wehub-resource-sync
1165 lines
41 KiB
Python
1165 lines
41 KiB
Python
# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Tests for operator dispatch."""
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import collections
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import typing
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import numpy as np
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from tensorflow.python.eager import context
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from tensorflow.python.framework import constant_op
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from tensorflow.python.framework import dtypes
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from tensorflow.python.framework import extension_type
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from tensorflow.python.framework import ops
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from tensorflow.python.framework import tensor as tensor_lib
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from tensorflow.python.framework import tensor_conversion
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from tensorflow.python.framework import test_util
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from tensorflow.python.ops import array_ops
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from tensorflow.python.ops import array_ops_stack
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from tensorflow.python.ops import bitwise_ops
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from tensorflow.python.ops import gen_math_ops
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from tensorflow.python.ops import math_ops
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from tensorflow.python.ops import variables
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from tensorflow.python.ops.linalg import linear_operator_diag
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from tensorflow.python.ops.proto_ops import decode_proto
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from tensorflow.python.platform import googletest
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from tensorflow.python.platform import test
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from tensorflow.python.platform import tf_logging
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from tensorflow.python.types import core as core_tf_types
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from tensorflow.python.util import deprecation
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from tensorflow.python.util import dispatch
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from tensorflow.python.util import nest
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from tensorflow.python.util.tf_export import get_canonical_name_for_symbol
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from tensorflow.python.util.tf_export import tf_export
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class CustomTensor(object):
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"""A fake composite tensor class, for testing type-based dispatching."""
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def __init__(self, tensor, score):
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self.tensor = ops.convert_to_tensor(tensor)
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self.score = score
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@tf_export("test_op")
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@dispatch.add_dispatch_support
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def test_op(x, y, z):
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"""A fake op for testing dispatch of Python ops."""
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return x + (2 * y) + (3 * z)
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@tf_export("test_op_with_optional")
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@dispatch.add_dispatch_support
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def test_op_with_optional(x, y, z, optional=None):
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"""A fake op for testing dispatch of Python ops."""
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del optional
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return x + (2 * y) + (3 * z)
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@tf_export("test_op_with_kwonly")
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@dispatch.add_dispatch_support
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def test_op_with_kwonly(*, x, y, z, optional=None):
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"""A fake op for testing dispatch of Python ops."""
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del optional
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return x + (2 * y) + (3 * z)
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class TensorTracer(object):
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"""An object used to trace TensorFlow graphs.
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This is an example class that is used to test global op dispatchers. The
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global op dispatcher for TensorTracers is defined below.
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"""
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def __init__(self, name, args=None, kwargs=None):
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self.name = name
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self.args = args
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self.kwargs = kwargs
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self.shape = array_ops.ones(shape=(4, 4)).shape
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self.dtype = dtypes.float32
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def __repr__(self):
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if self.args is None and self.kwargs is None:
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return self.name
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else:
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args = [str(x) for x in self.args]
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args += sorted(
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["{}={}".format(name, x) for (name, x) in self.kwargs.items()])
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return "{}({})".format(self.name, ", ".join(args))
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@property
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def is_tensor_like(self):
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return True
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@classmethod
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def _overload_all_operators(cls): # pylint: disable=invalid-name
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"""Register overloads for all operators."""
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for operator in tensor_lib.Tensor.OVERLOADABLE_OPERATORS:
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cls._overload_operator(operator)
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@classmethod
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def _overload_operator(cls, operator): # pylint: disable=invalid-name
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"""Overload an operator with the same overloading as `tensor_lib.Tensor`."""
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tensor_oper = getattr(tensor_lib.Tensor, operator)
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# Compatibility with Python 2:
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# Python 2 unbound methods have type checks for the first arg,
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# so we need to extract the underlying function
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tensor_oper = getattr(tensor_oper, "__func__", tensor_oper)
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setattr(cls, operator, tensor_oper)
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TensorTracer._overload_all_operators() # pylint: disable=protected-access
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class TensorTracerOpDispatcher(dispatch.GlobalOpDispatcher):
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"""Global op dispatcher for TensorTracer."""
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def _flatten_with_slice_flattening(self, x):
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flat = []
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for val in nest.flatten(x):
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if isinstance(val, slice):
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flat.extend((val.start, val.stop, val.step))
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else:
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flat.append(val)
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return flat
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def handle(self, op, args, kwargs):
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# Dispatcher only applies if at least one arg is a TensorTracer.
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if not (any(self.is_tensor_tracer_arg(x) for x in args) or
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any(self.is_tensor_tracer_arg(x) for x in kwargs.values())):
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return self.NOT_SUPPORTED
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symbol_name = get_canonical_name_for_symbol(op)
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return TensorTracer(symbol_name, args, kwargs)
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def is_tensor_tracer_arg(self, value):
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return any(
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isinstance(x, TensorTracer)
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for x in self._flatten_with_slice_flattening(value))
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@test_util.run_all_in_graph_and_eager_modes
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class DispatchTest(test_util.TensorFlowTestCase):
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def testAddDispatchForTypes_With_CppOp(self):
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original_handlers = gen_math_ops.atan2._tf_fallback_dispatchers[:]
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# Override the behavior of gen_math_ops.atan2 and make it look like add.
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@dispatch.dispatch_for_types(gen_math_ops.atan2, CustomTensor)
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def custom_atan2(y, x, name=None): # pylint: disable=unused-variable
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return CustomTensor(
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gen_math_ops.add(y.tensor, x.tensor, name), (x.score + y.score) / 2.0)
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self.assertEqual(
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len(math_ops.atan2._tf_fallback_dispatchers),
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len(original_handlers) + 1)
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# Test that we see the overridden behavior when using CustomTensors.
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x = CustomTensor([1., 2., 3.], 2.0)
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y = CustomTensor([7., 8., 2.], 0.0)
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x_plus_y = gen_math_ops.atan2(y, x)
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self.assertAllEqual(self.evaluate(x_plus_y.tensor), [8, 10, 5])
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self.assertNear(x_plus_y.score, 1.0, 0.001)
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# Test that we still get the right behavior when using normal Tensors.
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a = [1., 2., 3.]
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b = [7., 8., 2.]
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a_plus_b = gen_math_ops.atan2(a, b)
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self.assertAllClose(a_plus_b, [0.14189707, 0.24497867, 0.98279375])
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# Test that we still get a TypeError or ValueError if we pass some
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# type that's not supported by any dispatcher.
