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3f73c40412b82969da319482355bc2f9bef4688b
Stockfish/src/learn/learn.cpp
Tomasz Sobczyk 3f73c40412 More deterministic move accuracy validation.
2020-12-24 10:16:59 +09:00

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// Learning routines:
//
// 1) Automatic generation of game records in .bin format
// → "gensfen" command
//
// 2) Learning evaluation function parameters from the generated .bin files
// → "learn" command
//
// → Shuffle in the teacher phase is also an extension of this command.
// Example) "learn shuffle"
//
// 3) Automatic generation of fixed traces
// → "makebook think" command
// → implemented in extra/book/book.cpp
//
// 4) Post-station automatic review mode
// → I will not be involved in the engine because it is a problem that the GUI should assist.
// etc..
#include "learn.h"
#include "autograd.h"
#include "sfen_reader.h"
#include "misc.h"
#include "position.h"
#include "thread.h"
#include "tt.h"
#include "uci.h"
#include "search.h"
#include "timeman.h"
#include "nnue/evaluate_nnue.h"
#include "nnue/evaluate_nnue_learner.h"
#include "syzygy/tbprobe.h"
#include <chrono>
#include <climits>
#include <cmath> // std::exp(),std::pow(),std::log()
#include <cstring> // memcpy()
#include <fstream>
#include <iomanip>
#include <limits>
#include <list>
#include <memory>
#include <optional>
#include <random>
#include <regex>
#include <shared_mutex>
#include <sstream>
#include <unordered_set>
#include <iostream>
#include <map>
#include <algorithm>
#if defined (_OPENMP)
#include <omp.h>
#endif
using namespace std;
template <typename T>
T operator +=(std::atomic<T>& x, const T rhs)
{
T old = x.load(std::memory_order_consume);
// It is allowed that the value is rewritten from other thread at this timing.
// The idea that the value is not destroyed is good.
T desired = old + rhs;
while (!x.compare_exchange_weak(old, desired, std::memory_order_release, std::memory_order_consume))
desired = old + rhs;
return desired;
}
template <typename T>
T operator -= (std::atomic<T>& x, const T rhs) { return x += -rhs; }
namespace Learner
{
static double winning_probability_coefficient = 1.0 / PawnValueEg / 4.0 * std::log(10.0);
// Score scale factors. ex) If we set src_score_min_value = 0.0,
// src_score_max_value = 1.0, dest_score_min_value = 0.0,
// dest_score_max_value = 10000.0, [0.0, 1.0] will be scaled to [0, 10000].
static double src_score_min_value = 0.0;
static double src_score_max_value = 1.0;
static double dest_score_min_value = 0.0;
static double dest_score_max_value = 1.0;
// A constant used in elmo (WCSC27). Adjustment required.
// Since elmo does not internally divide the expression, the value is different.
// You can set this value with the learn command.
// 0.33 is equivalent to the constant (0.5) used in elmo (WCSC27)
static double elmo_lambda_low = 1.0;
static double elmo_lambda_high = 1.0;
static double elmo_lambda_limit = 32000;
// Using stockfish's WDL with win rate model instead of sigmoid
static bool use_wdl = false;
static void append_files_from_dir(
std::vector<std::string>& filenames,
const std::string& base_dir,
const std::string& target_dir)
{
string kif_base_dir = Path::combine(base_dir, target_dir);
sys::path p(kif_base_dir); // Origin of enumeration
std::for_each(sys::directory_iterator(p), sys::directory_iterator(),
[&](const sys::path& path) {
if (sys::is_regular_file(path))
filenames.push_back(Path::combine(target_dir, path.filename().generic_string()));
});
}
static void rebase_files(
std::vector<std::string>& filenames,
const std::string& base_dir)
{
for (auto& file : filenames)
{
file = Path::combine(base_dir, file);
}
}
static double calculate_lambda(double teacher_signal)
{
// If the evaluation value in deep search exceeds elmo_lambda_limit
// then apply elmo_lambda_high instead of elmo_lambda_low.
const double lambda =
(std::abs(teacher_signal) >= elmo_lambda_limit)
? elmo_lambda_high
: elmo_lambda_low;
return lambda;
}
// We use our own simple static autograd for automatic
// differentiation of the loss function. While it works it has it's caveats.
// To work fast enough it requires memoization and reference semantics.
// Memoization is mostly opaque to the user and is only per eval basis.
// As for reference semantics, we cannot copy every node,
// because we need a way to reuse computation.
// But we can't really use shared_ptr because of the overhead. That means
// that we have to ensure all parts of a loss expression are not destroyed
// before use. When lvalue references are used to construct a node it will
// store just a reference, it only perform a copy of the rvalue reference arguments.
// This means that we need some storage for the whole computation tree
// that keeps the values after function returns and never moves them to
// a different memory location. This means that we cannot use local
// variables and just return by value - because there may be dangling references left.
