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未验证 提交 572c466d 编写于 作者: W WangXi 提交者： GitHub
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[Prepare for MultiProcess xpu] unified gen nccl id, refine imperative reducer (#30455)

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paddle/fluid/imperative/all_reduce.cc
浏览文件 @ 572c466d
......@@ -16,8 +16,24 @@
#include "paddle/fluid/imperative/all_reduce.h"
#include <cuda.h>
#include <cuda_runtime.h>
#include <nccl.h>
#include <string>
#include <utility>
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/framework/scope.h"
#include "paddle/fluid/framework/selected_rows.h"
#include "paddle/fluid/framework/variable.h"
#include "paddle/fluid/imperative/nccl_context.h"
#include "paddle/fluid/platform/device_context.h"
#include "paddle/fluid/platform/nccl_helper.h"
#include "paddle/fluid/string/string_helper.h"
namespace paddle {
namespace imperative {
static const platform::Place &GetVarPlace(const framework::Variable &src) {
if (src.IsType<framework::LoDTensor>()) {
return src.Get<framework::LoDTensor>().place();
......
paddle/fluid/imperative/all_reduce.h
浏览文件 @ 572c466d
......@@ -16,21 +16,6 @@
#ifdef PADDLE_WITH_NCCL
#include <cuda.h>
#include <cuda_runtime.h>
#include <nccl.h>
#include <string>
#include <utility>
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/framework/scope.h"
#include "paddle/fluid/framework/selected_rows.h"
#include "paddle/fluid/framework/variable.h"
#include "paddle/fluid/imperative/nccl_context.h"
#include "paddle/fluid/platform/device_context.h"
#include "paddle/fluid/platform/nccl_helper.h"
#include "paddle/fluid/string/string_helper.h"
namespace paddle {
namespace framework {
class Variable;
......
paddle/fluid/imperative/nccl_context.cc
浏览文件 @ 572c466d
......@@ -14,175 +14,54 @@
#include "paddle/fluid/imperative/nccl_context.h"
namespace paddle {
namespace imperative {
#if defined(PADDLE_WITH_NCCL)
void NCCLParallelContext::RecvNCCLID(
const std::string &ep,
std::vector<ncclUniqueId> &nccl_ids) { // NOLINT
int nrings = nccl_ids.size();
auto addr = paddle::string::Split(ep, ':');
PADDLE_ENFORCE_EQ(
addr.size(), 2UL,
platform::errors::InvalidArgument(
"The endpoint should contain host and port, but got %s.", ep));
std::string host = addr[0];
int port = std::stoi(addr[1]);
int server_fd, new_socket;
struct sockaddr_in address;
int addrlen = sizeof(address);
char buffer[1024] = {0};
int opt = 0;
// creating socket fd
if ((server_fd = socket(AF_INET, SOCK_STREAM, 0)) == 0) {
PADDLE_THROW(
platform::errors::Unavailable("Create server file descriptor failed."));
}
if (setsockopt(server_fd, SOL_SOCKET, SO_REUSEADDR, &opt, sizeof(opt))) {
PADDLE_THROW(platform::errors::Unavailable("Set socket options failed."));
}
address.sin_family = AF_INET;
address.sin_addr.s_addr = INADDR_ANY;
address.sin_port = htons(port);
int try_times = 0;
int retry_time = 0;
while (true) {
if (bind(server_fd, (struct sockaddr *)&address, sizeof(address)) < 0) {
retry_time = 3 * (try_times + 1);
LOG(WARNING) << "Socket bind worker " << ep
<< (try_times < 9
? " failed, try again after " +
std::to_string(retry_time) + " seconds."
: " failed, try again after " +
std::to_string(retry_time) +
" seconds. Bind on endpoint " + ep +
" failed. Please confirm whether the "
"communication port or GPU card is occupied.");
std::this_thread::sleep_for(std::chrono::seconds(retry_time));
++try_times;
continue;
}
break;
}
VLOG(3) << "listening on: " << ep;
if (listen(server_fd, 3) < 0) {
PADDLE_THROW(platform::errors::Unavailable(
"Listen on server file descriptor failed."));
}
if ((new_socket =
accept(server_fd, reinterpret_cast<struct sockaddr *>(&address),
reinterpret_cast<socklen_t *>(&addrlen))) < 0) {
PADDLE_THROW(platform::errors::Unavailable(
"Accept the new socket file descriptor failed."));
}
if (read(new_socket, buffer, 1024) < 0) {
PADDLE_THROW(platform::errors::Unavailable("Read from socket failed."));
}
VLOG(3) << "recevived the ncclUniqueId";
memcpy(&nccl_ids[0], buffer, nrings * NCCL_UNIQUE_ID_BYTES);
VLOG(3) << "closing the socket server: " << ep;
close(server_fd);
}
void NCCLParallelContext::SendNCCLID(
const std::string &ep, const std::vector<ncclUniqueId> &nccl_ids) {
int nrings = nccl_ids.size();
auto addr = paddle::string::Split(ep, ':');
PADDLE_ENFORCE_EQ(
addr.size(), 2UL,
platform::errors::InvalidArgument(
"The endpoint should contain host and port, but got %s.", ep));
std::string host = addr[0];
int port = std::stoi(addr[1]);
int sock = 0;
struct sockaddr_in serv_addr;
char buffer[1024] = {0};
memcpy(buffer, &nccl_ids[0], nrings * NCCL_UNIQUE_ID_BYTES);
if ((sock = socket(AF_INET, SOCK_STREAM, 0)) < 0) {
PADDLE_THROW(platform::errors::Unavailable("Create socket failed."));
}
#include <string>
#include <utility>
#include <vector>
memset(&serv_addr, '0', sizeof(serv_addr));
serv_addr.sin_family = AF_INET;
serv_addr.sin_port = htons(port);
#if defined(PADDLE_WITH_NCCL)
#include "paddle/fluid/imperative/all_reduce.h"
#include "paddle/fluid/platform/collective_helper.h"
#include "paddle/fluid/platform/dynload/nccl.h"
#include "paddle/fluid/platform/gen_comm_id_helper.h"
#endif
char *ip = NULL;
struct hostent *hp;
if ((hp = gethostbyname(host.c_str())) == NULL) {
PADDLE_THROW(platform::errors::InvalidArgument(
"Fail to get host by name %s.", host));
}
int i = 0;
while (hp->h_addr_list[i] != NULL) {
ip = inet_ntoa(*(struct in_addr *)hp->h_addr_list[i]);
VLOG(3) << "gethostbyname host:" << host << " ->ip: " << ip;
break;
}
if (inet_pton(AF_INET, ip, &serv_addr.sin_addr) <= 0) {
PADDLE_THROW(platform::errors::Unavailable("Open address %s failed.", ep));
}
#include "paddle/fluid/framework/variable.h"
#include "paddle/fluid/platform/device_context.h"
#include "paddle/fluid/platform/place.h"
#include "paddle/fluid/string/split.h"
#include "paddle/fluid/string/string_helper.h"
int try_times = 0;
int retry_time = 0;
while (true) {
if (connect(sock, (struct sockaddr *)&serv_addr, sizeof(serv_addr)) < 0) {
retry_time = 3 * (try_times + 1);
LOG(WARNING)
<< "Socket connect worker " << ep
<< (try_times < 9
? " failed, try again after " + std::to_string(retry_time) +
" seconds."
