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Copy pathNetwork.cpp
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1043 lines (919 loc) · 37.5 KB
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/*
This file is part of Leela Zero.
Copyright (C) 2017-2019 Gian-Carlo Pascutto and contributors
Leela Zero is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
Leela Zero is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with Leela Zero. If not, see <http://www.gnu.org/licenses/>.
Additional permission under GNU GPL version 3 section 7
If you modify this Program, or any covered work, by linking or
combining it with NVIDIA Corporation's libraries from the
NVIDIA CUDA Toolkit and/or the NVIDIA CUDA Deep Neural
Network library and/or the NVIDIA TensorRT inference library
(or a modified version of those libraries), containing parts covered
by the terms of the respective license agreement, the licensors of
this Program grant you additional permission to convey the resulting
work.
*/
#include "config.h"
#include <algorithm>
#include <array>
#include <cassert>
#include <cmath>
#include <iterator>
#include <memory>
#include <sstream>
#include <string>
#include <boost/utility.hpp>
#include <boost/format.hpp>
#include <boost/spirit/home/x3.hpp>
#ifndef USE_BLAS
#include <Eigen/Dense>
#endif
#ifdef __APPLE__
#include <Accelerate/Accelerate.h>
#endif
#ifdef USE_MKL
#include <mkl.h>
#endif
#ifdef USE_OPENBLAS
#include <cblas.h>
#endif
#include "zlib.h"
#include "Network.h"
#include "CPUPipe.h"
#ifdef USE_OPENCL
#include "OpenCLScheduler.h"
#include "UCTNode.h"
#endif
#include "FastBoard.h"
#include "FastState.h"
#include "FullBoard.h"
#include "GameState.h"
#include "GTP.h"
#include "NNCache.h"
#include "Random.h"
#include "ThreadPool.h"
#include "Timing.h"
#include "Utils.h"
namespace x3 = boost::spirit::x3;
using namespace Utils;
#ifndef USE_BLAS
// Eigen helpers
template <typename T>
using EigenVectorMap =
Eigen::Map<Eigen::Matrix<T, Eigen::Dynamic, 1>>;
template <typename T>
using ConstEigenVectorMap =
Eigen::Map<const Eigen::Matrix<T, Eigen::Dynamic, 1>>;
template <typename T>
using ConstEigenMatrixMap =
Eigen::Map<const Eigen::Matrix<T, Eigen::Dynamic, Eigen::Dynamic>>;
#endif
// Symmetry helper
static std::array<std::array<int, NUM_INTERSECTIONS>,
Network::NUM_SYMMETRIES> symmetry_nn_idx_table;
float Network::benchmark_time(int centiseconds) {
const auto cpus = cfg_num_threads;
ThreadGroup tg(thread_pool);
std::atomic<int> runcount{0};
GameState state;
state.init_game(BOARD_SIZE, 7.5);
// As a sanity run, try one run with self check.
// Isn't enough to guarantee correctness but better than nothing,
// plus for large nets self-check takes a while (1~3 eval per second)
get_output(&state, Ensemble::RANDOM_SYMMETRY, -1, false, true, true);
const Time start;
for (auto i = size_t{0}; i < cpus; i++) {
tg.add_task([this, &runcount, start, centiseconds, state]() {
while (true) {
runcount++;
get_output(&state, Ensemble::RANDOM_SYMMETRY, -1, false);
const Time end;
const auto elapsed = Time::timediff_centis(start, end);
if (elapsed >= centiseconds) {
break;
}
}
});
}
tg.wait_all();
const Time end;
const auto elapsed = Time::timediff_centis(start, end);
return 100.0f * runcount.load() / elapsed;
}
void Network::benchmark(const GameState* const state, const int iterations) {
const auto cpus = cfg_num_threads;
const Time start;
ThreadGroup tg(thread_pool);
std::atomic<int> runcount{0};
for (auto i = size_t{0}; i < cpus; i++) {
tg.add_task([this, &runcount, iterations, state]() {
while (runcount < iterations) {
runcount++;
get_output(state, Ensemble::RANDOM_SYMMETRY, -1, false);
}
});
}
tg.wait_all();
const Time end;
const auto elapsed = Time::timediff_seconds(start, end);
myprintf("%5d evaluations in %5.2f seconds -> %d n/s\n",
runcount.load(), elapsed, int(runcount.load() / elapsed));
}
template<class container>
void process_bn_var(container& weights) {
constexpr float epsilon = 1e-5f;
for (auto&& w : weights) {
