wehub-resource-sync
96 lines
3.4 KiB
Python
96 lines
3.4 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 asynchronous compilation on the CPU and GPU devices."""
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import os
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from tensorflow.core.protobuf import config_pb2
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from tensorflow.python.client import session as session_lib
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from tensorflow.python.framework import dtypes
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from tensorflow.python.framework import function
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from tensorflow.python.framework import ops
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from tensorflow.python.ops import array_ops
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from tensorflow.python.ops import math_ops
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from tensorflow.python.platform import test
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def RunMetadataLabels(run_metadata):
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"""Returns all labels in run_metadata."""
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labels = []
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for dev_stats in run_metadata.step_stats.dev_stats:
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for node_stats in dev_stats.node_stats:
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labels.append(node_stats.timeline_label)
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return labels
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def InLabels(labels, substr):
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"""Returns true iff one of the labels contains substr."""
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return any(substr in x for x in labels)
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def MetadataHasXlaRunOp(run_metadata):
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"""Returns true if there are XlaRun kernels in run_metadata's timeline."""
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# TODO(phawkins): find a less hacky way to test whether a kernel ran.
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return InLabels(RunMetadataLabels(run_metadata), "_XlaRun")
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class AsyncCompilationTest(test.TestCase):
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# Asynchrobnous compilation uses the existing fallback path and existing
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# compiler. This test only tests that asynchronous compilation is performed.
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def testAsyncCompilationJit(self):
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@function.Defun(compiled=True)
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def CompiledFunction(x):
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return math_ops.log(x)
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with session_lib.Session() as sess:
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x = array_ops.placeholder(dtypes.float32)
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y = CompiledFunction(x)
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run_metadata = config_pb2.RunMetadata()
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sess.run(
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y,
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feed_dict={x: [0.] * 60},
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run_metadata=run_metadata,
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options=config_pb2.RunOptions(
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trace_level=config_pb2.RunOptions.FULL_TRACE))
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# For The first iteration, the fall back path is chosen.
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hasXlaRunOp = MetadataHasXlaRunOp(run_metadata)
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self.assertFalse(hasXlaRunOp)
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# Execute the session until after asynchronous compilation is finished
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# and the compiled cluster has been executed once.
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while (not hasXlaRunOp):
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run_metadata = config_pb2.RunMetadata()
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sess.run(
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y,
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feed_dict={x: [0.] * 60},
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run_metadata=run_metadata,
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options=config_pb2.RunOptions(
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trace_level=config_pb2.RunOptions.FULL_TRACE))
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hasXlaRunOp = MetadataHasXlaRunOp(run_metadata)
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if __name__ == "__main__":
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os.environ["TF_XLA_FLAGS"] = ("--tf_xla_async_compilation=true " +
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"--tf_xla_enable_lazy_compilation=true " +
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os.environ.get("TF_XLA_FLAGS", ""))
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# This test is using Tensorflow sessions which are not compatible with eager
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# mode.
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ops.disable_eager_execution()
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test.main()
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