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Synchronize printed info regions in the learner and sfen reader.

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This commit is contained in:
Tomasz Sobczyk
2020-10-24 13:34:10 +02:00
committed by nodchip
parent d824bd8ec5
commit 4b72658409
5 changed files with 66 additions and 45 deletions
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src/learn/learn.cpp
+21 -15
View File
@@ -138,7 +138,8 @@ namespace Learner
count = 0.0; count = 0.0;
} }
void print(const std::string& prefix, ostream& s) const template <typename StreamT>
void print(const std::string& prefix, StreamT& s) const
{ {
s << "==> " << prefix << "_cross_entropy_eval = " << cross_entropy_eval / count << endl; s << "==> " << prefix << "_cross_entropy_eval = " << cross_entropy_eval / count << endl;
s << "==> " << prefix << "_cross_entropy_win = " << cross_entropy_win / count << endl; s << "==> " << prefix << "_cross_entropy_win = " << cross_entropy_win / count << endl;
@@ -499,8 +500,9 @@ namespace Learner
if (validation_set_file_name.empty() if (validation_set_file_name.empty()
&& sfen_for_mse.size() != sfen_for_mse_size) && sfen_for_mse.size() != sfen_for_mse_size)
{ {
cout auto out = sync_region_cout.new_region();
<< "Error reading sfen_for_mse. Read " << sfen_for_mse.size() out
<< "INFO (learn): Error reading sfen_for_mse. Read " << sfen_for_mse.size()
<< " out of " << sfen_for_mse_size << '\n'; << " out of " << sfen_for_mse_size << '\n';
return; return;
@@ -514,7 +516,8 @@ namespace Learner
latest_loss_sum = 0.0; latest_loss_sum = 0.0;
latest_loss_count = 0; latest_loss_count = 0;
cout << "initial loss: " << best_loss << endl; auto out = sync_region_cout.new_region();
out << "INFO (learn): initial loss = " << best_loss << endl;
} }
stop_flag = false; stop_flag = false;
@@ -585,7 +588,8 @@ namespace Learner
if (pos.set_from_packed_sfen(ps.sfen, &si, &th) != 0) if (pos.set_from_packed_sfen(ps.sfen, &si, &th) != 0)
{ {
// Malformed sfen // Malformed sfen
cout << "Error! : illigal packed sfen = " << pos.fen() << endl; auto out = sync_region_cout.new_region();
out << "ERROR: illigal packed sfen = " << pos.fen() << endl;
goto RETRY_READ; goto RETRY_READ;
} }
@@ -674,14 +678,16 @@ namespace Learner
TT.new_search(); TT.new_search();
TimePoint elapsed = now() - Search::Limits.startTime + 1; TimePoint elapsed = now() - Search::Limits.startTime + 1;
cout << "\n"; auto out = sync_region_cout.new_region();
cout << "PROGRESS (calc_loss): " << now_string()
out << "\n";
out << "PROGRESS (calc_loss): " << now_string()
<< ", " << total_done << " sfens" << ", " << total_done << " sfens"
<< ", " << total_done * 1000 / elapsed << " sfens/second" << ", " << total_done * 1000 / elapsed << " sfens/second"
<< ", epoch " << epoch << ", epoch " << epoch
<< endl; << endl;
cout << "==> learning rate = " << global_learning_rate << endl; out << "==> learning rate = " << global_learning_rate << endl;
// For calculation of verification data loss // For calculation of verification data loss
