old_contents: # 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.cached_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()
  
new_contents: # 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" or platform.machine() == "aarch64":
        # Disabled denormal_test on power/s390x/aarch64 platform
        # Check relevant discussion - https://github.com/tensorflow/tensorflow/issues/11902
        return
      with self.cached_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()
  
subject: Disable denormal test on ARM until the architecture is supported.