Skip to content

P
Paddle

机器未来 / Paddle
与 Fork 源项目一致

Fork自 PaddlePaddle / Paddle

通知 1
Star 1
Fork 0
P
Paddle
未验证 提交 65bbf950 编写于 作者: G gongweibao 提交者： GitHub
浏览文件
操作

Add multi-ncclcomm and 2D ncclallreduce support. (#17263)

上级 b1bd483a
隐藏空白更改
内联 并排
Showing with 862 addition and 166 deletion
paddle/fluid/framework/details/all_reduce_op_handle.cc
浏览文件 @ 65bbf950
......@@ -35,16 +35,9 @@ namespace details {
AllReduceOpHandle::AllReduceOpHandle(ir::Node *node,
const std::vector<Scope *> &local_scopes,
const std::vector<platform::Place> &places,
const platform::NCCLContextMap *ctxs)
: OpHandleBase(node),
local_scopes_(local_scopes),
places_(places),
nccl_ctxs_(ctxs) {
if (nccl_ctxs_) {
for (auto &p : places_) {
this->SetDeviceContext(p, nccl_ctxs_->DevCtx(p));
}
}
const platform::MultiNCCLContextMap *ctxs)
: NCCLOpHandleBase(node, places, ctxs), local_scopes_(local_scopes) {
PADDLE_ENFORCE_EQ(places_.size(), local_scopes_.size());
}
#else
AllReduceOpHandle::AllReduceOpHandle(ir::Node *node,
......@@ -71,7 +64,9 @@ void AllReduceOpHandle::RunAllReduceFuncs(
if (FLAGS_sync_nccl_allreduce) {
for (auto &p : places_) {
int dev_id = boost::get<platform::CUDAPlace>(p).device;
auto &nccl_ctx = nccl_ctxs_->at(dev_id);
auto *nccl_ctxs =
nccl_ctxs_->GetRunEnvNCCLCtx(run_order_, use_hierarchical_allreduce_);
auto &nccl_ctx = nccl_ctxs->at(dev_id);
auto stream = nccl_ctx.stream();
cudaError_t e_sync = cudaStreamSynchronize(stream);
if (e_sync != 0) {
......@@ -134,19 +129,9 @@ void AllReduceOpHandle::RunImpl() {
numel = static_cast<size_t>(lod_tensor.numel());
}
int dev_id = boost::get<platform::CUDAPlace>(p).device;
auto &nccl_ctx = nccl_ctxs_->at(dev_id);
auto stream = nccl_ctx.stream();
auto comm = nccl_ctx.comm_;
VLOG(10) << "before all reduce buffer:" << buffer << ", numel:" << numel
<< ", dev_id:" << dev_id << ", dtype:" << dtype
<< ", place:" << p;
all_reduce_calls.emplace_back([=] {
PADDLE_ENFORCE(platform::dynload::ncclAllReduce(
buffer, buffer, numel, static_cast<ncclDataType_t>(dtype), ncclSum,
comm, stream));
NCCLAllReduce(p, buffer, buffer, numel,
static_cast<ncclDataType_t>(dtype), ncclSum);
});
}
RunAllReduceFuncs(all_reduce_calls);
......
paddle/fluid/framework/details/all_reduce_op_handle.h
浏览文件 @ 65bbf950
......@@ -21,6 +21,7 @@
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/framework/scope.h"
#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
#include "paddle/fluid/framework/details/nccl_op_handle.h"
#include "paddle/fluid/platform/nccl_helper.h"
#endif
......@@ -28,13 +29,15 @@ namespace paddle {
namespace framework {
namespace details {
class AllReduceOpHandle : public OpHandleBase {
public:
#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
class AllReduceOpHandle : public NCCLOpHandleBase {
public:
AllReduceOpHandle(ir::Node *node, const std::vector<Scope *> &local_scopes,
const std::vector<platform::Place> &places,
const platform::NCCLContextMap *ctxs);
const platform::MultiNCCLContextMap *ctxs);
#else
class AllReduceOpHandle : public OpHandleBase {
public:
AllReduceOpHandle(ir::Node *node, const std::vector<Scope *> &local_scopes,
const std::vector<platform::Place> &places);
#endif
......@@ -46,13 +49,17 @@ class AllReduceOpHandle : public OpHandleBase {
protected:
void RunImpl() override;
std::vector<Scope *> local_scopes_;
#if !(defined(PADDLE_WITH_CUDA) && !defined(_WIN32))
// NCCLOpHandleBase already have these attributes.
// Will polish it by class inheritance framework.
std::vector<platform::Place> places_;
#endif
#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
void RunAllReduceFuncs(
const std::vector<std::function<void()>> &all_reduce_calls);
const platform::NCCLContextMap *nccl_ctxs_;
#endif
};
......
paddle/fluid/framework/details/build_strategy.cc
浏览文件 @ 65bbf950
......@@ -256,16 +256,14 @@ bool BuildStrategy::IsMultiDevPass(const std::string &pass_name) const {
return framework::ir::MultiDevSSAGraphBuilder().count(pass_name) > 0;
}
ir::Graph *BuildStrategy::Apply(ir::Graph *graph,
const std::vector<platform::Place> &places,
const std::string &loss_var_name,
const std::vector<Scope *> &local_scopes,
const size_t &nranks,
ir::Graph *BuildStrategy::Apply(
ir::Graph *graph, const std::vector<platform::Place> &places,
const std::string &loss_var_name, const std::vector<Scope *> &local_scopes,
const size_t &nranks,
#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
const bool use_cuda,
platform::NCCLContextMap *nccl_ctxs) const {
const bool use_cuda, platform::MultiNCCLContextMap *nccl_ctxs) const {
#else
const bool use_cuda) const {
const bool use_cuda) const {
#endif
VLOG(3) << "apply all passes";
// Create a default one if not finalized by user.
......@@ -285,9 +283,9 @@ ir::Graph *BuildStrategy::Apply(ir::Graph *graph,
pass->Set<size_t>(ir::kNRanks, new size_t(nranks));
#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
platform::NCCLContextMap *nctx = use_cuda ? nccl_ctxs : nullptr;
platform::MultiNCCLContextMap *nctx = use_cuda ? nccl_ctxs : nullptr;
pass->Erase(kNCCLCtxs);
pass->SetNotOwned<platform::NCCLContextMap>(kNCCLCtxs, nctx);
pass->SetNotOwned<platform::MultiNCCLContextMap>(kNCCLCtxs, nctx);
#endif
} else if (pass->Type() == "alloc_continuous_space_for_grad_pass" ||
pass->Type() == "fuse_adam_op_pass" ||
......@@ -301,9 +299,12 @@ ir::Graph *BuildStrategy::Apply(ir::Graph *graph,
&local_scopes);
if (pass->Type() == "fuse_all_reduce_op_pass") {
#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
platform::NCCLContextMap *nctx = use_cuda ? nccl_ctxs : nullptr;
platform::MultiNCCLContextMap *nctx = use_cuda ? nccl_ctxs : nullptr;
pass->Erase(kNCCLCtxs);
pass->SetNotOwned<platform::NCCLContextMap>(kNCCLCtxs, nctx);
pass->SetNotOwned<platform::MultiNCCLContextMap>(kNCCLCtxs, nctx);
pass->Erase(kUseHierarchicalAllReduce);
pass->Set<bool>(kUseHierarchicalAllReduce,
new bool(use_hierarchical_allreduce_));
#endif
}
} else if (pass->Type() == "alloc_continuous_space_for_grad_pass") {
......@@ -316,6 +317,14 @@ ir::Graph *BuildStrategy::Apply(ir::Graph *graph,
LOG(INFO) << "set enable_sequential_execution:"
<< enable_sequential_execution_;
} else if (pass->Type() == "all_reduce_deps_pass") {
#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
platform::MultiNCCLContextMap *nctx = use_cuda ? nccl_ctxs : nullptr;
pass->Erase(kNCCLCtxs);
pass->SetNotOwned<platform::MultiNCCLContextMap>(kNCCLCtxs, nctx);
pass->Erase(kUseHierarchicalAllReduce);
pass->Set<bool>(kUseHierarchicalAllReduce,
new bool(use_hierarchical_allreduce_));
#endif
LOG(INFO) << "SeqOnlyAllReduceOps:" << SeqOnlyAllReduceOps(*this)
<< ", num_trainers:" << num_trainers_;
} else if (pass->Type() == "fuse_relu_depthwise_conv_pass") {
......
paddle/fluid/framework/details/build_strategy.h
浏览文件 @ 65bbf950
......@@ -111,6 +111,17 @@ struct BuildStrategy {
bool cache_runtime_context_{false};
std::unordered_set<std::string> mkldnn_enabled_op_types_;
size_t nccl_comm_num_{1};
// The picture is here:
// https://github.com/PaddlePaddle/Paddle/pull/17263#discussion_r285411396
bool use_hierarchical_allreduce_{false};
// Nccl ranks in a node when use hierarchical allreduce, it's setted to gpu
// cards' number in most cases.
size_t hierarchical_allreduce_inter_nranks_{0};
// Nccl ranks bewteen nodes when use hierarchical allreduce, it's setted to
// nodes number.
