wehub-resource-sync
108 lines
3.5 KiB
Python
108 lines
3.5 KiB
Python
# Copyright 2017 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 Grappler Constant Folding."""
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import numpy as np
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from tensorflow.python.eager import backprop
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from tensorflow.python.eager import context
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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 array_ops
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from tensorflow.python.ops import functional_ops
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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.ops import while_loop
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from tensorflow.python.platform import test
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class ConstantFoldingTest(test.TestCase):
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# See b/76008022.
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def testScanInsideWhile(self):
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def loop_cond(idx_step, *unused_args):
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return idx_step < 1
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def loop_body(idx_step, y):
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x = array_ops.zeros([10, 20, 30], dtype=dtypes.float32)
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x = functional_ops.scan(
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math_ops.add,
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x,
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initializer=array_ops.zeros([20, 30], dtype=dtypes.float32),
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back_prop=False,
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parallel_iterations=1)
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with ops.device('/cpu:0'):
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y = array_ops.identity(x)
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return idx_step + 1, y
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if test.is_gpu_available(cuda_only=True):
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init_y = array_ops.zeros([10, 20, 30], dtype=dtypes.float32)
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_, y = while_loop.while_loop(
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loop_cond,
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loop_body,
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loop_vars=[0, init_y],
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back_prop=False,
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parallel_iterations=1)
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y_v = self.evaluate(y)
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self.assertAllEqual(np.zeros([10, 20, 30]), y_v)
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# See b/159753857.
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def testGradientGraphOptimization(self):
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@def_function.function
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def f(x, y):
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with backprop.GradientTape() as tape:
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z = math_ops.mul(x, array_ops.zeros_like(x))
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l = math_ops.add(z, y)
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l = math_ops.reduce_sum(l)
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gx, gy = tape.gradient(l, [x, y])
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x.assign_add(gx)
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y.assign_add(gy)
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return x + y
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# XLA completely optimizes away the variable reads and
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# assignments, so skip the test.
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if test_util.is_xla_enabled():
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self.skipTest('Not relevant for XLA')
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with context.eager_mode():
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x = resource_variable_ops.ResourceVariable(
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np.random.uniform(size=[2, 2]), dtype=dtypes.float32)
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y = resource_variable_ops.ResourceVariable(
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np.random.uniform(size=[2, 2]), dtype=dtypes.float32)
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with context.collect_graphs(optimized=True) as graphs:
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f(x, y).numpy()
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self.assertLen(graphs, 1)
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assign_count = 0
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for node in graphs[0].node:
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if node.op == 'AssignAddVariableOp':
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self.assertEqual(node.input[0], 'y')
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assign_count += 1
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# Make sure that the only variable update that remains after
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# grappler optimization is that of y.
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self.assertEqual(assign_count, 1)
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self.assertLen(graphs[0].node, 11)
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if __name__ == '__main__':
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test.main()
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