Upload BottleneckT5LMWithPerturb

bottleneck_t5.py

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+import copy
+import torch
+import torch.nn as nn
+from torch.nn import CrossEntropyLoss
+from torch.nn import functional as F
+from typing import Optional, Tuple, Union
+
+from transformers import T5Config, T5Tokenizer, T5ForConditionalGeneration
+from transformers.models.t5.modeling_t5 import (
+    T5LayerNorm,
+    T5LayerFF,
+    T5Attention,
+    T5LayerSelfAttention,
+    T5LayerCrossAttention,
+    T5Block,
+    T5Stack,
+)
+from transformers.modeling_outputs import BaseModelOutput, Seq2SeqLMOutput
+
+class BottleneckCrossAttentionGate(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.gate = nn.Linear(2 * config.d_model, config.d_model, bias=False)
+        self.act = nn.Sigmoid()
+
+    def forward(self, query_states, latents):
+        latents = latents.unsqueeze(1).expand(query_states.shape)
+        query_latents = torch.cat([query_states, latents], dim=-1)
+        return 2 * self.act(self.gate(query_latents))
+
+class BottleneckT5Attention(T5Attention):
+    def __init__(self, config: T5Config, has_relative_attention_bias=False):
+        super(T5Attention, self).__init__()
+        self.is_decoder = config.is_decoder
+        self.has_relative_attention_bias = has_relative_attention_bias
+        self.relative_attention_num_buckets = config.relative_attention_num_buckets
+        self.relative_attention_max_distance = config.relative_attention_max_distance
+        self.d_model = config.d_model
+        self.key_value_proj_dim = config.d_kv
+        self.n_heads = config.num_heads
+        self.dropout = config.dropout_rate
+        self.inner_dim = self.n_heads * self.key_value_proj_dim
+
+        # Mesh TensorFlow initialization to avoid scaling before softmax
+        self.v = nn.Linear(self.d_model, self.inner_dim, bias=False)
+        self.o = nn.Linear(self.inner_dim, self.d_model, bias=False)
+
+        if self.has_relative_attention_bias:
+            self.relative_attention_bias = nn.Embedding(self.relative_attention_num_buckets, self.n_heads)
+        self.pruned_heads = set()
+        self.gradient_checkpointing = False
+
+    def prune_heads(self, heads):
+        if len(heads) == 0:
+            return
+        heads, index = find_pruneable_heads_and_indices(
+            heads, self.n_heads, self.key_value_proj_dim, self.pruned_heads
+        )
+        # Prune linear layers
+        self.v = prune_linear_layer(self.v, index)
+        self.o = prune_linear_layer(self.o, index, dim=1)
+        # Update hyper params
+        self.n_heads = self.n_heads - len(heads)
+        self.inner_dim = self.key_value_proj_dim * self.n_heads
+        self.pruned_heads = self.pruned_heads.union(heads)
+
+    def forward(
+        self,
+        hidden_states,
+        mask=None,
+        key_value_states=None,
+        position_bias=None,
+        past_key_value=None,
+        layer_head_mask=None,
+        query_length=None,
+        use_cache=False,
+        output_attentions=False,
+    ):
+        """
+        Self-attention (if key_value_states is None) or attention over source sentence (provided by key_value_states).
+        """
+        # Input is (batch_size, seq_length, dim)
+        # Mask is (batch_size, key_length) (non-causal) or (batch_size, key_length, key_length)
+        # past_key_value[0] is (batch_size, n_heads, q_len - 1, dim_per_head)
+        batch_size, seq_length = hidden_states.shape[:2]
+
+        real_seq_length = seq_length
+
+        if past_key_value is not None:
+            assert (
+                len(past_key_value) == 2
+            ), f"past_key_value should have 2 past states: keys and values. Got { len(past_key_value)} past states"
+            real_seq_length += past_key_value[0].shape[2] if query_length is None else query_length
+
+        key_length = real_seq_length if key_value_states is None else key_value_states.shape[1]
+
+        def shape(states):
+            """projection"""
+            return states.view(batch_size, -1, self.n_heads, self.key_value_proj_dim).transpose(1, 2)
+
+        def unshape(states):
+            """reshape"""
+            return states.transpose(1, 2).contiguous().view(batch_size, -1, self.inner_dim)
+
+        def project(hidden_states, proj_layer, key_value_states, past_key_value):
+            """projects hidden states correctly to key/query states"""
+            if key_value_states is None:
+                # self-attn
+                # (batch_size, n_heads, seq_length, dim_per_head)
+                hidden_states = shape(proj_layer(hidden_states))
+            elif past_key_value is None:
+                # cross-attn
+                # (batch_size, n_heads, seq_length, dim_per_head)
+                hidden_states = shape(proj_layer(key_value_states))
+
+            if past_key_value is not None:
+                if key_value_states is None:
