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Dual NNUE with L1-128 smallnet
Credit goes to @mstembera for: - writing the code enabling dual NNUE: https://github.com/official-stockfish/Stockfish/pull/4898 - the idea of trying L1-128 trained exclusively on high simple eval positions The L1-128 smallnet is: - epoch 399 of a single-stage training from scratch - trained only on positions from filtered data with high material difference - defined by abs(simple_eval) > 1000 ```yaml experiment-name: 128--S1-only-hse-v2 training-dataset: - /data/hse/S3/dfrc99-16tb7p-eval-filt-v2.min.high-simple-eval-1k.binpack - /data/hse/S3/leela96-filt-v2.min.high-simple-eval-1k.binpack - /data/hse/S3/test80-apr2022-16tb7p.min.high-simple-eval-1k.binpack - /data/hse/S7/test60-2020-2tb7p.v6-3072.high-simple-eval-1k.binpack - /data/hse/S7/test60-novdec2021-12tb7p-filter-v6-dd.min-mar2023.unmin.high-simple-eval-1k.binpack - /data/hse/S7/test77-nov2021-2tb7p.v6-3072.min.high-simple-eval-1k.binpack - /data/hse/S7/test77-dec2021-16tb7p-filter-v6-dd.min-mar2023.unmin.high-simple-eval-1k.binpack - /data/hse/S7/test77-jan2022-2tb7p.high-simple-eval-1k.binpack - /data/hse/S7/test78-jantomay2022-16tb7p-filter-v6-dd.min-mar2023.unmin.high-simple-eval-1k.binpack - /data/hse/S7/test78-juntosep2022-16tb7p-filter-v6-dd.min-mar2023.unmin.high-simple-eval-1k.binpack - /data/hse/S7/test79-apr2022-16tb7p-filter-v6-dd.min-mar2023.unmin.high-simple-eval-1k.binpack - /data/hse/S7/test79-may2022-16tb7p-filter-v6-dd.min-mar2023.unmin.high-simple-eval-1k.binpack # T80 2022 - /data/hse/S7/test80-may2022-16tb7p.high-simple-eval-1k.binpack - /data/hse/S7/test80-jun2022-16tb7p-filter-v6-dd.min-mar2023.unmin.high-simple-eval-1k.binpack - /data/hse/S7/test80-jul2022-16tb7p.v6-dd.min.high-simple-eval-1k.binpack - /data/hse/S7/test80-aug2022-16tb7p-filter-v6-dd.min-mar2023.unmin.high-simple-eval-1k.binpack - /data/hse/S7/test80-sep2022-16tb7p-filter-v6-dd.min-mar2023.unmin.high-simple-eval-1k.binpack - /data/hse/S7/test80-oct2022-16tb7p.v6-dd.high-simple-eval-1k.binpack - /data/hse/S7/test80-nov2022-16tb7p-v6-dd.min.high-simple-eval-1k.binpack # T80 2023 - /data/hse/S7/test80-jan2023-3of3-16tb7p-filter-v6-dd.min-mar2023.unmin.high-simple-eval-1k.binpack - /data/hse/S7/test80-feb2023-16tb7p-filter-v6-dd.min-mar2023.unmin.high-simple-eval-1k.binpack - /data/hse/S7/test80-mar2023-2tb7p.v6-sk16.min.high-simple-eval-1k.binpack - /data/hse/S7/test80-apr2023-2tb7p-filter-v6-sk16.min.high-simple-eval-1k.binpack - /data/hse/S7/test80-may2023-2tb7p.v6.min.high-simple-eval-1k.binpack - /data/hse/S7/test80-jun2023-2tb7p.v6-3072.min.high-simple-eval-1k.binpack - /data/hse/S7/test80-jul2023-2tb7p.v6-3072.min.high-simple-eval-1k.binpack - /data/hse/S7/test80-aug2023-2tb7p.v6.min.high-simple-eval-1k.binpack - /data/hse/S7/test80-sep2023-2tb7p.high-simple-eval-1k.binpack - /data/hse/S7/test80-oct2023-2tb7p.high-simple-eval-1k.binpack start-from-engine-test-net: False nnue-pytorch-branch: linrock/nnue-pytorch/L1-128 engine-test-branch: linrock/Stockfish/L1-128-nolazy engine-base-branch: linrock/Stockfish/L1-128 num-epochs: 500 lambda: 1.0 ``` Experiment yaml configs converted to easy_train.sh commands with: https://github.com/linrock/nnue-tools/blob/4339954/yaml_easy_train.py Binpacks interleaved at training time with: https://github.com/official-stockfish/nnue-pytorch/pull/259 Data filtered for high simple eval positions with: https://github.com/linrock/nnue-data/blob/32d6a68/filter_high_simple_eval_plain.py https://github.com/linrock/Stockfish/blob/61dbfe/src/tools/transform.cpp#L626-L655 Training data can be found at: https://robotmoon.com/nnue-training-data/ Local elo at 25k nodes per move of L1-128 smallnet (nnue-only eval) vs. L1-128 trained on standard S1 data: nn-epoch399.nnue : -318.1 +/- 2.1 Passed STC: https://tests.stockfishchess.org/tests/view/6574cb9d95ea6ba1fcd49e3b LLR: 2.93 (-2.94,2.94) <0.00,2.00> Total: 62432 W: 15875 L: 15521 D: 31036 Ptnml(0-2): 177, 7331, 15872, 7633, 203 Passed LTC: https://tests.stockfishchess.org/tests/view/6575da2d4d789acf40aaac6e LLR: 2.94 (-2.94,2.94) <0.50,2.50> Total: 64830 W: 16118 L: 15738 D: 32974 Ptnml(0-2): 43, 7129, 17697, 7497, 49 closes https://github.com/official-stockfish/Stockfish/pulls Bench: 1330050 Co-Authored-By: mstembera <5421953+mstembera@users.noreply.github.com>