AtomicLoss test_loss_sum{}; AtomicLoss test_loss_sum{};
@@ -694,11 +700,11 @@ namespace Learner
atomic<int> move_accord_count{0}; atomic<int> move_accord_count{0};
auto mainThread = Threads.main(); auto mainThread = Threads.main();
mainThread->execute_with_worker([](auto& th){ mainThread->execute_with_worker([&out](auto& th){
auto& pos = th.rootPos; auto& pos = th.rootPos;
StateInfo si; StateInfo si;
pos.set(StartFEN, false, &si, &th); pos.set(StartFEN, false, &si, &th);
cout << "==> startpos eval = " << Eval::evaluate(pos) << endl; out << "==> startpos eval = " << Eval::evaluate(pos) << endl;
}); });
mainThread->wait_for_worker_finished(); mainThread->wait_for_worker_finished();
@@ -721,19 +727,19 @@ namespace Learner
if (psv.size() && test_loss_sum.count > 0.0) if (psv.size() && test_loss_sum.count > 0.0)
{ {
test_loss_sum.print("test", cout); test_loss_sum.print("test", out);
if (learn_loss_sum.count > 0.0) if (learn_loss_sum.count > 0.0)
{ {
learn_loss_sum.print("learn", cout); learn_loss_sum.print("learn", out);
} }
cout << "==> norm = " << sum_norm << endl; out << "==> norm = " << sum_norm << endl;
cout << "==> move accuracy = " << (move_accord_count * 100.0 / psv.size()) << "%" << endl; out << "==> move accuracy = " << (move_accord_count * 100.0 / psv.size()) << "%" << endl;
} }
else else
{ {
cout << "Error! : psv.size() = " << psv.size() << " , done = " << test_loss_sum.count << endl; out << "ERROR: psv.size() = " << psv.size() << " , done = " << test_loss_sum.count << endl;
} }
learn_loss_sum.reset(); learn_loss_sum.reset();
src/learn/sfen_reader.h
+8 -5
View File
@@ -209,18 +209,19 @@ namespace Learner{
sfen_input_stream = open_sfen_input_file(currentFilename); sfen_input_stream = open_sfen_input_file(currentFilename);
auto out = sync_region_cout.new_region();
if (sfen_input_stream == nullptr) if (sfen_input_stream == nullptr)
{ {
std::cout << "INFO (sfen_reader): File does not exist: " << currentFilename << '\n'; out << "INFO (sfen_reader): File does not exist: " << currentFilename << '\n';
} }
else else
{ {
std::cout << "INFO (sfen_reader): Opened file for reading: " << currentFilename << '\n'; out << "INFO (sfen_reader): Opened file for reading: " << currentFilename << '\n';
// in case the file is empty or was deleted. // in case the file is empty or was deleted.
if (sfen_input_stream->eof()) if (sfen_input_stream->eof())
{ {
std::cout << "INFO (sfen_reader): File empty, nothing to read.\n"; out << "==> File empty, nothing to read.\n";
} }
else else
{ {
@@ -232,7 +233,8 @@ namespace Learner{
if (sfen_input_stream == nullptr && !open_next_file()) if (sfen_input_stream == nullptr && !open_next_file())
{ {
std::cout << "INFO (sfen_reader): End of files." << std::endl; auto out = sync_region_cout.new_region();
out << "INFO (sfen_reader): End of files." << std::endl;
end_of_files = true; end_of_files = true;
return; return;
} }
@@ -271,7 +273,8 @@ namespace Learner{
if(!open_next_file()) if(!open_next_file())
{ {
// There was no next file. Abort. // There was no next file. Abort.
