r y +Ġ J +' re +Ġh im +Ġ out +r a +ac k +at e +oo d +ver y +oo k +Ġli ke +Ġw or +Ġs a +re s +am e +i ve +Ġp l +Ġ us +Ġ ( +on g +' m +Ġwe re +â Ģ +a ke +o d +in k +c k +Ġs he +Ġp ro +Ġab out +Ġ E +as t +Ġg et +Ġe x +Ġ v +p e +I t +or t +ou nd +in d +ar d +it y +Ġd on +A nd +ic h +ri ght +Ġ O +Ġj ust +Ġ1 9 +i es +H e +Ġwh o +e ar +ea h +Ġd id +a k +Ġhe re +b er +Ġthe m +Ġn o +im e +in e +Ġm an +Ġw ant +Ġw ould +Ġ2 0 +Ġ if +Ġ ' +Ġw ill +u d +a p +Ġ K +Ġth ink +ou s +' ll +v en +Ġs ome +o st +W hat +e p +h ing +Ġin t +Ġan y +ow n +ic e +a g +Ġ right +m ent +id e +Ġg ot +Ġha s +t her +us e +it t +Ġwh ich +ou gh +Ġt r +f e +h i +c a +Ġse e +Ġwhe n +Ġsa id +u re +Ġa r +Ġa g +Ġp e +Ġs ay +w ay +i a +Ġt ime +Ġt w +i v +ĠH e +s o +Ġbe en +i an +Ġs p +a re +ac e +Ġn ow +o ve +ic k +op le +c i +Ġ U +W e +ĠI t +' ve +Ġthe ir +o se +Y eah +Ġl e +Ġl ook +- - +Ġc l +is h +l f +re at +n a +Ġm ore +Ġthe n +Ġgo ing +a ge +S o +it e +re e +Ġo ther +Ġpe ople +ar y +Ġc ould +Ġ = +ĠS t +N o +ot her +n e +Ġof f +u e +Ġb ack +h is +ac t +B ut +Ġo ver +ir st +O h +as s +Ġ qu +ou nt +on d +Ġ very +ing s +ca use +Ġ ro +b le +p er +Ġg r +Ġa f +p t +Ġf e +an s +Ġwor k +Ġtw o +Ġg ood +T hat +Ġto o +Ġb r +e nd +om et +Ġ en +er m +o g +p l +at ed +o ll +Ġ Ġ +t he +Ġf irst +ion s +or d +Ġint o +he d +as e +Ġp art +k ay +0 0 +Ġh ow +Ġd own +or n +al k +u b +w e +Ġu n +Ġw ay 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See all RoBERTa models at https://huggingface.co/models?filter=roberta +] + + +class StructRobertaConfig(RobertaConfig): + model_type = "roberta" + + def __init__( + self, + n_parser_layers=4, + conv_size=9, + relations=("head", "child"), + weight_act="softmax", + n_cntxt_layers=3, + n_cntxt_layers_2=0, + **kwargs,): + + super().__init__(**kwargs) + self.n_cntxt_layers = n_cntxt_layers + self.n_parser_layers = n_parser_layers + self.n_cntxt_layers_2 = n_cntxt_layers_2 + self.conv_size = conv_size + self.relations = relations + self.weight_act = weight_act + +class Conv1d(nn.Module): + """1D convolution layer.""" + + def __init__(self, hidden_size, kernel_size, dilation=1): + """Initialization. + + Args: + hidden_size: dimension of input embeddings + kernel_size: convolution kernel size + dilation: the spacing between the kernel points + """ + super(Conv1d, self).__init__() + + if kernel_size % 2 == 0: + padding = (kernel_size // 2) * dilation + self.shift = True + else: + padding = ((kernel_size - 1) // 2) * dilation + self.shift = False + self.conv = nn.Conv1d( + hidden_size, hidden_size, kernel_size, padding=padding, dilation=dilation + ) + + def forward(self, x): + """Compute convolution. + + Args: + x: input embeddings + Returns: + conv_output: convolution results + """ + + if self.shift: + return self.conv(x.transpose(1, 2)).transpose(1, 2)[:, 1:] + else: + return self.conv(x.transpose(1, 2)).transpose(1, 2) + + +class RobertaEmbeddings(nn.Module): + """ + Same as BertEmbeddings with a tiny tweak for positional embeddings indexing. + """ + + # Copied from transformers.models.bert.modeling_bert.BertEmbeddings.__init__ + def __init__(self, config): + super().__init__() + self.word_embeddings = nn.Embedding( + config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id + ) + self.position_embeddings = nn.Embedding( + config.max_position_embeddings, config.hidden_size + ) + self.token_type_embeddings = nn.Embedding( + config.type_vocab_size, config.hidden_size + ) + + # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load + # any TensorFlow checkpoint file + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + # position_ids (1, len position emb) is contiguous in memory and exported when serialized + self.position_embedding_type = getattr( + config, "position_embedding_type", "absolute" + ) + self.register_buffer( + "position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)) + ) + if version.parse(torch.__version__) > version.parse("1.6.0"): + self.register_buffer( + "token_type_ids", + torch.zeros(self.position_ids.size(), dtype=torch.long), + persistent=False, + ) + + # End copy + self.padding_idx = config.pad_token_id + self.position_embeddings = nn.Embedding( + config.max_position_embeddings, + config.hidden_size, + padding_idx=self.padding_idx, + ) + + def forward( + self, + input_ids=None, + token_type_ids=None, + position_ids=None, + inputs_embeds=None, + past_key_values_length=0, + ): + if position_ids is None: + if input_ids is not None: + # Create the position ids from the input token ids. Any padded tokens remain padded. + position_ids = create_position_ids_from_input_ids( + input_ids, self.padding_idx, past_key_values_length + ) + else: + position_ids = self.create_position_ids_from_inputs_embeds( + inputs_embeds + ) + + if input_ids is not None: + input_shape = input_ids.size() + else: + input_shape = inputs_embeds.size()[:-1] + + seq_length = input_shape[1] + + # Setting the token_type_ids to the registered buffer in constructor where it is all zeros, which usually occurs + # when its auto-generated, registered buffer helps users when tracing the model without passing token_type_ids, solves + # issue #5664 + if token_type_ids is None: + if hasattr(self, "token_type_ids"): + buffered_token_type_ids = self.token_type_ids[:, :seq_length] + buffered_token_type_ids_expanded = buffered_token_type_ids.expand( + input_shape[0], seq_length + ) + token_type_ids = buffered_token_type_ids_expanded + else: + token_type_ids = torch.zeros( + input_shape, dtype=torch.long, device=self.position_ids.device + ) + + if inputs_embeds is None: + inputs_embeds = self.word_embeddings(input_ids) + token_type_embeddings = self.token_type_embeddings(token_type_ids) + + embeddings = inputs_embeds + token_type_embeddings + if self.position_embedding_type == "absolute": + position_embeddings = self.position_embeddings(position_ids) + embeddings += position_embeddings + embeddings = self.LayerNorm(embeddings) + embeddings = self.dropout(embeddings) + return embeddings + + def create_position_ids_from_inputs_embeds(self, inputs_embeds): + """ + We are provided embeddings directly. We cannot infer which are padded so just generate sequential position ids. + + Args: + inputs_embeds: torch.Tensor + + Returns: torch.Tensor + """ + input_shape = inputs_embeds.size()[:-1] + sequence_length = input_shape[1] + + position_ids = torch.arange( + self.padding_idx + 1, + sequence_length + self.padding_idx + 1, + dtype=torch.long, + device=inputs_embeds.device, + ) + return position_ids.unsqueeze(0).expand(input_shape) + + +# Copied from transformers.models.bert.modeling_bert.BertSelfAttention with Bert->Roberta +class RobertaSelfAttention(nn.Module): + def __init__(self, config, position_embedding_type=None): + super().__init__() + if config.hidden_size % config.num_attention_heads != 0 and not hasattr( + config, "embedding_size" + ): + raise ValueError( + f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention " + f"heads ({config.num_attention_heads})" + ) + + self.num_attention_heads = config.num_attention_heads + self.attention_head_size = int(config.hidden_size / config.num_attention_heads) + self.all_head_size = self.num_attention_heads * self.attention_head_size + + self.query = nn.Linear(config.hidden_size, self.all_head_size) + self.key = nn.Linear(config.hidden_size, self.all_head_size) + self.value = nn.Linear(config.hidden_size, self.all_head_size) + + self.dropout = nn.Dropout(config.attention_probs_dropout_prob) + self.position_embedding_type = position_embedding_type or getattr( + config, "position_embedding_type", "absolute" + ) + if ( + self.position_embedding_type == "relative_key" + or self.position_embedding_type == "relative_key_query" + ): + self.max_position_embeddings = config.max_position_embeddings + self.distance_embedding = nn.Embedding( + 2 * config.max_position_embeddings - 1, self.attention_head_size + ) + + self.is_decoder = config.is_decoder + + def transpose_for_scores(self, x): + new_x_shape = x.size()[:-1] + ( + self.num_attention_heads, + self.attention_head_size, + ) + x = x.view(new_x_shape) + return x.permute(0, 2, 1, 3) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.FloatTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, + output_attentions: Optional[bool] = False, + parser_att_mask=None, + ) -> Tuple[torch.Tensor]: + mixed_query_layer = self.query(hidden_states) + + # If this is instantiated as a cross-attention module, the keys + # and values come from an encoder; the attention mask needs to be + # such that the encoder's padding tokens are not attended to. + is_cross_attention = encoder_hidden_states is not None + + if is_cross_attention and past_key_value is not None: + # reuse k,v, cross_attentions + key_layer = past_key_value[0] + value_layer = past_key_value[1] + attention_mask = encoder_attention_mask + elif is_cross_attention: + key_layer = self.transpose_for_scores(self.key(encoder_hidden_states)) + value_layer = self.transpose_for_scores(self.value(encoder_hidden_states)) + attention_mask = encoder_attention_mask + elif past_key_value is not None: + key_layer = self.transpose_for_scores(self.key(hidden_states)) + value_layer = self.transpose_for_scores(self.value(hidden_states)) + key_layer = torch.cat([past_key_value[0], key_layer], dim=2) + value_layer = torch.cat([past_key_value[1], value_layer], dim=2) + else: + key_layer = self.transpose_for_scores(self.key(hidden_states)) + value_layer = self.transpose_for_scores(self.value(hidden_states)) + + query_layer = self.transpose_for_scores(mixed_query_layer) + + if self.is_decoder: + # if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states. + # Further calls to cross_attention layer can then reuse all cross-attention + # key/value_states (first "if" case) + # if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of + # all previous decoder key/value_states. Further calls to uni-directional self-attention + # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case) + # if encoder bi-directional self-attention `past_key_value` is always `None` + past_key_value = (key_layer, value_layer) + + # Take the dot product between "query" and "key" to get the raw attention scores. + attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) + + if ( + self.position_embedding_type == "relative_key" + or self.position_embedding_type == "relative_key_query" + ): + seq_length = hidden_states.size()[1] + position_ids_l = torch.arange( + seq_length, dtype=torch.long, device=hidden_states.device + ).view(-1, 1) + position_ids_r = torch.arange( + seq_length, dtype=torch.long, device=hidden_states.device + ).view(1, -1) + distance = position_ids_l - position_ids_r + positional_embedding = self.distance_embedding( + distance + self.max_position_embeddings - 1 + ) + positional_embedding = positional_embedding.to( + dtype=query_layer.dtype + ) # fp16 compatibility + + if self.position_embedding_type == "relative_key": + relative_position_scores = torch.einsum( + "bhld,lrd->bhlr", query_layer, positional_embedding + ) + attention_scores = attention_scores + relative_position_scores + elif self.position_embedding_type == "relative_key_query": + relative_position_scores_query = torch.einsum( + "bhld,lrd->bhlr", query_layer, positional_embedding + ) + relative_position_scores_key = torch.einsum( + "bhrd,lrd->bhlr", key_layer, positional_embedding + ) + attention_scores = ( + attention_scores + + relative_position_scores_query + + relative_position_scores_key + ) + + attention_scores = attention_scores / math.sqrt(self.attention_head_size) + if attention_mask is not None: + # Apply the attention mask is (precomputed for all layers in RobertaModel forward() function) + attention_scores = attention_scores + attention_mask + + if parser_att_mask is None: + # Normalize the attention scores to probabilities. + attention_probs = nn.functional.softmax(attention_scores, dim=-1) + else: + attention_probs = torch.sigmoid(attention_scores) * parser_att_mask + + # This is actually dropping out entire tokens to attend to, which might + # seem a bit unusual, but is taken from the original Transformer paper. + attention_probs = self.dropout(attention_probs) + + # Mask heads if we want to + if head_mask is not None: + attention_probs = attention_probs * head_mask + + context_layer = torch.matmul(attention_probs, value_layer) + + context_layer = context_layer.permute(0, 2, 1, 3).contiguous() + new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) + context_layer = context_layer.view(new_context_layer_shape) + + outputs = ( + (context_layer, attention_probs) if output_attentions else (context_layer,) + ) + + if self.is_decoder: + outputs = outputs + (past_key_value,) + return outputs + + +# Copied from transformers.models.bert.modeling_bert.BertSelfOutput +class RobertaSelfOutput(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward( + self, hidden_states: torch.Tensor, input_tensor: torch.Tensor + ) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + hidden_states = self.LayerNorm(hidden_states + input_tensor) + return hidden_states + + +# Copied from transformers.models.bert.modeling_bert.BertAttention with Bert->Roberta +class RobertaAttention(nn.Module): + def __init__(self, config, position_embedding_type=None): + super().__init__() + self.self = RobertaSelfAttention( + config, position_embedding_type=position_embedding_type + ) + self.output = RobertaSelfOutput(config) + self.pruned_heads = set() + + def prune_heads(self, heads): + if len(heads) == 0: + return + heads, index = find_pruneable_heads_and_indices( + heads, + self.self.num_attention_heads, + self.self.attention_head_size, + self.pruned_heads, + ) + + # Prune linear layers + self.self.query = prune_linear_layer(self.self.query, index) + self.self.key = prune_linear_layer(self.self.key, index) + self.self.value = prune_linear_layer(self.self.value, index) + self.output.dense = prune_linear_layer(self.output.dense, index, dim=1) + + # Update hyper params and store pruned heads + self.self.num_attention_heads = self.self.num_attention_heads - len(heads) + self.self.all_head_size = ( + self.self.attention_head_size * self.self.num_attention_heads + ) + self.pruned_heads = self.pruned_heads.union(heads) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.FloatTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, + output_attentions: Optional[bool] = False, + parser_att_mask=None, + ) -> Tuple[torch.Tensor]: + self_outputs = self.self( + hidden_states, + attention_mask, + head_mask, + encoder_hidden_states, + encoder_attention_mask, + past_key_value, + output_attentions, + parser_att_mask=parser_att_mask, + ) + attention_output = self.output(self_outputs[0], hidden_states) + outputs = (attention_output,) + self_outputs[ + 1: + ] # add attentions if we output them + return outputs + + +# Copied from transformers.models.bert.modeling_bert.BertIntermediate +class RobertaIntermediate(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.intermediate_size) + if isinstance(config.hidden_act, str): + self.intermediate_act_fn = ACT2FN[config.hidden_act] + else: + self.intermediate_act_fn = config.hidden_act + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.intermediate_act_fn(hidden_states) + return hidden_states + + +# Copied from transformers.models.bert.modeling_bert.BertOutput +class RobertaOutput(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.intermediate_size, config.hidden_size) + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward( + self, hidden_states: torch.Tensor, input_tensor: torch.Tensor + ) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + hidden_states = self.LayerNorm(hidden_states + input_tensor) + return hidden_states + + +# Copied from transformers.models.bert.modeling_bert.BertLayer with Bert->Roberta +class RobertaLayer(nn.Module): + def __init__(self, config): + super().__init__() + self.chunk_size_feed_forward = config.chunk_size_feed_forward + self.seq_len_dim = 1 + self.attention = RobertaAttention(config) + self.is_decoder = config.is_decoder + self.add_cross_attention = config.add_cross_attention + if self.add_cross_attention: + if not self.is_decoder: + raise ValueError( + f"{self} should be used as a decoder model if cross attention is added" + ) + self.crossattention = RobertaAttention( + config, position_embedding_type="absolute" + ) + self.intermediate = RobertaIntermediate(config) + self.output = RobertaOutput(config) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.FloatTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, + output_attentions: Optional[bool] = False, + parser_att_mask=None, + ) -> Tuple[torch.Tensor]: + # decoder uni-directional self-attention cached key/values tuple is at positions 1,2 + self_attn_past_key_value = ( + past_key_value[:2] if past_key_value is not None else None + ) + self_attention_outputs = self.attention( + hidden_states, + attention_mask, + head_mask, + output_attentions=output_attentions, + past_key_value=self_attn_past_key_value, + parser_att_mask=parser_att_mask, + ) + attention_output = self_attention_outputs[0] + + # if decoder, the last output is tuple of self-attn cache + if self.is_decoder: + outputs = self_attention_outputs[1:-1] + present_key_value = self_attention_outputs[-1] + else: + outputs = self_attention_outputs[ + 1: + ] # add self attentions if we output attention weights + + cross_attn_present_key_value = None + if self.is_decoder and encoder_hidden_states is not None: + if not hasattr(self, "crossattention"): + raise ValueError( + f"If `encoder_hidden_states` are passed, {self} has to be instantiated with cross-attention layers by setting `config.add_cross_attention=True`" + ) + + # cross_attn cached key/values tuple is at positions 3,4 of past_key_value tuple + cross_attn_past_key_value = ( + past_key_value[-2:] if past_key_value is not None else None + ) + cross_attention_outputs = self.crossattention( + attention_output, + attention_mask, + head_mask, + encoder_hidden_states, + encoder_attention_mask, + cross_attn_past_key_value, + output_attentions, + ) + attention_output = cross_attention_outputs[0] + outputs = ( + outputs + cross_attention_outputs[1:-1] + ) # add cross attentions if we output attention weights + + # add cross-attn cache to positions 3,4 of present_key_value tuple + cross_attn_present_key_value = cross_attention_outputs[-1] + present_key_value = present_key_value + cross_attn_present_key_value + + layer_output = apply_chunking_to_forward( + self.feed_forward_chunk, + self.chunk_size_feed_forward, + self.seq_len_dim, + attention_output, + ) + outputs = (layer_output,) + outputs + + # if decoder, return the attn key/values as the last output + if self.is_decoder: + outputs = outputs + (present_key_value,) + + return outputs + + def feed_forward_chunk(self, attention_output): + intermediate_output = self.intermediate(attention_output) + layer_output = self.output(intermediate_output, attention_output) + return layer_output + + +# Copied from transformers.models.bert.modeling_bert.BertEncoder with Bert->Roberta +class RobertaEncoder(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + self.layer = nn.ModuleList( + [RobertaLayer(config) for _ in range(config.num_hidden_layers)] + ) + self.gradient_checkpointing = False + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.FloatTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = False, + output_hidden_states: Optional[bool] = False, + return_dict: Optional[bool] = True, + parser_att_mask=None, + ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPastAndCrossAttentions]: + all_hidden_states = () if output_hidden_states else None + all_self_attentions = () if output_attentions else None + all_cross_attentions = ( + () if output_attentions and self.config.add_cross_attention else None + ) + + next_decoder_cache = () if use_cache else None + for i, layer_module in enumerate(self.layer): + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + layer_head_mask = head_mask[i] if head_mask is not None else None + past_key_value = past_key_values[i] if past_key_values is not None else None + + if self.gradient_checkpointing and self.training: + + if use_cache: + logger.warning( + "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." + ) + use_cache = False + + def create_custom_forward(module): + def custom_forward(*inputs): + return module(*inputs, past_key_value, output_attentions) + + return custom_forward + + layer_outputs = torch.utils.checkpoint.checkpoint( + create_custom_forward(layer_module), + hidden_states, + attention_mask, + layer_head_mask, + encoder_hidden_states, + encoder_attention_mask, + ) + else: + if parser_att_mask is not None: + layer_outputs = layer_module( + hidden_states, + attention_mask, + layer_head_mask, + encoder_hidden_states, + encoder_attention_mask, + past_key_value, + output_attentions, + parser_att_mask=parser_att_mask[i]) + else: + layer_outputs = layer_module( + hidden_states, + attention_mask, + layer_head_mask, + encoder_hidden_states, + encoder_attention_mask, + past_key_value, + output_attentions, + parser_att_mask=None) + + + hidden_states = layer_outputs[0] + if use_cache: + next_decoder_cache += (layer_outputs[-1],) + if output_attentions: + all_self_attentions = all_self_attentions + (layer_outputs[1],) + if self.config.add_cross_attention: + all_cross_attentions = all_cross_attentions + (layer_outputs[2],) + + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + if not return_dict: + return tuple( + v + for v in [ + hidden_states, + next_decoder_cache, + all_hidden_states, + all_self_attentions, + all_cross_attentions, + ] + if v is not None + ) + return BaseModelOutputWithPastAndCrossAttentions( + last_hidden_state=hidden_states, + past_key_values=next_decoder_cache, + hidden_states=all_hidden_states, + attentions=all_self_attentions, + cross_attentions=all_cross_attentions, + ) + + +# Copied from transformers.models.bert.modeling_bert.BertPooler +class RobertaPooler(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.activation = nn.Tanh() + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + # We "pool" the model by simply taking the hidden state corresponding + # to the first token. + first_token_tensor = hidden_states[:, 0] + pooled_output = self.dense(first_token_tensor) + pooled_output = self.activation(pooled_output) + return pooled_output + + +class RobertaPreTrainedModel(PreTrainedModel): + """ + An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained + models. + """ + + config_class = RobertaConfig + base_model_prefix = "roberta" + supports_gradient_checkpointing = True + + # Copied from transformers.models.bert.modeling_bert.BertPreTrainedModel._init_weights + def _init_weights(self, module): + """Initialize the weights""" + if isinstance(module, nn.Linear): + # Slightly different from the TF version which uses truncated_normal for initialization + # cf https://github.com/pytorch/pytorch/pull/5617 + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.Embedding): + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.padding_idx is not None: + module.weight.data[module.padding_idx].zero_() + elif isinstance(module, nn.LayerNorm): + if module.bias is not None: + module.bias.data.zero_() + module.weight.data.fill_(1.0) + + def _set_gradient_checkpointing(self, module, value=False): + if isinstance(module, RobertaEncoder): + module.gradient_checkpointing = value + + def update_keys_to_ignore(self, config, del_keys_to_ignore): + """Remove some keys from ignore list""" + if not config.tie_word_embeddings: + # must make a new list, or the class variable gets modified! + self._keys_to_ignore_on_save = [ + k for k in self._keys_to_ignore_on_save if k not in del_keys_to_ignore + ] + self._keys_to_ignore_on_load_missing = [ + k + for k in self._keys_to_ignore_on_load_missing + if k not in del_keys_to_ignore + ] + + +ROBERTA_START_DOCSTRING = r""" + + This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the + library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads + etc.) + + This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. + Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage + and behavior. + + Parameters: + config ([`RobertaConfig`]): Model configuration class with all the parameters of the + model. Initializing with a config file does not load the weights associated with the model, only the + configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. +""" + + +ROBERTA_INPUTS_DOCSTRING = r""" + Args: + input_ids (`torch.LongTensor` of shape `({0})`): + Indices of input sequence tokens in the vocabulary. + + Indices can be obtained using [`RobertaTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + [What are input IDs?](../glossary#input-ids) + attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*): + Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + + [What are attention masks?](../glossary#attention-mask) + token_type_ids (`torch.LongTensor` of shape `({0})`, *optional*): + Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, + 1]`: + + - 0 corresponds to a *sentence A* token, + - 1 corresponds to a *sentence B* token. + + [What are token type IDs?](../glossary#token-type-ids) + position_ids (`torch.LongTensor` of shape `({0})`, *optional*): + Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, + config.max_position_embeddings - 1]`. + + [What are position IDs?](../glossary#position-ids) + head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): + Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: + + - 1 indicates the head is **not masked**, + - 0 indicates the head is **masked**. + + inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_size)`, *optional*): + Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This + is useful if you want more control over how to convert `input_ids` indices into associated vectors than the + model's internal embedding lookup matrix. + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned + tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for + more detail. + return_dict (`bool`, *optional*): + Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. +""" + + +class RobertaModel(RobertaPreTrainedModel): + """ + + The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of + cross-attention is added between the self-attention layers, following the architecture described in *Attention is + all you need*_ by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz + Kaiser and Illia Polosukhin. + + To behave as an decoder the model needs to be initialized with the `is_decoder` argument of the configuration set + to `True`. To be used in a Seq2Seq model, the model needs to initialized with both `is_decoder` argument and + `add_cross_attention` set to `True`; an `encoder_hidden_states` is then expected as an input to the forward pass. + + .. _*Attention is all you need*: https://arxiv.org/abs/1706.03762 + + """ + + _keys_to_ignore_on_load_missing = [r"position_ids"] + + # Copied from transformers.models.bert.modeling_bert.BertModel.__init__ with Bert->Roberta + def __init__(self, config, add_pooling_layer=True): + super().__init__(config) + self.config = config + + self.embeddings = RobertaEmbeddings(config) + self.encoder = RobertaEncoder(config) + + self.pooler = RobertaPooler(config) if add_pooling_layer else None + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.embeddings.word_embeddings + + def set_input_embeddings(self, value): + self.embeddings.word_embeddings = value + + def _prune_heads(self, heads_to_prune): + """ + Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base + class PreTrainedModel + """ + for layer, heads in heads_to_prune.items(): + self.encoder.layer[layer].attention.prune_heads(heads) + + # Copied from transformers.models.bert.modeling_bert.BertModel.forward + def forward( + self, + input_ids: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + token_type_ids: Optional[torch.Tensor] = None, + position_ids: Optional[torch.Tensor] = None, + head_mask: Optional[torch.Tensor] = None, + inputs_embeds: Optional[torch.Tensor] = None, + encoder_hidden_states: Optional[torch.Tensor] = None, + encoder_attention_mask: Optional[torch.Tensor] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + parser_att_mask=None, + ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPoolingAndCrossAttentions]: + r""" + encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): + Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if + the model is configured as a decoder. + encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*): + Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in + the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): + Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. + + If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that + don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all + `decoder_input_ids` of shape `(batch_size, sequence_length)`. + use_cache (`bool`, *optional*): + If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see + `past_key_values`). + """ + output_attentions = ( + output_attentions + if output_attentions is not None + else self.config.output_attentions + ) + output_hidden_states = ( + output_hidden_states + if output_hidden_states is not None + else self.config.output_hidden_states + ) + return_dict = ( + return_dict if return_dict is not None else self.config.use_return_dict + ) + + if self.config.is_decoder: + use_cache = use_cache if use_cache is not None else self.config.use_cache + else: + use_cache = False + + if input_ids is not None and inputs_embeds is not None: + raise ValueError( + "You cannot specify both input_ids and inputs_embeds at the same time" + ) + elif input_ids is not None: + input_shape = input_ids.size() + elif inputs_embeds is not None: + input_shape = inputs_embeds.size()[:-1] + else: + raise ValueError("You have to specify either input_ids or inputs_embeds") + + batch_size, seq_length = input_shape + device = input_ids.device if input_ids is not None else inputs_embeds.device + + # past_key_values_length + past_key_values_length = ( + past_key_values[0][0].shape[2] if past_key_values is not None else 0 + ) + + if attention_mask is None: + attention_mask = torch.ones( + ((batch_size, seq_length + past_key_values_length)), device=device + ) + + if token_type_ids is None: + if hasattr(self.embeddings, "token_type_ids"): + buffered_token_type_ids = self.embeddings.token_type_ids[:, :seq_length] + buffered_token_type_ids_expanded = buffered_token_type_ids.expand( + batch_size, seq_length + ) + token_type_ids = buffered_token_type_ids_expanded + else: + token_type_ids = torch.zeros( + input_shape, dtype=torch.long, device=device + ) + + # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length] + # ourselves in which case we just need to make it broadcastable to all heads. + extended_attention_mask: torch.Tensor = self.get_extended_attention_mask( + attention_mask, input_shape, device + ) + + # If a 2D or 3D attention mask is provided for the cross-attention + # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length] + if self.config.is_decoder and encoder_hidden_states is not None: + ( + encoder_batch_size, + encoder_sequence_length, + _, + ) = encoder_hidden_states.size() + encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length) + if encoder_attention_mask is None: + encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device) + encoder_extended_attention_mask = self.invert_attention_mask( + encoder_attention_mask + ) + else: + encoder_extended_attention_mask = None + + # Prepare head mask if needed + # 1.0 in head_mask indicate we keep the head + # attention_probs has shape bsz x n_heads x N x N + # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] + # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] + head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) + + embedding_output = self.embeddings( + input_ids=input_ids, + position_ids=position_ids, + token_type_ids=token_type_ids, + inputs_embeds=inputs_embeds, + past_key_values_length=past_key_values_length, + ) + encoder_outputs = self.encoder( + embedding_output, + attention_mask=extended_attention_mask, + head_mask=head_mask, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_extended_attention_mask, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + parser_att_mask=parser_att_mask, + ) + sequence_output = encoder_outputs[0] + pooled_output = ( + self.pooler(sequence_output) if self.pooler is not None else None + ) + + if not return_dict: + return (sequence_output, pooled_output) + encoder_outputs[1:] + + return BaseModelOutputWithPoolingAndCrossAttentions( + last_hidden_state=sequence_output, + pooler_output=pooled_output, + past_key_values=encoder_outputs.past_key_values, + hidden_states=encoder_outputs.hidden_states, + attentions=encoder_outputs.attentions, + cross_attentions=encoder_outputs.cross_attentions, + ) + + +class StructRoberta(RobertaPreTrainedModel): + _keys_to_ignore_on_save = [r"lm_head.decoder.weight", r"lm_head.decoder.bias"] + _keys_to_ignore_on_load_missing = [ + r"position_ids", + r"lm_head.decoder.weight", + r"lm_head.decoder.bias", + ] + _keys_to_ignore_on_load_unexpected = [r"pooler"] + + def __init__(self, config): + super().__init__(config) + + if config.is_decoder: + logger.warning( + "If you want to use `RobertaForMaskedLM` make sure `config.is_decoder=False` for " + "bi-directional self-attention." + ) + + + if config.n_cntxt_layers > 0: + config_cntxt = copy.deepcopy(config) + config_cntxt.num_hidden_layers = config.n_cntxt_layers + + self.cntxt_layers = RobertaModel(config_cntxt, add_pooling_layer=False) + + if config.n_cntxt_layers_2 > 0: + self.parser_layers_1 = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(int(config.n_parser_layers/2)) + ] + ) + + self.distance_ff_1 = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff_1 = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight_1 = nn.Parameter( + torch.zeros((config.n_cntxt_layers_2, config.num_attention_heads, n_rel)) + ) + self._rel_weight_1.data.normal_(0, 0.1) + + self._scaler_1 = nn.Parameter(torch.zeros(2)) + + config_cntxt_2 = copy.deepcopy(config) + config_cntxt_2.num_hidden_layers = config.n_cntxt_layers_2 + + self.cntxt_layers_2 = RobertaModel(config_cntxt_2, add_pooling_layer=False) + + + self.parser_layers_2 = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(int(config.n_parser_layers/2)) + ] + ) + + self.distance_ff_2 = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff_2 = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight_2 = nn.Parameter( + torch.zeros((config.num_hidden_layers, config.num_attention_heads, n_rel)) + ) + self._rel_weight_2.data.normal_(0, 0.1) + + self._scaler_2 = nn.Parameter(torch.zeros(2)) + + else: + self.parser_layers = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(config.n_parser_layers) + ] + ) + + self.distance_ff = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight = nn.Parameter( + torch.zeros((config.num_hidden_layers, config.num_attention_heads, n_rel)) + ) + self._rel_weight.data.normal_(0, 0.1) + + self._scaler = nn.Parameter(torch.zeros(2)) + + self.roberta = RobertaModel(config, add_pooling_layer=False) + + if config.n_cntxt_layers > 0: + self.cntxt_layers.embeddings = self.roberta.embeddings + if config.n_cntxt_layers_2 > 0: + self.cntxt_layers_2.embeddings = self.roberta.embeddings + + self.lm_head = RobertaLMHead(config) + + self.pad = config.pad_token_id + + # The LM head weights require special treatment only when they are tied with the word embeddings + self.update_keys_to_ignore(config, ["lm_head.decoder.weight"]) + + # Initialize weights and apply final processing + self.post_init() + + def get_output_embeddings(self): + return self.lm_head.decoder + + def set_output_embeddings(self, new_embeddings): + self.lm_head.decoder = new_embeddings + + @property + def scaler(self): + return self._scaler.exp() + + @property + def scaler_1(self): + return self._scaler_1.exp() + + @property + def scaler_2(self): + return self._scaler_2.exp() + + @property + def rel_weight(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight, dim=-1) + + @property + def rel_weight_1(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight_1) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight_1, dim=-1) + + + @property + def rel_weight_2(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight_2) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight_2, dim=-1) + + + def compute_block(self, distance, height, n_cntxt_layers=0): + """Compute constituents from distance and height.""" + + if n_cntxt_layers>0: + if n_cntxt_layers == 1: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler_1[0] + elif n_cntxt_layers == 2: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler_2[0] + else: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0] + + gamma = torch.sigmoid(-beta_logits) + ones = torch.ones_like(gamma) + + block_mask_left = cummin( + gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1 + ) + block_mask_left = block_mask_left - F.pad( + block_mask_left[:, :, :-1], (1, 0), value=0 + ) + block_mask_left.tril_(0) + + block_mask_right = cummin( + gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1 + ) + block_mask_right = block_mask_right - F.pad( + block_mask_right[:, :, 1:], (0, 1), value=0 + ) + block_mask_right.triu_(0) + + block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :] + block = cumsum(block_mask_left).tril(0) + cumsum( + block_mask_right, reverse=True + ).triu(1) + + return block_p, block + + def compute_head(self, height, n_cntxt_layers=0): + """Estimate head for each constituent.""" + + _, length = height.size() + if n_cntxt_layers>0: + if n_cntxt_layers == 1: + head_logits = height * self.scaler_1[1] + elif n_cntxt_layers == 2: + head_logits = height * self.scaler_2[1] + else: + head_logits = height * self.scaler[1] + index = torch.arange(length, device=height.device) + + mask = (index[:, None, None] <= index[None, None, :]) * ( + index[None, None, :] <= index[None, :, None] + ) + head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1) + head_logits.masked_fill_(~mask[None, :, :, :], -1e9) + + head_p = torch.softmax(head_logits, dim=-1) + + return head_p + + def parse(self, x, embs=None, n_cntxt_layers=0): + """Parse input sentence. + + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + distance: syntactic distance + height: syntactic height + """ + + mask = x != self.pad + mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0) + + if embs is None: + h = self.roberta.embeddings(x) + else: + h = embs + + if n_cntxt_layers > 0: + if n_cntxt_layers == 1: + parser_layers = self.parser_layers_1 + height_ff = self.height_ff_1 + distance_ff = self.distance_ff_1 + elif n_cntxt_layers == 2: + parser_layers = self.parser_layers_2 + height_ff = self.height_ff_2 + distance_ff = self.distance_ff_2 + for i in range(int(self.config.n_parser_layers/2)): + h = h.masked_fill(~mask[:, :, None], 0) + h = parser_layers[i](h) + + height = height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, dim=-1 + )[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max + ) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where( + margin_left > margin_right, margin_right, margin_left + ).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + else: + for i in range(self.config.n_parser_layers): + h = h.masked_fill(~mask[:, :, None], 0) + h = self.parser_layers[i](h) + + height = self.height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = self.distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, dim=-1 + )[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max + ) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where( + margin_left > margin_right, margin_right, margin_left + ).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + + return distance, height + + def generate_mask(self, x, distance, height, n_cntxt_layers=0): + """Compute head and cibling distribution for each token.""" + + bsz, length = x.size() + + eye = torch.eye(length, device=x.device, dtype=torch.bool) + eye = eye[None, :, :].expand((bsz, -1, -1)) + + block_p, block = self.compute_block(distance, height, n_cntxt_layers=n_cntxt_layers) + head_p = self.compute_head(height, n_cntxt_layers=n_cntxt_layers) + head = torch.einsum("blij,bijh->blh", block_p, head_p) + head = head.masked_fill(eye, 0) + child = head.transpose(1, 2) + cibling = torch.bmm(head, child).masked_fill(eye, 0) + + rel_list = [] + if "head" in self.config.relations: + rel_list.append(head) + if "child" in self.config.relations: + rel_list.append(child) + if "cibling" in self.config.relations: + rel_list.append(cibling) + + rel = torch.stack(rel_list, dim=1) + + if n_cntxt_layers > 0: + if n_cntxt_layers == 1: + rel_weight = self.rel_weight_1 + elif n_cntxt_layers == 2: + rel_weight = self.rel_weight_2 + else: + rel_weight = self.rel_weight + + dep = torch.einsum("lhr,brij->lbhij", rel_weight, rel) + + if n_cntxt_layers == 1: + num_layers = self.cntxt_layers_2.config.num_hidden_layers + else: + num_layers = self.roberta.config.num_hidden_layers + + att_mask = dep.reshape( + num_layers, + bsz, + self.config.num_attention_heads, + length, + length, + ) + + return att_mask, cibling, head, block + + def forward( + self, + input_ids: Optional[torch.LongTensor] = None, + attention_mask: Optional[torch.FloatTensor] = None, + token_type_ids: Optional[torch.LongTensor] = None, + position_ids: Optional[torch.LongTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, MaskedLMOutput]: + r""" + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ..., + config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the + loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]` + kwargs (`Dict[str, any]`, optional, defaults to *{}*): + Used to hide legacy arguments that have been deprecated. + """ + return_dict = ( + return_dict if return_dict is not None else self.config.use_return_dict + ) + + + if self.config.n_cntxt_layers > 0: + cntxt_outputs = self.cntxt_layers( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict) + + + if self.config.n_cntxt_layers_2 > 0: + distance_1, height_1 = self.parse(input_ids, cntxt_outputs[0], n_cntxt_layers=1) + att_mask_1, _, _, _ = self.generate_mask(input_ids, distance_1, height_1, n_cntxt_layers=1) + + cntxt_outputs_2 = self.cntxt_layers_2( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + parser_att_mask=att_mask_1) + + sequence_output = cntxt_outputs_2[0] + + distance_2, height_2 = self.parse(input_ids, sequence_output[0], n_cntxt_layers=2) + att_mask, _, _, _ = self.generate_mask(input_ids, distance_2, height_2, n_cntxt_layers=2) + + elif self.config.n_cntxt_layers > 0: + distance, height = self.parse(input_ids, cntxt_outputs[0]) + att_mask, _, _, _ = self.generate_mask(input_ids, distance, height) + else: + distance, height = self.parse(input_ids) + att_mask, _, _, _ = self.generate_mask(input_ids, distance, height) + + outputs = self.roberta( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + parser_att_mask=att_mask, + ) + sequence_output = outputs[0] + prediction_scores = self.lm_head(sequence_output) + + masked_lm_loss = None + if labels is not None: + loss_fct = CrossEntropyLoss() + masked_lm_loss = loss_fct( + prediction_scores.view(-1, self.config.vocab_size), labels.view(-1) + ) + + if not return_dict: + output = (prediction_scores,) + outputs[2:] + return ( + ((masked_lm_loss,) + output) if masked_lm_loss is not None else output + ) + + return MaskedLMOutput( + loss=masked_lm_loss, + logits=prediction_scores, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +class RobertaLMHead(nn.Module): + """Roberta Head for masked language modeling.""" + + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + + self.decoder = nn.Linear(config.hidden_size, config.vocab_size) + self.bias = nn.Parameter(torch.zeros(config.vocab_size)) + self.decoder.bias = self.bias + + def forward(self, features, **kwargs): + x = self.dense(features) + x = gelu(x) + x = self.layer_norm(x) + + # project back to size of vocabulary with bias + x = self.decoder(x) + + return x + + def _tie_weights(self): + # To tie those two weights if they get disconnected (on TPU or when the bias is resized) + self.bias = self.decoder.bias + + +class StructRobertaForSequenceClassification(RobertaPreTrainedModel): + _keys_to_ignore_on_load_missing = [r"position_ids"] + + def __init__(self, config): + super().__init__(config) + self.num_labels = config.num_labels + self.config = config + + if config.n_cntxt_layers > 0: + config_cntxt = copy.deepcopy(config) + config_cntxt.num_hidden_layers = config.n_cntxt_layers + + self.cntxt_layers = RobertaModel(config_cntxt, add_pooling_layer=False) + + if config.n_cntxt_layers_2 > 0: + self.parser_layers_1 = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(int(config.n_parser_layers/2)) + ] + ) + + self.distance_ff_1 = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff_1 = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight_1 = nn.Parameter( + torch.zeros((config.n_cntxt_layers_2, config.num_attention_heads, n_rel)) + ) + self._rel_weight_1.data.normal_(0, 0.1) + + self._scaler_1 = nn.Parameter(torch.zeros(2)) + + config_cntxt_2 = copy.deepcopy(config) + config_cntxt_2.num_hidden_layers = config.n_cntxt_layers_2 + + self.cntxt_layers_2 = RobertaModel(config_cntxt_2, add_pooling_layer=False) + + + self.parser_layers_2 = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(int(config.n_parser_layers/2)) + ] + ) + + self.distance_ff_2 = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff_2 = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight_2 = nn.Parameter( + torch.zeros((config.num_hidden_layers, config.num_attention_heads, n_rel)) + ) + self._rel_weight_2.data.normal_(0, 0.1) + + self._scaler_2 = nn.Parameter(torch.zeros(2)) + + else: + self.parser_layers = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(config.n_parser_layers) + ] + ) + + self.distance_ff = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight = nn.Parameter( + torch.zeros((config.num_hidden_layers, config.num_attention_heads, n_rel)) + ) + self._rel_weight.data.normal_(0, 0.1) + + self._scaler = nn.Parameter(torch.zeros(2)) + + self.roberta = RobertaModel(config, add_pooling_layer=False) + + if config.n_cntxt_layers > 0: + self.cntxt_layers.embeddings = self.roberta.embeddings + if config.n_cntxt_layers_2 > 0: + self.cntxt_layers_2.embeddings = self.roberta.embeddings + + + self.pad = config.pad_token_id + self.classifier = RobertaClassificationHead(config) + + # Initialize weights and apply final processing + self.post_init() + + + @property + def scaler(self): + return self._scaler.exp() + + @property + def scaler_1(self): + return self._scaler_1.exp() + + @property + def scaler_2(self): + return self._scaler_2.exp() + + @property + def rel_weight(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight, dim=-1) + + @property + def rel_weight_1(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight_1) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight_1, dim=-1) + + + @property + def rel_weight_2(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight_2) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight_2, dim=-1) + + + def compute_block(self, distance, height, n_cntxt_layers=0): + """Compute constituents from distance and height.""" + + if n_cntxt_layers>0: + if n_cntxt_layers == 1: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler_1[0] + elif n_cntxt_layers == 2: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler_2[0] + else: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0] + + gamma = torch.sigmoid(-beta_logits) + ones = torch.ones_like(gamma) + + block_mask_left = cummin( + gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1 + ) + block_mask_left = block_mask_left - F.pad( + block_mask_left[:, :, :-1], (1, 0), value=0 + ) + block_mask_left.tril_(0) + + block_mask_right = cummin( + gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1 + ) + block_mask_right = block_mask_right - F.pad( + block_mask_right[:, :, 1:], (0, 1), value=0 + ) + block_mask_right.triu_(0) + + block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :] + block = cumsum(block_mask_left).tril(0) + cumsum( + block_mask_right, reverse=True + ).triu(1) + + return block_p, block + + def compute_head(self, height, n_cntxt_layers=0): + """Estimate head for each constituent.""" + + _, length = height.size() + if n_cntxt_layers>0: + if n_cntxt_layers == 1: + head_logits = height * self.scaler_1[1] + elif n_cntxt_layers == 2: + head_logits = height * self.scaler_2[1] + else: + head_logits = height * self.scaler[1] + index = torch.arange(length, device=height.device) + + mask = (index[:, None, None] <= index[None, None, :]) * ( + index[None, None, :] <= index[None, :, None] + ) + head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1) + head_logits.masked_fill_(~mask[None, :, :, :], -1e9) + + head_p = torch.softmax(head_logits, dim=-1) + + return head_p + + def parse(self, x, embs=None, n_cntxt_layers=0): + """Parse input sentence. + + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + distance: syntactic distance + height: syntactic height + """ + + mask = x != self.pad + mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0) + + if embs is None: + h = self.roberta.embeddings(x) + else: + h = embs + + if n_cntxt_layers > 0: + if n_cntxt_layers == 1: + parser_layers = self.parser_layers_1 + height_ff = self.height_ff_1 + distance_ff = self.distance_ff_1 + elif n_cntxt_layers == 2: + parser_layers = self.parser_layers_2 + height_ff = self.height_ff_2 + distance_ff = self.distance_ff_2 + for i in range(int(self.config.n_parser_layers/2)): + h = h.masked_fill(~mask[:, :, None], 0) + h = parser_layers[i](h) + + height = height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, dim=-1 + )[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max + ) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where( + margin_left > margin_right, margin_right, margin_left + ).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + else: + for i in range(self.config.n_parser_layers): + h = h.masked_fill(~mask[:, :, None], 0) + h = self.parser_layers[i](h) + + height = self.height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = self.distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, dim=-1 + )[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max + ) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where( + margin_left > margin_right, margin_right, margin_left + ).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + + return distance, height + + def generate_mask(self, x, distance, height, n_cntxt_layers=0): + """Compute head and cibling distribution for each token.""" + + bsz, length = x.size() + + eye = torch.eye(length, device=x.device, dtype=torch.bool) + eye = eye[None, :, :].expand((bsz, -1, -1)) + + block_p, block = self.compute_block(distance, height, n_cntxt_layers=n_cntxt_layers) + head_p = self.compute_head(height, n_cntxt_layers=n_cntxt_layers) + head = torch.einsum("blij,bijh->blh", block_p, head_p) + head = head.masked_fill(eye, 0) + child = head.transpose(1, 2) + cibling = torch.bmm(head, child).masked_fill(eye, 0) + + rel_list = [] + if "head" in self.config.relations: + rel_list.append(head) + if "child" in self.config.relations: + rel_list.append(child) + if "cibling" in self.config.relations: + rel_list.append(cibling) + + rel = torch.stack(rel_list, dim=1) + + if n_cntxt_layers > 0: + if n_cntxt_layers == 1: + rel_weight = self.rel_weight_1 + elif n_cntxt_layers == 2: + rel_weight = self.rel_weight_2 + else: + rel_weight = self.rel_weight + + dep = torch.einsum("lhr,brij->lbhij", rel_weight, rel) + + if n_cntxt_layers == 1: + num_layers = self.cntxt_layers_2.config.num_hidden_layers + else: + num_layers = self.roberta.config.num_hidden_layers + + att_mask = dep.reshape( + num_layers, + bsz, + self.config.num_attention_heads, + length, + length, + ) + + return att_mask, cibling, head, block + + def forward( + self, + input_ids: Optional[torch.LongTensor] = None, + attention_mask: Optional[torch.FloatTensor] = None, + token_type_ids: Optional[torch.LongTensor] = None, + position_ids: Optional[torch.LongTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, SequenceClassifierOutput]: + r""" + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., + config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If + `config.num_labels > 1` a classification loss is computed (Cross-Entropy). + """ + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + if self.config.n_cntxt_layers > 0: + cntxt_outputs = self.cntxt_layers( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict) + + + if self.config.n_cntxt_layers_2 > 0: + distance_1, height_1 = self.parse(input_ids, cntxt_outputs[0], n_cntxt_layers=1) + att_mask_1, _, _, _ = self.generate_mask(input_ids, distance_1, height_1, n_cntxt_layers=1) + + cntxt_outputs_2 = self.cntxt_layers_2( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + parser_att_mask=att_mask_1) + + sequence_output = cntxt_outputs_2[0] + + distance_2, height_2 = self.parse(input_ids, sequence_output[0], n_cntxt_layers=2) + att_mask, _, _, _ = self.generate_mask(input_ids, distance_2, height_2, n_cntxt_layers=2) + + elif self.config.n_cntxt_layers > 0: + distance, height = self.parse(input_ids, cntxt_outputs[0]) + att_mask, _, _, _ = self.generate_mask(input_ids, distance, height) + else: + distance, height = self.parse(input_ids) + att_mask, _, _, _ = self.generate_mask(input_ids, distance, height) + + outputs = self.roberta( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + parser_att_mask=att_mask, + ) + sequence_output = outputs[0] + logits = self.classifier(sequence_output) + + loss = None + if labels is not None: + if self.config.problem_type is None: + if self.num_labels == 1: + self.config.problem_type = "regression" + elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): + self.config.problem_type = "single_label_classification" + else: + self.config.problem_type = "multi_label_classification" + + if self.config.problem_type == "regression": + loss_fct = MSELoss() + if self.num_labels == 1: + loss = loss_fct(logits.squeeze(), labels.squeeze()) + else: + loss = loss_fct(logits, labels) + elif self.config.problem_type == "single_label_classification": + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) + elif self.config.problem_type == "multi_label_classification": + loss_fct = BCEWithLogitsLoss() + loss = loss_fct(logits, labels) + + if not return_dict: + output = (logits,) + outputs[2:] + return ((loss,) + output) if loss is not None else output + + return SequenceClassifierOutput( + loss=loss, + logits=logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +class RobertaClassificationHead(nn.Module): + """Head for sentence-level classification tasks.""" + + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + classifier_dropout = ( + config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob + ) + self.dropout = nn.Dropout(classifier_dropout) + self.out_proj = nn.Linear(config.hidden_size, config.num_labels) + + def forward(self, features, **kwargs): + x = features[:, 0, :] # take token (equiv. to [CLS]) + x = self.dropout(x) + x = self.dense(x) + x = torch.tanh(x) + x = self.dropout(x) + x = self.out_proj(x) + return x + + +def create_position_ids_from_input_ids( + input_ids, padding_idx, past_key_values_length=0 +): + """ + Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols + are ignored. This is modified from fairseq's `utils.make_positions`. + + Args: + x: torch.Tensor x: + + Returns: torch.Tensor + """ + # The series of casts and type-conversions here are carefully balanced to both work with ONNX export and XLA. + mask = input_ids.ne(padding_idx).int() + incremental_indices = ( + torch.cumsum(mask, dim=1).type_as(mask) + past_key_values_length + ) * mask + return incremental_indices.long() + padding_idx + + +def cumprod(x, reverse=False, exclusive=False): + """cumulative product.""" + if reverse: + x = x.flip([-1]) + + if exclusive: + x = F.pad(x[:, :, :-1], (1, 0), value=1) + + cx = x.cumprod(-1) + + if reverse: + cx = cx.flip([-1]) + return cx + + +def cumsum(x, reverse=False, exclusive=False): + """cumulative sum.""" + bsz, _, length = x.size() + device = x.device + if reverse: + if exclusive: + w = torch.ones([bsz, length, length], device=device).tril(-1) + else: + w = torch.ones([bsz, length, length], device=device).tril(0) + cx = torch.bmm(x, w) + else: + if exclusive: + w = torch.ones([bsz, length, length], device=device).triu(1) + else: + w = torch.ones([bsz, length, length], device=device).triu(0) + cx = torch.bmm(x, w) + return cx + + +def cummin(x, reverse=False, exclusive=False, max_value=1e9): + """cumulative min.""" + if reverse: + if exclusive: + x = F.pad(x[:, :, 1:], (0, 1), value=max_value) + x = x.flip([-1]).cummin(-1)[0].flip([-1]) + else: + if exclusive: + x = F.pad(x[:, :, :-1], (1, 0), value=max_value) + x = x.cummin(-1)[0] + return x diff --git a/finetune/lexical_content_the_control/predict_results.txt b/finetune/lexical_content_the_control/predict_results.txt new file mode 100644 index 0000000000000000000000000000000000000000..900d4a9c06e6db7107ce5dafe79c75167f4f24b9 --- /dev/null +++ b/finetune/lexical_content_the_control/predict_results.txt @@ -0,0 +1,18201 @@ +index prediction +0 1 +1 0 +2 1 +3 0 +4 1 +5 0 +6 1 +7 0 +8 1 +9 0 +10 1 +11 0 +12 1 +13 0 +14 1 +15 0 +16 1 +17 0 +18 1 +19 0 +20 1 +21 0 +22 1 +23 0 +24 1 +25 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"Face": 23310, + "Fair": 15766, + "Faith": 30228, + "Fal": 29047, + "Fall": 20994, + "Family": 18034, + "Fan": 29687, + "Fant": 14340, + "Fantastic": 20705, + "Far": 11276, + "Farewell": 21512, + "Fast": 23643, + "Fat": 17693, + "Father": 6310, + "Fe": 9733, + "Fear": 26431, + "February": 14751, + "Feed": 31687, + "Feel": 16638, + "Fel": 23642, + "Female": 24740, + "Fen": 27396, + "Fer": 14039, + "Few": 31686, + "Fi": 17373, + "Fifteen": 23644, + "Fifty": 20010, + "Fig": 18589, + "Fight": 14040, + "Fighting": 31585, + "Fil": 14556, + "Film": 31591, + "Fin": 7495, + "Final": 21511, + "Finally": 11439, + "Find": 8513, + "Finding": 20443, + "Fine": 5197, + "Finish": 31491, + "Fire": 10247, + "First": 3946, + "Fish": 27930, + "Five": 7756, + "Fl": 6412, + "Flash": 29507, + "Fle": 31685, + "Flor": 21799, + "Flowers": 27770, + "Flying": 29688, + "Follow": 8333, + "Following": 14326, + "Food": 20726, + "Foot": 7641, + "Football": 17549, + "Footnote": 8577, + "For": 1451, + "Fore": 14750, + "Forget": 9176, + "Forgive": 13389, + "Form": 29048, + "Former": 31301, + "Fort": 11561, + "Fortunately": 20260, + "Forty": 18943, + "Found": 18590, + "Four": 6939, + "Fox": 26913, + "Fr": 14038, + "Fran": 6626, + "France": 14648, + "Frances": 15167, + "Francesco": 22665, + "Franche": 28357, + "Francis": 19953, + "Frank": 6880, + "Frankie": 25434, + "Franklin": 28909, + "Franz": 19095, + "Fre": 13598, + "Fred": 11064, + "Free": 16120, + "Freeze": 24738, + "French": 11562, + "Fres": 30289, + "Fresh": 26914, + "Friday": 19996, + "Fried": 19355, + "Friedberg": 31630, + "Friend": 20995, + "Friends": 24739, + "Fro": 26912, + "From": 3118, + "Front": 31688, + "Fuck": 4392, + "Fucking": 12998, + "Fujii": 25122, + "Full": 15765, + "Fun": 23995, + "Funny": 18246, + "Further": 20727, + "G": 43, + "GA": 13018, + "GAIN": 26366, + "GAN": 30982, + "GAR": 21803, + "GARTH": 30922, + "GAS": 15078, + "GASPS": 18770, + "GB": 16803, + "GBT": 28463, + "GC": 30981, + "GE": 5184, + "GEORGE": 18739, + "GET": 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"architectures": [ + "StructRobertaForSequenceClassification" + ], + "attention_probs_dropout_prob": 0.1, + "auto_map": { + "AutoConfig": "modeling_structroberta.StructRobertaConfig", + "AutoModelForMaskedLM": "modeling_structroberta.StructRoberta", + "AutoModelForSequenceClassification": "modeling_structroberta.StructRobertaForSequenceClassification" + }, + "bos_token_id": 0, + "classifier_dropout": null, + "conv_size": 9, + "eos_token_id": 2, + "hidden_act": "gelu", + "hidden_dropout_prob": 0.1, + "hidden_size": 768, + "id2label": { + "0": 0, + "1": 1 + }, + "initializer_range": 0.02, + "intermediate_size": 3072, + "label2id": { + "0": 0, + "1": 1 + }, + "layer_norm_eps": 1e-05, + "max_position_embeddings": 514, + "model_type": "roberta", + "n_cntxt_layers": 0, + "n_cntxt_layers_2": 0, + "n_parser_layers": 4, + "num_attention_heads": 12, + "num_hidden_layers": 12, + "pad_token_id": 1, + "position_embedding_type": "absolute", + "problem_type": "single_label_classification", + "relations": [ + "head", + "child" + ], + "torch_dtype": "float32", + "transformers_version": "4.26.1", + "type_vocab_size": 1, + "use_cache": true, + "vocab_size": 32000, + "weight_act": "softmax" +} diff --git a/finetune/main_verb_control/eval_results.json b/finetune/main_verb_control/eval_results.json new file mode 100644 index 0000000000000000000000000000000000000000..1096aac13ff6932c1b49e2b8d8ec42c21d3f59b1 --- /dev/null +++ b/finetune/main_verb_control/eval_results.json @@ -0,0 +1,11 @@ +{ + "epoch": 10.0, + "eval_accuracy": 0.9599388241767883, + "eval_f1": 0.9582669640012742, + "eval_loss": 0.264987975358963, + "eval_mcc": 0.9227506346584822, + "eval_runtime": 32.0536, + "eval_samples": 16350, + "eval_samples_per_second": 510.082, + "eval_steps_per_second": 63.768 +} \ No newline at end of file diff --git a/finetune/main_verb_control/merges.txt b/finetune/main_verb_control/merges.txt new file mode 100644 index 0000000000000000000000000000000000000000..94b4f252cb566a4dac62a08c78d85169ff4e91be --- /dev/null +++ b/finetune/main_verb_control/merges.txt @@ -0,0 +1,31794 @@ +#version: 0.2 +Ġ t +h e +Ġ a +i n +Ġt he +r e +Ġ w +o u +Ġ s +o n +e r +h a +n d +i s +i t +Ġ b +Ġ o +Ġ c +Ġ m +Ġ f +in g +o r +a n +e d +l l +Ġ d +e n +Ġt o +e s +Ġ p +Ġo f +Ġ y +Ġ in +a r +a t +Ġa nd +Ġ l +ha t +Ġy ou +a s +Ġ g +o m +Ġ n +Ġ I +i c +o w +l e +v e +o t +Ġ h +Ġ he +a l +e t +Ġ ha +' s +u s +Ġt h +a y +Ġ it +Ġ is +Ġb e +Ġt hat +Ġ on +i on +i d +Ġ e +g h +u t +l d +en t +i m +Ġ re +Ġw as +a c +Ġw e +v er +a m +T he +. . +Ġf or +l y +Ġ S +s t +r i +r o +i r +a d +s e +k e +Ġ . +Ġs t +gh t +o o +' t +Ġ A +ou t +it h +Ġm e +Ġ C +a ll +Ġ k +Ġ u +Ġa n +Ġ M +Ġw ith +u r +Ġa s +Ġd o +c t +he r +c e +i ll +t er +i f +c h +Ġw h +Ġs e +om e +Ġa re +Ġ H +i l +Ġn ot +Ġha ve +Ġ B +Ġ 1 +a nd +Ġa t +Ġth is +ou ld +an t +Ġg o +us t +at ion +Ġl i +o p +a in +Ġk n +Y ou +e a +Ġ P +Ġ T +Ġ , +u n +g e +o l +t h +Ġh is +u l +Ġ " +r om +.. . +o re +Ġ W +e ll +Ġn e +Ġ r +Ġkn ow +Ġ j +ou r +Ġ D +Ġd e +p p +Ġc an +Ġ G +Ġthe y +u m +Ġ The +Ġs o +es t +Ġ ? +Ġa l +Ġa ll +Ġa b +Ġm y +is t +er s +Ġ R +i e +Ġyou r +ar t +Ġb y +Ġf rom +es s +Ġs h +Ġo r +Ġ 2 +Ġ F +Ġw hat +n t +a b +o s +Ġ L +e l +Ġon e +Ġc on +Ġ N +Ġw he +Ġthe re +i g +i ght +ha n +all y +Ġu p +q u +e m +Ġc om +on e +Ġha d +Ġb ut +Ġhe r +Ġs u +re d +r y +Ġ J +' re +Ġh im +Ġ out +r a +ac k +at e +oo d +ver y +oo k +Ġli ke +Ġw or +Ġs a +re s +am e +i ve +Ġp l +Ġ us +Ġ ( +on g +' m +Ġwe re +â Ģ +a ke +o d +in k +c k +Ġs he +Ġp ro +Ġab out +Ġ E +as t +Ġg et +Ġe x +Ġ v +p e +I t +or t +ou nd +in d +ar d +it y +Ġd on +A nd +ic h +ri ght +Ġ O +Ġj ust +Ġ1 9 +i es +H e +Ġwh o +e ar +ea h +Ġd id +a k +Ġhe re +b er +Ġthe m +Ġn o +im e +in e +Ġm an +Ġw ant +Ġw ould +Ġ2 0 +Ġ if +Ġ ' +Ġw ill +u d +a p +Ġ K +Ġth ink +ou s +' ll +v en +Ġs ome +o st +W hat +e p +h ing +Ġin t +Ġan y +ow n +ic e +a g +Ġ right +m ent +id e +Ġg ot +Ġha s +t her +us e +it t +Ġwh ich +ou gh +Ġt r +f e +h i +c a +Ġse e +Ġwhe n +Ġsa id +u re +Ġa r +Ġa g +Ġp e +Ġs ay +w ay +i a +Ġt ime +Ġt w +i v +ĠH e +s o +Ġbe en +i an +Ġs p +a re +ac e +Ġn ow +o ve +ic k +op le +c i +Ġ U +W e +ĠI t +' ve +Ġthe ir +o se +Y eah +Ġl e +Ġl ook +- - +Ġc l +is h +l f +re at +n a +Ġm ore +Ġthe n +Ġgo ing +a ge +S o +it e +re e +Ġo ther +Ġpe ople +ar y +Ġc ould +Ġ = +ĠS t +N o +ot her +n e +Ġof f +u e +Ġb ack +h is +ac t +B ut +Ġo ver +ir st +O h +as s +Ġ qu +ou nt +on d +Ġ very +ing s +ca use +Ġ ro +b le +p er +Ġg r +Ġa f +p t +Ġf e +an s +Ġwor k +Ġtw o +Ġg ood +T hat +Ġto o +Ġb r +e nd +om et +Ġ en +er m +o g +p l +at ed +o ll +Ġ Ġ +t he +Ġf irst +ion s +or d +Ġint o +he d +as e +Ġp art +k ay +0 0 +Ġh ow +Ġd own +or n +al k +u b +w e +Ġu n +Ġw ay +ad e +Ġt han +i re +Ġal so +o y +f f +itt le +Ġc all +i z +m er +Ġa m +ou ght +Ġy ear +h y +at er +Ġp r +Ġcom p +o v +Ġc ome +W ell +Ġb l +Ġ our +an ce +Ġwhe re +nd er +ac h +if e +Ġag ain +Ġ im +n g +Ġon ly +Ġ V +Ġe very +ĠI n +Ġl ittle +Ġa d +c c +ur n 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See all RoBERTa models at https://huggingface.co/models?filter=roberta +] + + +class StructRobertaConfig(RobertaConfig): + model_type = "roberta" + + def __init__( + self, + n_parser_layers=4, + conv_size=9, + relations=("head", "child"), + weight_act="softmax", + n_cntxt_layers=3, + n_cntxt_layers_2=0, + **kwargs,): + + super().__init__(**kwargs) + self.n_cntxt_layers = n_cntxt_layers + self.n_parser_layers = n_parser_layers + self.n_cntxt_layers_2 = n_cntxt_layers_2 + self.conv_size = conv_size + self.relations = relations + self.weight_act = weight_act + +class Conv1d(nn.Module): + """1D convolution layer.""" + + def __init__(self, hidden_size, kernel_size, dilation=1): + """Initialization. + + Args: + hidden_size: dimension of input embeddings + kernel_size: convolution kernel size + dilation: the spacing between the kernel points + """ + super(Conv1d, self).__init__() + + if kernel_size % 2 == 0: + padding = (kernel_size // 2) * dilation + self.shift = True + else: + padding = ((kernel_size - 1) // 2) * dilation + self.shift = False + self.conv = nn.Conv1d( + hidden_size, hidden_size, kernel_size, padding=padding, dilation=dilation + ) + + def forward(self, x): + """Compute convolution. + + Args: + x: input embeddings + Returns: + conv_output: convolution results + """ + + if self.shift: + return self.conv(x.transpose(1, 2)).transpose(1, 2)[:, 1:] + else: + return self.conv(x.transpose(1, 2)).transpose(1, 2) + + +class RobertaEmbeddings(nn.Module): + """ + Same as BertEmbeddings with a tiny tweak for positional embeddings indexing. + """ + + # Copied from transformers.models.bert.modeling_bert.BertEmbeddings.__init__ + def __init__(self, config): + super().__init__() + self.word_embeddings = nn.Embedding( + config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id + ) + self.position_embeddings = nn.Embedding( + config.max_position_embeddings, config.hidden_size + ) + self.token_type_embeddings = nn.Embedding( + config.type_vocab_size, config.hidden_size + ) + + # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load + # any TensorFlow checkpoint file + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + # position_ids (1, len position emb) is contiguous in memory and exported when serialized + self.position_embedding_type = getattr( + config, "position_embedding_type", "absolute" + ) + self.register_buffer( + "position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)) + ) + if version.parse(torch.__version__) > version.parse("1.6.0"): + self.register_buffer( + "token_type_ids", + torch.zeros(self.position_ids.size(), dtype=torch.long), + persistent=False, + ) + + # End copy + self.padding_idx = config.pad_token_id + self.position_embeddings = nn.Embedding( + config.max_position_embeddings, + config.hidden_size, + padding_idx=self.padding_idx, + ) + + def forward( + self, + input_ids=None, + token_type_ids=None, + position_ids=None, + inputs_embeds=None, + past_key_values_length=0, + ): + if position_ids is None: + if input_ids is not None: + # Create the position ids from the input token ids. Any padded tokens remain padded. + position_ids = create_position_ids_from_input_ids( + input_ids, self.padding_idx, past_key_values_length + ) + else: + position_ids = self.create_position_ids_from_inputs_embeds( + inputs_embeds + ) + + if input_ids is not None: + input_shape = input_ids.size() + else: + input_shape = inputs_embeds.size()[:-1] + + seq_length = input_shape[1] + + # Setting the token_type_ids to the registered buffer in constructor where it is all zeros, which usually occurs + # when its auto-generated, registered buffer helps users when tracing the model without passing token_type_ids, solves + # issue #5664 + if token_type_ids is None: + if hasattr(self, "token_type_ids"): + buffered_token_type_ids = self.token_type_ids[:, :seq_length] + buffered_token_type_ids_expanded = buffered_token_type_ids.expand( + input_shape[0], seq_length + ) + token_type_ids = buffered_token_type_ids_expanded + else: + token_type_ids = torch.zeros( + input_shape, dtype=torch.long, device=self.position_ids.device + ) + + if inputs_embeds is None: + inputs_embeds = self.word_embeddings(input_ids) + token_type_embeddings = self.token_type_embeddings(token_type_ids) + + embeddings = inputs_embeds + token_type_embeddings + if self.position_embedding_type == "absolute": + position_embeddings = self.position_embeddings(position_ids) + embeddings += position_embeddings + embeddings = self.LayerNorm(embeddings) + embeddings = self.dropout(embeddings) + return embeddings + + def create_position_ids_from_inputs_embeds(self, inputs_embeds): + """ + We are provided embeddings directly. We cannot infer which are padded so just generate sequential position ids. + + Args: + inputs_embeds: torch.Tensor + + Returns: torch.Tensor + """ + input_shape = inputs_embeds.size()[:-1] + sequence_length = input_shape[1] + + position_ids = torch.arange( + self.padding_idx + 1, + sequence_length + self.padding_idx + 1, + dtype=torch.long, + device=inputs_embeds.device, + ) + return position_ids.unsqueeze(0).expand(input_shape) + + +# Copied from transformers.models.bert.modeling_bert.BertSelfAttention with Bert->Roberta +class RobertaSelfAttention(nn.Module): + def __init__(self, config, position_embedding_type=None): + super().__init__() + if config.hidden_size % config.num_attention_heads != 0 and not hasattr( + config, "embedding_size" + ): + raise ValueError( + f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention " + f"heads ({config.num_attention_heads})" + ) + + self.num_attention_heads = config.num_attention_heads + self.attention_head_size = int(config.hidden_size / config.num_attention_heads) + self.all_head_size = self.num_attention_heads * self.attention_head_size + + self.query = nn.Linear(config.hidden_size, self.all_head_size) + self.key = nn.Linear(config.hidden_size, self.all_head_size) + self.value = nn.Linear(config.hidden_size, self.all_head_size) + + self.dropout = nn.Dropout(config.attention_probs_dropout_prob) + self.position_embedding_type = position_embedding_type or getattr( + config, "position_embedding_type", "absolute" + ) + if ( + self.position_embedding_type == "relative_key" + or self.position_embedding_type == "relative_key_query" + ): + self.max_position_embeddings = config.max_position_embeddings + self.distance_embedding = nn.Embedding( + 2 * config.max_position_embeddings - 1, self.attention_head_size + ) + + self.is_decoder = config.is_decoder + + def transpose_for_scores(self, x): + new_x_shape = x.size()[:-1] + ( + self.num_attention_heads, + self.attention_head_size, + ) + x = x.view(new_x_shape) + return x.permute(0, 2, 1, 3) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.FloatTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, + output_attentions: Optional[bool] = False, + parser_att_mask=None, + ) -> Tuple[torch.Tensor]: + mixed_query_layer = self.query(hidden_states) + + # If this is instantiated as a cross-attention module, the keys + # and values come from an encoder; the attention mask needs to be + # such that the encoder's padding tokens are not attended to. + is_cross_attention = encoder_hidden_states is not None + + if is_cross_attention and past_key_value is not None: + # reuse k,v, cross_attentions + key_layer = past_key_value[0] + value_layer = past_key_value[1] + attention_mask = encoder_attention_mask + elif is_cross_attention: + key_layer = self.transpose_for_scores(self.key(encoder_hidden_states)) + value_layer = self.transpose_for_scores(self.value(encoder_hidden_states)) + attention_mask = encoder_attention_mask + elif past_key_value is not None: + key_layer = self.transpose_for_scores(self.key(hidden_states)) + value_layer = self.transpose_for_scores(self.value(hidden_states)) + key_layer = torch.cat([past_key_value[0], key_layer], dim=2) + value_layer = torch.cat([past_key_value[1], value_layer], dim=2) + else: + key_layer = self.transpose_for_scores(self.key(hidden_states)) + value_layer = self.transpose_for_scores(self.value(hidden_states)) + + query_layer = self.transpose_for_scores(mixed_query_layer) + + if self.is_decoder: + # if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states. + # Further calls to cross_attention layer can then reuse all cross-attention + # key/value_states (first "if" case) + # if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of + # all previous decoder key/value_states. Further calls to uni-directional self-attention + # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case) + # if encoder bi-directional self-attention `past_key_value` is always `None` + past_key_value = (key_layer, value_layer) + + # Take the dot product between "query" and "key" to get the raw attention scores. + attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) + + if ( + self.position_embedding_type == "relative_key" + or self.position_embedding_type == "relative_key_query" + ): + seq_length = hidden_states.size()[1] + position_ids_l = torch.arange( + seq_length, dtype=torch.long, device=hidden_states.device + ).view(-1, 1) + position_ids_r = torch.arange( + seq_length, dtype=torch.long, device=hidden_states.device + ).view(1, -1) + distance = position_ids_l - position_ids_r + positional_embedding = self.distance_embedding( + distance + self.max_position_embeddings - 1 + ) + positional_embedding = positional_embedding.to( + dtype=query_layer.dtype + ) # fp16 compatibility + + if self.position_embedding_type == "relative_key": + relative_position_scores = torch.einsum( + "bhld,lrd->bhlr", query_layer, positional_embedding + ) + attention_scores = attention_scores + relative_position_scores + elif self.position_embedding_type == "relative_key_query": + relative_position_scores_query = torch.einsum( + "bhld,lrd->bhlr", query_layer, positional_embedding + ) + relative_position_scores_key = torch.einsum( + "bhrd,lrd->bhlr", key_layer, positional_embedding + ) + attention_scores = ( + attention_scores + + relative_position_scores_query + + relative_position_scores_key + ) + + attention_scores = attention_scores / math.sqrt(self.attention_head_size) + if attention_mask is not None: + # Apply the attention mask is (precomputed for all layers in RobertaModel forward() function) + attention_scores = attention_scores + attention_mask + + if parser_att_mask is None: + # Normalize the attention scores to probabilities. + attention_probs = nn.functional.softmax(attention_scores, dim=-1) + else: + attention_probs = torch.sigmoid(attention_scores) * parser_att_mask + + # This is actually dropping out entire tokens to attend to, which might + # seem a bit unusual, but is taken from the original Transformer paper. + attention_probs = self.dropout(attention_probs) + + # Mask heads if we want to + if head_mask is not None: + attention_probs = attention_probs * head_mask + + context_layer = torch.matmul(attention_probs, value_layer) + + context_layer = context_layer.permute(0, 2, 1, 3).contiguous() + new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) + context_layer = context_layer.view(new_context_layer_shape) + + outputs = ( + (context_layer, attention_probs) if output_attentions else (context_layer,) + ) + + if self.is_decoder: + outputs = outputs + (past_key_value,) + return outputs + + +# Copied from transformers.models.bert.modeling_bert.BertSelfOutput +class RobertaSelfOutput(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward( + self, hidden_states: torch.Tensor, input_tensor: torch.Tensor + ) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + hidden_states = self.LayerNorm(hidden_states + input_tensor) + return hidden_states + + +# Copied from transformers.models.bert.modeling_bert.BertAttention with Bert->Roberta +class RobertaAttention(nn.Module): + def __init__(self, config, position_embedding_type=None): + super().__init__() + self.self = RobertaSelfAttention( + config, position_embedding_type=position_embedding_type + ) + self.output = RobertaSelfOutput(config) + self.pruned_heads = set() + + def prune_heads(self, heads): + if len(heads) == 0: + return + heads, index = find_pruneable_heads_and_indices( + heads, + self.self.num_attention_heads, + self.self.attention_head_size, + self.pruned_heads, + ) + + # Prune linear layers + self.self.query = prune_linear_layer(self.self.query, index) + self.self.key = prune_linear_layer(self.self.key, index) + self.self.value = prune_linear_layer(self.self.value, index) + self.output.dense = prune_linear_layer(self.output.dense, index, dim=1) + + # Update hyper params and store pruned heads + self.self.num_attention_heads = self.self.num_attention_heads - len(heads) + self.self.all_head_size = ( + self.self.attention_head_size * self.self.num_attention_heads + ) + self.pruned_heads = self.pruned_heads.union(heads) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.FloatTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, + output_attentions: Optional[bool] = False, + parser_att_mask=None, + ) -> Tuple[torch.Tensor]: + self_outputs = self.self( + hidden_states, + attention_mask, + head_mask, + encoder_hidden_states, + encoder_attention_mask, + past_key_value, + output_attentions, + parser_att_mask=parser_att_mask, + ) + attention_output = self.output(self_outputs[0], hidden_states) + outputs = (attention_output,) + self_outputs[ + 1: + ] # add attentions if we output them + return outputs + + +# Copied from transformers.models.bert.modeling_bert.BertIntermediate +class RobertaIntermediate(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.intermediate_size) + if isinstance(config.hidden_act, str): + self.intermediate_act_fn = ACT2FN[config.hidden_act] + else: + self.intermediate_act_fn = config.hidden_act + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.intermediate_act_fn(hidden_states) + return hidden_states + + +# Copied from transformers.models.bert.modeling_bert.BertOutput +class RobertaOutput(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.intermediate_size, config.hidden_size) + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward( + self, hidden_states: torch.Tensor, input_tensor: torch.Tensor + ) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + hidden_states = self.LayerNorm(hidden_states + input_tensor) + return hidden_states + + +# Copied from transformers.models.bert.modeling_bert.BertLayer with Bert->Roberta +class RobertaLayer(nn.Module): + def __init__(self, config): + super().__init__() + self.chunk_size_feed_forward = config.chunk_size_feed_forward + self.seq_len_dim = 1 + self.attention = RobertaAttention(config) + self.is_decoder = config.is_decoder + self.add_cross_attention = config.add_cross_attention + if self.add_cross_attention: + if not self.is_decoder: + raise ValueError( + f"{self} should be used as a decoder model if cross attention is added" + ) + self.crossattention = RobertaAttention( + config, position_embedding_type="absolute" + ) + self.intermediate = RobertaIntermediate(config) + self.output = RobertaOutput(config) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.FloatTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, + output_attentions: Optional[bool] = False, + parser_att_mask=None, + ) -> Tuple[torch.Tensor]: + # decoder uni-directional self-attention cached key/values tuple is at positions 1,2 + self_attn_past_key_value = ( + past_key_value[:2] if past_key_value is not None else None + ) + self_attention_outputs = self.attention( + hidden_states, + attention_mask, + head_mask, + output_attentions=output_attentions, + past_key_value=self_attn_past_key_value, + parser_att_mask=parser_att_mask, + ) + attention_output = self_attention_outputs[0] + + # if decoder, the last output is tuple of self-attn cache + if self.is_decoder: + outputs = self_attention_outputs[1:-1] + present_key_value = self_attention_outputs[-1] + else: + outputs = self_attention_outputs[ + 1: + ] # add self attentions if we output attention weights + + cross_attn_present_key_value = None + if self.is_decoder and encoder_hidden_states is not None: + if not hasattr(self, "crossattention"): + raise ValueError( + f"If `encoder_hidden_states` are passed, {self} has to be instantiated with cross-attention layers by setting `config.add_cross_attention=True`" + ) + + # cross_attn cached key/values tuple is at positions 3,4 of past_key_value tuple + cross_attn_past_key_value = ( + past_key_value[-2:] if past_key_value is not None else None + ) + cross_attention_outputs = self.crossattention( + attention_output, + attention_mask, + head_mask, + encoder_hidden_states, + encoder_attention_mask, + cross_attn_past_key_value, + output_attentions, + ) + attention_output = cross_attention_outputs[0] + outputs = ( + outputs + cross_attention_outputs[1:-1] + ) # add cross attentions if we output attention weights + + # add cross-attn cache to positions 3,4 of present_key_value tuple + cross_attn_present_key_value = cross_attention_outputs[-1] + present_key_value = present_key_value + cross_attn_present_key_value + + layer_output = apply_chunking_to_forward( + self.feed_forward_chunk, + self.chunk_size_feed_forward, + self.seq_len_dim, + attention_output, + ) + outputs = (layer_output,) + outputs + + # if decoder, return the attn key/values as the last output + if self.is_decoder: + outputs = outputs + (present_key_value,) + + return outputs + + def feed_forward_chunk(self, attention_output): + intermediate_output = self.intermediate(attention_output) + layer_output = self.output(intermediate_output, attention_output) + return layer_output + + +# Copied from transformers.models.bert.modeling_bert.BertEncoder with Bert->Roberta +class RobertaEncoder(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + self.layer = nn.ModuleList( + [RobertaLayer(config) for _ in range(config.num_hidden_layers)] + ) + self.gradient_checkpointing = False + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.FloatTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = False, + output_hidden_states: Optional[bool] = False, + return_dict: Optional[bool] = True, + parser_att_mask=None, + ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPastAndCrossAttentions]: + all_hidden_states = () if output_hidden_states else None + all_self_attentions = () if output_attentions else None + all_cross_attentions = ( + () if output_attentions and self.config.add_cross_attention else None + ) + + next_decoder_cache = () if use_cache else None + for i, layer_module in enumerate(self.layer): + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + layer_head_mask = head_mask[i] if head_mask is not None else None + past_key_value = past_key_values[i] if past_key_values is not None else None + + if self.gradient_checkpointing and self.training: + + if use_cache: + logger.warning( + "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." + ) + use_cache = False + + def create_custom_forward(module): + def custom_forward(*inputs): + return module(*inputs, past_key_value, output_attentions) + + return custom_forward + + layer_outputs = torch.utils.checkpoint.checkpoint( + create_custom_forward(layer_module), + hidden_states, + attention_mask, + layer_head_mask, + encoder_hidden_states, + encoder_attention_mask, + ) + else: + if parser_att_mask is not None: + layer_outputs = layer_module( + hidden_states, + attention_mask, + layer_head_mask, + encoder_hidden_states, + encoder_attention_mask, + past_key_value, + output_attentions, + parser_att_mask=parser_att_mask[i]) + else: + layer_outputs = layer_module( + hidden_states, + attention_mask, + layer_head_mask, + encoder_hidden_states, + encoder_attention_mask, + past_key_value, + output_attentions, + parser_att_mask=None) + + + hidden_states = layer_outputs[0] + if use_cache: + next_decoder_cache += (layer_outputs[-1],) + if output_attentions: + all_self_attentions = all_self_attentions + (layer_outputs[1],) + if self.config.add_cross_attention: + all_cross_attentions = all_cross_attentions + (layer_outputs[2],) + + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + if not return_dict: + return tuple( + v + for v in [ + hidden_states, + next_decoder_cache, + all_hidden_states, + all_self_attentions, + all_cross_attentions, + ] + if v is not None + ) + return BaseModelOutputWithPastAndCrossAttentions( + last_hidden_state=hidden_states, + past_key_values=next_decoder_cache, + hidden_states=all_hidden_states, + attentions=all_self_attentions, + cross_attentions=all_cross_attentions, + ) + + +# Copied from transformers.models.bert.modeling_bert.BertPooler +class RobertaPooler(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.activation = nn.Tanh() + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + # We "pool" the model by simply taking the hidden state corresponding + # to the first token. + first_token_tensor = hidden_states[:, 0] + pooled_output = self.dense(first_token_tensor) + pooled_output = self.activation(pooled_output) + return pooled_output + + +class RobertaPreTrainedModel(PreTrainedModel): + """ + An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained + models. + """ + + config_class = RobertaConfig + base_model_prefix = "roberta" + supports_gradient_checkpointing = True + + # Copied from transformers.models.bert.modeling_bert.BertPreTrainedModel._init_weights + def _init_weights(self, module): + """Initialize the weights""" + if isinstance(module, nn.Linear): + # Slightly different from the TF version which uses truncated_normal for initialization + # cf https://github.com/pytorch/pytorch/pull/5617 + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.Embedding): + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.padding_idx is not None: + module.weight.data[module.padding_idx].zero_() + elif isinstance(module, nn.LayerNorm): + if module.bias is not None: + module.bias.data.zero_() + module.weight.data.fill_(1.0) + + def _set_gradient_checkpointing(self, module, value=False): + if isinstance(module, RobertaEncoder): + module.gradient_checkpointing = value + + def update_keys_to_ignore(self, config, del_keys_to_ignore): + """Remove some keys from ignore list""" + if not config.tie_word_embeddings: + # must make a new list, or the class variable gets modified! + self._keys_to_ignore_on_save = [ + k for k in self._keys_to_ignore_on_save if k not in del_keys_to_ignore + ] + self._keys_to_ignore_on_load_missing = [ + k + for k in self._keys_to_ignore_on_load_missing + if k not in del_keys_to_ignore + ] + + +ROBERTA_START_DOCSTRING = r""" + + This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the + library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads + etc.) + + This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. + Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage + and behavior. + + Parameters: + config ([`RobertaConfig`]): Model configuration class with all the parameters of the + model. Initializing with a config file does not load the weights associated with the model, only the + configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. +""" + + +ROBERTA_INPUTS_DOCSTRING = r""" + Args: + input_ids (`torch.LongTensor` of shape `({0})`): + Indices of input sequence tokens in the vocabulary. + + Indices can be obtained using [`RobertaTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + [What are input IDs?](../glossary#input-ids) + attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*): + Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + + [What are attention masks?](../glossary#attention-mask) + token_type_ids (`torch.LongTensor` of shape `({0})`, *optional*): + Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, + 1]`: + + - 0 corresponds to a *sentence A* token, + - 1 corresponds to a *sentence B* token. + + [What are token type IDs?](../glossary#token-type-ids) + position_ids (`torch.LongTensor` of shape `({0})`, *optional*): + Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, + config.max_position_embeddings - 1]`. + + [What are position IDs?](../glossary#position-ids) + head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): + Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: + + - 1 indicates the head is **not masked**, + - 0 indicates the head is **masked**. + + inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_size)`, *optional*): + Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This + is useful if you want more control over how to convert `input_ids` indices into associated vectors than the + model's internal embedding lookup matrix. + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned + tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for + more detail. + return_dict (`bool`, *optional*): + Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. +""" + + +class RobertaModel(RobertaPreTrainedModel): + """ + + The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of + cross-attention is added between the self-attention layers, following the architecture described in *Attention is + all you need*_ by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz + Kaiser and Illia Polosukhin. + + To behave as an decoder the model needs to be initialized with the `is_decoder` argument of the configuration set + to `True`. To be used in a Seq2Seq model, the model needs to initialized with both `is_decoder` argument and + `add_cross_attention` set to `True`; an `encoder_hidden_states` is then expected as an input to the forward pass. + + .. _*Attention is all you need*: https://arxiv.org/abs/1706.03762 + + """ + + _keys_to_ignore_on_load_missing = [r"position_ids"] + + # Copied from transformers.models.bert.modeling_bert.BertModel.__init__ with Bert->Roberta + def __init__(self, config, add_pooling_layer=True): + super().__init__(config) + self.config = config + + self.embeddings = RobertaEmbeddings(config) + self.encoder = RobertaEncoder(config) + + self.pooler = RobertaPooler(config) if add_pooling_layer else None + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.embeddings.word_embeddings + + def set_input_embeddings(self, value): + self.embeddings.word_embeddings = value + + def _prune_heads(self, heads_to_prune): + """ + Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base + class PreTrainedModel + """ + for layer, heads in heads_to_prune.items(): + self.encoder.layer[layer].attention.prune_heads(heads) + + # Copied from transformers.models.bert.modeling_bert.BertModel.forward + def forward( + self, + input_ids: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + token_type_ids: Optional[torch.Tensor] = None, + position_ids: Optional[torch.Tensor] = None, + head_mask: Optional[torch.Tensor] = None, + inputs_embeds: Optional[torch.Tensor] = None, + encoder_hidden_states: Optional[torch.Tensor] = None, + encoder_attention_mask: Optional[torch.Tensor] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + parser_att_mask=None, + ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPoolingAndCrossAttentions]: + r""" + encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): + Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if + the model is configured as a decoder. + encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*): + Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in + the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): + Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. + + If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that + don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all + `decoder_input_ids` of shape `(batch_size, sequence_length)`. + use_cache (`bool`, *optional*): + If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see + `past_key_values`). + """ + output_attentions = ( + output_attentions + if output_attentions is not None + else self.config.output_attentions + ) + output_hidden_states = ( + output_hidden_states + if output_hidden_states is not None + else self.config.output_hidden_states + ) + return_dict = ( + return_dict if return_dict is not None else self.config.use_return_dict + ) + + if self.config.is_decoder: + use_cache = use_cache if use_cache is not None else self.config.use_cache + else: + use_cache = False + + if input_ids is not None and inputs_embeds is not None: + raise ValueError( + "You cannot specify both input_ids and inputs_embeds at the same time" + ) + elif input_ids is not None: + input_shape = input_ids.size() + elif inputs_embeds is not None: + input_shape = inputs_embeds.size()[:-1] + else: + raise ValueError("You have to specify either input_ids or inputs_embeds") + + batch_size, seq_length = input_shape + device = input_ids.device if input_ids is not None else inputs_embeds.device + + # past_key_values_length + past_key_values_length = ( + past_key_values[0][0].shape[2] if past_key_values is not None else 0 + ) + + if attention_mask is None: + attention_mask = torch.ones( + ((batch_size, seq_length + past_key_values_length)), device=device + ) + + if token_type_ids is None: + if hasattr(self.embeddings, "token_type_ids"): + buffered_token_type_ids = self.embeddings.token_type_ids[:, :seq_length] + buffered_token_type_ids_expanded = buffered_token_type_ids.expand( + batch_size, seq_length + ) + token_type_ids = buffered_token_type_ids_expanded + else: + token_type_ids = torch.zeros( + input_shape, dtype=torch.long, device=device + ) + + # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length] + # ourselves in which case we just need to make it broadcastable to all heads. + extended_attention_mask: torch.Tensor = self.get_extended_attention_mask( + attention_mask, input_shape, device + ) + + # If a 2D or 3D attention mask is provided for the cross-attention + # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length] + if self.config.is_decoder and encoder_hidden_states is not None: + ( + encoder_batch_size, + encoder_sequence_length, + _, + ) = encoder_hidden_states.size() + encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length) + if encoder_attention_mask is None: + encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device) + encoder_extended_attention_mask = self.invert_attention_mask( + encoder_attention_mask + ) + else: + encoder_extended_attention_mask = None + + # Prepare head mask if needed + # 1.0 in head_mask indicate we keep the head + # attention_probs has shape bsz x n_heads x N x N + # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] + # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] + head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) + + embedding_output = self.embeddings( + input_ids=input_ids, + position_ids=position_ids, + token_type_ids=token_type_ids, + inputs_embeds=inputs_embeds, + past_key_values_length=past_key_values_length, + ) + encoder_outputs = self.encoder( + embedding_output, + attention_mask=extended_attention_mask, + head_mask=head_mask, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_extended_attention_mask, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + parser_att_mask=parser_att_mask, + ) + sequence_output = encoder_outputs[0] + pooled_output = ( + self.pooler(sequence_output) if self.pooler is not None else None + ) + + if not return_dict: + return (sequence_output, pooled_output) + encoder_outputs[1:] + + return BaseModelOutputWithPoolingAndCrossAttentions( + last_hidden_state=sequence_output, + pooler_output=pooled_output, + past_key_values=encoder_outputs.past_key_values, + hidden_states=encoder_outputs.hidden_states, + attentions=encoder_outputs.attentions, + cross_attentions=encoder_outputs.cross_attentions, + ) + + +class StructRoberta(RobertaPreTrainedModel): + _keys_to_ignore_on_save = [r"lm_head.decoder.weight", r"lm_head.decoder.bias"] + _keys_to_ignore_on_load_missing = [ + r"position_ids", + r"lm_head.decoder.weight", + r"lm_head.decoder.bias", + ] + _keys_to_ignore_on_load_unexpected = [r"pooler"] + + def __init__(self, config): + super().__init__(config) + + if config.is_decoder: + logger.warning( + "If you want to use `RobertaForMaskedLM` make sure `config.is_decoder=False` for " + "bi-directional self-attention." + ) + + + if config.n_cntxt_layers > 0: + config_cntxt = copy.deepcopy(config) + config_cntxt.num_hidden_layers = config.n_cntxt_layers + + self.cntxt_layers = RobertaModel(config_cntxt, add_pooling_layer=False) + + if config.n_cntxt_layers_2 > 0: + self.parser_layers_1 = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(int(config.n_parser_layers/2)) + ] + ) + + self.distance_ff_1 = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff_1 = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight_1 = nn.Parameter( + torch.zeros((config.n_cntxt_layers_2, config.num_attention_heads, n_rel)) + ) + self._rel_weight_1.data.normal_(0, 0.1) + + self._scaler_1 = nn.Parameter(torch.zeros(2)) + + config_cntxt_2 = copy.deepcopy(config) + config_cntxt_2.num_hidden_layers = config.n_cntxt_layers_2 + + self.cntxt_layers_2 = RobertaModel(config_cntxt_2, add_pooling_layer=False) + + + self.parser_layers_2 = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(int(config.n_parser_layers/2)) + ] + ) + + self.distance_ff_2 = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff_2 = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight_2 = nn.Parameter( + torch.zeros((config.num_hidden_layers, config.num_attention_heads, n_rel)) + ) + self._rel_weight_2.data.normal_(0, 0.1) + + self._scaler_2 = nn.Parameter(torch.zeros(2)) + + else: + self.parser_layers = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(config.n_parser_layers) + ] + ) + + self.distance_ff = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight = nn.Parameter( + torch.zeros((config.num_hidden_layers, config.num_attention_heads, n_rel)) + ) + self._rel_weight.data.normal_(0, 0.1) + + self._scaler = nn.Parameter(torch.zeros(2)) + + self.roberta = RobertaModel(config, add_pooling_layer=False) + + if config.n_cntxt_layers > 0: + self.cntxt_layers.embeddings = self.roberta.embeddings + if config.n_cntxt_layers_2 > 0: + self.cntxt_layers_2.embeddings = self.roberta.embeddings + + self.lm_head = RobertaLMHead(config) + + self.pad = config.pad_token_id + + # The LM head weights require special treatment only when they are tied with the word embeddings + self.update_keys_to_ignore(config, ["lm_head.decoder.weight"]) + + # Initialize weights and apply final processing + self.post_init() + + def get_output_embeddings(self): + return self.lm_head.decoder + + def set_output_embeddings(self, new_embeddings): + self.lm_head.decoder = new_embeddings + + @property + def scaler(self): + return self._scaler.exp() + + @property + def scaler_1(self): + return self._scaler_1.exp() + + @property + def scaler_2(self): + return self._scaler_2.exp() + + @property + def rel_weight(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight, dim=-1) + + @property + def rel_weight_1(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight_1) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight_1, dim=-1) + + + @property + def rel_weight_2(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight_2) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight_2, dim=-1) + + + def compute_block(self, distance, height, n_cntxt_layers=0): + """Compute constituents from distance and height.""" + + if n_cntxt_layers>0: + if n_cntxt_layers == 1: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler_1[0] + elif n_cntxt_layers == 2: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler_2[0] + else: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0] + + gamma = torch.sigmoid(-beta_logits) + ones = torch.ones_like(gamma) + + block_mask_left = cummin( + gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1 + ) + block_mask_left = block_mask_left - F.pad( + block_mask_left[:, :, :-1], (1, 0), value=0 + ) + block_mask_left.tril_(0) + + block_mask_right = cummin( + gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1 + ) + block_mask_right = block_mask_right - F.pad( + block_mask_right[:, :, 1:], (0, 1), value=0 + ) + block_mask_right.triu_(0) + + block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :] + block = cumsum(block_mask_left).tril(0) + cumsum( + block_mask_right, reverse=True + ).triu(1) + + return block_p, block + + def compute_head(self, height, n_cntxt_layers=0): + """Estimate head for each constituent.""" + + _, length = height.size() + if n_cntxt_layers>0: + if n_cntxt_layers == 1: + head_logits = height * self.scaler_1[1] + elif n_cntxt_layers == 2: + head_logits = height * self.scaler_2[1] + else: + head_logits = height * self.scaler[1] + index = torch.arange(length, device=height.device) + + mask = (index[:, None, None] <= index[None, None, :]) * ( + index[None, None, :] <= index[None, :, None] + ) + head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1) + head_logits.masked_fill_(~mask[None, :, :, :], -1e9) + + head_p = torch.softmax(head_logits, dim=-1) + + return head_p + + def parse(self, x, embs=None, n_cntxt_layers=0): + """Parse input sentence. + + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + distance: syntactic distance + height: syntactic height + """ + + mask = x != self.pad + mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0) + + if embs is None: + h = self.roberta.embeddings(x) + else: + h = embs + + if n_cntxt_layers > 0: + if n_cntxt_layers == 1: + parser_layers = self.parser_layers_1 + height_ff = self.height_ff_1 + distance_ff = self.distance_ff_1 + elif n_cntxt_layers == 2: + parser_layers = self.parser_layers_2 + height_ff = self.height_ff_2 + distance_ff = self.distance_ff_2 + for i in range(int(self.config.n_parser_layers/2)): + h = h.masked_fill(~mask[:, :, None], 0) + h = parser_layers[i](h) + + height = height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, dim=-1 + )[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max + ) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where( + margin_left > margin_right, margin_right, margin_left + ).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + else: + for i in range(self.config.n_parser_layers): + h = h.masked_fill(~mask[:, :, None], 0) + h = self.parser_layers[i](h) + + height = self.height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = self.distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, dim=-1 + )[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max + ) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where( + margin_left > margin_right, margin_right, margin_left + ).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + + return distance, height + + def generate_mask(self, x, distance, height, n_cntxt_layers=0): + """Compute head and cibling distribution for each token.""" + + bsz, length = x.size() + + eye = torch.eye(length, device=x.device, dtype=torch.bool) + eye = eye[None, :, :].expand((bsz, -1, -1)) + + block_p, block = self.compute_block(distance, height, n_cntxt_layers=n_cntxt_layers) + head_p = self.compute_head(height, n_cntxt_layers=n_cntxt_layers) + head = torch.einsum("blij,bijh->blh", block_p, head_p) + head = head.masked_fill(eye, 0) + child = head.transpose(1, 2) + cibling = torch.bmm(head, child).masked_fill(eye, 0) + + rel_list = [] + if "head" in self.config.relations: + rel_list.append(head) + if "child" in self.config.relations: + rel_list.append(child) + if "cibling" in self.config.relations: + rel_list.append(cibling) + + rel = torch.stack(rel_list, dim=1) + + if n_cntxt_layers > 0: + if n_cntxt_layers == 1: + rel_weight = self.rel_weight_1 + elif n_cntxt_layers == 2: + rel_weight = self.rel_weight_2 + else: + rel_weight = self.rel_weight + + dep = torch.einsum("lhr,brij->lbhij", rel_weight, rel) + + if n_cntxt_layers == 1: + num_layers = self.cntxt_layers_2.config.num_hidden_layers + else: + num_layers = self.roberta.config.num_hidden_layers + + att_mask = dep.reshape( + num_layers, + bsz, + self.config.num_attention_heads, + length, + length, + ) + + return att_mask, cibling, head, block + + def forward( + self, + input_ids: Optional[torch.LongTensor] = None, + attention_mask: Optional[torch.FloatTensor] = None, + token_type_ids: Optional[torch.LongTensor] = None, + position_ids: Optional[torch.LongTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, MaskedLMOutput]: + r""" + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ..., + config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the + loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]` + kwargs (`Dict[str, any]`, optional, defaults to *{}*): + Used to hide legacy arguments that have been deprecated. + """ + return_dict = ( + return_dict if return_dict is not None else self.config.use_return_dict + ) + + + if self.config.n_cntxt_layers > 0: + cntxt_outputs = self.cntxt_layers( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict) + + + if self.config.n_cntxt_layers_2 > 0: + distance_1, height_1 = self.parse(input_ids, cntxt_outputs[0], n_cntxt_layers=1) + att_mask_1, _, _, _ = self.generate_mask(input_ids, distance_1, height_1, n_cntxt_layers=1) + + cntxt_outputs_2 = self.cntxt_layers_2( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + parser_att_mask=att_mask_1) + + sequence_output = cntxt_outputs_2[0] + + distance_2, height_2 = self.parse(input_ids, sequence_output[0], n_cntxt_layers=2) + att_mask, _, _, _ = self.generate_mask(input_ids, distance_2, height_2, n_cntxt_layers=2) + + elif self.config.n_cntxt_layers > 0: + distance, height = self.parse(input_ids, cntxt_outputs[0]) + att_mask, _, _, _ = self.generate_mask(input_ids, distance, height) + else: + distance, height = self.parse(input_ids) + att_mask, _, _, _ = self.generate_mask(input_ids, distance, height) + + outputs = self.roberta( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + parser_att_mask=att_mask, + ) + sequence_output = outputs[0] + prediction_scores = self.lm_head(sequence_output) + + masked_lm_loss = None + if labels is not None: + loss_fct = CrossEntropyLoss() + masked_lm_loss = loss_fct( + prediction_scores.view(-1, self.config.vocab_size), labels.view(-1) + ) + + if not return_dict: + output = (prediction_scores,) + outputs[2:] + return ( + ((masked_lm_loss,) + output) if masked_lm_loss is not None else output + ) + + return MaskedLMOutput( + loss=masked_lm_loss, + logits=prediction_scores, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +class RobertaLMHead(nn.Module): + """Roberta Head for masked language modeling.""" + + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + + self.decoder = nn.Linear(config.hidden_size, config.vocab_size) + self.bias = nn.Parameter(torch.zeros(config.vocab_size)) + self.decoder.bias = self.bias + + def forward(self, features, **kwargs): + x = self.dense(features) + x = gelu(x) + x = self.layer_norm(x) + + # project back to size of vocabulary with bias + x = self.decoder(x) + + return x + + def _tie_weights(self): + # To tie those two weights if they get disconnected (on TPU or when the bias is resized) + self.bias = self.decoder.bias + + +class StructRobertaForSequenceClassification(RobertaPreTrainedModel): + _keys_to_ignore_on_load_missing = [r"position_ids"] + + def __init__(self, config): + super().__init__(config) + self.num_labels = config.num_labels + self.config = config + + if config.n_cntxt_layers > 0: + config_cntxt = copy.deepcopy(config) + config_cntxt.num_hidden_layers = config.n_cntxt_layers + + self.cntxt_layers = RobertaModel(config_cntxt, add_pooling_layer=False) + + if config.n_cntxt_layers_2 > 0: + self.parser_layers_1 = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(int(config.n_parser_layers/2)) + ] + ) + + self.distance_ff_1 = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff_1 = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight_1 = nn.Parameter( + torch.zeros((config.n_cntxt_layers_2, config.num_attention_heads, n_rel)) + ) + self._rel_weight_1.data.normal_(0, 0.1) + + self._scaler_1 = nn.Parameter(torch.zeros(2)) + + config_cntxt_2 = copy.deepcopy(config) + config_cntxt_2.num_hidden_layers = config.n_cntxt_layers_2 + + self.cntxt_layers_2 = RobertaModel(config_cntxt_2, add_pooling_layer=False) + + + self.parser_layers_2 = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(int(config.n_parser_layers/2)) + ] + ) + + self.distance_ff_2 = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff_2 = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight_2 = nn.Parameter( + torch.zeros((config.num_hidden_layers, config.num_attention_heads, n_rel)) + ) + self._rel_weight_2.data.normal_(0, 0.1) + + self._scaler_2 = nn.Parameter(torch.zeros(2)) + + else: + self.parser_layers = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(config.n_parser_layers) + ] + ) + + self.distance_ff = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight = nn.Parameter( + torch.zeros((config.num_hidden_layers, config.num_attention_heads, n_rel)) + ) + self._rel_weight.data.normal_(0, 0.1) + + self._scaler = nn.Parameter(torch.zeros(2)) + + self.roberta = RobertaModel(config, add_pooling_layer=False) + + if config.n_cntxt_layers > 0: + self.cntxt_layers.embeddings = self.roberta.embeddings + if config.n_cntxt_layers_2 > 0: + self.cntxt_layers_2.embeddings = self.roberta.embeddings + + + self.pad = config.pad_token_id + self.classifier = RobertaClassificationHead(config) + + # Initialize weights and apply final processing + self.post_init() + + + @property + def scaler(self): + return self._scaler.exp() + + @property + def scaler_1(self): + return self._scaler_1.exp() + + @property + def scaler_2(self): + return self._scaler_2.exp() + + @property + def rel_weight(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight, dim=-1) + + @property + def rel_weight_1(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight_1) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight_1, dim=-1) + + + @property + def rel_weight_2(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight_2) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight_2, dim=-1) + + + def compute_block(self, distance, height, n_cntxt_layers=0): + """Compute constituents from distance and height.""" + + if n_cntxt_layers>0: + if n_cntxt_layers == 1: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler_1[0] + elif n_cntxt_layers == 2: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler_2[0] + else: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0] + + gamma = torch.sigmoid(-beta_logits) + ones = torch.ones_like(gamma) + + block_mask_left = cummin( + gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1 + ) + block_mask_left = block_mask_left - F.pad( + block_mask_left[:, :, :-1], (1, 0), value=0 + ) + block_mask_left.tril_(0) + + block_mask_right = cummin( + gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1 + ) + block_mask_right = block_mask_right - F.pad( + block_mask_right[:, :, 1:], (0, 1), value=0 + ) + block_mask_right.triu_(0) + + block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :] + block = cumsum(block_mask_left).tril(0) + cumsum( + block_mask_right, reverse=True + ).triu(1) + + return block_p, block + + def compute_head(self, height, n_cntxt_layers=0): + """Estimate head for each constituent.""" + + _, length = height.size() + if n_cntxt_layers>0: + if n_cntxt_layers == 1: + head_logits = height * self.scaler_1[1] + elif n_cntxt_layers == 2: + head_logits = height * self.scaler_2[1] + else: + head_logits = height * self.scaler[1] + index = torch.arange(length, device=height.device) + + mask = (index[:, None, None] <= index[None, None, :]) * ( + index[None, None, :] <= index[None, :, None] + ) + head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1) + head_logits.masked_fill_(~mask[None, :, :, :], -1e9) + + head_p = torch.softmax(head_logits, dim=-1) + + return head_p + + def parse(self, x, embs=None, n_cntxt_layers=0): + """Parse input sentence. + + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + distance: syntactic distance + height: syntactic height + """ + + mask = x != self.pad + mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0) + + if embs is None: + h = self.roberta.embeddings(x) + else: + h = embs + + if n_cntxt_layers > 0: + if n_cntxt_layers == 1: + parser_layers = self.parser_layers_1 + height_ff = self.height_ff_1 + distance_ff = self.distance_ff_1 + elif n_cntxt_layers == 2: + parser_layers = self.parser_layers_2 + height_ff = self.height_ff_2 + distance_ff = self.distance_ff_2 + for i in range(int(self.config.n_parser_layers/2)): + h = h.masked_fill(~mask[:, :, None], 0) + h = parser_layers[i](h) + + height = height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, dim=-1 + )[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max + ) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where( + margin_left > margin_right, margin_right, margin_left + ).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + else: + for i in range(self.config.n_parser_layers): + h = h.masked_fill(~mask[:, :, None], 0) + h = self.parser_layers[i](h) + + height = self.height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = self.distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, dim=-1 + )[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max + ) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where( + margin_left > margin_right, margin_right, margin_left + ).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + + return distance, height + + def generate_mask(self, x, distance, height, n_cntxt_layers=0): + """Compute head and cibling distribution for each token.""" + + bsz, length = x.size() + + eye = torch.eye(length, device=x.device, dtype=torch.bool) + eye = eye[None, :, :].expand((bsz, -1, -1)) + + block_p, block = self.compute_block(distance, height, n_cntxt_layers=n_cntxt_layers) + head_p = self.compute_head(height, n_cntxt_layers=n_cntxt_layers) + head = torch.einsum("blij,bijh->blh", block_p, head_p) + head = head.masked_fill(eye, 0) + child = head.transpose(1, 2) + cibling = torch.bmm(head, child).masked_fill(eye, 0) + + rel_list = [] + if "head" in self.config.relations: + rel_list.append(head) + if "child" in self.config.relations: + rel_list.append(child) + if "cibling" in self.config.relations: + rel_list.append(cibling) + + rel = torch.stack(rel_list, dim=1) + + if n_cntxt_layers > 0: + if n_cntxt_layers == 1: + rel_weight = self.rel_weight_1 + elif n_cntxt_layers == 2: + rel_weight = self.rel_weight_2 + else: + rel_weight = self.rel_weight + + dep = torch.einsum("lhr,brij->lbhij", rel_weight, rel) + + if n_cntxt_layers == 1: + num_layers = self.cntxt_layers_2.config.num_hidden_layers + else: + num_layers = self.roberta.config.num_hidden_layers + + att_mask = dep.reshape( + num_layers, + bsz, + self.config.num_attention_heads, + length, + length, + ) + + return att_mask, cibling, head, block + + def forward( + self, + input_ids: Optional[torch.LongTensor] = None, + attention_mask: Optional[torch.FloatTensor] = None, + token_type_ids: Optional[torch.LongTensor] = None, + position_ids: Optional[torch.LongTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, SequenceClassifierOutput]: + r""" + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., + config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If + `config.num_labels > 1` a classification loss is computed (Cross-Entropy). + """ + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + if self.config.n_cntxt_layers > 0: + cntxt_outputs = self.cntxt_layers( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict) + + + if self.config.n_cntxt_layers_2 > 0: + distance_1, height_1 = self.parse(input_ids, cntxt_outputs[0], n_cntxt_layers=1) + att_mask_1, _, _, _ = self.generate_mask(input_ids, distance_1, height_1, n_cntxt_layers=1) + + cntxt_outputs_2 = self.cntxt_layers_2( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + parser_att_mask=att_mask_1) + + sequence_output = cntxt_outputs_2[0] + + distance_2, height_2 = self.parse(input_ids, sequence_output[0], n_cntxt_layers=2) + att_mask, _, _, _ = self.generate_mask(input_ids, distance_2, height_2, n_cntxt_layers=2) + + elif self.config.n_cntxt_layers > 0: + distance, height = self.parse(input_ids, cntxt_outputs[0]) + att_mask, _, _, _ = self.generate_mask(input_ids, distance, height) + else: + distance, height = self.parse(input_ids) + att_mask, _, _, _ = self.generate_mask(input_ids, distance, height) + + outputs = self.roberta( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + parser_att_mask=att_mask, + ) + sequence_output = outputs[0] + logits = self.classifier(sequence_output) + + loss = None + if labels is not None: + if self.config.problem_type is None: + if self.num_labels == 1: + self.config.problem_type = "regression" + elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): + self.config.problem_type = "single_label_classification" + else: + self.config.problem_type = "multi_label_classification" + + if self.config.problem_type == "regression": + loss_fct = MSELoss() + if self.num_labels == 1: + loss = loss_fct(logits.squeeze(), labels.squeeze()) + else: + loss = loss_fct(logits, labels) + elif self.config.problem_type == "single_label_classification": + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) + elif self.config.problem_type == "multi_label_classification": + loss_fct = BCEWithLogitsLoss() + loss = loss_fct(logits, labels) + + if not return_dict: + output = (logits,) + outputs[2:] + return ((loss,) + output) if loss is not None else output + + return SequenceClassifierOutput( + loss=loss, + logits=logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +class RobertaClassificationHead(nn.Module): + """Head for sentence-level classification tasks.""" + + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + classifier_dropout = ( + config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob + ) + self.dropout = nn.Dropout(classifier_dropout) + self.out_proj = nn.Linear(config.hidden_size, config.num_labels) + + def forward(self, features, **kwargs): + x = features[:, 0, :] # take token (equiv. to [CLS]) + x = self.dropout(x) + x = self.dense(x) + x = torch.tanh(x) + x = self.dropout(x) + x = self.out_proj(x) + return x + + +def create_position_ids_from_input_ids( + input_ids, padding_idx, past_key_values_length=0 +): + """ + Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols + are ignored. This is modified from fairseq's `utils.make_positions`. + + Args: + x: torch.Tensor x: + + Returns: torch.Tensor + """ + # The series of casts and type-conversions here are carefully balanced to both work with ONNX export and XLA. + mask = input_ids.ne(padding_idx).int() + incremental_indices = ( + torch.cumsum(mask, dim=1).type_as(mask) + past_key_values_length + ) * mask + return incremental_indices.long() + padding_idx + + +def cumprod(x, reverse=False, exclusive=False): + """cumulative product.""" + if reverse: + x = x.flip([-1]) + + if exclusive: + x = F.pad(x[:, :, :-1], (1, 0), value=1) + + cx = x.cumprod(-1) + + if reverse: + cx = cx.flip([-1]) + return cx + + +def cumsum(x, reverse=False, exclusive=False): + """cumulative sum.""" + bsz, _, length = x.size() + device = x.device + if reverse: + if exclusive: + w = torch.ones([bsz, length, length], device=device).tril(-1) + else: + w = torch.ones([bsz, length, length], device=device).tril(0) + cx = torch.bmm(x, w) + else: + if exclusive: + w = torch.ones([bsz, length, length], device=device).triu(1) + else: + w = torch.ones([bsz, length, length], device=device).triu(0) + cx = torch.bmm(x, w) + return cx + + +def cummin(x, reverse=False, exclusive=False, max_value=1e9): + """cumulative min.""" + if reverse: + if exclusive: + x = F.pad(x[:, :, 1:], (0, 1), value=max_value) + x = x.flip([-1]).cummin(-1)[0].flip([-1]) + else: + if exclusive: + x = F.pad(x[:, :, :-1], (1, 0), value=max_value) + x = x.cummin(-1)[0] + return x diff --git a/finetune/main_verb_control/predict_results.txt b/finetune/main_verb_control/predict_results.txt new file mode 100644 index 0000000000000000000000000000000000000000..64d02ec6172e742daa78553b863f4b651a8aad13 --- /dev/null +++ b/finetune/main_verb_control/predict_results.txt @@ -0,0 +1,16351 @@ +index prediction +0 1 +1 0 +2 1 +3 0 +4 1 +5 0 +6 1 +7 0 +8 1 +9 0 +10 1 +11 0 +12 1 +13 0 +14 1 +15 0 +16 1 +17 0 +18 1 +19 0 +20 1 +21 0 +22 1 +23 0 +24 1 +25 0 +26 1 +27 0 +28 1 +29 1 +30 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b/finetune/main_verb_lexical_content_the/merges.txt new file mode 100644 index 0000000000000000000000000000000000000000..94b4f252cb566a4dac62a08c78d85169ff4e91be --- /dev/null +++ b/finetune/main_verb_lexical_content_the/merges.txt @@ -0,0 +1,31794 @@ +#version: 0.2 +Ġ t +h e +Ġ a +i n +Ġt he +r e +Ġ w +o u +Ġ s +o n +e r +h a +n d +i s +i t +Ġ b +Ġ o +Ġ c +Ġ m +Ġ f +in g +o r +a n +e d +l l +Ġ d +e n +Ġt o +e s +Ġ p +Ġo f +Ġ y +Ġ in +a r +a t +Ġa nd +Ġ l +ha t +Ġy ou +a s +Ġ g +o m +Ġ n +Ġ I +i c +o w +l e +v e +o t +Ġ h +Ġ he +a l +e t +Ġ ha +' s +u s +Ġt h +a y +Ġ it +Ġ is +Ġb e +Ġt hat +Ġ on +i on +i d +Ġ e +g h +u t +l d +en t +i m +Ġ re +Ġw as +a c +Ġw e +v er +a m +T he +. . +Ġf or +l y +Ġ S +s t +r i +r o +i r +a d +s e +k e +Ġ . +Ġs t +gh t +o o +' t +Ġ A +ou t +it h +Ġm e +Ġ C +a ll +Ġ k +Ġ u +Ġa n +Ġ M +Ġw ith +u r +Ġa s +Ġd o +c t +he r +c e +i ll +t er +i f +c h +Ġw h +Ġs e +om e +Ġa re +Ġ H +i l +Ġn ot +Ġha ve +Ġ B +Ġ 1 +a nd +Ġa t +Ġth is +ou ld +an t +Ġg o +us t +at ion +Ġl i +o p +a in +Ġk n +Y ou +e a +Ġ P +Ġ T +Ġ , +u n +g e +o l +t h +Ġh is +u l +Ġ " +r om +.. . +o re +Ġ W +e ll +Ġn e +Ġ r +Ġkn ow +Ġ j +ou r +Ġ D +Ġd e +p p +Ġc an +Ġ G +Ġthe y +u m +Ġ The +Ġs o +es t +Ġ ? +Ġa l +Ġa ll +Ġa b +Ġm y +is t +er s +Ġ R +i e +Ġyou r +ar t +Ġb y +Ġf rom +es s +Ġs h +Ġo r +Ġ 2 +Ġ F +Ġw hat +n t +a b +o s +Ġ L +e l +Ġon e +Ġc on +Ġ N +Ġw he +Ġthe re +i g +i ght +ha n +all y +Ġu p +q u +e m +Ġc om +on e +Ġha d +Ġb ut +Ġhe r +Ġs u +re d +r y +Ġ J +' re +Ġh im +Ġ out +r a +ac k +at e +oo d +ver y +oo k +Ġli ke +Ġw or +Ġs a +re s +am e +i ve +Ġp l +Ġ us +Ġ ( +on g +' m +Ġwe re +â Ģ +a ke +o d +in k +c k +Ġs he +Ġp ro +Ġab out +Ġ E +as t +Ġg et +Ġe x +Ġ v +p e +I t +or t +ou nd +in d +ar d +it y +Ġd on +A nd +ic h +ri ght +Ġ O +Ġj ust +Ġ1 9 +i es +H e +Ġwh o +e ar +ea h +Ġd id +a k +Ġhe re +b er +Ġthe m +Ġn o +im e +in e +Ġm an +Ġw ant +Ġw ould +Ġ2 0 +Ġ if +Ġ ' +Ġw ill +u d +a p +Ġ K +Ġth ink +ou s +' ll +v en +Ġs ome +o st +W hat +e p +h ing +Ġin t +Ġan y +ow n +ic e +a g +Ġ right +m ent +id e +Ġg ot +Ġha s +t her +us e +it t +Ġwh ich +ou gh +Ġt r +f e +h i +c a +Ġse e +Ġwhe n +Ġsa id +u re +Ġa r +Ġa g +Ġp e +Ġs ay +w ay +i a +Ġt ime +Ġt w +i v +ĠH e +s o +Ġbe en +i an +Ġs p +a re +ac e +Ġn ow +o ve +ic k +op le +c i +Ġ U +W e +ĠI t +' ve +Ġthe ir +o se +Y eah +Ġl e +Ġl ook +- - +Ġc l +is h +l f +re at +n a +Ġm ore +Ġthe n +Ġgo ing +a ge +S o +it e +re e +Ġo ther +Ġpe ople +ar y +Ġc ould +Ġ = +ĠS t +N o +ot her +n e +Ġof f +u e +Ġb ack +h is +ac t +B ut +Ġo ver +ir st +O h +as s +Ġ qu +ou nt +on d +Ġ very +ing s +ca use +Ġ ro +b le +p er +Ġg r +Ġa f +p t +Ġf e +an s +Ġwor k +Ġtw o +Ġg ood +T hat +Ġto o +Ġb r +e nd +om et +Ġ en +er m +o g +p l +at ed +o ll +Ġ Ġ +t he +Ġf irst +ion s +or d +Ġint o +he d +as e +Ġp art +k ay +0 0 +Ġh ow +Ġd own +or n +al k +u b +w e +Ġu n +Ġw ay +ad e +Ġt han +i re +Ġal so +o y +f f +itt le +Ġc all +i z +m er +Ġa m +ou ght +Ġy ear +h y +at er +Ġp r +Ġcom p +o v +Ġc ome +W ell +Ġb l +Ġ our +an ce +Ġwhe re +nd er 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See all RoBERTa models at https://huggingface.co/models?filter=roberta +] + + +class StructRobertaConfig(RobertaConfig): + model_type = "roberta" + + def __init__( + self, + n_parser_layers=4, + conv_size=9, + relations=("head", "child"), + weight_act="softmax", + n_cntxt_layers=3, + n_cntxt_layers_2=0, + **kwargs,): + + super().__init__(**kwargs) + self.n_cntxt_layers = n_cntxt_layers + self.n_parser_layers = n_parser_layers + self.n_cntxt_layers_2 = n_cntxt_layers_2 + self.conv_size = conv_size + self.relations = relations + self.weight_act = weight_act + +class Conv1d(nn.Module): + """1D convolution layer.""" + + def __init__(self, hidden_size, kernel_size, dilation=1): + """Initialization. + + Args: + hidden_size: dimension of input embeddings + kernel_size: convolution kernel size + dilation: the spacing between the kernel points + """ + super(Conv1d, self).