This commit is contained in:
Linmiao Xu
Disservin
153
src/evaluate.cpp
153
src/evaluate.cpp
@@ -23,6 +23,7 @@
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#include <cmath>
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#include <cstdlib>
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#include <fstream>
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#include <initializer_list>
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#include <iomanip>
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#include <iostream>
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#include <sstream>
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@@ -31,6 +32,7 @@
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#include "incbin/incbin.h"
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#include "misc.h"
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#include "nnue/evaluate_nnue.h"
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#include "nnue/nnue_architecture.h"
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#include "position.h"
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#include "thread.h"
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#include "types.h"
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@@ -44,11 +46,15 @@
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// const unsigned int gEmbeddedNNUESize; // the size of the embedded file
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// Note that this does not work in Microsoft Visual Studio.
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#if !defined(_MSC_VER) && !defined(NNUE_EMBEDDING_OFF)
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INCBIN(EmbeddedNNUE, EvalFileDefaultName);
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INCBIN(EmbeddedNNUEBig, EvalFileDefaultNameBig);
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INCBIN(EmbeddedNNUESmall, EvalFileDefaultNameSmall);
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#else
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const unsigned char gEmbeddedNNUEData[1] = {0x0};
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const unsigned char* const gEmbeddedNNUEEnd = &gEmbeddedNNUEData[1];
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const unsigned int gEmbeddedNNUESize = 1;
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const unsigned char gEmbeddedNNUEBigData[1] = {0x0};
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const unsigned char* const gEmbeddedNNUEBigEnd = &gEmbeddedNNUEBigData[1];
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const unsigned int gEmbeddedNNUEBigSize = 1;
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const unsigned char gEmbeddedNNUESmallData[1] = {0x0};
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const unsigned char* const gEmbeddedNNUESmallEnd = &gEmbeddedNNUESmallData[1];
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const unsigned int gEmbeddedNNUESmallSize = 1;
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#endif
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@@ -56,7 +62,9 @@ namespace Stockfish {
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namespace Eval {
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std::string currentEvalFileName = "None";
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std::string currentEvalFileName[2] = {"None", "None"};
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const std::string EvFiles[2] = {"EvalFile", "EvalFileSmall"};
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const std::string EvFileNames[2] = {EvalFileDefaultNameBig, EvalFileDefaultNameSmall};
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// Tries to load a NNUE network at startup time, or when the engine
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// receives a UCI command "setoption name EvalFile value nn-[a-z0-9]{12}.nnue"
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@@ -67,84 +75,96 @@ std::string currentEvalFileName = "None";
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// variable to have the engine search in a special directory in their distro.
