mirror of
https://github.com/HChaZZY/Stockfish.git
synced 2025-12-24 19:16:49 +08:00
Replace omp in trainer_feature_transformer
This commit is contained in:
Tomasz Sobczyk
nodchip
@@ -19,10 +19,6 @@
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#include <random>
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#include <set>
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#if defined(_OPENMP)
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#include <omp.h>
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#endif
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// Specialization for feature transformer of learning class template of NNUE evaluation function
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namespace Eval::NNUE {
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@@ -104,44 +100,45 @@ namespace Eval::NNUE {
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batch_ = &batch;
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// affine transform
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#pragma omp parallel for
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for (IndexType b = 0; b < batch.size(); ++b) {
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const IndexType batch_offset = kOutputDimensions * b;
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for (IndexType c = 0; c < 2; ++c) {
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const IndexType output_offset = batch_offset + kHalfDimensions * c;
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thread_pool.for_each_index_with_workers(
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0, batch.size(),
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[&](Thread&, int b) {
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const IndexType batch_offset = kOutputDimensions * b;
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for (IndexType c = 0; c < 2; ++c) {
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const IndexType output_offset = batch_offset + kHalfDimensions * c;
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#if defined(USE_BLAS)
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cblas_scopy(
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kHalfDimensions, biases_, 1, &output_[output_offset], 1
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);
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for (const auto& feature : batch[b].training_features[c]) {
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const IndexType weights_offset = kHalfDimensions * feature.get_index();
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cblas_saxpy(
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kHalfDimensions, (float)feature.get_count(),
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&weights_[weights_offset], 1, &output_[output_offset], 1
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cblas_scopy(
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kHalfDimensions, biases_, 1, &output_[output_offset], 1
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);
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}
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for (const auto& feature : batch[b].training_features[c]) {
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const IndexType weights_offset = kHalfDimensions * feature.get_index();
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cblas_saxpy(
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kHalfDimensions, (float)feature.get_count(),
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&weights_[weights_offset], 1, &output_[output_offset], 1
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);
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}
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#else
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Blas::scopy(
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thread_pool,
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kHalfDimensions, biases_, 1, &output_[output_offset], 1
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);
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for (const auto& feature : batch[b].training_features[c]) {
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const IndexType weights_offset = kHalfDimensions * feature.get_index();
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Blas::saxpy(
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thread_pool,
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kHalfDimensions, (float)feature.get_count(),
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&weights_[weights_offset], 1, &output_[output_offset], 1
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Blas::scopy(
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kHalfDimensions, biases_, 1, &output_[output_offset], 1
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);
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}
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for (const auto& feature : batch[b].training_features[c]) {
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const IndexType weights_offset = kHalfDimensions * feature.get_index();
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Blas::saxpy(
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kHalfDimensions, (float)feature.get_count(),
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&weights_[weights_offset], 1, &output_[output_offset], 1
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);
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}
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#endif
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}
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}
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}
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);
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thread_pool.wait_for_workers_finished();
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#if defined (USE_SSE2)
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@@ -358,6 +355,7 @@ namespace Eval::NNUE {
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cblas_sscal(
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kHalfDimensions, momentum_, biases_diff_, 1
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);
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for (IndexType b = 0; b < batch_->size(); ++b) {
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const IndexType batch_offset = kOutputDimensions * b;
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for (IndexType c = 0; c < 2; ++c) {
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@@ -374,36 +372,6 @@ namespace Eval::NNUE {
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biases_diff_, 1, biases_, 1
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);
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#pragma omp parallel
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{
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#if defined(_OPENMP)
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const IndexType num_threads = omp_get_num_threads();
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const IndexType thread_index = omp_get_thread_num();
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#endif
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for (IndexType b = 0; b < batch_->size(); ++b) {
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const IndexType batch_offset = kOutputDimensions * b;
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for (IndexType c = 0; c < 2; ++c) {
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const IndexType output_offset = batch_offset + kHalfDimensions * c;
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for (const auto& feature : (*batch_)[b].training_features[c]) {
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#if defined(_OPENMP)
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if (feature.get_index() % num_threads != thread_index)
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continue;
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#endif
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const IndexType weights_offset =
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kHalfDimensions * feature.get_index();
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const auto scale = static_cast<LearnFloatType>(
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effective_learning_rate / feature.get_count());
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cblas_saxpy(
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kHalfDimensions, -scale,
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&gradients_[output_offset], 1,
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&weights_[weights_offset], 1
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);
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}
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}
