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Merge pull request #35498 from lgeiger:cleanup-fwd-compat-tests 

PiperOrigin-RevId: 288339675
Change-Id: Id0b0bc64655cb52ab5dd5252528ec978dc1b9675
This commit is contained in:
TensorFlower Gardener 2020-01-06 11:38:33 -08:00
parent a979677021 19fb3ae6e2
commit 87cab44823
5 changed files with 101 additions and 111 deletions
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105
tensorflow/compiler/tests/binary_ops_test.py
View File

@ -23,7 +23,6 @@ import itertools
import numpy as np
from tensorflow.compiler.tests import xla_test
from tensorflow.python.compat import compat
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import errors
from tensorflow.python.ops import array_ops
@ -1113,59 +1112,57 @@ class BinaryOpsTest(xla_test.XLATestCase):
def testBatchMatMulBroadcast(self):
"""Tests broadcasting behavior of BatchMatMul."""
with compat.forward_compatibility_horizon(2019, 4, 26):
# [2, 3] @ [1, 3, 4] -> [1, 2, 4]
self._testBinary(
math_ops.matmul,
np.array([[10, 20, 30], [11, 21, 31]], dtype=np.float32),
np.array([[[1, 2, 3, 4], [2, 4, 6, 8], [3, 6, 9, 12]]],
dtype=np.float32),
expected=np.array([[[140, 280, 420, 560], [146, 292, 438, 584]]],
dtype=np.float32))
# [1, 2, 3] @ [3, 4] -> [1, 2, 4]
self._testBinary(
math_ops.matmul,
np.array([[[10, 20, 30], [11, 21, 31]]], dtype=np.float32),
np.array([[1, 2, 3, 4], [2, 4, 6, 8], [3, 6, 9, 12]],
dtype=np.float32),
expected=np.array([[[140, 280, 420, 560], [146, 292, 438, 584]]],
dtype=np.float32))
# [2, 1, 3] @ [3, 1] -> [2, 1, 1]
self._testBinary(
math_ops.matmul,
np.array([[[10, 20, 30]], [[11, 21, 31]]], dtype=np.float32),
np.array([[1], [2], [3]], dtype=np.float32),
expected=np.array([[[140]], [[146]]], dtype=np.float32))
# [2, 1, 3] @ [1, 3] -> [2, 1, 1] (adjoint_b)
self._testBinary(
lambda x, y: math_ops.matmul(x, y, adjoint_b=True),
np.array([[[10, 20, 30]], [[11, 21, 31]]], dtype=np.float32),
np.array([[1, 2, 3]], dtype=np.float32),
expected=np.array([[[140]], [[146]]], dtype=np.float32))
# [2, 3, 1] @ [3, 1] -> [2, 1, 1] (adjoint_a)
self._testBinary(
lambda x, y: math_ops.matmul(x, y, adjoint_a=True),
np.array([[[10], [20], [30]], [[11], [21], [31]]], dtype=np.float32),
np.array([[1], [2], [3]], dtype=np.float32),
expected=np.array([[[140]], [[146]]], dtype=np.float32))
# [2, 3, 1] @ [1, 3] -> [2, 1, 1] (adjoint_a and adjoint_b)
self._testBinary(
lambda x, y: math_ops.matmul(x, y, adjoint_a=True, adjoint_b=True),
np.array([[[10], [20], [30]], [[11], [21], [31]]], dtype=np.float32),
np.array([[1, 2, 3]], dtype=np.float32),
expected=np.array([[[140]], [[146]]], dtype=np.float32))
# [5, 1, 2, 3] @ [1, 7, 3, 4] -> [5, 7, 2, 4]
self._testBinary(
math_ops.matmul,
np.ones([5, 1, 2, 3], dtype=np.float32),
np.ones([1, 7, 3, 4], dtype=np.float32),
expected=np.full([5, 7, 2, 4], 3, dtype=np.float32))
# [4, 5, 1, 2, 3] @ [1, 1, 3, 5] -> [4, 5, 1, 2, 5]
self._testBinary(
math_ops.matmul,
np.full([4, 5, 1, 2, 3], 2., dtype=np.float32),
np.full([1, 1, 3, 5], 3., dtype=np.float32),
expected=np.full([4, 5, 1, 2, 5], 18., dtype=np.float32))
# [2, 3] @ [1, 3, 4] -> [1, 2, 4]
self._testBinary(
math_ops.matmul,
np.array([[10, 20, 30], [11, 21, 31]], dtype=np.float32),
np.array([[[1, 2, 3, 4], [2, 4, 6, 8], [3, 6, 9, 12]]],
dtype=np.float32),
expected=np.array([[[140, 280, 420, 560], [146, 292, 438, 584]]],
dtype=np.float32))
# [1, 2, 3] @ [3, 4] -> [1, 2, 4]
self._testBinary(
math_ops.matmul,
np.array([[[10, 20, 30], [11, 21, 31]]], dtype=np.float32),
np.array([[1, 2, 3, 4], [2, 4, 6, 8], [3, 6, 9, 12]], dtype=np.float32),
expected=np.array([[[140, 280, 420, 560], [146, 292, 438, 584]]],
dtype=np.float32))
# [2, 1, 3] @ [3, 1] -> [2, 1, 1]
self._testBinary(
math_ops.matmul,
np.array([[[10, 20, 30]], [[11, 21, 31]]], dtype=np.float32),
np.array([[1], [2], [3]], dtype=np.float32),
expected=np.array([[[140]], [[146]]], dtype=np.float32))
# [2, 1, 3] @ [1, 3] -> [2, 1, 1] (adjoint_b)
self._testBinary(
lambda x, y: math_ops.matmul(x, y, adjoint_b=True),
np.array([[[10, 20, 30]], [[11, 21, 31]]], dtype=np.float32),
np.array([[1, 2, 3]], dtype=np.float32),
expected=np.array([[[140]], [[146]]], dtype=np.float32))
# [2, 3, 1] @ [3, 1] -> [2, 1, 1] (adjoint_a)
self._testBinary(
lambda x, y: math_ops.matmul(x, y, adjoint_a=True),
np.array([[[10], [20], [30]], [[11], [21], [31]]], dtype=np.float32),
np.array([[1], [2], [3]], dtype=np.float32),
expected=np.array([[[140]], [[146]]], dtype=np.float32))
# [2, 3, 1] @ [1, 3] -> [2, 1, 1] (adjoint_a and adjoint_b)
self._testBinary(
lambda x, y: math_ops.matmul(x, y, adjoint_a=True, adjoint_b=True),
np.array([[[10], [20], [30]], [[11], [21], [31]]], dtype=np.float32),
np.array([[1, 2, 3]], dtype=np.float32),
expected=np.array([[[140]], [[146]]], dtype=np.float32))
# [5, 1, 2, 3] @ [1, 7, 3, 4] -> [5, 7, 2, 4]
self._testBinary(
math_ops.matmul,
np.ones([5, 1, 2, 3], dtype=np.float32),
np.ones([1, 7, 3, 4], dtype=np.float32),
expected=np.full([5, 7, 2, 4], 3, dtype=np.float32))
# [4, 5, 1, 2, 3] @ [1, 1, 3, 5] -> [4, 5, 1, 2, 5]
self._testBinary(
math_ops.matmul,
np.full([4, 5, 1, 2, 3], 2., dtype=np.float32),
np.full([1, 1, 3, 5], 3., dtype=np.float32),
expected=np.full([4, 5, 1, 2, 5], 18., dtype=np.float32))
def testPad(self):
for dtype, pad_type in itertools.product(