// We also cannot create a struct with this tree on demand because one cannot
// use `auto` as a struct members. This is a big issue, and the only way
// to solve it as of now is to use static thread_local variables and rely on the
// following assumptions:
// 1. the expression node must not change for the duration of the program
// within a single instance of a function. This is usually not a problem
// because almost all information is carried by the type. There is an
// exception though, we have ConstantRef and Constant nodes that
// do not encode the constants in the type, so it's possible
// that these nodes are different on the first call to the function
// then later. We MUST ensure that one function is only ever used
// for one specific expression.
// 2. thread_local variables are not expensive. Usually after creation
// it only requires a single unsynchronized boolean check and that's
// how most compilers implement it.
//
// So the general way to do things right now is to use static thread_local
// variables for all named autograd nodes. Results being nodes should be
// returned by reference, so that there's no need to copy the returned objects.
// Parameters being nodes should be taken by lvalue reference if they are
// used more than once (to enable reference semantics to reuse computation),
// but they can be rvalues and forward on first use if there's only one use
// of the node in the scope.
// We must keep in mind that the node tree created by such a function
// is never going to change as thread_local variables are initialized
// on first call. This means that one cannot use one function as a factory
// for different autograd expression trees.
template <typename ShallowT, typename TeacherT, typename ResultT, typename LambdaT>
static auto& cross_entropy_(
ShallowT& q_,
TeacherT& p_,
ResultT& t_,
LambdaT& lambda_
)
{
using namespace Learner::Autograd::UnivariateStatic;
constexpr double epsilon = 1e-12;
static thread_local auto teacher_entropy_ = -(p_ * log(p_ + epsilon) + (1.0 - p_) * log(1.0 - p_ + epsilon));
static thread_local auto outcome_entropy_ = -(t_ * log(t_ + epsilon) + (1.0 - t_) * log(1.0 - t_ + epsilon));
static thread_local auto teacher_loss_ = -(p_ * log(q_) + (1.0 - p_) * log(1.0 - q_));
static thread_local auto outcome_loss_ = -(t_ * log(q_) + (1.0 - t_) * log(1.0 - q_));
static thread_local auto result_ = lambda_ * teacher_loss_ + (1.0 - lambda_) * outcome_loss_;
static thread_local auto entropy_ = lambda_ * teacher_entropy_ + (1.0 - lambda_) * outcome_entropy_;
static thread_local auto cross_entropy_ = result_ - entropy_;
return cross_entropy_;
}
template <typename ValueT>
static auto& scale_score_(ValueT&& v_)
{
using namespace Learner::Autograd::UnivariateStatic;
// Normalize to [0.0, 1.0].
static thread_local auto normalized_ =
(std::forward<ValueT>(v_) - ConstantRef<double>(src_score_min_value))
/ (ConstantRef<double>(src_score_max_value) - ConstantRef<double>(src_score_min_value));
// Scale to [dest_score_min_value, dest_score_max_value].
static thread_local auto scaled_ =
normalized_
* (ConstantRef<double>(dest_score_max_value) - ConstantRef<double>(dest_score_min_value))
+ ConstantRef<double>(dest_score_min_value);
return scaled_;
}
static Value scale_score(Value v)
{
// Normalize to [0.0, 1.0].
auto normalized =
((double)v - src_score_min_value)
/ (src_score_max_value - src_score_min_value);
// Scale to [dest_score_min_value, dest_score_max_value].
auto scaled =
normalized
* (dest_score_max_value - dest_score_min_value)
+ dest_score_min_value;
return Value(scaled);
}
template <typename ValueT>
static auto& expected_perf_(ValueT&& v_)
{
using namespace Learner::Autograd::UnivariateStatic;
static thread_local auto perf_ = sigmoid(std::forward<ValueT>(v_) * ConstantRef<double>(winning_probability_coefficient));
return perf_;
}
template <typename ValueT, typename PlyT, typename T = typename ValueT::ValueType>
static auto& expected_perf_use_wdl_(
ValueT& v_,
PlyT&& ply_
)
{
using namespace Learner::Autograd::UnivariateStatic;
// Coefficients of a 3rd order polynomial fit based on fishtest data
// for two parameters needed to transform eval to the argument of a
// logistic function.
static constexpr T as[] = { -8.24404295, 64.23892342, -95.73056462, 153.86478679 };
static constexpr T bs[] = { -3.37154371, 28.44489198, -56.67657741, 72.05858751 };
// The model captures only up to 240 plies, so limit input (and rescale)
static thread_local auto m_ = std::forward<PlyT>(ply_) / 64.0;
static thread_local auto a_ = (((as[0] * m_ + as[1]) * m_ + as[2]) * m_) + as[3];
static thread_local auto b_ = (((bs[0] * m_ + bs[1]) * m_ + bs[2]) * m_) + bs[3];
// Return win rate in per mille
static thread_local auto sv_ = (v_ - a_) / b_;
static thread_local auto svn_ = (-v_ - a_) / b_;
static thread_local auto win_pct_ = sigmoid(sv_);
static thread_local auto loss_pct_ = sigmoid(svn_);
static thread_local auto draw_pct_ = 1.0 - win_pct_ - loss_pct_;
static thread_local auto perf_ = win_pct_ + draw_pct_ * 0.5;
return perf_;
}
static double expected_perf_use_wdl(
Value v,
int ply
)
{
// Coefficients of a 3rd order polynomial fit based on fishtest data
// for two parameters needed to transform eval to the argument of a
// logistic function.
