6e279f39dd
This significantly improves the ability of Grappler to optimize tf.function bodies. Fix a bug in ShapeOrHandleShape in training_ops.cc: If a resource argument does not have the handle_shapes available, we should return UnknownShape, rather than the shape of the argument itself, which is just a scalar resource handle. PiperOrigin-RevId: 291991651 Change-Id: Iae8f3c5c5cf75742f0f2ce8f99cef2ae1ece5648
51 lines
1.7 KiB
Python
51 lines
1.7 KiB
Python
# Copyright 2020 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Tests for Grappler Arithmetic Optimizer."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from tensorflow.python.eager import context
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from tensorflow.python.eager import def_function
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from tensorflow.python.ops import array_ops
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from tensorflow.python.ops import math_ops
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from tensorflow.python.platform import test
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class ArithmeticOptimizerTest(test.TestCase):
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# See b/146524878.
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def testFunctionArgShapeInference(self):
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@def_function.function
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def f(x, y):
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return math_ops.matmul(
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x, array_ops.reshape(array_ops.transpose(y), [384, 1536]))
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with context.eager_mode():
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x = array_ops.ones((1, 384))
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y = array_ops.ones((1536, 384))
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with context.collect_graphs(optimized=True) as graphs:
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f(x, y).numpy()
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self.assertLen(graphs, 1)
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self.assertLen(graphs[0].node, 4)
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self.assertEqual(graphs[0].node[2].name,
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'ArithmeticOptimizer/FoldTransposeIntoMatMul_MatMul')
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if __name__ == '__main__':
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test.main()
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