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Cleaner and more outputs during training initialization.

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This commit is contained in:
Tomasz Sobczyk
2020-10-24 14:21:59 +02:00
committed by nodchip
parent b882423005
commit 2c477d76ec
12 changed files with 129 additions and 94 deletions
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124
src/learn/learn.cpp
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@@ -141,12 +141,12 @@ namespace Learner
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_win = " << cross_entropy_win / count << endl;
s << "==> " << prefix << "_entropy_eval = " << entropy_eval / count << endl;
s << "==> " << prefix << "_entropy_win = " << entropy_win / count << endl;
s << "==> " << prefix << "_cross_entropy = " << cross_entropy / count << endl;
s << "==> " << prefix << "_entropy = " << entropy / count << endl;
s << " - " << prefix << "_cross_entropy_eval = " << cross_entropy_eval / count << endl;
s << " - " << prefix << "_cross_entropy_win = " << cross_entropy_win / count << endl;
s << " - " << prefix << "_entropy_eval = " << entropy_eval / count << endl;
s << " - " << prefix << "_entropy_win = " << entropy_win / count << endl;
s << " - " << prefix << "_cross_entropy = " << cross_entropy / count << endl;
s << " - " << prefix << "_entropy = " << entropy / count << endl;
}
};
}
@@ -687,7 +687,7 @@ namespace Learner
<< ", epoch " << epoch
<< endl;
out << "==> learning rate = " << global_learning_rate << endl;
out << " - learning rate = " << global_learning_rate << endl;
// For calculation of verification data loss
AtomicLoss test_loss_sum{};
@@ -704,7 +704,7 @@ namespace Learner
auto& pos = th.rootPos;
StateInfo si;
pos.set(StartFEN, false, &si, &th);
out << "==> startpos eval = " << Eval::evaluate(pos) << endl;
out << " - startpos eval = " << Eval::evaluate(pos) << endl;
});
mainThread->wait_for_worker_finished();
@@ -734,8 +734,8 @@ namespace Learner
learn_loss_sum.print("learn", out);
}
out << "==> norm = " << sum_norm << endl;
out << "==> move accuracy = " << (move_accord_count * 100.0 / psv.size()) << "%" << endl;
out << " - norm = " << sum_norm << endl;
out << " - move accuracy = " << (move_accord_count * 100.0 / psv.size()) << "%" << endl;
}
else
{
@@ -852,7 +852,7 @@ namespace Learner
latest_loss_sum = 0.0;
latest_loss_count = 0;
cout << "INFO (learning_rate):" << endl;
cout << "==> loss = " << latest_loss;
cout << " - loss = " << latest_loss;
auto tot = total_done;
if (auto_lr_drop)
{
@@ -882,7 +882,7 @@ namespace Learner
if (--trials > 0 && !is_final)
{
cout
<< "==> reducing learning rate from " << global_learning_rate
<< " - reducing learning rate from " << global_learning_rate
<< " to " << (global_learning_rate * newbob_decay)
<< " (" << trials << " more trials)" << endl;
@@ -892,7 +892,7 @@ namespace Learner
if (trials == 0)
{
cout << "==> converged" << endl;
cout << " - converged" << endl;
return true;
}
}
@@ -980,6 +980,8 @@ namespace Learner
string validation_set_file_name;
string seed;
auto out = sync_region_cout.new_region();
// Assume the filenames are staggered.
while (true)
{
@@ -1083,7 +1085,7 @@ namespace Learner
else if (option == "verbose") verbose = true;
else
{
cout << "Unknown option: " << option << ". Ignoring.\n";
out << "INFO: Unknown option: " << option << ". Ignoring.\n";
}
}
@@ -1092,11 +1094,14 @@ namespace Learner
loss_output_interval = LEARN_RMSE_OUTPUT_INTERVAL * mini_batch_size;
}
cout << "learn command , ";
// If reduction_gameply is set to 0, rand(0) will be divided by 0, so correct it to 1.
reduction_gameply = max(reduction_gameply, 1);
out << "INFO: Executing learn command\n";
// Issue a warning if OpenMP is disabled.
#if !defined(_OPENMP)
cout << "Warning! OpenMP disabled." << endl;
out << "WARNING: OpenMP disabled." << endl;
#endif
// Right now we only have the individual files.
