Remove expired forward compatibility horizons
This PR removes expired forward compatibility statements that will always evaluate to `True`.
This commit is contained in:
Lukas Geiger
parent
1d961d8b58
commit
503f3418ca
@ -21,7 +21,6 @@ from __future__ import print_function
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import numpy as np
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from tensorflow.compiler.tests import xla_test
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from tensorflow.python.compat import compat
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from tensorflow.python.framework import dtypes
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from tensorflow.python.ops import array_ops
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from tensorflow.python.ops import special_math_ops
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@ -61,13 +60,6 @@ class EinsumOpTest(xla_test.XLATestCase):
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np.array([[8]], dtype=dtype),
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expected=np.array([[-2]], dtype=dtype))
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with compat.forward_compatibility_horizon(2019, 10, 19):
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self._testBinary(
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lambda x, y: special_math_ops.einsum('ij,jk->ik', x, y),
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np.array([[-0.25]], dtype=dtype),
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np.array([[8]], dtype=dtype),
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expected=np.array([[-2]], dtype=dtype))
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def testImplicitForm(self):
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for dtype in self.float_types:
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self._testBinary(
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@ -76,13 +68,6 @@ class EinsumOpTest(xla_test.XLATestCase):
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np.array([[[1], [3], [2]], [[5], [6], [8]]], dtype=dtype),
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expected=np.array(128, dtype=dtype))
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with compat.forward_compatibility_horizon(2019, 10, 19):
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self._testBinary(
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lambda x, y: special_math_ops.einsum('ijk,kji', x, y),
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np.array([[[1, 3], [2, 5], [6, 8]]], dtype=dtype),
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np.array([[[1], [3], [2]], [[5], [6], [8]]], dtype=dtype),
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expected=np.array(128, dtype=dtype))
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def testReducedIndices(self):
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for dtype in self.float_types:
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self._testBinary(
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@ -91,13 +76,6 @@ class EinsumOpTest(xla_test.XLATestCase):
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np.array([3, 2], dtype=dtype),
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expected=np.array(59, dtype=dtype))
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with compat.forward_compatibility_horizon(2019, 10, 19):
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self._testBinary(
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lambda x, y: special_math_ops.einsum('ij,j->', x, y),
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np.array([[1, 3], [2, 5], [6, 8]], dtype=dtype),
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np.array([3, 2], dtype=dtype),
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expected=np.array(59, dtype=dtype))
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def testUnary(self):
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for dtype in self.float_types:
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self._testUnary(
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@ -105,12 +83,6 @@ class EinsumOpTest(xla_test.XLATestCase):
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np.array([[[1, 3], [2, 5], [6, 8]]], dtype=dtype),
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expected=np.array([[[1], [2], [6]], [[3], [5], [8]]], dtype=dtype))
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with compat.forward_compatibility_horizon(2019, 10, 19):
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self._testUnary(
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lambda x: special_math_ops.einsum('ijk->kji', x),
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np.array([[[1, 3], [2, 5], [6, 8]]], dtype=dtype),
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expected=np.array([[[1], [2], [6]], [[3], [5], [8]]], dtype=dtype))
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if __name__ == '__main__':
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googletest.main()
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@ -51,9 +51,6 @@ def make_unroll_batch_matmul_tests(options):
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[(4, 2, 2, 3), (4, 2, 2, 3), False, True],
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[(4, 2, 2, 3),
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(4, 2, 2, 3), True, False]] + broadcast_shape_params,
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# TODO(b/130887442): Improve the forward compatibility tests for every
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# ops.
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"forward_compatibility_test": [False, True],
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}]
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def build_graph(parameters):
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@ -73,14 +70,7 @@ def make_unroll_batch_matmul_tests(options):
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transpose_b=parameters["shape"][3])
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return [input_tensor1, input_tensor2], [out]
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if parameters["forward_compatibility_test"]:
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# This is hardcoded to the date after MatMulV2 is activated.
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# TODO(b/130887442): Improve the forward compatibility tests for every
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# ops, and remove the hardcoded date.
