mirror of
https://github.com/opelly27/Stockfish.git
synced 2026-05-20 05:07:46 +00:00
Tomasz Sobczyk
766 lines
31 KiB
C++
766 lines
31 KiB
C++
#ifndef _NNUE_TRAINER_FEATURE_TRANSFORMER_H_
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#define _NNUE_TRAINER_FEATURE_TRANSFORMER_H_
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#include "trainer.h"
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#include "extra/stockfish_blas.h"
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#include "features/all_factorizers.h"
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#include "learn/learn.h"
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#include "nnue/nnue_feature_transformer.h"
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#include "thread.h"
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#include <array>
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#include <bitset>
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#include <numeric>
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#include <random>
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#include <set>
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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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// Learning: Input feature converter
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template <>
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class Trainer<FeatureTransformer> {
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private:
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// Type of layer to learn
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using LayerType = FeatureTransformer;
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public:
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template <typename T>
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friend struct AlignedDeleter;
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template <typename T, typename... ArgumentTypes>
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friend std::shared_ptr<T> make_aligned_shared_ptr(ArgumentTypes&&... arguments);
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// factory function
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static std::shared_ptr<Trainer> create(LayerType* target_layer) {
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return make_aligned_shared_ptr<Trainer>(target_layer);
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}
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// Set options such as hyperparameters
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void send_message(Message* message) {
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if (receive_message("momentum", message)) {
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momentum_ = static_cast<LearnFloatType>(std::stod(message->value));
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}
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if (receive_message("learning_rate_scale", message)) {
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learning_rate_scale_ =
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static_cast<LearnFloatType>(std::stod(message->value));
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}
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if (receive_message("reset", message)) {
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dequantize_parameters();
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}
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if (receive_message("quantize_parameters", message)) {
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quantize_parameters();
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}
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if (receive_message("clear_unobserved_feature_weights", message)) {
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clear_unobserved_feature_weights();
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}
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if (receive_message("check_health", message)) {
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check_health();
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}
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}
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// Initialize the parameters with random numbers
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template <typename RNG>
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void initialize(RNG& rng) {
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std::fill(std::begin(weights_), std::end(weights_), +kZero);
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const double kSigma = 0.1 / std::sqrt(RawFeatures::kMaxActiveDimensions);
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auto distribution = std::normal_distribution<double>(0.0, kSigma);
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for (IndexType i = 0; i < kHalfDimensions * RawFeatures::kDimensions; ++i) {
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const auto weight = static_cast<LearnFloatType>(distribution(rng));
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weights_[i] = weight;
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}
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for (IndexType i = 0; i < kHalfDimensions; ++i) {
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biases_[i] = static_cast<LearnFloatType>(0.5);
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}
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quantize_parameters();
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}
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const LearnFloatType* step_start(ThreadPool& thread_pool, const std::vector<Example>& combined_batch)
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{
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if (output_.size() < kOutputDimensions * combined_batch.size()) {
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output_.resize(kOutputDimensions * combined_batch.size());
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gradients_.resize(kOutputDimensions * combined_batch.size());
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}
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if (thread_stat_states_.size() < thread_pool.size())
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{
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thread_stat_states_.resize(thread_pool.size());
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}
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if (thread_bias_states_.size() < thread_pool.size())
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{
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thread_bias_states_.resize(thread_pool.size());
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}
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batch_ = &combined_batch;
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auto& main_thread_bias_state = thread_bias_states_[0];
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#if defined(USE_BLAS)
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cblas_sscal(
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kHalfDimensions, momentum_, main_thread_bias_state.biases_diff_, 1
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);