size_t hierarchical_allreduce_exter_nranks_{0};
// NOTE:
// Before you add new options, think if it's a general strategy that works
// with other strategy. If not, the strategy should be created through
......@@ -136,7 +147,7 @@ struct BuildStrategy {
const size_t &nranks,
#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
const bool use_cuda,
platform::NCCLContextMap *nccl_ctxs) const;
platform::MultiNCCLContextMap *nccl_ctxs) const;
#else
const bool use_cuda) const;
#endif
......
paddle/fluid/framework/details/fast_threaded_ssa_graph_executor.cc
浏览文件 @ 65bbf950
......@@ -49,6 +49,7 @@ FastThreadedSSAGraphExecutor::FastThreadedSSAGraphExecutor(
FeedFetchList FastThreadedSSAGraphExecutor::Run(
const std::vector<std::string> &fetch_tensors) {
VLOG(3) << "enter FastThreadedSSAGraphExecutor Run";
std::unique_ptr<std::unordered_map<OpHandleBase *, std::atomic<int>>>
op_deps = atomic_op_deps_.get();
PrepareAtomicOpDeps();
......
paddle/fluid/framework/details/fused_all_reduce_op_handle.cc
浏览文件 @ 65bbf950
......@@ -44,17 +44,10 @@ typedef std::vector<std::vector<std::pair<std::string, const LoDTensor *>>>
FusedAllReduceOpHandle::FusedAllReduceOpHandle(
ir::Node *node, const std::vector<Scope *> &local_scopes,
const std::vector<platform::Place> &places, const size_t num_of_all_reduce,
const platform::NCCLContextMap *ctxs)
: OpHandleBase(node),
const platform::MultiNCCLContextMap *ctxs)
: NCCLOpHandleBase(node, places, ctxs),
local_scopes_(local_scopes),
places_(places),
num_of_all_reduce_(num_of_all_reduce),
nccl_ctxs_(ctxs) {
if (nccl_ctxs_) {
for (auto &p : places_) {
this->SetDeviceContext(p, nccl_ctxs_->DevCtx(p));
}
}
num_of_all_reduce_(num_of_all_reduce) {
PADDLE_ENFORCE_EQ(places_.size(), local_scopes_.size());
}
#else
......@@ -167,14 +160,9 @@ void FusedAllReduceOpHandle::RunImpl() {
auto &p = places_[i];
void *buffer = const_cast<void *>(lod_tensor_data.at(i));
int dev_id = boost::get<platform::CUDAPlace>(p).device;
auto &nccl_ctx = nccl_ctxs_->at(dev_id);
auto stream = nccl_ctx.stream();
auto comm = nccl_ctx.comm_;
all_reduce_calls.emplace_back([=] {
PADDLE_ENFORCE(platform::dynload::ncclAllReduce(
buffer, buffer, numel, static_cast<ncclDataType_t>(nccl_dtype),
ncclSum, comm, stream));
NCCLAllReduce(p, buffer, buffer, numel,
static_cast<ncclDataType_t>(nccl_dtype), ncclSum);
});
}
......
paddle/fluid/framework/details/fused_all_reduce_op_handle.h
浏览文件 @ 65bbf950
......@@ -21,6 +21,7 @@
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/framework/scope.h"
#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
#include "paddle/fluid/framework/details/nccl_op_handle.h"
#include "paddle/fluid/platform/nccl_helper.h"
#endif
......@@ -28,14 +29,15 @@ namespace paddle {
namespace framework {
namespace details {
struct FusedAllReduceOpHandle : public OpHandleBase {
#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
struct FusedAllReduceOpHandle : public NCCLOpHandleBase {
FusedAllReduceOpHandle(ir::Node *node,
const std::vector<Scope *> &local_scopes,
const std::vector<platform::Place> &places,
const size_t num_of_all_reduce,
const platform::NCCLContextMap *ctxs);
const platform::MultiNCCLContextMap *ctxs);
#else
struct FusedAllReduceOpHandle : public OpHandleBase {
FusedAllReduceOpHandle(ir::Node *node,
const std::vector<Scope *> &local_scopes,
const std::vector<platform::Place> &places,
......@@ -52,11 +54,12 @@ struct FusedAllReduceOpHandle : public OpHandleBase {
private:
std::vector<Scope *> local_scopes_;
#if !(defined(PADDLE_WITH_CUDA) && !defined(_WIN32))
// NCCLOpHandleBase already have these attributes.
// Will polish it by class inheritance framework.
std::vector<platform::Place> places_;
size_t num_of_all_reduce_;
#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
const platform::NCCLContextMap *nccl_ctxs_;
#endif
size_t num_of_all_reduce_;
// Check the dtype of the input
void GetDTypeAndNumel(
......
paddle/fluid/framework/details/multi_devices_helper.h
浏览文件 @ 65bbf950
......@@ -45,6 +45,7 @@ constexpr char kGraphVars[] = "vars";
constexpr char kPlaces[] = "places";
constexpr char kLocalScopes[] = "local_scopes";
constexpr char kNCCLCtxs[] = "nccl_ctxs";
constexpr char kUseHierarchicalAllReduce[] = "use_hierarchical_allreduce";
// aux variables to represent dependency. Useful to resolve data hazard.
typedef std::unordered_set<VarHandleBase *> GraphDepVars;
......
paddle/fluid/framework/details/nccl_op_handle.h 0 → 100644
浏览文件 @ 65bbf950
// 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.
#pragma once
#include <string>
#include <unordered_map>
#include <vector>
#include "paddle/fluid/framework/details/op_handle_base.h"
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/framework/scope.h"
#include "paddle/fluid/platform/dynload/nccl.h"
#include "paddle/fluid/platform/nccl_helper.h"
DECLARE_bool(sync_nccl_allreduce);
namespace paddle {
namespace framework {
namespace details {
class NCCLOpHandleBase : public OpHandleBase {
public:
NCCLOpHandleBase(ir::Node* node, const std::vector<platform::Place>& places,
const platform::MultiNCCLContextMap* nccl_ctxs)
: OpHandleBase(node), places_(places), nccl_ctxs_(nccl_ctxs) {
if (nccl_ctxs == nullptr) {
return;
}
// init device context
auto default_nccl_ctxs = nccl_ctxs_->DefaultFlatCtx();
for (auto& p : places_) {
this->SetDeviceContext(p, default_nccl_ctxs->DevCtx(p));
}
}
virtual ~NCCLOpHandleBase() {
for (auto& ev : inter_events_) {
PADDLE_ENFORCE(cudaEventDestroy(ev.second));
}
for (auto& ev : exter_events_) {
PADDLE_ENFORCE(cudaEventDestroy(ev.second));
}
}
void SetRunEnv(int run_order, bool use_hierarchical_allreduce) {
PADDLE_ENFORCE(run_order >= 0, "run_order must >= 0");
run_order_ = run_order;
use_hierarchical_allreduce_ = use_hierarchical_allreduce;
VLOG(10) << "SetRunEnv "
<< " run_order:" << run_order
<< ", use_hierarchical_allreduce:" << use_hierarchical_allreduce;
if (nccl_ctxs_ == nullptr) {
return;
}
if (!use_hierarchical_allreduce_) {
auto ctxs = nccl_ctxs_->GetFlatCtx(run_order);
for (auto& p : places_) {
this->SetDeviceContext(p, ctxs->DevCtx(p));
}
return;
}
PADDLE_ENFORCE(places_.size() == 1,
"HierarchicalAllReduce run one proc with one card mode.");
for (auto& p : places_) {
auto ctxs = nccl_ctxs_->GetHierarchicalInterCtx(run_order);
this->SetDeviceContext(p, ctxs->DevCtx(p));
}
for (auto& p : dev_ctxes_) {
int dev_id = boost::get<platform::CUDAPlace>(p.first).device;
if (inter_events_.find(dev_id) != inter_events_.end()) {
continue;
}
PADDLE_ENFORCE(cudaSetDevice(dev_id));
PADDLE_ENFORCE(cudaEventCreateWithFlags(&inter_events_[dev_id],
cudaEventDisableTiming));
PADDLE_ENFORCE(cudaEventCreateWithFlags(&exter_events_[dev_id],
cudaEventDisableTiming));
VLOG(10) << "Create events on dev_id:" << dev_id
<< ", inter_event:" << &inter_events_[dev_id]
<< ", exter_event:" << &exter_events_[dev_id];
}
}
void FlatNCCLAllReduce(platform::Place place, const void* sendbuff,
void* recvbuff, size_t count, ncclDataType_t datatype,
ncclRedOp_t op) {
PADDLE_ENFORCE(run_order_ >= 0, "run_order must > 0");
auto flat_nccl_ctxs = nccl_ctxs_->GetFlatCtx(run_order_);
int dev_id = boost::get<platform::CUDAPlace>(place).device;
auto& nccl_ctx = flat_nccl_ctxs->at(dev_id);
auto stream = nccl_ctx.stream();
auto comm = nccl_ctx.comm_;
VLOG(10) << "before all reduce buffer:" << sendbuff << ", numel:" << count
<< ", dev_id:" << dev_id << ", dtype:" << datatype
<< ", place:" << place;
PADDLE_ENFORCE(platform::dynload::ncclAllReduce(
sendbuff, recvbuff, count, datatype, op, comm, stream));
}
void NCCLAllReduce(platform::Place place, const void* sendbuff,
void* recvbuff, size_t count, ncclDataType_t datatype,
ncclRedOp_t op) {
PADDLE_ENFORCE(run_order_ >= 0, "run_order must > 0");
if (!use_hierarchical_allreduce_) {
FlatNCCLAllReduce(place, sendbuff, recvbuff, count, datatype, op);
return;
}
HierarchicalAllReduce(place, sendbuff, recvbuff, count, datatype, op);
}
void HierarchicalAllReduce(platform::Place place, const void* sendbuff,
void* recvbuff, size_t count,
ncclDataType_t datatype, ncclRedOp_t op) {
PADDLE_ENFORCE(run_order_ >= 0, "run_order must > 0");
InterReduce(place, sendbuff, recvbuff, count, datatype, op);
// When a trainer is not in exter allreduce ring
// they need not to call this.