: " failed, try again after " + std::to_string(retry_time) +
" seconds. Maybe that some process is occupied the "
"GPUs of this node now, and you should kill those "
"process manually.");
std::this_thread::sleep_for(std::chrono::seconds(retry_time));
++try_times;
continue;
}
VLOG(3) << "sending the ncclUniqueId to " << ep;
send(sock, buffer, NCCL_UNIQUE_ID_BYTES * nrings, 0);
break;
}
close(sock);
}
namespace paddle {
namespace imperative {
#if defined(PADDLE_WITH_NCCL)
void NCCLParallelContext::BcastNCCLId(
std::vector<ncclUniqueId> &nccl_ids, // NOLINT
int root) {
if (strategy_.local_rank_ == root) {
for (auto ep : strategy_.trainer_endpoints_) {
if (ep != strategy_.current_endpoint_) SendNCCLID(ep, nccl_ids);
std::vector<std::string> other_trainers;
for (auto &ep : strategy_.trainer_endpoints_) {
if (ep != strategy_.current_endpoint_) {
other_trainers.push_back(ep);
}
}
platform::SendBroadCastCommID(other_trainers, &nccl_ids);
} else {
RecvNCCLID(strategy_.current_endpoint_, nccl_ids);
platform::RecvBroadCastCommID(strategy_.current_endpoint_, &nccl_ids);
}
}
void NCCLParallelContext::Init() {
std::vector<ncclUniqueId> nccl_ids;
nccl_ids.resize(strategy_.nrings_);
if (strategy_.local_rank_ == 0) {
// generate the unique ncclid on the root worker
for (size_t i = 0; i < nccl_ids.size(); ++i) {
platform::dynload::ncclGetUniqueId(&nccl_ids[i]);
}
BcastNCCLId(nccl_ids, 0);
} else {
BcastNCCLId(nccl_ids, 0);
}
BcastNCCLId(nccl_ids, 0);
int gpu_id = BOOST_GET_CONST(platform::CUDAPlace, place_).device;
for (int ring_id = 0; ring_id < strategy_.nrings_; ring_id++) {
......@@ -193,6 +72,12 @@ void NCCLParallelContext::Init() {
platform::NCCLCommContext::Instance().CreateNCCLComm(
&nccl_ids[ring_id], strategy_.nranks_, strategy_.local_rank_, gpu_id,
ring_id);
compute_events_.emplace_back(
platform::CudaEventResourcePool::Instance().New(
BOOST_GET_CONST(platform::CUDAPlace, place_).device));
comm_events_.emplace_back(platform::CudaEventResourcePool::Instance().New(
BOOST_GET_CONST(platform::CUDAPlace, place_).device));
}
}
......@@ -206,11 +91,54 @@ void NCCLParallelContext::AllReduceByStream(const framework::Variable &src,
AllReduce(src, dst, strategy_, ring_id, use_calc_stream);
}
paddle::platform::CUDADeviceContext *NCCLParallelContext::GetDeviceContext(
paddle::platform::DeviceContext *NCCLParallelContext::GetDeviceContext(
int ring_id) {
return platform::NCCLCommContext::Instance()
return static_cast<platform::DeviceContext *>(
platform::NCCLCommContext::Instance()
.Get(ring_id, place_)
->dev_context();
->dev_context());
}
void NCCLParallelContext::WaitCompute(int ring_id) {
PADDLE_ENFORCE_GE(ring_id, 0, platform::errors::OutOfRange(
"ring id must >= 0, but got %d", ring_id));
PADDLE_ENFORCE_LT(ring_id, compute_events_.size(),
platform::errors::OutOfRange(
"ring id must < compute events size,"
"but got ring id = %d, compute events size = %d",
ring_id, compute_events_.size()));
auto compute_stream = static_cast<platform::CUDADeviceContext *>(
platform::DeviceContextPool::Instance().Get(place_))
->stream();
auto comm_stream =
platform::NCCLCommContext::Instance().Get(ring_id, place_)->stream();
auto event = compute_events_[ring_id].get();
// compute_stream-->event-->comm_stream
PADDLE_ENFORCE_CUDA_SUCCESS(cudaEventRecord(event, compute_stream));
PADDLE_ENFORCE_CUDA_SUCCESS(cudaStreamWaitEvent(comm_stream, event, 0));
}
void NCCLParallelContext::WaitComm(int ring_id) {
PADDLE_ENFORCE_GE(ring_id, 0, platform::errors::OutOfRange(
"ring id must >= 0, but got %d", ring_id));
PADDLE_ENFORCE_LT(ring_id, comm_events_.size(),
platform::errors::OutOfRange(
"ring id must < comm events size,"
"but got ring id = %d, comm events size = %d",
ring_id, comm_events_.size()));
auto compute_stream = static_cast<platform::CUDADeviceContext *>(
platform::DeviceContextPool::Instance().Get(place_))
->stream();
auto comm_stream =
platform::NCCLCommContext::Instance().Get(ring_id, place_)->stream();
auto event = comm_events_[ring_id].get();
// comm_stream-->event-->compute_stream
PADDLE_ENFORCE_CUDA_SUCCESS(cudaEventRecord(event, comm_stream));
PADDLE_ENFORCE_CUDA_SUCCESS(cudaStreamWaitEvent(compute_stream, event, 0));
}
#endif
......
paddle/fluid/imperative/nccl_context.h
浏览文件 @ 572c466d
......@@ -13,73 +13,20 @@
// limitations under the License.