w = 1.0f / std::sqrt(w + epsilon);
}
}
std::vector<float> Network::winograd_transform_f(const std::vector<float>& f,
const int outputs,
const int channels) {
// F(4x4, 3x3) Winograd filter transformation
// transpose(G.dot(f).dot(G.transpose()))
// U matrix is transposed for better memory layout in SGEMM
auto U = std::vector<float>(WINOGRAD_TILE * outputs * channels);
const auto G = std::array<float, 3 * WINOGRAD_ALPHA>
{ 1.0f, 0.0f, 0.0f,
-2.0f/3.0f, -SQ2/3.0f, -1.0f/3.0f,
-2.0f/3.0f, SQ2/3.0f, -1.0f/3.0f,
1.0f/6.0f, SQ2/6.0f, 1.0f/3.0f,
1.0f/6.0f, -SQ2/6.0f, 1.0f/3.0f,
0.0f, 0.0f, 1.0f};
auto temp = std::array<float, 3 * WINOGRAD_ALPHA>{};
constexpr auto max_buffersize = 8;
auto buffersize = max_buffersize;
if (outputs % buffersize != 0) {
buffersize = 1;
}
std::array<float, max_buffersize * WINOGRAD_ALPHA * WINOGRAD_ALPHA> buffer;
for (auto c = 0; c < channels; c++) {
for (auto o_b = 0; o_b < outputs/buffersize; o_b++) {
for (auto bufferline = 0; bufferline < buffersize; bufferline++) {
const auto o = o_b * buffersize + bufferline;
for (auto i = 0; i < WINOGRAD_ALPHA; i++) {
for (auto j = 0; j < 3; j++) {
auto acc = 0.0f;
for (auto k = 0; k < 3; k++) {
acc += G[i*3 + k] * f[o*channels*9 + c*9 + k*3 + j];
}
temp[i*3 + j] = acc;
}
}
for (auto xi = 0; xi < WINOGRAD_ALPHA; xi++) {
for (auto nu = 0; nu < WINOGRAD_ALPHA; nu++) {
auto acc = 0.0f;
for (auto k = 0; k < 3; k++) {
acc += temp[xi*3 + k] * G[nu*3 + k];
}
buffer[(xi * WINOGRAD_ALPHA + nu) * buffersize + bufferline] = acc;
}
}
}
for (auto i = 0; i < WINOGRAD_ALPHA * WINOGRAD_ALPHA; i++) {
for (auto entry = 0; entry < buffersize; entry++) {
const auto o = o_b * buffersize + entry;
U[i * outputs * channels
+ c * outputs
+ o] =
buffer[buffersize * i + entry];
}
}
}
}
return U;
}
std::pair<int, int> Network::load_v1_network(std::istream& wtfile) {
// Count size of the network
myprintf("Detecting residual layers...");
// We are version 1 or 2
if (m_has_se) {
myprintf("v%d: Minigo SE net...", 3);
} else if (m_value_head_not_stm) {
myprintf("v%d...", 2);
} else {
myprintf("v%d...", 1);
}
// First line was the version number
auto linecount = size_t{1};
auto channels = 0;
auto line = std::string{};
while (std::getline(wtfile, line)) {
auto iss = std::stringstream{line};
// Third line of parameters are the convolution layer biases,
// so this tells us the amount of channels in the residual layers.
// We are assuming all layers have the same amount of filters.
if (linecount == 2) {
auto count = std::distance(std::istream_iterator<std::string>(iss),
std::istream_iterator<std::string>());
myprintf("%d channels...", count);
channels = count;
}
linecount++;
}
// 1 format id, 1 input layer (4 x weights), 14 ending weights,
// possibly followed by SE weights;
// the rest are residuals, every residual has 8 x weight lines
auto residual_blocks = linecount - (1 + 4 + 14);
auto lines_per_block = m_has_se ? 12 : 8;
if (residual_blocks % lines_per_block != 0) {
myprintf("\nInconsistent number of weights in the file.\n");
return {0, 0};
}
residual_blocks /= lines_per_block;
myprintf("%d blocks.\n", residual_blocks);
// Re-read file and process
wtfile.clear();
wtfile.seekg(0, std::ios::beg);
// Get the file format id out of the way
std::getline(wtfile, line);
const auto plain_conv_layers = 1 + (residual_blocks * 2);
const auto plain_conv_wts = plain_conv_layers * 4;
const auto se_wts_begin = plain_conv_wts + 14;
linecount = 0;
while (std::getline(wtfile, line)) {
std::vector<float> weights;
auto it_line = line.cbegin();
const auto ok = phrase_parse(it_line, line.cend(),
*x3::float_, x3::space, weights);
if (!ok || it_line != line.cend()) {
myprintf("\nFailed to parse weight file. Error on line %d.\n",
linecount + 2); //+1 from version line, +1 from 0-indexing
return {0,0};
}
if (linecount < plain_conv_wts) {
if (linecount % 4 == 0) {
m_fwd_weights->m_conv_weights.emplace_back(weights);
} else if (linecount % 4 == 1) {
// Redundant in our model, but they encode the
// number of outputs so we have to read them in.