__init__() + + if kernel_size % 2 == 0: + padding = (kernel_size // 2) * dilation + self.shift = True + else: + padding = ((kernel_size - 1) // 2) * dilation + self.shift = False + self.conv = nn.Conv1d( + hidden_size, hidden_size, kernel_size, padding=padding, dilation=dilation + ) + + def forward(self, x): + """Compute convolution. + + Args: + x: input embeddings + Returns: + conv_output: convolution results + """ + + if self.shift: + return self.conv(x.transpose(1, 2)).transpose(1, 2)[:, 1:] + else: + return self.conv(x.transpose(1, 2)).transpose(1, 2) + + +class RobertaEmbeddings(nn.Module): + """ + Same as BertEmbeddings with a tiny tweak for positional embeddings indexing. + """ + + # Copied from transformers.models.bert.modeling_bert.BertEmbeddings.__init__ + def __init__(self, config): + super().__init__() + self.word_embeddings = nn.Embedding( + config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id + ) + self.position_embeddings = nn.Embedding( + config.max_position_embeddings, config.hidden_size + ) + self.token_type_embeddings = nn.Embedding( + config.type_vocab_size, config.hidden_size + ) + + # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load + # any TensorFlow checkpoint file + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + # position_ids (1, len position emb) is contiguous in memory and exported when serialized + self.position_embedding_type = getattr( + config, "position_embedding_type", "absolute" + ) + self.register_buffer( + "position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)) + ) + if version.parse(torch.__version__) > version.parse("1.6.0"): + self.register_buffer( + "token_type_ids", + torch.zeros(self.position_ids.size(), dtype=torch.long), + persistent=False, + ) + + # End copy + self.padding_idx = config.pad_token_id + self.position_embeddings = nn.Embedding( + config.max_position_embeddings, + config.hidden_size, + padding_idx=self.padding_idx, + ) + + def forward( + self, + input_ids=None, + token_type_ids=None, + position_ids=None, + inputs_embeds=None, + past_key_values_length=0, + ): + if position_ids is None: + if input_ids is not None: + # Create the position ids from the input token ids. Any padded tokens remain padded. + position_ids = create_position_ids_from_input_ids( + input_ids, self.padding_idx, past_key_values_length + ) + else: + position_ids = self.create_position_ids_from_inputs_embeds( + inputs_embeds + ) + + if input_ids is not None: + input_shape = input_ids.size() + else: + input_shape = inputs_embeds.size()[:-1] + + seq_length = input_shape[1] + + # Setting the token_type_ids to the registered buffer in constructor where it is all zeros, which usually occurs + # when its auto-generated, registered buffer helps users when tracing the model without passing token_type_ids, solves + # issue #5664 + if token_type_ids is None: + if hasattr(self, "token_type_ids"): + buffered_token_type_ids = self.token_type_ids[:, :seq_length] + buffered_token_type_ids_expanded = buffered_token_type_ids.expand( + input_shape[0], seq_length + ) + token_type_ids = buffered_token_type_ids_expanded + else: + token_type_ids = torch.zeros( + input_shape, dtype=torch.long, device=self.position_ids.device + ) + + if inputs_embeds is None: + inputs_embeds = self.word_embeddings(input_ids) + token_type_embeddings = self.token_type_embeddings(token_type_ids) + + embeddings = inputs_embeds + token_type_embeddings + if self.position_embedding_type == "absolute": + position_embeddings = self.position_embeddings(position_ids) + embeddings += position_embeddings + embeddings = self.LayerNorm(embeddings) + embeddings = self.dropout(embeddings) + return embeddings + + def create_position_ids_from_inputs_embeds(self, inputs_embeds): + """ + We are provided embeddings directly. We cannot infer which are padded so just generate sequential position ids. + + Args: + inputs_embeds: torch.Tensor + + Returns: torch.Tensor + """ + input_shape = inputs_embeds.size()[:-1] + sequence_length = input_shape[1] + + position_ids = torch.arange( + self.padding_idx + 1, + sequence_length + self.padding_idx + 1, + dtype=torch.long, + device=inputs_embeds.device, + ) + return position_ids.unsqueeze(0).expand(input_shape) + + +# Copied from transformers.models.bert.modeling_bert.BertSelfAttention with Bert->Roberta +class RobertaSelfAttention(nn.Module): + def __init__(self, config, position_embedding_type=None): + super().__init__() + if config.hidden_size % config.num_attention_heads != 0 and not hasattr( + config, "embedding_size" + ): + raise ValueError( + f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention " + f"heads ({config.num_attention_heads})" + ) + + self.num_attention_heads = config.num_attention_heads + self.attention_head_size = int(config.hidden_size / config.num_attention_heads) + self.all_head_size = self.num_attention_heads * self.attention_head_size + + self.query = nn.Linear(config.hidden_size, self.all_head_size) + self.key = nn.Linear(config.hidden_size, self.all_head_size) + self.value = nn.Linear(config.hidden_size, self.all_head_size) + + self.dropout = nn.Dropout(config.attention_probs_dropout_prob) + self.position_embedding_type = position_embedding_type or getattr( + config, "position_embedding_type", "absolute" + ) + if ( + self.position_embedding_type == "relative_key" + or self.position_embedding_type == "relative_key_query" + ): + self.max_position_embeddings = config.max_position_embeddings + self.distance_embedding = nn.Embedding( + 2 * config.max_position_embeddings - 1, self.attention_head_size + ) + + self.is_decoder = config.is_decoder + + def transpose_for_scores(self, x): + new_x_shape = x.size()[:-1] + ( + self.num_attention_heads, + self.attention_head_size, + ) + x = x.view(new_x_shape) + return x.permute(0, 2, 1, 3) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.FloatTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, + output_attentions: Optional[bool] = False, + parser_att_mask=None, + ) -> Tuple[torch.Tensor]: + mixed_query_layer = self.query(hidden_states) + + # If this is instantiated as a cross-attention module, the keys + # and values come from an encoder; the attention mask needs to be + # such that the encoder's padding tokens are not attended to. + is_cross_attention = encoder_hidden_states is not None + + if is_cross_attention and past_key_value is not None: + # reuse k,v, cross_attentions + key_layer = past_key_value[0] + value_layer = past_key_value[1] + attention_mask = encoder_attention_mask + elif is_cross_attention: + key_layer = self.transpose_for_scores(self.key(encoder_hidden_states)) + value_layer = self.transpose_for_scores(self.value(encoder_hidden_states)) + attention_mask = encoder_attention_mask + elif past_key_value is not None: + key_layer = self.transpose_for_scores(self.key(hidden_states)) + value_layer = self.transpose_for_scores(self.value(hidden_states)) + key_layer = torch.cat([past_key_value[0], key_layer], dim=2) + value_layer = torch.cat([past_key_value[1], value_layer], dim=2) + else: + key_layer = self.transpose_for_scores(self.key(hidden_states)) + value_layer = self.transpose_for_scores(self.value(hidden_states)) + + query_layer = self.transpose_for_scores(mixed_query_layer) + + if self.is_decoder: + # if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states. + # Further calls to cross_attention layer can then reuse all cross-attention + # key/value_states (first "if" case) + # if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of + # all previous decoder key/value_states. Further calls to uni-directional self-attention + # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case) + # if encoder bi-directional self-attention `past_key_value` is always `None` + past_key_value = (key_layer, value_layer) + + # Take the dot product between "query" and "key" to get the raw attention scores. + attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) + + if ( + self.position_embedding_type == "relative_key" + or self.position_embedding_type == "relative_key_query" + ): + seq_length = hidden_states.size()[1] + position_ids_l = torch.arange( + seq_length, dtype=torch.long, device=hidden_states.device + ).view(-1, 1) + position_ids_r = torch.arange( + seq_length, dtype=torch.long, device=hidden_states.device + ).view(1, -1) + distance = position_ids_l - position_ids_r + positional_embedding = self.distance_embedding( + distance + self.max_position_embeddings - 1 + ) + positional_embedding = positional_embedding.to( + dtype=query_layer.dtype + ) # fp16 compatibility + + if self.position_embedding_type == "relative_key": + relative_position_scores = torch.einsum( + "bhld,lrd->bhlr", query_layer, positional_embedding + ) + attention_scores = attention_scores + relative_position_scores + elif self.position_embedding_type == "relative_key_query": + relative_position_scores_query = torch.einsum( + "bhld,lrd->bhlr", query_layer, positional_embedding + ) + relative_position_scores_key = torch.einsum( + "bhrd,lrd->bhlr", key_layer, positional_embedding + ) + attention_scores = ( + attention_scores + + relative_position_scores_query + + relative_position_scores_key + ) + + attention_scores = attention_scores / math.sqrt(self.attention_head_size) + if attention_mask is not None: + # Apply the attention mask is (precomputed for all layers in RobertaModel forward() function) + attention_scores = attention_scores + attention_mask + + if parser_att_mask is None: + # Normalize the attention scores to probabilities. + attention_probs = nn.functional.softmax(attention_scores, dim=-1) + else: + attention_probs = torch.sigmoid(attention_scores) * parser_att_mask + + # This is actually dropping out entire tokens to attend to, which might + # seem a bit unusual, but is taken from the original Transformer paper. + attention_probs = self.dropout(attention_probs) + + # Mask heads if we want to + if head_mask is not None: + attention_probs = attention_probs * head_mask + + context_layer = torch.matmul(attention_probs, value_layer) + + context_layer = context_layer.permute(0, 2, 1, 3).contiguous() + new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) + context_layer = context_layer.view(new_context_layer_shape) + + outputs = ( + (context_layer, attention_probs) if output_attentions else (context_layer,) + ) + + if self.is_decoder: + outputs = outputs + (past_key_value,) + return outputs + + +# Copied from transformers.models.bert.modeling_bert.BertSelfOutput +class RobertaSelfOutput(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward( + self, hidden_states: torch.Tensor, input_tensor: torch.Tensor + ) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + hidden_states = self.LayerNorm(hidden_states + input_tensor) + return hidden_states + + +# Copied from transformers.models.bert.modeling_bert.BertAttention with Bert->Roberta +class RobertaAttention(nn.Module): + def __init__(self, config, position_embedding_type=None): + super().__init__() + self.self = RobertaSelfAttention( + config, position_embedding_type=position_embedding_type + ) + self.output = RobertaSelfOutput(config) + self.pruned_heads = set() + + def prune_heads(self, heads): + if len(heads) == 0: + return + heads, index = find_pruneable_heads_and_indices( + heads, + self.self.num_attention_heads, + self.self.attention_head_size, + self.pruned_heads, + ) + + # Prune linear layers + self.self.query = prune_linear_layer(self.self.query, index) + self.self.key = prune_linear_layer(self.self.key, index) + self.self.value = prune_linear_layer(self.self.value, index) + self.output.dense = prune_linear_layer(self.output.dense, index, dim=1) + + # Update hyper params and store pruned heads + self.self.num_attention_heads = self.self.num_attention_heads - len(heads) + self.self.all_head_size = ( + self.self.attention_head_size * self.self.num_attention_heads + ) + self.pruned_heads = self.pruned_heads.union(heads) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.FloatTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, + output_attentions: Optional[bool] = False, + parser_att_mask=None, + ) -> Tuple[torch.Tensor]: + self_outputs = self.self( + hidden_states, + attention_mask, + head_mask, + encoder_hidden_states, + encoder_attention_mask, + past_key_value, + output_attentions, + parser_att_mask=parser_att_mask, + ) + attention_output = self.output(self_outputs[0], hidden_states) + outputs = (attention_output,) + self_outputs[ + 1: + ] # add attentions if we output them + return outputs + + +# Copied from transformers.models.bert.modeling_bert.BertIntermediate +class RobertaIntermediate(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.intermediate_size) + if isinstance(config.hidden_act, str): + self.intermediate_act_fn = ACT2FN[config.hidden_act] + else: + self.intermediate_act_fn = config.hidden_act + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.intermediate_act_fn(hidden_states) + return hidden_states + + +# Copied from transformers.models.bert.modeling_bert.BertOutput +class RobertaOutput(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.intermediate_size, config.hidden_size) + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward( + self, hidden_states: torch.Tensor, input_tensor: torch.Tensor + ) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + hidden_states = self.LayerNorm(hidden_states + input_tensor) + return hidden_states + + +# Copied from transformers.models.bert.modeling_bert.BertLayer with Bert->Roberta +class RobertaLayer(nn.Module): + def __init__(self, config): + super().__init__() + self.chunk_size_feed_forward = config.chunk_size_feed_forward + self.seq_len_dim = 1 + self.attention = RobertaAttention(config) + self.is_decoder = config.is_decoder + self.add_cross_attention = config.add_cross_attention + if self.add_cross_attention: + if not self.is_decoder: + raise ValueError( + f"{self} should be used as a decoder model if cross attention is added" + ) + self.crossattention = RobertaAttention( + config, position_embedding_type="absolute" + ) + self.intermediate = RobertaIntermediate(config) + self.output = RobertaOutput(config) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.FloatTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, + output_attentions: Optional[bool] = False, + parser_att_mask=None, + ) -> Tuple[torch.Tensor]: + # decoder uni-directional self-attention cached key/values tuple is at positions 1,2 + self_attn_past_key_value = ( + past_key_value[:2] if past_key_value is not None else None + ) + self_attention_outputs = self.attention( + hidden_states, + attention_mask, + head_mask, + output_attentions=output_attentions, + past_key_value=self_attn_past_key_value, + parser_att_mask=parser_att_mask, + ) + attention_output = self_attention_outputs[0] + + # if decoder, the last output is tuple of self-attn cache + if self.is_decoder: + outputs = self_attention_outputs[1:-1] + present_key_value = self_attention_outputs[-1] + else: + outputs = self_attention_outputs[ + 1: + ] # add self attentions if we output attention weights + + cross_attn_present_key_value = None + if self.is_decoder and encoder_hidden_states is not None: + if not hasattr(self, "crossattention"): + raise ValueError( + f"If `encoder_hidden_states` are passed, {self} has to be instantiated with cross-attention layers by setting `config.add_cross_attention=True`" + ) + + # cross_attn cached key/values tuple is at positions 3,4 of past_key_value tuple + cross_attn_past_key_value = ( + past_key_value[-2:] if past_key_value is not None else None + ) + cross_attention_outputs = self.crossattention( + attention_output, + attention_mask, + head_mask, + encoder_hidden_states, + encoder_attention_mask, + cross_attn_past_key_value, + output_attentions, + ) + attention_output = cross_attention_outputs[0] + outputs = ( + outputs + cross_attention_outputs[1:-1] + ) # add cross attentions if we output attention weights + + # add cross-attn cache to positions 3,4 of present_key_value tuple + cross_attn_present_key_value = cross_attention_outputs[-1] + present_key_value = present_key_value + cross_attn_present_key_value + + layer_output = apply_chunking_to_forward( + self.feed_forward_chunk, + self.chunk_size_feed_forward, + self.seq_len_dim, + attention_output, + ) + outputs = (layer_output,) + outputs + + # if decoder, return the attn key/values as the last output + if self.is_decoder: + outputs = outputs + (present_key_value,) + + return outputs + + def feed_forward_chunk(self, attention_output): + intermediate_output = self.intermediate(attention_output) + layer_output = self.output(intermediate_output, attention_output) + return layer_output + + +# Copied from transformers.models.bert.modeling_bert.BertEncoder with Bert->Roberta +class RobertaEncoder(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + self.layer = nn.ModuleList( + [RobertaLayer(config) for _ in range(config.num_hidden_layers)] + ) + self.gradient_checkpointing = False + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.FloatTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = False, + output_hidden_states: Optional[bool] = False, + return_dict: Optional[bool] = True, + parser_att_mask=None, + ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPastAndCrossAttentions]: + all_hidden_states = () if output_hidden_states else None + all_self_attentions = () if output_attentions else None + all_cross_attentions = ( + () if output_attentions and self.config.add_cross_attention else None + ) + + next_decoder_cache = () if use_cache else None + for i, layer_module in enumerate(self.layer): + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + layer_head_mask = head_mask[i] if head_mask is not None else None + past_key_value = past_key_values[i] if past_key_values is not None else None + + if self.gradient_checkpointing and self.training: + + if use_cache: + logger.warning( + "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." + ) + use_cache = False + + def create_custom_forward(module): + def custom_forward(*inputs): + return module(*inputs, past_key_value, output_attentions) + + return custom_forward + + layer_outputs = torch.utils.checkpoint.checkpoint( + create_custom_forward(layer_module), + hidden_states, + attention_mask, + layer_head_mask, + encoder_hidden_states, + encoder_attention_mask, + ) + else: + if parser_att_mask is not None: + layer_outputs = layer_module( + hidden_states, + attention_mask, + layer_head_mask, + encoder_hidden_states, + encoder_attention_mask, + past_key_value, + output_attentions, + parser_att_mask=parser_att_mask[i]) + else: + layer_outputs = layer_module( + hidden_states, + attention_mask, + layer_head_mask, + encoder_hidden_states, + encoder_attention_mask, + past_key_value, + output_attentions, + parser_att_mask=None) + + + hidden_states = layer_outputs[0] + if use_cache: + next_decoder_cache += (layer_outputs[-1],) + if output_attentions: + all_self_attentions = all_self_attentions + (layer_outputs[1],) + if self.config.add_cross_attention: + all_cross_attentions = all_cross_attentions + (layer_outputs[2],) + + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + if not return_dict: + return tuple( + v + for v in [ + hidden_states, + next_decoder_cache, + all_hidden_states, + all_self_attentions, + all_cross_attentions, + ] + if v is not None + ) + return BaseModelOutputWithPastAndCrossAttentions( + last_hidden_state=hidden_states, + past_key_values=next_decoder_cache, + hidden_states=all_hidden_states, + attentions=all_self_attentions, + cross_attentions=all_cross_attentions, + ) + + +# Copied from transformers.models.bert.modeling_bert.BertPooler +class RobertaPooler(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.activation = nn.Tanh() + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + # We "pool" the model by simply taking the hidden state corresponding + # to the first token. + first_token_tensor = hidden_states[:, 0] + pooled_output = self.dense(first_token_tensor) + pooled_output = self.activation(pooled_output) + return pooled_output + + +class RobertaPreTrainedModel(PreTrainedModel): + """ + An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained + models. + """ + + config_class = RobertaConfig + base_model_prefix = "roberta" + supports_gradient_checkpointing = True + + # Copied from transformers.models.bert.modeling_bert.BertPreTrainedModel._init_weights + def _init_weights(self, module): + """Initialize the weights""" + if isinstance(module, nn.Linear): + # Slightly different from the TF version which uses truncated_normal for initialization + # cf https://github.com/pytorch/pytorch/pull/5617 + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.Embedding): + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.padding_idx is not None: + module.weight.data[module.padding_idx].zero_() + elif isinstance(module, nn.LayerNorm): + if module.bias is not None: + module.bias.data.zero_() + module.weight.data.fill_(1.0) + + def _set_gradient_checkpointing(self, module, value=False): + if isinstance(module, RobertaEncoder): + module.gradient_checkpointing = value + + def update_keys_to_ignore(self, config, del_keys_to_ignore): + """Remove some keys from ignore list""" + if not config.tie_word_embeddings: + # must make a new list, or the class variable gets modified! + self._keys_to_ignore_on_save = [ + k for k in self._keys_to_ignore_on_save if k not in del_keys_to_ignore + ] + self._keys_to_ignore_on_load_missing = [ + k + for k in self._keys_to_ignore_on_load_missing + if k not in del_keys_to_ignore + ] + + +ROBERTA_START_DOCSTRING = r""" + + This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the + library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads + etc.) + + This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. + Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage + and behavior. + + Parameters: + config ([`RobertaConfig`]): Model configuration class with all the parameters of the + model. Initializing with a config file does not load the weights associated with the model, only the + configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. +""" + + +ROBERTA_INPUTS_DOCSTRING = r""" + Args: + input_ids (`torch.LongTensor` of shape `({0})`): + Indices of input sequence tokens in the vocabulary. + + Indices can be obtained using [`RobertaTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + [What are input IDs?](../glossary#input-ids) + attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*): + Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + + [What are attention masks?](../glossary#attention-mask) + token_type_ids (`torch.LongTensor` of shape `({0})`, *optional*): + Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, + 1]`: + + - 0 corresponds to a *sentence A* token, + - 1 corresponds to a *sentence B* token. + + [What are token type IDs?](../glossary#token-type-ids) + position_ids (`torch.LongTensor` of shape `({0})`, *optional*): + Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, + config.max_position_embeddings - 1]`. + + [What are position IDs?](../glossary#position-ids) + head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): + Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: + + - 1 indicates the head is **not masked**, + - 0 indicates the head is **masked**. + + inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_size)`, *optional*): + Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This + is useful if you want more control over how to convert `input_ids` indices into associated vectors than the + model's internal embedding lookup matrix. + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned + tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for + more detail. + return_dict (`bool`, *optional*): + Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. +""" + + +class RobertaModel(RobertaPreTrainedModel): + """ + + The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of + cross-attention is added between the self-attention layers, following the architecture described in *Attention is + all you need*_ by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz + Kaiser and Illia Polosukhin. + + To behave as an decoder the model needs to be initialized with the `is_decoder` argument of the configuration set + to `True`. To be used in a Seq2Seq model, the model needs to initialized with both `is_decoder` argument and + `add_cross_attention` set to `True`; an `encoder_hidden_states` is then expected as an input to the forward pass. + + .. _*Attention is all you need*: https://arxiv.org/abs/1706.03762 + + """ + + _keys_to_ignore_on_load_missing = [r"position_ids"] + + # Copied from transformers.models.bert.modeling_bert.BertModel.__init__ with Bert->Roberta + def __init__(self, config, add_pooling_layer=True): + super().__init__(config) + self.config = config + + self.embeddings = RobertaEmbeddings(config) + self.encoder = RobertaEncoder(config) + + self.pooler = RobertaPooler(config) if add_pooling_layer else None + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.embeddings.word_embeddings + + def set_input_embeddings(self, value): + self.embeddings.word_embeddings = value + + def _prune_heads(self, heads_to_prune): + """ + Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base + class PreTrainedModel + """ + for layer, heads in heads_to_prune.items(): + self.encoder.layer[layer].attention.prune_heads(heads) + + # Copied from transformers.models.bert.modeling_bert.BertModel.forward + def forward( + self, + input_ids: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + token_type_ids: Optional[torch.Tensor] = None, + position_ids: Optional[torch.Tensor] = None, + head_mask: Optional[torch.Tensor] = None, + inputs_embeds: Optional[torch.Tensor] = None, + encoder_hidden_states: Optional[torch.Tensor] = None, + encoder_attention_mask: Optional[torch.Tensor] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + parser_att_mask=None, + ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPoolingAndCrossAttentions]: + r""" + encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): + Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if + the model is configured as a decoder. + encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*): + Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in + the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): + Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. + + If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that + don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all + `decoder_input_ids` of shape `(batch_size, sequence_length)`. + use_cache (`bool`, *optional*): + If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see + `past_key_values`). + """ + output_attentions = ( + output_attentions + if output_attentions is not None + else self.config.output_attentions + ) + output_hidden_states = ( + output_hidden_states + if output_hidden_states is not None + else self.config.output_hidden_states + ) + return_dict = ( + return_dict if return_dict is not None else self.config.use_return_dict + ) + + if self.config.is_decoder: + use_cache = use_cache if use_cache is not None else self.config.use_cache + else: + use_cache = False + + if input_ids is not None and inputs_embeds is not None: + raise ValueError( + "You cannot specify both input_ids and inputs_embeds at the same time" + ) + elif input_ids is not None: + input_shape = input_ids.size() + elif inputs_embeds is not None: + input_shape = inputs_embeds.size()[:-1] + else: + raise ValueError("You have to specify either input_ids or inputs_embeds") + + batch_size, seq_length = input_shape + device = input_ids.device if input_ids is not None else inputs_embeds.device + + # past_key_values_length + past_key_values_length = ( + past_key_values[0][0].shape[2] if past_key_values is not None else 0 + ) + + if attention_mask is None: + attention_mask = torch.ones( + ((batch_size, seq_length + past_key_values_length)), device=device + ) + + if token_type_ids is None: + if hasattr(self.embeddings, "token_type_ids"): + buffered_token_type_ids = self.embeddings.token_type_ids[:, :seq_length] + buffered_token_type_ids_expanded = buffered_token_type_ids.expand( + batch_size, seq_length + ) + token_type_ids = buffered_token_type_ids_expanded + else: + token_type_ids = torch.zeros( + input_shape, dtype=torch.long, device=device + ) + + # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length] + # ourselves in which case we just need to make it broadcastable to all heads. + extended_attention_mask: torch.Tensor = self.get_extended_attention_mask( + attention_mask, input_shape, device + ) + + # If a 2D or 3D attention mask is provided for the cross-attention + # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length] + if self.config.is_decoder and encoder_hidden_states is not None: + ( + encoder_batch_size, + encoder_sequence_length, + _, + ) = encoder_hidden_states.size() + encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length) + if encoder_attention_mask is None: + encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device) + encoder_extended_attention_mask = self.invert_attention_mask( + encoder_attention_mask + ) + else: + encoder_extended_attention_mask = None + + # Prepare head mask if needed + # 1.0 in head_mask indicate we keep the head + # attention_probs has shape bsz x n_heads x N x N + # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] + # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] + head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) + + embedding_output = self.embeddings( + input_ids=input_ids, + position_ids=position_ids, + token_type_ids=token_type_ids, + inputs_embeds=inputs_embeds, + past_key_values_length=past_key_values_length, + ) + encoder_outputs = self.encoder( + embedding_output, + attention_mask=extended_attention_mask, + head_mask=head_mask, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_extended_attention_mask, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + parser_att_mask=parser_att_mask, + ) + sequence_output = encoder_outputs[0] + pooled_output = ( + self.pooler(sequence_output) if self.pooler is not None else None + ) + + if not return_dict: + return (sequence_output, pooled_output) + encoder_outputs[1:] + + return BaseModelOutputWithPoolingAndCrossAttentions( + last_hidden_state=sequence_output, + pooler_output=pooled_output, + past_key_values=encoder_outputs.past_key_values, + hidden_states=encoder_outputs.hidden_states, + attentions=encoder_outputs.attentions, + cross_attentions=encoder_outputs.cross_attentions, + ) + + +class StructRoberta(RobertaPreTrainedModel): + _keys_to_ignore_on_save = [r"lm_head.decoder.weight", r"lm_head.decoder.bias"] + _keys_to_ignore_on_load_missing = [ + r"position_ids", + r"lm_head.decoder.weight", + r"lm_head.decoder.bias", + ] + _keys_to_ignore_on_load_unexpected = [r"pooler"] + + def __init__(self, config): + super().__init__(config) + + if config.is_decoder: + logger.warning( + "If you want to use `RobertaForMaskedLM` make sure `config.is_decoder=False` for " + "bi-directional self-attention." + ) + + + if config.n_cntxt_layers > 0: + config_cntxt = copy.deepcopy(config) + config_cntxt.num_hidden_layers = config.n_cntxt_layers + + self.cntxt_layers = RobertaModel(config_cntxt, add_pooling_layer=False) + + if config.n_cntxt_layers_2 > 0: + self.parser_layers_1 = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(int(config.n_parser_layers/2)) + ] + ) + + self.distance_ff_1 = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff_1 = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight_1 = nn.Parameter( + torch.zeros((config.n_cntxt_layers_2, config.num_attention_heads, n_rel)) + ) + self._rel_weight_1.data.normal_(0, 0.1) + + self._scaler_1 = nn.Parameter(torch.zeros(2)) + + config_cntxt_2 = copy.deepcopy(config) + config_cntxt_2.num_hidden_layers = config.n_cntxt_layers_2 + + self.cntxt_layers_2 = RobertaModel(config_cntxt_2, add_pooling_layer=False) + + + self.parser_layers_2 = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(int(config.n_parser_layers/2)) + ] + ) + + self.distance_ff_2 = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff_2 = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight_2 = nn.Parameter( + torch.zeros((config.num_hidden_layers, config.num_attention_heads, n_rel)) + ) + self._rel_weight_2.data.normal_(0, 0.1) + + self._scaler_2 = nn.Parameter(torch.zeros(2)) + + else: + self.parser_layers = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(config.n_parser_layers) + ] + ) + + self.distance_ff = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight = nn.Parameter( + torch.zeros((config.num_hidden_layers, config.num_attention_heads, n_rel)) + ) + self._rel_weight.data.normal_(0, 0.1) + + self._scaler = nn.Parameter(torch.zeros(2)) + + self.roberta = RobertaModel(config, add_pooling_layer=False) + + if config.n_cntxt_layers > 0: + self.cntxt_layers.embeddings = self.roberta.embeddings + if config.n_cntxt_layers_2 > 0: + self.cntxt_layers_2.embeddings = self.roberta.embeddings + + self.lm_head = RobertaLMHead(config) + + self.pad = config.pad_token_id + + # The LM head weights require special treatment only when they are tied with the word embeddings + self.update_keys_to_ignore(config, ["lm_head.decoder.weight"]) + + # Initialize weights and apply final processing + self.post_init() + + def get_output_embeddings(self): + return self.lm_head.decoder + + def set_output_embeddings(self, new_embeddings): + self.lm_head.decoder = new_embeddings + + @property + def scaler(self): + return self._scaler.exp() + + @property + def scaler_1(self): + return self._scaler_1.exp() + + @property + def scaler_2(self): + return self._scaler_2.exp() + + @property + def rel_weight(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight, dim=-1) + + @property + def rel_weight_1(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight_1) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight_1, dim=-1) + + + @property + def rel_weight_2(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight_2) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight_2, dim=-1) + + + def compute_block(self, distance, height, n_cntxt_layers=0): + """Compute constituents from distance and height.""" + + if n_cntxt_layers>0: + if n_cntxt_layers == 1: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler_1[0] + elif n_cntxt_layers == 2: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler_2[0] + else: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0] + + gamma = torch.sigmoid(-beta_logits) + ones = torch.ones_like(gamma) + + block_mask_left = cummin( + gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1 + ) + block_mask_left = block_mask_left - F.pad( + block_mask_left[:, :, :-1], (1, 0), value=0 + ) + block_mask_left.tril_(0) + + block_mask_right = cummin( + gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1 + ) + block_mask_right = block_mask_right - F.pad( + block_mask_right[:, :, 1:], (0, 1), value=0 + ) + block_mask_right.triu_(0) + + block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :] + block = cumsum(block_mask_left).tril(0) + cumsum( + block_mask_right, reverse=True + ).triu(1) + + return block_p, block + + def compute_head(self, height, n_cntxt_layers=0): + """Estimate head for each constituent.""" + + _, length = height.size() + if n_cntxt_layers>0: + if n_cntxt_layers == 1: + head_logits = height * self.scaler_1[1] + elif n_cntxt_layers == 2: + head_logits = height * self.scaler_2[1] + else: + head_logits = height * self.scaler[1] + index = torch.arange(length, device=height.device) + + mask = (index[:, None, None] <= index[None, None, :]) * ( + index[None, None, :] <= index[None, :, None] + ) + head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1) + head_logits.masked_fill_(~mask[None, :, :, :], -1e9) + + head_p = torch.softmax(head_logits, dim=-1) + + return head_p + + def parse(self, x, embs=None, n_cntxt_layers=0): + """Parse input sentence. + + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + distance: syntactic distance + height: syntactic height + """ + + mask = x != self.pad + mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0) + + if embs is None: + h = self.roberta.embeddings(x) + else: + h = embs + + if n_cntxt_layers > 0: + if n_cntxt_layers == 1: + parser_layers = self.parser_layers_1 + height_ff = self.height_ff_1 + distance_ff = self.distance_ff_1 + elif n_cntxt_layers == 2: + parser_layers = self.parser_layers_2 + height_ff = self.height_ff_2 + distance_ff = self.distance_ff_2 + for i in range(int(self.config.n_parser_layers/2)): + h = h.masked_fill(~mask[:, :, None], 0) + h = parser_layers[i](h) + + height = height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, dim=-1 + )[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max + ) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where( + margin_left > margin_right, margin_right, margin_left + ).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + else: + for i in range(self.config.n_parser_layers): + h = h.masked_fill(~mask[:, :, None], 0) + h = self.parser_layers[i](h) + + height = self.height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = self.distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, dim=-1 + )[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max + ) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where( + margin_left > margin_right, margin_right, margin_left + ).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + + return distance, height + + def generate_mask(self, x, distance, height, n_cntxt_layers=0): + """Compute head and cibling distribution for each token.""" + + bsz, length = x.size() + + eye = torch.eye(length, device=x.device, dtype=torch.bool) + eye = eye[None, :, :].expand((bsz, -1, -1)) + + block_p, block = self.compute_block(distance, height, n_cntxt_layers=n_cntxt_layers) + head_p = self.compute_head(height, n_cntxt_layers=n_cntxt_layers) + head = torch.einsum("blij,bijh->blh", block_p, head_p) + head = head.masked_fill(eye, 0) + child = head.transpose(1, 2) + cibling = torch.bmm(head, child).masked_fill(eye, 0) + + rel_list = [] + if "head" in self.config.relations: + rel_list.append(head) + if "child" in self.config.relations: + rel_list.append(child) + if "cibling" in self.config.relations: + rel_list.append(cibling) + + rel = torch.stack(rel_list, dim=1) + + if n_cntxt_layers > 0: + if n_cntxt_layers == 1: + rel_weight = self.rel_weight_1 + elif n_cntxt_layers == 2: + rel_weight = self.rel_weight_2 + else: + rel_weight = self.rel_weight + + dep = torch.einsum("lhr,brij->lbhij", rel_weight, rel) + + if n_cntxt_layers == 1: + num_layers = self.cntxt_layers_2.config.num_hidden_layers + else: + num_layers = self.roberta.config.num_hidden_layers + + att_mask = dep.reshape( + num_layers, + bsz, + self.config.num_attention_heads, + length, + length, + ) + + return att_mask, cibling, head, block + + def forward( + self, + input_ids: Optional[torch.LongTensor] = None, + attention_mask: Optional[torch.FloatTensor] = None, + token_type_ids: Optional[torch.LongTensor] = None, + position_ids: Optional[torch.LongTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, MaskedLMOutput]: + r""" + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ..., + config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the + loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]` + kwargs (`Dict[str, any]`, optional, defaults to *{}*): + Used to hide legacy arguments that have been deprecated. + """ + return_dict = ( + return_dict if return_dict is not None else self.config.use_return_dict + ) + + + if self.config.n_cntxt_layers > 0: + cntxt_outputs = self.cntxt_layers( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict) + + + if self.config.n_cntxt_layers_2 > 0: + distance_1, height_1 = self.parse(input_ids, cntxt_outputs[0], n_cntxt_layers=1) + att_mask_1, _, _, _ = self.generate_mask(input_ids, distance_1, height_1, n_cntxt_layers=1) + + cntxt_outputs_2 = self.cntxt_layers_2( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + parser_att_mask=att_mask_1) + + sequence_output = cntxt_outputs_2[0] + + distance_2, height_2 = self.parse(input_ids, sequence_output[0], n_cntxt_layers=2) + att_mask, _, _, _ = self.generate_mask(input_ids, distance_2, height_2, n_cntxt_layers=2) + + elif self.config.n_cntxt_layers > 0: + distance, height = self.parse(input_ids, cntxt_outputs[0]) + att_mask, _, _, _ = self.generate_mask(input_ids, distance, height) + else: + distance, height = self.parse(input_ids) + att_mask, _, _, _ = self.generate_mask(input_ids, distance, height) + + outputs = self.roberta( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + parser_att_mask=att_mask, + ) + sequence_output = outputs[0] + prediction_scores = self.lm_head(sequence_output) + + masked_lm_loss = None + if labels is not None: + loss_fct = CrossEntropyLoss() + masked_lm_loss = loss_fct( + prediction_scores.view(-1, self.config.vocab_size), labels.view(-1) + ) + + if not return_dict: + output = (prediction_scores,) + outputs[2:] + return ( + ((masked_lm_loss,) + output) if masked_lm_loss is not None else output + ) + + return MaskedLMOutput( + loss=masked_lm_loss, + logits=prediction_scores, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +class RobertaLMHead(nn.Module): + """Roberta Head for masked language modeling.""" + + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + + self.decoder = nn.Linear(config.hidden_size, config.vocab_size) + self.bias = nn.Parameter(torch.zeros(config.vocab_size)) + self.decoder.bias = self.bias + + def forward(self, features, **kwargs): + x = self.dense(features) + x = gelu(x) + x = self.layer_norm(x) + + # project back to size of vocabulary with bias + x = self.decoder(x) + + return x + + def _tie_weights(self): + # To tie those two weights if they get disconnected (on TPU or when the bias is resized) + self.bias = self.decoder.bias + + +class StructRobertaForSequenceClassification(RobertaPreTrainedModel): + _keys_to_ignore_on_load_missing = [r"position_ids"] + + def __init__(self, config): + super().__init__(config) + self.num_labels = config.num_labels + self.config = config + + if config.n_cntxt_layers > 0: + config_cntxt = copy.deepcopy(config) + config_cntxt.num_hidden_layers = config.n_cntxt_layers + + self.cntxt_layers = RobertaModel(config_cntxt, add_pooling_layer=False) + + if config.n_cntxt_layers_2 > 0: + self.parser_layers_1 = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(int(config.n_parser_layers/2)) + ] + ) + + self.distance_ff_1 = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff_1 = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight_1 = nn.Parameter( + torch.zeros((config.n_cntxt_layers_2, config.num_attention_heads, n_rel)) + ) + self._rel_weight_1.data.normal_(0, 0.1) + + self._scaler_1 = nn.Parameter(torch.zeros(2)) + + config_cntxt_2 = copy.deepcopy(config) + config_cntxt_2.num_hidden_layers = config.n_cntxt_layers_2 + + self.cntxt_layers_2 = RobertaModel(config_cntxt_2, add_pooling_layer=False) + + + self.parser_layers_2 = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(int(config.n_parser_layers/2)) + ] + ) + + self.distance_ff_2 = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff_2 = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight_2 = nn.Parameter( + torch.zeros((config.num_hidden_layers, config.num_attention_heads, n_rel)) + ) + self._rel_weight_2.data.normal_(0, 0.1) + + self._scaler_2 = nn.Parameter(torch.zeros(2)) + + else: + self.parser_layers = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(config.n_parser_layers) + ] + ) + + self.distance_ff = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight = nn.Parameter( + torch.zeros((config.num_hidden_layers, config.num_attention_heads, n_rel)) + ) + self._rel_weight.data.normal_(0, 0.1) + + self._scaler = nn.Parameter(torch.zeros(2)) + + self.roberta = RobertaModel(config, add_pooling_layer=False) + + if config.n_cntxt_layers > 0: + self.cntxt_layers.embeddings = self.roberta.embeddings + if config.n_cntxt_layers_2 > 0: + self.cntxt_layers_2.embeddings = self.roberta.embeddings + + + self.pad = config.pad_token_id + self.classifier = RobertaClassificationHead(config) + + # Initialize weights and apply final processing + self.post_init() + + + @property + def scaler(self): + return self._scaler.exp() + + @property + def scaler_1(self): + return self._scaler_1.exp() + + @property + def scaler_2(self): + return self._scaler_2.exp() + + @property + def rel_weight(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight, dim=-1) + + @property + def rel_weight_1(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight_1) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight_1, dim=-1) + + + @property + def rel_weight_2(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight_2) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight_2, dim=-1) + + + def compute_block(self, distance, height, n_cntxt_layers=0): + """Compute constituents from distance and height.""" + + if n_cntxt_layers>0: + if n_cntxt_layers == 1: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler_1[0] + elif n_cntxt_layers == 2: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler_2[0] + else: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0] + + gamma = torch.sigmoid(-beta_logits) + ones = torch.ones_like(gamma) + + block_mask_left = cummin( + gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1 + ) + block_mask_left = block_mask_left - F.pad( + block_mask_left[:, :, :-1], (1, 0), value=0 + ) + block_mask_left.tril_(0) + + block_mask_right = cummin( + gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1 + ) + block_mask_right = block_mask_right - F.pad( + block_mask_right[:, :, 1:], (0, 1), value=0 + ) + block_mask_right.triu_(0) + + block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :] + block = cumsum(block_mask_left).tril(0) + cumsum( + block_mask_right, reverse=True + ).triu(1) + + return block_p, block + + def compute_head(self, height, n_cntxt_layers=0): + """Estimate head for each constituent.""" + + _, length = height.size() + if n_cntxt_layers>0: + if n_cntxt_layers == 1: + head_logits = height * self.scaler_1[1] + elif n_cntxt_layers == 2: + head_logits = height * self.scaler_2[1] + else: + head_logits = height * self.scaler[1] + index = torch.arange(length, device=height.device) + + mask = (index[:, None, None] <= index[None, None, :]) * ( + index[None, None, :] <= index[None, :, None] + ) + head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1) + head_logits.masked_fill_(~mask[None, :, :, :], -1e9) + + head_p = torch.softmax(head_logits, dim=-1) + + return head_p + + def parse(self, x, embs=None, n_cntxt_layers=0): + """Parse input sentence. + + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + distance: syntactic distance + height: syntactic height + """ + + mask = x != self.pad + mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0) + + if embs is None: + h = self.roberta.embeddings(x) + else: + h = embs + + if n_cntxt_layers > 0: + if n_cntxt_layers == 1: + parser_layers = self.parser_layers_1 + height_ff = self.height_ff_1 + distance_ff = self.distance_ff_1 + elif n_cntxt_layers == 2: + parser_layers = self.parser_layers_2 + height_ff = self.height_ff_2 + distance_ff = self.distance_ff_2 + for i in range(int(self.config.n_parser_layers/2)): + h = h.masked_fill(~mask[:, :, None], 0) + h = parser_layers[i](h) + + height = height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, dim=-1 + )[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max + ) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where( + margin_left > margin_right, margin_right, margin_left + ).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + else: + for i in range(self.config.n_parser_layers): + h = h.masked_fill(~mask[:, :, None], 0) + h = self.parser_layers[i](h) + + height = self.height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = self.distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, dim=-1 + )[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max + ) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where( + margin_left > margin_right, margin_right, margin_left + ).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + + return distance, height + + def generate_mask(self, x, distance, height, n_cntxt_layers=0): + """Compute head and cibling distribution for each token.""" + + bsz, length = x.size() + + eye = torch.eye(length, device=x.device, dtype=torch.bool) + eye = eye[None, :, :].expand((bsz, -1, -1)) + + block_p, block = self.compute_block(distance, height, n_cntxt_layers=n_cntxt_layers) + head_p = self.compute_head(height, n_cntxt_layers=n_cntxt_layers) + head = torch.einsum("blij,bijh->blh", block_p, head_p) + head = head.masked_fill(eye, 0) + child = head.transpose(1, 2) + cibling = torch.bmm(head, child).masked_fill(eye, 0) + + rel_list = [] + if "head" in self.config.relations: + rel_list.append(head) + if "child" in self.config.relations: + rel_list.append(child) + if "cibling" in self.config.relations: + rel_list.append(cibling) + + rel = torch.stack(rel_list, dim=1) + + if n_cntxt_layers > 0: + if n_cntxt_layers == 1: + rel_weight = self.rel_weight_1 + elif n_cntxt_layers == 2: + rel_weight = self.rel_weight_2 + else: + rel_weight = self.rel_weight + + dep = torch.einsum("lhr,brij->lbhij", rel_weight, rel) + + if n_cntxt_layers == 1: + num_layers = self.cntxt_layers_2.config.num_hidden_layers + else: + num_layers = self.roberta.config.num_hidden_layers + + att_mask = dep.reshape( + num_layers, + bsz, + self.config.num_attention_heads, + length, + length, + ) + + return att_mask, cibling, head, block + + def forward( + self, + input_ids: Optional[torch.LongTensor] = None, + attention_mask: Optional[torch.FloatTensor] = None, + token_type_ids: Optional[torch.LongTensor] = None, + position_ids: Optional[torch.LongTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, SequenceClassifierOutput]: + r""" + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., + config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If + `config.num_labels > 1` a classification loss is computed (Cross-Entropy). + """ + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + if self.config.n_cntxt_layers > 0: + cntxt_outputs = self.cntxt_layers( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict) + + + if self.config.n_cntxt_layers_2 > 0: + distance_1, height_1 = self.parse(input_ids, cntxt_outputs[0], n_cntxt_layers=1) + att_mask_1, _, _, _ = self.generate_mask(input_ids, distance_1, height_1, n_cntxt_layers=1) + + cntxt_outputs_2 = self.cntxt_layers_2( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + parser_att_mask=att_mask_1) + + sequence_output = cntxt_outputs_2[0] + + distance_2, height_2 = self.parse(input_ids, sequence_output[0], n_cntxt_layers=2) + att_mask, _, _, _ = self.generate_mask(input_ids, distance_2, height_2, n_cntxt_layers=2) + + elif self.config.n_cntxt_layers > 0: + distance, height = self.parse(input_ids, cntxt_outputs[0]) + att_mask, _, _, _ = self.generate_mask(input_ids, distance, height) + else: + distance, height = self.parse(input_ids) + att_mask, _, _, _ = self.generate_mask(input_ids, distance, height) + + outputs = self.roberta( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + parser_att_mask=att_mask, + ) + sequence_output = outputs[0] + logits = self.classifier(sequence_output) + + loss = None + if labels is not None: + if self.config.problem_type is None: + if self.num_labels == 1: + self.config.problem_type = "regression" + elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): + self.config.problem_type = "single_label_classification" + else: + self.config.problem_type = "multi_label_classification" + + if self.config.problem_type == "regression": + loss_fct = MSELoss() + if self.num_labels == 1: + loss = loss_fct(logits.squeeze(), labels.squeeze()) + else: + loss = loss_fct(logits, labels) + elif self.config.problem_type == "single_label_classification": + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) + elif self.config.problem_type == "multi_label_classification": + loss_fct = BCEWithLogitsLoss() + loss = loss_fct(logits, labels) + + if not return_dict: + output = (logits,) + outputs[2:] + return ((loss,) + output) if loss is not None else output + + return SequenceClassifierOutput( + loss=loss, + logits=logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +class RobertaClassificationHead(nn.Module): + """Head for sentence-level classification tasks.""" + + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + classifier_dropout = ( + config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob + ) + self.dropout = nn.Dropout(classifier_dropout) + self.out_proj = nn.Linear(config.hidden_size, config.num_labels) + + def forward(self, features, **kwargs): + x = features[:, 0, :] # take token (equiv. to [CLS]) + x = self.dropout(x) + x = self.dense(x) + x = torch.tanh(x) + x = self.dropout(x) + x = self.out_proj(x) + return x + + +def create_position_ids_from_input_ids( + input_ids, padding_idx, past_key_values_length=0 +): + """ + Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols + are ignored. This is modified from fairseq's `utils.make_positions`. + + Args: + x: torch.Tensor x: + + Returns: torch.Tensor + """ + # The series of casts and type-conversions here are carefully balanced to both work with ONNX export and XLA. + mask = input_ids.ne(padding_idx).int() + incremental_indices = ( + torch.cumsum(mask, dim=1).type_as(mask) + past_key_values_length + ) * mask + return incremental_indices.long() + padding_idx + + +def cumprod(x, reverse=False, exclusive=False): + """cumulative product.""" + if reverse: + x = x.flip([-1]) + + if exclusive: + x = F.pad(x[:, :, :-1], (1, 0), value=1) + + cx = x.cumprod(-1) + + if reverse: + cx = cx.flip([-1]) + return cx + + +def cumsum(x, reverse=False, exclusive=False): + """cumulative sum.""" + bsz, _, length = x.size() + device = x.device + if reverse: + if exclusive: + w = torch.ones([bsz, length, length], device=device).tril(-1) + else: + w = torch.ones([bsz, length, length], device=device).tril(0) + cx = torch.bmm(x, w) + else: + if exclusive: + w = torch.ones([bsz, length, length], device=device).triu(1) + else: + w = torch.ones([bsz, length, length], device=device).triu(0) + cx = torch.bmm(x, w) + return cx + + +def cummin(x, reverse=False, exclusive=False, max_value=1e9): + """cumulative min.""" + if reverse: + if exclusive: + x = F.pad(x[:, :, 1:], (0, 1), value=max_value) + x = x.flip([-1]).cummin(-1)[0].flip([-1]) + else: + if exclusive: + x = F.pad(x[:, :, :-1], (1, 0), value=max_value) + x = x.cummin(-1)[0] + return x diff --git a/finetune/main_verb_lexical_content_the/predict_results.txt b/finetune/main_verb_lexical_content_the/predict_results.txt new file mode 100644 index 0000000000000000000000000000000000000000..68dcf1be02a6446073bf2a6ebefcbadbd83e3b51 --- /dev/null +++ b/finetune/main_verb_lexical_content_the/predict_results.txt @@ -0,0 +1,22100 @@ +index prediction +0 1 +1 0 +2 0 +3 1 +4 1 +5 0 +6 0 +7 1 +8 1 +9 0 +10 1 +11 0 +12 0 +13 1 +14 1 +15 0 +16 1 +17 0 +18 0 +19 1 +20 1 +21 0 +22 1 +23 0 +24 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--git a/finetune/main_verb_relative_token_position/all_results.json b/finetune/main_verb_relative_token_position/all_results.json new file mode 100644 index 0000000000000000000000000000000000000000..503233bb1e7ebd5737114f364b1f129b707e883f --- /dev/null +++ b/finetune/main_verb_relative_token_position/all_results.json @@ -0,0 +1,16 @@ +{ + "epoch": 10.0, + "eval_accuracy": 0.6683009266853333, + "eval_f1": 0.666749451550147, + "eval_loss": 3.100526809692383, + "eval_mcc": 0.3365875778956931, + "eval_runtime": 47.562, + "eval_samples": 24272, + "eval_samples_per_second": 510.323, + "eval_steps_per_second": 63.79, + "train_loss": 0.006852422207863866, + "train_runtime": 428.4842, + "train_samples": 8177, + "train_samples_per_second": 190.835, + "train_steps_per_second": 1.61 +} \ No newline at end of file diff --git a/finetune/main_verb_relative_token_position/config.json b/finetune/main_verb_relative_token_position/config.json new file mode 100644 index 0000000000000000000000000000000000000000..4a05363b2cdf362715477e08a3dc5678f8da6992 --- /dev/null +++ b/finetune/main_verb_relative_token_position/config.json @@ -0,0 +1,50 @@ +{ + "_name_or_path": "final_models/glue_models/structroberta_s1_final/", + "architectures": [ + "StructRobertaForSequenceClassification" + ], + "attention_probs_dropout_prob": 0.1, + "auto_map": { + "AutoConfig": "modeling_structroberta.StructRobertaConfig", + "AutoModelForMaskedLM": "modeling_structroberta.StructRoberta", + "AutoModelForSequenceClassification": "modeling_structroberta.StructRobertaForSequenceClassification" + }, + "bos_token_id": 0, + "classifier_dropout": null, + "conv_size": 9, + "eos_token_id": 2, + "hidden_act": "gelu", + "hidden_dropout_prob": 0.1, + "hidden_size": 768, + "id2label": { + "0": 0, + "1": 1 + }, + "initializer_range": 0.02, + "intermediate_size": 3072, + "label2id": { + "0": 0, + "1": 1 + }, + "layer_norm_eps": 1e-05, + "max_position_embeddings": 514, + "model_type": 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See all RoBERTa models at https://huggingface.co/models?filter=roberta +] + + +class StructRobertaConfig(RobertaConfig): + model_type = "roberta" + + def __init__( + self, + n_parser_layers=4, + conv_size=9, + relations=("head", "child"), + weight_act="softmax", + n_cntxt_layers=3, + n_cntxt_layers_2=0, + **kwargs,): + + super().__init__(**kwargs) + self.n_cntxt_layers = n_cntxt_layers + self.n_parser_layers = n_parser_layers + self.n_cntxt_layers_2 = n_cntxt_layers_2 + self.conv_size = conv_size + self.relations = relations + self.weight_act = weight_act + +class Conv1d(nn.Module): + """1D convolution layer.""" + + def __init__(self, hidden_size, kernel_size, dilation=1): + """Initialization. + + Args: + hidden_size: dimension of input embeddings + kernel_size: convolution kernel size + dilation: the spacing between the kernel points + """ + super(Conv1d, self).__init__() + + if kernel_size % 2 == 0: + padding = (kernel_size // 2) * dilation + self.shift = True + else: + padding = ((kernel_size - 1) // 2) * dilation + self.shift = False + self.conv = nn.Conv1d( + hidden_size, hidden_size, kernel_size, padding=padding, dilation=dilation + ) + + def forward(self, x): + """Compute convolution. + + Args: + x: input embeddings + Returns: + conv_output: convolution results + """ + + if self.shift: + return self.conv(x.transpose(1, 2)).transpose(1, 2)[:, 1:] + else: + return self.conv(x.transpose(1, 2)).transpose(1, 2) + + +class RobertaEmbeddings(nn.Module): + """ + Same as BertEmbeddings with a tiny tweak for positional embeddings indexing. + """ + + # Copied from transformers.models.bert.modeling_bert.BertEmbeddings.__init__ + def __init__(self, config): + super().__init__() + self.word_embeddings = nn.Embedding( + config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id + ) + self.position_embeddings = nn.Embedding( + config.max_position_embeddings, config.hidden_size + ) + self.token_type_embeddings = nn.Embedding( + config.type_vocab_size, config.hidden_size + ) + + # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load + # any TensorFlow checkpoint file + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + # position_ids (1, len position emb) is contiguous in memory and exported when serialized + self.position_embedding_type = getattr( + config, "position_embedding_type", "absolute" + ) + self.register_buffer( + "position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)) + ) + if version.parse(torch.__version__) > version.parse("1.6.0"): + self.register_buffer( + "token_type_ids", + torch.zeros(self.position_ids.size(), dtype=torch.long), + persistent=False, + ) + + # End copy + self.padding_idx = config.pad_token_id + self.position_embeddings = nn.Embedding( + config.max_position_embeddings, + config.hidden_size, + padding_idx=self.padding_idx, + ) + + def forward( + self, + input_ids=None, + token_type_ids=None, + position_ids=None, + inputs_embeds=None, + past_key_values_length=0, + ): + if position_ids is None: + if input_ids is not None: + # Create the position ids from the input token ids. Any padded tokens remain padded. + position_ids = create_position_ids_from_input_ids( + input_ids, self.padding_idx, past_key_values_length + ) + else: + position_ids = self.create_position_ids_from_inputs_embeds( + inputs_embeds + ) + + if input_ids is not None: + input_shape = input_ids.size() + else: + input_shape = inputs_embeds.size()[:-1] + + seq_length = input_shape[1] + + # Setting the token_type_ids to the registered buffer in constructor where it is all zeros, which usually occurs + # when its auto-generated, registered buffer helps users when tracing the model without passing token_type_ids, solves + # issue #5664 + if token_type_ids is None: + if hasattr(self, "token_type_ids"): + buffered_token_type_ids = self.token_type_ids[:, :seq_length] + buffered_token_type_ids_expanded = buffered_token_type_ids.expand( + input_shape[0], seq_length + ) + token_type_ids = buffered_token_type_ids_expanded + else: + token_type_ids = torch.zeros( + input_shape, dtype=torch.long, device=self.position_ids.device + ) + + if inputs_embeds is None: + inputs_embeds = self.word_embeddings(input_ids) + token_type_embeddings = self.token_type_embeddings(token_type_ids) + + embeddings = inputs_embeds + token_type_embeddings + if self.position_embedding_type == "absolute": + position_embeddings = self.position_embeddings(position_ids) + embeddings += position_embeddings + embeddings = self.LayerNorm(embeddings) + embeddings = self.dropout(embeddings) + return embeddings + + def create_position_ids_from_inputs_embeds(self, inputs_embeds): + """ + We are provided embeddings directly. We cannot infer which are padded so just generate sequential position ids. + + Args: + inputs_embeds: torch.Tensor + + Returns: torch.Tensor + """ + input_shape = inputs_embeds.size()[:-1] + sequence_length = input_shape[1] + + position_ids = torch.arange( + self.padding_idx + 1, + sequence_length + self.padding_idx + 1, + dtype=torch.long, + device=inputs_embeds.device, + ) + return position_ids.unsqueeze(0).expand(input_shape) + + +# Copied from transformers.models.bert.modeling_bert.BertSelfAttention with Bert->Roberta +class RobertaSelfAttention(nn.Module): + def __init__(self, config, position_embedding_type=None): + super().__init__() + if config.hidden_size % config.num_attention_heads != 0 and not hasattr( + config, "embedding_size" + ): + raise ValueError( + f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention " + f"heads ({config.num_attention_heads})" + ) + + self.num_attention_heads = config.num_attention_heads + self.attention_head_size = int(config.hidden_size / config.num_attention_heads) + self.all_head_size = self.num_attention_heads * self.attention_head_size + + self.query = nn.Linear(config.hidden_size, self.all_head_size) + self.key = nn.Linear(config.hidden_size, self.all_head_size) + self.value = nn.Linear(config.hidden_size, self.all_head_size) + + self.dropout = nn.Dropout(config.attention_probs_dropout_prob) + self.position_embedding_type = position_embedding_type or getattr( + config, "position_embedding_type", "absolute" + ) + if ( + self.position_embedding_type == "relative_key" + or self.position_embedding_type == "relative_key_query" + ): + self.max_position_embeddings = config.max_position_embeddings + self.distance_embedding = nn.Embedding( + 2 * config.max_position_embeddings - 1, self.attention_head_size + ) + + self.is_decoder = config.is_decoder + + def transpose_for_scores(self, x): + new_x_shape = x.size()[:-1] + ( + self.num_attention_heads, + self.attention_head_size, + ) + x = x.view(new_x_shape) + return x.permute(0, 2, 1, 3) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.FloatTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, + output_attentions: Optional[bool] = False, + parser_att_mask=None, + ) -> Tuple[torch.Tensor]: + mixed_query_layer = self.query(hidden_states) + + # If this is instantiated as a cross-attention module, the keys + # and values come from an encoder; the attention mask needs to be + # such that the encoder's padding tokens are not attended to. + is_cross_attention = encoder_hidden_states is not None + + if is_cross_attention and past_key_value is not None: + # reuse k,v, cross_attentions + key_layer = past_key_value[0] + value_layer = past_key_value[1] + attention_mask = encoder_attention_mask + elif is_cross_attention: + key_layer = self.transpose_for_scores(self.key(encoder_hidden_states)) + value_layer = self.transpose_for_scores(self.value(encoder_hidden_states)) + attention_mask = encoder_attention_mask + elif past_key_value is not None: + key_layer = self.transpose_for_scores(self.key(hidden_states)) + value_layer = self.transpose_for_scores(self.value(hidden_states)) + key_layer = torch.cat([past_key_value[0], key_layer], dim=2) + value_layer = torch.cat([past_key_value[1], value_layer], dim=2) + else: + key_layer = self.transpose_for_scores(self.key(hidden_states)) + value_layer = self.transpose_for_scores(self.value(hidden_states)) + + query_layer = self.transpose_for_scores(mixed_query_layer) + + if self.is_decoder: + # if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states. + # Further calls to cross_attention layer can then reuse all cross-attention + # key/value_states (first "if" case) + # if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of + # all previous decoder key/value_states. Further calls to uni-directional self-attention + # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case) + # if encoder bi-directional self-attention `past_key_value` is always `None` + past_key_value = (key_layer, value_layer) + + # Take the dot product between "query" and "key" to get the raw attention scores. + attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) + + if ( + self.position_embedding_type == "relative_key" + or self.position_embedding_type == "relative_key_query" + ): + seq_length = hidden_states.size()[1] + position_ids_l = torch.arange( + seq_length, dtype=torch.long, device=hidden_states.device + ).view(-1, 1) + position_ids_r = torch.arange( + seq_length, dtype=torch.long, device=hidden_states.device + ).view(1, -1) + distance = position_ids_l - position_ids_r + positional_embedding = self.distance_embedding( + distance + self.max_position_embeddings - 1 + ) + positional_embedding = positional_embedding.to( + dtype=query_layer.dtype + ) # fp16 compatibility + + if self.position_embedding_type == "relative_key": + relative_position_scores = torch.einsum( + "bhld,lrd->bhlr", query_layer, positional_embedding + ) + attention_scores = attention_scores + relative_position_scores + elif self.position_embedding_type == "relative_key_query": + relative_position_scores_query = torch.einsum( + "bhld,lrd->bhlr", query_layer, positional_embedding + ) + relative_position_scores_key = torch.einsum( + "bhrd,lrd->bhlr", key_layer, positional_embedding + ) + attention_scores = ( + attention_scores + + relative_position_scores_query + + relative_position_scores_key + ) + + attention_scores = attention_scores / math.sqrt(self.attention_head_size) + if attention_mask is not None: + # Apply the attention mask is (precomputed for all layers in RobertaModel forward() function) + attention_scores = attention_scores + attention_mask + + if parser_att_mask is None: + # Normalize the attention scores to probabilities. + attention_probs = nn.functional.softmax(attention_scores, dim=-1) + else: + attention_probs = torch.sigmoid(attention_scores) * parser_att_mask + + # This is actually dropping out entire tokens to attend to, which might + # seem a bit unusual, but is taken from the original Transformer paper. + attention_probs = self.dropout(attention_probs) + + # Mask heads if we want to + if head_mask is not None: + attention_probs = attention_probs * head_mask + + context_layer = torch.matmul(attention_probs, value_layer) + + context_layer = context_layer.permute(0, 2, 1, 3).contiguous() + new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) + context_layer = context_layer.view(new_context_layer_shape) + + outputs = ( + (context_layer, attention_probs) if output_attentions else (context_layer,) + ) + + if self.is_decoder: + outputs = outputs + (past_key_value,) + return outputs + + +# Copied from transformers.models.bert.modeling_bert.BertSelfOutput +class RobertaSelfOutput(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward( + self, hidden_states: torch.Tensor, input_tensor: torch.Tensor + ) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + hidden_states = self.LayerNorm(hidden_states + input_tensor) + return hidden_states + + +# Copied from transformers.models.bert.modeling_bert.BertAttention with Bert->Roberta +class RobertaAttention(nn.Module): + def __init__(self, config, position_embedding_type=None): + super().__init__() + self.self = RobertaSelfAttention( + config, position_embedding_type=position_embedding_type + ) + self.output = RobertaSelfOutput(config) + self.pruned_heads = set() + + def prune_heads(self, heads): + if len(heads) == 0: + return + heads, index = find_pruneable_heads_and_indices( + heads, + self.self.num_attention_heads, + self.self.attention_head_size, + self.pruned_heads, + ) + + # Prune linear layers + self.self.query = prune_linear_layer(self.self.query, index) + self.self.key = prune_linear_layer(self.self.key, index) + self.self.value = prune_linear_layer(self.self.value, index) + self.output.dense = prune_linear_layer(self.output.dense, index, dim=1) + + # Update hyper params and store pruned heads + self.self.num_attention_heads = self.self.num_attention_heads - len(heads) + self.self.all_head_size = ( + self.self.attention_head_size * self.self.num_attention_heads + ) + self.pruned_heads = self.pruned_heads.union(heads) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.FloatTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, + output_attentions: Optional[bool] = False, + parser_att_mask=None, + ) -> Tuple[torch.Tensor]: + self_outputs = self.self( + hidden_states, + attention_mask, + head_mask, + encoder_hidden_states, + encoder_attention_mask, + past_key_value, + output_attentions, + parser_att_mask=parser_att_mask, + ) + attention_output = self.output(self_outputs[0], hidden_states) + outputs = (attention_output,) + self_outputs[ + 1: + ] # add attentions if we output them + return outputs + + +# Copied from transformers.models.bert.modeling_bert.BertIntermediate +class RobertaIntermediate(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.intermediate_size) + if isinstance(config.hidden_act, str): + self.intermediate_act_fn = ACT2FN[config.hidden_act] + else: + self.intermediate_act_fn = config.hidden_act + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.intermediate_act_fn(hidden_states) + return hidden_states + + +# Copied from transformers.models.bert.modeling_bert.BertOutput +class RobertaOutput(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.intermediate_size, config.hidden_size) + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward( + self, hidden_states: torch.Tensor, input_tensor: torch.Tensor + ) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + hidden_states = self.LayerNorm(hidden_states + input_tensor) + return hidden_states + + +# Copied from transformers.models.bert.modeling_bert.BertLayer with Bert->Roberta +class RobertaLayer(nn.Module): + def __init__(self, config): + super().__init__() + self.chunk_size_feed_forward = config.chunk_size_feed_forward + self.seq_len_dim = 1 + self.attention = RobertaAttention(config) + self.is_decoder = config.is_decoder + self.add_cross_attention = config.add_cross_attention + if self.add_cross_attention: + if not self.is_decoder: + raise ValueError( + f"{self} should be used as a decoder model if cross attention is added" + ) + self.crossattention = RobertaAttention( + config, position_embedding_type="absolute" + ) + self.intermediate = RobertaIntermediate(config) + self.output = RobertaOutput(config) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.FloatTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, + output_attentions: Optional[bool] = False, + parser_att_mask=None, + ) -> Tuple[torch.Tensor]: + # decoder uni-directional self-attention cached key/values tuple is at positions 1,2 + self_attn_past_key_value = ( + past_key_value[:2] if past_key_value is not None else None + ) + self_attention_outputs = self.attention( + hidden_states, + attention_mask, + head_mask, + output_attentions=output_attentions, + past_key_value=self_attn_past_key_value, + parser_att_mask=parser_att_mask, + ) + attention_output = self_attention_outputs[0] + + # if decoder, the last output is tuple of self-attn cache + if self.is_decoder: + outputs = self_attention_outputs[1:-1] + present_key_value = self_attention_outputs[-1] + else: + outputs = self_attention_outputs[ + 1: + ] # add self attentions if we output attention weights + + cross_attn_present_key_value = None + if self.is_decoder and encoder_hidden_states is not None: + if not hasattr(self, "crossattention"): + raise ValueError( + f"If `encoder_hidden_states` are passed, {self} has to be instantiated with cross-attention layers by setting `config.add_cross_attention=True`" + ) + + # cross_attn cached key/values tuple is at positions 3,4 of past_key_value tuple + cross_attn_past_key_value = ( + past_key_value[-2:] if past_key_value is not None else None + ) + cross_attention_outputs = self.crossattention( + attention_output, + attention_mask, + head_mask, + encoder_hidden_states, + encoder_attention_mask, + cross_attn_past_key_value, + output_attentions, + ) + attention_output = cross_attention_outputs[0] + outputs = ( + outputs + cross_attention_outputs[1:-1] + ) # add cross attentions if we output attention weights + + # add cross-attn cache to positions 3,4 of present_key_value tuple + cross_attn_present_key_value = cross_attention_outputs[-1] + present_key_value = present_key_value + cross_attn_present_key_value + + layer_output = apply_chunking_to_forward( + self.feed_forward_chunk, + self.chunk_size_feed_forward, + self.seq_len_dim, + attention_output, + ) + outputs = (layer_output,) + outputs + + # if decoder, return the attn key/values as the last output + if self.is_decoder: + outputs = outputs + (present_key_value,) + + return outputs + + def feed_forward_chunk(self, attention_output): + intermediate_output = self.intermediate(attention_output) + layer_output = self.output(intermediate_output, attention_output) + return layer_output + + +# Copied from transformers.models.bert.modeling_bert.BertEncoder with Bert->Roberta +class RobertaEncoder(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + self.layer = nn.ModuleList( + [RobertaLayer(config) for _ in range(config.num_hidden_layers)] + ) + self.gradient_checkpointing = False + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.FloatTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = False, + output_hidden_states: Optional[bool] = False, + return_dict: Optional[bool] = True, + parser_att_mask=None, + ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPastAndCrossAttentions]: + all_hidden_states = () if output_hidden_states else None + all_self_attentions = () if output_attentions else None + all_cross_attentions = ( + () if output_attentions and self.config.add_cross_attention else None + ) + + next_decoder_cache = () if use_cache else None + for i, layer_module in enumerate(self.layer): + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + layer_head_mask = head_mask[i] if head_mask is not None else None + past_key_value = past_key_values[i] if past_key_values is not None else None + + if self.gradient_checkpointing and self.training: + + if use_cache: + logger.warning( + "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." + ) + use_cache = False + + def create_custom_forward(module): + def custom_forward(*inputs): + return module(*inputs, past_key_value, output_attentions) + + return custom_forward + + layer_outputs = torch.utils.checkpoint.checkpoint( + create_custom_forward(layer_module), + hidden_states, + attention_mask, + layer_head_mask, + encoder_hidden_states, + encoder_attention_mask, + ) + else: + if parser_att_mask is not None: + layer_outputs = layer_module( + hidden_states, + attention_mask, + layer_head_mask, + encoder_hidden_states, + encoder_attention_mask, + past_key_value, + output_attentions, + parser_att_mask=parser_att_mask[i]) + else: + layer_outputs = layer_module( + hidden_states, + attention_mask, + layer_head_mask, + encoder_hidden_states, + encoder_attention_mask, + past_key_value, + output_attentions, + parser_att_mask=None) + + + hidden_states = layer_outputs[0] + if use_cache: + next_decoder_cache += (layer_outputs[-1],) + if output_attentions: + all_self_attentions = all_self_attentions + (layer_outputs[1],) + if self.config.add_cross_attention: + all_cross_attentions = all_cross_attentions + (layer_outputs[2],) + + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + if not return_dict: + return tuple( + v + for v in [ + hidden_states, + next_decoder_cache, + all_hidden_states, + all_self_attentions, + all_cross_attentions, + ] + if v is not None + ) + return BaseModelOutputWithPastAndCrossAttentions( + last_hidden_state=hidden_states, + past_key_values=next_decoder_cache, + hidden_states=all_hidden_states, + attentions=all_self_attentions, + cross_attentions=all_cross_attentions, + ) + + +# Copied from transformers.models.bert.modeling_bert.BertPooler +class RobertaPooler(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.activation = nn.Tanh() + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + # We "pool" the model by simply taking the hidden state corresponding + # to the first token. + first_token_tensor = hidden_states[:, 0] + pooled_output = self.dense(first_token_tensor) + pooled_output = self.activation(pooled_output) + return pooled_output + + +class RobertaPreTrainedModel(PreTrainedModel): + """ + An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained + models. + """ + + config_class = RobertaConfig + base_model_prefix = "roberta" + supports_gradient_checkpointing = True + + # Copied from transformers.models.bert.modeling_bert.BertPreTrainedModel._init_weights + def _init_weights(self, module): + """Initialize the weights""" + if isinstance(module, nn.Linear): + # Slightly different from the TF version which uses truncated_normal for initialization + # cf https://github.com/pytorch/pytorch/pull/5617 + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.Embedding): + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.padding_idx is not None: + module.weight.data[module.padding_idx].zero_() + elif isinstance(module, nn.LayerNorm): + if module.bias is not None: + module.bias.data.zero_() + module.weight.data.fill_(1.0) + + def _set_gradient_checkpointing(self, module, value=False): + if isinstance(module, RobertaEncoder): + module.gradient_checkpointing = value + + def update_keys_to_ignore(self, config, del_keys_to_ignore): + """Remove some keys from ignore list""" + if not config.tie_word_embeddings: + # must make a new list, or the class variable gets modified! + self._keys_to_ignore_on_save = [ + k for k in self._keys_to_ignore_on_save if k not in del_keys_to_ignore + ] + self._keys_to_ignore_on_load_missing = [ + k + for k in self._keys_to_ignore_on_load_missing + if k not in del_keys_to_ignore + ] + + +ROBERTA_START_DOCSTRING = r""" + + This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the + library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads + etc.) + + This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. + Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage + and behavior. + + Parameters: + config ([`RobertaConfig`]): Model configuration class with all the parameters of the + model. Initializing with a config file does not load the weights associated with the model, only the + configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. +""" + + +ROBERTA_INPUTS_DOCSTRING = r""" + Args: + input_ids (`torch.LongTensor` of shape `({0})`): + Indices of input sequence tokens in the vocabulary. + + Indices can be obtained using [`RobertaTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + [What are input IDs?](../glossary#input-ids) + attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*): + Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + + [What are attention masks?](../glossary#attention-mask) + token_type_ids (`torch.LongTensor` of shape `({0})`, *optional*): + Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, + 1]`: + + - 0 corresponds to a *sentence A* token, + - 1 corresponds to a *sentence B* token. + + [What are token type IDs?](../glossary#token-type-ids) + position_ids (`torch.LongTensor` of shape `({0})`, *optional*): + Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, + config.max_position_embeddings - 1]`. + + [What are position IDs?](../glossary#position-ids) + head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): + Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: + + - 1 indicates the head is **not masked**, + - 0 indicates the head is **masked**. + + inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_size)`, *optional*): + Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This + is useful if you want more control over how to convert `input_ids` indices into associated vectors than the + model's internal embedding lookup matrix. + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned + tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for + more detail. + return_dict (`bool`, *optional*): + Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. +""" + + +class RobertaModel(RobertaPreTrainedModel): + """ + + The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of + cross-attention is added between the self-attention layers, following the architecture described in *Attention is + all you need*_ by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz + Kaiser and Illia Polosukhin. + + To behave as an decoder the model needs to be initialized with the `is_decoder` argument of the configuration set + to `True`. To be used in a Seq2Seq model, the model needs to initialized with both `is_decoder` argument and + `add_cross_attention` set to `True`; an `encoder_hidden_states` is then expected as an input to the forward pass. + + .. _*Attention is all you need*: https://arxiv.org/abs/1706.03762 + + """ + + _keys_to_ignore_on_load_missing = [r"position_ids"] + + # Copied from transformers.models.bert.modeling_bert.BertModel.__init__ with Bert->Roberta + def __init__(self, config, add_pooling_layer=True): + super().__init__(config) + self.config = config + + self.embeddings = RobertaEmbeddings(config) + self.encoder = RobertaEncoder(config) + + self.pooler = RobertaPooler(config) if add_pooling_layer else None + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.embeddings.word_embeddings + + def set_input_embeddings(self, value): + self.embeddings.word_embeddings = value + + def _prune_heads(self, heads_to_prune): + """ + Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base + class PreTrainedModel + """ + for layer, heads in heads_to_prune.items(): + self.encoder.layer[layer].attention.prune_heads(heads) + + # Copied from transformers.models.bert.modeling_bert.BertModel.forward + def forward( + self, + input_ids: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + token_type_ids: Optional[torch.Tensor] = None, + position_ids: Optional[torch.Tensor] = None, + head_mask: Optional[torch.Tensor] = None, + inputs_embeds: Optional[torch.Tensor] = None, + encoder_hidden_states: Optional[torch.Tensor] = None, + encoder_attention_mask: Optional[torch.Tensor] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + parser_att_mask=None, + ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPoolingAndCrossAttentions]: + r""" + encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): + Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if + the model is configured as a decoder. + encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*): + Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in + the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): + Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. + + If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that + don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all + `decoder_input_ids` of shape `(batch_size, sequence_length)`. + use_cache (`bool`, *optional*): + If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see + `past_key_values`). + """ + output_attentions = ( + output_attentions + if output_attentions is not None + else self.config.output_attentions + ) + output_hidden_states = ( + output_hidden_states + if output_hidden_states is not None + else self.config.output_hidden_states + ) + return_dict = ( + return_dict if return_dict is not None else self.config.use_return_dict + ) + + if self.config.is_decoder: + use_cache = use_cache if use_cache is not None else self.config.use_cache + else: + use_cache = False + + if input_ids is not None and inputs_embeds is not None: + raise ValueError( + "You cannot specify both input_ids and inputs_embeds at the same time" + ) + elif input_ids is not None: + input_shape = input_ids.size() + elif inputs_embeds is not None: + input_shape = inputs_embeds.size()[:-1] + else: + raise ValueError("You have to specify either input_ids or inputs_embeds") + + batch_size, seq_length = input_shape + device = input_ids.device if input_ids is not None else inputs_embeds.device + + # past_key_values_length + past_key_values_length = ( + past_key_values[0][0].shape[2] if past_key_values is not None else 0 + ) + + if attention_mask is None: + attention_mask = torch.ones( + ((batch_size, seq_length + past_key_values_length)), device=device + ) + + if token_type_ids is None: + if hasattr(self.embeddings, "token_type_ids"): + buffered_token_type_ids = self.embeddings.token_type_ids[:, :seq_length] + buffered_token_type_ids_expanded = buffered_token_type_ids.expand( + batch_size, seq_length + ) + token_type_ids = buffered_token_type_ids_expanded + else: + token_type_ids = torch.zeros( + input_shape, dtype=torch.long, device=device + ) + + # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length] + # ourselves in which case we just need to make it broadcastable to all heads. + extended_attention_mask: torch.Tensor = self.get_extended_attention_mask( + attention_mask, input_shape, device + ) + + # If a 2D or 3D attention mask is provided for the cross-attention + # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length] + if self.config.is_decoder and encoder_hidden_states is not None: + ( + encoder_batch_size, + encoder_sequence_length, + _, + ) = encoder_hidden_states.size() + encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length) + if encoder_attention_mask is None: + encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device) + encoder_extended_attention_mask = self.invert_attention_mask( + encoder_attention_mask + ) + else: + encoder_extended_attention_mask = None + + # Prepare head mask if needed + # 1.0 in head_mask indicate we keep the head + # attention_probs has shape bsz x n_heads x N x N + # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] + # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] + head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) + + embedding_output = self.embeddings( + input_ids=input_ids, + position_ids=position_ids, + token_type_ids=token_type_ids, + inputs_embeds=inputs_embeds, + past_key_values_length=past_key_values_length, + ) + encoder_outputs = self.encoder( + embedding_output, + attention_mask=extended_attention_mask, + head_mask=head_mask, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_extended_attention_mask, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + parser_att_mask=parser_att_mask, + ) + sequence_output = encoder_outputs[0] + pooled_output = ( + self.pooler(sequence_output) if self.pooler is not None else None + ) + + if not return_dict: + return (sequence_output, pooled_output) + encoder_outputs[1:] + + return BaseModelOutputWithPoolingAndCrossAttentions( + last_hidden_state=sequence_output, + pooler_output=pooled_output, + past_key_values=encoder_outputs.past_key_values, + hidden_states=encoder_outputs.hidden_states, + attentions=encoder_outputs.attentions, + cross_attentions=encoder_outputs.cross_attentions, + ) + + +class StructRoberta(RobertaPreTrainedModel): + _keys_to_ignore_on_save = [r"lm_head.decoder.weight", r"lm_head.decoder.bias"] + _keys_to_ignore_on_load_missing = [ + r"position_ids", + r"lm_head.decoder.weight", + r"lm_head.decoder.bias", + ] + _keys_to_ignore_on_load_unexpected = [r"pooler"] + + def __init__(self, config): + super().__init__(config) + + if config.is_decoder: + logger.warning( + "If you want to use `RobertaForMaskedLM` make sure `config.is_decoder=False` for " + "bi-directional self-attention." + ) + + + if config.n_cntxt_layers > 0: + config_cntxt = copy.deepcopy(config) + config_cntxt.num_hidden_layers = config.n_cntxt_layers + + self.cntxt_layers = RobertaModel(config_cntxt, add_pooling_layer=False) + + if config.n_cntxt_layers_2 > 0: + self.parser_layers_1 = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(int(config.n_parser_layers/2)) + ] + ) + + self.distance_ff_1 = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff_1 = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight_1 = nn.Parameter( + torch.zeros((config.n_cntxt_layers_2, config.num_attention_heads, n_rel)) + ) + self._rel_weight_1.data.normal_(0, 0.1) + + self._scaler_1 = nn.Parameter(torch.zeros(2)) + + config_cntxt_2 = copy.deepcopy(config) + config_cntxt_2.num_hidden_layers = config.n_cntxt_layers_2 + + self.cntxt_layers_2 = RobertaModel(config_cntxt_2, add_pooling_layer=False) + + + self.parser_layers_2 = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(int(config.n_parser_layers/2)) + ] + ) + + self.distance_ff_2 = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff_2 = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight_2 = nn.Parameter( + torch.zeros((config.num_hidden_layers, config.num_attention_heads, n_rel)) + ) + self._rel_weight_2.data.normal_(0, 0.1) + + self._scaler_2 = nn.Parameter(torch.zeros(2)) + + else: + self.parser_layers = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(config.n_parser_layers) + ] + ) + + self.distance_ff = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight = nn.Parameter( + torch.zeros((config.num_hidden_layers, config.num_attention_heads, n_rel)) + ) + self._rel_weight.data.normal_(0, 0.1) + + self._scaler = nn.Parameter(torch.zeros(2)) + + self.roberta = RobertaModel(config, add_pooling_layer=False) + + if config.n_cntxt_layers > 0: + self.cntxt_layers.embeddings = self.roberta.embeddings + if config.n_cntxt_layers_2 > 0: + self.cntxt_layers_2.embeddings = self.roberta.embeddings + + self.lm_head = RobertaLMHead(config) + + self.pad = config.pad_token_id + + # The LM head weights require special treatment only when they are tied with the word embeddings + self.update_keys_to_ignore(config, ["lm_head.decoder.weight"]) + + # Initialize weights and apply final processing + self.post_init() + + def get_output_embeddings(self): + return self.lm_head.decoder + + def set_output_embeddings(self, new_embeddings): + self.lm_head.decoder = new_embeddings + + @property + def scaler(self): + return self._scaler.exp() + + @property + def scaler_1(self): + return self._scaler_1.exp() + + @property + def scaler_2(self): + return self._scaler_2.exp() + + @property + def rel_weight(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight, dim=-1) + + @property + def rel_weight_1(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight_1) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight_1, dim=-1) + + + @property + def rel_weight_2(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight_2) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight_2, dim=-1) + + + def compute_block(self, distance, height, n_cntxt_layers=0): + """Compute constituents from distance and height.""" + + if n_cntxt_layers>0: + if n_cntxt_layers == 1: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler_1[0] + elif n_cntxt_layers == 2: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler_2[0] + else: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0] + + gamma = torch.sigmoid(-beta_logits) + ones = torch.ones_like(gamma) + + block_mask_left = cummin( + gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1 + ) + block_mask_left = block_mask_left - F.pad( + block_mask_left[:, :, :-1], (1, 0), value=0 + ) + block_mask_left.tril_(0) + + block_mask_right = cummin( + gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1 + ) + block_mask_right = block_mask_right - F.pad( + block_mask_right[:, :, 1:], (0, 1), value=0 + ) + block_mask_right.triu_(0) + + block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :] + block = cumsum(block_mask_left).tril(0) + cumsum( + block_mask_right, reverse=True + ).triu(1) + + return block_p, block + + def compute_head(self, height, n_cntxt_layers=0): + """Estimate head for each constituent.""" + + _, length = height.size() + if n_cntxt_layers>0: + if n_cntxt_layers == 1: + head_logits = height * self.scaler_1[1] + elif n_cntxt_layers == 2: + head_logits = height * self.scaler_2[1] + else: + head_logits = height * self.scaler[1] + index = torch.arange(length, device=height.device) + + mask = (index[:, None, None] <= index[None, None, :]) * ( + index[None, None, :] <= index[None, :, None] + ) + head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1) + head_logits.masked_fill_(~mask[None, :, :, :], -1e9) + + head_p = torch.softmax(head_logits, dim=-1) + + return head_p + + def parse(self, x, embs=None, n_cntxt_layers=0): + """Parse input sentence. + + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + distance: syntactic distance + height: syntactic height + """ + + mask = x != self.pad + mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0) + + if embs is None: + h = self.roberta.embeddings(x) + else: + h = embs + + if n_cntxt_layers > 0: + if n_cntxt_layers == 1: + parser_layers = self.parser_layers_1 + height_ff = self.height_ff_1 + distance_ff = self.distance_ff_1 + elif n_cntxt_layers == 2: + parser_layers = self.parser_layers_2 + height_ff = self.height_ff_2 + distance_ff = self.distance_ff_2 + for i in range(int(self.config.n_parser_layers/2)): + h = h.masked_fill(~mask[:, :, None], 0) + h = parser_layers[i](h) + + height = height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, dim=-1 + )[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max + ) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where( + margin_left > margin_right, margin_right, margin_left + ).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + else: + for i in range(self.config.n_parser_layers): + h = h.masked_fill(~mask[:, :, None], 0) + h = self.parser_layers[i](h) + + height = self.height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = self.distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, dim=-1 + )[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max + ) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where( + margin_left > margin_right, margin_right, margin_left + ).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + + return distance, height + + def generate_mask(self, x, distance, height, n_cntxt_layers=0): + """Compute head and cibling distribution for each token.""" + + bsz, length = x.size() + + eye = torch.eye(length, device=x.device, dtype=torch.bool) + eye = eye[None, :, :].expand((bsz, -1, -1)) + + block_p, block = self.compute_block(distance, height, n_cntxt_layers=n_cntxt_layers) + head_p = self.compute_head(height, n_cntxt_layers=n_cntxt_layers) + head = torch.einsum("blij,bijh->blh", block_p, head_p) + head = head.masked_fill(eye, 0) + child = head.transpose(1, 2) + cibling = torch.bmm(head, child).masked_fill(eye, 0) + + rel_list = [] + if "head" in self.config.relations: + rel_list.append(head) + if "child" in self.config.relations: + rel_list.append(child) + if "cibling" in self.config.relations: + rel_list.append(cibling) + + rel = torch.stack(rel_list, dim=1) + + if n_cntxt_layers > 0: + if n_cntxt_layers == 1: + rel_weight = self.rel_weight_1 + elif n_cntxt_layers == 2: + rel_weight = self.rel_weight_2 + else: + rel_weight = self.rel_weight + + dep = torch.einsum("lhr,brij->lbhij", rel_weight, rel) + + if n_cntxt_layers == 1: + num_layers = self.cntxt_layers_2.config.num_hidden_layers + else: + num_layers = self.roberta.config.num_hidden_layers + + att_mask = dep.reshape( + num_layers, + bsz, + self.config.num_attention_heads, + length, + length, + ) + + return att_mask, cibling, head, block + + def forward( + self, + input_ids: Optional[torch.LongTensor] = None, + attention_mask: Optional[torch.FloatTensor] = None, + token_type_ids: Optional[torch.LongTensor] = None, + position_ids: Optional[torch.LongTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, MaskedLMOutput]: + r""" + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ..., + config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the + loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]` + kwargs (`Dict[str, any]`, optional, defaults to *{}*): + Used to hide legacy arguments that have been deprecated. + """ + return_dict = ( + return_dict if return_dict is not None else self.config.use_return_dict + ) + + + if self.config.n_cntxt_layers > 0: + cntxt_outputs = self.cntxt_layers( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict) + + + if self.config.n_cntxt_layers_2 > 0: + distance_1, height_1 = self.parse(input_ids, cntxt_outputs[0], n_cntxt_layers=1) + att_mask_1, _, _, _ = self.generate_mask(input_ids, distance_1, height_1, n_cntxt_layers=1) + + cntxt_outputs_2 = self.cntxt_layers_2( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + parser_att_mask=att_mask_1) + + sequence_output = cntxt_outputs_2[0] + + distance_2, height_2 = self.parse(input_ids, sequence_output[0], n_cntxt_layers=2) + att_mask, _, _, _ = self.generate_mask(input_ids, distance_2, height_2, n_cntxt_layers=2) + + elif self.config.n_cntxt_layers > 0: + distance, height = self.parse(input_ids, cntxt_outputs[0]) + att_mask, _, _, _ = self.generate_mask(input_ids, distance, height) + else: + distance, height = self.parse(input_ids) + att_mask, _, _, _ = self.generate_mask(input_ids, distance, height) + + outputs = self.roberta( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + parser_att_mask=att_mask, + ) + sequence_output = outputs[0] + prediction_scores = self.lm_head(sequence_output) + + masked_lm_loss = None + if labels is not None: + loss_fct = CrossEntropyLoss() + masked_lm_loss = loss_fct( + prediction_scores.view(-1, self.config.vocab_size), labels.view(-1) + ) + + if not return_dict: + output = (prediction_scores,) + outputs[2:] + return ( + ((masked_lm_loss,) + output) if masked_lm_loss is not None else output + ) + + return MaskedLMOutput( + loss=masked_lm_loss, + logits=prediction_scores, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +class RobertaLMHead(nn.Module): + """Roberta Head for masked language modeling.""" + + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + + self.decoder = nn.Linear(config.hidden_size, config.vocab_size) + self.bias = nn.Parameter(torch.zeros(config.vocab_size)) + self.decoder.bias = self.bias + + def forward(self, features, **kwargs): + x = self.dense(features) + x = gelu(x) + x = self.layer_norm(x) + + # project back to size of vocabulary with bias + x = self.decoder(x) + + return x + + def _tie_weights(self): + # To tie those two weights if they get disconnected (on TPU or when the bias is resized) + self.bias = self.decoder.bias + + +class StructRobertaForSequenceClassification(RobertaPreTrainedModel): + _keys_to_ignore_on_load_missing = [r"position_ids"] + + def __init__(self, config): + super().__init__(config) + self.num_labels = config.num_labels + self.config = config + + if config.n_cntxt_layers > 0: + config_cntxt = copy.deepcopy(config) + config_cntxt.num_hidden_layers = config.n_cntxt_layers + + self.cntxt_layers = RobertaModel(config_cntxt, add_pooling_layer=False) + + if config.n_cntxt_layers_2 > 0: + self.parser_layers_1 = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(int(config.n_parser_layers/2)) + ] + ) + + self.distance_ff_1 = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff_1 = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight_1 = nn.Parameter( + torch.zeros((config.n_cntxt_layers_2, config.num_attention_heads, n_rel)) + ) + self._rel_weight_1.data.normal_(0, 0.1) + + self._scaler_1 = nn.Parameter(torch.zeros(2)) + + config_cntxt_2 = copy.deepcopy(config) + config_cntxt_2.num_hidden_layers = config.n_cntxt_layers_2 + + self.cntxt_layers_2 = RobertaModel(config_cntxt_2, add_pooling_layer=False) + + + self.parser_layers_2 = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(int(config.n_parser_layers/2)) + ] + ) + + self.distance_ff_2 = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff_2 = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight_2 = nn.Parameter( + torch.zeros((config.num_hidden_layers, config.num_attention_heads, n_rel)) + ) + self._rel_weight_2.data.normal_(0, 0.1) + + self._scaler_2 = nn.Parameter(torch.zeros(2)) + + else: + self.parser_layers = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(config.n_parser_layers) + ] + ) + + self.distance_ff = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight = nn.Parameter( + torch.zeros((config.num_hidden_layers, config.num_attention_heads, n_rel)) + ) + self._rel_weight.data.normal_(0, 0.1) + + self._scaler = nn.Parameter(torch.zeros(2)) + + self.roberta = RobertaModel(config, add_pooling_layer=False) + + if config.n_cntxt_layers > 0: + self.cntxt_layers.embeddings = self.roberta.embeddings + if config.n_cntxt_layers_2 > 0: + self.cntxt_layers_2.embeddings = self.roberta.embeddings + + + self.pad = config.pad_token_id + self.classifier = RobertaClassificationHead(config) + + # Initialize weights and apply final processing + self.post_init() + + + @property + def scaler(self): + return self._scaler.exp() + + @property + def scaler_1(self): + return self._scaler_1.exp() + + @property + def scaler_2(self): + return self._scaler_2.exp() + + @property + def rel_weight(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight, dim=-1) + + @property + def rel_weight_1(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight_1) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight_1, dim=-1) + + + @property + def rel_weight_2(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight_2) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight_2, dim=-1) + + + def compute_block(self, distance, height, n_cntxt_layers=0): + """Compute constituents from distance and height.""" + + if n_cntxt_layers>0: + if n_cntxt_layers == 1: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler_1[0] + elif n_cntxt_layers == 2: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler_2[0] + else: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0] + + gamma = torch.sigmoid(-beta_logits) + ones = torch.ones_like(gamma) + + block_mask_left = cummin( + gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1 + ) + block_mask_left = block_mask_left - F.pad( + block_mask_left[:, :, :-1], (1, 0), value=0 + ) + block_mask_left.tril_(0) + + block_mask_right = cummin( + gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1 + ) + block_mask_right = block_mask_right - F.pad( + block_mask_right[:, :, 1:], (0, 1), value=0 + ) + block_mask_right.triu_(0) + + block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :] + block = cumsum(block_mask_left).tril(0) + cumsum( + block_mask_right, reverse=True + ).triu(1) + + return block_p, block + + def compute_head(self, height, n_cntxt_layers=0): + """Estimate head for each constituent.""" + + _, length = height.size() + if n_cntxt_layers>0: + if n_cntxt_layers == 1: + head_logits = height * self.scaler_1[1] + elif n_cntxt_layers == 2: + head_logits = height * self.scaler_2[1] + else: + head_logits = height * self.scaler[1] + index = torch.arange(length, device=height.device) + + mask = (index[:, None, None] <= index[None, None, :]) * ( + index[None, None, :] <= index[None, :, None] + ) + head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1) + head_logits.masked_fill_(~mask[None, :, :, :], -1e9) + + head_p = torch.softmax(head_logits, dim=-1) + + return head_p + + def parse(self, x, embs=None, n_cntxt_layers=0): + """Parse input sentence. + + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + distance: syntactic distance + height: syntactic height + """ + + mask = x != self.pad + mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0) + + if embs is None: + h = self.roberta.embeddings(x) + else: + h = embs + + if n_cntxt_layers > 0: + if n_cntxt_layers == 1: + parser_layers = self.parser_layers_1 + height_ff = self.height_ff_1 + distance_ff = self.distance_ff_1 + elif n_cntxt_layers == 2: + parser_layers = self.parser_layers_2 + height_ff = self.height_ff_2 + distance_ff = self.distance_ff_2 + for i in range(int(self.config.n_parser_layers/2)): + h = h.masked_fill(~mask[:, :, None], 0) + h = parser_layers[i](h) + + height = height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, dim=-1 + )[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max + ) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where( + margin_left > margin_right, margin_right, margin_left + ).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + else: + for i in range(self.config.n_parser_layers): + h = h.masked_fill(~mask[:, :, None], 0) + h = self.parser_layers[i](h) + + height = self.height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = self.distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, dim=-1 + )[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max + ) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where( + margin_left > margin_right, margin_right, margin_left + ).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + + return distance, height + + def generate_mask(self, x, distance, height, n_cntxt_layers=0): + """Compute head and cibling distribution for each token.""" + + bsz, length = x.size() + + eye = torch.eye(length, device=x.device, dtype=torch.bool) + eye = eye[None, :, :].expand((bsz, -1, -1)) + + block_p, block = self.compute_block(distance, height, n_cntxt_layers=n_cntxt_layers) + head_p = self.compute_head(height, n_cntxt_layers=n_cntxt_layers) + head = torch.einsum("blij,bijh->blh", block_p, head_p) + head = head.masked_fill(eye, 0) + child = head.transpose(1, 2) + cibling = torch.bmm(head, child).masked_fill(eye, 0) + + rel_list = [] + if "head" in self.config.relations: + rel_list.append(head) + if "child" in self.config.relations: + rel_list.append(child) + if "cibling" in self.config.relations: + rel_list.append(cibling) + + rel = torch.stack(rel_list, dim=1) + + if n_cntxt_layers > 0: + if n_cntxt_layers == 1: + rel_weight = self.rel_weight_1 + elif n_cntxt_layers == 2: + rel_weight = self.rel_weight_2 + else: + rel_weight = self.rel_weight + + dep = torch.einsum("lhr,brij->lbhij", rel_weight, rel) + + if n_cntxt_layers == 1: + num_layers = self.cntxt_layers_2.config.num_hidden_layers + else: + num_layers = self.roberta.config.num_hidden_layers + + att_mask = dep.reshape( + num_layers, + bsz, + self.config.num_attention_heads, + length, + length, + ) + + return att_mask, cibling, head, block + + def forward( + self, + input_ids: Optional[torch.LongTensor] = None, + attention_mask: Optional[torch.FloatTensor] = None, + token_type_ids: Optional[torch.LongTensor] = None, + position_ids: Optional[torch.LongTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, SequenceClassifierOutput]: + r""" + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., + config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If + `config.num_labels > 1` a classification loss is computed (Cross-Entropy). + """ + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + if self.config.n_cntxt_layers > 0: + cntxt_outputs = self.cntxt_layers( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict) + + + if self.config.n_cntxt_layers_2 > 0: + distance_1, height_1 = self.parse(input_ids, cntxt_outputs[0], n_cntxt_layers=1) + att_mask_1, _, _, _ = self.generate_mask(input_ids, distance_1, height_1, n_cntxt_layers=1) + + cntxt_outputs_2 = self.cntxt_layers_2( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + parser_att_mask=att_mask_1) + + sequence_output = cntxt_outputs_2[0] + + distance_2, height_2 = self.parse(input_ids, sequence_output[0], n_cntxt_layers=2) + att_mask, _, _, _ = self.generate_mask(input_ids, distance_2, height_2, n_cntxt_layers=2) + + elif self.config.n_cntxt_layers > 0: + distance, height = self.parse(input_ids, cntxt_outputs[0]) + att_mask, _, _, _ = self.generate_mask(input_ids, distance, height) + else: + distance, height = self.parse(input_ids) + att_mask, _, _, _ = self.generate_mask(input_ids, distance, height) + + outputs = self.roberta( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + parser_att_mask=att_mask, + ) + sequence_output = outputs[0] + logits = self.classifier(sequence_output) + + loss = None + if labels is not None: + if self.config.problem_type is None: + if self.num_labels == 1: + self.config.problem_type = "regression" + elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): + self.config.problem_type = "single_label_classification" + else: + self.config.problem_type = "multi_label_classification" + + if self.config.problem_type == "regression": + loss_fct = MSELoss() + if self.num_labels == 1: + loss = loss_fct(logits.squeeze(), labels.squeeze()) + else: + loss = loss_fct(logits, labels) + elif self.config.problem_type == "single_label_classification": + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) + elif self.config.problem_type == "multi_label_classification": + loss_fct = BCEWithLogitsLoss() + loss = loss_fct(logits, labels) + + if not return_dict: + output = (logits,) + outputs[2:] + return ((loss,) + output) if loss is not None else output + + return SequenceClassifierOutput( + loss=loss, + logits=logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +class RobertaClassificationHead(nn.Module): + """Head for sentence-level classification tasks.""" + + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + classifier_dropout = ( + config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob + ) + self.dropout = nn.Dropout(classifier_dropout) + self.out_proj = nn.Linear(config.hidden_size, config.num_labels) + + def forward(self, features, **kwargs): + x = features[:, 0, :] # take token (equiv. to [CLS]) + x = self.dropout(x) + x = self.dense(x) + x = torch.tanh(x) + x = self.dropout(x) + x = self.out_proj(x) + return x + + +def create_position_ids_from_input_ids( + input_ids, padding_idx, past_key_values_length=0 +): + """ + Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols + are ignored. This is modified from fairseq's `utils.make_positions`. + + Args: + x: torch.Tensor x: + + Returns: torch.Tensor + """ + # The series of casts and type-conversions here are carefully balanced to both work with ONNX export and XLA. + mask = input_ids.ne(padding_idx).int() + incremental_indices = ( + torch.cumsum(mask, dim=1).type_as(mask) + past_key_values_length + ) * mask + return incremental_indices.long() + padding_idx + + +def cumprod(x, reverse=False, exclusive=False): + """cumulative product.""" + if reverse: + x = x.flip([-1]) + + if exclusive: + x = F.pad(x[:, :, :-1], (1, 0), value=1) + + cx = x.cumprod(-1) + + if reverse: + cx = cx.flip([-1]) + return cx + + +def cumsum(x, reverse=False, exclusive=False): + """cumulative sum.""" + bsz, _, length = x.size() + device = x.device + if reverse: + if exclusive: + w = torch.ones([bsz, length, length], device=device).tril(-1) + else: + w = torch.ones([bsz, length, length], device=device).tril(0) + cx = torch.bmm(x, w) + else: + if exclusive: + w = torch.ones([bsz, length, length], device=device).triu(1) + else: + w = torch.ones([bsz, length, length], device=device).triu(0) + cx = torch.bmm(x, w) + return cx + + +def cummin(x, reverse=False, exclusive=False, max_value=1e9): + """cumulative min.""" + if reverse: + if exclusive: + x = F.pad(x[:, :, 1:], (0, 1), value=max_value) + x = x.flip([-1]).cummin(-1)[0].flip([-1]) + else: + if exclusive: + x = F.pad(x[:, :, :-1], (1, 0), value=max_value) + x = x.cummin(-1)[0] + return x diff --git a/finetune/main_verb_relative_token_position/predict_results.txt b/finetune/main_verb_relative_token_position/predict_results.txt new file mode 100644 index 0000000000000000000000000000000000000000..18ab9123ba0cc38649637c805e4f0a1c6b803b49 --- /dev/null +++ b/finetune/main_verb_relative_token_position/predict_results.txt @@ -0,0 +1,24273 @@ +index prediction +0 0 +1 1 +2 1 +3 0 +4 1 +5 0 +6 0 +7 1 +8 1 +9 1 +10 1 +11 0 +12 0 +13 1 +14 0 +15 0 +16 1 +17 1 +18 1 +19 0 +20 1 +21 0 +22 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b/finetune/mnli-mm/eval_results.json @@ -0,0 +1,9 @@ +{ + "epoch": 4.99, + "eval_accuracy": 0.7272437810897827, + "eval_loss": 0.8128065466880798, + "eval_runtime": 12.3737, + "eval_samples": 6284, + "eval_samples_per_second": 507.852, + "eval_steps_per_second": 63.522 +} \ No newline at end of file diff --git a/finetune/mnli-mm/merges.txt b/finetune/mnli-mm/merges.txt new file mode 100644 index 0000000000000000000000000000000000000000..94b4f252cb566a4dac62a08c78d85169ff4e91be --- /dev/null +++ b/finetune/mnli-mm/merges.txt @@ -0,0 +1,31794 @@ +#version: 0.2 +Ġ t +h e +Ġ a +i n +Ġt he +r e +Ġ w +o u +Ġ s +o n +e r +h a +n d +i s +i t +Ġ b +Ġ o +Ġ c +Ġ m +Ġ f +in g +o r +a n +e d +l l +Ġ d +e n +Ġt o +e s +Ġ p +Ġo f +Ġ y +Ġ in +a r +a t +Ġa nd +Ġ l +ha t +Ġy ou +a s +Ġ g +o m +Ġ n +Ġ I +i c +o w +l e +v e +o t +Ġ h +Ġ he +a l +e t +Ġ ha +' s +u s +Ġt h +a y +Ġ it +Ġ is +Ġb e +Ġt hat +Ġ on +i on +i d +Ġ e +g h +u t +l d +en t +i m +Ġ re +Ġw as +a c +Ġw e +v er +a m +T he +. . +Ġf or +l y 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See all RoBERTa models at https://huggingface.co/models?filter=roberta +] + + +class StructRobertaConfig(RobertaConfig): + model_type = "roberta" + + def __init__( + self, + n_parser_layers=4, + conv_size=9, + relations=("head", "child"), + weight_act="softmax", + n_cntxt_layers=3, + n_cntxt_layers_2=0, + **kwargs,): + + super().__init__(**kwargs) + self.n_cntxt_layers = n_cntxt_layers + self.n_parser_layers = n_parser_layers + self.n_cntxt_layers_2 = n_cntxt_layers_2 + self.conv_size = conv_size + self.relations = relations + self.weight_act = weight_act + +class Conv1d(nn.Module): + """1D convolution layer.""" + + def __init__(self, hidden_size, kernel_size, dilation=1): + """Initialization. + + Args: + hidden_size: dimension of input embeddings + kernel_size: convolution kernel size + dilation: the spacing between the kernel points + """ + super(Conv1d, self).__init__() + + if kernel_size % 2 == 0: + padding = (kernel_size // 2) * dilation + self.shift = True + else: + padding = ((kernel_size - 1) // 2) * dilation + self.shift = False + self.conv = nn.Conv1d( + hidden_size, hidden_size, kernel_size, padding=padding, dilation=dilation + ) + + def forward(self, x): + """Compute convolution. + + Args: + x: input embeddings + Returns: + conv_output: convolution results + """ + + if self.shift: + return self.conv(x.transpose(1, 2)).transpose(1, 2)[:, 1:] + else: + return self.conv(x.transpose(1, 2)).transpose(1, 2) + + +class RobertaEmbeddings(nn.Module): + """ + Same as BertEmbeddings with a tiny tweak for positional embeddings indexing. + """ + + # Copied from transformers.models.bert.modeling_bert.BertEmbeddings.__init__ + def __init__(self, config): + super().__init__() + self.word_embeddings = nn.Embedding( + config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id + ) + self.position_embeddings = nn.Embedding( + config.max_position_embeddings, config.hidden_size + ) + self.token_type_embeddings = nn.Embedding( + config.type_vocab_size, config.hidden_size + ) + + # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load + # any TensorFlow checkpoint file + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + # position_ids (1, len position emb) is contiguous in memory and exported when serialized + self.position_embedding_type = getattr( + config, "position_embedding_type", "absolute" + ) + self.register_buffer( + "position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)) + ) + if version.parse(torch.__version__) > version.parse("1.6.0"): + self.register_buffer( + "token_type_ids", + torch.zeros(self.position_ids.size(), dtype=torch.long), + persistent=False, + ) + + # End copy + self.padding_idx = config.pad_token_id + self.position_embeddings = nn.Embedding( + config.max_position_embeddings, + config.hidden_size, + padding_idx=self.padding_idx, + ) + + def forward( + self, + input_ids=None, + token_type_ids=None, + position_ids=None, + inputs_embeds=None, + past_key_values_length=0, + ): + if position_ids is None: + if input_ids is not None: + # Create the position ids from the input token ids. Any padded tokens remain padded. + position_ids = create_position_ids_from_input_ids( + input_ids, self.padding_idx, past_key_values_length + ) + else: + position_ids = self.create_position_ids_from_inputs_embeds( + inputs_embeds + ) + + if input_ids is not None: + input_shape = input_ids.size() + else: + input_shape = inputs_embeds.size()[:-1] + + seq_length = input_shape[1] + + # Setting the token_type_ids to the registered buffer in constructor where it is all zeros, which usually occurs + # when its auto-generated, registered buffer helps users when tracing the model without passing token_type_ids, solves + # issue #5664 + if token_type_ids is None: + if hasattr(self, "token_type_ids"): + buffered_token_type_ids = self.token_type_ids[:, :seq_length] + buffered_token_type_ids_expanded = buffered_token_type_ids.expand( + input_shape[0], seq_length + ) + token_type_ids = buffered_token_type_ids_expanded + else: + token_type_ids = torch.zeros( + input_shape, dtype=torch.long, device=self.position_ids.device + ) + + if inputs_embeds is None: + inputs_embeds = self.word_embeddings(input_ids) + token_type_embeddings = self.token_type_embeddings(token_type_ids) + + embeddings = inputs_embeds + token_type_embeddings + if self.position_embedding_type == "absolute": + position_embeddings = self.position_embeddings(position_ids) + embeddings += position_embeddings + embeddings = self.LayerNorm(embeddings) + embeddings = self.dropout(embeddings) + return embeddings + + def create_position_ids_from_inputs_embeds(self, inputs_embeds): + """ + We are provided embeddings directly. We cannot infer which are padded so just generate sequential position ids. + + Args: + inputs_embeds: torch.Tensor + + Returns: torch.Tensor + """ + input_shape = inputs_embeds.size()[:-1] + sequence_length = input_shape[1] + + position_ids = torch.arange( + self.padding_idx + 1, + sequence_length + self.padding_idx + 1, + dtype=torch.long, + device=inputs_embeds.device, + ) + return position_ids.unsqueeze(0).expand(input_shape) + + +# Copied from transformers.models.bert.modeling_bert.BertSelfAttention with Bert->Roberta +class RobertaSelfAttention(nn.Module): + def __init__(self, config, position_embedding_type=None): + super().__init__() + if config.hidden_size % config.num_attention_heads != 0 and not hasattr( + config, "embedding_size" + ): + raise ValueError( + f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention " + f"heads ({config.num_attention_heads})" + ) + + self.num_attention_heads = config.num_attention_heads + self.attention_head_size = int(config.hidden_size / config.num_attention_heads) + self.all_head_size = self.num_attention_heads * self.attention_head_size + + self.query = nn.Linear(config.hidden_size, self.all_head_size) + self.key = nn.Linear(config.hidden_size, self.all_head_size) + self.value = nn.Linear(config.hidden_size, self.all_head_size) + + self.dropout = nn.Dropout(config.attention_probs_dropout_prob) + self.position_embedding_type = position_embedding_type or getattr( + config, "position_embedding_type", "absolute" + ) + if ( + self.position_embedding_type == "relative_key" + or self.position_embedding_type == "relative_key_query" + ): + self.max_position_embeddings = config.max_position_embeddings + self.distance_embedding = nn.Embedding( + 2 * config.max_position_embeddings - 1, self.attention_head_size + ) + + self.is_decoder = config.is_decoder + + def transpose_for_scores(self, x): + new_x_shape = x.size()[:-1] + ( + self.num_attention_heads, + self.attention_head_size, + ) + x = x.view(new_x_shape) + return x.permute(0, 2, 1, 3) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.FloatTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, + output_attentions: Optional[bool] = False, + parser_att_mask=None, + ) -> Tuple[torch.Tensor]: + mixed_query_layer = self.query(hidden_states) + + # If this is instantiated as a cross-attention module, the keys + # and values come from an encoder; the attention mask needs to be + # such that the encoder's padding tokens are not attended to. + is_cross_attention = encoder_hidden_states is not None + + if is_cross_attention and past_key_value is not None: + # reuse k,v, cross_attentions + key_layer = past_key_value[0] + value_layer = past_key_value[1] + attention_mask = encoder_attention_mask + elif is_cross_attention: + key_layer = self.transpose_for_scores(self.key(encoder_hidden_states)) + value_layer = self.transpose_for_scores(self.value(encoder_hidden_states)) + attention_mask = encoder_attention_mask + elif past_key_value is not None: + key_layer = self.transpose_for_scores(self.key(hidden_states)) + value_layer = self.transpose_for_scores(self.value(hidden_states)) + key_layer = torch.cat([past_key_value[0], key_layer], dim=2) + value_layer = torch.cat([past_key_value[1], value_layer], dim=2) + else: + key_layer = self.transpose_for_scores(self.key(hidden_states)) + value_layer = self.transpose_for_scores(self.value(hidden_states)) + + query_layer = self.transpose_for_scores(mixed_query_layer) + + if self.is_decoder: + # if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states. + # Further calls to cross_attention layer can then reuse all cross-attention + # key/value_states (first "if" case) + # if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of + # all previous decoder key/value_states. Further calls to uni-directional self-attention + # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case) + # if encoder bi-directional self-attention `past_key_value` is always `None` + past_key_value = (key_layer, value_layer) + + # Take the dot product between "query" and "key" to get the raw attention scores. + attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) + + if ( + self.position_embedding_type == "relative_key" + or self.position_embedding_type == "relative_key_query" + ): + seq_length = hidden_states.size()[1] + position_ids_l = torch.arange( + seq_length, dtype=torch.long, device=hidden_states.device + ).view(-1, 1) + position_ids_r = torch.arange( + seq_length, dtype=torch.long, device=hidden_states.device + ).view(1, -1) + distance = position_ids_l - position_ids_r + positional_embedding = self.distance_embedding( + distance + self.max_position_embeddings - 1 + ) + positional_embedding = positional_embedding.to( + dtype=query_layer.dtype + ) # fp16 compatibility + + if self.position_embedding_type == "relative_key": + relative_position_scores = torch.einsum( + "bhld,lrd->bhlr", query_layer, positional_embedding + ) + attention_scores = attention_scores + relative_position_scores + elif self.position_embedding_type == "relative_key_query": + relative_position_scores_query = torch.einsum( + "bhld,lrd->bhlr", query_layer, positional_embedding + ) + relative_position_scores_key = torch.einsum( + "bhrd,lrd->bhlr", key_layer, positional_embedding + ) + attention_scores = ( + attention_scores + + relative_position_scores_query + + relative_position_scores_key + ) + + attention_scores = attention_scores / math.sqrt(self.attention_head_size) + if attention_mask is not None: + # Apply the attention mask is (precomputed for all layers in RobertaModel forward() function) + attention_scores = attention_scores + attention_mask + + if parser_att_mask is None: + # Normalize the attention scores to probabilities. + attention_probs = nn.functional.softmax(attention_scores, dim=-1) + else: + attention_probs = torch.sigmoid(attention_scores) * parser_att_mask + + # This is actually dropping out entire tokens to attend to, which might + # seem a bit unusual, but is taken from the original Transformer paper. + attention_probs = self.dropout(attention_probs) + + # Mask heads if we want to + if head_mask is not None: + attention_probs = attention_probs * head_mask + + context_layer = torch.matmul(attention_probs, value_layer) + + context_layer = context_layer.permute(0, 2, 1, 3).contiguous() + new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) + context_layer = context_layer.view(new_context_layer_shape) + + outputs = ( + (context_layer, attention_probs) if output_attentions else (context_layer,) + ) + + if self.is_decoder: + outputs = outputs + (past_key_value,) + return outputs + + +# Copied from transformers.models.bert.modeling_bert.BertSelfOutput +class RobertaSelfOutput(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward( + self, hidden_states: torch.Tensor, input_tensor: torch.Tensor + ) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + hidden_states = self.LayerNorm(hidden_states + input_tensor) + return hidden_states + + +# Copied from transformers.models.bert.modeling_bert.BertAttention with Bert->Roberta +class RobertaAttention(nn.Module): + def __init__(self, config, position_embedding_type=None): + super().