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with self.assertRaises((TypeError, ValueError)):
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gen_math_ops.atan2(a, None)
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# Clean up
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gen_math_ops.atan2._tf_fallback_dispatchers = original_handlers
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def testAddDispatchForTypes_With_PythonOp(self):
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original_handlers = test_op._tf_fallback_dispatchers[:]
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def override_for_test_op(x, y, z): # pylint: disable=unused-variable
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return CustomTensor(
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test_op(x.tensor, y.tensor, z.tensor),
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(x.score + y.score + z.score) / 3.0)
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override = dispatch.dispatch_for_types(test_op, CustomTensor)(
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override_for_test_op
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)
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self.assertIs(override, override_for_test_op)
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x = CustomTensor([1, 2, 3], 0.2)
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y = CustomTensor([7, 8, 2], 0.4)
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z = CustomTensor([0, 1, 2], 0.6)
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result = test_op(x, y, z)
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self.assertAllEqual(self.evaluate(result.tensor), [15, 21, 13])
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self.assertNear(result.score, 0.4, 0.001)
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# Clean up
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test_op._tf_fallback_dispatchers = original_handlers
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def testDispatchForTypes_MissingArgs(self):
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original_handlers = test_op_with_optional._tf_fallback_dispatchers[:]
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def override_for_test_op(x, y, z): # pylint: disable=unused-variable
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return CustomTensor(
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test_op(x.tensor, y.tensor, z.tensor),
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(x.score + y.score + z.score) / 3.0,
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)
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override = dispatch.dispatch_for_types(test_op_with_optional, CustomTensor)(
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override_for_test_op
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)
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self.assertIs(override, override_for_test_op)
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x = CustomTensor([1, 2, 3], 0.2)
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y = CustomTensor([7, 8, 2], 0.4)
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z = CustomTensor([0, 1, 2], 0.6)
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result = test_op_with_optional(x, y, z)
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self.assertAllEqual(self.evaluate(result.tensor), [15, 21, 13])
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self.assertNear(result.score, 0.4, 0.001)
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# Clean up
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test_op_with_optional._tf_fallback_dispatchers = original_handlers
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def testDispatchForTypes_ProvidingMissingArgs(self):
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original_handlers = test_op_with_optional._tf_fallback_dispatchers[:]
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@dispatch.dispatch_for_types(test_op_with_optional, CustomTensor)
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def override_for_test_op(x, y, z): # pylint: disable=unused-variable
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return CustomTensor(
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test_op(x.tensor, y.tensor, z.tensor),
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(x.score + y.score + z.score) / 3.0,
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)
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x = CustomTensor([1, 2, 3], 0.2)
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y = CustomTensor([7, 8, 2], 0.4)
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z = CustomTensor([0, 1, 2], 0.6)
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with self.assertRaisesRegex(
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AssertionError,
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"Dispatched op is called with argument `optional` set to a non-default"
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" value, which is not supported by the decorated function",
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):
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test_op_with_optional(x, y, z, optional=3)
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# Clean up
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test_op_with_optional._tf_fallback_dispatchers = original_handlers
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def testDispatchForTypes_NewArgs(self):
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original_handlers = test_op_with_optional._tf_fallback_dispatchers[:]
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@dispatch.dispatch_for_types(test_op_with_optional, CustomTensor)
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def override_for_test_op(x, y, z, u=None): # pylint: disable=unused-variable
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del u
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return CustomTensor(
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test_op(x.tensor, y.tensor, z.tensor),
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(x.score + y.score + z.score) / 3.0,
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)
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x = CustomTensor([1, 2, 3], 0.2)
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y = CustomTensor([7, 8, 2], 0.4)
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z = CustomTensor([0, 1, 2], 0.6)
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result = test_op_with_optional(x, y, z)
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self.assertAllEqual(self.evaluate(result.tensor), [15, 21, 13])
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self.assertNear(result.score, 0.4, 0.001)
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# Clean up
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test_op_with_optional._tf_fallback_dispatchers = original_handlers
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def testDispatchForTypes_SignatureMismatchOrder(self):
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with self.assertRaisesRegex(
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AssertionError,
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"The decorated function's non-default arguments must be identical to"
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" that of the overridden op.",
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):
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@dispatch.dispatch_for_types(test_op, CustomTensor)
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def override_for_test_op(x, z, y): # pylint: disable=unused-variable
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return CustomTensor(
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test_op(x.tensor, y.tensor, z.tensor),
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(x.score + y.score + z.score) / 3.0,
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)
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def testDispatchForTypes_MissingKwOnly(self):
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with self.assertRaisesRegex(
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AssertionError,
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"The decorated function's non-default arguments must be identical to"
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" that of the overridden op.",
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):
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@dispatch.dispatch_for_types(test_op_with_kwonly, CustomTensor)
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def override_for_test_op(x, z, y): # pylint: disable=unused-variable
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return CustomTensor(
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test_op(x.tensor, y.tensor, z.tensor),
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(x.score + y.score + z.score) / 3.0,
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)
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def testDispatchForTypes_SignatureMismatchNames(self):
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with self.assertRaisesRegex(
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AssertionError,
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"The decorated function's non-default arguments must be identical to"
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" that of the overridden op.",
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):
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@dispatch.dispatch_for_types(test_op, CustomTensor)
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def override_for_test_op(a, b, c): # pylint: disable=unused-variable
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return CustomTensor(
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test_op(a.tensor, b.tensor, c.tensor),
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(a.score + b.score + c.score) / 3.0)
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def testDispatchForTypes_OpDoesNotSupportDispatch(self):
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def some_op(x, y):
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return x + y
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with self.assertRaisesRegex(AssertionError, "Dispatching not enabled for"):
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@dispatch.dispatch_for_types(some_op, CustomTensor)
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def override_for_some_op(x, y): # pylint: disable=unused-variable
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return x if x.score > 0 else y
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@test.mock.patch.object(tf_logging, "warning", autospec=True)
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def testInteractionWithDeprecationWarning(self, mock_warning):
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@deprecation.deprecated(date=None, instructions="Instructions")
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@dispatch.add_dispatch_support
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def some_op(x):
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return x
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some_op(5)
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message = mock_warning.call_args[0][0] % mock_warning.call_args[0][1:]
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self.assertRegex(
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message, r".*some_op \(from __main__\) is deprecated and will be "
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"removed in a future version.*")
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def testGlobalDispatcher(self):
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original_global_dispatchers = dispatch._GLOBAL_DISPATCHERS
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try:
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TensorTracerOpDispatcher().register()
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x = TensorTracer("x")
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y = TensorTracer("y")
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trace = math_ops.reduce_sum(math_ops.add(math_ops.abs(x), y), axis=3)
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self.assertEqual(
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str(trace), "math.reduce_sum(math.add(math.abs(x), y), axis=3)")
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proto_val = TensorTracer("proto")
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trace = decode_proto(proto_val, "message_type", ["field"], ["float32"])
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self.assertIn("io.decode_proto(bytes=proto,", str(trace))
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finally:
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# Clean up.
