code: # 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."""
  
  import numpy as np
  import platform
  
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import test_util
  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, dtypes):
      if (platform.machine() == "ppc64le" or platform.machine() == "s390x" or
          platform.machine() == "aarch64"):
        # Disabled denormal_test on power/s390x/aarch64 platform
        # Check relevant discussion - https://github.com/tensorflow/tensorflow/issues/11902
        return
      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(self.evaluate(flush), np.zeros(shape))
          # Make sure the flags don't leak out
          self.testPythonHasDenormals()
  
    @test_util.run_in_graph_and_eager_modes(use_gpu=False)
    def testFlushDenormalsCPU(self):
      # On CPUs, the processor flags flush for both single and double precision.
      self._flushDenormalsTest(dtypes=(np.float32, np.float64))
  
    @test_util.run_in_graph_and_eager_modes(use_gpu=True)
    def testFlushDenormalsGPU(self):
      # On GPUs, only single precision can flush to zero.
      self._flushDenormalsTest(dtypes=(np.float32,))
  
  
  if __name__ == "__main__":
    test.main()
  
apis: numpy.zeros tensorflow.python.framework.test_util.run_in_graph_and_eager_modes tensorflow.python.platform.test.main numpy.finfo tensorflow.python.framework.constant_op.constant