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#else
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Blas::sscal(
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kHalfDimensions, momentum_, main_thread_bias_state.biases_diff_, 1
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);
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#endif
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for (IndexType i = 1; i < thread_bias_states_.size(); ++i)
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thread_bias_states_[i].reset();
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return output_.data();
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}
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// forward propagation
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void propagate(Thread& th, uint64_t offset, uint64_t count) {
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auto& thread_stat_state = thread_stat_states_[th.thread_idx()];
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for (IndexType b = offset; b < offset + count; ++b)
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{
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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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);
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}
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#else
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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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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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#if defined (USE_SSE2)
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{
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static_assert(kHalfDimensions % 16 == 0, "This implementation assumes that it can process 16 floats at a time");
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auto m128_hmin_ps = [](__m128 x3210) {
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__m128 x0032 = _mm_shuffle_ps(x3210, x3210, _MM_SHUFFLE(0, 0, 3, 2));
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__m128 min_x_x_13_20 = _mm_min_ps(x3210, x0032);
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// a = [ # , # , min(x[1], x[3]) , min(x[2], x[0]) ]
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__m128 min_x_x_20_13 = _mm_shuffle_ps(min_x_x_13_20, min_x_x_13_20, _MM_SHUFFLE(0, 0, 0, 1));
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return _mm_cvtss_f32(_mm_min_ps(min_x_x_13_20, min_x_x_20_13));
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};
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auto m128_hmax_ps = [](__m128 x3210) {
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__m128 x0032 = _mm_shuffle_ps(x3210, x3210, _MM_SHUFFLE(0, 0, 3, 2));
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__m128 max_x_x_13_20 = _mm_max_ps(x3210, x0032);
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// a = [ # , # , max(x[1], x[3]) , max(x[2], x[0]) ]
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__m128 max_x_x_20_13 = _mm_shuffle_ps(max_x_x_13_20, max_x_x_13_20, _MM_SHUFFLE(0, 0, 0, 1));
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return _mm_cvtss_f32(_mm_max_ps(max_x_x_13_20, max_x_x_20_13));
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};
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const __m128 kZero4 = _mm_set1_ps(+kZero);
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const __m128 kOne4 = _mm_set1_ps(+kOne);
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__m128 min_pre_activation0 = _mm_set1_ps(thread_stat_state.min_pre_activation_);
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__m128 min_pre_activation1 = _mm_set1_ps(thread_stat_state.min_pre_activation_);
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__m128 max_pre_activation0 = _mm_set1_ps(thread_stat_state.max_pre_activation_);
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__m128 max_pre_activation1 = _mm_set1_ps(thread_stat_state.max_pre_activation_);
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for (IndexType b = offset; b < offset + count; ++b)
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{
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const IndexType batch_offset = kOutputDimensions * b;
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for (IndexType i = 0; i < kOutputDimensions; i += 16)
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{
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__m128 out0 = _mm_loadu_ps(&output_[batch_offset + i + 0]);
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__m128 out1 = _mm_loadu_ps(&output_[batch_offset + i + 4]);
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__m128 out2 = _mm_loadu_ps(&output_[batch_offset + i + 8]);
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__m128 out3 = _mm_loadu_ps(&output_[batch_offset + i + 12]);
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__m128 min01 = _mm_min_ps(out0, out1);
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__m128 min23 = _mm_min_ps(out2, out3);
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__m128 max01 = _mm_max_ps(out0, out1);
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__m128 max23 = _mm_max_ps(out2, out3);
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min_pre_activation0 = _mm_min_ps(min_pre_activation0, min01);
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min_pre_activation1 = _mm_min_ps(min_pre_activation1, min23);
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max_pre_activation0 = _mm_max_ps(max_pre_activation0, max01);
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max_pre_activation1 = _mm_max_ps(max_pre_activation1, max23);
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out0 = _mm_max_ps(kZero4, _mm_min_ps(kOne4, out0));
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out1 = _mm_max_ps(kZero4, _mm_min_ps(kOne4, out1));
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out2 = _mm_max_ps(kZero4, _mm_min_ps(kOne4, out2));
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out3 = _mm_max_ps(kZero4, _mm_min_ps(kOne4, out3));
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_mm_storeu_ps(&output_[batch_offset + i + 0], out0);
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_mm_storeu_ps(&output_[batch_offset + i + 4], out1);
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_mm_storeu_ps(&output_[batch_offset + i + 8], out2);
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_mm_storeu_ps(&output_[batch_offset + i + 12], out3);
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}
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}
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thread_stat_state.min_pre_activation_ = m128_hmin_ps(_mm_min_ps(min_pre_activation0, min_pre_activation1));
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thread_stat_state.max_pre_activation_ = m128_hmax_ps(_mm_max_ps(max_pre_activation0, max_pre_activation1));
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for (IndexType b = offset; b < offset + count; ++b)
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{
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const IndexType batch_offset = kOutputDimensions * b;
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for (IndexType half = 0; half < 2; ++half)
