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Align trainer arrays to cache line.

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This commit is contained in:
Tomasz Sobczyk
2020-10-27 19:37:19 +01:00
committed by nodchip
parent 8fac468259
commit f1e96cab55
5 changed files with 17 additions and 17 deletions
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12
src/nnue/trainer/trainer_affine_transform.h
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@@ -353,18 +353,18 @@ namespace Eval::NNUE {
LayerType* const target_layer_;
// parameter
LearnFloatType biases_[kOutputDimensions];
LearnFloatType weights_[kOutputDimensions * kInputDimensions];
alignas(kCacheLineSize) LearnFloatType biases_[kOutputDimensions];
alignas(kCacheLineSize) LearnFloatType weights_[kOutputDimensions * kInputDimensions];
// Buffer used for updating parameters
LearnFloatType biases_diff_[kOutputDimensions];
LearnFloatType weights_diff_[kOutputDimensions * kInputDimensions];
alignas(kCacheLineSize) LearnFloatType biases_diff_[kOutputDimensions];
alignas(kCacheLineSize) LearnFloatType weights_diff_[kOutputDimensions * kInputDimensions];
// Forward propagation buffer
std::vector<LearnFloatType> output_;
std::vector<LearnFloatType, CacheLineAlignedAllocator<LearnFloatType>> output_;
// buffer for back propagation
std::vector<LearnFloatType> gradients_;
std::vector<LearnFloatType, CacheLineAlignedAllocator<LearnFloatType>> gradients_;
// hyper parameter
LearnFloatType momentum_;

4
src/nnue/trainer/trainer_clipped_relu.h
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@@ -149,10 +149,10 @@ namespace Eval::NNUE {
LayerType* const target_layer_;
// Forward propagation buffer
std::vector<LearnFloatType> output_;
std::vector<LearnFloatType, CacheLineAlignedAllocator<LearnFloatType>> output_;
// buffer for back propagation
std::vector<LearnFloatType> gradients_;
std::vector<LearnFloatType, CacheLineAlignedAllocator<LearnFloatType>> gradients_;
// Health check statistics
LearnFloatType min_activations_[kOutputDimensions];

10
src/nnue/trainer/trainer_feature_transformer.h
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@@ -421,11 +421,11 @@ namespace Eval::NNUE {
LearnFloatType weights_[kHalfDimensions * kInputDimensions];
// Buffer used for updating parameters
LearnFloatType biases_diff_[kHalfDimensions];
std::vector<LearnFloatType> gradients_;
alignas(kCacheLineSize) LearnFloatType biases_diff_[kHalfDimensions];
std::vector<LearnFloatType, CacheLineAlignedAllocator<LearnFloatType>> gradients_;
// Forward propagation buffer
std::vector<LearnFloatType> output_;
std::vector<LearnFloatType, CacheLineAlignedAllocator<LearnFloatType>> output_;
// Features that appeared in the training data
std::bitset<kInputDimensions> observed_features;
@@ -437,8 +437,8 @@ namespace Eval::NNUE {
// Health check statistics
LearnFloatType min_pre_activation_;
LearnFloatType max_pre_activation_;
LearnFloatType min_activations_[kHalfDimensions];
LearnFloatType max_activations_[kHalfDimensions];
alignas(kCacheLineSize) LearnFloatType min_activations_[kHalfDimensions];
alignas(kCacheLineSize) LearnFloatType max_activations_[kHalfDimensions];
};
} // namespace Eval::NNUE

6
src/nnue/trainer/trainer_input_slice.h
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@@ -163,7 +163,7 @@ namespace Eval::NNUE {
const LearnFloatType* output_;
// buffer for back propagation
std::vector<LearnFloatType> gradients_;
std::vector<LearnFloatType, CacheLineAlignedAllocator<LearnFloatType>> gradients_;
};
// Learning: Input layer
@@ -256,10 +256,10 @@ namespace Eval::NNUE {
const std::shared_ptr<SharedInputTrainer> shared_input_trainer_;
// Forward propagation buffer
std::vector<LearnFloatType> output_;
std::vector<LearnFloatType, CacheLineAlignedAllocator<LearnFloatType>> output_;
// buffer for back propagation
std::vector<LearnFloatType> gradients_;
std::vector<LearnFloatType, CacheLineAlignedAllocator<LearnFloatType>> gradients_;
};
} // namespace Eval::NNUE

2
src/nnue/trainer/trainer_sum.h
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@@ -184,7 +184,7 @@ namespace Eval::NNUE {
LayerType* const target_layer_;
// Forward propagation buffer
std::vector<LearnFloatType> output_;
std::vector<LearnFloatType, CacheLineAlignedAllocator<LearnFloatType>> output_;
};
} // namespace Eval::NNUE