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with tf.compat.forward_compatibility_horizon(2019, 4, 26):
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return _build_graph()
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else:
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return _build_graph()
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return _build_graph()
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def build_inputs(parameters, sess, inputs, outputs):
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input_value1 = create_tensor_data(
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@ -64,9 +64,7 @@ class InjectPrefetchTest(test_base.DatasetTestBase, parameterized.TestCase):
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@combinations.generate(test_base.default_test_combinations())
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def testParallelInterleave(self):
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dataset = dataset_ops.Dataset.range(100)
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parallel_interleave = "ParallelInterleaveV2"
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if compat.forward_compatible(2020, 2, 20):
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parallel_interleave = "ParallelInterleaveV3"
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parallel_interleave = "ParallelInterleaveV3"
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if compat.forward_compatible(2020, 3, 6):
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parallel_interleave = "ParallelInterleaveV4"
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dataset = dataset.apply(
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@ -81,9 +79,7 @@ class InjectPrefetchTest(test_base.DatasetTestBase, parameterized.TestCase):
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@combinations.generate(test_base.default_test_combinations())
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def testChainedParallelDatasets(self):
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dataset = dataset_ops.Dataset.range(100)
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parallel_interleave = "ParallelInterleaveV2"
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if compat.forward_compatible(2020, 2, 20):
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parallel_interleave = "ParallelInterleaveV3"
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parallel_interleave = "ParallelInterleaveV3"
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if compat.forward_compatible(2020, 3, 6):
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parallel_interleave = "ParallelInterleaveV4"
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parallel_map = "ParallelMap"
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@ -17,8 +17,6 @@ from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from tensorflow.python.compat import compat
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from tensorflow.python.data.experimental.ops.distribute_options import AutoShardPolicy
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from tensorflow.python.data.experimental.ops.distribute_options import ExternalStatePolicy
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from tensorflow.python.data.ops import dataset_ops
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from tensorflow.python.data.util import nest
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@ -49,22 +47,13 @@ class _AutoShardDataset(dataset_ops.UnaryDataset):
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self._input_dataset = input_dataset
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self._element_spec = input_dataset.element_spec
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if (compat.forward_compatible(2019, 11, 25) or
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(input_dataset.options().experimental_distribute.auto_shard_policy !=
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AutoShardPolicy.AUTO)):
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variant_tensor = ged_ops.auto_shard_dataset(
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self._input_dataset._variant_tensor, # pylint: disable=protected-access
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num_workers=num_workers,
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index=index,
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auto_shard_policy=int(input_dataset.options().experimental_distribute
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.auto_shard_policy),
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**self._flat_structure)
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else:
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variant_tensor = ged_ops.auto_shard_dataset(
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self._input_dataset._variant_tensor, # pylint: disable=protected-access
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num_workers=num_workers,
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index=index,
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**self._flat_structure)
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variant_tensor = ged_ops.auto_shard_dataset(
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self._input_dataset._variant_tensor, # pylint: disable=protected-access
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num_workers=num_workers,
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index=index,
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auto_shard_policy=int(input_dataset.options().experimental_distribute
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.auto_shard_policy),
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**self._flat_structure)
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super(_AutoShardDataset, self).__init__(input_dataset, variant_tensor)
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@property
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@ -17,7 +17,6 @@ from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from tensorflow.python.compat import compat
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from tensorflow.python.data.ops import dataset_ops
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from tensorflow.python.framework import dtypes
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from tensorflow.python.framework import ops
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@ -78,42 +77,24 @@ class _SnapshotDataset(dataset_ops.UnaryUnchangedStructureDataset):
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self._input_dataset = input_dataset
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self._path = ops.convert_to_tensor(path, dtype=dtypes.string, name="path")
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if compat.forward_compatible(2020, 1, 10) or mode or snapshot_name:
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variant_tensor = ged_ops.snapshot_dataset(
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self._input_dataset._variant_tensor, # pylint: disable=protected-access
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path=self._path,
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compression=self._compression,
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reader_path_prefix=self._reader_path_prefix,
