Add specialization of method Array::FillRandom() for complex64 type.
Fix comments. PiperOrigin-RevId: 285447090 Change-Id: I6a328bedbdd19f7892a6cbe2dbde42067e252880
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@ -522,6 +522,7 @@ cc_library(
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cc_library(
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name = "array",
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srcs = ["array.cc"],
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hdrs = ["array.h"],
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deps = [
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":status",
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32
tensorflow/compiler/xla/array.cc
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32
tensorflow/compiler/xla/array.cc
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@ -0,0 +1,32 @@
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/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
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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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http://www.apache.org/licenses/LICENSE-2.0
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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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#include "tensorflow/compiler/xla/array.h"
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namespace xla {
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// Specialization of FillRandom() method for complex64 type. Uses real part of
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// the stddev parameter as the standard deviation value.
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template <>
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void Array<complex64>::FillRandom(const complex64& stddev, const double mean,
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const int seed) {
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std::mt19937 g(seed);
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std::normal_distribution<double> distribution(mean, std::real(stddev));
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for (int64 i = 0; i < num_elements(); ++i) {
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values_[i] = complex64(distribution(g), distribution(g));
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}
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}
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} // namespace xla
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@ -575,6 +575,12 @@ class Array {
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std::unique_ptr<T[]> values_;
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};
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// Specialization of FillRandom() method for complex64 type. Uses real part of
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// the stddev parameter as the standard deviation value.
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template <>
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void Array<complex64>::FillRandom(const complex64& stddev, const double mean,
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const int seed);
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} // namespace xla
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#endif // TENSORFLOW_COMPILER_XLA_ARRAY_H_
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@ -1103,8 +1103,7 @@ class HloConvolutionInstruction : public HloInstruction {
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void set_feature_group_count(int64 num_feature_groups) {
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feature_group_count_ = num_feature_groups;
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}
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// The number of feature groups. Must be a divisor of the input batch
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// dimension.
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// The number of batch groups. Must be a divisor of the input batch dimension.
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int64 batch_group_count() const { return batch_group_count_; }
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void set_batch_group_count(int64 num_batch_groups) {
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batch_group_count_ = num_batch_groups;
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@ -1138,8 +1137,7 @@ class HloConvolutionInstruction : public HloInstruction {
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// The number of feature groups. Must be a divisor of the input feature
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// dimension and output feature dimension.
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int64 feature_group_count_;
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// The number of feature groups. Must be a divisor of the input batch
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// dimension.
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// The number of batch groups. Must be a divisor of the input batch dimension.
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int64 batch_group_count_;
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// Describes the window used for a convolution.
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Window window_;
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