diff --git a/src/transformers/models/m2m_100/configuration_m2m_100.py b/src/transformers/models/m2m_100/configuration_m2m_100.py index 62a63d248b90..180950f8c7b9 100644 --- a/src/transformers/models/m2m_100/configuration_m2m_100.py +++ b/src/transformers/models/m2m_100/configuration_m2m_100.py @@ -198,13 +198,13 @@ def _generate_dummy_inputs_for_sequence_classification_and_question_answering( # 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 + 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 + seq_length, fixed_dimension=OnnxConfig.default_fixed_sequence, num_token_to_add=token_to_add ) # Generate dummy inputs according to compute batch and sequence diff --git a/src/transformers/onnx/convert.py b/src/transformers/onnx/convert.py index 42b57d2c5402..cb646948a821 100644 --- a/src/transformers/onnx/convert.py +++ b/src/transformers/onnx/convert.py @@ -22,6 +22,7 @@ from packaging.version import Version, parse from ..file_utils import TensorType, is_tf_available, is_torch_available, is_torch_onnx_dict_inputs_support_available +from ..tokenization_utils_base import PreTrainedTokenizerBase from ..utils import logging from .config import OnnxConfig @@ -100,11 +101,17 @@ def export_pytorch( `Tuple[List[str], List[str]]`: A tuple with an ordered list of the model's inputs, and the named inputs from the ONNX configuration. """ + + if isinstance(preprocessor, PreTrainedTokenizerBase) and tokenizer is not None: + raise ValueError("You cannot provide both a tokenizer and a preprocessor to export the model.") if tokenizer is not None: warnings.warn( "The `tokenizer` argument is deprecated and will be removed in version 5 of Transformers. Use `preprocessor` instead.", FutureWarning, ) + logger.info("Overwriting the `preprocessor` argument with `tokenizer` to generate dummmy inputs.") + preprocessor = tokenizer + if issubclass(type(model), PreTrainedModel): import torch from torch.onnx import export as onnx_export @@ -123,9 +130,7 @@ def export_pytorch( # Ensure inputs match # TODO: Check when exporting QA we provide "is_pair=True" - model_inputs = config.generate_dummy_inputs( - preprocessor, tokenizer=tokenizer, framework=TensorType.PYTORCH - ) + model_inputs = config.generate_dummy_inputs(preprocessor, framework=TensorType.PYTORCH) inputs_match, matched_inputs = ensure_model_and_config_inputs_match(model, model_inputs.keys()) onnx_outputs = list(config.outputs.keys()) @@ -213,11 +218,15 @@ def export_tensorflow( import onnx import tf2onnx + if isinstance(preprocessor, PreTrainedTokenizerBase) and tokenizer is not None: + raise ValueError("You cannot provide both a tokenizer and preprocessor to export the model.") if tokenizer is not None: warnings.warn( "The `tokenizer` argument is deprecated and will be removed in version 5 of Transformers. Use `preprocessor` instead.", FutureWarning, ) + logger.info("Overwriting the `preprocessor` argument with `tokenizer` to generate dummmy inputs.") + preprocessor = tokenizer model.config.return_dict = True @@ -229,7 +238,7 @@ def export_tensorflow( setattr(model.config, override_config_key, override_config_value) # Ensure inputs match - model_inputs = config.generate_dummy_inputs(preprocessor, tokenizer=tokenizer, framework=TensorType.TENSORFLOW) + model_inputs = config.generate_dummy_inputs(preprocessor, framework=TensorType.TENSORFLOW) inputs_match, matched_inputs = ensure_model_and_config_inputs_match(model, model_inputs.keys()) onnx_outputs = list(config.outputs.keys()) @@ -273,11 +282,16 @@ def export( "Cannot convert because neither PyTorch nor TensorFlow are not installed. " "Please install torch or tensorflow first." ) + + if isinstance(preprocessor, PreTrainedTokenizerBase) and tokenizer is not None: + raise ValueError("You cannot provide both a tokenizer and a preprocessor to export the model.") if tokenizer is not None: warnings.warn( "The `tokenizer` argument is deprecated and will be removed in version 5 of Transformers. Use `preprocessor` instead.", FutureWarning, ) + logger.info("Overwriting the `preprocessor` argument with `tokenizer` to generate dummmy inputs.") + preprocessor = tokenizer if is_torch_available(): from ..file_utils import torch_version @@ -309,16 +323,22 @@ def validate_model_outputs( logger.info("Validating ONNX model...") + if isinstance(preprocessor, PreTrainedTokenizerBase) and tokenizer is not None: + raise