m_fwd_weights->m_conv_biases.emplace_back(weights);
} else if (linecount % 4 == 2) {
m_fwd_weights->m_batchnorm_means.emplace_back(weights);
} else if (linecount % 4 == 3) {
process_bn_var(weights);
m_fwd_weights->m_batchnorm_stddevs.emplace_back(weights);
}
} else if (linecount < se_wts_begin) {
switch (linecount - plain_conv_wts) {
case 0: m_fwd_weights->m_conv_pol_w = std::move(weights); break;
case 1: m_fwd_weights->m_conv_pol_b = std::move(weights); break;
case 2: std::copy(cbegin(weights), cend(weights),
begin(m_bn_pol_w1)); break;
case 3: std::copy(cbegin(weights), cend(weights),
begin(m_bn_pol_w2)); break;
case 4: std::copy(cbegin(weights), cend(weights),
begin(m_ip_pol_w)); break;
case 5: std::copy(cbegin(weights), cend(weights),
begin(m_ip_pol_b)); break;
case 6: m_fwd_weights->m_conv_val_w = std::move(weights); break;
case 7: m_fwd_weights->m_conv_val_b = std::move(weights); break;
case 8: std::copy(cbegin(weights), cend(weights),
begin(m_bn_val_w1)); break;
case 9: std::copy(cbegin(weights), cend(weights),
begin(m_bn_val_w2)); break;
case 10: std::copy(cbegin(weights), cend(weights),
begin(m_ip1_val_w)); break;
case 11: std::copy(cbegin(weights), cend(weights),
begin(m_ip1_val_b)); break;
case 12: std::copy(cbegin(weights), cend(weights),
begin(m_ip2_val_w)); break;
case 13: std::copy(cbegin(weights), cend(weights),
begin(m_ip2_val_b)); break;
}
} else {
switch ((linecount - se_wts_begin) % 4) {
case 0: m_fwd_weights->m_se_fc1_weights.emplace_back(weights); break;
case 1: m_fwd_weights->m_se_fc1_biases.emplace_back(weights); break;
case 2: m_fwd_weights->m_se_fc2_weights.emplace_back(weights); break;
case 3: m_fwd_weights->m_se_fc2_biases.emplace_back(weights); break;
}
}
linecount++;
}
process_bn_var(m_bn_pol_w2);
process_bn_var(m_bn_val_w2);
return {channels, static_cast<int>(residual_blocks)};
}
std::pair<int, int> Network::load_network_file(const std::string& filename) {
// gzopen supports both gz and non-gz files, will decompress
// or just read directly as needed.
auto gzhandle = gzopen(filename.c_str(), "rb");
if (gzhandle == nullptr) {
myprintf("Could not open weights file: %s\n", filename.c_str());
return {0, 0};
}
// Stream the gz file in to a memory buffer stream.
auto buffer = std::stringstream{};
constexpr auto chunkBufferSize = 64 * 1024;
std::vector<char> chunkBuffer(chunkBufferSize);
while (true) {
auto bytesRead = gzread(gzhandle, chunkBuffer.data(), chunkBufferSize);
if (bytesRead == 0) break;
if (bytesRead < 0) {
myprintf("Failed to decompress or read: %s\n", filename.c_str());
gzclose(gzhandle);
return {0, 0};
}
assert(bytesRead <= chunkBufferSize);
buffer.write(chunkBuffer.data(), bytesRead);
}
gzclose(gzhandle);
// Read format version
auto line = std::string{};
auto format_version = -1;
if (std::getline(buffer, line)) {
auto iss = std::stringstream{line};
// First line is the file format version id
iss >> format_version;
if (iss.fail() || (format_version <= 0 || format_version > 3)) {
myprintf("Weights file is the wrong version.\n");
return {0, 0};
} else {
// Version 2 networks are identical to v1, except
// that they return the value for black instead of
// the player to move. This is used by ELF Open Go.
