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180 lines (152 loc) · 7.04 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.
*/
#ifndef NETWORK_H_INCLUDED
#define NETWORK_H_INCLUDED
#include "config.h"
#include <deque>
#include <array>
#include <memory>
#include <string>
#include <utility>
#include <vector>
#include <fstream>
#include "NNCache.h"
#include "FastState.h"
#ifdef USE_OPENCL
#include "OpenCLScheduler.h"
#endif
#include "GameState.h"
#include "ForwardPipe.h"
#ifdef USE_OPENCL
#include "OpenCLScheduler.h"
#endif
#ifdef USE_OPENCL_SELFCHECK
#include "SMP.h"
#endif
// Winograd filter transformation changes 3x3 filters to M + 3 - 1
constexpr auto WINOGRAD_M = 4;
constexpr auto WINOGRAD_ALPHA = WINOGRAD_M + 3 - 1;
constexpr auto WINOGRAD_WTILES = BOARD_SIZE / WINOGRAD_M + (BOARD_SIZE % WINOGRAD_M != 0);
constexpr auto WINOGRAD_TILE = WINOGRAD_ALPHA * WINOGRAD_ALPHA;
constexpr auto WINOGRAD_P = WINOGRAD_WTILES * WINOGRAD_WTILES;
constexpr auto SQ2 = 1.4142135623730951f; // Square root of 2
class Network {
using ForwardPipeWeights = ForwardPipe::ForwardPipeWeights;
public:
static constexpr auto NUM_SYMMETRIES = 8;
static constexpr auto IDENTITY_SYMMETRY = 0;
enum Ensemble {
DIRECT, RANDOM_SYMMETRY, AVERAGE
};
using PolicyVertexPair = std::pair<float,int>;
using Netresult = NNCache::Netresult;
Netresult get_output(const GameState* const state,
const Ensemble ensemble,
const int symmetry = -1,
const bool read_cache = true,
const bool write_cache = true,
const bool force_selfcheck = false);
static constexpr auto INPUT_MOVES = 8;
static constexpr auto INPUT_CHANNELS = 2 * INPUT_MOVES + 2;
static constexpr auto OUTPUTS_POLICY = 2;
static constexpr auto OUTPUTS_VALUE = 1;
static constexpr auto VALUE_LAYER = 256;
void initialize(int playouts, const std::string & weightsfile);
float benchmark_time(int centiseconds);
void benchmark(const GameState * const state,
const int iterations = 1600);
static void show_heatmap(const FastState * const state,
const Netresult & netres, const bool topmoves);
static std::vector<float> gather_features(const GameState* const state,
const int symmetry);
static std::pair<int, int> get_symmetry(const std::pair<int, int>& vertex,
const int symmetry,
const int board_size = BOARD_SIZE);
size_t get_estimated_size();
size_t get_estimated_cache_size();
void nncache_resize(int max_count);
private:
std::pair<int, int> load_v1_network(std::istream& wtfile);
std::pair<int, int> load_network_file(const std::string& filename);
static std::vector<float> winograd_transform_f(const std::vector<float>& f,
const int outputs, const int channels);
static std::vector<float> zeropad_U(const std::vector<float>& U,
const int outputs, const int channels,
const int outputs_pad, const int channels_pad);
static void winograd_transform_in(const std::vector<float>& in,
std::vector<float>& V,
const int C);
static void winograd_transform_out(const std::vector<float>& M,
std::vector<float>& Y,
const int K);
static void winograd_convolve3(const int outputs,
const std::vector<float>& input,
const std::vector<float>& U,
std::vector<float>& V,
std::vector<float>& M,
std::vector<float>& output);
static void winograd_sgemm(const std::vector<float>& U,
const std::vector<float>& V,
std::vector<float>& M, const int C, const int K);
Netresult get_output_internal(const GameState* const state,
const int symmetry, bool selfcheck = false);
static void fill_input_plane_pair(const FullBoard& board,
std::vector<float>::iterator black,
std::vector<float>::iterator white,
const int symmetry);
bool probe_cache(const GameState* const state, Network::Netresult& result);
std::unique_ptr<ForwardPipe>&& init_net(int channels,
std::unique_ptr<ForwardPipe>&& pipe);
#ifdef USE_HALF
void select_precision(int channels);
#endif
std::unique_ptr<ForwardPipe> m_forward;
#ifdef USE_OPENCL_SELFCHECK
void compare_net_outputs(const Netresult& data, const Netresult& ref);
std::unique_ptr<ForwardPipe> m_forward_cpu;
#endif
NNCache m_nncache;
size_t estimated_size{0};
// Residual tower
std::shared_ptr<ForwardPipeWeights> m_fwd_weights;
// Policy head
std::array<float, OUTPUTS_POLICY> m_bn_pol_w1;
std::array<float, OUTPUTS_POLICY> m_bn_pol_w2;
std::array<float, OUTPUTS_POLICY
* NUM_INTERSECTIONS
* POTENTIAL_MOVES> m_ip_pol_w;
std::array<float, POTENTIAL_MOVES> m_ip_pol_b;
// Value head
std::array<float, OUTPUTS_VALUE> m_bn_val_w1;
std::array<float, OUTPUTS_VALUE> m_bn_val_w2;
std::array<float, OUTPUTS_VALUE
* NUM_INTERSECTIONS
* VALUE_LAYER> m_ip1_val_w;
std::array<float, VALUE_LAYER> m_ip1_val_b;
std::array<float, VALUE_LAYER> m_ip2_val_w;
std::array<float, 1> m_ip2_val_b;
bool m_value_head_not_stm;
bool m_has_se{false};
};
#endif