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// Copyright 2018 Google LLC
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include "cc/mcts_player.h"
#include <algorithm>
#include <cmath>
#include <iostream>
#include <sstream>
#include <utility>
#include "absl/memory/memory.h"
#include "absl/strings/str_cat.h"
#include "absl/strings/str_format.h"
#include "absl/strings/str_join.h"
#include "absl/time/clock.h"
#include "cc/check.h"
#include "cc/random.h"
#include "cc/symmetries.h"
namespace minigo {
std::ostream& operator<<(std::ostream& os, const MctsPlayer::Options& options) {
os << "name:" << options.name << " inject_noise:" << options.inject_noise
<< " soft_pick:" << options.soft_pick
<< " random_symmetry:" << options.random_symmetry
<< &q
8000
uot; resign_threshold:" << options.resign_threshold
<< " resign_enabled:" << options.resign_enabled
<< " batch_size:" << options.batch_size << " komi:" << options.komi
<< " num_readouts:" << options.num_readouts
<< " seconds_per_move:" << options.seconds_per_move
<< " time_limit:" << options.time_limit
<< " decay_factor:" << options.decay_factor
<< " random_seed:" << options.random_seed;
return os;
}
float TimeRecommendation(int move_num, float seconds_per_move, float time_limit,
float decay_factor) {
// Divide by two since you only play half the moves in a game.
int player_move_num = move_num / 2;
// Sum of geometric series maxes out at endgame_time seconds.
float endgame_time = seconds_per_move / (1.0f - decay_factor);
float base_time;
int core_moves;
if (endgame_time > time_limit) {
// There is so little main time that we're already in 'endgame' mode.
base_time = time_limit * (1.0f - decay_factor);
core_moves = 0;
} else {
// Leave over endgame_time seconds for the end, and play at
// seconds_per_move for as long as possible.
base_time = seconds_per_move;
core_moves = (time_limit - endgame_time) / seconds_per_move;
}
return base_time *
std::pow(decay_factor, std::max(player_move_num - core_moves, 0));
}
MctsPlayer::MctsPlayer(std::unique_ptr<DualNet> network, const Options& options)
: network_(std::move(network)),
game_root_(&dummy_stats_, {&bv_, &gv_, Color::kBlack}),
rnd_(options.random_seed),
options_(options) {
options_.resign_threshold = -std::abs(options_.resign_threshold);
// When to do deterministic move selection: 30 moves on a 19x19, 6 on 9x9.
// divide 2, multiply 2 guarentees that white and black do even number.
temperature_cutoff_ = !options_.soft_pick ? -1 : (((kN * kN / 12) / 2) * 2);
root_ = &game_root_;
if (options_.verbose) {
std::cerr << "MctsPlayer options: " << options_ << "\n";
std::cerr << "Random seed used: " << rnd_.seed() << "\n";
}
InitializeGame({&bv_, &gv_, Color::kBlack});
}
MctsPlayer::~MctsPlayer() {
if (options_.verbose) {
std::cerr << "Inference history:" << std::endl;
for (const auto& info : inferences_) {
std::cerr << info.model << " [" << info.first_move << ", "
<< info.last_move << "]" << std::endl;
}
}
}
void MctsPlayer::InitializeGame(const Position& position) {
game_root_ = {&dummy_stats_, Position(&bv_, &gv_, position)};
root_ = &game_root_;
game_over_ = false;
}
void MctsPlayer::NewGame() {
game_root_ = MctsNode(&dummy_stats_, {&bv_, &gv_, Color::kBlack});
root_ = &game_root_;
game_over_ = false;
history_.clear();
}
Coord MctsPlayer::SuggestMove() {
auto start = absl::Now();
// In order to correctly count the number of reads performed, the root node
// must be expanded. The root will always be expanded unless this is the first
// time SuggestMove has been called for a game, or PlayMove was called without
// a prior call to SuggestMove.
if (!root_->is_expanded) {
auto* first_node = root_->SelectLeaf();
ProcessLeaves({&first_node, 1});
}
if (options_.inject_noise) {
std::array<float, kNumMoves> noise;
rnd_.Dirichlet(kDirichletAlpha, &noise);
root_->InjectNoise(noise);
}
int current_readouts = root_->N();
if (options_.seconds_per_move > 0) {
// Use time to limit the number of reads.
float seconds_per_move = options_.seconds_per_move;
if (options_.time_limit > 0) {
seconds_per_move =
TimeRecommendation(root_->position.n(), seconds_per_move,
options_.time_limit, options_.decay_factor);
}
while (absl::Now() - start < absl::Seconds(seconds_per_move)) {
TreeSearch();
}
} else {
// Use a fixed number of reads.
while (root_->N() < current_readouts + options_.num_readouts) {
TreeSearch();
}
}
int num_readouts = root_->N() - current_readouts;
auto elapsed = absl::Now() - start;
elapsed = elapsed * 100 / num_readouts;
if (options_.verbose) {
std::cerr << "Milliseconds per 100 reads: "
<< absl::ToInt64Milliseconds(elapsed) << "ms"
<< " over " << num_readouts
<< " readouts (batched: " << options_.batch_size << ")"
<< std::endl;
}
if (ShouldResign()) {
return Coord::kResign;
}
return PickMove();
}
Coord MctsPlayer::PickMove() {
if (root_->position.n() >= temperature_cutoff_) {
Coord c = root_->GetMostVisitedMove();
if (options_.verbose) {
std::cerr << "Picked arg_max " << c << std::endl;
}
return c;
}
// Select from the first kN * kN moves (instead of kNumMoves) to avoid
// randomly choosing to pass early on in the game.
std::array<float, kN * kN> cdf;
// For moves before the temperature cutoff, exponentiate the probabilities by
// a temperature slightly larger than unity to encourage diversity in early
// play and hopefully to move away from 3-3s.
for (size_t i = 0; i < cdf.size(); ++i) {
cdf[i] = std::pow(root_->child_N(i), kVisitCountSquash);
}
for (size_t i = 1; i < cdf.size(); ++i) {
cdf[i] += cdf[i - 1];
}
float e = rnd_();
Coord c = SearchSorted(cdf, e * cdf.back());
if (options_.verbose) {
std::cerr << "Picked rnd(" << e << ") " << c << std::endl;
}
MG_DCHECK(root_->child_N(c) != 0);
return c;
}
absl::Span<MctsNode* const> MctsPlayer::TreeSearch() {
int batch_size = options_.batch_size;
int max_iterations = batch_size * 2;
leaves_.resize(0);
for (int i = 0; i < max_iterations; ++i) {
auto* leaf = root_->SelectLeaf();
if (leaf == nullptr) {
continue;
}
if (leaf->position.is_game_over() ||
leaf->position.n() >= kMaxSearchDepth) {
float value = leaf->position.CalculateScore(options_.komi) > 0 ? 1 : -1;
leaf->IncorporateEndGameResult(value, root_);
} else {
leaf->AddVirtualLoss(root_);
leaves_.push_back(leaf);
if (static_cast<int>(leaves_.size()) == batch_size) {
break;
}
}
}
if (!leaves_.empty()) {
ProcessLeaves(absl::MakeSpan(leaves_));
for (auto* leaf : leaves_) {
leaf->RevertVirtualLoss(root_);
}
}
return absl::MakeConstSpan(leaves_);
}
bool MctsPlayer::ShouldResign() const {
return options_.resign_enabled &&
root_->Q_perspective() < options_.resign_threshold;
}
void MctsPlayer::PlayMove(Coord c) {
if (game_over_) {
std::cerr << "ERROR: can't play move " << c << ", game is over"
<< std::endl;
return;
}
// Handle resignations.
if (c == Coord::kResign) {
if (root_->position.to_play() == Color::kBlack) {
result_ = -1;
result_string_ = "W+R";
} else {
result_ = 1;
result_string_ = "B+R";
}
game_over_ = true;
return;
}
PushHistory(c);
root_ = root_->MaybeAddChild(c);
// Don't need to keep the parent's children around anymore because we'll
// never revisit them.
root_->parent->PruneChildren(c);
if (options_.verbose) {
std::cerr << absl::StreamFormat("%s Q: %0.5f\n", name(), root_->Q());
std::cerr << "Played >>" << c << std::endl;
}
// Handle consecutive passing.
if (root_->position.is_game_over() ||
root_->position.n() >= kMaxSearchDepth) {
float score = root_->position.CalculateScore(options_.komi);
result_string_ = FormatScore(score);
result_ = score < 0 ? -1 : score > 0 ? 1 : 0;
game_over_ = true;
}
}
std::string MctsPlayer::FormatScore(float score) const {
return absl::StrFormat("%c+%.1f", score > 0 ? 'B' : 'W', std::abs(score));
}
void MctsPlayer::PushHistory(Coord c) {
history_.emplace_back();
History& history = history_.back();
history.c = c;
history.comment = root_->Describe();
history.node = root_;
if (!inferences_.empty()) {
// Record which model(s) were used when running tree search for this move.
std::vector<std::string> models;
for (auto it = inferences_.rbegin(); it != inferences_.rend(); ++it) {
if (it->last_move < root_->position.n()) {
break;
}
models.push_back(it->model);
}
std::reverse(models.begin(), models.end());
auto model_comment = absl::StrCat("models:", absl::StrJoin(models, ","));
history.comment = absl::StrCat(model_comment, "\n", history.comment);
if (options_.verbose) {
std::cerr << model_comment << std::endl;
}
}
// Convert child visit counts to a probability distribution, pi.
if (root_->position.n() < temperature_cutoff_) {
// Squash counts before normalizing to match softpick behavior in PickMove.
for (int i = 0; i < kNumMoves; ++i) {
history.search_pi[i] = std::pow(root_->child_N(i), kVisitCountSquash);
}
} else {
for (int i = 0; i < kNumMoves; ++i) {
history.search_pi[i] = root_->child_N(i);
}
}
// Normalize counts.
float sum = 0;
for (int i = 0; i < kNumMoves; ++i) {
sum += history.search_pi[i];
}
for (int i = 0; i < kNumMoves; ++i) {
history.search_pi[i] /= sum;
}
}
void MctsPlayer::ProcessLeaves(absl::Span<MctsNode*> leaves) {
// Select symmetry operations to apply.
symmetries_used_.resize(0);
if (options_.random_symmetry) {
symmetries_used_.reserve(leaves.size());
for (size_t i = 0; i < leaves.size(); ++i) {
symmetries_used_.push_back(static_cast<symmetry::Symmetry>(
rnd_.UniformInt(0, symmetry::kNumSymmetries - 1)));
}
} else {
symmetries_used_.resize(leaves.size(), symmetry::kIdentity);
}
// Build input features for each leaf, applying random symmetries if
// requested.
DualNet::BoardFeatures raw_features;
features_.resize(leaves.size());
for (size_t i = 0; i < leaves.size(); ++i) {
leaves[i]->GetMoveHistory(DualNet::kMoveHistory, &recent_positions_);
DualNet::SetFeatures(recent_positions_, leaves[i]->position.to_play(),
&raw_features);
symmetry::ApplySymmetry<float, kN, DualNet::kNumStoneFeatures>(
symmetries_used_[i], raw_features.data(), features_[i].data());
}
std::vector<const DualNet::BoardFeatures*> feature_ptrs;
feature_ptrs.reserve(features_.size());
for (const auto& feature : features_) {
feature_ptrs.push_back(&feature);
}
outputs_.resize(leaves.size());
std::vector<DualNet::Output*> output_ptrs;
output_ptrs.reserve(outputs_.size());
for (auto& output : outputs_) {
output_ptrs.push_back(&output);
}
// Run inference.
network_->RunMany(std::move(feature_ptrs), std::move(output_ptrs), &model_);
// Record some information about the inference.
if (!model_.empty()) {
if (inferences_.empty() || model_ != inferences_.back().model) {
inferences_.emplace_back(model_, root_->position.n());
}
inferences_.back().last_move = root_->position.n();
inferences_.back().total_count += leaves.size();
}
// Incorporate the inference outputs back into tree search, undoing any
// previously applied random symmetries.
std::array<float, kNumMoves> raw_policy;
for (size_t i = 0; i < leaves.size(); ++i) {
MctsNode* leaf = leaves[i];
const auto& output = outputs_[i];
symmetry::ApplySymmetry<float, kN, 1>(
symmetry::Inverse(symmetries_used_[i]), output.policy.data(),
raw_policy.data());
raw_policy[Coord::kPass] = output.policy[Coord::kPass];
leaf->IncorporateResults(raw_policy, output.value, root_);
}
}
} // namespace minigo