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PaddleDetection
提交 c9b79989 编写于 作者: D dongdaxiang
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fix tag in async_executor

上级 95b887c4
隐藏空白更改
内联 并排
Showing with 336 addition and 334 deletion
paddle/fluid/framework/async_executor.cc
浏览文件 @ c9b79989
......@@ -102,139 +102,139 @@ void AsyncExecutor::GatherServers(
}
void AsyncExecutor::InitParamConfig() {
for (int i = 0; i <
_pslib_ptr->get_param()->server_param().\
downpour_server_param().\
downpour_table_param_size();
++i) {
if (_pslib_ptr->get_param()->server_param().\
downpour_server_param().downpour_table_param(i).\
table_class().find("SparseTable") != -1) {
_param_config.fea_dim = _pslib_ptr->get_param()->server_param().\
downpour_server_param().\
downpour_table_param(i).\
accessor().fea_dim();
break;
}
for (int i = 0; i <
_pslib_ptr->get_param()->server_param(). \
downpour_server_param(). \
downpour_table_param_size();
++i) {
if (_pslib_ptr->get_param()->server_param(). \
downpour_server_param().downpour_table_param(i). \
table_class().find("SparseTable") != -1) {
_param_config.fea_dim = _pslib_ptr->get_param()->server_param(). \
downpour_server_param(). \
downpour_table_param(i). \
accessor().fea_dim();
break;
}
_param_config.slot_dim = _param_config.fea_dim - 2;
_param_config.tmp_push_dense_wait_times = static_cast<int32_t>(
_pslib_ptr->get_param()->trainer_param().push_dense_per_batch());
_param_config.tmp_push_sparse_wait_times = static_cast<int32_t>(
_pslib_ptr->get_param()->trainer_param().push_sparse_per_batch());
for (auto t = 0u;
t < _pslib_ptr->get_param()->trainer_param().skip_op_size();
++t) {
_param_config.skip_op.push_back(
_pslib_ptr->get_param()->trainer_param().skip_op(t));
}
_param_config.slot_dim = _param_config.fea_dim - 2;
_param_config.tmp_push_dense_wait_times = static_cast<int32_t>(
_pslib_ptr->get_param()->trainer_param().push_dense_per_batch());
_param_config.tmp_push_sparse_wait_times = static_cast<int32_t>(
_pslib_ptr->get_param()->trainer_param().push_sparse_per_batch());
for (auto t = 0u;
t < _pslib_ptr->get_param()->trainer_param().skip_op_size();
++t) {
_param_config.skip_op.push_back(
_pslib_ptr->get_param()->trainer_param().skip_op(t));
}
for (auto t = 0u;
t < _pslib_ptr->get_param()->trainer_param().sparse_table_size();
++t) {
auto& table = _pslib_ptr->get_param()->trainer_param().sparse_table(t);
std::vector<std::string> tmp_sparse_variable_name;
for (int i = 0u; i < table.slot_value_size(); ++i) {
tmp_sparse_variable_name.push_back(table.slot_value(i));
_param_config.slot_alias_to_table[table.slot_key(i)] =
table.table_id();
}
for (auto t = 0u;
t < _pslib_ptr->get_param()->trainer_param().sparse_table_size();
++t) {
auto& table = _pslib_ptr->get_param()->trainer_param().sparse_table(t);
std::vector<std::string> tmp_sparse_variable_name;
for (int i = 0u; i < table.slot_value_size(); ++i) {
tmp_sparse_variable_name.push_back(table.slot_value(i));
_param_config.slot_alias_to_table[table.slot_key(i)] =
table.table_id();
}
std::vector<std::string> tmp_sparse_gradient_variable_name;
for (auto i = 0u; i < table.slot_gradient_size(); ++i) {
tmp_sparse_gradient_variable_name.push_back(
table.slot_gradient(i));
}
_param_config.slot_input_vec[table.table_id()] =
