support ONNX export of XDropout in deberta{,_v2} and sew_d (#17502)
* support ONNX export of XDropout in deberta{,_v2}
* black
* copy to sew_d
* add test
* isort
* use pytest.mark.filterwarnings
* review comments
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@@ -1,3 +1,4 @@
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import os
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from pathlib import Path
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from tempfile import NamedTemporaryFile
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from unittest import TestCase
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@@ -26,6 +27,11 @@ from transformers.testing_utils import require_onnx, require_rjieba, require_tf,
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if is_torch_available() or is_tf_available():
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from transformers.onnx.features import FeaturesManager
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if is_torch_available():
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import torch
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from transformers.models.deberta import modeling_deberta
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@require_onnx
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class OnnxUtilsTestCaseV2(TestCase):
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@@ -356,3 +362,40 @@ class OnnxExportTestCaseV2(TestCase):
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self, test_name, name, model_name, feature, onnx_config_class_constructor
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):
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self._onnx_export(test_name, name, model_name, feature, onnx_config_class_constructor)
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class StableDropoutTestCase(TestCase):
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"""Tests export of StableDropout module."""
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@require_torch
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@pytest.mark.filterwarnings("ignore:.*Dropout.*:UserWarning:torch.onnx.*") # torch.onnx is spammy.
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def test_training(self):
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"""Tests export of StableDropout in training mode."""
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devnull = open(os.devnull, "wb")
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# drop_prob must be > 0 for the test to be meaningful
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sd = modeling_deberta.StableDropout(0.1)
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# Avoid warnings in training mode
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do_constant_folding = False
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# Dropout is a no-op in inference mode
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training = torch.onnx.TrainingMode.PRESERVE
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input = (torch.randn(2, 2),)
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torch.onnx.export(
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sd,
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input,
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devnull,
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opset_version=12, # Minimum supported
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do_constant_folding=do_constant_folding,
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training=training,
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)
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# Expected to fail with opset_version < 12
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with self.assertRaises(Exception):
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torch.onnx.export(
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sd,
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input,
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devnull,
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opset_version=11,
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do_constant_folding=do_constant_folding,
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training=training,
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)
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