#pragma once
// network header files
#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
#include <arpa/inet.h>
#include <netdb.h>
#include <netinet/in.h>
#include <stdlib.h>
#include <sys/socket.h>
#endif
#include <memory>
#include <string>
#include <utility>
#include <vector>
#include "paddle/fluid/framework/scope.h"
#include "paddle/fluid/framework/variable.h"
#include "paddle/fluid/platform/device_context.h"
#if defined(PADDLE_WITH_NCCL)
#include "paddle/fluid/imperative/all_reduce.h"
#include "paddle/fluid/platform/cuda_resource_pool.h"
#include "paddle/fluid/platform/dynload/nccl.h"
#include "paddle/fluid/platform/nccl_helper.h"
#endif
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/framework/selected_rows.h"
#include "paddle/fluid/platform/collective_helper.h"
#include "paddle/fluid/platform/place.h"
#include "paddle/fluid/string/split.h"
#include "paddle/fluid/string/string_helper.h"
#include "paddle/fluid/imperative/parallel_context.h"
namespace paddle {
namespace imperative {
struct ParallelStrategy {
int nranks_{1};
int local_rank_{0};
std::vector<std::string> trainer_endpoints_{};
std::string current_endpoint_{""};
// TODO(shenliang03): support multi stream communication
int nrings_{1};
};
class ParallelContext {
public:
explicit ParallelContext(const ParallelStrategy& strategy,
const platform::Place& place)
: strategy_(strategy), place_(place) {}
virtual ~ParallelContext() {}
virtual void Init() = 0;
virtual void AllReduceByStream(const framework::Variable& src,
framework::Variable* dst, int ring_id = 0,
bool use_calc_stream = false) = 0;
#if defined(PADDLE_WITH_NCCL)
virtual paddle::platform::CUDADeviceContext* GetDeviceContext(
int ring_id) = 0;
#endif
inline int GetNRings() { return strategy_.nrings_; }
protected:
ParallelStrategy strategy_;
platform::Place place_;
};
#if defined(PADDLE_WITH_NCCL)
class NCCLParallelContext : public ParallelContext {
public:
......@@ -87,7 +34,7 @@ class NCCLParallelContext : public ParallelContext {
const platform::Place& place)
: ParallelContext(strategy, place) {}
~NCCLParallelContext() {}
~NCCLParallelContext() override = default;
void BcastNCCLId(std::vector<ncclUniqueId>& nccl_ids, int root); // NOLINT
......@@ -97,14 +44,18 @@ class NCCLParallelContext : public ParallelContext {
framework::Variable* dst, int ring_id,
bool use_calc_stream) override;
paddle::platform::CUDADeviceContext* GetDeviceContext(int ring_id) override;
paddle::platform::DeviceContext* GetDeviceContext(int ring_id) override;
void WaitCompute(int ring_id) override;
void WaitComm(int ring_id) override;
protected:
void RecvNCCLID(const std::string& endpoint,
std::vector<ncclUniqueId>& nccl_ids); // NOLINT
private:
// used for comm wait compute, compute_stream-->event-->comm_stream[ring_id]
std::vector<std::shared_ptr<platform::CudaEventObject>> compute_events_;
void SendNCCLID(const std::string& endpoint,
const std::vector<ncclUniqueId>& nccl_ids);
// used for compute wait comm, comm_stream[ring_id]-->event-->compute_stream
std::vector<std::shared_ptr<platform::CudaEventObject>> comm_events_;
};
#endif
......
paddle/fluid/imperative/parallel_context.h 0 → 100644
浏览文件 @ 572c466d
// Copyright (c) 2020 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 <vector>
#include "paddle/fluid/platform/place.h"
namespace paddle {
namespace platform {
class DeviceContext;
} // namespace platform
namespace framework {
class Variable;
} // namespace framework
} // namespace paddle
namespace paddle {
namespace imperative {
struct ParallelStrategy {
int nranks_{1};
int local_rank_{0};
std::vector<std::string> trainer_endpoints_{};
std::string current_endpoint_{""};
int nrings_{1};
};
class ParallelContext {
public:
explicit ParallelContext(const ParallelStrategy& strategy,
const platform::Place& place)
: strategy_(strategy), place_(place) {}
virtual ~ParallelContext() = default;
virtual void Init() = 0;
virtual void AllReduceByStream(const framework::Variable& src,
framework::Variable* dst, int ring_id,
bool use_calc_stream) = 0;
virtual paddle::platform::DeviceContext* GetDeviceContext(int ring_id) = 0;
// comm_stream[ring_id] wait compute_stream.
// if CPU, should do nothing.
virtual void WaitCompute(int ring_id) = 0;
// compute_stream wait comm_stream[ring_id]
// if CPU, should do nothing.
