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Properly apply gradient norm clipping after it's scaled in the update_parameters.

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This commit is contained in:
Tomasz Sobczyk
2020-11-30 20:39:46 +01:00
committed by nodchip
parent de675e3503
commit 256c4b55ec
3 changed files with 10 additions and 8 deletions
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11
src/learn/learn.cpp
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@@ -95,8 +95,6 @@ namespace Learner
static double elmo_lambda_high = 1.0;
static double elmo_lambda_limit = 32000;
static double max_grad = 1.0;
// Using stockfish's WDL with win rate model instead of sigmoid
static bool use_wdl = false;
@@ -280,7 +278,7 @@ namespace Learner
calculate_lambda(teacher_signal)
);
return loss_.eval(args).clamp_grad(max_grad);
return loss_.eval(args);
}
static auto get_loss(
@@ -334,6 +332,7 @@ namespace Learner
bool smart_fen_skipping = false;
double learning_rate = 1.0;
double max_grad = 1.0;
string validation_set_file_name;
string seed;
@@ -651,7 +650,7 @@ namespace Learner
// should be no real issues happening since
// the read/write phases are isolated.
atomic_thread_fence(memory_order_seq_cst);
Eval::NNUE::update_parameters(Threads, epoch, params.verbose, params.learning_rate, get_loss);
Eval::NNUE::update_parameters(Threads, epoch, params.verbose, params.learning_rate, params.max_grad, get_loss);
atomic_thread_fence(memory_order_seq_cst);
if (++save_count * params.mini_batch_size >= params.eval_save_interval)
@@ -985,6 +984,7 @@ namespace Learner
// learning rate
else if (option == "lr") is >> params.learning_rate;
else if (option == "max_grad") is >> params.max_grad;
// Accept also the old option name.
else if (option == "use_draw_in_training"
@@ -1012,7 +1012,6 @@ namespace Learner
else if (option == "lambda") is >> elmo_lambda_low;
else if (option == "lambda2") is >> elmo_lambda_high;
else if (option == "lambda_limit") is >> elmo_lambda_limit;
else if (option == "max_grad") is >> max_grad;
else if (option == "reduction_gameply") is >> params.reduction_gameply;
@@ -1100,6 +1099,7 @@ namespace Learner
out << " - nn_options : " << nn_options << endl;
out << " - learning rate : " << params.learning_rate << endl;
out << " - max_grad : " << params.max_grad << endl;
out << " - use draws in training : " << params.use_draw_games_in_training << endl;
out << " - use draws in validation : " << params.use_draw_games_in_validation << endl;
out << " - skip repeated positions : " << params.skip_duplicated_positions_in_training << endl;
@@ -1117,7 +1117,6 @@ namespace Learner
out << " - elmo_lambda_low : " << elmo_lambda_low << endl;
out << " - elmo_lambda_high : " << elmo_lambda_high << endl;
out << " - elmo_lambda_limit : " << elmo_lambda_limit << endl;
out << " - max_grad : " << max_grad << endl;
out << " - eval_save_interval : " << params.eval_save_interval << " sfens" << endl;
out << " - loss_output_interval : " << params.loss_output_interval << " sfens" << endl;

6
src/nnue/evaluate_nnue_learner.cpp
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@@ -195,6 +195,7 @@ namespace Eval::NNUE {
uint64_t epoch,
bool verbose,
double learning_rate,
double max_grad,
Learner::CalcLossFunc calc_loss)
{
using namespace Learner::Autograd::UnivariateStatic;
@@ -237,8 +238,9 @@ namespace Eval::NNUE {
const auto discrete = e.sign * e.discrete_nn_eval;
const auto& psv = e.psv;
const auto loss = calc_loss(shallow, (Value)psv.score, psv.game_result, psv.gamePly);
const double gradient = loss.grad * e.sign * kPonanzaConstant;
gradients[b] = static_cast<LearnFloatType>(gradient * e.weight);
const double gradient = std::clamp(
loss.grad * e.sign * kPonanzaConstant * e.weight, -max_grad, max_grad);
gradients[b] = static_cast<LearnFloatType>(gradient);
// The discrete eval will only be valid before first backpropagation,

1
src/nnue/evaluate_nnue_learner.h
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@@ -38,6 +38,7 @@ namespace Eval::NNUE {
uint64_t epoch,
bool verbose,
double learning_rate,
double max_grad,
Learner::CalcLossFunc calc_loss);
// Check if there are any problems with learning