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writer_path_prefix=self._writer_path_prefix,
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shard_size_bytes=self._shard_size_bytes,
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pending_snapshot_expiry_seconds=self._pending_snapshot_expiry_seconds,
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num_reader_threads=self._num_reader_threads,
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reader_buffer_size=self._reader_buffer_size,
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num_writer_threads=self._num_writer_threads,
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writer_buffer_size=self._writer_buffer_size,
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shuffle_on_read=self._shuffle_on_read,
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seed=self._seed,
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seed2=self._seed2,
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mode=self._mode,
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snapshot_name=self._snapshot_name,
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**self._flat_structure)
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else:
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variant_tensor = ged_ops.snapshot_dataset(
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self._input_dataset._variant_tensor, # pylint: disable=protected-access
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path=self._path,
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compression=self._compression,
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reader_path_prefix=self._reader_path_prefix,
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writer_path_prefix=self._writer_path_prefix,
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shard_size_bytes=self._shard_size_bytes,
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pending_snapshot_expiry_seconds=self._pending_snapshot_expiry_seconds,
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num_reader_threads=self._num_reader_threads,
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reader_buffer_size=self._reader_buffer_size,
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num_writer_threads=self._num_writer_threads,
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writer_buffer_size=self._writer_buffer_size,
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shuffle_on_read=self._shuffle_on_read,
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seed=self._seed,
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seed2=self._seed2,
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**self._flat_structure)
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variant_tensor = ged_ops.snapshot_dataset(
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self._input_dataset._variant_tensor, # pylint: disable=protected-access
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path=self._path,
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compression=self._compression,
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reader_path_prefix=self._reader_path_prefix,
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writer_path_prefix=self._writer_path_prefix,
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shard_size_bytes=self._shard_size_bytes,
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pending_snapshot_expiry_seconds=self._pending_snapshot_expiry_seconds,
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num_reader_threads=self._num_reader_threads,
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reader_buffer_size=self._reader_buffer_size,
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num_writer_threads=self._num_writer_threads,
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writer_buffer_size=self._writer_buffer_size,
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shuffle_on_read=self._shuffle_on_read,
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seed=self._seed,
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seed2=self._seed2,
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mode=self._mode,
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snapshot_name=self._snapshot_name,
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**self._flat_structure)
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super(_SnapshotDataset, self).__init__(input_dataset, variant_tensor)
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@ -1374,7 +1374,7 @@ class DatasetV2(tracking_base.Trackable, composite_tensor.CompositeTensor):
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Raises:
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InvalidArgumentError: if `num_shards` or `index` are illegal values.
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Note: error checking is done on a best-effort basis, and errors aren't
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guaranteed to be caught upon dataset creation. (e.g. providing in a
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placeholder tensor bypasses the early checking, and will instead result
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@ -4269,7 +4269,7 @@ class ParallelInterleaveDataset(UnaryDataset):
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f=self._map_func.function,
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deterministic=deterministic_string,
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**self._flat_structure)
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elif deterministic is not None or compat.forward_compatible(2020, 2, 20):
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else:
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variant_tensor = gen_dataset_ops.parallel_interleave_dataset_v3(
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input_dataset._variant_tensor, # pylint: disable=protected-access
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self._map_func.function.captured_inputs, # pylint: disable=protected-access
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@ -4279,15 +4279,6 @@ class ParallelInterleaveDataset(UnaryDataset):
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f=self._map_func.function,
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deterministic=deterministic_string,
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**self._flat_structure)
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else:
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variant_tensor = gen_dataset_ops.parallel_interleave_dataset_v2(
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input_dataset._variant_tensor, # pylint: disable=protected-access
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self._map_func.function.captured_inputs, # pylint: disable=protected-access
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self._cycle_length,
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self._block_length,
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self._num_parallel_calls,
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f=self._map_func.function,
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**self._flat_structure)
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super(ParallelInterleaveDataset, self).__init__(input_dataset,
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variant_tensor)
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