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Fix sendrecv port bind #9595

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merged 4 commits into from
Apr 4, 2018
Merged

Fix sendrecv port bind #9595

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94eea16
fix sendrecv port bind
typhoonzero Apr 3, 2018
00f8e63
update
typhoonzero Apr 3, 2018
b853ac8
Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into…
typhoonzero Apr 3, 2018
b03fa88
Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into…
typhoonzero Apr 4, 2018
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9 changes: 7 additions & 2 deletions paddle/fluid/operators/detail/grpc_server.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -186,7 +186,8 @@ void AsyncGRPCServer::WaitClientGet(int count) {

void AsyncGRPCServer::RunSyncUpdate() {
::grpc::ServerBuilder builder;
builder.AddListeningPort(address_, ::grpc::InsecureServerCredentials());
builder.AddListeningPort(address_, ::grpc::InsecureServerCredentials(),
&selected_port_);
builder.SetMaxSendMessageSize(std::numeric_limits<int>::max());
builder.SetMaxReceiveMessageSize(std::numeric_limits<int>::max());
builder.RegisterService(&service_);
Expand All @@ -196,7 +197,8 @@ void AsyncGRPCServer::RunSyncUpdate() {
cq_prefetch_ = builder.AddCompletionQueue();

server_ = builder.BuildAndStart();
LOG(INFO) << "Server listening on " << address_ << std::endl;
LOG(INFO) << "Server listening on " << address_
<< " selected port: " << selected_port_;

std::function<void()> send_register =
std::bind(&AsyncGRPCServer::TryToRegisterNewSendOne, this);
Expand Down Expand Up @@ -242,6 +244,9 @@ void AsyncGRPCServer::TryToRegisterNewSendOne() {
VLOG(3) << "shutdown, do not TryToRegisterNewSendOne";
return;
}
while (scope_ == nullptr) {
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These should be deleted.

sleep(0.01);
}
RequestSend* send = new RequestSend(&service_, cq_send_.get(), scope_,
&var_recv_queue_, dev_ctx_);
VLOG(4) << "Create RequestSend status:" << send->Status();
Expand Down
3 changes: 3 additions & 0 deletions paddle/fluid/operators/detail/grpc_server.h
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Original file line number Diff line number Diff line change
Expand Up @@ -62,6 +62,8 @@ class AsyncGRPCServer final {

void SetExecutor(framework::Executor *executor) { executor_ = executor; }

int GetSelectedPort() { return selected_port_; }

const ReceivedMessage Get() { return this->var_recv_queue_.Pop(); }

void Push(const std::string &msg_name) {
Expand Down Expand Up @@ -109,6 +111,7 @@ class AsyncGRPCServer final {
int prefetch_blk_id_;
framework::ProgramDesc *program_;
framework::Executor *executor_;
int selected_port_;
};

}; // namespace detail
Expand Down
272 changes: 116 additions & 156 deletions paddle/fluid/operators/listen_and_serv_op.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -12,185 +12,145 @@ 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 <stdint.h>
#include <ostream>
#include <thread>

#include "paddle/fluid/framework/executor.h"
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/threadpool.h"
#include "paddle/fluid/operators/detail/grpc_server.h"
#include "paddle/fluid/operators/listen_and_serv_op.h"

namespace paddle {
namespace operators {

constexpr char kOptimizeBlock[] = "OptimizeBlock";

void RunServer(std::shared_ptr<detail::AsyncGRPCServer> service) {
service->RunSyncUpdate();
VLOG(4) << "RunServer thread end";
}

static void CreateTensorFromMessageType(framework::Variable *var,
sendrecv::VarType var_type) {
if (var_type == sendrecv::VarType::LOD_TENSOR) {
var->GetMutable<framework::LoDTensor>();
} else if (var_type == sendrecv::VarType::SELECTED_ROWS) {
var->GetMutable<framework::SelectedRows>();
} else {
PADDLE_THROW(
"VariableMessage type %d is not in "
"[LoDTensor, SelectedRows]",
var_type);
}
ListenAndServOp::ListenAndServOp(const std::string &type,
const framework::VariableNameMap &inputs,
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs)
: OperatorBase(type, inputs, outputs, attrs) {}

int ListenAndServOp::GetSelectedPort() {
return rpc_service_->GetSelectedPort();
}

static void ParallelExecuteBlocks(const std::vector<size_t> &parallel_blkids,
framework::Executor *executor,
framework::ProgramDesc *program,
framework::Scope *scope) {
std::vector<std::future<void>> fs;
for (size_t idx : parallel_blkids) {
fs.push_back(framework::Async([&executor, &program, &scope, idx]() {
int run_block = idx; // thread local
try {
executor->Run(*program, scope, run_block, false, false);
} catch (std::exception &e) {
LOG(ERROR) << "run sub program error " << e.what();
}
}));
}
for (size_t i = 0; i < fs.size(); ++i) fs[i].wait();
void ListenAndServOp::Stop() {
rpc_service_->Push(LISTEN_TERMINATE_MESSAGE);
server_thread_->join();
}

class ListenAndServOp : public framework::OperatorBase {
public:
ListenAndServOp(const std::string &type,
const framework::VariableNameMap &inputs,
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs)
: OperatorBase(type, inputs, outputs, attrs) {
if (!rpc_service_) {
std::string endpoint = Attr<std::string>("endpoint");
rpc_service_.reset(new detail::AsyncGRPCServer(endpoint));
server_thread_.reset(new std::thread(RunServer, rpc_service_));
}
}
void ListenAndServOp::RunImpl(const framework::Scope &scope,
const platform::Place &dev_place) const {
platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance();
auto &dev_ctx = *pool.Get(dev_place);
framework::Scope &recv_scope = scope.NewScope();
LOG(INFO) << "created recv scope: " << &recv_scope;

void Stop() override {
rpc_service_->Push(LISTEN_TERMINATE_MESSAGE);
server_thread_->join();
if (!rpc_service_) {
std::string endpoint = Attr<std::string>("endpoint");
rpc_service_.reset(new detail::AsyncGRPCServer(endpoint));
}

void RunImpl(const framework::Scope &scope,
const platform::Place &dev_place) const override {
platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance();
auto &dev_ctx = *pool.Get(dev_place);
framework::Scope &recv_scope = scope.NewScope();

// FIXME(Yancey1989): initialize rpc server with lazy mode.
rpc_service_->SetScope(&recv_scope);
rpc_service_->SetDevCtx(&dev_ctx);
auto ins = Inputs("X");
auto fan_in = Attr<int>("Fanin");

auto *block = Attr<framework::BlockDesc *>(kOptimizeBlock);
auto *program = block->Program();
int num_blocks = program->Size();
PADDLE_ENFORCE_GE(num_blocks, 2,
"server program should have at least 2 blocks");

framework::Executor executor(dev_place);

// TODO(qiao) set proper fields for table lookup and update
rpc_service_->SetExecutor(&executor);
rpc_service_->SetPrefetchBlkdId(0);
rpc_service_->SetProgram(program);

// TODO(typhoonzero): change this to a while_op for every cluster-batch.
bool exit_flag = false;
// Record received sparse variables, so that
// we could reset those after execute optimize program
std::vector<framework::Variable *> sparse_vars;
while (!exit_flag) {
// Get from multiple trainers, we don't care about the order in which
// the gradients arrives, just add suffix 0~n and merge the gradient.
rpc_service_->SetCond(0);
size_t recv_var_cnt = 0;
int batch_barrier = 0;
while (batch_barrier != fan_in) {
const detail::ReceivedMessage v = rpc_service_->Get();
auto recv_var_name = v.first;
if (recv_var_name == LISTEN_TERMINATE_MESSAGE) {
LOG(INFO) << "received terminate message and exit";
exit_flag = true;
break;
} else if (recv_var_name == BATCH_BARRIER_MESSAGE) {
VLOG(3) << "recv batch barrier message";
batch_barrier++;
continue;
} else {
VLOG(3) << "received grad: " << recv_var_name;
recv_var_cnt++;
auto var = v.second->GetVar();
if (var == nullptr) {
LOG(ERROR) << "Can not find server side var: " << recv_var_name;
PADDLE_THROW("Can not find server side var");
}
if (var->IsType<framework::SelectedRows>()) {
sparse_vars.push_back(var);
}
}
}
if (exit_flag) {
rpc_service_->SetCond(1);
rpc_service_->ShutDown();
auto ins = Inputs("X");
auto fan_in = Attr<int>("Fanin");
auto *block = Attr<framework::BlockDesc *>(kOptimizeBlock);
auto *program = block->Program();
size_t num_blocks = program->Size();
PADDLE_ENFORCE_GE(num_blocks, 2,
"server program should have at least 2 blocks");

framework::Executor executor(dev_place);

// FIXME(Yancey1989): initialize rpc server with lazy mode.
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This line is not useful.

rpc_service_->SetScope(&recv_scope);
rpc_service_->SetDevCtx(&dev_ctx);
// TODO(qiao) set proper fields for table lookup and update
rpc_service_->SetExecutor(&executor);
rpc_service_->SetPrefetchBlkdId(0);
rpc_service_->SetProgram(program);
// start the server listening after all member initialized.
server_thread_.reset(new std::thread(RunServer, rpc_service_));
// FIXME(typhoonzero): do we need to wait until the server port is ready?
sleep(5);

// TODO(typhoonzero): change this to a while_op for every cluster-batch.
bool exit_flag = false;
// Record received sparse variables, so that
// we could reset those after execute optimize program
std::vector<framework::Variable *> sparse_vars;
while (!exit_flag) {
// Get from multiple trainers, we don't care about the order in which
// the gradients arrives, just add suffix 0~n and merge the gradient.
rpc_service_->SetCond(0);
size_t recv_var_cnt = 0;
int batch_barrier = 0;
while (batch_barrier != fan_in) {
const detail::ReceivedMessage v = rpc_service_->Get();
auto recv_var_name = v.first;
if (recv_var_name == LISTEN_TERMINATE_MESSAGE) {
LOG(INFO) << "received terminate message and exit";
exit_flag = true;
break;
}

// NOTE: if is_gpu_place, CUDA kernels are laugched by multiple threads
// and this will still work.

// The optimize blocks which have the same parent ID would run parallel
// TODO(Yancey1989): need to use ParallelExecutor for future
size_t last_parent_blkid = program->Block(1).Parent();
std::vector<size_t> parallel_blkids;
parallel_blkids.push_back(1);
double ts = detail::GetTimestamp();
for (size_t blkid = 2; blkid < num_blocks; ++blkid) {
if (program->Block(blkid).Parent() != last_parent_blkid) {
for (size_t idx : parallel_blkids) VLOG(3) << idx;
ParallelExecuteBlocks(parallel_blkids, &executor, program,
&recv_scope);
parallel_blkids.clear();
last_parent_blkid = program->Block(blkid).Parent();
} else if (recv_var_name == BATCH_BARRIER_MESSAGE) {
VLOG(3) << "recv batch barrier message";
batch_barrier++;
continue;
} else {
VLOG(3) << "received grad: " << recv_var_name;
recv_var_cnt++;
auto var = v.second->GetVar();
if (var == nullptr) {
LOG(ERROR) << "Can not find server side var: " << recv_var_name;
PADDLE_THROW("Can not find server side var");
}
if (var->IsType<framework::SelectedRows>()) {
sparse_vars.push_back(var);
}
parallel_blkids.push_back(blkid);
}
ParallelExecuteBlocks(parallel_blkids, &executor, program, &recv_scope);

VLOG(3) << "run all blocks spent " << detail::GetTimestamp() - ts
<< "(ms)";

// Reset the received sparse variables, the sum operator would not
// sum the input sparse variables which rows is empty at the next
// mini-batch.
// TODO(Yancey1989): move the reset action into an operator, we couldn't
// have any hide logic in the operator.
for (auto &var : sparse_vars) {
var->GetMutable<framework::SelectedRows>()->mutable_rows()->clear();
}
}
if (exit_flag) {
rpc_service_->SetCond(1);
// FIXME(typhoonzero): use another condition to sync wait clients get.
rpc_service_->WaitClientGet(fan_in);
sparse_vars.clear();
} // while(true)
}
rpc_service_->ShutDown();
break;
}

protected:
std::shared_ptr<detail::AsyncGRPCServer> rpc_service_;
std::shared_ptr<std::thread> server_thread_;
};
// NOTE: if is_gpu_place, CUDA kernels are laugched by multiple threads
// and this will still work.

// The optimize blocks which have the same parent ID would run parallel
// TODO(Yancey1989): need to use ParallelExecutor for future
int32_t last_parent_blkid = program->Block(1).Parent();
std::vector<size_t> parallel_blkids;
parallel_blkids.push_back(1);
double ts = detail::GetTimestamp();
for (size_t blkid = 2; blkid < num_blocks; ++blkid) {
if (program->Block(blkid).Parent() != last_parent_blkid) {
for (size_t idx : parallel_blkids) VLOG(3) << idx;
ParallelExecuteBlocks(parallel_blkids, &executor, program, &recv_scope);
parallel_blkids.clear();
last_parent_blkid = program->Block(blkid).Parent();
}
parallel_blkids.push_back(blkid);
}
ParallelExecuteBlocks(parallel_blkids, &executor, program, &recv_scope);

VLOG(3) << "run all blocks spent " << detail::GetTimestamp() - ts << "(ms)";

// Reset the received sparse variables, the sum operator would not
// sum the input sparse variables which rows is empty at the next
// mini-batch.
// TODO(Yancey1989): move the reset action into an operator, we couldn't
// have any hide logic in the operator.
for (auto &var : sparse_vars) {
var->GetMutable<framework::SelectedRows>()->mutable_rows()->clear();
}
rpc_service_->SetCond(1);
// FIXME(typhoonzero): use another condition to sync wait clients get.
rpc_service_->WaitClientGet(fan_in);
sparse_vars.clear();
} // while(true)
}

class ListenAndServOpMaker : public framework::OpProtoAndCheckerMaker {
public:
Expand Down
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