__init__() + self.self = RobertaSelfAttention( + config, position_embedding_type=position_embedding_type + ) + self.output = RobertaSelfOutput(config) + self.pruned_heads = set() + + def prune_heads(self, heads): + if len(heads) == 0: + return + heads, index = find_pruneable_heads_and_indices( + heads, + self.self.num_attention_heads, + self.self.attention_head_size, + self.pruned_heads, + ) + + # Prune linear layers + self.self.query = prune_linear_layer(self.self.query, index) + self.self.key = prune_linear_layer(self.self.key, index) + self.self.value = prune_linear_layer(self.self.value, index) + self.output.dense = prune_linear_layer(self.output.dense, index, dim=1) + + # Update hyper params and store pruned heads + self.self.num_attention_heads = self.self.num_attention_heads - len(heads) + self.self.all_head_size = ( + self.self.attention_head_size * self.self.num_attention_heads + ) + self.pruned_heads = self.pruned_heads.union(heads) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.FloatTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, + output_attentions: Optional[bool] = False, + parser_att_mask=None, + ) -> Tuple[torch.Tensor]: + self_outputs = self.self( + hidden_states, + attention_mask, + head_mask, + encoder_hidden_states, + encoder_attention_mask, + past_key_value, + output_attentions, + parser_att_mask=parser_att_mask, + ) + attention_output = self.output(self_outputs[0], hidden_states) + outputs = (attention_output,) + self_outputs[ + 1: + ] # add attentions if we output them + return outputs + + +# Copied from transformers.models.bert.modeling_bert.BertIntermediate +class RobertaIntermediate(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.intermediate_size) + if isinstance(config.hidden_act, str): + self.intermediate_act_fn = ACT2FN[config.hidden_act] + else: + self.intermediate_act_fn = config.hidden_act + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.intermediate_act_fn(hidden_states) + return hidden_states + + +# Copied from transformers.models.bert.modeling_bert.BertOutput +class RobertaOutput(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.intermediate_size, config.hidden_size) + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward( + self, hidden_states: torch.Tensor, input_tensor: torch.Tensor + ) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + hidden_states = self.LayerNorm(hidden_states + input_tensor) + return hidden_states + + +# Copied from transformers.models.bert.modeling_bert.BertLayer with Bert->Roberta +class RobertaLayer(nn.Module): + def __init__(self, config): + super().__init__() + self.chunk_size_feed_forward = config.chunk_size_feed_forward + self.seq_len_dim = 1 + self.attention = RobertaAttention(config) + self.is_decoder = config.is_decoder + self.add_cross_attention = config.add_cross_attention + if self.add_cross_attention: + if not self.is_decoder: + raise ValueError( + f"{self} should be used as a decoder model if cross attention is added" + ) + self.crossattention = RobertaAttention( + config, position_embedding_type="absolute" + ) + self.intermediate = RobertaIntermediate(config) + self.output = RobertaOutput(config) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.FloatTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, + output_attentions: Optional[bool] = False, + parser_att_mask=None, + ) -> Tuple[torch.Tensor]: + # decoder uni-directional self-attention cached key/values tuple is at positions 1,2 + self_attn_past_key_value = ( + past_key_value[:2] if past_key_value is not None else None + ) + self_attention_outputs = self.attention( + hidden_states, + attention_mask, + head_mask, + output_attentions=output_attentions, + past_key_value=self_attn_past_key_value, + parser_att_mask=parser_att_mask, + ) + attention_output = self_attention_outputs[0] + + # if decoder, the last output is tuple of self-attn cache + if self.is_decoder: + outputs = self_attention_outputs[1:-1] + present_key_value = self_attention_outputs[-1] + else: + outputs = self_attention_outputs[ + 1: + ] # add self attentions if we output attention weights + + cross_attn_present_key_value = None + if self.is_decoder and encoder_hidden_states is not None: + if not hasattr(self, "crossattention"): + raise ValueError( + f"If `encoder_hidden_states` are passed, {self} has to be instantiated with cross-attention layers by setting `config.add_cross_attention=True`" + ) + + # cross_attn cached key/values tuple is at positions 3,4 of past_key_value tuple + cross_attn_past_key_value = ( + past_key_value[-2:] if past_key_value is not None else None + ) + cross_attention_outputs = self.crossattention( + attention_output, + attention_mask, + head_mask, + encoder_hidden_states, + encoder_attention_mask, + cross_attn_past_key_value, + output_attentions, + ) + attention_output = cross_attention_outputs[0] + outputs = ( + outputs + cross_attention_outputs[1:-1] + ) # add cross attentions if we output attention weights + + # add cross-attn cache to positions 3,4 of present_key_value tuple + cross_attn_present_key_value = cross_attention_outputs[-1] + present_key_value = present_key_value + cross_attn_present_key_value + + layer_output = apply_chunking_to_forward( + self.feed_forward_chunk, + self.chunk_size_feed_forward, + self.seq_len_dim, + attention_output, + ) + outputs = (layer_output,) + outputs + + # if decoder, return the attn key/values as the last output + if self.is_decoder: + outputs = outputs + (present_key_value,) + + return outputs + + def feed_forward_chunk(self, attention_output): + intermediate_output = self.intermediate(attention_output) + layer_output = self.output(intermediate_output, attention_output) + return layer_output + + +# Copied from transformers.models.bert.modeling_bert.BertEncoder with Bert->Roberta +class RobertaEncoder(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + self.layer = nn.ModuleList( + [RobertaLayer(config) for _ in range(config.num_hidden_layers)] + ) + self.gradient_checkpointing = False + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.FloatTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = False, + output_hidden_states: Optional[bool] = False, + return_dict: Optional[bool] = True, + parser_att_mask=None, + ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPastAndCrossAttentions]: + all_hidden_states = () if output_hidden_states else None + all_self_attentions = () if output_attentions else None + all_cross_attentions = ( + () if output_attentions and self.config.add_cross_attention else None + ) + + next_decoder_cache = () if use_cache else None + for i, layer_module in enumerate(self.layer): + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + layer_head_mask = head_mask[i] if head_mask is not None else None + past_key_value = past_key_values[i] if past_key_values is not None else None + + if self.gradient_checkpointing and self.training: + + if use_cache: + logger.warning( + "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." + ) + use_cache = False + + def create_custom_forward(module): + def custom_forward(*inputs): + return module(*inputs, past_key_value, output_attentions) + + return custom_forward + + layer_outputs = torch.utils.checkpoint.checkpoint( + create_custom_forward(layer_module), + hidden_states, + attention_mask, + layer_head_mask, + encoder_hidden_states, + encoder_attention_mask, + ) + else: + if parser_att_mask is not None: + layer_outputs = layer_module( + hidden_states, + attention_mask, + layer_head_mask, + encoder_hidden_states, + encoder_attention_mask, + past_key_value, + output_attentions, + parser_att_mask=parser_att_mask[i]) + else: + layer_outputs = layer_module( + hidden_states, + attention_mask, + layer_head_mask, + encoder_hidden_states, + encoder_attention_mask, + past_key_value, + output_attentions, + parser_att_mask=None) + + + hidden_states = layer_outputs[0] + if use_cache: + next_decoder_cache += (layer_outputs[-1],) + if output_attentions: + all_self_attentions = all_self_attentions + (layer_outputs[1],) + if self.config.add_cross_attention: + all_cross_attentions = all_cross_attentions + (layer_outputs[2],) + + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + if not return_dict: + return tuple( + v + for v in [ + hidden_states, + next_decoder_cache, + all_hidden_states, + all_self_attentions, + all_cross_attentions, + ] + if v is not None + ) + return BaseModelOutputWithPastAndCrossAttentions( + last_hidden_state=hidden_states, + past_key_values=next_decoder_cache, + hidden_states=all_hidden_states, + attentions=all_self_attentions, + cross_attentions=all_cross_attentions, + ) + + +# Copied from transformers.models.bert.modeling_bert.BertPooler +class RobertaPooler(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.activation = nn.Tanh() + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + # We "pool" the model by simply taking the hidden state corresponding + # to the first token. + first_token_tensor = hidden_states[:, 0] + pooled_output = self.dense(first_token_tensor) + pooled_output = self.activation(pooled_output) + return pooled_output + + +class RobertaPreTrainedModel(PreTrainedModel): + """ + An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained + models. + """ + + config_class = RobertaConfig + base_model_prefix = "roberta" + supports_gradient_checkpointing = True + + # Copied from transformers.models.bert.modeling_bert.BertPreTrainedModel._init_weights + def _init_weights(self, module): + """Initialize the weights""" + if isinstance(module, nn.Linear): + # Slightly different from the TF version which uses truncated_normal for initialization + # cf https://github.com/pytorch/pytorch/pull/5617 + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.Embedding): + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.padding_idx is not None: + module.weight.data[module.padding_idx].zero_() + elif isinstance(module, nn.LayerNorm): + if module.bias is not None: + module.bias.data.zero_() + module.weight.data.fill_(1.0) + + def _set_gradient_checkpointing(self, module, value=False): + if isinstance(module, RobertaEncoder): + module.gradient_checkpointing = value + + def update_keys_to_ignore(self, config, del_keys_to_ignore): + """Remove some keys from ignore list""" + if not config.tie_word_embeddings: + # must make a new list, or the class variable gets modified! + self._keys_to_ignore_on_save = [ + k for k in self._keys_to_ignore_on_save if k not in del_keys_to_ignore + ] + self._keys_to_ignore_on_load_missing = [ + k + for k in self._keys_to_ignore_on_load_missing + if k not in del_keys_to_ignore + ] + + +ROBERTA_START_DOCSTRING = r""" + + This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the + library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads + etc.) + + This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. + Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage + and behavior. + + Parameters: + config ([`RobertaConfig`]): Model configuration class with all the parameters of the + model. Initializing with a config file does not load the weights associated with the model, only the + configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. +""" + + +ROBERTA_INPUTS_DOCSTRING = r""" + Args: + input_ids (`torch.LongTensor` of shape `({0})`): + Indices of input sequence tokens in the vocabulary. + + Indices can be obtained using [`RobertaTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + [What are input IDs?](../glossary#input-ids) + attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*): + Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + + [What are attention masks?](../glossary#attention-mask) + token_type_ids (`torch.LongTensor` of shape `({0})`, *optional*): + Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, + 1]`: + + - 0 corresponds to a *sentence A* token, + - 1 corresponds to a *sentence B* token. + + [What are token type IDs?](../glossary#token-type-ids) + position_ids (`torch.LongTensor` of shape `({0})`, *optional*): + Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, + config.max_position_embeddings - 1]`. + + [What are position IDs?](../glossary#position-ids) + head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): + Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: + + - 1 indicates the head is **not masked**, + - 0 indicates the head is **masked**. + + inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_size)`, *optional*): + Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This + is useful if you want more control over how to convert `input_ids` indices into associated vectors than the + model's internal embedding lookup matrix. + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned + tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for + more detail. + return_dict (`bool`, *optional*): + Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. +""" + + +class RobertaModel(RobertaPreTrainedModel): + """ + + The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of + cross-attention is added between the self-attention layers, following the architecture described in *Attention is + all you need*_ by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz + Kaiser and Illia Polosukhin. + + To behave as an decoder the model needs to be initialized with the `is_decoder` argument of the configuration set + to `True`. To be used in a Seq2Seq model, the model needs to initialized with both `is_decoder` argument and + `add_cross_attention` set to `True`; an `encoder_hidden_states` is then expected as an input to the forward pass. + + .. _*Attention is all you need*: https://arxiv.org/abs/1706.03762 + + """ + + _keys_to_ignore_on_load_missing = [r"position_ids"] + + # Copied from transformers.models.bert.modeling_bert.BertModel.__init__ with Bert->Roberta + def __init__(self, config, add_pooling_layer=True): + super().__init__(config) + self.config = config + + self.embeddings = RobertaEmbeddings(config) + self.encoder = RobertaEncoder(config) + + self.pooler = RobertaPooler(config) if add_pooling_layer else None + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.embeddings.word_embeddings + + def set_input_embeddings(self, value): + self.embeddings.word_embeddings = value + + def _prune_heads(self, heads_to_prune): + """ + Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base + class PreTrainedModel + """ + for layer, heads in heads_to_prune.items(): + self.encoder.layer[layer].attention.prune_heads(heads) + + # Copied from transformers.models.bert.modeling_bert.BertModel.forward + def forward( + self, + input_ids: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + token_type_ids: Optional[torch.Tensor] = None, + position_ids: Optional[torch.Tensor] = None, + head_mask: Optional[torch.Tensor] = None, + inputs_embeds: Optional[torch.Tensor] = None, + encoder_hidden_states: Optional[torch.Tensor] = None, + encoder_attention_mask: Optional[torch.Tensor] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + parser_att_mask=None, + ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPoolingAndCrossAttentions]: + r""" + encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): + Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if + the model is configured as a decoder. + encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*): + Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in + the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): + Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. + + If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that + don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all + `decoder_input_ids` of shape `(batch_size, sequence_length)`. + use_cache (`bool`, *optional*): + If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see + `past_key_values`). + """ + output_attentions = ( + output_attentions + if output_attentions is not None + else self.config.output_attentions + ) + output_hidden_states = ( + output_hidden_states + if output_hidden_states is not None + else self.config.output_hidden_states + ) + return_dict = ( + return_dict if return_dict is not None else self.config.use_return_dict + ) + + if self.config.is_decoder: + use_cache = use_cache if use_cache is not None else self.config.use_cache + else: + use_cache = False + + if input_ids is not None and inputs_embeds is not None: + raise ValueError( + "You cannot specify both input_ids and inputs_embeds at the same time" + ) + elif input_ids is not None: + input_shape = input_ids.size() + elif inputs_embeds is not None: + input_shape = inputs_embeds.size()[:-1] + else: + raise ValueError("You have to specify either input_ids or inputs_embeds") + + batch_size, seq_length = input_shape + device = input_ids.device if input_ids is not None else inputs_embeds.device + + # past_key_values_length + past_key_values_length = ( + past_key_values[0][0].shape[2] if past_key_values is not None else 0 + ) + + if attention_mask is None: + attention_mask = torch.ones( + ((batch_size, seq_length + past_key_values_length)), device=device + ) + + if token_type_ids is None: + if hasattr(self.embeddings, "token_type_ids"): + buffered_token_type_ids = self.embeddings.token_type_ids[:, :seq_length] + buffered_token_type_ids_expanded = buffered_token_type_ids.expand( + batch_size, seq_length + ) + token_type_ids = buffered_token_type_ids_expanded + else: + token_type_ids = torch.zeros( + input_shape, dtype=torch.long, device=device + ) + + # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length] + # ourselves in which case we just need to make it broadcastable to all heads. + extended_attention_mask: torch.Tensor = self.get_extended_attention_mask( + attention_mask, input_shape, device + ) + + # If a 2D or 3D attention mask is provided for the cross-attention + # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length] + if self.config.is_decoder and encoder_hidden_states is not None: + ( + encoder_batch_size, + encoder_sequence_length, + _, + ) = encoder_hidden_states.size() + encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length) + if encoder_attention_mask is None: + encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device) + encoder_extended_attention_mask = self.invert_attention_mask( + encoder_attention_mask + ) + else: + encoder_extended_attention_mask = None + + # Prepare head mask if needed + # 1.0 in head_mask indicate we keep the head + # attention_probs has shape bsz x n_heads x N x N + # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] + # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] + head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) + + embedding_output = self.embeddings( + input_ids=input_ids, + position_ids=position_ids, + token_type_ids=token_type_ids, + inputs_embeds=inputs_embeds, + past_key_values_length=past_key_values_length, + ) + encoder_outputs = self.encoder( + embedding_output, + attention_mask=extended_attention_mask, + head_mask=head_mask, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_extended_attention_mask, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + parser_att_mask=parser_att_mask, + ) + sequence_output = encoder_outputs[0] + pooled_output = ( + self.pooler(sequence_output) if self.pooler is not None else None + ) + + if not return_dict: + return (sequence_output, pooled_output) + encoder_outputs[1:] + + return BaseModelOutputWithPoolingAndCrossAttentions( + last_hidden_state=sequence_output, + pooler_output=pooled_output, + past_key_values=encoder_outputs.past_key_values, + hidden_states=encoder_outputs.hidden_states, + attentions=encoder_outputs.attentions, + cross_attentions=encoder_outputs.cross_attentions, + ) + + +class StructRoberta(RobertaPreTrainedModel): + _keys_to_ignore_on_save = [r"lm_head.decoder.weight", r"lm_head.decoder.bias"] + _keys_to_ignore_on_load_missing = [ + r"position_ids", + r"lm_head.decoder.weight", + r"lm_head.decoder.bias", + ] + _keys_to_ignore_on_load_unexpected = [r"pooler"] + + def __init__(self, config): + super().__init__(config) + + if config.is_decoder: + logger.warning( + "If you want to use `RobertaForMaskedLM` make sure `config.is_decoder=False` for " + "bi-directional self-attention." + ) + + + if config.n_cntxt_layers > 0: + config_cntxt = copy.deepcopy(config) + config_cntxt.num_hidden_layers = config.n_cntxt_layers + + self.cntxt_layers = RobertaModel(config_cntxt, add_pooling_layer=False) + + if config.n_cntxt_layers_2 > 0: + self.parser_layers_1 = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(int(config.n_parser_layers/2)) + ] + ) + + self.distance_ff_1 = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff_1 = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight_1 = nn.Parameter( + torch.zeros((config.n_cntxt_layers_2, config.num_attention_heads, n_rel)) + ) + self._rel_weight_1.data.normal_(0, 0.1) + + self._scaler_1 = nn.Parameter(torch.zeros(2)) + + config_cntxt_2 = copy.deepcopy(config) + config_cntxt_2.num_hidden_layers = config.n_cntxt_layers_2 + + self.cntxt_layers_2 = RobertaModel(config_cntxt_2, add_pooling_layer=False) + + + self.parser_layers_2 = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(int(config.n_parser_layers/2)) + ] + ) + + self.distance_ff_2 = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff_2 = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight_2 = nn.Parameter( + torch.zeros((config.num_hidden_layers, config.num_attention_heads, n_rel)) + ) + self._rel_weight_2.data.normal_(0, 0.1) + + self._scaler_2 = nn.Parameter(torch.zeros(2)) + + else: + self.parser_layers = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(config.n_parser_layers) + ] + ) + + self.distance_ff = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight = nn.Parameter( + torch.zeros((config.num_hidden_layers, config.num_attention_heads, n_rel)) + ) + self._rel_weight.data.normal_(0, 0.1) + + self._scaler = nn.Parameter(torch.zeros(2)) + + self.roberta = RobertaModel(config, add_pooling_layer=False) + + if config.n_cntxt_layers > 0: + self.cntxt_layers.embeddings = self.roberta.embeddings + if config.n_cntxt_layers_2 > 0: + self.cntxt_layers_2.embeddings = self.roberta.embeddings + + self.lm_head = RobertaLMHead(config) + + self.pad = config.pad_token_id + + # The LM head weights require special treatment only when they are tied with the word embeddings + self.update_keys_to_ignore(config, ["lm_head.decoder.weight"]) + + # Initialize weights and apply final processing + self.post_init() + + def get_output_embeddings(self): + return self.lm_head.decoder + + def set_output_embeddings(self, new_embeddings): + self.lm_head.decoder = new_embeddings + + @property + def scaler(self): + return self._scaler.exp() + + @property + def scaler_1(self): + return self._scaler_1.exp() + + @property + def scaler_2(self): + return self._scaler_2.exp() + + @property + def rel_weight(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight, dim=-1) + + @property + def rel_weight_1(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight_1) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight_1, dim=-1) + + + @property + def rel_weight_2(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight_2) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight_2, dim=-1) + + + def compute_block(self, distance, height, n_cntxt_layers=0): + """Compute constituents from distance and height.""" + + if n_cntxt_layers>0: + if n_cntxt_layers == 1: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler_1[0] + elif n_cntxt_layers == 2: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler_2[0] + else: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0] + + gamma = torch.sigmoid(-beta_logits) + ones = torch.ones_like(gamma) + + block_mask_left = cummin( + gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1 + ) + block_mask_left = block_mask_left - F.pad( + block_mask_left[:, :, :-1], (1, 0), value=0 + ) + block_mask_left.tril_(0) + + block_mask_right = cummin( + gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1 + ) + block_mask_right = block_mask_right - F.pad( + block_mask_right[:, :, 1:], (0, 1), value=0 + ) + block_mask_right.triu_(0) + + block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :] + block = cumsum(block_mask_left).tril(0) + cumsum( + block_mask_right, reverse=True + ).triu(1) + + return block_p, block + + def compute_head(self, height, n_cntxt_layers=0): + """Estimate head for each constituent.""" + + _, length = height.size() + if n_cntxt_layers>0: + if n_cntxt_layers == 1: + head_logits = height * self.scaler_1[1] + elif n_cntxt_layers == 2: + head_logits = height * self.scaler_2[1] + else: + head_logits = height * self.scaler[1] + index = torch.arange(length, device=height.device) + + mask = (index[:, None, None] <= index[None, None, :]) * ( + index[None, None, :] <= index[None, :, None] + ) + head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1) + head_logits.masked_fill_(~mask[None, :, :, :], -1e9) + + head_p = torch.softmax(head_logits, dim=-1) + + return head_p + + def parse(self, x, embs=None, n_cntxt_layers=0): + """Parse input sentence. + + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + distance: syntactic distance + height: syntactic height + """ + + mask = x != self.pad + mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0) + + if embs is None: + h = self.roberta.embeddings(x) + else: + h = embs + + if n_cntxt_layers > 0: + if n_cntxt_layers == 1: + parser_layers = self.parser_layers_1 + height_ff = self.height_ff_1 + distance_ff = self.distance_ff_1 + elif n_cntxt_layers == 2: + parser_layers = self.parser_layers_2 + height_ff = self.height_ff_2 + distance_ff = self.distance_ff_2 + for i in range(int(self.config.n_parser_layers/2)): + h = h.masked_fill(~mask[:, :, None], 0) + h = parser_layers[i](h) + + height = height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, dim=-1 + )[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max + ) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where( + margin_left > margin_right, margin_right, margin_left + ).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + else: + for i in range(self.config.n_parser_layers): + h = h.masked_fill(~mask[:, :, None], 0) + h = self.parser_layers[i](h) + + height = self.height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = self.distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, dim=-1 + )[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max + ) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where( + margin_left > margin_right, margin_right, margin_left + ).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + + return distance, height + + def generate_mask(self, x, distance, height, n_cntxt_layers=0): + """Compute head and cibling distribution for each token.""" + + bsz, length = x.size() + + eye = torch.eye(length, device=x.device, dtype=torch.bool) + eye = eye[None, :, :].expand((bsz, -1, -1)) + + block_p, block = self.compute_block(distance, height, n_cntxt_layers=n_cntxt_layers) + head_p = self.compute_head(height, n_cntxt_layers=n_cntxt_layers) + head = torch.einsum("blij,bijh->blh", block_p, head_p) + head = head.masked_fill(eye, 0) + child = head.transpose(1, 2) + cibling = torch.bmm(head, child).masked_fill(eye, 0) + + rel_list = [] + if "head" in self.config.relations: + rel_list.append(head) + if "child" in self.config.relations: + rel_list.append(child) + if "cibling" in self.config.relations: + rel_list.append(cibling) + + rel = torch.stack(rel_list, dim=1) + + if n_cntxt_layers > 0: + if n_cntxt_layers == 1: + rel_weight = self.rel_weight_1 + elif n_cntxt_layers == 2: + rel_weight = self.rel_weight_2 + else: + rel_weight = self.rel_weight + + dep = torch.einsum("lhr,brij->lbhij", rel_weight, rel) + + if n_cntxt_layers == 1: + num_layers = self.cntxt_layers_2.config.num_hidden_layers + else: + num_layers = self.roberta.config.num_hidden_layers + + att_mask = dep.reshape( + num_layers, + bsz, + self.config.num_attention_heads, + length, + length, + ) + + return att_mask, cibling, head, block + + def forward( + self, + input_ids: Optional[torch.LongTensor] = None, + attention_mask: Optional[torch.FloatTensor] = None, + token_type_ids: Optional[torch.LongTensor] = None, + position_ids: Optional[torch.LongTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, MaskedLMOutput]: + r""" + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ..., + config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the + loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]` + kwargs (`Dict[str, any]`, optional, defaults to *{}*): + Used to hide legacy arguments that have been deprecated. + """ + return_dict = ( + return_dict if return_dict is not None else self.config.use_return_dict + ) + + + if self.config.n_cntxt_layers > 0: + cntxt_outputs = self.cntxt_layers( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict) + + + if self.config.n_cntxt_layers_2 > 0: + distance_1, height_1 = self.parse(input_ids, cntxt_outputs[0], n_cntxt_layers=1) + att_mask_1, _, _, _ = self.generate_mask(input_ids, distance_1, height_1, n_cntxt_layers=1) + + cntxt_outputs_2 = self.cntxt_layers_2( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + parser_att_mask=att_mask_1) + + sequence_output = cntxt_outputs_2[0] + + distance_2, height_2 = self.parse(input_ids, sequence_output[0], n_cntxt_layers=2) + att_mask, _, _, _ = self.generate_mask(input_ids, distance_2, height_2, n_cntxt_layers=2) + + elif self.config.n_cntxt_layers > 0: + distance, height = self.parse(input_ids, cntxt_outputs[0]) + att_mask, _, _, _ = self.generate_mask(input_ids, distance, height) + else: + distance, height = self.parse(input_ids) + att_mask, _, _, _ = self.generate_mask(input_ids, distance, height) + + outputs = self.roberta( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + parser_att_mask=att_mask, + ) + sequence_output = outputs[0] + prediction_scores = self.lm_head(sequence_output) + + masked_lm_loss = None + if labels is not None: + loss_fct = CrossEntropyLoss() + masked_lm_loss = loss_fct( + prediction_scores.view(-1, self.config.vocab_size), labels.view(-1) + ) + + if not return_dict: + output = (prediction_scores,) + outputs[2:] + return ( + ((masked_lm_loss,) + output) if masked_lm_loss is not None else output + ) + + return MaskedLMOutput( + loss=masked_lm_loss, + logits=prediction_scores, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +class RobertaLMHead(nn.Module): + """Roberta Head for masked language modeling.""" + + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + + self.decoder = nn.Linear(config.hidden_size, config.vocab_size) + self.bias = nn.Parameter(torch.zeros(config.vocab_size)) + self.decoder.bias = self.bias + + def forward(self, features, **kwargs): + x = self.dense(features) + x = gelu(x) + x = self.layer_norm(x) + + # project back to size of vocabulary with bias + x = self.decoder(x) + + return x + + def _tie_weights(self): + # To tie those two weights if they get disconnected (on TPU or when the bias is resized) + self.bias = self.decoder.bias + + +class StructRobertaForSequenceClassification(RobertaPreTrainedModel): + _keys_to_ignore_on_load_missing = [r"position_ids"] + + def __init__(self, config): + super().__init__(config) + self.num_labels = config.num_labels + self.config = config + + if config.n_cntxt_layers > 0: + config_cntxt = copy.deepcopy(config) + config_cntxt.num_hidden_layers = config.n_cntxt_layers + + self.cntxt_layers = RobertaModel(config_cntxt, add_pooling_layer=False) + + if config.n_cntxt_layers_2 > 0: + self.parser_layers_1 = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(int(config.n_parser_layers/2)) + ] + ) + + self.distance_ff_1 = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff_1 = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight_1 = nn.Parameter( + torch.zeros((config.n_cntxt_layers_2, config.num_attention_heads, n_rel)) + ) + self._rel_weight_1.data.normal_(0, 0.1) + + self._scaler_1 = nn.Parameter(torch.zeros(2)) + + config_cntxt_2 = copy.deepcopy(config) + config_cntxt_2.num_hidden_layers = config.n_cntxt_layers_2 + + self.cntxt_layers_2 = RobertaModel(config_cntxt_2, add_pooling_layer=False) + + + self.parser_layers_2 = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(int(config.n_parser_layers/2)) + ] + ) + + self.distance_ff_2 = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff_2 = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight_2 = nn.Parameter( + torch.zeros((config.num_hidden_layers, config.num_attention_heads, n_rel)) + ) + self._rel_weight_2.data.normal_(0, 0.1) + + self._scaler_2 = nn.Parameter(torch.zeros(2)) + + else: + self.parser_layers = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(config.n_parser_layers) + ] + ) + + self.distance_ff = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight = nn.Parameter( + torch.zeros((config.num_hidden_layers, config.num_attention_heads, n_rel)) + ) + self._rel_weight.data.normal_(0, 0.1) + + self._scaler = nn.Parameter(torch.zeros(2)) + + self.roberta = RobertaModel(config, add_pooling_layer=False) + + if config.n_cntxt_layers > 0: + self.cntxt_layers.embeddings = self.roberta.embeddings + if config.n_cntxt_layers_2 > 0: + self.cntxt_layers_2.embeddings = self.roberta.embeddings + + + self.pad = config.pad_token_id + self.classifier = RobertaClassificationHead(config) + + # Initialize weights and apply final processing + self.post_init() + + + @property + def scaler(self): + return self._scaler.exp() + + @property + def scaler_1(self): + return self._scaler_1.exp() + + @property + def scaler_2(self): + return self._scaler_2.exp() + + @property + def rel_weight(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight, dim=-1) + + @property + def rel_weight_1(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight_1) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight_1, dim=-1) + + + @property + def rel_weight_2(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight_2) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight_2, dim=-1) + + + def compute_block(self, distance, height, n_cntxt_layers=0): + """Compute constituents from distance and height.""" + + if n_cntxt_layers>0: + if n_cntxt_layers == 1: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler_1[0] + elif n_cntxt_layers == 2: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler_2[0] + else: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0] + + gamma = torch.sigmoid(-beta_logits) + ones = torch.ones_like(gamma) + + block_mask_left = cummin( + gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1 + ) + block_mask_left = block_mask_left - F.pad( + block_mask_left[:, :, :-1], (1, 0), value=0 + ) + block_mask_left.tril_(0) + + block_mask_right = cummin( + gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1 + ) + block_mask_right = block_mask_right - F.pad( + block_mask_right[:, :, 1:], (0, 1), value=0 + ) + block_mask_right.triu_(0) + + block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :] + block = cumsum(block_mask_left).tril(0) + cumsum( + block_mask_right, reverse=True + ).triu(1) + + return block_p, block + + def compute_head(self, height, n_cntxt_layers=0): + """Estimate head for each constituent.""" + + _, length = height.size() + if n_cntxt_layers>0: + if n_cntxt_layers == 1: + head_logits = height * self.scaler_1[1] + elif n_cntxt_layers == 2: + head_logits = height * self.scaler_2[1] + else: + head_logits = height * self.scaler[1] + index = torch.arange(length, device=height.device) + + mask = (index[:, None, None] <= index[None, None, :]) * ( + index[None, None, :] <= index[None, :, None] + ) + head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1) + head_logits.masked_fill_(~mask[None, :, :, :], -1e9) + + head_p = torch.softmax(head_logits, dim=-1) + + return head_p + + def parse(self, x, embs=None, n_cntxt_layers=0): + """Parse input sentence. + + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + distance: syntactic distance + height: syntactic height + """ + + mask = x != self.pad + mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0) + + if embs is None: + h = self.roberta.embeddings(x) + else: + h = embs + + if n_cntxt_layers > 0: + if n_cntxt_layers == 1: + parser_layers = self.parser_layers_1 + height_ff = self.height_ff_1 + distance_ff = self.distance_ff_1 + elif n_cntxt_layers == 2: + parser_layers = self.parser_layers_2 + height_ff = self.height_ff_2 + distance_ff = self.distance_ff_2 + for i in range(int(self.config.n_parser_layers/2)): + h = h.masked_fill(~mask[:, :, None], 0) + h = parser_layers[i](h) + + height = height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, dim=-1 + )[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max + ) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where( + margin_left > margin_right, margin_right, margin_left + ).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + else: + for i in range(self.config.n_parser_layers): + h = h.masked_fill(~mask[:, :, None], 0) + h = self.parser_layers[i](h) + + height = self.height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = self.distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, dim=-1 + )[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max + ) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where( + margin_left > margin_right, margin_right, margin_left + ).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + + return distance, height + + def generate_mask(self, x, distance, height, n_cntxt_layers=0): + """Compute head and cibling distribution for each token.""" + + bsz, length = x.size() + + eye = torch.eye(length, device=x.device, dtype=torch.bool) + eye = eye[None, :, :].expand((bsz, -1, -1)) + + block_p, block = self.compute_block(distance, height, n_cntxt_layers=n_cntxt_layers) + head_p = self.compute_head(height, n_cntxt_layers=n_cntxt_layers) + head = torch.einsum("blij,bijh->blh", block_p, head_p) + head = head.masked_fill(eye, 0) + child = head.transpose(1, 2) + cibling = torch.bmm(head, child).masked_fill(eye, 0) + + rel_list = [] + if "head" in self.config.relations: + rel_list.append(head) + if "child" in self.config.relations: + rel_list.append(child) + if "cibling" in self.config.relations: + rel_list.append(cibling) + + rel = torch.stack(rel_list, dim=1) + + if n_cntxt_layers > 0: + if n_cntxt_layers == 1: + rel_weight = self.rel_weight_1 + elif n_cntxt_layers == 2: + rel_weight = self.rel_weight_2 + else: + rel_weight = self.rel_weight + + dep = torch.einsum("lhr,brij->lbhij", rel_weight, rel) + + if n_cntxt_layers == 1: + num_layers = self.cntxt_layers_2.config.num_hidden_layers + else: + num_layers = self.roberta.config.num_hidden_layers + + att_mask = dep.reshape( + num_layers, + bsz, + self.config.num_attention_heads, + length, + length, + ) + + return att_mask, cibling, head, block + + def forward( + self, + input_ids: Optional[torch.LongTensor] = None, + attention_mask: Optional[torch.FloatTensor] = None, + token_type_ids: Optional[torch.LongTensor] = None, + position_ids: Optional[torch.LongTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, SequenceClassifierOutput]: + r""" + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., + config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If + `config.num_labels > 1` a classification loss is computed (Cross-Entropy). + """ + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + if self.config.n_cntxt_layers > 0: + cntxt_outputs = self.cntxt_layers( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict) + + + if self.config.n_cntxt_layers_2 > 0: + distance_1, height_1 = self.parse(input_ids, cntxt_outputs[0], n_cntxt_layers=1) + att_mask_1, _, _, _ = self.generate_mask(input_ids, distance_1, height_1, n_cntxt_layers=1) + + cntxt_outputs_2 = self.cntxt_layers_2( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + parser_att_mask=att_mask_1) + + sequence_output = cntxt_outputs_2[0] + + distance_2, height_2 = self.parse(input_ids, sequence_output[0], n_cntxt_layers=2) + att_mask, _, _, _ = self.generate_mask(input_ids, distance_2, height_2, n_cntxt_layers=2) + + elif self.config.n_cntxt_layers > 0: + distance, height = self.parse(input_ids, cntxt_outputs[0]) + att_mask, _, _, _ = self.generate_mask(input_ids, distance, height) + else: + distance, height = self.parse(input_ids) + att_mask, _, _, _ = self.generate_mask(input_ids, distance, height) + + outputs = self.roberta( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + parser_att_mask=att_mask, + ) + sequence_output = outputs[0] + logits = self.classifier(sequence_output) + + loss = None + if labels is not None: + if self.config.problem_type is None: + if self.num_labels == 1: + self.config.problem_type = "regression" + elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): + self.config.problem_type = "single_label_classification" + else: + self.config.problem_type = "multi_label_classification" + + if self.config.problem_type == "regression": + loss_fct = MSELoss() + if self.num_labels == 1: + loss = loss_fct(logits.squeeze(), labels.squeeze()) + else: + loss = loss_fct(logits, labels) + elif self.config.problem_type == "single_label_classification": + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) + elif self.config.problem_type == "multi_label_classification": + loss_fct = BCEWithLogitsLoss() + loss = loss_fct(logits, labels) + + if not return_dict: + output = (logits,) + outputs[2:] + return ((loss,) + output) if loss is not None else output + + return SequenceClassifierOutput( + loss=loss, + logits=logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +class RobertaClassificationHead(nn.Module): + """Head for sentence-level classification tasks.""" + + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + classifier_dropout = ( + config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob + ) + self.dropout = nn.Dropout(classifier_dropout) + self.out_proj = nn.Linear(config.hidden_size, config.num_labels) + + def forward(self, features, **kwargs): + x = features[:, 0, :] # take token (equiv. to [CLS]) + x = self.dropout(x) + x = self.dense(x) + x = torch.tanh(x) + x = self.dropout(x) + x = self.out_proj(x) + return x + + +def create_position_ids_from_input_ids( + input_ids, padding_idx, past_key_values_length=0 +): + """ + Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols + are ignored. This is modified from fairseq's `utils.make_positions`. + + Args: + x: torch.Tensor x: + + Returns: torch.Tensor + """ + # The series of casts and type-conversions here are carefully balanced to both work with ONNX export and XLA. + mask = input_ids.ne(padding_idx).int() + incremental_indices = ( + torch.cumsum(mask, dim=1).type_as(mask) + past_key_values_length + ) * mask + return incremental_indices.long() + padding_idx + + +def cumprod(x, reverse=False, exclusive=False): + """cumulative product.""" + if reverse: + x = x.flip([-1]) + + if exclusive: + x = F.pad(x[:, :, :-1], (1, 0), value=1) + + cx = x.cumprod(-1) + + if reverse: + cx = cx.flip([-1]) + return cx + + +def cumsum(x, reverse=False, exclusive=False): + """cumulative sum.""" + bsz, _, length = x.size() + device = x.device + if reverse: + if exclusive: + w = torch.ones([bsz, length, length], device=device).tril(-1) + else: + w = torch.ones([bsz, length, length], device=device).tril(0) + cx = torch.bmm(x, w) + else: + if exclusive: + w = torch.ones([bsz, length, length], device=device).triu(1) + else: + w = torch.ones([bsz, length, length], device=device).triu(0) + cx = torch.bmm(x, w) + return cx + + +def cummin(x, reverse=False, exclusive=False, max_value=1e9): + """cumulative min.""" + if reverse: + if exclusive: + x = F.pad(x[:, :, 1:], (0, 1), value=max_value) + x = x.flip([-1]).cummin(-1)[0].flip([-1]) + else: + if exclusive: + x = F.pad(x[:, :, :-1], (1, 0), value=max_value) + x = x.cummin(-1)[0] + return x diff --git a/finetune/mnli-mm/predict_results.txt b/finetune/mnli-mm/predict_results.txt new file mode 100644 index 0000000000000000000000000000000000000000..7558b7cc4ac04d79e962fb9fb802f71c7698b432 --- /dev/null +++ b/finetune/mnli-mm/predict_results.txt @@ -0,0 +1,6285 @@ +index prediction +0 2 +1 0 +2 2 +3 1 +4 2 +5 1 +6 1 +7 2 +8 1 +9 1 +10 2 +11 1 +12 2 +13 1 +14 0 +15 2 +16 2 +17 1 +18 2 +19 1 +20 0 +21 0 +22 2 +23 1 +24 0 +25 0 +26 2 +27 1 +28 0 +29 1 +30 1 +31 1 +32 0 +33 2 +34 1 +35 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+u n +g e +o l +t h +Ġh is +u l +Ġ " +r om +.. . +o re +Ġ W +e ll +Ġn e +Ġ r +Ġkn ow +Ġ j +ou r +Ġ D +Ġd e +p p +Ġc an +Ġ G +Ġthe y +u m +Ġ The +Ġs o +es t +Ġ ? +Ġa l +Ġa ll +Ġa b +Ġm y +is t +er s +Ġ R +i e +Ġyou r +ar t +Ġb y +Ġf rom +es s +Ġs h +Ġo r +Ġ 2 +Ġ F +Ġw hat +n t +a b +o s +Ġ L +e l +Ġon e +Ġc on +Ġ N +Ġw he +Ġthe re +i g +i ght +ha n +all y +Ġu p +q u +e m +Ġc om +on e +Ġha d +Ġb ut +Ġhe r +Ġs u +re d +r y +Ġ J +' re +Ġh im +Ġ out +r a +ac k +at e +oo d +ver y +oo k +Ġli ke +Ġw or +Ġs a +re s +am e +i ve +Ġp l +Ġ us +Ġ ( +on g +' m +Ġwe re +â Ģ +a ke +o d +in k +c k +Ġs he +Ġp ro +Ġab out +Ġ E +as t +Ġg et +Ġe x +Ġ v +p e +I t +or t +ou nd +in d +ar d +it y +Ġd on +A nd +ic h +ri ght +Ġ O +Ġj ust +Ġ1 9 +i es +H e +Ġwh o +e ar +ea h +Ġd id +a k +Ġhe re +b er +Ġthe m +Ġn o +im e +in e +Ġm an +Ġw ant +Ġw ould +Ġ2 0 +Ġ if +Ġ ' +Ġw ill +u d +a p +Ġ K +Ġth ink +ou s +' ll +v en +Ġs ome +o st +W hat +e p +h ing +Ġin t +Ġan y +ow n +ic e +a g +Ġ right +m ent +id e +Ġg ot +Ġha s 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See all RoBERTa models at https://huggingface.co/models?filter=roberta +] + + +class StructRobertaConfig(RobertaConfig): + model_type = "roberta" + + def __init__( + self, + n_parser_layers=4, + conv_size=9, + relations=("head", "child"), + weight_act="softmax", + n_cntxt_layers=3, + n_cntxt_layers_2=0, + **kwargs,): + + super().__init__(**kwargs) + self.n_cntxt_layers = n_cntxt_layers + self.n_parser_layers = n_parser_layers + self.n_cntxt_layers_2 = n_cntxt_layers_2 + self.conv_size = conv_size + self.relations = relations + self.weight_act = weight_act + +class Conv1d(nn.Module): + """1D convolution layer.""" + + def __init__(self, hidden_size, kernel_size, dilation=1): + """Initialization. + + Args: + hidden_size: dimension of input embeddings + kernel_size: convolution kernel size + dilation: the spacing between the kernel points + """ + super(Conv1d, self).__init__() + + if kernel_size % 2 == 0: + padding = (kernel_size // 2) * dilation + self.shift = True + else: + padding = ((kernel_size - 1) // 2) * dilation + self.shift = False + self.conv = nn.Conv1d( + hidden_size, hidden_size, kernel_size, padding=padding, dilation=dilation + ) + + def forward(self, x): + """Compute convolution. + + Args: + x: input embeddings + Returns: + conv_output: convolution results + """ + + if self.shift: + return self.conv(x.transpose(1, 2)).transpose(1, 2)[:, 1:] + else: + return self.conv(x.transpose(1, 2)).transpose(1, 2) + + +class RobertaEmbeddings(nn.Module): + """ + Same as BertEmbeddings with a tiny tweak for positional embeddings indexing. + """ + + # Copied from transformers.models.bert.modeling_bert.BertEmbeddings.__init__ + def __init__(self, config): + super().__init__() + self.word_embeddings = nn.Embedding( + config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id + ) + self.position_embeddings = nn.Embedding( + config.max_position_embeddings, config.hidden_size + ) + self.token_type_embeddings = nn.Embedding( + config.type_vocab_size, config.hidden_size + ) + + # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load + # any TensorFlow checkpoint file + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + # position_ids (1, len position emb) is contiguous in memory and exported when serialized + self.position_embedding_type = getattr( + config, "position_embedding_type", "absolute" + ) + self.register_buffer( + "position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)) + ) + if version.parse(torch.__version__) > version.parse("1.6.0"): + self.register_buffer( + "token_type_ids", + torch.zeros(self.position_ids.size(), dtype=torch.long), + persistent=False, + ) + + # End copy + self.padding_idx = config.pad_token_id + self.position_embeddings = nn.Embedding( + config.max_position_embeddings, + config.hidden_size, + padding_idx=self.padding_idx, + ) + + def forward( + self, + input_ids=None, + token_type_ids=None, + position_ids=None, + inputs_embeds=None, + past_key_values_length=0, + ): + if position_ids is None: + if input_ids is not None: + # Create the position ids from the input token ids. Any padded tokens remain padded. + position_ids = create_position_ids_from_input_ids( + input_ids, self.padding_idx, past_key_values_length + ) + else: + position_ids = self.create_position_ids_from_inputs_embeds( + inputs_embeds + ) + + if input_ids is not None: + input_shape = input_ids.size() + else: + input_shape = inputs_embeds.size()[:-1] + + seq_length = input_shape[1] + + # Setting the token_type_ids to the registered buffer in constructor where it is all zeros, which usually occurs + # when its auto-generated, registered buffer helps users when tracing the model without passing token_type_ids, solves + # issue #5664 + if token_type_ids is None: + if hasattr(self, "token_type_ids"): + buffered_token_type_ids = self.token_type_ids[:, :seq_length] + buffered_token_type_ids_expanded = buffered_token_type_ids.expand( + input_shape[0], seq_length + ) + token_type_ids = buffered_token_type_ids_expanded + else: + token_type_ids = torch.zeros( + input_shape, dtype=torch.long, device=self.position_ids.device + ) + + if inputs_embeds is None: + inputs_embeds = self.word_embeddings(input_ids) + token_type_embeddings = self.token_type_embeddings(token_type_ids) + + embeddings = inputs_embeds + token_type_embeddings + if self.position_embedding_type == "absolute": + position_embeddings = self.position_embeddings(position_ids) + embeddings += position_embeddings + embeddings = self.LayerNorm(embeddings) + embeddings = self.dropout(embeddings) + return embeddings + + def create_position_ids_from_inputs_embeds(self, inputs_embeds): + """ + We are provided embeddings directly. We cannot infer which are padded so just generate sequential position ids. + + Args: + inputs_embeds: torch.Tensor + + Returns: torch.Tensor + """ + input_shape = inputs_embeds.size()[:-1] + sequence_length = input_shape[1] + + position_ids = torch.arange( + self.padding_idx + 1, + sequence_length + self.padding_idx + 1, + dtype=torch.long, + device=inputs_embeds.device, + ) + return position_ids.unsqueeze(0).expand(input_shape) + + +# Copied from transformers.models.bert.modeling_bert.BertSelfAttention with Bert->Roberta +class RobertaSelfAttention(nn.Module): + def __init__(self, config, position_embedding_type=None): + super().__init__() + if config.hidden_size % config.num_attention_heads != 0 and not hasattr( + config, "embedding_size" + ): + raise ValueError( + f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention " + f"heads ({config.num_attention_heads})" + ) + + self.num_attention_heads = config.num_attention_heads + self.attention_head_size = int(config.hidden_size / config.num_attention_heads) + self.all_head_size = self.num_attention_heads * self.attention_head_size + + self.query = nn.Linear(config.hidden_size, self.all_head_size) + self.key = nn.Linear(config.hidden_size, self.all_head_size) + self.value = nn.Linear(config.hidden_size, self.all_head_size) + + self.dropout = nn.Dropout(config.attention_probs_dropout_prob) + self.position_embedding_type = position_embedding_type or getattr( + config, "position_embedding_type", "absolute" + ) + if ( + self.position_embedding_type == "relative_key" + or self.position_embedding_type == "relative_key_query" + ): + self.max_position_embeddings = config.max_position_embeddings + self.distance_embedding = nn.Embedding( + 2 * config.max_position_embeddings - 1, self.attention_head_size + ) + + self.is_decoder = config.is_decoder + + def transpose_for_scores(self, x): + new_x_shape = x.size()[:-1] + ( + self.num_attention_heads, + self.attention_head_size, + ) + x = x.view(new_x_shape) + return x.permute(0, 2, 1, 3) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.FloatTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, + output_attentions: Optional[bool] = False, + parser_att_mask=None, + ) -> Tuple[torch.Tensor]: + mixed_query_layer = self.query(hidden_states) + + # If this is instantiated as a cross-attention module, the keys + # and values come from an encoder; the attention mask needs to be + # such that the encoder's padding tokens are not attended to. + is_cross_attention = encoder_hidden_states is not None + + if is_cross_attention and past_key_value is not None: + # reuse k,v, cross_attentions + key_layer = past_key_value[0] + value_layer = past_key_value[1] + attention_mask = encoder_attention_mask + elif is_cross_attention: + key_layer = self.transpose_for_scores(self.key(encoder_hidden_states)) + value_layer = self.transpose_for_scores(self.value(encoder_hidden_states)) + attention_mask = encoder_attention_mask + elif past_key_value is not None: + key_layer = self.transpose_for_scores(self.key(hidden_states)) + value_layer = self.transpose_for_scores(self.value(hidden_states)) + key_layer = torch.cat([past_key_value[0], key_layer], dim=2) + value_layer = torch.cat([past_key_value[1], value_layer], dim=2) + else: + key_layer = self.transpose_for_scores(self.key(hidden_states)) + value_layer = self.transpose_for_scores(self.value(hidden_states)) + + query_layer = self.transpose_for_scores(mixed_query_layer) + + if self.is_decoder: + # if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states. + # Further calls to cross_attention layer can then reuse all cross-attention + # key/value_states (first "if" case) + # if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of + # all previous decoder key/value_states. Further calls to uni-directional self-attention + # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case) + # if encoder bi-directional self-attention `past_key_value` is always `None` + past_key_value = (key_layer, value_layer) + + # Take the dot product between "query" and "key" to get the raw attention scores. + attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) + + if ( + self.position_embedding_type == "relative_key" + or self.position_embedding_type == "relative_key_query" + ): + seq_length = hidden_states.size()[1] + position_ids_l = torch.arange( + seq_length, dtype=torch.long, device=hidden_states.device + ).view(-1, 1) + position_ids_r = torch.arange( + seq_length, dtype=torch.long, device=hidden_states.device + ).view(1, -1) + distance = position_ids_l - position_ids_r + positional_embedding = self.distance_embedding( + distance + self.max_position_embeddings - 1 + ) + positional_embedding = positional_embedding.to( + dtype=query_layer.dtype + ) # fp16 compatibility + + if self.position_embedding_type == "relative_key": + relative_position_scores = torch.einsum( + "bhld,lrd->bhlr", query_layer, positional_embedding + ) + attention_scores = attention_scores + relative_position_scores + elif self.position_embedding_type == "relative_key_query": + relative_position_scores_query = torch.einsum( + "bhld,lrd->bhlr", query_layer, positional_embedding + ) + relative_position_scores_key = torch.einsum( + "bhrd,lrd->bhlr", key_layer, positional_embedding + ) + attention_scores = ( + attention_scores + + relative_position_scores_query + + relative_position_scores_key + ) + + attention_scores = attention_scores / math.sqrt(self.attention_head_size) + if attention_mask is not None: + # Apply the attention mask is (precomputed for all layers in RobertaModel forward() function) + attention_scores = attention_scores + attention_mask + + if parser_att_mask is None: + # Normalize the attention scores to probabilities. + attention_probs = nn.functional.softmax(attention_scores, dim=-1) + else: + attention_probs = torch.sigmoid(attention_scores) * parser_att_mask + + # This is actually dropping out entire tokens to attend to, which might + # seem a bit unusual, but is taken from the original Transformer paper. + attention_probs = self.dropout(attention_probs) + + # Mask heads if we want to + if head_mask is not None: + attention_probs = attention_probs * head_mask + + context_layer = torch.matmul(attention_probs, value_layer) + + context_layer = context_layer.permute(0, 2, 1, 3).contiguous() + new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) + context_layer = context_layer.view(new_context_layer_shape) + + outputs = ( + (context_layer, attention_probs) if output_attentions else (context_layer,) + ) + + if self.is_decoder: + outputs = outputs + (past_key_value,) + return outputs + + +# Copied from transformers.models.bert.modeling_bert.BertSelfOutput +class RobertaSelfOutput(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward( + self, hidden_states: torch.Tensor, input_tensor: torch.Tensor + ) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + hidden_states = self.LayerNorm(hidden_states + input_tensor) + return hidden_states + + +# Copied from transformers.models.bert.modeling_bert.BertAttention with Bert->Roberta +class RobertaAttention(nn.Module): + def __init__(self, config, position_embedding_type=None): + super().__init__() + self.self = RobertaSelfAttention( + config, position_embedding_type=position_embedding_type + ) + self.output = RobertaSelfOutput(config) + self.pruned_heads = set() + + def prune_heads(self, heads): + if len(heads) == 0: + return + heads, index = find_pruneable_heads_and_indices( + heads, + self.self.num_attention_heads, + self.self.attention_head_size, + self.pruned_heads, + ) + + # Prune linear layers + self.self.query = prune_linear_layer(self.self.query, index) + self.self.key = prune_linear_layer(self.self.key, index) + self.self.value = prune_linear_layer(self.self.value, index) + self.output.dense = prune_linear_layer(self.output.dense, index, dim=1) + + # Update hyper params and store pruned heads + self.self.num_attention_heads = self.self.num_attention_heads - len(heads) + self.self.all_head_size = ( + self.self.attention_head_size * self.self.num_attention_heads + ) + self.pruned_heads = self.pruned_heads.union(heads) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.FloatTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, + output_attentions: Optional[bool] = False, + parser_att_mask=None, + ) -> Tuple[torch.Tensor]: + self_outputs = self.self( + hidden_states, + attention_mask, + head_mask, + encoder_hidden_states, + encoder_attention_mask, + past_key_value, + output_attentions, + parser_att_mask=parser_att_mask, + ) + attention_output = self.output(self_outputs[0], hidden_states) + outputs = (attention_output,) + self_outputs[ + 1: + ] # add attentions if we output them + return outputs + + +# Copied from transformers.models.bert.modeling_bert.BertIntermediate +class RobertaIntermediate(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.intermediate_size) + if isinstance(config.hidden_act, str): + self.intermediate_act_fn = ACT2FN[config.hidden_act] + else: + self.intermediate_act_fn = config.hidden_act + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.intermediate_act_fn(hidden_states) + return hidden_states + + +# Copied from transformers.models.bert.modeling_bert.BertOutput +class RobertaOutput(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.intermediate_size, config.hidden_size) + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward( + self, hidden_states: torch.Tensor, input_tensor: torch.Tensor + ) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + hidden_states = self.LayerNorm(hidden_states + input_tensor) + return hidden_states + + +# Copied from transformers.models.bert.modeling_bert.BertLayer with Bert->Roberta +class RobertaLayer(nn.Module): + def __init__(self, config): + super().__init__() + self.chunk_size_feed_forward = config.chunk_size_feed_forward + self.seq_len_dim = 1 + self.attention = RobertaAttention(config) + self.is_decoder = config.is_decoder + self.add_cross_attention = config.add_cross_attention + if self.add_cross_attention: + if not self.is_decoder: + raise ValueError( + f"{self} should be used as a decoder model if cross attention is added" + ) + self.crossattention = RobertaAttention( + config, position_embedding_type="absolute" + ) + self.intermediate = RobertaIntermediate(config) + self.output = RobertaOutput(config) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.FloatTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, + output_attentions: Optional[bool] = False, + parser_att_mask=None, + ) -> Tuple[torch.Tensor]: + # decoder uni-directional self-attention cached key/values tuple is at positions 1,2 + self_attn_past_key_value = ( + past_key_value[:2] if past_key_value is not None else None + ) + self_attention_outputs = self.attention( + hidden_states, + attention_mask, + head_mask, + output_attentions=output_attentions, + past_key_value=self_attn_past_key_value, + parser_att_mask=parser_att_mask, + ) + attention_output = self_attention_outputs[0] + + # if decoder, the last output is tuple of self-attn cache + if self.is_decoder: + outputs = self_attention_outputs[1:-1] + present_key_value = self_attention_outputs[-1] + else: + outputs = self_attention_outputs[ + 1: + ] # add self attentions if we output attention weights + + cross_attn_present_key_value = None + if self.is_decoder and encoder_hidden_states is not None: + if not hasattr(self, "crossattention"): + raise ValueError( + f"If `encoder_hidden_states` are passed, {self} has to be instantiated with cross-attention layers by setting `config.add_cross_attention=True`" + ) + + # cross_attn cached key/values tuple is at positions 3,4 of past_key_value tuple + cross_attn_past_key_value = ( + past_key_value[-2:] if past_key_value is not None else None + ) + cross_attention_outputs = self.crossattention( + attention_output, + attention_mask, + head_mask, + encoder_hidden_states, + encoder_attention_mask, + cross_attn_past_key_value, + output_attentions, + ) + attention_output = cross_attention_outputs[0] + outputs = ( + outputs + cross_attention_outputs[1:-1] + ) # add cross attentions if we output attention weights + + # add cross-attn cache to positions 3,4 of present_key_value tuple + cross_attn_present_key_value = cross_attention_outputs[-1] + present_key_value = present_key_value + cross_attn_present_key_value + + layer_output = apply_chunking_to_forward( + self.feed_forward_chunk, + self.chunk_size_feed_forward, + self.seq_len_dim, + attention_output, + ) + outputs = (layer_output,) + outputs + + # if decoder, return the attn key/values as the last output + if self.is_decoder: + outputs = outputs + (present_key_value,) + + return outputs + + def feed_forward_chunk(self, attention_output): + intermediate_output = self.intermediate(attention_output) + layer_output = self.output(intermediate_output, attention_output) + return layer_output + + +# Copied from transformers.models.bert.modeling_bert.BertEncoder with Bert->Roberta +class RobertaEncoder(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + self.layer = nn.ModuleList( + [RobertaLayer(config) for _ in range(config.num_hidden_layers)] + ) + self.gradient_checkpointing = False + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.FloatTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = False, + output_hidden_states: Optional[bool] = False, + return_dict: Optional[bool] = True, + parser_att_mask=None, + ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPastAndCrossAttentions]: + all_hidden_states = () if output_hidden_states else None + all_self_attentions = () if output_attentions else None + all_cross_attentions = ( + () if output_attentions and self.config.add_cross_attention else None + ) + + next_decoder_cache = () if use_cache else None + for i, layer_module in enumerate(self.layer): + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + layer_head_mask = head_mask[i] if head_mask is not None else None + past_key_value = past_key_values[i] if past_key_values is not None else None + + if self.gradient_checkpointing and self.training: + + if use_cache: + logger.warning( + "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." + ) + use_cache = False + + def create_custom_forward(module): + def custom_forward(*inputs): + return module(*inputs, past_key_value, output_attentions) + + return custom_forward + + layer_outputs = torch.utils.checkpoint.checkpoint( + create_custom_forward(layer_module), + hidden_states, + attention_mask, + layer_head_mask, + encoder_hidden_states, + encoder_attention_mask, + ) + else: + if parser_att_mask is not None: + layer_outputs = layer_module( + hidden_states, + attention_mask, + layer_head_mask, + encoder_hidden_states, + encoder_attention_mask, + past_key_value, + output_attentions, + parser_att_mask=parser_att_mask[i]) + else: + layer_outputs = layer_module( + hidden_states, + attention_mask, + layer_head_mask, + encoder_hidden_states, + encoder_attention_mask, + past_key_value, + output_attentions, + parser_att_mask=None) + + + hidden_states = layer_outputs[0] + if use_cache: + next_decoder_cache += (layer_outputs[-1],) + if output_attentions: + all_self_attentions = all_self_attentions + (layer_outputs[1],) + if self.config.add_cross_attention: + all_cross_attentions = all_cross_attentions + (layer_outputs[2],) + + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + if not return_dict: + return tuple( + v + for v in [ + hidden_states, + next_decoder_cache, + all_hidden_states, + all_self_attentions, + all_cross_attentions, + ] + if v is not None + ) + return BaseModelOutputWithPastAndCrossAttentions( + last_hidden_state=hidden_states, + past_key_values=next_decoder_cache, + hidden_states=all_hidden_states, + attentions=all_self_attentions, + cross_attentions=all_cross_attentions, + ) + + +# Copied from transformers.models.bert.modeling_bert.BertPooler +class RobertaPooler(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.activation = nn.Tanh() + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + # We "pool" the model by simply taking the hidden state corresponding + # to the first token. + first_token_tensor = hidden_states[:, 0] + pooled_output = self.dense(first_token_tensor) + pooled_output = self.activation(pooled_output) + return pooled_output + + +class RobertaPreTrainedModel(PreTrainedModel): + """ + An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained + models. + """ + + config_class = RobertaConfig + base_model_prefix = "roberta" + supports_gradient_checkpointing = True + + # Copied from transformers.models.bert.modeling_bert.BertPreTrainedModel._init_weights + def _init_weights(self, module): + """Initialize the weights""" + if isinstance(module, nn.Linear): + # Slightly different from the TF version which uses truncated_normal for initialization + # cf https://github.com/pytorch/pytorch/pull/5617 + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.Embedding): + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.padding_idx is not None: + module.weight.data[module.padding_idx].zero_() + elif isinstance(module, nn.LayerNorm): + if module.bias is not None: + module.bias.data.zero_() + module.weight.data.fill_(1.0) + + def _set_gradient_checkpointing(self, module, value=False): + if isinstance(module, RobertaEncoder): + module.gradient_checkpointing = value + + def update_keys_to_ignore(self, config, del_keys_to_ignore): + """Remove some keys from ignore list""" + if not config.tie_word_embeddings: + # must make a new list, or the class variable gets modified! + self._keys_to_ignore_on_save = [ + k for k in self._keys_to_ignore_on_save if k not in del_keys_to_ignore + ] + self._keys_to_ignore_on_load_missing = [ + k + for k in self._keys_to_ignore_on_load_missing + if k not in del_keys_to_ignore + ] + + +ROBERTA_START_DOCSTRING = r""" + + This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the + library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads + etc.) + + This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. + Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage + and behavior. + + Parameters: + config ([`RobertaConfig`]): Model configuration class with all the parameters of the + model. Initializing with a config file does not load the weights associated with the model, only the + configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. +""" + + +ROBERTA_INPUTS_DOCSTRING = r""" + Args: + input_ids (`torch.LongTensor` of shape `({0})`): + Indices of input sequence tokens in the vocabulary. + + Indices can be obtained using [`RobertaTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + [What are input IDs?](../glossary#input-ids) + attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*): + Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + + [What are attention masks?](../glossary#attention-mask) + token_type_ids (`torch.LongTensor` of shape `({0})`, *optional*): + Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, + 1]`: + + - 0 corresponds to a *sentence A* token, + - 1 corresponds to a *sentence B* token. + + [What are token type IDs?](../glossary#token-type-ids) + position_ids (`torch.LongTensor` of shape `({0})`, *optional*): + Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, + config.max_position_embeddings - 1]`. + + [What are position IDs?](../glossary#position-ids) + head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): + Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: + + - 1 indicates the head is **not masked**, + - 0 indicates the head is **masked**. + + inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_size)`, *optional*): + Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This + is useful if you want more control over how to convert `input_ids` indices into associated vectors than the + model's internal embedding lookup matrix. + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned + tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for + more detail. + return_dict (`bool`, *optional*): + Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. +""" + + +class RobertaModel(RobertaPreTrainedModel): + """ + + The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of + cross-attention is added between the self-attention layers, following the architecture described in *Attention is + all you need*_ by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz + Kaiser and Illia Polosukhin. + + To behave as an decoder the model needs to be initialized with the `is_decoder` argument of the configuration set + to `True`. To be used in a Seq2Seq model, the model needs to initialized with both `is_decoder` argument and + `add_cross_attention` set to `True`; an `encoder_hidden_states` is then expected as an input to the forward pass. + + .. _*Attention is all you need*: https://arxiv.org/abs/1706.03762 + + """ + + _keys_to_ignore_on_load_missing = [r"position_ids"] + + # Copied from transformers.models.bert.modeling_bert.BertModel.__init__ with Bert->Roberta + def __init__(self, config, add_pooling_layer=True): + super().__init__(config) + self.config = config + + self.embeddings = RobertaEmbeddings(config) + self.encoder = RobertaEncoder(config) + + self.pooler = RobertaPooler(config) if add_pooling_layer else None + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.embeddings.word_embeddings + + def set_input_embeddings(self, value): + self.embeddings.word_embeddings = value + + def _prune_heads(self, heads_to_prune): + """ + Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base + class PreTrainedModel + """ + for layer, heads in heads_to_prune.items(): + self.encoder.layer[layer].attention.prune_heads(heads) + + # Copied from transformers.models.bert.modeling_bert.BertModel.forward + def forward( + self, + input_ids: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + token_type_ids: Optional[torch.Tensor] = None, + position_ids: Optional[torch.Tensor] = None, + head_mask: Optional[torch.Tensor] = None, + inputs_embeds: Optional[torch.Tensor] = None, + encoder_hidden_states: Optional[torch.Tensor] = None, + encoder_attention_mask: Optional[torch.Tensor] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + parser_att_mask=None, + ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPoolingAndCrossAttentions]: + r""" + encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): + Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if + the model is configured as a decoder. + encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*): + Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in + the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): + Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. + + If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that + don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all + `decoder_input_ids` of shape `(batch_size, sequence_length)`. + use_cache (`bool`, *optional*): + If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see + `past_key_values`). + """ + output_attentions = ( + output_attentions + if output_attentions is not None + else self.config.output_attentions + ) + output_hidden_states = ( + output_hidden_states + if output_hidden_states is not None + else self.config.output_hidden_states + ) + return_dict = ( + return_dict if return_dict is not None else self.config.use_return_dict + ) + + if self.config.is_decoder: + use_cache = use_cache if use_cache is not None else self.config.use_cache + else: + use_cache = False + + if input_ids is not None and inputs_embeds is not None: + raise ValueError( + "You cannot specify both input_ids and inputs_embeds at the same time" + ) + elif input_ids is not None: + input_shape = input_ids.size() + elif inputs_embeds is not None: + input_shape = inputs_embeds.size()[:-1] + else: + raise ValueError("You have to specify either input_ids or inputs_embeds") + + batch_size, seq_length = input_shape + device = input_ids.device if input_ids is not None else inputs_embeds.device + + # past_key_values_length + past_key_values_length = ( + past_key_values[0][0].shape[2] if past_key_values is not None else 0 + ) + + if attention_mask is None: + attention_mask = torch.ones( + ((batch_size, seq_length + past_key_values_length)), device=device + ) + + if token_type_ids is None: + if hasattr(self.embeddings, "token_type_ids"): + buffered_token_type_ids = self.embeddings.token_type_ids[:, :seq_length] + buffered_token_type_ids_expanded = buffered_token_type_ids.expand( + batch_size, seq_length + ) + token_type_ids = buffered_token_type_ids_expanded + else: + token_type_ids = torch.zeros( + input_shape, dtype=torch.long, device=device + ) + + # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length] + # ourselves in which case we just need to make it broadcastable to all heads. + extended_attention_mask: torch.Tensor = self.get_extended_attention_mask( + attention_mask, input_shape, device + ) + + # If a 2D or 3D attention mask is provided for the cross-attention + # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length] + if self.config.is_decoder and encoder_hidden_states is not None: + ( + encoder_batch_size, + encoder_sequence_length, + _, + ) = encoder_hidden_states.size() + encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length) + if encoder_attention_mask is None: + encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device) + encoder_extended_attention_mask = self.invert_attention_mask( + encoder_attention_mask + ) + else: + encoder_extended_attention_mask = None + + # Prepare head mask if needed + # 1.0 in head_mask indicate we keep the head + # attention_probs has shape bsz x n_heads x N x N + # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] + # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] + head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) + + embedding_output = self.embeddings( + input_ids=input_ids, + position_ids=position_ids, + token_type_ids=token_type_ids, + inputs_embeds=inputs_embeds, + past_key_values_length=past_key_values_length, + ) + encoder_outputs = self.encoder( + embedding_output, + attention_mask=extended_attention_mask, + head_mask=head_mask, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_extended_attention_mask, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + parser_att_mask=parser_att_mask, + ) + sequence_output = encoder_outputs[0] + pooled_output = ( + self.pooler(sequence_output) if self.pooler is not None else None + ) + + if not return_dict: + return (sequence_output, pooled_output) + encoder_outputs[1:] + + return BaseModelOutputWithPoolingAndCrossAttentions( + last_hidden_state=sequence_output, + pooler_output=pooled_output, + past_key_values=encoder_outputs.past_key_values, + hidden_states=encoder_outputs.hidden_states, + attentions=encoder_outputs.attentions, + cross_attentions=encoder_outputs.cross_attentions, + ) + + +class StructRoberta(RobertaPreTrainedModel): + _keys_to_ignore_on_save = [r"lm_head.decoder.weight", r"lm_head.decoder.bias"] + _keys_to_ignore_on_load_missing = [ + r"position_ids", + r"lm_head.decoder.weight", + r"lm_head.decoder.bias", + ] + _keys_to_ignore_on_load_unexpected = [r"pooler"] + + def __init__(self, config): + super().__init__(config) + + if config.is_decoder: + logger.warning( + "If you want to use `RobertaForMaskedLM` make sure `config.is_decoder=False` for " + "bi-directional self-attention." + ) + + + if config.n_cntxt_layers > 0: + config_cntxt = copy.deepcopy(config) + config_cntxt.num_hidden_layers = config.n_cntxt_layers + + self.cntxt_layers = RobertaModel(config_cntxt, add_pooling_layer=False) + + if config.n_cntxt_layers_2 > 0: + self.parser_layers_1 = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(int(config.n_parser_layers/2)) + ] + ) + + self.distance_ff_1 = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff_1 = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight_1 = nn.Parameter( + torch.zeros((config.n_cntxt_layers_2, config.num_attention_heads, n_rel)) + ) + self._rel_weight_1.data.normal_(0, 0.1) + + self._scaler_1 = nn.Parameter(torch.zeros(2)) + + config_cntxt_2 = copy.deepcopy(config) + config_cntxt_2.num_hidden_layers = config.n_cntxt_layers_2 + + self.cntxt_layers_2 = RobertaModel(config_cntxt_2, add_pooling_layer=False) + + + self.parser_layers_2 = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(int(config.n_parser_layers/2)) + ] + ) + + self.distance_ff_2 = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff_2 = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight_2 = nn.Parameter( + torch.zeros((config.num_hidden_layers, config.num_attention_heads, n_rel)) + ) + self._rel_weight_2.data.normal_(0, 0.1) + + self._scaler_2 = nn.Parameter(torch.zeros(2)) + + else: + self.parser_layers = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(config.n_parser_layers) + ] + ) + + self.distance_ff = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight = nn.Parameter( + torch.zeros((config.num_hidden_layers, config.num_attention_heads, n_rel)) + ) + self._rel_weight.data.normal_(0, 0.1) + + self._scaler = nn.Parameter(torch.zeros(2)) + + self.roberta = RobertaModel(config, add_pooling_layer=False) + + if config.n_cntxt_layers > 0: + self.cntxt_layers.embeddings = self.roberta.embeddings + if config.n_cntxt_layers_2 > 0: + self.cntxt_layers_2.embeddings = self.roberta.embeddings + + self.lm_head = RobertaLMHead(config) + + self.pad = config.pad_token_id + + # The LM head weights require special treatment only when they are tied with the word embeddings + self.update_keys_to_ignore(config, ["lm_head.decoder.weight"]) + + # Initialize weights and apply final processing + self.post_init() + + def get_output_embeddings(self): + return self.lm_head.decoder + + def set_output_embeddings(self, new_embeddings): + self.lm_head.decoder = new_embeddings + + @property + def scaler(self): + return self._scaler.exp() + + @property + def scaler_1(self): + return self._scaler_1.exp() + + @property + def scaler_2(self): + return self._scaler_2.exp() + + @property + def rel_weight(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight, dim=-1) + + @property + def rel_weight_1(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight_1) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight_1, dim=-1) + + + @property + def rel_weight_2(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight_2) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight_2, dim=-1) + + + def compute_block(self, distance, height, n_cntxt_layers=0): + """Compute constituents from distance and height.""" + + if n_cntxt_layers>0: + if n_cntxt_layers == 1: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler_1[0] + elif n_cntxt_layers == 2: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler_2[0] + else: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0] + + gamma = torch.sigmoid(-beta_logits) + ones = torch.ones_like(gamma) + + block_mask_left = cummin( + gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1 + ) + block_mask_left = block_mask_left - F.pad( + block_mask_left[:, :, :-1], (1, 0), value=0 + ) + block_mask_left.tril_(0) + + block_mask_right = cummin( + gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1 + ) + block_mask_right = block_mask_right - F.pad( + block_mask_right[:, :, 1:], (0, 1), value=0 + ) + block_mask_right.triu_(0) + + block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :] + block = cumsum(block_mask_left).tril(0) + cumsum( + block_mask_right, reverse=True + ).triu(1) + + return block_p, block + + def compute_head(self, height, n_cntxt_layers=0): + """Estimate head for each constituent.""" + + _, length = height.size() + if n_cntxt_layers>0: + if n_cntxt_layers == 1: + head_logits = height * self.scaler_1[1] + elif n_cntxt_layers == 2: + head_logits = height * self.scaler_2[1] + else: + head_logits = height * self.scaler[1] + index = torch.arange(length, device=height.device) + + mask = (index[:, None, None] <= index[None, None, :]) * ( + index[None, None, :] <= index[None, :, None] + ) + head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1) + head_logits.masked_fill_(~mask[None, :, :, :], -1e9) + + head_p = torch.softmax(head_logits, dim=-1) + + return head_p + + def parse(self, x, embs=None, n_cntxt_layers=0): + """Parse input sentence. + + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + distance: syntactic distance + height: syntactic height + """ + + mask = x != self.pad + mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0) + + if embs is None: + h = self.roberta.embeddings(x) + else: + h = embs + + if n_cntxt_layers > 0: + if n_cntxt_layers == 1: + parser_layers = self.parser_layers_1 + height_ff = self.height_ff_1 + distance_ff = self.distance_ff_1 + elif n_cntxt_layers == 2: + parser_layers = self.parser_layers_2 + height_ff = self.height_ff_2 + distance_ff = self.distance_ff_2 + for i in range(int(self.config.n_parser_layers/2)): + h = h.masked_fill(~mask[:, :, None], 0) + h = parser_layers[i](h) + + height = height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, dim=-1 + )[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max + ) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where( + margin_left > margin_right, margin_right, margin_left + ).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + else: + for i in range(self.config.n_parser_layers): + h = h.masked_fill(~mask[:, :, None], 0) + h = self.parser_layers[i](h) + + height = self.height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = self.distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, dim=-1 + )[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max + ) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where( + margin_left > margin_right, margin_right, margin_left + ).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + + return distance, height + + def generate_mask(self, x, distance, height, n_cntxt_layers=0): + """Compute head and cibling distribution for each token.""" + + bsz, length = x.size() + + eye = torch.eye(length, device=x.device, dtype=torch.bool) + eye = eye[None, :, :].expand((bsz, -1, -1)) + + block_p, block = self.compute_block(distance, height, n_cntxt_layers=n_cntxt_layers) + head_p = self.compute_head(height, n_cntxt_layers=n_cntxt_layers) + head = torch.einsum("blij,bijh->blh", block_p, head_p) + head = head.masked_fill(eye, 0) + child = head.transpose(1, 2) + cibling = torch.bmm(head, child).masked_fill(eye, 0) + + rel_list = [] + if "head" in self.config.relations: + rel_list.append(head) + if "child" in self.config.relations: + rel_list.append(child) + if "cibling" in self.config.relations: + rel_list.append(cibling) + + rel = torch.stack(rel_list, dim=1) + + if n_cntxt_layers > 0: + if n_cntxt_layers == 1: + rel_weight = self.rel_weight_1 + elif n_cntxt_layers == 2: + rel_weight = self.rel_weight_2 + else: + rel_weight = self.rel_weight + + dep = torch.einsum("lhr,brij->lbhij", rel_weight, rel) + + if n_cntxt_layers == 1: + num_layers = self.cntxt_layers_2.config.num_hidden_layers + else: + num_layers = self.roberta.config.num_hidden_layers + + att_mask = dep.reshape( + num_layers, + bsz, + self.config.num_attention_heads, + length, + length, + ) + + return att_mask, cibling, head, block + + def forward( + self, + input_ids: Optional[torch.LongTensor] = None, + attention_mask: Optional[torch.FloatTensor] = None, + token_type_ids: Optional[torch.LongTensor] = None, + position_ids: Optional[torch.LongTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, MaskedLMOutput]: + r""" + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ..., + config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the + loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]` + kwargs (`Dict[str, any]`, optional, defaults to *{}*): + Used to hide legacy arguments that have been deprecated. + """ + return_dict = ( + return_dict if return_dict is not None else self.config.use_return_dict + ) + + + if self.config.n_cntxt_layers > 0: + cntxt_outputs = self.cntxt_layers( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict) + + + if self.config.n_cntxt_layers_2 > 0: + distance_1, height_1 = self.parse(input_ids, cntxt_outputs[0], n_cntxt_layers=1) + att_mask_1, _, _, _ = self.generate_mask(input_ids, distance_1, height_1, n_cntxt_layers=1) + + cntxt_outputs_2 = self.cntxt_layers_2( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + parser_att_mask=att_mask_1) + + sequence_output = cntxt_outputs_2[0] + + distance_2, height_2 = self.parse(input_ids, sequence_output[0], n_cntxt_layers=2) + att_mask, _, _, _ = self.generate_mask(input_ids, distance_2, height_2, n_cntxt_layers=2) + + elif self.config.n_cntxt_layers > 0: + distance, height = self.parse(input_ids, cntxt_outputs[0]) + att_mask, _, _, _ = self.generate_mask(input_ids, distance, height) + else: + distance, height = self.parse(input_ids) + att_mask, _, _, _ = self.generate_mask(input_ids, distance, height) + + outputs = self.roberta( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + parser_att_mask=att_mask, + ) + sequence_output = outputs[0] + prediction_scores = self.lm_head(sequence_output) + + masked_lm_loss = None + if labels is not None: + loss_fct = CrossEntropyLoss() + masked_lm_loss = loss_fct( + prediction_scores.view(-1, self.config.vocab_size), labels.view(-1) + ) + + if not return_dict: + output = (prediction_scores,) + outputs[2:] + return ( + ((masked_lm_loss,) + output) if masked_lm_loss is not None else output + ) + + return MaskedLMOutput( + loss=masked_lm_loss, + logits=prediction_scores, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +class RobertaLMHead(nn.Module): + """Roberta Head for masked language modeling.""" + + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + + self.decoder = nn.Linear(config.hidden_size, config.vocab_size) + self.bias = nn.Parameter(torch.zeros(config.vocab_size)) + self.decoder.bias = self.bias + + def forward(self, features, **kwargs): + x = self.dense(features) + x = gelu(x) + x = self.layer_norm(x) + + # project back to size of vocabulary with bias + x = self.decoder(x) + + return x + + def _tie_weights(self): + # To tie those two weights if they get disconnected (on TPU or when the bias is resized) + self.bias = self.decoder.bias + + +class StructRobertaForSequenceClassification(RobertaPreTrainedModel): + _keys_to_ignore_on_load_missing = [r"position_ids"] + + def __init__(self, config): + super().__init__(config) + self.num_labels = config.num_labels + self.config = config + + if config.n_cntxt_layers > 0: + config_cntxt = copy.deepcopy(config) + config_cntxt.num_hidden_layers = config.n_cntxt_layers + + self.cntxt_layers = RobertaModel(config_cntxt, add_pooling_layer=False) + + if config.n_cntxt_layers_2 > 0: + self.parser_layers_1 = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(int(config.n_parser_layers/2)) + ] + ) + + self.distance_ff_1 = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff_1 = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight_1 = nn.Parameter( + torch.zeros((config.n_cntxt_layers_2, config.num_attention_heads, n_rel)) + ) + self._rel_weight_1.data.normal_(0, 0.1) + + self._scaler_1 = nn.Parameter(torch.zeros(2)) + + config_cntxt_2 = copy.deepcopy(config) + config_cntxt_2.num_hidden_layers = config.n_cntxt_layers_2 + + self.cntxt_layers_2 = RobertaModel(config_cntxt_2, add_pooling_layer=False) + + + self.parser_layers_2 = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(int(config.n_parser_layers/2)) + ] + ) + + self.distance_ff_2 = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff_2 = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight_2 = nn.Parameter( + torch.zeros((config.num_hidden_layers, config.num_attention_heads, n_rel)) + ) + self._rel_weight_2.data.normal_(0, 0.1) + + self._scaler_2 = nn.Parameter(torch.zeros(2)) + + else: + self.parser_layers = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(config.n_parser_layers) + ] + ) + + self.distance_ff = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight = nn.Parameter( + torch.zeros((config.num_hidden_layers, config.num_attention_heads, n_rel)) + ) + self._rel_weight.data.normal_(0, 0.1) + + self._scaler = nn.Parameter(torch.zeros(2)) + + self.roberta = RobertaModel(config, add_pooling_layer=False) + + if config.n_cntxt_layers > 0: + self.cntxt_layers.embeddings = self.roberta.embeddings + if config.n_cntxt_layers_2 > 0: + self.cntxt_layers_2.embeddings = self.roberta.embeddings + + + self.pad = config.pad_token_id + self.classifier = RobertaClassificationHead(config) + + # Initialize weights and apply final processing + self.post_init() + + + @property + def scaler(self): + return self._scaler.exp() + + @property + def scaler_1(self): + return self._scaler_1.exp() + + @property + def scaler_2(self): + return self._scaler_2.exp() + + @property + def rel_weight(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight, dim=-1) + + @property + def rel_weight_1(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight_1) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight_1, dim=-1) + + + @property + def rel_weight_2(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight_2) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight_2, dim=-1) + + + def compute_block(self, distance, height, n_cntxt_layers=0): + """Compute constituents from distance and height.""" + + if n_cntxt_layers>0: + if n_cntxt_layers == 1: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler_1[0] + elif n_cntxt_layers == 2: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler_2[0] + else: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0] + + gamma = torch.sigmoid(-beta_logits) + ones = torch.ones_like(gamma) + + block_mask_left = cummin( + gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1 + ) + block_mask_left = block_mask_left - F.pad( + block_mask_left[:, :, :-1], (1, 0), value=0 + ) + block_mask_left.tril_(0) + + block_mask_right = cummin( + gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1 + ) + block_mask_right = block_mask_right - F.pad( + block_mask_right[:, :, 1:], (0, 1), value=0 + ) + block_mask_right.triu_(0) + + block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :] + block = cumsum(block_mask_left).tril(0) + cumsum( + block_mask_right, reverse=True + ).triu(1) + + return block_p, block + + def compute_head(self, height, n_cntxt_layers=0): + """Estimate head for each constituent.""" + + _, length = height.size() + if n_cntxt_layers>0: + if n_cntxt_layers == 1: + head_logits = height * self.scaler_1[1] + elif n_cntxt_layers == 2: + head_logits = height * self.scaler_2[1] + else: + head_logits = height * self.scaler[1] + index = torch.arange(length, device=height.device) + + mask = (index[:, None, None] <= index[None, None, :]) * ( + index[None, None, :] <= index[None, :, None] + ) + head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1) + head_logits.masked_fill_(~mask[None, :, :, :], -1e9) + + head_p = torch.softmax(head_logits, dim=-1) + + return head_p + + def parse(self, x, embs=None, n_cntxt_layers=0): + """Parse input sentence. + + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + distance: syntactic distance + height: syntactic height + """ + + mask = x != self.pad + mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0) + + if embs is None: + h = self.roberta.embeddings(x) + else: + h = embs + + if n_cntxt_layers > 0: + if n_cntxt_layers == 1: + parser_layers = self.parser_layers_1 + height_ff = self.height_ff_1 + distance_ff = self.distance_ff_1 + elif n_cntxt_layers == 2: + parser_layers = self.parser_layers_2 + height_ff = self.height_ff_2 + distance_ff = self.distance_ff_2 + for i in range(int(self.config.n_parser_layers/2)): + h = h.masked_fill(~mask[:, :, None], 0) + h = parser_layers[i](h) + + height = height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, dim=-1 + )[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max + ) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where( + margin_left > margin_right, margin_right, margin_left + ).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + else: + for i in range(self.config.n_parser_layers): + h = h.masked_fill(~mask[:, :, None], 0) + h = self.parser_layers[i](h) + + height = self.height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = self.distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, dim=-1 + )[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max + ) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where( + margin_left > margin_right, margin_right, margin_left + ).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + + return distance, height + + def generate_mask(self, x, distance, height, n_cntxt_layers=0): + """Compute head and cibling distribution for each token.""" + + bsz, length = x.size() + + eye = torch.eye(length, device=x.device, dtype=torch.bool) + eye = eye[None, :, :].expand((bsz, -1, -1)) + + block_p, block = self.compute_block(distance, height, n_cntxt_layers=n_cntxt_layers) + head_p = self.compute_head(height, n_cntxt_layers=n_cntxt_layers) + head = torch.einsum("blij,bijh->blh", block_p, head_p) + head = head.masked_fill(eye, 0) + child = head.transpose(1, 2) + cibling = torch.bmm(head, child).masked_fill(eye, 0) + + rel_list = [] + if "head" in self.config.relations: + rel_list.append(head) + if "child" in self.config.relations: + rel_list.append(child) + if "cibling" in self.config.relations: + rel_list.append(cibling) + + rel = torch.stack(rel_list, dim=1) + + if n_cntxt_layers > 0: + if n_cntxt_layers == 1: + rel_weight = self.rel_weight_1 + elif n_cntxt_layers == 2: + rel_weight = self.rel_weight_2 + else: + rel_weight = self.rel_weight + + dep = torch.einsum("lhr,brij->lbhij", rel_weight, rel) + + if n_cntxt_layers == 1: + num_layers = self.cntxt_layers_2.config.num_hidden_layers + else: + num_layers = self.roberta.config.num_hidden_layers + + att_mask = dep.reshape( + num_layers, + bsz, + self.config.num_attention_heads, + length, + length, + ) + + return att_mask, cibling, head, block + + def forward( + self, + input_ids: Optional[torch.LongTensor] = None, + attention_mask: Optional[torch.FloatTensor] = None, + token_type_ids: Optional[torch.LongTensor] = None, + position_ids: Optional[torch.LongTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, SequenceClassifierOutput]: + r""" + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., + config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If + `config.num_labels > 1` a classification loss is computed (Cross-Entropy). + """ + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + if self.config.n_cntxt_layers > 0: + cntxt_outputs = self.cntxt_layers( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict) + + + if self.config.n_cntxt_layers_2 > 0: + distance_1, height_1 = self.parse(input_ids, cntxt_outputs[0], n_cntxt_layers=1) + att_mask_1, _, _, _ = self.generate_mask(input_ids, distance_1, height_1, n_cntxt_layers=1) + + cntxt_outputs_2 = self.cntxt_layers_2( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + parser_att_mask=att_mask_1) + + sequence_output = cntxt_outputs_2[0] + + distance_2, height_2 = self.parse(input_ids, sequence_output[0], n_cntxt_layers=2) + att_mask, _, _, _ = self.generate_mask(input_ids, distance_2, height_2, n_cntxt_layers=2) + + elif self.config.n_cntxt_layers > 0: + distance, height = self.parse(input_ids, cntxt_outputs[0]) + att_mask, _, _, _ = self.generate_mask(input_ids, distance, height) + else: + distance, height = self.parse(input_ids) + att_mask, _, _, _ = self.generate_mask(input_ids, distance, height) + + outputs = self.roberta( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + parser_att_mask=att_mask, + ) + sequence_output = outputs[0] + logits = self.classifier(sequence_output) + + loss = None + if labels is not None: + if self.config.problem_type is None: + if self.num_labels == 1: + self.config.problem_type = "regression" + elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): + self.config.problem_type = "single_label_classification" + else: + self.config.problem_type = "multi_label_classification" + + if self.config.problem_type == "regression": + loss_fct = MSELoss() + if self.num_labels == 1: + loss = loss_fct(logits.squeeze(), labels.squeeze()) + else: + loss = loss_fct(logits, labels) + elif self.config.problem_type == "single_label_classification": + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) + elif self.config.problem_type == "multi_label_classification": + loss_fct = BCEWithLogitsLoss() + loss = loss_fct(logits, labels) + + if not return_dict: + output = (logits,) + outputs[2:] + return ((loss,) + output) if loss is not None else output + + return SequenceClassifierOutput( + loss=loss, + logits=logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +class RobertaClassificationHead(nn.Module): + """Head for sentence-level classification tasks.""" + + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + classifier_dropout = ( + config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob + ) + self.dropout = nn.Dropout(classifier_dropout) + self.out_proj = nn.Linear(config.hidden_size, config.num_labels) + + def forward(self, features, **kwargs): + x = features[:, 0, :] # take token (equiv. to [CLS]) + x = self.dropout(x) + x = self.dense(x) + x = torch.tanh(x) + x = self.dropout(x) + x = self.out_proj(x) + return x + + +def create_position_ids_from_input_ids( + input_ids, padding_idx, past_key_values_length=0 +): + """ + Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols + are ignored. This is modified from fairseq's `utils.make_positions`. + + Args: + x: torch.Tensor x: + + Returns: torch.Tensor + """ + # The series of casts and type-conversions here are carefully balanced to both work with ONNX export and XLA. + mask = input_ids.ne(padding_idx).int() + incremental_indices = ( + torch.cumsum(mask, dim=1).type_as(mask) + past_key_values_length + ) * mask + return incremental_indices.long() + padding_idx + + +def cumprod(x, reverse=False, exclusive=False): + """cumulative product.""" + if reverse: + x = x.flip([-1]) + + if exclusive: + x = F.pad(x[:, :, :-1], (1, 0), value=1) + + cx = x.cumprod(-1) + + if reverse: + cx = cx.flip([-1]) + return cx + + +def cumsum(x, reverse=False, exclusive=False): + """cumulative sum.""" + bsz, _, length = x.size() + device = x.device + if reverse: + if exclusive: + w = torch.ones([bsz, length, length], device=device).tril(-1) + else: + w = torch.ones([bsz, length, length], device=device).tril(0) + cx = torch.bmm(x, w) + else: + if exclusive: + w = torch.ones([bsz, length, length], device=device).triu(1) + else: + w = torch.ones([bsz, length, length], device=device).triu(0) + cx = torch.bmm(x, w) + return cx + + +def cummin(x, reverse=False, exclusive=False, max_value=1e9): + """cumulative min.""" + if reverse: + if exclusive: + x = F.pad(x[:, :, 1:], (0, 1), value=max_value) + x = x.flip([-1]).cummin(-1)[0].flip([-1]) + else: + if exclusive: + x = F.pad(x[:, :, :-1], (1, 0), value=max_value) + x = x.cummin(-1)[0] + return x diff --git a/finetune/mnli/predict_results.txt b/finetune/mnli/predict_results.txt new file mode 100644 index 0000000000000000000000000000000000000000..5e0bd284e23c43cc1fbdc5dfd95cb012fde18faa --- /dev/null +++ b/finetune/mnli/predict_results.txt @@ -0,0 +1,6563 @@ +index prediction +0 2 +1 2 +2 0 +3 1 +4 2 +5 2 +6 2 +7 0 +8 0 +9 2 +10 0 +11 0 +12 0 +13 0 +14 1 +15 1 +16 0 +17 1 +18 1 +19 2 +20 1 +21 1 +22 0 +23 0 +24 1 +25 2 +26 1 +27 1 +28 1 +29 2 +30 2 +31 1 +32 1 +33 0 +34 1 +35 0 +36 0 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"child" + ], + "torch_dtype": "float32", + "transformers_version": "4.26.1", + "type_vocab_size": 1, + "use_cache": true, + "vocab_size": 32000, + "weight_act": "softmax" +} diff --git a/finetune/mrpc/eval_results.json b/finetune/mrpc/eval_results.json new file mode 100644 index 0000000000000000000000000000000000000000..37e7910024b4cdba10437241d0ae31e5999e7160 --- /dev/null +++ b/finetune/mrpc/eval_results.json @@ -0,0 +1,11 @@ +{ + "epoch": 10.0, + "eval_accuracy": 0.6666666865348816, + "eval_f1": 0.7649402390438248, + "eval_loss": 1.3271363973617554, + "eval_mcc": 0.19335799090970276, + "eval_runtime": 0.3788, + "eval_samples": 177, + "eval_samples_per_second": 467.21, + "eval_steps_per_second": 60.711 +} \ No newline at end of file diff --git a/finetune/mrpc/merges.txt b/finetune/mrpc/merges.txt new file mode 100644 index 0000000000000000000000000000000000000000..94b4f252cb566a4dac62a08c78d85169ff4e91be --- /dev/null +++ b/finetune/mrpc/merges.txt @@ -0,0 +1,31794 @@ +#version: 0.2 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". +A gh +A ld +A unty +B erm +B ah +B OY +C arr +C ulture +C rying +C atholic +D ang +F le +F ew +F eed +F ront +G ab +G ray +G ibbs +H ong +H erm +I ra +I aw +J or +J ana +K y +K night +K askaskia +K ELLY +L ED +L osing +L anguage +M onday +M ATT +N S +N urse +N othin +P ha +P ap +P ont +P rem +P EOPLE +R i +R uss +S ou +S ty +S alt +T ie +T unes +V i +W el +W ARD +Y awang +b able +b ahn +c ress +c ole +c rit +c ÃŃa +d oc +d ling +e ff +f ool +f ired +f urther +f amous +f emale +g is +g all +g um +g irlfriend +h oun +h wa +i ations +j ord +j ah +k il +k ai +k ids +m and +m ap +m oving +m ummy +o form +s and +t ha +t hang +u aries +v id +v ine +v able +v ati +w ine +w ag +z le + ¡ +à ¬ +Å Ħ +Ġt umble +he x +he id +in ine +in stead +Ġw izard +ou ching +on tain +ha ha +is cher +Ġb irl +Ġb ert +Ġb raid +Ġo t +Ġo mitted +Ġc his +Ġm asks +Ġm uttering +Ġf oll +an ley +Ġd azed +Ġd ouche +es on +Ġp he +Ġp au +ar ina +ar oni +ar von +at io +Ġl icked +Ġl icking +Ġg it +Ġn il +ĠI ld +le ments +Ġh uts +Ġhe ur +al ming +Ġit ching +Ġit chy +Ġe up +ut sc +Ġwe ights +Ġfor fe +Ġfor ci +ĠS ter +ĠS ino +ĠS ales +ĠS hib +ĠS iri +st ed +st ack +st range +st icks +ri y +ad itionally +Ġst if +Ġst rew +Ġst ains +Ġst ables +Ġst unning +oo ges +ĠA part +ĠA men +ĠA cha +ĠC ertain +ĠC ey +ĠC LA +ĠC ARR +Ġu gh +ĠM ist +ĠM OM +ĠM IGHT +Ġas semble +ill ors +Ġse ag +ĠH ollow +ĠH ades +ĠH ym +il ization +ĠB old +Ġli ability +op ping +ĠP K +ĠP ill +ĠP ew +ĠP ied +ĠP oe +ĠP OWER +ĠP iazza +ĠT il +ĠT ia +ĠT ick +un er +ĠW ap +Ġr ushes +Ġj est +Ġj ets +ĠD M +ĠD ort +ĠD OU +ĠD IS +ĠD uart +ĠD izzy +ĠG ina +um en +Ġso ils +Ġso aring +est us +Ġal igned +Ġall erg +ist ered +ĠR T +ĠR im +ĠR ac +ĠR iding +ĠR umble +ie ving +art y +ab lan +ĠL ac +ĠL ust +ĠL um +ĠL em +ĠL ions +ĠL ars +Ġcon gest +ĠN ights +ig l +han na +han sa +Ġup hold +Ġcom ical +Ġher ring +ĠJ ude +Ġout look +Ġout lined +ra ult +Ġpl umber +Ġpl aus +od i +ink meaner +Ġpro state +Ġpro stitutes +Ġex clude +Ġv ener +Ġv ines +Ġv ipers +ard y +ity a +ĠO D +ies selin +Ġman nik +ud ers +ud eau +ap o +ĠK iri +ven lock +Ġtr acing +ure en +ĠHe w +ĠHe gel +ove l +ick i +ci us +ĠU CLA +reat ic +ĠSt ras +ĠSt empniak +No ll +other wise +ne ither +Ġback grounds +Ġover d +Ġgr inding +That s +Ġen act +Ġen cl +Ġen vious +og g +og un +pl anet +ord ered +Ġdown stream +Ġun real +Ġun wanted +Ġun ravel +iz oph +Ġam iable +Ġcomp osing +Ġbl ush +ĠV on +ĠV ig +ĠIn v +urn al +Ġplay doh +ress ions +av or +av ius +Ġdis ks +Ġgu iding +ĠY ek +be es +ip ani +ip hon +Ġfl aws +ect ing +vers ation +ks het +Ġrem nants +ix by +Ġsha ving +Ġsec urities +Ġspe ars +Ġlast ly +li k +Ġend ocr +Ġlea ked +Ġfam ine +Ġnight gown +ny a +de gree +Ġ18 10 +Ġ18 28 +Ġ18 32 +gg les +ĠShe ldon +ĠAr um +Ġmar ries diff --git a/finetune/mrpc/modeling_structroberta.py b/finetune/mrpc/modeling_structroberta.py new file mode 100644 index 0000000000000000000000000000000000000000..9195a79dc7ae5e8c606bb77a05b8a1d5672bfdc0 --- /dev/null +++ b/finetune/mrpc/modeling_structroberta.py @@ -0,0 +1,2146 @@ +import copy +import math +from typing import List, Optional, Tuple, Union +import torch +import torch.utils.checkpoint +from packaging import version +from torch import nn +import torch.nn.functional as F +from torch.nn import CrossEntropyLoss, MSELoss, BCEWithLogitsLoss +from transformers.activations import ACT2FN, gelu +from transformers.modeling_outputs import ( + BaseModelOutputWithPastAndCrossAttentions, + BaseModelOutputWithPoolingAndCrossAttentions, + MaskedLMOutput, + SequenceClassifierOutput +) +from transformers.modeling_utils import ( + PreTrainedModel, + apply_chunking_to_forward, + find_pruneable_heads_and_indices, + prune_linear_layer, +) +from transformers.utils import logging +from transformers import RobertaConfig + + +logger = logging.get_logger(__name__) +ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST = [ + "roberta-base", + "roberta-large", + "roberta-large-mnli", + "distilroberta-base", + "roberta-base-openai-detector", + "roberta-large-openai-detector", + # See all RoBERTa models at https://huggingface.co/models?filter=roberta +] + + +class StructRobertaConfig(RobertaConfig): + model_type = "roberta" + + def __init__( + self, + n_parser_layers=4, + conv_size=9, + relations=("head", "child"), + weight_act="softmax", + n_cntxt_layers=3, + n_cntxt_layers_2=0, + **kwargs,): + + super().__init__(**kwargs) + self.n_cntxt_layers = n_cntxt_layers + self.n_parser_layers = n_parser_layers + self.n_cntxt_layers_2 = n_cntxt_layers_2 + self.conv_size = conv_size + self.relations = relations + self.weight_act = weight_act + +class Conv1d(nn.Module): + """1D convolution layer.""" + + def __init__(self, hidden_size, kernel_size, dilation=1): + """Initialization. + + Args: + hidden_size: dimension of input embeddings + kernel_size: convolution kernel size + dilation: the spacing between the kernel points + """ + super(Conv1d, self).__init__() + + if kernel_size % 2 == 0: + padding = (kernel_size // 2) * dilation + self.shift = True + else: + padding = ((kernel_size - 1) // 2) * dilation + self.shift = False + self.conv = nn.Conv1d( + hidden_size, hidden_size, kernel_size, padding=padding, dilation=dilation + ) + + def forward(self, x): + """Compute convolution. + + Args: + x: input embeddings + Returns: + conv_output: convolution results + """ + + if self.shift: + return self.conv(x.transpose(1, 2)).transpose(1, 2)[:, 1:] + else: + return self.conv(x.transpose(1, 2)).transpose(1, 2) + + +class RobertaEmbeddings(nn.Module): + """ + Same as BertEmbeddings with a tiny tweak for positional embeddings indexing. + """ + + # Copied from transformers.models.bert.modeling_bert.BertEmbeddings.__init__ + def __init__(self, config): + super().__init__() + self.word_embeddings = nn.Embedding( + config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id + ) + self.position_embeddings = nn.Embedding( + config.max_position_embeddings, config.hidden_size + ) + self.token_type_embeddings = nn.Embedding( + config.type_vocab_size, config.hidden_size + ) + + # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load + # any TensorFlow checkpoint file + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + # position_ids (1, len position emb) is contiguous in memory and exported when serialized + self.position_embedding_type = getattr( + config, "position_embedding_type", "absolute" + ) + self.register_buffer( + "position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)) + ) + if version.parse(torch.__version__) > version.parse("1.6.0"): + self.register_buffer( + "token_type_ids", + torch.zeros(self.position_ids.size(), dtype=torch.long), + persistent=False, + ) + + # End copy + self.padding_idx = config.pad_token_id + self.position_embeddings = nn.Embedding( + config.max_position_embeddings, + config.hidden_size, + padding_idx=self.padding_idx, + ) + + def forward( + self, + input_ids=None, + token_type_ids=None, + position_ids=None, + inputs_embeds=None, + past_key_values_length=0, + ): + if position_ids is None: + if input_ids is not None: + # Create the position ids from the input token ids. Any padded tokens remain padded. + position_ids = create_position_ids_from_input_ids( + input_ids, self.padding_idx, past_key_values_length + ) + else: + position_ids = self.create_position_ids_from_inputs_embeds( + inputs_embeds + ) + + if input_ids is not None: + input_shape = input_ids.size() + else: + input_shape = inputs_embeds.size()[:-1] + + seq_length = input_shape[1] + + # Setting the token_type_ids to the registered buffer in constructor where it is all zeros, which usually occurs + # when its auto-generated, registered buffer helps users when tracing the model without passing token_type_ids, solves + # issue #5664 + if token_type_ids is None: + if hasattr(self, "token_type_ids"): + buffered_token_type_ids = self.token_type_ids[:, :seq_length] + buffered_token_type_ids_expanded = buffered_token_type_ids.expand( + input_shape[0], seq_length + ) + token_type_ids = buffered_token_type_ids_expanded + else: + token_type_ids = torch.zeros( + input_shape, dtype=torch.long, device=self.position_ids.device + ) + + if inputs_embeds is None: + inputs_embeds = self.word_embeddings(input_ids) + token_type_embeddings = self.token_type_embeddings(token_type_ids) + + embeddings = inputs_embeds + token_type_embeddings + if self.position_embedding_type == "absolute": + position_embeddings = self.position_embeddings(position_ids) + embeddings += position_embeddings + embeddings = self.LayerNorm(embeddings) + embeddings = self.dropout(embeddings) + return embeddings + + def create_position_ids_from_inputs_embeds(self, inputs_embeds): + """ + We are provided embeddings directly. We cannot infer which are padded so just generate sequential position ids. + + Args: + inputs_embeds: torch.Tensor + + Returns: torch.Tensor + """ + input_shape = inputs_embeds.size()[:-1] + sequence_length = input_shape[1] + + position_ids = torch.arange( + self.padding_idx + 1, + sequence_length + self.padding_idx + 1, + dtype=torch.long, + device=inputs_embeds.device, + ) + return position_ids.unsqueeze(0).expand(input_shape) + + +# Copied from transformers.models.bert.modeling_bert.BertSelfAttention with Bert->Roberta +class RobertaSelfAttention(nn.Module): + def __init__(self, config, position_embedding_type=None): + super().__init__() + if config.hidden_size % config.num_attention_heads != 0 and not hasattr( + config, "embedding_size" + ): + raise ValueError( + f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention " + f"heads ({config.num_attention_heads})" + ) + + self.num_attention_heads = config.num_attention_heads + self.attention_head_size = int(config.hidden_size / config.num_attention_heads) + self.all_head_size = self.num_attention_heads * self.attention_head_size + + self.query = nn.Linear(config.hidden_size, self.all_head_size) + self.key = nn.Linear(config.hidden_size, self.all_head_size) + self.value = nn.Linear(config.hidden_size, self.all_head_size) + + self.dropout = nn.Dropout(config.attention_probs_dropout_prob) + self.position_embedding_type = position_embedding_type or getattr( + config, "position_embedding_type", "absolute" + ) + if ( + self.position_embedding_type == "relative_key" + or self.position_embedding_type == "relative_key_query" + ): + self.max_position_embeddings = config.max_position_embeddings + self.distance_embedding = nn.Embedding( + 2 * config.max_position_embeddings - 1, self.attention_head_size + ) + + self.is_decoder = config.is_decoder + + def transpose_for_scores(self, x): + new_x_shape = x.size()[:-1] + ( + self.num_attention_heads, + self.attention_head_size, + ) + x = x.view(new_x_shape) + return x.permute(0, 2, 1, 3) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.FloatTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, + output_attentions: Optional[bool] = False, + parser_att_mask=None, + ) -> Tuple[torch.Tensor]: + mixed_query_layer = self.query(hidden_states) + + # If this is instantiated as a cross-attention module, the keys + # and values come from an encoder; the attention mask needs to be + # such that the encoder's padding tokens are not attended to. + is_cross_attention = encoder_hidden_states is not None + + if is_cross_attention and past_key_value is not None: + # reuse k,v, cross_attentions + key_layer = past_key_value[0] + value_layer = past_key_value[1] + attention_mask = encoder_attention_mask + elif is_cross_attention: + key_layer = self.transpose_for_scores(self.key(encoder_hidden_states)) + value_layer = self.transpose_for_scores(self.value(encoder_hidden_states)) + attention_mask = encoder_attention_mask + elif past_key_value is not None: + key_layer = self.transpose_for_scores(self.key(hidden_states)) + value_layer = self.transpose_for_scores(self.value(hidden_states)) + key_layer = torch.cat([past_key_value[0], key_layer], dim=2) + value_layer = torch.cat([past_key_value[1], value_layer], dim=2) + else: + key_layer = self.transpose_for_scores(self.key(hidden_states)) + value_layer = self.transpose_for_scores(self.value(hidden_states)) + + query_layer = self.transpose_for_scores(mixed_query_layer) + + if self.is_decoder: + # if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states. + # Further calls to cross_attention layer can then reuse all cross-attention + # key/value_states (first "if" case) + # if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of + # all previous decoder key/value_states. Further calls to uni-directional self-attention + # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case) + # if encoder bi-directional self-attention `past_key_value` is always `None` + past_key_value = (key_layer, value_layer) + + # Take the dot product between "query" and "key" to get the raw attention scores. + attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) + + if ( + self.position_embedding_type == "relative_key" + or self.position_embedding_type == "relative_key_query" + ): + seq_length = hidden_states.size()[1] + position_ids_l = torch.arange( + seq_length, dtype=torch.long, device=hidden_states.device + ).view(-1, 1) + position_ids_r = torch.arange( + seq_length, dtype=torch.long, device=hidden_states.device + ).view(1, -1) + distance = position_ids_l - position_ids_r + positional_embedding = self.distance_embedding( + distance + self.max_position_embeddings - 1 + ) + positional_embedding = positional_embedding.to( + dtype=query_layer.dtype + ) # fp16 compatibility + + if self.position_embedding_type == "relative_key": + relative_position_scores = torch.einsum( + "bhld,lrd->bhlr", query_layer, positional_embedding + ) + attention_scores = attention_scores + relative_position_scores + elif self.position_embedding_type == "relative_key_query": + relative_position_scores_query = torch.einsum( + "bhld,lrd->bhlr", query_layer, positional_embedding + ) + relative_position_scores_key = torch.einsum( + "bhrd,lrd->bhlr", key_layer, positional_embedding + ) + attention_scores = ( + attention_scores + + relative_position_scores_query + + relative_position_scores_key + ) + + attention_scores = attention_scores / math.sqrt(self.attention_head_size) + if attention_mask is not None: + # Apply the attention mask is (precomputed for all layers in RobertaModel forward() function) + attention_scores = attention_scores + attention_mask + + if parser_att_mask is None: + # Normalize the attention scores to probabilities. + attention_probs = nn.functional.softmax(attention_scores, dim=-1) + else: + attention_probs = torch.sigmoid(attention_scores) * parser_att_mask + + # This is actually dropping out entire tokens to attend to, which might + # seem a bit unusual, but is taken from the original Transformer paper. + attention_probs = self.dropout(attention_probs) + + # Mask heads if we want to + if head_mask is not None: + attention_probs = attention_probs * head_mask + + context_layer = torch.matmul(attention_probs, value_layer) + + context_layer = context_layer.permute(0, 2, 1, 3).contiguous() + new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) + context_layer = context_layer.view(new_context_layer_shape) + + outputs = ( + (context_layer, attention_probs) if output_attentions else (context_layer,) + ) + + if self.is_decoder: + outputs = outputs + (past_key_value,) + return outputs + + +# Copied from transformers.models.bert.modeling_bert.BertSelfOutput +class RobertaSelfOutput(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward( + self, hidden_states: torch.Tensor, input_tensor: torch.Tensor + ) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + hidden_states = self.LayerNorm(hidden_states + input_tensor) + return hidden_states + + +# Copied from transformers.models.bert.modeling_bert.BertAttention with Bert->Roberta +class RobertaAttention(nn.Module): + def __init__(self, config, position_embedding_type=None): + super().__init__() + self.self = RobertaSelfAttention( + config, position_embedding_type=position_embedding_type + ) + self.output = RobertaSelfOutput(config) + self.pruned_heads = set() + + def prune_heads(self, heads): + if len(heads) == 0: + return + heads, index = find_pruneable_heads_and_indices( + heads, + self.self.num_attention_heads, + self.self.attention_head_size, + self.pruned_heads, + ) + + # Prune linear layers + self.self.query = prune_linear_layer(self.self.query, index) + self.self.key = prune_linear_layer(self.self.key, index) + self.self.value = prune_linear_layer(self.self.value, index) + self.output.dense = prune_linear_layer(self.output.dense, index, dim=1) + + # Update hyper params and store pruned heads + self.self.num_attention_heads = self.self.num_attention_heads - len(heads) + self.self.all_head_size = ( + self.self.attention_head_size * self.self.num_attention_heads + ) + self.pruned_heads = self.pruned_heads.union(heads) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.FloatTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, + output_attentions: Optional[bool] = False, + parser_att_mask=None, + ) -> Tuple[torch.Tensor]: + self_outputs = self.self( + hidden_states, + attention_mask, + head_mask, + encoder_hidden_states, + encoder_attention_mask, + past_key_value, + output_attentions, + parser_att_mask=parser_att_mask, + ) + attention_output = self.output(self_outputs[0], hidden_states) + outputs = (attention_output,) + self_outputs[ + 1: + ] # add attentions if we output them + return outputs + + +# Copied from transformers.models.bert.modeling_bert.BertIntermediate +class RobertaIntermediate(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.intermediate_size) + if isinstance(config.hidden_act, str): + self.intermediate_act_fn = ACT2FN[config.hidden_act] + else: + self.intermediate_act_fn = config.hidden_act + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.intermediate_act_fn(hidden_states) + return hidden_states + + +# Copied from transformers.models.bert.modeling_bert.BertOutput +class RobertaOutput(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.intermediate_size, config.hidden_size) + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward( + self, hidden_states: torch.Tensor, input_tensor: torch.Tensor + ) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + hidden_states = self.LayerNorm(hidden_states + input_tensor) + return hidden_states + + +# Copied from transformers.models.bert.modeling_bert.BertLayer with Bert->Roberta +class RobertaLayer(nn.Module): + def __init__(self, config): + super().