未验证 提交 b20fa022 编写于 作者: T tangwei12 提交者: GitHub

Merge pull request #11490 from seiriosPlus/ckpt_m2

Checkpoint M2: lookup table checkpoint
......@@ -195,7 +195,7 @@ if(WITH_DISTRIBUTE)
endif()
set(DISTRIBUTE_COMPILE_FLAGS "-Wno-non-virtual-dtor -Wno-error=non-virtual-dtor -Wno-error=delete-non-virtual-dtor")
foreach(dist_op "prefetch_op" "listen_and_serv_op" "send_op" "recv_op" "send_barrier_op" "fetch_barrier_op")
foreach(dist_op "prefetch_op" "checkpoint_notify_op" "listen_and_serv_op" "send_op" "recv_op" "send_barrier_op" "fetch_barrier_op")
op_library(${dist_op} DEPS ${DISTRIBUTE_DEPS})
set_source_files_properties(${dist_op}.cc PROPERTIES COMPILE_FLAGS ${DISTRIBUTE_COMPILE_FLAGS})
endforeach()
......@@ -216,7 +216,7 @@ if(WITH_DISTRIBUTE)
set(DEPS_OPS ${DEPS_OPS} gen_nccl_id_op)
endif()
else()
set(DEPS_OPS ${DEPS_OPS} prefetch_op recv_op listen_and_serv_op send_op send_barrier_op fetch_barrier_op gen_nccl_id_op)
set(DEPS_OPS ${DEPS_OPS} checkpoint_notify_op prefetch_op recv_op listen_and_serv_op send_op send_barrier_op fetch_barrier_op gen_nccl_id_op)
endif()
op_library(cross_entropy_op DEPS cross_entropy)
......
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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 <future> // NOLINT
#include <ostream>
#include "paddle/fluid/framework/data_type.h"
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/detail/macros.h"
#include "paddle/fluid/operators/send_recv_util.h"
#include "paddle/fluid/string/printf.h"
namespace paddle {
namespace operators {
class CheckpointNotifyOp : public framework::OperatorBase {
public:
CheckpointNotifyOp(const std::string& type,
const framework::VariableNameMap& inputs,
const framework::VariableNameMap& outputs,
const framework::AttributeMap& attrs)
: OperatorBase(type, inputs, outputs, attrs) {}
void RunImpl(const framework::Scope& scope,
const platform::Place& place) const override {
std::vector<std::string> epmap = Attr<std::vector<std::string>>("epmap");
std::string dir = Attr<std::string>("dir");
std::string lookup_table_name = Attr<std::string>("lookup_table");
distributed::RPCClient* rpc_client =
distributed::RPCClient::GetInstance<RPCCLIENT_T>();
for (size_t i = 0; i < epmap.size(); i++) {
auto lookup_table_save_dir =
string::Sprintf("%s/%s_%d", dir, lookup_table_name, i);
rpc_client->AsyncCheckpointNotify(epmap[i], lookup_table_save_dir);
VLOG(3) << "checkpoint notify sending lookup table: " << lookup_table_name
<< " and dir:" << dir << " to " << epmap[i];
}
rpc_client->Wait();
}
};
class CheckpointNotifyOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() {
AddAttr<std::vector<std::string>>("epmap",
"(string vector, default 127.0.0.1:6164)"
"Parameter Server endpoints in the order")
.SetDefault({"127.0.0.1:6164"});
AddAttr<std::string>(
"dir", "(string, default '') indicate the folder checkpoint will use");
AddAttr<std::string>("lookup_table",
"(string, default '') the lookup table name");
AddComment(R"DOC(
CheckpointNotify operator
This operator will send lookup table and it's checkpoint direcoty to listen_and_serve op at
the parameter server.
)DOC");
}
};
class CheckpointNotifyOpShapeInference : public framework::InferShapeBase {
public:
void operator()(framework::InferShapeContext* ctx) const override {}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(checkpoint_notify, ops::CheckpointNotifyOp,
paddle::framework::EmptyGradOpMaker,
ops::CheckpointNotifyOpMaker,
ops::CheckpointNotifyOpShapeInference);
......@@ -239,6 +239,23 @@ void GRPCClient::AsyncSendComplete(const std::string& ep, int64_t time_out) {
req_count_++;
}
void GRPCClient::AsyncCheckpointNotify(const std::string& ep,
const std::string& dir,
int64_t time_out) {
const auto ch = GetChannel(ep);
CheckpointNotifyProcessor* s = new CheckpointNotifyProcessor(ch);
s->Prepare(time_out);
sendrecv::VariableMessage req;
req.set_varname(CHECKPOINT_SAVE_MESSAGE);
req.set_out_varname(dir);
auto rpc = s->stub_->AsyncCheckpointNotify(s->context_.get(), req, &cq_);
rpc->Finish(&s->reply_, &s->status_, reinterpret_cast<void*>(s));
req_count_++;
}
void GRPCClient::Wait() {
std::unique_lock<std::mutex> lk(sync_mutex_);
sync_cond_.wait(lk, [this] { return req_count_ == 0; });
......
......@@ -171,6 +171,20 @@ class FetchBarrierProcessor : public BaseProcessor {
std::unique_ptr<sendrecv::SendRecvService::Stub> stub_;
};
class CheckpointNotifyProcessor : public BaseProcessor {
public:
explicit CheckpointNotifyProcessor(std::shared_ptr<grpc::Channel> ch)
: BaseProcessor(ch) {
stub_ = sendrecv::SendRecvService::NewStub(ch);
}
virtual ~CheckpointNotifyProcessor() {}
virtual void Process() {}
sendrecv::VoidMessage reply_;
std::unique_ptr<sendrecv::SendRecvService::Stub> stub_;
};
class GRPCClient : public RPCClient {
public:
GRPCClient() {}
......@@ -197,6 +211,9 @@ class GRPCClient : public RPCClient {
void AsyncSendFetchBarrier(const std::string& ep,
int64_t time_out = FLAGS_rpc_deadline) override;
void AsyncCheckpointNotify(const std::string& ep, const std::string& dir,
int64_t time_out = FLAGS_grpc_deadline) override;
void Wait() override;
void SendComplete() override;
......