if (format_version == 3) {
m_has_se = true;
}
if (format_version == 2 || format_version == 3) {
m_value_head_not_stm = true;
} else {
m_value_head_not_stm = false;
}
return load_v1_network(buffer);
}
}
return {0, 0};
}
std::unique_ptr<ForwardPipe>&& Network::init_net(int channels,
std::unique_ptr<ForwardPipe>&& pipe) {
pipe->initialize(channels);
pipe->push_weights(WINOGRAD_ALPHA, INPUT_CHANNELS, channels, m_fwd_weights);
return std::move(pipe);
}
#ifdef USE_HALF
void Network::select_precision(int channels) {
if (cfg_precision == precision_t::AUTO) {
auto score_fp16 = float{-1.0};
auto score_fp32 = float{-1.0};
myprintf("Initializing OpenCL (autodetecting precision).\n");
// Setup fp16 here so that we can see if we can skip autodetect.
// However, if fp16 sanity check fails we will return a fp32 and pray it works.
auto fp16_net = std::make_unique<OpenCLScheduler<half_float::half>>();
if (!fp16_net->needs_autodetect()) {
try {
myprintf("OpenCL: using fp16/half compute support.\n");
m_forward = init_net(channels, std::move(fp16_net));
benchmark_time(1); // a sanity check run
} catch (...) {
myprintf("OpenCL: fp16/half failed despite driver claiming support.\n");
myprintf("Falling back to single precision\n");
m_forward.reset();
m_forward = init_net(channels,
std::make_unique<OpenCLScheduler<float>>());
}
return;
}
// Start by setting up fp32.
try {
m_forward.reset();
m_forward = init_net(channels,
std::make_unique<OpenCLScheduler<float>>());
score_fp32 = benchmark_time(100);
} catch (...) {
// empty - if exception thrown just throw away fp32 net
}
// Now benchmark fp16.
try {
m_forward.reset();
m_forward = init_net(channels, std::move(fp16_net));
score_fp16 = benchmark_time(100);
} catch (...) {
// empty - if exception thrown just throw away fp16 net
}
if (score_fp16 < 0.0f && score_fp32 < 0.0f) {
myprintf("Both single precision and half precision failed to run.\n");
throw std::runtime_error("Failed to initialize net.");
} else if (score_fp16 < 0.0f) {
myprintf("Using OpenCL single precision (half precision failed to run).\n");
m_forward.reset();
m_forward = init_net(channels,
std::make_unique<OpenCLScheduler<float>>());
} else if (score_fp32 < 0.0f) {
myprintf("Using OpenCL half precision (single precision failed to run).\n");
} else if (score_fp32 * 1.05f > score_fp16) {
myprintf("Using OpenCL single precision (less than 5%% slower than half).\n");
m_forward.reset();
m_forward = init_net(channels,
std::make_unique<OpenCLScheduler<float>>());
} else {
myprintf("Using OpenCL half precision (at least 5%% faster than single).\n");
}
return;
} else if (cfg_precision == precision_t::SINGLE) {
myprintf("Initializing OpenCL (single precision).\n");
m_forward = init_net(channels,
std::make_unique<OpenCLScheduler<float>>());
return;
} else if (cfg_precision == precision_t::HALF) {
myprintf("Initializing OpenCL (half precision).\n");
m_forward = init_net(channels,
std::make_unique<OpenCLScheduler<half_float::half>>());
return;
}
}
#endif
void Network::initialize(int playouts, const std::string & weightsfile) {
#ifdef USE_BLAS
#ifndef __APPLE__
#ifdef USE_OPENBLAS
openblas_set_num_threads(1);
myprintf("BLAS Core: %s\n", openblas_get_corename());
#endif
#ifdef USE_MKL
//mkl_set_threading_layer(MKL_THREADING_SEQUENTIAL);
mkl_set_num_threads(1);
MKLVersion Version;
mkl_get_version(&Version);
myprintf("BLAS core: MKL %s\n", Version.Processor);
#endif
#endif
#else
myprintf("BLAS Core: built-in Eigen %d.%d.%d library.\n",
EIGEN_WORLD_VERSION, EIGEN_MAJOR_VERSION, EIGEN_MINOR_VERSION);
#endif
m_fwd_weights = std::make_shared<ForwardPipeWeights>();
// Make a guess at a good size as long as the user doesn't
// explicitly set a maximum memory usage.
