193 lines
6.8 KiB
Python
193 lines
6.8 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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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.framework import ops
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from tensorflow.python.framework import test_util
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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.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.util import deprecation
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from tensorflow.python.util import dispatch
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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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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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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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class TensorTracerOpDispatcher(dispatch.GlobalOpDispatcher):
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"""Global op dispatcher for TensorTracer."""
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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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return TensorTracer(op.__name__, args, kwargs)
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def is_tensor_tracer_arg(self, value):
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if isinstance(value, TensorTracer):
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return True
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if isinstance(value, (list, tuple)):
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if any(isinstance(x, TensorTracer) for x in value):
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return True
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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.add._tf_dispatchers[:]
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# Override the behavior of gen_math_ops.add.
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@dispatch.dispatch_for_types(gen_math_ops.add, CustomTensor)
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def custom_add(x, y, name=None): # pylint: disable=unused-variable
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return CustomTensor(gen_math_ops.add(x.tensor, y.tensor, name),
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(x.score+y.score) / 2.0)
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self.assertEqual(len(math_ops.add._tf_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.add(x, y)
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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 = [4, 5, 6]
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a_plus_b = gen_math_ops.add(a, b)
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self.assertAllEqual(a_plus_b, [5, 7, 9])
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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.add(a, None)
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# Clean up
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gen_math_ops.add._tf_dispatchers = original_handlers
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def testAddDispatchForTypes_With_PythonOp(self):
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original_handlers = test_op._tf_dispatchers[:]
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@dispatch.dispatch_for_types(test_op, CustomTensor)
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def override_for_test_op(x, y, z): # pylint: disable=unused-variable
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return CustomTensor(test_op(x.tensor, y.tensor, z.tensor),
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(x.score + y.score + z.score) / 3.0)
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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_dispatchers = original_handlers
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def testDispatchForTypes_SignatureMismatch(self):
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with self.assertRaisesRegexp(AssertionError, "The decorated function's "
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"signature must exactly match.*"):
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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(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.assertRaisesRegexp(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.assertRegexpMatches(
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message,
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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), "reduce_sum(add(name=None, x=abs(x), y=y), axis=3)")
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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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if __name__ == "__main__":
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googletest.main()
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