......@@ -200,6 +200,45 @@ class RequestPrefetch final : public RequestBase {
framework::Scope* local_scope_;
};
class RequestCheckpointNotify final : public RequestBase {
public:
explicit RequestCheckpointNotify(GrpcService::AsyncService* service,
::grpc::ServerCompletionQueue* cq,
RequestHandler* request_handler, int req_id)
: RequestBase(service, cq, request_handler, req_id), responder_(&ctx_) {
request_.reset(new VariableResponse(request_handler->scope(),
request_handler->dev_ctx()));
int method_id =
static_cast<int>(distributed::GrpcMethod::kCheckpointNotify);
service_->RequestAsyncUnary(
method_id, &ctx_, request_.get(), &responder_, cq_, cq_,
reinterpret_cast<void*>(static_cast<intptr_t>(req_id)));
}
virtual ~RequestCheckpointNotify() {}
std::string GetReqName() override { return request_->Varname(); }
void Process() override {
auto scope = request_->GetMutableLocalScope();
std::string checkpoint_notify = request_->Varname();
std::string checkpoint_dir = request_->OutVarname();
VLOG(4) << "RequestCheckpointNotify notify: " << checkpoint_notify
<< ", dir: " << checkpoint_dir;
request_handler_->Handle(checkpoint_notify, scope, nullptr, nullptr,
checkpoint_dir);
Finish(reply_, &responder_);
}
protected:
std::shared_ptr<VariableResponse> request_;
sendrecv::VoidMessage reply_;
ServerAsyncResponseWriter<sendrecv::VoidMessage> responder_;
};
void AsyncGRPCServer::WaitServerReady() {
VLOG(4) << "AsyncGRPCServer is wait server ready";
std::unique_lock<std::mutex> lock(this->mutex_ready_);
......@@ -237,6 +276,7 @@ void AsyncGRPCServer::StartServer() {
reqs.reserve(kRequestBufSize);
for (int i = 0; i < kRequestBufSize; i++) {
VLOG(6) << "TryToRegisterNewOne on RPC NAME: " << rpc_name << " I: " << i;
TryToRegisterNewOne(rpc_name, i);
}
......@@ -289,8 +329,8 @@ void AsyncGRPCServer::TryToRegisterNewOne(const std::string& rpc_name,
return;
}
VLOG(4) << "register send rpc_name:" << rpc_name
<< ", handler:" << rpc_call_map_[kRequestSend];
VLOG(4) << "TryToRegisterNewOne on RPC NAME: " << rpc_name
<< " REQ ID: " << req_id;
auto& reqs = rpc_reqs_[rpc_name];
auto& handler = rpc_call_map_[rpc_name];
......@@ -303,6 +343,8 @@ void AsyncGRPCServer::TryToRegisterNewOne(const std::string& rpc_name,
b = new RequestGet(&service_, cq.get(), handler, req_id);
} else if (rpc_name == kRequestPrefetch) {
b = new RequestPrefetch(&service_, cq.get(), handler, req_id);
} else if (rpc_name == kRequestCheckpoint) {
b = new RequestCheckpointNotify(&service_, cq.get(), handler, req_id);
} else {
PADDLE_ENFORCE(false, "not supported rpc");
}
......@@ -321,7 +363,7 @@ void AsyncGRPCServer::HandleRequest(
while (true) {
VLOG(4) << "HandleRequest " << rpc_name << " wait next";
if (!cq->Next(&tag, &ok)) {
LOG(INFO) << "CompletionQueue " << rpc_name << " shutdown!";
VLOG(3) << "CompletionQueue " << rpc_name << " shutdown!";
break;
}
......
......@@ -80,10 +80,11 @@ enum class GrpcMethod {
kSendVariable,
kGetVariable,
kPrefetchVariable,
kCheckpointNotify,
};
static const int kGrpcNumMethods =
static_cast<int>(GrpcMethod::kPrefetchVariable) + 1;
static_cast<int>(GrpcMethod::kCheckpointNotify) + 1;
inline const char* GrpcMethodName(GrpcMethod id) {
switch (id) {
......@@ -93,6 +94,8 @@ inline const char* GrpcMethodName(GrpcMethod id) {
return "/sendrecv.SendRecvService/GetVariable";
case GrpcMethod::kPrefetchVariable:
return "/sendrecv.SendRecvService/PrefetchVariable";
case GrpcMethod::kCheckpointNotify:
return "/sendrecv.SendRecvService/CheckpointNotify";
}
// Shouldn't be reached.
......
......@@ -36,12 +36,16 @@ namespace distributed {
constexpr char kRequestSend[] = "RequestSend";
constexpr char kRequestGet[] = "RequestGet";
constexpr char kRequestPrefetch[] = "RequestPrefetch";
constexpr char kRequestCheckpoint[] = "RequestCheckpoint";
#define LISTEN_TERMINATE_MESSAGE "TERMINATE@RECV"
#define BATCH_BARRIER_MESSAGE "BATCH_BARRIER@RECV"
#define FETCH_BARRIER_MESSAGE "FETCH_BARRIER@RECV"
#define COMPLETE_MESSAGE "COMPLETE@RECV"
#define CHECKPOINT_SAVE_MESSAGE "SAVE@CHECKPOINTNOTIFY"
#define CHECKPOINT_LOAD_MESSAGE "LOAD@CHECKPOINTNOTIFY"
class RPCServer;
class RequestHandler {
......@@ -69,6 +73,11 @@ class RequestHandler {
prefetch_var_name_to_prepared_ctx_ = g;
}
void SetCheckpointNotifyPreparedCtx(
std::shared_ptr<framework::ExecutorPrepareContext> g) {
checkpoint_prepared_ctx_ = g;
}
// Used for async.
void SetGradToPreparedCtx(
std::unordered_map<
......@@ -115,6 +124,8 @@ class RequestHandler {
std::unordered_map<std::string,
std::shared_ptr<framework::ExecutorPrepareContext>>*
prefetch_var_name_to_prepared_ctx_;
// used for checkpoint notify
std::shared_ptr<framework::ExecutorPrepareContext> checkpoint_prepared_ctx_;
// Used for async.
std::unordered_map<std::string,
......
