def test_skipped_ops(self):
with context.eager_mode():
x = constant_op.constant(np.ones((1, 1, 1, 1)).astype(np.float32))
# Cast is on the hardcoded list of ops to skip
gen_math_ops.cast(x, dtypes.float64)
self.assertEmpty(self._get_new_node_defs())
gen_nn_ops.conv2d(x, x, [1, 1, 1, 1], 'SAME')
y = constant_op.constant(np.zeros((1, 1, 1, 1)).astype(np.float32))
# Duplicate ops are skipped, even if input values are different
gen_nn_ops.conv2d(x, y, [1, 1, 1, 1], 'SAME')
if not IsMklEnabled():
self.assertLen(self._get_new_node_defs(), 1)
else:
ndefs = self._get_new_node_defs()
if (len(ndefs) >= 1 and ndefs[0].op != ndefs[1].op):
# One of the ops got rewritten by oneDNN optimization pass
self.assertLen(ndefs, 2)
else:
self.assertLen(ndefs, 1)
x = constant_op.constant(
np.ones((1, 1, 1, 1, 1, 1)).astype(np.float32))
paddings = constant_op.constant(np.ones((6, 2)).astype(np.int32))
constant_values = constant_op.constant(0.)
# If an host int32 input has more than 10 elements, the op is skipped
gen_array_ops.pad_v2(x, paddings, constant_values)
self.assertEmpty(self._get_new_node_defs())
def test_host_int32_inputs(self):
with context.eager_mode():
x = constant_op.constant(np.ones((2, 2)).astype(np.float32))
paddings = constant_op.constant([[1, 2], [3, 4]])
constant_values = constant_op.constant(0.)
gen_array_ops.pad_v2(x, paddings, constant_values)
node_defs = self._get_new_node_defs()
self.assertLen(node_defs, 1)
(node_def, ) = node_defs # pylint: disable=unbalanced-tuple-unpacking
self.assertEqual(node_def.op, 'PadV2')
self.assertEqual(self._get_input_dtypes(node_def),
[dtypes.float32, dtypes.int32, dtypes.float32])
self.assertEqual(self._get_input_shapes(node_def), [(2, 2), (2, 2),
()])
self.assertIsNone(self._get_input_tensor(node_def, 0))
self.assertAllEqual(self._get_input_tensor(node_def, 1),
np.array([[1, 2], [3, 4]]))
self.assertIsNone(self._get_input_tensor(node_def, 2))
def test_skipped_ops(self):
with context.eager_mode():
x = constant_op.constant(np.ones((1, 1, 1, 1)).astype(np.float32))
# Cast is on the hardcoded list of ops to skip
gen_math_ops.cast(x, dtypes.float64)
self.assertEmpty(self._get_new_node_defs())
gen_nn_ops.conv2d(x, x, [1, 1, 1, 1], 'SAME')
y = constant_op.constant(np.zeros((1, 1, 1, 1)).astype(np.float32))
# Duplicate ops are skipped, even if input values are different
gen_nn_ops.conv2d(x, y, [1, 1, 1, 1], 'SAME')
self.assertLen(self._get_new_node_defs(), 1)
x = constant_op.constant(
np.ones((1, 1, 1, 1, 1, 1)).astype(np.float32))
paddings = constant_op.constant(np.ones((6, 2)).astype(np.int32))
constant_values = constant_op.constant(0.)
# If an host int32 input has more than 10 elements, the op is skipped
gen_array_ops.pad_v2(x, paddings, constant_values)
self.assertEmpty(self._get_new_node_defs())