static constexpr double as[] = { -8.24404295, 64.23892342, -95.73056462, 153.86478679 };
static constexpr double bs[] = { -3.37154371, 28.44489198, -56.67657741, 72.05858751 };
// The model captures only up to 240 plies, so limit input (and rescale)
auto m = ply / 64.0;
auto a = (((as[0] * m + as[1]) * m + as[2]) * m) + as[3];
auto b = (((bs[0] * m + bs[1]) * m + bs[2]) * m) + bs[3];
// Return win rate in per mille
auto sv = ((double)v - a) / b;
auto svn = ((double)-v - a) / b;
auto win_pct = Math::sigmoid(sv);
auto loss_pct = Math::sigmoid(svn);
auto draw_pct = 1.0 - win_pct - loss_pct;
auto perf = win_pct + draw_pct * 0.5;
return perf;
}
[[maybe_unused]] static ValueWithGrad<double> get_loss_noob(
Value shallow, Value teacher_signal, int result, int /* ply */)
{
using namespace Learner::Autograd::UnivariateStatic;
static thread_local auto q_ = VariableParameter<double, 0>{};
static thread_local auto p_ = ConstantParameter<double, 1>{};
static thread_local auto loss_ = pow(q_ - p_, 2.0) * (1.0 / (2400.0 * 2.0 * 600.0));
auto args = std::tuple(
(double)shallow,
(double)teacher_signal,
(double)result,
calculate_lambda(teacher_signal)
);
return loss_.eval(args);
}
static auto& get_loss_cross_entropy_()
{
using namespace Learner::Autograd::UnivariateStatic;
static thread_local auto& q_ = expected_perf_(VariableParameter<double, 0>{});
static thread_local auto& p_ = expected_perf_(scale_score_(ConstantParameter<double, 1>{}));
static thread_local auto t_ = (ConstantParameter<double, 2>{} + 1.0) * 0.5;
static thread_local auto lambda_ = ConstantParameter<double, 3>{};
static thread_local auto& loss_ = cross_entropy_(q_, p_, t_, lambda_);
return loss_;
}
static auto get_loss_cross_entropy_args(
Value shallow, Value teacher_signal, int result)
{
return std::tuple(
(double)shallow,
(double)teacher_signal,
(double)result,
calculate_lambda(teacher_signal)
);
}
static ValueWithGrad<double> get_loss_cross_entropy(
Value shallow, Value teacher_signal, int result, int /* ply */)
{
using namespace Learner::Autograd::UnivariateStatic;
static thread_local auto& loss_ = get_loss_cross_entropy_();
auto args = get_loss_cross_entropy_args(shallow, teacher_signal, result);
return loss_.eval(args);
}
static ValueWithGrad<double> get_loss_cross_entropy_no_grad(
Value shallow, Value teacher_signal, int result, int /* ply */)
{
using namespace Learner::Autograd::UnivariateStatic;
static thread_local auto& loss_ = get_loss_cross_entropy_();
auto args = get_loss_cross_entropy_args(shallow, teacher_signal, result);
return { loss_.value(args), 0.0 };
}
static auto& get_loss_cross_entropy_use_wdl_()
{
using namespace Learner::Autograd::UnivariateStatic;
static thread_local auto ply_ = ConstantParameter<double, 4>{};
static thread_local auto shallow_ = VariableParameter<double, 0>{};
static thread_local auto& q_ = expected_perf_use_wdl_(shallow_, ply_);
// We could do just this but MSVC crashes with an internal compiler error :(
// static thread_local auto& scaled_teacher_ = scale_score_(ConstantParameter<double, 1>{});
// static thread_local auto& p_ = expected_perf_use_wdl_(scaled_teacher_, ply_);
static thread_local auto p_ = ConstantParameter<double, 1>{};
static thread_local auto t_ = (ConstantParameter<double, 2>{} + 1.0) * 0.5;
static thread_local auto lambda_ = ConstantParameter<double, 3>{};
static thread_local auto& loss_ = cross_entropy_(q_, p_, t_, lambda_);
return loss_;
}
static auto get_loss_cross_entropy_use_wdl_args(
Value shallow, Value teacher_signal, int result, int ply)
{
return std::tuple(
(double)shallow,
// This is required because otherwise MSVC crashes :(
expected_perf_use_wdl(scale_score(teacher_signal), ply),
(double)result,
calculate_lambda(teacher_signal),
(double)std::min(240, ply)
);
}
static ValueWithGrad<double> get_loss_cross_entropy_use_wdl(
Value shallow, Value teacher_signal, int result, int ply)
{
using namespace Learner::Autograd::UnivariateStatic;
static thread_local auto& loss_ = get_loss_cross_entropy_use_wdl_();
auto args = get_loss_cross_entropy_use_wdl_args(shallow, teacher_signal, result, ply);
return loss_.eval(args);
}
static ValueWithGrad<double> get_loss_cross_entropy_use_wdl_no_grad(
Value shallow, Value teacher_signal, int result, int ply)
{
using namespace Learner::Autograd::UnivariateStatic;
static thread_local auto& loss_ = get_loss_cross_entropy_use_wdl_();
auto args = get_loss_cross_entropy_use_wdl_args(shallow, teacher_signal, result, ply);
return { loss_.value(args), 0.0 };
}
static auto get_loss(Value shallow, Value teacher_signal, int result, int ply)
{
using namespace Learner::Autograd::UnivariateStatic;
if (use_wdl)
{
return get_loss_cross_entropy_use_wdl(shallow, teacher_signal, result, ply);
}
else
{
return get_loss_cross_entropy(shallow, teacher_signal, result, ply);
}
}