ValueError("You cannot provide both a tokenizer and a preprocessor to validatethe model outputs.") + if tokenizer is not None: + warnings.warn( + "The `tokenizer` argument is deprecated and will be removed in version 5 of Transformers. Use `preprocessor` instead.", + FutureWarning, + ) + logger.info("Overwriting the `preprocessor` argument with `tokenizer` to generate dummmy inputs.") + preprocessor = tokenizer + # TODO: generate inputs with a different batch_size and seq_len that was used for conversion to properly test # dynamic input shapes. - if issubclass(type(reference_model), PreTrainedModel): - reference_model_inputs = config.generate_dummy_inputs( - preprocessor, tokenizer=tokenizer, framework=TensorType.PYTORCH - ) + if is_torch_available() and issubclass(type(reference_model), PreTrainedModel): + reference_model_inputs = config.generate_dummy_inputs(preprocessor, framework=TensorType.PYTORCH) else: - reference_model_inputs = config.generate_dummy_inputs( - preprocessor, tokenizer=tokenizer, framework=TensorType.TENSORFLOW - ) + reference_model_inputs = config.generate_dummy_inputs(preprocessor, framework=TensorType.TENSORFLOW) # Create ONNX Runtime session options = SessionOptions() @@ -368,7 +388,7 @@ def validate_model_outputs( # Check the shape and values match for name, ort_value in zip(onnx_named_outputs, onnx_outputs): - if issubclass(type(reference_model), PreTrainedModel): + if is_torch_available() and issubclass(type(reference_model), PreTrainedModel): ref_value = ref_outputs_dict[name].detach().numpy() else: ref_value = ref_outputs_dict[name].numpy() @@ -402,7 +422,7 @@ def ensure_model_and_config_inputs_match( :param model_inputs: :param config_inputs: :return: """ - if issubclass(type(model), PreTrainedModel): + if is_torch_available() and issubclass(type(model), PreTrainedModel): forward_parameters = signature(model.forward).parameters else: forward_parameters = signature(model.call).parameters diff --git a/tests/onnx/test_onnx_v2.py b/tests/onnx/test_onnx_v2.py index a0a5e0f943a5..26ef4370e272 100644 --- a/tests/onnx/test_onnx_v2.py +++ b/tests/onnx/test_onnx_v2.py @@ -196,28 +196,19 @@ def test_values_override(self): ("m2m-100", "facebook/m2m100_418M"), } +# TODO(lewtun): Include the same model types in `PYTORCH_EXPORT_MODELS` once TensorFlow has parity with the PyTorch model implementations. TENSORFLOW_EXPORT_DEFAULT_MODELS = { ("albert", "hf-internal-testing/tiny-albert"), ("bert", "bert-base-cased"), - ("ibert", "kssteven/ibert-roberta-base"), - ("camembert", "camembert-base"), ("distilbert", "distilbert-base-cased"), ("roberta", "roberta-base"), - ("xlm-roberta", "xlm-roberta-base"), - ("layoutlm", "microsoft/layoutlm-base-uncased"), } -TENSORFLOW_EXPORT_WITH_PAST_MODELS = { - ("gpt2", "gpt2"), - ("gpt-neo", "EleutherAI/gpt-neo-125M"), -} +# TODO(lewtun): Include the same model types in `PYTORCH_EXPORT_WITH_PAST_MODELS` once TensorFlow has parity with the PyTorch model implementations. +TENSORFLOW_EXPORT_WITH_PAST_MODELS = {} -TENSORFLOW_EXPORT_SEQ2SEQ_WITH_PAST_MODELS = { - ("bart", "facebook/bart-base"), - ("mbart", "sshleifer/tiny-mbart"), - ("t5", "t5-small"), - ("marian", "Helsinki-NLP/opus-mt-en-de"), -} +# TODO(lewtun): Include the same model types in `PYTORCH_EXPORT_SEQ2SEQ_WITH_PAST_MODELS` once TensorFlow has parity with the PyTorch model implementations. +TENSORFLOW_EXPORT_SEQ2SEQ_WITH_PAST_MODELS = {} def _get_models_to_test(export_models_list): @@ -312,13 +303,13 @@ def test_pytorch_export_seq2seq_with_past( def test_tensorflow_export(self, test_name, name, model_name, feature, onnx_config_class_constructor): self._onnx_export(test_name, name, model_name, feature, onnx_config_class_constructor) - @parameterized.expand(_get_models_to_test(TENSORFLOW_EXPORT_WITH_PAST_MODELS)) + @parameterized.expand(_get_models_to_test(TENSORFLOW_EXPORT_WITH_PAST_MODELS), skip_on_empty=True) @slow @require_tf def test_tensorflow_export_with_past(self, test_name, name, model_name, feature, onnx_config_class_constructor): self._onnx_export(test_name, name, model_name, feature, onnx_config_class_constructor) - @parameterized.expand(_get_models_to_test(TENSORFLOW_EXPORT_SEQ2SEQ_WITH_PAST_MODELS)) + @parameterized.expand(_get_models_to_test(TENSORFLOW_EXPORT_SEQ2SEQ_WITH_PAST_MODELS), skip_on_empty=True) @slow @require_tf def test_tensorflow_export_seq2seq_with_past(