65 lines
2.4 KiB
Python
65 lines
2.4 KiB
Python
# Copyright 2015 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 denormal handling."""
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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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import numpy as np
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import platform
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from tensorflow.python.framework import constant_op
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from tensorflow.python.framework import test_util
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from tensorflow.python.platform import test
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class DenormalTest(test.TestCase):
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def testPythonHasDenormals(self):
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"""Non-tf numpy code should treat denormals correctly."""
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for dtype in np.float32, np.float64:
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tiny = np.finfo(dtype).tiny
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self.assertEqual(tiny, tiny / 16 * 16)
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def _flushDenormalsTest(self, dtypes):
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if (platform.machine() == "ppc64le" or platform.machine() == "s390x" or
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platform.machine() == "aarch64"):
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# Disabled denormal_test on power/s390x/aarch64 platform
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# Check relevant discussion - https://github.com/tensorflow/tensorflow/issues/11902
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return
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for dtype in dtypes:
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tiny = np.finfo(dtype).tiny
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# Small shape to test main thread, large shape to test thread pool
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for shape in (), (1 << 20,):
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flush = 0.1 * constant_op.constant(tiny, shape=shape)
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self.assertAllEqual(self.evaluate(flush), np.zeros(shape))
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# Make sure the flags don't leak out
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self.testPythonHasDenormals()
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@test_util.run_in_graph_and_eager_modes(use_gpu=False)
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def testFlushDenormalsCPU(self):
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# On CPUs, the processor flags flush for both single and double precision.
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self._flushDenormalsTest(dtypes=(np.float32, np.float64))
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@test_util.run_in_graph_and_eager_modes(use_gpu=True)
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def testFlushDenormalsGPU(self):
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# On GPUs, only single precision can flush to zero.
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self._flushDenormalsTest(dtypes=(np.float32,))
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if __name__ == "__main__":
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test.main()
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