static auto get_loss_no_grad(Value shallow, Value teacher_signal, int result, int ply)
{
using namespace Learner::Autograd::UnivariateStatic;
if (use_wdl)
{
return get_loss_cross_entropy_use_wdl_no_grad(shallow, teacher_signal, result, ply);
}
else
{
return get_loss_cross_entropy_no_grad(shallow, teacher_signal, result, ply);
}
}
[[maybe_unused]] static auto get_loss(
Value teacher_signal,
Value shallow,
const PackedSfenValue& psv)
{
return get_loss(shallow, teacher_signal, psv.game_result, psv.gamePly);
}
static auto get_loss_no_grad(
Value teacher_signal,
Value shallow,
const PackedSfenValue& psv)
{
return get_loss_no_grad(shallow, teacher_signal, psv.game_result, psv.gamePly);
}
// Class to generate sfen with multiple threads
struct LearnerThink
{
struct Params
{
// Mini batch size size. Be sure to set it on the side that uses this class.
uint64_t mini_batch_size = LEARN_MINI_BATCH_SIZE;
// Number of phases used for calculation such as mse
// mini-batch size = 1M is standard, so 0.2% of that should be negligible in terms of time.
// Since search() is performed with depth = 1 in calculation of
// move match rate, simple comparison is not possible...
uint64_t validation_count = 2000;
// Option to exclude early stage from learning
int reduction_gameply = 1;
// If the absolute value of the evaluation value of the deep search
// of the teacher phase exceeds this value, discard the teacher phase.
int eval_limit = 32000;
// Flag whether to dig a folder each time the evaluation function is saved.
// If true, do not dig the folder.
bool save_only_once = false;
bool shuffle = true;
bool verbose = false;
double newbob_decay = 0.5;
int newbob_num_trials = 4;
uint64_t auto_lr_drop = 0;
std::string best_nn_directory;
uint64_t eval_save_interval = LEARN_EVAL_SAVE_INTERVAL;
uint64_t loss_output_interval = 1'000'000;
size_t sfen_read_size = SfenReader::DEFAULT_SFEN_READ_SIZE;
size_t thread_buffer_size = SfenReader::DEFAULT_THREAD_BUFFER_SIZE;
bool use_draw_games_in_training = true;
bool use_draw_games_in_validation = true;
bool skip_duplicated_positions_in_training = true;
bool assume_quiet = false;
bool smart_fen_skipping = false;
bool smart_fen_skipping_for_validation = false;
double learning_rate = 1.0;
double max_grad = 1.0;
string validation_set_file_name;
string seed;
std::vector<std::string> filenames;
uint64_t num_threads;
void enforce_constraints()
{
num_threads = Options["Threads"];
if (loss_output_interval == 0)
{
loss_output_interval = LEARN_RMSE_OUTPUT_INTERVAL * mini_batch_size;
}
// 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);
if (newbob_decay != 1.0 && !Options["SkipLoadingEval"]) {
// Save the current net to [EvalSaveDir]\original.
Eval::NNUE::save_eval("original");
// Set the folder above to best_nn_directory so that the trainer can
// resotre the network parameters from the original net file.
best_nn_directory =
Path::combine(Options["EvalSaveDir"], "original");
}
}
};
LearnerThink(const Params& prm) :
params(prm),
prng(prm.seed),
train_sr(
prm.filenames,
prm.shuffle,
SfenReaderMode::Cyclic,
prm.num_threads,
std::to_string(prng.next_random_seed()),
prm.sfen_read_size,
prm.thread_buffer_size),
validation_sr(
prm.validation_set_file_name.empty() ? prm.filenames : std::vector<std::string>{ prm.validation_set_file_name },
prm.shuffle,
SfenReaderMode::Cyclic,
1,
std::to_string(prng.next_random_seed()),
prm.sfen_read_size,
prm.thread_buffer_size),
learn_loss_sum{}
{
save_count = 0;
loss_output_count = 0;
last_lr_drop = 0;
best_loss = std::numeric_limits<double>::infinity();
latest_loss_sum = 0.0;
latest_loss_count = 0;
total_done = 0;
trials = params.newbob_num_trials;
dir_number = 0;
}
void learn(uint64_t epochs);
private:
static void set_learning_search_limits();
PSVector fetch_next_validation_set();
void learn_worker(Thread& th, std::atomic<uint64_t>& counter, uint64_t limit);
void update_weights(const PSVector& psv, uint64_t epoch);
void calc_loss(const PSVector& psv, uint64_t epoch);
void calc_loss_worker(
Thread& th,
std::atomic<uint64_t>& counter,
const PSVector& psv,
Loss& test_loss_sum,
atomic<double>& sum_norm,
atomic<int>& move_accord_count
);
bool has_depth1_move_agreement(Position& pos, Move pvmove);
bool check_progress();
// save merit function parameters to a file
bool save(bool is_final = false);
Params params;
PRNG prng;
// sfen reader
SfenReader train_sr;
SfenReader validation_sr;
uint64_t save_count;
uint64_t loss_output_count;
std::atomic<bool> stop_flag;
uint64_t total_done;
uint64_t last_lr_drop;
double best_loss;
double latest_loss_sum;
uint64_t latest_loss_count;
int trials;
int dir_number;
// For calculation of learning data loss
Loss learn_loss_sum;
};
void LearnerThink::set_learning_search_limits()
{
Threads.main()->ponder = false;
// About Search::Limits
// Be careful because this member variable is global and affects other threads.