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dispatch._GLOBAL_DISPATCHERS = original_global_dispatchers
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def testGlobalDispatcherConvertToTensor(self):
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original_global_dispatchers = dispatch._GLOBAL_DISPATCHERS
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try:
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TensorTracerOpDispatcher().register()
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x = TensorTracer("x")
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y = TensorTracer("y")
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trace = math_ops.add(
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math_ops.abs(tensor_conversion.convert_to_tensor_v2_with_dispatch(x)),
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y,
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)
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self.assertEqual(
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str(trace), "math.add(math.abs(convert_to_tensor(x)), y)")
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finally:
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# Clean up.
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dispatch._GLOBAL_DISPATCHERS = original_global_dispatchers
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def testGlobalDispatcherGetItem(self):
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original_global_dispatchers = dispatch._GLOBAL_DISPATCHERS
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try:
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TensorTracerOpDispatcher().register()
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x = TensorTracer("x")
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trace = x[0]
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self.assertEqual(str(trace), "__operators__.getitem(x, 0)")
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x = TensorTracer("x")
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y = TensorTracer("y")
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trace = x[y]
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self.assertEqual(str(trace), "__operators__.getitem(x, y)")
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x = TensorTracer("x")
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y = TensorTracer("y")
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trace = x[:y] # pylint: disable=invalid-slice-index
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self.assertEqual(
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str(trace), "__operators__.getitem(x, slice(None, y, None))")
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x = array_ops.ones(shape=(3, 3))
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y = TensorTracer("y")
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trace = x[y]
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self.assertEqual(str(trace), "__operators__.getitem(%s, y)" % x)
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trace = x[:y] # pylint: disable=invalid-slice-index
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self.assertEqual(
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str(trace), "__operators__.getitem(%s, slice(None, y, None))" % x)
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finally:
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# Clean up.
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dispatch._GLOBAL_DISPATCHERS = original_global_dispatchers
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def testGlobalDispatcherLinearOperators(self):
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original_global_dispatchers = dispatch._GLOBAL_DISPATCHERS
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try:
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TensorTracerOpDispatcher().register()
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x = TensorTracer("x")
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# To grab the eigenvalues the diag operator just calls convert_to_tensor
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# (twice) in this case.
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trace = linear_operator_diag.LinearOperatorDiag(x).eigvals()
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self.assertEqual(
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str(trace),
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"convert_to_tensor(convert_to_tensor(x, dtype=None, dtype_hint=None, "
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"name=diag))")
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# The diagonal tensor addition gets traced even though the linear_operator
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# API only uses dispatchable ops instead of directly exposing dispatching.
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trace = linear_operator_diag.LinearOperatorDiag(x).add_to_tensor(x)
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self.assertIn(
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"linalg.set_diag(convert_to_tensor(x, name=x), __operators__.add("
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"convert_to_tensor(x, dtype=None, dtype_hint=None, name=diag), "
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"linalg.diag_part(convert_to_tensor(x, name=x)), "
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"name=", str(trace))
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# The dispatch-supporting ops the non-singular check calls out to
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# get traced.
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trace = linear_operator_diag.LinearOperatorDiag(x).assert_non_singular()
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self.assertIn("debugging.assert_less", str(trace))
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self.assertIn(
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"message=Singular operator: Diagonal contained zero values.",
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str(trace))
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finally:
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# Clean up.
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dispatch._GLOBAL_DISPATCHERS = original_global_dispatchers
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class MaskedTensor(extension_type.ExtensionType):
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"""Simple ExtensionType for testing v2 dispatch."""
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values: tensor_lib.Tensor
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mask: tensor_lib.Tensor
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class SillyTensor(extension_type.ExtensionType):
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"""Simple ExtensionType for testing v2 dispatch."""
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value: tensor_lib.Tensor
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how_silly: float
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@test_util.run_all_in_graph_and_eager_modes
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class DispatchV2Test(test_util.TensorFlowTestCase):
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|
def testDispatchForOneSignature(self):
|
|
|
|
@dispatch.dispatch_for_api(math_ops.add, {
|
|
"x": MaskedTensor,
|
|
"y": MaskedTensor
|
|
})
|
|
def masked_add(x, y, name=None):
|
|
with ops.name_scope(name):
|
|
return MaskedTensor(x.values + y.values, x.mask & y.mask)
|
|
|
|
try:
|
|
x = MaskedTensor([1, 2, 3, 4, 5], [1, 0, 1, 1, 1])
|
|
y = MaskedTensor([1, 1, 1, 1, 1], [1, 1, 0, 1, 0])
|
|
z = math_ops.add(x, y)
|
|
self.assertAllEqual(z.values, x.values + y.values)
|
|
self.assertAllEqual(z.mask, x.mask & y.mask)
|
|
|
|
finally:
|
|
# Clean up dispatch table.
|
|
dispatch.unregister_dispatch_for(masked_add)
|
|
|
|
def testDispatchSignatureWithUnspecifiedParameter(self):
|
|
|
|
@dispatch.dispatch_for_api(math_ops.add, {"x": MaskedTensor})
|
|
def masked_add(x, y):
|
|
if y is None:
|
|
return x
|
|
y_values = y.values if isinstance(y, MaskedTensor) else y
|
|
y_mask = y.mask if isinstance(y, MaskedTensor) else True
|
|
return MaskedTensor(x.values + y_values, x.mask & y_mask)
|
|
|
|
try:
|
|
a = MaskedTensor([1, 2, 3, 4, 5], [1, 0, 1, 1, 1])
|
|
b = constant_op.constant([10, 20, 30, 40, 50])
|
|
c = [10, 20, 30, 40, 50]
|
|
d = 50
|
|
e = None
|
|
# As long as `x` is a MaskedTensor, the dispatcher will be called
|
|
# (regardless of the type for `y`):
|
|
self.assertAllEqual(math_ops.add(a, b).values, [11, 22, 33, 44, 55])
|
|
self.assertAllEqual(math_ops.add(a, c).values, [11, 22, 33, 44, 55])
|
|
self.assertAllEqual(math_ops.add(a, d).values, [51, 52, 53, 54, 55])
|
|
self.assertAllEqual(math_ops.add(a, e).values, [1, 2, 3, 4, 5])
|
|
|
|
finally:
|
|
# Clean up dispatch table.
|
|
dispatch.unregister_dispatch_for(masked_add)
|
|
|
|
def testDispatchForMultipleSignatures(self):
|
|
|
|
@dispatch.dispatch_for_api(math_ops.add, {"x": MaskedTensor},
|
|
{"y": MaskedTensor})
|
|
def masked_add(x, y, name=None):
|
|
with ops.name_scope(name):
|
|
x_values = x.values if isinstance(x, MaskedTensor) else x
|
|
x_mask = x.mask if isinstance(x, MaskedTensor) else True
|
|
y_values = y.values if isinstance(y, MaskedTensor) else y
|
|
y_mask = y.mask if isinstance(y, MaskedTensor) else True
|
|
return MaskedTensor(x_values + y_values, x_mask & y_mask)
|
|
|
|
try:
|
|
x = MaskedTensor([1, 2, 3, 4, 5], [1, 0, 1, 1, 1])
|
|
y = constant_op.constant([10, 20, 30, 40, 50])
|
|
z = math_ops.add(x, y)
|
|
self.assertAllEqual(z.values, x.values + y)
|
|
self.assertAllEqual(z.mask, x.mask)
|
|
|
|
finally:
|
|
# Clean up dispatch table.
|
|
dispatch.unregister_dispatch_for(masked_add)
|
|
|
|
def testDispatchForList(self):
|
|
|
|
@dispatch.dispatch_for_api(array_ops.concat,
|
|
{"values": typing.List[MaskedTensor]})
|
|
def masked_concat(values, axis, name=None):
|
|
with ops.name_scope(name):
|
|
return MaskedTensor(
|
|
array_ops.concat([v.values for v in values], axis),
|
|
array_ops.concat([v.mask for v in values], axis))
|
|
|
|
try:
|
|
x = MaskedTensor([1, 2, 3, 4, 5], [1, 0, 1, 1, 1])
|
|
y = MaskedTensor([1, 1, 1], [1, 1, 0])
|
|
z = array_ops.concat([x, y], axis=0)
|
|
self.assertAllEqual(z.values, array_ops.concat([x.values, y.values], 0))
|
|
self.assertAllEqual(z.mask, array_ops.concat([x.mask, y.mask], 0))
|
|
|
|
finally:
|
|
# Clean up dispatch table.
|
|
dispatch.unregister_dispatch_for(masked_concat)
|
|
|
|
def testDispatchForUnion(self):
|
|
MaybeMasked = typing.Union[MaskedTensor, tensor_lib.Tensor]
|
|
|
|
@dispatch.dispatch_for_api(math_ops.add, {
|
|
"x": MaybeMasked,
|
|
"y": MaybeMasked
|
|
})
|
|
def masked_add(x, y, name=None):
|
|
with ops.name_scope(name):
|
|
x_values = x.values if isinstance(x, MaskedTensor) else x
|
|
x_mask = x.mask if isinstance(x, MaskedTensor) else True
|
|
y_values = y.values if isinstance(y, MaskedTensor) else y
|
|
y_mask = y.mask if isinstance(y, MaskedTensor) else True
|
|
return MaskedTensor(x_values + y_values, x_mask & y_mask)
|
|
|
|
try:
|
|
x = MaskedTensor([1, 2, 3, 4, 5], [1, 0, 1, 1, 1])
|
|
y = constant_op.constant([10, 20, 30, 40, 50])
|
|
z = math_ops.add(x, y)
|
|
self.assertAllEqual(z.values, x.values + y)
|
|
self.assertAllEqual(z.mask, x.mask)
|
|
|
|
finally:
|
|
# Clean up dispatch table.
|
|
dispatch.unregister_dispatch_for(masked_add)
|
|
|
|
def testDispatchForTensorLike(self):
|
|
MaskedOrTensorLike = typing.Union[MaskedTensor, core_tf_types.TensorLike]
|
|
|
|
@dispatch.dispatch_for_api(math_ops.add)
|
|
def masked_add(x: MaskedOrTensorLike, y: MaskedOrTensorLike, name=None):
|
|
with ops.name_scope(name):
|
|
x_values = x.values if isinstance(x, MaskedTensor) else x
|
|
x_mask = x.mask if isinstance(x, MaskedTensor) else True
|
|
y_values = y.values if isinstance(y, MaskedTensor) else y
|
|
y_mask = y.mask if isinstance(y, MaskedTensor) else True
|
|
return MaskedTensor(x_values + y_values, x_mask & y_mask)
|
|
|
|
try:
|
|
x = MaskedTensor([1, 2, 3, 4, 5], [1, 0, 1, 1, 1])
|
|
y1 = [10, 20, 30, 40, 50]
|
|
y2 = np.array([10, 20, 30, 40, 50])
|
|
y3 = constant_op.constant([10, 20, 30, 40, 50])
|
|
y4 = variables.Variable([5, 4, 3, 2, 1])
|
|
if not context.executing_eagerly():
|
|
self.evaluate(variables.global_variables_initializer())
|
|
for y in [y1, y2, y3, y4]:
|
|
z = math_ops.add(x, y)
|
|
self.assertAllEqual(z.values, x.values + y)
|
|
self.assertAllEqual(z.mask, x.mask)
|
|
|
|
finally:
|
|
# Clean up dispatch table.
|
|
dispatch.unregister_dispatch_for(masked_add)
|
|
|
|
def testDispatchForOptional(self):
|
|
# Note: typing.Optional[X] == typing.Union[X, NoneType].
|
|
|
|
@dispatch.dispatch_for_api(
|
|
array_ops.where_v2, {
|
|
"condition": MaskedTensor,
|
|
"x": typing.Optional[MaskedTensor],
|
|
"y": typing.Optional[MaskedTensor]
|
|
})
|
|
def masked_where(condition, x=None, y=None, name=None):
|
|
del condition, x, y, name
|
|
return "stub"
|
|
|
|
try:
|
|
x = MaskedTensor([True, False, True, True, True], [1, 0, 1, 1, 1])
|
|
self.assertEqual(array_ops.where_v2(x), "stub")
|
|
self.assertEqual(array_ops.where_v2(x, x, x), "stub")
|
|
|
|
finally:
