Enable export of model with fixed shape

docs/source/exporters/onnx/overview.mdx

@@ -121,6 +121,7 @@ Supported architectures from [🤗 Timm](https://huggingface.co/docs/timm/index)
 - EfficientNet
 - EfficientNet (Knapsack Pruned)
 - Ensemble Adversarial Inception ResNet v2
+- ESE-VoVNet (Partial support with static shapes)
 - FBNet
 - (Gluon) Inception v3
 - (Gluon) ResNet

optimum/commands/export/onnx.py

@@ -150,6 +150,9 @@ def parse_args_onnx(parser):
             "Also disable the use of position_ids for text-generation models that require it for batched generation. This argument is introduced for backward compatibility and will be removed in a future release of Optimum."
         ),
     )
+    optional_group.add_argument(
+        "--no-dynamic-axes", action="store_true", help="Disable dynamic axes during ONNX export"
+    )
 
     input_group = parser.add_argument_group(
         "Input shapes (if necessary, this allows to override the shapes of the input given to the ONNX exporter, that requires an example input)."
@@ -263,6 +266,7 @@ def run(self):
             _variant=self.args.variant,
             library_name=self.args.library_name,
             legacy=self.args.legacy,
+            no_dynamic_axes=self.args.no_dynamic_axes,
             model_kwargs=self.args.model_kwargs,
             **input_shapes,
         )

optimum/exporters/onnx/__main__.py

@@ -190,6 +190,7 @@ def main_export(
     _variant: str = "default",
     library_name: Optional[str] = None,
     legacy: bool = False,
+    no_dynamic_axes: bool = False,
     **kwargs_shapes,
 ):
     """
@@ -270,6 +271,8 @@ def main_export(
             The library of the model (`"transformers"` or `"diffusers"` or `"timm"` or `"sentence_transformers"`). If not provided, will attempt to automatically detect the library name for the checkpoint.
         legacy (`bool`, defaults to `False`):
             Disable the use of position_ids for text-generation models that require it for batched generation. Also enable to export decoder only models in three files (without + with past and the merged model). This argument is introduced for backward compatibility and will be removed in a future release of Optimum.
+        no_dynamic_axes (bool, defaults to `False`):
+            If True, disables the use of dynamic axes during ONNX export.
         **kwargs_shapes (`Dict`):
             Shapes to use during inference. This argument allows to override the default shapes used during the ONNX export.
 
@@ -556,6 +559,7 @@ def main_export(
         input_shapes=input_shapes,
         device=device,
         dtype="fp16" if fp16 is True else None,
+        no_dynamic_axes=no_dynamic_axes,
         model_kwargs=model_kwargs,
     )
 

optimum/exporters/onnx/convert.py

@@ -487,6 +487,7 @@ def export_pytorch(
     device: str = "cpu",
     dtype: Optional["torch.dtype"] = None,
     input_shapes: Optional[Dict] = None,
+    no_dynamic_axes: bool = False,
     model_kwargs: Optional[Dict[str, Any]] = None,
 ) -> Tuple[List[str], List[str]]:
     """
@@ -508,6 +509,8 @@ def export_pytorch(
             Data type to remap the model inputs to. PyTorch-only. Only `torch.float16` is supported.
         input_shapes (`Optional[Dict]`, defaults to `None`):
             If specified, allows to use specific shapes for the example input provided to the ONNX exporter.
+        no_dynamic_axes (bool, defaults to `False`):
+            If True, disables the use of dynamic axes during ONNX export.
         model_kwargs (`Optional[Dict[str, Any]]`, defaults to `None`):
             Experimental usage: keyword arguments to pass to the model during
             the export. This argument should be used along the `custom_onnx_config` argument
@@ -562,6 +565,11 @@ def remap(value):
             input_names = list(inputs.keys())
             output_names = list(config.outputs.keys())
 
