move check_dummy_inputs_allowed to common export utils

optimum/exporters/onnx/base.py

@@ -27,16 +27,12 @@
 from typing import TYPE_CHECKING, Any, Dict, Iterable, List, Optional, Tuple, Union
 
 import numpy as np
-import onnx
 from transformers.utils import is_accelerate_available, is_torch_available
 
-from ...onnx import remove_duplicate_weights_from_tied_info
-
 
 if is_torch_available():
     import torch.nn as nn
 
-from ...onnx import merge_decoders
 from ...utils import (
     DEFAULT_DUMMY_SHAPES,
     DummyInputGenerator,
@@ -542,6 +538,10 @@ def post_process_exported_models(
         first_key = next(iter(models_and_onnx_configs))
         if is_torch_available() and isinstance(models_and_onnx_configs[first_key][0], nn.Module):
             if is_accelerate_available():
+                import onnx
+
+                from ...onnx import remove_duplicate_weights_from_tied_info
+
                 logger.info("Deduplicating shared (tied) weights...")
                 for subpath, key in zip(onnx_files_subpaths, models_and_onnx_configs):
                     torch_model = models_and_onnx_configs[key][0]
@@ -934,6 +934,8 @@ def post_process_exported_models(
             decoder_with_past_path = Path(path, onnx_files_subpaths[2])
             decoder_merged_path = Path(path, ONNX_DECODER_MERGED_NAME + ".onnx")
             try:
+                from ...onnx import merge_decoders
+
                 # The decoder with past does not output the cross attention past key values as they are constant,
                 # hence the need for strict=False
                 merge_decoders(

optimum/exporters/onnx/config.py

@@ -20,7 +20,6 @@
 
 from transformers.utils import is_tf_available
 
-from ...onnx import merge_decoders
 from ...utils import (
     DummyAudioInputGenerator,
     DummyBboxInputGenerator,
@@ -129,6 +128,8 @@ def post_process_exported_models(
 
         # Attempt to merge only if the decoder-only was exported separately without/with past
         if self.use_past is True and len(models_and_onnx_configs) == 2:
+            from ...onnx import merge_decoders
+
             decoder_path = Path(path, onnx_files_subpaths[0])
             decoder_with_past_path = Path(path, onnx_files_subpaths[1])
             decoder_merged_path = Path(path, ONNX_DECODER_MERGED_NAME + ".onnx")

optimum/exporters/onnx/convert.py

@@ -22,7 +22,7 @@
 from inspect import signature
 from itertools import chain
 from pathlib import Path
-from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple, Union
+from typing import Any, Callable, Dict, List, Optional, Tuple, Union
 
 import numpy as np
 import onnx
@@ -45,6 +45,7 @@
 from ...utils.save_utils import maybe_save_preprocessors
 from ..error_utils import AtolError, MinimumVersionError, OutputMatchError, ShapeError
 from ..tasks import TasksManager
+from ..utils import check_dummy_inputs_are_allowed
 from .base import OnnxConfig
 from .constants import UNPICKABLE_ARCHS
 from .model_configs import SpeechT5OnnxConfig
@@ -75,30 +76,6 @@ class DynamicAxisNameError(ValueError):
     pass
 
 
-def check_dummy_inputs_are_allowed(
-    model: Union["PreTrainedModel", "TFPreTrainedModel", "ModelMixin"], dummy_input_names: Iterable[str]
-):
-    """
-    Checks that the dummy inputs from the ONNX config is a subset of the allowed inputs for `model`.
-    Args:
-        model (`Union[transformers.PreTrainedModel, transformers.TFPreTrainedModel`]):
-            The model instance.
-        model_inputs (`Iterable[str]`):
-            The model input names.
-    """
-
-    forward = model.forward if is_torch_available() and isinstance(model, nn.Module) else model.call
-    forward_parameters = signature(forward).parameters
-    forward_inputs_set = set(forward_parameters.keys())
-    dummy_input_names = set(dummy_input_names)
-
-    # We are fine if config_inputs has more keys than model_inputs
-    if not dummy_input_names.issubset(forward_inputs_set):
-        raise ValueError(
-            f"Config dummy inputs are not a subset of the model inputs: {dummy_input_names} vs {forward_inputs_set}"
-        )
-
-
 def validate_models_outputs(
     models_and_onnx_configs: Dict[
         str, Tuple[Union["PreTrainedModel", "TFPreTrainedModel", "ModelMixin"], "OnnxConfig"]

optimum/exporters/onnx/model_configs.py

@@ -21,7 +21,6 @@
 from packaging import version
 from transformers.utils import is_tf_available
 
-from ...onnx import merge_decoders
 from ...utils import (
     DEFAULT_DUMMY_SHAPES,
     BloomDummyPastKeyValuesGenerator,
@@ -1875,6 +1874,8 @@ def post_process_exported_models(
             decoder_with_past_path = Path(path, onnx_files_subpaths[3])
             decoder_merged_path = Path(path, ONNX_DECODER_MERGED_NAME + ".onnx")
             try:
+                from ...onnx import merge_decoders
+
                 # The decoder with past does not output the cross attention past key values as they are constant,
                 # hence the need for strict=False
                 merge_decoders(

optimum/exporters/utils.py

@@ -16,7 +16,8 @@
 """Utilities for model preparation to export."""
 
 import copy
-from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Tuple, Union
+from inspect import signature
+from typing import TYPE_CHECKING, Any, Callable, Dict, Iterable, List, Optional, Tuple, Union
 
 import torch
 from packaging import version
@@ -675,3 +676,27 @@ def _get_submodels_and_export_configs(
         export_config = next(iter(models_and_export_configs.values()))[1]
 
     return export_config, models_and_export_configs
+
+
+def check_dummy_inputs_are_allowed(
+    model: Union["PreTrainedModel", "TFPreTrainedModel", "ModelMixin"], dummy_input_names: Iterable[str]
+):
+    """
+    Checks that the dummy inputs from the ONNX config is a subset of the allowed inputs for `model`.
+    Args:
+        model (`Union[transformers.PreTrainedModel, transformers.TFPreTrainedModel`]):
+            The model instance.
+        model_inputs (`Iterable[str]`):
+            The model input names.
+    """
+
+    forward = model.forward if is_torch_available() and isinstance(model, torch.nn.Module) else model.call
+    forward_parameters = signature(forward).parameters
+    forward_inputs_set = set(forward_parameters.keys())
+    dummy_input_names = set(dummy_input_names)
+
+    # We are fine if config_inputs has more keys than model_inputs
+    if not dummy_input_names.issubset(forward_inputs_set):
+        raise ValueError(
+            f"Config dummy inputs are not a subset of the model inputs: {dummy_input_names} vs {forward_inputs_set}"
+        )