|
|
# Clean up dispatch table.
|
|
dispatch.unregister_dispatch_for(masked_where)
|
|
|
|
def testDispatchForSignatureFromAnnotations(self):
|
|
|
|
@dispatch.dispatch_for_api(math_ops.add)
|
|
def masked_add(x: MaskedTensor, y: MaskedTensor, name=None):
|
|
with ops.name_scope(name):
|
|
return MaskedTensor(x.values + y.values, x.mask & y.mask)
|
|
|
|
try:
|
|
x = MaskedTensor([1, 2, 3, 4, 5], [1, 0, 1, 1, 1])
|
|
y = MaskedTensor([1, 1, 1, 1, 1], [1, 1, 0, 1, 0])
|
|
z = math_ops.add(x, y)
|
|
self.assertAllEqual(z.values, x.values + y.values)
|
|
self.assertAllEqual(z.mask, x.mask & y.mask)
|
|
|
|
finally:
|
|
# Clean up dispatch table.
|
|
dispatch.unregister_dispatch_for(masked_add)
|
|
|
|
def testDispatchForPositionalSignature(self):
|
|
|
|
@dispatch.dispatch_for_api(math_ops.add, {0: MaskedTensor, 1: MaskedTensor})
|
|
def masked_add(x, y, name=None):
|
|
with ops.name_scope(name):
|
|
return MaskedTensor(x.values + y.values, x.mask & y.mask)
|
|
|
|
try:
|
|
x = MaskedTensor([1, 2, 3, 4, 5], [1, 0, 1, 1, 1])
|
|
y = MaskedTensor([1, 1, 1, 1, 1], [1, 1, 0, 1, 0])
|
|
z = math_ops.add(x, y)
|
|
self.assertAllEqual(z.values, x.values + y.values)
|
|
self.assertAllEqual(z.mask, x.mask & y.mask)
|
|
|
|
finally:
|
|
# Clean up dispatch table.
|
|
dispatch.unregister_dispatch_for(masked_add)
|
|
|
|
def testDispatchWithVarargs(self):
|
|
|
|
@dispatch.dispatch_for_api(math_ops.add, {
|
|
"x": MaskedTensor,
|
|
"y": MaskedTensor
|
|
})
|
|
def masked_add(*args, **kwargs):
|
|
self.assertAllEqual(args[0].values, x.values)
|
|
self.assertAllEqual(args[1].values, y.values)
|
|
self.assertEmpty(kwargs)
|
|
return "stub"
|
|
|
|
try:
|
|
x = MaskedTensor([1, 2, 3, 4, 5], [1, 0, 1, 1, 1])
|
|
y = MaskedTensor([1, 1, 1, 1, 1], [1, 1, 0, 1, 0])
|
|
self.assertEqual(math_ops.add(x, y), "stub")
|
|
|
|
finally:
|
|
# Clean up dispatch table.
|
|
dispatch.unregister_dispatch_for(masked_add)
|
|
|
|
def testDispatchWithKwargs(self):
|
|
|
|
@dispatch.dispatch_for_api(math_ops.add, {
|
|
"x": MaskedTensor,
|
|
"y": MaskedTensor
|
|
})
|
|
def masked_add(*args, **kwargs):
|
|
self.assertAllEqual(kwargs["x"].values, x.values)
|
|
self.assertAllEqual(kwargs["y"].values, y.values)
|
|
self.assertEmpty(args)
|
|
return "stub"
|
|
|
|
try:
|
|
x = MaskedTensor([1, 2, 3, 4, 5], [1, 0, 1, 1, 1])
|
|
y = MaskedTensor([1, 1, 1, 1, 1], [1, 1, 0, 1, 0])
|
|
self.assertEqual(math_ops.add(x=x, y=y), "stub")
|
|
|
|
finally:
|
|
# Clean up dispatch table.
|
|
dispatch.unregister_dispatch_for(masked_add)
|
|
|
|
def testDispatchErrorForBadAPI(self):
|
|
|
|
def api_without_dispatch_support(x):
|
|
return x + 1
|
|
|
|
with self.assertRaisesRegex(ValueError, ".* does not support dispatch."):
|
|
|
|
@dispatch.dispatch_for_api(api_without_dispatch_support,
|
|
{"x": MaskedTensor})
|
|
def my_version(x): # pylint: disable=unused-variable
|
|
del x
|
|
|
|
def testDispatchErrorForNoSignature(self):
|
|
with self.assertRaisesRegex(ValueError,
|
|
"must be called with at least one signature"):
|
|
|
|
@dispatch.dispatch_for_api(math_ops.add)
|
|
def my_add(x, y, name=None): # pylint: disable=unused-variable
|
|
del x, y, name
|
|
|
|
def testDispatchErrorSignatureMismatchParamName(self):
|
|
with self.assertRaisesRegex(
|
|
ValueError, r"Dispatch function's signature \(x, why, name=None\) does "
|
|
r"not match API's signature \(x, y, name=None\)."):
|
|
|
|
@dispatch.dispatch_for_api(math_ops.add, {"x": MaskedTensor})
|
|
def my_add(x, why, name=None): # pylint: disable=unused-variable
|
|
del x, why, name
|
|
|
|
def testDispatchErrorSignatureMismatchExtraParam(self):
|
|
with self.assertRaisesRegex(
|
|
ValueError, r"Dispatch function's signature \(x, y, name=None, extra_"
|
|
r"arg=None\) does not match API's signature \(x, y, name=None\)."):
|
|
|
|
@dispatch.dispatch_for_api(math_ops.add, {"x": MaskedTensor})
|
|
def my_add(x, y, name=None, extra_arg=None): # pylint: disable=unused-variable
|
|
del x, y, name, extra_arg
|
|
|
|
def testDispatchErrorForUnsupportedTypeAnnotation(self):
|
|
with self.assertRaisesRegex(
|
|
ValueError,
|
|
"Type annotation .* is not currently supported by dispatch."):
|
|
|
|
@dispatch.dispatch_for_api(math_ops.add,
|
|
{"x": typing.Tuple[MaskedTensor]})
|
|
def my_add(x, y, name=None): # pylint: disable=unused-variable
|
|
del x, y, name
|
|
|
|
def testDispatchErrorForUnknownParameter(self):
|
|
with self.assertRaisesRegex(
|
|
ValueError, "signature includes annotation for unknown parameter 'z'."):
|
|
|
|
@dispatch.dispatch_for_api(math_ops.add, {"z": MaskedTensor})
|
|
def my_add(x, y, name=None): # pylint: disable=unused-variable
|
|
del x, y, name
|
|
|
|
def testDispatchErrorUnsupportedKeywordOnlyAnnotation(self):
