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Removed EVAL_NNUE macro.

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nodchip
2020-09-08 22:45:29 +09:00
parent 41b7674aee
commit a6013557f2
31 changed files with 65 additions and 223 deletions
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133
src/learn/learner.cpp
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@@ -17,45 +17,40 @@
// → I will not be involved in the engine because it is a problem that the GUI should assist.
// etc..
#define EVAL_LEARN
#if defined(EVAL_LEARN)
#include "../eval/evaluate_common.h"
#include "../misc.h"
#include "../nnue/evaluate_nnue_learner.h"
#include "../position.h"
#include "../syzygy/tbprobe.h"
#include "../thread.h"
#include "../tt.h"
#include "../uci.h"
#include "learn.h"
#include "multi_think.h"
#include "../uci.h"
#include "../syzygy/tbprobe.h"
#include "../misc.h"
#include "../thread.h"
#include "../position.h"
#include "../tt.h"
#include <chrono>
#include <random>
#include <regex>
#include <sstream>
#include <fstream>
#include <unordered_set>
#include <iomanip>
#include <list>
#include <climits>
#include <cmath> // std::exp(),std::pow(),std::log()
#include <cstring> // memcpy()
#include <memory>
#include <limits>
#include <optional>
#include <filesystem>
#include <fstream>
#include <iomanip>
#include <limits>
#include <list>
#include <memory>
#include <optional>
#include <random>
#include <regex>
#include <shared_mutex>
#include <sstream>
#include <unordered_set>
#if defined (_OPENMP)
#include <omp.h>
#endif
#if defined(EVAL_NNUE)
#include "../nnue/evaluate_nnue_learner.h"
#include <climits>
#include <shared_mutex>
#endif
using namespace std;
@@ -724,14 +719,12 @@ namespace Learner
learn_sum_entropy = 0.0;
#endif
#if defined(EVAL_NNUE)
newbob_scale = 1.0;
newbob_decay = 1.0;
newbob_num_trials = 2;
best_loss = std::numeric_limits<double>::infinity();
latest_loss_sum = 0.0;
latest_loss_count = 0;
#endif
}
virtual void thread_worker(size_t thread_id);
@@ -787,7 +780,6 @@ namespace Learner
atomic<double> learn_sum_entropy;
#endif
#if defined(EVAL_NNUE)
shared_timed_mutex nn_mutex;
double newbob_scale;
double newbob_decay;
@@ -796,7 +788,6 @@ namespace Learner
double latest_loss_sum;
uint64_t latest_loss_count;
std::string best_nn_directory;
#endif
uint64_t eval_save_interval;
uint64_t loss_output_interval;
@@ -844,13 +835,10 @@ namespace Learner
// It doesn't matter if you have disabled the substitution table.
TT.new_search();
#if defined(EVAL_NNUE)
std::cout << "PROGRESS: " << now_string() << ", ";
std::cout << sr.total_done << " sfens";
std::cout << ", iteration " << epoch;
std::cout << ", eta = " << Eval::get_eta() << ", ";
#endif
#if !defined(LOSS_FUNCTION_IS_ELMO_METHOD)
double sum_error = 0;
@@ -1009,10 +997,8 @@ namespace Learner
#endif
#if defined(LOSS_FUNCTION_IS_ELMO_METHOD)
#if defined(EVAL_NNUE)
latest_loss_sum += test_sum_cross_entropy - test_sum_entropy;
latest_loss_count += sr.sfen_for_mse.size();
#endif
// learn_cross_entropy may be called train cross
// entropy in the world of machine learning,
@@ -1074,14 +1060,10 @@ namespace Learner
// display mse (this is sometimes done only for thread 0)
// Immediately after being read from the file...