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}
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}
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#else
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Blas::sscal(
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@@ -429,38 +397,47 @@ namespace Eval::NNUE {
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biases_diff_, 1, biases_, 1
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);
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#pragma omp parallel
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{
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#if defined(_OPENMP)
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const IndexType num_threads = omp_get_num_threads();
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const IndexType thread_index = omp_get_thread_num();
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#endif
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for (IndexType b = 0; b < batch_->size(); ++b) {
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const IndexType batch_offset = kOutputDimensions * b;
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for (IndexType c = 0; c < 2; ++c) {
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const IndexType output_offset = batch_offset + kHalfDimensions * c;
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for (const auto& feature : (*batch_)[b].training_features[c]) {
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#if defined(_OPENMP)
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if (feature.get_index() % num_threads != thread_index)
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continue;
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#endif
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const IndexType weights_offset =
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kHalfDimensions * feature.get_index();
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const auto scale = static_cast<LearnFloatType>(
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effective_learning_rate / feature.get_count());
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Blas::saxpy(
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thread_pool,
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kHalfDimensions, -scale,
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&gradients_[output_offset], 1,
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&weights_[weights_offset], 1
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);
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thread_pool.execute_with_workers(
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[&, num_threads = thread_pool.size()](Thread& th) {
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const auto thread_index = th.thread_idx();
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for (IndexType b = 0; b < batch_->size(); ++b) {
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const IndexType batch_offset = kOutputDimensions * b;
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for (IndexType c = 0; c < 2; ++c) {
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const IndexType output_offset = batch_offset + kHalfDimensions * c;
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for (const auto& feature : (*batch_)[b].training_features[c]) {
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if (feature.get_index() % num_threads != thread_index)
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continue;
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const IndexType weights_offset =
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kHalfDimensions * feature.get_index();
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const auto scale = static_cast<LearnFloatType>(
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effective_learning_rate / feature.get_count());
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#if defined (USE_BLAS)
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cblas_saxpy(
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kHalfDimensions, -scale,
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&gradients_[output_offset], 1,
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&weights_[weights_offset], 1
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);
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#else
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Blas::saxpy(
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kHalfDimensions, -scale,
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&gradients_[output_offset], 1,
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&weights_[weights_offset], 1
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);
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#endif
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}
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}
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}
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}
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}
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);
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#endif
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for (IndexType b = 0; b < batch_->size(); ++b) {
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for (IndexType c = 0; c < 2; ++c) {
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for (const auto& feature : (*batch_)[b].training_features[c]) {
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@@ -468,6 +445,8 @@ namespace Eval::NNUE {
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}
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}
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}
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thread_pool.wait_for_workers_finished();
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}
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private:
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@@ -493,22 +472,25 @@ namespace Eval::NNUE {
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std::vector<TrainingFeature> training_features;
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#pragma omp parallel for private(training_features)
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for (IndexType j = 0; j < RawFeatures::kDimensions; ++j) {
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training_features.clear();
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Features::Factorizer<RawFeatures>::append_training_features(
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j, &training_features);
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Threads.for_each_index_with_workers(
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0, RawFeatures::kDimensions,
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[this, training_features](Thread&, int j) mutable {
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training_features.clear();
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Features::Factorizer<RawFeatures>::append_training_features(
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j, &training_features);
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for (IndexType i = 0; i < kHalfDimensions; ++i) {
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double sum = 0.0;
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for (const auto& feature : training_features) {
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sum += weights_[kHalfDimensions * feature.get_index() + i];
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for (IndexType i = 0; i < kHalfDimensions; ++i) {
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double sum = 0.0;
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for (const auto& feature : training_features) {
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sum += weights_[kHalfDimensions * feature.get_index() + i];
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}
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target_layer_->weights_[kHalfDimensions * j + i] =
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round<typename LayerType::WeightType>(sum * kWeightScale);
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}
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target_layer_->weights_[kHalfDimensions * j + i] =
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round<typename LayerType::WeightType>(sum * kWeightScale);
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}
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}
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);
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Threads.wait_for_workers_finished();
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}
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void reset_stats() {
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