|
|
|
|
@dispatch.add_dispatch_support
|
|
def foo(x, *, y):
|
|
return x + y
|
|
|
|
with self.assertRaisesRegex(
|
|
ValueError, "Dispatch currently only supports type "
|
|
"annotations for positional parameters"):
|
|
|
|
@dispatch.dispatch_for_api(foo, {"y": MaskedTensor})
|
|
def masked_foo(x, *, y): # pylint: disable=unused-variable
|
|
del x, y
|
|
|
|
def testDispatchErrorBadSignatureType(self):
|
|
with self.assertRaisesRegex(
|
|
TypeError, "signatures must be dictionaries mapping parameter "
|
|
"names to type annotations"):
|
|
|
|
@dispatch.dispatch_for_api(math_ops.add, [MaskedTensor])
|
|
def my_add(x, y, name=None): # pylint: disable=unused-variable
|
|
del x, y, name
|
|
|
|
with self.assertRaisesRegex(
|
|
TypeError, "signatures must be dictionaries mapping parameter "
|
|
"names to type annotations"):
|
|
|
|
@dispatch.dispatch_for_api(math_ops.multiply, {None: MaskedTensor})
|
|
def my_multiply(x, y, name=None): # pylint: disable=unused-variable
|
|
del x, y, name
|
|
|
|
def testDispatchErrorNotCallable(self):
|
|
with self.assertRaisesRegex(TypeError,
|
|
"Expected dispatch_target to be callable"):
|
|
dispatch.dispatch_for_api(math_ops.abs, {0: MaskedTensor})("not_callable")
|
|
|
|
def testRegisterDispatchableType(self):
|
|
Car = collections.namedtuple("Car", ["size", "speed"])
|
|
dispatch.register_dispatchable_type(Car)
|
|
|
|
@dispatch.dispatch_for_api(math_ops.add, {"x": Car, "y": Car})
|
|
def add_car(x, y, name=None):
|
|
with ops.name_scope(name):
|
|
return Car(x.size + y.size, x.speed + y.speed)
|
|
|
|
try:
|
|
x = Car(constant_op.constant(1), constant_op.constant(3))
|
|
y = Car(constant_op.constant(10), constant_op.constant(20))
|
|
z = math_ops.add(x, y)
|
|
self.assertAllEqual(z.size, 11)
|
|
self.assertAllEqual(z.speed, 23)
|
|
|
|
finally:
|
|
# Clean up dispatch table.
|
|
dispatch.unregister_dispatch_for(add_car)
|
|
|
|
def testTypeCheckersAreCached(self):
|
|
checker1 = dispatch.make_type_checker(int)
|
|
checker2 = dispatch.make_type_checker(int)
|
|
self.assertIs(checker1, checker2)
|
|
|
|
def testDispatchTargetWithNoNameArgument(self):
|
|
|
|
@dispatch.dispatch_for_api(math_ops.add, {
|
|
"x": MaskedTensor,
|
|
"y": MaskedTensor
|
|
})
|
|
def masked_add(x, y):
|
|
return MaskedTensor(x.values + y.values, x.mask & y.mask)
|
|
|
|
try:
|
|
x = MaskedTensor([1, 2, 3, 4, 5], [1, 0, 1, 1, 1])
|
|
y = MaskedTensor([1, 1, 1, 1, 1], [1, 1, 0, 1, 0])
|
|
|
|
# pass name w/ keyword arg
|
|
a = math_ops.add(x, y, name="MyAdd")
|
|
if not context.executing_eagerly(): # names not defined in eager mode.
|
|
self.assertRegex(a.values.name, r"^MyAdd/add.*")
|
|
self.assertRegex(a.mask.name, r"^MyAdd/and.*")
|
|
|
|
# pass name w/ positional arg
|
|
b = math_ops.add(x, y, "B")
|
|
if not context.executing_eagerly(): # names not defined in eager mode.
|
|
self.assertRegex(b.values.name, r"^B/add.*")
|
|
self.assertRegex(b.mask.name, r"^B/and.*")
|
|
|
|
# default name value
|
|
c = math_ops.add(x, y)
|
|
if not context.executing_eagerly(): # names not defined in eager mode.
|
|
self.assertRegex(c.values.name, r"^add.*")
|
|
self.assertRegex(c.mask.name, r"^and.*")
|
|
|
|
finally:
|
|
# Clean up dispatch table.
|
|
dispatch.unregister_dispatch_for(masked_add)
|
|
|
|
def testDispatchApiWithNoNameArg(self):
|
|
# Note: The "tensor_equals" API has no "name" argument.
|
|
signature = {"self": MaskedTensor, "other": MaskedTensor}
|
|
|
|
@dispatch.dispatch_for_api(math_ops.tensor_equals, signature)
|
|
def masked_tensor_equals(self, other):
|
|
del self, other
|
|
|
|
dispatch.unregister_dispatch_for(masked_tensor_equals) # clean up.
|
|
|
|
with self.assertRaisesRegex(
|
|
ValueError, r"Dispatch function's signature \(self, other, name=None\) "
|
|
r"does not match API's signature \(self, other\)\."):
|
|
|
|
@dispatch.dispatch_for_api(math_ops.tensor_equals, signature)
|
|
def masked_tensor_equals_2(self, other, name=None):
|
|
del self, other, name
|
|
|
|
del masked_tensor_equals_2 # avoid pylint unused variable warning.
|
|
|
|
def testDispatchWithIterableParams(self):
|
|
# The add_n API supports having `inputs` be an iterable (and not just
|
|
# a sequence).
|
|
@dispatch.dispatch_for_api(math_ops.add_n,
|
|
{"inputs": typing.List[MaskedTensor]})
|
|
def masked_add_n(inputs):
|
|
masks = array_ops_stack.stack([x.mask for x in inputs])
|
|
return MaskedTensor(
|
|
math_ops.add_n([x.values for x in inputs]),
|
|
math_ops.reduce_all(masks, axis=0))
|
|
|
|
try:
|
|
generator = (MaskedTensor([i], [True]) for i in range(5))
|
|
y = math_ops.add_n(generator)
|
|
self.assertAllEqual(y.values, [0 + 1 + 2 + 3 + 4])
|
|
self.assertAllEqual(y.mask, [True])
|
|
|
|
finally:
|
|
# Clean up dispatch table.
|
|
dispatch.unregister_dispatch_for(masked_add_n)
|
|
|
|
def testBadIterableParametersError(self):
|
|
fn = lambda x: [t + 1 for t in x]
|
|
with self.assertRaisesRegex(
|
|
TypeError, "iterable_parameters should be a list or tuple of string"):
|
|
dispatch.add_dispatch_support(iterable_parameters="x")(fn)
|
|
|
|
def testUnregisterDispatchTargetBadTargetError(self):
|
|
fn = lambda x: x + 1
|
|
with self.assertRaisesRegex(ValueError, "Function .* was not registered"):
|
|
dispatch.unregister_dispatch_for(fn)
|
|
|
|
def testAddDuplicateApiDisptacherError(self):
|
|
some_op = lambda x: x
|
|
some_op = dispatch.add_type_based_api_dispatcher(some_op)
|
|
with self.assertRaisesRegex(
|
|
ValueError, ".* already has a type-based API dispatcher."):
|
|
some_op = dispatch.add_type_based_api_dispatcher(some_op)
|
|
|
|
def testGetApisWithTypeBasedDispatch(self):
|
|
dispatch_apis = dispatch.apis_with_type_based_dispatch()
|
|
self.assertIn(math_ops.add, dispatch_apis)
|
|
self.assertIn(array_ops.concat, dispatch_apis)
|
|
|
|
def testTypeBasedDispatchTargetsFor(self):
|
|
MaskedTensorList = typing.List[
|
|
typing.Union[MaskedTensor, tensor_lib.Tensor]]
|
|
try:
|
|
|
|
@dispatch.dispatch_for_api(math_ops.add)
|
|
def masked_add(x: MaskedTensor, y: MaskedTensor):
|
|
del x, y
|
|
|
|
@dispatch.dispatch_for_api(array_ops.concat)
|
|
def masked_concat(values: MaskedTensorList, axis):
|
|
del values, axis
|
|
|
|
@dispatch.dispatch_for_api(math_ops.add)
|
|
def silly_add(x: SillyTensor, y: SillyTensor):
|
|
del x, y
|
|
|
|
@dispatch.dispatch_for_api(math_ops.abs)
|
|
def silly_abs(x: SillyTensor):
|
|
del x
|
|
|
|
# Note: `expected` does not contain keys or values from SillyTensor.
|
|
targets = dispatch.type_based_dispatch_signatures_for(MaskedTensor)
|
|
expected = {math_ops.add: [{"x": MaskedTensor, "y": MaskedTensor}],
|
|
array_ops.concat: [{"values": MaskedTensorList}]}
|
|
self.assertEqual(targets, expected)
|
|
|
|
finally:
|
|
# Clean up dispatch table.
|
|
dispatch.unregister_dispatch_for(masked_add)
|
|
dispatch.unregister_dispatch_for(masked_concat)
|
|
dispatch.unregister_dispatch_for(silly_add)
|
|
dispatch.unregister_dispatch_for(silly_abs)
|
|
|
|
def testDispatchForUnaryElementwiseAPIs(self):
|
|
|
|
@dispatch.dispatch_for_unary_elementwise_apis(MaskedTensor)
|
|
def unary_elementwise_api_handler(api_func, x):
|
|
return MaskedTensor(api_func(x.values), x.mask)
|
|
|
|
try:
|
|
x = MaskedTensor([1, -2, -3], [True, True, False])
|
|
# Test calls with positional & keyword argument (& combinations)
|
|
abs_x = math_ops.abs(x)
|
|
sign_x = math_ops.sign(x=x)
|
|
neg_x = math_ops.negative(x, "neg_x")
|
|
invert_x = bitwise_ops.invert(x, name="invert_x")
|
|
ones_like_x = array_ops.ones_like(x, name="ones_like_x")
|
|
ones_like_x_float = array_ops.ones_like(
|
|
x, dtypes.float32, name="ones_like_x_float")
|
|
self.assertAllEqual(abs_x.values, [1, 2, 3])
|
|
self.assertAllEqual(sign_x.values, [1, -1, -1])
|
|
self.assertAllEqual(neg_x.values, [-1, 2, 3])
|
|
self.assertAllEqual(invert_x.values, [-2, 1, 2])
|
|
self.assertAllEqual(ones_like_x.values, [1, 1, 1])
|
|
self.assertAllEqual(ones_like_x_float.values, [1., 1., 1.])
|
|
for result in [
|
|
abs_x, sign_x, neg_x, invert_x, ones_like_x, ones_like_x_float
|
|
]:
|
|
self.assertAllEqual(result.mask, [True, True, False])
|
|
if not context.executing_eagerly(): # names not defined in eager mode.
|
|
self.assertRegex(neg_x.values.name, r"^neg_x/Neg:.*")
|
|
self.assertRegex(invert_x.values.name, r"^invert_x/.*")
|
|
self.assertRegex(ones_like_x.values.name, r"^ones_like_x/.*")
|
|
self.assertRegex(ones_like_x_float.values.name,
|
|
r"^ones_like_x_float/.*")
|
|
|
|
finally:
|
|
dispatch.unregister_dispatch_for(unary_elementwise_api_handler)
|
|
|
|
def testDispatchForBinaryElementwiseAPIs(self):
|
|
|
|
@dispatch.dispatch_for_binary_elementwise_apis(MaskedTensor, MaskedTensor)
|
|
def binary_elementwise_api_handler(api_func, x, y):
|
|
return MaskedTensor(api_func(x.values, y.values), x.mask & y.mask)
|
|
|
|
try:
|
|
x = MaskedTensor([1, -2, -3], [True, True, False])
|
|
y = MaskedTensor([10, 20, 30], [True, False, True])
|
|
# Test calls with positional & keyword arguments (& combinations)
|
|
x_times_y = math_ops.multiply(x, y)
|
|
x_plus_y = math_ops.add(x, y=y)
|
|
x_minus_y = math_ops.subtract(x=x, y=y)
|
|
min_x_y = math_ops.minimum(x, y, "min_x_y")
|
|
y_times_x = math_ops.multiply(y, x, name="y_times_x")
|
|
y_plus_x = math_ops.add(y, y=x, name="y_plus_x")
|
|
y_minus_x = math_ops.subtract(x=y, y=x, name="y_minus_x")
|
|
self.assertAllEqual(x_times_y.values, [10, -40, -90])
|
|
self.assertAllEqual(x_plus_y.values, [11, 18, 27])
|
|
self.assertAllEqual(x_minus_y.values, [-9, -22, -33])
|
|
self.assertAllEqual(min_x_y.values, [1, -2, -3])
|
|
self.assertAllEqual(y_times_x.values, [10, -40, -90])
|
|
self.assertAllEqual(y_plus_x.values, [11, 18, 27])
|
|
self.assertAllEqual(y_minus_x.values, [9, 22, 33])
|
|
for result in [
|
|
x_times_y, x_plus_y, x_minus_y, min_x_y, y_times_x, y_plus_x,
|
|
y_minus_x
|
|
]:
|
|
self.assertAllEqual(result.mask, [True, False, False])
|
|
if not context.executing_eagerly(): # names not defined in eager mode.
