custom_bart/bart_onnx.py

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
            )