32
tensorflow/python/data/experimental/kernel_tests/copy_to_device_test.py
View File

@ -20,7 +20,6 @@ from __future__ import print_function
from absl.testing import parameterized
from tensorflow.core.protobuf import config_pb2
from tensorflow.python.compat import compat
from tensorflow.python.data.experimental.ops import prefetching_ops
from tensorflow.python.data.kernel_tests import test_base
from tensorflow.python.data.ops import dataset_ops
@ -420,24 +419,23 @@ class CopyToDeviceTest(test_base.DatasetTestBase, parameterized.TestCase):
if not test_util.is_gpu_available():
self.skipTest("No GPU available")
with compat.forward_compatibility_horizon(2018, 8, 4):
host_dataset = dataset_ops.Dataset.range(10)
device_dataset = host_dataset.apply(
prefetching_ops.copy_to_device("/gpu:0", source_device="/cpu:0"))
back_to_cpu_dataset = device_dataset.apply(
prefetching_ops.copy_to_device("/cpu:0", source_device="/gpu:0"))
host_dataset = dataset_ops.Dataset.range(10)
device_dataset = host_dataset.apply(
prefetching_ops.copy_to_device("/gpu:0", source_device="/cpu:0"))
back_to_cpu_dataset = device_dataset.apply(
prefetching_ops.copy_to_device("/cpu:0", source_device="/gpu:0"))
with ops.device("/cpu:0"):
iterator = dataset_ops.make_initializable_iterator(back_to_cpu_dataset)
next_element = iterator.get_next()
with ops.device("/cpu:0"):
iterator = dataset_ops.make_initializable_iterator(back_to_cpu_dataset)
next_element = iterator.get_next()
with self.cached_session(
config=config_pb2.ConfigProto(allow_soft_placement=False)):
self.evaluate(iterator.initializer)
for i in range(10):
self.assertEqual(i, self.evaluate(next_element))
with self.assertRaises(errors.OutOfRangeError):
self.evaluate(next_element)
with self.cached_session(
config=config_pb2.ConfigProto(allow_soft_placement=False)):
self.evaluate(iterator.initializer)
for i in range(10):
self.assertEqual(i, self.evaluate(next_element))
with self.assertRaises(errors.OutOfRangeError):
self.evaluate(next_element)
@combinations.generate(test_base.graph_only_combinations())
def testCopyToDeviceWithReInit(self):

