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# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Tests for denormal handling."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import platform
from tensorflow.python.framework import constant_op
from tensorflow.python.ops import array_ops
from tensorflow.python.platform import test
class DenormalTest(test.TestCase):
def testPythonHasDenormals(self):
"""Non-tf numpy code should treat denormals correctly."""
for dtype in np.float32, np.float64:
tiny = np.finfo(dtype).tiny
self.assertEqual(tiny, tiny / 16 * 16)
def _flushDenormalsTest(self, use_gpu, dtypes):
if platform.machine() == "ppc64le" or platform.machine() == "s390x":
# Disabled denormal_test on power/s390x platform
# Check relevant discussion - https://github.com/tensorflow/tensorflow/issues/11902
return
with self.test_session(use_gpu=use_gpu):
array_ops.identity(7).eval()
for dtype in dtypes:
tiny = np.finfo(dtype).tiny
# Small shape to test main thread, large shape to test thread pool
for shape in (), (1 << 20,):
flush = 0.1 * constant_op.constant(tiny, shape=shape)
self.assertAllEqual(flush.eval(), np.zeros(shape))
# Make sure the flags don't leak out
self.testPythonHasDenormals()
def testFlushDenormalsCPU(self):
# On CPUs, the processor flags flush for both single and double precision.
self._flushDenormalsTest(use_gpu=False, dtypes=(np.float32, np.float64))
def testFlushDenormalsGPU(self):
# On GPUs, only single precision can flush to zero.
self._flushDenormalsTest(use_gpu=True, dtypes=(np.float32,))
if __name__ == "__main__":
test.main()
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