#if defined(EVAL_NNUE)
// Lock the evaluation function so that it is not used during updating.
shared_lock<shared_timed_mutex> read_lock(nn_mutex, defer_lock);
if (sr.next_update_weights <= sr.total_done ||
(thread_id != 0 && !read_lock.try_lock()))
#else
if (sr.next_update_weights <= sr.total_done)
#endif
{
if (thread_id != 0)
{
@@ -1105,18 +1087,6 @@ namespace Learner
continue;
}
#if !defined(EVAL_NNUE)
// Output the current time. Output every time.
std::cout << sr.total_done << " sfens , at " << now_string() << std::endl;
// Reflect the gradient in the weight array at this timing.
// The calculation of the gradient is just right for
// each 1M phase in terms of mini-batch.
Eval::update_weights(epoch, freeze);
// Display epoch and current eta for debugging.
std::cout << "epoch = " << epoch << " , eta = " << Eval::get_eta() << std::endl;
#else
{
// update parameters
@@ -1124,7 +1094,7 @@ namespace Learner
lock_guard<shared_timed_mutex> write_lock(nn_mutex);
Eval::NNUE::UpdateParameters(epoch);
}
#endif
++epoch;
// However, the elapsed time during update_weights() and calc_rmse() is ignored.
@@ -1156,9 +1126,7 @@ namespace Learner
// loss calculation
calc_loss(thread_id, done);
#if defined(EVAL_NNUE)
Eval::NNUE::CheckHealth();
#endif
// Make a note of how far you have totaled.
sr.last_done = sr.total_done;
@@ -1216,25 +1184,6 @@ namespace Learner
goto RETRY_READ;
}
#if !defined(EVAL_NNUE)
if (skip_duplicated_positions_in_training)
{
const auto key = pos.key();
// Exclude the phase used for rmse calculation.
if (sr.is_for_rmse(key))
goto RETRY_READ;
// Exclude the most recently used aspect.
const auto hash_index = size_t(key & (sr.READ_SFEN_HASH_SIZE - 1));
const auto key2 = sr.hash[hash_index];
if (key == key2)
goto RETRY_READ;
sr.hash[hash_index] = key; // Replace with the current key.
}
#endif
// There is a possibility that all the pieces are blocked and stuck.
// Also, the declaration win phase is excluded from
// learning because you cannot go to leaf with PV moves.
@@ -1326,25 +1275,9 @@ namespace Learner
learn_sum_entropy += learn_entropy;
#endif
#if !defined(EVAL_NNUE)
// Slope
double dj_dw = calc_grad(deep_value, shallow_value, ps);
// Add jd_dw as the gradient (∂J/∂Wj) for the
// feature vector currently appearing in the leaf node.
// If it is not PV termination, apply a discount rate.
if (discount_rate != 0 && ply != (int)pv.size())
dj_dw *= discount_rate;
// Since we have reached leaf, add the gradient to the features that appear in this phase.
// Update based on gradient later.