m_nncache.set_size_from_playouts(playouts);
// Prepare symmetry table
for (auto s = 0; s < NUM_SYMMETRIES; ++s) {
for (auto v = 0; v < NUM_INTERSECTIONS; ++v) {
const auto newvtx =
get_symmetry({v % BOARD_SIZE, v / BOARD_SIZE}, s);
symmetry_nn_idx_table[s][v] =
(newvtx.second * BOARD_SIZE) + newvtx.first;
assert(symmetry_nn_idx_table[s][v] >= 0
&& symmetry_nn_idx_table[s][v] < NUM_INTERSECTIONS);
}
}
// Load network from file
size_t channels, residual_blocks;
std::tie(channels, residual_blocks) = load_network_file(weightsfile);
if (channels == 0) {
exit(EXIT_FAILURE);
}
auto weight_index = size_t{0};
// Input convolution
// Winograd transform convolution weights
m_fwd_weights->m_conv_weights[weight_index] =
winograd_transform_f(m_fwd_weights->m_conv_weights[weight_index],
channels, INPUT_CHANNELS);
weight_index++;
// Residual block convolutions
for (auto i = size_t{0}; i < residual_blocks * 2; i++) {
m_fwd_weights->m_conv_weights[weight_index] =
winograd_transform_f(m_fwd_weights->m_conv_weights[weight_index],
channels, channels);
weight_index++;
}
// Biases are not calculated and are typically zero but some networks might
// still have non-zero biases.
// Move biases to batchnorm means to make the output match without having
// to separately add the biases.
auto bias_size = m_fwd_weights->m_conv_biases.size();
for (auto i = size_t{0}; i < bias_size; i++) {
auto means_size = m_fwd_weights->m_batchnorm_means[i].size();
for (auto j = size_t{0}; j < means_size; j++) {
m_fwd_weights->m_batchnorm_means[i][j] -= m_fwd_weights->m_conv_biases[i][j];
m_fwd_weights->m_conv_biases[i][j] = 0.0f;
}
}
for (auto i = size_t{0}; i < m_bn_val_w1.size(); i++) {
m_bn_val_w1[i] -= m_fwd_weights->m_conv_val_b[i];
m_fwd_weights->m_conv_val_b[i] = 0.0f;
}
for (auto i = size_t{0}; i < m_bn_pol_w1.size(); i++) {
m_bn_pol_w1[i] -= m_fwd_weights->m_conv_pol_b[i];
m_fwd_weights->m_conv_pol_b[i] = 0.0f;
}
#ifdef USE_OPENCL
if (cfg_cpu_only) {
myprintf("Initializing CPU-only evaluation.\n");
m_forward = init_net(channels, std::make_unique<CPUPipe>());
} else {
#ifdef USE_OPENCL_SELFCHECK
// initialize CPU reference first, so that we can self-check
// when doing fp16 vs. fp32 detections
m_forward_cpu = init_net(channels, std::make_unique<CPUPipe>());
#endif
#ifdef USE_HALF
// HALF support is enabled, and we are using the GPU.
// Select the precision to use at runtime.
select_precision(channels);
#else
myprintf("Initializing OpenCL (single precision).\n");
m_forward = init_net(channels,
std::make_unique<OpenCLScheduler<float>>());
#endif
}
#else //!USE_OPENCL
myprintf("Initializing CPU-only evaluation.\n");
m_forward = init_net(channels, std::make_unique<CPUPipe>());
#endif
// Need to estimate size before clearing up the pipe.
get_estimated_size();
m_fwd_weights.reset();
}
template<unsigned int inputs,
unsigned int outputs,
bool ReLU,
size_t W>
std::vector<float> innerproduct(const std::vector<float>& input,
const std::array<float, W>& weights,
const std::array<float, outputs>& biases) {
std::vector<float> output(outputs);
#ifdef USE_BLAS
cblas_sgemv(CblasRowMajor, CblasNoTrans,
// M K
outputs, inputs,
1.0f, &weights[0], inputs,
&input[0], 1,
0.0f, &output[0], 1);
#else
EigenVectorMap<float> y(output.data(), outputs);
y.noalias() =
ConstEigenMatrixMap<float>(weights.data(),
inputs,
outputs).transpose()
* ConstEigenVectorMap<float>(input.data(), inputs);
#endif
const auto lambda_ReLU = [](const auto val) { return (val > 0.0f) ?