+                    # self-attn
+                    # (batch_size, n_heads, key_length, dim_per_head)
+                    hidden_states = torch.cat([past_key_value, hidden_states], dim=2)
+                else:
+                    # cross-attn
+                    hidden_states = past_key_value
+            return hidden_states
+
+        # key/value states
+        key_states = torch.zeros((batch_size, self.n_heads, seq_length, key_length), device=hidden_states.device)
+        value_states = project(
+            hidden_states, self.v, key_value_states, past_key_value[1] if past_key_value is not None else None
+        )
+
+        # compute scores
+        scores = torch.ones((batch_size, self.n_heads, seq_length, key_length), device=hidden_states.device)
+
+        if position_bias is None:
+            if not self.has_relative_attention_bias:
+                position_bias = torch.zeros(
+                    (1, self.n_heads, real_seq_length, key_length), device=scores.device, dtype=scores.dtype
+                )
+                if self.gradient_checkpointing and self.training:
+                    position_bias.requires_grad = True
+            else:
+                position_bias = self.compute_bias(real_seq_length, key_length, device=scores.device)
+
+            # if key and values are already calculated
+            # we want only the last query position bias
+            if past_key_value is not None:
+                position_bias = position_bias[:, :, -hidden_states.size(1) :, :]
+
+            if mask is not None:
+                position_bias = position_bias + mask  # (batch_size, n_heads, seq_length, key_length)
+
+        if self.pruned_heads:
+            mask = torch.ones(position_bias.shape[1])
+            mask[list(self.pruned_heads)] = 0
+            position_bias_masked = position_bias[:, mask.bool()]
+        else:
+            position_bias_masked = position_bias
+
+        scores += position_bias_masked
+        attn_weights = nn.functional.softmax(scores.float(), dim=-1).type_as(
+            scores
+        )  # (batch_size, n_heads, seq_length, key_length)
+        attn_weights = nn.functional.dropout(
+            attn_weights, p=self.dropout, training=self.training
+        )  # (batch_size, n_heads, seq_length, key_length)
+
+        # Mask heads if we want to
+        if layer_head_mask is not None:
+            attn_weights = attn_weights * layer_head_mask
+
+        attn_output = unshape(torch.matmul(attn_weights, value_states))  # (batch_size, seq_length, dim)
+        attn_output = self.o(attn_output)
+
+        present_key_value_state = (key_states, value_states) if (self.is_decoder and use_cache) else None
+        outputs = (attn_output,) + (present_key_value_state,) + (position_bias,)
+
+        if output_attentions:
+            outputs = outputs + (attn_weights,)
+        return outputs
+
+class BottleneckT5LayerCrossAttention(T5LayerCrossAttention):
+    def __init__(self, config):
+        super(T5LayerCrossAttention, self).__init__()
+        self.EncDecAttention = BottleneckT5Attention(config, has_relative_attention_bias=False)
+        self.layer_norm = T5LayerNorm(config.d_model, eps=config.layer_norm_epsilon)
+        self.gate = BottleneckCrossAttentionGate(config)
+        self.dropout = nn.Dropout(config.dropout_rate)
+
+    def forward(
+        self,
+        hidden_states,
+        key_value_states,
+        attention_mask=None,
+        position_bias=None,
+        layer_head_mask=None,
+        past_key_value=None,
+        use_cache=False,
+        query_length=None,
+        output_attentions=False,
+    ):
+        normed_hidden_states = self.layer_norm(hidden_states)
+        attention_output = self.EncDecAttention(
+            normed_hidden_states,
+            mask=attention_mask,
+            key_value_states=key_value_states,
+            position_bias=position_bias,
+            layer_head_mask=layer_head_mask,
+            past_key_value=past_key_value,
+            use_cache=use_cache,
+            query_length=query_length,
+            output_attentions=output_attentions,
+        )
+        latents = key_value_states[:, 0]
+        layer_output = hidden_states + self.dropout(self.gate(normed_hidden_states, latents) * attention_output[0])
+        outputs = (layer_output,) + attention_output[1:]  # add attentions if we output them
+        return outputs
+
+class BottleneckT5Block(T5Block):
+    def __init__(self, config, has_relative_attention_bias=False):
+        super(T5Block, self).__init__()
+        self.is_decoder = config.is_decoder
+        self.layer = nn.ModuleList()
+        self.layer.append(T5LayerSelfAttention(config, has_relative_attention_bias=has_relative_attention_bias))
+        if self.is_decoder:
+            self.layer.append(BottleneckT5LayerCrossAttention(config))
+
+        self.layer.append(T5LayerFF(config))
+
+class BottleneckT5Stack(T5Stack):
