from collections import OrderedDict from typing import Any, Mapping, Optional import torch from transformers import PreTrainedTokenizer from transformers.onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeq2SeqConfigWithPast from transformers.onnx.utils import compute_effective_axis_dimension from transformers.utils.generic import TensorType from transformers.utils.import_utils import is_torch_available class BartCustumOnnxConfig(OnnxSeq2SeqConfigWithPast): @property def inputs(self) -> Mapping[str, Mapping[int, str]]: if self.task in ["default", "seq2seq-lm"]: common_inputs = OrderedDict( [ ("input_ids", {0: "batch", 1: "encoder_sequence"}), ("attention_mask", {0: "batch", 1: "encoder_sequence"}), ("input_commonsense_relations", {0: "batch", 1: "encoder_sequence", 2: "encoder_sequence"}), ] ) if self.use_past: common_inputs["decoder_input_ids"] = {0: "batch"} common_inputs["decoder_attention_mask"] = {0: "batch", 1: "past_decoder_sequence + sequence"} else: common_inputs["decoder_input_ids"] = {0: "batch", 1: "decoder_sequence"} common_inputs["decoder_attention_mask"] = {0: "batch", 1: "decoder_sequence"} if self.use_past: self.fill_with_past_key_values_(common_inputs, direction="inputs") elif self.task == "causal-lm": # TODO: figure this case out. common_inputs = OrderedDict( [ ("input_ids", {0: "batch", 1: "encoder_sequence"}), ("attention_mask", {0: "batch", 1: "encoder_sequence"}), ] ) if self.use_past: num_encoder_layers, _ = self.num_layers for i in range(num_encoder_layers): common_inputs[f"past_key_values.{i}.key"] = {0: "batch", 2: "past_sequence + sequence"} common_inputs[f"past_key_values.{i}.value"] = {0: "batch", 2: "past_sequence + sequence"} else: common_inputs = OrderedDict( [ ("input_ids", {0: "batch", 1: "encoder_sequence"}), ("attention_mask", {0: "batch", 1: "encoder_sequence"}), ("input_commonsense_relations", {0: "batch", 2: "encoder_sequence", 3: "encoder_sequence"}), ("decoder_input_ids", {0: "batch", 1: "decoder_sequence"}), ("decoder_attention_mask", {0: "batch", 1: "decoder_sequence"}), ] ) return common_inputs @property def outputs(self) -> Mapping[str, Mapping[int, str]]: if self.task in ["default", "seq2seq-lm"]: common_outputs = super().outputs else: common_outputs = super(OnnxConfigWithPast, self).outputs if self.use_past: num_encoder_layers, _ = self.num_layers for i in range(num_encoder_layers): common_outputs[f"present.{i}.key"] = {0: "batch", 2: "past_sequence + sequence"} common_outputs[f"present.{i}.value"] = {0: "batch", 2: "past_sequence + sequence"} return common_outputs def _generate_dummy_inputs_for_default_and_seq2seq_lm( self, tokenizer: PreTrainedTokenizer, batch_size: int = -1, seq_length: int = -1, is_pair: bool = False, framework: Optional[TensorType] = None, ) -> Mapping[str, Any]: encoder_inputs = self._generate_dummy_inputs_for_sequence_classification_and_question_answering( tokenizer, batch_size, seq_length, is_pair, framework ) # Generate decoder inputs decoder_seq_length = seq_length if not self.use_past else 1 decoder_inputs = self._generate_dummy_inputs_for_sequence_classification_and_question_answering( tokenizer, batch_size, decoder_seq_length, is_pair, framework ) decoder_inputs = {f"decoder_{name}": tensor for name, tensor in decoder_inputs.items()} common_inputs = dict(**encoder_inputs, **decoder_inputs) if self.use_past: if not is_torch_available(): raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed.") else: import torch batch, encoder_seq_length = common_inputs["input_ids"].shape decoder_seq_length = common_inputs["decoder_input_ids"].shape[1] num_encoder_attention_heads, num_decoder_attention_heads = self.num_attention_heads encoder_shape = ( batch, num_encoder_attention_heads, encoder_seq_length, self._config.hidden_size // num_encoder_attention_heads, ) decoder_past_length = decoder_seq_length + 3 decoder_shape = ( batch, num_decoder_attention_heads, decoder_past_length, self._config.hidden_size // num_decoder_attention_heads, ) common_inputs["decoder_attention_mask"] = torch.cat( [common_inputs["decoder_attention_mask"], torch.ones(batch, decoder_past_length)], dim=1 ) common_inputs["past_key_values"] = [] # If the number of encoder and decoder layers are present in the model configuration, both are considered num_encoder_layers, num_decoder_layers = self.num_layers min_num_layers = min(num_encoder_layers, num_decoder_layers) max_num_layers = max(num_encoder_layers, num_decoder_layers) - min_num_layers remaining_side_name = "encoder" if num_encoder_layers > num_decoder_layers else "decoder" for _ in range(min_num_layers): common_inputs["past_key_values"].append( ( torch.zeros(decoder_shape), torch.zeros(decoder_shape), torch.zeros(encoder_shape), torch.zeros(encoder_shape), ) ) # TODO: test this. shape = encoder_shape if remaining_side_name == "encoder" else decoder_shape for _ in range(min_num_layers, max_num_layers): common_inputs["past_key_values"].append((torch.zeros(shape), torch.zeros(shape))) return common_inputs def _generate_dummy_inputs_for_causal_lm( self, tokenizer: PreTrainedTokenizer, batch_size: int = -1, seq_length: int = -1, is_pair: bool = False, framework: Optional[TensorType] = None, ) -> Mapping[str, Any]: common_inputs = self._generate_dummy_inputs_for_sequence_classification_and_question_answering( tokenizer, batch_size, seq_length, is_pair, framework ) if self.use_past: if not is_torch_available(): raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed.") else: import torch batch, seqlen = common_inputs["input_ids"].shape # Not using the same length for past_key_values past_key_values_length = seqlen + 2 num_encoder_layers, _ = self.num_layers num_encoder_attention_heads, _ = self.num_attention_heads past_shape = ( batch, num_encoder_attention_heads, past_key_values_length, self._config.hidden_size // num_encoder_attention_heads, ) mask_dtype = common_inputs["attention_mask"].dtype common_inputs["attention_mask"] = torch.cat( [common_inputs["attention_mask"], torch.ones(batch, past_key_values_length, dtype=mask_dtype)], dim=1 ) common_inputs["past_key_values"] = [ (torch.zeros(past_shape), torch.zeros(past_shape)) for _ in range(num_encoder_layers) ] return common_inputs def _generate_dummy_inputs_for_sequence_classification_and_question_answering( self, tokenizer: PreTrainedTokenizer, batch_size: int = -1, seq_length: int = -1, is_pair: bool = False, framework: Optional[TensorType] = None, ) -> Mapping[str, Any]: # Copied from OnnxConfig.generate_dummy_inputs # Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity. # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX batch_size = compute_effective_axis_dimension( batch_size, fixed_dimension=OnnxConfig.default_fixed_batch, num_token_to_add=0 ) # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX token_to_add = tokenizer.num_special_tokens_to_add(is_pair) seq_length = compute_effective_axis_dimension( seq_length, fixed_dimension=OnnxConfig.default_fixed_sequence, num_token_to_add=token_to_add ) # Generate dummy inputs according to compute batch and sequence dummy_input = [" ".join([tokenizer.unk_token]) * seq_length] * batch_size tmp_seq_length = seq_length + 2 commonsense_relation= torch.IntTensor([[[0] * tmp_seq_length] * tmp_seq_length]* batch_size) common_inputs = dict(tokenizer(dummy_input, return_tensors=framework)) common_inputs['input_commonsense_relations'] = commonsense_relation print('here:', common_inputs) return common_inputs def generate_dummy_inputs( self, tokenizer: PreTrainedTokenizer, batch_size: int = -1, seq_length: int = -1, is_pair: bool = False, framework: Optional[TensorType] = None, ) -> Mapping[str, Any]: if self.task in ["default", "seq2seq-lm"]: common_inputs = self._generate_dummy_inputs_for_default_and_seq2seq_lm( tokenizer, batch_size=batch_size, seq_length=seq_length, is_pair=is_pair, framework=framework ) elif self.task == "causal-lm": common_inputs = self._generate_dummy_inputs_for_causal_lm( tokenizer, batch_size=batch_size, seq_length=seq_length, is_pair=is_pair, framework=framework ) else: common_inputs = self._generate_dummy_inputs_for_sequence_classification_and_question_answering( tokenizer, batch_size=batch_size, seq_length=seq_length, is_pair=is_pair, framework=framework ) if 'decoder_input_commonsense_relations' in common_inputs: del common_inputs['decoder_input_commonsense_relations'] return common_inputs def _flatten_past_key_values_(self, flattened_output, name, idx, t): if self.task in ["default", "seq2seq-lm"]: flattened_output = super()._flatten_past_key_values_(flattened_output, name, idx, t) else: flattened_output = super(OnnxSeq2SeqConfigWithPast, self)._flatten_past_key_values_( flattened_output, name, idx, t )