5e9107066d
After inlining, we convert control dependencies to the function call to depend on all control outputs. Although read variable ops doesn't have side effect itself, it must be executed before variable updates after the function call. Note that it's not enough to convert control dependencies to the function call to depend on both control outputs and data outputs of the function call. The read variable ops can be inputs to ops that have side effects, e.g. assert and print, which are not the function data outputs. PiperOrigin-RevId: 338391537 Change-Id: Ibc843c1a584088e54010685ebce678b047f7d94d
112 lines
3.6 KiB
Python
112 lines
3.6 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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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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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 control_flow_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.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 = control_flow_ops.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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