+            if no_dynamic_axes:
+                dynamix_axes = None
+            else:
+                dynamix_axes = dict(chain(inputs.items(), config.outputs.items()))
+
             # Export can work with named args but the dict containing named args has to be the last element of the args
             # tuple.
             onnx_export(
@@ -570,7 +578,7 @@ def remap(value):
                 f=output.as_posix(),
                 input_names=input_names,
                 output_names=output_names,
-                dynamic_axes=dict(chain(inputs.items(), config.outputs.items())),
+                dynamic_axes=dynamix_axes,
                 do_constant_folding=True,
                 opset_version=opset,
             )
@@ -694,6 +702,7 @@ def export_models(
     input_shapes: Optional[Dict] = None,
     disable_dynamic_axes_fix: Optional[bool] = False,
     dtype: Optional[str] = None,
+    no_dynamic_axes: bool = False,
     model_kwargs: Optional[Dict[str, Any]] = None,
 ) -> Tuple[List[List[str]], List[List[str]]]:
     """
@@ -720,6 +729,8 @@ def export_models(
             Whether to disable the default dynamic axes fixing.
         dtype (`Optional[str]`, defaults to `None`):
             Data type to remap the model inputs to. PyTorch-only. Only `fp16` is supported.
+        no_dynamic_axes (bool, defaults to `False`):
+            If True, disables the use of dynamic axes during ONNX export.
         model_kwargs (`Optional[Dict[str, Any]]`, defaults to `None`):
             Experimental usage: keyword arguments to pass to the model during
             the export. This argument should be used along the `custom_onnx_config` argument
@@ -753,6 +764,7 @@ def export_models(
                 input_shapes=input_shapes,
                 disable_dynamic_axes_fix=disable_dynamic_axes_fix,
                 dtype=dtype,
+                no_dynamic_axes=no_dynamic_axes,
                 model_kwargs=model_kwargs,
             )
         )
@@ -770,6 +782,7 @@ def export(
     input_shapes: Optional[Dict] = None,
     disable_dynamic_axes_fix: Optional[bool] = False,
     dtype: Optional[str] = None,
+    no_dynamic_axes: bool = False,
     model_kwargs: Optional[Dict[str, Any]] = None,
 ) -> Tuple[List[str], List[str]]:
     """
@@ -793,6 +806,8 @@ def export(
             Whether to disable the default dynamic axes fixing.
         dtype (`Optional[str]`, defaults to `None`):
             Data type to remap the model inputs to. PyTorch-only. Only `fp16` is supported.
+        no_dynamic_axes (bool, defaults to `False`):
+            If True, disables the use of dynamic axes during ONNX export.
         model_kwargs (`Optional[Dict[str, Any]]`, defaults to `None`):
             Experimental usage: keyword arguments to pass to the model during
             the export. This argument should be used along the `custom_onnx_config` argument
@@ -855,6 +870,7 @@ def export(
             device=device,
             input_shapes=input_shapes,
             dtype=torch_dtype,
+            no_dynamic_axes=no_dynamic_axes,
             model_kwargs=model_kwargs,
         )
 

tests/exporters/exporters_utils.py

@@ -28,6 +28,11 @@
     "num_choices": [4],
 }
 
+NO_DYNAMIC_AXES_EXPORT_SHAPES_TRANSFORMERS = {
+    "batch_size": [1, 3, 5],
+    "num_choices": [2, 4],
+    "sequence_length": [8, 33, 96],
+}
 
 PYTORCH_EXPORT_MODELS_TINY = {
     "albert": "hf-internal-testing/tiny-random-AlbertModel",
@@ -325,3 +330,19 @@
     "sentence-transformers-clip": "sentence-transformers/all-MiniLM-L6-v2",
     "sentence-transformers-transformer": "sentence-transformers/clip-ViT-B-32-multilingual-v1",
 }
+
+
+PYTORCH_TRANSFORMERS_MODEL_NO_DYNAMIC_AXES = {
+    "albert": "hf-internal-testing/tiny-random-AlbertModel",
+    "gpt2": "hf-internal-testing/tiny-random-gpt2",
+    "roberta": "hf-internal-testing/tiny-random-RobertaModel",
+    "roformer": "hf-internal-testing/tiny-random-RoFormerModel",
+}
+
+
+PYTORCH_TIMM_MODEL_NO_DYNAMIC_AXES = {
+    "default-timm-config": {
+        "timm/ese_vovnet39b.ra_in1k": ["image-classification"],
+        "timm/ese_vovnet19b_dw.ra_in1k": ["image-classification"],
+    }
+}

tests/exporters/onnx/test_exporters_onnx_cli.py

@@ -33,17 +33,20 @@
     ONNX_DECODER_WITH_PAST_NAME,
     ONNX_ENCODER_NAME,
 )
-from optimum.utils.testing_utils import require_diffusers, require_sentence_transformers, require_timm
+from optimum.utils.testing_utils import grid_parameters, require_diffusers, require_sentence_transformers, require_timm
 
 
 if is_torch_available():
     from optimum.exporters.tasks import TasksManager
 
 from ..exporters_utils import (
+    NO_DYNAMIC_AXES_EXPORT_SHAPES_TRANSFORMERS,
     PYTORCH_EXPORT_MODELS_TINY,
     PYTORCH_SENTENCE_TRANSFORMERS_MODEL,
     PYTORCH_STABLE_DIFFUSION_MODEL,
     PYTORCH_TIMM_MODEL,
+    PYTORCH_TIMM_MODEL_NO_DYNAMIC_AXES,
+    PYTORCH_TRANSFORMERS_MODEL_NO_DYNAMIC_AXES,
 )
 
 
@@ -179,6 +182,7 @@ def _onnx_export(
         device: str = "cpu",
         fp16: bool = False,
         variant: str = "default",
+        no_dynamic_axes: bool = False,
         model_kwargs: Optional[Dict] = None,
     ):
         with TemporaryDirectory() as tmpdir:
@@ -193,11 +197,54 @@ def _onnx_export(
                     monolith=monolith,
                     no_post_process=no_post_process,
                     _variant=variant,
+                    no_dynamic_axes=no_dynamic_axes,
                     model_kwargs=model_kwargs,
                 )
             except MinimumVersionError as e:
                 pytest.skip(f"Skipping due to minimum version requirements not met. Full error: {e}")
 