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{
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const IndexType half_offset = batch_offset + half * kHalfDimensions;
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for (IndexType i = 0; i < kHalfDimensions; i += 16)
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{
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const __m128 out0 = _mm_loadu_ps(&output_[i + 0 + half_offset]);
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const __m128 out1 = _mm_loadu_ps(&output_[i + 4 + half_offset]);
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const __m128 out2 = _mm_loadu_ps(&output_[i + 8 + half_offset]);
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const __m128 out3 = _mm_loadu_ps(&output_[i + 12 + half_offset]);
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__m128 minact0 = _mm_loadu_ps(&thread_stat_state.min_activations_[i + 0]);
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__m128 minact1 = _mm_loadu_ps(&thread_stat_state.min_activations_[i + 4]);
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__m128 minact2 = _mm_loadu_ps(&thread_stat_state.min_activations_[i + 8]);
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__m128 minact3 = _mm_loadu_ps(&thread_stat_state.min_activations_[i + 12]);
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__m128 maxact0 = _mm_loadu_ps(&thread_stat_state.max_activations_[i + 0]);
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__m128 maxact1 = _mm_loadu_ps(&thread_stat_state.max_activations_[i + 4]);
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__m128 maxact2 = _mm_loadu_ps(&thread_stat_state.max_activations_[i + 8]);
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__m128 maxact3 = _mm_loadu_ps(&thread_stat_state.max_activations_[i + 12]);
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minact0 = _mm_min_ps(out0, minact0);
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minact1 = _mm_min_ps(out1, minact1);
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minact2 = _mm_min_ps(out2, minact2);
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minact3 = _mm_min_ps(out3, minact3);
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maxact0 = _mm_max_ps(out0, maxact0);
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maxact1 = _mm_max_ps(out1, maxact1);
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maxact2 = _mm_max_ps(out2, maxact2);
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maxact3 = _mm_max_ps(out3, maxact3);
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_mm_storeu_ps(&thread_stat_state.min_activations_[i + 0], minact0);
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_mm_storeu_ps(&thread_stat_state.min_activations_[i + 4], minact1);
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_mm_storeu_ps(&thread_stat_state.min_activations_[i + 8], minact2);
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_mm_storeu_ps(&thread_stat_state.min_activations_[i + 12], minact3);
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_mm_storeu_ps(&thread_stat_state.max_activations_[i + 0], maxact0);
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_mm_storeu_ps(&thread_stat_state.max_activations_[i + 4], maxact1);
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_mm_storeu_ps(&thread_stat_state.max_activations_[i + 8], maxact2);
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_mm_storeu_ps(&thread_stat_state.max_activations_[i + 12], maxact3);
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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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// clipped ReLU
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for (IndexType b = offset; b < offset + count; ++b) {
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const IndexType batch_offset = kOutputDimensions * b;
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for (IndexType i = 0; i < kOutputDimensions; ++i) {
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const IndexType index = batch_offset + i;
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thread_stat_state.min_pre_activation_ = std::min(thread_stat_state.min_pre_activation_, output_[index]);
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thread_stat_state.max_pre_activation_ = std::max(thread_stat_state.max_pre_activation_, output_[index]);
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output_[index] = std::max(+kZero, std::min(+kOne, output_[index]));
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const IndexType t = i % kHalfDimensions;
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thread_stat_state.min_activations_[t] = std::min(thread_stat_state.min_activations_[t], output_[index]);
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thread_stat_state.max_activations_[t] = std::max(thread_stat_state.max_activations_[t], output_[index]);
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}
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}
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#endif
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}
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// backpropagation
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void backpropagate(Thread& th,
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const LearnFloatType* gradients,
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uint64_t offset,
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uint64_t count) {
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auto& thread_stat_state = thread_stat_states_[th.thread_idx()];
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auto& thread_bias_state = thread_bias_states_[th.thread_idx()];
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#if defined (USE_SSE2)
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{
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static_assert(kHalfDimensions % 16 == 0, "This implementation assumes that it can process 16 floats at a time");
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const __m128 kZero4 = _mm_set1_ps(+kZero);
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const __m128 kOne4 = _mm_set1_ps(+kOne);
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for (IndexType b = offset; b < offset + count; ++b)
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{
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const IndexType batch_offset = kOutputDimensions * b;
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for (IndexType i = 0; i < kOutputDimensions; i += 16)
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{
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__m128 out0 = _mm_loadu_ps(&output_[batch_offset + i + 0]);
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__m128 out1 = _mm_loadu_ps(&output_[batch_offset + i + 4]);
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__m128 out2 = _mm_loadu_ps(&output_[batch_offset + i + 8]);
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__m128 out3 = _mm_loadu_ps(&output_[batch_offset + i + 12]);
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__m128 clipped0 = _mm_or_ps(_mm_cmple_ps(out0, kZero4), _mm_cmpge_ps(out0, kOne4));
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__m128 clipped1 = _mm_or_ps(_mm_cmple_ps(out1, kZero4), _mm_cmpge_ps(out1, kOne4));