val : 0.0f; };
for (unsigned int o = 0; o < outputs; o++) {
auto val = biases[o] + output[o];
if (ReLU) {
val = lambda_ReLU(val);
}
output[o] = val;
}
return output;
}
template <size_t spatial_size>
void batchnorm(const size_t channels,
std::vector<float>& data,
const float* const means,
const float* const stddivs,
const float* const eltwise = nullptr) {
const auto lambda_ReLU = [](const auto val) { return (val > 0.0f) ?
val : 0.0f; };
for (auto c = size_t{0}; c < channels; ++c) {
const auto mean = means[c];
const auto scale_stddiv = stddivs[c];
const auto arr = &data[c * spatial_size];
if (eltwise == nullptr) {
// Classical BN
for (auto b = size_t{0}; b < spatial_size; b++) {
arr[b] = lambda_ReLU(scale_stddiv * (arr[b] - mean));
}
} else {
// BN + residual add
const auto res = &eltwise[c * spatial_size];
for (auto b = size_t{0}; b < spatial_size; b++) {
arr[b] = lambda_ReLU((scale_stddiv * (arr[b] - mean)) + res[b]);
}
}
}
}
#ifdef USE_OPENCL_SELFCHECK
void Network::compare_net_outputs(const Netresult& data,
const Netresult& ref) {
// Calculates L2-norm between data and ref.
constexpr auto max_error = 0.2f;
auto error = 0.0f;
for (auto idx = size_t{0}; idx < data.policy.size(); ++idx) {
const auto diff = data.policy[idx] - ref.policy[idx];
error += diff * diff;
}
const auto diff_pass = data.policy_pass - ref.policy_pass;
const auto diff_winrate = data.winrate - ref.winrate;
error += diff_pass * diff_pass;
error += diff_winrate * diff_winrate;
error = std::sqrt(error);
if (error > max_error || std::isnan(error)) {
printf("Error in OpenCL calculation: Update your device's OpenCL drivers "
"or reduce the amount of games played simultaneously.\n");
throw std::runtime_error("OpenCL self-check mismatch.");
}
}
#endif
std::vector<float> softmax(const std::vector<float>& input,
const float temperature = 1.0f) {
auto output = std::vector<float>{};
output.reserve(input.size());
const auto alpha = *std::max_element(cbegin(input), cend(input));
auto denom = 0.0f;
for (const auto in_val : input) {
auto val = std::exp((in_val - alpha) / temperature);
denom += val;
output.push_back(val);
}
for (auto& out : output) {
out /= denom;
}
return output;
}
bool Network::probe_cache(const GameState* const state,
Network::Netresult& result) {
if (m_nncache.lookup(state->board.get_hash(), result)) {
return true;
}
// If we are not generating a self-play game, try to find
// symmetries if we are in the early opening.
if (!cfg_noise && !cfg_random_cnt
&& state->get_movenum()
< (state->get_timecontrol().opening_moves(BOARD_SIZE) / 2)) {
for (auto sym = 0; sym < Network::NUM_SYMMETRIES; ++sym) {
if (sym == Network::IDENTITY_SYMMETRY) {
continue;
}
const auto hash = state->get_symmetry_hash(sym);
if (m_nncache.lookup(hash, result)) {
decltype(result.policy) corrected_policy;
for (auto idx = size_t{0}; idx < NUM_INTERSECTIONS; ++idx) {
const auto sym_idx = symmetry_nn_idx_table[sym][idx];
corrected_policy[idx] = result.policy[sym_idx];
}
result.policy = std::move(corrected_policy);
return true;
}
}
}
return false;
}
Network::Netresult Network::get_output(
const GameState* const state, const Ensemble ensemble, const int symmetry,
const bool read_cache, const bool write_cache, const bool force_selfcheck) {
Netresult result;
if (state->board.get_boardsize() != BOARD_SIZE) {
return result;
}
if (read_cache) {
// See if we already have this in the cache.