@@ -1107,65 +1112,80 @@ namespace Learner
}
rebase_files(filenames, base_dir);
cout << "learn from ";
out << "INFO: Input files:\n";
for (auto s : filenames)
cout << s << " , ";
out << " - " << s << '\n';
cout << endl;
out << "INFO: Parameters:\n";
if (!validation_set_file_name.empty())
{
cout << "validation set : " << validation_set_file_name << endl;
out << " - validation set : " << validation_set_file_name << endl;
}
cout << "base dir : " << base_dir << endl;
cout << "target dir : " << target_dir << endl;
out << " - epochs : " << epochs << endl;
out << " - epochs * minibatch size : " << epochs * mini_batch_size << endl;
out << " - eval_limit : " << eval_limit << endl;
out << " - save_only_once : " << (save_only_once ? "true" : "false") << endl;
out << " - shuffle on read : " << (no_shuffle ? "false" : "true") << endl;
cout << "epochs : " << epochs << endl;
cout << "eval_limit : " << eval_limit << endl;
cout << "save_only_once : " << (save_only_once ? "true" : "false") << endl;
cout << "no_shuffle : " << (no_shuffle ? "true" : "false") << endl;
out << " - Loss Function : " << LOSS_FUNCTION << endl;
out << " - minibatch size : " << mini_batch_size << endl;
cout << "Loss Function : " << LOSS_FUNCTION << endl;
cout << "mini-batch size : " << mini_batch_size << endl;
out << " - nn_batch_size : " << nn_batch_size << endl;
out << " - nn_options : " << nn_options << endl;
cout << "nn_batch_size : " << nn_batch_size << endl;
cout << "nn_options : " << nn_options << endl;
out << " - learning rate : " << global_learning_rate << endl;
out << " - use draws in training : " << use_draw_games_in_training << endl;
out << " - use draws in validation : " << use_draw_games_in_validation << endl;
out << " - skip repeated positions : " << skip_duplicated_positions_in_training << endl;
cout << "learning rate : " << global_learning_rate << endl;
cout << "use_draw_games_in_training : " << use_draw_games_in_training << endl;
cout << "use_draw_games_in_validation : " << use_draw_games_in_validation << endl;
cout << "skip_duplicated_positions_in_training : " << skip_duplicated_positions_in_training << endl;
out << " - winning prob coeff : " << winning_probability_coefficient << endl;
out << " - use_wdl : " << use_wdl << endl;
if (newbob_decay != 1.0) {
cout << "scheduling : newbob with decay = " << newbob_decay
<< ", " << newbob_num_trials << " trials" << endl;
out << " - src_score_min_value : " << src_score_min_value << endl;
out << " - src_score_max_value : " << src_score_max_value << endl;
out << " - dest_score_min_value : " << dest_score_min_value << endl;
out << " - dest_score_max_value : " << dest_score_max_value << endl;
out << " - reduction_gameply : " << reduction_gameply << endl;
out << " - LAMBDA : " << ELMO_LAMBDA << endl;
out << " - LAMBDA2 : " << ELMO_LAMBDA2 << endl;
out << " - LAMBDA_LIMIT : " << ELMO_LAMBDA_LIMIT << endl;
out << " - eval_save_interval : " << eval_save_interval << " sfens" << endl;
out << " - loss_output_interval : " << loss_output_interval << " sfens" << endl;
out << " - sfen_read_size : " << sfen_read_size << endl;
out << " - thread_buffer_size : " << thread_buffer_size << endl;
out << " - seed : " << seed << endl;
out << " - verbose : " << (verbose ? "true" : "false") << endl;
if (auto_lr_drop) {
out << " - learning rate scheduling : every " << auto_lr_drop << " sfens" << endl;
}
else if (newbob_decay != 1.0) {
out << " - learning rate scheduling : newbob with decay" << endl;
out << " - newbob_decay : " << newbob_decay << endl;
out << " - newbob_num_trials : " << newbob_num_trials << endl;
}
else {
cout << "scheduling : default" << endl;
out << " - learning rate scheduling : fixed learning rate" << endl;
}
// If reduction_gameply is set to 0, rand(0) will be divided by 0, so correct it to 1.
reduction_gameply = max(reduction_gameply, 1);
cout << "reduction_gameply : " << reduction_gameply << endl;
cout << "LAMBDA : " << ELMO_LAMBDA << endl;
cout << "LAMBDA2 : " << ELMO_LAMBDA2 << endl;
cout << "LAMBDA_LIMIT : " << ELMO_LAMBDA_LIMIT << endl;
cout << "eval_save_interval : " << eval_save_interval << " sfens" << endl;
cout << "loss_output_interval: " << loss_output_interval << " sfens" << endl;
out << endl;
// -----------------------------------
// various initialization
// -----------------------------------
cout << "init.." << endl;
out << "INFO: Started initialization." << endl;
Threads.main()->ponder = false;
set_learning_search_limits();
cout << "init_training.." << endl;
Eval::NNUE::initialize_training(seed);
Eval::NNUE::initialize_training(seed, out);
Eval::NNUE::set_batch_size(nn_batch_size);
Eval::NNUE::set_options(nn_options);
@@ -1204,7 +1224,9 @@ namespace Learner
learn_think.verbose = verbose;
cout << "init done." << endl;
out << "Finished initialization." << endl;
out.unlock();
// Start learning.
learn_think.learn(epochs);