auto& limits = Search::Limits;
limits.startTime = now();
// Make the search equivalent to the "go infinite" command. (Because it is troublesome if time management is done)
limits.infinite = true;
// Since PV is an obstacle when displayed, erase it.
limits.silent = true;
// If you use this, it will be compared with the accumulated nodes of each thread. Therefore, do not use it.
limits.nodes = 0;
// depth is also processed by the one passed as an argument of Learner::search().
limits.depth = 0;
}
PSVector LearnerThink::fetch_next_validation_set()
{
PSVector validation_data;
auto mainThread = Threads.main();
mainThread->execute_with_worker([&validation_data, this](auto& th){
auto do_include_predicate = [&th, this](const PackedSfenValue& ps) -> bool {
if (params.eval_limit < abs(ps.score))
return false;
if (!params.use_draw_games_in_validation && ps.game_result == 0)
return false;
if (params.smart_fen_skipping_for_validation)
{
StateInfo si;
auto& pos = th.rootPos;
if (pos.set_from_packed_sfen(ps.sfen, &si, &th) != 0)
return false;
if (pos.capture_or_promotion((Move)ps.move) || pos.checkers())
return false;
}
return true;
};
validation_data = validation_sr.read_some(
params.validation_count,
params.validation_count * 100, // to have a reasonable bound on the running time.
do_include_predicate
);
});
mainThread->wait_for_worker_finished();
return validation_data;
}
void LearnerThink::learn(uint64_t epochs)
{
#if defined(_OPENMP)
omp_set_num_threads((int)Options["Threads"]);
#endif
set_learning_search_limits();
Eval::NNUE::verify_any_net_loaded();
const PSVector validation_data = fetch_next_validation_set();
if (validation_data.size() != params.validation_count)
{
auto out = sync_region_cout.new_region();
out
<< "INFO (learn): Error reading validation data. Read " << validation_data.size()
<< " out of " << params.validation_count << '\n'
<< "INFO (learn): This either means that less than 1% of the validation data passed the filter"
<< " or the file is empty\n";
return;
}
if (params.newbob_decay != 1.0) {
calc_loss(validation_data, 0);
best_loss = latest_loss_sum / latest_loss_count;
latest_loss_sum = 0.0;
latest_loss_count = 0;
auto out = sync_region_cout.new_region();
out << "INFO (learn): initial loss = " << best_loss << endl;
}
stop_flag = false;
for(uint64_t epoch = 1; epoch <= epochs; ++epoch)
{
std::atomic<uint64_t> counter{0};
Threads.execute_with_workers([this, &counter](auto& th){
learn_worker(th, counter, params.mini_batch_size);
});
total_done += params.mini_batch_size;
Threads.wait_for_workers_finished();
if (stop_flag)
break;
update_weights(validation_data, epoch);
if (stop_flag)
break;
}
Eval::NNUE::finalize_net();
save(true);
}
void LearnerThink::learn_worker(Thread& th, std::atomic<uint64_t>& counter, uint64_t limit)
{
const auto thread_id = th.thread_idx();
auto& pos = th.rootPos;
std::vector<StateInfo, AlignedAllocator<StateInfo>> state(MAX_PLY);
while(!stop_flag)
{
const auto iter = counter.fetch_add(1);
if (iter >= limit)
break;
PackedSfenValue ps;
RETRY_READ:;
if (!train_sr.read_to_thread_buffer(thread_id, ps))
{
// If we ran out of data we stop completely
// because there's nothing left to do.
stop_flag = true;
break;
}
if (params.eval_limit < abs(ps.score))
goto RETRY_READ;
if (!params.use_draw_games_in_training && ps.game_result == 0)
goto RETRY_READ;
// Skip over the opening phase
if (ps.gamePly < prng.rand(params.reduction_gameply))
goto RETRY_READ;
StateInfo si;
if (pos.set_from_packed_sfen(ps.sfen, &si, &th) != 0)
{
// Malformed sfen
auto out = sync_region_cout.new_region();
out << "ERROR: illigal packed sfen = " << pos.fen() << endl;
goto RETRY_READ;
}
const auto rootColor = pos.side_to_move();
// A function that adds the current `pos` and `ps`
// to the training set.
auto pos_add_grad = [&]() {
// Evaluation value of deep search
const Value shallow_value = Eval::evaluate(pos);
Eval::NNUE::add_example(pos, rootColor, shallow_value, ps, 1.0);
};
if (!pos.pseudo_legal((Move)ps.move) || !pos.legal((Move)ps.move))
{
goto RETRY_READ;
}
// We don't need to qsearch when doing smart skipping
if (!params.assume_quiet && !params.smart_fen_skipping)
{
int ply = 0;
pos.do_move((Move)ps.move, state[ply++]);
// Evaluation value of shallow search (qsearch)
const auto [_, pv] = Search::qsearch(pos);
for (auto m : pv)
{
pos.do_move(m, state[ply++]);
}
}
if (params.smart_fen_skipping
&& (pos.capture_or_promotion((Move)ps.move)
|| pos.checkers()))
{
goto RETRY_READ;
}
// We want to position being trained on not to be terminal
if (MoveList<LEGAL>(pos).size() == 0)
goto RETRY_READ;
// Since we have reached the end phase of PV, add the slope here.
pos_add_grad();
}
}
void LearnerThink::update_weights(const PSVector& psv, uint64_t epoch)
{
// I'm not sure this fencing is correct. But either way there
// should be no real issues happening since
// the read/write phases are isolated.
atomic_thread_fence(memory_order_seq_cst);
learn_loss_sum += 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)
{
save_count = 0;
const bool converged = save();
if (converged)
{
stop_flag = true;
return;
}
}
if (++loss_output_count * params.mini_batch_size >= params.loss_output_interval)
{
loss_output_count = 0;
// loss calculation
calc_loss(psv, epoch);
Eval::NNUE::check_health();
}
}
void LearnerThink::calc_loss(const PSVector& psv, uint64_t epoch)
{
TT.new_search();
TimePoint elapsed = now() - Search::Limits.startTime + 1;
auto out = sync_region_cout.new_region();
out << "\n";
out << "PROGRESS (calc_loss): " << now_string()
<< ", " << total_done << " sfens"
<< ", " << total_done * 1000 / elapsed << " sfens/second"
<< ", epoch " << epoch
<< endl;
out << " - learning rate = " << params.learning_rate << endl;
// For calculation of verification data loss
Loss test_loss_sum{};
// norm for learning
atomic<double> sum_norm{0.0};
// The number of times the pv first move of deep
// search matches the pv first move of search(1).
atomic<int> move_accord_count{0};
auto mainThread = Threads.main();
mainThread->execute_with_worker([&out](auto& th){
auto& pos = th.rootPos;
StateInfo si;
pos.set(StartFEN, false, &si, &th);
out << " - startpos eval = " << Eval::evaluate(pos) << endl;
});
mainThread->wait_for_worker_finished();
// The number of tasks to do.
atomic<uint64_t> counter{0};
Threads.execute_with_workers([&](auto& th){
calc_loss_worker(
th,
counter,
psv,
test_loss_sum,
sum_norm,
move_accord_count
);
});
Threads.wait_for_workers_finished();
latest_loss_sum += test_loss_sum.value();
latest_loss_count += psv.size();
if (psv.size() && test_loss_sum.count() > 0)
{
test_loss_sum.print_only_loss("val", out);
if (learn_loss_sum.count() > 0)
{
learn_loss_sum.print_with_grad("train", out);
}
out << " - norm = " << sum_norm << endl;
out << " - move accuracy = " << (move_accord_count * 100.0 / psv.size()) << "%" << endl;
}
else
{
out << "ERROR: psv.size() = " << psv.size() << " , done = " << test_loss_sum.count() << endl;
}
learn_loss_sum.reset();
}
void LearnerThink::calc_loss_worker(
Thread& th,
std::atomic<uint64_t>& counter,
const PSVector& psv,
Loss& test_loss_sum,
atomic<double>& sum_norm,
atomic<int>& move_accord_count
)
{
Loss local_loss_sum{};
auto& pos = th.rootPos;
for(;;)
{
const auto task_id = counter.fetch_add(1);
if (task_id >= psv.size())
{
break;
}
const auto& ps = psv[task_id];
StateInfo si;
if (pos.set_from_packed_sfen(ps.sfen, &si, &th) != 0)
{
cout << "Error! : illegal packed sfen " << pos.fen() << endl;
continue;
}
const Value shallow_value = Eval::evaluate(pos);
// Evaluation value of deep search
const auto deep_value = (Value)ps.score;
const auto loss = get_loss_no_grad(
deep_value,
shallow_value,
ps);
local_loss_sum += loss;
sum_norm += (double)abs(shallow_value);
// Threat all moves with equal scores as first. This is up to move ordering.
if (has_depth1_move_agreement(pos, (Move)ps.move))
move_accord_count.fetch_add(1, std::memory_order_relaxed);
}
test_loss_sum += local_loss_sum;
}
bool LearnerThink::has_depth1_move_agreement(Position& pos, Move pvmove)
{
// Determine if the depth 1 search pv matches the move from the dataset.
