9df9d06e27
- Just `compile` is a Python built-in, and is already overloaded by the Keras `compile` method. - `jit` is closer to other ML frameworks. - Technically speaking, `jit=True` is not self-descriptive (it does not specify what is it doing just-in-time) - Moreover, `tf.function` by itself without compilation could be described as a JIT. - Also `jit` by itself is less grep'able. - Thus `@tf.function(jit_compile=True)` is the preferred spelling. PiperOrigin-RevId: 340503501 Change-Id: I7bffe60aca69be6640390f6e6c4af40c6c4dbfda
79 lines
3.1 KiB
Python
79 lines
3.1 KiB
Python
# Copyright 2020 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 Collective Operations with XLA."""
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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.core.protobuf import config_pb2
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from tensorflow.core.protobuf import rewriter_config_pb2
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from tensorflow.python.eager import def_function
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from tensorflow.python.framework import constant_op
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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 collective_ops
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from tensorflow.python.platform import test
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class CollectiveOpXlaTest(test.TestCase):
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def testScopedAllocatorWithXla(self):
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group_size = 2
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group_key = 1
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instance_key1 = 1
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instance_key2 = 2
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tensor_size = 10
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graph_options = config_pb2.GraphOptions(
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optimizer_options=config_pb2.OptimizerOptions(
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do_constant_folding=False))
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cfg = config_pb2.ConfigProto(device_count={'CPU': group_size},
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graph_options=graph_options)
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rewrite_options = cfg.graph_options.rewrite_options
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rewrite_options.scoped_allocator_optimization = (
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rewriter_config_pb2.RewriterConfig.ON)
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del rewrite_options.scoped_allocator_opts.enable_op[:]
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rewrite_options.scoped_allocator_opts.enable_op.append('CollectiveReduce')
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# Tests that execute collectives need to be enclosed in graph or tf.function
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with ops.Graph().as_default(), self.session(config=cfg) as sess:
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run_ops = []
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for i in range(group_size):
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with ops.device('CPU:%d' % i):
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tensor_val = [i + 1.] * tensor_size
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constant = constant_op.constant(tensor_val)
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@def_function.function(jit_compile=True)
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def f(x):
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return 2 * x + 1
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input_tensor1 = array_ops.identity(f(constant))
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input_tensor2 = array_ops.identity(f(constant))
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reduced_tensor1 = collective_ops.all_reduce(
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input_tensor1, group_size, group_key, instance_key1, 'Add', 'Id')
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reduced_tensor2 = collective_ops.all_reduce(
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input_tensor2, group_size, group_key, instance_key2, 'Add', 'Id')
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run_ops.append(array_ops.identity(reduced_tensor1))
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run_ops.append(array_ops.identity(reduced_tensor2))
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results = sess.run(run_ops)
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for result in results:
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for result_val in result:
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self.assertEqual(result_val, 8.)
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if __name__ == '__main__':
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test.main()
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