std::move(tmp_sparse_variable_name);
_param_config.gradient_var[table.table_id()] =
std::move(tmp_sparse_gradient_variable_name);
_param_config.sparse_table_id.push_back(table.table_id());
std::vector<std::string> tmp_sparse_gradient_variable_name;
for (auto i = 0u; i < table.slot_gradient_size(); ++i) {
tmp_sparse_gradient_variable_name.push_back(
table.slot_gradient(i));
}
for (auto t = 0u;
t < _pslib_ptr->get_param()->trainer_param().dense_table_size();
++t) {
auto& table = _pslib_ptr->get_param()->trainer_param().dense_table(t);
std::vector<std::string> tmp_dense_variable_name;
for (int i = 0u; i < table.dense_variable_name_size(); ++i) {
tmp_dense_variable_name.push_back(table.dense_variable_name(i));
}
std::vector<std::string> tmp_dense_gradient_variable_name;
for (auto i = 0u; i < table.dense_gradient_variable_name_size(); ++i) {
tmp_dense_gradient_variable_name.push_back(
table.dense_gradient_variable_name(i));
}
_param_config.dense_variable_name[table.table_id()] =
std::move(tmp_dense_variable_name);
_param_config.dense_gradient_variable_name[table.table_id()] =
std::move(tmp_dense_gradient_variable_name);
_param_config.dense_table_id.push_back(table.table_id());
_param_config.dense_table_size.push_back(table.fea_dim());
_param_config.slot_input_vec[table.table_id()] =
std::move(tmp_sparse_variable_name);
_param_config.gradient_var[table.table_id()] =
std::move(tmp_sparse_gradient_variable_name);
_param_config.sparse_table_id.push_back(table.table_id());
}
for (auto t = 0u;
t < _pslib_ptr->get_param()->trainer_param().dense_table_size();
++t) {
auto& table = _pslib_ptr->get_param()->trainer_param().dense_table(t);
std::vector<std::string> tmp_dense_variable_name;
for (int i = 0u; i < table.dense_variable_name_size(); ++i) {
tmp_dense_variable_name.push_back(table.dense_variable_name(i));
}
std::vector<std::string> tmp_dense_gradient_variable_name;
for (auto i = 0u; i < table.dense_gradient_variable_name_size(); ++i) {
tmp_dense_gradient_variable_name.push_back(
table.dense_gradient_variable_name(i));
}
_param_config.dense_variable_name[table.table_id()] =
std::move(tmp_dense_variable_name);
_param_config.dense_gradient_variable_name[table.table_id()] =
std::move(tmp_dense_gradient_variable_name);
_param_config.dense_table_id.push_back(table.table_id());
_param_config.dense_table_size.push_back(table.fea_dim());
}
}
void AsyncExecutor::InitModel() {
for (auto table_id : _param_config.dense_table_id) {
std::vector<paddle::ps::Region> regions;
for (auto& t : _param_config.dense_variable_name[table_id]) {
Variable* var = root_scope_->FindVar(t);
CHECK(var != nullptr) << "var[" << t << "] not found";
LoDTensor* tensor = var->GetMutable<LoDTensor>();
float* g = tensor->data<float>();
CHECK(g != nullptr) << "var[" << t << "] value not initialized";
float init_range = 0.2;
int rown = tensor->dims()[0];
init_range /= sqrt(rown);
std::normal_distribution<float> ndistr(0.0, 1.0);
for (auto i = 0u; i < tensor->numel(); ++i) {
g[i] = ndistr(local_random_engine()) * init_range;
}
paddle::ps::Region reg(g, tensor->numel());
regions.emplace_back(std::move(reg));
}
auto push_status =
_pslib_ptr->_worker_ptr->push_dense_param(
regions.data(), regions.size(), table_id);
push_status.wait();
auto status = push_status.get();
if (status != 0) {
LOG(FATAL) << "push dense param failed, status[" << status << "]";
exit(-1);
}
for (auto table_id : _param_config.dense_table_id) {
std::vector<paddle::ps::Region> regions;
for (auto& t : _param_config.dense_variable_name[table_id]) {
Variable* var = root_scope_->FindVar(t);
CHECK(var != nullptr) << "var[" << t << "] not found";
LoDTensor* tensor = var->GetMutable<LoDTensor>();
float* g = tensor->data<float>();
CHECK(g != nullptr) << "var[" << t << "] value not initialized";
float init_range = 0.2;
int rown = tensor->dims()[0];
init_range /= sqrt(rown);
std::normal_distribution<float> ndistr(0.0, 1.0);
for (auto i = 0u; i < tensor->numel(); ++i) {
g[i] = ndistr(local_random_engine()) * init_range;
}
paddle::ps::Region reg(g, tensor->numel());
regions.emplace_back(std::move(reg));
}
auto push_status =
_pslib_ptr->_worker_ptr->push_dense_param(
regions.data(), regions.size(), table_id);
push_status.wait();
auto status = push_status.get();
if (status != 0) {
LOG(FATAL) << "push dense param failed, status[" << status << "]";
exit(-1);
}
}
}
void AsyncExecutor::SaveModel(const std::string& path) {
auto ret = _pslib_ptr->_worker_ptr->flush();
ret.wait();
ret = _pslib_ptr->_worker_ptr->save(path, 0);
ret.wait();
int32_t feasign_cnt = ret.get();
if (feasign_cnt == -1) { // (colourful-tree) TODO should be feasign_cnt < 0
LOG(FATAL) << "save model failed";
exit(-1);
}
auto ret = _pslib_ptr->_worker_ptr->flush();
ret.wait();
ret = _pslib_ptr->_worker_ptr->save(path, 0);
ret.wait();
int32_t feasign_cnt = ret.get();
if (feasign_cnt == -1) { // (colourful-tree) TODO should be feasign_cnt < 0
LOG(FATAL) << "save model failed";
exit(-1);
}
}
void AsyncExecutor::PrepareDenseThread(const std::string& mode) {
if (mode == "mpi") {
DensePullThreadParam param;
param.ps_client = _pslib_ptr->_worker_ptr;;
param.threshold = 1;
param.training_thread_num = actual_thread_num;
param.root_scope = root_scope_;
param.dense_params = &_param_config.dense_variable_name;
_pull_dense_thread = std::shared_ptr<DensePullThread>(
new DensePullThread(param));
_pull_dense_thread->start();
}
if (mode == "mpi") {
DensePullThreadParam param;
param.ps_client = _pslib_ptr->_worker_ptr;;
param.threshold = 1;
param.training_thread_num = actual_thread_num;
param.root_scope = root_scope_;
param.dense_params = &_param_config.dense_variable_name;
_pull_dense_thread = std::shared_ptr<DensePullThread>(
new DensePullThread(param));
_pull_dense_thread->start();
}
}
#endif
......
paddle/fluid/framework/async_executor.h
浏览文件 @ c9b79989
......@@ -45,7 +45,8 @@ inline std::default_random_engine& local_random_engine() {
engine_wrapper_t() {
static std::atomic<uint64_t> x(0);
std::seed_seq sseq = {x++, x++, x++,
static_cast<uint64_t>(current_realtime() * 1000)};
static_cast<uint64_t>(
current_realtime() * 1000)};
engine.seed(sseq);
}
};
......@@ -77,6 +78,7 @@ class AsyncExecutor {
void SaveModel(const std::string& path);
void InitParamConfig();
#endif
private:
void CreateThreads(ExecutorThreadWorker* worker,
const ProgramDesc& main_program,
......@@ -87,6 +89,7 @@ class AsyncExecutor {
#ifdef PADDLE_WITH_PSLIB
void PrepareDenseThread(const std::string& mode);
#endif
public:
#ifdef PADDLE_WITH_PSLIB
std::shared_ptr<paddle::distributed::PSlib> _pslib_ptr;
......
paddle/fluid/framework/executor_thread_worker.cc
浏览文件 @ c9b79989
......@@ -33,87 +33,87 @@ namespace framework {
#ifdef PADDLE_WITH_PSLIB
int DensePullThread::start() {
_running = true;
_t = std::thread(&DensePullThread::run, this);
return 0;
_running = true;
_t = std::thread(&DensePullThread::run, this);
return 0;
}
void DensePullThread::run() {
while (_running) {
_pull_dense_status.resize(0);
for (auto& t : _dense_variable_name) {
if (check_update_param(t.first)) {
auto status = pull_dense(t.first);
_pull_dense_status.emplace_back(std::move(status));
reset_thread_version(t.first);
}
}
if (_pull_dense_status.size() != 0) {
wait_all();
}
usleep(_sleep_time_ms * 1000);
while (_running) {
_pull_dense_status.resize(0);
for (auto& t : _dense_variable_name) {
if (check_update_param(t.first)) {
auto status = pull_dense(t.first);
_pull_dense_status.emplace_back(std::move(status));
reset_thread_version(t.first);
}
}
if (_pull_dense_status.size() != 0) {
wait_all();
}
usleep(_sleep_time_ms * 1000);
}
}
bool DensePullThread::check_update_param(uint64_t table_id) {
{
std::lock_guard<std::mutex> lock(_mutex_for_version);
auto& version = _training_versions[table_id];
_current_version[table_id] =
*(std::min_element(version.begin(), version.end()));
}
if (_current_version[table_id] - _last_versions[table_id] < _threshold) {
return false;
}
return true;
{
std::lock_guard<std::mutex> lock(_mutex_for_version);
auto& version = _training_versions[table_id];
_current_version[table_id] =
*(std::min_element(version.begin(), version.end()));
}
if (_current_version[table_id] - _last_versions[table_id] < _threshold) {
return false;
}
return true;
}
void DensePullThread::reset_thread_version(uint64_t table_id) {
std::lock_guard<std::mutex> lock(_mutex_for_version);
_last_versions[table_id] = _current_version[table_id];
std::lock_guard<std::mutex> lock(_mutex_for_version);
_last_versions[table_id] = _current_version[table_id];
}
std::future<int32_t> DensePullThread::pull_dense(uint64_t table_id) {
auto& regions = _regions[table_id];
regions.clear();
auto& variables = _dense_variable_name[table_id];
regions.resize(variables.size());
for (auto i = 0u; i < variables.size(); ++i) {
auto& t = variables[i];
Variable* var = _root_scope->FindVar(t);
LoDTensor* tensor = var->GetMutable<LoDTensor>();
float* w = tensor->data<float>();
paddle::ps::Region reg(w, tensor->numel());
regions[i] = std::move(reg);
}
return _ps_client->pull_dense(regions.data(), regions.size(), table_id);
auto& regions = _regions[table_id];
regions.clear();
auto& variables = _dense_variable_name[table_id];
regions.resize(variables.size());
for (auto i = 0u; i < variables.size(); ++i) {
auto& t = variables[i];
Variable* var = _root_scope->FindVar(t);
LoDTensor* tensor = var->GetMutable<LoDTensor>();
float* w = tensor->data<float>();
paddle::ps::Region reg(w, tensor->numel());
regions[i] = std::move(reg);
}
return _ps_client->pull_dense(regions.data(), regions.size(), table_id);
}
void DensePullThread::wait_all() {
for (auto& t : _pull_dense_status) {
t.wait();
auto status = t.get();
if (status != 0) {
LOG(WARNING) << "pull dense failed times:" <<
++_pull_dense_fail_times;
}
for (auto& t : _pull_dense_status) {
t.wait();
auto status = t.get();
if (status != 0) {
LOG(WARNING) << "pull dense failed times:" <<
++_pull_dense_fail_times;
}
if (_pull_dense_fail_times > 20) {
LOG(FATAL) << "pull dense failed times more than 20 times";
exit(-1);
}
_pull_dense_status.resize(0);
}
if (_pull_dense_fail_times > 20) {
LOG(FATAL) << "pull dense failed times more than 20 times";
exit(-1);
}
_pull_dense_status.resize(0);
}
void DensePullThread::increase_thread_version(
int thread_id, uint64_t table_id) {
std::lock_guard<std::mutex> lock(_mutex_for_version);
_training_versions[table_id][thread_id]++;
std::lock_guard<std::mutex> lock(_mutex_for_version);
_training_versions[table_id][thread_id]++;
}
#endif
#endif
void ExecutorThreadWorker::CreateThreadOperators(const ProgramDesc& program) {
auto& block = program.Block(0);
......@@ -336,56 +336,56 @@ void AsyncExecutorThreadWorker::TrainFiles() {
void AsyncExecutorThreadWorker::SetPSlibPtr(
std::shared_ptr<paddle::distributed::PSlib> pslib_ptr) {
_pslib_ptr = pslib_ptr;
_pslib_ptr = pslib_ptr;
}
void AsyncExecutorThreadWorker::SetPullDenseThread(
std::shared_ptr<DensePullThread> dpt) {
_pull_dense_thread = dpt;
_pull_dense_thread = dpt;
}
void AsyncExecutorThreadWorker::TrainOneNetwork() {
PrepareParams();
for (auto& op : ops_) {
if (op->Type().find("sgd") != std::string::npos) {
continue;
}
bool need_skip = false;
for (auto t = 0u; t < _param_config->skip_op.size(); ++t) {
if (op->Type().find(_param_config->skip_op[t]) !=
std::string::npos) {
need_skip = true;
break;
}
}
if (!need_skip) {
op->Run(*thread_scope_, place_);
}
PrepareParams();
for (auto& op : ops_) {
if (op->Type().find("sgd") != std::string::npos) {
continue;
}
bool need_skip = false;
for (auto t = 0u; t < _param_config->skip_op.size(); ++t) {
if (op->Type().find(_param_config->skip_op[t]) !=
std::string::npos) {
need_skip = true;
break;
}
}
if (!need_skip) {
op->Run(*thread_scope_, place_);
}
UpdateParams();
}
UpdateParams();
}
void AsyncExecutorThreadWorker::SetParamConfig(
AsyncWorkerParamConfig* param_config) {
_param_config = param_config;
_param_config = param_config;
}
void AsyncExecutorThreadWorker::PrepareParams() {
for (auto table_id : _param_config->sparse_table_id) {
PullSparse(table_id);
for (auto& t : _pull_sparse_status) {
t.wait();
auto status = t.get();
if (status != 0) {
LOG(ERROR) << "pull sparse failed, status[" << status << "]";
exit(-1);
}
}
for (auto table_id : _param_config->sparse_table_id) {
PullSparse(table_id);
for (auto& t : _pull_sparse_status) {
t.wait();
auto status = t.get();
if (status != 0) {
LOG(ERROR) << "pull sparse failed, status[" << status << "]";
exit(-1);
}
}
_pull_sparse_status.resize(0);
}
_pull_sparse_status.resize(0);
for (auto table_id : _param_config->sparse_table_id) {
FillSparse(table_id);
}
for (auto table_id : _param_config->sparse_table_id) {
FillSparse(table_id);
}
}
void AsyncExecutorThreadWorker::UpdateParams() {
......@@ -426,21 +426,20 @@ void AsyncExecutorThreadWorker::UpdateParams() {
}
void AsyncExecutorThreadWorker::PushDense(int table_id) {
std::vector<paddle::ps::Region> regions;
for (auto& t : _param_config->dense_gradient_variable_name[table_id]) {
Variable* var = thread_scope_->FindVar(t);
CHECK(var != nullptr) << "var[" << t << "] not found";
LoDTensor* tensor = var->GetMutable<LoDTensor>();
int count = tensor->numel();
float* g = tensor->data<float>();
paddle::ps::Region reg(g, count);
regions.emplace_back(std::move(reg));
}
auto status = _pslib_ptr->_worker_ptr->push_dense(
regions.data(), regions.size(), table_id);
_push_dense_status.push_back(std::move(status));
std::vector<paddle::ps::Region> regions;
for (auto& t : _param_config->dense_gradient_variable_name[table_id]) {
Variable* var = thread_scope_->FindVar(t);
CHECK(var != nullptr) << "var[" << t << "] not found";
LoDTensor* tensor = var->GetMutable<LoDTensor>();
int count = tensor->numel();
float* g = tensor->data<float>();
paddle::ps::Region reg(g, count);
regions.emplace_back(std::move(reg));
}
auto status = _pslib_ptr->_worker_ptr->push_dense(
regions.data(), regions.size(), table_id);
_push_dense_status.push_back(std::move(status));
}
void AsyncExecutorThreadWorker::PullSparse(int table_id) {
......@@ -643,24 +642,24 @@ void AsyncExecutorThreadWorker::check_pull_push_memory(
const std::vector<uint64_t>& features,
std::vector<std::vector<float>>& push_g,
int dim) {
push_g.resize(features.size() + 1);
for (auto& t : push_g) {
t.resize(dim);
}
push_g.resize(features.size() + 1);
for (auto& t : push_g) {
t.resize(dim);
}
}
void AsyncExecutorThreadWorker::check_pull_push_memory(
const std::vector<uint64_t>& features,
std::vector<float*>& push_g,
int dim) {
if (features.size() > push_g.size()) {
push_g.reserve(features.size() + 1);
auto size = features.size() - push_g.size() + 1;
for (auto i = 0u; i < size; ++i) {
float* ptr = new float[dim];
push_g.push_back(ptr);
}
const std::vector<uint64_t>& features,
std::vector<float*>& push_g,
int dim) {
if (features.size() > push_g.size()) {
push_g.reserve(features.size() + 1);
auto size = features.size() - push_g.size() + 1;
for (auto i = 0u; i < size; ++i) {
float* ptr = new float[dim];
push_g.push_back(ptr);
}
}
}
#endif
......