__init__() + self.chunk_size_feed_forward = config.chunk_size_feed_forward + self.seq_len_dim = 1 + self.attention = RobertaAttention(config) + self.is_decoder = config.is_decoder + self.add_cross_attention = config.add_cross_attention + if self.add_cross_attention: + if not self.is_decoder: + raise ValueError( + f"{self} should be used as a decoder model if cross attention is added" + ) + self.crossattention = RobertaAttention( + config, position_embedding_type="absolute" + ) + self.intermediate = RobertaIntermediate(config) + self.output = RobertaOutput(config) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.FloatTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, + output_attentions: Optional[bool] = False, + parser_att_mask=None, + ) -> Tuple[torch.Tensor]: + # decoder uni-directional self-attention cached key/values tuple is at positions 1,2 + self_attn_past_key_value = ( + past_key_value[:2] if past_key_value is not None else None + ) + self_attention_outputs = self.attention( + hidden_states, + attention_mask, + head_mask, + output_attentions=output_attentions, + past_key_value=self_attn_past_key_value, + parser_att_mask=parser_att_mask, + ) + attention_output = self_attention_outputs[0] + + # if decoder, the last output is tuple of self-attn cache + if self.is_decoder: + outputs = self_attention_outputs[1:-1] + present_key_value = self_attention_outputs[-1] + else: + outputs = self_attention_outputs[ + 1: + ] # add self attentions if we output attention weights + + cross_attn_present_key_value = None + if self.is_decoder and encoder_hidden_states is not None: + if not hasattr(self, "crossattention"): + raise ValueError( + f"If `encoder_hidden_states` are passed, {self} has to be instantiated with cross-attention layers by setting `config.add_cross_attention=True`" + ) + + # cross_attn cached key/values tuple is at positions 3,4 of past_key_value tuple + cross_attn_past_key_value = ( + past_key_value[-2:] if past_key_value is not None else None + ) + cross_attention_outputs = self.crossattention( + attention_output, + attention_mask, + head_mask, + encoder_hidden_states, + encoder_attention_mask, + cross_attn_past_key_value, + output_attentions, + ) + attention_output = cross_attention_outputs[0] + outputs = ( + outputs + cross_attention_outputs[1:-1] + ) # add cross attentions if we output attention weights + + # add cross-attn cache to positions 3,4 of present_key_value tuple + cross_attn_present_key_value = cross_attention_outputs[-1] + present_key_value = present_key_value + cross_attn_present_key_value + + layer_output = apply_chunking_to_forward( + self.feed_forward_chunk, + self.chunk_size_feed_forward, + self.seq_len_dim, + attention_output, + ) + outputs = (layer_output,) + outputs + + # if decoder, return the attn key/values as the last output + if self.is_decoder: + outputs = outputs + (present_key_value,) + + return outputs + + def feed_forward_chunk(self, attention_output): + intermediate_output = self.intermediate(attention_output) + layer_output = self.output(intermediate_output, attention_output) + return layer_output + + +# Copied from transformers.models.bert.modeling_bert.BertEncoder with Bert->Roberta +class RobertaEncoder(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + self.layer = nn.ModuleList( + [RobertaLayer(config) for _ in range(config.num_hidden_layers)] + ) + self.gradient_checkpointing = False + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.FloatTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = False, + output_hidden_states: Optional[bool] = False, + return_dict: Optional[bool] = True, + parser_att_mask=None, + ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPastAndCrossAttentions]: + all_hidden_states = () if output_hidden_states else None + all_self_attentions = () if output_attentions else None + all_cross_attentions = ( + () if output_attentions and self.config.add_cross_attention else None + ) + + next_decoder_cache = () if use_cache else None + for i, layer_module in enumerate(self.layer): + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + layer_head_mask = head_mask[i] if head_mask is not None else None + past_key_value = past_key_values[i] if past_key_values is not None else None + + if self.gradient_checkpointing and self.training: + + if use_cache: + logger.warning( + "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." + ) + use_cache = False + + def create_custom_forward(module): + def custom_forward(*inputs): + return module(*inputs, past_key_value, output_attentions) + + return custom_forward + + layer_outputs = torch.utils.checkpoint.checkpoint( + create_custom_forward(layer_module), + hidden_states, + attention_mask, + layer_head_mask, + encoder_hidden_states, + encoder_attention_mask, + ) + else: + if parser_att_mask is not None: + layer_outputs = layer_module( + hidden_states, + attention_mask, + layer_head_mask, + encoder_hidden_states, + encoder_attention_mask, + past_key_value, + output_attentions, + parser_att_mask=parser_att_mask[i]) + else: + layer_outputs = layer_module( + hidden_states, + attention_mask, + layer_head_mask, + encoder_hidden_states, + encoder_attention_mask, + past_key_value, + output_attentions, + parser_att_mask=None) + + + hidden_states = layer_outputs[0] + if use_cache: + next_decoder_cache += (layer_outputs[-1],) + if output_attentions: + all_self_attentions = all_self_attentions + (layer_outputs[1],) + if self.config.add_cross_attention: + all_cross_attentions = all_cross_attentions + (layer_outputs[2],) + + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + if not return_dict: + return tuple( + v + for v in [ + hidden_states, + next_decoder_cache, + all_hidden_states, + all_self_attentions, + all_cross_attentions, + ] + if v is not None + ) + return BaseModelOutputWithPastAndCrossAttentions( + last_hidden_state=hidden_states, + past_key_values=next_decoder_cache, + hidden_states=all_hidden_states, + attentions=all_self_attentions, + cross_attentions=all_cross_attentions, + ) + + +# Copied from transformers.models.bert.modeling_bert.BertPooler +class RobertaPooler(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.activation = nn.Tanh() + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + # We "pool" the model by simply taking the hidden state corresponding + # to the first token. + first_token_tensor = hidden_states[:, 0] + pooled_output = self.dense(first_token_tensor) + pooled_output = self.activation(pooled_output) + return pooled_output + + +class RobertaPreTrainedModel(PreTrainedModel): + """ + An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained + models. + """ + + config_class = RobertaConfig + base_model_prefix = "roberta" + supports_gradient_checkpointing = True + + # Copied from transformers.models.bert.modeling_bert.BertPreTrainedModel._init_weights + def _init_weights(self, module): + """Initialize the weights""" + if isinstance(module, nn.Linear): + # Slightly different from the TF version which uses truncated_normal for initialization + # cf https://github.com/pytorch/pytorch/pull/5617 + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.Embedding): + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.padding_idx is not None: + module.weight.data[module.padding_idx].zero_() + elif isinstance(module, nn.LayerNorm): + if module.bias is not None: + module.bias.data.zero_() + module.weight.data.fill_(1.0) + + def _set_gradient_checkpointing(self, module, value=False): + if isinstance(module, RobertaEncoder): + module.gradient_checkpointing = value + + def update_keys_to_ignore(self, config, del_keys_to_ignore): + """Remove some keys from ignore list""" + if not config.tie_word_embeddings: + # must make a new list, or the class variable gets modified! + self._keys_to_ignore_on_save = [ + k for k in self._keys_to_ignore_on_save if k not in del_keys_to_ignore + ] + self._keys_to_ignore_on_load_missing = [ + k + for k in self._keys_to_ignore_on_load_missing + if k not in del_keys_to_ignore + ] + + +ROBERTA_START_DOCSTRING = r""" + + This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the + library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads + etc.) + + This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. + Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage + and behavior. + + Parameters: + config ([`RobertaConfig`]): Model configuration class with all the parameters of the + model. Initializing with a config file does not load the weights associated with the model, only the + configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. +""" + + +ROBERTA_INPUTS_DOCSTRING = r""" + Args: + input_ids (`torch.LongTensor` of shape `({0})`): + Indices of input sequence tokens in the vocabulary. + + Indices can be obtained using [`RobertaTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + [What are input IDs?](../glossary#input-ids) + attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*): + Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + + [What are attention masks?](../glossary#attention-mask) + token_type_ids (`torch.LongTensor` of shape `({0})`, *optional*): + Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, + 1]`: + + - 0 corresponds to a *sentence A* token, + - 1 corresponds to a *sentence B* token. + + [What are token type IDs?](../glossary#token-type-ids) + position_ids (`torch.LongTensor` of shape `({0})`, *optional*): + Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, + config.max_position_embeddings - 1]`. + + [What are position IDs?](../glossary#position-ids) + head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): + Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: + + - 1 indicates the head is **not masked**, + - 0 indicates the head is **masked**. + + inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_size)`, *optional*): + Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This + is useful if you want more control over how to convert `input_ids` indices into associated vectors than the + model's internal embedding lookup matrix. + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned + tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for + more detail. + return_dict (`bool`, *optional*): + Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. +""" + + +class RobertaModel(RobertaPreTrainedModel): + """ + + The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of + cross-attention is added between the self-attention layers, following the architecture described in *Attention is + all you need*_ by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz + Kaiser and Illia Polosukhin. + + To behave as an decoder the model needs to be initialized with the `is_decoder` argument of the configuration set + to `True`. To be used in a Seq2Seq model, the model needs to initialized with both `is_decoder` argument and + `add_cross_attention` set to `True`; an `encoder_hidden_states` is then expected as an input to the forward pass. + + .. _*Attention is all you need*: https://arxiv.org/abs/1706.03762 + + """ + + _keys_to_ignore_on_load_missing = [r"position_ids"] + + # Copied from transformers.models.bert.modeling_bert.BertModel.__init__ with Bert->Roberta + def __init__(self, config, add_pooling_layer=True): + super().__init__(config) + self.config = config + + self.embeddings = RobertaEmbeddings(config) + self.encoder = RobertaEncoder(config) + + self.pooler = RobertaPooler(config) if add_pooling_layer else None + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.embeddings.word_embeddings + + def set_input_embeddings(self, value): + self.embeddings.word_embeddings = value + + def _prune_heads(self, heads_to_prune): + """ + Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base + class PreTrainedModel + """ + for layer, heads in heads_to_prune.items(): + self.encoder.layer[layer].attention.prune_heads(heads) + + # Copied from transformers.models.bert.modeling_bert.BertModel.forward + def forward( + self, + input_ids: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + token_type_ids: Optional[torch.Tensor] = None, + position_ids: Optional[torch.Tensor] = None, + head_mask: Optional[torch.Tensor] = None, + inputs_embeds: Optional[torch.Tensor] = None, + encoder_hidden_states: Optional[torch.Tensor] = None, + encoder_attention_mask: Optional[torch.Tensor] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + parser_att_mask=None, + ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPoolingAndCrossAttentions]: + r""" + encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): + Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if + the model is configured as a decoder. + encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*): + Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in + the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): + Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. + + If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that + don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all + `decoder_input_ids` of shape `(batch_size, sequence_length)`. + use_cache (`bool`, *optional*): + If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see + `past_key_values`). + """ + output_attentions = ( + output_attentions + if output_attentions is not None + else self.config.output_attentions + ) + output_hidden_states = ( + output_hidden_states + if output_hidden_states is not None + else self.config.output_hidden_states + ) + return_dict = ( + return_dict if return_dict is not None else self.config.use_return_dict + ) + + if self.config.is_decoder: + use_cache = use_cache if use_cache is not None else self.config.use_cache + else: + use_cache = False + + if input_ids is not None and inputs_embeds is not None: + raise ValueError( + "You cannot specify both input_ids and inputs_embeds at the same time" + ) + elif input_ids is not None: + input_shape = input_ids.size() + elif inputs_embeds is not None: + input_shape = inputs_embeds.size()[:-1] + else: + raise ValueError("You have to specify either input_ids or inputs_embeds") + + batch_size, seq_length = input_shape + device = input_ids.device if input_ids is not None else inputs_embeds.device + + # past_key_values_length + past_key_values_length = ( + past_key_values[0][0].shape[2] if past_key_values is not None else 0 + ) + + if attention_mask is None: + attention_mask = torch.ones( + ((batch_size, seq_length + past_key_values_length)), device=device + ) + + if token_type_ids is None: + if hasattr(self.embeddings, "token_type_ids"): + buffered_token_type_ids = self.embeddings.token_type_ids[:, :seq_length] + buffered_token_type_ids_expanded = buffered_token_type_ids.expand( + batch_size, seq_length + ) + token_type_ids = buffered_token_type_ids_expanded + else: + token_type_ids = torch.zeros( + input_shape, dtype=torch.long, device=device + ) + + # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length] + # ourselves in which case we just need to make it broadcastable to all heads. + extended_attention_mask: torch.Tensor = self.get_extended_attention_mask( + attention_mask, input_shape, device + ) + + # If a 2D or 3D attention mask is provided for the cross-attention + # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length] + if self.config.is_decoder and encoder_hidden_states is not None: + ( + encoder_batch_size, + encoder_sequence_length, + _, + ) = encoder_hidden_states.size() + encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length) + if encoder_attention_mask is None: + encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device) + encoder_extended_attention_mask = self.invert_attention_mask( + encoder_attention_mask + ) + else: + encoder_extended_attention_mask = None + + # Prepare head mask if needed + # 1.0 in head_mask indicate we keep the head + # attention_probs has shape bsz x n_heads x N x N + # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] + # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] + head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) + + embedding_output = self.embeddings( + input_ids=input_ids, + position_ids=position_ids, + token_type_ids=token_type_ids, + inputs_embeds=inputs_embeds, + past_key_values_length=past_key_values_length, + ) + encoder_outputs = self.encoder( + embedding_output, + attention_mask=extended_attention_mask, + head_mask=head_mask, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_extended_attention_mask, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + parser_att_mask=parser_att_mask, + ) + sequence_output = encoder_outputs[0] + pooled_output = ( + self.pooler(sequence_output) if self.pooler is not None else None + ) + + if not return_dict: + return (sequence_output, pooled_output) + encoder_outputs[1:] + + return BaseModelOutputWithPoolingAndCrossAttentions( + last_hidden_state=sequence_output, + pooler_output=pooled_output, + past_key_values=encoder_outputs.past_key_values, + hidden_states=encoder_outputs.hidden_states, + attentions=encoder_outputs.attentions, + cross_attentions=encoder_outputs.cross_attentions, + ) + + +class StructRoberta(RobertaPreTrainedModel): + _keys_to_ignore_on_save = [r"lm_head.decoder.weight", r"lm_head.decoder.bias"] + _keys_to_ignore_on_load_missing = [ + r"position_ids", + r"lm_head.decoder.weight", + r"lm_head.decoder.bias", + ] + _keys_to_ignore_on_load_unexpected = [r"pooler"] + + def __init__(self, config): + super().__init__(config) + + if config.is_decoder: + logger.warning( + "If you want to use `RobertaForMaskedLM` make sure `config.is_decoder=False` for " + "bi-directional self-attention." + ) + + + if config.n_cntxt_layers > 0: + config_cntxt = copy.deepcopy(config) + config_cntxt.num_hidden_layers = config.n_cntxt_layers + + self.cntxt_layers = RobertaModel(config_cntxt, add_pooling_layer=False) + + if config.n_cntxt_layers_2 > 0: + self.parser_layers_1 = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(int(config.n_parser_layers/2)) + ] + ) + + self.distance_ff_1 = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff_1 = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight_1 = nn.Parameter( + torch.zeros((config.n_cntxt_layers_2, config.num_attention_heads, n_rel)) + ) + self._rel_weight_1.data.normal_(0, 0.1) + + self._scaler_1 = nn.Parameter(torch.zeros(2)) + + config_cntxt_2 = copy.deepcopy(config) + config_cntxt_2.num_hidden_layers = config.n_cntxt_layers_2 + + self.cntxt_layers_2 = RobertaModel(config_cntxt_2, add_pooling_layer=False) + + + self.parser_layers_2 = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(int(config.n_parser_layers/2)) + ] + ) + + self.distance_ff_2 = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff_2 = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight_2 = nn.Parameter( + torch.zeros((config.num_hidden_layers, config.num_attention_heads, n_rel)) + ) + self._rel_weight_2.data.normal_(0, 0.1) + + self._scaler_2 = nn.Parameter(torch.zeros(2)) + + else: + self.parser_layers = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(config.n_parser_layers) + ] + ) + + self.distance_ff = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight = nn.Parameter( + torch.zeros((config.num_hidden_layers, config.num_attention_heads, n_rel)) + ) + self._rel_weight.data.normal_(0, 0.1) + + self._scaler = nn.Parameter(torch.zeros(2)) + + self.roberta = RobertaModel(config, add_pooling_layer=False) + + if config.n_cntxt_layers > 0: + self.cntxt_layers.embeddings = self.roberta.embeddings + if config.n_cntxt_layers_2 > 0: + self.cntxt_layers_2.embeddings = self.roberta.embeddings + + self.lm_head = RobertaLMHead(config) + + self.pad = config.pad_token_id + + # The LM head weights require special treatment only when they are tied with the word embeddings + self.update_keys_to_ignore(config, ["lm_head.decoder.weight"]) + + # Initialize weights and apply final processing + self.post_init() + + def get_output_embeddings(self): + return self.lm_head.decoder + + def set_output_embeddings(self, new_embeddings): + self.lm_head.decoder = new_embeddings + + @property + def scaler(self): + return self._scaler.exp() + + @property + def scaler_1(self): + return self._scaler_1.exp() + + @property + def scaler_2(self): + return self._scaler_2.exp() + + @property + def rel_weight(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight, dim=-1) + + @property + def rel_weight_1(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight_1) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight_1, dim=-1) + + + @property + def rel_weight_2(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight_2) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight_2, dim=-1) + + + def compute_block(self, distance, height, n_cntxt_layers=0): + """Compute constituents from distance and height.""" + + if n_cntxt_layers>0: + if n_cntxt_layers == 1: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler_1[0] + elif n_cntxt_layers == 2: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler_2[0] + else: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0] + + gamma = torch.sigmoid(-beta_logits) + ones = torch.ones_like(gamma) + + block_mask_left = cummin( + gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1 + ) + block_mask_left = block_mask_left - F.pad( + block_mask_left[:, :, :-1], (1, 0), value=0 + ) + block_mask_left.tril_(0) + + block_mask_right = cummin( + gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1 + ) + block_mask_right = block_mask_right - F.pad( + block_mask_right[:, :, 1:], (0, 1), value=0 + ) + block_mask_right.triu_(0) + + block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :] + block = cumsum(block_mask_left).tril(0) + cumsum( + block_mask_right, reverse=True + ).triu(1) + + return block_p, block + + def compute_head(self, height, n_cntxt_layers=0): + """Estimate head for each constituent.""" + + _, length = height.size() + if n_cntxt_layers>0: + if n_cntxt_layers == 1: + head_logits = height * self.scaler_1[1] + elif n_cntxt_layers == 2: + head_logits = height * self.scaler_2[1] + else: + head_logits = height * self.scaler[1] + index = torch.arange(length, device=height.device) + + mask = (index[:, None, None] <= index[None, None, :]) * ( + index[None, None, :] <= index[None, :, None] + ) + head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1) + head_logits.masked_fill_(~mask[None, :, :, :], -1e9) + + head_p = torch.softmax(head_logits, dim=-1) + + return head_p + + def parse(self, x, embs=None, n_cntxt_layers=0): + """Parse input sentence. + + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + distance: syntactic distance + height: syntactic height + """ + + mask = x != self.pad + mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0) + + if embs is None: + h = self.roberta.embeddings(x) + else: + h = embs + + if n_cntxt_layers > 0: + if n_cntxt_layers == 1: + parser_layers = self.parser_layers_1 + height_ff = self.height_ff_1 + distance_ff = self.distance_ff_1 + elif n_cntxt_layers == 2: + parser_layers = self.parser_layers_2 + height_ff = self.height_ff_2 + distance_ff = self.distance_ff_2 + for i in range(int(self.config.n_parser_layers/2)): + h = h.masked_fill(~mask[:, :, None], 0) + h = parser_layers[i](h) + + height = height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, dim=-1 + )[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max + ) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where( + margin_left > margin_right, margin_right, margin_left + ).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + else: + for i in range(self.config.n_parser_layers): + h = h.masked_fill(~mask[:, :, None], 0) + h = self.parser_layers[i](h) + + height = self.height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = self.distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, dim=-1 + )[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max + ) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where( + margin_left > margin_right, margin_right, margin_left + ).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + + return distance, height + + def generate_mask(self, x, distance, height, n_cntxt_layers=0): + """Compute head and cibling distribution for each token.""" + + bsz, length = x.size() + + eye = torch.eye(length, device=x.device, dtype=torch.bool) + eye = eye[None, :, :].expand((bsz, -1, -1)) + + block_p, block = self.compute_block(distance, height, n_cntxt_layers=n_cntxt_layers) + head_p = self.compute_head(height, n_cntxt_layers=n_cntxt_layers) + head = torch.einsum("blij,bijh->blh", block_p, head_p) + head = head.masked_fill(eye, 0) + child = head.transpose(1, 2) + cibling = torch.bmm(head, child).masked_fill(eye, 0) + + rel_list = [] + if "head" in self.config.relations: + rel_list.append(head) + if "child" in self.config.relations: + rel_list.append(child) + if "cibling" in self.config.relations: + rel_list.append(cibling) + + rel = torch.stack(rel_list, dim=1) + + if n_cntxt_layers > 0: + if n_cntxt_layers == 1: + rel_weight = self.rel_weight_1 + elif n_cntxt_layers == 2: + rel_weight = self.rel_weight_2 + else: + rel_weight = self.rel_weight + + dep = torch.einsum("lhr,brij->lbhij", rel_weight, rel) + + if n_cntxt_layers == 1: + num_layers = self.cntxt_layers_2.config.num_hidden_layers + else: + num_layers = self.roberta.config.num_hidden_layers + + att_mask = dep.reshape( + num_layers, + bsz, + self.config.num_attention_heads, + length, + length, + ) + + return att_mask, cibling, head, block + + def forward( + self, + input_ids: Optional[torch.LongTensor] = None, + attention_mask: Optional[torch.FloatTensor] = None, + token_type_ids: Optional[torch.LongTensor] = None, + position_ids: Optional[torch.LongTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, MaskedLMOutput]: + r""" + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ..., + config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the + loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]` + kwargs (`Dict[str, any]`, optional, defaults to *{}*): + Used to hide legacy arguments that have been deprecated. + """ + return_dict = ( + return_dict if return_dict is not None else self.config.use_return_dict + ) + + + if self.config.n_cntxt_layers > 0: + cntxt_outputs = self.cntxt_layers( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict) + + + if self.config.n_cntxt_layers_2 > 0: + distance_1, height_1 = self.parse(input_ids, cntxt_outputs[0], n_cntxt_layers=1) + att_mask_1, _, _, _ = self.generate_mask(input_ids, distance_1, height_1, n_cntxt_layers=1) + + cntxt_outputs_2 = self.cntxt_layers_2( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + parser_att_mask=att_mask_1) + + sequence_output = cntxt_outputs_2[0] + + distance_2, height_2 = self.parse(input_ids, sequence_output[0], n_cntxt_layers=2) + att_mask, _, _, _ = self.generate_mask(input_ids, distance_2, height_2, n_cntxt_layers=2) + + elif self.config.n_cntxt_layers > 0: + distance, height = self.parse(input_ids, cntxt_outputs[0]) + att_mask, _, _, _ = self.generate_mask(input_ids, distance, height) + else: + distance, height = self.parse(input_ids) + att_mask, _, _, _ = self.generate_mask(input_ids, distance, height) + + outputs = self.roberta( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + parser_att_mask=att_mask, + ) + sequence_output = outputs[0] + prediction_scores = self.lm_head(sequence_output) + + masked_lm_loss = None + if labels is not None: + loss_fct = CrossEntropyLoss() + masked_lm_loss = loss_fct( + prediction_scores.view(-1, self.config.vocab_size), labels.view(-1) + ) + + if not return_dict: + output = (prediction_scores,) + outputs[2:] + return ( + ((masked_lm_loss,) + output) if masked_lm_loss is not None else output + ) + + return MaskedLMOutput( + loss=masked_lm_loss, + logits=prediction_scores, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +class RobertaLMHead(nn.Module): + """Roberta Head for masked language modeling.""" + + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + + self.decoder = nn.Linear(config.hidden_size, config.vocab_size) + self.bias = nn.Parameter(torch.zeros(config.vocab_size)) + self.decoder.bias = self.bias + + def forward(self, features, **kwargs): + x = self.dense(features) + x = gelu(x) + x = self.layer_norm(x) + + # project back to size of vocabulary with bias + x = self.decoder(x) + + return x + + def _tie_weights(self): + # To tie those two weights if they get disconnected (on TPU or when the bias is resized) + self.bias = self.decoder.bias + + +class StructRobertaForSequenceClassification(RobertaPreTrainedModel): + _keys_to_ignore_on_load_missing = [r"position_ids"] + + def __init__(self, config): + super().__init__(config) + self.num_labels = config.num_labels + self.config = config + + if config.n_cntxt_layers > 0: + config_cntxt = copy.deepcopy(config) + config_cntxt.num_hidden_layers = config.n_cntxt_layers + + self.cntxt_layers = RobertaModel(config_cntxt, add_pooling_layer=False) + + if config.n_cntxt_layers_2 > 0: + self.parser_layers_1 = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(int(config.n_parser_layers/2)) + ] + ) + + self.distance_ff_1 = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff_1 = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight_1 = nn.Parameter( + torch.zeros((config.n_cntxt_layers_2, config.num_attention_heads, n_rel)) + ) + self._rel_weight_1.data.normal_(0, 0.1) + + self._scaler_1 = nn.Parameter(torch.zeros(2)) + + config_cntxt_2 = copy.deepcopy(config) + config_cntxt_2.num_hidden_layers = config.n_cntxt_layers_2 + + self.cntxt_layers_2 = RobertaModel(config_cntxt_2, add_pooling_layer=False) + + + self.parser_layers_2 = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(int(config.n_parser_layers/2)) + ] + ) + + self.distance_ff_2 = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff_2 = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight_2 = nn.Parameter( + torch.zeros((config.num_hidden_layers, config.num_attention_heads, n_rel)) + ) + self._rel_weight_2.data.normal_(0, 0.1) + + self._scaler_2 = nn.Parameter(torch.zeros(2)) + + else: + self.parser_layers = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(config.n_parser_layers) + ] + ) + + self.distance_ff = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight = nn.Parameter( + torch.zeros((config.num_hidden_layers, config.num_attention_heads, n_rel)) + ) + self._rel_weight.data.normal_(0, 0.1) + + self._scaler = nn.Parameter(torch.zeros(2)) + + self.roberta = RobertaModel(config, add_pooling_layer=False) + + if config.n_cntxt_layers > 0: + self.cntxt_layers.embeddings = self.roberta.embeddings + if config.n_cntxt_layers_2 > 0: + self.cntxt_layers_2.embeddings = self.roberta.embeddings + + + self.pad = config.pad_token_id + self.classifier = RobertaClassificationHead(config) + + # Initialize weights and apply final processing + self.post_init() + + + @property + def scaler(self): + return self._scaler.exp() + + @property + def scaler_1(self): + return self._scaler_1.exp() + + @property + def scaler_2(self): + return self._scaler_2.exp() + + @property + def rel_weight(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight, dim=-1) + + @property + def rel_weight_1(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight_1) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight_1, dim=-1) + + + @property + def rel_weight_2(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight_2) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight_2, dim=-1) + + + def compute_block(self, distance, height, n_cntxt_layers=0): + """Compute constituents from distance and height.""" + + if n_cntxt_layers>0: + if n_cntxt_layers == 1: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler_1[0] + elif n_cntxt_layers == 2: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler_2[0] + else: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0] + + gamma = torch.sigmoid(-beta_logits) + ones = torch.ones_like(gamma) + + block_mask_left = cummin( + gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1 + ) + block_mask_left = block_mask_left - F.pad( + block_mask_left[:, :, :-1], (1, 0), value=0 + ) + block_mask_left.tril_(0) + + block_mask_right = cummin( + gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1 + ) + block_mask_right = block_mask_right - F.pad( + block_mask_right[:, :, 1:], (0, 1), value=0 + ) + block_mask_right.triu_(0) + + block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :] + block = cumsum(block_mask_left).tril(0) + cumsum( + block_mask_right, reverse=True + ).triu(1) + + return block_p, block + + def compute_head(self, height, n_cntxt_layers=0): + """Estimate head for each constituent.""" + + _, length = height.size() + if n_cntxt_layers>0: + if n_cntxt_layers == 1: + head_logits = height * self.scaler_1[1] + elif n_cntxt_layers == 2: + head_logits = height * self.scaler_2[1] + else: + head_logits = height * self.scaler[1] + index = torch.arange(length, device=height.device) + + mask = (index[:, None, None] <= index[None, None, :]) * ( + index[None, None, :] <= index[None, :, None] + ) + head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1) + head_logits.masked_fill_(~mask[None, :, :, :], -1e9) + + head_p = torch.softmax(head_logits, dim=-1) + + return head_p + + def parse(self, x, embs=None, n_cntxt_layers=0): + """Parse input sentence. + + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + distance: syntactic distance + height: syntactic height + """ + + mask = x != self.pad + mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0) + + if embs is None: + h = self.roberta.embeddings(x) + else: + h = embs + + if n_cntxt_layers > 0: + if n_cntxt_layers == 1: + parser_layers = self.parser_layers_1 + height_ff = self.height_ff_1 + distance_ff = self.distance_ff_1 + elif n_cntxt_layers == 2: + parser_layers = self.parser_layers_2 + height_ff = self.height_ff_2 + distance_ff = self.distance_ff_2 + for i in range(int(self.config.n_parser_layers/2)): + h = h.masked_fill(~mask[:, :, None], 0) + h = parser_layers[i](h) + + height = height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, dim=-1 + )[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max + ) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where( + margin_left > margin_right, margin_right, margin_left + ).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + else: + for i in range(self.config.n_parser_layers): + h = h.masked_fill(~mask[:, :, None], 0) + h = self.parser_layers[i](h) + + height = self.height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = self.distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, dim=-1 + )[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max + ) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where( + margin_left > margin_right, margin_right, margin_left + ).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + + return distance, height + + def generate_mask(self, x, distance, height, n_cntxt_layers=0): + """Compute head and cibling distribution for each token.""" + + bsz, length = x.size() + + eye = torch.eye(length, device=x.device, dtype=torch.bool) + eye = eye[None, :, :].expand((bsz, -1, -1)) + + block_p, block = self.compute_block(distance, height, n_cntxt_layers=n_cntxt_layers) + head_p = self.compute_head(height, n_cntxt_layers=n_cntxt_layers) + head = torch.einsum("blij,bijh->blh", block_p, head_p) + head = head.masked_fill(eye, 0) + child = head.transpose(1, 2) + cibling = torch.bmm(head, child).masked_fill(eye, 0) + + rel_list = [] + if "head" in self.config.relations: + rel_list.append(head) + if "child" in self.config.relations: + rel_list.append(child) + if "cibling" in self.config.relations: + rel_list.append(cibling) + + rel = torch.stack(rel_list, dim=1) + + if n_cntxt_layers > 0: + if n_cntxt_layers == 1: + rel_weight = self.rel_weight_1 + elif n_cntxt_layers == 2: + rel_weight = self.rel_weight_2 + else: + rel_weight = self.rel_weight + + dep = torch.einsum("lhr,brij->lbhij", rel_weight, rel) + + if n_cntxt_layers == 1: + num_layers = self.cntxt_layers_2.config.num_hidden_layers + else: + num_layers = self.roberta.config.num_hidden_layers + + att_mask = dep.reshape( + num_layers, + bsz, + self.config.num_attention_heads, + length, + length, + ) + + return att_mask, cibling, head, block + + def forward( + self, + input_ids: Optional[torch.LongTensor] = None, + attention_mask: Optional[torch.FloatTensor] = None, + token_type_ids: Optional[torch.LongTensor] = None, + position_ids: Optional[torch.LongTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, SequenceClassifierOutput]: + r""" + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., + config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If + `config.num_labels > 1` a classification loss is computed (Cross-Entropy). + """ + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + if self.config.n_cntxt_layers > 0: + cntxt_outputs = self.cntxt_layers( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict) + + + if self.config.n_cntxt_layers_2 > 0: + distance_1, height_1 = self.parse(input_ids, cntxt_outputs[0], n_cntxt_layers=1) + att_mask_1, _, _, _ = self.generate_mask(input_ids, distance_1, height_1, n_cntxt_layers=1) + + cntxt_outputs_2 = self.cntxt_layers_2( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + parser_att_mask=att_mask_1) + + sequence_output = cntxt_outputs_2[0] + + distance_2, height_2 = self.parse(input_ids, sequence_output[0], n_cntxt_layers=2) + att_mask, _, _, _ = self.generate_mask(input_ids, distance_2, height_2, n_cntxt_layers=2) + + elif self.config.n_cntxt_layers > 0: + distance, height = self.parse(input_ids, cntxt_outputs[0]) + att_mask, _, _, _ = self.generate_mask(input_ids, distance, height) + else: + distance, height = self.parse(input_ids) + att_mask, _, _, _ = self.generate_mask(input_ids, distance, height) + + outputs = self.roberta( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + parser_att_mask=att_mask, + ) + sequence_output = outputs[0] + logits = self.classifier(sequence_output) + + loss = None + if labels is not None: + if self.config.problem_type is None: + if self.num_labels == 1: + self.config.problem_type = "regression" + elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): + self.config.problem_type = "single_label_classification" + else: + self.config.problem_type = "multi_label_classification" + + if self.config.problem_type == "regression": + loss_fct = MSELoss() + if self.num_labels == 1: + loss = loss_fct(logits.squeeze(), labels.squeeze()) + else: + loss = loss_fct(logits, labels) + elif self.config.problem_type == "single_label_classification": + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) + elif self.config.problem_type == "multi_label_classification": + loss_fct = BCEWithLogitsLoss() + loss = loss_fct(logits, labels) + + if not return_dict: + output = (logits,) + outputs[2:] + return ((loss,) + output) if loss is not None else output + + return SequenceClassifierOutput( + loss=loss, + logits=logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +class RobertaClassificationHead(nn.Module): + """Head for sentence-level classification tasks.""" + + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + classifier_dropout = ( + config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob + ) + self.dropout = nn.Dropout(classifier_dropout) + self.out_proj = nn.Linear(config.hidden_size, config.num_labels) + + def forward(self, features, **kwargs): + x = features[:, 0, :] # take token (equiv. to [CLS]) + x = self.dropout(x) + x = self.dense(x) + x = torch.tanh(x) + x = self.dropout(x) + x = self.out_proj(x) + return x + + +def create_position_ids_from_input_ids( + input_ids, padding_idx, past_key_values_length=0 +): + """ + Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols + are ignored. This is modified from fairseq's `utils.make_positions`. + + Args: + x: torch.Tensor x: + + Returns: torch.Tensor + """ + # The series of casts and type-conversions here are carefully balanced to both work with ONNX export and XLA. + mask = input_ids.ne(padding_idx).int() + incremental_indices = ( + torch.cumsum(mask, dim=1).type_as(mask) + past_key_values_length + ) * mask + return incremental_indices.long() + padding_idx + + +def cumprod(x, reverse=False, exclusive=False): + """cumulative product.""" + if reverse: + x = x.flip([-1]) + + if exclusive: + x = F.pad(x[:, :, :-1], (1, 0), value=1) + + cx = x.cumprod(-1) + + if reverse: + cx = cx.flip([-1]) + return cx + + +def cumsum(x, reverse=False, exclusive=False): + """cumulative sum.""" + bsz, _, length = x.size() + device = x.device + if reverse: + if exclusive: + w = torch.ones([bsz, length, length], device=device).tril(-1) + else: + w = torch.ones([bsz, length, length], device=device).tril(0) + cx = torch.bmm(x, w) + else: + if exclusive: + w = torch.ones([bsz, length, length], device=device).triu(1) + else: + w = torch.ones([bsz, length, length], device=device).triu(0) + cx = torch.bmm(x, w) + return cx + + +def cummin(x, reverse=False, exclusive=False, max_value=1e9): + """cumulative min.""" + if reverse: + if exclusive: + x = F.pad(x[:, :, 1:], (0, 1), value=max_value) + x = x.flip([-1]).cummin(-1)[0].flip([-1]) + else: + if exclusive: + x = F.pad(x[:, :, :-1], (1, 0), value=max_value) + x = x.cummin(-1)[0] + return x diff --git a/finetune/mrpc/predict_results.txt b/finetune/mrpc/predict_results.txt new file mode 100644 index 0000000000000000000000000000000000000000..d9ad20926578da0dcfa3ad4bdc6f41867a48a886 --- /dev/null +++ b/finetune/mrpc/predict_results.txt @@ -0,0 +1,178 @@ +index prediction +0 1 +1 1 +2 1 +3 0 +4 1 +5 1 +6 1 +7 1 +8 1 +9 1 +10 1 +11 1 +12 1 +13 0 +14 0 +15 0 +16 0 +17 0 +18 1 +19 0 +20 1 +21 0 +22 1 +23 1 +24 1 +25 1 +26 0 +27 1 +28 0 +29 1 +30 1 +31 1 +32 1 +33 0 +34 1 +35 1 +36 1 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file mode 100644 index 0000000000000000000000000000000000000000..24fe0c382fc58d7ee1ff41e74db395ef502429cf --- /dev/null +++ b/finetune/mrpc/pytorch_model.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:5684f5b999d09435859dab57ba641e93f55a51af345cca0d59d2f1d1004595db +size 534594121 diff --git a/finetune/mrpc/special_tokens_map.json b/finetune/mrpc/special_tokens_map.json new file mode 100644 index 0000000000000000000000000000000000000000..d5698132694f4f1bcff08fa7d937b1701812598e --- /dev/null +++ b/finetune/mrpc/special_tokens_map.json @@ -0,0 +1,15 @@ +{ + "bos_token": "", + "cls_token": "", + "eos_token": "", + "mask_token": { + "content": "", + "lstrip": true, + "normalized": false, + "rstrip": false, + "single_word": false + }, + "pad_token": "", + "sep_token": "", + "unk_token": "" +} diff --git a/finetune/mrpc/tokenizer_config.json b/finetune/mrpc/tokenizer_config.json new file mode 100644 index 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+Ġ " +r om +.. . +o re +Ġ W +e ll +Ġn e +Ġ r +Ġkn ow +Ġ j +ou r +Ġ D +Ġd e +p p +Ġc an +Ġ G +Ġthe y +u m +Ġ The +Ġs o +es t +Ġ ? +Ġa l +Ġa ll +Ġa b +Ġm y +is t +er s +Ġ R +i e +Ġyou r +ar t +Ġb y +Ġf rom +es s +Ġs h +Ġo r +Ġ 2 +Ġ F +Ġw hat +n t +a b +o s +Ġ L +e l +Ġon e +Ġc on +Ġ N +Ġw he +Ġthe re +i g +i ght +ha n +all y +Ġu p +q u +e m +Ġc om +on e +Ġha d +Ġb ut +Ġhe r +Ġs u +re d +r y +Ġ J +' re +Ġh im +Ġ out +r a +ac k +at e +oo d +ver y +oo k +Ġli ke +Ġw or +Ġs a +re s +am e +i ve +Ġp l +Ġ us +Ġ ( +on g +' m +Ġwe re +â Ģ +a ke +o d +in k +c k +Ġs he +Ġp ro +Ġab out +Ġ E +as t +Ġg et +Ġe x +Ġ v +p e +I t +or t +ou nd +in d +ar d +it y +Ġd on +A nd +ic h +ri ght +Ġ O +Ġj ust +Ġ1 9 +i es +H e +Ġwh o +e ar +ea h +Ġd id +a k +Ġhe re +b er +Ġthe m +Ġn o +im e +in e +Ġm an +Ġw ant +Ġw ould +Ġ2 0 +Ġ if +Ġ ' +Ġw ill +u d +a p +Ġ K +Ġth ink +ou s +' ll +v en +Ġs ome +o st +W hat +e p +h ing +Ġin t +Ġan y +ow n +ic e +a g +Ġ right +m ent +id e +Ġg ot +Ġha s +t her +us e +it t +Ġwh ich +ou 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See all RoBERTa models at https://huggingface.co/models?filter=roberta +] + + +class StructRobertaConfig(RobertaConfig): + model_type = "roberta" + + def __init__( + self, + n_parser_layers=4, + conv_size=9, + relations=("head", "child"), + weight_act="softmax", + n_cntxt_layers=3, + n_cntxt_layers_2=0, + **kwargs,): + + super().__init__(**kwargs) + self.n_cntxt_layers = n_cntxt_layers + self.n_parser_layers = n_parser_layers + self.n_cntxt_layers_2 = n_cntxt_layers_2 + self.conv_size = conv_size + self.relations = relations + self.weight_act = weight_act + +class Conv1d(nn.Module): + """1D convolution layer.""" + + def __init__(self, hidden_size, kernel_size, dilation=1): + """Initialization. + + Args: + hidden_size: dimension of input embeddings + kernel_size: convolution kernel size + dilation: the spacing between the kernel points + """ + super(Conv1d, self).__init__() + + if kernel_size % 2 == 0: + padding = (kernel_size // 2) * dilation + self.shift = True + else: + padding = ((kernel_size - 1) // 2) * dilation + self.shift = False + self.conv = nn.Conv1d( + hidden_size, hidden_size, kernel_size, padding=padding, dilation=dilation + ) + + def forward(self, x): + """Compute convolution. + + Args: + x: input embeddings + Returns: + conv_output: convolution results + """ + + if self.shift: + return self.conv(x.transpose(1, 2)).transpose(1, 2)[:, 1:] + else: + return self.conv(x.transpose(1, 2)).transpose(1, 2) + + +class RobertaEmbeddings(nn.Module): + """ + Same as BertEmbeddings with a tiny tweak for positional embeddings indexing. + """ + + # Copied from transformers.models.bert.modeling_bert.BertEmbeddings.__init__ + def __init__(self, config): + super().__init__() + self.word_embeddings = nn.Embedding( + config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id + ) + self.position_embeddings = nn.Embedding( + config.max_position_embeddings, config.hidden_size + ) + self.token_type_embeddings = nn.Embedding( + config.type_vocab_size, config.hidden_size + ) + + # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load + # any TensorFlow checkpoint file + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + # position_ids (1, len position emb) is contiguous in memory and exported when serialized + self.position_embedding_type = getattr( + config, "position_embedding_type", "absolute" + ) + self.register_buffer( + "position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)) + ) + if version.parse(torch.__version__) > version.parse("1.6.0"): + self.register_buffer( + "token_type_ids", + torch.zeros(self.position_ids.size(), dtype=torch.long), + persistent=False, + ) + + # End copy + self.padding_idx = config.pad_token_id + self.position_embeddings = nn.Embedding( + config.max_position_embeddings, + config.hidden_size, + padding_idx=self.padding_idx, + ) + + def forward( + self, + input_ids=None, + token_type_ids=None, + position_ids=None, + inputs_embeds=None, + past_key_values_length=0, + ): + if position_ids is None: + if input_ids is not None: + # Create the position ids from the input token ids. Any padded tokens remain padded. + position_ids = create_position_ids_from_input_ids( + input_ids, self.padding_idx, past_key_values_length + ) + else: + position_ids = self.create_position_ids_from_inputs_embeds( + inputs_embeds + ) + + if input_ids is not None: + input_shape = input_ids.size() + else: + input_shape = inputs_embeds.size()[:-1] + + seq_length = input_shape[1] + + # Setting the token_type_ids to the registered buffer in constructor where it is all zeros, which usually occurs + # when its auto-generated, registered buffer helps users when tracing the model without passing token_type_ids, solves + # issue #5664 + if token_type_ids is None: + if hasattr(self, "token_type_ids"): + buffered_token_type_ids = self.token_type_ids[:, :seq_length] + buffered_token_type_ids_expanded = buffered_token_type_ids.expand( + input_shape[0], seq_length + ) + token_type_ids = buffered_token_type_ids_expanded + else: + token_type_ids = torch.zeros( + input_shape, dtype=torch.long, device=self.position_ids.device + ) + + if inputs_embeds is None: + inputs_embeds = self.word_embeddings(input_ids) + token_type_embeddings = self.token_type_embeddings(token_type_ids) + + embeddings = inputs_embeds + token_type_embeddings + if self.position_embedding_type == "absolute": + position_embeddings = self.position_embeddings(position_ids) + embeddings += position_embeddings + embeddings = self.LayerNorm(embeddings) + embeddings = self.dropout(embeddings) + return embeddings + + def create_position_ids_from_inputs_embeds(self, inputs_embeds): + """ + We are provided embeddings directly. We cannot infer which are padded so just generate sequential position ids. + + Args: + inputs_embeds: torch.Tensor + + Returns: torch.Tensor + """ + input_shape = inputs_embeds.size()[:-1] + sequence_length = input_shape[1] + + position_ids = torch.arange( + self.padding_idx + 1, + sequence_length + self.padding_idx + 1, + dtype=torch.long, + device=inputs_embeds.device, + ) + return position_ids.unsqueeze(0).expand(input_shape) + + +# Copied from transformers.models.bert.modeling_bert.BertSelfAttention with Bert->Roberta +class RobertaSelfAttention(nn.Module): + def __init__(self, config, position_embedding_type=None): + super().__init__() + if config.hidden_size % config.num_attention_heads != 0 and not hasattr( + config, "embedding_size" + ): + raise ValueError( + f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention " + f"heads ({config.num_attention_heads})" + ) + + self.num_attention_heads = config.num_attention_heads + self.attention_head_size = int(config.hidden_size / config.num_attention_heads) + self.all_head_size = self.num_attention_heads * self.attention_head_size + + self.query = nn.Linear(config.hidden_size, self.all_head_size) + self.key = nn.Linear(config.hidden_size, self.all_head_size) + self.value = nn.Linear(config.hidden_size, self.all_head_size) + + self.dropout = nn.Dropout(config.attention_probs_dropout_prob) + self.position_embedding_type = position_embedding_type or getattr( + config, "position_embedding_type", "absolute" + ) + if ( + self.position_embedding_type == "relative_key" + or self.position_embedding_type == "relative_key_query" + ): + self.max_position_embeddings = config.max_position_embeddings + self.distance_embedding = nn.Embedding( + 2 * config.max_position_embeddings - 1, self.attention_head_size + ) + + self.is_decoder = config.is_decoder + + def transpose_for_scores(self, x): + new_x_shape = x.size()[:-1] + ( + self.num_attention_heads, + self.attention_head_size, + ) + x = x.view(new_x_shape) + return x.permute(0, 2, 1, 3) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.FloatTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, + output_attentions: Optional[bool] = False, + parser_att_mask=None, + ) -> Tuple[torch.Tensor]: + mixed_query_layer = self.query(hidden_states) + + # If this is instantiated as a cross-attention module, the keys + # and values come from an encoder; the attention mask needs to be + # such that the encoder's padding tokens are not attended to. + is_cross_attention = encoder_hidden_states is not None + + if is_cross_attention and past_key_value is not None: + # reuse k,v, cross_attentions + key_layer = past_key_value[0] + value_layer = past_key_value[1] + attention_mask = encoder_attention_mask + elif is_cross_attention: + key_layer = self.transpose_for_scores(self.key(encoder_hidden_states)) + value_layer = self.transpose_for_scores(self.value(encoder_hidden_states)) + attention_mask = encoder_attention_mask + elif past_key_value is not None: + key_layer = self.transpose_for_scores(self.key(hidden_states)) + value_layer = self.transpose_for_scores(self.value(hidden_states)) + key_layer = torch.cat([past_key_value[0], key_layer], dim=2) + value_layer = torch.cat([past_key_value[1], value_layer], dim=2) + else: + key_layer = self.transpose_for_scores(self.key(hidden_states)) + value_layer = self.transpose_for_scores(self.value(hidden_states)) + + query_layer = self.transpose_for_scores(mixed_query_layer) + + if self.is_decoder: + # if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states. + # Further calls to cross_attention layer can then reuse all cross-attention + # key/value_states (first "if" case) + # if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of + # all previous decoder key/value_states. Further calls to uni-directional self-attention + # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case) + # if encoder bi-directional self-attention `past_key_value` is always `None` + past_key_value = (key_layer, value_layer) + + # Take the dot product between "query" and "key" to get the raw attention scores. + attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) + + if ( + self.position_embedding_type == "relative_key" + or self.position_embedding_type == "relative_key_query" + ): + seq_length = hidden_states.size()[1] + position_ids_l = torch.arange( + seq_length, dtype=torch.long, device=hidden_states.device + ).view(-1, 1) + position_ids_r = torch.arange( + seq_length, dtype=torch.long, device=hidden_states.device + ).view(1, -1) + distance = position_ids_l - position_ids_r + positional_embedding = self.distance_embedding( + distance + self.max_position_embeddings - 1 + ) + positional_embedding = positional_embedding.to( + dtype=query_layer.dtype + ) # fp16 compatibility + + if self.position_embedding_type == "relative_key": + relative_position_scores = torch.einsum( + "bhld,lrd->bhlr", query_layer, positional_embedding + ) + attention_scores = attention_scores + relative_position_scores + elif self.position_embedding_type == "relative_key_query": + relative_position_scores_query = torch.einsum( + "bhld,lrd->bhlr", query_layer, positional_embedding + ) + relative_position_scores_key = torch.einsum( + "bhrd,lrd->bhlr", key_layer, positional_embedding + ) + attention_scores = ( + attention_scores + + relative_position_scores_query + + relative_position_scores_key + ) + + attention_scores = attention_scores / math.sqrt(self.attention_head_size) + if attention_mask is not None: + # Apply the attention mask is (precomputed for all layers in RobertaModel forward() function) + attention_scores = attention_scores + attention_mask + + if parser_att_mask is None: + # Normalize the attention scores to probabilities. + attention_probs = nn.functional.softmax(attention_scores, dim=-1) + else: + attention_probs = torch.sigmoid(attention_scores) * parser_att_mask + + # This is actually dropping out entire tokens to attend to, which might + # seem a bit unusual, but is taken from the original Transformer paper. + attention_probs = self.dropout(attention_probs) + + # Mask heads if we want to + if head_mask is not None: + attention_probs = attention_probs * head_mask + + context_layer = torch.matmul(attention_probs, value_layer) + + context_layer = context_layer.permute(0, 2, 1, 3).contiguous() + new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) + context_layer = context_layer.view(new_context_layer_shape) + + outputs = ( + (context_layer, attention_probs) if output_attentions else (context_layer,) + ) + + if self.is_decoder: + outputs = outputs + (past_key_value,) + return outputs + + +# Copied from transformers.models.bert.modeling_bert.BertSelfOutput +class RobertaSelfOutput(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward( + self, hidden_states: torch.Tensor, input_tensor: torch.Tensor + ) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + hidden_states = self.LayerNorm(hidden_states + input_tensor) + return hidden_states + + +# Copied from transformers.models.bert.modeling_bert.BertAttention with Bert->Roberta +class RobertaAttention(nn.Module): + def __init__(self, config, position_embedding_type=None): + super().__init__() + self.self = RobertaSelfAttention( + config, position_embedding_type=position_embedding_type + ) + self.output = RobertaSelfOutput(config) + self.pruned_heads = set() + + def prune_heads(self, heads): + if len(heads) == 0: + return + heads, index = find_pruneable_heads_and_indices( + heads, + self.self.num_attention_heads, + self.self.attention_head_size, + self.pruned_heads, + ) + + # Prune linear layers + self.self.query = prune_linear_layer(self.self.query, index) + self.self.key = prune_linear_layer(self.self.key, index) + self.self.value = prune_linear_layer(self.self.value, index) + self.output.dense = prune_linear_layer(self.output.dense, index, dim=1) + + # Update hyper params and store pruned heads + self.self.num_attention_heads = self.self.num_attention_heads - len(heads) + self.self.all_head_size = ( + self.self.attention_head_size * self.self.num_attention_heads + ) + self.pruned_heads = self.pruned_heads.union(heads) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.FloatTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, + output_attentions: Optional[bool] = False, + parser_att_mask=None, + ) -> Tuple[torch.Tensor]: + self_outputs = self.self( + hidden_states, + attention_mask, + head_mask, + encoder_hidden_states, + encoder_attention_mask, + past_key_value, + output_attentions, + parser_att_mask=parser_att_mask, + ) + attention_output = self.output(self_outputs[0], hidden_states) + outputs = (attention_output,) + self_outputs[ + 1: + ] # add attentions if we output them + return outputs + + +# Copied from transformers.models.bert.modeling_bert.BertIntermediate +class RobertaIntermediate(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.intermediate_size) + if isinstance(config.hidden_act, str): + self.intermediate_act_fn = ACT2FN[config.hidden_act] + else: + self.intermediate_act_fn = config.hidden_act + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.intermediate_act_fn(hidden_states) + return hidden_states + + +# Copied from transformers.models.bert.modeling_bert.BertOutput +class RobertaOutput(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.intermediate_size, config.hidden_size) + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward( + self, hidden_states: torch.Tensor, input_tensor: torch.Tensor + ) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + hidden_states = self.LayerNorm(hidden_states + input_tensor) + return hidden_states + + +# Copied from transformers.models.bert.modeling_bert.BertLayer with Bert->Roberta +class RobertaLayer(nn.Module): + def __init__(self, config): + super().__init__() + self.chunk_size_feed_forward = config.chunk_size_feed_forward + self.seq_len_dim = 1 + self.attention = RobertaAttention(config) + self.is_decoder = config.is_decoder + self.add_cross_attention = config.add_cross_attention + if self.add_cross_attention: + if not self.is_decoder: + raise ValueError( + f"{self} should be used as a decoder model if cross attention is added" + ) + self.crossattention = RobertaAttention( + config, position_embedding_type="absolute" + ) + self.intermediate = RobertaIntermediate(config) + self.output = RobertaOutput(config) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.FloatTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, + output_attentions: Optional[bool] = False, + parser_att_mask=None, + ) -> Tuple[torch.Tensor]: + # decoder uni-directional self-attention cached key/values tuple is at positions 1,2 + self_attn_past_key_value = ( + past_key_value[:2] if past_key_value is not None else None + ) + self_attention_outputs = self.attention( + hidden_states, + attention_mask, + head_mask, + output_attentions=output_attentions, + past_key_value=self_attn_past_key_value, + parser_att_mask=parser_att_mask, + ) + attention_output = self_attention_outputs[0] + + # if decoder, the last output is tuple of self-attn cache + if self.is_decoder: + outputs = self_attention_outputs[1:-1] + present_key_value = self_attention_outputs[-1] + else: + outputs = self_attention_outputs[ + 1: + ] # add self attentions if we output attention weights + + cross_attn_present_key_value = None + if self.is_decoder and encoder_hidden_states is not None: + if not hasattr(self, "crossattention"): + raise ValueError( + f"If `encoder_hidden_states` are passed, {self} has to be instantiated with cross-attention layers by setting `config.add_cross_attention=True`" + ) + + # cross_attn cached key/values tuple is at positions 3,4 of past_key_value tuple + cross_attn_past_key_value = ( + past_key_value[-2:] if past_key_value is not None else None + ) + cross_attention_outputs = self.crossattention( + attention_output, + attention_mask, + head_mask, + encoder_hidden_states, + encoder_attention_mask, + cross_attn_past_key_value, + output_attentions, + ) + attention_output = cross_attention_outputs[0] + outputs = ( + outputs + cross_attention_outputs[1:-1] + ) # add cross attentions if we output attention weights + + # add cross-attn cache to positions 3,4 of present_key_value tuple + cross_attn_present_key_value = cross_attention_outputs[-1] + present_key_value = present_key_value + cross_attn_present_key_value + + layer_output = apply_chunking_to_forward( + self.feed_forward_chunk, + self.chunk_size_feed_forward, + self.seq_len_dim, + attention_output, + ) + outputs = (layer_output,) + outputs + + # if decoder, return the attn key/values as the last output + if self.is_decoder: + outputs = outputs + (present_key_value,) + + return outputs + + def feed_forward_chunk(self, attention_output): + intermediate_output = self.intermediate(attention_output) + layer_output = self.output(intermediate_output, attention_output) + return layer_output + + +# Copied from transformers.models.bert.modeling_bert.BertEncoder with Bert->Roberta +class RobertaEncoder(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + self.layer = nn.ModuleList( + [RobertaLayer(config) for _ in range(config.num_hidden_layers)] + ) + self.gradient_checkpointing = False + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.FloatTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = False, + output_hidden_states: Optional[bool] = False, + return_dict: Optional[bool] = True, + parser_att_mask=None, + ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPastAndCrossAttentions]: + all_hidden_states = () if output_hidden_states else None + all_self_attentions = () if output_attentions else None + all_cross_attentions = ( + () if output_attentions and self.config.add_cross_attention else None + ) + + next_decoder_cache = () if use_cache else None + for i, layer_module in enumerate(self.layer): + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + layer_head_mask = head_mask[i] if head_mask is not None else None + past_key_value = past_key_values[i] if past_key_values is not None else None + + if self.gradient_checkpointing and self.training: + + if use_cache: + logger.warning( + "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." + ) + use_cache = False + + def create_custom_forward(module): + def custom_forward(*inputs): + return module(*inputs, past_key_value, output_attentions) + + return custom_forward + + layer_outputs = torch.utils.checkpoint.checkpoint( + create_custom_forward(layer_module), + hidden_states, + attention_mask, + layer_head_mask, + encoder_hidden_states, + encoder_attention_mask, + ) + else: + if parser_att_mask is not None: + layer_outputs = layer_module( + hidden_states, + attention_mask, + layer_head_mask, + encoder_hidden_states, + encoder_attention_mask, + past_key_value, + output_attentions, + parser_att_mask=parser_att_mask[i]) + else: + layer_outputs = layer_module( + hidden_states, + attention_mask, + layer_head_mask, + encoder_hidden_states, + encoder_attention_mask, + past_key_value, + output_attentions, + parser_att_mask=None) + + + hidden_states = layer_outputs[0] + if use_cache: + next_decoder_cache += (layer_outputs[-1],) + if output_attentions: + all_self_attentions = all_self_attentions + (layer_outputs[1],) + if self.config.add_cross_attention: + all_cross_attentions = all_cross_attentions + (layer_outputs[2],) + + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + if not return_dict: + return tuple( + v + for v in [ + hidden_states, + next_decoder_cache, + all_hidden_states, + all_self_attentions, + all_cross_attentions, + ] + if v is not None + ) + return BaseModelOutputWithPastAndCrossAttentions( + last_hidden_state=hidden_states, + past_key_values=next_decoder_cache, + hidden_states=all_hidden_states, + attentions=all_self_attentions, + cross_attentions=all_cross_attentions, + ) + + +# Copied from transformers.models.bert.modeling_bert.BertPooler +class RobertaPooler(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.activation = nn.Tanh() + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + # We "pool" the model by simply taking the hidden state corresponding + # to the first token. + first_token_tensor = hidden_states[:, 0] + pooled_output = self.dense(first_token_tensor) + pooled_output = self.activation(pooled_output) + return pooled_output + + +class RobertaPreTrainedModel(PreTrainedModel): + """ + An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained + models. + """ + + config_class = RobertaConfig + base_model_prefix = "roberta" + supports_gradient_checkpointing = True + + # Copied from transformers.models.bert.modeling_bert.BertPreTrainedModel._init_weights + def _init_weights(self, module): + """Initialize the weights""" + if isinstance(module, nn.Linear): + # Slightly different from the TF version which uses truncated_normal for initialization + # cf https://github.com/pytorch/pytorch/pull/5617 + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.Embedding): + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.padding_idx is not None: + module.weight.data[module.padding_idx].zero_() + elif isinstance(module, nn.LayerNorm): + if module.bias is not None: + module.bias.data.zero_() + module.weight.data.fill_(1.0) + + def _set_gradient_checkpointing(self, module, value=False): + if isinstance(module, RobertaEncoder): + module.gradient_checkpointing = value + + def update_keys_to_ignore(self, config, del_keys_to_ignore): + """Remove some keys from ignore list""" + if not config.tie_word_embeddings: + # must make a new list, or the class variable gets modified! + self._keys_to_ignore_on_save = [ + k for k in self._keys_to_ignore_on_save if k not in del_keys_to_ignore + ] + self._keys_to_ignore_on_load_missing = [ + k + for k in self._keys_to_ignore_on_load_missing + if k not in del_keys_to_ignore + ] + + +ROBERTA_START_DOCSTRING = r""" + + This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the + library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads + etc.) + + This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. + Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage + and behavior. + + Parameters: + config ([`RobertaConfig`]): Model configuration class with all the parameters of the + model. Initializing with a config file does not load the weights associated with the model, only the + configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. +""" + + +ROBERTA_INPUTS_DOCSTRING = r""" + Args: + input_ids (`torch.LongTensor` of shape `({0})`): + Indices of input sequence tokens in the vocabulary. + + Indices can be obtained using [`RobertaTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + [What are input IDs?](../glossary#input-ids) + attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*): + Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + + [What are attention masks?](../glossary#attention-mask) + token_type_ids (`torch.LongTensor` of shape `({0})`, *optional*): + Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, + 1]`: + + - 0 corresponds to a *sentence A* token, + - 1 corresponds to a *sentence B* token. + + [What are token type IDs?](../glossary#token-type-ids) + position_ids (`torch.LongTensor` of shape `({0})`, *optional*): + Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, + config.max_position_embeddings - 1]`. + + [What are position IDs?](../glossary#position-ids) + head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): + Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: + + - 1 indicates the head is **not masked**, + - 0 indicates the head is **masked**. + + inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_size)`, *optional*): + Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This + is useful if you want more control over how to convert `input_ids` indices into associated vectors than the + model's internal embedding lookup matrix. + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned + tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for + more detail. + return_dict (`bool`, *optional*): + Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. +""" + + +class RobertaModel(RobertaPreTrainedModel): + """ + + The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of + cross-attention is added between the self-attention layers, following the architecture described in *Attention is + all you need*_ by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz + Kaiser and Illia Polosukhin. + + To behave as an decoder the model needs to be initialized with the `is_decoder` argument of the configuration set + to `True`. To be used in a Seq2Seq model, the model needs to initialized with both `is_decoder` argument and + `add_cross_attention` set to `True`; an `encoder_hidden_states` is then expected as an input to the forward pass. + + .. _*Attention is all you need*: https://arxiv.org/abs/1706.03762 + + """ + + _keys_to_ignore_on_load_missing = [r"position_ids"] + + # Copied from transformers.models.bert.modeling_bert.BertModel.__init__ with Bert->Roberta + def __init__(self, config, add_pooling_layer=True): + super().__init__(config) + self.config = config + + self.embeddings = RobertaEmbeddings(config) + self.encoder = RobertaEncoder(config) + + self.pooler = RobertaPooler(config) if add_pooling_layer else None + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.embeddings.word_embeddings + + def set_input_embeddings(self, value): + self.embeddings.word_embeddings = value + + def _prune_heads(self, heads_to_prune): + """ + Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base + class PreTrainedModel + """ + for layer, heads in heads_to_prune.items(): + self.encoder.layer[layer].attention.prune_heads(heads) + + # Copied from transformers.models.bert.modeling_bert.BertModel.forward + def forward( + self, + input_ids: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + token_type_ids: Optional[torch.Tensor] = None, + position_ids: Optional[torch.Tensor] = None, + head_mask: Optional[torch.Tensor] = None, + inputs_embeds: Optional[torch.Tensor] = None, + encoder_hidden_states: Optional[torch.Tensor] = None, + encoder_attention_mask: Optional[torch.Tensor] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + parser_att_mask=None, + ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPoolingAndCrossAttentions]: + r""" + encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): + Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if + the model is configured as a decoder. + encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*): + Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in + the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): + Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. + + If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that + don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all + `decoder_input_ids` of shape `(batch_size, sequence_length)`. + use_cache (`bool`, *optional*): + If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see + `past_key_values`). + """ + output_attentions = ( + output_attentions + if output_attentions is not None + else self.config.output_attentions + ) + output_hidden_states = ( + output_hidden_states + if output_hidden_states is not None + else self.config.output_hidden_states + ) + return_dict = ( + return_dict if return_dict is not None else self.config.use_return_dict + ) + + if self.config.is_decoder: + use_cache = use_cache if use_cache is not None else self.config.use_cache + else: + use_cache = False + + if input_ids is not None and inputs_embeds is not None: + raise ValueError( + "You cannot specify both input_ids and inputs_embeds at the same time" + ) + elif input_ids is not None: + input_shape = input_ids.size() + elif inputs_embeds is not None: + input_shape = inputs_embeds.size()[:-1] + else: + raise ValueError("You have to specify either input_ids or inputs_embeds") + + batch_size, seq_length = input_shape + device = input_ids.device if input_ids is not None else inputs_embeds.device + + # past_key_values_length + past_key_values_length = ( + past_key_values[0][0].shape[2] if past_key_values is not None else 0 + ) + + if attention_mask is None: + attention_mask = torch.ones( + ((batch_size, seq_length + past_key_values_length)), device=device + ) + + if token_type_ids is None: + if hasattr(self.embeddings, "token_type_ids"): + buffered_token_type_ids = self.embeddings.token_type_ids[:, :seq_length] + buffered_token_type_ids_expanded = buffered_token_type_ids.expand( + batch_size, seq_length + ) + token_type_ids = buffered_token_type_ids_expanded + else: + token_type_ids = torch.zeros( + input_shape, dtype=torch.long, device=device + ) + + # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length] + # ourselves in which case we just need to make it broadcastable to all heads. + extended_attention_mask: torch.Tensor = self.get_extended_attention_mask( + attention_mask, input_shape, device + ) + + # If a 2D or 3D attention mask is provided for the cross-attention + # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length] + if self.config.is_decoder and encoder_hidden_states is not None: + ( + encoder_batch_size, + encoder_sequence_length, + _, + ) = encoder_hidden_states.size() + encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length) + if encoder_attention_mask is None: + encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device) + encoder_extended_attention_mask = self.invert_attention_mask( + encoder_attention_mask + ) + else: + encoder_extended_attention_mask = None + + # Prepare head mask if needed + # 1.0 in head_mask indicate we keep the head + # attention_probs has shape bsz x n_heads x N x N + # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] + # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] + head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) + + embedding_output = self.embeddings( + input_ids=input_ids, + position_ids=position_ids, + token_type_ids=token_type_ids, + inputs_embeds=inputs_embeds, + past_key_values_length=past_key_values_length, + ) + encoder_outputs = self.encoder( + embedding_output, + attention_mask=extended_attention_mask, + head_mask=head_mask, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_extended_attention_mask, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + parser_att_mask=parser_att_mask, + ) + sequence_output = encoder_outputs[0] + pooled_output = ( + self.pooler(sequence_output) if self.pooler is not None else None + ) + + if not return_dict: + return (sequence_output, pooled_output) + encoder_outputs[1:] + + return BaseModelOutputWithPoolingAndCrossAttentions( + last_hidden_state=sequence_output, + pooler_output=pooled_output, + past_key_values=encoder_outputs.past_key_values, + hidden_states=encoder_outputs.hidden_states, + attentions=encoder_outputs.attentions, + cross_attentions=encoder_outputs.cross_attentions, + ) + + +class StructRoberta(RobertaPreTrainedModel): + _keys_to_ignore_on_save = [r"lm_head.decoder.weight", r"lm_head.decoder.bias"] + _keys_to_ignore_on_load_missing = [ + r"position_ids", + r"lm_head.decoder.weight", + r"lm_head.decoder.bias", + ] + _keys_to_ignore_on_load_unexpected = [r"pooler"] + + def __init__(self, config): + super().__init__(config) + + if config.is_decoder: + logger.warning( + "If you want to use `RobertaForMaskedLM` make sure `config.is_decoder=False` for " + "bi-directional self-attention." + ) + + + if config.n_cntxt_layers > 0: + config_cntxt = copy.deepcopy(config) + config_cntxt.num_hidden_layers = config.n_cntxt_layers + + self.cntxt_layers = RobertaModel(config_cntxt, add_pooling_layer=False) + + if config.n_cntxt_layers_2 > 0: + self.parser_layers_1 = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(int(config.n_parser_layers/2)) + ] + ) + + self.distance_ff_1 = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff_1 = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight_1 = nn.Parameter( + torch.zeros((config.n_cntxt_layers_2, config.num_attention_heads, n_rel)) + ) + self._rel_weight_1.data.normal_(0, 0.1) + + self._scaler_1 = nn.Parameter(torch.zeros(2)) + + config_cntxt_2 = copy.deepcopy(config) + config_cntxt_2.num_hidden_layers = config.n_cntxt_layers_2 + + self.cntxt_layers_2 = RobertaModel(config_cntxt_2, add_pooling_layer=False) + + + self.parser_layers_2 = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(int(config.n_parser_layers/2)) + ] + ) + + self.distance_ff_2 = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff_2 = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight_2 = nn.Parameter( + torch.zeros((config.num_hidden_layers, config.num_attention_heads, n_rel)) + ) + self._rel_weight_2.data.normal_(0, 0.1) + + self._scaler_2 = nn.Parameter(torch.zeros(2)) + + else: + self.parser_layers = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(config.n_parser_layers) + ] + ) + + self.distance_ff = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight = nn.Parameter( + torch.zeros((config.num_hidden_layers, config.num_attention_heads, n_rel)) + ) + self._rel_weight.data.normal_(0, 0.1) + + self._scaler = nn.Parameter(torch.zeros(2)) + + self.roberta = RobertaModel(config, add_pooling_layer=False) + + if config.n_cntxt_layers > 0: + self.cntxt_layers.embeddings = self.roberta.embeddings + if config.n_cntxt_layers_2 > 0: + self.cntxt_layers_2.embeddings = self.roberta.embeddings + + self.lm_head = RobertaLMHead(config) + + self.pad = config.pad_token_id + + # The LM head weights require special treatment only when they are tied with the word embeddings + self.update_keys_to_ignore(config, ["lm_head.decoder.weight"]) + + # Initialize weights and apply final processing + self.post_init() + + def get_output_embeddings(self): + return self.lm_head.decoder + + def set_output_embeddings(self, new_embeddings): + self.lm_head.decoder = new_embeddings + + @property + def scaler(self): + return self._scaler.exp() + + @property + def scaler_1(self): + return self._scaler_1.exp() + + @property + def scaler_2(self): + return self._scaler_2.exp() + + @property + def rel_weight(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight, dim=-1) + + @property + def rel_weight_1(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight_1) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight_1, dim=-1) + + + @property + def rel_weight_2(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight_2) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight_2, dim=-1) + + + def compute_block(self, distance, height, n_cntxt_layers=0): + """Compute constituents from distance and height.""" + + if n_cntxt_layers>0: + if n_cntxt_layers == 1: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler_1[0] + elif n_cntxt_layers == 2: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler_2[0] + else: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0] + + gamma = torch.sigmoid(-beta_logits) + ones = torch.ones_like(gamma) + + block_mask_left = cummin( + gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1 + ) + block_mask_left = block_mask_left - F.pad( + block_mask_left[:, :, :-1], (1, 0), value=0 + ) + block_mask_left.tril_(0) + + block_mask_right = cummin( + gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1 + ) + block_mask_right = block_mask_right - F.pad( + block_mask_right[:, :, 1:], (0, 1), value=0 + ) + block_mask_right.triu_(0) + + block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :] + block = cumsum(block_mask_left).tril(0) + cumsum( + block_mask_right, reverse=True + ).triu(1) + + return block_p, block + + def compute_head(self, height, n_cntxt_layers=0): + """Estimate head for each constituent.""" + + _, length = height.size() + if n_cntxt_layers>0: + if n_cntxt_layers == 1: + head_logits = height * self.scaler_1[1] + elif n_cntxt_layers == 2: + head_logits = height * self.scaler_2[1] + else: + head_logits = height * self.scaler[1] + index = torch.arange(length, device=height.device) + + mask = (index[:, None, None] <= index[None, None, :]) * ( + index[None, None, :] <= index[None, :, None] + ) + head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1) + head_logits.masked_fill_(~mask[None, :, :, :], -1e9) + + head_p = torch.softmax(head_logits, dim=-1) + + return head_p + + def parse(self, x, embs=None, n_cntxt_layers=0): + """Parse input sentence. + + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + distance: syntactic distance + height: syntactic height + """ + + mask = x != self.pad + mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0) + + if embs is None: + h = self.roberta.embeddings(x) + else: + h = embs + + if n_cntxt_layers > 0: + if n_cntxt_layers == 1: + parser_layers = self.parser_layers_1 + height_ff = self.height_ff_1 + distance_ff = self.distance_ff_1 + elif n_cntxt_layers == 2: + parser_layers = self.parser_layers_2 + height_ff = self.height_ff_2 + distance_ff = self.distance_ff_2 + for i in range(int(self.config.n_parser_layers/2)): + h = h.masked_fill(~mask[:, :, None], 0) + h = parser_layers[i](h) + + height = height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, dim=-1 + )[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max + ) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where( + margin_left > margin_right, margin_right, margin_left + ).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + else: + for i in range(self.config.n_parser_layers): + h = h.masked_fill(~mask[:, :, None], 0) + h = self.parser_layers[i](h) + + height = self.height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = self.distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, dim=-1 + )[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max + ) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where( + margin_left > margin_right, margin_right, margin_left + ).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + + return distance, height + + def generate_mask(self, x, distance, height, n_cntxt_layers=0): + """Compute head and cibling distribution for each token.""" + + bsz, length = x.size() + + eye = torch.eye(length, device=x.device, dtype=torch.bool) + eye = eye[None, :, :].expand((bsz, -1, -1)) + + block_p, block = self.compute_block(distance, height, n_cntxt_layers=n_cntxt_layers) + head_p = self.compute_head(height, n_cntxt_layers=n_cntxt_layers) + head = torch.einsum("blij,bijh->blh", block_p, head_p) + head = head.masked_fill(eye, 0) + child = head.transpose(1, 2) + cibling = torch.bmm(head, child).masked_fill(eye, 0) + + rel_list = [] + if "head" in self.config.relations: + rel_list.append(head) + if "child" in self.config.relations: + rel_list.append(child) + if "cibling" in self.config.relations: + rel_list.append(cibling) + + rel = torch.stack(rel_list, dim=1) + + if n_cntxt_layers > 0: + if n_cntxt_layers == 1: + rel_weight = self.rel_weight_1 + elif n_cntxt_layers == 2: + rel_weight = self.rel_weight_2 + else: + rel_weight = self.rel_weight + + dep = torch.einsum("lhr,brij->lbhij", rel_weight, rel) + + if n_cntxt_layers == 1: + num_layers = self.cntxt_layers_2.config.num_hidden_layers + else: + num_layers = self.roberta.config.num_hidden_layers + + att_mask = dep.reshape( + num_layers, + bsz, + self.config.num_attention_heads, + length, + length, + ) + + return att_mask, cibling, head, block + + def forward( + self, + input_ids: Optional[torch.LongTensor] = None, + attention_mask: Optional[torch.FloatTensor] = None, + token_type_ids: Optional[torch.LongTensor] = None, + position_ids: Optional[torch.LongTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, MaskedLMOutput]: + r""" + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ..., + config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the + loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]` + kwargs (`Dict[str, any]`, optional, defaults to *{}*): + Used to hide legacy arguments that have been deprecated. + """ + return_dict = ( + return_dict if return_dict is not None else self.config.use_return_dict + ) + + + if self.config.n_cntxt_layers > 0: + cntxt_outputs = self.cntxt_layers( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict) + + + if self.config.n_cntxt_layers_2 > 0: + distance_1, height_1 = self.parse(input_ids, cntxt_outputs[0], n_cntxt_layers=1) + att_mask_1, _, _, _ = self.generate_mask(input_ids, distance_1, height_1, n_cntxt_layers=1) + + cntxt_outputs_2 = self.cntxt_layers_2( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + parser_att_mask=att_mask_1) + + sequence_output = cntxt_outputs_2[0] + + distance_2, height_2 = self.parse(input_ids, sequence_output[0], n_cntxt_layers=2) + att_mask, _, _, _ = self.generate_mask(input_ids, distance_2, height_2, n_cntxt_layers=2) + + elif self.config.n_cntxt_layers > 0: + distance, height = self.parse(input_ids, cntxt_outputs[0]) + att_mask, _, _, _ = self.generate_mask(input_ids, distance, height) + else: + distance, height = self.parse(input_ids) + att_mask, _, _, _ = self.generate_mask(input_ids, distance, height) + + outputs = self.roberta( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + parser_att_mask=att_mask, + ) + sequence_output = outputs[0] + prediction_scores = self.lm_head(sequence_output) + + masked_lm_loss = None + if labels is not None: + loss_fct = CrossEntropyLoss() + masked_lm_loss = loss_fct( + prediction_scores.view(-1, self.config.vocab_size), labels.view(-1) + ) + + if not return_dict: + output = (prediction_scores,) + outputs[2:] + return ( + ((masked_lm_loss,) + output) if masked_lm_loss is not None else output + ) + + return MaskedLMOutput( + loss=masked_lm_loss, + logits=prediction_scores, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +class RobertaLMHead(nn.Module): + """Roberta Head for masked language modeling.""" + + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + + self.decoder = nn.Linear(config.hidden_size, config.vocab_size) + self.bias = nn.Parameter(torch.zeros(config.vocab_size)) + self.decoder.bias = self.bias + + def forward(self, features, **kwargs): + x = self.dense(features) + x = gelu(x) + x = self.layer_norm(x) + + # project back to size of vocabulary with bias + x = self.decoder(x) + + return x + + def _tie_weights(self): + # To tie those two weights if they get disconnected (on TPU or when the bias is resized) + self.bias = self.decoder.bias + + +class StructRobertaForSequenceClassification(RobertaPreTrainedModel): + _keys_to_ignore_on_load_missing = [r"position_ids"] + + def __init__(self, config): + super().__init__(config) + self.num_labels = config.num_labels + self.config = config + + if config.n_cntxt_layers > 0: + config_cntxt = copy.deepcopy(config) + config_cntxt.num_hidden_layers = config.n_cntxt_layers + + self.cntxt_layers = RobertaModel(config_cntxt, add_pooling_layer=False) + + if config.n_cntxt_layers_2 > 0: + self.parser_layers_1 = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(int(config.n_parser_layers/2)) + ] + ) + + self.distance_ff_1 = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff_1 = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight_1 = nn.Parameter( + torch.zeros((config.n_cntxt_layers_2, config.num_attention_heads, n_rel)) + ) + self._rel_weight_1.data.normal_(0, 0.1) + + self._scaler_1 = nn.Parameter(torch.zeros(2)) + + config_cntxt_2 = copy.deepcopy(config) + config_cntxt_2.num_hidden_layers = config.n_cntxt_layers_2 + + self.cntxt_layers_2 = RobertaModel(config_cntxt_2, add_pooling_layer=False) + + + self.parser_layers_2 = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(int(config.n_parser_layers/2)) + ] + ) + + self.distance_ff_2 = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff_2 = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight_2 = nn.Parameter( + torch.zeros((config.num_hidden_layers, config.num_attention_heads, n_rel)) + ) + self._rel_weight_2.data.normal_(0, 0.1) + + self._scaler_2 = nn.Parameter(torch.zeros(2)) + + else: + self.parser_layers = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(config.n_parser_layers) + ] + ) + + self.distance_ff = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight = nn.Parameter( + torch.zeros((config.num_hidden_layers, config.num_attention_heads, n_rel)) + ) + self._rel_weight.data.normal_(0, 0.1) + + self._scaler = nn.Parameter(torch.zeros(2)) + + self.roberta = RobertaModel(config, add_pooling_layer=False) + + if config.n_cntxt_layers > 0: + self.cntxt_layers.embeddings = self.roberta.embeddings + if config.n_cntxt_layers_2 > 0: + self.cntxt_layers_2.embeddings = self.roberta.embeddings + + + self.pad = config.pad_token_id + self.classifier = RobertaClassificationHead(config) + + # Initialize weights and apply final processing + self.post_init() + + + @property + def scaler(self): + return self._scaler.exp() + + @property + def scaler_1(self): + return self._scaler_1.exp() + + @property + def scaler_2(self): + return self._scaler_2.exp() + + @property + def rel_weight(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight, dim=-1) + + @property + def rel_weight_1(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight_1) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight_1, dim=-1) + + + @property + def rel_weight_2(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight_2) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight_2, dim=-1) + + + def compute_block(self, distance, height, n_cntxt_layers=0): + """Compute constituents from distance and height.""" + + if n_cntxt_layers>0: + if n_cntxt_layers == 1: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler_1[0] + elif n_cntxt_layers == 2: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler_2[0] + else: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0] + + gamma = torch.sigmoid(-beta_logits) + ones = torch.ones_like(gamma) + + block_mask_left = cummin( + gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1 + ) + block_mask_left = block_mask_left - F.pad( + block_mask_left[:, :, :-1], (1, 0), value=0 + ) + block_mask_left.tril_(0) + + block_mask_right = cummin( + gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1 + ) + block_mask_right = block_mask_right - F.pad( + block_mask_right[:, :, 1:], (0, 1), value=0 + ) + block_mask_right.triu_(0) + + block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :] + block = cumsum(block_mask_left).tril(0) + cumsum( + block_mask_right, reverse=True + ).triu(1) + + return block_p, block + + def compute_head(self, height, n_cntxt_layers=0): + """Estimate head for each constituent.""" + + _, length = height.size() + if n_cntxt_layers>0: + if n_cntxt_layers == 1: + head_logits = height * self.scaler_1[1] + elif n_cntxt_layers == 2: + head_logits = height * self.scaler_2[1] + else: + head_logits = height * self.scaler[1] + index = torch.arange(length, device=height.device) + + mask = (index[:, None, None] <= index[None, None, :]) * ( + index[None, None, :] <= index[None, :, None] + ) + head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1) + head_logits.masked_fill_(~mask[None, :, :, :], -1e9) + + head_p = torch.softmax(head_logits, dim=-1) + + return head_p + + def parse(self, x, embs=None, n_cntxt_layers=0): + """Parse input sentence. + + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + distance: syntactic distance + height: syntactic height + """ + + mask = x != self.pad + mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0) + + if embs is None: + h = self.roberta.embeddings(x) + else: + h = embs + + if n_cntxt_layers > 0: + if n_cntxt_layers == 1: + parser_layers = self.parser_layers_1 + height_ff = self.height_ff_1 + distance_ff = self.distance_ff_1 + elif n_cntxt_layers == 2: + parser_layers = self.parser_layers_2 + height_ff = self.height_ff_2 + distance_ff = self.distance_ff_2 + for i in range(int(self.config.n_parser_layers/2)): + h = h.masked_fill(~mask[:, :, None], 0) + h = parser_layers[i](h) + + height = height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, dim=-1 + )[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max + ) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where( + margin_left > margin_right, margin_right, margin_left + ).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + else: + for i in range(self.config.n_parser_layers): + h = h.masked_fill(~mask[:, :, None], 0) + h = self.parser_layers[i](h) + + height = self.height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = self.distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, dim=-1 + )[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max + ) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where( + margin_left > margin_right, margin_right, margin_left + ).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + + return distance, height + + def generate_mask(self, x, distance, height, n_cntxt_layers=0): + """Compute head and cibling distribution for each token.""" + + bsz, length = x.size() + + eye = torch.eye(length, device=x.device, dtype=torch.bool) + eye = eye[None, :, :].expand((bsz, -1, -1)) + + block_p, block = self.compute_block(distance, height, n_cntxt_layers=n_cntxt_layers) + head_p = self.compute_head(height, n_cntxt_layers=n_cntxt_layers) + head = torch.einsum("blij,bijh->blh", block_p, head_p) + head = head.masked_fill(eye, 0) + child = head.transpose(1, 2) + cibling = torch.bmm(head, child).masked_fill(eye, 0) + + rel_list = [] + if "head" in self.config.relations: + rel_list.append(head) + if "child" in self.config.relations: + rel_list.append(child) + if "cibling" in self.config.relations: + rel_list.append(cibling) + + rel = torch.stack(rel_list, dim=1) + + if n_cntxt_layers > 0: + if n_cntxt_layers == 1: + rel_weight = self.rel_weight_1 + elif n_cntxt_layers == 2: + rel_weight = self.rel_weight_2 + else: + rel_weight = self.rel_weight + + dep = torch.einsum("lhr,brij->lbhij", rel_weight, rel) + + if n_cntxt_layers == 1: + num_layers = self.cntxt_layers_2.config.num_hidden_layers + else: + num_layers = self.roberta.config.num_hidden_layers + + att_mask = dep.reshape( + num_layers, + bsz, + self.config.num_attention_heads, + length, + length, + ) + + return att_mask, cibling, head, block + + def forward( + self, + input_ids: Optional[torch.LongTensor] = None, + attention_mask: Optional[torch.FloatTensor] = None, + token_type_ids: Optional[torch.LongTensor] = None, + position_ids: Optional[torch.LongTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, SequenceClassifierOutput]: + r""" + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., + config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If + `config.num_labels > 1` a classification loss is computed (Cross-Entropy). + """ + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + if self.config.n_cntxt_layers > 0: + cntxt_outputs = self.cntxt_layers( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict) + + + if self.config.n_cntxt_layers_2 > 0: + distance_1, height_1 = self.parse(input_ids, cntxt_outputs[0], n_cntxt_layers=1) + att_mask_1, _, _, _ = self.generate_mask(input_ids, distance_1, height_1, n_cntxt_layers=1) + + cntxt_outputs_2 = self.cntxt_layers_2( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + parser_att_mask=att_mask_1) + + sequence_output = cntxt_outputs_2[0] + + distance_2, height_2 = self.parse(input_ids, sequence_output[0], n_cntxt_layers=2) + att_mask, _, _, _ = self.generate_mask(input_ids, distance_2, height_2, n_cntxt_layers=2) + + elif self.config.n_cntxt_layers > 0: + distance, height = self.parse(input_ids, cntxt_outputs[0]) + att_mask, _, _, _ = self.generate_mask(input_ids, distance, height) + else: + distance, height = self.parse(input_ids) + att_mask, _, _, _ = self.generate_mask(input_ids, distance, height) + + outputs = self.roberta( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + parser_att_mask=att_mask, + ) + sequence_output = outputs[0] + logits = self.classifier(sequence_output) + + loss = None + if labels is not None: + if self.config.problem_type is None: + if self.num_labels == 1: + self.config.problem_type = "regression" + elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): + self.config.problem_type = "single_label_classification" + else: + self.config.problem_type = "multi_label_classification" + + if self.config.problem_type == "regression": + loss_fct = MSELoss() + if self.num_labels == 1: + loss = loss_fct(logits.squeeze(), labels.squeeze()) + else: + loss = loss_fct(logits, labels) + elif self.config.problem_type == "single_label_classification": + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) + elif self.config.problem_type == "multi_label_classification": + loss_fct = BCEWithLogitsLoss() + loss = loss_fct(logits, labels) + + if not return_dict: + output = (logits,) + outputs[2:] + return ((loss,) + output) if loss is not None else output + + return SequenceClassifierOutput( + loss=loss, + logits=logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +class RobertaClassificationHead(nn.Module): + """Head for sentence-level classification tasks.""" + + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + classifier_dropout = ( + config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob + ) + self.dropout = nn.Dropout(classifier_dropout) + self.out_proj = nn.Linear(config.hidden_size, config.num_labels) + + def forward(self, features, **kwargs): + x = features[:, 0, :] # take token (equiv. to [CLS]) + x = self.dropout(x) + x = self.dense(x) + x = torch.tanh(x) + x = self.dropout(x) + x = self.out_proj(x) + return x + + +def create_position_ids_from_input_ids( + input_ids, padding_idx, past_key_values_length=0 +): + """ + Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols + are ignored. This is modified from fairseq's `utils.make_positions`. + + Args: + x: torch.Tensor x: + + Returns: torch.Tensor + """ + # The series of casts and type-conversions here are carefully balanced to both work with ONNX export and XLA. + mask = input_ids.ne(padding_idx).int() + incremental_indices = ( + torch.cumsum(mask, dim=1).type_as(mask) + past_key_values_length + ) * mask + return incremental_indices.long() + padding_idx + + +def cumprod(x, reverse=False, exclusive=False): + """cumulative product.""" + if reverse: + x = x.flip([-1]) + + if exclusive: + x = F.pad(x[:, :, :-1], (1, 0), value=1) + + cx = x.cumprod(-1) + + if reverse: + cx = cx.flip([-1]) + return cx + + +def cumsum(x, reverse=False, exclusive=False): + """cumulative sum.""" + bsz, _, length = x.size() + device = x.device + if reverse: + if exclusive: + w = torch.ones([bsz, length, length], device=device).tril(-1) + else: + w = torch.ones([bsz, length, length], device=device).tril(0) + cx = torch.bmm(x, w) + else: + if exclusive: + w = torch.ones([bsz, length, length], device=device).triu(1) + else: + w = torch.ones([bsz, length, length], device=device).triu(0) + cx = torch.bmm(x, w) + return cx + + +def cummin(x, reverse=False, exclusive=False, max_value=1e9): + """cumulative min.""" + if reverse: + if exclusive: + x = F.pad(x[:, :, 1:], (0, 1), value=max_value) + x = x.flip([-1]).cummin(-1)[0].flip([-1]) + else: + if exclusive: + x = F.pad(x[:, :, :-1], (1, 0), value=max_value) + x = x.cummin(-1)[0] + return x diff --git a/finetune/multirc/predict_results.txt b/finetune/multirc/predict_results.txt new file mode 100644 index 0000000000000000000000000000000000000000..5c4f3445e091c37f4eda52c9365ece60266f1a28 --- /dev/null +++ b/finetune/multirc/predict_results.txt @@ -0,0 +1,914 @@ +index prediction +0 0 +1 1 +2 1 +3 1 +4 0 +5 0 +6 1 +7 1 +8 0 +9 1 +10 1 +11 1 +12 1 +13 0 +14 0 +15 0 +16 1 +17 1 +18 0 +19 0 +20 0 +21 0 +22 1 +23 1 +24 0 +25 0 +26 1 +27 0 +28 1 +29 1 +30 1 +31 0 +32 1 +33 1 +34 0 +35 1 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7091, + "Chair": 20941, + "Chairman": 31640, + "Cham": 27391, + "Champagne": 26908, + "Chan": 10002, + "Chand": 24367, + "Chang": 23641, + "Change": 22340, + "Chapter": 31484, + "Char": 4847, + "Charl": 22012, + "Charles": 9843, + "Charlie": 15618, + "Charlotte": 29026, + "Chat": 19557, + "Che": 4490, + "Check": 9112, + "Cheers": 13079, + "Chel": 29684, + "Ches": 29044, + "Chess": 26907, + "Chi": 18777, + "Chicago": 26226, + "Chicken": 28241, + "Chief": 12919, + "Child": 18587, + "Children": 15321, + "China": 23519, + "Chinese": 22882, + "Cho": 15188, + "Choose": 31336, + "Chp": 16315, + "Chris": 14514, + "Christ": 5128, + "Christian": 11908, + "Christine": 24997, + "Christmas": 24260, + "Christopher": 21184, + "Chuck": 17317, + "Chuckles": 13220, + "Chuckling": 31617, + "Chun": 26672, + "Church": 19483, + "Cir": 27390, + "City": 20257, + "Civil": 30285, + "Cl": 2870, + "Cla": 13016, + "Claire": 22653, + "Clara": 25793, + "Clare": 25792, + "Clark": 26696, + "Class": 14413, + "Clause": 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See all RoBERTa models at https://huggingface.co/models?filter=roberta +] + + +class StructRobertaConfig(RobertaConfig): + model_type = "roberta" + + def __init__( + self, + n_parser_layers=4, + conv_size=9, + relations=("head", "child"), + weight_act="softmax", + n_cntxt_layers=3, + n_cntxt_layers_2=0, + **kwargs,): + + super().__init__(**kwargs) + self.n_cntxt_layers = n_cntxt_layers + self.n_parser_layers = n_parser_layers + self.n_cntxt_layers_2 = n_cntxt_layers_2 + self.conv_size = conv_size + self.relations = relations + self.weight_act = weight_act + +class Conv1d(nn.Module): + """1D convolution layer.""" + + def __init__(self, hidden_size, kernel_size, dilation=1): + """Initialization. + + Args: + hidden_size: dimension of input embeddings + kernel_size: convolution kernel size + dilation: the spacing between the kernel points + """ + super(Conv1d, self).__init__() + + if kernel_size % 2 == 0: + padding = (kernel_size // 2) * dilation + self.shift = True + else: + padding = ((kernel_size - 1) // 2) * dilation + self.shift = False + self.conv = nn.Conv1d( + hidden_size, hidden_size, kernel_size, padding=padding, dilation=dilation + ) + + def forward(self, x): + """Compute convolution. + + Args: + x: input embeddings + Returns: + conv_output: convolution results + """ + + if self.shift: + return self.conv(x.transpose(1, 2)).transpose(1, 2)[:, 1:] + else: + return self.conv(x.transpose(1, 2)).transpose(1, 2) + + +class RobertaEmbeddings(nn.Module): + """ + Same as BertEmbeddings with a tiny tweak for positional embeddings indexing. + """ + + # Copied from transformers.models.bert.modeling_bert.BertEmbeddings.__init__ + def __init__(self, config): + super().__init__() + self.word_embeddings = nn.Embedding( + config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id + ) + self.position_embeddings = nn.Embedding( + config.max_position_embeddings, config.hidden_size + ) + self.token_type_embeddings = nn.Embedding( + config.type_vocab_size, config.hidden_size + ) + + # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load + # any TensorFlow checkpoint file + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + # position_ids (1, len position emb) is contiguous in memory and exported when serialized + self.position_embedding_type = getattr( + config, "position_embedding_type", "absolute" + ) + self.register_buffer( + "position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)) + ) + if version.parse(torch.__version__) > version.parse("1.6.0"): + self.register_buffer( + "token_type_ids", + torch.zeros(self.position_ids.size(), dtype=torch.long), + persistent=False, + ) + + # End copy + self.padding_idx = config.pad_token_id + self.position_embeddings = nn.Embedding( + config.max_position_embeddings, + config.hidden_size, + padding_idx=self.padding_idx, + ) + + def forward( + self, + input_ids=None, + token_type_ids=None, + position_ids=None, + inputs_embeds=None, + past_key_values_length=0, + ): + if position_ids is None: + if input_ids is not None: + # Create the position ids from the input token ids. Any padded tokens remain padded. + position_ids = create_position_ids_from_input_ids( + input_ids, self.padding_idx, past_key_values_length + ) + else: + position_ids = self.create_position_ids_from_inputs_embeds( + inputs_embeds + ) + + if input_ids is not None: + input_shape = input_ids.size() + else: + input_shape = inputs_embeds.size()[:-1] + + seq_length = input_shape[1] + + # Setting the token_type_ids to the registered buffer in constructor where it is all zeros, which usually occurs + # when its auto-generated, registered buffer helps users when tracing the model without passing token_type_ids, solves + # issue #5664 + if token_type_ids is None: + if hasattr(self, "token_type_ids"): + buffered_token_type_ids = self.token_type_ids[:, :seq_length] + buffered_token_type_ids_expanded = buffered_token_type_ids.expand( + input_shape[0], seq_length + ) + token_type_ids = buffered_token_type_ids_expanded + else: + token_type_ids = torch.zeros( + input_shape, dtype=torch.long, device=self.position_ids.device + ) + + if inputs_embeds is None: + inputs_embeds = self.word_embeddings(input_ids) + token_type_embeddings = self.token_type_embeddings(token_type_ids) + + embeddings = inputs_embeds + token_type_embeddings + if self.position_embedding_type == "absolute": + position_embeddings = self.position_embeddings(position_ids) + embeddings += position_embeddings + embeddings = self.LayerNorm(embeddings) + embeddings = self.dropout(embeddings) + return embeddings + + def create_position_ids_from_inputs_embeds(self, inputs_embeds): + """ + We are provided embeddings directly. We cannot infer which are padded so just generate sequential position ids. + + Args: + inputs_embeds: torch.Tensor + + Returns: torch.Tensor + """ + input_shape = inputs_embeds.size()[:-1] + sequence_length = input_shape[1] + + position_ids = torch.arange( + self.padding_idx + 1, + sequence_length + self.padding_idx + 1, + dtype=torch.long, + device=inputs_embeds.device, + ) + return position_ids.unsqueeze(0).expand(input_shape) + + +# Copied from transformers.models.bert.modeling_bert.BertSelfAttention with Bert->Roberta +class RobertaSelfAttention(nn.Module): + def __init__(self, config, position_embedding_type=None): + super().__init__() + if config.hidden_size % config.num_attention_heads != 0 and not hasattr( + config, "embedding_size" + ): + raise ValueError( + f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention " + f"heads ({config.num_attention_heads})" + ) + + self.num_attention_heads = config.num_attention_heads + self.attention_head_size = int(config.hidden_size / config.num_attention_heads) + self.all_head_size = self.num_attention_heads * self.attention_head_size + + self.query = nn.Linear(config.hidden_size, self.all_head_size) + self.key = nn.Linear(config.hidden_size, self.all_head_size) + self.value = nn.Linear(config.hidden_size, self.all_head_size) + + self.dropout = nn.Dropout(config.attention_probs_dropout_prob) + self.position_embedding_type = position_embedding_type or getattr( + config, "position_embedding_type", "absolute" + ) + if ( + self.position_embedding_type == "relative_key" + or self.position_embedding_type == "relative_key_query" + ): + self.max_position_embeddings = config.max_position_embeddings + self.distance_embedding = nn.Embedding( + 2 * config.max_position_embeddings - 1, self.attention_head_size + ) + + self.is_decoder = config.is_decoder + + def transpose_for_scores(self, x): + new_x_shape = x.size()[:-1] + ( + self.num_attention_heads, + self.attention_head_size, + ) + x = x.view(new_x_shape) + return x.permute(0, 2, 1, 3) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.FloatTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, + output_attentions: Optional[bool] = False, + parser_att_mask=None, + ) -> Tuple[torch.Tensor]: + mixed_query_layer = self.query(hidden_states) + + # If this is instantiated as a cross-attention module, the keys + # and values come from an encoder; the attention mask needs to be + # such that the encoder's padding tokens are not attended to. + is_cross_attention = encoder_hidden_states is not None + + if is_cross_attention and past_key_value is not None: + # reuse k,v, cross_attentions + key_layer = past_key_value[0] + value_layer = past_key_value[1] + attention_mask = encoder_attention_mask + elif is_cross_attention: + key_layer = self.transpose_for_scores(self.key(encoder_hidden_states)) + value_layer = self.transpose_for_scores(self.value(encoder_hidden_states)) + attention_mask = encoder_attention_mask + elif past_key_value is not None: + key_layer = self.transpose_for_scores(self.key(hidden_states)) + value_layer = self.transpose_for_scores(self.value(hidden_states)) + key_layer = torch.cat([past_key_value[0], key_layer], dim=2) + value_layer = torch.cat([past_key_value[1], value_layer], dim=2) + else: + key_layer = self.transpose_for_scores(self.key(hidden_states)) + value_layer = self.transpose_for_scores(self.value(hidden_states)) + + query_layer = self.transpose_for_scores(mixed_query_layer) + + if self.is_decoder: + # if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states. + # Further calls to cross_attention layer can then reuse all cross-attention + # key/value_states (first "if" case) + # if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of + # all previous decoder key/value_states. Further calls to uni-directional self-attention + # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case) + # if encoder bi-directional self-attention `past_key_value` is always `None` + past_key_value = (key_layer, value_layer) + + # Take the dot product between "query" and "key" to get the raw attention scores. + attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) + + if ( + self.position_embedding_type == "relative_key" + or self.position_embedding_type == "relative_key_query" + ): + seq_length = hidden_states.size()[1] + position_ids_l = torch.arange( + seq_length, dtype=torch.long, device=hidden_states.device + ).view(-1, 1) + position_ids_r = torch.arange( + seq_length, dtype=torch.long, device=hidden_states.device + ).view(1, -1) + distance = position_ids_l - position_ids_r + positional_embedding = self.distance_embedding( + distance + self.max_position_embeddings - 1 + ) + positional_embedding = positional_embedding.to( + dtype=query_layer.dtype + ) # fp16 compatibility + + if self.position_embedding_type == "relative_key": + relative_position_scores = torch.einsum( + "bhld,lrd->bhlr", query_layer, positional_embedding + ) + attention_scores = attention_scores + relative_position_scores + elif self.position_embedding_type == "relative_key_query": + relative_position_scores_query = torch.einsum( + "bhld,lrd->bhlr", query_layer, positional_embedding + ) + relative_position_scores_key = torch.einsum( + "bhrd,lrd->bhlr", key_layer, positional_embedding + ) + attention_scores = ( + attention_scores + + relative_position_scores_query + + relative_position_scores_key + ) + + attention_scores = attention_scores / math.sqrt(self.attention_head_size) + if attention_mask is not None: + # Apply the attention mask is (precomputed for all layers in RobertaModel forward() function) + attention_scores = attention_scores + attention_mask + + if parser_att_mask is None: + # Normalize the attention scores to probabilities. + attention_probs = nn.functional.softmax(attention_scores, dim=-1) + else: + attention_probs = torch.sigmoid(attention_scores) * parser_att_mask + + # This is actually dropping out entire tokens to attend to, which might + # seem a bit unusual, but is taken from the original Transformer paper. + attention_probs = self.dropout(attention_probs) + + # Mask heads if we want to + if head_mask is not None: + attention_probs = attention_probs * head_mask + + context_layer = torch.matmul(attention_probs, value_layer) + + context_layer = context_layer.permute(0, 2, 1, 3).contiguous() + new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) + context_layer = context_layer.view(new_context_layer_shape) + + outputs = ( + (context_layer, attention_probs) if output_attentions else (context_layer,) + ) + + if self.is_decoder: + outputs = outputs + (past_key_value,) + return outputs + + +# Copied from transformers.models.bert.modeling_bert.BertSelfOutput +class RobertaSelfOutput(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward( + self, hidden_states: torch.Tensor, input_tensor: torch.Tensor + ) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + hidden_states = self.LayerNorm(hidden_states + input_tensor) + return hidden_states + + +# Copied from transformers.models.bert.modeling_bert.BertAttention with Bert->Roberta +class RobertaAttention(nn.Module): + def __init__(self, config, position_embedding_type=None): + super().__init__() + self.self = RobertaSelfAttention( + config, position_embedding_type=position_embedding_type + ) + self.output = RobertaSelfOutput(config) + self.pruned_heads = set() + + def prune_heads(self, heads): + if len(heads) == 0: + return + heads, index = find_pruneable_heads_and_indices( + heads, + self.self.num_attention_heads, + self.self.attention_head_size, + self.pruned_heads, + ) + + # Prune linear layers + self.self.query = prune_linear_layer(self.self.query, index) + self.self.key = prune_linear_layer(self.self.key, index) + self.self.value = prune_linear_layer(self.self.value, index) + self.output.dense = prune_linear_layer(self.output.dense, index, dim=1) + + # Update hyper params and store pruned heads + self.self.num_attention_heads = self.self.num_attention_heads - len(heads) + self.self.all_head_size = ( + self.self.attention_head_size * self.self.num_attention_heads + ) + self.pruned_heads = self.pruned_heads.union(heads) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.FloatTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, + output_attentions: Optional[bool] = False, + parser_att_mask=None, + ) -> Tuple[torch.Tensor]: + self_outputs = self.self( + hidden_states, + attention_mask, + head_mask, + encoder_hidden_states, + encoder_attention_mask, + past_key_value, + output_attentions, + parser_att_mask=parser_att_mask, + ) + attention_output = self.output(self_outputs[0], hidden_states) + outputs = (attention_output,) + self_outputs[ + 1: + ] # add attentions if we output them + return outputs + + +# Copied from transformers.models.bert.modeling_bert.BertIntermediate +class RobertaIntermediate(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.intermediate_size) + if isinstance(config.hidden_act, str): + self.intermediate_act_fn = ACT2FN[config.hidden_act] + else: + self.intermediate_act_fn = config.hidden_act + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.intermediate_act_fn(hidden_states) + return hidden_states + + +# Copied from transformers.models.bert.modeling_bert.BertOutput +class RobertaOutput(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.intermediate_size, config.hidden_size) + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward( + self, hidden_states: torch.Tensor, input_tensor: torch.Tensor + ) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + hidden_states = self.LayerNorm(hidden_states + input_tensor) + return hidden_states + + +# Copied from transformers.models.bert.modeling_bert.BertLayer with Bert->Roberta +class RobertaLayer(nn.Module): + def __init__(self, config): + super().__init__() + self.chunk_size_feed_forward = config.chunk_size_feed_forward + self.seq_len_dim = 1 + self.attention = RobertaAttention(config) + self.is_decoder = config.is_decoder + self.add_cross_attention = config.add_cross_attention + if self.add_cross_attention: + if not self.is_decoder: + raise ValueError( + f"{self} should be used as a decoder model if cross attention is added" + ) + self.crossattention = RobertaAttention( + config, position_embedding_type="absolute" + ) + self.intermediate = RobertaIntermediate(config) + self.output = RobertaOutput(config) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.FloatTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, + output_attentions: Optional[bool] = False, + parser_att_mask=None, + ) -> Tuple[torch.Tensor]: + # decoder uni-directional self-attention cached key/values tuple is at positions 1,2 + self_attn_past_key_value = ( + past_key_value[:2] if past_key_value is not None else None + ) + self_attention_outputs = self.attention( + hidden_states, + attention_mask, + head_mask, + output_attentions=output_attentions, + past_key_value=self_attn_past_key_value, + parser_att_mask=parser_att_mask, + ) + attention_output = self_attention_outputs[0] + + # if decoder, the last output is tuple of self-attn cache + if self.is_decoder: + outputs = self_attention_outputs[1:-1] + present_key_value = self_attention_outputs[-1] + else: + outputs = self_attention_outputs[ + 1: + ] # add self attentions if we output attention weights + + cross_attn_present_key_value = None + if self.is_decoder and encoder_hidden_states is not None: + if not hasattr(self, "crossattention"): + raise ValueError( + f"If `encoder_hidden_states` are passed, {self} has to be instantiated with cross-attention layers by setting `config.add_cross_attention=True`" + ) + + # cross_attn cached key/values tuple is at positions 3,4 of past_key_value tuple + cross_attn_past_key_value = ( + past_key_value[-2:] if past_key_value is not None else None + ) + cross_attention_outputs = self.crossattention( + attention_output, + attention_mask, + head_mask, + encoder_hidden_states, + encoder_attention_mask, + cross_attn_past_key_value, + output_attentions, + ) + attention_output = cross_attention_outputs[0] + outputs = ( + outputs + cross_attention_outputs[1:-1] + ) # add cross attentions if we output attention weights + + # add cross-attn cache to positions 3,4 of present_key_value tuple + cross_attn_present_key_value = cross_attention_outputs[-1] + present_key_value = present_key_value + cross_attn_present_key_value + + layer_output = apply_chunking_to_forward( + self.feed_forward_chunk, + self.chunk_size_feed_forward, + self.seq_len_dim, + attention_output, + ) + outputs = (layer_output,) + outputs + + # if decoder, return the attn key/values as the last output + if self.is_decoder: + outputs = outputs + (present_key_value,) + + return outputs + + def feed_forward_chunk(self, attention_output): + intermediate_output = self.intermediate(attention_output) + layer_output = self.output(intermediate_output, attention_output) + return layer_output + + +# Copied from transformers.models.bert.modeling_bert.BertEncoder with Bert->Roberta +class RobertaEncoder(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + self.layer = nn.ModuleList( + [RobertaLayer(config) for _ in range(config.num_hidden_layers)] + ) + self.gradient_checkpointing = False + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.FloatTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = False, + output_hidden_states: Optional[bool] = False, + return_dict: Optional[bool] = True, + parser_att_mask=None, + ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPastAndCrossAttentions]: + all_hidden_states = () if output_hidden_states else None + all_self_attentions = () if output_attentions else None + all_cross_attentions = ( + () if output_attentions and self.config.add_cross_attention else None + ) + + next_decoder_cache = () if use_cache else None + for i, layer_module in enumerate(self.layer): + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + layer_head_mask = head_mask[i] if head_mask is not None else None + past_key_value = past_key_values[i] if past_key_values is not None else None + + if self.gradient_checkpointing and self.training: + + if use_cache: + logger.warning( + "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." + ) + use_cache = False + + def create_custom_forward(module): + def custom_forward(*inputs): + return module(*inputs, past_key_value, output_attentions) + + return custom_forward + + layer_outputs = torch.utils.checkpoint.checkpoint( + create_custom_forward(layer_module), + hidden_states, + attention_mask, + layer_head_mask, + encoder_hidden_states, + encoder_attention_mask, + ) + else: + if parser_att_mask is not None: + layer_outputs = layer_module( + hidden_states, + attention_mask, + layer_head_mask, + encoder_hidden_states, + encoder_attention_mask, + past_key_value, + output_attentions, + parser_att_mask=parser_att_mask[i]) + else: + layer_outputs = layer_module( + hidden_states, + attention_mask, + layer_head_mask, + encoder_hidden_states, + encoder_attention_mask, + past_key_value, + output_attentions, + parser_att_mask=None) + + + hidden_states = layer_outputs[0] + if use_cache: + next_decoder_cache += (layer_outputs[-1],) + if output_attentions: + all_self_attentions = all_self_attentions + (layer_outputs[1],) + if self.config.add_cross_attention: + all_cross_attentions = all_cross_attentions + (layer_outputs[2],) + + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + if not return_dict: + return tuple( + v + for v in [ + hidden_states, + next_decoder_cache, + all_hidden_states, + all_self_attentions, + all_cross_attentions, + ] + if v is not None + ) + return BaseModelOutputWithPastAndCrossAttentions( + last_hidden_state=hidden_states, + past_key_values=next_decoder_cache, + hidden_states=all_hidden_states, + attentions=all_self_attentions, + cross_attentions=all_cross_attentions, + ) + + +# Copied from transformers.models.bert.modeling_bert.BertPooler +class RobertaPooler(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.activation = nn.Tanh() + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + # We "pool" the model by simply taking the hidden state corresponding + # to the first token. + first_token_tensor = hidden_states[:, 0] + pooled_output = self.dense(first_token_tensor) + pooled_output = self.activation(pooled_output) + return pooled_output + + +class RobertaPreTrainedModel(PreTrainedModel): + """ + An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained + models. + """ + + config_class = RobertaConfig + base_model_prefix = "roberta" + supports_gradient_checkpointing = True + + # Copied from transformers.models.bert.modeling_bert.BertPreTrainedModel._init_weights + def _init_weights(self, module): + """Initialize the weights""" + if isinstance(module, nn.Linear): + # Slightly different from the TF version which uses truncated_normal for initialization + # cf https://github.com/pytorch/pytorch/pull/5617 + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.Embedding): + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.padding_idx is not None: + module.weight.data[module.padding_idx].zero_() + elif isinstance(module, nn.LayerNorm): + if module.bias is not None: + module.bias.data.zero_() + module.weight.data.fill_(1.0) + + def _set_gradient_checkpointing(self, module, value=False): + if isinstance(module, RobertaEncoder): + module.gradient_checkpointing = value + + def update_keys_to_ignore(self, config, del_keys_to_ignore): + """Remove some keys from ignore list""" + if not config.tie_word_embeddings: + # must make a new list, or the class variable gets modified! + self._keys_to_ignore_on_save = [ + k for k in self._keys_to_ignore_on_save if k not in del_keys_to_ignore + ] + self._keys_to_ignore_on_load_missing = [ + k + for k in self._keys_to_ignore_on_load_missing + if k not in del_keys_to_ignore + ] + + +ROBERTA_START_DOCSTRING = r""" + + This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the + library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads + etc.) + + This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. + Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage + and behavior. + + Parameters: + config ([`RobertaConfig`]): Model configuration class with all the parameters of the + model. Initializing with a config file does not load the weights associated with the model, only the + configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. +""" + + +ROBERTA_INPUTS_DOCSTRING = r""" + Args: + input_ids (`torch.LongTensor` of shape `({0})`): + Indices of input sequence tokens in the vocabulary. + + Indices can be obtained using [`RobertaTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + [What are input IDs?](../glossary#input-ids) + attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*): + Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + + [What are attention masks?](../glossary#attention-mask) + token_type_ids (`torch.LongTensor` of shape `({0})`, *optional*): + Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, + 1]`: + + - 0 corresponds to a *sentence A* token, + - 1 corresponds to a *sentence B* token. + + [What are token type IDs?](../glossary#token-type-ids) + position_ids (`torch.LongTensor` of shape `({0})`, *optional*): + Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, + config.max_position_embeddings - 1]`. + + [What are position IDs?](../glossary#position-ids) + head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): + Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: + + - 1 indicates the head is **not masked**, + - 0 indicates the head is **masked**. + + inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_size)`, *optional*): + Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This + is useful if you want more control over how to convert `input_ids` indices into associated vectors than the + model's internal embedding lookup matrix. + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned + tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for + more detail. + return_dict (`bool`, *optional*): + Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. +""" + + +class RobertaModel(RobertaPreTrainedModel): + """ + + The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of + cross-attention is added between the self-attention layers, following the architecture described in *Attention is + all you need*_ by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz + Kaiser and Illia Polosukhin. + + To behave as an decoder the model needs to be initialized with the `is_decoder` argument of the configuration set + to `True`. To be used in a Seq2Seq model, the model needs to initialized with both `is_decoder` argument and + `add_cross_attention` set to `True`; an `encoder_hidden_states` is then expected as an input to the forward pass. + + .. _*Attention is all you need*: https://arxiv.org/abs/1706.03762 + + """ + + _keys_to_ignore_on_load_missing = [r"position_ids"] + + # Copied from transformers.models.bert.modeling_bert.BertModel.