......@@ -22,11 +22,16 @@
#include "paddle/fluid/framework/selected_rows.h"
#include "paddle/fluid/operators/distributed/request_handler_impl.h"
#include "paddle/fluid/operators/distributed/rpc_server.h"
#include "paddle/fluid/string/printf.h"
namespace paddle {
namespace operators {
namespace distributed {
// define LOOKUP_TABLE_PATH for checkpoint notify to save lookup table variables
// to directory specified.
constexpr char LOOKUP_TABLE_PATH[] = "kLookupTablePath";
bool RequestSendHandler::Handle(const std::string& varname,
framework::Scope* scope,
framework::Variable* invar,
......@@ -119,6 +124,24 @@ bool RequestPrefetchHandler::Handle(const std::string& varname,
return true;
}
bool RequestCheckpointHandler::Handle(const std::string& varname,
framework::Scope* scope,
framework::Variable* invar,
framework::Variable** outvar,
const std::string& out_var_name) {
PADDLE_ENFORCE(
checkpoint_notify_id != -1,
"when checkpoint_notify_id = -1, there should be no RPC invoke.");
auto* lt_var = scope->FindVar(LOOKUP_TABLE_PATH)->GetMutable<std::string>();
lt_var->clear();
lt_var->append(out_var_name);
VLOG(4) << "RequestCheckpointHandler update var kLookupTablePath to: "
<< out_var_name;
executor_->RunPreparedContext(checkpoint_prepared_ctx_.get(), scope);
return true;
}
} // namespace distributed
} // namespace operators
} // namespace paddle
......@@ -66,6 +66,21 @@ class RequestPrefetchHandler final : public RequestHandler {
const std::string& out_var_name = "") override;
};
class RequestCheckpointHandler final : public RequestHandler {
public:
explicit RequestCheckpointHandler(bool sync_mode, int checkpoint_notify_id)
: RequestHandler(sync_mode) {
this->checkpoint_notify_id = checkpoint_notify_id;
}
virtual ~RequestCheckpointHandler() {}
bool Handle(const std::string& varname, framework::Scope* scope,
framework::Variable* var, framework::Variable** outvar,
const std::string& out_var_name = "") override;
private:
int checkpoint_notify_id;
};
} // namespace distributed
} // namespace operators
} // namespace paddle
......@@ -56,6 +56,10 @@ class RPCClient {
virtual void AsyncSendFetchBarrier(const std::string& ep,
int64_t time_out = FLAGS_rpc_deadline) = 0;
virtual void AsyncCheckpointNotify(
const std::string& ep, const std::string& dir,
int64_t time_out = FLAGS_grpc_deadline) = 0;
// SendComplete tells all the server that current trainer have no more data
// to train, so that the pserver can reduce it's barrier count, and continue
// to train with other trainers.
......
......@@ -25,6 +25,8 @@ service SendRecvService {
rpc GetVariable(VariableMessage) returns (VariableMessage) {}
// pre-fetch variable by given variable name and Ids
rpc PrefetchVariable(VariableMessage) returns (VariableMessage) {}
rpc CheckpointNotify(VariableMessage) returns (VoidMessage) {}
}
// VariableMessage is serialized paddle variable message.
......
......@@ -99,7 +99,8 @@ static int64_t GetTimestamp() {
void ListenAndServOp::RunSyncLoop(
framework::Executor *executor, framework::ProgramDesc *program,
framework::Scope *recv_scope,
const std::vector<int> &prefetch_block_id_list) const {
const std::vector<int> &prefetch_block_id_list,
const int checkpoint_point_block_id) const {
size_t num_blocks = program->Size();
auto optimize_blocks =
Attr<std::vector<framework::BlockDesc *>>(kOptimizeBlocks);
......@@ -208,7 +209,7 @@ void ListenAndServOp::RunAsyncLoop(framework::Executor *executor,
while (true) {
if (rpc_service_->IsExit()) {
LOG(INFO) << "get exit!rpc_processor break!";
VLOG(4) << "get exit!rpc_processor break!";
break;
}
......@@ -223,6 +224,7 @@ static void FillRequestCtx(
std::unordered_map<std::string,
std::shared_ptr<framework::ExecutorPrepareContext>>
*prefetch_ctx,
std::shared_ptr<framework::ExecutorPrepareContext> checkpoint_ctx,
distributed::RPCServer *rpc_server) {
h->SetScope(scope);
h->SetDevCtx(dev_ctx);
......@@ -230,6 +232,7 @@ static void FillRequestCtx(
h->SetProgram(program);
h->SetPrefetchPreparedCtx(prefetch_ctx);
h->SetRPCServer(rpc_server);
h->SetCheckpointNotifyPreparedCtx(checkpoint_ctx);
}
void ListenAndServOp::RunImpl(const framework::Scope &scope,
......@@ -245,9 +248,11 @@ void ListenAndServOp::RunImpl(const framework::Scope &scope,
PADDLE_ENFORCE(!rpc_service_);
std::string endpoint = Attr<std::string>("endpoint");
int checkpoint_block_id = Attr<int>(kCheckpointBlockId);
LOG(INFO) << "sync_mode:" << sync_mode << ", fan_in:" << fan_in
<< ", end_point:" << endpoint;
VLOG(4) << "sync_mode:" << sync_mode << ", fan_in:" << fan_in
<< ", end_point:" << endpoint
<< ", checkpoint_block_id: " << checkpoint_block_id;
rpc_service_.reset(new RPCSERVER_T(endpoint, fan_in));
......@@ -255,6 +260,8 @@ void ListenAndServOp::RunImpl(const framework::Scope &scope,
request_get_handler_.reset(new distributed::RequestGetHandler(sync_mode));
request_prefetch_handler_.reset(
new distributed::RequestPrefetchHandler(sync_mode));
request_checkpoint_handler_.reset(new distributed::RequestCheckpointHandler(
sync_mode, checkpoint_block_id));
rpc_service_->RegisterRPC(distributed::kRequestSend,
request_send_handler_.get());
......@@ -262,6 +269,8 @@ void ListenAndServOp::RunImpl(const framework::Scope &scope,
request_get_handler_.get());
rpc_service_->RegisterRPC(distributed::kRequestPrefetch,
request_prefetch_handler_.get());
rpc_service_->RegisterRPC(distributed::kRequestCheckpoint,
request_checkpoint_handler_.get());
auto optimize_blocks =
Attr<std::vector<framework::BlockDesc *>>(kOptimizeBlocks);
......@@ -270,6 +279,13 @@ void ListenAndServOp::RunImpl(const framework::Scope &scope,
auto *program = optimize_blocks[0]->Program();
framework::Executor executor(dev_place);
std::shared_ptr<framework::ExecutorPrepareContext> ckpt_pre_context = nullptr;
if (checkpoint_block_id != -1) {
auto ctx = executor.Prepare(*program, checkpoint_block_id);
// see: https://stackoverflow.com/a/14856553
ckpt_pre_context = std::move(ctx);
}
// prepare for prefetch
std::vector<int> prefetch_block_id_list;
std::unordered_map<int, std::string> block_id_to_prefetch_var_name;
......@@ -300,13 +316,15 @@ void ListenAndServOp::RunImpl(const framework::Scope &scope,
prefetch_var_name_to_prepared_ctx[prefetch_var_name] = prefetch_prepared[i];
}
auto f = std::bind(FillRequestCtx, std::placeholders::_1, &recv_scope,
&dev_ctx, &executor, program,
&prefetch_var_name_to_prepared_ctx, rpc_service_.get());
auto f =
std::bind(FillRequestCtx, std::placeholders::_1, &recv_scope, &dev_ctx,
&executor, program, &prefetch_var_name_to_prepared_ctx,
ckpt_pre_context, rpc_service_.get());
f(request_send_handler_.get());
f(request_get_handler_.get());
f(request_prefetch_handler_.get());
f(request_checkpoint_handler_.get());
// start the server listening after all member initialized.
server_thread_.reset(new std::thread(RunServer, rpc_service_));
......@@ -320,7 +338,8 @@ void ListenAndServOp::RunImpl(const framework::Scope &scope,
// Write to a file of server selected port for python use.
SavePort();
if (sync_mode) {
RunSyncLoop(&executor, program, &recv_scope, prefetch_block_id_list);
RunSyncLoop(&executor, program, &recv_scope, prefetch_block_id_list,
checkpoint_block_id);
} else {
RunAsyncLoop(&executor, program, &recv_scope);
}
......@@ -352,6 +371,9 @@ class ListenAndServOpMaker : public framework::OpProtoAndCheckerMaker {
.SetDefault({});
AddAttr<int>("Fanin", "How many clients send to this server.")
.SetDefault(1);
AddAttr<int>(kCheckpointBlockId,
"BolckID to run save checkpoint on pserer.")
.SetDefault(-1);
}
};
......
......@@ -32,6 +32,7 @@ namespace operators {
constexpr char kOptimizeBlocks[] = "optimize_blocks";
constexpr char kPrefetchVarNameToBlockId[] = "prefetch_var_name_to_block_id";
constexpr char kCheckpointBlockId[] = "checkpint_block_id";
void RunServer(std::shared_ptr<distributed::RPCServer> service);
......@@ -47,7 +48,8 @@ class ListenAndServOp : public framework::OperatorBase {
void RunSyncLoop(framework::Executor* executor,
framework::ProgramDesc* program,
framework::Scope* recv_scope,
const std::vector<int>& prefetch_block_id_list) const;
const std::vector<int>& prefetch_block_id_list,
const int checkpoint_point_block_id) const;
void RunAsyncLoop(framework::Executor* executor,
framework::ProgramDesc* program,
......@@ -68,6 +70,8 @@ class ListenAndServOp : public framework::OperatorBase {
mutable std::shared_ptr<distributed::RequestHandler> request_get_handler_;
mutable std::shared_ptr<distributed::RequestHandler>
request_prefetch_handler_;
mutable std::shared_ptr<distributed::RequestHandler>
request_checkpoint_handler_;
mutable std::shared_ptr<std::thread> server_thread_;
};
......
......@@ -34,6 +34,8 @@ class LoadOp : public framework::OperatorBase {
auto *dev_ctx = platform::DeviceContextPool::Instance().Get(place);
platform::RecordEvent record_event(Type(), dev_ctx);
// FIXME(yuyang18): We save variable to local file now, but we should change
// it to save an output stream.
auto filename = Attr<std::string>("file_path");
std::ifstream fin(filename);
PADDLE_ENFORCE(static_cast<bool>(fin), "Cannot open file %s for load op",
......@@ -44,9 +46,25 @@ class LoadOp : public framework::OperatorBase {
PADDLE_ENFORCE(out_var != nullptr, "Output variable %s cannot be found",
out_var_name);
auto *tensor = out_var->GetMutable<framework::LoDTensor>();
if (out_var->IsType<framework::LoDTensor>()) {
LoadLodTensor(fin, place, out_var);
} else if (out_var->IsType<framework::SelectedRows>()) {
LoadSelectedRows(fin, place, out_var);
} else {
PADDLE_ENFORCE(
false,
"Load only support LoDTensor and SelectedRows, %s has wrong type",
out_var_name);
}
}
DeserializeFromStream(fin, tensor, *dev_ctx);
void LoadLodTensor(std::istream &fin, const platform::Place &place,
framework::Variable *var) const {
// get device context from pool
platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance();
auto &dev_ctx = *pool.Get(place);
auto *tensor = var->GetMutable<framework::LoDTensor>();
DeserializeFromStream(fin, tensor, dev_ctx);
auto load_as_fp16 = Attr<bool>("load_as_fp16");
auto in_dtype = framework::ToDataType(tensor->type());
......@@ -63,18 +81,27 @@ class LoadOp : public framework::OperatorBase {
&fp16_tensor);
// reset output tensor
out_var->Clear();
tensor = out_var->GetMutable<framework::LoDTensor>();
var->Clear();
tensor = var->GetMutable<framework::LoDTensor>();
tensor->set_lod(fp16_tensor.lod());
tensor->ShareDataWith(fp16_tensor);
}
}
void LoadSelectedRows(std::istream &fin, const platform::Place &place,
framework::Variable *var) const {
auto *selectedRows = var->GetMutable<framework::SelectedRows>();
// get device context from pool
platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance();
auto &dev_ctx = *pool.Get(place);
framework::DeserializeFromStream(fin, selectedRows, dev_ctx);
}
};
class LoadOpProtoMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddOutput("Out", "The tensor need to be loaded");
AddOutput("Out", "The LoDTensor / SelectedRows need to be loaded");
AddAttr<bool>(
"load_as_fp16",
"If true, the tensor will be first loaded and then "
......@@ -85,7 +112,9 @@ class LoadOpProtoMaker : public framework::OpProtoAndCheckerMaker {
R"(Variable will be loaded from "file_path")")
.AddCustomChecker(
[](const std::string &path) { return !path.empty(); });
AddComment("Load operator will load a tensor variable from disk file.");
AddComment(
"Load operator will load a LoDTensor / SelectedRows variable from disk "
"file.");
}
};
} // namespace operators
......
......@@ -139,6 +139,7 @@ TEST(LoadFP16Op, CPU) {
save_op->Run(scope, place);
auto load_var = scope.Var("out_var");
load_var->GetMutable<paddle::framework::LoDTensor>();
auto load_op = paddle::framework::OpRegistry::CreateOp(
"load", {}, {{"Out", {"out_var"}}}, attrs);
load_op->Run(scope, place);
......
......@@ -22,11 +22,17 @@ limitations under the License. */
#include "paddle/fluid/framework/framework.pb.h"
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/selected_rows.h"
#include "paddle/fluid/framework/variable.h"
#include "paddle/fluid/platform/device_context.h"
namespace paddle {
namespace operators {
// define LOOKUP_TABLE_PATH for checkpoint notify to save lookup table variables
// to directory specified.
constexpr char LOOKUP_TABLE_PATH[] = "kLookupTablePath";
// TODO(yuyang18): If the functions below are needed by other files, move them
// to paddle::filesystem namespace.
constexpr char kSEP = '/';
......@@ -67,9 +73,27 @@ class SaveOp : public framework::OperatorBase {
private:
void RunImpl(const framework::Scope &scope,
const platform::Place &place) const override {
auto iname = Input("X");
auto *var = scope.FindVar(iname);
PADDLE_ENFORCE(var != nullptr, "Cannot find variable %s for save_op",
iname);
if (var->IsType<framework::LoDTensor>()) {
SaveLodTensor(place, var);
} else if (var->IsType<framework::SelectedRows>()) {
SaveSelectedRows(scope, place, var);
} else {
PADDLE_ENFORCE(
false,
"SaveOp only support LoDTensor and SelectedRows, %s has wrong type",
iname);
}
}
void SaveLodTensor(const platform::Place &place,
framework::Variable *var) const {
auto filename = Attr<std::string>("file_path");
auto overwrite = Attr<bool>("overwrite");
auto save_as_fp16 = Attr<bool>("save_as_fp16");
if (FileExists(filename) && !overwrite) {
PADDLE_THROW("%s is existed, cannot save to it when overwrite=false",
......@@ -78,26 +102,19 @@ class SaveOp : public framework::OperatorBase {
MkDirRecursively(DirName(filename).c_str());
// FIXME(yuyang18): We save variable to local file now, but we should change
// it to save an output stream.
std::ofstream fout(filename);
PADDLE_ENFORCE(static_cast<bool>(fout), "Cannot open %s to write",
filename);
auto iname = Input("X");
auto *var = scope.FindVar(iname);
PADDLE_ENFORCE(var != nullptr, "Cannot find variable %s for save_op",
iname);
PADDLE_ENFORCE(var->IsType<framework::LoDTensor>(),
"SaveOp only support LoDTensor, %s has wrong type", iname);
auto &tensor = var->Get<framework::LoDTensor>();
// get device context from pool
platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance();
auto &dev_ctx = *pool.Get(place);
// FIXME(yuyang18): We save variable to local file now, but we should change
// it to save an output stream.
std::ofstream fout(filename);
PADDLE_ENFORCE(static_cast<bool>(fout), "Cannot open %s to write",
filename);
auto save_as_fp16 = Attr<bool>("save_as_fp16");
auto in_dtype = framework::ToDataType(tensor.type());
auto out_dtype = save_as_fp16 ? framework::proto::VarType::FP16 : in_dtype;
......@@ -112,17 +129,43 @@ class SaveOp : public framework::OperatorBase {
} else {
framework::SerializeToStream(fout, tensor, dev_ctx);
}
fout.close();
}
void SaveSelectedRows(const framework::Scope &scope,
const platform::Place &place,
framework::Variable *var) const {
auto *lt_var = scope.FindVar(LOOKUP_TABLE_PATH)->GetMutable<std::string>();
PADDLE_ENFORCE(
lt_var != nullptr,
"Can not find variable kLookupTablePath for SaveSelectedRows");
std::string filename = lt_var->data();
VLOG(4) << "SaveSelectedRows get File name: " << filename;
auto &selectedRows = var->Get<framework::SelectedRows>();
// get device context from pool
platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance();
auto &dev_ctx = *pool.Get(place);
// FIXME(yuyang18): We save variable to local file now, but we should change
// it to save an output stream.
std::ofstream fout(filename);
PADDLE_ENFORCE(static_cast<bool>(fout), "Cannot open %s to write",
filename);
framework::SerializeToStream(fout, selectedRows, dev_ctx);
fout.close();
}
};
class SaveOpProtoMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("X", "(Tensor ) Input tensor to be saved");
AddInput("X", "(Tensor ) Input LoDTensor and SelectedRows to be saved");
AddComment(R"DOC(
Save operator
This operator will serialize and write a tensor variable to file on disk.
This operator will serialize and write LoDTensor / SelectedRows variable to file on disk.
)DOC");
AddAttr<bool>("overwrite",
"(boolean, default true)"
......@@ -142,9 +185,26 @@ This operator will serialize and write a tensor variable to file on disk.
}
};
class SaveOpVarTypeInference : public framework::VarTypeInference {
public:
void operator()(const framework::OpDesc &op_desc,
framework::BlockDesc *block) const override {
auto out_var_name = op_desc.Output(LOOKUP_TABLE_PATH).front();
auto &out_var = block->FindRecursiveOrCreateVar(out_var_name);
auto var_type = framework::proto::VarType::RAW;
out_var.SetType(var_type);
}
};
class SaveOpShapeInference : public framework::InferShapeBase {
public:
void operator()(framework::InferShapeContext *ctx) const override {}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(save, ops::SaveOp, ops::SaveOpProtoMaker);
REGISTER_OPERATOR(save, ops::SaveOp, paddle::framework::EmptyGradOpMaker,
ops::SaveOpProtoMaker, ops::SaveOpVarTypeInference,
ops::SaveOpShapeInference);
......@@ -454,7 +454,7 @@ class Operator(object):
'rnn_memory_helper_grad', 'conditional_block', 'while', 'send', 'recv',
'listen_and_serv', 'parallel_do', 'save_combine', 'load_combine',
'ncclInit', 'channel_create', 'channel_close', 'channel_send',
'channel_recv', 'select', 'gen_nccl_id'
'channel_recv', 'select', 'checkpoint_notify', 'gen_nccl_id'
}
def __init__(self,
......@@ -1214,6 +1214,9 @@ class Block(object):
if var.type == core.VarDesc.VarType.STEP_SCOPES:
ret_var = self.create_var(
name=var.name, persistable=var.persistable, type=var.type)
elif var.type == core.VarDesc.VarType.RAW:
ret_var = self.create_var(
name=var.name, persistable=var.persistable, type=var.type)
elif var.type == core.VarDesc.VarType.SELECTED_ROWS:
ret_var = self.create_var(
name=var.name,
......@@ -1923,7 +1926,7 @@ def get_var(name, program=None):
Args:
name(str): name of the variable
program(Program|None): program object.
If None, default_global_program() will be used.
If None, default_global_program() will be used.
Returns:
Variable
......
......@@ -13,6 +13,7 @@
# limitations under the License.
import os
import errno
import time
import shutil
......@@ -25,7 +26,8 @@ __all__ = [
'load_persistables', 'save_inference_model', 'load_inference_model',
'get_inference_program', 'save_checkpoint', 'load_checkpoint',
'clean_checkpoint', 'load_persist_vars_without_grad',
'save_persist_vars_without_grad', 'get_latest_checkpoint_serial'
'load_lookup_table_vars', 'save_persist_vars_without_grad',
'get_latest_checkpoint_serial'
]
......@@ -795,6 +797,7 @@ def get_parameter_value_by_name(name, executor, program=None):
SUCCESS_MARK_FILENAME = "_SUCCESS"
CHECKPOINT_PREFIX = "checkpoint"
MODEL_DIR = "__model__"
LOOKUP_TABLE_DIR = "__lookup_table__"
TRAINER_PREFIX = "trainer"
CHECKPOINT_SEPARATOR = "_"
......@@ -804,7 +807,9 @@ def save_checkpoint(executor,
trainer_id,
trainer_args=None,
main_program=None,
max_num_checkpoints=3):
max_num_checkpoints=3,
lookup_table=None,
ps_endpoint_list=None):
"""
This function filters out all checkpoint variables from the give
main_program and then saves these variables to the `checkpoint_dir`
......@@ -836,6 +841,12 @@ def save_checkpoint(executor,
max_num_checkpoints(int): The max number of total number of existing
checkpoints.
Default: 3
lookup_table(string|None): the lookup table name, when use distribute
lookup table, we can get lookup table name by DistributeTranspiler.
table_name
ps_endpoint_list(list|None): the parameter server ip:port list.
when use distribute lookup table, we can get ps_endpoint_list by
distribute arguments.
Returns:
None
......@@ -852,30 +863,40 @@ def save_checkpoint(executor,
prog = fluid.default_main_program()
trainer_args = {"epoch_id": 200,
"step_id": 20} # just an example
table_name = "share_w"
ps_endpoints = ["127.0.0.1:6000","127.0.0.1:6001"]
fluid.io.save_checkpoint(executor=exe,
checkpoint_dir=path,
trainer_id=0,
trainer_args=trainer_args,
main_program=prog,
max_num_checkpoints=3)
max_num_checkpoints=3,
lookup_table=table_name,
ps_endpoint_list = ps_endpoints)
"""
if checkpoint_dir is None:
raise ValueError("'checkpoint_dir' should not be None")
assert checkpoint_dir
if trainer_args:
assert isinstance(trainer_args, dict)
if not os.path.isdir(checkpoint_dir):
os.makedirs(checkpoint_dir)
is_chief = trainer_id == 0
_make_chekcpoint_dirs(checkpoint_dir)
serial = get_latest_checkpoint_serial(checkpoint_dir) + 1
cur_dir = _get_serial_dir(checkpoint_dir, serial)
save_trainer_args(cur_dir, trainer_id, trainer_args)
if trainer_id == 0:
if is_chief:
save_persist_vars_without_grad(executor, cur_dir, main_program)
if is_chief and lookup_table and ps_endpoint_list:
save_pserver_vars_by_notify(executor, cur_dir, lookup_table,
ps_endpoint_list)
_scroll_delete(checkpoint_dir, max_num_checkpoints)
......@@ -942,8 +963,9 @@ def load_checkpoint(executor, checkpoint_dir, serial, main_program):
def clean_checkpoint(checkpoint_dir, delete_dir=False):
"""
clean the checkpoint dir, when the train exits normally, the trainer will call clean_checkpoint to delete checkpoint directory saved before.
delete_dir only works when the directory is empty, otherwise, OSError is raised.
clean the checkpoint dir, when the train exits normally,
the trainer will call clean_checkpoint to delete checkpoint directory saved before.
delete_dir only works when the directory is empty, otherwise, OSError is raised.
: param checkpoint_dir
: param delete_dir
......@@ -1009,6 +1031,56 @@ def load_persist_vars_without_grad(executor,
filename=None)
def load_lookup_table_vars(executor, dirname, program, pserver_id, table_name):
"""
The parameter server will load lookup table's local file in
selectedrows variable.
Args:
executor(Executor): The executor to run for loading persistable variables
dirname(str): The directory path
main_program(Program): Find the variable named table_name in main_program
pserver_id(int): the serial number in pserver_endpoints list
table_name(str): lookup table name
Returns:
None
Examples:
.. code-block:: python
exe = fluid.Executor(fluid.CPUPlace())
dirname = "./checkpoints/checkpoint_9/__model__"
prog = fluid.default_main_program()
pserver_id = 1
table_name = "share_w"
fluid.io.load_lookup_table_vars(executor=exe,
dirname=dirname, program=prog, pserver_id=pserver_id,
table_name=table_name)
"""
for var in program.list_vars():
if var.name == table_name:
lookup_table_var = var
break
assert lookup_table_var is not None
lookup_table_dir = os.path.join(dirname, LOOKUP_TABLE_DIR)
table_file = table_name + CHECKPOINT_SEPARATOR + str(pserver_id)
load_prog = Program()
load_block = load_prog.global_block()
load_block.append_op(
type='load',
inputs={},
outputs={'Out': [lookup_table_var]},
attrs={'file_path': os.path.join(lookup_table_dir, table_file)})
executor.run(load_prog)
def save_persist_vars_without_grad(executor, dirname, program):
"""
This function filters out all checkpoint variables from the give
......@@ -1055,6 +1127,54 @@ def save_persist_vars_without_grad(executor, dirname, program):
_write_success(cur_dir)
def save_pserver_vars_by_notify(executor, dirname, lookup_table,
ps_endpoint_list):
"""
This function will send checkpoint notify message from Trainer 0
to all the pservers.
The checkpoint notify message contains lookup table name,
the absolute path on pserver to save lookup_table.
Args:
executor(Executor): The executor to run for send checkpoint notify.
dirname(str): The folder where to save checkpoints.
lookup_table(string): the lookup table name, when use distribute
lookup table, we can get lookup table name by DistributeTranspiler.
table_name
ps_endpoint_list(list): the parameter server ip:port list.
when use distribute lookup table, we can get ps_endpoint_list by
distribute arguments.
Return:
None
Examples:
.. code-block:: python
exe = fluid.Executor(fluid.CPUPlace())
param_path = "./my_paddle_model"
prog = fluid.default_main_program()
table_name = "share_w"
ps_endpoints = ["127.0.0.1:6000","127.0.0.1:6001"]
fluid.io.save_pserver_vars_by_notify(executor=exe,
dirname=param_path, lookup_table=table_name,
ps_endpoint_list=ps_endpoints)
"""
cur_dir = _get_lookuptable_dir(dirname)
checkpoint_notify_program = Program()
checkpoint_notify_block = checkpoint_notify_program.global_block()
attrs = {}
attrs['epmap'] = ps_endpoint_list
attrs['dir'] = cur_dir
attrs['lookup_table'] = lookup_table
checkpoint_notify_block.append_op(
type='checkpoint_notify', inputs={}, outputs={}, attrs=attrs)
executor.run(checkpoint_notify_program)
def save_trainer_args(dirname, trainer_id, trainer_args):
assert isinstance(trainer_args, dict)
......@@ -1068,6 +1188,29 @@ def save_trainer_args(dirname, trainer_id, trainer_args):
def load_trainer_args(checkpoint_dir, serial, trainer_id, trainer_args):
"""
trainer will load some args from it's independent directory,
such as epoch_id and step_id.
Args:
checkpoint_dir(str): The folder where all checkpoints are.
serial(int): The serial of checkpoint you would like to load.
trainer_id(int): current trainer id.
trainer_args(list): list about load trainer args
Return:
None
Examples:
.. code-block:: python
param_path = "./checkpoint/"
serial = 7
trainer_id = 2
trainer_args = ["epoch_id", "step_id"]
fluid.io.load_trainer_args(checkpoint_dir=param_path, serial=serial,
trainer_id=trainer_id, trainer_args=trainer_args)
"""
assert isinstance(trainer_args, list)
cur_dir = _get_serial_dir(checkpoint_dir, serial)
......@@ -1088,7 +1231,7 @@ def _is_checkpoint_var(var):
the checkpoint will not save or load all the variables.
var type is FEED_MINIBATCH/FETCH_LIST/RAW or var name ends with @GRAD are discarded.
: param var
: param var(Variable)
"""
if var.desc.type() == core.VarDesc.VarType.FEED_MINIBATCH or \
var.desc.type() == core.VarDesc.VarType.FETCH_LIST or \
......@@ -1108,6 +1251,23 @@ def _is_checkpoint_var(var):
return var.persistable
def _make_chekcpoint_dirs(dirs):
"""
_make_chekcpoint_dirs will makdir local directory directly, when the directory is exist, it will igore it.