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__m128 clipped2 = _mm_or_ps(_mm_cmple_ps(out2, kZero4), _mm_cmpge_ps(out2, kOne4));
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__m128 clipped3 = _mm_or_ps(_mm_cmple_ps(out3, kZero4), _mm_cmpge_ps(out3, kOne4));
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__m128 grad0 = _mm_loadu_ps(&gradients[batch_offset + i + 0]);
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__m128 grad1 = _mm_loadu_ps(&gradients[batch_offset + i + 4]);
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__m128 grad2 = _mm_loadu_ps(&gradients[batch_offset + i + 8]);
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__m128 grad3 = _mm_loadu_ps(&gradients[batch_offset + i + 12]);
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grad0 = _mm_andnot_ps(clipped0, grad0);
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grad1 = _mm_andnot_ps(clipped1, grad1);
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grad2 = _mm_andnot_ps(clipped2, grad2);
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grad3 = _mm_andnot_ps(clipped3, grad3);
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_mm_storeu_ps(&gradients_[batch_offset + i + 0], grad0);
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_mm_storeu_ps(&gradients_[batch_offset + i + 4], grad1);
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_mm_storeu_ps(&gradients_[batch_offset + i + 8], grad2);
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_mm_storeu_ps(&gradients_[batch_offset + i + 12], grad3);
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const int clipped_mask =
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(_mm_movemask_ps(clipped0) << 0)
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| (_mm_movemask_ps(clipped1) << 4)
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| (_mm_movemask_ps(clipped2) << 8)
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| (_mm_movemask_ps(clipped3) << 12);
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thread_stat_state.num_clipped_ += popcount(clipped_mask);
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}
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}
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}
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#else
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for (IndexType b = offset; b < offset + count; ++b) {
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const IndexType batch_offset = kOutputDimensions * b;
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for (IndexType i = 0; i < kOutputDimensions; ++i) {
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const IndexType index = batch_offset + i;
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const bool clipped = (output_[index] <= kZero) | (output_[index] >= kOne);
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gradients_[index] = gradients[index] * !clipped;
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thread_stat_state.num_clipped_ += clipped;
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}
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}
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#endif
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thread_stat_state.num_total_ += count * kOutputDimensions;
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#if defined(USE_BLAS)
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for (IndexType b = offset; b < offset + count; ++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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cblas_saxpy(
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kHalfDimensions, 1.0,
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&gradients_[output_offset], 1, thread_bias_state.biases_diff_, 1
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);
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}
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}
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#else
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for (IndexType b = offset; b < offset + count; ++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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Blas::saxpy(
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kHalfDimensions, 1.0,
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&gradients_[output_offset], 1, thread_bias_state.biases_diff_, 1
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);
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}
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}
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#endif
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}
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void reduce_thread_stat_state()
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{
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for (IndexType i = 1; i < thread_stat_states_.size(); ++i)
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{
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thread_stat_states_[0] += thread_stat_states_[i];
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}
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}
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void reduce_thread_bias_state()
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{
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for (IndexType i = 1; i < thread_bias_states_.size(); ++i)
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{
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thread_bias_states_[0] += thread_bias_states_[i];
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}
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}
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void step_end(ThreadPool& thread_pool, LearnFloatType learning_rate) {
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const LearnFloatType local_learning_rate =
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learning_rate * learning_rate_scale_;
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// Since the weight matrix updates only the columns corresponding to the features that appeared in the input,
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// Correct the learning rate and adjust the scale without using momentum
|
|
const LearnFloatType effective_learning_rate =
|
|
static_cast<LearnFloatType>(local_learning_rate / (1.0 - momentum_));
|
|
|
|
reduce_thread_bias_state();
|
|
|
|
auto& main_thread_state = thread_bias_states_[0];
|
|
|
|
#if defined(USE_BLAS)
|
|
|
|
cblas_saxpy(
|
|
kHalfDimensions, -local_learning_rate,
|
|
main_thread_state.biases_diff_, 1, biases_, 1
|
|
);
|
|
|
|
#else
|
|
|
|
Blas::saxpy(
|
|
kHalfDimensions, -local_learning_rate,
|
|
main_thread_state.biases_diff_, 1, biases_, 1
|
|
);
|
|
|
|
#endif
|
|
|
|
thread_pool.execute_with_workers(
|
|
[&, num_threads = thread_pool.size()](Thread& th) {
|
|
const auto thread_index = th.thread_idx();
|
|
|
|
for (IndexType b = 0; b < batch_->size(); ++b) {
|
|
const IndexType batch_offset = kOutputDimensions * b;
|
|
for (IndexType c = 0; c < 2; ++c) {
|
|
const IndexType output_offset = batch_offset + kHalfDimensions * c;
|
|
for (const auto& feature : (*batch_)[b].training_features[c]) {
|
|
const IndexType feature_index = feature.get_index();
|
|
|
|
// We assign each bucket a continuous range of bits at least
|
|
// of cache line size to prevent false sharing.