virtual void WaitComm(int ring_id) = 0;
inline int GetNRings() const { return strategy_.nrings_; }
protected:
ParallelStrategy strategy_;
platform::Place place_;
};
} // namespace imperative
} // namespace paddle
paddle/fluid/imperative/reducer.cc
浏览文件 @ 572c466d
......@@ -14,60 +14,170 @@
#include "paddle/fluid/imperative/reducer.h"
#include <algorithm>
#include <iostream>
#include <map>
#include <memory>
#include <string>
#include <unordered_map>
#include <utility>
#include <vector>
#include "paddle/fluid/framework/data_type.h"
#include "paddle/fluid/imperative/layer.h"
#include "paddle/fluid/imperative/op_base.h"
#include "paddle/fluid/imperative/variable_wrapper.h"
#include "paddle/fluid/memory/memory.h"
#include "paddle/fluid/string/string_helper.h"
#if defined(PADDLE_WITH_NCCL)
#include "paddle/fluid/operators/math/concat_and_split.h"
#include "paddle/fluid/operators/strided_memcpy.h"
#endif
#include "paddle/fluid/imperative/parallel_context.h"
namespace paddle {
namespace imperative {
#if defined(PADDLE_WITH_NCCL)
std::shared_ptr<Reducer> Reducer::s_instance_ = NULL;
// context is used to select the stream for concat
void Group::ConcatTensors(const platform::CUDADeviceContext &context) {
VLOG(3) << "Before concat, set output tensor size is " << all_length_;
auto tensor = dense_contents_.GetMutable<framework::LoDTensor>();
tensor->Resize(framework::make_ddim({all_length_}))
.mutable_data(context.GetPlace(), dtype_);
template <typename DeviceContext, typename T>
static void ConcatTensorsForAllReduce(
const DeviceContext &context,
const std::vector<framework::Tensor> &dense_tensors_,
framework::Variable *p_dense_contents) {
operators::math::ConcatFunctor<DeviceContext, T> concat_functor_;
concat_functor_(context, dense_tensors_, 0,
p_dense_contents->GetMutable<framework::LoDTensor>());
}
switch (dtype_) {
template <typename DeviceContext, typename T>
static void SplitTensorsForAllReduce(
const DeviceContext &context, framework::Variable *p_dense_contents,
std::vector<framework::Tensor> *p_dense_tensors) {
auto *in = p_dense_contents->GetMutable<framework::LoDTensor>();
std::vector<framework::Tensor *> outs;
std::vector<const framework::Tensor *> shape_refer;
outs.reserve(p_dense_tensors->size());
shape_refer.reserve(p_dense_tensors->size());
for (auto &tensor : *p_dense_tensors) {
outs.emplace_back(&tensor);
shape_refer.emplace_back(&tensor);
}
// Sometimes direct copies will be faster
if (p_dense_tensors->size() < 10) {
operators::StridedMemcpyWithAxis0<T>(context, *in, shape_refer, &outs);
} else {
operators::math::SplitFunctor<DeviceContext, T> split_functor_;
split_functor_(context, *in, shape_refer, 0, &outs);
}
}
// context is used to select the stream for concat
template <typename DeviceContext>
static void ConcatTensorsWithType(
const DeviceContext &context,
const std::vector<framework::Tensor> &dense_tensors_,
framework::Variable *p_dense_contents,
framework::proto::VarType::Type type) {
switch (type) {
case framework::proto::VarType::FP16:
ConcatTensorsForAllReduce<platform::float16>(context, dense_tensors_,
&dense_contents_);
ConcatTensorsForAllReduce<DeviceContext, platform::float16>(
context, dense_tensors_, p_dense_contents);
break;
case framework::proto::VarType::FP32:
ConcatTensorsForAllReduce<float>(context, dense_tensors_,
&dense_contents_);
ConcatTensorsForAllReduce<DeviceContext, float>(context, dense_tensors_,
p_dense_contents);
break;
case framework::proto::VarType::FP64:
ConcatTensorsForAllReduce<double>(context, dense_tensors_,
&dense_contents_);
ConcatTensorsForAllReduce<DeviceContext, double>(context, dense_tensors_,
p_dense_contents);
break;
default:
PADDLE_THROW(platform::errors::Unimplemented(
"Data type (%s) is not supported when it concats tensors for "
"allreduce.",
framework::DataTypeToString(dtype_)));
framework::DataTypeToString(type)));
}
}
// context is used to select the stream for split
void Group::SplitTensors(const platform::CUDADeviceContext &context) {
switch (dtype_) {
template <typename DeviceContext>
static void SplitTensorsWithType(
const DeviceContext &context, framework::Variable *p_dense_contents,
std::vector<framework::Tensor> *p_dense_tensors,
framework::proto::VarType::Type type) {
switch (type) {
case framework::proto::VarType::FP16:
SplitTensorsForAllReduce<platform::float16>(context, &dense_contents_,
&dense_tensors_);
SplitTensorsForAllReduce<DeviceContext, platform::float16>(
context, p_dense_contents, p_dense_tensors);
break;
case framework::proto::VarType::FP32:
SplitTensorsForAllReduce<float>(context, &dense_contents_,
&dense_tensors_);
SplitTensorsForAllReduce<DeviceContext, float>(context, p_dense_contents,
p_dense_tensors);
break;
case framework::proto::VarType::FP64:
SplitTensorsForAllReduce<double>(context, &dense_contents_,
&dense_tensors_);
SplitTensorsForAllReduce<DeviceContext, double>(context, p_dense_contents,
p_dense_tensors);
break;
default:
PADDLE_THROW(platform::errors::Unimplemented(
"Data type (%s) is not supported when it splits tensors for "
"allreduce.",
framework::DataTypeToString(dtype_)));
framework::DataTypeToString(type)));
}
}
void Group::ConcatTensors(const platform::DeviceContext &context) {
VLOG(3) << "Before concat, set output tensor size is " << all_length_;
auto tensor = dense_contents_.GetMutable<framework::LoDTensor>();
tensor->Resize(framework::make_ddim({all_length_}))
.mutable_data(context.GetPlace(), dtype_);
auto place = context.GetPlace();
if (platform::is_gpu_place(place)) {
#ifdef PADDLE_WITH_NCCL
ConcatTensorsWithType(
static_cast<const platform::CUDADeviceContext &>(context),
dense_tensors_, &dense_contents_, dtype_);
#else
PADDLE_THROW(platform::errors::PermissionDenied(
"Paddle can't concat grad tensors since it's not compiled with NCCL,"
"Please recompile or reinstall Paddle with NCCL support."));
#endif
} else if (platform::is_cpu_place(place)) {
ConcatTensorsWithType(
static_cast<const platform::CPUDeviceContext &>(context),
dense_tensors_, &dense_contents_, dtype_);
} else {
PADDLE_THROW(platform::errors::Unimplemented(
"Concat grad tensor not supported on place (%s)", place));
}
}
void Group::SplitTensors(const platform::DeviceContext &context) {
auto place = context.GetPlace();
if (platform::is_gpu_place(place)) {
#ifdef PADDLE_WITH_NCCL
SplitTensorsWithType(
static_cast<const platform::CUDADeviceContext &>(context),
&dense_contents_, &dense_tensors_, dtype_);
#else
PADDLE_THROW(platform::errors::PermissionDenied(
"Paddle can't split grad tensor since it's not compiled with NCCL,"
"Please recompile or reinstall Paddle with NCCL support."));
#endif
} else if (platform::is_cpu_place(place)) {
SplitTensorsWithType(
