Onnx fix test

src/transformers/convert_graph_to_onnx.py

@@ -222,7 +222,9 @@ def build_shape_dict(name: str, tensor, is_input: bool, seq_len: int):
     return input_vars, output_names, dynamic_axes, tokens
 
 
-def load_graph_from_args(pipeline_name: str, framework: str, model: str, tokenizer: Optional[str] = None) -> Pipeline:
+def load_graph_from_args(
+    pipeline_name: str, framework: str, model: str, tokenizer: Optional[str] = None, **models_kwargs
+) -> Pipeline:
     """
     Convert the set of arguments provided through the CLI to an actual pipeline reference (tokenizer + model
 
@@ -248,7 +250,7 @@ def load_graph_from_args(pipeline_name: str, framework: str, model: str, tokeniz
     print(f"Loading pipeline (model: {model}, tokenizer: {tokenizer})")
 
     # Allocate tokenizer and model
-    return pipeline(pipeline_name, model=model, tokenizer=tokenizer, framework=framework)
+    return pipeline(pipeline_name, model=model, tokenizer=tokenizer, framework=framework, model_kwargs=models_kwargs)
 
 
 def convert_pytorch(nlp: Pipeline, opset: int, output: Path, use_external_format: bool):
@@ -335,6 +337,7 @@ def convert(
     tokenizer: Optional[str] = None,
     use_external_format: bool = False,
     pipeline_name: str = "feature-extraction",
+    **model_kwargs
 ):
     """
     Convert the pipeline object to the ONNX Intermediate Representation (IR) format
@@ -347,14 +350,15 @@ def convert(
         tokenizer: The name of the model to load for the pipeline, default to the model's name if not provided
         use_external_format: Split the model definition from its parameters to allow model bigger than 2GB (PyTorch only)
         pipeline_name: The kind of pipeline to instantiate (ner, question-answering, etc.)
+        model_kwargs: Keyword arguments to be forwarded to the model constructor
 
     Returns:
 
     """
     print(f"ONNX opset version set to: {opset}")
 
     # Load the pipeline
-    nlp = load_graph_from_args(pipeline_name, framework, model, tokenizer)
+    nlp = load_graph_from_args(pipeline_name, framework, model, tokenizer, **model_kwargs)
 
     if not output.parent.exists():
         print(f"Creating folder {output.parent}")

src/transformers/pipelines/__init__.py

@@ -246,6 +246,7 @@ def pipeline(
     framework: Optional[str] = None,
     revision: Optional[str] = None,
     use_fast: bool = True,
+    model_kwargs: Dict[str, Any] = {},
     **kwargs
 ) -> Pipeline:
     """
@@ -307,6 +308,9 @@ def pipeline(
             artifacts on huggingface.co, so ``revision`` can be any identifier allowed by git.
         use_fast (:obj:`bool`, `optional`, defaults to :obj:`True`):
             Whether or not to use a Fast tokenizer if possible (a :class:`~transformers.PreTrainedTokenizerFast`).
+        model_kwargs:
+            Additional dictionary of keyword arguments passed along to the model's :obj:`from_pretrained(...,
+            **model_kwargs)` function.
         kwargs:
             Additional keyword arguments passed along to the specific pipeline init (see the documentation for the
             corresponding pipeline class for possible values).
@@ -383,7 +387,6 @@ def pipeline(
     # Instantiate model if needed
     if isinstance(model, str):
         # Handle transparent TF/PT model conversion
-        model_kwargs = {}
         if framework == "pt" and model.endswith(".h5"):
             model_kwargs["from_tf"] = True
             logger.warning(

tests/test_onnx.py

@@ -38,19 +38,23 @@ def forward(self, input_ids, some_other_args, token_type_ids, attention_mask):
 
 
 class OnnxExportTestCase(unittest.TestCase):
-    MODEL_TO_TEST = ["bert-base-cased", "gpt2", "roberta-base"]
+    MODEL_TO_TEST = [
+        # (model_name, model_kwargs)
+        ("bert-base-cased", {}),
+        ("gpt2", {"use_cache": False}),  # We don't support exporting GPT2 past keys anymore
+    ]
 
     @require_tf
     @slow
     def test_export_tensorflow(self):
-        for model in OnnxExportTestCase.MODEL_TO_TEST:
-            self._test_export(model, "tf", 12)
+        for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
+            self._test_export(model, "tf", 12, **model_kwargs)
 
     @require_torch
     @slow
     def test_export_pytorch(self):
-        for model in OnnxExportTestCase.MODEL_TO_TEST:
-            self._test_export(model, "pt", 12)
+        for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
+            self._test_export(model, "pt", 12, **model_kwargs)
 
     @require_torch
     @slow
@@ -71,8 +75,8 @@ def test_export_custom_bert_model(self):
     @require_tf
     @slow
     def test_quantize_tf(self):
-        for model in OnnxExportTestCase.MODEL_TO_TEST:
-            path = self._test_export(model, "tf", 12)
+        for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
+            path = self._test_export(model, "tf", 12, **model_kwargs)
             quantized_path = quantize(Path(path))
 
             # Ensure the actual quantized model is not bigger than the original one
@@ -82,15 +86,15 @@ def test_quantize_tf(self):
     @require_torch
     @slow
     def test_quantize_pytorch(self):
-        for model in OnnxExportTestCase.MODEL_TO_TEST:
-            path = self._test_export(model, "pt", 12)
+        for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
+            path = self._test_export(model, "pt", 12, **model_kwargs)
             quantized_path = quantize(path)
 
             # Ensure the actual quantized model is not bigger than the original one
             if quantized_path.stat().st_size >= Path(path).stat().st_size:
                 self.fail("Quantized model is bigger than initial ONNX model")
 
-    def _test_export(self, model, framework, opset, tokenizer=None):
+    def _test_export(self, model, framework, opset, tokenizer=None, **model_kwargs):
         try:
             # Compute path
             with TemporaryDirectory() as tempdir:
@@ -101,7 +105,7 @@ def _test_export(self, model, framework, opset, tokenizer=None):
                 path.parent.rmdir()
 
             # Export
-            convert(framework, model, path, opset, tokenizer)
+            convert(framework, model, path, opset, tokenizer, **model_kwargs)
 
             return path
         except Exception as e: