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Network.cpp
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#include "config.h"
#include <algorithm>
#include <cassert>
#include <list>
#include <set>
#include <iostream>
#include <fstream>
#include <memory>
#include <cmath>
#include <array>
#include <thread>
#include <boost/utility.hpp>
#include <boost/format.hpp>
#ifdef USE_CAFFE
#include <caffe/proto/caffe.pb.h>
#include <caffe/util/db.hpp>
#include <caffe/util/io.hpp>
#include <caffe/blob.hpp>
using namespace caffe;
#endif
#include "Im2Col.h"
#ifdef __APPLE__
#include <Accelerate/Accelerate.h>
#endif
#ifdef USE_MKL
#include <mkl.h>
#endif
#ifdef USE_OPENBLAS
#include <cblas.h>
#endif
#ifdef USE_OPENCL
#include "OpenCL.h"
#include "UCTNode.h"
#endif
#include "SGFTree.h"
#include "SGFParser.h"
#include "Utils.h"
#include "FastBoard.h"
#include "Random.h"
#include "Network.h"
#include "GTP.h"
#include "Utils.h"
using namespace Utils;
Network* Network::s_Net = nullptr;
#ifdef USE_CAFFE
std::unique_ptr<caffe::Net> Network::s_net;
#endif
#ifdef USE_OPENCL
extern const std::array<float, 102400> conv1_w;
extern const std::array<float, 128> conv1_b;
extern const std::array<float, 221184> conv2_w;
extern const std::array<float, 192> conv2_b;
extern const std::array<float, 331776> conv3_w;
extern const std::array<float, 192> conv3_b;
extern const std::array<float, 331776> conv4_w;
extern const std::array<float, 192> conv4_b;
extern const std::array<float, 331776> conv5_w;
extern const std::array<float, 192> conv5_b;
extern const std::array<float, 331776> conv6_w;
extern const std::array<float, 192> conv6_b;
extern const std::array<float, 331776> conv7_w;
extern const std::array<float, 192> conv7_b;
extern const std::array<float, 331776> conv8_w;
extern const std::array<float, 192> conv8_b;
extern const std::array<float, 331776> conv9_w;
extern const std::array<float, 192> conv9_b;
extern const std::array<float, 331776> conv10_w;
extern const std::array<float, 192> conv10_b;
extern const std::array<float, 331776> conv11_w;
extern const std::array<float, 192> conv11_b;
extern const std::array<float, 331776> conv12_w;
extern const std::array<float, 192> conv12_b;
extern const std::array<float, 1728> conv13_w;
extern const std::array<float, 1> conv13_b;
#else
extern const std::array<float, 76800> conv1_w;
extern const std::array<float, 96> conv1_b;
extern const std::array<float, 110592> conv2_w;
extern const std::array<float, 128> conv2_b;
extern const std::array<float, 147456> conv3_w;
extern const std::array<float, 128> conv3_b;
extern const std::array<float, 147456> conv4_w;
extern const std::array<float, 128> conv4_b;
extern const std::array<float, 147456> conv5_w;
extern const std::array<float, 128> conv5_b;
extern const std::array<float, 147456> conv6_w;
extern const std::array<float, 128> conv6_b;
extern const std::array<float, 147456> conv7_w;
extern const std::array<float, 128> conv7_b;
extern const std::array<float, 147456> conv8_w;
extern const std::array<float, 128> conv8_b;
extern const std::array<float, 147456> conv9_w;
extern const std::array<float, 128> conv9_b;
extern const std::array<float, 147456> conv10_w;
extern const std::array<float, 128> conv10_b;
extern const std::array<float, 147456> conv11_w;
extern const std::array<float, 128> conv11_b;
extern const std::array<float, 147456> conv12_w;
extern const std::array<float, 128> conv12_b;
extern const std::array<float, 1152> conv13_w;
extern const std::array<float, 1> conv13_b;
#endif
extern const std::array<float, 51200> val_conv1_w;
extern const std::array<float, 64> val_conv1_b;
extern const std::array<float, 36864> val_conv2_w;
extern const std::array<float, 64> val_conv2_b;
extern const std::array<float, 36864> val_conv3_w;
extern const std::array<float, 64> val_conv3_b;
extern const std::array<float, 36864> val_conv4_w;
extern const std::array<float, 64> val_conv4_b;
extern const std::array<float, 36864> val_conv5_w;
extern const std::array<float, 64> val_conv5_b;
extern const std::array<float, 36864> val_conv6_w;
extern const std::array<float, 64> val_conv6_b;
extern const std::array<float, 36864> val_conv7_w;
extern const std::array<float, 64> val_conv7_b;
extern const std::array<float, 36864> val_conv8_w;
extern const std::array<float, 64> val_conv8_b;
extern const std::array<float, 36864> val_conv9_w;
extern const std::array<float, 64> val_conv9_b;
extern const std::array<float, 36864> val_conv10_w;
extern const std::array<float, 64> val_conv10_b;
extern const std::array<float, 36864> val_conv11_w;
extern const std::array<float, 64> val_conv11_b;
extern const std::array<float, 576> val_conv12_w;
extern const std::array<float, 1> val_conv12_b;
extern const std::array<float, 92416> val_ip13_w;
extern const std::array<float, 256> val_ip13_b;
extern const std::array<float, 256> val_ip14_w;
extern const std::array<float, 1> val_ip14_b;
Network * Network::get_Network(void) {
if (!s_Net) {
s_Net = new Network();
s_Net->initialize();
}
return s_Net;
}
void Network::benchmark(FastState * state) {
{
// Policy
int BENCH_AMOUNT = 2000;
int cpus = cfg_num_threads;
int iters_per_thread = (BENCH_AMOUNT + (cpus - 1)) / cpus;
Time start;
ThreadGroup tg(thread_pool);
for (int i = 0; i < cpus; i++) {
tg.add_task([iters_per_thread, state]() {
FastState mystate = *state;
for (int loop = 0; loop < iters_per_thread; loop++) {
auto vec = get_scored_moves(&mystate, Ensemble::RANDOM_ROTATION);
}
});
};
tg.wait_all();
Time end;
myprintf("%5d predictions in %5.2f seconds -> %d p/s\n",
BENCH_AMOUNT,
(float)Time::timediff(start,end)/100.0,
(int)((float)BENCH_AMOUNT/((float)Time::timediff(start,end)/100.0)));
}
{
// Value
int BENCH_AMOUNT = 10000;
int cpus = cfg_num_threads;
int iters_per_thread = (BENCH_AMOUNT + (cpus - 1)) / cpus;
Time start;
ThreadGroup tg(thread_pool);
for (int i = 0; i < cpus; i++) {
tg.add_task([iters_per_thread, state]() {
FastState mystate = *state;
for (int loop = 0; loop < iters_per_thread; loop++) {
auto vec = get_value(&mystate, Ensemble::RANDOM_ROTATION);
}
});
};
tg.wait_all();
Time end;
myprintf("%5d evaluations in %5.2f seconds -> %d p/s\n",
BENCH_AMOUNT,
(float)Time::timediff(start,end)/100.0,
(int)((float)BENCH_AMOUNT/((float)Time::timediff(start,end)/100.0)));
}
}
void Network::initialize(void) {
#ifdef USE_OPENCL
myprintf("Initializing OpenCL\n");
opencl.initialize();
myprintf("Transferring weights to GPU...");
opencl_policy_net.push_convolve(5, conv1_w, conv1_b);
opencl_policy_net.push_convolve(3, conv2_w, conv2_b);
opencl_policy_net.push_convolve(3, conv3_w, conv3_b);
opencl_policy_net.push_convolve(3, conv4_w, conv4_b);
opencl_policy_net.push_convolve(3, conv5_w, conv5_b);
opencl_policy_net.push_convolve(3, conv6_w, conv6_b);
opencl_policy_net.push_convolve(3, conv7_w, conv7_b);
opencl_policy_net.push_convolve(3, conv8_w, conv8_b);
opencl_policy_net.push_convolve(3, conv9_w, conv9_b);
opencl_policy_net.push_convolve(3, conv10_w, conv10_b);
opencl_policy_net.push_convolve(3, conv11_w, conv11_b);
opencl_policy_net.push_convolve(3, conv12_w, conv12_b);
opencl_policy_net.push_convolve(3, conv13_w, conv13_b);
opencl_value_net.push_convolve(5, val_conv1_w, val_conv1_b);
opencl_value_net.push_convolve(3, val_conv2_w, val_conv2_b);
opencl_value_net.push_convolve(3, val_conv3_w, val_conv3_b);
opencl_value_net.push_convolve(3, val_conv4_w, val_conv4_b);
opencl_value_net.push_convolve(3, val_conv5_w, val_conv5_b);
opencl_value_net.push_convolve(3, val_conv6_w, val_conv6_b);
opencl_value_net.push_convolve(3, val_conv7_w, val_conv7_b);
opencl_value_net.push_convolve(3, val_conv8_w, val_conv8_b);
opencl_value_net.push_convolve(3, val_conv9_w, val_conv9_b);
opencl_value_net.push_convolve(3, val_conv10_w, val_conv10_b);
opencl_value_net.push_convolve(3, val_conv11_w, val_conv11_b);
opencl_value_net.push_convolve(3, val_conv12_w, val_conv12_b);
opencl_value_net.push_innerproduct(val_ip13_w, val_ip13_b);
opencl_value_net.push_innerproduct(val_ip14_w, val_ip14_b);
myprintf("done\n");
#endif
#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
#endif
#ifdef USE_CAFFE
myprintf("Initializing DCNN...");
Caffe::set_mode(Caffe::GPU);
s_net.reset(new Net("model_5722.txt", TEST));
s_net->CopyTrainedLayersFrom("model_5779.caffemodel");
myprintf("Inputs: %d Outputs: %d\n",
s_net->num_inputs(), s_net->num_outputs());
Blob* input_layer = s_net->input_blobs()[0];
int num_channels = input_layer->channels();
int width = input_layer->width();
int height = input_layer->height();
myprintf("Input: channels=%d, width=%d, height=%d\n", num_channels, width, height);
for (int i = 0; i < s_net->num_outputs(); i++) {
Blob* output_layer = s_net->output_blobs()[i];
int num_out_channels = output_layer->channels();
width = output_layer->width();
height = output_layer->height();
myprintf("Output: channels=%d, width=%d, height=%d\n", num_out_channels, width, height);
}
//#define WRITE_WEIGHTS
#ifdef WRITE_WEIGHTS
std::ofstream out("weights.txt");
out << "#include <array>" << std::endl << std::endl;
#endif
int total_weights = 0;
auto & layers = s_net->layers();
myprintf("%d layers:\n", layers.size());
int layer_num = 1;
#ifdef WRITE_WEIGHTS
int conv_count = 0;
#endif
for (auto it = layers.begin(); it != layers.end(); ++it, ++layer_num) {
myprintf("layer %d (%s)", layer_num, (*it)->type());
auto & blobs = (*it)->blobs();
if (blobs.size() > 0) myprintf(" = ");
for (auto pars = blobs.begin(); pars != blobs.end(); ++pars) {
const Blob & blob = *(*pars);
total_weights += blob.count();
myprintf("%s ", blob.shape_string().c_str());
if (boost::next(pars) != blobs.end()) myprintf("+ ");
#ifdef WRITE_WEIGHTS
out << "// " << blob.shape_string() << std::endl;
if (strcmp((*it)->type(), "Convolution") == 0) {
if (pars == blobs.begin()) {
conv_count++;
out << "std::array<float, " << blob.count()
<< "> val_conv" << conv_count << "_w = {{" << std::endl;
} else {
out << "std::array<float, " << blob.count()
<< "> val_conv" << conv_count << "_b = {{" << std::endl;
}
} else if (strcmp((*it)->type(), "BatchNorm") == 0) {
out << "std::array<float, " << blob.count()
<< "> val_bn" << conv_count << "_w" << (pars - blobs.begin()) + 1
<< " = {{" << std::endl;
} else if (strcmp((*it)->type(), "InnerProduct") == 0) {
if (pars == blobs.begin()) {
conv_count++;
out << "std::array<float, " << blob.count()
<< "> val_ip" << conv_count << "_w = {{" << std::endl;
} else {
out << "std::array<float, " << blob.count()
<< "> val_ip" << conv_count << "_b = {{" << std::endl;
}
} else {
out << "std::array<float, " << blob.count()
<< "> val_sc" << conv_count << "_w" << (pars - blobs.begin()) + 1
<< " = {{" << std::endl;
}
for (int idx = 0; idx < blob.count(); idx++) {
out << blob.cpu_data<float>()[idx];
if (idx != blob.count() - 1) out << ", ";
else out << " }};" << std::endl;
}
out << std::endl;
#endif
}
myprintf("\n");
}
#ifdef WRITE_WEIGHTS
out.close();
#endif
myprintf("%d total DCNN weights\n", total_weights);
#endif
}
#ifdef USE_BLAS
template<unsigned int filter_size,
unsigned int channels, unsigned int outputs,
size_t W, size_t B>
void convolve(std::vector<float>& input,
const std::array<float, W>& weights,
const std::array<float, B>& biases,
std::vector<float>& output) {
// fixed for 19x19
constexpr unsigned int width = 19;
constexpr unsigned int height = 19;
constexpr unsigned int spatial_out = width * height;
constexpr unsigned int filter_len = filter_size * filter_size;
constexpr unsigned int filter_dim = filter_len * channels;
std::vector<float> col(filter_dim * width * height);
im2col<channels, filter_size>(input, col);
// Weight shape (output, input, filter_size, filter_size)
// 96 22 5 5
// outputs[96,19x19] = weights[96,22x9] x col[22x9,19x19]
// C←αAB + βC
// M Number of rows in matrices A and C.
// N Number of columns in matrices B and C.
// K Number of columns in matrix A; number of rows in matrix B.
// lda The size of the first dimention of matrix A; if you are
// passing a matrix A[m][n], the value should be m.
// cblas_sgemm(CblasRowMajor, TransA, TransB, M, N, K, alpha, A, lda, B,
// ldb, beta, C, N);
cblas_sgemm(CblasRowMajor, CblasNoTrans, CblasNoTrans,
// M N K
outputs, spatial_out, filter_dim,
1.0f, &weights[0], filter_dim,
&col[0], spatial_out,
0.0f, &output[0], spatial_out);
auto lambda_ELU = [](float val) { return (val > 0.0f) ?
val : 1.0f * (std::exp(val) - 1.0f); };
//auto lambda_ReLU = [](float val) { return (val > 0.0f) ?
// val : 0.0f; };
for (unsigned int o = 0; o < outputs; o++) {
for (unsigned int b = 0; b < spatial_out; b++) {
output[(o * spatial_out) + b] =
lambda_ELU(biases[o] + output[(o * spatial_out) + b]);
}
}
}
template<unsigned int inputs,
unsigned int outputs,
size_t W, size_t B>
void innerproduct(std::vector<float>& input,
const std::array<float, W>& weights,
const std::array<float, B>& biases,
std::vector<float>& output) {
assert(B == outputs);
cblas_sgemv(CblasRowMajor, CblasNoTrans,
// M K
outputs, inputs,
1.0f, &weights[0], inputs,
&input[0], 1,
0.0f, &output[0], 1);
auto lambda_ELU = [](float val) { return (val > 0.0f) ?
val : 1.0f * (std::exp(val) - 1.0f); };
//auto lambda_ReLU = [](float val) { return (val > 0.0f) ?
// val : 0.0f; };
for (unsigned int o = 0; o < outputs; o++) {
float val = biases[o] + output[o];
if (outputs > 1) {
val = lambda_ELU(val);
}
output[o] = val;
}
}
template<unsigned int channels,
unsigned int spatial_size>
void batchnorm(std::vector<float>& input,
std::array<float, channels>& means,
std::array<float, channels>& variances,
std::array<float, 1> scale,
std::vector<float>& output)
{
constexpr float epsilon = 1e-5;
for (unsigned int c = 0; c < channels; ++c) {
float mean = means[c] / scale[0];
float variance = variances[c] / scale[0];
variance += epsilon;
float scale_stddiv = 1.0f / std::sqrt(variance);
float * out = &output[c * spatial_size];
float const * in = &input[c * spatial_size];
for (unsigned int b = 0; b < spatial_size; b++) {
out[b] = scale_stddiv * (in[b] - mean);
}
}
}
#endif
void Network::softmax(std::vector<float>& input,
std::vector<float>& output,
float temperature) {
assert(&input != &output);
float alpha = *std::max_element(input.begin(),
input.begin() + output.size());
alpha /= temperature;
float denom = 0.0f;
std::vector<float> helper(output.size());
for (size_t i = 0; i < output.size(); i++) {
float val = std::exp((input[i]/temperature) - alpha);
helper[i] = val;
denom += val;
}
for (size_t i = 0; i < output.size(); i++) {
output[i] = helper[i] / denom;
}
}
#ifdef USE_OPENCL
class CallbackData {
public:
std::atomic<int> * m_nodecount;
FastState m_state;
UCTNode * m_node;
int m_rotation;
std::atomic<int> * m_thread_results_outstanding;
std::vector<float> m_output_data;
std::vector<float> m_input_data;
Network::BoardPlane m_ladder;
};
extern "C" void CL_CALLBACK forward_cb(cl_event event, cl_int status,
void* data) {
CallbackData * cb_data = static_cast<CallbackData*>(data);
// Mark the kernels as available
cb_data->m_thread_results_outstanding->fetch_sub(1, std::memory_order_release);
constexpr int width = 19;
constexpr int height = 19;
std::vector<float> softmax_data(width * height);
Network::softmax(cb_data->m_output_data, softmax_data, cfg_softmax_temp);
std::vector<float>& outputs = softmax_data;
Network::Netresult result;
for (size_t idx = 0; idx < outputs.size(); idx++) {
int rot_idx = Network::rev_rotate_nn_idx(idx, cb_data->m_rotation);
float val = outputs[rot_idx];
int x = idx % 19;
int y = idx / 19;
int vtx = cb_data->m_state.board.get_vertex(x, y);
if (cb_data->m_state.board.get_square(vtx) == FastBoard::EMPTY) {
result.push_back(std::make_pair(val, vtx));
}
}
/* prune losing ladders completely */
for (auto & sm : result) {
std::pair<int, int> xy = cb_data->m_state.board.get_xy(sm.second);
int bitmappos = (xy.second * 19) + xy.first;
if (cb_data->m_ladder[bitmappos]) {
//myprintf("Ladder at %s (%d) score %f\n",
// state->board.move_to_text(sm.second).c_str(),
// sm.second,
// sm.first);
sm.first = 0.0f;
}
}
// Network::show_heatmap(&cb_data->m_state, result, false);
cb_data->m_node->scoring_cb(cb_data->m_nodecount, cb_data->m_state,
result, false);
delete cb_data;
// Reduce the count of things having pointers to UCTNodes
// or UCTSearch. We cannot destroy the search till these
// have finished.
opencl.callback_finished();
}
void Network::async_scored_moves(std::atomic<int> * nodecount,
FastState * state,
UCTNode * node,
Ensemble ensemble,
int rotation) {
if (state->board.get_boardsize() != 19) {
return;
}
assert(ensemble == DIRECT || ensemble == RANDOM_ROTATION);
if (ensemble == RANDOM_ROTATION) {
assert(rotation == -1);
rotation = Random::get_Rng()->randfix<8>();
} else {
assert(ensemble == DIRECT);
}
CallbackData * cb_data = new CallbackData();
NNPlanes planes;
BoardPlane *ladder;
gather_features_policy(state, planes, &ladder);
constexpr int width = 19;
constexpr int height = 19;
cb_data->m_nodecount = nodecount;
cb_data->m_state = *state;
cb_data->m_node = node;
cb_data->m_input_data.resize(Network::MAX_CHANNELS * 19 * 19);
cb_data->m_output_data.resize(Network::MAX_CHANNELS * 19 * 19);
cb_data->m_thread_results_outstanding = opencl.get_thread_results_outstanding();
//assert(cb_data->m_thread_result_outstanding.load(boost::memory_order_acquire) == 0);
cb_data->m_rotation = rotation;
cb_data->m_ladder = *ladder;
for (int c = 0; c < Network::POLICY_CHANNELS; ++c) {
for (int h = 0; h < height; ++h) {
for (int w = 0; w < width; ++w) {
int vtx = rotate_nn_idx(h * 19 + w, rotation);
cb_data->m_input_data[(c * height + h) * width + w] =
(float)planes[c][vtx];
}
}
}
void * data = static_cast<void*>(cb_data);
opencl_policy_net.forward(cb_data->m_input_data,
cb_data->m_output_data,
forward_cb, data);
}
#endif
float Network::get_value(FastState * state, Ensemble ensemble) {
if (state->board.get_boardsize() != 19) {
assert(false);
return 0.5f;
}
NNPlanes planes;
gather_features_value(state, planes);
float result;
if (ensemble == DIRECT) {
result = get_value_internal(state, planes, 0);
} else if (ensemble == RANDOM_ROTATION) {
int rotation = Random::get_Rng()->randfix<8>();
result = get_value_internal(state, planes, rotation);
} else {
assert(ensemble == AVERAGE_ALL);
result = get_value_internal(state, planes, 0);
//myprintf("%5.4f ", result);
for (int r = 1; r < 8; r++) {
float sum_res = get_value_internal(state, planes, r);
//myprintf("%5.4f ", sum_res);
result += sum_res;
}
result /= 8.0f;
}
//if (ensemble == AVERAGE_ALL || ensemble == DIRECT) {
// myprintf("==> %5.4f\n", result);
//}
return result;
}
Network::Netresult Network::get_scored_moves(
FastState * state, Ensemble ensemble, int rotation) {
Netresult result;
if (state->board.get_boardsize() != 19) {
return result;
}
NNPlanes planes;
BoardPlane* ladder;
gather_features_policy(state, planes, &ladder);
if (ensemble == DIRECT) {
assert(rotation >= 0 && rotation <= 7);
result = get_scored_moves_internal(state, planes, rotation);
} else if (ensemble == RANDOM_ROTATION) {
assert(rotation == -1);
int rand_rot = Random::get_Rng()->randfix<8>();
result = get_scored_moves_internal(state, planes, rand_rot);
} else {
assert(ensemble == AVERAGE_ALL);
result = get_scored_moves_internal(state, planes, 0);
for (int r = 1; r < 8; r++) {
auto sum_res = get_scored_moves_internal(state, planes, r);
for (size_t i = 0; i < sum_res.size(); i++) {
assert(result[i].second == sum_res[i].second);
result[i].first += sum_res[i].first;
}
}
std::for_each(result.begin(), result.end(),
[](scored_node & sn){ sn.first /= 8.0f; });
}
/* prune losing ladders completely */
for (auto & sm : result) {
std::pair<int, int> xy = state->board.get_xy(sm.second);
int bitmappos = (xy.second * 19) + xy.first;
if ((*ladder)[bitmappos]) {
//myprintf("Ladder at %s (%d) score %f\n",
// state->board.move_to_text(sm.second).c_str(),
// sm.second,
// sm.first);
sm.first = 0.0f;
}
}
// if (ensemble == AVERAGE_ALL || ensemble == DIRECT) {
// show_heatmap(state, result, true);
// }
return result;
}
float Network::get_value_internal(
FastState * state, NNPlanes & planes, int rotation) {
assert(rotation >= 0 && rotation <= 7);
float result;
constexpr int channels = VALUE_CHANNELS;
constexpr int width = 19;
constexpr int height = 19;
constexpr int max_channels = MAX_VALUE_CHANNELS;
std::vector<float> orig_input_data(planes.size() * width * height);
std::vector<float> input_data(max_channels * width * height);
std::vector<float> output_data(max_channels * width * height);
std::vector<float> winrate_data(256);
std::vector<float> winrate_out(1);
for (int c = 0; c < channels; ++c) {
for (int h = 0; h < height; ++h) {
for (int w = 0; w < width; ++w) {
int vtx = rotate_nn_idx(h * 19 + w, rotation);
orig_input_data[(c * height + h) * width + w] =
(float)planes[c][vtx];
}
}
}
#ifdef USE_OPENCL
std::copy(orig_input_data.begin(), orig_input_data.end(), input_data.begin());
opencl_value_net.forward(input_data, output_data, nullptr, nullptr);
// Sigmoid
float winrate_sig = (1.0f + std::tanh(output_data[0])) / 2.0f;
result = winrate_sig;
#elif defined(USE_BLAS)
std::copy(orig_input_data.begin(), orig_input_data.end(), input_data.begin());
convolve<5, 32, 64>(input_data, val_conv1_w, val_conv1_b, output_data);
std::swap(input_data, output_data);
convolve<3, 64, 64>(input_data, val_conv2_w, val_conv2_b, output_data);
std::swap(input_data, output_data);
convolve<3, 64, 64>(input_data, val_conv3_w, val_conv3_b, output_data);
std::swap(input_data, output_data);
convolve<3, 64, 64>(input_data, val_conv4_w, val_conv4_b, output_data);
std::swap(input_data, output_data);
convolve<3, 64, 64>(input_data, val_conv5_w, val_conv5_b, output_data);
std::swap(input_data, output_data);
convolve<3, 64, 64>(input_data, val_conv6_w, val_conv6_b, output_data);
std::swap(input_data, output_data);
convolve<3, 64, 64>(input_data, val_conv7_w, val_conv7_b, output_data);
std::swap(input_data, output_data);
convolve<3, 64, 64>(input_data, val_conv8_w, val_conv8_b, output_data);
std::swap(input_data, output_data);
convolve<3, 64, 64>(input_data, val_conv9_w, val_conv9_b, output_data);
std::swap(input_data, output_data);
convolve<3, 64, 64>(input_data, val_conv10_w, val_conv10_b, output_data);
std::swap(input_data, output_data);
convolve<3, 64, 64>(input_data, val_conv11_w, val_conv11_b, output_data);
std::swap(input_data, output_data);
convolve<3, 64, 1>(input_data, val_conv12_w, val_conv12_b, output_data);
// Now get the score
innerproduct<361, 256>(output_data, val_ip13_w, val_ip13_b, winrate_data);
innerproduct<256, 1>(winrate_data, val_ip14_w, val_ip14_b, winrate_out);
// Sigmoid
float winrate_sig = (1.0f + std::tanh(winrate_out[0])) / 2.0f;
result = winrate_sig;
#endif
return result;
}
Network::Netresult Network::get_scored_moves_internal(
FastState * state, NNPlanes & planes, int rotation) {
Netresult result;
assert(rotation >= 0 && rotation <= 7);
#ifdef USE_CAFFE
Blob* input_layer = s_net->input_blobs()[0];
int channels = input_layer->channels();
int width = input_layer->width();
int height = input_layer->height();
assert(channels == (int)planes.size());
assert(width == state->board.get_boardsize());
assert(height == state->board.get_boardsize());
float* orig_input_data = input_layer->mutable_cpu_data<float>();
#else
constexpr int channels = POLICY_CHANNELS;
constexpr int width = 19;
constexpr int height = 19;
constexpr int max_channels = MAX_CHANNELS;
std::vector<float> orig_input_data(planes.size() * width * height);
std::vector<float> input_data(max_channels * width * height);
std::vector<float> output_data(max_channels * width * height);
std::vector<float> softmax_data(width * height);
#endif
for (int c = 0; c < channels; ++c) {
for (int h = 0; h < height; ++h) {
for (int w = 0; w < width; ++w) {
int vtx = rotate_nn_idx(h * 19 + w, rotation);
orig_input_data[(c * height + h) * width + w] =
(float)planes[c][vtx];
}
}
}
#ifdef USE_OPENCL
std::copy(orig_input_data.begin(), orig_input_data.end(), input_data.begin());
opencl_policy_net.forward(input_data, output_data, nullptr, nullptr);
softmax(output_data, softmax_data, cfg_softmax_temp);
std::vector<float>& outputs = softmax_data;
#elif defined(USE_BLAS)
// XXX really only need the first 24
std::copy(orig_input_data.begin(), orig_input_data.end(), input_data.begin());
convolve<5, 32, 96>(input_data, conv1_w, conv1_b, output_data);
std::swap(input_data, output_data);
convolve<3, 96, 128>(input_data, conv2_w, conv2_b, output_data);
std::swap(input_data, output_data);
convolve<3, 128, 128>(input_data, conv3_w, conv3_b, output_data);
std::swap(input_data, output_data);
convolve<3, 128, 128>(input_data, conv4_w, conv4_b, output_data);
std::swap(input_data, output_data);
convolve<3, 128, 128>(input_data, conv5_w, conv5_b, output_data);
std::swap(input_data, output_data);
convolve<3, 128, 128>(input_data, conv6_w, conv6_b, output_data);
std::swap(input_data, output_data);
convolve<3, 128, 128>(input_data, conv7_w, conv7_b, output_data);
std::swap(input_data, output_data);
convolve<3, 128, 128>(input_data, conv8_w, conv8_b, output_data);
std::swap(input_data, output_data);
convolve<3, 128, 128>(input_data, conv9_w, conv9_b, output_data);
std::swap(input_data, output_data);
convolve<3, 128, 128>(input_data, conv10_w, conv10_b, output_data);
std::swap(input_data, output_data);
convolve<3, 128, 128>(input_data, conv11_w, conv11_b, output_data);
std::swap(input_data, output_data);
convolve<3, 128, 128>(input_data, conv12_w, conv12_b, output_data);
std::swap(input_data, output_data);
convolve<3, 128, 1>(input_data, conv13_w, conv13_b, output_data);
softmax(output_data, softmax_data, cfg_softmax_temp);
// Move scores
std::vector<float>& outputs = softmax_data;
#endif
#ifdef USE_CAFFE
s_net->Forward();
Blob* output_layer = s_net->output_blobs()[0];
const float* begin = output_layer->cpu_data<float>();
const float* end = begin + output_layer->channels();
auto outputs = std::vector<float>(begin, end);
#endif
for (size_t idx = 0; idx < outputs.size(); idx++) {
int rot_idx = rev_rotate_nn_idx(idx, rotation);
float val = outputs[rot_idx];
int x = idx % 19;
int y = idx / 19;
int vtx = state->board.get_vertex(x, y);
if (state->board.get_square(vtx) == FastBoard::EMPTY) {
result.push_back(std::make_pair(val, vtx));
}
}
return result;
}
void Network::show_heatmap(FastState * state, Netresult& result, bool topmoves) {
auto moves = result;
std::vector<std::string> display_map;
std::string line;
for (unsigned int y = 0; y < 19; y++) {
for (unsigned int x = 0; x < 19; x++) {
int vtx = state->board.get_vertex(x, y);
auto item = std::find_if(moves.cbegin(), moves.cend(),
[&vtx](scored_node const & item) {
return item.second == vtx;
});
float score = 0.0f;
// Non-empty squares won't be scored
if (item != moves.end()) {
score = item->first;
assert(vtx == item->second);
}
line += boost::str(boost::format("%3d ") % int(score * 1000));
if (x == 18) {
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());
}
if (topmoves) {
std::stable_sort(moves.rbegin(), moves.rend());
float cum = 0.0f;
size_t tried = 0;
while (cum < 0.85f && tried < moves.size()) {
if (moves[tried].first < 0.01f) break;
myprintf("%1.3f (%s)\n",
moves[tried].first,
state->board.move_to_text(moves[tried].second).c_str());
cum += moves[tried].first;
tried++;
}
}
}
void Network::gather_features_policy(FastState * state, NNPlanes & planes,
BoardPlane** ladder_out) {
planes.resize(32);
BoardPlane& empt_color = planes[0];
BoardPlane& move_color = planes[1];
BoardPlane& othr_color = planes[2];
BoardPlane& libs_1 = planes[3];
BoardPlane& libs_2 = planes[4];
BoardPlane& libs_3 = planes[5];
BoardPlane& libs_4 = planes[6];
BoardPlane& libs_5p = planes[7];
BoardPlane& libs_1_e = planes[8];
BoardPlane& libs_2_e = planes[9];
BoardPlane& libs_3_e = planes[10];
BoardPlane& libs_4_e = planes[11];
BoardPlane& libs_5p_e = planes[12];
BoardPlane& after_1 = planes[13];
BoardPlane& after_2 = planes[14];
BoardPlane& after_3 = planes[15];
BoardPlane& after_4 = planes[16];
BoardPlane& after_5 = planes[17];
BoardPlane& after_6p = planes[18];
BoardPlane& after_1_e = planes[19];
BoardPlane& after_2_e = planes[20];
BoardPlane& after_3_e = planes[21];
BoardPlane& after_4_e = planes[22];
BoardPlane& after_5_e = planes[23];
BoardPlane& after_6p_e = planes[24];
BoardPlane& ladder = planes[25];
BoardPlane& ladder_win = planes[26];
BoardPlane& komove = planes[27];
BoardPlane& movehist1 = planes[28];
BoardPlane& movehist2 = planes[29];
BoardPlane& has_komi = planes[30];
BoardPlane& line_3 = planes[31];
if (ladder_out) {
*ladder_out = &ladder;
}
bool white_has_komi = true;
if (std::fabs(state->get_komi()) <= 0.75f) {
white_has_komi = false;
}
int tomove = state->get_to_move();
// collect white, black occupation planes
for (int j = 0; j < 19; j++) {
for(int i = 0; i < 19; i++) {
int vtx = state->board.get_vertex(i, j);
FastBoard::square_t color =
state->board.get_square(vtx);
int idx = j * 19 + i;
if (i == 2 || i == 16 || j == 2 || j == 16) {
line_3[idx] = true;
}
if (color != FastBoard::EMPTY) {
// White gets extra points in scoring
if (color == FastBoard::WHITE && white_has_komi) {
has_komi[idx] = true;
}
if (color == tomove) {
move_color[idx] = true;
} else {
othr_color[idx] = true;
}
int rlibs = state->board.count_rliberties(vtx);
if (rlibs == 1) {
if (color == tomove) {
libs_1[idx] = true;
} else {
libs_1_e[idx] = true;
}
} else if (rlibs == 2) {
if (color == tomove) {
libs_2[idx] = true;
} else {
libs_2_e[idx] = true;
}
} else if (rlibs == 3) {
if (color == tomove) {
libs_3[idx] = true;
} else {
libs_3_e[idx] = true;
}
} else if (rlibs == 4) {
if (color == tomove) {
libs_4[idx] = true;
} else {
libs_4_e[idx] = true;
}
} else if (rlibs >= 5) {
if (color == tomove) {
libs_5p[idx] = true;
} else {
libs_5p_e[idx] = true;
}
}
} else {
empt_color[idx] = true;
std::pair<int, int> p =
state->board.after_liberties(tomove, vtx);
int al = p.first;
if (al == 1) {
after_1[idx] = true;
} else if (al == 2) {
after_2[idx] = true;
} else if (al == 3) {
after_3[idx] = true;
} else if (al == 4) {
after_4[idx] = true;
} else if (al == 5) {
after_5[idx] = true;
} else if (al >= 6) {
after_6p[idx] = true;
}
int at = p.second;