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void NNUE::init() {
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std::string eval_file = std::string(Options["EvalFile"]);
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if (eval_file.empty())
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eval_file = EvalFileDefaultName;
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for (NetSize netSize : {Big, Small})
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{
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// change after fishtest supports EvalFileSmall
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std::string eval_file =
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std::string(netSize == Small ? EvalFileDefaultNameSmall : Options[EvFiles[netSize]]);
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if (eval_file.empty())
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eval_file = EvFileNames[netSize];
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#if defined(DEFAULT_NNUE_DIRECTORY)
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std::vector<std::string> dirs = {"<internal>", "", CommandLine::binaryDirectory,
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stringify(DEFAULT_NNUE_DIRECTORY)};
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std::vector<std::string> dirs = {"<internal>", "", CommandLine::binaryDirectory,
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stringify(DEFAULT_NNUE_DIRECTORY)};
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#else
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std::vector<std::string> dirs = {"<internal>", "", CommandLine::binaryDirectory};
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std::vector<std::string> dirs = {"<internal>", "", CommandLine::binaryDirectory};
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#endif
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for (const std::string& directory : dirs)
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if (currentEvalFileName != eval_file)
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for (const std::string& directory : dirs)
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{
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if (directory != "<internal>")
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if (currentEvalFileName[netSize] != eval_file)
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{
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std::ifstream stream(directory + eval_file, std::ios::binary);
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if (NNUE::load_eval(eval_file, stream))
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currentEvalFileName = eval_file;
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}
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if (directory != "<internal>")
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{
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std::ifstream stream(directory + eval_file, std::ios::binary);
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if (NNUE::load_eval(eval_file, stream, netSize))
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currentEvalFileName[netSize] = eval_file;
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}
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if (directory == "<internal>" && eval_file == EvalFileDefaultName)
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{
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// C++ way to prepare a buffer for a memory stream
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class MemoryBuffer: public std::basic_streambuf<char> {
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public:
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MemoryBuffer(char* p, size_t n) {
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setg(p, p, p + n);
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setp(p, p + n);
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}
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};
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if (directory == "<internal>" && eval_file == EvFileNames[netSize])
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{
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// C++ way to prepare a buffer for a memory stream
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class MemoryBuffer: public std::basic_streambuf<char> {
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public:
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MemoryBuffer(char* p, size_t n) {
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setg(p, p, p + n);
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setp(p, p + n);
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}
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};
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MemoryBuffer buffer(
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const_cast<char*>(reinterpret_cast<const char*>(gEmbeddedNNUEData)),
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size_t(gEmbeddedNNUESize));
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(void) gEmbeddedNNUEEnd; // Silence warning on unused variable
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MemoryBuffer buffer(
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const_cast<char*>(reinterpret_cast<const char*>(
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netSize == Small ? gEmbeddedNNUESmallData : gEmbeddedNNUEBigData)),
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size_t(netSize == Small ? gEmbeddedNNUESmallSize : gEmbeddedNNUEBigSize));
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(void) gEmbeddedNNUEBigEnd; // Silence warning on unused variable
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(void) gEmbeddedNNUESmallEnd;
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std::istream stream(&buffer);
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if (NNUE::load_eval(eval_file, stream))
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currentEvalFileName = eval_file;
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std::istream stream(&buffer);
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if (NNUE::load_eval(eval_file, stream, netSize))
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currentEvalFileName[netSize] = eval_file;
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}
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}
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}
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}
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}
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// Verifies that the last net used was loaded successfully
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void NNUE::verify() {
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std::string eval_file = std::string(Options["EvalFile"]);
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if (eval_file.empty())
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eval_file = EvalFileDefaultName;
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if (currentEvalFileName != eval_file)
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for (NetSize netSize : {Big, Small})
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{