Eval::add_grad(pos, rootColor, dj_dw, freeze);
#else
const double example_weight =
(discount_rate != 0 && ply != (int)pv.size()) ? discount_rate : 1.0;
Eval::NNUE::AddExample(pos, rootColor, ps, example_weight);
#endif
// Since the processing is completed, the counter of the processed number is incremented
sr.total_done++;
@@ -1425,7 +1358,6 @@ namespace Learner
const std::string dir_name = std::to_string(dir_number++);
Eval::save_eval(dir_name);
#if defined(EVAL_NNUE)
if (newbob_decay != 1.0 && latest_loss_count > 0) {
static int trials = newbob_num_trials;
const double latest_loss = latest_loss_sum / latest_loss_count;
@@ -1470,7 +1402,6 @@ namespace Learner
return true;
}
}
#endif
}
return false;
}
@@ -1817,12 +1748,10 @@ namespace Learner
// Optional item that does not let you learn KK/KKP/KPP/KPPP
array<bool, 4> freeze = {};
#if defined(EVAL_NNUE)
uint64_t nn_batch_size = 1000;
double newbob_decay = 1.0;
int newbob_num_trials = 2;
string nn_options;
#endif
uint64_t eval_save_interval = LEARN_EVAL_SAVE_INTERVAL;
uint64_t loss_output_interval = 0;
@@ -1922,12 +1851,11 @@ namespace Learner
else if (option == "save_only_once") save_only_once = true;
else if (option == "no_shuffle") no_shuffle = true;
#if defined(EVAL_NNUE)
else if (option == "nn_batch_size") is >> nn_batch_size;
else if (option == "newbob_decay") is >> newbob_decay;
else if (option == "newbob_num_trials") is >> newbob_num_trials;
else if (option == "nn_options") is >> nn_options;
#endif
else if (option == "eval_save_interval") is >> eval_save_interval;
else if (option == "loss_output_interval") is >> loss_output_interval;
else if (option == "mirror_percentage") is >> mirror_percentage;
@@ -2074,23 +2002,18 @@ namespace Learner
}
}
#if !defined(EVAL_NNUE)
cout << "Gradient Method : " << LEARN_UPDATE << endl;
#endif
cout << "Loss Function : " << LOSS_FUNCTION << endl;
cout << "mini-batch size : " << mini_batch_size << endl;
#if defined(EVAL_NNUE)
cout << "nn_batch_size : " << nn_batch_size << endl;
cout << "nn_options : " << nn_options << endl;
#endif
cout << "learning rate : " << eta1 << " , " << eta2 << " , " << eta3 << endl;
cout << "eta_epoch : " << eta1_epoch << " , " << eta2_epoch << 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;
#if defined(EVAL_NNUE)
if (newbob_decay != 1.0) {
cout << "scheduling : newbob with decay = " << newbob_decay
<< ", " << newbob_num_trials << " trials" << endl;
@@ -2098,7 +2021,6 @@ namespace Learner
else {
cout << "scheduling : default" << endl;
}
#endif
cout << "discount rate : " << discount_rate << endl;
@@ -2133,12 +2055,6 @@ namespace Learner
// Read evaluation function parameters
Eval::init_NNUE();
#if !defined(EVAL_NNUE)
cout << "init_grad.." << endl;
// Initialize gradient array of merit function parameters
Eval::init_grad(eta1, eta1_epoch, eta2, eta2_epoch, eta3);
#else
cout << "init_training.." << endl;
Eval::NNUE::InitializeTraining(eta1, eta1_epoch, eta2, eta2_epoch, eta3);
Eval::NNUE::SetBatchSize(nn_batch_size);
@@ -2146,7 +2062,6 @@ namespace Learner
if (newbob_decay != 1.0 && !Options["SkipLoadingEval"]) {
learn_think.best_nn_directory = std::string(Options["EvalDir"]);
}
#endif
#if 0
// A test to give a gradient of 1.0 to the initial stage of Hirate.
@@ -2170,11 +2085,9 @@ namespace Learner
learn_think.freeze = freeze;
learn_think.reduction_gameply = reduction_gameply;
#if defined(EVAL_NNUE)
learn_think.newbob_scale = 1.0;
learn_think.newbob_decay = newbob_decay;
learn_think.newbob_num_trials = newbob_num_trials;
#endif
learn_think.eval_save_interval = eval_save_interval;
learn_think.loss_output_interval = loss_output_interval;
@@ -2199,7 +2112,6 @@ namespace Learner
// Calculate rmse once at this point (timing of 0 sfen)
// sr.calc_rmse();
#if defined(EVAL_NNUE)
if (newbob_decay != 1.0) {
learn_think.calc_loss(0, -1);
learn_think.best_loss = learn_think.latest_loss_sum / learn_think.latest_loss_count;
@@ -2207,7 +2119,6 @@ namespace Learner
learn_think.latest_loss_count = 0;
cout << "initial loss: " << learn_think.best_loss << endl;
}
#endif
// -----------------------------------
// start learning evaluation function parameters