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Flush denormals to zero in eager mode. 

PiperOrigin-RevId: 311364546
Change-Id: I42efa6b19b8193c49bc581879b04ce3d05a13607
This commit is contained in:
A. Unique TensorFlower 2020-05-13 11:06:08 -07:00 committed by TensorFlower Gardener
parent 88c4ee0102
commit b97bf5ae0b
2 changed files with 19 additions and 18 deletions
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4
tensorflow/core/common_runtime/eager/kernel_and_device.cc
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@ -35,8 +35,10 @@ limitations under the License.
#include "tensorflow/core/lib/gtl/cleanup.h"
#include "tensorflow/core/lib/gtl/map_util.h"
#include "tensorflow/core/lib/random/random.h"
#include "tensorflow/core/platform/denormal.h"
#include "tensorflow/core/platform/errors.h"
#include "tensorflow/core/platform/fingerprint.h"
#include "tensorflow/core/platform/setround.h"
#include "tensorflow/core/profiler/lib/annotated_traceme.h"
#include "tensorflow/core/profiler/lib/traceme.h"
#include "tensorflow/core/public/version.h"
@ -281,6 +283,8 @@ Status KernelAndDeviceOp::Run(
OpKernelContext context(&params);
{
port::ScopedFlushDenormal flush;
port::ScopedSetRound round(FE_TONEAREST);
// 'AnnotatedTraceMe' will trace both scheduling time on host and execution
// time on device of the OpKernel.
profiler::AnnotatedTraceMe activity(

33
tensorflow/python/kernel_tests/denormal_test.py
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@ -23,7 +23,6 @@ import platform
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import test_util
from tensorflow.python.ops import array_ops
from tensorflow.python.platform import test
@ -35,32 +34,30 @@ class DenormalTest(test.TestCase):
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":
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
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()
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_deprecated_v1
@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(use_gpu=False, dtypes=(np.float32, np.float64))
self._flushDenormalsTest(dtypes=(np.float32, np.float64))
@test_util.run_deprecated_v1
@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(use_gpu=True, dtypes=(np.float32,))
self._flushDenormalsTest(dtypes=(np.float32,))
if __name__ == "__main__":