1 # Copyright 2015 The TensorFlow Authors. All Rights Reserved. 2 # 3 # Licensed under the Apache License, Version 2.0 (the "License"); 4 # you may not use this file except in compliance with the License. 5 # You may obtain a copy of the License at 6 # 7 # http://www.apache.org/licenses/LICENSE-2.0 8 # 9 # Unless required by applicable law or agreed to in writing, software 10 # distributed under the License is distributed on an "AS IS" BASIS, 11 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 # See the License for the specific language governing permissions and 13 # limitations under the License. 14 # ============================================================================== 15 """Tests for denormal handling.""" 16 17 from __future__ import absolute_import 18 from __future__ import division 19 from __future__ import print_function 20 21 import numpy as np 22 import platform 23 24 from tensorflow.python.framework import constant_op 25 from tensorflow.python.ops import array_ops 26 from tensorflow.python.platform import test 27 28 29 class DenormalTest(test.TestCase): 30 31 def testPythonHasDenormals(self): 32 """Non-tf numpy code should treat denormals correctly.""" 33 for dtype in np.float32, np.float64: 34 tiny = np.finfo(dtype).tiny 35 self.assertEqual(tiny, tiny / 16 * 16) 36 37 def _flushDenormalsTest(self, use_gpu, dtypes): 38 if platform.machine() == "ppc64le" or platform.machine() == "s390x": 39 # Disabled denormal_test on power/s390x platform 40 # Check relevant discussion - https://github.com/tensorflow/tensorflow/issues/11902 41 return 42 with self.test_session(use_gpu=use_gpu): 43 array_ops.identity(7).eval() 44 for dtype in dtypes: 45 tiny = np.finfo(dtype).tiny 46 # Small shape to test main thread, large shape to test thread pool 47 for shape in (), (1 << 20,): 48 flush = 0.1 * constant_op.constant(tiny, shape=shape) 49 self.assertAllEqual(flush.eval(), np.zeros(shape)) 50 # Make sure the flags don't leak out 51 self.testPythonHasDenormals() 52 53 def testFlushDenormalsCPU(self): 54 # On CPUs, the processor flags flush for both single and double precision. 55 self._flushDenormalsTest(use_gpu=False, dtypes=(np.float32, np.float64)) 56 57 def testFlushDenormalsGPU(self): 58 # On GPUs, only single precision can flush to zero. 59 self._flushDenormalsTest(use_gpu=True, dtypes=(np.float32,)) 60 61 62 if __name__ == "__main__": 63 test.main() 64