|
|
// For HalfKP this is enough to saturate about 80 threads.
|
|
const IndexType thread_bucket =
|
|
(feature_index / BitsetType::best_concurrent_access_stride)
|
|
% num_threads;
|
|
|
|
if (thread_bucket != thread_index)
|
|
continue;
|
|
|
|
// This operation can be performed safely because
|
|
// each thread accesses a different memory location
|
|
// (even a different cache line)
|
|
observed_features.set(feature_index);
|
|
|
|
const IndexType weights_offset =
|
|
kHalfDimensions * feature_index;
|
|
|
|
const auto scale = static_cast<LearnFloatType>(
|
|
effective_learning_rate / feature.get_count());
|
|
|
|
#if defined (USE_BLAS)
|
|
|
|
cblas_saxpy(
|
|
kHalfDimensions, -scale,
|
|
&gradients_[output_offset], 1,
|
|
&weights_[weights_offset], 1
|
|
);
|
|
|
|
#else
|
|
|
|
Blas::saxpy(
|
|
kHalfDimensions, -scale,
|
|
&gradients_[output_offset], 1,
|
|
&weights_[weights_offset], 1
|
|
);
|
|
|
|
#endif
|
|
}
|
|
}
|
|
}
|
|
}
|
|
);
|
|
|
|
thread_pool.wait_for_workers_finished();
|
|
}
|
|
|
|
private:
|
|
// constructor
|
|
Trainer(LayerType* target_layer) :
|
|
batch_(nullptr),
|
|
target_layer_(target_layer),
|
|
biases_(),
|
|
weights_(),
|
|
momentum_(0.2),
|
|
learning_rate_scale_(1.0) {
|
|
|
|
dequantize_parameters();
|
|
}
|
|
|
|
// Weight saturation and parameterization
|
|
void quantize_parameters() {
|
|
for (IndexType i = 0; i < kHalfDimensions; ++i) {
|
|
target_layer_->biases_[i] =
|
|
round<typename LayerType::BiasType>(biases_[i] * kBiasScale);
|
|
}
|
|
|
|
std::vector<TrainingFeature> training_features;
|
|
|
|
Threads.for_each_index_with_workers(
|
|
0, RawFeatures::kDimensions,
|
|
[this, training_features](Thread&, int j) mutable {
|
|
training_features.clear();
|
|
Features::Factorizer<RawFeatures>::append_training_features(
|
|
j, &training_features);
|
|
|
|
for (IndexType i = 0; i < kHalfDimensions; ++i) {
|
|
double sum = 0.0;
|
|
for (const auto& feature : training_features) {
|
|
sum += weights_[kHalfDimensions * feature.get_index() + i];
|
|
}
|
|
|
|
target_layer_->weights_[kHalfDimensions * j + i] =
|
|
round<typename LayerType::WeightType>(sum * kWeightScale);
|
|
}
|
|
}
|
|
);
|
|
Threads.wait_for_workers_finished();
|
|
}
|
|
|
|
void reset_stats() {
|
|
for (auto& state : thread_stat_states_)
|
|
state.reset();
|
|
}
|
|
|
|
// read parameterized integer
|
|
void dequantize_parameters() {
|
|
for (IndexType i = 0; i < kHalfDimensions; ++i) {
|
|
biases_[i] = static_cast<LearnFloatType>(
|
|
target_layer_->biases_[i] / kBiasScale);
|
|
}
|
|
|
|
std::fill(std::begin(weights_), std::end(weights_), +kZero);
|
|
|
|
for (IndexType i = 0; i < kHalfDimensions * RawFeatures::kDimensions; ++i) {
|
|
weights_[i] = static_cast<LearnFloatType>(
|
|
target_layer_->weights_[i] / kWeightScale);
|
|
}
|
|
|
|
reset_stats();
|
|
|
|
for (auto& state : thread_bias_states_)
|
|
state.reset();
|
|
}
|
|
|
|
// Set the weight corresponding to the feature that does not appear in the learning data to 0
|
|
void clear_unobserved_feature_weights() {
