提交 43c82376 编写于 作者: Q Qiao Longfei

use one graph

上级 10393dd0
......@@ -21,15 +21,14 @@ namespace details {
AsyncSSAGraphExecutor::AsyncSSAGraphExecutor(
const ExecutionStrategy &strategy, const std::vector<Scope *> &local_scopes,
const std::vector<platform::Place> &places,
std::vector<std::unique_ptr<ir::Graph>> &&graphs)
std::unique_ptr<ir::Graph> &&graph)
: strategy_(std::move(strategy)),
local_scopes_(std::move(local_scopes)),
pool_(places.size() >= 2 ? new ::ThreadPool(places.size()) : nullptr),
places_(std::move(places)),
graphs_(std::move(graphs)) {
graph_(std::move(graph)) {
VLOG(3) << "build AsyncSSAGraphExecutor";
PADDLE_ENFORCE_EQ(places_.size(), local_scopes_.size());
PADDLE_ENFORCE_EQ(graphs_.size(), local_scopes_.size());
// set the correct size of thread pool to each device.
strategy_.num_threads_ = strategy_.num_threads_ < places_.size()
......@@ -39,7 +38,7 @@ AsyncSSAGraphExecutor::AsyncSSAGraphExecutor(
<< " to run the operators of the graph on each device.";
for (size_t i = 0; i < places.size(); ++i) {
executors_.emplace_back(new details::ThreadedSSAGraphExecutor(
strategy_, {local_scopes_[i]}, {places_[i]}, std::move(graphs_[i])));
strategy_, {local_scopes_[i]}, {places_[i]}, graph_.get()));
}
}
......
......@@ -29,9 +29,9 @@ class AsyncSSAGraphExecutor : public SSAGraphExecutor {
AsyncSSAGraphExecutor(const ExecutionStrategy &strategy,
const std::vector<Scope *> &local_scopes,
const std::vector<platform::Place> &places,
std::vector<std::unique_ptr<ir::Graph>> &&graphs);
std::unique_ptr<ir::Graph> &&graph);
~AsyncSSAGraphExecutor() final = default;
const ir::Graph &Graph() const override { return *graphs_[0]; }
const ir::Graph &Graph() const override { return *graph_; }
FeedFetchList Run(const std::vector<std::string> &fetch_tensors) override;
......@@ -40,7 +40,7 @@ class AsyncSSAGraphExecutor : public SSAGraphExecutor {
std::vector<Scope *> local_scopes_;
std::unique_ptr<::ThreadPool> pool_{nullptr};
std::vector<platform::Place> places_;
std::vector<std::unique_ptr<ir::Graph>> graphs_;
std::unique_ptr<ir::Graph> graph_;
std::vector<std::unique_ptr<details::ThreadedSSAGraphExecutor>> executors_;
ExceptionHolder exception_holder_;
......
......@@ -264,71 +264,59 @@ ParallelExecutor::ParallelExecutor(
// Step 2. Convert main_program to SSA form and dependency graph. Also, insert
// ncclOp
std::vector<std::unique_ptr<ir::Graph>> graphs;
std::unique_ptr<ir::Graph> graph;
#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
if (build_strategy.async_mode_ && !build_strategy.is_distribution_) {
VLOG(3) << "use local async mode";
for (size_t i = 0; i < member_->places_.size(); ++i) {
std::unique_ptr<ir::Graph> graph = build_strategy.Apply(
main_program, {member_->places_[i]}, loss_var_name,
{member_->local_scopes_[i]}, member_->nranks_, member_->use_cuda_,
member_->nccl_ctxs_.get());
graphs.push_back(std::move(graph));
}
graph =
build_strategy.Apply(main_program, {member_->places_[0]}, loss_var_name,
{member_->local_scopes_[0]}, member_->nranks_,
member_->use_cuda_, member_->nccl_ctxs_.get());
} else {
std::unique_ptr<ir::Graph> graph = build_strategy.Apply(
main_program, member_->places_, loss_var_name, member_->local_scopes_,
member_->nranks_, member_->use_cuda_, member_->nccl_ctxs_.get());
graphs.push_back(std::move(graph));
graph = build_strategy.Apply(main_program, member_->places_, loss_var_name,
member_->local_scopes_, member_->nranks_,
member_->use_cuda_, member_->nccl_ctxs_.get());
}
#else
if (build_strategy.async_mode_ && !build_strategy.is_distribution_) {
VLOG(3) << "use local async mode";
for (size_t i = 0; i < member_->places_.size(); ++i) {
std::unique_ptr<ir::Graph> graph = build_strategy.Apply(
main_program, {member_->places_[i]}, loss_var_name,
{member_->local_scopes_[i]}, member_->nranks_, member_->use_cuda_);
graphs.push_back(std::move(graph));
}
graph = build_strategy.Apply(main_program, {member_->places_[0]},
loss_var_name, {member_->local_scopes_[0]},
member_->nranks_, member_->use_cuda_);
} else {
std::unique_ptr<ir::Graph> graph = build_strategy.Apply(
main_program, member_->places_, loss_var_name, member_->local_scopes_,
member_->nranks_, member_->use_cuda_);
graphs.push_back(std::move(graph));
graph = build_strategy.Apply(main_program, member_->places_, loss_var_name,
member_->local_scopes_, member_->nranks_,
member_->use_cuda_);
}
#endif
auto max_memory_size = GetEagerDeletionThreshold();
VLOG(10) << "Eager Deletion Threshold "
<< static_cast<float>(max_memory_size) / (1 << 30);
if (max_memory_size >= 0) {
for (size_t i = 0; i < graphs.size(); ++i) {
graphs[i] = member_->PrepareGCAndRefCnts(
std::move(graphs[i]), static_cast<size_t>(max_memory_size));
}
graph = member_->PrepareGCAndRefCnts(std::move(graph),
static_cast<size_t>(max_memory_size));
}
// Step 3. Create vars in each scope. Passes may also create new vars.
// skip control vars and empty vars
std::vector<details::VariableInfo> var_infos;
for (auto &graph : graphs) {
for (auto &node : graph->Nodes()) {
if (node->IsVar() && !node->IsCtrlVar() && node->Var()) {
var_infos.emplace_back();
var_infos.back().name_ = node->Var()->Name();
var_infos.back().type_ = node->Var()->GetType();
var_infos.back().persistable_ = node->Var()->Persistable();
}
for (auto &node : graph->Nodes()) {
if (node->IsVar() && !node->IsCtrlVar() && node->Var()) {
var_infos.emplace_back();
var_infos.back().name_ = node->Var()->Name();
var_infos.back().type_ = node->Var()->GetType();
var_infos.back().persistable_ = node->Var()->Persistable();
}
}
// If the loss_var_name is given, the number of graph should be only one.
if (loss_var_name.size()) {
size_t graph_num = ir::GraphNum(*graphs[0]);
size_t graph_num = ir::GraphNum(*graph);
if (graph_num > 1) {
LOG(WARNING)
<< "The number of graph should be only one, "
"but the current graph has "
<< ir::GraphNum(*graphs[0])
<< ir::GraphNum(*graph)
<< " sub_graphs. If you want to see the nodes of the "
"sub_graphs, you should use 'FLAGS_print_sub_graph_dir' "
"to specify the output dir. NOTES: if you not do training, "
......@@ -340,7 +328,7 @@ ParallelExecutor::ParallelExecutor(
VLOG(3) << "use AsyncSSAGraphExecutor";
member_->executor_.reset(new details::AsyncSSAGraphExecutor(
exec_strategy, member_->local_scopes_, member_->places_,
std::move(graphs)));
std::move(graph)));
} else if (build_strategy.enable_parallel_graph_) {
VLOG(3) << "use ParallelSSAGraphExecutor";
#ifdef PADDLE_WITH_CUDA
......@@ -358,12 +346,12 @@ ParallelExecutor::ParallelExecutor(
VLOG(3) << "use ThreadedSSAGraphExecutor";
member_->executor_.reset(new details::ThreadedSSAGraphExecutor(
exec_strategy, member_->local_scopes_, member_->places_,
std::move(graphs[0])));
std::move(graph)));
} else {
VLOG(3) << "use FastThreadedSSAGraphExecutor";
member_->executor_.reset(new details::FastThreadedSSAGraphExecutor(
exec_strategy, member_->local_scopes_, member_->places_,
std::move(graphs[0])));
std::move(graph)));
}
}
......
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