paddle/fluid/framework/executor_thread_worker.h
浏览文件 @ c9b79989
......@@ -67,79 +67,79 @@ struct DensePullThreadParam {
class DensePullThread {
public:
explicit DensePullThread(const DensePullThreadParam& param) :
_running(false) {
_ps_client = param.ps_client;
_threshold = param.threshold;
_thread_num = param.training_thread_num;
_root_scope = param.root_scope;
_sleep_time_ms = param.sleep_time_ms;
for (auto& t : *param.dense_params) {
_dense_variable_name[t.first].insert(
_dense_variable_name[t.first].end(),
t.second.begin(), t.second.end());
_training_versions[t.first].resize(_thread_num, 0);
_last_versions[t.first] = 0;
_current_version[t.first] = 0;
}
_running(false) {
_ps_client = param.ps_client;
_threshold = param.threshold;
_thread_num = param.training_thread_num;
_root_scope = param.root_scope;
_sleep_time_ms = param.sleep_time_ms;
for (auto& t : *param.dense_params) {
_dense_variable_name[t.first].insert(
_dense_variable_name[t.first].end(),
t.second.begin(), t.second.end());
_training_versions[t.first].resize(_thread_num, 0);
_last_versions[t.first] = 0;
_current_version[t.first] = 0;
}
int start();
void stop() {
if (_running) {
_running = false;
_t.join();
}
}
int start();
void stop() {
if (_running) {
_running = false;
_t.join();
}
void increase_thread_version(int thread_id, uint64_t table_id);
void reset_thread_version(uint64_t table_id);
std::future<int32_t> pull_dense(uint64_t table_id);
void pull_dense2(uint64_t table_id);
void wait_all();
}
void increase_thread_version(int thread_id, uint64_t table_id);
void reset_thread_version(uint64_t table_id);
std::future<int32_t> pull_dense(uint64_t table_id);
void pull_dense2(uint64_t table_id);
void wait_all();
private:
void run();
bool check_update_param(uint64_t table_id);
void run();
bool check_update_param(uint64_t table_id);
private:
std::shared_ptr<paddle::ps::PSClient> _ps_client;
int _thread_num;
int _threshold;
int _sleep_time_ms;
Scope* _root_scope;
bool _running;
std::map<uint64_t, uint64_t> _last_versions;
std::map<uint64_t, uint64_t> _current_version;
std::mutex _mutex_for_version;
std::map<uint64_t, std::vector<uint64_t>> _training_versions;
std::map<uint64_t, std::vector<std::string>> _dense_variable_name;
std::thread _t;
std::vector<::std::future<int32_t>> _pull_dense_status;
std::map<uint64_t, std::vector<paddle::ps::Region>> _regions;
uint32_t _pull_dense_fail_times = 0;
std::vector<float> _base_norm_param;
std::vector<float> _mean;
std::vector<float> _scale;
float _squared_sum_epsilon = 1e-4;
std::mutex _mutex_for_mean_scale;
float _total_batch_num = 0;
std::shared_ptr<paddle::ps::PSClient> _ps_client;
int _thread_num;
int _threshold;
int _sleep_time_ms;
Scope* _root_scope;
bool _running;
std::map<uint64_t, uint64_t> _last_versions;
std::map<uint64_t, uint64_t> _current_version;
std::mutex _mutex_for_version;
std::map<uint64_t, std::vector<uint64_t>> _training_versions;
std::map<uint64_t, std::vector<std::string>> _dense_variable_name;