static_cast<const platform::CPUDeviceContext &>(context),
&dense_contents_, &dense_tensors_, dtype_);
} else {
PADDLE_THROW(platform::errors::Unimplemented(
"Split grad tensor not supported on place (%s)", place));
}
}
......@@ -115,44 +225,13 @@ Reducer::Reducer(const std::vector<std::shared_ptr<imperative::VarBase>> &vars,
})));
var_index_map_[var->GradVarBase()->SharedVar().get()] = global_var_index;
}
// create streams
compute_stream_ = static_cast<platform::CUDADeviceContext *>(
platform::DeviceContextPool::Instance().Get(place_))
->stream();
for (int i = 0; i < nrings_; ++i) {
comm_streams_.emplace_back(
platform::NCCLCommContext::Instance().Get(i, place_)->stream());
comm_events_.emplace_back(platform::CudaEventResourcePool::Instance().New(
BOOST_GET_CONST(platform::CUDAPlace, place_).device));
}
CreateGroupEvents(group_indices.size());
std::call_once(once_flag_, []() {
std::atexit([]() { Reducer::GetInstance()->ReleaseReducer(); });
});
}
void Reducer::ReleaseReducer() {
for (auto &event : group_events_) {
event.reset();
}
for (auto &event : comm_events_) {
event.reset();
}
}
void Reducer::CreateGroupEvents(int group_num) {
// release old events
for (auto &event : group_events_) {
event.reset();
}
group_events_.clear();
group_events_.resize(group_num);
for (auto &event : group_events_) {
event = platform::CudaEventResourcePool::Instance().New(
BOOST_GET_CONST(platform::CUDAPlace, place_).device);
}
}
void Reducer::ReleaseReducer() { parallel_ctx_.reset(); }
void Reducer::InitializeDenseGroups(
const std::vector<size_t> &variable_indices_, Group *p_group) {
......@@ -455,18 +534,18 @@ void Reducer::MarkGroupReady(size_t group_index) {
return;
}
PADDLE_ENFORCE_CUDA_SUCCESS(
cudaEventRecord(group_events_[group_index].get(), compute_stream_));
for (int i = 0; i < nrings_; ++i) {
PADDLE_ENFORCE_CUDA_SUCCESS(cudaStreamWaitEvent(
comm_streams_[i], group_events_[group_index].get(), 0));
}
for (; next_group_ < groups_.size() && groups_[next_group_].pending_ == 0;
++next_group_) {
auto &group = groups_[next_group_];
int run_order = next_group_ % nrings_;
// For CUDA or XPU, compute_stream --> comm_stream.
// For CPU, do nothing.
// NOTE. Because concat uses the comm_stream,
// so we expose WaitCompute() interface and call
// it here.
parallel_ctx_->WaitCompute(run_order);
if (group.is_sparse_) {
if (group.sparse_contents_ != nullptr) {
VLOG(3) << "sparse group [" << next_group_
......@@ -526,20 +605,13 @@ void Reducer::FinalizeBackward() {
all_group_ready_ = false;
// Must prevent compute_stream_ starting until all comm streams have finished
for (int i = 0; i < nrings_; ++i) {
PADDLE_ENFORCE_CUDA_SUCCESS(
cudaEventRecord(comm_events_[i].get(), comm_streams_[i]));
}
for (int i = 0; i < nrings_; ++i) {
PADDLE_ENFORCE_CUDA_SUCCESS(
cudaStreamWaitEvent(compute_stream_, comm_events_[i].get(), 0));
parallel_ctx_->WaitComm(i);
}
if (NeedRebuildGroup()) {
VLOG(3) << "Start rebuilding the groups";
auto rebuild_group_indices = RebuildGruops();
auto rebuild_group_number = rebuild_group_indices.size();
group_indices_ = std::move(rebuild_group_indices);
CreateGroupEvents(rebuild_group_number);
InitializeGroups(group_indices_);
}
......
paddle/fluid/imperative/reducer.h
浏览文件 @ 572c466d
......@@ -24,60 +24,27 @@
#include <unordered_set>
#include <utility>
#include <vector>
#include "paddle/fluid/framework/data_type.h"
#include "paddle/fluid/imperative/layer.h"
#include "paddle/fluid/imperative/op_base.h"
#include "paddle/fluid/imperative/variable_wrapper.h"
#include "paddle/fluid/memory/memory.h"
#include "paddle/fluid/string/string_helper.h"
#include "paddle/fluid/framework/tensor.h"
#include "paddle/fluid/framework/variable.h"
#if defined(PADDLE_WITH_NCCL)
#include "paddle/fluid/imperative/all_reduce.h"
#include "paddle/fluid/operators/math/concat_and_split.h"
#include "paddle/fluid/operators/strided_memcpy.h"
#include "paddle/fluid/platform/cuda_resource_pool.h"
#endif
namespace paddle {
namespace platform {
class DeviceContext;
} // namespace platform
namespace imperative {
class ParallelContext;
class VarBase;
class VariableWrapper;
} // namespace imperative
} // namespace paddle
namespace paddle {
namespace imperative {
#if defined(PADDLE_WITH_NCCL)
template <typename T>
void ConcatTensorsForAllReduce(
const platform::CUDADeviceContext& context,
const std::vector<framework::Tensor>& dense_tensors_,
framework::Variable* p_dense_contents) {
operators::math::ConcatFunctor<platform::CUDADeviceContext, T>
concat_functor_;
concat_functor_(context, dense_tensors_, 0,
p_dense_contents->GetMutable<framework::LoDTensor>());
}
template <typename T>
void SplitTensorsForAllReduce(const platform::CUDADeviceContext& context,
framework::Variable* p_dense_contents,
std::vector<framework::Tensor>* p_dense_tensors) {
auto* in = p_dense_contents->GetMutable<framework::LoDTensor>();
std::vector<framework::Tensor*> outs;
std::vector<const framework::Tensor*> shape_refer;
outs.reserve(p_dense_tensors->size());
shape_refer.reserve(p_dense_tensors->size());
for (auto& tensor : *p_dense_tensors) {
outs.emplace_back(&tensor);
shape_refer.emplace_back(&tensor);
}
// Sometimes direct copies will be faster
if (p_dense_tensors->size() < 10) {
operators::StridedMemcpyWithAxis0<T>(context, *in, shape_refer, &outs);
} else {
operators::math::SplitFunctor<platform::CUDADeviceContext, T>
split_functor_;
split_functor_(context, *in, shape_refer, 0, &outs);
}
}
class Group {
public:
// Here, we use dense_contents_ & sparse_contents_ to
......@@ -104,10 +71,10 @@ class Group {
framework::proto::VarType::Type dtype_;
// context is used to select the stream for concat
void ConcatTensors(const platform::CUDADeviceContext& context);
void ConcatTensors(const platform::DeviceContext& context);
// context is used to select the stream for split
void SplitTensors(const platform::CUDADeviceContext& context);
void SplitTensors(const platform::DeviceContext& context);
friend std::ostream& operator<<(std::ostream&, const Group&);
};
......@@ -155,8 +122,6 @@ class Reducer {
std::vector<std::vector<size_t>> RebuildGruops();
void CreateGroupEvents(int group_num);
inline bool NeedRebuildGroup() { return !has_rebuilt_group_; }
// Reducer Singleton
......@@ -193,11 +158,6 @@ class Reducer {
std::shared_ptr<imperative::ParallelContext> parallel_ctx_;
std::vector<VariableLocator> variable_locators_;
// Following variables are to help sync stream
std::vector<std::shared_ptr<platform::CudaEventObject>> group_events_;
std::vector<std::shared_ptr<platform::CudaEventObject>> comm_events_;
cudaStream_t compute_stream_;
std::vector<cudaStream_t> comm_streams_;
int nrings_ = 1;
// Following variables are to help rebuild group
......
paddle/fluid/imperative/tests/nccl_context_test.cc
浏览文件 @ 572c466d
......@@ -12,6 +12,8 @@
// See the License for the specific language governing permissions and
// limitations under the License.
#include <thread> // NOLINT
#include "paddle/fluid/imperative/nccl_context.h"
#include "gtest/gtest.h"
......
paddle/fluid/imperative/tests/test_group.cc
浏览文件 @ 572c466d
......@@ -60,6 +60,109 @@ TEST(TestGroup, TestPrintGroupMessage) {
ASSERT_STREQ(stream2.str().c_str(), head.c_str());
}
template <typename T, typename Place>
void GroupConcatSplit(Place place, size_t size) {
platform::CPUPlace cpu_place;
Group group;
// [[0.0], [0.0, 1.0], [0.0, 1.0, 2.0] .. ]
std::vector<framework::Variable> vars;
vars.resize(size);
for (size_t i = 0; i < size; ++i) {
auto len = i + 1;
auto* tensor = vars[i].GetMutable<framework::LoDTensor>();
tensor->Resize({static_cast<int64_t>(len)});
auto* data = tensor->mutable_data<T>(place);
std::vector<T> value;
for (size_t j = 0; j < len; ++j) {
value.push_back(static_cast<T>(1.0 * j));
}
if (std::is_same<Place, platform::CUDAPlace>::value) {
paddle::memory::Copy(place, data, cpu_place, value.data(),
sizeof(T) * value.size(), 0);
} else {
paddle::memory::Copy(place, data, cpu_place, value.data(),
sizeof(T) * value.size());
}
framework::Tensor tmp;
tmp.ShareDataWith(*tensor).Resize({static_cast<int64_t>(len)});
group.dense_tensors_.push_back(std::move(tmp));
group.all_length_ += len;
group.dtype_ = tensor->type();
}
paddle::platform::DeviceContextPool& pool =
paddle::platform::DeviceContextPool::Instance();
auto* dev_ctx = pool.Get(place);
{ // concat
group.ConcatTensors(*dev_ctx);
auto* tensor = group.dense_contents_.GetMutable<framework::LoDTensor>();
framework::Tensor tmp;
framework::TensorCopySync(*tensor, cpu_place, &tmp);
auto* data = tmp.data<T>();
size_t offset = 0;
for (size_t i = 0; i < size; ++i) {
auto len = i + 1;
for (size_t j = 0; j < len; ++j) {
EXPECT_EQ(data[offset + j], static_cast<T>(1.0 * j));
// [[-0.0], [-0.0, -1.0], [-0.0, -1.0, -2.0] .. ]
data[offset + j] = -data[offset + j];
}
offset += len;
}
framework::TensorCopySync(tmp, place, tensor);
}
{ // split
group.SplitTensors(*dev_ctx);
for (size_t i = 0; i < size; ++i) {
auto len = i + 1;
auto& tensor = group.dense_tensors_[i];
framework::Tensor tmp;
framework::TensorCopySync(tensor, cpu_place, &tmp);
auto* data = tmp.data<T>();
for (size_t j = 0; j < len; ++j) {
EXPECT_EQ(data[j], static_cast<T>(-1.0 * j));
}
}
}
}
TEST(TestGroup, TestConcatSplit) {
platform::CUDAPlace cuda_place(0);
platform::CPUPlace cpu_place;
int size = 3;
GroupConcatSplit<float>(cpu_place, size);
GroupConcatSplit<double>(cpu_place, size);
GroupConcatSplit<platform::float16>(cpu_place, size);
GroupConcatSplit<float>(cuda_place, size);
GroupConcatSplit<double>(cuda_place, size);
GroupConcatSplit<platform::float16>(cuda_place, size);
size = 15;
GroupConcatSplit<float>(cpu_place, size);
GroupConcatSplit<double>(cpu_place, size);
GroupConcatSplit<platform::float16>(cpu_place, size);
GroupConcatSplit<float>(cuda_place, size);
GroupConcatSplit<double>(cuda_place, size);
GroupConcatSplit<platform::float16>(cuda_place, size);
}
TEST(TestGroup, TestConcatSplitException) {
platform::CUDAPinnedPlace place;
int size = 3;
ASSERT_ANY_THROW(GroupConcatSplit<float>(place, size));
}
#endif
} // namespace imperative
......
paddle/fluid/operators/collective/CMakeLists.txt
浏览文件 @ 572c466d
......@@ -15,9 +15,8 @@ register_operators(EXCLUDES c_gen_nccl_id_op gen_nccl_id_op DEPS ${COLLECTIVE_DE
if(WITH_NCCL)
set(COLLECTIVE_DEPS ${COLLECTIVE_DEPS} nccl_common collective_helper)
cc_library(gen_nccl_id_op_helper SRCS gen_nccl_id_op_helper.cc DEPS nccl_common)
op_library(c_gen_nccl_id_op DEPS ${COLLECTIVE_DEPS} gen_nccl_id_op_helper)
op_library(gen_nccl_id_op DEPS ${COLLECTIVE_DEPS} gen_nccl_id_op_helper)
op_library(c_gen_nccl_id_op DEPS ${COLLECTIVE_DEPS})
op_library(gen_nccl_id_op DEPS ${COLLECTIVE_DEPS})
endif()
if(WITH_GLOO)
......
paddle/fluid/operators/collective/c_gen_nccl_id_op.cc
浏览文件 @ 572c466d
......@@ -23,11 +23,32 @@ limitations under the License. */
#include "paddle/fluid/platform/enforce.h"
#include "paddle/fluid/platform/place.h"
#include "paddle/fluid/operators/collective/gen_nccl_id_op_helper.h"
#include "paddle/fluid/platform/gen_comm_id_helper.h"
namespace paddle {
namespace operators {
static void GenNCCLID(std::vector<ncclUniqueId>* nccl_ids) {
for (size_t i = 0; i < nccl_ids->size(); ++i) {
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::ncclGetUniqueId(&(*nccl_ids)[i]));
}
}
static void CopyNCCLIDToVar(const std::vector<ncclUniqueId>& nccl_ids,
std::function<std::string(size_t)> func,
const framework::Scope& scope) {
for (size_t i = 0; i < nccl_ids.size(); ++i) {
std::string var_name = func(i);
auto var = scope.FindVar(var_name);
PADDLE_ENFORCE_NOT_NULL(
var, platform::errors::NotFound("Variable with name %s is not found",
var_name.c_str()));
auto nccl_id = var->GetMutable<ncclUniqueId>();
memcpy(nccl_id, &nccl_ids[i], sizeof(ncclUniqueId));
}
}
class CGenNCCLIdOp : public framework::OperatorBase {
public:
CGenNCCLIdOp(const std::string& type,
......@@ -45,14 +66,20 @@ class CGenNCCLIdOp : public framework::OperatorBase {
return Output("Out");
};
std::vector<ncclUniqueId> nccl_ids;
nccl_ids.resize(1);
if (rank == 0) {
GenNCCLID(&nccl_ids);
std::vector<std::string> endpoint_list =
Attr<std::vector<std::string>>("other_endpoints");
SendBroadCastNCCLID(endpoint_list, 1, func, local_scope);
platform::SendBroadCastCommID(endpoint_list, &nccl_ids);
} else {
std::string endpoint = Attr<std::string>("endpoint");
RecvBroadCastNCCLID(endpoint, 1, func, local_scope);
platform::RecvBroadCastCommID(endpoint, &nccl_ids);
}
CopyNCCLIDToVar(nccl_ids, func, scope);
scope.DeleteScope(&local_scope);
}
};
......
paddle/fluid/operators/collective/gen_nccl_id_op.cc
浏览文件 @ 572c466d
......@@ -27,11 +27,32 @@ limitations under the License. */
#include "paddle/fluid/platform/place.h"
#include "paddle/fluid/string/split.h"
#include "paddle/fluid/operators/collective/gen_nccl_id_op_helper.h"
#include "paddle/fluid/platform/gen_comm_id_helper.h"
namespace paddle {
namespace operators {
static void GenNCCLID(std::vector<ncclUniqueId>* nccl_ids) {
for (size_t i = 0; i < nccl_ids->size(); ++i) {
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::ncclGetUniqueId(&(*nccl_ids)[i]));
}
}
static void CopyNCCLIDToVar(const std::vector<ncclUniqueId>& nccl_ids,
std::function<std::string(size_t)> func,
const framework::Scope& scope) {
for (size_t i = 0; i < nccl_ids.size(); ++i) {
std::string var_name = func(i);
auto var = scope.FindVar(var_name);
PADDLE_ENFORCE_NOT_NULL(
var, platform::errors::NotFound("Variable with name %s is not found",
var_name.c_str()));
auto nccl_id = var->GetMutable<ncclUniqueId>();
memcpy(nccl_id, &nccl_ids[i], sizeof(ncclUniqueId));
}
}
class GenNCCLIdOp : public framework::OperatorBase {
public:
GenNCCLIdOp(const std::string& type, const framework::VariableNameMap& inputs,
......@@ -98,19 +119,25 @@ class GenNCCLIdOp : public framework::OperatorBase {
<< ", trainers:" << ss.str();
int server_fd = -1;
std::vector<ncclUniqueId> nccl_ids;
nccl_ids.resize(nccl_comm_num);
/// 1. init flat
std::function<std::string(size_t)> func = platform::GetFlatNCCLVarName;
// broadcast unique id
if (trainer_id == 0) {
GenNCCLID(&nccl_ids);
// server endpoints
std::vector<std::string> flat_endpoints;
flat_endpoints.insert(flat_endpoints.begin(), trainers.begin() + 1,
trainers.end());
SendBroadCastNCCLID(flat_endpoints, nccl_comm_num, func, scope);
platform::SendBroadCastCommID(flat_endpoints, &nccl_ids);
} else {
server_fd = CreateListenSocket(endpoint);
RecvBroadCastNCCLID(server_fd, endpoint, nccl_comm_num, func, scope);
server_fd = platform::CreateListenSocket(endpoint);
platform::RecvBroadCastCommID(server_fd, endpoint, &nccl_ids);
}
CopyNCCLIDToVar(nccl_ids, func, scope);
/// 2. hierarchical inter ncclid
func = platform::GetHierarchicalInterNCCLVarName;
......@@ -127,10 +154,13 @@ class GenNCCLIdOp : public framework::OperatorBase {
}
VLOG(1) << "Hierarchical inter ring endpoints:" << ss.str();
SendBroadCastNCCLID(inter_endpoints, nccl_comm_num, func, scope);
GenNCCLID(&nccl_ids);
platform::SendBroadCastCommID(inter_endpoints, &nccl_ids);
CopyNCCLIDToVar(nccl_ids, func, scope);
} else if (inter_trainer_id > 0) {
VLOG(1) << "Hierarchical inter ring";
RecvBroadCastNCCLID(server_fd, endpoint, nccl_comm_num, func, scope);
platform::RecvBroadCastCommID(server_fd, endpoint, &nccl_ids);
CopyNCCLIDToVar(nccl_ids, func, scope);
}
/// 3. hierarchical exter ncclid
......@@ -146,15 +176,18 @@ class GenNCCLIdOp : public framework::OperatorBase {
}
VLOG(1) << "Hierarchical exter ring endpoints:" << ss.str();
SendBroadCastNCCLID(exter_endpoints, nccl_comm_num, func, scope);
GenNCCLID(&nccl_ids);
platform::SendBroadCastCommID(exter_endpoints, &nccl_ids);
CopyNCCLIDToVar(nccl_ids, func, scope);
} else if (exter_trainer_id > 0) {
VLOG(1) << "Hierarchical exter ring";
RecvBroadCastNCCLID(server_fd, endpoint, nccl_comm_num, func, scope);
platform::RecvBroadCastCommID(server_fd, endpoint, &nccl_ids);
CopyNCCLIDToVar(nccl_ids, func, scope);
}
// close socket server
if (trainer_id != 0) {
CloseSocket(server_fd);
platform::CloseSocket(server_fd);
}
}
};
......