+    def _onnx_export_no_dynamic_axes(
+        self,
+        model_name: str,
+        task: str,
+        input_shape: dict,
+        input_shape_for_validation: tuple,
+        monolith: bool = False,
+        no_post_process: bool = False,
+        optimization_level: Optional[str] = None,
+        device: str = "cpu",
+        fp16: bool = False,
+        variant: str = "default",
+        model_kwargs: Optional[Dict] = None,
+    ):
+        with TemporaryDirectory() as tmpdir:
+            try:
+                main_export(
+                    model_name_or_path=model_name,
+                    output=tmpdir,
+                    task=task,
+                    device=device,
+                    fp16=fp16,
+                    optimize=optimization_level,
+                    monolith=monolith,
+                    no_post_process=no_post_process,
+                    _variant=variant,
+                    no_dynamic_axes=True,
+                    model_kwargs=model_kwargs,
+                    **input_shape,
+                )
+
+                model = onnx.load(Path(tmpdir) / "model.onnx")
+
+                is_dynamic = any(dim.dim_param for dim in model.graph.input[0].type.tensor_type.shape.dim)
+                self.assertFalse(is_dynamic)
+
+                model_input_shape = [dim.dim_value for dim in model.graph.input[0].type.tensor_type.shape.dim]
+                self.assertEqual(model_input_shape, input_shape_for_validation)
+
+            except MinimumVersionError as e:
+                pytest.skip(f"Skipping due to minimum version requirements not met. Full error: {e}")
+
     @parameterized.expand(PYTORCH_STABLE_DIFFUSION_MODEL.items())
     @require_torch
     @require_vision
@@ -258,6 +305,32 @@ def test_exporters_cli_pytorch_cpu_timm(
     ):
         self._onnx_export(model_name, task, monolith, no_post_process, variant=variant)
 
+    @parameterized.expand(_get_models_to_test(PYTORCH_TIMM_MODEL_NO_DYNAMIC_AXES, library_name="timm"))
+    @require_torch
+    @require_vision
+    @require_timm
+    @slow
+    @pytest.mark.timm_test
+    @pytest.mark.run_slow
+    def test_exporters_cli_pytorch_cpu_timm_no_dynamic_axes(
+        self,
+        test_name: str,
+        model_type: str,
+        model_name: str,
+        task: str,
+        variant: str,
+        monolith: bool,
+        no_post_process: bool,
+    ):
+        input_shapes_iterator = grid_parameters({"batch_size": [1, 3, 5]}, yield_dict=True, add_test_name=False)
+        for input_shape in input_shapes_iterator:
+            # NOTE: The timm models use input shapes from the model config, so we need to fix the other shapes of the model.
+            input_shape_for_validation = [input_shape["batch_size"], 3, 224, 224]
+
+            self._onnx_export_no_dynamic_axes(
+                model_name, task, input_shape, input_shape_for_validation, monolith, no_post_process, variant=variant
+            )
+
     @parameterized.expand(_get_models_to_test(PYTORCH_TIMM_MODEL, library_name="timm"))
     @require_torch_gpu
     @require_vision
@@ -322,6 +395,36 @@ def test_exporters_cli_pytorch_cpu(
 
         self._onnx_export(model_name, task, monolith, no_post_process, variant=variant, model_kwargs=model_kwargs)
 
+    @parameterized.expand(_get_models_to_test(PYTORCH_TRANSFORMERS_MODEL_NO_DYNAMIC_AXES))
+    @require_torch
+    @require_vision
+    def test_exporters_cli_pytorch_cpu_no_dynamic_axes(
+        self,
+        test_name: str,
+        model_type: str,
+        model_name: str,
+        task: str,
+        variant: str,
+        monolith: bool,
+        no_post_process: bool,
+    ):
+        input_shapes_iterator = grid_parameters(
+            NO_DYNAMIC_AXES_EXPORT_SHAPES_TRANSFORMERS, yield_dict=True, add_test_name=False
+        )
+        for input_shape in input_shapes_iterator:
+            if task == "multiple-choice":
+                input_shape_for_validation = [
+                    input_shape["batch_size"],
+                    input_shape["num_choices"],
+                    input_shape["sequence_length"],
+                ]
+            else:
+                input_shape_for_validation = [input_shape["batch_size"], input_shape["sequence_length"]]
+
+            self._onnx_export_no_dynamic_axes(
+                model_name, task, input_shape, input_shape_for_validation, monolith, no_post_process, variant=variant
+            )
+
     @parameterized.expand(_get_models_to_test(PYTORCH_EXPORT_MODELS_TINY))
     @require_vision
     @require_torch_gpu