|
|
for (IndexType i = 0; i < kInputDimensions; ++i) {
|
|
if (!observed_features.test(i)) {
|
|
std::fill(std::begin(weights_) + kHalfDimensions * i,
|
|
std::begin(weights_) + kHalfDimensions * (i + 1), +kZero);
|
|
}
|
|
}
|
|
|
|
quantize_parameters();
|
|
}
|
|
|
|
// Check if there are any problems with learning
|
|
void check_health() {
|
|
|
|
constexpr LearnFloatType kPreActivationLimit =
|
|
std::numeric_limits<typename LayerType::WeightType>::max() /
|
|
kWeightScale;
|
|
|
|
reduce_thread_stat_state();
|
|
|
|
auto& main_thread_state = thread_stat_states_[0];
|
|
|
|
const auto largest_min_activation = *std::max_element(
|
|
std::begin(main_thread_state.min_activations_), std::end(main_thread_state.min_activations_));
|
|
const auto smallest_max_activation = *std::min_element(
|
|
std::begin(main_thread_state.max_activations_), std::end(main_thread_state.max_activations_));
|
|
|
|
double abs_bias_sum = 0.0;
|
|
double abs_weight_sum = 0.0;
|
|
|
|
for(auto b : biases_)
|
|
abs_bias_sum += std::abs(b);
|
|
|
|
for(auto w : weights_)
|
|
abs_weight_sum += std::abs(w);
|
|
|
|
auto out = sync_region_cout.new_region();
|
|
|
|
out << "INFO (check_health):"
|
|
<< " layer " << LayerType::kLayerIndex
|
|
<< " - " << LayerType::get_name()
|
|
<< std::endl;
|
|
|
|
out << " - observed " << observed_features.count()
|
|
<< " (out of " << kInputDimensions << ") features"
|
|
<< std::endl;
|
|
|
|
out << " - (min, max) of pre-activations = "
|
|
<< main_thread_state.min_pre_activation_ << ", "
|
|
<< main_thread_state.max_pre_activation_ << " (limit = "
|
|
<< kPreActivationLimit << ")"
|
|
<< std::endl;
|
|
|
|
out << " - largest min activation = " << largest_min_activation
|
|
<< " , smallest max activation = " << smallest_max_activation
|
|
<< std::endl;
|
|
|
|
out << " - avg_abs_bias = " << abs_bias_sum / std::size(biases_) << std::endl;
|
|
out << " - avg_abs_weight = " << abs_weight_sum / std::size(weights_) << std::endl;
|
|
|
|
out << " - clipped " << static_cast<double>(main_thread_state.num_clipped_) / main_thread_state.num_total_ * 100.0 << "% of outputs"
|
|
<< std::endl;
|
|
|
|
out.unlock();
|
|
|
|
reset_stats();
|
|
}
|
|
|
|
// number of input/output dimensions
|
|
static constexpr IndexType kInputDimensions =
|
|
Features::Factorizer<RawFeatures>::get_dimensions();
|
|
static constexpr IndexType kOutputDimensions = LayerType::kOutputDimensions;
|
|
static constexpr IndexType kHalfDimensions = LayerType::kHalfDimensions;
|
|
|
|
// Coefficient used for parameterization
|
|
static constexpr LearnFloatType kActivationScale =
|
|
std::numeric_limits<std::int8_t>::max();
|
|
static constexpr LearnFloatType kBiasScale = kActivationScale;
|
|
static constexpr LearnFloatType kWeightScale = kActivationScale;
|
|
|
|
// LearnFloatType constant
|
|
static constexpr LearnFloatType kZero = static_cast<LearnFloatType>(0.0);
|
|
static constexpr LearnFloatType kOne = static_cast<LearnFloatType>(1.0);
|
|
|
|
// mini batch
|
|