if (nccl_ctxs_->NeedExterAllReduce()) {
ExterAllReduce(place, recvbuff, recvbuff, count, datatype, op);
}
InterBroadCast(place, recvbuff, count, datatype, op);
}
protected:
void InterReduce(platform::Place place, const void* sendbuff, void* recvbuff,
size_t count, ncclDataType_t datatype, ncclRedOp_t op) {
auto nccl_ctxs = nccl_ctxs_->GetHierarchicalInterCtx(run_order_);
int dev_id = boost::get<platform::CUDAPlace>(place).device;
auto& nccl_ctx = nccl_ctxs->at(dev_id);
auto stream = nccl_ctx.stream();
auto comm = nccl_ctx.comm_;
VLOG(10) << "before all reduce"
<< " run_order:" << run_order_ << ", buffer:" << sendbuff
<< ", numel:" << count << ", dev_id:" << dev_id
<< ", dtype:" << datatype << ", place:" << place
<< ", stream:" << stream;
PADDLE_ENFORCE(platform::dynload::ncclReduce(
sendbuff, recvbuff, count, datatype, ncclSum, 0, comm, stream));
cudaEventRecord(inter_events_.at(dev_id), stream);
if (FLAGS_sync_nccl_allreduce) {
PADDLE_ENFORCE(cudaStreamSynchronize(stream),
"sync HierarchicalAllReduce inter stream error");
}
}
void ExterAllReduce(platform::Place place, const void* sendbuff,
void* recvbuff, size_t count, ncclDataType_t datatype,
ncclRedOp_t op) {
auto nccl_ctxs = nccl_ctxs_->GetHierarchicalExterCtx(run_order_);
PADDLE_ENFORCE(nccl_ctxs_, "can't get exter %d nccl_ctxs", run_order_);
int dev_id = boost::get<platform::CUDAPlace>(place).device;
auto& nccl_ctx = nccl_ctxs->at(dev_id);
auto stream = nccl_ctx.stream();
auto comm = nccl_ctx.comm_;
VLOG(10) << "before all reduce run_order:" << run_order_
<< "buffer:" << sendbuff << ", numel:" << count
<< ", dev_id:" << dev_id << ", dtype:" << datatype
<< ", place:" << place << ", stream:" << stream;
cudaStreamWaitEvent(stream, inter_events_.at(dev_id), 0);
PADDLE_ENFORCE(platform::dynload::ncclAllReduce(
sendbuff, recvbuff, count, datatype, op, comm, stream));
cudaEventRecord(exter_events_.at(dev_id), stream);
if (FLAGS_sync_nccl_allreduce) {
PADDLE_ENFORCE(cudaStreamSynchronize(stream),
"sync HierarchicalAllReduce exter stream error");
}
}
void InterBroadCast(platform::Place place, void* sendbuff, size_t count,
ncclDataType_t datatype, ncclRedOp_t op) {
auto nccl_ctxs = nccl_ctxs_->GetHierarchicalInterCtx(run_order_);
int dev_id = boost::get<platform::CUDAPlace>(place).device;
auto& nccl_ctx = nccl_ctxs->at(dev_id);
auto stream = nccl_ctx.stream();
auto comm = nccl_ctx.comm_;
VLOG(10) << "before InterBroadCast buffer:" << sendbuff
<< ", numel:" << count << ", dev_id:" << dev_id
<< ", dtype:" << datatype << ", place:" << place
<< ", stream:" << stream;
cudaStreamWaitEvent(stream, exter_events_.at(dev_id), 0);
PADDLE_ENFORCE(platform::dynload::ncclBcast(sendbuff, count, datatype, 0,
comm, stream));
}
protected:
std::vector<platform::Place> places_;
const platform::MultiNCCLContextMap* nccl_ctxs_{nullptr};
// When multi trainer call collective function, they need run the same order.
// Or the program will hang.So we use allreduce_deps_pass to set this
// run_order_.
int run_order_{0};
// Use 2d allreduce or not.
bool use_hierarchical_allreduce_{false};
private:
// hierarchical needed events
std::unordered_map<int, cudaEvent_t> inter_events_;
std::unordered_map<int, cudaEvent_t> exter_events_;
};
} // namespace details
} // namespace framework
} // namespace paddle
paddle/fluid/framework/details/op_handle_base.cc
浏览文件 @ 65bbf950
......@@ -187,6 +187,11 @@ void OpHandleBase::RunAndRecordEvent(const std::function<void()> &callback) {
std::function<void()> method = callback;
for (auto &p : dev_ctxes_) {
method = [method, p, this]() {
VLOG(10) << "cudadevicecontext:"
<< static_cast<platform::CUDADeviceContext *>(p.second)
<< ", dev_id:"
<< boost::get<platform::CUDAPlace>(p.first).device;
static_cast<platform::CUDADeviceContext *>(p.second)->RecordEvent(
events_.at(boost::get<platform::CUDAPlace>(p.first).device),
method);
......
paddle/fluid/framework/details/parallel_ssa_graph_executor.cc
浏览文件 @ 65bbf950
......@@ -95,6 +95,7 @@ ParallelSSAGraphExecutor::ParallelSSAGraphExecutor(
auto seq_allreduce_pass =
ir::PassRegistry::Instance().Get("all_reduce_deps_pass");
seq_allreduce_pass->Set<bool>(kUseHierarchicalAllReduce, new bool(false));
for (size_t i = 0; i < graphs_.size(); ++i) {
graphs_[i].reset(seq_allreduce_pass->Apply(graphs_[i].release()));
}
......
paddle/fluid/framework/details/sparse_all_reduce_op_handle.cc
浏览文件 @ 65bbf950
......@@ -30,7 +30,7 @@ namespace details {
SparseAllReduceOpHandle::SparseAllReduceOpHandle(
ir::Node *node, const std::vector<Scope *> &local_scopes,
const std::vector<platform::Place> &places,
const platform::NCCLContextMap *ctxs, bool is_encoded, int nranks)
const platform::MultiNCCLContextMap *ctxs, bool is_encoded, int nranks)
: AllReduceOpHandle(node, local_scopes, places, ctxs),
is_encoded_(is_encoded),
nranks_(nranks) {
......@@ -102,7 +102,8 @@ void SparseAllReduceOpHandle::RunImplEncoded() {
out_numel = (out_numel == 0) ? static_cast<size_t>(out.numel()) : out_numel;
int dev_id = boost::get<platform::CUDAPlace>(place).device;
auto &nccl_ctx = nccl_ctxs_->at(dev_id);
auto *nccl_ctxs = nccl_ctxs_->GetRunEnvNCCLCtx(run_order_, false);
auto &nccl_ctx = nccl_ctxs->at(dev_id);
auto stream = nccl_ctx.stream();
auto comm = nccl_ctx.comm_;
......
paddle/fluid/framework/details/sparse_all_reduce_op_handle.h
浏览文件 @ 65bbf950
......@@ -32,7 +32,7 @@ class SparseAllReduceOpHandle : public AllReduceOpHandle {
SparseAllReduceOpHandle(ir::Node *node,
const std::vector<Scope *> &local_scopes,
const std::vector<platform::Place> &places,
const platform::NCCLContextMap *ctxs,
const platform::MultiNCCLContextMap *ctxs,
bool is_encoded = false, int nranks = -1);
std::string Name() const override;
......
paddle/fluid/framework/ir/multi_devices_graph_pass/all_reduce_deps_pass.cc
浏览文件 @ 65bbf950
......@@ -22,6 +22,7 @@
#include "paddle/fluid/framework/details/all_reduce_op_handle.h"
#include "paddle/fluid/framework/details/container_cast.h"
#include "paddle/fluid/framework/details/fused_all_reduce_op_handle.h"
#include "paddle/fluid/framework/details/multi_devices_helper.h"
#include "paddle/fluid/framework/ir/graph.h"
#include "paddle/fluid/framework/ir/graph_helper.h"
......@@ -35,9 +36,20 @@ namespace ir {
class AllReduceDepsPass : public ir::Pass {
protected:
void ApplyImpl(ir::Graph* graph) const override {
std::vector<details::AllReduceOpHandle*> all_reduce_op_handles =
std::vector<details::OpHandleBase*> all_reduce_op_handles =
GetSortedAllReduceOps(*graph);
#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
auto use_hierarchical_allreduce =
Get<bool>(details::kUseHierarchicalAllReduce);
for (size_t i = 0; i < all_reduce_op_handles.size(); ++i) {
auto op_handle =
dynamic_cast<details::NCCLOpHandleBase*>(all_reduce_op_handles[i]);
PADDLE_ENFORCE(op_handle, "op_handle must be NCCLOpHandleBase");
op_handle->SetRunEnv(i, use_hierarchical_allreduce);
}
#endif
for (size_t i = 1; i < all_reduce_op_handles.size(); ++i) {
auto* dep_var = new details::DummyVarHandle(graph->CreateControlDepVar());
graph->Get<details::GraphDepVars>(details::kGraphDepVars)
......@@ -51,13 +63,12 @@ class AllReduceDepsPass : public ir::Pass {
}
}
std::vector<details::AllReduceOpHandle*> GetSortedAllReduceOps(
std::vector<details::OpHandleBase*> GetSortedAllReduceOps(
const ir::Graph& graph) const {
std::vector<details::AllReduceOpHandle*> all_reduce_op_handles;
std::vector<details::OpHandleBase*> all_reduce_op_handles;
std::unordered_map<details::OpHandleBase*, size_t> pending_ops;
std::unordered_set<details::OpHandleBase*> ready_ops;
std::unordered_set<details::OpHandleBase*> next_ready_ops;
auto op_handles = ir::FilterByNodeWrapper<details::OpHandleBase>(graph);
size_t num_of_ops = op_handles.size();
for (details::OpHandleBase* op : op_handles) {
......@@ -95,13 +106,16 @@ class AllReduceDepsPass : public ir::Pass {
void GetSortedAllReduceOps(
const std::unordered_set<details::OpHandleBase*>& ready_ops,
std::vector<details::AllReduceOpHandle*>* all_reduce_op_handles) const {
std::vector<details::AllReduceOpHandle*> current_all_reduce_op_handles;
std::vector<details::OpHandleBase*>* all_reduce_op_handles) const {
std::vector<details::OpHandleBase*> current_all_reduce_op_handles;
for (auto& op_handle : ready_ops) {
auto all_reduce_op_handle =
dynamic_cast<details::AllReduceOpHandle*>(op_handle);
if (all_reduce_op_handle) {
current_all_reduce_op_handles.emplace_back(all_reduce_op_handle);
auto fused_all_reduce_op_handle =
dynamic_cast<details::FusedAllReduceOpHandle*>(op_handle);
if (all_reduce_op_handle || fused_all_reduce_op_handle) {
current_all_reduce_op_handles.emplace_back(op_handle);
}
}
......@@ -110,8 +124,8 @@ class AllReduceDepsPass : public ir::Pass {
// Sort the current_all_reduce_op_handles according to the name of input.
sort(current_all_reduce_op_handles.begin(),
current_all_reduce_op_handles.end(),
[](const details::AllReduceOpHandle* left,
const details::AllReduceOpHandle* right) -> bool {
[](const details::OpHandleBase* left,
const details::OpHandleBase* right) -> bool {
auto left_in_vars =
details::DynamicCast<details::VarHandle>(left->Inputs());
auto right_in_vars =
......@@ -126,9 +140,9 @@ class AllReduceDepsPass : public ir::Pass {
current_all_reduce_op_handles.end());
}
void DebugString(const ir::Graph& graph,
const std::vector<details::AllReduceOpHandle*>&
all_reduce_op_handles) const {
void DebugString(
const ir::Graph& graph,
const std::vector<details::OpHandleBase*>& all_reduce_op_handles) const {
// get vars order
std::map<int, std::vector<std::string>> vars =
GetSoredGradientsFromStaleProgram(graph);
......
paddle/fluid/framework/ir/multi_devices_graph_pass/fuse_all_reduce_op_pass.cc
浏览文件 @ 65bbf950
......@@ -34,7 +34,8 @@ class FuseAllReduceOpPass : public ir::Pass {
auto &places = Get<const std::vector<platform::Place>>(details::kPlaces);
auto &local_scopes = Get<const std::vector<Scope *>>(details::kLocalScopes);
#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
auto *nccl_ctxs = &Get<platform::NCCLContextMap>(details::kNCCLCtxs);
auto *multi_nccl_ctxs =
&Get<platform::MultiNCCLContextMap>(details::kNCCLCtxs);
#endif
std::unordered_set<std::string> grads;
......@@ -94,7 +95,7 @@ class FuseAllReduceOpPass : public ir::Pass {
}
#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
InsertFusedAllReduce(places, local_scopes, group_size,
group_all_reduce_ops, nccl_ctxs, &result);
group_all_reduce_ops, multi_nccl_ctxs, &result);
#else
InsertFusedAllReduce(places, local_scopes, group_size,
group_all_reduce_ops, &result);
......@@ -102,14 +103,14 @@ class FuseAllReduceOpPass : public ir::Pass {
}
}
void InsertFusedAllReduce(const std::vector<platform::Place> &places,
const std::vector<Scope *> &local_scopes,
const size_t num_of_all_reduce,
const std::vector<ir::Node *> &all_reduce_ops,
void InsertFusedAllReduce(
const std::vector<platform::Place> &places,
const std::vector<Scope *> &local_scopes, const size_t num_of_all_reduce,
const std::vector<ir::Node *> &all_reduce_ops,
#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
const platform::NCCLContextMap *nccl_ctxs,
const platform::MultiNCCLContextMap *multi_nccl_ctxs,
#endif
ir::Graph *result) const {
ir::Graph *result) const {
std::vector<details::VarHandleBase *> inputs;
std::vector<details::VarHandleBase *> outputs;
for (auto &op : all_reduce_ops) {
......@@ -135,7 +136,7 @@ class FuseAllReduceOpPass : public ir::Pass {
#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
CreateFusedAllReduceOp(inputs, outputs, num_of_all_reduce, places,
local_scopes, nccl_ctxs, result);
local_scopes, multi_nccl_ctxs, result);
#else
CreateFusedAllReduceOp(inputs, outputs, num_of_all_reduce, places,
local_scopes, result);
......@@ -150,13 +151,13 @@ class FuseAllReduceOpPass : public ir::Pass {
const std::vector<platform::Place> &places,
const std::vector<Scope *> &local_scopes,
#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
const platform::NCCLContextMap *nccl_ctxs,
const platform::MultiNCCLContextMap *multi_nccl_ctxs,
#endif
ir::Graph *result) const {
#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
auto *op_handle = new details::FusedAllReduceOpHandle(
result->CreateEmptyNode("fused_all_reduce", ir::Node::Type::kOperation),
local_scopes, places, num_of_all_reduce, nccl_ctxs);
local_scopes, places, num_of_all_reduce, multi_nccl_ctxs);
#else
auto *op_handle = new details::FusedAllReduceOpHandle(
result->CreateEmptyNode("fused_all_reduce", ir::Node::Type::kOperation),
......@@ -172,7 +173,7 @@ class FuseAllReduceOpPass : public ir::Pass {
}
#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
if (!nccl_ctxs) {
if (!multi_nccl_ctxs) {
SetCommunicationContext(places, op_handle);
}
#else
......
paddle/fluid/framework/ir/multi_devices_graph_pass/multi_devices_graph_pass.cc
浏览文件 @ 65bbf950
......@@ -157,7 +157,11 @@ void MultiDevSSAGraphBuilderBase::Init() const {
local_scopes_ = Get<const std::vector<Scope *>>(details::kLocalScopes);
strategy_ = Get<const details::BuildStrategy>(kStrategy);
#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
nccl_ctxs_ = &Get<platform::NCCLContextMap>(details::kNCCLCtxs);
multi_nccl_ctxs_ = &Get<platform::MultiNCCLContextMap>(details::kNCCLCtxs);
nccl_ctxs_ = nullptr;
if (multi_nccl_ctxs_) {
nccl_ctxs_ = multi_nccl_ctxs_->DefaultFlatCtx();
}
#endif
PADDLE_ENFORCE_EQ(places_.size(), local_scopes_.size());
}
......@@ -460,20 +464,20 @@ void MultiDevSSAGraphBuilderBase::CreateAllReduceOp(ir::Graph *result,
result->Get<GraphOps>(kGraphOps).emplace_back(
new details::SparseAllReduceOpHandle(
result->CreateEmptyNode("allreduce", ir::Node::Type::kOperation),
scopes, places, nccl_ctxs_, is_encoded,
scopes, places, multi_nccl_ctxs_, is_encoded,
static_cast<int>(strategy_.trainers_endpoints_.size()) *
places_.size()));
} else {
result->Get<GraphOps>(kGraphOps).emplace_back(
new details::AllReduceOpHandle(
result->CreateEmptyNode("allreduce", ir::Node::Type::kOperation),
scopes, places, nccl_ctxs_));
scopes, places, multi_nccl_ctxs_));
}
#elif defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
result->Get<GraphOps>(kGraphOps).emplace_back(
new details::AllReduceOpHandle(
result->CreateEmptyNode("allreduce", ir::Node::Type::kOperation),
scopes, places, nccl_ctxs_));
scopes, places, multi_nccl_ctxs_));
#else
result->Get<GraphOps>(kGraphOps).emplace_back(
new details::AllReduceOpHandle(
......
