diff --git a/tensorflow/compiler/tests/binary_ops_test.py b/tensorflow/compiler/tests/binary_ops_test.py index 65a95c01723..444948c4078 100644 --- a/tensorflow/compiler/tests/binary_ops_test.py +++ b/tensorflow/compiler/tests/binary_ops_test.py @@ -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( diff --git a/tensorflow/python/data/experimental/kernel_tests/copy_to_device_test.py b/tensorflow/python/data/experimental/kernel_tests/copy_to_device_test.py index 2fa149fcbaa..b325474daab 100644 --- a/tensorflow/python/data/experimental/kernel_tests/copy_to_device_test.py +++ b/tensorflow/python/data/experimental/kernel_tests/copy_to_device_test.py @@ -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): diff --git a/tensorflow/python/grappler/layout_optimizer_test.py b/tensorflow/python/grappler/layout_optimizer_test.py index b4d73bfad0d..10f869805d8 100644 --- a/tensorflow/python/grappler/layout_optimizer_test.py +++ b/tensorflow/python/grappler/layout_optimizer_test.py @@ -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): diff --git a/tensorflow/python/kernel_tests/gather_nd_op_test.py b/tensorflow/python/kernel_tests/gather_nd_op_test.py index cfbdeb50ff4..79b1915dcbc 100644 --- a/tensorflow/python/kernel_tests/gather_nd_op_test.py +++ b/tensorflow/python/kernel_tests/gather_nd_op_test.py @@ -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): diff --git a/tensorflow/python/kernel_tests/regex_full_match_op_test.py b/tensorflow/python/kernel_tests/regex_full_match_op_test.py index 9976e57b100..ae4a8e4e422 100644 --- a/tensorflow/python/kernel_tests/regex_full_match_op_test.py +++ b/tensorflow/python/kernel_tests/regex_full_match_op_test.py @@ -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()