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// change after fishtest supports EvalFileSmall
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std::string eval_file =
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std::string(netSize == Small ? EvalFileDefaultNameSmall : Options[EvFiles[netSize]]);
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if (eval_file.empty())
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eval_file = EvFileNames[netSize];
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std::string msg1 =
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"Network evaluation parameters compatible with the engine must be available.";
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std::string msg2 = "The network file " + eval_file + " was not loaded successfully.";
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std::string msg3 = "The UCI option EvalFile might need to specify the full path, "
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"including the directory name, to the network file.";
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std::string msg4 = "The default net can be downloaded from: "
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"https://tests.stockfishchess.org/api/nn/"
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+ std::string(EvalFileDefaultName);
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std::string msg5 = "The engine will be terminated now.";
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if (currentEvalFileName[netSize] != eval_file)
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{
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std::string msg1 =
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"Network evaluation parameters compatible with the engine must be available.";
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std::string msg2 = "The network file " + eval_file + " was not loaded successfully.";
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std::string msg3 = "The UCI option EvalFile might need to specify the full path, "
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"including the directory name, to the network file.";
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std::string msg4 = "The default net can be downloaded from: "
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"https://tests.stockfishchess.org/api/nn/"
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+ std::string(EvFileNames[netSize]);
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std::string msg5 = "The engine will be terminated now.";
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sync_cout << "info string ERROR: " << msg1 << sync_endl;
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sync_cout << "info string ERROR: " << msg2 << sync_endl;
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sync_cout << "info string ERROR: " << msg3 << sync_endl;
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sync_cout << "info string ERROR: " << msg4 << sync_endl;
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sync_cout << "info string ERROR: " << msg5 << sync_endl;
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sync_cout << "info string ERROR: " << msg1 << sync_endl;
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sync_cout << "info string ERROR: " << msg2 << sync_endl;
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sync_cout << "info string ERROR: " << msg3 << sync_endl;
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sync_cout << "info string ERROR: " << msg4 << sync_endl;
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sync_cout << "info string ERROR: " << msg5 << sync_endl;
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exit(EXIT_FAILURE);
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exit(EXIT_FAILURE);
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}
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sync_cout << "info string NNUE evaluation using " << eval_file << sync_endl;
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}
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sync_cout << "info string NNUE evaluation using " << eval_file << sync_endl;
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}
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}
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// Returns a static, purely materialistic evaluation of the position from
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// the point of view of the given color. It can be divided by PawnValue to get
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// an approximation of the material advantage on the board in terms of pawns.
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@@ -163,18 +183,19 @@ Value Eval::evaluate(const Position& pos) {
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int v;
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Color stm = pos.side_to_move();
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int shuffling = pos.rule50_count();
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int simpleEval = simple_eval(pos, stm) + (int(pos.key() & 7) - 3);
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bool lazy = std::abs(simpleEval) >= RookValue + KnightValue + 16 * shuffling * shuffling
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+ std::abs(pos.this_thread()->bestValue)
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+ std::abs(pos.this_thread()->rootSimpleEval);
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int simpleEval = simple_eval(pos, stm);
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bool lazy = std::abs(simpleEval) > 2300;
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if (lazy)
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v = simpleEval;
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else
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{
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int nnueComplexity;
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Value nnue = NNUE::evaluate(pos, true, &nnueComplexity);
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bool smallNet = std::abs(simpleEval) > 1100;
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int nnueComplexity;
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Value nnue = smallNet ? NNUE::evaluate<NNUE::Small>(pos, true, &nnueComplexity)
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: NNUE::evaluate<NNUE::Big>(pos, true, &nnueComplexity);
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int optimism = pos.this_thread()->optimism[stm];
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@@ -217,7 +238,7 @@ std::string Eval::trace(Position& pos) {
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ss << std::showpoint << std::showpos << std::fixed << std::setprecision(2) << std::setw(15);
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Value v;
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v = NNUE::evaluate(pos, false);
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v = NNUE::evaluate<NNUE::Big>(pos, false);
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v = pos.side_to_move() == WHITE ? v : -v;
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ss << "NNUE evaluation " << 0.01 * UCI::to_cp(v) << " (white side)\n";
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