+    def __init__(self, config, embed_tokens=None):
+        super(T5Stack, self).__init__(config)
+
+        self.embed_tokens = embed_tokens
+        self.is_decoder = config.is_decoder
+
+        self.block = nn.ModuleList(
+            [BottleneckT5Block(config, has_relative_attention_bias=bool(i == 0)) for i in range(config.num_layers)]
+        )
+        self.final_layer_norm = T5LayerNorm(config.d_model, eps=config.layer_norm_epsilon)
+        self.dropout = nn.Dropout(config.dropout_rate)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+        # Model parallel
+        self.model_parallel = False
+        self.device_map = None
+        self.gradient_checkpointing = False
+
+class BottleneckT5LMWithPerturb(T5ForConditionalGeneration):
+    def __init__(self, config: T5Config):
+        super(T5ForConditionalGeneration, self).__init__(config)
+        self.model_dim = config.d_model
+
+        self.shared = nn.Embedding(config.vocab_size, config.d_model)
+        encoder_config = copy.deepcopy(config)
+        encoder_config.is_decoder = False
+        encoder_config.use_cache = False
+        encoder_config.is_encoder_decoder = False
+        self.encoder = T5Stack(encoder_config, self.shared)
+
+        # New in Contra: MHA bottleneck block
+        self.num_heads = 8
+        self.bottleneck = nn.MultiheadAttention(config.d_model,
+                num_heads=self.num_heads,
+                dropout=config.dropout_rate,
+                bias=False,
+                batch_first=True)
+        self.bottleneck_scale = nn.Parameter(torch.ones(1))
+
+        self.dec_emb = nn.Embedding(config.vocab_size, config.d_model)
+        decoder_config = copy.deepcopy(config)
+        decoder_config.is_decoder = True
+        decoder_config.is_encoder_decoder = False
+        decoder_config.num_layers = config.num_decoder_layers
+        self.decoder = BottleneckT5Stack(decoder_config, self.dec_emb)
+
+        self.lm_head = nn.Linear(config.d_model, config.vocab_size, bias=False)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+        # Model parallel
+        self.model_parallel = False
+        self.device_map = None
+
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        decoder_input_ids: Optional[torch.LongTensor] = None,
+        decoder_attention_mask: Optional[torch.BoolTensor] = None,
+        head_mask: Optional[torch.FloatTensor] = None,
+        decoder_head_mask: Optional[torch.FloatTensor] = None,
+        cross_attn_head_mask: Optional[torch.Tensor] = None,
+        encoder_outputs: Optional[Tuple[Tuple[torch.Tensor]]] = None,
+        past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        perturb_vector: Optional[torch.FloatTensor] = None,
+        encode_only: Optional[bool] = None,
+    ) -> Union[Tuple[torch.FloatTensor], Seq2SeqLMOutput]:
+        use_cache = use_cache if use_cache is not None else self.config.use_cache
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        # FutureWarning: head_mask was separated into two input args - head_mask, decoder_head_mask
+        if head_mask is not None and decoder_head_mask is None:
+            if self.config.num_layers == self.config.num_decoder_layers:
+                warnings.warn(__HEAD_MASK_WARNING_MSG, FutureWarning)
+                decoder_head_mask = head_mask
+
+        # Encode if needed (training, first prediction pass)
+        if encoder_outputs is None:
+            # Convert encoder inputs in embeddings if needed
+            encoder_outputs = self.encoder(
+                input_ids=input_ids,
+                attention_mask=attention_mask,
+                inputs_embeds=inputs_embeds,
+                head_mask=head_mask,
+                output_attentions=output_attentions,
+                output_hidden_states=output_hidden_states,
+                return_dict=return_dict,
+            )
+        elif return_dict and not isinstance(encoder_outputs, BaseModelOutput):
+            encoder_outputs = BaseModelOutput(
+                last_hidden_state=encoder_outputs[0],
+                hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None,
+                attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None,
+            )
+
+        hidden_states = encoder_outputs[0]
+
+        # MHA across token embeddings + embedding normalization + broadcast
+        hidden_states = hidden_states.repeat(
+                attention_mask.shape[0] // hidden_states.shape[0],
+                1, 1) # during contrastive search, attn mask can have higher batch size than hidden_state
+        mask_expanded = attention_mask.float().unsqueeze(-1).expand(hidden_states.shape)
+        mean_pooled_embedding = torch.sum(hidden_states * mask_expanded, 1) / torch.clamp(mask_expanded.sum(1), min=1e-9)
+        unscaled_latent, attn_weights = self.bottleneck(mean_pooled_embedding.unsqueeze(1), hidden_states, hidden_states,
+                need_weights=False,