if (probe_cache(state, result)) {
return result;
}
}
if (ensemble == DIRECT) {
assert(symmetry >= 0 && symmetry < NUM_SYMMETRIES);
result = get_output_internal(state, symmetry);
} else if (ensemble == AVERAGE) {
assert(symmetry == -1);
for (auto sym = 0; sym < NUM_SYMMETRIES; ++sym) {
auto tmpresult = get_output_internal(state, sym);
result.winrate +=
tmpresult.winrate / static_cast<float>(NUM_SYMMETRIES);
result.policy_pass +=
tmpresult.policy_pass / static_cast<float>(NUM_SYMMETRIES);
for (auto idx = size_t{0}; idx < NUM_INTERSECTIONS; idx++) {
result.policy[idx] +=
tmpresult.policy[idx] / static_cast<float>(NUM_SYMMETRIES);
}
}
} else {
assert(ensemble == RANDOM_SYMMETRY);
assert(symmetry == -1);
const auto rand_sym = Random::get_Rng().randfix<NUM_SYMMETRIES>();
result = get_output_internal(state, rand_sym);
#ifdef USE_OPENCL_SELFCHECK
// Both implementations are available, self-check the OpenCL driver by
// running both with a probability of 1/2000.
// selfcheck is done here because this is the only place NN
// evaluation is done on actual gameplay.
if (m_forward_cpu != nullptr
&& (force_selfcheck || Random::get_Rng().randfix<SELFCHECK_PROBABILITY>() == 0)
) {
auto result_ref = get_output_internal(state, rand_sym, true);
compare_net_outputs(result, result_ref);
}
#else
(void)force_selfcheck;
#endif
}
// v2 format (ELF Open Go) returns black value, not stm
if (m_value_head_not_stm) {
if (state->board.get_to_move() == FastBoard::WHITE) {
result.winrate = 1.0f - result.winrate;
}
}
if (write_cache) {
// Insert result into cache.
m_nncache.insert(state->board.get_hash(), result);
}
return result;
}
Network::Netresult Network::get_output_internal(
const GameState* const state, const int symmetry, bool selfcheck) {
assert(symmetry >= 0 && symmetry < NUM_SYMMETRIES);
constexpr auto width = BOARD_SIZE;
constexpr auto height = BOARD_SIZE;
const auto input_data = gather_features(state, symmetry);
std::vector<float> policy_data(OUTPUTS_POLICY * width * height);
std::vector<float> value_data(OUTPUTS_VALUE * width * height);
#ifdef USE_OPENCL_SELFCHECK
if (selfcheck) {
m_forward_cpu->forward(input_data, policy_data, value_data);
} else {
m_forward->forward(input_data, policy_data, value_data);
}
#else
m_forward->forward(input_data, policy_data, value_data);
(void) selfcheck;
#endif
// Get the moves
batchnorm<NUM_INTERSECTIONS>(OUTPUTS_POLICY, policy_data,
m_bn_pol_w1.data(), m_bn_pol_w2.data());
const auto policy_out =
innerproduct<OUTPUTS_POLICY * NUM_INTERSECTIONS, POTENTIAL_MOVES, false>(
policy_data, m_ip_pol_w, m_ip_pol_b);
const auto outputs = softmax(policy_out, cfg_softmax_temp);
// Now get the value
batchnorm<NUM_INTERSECTIONS>(OUTPUTS_VALUE, value_data,
m_bn_val_w1.data(), m_bn_val_w2.data());
const auto winrate_data =
innerproduct<OUTPUTS_VALUE * NUM_INTERSECTIONS, VALUE_LAYER, true>(
value_data, m_ip1_val_w, m_ip1_val_b);
const auto winrate_out =
innerproduct<VALUE_LAYER, 1, false>(winrate_data, m_ip2_val_w, m_ip2_val_b);
// Map TanH output range [-1..1] to [0..1] range
const auto winrate = (1.0f + std::tanh(winrate_out[0])) / 2.0f;
Netresult result;
for (auto idx = size_t{0}; idx < NUM_INTERSECTIONS; idx++) {
const auto sym_idx = symmetry_nn_idx_table[symmetry][idx];
result.policy[sym_idx] = outputs[idx];
}
result.policy_pass = outputs[NUM_INTERSECTIONS];
result.winrate = winrate;
return result;
}
void Network::show_heatmap(const FastState* const state,
const Netresult& result,
const bool topmoves) {
std::vector<std::string> display_map;
std::string line;
for (unsigned int y = 0; y < BOARD_SIZE; y++) {
for (unsigned int x = 0; x < BOARD_SIZE; x++) {
