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PascalCase -> snake_case for consistency with the rest of the codebase.

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This commit is contained in:
Tomasz Sobczyk
2020-10-19 18:37:23 +09:00
committed by nodchip
parent 2398d34e87
commit 146a6b056e
37 changed files with 844 additions and 737 deletions
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src/nnue/trainer/trainer_affine_transform.h
+22 -22
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@@ -21,7 +21,7 @@ namespace Eval::NNUE {
public:
// factory function
static std::shared_ptr<Trainer> Create(
static std::shared_ptr<Trainer> create(
LayerType* target_layer, FeatureTransformer* ft) {
return std::shared_ptr<Trainer>(
@@ -29,31 +29,31 @@ namespace Eval::NNUE {
}
// Set options such as hyperparameters
void SendMessage(Message* message) {
previous_layer_trainer_->SendMessage(message);
void send_message(Message* message) {
previous_layer_trainer_->send_message(message);
if (ReceiveMessage("momentum", message)) {
if (receive_message("momentum", message)) {
momentum_ = static_cast<LearnFloatType>(std::stod(message->value));
}
if (ReceiveMessage("learning_rate_scale", message)) {
if (receive_message("learning_rate_scale", message)) {
learning_rate_scale_ =
static_cast<LearnFloatType>(std::stod(message->value));
}
if (ReceiveMessage("reset", message)) {
DequantizeParameters();
if (receive_message("reset", message)) {
dequantize_parameters();
}
if (ReceiveMessage("quantize_parameters", message)) {
QuantizeParameters();
if (receive_message("quantize_parameters", message)) {
quantize_parameters();
}
}
// Initialize the parameters with random numbers
template <typename RNG>
void Initialize(RNG& rng) {
previous_layer_trainer_->Initialize(rng);
void initialize(RNG& rng) {
previous_layer_trainer_->initialize(rng);
if (kIsOutputLayer) {
// Initialize output layer with 0
@@ -80,18 +80,18 @@ namespace Eval::NNUE {
}
}
QuantizeParameters();
quantize_parameters();
}
// forward propagation
const LearnFloatType* Propagate(const std::vector<Example>& batch) {
const LearnFloatType* propagate(const std::vector<Example>& batch) {
if (output_.size() < kOutputDimensions * batch.size()) {
output_.resize(kOutputDimensions * batch.size());
gradients_.resize(kInputDimensions * batch.size());
}
batch_size_ = static_cast<IndexType>(batch.size());
batch_input_ = previous_layer_trainer_->Propagate(batch);
batch_input_ = previous_layer_trainer_->propagate(batch);
#if defined(USE_BLAS)
for (IndexType b = 0; b < batch_size_; ++b) {
const IndexType batch_offset = kOutputDimensions * b;
@@ -123,7 +123,7 @@ namespace Eval::NNUE {
}
// backpropagation
void Backpropagate(const LearnFloatType* gradients,
void backpropagate(const LearnFloatType* gradients,
LearnFloatType learning_rate) {
const LearnFloatType local_learning_rate =
@@ -206,7 +206,7 @@ namespace Eval::NNUE {
}
#endif
previous_layer_trainer_->Backpropagate(gradients_.data(), learning_rate);
previous_layer_trainer_->backpropagate(gradients_.data(), learning_rate);
}
private:
@@ -214,7 +214,7 @@ namespace Eval::NNUE {
Trainer(LayerType* target_layer, FeatureTransformer* ft) :
batch_size_(0),
batch_input_(nullptr),
previous_layer_trainer_(Trainer<PreviousLayer>::Create(
previous_layer_trainer_(Trainer<PreviousLayer>::create(
&target_layer->previous_layer_, ft)),
target_layer_(target_layer),
biases_(),
@@ -224,11 +224,11 @@ namespace Eval::NNUE {
momentum_(0.2),
learning_rate_scale_(1.0) {
DequantizeParameters();
dequantize_parameters();
}
// Weight saturation and parameterization
void QuantizeParameters() {
void quantize_parameters() {
for (IndexType i = 0; i < kOutputDimensions * kInputDimensions; ++i) {
weights_[i] = std::max(-kMaxWeightMagnitude,
std::min(+kMaxWeightMagnitude, weights_[i]));
@@ -236,7 +236,7 @@ namespace Eval::NNUE {
for (IndexType i = 0; i < kOutputDimensions; ++i) {
target_layer_->biases_[i] =
Round<typename LayerType::BiasType>(biases_[i] * kBiasScale);
round<typename LayerType::BiasType>(biases_[i] * kBiasScale);
}
for (IndexType i = 0; i < kOutputDimensions; ++i) {
@@ -244,14 +244,14 @@ namespace Eval::NNUE {
const auto padded_offset = LayerType::kPaddedInputDimensions * i;
for (IndexType j = 0; j < kInputDimensions; ++j) {
target_layer_->weights_[padded_offset + j] =
Round<typename LayerType::WeightType>(
round<typename LayerType::WeightType>(
weights_[offset + j] * kWeightScale);
}
}
}
// read parameterized integer
void DequantizeParameters() {
void dequantize_parameters() {
for (IndexType i = 0; i < kOutputDimensions; ++i) {
biases_[i] = static_cast<LearnFloatType>(
target_layer_->biases_[i] / kBiasScale);