// Do a manual depth 1 search so we're not affected by previous searches.
std::vector<std::pair<Move, Value>> child_scores;
// Call evaluate once for the rootpos so that the evals
// for children moves use incremental feature transformer updates.
(void)Eval::evaluate(pos);
// Just to get guaranteed alignment.
std::vector<StateInfo, AlignedAllocator<StateInfo>> states(1);
auto legal_moves = MoveList<LEGAL>(pos);
for (auto m : legal_moves)
{
pos.do_move(m, states[0]);
// We don't care if the king is in check or stuff like that.
// not a big issue and nnue should digest all.
auto value = -Eval::evaluate(pos);
child_scores.emplace_back(m, value);
pos.undo_move(m);
}
if (child_scores.empty())
return false;
std::sort(
child_scores.begin(),
child_scores.end(),
[](auto& lhs, auto& rhs) { return lhs.second > rhs.second; }
);
// Require the best move to have strictly higher score than the next one.
return
child_scores[0].first == pvmove
&& (child_scores.size() == 1
|| child_scores[1].second != child_scores[0].second);
}
bool LearnerThink::check_progress()
{
auto out = sync_region_cout.new_region();
const double latest_loss = latest_loss_sum / latest_loss_count;
bool converged = false;
latest_loss_sum = 0.0;
latest_loss_count = 0;
auto drop_lr = [&]() {
last_lr_drop = total_done;
out
<< " - reducing learning rate from " << params.learning_rate
<< " to " << (params.learning_rate * params.newbob_decay)
<< " (" << trials << " more trials)" << endl;
params.learning_rate *= params.newbob_decay;
};
auto accept = [&]() {
out << " - loss = " << latest_loss << " < best (" << best_loss << "), accepted" << endl;
best_loss = latest_loss;
trials = params.newbob_num_trials;
};
auto reject = [&]() {
out << " - loss = " << latest_loss << " >= best (" << best_loss << "), rejected" << endl;
--trials;
if (trials > 0)
{
drop_lr();
return false;
}
else
{
return true;
}
};
out << "INFO (learning_rate):" << endl;
if (params.auto_lr_drop)
{
accept();
if (total_done >= last_lr_drop + params.auto_lr_drop)
{
drop_lr();
}
}
else if (latest_loss < best_loss)
{
accept();
}
else
{
converged = reject();
}
if (converged)
{
out << " - converged" << endl;
}
return converged;
}
// Write evaluation function file.
bool LearnerThink::save(bool is_final)
{
// Each time you save, change the extension part of the file name like "0","1","2",..
// (Because I want to compare the winning rate for each evaluation function parameter later)
bool converged = false;
if (params.save_only_once)
{
// When EVAL_SAVE_ONLY_ONCE is defined,
// Do not dig a subfolder because I want to save it only once.
Eval::NNUE::save_eval("");
}
else if (is_final)
{
Eval::NNUE::save_eval("final");
converged = true;
}
else
{
// TODO: consider naming the output directory by epoch.
const std::string dir_name = std::to_string(dir_number++);
Eval::NNUE::save_eval(dir_name);
if (params.newbob_decay != 1.0 && latest_loss_count > 0)
{
converged = check_progress();
params.best_nn_directory = Path::combine((std::string)Options["EvalSaveDir"], dir_name);
}
}
return converged;
}
// Learning from the generated game record
void learn(istringstream& is)
{
LearnerThink::Params params;
// Number of epochs
uint64_t epochs = std::numeric_limits<uint64_t>::max();
// Game file storage folder (get game file with relative path from here)
string base_dir;
string target_dir;
uint64_t nn_batch_size = 1000;
string nn_options;
auto out = sync_region_cout.new_region();
// Assume the filenames are staggered.
while (true)
{
string option;
is >> option;
if (option == "")
break;
// specify the number of phases of mini-batch
if (option == "bat")
{
is >> params.mini_batch_size;
params.mini_batch_size *= 10000; // Unit is ten thousand
}
// Specify the folder in which the game record is stored and make it the rooting target.
else if (option == "targetdir") is >> target_dir;
else if (option == "targetfile")
{
std::string filename;
is >> filename;
params.filenames.push_back(filename);
}
else if (option == "validation_count") is >> params.validation_count;
// Specify the number of loops
else if (option == "epochs") is >> epochs;
// Game file storage folder (get game file with relative path from here)
else if (option == "basedir") is >> base_dir;
// Mini batch size
else if (option == "batchsize") is >> params.mini_batch_size;
// 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"
|| option == "use_draw_games_in_training")
is >> params.use_draw_games_in_training;
// Accept also the old option name.
else if (option == "use_draw_in_validation"
|| option == "use_draw_games_in_validation")
is >> params.use_draw_games_in_validation;
// Accept also the old option name.