__init__ with Bert->Roberta + def __init__(self, config, add_pooling_layer=True): + super().__init__(config) + self.config = config + + self.embeddings = RobertaEmbeddings(config) + self.encoder = RobertaEncoder(config) + + self.pooler = RobertaPooler(config) if add_pooling_layer else None + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.embeddings.word_embeddings + + def set_input_embeddings(self, value): + self.embeddings.word_embeddings = value + + def _prune_heads(self, heads_to_prune): + """ + Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base + class PreTrainedModel + """ + for layer, heads in heads_to_prune.items(): + self.encoder.layer[layer].attention.prune_heads(heads) + + # Copied from transformers.models.bert.modeling_bert.BertModel.forward + def forward( + self, + input_ids: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + token_type_ids: Optional[torch.Tensor] = None, + position_ids: Optional[torch.Tensor] = None, + head_mask: Optional[torch.Tensor] = None, + inputs_embeds: Optional[torch.Tensor] = None, + encoder_hidden_states: Optional[torch.Tensor] = None, + encoder_attention_mask: Optional[torch.Tensor] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + parser_att_mask=None, + ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPoolingAndCrossAttentions]: + r""" + encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): + Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if + the model is configured as a decoder. + encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*): + Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in + the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): + Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. + + If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that + don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all + `decoder_input_ids` of shape `(batch_size, sequence_length)`. + use_cache (`bool`, *optional*): + If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see + `past_key_values`). + """ + output_attentions = ( + output_attentions + if output_attentions is not None + else self.config.output_attentions + ) + output_hidden_states = ( + output_hidden_states + if output_hidden_states is not None + else self.config.output_hidden_states + ) + return_dict = ( + return_dict if return_dict is not None else self.config.use_return_dict + ) + + if self.config.is_decoder: + use_cache = use_cache if use_cache is not None else self.config.use_cache + else: + use_cache = False + + if input_ids is not None and inputs_embeds is not None: + raise ValueError( + "You cannot specify both input_ids and inputs_embeds at the same time" + ) + elif input_ids is not None: + input_shape = input_ids.size() + elif inputs_embeds is not None: + input_shape = inputs_embeds.size()[:-1] + else: + raise ValueError("You have to specify either input_ids or inputs_embeds") + + batch_size, seq_length = input_shape + device = input_ids.device if input_ids is not None else inputs_embeds.device + + # past_key_values_length + past_key_values_length = ( + past_key_values[0][0].shape[2] if past_key_values is not None else 0 + ) + + if attention_mask is None: + attention_mask = torch.ones( + ((batch_size, seq_length + past_key_values_length)), device=device + ) + + if token_type_ids is None: + if hasattr(self.embeddings, "token_type_ids"): + buffered_token_type_ids = self.embeddings.token_type_ids[:, :seq_length] + buffered_token_type_ids_expanded = buffered_token_type_ids.expand( + batch_size, seq_length + ) + token_type_ids = buffered_token_type_ids_expanded + else: + token_type_ids = torch.zeros( + input_shape, dtype=torch.long, device=device + ) + + # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length] + # ourselves in which case we just need to make it broadcastable to all heads. + extended_attention_mask: torch.Tensor = self.get_extended_attention_mask( + attention_mask, input_shape, device + ) + + # If a 2D or 3D attention mask is provided for the cross-attention + # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length] + if self.config.is_decoder and encoder_hidden_states is not None: + ( + encoder_batch_size, + encoder_sequence_length, + _, + ) = encoder_hidden_states.size() + encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length) + if encoder_attention_mask is None: + encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device) + encoder_extended_attention_mask = self.invert_attention_mask( + encoder_attention_mask + ) + else: + encoder_extended_attention_mask = None + + # Prepare head mask if needed + # 1.0 in head_mask indicate we keep the head + # attention_probs has shape bsz x n_heads x N x N + # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] + # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] + head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) + + embedding_output = self.embeddings( + input_ids=input_ids, + position_ids=position_ids, + token_type_ids=token_type_ids, + inputs_embeds=inputs_embeds, + past_key_values_length=past_key_values_length, + ) + encoder_outputs = self.encoder( + embedding_output, + attention_mask=extended_attention_mask, + head_mask=head_mask, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_extended_attention_mask, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + parser_att_mask=parser_att_mask, + ) + sequence_output = encoder_outputs[0] + pooled_output = ( + self.pooler(sequence_output) if self.pooler is not None else None + ) + + if not return_dict: + return (sequence_output, pooled_output) + encoder_outputs[1:] + + return BaseModelOutputWithPoolingAndCrossAttentions( + last_hidden_state=sequence_output, + pooler_output=pooled_output, + past_key_values=encoder_outputs.past_key_values, + hidden_states=encoder_outputs.hidden_states, + attentions=encoder_outputs.attentions, + cross_attentions=encoder_outputs.cross_attentions, + ) + + +class StructRoberta(RobertaPreTrainedModel): + _keys_to_ignore_on_save = [r"lm_head.decoder.weight", r"lm_head.decoder.bias"] + _keys_to_ignore_on_load_missing = [ + r"position_ids", + r"lm_head.decoder.weight", + r"lm_head.decoder.bias", + ] + _keys_to_ignore_on_load_unexpected = [r"pooler"] + + def __init__(self, config): + super().__init__(config) + + if config.is_decoder: + logger.warning( + "If you want to use `RobertaForMaskedLM` make sure `config.is_decoder=False` for " + "bi-directional self-attention." + ) + + + if config.n_cntxt_layers > 0: + config_cntxt = copy.deepcopy(config) + config_cntxt.num_hidden_layers = config.n_cntxt_layers + + self.cntxt_layers = RobertaModel(config_cntxt, add_pooling_layer=False) + + if config.n_cntxt_layers_2 > 0: + self.parser_layers_1 = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(int(config.n_parser_layers/2)) + ] + ) + + self.distance_ff_1 = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff_1 = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight_1 = nn.Parameter( + torch.zeros((config.n_cntxt_layers_2, config.num_attention_heads, n_rel)) + ) + self._rel_weight_1.data.normal_(0, 0.1) + + self._scaler_1 = nn.Parameter(torch.zeros(2)) + + config_cntxt_2 = copy.deepcopy(config) + config_cntxt_2.num_hidden_layers = config.n_cntxt_layers_2 + + self.cntxt_layers_2 = RobertaModel(config_cntxt_2, add_pooling_layer=False) + + + self.parser_layers_2 = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(int(config.n_parser_layers/2)) + ] + ) + + self.distance_ff_2 = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff_2 = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight_2 = nn.Parameter( + torch.zeros((config.num_hidden_layers, config.num_attention_heads, n_rel)) + ) + self._rel_weight_2.data.normal_(0, 0.1) + + self._scaler_2 = nn.Parameter(torch.zeros(2)) + + else: + self.parser_layers = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(config.n_parser_layers) + ] + ) + + self.distance_ff = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight = nn.Parameter( + torch.zeros((config.num_hidden_layers, config.num_attention_heads, n_rel)) + ) + self._rel_weight.data.normal_(0, 0.1) + + self._scaler = nn.Parameter(torch.zeros(2)) + + self.roberta = RobertaModel(config, add_pooling_layer=False) + + if config.n_cntxt_layers > 0: + self.cntxt_layers.embeddings = self.roberta.embeddings + if config.n_cntxt_layers_2 > 0: + self.cntxt_layers_2.embeddings = self.roberta.embeddings + + self.lm_head = RobertaLMHead(config) + + self.pad = config.pad_token_id + + # The LM head weights require special treatment only when they are tied with the word embeddings + self.update_keys_to_ignore(config, ["lm_head.decoder.weight"]) + + # Initialize weights and apply final processing + self.post_init() + + def get_output_embeddings(self): + return self.lm_head.decoder + + def set_output_embeddings(self, new_embeddings): + self.lm_head.decoder = new_embeddings + + @property + def scaler(self): + return self._scaler.exp() + + @property + def scaler_1(self): + return self._scaler_1.exp() + + @property + def scaler_2(self): + return self._scaler_2.exp() + + @property + def rel_weight(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight, dim=-1) + + @property + def rel_weight_1(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight_1) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight_1, dim=-1) + + + @property + def rel_weight_2(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight_2) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight_2, dim=-1) + + + def compute_block(self, distance, height, n_cntxt_layers=0): + """Compute constituents from distance and height.""" + + if n_cntxt_layers>0: + if n_cntxt_layers == 1: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler_1[0] + elif n_cntxt_layers == 2: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler_2[0] + else: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0] + + gamma = torch.sigmoid(-beta_logits) + ones = torch.ones_like(gamma) + + block_mask_left = cummin( + gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1 + ) + block_mask_left = block_mask_left - F.pad( + block_mask_left[:, :, :-1], (1, 0), value=0 + ) + block_mask_left.tril_(0) + + block_mask_right = cummin( + gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1 + ) + block_mask_right = block_mask_right - F.pad( + block_mask_right[:, :, 1:], (0, 1), value=0 + ) + block_mask_right.triu_(0) + + block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :] + block = cumsum(block_mask_left).tril(0) + cumsum( + block_mask_right, reverse=True + ).triu(1) + + return block_p, block + + def compute_head(self, height, n_cntxt_layers=0): + """Estimate head for each constituent.""" + + _, length = height.size() + if n_cntxt_layers>0: + if n_cntxt_layers == 1: + head_logits = height * self.scaler_1[1] + elif n_cntxt_layers == 2: + head_logits = height * self.scaler_2[1] + else: + head_logits = height * self.scaler[1] + index = torch.arange(length, device=height.device) + + mask = (index[:, None, None] <= index[None, None, :]) * ( + index[None, None, :] <= index[None, :, None] + ) + head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1) + head_logits.masked_fill_(~mask[None, :, :, :], -1e9) + + head_p = torch.softmax(head_logits, dim=-1) + + return head_p + + def parse(self, x, embs=None, n_cntxt_layers=0): + """Parse input sentence. + + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + distance: syntactic distance + height: syntactic height + """ + + mask = x != self.pad + mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0) + + if embs is None: + h = self.roberta.embeddings(x) + else: + h = embs + + if n_cntxt_layers > 0: + if n_cntxt_layers == 1: + parser_layers = self.parser_layers_1 + height_ff = self.height_ff_1 + distance_ff = self.distance_ff_1 + elif n_cntxt_layers == 2: + parser_layers = self.parser_layers_2 + height_ff = self.height_ff_2 + distance_ff = self.distance_ff_2 + for i in range(int(self.config.n_parser_layers/2)): + h = h.masked_fill(~mask[:, :, None], 0) + h = parser_layers[i](h) + + height = height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, dim=-1 + )[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max + ) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where( + margin_left > margin_right, margin_right, margin_left + ).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + else: + for i in range(self.config.n_parser_layers): + h = h.masked_fill(~mask[:, :, None], 0) + h = self.parser_layers[i](h) + + height = self.height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = self.distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, dim=-1 + )[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max + ) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where( + margin_left > margin_right, margin_right, margin_left + ).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + + return distance, height + + def generate_mask(self, x, distance, height, n_cntxt_layers=0): + """Compute head and cibling distribution for each token.""" + + bsz, length = x.size() + + eye = torch.eye(length, device=x.device, dtype=torch.bool) + eye = eye[None, :, :].expand((bsz, -1, -1)) + + block_p, block = self.compute_block(distance, height, n_cntxt_layers=n_cntxt_layers) + head_p = self.compute_head(height, n_cntxt_layers=n_cntxt_layers) + head = torch.einsum("blij,bijh->blh", block_p, head_p) + head = head.masked_fill(eye, 0) + child = head.transpose(1, 2) + cibling = torch.bmm(head, child).masked_fill(eye, 0) + + rel_list = [] + if "head" in self.config.relations: + rel_list.append(head) + if "child" in self.config.relations: + rel_list.append(child) + if "cibling" in self.config.relations: + rel_list.append(cibling) + + rel = torch.stack(rel_list, dim=1) + + if n_cntxt_layers > 0: + if n_cntxt_layers == 1: + rel_weight = self.rel_weight_1 + elif n_cntxt_layers == 2: + rel_weight = self.rel_weight_2 + else: + rel_weight = self.rel_weight + + dep = torch.einsum("lhr,brij->lbhij", rel_weight, rel) + + if n_cntxt_layers == 1: + num_layers = self.cntxt_layers_2.config.num_hidden_layers + else: + num_layers = self.roberta.config.num_hidden_layers + + att_mask = dep.reshape( + num_layers, + bsz, + self.config.num_attention_heads, + length, + length, + ) + + return att_mask, cibling, head, block + + def forward( + self, + input_ids: Optional[torch.LongTensor] = None, + attention_mask: Optional[torch.FloatTensor] = None, + token_type_ids: Optional[torch.LongTensor] = None, + position_ids: Optional[torch.LongTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, MaskedLMOutput]: + r""" + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ..., + config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the + loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]` + kwargs (`Dict[str, any]`, optional, defaults to *{}*): + Used to hide legacy arguments that have been deprecated. + """ + return_dict = ( + return_dict if return_dict is not None else self.config.use_return_dict + ) + + + if self.config.n_cntxt_layers > 0: + cntxt_outputs = self.cntxt_layers( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict) + + + if self.config.n_cntxt_layers_2 > 0: + distance_1, height_1 = self.parse(input_ids, cntxt_outputs[0], n_cntxt_layers=1) + att_mask_1, _, _, _ = self.generate_mask(input_ids, distance_1, height_1, n_cntxt_layers=1) + + cntxt_outputs_2 = self.cntxt_layers_2( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + parser_att_mask=att_mask_1) + + sequence_output = cntxt_outputs_2[0] + + distance_2, height_2 = self.parse(input_ids, sequence_output[0], n_cntxt_layers=2) + att_mask, _, _, _ = self.generate_mask(input_ids, distance_2, height_2, n_cntxt_layers=2) + + elif self.config.n_cntxt_layers > 0: + distance, height = self.parse(input_ids, cntxt_outputs[0]) + att_mask, _, _, _ = self.generate_mask(input_ids, distance, height) + else: + distance, height = self.parse(input_ids) + att_mask, _, _, _ = self.generate_mask(input_ids, distance, height) + + outputs = self.roberta( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + parser_att_mask=att_mask, + ) + sequence_output = outputs[0] + prediction_scores = self.lm_head(sequence_output) + + masked_lm_loss = None + if labels is not None: + loss_fct = CrossEntropyLoss() + masked_lm_loss = loss_fct( + prediction_scores.view(-1, self.config.vocab_size), labels.view(-1) + ) + + if not return_dict: + output = (prediction_scores,) + outputs[2:] + return ( + ((masked_lm_loss,) + output) if masked_lm_loss is not None else output + ) + + return MaskedLMOutput( + loss=masked_lm_loss, + logits=prediction_scores, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +class RobertaLMHead(nn.Module): + """Roberta Head for masked language modeling.""" + + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + + self.decoder = nn.Linear(config.hidden_size, config.vocab_size) + self.bias = nn.Parameter(torch.zeros(config.vocab_size)) + self.decoder.bias = self.bias + + def forward(self, features, **kwargs): + x = self.dense(features) + x = gelu(x) + x = self.layer_norm(x) + + # project back to size of vocabulary with bias + x = self.decoder(x) + + return x + + def _tie_weights(self): + # To tie those two weights if they get disconnected (on TPU or when the bias is resized) + self.bias = self.decoder.bias + + +class StructRobertaForSequenceClassification(RobertaPreTrainedModel): + _keys_to_ignore_on_load_missing = [r"position_ids"] + + def __init__(self, config): + super().__init__(config) + self.num_labels = config.num_labels + self.config = config + + if config.n_cntxt_layers > 0: + config_cntxt = copy.deepcopy(config) + config_cntxt.num_hidden_layers = config.n_cntxt_layers + + self.cntxt_layers = RobertaModel(config_cntxt, add_pooling_layer=False) + + if config.n_cntxt_layers_2 > 0: + self.parser_layers_1 = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(int(config.n_parser_layers/2)) + ] + ) + + self.distance_ff_1 = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff_1 = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight_1 = nn.Parameter( + torch.zeros((config.n_cntxt_layers_2, config.num_attention_heads, n_rel)) + ) + self._rel_weight_1.data.normal_(0, 0.1) + + self._scaler_1 = nn.Parameter(torch.zeros(2)) + + config_cntxt_2 = copy.deepcopy(config) + config_cntxt_2.num_hidden_layers = config.n_cntxt_layers_2 + + self.cntxt_layers_2 = RobertaModel(config_cntxt_2, add_pooling_layer=False) + + + self.parser_layers_2 = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(int(config.n_parser_layers/2)) + ] + ) + + self.distance_ff_2 = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff_2 = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight_2 = nn.Parameter( + torch.zeros((config.num_hidden_layers, config.num_attention_heads, n_rel)) + ) + self._rel_weight_2.data.normal_(0, 0.1) + + self._scaler_2 = nn.Parameter(torch.zeros(2)) + + else: + self.parser_layers = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(config.n_parser_layers) + ] + ) + + self.distance_ff = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight = nn.Parameter( + torch.zeros((config.num_hidden_layers, config.num_attention_heads, n_rel)) + ) + self._rel_weight.data.normal_(0, 0.1) + + self._scaler = nn.Parameter(torch.zeros(2)) + + self.roberta = RobertaModel(config, add_pooling_layer=False) + + if config.n_cntxt_layers > 0: + self.cntxt_layers.embeddings = self.roberta.embeddings + if config.n_cntxt_layers_2 > 0: + self.cntxt_layers_2.embeddings = self.roberta.embeddings + + + self.pad = config.pad_token_id + self.classifier = RobertaClassificationHead(config) + + # Initialize weights and apply final processing + self.post_init() + + + @property + def scaler(self): + return self._scaler.exp() + + @property + def scaler_1(self): + return self._scaler_1.exp() + + @property + def scaler_2(self): + return self._scaler_2.exp() + + @property + def rel_weight(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight, dim=-1) + + @property + def rel_weight_1(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight_1) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight_1, dim=-1) + + + @property + def rel_weight_2(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight_2) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight_2, dim=-1) + + + def compute_block(self, distance, height, n_cntxt_layers=0): + """Compute constituents from distance and height.""" + + if n_cntxt_layers>0: + if n_cntxt_layers == 1: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler_1[0] + elif n_cntxt_layers == 2: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler_2[0] + else: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0] + + gamma = torch.sigmoid(-beta_logits) + ones = torch.ones_like(gamma) + + block_mask_left = cummin( + gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1 + ) + block_mask_left = block_mask_left - F.pad( + block_mask_left[:, :, :-1], (1, 0), value=0 + ) + block_mask_left.tril_(0) + + block_mask_right = cummin( + gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1 + ) + block_mask_right = block_mask_right - F.pad( + block_mask_right[:, :, 1:], (0, 1), value=0 + ) + block_mask_right.triu_(0) + + block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :] + block = cumsum(block_mask_left).tril(0) + cumsum( + block_mask_right, reverse=True + ).triu(1) + + return block_p, block + + def compute_head(self, height, n_cntxt_layers=0): + """Estimate head for each constituent.""" + + _, length = height.size() + if n_cntxt_layers>0: + if n_cntxt_layers == 1: + head_logits = height * self.scaler_1[1] + elif n_cntxt_layers == 2: + head_logits = height * self.scaler_2[1] + else: + head_logits = height * self.scaler[1] + index = torch.arange(length, device=height.device) + + mask = (index[:, None, None] <= index[None, None, :]) * ( + index[None, None, :] <= index[None, :, None] + ) + head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1) + head_logits.masked_fill_(~mask[None, :, :, :], -1e9) + + head_p = torch.softmax(head_logits, dim=-1) + + return head_p + + def parse(self, x, embs=None, n_cntxt_layers=0): + """Parse input sentence. + + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + distance: syntactic distance + height: syntactic height + """ + + mask = x != self.pad + mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0) + + if embs is None: + h = self.roberta.embeddings(x) + else: + h = embs + + if n_cntxt_layers > 0: + if n_cntxt_layers == 1: + parser_layers = self.parser_layers_1 + height_ff = self.height_ff_1 + distance_ff = self.distance_ff_1 + elif n_cntxt_layers == 2: + parser_layers = self.parser_layers_2 + height_ff = self.height_ff_2 + distance_ff = self.distance_ff_2 + for i in range(int(self.config.n_parser_layers/2)): + h = h.masked_fill(~mask[:, :, None], 0) + h = parser_layers[i](h) + + height = height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, dim=-1 + )[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max + ) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where( + margin_left > margin_right, margin_right, margin_left + ).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + else: + for i in range(self.config.n_parser_layers): + h = h.masked_fill(~mask[:, :, None], 0) + h = self.parser_layers[i](h) + + height = self.height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = self.distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, dim=-1 + )[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max + ) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where( + margin_left > margin_right, margin_right, margin_left + ).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + + return distance, height + + def generate_mask(self, x, distance, height, n_cntxt_layers=0): + """Compute head and cibling distribution for each token.""" + + bsz, length = x.size() + + eye = torch.eye(length, device=x.device, dtype=torch.bool) + eye = eye[None, :, :].expand((bsz, -1, -1)) + + block_p, block = self.compute_block(distance, height, n_cntxt_layers=n_cntxt_layers) + head_p = self.compute_head(height, n_cntxt_layers=n_cntxt_layers) + head = torch.einsum("blij,bijh->blh", block_p, head_p) + head = head.masked_fill(eye, 0) + child = head.transpose(1, 2) + cibling = torch.bmm(head, child).masked_fill(eye, 0) + + rel_list = [] + if "head" in self.config.relations: + rel_list.append(head) + if "child" in self.config.relations: + rel_list.append(child) + if "cibling" in self.config.relations: + rel_list.append(cibling) + + rel = torch.stack(rel_list, dim=1) + + if n_cntxt_layers > 0: + if n_cntxt_layers == 1: + rel_weight = self.rel_weight_1 + elif n_cntxt_layers == 2: + rel_weight = self.rel_weight_2 + else: + rel_weight = self.rel_weight + + dep = torch.einsum("lhr,brij->lbhij", rel_weight, rel) + + if n_cntxt_layers == 1: + num_layers = self.cntxt_layers_2.config.num_hidden_layers + else: + num_layers = self.roberta.config.num_hidden_layers + + att_mask = dep.reshape( + num_layers, + bsz, + self.config.num_attention_heads, + length, + length, + ) + + return att_mask, cibling, head, block + + def forward( + self, + input_ids: Optional[torch.LongTensor] = None, + attention_mask: Optional[torch.FloatTensor] = None, + token_type_ids: Optional[torch.LongTensor] = None, + position_ids: Optional[torch.LongTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, SequenceClassifierOutput]: + r""" + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., + config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If + `config.num_labels > 1` a classification loss is computed (Cross-Entropy). + """ + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + if self.config.n_cntxt_layers > 0: + cntxt_outputs = self.cntxt_layers( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict) + + + if self.config.n_cntxt_layers_2 > 0: + distance_1, height_1 = self.parse(input_ids, cntxt_outputs[0], n_cntxt_layers=1) + att_mask_1, _, _, _ = self.generate_mask(input_ids, distance_1, height_1, n_cntxt_layers=1) + + cntxt_outputs_2 = self.cntxt_layers_2( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + parser_att_mask=att_mask_1) + + sequence_output = cntxt_outputs_2[0] + + distance_2, height_2 = self.parse(input_ids, sequence_output[0], n_cntxt_layers=2) + att_mask, _, _, _ = self.generate_mask(input_ids, distance_2, height_2, n_cntxt_layers=2) + + elif self.config.n_cntxt_layers > 0: + distance, height = self.parse(input_ids, cntxt_outputs[0]) + att_mask, _, _, _ = self.generate_mask(input_ids, distance, height) + else: + distance, height = self.parse(input_ids) + att_mask, _, _, _ = self.generate_mask(input_ids, distance, height) + + outputs = self.roberta( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + parser_att_mask=att_mask, + ) + sequence_output = outputs[0] + logits = self.classifier(sequence_output) + + loss = None + if labels is not None: + if self.config.problem_type is None: + if self.num_labels == 1: + self.config.problem_type = "regression" + elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): + self.config.problem_type = "single_label_classification" + else: + self.config.problem_type = "multi_label_classification" + + if self.config.problem_type == "regression": + loss_fct = MSELoss() + if self.num_labels == 1: + loss = loss_fct(logits.squeeze(), labels.squeeze()) + else: + loss = loss_fct(logits, labels) + elif self.config.problem_type == "single_label_classification": + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) + elif self.config.problem_type == "multi_label_classification": + loss_fct = BCEWithLogitsLoss() + loss = loss_fct(logits, labels) + + if not return_dict: + output = (logits,) + outputs[2:] + return ((loss,) + output) if loss is not None else output + + return SequenceClassifierOutput( + loss=loss, + logits=logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +class RobertaClassificationHead(nn.Module): + """Head for sentence-level classification tasks.""" + + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + classifier_dropout = ( + config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob + ) + self.dropout = nn.Dropout(classifier_dropout) + self.out_proj = nn.Linear(config.hidden_size, config.num_labels) + + def forward(self, features, **kwargs): + x = features[:, 0, :] # take token (equiv. to [CLS]) + x = self.dropout(x) + x = self.dense(x) + x = torch.tanh(x) + x = self.dropout(x) + x = self.out_proj(x) + return x + + +def create_position_ids_from_input_ids( + input_ids, padding_idx, past_key_values_length=0 +): + """ + Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols + are ignored. This is modified from fairseq's `utils.make_positions`. + + Args: + x: torch.Tensor x: + + Returns: torch.Tensor + """ + # The series of casts and type-conversions here are carefully balanced to both work with ONNX export and XLA. + mask = input_ids.ne(padding_idx).int() + incremental_indices = ( + torch.cumsum(mask, dim=1).type_as(mask) + past_key_values_length + ) * mask + return incremental_indices.long() + padding_idx + + +def cumprod(x, reverse=False, exclusive=False): + """cumulative product.""" + if reverse: + x = x.flip([-1]) + + if exclusive: + x = F.pad(x[:, :, :-1], (1, 0), value=1) + + cx = x.cumprod(-1) + + if reverse: + cx = cx.flip([-1]) + return cx + + +def cumsum(x, reverse=False, exclusive=False): + """cumulative sum.""" + bsz, _, length = x.size() + device = x.device + if reverse: + if exclusive: + w = torch.ones([bsz, length, length], device=device).tril(-1) + else: + w = torch.ones([bsz, length, length], device=device).tril(0) + cx = torch.bmm(x, w) + else: + if exclusive: + w = torch.ones([bsz, length, length], device=device).triu(1) + else: + w = torch.ones([bsz, length, length], device=device).triu(0) + cx = torch.bmm(x, w) + return cx + + +def cummin(x, reverse=False, exclusive=False, max_value=1e9): + """cumulative min.""" + if reverse: + if exclusive: + x = F.pad(x[:, :, 1:], (0, 1), value=max_value) + x = x.flip([-1]).cummin(-1)[0].flip([-1]) + else: + if exclusive: + x = F.pad(x[:, :, :-1], (1, 0), value=max_value) + x = x.cummin(-1)[0] + return x diff --git a/finetune/qnli/predict_results.txt b/finetune/qnli/predict_results.txt new file mode 100644 index 0000000000000000000000000000000000000000..e59f02944813e8ac2b5d855577729c5e7a18d21a --- /dev/null +++ b/finetune/qnli/predict_results.txt @@ -0,0 +1,2287 @@ +index prediction +0 1 +1 1 +2 1 +3 0 +4 0 +5 1 +6 1 +7 1 +8 0 +9 0 +10 0 +11 1 +12 1 +13 1 +14 1 +15 0 +16 1 +17 1 +18 0 +19 1 +20 0 +21 0 +22 1 +23 1 +24 1 +25 0 +26 0 +27 1 +28 0 +29 0 +30 1 +31 1 +32 1 +33 1 +34 1 +35 0 +36 0 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See all RoBERTa models at https://huggingface.co/models?filter=roberta +] + + +class StructRobertaConfig(RobertaConfig): + model_type = "roberta" + + def __init__( + self, + n_parser_layers=4, + conv_size=9, + relations=("head", "child"), + weight_act="softmax", + n_cntxt_layers=3, + n_cntxt_layers_2=0, + **kwargs,): + + super().__init__(**kwargs) + self.n_cntxt_layers = n_cntxt_layers + self.n_parser_layers = n_parser_layers + self.n_cntxt_layers_2 = n_cntxt_layers_2 + self.conv_size = conv_size + self.relations = relations + self.weight_act = weight_act + +class Conv1d(nn.Module): + """1D convolution layer.""" + + def __init__(self, hidden_size, kernel_size, dilation=1): + """Initialization. + + Args: + hidden_size: dimension of input embeddings + kernel_size: convolution kernel size + dilation: the spacing between the kernel points + """ + super(Conv1d, self).__init__() + + if kernel_size % 2 == 0: + padding = (kernel_size // 2) * dilation + self.shift = True + else: + padding = ((kernel_size - 1) // 2) * dilation + self.shift = False + self.conv = nn.Conv1d( + hidden_size, hidden_size, kernel_size, padding=padding, dilation=dilation + ) + + def forward(self, x): + """Compute convolution. + + Args: + x: input embeddings + Returns: + conv_output: convolution results + """ + + if self.shift: + return self.conv(x.transpose(1, 2)).transpose(1, 2)[:, 1:] + else: + return self.conv(x.transpose(1, 2)).transpose(1, 2) + + +class RobertaEmbeddings(nn.Module): + """ + Same as BertEmbeddings with a tiny tweak for positional embeddings indexing. + """ + + # Copied from transformers.models.bert.modeling_bert.BertEmbeddings.__init__ + def __init__(self, config): + super().__init__() + self.word_embeddings = nn.Embedding( + config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id + ) + self.position_embeddings = nn.Embedding( + config.max_position_embeddings, config.hidden_size + ) + self.token_type_embeddings = nn.Embedding( + config.type_vocab_size, config.hidden_size + ) + + # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load + # any TensorFlow checkpoint file + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + # position_ids (1, len position emb) is contiguous in memory and exported when serialized + self.position_embedding_type = getattr( + config, "position_embedding_type", "absolute" + ) + self.register_buffer( + "position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)) + ) + if version.parse(torch.__version__) > version.parse("1.6.0"): + self.register_buffer( + "token_type_ids", + torch.zeros(self.position_ids.size(), dtype=torch.long), + persistent=False, + ) + + # End copy + self.padding_idx = config.pad_token_id + self.position_embeddings = nn.Embedding( + config.max_position_embeddings, + config.hidden_size, + padding_idx=self.padding_idx, + ) + + def forward( + self, + input_ids=None, + token_type_ids=None, + position_ids=None, + inputs_embeds=None, + past_key_values_length=0, + ): + if position_ids is None: + if input_ids is not None: + # Create the position ids from the input token ids. Any padded tokens remain padded. + position_ids = create_position_ids_from_input_ids( + input_ids, self.padding_idx, past_key_values_length + ) + else: + position_ids = self.create_position_ids_from_inputs_embeds( + inputs_embeds + ) + + if input_ids is not None: + input_shape = input_ids.size() + else: + input_shape = inputs_embeds.size()[:-1] + + seq_length = input_shape[1] + + # Setting the token_type_ids to the registered buffer in constructor where it is all zeros, which usually occurs + # when its auto-generated, registered buffer helps users when tracing the model without passing token_type_ids, solves + # issue #5664 + if token_type_ids is None: + if hasattr(self, "token_type_ids"): + buffered_token_type_ids = self.token_type_ids[:, :seq_length] + buffered_token_type_ids_expanded = buffered_token_type_ids.expand( + input_shape[0], seq_length + ) + token_type_ids = buffered_token_type_ids_expanded + else: + token_type_ids = torch.zeros( + input_shape, dtype=torch.long, device=self.position_ids.device + ) + + if inputs_embeds is None: + inputs_embeds = self.word_embeddings(input_ids) + token_type_embeddings = self.token_type_embeddings(token_type_ids) + + embeddings = inputs_embeds + token_type_embeddings + if self.position_embedding_type == "absolute": + position_embeddings = self.position_embeddings(position_ids) + embeddings += position_embeddings + embeddings = self.LayerNorm(embeddings) + embeddings = self.dropout(embeddings) + return embeddings + + def create_position_ids_from_inputs_embeds(self, inputs_embeds): + """ + We are provided embeddings directly. We cannot infer which are padded so just generate sequential position ids. + + Args: + inputs_embeds: torch.Tensor + + Returns: torch.Tensor + """ + input_shape = inputs_embeds.size()[:-1] + sequence_length = input_shape[1] + + position_ids = torch.arange( + self.padding_idx + 1, + sequence_length + self.padding_idx + 1, + dtype=torch.long, + device=inputs_embeds.device, + ) + return position_ids.unsqueeze(0).expand(input_shape) + + +# Copied from transformers.models.bert.modeling_bert.BertSelfAttention with Bert->Roberta +class RobertaSelfAttention(nn.Module): + def __init__(self, config, position_embedding_type=None): + super().__init__() + if config.hidden_size % config.num_attention_heads != 0 and not hasattr( + config, "embedding_size" + ): + raise ValueError( + f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention " + f"heads ({config.num_attention_heads})" + ) + + self.num_attention_heads = config.num_attention_heads + self.attention_head_size = int(config.hidden_size / config.num_attention_heads) + self.all_head_size = self.num_attention_heads * self.attention_head_size + + self.query = nn.Linear(config.hidden_size, self.all_head_size) + self.key = nn.Linear(config.hidden_size, self.all_head_size) + self.value = nn.Linear(config.hidden_size, self.all_head_size) + + self.dropout = nn.Dropout(config.attention_probs_dropout_prob) + self.position_embedding_type = position_embedding_type or getattr( + config, "position_embedding_type", "absolute" + ) + if ( + self.position_embedding_type == "relative_key" + or self.position_embedding_type == "relative_key_query" + ): + self.max_position_embeddings = config.max_position_embeddings + self.distance_embedding = nn.Embedding( + 2 * config.max_position_embeddings - 1, self.attention_head_size + ) + + self.is_decoder = config.is_decoder + + def transpose_for_scores(self, x): + new_x_shape = x.size()[:-1] + ( + self.num_attention_heads, + self.attention_head_size, + ) + x = x.view(new_x_shape) + return x.permute(0, 2, 1, 3) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.FloatTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, + output_attentions: Optional[bool] = False, + parser_att_mask=None, + ) -> Tuple[torch.Tensor]: + mixed_query_layer = self.query(hidden_states) + + # If this is instantiated as a cross-attention module, the keys + # and values come from an encoder; the attention mask needs to be + # such that the encoder's padding tokens are not attended to. + is_cross_attention = encoder_hidden_states is not None + + if is_cross_attention and past_key_value is not None: + # reuse k,v, cross_attentions + key_layer = past_key_value[0] + value_layer = past_key_value[1] + attention_mask = encoder_attention_mask + elif is_cross_attention: + key_layer = self.transpose_for_scores(self.key(encoder_hidden_states)) + value_layer = self.transpose_for_scores(self.value(encoder_hidden_states)) + attention_mask = encoder_attention_mask + elif past_key_value is not None: + key_layer = self.transpose_for_scores(self.key(hidden_states)) + value_layer = self.transpose_for_scores(self.value(hidden_states)) + key_layer = torch.cat([past_key_value[0], key_layer], dim=2) + value_layer = torch.cat([past_key_value[1], value_layer], dim=2) + else: + key_layer = self.transpose_for_scores(self.key(hidden_states)) + value_layer = self.transpose_for_scores(self.value(hidden_states)) + + query_layer = self.transpose_for_scores(mixed_query_layer) + + if self.is_decoder: + # if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states. + # Further calls to cross_attention layer can then reuse all cross-attention + # key/value_states (first "if" case) + # if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of + # all previous decoder key/value_states. Further calls to uni-directional self-attention + # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case) + # if encoder bi-directional self-attention `past_key_value` is always `None` + past_key_value = (key_layer, value_layer) + + # Take the dot product between "query" and "key" to get the raw attention scores. + attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) + + if ( + self.position_embedding_type == "relative_key" + or self.position_embedding_type == "relative_key_query" + ): + seq_length = hidden_states.size()[1] + position_ids_l = torch.arange( + seq_length, dtype=torch.long, device=hidden_states.device + ).view(-1, 1) + position_ids_r = torch.arange( + seq_length, dtype=torch.long, device=hidden_states.device + ).view(1, -1) + distance = position_ids_l - position_ids_r + positional_embedding = self.distance_embedding( + distance + self.max_position_embeddings - 1 + ) + positional_embedding = positional_embedding.to( + dtype=query_layer.dtype + ) # fp16 compatibility + + if self.position_embedding_type == "relative_key": + relative_position_scores = torch.einsum( + "bhld,lrd->bhlr", query_layer, positional_embedding + ) + attention_scores = attention_scores + relative_position_scores + elif self.position_embedding_type == "relative_key_query": + relative_position_scores_query = torch.einsum( + "bhld,lrd->bhlr", query_layer, positional_embedding + ) + relative_position_scores_key = torch.einsum( + "bhrd,lrd->bhlr", key_layer, positional_embedding + ) + attention_scores = ( + attention_scores + + relative_position_scores_query + + relative_position_scores_key + ) + + attention_scores = attention_scores / math.sqrt(self.attention_head_size) + if attention_mask is not None: + # Apply the attention mask is (precomputed for all layers in RobertaModel forward() function) + attention_scores = attention_scores + attention_mask + + if parser_att_mask is None: + # Normalize the attention scores to probabilities. + attention_probs = nn.functional.softmax(attention_scores, dim=-1) + else: + attention_probs = torch.sigmoid(attention_scores) * parser_att_mask + + # This is actually dropping out entire tokens to attend to, which might + # seem a bit unusual, but is taken from the original Transformer paper. + attention_probs = self.dropout(attention_probs) + + # Mask heads if we want to + if head_mask is not None: + attention_probs = attention_probs * head_mask + + context_layer = torch.matmul(attention_probs, value_layer) + + context_layer = context_layer.permute(0, 2, 1, 3).contiguous() + new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) + context_layer = context_layer.view(new_context_layer_shape) + + outputs = ( + (context_layer, attention_probs) if output_attentions else (context_layer,) + ) + + if self.is_decoder: + outputs = outputs + (past_key_value,) + return outputs + + +# Copied from transformers.models.bert.modeling_bert.BertSelfOutput +class RobertaSelfOutput(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward( + self, hidden_states: torch.Tensor, input_tensor: torch.Tensor + ) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + hidden_states = self.LayerNorm(hidden_states + input_tensor) + return hidden_states + + +# Copied from transformers.models.bert.modeling_bert.BertAttention with Bert->Roberta +class RobertaAttention(nn.Module): + def __init__(self, config, position_embedding_type=None): + super().__init__() + self.self = RobertaSelfAttention( + config, position_embedding_type=position_embedding_type + ) + self.output = RobertaSelfOutput(config) + self.pruned_heads = set() + + def prune_heads(self, heads): + if len(heads) == 0: + return + heads, index = find_pruneable_heads_and_indices( + heads, + self.self.num_attention_heads, + self.self.attention_head_size, + self.pruned_heads, + ) + + # Prune linear layers + self.self.query = prune_linear_layer(self.self.query, index) + self.self.key = prune_linear_layer(self.self.key, index) + self.self.value = prune_linear_layer(self.self.value, index) + self.output.dense = prune_linear_layer(self.output.dense, index, dim=1) + + # Update hyper params and store pruned heads + self.self.num_attention_heads = self.self.num_attention_heads - len(heads) + self.self.all_head_size = ( + self.self.attention_head_size * self.self.num_attention_heads + ) + self.pruned_heads = self.pruned_heads.union(heads) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.FloatTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, + output_attentions: Optional[bool] = False, + parser_att_mask=None, + ) -> Tuple[torch.Tensor]: + self_outputs = self.self( + hidden_states, + attention_mask, + head_mask, + encoder_hidden_states, + encoder_attention_mask, + past_key_value, + output_attentions, + parser_att_mask=parser_att_mask, + ) + attention_output = self.output(self_outputs[0], hidden_states) + outputs = (attention_output,) + self_outputs[ + 1: + ] # add attentions if we output them + return outputs + + +# Copied from transformers.models.bert.modeling_bert.BertIntermediate +class RobertaIntermediate(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.intermediate_size) + if isinstance(config.hidden_act, str): + self.intermediate_act_fn = ACT2FN[config.hidden_act] + else: + self.intermediate_act_fn = config.hidden_act + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.intermediate_act_fn(hidden_states) + return hidden_states + + +# Copied from transformers.models.bert.modeling_bert.BertOutput +class RobertaOutput(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.intermediate_size, config.hidden_size) + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward( + self, hidden_states: torch.Tensor, input_tensor: torch.Tensor + ) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + hidden_states = self.LayerNorm(hidden_states + input_tensor) + return hidden_states + + +# Copied from transformers.models.bert.modeling_bert.BertLayer with Bert->Roberta +class RobertaLayer(nn.Module): + def __init__(self, config): + super().__init__() + self.chunk_size_feed_forward = config.chunk_size_feed_forward + self.seq_len_dim = 1 + self.attention = RobertaAttention(config) + self.is_decoder = config.is_decoder + self.add_cross_attention = config.add_cross_attention + if self.add_cross_attention: + if not self.is_decoder: + raise ValueError( + f"{self} should be used as a decoder model if cross attention is added" + ) + self.crossattention = RobertaAttention( + config, position_embedding_type="absolute" + ) + self.intermediate = RobertaIntermediate(config) + self.output = RobertaOutput(config) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.FloatTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, + output_attentions: Optional[bool] = False, + parser_att_mask=None, + ) -> Tuple[torch.Tensor]: + # decoder uni-directional self-attention cached key/values tuple is at positions 1,2 + self_attn_past_key_value = ( + past_key_value[:2] if past_key_value is not None else None + ) + self_attention_outputs = self.attention( + hidden_states, + attention_mask, + head_mask, + output_attentions=output_attentions, + past_key_value=self_attn_past_key_value, + parser_att_mask=parser_att_mask, + ) + attention_output = self_attention_outputs[0] + + # if decoder, the last output is tuple of self-attn cache + if self.is_decoder: + outputs = self_attention_outputs[1:-1] + present_key_value = self_attention_outputs[-1] + else: + outputs = self_attention_outputs[ + 1: + ] # add self attentions if we output attention weights + + cross_attn_present_key_value = None + if self.is_decoder and encoder_hidden_states is not None: + if not hasattr(self, "crossattention"): + raise ValueError( + f"If `encoder_hidden_states` are passed, {self} has to be instantiated with cross-attention layers by setting `config.add_cross_attention=True`" + ) + + # cross_attn cached key/values tuple is at positions 3,4 of past_key_value tuple + cross_attn_past_key_value = ( + past_key_value[-2:] if past_key_value is not None else None + ) + cross_attention_outputs = self.crossattention( + attention_output, + attention_mask, + head_mask, + encoder_hidden_states, + encoder_attention_mask, + cross_attn_past_key_value, + output_attentions, + ) + attention_output = cross_attention_outputs[0] + outputs = ( + outputs + cross_attention_outputs[1:-1] + ) # add cross attentions if we output attention weights + + # add cross-attn cache to positions 3,4 of present_key_value tuple + cross_attn_present_key_value = cross_attention_outputs[-1] + present_key_value = present_key_value + cross_attn_present_key_value + + layer_output = apply_chunking_to_forward( + self.feed_forward_chunk, + self.chunk_size_feed_forward, + self.seq_len_dim, + attention_output, + ) + outputs = (layer_output,) + outputs + + # if decoder, return the attn key/values as the last output + if self.is_decoder: + outputs = outputs + (present_key_value,) + + return outputs + + def feed_forward_chunk(self, attention_output): + intermediate_output = self.intermediate(attention_output) + layer_output = self.output(intermediate_output, attention_output) + return layer_output + + +# Copied from transformers.models.bert.modeling_bert.BertEncoder with Bert->Roberta +class RobertaEncoder(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + self.layer = nn.ModuleList( + [RobertaLayer(config) for _ in range(config.num_hidden_layers)] + ) + self.gradient_checkpointing = False + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.FloatTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = False, + output_hidden_states: Optional[bool] = False, + return_dict: Optional[bool] = True, + parser_att_mask=None, + ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPastAndCrossAttentions]: + all_hidden_states = () if output_hidden_states else None + all_self_attentions = () if output_attentions else None + all_cross_attentions = ( + () if output_attentions and self.config.add_cross_attention else None + ) + + next_decoder_cache = () if use_cache else None + for i, layer_module in enumerate(self.layer): + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + layer_head_mask = head_mask[i] if head_mask is not None else None + past_key_value = past_key_values[i] if past_key_values is not None else None + + if self.gradient_checkpointing and self.training: + + if use_cache: + logger.warning( + "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." + ) + use_cache = False + + def create_custom_forward(module): + def custom_forward(*inputs): + return module(*inputs, past_key_value, output_attentions) + + return custom_forward + + layer_outputs = torch.utils.checkpoint.checkpoint( + create_custom_forward(layer_module), + hidden_states, + attention_mask, + layer_head_mask, + encoder_hidden_states, + encoder_attention_mask, + ) + else: + if parser_att_mask is not None: + layer_outputs = layer_module( + hidden_states, + attention_mask, + layer_head_mask, + encoder_hidden_states, + encoder_attention_mask, + past_key_value, + output_attentions, + parser_att_mask=parser_att_mask[i]) + else: + layer_outputs = layer_module( + hidden_states, + attention_mask, + layer_head_mask, + encoder_hidden_states, + encoder_attention_mask, + past_key_value, + output_attentions, + parser_att_mask=None) + + + hidden_states = layer_outputs[0] + if use_cache: + next_decoder_cache += (layer_outputs[-1],) + if output_attentions: + all_self_attentions = all_self_attentions + (layer_outputs[1],) + if self.config.add_cross_attention: + all_cross_attentions = all_cross_attentions + (layer_outputs[2],) + + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + if not return_dict: + return tuple( + v + for v in [ + hidden_states, + next_decoder_cache, + all_hidden_states, + all_self_attentions, + all_cross_attentions, + ] + if v is not None + ) + return BaseModelOutputWithPastAndCrossAttentions( + last_hidden_state=hidden_states, + past_key_values=next_decoder_cache, + hidden_states=all_hidden_states, + attentions=all_self_attentions, + cross_attentions=all_cross_attentions, + ) + + +# Copied from transformers.models.bert.modeling_bert.BertPooler +class RobertaPooler(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.activation = nn.Tanh() + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + # We "pool" the model by simply taking the hidden state corresponding + # to the first token. + first_token_tensor = hidden_states[:, 0] + pooled_output = self.dense(first_token_tensor) + pooled_output = self.activation(pooled_output) + return pooled_output + + +class RobertaPreTrainedModel(PreTrainedModel): + """ + An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained + models. + """ + + config_class = RobertaConfig + base_model_prefix = "roberta" + supports_gradient_checkpointing = True + + # Copied from transformers.models.bert.modeling_bert.BertPreTrainedModel._init_weights + def _init_weights(self, module): + """Initialize the weights""" + if isinstance(module, nn.Linear): + # Slightly different from the TF version which uses truncated_normal for initialization + # cf https://github.com/pytorch/pytorch/pull/5617 + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.Embedding): + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.padding_idx is not None: + module.weight.data[module.padding_idx].zero_() + elif isinstance(module, nn.LayerNorm): + if module.bias is not None: + module.bias.data.zero_() + module.weight.data.fill_(1.0) + + def _set_gradient_checkpointing(self, module, value=False): + if isinstance(module, RobertaEncoder): + module.gradient_checkpointing = value + + def update_keys_to_ignore(self, config, del_keys_to_ignore): + """Remove some keys from ignore list""" + if not config.tie_word_embeddings: + # must make a new list, or the class variable gets modified! + self._keys_to_ignore_on_save = [ + k for k in self._keys_to_ignore_on_save if k not in del_keys_to_ignore + ] + self._keys_to_ignore_on_load_missing = [ + k + for k in self._keys_to_ignore_on_load_missing + if k not in del_keys_to_ignore + ] + + +ROBERTA_START_DOCSTRING = r""" + + This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the + library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads + etc.) + + This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. + Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage + and behavior. + + Parameters: + config ([`RobertaConfig`]): Model configuration class with all the parameters of the + model. Initializing with a config file does not load the weights associated with the model, only the + configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. +""" + + +ROBERTA_INPUTS_DOCSTRING = r""" + Args: + input_ids (`torch.LongTensor` of shape `({0})`): + Indices of input sequence tokens in the vocabulary. + + Indices can be obtained using [`RobertaTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + [What are input IDs?](../glossary#input-ids) + attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*): + Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + + [What are attention masks?](../glossary#attention-mask) + token_type_ids (`torch.LongTensor` of shape `({0})`, *optional*): + Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, + 1]`: + + - 0 corresponds to a *sentence A* token, + - 1 corresponds to a *sentence B* token. + + [What are token type IDs?](../glossary#token-type-ids) + position_ids (`torch.LongTensor` of shape `({0})`, *optional*): + Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, + config.max_position_embeddings - 1]`. + + [What are position IDs?](../glossary#position-ids) + head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): + Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: + + - 1 indicates the head is **not masked**, + - 0 indicates the head is **masked**. + + inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_size)`, *optional*): + Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This + is useful if you want more control over how to convert `input_ids` indices into associated vectors than the + model's internal embedding lookup matrix. + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned + tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for + more detail. + return_dict (`bool`, *optional*): + Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. +""" + + +class RobertaModel(RobertaPreTrainedModel): + """ + + The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of + cross-attention is added between the self-attention layers, following the architecture described in *Attention is + all you need*_ by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz + Kaiser and Illia Polosukhin. + + To behave as an decoder the model needs to be initialized with the `is_decoder` argument of the configuration set + to `True`. To be used in a Seq2Seq model, the model needs to initialized with both `is_decoder` argument and + `add_cross_attention` set to `True`; an `encoder_hidden_states` is then expected as an input to the forward pass. + + .. _*Attention is all you need*: https://arxiv.org/abs/1706.03762 + + """ + + _keys_to_ignore_on_load_missing = [r"position_ids"] + + # Copied from transformers.models.bert.modeling_bert.BertModel.__init__ with Bert->Roberta + def __init__(self, config, add_pooling_layer=True): + super().__init__(config) + self.config = config + + self.embeddings = RobertaEmbeddings(config) + self.encoder = RobertaEncoder(config) + + self.pooler = RobertaPooler(config) if add_pooling_layer else None + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.embeddings.word_embeddings + + def set_input_embeddings(self, value): + self.embeddings.word_embeddings = value + + def _prune_heads(self, heads_to_prune): + """ + Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base + class PreTrainedModel + """ + for layer, heads in heads_to_prune.items(): + self.encoder.layer[layer].attention.prune_heads(heads) + + # Copied from transformers.models.bert.modeling_bert.BertModel.forward + def forward( + self, + input_ids: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + token_type_ids: Optional[torch.Tensor] = None, + position_ids: Optional[torch.Tensor] = None, + head_mask: Optional[torch.Tensor] = None, + inputs_embeds: Optional[torch.Tensor] = None, + encoder_hidden_states: Optional[torch.Tensor] = None, + encoder_attention_mask: Optional[torch.Tensor] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + parser_att_mask=None, + ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPoolingAndCrossAttentions]: + r""" + encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): + Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if + the model is configured as a decoder. + encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*): + Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in + the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): + Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. + + If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that + don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all + `decoder_input_ids` of shape `(batch_size, sequence_length)`. + use_cache (`bool`, *optional*): + If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see + `past_key_values`). + """ + output_attentions = ( + output_attentions + if output_attentions is not None + else self.config.output_attentions + ) + output_hidden_states = ( + output_hidden_states + if output_hidden_states is not None + else self.config.output_hidden_states + ) + return_dict = ( + return_dict if return_dict is not None else self.config.use_return_dict + ) + + if self.config.is_decoder: + use_cache = use_cache if use_cache is not None else self.config.use_cache + else: + use_cache = False + + if input_ids is not None and inputs_embeds is not None: + raise ValueError( + "You cannot specify both input_ids and inputs_embeds at the same time" + ) + elif input_ids is not None: + input_shape = input_ids.size() + elif inputs_embeds is not None: + input_shape = inputs_embeds.size()[:-1] + else: + raise ValueError("You have to specify either input_ids or inputs_embeds") + + batch_size, seq_length = input_shape + device = input_ids.device if input_ids is not None else inputs_embeds.device + + # past_key_values_length + past_key_values_length = ( + past_key_values[0][0].shape[2] if past_key_values is not None else 0 + ) + + if attention_mask is None: + attention_mask = torch.ones( + ((batch_size, seq_length + past_key_values_length)), device=device + ) + + if token_type_ids is None: + if hasattr(self.embeddings, "token_type_ids"): + buffered_token_type_ids = self.embeddings.token_type_ids[:, :seq_length] + buffered_token_type_ids_expanded = buffered_token_type_ids.expand( + batch_size, seq_length + ) + token_type_ids = buffered_token_type_ids_expanded + else: + token_type_ids = torch.zeros( + input_shape, dtype=torch.long, device=device + ) + + # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length] + # ourselves in which case we just need to make it broadcastable to all heads. + extended_attention_mask: torch.Tensor = self.get_extended_attention_mask( + attention_mask, input_shape, device + ) + + # If a 2D or 3D attention mask is provided for the cross-attention + # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length] + if self.config.is_decoder and encoder_hidden_states is not None: + ( + encoder_batch_size, + encoder_sequence_length, + _, + ) = encoder_hidden_states.size() + encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length) + if encoder_attention_mask is None: + encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device) + encoder_extended_attention_mask = self.invert_attention_mask( + encoder_attention_mask + ) + else: + encoder_extended_attention_mask = None + + # Prepare head mask if needed + # 1.0 in head_mask indicate we keep the head + # attention_probs has shape bsz x n_heads x N x N + # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] + # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] + head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) + + embedding_output = self.embeddings( + input_ids=input_ids, + position_ids=position_ids, + token_type_ids=token_type_ids, + inputs_embeds=inputs_embeds, + past_key_values_length=past_key_values_length, + ) + encoder_outputs = self.encoder( + embedding_output, + attention_mask=extended_attention_mask, + head_mask=head_mask, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_extended_attention_mask, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + parser_att_mask=parser_att_mask, + ) + sequence_output = encoder_outputs[0] + pooled_output = ( + self.pooler(sequence_output) if self.pooler is not None else None + ) + + if not return_dict: + return (sequence_output, pooled_output) + encoder_outputs[1:] + + return BaseModelOutputWithPoolingAndCrossAttentions( + last_hidden_state=sequence_output, + pooler_output=pooled_output, + past_key_values=encoder_outputs.past_key_values, + hidden_states=encoder_outputs.hidden_states, + attentions=encoder_outputs.attentions, + cross_attentions=encoder_outputs.cross_attentions, + ) + + +class StructRoberta(RobertaPreTrainedModel): + _keys_to_ignore_on_save = [r"lm_head.decoder.weight", r"lm_head.decoder.bias"] + _keys_to_ignore_on_load_missing = [ + r"position_ids", + r"lm_head.decoder.weight", + r"lm_head.decoder.bias", + ] + _keys_to_ignore_on_load_unexpected = [r"pooler"] + + def __init__(self, config): + super().__init__(config) + + if config.is_decoder: + logger.warning( + "If you want to use `RobertaForMaskedLM` make sure `config.is_decoder=False` for " + "bi-directional self-attention." + ) + + + if config.n_cntxt_layers > 0: + config_cntxt = copy.deepcopy(config) + config_cntxt.num_hidden_layers = config.n_cntxt_layers + + self.cntxt_layers = RobertaModel(config_cntxt, add_pooling_layer=False) + + if config.n_cntxt_layers_2 > 0: + self.parser_layers_1 = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(int(config.n_parser_layers/2)) + ] + ) + + self.distance_ff_1 = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff_1 = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight_1 = nn.Parameter( + torch.zeros((config.n_cntxt_layers_2, config.num_attention_heads, n_rel)) + ) + self._rel_weight_1.data.normal_(0, 0.1) + + self._scaler_1 = nn.Parameter(torch.zeros(2)) + + config_cntxt_2 = copy.deepcopy(config) + config_cntxt_2.num_hidden_layers = config.n_cntxt_layers_2 + + self.cntxt_layers_2 = RobertaModel(config_cntxt_2, add_pooling_layer=False) + + + self.parser_layers_2 = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(int(config.n_parser_layers/2)) + ] + ) + + self.distance_ff_2 = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff_2 = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight_2 = nn.Parameter( + torch.zeros((config.num_hidden_layers, config.num_attention_heads, n_rel)) + ) + self._rel_weight_2.data.normal_(0, 0.1) + + self._scaler_2 = nn.Parameter(torch.zeros(2)) + + else: + self.parser_layers = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(config.n_parser_layers) + ] + ) + + self.distance_ff = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight = nn.Parameter( + torch.zeros((config.num_hidden_layers, config.num_attention_heads, n_rel)) + ) + self._rel_weight.data.normal_(0, 0.1) + + self._scaler = nn.Parameter(torch.zeros(2)) + + self.roberta = RobertaModel(config, add_pooling_layer=False) + + if config.n_cntxt_layers > 0: + self.cntxt_layers.embeddings = self.roberta.embeddings + if config.n_cntxt_layers_2 > 0: + self.cntxt_layers_2.embeddings = self.roberta.embeddings + + self.lm_head = RobertaLMHead(config) + + self.pad = config.pad_token_id + + # The LM head weights require special treatment only when they are tied with the word embeddings + self.update_keys_to_ignore(config, ["lm_head.decoder.weight"]) + + # Initialize weights and apply final processing + self.post_init() + + def get_output_embeddings(self): + return self.lm_head.decoder + + def set_output_embeddings(self, new_embeddings): + self.lm_head.decoder = new_embeddings + + @property + def scaler(self): + return self._scaler.exp() + + @property + def scaler_1(self): + return self._scaler_1.exp() + + @property + def scaler_2(self): + return self._scaler_2.exp() + + @property + def rel_weight(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight, dim=-1) + + @property + def rel_weight_1(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight_1) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight_1, dim=-1) + + + @property + def rel_weight_2(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight_2) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight_2, dim=-1) + + + def compute_block(self, distance, height, n_cntxt_layers=0): + """Compute constituents from distance and height.""" + + if n_cntxt_layers>0: + if n_cntxt_layers == 1: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler_1[0] + elif n_cntxt_layers == 2: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler_2[0] + else: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0] + + gamma = torch.sigmoid(-beta_logits) + ones = torch.ones_like(gamma) + + block_mask_left = cummin( + gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1 + ) + block_mask_left = block_mask_left - F.pad( + block_mask_left[:, :, :-1], (1, 0), value=0 + ) + block_mask_left.tril_(0) + + block_mask_right = cummin( + gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1 + ) + block_mask_right = block_mask_right - F.pad( + block_mask_right[:, :, 1:], (0, 1), value=0 + ) + block_mask_right.triu_(0) + + block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :] + block = cumsum(block_mask_left).tril(0) + cumsum( + block_mask_right, reverse=True + ).triu(1) + + return block_p, block + + def compute_head(self, height, n_cntxt_layers=0): + """Estimate head for each constituent.""" + + _, length = height.size() + if n_cntxt_layers>0: + if n_cntxt_layers == 1: + head_logits = height * self.scaler_1[1] + elif n_cntxt_layers == 2: + head_logits = height * self.scaler_2[1] + else: + head_logits = height * self.scaler[1] + index = torch.arange(length, device=height.device) + + mask = (index[:, None, None] <= index[None, None, :]) * ( + index[None, None, :] <= index[None, :, None] + ) + head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1) + head_logits.masked_fill_(~mask[None, :, :, :], -1e9) + + head_p = torch.softmax(head_logits, dim=-1) + + return head_p + + def parse(self, x, embs=None, n_cntxt_layers=0): + """Parse input sentence. + + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + distance: syntactic distance + height: syntactic height + """ + + mask = x != self.pad + mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0) + + if embs is None: + h = self.roberta.embeddings(x) + else: + h = embs + + if n_cntxt_layers > 0: + if n_cntxt_layers == 1: + parser_layers = self.parser_layers_1 + height_ff = self.height_ff_1 + distance_ff = self.distance_ff_1 + elif n_cntxt_layers == 2: + parser_layers = self.parser_layers_2 + height_ff = self.height_ff_2 + distance_ff = self.distance_ff_2 + for i in range(int(self.config.n_parser_layers/2)): + h = h.masked_fill(~mask[:, :, None], 0) + h = parser_layers[i](h) + + height = height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, dim=-1 + )[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max + ) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where( + margin_left > margin_right, margin_right, margin_left + ).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + else: + for i in range(self.config.n_parser_layers): + h = h.masked_fill(~mask[:, :, None], 0) + h = self.parser_layers[i](h) + + height = self.height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = self.distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, dim=-1 + )[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max + ) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where( + margin_left > margin_right, margin_right, margin_left + ).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + + return distance, height + + def generate_mask(self, x, distance, height, n_cntxt_layers=0): + """Compute head and cibling distribution for each token.""" + + bsz, length = x.size() + + eye = torch.eye(length, device=x.device, dtype=torch.bool) + eye = eye[None, :, :].expand((bsz, -1, -1)) + + block_p, block = self.compute_block(distance, height, n_cntxt_layers=n_cntxt_layers) + head_p = self.compute_head(height, n_cntxt_layers=n_cntxt_layers) + head = torch.einsum("blij,bijh->blh", block_p, head_p) + head = head.masked_fill(eye, 0) + child = head.transpose(1, 2) + cibling = torch.bmm(head, child).masked_fill(eye, 0) + + rel_list = [] + if "head" in self.config.relations: + rel_list.append(head) + if "child" in self.config.relations: + rel_list.append(child) + if "cibling" in self.config.relations: + rel_list.append(cibling) + + rel = torch.stack(rel_list, dim=1) + + if n_cntxt_layers > 0: + if n_cntxt_layers == 1: + rel_weight = self.rel_weight_1 + elif n_cntxt_layers == 2: + rel_weight = self.rel_weight_2 + else: + rel_weight = self.rel_weight + + dep = torch.einsum("lhr,brij->lbhij", rel_weight, rel) + + if n_cntxt_layers == 1: + num_layers = self.cntxt_layers_2.config.num_hidden_layers + else: + num_layers = self.roberta.config.num_hidden_layers + + att_mask = dep.reshape( + num_layers, + bsz, + self.config.num_attention_heads, + length, + length, + ) + + return att_mask, cibling, head, block + + def forward( + self, + input_ids: Optional[torch.LongTensor] = None, + attention_mask: Optional[torch.FloatTensor] = None, + token_type_ids: Optional[torch.LongTensor] = None, + position_ids: Optional[torch.LongTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, MaskedLMOutput]: + r""" + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ..., + config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the + loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]` + kwargs (`Dict[str, any]`, optional, defaults to *{}*): + Used to hide legacy arguments that have been deprecated. + """ + return_dict = ( + return_dict if return_dict is not None else self.config.use_return_dict + ) + + + if self.config.n_cntxt_layers > 0: + cntxt_outputs = self.cntxt_layers( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict) + + + if self.config.n_cntxt_layers_2 > 0: + distance_1, height_1 = self.parse(input_ids, cntxt_outputs[0], n_cntxt_layers=1) + att_mask_1, _, _, _ = self.generate_mask(input_ids, distance_1, height_1, n_cntxt_layers=1) + + cntxt_outputs_2 = self.cntxt_layers_2( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + parser_att_mask=att_mask_1) + + sequence_output = cntxt_outputs_2[0] + + distance_2, height_2 = self.parse(input_ids, sequence_output[0], n_cntxt_layers=2) + att_mask, _, _, _ = self.generate_mask(input_ids, distance_2, height_2, n_cntxt_layers=2) + + elif self.config.n_cntxt_layers > 0: + distance, height = self.parse(input_ids, cntxt_outputs[0]) + att_mask, _, _, _ = self.generate_mask(input_ids, distance, height) + else: + distance, height = self.parse(input_ids) + att_mask, _, _, _ = self.generate_mask(input_ids, distance, height) + + outputs = self.roberta( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + parser_att_mask=att_mask, + ) + sequence_output = outputs[0] + prediction_scores = self.lm_head(sequence_output) + + masked_lm_loss = None + if labels is not None: + loss_fct = CrossEntropyLoss() + masked_lm_loss = loss_fct( + prediction_scores.view(-1, self.config.vocab_size), labels.view(-1) + ) + + if not return_dict: + output = (prediction_scores,) + outputs[2:] + return ( + ((masked_lm_loss,) + output) if masked_lm_loss is not None else output + ) + + return MaskedLMOutput( + loss=masked_lm_loss, + logits=prediction_scores, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +class RobertaLMHead(nn.Module): + """Roberta Head for masked language modeling.""" + + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + + self.decoder = nn.Linear(config.hidden_size, config.vocab_size) + self.bias = nn.Parameter(torch.zeros(config.vocab_size)) + self.decoder.bias = self.bias + + def forward(self, features, **kwargs): + x = self.dense(features) + x = gelu(x) + x = self.layer_norm(x) + + # project back to size of vocabulary with bias + x = self.decoder(x) + + return x + + def _tie_weights(self): + # To tie those two weights if they get disconnected (on TPU or when the bias is resized) + self.bias = self.decoder.bias + + +class StructRobertaForSequenceClassification(RobertaPreTrainedModel): + _keys_to_ignore_on_load_missing = [r"position_ids"] + + def __init__(self, config): + super().__init__(config) + self.num_labels = config.num_labels + self.config = config + + if config.n_cntxt_layers > 0: + config_cntxt = copy.deepcopy(config) + config_cntxt.num_hidden_layers = config.n_cntxt_layers + + self.cntxt_layers = RobertaModel(config_cntxt, add_pooling_layer=False) + + if config.n_cntxt_layers_2 > 0: + self.parser_layers_1 = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(int(config.n_parser_layers/2)) + ] + ) + + self.distance_ff_1 = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff_1 = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight_1 = nn.Parameter( + torch.zeros((config.n_cntxt_layers_2, config.num_attention_heads, n_rel)) + ) + self._rel_weight_1.data.normal_(0, 0.1) + + self._scaler_1 = nn.Parameter(torch.zeros(2)) + + config_cntxt_2 = copy.deepcopy(config) + config_cntxt_2.num_hidden_layers = config.n_cntxt_layers_2 + + self.cntxt_layers_2 = RobertaModel(config_cntxt_2, add_pooling_layer=False) + + + self.parser_layers_2 = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(int(config.n_parser_layers/2)) + ] + ) + + self.distance_ff_2 = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff_2 = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight_2 = nn.Parameter( + torch.zeros((config.num_hidden_layers, config.num_attention_heads, n_rel)) + ) + self._rel_weight_2.data.normal_(0, 0.1) + + self._scaler_2 = nn.Parameter(torch.zeros(2)) + + else: + self.parser_layers = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(config.n_parser_layers) + ] + ) + + self.distance_ff = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight = nn.Parameter( + torch.zeros((config.num_hidden_layers, config.num_attention_heads, n_rel)) + ) + self._rel_weight.data.normal_(0, 0.1) + + self._scaler = nn.Parameter(torch.zeros(2)) + + self.roberta = RobertaModel(config, add_pooling_layer=False) + + if config.n_cntxt_layers > 0: + self.cntxt_layers.embeddings = self.roberta.embeddings + if config.n_cntxt_layers_2 > 0: + self.cntxt_layers_2.embeddings = self.roberta.embeddings + + + self.pad = config.pad_token_id + self.classifier = RobertaClassificationHead(config) + + # Initialize weights and apply final processing + self.post_init() + + + @property + def scaler(self): + return self._scaler.exp() + + @property + def scaler_1(self): + return self._scaler_1.exp() + + @property + def scaler_2(self): + return self._scaler_2.exp() + + @property + def rel_weight(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight, dim=-1) + + @property + def rel_weight_1(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight_1) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight_1, dim=-1) + + + @property + def rel_weight_2(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight_2) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight_2, dim=-1) + + + def compute_block(self, distance, height, n_cntxt_layers=0): + """Compute constituents from distance and height.""" + + if n_cntxt_layers>0: + if n_cntxt_layers == 1: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler_1[0] + elif n_cntxt_layers == 2: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler_2[0] + else: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0] + + gamma = torch.sigmoid(-beta_logits) + ones = torch.ones_like(gamma) + + block_mask_left = cummin( + gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1 + ) + block_mask_left = block_mask_left - F.pad( + block_mask_left[:, :, :-1], (1, 0), value=0 + ) + block_mask_left.tril_(0) + + block_mask_right = cummin( + gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1 + ) + block_mask_right = block_mask_right - F.pad( + block_mask_right[:, :, 1:], (0, 1), value=0 + ) + block_mask_right.triu_(0) + + block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :] + block = cumsum(block_mask_left).tril(0) + cumsum( + block_mask_right, reverse=True + ).triu(1) + + return block_p, block + + def compute_head(self, height, n_cntxt_layers=0): + """Estimate head for each constituent.""" + + _, length = height.size() + if n_cntxt_layers>0: + if n_cntxt_layers == 1: + head_logits = height * self.scaler_1[1] + elif n_cntxt_layers == 2: + head_logits = height * self.scaler_2[1] + else: + head_logits = height * self.scaler[1] + index = torch.arange(length, device=height.device) + + mask = (index[:, None, None] <= index[None, None, :]) * ( + index[None, None, :] <= index[None, :, None] + ) + head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1) + head_logits.masked_fill_(~mask[None, :, :, :], -1e9) + + head_p = torch.softmax(head_logits, dim=-1) + + return head_p + + def parse(self, x, embs=None, n_cntxt_layers=0): + """Parse input sentence. + + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + distance: syntactic distance + height: syntactic height + """ + + mask = x != self.pad + mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0) + + if embs is None: + h = self.roberta.embeddings(x) + else: + h = embs + + if n_cntxt_layers > 0: + if n_cntxt_layers == 1: + parser_layers = self.parser_layers_1 + height_ff = self.height_ff_1 + distance_ff = self.distance_ff_1 + elif n_cntxt_layers == 2: + parser_layers = self.parser_layers_2 + height_ff = self.height_ff_2 + distance_ff = self.distance_ff_2 + for i in range(int(self.config.n_parser_layers/2)): + h = h.masked_fill(~mask[:, :, None], 0) + h = parser_layers[i](h) + + height = height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, dim=-1 + )[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max + ) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where( + margin_left > margin_right, margin_right, margin_left + ).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + else: + for i in range(self.config.n_parser_layers): + h = h.masked_fill(~mask[:, :, None], 0) + h = self.parser_layers[i](h) + + height = self.height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = self.distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, dim=-1 + )[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max + ) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where( + margin_left > margin_right, margin_right, margin_left + ).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + + return distance, height + + def generate_mask(self, x, distance, height, n_cntxt_layers=0): + """Compute head and cibling distribution for each token.""" + + bsz, length = x.size() + + eye = torch.eye(length, device=x.device, dtype=torch.bool) + eye = eye[None, :, :].expand((bsz, -1, -1)) + + block_p, block = self.compute_block(distance, height, n_cntxt_layers=n_cntxt_layers) + head_p = self.compute_head(height, n_cntxt_layers=n_cntxt_layers) + head = torch.einsum("blij,bijh->blh", block_p, head_p) + head = head.masked_fill(eye, 0) + child = head.transpose(1, 2) + cibling = torch.bmm(head, child).masked_fill(eye, 0) + + rel_list = [] + if "head" in self.config.relations: + rel_list.append(head) + if "child" in self.config.relations: + rel_list.append(child) + if "cibling" in self.config.relations: + rel_list.append(cibling) + + rel = torch.stack(rel_list, dim=1) + + if n_cntxt_layers > 0: + if n_cntxt_layers == 1: + rel_weight = self.rel_weight_1 + elif n_cntxt_layers == 2: + rel_weight = self.rel_weight_2 + else: + rel_weight = self.rel_weight + + dep = torch.einsum("lhr,brij->lbhij", rel_weight, rel) + + if n_cntxt_layers == 1: + num_layers = self.cntxt_layers_2.config.num_hidden_layers + else: + num_layers = self.roberta.config.num_hidden_layers + + att_mask = dep.reshape( + num_layers, + bsz, + self.config.num_attention_heads, + length, + length, + ) + + return att_mask, cibling, head, block + + def forward( + self, + input_ids: Optional[torch.LongTensor] = None, + attention_mask: Optional[torch.FloatTensor] = None, + token_type_ids: Optional[torch.LongTensor] = None, + position_ids: Optional[torch.LongTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, SequenceClassifierOutput]: + r""" + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., + config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If + `config.num_labels > 1` a classification loss is computed (Cross-Entropy). + """ + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + if self.config.n_cntxt_layers > 0: + cntxt_outputs = self.cntxt_layers( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict) + + + if self.config.n_cntxt_layers_2 > 0: + distance_1, height_1 = self.parse(input_ids, cntxt_outputs[0], n_cntxt_layers=1) + att_mask_1, _, _, _ = self.generate_mask(input_ids, distance_1, height_1, n_cntxt_layers=1) + + cntxt_outputs_2 = self.cntxt_layers_2( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + parser_att_mask=att_mask_1) + + sequence_output = cntxt_outputs_2[0] + + distance_2, height_2 = self.parse(input_ids, sequence_output[0], n_cntxt_layers=2) + att_mask, _, _, _ = self.generate_mask(input_ids, distance_2, height_2, n_cntxt_layers=2) + + elif self.config.n_cntxt_layers > 0: + distance, height = self.parse(input_ids, cntxt_outputs[0]) + att_mask, _, _, _ = self.generate_mask(input_ids, distance, height) + else: + distance, height = self.parse(input_ids) + att_mask, _, _, _ = self.generate_mask(input_ids, distance, height) + + outputs = self.roberta( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + parser_att_mask=att_mask, + ) + sequence_output = outputs[0] + logits = self.classifier(sequence_output) + + loss = None + if labels is not None: + if self.config.problem_type is None: + if self.num_labels == 1: + self.config.problem_type = "regression" + elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): + self.config.problem_type = "single_label_classification" + else: + self.config.problem_type = "multi_label_classification" + + if self.config.problem_type == "regression": + loss_fct = MSELoss() + if self.num_labels == 1: + loss = loss_fct(logits.squeeze(), labels.squeeze()) + else: + loss = loss_fct(logits, labels) + elif self.config.problem_type == "single_label_classification": + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) + elif self.config.problem_type == "multi_label_classification": + loss_fct = BCEWithLogitsLoss() + loss = loss_fct(logits, labels) + + if not return_dict: + output = (logits,) + outputs[2:] + return ((loss,) + output) if loss is not None else output + + return SequenceClassifierOutput( + loss=loss, + logits=logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +class RobertaClassificationHead(nn.Module): + """Head for sentence-level classification tasks.""" + + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + classifier_dropout = ( + config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob + ) + self.dropout = nn.Dropout(classifier_dropout) + self.out_proj = nn.Linear(config.hidden_size, config.num_labels) + + def forward(self, features, **kwargs): + x = features[:, 0, :] # take token (equiv. to [CLS]) + x = self.dropout(x) + x = self.dense(x) + x = torch.tanh(x) + x = self.dropout(x) + x = self.out_proj(x) + return x + + +def create_position_ids_from_input_ids( + input_ids, padding_idx, past_key_values_length=0 +): + """ + Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols + are ignored. This is modified from fairseq's `utils.make_positions`. + + Args: + x: torch.Tensor x: + + Returns: torch.Tensor + """ + # The series of casts and type-conversions here are carefully balanced to both work with ONNX export and XLA. + mask = input_ids.ne(padding_idx).int() + incremental_indices = ( + torch.cumsum(mask, dim=1).type_as(mask) + past_key_values_length + ) * mask + return incremental_indices.long() + padding_idx + + +def cumprod(x, reverse=False, exclusive=False): + """cumulative product.""" + if reverse: + x = x.flip([-1]) + + if exclusive: + x = F.pad(x[:, :, :-1], (1, 0), value=1) + + cx = x.cumprod(-1) + + if reverse: + cx = cx.flip([-1]) + return cx + + +def cumsum(x, reverse=False, exclusive=False): + """cumulative sum.""" + bsz, _, length = x.size() + device = x.device + if reverse: + if exclusive: + w = torch.ones([bsz, length, length], device=device).tril(-1) + else: + w = torch.ones([bsz, length, length], device=device).tril(0) + cx = torch.bmm(x, w) + else: + if exclusive: + w = torch.ones([bsz, length, length], device=device).triu(1) + else: + w = torch.ones([bsz, length, length], device=device).triu(0) + cx = torch.bmm(x, w) + return cx + + +def cummin(x, reverse=False, exclusive=False, max_value=1e9): + """cumulative min.""" + if reverse: + if exclusive: + x = F.pad(x[:, :, 1:], (0, 1), value=max_value) + x = x.flip([-1]).cummin(-1)[0].flip([-1]) + else: + if exclusive: + x = F.pad(x[:, :, :-1], (1, 0), value=max_value) + x = x.cummin(-1)[0] + return x diff --git a/finetune/qqp/predict_results.txt b/finetune/qqp/predict_results.txt new file mode 100644 index 0000000000000000000000000000000000000000..5cf3b77b9f606facc31dea0a824175c009bb9888 --- /dev/null +++ b/finetune/qqp/predict_results.txt @@ -0,0 +1,26890 @@ +index prediction +0 0 +1 0 +2 1 +3 1 +4 1 +5 1 +6 1 +7 0 +8 0 +9 0 +10 0 +11 1 +12 0 +13 0 +14 0 +15 0 +16 0 +17 0 +18 0 +19 0 +20 0 +21 1 +22 0 +23 0 +24 0 +25 0 +26 0 +27 1 +28 1 +29 1 +30 1 +31 1 +32 0 +33 1 +34 1 +35 0 +36 0 +37 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See all RoBERTa models at https://huggingface.co/models?filter=roberta +] + + +class StructRobertaConfig(RobertaConfig): + model_type = "roberta" + + def __init__( + self, + n_parser_layers=4, + conv_size=9, + relations=("head", "child"), + weight_act="softmax", + n_cntxt_layers=3, + n_cntxt_layers_2=0, + **kwargs,): + + super().__init__(**kwargs) + self.n_cntxt_layers = n_cntxt_layers + self.n_parser_layers = n_parser_layers + self.n_cntxt_layers_2 = n_cntxt_layers_2 + self.conv_size = conv_size + self.relations = relations + self.weight_act = weight_act + +class Conv1d(nn.Module): + """1D convolution layer.""" + + def __init__(self, hidden_size, kernel_size, dilation=1): + """Initialization. + + Args: + hidden_size: dimension of input embeddings + kernel_size: convolution kernel size + dilation: the spacing between the kernel points + """ + super(Conv1d, self).__init__() + + if kernel_size % 2 == 0: + padding = (kernel_size // 2) * dilation + self.shift = True + else: + padding = ((kernel_size - 1) // 2) * dilation + self.shift = False + self.conv = nn.Conv1d( + hidden_size, hidden_size, kernel_size, padding=padding, dilation=dilation + ) + + def forward(self, x): + """Compute convolution. + + Args: + x: input embeddings + Returns: + conv_output: convolution results + """ + + if self.shift: + return self.conv(x.transpose(1, 2)).transpose(1, 2)[:, 1:] + else: + return self.conv(x.transpose(1, 2)).transpose(1, 2) + + +class RobertaEmbeddings(nn.Module): + """ + Same as BertEmbeddings with a tiny tweak for positional embeddings indexing. + """ + + # Copied from transformers.models.bert.modeling_bert.BertEmbeddings.__init__ + def __init__(self, config): + super().__init__() + self.word_embeddings = nn.Embedding( + config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id + ) + self.position_embeddings = nn.Embedding( + config.max_position_embeddings, config.hidden_size + ) + self.token_type_embeddings = nn.Embedding( + config.type_vocab_size, config.hidden_size + ) + + # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load + # any TensorFlow checkpoint file + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + # position_ids (1, len position emb) is contiguous in memory and exported when serialized + self.position_embedding_type = getattr( + config, "position_embedding_type", "absolute" + ) + self.register_buffer( + "position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)) + ) + if version.parse(torch.__version__) > version.parse("1.6.0"): + self.register_buffer( + "token_type_ids", + torch.zeros(self.position_ids.size(), dtype=torch.long), + persistent=False, + ) + + # End copy + self.padding_idx = config.pad_token_id + self.position_embeddings = nn.Embedding( + config.max_position_embeddings, + config.hidden_size, + padding_idx=self.padding_idx, + ) + + def forward( + self, + input_ids=None, + token_type_ids=None, + position_ids=None, + inputs_embeds=None, + past_key_values_length=0, + ): + if position_ids is None: + if input_ids is not None: + # Create the position ids from the input token ids. Any padded tokens remain padded. + position_ids = create_position_ids_from_input_ids( + input_ids, self.padding_idx, past_key_values_length + ) + else: + position_ids = self.create_position_ids_from_inputs_embeds( + inputs_embeds + ) + + if input_ids is not None: + input_shape = input_ids.size() + else: + input_shape = inputs_embeds.size()[:-1] + + seq_length = input_shape[1] + + # Setting the token_type_ids to the registered buffer in constructor where it is all zeros, which usually occurs + # when its auto-generated, registered buffer helps users when tracing the model without passing token_type_ids, solves + # issue #5664 + if token_type_ids is None: + if hasattr(self, "token_type_ids"): + buffered_token_type_ids = self.token_type_ids[:, :seq_length] + buffered_token_type_ids_expanded = buffered_token_type_ids.expand( + input_shape[0], seq_length + ) + token_type_ids = buffered_token_type_ids_expanded + else: + token_type_ids = torch.zeros( + input_shape, dtype=torch.long, device=self.position_ids.device + ) + + if inputs_embeds is None: + inputs_embeds = self.word_embeddings(input_ids) + token_type_embeddings = self.token_type_embeddings(token_type_ids) + + embeddings = inputs_embeds + token_type_embeddings + if self.position_embedding_type == "absolute": + position_embeddings = self.position_embeddings(position_ids) + embeddings += position_embeddings + embeddings = self.LayerNorm(embeddings) + embeddings = self.dropout(embeddings) + return embeddings + + def create_position_ids_from_inputs_embeds(self, inputs_embeds): + """ + We are provided embeddings directly. We cannot infer which are padded so just generate sequential position ids. + + Args: + inputs_embeds: torch.Tensor + + Returns: torch.Tensor + """ + input_shape = inputs_embeds.size()[:-1] + sequence_length = input_shape[1] + + position_ids = torch.arange( + self.padding_idx + 1, + sequence_length + self.padding_idx + 1, + dtype=torch.long, + device=inputs_embeds.device, + ) + return position_ids.unsqueeze(0).expand(input_shape) + + +# Copied from transformers.models.bert.modeling_bert.BertSelfAttention with Bert->Roberta +class RobertaSelfAttention(nn.Module): + def __init__(self, config, position_embedding_type=None): + super().__init__() + if config.hidden_size % config.num_attention_heads != 0 and not hasattr( + config, "embedding_size" + ): + raise ValueError( + f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention " + f"heads ({config.num_attention_heads})" + ) + + self.num_attention_heads = config.num_attention_heads + self.attention_head_size = int(config.hidden_size / config.num_attention_heads) + self.all_head_size = self.num_attention_heads * self.attention_head_size + + self.query = nn.Linear(config.hidden_size, self.all_head_size) + self.key = nn.Linear(config.hidden_size, self.all_head_size) + self.value = nn.Linear(config.hidden_size, self.all_head_size) + + self.dropout = nn.Dropout(config.attention_probs_dropout_prob) + self.position_embedding_type = position_embedding_type or getattr( + config, "position_embedding_type", "absolute" + ) + if ( + self.position_embedding_type == "relative_key" + or self.position_embedding_type == "relative_key_query" + ): + self.max_position_embeddings = config.max_position_embeddings + self.distance_embedding = nn.Embedding( + 2 * config.max_position_embeddings - 1, self.attention_head_size + ) + + self.is_decoder = config.is_decoder + + def transpose_for_scores(self, x): + new_x_shape = x.size()[:-1] + ( + self.num_attention_heads, + self.attention_head_size, + ) + x = x.view(new_x_shape) + return x.permute(0, 2, 1, 3) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.FloatTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, + output_attentions: Optional[bool] = False, + parser_att_mask=None, + ) -> Tuple[torch.Tensor]: + mixed_query_layer = self.query(hidden_states) + + # If this is instantiated as a cross-attention module, the keys + # and values come from an encoder; the attention mask needs to be + # such that the encoder's padding tokens are not attended to. + is_cross_attention = encoder_hidden_states is not None + + if is_cross_attention and past_key_value is not None: + # reuse k,v, cross_attentions + key_layer = past_key_value[0] + value_layer = past_key_value[1] + attention_mask = encoder_attention_mask + elif is_cross_attention: + key_layer = self.transpose_for_scores(self.key(encoder_hidden_states)) + value_layer = self.transpose_for_scores(self.value(encoder_hidden_states)) + attention_mask = encoder_attention_mask + elif past_key_value is not None: + key_layer = self.transpose_for_scores(self.key(hidden_states)) + value_layer = self.transpose_for_scores(self.value(hidden_states)) + key_layer = torch.cat([past_key_value[0], key_layer], dim=2) + value_layer = torch.cat([past_key_value[1], value_layer], dim=2) + else: + key_layer = self.transpose_for_scores(self.key(hidden_states)) + value_layer = self.transpose_for_scores(self.value(hidden_states)) + + query_layer = self.transpose_for_scores(mixed_query_layer) + + if self.is_decoder: + # if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states. + # Further calls to cross_attention layer can then reuse all cross-attention + # key/value_states (first "if" case) + # if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of + # all previous decoder key/value_states. Further calls to uni-directional self-attention + # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case) + # if encoder bi-directional self-attention `past_key_value` is always `None` + past_key_value = (key_layer, value_layer) + + # Take the dot product between "query" and "key" to get the raw attention scores. + attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) + + if ( + self.position_embedding_type == "relative_key" + or self.position_embedding_type == "relative_key_query" + ): + seq_length = hidden_states.size()[1] + position_ids_l = torch.arange( + seq_length, dtype=torch.long, device=hidden_states.device + ).view(-1, 1) + position_ids_r = torch.arange( + seq_length, dtype=torch.long, device=hidden_states.device + ).view(1, -1) + distance = position_ids_l - position_ids_r + positional_embedding = self.distance_embedding( + distance + self.max_position_embeddings - 1 + ) + positional_embedding = positional_embedding.to( + dtype=query_layer.dtype + ) # fp16 compatibility + + if self.position_embedding_type == "relative_key": + relative_position_scores = torch.einsum( + "bhld,lrd->bhlr", query_layer, positional_embedding + ) + attention_scores = attention_scores + relative_position_scores + elif self.position_embedding_type == "relative_key_query": + relative_position_scores_query = torch.einsum( + "bhld,lrd->bhlr", query_layer, positional_embedding + ) + relative_position_scores_key = torch.einsum( + "bhrd,lrd->bhlr", key_layer, positional_embedding + ) + attention_scores = ( + attention_scores + + relative_position_scores_query + + relative_position_scores_key + ) + + attention_scores = attention_scores / math.sqrt(self.attention_head_size) + if attention_mask is not None: + # Apply the attention mask is (precomputed for all layers in RobertaModel forward() function) + attention_scores = attention_scores + attention_mask + + if parser_att_mask is None: + # Normalize the attention scores to probabilities. + attention_probs = nn.functional.softmax(attention_scores, dim=-1) + else: + attention_probs = torch.sigmoid(attention_scores) * parser_att_mask + + # This is actually dropping out entire tokens to attend to, which might + # seem a bit unusual, but is taken from the original Transformer paper. + attention_probs = self.dropout(attention_probs) + + # Mask heads if we want to + if head_mask is not None: + attention_probs = attention_probs * head_mask + + context_layer = torch.matmul(attention_probs, value_layer) + + context_layer = context_layer.permute(0, 2, 1, 3).contiguous() + new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) + context_layer = context_layer.view(new_context_layer_shape) + + outputs = ( + (context_layer, attention_probs) if output_attentions else (context_layer,) + ) + + if self.is_decoder: + outputs = outputs + (past_key_value,) + return outputs + + +# Copied from transformers.models.bert.modeling_bert.BertSelfOutput +class RobertaSelfOutput(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward( + self, hidden_states: torch.Tensor, input_tensor: torch.Tensor + ) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + hidden_states = self.LayerNorm(hidden_states + input_tensor) + return hidden_states + + +# Copied from transformers.models.bert.modeling_bert.BertAttention with Bert->Roberta +class RobertaAttention(nn.Module): + def __init__(self, config, position_embedding_type=None): + super().__init__() + self.self = RobertaSelfAttention( + config, position_embedding_type=position_embedding_type + ) + self.output = RobertaSelfOutput(config) + self.pruned_heads = set() + + def prune_heads(self, heads): + if len(heads) == 0: + return + heads, index = find_pruneable_heads_and_indices( + heads, + self.self.num_attention_heads, + self.self.attention_head_size, + self.pruned_heads, + ) + + # Prune linear layers + self.self.query = prune_linear_layer(self.self.query, index) + self.self.key = prune_linear_layer(self.self.key, index) + self.self.value = prune_linear_layer(self.self.value, index) + self.output.dense = prune_linear_layer(self.output.dense, index, dim=1) + + # Update hyper params and store pruned heads + self.self.num_attention_heads = self.self.num_attention_heads - len(heads) + self.self.all_head_size = ( + self.self.attention_head_size * self.self.num_attention_heads + ) + self.pruned_heads = self.pruned_heads.union(heads) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.FloatTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, + output_attentions: Optional[bool] = False, + parser_att_mask=None, + ) -> Tuple[torch.Tensor]: + self_outputs = self.self( + hidden_states, + attention_mask, + head_mask, + encoder_hidden_states, + encoder_attention_mask, + past_key_value, + output_attentions, + parser_att_mask=parser_att_mask, + ) + attention_output = self.output(self_outputs[0], hidden_states) + outputs = (attention_output,) + self_outputs[ + 1: + ] # add attentions if we output them + return outputs + + +# Copied from transformers.models.bert.modeling_bert.BertIntermediate +class RobertaIntermediate(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.intermediate_size) + if isinstance(config.hidden_act, str): + self.intermediate_act_fn = ACT2FN[config.hidden_act] + else: + self.intermediate_act_fn = config.hidden_act + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.intermediate_act_fn(hidden_states) + return hidden_states + + +# Copied from transformers.models.bert.modeling_bert.BertOutput +class RobertaOutput(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.intermediate_size, config.hidden_size) + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward( + self, hidden_states: torch.Tensor, input_tensor: torch.Tensor + ) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + hidden_states = self.LayerNorm(hidden_states + input_tensor) + return hidden_states + + +# Copied from transformers.models.bert.modeling_bert.BertLayer with Bert->Roberta +class RobertaLayer(nn.Module): + def __init__(self, config): + super().__init__() + self.chunk_size_feed_forward = config.chunk_size_feed_forward + self.seq_len_dim = 1 + self.attention = RobertaAttention(config) + self.is_decoder = config.is_decoder + self.add_cross_attention = config.add_cross_attention + if self.add_cross_attention: + if not self.is_decoder: + raise ValueError( + f"{self} should be used as a decoder model if cross attention is added" + ) + self.crossattention = RobertaAttention( + config, position_embedding_type="absolute" + ) + self.intermediate = RobertaIntermediate(config) + self.output = RobertaOutput(config) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.FloatTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, + output_attentions: Optional[bool] = False, + parser_att_mask=None, + ) -> Tuple[torch.Tensor]: + # decoder uni-directional self-attention cached key/values tuple is at positions 1,2 + self_attn_past_key_value = ( + past_key_value[:2] if past_key_value is not None else None + ) + self_attention_outputs = self.attention( + hidden_states, + attention_mask, + head_mask, + output_attentions=output_attentions, + past_key_value=self_attn_past_key_value, + parser_att_mask=parser_att_mask, + ) + attention_output = self_attention_outputs[0] + + # if decoder, the last output is tuple of self-attn cache + if self.is_decoder: + outputs = self_attention_outputs[1:-1] + present_key_value = self_attention_outputs[-1] + else: + outputs = self_attention_outputs[ + 1: + ] # add self attentions if we output attention weights + + cross_attn_present_key_value = None + if self.is_decoder and encoder_hidden_states is not None: + if not hasattr(self, "crossattention"): + raise ValueError( + f"If `encoder_hidden_states` are passed, {self} has to be instantiated with cross-attention layers by setting `config.add_cross_attention=True`" + ) + + # cross_attn cached key/values tuple is at positions 3,4 of past_key_value tuple + cross_attn_past_key_value = ( + past_key_value[-2:] if past_key_value is not None else None + ) + cross_attention_outputs = self.crossattention( + attention_output, + attention_mask, + head_mask, + encoder_hidden_states, + encoder_attention_mask, + cross_attn_past_key_value, + output_attentions, + ) + attention_output = cross_attention_outputs[0] + outputs = ( + outputs + cross_attention_outputs[1:-1] + ) # add cross attentions if we output attention weights + + # add cross-attn cache to positions 3,4 of present_key_value tuple + cross_attn_present_key_value = cross_attention_outputs[-1] + present_key_value = present_key_value + cross_attn_present_key_value + + layer_output = apply_chunking_to_forward( + self.feed_forward_chunk, + self.chunk_size_feed_forward, + self.seq_len_dim, + attention_output, + ) + outputs = (layer_output,) + outputs + + # if decoder, return the attn key/values as the last output + if self.is_decoder: + outputs = outputs + (present_key_value,) + + return outputs + + def feed_forward_chunk(self, attention_output): + intermediate_output = self.intermediate(attention_output) + layer_output = self.output(intermediate_output, attention_output) + return layer_output + + +# Copied from transformers.models.bert.modeling_bert.BertEncoder with Bert->Roberta +class RobertaEncoder(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + self.layer = nn.ModuleList( + [RobertaLayer(config) for _ in range(config.num_hidden_layers)] + ) + self.gradient_checkpointing = False + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.FloatTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = False, + output_hidden_states: Optional[bool] = False, + return_dict: Optional[bool] = True, + parser_att_mask=None, + ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPastAndCrossAttentions]: + all_hidden_states = () if output_hidden_states else None + all_self_attentions = () if output_attentions else None + all_cross_attentions = ( + () if output_attentions and self.config.add_cross_attention else None + ) + + next_decoder_cache = () if use_cache else None + for i, layer_module in enumerate(self.layer): + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + layer_head_mask = head_mask[i] if head_mask is not None else None + past_key_value = past_key_values[i] if past_key_values is not None else None + + if self.gradient_checkpointing and self.training: + + if use_cache: + logger.warning( + "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." + ) + use_cache = False + + def create_custom_forward(module): + def custom_forward(*inputs): + return module(*inputs, past_key_value, output_attentions) + + return custom_forward + + layer_outputs = torch.utils.checkpoint.checkpoint( + create_custom_forward(layer_module), + hidden_states, + attention_mask, + layer_head_mask, + encoder_hidden_states, + encoder_attention_mask, + ) + else: + if parser_att_mask is not None: + layer_outputs = layer_module( + hidden_states, + attention_mask, + layer_head_mask, + encoder_hidden_states, + encoder_attention_mask, + past_key_value, + output_attentions, + parser_att_mask=parser_att_mask[i]) + else: + layer_outputs = layer_module( + hidden_states, + attention_mask, + layer_head_mask, + encoder_hidden_states, + encoder_attention_mask, + past_key_value, + output_attentions, + parser_att_mask=None) + + + hidden_states = layer_outputs[0] + if use_cache: + next_decoder_cache += (layer_outputs[-1],) + if output_attentions: + all_self_attentions = all_self_attentions + (layer_outputs[1],) + if self.config.add_cross_attention: + all_cross_attentions = all_cross_attentions + (layer_outputs[2],) + + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + if not return_dict: + return tuple( + v + for v in [ + hidden_states, + next_decoder_cache, + all_hidden_states, + all_self_attentions, + all_cross_attentions, + ] + if v is not None + ) + return BaseModelOutputWithPastAndCrossAttentions( + last_hidden_state=hidden_states, + past_key_values=next_decoder_cache, + hidden_states=all_hidden_states, + attentions=all_self_attentions, + cross_attentions=all_cross_attentions, + ) + + +# Copied from transformers.models.bert.modeling_bert.BertPooler +class RobertaPooler(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.activation = nn.Tanh() + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + # We "pool" the model by simply taking the hidden state corresponding + # to the first token. + first_token_tensor = hidden_states[:, 0] + pooled_output = self.dense(first_token_tensor) + pooled_output = self.activation(pooled_output) + return pooled_output + + +class RobertaPreTrainedModel(PreTrainedModel): + """ + An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained + models. + """ + + config_class = RobertaConfig + base_model_prefix = "roberta" + supports_gradient_checkpointing = True + + # Copied from transformers.models.bert.modeling_bert.BertPreTrainedModel._init_weights + def _init_weights(self, module): + """Initialize the weights""" + if isinstance(module, nn.Linear): + # Slightly different from the TF version which uses truncated_normal for initialization + # cf https://github.com/pytorch/pytorch/pull/5617 + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.Embedding): + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.padding_idx is not None: + module.weight.data[module.padding_idx].zero_() + elif isinstance(module, nn.LayerNorm): + if module.bias is not None: + module.bias.data.zero_() + module.weight.data.fill_(1.0) + + def _set_gradient_checkpointing(self, module, value=False): + if isinstance(module, RobertaEncoder): + module.gradient_checkpointing = value + + def update_keys_to_ignore(self, config, del_keys_to_ignore): + """Remove some keys from ignore list""" + if not config.tie_word_embeddings: + # must make a new list, or the class variable gets modified! + self._keys_to_ignore_on_save = [ + k for k in self._keys_to_ignore_on_save if k not in del_keys_to_ignore + ] + self._keys_to_ignore_on_load_missing = [ + k + for k in self._keys_to_ignore_on_load_missing + if k not in del_keys_to_ignore + ] + + +ROBERTA_START_DOCSTRING = r""" + + This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the + library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads + etc.) + + This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. + Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage + and behavior. + + Parameters: + config ([`RobertaConfig`]): Model configuration class with all the parameters of the + model. Initializing with a config file does not load the weights associated with the model, only the + configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. +""" + + +ROBERTA_INPUTS_DOCSTRING = r""" + Args: + input_ids (`torch.LongTensor` of shape `({0})`): + Indices of input sequence tokens in the vocabulary. + + Indices can be obtained using [`RobertaTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + [What are input IDs?](../glossary#input-ids) + attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*): + Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + + [What are attention masks?](../glossary#attention-mask) + token_type_ids (`torch.LongTensor` of shape `({0})`, *optional*): + Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, + 1]`: + + - 0 corresponds to a *sentence A* token, + - 1 corresponds to a *sentence B* token. + + [What are token type IDs?](../glossary#token-type-ids) + position_ids (`torch.LongTensor` of shape `({0})`, *optional*): + Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, + config.max_position_embeddings - 1]`. + + [What are position IDs?](../glossary#position-ids) + head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): + Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: + + - 1 indicates the head is **not masked**, + - 0 indicates the head is **masked**. + + inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_size)`, *optional*): + Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This + is useful if you want more control over how to convert `input_ids` indices into associated vectors than the + model's internal embedding lookup matrix. + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned + tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for + more detail. + return_dict (`bool`, *optional*): + Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. +""" + + +class RobertaModel(RobertaPreTrainedModel): + """ + + The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of + cross-attention is added between the self-attention layers, following the architecture described in *Attention is + all you need*_ by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz + Kaiser and Illia Polosukhin. + + To behave as an decoder the model needs to be initialized with the `is_decoder` argument of the configuration set + to `True`. To be used in a Seq2Seq model, the model needs to initialized with both `is_decoder` argument and + `add_cross_attention` set to `True`; an `encoder_hidden_states` is then expected as an input to the forward pass. + + .. _*Attention is all you need*: https://arxiv.org/abs/1706.03762 + + """ + + _keys_to_ignore_on_load_missing = [r"position_ids"] + + # Copied from transformers.models.bert.modeling_bert.BertModel.__init__ with Bert->Roberta + def __init__(self, config, add_pooling_layer=True): + super().__init__(config) + self.config = config + + self.embeddings = RobertaEmbeddings(config) + self.encoder = RobertaEncoder(config) + + self.pooler = RobertaPooler(config) if add_pooling_layer else None + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.embeddings.word_embeddings + + def set_input_embeddings(self, value): + self.embeddings.word_embeddings = value + + def _prune_heads(self, heads_to_prune): + """ + Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base + class PreTrainedModel + """ + for layer, heads in heads_to_prune.items(): + self.encoder.layer[layer].attention.prune_heads(heads) + + # Copied from transformers.models.bert.modeling_bert.BertModel.forward + def forward( + self, + input_ids: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + token_type_ids: Optional[torch.Tensor] = None, + position_ids: Optional[torch.Tensor] = None, + head_mask: Optional[torch.Tensor] = None, + inputs_embeds: Optional[torch.Tensor] = None, + encoder_hidden_states: Optional[torch.Tensor] = None, + encoder_attention_mask: Optional[torch.Tensor] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + parser_att_mask=None, + ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPoolingAndCrossAttentions]: + r""" + encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): + Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if + the model is configured as a decoder. + encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*): + Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in + the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): + Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. + + If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that + don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all + `decoder_input_ids` of shape `(batch_size, sequence_length)`. + use_cache (`bool`, *optional*): + If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see + `past_key_values`). + """ + output_attentions = ( + output_attentions + if output_attentions is not None + else self.config.output_attentions + ) + output_hidden_states = ( + output_hidden_states + if output_hidden_states is not None + else self.config.output_hidden_states + ) + return_dict = ( + return_dict if return_dict is not None else self.config.use_return_dict + ) + + if self.config.is_decoder: + use_cache = use_cache if use_cache is not None else self.config.use_cache + else: + use_cache = False + + if input_ids is not None and inputs_embeds is not None: + raise ValueError( + "You cannot specify both input_ids and inputs_embeds at the same time" + ) + elif input_ids is not None: + input_shape = input_ids.size() + elif inputs_embeds is not None: + input_shape = inputs_embeds.size()[:-1] + else: + raise ValueError("You have to specify either input_ids or inputs_embeds") + + batch_size, seq_length = input_shape + device = input_ids.device if input_ids is not None else inputs_embeds.device + + # past_key_values_length + past_key_values_length = ( + past_key_values[0][0].shape[2] if past_key_values is not None else 0 + ) + + if attention_mask is None: + attention_mask = torch.ones( + ((batch_size, seq_length + past_key_values_length)), device=device + ) + + if token_type_ids is None: + if hasattr(self.embeddings, "token_type_ids"): + buffered_token_type_ids = self.embeddings.token_type_ids[:, :seq_length] + buffered_token_type_ids_expanded = buffered_token_type_ids.expand( + batch_size, seq_length + ) + token_type_ids = buffered_token_type_ids_expanded + else: + token_type_ids = torch.zeros( + input_shape, dtype=torch.long, device=device + ) + + # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length] + # ourselves in which case we just need to make it broadcastable to all heads. + extended_attention_mask: torch.Tensor = self.get_extended_attention_mask( + attention_mask, input_shape, device + ) + + # If a 2D or 3D attention mask is provided for the cross-attention + # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length] + if self.config.is_decoder and encoder_hidden_states is not None: + ( + encoder_batch_size, + encoder_sequence_length, + _, + ) = encoder_hidden_states.size() + encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length) + if encoder_attention_mask is None: + encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device) + encoder_extended_attention_mask = self.invert_attention_mask( + encoder_attention_mask + ) + else: + encoder_extended_attention_mask = None + + # Prepare head mask if needed + # 1.0 in head_mask indicate we keep the head + # attention_probs has shape bsz x n_heads x N x N + # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] + # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] + head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) + + embedding_output = self.embeddings( + input_ids=input_ids, + position_ids=position_ids, + token_type_ids=token_type_ids, + inputs_embeds=inputs_embeds, + past_key_values_length=past_key_values_length, + ) + encoder_outputs = self.encoder( + embedding_output, + attention_mask=extended_attention_mask, + head_mask=head_mask, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_extended_attention_mask, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + parser_att_mask=parser_att_mask, + ) + sequence_output = encoder_outputs[0] + pooled_output = ( + self.pooler(sequence_output) if self.pooler is not None else None + ) + + if not return_dict: + return (sequence_output, pooled_output) + encoder_outputs[1:] + + return BaseModelOutputWithPoolingAndCrossAttentions( + last_hidden_state=sequence_output, + pooler_output=pooled_output, + past_key_values=encoder_outputs.past_key_values, + hidden_states=encoder_outputs.hidden_states, + attentions=encoder_outputs.attentions, + cross_attentions=encoder_outputs.cross_attentions, + ) + + +class StructRoberta(RobertaPreTrainedModel): + _keys_to_ignore_on_save = [r"lm_head.decoder.weight", r"lm_head.decoder.bias"] + _keys_to_ignore_on_load_missing = [ + r"position_ids", + r"lm_head.decoder.weight", + r"lm_head.decoder.bias", + ] + _keys_to_ignore_on_load_unexpected = [r"pooler"] + + def __init__(self, config): + super().__init__(config) + + if config.is_decoder: + logger.warning( + "If you want to use `RobertaForMaskedLM` make sure `config.is_decoder=False` for " + "bi-directional self-attention." + ) + + + if config.n_cntxt_layers > 0: + config_cntxt = copy.deepcopy(config) + config_cntxt.num_hidden_layers = config.n_cntxt_layers + + self.cntxt_layers = RobertaModel(config_cntxt, add_pooling_layer=False) + + if config.n_cntxt_layers_2 > 0: + self.parser_layers_1 = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(int(config.n_parser_layers/2)) + ] + ) + + self.distance_ff_1 = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff_1 = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight_1 = nn.Parameter( + torch.zeros((config.n_cntxt_layers_2, config.num_attention_heads, n_rel)) + ) + self._rel_weight_1.data.normal_(0, 0.1) + + self._scaler_1 = nn.Parameter(torch.zeros(2)) + + config_cntxt_2 = copy.deepcopy(config) + config_cntxt_2.num_hidden_layers = config.n_cntxt_layers_2 + + self.cntxt_layers_2 = RobertaModel(config_cntxt_2, add_pooling_layer=False) + + + self.parser_layers_2 = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(int(config.n_parser_layers/2)) + ] + ) + + self.distance_ff_2 = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff_2 = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight_2 = nn.Parameter( + torch.zeros((config.num_hidden_layers, config.num_attention_heads, n_rel)) + ) + self._rel_weight_2.data.normal_(0, 0.1) + + self._scaler_2 = nn.Parameter(torch.zeros(2)) + + else: + self.parser_layers = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(config.n_parser_layers) + ] + ) + + self.distance_ff = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight = nn.Parameter( + torch.zeros((config.num_hidden_layers, config.num_attention_heads, n_rel)) + ) + self._rel_weight.data.normal_(0, 0.1) + + self._scaler = nn.Parameter(torch.zeros(2)) + + self.roberta = RobertaModel(config, add_pooling_layer=False) + + if config.n_cntxt_layers > 0: + self.cntxt_layers.embeddings = self.roberta.embeddings + if config.n_cntxt_layers_2 > 0: + self.cntxt_layers_2.embeddings = self.roberta.embeddings + + self.lm_head = RobertaLMHead(config) + + self.pad = config.pad_token_id + + # The LM head weights require special treatment only when they are tied with the word embeddings + self.update_keys_to_ignore(config, ["lm_head.decoder.weight"]) + + # Initialize weights and apply final processing + self.post_init() + + def get_output_embeddings(self): + return self.lm_head.decoder + + def set_output_embeddings(self, new_embeddings): + self.lm_head.decoder = new_embeddings + + @property + def scaler(self): + return self._scaler.exp() + + @property + def scaler_1(self): + return self._scaler_1.exp() + + @property + def scaler_2(self): + return self._scaler_2.exp() + + @property + def rel_weight(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight, dim=-1) + + @property + def rel_weight_1(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight_1) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight_1, dim=-1) + + + @property + def rel_weight_2(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight_2) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight_2, dim=-1) + + + def compute_block(self, distance, height, n_cntxt_layers=0): + """Compute constituents from distance and height.""" + + if n_cntxt_layers>0: + if n_cntxt_layers == 1: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler_1[0] + elif n_cntxt_layers == 2: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler_2[0] + else: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0] + + gamma = torch.sigmoid(-beta_logits) + ones = torch.ones_like(gamma) + + block_mask_left = cummin( + gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1 + ) + block_mask_left = block_mask_left - F.pad( + block_mask_left[:, :, :-1], (1, 0), value=0 + ) + block_mask_left.tril_(0) + + block_mask_right = cummin( + gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1 + ) + block_mask_right = block_mask_right - F.pad( + block_mask_right[:, :, 1:], (0, 1), value=0 + ) + block_mask_right.triu_(0) + + block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :] + block = cumsum(block_mask_left).tril(0) + cumsum( + block_mask_right, reverse=True + ).triu(1) + + return block_p, block + + def compute_head(self, height, n_cntxt_layers=0): + """Estimate head for each constituent.""" + + _, length = height.size() + if n_cntxt_layers>0: + if n_cntxt_layers == 1: + head_logits = height * self.scaler_1[1] + elif n_cntxt_layers == 2: + head_logits = height * self.scaler_2[1] + else: + head_logits = height * self.scaler[1] + index = torch.arange(length, device=height.device) + + mask = (index[:, None, None] <= index[None, None, :]) * ( + index[None, None, :] <= index[None, :, None] + ) + head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1) + head_logits.masked_fill_(~mask[None, :, :, :], -1e9) + + head_p = torch.softmax(head_logits, dim=-1) + + return head_p + + def parse(self, x, embs=None, n_cntxt_layers=0): + """Parse input sentence. + + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + distance: syntactic distance + height: syntactic height + """ + + mask = x != self.pad + mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0) + + if embs is None: + h = self.roberta.embeddings(x) + else: + h = embs + + if n_cntxt_layers > 0: + if n_cntxt_layers == 1: + parser_layers = self.parser_layers_1 + height_ff = self.height_ff_1 + distance_ff = self.distance_ff_1 + elif n_cntxt_layers == 2: + parser_layers = self.parser_layers_2 + height_ff = self.height_ff_2 + distance_ff = self.distance_ff_2 + for i in range(int(self.config.n_parser_layers/2)): + h = h.masked_fill(~mask[:, :, None], 0) + h = parser_layers[i](h) + + height = height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, dim=-1 + )[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max + ) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where( + margin_left > margin_right, margin_right, margin_left + ).