paddle/fluid/framework/ir/multi_devices_graph_pass/multi_devices_graph_pass.h
浏览文件 @ 65bbf950
......@@ -96,7 +96,8 @@ class MultiDevSSAGraphBuilderBase : public ir::Pass {
size_t device_id) const;
#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
mutable platform::NCCLContextMap *nccl_ctxs_;
mutable platform::NCCLContextMap *nccl_ctxs_{nullptr};
mutable platform::MultiNCCLContextMap *multi_nccl_ctxs_{nullptr};
#endif
mutable std::string loss_var_name_;
......
paddle/fluid/framework/parallel_executor.cc
浏览文件 @ 65bbf950
......@@ -94,6 +94,89 @@ class ParallelExecutorPrivate {
}
}
#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
void InitNCCLCtxs(framework::Scope *scope, const BuildStrategy &bst) {
VLOG(1) << "nccl comm num:" << bst.nccl_comm_num_ << ", nranks:" << nranks_
<< ", num_trainers:" << bst.num_trainers_
<< ", trainer_id:" << bst.trainer_id_;
if (bst.use_hierarchical_allreduce_) {
VLOG(1) << ", use_hierarchical_allreduce:"
<< bst.use_hierarchical_allreduce_ << ", inter_trainers_num:"
<< bst.hierarchical_allreduce_inter_nranks_
<< ", exter_trainers_num:"
<< bst.hierarchical_allreduce_exter_nranks_;
}
std::vector<ncclUniqueId *> flat_nccl_ids;
if (nranks_ == 1) {
// FIXME(gongwb): need not to create ncclid when nranks==1
nccl_ctxs_.InitFlatCtxs(places_, flat_nccl_ids, bst.num_trainers_,
bst.trainer_id_);
return;
}
if (bst.enable_parallel_graph_) {
VLOG(1) << "use only one ncclid in pg model";
ncclUniqueId *nccl_id = nullptr;
std::string var_name = platform::GetFlatNCCLVarName(0);
auto nccl_id_var = scope->FindVar(var_name);
if (nccl_id_var) {
nccl_id = nccl_id_var->GetMutable<ncclUniqueId>();
} else {
nccl_id = new ncclUniqueId();
PADDLE_ENFORCE(platform::dynload::ncclGetUniqueId(nccl_id));
}
flat_nccl_ids.push_back(nccl_id);
nccl_ctxs_.InitFlatCtxs(places_, flat_nccl_ids, bst.num_trainers_,
bst.trainer_id_);
VLOG(1) << "init bst nccl context complete!";
return;
}
// num_trainers ==1 && places > 1
if (bst.num_trainers_ == 1) {
nccl_ctxs_.InitFlatCtxs(places_, flat_nccl_ids, bst.num_trainers_,
bst.trainer_id_);
return;
}
for (int i = 0; i < static_cast<int>(bst.nccl_comm_num_); i++) {
std::string var_name = platform::GetFlatNCCLVarName(i);
auto nccl_id_var = scope->FindVar(var_name);
PADDLE_ENFORCE(nccl_id_var, "can't find %s nccl_id_var", var_name);
auto nccl_id = nccl_id_var->GetMutable<ncclUniqueId>();
flat_nccl_ids.push_back(nccl_id);
}
nccl_ctxs_.InitFlatCtxs(places_, flat_nccl_ids, bst.num_trainers_,
bst.trainer_id_);
if (bst.use_hierarchical_allreduce_) {
std::string var_name = platform::GetHierarchicalInterNCCLVarName();
auto nccl_id_var = scope->FindVar(var_name);
auto inter_nccl_id = nccl_id_var->GetMutable<ncclUniqueId>();
std::vector<ncclUniqueId *> exter_nccl_ids;
for (int i = 0; i < static_cast<int>(bst.nccl_comm_num_); i++) {
std::string var_name = platform::GetHierarchicalExterNCCLVarName(i);
auto nccl_id_var = scope->FindVar(var_name);
PADDLE_ENFORCE(nccl_id_var, "can't find %s nccl_id_var", var_name);
auto nccl_id = nccl_id_var->GetMutable<ncclUniqueId>();
exter_nccl_ids.push_back(nccl_id);
}
nccl_ctxs_.InitHierarchicalCtxs(places_, inter_nccl_id, exter_nccl_ids,
bst.num_trainers_, bst.trainer_id_,
bst.hierarchical_allreduce_inter_nranks_,
bst.hierarchical_allreduce_exter_nranks_);
}
}
#endif
BuildStrategy build_strategy_;
std::vector<platform::Place> places_;
std::vector<Scope *> local_scopes_;
......@@ -101,7 +184,7 @@ class ParallelExecutorPrivate {
std::unique_ptr<details::SSAGraphExecutor> executor_;
#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
std::unique_ptr<platform::NCCLContextMap> nccl_ctxs_;
platform::MultiNCCLContextMap nccl_ctxs_;
#endif
bool own_local_scope_;
bool use_cuda_;
......@@ -254,24 +337,7 @@ ParallelExecutor::ParallelExecutor(const std::vector<platform::Place> &places,
if (member_->use_cuda_) {
// Bcast Parameters to all GPUs
#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
ncclUniqueId *nccl_id = nullptr;
// gen_nccl_id operator can broadcast the ncclUniqueId for nccl2 collective
// distributed training
auto *nccl_id_var = scope->FindVar(NCCL_ID_VARNAME);
if (nccl_id_var != nullptr) {
nccl_id = nccl_id_var->GetMutable<ncclUniqueId>();
}
if (build_strategy.enable_parallel_graph_ && member_->nranks_ > 1UL) {
if (nccl_id == nullptr) {
local_nccl_id_.reset(new ncclUniqueId());
platform::dynload::ncclGetUniqueId(local_nccl_id_.get());
nccl_id = local_nccl_id_.get();
}
}
member_->nccl_ctxs_.reset(new platform::NCCLContextMap(
member_->places_, nccl_id, build_strategy.num_trainers_,
build_strategy.trainer_id_));
member_->InitNCCLCtxs(scope, build_strategy);
// Initialize device context's nccl comm, will be used by normal
// Operators like sync_batch_norm, and collective ops.
......@@ -280,22 +346,14 @@ ParallelExecutor::ParallelExecutor(const std::vector<platform::Place> &places,
// NOTE: NCCL group-calls and non-group-calls can not use the same
// NCCL communicator, so for ParallelGraph and Multi-Process mode, re-use
// same communicators.
std::unique_ptr<platform::NCCLContextMap> dev_nccl_ctxs;
if (nccl_id == nullptr) {
dev_nccl_ctxs.reset(new platform::NCCLContextMap(member_->places_));
}
for (size_t dev_id = 0; dev_id < member_->places_.size(); ++dev_id) {
platform::DeviceContextPool &pool =
platform::DeviceContextPool::Instance();
auto *dev_ctx = static_cast<platform::CUDADeviceContext *>(
pool.Get(member_->places_[dev_id]));
if (nccl_id != nullptr) {
auto &nccl_ctx = member_->nccl_ctxs_->at(member_->places_[dev_id]);
dev_ctx->set_nccl_comm(nccl_ctx.comm());
} else {
auto &nccl_ctx = dev_nccl_ctxs->at(member_->places_[dev_id]);
dev_ctx->set_nccl_comm(nccl_ctx.comm());
}
auto &nccl_ctx =
member_->nccl_ctxs_.DefaultFlatCtx()->at(member_->places_[dev_id]);
dev_ctx->set_nccl_comm(nccl_ctx.comm());
}
#else
PADDLE_THROW("Not compiled with CUDA");
......@@ -327,18 +385,18 @@ ParallelExecutor::ParallelExecutor(const std::vector<platform::Place> &places,
VLOG(3) << "use local async mode";
graph = build_strategy.Apply(graph, {member_->places_[0]}, loss_var_name,
{member_->local_scopes_[0]}, 1,
member_->use_cuda_, member_->nccl_ctxs_.get());
member_->use_cuda_, &member_->nccl_ctxs_);
for (size_t i = 1; i < member_->places_.size(); ++i) {
graphs[i] =
build_strategy.Apply(graphs[i], {member_->places_[i]}, loss_var_name,
{member_->local_scopes_[i]}, 1,
member_->use_cuda_, member_->nccl_ctxs_.get());
member_->use_cuda_, &member_->nccl_ctxs_);
async_graphs[i] = graphs[i];
}
} else {
graph = build_strategy.Apply(graph, member_->places_, loss_var_name,
member_->local_scopes_, member_->nranks_,
member_->use_cuda_, member_->nccl_ctxs_.get());
member_->use_cuda_, &member_->nccl_ctxs_);
}
#else
if (build_strategy.async_mode_) {
......@@ -471,13 +529,14 @@ void ParallelExecutor::BCastParamsToDevices(
PADDLE_ENFORCE_EQ(member_->places_.size(), buffers.size(),
"variables' buffer size to bcast NOT equal to places");
{
auto *nccl_ctxs = member_->nccl_ctxs_.DefaultFlatCtx();
platform::NCCLGroupGuard guard;
for (size_t i = 0; i < member_->places_.size(); ++i) {
auto &nccl_ctx = member_->nccl_ctxs_->at(member_->places_[i]);
auto &nccl_ctx = nccl_ctxs->at(member_->places_[i]);
platform::dynload::ncclBcast(buffers[i], numel, data_type, 0,
nccl_ctx.comm_, nccl_ctx.stream());
}
member_->nccl_ctxs_->WaitAll();
nccl_ctxs->WaitAll();
}
#else
PADDLE_THROW("Not compiled with CUDA");
......@@ -512,6 +571,7 @@ void ParallelExecutor::BCastParamsToDevices(
void ParallelExecutor::Run(const std::vector<std::string> &fetch_tensors,
const std::string &fetched_var_name) {
VLOG(3) << "enter ParallelExecutor Run";
#ifdef WITH_GPERFTOOLS
if (gProfileStarted) {
ProfilerFlush();
......@@ -522,6 +582,8 @@ void ParallelExecutor::Run(const std::vector<std::string> &fetch_tensors,
if (member_->HasGarbageCollectors()) {
member_->ResetRuntimeReferenceCount(fetch_tensors, fetched_var_name);
}
VLOG(3) << "ParallelExecutor begin to run member_->executor_->Run";
auto fetch_data = member_->executor_->Run(fetch_tensors);
*member_->global_scope_->Var(fetched_var_name)->GetMutable<FeedFetchList>() =
fetch_data;
......
paddle/fluid/framework/parallel_executor.h
浏览文件 @ 65bbf950
......@@ -87,9 +87,6 @@ class ParallelExecutor {
ParallelExecutorPrivate *member_;
std::vector<std::unique_ptr<ir::Graph>> async_graphs_;
#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
std::unique_ptr<ncclUniqueId> local_nccl_id_;
#endif
};
} // namespace framework
......
paddle/fluid/operators/distributed_ops/gen_nccl_id_op.cc
浏览文件 @ 65bbf950
......@@ -41,31 +41,129 @@ class GenNCCLIdOp : public framework::OperatorBase {
// put nccl id in CPUPlace
auto& dev_ctx = *pool.Get(platform::CPUPlace());
int trainer_id = Attr<int>("trainer_id");
std::vector<std::string> trainers =
Attr<std::vector<std::string>>("trainers");
PADDLE_ENFORCE(
trainer_id >= 0 && trainer_id < static_cast<int>(trainers.size()),
"trainer_id:%d must be in trainers.size range", trainer_id);
std::string endpoint = trainers[trainer_id];
framework::Scope& local_scope = scope.NewScope();
int nccl_comm_num = Attr<int>("nccl_comm_num");
int use_hierarchical_allreduce = Attr<bool>("use_hierarchical_allreduce");
int inter_nranks = Attr<int>("hierarchical_allreduce_inter_nranks");
int inter_trainer_id = -1;
int exter_trainer_id = -1;
if (use_hierarchical_allreduce) {
PADDLE_ENFORCE(trainers.size() > 1, "trainers.size():%llu < 1",
trainers.size());
PADDLE_ENFORCE(inter_nranks > 1, "inter_nranks:%d < 1", inter_nranks);
PADDLE_ENFORCE((trainers.size() % inter_nranks == 0),
"trainers.size():%llu mod inter_nranks:%d != 0",
trainers.size(), inter_nranks);
inter_trainer_id = trainer_id % inter_nranks;
if (trainer_id % inter_nranks == 0) {
exter_trainer_id = trainer_id / inter_nranks;
}
}
if (trainer_id != 0) {
GetIdByServer(endpoint, &local_scope, dev_ctx, nccl_comm_num,
use_hierarchical_allreduce, trainer_id, inter_trainer_id,
exter_trainer_id);
}
std::ostringstream ss;
for (size_t i = 0; i < trainers.size(); i++) {
ss << trainers[i] << ",";
}
VLOG(1) << "trainer_id:" << trainer_id
<< ", use_hierarchical_allreduce:" << use_hierarchical_allreduce
<< ", inter_nranks:" << inter_nranks
<< ", inter_trainer_id:" << inter_trainer_id
<< ", exter_trainer_id:" << exter_trainer_id
<< ", trainers:" << ss.str();
// init flat
if (trainer_id == 0) {
GenerateAndSend(&local_scope, dev_ctx);
} else {
GetIdByServer(&local_scope, dev_ctx);
std::vector<std::string> flat_endpoints;
flat_endpoints.insert(flat_endpoints.begin(), trainers.begin() + 1,
trainers.end());
// flat nccl_id
for (int i = 0; i < nccl_comm_num; i++) {
std::string var_name = platform::GetFlatNCCLVarName(i);
GenerateAndSend(&local_scope, dev_ctx, var_name, flat_endpoints);
}
}
if (!use_hierarchical_allreduce) {
return;
}
PADDLE_ENFORCE(trainers.size() % inter_nranks == 0,
"enpoints.size:%llu mod inter_nranks:%d should ==0",
trainers.size(), inter_nranks);
PADDLE_ENFORCE(inter_nranks > 1, "inter_nranks:%d must > 1", inter_nranks);
// hierarchical inter ncclid
if (inter_trainer_id == 0) {
std::ostringstream ss;
ss << endpoint;
std::vector<std::string> inter_endpoints;
for (int i = trainer_id + 1; i < trainer_id + inter_nranks &&
i < static_cast<int>(trainers.size());
i++) {
ss << ",";
inter_endpoints.push_back(trainers[i]);
ss << trainers[i];
}
VLOG(1) << "Hierarchical inter ring endpoints:" << ss.str();
std::string nccl_var_name = platform::GetHierarchicalInterNCCLVarName();
GenerateAndSend(&local_scope, dev_ctx, nccl_var_name, inter_endpoints);
}
// hierarchical exter ncclid
if (exter_trainer_id == 0) {
std::ostringstream ss;
std::vector<std::string> exter_endpoints;
ss << endpoint;
for (size_t i = inter_nranks; i < trainers.size(); i += inter_nranks) {
ss << ",";
exter_endpoints.push_back(trainers[i]);
ss << trainers[i];
}
VLOG(1) << "Hierarchical exter ring endpoints:" << ss.str();
for (int i = 0; i < nccl_comm_num; i++) {
std::string nccl_var_name =
platform::GetHierarchicalExterNCCLVarName(i);
GenerateAndSend(&local_scope, dev_ctx, nccl_var_name, exter_endpoints);
}
}
}
private:
void GenerateAndSend(framework::Scope* scope,
const platform::DeviceContext& dev_ctx) const {
auto var = scope->FindVar(NCCL_ID_VARNAME);
PADDLE_ENFORCE_NOT_NULL(var);
const platform::DeviceContext& dev_ctx,
const std::string& nccl_id_name,
const std::vector<std::string>& endpoint_list) const {
auto var = scope->FindVar(nccl_id_name);
PADDLE_ENFORCE_NOT_NULL(var, "can't find nccl_id_var_name:%s",
nccl_id_name);
auto id = var->GetMutable<ncclUniqueId>();
PADDLE_ENFORCE(platform::dynload::ncclGetUniqueId(id));
std::vector<std::string> endpoint_list =
Attr<std::vector<std::string>>("endpoint_list");
distributed::RPCClient* client =
distributed::RPCClient::GetInstance<RPCCLIENT_T>(0);
for (auto& ep : endpoint_list) {
VLOG(3) << "sending nccl id to " << ep;
client->AsyncSendVar(ep, dev_ctx, *scope, NCCL_ID_VARNAME);
VLOG(3) << "sending nccl_id_var:" << nccl_id_name << " to " << ep;
client->AsyncSendVar(ep, dev_ctx, *scope, nccl_id_name);
}
client->Wait();
for (auto& ep : endpoint_list) {
......@@ -75,9 +173,11 @@ class GenNCCLIdOp : public framework::OperatorBase {
VLOG(3) << "sending completed...";
}
void GetIdByServer(framework::Scope* scope,
const platform::DeviceContext& dev_ctx) const {
std::string endpoint = Attr<std::string>("endpoint");
void GetIdByServer(const std::string& endpoint, framework::Scope* scope,
const platform::DeviceContext& dev_ctx, int nccl_comm_num,
bool use_hierarchical_allreduce, int trainer_id,
int inter_trainer_id, int exter_trainer_id) const {
// std::string endpoint = Attr<std::string>("endpoint");
// NOTE: Can not use unique_ptr here because the default
// deleter will call GRPC Server's base class's dtor and
// that will cause a wired crash.
......@@ -98,10 +198,42 @@ class GenNCCLIdOp : public framework::OperatorBase {
std::thread server_thread(
std::bind(&distributed::RPCServer::StartServer, rpc_service.get()));
rpc_service->SetCond(distributed::kRequestSend);
VLOG(3) << "start getting nccl id from trainer 0...";
rpc_service->WaitBarrier(distributed::kRequestSend);
VLOG(3) << "got nccl id and stop server...";
for (int i = 0; i < nccl_comm_num; i++) {
rpc_service->SetCond(distributed::kRequestSend);
VLOG(3) << "trainer_id:" << trainer_id
<< " start getting nccl id from trainer 0, nccl_comm_no:" << i;
rpc_service->WaitBarrier(distributed::kRequestSend);
rpc_service->ResetBarrierCounter();
}
if (use_hierarchical_allreduce) {
if (inter_trainer_id > 0) {
rpc_service->SetCond(distributed::kRequestSend);
VLOG(3) << "trainer_id:" << trainer_id
<< ", inter_trainer_id:" << inter_trainer_id
<< " start getting nccl id from inter_trainer 0";
rpc_service->WaitBarrier(distributed::kRequestSend);
rpc_service->ResetBarrierCounter();
}
if (exter_trainer_id > 0) {
for (int i = 0; i < nccl_comm_num; i++) {
rpc_service->SetCond(distributed::kRequestSend);
VLOG(3)
<< "trainer_id:" << trainer_id
<< ", exter_trainer_id:" << exter_trainer_id
<< " start getting nccl id from exter_trainer 0, nccl_comm_no:"
<< i;
rpc_service->WaitBarrier(distributed::kRequestSend);
rpc_service->ResetBarrierCounter();
}
}
}
VLOG(3) << "traier_id:" << trainer_id
<< ", inter_trainer_id:" << inter_trainer_id
<< ", exter_trainer_id:" << exter_trainer_id
<< " got nccl id and stop server...";
rpc_service->ShutDown();
VLOG(3) << "rpc server stopped";
server_thread.join();
......@@ -118,18 +250,26 @@ GenNCCLId operator
For trainer 0: generate a new UniqueId and send it to all the other trainers.
For trainer 1~n: start a gRPC server to get the UniqueId, once got, stop the server.
)DOC");
AddAttr<std::string>("endpoint",
"(string), e.g. 127.0.0.1:6175 "
"current listen endpoint");
AddAttr<std::vector<std::string>>(
"endpoint_list",
"['trainer1_ip:port', 'trainer2_ip:port', ...] "
"list of trainer endpoints start from trainer 1")
"trainers",
"['trainer0_ip:port', 'trainer1_ip:port', ...] "
"list of all trainer endpoints")
.SetDefault({});
AddAttr<int>("trainer_id",
"(int default 0) "
"The index of the trainer in distributed training.")
.SetDefault(0);
"(int) "
"The index of the trainer in distributed training.");
AddAttr<int>("nccl_comm_num",
"(int default 1) "
"The number of nccl communicator num.")
.SetDefault(1);
AddAttr<bool>("use_hierarchical_allreduce",
"(bool default false) "
"Wheter to use hierarchical allreduce.")
.SetDefault(false);
AddAttr<int>("hierarchical_allreduce_inter_nranks",
"(int default 1) "
"Wheter to use hierarchical allreduce.")
.SetDefault(-1);
}
};
......
paddle/fluid/platform/nccl_helper.h
浏览文件 @ 65bbf950
......@@ -124,8 +124,8 @@ struct NCCLContextMap {
} else {
rank = trainer_id;
}
VLOG(3) << "init nccl rank: " << rank << " nranks: " << nranks
<< " gpu id: " << gpu_id;
VLOG(1) << "init nccl rank:" << rank << ", nranks:" << nranks
<< ", gpu_id:" << gpu_id << ", dev_id:" << order_[i];
PADDLE_ENFORCE(cudaSetDevice(gpu_id));
PADDLE_ENFORCE(platform::dynload::ncclCommInitRank(
comms.get() + i, nranks, *nccl_id, rank));
......@@ -160,6 +160,134 @@ struct NCCLContextMap {
}
};
inline std::string GetFlatNCCLVarName(size_t pos) {
if (pos == 0) {
return NCCL_ID_VARNAME;
}
return string::Sprintf("%s_%d", NCCL_ID_VARNAME, static_cast<int>(pos));
}
inline std::string GetHierarchicalExterNCCLVarName(size_t pos) {
return string::Sprintf("Hierarchical_exter_%s_%d", NCCL_ID_VARNAME,
static_cast<int>(pos));
}
inline std::string GetHierarchicalInterNCCLVarName() {
return string::Sprintf("Hierarchical_inter_%s", NCCL_ID_VARNAME);
}
class MultiNCCLContextMap {
public:
MultiNCCLContextMap() {}
virtual ~MultiNCCLContextMap() {}
NCCLContextMap *DefaultFlatCtx() const {
if (flat_ctxs_.size() == 0) {
return nullptr;
}
return flat_ctxs_[0].get();
}
std::vector<std::unique_ptr<NCCLContextMap>> *GetFlatCtxs() {
return &flat_ctxs_;
}
NCCLContextMap *GetFlatCtx(size_t run_order) const {
return flat_ctxs_[run_order % flat_ctxs_.size()].get();
}
NCCLContextMap *GetRunEnvNCCLCtx(size_t run_order,
bool use_hierarchical_allreduce) const {
if (!use_hierarchical_allreduce) {
return GetFlatCtx(run_order);
}
return GetHierarchicalInterCtx(run_order);
}
void InitFlatCtxs(const std::vector<platform::Place> &places,
const std::vector<ncclUniqueId *> &nccl_ids,
size_t trainers_num, size_t trainer_id) {
if (nccl_ids.size() == 0) {
auto ptr = new platform::NCCLContextMap(places);
VLOG(1) << "init local trainer";
flat_ctxs_.emplace_back(ptr);
return;
}
for (size_t i = 0; i < nccl_ids.size(); i++) {
auto ptr = new platform::NCCLContextMap(places, nccl_ids[i], trainers_num,
trainer_id);
VLOG(1) << "init trainer_id:" << trainer_id << ", comm no:" << i;
flat_ctxs_.emplace_back(ptr);
}
}
void InitHierarchicalCtxs(const std::vector<platform::Place> &places,
ncclUniqueId *inter_nccl_id,
const std::vector<ncclUniqueId *> &exter_nccl_id,
size_t trainers_num, size_t trainer_id,
size_t inter_trainers_num,
size_t exter_trainers_num) {
PADDLE_ENFORCE(trainers_num == inter_trainers_num * exter_trainers_num,
"trainers_num:%llu != inter_trainers_num:%llu * "
"exter_trainers_num:%llu",
trainers_num, inter_trainers_num, exter_trainers_num);
PADDLE_ENFORCE(inter_trainers_num > 1, "inter_trainers_num:%llu must > 1",
inter_trainers_num);
int inter_trainer_id = trainer_id % inter_trainers_num;
VLOG(1) << "init inter_trainer_id:" << inter_trainer_id;
auto local = new NCCLContextMap(places, inter_nccl_id, inter_trainers_num,
inter_trainer_id);
h_inter_ctxs_.emplace_back(local);
int exter_trainer_id = -1;
if (trainer_id % inter_trainers_num == 0) {
exter_trainer_id = trainer_id / inter_trainers_num;
}
if (exter_trainer_id >= 0) {
for (size_t i = 0; i < exter_nccl_id.size(); i++) {
auto ex = new NCCLContextMap(places, exter_nccl_id[i],
exter_trainers_num, exter_trainer_id);
VLOG(1) << "init exter_trainer_id:" << exter_trainer_id
<< ", comm no:" << i;
h_exter_ctxs_.emplace_back(ex);
}
}
}
bool NeedExterAllReduce() const { return h_exter_ctxs_.size() > 0; }
NCCLContextMap *GetHierarchicalInterCtx(size_t run_order) const {
return h_inter_ctxs_[run_order % h_inter_ctxs_.size()].get();
}
NCCLContextMap *GetHierarchicalExterCtx(size_t run_order) const {
return h_exter_ctxs_[run_order % h_exter_ctxs_.size()].get();
}
std::vector<std::unique_ptr<NCCLContextMap>> *GetHierarchicalInterCtxs() {
return &h_inter_ctxs_;
}
std::vector<std::unique_ptr<NCCLContextMap>> *GetHierarchicalExterCtxs() {
return &h_exter_ctxs_;
}
protected:
// Support multi nccl comm on default nccl ring while NCCLContextMap can't.
std::vector<std::unique_ptr<NCCLContextMap>> flat_ctxs_;
// h_inter_ctxs_ and h_exter_ctxs_ are for 2d allreduce.
// And h_exter_ctxs_ can support multi comm too.
std::vector<std::unique_ptr<NCCLContextMap>> h_inter_ctxs_;
std::vector<std::unique_ptr<NCCLContextMap>> h_exter_ctxs_;
};
} // namespace platform
} // namespace paddle
#endif
paddle/fluid/pybind/pybind.cc
浏览文件 @ 65bbf950
......@@ -1483,6 +1483,34 @@ All parameter, weight, gradient are variables in Paddle.