std::thread _t;
std::vector<::std::future<int32_t>> _pull_dense_status;
std::map<uint64_t, std::vector<paddle::ps::Region>> _regions;
uint32_t _pull_dense_fail_times = 0;
std::vector<float> _base_norm_param;
std::vector<float> _mean;
std::vector<float> _scale;
float _squared_sum_epsilon = 1e-4;
std::mutex _mutex_for_mean_scale;
float _total_batch_num = 0;
};
#endif
class ExecutorThreadWorker {
public:
ExecutorThreadWorker()
: thread_id_(-1), root_scope_(NULL), thread_scope_(NULL), debug_(false) {}
ExecutorThreadWorker()
: thread_id_(-1), root_scope_(NULL), thread_scope_(NULL), debug_(false) {}
virtual ~ExecutorThreadWorker() {}
void CreateThreadResource(const framework::ProgramDesc& program,
const paddle::platform::Place& place);
void SetThreadId(int tid);
......@@ -160,7 +160,7 @@ class ExecutorThreadWorker {
void SetFetchVarNames(const std::vector<std::string>& fetch_var_names);
#ifdef PADDLE_WITH_PSLIB
virtual void SetPSlibPtr(
std::shared_ptr<paddle::distributed::PSlib> pslib_ptr) {};
std::shared_ptr<paddle::distributed::PSlib> pslib_ptr) {}
virtual void SetPullDenseThread(
std::shared_ptr<DensePullThread> dpt) {}
virtual void SetParamConfig(
......@@ -218,32 +218,32 @@ class AsyncExecutorThreadWorker: public ExecutorThreadWorker {
void check_pull_push_memory(const std::vector<uint64_t>& features,
std::vector<std::vector<float>>& push_g,
int dim);
void collect_feasign_info(int table_id);
void collect_feasign_info(int table_id);
private:
struct FeasignInfo {
uint32_t slot;
uint32_t ins;
int64_t label;
};
std::map<uint64_t, std::vector<uint64_t>> _features;
std::map<uint64_t, std::vector<FeasignInfo>> _fea_info;
std::map<uint64_t, std::vector<std::vector<float>>> _feature_value;
std::map<uint64_t, std::vector<std::vector<float>>> _feature_push_value;
std::shared_ptr<paddle::distributed::PSlib> _pslib_ptr;
std::shared_ptr<DensePullThread> _pull_dense_thread;
std::vector<::std::future<int32_t>> _pull_sparse_status;
std::vector<::std::future<int32_t>> _pull_dense_status;
std::vector<::std::future<int32_t>> _push_sparse_status;
std::vector<::std::future<int32_t>> _push_dense_status;
AsyncWorkerParamConfig* _param_config;
struct FeasignInfo {
uint32_t slot;
uint32_t ins;
int64_t label;
};
std::map<uint64_t, std::vector<uint64_t>> _features;
std::map<uint64_t, std::vector<FeasignInfo>> _fea_info;
std::map<uint64_t, std::vector<std::vector<float>>> _feature_value;
std::map<uint64_t, std::vector<std::vector<float>>> _feature_push_value;
std::shared_ptr<paddle::distributed::PSlib> _pslib_ptr;
std::shared_ptr<DensePullThread> _pull_dense_thread;
std::vector<::std::future<int32_t>> _pull_sparse_status;
std::vector<::std::future<int32_t>> _pull_dense_status;
std::vector<::std::future<int32_t>> _push_sparse_status;
std::vector<::std::future<int32_t>> _push_dense_status;
AsyncWorkerParamConfig* _param_config;
};
#endif
......
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