else if (option == "use_hash_in_training"
|| option == "skip_duplicated_positions_in_training")
is >> params.skip_duplicated_positions_in_training;
else if (option == "winning_probability_coefficient")
is >> winning_probability_coefficient;
// Using WDL with win rate model instead of sigmoid
else if (option == "use_wdl") is >> use_wdl;
// LAMBDA
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 == "reduction_gameply") is >> params.reduction_gameply;
else if (option == "eval_limit") is >> params.eval_limit;
else if (option == "save_only_once") params.save_only_once = true;
else if (option == "no_shuffle") params.shuffle = false;
else if (option == "nn_batch_size") is >> nn_batch_size;
else if (option == "newbob_decay") is >> params.newbob_decay;
else if (option == "newbob_num_trials") is >> params.newbob_num_trials;
else if (option == "nn_options") is >> nn_options;
else if (option == "auto_lr_drop") is >> params.auto_lr_drop;
else if (option == "eval_save_interval") is >> params.eval_save_interval;
else if (option == "loss_output_interval") is >> params.loss_output_interval;
else if (option == "validation_set_file_name") is >> params.validation_set_file_name;
else if (option == "src_score_min_value") is >> src_score_min_value;
else if (option == "src_score_max_value") is >> src_score_max_value;
else if (option == "dest_score_min_value") is >> dest_score_min_value;
else if (option == "dest_score_max_value") is >> dest_score_max_value;
else if (option == "sfen_read_size") is >> params.sfen_read_size;
else if (option == "thread_buffer_size") is >> params.thread_buffer_size;
else if (option == "seed") is >> params.seed;
else if (option == "set_recommended_uci_options")
{
UCI::setoption("Use NNUE", "pure");
UCI::setoption("MultiPV", "1");
UCI::setoption("Contempt", "0");
UCI::setoption("Skill Level", "20");
UCI::setoption("UCI_Chess960", "false");
UCI::setoption("UCI_AnalyseMode", "false");
UCI::setoption("UCI_LimitStrength", "false");
UCI::setoption("PruneAtShallowDepth", "false");
UCI::setoption("EnableTranspositionTable", "false");
}
else if (option == "verbose") params.verbose = true;
else if (option == "assume_quiet") params.assume_quiet = true;
else if (option == "smart_fen_skipping") params.smart_fen_skipping = true;
else if (option == "smart_fen_skipping_for_validation") params.smart_fen_skipping_for_validation = true;
else
{
out << "INFO: Unknown option: " << option << ". Ignoring.\n";
}
}
out << "INFO: Executing learn command\n";
// Issue a warning if OpenMP is disabled.
#if !defined(_OPENMP)
out << "WARNING: OpenMP disabled." << endl;
#endif
params.enforce_constraints();
// Right now we only have the individual files.
// We need to apply base_dir here
if (!target_dir.empty())
{
append_files_from_dir(params.filenames, base_dir, target_dir);
}
rebase_files(params.filenames, base_dir);
out << "INFO: Input files:\n";
for (auto s : params.filenames)
out << " - " << s << '\n';
out << "INFO: Parameters:\n";
if (!params.validation_set_file_name.empty())
{
out << " - validation set : " << params.validation_set_file_name << endl;
}
out << " - validation count : " << params.validation_count << endl;
out << " - epochs : " << epochs << endl;
out << " - epochs * minibatch size : " << epochs * params.mini_batch_size << endl;
out << " - eval_limit : " << params.eval_limit << endl;
out << " - save_only_once : " << (params.save_only_once ? "true" : "false") << endl;
out << " - shuffle on read : " << (params.shuffle ? "true" : "false") << endl;
out << " - Loss Function : " << LOSS_FUNCTION << endl;
out << " - minibatch size : " << params.mini_batch_size << endl;
out << " - nn_batch_size : " << nn_batch_size << endl;
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;
out << " - winning prob coeff : " << winning_probability_coefficient << endl;
out << " - use_wdl : " << use_wdl << 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 : " << params.reduction_gameply << endl;
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 << " - eval_save_interval : " << params.eval_save_interval << " sfens" << endl;
out << " - loss_output_interval : " << params.loss_output_interval << " sfens" << endl;
out << " - sfen_read_size : " << params.sfen_read_size << endl;
out << " - thread_buffer_size : " << params.thread_buffer_size << endl;
out << " - smart_fen_skipping : " << params.smart_fen_skipping << endl;
out << " - smart_fen_skipping_val : " << params.smart_fen_skipping_for_validation << endl;
out << " - seed : " << params.seed << endl;
out << " - verbose : " << (params.verbose ? "true" : "false") << endl;
if (params.auto_lr_drop) {
out << " - learning rate scheduling : every " << params.auto_lr_drop << " sfens" << endl;
}
else if (params.newbob_decay != 1.0) {
out << " - learning rate scheduling : newbob with decay" << endl;
out << " - newbob_decay : " << params.newbob_decay << endl;
out << " - newbob_num_trials : " << params.newbob_num_trials << endl;
}
else {
out << " - learning rate scheduling : fixed learning rate" << endl;
}
out << endl;
out << "INFO: Started initialization." << endl;
Eval::NNUE::initialize_training(params.seed, out);
Eval::NNUE::set_batch_size(nn_batch_size);
Eval::NNUE::set_options(nn_options);
LearnerThink learn_think(params);
out << "Finished initialization." << endl;
out.unlock();
// Start learning.
learn_think.learn(epochs);
}
} // namespace Learner
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