const std::vector<Example>* batch_;
|
|
|
|
// layer to learn
|
|
LayerType* const target_layer_;
|
|
|
|
IndexType num_total_;
|
|
|
|
// parameter
|
|
alignas(kCacheLineSize) LearnFloatType biases_[kHalfDimensions];
|
|
alignas(kCacheLineSize)
|
|
LearnFloatType weights_[kHalfDimensions * kInputDimensions];
|
|
|
|
// Buffer used for updating parameters
|
|
std::vector<LearnFloatType, CacheLineAlignedAllocator<LearnFloatType>> gradients_;
|
|
|
|
// Forward propagation buffer
|
|
std::vector<LearnFloatType, CacheLineAlignedAllocator<LearnFloatType>> output_;
|
|
|
|
// Features that appeared in the training data
|
|
using BitsetType = LargeBitset<kInputDimensions>;
|
|
BitsetType observed_features;
|
|
|
|
// hyper parameter
|
|
LearnFloatType momentum_;
|
|
LearnFloatType learning_rate_scale_;
|
|
|
|
struct alignas(kCacheLineSize) ThreadStatState
|
|
{
|
|
alignas(kCacheLineSize) LearnFloatType min_activations_[kHalfDimensions];
|
|
alignas(kCacheLineSize) LearnFloatType max_activations_[kHalfDimensions];
|
|
LearnFloatType min_pre_activation_;
|
|
LearnFloatType max_pre_activation_;
|
|
IndexType num_clipped_;
|
|
IndexType num_total_;
|
|
|
|
ThreadStatState() { reset(); }
|
|
|
|
ThreadStatState& operator+=(const ThreadStatState& other)
|
|
{
|
|
for (IndexType i = 0; i < kHalfDimensions; ++i)
|
|
{
|
|
min_activations_[i] = std::min(min_activations_[i], other.min_activations_[i]);
|
|
}
|
|
|
|
for (IndexType i = 0; i < kHalfDimensions; ++i)
|
|
{
|
|
max_activations_[i] = std::max(max_activations_[i], other.max_activations_[i]);
|
|
}
|
|
|
|
min_pre_activation_ = std::min(min_pre_activation_, other.min_pre_activation_);
|
|
max_pre_activation_ = std::max(max_pre_activation_, other.max_pre_activation_);
|
|
|
|
num_clipped_ += other.num_clipped_;
|
|
num_total_ += other.num_total_;
|
|
|
|
return *this;
|
|
}
|
|
|
|
void reset()
|
|
{
|
|
std::fill(std::begin(min_activations_), std::end(min_activations_), std::numeric_limits<float>::max());
|
|
std::fill(std::begin(max_activations_), std::end(max_activations_), std::numeric_limits<float>::lowest());
|
|
min_pre_activation_ = std::numeric_limits<float>::max();
|
|
max_pre_activation_ = std::numeric_limits<float>::lowest();
|
|
num_clipped_ = 0;
|
|
num_total_ = 0;
|
|
}
|
|
};
|
|
|
|
struct alignas(kCacheLineSize) ThreadBiasState
|
|
{
|
|
alignas(kCacheLineSize) LearnFloatType biases_diff_[kHalfDimensions];
|
|
|
|
ThreadBiasState() { reset(); }
|
|
|
|
ThreadBiasState& operator+=(const ThreadBiasState& other)
|
|
{
|
|
for (IndexType i = 0; i < kHalfDimensions; ++i)
|
|
{
|
|
biases_diff_[i] += other.biases_diff_[i];
|
|
}
|
|
|
|
return *this;
|
|
}
|
|
|
|
void reset()
|
|
{
|
|
std::fill(std::begin(biases_diff_), std::end(biases_diff_), 0.0f);
|
|
}
|
|
};
|
|
|
|
std::vector<ThreadStatState, CacheLineAlignedAllocator<ThreadStatState>> thread_stat_states_;
|
|
std::vector<ThreadBiasState, CacheLineAlignedAllocator<ThreadBiasState>> thread_bias_states_;
|
|
};
|
|
|
|
} // namespace Eval::NNUE
|
|
|
|
#endif
|