std::cout << "INFO (sfen_reader): End of files." << std::endl; auto out = sync_region_cout.new_region();
out << "INFO (sfen_reader): End of files." << std::endl;
end_of_files = true; end_of_files = true;
return; return;
} }
src/nnue/evaluate_nnue_learner.cpp
+3 -1
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@@ -224,7 +224,9 @@ namespace Eval::NNUE {
const double avg_abs_eval_diff = abs_eval_diff_sum / batch_size; const double avg_abs_eval_diff = abs_eval_diff_sum / batch_size;
const double avg_abs_discrete_eval = abs_discrete_eval_sum / batch_size; const double avg_abs_discrete_eval = abs_discrete_eval_sum / batch_size;
std::cout << "INFO (update_parameters):" auto out = sync_region_cout.new_region();
out << "INFO (update_parameters):"
<< " epoch = " << epoch << " epoch = " << epoch
<< " , avg_abs(trainer_eval-nnue_eval) = " << avg_abs_eval_diff << " , avg_abs(trainer_eval-nnue_eval) = " << avg_abs_eval_diff
<< " , avg_abs(nnue_eval) = " << avg_abs_discrete_eval << " , avg_abs(nnue_eval) = " << avg_abs_discrete_eval
src/nnue/trainer/trainer_clipped_relu.h
+12 -7
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@@ -94,19 +94,24 @@ namespace Eval::NNUE {
// Check if there are any problems with learning // Check if there are any problems with learning
void check_health() { void check_health() {
const auto largest_min_activation = *std::max_element( const auto largest_min_activation = *std::max_element(
std::begin(min_activations_), std::end(min_activations_)); std::begin(min_activations_), std::end(min_activations_));
const auto smallest_max_activation = *std::min_element( const auto smallest_max_activation = *std::min_element(
std::begin(max_activations_), std::end(max_activations_)); std::begin(max_activations_), std::end(max_activations_));
std::cout << "INFO (check_health):" auto out = sync_region_cout.new_region();
<< " layer " << LayerType::kLayerIndex
<< " - " << LayerType::get_name()
<< std::endl;
std::cout << "==> largest min activation = " << largest_min_activation out << "INFO (check_health):"
<< " , smallest max activation = " << smallest_max_activation << " layer " << LayerType::kLayerIndex
<< std::endl; << " - " << LayerType::get_name()
<< std::endl;
out << "==> largest min activation = " << largest_min_activation
<< " , smallest max activation = " << smallest_max_activation
<< std::endl;
out.unlock();
std::fill(std::begin(min_activations_), std::end(min_activations_), std::fill(std::begin(min_activations_), std::end(min_activations_),
std::numeric_limits<LearnFloatType>::max()); std::numeric_limits<LearnFloatType>::max());
src/nnue/trainer/trainer_feature_transformer.h
+22 -17
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@@ -330,33 +330,38 @@ namespace Eval::NNUE {
// Check if there are any problems with learning // Check if there are any problems with learning
void check_health() { void check_health() {
std::cout << "INFO (check_health):"
<< " layer " << LayerType::kLayerIndex
<< " - " << LayerType::get_name()
<< std::endl;
std::cout << "==> observed " << observed_features.count()
<< " (out of " << kInputDimensions << ") features"
<< std::endl;
constexpr LearnFloatType kPreActivationLimit = constexpr LearnFloatType kPreActivationLimit =
std::numeric_limits<typename LayerType::WeightType>::max() / std::numeric_limits<typename LayerType::WeightType>::max() /
kWeightScale; kWeightScale;
std::cout << "==> (min, max) of pre-activations = "
<< min_pre_activation_ << ", "
<< max_pre_activation_ << " (limit = "
<< kPreActivationLimit << ")"
<< std::endl;
const auto largest_min_activation = *std::max_element( const auto largest_min_activation = *std::max_element(
std::begin(min_activations_), std::end(min_activations_)); std::begin(min_activations_), std::end(min_activations_));
const auto smallest_max_activation = *std::min_element( const auto smallest_max_activation = *std::min_element(
std::begin(max_activations_), std::end(max_activations_)); std::begin(max_activations_), std::end(max_activations_));
std::cout << "==> largest min activation = " << largest_min_activation auto out = sync_region_cout.new_region();
<< " , smallest max activation = " << smallest_max_activation
<< std::endl; 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 = "
<< min_pre_activation_ << ", "
<< max_pre_activation_ << " (limit = "
<< kPreActivationLimit << ")"
<< std::endl;
out << "==> largest min activation = " << largest_min_activation
<< " , smallest max activation = " << smallest_max_activation
<< std::endl;
out.unlock();
std::fill(std::begin(min_activations_), std::end(min_activations_), std::fill(std::begin(min_activations_), std::end(min_activations_),
std::numeric_limits<LearnFloatType>::max()); std::numeric_limits<LearnFloatType>::max());