|
|
self.assertRegex(min_x_y.values.name, r"^min_x_y/Minimum:.*")
|
|
self.assertRegex(min_x_y.mask.name, r"^min_x_y/and:.*")
|
|
self.assertRegex(y_times_x.values.name, r"^y_times_x/.*")
|
|
self.assertRegex(y_plus_x.values.name, r"^y_plus_x/.*")
|
|
self.assertRegex(y_minus_x.values.name, r"^y_minus_x/.*")
|
|
|
|
finally:
|
|
dispatch.unregister_dispatch_for(binary_elementwise_api_handler)
|
|
|
|
def testDuplicateDispatchForUnaryElementwiseAPIsError(self):
|
|
|
|
@dispatch.dispatch_for_unary_elementwise_apis(MaskedTensor)
|
|
def handler(api_func, x):
|
|
return MaskedTensor(api_func(x.values), x.mask)
|
|
|
|
try:
|
|
with self.assertRaisesRegex(
|
|
ValueError, r"A unary elementwise dispatch handler \(.*\) has "
|
|
"already been registered for .*"):
|
|
|
|
@dispatch.dispatch_for_unary_elementwise_apis(MaskedTensor)
|
|
def another_handler(api_func, x):
|
|
return MaskedTensor(api_func(x.values), ~x.mask)
|
|
|
|
del another_handler
|
|
|
|
finally:
|
|
dispatch.unregister_dispatch_for(handler)
|
|
|
|
def testDuplicateDispatchForBinaryElementwiseAPIsError(self):
|
|
|
|
@dispatch.dispatch_for_binary_elementwise_apis(MaskedTensor, MaskedTensor)
|
|
def handler(api_func, x, y):
|
|
return MaskedTensor(api_func(x.values, y.values), x.mask & y.mask)
|
|
|
|
try:
|
|
with self.assertRaisesRegex(
|
|
ValueError, r"A binary elementwise dispatch handler \(.*\) has "
|
|
"already been registered for .*"):
|
|
|
|
@dispatch.dispatch_for_binary_elementwise_apis(MaskedTensor,
|
|
MaskedTensor)
|
|
def another_handler(api_func, x, y):
|
|
return MaskedTensor(api_func(x.values, y.values), x.mask)
|
|
|
|
del another_handler
|
|
|
|
finally:
|
|
dispatch.unregister_dispatch_for(handler)
|
|
|
|
def testRegisterUnaryElementwiseApiAfterHandler(self):
|
|
# Test that it's ok to call register_unary_elementwise_api after
|
|
# dispatch_for_unary_elementwise_apis.
|
|
|
|
@dispatch.dispatch_for_unary_elementwise_apis(MaskedTensor)
|
|
def handler(api_func, x):
|
|
return MaskedTensor(api_func(x.values), x.mask)
|
|
|
|
try:
|
|
|
|
@dispatch.register_unary_elementwise_api
|
|
@dispatch.add_dispatch_support
|
|
def some_op(x):
|
|
return x * 2
|
|
|
|
x = MaskedTensor([1, 2, 3], [True, False, True])
|
|
y = some_op(x)
|
|
self.assertAllEqual(y.values, [2, 4, 6])
|
|
self.assertAllEqual(y.mask, [True, False, True])
|
|
|
|
finally:
|
|
dispatch.unregister_dispatch_for(handler)
|
|
|
|
def testRegisterBinaryElementwiseApiAfterHandler(self):
|
|
# Test that it's ok to call register_binary_elementwise_api after
|
|
# dispatch_for_binary_elementwise_apis.
|
|
|
|
@dispatch.dispatch_for_binary_elementwise_apis(MaskedTensor, MaskedTensor)
|
|
def handler(api_func, x, y):
|
|
return MaskedTensor(api_func(x.values, y.values), x.mask & y.mask)
|
|
|
|
try:
|
|
|
|
@dispatch.register_binary_elementwise_api
|
|
@dispatch.add_dispatch_support
|
|
def some_op(x, y):
|
|
return x * 2 + y
|
|
|
|
x = MaskedTensor([1, 2, 3], [True, False, True])
|
|
y = MaskedTensor([10, 20, 30], [True, True, False])
|
|
z = some_op(x, y)
|
|
self.assertAllEqual(z.values, [12, 24, 36])
|
|
self.assertAllEqual(z.mask, [True, False, False])
|
|
|
|
finally:
|
|
dispatch.unregister_dispatch_for(handler)
|
|
|
|
def testElementwiseApiLists(self):
|
|
self.assertIn(math_ops.abs, dispatch.unary_elementwise_apis())
|
|
self.assertIn(math_ops.cos, dispatch.unary_elementwise_apis())
|
|
self.assertIn(math_ops.add, dispatch.binary_elementwise_apis())
|
|
self.assertIn(math_ops.multiply, dispatch.binary_elementwise_apis())
|
|
|
|
def testUpdateDocstringsWithAPILists(self):
|
|
dispatch.update_docstrings_with_api_lists()
|
|
self.assertRegex(
|
|
dispatch.dispatch_for_api.__doc__,
|
|
r"(?s)The TensorFlow APIs that may be overridden "
|
|
r"by `@dispatch_for_api` are:.*"
|
|
r"\* `tf\.concat\(values, axis, name\)`.*"
|
|
r"\* `tf\.math\.add\(x, y, name\)`.*")
|
|
self.assertRegex(
|
|
dispatch.dispatch_for_unary_elementwise_apis.__doc__,
|
|
r"(?s)The unary elementwise APIs are:.*"
|
|
r"\* `tf\.math\.abs\(x, name\)`.*"
|
|
r"\* `tf\.math\.cos\(x, name\)`.*")
|
|
self.assertRegex(
|
|
dispatch.dispatch_for_binary_elementwise_apis.__doc__,
|
|
r"(?s)The binary elementwise APIs are:.*"
|
|
r"\* `tf\.math\.add\(x, y, name\)`.*"
|
|
r"\* `tf\.math\.multiply\(x, y, name\)`.*")
|
|
|
|
|
|
if __name__ == "__main__":
|
|
googletest.main()
|
|
|