38
tensorflow/python/grappler/layout_optimizer_test.py
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@ -25,7 +25,6 @@ from tensorflow.core.protobuf import device_properties_pb2
from tensorflow.core.protobuf import rewriter_config_pb2
from tensorflow.core.protobuf import saver_pb2
from tensorflow.python.client import session
from tensorflow.python.compat import compat
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
@ -1457,29 +1456,28 @@ class LayoutOptimizerTest(test.TestCase):
@test_util.deprecated_graph_mode_only
def testBinaryOpSecondPort(self):
with compat.forward_compatibility_horizon(2019, 6, 7):
if test.is_gpu_available(cuda_only=True):
output = _model_with_second_port()
if test.is_gpu_available(cuda_only=True):
output = _model_with_second_port()
with session.Session(config=_get_config(False)) as sess:
output_val_ref = self.evaluate(output)
with session.Session(config=_get_config(False)) as sess:
output_val_ref = self.evaluate(output)
with session.Session(config=_get_config()) as sess:
metadata = config_pb2.RunMetadata()
output_val = sess.run(output, run_metadata=metadata)
with session.Session(config=_get_config()) as sess:
metadata = config_pb2.RunMetadata()
output_val = sess.run(output, run_metadata=metadata)
nodes = []
num_transposes = 0
for node in metadata.cost_graph.node:
if _is_transpose(node.name):
num_transposes += 1
nodes.append(node.name)
nodes = []
num_transposes = 0
for node in metadata.cost_graph.node:
if _is_transpose(node.name):
num_transposes += 1
nodes.append(node.name)
expected_num_transposes = 2
self.assertEqual(expected_num_transposes, num_transposes)
self._assert_trans_nhwc_to_nchw('FusedBatchNormV3-0', nodes)
self._assert_trans_nchw_to_nhwc('Add-0-0', nodes)
self.assertAllClose(output_val_ref, output_val, atol=1e-3)
expected_num_transposes = 2
self.assertEqual(expected_num_transposes, num_transposes)
self._assert_trans_nhwc_to_nchw('FusedBatchNormV3-0', nodes)
self._assert_trans_nchw_to_nhwc('Add-0-0', nodes)
self.assertAllClose(output_val_ref, output_val, atol=1e-3)
@test_util.deprecated_graph_mode_only
def testGradient(self):

18
tensorflow/python/kernel_tests/gather_nd_op_test.py
View File

@ -23,7 +23,6 @@ import time
import numpy as np
from tensorflow.python.client import session
from tensorflow.python.compat import compat
from tensorflow.python.eager import context
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
@ -375,15 +374,14 @@ class GatherNdTest(test.TestCase):
@test_util.run_in_graph_and_eager_modes
def testGatherNdResourceVariable(self):
with compat.forward_compatibility_horizon(2019, 4, 30):
with self.cached_session():
v = resource_variable_ops.ResourceVariable(
constant_op.constant([[1, 2], [3, 4], [5, 6]]))
self.evaluate(variables.global_variables_initializer())
gather = array_ops.gather_nd(v, [[0, 1], [2, 0]])
if not context.executing_eagerly(): # .op doesn't make sense in Eager
self.assertEqual("ResourceGatherNd", gather.op.inputs[0].op.type)
self.assertAllEqual([2, 5], gather)
with self.cached_session():
v = resource_variable_ops.ResourceVariable(
constant_op.constant([[1, 2], [3, 4], [5, 6]]))
self.evaluate(variables.global_variables_initializer())
gather = array_ops.gather_nd(v, [[0, 1], [2, 0]])
if not context.executing_eagerly(): # .op doesn't make sense in Eager
self.assertEqual("ResourceGatherNd", gather.op.inputs[0].op.type)
self.assertAllEqual([2, 5], gather)
class GatherNdOpBenchmark(test.Benchmark):

19
tensorflow/python/kernel_tests/regex_full_match_op_test.py
View File

@ -20,7 +20,6 @@ from __future__ import print_function
from absl.testing import parameterized
from tensorflow.python.compat import compat
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import test_util
@ -85,16 +84,16 @@ class RegexFullMatchOpTest(test.TestCase):
@test_util.run_deprecated_v1
def testStaticRegexFullMatchDelegation(self):
with compat.forward_compatibility_horizon(2018, 11, 20):
with self.cached_session():
input_tensor = constant_op.constant("foo", dtypes.string)
pattern = "[a-z]*"
op = string_ops.regex_full_match(input_tensor, pattern)
self.assertTrue(op.name.startswith("StaticRegexFullMatch"), op.name)
with self.cached_session():
input_tensor = constant_op.constant("foo", dtypes.string)
pattern = "[a-z]*"
op = string_ops.regex_full_match(input_tensor, pattern)
self.assertTrue(op.name.startswith("StaticRegexFullMatch"), op.name)
pattern_tensor = constant_op.constant("[a-z]*", dtypes.string)
op_vec = string_ops.regex_full_match(input_tensor, pattern_tensor)
self.assertTrue(op_vec.name.startswith("RegexFullMatch"), op.name)
pattern_tensor = constant_op.constant("[a-z]*", dtypes.string)
op_vec = string_ops.regex_full_match(input_tensor, pattern_tensor)
self.assertTrue(op_vec.name.startswith("RegexFullMatch"), op.name)
if __name__ == "__main__":
test.main()