+                # torch MHA attn_mask has opposite signs to HF T5 masks... sigh
+                attn_mask=attention_mask.float().unsqueeze(1).repeat_interleave(self.num_heads, dim=0))
+        latent = self.bottleneck_scale * F.normalize(unscaled_latent, p=2, dim=2)
+        if encode_only:
+            return latent.squeeze(1)
+        hidden_states = latent.expand(hidden_states.shape)
+
+        if hasattr(self, 'perturb_vector'):
+            hidden_states = self.bottleneck_scale * F.normalize(hidden_states + self.perturb_vector, p=2, dim=2)
+
+        if self.model_parallel:
+            torch.cuda.set_device(self.decoder.first_device)
+
+        if labels is not None and decoder_input_ids is None and decoder_inputs_embeds is None:
+            # get decoder inputs from shifting lm labels to the right
+            decoder_input_ids = self._shift_right(labels)
+
+        # Set device for model parallelism
+        if self.model_parallel:
+            torch.cuda.set_device(self.decoder.first_device)
+            hidden_states = hidden_states.to(self.decoder.first_device)
+            if decoder_input_ids is not None:
+                decoder_input_ids = decoder_input_ids.to(self.decoder.first_device)
+            if attention_mask is not None:
+                attention_mask = attention_mask.to(self.decoder.first_device)
+            if decoder_attention_mask is not None:
+                decoder_attention_mask = decoder_attention_mask.to(self.decoder.first_device)
+
+        # Decode
+        decoder_outputs = self.decoder(
+            input_ids=decoder_input_ids,
+            attention_mask=decoder_attention_mask,
+            inputs_embeds=decoder_inputs_embeds,
+            past_key_values=past_key_values,
+            encoder_hidden_states=hidden_states,
+            encoder_attention_mask=attention_mask,
+            head_mask=decoder_head_mask,
+            cross_attn_head_mask=cross_attn_head_mask,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        sequence_output = decoder_outputs[0]
+
+        # Set device for model parallelism
+        if self.model_parallel:
+            torch.cuda.set_device(self.encoder.first_device)
+            self.lm_head = self.lm_head.to(self.encoder.first_device)
+            sequence_output = sequence_output.to(self.lm_head.weight.device)
+
+        if self.config.tie_word_embeddings:
+            # Rescale output before projecting on vocab
+            # See https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/transformer/transformer.py#L586
+            sequence_output = sequence_output * (self.model_dim**-0.5)
+
+        lm_logits = self.lm_head(sequence_output)
+
+        loss = None
+        if labels is not None:
+            loss_fct = CrossEntropyLoss(ignore_index=-100)
+            loss = loss_fct(lm_logits.view(-1, lm_logits.size(-1)), labels.view(-1))
+            # TODO(thom): Add z_loss https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/layers.py#L666
+
+        if not return_dict:
+            output = (lm_logits,) + decoder_outputs[1:] + encoder_outputs
+            return ((loss,) + output) if loss is not None else output
+
+        return Seq2SeqLMOutput(
+            loss=loss,
+            logits=lm_logits,
+            past_key_values=decoder_outputs.past_key_values,
+            decoder_hidden_states=decoder_outputs.hidden_states,
+            decoder_attentions=decoder_outputs.attentions,
+            cross_attentions=decoder_outputs.cross_attentions,
+            encoder_last_hidden_state=encoder_outputs.last_hidden_state,
+            encoder_hidden_states=encoder_outputs.hidden_states,
+            encoder_attentions=encoder_outputs.attentions,
+        )
+

config.json

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+{
+  "_name_or_path": "./bottleneck-t5",
+  "architectures": [
+    "BottleneckT5LMWithPerturb"
+  ],
+  "auto_map": {
+    "AutoModelForCausalLM": "bottleneck_t5.BottleneckT5LMWithPerturb"
+  },
+  "classifier_dropout": 0.0,
+  "d_ff": 1024,
+  "d_kv": 64,
+  "d_model": 512,
+  "decoder_start_token_id": 0,
+  "dense_act_fn": "gelu_new",
+  "dropout_rate": 0.1,
+  "eos_token_id": 1,
+  "feed_forward_proj": "gated-gelu",
+  "initializer_factor": 1.0,
+  "is_encoder_decoder": true,
+  "is_gated_act": true,
+  "layer_norm_epsilon": 1e-06,
+  "model_type": "t5",
+  "num_decoder_layers": 8,
+  "num_heads": 6,
+  "num_layers": 8,
+  "output_past": true,
+  "pad_token_id": 0,
+  "relative_attention_max_distance": 128,
+  "relative_attention_num_buckets": 32,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float32",
+  "transformers_version": "4.33.3",
+  "use_cache": true,
+  "vocab_size": 32128
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "decoder_start_token_id": 0,
+  "eos_token_id": 1,
+  "pad_token_id": 0,
+  "transformers_version": "4.33.3"
+}

pytorch_model.bin

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