Fix duplicate arguments passed to dummy inputs in ONNX export (#16045)

* Fix duplicate arguments passed to dummy inputs in ONNX export

* Fix M2M100 ONNX config

* Ensure we check PreTrained model only if torch is available

* Remove TensorFlow tests for models without PyTorch parity

src/transformers/models/m2m_100/configuration_m2m_100.py

@@ -198,13 +198,13 @@ class M2M100OnnxConfig(OnnxSeq2SeqConfigWithPast):
         # 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

src/transformers/onnx/convert.py

@@ -22,6 +22,7 @@ import numpy as np
 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

tests/onnx/test_onnx_v2.py

@@ -196,28 +196,19 @@ PYTORCH_EXPORT_SEQ2SEQ_WITH_PAST_MODELS = {
     ("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 @@ class OnnxExportTestCaseV2(TestCase):
     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(