[](BuildStrategy &self, int trainer_id) {
self.trainer_id_ = trainer_id;
})
.def_property(
"nccl_comm_num",
[](const BuildStrategy &self) { return self.nccl_comm_num_; },
[](BuildStrategy &self, int nccl_comm_num) {
self.nccl_comm_num_ = nccl_comm_num;
})
.def_property("use_hierarchical_allreduce_",
[](const BuildStrategy &self) {
return self.use_hierarchical_allreduce_;
},
[](BuildStrategy &self, bool use) {
self.use_hierarchical_allreduce_ = use;
})
.def_property("hierarchical_allreduce_inter_nranks_",
[](const BuildStrategy &self) {
return self.hierarchical_allreduce_inter_nranks_;
},
[](BuildStrategy &self, int nranks) {
self.hierarchical_allreduce_inter_nranks_ = nranks;
})
.def_property("hierarchical_allreduce_exter_nranks_",
[](const BuildStrategy &self) {
return self.hierarchical_allreduce_exter_nranks_;
},
[](BuildStrategy &self, int nranks) {
self.hierarchical_allreduce_exter_nranks_ = nranks;
})
.def_property(
"fuse_elewise_add_act_ops",
[](const BuildStrategy &self) {
......
python/paddle/fluid/compiler.py
浏览文件 @ 65bbf950
......@@ -98,6 +98,7 @@ class CompiledProgram(object):
def __init__(self, program_or_graph):
if isinstance(program_or_graph, core.Graph):
self._graph = program_or_graph
# don't not create a new program here.
self._program = None
elif isinstance(program_or_graph, framework.Program):
self._graph = core.Graph(program_or_graph.desc)
......@@ -299,6 +300,7 @@ class CompiledProgram(object):
# TODO(wuyi): trainer endpoings should be passed in through
# build_strategy, not program.xxx.
# TODO(gongwb): let user to set them once.
if self._program and self._build_strategy.num_trainers > 1 and \
self._program._trainers_endpoints:
tps = self._program._trainers_endpoints
......@@ -307,6 +309,12 @@ class CompiledProgram(object):
tps), "num_trainers == len(end_points)"
self._build_strategy.trainers_endpoints = tps
if self._program:
self._build_strategy.nccl_comm_num = self._program._nccl_comm_num
self._build_strategy.use_hierarchical_allreduce_ = self._program._use_hierarchical_allreduce
self._build_strategy.hierarchical_allreduce_inter_nranks_ = self._program._hierarchical_allreduce_inter_nranks
self._build_strategy.hierarchical_allreduce_exter_nranks_ = self._program._hierarchical_allreduce_exter_nranks
if self._build_strategy.sync_batch_norm:
self._build_strategy.enable_sequential_execution = True
......
python/paddle/fluid/framework.py
浏览文件 @ 65bbf950
......@@ -2762,6 +2762,10 @@ class Program(object):
# use Deep gradient comrepssion or not
self._enable_dgc = False
self._nccl_comm_num = 1
self._use_hierarchical_allreduce = False
self._hierarchical_allreduce_inter_nranks = 0
self._hierarchical_allreduce_exter_nranks = 0
# @deprecated(the python memory optimize transpiler is deprecated)
# whether the program is optimized by memory_optimize_transpiler
......
python/paddle/fluid/tests/unittests/test_dist_base.py
浏览文件 @ 65bbf950
......@@ -51,11 +51,14 @@ class TestDistRunnerBase(object):
trainers,
sync_mode,
dc_asgd=False,
current_endpoint=None):
current_endpoint=None,
nccl_comm_num=1):
# NOTE: import fluid until runtime, or else forking processes will cause error.
config = fluid.DistributeTranspilerConfig()
config.enable_dc_asgd = dc_asgd
config.sync_mode = sync_mode
if nccl_comm_num > 1:
config.nccl_comm_num = nccl_comm_num
# config.runtime_split_send_recv = True
t = fluid.DistributeTranspiler(config=config)
t.transpile(
......@@ -106,6 +109,7 @@ class TestDistRunnerBase(object):
# transpile for nccl2
config = fluid.DistributeTranspilerConfig()
config.mode = "nccl2"
config.nccl_comm_num = args.nccl_comm_num
nccl2_t = fluid.DistributeTranspiler(config=config)
nccl2_t.transpile(
args.trainer_id,
......@@ -113,6 +117,7 @@ class TestDistRunnerBase(object):
startup_program=fluid.default_startup_program(),
trainers=args.endpoints,
current_endpoint=args.current_endpoint)
trainer_prog = fluid.default_main_program()
else:
trainer_prog = fluid.default_main_program()
......@@ -268,6 +273,7 @@ def runtime_main(test_class):
choices=["pserver", "nccl2", "local", "nccl2_reduce_layer"])
parser.add_argument('--trainer_id', type=int, required=False, default=0)
parser.add_argument('--trainers', type=int, required=False, default=1)
parser.add_argument('--nccl_comm_num', type=int, required=False, default=1)
parser.add_argument(
'--current_endpoint', type=str, required=False, default="")
parser.add_argument('--sync_mode', action='store_true')
......@@ -345,6 +351,7 @@ class TestDistBase(unittest.TestCase):
self._lr = 0.001
self._use_dgc = False
self._dygraph = False
self._nccl_comm_num = 1
self._setup_config()
self._after_setup_config()
......@@ -590,6 +597,11 @@ class TestDistBase(unittest.TestCase):
if self._use_dgc:
tr0_cmd += " --use_dgc"
tr1_cmd += " --use_dgc"
if self._nccl_comm_num > 1:
tr0_cmd += " --nccl_comm_num {}".format(self._nccl_comm_num)
tr1_cmd += " --nccl_comm_num {}".format(self._nccl_comm_num)
if self._mp_mode:
env0 = {"FLAGS_selected_gpus": "0"}
env1 = {"FLAGS_selected_gpus": "1"}
......
python/paddle/fluid/tests/unittests/test_dist_mnist.py
浏览文件 @ 65bbf950
......@@ -39,6 +39,20 @@ class TestDistMnistNCCL2(TestDistBase):
self.check_with_place("dist_mnist.py", delta=1e-5)
class TestDistMnistNCCL2MultiNCCLComm(TestDistBase):
def _setup_config(self):
self._sync_mode = True
self._use_reduce = False
self._use_reader_alloc = False
self._nccl2_mode = True
self._nccl_comm_num = 3
def test_dist_train(self):
import paddle.fluid as fluid
if fluid.core.is_compiled_with_cuda():
self.check_with_place("dist_mnist.py", delta=1e-5)
class TestDistMnistNCCL2DGC(TestDistBase):
def _setup_config(self):
self._sync_mode = True
......
python/paddle/fluid/transpiler/distribute_transpiler.py
浏览文件 @ 65bbf950
......@@ -165,6 +165,15 @@ class DistributeTranspilerConfig(object):
runtime_split_send_recv = False
sync_mode = True
nccl_comm_num = 1
#The picture here illustrates the principle:
#https://github.com/PaddlePaddle/Paddle/pull/17263#discussion_r285411396
use_hierarchical_allreduce = False
#Nccl ranks in a node when use hierarchical allreduce, it's setted to gpu cards' number in most cases.
hierarchical_allreduce_inter_nranks = 0
#Nccl ranks bewteen nodes when use hierarchical allreduce, it's setted to nodes number.
hierarchical_allreduce_exter_nranks = 0
class DistributeTranspiler(object):
"""
......@@ -261,14 +270,36 @@ class DistributeTranspiler(object):
nccl_id_var = startup_program.global_block().create_var(
name="NCCLID", persistable=True, type=core.VarDesc.VarType.RAW)
for i in range(1, self.config.nccl_comm_num):
startup_program.global_block().create_var(
name="NCCLID_{}".format(i),
persistable=True,
type=core.VarDesc.VarType.RAW)
if self.config.use_hierarchical_allreduce:
startup_program.global_block().create_var(
name="Hierarchical_inter_NCCLID",
persistable=True,
type=core.VarDesc.VarType.RAW)
for i in range(0, self.config.nccl_comm_num):
startup_program.global_block().create_var(
name="Hierarchical_exter_NCCLID_{}".format(i),
persistable=True,
type=core.VarDesc.VarType.RAW)
startup_program.global_block().append_op(
type="gen_nccl_id",
inputs={},
outputs={"NCCLID": nccl_id_var},
attrs={
"endpoint": current_endpoint,
"endpoint_list": worker_endpoints,
"trainer_id": trainer_id
"trainers": trainers.split(","),
"trainer_id": trainer_id,
"nccl_comm_num": self.config.nccl_comm_num,
"use_hierarchical_allreduce":
self.config.use_hierarchical_allreduce,
"hierarchical_allreduce_inter_nranks":
self.config.hierarchical_allreduce_inter_nranks
})
return nccl_id_var
else:
......@@ -350,6 +381,12 @@ class DistributeTranspiler(object):
if self.config.mode == "nccl2":
assert (isinstance(trainers, str))
self.origin_program._trainers_endpoints = trainers.split(",")
self.origin_program._nccl_comm_num = self.config.nccl_comm_num
self.origin_program._use_hierarchical_allreduce = self.config.use_hierarchical_allreduce
self.origin_program._hierarchical_allreduce_inter_nranks = \
int(self.config.hierarchical_allreduce_inter_nranks)
self.origin_program._hierarchical_allreduce_exter_nranks = \
int(self.config.hierarchical_allreduce_exter_nranks)
self._transpile_nccl2(
trainer_id,
trainers,
......
Markdown is supported
0% 或 .
You are about to add 0 people to the discussion. Proceed with caution.
先完成此消息的编辑！
想要评论请 注册 或