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + else: + for i in range(self.config.n_parser_layers): + h = h.masked_fill(~mask[:, :, None], 0) + h = self.parser_layers[i](h) + + height = self.height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = self.distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, dim=-1 + )[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max + ) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where( + margin_left > margin_right, margin_right, margin_left + ).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + + return distance, height + + def generate_mask(self, x, distance, height, n_cntxt_layers=0): + """Compute head and cibling distribution for each token.""" + + bsz, length = x.size() + + eye = torch.eye(length, device=x.device, dtype=torch.bool) + eye = eye[None, :, :].expand((bsz, -1, -1)) + + block_p, block = self.compute_block(distance, height, n_cntxt_layers=n_cntxt_layers) + head_p = self.compute_head(height, n_cntxt_layers=n_cntxt_layers) + head = torch.einsum("blij,bijh->blh", block_p, head_p) + head = head.masked_fill(eye, 0) + child = head.transpose(1, 2) + cibling = torch.bmm(head, child).masked_fill(eye, 0) + + rel_list = [] + if "head" in self.config.relations: + rel_list.append(head) + if "child" in self.config.relations: + rel_list.append(child) + if "cibling" in self.config.relations: + rel_list.append(cibling) + + rel = torch.stack(rel_list, dim=1) + + if n_cntxt_layers > 0: + if n_cntxt_layers == 1: + rel_weight = self.rel_weight_1 + elif n_cntxt_layers == 2: + rel_weight = self.rel_weight_2 + else: + rel_weight = self.rel_weight + + dep = torch.einsum("lhr,brij->lbhij", rel_weight, rel) + + if n_cntxt_layers == 1: + num_layers = self.cntxt_layers_2.config.num_hidden_layers + else: + num_layers = self.roberta.config.num_hidden_layers + + att_mask = dep.reshape( + num_layers, + bsz, + self.config.num_attention_heads, + length, + length, + ) + + return att_mask, cibling, head, block + + def forward( + self, + input_ids: Optional[torch.LongTensor] = None, + attention_mask: Optional[torch.FloatTensor] = None, + token_type_ids: Optional[torch.LongTensor] = None, + position_ids: Optional[torch.LongTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, MaskedLMOutput]: + r""" + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ..., + config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the + loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]` + kwargs (`Dict[str, any]`, optional, defaults to *{}*): + Used to hide legacy arguments that have been deprecated. + """ + return_dict = ( + return_dict if return_dict is not None else self.config.use_return_dict + ) + + + if self.config.n_cntxt_layers > 0: + cntxt_outputs = self.cntxt_layers( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict) + + + if self.config.n_cntxt_layers_2 > 0: + distance_1, height_1 = self.parse(input_ids, cntxt_outputs[0], n_cntxt_layers=1) + att_mask_1, _, _, _ = self.generate_mask(input_ids, distance_1, height_1, n_cntxt_layers=1) + + cntxt_outputs_2 = self.cntxt_layers_2( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + parser_att_mask=att_mask_1) + + sequence_output = cntxt_outputs_2[0] + + distance_2, height_2 = self.parse(input_ids, sequence_output[0], n_cntxt_layers=2) + att_mask, _, _, _ = self.generate_mask(input_ids, distance_2, height_2, n_cntxt_layers=2) + + elif self.config.n_cntxt_layers > 0: + distance, height = self.parse(input_ids, cntxt_outputs[0]) + att_mask, _, _, _ = self.generate_mask(input_ids, distance, height) + else: + distance, height = self.parse(input_ids) + att_mask, _, _, _ = self.generate_mask(input_ids, distance, height) + + outputs = self.roberta( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + parser_att_mask=att_mask, + ) + sequence_output = outputs[0] + prediction_scores = self.lm_head(sequence_output) + + masked_lm_loss = None + if labels is not None: + loss_fct = CrossEntropyLoss() + masked_lm_loss = loss_fct( + prediction_scores.view(-1, self.config.vocab_size), labels.view(-1) + ) + + if not return_dict: + output = (prediction_scores,) + outputs[2:] + return ( + ((masked_lm_loss,) + output) if masked_lm_loss is not None else output + ) + + return MaskedLMOutput( + loss=masked_lm_loss, + logits=prediction_scores, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +class RobertaLMHead(nn.Module): + """Roberta Head for masked language modeling.""" + + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + + self.decoder = nn.Linear(config.hidden_size, config.vocab_size) + self.bias = nn.Parameter(torch.zeros(config.vocab_size)) + self.decoder.bias = self.bias + + def forward(self, features, **kwargs): + x = self.dense(features) + x = gelu(x) + x = self.layer_norm(x) + + # project back to size of vocabulary with bias + x = self.decoder(x) + + return x + + def _tie_weights(self): + # To tie those two weights if they get disconnected (on TPU or when the bias is resized) + self.bias = self.decoder.bias + + +class StructRobertaForSequenceClassification(RobertaPreTrainedModel): + _keys_to_ignore_on_load_missing = [r"position_ids"] + + def __init__(self, config): + super().__init__(config) + self.num_labels = config.num_labels + self.config = config + + if config.n_cntxt_layers > 0: + config_cntxt = copy.deepcopy(config) + config_cntxt.num_hidden_layers = config.n_cntxt_layers + + self.cntxt_layers = RobertaModel(config_cntxt, add_pooling_layer=False) + + if config.n_cntxt_layers_2 > 0: + self.parser_layers_1 = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(int(config.n_parser_layers/2)) + ] + ) + + self.distance_ff_1 = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff_1 = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight_1 = nn.Parameter( + torch.zeros((config.n_cntxt_layers_2, config.num_attention_heads, n_rel)) + ) + self._rel_weight_1.data.normal_(0, 0.1) + + self._scaler_1 = nn.Parameter(torch.zeros(2)) + + config_cntxt_2 = copy.deepcopy(config) + config_cntxt_2.num_hidden_layers = config.n_cntxt_layers_2 + + self.cntxt_layers_2 = RobertaModel(config_cntxt_2, add_pooling_layer=False) + + + self.parser_layers_2 = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(int(config.n_parser_layers/2)) + ] + ) + + self.distance_ff_2 = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff_2 = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight_2 = nn.Parameter( + torch.zeros((config.num_hidden_layers, config.num_attention_heads, n_rel)) + ) + self._rel_weight_2.data.normal_(0, 0.1) + + self._scaler_2 = nn.Parameter(torch.zeros(2)) + + else: + self.parser_layers = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(config.n_parser_layers) + ] + ) + + self.distance_ff = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight = nn.Parameter( + torch.zeros((config.num_hidden_layers, config.num_attention_heads, n_rel)) + ) + self._rel_weight.data.normal_(0, 0.1) + + self._scaler = nn.Parameter(torch.zeros(2)) + + self.roberta = RobertaModel(config, add_pooling_layer=False) + + if config.n_cntxt_layers > 0: + self.cntxt_layers.embeddings = self.roberta.embeddings + if config.n_cntxt_layers_2 > 0: + self.cntxt_layers_2.embeddings = self.roberta.embeddings + + + self.pad = config.pad_token_id + self.classifier = RobertaClassificationHead(config) + + # Initialize weights and apply final processing + self.post_init() + + + @property + def scaler(self): + return self._scaler.exp() + + @property + def scaler_1(self): + return self._scaler_1.exp() + + @property + def scaler_2(self): + return self._scaler_2.exp() + + @property + def rel_weight(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight, dim=-1) + + @property + def rel_weight_1(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight_1) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight_1, dim=-1) + + + @property + def rel_weight_2(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight_2) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight_2, dim=-1) + + + def compute_block(self, distance, height, n_cntxt_layers=0): + """Compute constituents from distance and height.""" + + if n_cntxt_layers>0: + if n_cntxt_layers == 1: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler_1[0] + elif n_cntxt_layers == 2: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler_2[0] + else: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0] + + gamma = torch.sigmoid(-beta_logits) + ones = torch.ones_like(gamma) + + block_mask_left = cummin( + gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1 + ) + block_mask_left = block_mask_left - F.pad( + block_mask_left[:, :, :-1], (1, 0), value=0 + ) + block_mask_left.tril_(0) + + block_mask_right = cummin( + gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1 + ) + block_mask_right = block_mask_right - F.pad( + block_mask_right[:, :, 1:], (0, 1), value=0 + ) + block_mask_right.triu_(0) + + block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :] + block = cumsum(block_mask_left).tril(0) + cumsum( + block_mask_right, reverse=True + ).triu(1) + + return block_p, block + + def compute_head(self, height, n_cntxt_layers=0): + """Estimate head for each constituent.""" + + _, length = height.size() + if n_cntxt_layers>0: + if n_cntxt_layers == 1: + head_logits = height * self.scaler_1[1] + elif n_cntxt_layers == 2: + head_logits = height * self.scaler_2[1] + else: + head_logits = height * self.scaler[1] + index = torch.arange(length, device=height.device) + + mask = (index[:, None, None] <= index[None, None, :]) * ( + index[None, None, :] <= index[None, :, None] + ) + head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1) + head_logits.masked_fill_(~mask[None, :, :, :], -1e9) + + head_p = torch.softmax(head_logits, dim=-1) + + return head_p + + def parse(self, x, embs=None, n_cntxt_layers=0): + """Parse input sentence. + + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + distance: syntactic distance + height: syntactic height + """ + + mask = x != self.pad + mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0) + + if embs is None: + h = self.roberta.embeddings(x) + else: + h = embs + + if n_cntxt_layers > 0: + if n_cntxt_layers == 1: + parser_layers = self.parser_layers_1 + height_ff = self.height_ff_1 + distance_ff = self.distance_ff_1 + elif n_cntxt_layers == 2: + parser_layers = self.parser_layers_2 + height_ff = self.height_ff_2 + distance_ff = self.distance_ff_2 + for i in range(int(self.config.n_parser_layers/2)): + h = h.masked_fill(~mask[:, :, None], 0) + h = parser_layers[i](h) + + height = height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, dim=-1 + )[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max + ) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where( + margin_left > margin_right, margin_right, margin_left + ).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + else: + for i in range(self.config.n_parser_layers): + h = h.masked_fill(~mask[:, :, None], 0) + h = self.parser_layers[i](h) + + height = self.height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = self.distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, dim=-1 + )[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max + ) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where( + margin_left > margin_right, margin_right, margin_left + ).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + + return distance, height + + def generate_mask(self, x, distance, height, n_cntxt_layers=0): + """Compute head and cibling distribution for each token.""" + + bsz, length = x.size() + + eye = torch.eye(length, device=x.device, dtype=torch.bool) + eye = eye[None, :, :].expand((bsz, -1, -1)) + + block_p, block = self.compute_block(distance, height, n_cntxt_layers=n_cntxt_layers) + head_p = self.compute_head(height, n_cntxt_layers=n_cntxt_layers) + head = torch.einsum("blij,bijh->blh", block_p, head_p) + head = head.masked_fill(eye, 0) + child = head.transpose(1, 2) + cibling = torch.bmm(head, child).masked_fill(eye, 0) + + rel_list = [] + if "head" in self.config.relations: + rel_list.append(head) + if "child" in self.config.relations: + rel_list.append(child) + if "cibling" in self.config.relations: + rel_list.append(cibling) + + rel = torch.stack(rel_list, dim=1) + + if n_cntxt_layers > 0: + if n_cntxt_layers == 1: + rel_weight = self.rel_weight_1 + elif n_cntxt_layers == 2: + rel_weight = self.rel_weight_2 + else: + rel_weight = self.rel_weight + + dep = torch.einsum("lhr,brij->lbhij", rel_weight, rel) + + if n_cntxt_layers == 1: + num_layers = self.cntxt_layers_2.config.num_hidden_layers + else: + num_layers = self.roberta.config.num_hidden_layers + + att_mask = dep.reshape( + num_layers, + bsz, + self.config.num_attention_heads, + length, + length, + ) + + return att_mask, cibling, head, block + + def forward( + self, + input_ids: Optional[torch.LongTensor] = None, + attention_mask: Optional[torch.FloatTensor] = None, + token_type_ids: Optional[torch.LongTensor] = None, + position_ids: Optional[torch.LongTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, SequenceClassifierOutput]: + r""" + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., + config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If + `config.num_labels > 1` a classification loss is computed (Cross-Entropy). + """ + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + if self.config.n_cntxt_layers > 0: + cntxt_outputs = self.cntxt_layers( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict) + + + if self.config.n_cntxt_layers_2 > 0: + distance_1, height_1 = self.parse(input_ids, cntxt_outputs[0], n_cntxt_layers=1) + att_mask_1, _, _, _ = self.generate_mask(input_ids, distance_1, height_1, n_cntxt_layers=1) + + cntxt_outputs_2 = self.cntxt_layers_2( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + parser_att_mask=att_mask_1) + + sequence_output = cntxt_outputs_2[0] + + distance_2, height_2 = self.parse(input_ids, sequence_output[0], n_cntxt_layers=2) + att_mask, _, _, _ = self.generate_mask(input_ids, distance_2, height_2, n_cntxt_layers=2) + + elif self.config.n_cntxt_layers > 0: + distance, height = self.parse(input_ids, cntxt_outputs[0]) + att_mask, _, _, _ = self.generate_mask(input_ids, distance, height) + else: + distance, height = self.parse(input_ids) + att_mask, _, _, _ = self.generate_mask(input_ids, distance, height) + + outputs = self.roberta( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + parser_att_mask=att_mask, + ) + sequence_output = outputs[0] + logits = self.classifier(sequence_output) + + loss = None + if labels is not None: + if self.config.problem_type is None: + if self.num_labels == 1: + self.config.problem_type = "regression" + elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): + self.config.problem_type = "single_label_classification" + else: + self.config.problem_type = "multi_label_classification" + + if self.config.problem_type == "regression": + loss_fct = MSELoss() + if self.num_labels == 1: + loss = loss_fct(logits.squeeze(), labels.squeeze()) + else: + loss = loss_fct(logits, labels) + elif self.config.problem_type == "single_label_classification": + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) + elif self.config.problem_type == "multi_label_classification": + loss_fct = BCEWithLogitsLoss() + loss = loss_fct(logits, labels) + + if not return_dict: + output = (logits,) + outputs[2:] + return ((loss,) + output) if loss is not None else output + + return SequenceClassifierOutput( + loss=loss, + logits=logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +class RobertaClassificationHead(nn.Module): + """Head for sentence-level classification tasks.""" + + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + classifier_dropout = ( + config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob + ) + self.dropout = nn.Dropout(classifier_dropout) + self.out_proj = nn.Linear(config.hidden_size, config.num_labels) + + def forward(self, features, **kwargs): + x = features[:, 0, :] # take token (equiv. to [CLS]) + x = self.dropout(x) + x = self.dense(x) + x = torch.tanh(x) + x = self.dropout(x) + x = self.out_proj(x) + return x + + +def create_position_ids_from_input_ids( + input_ids, padding_idx, past_key_values_length=0 +): + """ + Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols + are ignored. This is modified from fairseq's `utils.make_positions`. + + Args: + x: torch.Tensor x: + + Returns: torch.Tensor + """ + # The series of casts and type-conversions here are carefully balanced to both work with ONNX export and XLA. + mask = input_ids.ne(padding_idx).int() + incremental_indices = ( + torch.cumsum(mask, dim=1).type_as(mask) + past_key_values_length + ) * mask + return incremental_indices.long() + padding_idx + + +def cumprod(x, reverse=False, exclusive=False): + """cumulative product.""" + if reverse: + x = x.flip([-1]) + + if exclusive: + x = F.pad(x[:, :, :-1], (1, 0), value=1) + + cx = x.cumprod(-1) + + if reverse: + cx = cx.flip([-1]) + return cx + + +def cumsum(x, reverse=False, exclusive=False): + """cumulative sum.""" + bsz, _, length = x.size() + device = x.device + if reverse: + if exclusive: + w = torch.ones([bsz, length, length], device=device).tril(-1) + else: + w = torch.ones([bsz, length, length], device=device).tril(0) + cx = torch.bmm(x, w) + else: + if exclusive: + w = torch.ones([bsz, length, length], device=device).triu(1) + else: + w = torch.ones([bsz, length, length], device=device).triu(0) + cx = torch.bmm(x, w) + return cx + + +def cummin(x, reverse=False, exclusive=False, max_value=1e9): + """cumulative min.""" + if reverse: + if exclusive: + x = F.pad(x[:, :, 1:], (0, 1), value=max_value) + x = x.flip([-1]).cummin(-1)[0].flip([-1]) + else: + if exclusive: + x = F.pad(x[:, :, :-1], (1, 0), value=max_value) + x = x.cummin(-1)[0] + return x diff --git a/finetune/relative_position_control/predict_results.txt b/finetune/relative_position_control/predict_results.txt new file mode 100644 index 0000000000000000000000000000000000000000..70ea7b7659785bc357f53e5ba94204c046905f06 --- /dev/null +++ b/finetune/relative_position_control/predict_results.txt @@ -0,0 +1,18093 @@ +index prediction +0 1 +1 0 +2 1 +3 0 +4 1 +5 0 +6 1 +7 0 +8 1 +9 0 +10 0 +11 0 +12 1 +13 0 +14 1 +15 0 +16 1 +17 0 +18 1 +19 0 +20 1 +21 0 +22 1 +23 0 +24 1 +25 0 +26 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". +A gh +A ld +A unty +B erm +B ah +B OY +C arr +C ulture +C rying +C atholic +D ang +F le +F ew +F eed +F ront +G ab +G ray +G ibbs +H ong +H erm +I ra +I aw +J or +J ana +K y +K night +K askaskia +K ELLY +L ED +L osing +L anguage +M onday +M ATT +N S +N urse +N othin +P ha +P ap +P ont +P rem +P EOPLE +R i +R uss +S ou +S ty +S alt +T ie +T unes +V i +W el +W ARD +Y awang +b able +b ahn +c ress +c ole +c rit +c ÃŃa +d oc +d ling +e ff +f ool +f ired +f urther +f amous +f emale +g is +g all +g um +g irlfriend +h oun +h wa +i ations +j ord +j ah +k il +k ai +k ids +m and +m ap +m oving +m ummy +o form +s and +t ha +t hang +u aries +v id +v ine +v able +v ati +w ine +w ag +z le + ¡ +à ¬ +Å Ħ +Ġt umble +he x +he id +in ine +in stead +Ġw izard +ou ching +on tain +ha ha +is cher +Ġb irl +Ġb ert +Ġb raid +Ġo t +Ġo mitted +Ġc his +Ġm asks +Ġm uttering +Ġf oll +an ley +Ġd azed +Ġd ouche +es on +Ġp he +Ġp au +ar ina +ar oni +ar von +at io +Ġl icked +Ġl icking +Ġg it +Ġn il +ĠI ld +le ments +Ġh uts +Ġhe ur +al ming +Ġit ching +Ġit chy +Ġe up +ut sc +Ġwe ights +Ġfor fe +Ġfor ci +ĠS ter +ĠS ino +ĠS ales +ĠS hib +ĠS iri +st ed +st ack +st range +st icks +ri y +ad itionally +Ġst if +Ġst rew +Ġst ains +Ġst ables +Ġst unning +oo ges +ĠA part +ĠA men +ĠA cha +ĠC ertain +ĠC ey +ĠC LA +ĠC ARR +Ġu gh +ĠM ist +ĠM OM +ĠM IGHT +Ġas semble +ill ors +Ġse ag +ĠH ollow +ĠH ades +ĠH ym +il ization +ĠB old +Ġli ability +op ping +ĠP K +ĠP ill +ĠP ew +ĠP ied +ĠP oe +ĠP OWER +ĠP iazza +ĠT il +ĠT ia +ĠT ick +un er +ĠW ap +Ġr ushes +Ġj est +Ġj ets +ĠD M +ĠD ort +ĠD OU +ĠD IS +ĠD uart +ĠD izzy +ĠG ina +um en +Ġso ils +Ġso aring +est us +Ġal igned +Ġall erg +ist ered +ĠR T +ĠR im +ĠR ac +ĠR iding +ĠR umble +ie ving +art y +ab lan +ĠL ac +ĠL ust +ĠL um +ĠL em +ĠL ions +ĠL ars +Ġcon gest +ĠN ights +ig l +han na +han sa +Ġup hold +Ġcom ical +Ġher ring +ĠJ ude +Ġout look +Ġout lined +ra ult +Ġpl umber +Ġpl aus +od i +ink meaner +Ġpro state +Ġpro stitutes +Ġex clude +Ġv ener +Ġv ines +Ġv ipers +ard y +ity a +ĠO D +ies selin +Ġman nik +ud ers +ud eau +ap o +ĠK iri +ven lock +Ġtr acing +ure en +ĠHe w +ĠHe gel +ove l +ick i +ci us +ĠU CLA +reat ic +ĠSt ras +ĠSt empniak +No ll +other wise +ne ither +Ġback grounds +Ġover d +Ġgr inding +That s +Ġen act +Ġen cl +Ġen vious +og g +og un +pl anet +ord ered +Ġdown stream +Ġun real +Ġun wanted +Ġun ravel +iz oph +Ġam iable +Ġcomp osing +Ġbl ush +ĠV on +ĠV ig +ĠIn v +urn al +Ġplay doh +ress ions +av or +av ius +Ġdis ks +Ġgu iding +ĠY ek +be es +ip ani +ip hon +Ġfl aws +ect ing +vers ation +ks het +Ġrem nants +ix by +Ġsha ving +Ġsec urities +Ġspe ars +Ġlast ly +li k +Ġend ocr +Ġlea ked +Ġfam ine +Ġnight gown +ny a +de gree +Ġ18 10 +Ġ18 28 +Ġ18 32 +gg les +ĠShe ldon +ĠAr um +Ġmar ries diff --git a/finetune/rte/modeling_structroberta.py b/finetune/rte/modeling_structroberta.py new file mode 100644 index 0000000000000000000000000000000000000000..9195a79dc7ae5e8c606bb77a05b8a1d5672bfdc0 --- /dev/null +++ b/finetune/rte/modeling_structroberta.py @@ -0,0 +1,2146 @@ +import copy +import math +from typing import List, Optional, Tuple, Union +import torch +import torch.utils.checkpoint +from packaging import version +from torch import nn +import torch.nn.functional as F +from torch.nn import CrossEntropyLoss, MSELoss, BCEWithLogitsLoss +from transformers.activations import ACT2FN, gelu +from transformers.modeling_outputs import ( + BaseModelOutputWithPastAndCrossAttentions, + BaseModelOutputWithPoolingAndCrossAttentions, + MaskedLMOutput, + SequenceClassifierOutput +) +from transformers.modeling_utils import ( + PreTrainedModel, + apply_chunking_to_forward, + find_pruneable_heads_and_indices, + prune_linear_layer, +) +from transformers.utils import logging +from transformers import RobertaConfig + + +logger = logging.get_logger(__name__) +ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST = [ + "roberta-base", + "roberta-large", + "roberta-large-mnli", + "distilroberta-base", + "roberta-base-openai-detector", + "roberta-large-openai-detector", + # See all RoBERTa models at https://huggingface.co/models?filter=roberta +] + + +class StructRobertaConfig(RobertaConfig): + model_type = "roberta" + + def __init__( + self, + n_parser_layers=4, + conv_size=9, + relations=("head", "child"), + weight_act="softmax", + n_cntxt_layers=3, + n_cntxt_layers_2=0, + **kwargs,): + + super().__init__(**kwargs) + self.n_cntxt_layers = n_cntxt_layers + self.n_parser_layers = n_parser_layers + self.n_cntxt_layers_2 = n_cntxt_layers_2 + self.conv_size = conv_size + self.relations = relations + self.weight_act = weight_act + +class Conv1d(nn.Module): + """1D convolution layer.""" + + def __init__(self, hidden_size, kernel_size, dilation=1): + """Initialization. + + Args: + hidden_size: dimension of input embeddings + kernel_size: convolution kernel size + dilation: the spacing between the kernel points + """ + super(Conv1d, self).__init__() + + if kernel_size % 2 == 0: + padding = (kernel_size // 2) * dilation + self.shift = True + else: + padding = ((kernel_size - 1) // 2) * dilation + self.shift = False + self.conv = nn.Conv1d( + hidden_size, hidden_size, kernel_size, padding=padding, dilation=dilation + ) + + def forward(self, x): + """Compute convolution. + + Args: + x: input embeddings + Returns: + conv_output: convolution results + """ + + if self.shift: + return self.conv(x.transpose(1, 2)).transpose(1, 2)[:, 1:] + else: + return self.conv(x.transpose(1, 2)).transpose(1, 2) + + +class RobertaEmbeddings(nn.Module): + """ + Same as BertEmbeddings with a tiny tweak for positional embeddings indexing. + """ + + # Copied from transformers.models.bert.modeling_bert.BertEmbeddings.__init__ + def __init__(self, config): + super().__init__() + self.word_embeddings = nn.Embedding( + config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id + ) + self.position_embeddings = nn.Embedding( + config.max_position_embeddings, config.hidden_size + ) + self.token_type_embeddings = nn.Embedding( + config.type_vocab_size, config.hidden_size + ) + + # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load + # any TensorFlow checkpoint file + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + # position_ids (1, len position emb) is contiguous in memory and exported when serialized + self.position_embedding_type = getattr( + config, "position_embedding_type", "absolute" + ) + self.register_buffer( + "position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)) + ) + if version.parse(torch.__version__) > version.parse("1.6.0"): + self.register_buffer( + "token_type_ids", + torch.zeros(self.position_ids.size(), dtype=torch.long), + persistent=False, + ) + + # End copy + self.padding_idx = config.pad_token_id + self.position_embeddings = nn.Embedding( + config.max_position_embeddings, + config.hidden_size, + padding_idx=self.padding_idx, + ) + + def forward( + self, + input_ids=None, + token_type_ids=None, + position_ids=None, + inputs_embeds=None, + past_key_values_length=0, + ): + if position_ids is None: + if input_ids is not None: + # Create the position ids from the input token ids. Any padded tokens remain padded. + position_ids = create_position_ids_from_input_ids( + input_ids, self.padding_idx, past_key_values_length + ) + else: + position_ids = self.create_position_ids_from_inputs_embeds( + inputs_embeds + ) + + if input_ids is not None: + input_shape = input_ids.size() + else: + input_shape = inputs_embeds.size()[:-1] + + seq_length = input_shape[1] + + # Setting the token_type_ids to the registered buffer in constructor where it is all zeros, which usually occurs + # when its auto-generated, registered buffer helps users when tracing the model without passing token_type_ids, solves + # issue #5664 + if token_type_ids is None: + if hasattr(self, "token_type_ids"): + buffered_token_type_ids = self.token_type_ids[:, :seq_length] + buffered_token_type_ids_expanded = buffered_token_type_ids.expand( + input_shape[0], seq_length + ) + token_type_ids = buffered_token_type_ids_expanded + else: + token_type_ids = torch.zeros( + input_shape, dtype=torch.long, device=self.position_ids.device + ) + + if inputs_embeds is None: + inputs_embeds = self.word_embeddings(input_ids) + token_type_embeddings = self.token_type_embeddings(token_type_ids) + + embeddings = inputs_embeds + token_type_embeddings + if self.position_embedding_type == "absolute": + position_embeddings = self.position_embeddings(position_ids) + embeddings += position_embeddings + embeddings = self.LayerNorm(embeddings) + embeddings = self.dropout(embeddings) + return embeddings + + def create_position_ids_from_inputs_embeds(self, inputs_embeds): + """ + We are provided embeddings directly. We cannot infer which are padded so just generate sequential position ids. + + Args: + inputs_embeds: torch.Tensor + + Returns: torch.Tensor + """ + input_shape = inputs_embeds.size()[:-1] + sequence_length = input_shape[1] + + position_ids = torch.arange( + self.padding_idx + 1, + sequence_length + self.padding_idx + 1, + dtype=torch.long, + device=inputs_embeds.device, + ) + return position_ids.unsqueeze(0).expand(input_shape) + + +# Copied from transformers.models.bert.modeling_bert.BertSelfAttention with Bert->Roberta +class RobertaSelfAttention(nn.Module): + def __init__(self, config, position_embedding_type=None): + super().__init__() + if config.hidden_size % config.num_attention_heads != 0 and not hasattr( + config, "embedding_size" + ): + raise ValueError( + f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention " + f"heads ({config.num_attention_heads})" + ) + + self.num_attention_heads = config.num_attention_heads + self.attention_head_size = int(config.hidden_size / config.num_attention_heads) + self.all_head_size = self.num_attention_heads * self.attention_head_size + + self.query = nn.Linear(config.hidden_size, self.all_head_size) + self.key = nn.Linear(config.hidden_size, self.all_head_size) + self.value = nn.Linear(config.hidden_size, self.all_head_size) + + self.dropout = nn.Dropout(config.attention_probs_dropout_prob) + self.position_embedding_type = position_embedding_type or getattr( + config, "position_embedding_type", "absolute" + ) + if ( + self.position_embedding_type == "relative_key" + or self.position_embedding_type == "relative_key_query" + ): + self.max_position_embeddings = config.max_position_embeddings + self.distance_embedding = nn.Embedding( + 2 * config.max_position_embeddings - 1, self.attention_head_size + ) + + self.is_decoder = config.is_decoder + + def transpose_for_scores(self, x): + new_x_shape = x.size()[:-1] + ( + self.num_attention_heads, + self.attention_head_size, + ) + x = x.view(new_x_shape) + return x.permute(0, 2, 1, 3) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.FloatTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, + output_attentions: Optional[bool] = False, + parser_att_mask=None, + ) -> Tuple[torch.Tensor]: + mixed_query_layer = self.query(hidden_states) + + # If this is instantiated as a cross-attention module, the keys + # and values come from an encoder; the attention mask needs to be + # such that the encoder's padding tokens are not attended to. + is_cross_attention = encoder_hidden_states is not None + + if is_cross_attention and past_key_value is not None: + # reuse k,v, cross_attentions + key_layer = past_key_value[0] + value_layer = past_key_value[1] + attention_mask = encoder_attention_mask + elif is_cross_attention: + key_layer = self.transpose_for_scores(self.key(encoder_hidden_states)) + value_layer = self.transpose_for_scores(self.value(encoder_hidden_states)) + attention_mask = encoder_attention_mask + elif past_key_value is not None: + key_layer = self.transpose_for_scores(self.key(hidden_states)) + value_layer = self.transpose_for_scores(self.value(hidden_states)) + key_layer = torch.cat([past_key_value[0], key_layer], dim=2) + value_layer = torch.cat([past_key_value[1], value_layer], dim=2) + else: + key_layer = self.transpose_for_scores(self.key(hidden_states)) + value_layer = self.transpose_for_scores(self.value(hidden_states)) + + query_layer = self.transpose_for_scores(mixed_query_layer) + + if self.is_decoder: + # if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states. + # Further calls to cross_attention layer can then reuse all cross-attention + # key/value_states (first "if" case) + # if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of + # all previous decoder key/value_states. Further calls to uni-directional self-attention + # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case) + # if encoder bi-directional self-attention `past_key_value` is always `None` + past_key_value = (key_layer, value_layer) + + # Take the dot product between "query" and "key" to get the raw attention scores. + attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) + + if ( + self.position_embedding_type == "relative_key" + or self.position_embedding_type == "relative_key_query" + ): + seq_length = hidden_states.size()[1] + position_ids_l = torch.arange( + seq_length, dtype=torch.long, device=hidden_states.device + ).view(-1, 1) + position_ids_r = torch.arange( + seq_length, dtype=torch.long, device=hidden_states.device + ).view(1, -1) + distance = position_ids_l - position_ids_r + positional_embedding = self.distance_embedding( + distance + self.max_position_embeddings - 1 + ) + positional_embedding = positional_embedding.to( + dtype=query_layer.dtype + ) # fp16 compatibility + + if self.position_embedding_type == "relative_key": + relative_position_scores = torch.einsum( + "bhld,lrd->bhlr", query_layer, positional_embedding + ) + attention_scores = attention_scores + relative_position_scores + elif self.position_embedding_type == "relative_key_query": + relative_position_scores_query = torch.einsum( + "bhld,lrd->bhlr", query_layer, positional_embedding + ) + relative_position_scores_key = torch.einsum( + "bhrd,lrd->bhlr", key_layer, positional_embedding + ) + attention_scores = ( + attention_scores + + relative_position_scores_query + + relative_position_scores_key + ) + + attention_scores = attention_scores / math.sqrt(self.attention_head_size) + if attention_mask is not None: + # Apply the attention mask is (precomputed for all layers in RobertaModel forward() function) + attention_scores = attention_scores + attention_mask + + if parser_att_mask is None: + # Normalize the attention scores to probabilities. + attention_probs = nn.functional.softmax(attention_scores, dim=-1) + else: + attention_probs = torch.sigmoid(attention_scores) * parser_att_mask + + # This is actually dropping out entire tokens to attend to, which might + # seem a bit unusual, but is taken from the original Transformer paper. + attention_probs = self.dropout(attention_probs) + + # Mask heads if we want to + if head_mask is not None: + attention_probs = attention_probs * head_mask + + context_layer = torch.matmul(attention_probs, value_layer) + + context_layer = context_layer.permute(0, 2, 1, 3).contiguous() + new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) + context_layer = context_layer.view(new_context_layer_shape) + + outputs = ( + (context_layer, attention_probs) if output_attentions else (context_layer,) + ) + + if self.is_decoder: + outputs = outputs + (past_key_value,) + return outputs + + +# Copied from transformers.models.bert.modeling_bert.BertSelfOutput +class RobertaSelfOutput(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward( + self, hidden_states: torch.Tensor, input_tensor: torch.Tensor + ) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + hidden_states = self.LayerNorm(hidden_states + input_tensor) + return hidden_states + + +# Copied from transformers.models.bert.modeling_bert.BertAttention with Bert->Roberta +class RobertaAttention(nn.Module): + def __init__(self, config, position_embedding_type=None): + super().__init__() + self.self = RobertaSelfAttention( + config, position_embedding_type=position_embedding_type + ) + self.output = RobertaSelfOutput(config) + self.pruned_heads = set() + + def prune_heads(self, heads): + if len(heads) == 0: + return + heads, index = find_pruneable_heads_and_indices( + heads, + self.self.num_attention_heads, + self.self.attention_head_size, + self.pruned_heads, + ) + + # Prune linear layers + self.self.query = prune_linear_layer(self.self.query, index) + self.self.key = prune_linear_layer(self.self.key, index) + self.self.value = prune_linear_layer(self.self.value, index) + self.output.dense = prune_linear_layer(self.output.dense, index, dim=1) + + # Update hyper params and store pruned heads + self.self.num_attention_heads = self.self.num_attention_heads - len(heads) + self.self.all_head_size = ( + self.self.attention_head_size * self.self.num_attention_heads + ) + self.pruned_heads = self.pruned_heads.union(heads) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.FloatTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, + output_attentions: Optional[bool] = False, + parser_att_mask=None, + ) -> Tuple[torch.Tensor]: + self_outputs = self.self( + hidden_states, + attention_mask, + head_mask, + encoder_hidden_states, + encoder_attention_mask, + past_key_value, + output_attentions, + parser_att_mask=parser_att_mask, + ) + attention_output = self.output(self_outputs[0], hidden_states) + outputs = (attention_output,) + self_outputs[ + 1: + ] # add attentions if we output them + return outputs + + +# Copied from transformers.models.bert.modeling_bert.BertIntermediate +class RobertaIntermediate(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.intermediate_size) + if isinstance(config.hidden_act, str): + self.intermediate_act_fn = ACT2FN[config.hidden_act] + else: + self.intermediate_act_fn = config.hidden_act + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.intermediate_act_fn(hidden_states) + return hidden_states + + +# Copied from transformers.models.bert.modeling_bert.BertOutput +class RobertaOutput(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.intermediate_size, config.hidden_size) + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward( + self, hidden_states: torch.Tensor, input_tensor: torch.Tensor + ) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + hidden_states = self.LayerNorm(hidden_states + input_tensor) + return hidden_states + + +# Copied from transformers.models.bert.modeling_bert.BertLayer with Bert->Roberta +class RobertaLayer(nn.Module): + def __init__(self, config): + super().__init__() + self.chunk_size_feed_forward = config.chunk_size_feed_forward + self.seq_len_dim = 1 + self.attention = RobertaAttention(config) + self.is_decoder = config.is_decoder + self.add_cross_attention = config.add_cross_attention + if self.add_cross_attention: + if not self.is_decoder: + raise ValueError( + f"{self} should be used as a decoder model if cross attention is added" + ) + self.crossattention = RobertaAttention( + config, position_embedding_type="absolute" + ) + self.intermediate = RobertaIntermediate(config) + self.output = RobertaOutput(config) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.FloatTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, + output_attentions: Optional[bool] = False, + parser_att_mask=None, + ) -> Tuple[torch.Tensor]: + # decoder uni-directional self-attention cached key/values tuple is at positions 1,2 + self_attn_past_key_value = ( + past_key_value[:2] if past_key_value is not None else None + ) + self_attention_outputs = self.attention( + hidden_states, + attention_mask, + head_mask, + output_attentions=output_attentions, + past_key_value=self_attn_past_key_value, + parser_att_mask=parser_att_mask, + ) + attention_output = self_attention_outputs[0] + + # if decoder, the last output is tuple of self-attn cache + if self.is_decoder: + outputs = self_attention_outputs[1:-1] + present_key_value = self_attention_outputs[-1] + else: + outputs = self_attention_outputs[ + 1: + ] # add self attentions if we output attention weights + + cross_attn_present_key_value = None + if self.is_decoder and encoder_hidden_states is not None: + if not hasattr(self, "crossattention"): + raise ValueError( + f"If `encoder_hidden_states` are passed, {self} has to be instantiated with cross-attention layers by setting `config.add_cross_attention=True`" + ) + + # cross_attn cached key/values tuple is at positions 3,4 of past_key_value tuple + cross_attn_past_key_value = ( + past_key_value[-2:] if past_key_value is not None else None + ) + cross_attention_outputs = self.crossattention( + attention_output, + attention_mask, + head_mask, + encoder_hidden_states, + encoder_attention_mask, + cross_attn_past_key_value, + output_attentions, + ) + attention_output = cross_attention_outputs[0] + outputs = ( + outputs + cross_attention_outputs[1:-1] + ) # add cross attentions if we output attention weights + + # add cross-attn cache to positions 3,4 of present_key_value tuple + cross_attn_present_key_value = cross_attention_outputs[-1] + present_key_value = present_key_value + cross_attn_present_key_value + + layer_output = apply_chunking_to_forward( + self.feed_forward_chunk, + self.chunk_size_feed_forward, + self.seq_len_dim, + attention_output, + ) + outputs = (layer_output,) + outputs + + # if decoder, return the attn key/values as the last output + if self.is_decoder: + outputs = outputs + (present_key_value,) + + return outputs + + def feed_forward_chunk(self, attention_output): + intermediate_output = self.intermediate(attention_output) + layer_output = self.output(intermediate_output, attention_output) + return layer_output + + +# Copied from transformers.models.bert.modeling_bert.BertEncoder with Bert->Roberta +class RobertaEncoder(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + self.layer = nn.ModuleList( + [RobertaLayer(config) for _ in range(config.num_hidden_layers)] + ) + self.gradient_checkpointing = False + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.FloatTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = False, + output_hidden_states: Optional[bool] = False, + return_dict: Optional[bool] = True, + parser_att_mask=None, + ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPastAndCrossAttentions]: + all_hidden_states = () if output_hidden_states else None + all_self_attentions = () if output_attentions else None + all_cross_attentions = ( + () if output_attentions and self.config.add_cross_attention else None + ) + + next_decoder_cache = () if use_cache else None + for i, layer_module in enumerate(self.layer): + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + layer_head_mask = head_mask[i] if head_mask is not None else None + past_key_value = past_key_values[i] if past_key_values is not None else None + + if self.gradient_checkpointing and self.training: + + if use_cache: + logger.warning( + "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." + ) + use_cache = False + + def create_custom_forward(module): + def custom_forward(*inputs): + return module(*inputs, past_key_value, output_attentions) + + return custom_forward + + layer_outputs = torch.utils.checkpoint.checkpoint( + create_custom_forward(layer_module), + hidden_states, + attention_mask, + layer_head_mask, + encoder_hidden_states, + encoder_attention_mask, + ) + else: + if parser_att_mask is not None: + layer_outputs = layer_module( + hidden_states, + attention_mask, + layer_head_mask, + encoder_hidden_states, + encoder_attention_mask, + past_key_value, + output_attentions, + parser_att_mask=parser_att_mask[i]) + else: + layer_outputs = layer_module( + hidden_states, + attention_mask, + layer_head_mask, + encoder_hidden_states, + encoder_attention_mask, + past_key_value, + output_attentions, + parser_att_mask=None) + + + hidden_states = layer_outputs[0] + if use_cache: + next_decoder_cache += (layer_outputs[-1],) + if output_attentions: + all_self_attentions = all_self_attentions + (layer_outputs[1],) + if self.config.add_cross_attention: + all_cross_attentions = all_cross_attentions + (layer_outputs[2],) + + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + if not return_dict: + return tuple( + v + for v in [ + hidden_states, + next_decoder_cache, + all_hidden_states, + all_self_attentions, + all_cross_attentions, + ] + if v is not None + ) + return BaseModelOutputWithPastAndCrossAttentions( + last_hidden_state=hidden_states, + past_key_values=next_decoder_cache, + hidden_states=all_hidden_states, + attentions=all_self_attentions, + cross_attentions=all_cross_attentions, + ) + + +# Copied from transformers.models.bert.modeling_bert.BertPooler +class RobertaPooler(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.activation = nn.Tanh() + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + # We "pool" the model by simply taking the hidden state corresponding + # to the first token. + first_token_tensor = hidden_states[:, 0] + pooled_output = self.dense(first_token_tensor) + pooled_output = self.activation(pooled_output) + return pooled_output + + +class RobertaPreTrainedModel(PreTrainedModel): + """ + An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained + models. + """ + + config_class = RobertaConfig + base_model_prefix = "roberta" + supports_gradient_checkpointing = True + + # Copied from transformers.models.bert.modeling_bert.BertPreTrainedModel._init_weights + def _init_weights(self, module): + """Initialize the weights""" + if isinstance(module, nn.Linear): + # Slightly different from the TF version which uses truncated_normal for initialization + # cf https://github.com/pytorch/pytorch/pull/5617 + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.Embedding): + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.padding_idx is not None: + module.weight.data[module.padding_idx].zero_() + elif isinstance(module, nn.LayerNorm): + if module.bias is not None: + module.bias.data.zero_() + module.weight.data.fill_(1.0) + + def _set_gradient_checkpointing(self, module, value=False): + if isinstance(module, RobertaEncoder): + module.gradient_checkpointing = value + + def update_keys_to_ignore(self, config, del_keys_to_ignore): + """Remove some keys from ignore list""" + if not config.tie_word_embeddings: + # must make a new list, or the class variable gets modified! + self._keys_to_ignore_on_save = [ + k for k in self._keys_to_ignore_on_save if k not in del_keys_to_ignore + ] + self._keys_to_ignore_on_load_missing = [ + k + for k in self._keys_to_ignore_on_load_missing + if k not in del_keys_to_ignore + ] + + +ROBERTA_START_DOCSTRING = r""" + + This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the + library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads + etc.) + + This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. + Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage + and behavior. + + Parameters: + config ([`RobertaConfig`]): Model configuration class with all the parameters of the + model. Initializing with a config file does not load the weights associated with the model, only the + configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. +""" + + +ROBERTA_INPUTS_DOCSTRING = r""" + Args: + input_ids (`torch.LongTensor` of shape `({0})`): + Indices of input sequence tokens in the vocabulary. + + Indices can be obtained using [`RobertaTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + [What are input IDs?](../glossary#input-ids) + attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*): + Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + + [What are attention masks?](../glossary#attention-mask) + token_type_ids (`torch.LongTensor` of shape `({0})`, *optional*): + Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, + 1]`: + + - 0 corresponds to a *sentence A* token, + - 1 corresponds to a *sentence B* token. + + [What are token type IDs?](../glossary#token-type-ids) + position_ids (`torch.LongTensor` of shape `({0})`, *optional*): + Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, + config.max_position_embeddings - 1]`. + + [What are position IDs?](../glossary#position-ids) + head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): + Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: + + - 1 indicates the head is **not masked**, + - 0 indicates the head is **masked**. + + inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_size)`, *optional*): + Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This + is useful if you want more control over how to convert `input_ids` indices into associated vectors than the + model's internal embedding lookup matrix. + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned + tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for + more detail. + return_dict (`bool`, *optional*): + Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. +""" + + +class RobertaModel(RobertaPreTrainedModel): + """ + + The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of + cross-attention is added between the self-attention layers, following the architecture described in *Attention is + all you need*_ by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz + Kaiser and Illia Polosukhin. + + To behave as an decoder the model needs to be initialized with the `is_decoder` argument of the configuration set + to `True`. To be used in a Seq2Seq model, the model needs to initialized with both `is_decoder` argument and + `add_cross_attention` set to `True`; an `encoder_hidden_states` is then expected as an input to the forward pass. + + .. _*Attention is all you need*: https://arxiv.org/abs/1706.03762 + + """ + + _keys_to_ignore_on_load_missing = [r"position_ids"] + + # Copied from transformers.models.bert.modeling_bert.BertModel.__init__ with Bert->Roberta + def __init__(self, config, add_pooling_layer=True): + super().__init__(config) + self.config = config + + self.embeddings = RobertaEmbeddings(config) + self.encoder = RobertaEncoder(config) + + self.pooler = RobertaPooler(config) if add_pooling_layer else None + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.embeddings.word_embeddings + + def set_input_embeddings(self, value): + self.embeddings.word_embeddings = value + + def _prune_heads(self, heads_to_prune): + """ + Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base + class PreTrainedModel + """ + for layer, heads in heads_to_prune.items(): + self.encoder.layer[layer].attention.prune_heads(heads) + + # Copied from transformers.models.bert.modeling_bert.BertModel.forward + def forward( + self, + input_ids: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + token_type_ids: Optional[torch.Tensor] = None, + position_ids: Optional[torch.Tensor] = None, + head_mask: Optional[torch.Tensor] = None, + inputs_embeds: Optional[torch.Tensor] = None, + encoder_hidden_states: Optional[torch.Tensor] = None, + encoder_attention_mask: Optional[torch.Tensor] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + parser_att_mask=None, + ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPoolingAndCrossAttentions]: + r""" + encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): + Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if + the model is configured as a decoder. + encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*): + Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in + the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): + Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. + + If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that + don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all + `decoder_input_ids` of shape `(batch_size, sequence_length)`. + use_cache (`bool`, *optional*): + If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see + `past_key_values`). + """ + output_attentions = ( + output_attentions + if output_attentions is not None + else self.config.output_attentions + ) + output_hidden_states = ( + output_hidden_states + if output_hidden_states is not None + else self.config.output_hidden_states + ) + return_dict = ( + return_dict if return_dict is not None else self.config.use_return_dict + ) + + if self.config.is_decoder: + use_cache = use_cache if use_cache is not None else self.config.use_cache + else: + use_cache = False + + if input_ids is not None and inputs_embeds is not None: + raise ValueError( + "You cannot specify both input_ids and inputs_embeds at the same time" + ) + elif input_ids is not None: + input_shape = input_ids.size() + elif inputs_embeds is not None: + input_shape = inputs_embeds.size()[:-1] + else: + raise ValueError("You have to specify either input_ids or inputs_embeds") + + batch_size, seq_length = input_shape + device = input_ids.device if input_ids is not None else inputs_embeds.device + + # past_key_values_length + past_key_values_length = ( + past_key_values[0][0].shape[2] if past_key_values is not None else 0 + ) + + if attention_mask is None: + attention_mask = torch.ones( + ((batch_size, seq_length + past_key_values_length)), device=device + ) + + if token_type_ids is None: + if hasattr(self.embeddings, "token_type_ids"): + buffered_token_type_ids = self.embeddings.token_type_ids[:, :seq_length] + buffered_token_type_ids_expanded = buffered_token_type_ids.expand( + batch_size, seq_length + ) + token_type_ids = buffered_token_type_ids_expanded + else: + token_type_ids = torch.zeros( + input_shape, dtype=torch.long, device=device + ) + + # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length] + # ourselves in which case we just need to make it broadcastable to all heads. + extended_attention_mask: torch.Tensor = self.get_extended_attention_mask( + attention_mask, input_shape, device + ) + + # If a 2D or 3D attention mask is provided for the cross-attention + # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length] + if self.config.is_decoder and encoder_hidden_states is not None: + ( + encoder_batch_size, + encoder_sequence_length, + _, + ) = encoder_hidden_states.size() + encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length) + if encoder_attention_mask is None: + encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device) + encoder_extended_attention_mask = self.invert_attention_mask( + encoder_attention_mask + ) + else: + encoder_extended_attention_mask = None + + # Prepare head mask if needed + # 1.0 in head_mask indicate we keep the head + # attention_probs has shape bsz x n_heads x N x N + # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] + # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] + head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) + + embedding_output = self.embeddings( + input_ids=input_ids, + position_ids=position_ids, + token_type_ids=token_type_ids, + inputs_embeds=inputs_embeds, + past_key_values_length=past_key_values_length, + ) + encoder_outputs = self.encoder( + embedding_output, + attention_mask=extended_attention_mask, + head_mask=head_mask, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_extended_attention_mask, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + parser_att_mask=parser_att_mask, + ) + sequence_output = encoder_outputs[0] + pooled_output = ( + self.pooler(sequence_output) if self.pooler is not None else None + ) + + if not return_dict: + return (sequence_output, pooled_output) + encoder_outputs[1:] + + return BaseModelOutputWithPoolingAndCrossAttentions( + last_hidden_state=sequence_output, + pooler_output=pooled_output, + past_key_values=encoder_outputs.past_key_values, + hidden_states=encoder_outputs.hidden_states, + attentions=encoder_outputs.attentions, + cross_attentions=encoder_outputs.cross_attentions, + ) + + +class StructRoberta(RobertaPreTrainedModel): + _keys_to_ignore_on_save = [r"lm_head.decoder.weight", r"lm_head.decoder.bias"] + _keys_to_ignore_on_load_missing = [ + r"position_ids", + r"lm_head.decoder.weight", + r"lm_head.decoder.bias", + ] + _keys_to_ignore_on_load_unexpected = [r"pooler"] + + def __init__(self, config): + super().__init__(config) + + if config.is_decoder: + logger.warning( + "If you want to use `RobertaForMaskedLM` make sure `config.is_decoder=False` for " + "bi-directional self-attention." + ) + + + if config.n_cntxt_layers > 0: + config_cntxt = copy.deepcopy(config) + config_cntxt.num_hidden_layers = config.n_cntxt_layers + + self.cntxt_layers = RobertaModel(config_cntxt, add_pooling_layer=False) + + if config.n_cntxt_layers_2 > 0: + self.parser_layers_1 = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(int(config.n_parser_layers/2)) + ] + ) + + self.distance_ff_1 = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff_1 = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight_1 = nn.Parameter( + torch.zeros((config.n_cntxt_layers_2, config.num_attention_heads, n_rel)) + ) + self._rel_weight_1.data.normal_(0, 0.1) + + self._scaler_1 = nn.Parameter(torch.zeros(2)) + + config_cntxt_2 = copy.deepcopy(config) + config_cntxt_2.num_hidden_layers = config.n_cntxt_layers_2 + + self.cntxt_layers_2 = RobertaModel(config_cntxt_2, add_pooling_layer=False) + + + self.parser_layers_2 = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(int(config.n_parser_layers/2)) + ] + ) + + self.distance_ff_2 = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff_2 = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight_2 = nn.Parameter( + torch.zeros((config.num_hidden_layers, config.num_attention_heads, n_rel)) + ) + self._rel_weight_2.data.normal_(0, 0.1) + + self._scaler_2 = nn.Parameter(torch.zeros(2)) + + else: + self.parser_layers = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(config.n_parser_layers) + ] + ) + + self.distance_ff = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight = nn.Parameter( + torch.zeros((config.num_hidden_layers, config.num_attention_heads, n_rel)) + ) + self._rel_weight.data.normal_(0, 0.1) + + self._scaler = nn.Parameter(torch.zeros(2)) + + self.roberta = RobertaModel(config, add_pooling_layer=False) + + if config.n_cntxt_layers > 0: + self.cntxt_layers.embeddings = self.roberta.embeddings + if config.n_cntxt_layers_2 > 0: + self.cntxt_layers_2.embeddings = self.roberta.embeddings + + self.lm_head = RobertaLMHead(config) + + self.pad = config.pad_token_id + + # The LM head weights require special treatment only when they are tied with the word embeddings + self.update_keys_to_ignore(config, ["lm_head.decoder.weight"]) + + # Initialize weights and apply final processing + self.post_init() + + def get_output_embeddings(self): + return self.lm_head.decoder + + def set_output_embeddings(self, new_embeddings): + self.lm_head.decoder = new_embeddings + + @property + def scaler(self): + return self._scaler.exp() + + @property + def scaler_1(self): + return self._scaler_1.exp() + + @property + def scaler_2(self): + return self._scaler_2.exp() + + @property + def rel_weight(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight, dim=-1) + + @property + def rel_weight_1(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight_1) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight_1, dim=-1) + + + @property + def rel_weight_2(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight_2) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight_2, dim=-1) + + + def compute_block(self, distance, height, n_cntxt_layers=0): + """Compute constituents from distance and height.""" + + if n_cntxt_layers>0: + if n_cntxt_layers == 1: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler_1[0] + elif n_cntxt_layers == 2: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler_2[0] + else: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0] + + gamma = torch.sigmoid(-beta_logits) + ones = torch.ones_like(gamma) + + block_mask_left = cummin( + gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1 + ) + block_mask_left = block_mask_left - F.pad( + block_mask_left[:, :, :-1], (1, 0), value=0 + ) + block_mask_left.tril_(0) + + block_mask_right = cummin( + gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1 + ) + block_mask_right = block_mask_right - F.pad( + block_mask_right[:, :, 1:], (0, 1), value=0 + ) + block_mask_right.triu_(0) + + block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :] + block = cumsum(block_mask_left).tril(0) + cumsum( + block_mask_right, reverse=True + ).triu(1) + + return block_p, block + + def compute_head(self, height, n_cntxt_layers=0): + """Estimate head for each constituent.""" + + _, length = height.size() + if n_cntxt_layers>0: + if n_cntxt_layers == 1: + head_logits = height * self.scaler_1[1] + elif n_cntxt_layers == 2: + head_logits = height * self.scaler_2[1] + else: + head_logits = height * self.scaler[1] + index = torch.arange(length, device=height.device) + + mask = (index[:, None, None] <= index[None, None, :]) * ( + index[None, None, :] <= index[None, :, None] + ) + head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1) + head_logits.masked_fill_(~mask[None, :, :, :], -1e9) + + head_p = torch.softmax(head_logits, dim=-1) + + return head_p + + def parse(self, x, embs=None, n_cntxt_layers=0): + """Parse input sentence. + + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + distance: syntactic distance + height: syntactic height + """ + + mask = x != self.pad + mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0) + + if embs is None: + h = self.roberta.embeddings(x) + else: + h = embs + + if n_cntxt_layers > 0: + if n_cntxt_layers == 1: + parser_layers = self.parser_layers_1 + height_ff = self.height_ff_1 + distance_ff = self.distance_ff_1 + elif n_cntxt_layers == 2: + parser_layers = self.parser_layers_2 + height_ff = self.height_ff_2 + distance_ff = self.distance_ff_2 + for i in range(int(self.config.n_parser_layers/2)): + h = h.masked_fill(~mask[:, :, None], 0) + h = parser_layers[i](h) + + height = height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, dim=-1 + )[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max + ) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where( + margin_left > margin_right, margin_right, margin_left + ).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + else: + for i in range(self.config.n_parser_layers): + h = h.masked_fill(~mask[:, :, None], 0) + h = self.parser_layers[i](h) + + height = self.height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = self.distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, dim=-1 + )[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max + ) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where( + margin_left > margin_right, margin_right, margin_left + ).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + + return distance, height + + def generate_mask(self, x, distance, height, n_cntxt_layers=0): + """Compute head and cibling distribution for each token.""" + + bsz, length = x.size() + + eye = torch.eye(length, device=x.device, dtype=torch.bool) + eye = eye[None, :, :].expand((bsz, -1, -1)) + + block_p, block = self.compute_block(distance, height, n_cntxt_layers=n_cntxt_layers) + head_p = self.compute_head(height, n_cntxt_layers=n_cntxt_layers) + head = torch.einsum("blij,bijh->blh", block_p, head_p) + head = head.masked_fill(eye, 0) + child = head.transpose(1, 2) + cibling = torch.bmm(head, child).masked_fill(eye, 0) + + rel_list = [] + if "head" in self.config.relations: + rel_list.append(head) + if "child" in self.config.relations: + rel_list.append(child) + if "cibling" in self.config.relations: + rel_list.append(cibling) + + rel = torch.stack(rel_list, dim=1) + + if n_cntxt_layers > 0: + if n_cntxt_layers == 1: + rel_weight = self.rel_weight_1 + elif n_cntxt_layers == 2: + rel_weight = self.rel_weight_2 + else: + rel_weight = self.rel_weight + + dep = torch.einsum("lhr,brij->lbhij", rel_weight, rel) + + if n_cntxt_layers == 1: + num_layers = self.cntxt_layers_2.config.num_hidden_layers + else: + num_layers = self.roberta.config.num_hidden_layers + + att_mask = dep.reshape( + num_layers, + bsz, + self.config.num_attention_heads, + length, + length, + ) + + return att_mask, cibling, head, block + + def forward( + self, + input_ids: Optional[torch.LongTensor] = None, + attention_mask: Optional[torch.FloatTensor] = None, + token_type_ids: Optional[torch.LongTensor] = None, + position_ids: Optional[torch.LongTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, MaskedLMOutput]: + r""" + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ..., + config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the + loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]` + kwargs (`Dict[str, any]`, optional, defaults to *{}*): + Used to hide legacy arguments that have been deprecated. + """ + return_dict = ( + return_dict if return_dict is not None else self.config.use_return_dict + ) + + + if self.config.n_cntxt_layers > 0: + cntxt_outputs = self.cntxt_layers( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict) + + + if self.config.n_cntxt_layers_2 > 0: + distance_1, height_1 = self.parse(input_ids, cntxt_outputs[0], n_cntxt_layers=1) + att_mask_1, _, _, _ = self.generate_mask(input_ids, distance_1, height_1, n_cntxt_layers=1) + + cntxt_outputs_2 = self.cntxt_layers_2( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + parser_att_mask=att_mask_1) + + sequence_output = cntxt_outputs_2[0] + + distance_2, height_2 = self.parse(input_ids, sequence_output[0], n_cntxt_layers=2) + att_mask, _, _, _ = self.generate_mask(input_ids, distance_2, height_2, n_cntxt_layers=2) + + elif self.config.n_cntxt_layers > 0: + distance, height = self.parse(input_ids, cntxt_outputs[0]) + att_mask, _, _, _ = self.generate_mask(input_ids, distance, height) + else: + distance, height = self.parse(input_ids) + att_mask, _, _, _ = self.generate_mask(input_ids, distance, height) + + outputs = self.roberta( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + parser_att_mask=att_mask, + ) + sequence_output = outputs[0] + prediction_scores = self.lm_head(sequence_output) + + masked_lm_loss = None + if labels is not None: + loss_fct = CrossEntropyLoss() + masked_lm_loss = loss_fct( + prediction_scores.view(-1, self.config.vocab_size), labels.view(-1) + ) + + if not return_dict: + output = (prediction_scores,) + outputs[2:] + return ( + ((masked_lm_loss,) + output) if masked_lm_loss is not None else output + ) + + return MaskedLMOutput( + loss=masked_lm_loss, + logits=prediction_scores, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +class RobertaLMHead(nn.Module): + """Roberta Head for masked language modeling.""" + + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + + self.decoder = nn.Linear(config.hidden_size, config.vocab_size) + self.bias = nn.Parameter(torch.zeros(config.vocab_size)) + self.decoder.bias = self.bias + + def forward(self, features, **kwargs): + x = self.dense(features) + x = gelu(x) + x = self.layer_norm(x) + + # project back to size of vocabulary with bias + x = self.decoder(x) + + return x + + def _tie_weights(self): + # To tie those two weights if they get disconnected (on TPU or when the bias is resized) + self.bias = self.decoder.bias + + +class StructRobertaForSequenceClassification(RobertaPreTrainedModel): + _keys_to_ignore_on_load_missing = [r"position_ids"] + + def __init__(self, config): + super().__init__(config) + self.num_labels = config.num_labels + self.config = config + + if config.n_cntxt_layers > 0: + config_cntxt = copy.deepcopy(config) + config_cntxt.num_hidden_layers = config.n_cntxt_layers + + self.cntxt_layers = RobertaModel(config_cntxt, add_pooling_layer=False) + + if config.n_cntxt_layers_2 > 0: + self.parser_layers_1 = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(int(config.n_parser_layers/2)) + ] + ) + + self.distance_ff_1 = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff_1 = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight_1 = nn.Parameter( + torch.zeros((config.n_cntxt_layers_2, config.num_attention_heads, n_rel)) + ) + self._rel_weight_1.data.normal_(0, 0.1) + + self._scaler_1 = nn.Parameter(torch.zeros(2)) + + config_cntxt_2 = copy.deepcopy(config) + config_cntxt_2.num_hidden_layers = config.n_cntxt_layers_2 + + self.cntxt_layers_2 = RobertaModel(config_cntxt_2, add_pooling_layer=False) + + + self.parser_layers_2 = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(int(config.n_parser_layers/2)) + ] + ) + + self.distance_ff_2 = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff_2 = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight_2 = nn.Parameter( + torch.zeros((config.num_hidden_layers, config.num_attention_heads, n_rel)) + ) + self._rel_weight_2.data.normal_(0, 0.1) + + self._scaler_2 = nn.Parameter(torch.zeros(2)) + + else: + self.parser_layers = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(config.n_parser_layers) + ] + ) + + self.distance_ff = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight = nn.Parameter( + torch.zeros((config.num_hidden_layers, config.num_attention_heads, n_rel)) + ) + self._rel_weight.data.normal_(0, 0.1) + + self._scaler = nn.Parameter(torch.zeros(2)) + + self.roberta = RobertaModel(config, add_pooling_layer=False) + + if config.n_cntxt_layers > 0: + self.cntxt_layers.embeddings = self.roberta.embeddings + if config.n_cntxt_layers_2 > 0: + self.cntxt_layers_2.embeddings = self.roberta.embeddings + + + self.pad = config.pad_token_id + self.classifier = RobertaClassificationHead(config) + + # Initialize weights and apply final processing + self.post_init() + + + @property + def scaler(self): + return self._scaler.exp() + + @property + def scaler_1(self): + return self._scaler_1.exp() + + @property + def scaler_2(self): + return self._scaler_2.exp() + + @property + def rel_weight(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight, dim=-1) + + @property + def rel_weight_1(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight_1) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight_1, dim=-1) + + + @property + def rel_weight_2(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight_2) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight_2, dim=-1) + + + def compute_block(self, distance, height, n_cntxt_layers=0): + """Compute constituents from distance and height.""" + + if n_cntxt_layers>0: + if n_cntxt_layers == 1: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler_1[0] + elif n_cntxt_layers == 2: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler_2[0] + else: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0] + + gamma = torch.sigmoid(-beta_logits) + ones = torch.ones_like(gamma) + + block_mask_left = cummin( + gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1 + ) + block_mask_left = block_mask_left - F.pad( + block_mask_left[:, :, :-1], (1, 0), value=0 + ) + block_mask_left.tril_(0) + + block_mask_right = cummin( + gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1 + ) + block_mask_right = block_mask_right - F.pad( + block_mask_right[:, :, 1:], (0, 1), value=0 + ) + block_mask_right.triu_(0) + + block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :] + block = cumsum(block_mask_left).tril(0) + cumsum( + block_mask_right, reverse=True + ).triu(1) + + return block_p, block + + def compute_head(self, height, n_cntxt_layers=0): + """Estimate head for each constituent.""" + + _, length = height.size() + if n_cntxt_layers>0: + if n_cntxt_layers == 1: + head_logits = height * self.scaler_1[1] + elif n_cntxt_layers == 2: + head_logits = height * self.scaler_2[1] + else: + head_logits = height * self.scaler[1] + index = torch.arange(length, device=height.device) + + mask = (index[:, None, None] <= index[None, None, :]) * ( + index[None, None, :] <= index[None, :, None] + ) + head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1) + head_logits.masked_fill_(~mask[None, :, :, :], -1e9) + + head_p = torch.softmax(head_logits, dim=-1) + + return head_p + + def parse(self, x, embs=None, n_cntxt_layers=0): + """Parse input sentence. + + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + distance: syntactic distance + height: syntactic height + """ + + mask = x != self.pad + mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0) + + if embs is None: + h = self.roberta.embeddings(x) + else: + h = embs + + if n_cntxt_layers > 0: + if n_cntxt_layers == 1: + parser_layers = self.parser_layers_1 + height_ff = self.height_ff_1 + distance_ff = self.distance_ff_1 + elif n_cntxt_layers == 2: + parser_layers = self.parser_layers_2 + height_ff = self.height_ff_2 + distance_ff = self.distance_ff_2 + for i in range(int(self.config.n_parser_layers/2)): + h = h.masked_fill(~mask[:, :, None], 0) + h = parser_layers[i](h) + + height = height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, dim=-1 + )[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max + ) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where( + margin_left > margin_right, margin_right, margin_left + ).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + else: + for i in range(self.config.n_parser_layers): + h = h.masked_fill(~mask[:, :, None], 0) + h = self.parser_layers[i](h) + + height = self.height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = self.distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, dim=-1 + )[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max + ) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where( + margin_left > margin_right, margin_right, margin_left + ).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + + return distance, height + + def generate_mask(self, x, distance, height, n_cntxt_layers=0): + """Compute head and cibling distribution for each token.""" + + bsz, length = x.size() + + eye = torch.eye(length, device=x.device, dtype=torch.bool) + eye = eye[None, :, :].expand((bsz, -1, -1)) + + block_p, block = self.compute_block(distance, height, n_cntxt_layers=n_cntxt_layers) + head_p = self.compute_head(height, n_cntxt_layers=n_cntxt_layers) + head = torch.einsum("blij,bijh->blh", block_p, head_p) + head = head.masked_fill(eye, 0) + child = head.transpose(1, 2) + cibling = torch.bmm(head, child).masked_fill(eye, 0) + + rel_list = [] + if "head" in self.config.relations: + rel_list.append(head) + if "child" in self.config.relations: + rel_list.append(child) + if "cibling" in self.config.relations: + rel_list.append(cibling) + + rel = torch.stack(rel_list, dim=1) + + if n_cntxt_layers > 0: + if n_cntxt_layers == 1: + rel_weight = self.rel_weight_1 + elif n_cntxt_layers == 2: + rel_weight = self.rel_weight_2 + else: + rel_weight = self.rel_weight + + dep = torch.einsum("lhr,brij->lbhij", rel_weight, rel) + + if n_cntxt_layers == 1: + num_layers = self.cntxt_layers_2.config.num_hidden_layers + else: + num_layers = self.roberta.config.num_hidden_layers + + att_mask = dep.reshape( + num_layers, + bsz, + self.config.num_attention_heads, + length, + length, + ) + + return att_mask, cibling, head, block + + def forward( + self, + input_ids: Optional[torch.LongTensor] = None, + attention_mask: Optional[torch.FloatTensor] = None, + token_type_ids: Optional[torch.LongTensor] = None, + position_ids: Optional[torch.LongTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, SequenceClassifierOutput]: + r""" + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., + config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If + `config.num_labels > 1` a classification loss is computed (Cross-Entropy). + """ + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + if self.config.n_cntxt_layers > 0: + cntxt_outputs = self.cntxt_layers( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict) + + + if self.config.n_cntxt_layers_2 > 0: + distance_1, height_1 = self.parse(input_ids, cntxt_outputs[0], n_cntxt_layers=1) + att_mask_1, _, _, _ = self.generate_mask(input_ids, distance_1, height_1, n_cntxt_layers=1) + + cntxt_outputs_2 = self.cntxt_layers_2( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + parser_att_mask=att_mask_1) + + sequence_output = cntxt_outputs_2[0] + + distance_2, height_2 = self.parse(input_ids, sequence_output[0], n_cntxt_layers=2) + att_mask, _, _, _ = self.generate_mask(input_ids, distance_2, height_2, n_cntxt_layers=2) + + elif self.config.n_cntxt_layers > 0: + distance, height = self.parse(input_ids, cntxt_outputs[0]) + att_mask, _, _, _ = self.generate_mask(input_ids, distance, height) + else: + distance, height = self.parse(input_ids) + att_mask, _, _, _ = self.generate_mask(input_ids, distance, height) + + outputs = self.roberta( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + parser_att_mask=att_mask, + ) + sequence_output = outputs[0] + logits = self.classifier(sequence_output) + + loss = None + if labels is not None: + if self.config.problem_type is None: + if self.num_labels == 1: + self.config.problem_type = "regression" + elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): + self.config.problem_type = "single_label_classification" + else: + self.config.problem_type = "multi_label_classification" + + if self.config.problem_type == "regression": + loss_fct = MSELoss() + if self.num_labels == 1: + loss = loss_fct(logits.squeeze(), labels.squeeze()) + else: + loss = loss_fct(logits, labels) + elif self.config.problem_type == "single_label_classification": + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) + elif self.config.problem_type == "multi_label_classification": + loss_fct = BCEWithLogitsLoss() + loss = loss_fct(logits, labels) + + if not return_dict: + output = (logits,) + outputs[2:] + return ((loss,) + output) if loss is not None else output + + return SequenceClassifierOutput( + loss=loss, + logits=logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +class RobertaClassificationHead(nn.Module): + """Head for sentence-level classification tasks.""" + + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + classifier_dropout = ( + config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob + ) + self.dropout = nn.Dropout(classifier_dropout) + self.out_proj = nn.Linear(config.hidden_size, config.num_labels) + + def forward(self, features, **kwargs): + x = features[:, 0, :] # take token (equiv. to [CLS]) + x = self.dropout(x) + x = self.dense(x) + x = torch.tanh(x) + x = self.dropout(x) + x = self.out_proj(x) + return x + + +def create_position_ids_from_input_ids( + input_ids, padding_idx, past_key_values_length=0 +): + """ + Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols + are ignored. This is modified from fairseq's `utils.make_positions`. + + Args: + x: torch.Tensor x: + + Returns: torch.Tensor + """ + # The series of casts and type-conversions here are carefully balanced to both work with ONNX export and XLA. + mask = input_ids.ne(padding_idx).int() + incremental_indices = ( + torch.cumsum(mask, dim=1).type_as(mask) + past_key_values_length + ) * mask + return incremental_indices.long() + padding_idx + + +def cumprod(x, reverse=False, exclusive=False): + """cumulative product.""" + if reverse: + x = x.flip([-1]) + + if exclusive: + x = F.pad(x[:, :, :-1], (1, 0), value=1) + + cx = x.cumprod(-1) + + if reverse: + cx = cx.flip([-1]) + return cx + + +def cumsum(x, reverse=False, exclusive=False): + """cumulative sum.""" + bsz, _, length = x.size() + device = x.device + if reverse: + if exclusive: + w = torch.ones([bsz, length, length], device=device).tril(-1) + else: + w = torch.ones([bsz, length, length], device=device).tril(0) + cx = torch.bmm(x, w) + else: + if exclusive: + w = torch.ones([bsz, length, length], device=device).triu(1) + else: + w = torch.ones([bsz, length, length], device=device).triu(0) + cx = torch.bmm(x, w) + return cx + + +def cummin(x, reverse=False, exclusive=False, max_value=1e9): + """cumulative min.""" + if reverse: + if exclusive: + x = F.pad(x[:, :, 1:], (0, 1), value=max_value) + x = x.flip([-1]).cummin(-1)[0].flip([-1]) + else: + if exclusive: + x = F.pad(x[:, :, :-1], (1, 0), value=max_value) + x = x.cummin(-1)[0] + return x diff --git a/finetune/rte/predict_results.txt b/finetune/rte/predict_results.txt new file mode 100644 index 0000000000000000000000000000000000000000..23233be5b562e895059cb8434b5c0df18aeef808 --- /dev/null +++ b/finetune/rte/predict_results.txt @@ -0,0 +1,100 @@ +index prediction +0 0 +1 1 +2 0 +3 1 +4 1 +5 1 +6 1 +7 1 +8 0 +9 0 +10 0 +11 1 +12 0 +13 1 +14 1 +15 0 +16 1 +17 0 +18 1 +19 1 +20 1 +21 1 +22 1 +23 1 +24 0 +25 0 +26 1 +27 0 +28 0 +29 0 +30 1 +31 1 +32 0 +33 0 +34 1 +35 1 +36 1 +37 0 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--- /dev/null +++ b/finetune/sst2/eval_results.json @@ -0,0 +1,11 @@ +{ + "epoch": 10.0, + "eval_accuracy": 0.874015748500824, + "eval_f1": 0.875968992248062, + "eval_loss": 0.5028718709945679, + "eval_mcc": 0.7486469063845578, + "eval_runtime": 1.0167, + "eval_samples": 508, + "eval_samples_per_second": 499.666, + "eval_steps_per_second": 62.95 +} \ No newline at end of file diff --git a/finetune/sst2/merges.txt b/finetune/sst2/merges.txt new file mode 100644 index 0000000000000000000000000000000000000000..94b4f252cb566a4dac62a08c78d85169ff4e91be --- /dev/null +++ b/finetune/sst2/merges.txt @@ -0,0 +1,31794 @@ +#version: 0.2 +Ġ t +h e +Ġ a +i n +Ġt he +r e +Ġ w +o u +Ġ s +o n +e r +h a +n d +i s +i t +Ġ b +Ġ o +Ġ c +Ġ m +Ġ f +in g +o r +a n +e d +l l +Ġ d +e n +Ġt o +e s +Ġ p +Ġo f +Ġ y +Ġ in +a r +a t +Ġa nd +Ġ l +ha t +Ġy ou +a s +Ġ g +o m +Ġ n +Ġ I +i c +o w +l e +v e +o t +Ġ h +Ġ he +a l +e t +Ġ ha +' s +u s +Ġt h +a y +Ġ it +Ġ is +Ġb e +Ġt hat +Ġ on +i on +i d +Ġ e +g h +u t +l d +en t +i m +Ġ re +Ġw as +a c +Ġw e +v er +a m +T he +. . +Ġf or +l y +Ġ S +s t +r i +r o +i r +a d +s e +k e +Ġ . +Ġs t +gh t +o o +' t +Ġ A +ou t +it h +Ġm e +Ġ C +a ll +Ġ k +Ġ u +Ġa n +Ġ M +Ġw ith +u r +Ġa s +Ġd o +c t +he r +c e +i ll +t er +i f +c h +Ġw h +Ġs e +om e +Ġa re +Ġ H +i l +Ġn ot +Ġha ve +Ġ B +Ġ 1 +a nd +Ġa t +Ġth is +ou ld +an t +Ġg o +us t +at ion +Ġl i +o p +a in +Ġk n +Y ou +e a +Ġ P +Ġ T +Ġ , +u n +g e +o l +t h +Ġh is +u l +Ġ " +r om +.. . +o re +Ġ W +e ll +Ġn e +Ġ r +Ġkn ow +Ġ j +ou r +Ġ D +Ġd e +p p +Ġc an +Ġ G +Ġthe y +u m +Ġ The +Ġs o +es t +Ġ ? +Ġa l +Ġa ll +Ġa b +Ġm y +is t +er s +Ġ R +i e +Ġyou r +ar t +Ġb y +Ġf rom +es s +Ġs h +Ġo r +Ġ 2 +Ġ F +Ġw hat +n t +a b +o s +Ġ L +e l +Ġon e +Ġc on +Ġ N +Ġw he +Ġthe re +i g +i ght +ha n +all y +Ġu p +q u +e m +Ġc om +on e +Ġha d +Ġb ut +Ġhe r +Ġs u +re d +r y +Ġ J +' re +Ġh im +Ġ out +r a +ac k +at e +oo d +ver y +oo k +Ġli ke +Ġw or +Ġs a +re s +am e +i ve +Ġp l +Ġ us +Ġ ( +on g +' m +Ġwe re +â Ģ +a ke +o d +in k +c k +Ġs he +Ġp ro +Ġab out +Ġ E +as t +Ġg et +Ġe x +Ġ v +p e +I t +or t +ou nd +in d +ar d +it y +Ġd on +A nd +ic h +ri ght +Ġ O +Ġj ust +Ġ1 9 +i es +H e +Ġwh o +e ar +ea h +Ġd id +a k +Ġhe re +b er +Ġthe m +Ġn o +im e +in e +Ġm an +Ġw ant +Ġw ould +Ġ2 0 +Ġ if +Ġ ' +Ġw ill +u d +a p +Ġ K +Ġth ink +ou s +' ll +v en +Ġs ome +o st +W hat +e p +h ing +Ġin t +Ġan y +ow n +ic e +a g +Ġ right +m ent +id e +Ġg ot +Ġha s +t her +us e +it t +Ġwh ich +ou gh +Ġt r +f e +h i +c a +Ġse e +Ġwhe n +Ġsa id +u re +Ġa r +Ġa g +Ġp e +Ġs ay +w ay +i a +Ġt ime +Ġt w +i v +ĠH e +s o +Ġbe en +i an +Ġs p +a re +ac e +Ġn ow +o ve +ic k +op le +c i +Ġ U +W e +ĠI t +' ve +Ġthe ir +o se +Y eah +Ġl e +Ġl ook +- - +Ġc l +is h +l f +re at +n a +Ġm ore +Ġthe n +Ġgo ing +a ge +S o +it e +re e +Ġo ther +Ġpe ople +ar y +Ġc ould +Ġ = +ĠS t +N o +ot her +n e +Ġof f +u e +Ġb ack +h is +ac t +B ut +Ġo ver +ir st +O h +as s +Ġ qu +ou nt +on d +Ġ very +ing s +ca use +Ġ ro +b le +p er +Ġg r +Ġa f +p t +Ġf e +an s +Ġwor k +Ġtw o 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See all RoBERTa models at https://huggingface.co/models?filter=roberta +] + + +class StructRobertaConfig(RobertaConfig): + model_type = "roberta" + + def __init__( + self, + n_parser_layers=4, + conv_size=9, + relations=("head", "child"), + weight_act="softmax", + n_cntxt_layers=3, + n_cntxt_layers_2=0, + **kwargs,): + + super().__init__(**kwargs) + self.n_cntxt_layers = n_cntxt_layers + self.n_parser_layers = n_parser_layers + self.n_cntxt_layers_2 = n_cntxt_layers_2 + self.conv_size = conv_size + self.relations = relations + self.weight_act = weight_act + +class Conv1d(nn.Module): + """1D convolution layer.""" + + def __init__(self, hidden_size, kernel_size, dilation=1): + """Initialization. + + Args: + hidden_size: dimension of input embeddings + kernel_size: convolution kernel size + dilation: the spacing between the kernel points + """ + super(Conv1d, self).__init__() + + if kernel_size % 2 == 0: + padding = (kernel_size // 2) * dilation + self.shift = True + else: + padding = ((kernel_size - 1) // 2) * dilation + self.shift = False + self.conv = nn.Conv1d( + hidden_size, hidden_size, kernel_size, padding=padding, dilation=dilation + ) + + def forward(self, x): + """Compute convolution. + + Args: + x: input embeddings + Returns: + conv_output: convolution results + """ + + if self.shift: + return self.conv(x.transpose(1, 2)).transpose(1, 2)[:, 1:] + else: + return self.conv(x.transpose(1, 2)).transpose(1, 2) + + +class RobertaEmbeddings(nn.Module): + """ + Same as BertEmbeddings with a tiny tweak for positional embeddings indexing. + """ + + # Copied from transformers.models.bert.modeling_bert.BertEmbeddings.__init__ + def __init__(self, config): + super().__init__() + self.word_embeddings = nn.Embedding( + config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id + ) + self.position_embeddings = nn.Embedding( + config.max_position_embeddings, config.hidden_size + ) + self.token_type_embeddings = nn.Embedding( + config.type_vocab_size, config.hidden_size + ) + + # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load + # any TensorFlow checkpoint file + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + # position_ids (1, len position emb) is contiguous in memory and exported when serialized + self.position_embedding_type = getattr( + config, "position_embedding_type", "absolute" + ) + self.register_buffer( + "position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)) + ) + if version.parse(torch.__version__) > version.parse("1.6.0"): + self.register_buffer( + "token_type_ids", + torch.zeros(self.position_ids.size(), dtype=torch.long), + persistent=False, + ) + + # End copy + self.padding_idx = config.pad_token_id + self.position_embeddings = nn.Embedding( + config.max_position_embeddings, + config.hidden_size, + padding_idx=self.padding_idx, + ) + + def forward( + self, + input_ids=None, + token_type_ids=None, + position_ids=None, + inputs_embeds=None, + past_key_values_length=0, + ): + if position_ids is None: + if input_ids is not None: + # Create the position ids from the input token ids. Any padded tokens remain padded. + position_ids = create_position_ids_from_input_ids( + input_ids, self.padding_idx, past_key_values_length + ) + else: + position_ids = self.create_position_ids_from_inputs_embeds( + inputs_embeds + ) + + if input_ids is not None: + input_shape = input_ids.size() + else: + input_shape = inputs_embeds.size()[:-1] + + seq_length = input_shape[1] + + # Setting the token_type_ids to the registered buffer in constructor where it is all zeros, which usually occurs + # when its auto-generated, registered buffer helps users when tracing the model without passing token_type_ids, solves + # issue #5664 + if token_type_ids is None: + if hasattr(self, "token_type_ids"): + buffered_token_type_ids = self.token_type_ids[:, :seq_length] + buffered_token_type_ids_expanded = buffered_token_type_ids.expand( + input_shape[0], seq_length + ) + token_type_ids = buffered_token_type_ids_expanded + else: + token_type_ids = torch.zeros( + input_shape, dtype=torch.long, device=self.position_ids.device + ) + + if inputs_embeds is None: + inputs_embeds = self.word_embeddings(input_ids) + token_type_embeddings = self.token_type_embeddings(token_type_ids) + + embeddings = inputs_embeds + token_type_embeddings + if self.position_embedding_type == "absolute": + position_embeddings = self.position_embeddings(position_ids) + embeddings += position_embeddings + embeddings = self.LayerNorm(embeddings) + embeddings = self.dropout(embeddings) + return embeddings + + def create_position_ids_from_inputs_embeds(self, inputs_embeds): + """ + We are provided embeddings directly. We cannot infer which are padded so just generate sequential position ids. + + Args: + inputs_embeds: torch.Tensor + + Returns: torch.Tensor + """ + input_shape = inputs_embeds.size()[:-1] + sequence_length = input_shape[1] + + position_ids = torch.arange( + self.padding_idx + 1, + sequence_length + self.padding_idx + 1, + dtype=torch.long, + device=inputs_embeds.device, + ) + return position_ids.unsqueeze(0).expand(input_shape) + + +# Copied from transformers.models.bert.modeling_bert.BertSelfAttention with Bert->Roberta +class RobertaSelfAttention(nn.Module): + def __init__(self, config, position_embedding_type=None): + super().__init__() + if config.hidden_size % config.num_attention_heads != 0 and not hasattr( + config, "embedding_size" + ): + raise ValueError( + f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention " + f"heads ({config.num_attention_heads})" + ) + + self.num_attention_heads = config.num_attention_heads + self.attention_head_size = int(config.hidden_size / config.num_attention_heads) + self.all_head_size = self.num_attention_heads * self.attention_head_size + + self.query = nn.Linear(config.hidden_size, self.all_head_size) + self.key = nn.Linear(config.hidden_size, self.all_head_size) + self.value = nn.Linear(config.hidden_size, self.all_head_size) + + self.dropout = nn.Dropout(config.attention_probs_dropout_prob) + self.position_embedding_type = position_embedding_type or getattr( + config, "position_embedding_type", "absolute" + ) + if ( + self.position_embedding_type == "relative_key" + or self.position_embedding_type == "relative_key_query" + ): + self.max_position_embeddings = config.max_position_embeddings + self.distance_embedding = nn.Embedding( + 2 * config.max_position_embeddings - 1, self.attention_head_size + ) + + self.is_decoder = config.is_decoder + + def transpose_for_scores(self, x): + new_x_shape = x.size()[:-1] + ( + self.num_attention_heads, + self.attention_head_size, + ) + x = x.view(new_x_shape) + return x.permute(0, 2, 1, 3) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.FloatTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, + output_attentions: Optional[bool] = False, + parser_att_mask=None, + ) -> Tuple[torch.Tensor]: + mixed_query_layer = self.query(hidden_states) + + # If this is instantiated as a cross-attention module, the keys + # and values come from an encoder; the attention mask needs to be + # such that the encoder's padding tokens are not attended to. + is_cross_attention = encoder_hidden_states is not None + + if is_cross_attention and past_key_value is not None: + # reuse k,v, cross_attentions + key_layer = past_key_value[0] + value_layer = past_key_value[1] + attention_mask = encoder_attention_mask + elif is_cross_attention: + key_layer = self.transpose_for_scores(self.key(encoder_hidden_states)) + value_layer = self.transpose_for_scores(self.value(encoder_hidden_states)) + attention_mask = encoder_attention_mask + elif past_key_value is not None: + key_layer = self.transpose_for_scores(self.key(hidden_states)) + value_layer = self.transpose_for_scores(self.value(hidden_states)) + key_layer = torch.cat([past_key_value[0], key_layer], dim=2) + value_layer = torch.cat([past_key_value[1], value_layer], dim=2) + else: + key_layer = self.transpose_for_scores(self.key(hidden_states)) + value_layer = self.transpose_for_scores(self.value(hidden_states)) + + query_layer = self.transpose_for_scores(mixed_query_layer) + + if self.is_decoder: + # if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states. + # Further calls to cross_attention layer can then reuse all cross-attention + # key/value_states (first "if" case) + # if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of + # all previous decoder key/value_states. Further calls to uni-directional self-attention + # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case) + # if encoder bi-directional self-attention `past_key_value` is always `None` + past_key_value = (key_layer, value_layer) + + # Take the dot product between "query" and "key" to get the raw attention scores. + attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) + + if ( + self.position_embedding_type == "relative_key" + or self.position_embedding_type == "relative_key_query" + ): + seq_length = hidden_states.size()[1] + position_ids_l = torch.arange( + seq_length, dtype=torch.long, device=hidden_states.device + ).view(-1, 1) + position_ids_r = torch.arange( + seq_length, dtype=torch.long, device=hidden_states.device + ).view(1, -1) + distance = position_ids_l - position_ids_r + positional_embedding = self.distance_embedding( + distance + self.max_position_embeddings - 1 + ) + positional_embedding = positional_embedding.to( + dtype=query_layer.dtype + ) # fp16 compatibility + + if self.position_embedding_type == "relative_key": + relative_position_scores = torch.einsum( + "bhld,lrd->bhlr", query_layer, positional_embedding + ) + attention_scores = attention_scores + relative_position_scores + elif self.position_embedding_type == "relative_key_query": + relative_position_scores_query = torch.einsum( + "bhld,lrd->bhlr", query_layer, positional_embedding + ) + relative_position_scores_key = torch.einsum( + "bhrd,lrd->bhlr", key_layer, positional_embedding + ) + attention_scores = ( + attention_scores + + relative_position_scores_query + + relative_position_scores_key + ) + + attention_scores = attention_scores / math.sqrt(self.attention_head_size) + if attention_mask is not None: + # Apply the attention mask is (precomputed for all layers in RobertaModel forward() function) + attention_scores = attention_scores + attention_mask + + if parser_att_mask is None: + # Normalize the attention scores to probabilities. + attention_probs = nn.functional.softmax(attention_scores, dim=-1) + else: + attention_probs = torch.sigmoid(attention_scores) * parser_att_mask + + # This is actually dropping out entire tokens to attend to, which might + # seem a bit unusual, but is taken from the original Transformer paper. + attention_probs = self.dropout(attention_probs) + + # Mask heads if we want to + if head_mask is not None: + attention_probs = attention_probs * head_mask + + context_layer = torch.matmul(attention_probs, value_layer) + + context_layer = context_layer.permute(0, 2, 1, 3).contiguous() + new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) + context_layer = context_layer.view(new_context_layer_shape) + + outputs = ( + (context_layer, attention_probs) if output_attentions else (context_layer,) + ) + + if self.is_decoder: + outputs = outputs + (past_key_value,) + return outputs + + +# Copied from transformers.models.bert.modeling_bert.BertSelfOutput +class RobertaSelfOutput(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward( + self, hidden_states: torch.Tensor, input_tensor: torch.Tensor + ) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + hidden_states = self.LayerNorm(hidden_states + input_tensor) + return hidden_states + + +# Copied from transformers.models.bert.modeling_bert.BertAttention with Bert->Roberta +class RobertaAttention(nn.Module): + def __init__(self, config, position_embedding_type=None): + super().__init__() + self.self = RobertaSelfAttention( + config, position_embedding_type=position_embedding_type + ) + self.output = RobertaSelfOutput(config) + self.pruned_heads = set() + + def prune_heads(self, heads): + if len(heads) == 0: + return + heads, index = find_pruneable_heads_and_indices( + heads, + self.self.num_attention_heads, + self.self.attention_head_size, + self.pruned_heads, + ) + + # Prune linear layers + self.self.query = prune_linear_layer(self.self.query, index) + self.self.key = prune_linear_layer(self.self.key, index) + self.self.value = prune_linear_layer(self.self.value, index) + self.output.dense = prune_linear_layer(self.output.dense, index, dim=1) + + # Update hyper params and store pruned heads + self.self.num_attention_heads = self.self.num_attention_heads - len(heads) + self.self.all_head_size = ( + self.self.attention_head_size * self.self.num_attention_heads + ) + self.pruned_heads = self.pruned_heads.union(heads) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.FloatTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, + output_attentions: Optional[bool] = False, + parser_att_mask=None, + ) -> Tuple[torch.Tensor]: + self_outputs = self.self( + hidden_states, + attention_mask, + head_mask, + encoder_hidden_states, + encoder_attention_mask, + past_key_value, + output_attentions, + parser_att_mask=parser_att_mask, + ) + attention_output = self.output(self_outputs[0], hidden_states) + outputs = (attention_output,) + self_outputs[ + 1: + ] # add attentions if we output them + return outputs + + +# Copied from transformers.models.bert.modeling_bert.BertIntermediate +class RobertaIntermediate(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.intermediate_size) + if isinstance(config.hidden_act, str): + self.intermediate_act_fn = ACT2FN[config.hidden_act] + else: + self.intermediate_act_fn = config.hidden_act + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.intermediate_act_fn(hidden_states) + return hidden_states + + +# Copied from transformers.models.bert.modeling_bert.BertOutput +class RobertaOutput(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.intermediate_size, config.hidden_size) + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward( + self, hidden_states: torch.Tensor, input_tensor: torch.Tensor + ) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + hidden_states = self.LayerNorm(hidden_states + input_tensor) + return hidden_states + + +# Copied from transformers.models.bert.modeling_bert.BertLayer with Bert->Roberta +class RobertaLayer(nn.Module): + def __init__(self, config): + super().__init__() + self.chunk_size_feed_forward = config.chunk_size_feed_forward + self.seq_len_dim = 1 + self.attention = RobertaAttention(config) + self.is_decoder = config.is_decoder + self.add_cross_attention = config.add_cross_attention + if self.add_cross_attention: + if not self.is_decoder: + raise ValueError( + f"{self} should be used as a decoder model if cross attention is added" + ) + self.crossattention = RobertaAttention( + config, position_embedding_type="absolute" + ) + self.intermediate = RobertaIntermediate(config) + self.output = RobertaOutput(config) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.FloatTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, + output_attentions: Optional[bool] = False, + parser_att_mask=None, + ) -> Tuple[torch.Tensor]: + # decoder uni-directional self-attention cached key/values tuple is at positions 1,2 + self_attn_past_key_value = ( + past_key_value[:2] if past_key_value is not None else None + ) + self_attention_outputs = self.attention( + hidden_states, + attention_mask, + head_mask, + output_attentions=output_attentions, + past_key_value=self_attn_past_key_value, + parser_att_mask=parser_att_mask, + ) + attention_output = self_attention_outputs[0] + + # if decoder, the last output is tuple of self-attn cache + if self.is_decoder: + outputs = self_attention_outputs[1:-1] + present_key_value = self_attention_outputs[-1] + else: + outputs = self_attention_outputs[ + 1: + ] # add self attentions if we output attention weights + + cross_attn_present_key_value = None + if self.is_decoder and encoder_hidden_states is not None: + if not hasattr(self, "crossattention"): + raise ValueError( + f"If `encoder_hidden_states` are passed, {self} has to be instantiated with cross-attention layers by setting `config.add_cross_attention=True`" + ) + + # cross_attn cached key/values tuple is at positions 3,4 of past_key_value tuple + cross_attn_past_key_value = ( + past_key_value[-2:] if past_key_value is not None else None + ) + cross_attention_outputs = self.crossattention( + attention_output, + attention_mask, + head_mask, + encoder_hidden_states, + encoder_attention_mask, + cross_attn_past_key_value, + output_attentions, + ) + attention_output = cross_attention_outputs[0] + outputs = ( + outputs + cross_attention_outputs[1:-1] + ) # add cross attentions if we output attention weights + + # add cross-attn cache to positions 3,4 of present_key_value tuple + cross_attn_present_key_value = cross_attention_outputs[-1] + present_key_value = present_key_value + cross_attn_present_key_value + + layer_output = apply_chunking_to_forward( + self.feed_forward_chunk, + self.chunk_size_feed_forward, + self.seq_len_dim, + attention_output, + ) + outputs = (layer_output,) + outputs + + # if decoder, return the attn key/values as the last output + if self.is_decoder: + outputs = outputs + (present_key_value,) + + return outputs + + def feed_forward_chunk(self, attention_output): + intermediate_output = self.intermediate(attention_output) + layer_output = self.output(intermediate_output, attention_output) + return layer_output + + +# Copied from transformers.models.bert.modeling_bert.BertEncoder with Bert->Roberta +class RobertaEncoder(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + self.layer = nn.ModuleList( + [RobertaLayer(config) for _ in range(config.num_hidden_layers)] + ) + self.gradient_checkpointing = False + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.FloatTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = False, + output_hidden_states: Optional[bool] = False, + return_dict: Optional[bool] = True, + parser_att_mask=None, + ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPastAndCrossAttentions]: + all_hidden_states = () if output_hidden_states else None + all_self_attentions = () if output_attentions else None + all_cross_attentions = ( + () if output_attentions and self.config.add_cross_attention else None + ) + + next_decoder_cache = () if use_cache else None + for i, layer_module in enumerate(self.layer): + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + layer_head_mask = head_mask[i] if head_mask is not None else None + past_key_value = past_key_values[i] if past_key_values is not None else None + + if self.gradient_checkpointing and self.training: + + if use_cache: + logger.warning( + "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." + ) + use_cache = False + + def create_custom_forward(module): + def custom_forward(*inputs): + return module(*inputs, past_key_value, output_attentions) + + return custom_forward + + layer_outputs = torch.utils.checkpoint.checkpoint( + create_custom_forward(layer_module), + hidden_states, + attention_mask, + layer_head_mask, + encoder_hidden_states, + encoder_attention_mask, + ) + else: + if parser_att_mask is not None: + layer_outputs = layer_module( + hidden_states, + attention_mask, + layer_head_mask, + encoder_hidden_states, + encoder_attention_mask, + past_key_value, + output_attentions, + parser_att_mask=parser_att_mask[i]) + else: + layer_outputs = layer_module( + hidden_states, + attention_mask, + layer_head_mask, + encoder_hidden_states, + encoder_attention_mask, + past_key_value, + output_attentions, + parser_att_mask=None) + + + hidden_states = layer_outputs[0] + if use_cache: + next_decoder_cache += (layer_outputs[-1],) + if output_attentions: + all_self_attentions = all_self_attentions + (layer_outputs[1],) + if self.config.add_cross_attention: + all_cross_attentions = all_cross_attentions + (layer_outputs[2],) + + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + if not return_dict: + return tuple( + v + for v in [ + hidden_states, + next_decoder_cache, + all_hidden_states, + all_self_attentions, + all_cross_attentions, + ] + if v is not None + ) + return BaseModelOutputWithPastAndCrossAttentions( + last_hidden_state=hidden_states, + past_key_values=next_decoder_cache, + hidden_states=all_hidden_states, + attentions=all_self_attentions, + cross_attentions=all_cross_attentions, + ) + + +# Copied from transformers.models.bert.modeling_bert.BertPooler +class RobertaPooler(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.activation = nn.Tanh() + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + # We "pool" the model by simply taking the hidden state corresponding + # to the first token. + first_token_tensor = hidden_states[:, 0] + pooled_output = self.dense(first_token_tensor) + pooled_output = self.activation(pooled_output) + return pooled_output + + +class RobertaPreTrainedModel(PreTrainedModel): + """ + An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained + models. + """ + + config_class = RobertaConfig + base_model_prefix = "roberta" + supports_gradient_checkpointing = True + + # Copied from transformers.models.bert.modeling_bert.BertPreTrainedModel._init_weights + def _init_weights(self, module): + """Initialize the weights""" + if isinstance(module, nn.Linear): + # Slightly different from the TF version which uses truncated_normal for initialization + # cf https://github.com/pytorch/pytorch/pull/5617 + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.Embedding): + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.padding_idx is not None: + module.weight.data[module.padding_idx].zero_() + elif isinstance(module, nn.LayerNorm): + if module.bias is not None: + module.bias.data.zero_() + module.weight.data.fill_(1.0) + + def _set_gradient_checkpointing(self, module, value=False): + if isinstance(module, RobertaEncoder): + module.gradient_checkpointing = value + + def update_keys_to_ignore(self, config, del_keys_to_ignore): + """Remove some keys from ignore list""" + if not config.tie_word_embeddings: + # must make a new list, or the class variable gets modified! + self._keys_to_ignore_on_save = [ + k for k in self._keys_to_ignore_on_save if k not in del_keys_to_ignore + ] + self._keys_to_ignore_on_load_missing = [ + k + for k in self._keys_to_ignore_on_load_missing + if k not in del_keys_to_ignore + ] + + +ROBERTA_START_DOCSTRING = r""" + + This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the + library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads + etc.) + + This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. + Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage + and behavior. + + Parameters: + config ([`RobertaConfig`]): Model configuration class with all the parameters of the + model. Initializing with a config file does not load the weights associated with the model, only the + configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. +""" + + +ROBERTA_INPUTS_DOCSTRING = r""" + Args: + input_ids (`torch.LongTensor` of shape `({0})`): + Indices of input sequence tokens in the vocabulary. + + Indices can be obtained using [`RobertaTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + [What are input IDs?](../glossary#input-ids) + attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*): + Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + + [What are attention masks?](../glossary#attention-mask) + token_type_ids (`torch.LongTensor` of shape `({0})`, *optional*): + Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, + 1]`: + + - 0 corresponds to a *sentence A* token, + - 1 corresponds to a *sentence B* token. + + [What are token type IDs?](../glossary#token-type-ids) + position_ids (`torch.LongTensor` of shape `({0})`, *optional*): + Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, + config.max_position_embeddings - 1]`. + + [What are position IDs?](../glossary#position-ids) + head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): + Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: + + - 1 indicates the head is **not masked**, + - 0 indicates the head is **masked**. + + inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_size)`, *optional*): + Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This + is useful if you want more control over how to convert `input_ids` indices into associated vectors than the + model's internal embedding lookup matrix. + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned + tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for + more detail. + return_dict (`bool`, *optional*): + Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. +""" + + +class RobertaModel(RobertaPreTrainedModel): + """ + + The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of + cross-attention is added between the self-attention layers, following the architecture described in *Attention is + all you need*_ by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz + Kaiser and Illia Polosukhin. + + To behave as an decoder the model needs to be initialized with the `is_decoder` argument of the configuration set + to `True`. To be used in a Seq2Seq model, the model needs to initialized with both `is_decoder` argument and + `add_cross_attention` set to `True`; an `encoder_hidden_states` is then expected as an input to the forward pass. + + .. _*Attention is all you need*: https://arxiv.org/abs/1706.03762 + + """ + + _keys_to_ignore_on_load_missing = [r"position_ids"] + + # Copied from transformers.models.bert.modeling_bert.BertModel.__init__ with Bert->Roberta + def __init__(self, config, add_pooling_layer=True): + super().__init__(config) + self.config = config + + self.embeddings = RobertaEmbeddings(config) + self.encoder = RobertaEncoder(config) + + self.pooler = RobertaPooler(config) if add_pooling_layer else None + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.embeddings.word_embeddings + + def set_input_embeddings(self, value): + self.embeddings.word_embeddings = value + + def _prune_heads(self, heads_to_prune): + """ + Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base + class PreTrainedModel + """ + for layer, heads in heads_to_prune.items(): + self.encoder.layer[layer].attention.prune_heads(heads) + + # Copied from transformers.models.bert.modeling_bert.BertModel.forward + def forward( + self, + input_ids: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + token_type_ids: Optional[torch.Tensor] = None, + position_ids: Optional[torch.Tensor] = None, + head_mask: Optional[torch.Tensor] = None, + inputs_embeds: Optional[torch.Tensor] = None, + encoder_hidden_states: Optional[torch.Tensor] = None, + encoder_attention_mask: Optional[torch.Tensor] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + parser_att_mask=None, + ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPoolingAndCrossAttentions]: + r""" + encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): + Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if + the model is configured as a decoder. + encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*): + Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in + the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): + Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. + + If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that + don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all + `decoder_input_ids` of shape `(batch_size, sequence_length)`. + use_cache (`bool`, *optional*): + If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see + `past_key_values`). + """ + output_attentions = ( + output_attentions + if output_attentions is not None + else self.config.output_attentions + ) + output_hidden_states = ( + output_hidden_states + if output_hidden_states is not None + else self.config.output_hidden_states + ) + return_dict = ( + return_dict if return_dict is not None else self.config.use_return_dict + ) + + if self.config.is_decoder: + use_cache = use_cache if use_cache is not None else self.config.use_cache + else: + use_cache = False + + if input_ids is not None and inputs_embeds is not None: + raise ValueError( + "You cannot specify both input_ids and inputs_embeds at the same time" + ) + elif input_ids is not None: + input_shape = input_ids.size() + elif inputs_embeds is not None: + input_shape = inputs_embeds.size()[:-1] + else: + raise ValueError("You have to specify either input_ids or inputs_embeds") + + batch_size, seq_length = input_shape + device = input_ids.device if input_ids is not None else inputs_embeds.device + + # past_key_values_length + past_key_values_length = ( + past_key_values[0][0].shape[2] if past_key_values is not None else 0 + ) + + if attention_mask is None: + attention_mask = torch.ones( + ((batch_size, seq_length + past_key_values_length)), device=device + ) + + if token_type_ids is None: + if hasattr(self.embeddings, "token_type_ids"): + buffered_token_type_ids = self.embeddings.token_type_ids[:, :seq_length] + buffered_token_type_ids_expanded = buffered_token_type_ids.expand( + batch_size, seq_length + ) + token_type_ids = buffered_token_type_ids_expanded + else: + token_type_ids = torch.zeros( + input_shape, dtype=torch.long, device=device + ) + + # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length] + # ourselves in which case we just need to make it broadcastable to all heads. + extended_attention_mask: torch.Tensor = self.get_extended_attention_mask( + attention_mask, input_shape, device + ) + + # If a 2D or 3D attention mask is provided for the cross-attention + # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length] + if self.config.is_decoder and encoder_hidden_states is not None: + ( + encoder_batch_size, + encoder_sequence_length, + _, + ) = encoder_hidden_states.size() + encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length) + if encoder_attention_mask is None: + encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device) + encoder_extended_attention_mask = self.invert_attention_mask( + encoder_attention_mask + ) + else: + encoder_extended_attention_mask = None + + # Prepare head mask if needed + # 1.0 in head_mask indicate we keep the head + # attention_probs has shape bsz x n_heads x N x N + # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] + # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] + head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) + + embedding_output = self.embeddings( + input_ids=input_ids, + position_ids=position_ids, + token_type_ids=token_type_ids, + inputs_embeds=inputs_embeds, + past_key_values_length=past_key_values_length, + ) + encoder_outputs = self.encoder( + embedding_output, + attention_mask=extended_attention_mask, + head_mask=head_mask, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_extended_attention_mask, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + parser_att_mask=parser_att_mask, + ) + sequence_output = encoder_outputs[0] + pooled_output = ( + self.pooler(sequence_output) if self.pooler is not None else None + ) + + if not return_dict: + return (sequence_output, pooled_output) + encoder_outputs[1:] + + return BaseModelOutputWithPoolingAndCrossAttentions( + last_hidden_state=sequence_output, + pooler_output=pooled_output, + past_key_values=encoder_outputs.past_key_values, + hidden_states=encoder_outputs.hidden_states, + attentions=encoder_outputs.attentions, + cross_attentions=encoder_outputs.cross_attentions, + ) + + +class StructRoberta(RobertaPreTrainedModel): + _keys_to_ignore_on_save = [r"lm_head.decoder.weight", r"lm_head.decoder.bias"] + _keys_to_ignore_on_load_missing = [ + r"position_ids", + r"lm_head.decoder.weight", + r"lm_head.decoder.bias", + ] + _keys_to_ignore_on_load_unexpected = [r"pooler"] + + def __init__(self, config): + super().__init__(config) + + if config.is_decoder: + logger.warning( + "If you want to use `RobertaForMaskedLM` make sure `config.is_decoder=False` for " + "bi-directional self-attention." + ) + + + if config.n_cntxt_layers > 0: + config_cntxt = copy.deepcopy(config) + config_cntxt.num_hidden_layers = config.n_cntxt_layers + + self.cntxt_layers = RobertaModel(config_cntxt, add_pooling_layer=False) + + if config.n_cntxt_layers_2 > 0: + self.parser_layers_1 = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(int(config.n_parser_layers/2)) + ] + ) + + self.distance_ff_1 = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff_1 = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight_1 = nn.Parameter( + torch.zeros((config.n_cntxt_layers_2, config.num_attention_heads, n_rel)) + ) + self._rel_weight_1.data.normal_(0, 0.1) + + self._scaler_1 = nn.Parameter(torch.zeros(2)) + + config_cntxt_2 = copy.deepcopy(config) + config_cntxt_2.num_hidden_layers = config.n_cntxt_layers_2 + + self.cntxt_layers_2 = RobertaModel(config_cntxt_2, add_pooling_layer=False) + + + self.parser_layers_2 = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(int(config.n_parser_layers/2)) + ] + ) + + self.distance_ff_2 = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff_2 = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight_2 = nn.Parameter( + torch.zeros((config.num_hidden_layers, config.num_attention_heads, n_rel)) + ) + self._rel_weight_2.data.normal_(0, 0.1) + + self._scaler_2 = nn.Parameter(torch.zeros(2)) + + else: + self.parser_layers = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(config.n_parser_layers) + ] + ) + + self.distance_ff = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight = nn.Parameter( + torch.zeros((config.num_hidden_layers, config.num_attention_heads, n_rel)) + ) + self._rel_weight.data.normal_(0, 0.1) + + self._scaler = nn.Parameter(torch.zeros(2)) + + self.roberta = RobertaModel(config, add_pooling_layer=False) + + if config.n_cntxt_layers > 0: + self.cntxt_layers.embeddings = self.roberta.embeddings + if config.n_cntxt_layers_2 > 0: + self.cntxt_layers_2.embeddings = self.roberta.embeddings + + self.lm_head = RobertaLMHead(config) + + self.pad = config.pad_token_id + + # The LM head weights require special treatment only when they are tied with the word embeddings + self.update_keys_to_ignore(config, ["lm_head.decoder.weight"]) + + # Initialize weights and apply final processing + self.post_init() + + def get_output_embeddings(self): + return self.lm_head.decoder + + def set_output_embeddings(self, new_embeddings): + self.lm_head.decoder = new_embeddings + + @property + def scaler(self): + return self._scaler.exp() + + @property + def scaler_1(self): + return self._scaler_1.exp() + + @property + def scaler_2(self): + return self._scaler_2.exp() + + @property + def rel_weight(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight, dim=-1) + + @property + def rel_weight_1(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight_1) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight_1, dim=-1) + + + @property + def rel_weight_2(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight_2) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight_2, dim=-1) + + + def compute_block(self, distance, height, n_cntxt_layers=0): + """Compute constituents from distance and height.""" + + if n_cntxt_layers>0: + if n_cntxt_layers == 1: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler_1[0] + elif n_cntxt_layers == 2: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler_2[0] + else: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0] + + gamma = torch.sigmoid(-beta_logits) + ones = torch.ones_like(gamma) + + block_mask_left = cummin( + gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1 + ) + block_mask_left = block_mask_left - F.pad( + block_mask_left[:, :, :-1], (1, 0), value=0 + ) + block_mask_left.tril_(0) + + block_mask_right = cummin( + gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1 + ) + block_mask_right = block_mask_right - F.pad( + block_mask_right[:, :, 1:], (0, 1), value=0 + ) + block_mask_right.triu_(0) + + block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :] + block = cumsum(block_mask_left).tril(0) + cumsum( + block_mask_right, reverse=True + ).triu(1) + + return block_p, block + + def compute_head(self, height, n_cntxt_layers=0): + """Estimate head for each constituent.""" + + _, length = height.size() + if n_cntxt_layers>0: + if n_cntxt_layers == 1: + head_logits = height * self.scaler_1[1] + elif n_cntxt_layers == 2: + head_logits = height * self.scaler_2[1] + else: + head_logits = height * self.scaler[1] + index = torch.arange(length, device=height.device) + + mask = (index[:, None, None] <= index[None, None, :]) * ( + index[None, None, :] <= index[None, :, None] + ) + head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1) + head_logits.masked_fill_(~mask[None, :, :, :], -1e9) + + head_p = torch.softmax(head_logits, dim=-1) + + return head_p + + def parse(self, x, embs=None, n_cntxt_layers=0): + """Parse input sentence. + + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + distance: syntactic distance + height: syntactic height + """ + + mask = x != self.pad + mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0) + + if embs is None: + h = self.roberta.embeddings(x) + else: + h = embs + + if n_cntxt_layers > 0: + if n_cntxt_layers == 1: + parser_layers = self.parser_layers_1 + height_ff = self.height_ff_1 + distance_ff = self.distance_ff_1 + elif n_cntxt_layers == 2: + parser_layers = self.parser_layers_2 + height_ff = self.height_ff_2 + distance_ff = self.distance_ff_2 + for i in range(int(self.config.n_parser_layers/2)): + h = h.masked_fill(~mask[:, :, None], 0) + h = parser_layers[i](h) + + height = height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, dim=-1 + )[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max + ) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where( + margin_left > margin_right, margin_right, margin_left + ).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + else: + for i in range(self.config.n_parser_layers): + h = h.masked_fill(~mask[:, :, None], 0) + h = self.parser_layers[i](h) + + height = self.height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = self.distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, dim=-1 + )[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max + ) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where( + margin_left > margin_right, margin_right, margin_left + ).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + + return distance, height + + def generate_mask(self, x, distance, height, n_cntxt_layers=0): + """Compute head and cibling distribution for each token.""" + + bsz, length = x.size() + + eye = torch.eye(length, device=x.device, dtype=torch.bool) + eye = eye[None, :, :].expand((bsz, -1, -1)) + + block_p, block = self.compute_block(distance, height, n_cntxt_layers=n_cntxt_layers) + head_p = self.compute_head(height, n_cntxt_layers=n_cntxt_layers) + head = torch.einsum("blij,bijh->blh", block_p, head_p) + head = head.masked_fill(eye, 0) + child = head.transpose(1, 2) + cibling = torch.bmm(head, child).masked_fill(eye, 0) + + rel_list = [] + if "head" in self.config.relations: + rel_list.append(head) + if "child" in self.config.relations: + rel_list.append(child) + if "cibling" in self.config.relations: + rel_list.append(cibling) + + rel = torch.stack(rel_list, dim=1) + + if n_cntxt_layers > 0: + if n_cntxt_layers == 1: + rel_weight = self.rel_weight_1 + elif n_cntxt_layers == 2: + rel_weight = self.rel_weight_2 + else: + rel_weight = self.rel_weight + + dep = torch.einsum("lhr,brij->lbhij", rel_weight, rel) + + if n_cntxt_layers == 1: + num_layers = self.cntxt_layers_2.config.num_hidden_layers + else: + num_layers = self.roberta.config.num_hidden_layers + + att_mask = dep.reshape( + num_layers, + bsz, + self.config.num_attention_heads, + length, + length, + ) + + return att_mask, cibling, head, block + + def forward( + self, + input_ids: Optional[torch.LongTensor] = None, + attention_mask: Optional[torch.FloatTensor] = None, + token_type_ids: Optional[torch.LongTensor] = None, + position_ids: Optional[torch.LongTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, MaskedLMOutput]: + r""" + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ..., + config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the + loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]` + kwargs (`Dict[str, any]`, optional, defaults to *{}*): + Used to hide legacy arguments that have been deprecated. + """ + return_dict = ( + return_dict if return_dict is not None else self.config.use_return_dict + ) + + + if self.config.n_cntxt_layers > 0: + cntxt_outputs = self.cntxt_layers( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict) + + + if self.config.n_cntxt_layers_2 > 0: + distance_1, height_1 = self.parse(input_ids, cntxt_outputs[0], n_cntxt_layers=1) + att_mask_1, _, _, _ = self.generate_mask(input_ids, distance_1, height_1, n_cntxt_layers=1) + + cntxt_outputs_2 = self.cntxt_layers_2( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + parser_att_mask=att_mask_1) + + sequence_output = cntxt_outputs_2[0] + + distance_2, height_2 = self.parse(input_ids, sequence_output[0], n_cntxt_layers=2) + att_mask, _, _, _ = self.generate_mask(input_ids, distance_2, height_2, n_cntxt_layers=2) + + elif self.config.n_cntxt_layers > 0: + distance, height = self.parse(input_ids, cntxt_outputs[0]) + att_mask, _, _, _ = self.generate_mask(input_ids, distance, height) + else: + distance, height = self.parse(input_ids) + att_mask, _, _, _ = self.generate_mask(input_ids, distance, height) + + outputs = self.roberta( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + parser_att_mask=att_mask, + ) + sequence_output = outputs[0] + prediction_scores = self.lm_head(sequence_output) + + masked_lm_loss = None + if labels is not None: + loss_fct = CrossEntropyLoss() + masked_lm_loss = loss_fct( + prediction_scores.view(-1, self.config.vocab_size), labels.view(-1) + ) + + if not return_dict: + output = (prediction_scores,) + outputs[2:] + return ( + ((masked_lm_loss,) + output) if masked_lm_loss is not None else output + ) + + return MaskedLMOutput( + loss=masked_lm_loss, + logits=prediction_scores, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +class RobertaLMHead(nn.Module): + """Roberta Head for masked language modeling.""" + + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + + self.decoder = nn.Linear(config.hidden_size, config.vocab_size) + self.bias = nn.Parameter(torch.zeros(config.vocab_size)) + self.decoder.bias = self.bias + + def forward(self, features, **kwargs): + x = self.dense(features) + x = gelu(x) + x = self.layer_norm(x) + + # project back to size of vocabulary with bias + x = self.decoder(x) + + return x + + def _tie_weights(self): + # To tie those two weights if they get disconnected (on TPU or when the bias is resized) + self.bias = self.decoder.bias + + +class StructRobertaForSequenceClassification(RobertaPreTrainedModel): + _keys_to_ignore_on_load_missing = [r"position_ids"] + + def __init__(self, config): + super().