wehub-resource-sync
100 lines
3.4 KiB
Python
100 lines
3.4 KiB
Python
# Copyright 2015 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 tensorflow.ops.embedding_ops."""
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import numpy as np
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from tensorflow.python.eager import def_function
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from tensorflow.python.framework import dtypes
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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 embedding_ops
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from tensorflow.python.ops import gradients
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from tensorflow.python.ops import math_ops
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from tensorflow.python.ops import resource_variable_ops
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from tensorflow.python.platform import googletest
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@test_util.run_all_in_graph_and_eager_modes
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class EmbeddingLookupTest(test_util.TensorFlowTestCase):
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def testEmbeddingLookupOnUninitializedVariableDoesSparseRead(self):
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x = resource_variable_ops.UninitializedVariable(
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trainable=True, shape=[3, 3], dtype=dtypes.float32)
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@def_function.function(input_signature=[])
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def _init():
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return x.assign(np.zeros([3, 3]))
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@def_function.function(input_signature=[])
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def _call():
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return embedding_ops.embedding_lookup_v2(x, [0])
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self.assertAllClose(self.evaluate(_init()), np.zeros([3, 3]))
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concrete_call = _call.get_concrete_function()
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self.assertAllClose(self.evaluate(concrete_call()), [[0., 0., 0.]])
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resource_gather_node = []
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read_var_node = []
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graph = concrete_call.graph.as_graph_def()
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for n in graph.node:
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if n.op == "ResourceGather":
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resource_gather_node.append(n)
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if n.op == "ReadVariableOp":
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read_var_node.append(n)
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for f in graph.library.function:
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for n in f.node_def:
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if n.op == "ResourceGather":
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resource_gather_node.append(n)
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if n.op == "ReadVariableOp":
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read_var_node.append(n)
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# There should be a single ResourceGather, but no ReadVariableOp
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# (dense read).
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self.assertLen(resource_gather_node, 1)
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self.assertLen(read_var_node, 0)
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def testEmbeddingLookupGradientsHaveKnownShape(self):
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x = resource_variable_ops.ResourceVariable(
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initial_value=np.zeros([3, 3]),
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trainable=True,
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shape=[3, 3],
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dtype=dtypes.float32)
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@def_function.function(input_signature=[])
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def _init():
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return x.assign(np.zeros([3, 3]))
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@def_function.function(input_signature=[])
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def _call():
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with gradients.GradientTape() as tape:
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y = embedding_ops.embedding_lookup_v2(x, [0])
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loss = math_ops.reduce_sum(y)
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grads = tape.gradient(loss, x)
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self.assertAllEqual(grads.shape, [3, 3])
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return ops.convert_to_tensor(grads)
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self.assertAllClose(self.evaluate(_init()), np.zeros([3, 3]))
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concrete_call = _call.get_concrete_function()
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self.assertAllClose(
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self.evaluate(concrete_call()),
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[[1., 1., 1.], [0., 0., 0.], [0., 0., 0.]])
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if __name__ == "__main__":
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googletest.main()
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