"""
assert dirs is not None
if os.path.isfile(dirs):
raise OSError(errno.ENOTDIR, "dirs path shoule be a Directory.", dirs)
if not os.path.isdir(dirs):
try:
os.makedirs(dirs)
except OSError as err:
if err.errno != errno.EEXIST:
raise err
def _get_dir_serial(dirname):
_, serial = dirname.split(CHECKPOINT_SEPARATOR)
......@@ -1121,29 +1281,27 @@ def _get_dir_serial(dirname):
def _get_serial_dir(dirname, serial):
serial_folder = CHECKPOINT_PREFIX + CHECKPOINT_SEPARATOR + str(serial)
serial_dir = os.path.join(dirname, serial_folder)
if not os.path.isdir(serial_dir):
os.makedirs(serial_dir)
_make_chekcpoint_dirs(serial_dir)
return serial_dir
def _get_model_dir(dirname):
model_dir = os.path.join(dirname, MODEL_DIR)
_make_chekcpoint_dirs(model_dir)
return model_dir
if not os.path.isdir(model_dir):
os.makedirs(model_dir)
return model_dir
def _get_lookuptable_dir(dirname):
lookuptable_dir = os.path.join(dirname, LOOKUP_TABLE_DIR)
_make_chekcpoint_dirs(lookuptable_dir)
return lookuptable_dir
def _get_trainer_dir(dirname, trainer_id):
trainer_folder = TRAINER_PREFIX + CHECKPOINT_SEPARATOR + str(trainer_id)
trainer_dir = os.path.join(dirname, trainer_folder)
if not os.path.isdir(trainer_dir):
os.makedirs(trainer_dir)
_make_chekcpoint_dirs(trainer_dir)
return trainer_dir
......@@ -1162,7 +1320,11 @@ def _scroll_delete(dirname, max_num_checkpoints=3):
serials = serials[max_num_checkpoints:]
for serial in serials:
cur_dir = _get_serial_dir(dirname, serial)
shutil.rmtree(cur_dir)
try:
shutil.rmtree(cur_dir)
except OSError as err:
if err.errno != errno.ENOENT:
raise err
def _write_success(dirname):
......
......@@ -119,27 +119,20 @@ class CheckpointConfig(object):
max_num_checkpoints=3,
epoch_interval=1,
step_interval=10):
if checkpoint_dir is None:
self.checkpoint_dir = os.getcwd()
else:
self.checkpoint_dir = checkpoint_dir
self.max_num_checkpoints = max_num_checkpoints
if epoch_interval < 1:
self.epoch_interval = 1
else:
self.epoch_interval = epoch_interval
if step_interval < 1:
self.step_interval = 10
else:
self.step_interval = step_interval
assert epoch_interval >= 1
assert step_interval >= 1
self.checkpoint_dir = checkpoint_dir \
if checkpoint_dir is not None else os.getcwd()
self.max_num_checkpoints = max_num_checkpoints
self.epoch_interval = epoch_interval
self.step_interval = step_interval
self.epoch_id = 0
self.step_id = 0
self.load_serial = None
self.is_pserver = False
self.pserver_id = None
self.lookup_table_name = None
def check_and_get_place(place):
......@@ -290,13 +283,20 @@ class Trainer(object):
self.checkpoint_cfg.load_serial,
self.startup_program)
if not self.checkpoint_cfg.is_pserver:
epoch_id, step_id = io.load_trainer_args(
self.checkpoint_cfg.checkpoint_dir,
self.checkpoint_cfg.load_serial, self.trainer_id,
self._get_checkpoint_load_args())
self.checkpoint_cfg.epoch_id = int(epoch_id)
self.checkpoint_cfg.step_id = int(step_id)
if not self.checkpoint_cfg.pserver_id:
epoch_id, step_id = io.load_trainer_args(
self.checkpoint_cfg.checkpoint_dir,
self.checkpoint_cfg.load_serial, self.trainer_id,
self._get_checkpoint_load_args())
self.checkpoint_cfg.epoch_id = int(epoch_id)
self.checkpoint_cfg.step_id = int(step_id)
else:
if self.checkpoint_cfg.lookup_table_name:
io.load_lookup_table_vars(
exe, self.checkpoint_cfg.checkpoint_dir,
self.startup_program,
self.checkpoint_cfg.pserver_id,
self.checkpoint_cfg.lookup_table_name)
if param_path and os.path.isdir(param_path):
# load params from param_path into scope
......@@ -366,7 +366,10 @@ class Trainer(object):
self.trainer_id, pservers=pserver_endpoints, trainers=trainers)
if training_role == "PSERVER":
if self.checkpoint_cfg:
self.is_pserver = True
pserver_id = eplist.index(current_endpoint)
self.checkpoint_cfg.pserver_id = pserver_id
if t.has_distributed_lookup_table:
self.checkpoint_cfg.lookup_table_name = t.table_name
self.train_program = t.get_pserver_program(current_endpoint)
self.startup_program = t.get_startup_program(current_endpoint,
......@@ -566,7 +569,8 @@ class Trainer(object):
def _save_checkpoint(self, epoch_id, step_id):
assert self.checkpoint_cfg
if epoch_id % self.checkpoint_cfg.epoch_interval == 0 and step_id % self.checkpoint_cfg.step_interval == 0:
if epoch_id % self.checkpoint_cfg.epoch_interval == 0 \
and step_id % self.checkpoint_cfg.step_interval == 0:
exe = executor.Executor(self.place)
io.save_checkpoint(
executor=exe,
......
......@@ -471,6 +471,8 @@ class DistributeTranspiler(object):
pserver_index, pserver_program, pre_block_idx, grad_to_block_id)
prefetch_var_name_to_block_id = self._create_prefetch_block(
pserver_index, pserver_program, table_opt_block)
checkpoint_block_id = self._create_checkpoint_save_block(
pserver_program, table_opt_block.idx)
# NOTE: if has_distributed_lookup_table is False, then prefetch_block will
# not be executed, so it's safe to use optimize_block to hold the place
......@@ -489,6 +491,7 @@ class DistributeTranspiler(object):
if len(prefetch_var_name_to_block_id) > 0:
attrs['prefetch_var_name_to_block_id'] \
= prefetch_var_name_to_block_id
attrs['checkpint_block_id'] = checkpoint_block_id
# step5 append the listen_and_serv op
pserver_program.global_block().append_op(
......@@ -910,6 +913,27 @@ class DistributeTranspiler(object):
return table_opt_block
def _create_checkpoint_save_block(self, pserver_program, pre_block_idx):
"""
create a new block to handle save checkpoint.
"""
import os
pserver_program.global_block().create_var(
name="kLookupTablePath",
persistable=True,
type=core.VarDesc.VarType.RAW)
checkpoint_save_block = pserver_program.create_block(pre_block_idx)
# this 'file_path' do not be used in save lookup table variable
checkpoint_save_block.append_op(
type='save',
inputs={'X': [self.table_name]},
outputs={},
attrs={'file_path': "none"})
return checkpoint_save_block.idx
def _create_vars_from_blocklist(self,
program,
block_list,
......
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