__init__(config) + self.num_labels = config.num_labels + self.config = config + + if config.n_cntxt_layers > 0: + config_cntxt = copy.deepcopy(config) + config_cntxt.num_hidden_layers = config.n_cntxt_layers + + self.cntxt_layers = RobertaModel(config_cntxt, add_pooling_layer=False) + + if config.n_cntxt_layers_2 > 0: + self.parser_layers_1 = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(int(config.n_parser_layers/2)) + ] + ) + + self.distance_ff_1 = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff_1 = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight_1 = nn.Parameter( + torch.zeros((config.n_cntxt_layers_2, config.num_attention_heads, n_rel)) + ) + self._rel_weight_1.data.normal_(0, 0.1) + + self._scaler_1 = nn.Parameter(torch.zeros(2)) + + config_cntxt_2 = copy.deepcopy(config) + config_cntxt_2.num_hidden_layers = config.n_cntxt_layers_2 + + self.cntxt_layers_2 = RobertaModel(config_cntxt_2, add_pooling_layer=False) + + + self.parser_layers_2 = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(int(config.n_parser_layers/2)) + ] + ) + + self.distance_ff_2 = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff_2 = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight_2 = nn.Parameter( + torch.zeros((config.num_hidden_layers, config.num_attention_heads, n_rel)) + ) + self._rel_weight_2.data.normal_(0, 0.1) + + self._scaler_2 = nn.Parameter(torch.zeros(2)) + + else: + self.parser_layers = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(config.n_parser_layers) + ] + ) + + self.distance_ff = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight = nn.Parameter( + torch.zeros((config.num_hidden_layers, config.num_attention_heads, n_rel)) + ) + self._rel_weight.data.normal_(0, 0.1) + + self._scaler = nn.Parameter(torch.zeros(2)) + + self.roberta = RobertaModel(config, add_pooling_layer=False) + + if config.n_cntxt_layers > 0: + self.cntxt_layers.embeddings = self.roberta.embeddings + if config.n_cntxt_layers_2 > 0: + self.cntxt_layers_2.embeddings = self.roberta.embeddings + + + self.pad = config.pad_token_id + self.classifier = RobertaClassificationHead(config) + + # Initialize weights and apply final processing + self.post_init() + + + @property + def scaler(self): + return self._scaler.exp() + + @property + def scaler_1(self): + return self._scaler_1.exp() + + @property + def scaler_2(self): + return self._scaler_2.exp() + + @property + def rel_weight(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight, dim=-1) + + @property + def rel_weight_1(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight_1) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight_1, dim=-1) + + + @property + def rel_weight_2(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight_2) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight_2, dim=-1) + + + def compute_block(self, distance, height, n_cntxt_layers=0): + """Compute constituents from distance and height.""" + + if n_cntxt_layers>0: + if n_cntxt_layers == 1: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler_1[0] + elif n_cntxt_layers == 2: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler_2[0] + else: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0] + + gamma = torch.sigmoid(-beta_logits) + ones = torch.ones_like(gamma) + + block_mask_left = cummin( + gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1 + ) + block_mask_left = block_mask_left - F.pad( + block_mask_left[:, :, :-1], (1, 0), value=0 + ) + block_mask_left.tril_(0) + + block_mask_right = cummin( + gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1 + ) + block_mask_right = block_mask_right - F.pad( + block_mask_right[:, :, 1:], (0, 1), value=0 + ) + block_mask_right.triu_(0) + + block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :] + block = cumsum(block_mask_left).tril(0) + cumsum( + block_mask_right, reverse=True + ).triu(1) + + return block_p, block + + def compute_head(self, height, n_cntxt_layers=0): + """Estimate head for each constituent.""" + + _, length = height.size() + if n_cntxt_layers>0: + if n_cntxt_layers == 1: + head_logits = height * self.scaler_1[1] + elif n_cntxt_layers == 2: + head_logits = height * self.scaler_2[1] + else: + head_logits = height * self.scaler[1] + index = torch.arange(length, device=height.device) + + mask = (index[:, None, None] <= index[None, None, :]) * ( + index[None, None, :] <= index[None, :, None] + ) + head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1) + head_logits.masked_fill_(~mask[None, :, :, :], -1e9) + + head_p = torch.softmax(head_logits, dim=-1) + + return head_p + + def parse(self, x, embs=None, n_cntxt_layers=0): + """Parse input sentence. + + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + distance: syntactic distance + height: syntactic height + """ + + mask = x != self.pad + mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0) + + if embs is None: + h = self.roberta.embeddings(x) + else: + h = embs + + if n_cntxt_layers > 0: + if n_cntxt_layers == 1: + parser_layers = self.parser_layers_1 + height_ff = self.height_ff_1 + distance_ff = self.distance_ff_1 + elif n_cntxt_layers == 2: + parser_layers = self.parser_layers_2 + height_ff = self.height_ff_2 + distance_ff = self.distance_ff_2 + for i in range(int(self.config.n_parser_layers/2)): + h = h.masked_fill(~mask[:, :, None], 0) + h = parser_layers[i](h) + + height = height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, dim=-1 + )[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max + ) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where( + margin_left > margin_right, margin_right, margin_left + ).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + else: + for i in range(self.config.n_parser_layers): + h = h.masked_fill(~mask[:, :, None], 0) + h = self.parser_layers[i](h) + + height = self.height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = self.distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, dim=-1 + )[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max + ) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where( + margin_left > margin_right, margin_right, margin_left + ).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + + return distance, height + + def generate_mask(self, x, distance, height, n_cntxt_layers=0): + """Compute head and cibling distribution for each token.""" + + bsz, length = x.size() + + eye = torch.eye(length, device=x.device, dtype=torch.bool) + eye = eye[None, :, :].expand((bsz, -1, -1)) + + block_p, block = self.compute_block(distance, height, n_cntxt_layers=n_cntxt_layers) + head_p = self.compute_head(height, n_cntxt_layers=n_cntxt_layers) + head = torch.einsum("blij,bijh->blh", block_p, head_p) + head = head.masked_fill(eye, 0) + child = head.transpose(1, 2) + cibling = torch.bmm(head, child).masked_fill(eye, 0) + + rel_list = [] + if "head" in self.config.relations: + rel_list.append(head) + if "child" in self.config.relations: + rel_list.append(child) + if "cibling" in self.config.relations: + rel_list.append(cibling) + + rel = torch.stack(rel_list, dim=1) + + if n_cntxt_layers > 0: + if n_cntxt_layers == 1: + rel_weight = self.rel_weight_1 + elif n_cntxt_layers == 2: + rel_weight = self.rel_weight_2 + else: + rel_weight = self.rel_weight + + dep = torch.einsum("lhr,brij->lbhij", rel_weight, rel) + + if n_cntxt_layers == 1: + num_layers = self.cntxt_layers_2.config.num_hidden_layers + else: + num_layers = self.roberta.config.num_hidden_layers + + att_mask = dep.reshape( + num_layers, + bsz, + self.config.num_attention_heads, + length, + length, + ) + + return att_mask, cibling, head, block + + def forward( + self, + input_ids: Optional[torch.LongTensor] = None, + attention_mask: Optional[torch.FloatTensor] = None, + token_type_ids: Optional[torch.LongTensor] = None, + position_ids: Optional[torch.LongTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, SequenceClassifierOutput]: + r""" + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., + config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If + `config.num_labels > 1` a classification loss is computed (Cross-Entropy). + """ + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + if self.config.n_cntxt_layers > 0: + cntxt_outputs = self.cntxt_layers( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict) + + + if self.config.n_cntxt_layers_2 > 0: + distance_1, height_1 = self.parse(input_ids, cntxt_outputs[0], n_cntxt_layers=1) + att_mask_1, _, _, _ = self.generate_mask(input_ids, distance_1, height_1, n_cntxt_layers=1) + + cntxt_outputs_2 = self.cntxt_layers_2( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + parser_att_mask=att_mask_1) + + sequence_output = cntxt_outputs_2[0] + + distance_2, height_2 = self.parse(input_ids, sequence_output[0], n_cntxt_layers=2) + att_mask, _, _, _ = self.generate_mask(input_ids, distance_2, height_2, n_cntxt_layers=2) + + elif self.config.n_cntxt_layers > 0: + distance, height = self.parse(input_ids, cntxt_outputs[0]) + att_mask, _, _, _ = self.generate_mask(input_ids, distance, height) + else: + distance, height = self.parse(input_ids) + att_mask, _, _, _ = self.generate_mask(input_ids, distance, height) + + outputs = self.roberta( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + parser_att_mask=att_mask, + ) + sequence_output = outputs[0] + logits = self.classifier(sequence_output) + + loss = None + if labels is not None: + if self.config.problem_type is None: + if self.num_labels == 1: + self.config.problem_type = "regression" + elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): + self.config.problem_type = "single_label_classification" + else: + self.config.problem_type = "multi_label_classification" + + if self.config.problem_type == "regression": + loss_fct = MSELoss() + if self.num_labels == 1: + loss = loss_fct(logits.squeeze(), labels.squeeze()) + else: + loss = loss_fct(logits, labels) + elif self.config.problem_type == "single_label_classification": + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) + elif self.config.problem_type == "multi_label_classification": + loss_fct = BCEWithLogitsLoss() + loss = loss_fct(logits, labels) + + if not return_dict: + output = (logits,) + outputs[2:] + return ((loss,) + output) if loss is not None else output + + return SequenceClassifierOutput( + loss=loss, + logits=logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +class RobertaClassificationHead(nn.Module): + """Head for sentence-level classification tasks.""" + + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + classifier_dropout = ( + config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob + ) + self.dropout = nn.Dropout(classifier_dropout) + self.out_proj = nn.Linear(config.hidden_size, config.num_labels) + + def forward(self, features, **kwargs): + x = features[:, 0, :] # take token (equiv. to [CLS]) + x = self.dropout(x) + x = self.dense(x) + x = torch.tanh(x) + x = self.dropout(x) + x = self.out_proj(x) + return x + + +def create_position_ids_from_input_ids( + input_ids, padding_idx, past_key_values_length=0 +): + """ + Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols + are ignored. This is modified from fairseq's `utils.make_positions`. + + Args: + x: torch.Tensor x: + + Returns: torch.Tensor + """ + # The series of casts and type-conversions here are carefully balanced to both work with ONNX export and XLA. + mask = input_ids.ne(padding_idx).int() + incremental_indices = ( + torch.cumsum(mask, dim=1).type_as(mask) + past_key_values_length + ) * mask + return incremental_indices.long() + padding_idx + + +def cumprod(x, reverse=False, exclusive=False): + """cumulative product.""" + if reverse: + x = x.flip([-1]) + + if exclusive: + x = F.pad(x[:, :, :-1], (1, 0), value=1) + + cx = x.cumprod(-1) + + if reverse: + cx = cx.flip([-1]) + return cx + + +def cumsum(x, reverse=False, exclusive=False): + """cumulative sum.""" + bsz, _, length = x.size() + device = x.device + if reverse: + if exclusive: + w = torch.ones([bsz, length, length], device=device).tril(-1) + else: + w = torch.ones([bsz, length, length], device=device).tril(0) + cx = torch.bmm(x, w) + else: + if exclusive: + w = torch.ones([bsz, length, length], device=device).triu(1) + else: + w = torch.ones([bsz, length, length], device=device).triu(0) + cx = torch.bmm(x, w) + return cx + + +def cummin(x, reverse=False, exclusive=False, max_value=1e9): + """cumulative min.""" + if reverse: + if exclusive: + x = F.pad(x[:, :, 1:], (0, 1), value=max_value) + x = x.flip([-1]).cummin(-1)[0].flip([-1]) + else: + if exclusive: + x = F.pad(x[:, :, :-1], (1, 0), value=max_value) + x = x.cummin(-1)[0] + return x diff --git a/finetune/sst2/predict_results.txt b/finetune/sst2/predict_results.txt new file mode 100644 index 0000000000000000000000000000000000000000..dbcf0aa1e4eaad1b15a9b1c527087a6f3b4d0db5 --- /dev/null +++ b/finetune/sst2/predict_results.txt @@ -0,0 +1,509 @@ +index prediction +0 1 +1 0 +2 1 +3 1 +4 0 +5 0 +6 0 +7 1 +8 0 +9 1 +10 0 +11 0 +12 0 +13 1 +14 1 +15 0 +16 0 +17 1 +18 1 +19 1 +20 1 +21 1 +22 0 +23 1 +24 0 +25 0 +26 0 +27 1 +28 1 +29 1 +30 1 +31 1 +32 1 +33 1 +34 0 +35 1 +36 1 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"ľ": 201, + "Ŀ": 202, + "ŀ": 203, + "Ł": 204, + "ł": 205, + "Ń": 206 +} diff --git a/finetune/syntactic_category_control/all_results.json b/finetune/syntactic_category_control/all_results.json new file mode 100644 index 0000000000000000000000000000000000000000..a9828b4d39fdbe73646471153e761b0888a33efa --- /dev/null +++ b/finetune/syntactic_category_control/all_results.json @@ -0,0 +1,16 @@ +{ + "epoch": 10.0, + "eval_accuracy": 0.9465081095695496, + "eval_f1": 0.9486610765485528, + "eval_loss": 0.4224085509777069, + "eval_mcc": 0.8963104681610891, + "eval_runtime": 34.7369, + "eval_samples": 17741, + "eval_samples_per_second": 510.725, + "eval_steps_per_second": 63.851, + "train_loss": 0.004904636573046446, + "train_runtime": 449.9912, + "train_samples": 8930, + "train_samples_per_second": 198.448, + "train_steps_per_second": 1.667 +} \ No newline at end of file diff --git a/finetune/syntactic_category_control/config.json b/finetune/syntactic_category_control/config.json new file mode 100644 index 0000000000000000000000000000000000000000..4a05363b2cdf362715477e08a3dc5678f8da6992 --- /dev/null +++ b/finetune/syntactic_category_control/config.json @@ -0,0 +1,50 @@ +{ + "_name_or_path": "final_models/glue_models/structroberta_s1_final/", + "architectures": [ + "StructRobertaForSequenceClassification" + ], + "attention_probs_dropout_prob": 0.1, + "auto_map": { + "AutoConfig": "modeling_structroberta.StructRobertaConfig", + "AutoModelForMaskedLM": "modeling_structroberta.StructRoberta", + "AutoModelForSequenceClassification": "modeling_structroberta.StructRobertaForSequenceClassification" + }, + "bos_token_id": 0, + "classifier_dropout": null, + "conv_size": 9, + "eos_token_id": 2, + "hidden_act": "gelu", + "hidden_dropout_prob": 0.1, + "hidden_size": 768, + "id2label": { + "0": 0, + "1": 1 + }, + "initializer_range": 0.02, + "intermediate_size": 3072, + "label2id": { + "0": 0, + "1": 1 + }, + "layer_norm_eps": 1e-05, + "max_position_embeddings": 514, + "model_type": 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"eval_samples_per_second": 510.725, + "eval_steps_per_second": 63.851 +} \ No newline at end of file diff --git a/finetune/syntactic_category_control/merges.txt b/finetune/syntactic_category_control/merges.txt new file mode 100644 index 0000000000000000000000000000000000000000..94b4f252cb566a4dac62a08c78d85169ff4e91be --- /dev/null +++ b/finetune/syntactic_category_control/merges.txt @@ -0,0 +1,31794 @@ +#version: 0.2 +Ġ t +h e +Ġ a +i n +Ġt he +r e +Ġ w +o u +Ġ s +o n +e r +h a +n d +i s +i t +Ġ b +Ġ o +Ġ c +Ġ m +Ġ f +in g +o r +a n +e d +l l +Ġ d +e n +Ġt o +e s +Ġ p +Ġo f +Ġ y +Ġ in +a r +a t +Ġa nd +Ġ l +ha t +Ġy ou +a s +Ġ g +o m +Ġ n +Ġ I +i c +o w +l e +v e +o t +Ġ h +Ġ he +a l +e t +Ġ ha +' s +u s +Ġt h +a y +Ġ it +Ġ is +Ġb e +Ġt hat +Ġ on +i on +i d +Ġ e +g h +u t +l d +en t +i m +Ġ re +Ġw as +a c +Ġw e +v er +a m +T he +. . +Ġf or +l y +Ġ S +s t +r i +r o +i r +a d +s e +k e +Ġ . +Ġs t +gh t +o o +' t +Ġ A +ou t +it h +Ġm e +Ġ C +a ll +Ġ k +Ġ u +Ġa n +Ġ M +Ġw ith +u r +Ġa s 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See all RoBERTa models at https://huggingface.co/models?filter=roberta +] + + +class StructRobertaConfig(RobertaConfig): + model_type = "roberta" + + def __init__( + self, + n_parser_layers=4, + conv_size=9, + relations=("head", "child"), + weight_act="softmax", + n_cntxt_layers=3, + n_cntxt_layers_2=0, + **kwargs,): + + super().__init__(**kwargs) + self.n_cntxt_layers = n_cntxt_layers + self.n_parser_layers = n_parser_layers + self.n_cntxt_layers_2 = n_cntxt_layers_2 + self.conv_size = conv_size + self.relations = relations + self.weight_act = weight_act + +class Conv1d(nn.Module): + """1D convolution layer.""" + + def __init__(self, hidden_size, kernel_size, dilation=1): + """Initialization. + + Args: + hidden_size: dimension of input embeddings + kernel_size: convolution kernel size + dilation: the spacing between the kernel points + """ + super(Conv1d, self).__init__() + + if kernel_size % 2 == 0: + padding = (kernel_size // 2) * dilation + self.shift = True + else: + padding = ((kernel_size - 1) // 2) * dilation + self.shift = False + self.conv = nn.Conv1d( + hidden_size, hidden_size, kernel_size, padding=padding, dilation=dilation + ) + + def forward(self, x): + """Compute convolution. + + Args: + x: input embeddings + Returns: + conv_output: convolution results + """ + + if self.shift: + return self.conv(x.transpose(1, 2)).transpose(1, 2)[:, 1:] + else: + return self.conv(x.transpose(1, 2)).transpose(1, 2) + + +class RobertaEmbeddings(nn.Module): + """ + Same as BertEmbeddings with a tiny tweak for positional embeddings indexing. + """ + + # Copied from transformers.models.bert.modeling_bert.BertEmbeddings.__init__ + def __init__(self, config): + super().__init__() + self.word_embeddings = nn.Embedding( + config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id + ) + self.position_embeddings = nn.Embedding( + config.max_position_embeddings, config.hidden_size + ) + self.token_type_embeddings = nn.Embedding( + config.type_vocab_size, config.hidden_size + ) + + # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load + # any TensorFlow checkpoint file + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + # position_ids (1, len position emb) is contiguous in memory and exported when serialized + self.position_embedding_type = getattr( + config, "position_embedding_type", "absolute" + ) + self.register_buffer( + "position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)) + ) + if version.parse(torch.__version__) > version.parse("1.6.0"): + self.register_buffer( + "token_type_ids", + torch.zeros(self.position_ids.size(), dtype=torch.long), + persistent=False, + ) + + # End copy + self.padding_idx = config.pad_token_id + self.position_embeddings = nn.Embedding( + config.max_position_embeddings, + config.hidden_size, + padding_idx=self.padding_idx, + ) + + def forward( + self, + input_ids=None, + token_type_ids=None, + position_ids=None, + inputs_embeds=None, + past_key_values_length=0, + ): + if position_ids is None: + if input_ids is not None: + # Create the position ids from the input token ids. Any padded tokens remain padded. + position_ids = create_position_ids_from_input_ids( + input_ids, self.padding_idx, past_key_values_length + ) + else: + position_ids = self.create_position_ids_from_inputs_embeds( + inputs_embeds + ) + + if input_ids is not None: + input_shape = input_ids.size() + else: + input_shape = inputs_embeds.size()[:-1] + + seq_length = input_shape[1] + + # Setting the token_type_ids to the registered buffer in constructor where it is all zeros, which usually occurs + # when its auto-generated, registered buffer helps users when tracing the model without passing token_type_ids, solves + # issue #5664 + if token_type_ids is None: + if hasattr(self, "token_type_ids"): + buffered_token_type_ids = self.token_type_ids[:, :seq_length] + buffered_token_type_ids_expanded = buffered_token_type_ids.expand( + input_shape[0], seq_length + ) + token_type_ids = buffered_token_type_ids_expanded + else: + token_type_ids = torch.zeros( + input_shape, dtype=torch.long, device=self.position_ids.device + ) + + if inputs_embeds is None: + inputs_embeds = self.word_embeddings(input_ids) + token_type_embeddings = self.token_type_embeddings(token_type_ids) + + embeddings = inputs_embeds + token_type_embeddings + if self.position_embedding_type == "absolute": + position_embeddings = self.position_embeddings(position_ids) + embeddings += position_embeddings + embeddings = self.LayerNorm(embeddings) + embeddings = self.dropout(embeddings) + return embeddings + + def create_position_ids_from_inputs_embeds(self, inputs_embeds): + """ + We are provided embeddings directly. We cannot infer which are padded so just generate sequential position ids. + + Args: + inputs_embeds: torch.Tensor + + Returns: torch.Tensor + """ + input_shape = inputs_embeds.size()[:-1] + sequence_length = input_shape[1] + + position_ids = torch.arange( + self.padding_idx + 1, + sequence_length + self.padding_idx + 1, + dtype=torch.long, + device=inputs_embeds.device, + ) + return position_ids.unsqueeze(0).expand(input_shape) + + +# Copied from transformers.models.bert.modeling_bert.BertSelfAttention with Bert->Roberta +class RobertaSelfAttention(nn.Module): + def __init__(self, config, position_embedding_type=None): + super().__init__() + if config.hidden_size % config.num_attention_heads != 0 and not hasattr( + config, "embedding_size" + ): + raise ValueError( + f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention " + f"heads ({config.num_attention_heads})" + ) + + self.num_attention_heads = config.num_attention_heads + self.attention_head_size = int(config.hidden_size / config.num_attention_heads) + self.all_head_size = self.num_attention_heads * self.attention_head_size + + self.query = nn.Linear(config.hidden_size, self.all_head_size) + self.key = nn.Linear(config.hidden_size, self.all_head_size) + self.value = nn.Linear(config.hidden_size, self.all_head_size) + + self.dropout = nn.Dropout(config.attention_probs_dropout_prob) + self.position_embedding_type = position_embedding_type or getattr( + config, "position_embedding_type", "absolute" + ) + if ( + self.position_embedding_type == "relative_key" + or self.position_embedding_type == "relative_key_query" + ): + self.max_position_embeddings = config.max_position_embeddings + self.distance_embedding = nn.Embedding( + 2 * config.max_position_embeddings - 1, self.attention_head_size + ) + + self.is_decoder = config.is_decoder + + def transpose_for_scores(self, x): + new_x_shape = x.size()[:-1] + ( + self.num_attention_heads, + self.attention_head_size, + ) + x = x.view(new_x_shape) + return x.permute(0, 2, 1, 3) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.FloatTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, + output_attentions: Optional[bool] = False, + parser_att_mask=None, + ) -> Tuple[torch.Tensor]: + mixed_query_layer = self.query(hidden_states) + + # If this is instantiated as a cross-attention module, the keys + # and values come from an encoder; the attention mask needs to be + # such that the encoder's padding tokens are not attended to. + is_cross_attention = encoder_hidden_states is not None + + if is_cross_attention and past_key_value is not None: + # reuse k,v, cross_attentions + key_layer = past_key_value[0] + value_layer = past_key_value[1] + attention_mask = encoder_attention_mask + elif is_cross_attention: + key_layer = self.transpose_for_scores(self.key(encoder_hidden_states)) + value_layer = self.transpose_for_scores(self.value(encoder_hidden_states)) + attention_mask = encoder_attention_mask + elif past_key_value is not None: + key_layer = self.transpose_for_scores(self.key(hidden_states)) + value_layer = self.transpose_for_scores(self.value(hidden_states)) + key_layer = torch.cat([past_key_value[0], key_layer], dim=2) + value_layer = torch.cat([past_key_value[1], value_layer], dim=2) + else: + key_layer = self.transpose_for_scores(self.key(hidden_states)) + value_layer = self.transpose_for_scores(self.value(hidden_states)) + + query_layer = self.transpose_for_scores(mixed_query_layer) + + if self.is_decoder: + # if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states. + # Further calls to cross_attention layer can then reuse all cross-attention + # key/value_states (first "if" case) + # if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of + # all previous decoder key/value_states. Further calls to uni-directional self-attention + # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case) + # if encoder bi-directional self-attention `past_key_value` is always `None` + past_key_value = (key_layer, value_layer) + + # Take the dot product between "query" and "key" to get the raw attention scores. + attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) + + if ( + self.position_embedding_type == "relative_key" + or self.position_embedding_type == "relative_key_query" + ): + seq_length = hidden_states.size()[1] + position_ids_l = torch.arange( + seq_length, dtype=torch.long, device=hidden_states.device + ).view(-1, 1) + position_ids_r = torch.arange( + seq_length, dtype=torch.long, device=hidden_states.device + ).view(1, -1) + distance = position_ids_l - position_ids_r + positional_embedding = self.distance_embedding( + distance + self.max_position_embeddings - 1 + ) + positional_embedding = positional_embedding.to( + dtype=query_layer.dtype + ) # fp16 compatibility + + if self.position_embedding_type == "relative_key": + relative_position_scores = torch.einsum( + "bhld,lrd->bhlr", query_layer, positional_embedding + ) + attention_scores = attention_scores + relative_position_scores + elif self.position_embedding_type == "relative_key_query": + relative_position_scores_query = torch.einsum( + "bhld,lrd->bhlr", query_layer, positional_embedding + ) + relative_position_scores_key = torch.einsum( + "bhrd,lrd->bhlr", key_layer, positional_embedding + ) + attention_scores = ( + attention_scores + + relative_position_scores_query + + relative_position_scores_key + ) + + attention_scores = attention_scores / math.sqrt(self.attention_head_size) + if attention_mask is not None: + # Apply the attention mask is (precomputed for all layers in RobertaModel forward() function) + attention_scores = attention_scores + attention_mask + + if parser_att_mask is None: + # Normalize the attention scores to probabilities. + attention_probs = nn.functional.softmax(attention_scores, dim=-1) + else: + attention_probs = torch.sigmoid(attention_scores) * parser_att_mask + + # This is actually dropping out entire tokens to attend to, which might + # seem a bit unusual, but is taken from the original Transformer paper. + attention_probs = self.dropout(attention_probs) + + # Mask heads if we want to + if head_mask is not None: + attention_probs = attention_probs * head_mask + + context_layer = torch.matmul(attention_probs, value_layer) + + context_layer = context_layer.permute(0, 2, 1, 3).contiguous() + new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) + context_layer = context_layer.view(new_context_layer_shape) + + outputs = ( + (context_layer, attention_probs) if output_attentions else (context_layer,) + ) + + if self.is_decoder: + outputs = outputs + (past_key_value,) + return outputs + + +# Copied from transformers.models.bert.modeling_bert.BertSelfOutput +class RobertaSelfOutput(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward( + self, hidden_states: torch.Tensor, input_tensor: torch.Tensor + ) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + hidden_states = self.LayerNorm(hidden_states + input_tensor) + return hidden_states + + +# Copied from transformers.models.bert.modeling_bert.BertAttention with Bert->Roberta +class RobertaAttention(nn.Module): + def __init__(self, config, position_embedding_type=None): + super().__init__() + self.self = RobertaSelfAttention( + config, position_embedding_type=position_embedding_type + ) + self.output = RobertaSelfOutput(config) + self.pruned_heads = set() + + def prune_heads(self, heads): + if len(heads) == 0: + return + heads, index = find_pruneable_heads_and_indices( + heads, + self.self.num_attention_heads, + self.self.attention_head_size, + self.pruned_heads, + ) + + # Prune linear layers + self.self.query = prune_linear_layer(self.self.query, index) + self.self.key = prune_linear_layer(self.self.key, index) + self.self.value = prune_linear_layer(self.self.value, index) + self.output.dense = prune_linear_layer(self.output.dense, index, dim=1) + + # Update hyper params and store pruned heads + self.self.num_attention_heads = self.self.num_attention_heads - len(heads) + self.self.all_head_size = ( + self.self.attention_head_size * self.self.num_attention_heads + ) + self.pruned_heads = self.pruned_heads.union(heads) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.FloatTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, + output_attentions: Optional[bool] = False, + parser_att_mask=None, + ) -> Tuple[torch.Tensor]: + self_outputs = self.self( + hidden_states, + attention_mask, + head_mask, + encoder_hidden_states, + encoder_attention_mask, + past_key_value, + output_attentions, + parser_att_mask=parser_att_mask, + ) + attention_output = self.output(self_outputs[0], hidden_states) + outputs = (attention_output,) + self_outputs[ + 1: + ] # add attentions if we output them + return outputs + + +# Copied from transformers.models.bert.modeling_bert.BertIntermediate +class RobertaIntermediate(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.intermediate_size) + if isinstance(config.hidden_act, str): + self.intermediate_act_fn = ACT2FN[config.hidden_act] + else: + self.intermediate_act_fn = config.hidden_act + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.intermediate_act_fn(hidden_states) + return hidden_states + + +# Copied from transformers.models.bert.modeling_bert.BertOutput +class RobertaOutput(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.intermediate_size, config.hidden_size) + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward( + self, hidden_states: torch.Tensor, input_tensor: torch.Tensor + ) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + hidden_states = self.LayerNorm(hidden_states + input_tensor) + return hidden_states + + +# Copied from transformers.models.bert.modeling_bert.BertLayer with Bert->Roberta +class RobertaLayer(nn.Module): + def __init__(self, config): + super().__init__() + self.chunk_size_feed_forward = config.chunk_size_feed_forward + self.seq_len_dim = 1 + self.attention = RobertaAttention(config) + self.is_decoder = config.is_decoder + self.add_cross_attention = config.add_cross_attention + if self.add_cross_attention: + if not self.is_decoder: + raise ValueError( + f"{self} should be used as a decoder model if cross attention is added" + ) + self.crossattention = RobertaAttention( + config, position_embedding_type="absolute" + ) + self.intermediate = RobertaIntermediate(config) + self.output = RobertaOutput(config) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.FloatTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, + output_attentions: Optional[bool] = False, + parser_att_mask=None, + ) -> Tuple[torch.Tensor]: + # decoder uni-directional self-attention cached key/values tuple is at positions 1,2 + self_attn_past_key_value = ( + past_key_value[:2] if past_key_value is not None else None + ) + self_attention_outputs = self.attention( + hidden_states, + attention_mask, + head_mask, + output_attentions=output_attentions, + past_key_value=self_attn_past_key_value, + parser_att_mask=parser_att_mask, + ) + attention_output = self_attention_outputs[0] + + # if decoder, the last output is tuple of self-attn cache + if self.is_decoder: + outputs = self_attention_outputs[1:-1] + present_key_value = self_attention_outputs[-1] + else: + outputs = self_attention_outputs[ + 1: + ] # add self attentions if we output attention weights + + cross_attn_present_key_value = None + if self.is_decoder and encoder_hidden_states is not None: + if not hasattr(self, "crossattention"): + raise ValueError( + f"If `encoder_hidden_states` are passed, {self} has to be instantiated with cross-attention layers by setting `config.add_cross_attention=True`" + ) + + # cross_attn cached key/values tuple is at positions 3,4 of past_key_value tuple + cross_attn_past_key_value = ( + past_key_value[-2:] if past_key_value is not None else None + ) + cross_attention_outputs = self.crossattention( + attention_output, + attention_mask, + head_mask, + encoder_hidden_states, + encoder_attention_mask, + cross_attn_past_key_value, + output_attentions, + ) + attention_output = cross_attention_outputs[0] + outputs = ( + outputs + cross_attention_outputs[1:-1] + ) # add cross attentions if we output attention weights + + # add cross-attn cache to positions 3,4 of present_key_value tuple + cross_attn_present_key_value = cross_attention_outputs[-1] + present_key_value = present_key_value + cross_attn_present_key_value + + layer_output = apply_chunking_to_forward( + self.feed_forward_chunk, + self.chunk_size_feed_forward, + self.seq_len_dim, + attention_output, + ) + outputs = (layer_output,) + outputs + + # if decoder, return the attn key/values as the last output + if self.is_decoder: + outputs = outputs + (present_key_value,) + + return outputs + + def feed_forward_chunk(self, attention_output): + intermediate_output = self.intermediate(attention_output) + layer_output = self.output(intermediate_output, attention_output) + return layer_output + + +# Copied from transformers.models.bert.modeling_bert.BertEncoder with Bert->Roberta +class RobertaEncoder(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + self.layer = nn.ModuleList( + [RobertaLayer(config) for _ in range(config.num_hidden_layers)] + ) + self.gradient_checkpointing = False + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.FloatTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = False, + output_hidden_states: Optional[bool] = False, + return_dict: Optional[bool] = True, + parser_att_mask=None, + ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPastAndCrossAttentions]: + all_hidden_states = () if output_hidden_states else None + all_self_attentions = () if output_attentions else None + all_cross_attentions = ( + () if output_attentions and self.config.add_cross_attention else None + ) + + next_decoder_cache = () if use_cache else None + for i, layer_module in enumerate(self.layer): + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + layer_head_mask = head_mask[i] if head_mask is not None else None + past_key_value = past_key_values[i] if past_key_values is not None else None + + if self.gradient_checkpointing and self.training: + + if use_cache: + logger.warning( + "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." + ) + use_cache = False + + def create_custom_forward(module): + def custom_forward(*inputs): + return module(*inputs, past_key_value, output_attentions) + + return custom_forward + + layer_outputs = torch.utils.checkpoint.checkpoint( + create_custom_forward(layer_module), + hidden_states, + attention_mask, + layer_head_mask, + encoder_hidden_states, + encoder_attention_mask, + ) + else: + if parser_att_mask is not None: + layer_outputs = layer_module( + hidden_states, + attention_mask, + layer_head_mask, + encoder_hidden_states, + encoder_attention_mask, + past_key_value, + output_attentions, + parser_att_mask=parser_att_mask[i]) + else: + layer_outputs = layer_module( + hidden_states, + attention_mask, + layer_head_mask, + encoder_hidden_states, + encoder_attention_mask, + past_key_value, + output_attentions, + parser_att_mask=None) + + + hidden_states = layer_outputs[0] + if use_cache: + next_decoder_cache += (layer_outputs[-1],) + if output_attentions: + all_self_attentions = all_self_attentions + (layer_outputs[1],) + if self.config.add_cross_attention: + all_cross_attentions = all_cross_attentions + (layer_outputs[2],) + + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + if not return_dict: + return tuple( + v + for v in [ + hidden_states, + next_decoder_cache, + all_hidden_states, + all_self_attentions, + all_cross_attentions, + ] + if v is not None + ) + return BaseModelOutputWithPastAndCrossAttentions( + last_hidden_state=hidden_states, + past_key_values=next_decoder_cache, + hidden_states=all_hidden_states, + attentions=all_self_attentions, + cross_attentions=all_cross_attentions, + ) + + +# Copied from transformers.models.bert.modeling_bert.BertPooler +class RobertaPooler(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.activation = nn.Tanh() + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + # We "pool" the model by simply taking the hidden state corresponding + # to the first token. + first_token_tensor = hidden_states[:, 0] + pooled_output = self.dense(first_token_tensor) + pooled_output = self.activation(pooled_output) + return pooled_output + + +class RobertaPreTrainedModel(PreTrainedModel): + """ + An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained + models. + """ + + config_class = RobertaConfig + base_model_prefix = "roberta" + supports_gradient_checkpointing = True + + # Copied from transformers.models.bert.modeling_bert.BertPreTrainedModel._init_weights + def _init_weights(self, module): + """Initialize the weights""" + if isinstance(module, nn.Linear): + # Slightly different from the TF version which uses truncated_normal for initialization + # cf https://github.com/pytorch/pytorch/pull/5617 + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.Embedding): + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.padding_idx is not None: + module.weight.data[module.padding_idx].zero_() + elif isinstance(module, nn.LayerNorm): + if module.bias is not None: + module.bias.data.zero_() + module.weight.data.fill_(1.0) + + def _set_gradient_checkpointing(self, module, value=False): + if isinstance(module, RobertaEncoder): + module.gradient_checkpointing = value + + def update_keys_to_ignore(self, config, del_keys_to_ignore): + """Remove some keys from ignore list""" + if not config.tie_word_embeddings: + # must make a new list, or the class variable gets modified! + self._keys_to_ignore_on_save = [ + k for k in self._keys_to_ignore_on_save if k not in del_keys_to_ignore + ] + self._keys_to_ignore_on_load_missing = [ + k + for k in self._keys_to_ignore_on_load_missing + if k not in del_keys_to_ignore + ] + + +ROBERTA_START_DOCSTRING = r""" + + This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the + library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads + etc.) + + This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. + Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage + and behavior. + + Parameters: + config ([`RobertaConfig`]): Model configuration class with all the parameters of the + model. Initializing with a config file does not load the weights associated with the model, only the + configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. +""" + + +ROBERTA_INPUTS_DOCSTRING = r""" + Args: + input_ids (`torch.LongTensor` of shape `({0})`): + Indices of input sequence tokens in the vocabulary. + + Indices can be obtained using [`RobertaTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + [What are input IDs?](../glossary#input-ids) + attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*): + Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + + [What are attention masks?](../glossary#attention-mask) + token_type_ids (`torch.LongTensor` of shape `({0})`, *optional*): + Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, + 1]`: + + - 0 corresponds to a *sentence A* token, + - 1 corresponds to a *sentence B* token. + + [What are token type IDs?](../glossary#token-type-ids) + position_ids (`torch.LongTensor` of shape `({0})`, *optional*): + Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, + config.max_position_embeddings - 1]`. + + [What are position IDs?](../glossary#position-ids) + head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): + Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: + + - 1 indicates the head is **not masked**, + - 0 indicates the head is **masked**. + + inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_size)`, *optional*): + Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This + is useful if you want more control over how to convert `input_ids` indices into associated vectors than the + model's internal embedding lookup matrix. + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned + tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for + more detail. + return_dict (`bool`, *optional*): + Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. +""" + + +class RobertaModel(RobertaPreTrainedModel): + """ + + The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of + cross-attention is added between the self-attention layers, following the architecture described in *Attention is + all you need*_ by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz + Kaiser and Illia Polosukhin. + + To behave as an decoder the model needs to be initialized with the `is_decoder` argument of the configuration set + to `True`. To be used in a Seq2Seq model, the model needs to initialized with both `is_decoder` argument and + `add_cross_attention` set to `True`; an `encoder_hidden_states` is then expected as an input to the forward pass. + + .. _*Attention is all you need*: https://arxiv.org/abs/1706.03762 + + """ + + _keys_to_ignore_on_load_missing = [r"position_ids"] + + # Copied from transformers.models.bert.modeling_bert.BertModel.__init__ with Bert->Roberta + def __init__(self, config, add_pooling_layer=True): + super().__init__(config) + self.config = config + + self.embeddings = RobertaEmbeddings(config) + self.encoder = RobertaEncoder(config) + + self.pooler = RobertaPooler(config) if add_pooling_layer else None + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.embeddings.word_embeddings + + def set_input_embeddings(self, value): + self.embeddings.word_embeddings = value + + def _prune_heads(self, heads_to_prune): + """ + Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base + class PreTrainedModel + """ + for layer, heads in heads_to_prune.items(): + self.encoder.layer[layer].attention.prune_heads(heads) + + # Copied from transformers.models.bert.modeling_bert.BertModel.forward + def forward( + self, + input_ids: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + token_type_ids: Optional[torch.Tensor] = None, + position_ids: Optional[torch.Tensor] = None, + head_mask: Optional[torch.Tensor] = None, + inputs_embeds: Optional[torch.Tensor] = None, + encoder_hidden_states: Optional[torch.Tensor] = None, + encoder_attention_mask: Optional[torch.Tensor] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + parser_att_mask=None, + ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPoolingAndCrossAttentions]: + r""" + encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): + Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if + the model is configured as a decoder. + encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*): + Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in + the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): + Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. + + If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that + don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all + `decoder_input_ids` of shape `(batch_size, sequence_length)`. + use_cache (`bool`, *optional*): + If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see + `past_key_values`). + """ + output_attentions = ( + output_attentions + if output_attentions is not None + else self.config.output_attentions + ) + output_hidden_states = ( + output_hidden_states + if output_hidden_states is not None + else self.config.output_hidden_states + ) + return_dict = ( + return_dict if return_dict is not None else self.config.use_return_dict + ) + + if self.config.is_decoder: + use_cache = use_cache if use_cache is not None else self.config.use_cache + else: + use_cache = False + + if input_ids is not None and inputs_embeds is not None: + raise ValueError( + "You cannot specify both input_ids and inputs_embeds at the same time" + ) + elif input_ids is not None: + input_shape = input_ids.size() + elif inputs_embeds is not None: + input_shape = inputs_embeds.size()[:-1] + else: + raise ValueError("You have to specify either input_ids or inputs_embeds") + + batch_size, seq_length = input_shape + device = input_ids.device if input_ids is not None else inputs_embeds.device + + # past_key_values_length + past_key_values_length = ( + past_key_values[0][0].shape[2] if past_key_values is not None else 0 + ) + + if attention_mask is None: + attention_mask = torch.ones( + ((batch_size, seq_length + past_key_values_length)), device=device + ) + + if token_type_ids is None: + if hasattr(self.embeddings, "token_type_ids"): + buffered_token_type_ids = self.embeddings.token_type_ids[:, :seq_length] + buffered_token_type_ids_expanded = buffered_token_type_ids.expand( + batch_size, seq_length + ) + token_type_ids = buffered_token_type_ids_expanded + else: + token_type_ids = torch.zeros( + input_shape, dtype=torch.long, device=device + ) + + # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length] + # ourselves in which case we just need to make it broadcastable to all heads. + extended_attention_mask: torch.Tensor = self.get_extended_attention_mask( + attention_mask, input_shape, device + ) + + # If a 2D or 3D attention mask is provided for the cross-attention + # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length] + if self.config.is_decoder and encoder_hidden_states is not None: + ( + encoder_batch_size, + encoder_sequence_length, + _, + ) = encoder_hidden_states.size() + encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length) + if encoder_attention_mask is None: + encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device) + encoder_extended_attention_mask = self.invert_attention_mask( + encoder_attention_mask + ) + else: + encoder_extended_attention_mask = None + + # Prepare head mask if needed + # 1.0 in head_mask indicate we keep the head + # attention_probs has shape bsz x n_heads x N x N + # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] + # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] + head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) + + embedding_output = self.embeddings( + input_ids=input_ids, + position_ids=position_ids, + token_type_ids=token_type_ids, + inputs_embeds=inputs_embeds, + past_key_values_length=past_key_values_length, + ) + encoder_outputs = self.encoder( + embedding_output, + attention_mask=extended_attention_mask, + head_mask=head_mask, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_extended_attention_mask, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + parser_att_mask=parser_att_mask, + ) + sequence_output = encoder_outputs[0] + pooled_output = ( + self.pooler(sequence_output) if self.pooler is not None else None + ) + + if not return_dict: + return (sequence_output, pooled_output) + encoder_outputs[1:] + + return BaseModelOutputWithPoolingAndCrossAttentions( + last_hidden_state=sequence_output, + pooler_output=pooled_output, + past_key_values=encoder_outputs.past_key_values, + hidden_states=encoder_outputs.hidden_states, + attentions=encoder_outputs.attentions, + cross_attentions=encoder_outputs.cross_attentions, + ) + + +class StructRoberta(RobertaPreTrainedModel): + _keys_to_ignore_on_save = [r"lm_head.decoder.weight", r"lm_head.decoder.bias"] + _keys_to_ignore_on_load_missing = [ + r"position_ids", + r"lm_head.decoder.weight", + r"lm_head.decoder.bias", + ] + _keys_to_ignore_on_load_unexpected = [r"pooler"] + + def __init__(self, config): + super().__init__(config) + + if config.is_decoder: + logger.warning( + "If you want to use `RobertaForMaskedLM` make sure `config.is_decoder=False` for " + "bi-directional self-attention." + ) + + + if config.n_cntxt_layers > 0: + config_cntxt = copy.deepcopy(config) + config_cntxt.num_hidden_layers = config.n_cntxt_layers + + self.cntxt_layers = RobertaModel(config_cntxt, add_pooling_layer=False) + + if config.n_cntxt_layers_2 > 0: + self.parser_layers_1 = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(int(config.n_parser_layers/2)) + ] + ) + + self.distance_ff_1 = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff_1 = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight_1 = nn.Parameter( + torch.zeros((config.n_cntxt_layers_2, config.num_attention_heads, n_rel)) + ) + self._rel_weight_1.data.normal_(0, 0.1) + + self._scaler_1 = nn.Parameter(torch.zeros(2)) + + config_cntxt_2 = copy.deepcopy(config) + config_cntxt_2.num_hidden_layers = config.n_cntxt_layers_2 + + self.cntxt_layers_2 = RobertaModel(config_cntxt_2, add_pooling_layer=False) + + + self.parser_layers_2 = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(int(config.n_parser_layers/2)) + ] + ) + + self.distance_ff_2 = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff_2 = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight_2 = nn.Parameter( + torch.zeros((config.num_hidden_layers, config.num_attention_heads, n_rel)) + ) + self._rel_weight_2.data.normal_(0, 0.1) + + self._scaler_2 = nn.Parameter(torch.zeros(2)) + + else: + self.parser_layers = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(config.n_parser_layers) + ] + ) + + self.distance_ff = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight = nn.Parameter( + torch.zeros((config.num_hidden_layers, config.num_attention_heads, n_rel)) + ) + self._rel_weight.data.normal_(0, 0.1) + + self._scaler = nn.Parameter(torch.zeros(2)) + + self.roberta = RobertaModel(config, add_pooling_layer=False) + + if config.n_cntxt_layers > 0: + self.cntxt_layers.embeddings = self.roberta.embeddings + if config.n_cntxt_layers_2 > 0: + self.cntxt_layers_2.embeddings = self.roberta.embeddings + + self.lm_head = RobertaLMHead(config) + + self.pad = config.pad_token_id + + # The LM head weights require special treatment only when they are tied with the word embeddings + self.update_keys_to_ignore(config, ["lm_head.decoder.weight"]) + + # Initialize weights and apply final processing + self.post_init() + + def get_output_embeddings(self): + return self.lm_head.decoder + + def set_output_embeddings(self, new_embeddings): + self.lm_head.decoder = new_embeddings + + @property + def scaler(self): + return self._scaler.exp() + + @property + def scaler_1(self): + return self._scaler_1.exp() + + @property + def scaler_2(self): + return self._scaler_2.exp() + + @property + def rel_weight(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight, dim=-1) + + @property + def rel_weight_1(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight_1) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight_1, dim=-1) + + + @property + def rel_weight_2(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight_2) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight_2, dim=-1) + + + def compute_block(self, distance, height, n_cntxt_layers=0): + """Compute constituents from distance and height.""" + + if n_cntxt_layers>0: + if n_cntxt_layers == 1: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler_1[0] + elif n_cntxt_layers == 2: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler_2[0] + else: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0] + + gamma = torch.sigmoid(-beta_logits) + ones = torch.ones_like(gamma) + + block_mask_left = cummin( + gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1 + ) + block_mask_left = block_mask_left - F.pad( + block_mask_left[:, :, :-1], (1, 0), value=0 + ) + block_mask_left.tril_(0) + + block_mask_right = cummin( + gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1 + ) + block_mask_right = block_mask_right - F.pad( + block_mask_right[:, :, 1:], (0, 1), value=0 + ) + block_mask_right.triu_(0) + + block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :] + block = cumsum(block_mask_left).tril(0) + cumsum( + block_mask_right, reverse=True + ).triu(1) + + return block_p, block + + def compute_head(self, height, n_cntxt_layers=0): + """Estimate head for each constituent.""" + + _, length = height.size() + if n_cntxt_layers>0: + if n_cntxt_layers == 1: + head_logits = height * self.scaler_1[1] + elif n_cntxt_layers == 2: + head_logits = height * self.scaler_2[1] + else: + head_logits = height * self.scaler[1] + index = torch.arange(length, device=height.device) + + mask = (index[:, None, None] <= index[None, None, :]) * ( + index[None, None, :] <= index[None, :, None] + ) + head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1) + head_logits.masked_fill_(~mask[None, :, :, :], -1e9) + + head_p = torch.softmax(head_logits, dim=-1) + + return head_p + + def parse(self, x, embs=None, n_cntxt_layers=0): + """Parse input sentence. + + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + distance: syntactic distance + height: syntactic height + """ + + mask = x != self.pad + mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0) + + if embs is None: + h = self.roberta.embeddings(x) + else: + h = embs + + if n_cntxt_layers > 0: + if n_cntxt_layers == 1: + parser_layers = self.parser_layers_1 + height_ff = self.height_ff_1 + distance_ff = self.distance_ff_1 + elif n_cntxt_layers == 2: + parser_layers = self.parser_layers_2 + height_ff = self.height_ff_2 + distance_ff = self.distance_ff_2 + for i in range(int(self.config.n_parser_layers/2)): + h = h.masked_fill(~mask[:, :, None], 0) + h = parser_layers[i](h) + + height = height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, dim=-1 + )[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max + ) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where( + margin_left > margin_right, margin_right, margin_left + ).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + else: + for i in range(self.config.n_parser_layers): + h = h.masked_fill(~mask[:, :, None], 0) + h = self.parser_layers[i](h) + + height = self.height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = self.distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, dim=-1 + )[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max + ) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where( + margin_left > margin_right, margin_right, margin_left + ).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + + return distance, height + + def generate_mask(self, x, distance, height, n_cntxt_layers=0): + """Compute head and cibling distribution for each token.""" + + bsz, length = x.size() + + eye = torch.eye(length, device=x.device, dtype=torch.bool) + eye = eye[None, :, :].expand((bsz, -1, -1)) + + block_p, block = self.compute_block(distance, height, n_cntxt_layers=n_cntxt_layers) + head_p = self.compute_head(height, n_cntxt_layers=n_cntxt_layers) + head = torch.einsum("blij,bijh->blh", block_p, head_p) + head = head.masked_fill(eye, 0) + child = head.transpose(1, 2) + cibling = torch.bmm(head, child).masked_fill(eye, 0) + + rel_list = [] + if "head" in self.config.relations: + rel_list.append(head) + if "child" in self.config.relations: + rel_list.append(child) + if "cibling" in self.config.relations: + rel_list.append(cibling) + + rel = torch.stack(rel_list, dim=1) + + if n_cntxt_layers > 0: + if n_cntxt_layers == 1: + rel_weight = self.rel_weight_1 + elif n_cntxt_layers == 2: + rel_weight = self.rel_weight_2 + else: + rel_weight = self.rel_weight + + dep = torch.einsum("lhr,brij->lbhij", rel_weight, rel) + + if n_cntxt_layers == 1: + num_layers = self.cntxt_layers_2.config.num_hidden_layers + else: + num_layers = self.roberta.config.num_hidden_layers + + att_mask = dep.reshape( + num_layers, + bsz, + self.config.num_attention_heads, + length, + length, + ) + + return att_mask, cibling, head, block + + def forward( + self, + input_ids: Optional[torch.LongTensor] = None, + attention_mask: Optional[torch.FloatTensor] = None, + token_type_ids: Optional[torch.LongTensor] = None, + position_ids: Optional[torch.LongTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, MaskedLMOutput]: + r""" + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ..., + config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the + loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]` + kwargs (`Dict[str, any]`, optional, defaults to *{}*): + Used to hide legacy arguments that have been deprecated. + """ + return_dict = ( + return_dict if return_dict is not None else self.config.use_return_dict + ) + + + if self.config.n_cntxt_layers > 0: + cntxt_outputs = self.cntxt_layers( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict) + + + if self.config.n_cntxt_layers_2 > 0: + distance_1, height_1 = self.parse(input_ids, cntxt_outputs[0], n_cntxt_layers=1) + att_mask_1, _, _, _ = self.generate_mask(input_ids, distance_1, height_1, n_cntxt_layers=1) + + cntxt_outputs_2 = self.cntxt_layers_2( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + parser_att_mask=att_mask_1) + + sequence_output = cntxt_outputs_2[0] + + distance_2, height_2 = self.parse(input_ids, sequence_output[0], n_cntxt_layers=2) + att_mask, _, _, _ = self.generate_mask(input_ids, distance_2, height_2, n_cntxt_layers=2) + + elif self.config.n_cntxt_layers > 0: + distance, height = self.parse(input_ids, cntxt_outputs[0]) + att_mask, _, _, _ = self.generate_mask(input_ids, distance, height) + else: + distance, height = self.parse(input_ids) + att_mask, _, _, _ = self.generate_mask(input_ids, distance, height) + + outputs = self.roberta( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + parser_att_mask=att_mask, + ) + sequence_output = outputs[0] + prediction_scores = self.lm_head(sequence_output) + + masked_lm_loss = None + if labels is not None: + loss_fct = CrossEntropyLoss() + masked_lm_loss = loss_fct( + prediction_scores.view(-1, self.config.vocab_size), labels.view(-1) + ) + + if not return_dict: + output = (prediction_scores,) + outputs[2:] + return ( + ((masked_lm_loss,) + output) if masked_lm_loss is not None else output + ) + + return MaskedLMOutput( + loss=masked_lm_loss, + logits=prediction_scores, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +class RobertaLMHead(nn.Module): + """Roberta Head for masked language modeling.""" + + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + + self.decoder = nn.Linear(config.hidden_size, config.vocab_size) + self.bias = nn.Parameter(torch.zeros(config.vocab_size)) + self.decoder.bias = self.bias + + def forward(self, features, **kwargs): + x = self.dense(features) + x = gelu(x) + x = self.layer_norm(x) + + # project back to size of vocabulary with bias + x = self.decoder(x) + + return x + + def _tie_weights(self): + # To tie those two weights if they get disconnected (on TPU or when the bias is resized) + self.bias = self.decoder.bias + + +class StructRobertaForSequenceClassification(RobertaPreTrainedModel): + _keys_to_ignore_on_load_missing = [r"position_ids"] + + def __init__(self, config): + super().__init__(config) + self.num_labels = config.num_labels + self.config = config + + if config.n_cntxt_layers > 0: + config_cntxt = copy.deepcopy(config) + config_cntxt.num_hidden_layers = config.n_cntxt_layers + + self.cntxt_layers = RobertaModel(config_cntxt, add_pooling_layer=False) + + if config.n_cntxt_layers_2 > 0: + self.parser_layers_1 = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(int(config.n_parser_layers/2)) + ] + ) + + self.distance_ff_1 = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff_1 = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight_1 = nn.Parameter( + torch.zeros((config.n_cntxt_layers_2, config.num_attention_heads, n_rel)) + ) + self._rel_weight_1.data.normal_(0, 0.1) + + self._scaler_1 = nn.Parameter(torch.zeros(2)) + + config_cntxt_2 = copy.deepcopy(config) + config_cntxt_2.num_hidden_layers = config.n_cntxt_layers_2 + + self.cntxt_layers_2 = RobertaModel(config_cntxt_2, add_pooling_layer=False) + + + self.parser_layers_2 = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(int(config.n_parser_layers/2)) + ] + ) + + self.distance_ff_2 = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff_2 = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight_2 = nn.Parameter( + torch.zeros((config.num_hidden_layers, config.num_attention_heads, n_rel)) + ) + self._rel_weight_2.data.normal_(0, 0.1) + + self._scaler_2 = nn.Parameter(torch.zeros(2)) + + else: + self.parser_layers = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(config.n_parser_layers) + ] + ) + + self.distance_ff = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight = nn.Parameter( + torch.zeros((config.num_hidden_layers, config.num_attention_heads, n_rel)) + ) + self._rel_weight.data.normal_(0, 0.1) + + self._scaler = nn.Parameter(torch.zeros(2)) + + self.roberta = RobertaModel(config, add_pooling_layer=False) + + if config.n_cntxt_layers > 0: + self.cntxt_layers.embeddings = self.roberta.embeddings + if config.n_cntxt_layers_2 > 0: + self.cntxt_layers_2.embeddings = self.roberta.embeddings + + + self.pad = config.pad_token_id + self.classifier = RobertaClassificationHead(config) + + # Initialize weights and apply final processing + self.post_init() + + + @property + def scaler(self): + return self._scaler.exp() + + @property + def scaler_1(self): + return self._scaler_1.exp() + + @property + def scaler_2(self): + return self._scaler_2.exp() + + @property + def rel_weight(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight, dim=-1) + + @property + def rel_weight_1(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight_1) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight_1, dim=-1) + + + @property + def rel_weight_2(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight_2) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight_2, dim=-1) + + + def compute_block(self, distance, height, n_cntxt_layers=0): + """Compute constituents from distance and height.""" + + if n_cntxt_layers>0: + if n_cntxt_layers == 1: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler_1[0] + elif n_cntxt_layers == 2: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler_2[0] + else: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0] + + gamma = torch.sigmoid(-beta_logits) + ones = torch.ones_like(gamma) + + block_mask_left = cummin( + gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1 + ) + block_mask_left = block_mask_left - F.pad( + block_mask_left[:, :, :-1], (1, 0), value=0 + ) + block_mask_left.tril_(0) + + block_mask_right = cummin( + gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1 + ) + block_mask_right = block_mask_right - F.pad( + block_mask_right[:, :, 1:], (0, 1), value=0 + ) + block_mask_right.triu_(0) + + block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :] + block = cumsum(block_mask_left).tril(0) + cumsum( + block_mask_right, reverse=True + ).triu(1) + + return block_p, block + + def compute_head(self, height, n_cntxt_layers=0): + """Estimate head for each constituent.""" + + _, length = height.size() + if n_cntxt_layers>0: + if n_cntxt_layers == 1: + head_logits = height * self.scaler_1[1] + elif n_cntxt_layers == 2: + head_logits = height * self.scaler_2[1] + else: + head_logits = height * self.scaler[1] + index = torch.arange(length, device=height.device) + + mask = (index[:, None, None] <= index[None, None, :]) * ( + index[None, None, :] <= index[None, :, None] + ) + head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1) + head_logits.masked_fill_(~mask[None, :, :, :], -1e9) + + head_p = torch.softmax(head_logits, dim=-1) + + return head_p + + def parse(self, x, embs=None, n_cntxt_layers=0): + """Parse input sentence. + + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + distance: syntactic distance + height: syntactic height + """ + + mask = x != self.pad + mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0) + + if embs is None: + h = self.roberta.embeddings(x) + else: + h = embs + + if n_cntxt_layers > 0: + if n_cntxt_layers == 1: + parser_layers = self.parser_layers_1 + height_ff = self.height_ff_1 + distance_ff = self.distance_ff_1 + elif n_cntxt_layers == 2: + parser_layers = self.parser_layers_2 + height_ff = self.height_ff_2 + distance_ff = self.distance_ff_2 + for i in range(int(self.config.n_parser_layers/2)): + h = h.masked_fill(~mask[:, :, None], 0) + h = parser_layers[i](h) + + height = height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, dim=-1 + )[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max + ) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where( + margin_left > margin_right, margin_right, margin_left + ).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + else: + for i in range(self.config.n_parser_layers): + h = h.masked_fill(~mask[:, :, None], 0) + h = self.parser_layers[i](h) + + height = self.height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = self.distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, dim=-1 + )[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max + ) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where( + margin_left > margin_right, margin_right, margin_left + ).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + + return distance, height + + def generate_mask(self, x, distance, height, n_cntxt_layers=0): + """Compute head and cibling distribution for each token.""" + + bsz, length = x.size() + + eye = torch.eye(length, device=x.device, dtype=torch.bool) + eye = eye[None, :, :].expand((bsz, -1, -1)) + + block_p, block = self.compute_block(distance, height, n_cntxt_layers=n_cntxt_layers) + head_p = self.compute_head(height, n_cntxt_layers=n_cntxt_layers) + head = torch.einsum("blij,bijh->blh", block_p, head_p) + head = head.masked_fill(eye, 0) + child = head.transpose(1, 2) + cibling = torch.bmm(head, child).masked_fill(eye, 0) + + rel_list = [] + if "head" in self.config.relations: + rel_list.append(head) + if "child" in self.config.relations: + rel_list.append(child) + if "cibling" in self.config.relations: + rel_list.append(cibling) + + rel = torch.stack(rel_list, dim=1) + + if n_cntxt_layers > 0: + if n_cntxt_layers == 1: + rel_weight = self.rel_weight_1 + elif n_cntxt_layers == 2: + rel_weight = self.rel_weight_2 + else: + rel_weight = self.rel_weight + + dep = torch.einsum("lhr,brij->lbhij", rel_weight, rel) + + if n_cntxt_layers == 1: + num_layers = self.cntxt_layers_2.config.num_hidden_layers + else: + num_layers = self.roberta.config.num_hidden_layers + + att_mask = dep.reshape( + num_layers, + bsz, + self.config.num_attention_heads, + length, + length, + ) + + return att_mask, cibling, head, block + + def forward( + self, + input_ids: Optional[torch.LongTensor] = None, + attention_mask: Optional[torch.FloatTensor] = None, + token_type_ids: Optional[torch.LongTensor] = None, + position_ids: Optional[torch.LongTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, SequenceClassifierOutput]: + r""" + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., + config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If + `config.num_labels > 1` a classification loss is computed (Cross-Entropy). + """ + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + if self.config.n_cntxt_layers > 0: + cntxt_outputs = self.cntxt_layers( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict) + + + if self.config.n_cntxt_layers_2 > 0: + distance_1, height_1 = self.parse(input_ids, cntxt_outputs[0], n_cntxt_layers=1) + att_mask_1, _, _, _ = self.generate_mask(input_ids, distance_1, height_1, n_cntxt_layers=1) + + cntxt_outputs_2 = self.cntxt_layers_2( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + parser_att_mask=att_mask_1) + + sequence_output = cntxt_outputs_2[0] + + distance_2, height_2 = self.parse(input_ids, sequence_output[0], n_cntxt_layers=2) + att_mask, _, _, _ = self.generate_mask(input_ids, distance_2, height_2, n_cntxt_layers=2) + + elif self.config.n_cntxt_layers > 0: + distance, height = self.parse(input_ids, cntxt_outputs[0]) + att_mask, _, _, _ = self.generate_mask(input_ids, distance, height) + else: + distance, height = self.parse(input_ids) + att_mask, _, _, _ = self.generate_mask(input_ids, distance, height) + + outputs = self.roberta( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + parser_att_mask=att_mask, + ) + sequence_output = outputs[0] + logits = self.classifier(sequence_output) + + loss = None + if labels is not None: + if self.config.problem_type is None: + if self.num_labels == 1: + self.config.problem_type = "regression" + elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): + self.config.problem_type = "single_label_classification" + else: + self.config.problem_type = "multi_label_classification" + + if self.config.problem_type == "regression": + loss_fct = MSELoss() + if self.num_labels == 1: + loss = loss_fct(logits.squeeze(), labels.squeeze()) + else: + loss = loss_fct(logits, labels) + elif self.config.problem_type == "single_label_classification": + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) + elif self.config.problem_type == "multi_label_classification": + loss_fct = BCEWithLogitsLoss() + loss = loss_fct(logits, labels) + + if not return_dict: + output = (logits,) + outputs[2:] + return ((loss,) + output) if loss is not None else output + + return SequenceClassifierOutput( + loss=loss, + logits=logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +class RobertaClassificationHead(nn.Module): + """Head for sentence-level classification tasks.""" + + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + classifier_dropout = ( + config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob + ) + self.dropout = nn.Dropout(classifier_dropout) + self.out_proj = nn.Linear(config.hidden_size, config.num_labels) + + def forward(self, features, **kwargs): + x = features[:, 0, :] # take token (equiv. to [CLS]) + x = self.dropout(x) + x = self.dense(x) + x = torch.tanh(x) + x = self.dropout(x) + x = self.out_proj(x) + return x + + +def create_position_ids_from_input_ids( + input_ids, padding_idx, past_key_values_length=0 +): + """ + Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols + are ignored. This is modified from fairseq's `utils.make_positions`. + + Args: + x: torch.Tensor x: + + Returns: torch.Tensor + """ + # The series of casts and type-conversions here are carefully balanced to both work with ONNX export and XLA. + mask = input_ids.ne(padding_idx).int() + incremental_indices = ( + torch.cumsum(mask, dim=1).type_as(mask) + past_key_values_length + ) * mask + return incremental_indices.long() + padding_idx + + +def cumprod(x, reverse=False, exclusive=False): + """cumulative product.""" + if reverse: + x = x.flip([-1]) + + if exclusive: + x = F.pad(x[:, :, :-1], (1, 0), value=1) + + cx = x.cumprod(-1) + + if reverse: + cx = cx.flip([-1]) + return cx + + +def cumsum(x, reverse=False, exclusive=False): + """cumulative sum.""" + bsz, _, length = x.size() + device = x.device + if reverse: + if exclusive: + w = torch.ones([bsz, length, length], device=device).tril(-1) + else: + w = torch.ones([bsz, length, length], device=device).tril(0) + cx = torch.bmm(x, w) + else: + if exclusive: + w = torch.ones([bsz, length, length], device=device).triu(1) + else: + w = torch.ones([bsz, length, length], device=device).triu(0) + cx = torch.bmm(x, w) + return cx + + +def cummin(x, reverse=False, exclusive=False, max_value=1e9): + """cumulative min.""" + if reverse: + if exclusive: + x = F.pad(x[:, :, 1:], (0, 1), value=max_value) + x = x.flip([-1]).cummin(-1)[0].flip([-1]) + else: + if exclusive: + x = F.pad(x[:, :, :-1], (1, 0), value=max_value) + x = x.cummin(-1)[0] + return x diff --git a/finetune/syntactic_category_control/predict_results.txt b/finetune/syntactic_category_control/predict_results.txt new file mode 100644 index 0000000000000000000000000000000000000000..afebe457943a7989c341867a63c0870128213706 --- /dev/null +++ b/finetune/syntactic_category_control/predict_results.txt @@ -0,0 +1,17742 @@ +index prediction +0 1 +1 0 +2 1 +3 0 +4 1 +5 0 +6 1 +7 0 +8 1 +9 0 +10 1 +11 0 +12 1 +13 0 +14 1 +15 1 +16 0 +17 1 +18 0 +19 1 +20 0 +21 1 +22 0 +23 1 +24 0 +25 1 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b/finetune/syntactic_category_lexical_content_the/merges.txt @@ -0,0 +1,31794 @@ +#version: 0.2 +Ġ t +h e +Ġ a +i n +Ġt he +r e +Ġ w +o u +Ġ s +o n +e r +h a +n d +i s +i t +Ġ b +Ġ o +Ġ c +Ġ m +Ġ f +in g +o r +a n +e d +l l +Ġ d +e n +Ġt o +e s +Ġ p +Ġo f +Ġ y +Ġ in +a r +a t +Ġa nd +Ġ l +ha t +Ġy ou +a s +Ġ g +o m +Ġ n +Ġ I +i c +o w +l e +v e +o t +Ġ h +Ġ he +a l +e t +Ġ ha +' s +u s +Ġt h +a y +Ġ it +Ġ is +Ġb e +Ġt hat +Ġ on +i on +i d +Ġ e +g h +u t +l d +en t +i m +Ġ re +Ġw as +a c +Ġw e +v er +a m +T he +. . +Ġf or +l y +Ġ S +s t +r i +r o +i r +a d +s e +k e +Ġ . +Ġs t +gh t +o o +' t +Ġ A +ou t +it h +Ġm e +Ġ C +a ll +Ġ k +Ġ u +Ġa n +Ġ M +Ġw ith +u r +Ġa s +Ġd o +c t +he r +c e +i ll +t er +i f +c h +Ġw h +Ġs e +om e +Ġa re +Ġ H +i l +Ġn ot +Ġha ve +Ġ B +Ġ 1 +a nd +Ġa t +Ġth is +ou ld +an t +Ġg o +us t +at ion +Ġl i +o p +a in +Ġk n +Y ou +e a +Ġ P +Ġ T +Ġ , +u n +g e +o l +t h +Ġh is +u l +Ġ " +r om +.. . +o re +Ġ W +e ll +Ġn e +Ġ r +Ġkn ow +Ġ j +ou r +Ġ D +Ġd e +p p +Ġc an +Ġ G +Ġthe y +u m +Ġ The +Ġs o +es t +Ġ ? +Ġa l +Ġa ll +Ġa b +Ġm y +is t +er s +Ġ R +i e +Ġyou r +ar t +Ġb y +Ġf rom +es s +Ġs h +Ġo r +Ġ 2 +Ġ F +Ġw hat +n t +a b +o s +Ġ L +e l +Ġon e +Ġc on +Ġ N +Ġw he +Ġthe re +i g +i ght +ha n +all y +Ġu p +q u +e m +Ġc om +on e +Ġha d +Ġb ut +Ġhe r +Ġs u +re d +r y +Ġ J +' re +Ġh im +Ġ out +r a +ac k +at e +oo d +ver y +oo k +Ġli ke +Ġw or +Ġs a +re s +am e +i ve +Ġp l +Ġ us +Ġ ( +on g +' m +Ġwe re +â Ģ +a ke +o d +in k +c k +Ġs he +Ġp ro +Ġab out +Ġ E +as t +Ġg et +Ġe x +Ġ v +p e +I t +or t +ou nd +in d +ar d +it y +Ġd on +A nd +ic h +ri ght +Ġ O +Ġj ust +Ġ1 9 +i es +H e +Ġwh o +e ar +ea h +Ġd id +a k +Ġhe re +b er +Ġthe m +Ġn o +im e +in e +Ġm an +Ġw ant +Ġw ould +Ġ2 0 +Ġ if +Ġ ' +Ġw ill +u d +a p +Ġ K +Ġth ink +ou s +' ll +v en +Ġs ome +o st +W hat +e p +h ing +Ġin t +Ġan y +ow n +ic e +a g +Ġ right +m ent +id e +Ġg ot +Ġha s +t her +us e +it t +Ġwh ich +ou gh +Ġt r +f e +h i +c a +Ġse e +Ġwhe n +Ġsa id +u re +Ġa r +Ġa g +Ġp e +Ġs ay +w ay +i a +Ġt ime +Ġt w +i v +ĠH e +s o +Ġbe en +i an +Ġs p +a re +ac e +Ġn ow +o ve +ic k +op le +c i +Ġ U +W e +ĠI t +' ve +Ġthe ir +o se +Y eah +Ġl e +Ġl ook +- - +Ġc l +is h +l f +re at +n a +Ġm ore +Ġthe n +Ġgo ing +a ge +S o +it e +re e +Ġo ther +Ġpe ople +ar y +Ġc ould +Ġ = +ĠS t +N o +ot her +n e +Ġof f +u e +Ġb ack +h is +ac t +B ut +Ġo ver +ir st +O h +as s +Ġ qu +ou nt +on d +Ġ very +ing s +ca use +Ġ ro +b le +p er +Ġg r +Ġa f +p t +Ġf e +an s +Ġwor k +Ġtw o +Ġg ood +T hat +Ġto o +Ġb r +e nd +om et +Ġ en +er m +o g +p l +at ed +o ll +Ġ Ġ +t he +Ġf irst +ion s +or d +Ġint o +he d +as e +Ġp art +k ay +0 0 +Ġh ow +Ġd own +or n +al k +u b +w e +Ġu n +Ġw ay +ad e +Ġt han +i re +Ġal so +o y +f f +itt le +Ġc all +i z +m er +Ġa m +ou ght +Ġy ear +h y +at er +Ġp r +Ġcom p +o v +Ġc ome +W ell +Ġb l +Ġ our +an ce +Ġwhe re +nd er +ac h +if e +Ġag ain +Ġ im +n g +Ġon ly +Ġ V +Ġe very +ĠI n +Ġl ittle +Ġa d +c c +ur n +ha r +o h +Ġs omet +r u +Ġd ay +w n +Ġb et +an g +Ġpl ay +h o +Ġwe ll +am p +he 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See all RoBERTa models at https://huggingface.co/models?filter=roberta +] + + +class StructRobertaConfig(RobertaConfig): + model_type = "roberta" + + def __init__( + self, + n_parser_layers=4, + conv_size=9, + relations=("head", "child"), + weight_act="softmax", + n_cntxt_layers=3, + n_cntxt_layers_2=0, + **kwargs,): + + super().__init__(**kwargs) + self.n_cntxt_layers = n_cntxt_layers + self.n_parser_layers = n_parser_layers + self.n_cntxt_layers_2 = n_cntxt_layers_2 + self.conv_size = conv_size + self.relations = relations + self.weight_act = weight_act + +class Conv1d(nn.Module): + """1D convolution layer.""" + + def __init__(self, hidden_size, kernel_size, dilation=1): + """Initialization. + + Args: + hidden_size: dimension of input embeddings + kernel_size: convolution kernel size + dilation: the spacing between the kernel points + """ + super(Conv1d, self).__init__() + + if kernel_size % 2 == 0: + padding = (kernel_size // 2) * dilation + self.shift = True + else: + padding = ((kernel_size - 1) // 2) * dilation + self.shift = False + self.conv = nn.Conv1d( + hidden_size, hidden_size, kernel_size, padding=padding, dilation=dilation + ) + + def forward(self, x): + """Compute convolution. + + Args: + x: input embeddings + Returns: + conv_output: convolution results + """ + + if self.shift: + return self.conv(x.transpose(1, 2)).transpose(1, 2)[:, 1:] + else: + return self.conv(x.transpose(1, 2)).transpose(1, 2) + + +class RobertaEmbeddings(nn.Module): + """ + Same as BertEmbeddings with a tiny tweak for positional embeddings indexing. + """ + + # Copied from transformers.models.bert.modeling_bert.BertEmbeddings.__init__ + def __init__(self, config): + super().__init__() + self.word_embeddings = nn.Embedding( + config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id + ) + self.position_embeddings = nn.Embedding( + config.max_position_embeddings, config.hidden_size + ) + self.token_type_embeddings = nn.Embedding( + config.type_vocab_size, config.hidden_size + ) + + # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load + # any TensorFlow checkpoint file + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + # position_ids (1, len position emb) is contiguous in memory and exported when serialized + self.position_embedding_type = getattr( + config, "position_embedding_type", "absolute" + ) + self.register_buffer( + "position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)) + ) + if version.parse(torch.__version__) > version.parse("1.6.0"): + self.register_buffer( + "token_type_ids", + torch.zeros(self.position_ids.size(), dtype=torch.long), + persistent=False, + ) + + # End copy + self.padding_idx = config.pad_token_id + self.position_embeddings = nn.Embedding( + config.max_position_embeddings, + config.hidden_size, + padding_idx=self.padding_idx, + ) + + def forward( + self, + input_ids=None, + token_type_ids=None, + position_ids=None, + inputs_embeds=None, + past_key_values_length=0, + ): + if position_ids is None: + if input_ids is not None: + # Create the position ids from the input token ids. Any padded tokens remain padded. + position_ids = create_position_ids_from_input_ids( + input_ids, self.padding_idx, past_key_values_length + ) + else: + position_ids = self.create_position_ids_from_inputs_embeds( + inputs_embeds + ) + + if input_ids is not None: + input_shape = input_ids.size() + else: + input_shape = inputs_embeds.size()[:-1] + + seq_length = input_shape[1] + + # Setting the token_type_ids to the registered buffer in constructor where it is all zeros, which usually occurs + # when its auto-generated, registered buffer helps users when tracing the model without passing token_type_ids, solves + # issue #5664 + if token_type_ids is None: + if hasattr(self, "token_type_ids"): + buffered_token_type_ids = self.token_type_ids[:, :seq_length] + buffered_token_type_ids_expanded = buffered_token_type_ids.expand( + input_shape[0], seq_length + ) + token_type_ids = buffered_token_type_ids_expanded + else: + token_type_ids = torch.zeros( + input_shape, dtype=torch.long, device=self.position_ids.device + ) + + if inputs_embeds is None: + inputs_embeds = self.word_embeddings(input_ids) + token_type_embeddings = self.token_type_embeddings(token_type_ids) + + embeddings = inputs_embeds + token_type_embeddings + if self.position_embedding_type == "absolute": + position_embeddings = self.position_embeddings(position_ids) + embeddings += position_embeddings + embeddings = self.LayerNorm(embeddings) + embeddings = self.dropout(embeddings) + return embeddings + + def create_position_ids_from_inputs_embeds(self, inputs_embeds): + """ + We are provided embeddings directly. We cannot infer which are padded so just generate sequential position ids. + + Args: + inputs_embeds: torch.Tensor + + Returns: torch.Tensor + """ + input_shape = inputs_embeds.size()[:-1] + sequence_length = input_shape[1] + + position_ids = torch.arange( + self.padding_idx + 1, + sequence_length + self.padding_idx + 1, + dtype=torch.long, + device=inputs_embeds.device, + ) + return position_ids.unsqueeze(0).expand(input_shape) + + +# Copied from transformers.models.bert.modeling_bert.BertSelfAttention with Bert->Roberta +class RobertaSelfAttention(nn.Module): + def __init__(self, config, position_embedding_type=None): + super().__init__() + if config.hidden_size % config.num_attention_heads != 0 and not hasattr( + config, "embedding_size" + ): + raise ValueError( + f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention " + f"heads ({config.num_attention_heads})" + ) + + self.num_attention_heads = config.num_attention_heads + self.attention_head_size = int(config.hidden_size / config.num_attention_heads) + self.all_head_size = self.num_attention_heads * self.attention_head_size + + self.query = nn.Linear(config.hidden_size, self.all_head_size) + self.key = nn.Linear(config.hidden_size, self.all_head_size) + self.value = nn.Linear(config.hidden_size, self.all_head_size) + + self.dropout = nn.Dropout(config.attention_probs_dropout_prob) + self.position_embedding_type = position_embedding_type or getattr( + config, "position_embedding_type", "absolute" + ) + if ( + self.position_embedding_type == "relative_key" + or self.position_embedding_type == "relative_key_query" + ): + self.max_position_embeddings = config.max_position_embeddings + self.distance_embedding = nn.Embedding( + 2 * config.max_position_embeddings - 1, self.attention_head_size + ) + + self.is_decoder = config.is_decoder + + def transpose_for_scores(self, x): + new_x_shape = x.size()[:-1] + ( + self.num_attention_heads, + self.attention_head_size, + ) + x = x.view(new_x_shape) + return x.permute(0, 2, 1, 3) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.FloatTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, + output_attentions: Optional[bool] = False, + parser_att_mask=None, + ) -> Tuple[torch.Tensor]: + mixed_query_layer = self.query(hidden_states) + + # If this is instantiated as a cross-attention module, the keys + # and values come from an encoder; the attention mask needs to be + # such that the encoder's padding tokens are not attended to. + is_cross_attention = encoder_hidden_states is not None + + if is_cross_attention and past_key_value is not None: + # reuse k,v, cross_attentions + key_layer = past_key_value[0] + value_layer = past_key_value[1] + attention_mask = encoder_attention_mask + elif is_cross_attention: + key_layer = self.transpose_for_scores(self.key(encoder_hidden_states)) + value_layer = self.transpose_for_scores(self.value(encoder_hidden_states)) + attention_mask = encoder_attention_mask + elif past_key_value is not None: + key_layer = self.transpose_for_scores(self.key(hidden_states)) + value_layer = self.transpose_for_scores(self.value(hidden_states)) + key_layer = torch.cat([past_key_value[0], key_layer], dim=2) + value_layer = torch.cat([past_key_value[1], value_layer], dim=2) + else: + key_layer = self.transpose_for_scores(self.key(hidden_states)) + value_layer = self.transpose_for_scores(self.value(hidden_states)) + + query_layer = self.transpose_for_scores(mixed_query_layer) + + if self.is_decoder: + # if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states. + # Further calls to cross_attention layer can then reuse all cross-attention + # key/value_states (first "if" case) + # if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of + # all previous decoder key/value_states. Further calls to uni-directional self-attention + # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case) + # if encoder bi-directional self-attention `past_key_value` is always `None` + past_key_value = (key_layer, value_layer) + + # Take the dot product between "query" and "key" to get the raw attention scores. + attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) + + if ( + self.position_embedding_type == "relative_key" + or self.position_embedding_type == "relative_key_query" + ): + seq_length = hidden_states.size()[1] + position_ids_l = torch.arange( + seq_length, dtype=torch.long, device=hidden_states.device + ).view(-1, 1) + position_ids_r = torch.arange( + seq_length, dtype=torch.long, device=hidden_states.device + ).view(1, -1) + distance = position_ids_l - position_ids_r + positional_embedding = self.distance_embedding( + distance + self.max_position_embeddings - 1 + ) + positional_embedding = positional_embedding.to( + dtype=query_layer.dtype + ) # fp16 compatibility + + if self.position_embedding_type == "relative_key": + relative_position_scores = torch.einsum( + "bhld,lrd->bhlr", query_layer, positional_embedding + ) + attention_scores = attention_scores + relative_position_scores + elif self.position_embedding_type == "relative_key_query": + relative_position_scores_query = torch.einsum( + "bhld,lrd->bhlr", query_layer, positional_embedding + ) + relative_position_scores_key = torch.einsum( + "bhrd,lrd->bhlr", key_layer, positional_embedding + ) + attention_scores = ( + attention_scores + + relative_position_scores_query + + relative_position_scores_key + ) + + attention_scores = attention_scores / math.sqrt(self.attention_head_size) + if attention_mask is not None: + # Apply the attention mask is (precomputed for all layers in RobertaModel forward() function) + attention_scores = attention_scores + attention_mask + + if parser_att_mask is None: + # Normalize the attention scores to probabilities. + attention_probs = nn.functional.softmax(attention_scores, dim=-1) + else: + attention_probs = torch.sigmoid(attention_scores) * parser_att_mask + + # This is actually dropping out entire tokens to attend to, which might + # seem a bit unusual, but is taken from the original Transformer paper. + attention_probs = self.dropout(attention_probs) + + # Mask heads if we want to + if head_mask is not None: + attention_probs = attention_probs * head_mask + + context_layer = torch.matmul(attention_probs, value_layer) + + context_layer = context_layer.permute(0, 2, 1, 3).contiguous() + new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) + context_layer = context_layer.view(new_context_layer_shape) + + outputs = ( + (context_layer, attention_probs) if output_attentions else (context_layer,) + ) + + if self.is_decoder: + outputs = outputs + (past_key_value,) + return outputs + + +# Copied from transformers.models.bert.modeling_bert.BertSelfOutput +class RobertaSelfOutput(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward( + self, hidden_states: torch.Tensor, input_tensor: torch.Tensor + ) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + hidden_states = self.LayerNorm(hidden_states + input_tensor) + return hidden_states + + +# Copied from transformers.models.bert.modeling_bert.BertAttention with Bert->Roberta +class RobertaAttention(nn.Module): + def __init__(self, config, position_embedding_type=None): + super().__init__() + self.self = RobertaSelfAttention( + config, position_embedding_type=position_embedding_type + ) + self.output = RobertaSelfOutput(config) + self.pruned_heads = set() + + def prune_heads(self, heads): + if len(heads) == 0: + return + heads, index = find_pruneable_heads_and_indices( + heads, + self.self.num_attention_heads, + self.self.attention_head_size, + self.pruned_heads, + ) + + # Prune linear layers + self.self.query = prune_linear_layer(self.self.query, index) + self.self.key = prune_linear_layer(self.self.key, index) + self.self.value = prune_linear_layer(self.self.value, index) + self.output.dense = prune_linear_layer(self.output.dense, index, dim=1) + + # Update hyper params and store pruned heads + self.self.num_attention_heads = self.self.num_attention_heads - len(heads) + self.self.all_head_size = ( + self.self.attention_head_size * self.self.num_attention_heads + ) + self.pruned_heads = self.pruned_heads.union(heads) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.FloatTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, + output_attentions: Optional[bool] = False, + parser_att_mask=None, + ) -> Tuple[torch.Tensor]: + self_outputs = self.self( + hidden_states, + attention_mask, + head_mask, + encoder_hidden_states, + encoder_attention_mask, + past_key_value, + output_attentions, + parser_att_mask=parser_att_mask, + ) + attention_output = self.output(self_outputs[0], hidden_states) + outputs = (attention_output,) + self_outputs[ + 1: + ] # add attentions if we output them + return outputs + + +# Copied from transformers.models.bert.modeling_bert.BertIntermediate +class RobertaIntermediate(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.intermediate_size) + if isinstance(config.hidden_act, str): + self.intermediate_act_fn = ACT2FN[config.hidden_act] + else: + self.intermediate_act_fn = config.hidden_act + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.intermediate_act_fn(hidden_states) + return hidden_states + + +# Copied from transformers.models.bert.modeling_bert.BertOutput +class RobertaOutput(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.intermediate_size, config.hidden_size) + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward( + self, hidden_states: torch.Tensor, input_tensor: torch.Tensor + ) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + hidden_states = self.LayerNorm(hidden_states + input_tensor) + return hidden_states + + +# Copied from transformers.models.bert.modeling_bert.BertLayer with Bert->Roberta +class RobertaLayer(nn.Module): + def __init__(self, config): + super().__init__() + self.chunk_size_feed_forward = config.chunk_size_feed_forward + self.seq_len_dim = 1 + self.attention = RobertaAttention(config) + self.is_decoder = config.is_decoder + self.add_cross_attention = config.add_cross_attention + if self.add_cross_attention: + if not self.is_decoder: + raise ValueError( + f"{self} should be used as a decoder model if cross attention is added" + ) + self.crossattention = RobertaAttention( + config, position_embedding_type="absolute" + ) + self.intermediate = RobertaIntermediate(config) + self.output = RobertaOutput(config) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.FloatTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, + output_attentions: Optional[bool] = False, + parser_att_mask=None, + ) -> Tuple[torch.Tensor]: + # decoder uni-directional self-attention cached key/values tuple is at positions 1,2 + self_attn_past_key_value = ( + past_key_value[:2] if past_key_value is not None else None + ) + self_attention_outputs = self.attention( + hidden_states, + attention_mask, + head_mask, + output_attentions=output_attentions, + past_key_value=self_attn_past_key_value, + parser_att_mask=parser_att_mask, + ) + attention_output = self_attention_outputs[0] + + # if decoder, the last output is tuple of self-attn cache + if self.is_decoder: + outputs = self_attention_outputs[1:-1] + present_key_value = self_attention_outputs[-1] + else: + outputs = self_attention_outputs[ + 1: + ] # add self attentions if we output attention weights + + cross_attn_present_key_value = None + if self.is_decoder and encoder_hidden_states is not None: + if not hasattr(self, "crossattention"): + raise ValueError( + f"If `encoder_hidden_states` are passed, {self} has to be instantiated with cross-attention layers by setting `config.add_cross_attention=True`" + ) + + # cross_attn cached key/values tuple is at positions 3,4 of past_key_value tuple + cross_attn_past_key_value = ( + past_key_value[-2:] if past_key_value is not None else None + ) + cross_attention_outputs = self.crossattention( + attention_output, + attention_mask, + head_mask, + encoder_hidden_states, + encoder_attention_mask, + cross_attn_past_key_value, + output_attentions, + ) + attention_output = cross_attention_outputs[0] + outputs = ( + outputs + cross_attention_outputs[1:-1] + ) # add cross attentions if we output attention weights + + # add cross-attn cache to positions 3,4 of present_key_value tuple + cross_attn_present_key_value = cross_attention_outputs[-1] + present_key_value = present_key_value + cross_attn_present_key_value + + layer_output = apply_chunking_to_forward( + self.feed_forward_chunk, + self.chunk_size_feed_forward, + self.seq_len_dim, + attention_output, + ) + outputs = (layer_output,) + outputs + + # if decoder, return the attn key/values as the last output + if self.is_decoder: + outputs = outputs + (present_key_value,) + + return outputs + + def feed_forward_chunk(self, attention_output): + intermediate_output = self.intermediate(attention_output) + layer_output = self.output(intermediate_output, attention_output) + return layer_output + + +# Copied from transformers.models.bert.modeling_bert.BertEncoder with Bert->Roberta +class RobertaEncoder(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + self.layer = nn.ModuleList( + [RobertaLayer(config) for _ in range(config.num_hidden_layers)] + ) + self.gradient_checkpointing = False + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.FloatTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = False, + output_hidden_states: Optional[bool] = False, + return_dict: Optional[bool] = True, + parser_att_mask=None, + ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPastAndCrossAttentions]: + all_hidden_states = () if output_hidden_states else None + all_self_attentions = () if output_attentions else None + all_cross_attentions = ( + () if output_attentions and self.config.add_cross_attention else None + ) + + next_decoder_cache = () if use_cache else None + for i, layer_module in enumerate(self.layer): + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + layer_head_mask = head_mask[i] if head_mask is not None else None + past_key_value = past_key_values[i] if past_key_values is not None else None + + if self.gradient_checkpointing and self.training: + + if use_cache: + logger.warning( + "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." + ) + use_cache = False + + def create_custom_forward(module): + def custom_forward(*inputs): + return module(*inputs, past_key_value, output_attentions) + + return custom_forward + + layer_outputs = torch.utils.checkpoint.checkpoint( + create_custom_forward(layer_module), + hidden_states, + attention_mask, + layer_head_mask, + encoder_hidden_states, + encoder_attention_mask, + ) + else: + if parser_att_mask is not None: + layer_outputs = layer_module( + hidden_states, + attention_mask, + layer_head_mask, + encoder_hidden_states, + encoder_attention_mask, + past_key_value, + output_attentions, + parser_att_mask=parser_att_mask[i]) + else: + layer_outputs = layer_module( + hidden_states, + attention_mask, + layer_head_mask, + encoder_hidden_states, + encoder_attention_mask, + past_key_value, + output_attentions, + parser_att_mask=None) + + + hidden_states = layer_outputs[0] + if use_cache: + next_decoder_cache += (layer_outputs[-1],) + if output_attentions: + all_self_attentions = all_self_attentions + (layer_outputs[1],) + if self.config.add_cross_attention: + all_cross_attentions = all_cross_attentions + (layer_outputs[2],) + + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + if not return_dict: + return tuple( + v + for v in [ + hidden_states, + next_decoder_cache, + all_hidden_states, + all_self_attentions, + all_cross_attentions, + ] + if v is not None + ) + return BaseModelOutputWithPastAndCrossAttentions( + last_hidden_state=hidden_states, + past_key_values=next_decoder_cache, + hidden_states=all_hidden_states, + attentions=all_self_attentions, + cross_attentions=all_cross_attentions, + ) + + +# Copied from transformers.models.bert.modeling_bert.BertPooler +class RobertaPooler(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.activation = nn.Tanh() + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + # We "pool" the model by simply taking the hidden state corresponding + # to the first token. + first_token_tensor = hidden_states[:, 0] + pooled_output = self.dense(first_token_tensor) + pooled_output = self.activation(pooled_output) + return pooled_output + + +class RobertaPreTrainedModel(PreTrainedModel): + """ + An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained + models. + """ + + config_class = RobertaConfig + base_model_prefix = "roberta" + supports_gradient_checkpointing = True + + # Copied from transformers.models.bert.modeling_bert.BertPreTrainedModel._init_weights + def _init_weights(self, module): + """Initialize the weights""" + if isinstance(module, nn.Linear): + # Slightly different from the TF version which uses truncated_normal for initialization + # cf https://github.com/pytorch/pytorch/pull/5617 + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.Embedding): + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.padding_idx is not None: + module.weight.data[module.padding_idx].zero_() + elif isinstance(module, nn.LayerNorm): + if module.bias is not None: + module.bias.data.zero_() + module.weight.data.fill_(1.0) + + def _set_gradient_checkpointing(self, module, value=False): + if isinstance(module, RobertaEncoder): + module.gradient_checkpointing = value + + def update_keys_to_ignore(self, config, del_keys_to_ignore): + """Remove some keys from ignore list""" + if not config.tie_word_embeddings: + # must make a new list, or the class variable gets modified! + self._keys_to_ignore_on_save = [ + k for k in self._keys_to_ignore_on_save if k not in del_keys_to_ignore + ] + self._keys_to_ignore_on_load_missing = [ + k + for k in self._keys_to_ignore_on_load_missing + if k not in del_keys_to_ignore + ] + + +ROBERTA_START_DOCSTRING = r""" + + This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the + library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads + etc.) + + This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. + Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage + and behavior. + + Parameters: + config ([`RobertaConfig`]): Model configuration class with all the parameters of the + model. Initializing with a config file does not load the weights associated with the model, only the + configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. +""" + + +ROBERTA_INPUTS_DOCSTRING = r""" + Args: + input_ids (`torch.LongTensor` of shape `({0})`): + Indices of input sequence tokens in the vocabulary. + + Indices can be obtained using [`RobertaTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + [What are input IDs?](../glossary#input-ids) + attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*): + Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + + [What are attention masks?](../glossary#attention-mask) + token_type_ids (`torch.LongTensor` of shape `({0})`, *optional*): + Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, + 1]`: + + - 0 corresponds to a *sentence A* token, + - 1 corresponds to a *sentence B* token. + + [What are token type IDs?](../glossary#token-type-ids) + position_ids (`torch.LongTensor` of shape `({0})`, *optional*): + Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, + config.max_position_embeddings - 1]`. + + [What are position IDs?](../glossary#position-ids) + head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): + Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: + + - 1 indicates the head is **not masked**, + - 0 indicates the head is **masked**. + + inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_size)`, *optional*): + Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This + is useful if you want more control over how to convert `input_ids` indices into associated vectors than the + model's internal embedding lookup matrix. + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned + tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for + more detail. + return_dict (`bool`, *optional*): + Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. +""" + + +class RobertaModel(RobertaPreTrainedModel): + """ + + The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of + cross-attention is added between the self-attention layers, following the architecture described in *Attention is + all you need*_ by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz + Kaiser and Illia Polosukhin. + + To behave as an decoder the model needs to be initialized with the `is_decoder` argument of the configuration set + to `True`. To be used in a Seq2Seq model, the model needs to initialized with both `is_decoder` argument and + `add_cross_attention` set to `True`; an `encoder_hidden_states` is then expected as an input to the forward pass. + + .. _*Attention is all you need*: https://arxiv.org/abs/1706.03762 + + """ + + _keys_to_ignore_on_load_missing = [r"position_ids"] + + # Copied from transformers.models.bert.modeling_bert.BertModel.__init__ with Bert->Roberta + def __init__(self, config, add_pooling_layer=True): + super().__init__(config) + self.config = config + + self.embeddings = RobertaEmbeddings(config) + self.encoder = RobertaEncoder(config) + + self.pooler = RobertaPooler(config) if add_pooling_layer else None + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.embeddings.word_embeddings + + def set_input_embeddings(self, value): + self.embeddings.word_embeddings = value + + def _prune_heads(self, heads_to_prune): + """ + Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base + class PreTrainedModel + """ + for layer, heads in heads_to_prune.items(): + self.encoder.layer[layer].attention.prune_heads(heads) + + # Copied from transformers.models.bert.modeling_bert.BertModel.forward + def forward( + self, + input_ids: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + token_type_ids: Optional[torch.Tensor] = None, + position_ids: Optional[torch.Tensor] = None, + head_mask: Optional[torch.Tensor] = None, + inputs_embeds: Optional[torch.Tensor] = None, + encoder_hidden_states: Optional[torch.Tensor] = None, + encoder_attention_mask: Optional[torch.Tensor] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + parser_att_mask=None, + ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPoolingAndCrossAttentions]: + r""" + encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): + Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if + the model is configured as a decoder. + encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*): + Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in + the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): + Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. + + If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that + don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all + `decoder_input_ids` of shape `(batch_size, sequence_length)`. + use_cache (`bool`, *optional*): + If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see + `past_key_values`). + """ + output_attentions = ( + output_attentions + if output_attentions is not None + else self.config.output_attentions + ) + output_hidden_states = ( + output_hidden_states + if output_hidden_states is not None + else self.config.output_hidden_states + ) + return_dict = ( + return_dict if return_dict is not None else self.config.use_return_dict + ) + + if self.config.is_decoder: + use_cache = use_cache if use_cache is not None else self.config.use_cache + else: + use_cache = False + + if input_ids is not None and inputs_embeds is not None: + raise ValueError( + "You cannot specify both input_ids and inputs_embeds at the same time" + ) + elif input_ids is not None: + input_shape = input_ids.size() + elif inputs_embeds is not None: + input_shape = inputs_embeds.size()[:-1] + else: + raise ValueError("You have to specify either input_ids or inputs_embeds") + + batch_size, seq_length = input_shape + device = input_ids.device if input_ids is not None else inputs_embeds.device + + # past_key_values_length + past_key_values_length = ( + past_key_values[0][0].shape[2] if past_key_values is not None else 0 + ) + + if attention_mask is None: + attention_mask = torch.ones( + ((batch_size, seq_length + past_key_values_length)), device=device + ) + + if token_type_ids is None: + if hasattr(self.embeddings, "token_type_ids"): + buffered_token_type_ids = self.embeddings.token_type_ids[:, :seq_length] + buffered_token_type_ids_expanded = buffered_token_type_ids.expand( + batch_size, seq_length + ) + token_type_ids = buffered_token_type_ids_expanded + else: + token_type_ids = torch.zeros( + input_shape, dtype=torch.long, device=device + ) + + # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length] + # ourselves in which case we just need to make it broadcastable to all heads. + extended_attention_mask: torch.Tensor = self.get_extended_attention_mask( + attention_mask, input_shape, device + ) + + # If a 2D or 3D attention mask is provided for the cross-attention + # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length] + if self.config.is_decoder and encoder_hidden_states is not None: + ( + encoder_batch_size, + encoder_sequence_length, + _, + ) = encoder_hidden_states.size() + encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length) + if encoder_attention_mask is None: + encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device) + encoder_extended_attention_mask = self.invert_attention_mask( + encoder_attention_mask + ) + else: + encoder_extended_attention_mask = None + + # Prepare head mask if needed + # 1.0 in head_mask indicate we keep the head + # attention_probs has shape bsz x n_heads x N x N + # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] + # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] + head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) + + embedding_output = self.embeddings( + input_ids=input_ids, + position_ids=position_ids, + token_type_ids=token_type_ids, + inputs_embeds=inputs_embeds, + past_key_values_length=past_key_values_length, + ) + encoder_outputs = self.encoder( + embedding_output, + attention_mask=extended_attention_mask, + head_mask=head_mask, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_extended_attention_mask, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + parser_att_mask=parser_att_mask, + ) + sequence_output = encoder_outputs[0] + pooled_output = ( + self.pooler(sequence_output) if self.pooler is not None else None + ) + + if not return_dict: + return (sequence_output, pooled_output) + encoder_outputs[1:] + + return BaseModelOutputWithPoolingAndCrossAttentions( + last_hidden_state=sequence_output, + pooler_output=pooled_output, + past_key_values=encoder_outputs.past_key_values, + hidden_states=encoder_outputs.hidden_states, + attentions=encoder_outputs.attentions, + cross_attentions=encoder_outputs.cross_attentions, + ) + + +class StructRoberta(RobertaPreTrainedModel): + _keys_to_ignore_on_save = [r"lm_head.decoder.weight", r"lm_head.decoder.bias"] + _keys_to_ignore_on_load_missing = [ + r"position_ids", + r"lm_head.decoder.weight", + r"lm_head.decoder.bias", + ] + _keys_to_ignore_on_load_unexpected = [r"pooler"] + + def __init__(self, config): + super().__init__(config) + + if config.is_decoder: + logger.warning( + "If you want to use `RobertaForMaskedLM` make sure `config.is_decoder=False` for " + "bi-directional self-attention." + ) + + + if config.n_cntxt_layers > 0: + config_cntxt = copy.deepcopy(config) + config_cntxt.num_hidden_layers = config.n_cntxt_layers + + self.cntxt_layers = RobertaModel(config_cntxt, add_pooling_layer=False) + + if config.n_cntxt_layers_2 > 0: + self.parser_layers_1 = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(int(config.n_parser_layers/2)) + ] + ) + + self.distance_ff_1 = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff_1 = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight_1 = nn.Parameter( + torch.zeros((config.n_cntxt_layers_2, config.num_attention_heads, n_rel)) + ) + self._rel_weight_1.data.normal_(0, 0.1) + + self._scaler_1 = nn.Parameter(torch.zeros(2)) + + config_cntxt_2 = copy.deepcopy(config) + config_cntxt_2.num_hidden_layers = config.n_cntxt_layers_2 + + self.cntxt_layers_2 = RobertaModel(config_cntxt_2, add_pooling_layer=False) + + + self.parser_layers_2 = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(int(config.n_parser_layers/2)) + ] + ) + + self.distance_ff_2 = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff_2 = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight_2 = nn.Parameter( + torch.zeros((config.num_hidden_layers, config.num_attention_heads, n_rel)) + ) + self._rel_weight_2.data.normal_(0, 0.1) + + self._scaler_2 = nn.Parameter(torch.zeros(2)) + + else: + self.parser_layers = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(config.n_parser_layers) + ] + ) + + self.distance_ff = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight = nn.Parameter( + torch.zeros((config.num_hidden_layers, config.num_attention_heads, n_rel)) + ) + self._rel_weight.data.normal_(0, 0.1) + + self._scaler = nn.Parameter(torch.zeros(2)) + + self.roberta = RobertaModel(config, add_pooling_layer=False) + + if config.n_cntxt_layers > 0: + self.cntxt_layers.embeddings = self.roberta.embeddings + if config.n_cntxt_layers_2 > 0: + self.cntxt_layers_2.embeddings = self.roberta.embeddings + + self.lm_head = RobertaLMHead(config) + + self.pad = config.pad_token_id + + # The LM head weights require special treatment only when they are tied with the word embeddings + self.update_keys_to_ignore(config, ["lm_head.decoder.weight"]) + + # Initialize weights and apply final processing + self.post_init() + + def get_output_embeddings(self): + return self.lm_head.decoder + + def set_output_embeddings(self, new_embeddings): + self.lm_head.decoder = new_embeddings + + @property + def scaler(self): + return self._scaler.exp() + + @property + def scaler_1(self): + return self._scaler_1.exp() + + @property + def scaler_2(self): + return self._scaler_2.exp() + + @property + def rel_weight(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight, dim=-1) + + @property + def rel_weight_1(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight_1) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight_1, dim=-1) + + + @property + def rel_weight_2(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight_2) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight_2, dim=-1) + + + def compute_block(self, distance, height, n_cntxt_layers=0): + """Compute constituents from distance and height.""" + + if n_cntxt_layers>0: + if n_cntxt_layers == 1: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler_1[0] + elif n_cntxt_layers == 2: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler_2[0] + else: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0] + + gamma = torch.sigmoid(-beta_logits) + ones = torch.ones_like(gamma) + + block_mask_left = cummin( + gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1 + ) + block_mask_left = block_mask_left - F.pad( + block_mask_left[:, :, :-1], (1, 0), value=0 + ) + block_mask_left.tril_(0) + + block_mask_right = cummin( + gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1 + ) + block_mask_right = block_mask_right - F.pad( + block_mask_right[:, :, 1:], (0, 1), value=0 + ) + block_mask_right.triu_(0) + + block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :] + block = cumsum(block_mask_left).tril(0) + cumsum( + block_mask_right, reverse=True + ).triu(1) + + return block_p, block + + def compute_head(self, height, n_cntxt_layers=0): + """Estimate head for each constituent.""" + + _, length = height.size() + if n_cntxt_layers>0: + if n_cntxt_layers == 1: + head_logits = height * self.scaler_1[1] + elif n_cntxt_layers == 2: + head_logits = height * self.scaler_2[1] + else: + head_logits = height * self.scaler[1] + index = torch.arange(length, device=height.device) + + mask = (index[:, None, None] <= index[None, None, :]) * ( + index[None, None, :] <= index[None, :, None] + ) + head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1) + head_logits.masked_fill_(~mask[None, :, :, :], -1e9) + + head_p = torch.softmax(head_logits, dim=-1) + + return head_p + + def parse(self, x, embs=None, n_cntxt_layers=0): + """Parse input sentence. + + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + distance: syntactic distance + height: syntactic height + """ + + mask = x != self.pad + mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0) + + if embs is None: + h = self.roberta.embeddings(x) + else: + h = embs + + if n_cntxt_layers > 0: + if n_cntxt_layers == 1: + parser_layers = self.parser_layers_1 + height_ff = self.height_ff_1 + distance_ff = self.distance_ff_1 + elif n_cntxt_layers == 2: + parser_layers = self.parser_layers_2 + height_ff = self.height_ff_2 + distance_ff = self.distance_ff_2 + for i in range(int(self.config.n_parser_layers/2)): + h = h.masked_fill(~mask[:, :, None], 0) + h = parser_layers[i](h) + + height = height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, dim=-1 + )[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max + ) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where( + margin_left > margin_right, margin_right, margin_left + ).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + else: + for i in range(self.config.n_parser_layers): + h = h.masked_fill(~mask[:, :, None], 0) + h = self.parser_layers[i](h) + + height = self.height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = self.distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, dim=-1 + )[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max + ) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where( + margin_left > margin_right, margin_right, margin_left + ).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + + return distance, height + + def generate_mask(self, x, distance, height, n_cntxt_layers=0): + """Compute head and cibling distribution for each token.""" + + bsz, length = x.size() + + eye = torch.eye(length, device=x.device, dtype=torch.bool) + eye = eye[None, :, :].expand((bsz, -1, -1)) + + block_p, block = self.compute_block(distance, height, n_cntxt_layers=n_cntxt_layers) + head_p = self.compute_head(height, n_cntxt_layers=n_cntxt_layers) + head = torch.einsum("blij,bijh->blh", block_p, head_p) + head = head.masked_fill(eye, 0) + child = head.transpose(1, 2) + cibling = torch.bmm(head, child).masked_fill(eye, 0) + + rel_list = [] + if "head" in self.config.relations: + rel_list.append(head) + if "child" in self.config.relations: + rel_list.append(child) + if "cibling" in self.config.relations: + rel_list.append(cibling) + + rel = torch.stack(rel_list, dim=1) + + if n_cntxt_layers > 0: + if n_cntxt_layers == 1: + rel_weight = self.rel_weight_1 + elif n_cntxt_layers == 2: + rel_weight = self.rel_weight_2 + else: + rel_weight = self.rel_weight + + dep = torch.einsum("lhr,brij->lbhij", rel_weight, rel) + + if n_cntxt_layers == 1: + num_layers = self.cntxt_layers_2.config.num_hidden_layers + else: + num_layers = self.roberta.config.num_hidden_layers + + att_mask = dep.reshape( + num_layers, + bsz, + self.config.num_attention_heads, + length, + length, + ) + + return att_mask, cibling, head, block + + def forward( + self, + input_ids: Optional[torch.LongTensor] = None, + attention_mask: Optional[torch.FloatTensor] = None, + token_type_ids: Optional[torch.LongTensor] = None, + position_ids: Optional[torch.LongTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, MaskedLMOutput]: + r""" + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ..., + config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the + loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]` + kwargs (`Dict[str, any]`, optional, defaults to *{}*): + Used to hide legacy arguments that have been deprecated. + """ + return_dict = ( + return_dict if return_dict is not None else self.config.use_return_dict + ) + + + if self.config.n_cntxt_layers > 0: + cntxt_outputs = self.cntxt_layers( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict) + + + if self.config.n_cntxt_layers_2 > 0: + distance_1, height_1 = self.parse(input_ids, cntxt_outputs[0], n_cntxt_layers=1) + att_mask_1, _, _, _ = self.generate_mask(input_ids, distance_1, height_1, n_cntxt_layers=1) + + cntxt_outputs_2 = self.cntxt_layers_2( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + parser_att_mask=att_mask_1) + + sequence_output = cntxt_outputs_2[0] + + distance_2, height_2 = self.parse(input_ids, sequence_output[0], n_cntxt_layers=2) + att_mask, _, _, _ = self.generate_mask(input_ids, distance_2, height_2, n_cntxt_layers=2) + + elif self.config.n_cntxt_layers > 0: + distance, height = self.parse(input_ids, cntxt_outputs[0]) + att_mask, _, _, _ = self.generate_mask(input_ids, distance, height) + else: + distance, height = self.parse(input_ids) + att_mask, _, _, _ = self.generate_mask(input_ids, distance, height) + + outputs = self.roberta( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + parser_att_mask=att_mask, + ) + sequence_output = outputs[0] + prediction_scores = self.lm_head(sequence_output) + + masked_lm_loss = None + if labels is not None: + loss_fct = CrossEntropyLoss() + masked_lm_loss = loss_fct( + prediction_scores.view(-1, self.config.vocab_size), labels.view(-1) + ) + + if not return_dict: + output = (prediction_scores,) + outputs[2:] + return ( + ((masked_lm_loss,) + output) if masked_lm_loss is not None else output + ) + + return MaskedLMOutput( + loss=masked_lm_loss, + logits=prediction_scores, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +class RobertaLMHead(nn.Module): + """Roberta Head for masked language modeling.""" + + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + + self.decoder = nn.Linear(config.hidden_size, config.vocab_size) + self.bias = nn.Parameter(torch.zeros(config.vocab_size)) + self.decoder.bias = self.bias + + def forward(self, features, **kwargs): + x = self.dense(features) + x = gelu(x) + x = self.layer_norm(x) + + # project back to size of vocabulary with bias + x = self.decoder(x) + + return x + + def _tie_weights(self): + # To tie those two weights if they get disconnected (on TPU or when the bias is resized) + self.bias = self.decoder.bias + + +class StructRobertaForSequenceClassification(RobertaPreTrainedModel): + _keys_to_ignore_on_load_missing = [r"position_ids"] + + def __init__(self, config): + super().__init__(config) + self.num_labels = config.num_labels + self.config = config + + if config.n_cntxt_layers > 0: + config_cntxt = copy.deepcopy(config) + config_cntxt.num_hidden_layers = config.n_cntxt_layers + + self.cntxt_layers = RobertaModel(config_cntxt, add_pooling_layer=False) + + if config.n_cntxt_layers_2 > 0: + self.parser_layers_1 = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(int(config.n_parser_layers/2)) + ] + ) + + self.distance_ff_1 = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff_1 = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight_1 = nn.Parameter( + torch.zeros((config.n_cntxt_layers_2, config.num_attention_heads, n_rel)) + ) + self._rel_weight_1.data.normal_(0, 0.1) + + self._scaler_1 = nn.Parameter(torch.zeros(2)) + + config_cntxt_2 = copy.deepcopy(config) + config_cntxt_2.num_hidden_layers = config.n_cntxt_layers_2 + + self.cntxt_layers_2 = RobertaModel(config_cntxt_2, add_pooling_layer=False) + + + self.parser_layers_2 = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(int(config.n_parser_layers/2)) + ] + ) + + self.distance_ff_2 = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff_2 = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight_2 = nn.Parameter( + torch.zeros((config.num_hidden_layers, config.num_attention_heads, n_rel)) + ) + self._rel_weight_2.data.normal_(0, 0.1) + + self._scaler_2 = nn.Parameter(torch.zeros(2)) + + else: + self.parser_layers = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(config.n_parser_layers) + ] + ) + + self.distance_ff = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight = nn.Parameter( + torch.zeros((config.num_hidden_layers, config.num_attention_heads, n_rel)) + ) + self._rel_weight.data.normal_(0, 0.1) + + self._scaler = nn.Parameter(torch.zeros(2)) + + self.roberta = RobertaModel(config, add_pooling_layer=False) + + if config.n_cntxt_layers > 0: + self.cntxt_layers.embeddings = self.roberta.embeddings + if config.n_cntxt_layers_2 > 0: + self.cntxt_layers_2.embeddings = self.roberta.embeddings + + + self.pad = config.pad_token_id + self.classifier = RobertaClassificationHead(config) + + # Initialize weights and apply final processing + self.post_init() + + + @property + def scaler(self): + return self._scaler.exp() + + @property + def scaler_1(self): + return self._scaler_1.exp() + + @property + def scaler_2(self): + return self._scaler_2.exp() + + @property + def rel_weight(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight, dim=-1) + + @property + def rel_weight_1(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight_1) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight_1, dim=-1) + + + @property + def rel_weight_2(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight_2) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight_2, dim=-1) + + + def compute_block(self, distance, height, n_cntxt_layers=0): + """Compute constituents from distance and height.""" + + if n_cntxt_layers>0: + if n_cntxt_layers == 1: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler_1[0] + elif n_cntxt_layers == 2: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler_2[0] + else: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0] + + gamma = torch.sigmoid(-beta_logits) + ones = torch.ones_like(gamma) + + block_mask_left = cummin( + gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1 + ) + block_mask_left = block_mask_left - F.pad( + block_mask_left[:, :, :-1], (1, 0), value=0 + ) + block_mask_left.tril_(0) + + block_mask_right = cummin( + gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1 + ) + block_mask_right = block_mask_right - F.pad( + block_mask_right[:, :, 1:], (0, 1), value=0 + ) + block_mask_right.triu_(0) + + block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :] + block = cumsum(block_mask_left).tril(0) + cumsum( + block_mask_right, reverse=True + ).triu(1) + + return block_p, block + + def compute_head(self, height, n_cntxt_layers=0): + """Estimate head for each constituent.""" + + _, length = height.size() + if n_cntxt_layers>0: + if n_cntxt_layers == 1: + head_logits = height * self.scaler_1[1] + elif n_cntxt_layers == 2: + head_logits = height * self.scaler_2[1] + else: + head_logits = height * self.scaler[1] + index = torch.arange(length, device=height.device) + + mask = (index[:, None, None] <= index[None, None, :]) * ( + index[None, None, :] <= index[None, :, None] + ) + head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1) + head_logits.masked_fill_(~mask[None, :, :, :], -1e9) + + head_p = torch.softmax(head_logits, dim=-1) + + return head_p + + def parse(self, x, embs=None, n_cntxt_layers=0): + """Parse input sentence. + + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + distance: syntactic distance + height: syntactic height + """ + + mask = x != self.pad + mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0) + + if embs is None: + h = self.roberta.embeddings(x) + else: + h = embs + + if n_cntxt_layers > 0: + if n_cntxt_layers == 1: + parser_layers = self.parser_layers_1 + height_ff = self.height_ff_1 + distance_ff = self.distance_ff_1 + elif n_cntxt_layers == 2: + parser_layers = self.parser_layers_2 + height_ff = self.height_ff_2 + distance_ff = self.distance_ff_2 + for i in range(int(self.config.n_parser_layers/2)): + h = h.masked_fill(~mask[:, :, None], 0) + h = parser_layers[i](h) + + height = height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, dim=-1 + )[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max + ) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where( + margin_left > margin_right, margin_right, margin_left + ).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + else: + for i in range(self.config.n_parser_layers): + h = h.masked_fill(~mask[:, :, None], 0) + h = self.parser_layers[i](h) + + height = self.height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = self.distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, dim=-1 + )[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max + ) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where( + margin_left > margin_right, margin_right, margin_left + ).