auto policy = 0;
const auto vertex = state->board.get_vertex(x, y);
if (state->board.get_state(vertex) == FastBoard::EMPTY) {
policy = result.policy[y * BOARD_SIZE + x] * 1000;
}
line += boost::str(boost::format("%3d ") % policy);
}
display_map.push_back(line);
line.clear();
}
for (int i = display_map.size() - 1; i >= 0; --i) {
myprintf("%s\n", display_map[i].c_str());
}
const auto pass_policy = int(result.policy_pass * 1000);
myprintf("pass: %d\n", pass_policy);
myprintf("winrate: %f\n", result.winrate);
if (topmoves) {
std::vector<Network::PolicyVertexPair> moves;
for (auto i=0; i < NUM_INTERSECTIONS; i++) {
const auto x = i % BOARD_SIZE;
const auto y = i / BOARD_SIZE;
const auto vertex = state->board.get_vertex(x, y);
if (state->board.get_state(vertex) == FastBoard::EMPTY) {
moves.emplace_back(result.policy[i], vertex);
}
}
moves.emplace_back(result.policy_pass, FastBoard::PASS);
std::stable_sort(rbegin(moves), rend(moves));
auto cum = 0.0f;
for (const auto& move : moves) {
if (cum > 0.85f || move.first < 0.01f) break;
myprintf("%1.3f (%s)\n",
move.first,
state->board.move_to_text(move.second).c_str());
cum += move.first;
}
}
}
void Network::fill_input_plane_pair(const FullBoard& board,
std::vector<float>::iterator black,
std::vector<float>::iterator white,
const int symmetry) {
for (auto idx = 0; idx < NUM_INTERSECTIONS; idx++) {
const auto sym_idx = symmetry_nn_idx_table[symmetry][idx];
const auto x = sym_idx % BOARD_SIZE;
const auto y = sym_idx / BOARD_SIZE;
const auto color = board.get_state(x, y);
if (color == FastBoard::BLACK) {
black[idx] = float(true);
} else if (color == FastBoard::WHITE) {
white[idx] = float(true);
}
}
}
std::vector<float> Network::gather_features(const GameState* const state,
const int symmetry) {
assert(symmetry >= 0 && symmetry < NUM_SYMMETRIES);
auto input_data = std::vector<float>(INPUT_CHANNELS * NUM_INTERSECTIONS);
const auto to_move = state->get_to_move();
const auto blacks_move = to_move == FastBoard::BLACK;
const auto black_it = blacks_move ?
begin(input_data) :
begin(input_data) + INPUT_MOVES * NUM_INTERSECTIONS;
const auto white_it = blacks_move ?
begin(input_data) + INPUT_MOVES * NUM_INTERSECTIONS :
begin(input_data);
const auto to_move_it = blacks_move ?
begin(input_data) + 2 * INPUT_MOVES * NUM_INTERSECTIONS :
begin(input_data) + (2 * INPUT_MOVES + 1) * NUM_INTERSECTIONS;
const auto moves = std::min<size_t>(state->get_movenum() + 1, INPUT_MOVES);
// Go back in time, fill history boards
for (auto h = size_t{0}; h < moves; h++) {
// collect white, black occupation planes
fill_input_plane_pair(state->get_past_board(h),
black_it + h * NUM_INTERSECTIONS,
white_it + h * NUM_INTERSECTIONS,
symmetry);
}
std::fill(to_move_it, to_move_it + NUM_INTERSECTIONS, float(true));
return input_data;
}
std::pair<int, int> Network::get_symmetry(const std::pair<int, int>& vertex,
const int symmetry,
const int board_size) {
auto x = vertex.first;
auto y = vertex.second;
assert(x >= 0 && x < board_size);
assert(y >= 0 && y < board_size);
assert(symmetry >= 0 && symmetry < NUM_SYMMETRIES);
if ((symmetry & 4) != 0) {
std::swap(x, y);
}
if ((symmetry & 2) != 0) {
x = board_size - x - 1;
}
if ((symmetry & 1) != 0) {
y = board_size - y - 1;
}
assert(x >= 0 && x < board_size);
assert(y >= 0 && y < board_size);
assert(symmetry != IDENTITY_SYMMETRY || vertex == std::make_pair(x, y));
return {x, y};
}
size_t Network::get_estimated_size() {
if (estimated_size != 0) {
return estimated_size;
}
auto result = size_t{0};
const auto lambda_vector_size = [](const std::vector<std::vector<float>> &v) {
auto result = size_t{0};