ONNX v2 raises an Exception when using PyTorch < 1.8.0 (#12933)

* Raise an issue if the pytorch version is < 1.8.0

* Attempt to add a test to ensure it correctly raises.

* Missing docstring.

* Second attempt, patch with string absolute import.

* Let's do the call before checking it was called ...

* use the correct function ... 🤦

* Raise ImportError and AssertionError respectively when unable to find torch and torch version is not sufficient.

* Correct path mock patching

* relax constraint for torch_onnx_dict_inputs to ge instead of eq.

* Style.

* Split each version requirements for torch.

* Let's compare version directly.

* Import torch_version after checking pytorch is installed.

* @require_torch

src/transformers/file_utils.py

@@ -274,8 +274,9 @@ PRESET_MIRROR_DICT = {
     "bfsu": "https://mirrors.bfsu.edu.cn/hugging-face-models",
 }
 
-# This is the version of torch required to run torch.fx features.
+# This is the version of torch required to run torch.fx features and torch.onnx with dictionary inputs.
 TORCH_FX_REQUIRED_VERSION = version.parse("1.8")
+TORCH_ONNX_DICT_INPUTS_MINIMUM_VERSION = version.parse("1.8")
 
 _is_offline_mode = True if os.environ.get("TRANSFORMERS_OFFLINE", "0").upper() in ENV_VARS_TRUE_VALUES else False
 
@@ -297,7 +298,7 @@ def is_torch_cuda_available():
         return False
 
 
-_torch_fx_available = False
+_torch_fx_available = _torch_onnx_dict_inputs_support_available = False
 if _torch_available:
     torch_version = version.parse(importlib_metadata.version("torch"))
     _torch_fx_available = (torch_version.major, torch_version.minor) == (
@@ -305,11 +306,17 @@ if _torch_available:
         TORCH_FX_REQUIRED_VERSION.minor,
     )
 
+    _torch_onnx_dict_inputs_support_available = torch_version >= TORCH_ONNX_DICT_INPUTS_MINIMUM_VERSION
+
 
 def is_torch_fx_available():
     return _torch_fx_available
 
 
+def is_torch_onnx_dict_inputs_support_available():
+    return _torch_onnx_dict_inputs_support_available
+
+
 def is_tf_available():
     return _tf_available
 

src/transformers/onnx/convert.py

@@ -21,6 +21,7 @@ import numpy as np
 from packaging.version import Version, parse
 
 from .. import PreTrainedModel, PreTrainedTokenizer, TensorType, TFPreTrainedModel, is_torch_available
+from ..file_utils import is_torch_onnx_dict_inputs_support_available
 from ..utils import logging
 from .config import OnnxConfig
 from .utils import flatten_output_collection_property
@@ -79,11 +80,16 @@ def export(
 
     """
     if not is_torch_available():
-        raise Exception("Cannot convert because PyTorch is not installed. Please install torch first.")
+        raise ImportError("Cannot convert because PyTorch is not installed. Please install torch first.")
 
     import torch
     from torch.onnx import export
 
+    from ..file_utils import torch_version
+
+    if not is_torch_onnx_dict_inputs_support_available():
+        raise AssertionError(f"Unsupported PyTorch version, minimum required is 1.8.0, got: {torch_version}")
+
     logger.info(f"Using framework PyTorch: {torch.__version__}")
     torch.set_grad_enabled(False)
     model.config.return_dict = True

tests/test_onnx_v2.py

@@ -24,7 +24,13 @@ from transformers.models.roberta import RobertaOnnxConfig
 
 # from transformers.models.t5 import T5OnnxConfig
 from transformers.models.xlm_roberta import XLMRobertaOnnxConfig
-from transformers.onnx import EXTERNAL_DATA_FORMAT_SIZE_LIMIT, OnnxConfig, ParameterFormat, validate_model_outputs
+from transformers.onnx import (
+    EXTERNAL_DATA_FORMAT_SIZE_LIMIT,
+    OnnxConfig,
+    ParameterFormat,
+    export,
+    validate_model_outputs,
+)
 from transformers.onnx.config import DEFAULT_ONNX_OPSET, OnnxConfigWithPast
 from transformers.onnx.utils import (
     compute_effective_axis_dimension,
@@ -40,6 +46,15 @@ class OnnxUtilsTestCaseV2(TestCase):
     Cover all the utilities involved to export ONNX models
     """
 
+    @require_torch
+    @patch("transformers.onnx.convert.is_torch_onnx_dict_inputs_support_available", return_value=False)
+    def test_ensure_pytorch_version_ge_1_8_0(self, mock_is_torch_onnx_dict_inputs_support_available):
+        """
+        Ensure we raise an Exception if the pytorch version is unsupported (< 1.8.0)
+        """
+        self.assertRaises(AssertionError, export, None, None, None, None, None)
+        mock_is_torch_onnx_dict_inputs_support_available.assert_called()
+
     def test_compute_effective_axis_dimension(self):
         """
         When exporting ONNX model with dynamic axis (batch or sequence) we set batch_size and/or sequence_length = -1.