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + + return distance, height + + def generate_mask(self, x, distance, height, n_cntxt_layers=0): + """Compute head and cibling distribution for each token.""" + + bsz, length = x.size() + + eye = torch.eye(length, device=x.device, dtype=torch.bool) + eye = eye[None, :, :].expand((bsz, -1, -1)) + + block_p, block = self.compute_block(distance, height, n_cntxt_layers=n_cntxt_layers) + head_p = self.compute_head(height, n_cntxt_layers=n_cntxt_layers) + head = torch.einsum("blij,bijh->blh", block_p, head_p) + head = head.masked_fill(eye, 0) + child = head.transpose(1, 2) + cibling = torch.bmm(head, child).masked_fill(eye, 0) + + rel_list = [] + if "head" in self.config.relations: + rel_list.append(head) + if "child" in self.config.relations: + rel_list.append(child) + if "cibling" in self.config.relations: + rel_list.append(cibling) + + rel = torch.stack(rel_list, dim=1) + + if n_cntxt_layers > 0: + if n_cntxt_layers == 1: + rel_weight = self.rel_weight_1 + elif n_cntxt_layers == 2: + rel_weight = self.rel_weight_2 + else: + rel_weight = self.rel_weight + + dep = torch.einsum("lhr,brij->lbhij", rel_weight, rel) + + if n_cntxt_layers == 1: + num_layers = self.cntxt_layers_2.config.num_hidden_layers + else: + num_layers = self.roberta.config.num_hidden_layers + + att_mask = dep.reshape( + num_layers, + bsz, + self.config.num_attention_heads, + length, + length, + ) + + return att_mask, cibling, head, block + + def forward( + self, + input_ids: Optional[torch.LongTensor] = None, + attention_mask: Optional[torch.FloatTensor] = None, + token_type_ids: Optional[torch.LongTensor] = None, + position_ids: Optional[torch.LongTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, SequenceClassifierOutput]: + r""" + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., + config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If + `config.num_labels > 1` a classification loss is computed (Cross-Entropy). + """ + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + if self.config.n_cntxt_layers > 0: + cntxt_outputs = self.cntxt_layers( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict) + + + if self.config.n_cntxt_layers_2 > 0: + distance_1, height_1 = self.parse(input_ids, cntxt_outputs[0], n_cntxt_layers=1) + att_mask_1, _, _, _ = self.generate_mask(input_ids, distance_1, height_1, n_cntxt_layers=1) + + cntxt_outputs_2 = self.cntxt_layers_2( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + parser_att_mask=att_mask_1) + + sequence_output = cntxt_outputs_2[0] + + distance_2, height_2 = self.parse(input_ids, sequence_output[0], n_cntxt_layers=2) + att_mask, _, _, _ = self.generate_mask(input_ids, distance_2, height_2, n_cntxt_layers=2) + + elif self.config.n_cntxt_layers > 0: + distance, height = self.parse(input_ids, cntxt_outputs[0]) + att_mask, _, _, _ = self.generate_mask(input_ids, distance, height) + else: + distance, height = self.parse(input_ids) + att_mask, _, _, _ = self.generate_mask(input_ids, distance, height) + + outputs = self.roberta( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + parser_att_mask=att_mask, + ) + sequence_output = outputs[0] + logits = self.classifier(sequence_output) + + loss = None + if labels is not None: + if self.config.problem_type is None: + if self.num_labels == 1: + self.config.problem_type = "regression" + elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): + self.config.problem_type = "single_label_classification" + else: + self.config.problem_type = "multi_label_classification" + + if self.config.problem_type == "regression": + loss_fct = MSELoss() + if self.num_labels == 1: + loss = loss_fct(logits.squeeze(), labels.squeeze()) + else: + loss = loss_fct(logits, labels) + elif self.config.problem_type == "single_label_classification": + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) + elif self.config.problem_type == "multi_label_classification": + loss_fct = BCEWithLogitsLoss() + loss = loss_fct(logits, labels) + + if not return_dict: + output = (logits,) + outputs[2:] + return ((loss,) + output) if loss is not None else output + + return SequenceClassifierOutput( + loss=loss, + logits=logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +class RobertaClassificationHead(nn.Module): + """Head for sentence-level classification tasks.""" + + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + classifier_dropout = ( + config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob + ) + self.dropout = nn.Dropout(classifier_dropout) + self.out_proj = nn.Linear(config.hidden_size, config.num_labels) + + def forward(self, features, **kwargs): + x = features[:, 0, :] # take token (equiv. to [CLS]) + x = self.dropout(x) + x = self.dense(x) + x = torch.tanh(x) + x = self.dropout(x) + x = self.out_proj(x) + return x + + +def create_position_ids_from_input_ids( + input_ids, padding_idx, past_key_values_length=0 +): + """ + Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols + are ignored. This is modified from fairseq's `utils.make_positions`. + + Args: + x: torch.Tensor x: + + Returns: torch.Tensor + """ + # The series of casts and type-conversions here are carefully balanced to both work with ONNX export and XLA. + mask = input_ids.ne(padding_idx).int() + incremental_indices = ( + torch.cumsum(mask, dim=1).type_as(mask) + past_key_values_length + ) * mask + return incremental_indices.long() + padding_idx + + +def cumprod(x, reverse=False, exclusive=False): + """cumulative product.""" + if reverse: + x = x.flip([-1]) + + if exclusive: + x = F.pad(x[:, :, :-1], (1, 0), value=1) + + cx = x.cumprod(-1) + + if reverse: + cx = cx.flip([-1]) + return cx + + +def cumsum(x, reverse=False, exclusive=False): + """cumulative sum.""" + bsz, _, length = x.size() + device = x.device + if reverse: + if exclusive: + w = torch.ones([bsz, length, length], device=device).tril(-1) + else: + w = torch.ones([bsz, length, length], device=device).tril(0) + cx = torch.bmm(x, w) + else: + if exclusive: + w = torch.ones([bsz, length, length], device=device).triu(1) + else: + w = torch.ones([bsz, length, length], device=device).triu(0) + cx = torch.bmm(x, w) + return cx + + +def cummin(x, reverse=False, exclusive=False, max_value=1e9): + """cumulative min.""" + if reverse: + if exclusive: + x = F.pad(x[:, :, 1:], (0, 1), value=max_value) + x = x.flip([-1]).cummin(-1)[0].flip([-1]) + else: + if exclusive: + x = F.pad(x[:, :, :-1], (1, 0), value=max_value) + x = x.cummin(-1)[0] + return x diff --git a/finetune/syntactic_category_lexical_content_the/predict_results.txt b/finetune/syntactic_category_lexical_content_the/predict_results.txt new file mode 100644 index 0000000000000000000000000000000000000000..de90d8da730ce1f5c3033fd862db957771efc6bb --- /dev/null +++ b/finetune/syntactic_category_lexical_content_the/predict_results.txt @@ -0,0 +1,23342 @@ +index prediction +0 1 +1 0 +2 0 +3 1 +4 1 +5 0 +6 1 +7 0 +8 0 +9 1 +10 0 +11 0 +12 1 +13 0 +14 0 +15 1 +16 1 +17 0 +18 1 +19 0 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a/finetune/syntactic_category_relative_position/eval_results.json b/finetune/syntactic_category_relative_position/eval_results.json new file mode 100644 index 0000000000000000000000000000000000000000..922ad38f3a7dc01c6d832760fb74c3809b58aecb --- /dev/null +++ b/finetune/syntactic_category_relative_position/eval_results.json @@ -0,0 +1,11 @@ +{ + "epoch": 10.0, + "eval_accuracy": 0.6505087018013, + "eval_f1": 0.6783450829594816, + "eval_loss": 3.1766340732574463, + "eval_mcc": 0.30880477112694915, + "eval_runtime": 50.7539, + "eval_samples": 25849, + "eval_samples_per_second": 509.301, + "eval_steps_per_second": 63.68 +} \ No newline at end of file diff --git a/finetune/syntactic_category_relative_position/merges.txt b/finetune/syntactic_category_relative_position/merges.txt new file mode 100644 index 0000000000000000000000000000000000000000..94b4f252cb566a4dac62a08c78d85169ff4e91be --- /dev/null +++ b/finetune/syntactic_category_relative_position/merges.txt @@ -0,0 +1,31794 @@ +#version: 0.2 +Ġ t +h e +Ġ a +i n +Ġt he +r e +Ġ w +o u +Ġ s +o n +e r +h a +n d +i s +i t +Ġ b +Ġ o +Ġ c +Ġ m +Ġ f +in g +o r +a n +e d +l l +Ġ d +e n +Ġt o +e s +Ġ p +Ġo f +Ġ y +Ġ in +a r +a t +Ġa nd +Ġ l +ha t +Ġy ou +a s +Ġ g +o m +Ġ n +Ġ I +i c +o w +l e +v e +o t +Ġ h +Ġ he +a l +e t +Ġ ha +' s +u s +Ġt h +a y +Ġ it +Ġ is +Ġb e +Ġt hat +Ġ on +i on +i d +Ġ e +g h +u t +l d +en t +i m +Ġ re +Ġw as +a c +Ġw e +v er +a m +T he +. . +Ġf or +l y +Ġ S +s t +r i +r o +i r +a d +s e +k e +Ġ . +Ġs t +gh t +o o +' t +Ġ A +ou t +it h +Ġm e +Ġ C +a ll +Ġ k +Ġ u +Ġa n +Ġ M +Ġw ith +u r +Ġa s +Ġd o +c t +he r +c e +i ll +t er +i f +c h +Ġw h +Ġs e +om e +Ġa re +Ġ H +i l +Ġn ot +Ġha ve +Ġ B +Ġ 1 +a nd +Ġa t +Ġth is +ou ld +an t +Ġg o +us t +at ion +Ġl i +o p +a in +Ġk n +Y ou +e a +Ġ P +Ġ T +Ġ , +u n +g e +o l +t h +Ġh is +u l +Ġ " +r om +.. . +o re +Ġ W +e ll +Ġn e +Ġ r +Ġkn ow +Ġ j +ou r +Ġ D +Ġd e +p p +Ġc an +Ġ G +Ġthe y +u m +Ġ The +Ġs o +es t +Ġ ? +Ġa l +Ġa ll +Ġa b +Ġm y +is t +er s +Ġ R +i e +Ġyou r +ar t +Ġb y +Ġf rom +es s +Ġs h +Ġo r +Ġ 2 +Ġ F +Ġw hat +n t +a b +o s +Ġ L +e l +Ġon e +Ġc on +Ġ N +Ġw he +Ġthe re +i g +i ght +ha n +all y +Ġu p +q u +e m +Ġc om +on e +Ġha d +Ġb ut +Ġhe r +Ġs u +re d +r y +Ġ J +' re +Ġh im +Ġ out +r a +ac k +at e +oo d +ver y +oo k +Ġli ke +Ġw or +Ġs a +re s +am e +i ve +Ġp l +Ġ us +Ġ ( +on g +' m +Ġwe re +â Ģ +a ke +o d +in k +c k +Ġs he +Ġp ro +Ġab out +Ġ E +as t +Ġg et +Ġe x +Ġ v +p e +I t +or t +ou nd +in d +ar d +it y +Ġd on +A nd +ic h +ri ght +Ġ O +Ġj ust +Ġ1 9 +i es +H e +Ġwh o +e ar +ea h +Ġd id +a k +Ġhe re +b er +Ġthe m +Ġn o +im e +in e +Ġm an +Ġw ant +Ġw ould +Ġ2 0 +Ġ if +Ġ ' +Ġw ill +u d +a p +Ġ K +Ġth ink +ou s +' ll +v en +Ġs ome +o st +W hat +e p +h ing +Ġin t +Ġan y +ow n +ic e +a g +Ġ right +m ent +id e +Ġg ot +Ġha s +t her +us e +it t +Ġwh ich +ou gh +Ġt r +f e +h i +c a +Ġse e +Ġwhe n +Ġsa id +u re +Ġa r +Ġa g +Ġp e +Ġs ay +w ay +i a +Ġt ime +Ġt w +i v +ĠH e +s o +Ġbe en +i an +Ġs p +a re +ac e +Ġn ow +o ve +ic k +op le +c i +Ġ U +W e +ĠI t +' ve +Ġthe ir +o se +Y eah +Ġl e +Ġl ook +- - +Ġc l +is h +l f +re at +n a +Ġm ore +Ġthe n +Ġgo ing +a ge +S o +it e +re e +Ġo ther +Ġpe ople +ar y +Ġc ould +Ġ = +ĠS t +N o +ot her +n e +Ġof f +u e +Ġb ack +h is +ac t +B ut +Ġo ver +ir st +O h +as s +Ġ qu +ou nt +on d +Ġ very +ing s +ca use +Ġ ro +b le +p er +Ġg r +Ġa f +p t +Ġf e +an s +Ġwor k +Ġtw o +Ġg ood +T hat +Ġto o +Ġb r +e nd +om et +Ġ en +er m +o g +p l +at ed +o ll +Ġ Ġ +t he +Ġf irst +ion s +or d +Ġint o +he d +as e +Ġp art +k ay +0 0 +Ġh ow +Ġd own +or n +al k +u b +w e +Ġu n +Ġw ay +ad e +Ġt han +i re +Ġal so +o y +f f +itt le +Ġc all +i z +m er +Ġa m +ou ght +Ġy ear +h y +at er +Ġp r +Ġcom p +o v +Ġc ome +W ell +Ġb l +Ġ our +an ce +Ġwhe re +nd er +ac h +if e +Ġag ain +Ġ im +n g +Ġon ly +Ġ V +Ġe very +ĠI n +Ġl ittle +Ġa d +c c +ur n +ha r +o h +Ġs omet +r u +Ġd ay +w n +Ġb et +an g +Ġpl ay +h o +Ġwe ll +am p +he re +or m +Ġt e +Ġa ct +Ġne ed +en ce +res s +u ch +a v +Ġcon t +Ġbe cause +Ġd is 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See all RoBERTa models at https://huggingface.co/models?filter=roberta +] + + +class StructRobertaConfig(RobertaConfig): + model_type = "roberta" + + def __init__( + self, + n_parser_layers=4, + conv_size=9, + relations=("head", "child"), + weight_act="softmax", + n_cntxt_layers=3, + n_cntxt_layers_2=0, + **kwargs,): + + super().__init__(**kwargs) + self.n_cntxt_layers = n_cntxt_layers + self.n_parser_layers = n_parser_layers + self.n_cntxt_layers_2 = n_cntxt_layers_2 + self.conv_size = conv_size + self.relations = relations + self.weight_act = weight_act + +class Conv1d(nn.Module): + """1D convolution layer.""" + + def __init__(self, hidden_size, kernel_size, dilation=1): + """Initialization. + + Args: + hidden_size: dimension of input embeddings + kernel_size: convolution kernel size + dilation: the spacing between the kernel points + """ + super(Conv1d, self).__init__() + + if kernel_size % 2 == 0: + padding = (kernel_size // 2) * dilation + self.shift = True + else: + padding = ((kernel_size - 1) // 2) * dilation + self.shift = False + self.conv = nn.Conv1d( + hidden_size, hidden_size, kernel_size, padding=padding, dilation=dilation + ) + + def forward(self, x): + """Compute convolution. + + Args: + x: input embeddings + Returns: + conv_output: convolution results + """ + + if self.shift: + return self.conv(x.transpose(1, 2)).transpose(1, 2)[:, 1:] + else: + return self.conv(x.transpose(1, 2)).transpose(1, 2) + + +class RobertaEmbeddings(nn.Module): + """ + Same as BertEmbeddings with a tiny tweak for positional embeddings indexing. + """ + + # Copied from transformers.models.bert.modeling_bert.BertEmbeddings.__init__ + def __init__(self, config): + super().__init__() + self.word_embeddings = nn.Embedding( + config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id + ) + self.position_embeddings = nn.Embedding( + config.max_position_embeddings, config.hidden_size + ) + self.token_type_embeddings = nn.Embedding( + config.type_vocab_size, config.hidden_size + ) + + # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load + # any TensorFlow checkpoint file + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + # position_ids (1, len position emb) is contiguous in memory and exported when serialized + self.position_embedding_type = getattr( + config, "position_embedding_type", "absolute" + ) + self.register_buffer( + "position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)) + ) + if version.parse(torch.__version__) > version.parse("1.6.0"): + self.register_buffer( + "token_type_ids", + torch.zeros(self.position_ids.size(), dtype=torch.long), + persistent=False, + ) + + # End copy + self.padding_idx = config.pad_token_id + self.position_embeddings = nn.Embedding( + config.max_position_embeddings, + config.hidden_size, + padding_idx=self.padding_idx, + ) + + def forward( + self, + input_ids=None, + token_type_ids=None, + position_ids=None, + inputs_embeds=None, + past_key_values_length=0, + ): + if position_ids is None: + if input_ids is not None: + # Create the position ids from the input token ids. Any padded tokens remain padded. + position_ids = create_position_ids_from_input_ids( + input_ids, self.padding_idx, past_key_values_length + ) + else: + position_ids = self.create_position_ids_from_inputs_embeds( + inputs_embeds + ) + + if input_ids is not None: + input_shape = input_ids.size() + else: + input_shape = inputs_embeds.size()[:-1] + + seq_length = input_shape[1] + + # Setting the token_type_ids to the registered buffer in constructor where it is all zeros, which usually occurs + # when its auto-generated, registered buffer helps users when tracing the model without passing token_type_ids, solves + # issue #5664 + if token_type_ids is None: + if hasattr(self, "token_type_ids"): + buffered_token_type_ids = self.token_type_ids[:, :seq_length] + buffered_token_type_ids_expanded = buffered_token_type_ids.expand( + input_shape[0], seq_length + ) + token_type_ids = buffered_token_type_ids_expanded + else: + token_type_ids = torch.zeros( + input_shape, dtype=torch.long, device=self.position_ids.device + ) + + if inputs_embeds is None: + inputs_embeds = self.word_embeddings(input_ids) + token_type_embeddings = self.token_type_embeddings(token_type_ids) + + embeddings = inputs_embeds + token_type_embeddings + if self.position_embedding_type == "absolute": + position_embeddings = self.position_embeddings(position_ids) + embeddings += position_embeddings + embeddings = self.LayerNorm(embeddings) + embeddings = self.dropout(embeddings) + return embeddings + + def create_position_ids_from_inputs_embeds(self, inputs_embeds): + """ + We are provided embeddings directly. We cannot infer which are padded so just generate sequential position ids. + + Args: + inputs_embeds: torch.Tensor + + Returns: torch.Tensor + """ + input_shape = inputs_embeds.size()[:-1] + sequence_length = input_shape[1] + + position_ids = torch.arange( + self.padding_idx + 1, + sequence_length + self.padding_idx + 1, + dtype=torch.long, + device=inputs_embeds.device, + ) + return position_ids.unsqueeze(0).expand(input_shape) + + +# Copied from transformers.models.bert.modeling_bert.BertSelfAttention with Bert->Roberta +class RobertaSelfAttention(nn.Module): + def __init__(self, config, position_embedding_type=None): + super().__init__() + if config.hidden_size % config.num_attention_heads != 0 and not hasattr( + config, "embedding_size" + ): + raise ValueError( + f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention " + f"heads ({config.num_attention_heads})" + ) + + self.num_attention_heads = config.num_attention_heads + self.attention_head_size = int(config.hidden_size / config.num_attention_heads) + self.all_head_size = self.num_attention_heads * self.attention_head_size + + self.query = nn.Linear(config.hidden_size, self.all_head_size) + self.key = nn.Linear(config.hidden_size, self.all_head_size) + self.value = nn.Linear(config.hidden_size, self.all_head_size) + + self.dropout = nn.Dropout(config.attention_probs_dropout_prob) + self.position_embedding_type = position_embedding_type or getattr( + config, "position_embedding_type", "absolute" + ) + if ( + self.position_embedding_type == "relative_key" + or self.position_embedding_type == "relative_key_query" + ): + self.max_position_embeddings = config.max_position_embeddings + self.distance_embedding = nn.Embedding( + 2 * config.max_position_embeddings - 1, self.attention_head_size + ) + + self.is_decoder = config.is_decoder + + def transpose_for_scores(self, x): + new_x_shape = x.size()[:-1] + ( + self.num_attention_heads, + self.attention_head_size, + ) + x = x.view(new_x_shape) + return x.permute(0, 2, 1, 3) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.FloatTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, + output_attentions: Optional[bool] = False, + parser_att_mask=None, + ) -> Tuple[torch.Tensor]: + mixed_query_layer = self.query(hidden_states) + + # If this is instantiated as a cross-attention module, the keys + # and values come from an encoder; the attention mask needs to be + # such that the encoder's padding tokens are not attended to. + is_cross_attention = encoder_hidden_states is not None + + if is_cross_attention and past_key_value is not None: + # reuse k,v, cross_attentions + key_layer = past_key_value[0] + value_layer = past_key_value[1] + attention_mask = encoder_attention_mask + elif is_cross_attention: + key_layer = self.transpose_for_scores(self.key(encoder_hidden_states)) + value_layer = self.transpose_for_scores(self.value(encoder_hidden_states)) + attention_mask = encoder_attention_mask + elif past_key_value is not None: + key_layer = self.transpose_for_scores(self.key(hidden_states)) + value_layer = self.transpose_for_scores(self.value(hidden_states)) + key_layer = torch.cat([past_key_value[0], key_layer], dim=2) + value_layer = torch.cat([past_key_value[1], value_layer], dim=2) + else: + key_layer = self.transpose_for_scores(self.key(hidden_states)) + value_layer = self.transpose_for_scores(self.value(hidden_states)) + + query_layer = self.transpose_for_scores(mixed_query_layer) + + if self.is_decoder: + # if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states. + # Further calls to cross_attention layer can then reuse all cross-attention + # key/value_states (first "if" case) + # if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of + # all previous decoder key/value_states. Further calls to uni-directional self-attention + # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case) + # if encoder bi-directional self-attention `past_key_value` is always `None` + past_key_value = (key_layer, value_layer) + + # Take the dot product between "query" and "key" to get the raw attention scores. + attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) + + if ( + self.position_embedding_type == "relative_key" + or self.position_embedding_type == "relative_key_query" + ): + seq_length = hidden_states.size()[1] + position_ids_l = torch.arange( + seq_length, dtype=torch.long, device=hidden_states.device + ).view(-1, 1) + position_ids_r = torch.arange( + seq_length, dtype=torch.long, device=hidden_states.device + ).view(1, -1) + distance = position_ids_l - position_ids_r + positional_embedding = self.distance_embedding( + distance + self.max_position_embeddings - 1 + ) + positional_embedding = positional_embedding.to( + dtype=query_layer.dtype + ) # fp16 compatibility + + if self.position_embedding_type == "relative_key": + relative_position_scores = torch.einsum( + "bhld,lrd->bhlr", query_layer, positional_embedding + ) + attention_scores = attention_scores + relative_position_scores + elif self.position_embedding_type == "relative_key_query": + relative_position_scores_query = torch.einsum( + "bhld,lrd->bhlr", query_layer, positional_embedding + ) + relative_position_scores_key = torch.einsum( + "bhrd,lrd->bhlr", key_layer, positional_embedding + ) + attention_scores = ( + attention_scores + + relative_position_scores_query + + relative_position_scores_key + ) + + attention_scores = attention_scores / math.sqrt(self.attention_head_size) + if attention_mask is not None: + # Apply the attention mask is (precomputed for all layers in RobertaModel forward() function) + attention_scores = attention_scores + attention_mask + + if parser_att_mask is None: + # Normalize the attention scores to probabilities. + attention_probs = nn.functional.softmax(attention_scores, dim=-1) + else: + attention_probs = torch.sigmoid(attention_scores) * parser_att_mask + + # This is actually dropping out entire tokens to attend to, which might + # seem a bit unusual, but is taken from the original Transformer paper. + attention_probs = self.dropout(attention_probs) + + # Mask heads if we want to + if head_mask is not None: + attention_probs = attention_probs * head_mask + + context_layer = torch.matmul(attention_probs, value_layer) + + context_layer = context_layer.permute(0, 2, 1, 3).contiguous() + new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) + context_layer = context_layer.view(new_context_layer_shape) + + outputs = ( + (context_layer, attention_probs) if output_attentions else (context_layer,) + ) + + if self.is_decoder: + outputs = outputs + (past_key_value,) + return outputs + + +# Copied from transformers.models.bert.modeling_bert.BertSelfOutput +class RobertaSelfOutput(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward( + self, hidden_states: torch.Tensor, input_tensor: torch.Tensor + ) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + hidden_states = self.LayerNorm(hidden_states + input_tensor) + return hidden_states + + +# Copied from transformers.models.bert.modeling_bert.BertAttention with Bert->Roberta +class RobertaAttention(nn.Module): + def __init__(self, config, position_embedding_type=None): + super().__init__() + self.self = RobertaSelfAttention( + config, position_embedding_type=position_embedding_type + ) + self.output = RobertaSelfOutput(config) + self.pruned_heads = set() + + def prune_heads(self, heads): + if len(heads) == 0: + return + heads, index = find_pruneable_heads_and_indices( + heads, + self.self.num_attention_heads, + self.self.attention_head_size, + self.pruned_heads, + ) + + # Prune linear layers + self.self.query = prune_linear_layer(self.self.query, index) + self.self.key = prune_linear_layer(self.self.key, index) + self.self.value = prune_linear_layer(self.self.value, index) + self.output.dense = prune_linear_layer(self.output.dense, index, dim=1) + + # Update hyper params and store pruned heads + self.self.num_attention_heads = self.self.num_attention_heads - len(heads) + self.self.all_head_size = ( + self.self.attention_head_size * self.self.num_attention_heads + ) + self.pruned_heads = self.pruned_heads.union(heads) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.FloatTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, + output_attentions: Optional[bool] = False, + parser_att_mask=None, + ) -> Tuple[torch.Tensor]: + self_outputs = self.self( + hidden_states, + attention_mask, + head_mask, + encoder_hidden_states, + encoder_attention_mask, + past_key_value, + output_attentions, + parser_att_mask=parser_att_mask, + ) + attention_output = self.output(self_outputs[0], hidden_states) + outputs = (attention_output,) + self_outputs[ + 1: + ] # add attentions if we output them + return outputs + + +# Copied from transformers.models.bert.modeling_bert.BertIntermediate +class RobertaIntermediate(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.intermediate_size) + if isinstance(config.hidden_act, str): + self.intermediate_act_fn = ACT2FN[config.hidden_act] + else: + self.intermediate_act_fn = config.hidden_act + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.intermediate_act_fn(hidden_states) + return hidden_states + + +# Copied from transformers.models.bert.modeling_bert.BertOutput +class RobertaOutput(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.intermediate_size, config.hidden_size) + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward( + self, hidden_states: torch.Tensor, input_tensor: torch.Tensor + ) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + hidden_states = self.LayerNorm(hidden_states + input_tensor) + return hidden_states + + +# Copied from transformers.models.bert.modeling_bert.BertLayer with Bert->Roberta +class RobertaLayer(nn.Module): + def __init__(self, config): + super().__init__() + self.chunk_size_feed_forward = config.chunk_size_feed_forward + self.seq_len_dim = 1 + self.attention = RobertaAttention(config) + self.is_decoder = config.is_decoder + self.add_cross_attention = config.add_cross_attention + if self.add_cross_attention: + if not self.is_decoder: + raise ValueError( + f"{self} should be used as a decoder model if cross attention is added" + ) + self.crossattention = RobertaAttention( + config, position_embedding_type="absolute" + ) + self.intermediate = RobertaIntermediate(config) + self.output = RobertaOutput(config) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.FloatTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, + output_attentions: Optional[bool] = False, + parser_att_mask=None, + ) -> Tuple[torch.Tensor]: + # decoder uni-directional self-attention cached key/values tuple is at positions 1,2 + self_attn_past_key_value = ( + past_key_value[:2] if past_key_value is not None else None + ) + self_attention_outputs = self.attention( + hidden_states, + attention_mask, + head_mask, + output_attentions=output_attentions, + past_key_value=self_attn_past_key_value, + parser_att_mask=parser_att_mask, + ) + attention_output = self_attention_outputs[0] + + # if decoder, the last output is tuple of self-attn cache + if self.is_decoder: + outputs = self_attention_outputs[1:-1] + present_key_value = self_attention_outputs[-1] + else: + outputs = self_attention_outputs[ + 1: + ] # add self attentions if we output attention weights + + cross_attn_present_key_value = None + if self.is_decoder and encoder_hidden_states is not None: + if not hasattr(self, "crossattention"): + raise ValueError( + f"If `encoder_hidden_states` are passed, {self} has to be instantiated with cross-attention layers by setting `config.add_cross_attention=True`" + ) + + # cross_attn cached key/values tuple is at positions 3,4 of past_key_value tuple + cross_attn_past_key_value = ( + past_key_value[-2:] if past_key_value is not None else None + ) + cross_attention_outputs = self.crossattention( + attention_output, + attention_mask, + head_mask, + encoder_hidden_states, + encoder_attention_mask, + cross_attn_past_key_value, + output_attentions, + ) + attention_output = cross_attention_outputs[0] + outputs = ( + outputs + cross_attention_outputs[1:-1] + ) # add cross attentions if we output attention weights + + # add cross-attn cache to positions 3,4 of present_key_value tuple + cross_attn_present_key_value = cross_attention_outputs[-1] + present_key_value = present_key_value + cross_attn_present_key_value + + layer_output = apply_chunking_to_forward( + self.feed_forward_chunk, + self.chunk_size_feed_forward, + self.seq_len_dim, + attention_output, + ) + outputs = (layer_output,) + outputs + + # if decoder, return the attn key/values as the last output + if self.is_decoder: + outputs = outputs + (present_key_value,) + + return outputs + + def feed_forward_chunk(self, attention_output): + intermediate_output = self.intermediate(attention_output) + layer_output = self.output(intermediate_output, attention_output) + return layer_output + + +# Copied from transformers.models.bert.modeling_bert.BertEncoder with Bert->Roberta +class RobertaEncoder(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + self.layer = nn.ModuleList( + [RobertaLayer(config) for _ in range(config.num_hidden_layers)] + ) + self.gradient_checkpointing = False + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.FloatTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = False, + output_hidden_states: Optional[bool] = False, + return_dict: Optional[bool] = True, + parser_att_mask=None, + ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPastAndCrossAttentions]: + all_hidden_states = () if output_hidden_states else None + all_self_attentions = () if output_attentions else None + all_cross_attentions = ( + () if output_attentions and self.config.add_cross_attention else None + ) + + next_decoder_cache = () if use_cache else None + for i, layer_module in enumerate(self.layer): + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + layer_head_mask = head_mask[i] if head_mask is not None else None + past_key_value = past_key_values[i] if past_key_values is not None else None + + if self.gradient_checkpointing and self.training: + + if use_cache: + logger.warning( + "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." + ) + use_cache = False + + def create_custom_forward(module): + def custom_forward(*inputs): + return module(*inputs, past_key_value, output_attentions) + + return custom_forward + + layer_outputs = torch.utils.checkpoint.checkpoint( + create_custom_forward(layer_module), + hidden_states, + attention_mask, + layer_head_mask, + encoder_hidden_states, + encoder_attention_mask, + ) + else: + if parser_att_mask is not None: + layer_outputs = layer_module( + hidden_states, + attention_mask, + layer_head_mask, + encoder_hidden_states, + encoder_attention_mask, + past_key_value, + output_attentions, + parser_att_mask=parser_att_mask[i]) + else: + layer_outputs = layer_module( + hidden_states, + attention_mask, + layer_head_mask, + encoder_hidden_states, + encoder_attention_mask, + past_key_value, + output_attentions, + parser_att_mask=None) + + + hidden_states = layer_outputs[0] + if use_cache: + next_decoder_cache += (layer_outputs[-1],) + if output_attentions: + all_self_attentions = all_self_attentions + (layer_outputs[1],) + if self.config.add_cross_attention: + all_cross_attentions = all_cross_attentions + (layer_outputs[2],) + + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + if not return_dict: + return tuple( + v + for v in [ + hidden_states, + next_decoder_cache, + all_hidden_states, + all_self_attentions, + all_cross_attentions, + ] + if v is not None + ) + return BaseModelOutputWithPastAndCrossAttentions( + last_hidden_state=hidden_states, + past_key_values=next_decoder_cache, + hidden_states=all_hidden_states, + attentions=all_self_attentions, + cross_attentions=all_cross_attentions, + ) + + +# Copied from transformers.models.bert.modeling_bert.BertPooler +class RobertaPooler(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.activation = nn.Tanh() + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + # We "pool" the model by simply taking the hidden state corresponding + # to the first token. + first_token_tensor = hidden_states[:, 0] + pooled_output = self.dense(first_token_tensor) + pooled_output = self.activation(pooled_output) + return pooled_output + + +class RobertaPreTrainedModel(PreTrainedModel): + """ + An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained + models. + """ + + config_class = RobertaConfig + base_model_prefix = "roberta" + supports_gradient_checkpointing = True + + # Copied from transformers.models.bert.modeling_bert.BertPreTrainedModel._init_weights + def _init_weights(self, module): + """Initialize the weights""" + if isinstance(module, nn.Linear): + # Slightly different from the TF version which uses truncated_normal for initialization + # cf https://github.com/pytorch/pytorch/pull/5617 + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.Embedding): + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.padding_idx is not None: + module.weight.data[module.padding_idx].zero_() + elif isinstance(module, nn.LayerNorm): + if module.bias is not None: + module.bias.data.zero_() + module.weight.data.fill_(1.0) + + def _set_gradient_checkpointing(self, module, value=False): + if isinstance(module, RobertaEncoder): + module.gradient_checkpointing = value + + def update_keys_to_ignore(self, config, del_keys_to_ignore): + """Remove some keys from ignore list""" + if not config.tie_word_embeddings: + # must make a new list, or the class variable gets modified! + self._keys_to_ignore_on_save = [ + k for k in self._keys_to_ignore_on_save if k not in del_keys_to_ignore + ] + self._keys_to_ignore_on_load_missing = [ + k + for k in self._keys_to_ignore_on_load_missing + if k not in del_keys_to_ignore + ] + + +ROBERTA_START_DOCSTRING = r""" + + This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the + library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads + etc.) + + This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. + Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage + and behavior. + + Parameters: + config ([`RobertaConfig`]): Model configuration class with all the parameters of the + model. Initializing with a config file does not load the weights associated with the model, only the + configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. +""" + + +ROBERTA_INPUTS_DOCSTRING = r""" + Args: + input_ids (`torch.LongTensor` of shape `({0})`): + Indices of input sequence tokens in the vocabulary. + + Indices can be obtained using [`RobertaTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + [What are input IDs?](../glossary#input-ids) + attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*): + Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + + [What are attention masks?](../glossary#attention-mask) + token_type_ids (`torch.LongTensor` of shape `({0})`, *optional*): + Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, + 1]`: + + - 0 corresponds to a *sentence A* token, + - 1 corresponds to a *sentence B* token. + + [What are token type IDs?](../glossary#token-type-ids) + position_ids (`torch.LongTensor` of shape `({0})`, *optional*): + Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, + config.max_position_embeddings - 1]`. + + [What are position IDs?](../glossary#position-ids) + head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): + Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: + + - 1 indicates the head is **not masked**, + - 0 indicates the head is **masked**. + + inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_size)`, *optional*): + Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This + is useful if you want more control over how to convert `input_ids` indices into associated vectors than the + model's internal embedding lookup matrix. + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned + tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for + more detail. + return_dict (`bool`, *optional*): + Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. +""" + + +class RobertaModel(RobertaPreTrainedModel): + """ + + The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of + cross-attention is added between the self-attention layers, following the architecture described in *Attention is + all you need*_ by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz + Kaiser and Illia Polosukhin. + + To behave as an decoder the model needs to be initialized with the `is_decoder` argument of the configuration set + to `True`. To be used in a Seq2Seq model, the model needs to initialized with both `is_decoder` argument and + `add_cross_attention` set to `True`; an `encoder_hidden_states` is then expected as an input to the forward pass. + + .. _*Attention is all you need*: https://arxiv.org/abs/1706.03762 + + """ + + _keys_to_ignore_on_load_missing = [r"position_ids"] + + # Copied from transformers.models.bert.modeling_bert.BertModel.__init__ with Bert->Roberta + def __init__(self, config, add_pooling_layer=True): + super().__init__(config) + self.config = config + + self.embeddings = RobertaEmbeddings(config) + self.encoder = RobertaEncoder(config) + + self.pooler = RobertaPooler(config) if add_pooling_layer else None + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.embeddings.word_embeddings + + def set_input_embeddings(self, value): + self.embeddings.word_embeddings = value + + def _prune_heads(self, heads_to_prune): + """ + Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base + class PreTrainedModel + """ + for layer, heads in heads_to_prune.items(): + self.encoder.layer[layer].attention.prune_heads(heads) + + # Copied from transformers.models.bert.modeling_bert.BertModel.forward + def forward( + self, + input_ids: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + token_type_ids: Optional[torch.Tensor] = None, + position_ids: Optional[torch.Tensor] = None, + head_mask: Optional[torch.Tensor] = None, + inputs_embeds: Optional[torch.Tensor] = None, + encoder_hidden_states: Optional[torch.Tensor] = None, + encoder_attention_mask: Optional[torch.Tensor] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + parser_att_mask=None, + ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPoolingAndCrossAttentions]: + r""" + encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): + Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if + the model is configured as a decoder. + encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*): + Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in + the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): + Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. + + If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that + don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all + `decoder_input_ids` of shape `(batch_size, sequence_length)`. + use_cache (`bool`, *optional*): + If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see + `past_key_values`). + """ + output_attentions = ( + output_attentions + if output_attentions is not None + else self.config.output_attentions + ) + output_hidden_states = ( + output_hidden_states + if output_hidden_states is not None + else self.config.output_hidden_states + ) + return_dict = ( + return_dict if return_dict is not None else self.config.use_return_dict + ) + + if self.config.is_decoder: + use_cache = use_cache if use_cache is not None else self.config.use_cache + else: + use_cache = False + + if input_ids is not None and inputs_embeds is not None: + raise ValueError( + "You cannot specify both input_ids and inputs_embeds at the same time" + ) + elif input_ids is not None: + input_shape = input_ids.size() + elif inputs_embeds is not None: + input_shape = inputs_embeds.size()[:-1] + else: + raise ValueError("You have to specify either input_ids or inputs_embeds") + + batch_size, seq_length = input_shape + device = input_ids.device if input_ids is not None else inputs_embeds.device + + # past_key_values_length + past_key_values_length = ( + past_key_values[0][0].shape[2] if past_key_values is not None else 0 + ) + + if attention_mask is None: + attention_mask = torch.ones( + ((batch_size, seq_length + past_key_values_length)), device=device + ) + + if token_type_ids is None: + if hasattr(self.embeddings, "token_type_ids"): + buffered_token_type_ids = self.embeddings.token_type_ids[:, :seq_length] + buffered_token_type_ids_expanded = buffered_token_type_ids.expand( + batch_size, seq_length + ) + token_type_ids = buffered_token_type_ids_expanded + else: + token_type_ids = torch.zeros( + input_shape, dtype=torch.long, device=device + ) + + # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length] + # ourselves in which case we just need to make it broadcastable to all heads. + extended_attention_mask: torch.Tensor = self.get_extended_attention_mask( + attention_mask, input_shape, device + ) + + # If a 2D or 3D attention mask is provided for the cross-attention + # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length] + if self.config.is_decoder and encoder_hidden_states is not None: + ( + encoder_batch_size, + encoder_sequence_length, + _, + ) = encoder_hidden_states.size() + encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length) + if encoder_attention_mask is None: + encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device) + encoder_extended_attention_mask = self.invert_attention_mask( + encoder_attention_mask + ) + else: + encoder_extended_attention_mask = None + + # Prepare head mask if needed + # 1.0 in head_mask indicate we keep the head + # attention_probs has shape bsz x n_heads x N x N + # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] + # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] + head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) + + embedding_output = self.embeddings( + input_ids=input_ids, + position_ids=position_ids, + token_type_ids=token_type_ids, + inputs_embeds=inputs_embeds, + past_key_values_length=past_key_values_length, + ) + encoder_outputs = self.encoder( + embedding_output, + attention_mask=extended_attention_mask, + head_mask=head_mask, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_extended_attention_mask, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + parser_att_mask=parser_att_mask, + ) + sequence_output = encoder_outputs[0] + pooled_output = ( + self.pooler(sequence_output) if self.pooler is not None else None + ) + + if not return_dict: + return (sequence_output, pooled_output) + encoder_outputs[1:] + + return BaseModelOutputWithPoolingAndCrossAttentions( + last_hidden_state=sequence_output, + pooler_output=pooled_output, + past_key_values=encoder_outputs.past_key_values, + hidden_states=encoder_outputs.hidden_states, + attentions=encoder_outputs.attentions, + cross_attentions=encoder_outputs.cross_attentions, + ) + + +class StructRoberta(RobertaPreTrainedModel): + _keys_to_ignore_on_save = [r"lm_head.decoder.weight", r"lm_head.decoder.bias"] + _keys_to_ignore_on_load_missing = [ + r"position_ids", + r"lm_head.decoder.weight", + r"lm_head.decoder.bias", + ] + _keys_to_ignore_on_load_unexpected = [r"pooler"] + + def __init__(self, config): + super().__init__(config) + + if config.is_decoder: + logger.warning( + "If you want to use `RobertaForMaskedLM` make sure `config.is_decoder=False` for " + "bi-directional self-attention." + ) + + + if config.n_cntxt_layers > 0: + config_cntxt = copy.deepcopy(config) + config_cntxt.num_hidden_layers = config.n_cntxt_layers + + self.cntxt_layers = RobertaModel(config_cntxt, add_pooling_layer=False) + + if config.n_cntxt_layers_2 > 0: + self.parser_layers_1 = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(int(config.n_parser_layers/2)) + ] + ) + + self.distance_ff_1 = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff_1 = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight_1 = nn.Parameter( + torch.zeros((config.n_cntxt_layers_2, config.num_attention_heads, n_rel)) + ) + self._rel_weight_1.data.normal_(0, 0.1) + + self._scaler_1 = nn.Parameter(torch.zeros(2)) + + config_cntxt_2 = copy.deepcopy(config) + config_cntxt_2.num_hidden_layers = config.n_cntxt_layers_2 + + self.cntxt_layers_2 = RobertaModel(config_cntxt_2, add_pooling_layer=False) + + + self.parser_layers_2 = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(int(config.n_parser_layers/2)) + ] + ) + + self.distance_ff_2 = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff_2 = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight_2 = nn.Parameter( + torch.zeros((config.num_hidden_layers, config.num_attention_heads, n_rel)) + ) + self._rel_weight_2.data.normal_(0, 0.1) + + self._scaler_2 = nn.Parameter(torch.zeros(2)) + + else: + self.parser_layers = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(config.n_parser_layers) + ] + ) + + self.distance_ff = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight = nn.Parameter( + torch.zeros((config.num_hidden_layers, config.num_attention_heads, n_rel)) + ) + self._rel_weight.data.normal_(0, 0.1) + + self._scaler = nn.Parameter(torch.zeros(2)) + + self.roberta = RobertaModel(config, add_pooling_layer=False) + + if config.n_cntxt_layers > 0: + self.cntxt_layers.embeddings = self.roberta.embeddings + if config.n_cntxt_layers_2 > 0: + self.cntxt_layers_2.embeddings = self.roberta.embeddings + + self.lm_head = RobertaLMHead(config) + + self.pad = config.pad_token_id + + # The LM head weights require special treatment only when they are tied with the word embeddings + self.update_keys_to_ignore(config, ["lm_head.decoder.weight"]) + + # Initialize weights and apply final processing + self.post_init() + + def get_output_embeddings(self): + return self.lm_head.decoder + + def set_output_embeddings(self, new_embeddings): + self.lm_head.decoder = new_embeddings + + @property + def scaler(self): + return self._scaler.exp() + + @property + def scaler_1(self): + return self._scaler_1.exp() + + @property + def scaler_2(self): + return self._scaler_2.exp() + + @property + def rel_weight(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight, dim=-1) + + @property + def rel_weight_1(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight_1) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight_1, dim=-1) + + + @property + def rel_weight_2(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight_2) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight_2, dim=-1) + + + def compute_block(self, distance, height, n_cntxt_layers=0): + """Compute constituents from distance and height.""" + + if n_cntxt_layers>0: + if n_cntxt_layers == 1: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler_1[0] + elif n_cntxt_layers == 2: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler_2[0] + else: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0] + + gamma = torch.sigmoid(-beta_logits) + ones = torch.ones_like(gamma) + + block_mask_left = cummin( + gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1 + ) + block_mask_left = block_mask_left - F.pad( + block_mask_left[:, :, :-1], (1, 0), value=0 + ) + block_mask_left.tril_(0) + + block_mask_right = cummin( + gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1 + ) + block_mask_right = block_mask_right - F.pad( + block_mask_right[:, :, 1:], (0, 1), value=0 + ) + block_mask_right.triu_(0) + + block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :] + block = cumsum(block_mask_left).tril(0) + cumsum( + block_mask_right, reverse=True + ).triu(1) + + return block_p, block + + def compute_head(self, height, n_cntxt_layers=0): + """Estimate head for each constituent.""" + + _, length = height.size() + if n_cntxt_layers>0: + if n_cntxt_layers == 1: + head_logits = height * self.scaler_1[1] + elif n_cntxt_layers == 2: + head_logits = height * self.scaler_2[1] + else: + head_logits = height * self.scaler[1] + index = torch.arange(length, device=height.device) + + mask = (index[:, None, None] <= index[None, None, :]) * ( + index[None, None, :] <= index[None, :, None] + ) + head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1) + head_logits.masked_fill_(~mask[None, :, :, :], -1e9) + + head_p = torch.softmax(head_logits, dim=-1) + + return head_p + + def parse(self, x, embs=None, n_cntxt_layers=0): + """Parse input sentence. + + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + distance: syntactic distance + height: syntactic height + """ + + mask = x != self.pad + mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0) + + if embs is None: + h = self.roberta.embeddings(x) + else: + h = embs + + if n_cntxt_layers > 0: + if n_cntxt_layers == 1: + parser_layers = self.parser_layers_1 + height_ff = self.height_ff_1 + distance_ff = self.distance_ff_1 + elif n_cntxt_layers == 2: + parser_layers = self.parser_layers_2 + height_ff = self.height_ff_2 + distance_ff = self.distance_ff_2 + for i in range(int(self.config.n_parser_layers/2)): + h = h.masked_fill(~mask[:, :, None], 0) + h = parser_layers[i](h) + + height = height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, dim=-1 + )[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max + ) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where( + margin_left > margin_right, margin_right, margin_left + ).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + else: + for i in range(self.config.n_parser_layers): + h = h.masked_fill(~mask[:, :, None], 0) + h = self.parser_layers[i](h) + + height = self.height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = self.distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, dim=-1 + )[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max + ) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where( + margin_left > margin_right, margin_right, margin_left + ).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + + return distance, height + + def generate_mask(self, x, distance, height, n_cntxt_layers=0): + """Compute head and cibling distribution for each token.""" + + bsz, length = x.size() + + eye = torch.eye(length, device=x.device, dtype=torch.bool) + eye = eye[None, :, :].expand((bsz, -1, -1)) + + block_p, block = self.compute_block(distance, height, n_cntxt_layers=n_cntxt_layers) + head_p = self.compute_head(height, n_cntxt_layers=n_cntxt_layers) + head = torch.einsum("blij,bijh->blh", block_p, head_p) + head = head.masked_fill(eye, 0) + child = head.transpose(1, 2) + cibling = torch.bmm(head, child).masked_fill(eye, 0) + + rel_list = [] + if "head" in self.config.relations: + rel_list.append(head) + if "child" in self.config.relations: + rel_list.append(child) + if "cibling" in self.config.relations: + rel_list.append(cibling) + + rel = torch.stack(rel_list, dim=1) + + if n_cntxt_layers > 0: + if n_cntxt_layers == 1: + rel_weight = self.rel_weight_1 + elif n_cntxt_layers == 2: + rel_weight = self.rel_weight_2 + else: + rel_weight = self.rel_weight + + dep = torch.einsum("lhr,brij->lbhij", rel_weight, rel) + + if n_cntxt_layers == 1: + num_layers = self.cntxt_layers_2.config.num_hidden_layers + else: + num_layers = self.roberta.config.num_hidden_layers + + att_mask = dep.reshape( + num_layers, + bsz, + self.config.num_attention_heads, + length, + length, + ) + + return att_mask, cibling, head, block + + def forward( + self, + input_ids: Optional[torch.LongTensor] = None, + attention_mask: Optional[torch.FloatTensor] = None, + token_type_ids: Optional[torch.LongTensor] = None, + position_ids: Optional[torch.LongTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, MaskedLMOutput]: + r""" + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ..., + config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the + loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]` + kwargs (`Dict[str, any]`, optional, defaults to *{}*): + Used to hide legacy arguments that have been deprecated. + """ + return_dict = ( + return_dict if return_dict is not None else self.config.use_return_dict + ) + + + if self.config.n_cntxt_layers > 0: + cntxt_outputs = self.cntxt_layers( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict) + + + if self.config.n_cntxt_layers_2 > 0: + distance_1, height_1 = self.parse(input_ids, cntxt_outputs[0], n_cntxt_layers=1) + att_mask_1, _, _, _ = self.generate_mask(input_ids, distance_1, height_1, n_cntxt_layers=1) + + cntxt_outputs_2 = self.cntxt_layers_2( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + parser_att_mask=att_mask_1) + + sequence_output = cntxt_outputs_2[0] + + distance_2, height_2 = self.parse(input_ids, sequence_output[0], n_cntxt_layers=2) + att_mask, _, _, _ = self.generate_mask(input_ids, distance_2, height_2, n_cntxt_layers=2) + + elif self.config.n_cntxt_layers > 0: + distance, height = self.parse(input_ids, cntxt_outputs[0]) + att_mask, _, _, _ = self.generate_mask(input_ids, distance, height) + else: + distance, height = self.parse(input_ids) + att_mask, _, _, _ = self.generate_mask(input_ids, distance, height) + + outputs = self.roberta( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + parser_att_mask=att_mask, + ) + sequence_output = outputs[0] + prediction_scores = self.lm_head(sequence_output) + + masked_lm_loss = None + if labels is not None: + loss_fct = CrossEntropyLoss() + masked_lm_loss = loss_fct( + prediction_scores.view(-1, self.config.vocab_size), labels.view(-1) + ) + + if not return_dict: + output = (prediction_scores,) + outputs[2:] + return ( + ((masked_lm_loss,) + output) if masked_lm_loss is not None else output + ) + + return MaskedLMOutput( + loss=masked_lm_loss, + logits=prediction_scores, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +class RobertaLMHead(nn.Module): + """Roberta Head for masked language modeling.""" + + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + + self.decoder = nn.Linear(config.hidden_size, config.vocab_size) + self.bias = nn.Parameter(torch.zeros(config.vocab_size)) + self.decoder.bias = self.bias + + def forward(self, features, **kwargs): + x = self.dense(features) + x = gelu(x) + x = self.layer_norm(x) + + # project back to size of vocabulary with bias + x = self.decoder(x) + + return x + + def _tie_weights(self): + # To tie those two weights if they get disconnected (on TPU or when the bias is resized) + self.bias = self.decoder.bias + + +class StructRobertaForSequenceClassification(RobertaPreTrainedModel): + _keys_to_ignore_on_load_missing = [r"position_ids"] + + def __init__(self, config): + super().__init__(config) + self.num_labels = config.num_labels + self.config = config + + if config.n_cntxt_layers > 0: + config_cntxt = copy.deepcopy(config) + config_cntxt.num_hidden_layers = config.n_cntxt_layers + + self.cntxt_layers = RobertaModel(config_cntxt, add_pooling_layer=False) + + if config.n_cntxt_layers_2 > 0: + self.parser_layers_1 = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(int(config.n_parser_layers/2)) + ] + ) + + self.distance_ff_1 = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff_1 = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight_1 = nn.Parameter( + torch.zeros((config.n_cntxt_layers_2, config.num_attention_heads, n_rel)) + ) + self._rel_weight_1.data.normal_(0, 0.1) + + self._scaler_1 = nn.Parameter(torch.zeros(2)) + + config_cntxt_2 = copy.deepcopy(config) + config_cntxt_2.num_hidden_layers = config.n_cntxt_layers_2 + + self.cntxt_layers_2 = RobertaModel(config_cntxt_2, add_pooling_layer=False) + + + self.parser_layers_2 = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(int(config.n_parser_layers/2)) + ] + ) + + self.distance_ff_2 = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff_2 = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight_2 = nn.Parameter( + torch.zeros((config.num_hidden_layers, config.num_attention_heads, n_rel)) + ) + self._rel_weight_2.data.normal_(0, 0.1) + + self._scaler_2 = nn.Parameter(torch.zeros(2)) + + else: + self.parser_layers = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(config.n_parser_layers) + ] + ) + + self.distance_ff = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight = nn.Parameter( + torch.zeros((config.num_hidden_layers, config.num_attention_heads, n_rel)) + ) + self._rel_weight.data.normal_(0, 0.1) + + self._scaler = nn.Parameter(torch.zeros(2)) + + self.roberta = RobertaModel(config, add_pooling_layer=False) + + if config.n_cntxt_layers > 0: + self.cntxt_layers.embeddings = self.roberta.embeddings + if config.n_cntxt_layers_2 > 0: + self.cntxt_layers_2.embeddings = self.roberta.embeddings + + + self.pad = config.pad_token_id + self.classifier = RobertaClassificationHead(config) + + # Initialize weights and apply final processing + self.post_init() + + + @property + def scaler(self): + return self._scaler.exp() + + @property + def scaler_1(self): + return self._scaler_1.exp() + + @property + def scaler_2(self): + return self._scaler_2.exp() + + @property + def rel_weight(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight, dim=-1) + + @property + def rel_weight_1(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight_1) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight_1, dim=-1) + + + @property + def rel_weight_2(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight_2) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight_2, dim=-1) + + + def compute_block(self, distance, height, n_cntxt_layers=0): + """Compute constituents from distance and height.""" + + if n_cntxt_layers>0: + if n_cntxt_layers == 1: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler_1[0] + elif n_cntxt_layers == 2: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler_2[0] + else: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0] + + gamma = torch.sigmoid(-beta_logits) + ones = torch.ones_like(gamma) + + block_mask_left = cummin( + gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1 + ) + block_mask_left = block_mask_left - F.pad( + block_mask_left[:, :, :-1], (1, 0), value=0 + ) + block_mask_left.tril_(0) + + block_mask_right = cummin( + gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1 + ) + block_mask_right = block_mask_right - F.pad( + block_mask_right[:, :, 1:], (0, 1), value=0 + ) + block_mask_right.triu_(0) + + block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :] + block = cumsum(block_mask_left).tril(0) + cumsum( + block_mask_right, reverse=True + ).triu(1) + + return block_p, block + + def compute_head(self, height, n_cntxt_layers=0): + """Estimate head for each constituent.""" + + _, length = height.size() + if n_cntxt_layers>0: + if n_cntxt_layers == 1: + head_logits = height * self.scaler_1[1] + elif n_cntxt_layers == 2: + head_logits = height * self.scaler_2[1] + else: + head_logits = height * self.scaler[1] + index = torch.arange(length, device=height.device) + + mask = (index[:, None, None] <= index[None, None, :]) * ( + index[None, None, :] <= index[None, :, None] + ) + head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1) + head_logits.masked_fill_(~mask[None, :, :, :], -1e9) + + head_p = torch.softmax(head_logits, dim=-1) + + return head_p + + def parse(self, x, embs=None, n_cntxt_layers=0): + """Parse input sentence. + + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + distance: syntactic distance + height: syntactic height + """ + + mask = x != self.pad + mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0) + + if embs is None: + h = self.roberta.embeddings(x) + else: + h = embs + + if n_cntxt_layers > 0: + if n_cntxt_layers == 1: + parser_layers = self.parser_layers_1 + height_ff = self.height_ff_1 + distance_ff = self.distance_ff_1 + elif n_cntxt_layers == 2: + parser_layers = self.parser_layers_2 + height_ff = self.height_ff_2 + distance_ff = self.distance_ff_2 + for i in range(int(self.config.n_parser_layers/2)): + h = h.masked_fill(~mask[:, :, None], 0) + h = parser_layers[i](h) + + height = height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, dim=-1 + )[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max + ) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where( + margin_left > margin_right, margin_right, margin_left + ).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + else: + for i in range(self.config.n_parser_layers): + h = h.masked_fill(~mask[:, :, None], 0) + h = self.parser_layers[i](h) + + height = self.height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = self.distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, dim=-1 + )[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max + ) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where( + margin_left > margin_right, margin_right, margin_left + ).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + + return distance, height + + def generate_mask(self, x, distance, height, n_cntxt_layers=0): + """Compute head and cibling distribution for each token.""" + + bsz, length = x.size() + + eye = torch.eye(length, device=x.device, dtype=torch.bool) + eye = eye[None, :, :].expand((bsz, -1, -1)) + + block_p, block = self.compute_block(distance, height, n_cntxt_layers=n_cntxt_layers) + head_p = self.compute_head(height, n_cntxt_layers=n_cntxt_layers) + head = torch.einsum("blij,bijh->blh", block_p, head_p) + head = head.masked_fill(eye, 0) + child = head.transpose(1, 2) + cibling = torch.bmm(head, child).masked_fill(eye, 0) + + rel_list = [] + if "head" in self.config.relations: + rel_list.append(head) + if "child" in self.config.relations: + rel_list.append(child) + if "cibling" in self.config.relations: + rel_list.append(cibling) + + rel = torch.stack(rel_list, dim=1) + + if n_cntxt_layers > 0: + if n_cntxt_layers == 1: + rel_weight = self.rel_weight_1 + elif n_cntxt_layers == 2: + rel_weight = self.rel_weight_2 + else: + rel_weight = self.rel_weight + + dep = torch.einsum("lhr,brij->lbhij", rel_weight, rel) + + if n_cntxt_layers == 1: + num_layers = self.cntxt_layers_2.config.num_hidden_layers + else: + num_layers = self.roberta.config.num_hidden_layers + + att_mask = dep.reshape( + num_layers, + bsz, + self.config.num_attention_heads, + length, + length, + ) + + return att_mask, cibling, head, block + + def forward( + self, + input_ids: Optional[torch.LongTensor] = None, + attention_mask: Optional[torch.FloatTensor] = None, + token_type_ids: Optional[torch.LongTensor] = None, + position_ids: Optional[torch.LongTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, SequenceClassifierOutput]: + r""" + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., + config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If + `config.num_labels > 1` a classification loss is computed (Cross-Entropy). + """ + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + if self.config.n_cntxt_layers > 0: + cntxt_outputs = self.cntxt_layers( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict) + + + if self.config.n_cntxt_layers_2 > 0: + distance_1, height_1 = self.parse(input_ids, cntxt_outputs[0], n_cntxt_layers=1) + att_mask_1, _, _, _ = self.generate_mask(input_ids, distance_1, height_1, n_cntxt_layers=1) + + cntxt_outputs_2 = self.cntxt_layers_2( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + parser_att_mask=att_mask_1) + + sequence_output = cntxt_outputs_2[0] + + distance_2, height_2 = self.parse(input_ids, sequence_output[0], n_cntxt_layers=2) + att_mask, _, _, _ = self.generate_mask(input_ids, distance_2, height_2, n_cntxt_layers=2) + + elif self.config.n_cntxt_layers > 0: + distance, height = self.parse(input_ids, cntxt_outputs[0]) + att_mask, _, _, _ = self.generate_mask(input_ids, distance, height) + else: + distance, height = self.parse(input_ids) + att_mask, _, _, _ = self.generate_mask(input_ids, distance, height) + + outputs = self.roberta( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + parser_att_mask=att_mask, + ) + sequence_output = outputs[0] + logits = self.classifier(sequence_output) + + loss = None + if labels is not None: + if self.config.problem_type is None: + if self.num_labels == 1: + self.config.problem_type = "regression" + elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): + self.config.problem_type = "single_label_classification" + else: + self.config.problem_type = "multi_label_classification" + + if self.config.problem_type == "regression": + loss_fct = MSELoss() + if self.num_labels == 1: + loss = loss_fct(logits.squeeze(), labels.squeeze()) + else: + loss = loss_fct(logits, labels) + elif self.config.problem_type == "single_label_classification": + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) + elif self.config.problem_type == "multi_label_classification": + loss_fct = BCEWithLogitsLoss() + loss = loss_fct(logits, labels) + + if not return_dict: + output = (logits,) + outputs[2:] + return ((loss,) + output) if loss is not None else output + + return SequenceClassifierOutput( + loss=loss, + logits=logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +class RobertaClassificationHead(nn.Module): + """Head for sentence-level classification tasks.""" + + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + classifier_dropout = ( + config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob + ) + self.dropout = nn.Dropout(classifier_dropout) + self.out_proj = nn.Linear(config.hidden_size, config.num_labels) + + def forward(self, features, **kwargs): + x = features[:, 0, :] # take token (equiv. to [CLS]) + x = self.dropout(x) + x = self.dense(x) + x = torch.tanh(x) + x = self.dropout(x) + x = self.out_proj(x) + return x + + +def create_position_ids_from_input_ids( + input_ids, padding_idx, past_key_values_length=0 +): + """ + Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols + are ignored. This is modified from fairseq's `utils.make_positions`. + + Args: + x: torch.Tensor x: + + Returns: torch.Tensor + """ + # The series of casts and type-conversions here are carefully balanced to both work with ONNX export and XLA. + mask = input_ids.ne(padding_idx).int() + incremental_indices = ( + torch.cumsum(mask, dim=1).type_as(mask) + past_key_values_length + ) * mask + return incremental_indices.long() + padding_idx + + +def cumprod(x, reverse=False, exclusive=False): + """cumulative product.""" + if reverse: + x = x.flip([-1]) + + if exclusive: + x = F.pad(x[:, :, :-1], (1, 0), value=1) + + cx = x.cumprod(-1) + + if reverse: + cx = cx.flip([-1]) + return cx + + +def cumsum(x, reverse=False, exclusive=False): + """cumulative sum.""" + bsz, _, length = x.size() + device = x.device + if reverse: + if exclusive: + w = torch.ones([bsz, length, length], device=device).tril(-1) + else: + w = torch.ones([bsz, length, length], device=device).tril(0) + cx = torch.bmm(x, w) + else: + if exclusive: + w = torch.ones([bsz, length, length], device=device).triu(1) + else: + w = torch.ones([bsz, length, length], device=device).triu(0) + cx = torch.bmm(x, w) + return cx + + +def cummin(x, reverse=False, exclusive=False, max_value=1e9): + """cumulative min.""" + if reverse: + if exclusive: + x = F.pad(x[:, :, 1:], (0, 1), value=max_value) + x = x.flip([-1]).cummin(-1)[0].flip([-1]) + else: + if exclusive: + x = F.pad(x[:, :, :-1], (1, 0), value=max_value) + x = x.cummin(-1)[0] + return x diff --git a/finetune/syntactic_category_relative_position/predict_results.txt b/finetune/syntactic_category_relative_position/predict_results.txt new file mode 100644 index 0000000000000000000000000000000000000000..a938f53975f699314f10b837e7d1a240a80ea1ba --- /dev/null +++ b/finetune/syntactic_category_relative_position/predict_results.txt @@ -0,0 +1,25850 @@ +index prediction +0 1 +1 0 +2 0 +3 1 +4 1 +5 0 +6 1 +7 0 +8 1 +9 1 +10 0 +11 1 +12 0 +13 0 +14 1 +15 1 +16 1 +17 0 +18 1 +19 0 +20 1 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See all RoBERTa models at https://huggingface.co/models?filter=roberta +] + + +class StructRobertaConfig(RobertaConfig): + model_type = "roberta" + + def __init__( + self, + n_parser_layers=4, + conv_size=9, + relations=("head", "child"), + weight_act="softmax", + n_cntxt_layers=3, + n_cntxt_layers_2=0, + **kwargs,): + + super().__init__(**kwargs) + self.n_cntxt_layers = n_cntxt_layers + self.n_parser_layers = n_parser_layers + self.n_cntxt_layers_2 = n_cntxt_layers_2 + self.conv_size = conv_size + self.relations = relations + self.weight_act = weight_act + +class Conv1d(nn.Module): + """1D convolution layer.""" + + def __init__(self, hidden_size, kernel_size, dilation=1): + """Initialization. + + Args: + hidden_size: dimension of input embeddings + kernel_size: convolution kernel size + dilation: the spacing between the kernel points + """ + super(Conv1d, self).__init__() + + if kernel_size % 2 == 0: + padding = (kernel_size // 2) * dilation + self.shift = True + else: + padding = ((kernel_size - 1) // 2) * dilation + self.shift = False + self.conv = nn.Conv1d( + hidden_size, hidden_size, kernel_size, padding=padding, dilation=dilation + ) + + def forward(self, x): + """Compute convolution. + + Args: + x: input embeddings + Returns: + conv_output: convolution results + """ + + if self.shift: + return self.conv(x.transpose(1, 2)).transpose(1, 2)[:, 1:] + else: + return self.conv(x.transpose(1, 2)).transpose(1, 2) + + +class RobertaEmbeddings(nn.Module): + """ + Same as BertEmbeddings with a tiny tweak for positional embeddings indexing. + """ + + # Copied from transformers.models.bert.modeling_bert.BertEmbeddings.__init__ + def __init__(self, config): + super().__init__() + self.word_embeddings = nn.Embedding( + config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id + ) + self.position_embeddings = nn.Embedding( + config.max_position_embeddings, config.hidden_size + ) + self.token_type_embeddings = nn.Embedding( + config.type_vocab_size, config.hidden_size + ) + + # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load + # any TensorFlow checkpoint file + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + # position_ids (1, len position emb) is contiguous in memory and exported when serialized + self.position_embedding_type = getattr( + config, "position_embedding_type", "absolute" + ) + self.register_buffer( + "position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)) + ) + if version.parse(torch.__version__) > version.parse("1.6.0"): + self.register_buffer( + "token_type_ids", + torch.zeros(self.position_ids.size(), dtype=torch.long), + persistent=False, + ) + + # End copy + self.padding_idx = config.pad_token_id + self.position_embeddings = nn.Embedding( + config.max_position_embeddings, + config.hidden_size, + padding_idx=self.padding_idx, + ) + + def forward( + self, + input_ids=None, + token_type_ids=None, + position_ids=None, + inputs_embeds=None, + past_key_values_length=0, + ): + if position_ids is None: + if input_ids is not None: + # Create the position ids from the input token ids. Any padded tokens remain padded. + position_ids = create_position_ids_from_input_ids( + input_ids, self.padding_idx, past_key_values_length + ) + else: + position_ids = self.create_position_ids_from_inputs_embeds( + inputs_embeds + ) + + if input_ids is not None: + input_shape = input_ids.size() + else: + input_shape = inputs_embeds.size()[:-1] + + seq_length = input_shape[1] + + # Setting the token_type_ids to the registered buffer in constructor where it is all zeros, which usually occurs + # when its auto-generated, registered buffer helps users when tracing the model without passing token_type_ids, solves + # issue #5664 + if token_type_ids is None: + if hasattr(self, "token_type_ids"): + buffered_token_type_ids = self.token_type_ids[:, :seq_length] + buffered_token_type_ids_expanded = buffered_token_type_ids.expand( + input_shape[0], seq_length + ) + token_type_ids = buffered_token_type_ids_expanded + else: + token_type_ids = torch.zeros( + input_shape, dtype=torch.long, device=self.position_ids.device + ) + + if inputs_embeds is None: + inputs_embeds = self.word_embeddings(input_ids) + token_type_embeddings = self.token_type_embeddings(token_type_ids) + + embeddings = inputs_embeds + token_type_embeddings + if self.position_embedding_type == "absolute": + position_embeddings = self.position_embeddings(position_ids) + embeddings += position_embeddings + embeddings = self.LayerNorm(embeddings) + embeddings = self.dropout(embeddings) + return embeddings + + def create_position_ids_from_inputs_embeds(self, inputs_embeds): + """ + We are provided embeddings directly. We cannot infer which are padded so just generate sequential position ids. + + Args: + inputs_embeds: torch.Tensor + + Returns: torch.Tensor + """ + input_shape = inputs_embeds.size()[:-1] + sequence_length = input_shape[1] + + position_ids = torch.arange( + self.padding_idx + 1, + sequence_length + self.padding_idx + 1, + dtype=torch.long, + device=inputs_embeds.device, + ) + return position_ids.unsqueeze(0).expand(input_shape) + + +# Copied from transformers.models.bert.modeling_bert.BertSelfAttention with Bert->Roberta +class RobertaSelfAttention(nn.Module): + def __init__(self, config, position_embedding_type=None): + super().__init__() + if config.hidden_size % config.num_attention_heads != 0 and not hasattr( + config, "embedding_size" + ): + raise ValueError( + f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention " + f"heads ({config.num_attention_heads})" + ) + + self.num_attention_heads = config.num_attention_heads + self.attention_head_size = int(config.hidden_size / config.num_attention_heads) + self.all_head_size = self.num_attention_heads * self.attention_head_size + + self.query = nn.Linear(config.hidden_size, self.all_head_size) + self.key = nn.Linear(config.hidden_size, self.all_head_size) + self.value = nn.Linear(config.hidden_size, self.all_head_size) + + self.dropout = nn.Dropout(config.attention_probs_dropout_prob) + self.position_embedding_type = position_embedding_type or getattr( + config, "position_embedding_type", "absolute" + ) + if ( + self.position_embedding_type == "relative_key" + or self.position_embedding_type == "relative_key_query" + ): + self.max_position_embeddings = config.max_position_embeddings + self.distance_embedding = nn.Embedding( + 2 * config.max_position_embeddings - 1, self.attention_head_size + ) + + self.is_decoder = config.is_decoder + + def transpose_for_scores(self, x): + new_x_shape = x.size()[:-1] + ( + self.num_attention_heads, + self.attention_head_size, + ) + x = x.view(new_x_shape) + return x.permute(0, 2, 1, 3) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.FloatTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, + output_attentions: Optional[bool] = False, + parser_att_mask=None, + ) -> Tuple[torch.Tensor]: + mixed_query_layer = self.query(hidden_states) + + # If this is instantiated as a cross-attention module, the keys + # and values come from an encoder; the attention mask needs to be + # such that the encoder's padding tokens are not attended to. + is_cross_attention = encoder_hidden_states is not None + + if is_cross_attention and past_key_value is not None: + # reuse k,v, cross_attentions + key_layer = past_key_value[0] + value_layer = past_key_value[1] + attention_mask = encoder_attention_mask + elif is_cross_attention: + key_layer = self.transpose_for_scores(self.key(encoder_hidden_states)) + value_layer = self.transpose_for_scores(self.value(encoder_hidden_states)) + attention_mask = encoder_attention_mask + elif past_key_value is not None: + key_layer = self.transpose_for_scores(self.key(hidden_states)) + value_layer = self.transpose_for_scores(self.value(hidden_states)) + key_layer = torch.cat([past_key_value[0], key_layer], dim=2) + value_layer = torch.cat([past_key_value[1], value_layer], dim=2) + else: + key_layer = self.transpose_for_scores(self.key(hidden_states)) + value_layer = self.transpose_for_scores(self.value(hidden_states)) + + query_layer = self.transpose_for_scores(mixed_query_layer) + + if self.is_decoder: + # if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states. + # Further calls to cross_attention layer can then reuse all cross-attention + # key/value_states (first "if" case) + # if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of + # all previous decoder key/value_states. Further calls to uni-directional self-attention + # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case) + # if encoder bi-directional self-attention `past_key_value` is always `None` + past_key_value = (key_layer, value_layer) + + # Take the dot product between "query" and "key" to get the raw attention scores. + attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) + + if ( + self.position_embedding_type == "relative_key" + or self.position_embedding_type == "relative_key_query" + ): + seq_length = hidden_states.size()[1] + position_ids_l = torch.arange( + seq_length, dtype=torch.long, device=hidden_states.device + ).view(-1, 1) + position_ids_r = torch.arange( + seq_length, dtype=torch.long, device=hidden_states.device + ).view(1, -1) + distance = position_ids_l - position_ids_r + positional_embedding = self.distance_embedding( + distance + self.max_position_embeddings - 1 + ) + positional_embedding = positional_embedding.to( + dtype=query_layer.dtype + ) # fp16 compatibility + + if self.position_embedding_type == "relative_key": + relative_position_scores = torch.einsum( + "bhld,lrd->bhlr", query_layer, positional_embedding + ) + attention_scores = attention_scores + relative_position_scores + elif self.position_embedding_type == "relative_key_query": + relative_position_scores_query = torch.einsum( + "bhld,lrd->bhlr", query_layer, positional_embedding + ) + relative_position_scores_key = torch.einsum( + "bhrd,lrd->bhlr", key_layer, positional_embedding + ) + attention_scores = ( + attention_scores + + relative_position_scores_query + + relative_position_scores_key + ) + + attention_scores = attention_scores / math.sqrt(self.attention_head_size) + if attention_mask is not None: + # Apply the attention mask is (precomputed for all layers in RobertaModel forward() function) + attention_scores = attention_scores + attention_mask + + if parser_att_mask is None: + # Normalize the attention scores to probabilities. + attention_probs = nn.functional.softmax(attention_scores, dim=-1) + else: + attention_probs = torch.sigmoid(attention_scores) * parser_att_mask + + # This is actually dropping out entire tokens to attend to, which might + # seem a bit unusual, but is taken from the original Transformer paper. + attention_probs = self.dropout(attention_probs) + + # Mask heads if we want to + if head_mask is not None: + attention_probs = attention_probs * head_mask + + context_layer = torch.matmul(attention_probs, value_layer) + + context_layer = context_layer.permute(0, 2, 1, 3).contiguous() + new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) + context_layer = context_layer.view(new_context_layer_shape) + + outputs = ( + (context_layer, attention_probs) if output_attentions else (context_layer,) + ) + + if self.is_decoder: + outputs = outputs + (past_key_value,) + return outputs + + +# Copied from transformers.models.bert.modeling_bert.BertSelfOutput +class RobertaSelfOutput(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward( + self, hidden_states: torch.Tensor, input_tensor: torch.Tensor + ) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + hidden_states = self.LayerNorm(hidden_states + input_tensor) + return hidden_states + + +# Copied from transformers.models.bert.modeling_bert.BertAttention with Bert->Roberta +class RobertaAttention(nn.Module): + def __init__(self, config, position_embedding_type=None): + super().__init__() + self.self = RobertaSelfAttention( + config, position_embedding_type=position_embedding_type + ) + self.output = RobertaSelfOutput(config) + self.pruned_heads = set() + + def prune_heads(self, heads): + if len(heads) == 0: + return + heads, index = find_pruneable_heads_and_indices( + heads, + self.self.num_attention_heads, + self.self.attention_head_size, + self.pruned_heads, + ) + + # Prune linear layers + self.self.query = prune_linear_layer(self.self.query, index) + self.self.key = prune_linear_layer(self.self.key, index) + self.self.value = prune_linear_layer(self.self.value, index) + self.output.dense = prune_linear_layer(self.output.dense, index, dim=1) + + # Update hyper params and store pruned heads + self.self.num_attention_heads = self.self.num_attention_heads - len(heads) + self.self.all_head_size = ( + self.self.attention_head_size * self.self.num_attention_heads + ) + self.pruned_heads = self.pruned_heads.union(heads) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.FloatTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, + output_attentions: Optional[bool] = False, + parser_att_mask=None, + ) -> Tuple[torch.Tensor]: + self_outputs = self.self( + hidden_states, + attention_mask, + head_mask, + encoder_hidden_states, + encoder_attention_mask, + past_key_value, + output_attentions, + parser_att_mask=parser_att_mask, + ) + attention_output = self.output(self_outputs[0], hidden_states) + outputs = (attention_output,) + self_outputs[ + 1: + ] # add attentions if we output them + return outputs + + +# Copied from transformers.models.bert.modeling_bert.BertIntermediate +class RobertaIntermediate(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.intermediate_size) + if isinstance(config.hidden_act, str): + self.intermediate_act_fn = ACT2FN[config.hidden_act] + else: + self.intermediate_act_fn = config.hidden_act + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.intermediate_act_fn(hidden_states) + return hidden_states + + +# Copied from transformers.models.bert.modeling_bert.BertOutput +class RobertaOutput(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.intermediate_size, config.hidden_size) + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward( + self, hidden_states: torch.Tensor, input_tensor: torch.Tensor + ) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + hidden_states = self.LayerNorm(hidden_states + input_tensor) + return hidden_states + + +# Copied from transformers.models.bert.modeling_bert.BertLayer with Bert->Roberta +class RobertaLayer(nn.Module): + def __init__(self, config): + super().__init__() + self.chunk_size_feed_forward = config.chunk_size_feed_forward + self.seq_len_dim = 1 + self.attention = RobertaAttention(config) + self.is_decoder = config.is_decoder + self.add_cross_attention = config.add_cross_attention + if self.add_cross_attention: + if not self.is_decoder: + raise ValueError( + f"{self} should be used as a decoder model if cross attention is added" + ) + self.crossattention = RobertaAttention( + config, position_embedding_type="absolute" + ) + self.intermediate = RobertaIntermediate(config) + self.output = RobertaOutput(config) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.FloatTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, + output_attentions: Optional[bool] = False, + parser_att_mask=None, + ) -> Tuple[torch.Tensor]: + # decoder uni-directional self-attention cached key/values tuple is at positions 1,2 + self_attn_past_key_value = ( + past_key_value[:2] if past_key_value is not None else None + ) + self_attention_outputs = self.attention( + hidden_states, + attention_mask, + head_mask, + output_attentions=output_attentions, + past_key_value=self_attn_past_key_value, + parser_att_mask=parser_att_mask, + ) + attention_output = self_attention_outputs[0] + + # if decoder, the last output is tuple of self-attn cache + if self.is_decoder: + outputs = self_attention_outputs[1:-1] + present_key_value = self_attention_outputs[-1] + else: + outputs = self_attention_outputs[ + 1: + ] # add self attentions if we output attention weights + + cross_attn_present_key_value = None + if self.is_decoder and encoder_hidden_states is not None: + if not hasattr(self, "crossattention"): + raise ValueError( + f"If `encoder_hidden_states` are passed, {self} has to be instantiated with cross-attention layers by setting `config.add_cross_attention=True`" + ) + + # cross_attn cached key/values tuple is at positions 3,4 of past_key_value tuple + cross_attn_past_key_value = ( + past_key_value[-2:] if past_key_value is not None else None + ) + cross_attention_outputs = self.crossattention( + attention_output, + attention_mask, + head_mask, + encoder_hidden_states, + encoder_attention_mask, + cross_attn_past_key_value, + output_attentions, + ) + attention_output = cross_attention_outputs[0] + outputs = ( + outputs + cross_attention_outputs[1:-1] + ) # add cross attentions if we output attention weights + + # add cross-attn cache to positions 3,4 of present_key_value tuple + cross_attn_present_key_value = cross_attention_outputs[-1] + present_key_value = present_key_value + cross_attn_present_key_value + + layer_output = apply_chunking_to_forward( + self.feed_forward_chunk, + self.chunk_size_feed_forward, + self.seq_len_dim, + attention_output, + ) + outputs = (layer_output,) + outputs + + # if decoder, return the attn key/values as the last output + if self.is_decoder: + outputs = outputs + (present_key_value,) + + return outputs + + def feed_forward_chunk(self, attention_output): + intermediate_output = self.intermediate(attention_output) + layer_output = self.output(intermediate_output, attention_output) + return layer_output + + +# Copied from transformers.models.bert.modeling_bert.BertEncoder with Bert->Roberta +class RobertaEncoder(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + self.layer = nn.ModuleList( + [RobertaLayer(config) for _ in range(config.num_hidden_layers)] + ) + self.gradient_checkpointing = False + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.FloatTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = False, + output_hidden_states: Optional[bool] = False, + return_dict: Optional[bool] = True, + parser_att_mask=None, + ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPastAndCrossAttentions]: + all_hidden_states = () if output_hidden_states else None + all_self_attentions = () if output_attentions else None + all_cross_attentions = ( + () if output_attentions and self.config.add_cross_attention else None + ) + + next_decoder_cache = () if use_cache else None + for i, layer_module in enumerate(self.layer): + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + layer_head_mask = head_mask[i] if head_mask is not None else None + past_key_value = past_key_values[i] if past_key_values is not None else None + + if self.gradient_checkpointing and self.training: + + if use_cache: + logger.warning( + "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." + ) + use_cache = False + + def create_custom_forward(module): + def custom_forward(*inputs): + return module(*inputs, past_key_value, output_attentions) + + return custom_forward + + layer_outputs = torch.utils.checkpoint.checkpoint( + create_custom_forward(layer_module), + hidden_states, + attention_mask, + layer_head_mask, + encoder_hidden_states, + encoder_attention_mask, + ) + else: + if parser_att_mask is not None: + layer_outputs = layer_module( + hidden_states, + attention_mask, + layer_head_mask, + encoder_hidden_states, + encoder_attention_mask, + past_key_value, + output_attentions, + parser_att_mask=parser_att_mask[i]) + else: + layer_outputs = layer_module( + hidden_states, + attention_mask, + layer_head_mask, + encoder_hidden_states, + encoder_attention_mask, + past_key_value, + output_attentions, + parser_att_mask=None) + + + hidden_states = layer_outputs[0] + if use_cache: + next_decoder_cache += (layer_outputs[-1],) + if output_attentions: + all_self_attentions = all_self_attentions + (layer_outputs[1],) + if self.config.add_cross_attention: + all_cross_attentions = all_cross_attentions + (layer_outputs[2],) + + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + if not return_dict: + return tuple( + v + for v in [ + hidden_states, + next_decoder_cache, + all_hidden_states, + all_self_attentions, + all_cross_attentions, + ] + if v is not None + ) + return BaseModelOutputWithPastAndCrossAttentions( + last_hidden_state=hidden_states, + past_key_values=next_decoder_cache, + hidden_states=all_hidden_states, + attentions=all_self_attentions, + cross_attentions=all_cross_attentions, + ) + + +# Copied from transformers.models.bert.modeling_bert.BertPooler +class RobertaPooler(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.activation = nn.Tanh() + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + # We "pool" the model by simply taking the hidden state corresponding + # to the first token. + first_token_tensor = hidden_states[:, 0] + pooled_output = self.dense(first_token_tensor) + pooled_output = self.activation(pooled_output) + return pooled_output + + +class RobertaPreTrainedModel(PreTrainedModel): + """ + An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained + models. + """ + + config_class = RobertaConfig + base_model_prefix = "roberta" + supports_gradient_checkpointing = True + + # Copied from transformers.models.bert.modeling_bert.BertPreTrainedModel._init_weights + def _init_weights(self, module): + """Initialize the weights""" + if isinstance(module, nn.Linear): + # Slightly different from the TF version which uses truncated_normal for initialization + # cf https://github.com/pytorch/pytorch/pull/5617 + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.Embedding): + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.padding_idx is not None: + module.weight.data[module.padding_idx].zero_() + elif isinstance(module, nn.LayerNorm): + if module.bias is not None: + module.bias.data.zero_() + module.weight.data.fill_(1.0) + + def _set_gradient_checkpointing(self, module, value=False): + if isinstance(module, RobertaEncoder): + module.gradient_checkpointing = value + + def update_keys_to_ignore(self, config, del_keys_to_ignore): + """Remove some keys from ignore list""" + if not config.tie_word_embeddings: + # must make a new list, or the class variable gets modified! + self._keys_to_ignore_on_save = [ + k for k in self._keys_to_ignore_on_save if k not in del_keys_to_ignore + ] + self._keys_to_ignore_on_load_missing = [ + k + for k in self._keys_to_ignore_on_load_missing + if k not in del_keys_to_ignore + ] + + +ROBERTA_START_DOCSTRING = r""" + + This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the + library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads + etc.) + + This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. + Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage + and behavior. + + Parameters: + config ([`RobertaConfig`]): Model configuration class with all the parameters of the + model. Initializing with a config file does not load the weights associated with the model, only the + configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. +""" + + +ROBERTA_INPUTS_DOCSTRING = r""" + Args: + input_ids (`torch.LongTensor` of shape `({0})`): + Indices of input sequence tokens in the vocabulary. + + Indices can be obtained using [`RobertaTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + [What are input IDs?](../glossary#input-ids) + attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*): + Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + + [What are attention masks?](../glossary#attention-mask) + token_type_ids (`torch.LongTensor` of shape `({0})`, *optional*): + Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, + 1]`: + + - 0 corresponds to a *sentence A* token, + - 1 corresponds to a *sentence B* token. + + [What are token type IDs?](../glossary#token-type-ids) + position_ids (`torch.LongTensor` of shape `({0})`, *optional*): + Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, + config.max_position_embeddings - 1]`. + + [What are position IDs?](../glossary#position-ids) + head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): + Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: + + - 1 indicates the head is **not masked**, + - 0 indicates the head is **masked**. + + inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_size)`, *optional*): + Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This + is useful if you want more control over how to convert `input_ids` indices into associated vectors than the + model's internal embedding lookup matrix. + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned + tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for + more detail. + return_dict (`bool`, *optional*): + Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. +""" + + +class RobertaModel(RobertaPreTrainedModel): + """ + + The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of + cross-attention is added between the self-attention layers, following the architecture described in *Attention is + all you need*_ by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz + Kaiser and Illia Polosukhin. + + To behave as an decoder the model needs to be initialized with the `is_decoder` argument of the configuration set + to `True`. To be used in a Seq2Seq model, the model needs to initialized with both `is_decoder` argument and + `add_cross_attention` set to `True`; an `encoder_hidden_states` is then expected as an input to the forward pass. + + .. _*Attention is all you need*: https://arxiv.org/abs/1706.03762 + + """ + + _keys_to_ignore_on_load_missing = [r"position_ids"] + + # Copied from transformers.models.bert.modeling_bert.BertModel.__init__ with Bert->Roberta + def __init__(self, config, add_pooling_layer=True): + super().__init__(config) + self.config = config + + self.embeddings = RobertaEmbeddings(config) + self.encoder = RobertaEncoder(config) + + self.pooler = RobertaPooler(config) if add_pooling_layer else None + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.embeddings.word_embeddings + + def set_input_embeddings(self, value): + self.embeddings.word_embeddings = value + + def _prune_heads(self, heads_to_prune): + """ + Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base + class PreTrainedModel + """ + for layer, heads in heads_to_prune.items(): + self.encoder.layer[layer].attention.prune_heads(heads) + + # Copied from transformers.models.bert.modeling_bert.BertModel.forward + def forward( + self, + input_ids: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + token_type_ids: Optional[torch.Tensor] = None, + position_ids: Optional[torch.Tensor] = None, + head_mask: Optional[torch.Tensor] = None, + inputs_embeds: Optional[torch.Tensor] = None, + encoder_hidden_states: Optional[torch.Tensor] = None, + encoder_attention_mask: Optional[torch.Tensor] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + parser_att_mask=None, + ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPoolingAndCrossAttentions]: + r""" + encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): + Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if + the model is configured as a decoder. + encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*): + Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in + the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): + Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. + + If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that + don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all + `decoder_input_ids` of shape `(batch_size, sequence_length)`. + use_cache (`bool`, *optional*): + If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see + `past_key_values`). + """ + output_attentions = ( + output_attentions + if output_attentions is not None + else self.config.output_attentions + ) + output_hidden_states = ( + output_hidden_states + if output_hidden_states is not None + else self.config.output_hidden_states + ) + return_dict = ( + return_dict if return_dict is not None else self.config.use_return_dict + ) + + if self.config.is_decoder: + use_cache = use_cache if use_cache is not None else self.config.use_cache + else: + use_cache = False + + if input_ids is not None and inputs_embeds is not None: + raise ValueError( + "You cannot specify both input_ids and inputs_embeds at the same time" + ) + elif input_ids is not None: + input_shape = input_ids.size() + elif inputs_embeds is not None: + input_shape = inputs_embeds.size()[:-1] + else: + raise ValueError("You have to specify either input_ids or inputs_embeds") + + batch_size, seq_length = input_shape + device = input_ids.device if input_ids is not None else inputs_embeds.device + + # past_key_values_length + past_key_values_length = ( + past_key_values[0][0].shape[2] if past_key_values is not None else 0 + ) + + if attention_mask is None: + attention_mask = torch.ones( + ((batch_size, seq_length + past_key_values_length)), device=device + ) + + if token_type_ids is None: + if hasattr(self.embeddings, "token_type_ids"): + buffered_token_type_ids = self.embeddings.token_type_ids[:, :seq_length] + buffered_token_type_ids_expanded = buffered_token_type_ids.expand( + batch_size, seq_length + ) + token_type_ids = buffered_token_type_ids_expanded + else: + token_type_ids = torch.zeros( + input_shape, dtype=torch.long, device=device + ) + + # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length] + # ourselves in which case we just need to make it broadcastable to all heads. + extended_attention_mask: torch.Tensor = self.get_extended_attention_mask( + attention_mask, input_shape, device + ) + + # If a 2D or 3D attention mask is provided for the cross-attention + # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length] + if self.config.is_decoder and encoder_hidden_states is not None: + ( + encoder_batch_size, + encoder_sequence_length, + _, + ) = encoder_hidden_states.size() + encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length) + if encoder_attention_mask is None: + encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device) + encoder_extended_attention_mask = self.invert_attention_mask( + encoder_attention_mask + ) + else: + encoder_extended_attention_mask = None + + # Prepare head mask if needed + # 1.0 in head_mask indicate we keep the head + # attention_probs has shape bsz x n_heads x N x N + # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] + # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] + head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) + + embedding_output = self.embeddings( + input_ids=input_ids, + position_ids=position_ids, + token_type_ids=token_type_ids, + inputs_embeds=inputs_embeds, + past_key_values_length=past_key_values_length, + ) + encoder_outputs = self.encoder( + embedding_output, + attention_mask=extended_attention_mask, + head_mask=head_mask, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_extended_attention_mask, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + parser_att_mask=parser_att_mask, + ) + sequence_output = encoder_outputs[0] + pooled_output = ( + self.pooler(sequence_output) if self.pooler is not None else None + ) + + if not return_dict: + return (sequence_output, pooled_output) + encoder_outputs[1:] + + return BaseModelOutputWithPoolingAndCrossAttentions( + last_hidden_state=sequence_output, + pooler_output=pooled_output, + past_key_values=encoder_outputs.past_key_values, + hidden_states=encoder_outputs.hidden_states, + attentions=encoder_outputs.attentions, + cross_attentions=encoder_outputs.cross_attentions, + ) + + +class StructRoberta(RobertaPreTrainedModel): + _keys_to_ignore_on_save = [r"lm_head.decoder.weight", r"lm_head.decoder.bias"] + _keys_to_ignore_on_load_missing = [ + r"position_ids", + r"lm_head.decoder.weight", + r"lm_head.decoder.bias", + ] + _keys_to_ignore_on_load_unexpected = [r"pooler"] + + def __init__(self, config): + super().__init__(config) + + if config.is_decoder: + logger.warning( + "If you want to use `RobertaForMaskedLM` make sure `config.is_decoder=False` for " + "bi-directional self-attention." + ) + + + if config.n_cntxt_layers > 0: + config_cntxt = copy.deepcopy(config) + config_cntxt.num_hidden_layers = config.n_cntxt_layers + + self.cntxt_layers = RobertaModel(config_cntxt, add_pooling_layer=False) + + if config.n_cntxt_layers_2 > 0: + self.parser_layers_1 = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(int(config.n_parser_layers/2)) + ] + ) + + self.distance_ff_1 = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff_1 = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight_1 = nn.Parameter( + torch.zeros((config.n_cntxt_layers_2, config.num_attention_heads, n_rel)) + ) + self._rel_weight_1.data.normal_(0, 0.1) + + self._scaler_1 = nn.Parameter(torch.zeros(2)) + + config_cntxt_2 = copy.deepcopy(config) + config_cntxt_2.num_hidden_layers = config.n_cntxt_layers_2 + + self.cntxt_layers_2 = RobertaModel(config_cntxt_2, add_pooling_layer=False) + + + self.parser_layers_2 = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(int(config.n_parser_layers/2)) + ] + ) + + self.distance_ff_2 = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff_2 = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight_2 = nn.Parameter( + torch.zeros((config.num_hidden_layers, config.num_attention_heads, n_rel)) + ) + self._rel_weight_2.data.normal_(0, 0.1) + + self._scaler_2 = nn.Parameter(torch.zeros(2)) + + else: + self.parser_layers = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(config.n_parser_layers) + ] + ) + + self.distance_ff = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight = nn.Parameter( + torch.zeros((config.num_hidden_layers, config.num_attention_heads, n_rel)) + ) + self._rel_weight.data.normal_(0, 0.1) + + self._scaler = nn.Parameter(torch.zeros(2)) + + self.roberta = RobertaModel(config, add_pooling_layer=False) + + if config.n_cntxt_layers > 0: + self.cntxt_layers.embeddings = self.roberta.embeddings + if config.n_cntxt_layers_2 > 0: + self.cntxt_layers_2.embeddings = self.roberta.embeddings + + self.lm_head = RobertaLMHead(config) + + self.pad = config.pad_token_id + + # The LM head weights require special treatment only when they are tied with the word embeddings + self.update_keys_to_ignore(config, ["lm_head.decoder.weight"]) + + # Initialize weights and apply final processing + self.post_init() + + def get_output_embeddings(self): + return self.lm_head.decoder + + def set_output_embeddings(self, new_embeddings): + self.lm_head.decoder = new_embeddings + + @property + def scaler(self): + return self._scaler.exp() + + @property + def scaler_1(self): + return self._scaler_1.exp() + + @property + def scaler_2(self): + return self._scaler_2.exp() + + @property + def rel_weight(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight, dim=-1) + + @property + def rel_weight_1(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight_1) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight_1, dim=-1) + + + @property + def rel_weight_2(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight_2) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight_2, dim=-1) + + + def compute_block(self, distance, height, n_cntxt_layers=0): + """Compute constituents from distance and height.""" + + if n_cntxt_layers>0: + if n_cntxt_layers == 1: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler_1[0] + elif n_cntxt_layers == 2: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler_2[0] + else: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0] + + gamma = torch.sigmoid(-beta_logits) + ones = torch.ones_like(gamma) + + block_mask_left = cummin( + gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1 + ) + block_mask_left = block_mask_left - F.pad( + block_mask_left[:, :, :-1], (1, 0), value=0 + ) + block_mask_left.tril_(0) + + block_mask_right = cummin( + gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1 + ) + block_mask_right = block_mask_right - F.pad( + block_mask_right[:, :, 1:], (0, 1), value=0 + ) + block_mask_right.triu_(0) + + block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :] + block = cumsum(block_mask_left).tril(0) + cumsum( + block_mask_right, reverse=True + ).triu(1) + + return block_p, block + + def compute_head(self, height, n_cntxt_layers=0): + """Estimate head for each constituent.""" + + _, length = height.size() + if n_cntxt_layers>0: + if n_cntxt_layers == 1: + head_logits = height * self.scaler_1[1] + elif n_cntxt_layers == 2: + head_logits = height * self.scaler_2[1] + else: + head_logits = height * self.scaler[1] + index = torch.arange(length, device=height.device) + + mask = (index[:, None, None] <= index[None, None, :]) * ( + index[None, None, :] <= index[None, :, None] + ) + head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1) + head_logits.masked_fill_(~mask[None, :, :, :], -1e9) + + head_p = torch.softmax(head_logits, dim=-1) + + return head_p + + def parse(self, x, embs=None, n_cntxt_layers=0): + """Parse input sentence. + + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + distance: syntactic distance + height: syntactic height + """ + + mask = x != self.pad + mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0) + + if embs is None: + h = self.roberta.embeddings(x) + else: + h = embs + + if n_cntxt_layers > 0: + if n_cntxt_layers == 1: + parser_layers = self.parser_layers_1 + height_ff = self.height_ff_1 + distance_ff = self.distance_ff_1 + elif n_cntxt_layers == 2: + parser_layers = self.parser_layers_2 + height_ff = self.height_ff_2 + distance_ff = self.distance_ff_2 + for i in range(int(self.config.n_parser_layers/2)): + h = h.masked_fill(~mask[:, :, None], 0) + h = parser_layers[i](h) + + height = height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, dim=-1 + )[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max + ) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where( + margin_left > margin_right, margin_right, margin_left + ).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + else: + for i in range(self.config.n_parser_layers): + h = h.masked_fill(~mask[:, :, None], 0) + h = self.parser_layers[i](h) + + height = self.height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = self.distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, dim=-1 + )[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max + ) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where( + margin_left > margin_right, margin_right, margin_left + ).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + + return distance, height + + def generate_mask(self, x, distance, height, n_cntxt_layers=0): + """Compute head and cibling distribution for each token.""" + + bsz, length = x.size() + + eye = torch.eye(length, device=x.device, dtype=torch.bool) + eye = eye[None, :, :].expand((bsz, -1, -1)) + + block_p, block = self.compute_block(distance, height, n_cntxt_layers=n_cntxt_layers) + head_p = self.compute_head(height, n_cntxt_layers=n_cntxt_layers) + head = torch.einsum("blij,bijh->blh", block_p, head_p) + head = head.masked_fill(eye, 0) + child = head.transpose(1, 2) + cibling = torch.bmm(head, child).masked_fill(eye, 0) + + rel_list = [] + if "head" in self.config.relations: + rel_list.append(head) + if "child" in self.config.relations: + rel_list.append(child) + if "cibling" in self.config.relations: + rel_list.append(cibling) + + rel = torch.stack(rel_list, dim=1) + + if n_cntxt_layers > 0: + if n_cntxt_layers == 1: + rel_weight = self.rel_weight_1 + elif n_cntxt_layers == 2: + rel_weight = self.rel_weight_2 + else: + rel_weight = self.rel_weight + + dep = torch.einsum("lhr,brij->lbhij", rel_weight, rel) + + if n_cntxt_layers == 1: + num_layers = self.cntxt_layers_2.config.num_hidden_layers + else: + num_layers = self.roberta.config.num_hidden_layers + + att_mask = dep.reshape( + num_layers, + bsz, + self.config.num_attention_heads, + length, + length, + ) + + return att_mask, cibling, head, block + + def forward( + self, + input_ids: Optional[torch.LongTensor] = None, + attention_mask: Optional[torch.FloatTensor] = None, + token_type_ids: Optional[torch.LongTensor] = None, + position_ids: Optional[torch.LongTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, MaskedLMOutput]: + r""" + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ..., + config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the + loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]` + kwargs (`Dict[str, any]`, optional, defaults to *{}*): + Used to hide legacy arguments that have been deprecated. + """ + return_dict = ( + return_dict if return_dict is not None else self.config.use_return_dict + ) + + + if self.config.n_cntxt_layers > 0: + cntxt_outputs = self.cntxt_layers( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict) + + + if self.config.n_cntxt_layers_2 > 0: + distance_1, height_1 = self.parse(input_ids, cntxt_outputs[0], n_cntxt_layers=1) + att_mask_1, _, _, _ = self.generate_mask(input_ids, distance_1, height_1, n_cntxt_layers=1) + + cntxt_outputs_2 = self.cntxt_layers_2( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + parser_att_mask=att_mask_1) + + sequence_output = cntxt_outputs_2[0] + + distance_2, height_2 = self.parse(input_ids, sequence_output[0], n_cntxt_layers=2) + att_mask, _, _, _ = self.generate_mask(input_ids, distance_2, height_2, n_cntxt_layers=2) + + elif self.config.n_cntxt_layers > 0: + distance, height = self.parse(input_ids, cntxt_outputs[0]) + att_mask, _, _, _ = self.generate_mask(input_ids, distance, height) + else: + distance, height = self.parse(input_ids) + att_mask, _, _, _ = self.generate_mask(input_ids, distance, height) + + outputs = self.roberta( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + parser_att_mask=att_mask, + ) + sequence_output = outputs[0] + prediction_scores = self.lm_head(sequence_output) + + masked_lm_loss = None + if labels is not None: + loss_fct = CrossEntropyLoss() + masked_lm_loss = loss_fct( + prediction_scores.view(-1, self.config.vocab_size), labels.view(-1) + ) + + if not return_dict: + output = (prediction_scores,) + outputs[2:] + return ( + ((masked_lm_loss,) + output) if masked_lm_loss is not None else output + ) + + return MaskedLMOutput( + loss=masked_lm_loss, + logits=prediction_scores, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +class RobertaLMHead(nn.Module): + """Roberta Head for masked language modeling.""" + + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + + self.decoder = nn.Linear(config.hidden_size, config.vocab_size) + self.bias = nn.Parameter(torch.zeros(config.vocab_size)) + self.decoder.bias = self.bias + + def forward(self, features, **kwargs): + x = self.dense(features) + x = gelu(x) + x = self.layer_norm(x) + + # project back to size of vocabulary with bias + x = self.decoder(x) + + return x + + def _tie_weights(self): + # To tie those two weights if they get disconnected (on TPU or when the bias is resized) + self.bias = self.decoder.bias + + +class StructRobertaForSequenceClassification(RobertaPreTrainedModel): + _keys_to_ignore_on_load_missing = [r"position_ids"] + + def __init__(self, config): + super().__init__(config) + self.num_labels = config.num_labels + self.config = config + + if config.n_cntxt_layers > 0: + config_cntxt = copy.deepcopy(config) + config_cntxt.num_hidden_layers = config.n_cntxt_layers + + self.cntxt_layers = RobertaModel(config_cntxt, add_pooling_layer=False) + + if config.n_cntxt_layers_2 > 0: + self.parser_layers_1 = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(int(config.n_parser_layers/2)) + ] + ) + + self.distance_ff_1 = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff_1 = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight_1 = nn.Parameter( + torch.zeros((config.n_cntxt_layers_2, config.num_attention_heads, n_rel)) + ) + self._rel_weight_1.data.normal_(0, 0.1) + + self._scaler_1 = nn.Parameter(torch.zeros(2)) + + config_cntxt_2 = copy.deepcopy(config) + config_cntxt_2.num_hidden_layers = config.n_cntxt_layers_2 + + self.cntxt_layers_2 = RobertaModel(config_cntxt_2, add_pooling_layer=False) + + + self.parser_layers_2 = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(int(config.n_parser_layers/2)) + ] + ) + + self.distance_ff_2 = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff_2 = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight_2 = nn.Parameter( + torch.zeros((config.num_hidden_layers, config.num_attention_heads, n_rel)) + ) + self._rel_weight_2.data.normal_(0, 0.1) + + self._scaler_2 = nn.Parameter(torch.zeros(2)) + + else: + self.parser_layers = nn.ModuleList( + [ + nn.Sequential( + Conv1d(config.hidden_size, config.conv_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + ) + for i in range(config.n_parser_layers) + ] + ) + + self.distance_ff = nn.Sequential( + Conv1d(config.hidden_size, 2), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + self.height_ff = nn.Sequential( + nn.Linear(config.hidden_size, config.hidden_size), + nn.LayerNorm(config.hidden_size, elementwise_affine=False), + nn.Tanh(), + nn.Linear(config.hidden_size, 1), + ) + + n_rel = len(config.relations) + self._rel_weight = nn.Parameter( + torch.zeros((config.num_hidden_layers, config.num_attention_heads, n_rel)) + ) + self._rel_weight.data.normal_(0, 0.1) + + self._scaler = nn.Parameter(torch.zeros(2)) + + self.roberta = RobertaModel(config, add_pooling_layer=False) + + if config.n_cntxt_layers > 0: + self.cntxt_layers.embeddings = self.roberta.embeddings + if config.n_cntxt_layers_2 > 0: + self.cntxt_layers_2.embeddings = self.roberta.embeddings + + + self.pad = config.pad_token_id + self.classifier = RobertaClassificationHead(config) + + # Initialize weights and apply final processing + self.post_init() + + + @property + def scaler(self): + return self._scaler.exp() + + @property + def scaler_1(self): + return self._scaler_1.exp() + + @property + def scaler_2(self): + return self._scaler_2.exp() + + @property + def rel_weight(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight, dim=-1) + + @property + def rel_weight_1(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight_1) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight_1, dim=-1) + + + @property + def rel_weight_2(self): + if self.config.weight_act == "sigmoid": + return torch.sigmoid(self._rel_weight_2) + elif self.config.weight_act == "softmax": + return torch.softmax(self._rel_weight_2, dim=-1) + + + def compute_block(self, distance, height, n_cntxt_layers=0): + """Compute constituents from distance and height.""" + + if n_cntxt_layers>0: + if n_cntxt_layers == 1: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler_1[0] + elif n_cntxt_layers == 2: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler_2[0] + else: + beta_logits = (distance[:, None, :] - height[:, :, None]) * self.scaler[0] + + gamma = torch.sigmoid(-beta_logits) + ones = torch.ones_like(gamma) + + block_mask_left = cummin( + gamma.tril(-1) + ones.triu(0), reverse=True, max_value=1 + ) + block_mask_left = block_mask_left - F.pad( + block_mask_left[:, :, :-1], (1, 0), value=0 + ) + block_mask_left.tril_(0) + + block_mask_right = cummin( + gamma.triu(0) + ones.tril(-1), exclusive=True, max_value=1 + ) + block_mask_right = block_mask_right - F.pad( + block_mask_right[:, :, 1:], (0, 1), value=0 + ) + block_mask_right.triu_(0) + + block_p = block_mask_left[:, :, :, None] * block_mask_right[:, :, None, :] + block = cumsum(block_mask_left).tril(0) + cumsum( + block_mask_right, reverse=True + ).triu(1) + + return block_p, block + + def compute_head(self, height, n_cntxt_layers=0): + """Estimate head for each constituent.""" + + _, length = height.size() + if n_cntxt_layers>0: + if n_cntxt_layers == 1: + head_logits = height * self.scaler_1[1] + elif n_cntxt_layers == 2: + head_logits = height * self.scaler_2[1] + else: + head_logits = height * self.scaler[1] + index = torch.arange(length, device=height.device) + + mask = (index[:, None, None] <= index[None, None, :]) * ( + index[None, None, :] <= index[None, :, None] + ) + head_logits = head_logits[:, None, None, :].repeat(1, length, length, 1) + head_logits.masked_fill_(~mask[None, :, :, :], -1e9) + + head_p = torch.softmax(head_logits, dim=-1) + + return head_p + + def parse(self, x, embs=None, n_cntxt_layers=0): + """Parse input sentence. + + Args: + x: input tokens (required). + pos: position for each token (optional). + Returns: + distance: syntactic distance + height: syntactic height + """ + + mask = x != self.pad + mask_shifted = F.pad(mask[:, 1:], (0, 1), value=0) + + if embs is None: + h = self.roberta.embeddings(x) + else: + h = embs + + if n_cntxt_layers > 0: + if n_cntxt_layers == 1: + parser_layers = self.parser_layers_1 + height_ff = self.height_ff_1 + distance_ff = self.distance_ff_1 + elif n_cntxt_layers == 2: + parser_layers = self.parser_layers_2 + height_ff = self.height_ff_2 + distance_ff = self.distance_ff_2 + for i in range(int(self.config.n_parser_layers/2)): + h = h.masked_fill(~mask[:, :, None], 0) + h = parser_layers[i](h) + + height = height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, dim=-1 + )[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max + ) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where( + margin_left > margin_right, margin_right, margin_left + ).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + else: + for i in range(self.config.n_parser_layers): + h = h.masked_fill(~mask[:, :, None], 0) + h = self.parser_layers[i](h) + + height = self.height_ff(h).squeeze(-1) + height.masked_fill_(~mask, -1e9) + + distance = self.distance_ff(h).squeeze(-1) + distance.masked_fill_(~mask_shifted, 1e9) + + # Calbrating the distance and height to the same level + length = distance.size(1) + height_max = height[:, None, :].expand(-1, length, -1) + height_max = torch.cummax( + height_max.triu(0) - torch.ones_like(height_max).tril(-1) * 1e9, dim=-1 + )[0].triu(0) + + margin_left = torch.relu( + F.pad(distance[:, :-1, None], (0, 0, 1, 0), value=1e9) - height_max + ) + margin_right = torch.relu(distance[:, None, :] - height_max) + margin = torch.where( + margin_left > margin_right, margin_right, margin_left + ).triu(0) + + margin_mask = torch.stack([mask_shifted] + [mask] * (length - 1), dim=1) + margin.masked_fill_(~margin_mask, 0) + margin = margin.max() + + distance = distance - margin + + return distance, height + + def generate_mask(self, x, distance, height, n_cntxt_layers=0): + """Compute head and cibling distribution for each token.""" + + bsz, length = x.size() + + eye = torch.eye(length, device=x.device, dtype=torch.bool) + eye = eye[None, :, :].expand((bsz, -1, -1)) + + block_p, block = self.compute_block(distance, height, n_cntxt_layers=n_cntxt_layers) + head_p = self.compute_head(height, n_cntxt_layers=n_cntxt_layers) + head = torch.einsum("blij,bijh->blh", block_p, head_p) + head = head.masked_fill(eye, 0) + child = head.transpose(1, 2) + cibling = torch.bmm(head, child).masked_fill(eye, 0) + + rel_list = [] + if "head" in self.config.relations: + rel_list.append(head) + if "child" in self.config.relations: + rel_list.append(child) + if "cibling" in self.config.relations: + rel_list.append(cibling) + + rel = torch.stack(rel_list, dim=1) + + if n_cntxt_layers > 0: + if n_cntxt_layers == 1: + rel_weight = self.rel_weight_1 + elif n_cntxt_layers == 2: + rel_weight = self.rel_weight_2 + else: + rel_weight = self.rel_weight + + dep = torch.einsum("lhr,brij->lbhij", rel_weight, rel) + + if n_cntxt_layers == 1: + num_layers = self.cntxt_layers_2.config.num_hidden_layers + else: + num_layers = self.roberta.config.num_hidden_layers + + att_mask = dep.reshape( + num_layers, + bsz, + self.config.num_attention_heads, + length, + length, + ) + + return att_mask, cibling, head, block + + def forward( + self, + input_ids: Optional[torch.LongTensor] = None, + attention_mask: Optional[torch.FloatTensor] = None, + token_type_ids: Optional[torch.LongTensor] = None, + position_ids: Optional[torch.LongTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, SequenceClassifierOutput]: + r""" + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., + config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If + `config.num_labels > 1` a classification loss is computed (Cross-Entropy). + """ + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + if self.config.n_cntxt_layers > 0: + cntxt_outputs = self.cntxt_layers( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict) + + + if self.config.n_cntxt_layers_2 > 0: + distance_1, height_1 = self.parse(input_ids, cntxt_outputs[0], n_cntxt_layers=1) + att_mask_1, _, _, _ = self.generate_mask(input_ids, distance_1, height_1, n_cntxt_layers=1) + + cntxt_outputs_2 = self.cntxt_layers_2( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + parser_att_mask=att_mask_1) + + sequence_output = cntxt_outputs_2[0] + + distance_2, height_2 = self.parse(input_ids, sequence_output[0], n_cntxt_layers=2) + att_mask, _, _, _ = self.generate_mask(input_ids, distance_2, height_2, n_cntxt_layers=2) + + elif self.config.n_cntxt_layers > 0: + distance, height = self.parse(input_ids, cntxt_outputs[0]) + att_mask, _, _, _ = self.generate_mask(input_ids, distance, height) + else: + distance, height = self.parse(input_ids) + att_mask, _, _, _ = self.generate_mask(input_ids, distance, height) + + outputs = self.roberta( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + parser_att_mask=att_mask, + ) + sequence_output = outputs[0] + logits = self.classifier(sequence_output) + + loss = None + if labels is not None: + if self.config.problem_type is None: + if self.num_labels == 1: + self.config.problem_type = "regression" + elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): + self.config.problem_type = "single_label_classification" + else: + self.config.problem_type = "multi_label_classification" + + if self.config.problem_type == "regression": + loss_fct = MSELoss() + if self.num_labels == 1: + loss = loss_fct(logits.squeeze(), labels.squeeze()) + else: + loss = loss_fct(logits, labels) + elif self.config.problem_type == "single_label_classification": + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) + elif self.config.problem_type == "multi_label_classification": + loss_fct = BCEWithLogitsLoss() + loss = loss_fct(logits, labels) + + if not return_dict: + output = (logits,) + outputs[2:] + return ((loss,) + output) if loss is not None else output + + return SequenceClassifierOutput( + loss=loss, + logits=logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +class RobertaClassificationHead(nn.Module): + """Head for sentence-level classification tasks.""" + + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + classifier_dropout = ( + config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob + ) + self.dropout = nn.Dropout(classifier_dropout) + self.out_proj = nn.Linear(config.hidden_size, config.num_labels) + + def forward(self, features, **kwargs): + x = features[:, 0, :] # take token (equiv. to [CLS]) + x = self.dropout(x) + x = self.dense(x) + x = torch.tanh(x) + x = self.dropout(x) + x = self.out_proj(x) + return x + + +def create_position_ids_from_input_ids( + input_ids, padding_idx, past_key_values_length=0 +): + """ + Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols + are ignored. This is modified from fairseq's `utils.make_positions`. + + Args: + x: torch.Tensor x: + + Returns: torch.Tensor + """ + # The series of casts and type-conversions here are carefully balanced to both work with ONNX export and XLA. + mask = input_ids.ne(padding_idx).int() + incremental_indices = ( + torch.cumsum(mask, dim=1).type_as(mask) + past_key_values_length + ) * mask + return incremental_indices.long() + padding_idx + + +def cumprod(x, reverse=False, exclusive=False): + """cumulative product.""" + if reverse: + x = x.flip([-1]) + + if exclusive: + x = F.pad(x[:, :, :-1], (1, 0), value=1) + + cx = x.cumprod(-1) + + if reverse: + cx = cx.flip([-1]) + return cx + + +def cumsum(x, reverse=False, exclusive=False): + """cumulative sum.""" + bsz, _, length = x.size() + device = x.device + if reverse: + if exclusive: + w = torch.ones([bsz, length, length], device=device).tril(-1) + else: + w = torch.ones([bsz, length, length], device=device).tril(0) + cx = torch.bmm(x, w) + else: + if exclusive: + w = torch.ones([bsz, length, length], device=device).triu(1) + else: + w = torch.ones([bsz, length, length], device=device).triu(0) + cx = torch.bmm(x, w) + return cx + + +def cummin(x, reverse=False, exclusive=False, max_value=1e9): + """cumulative min.""" + if reverse: + if exclusive: + x = F.pad(x[:, :, 1:], (0, 1), value=max_value) + x = x.flip([-1]).cummin(-1)[0].flip([-1]) + else: + if exclusive: + x = F.pad(x[:, :, :-1], (1, 0), value=max_value) + x = x.cummin(-1)[0] + return x diff --git a/finetune/wsc/predict_results.txt b/finetune/wsc/predict_results.txt new file mode 100644 index 0000000000000000000000000000000000000000..dcca29cce53336522caef212389e1068064372ac --- /dev/null +++ b/finetune/wsc/predict_results.txt @@ -0,0 +1,84 @@ +index prediction +0 0 +1 0 +2 0 +3 0 +4 0 +5 0 +6 0 +7 0 +8 1 +9 0 +10 0 +11 0 +12 0 +13 0 +14 0 +15 0 +16 0 +17 0 +18 0 +19 0 +20 0 +21 0 +22 0 +23 0 +24 1 +25 0 +26 0 +27 0 +28 1 +29 0 +30 0 +31 0 +32 0 +33 0 +34 0 +35 0 +36 0 +37 0 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