paddle/fluid/platform/CMakeLists.txt
浏览文件 @ 572c466d
......@@ -101,7 +101,7 @@ cc_library(device_context SRCS device_context.cc init.cc DEPS simple_threadpool
place eigen3 stringpiece cpu_helper cpu_info framework_proto ${GPU_CTX_DEPS} ${MKLDNN_CTX_DEPS}
${dgc_deps} dlpack cudnn_workspace_helper ${XPU_CTX_DEPS})
cc_library(collective_helper SRCS collective_helper.cc DEPS framework_proto device_context enforce)
cc_library(collective_helper SRCS collective_helper.cc gen_comm_id_helper.cc DEPS framework_proto device_context enforce)
if(WITH_GPU)
cc_library(cuda_resource_pool SRCS cuda_resource_pool.cc DEPS gpu_info)
......
paddle/fluid/operators/collective/gen_nccl_id_op_helper.cc → paddle/fluid/platform/gen_comm_id_helper.cc
浏览文件 @ 572c466d
......@@ -12,7 +12,8 @@ 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 "paddle/fluid/operators/collective/gen_nccl_id_op_helper.h"
#ifdef PADDLE_WITH_NCCL
#include "paddle/fluid/platform/gen_comm_id_helper.h"
#include <arpa/inet.h>
#include <netdb.h>
......@@ -31,7 +32,7 @@ limitations under the License. */
#include "paddle/fluid/string/split.h"
namespace paddle {
namespace operators {
namespace platform {
constexpr char COMM_HEAD[] = "_pd_gen_comm_id_";
......@@ -257,26 +258,29 @@ static int ConnectAddr(const std::string& ep, const char* head) {
return sock;
}
static void RecvNCCLID(int conn, ncclUniqueId* nccl_id) {
template <typename CommUniqueId>
static void RecvCommID(int conn, CommUniqueId* nccl_id) {
char buffer[1024] = {0};
static_assert(NCCL_UNIQUE_ID_BYTES <= 1024,
static_assert(sizeof(CommUniqueId) <= 1024,
"nccl id bytes must <= buffer size");
CHECK_SYS_CALL(SocketRecv(conn, buffer, NCCL_UNIQUE_ID_BYTES), "recv ncc id");
memcpy(nccl_id, buffer, NCCL_UNIQUE_ID_BYTES);
CHECK_SYS_CALL(SocketRecv(conn, buffer, sizeof(CommUniqueId)),
"recv comm unique id");
memcpy(nccl_id, buffer, sizeof(CommUniqueId));
}
static void SendNCCLID(int conn, ncclUniqueId* nccl_id) {
template <typename CommUniqueId>
static void SendCommID(int conn, CommUniqueId* nccl_id) {
char buffer[1024] = {0};
memcpy(buffer, nccl_id, NCCL_UNIQUE_ID_BYTES);
memcpy(buffer, nccl_id, sizeof(CommUniqueId));
CHECK_SYS_CALL(SocketSend(conn, buffer, NCCL_UNIQUE_ID_BYTES),
"send nccl id");
CHECK_SYS_CALL(SocketSend(conn, buffer, sizeof(CommUniqueId)),
"send comm unique id");
}
void SendBroadCastNCCLID(std::vector<std::string> servers, int nccl_comm_num,
std::function<std::string(size_t)> func,
const framework::Scope& scope) {
template <typename CommUniqueId>
void SendBroadCastCommID(std::vector<std::string> servers,
std::vector<CommUniqueId>* nccl_ids) {
// connect with server
std::vector<int> connects;
for (auto server : servers) {
......@@ -286,23 +290,13 @@ void SendBroadCastNCCLID(std::vector<std::string> servers, int nccl_comm_num,
}
VLOG(3) << "connecting completed...";
for (int i = 0; i < nccl_comm_num; ++i) {
std::string var_name = func(i);
auto var = scope.FindVar(var_name);
PADDLE_ENFORCE_NOT_NULL(
var, platform::errors::NotFound("Variable with name %s is not found",
var_name.c_str()));
auto nccl_id = var->GetMutable<ncclUniqueId>();
PADDLE_ENFORCE_CUDA_SUCCESS(platform::dynload::ncclGetUniqueId(nccl_id));
for (size_t i = 0; i < nccl_ids->size(); ++i) {
int j = 0;
for (auto conn : connects) {
VLOG(3) << "sending nccl_id_var: " << var_name << " to " << servers[j]
<< " nccl_comm_no: " << i;
SendNCCLID(conn, nccl_id);
VLOG(3) << "sending comm_id to " << servers[j] << " nccl_comm_no: " << i;
SendCommID(conn, &(*nccl_ids)[i]);
++j;
}
VLOG(3) << "sending completed...";
}
// close client
......@@ -311,34 +305,43 @@ void SendBroadCastNCCLID(std::vector<std::string> servers, int nccl_comm_num,
}
}
void RecvBroadCastNCCLID(std::string endpoint, int nccl_comm_num,
std::function<std::string(size_t)> func,
const framework::Scope& scope) {
template <typename CommUniqueId>
void RecvBroadCastCommID(std::string endpoint,
std::vector<CommUniqueId>* nccl_ids) {
int server = CreateListenSocket(endpoint);
RecvBroadCastNCCLID(server, endpoint, nccl_comm_num, func, scope);
RecvBroadCastCommID(server, endpoint, nccl_ids);
CloseSocket(server);
}
void RecvBroadCastNCCLID(int server_fd, std::string endpoint, int nccl_comm_num,
std::function<std::string(size_t)> func,
const framework::Scope& scope) {
template <typename CommUniqueId>
void RecvBroadCastCommID(int server_fd, std::string endpoint,
std::vector<CommUniqueId>* nccl_ids) {
int client = SocketAccept(server_fd, COMM_HEAD);
for (int i = 0; i < nccl_comm_num; ++i) {
std::string var_name = func(i);
auto var = scope.FindVar(var_name);
PADDLE_ENFORCE_NOT_NULL(
var, platform::errors::NotFound("Variable with name %s is not found",
var_name.c_str()));
auto nccl_id = var->GetMutable<ncclUniqueId>();
VLOG(3) << "trainer: " << endpoint << " receiving nccl_id_var: " << var_name
<< " from trainer 0, nccl_comm_no: " << i;
RecvNCCLID(client, nccl_id);
for (size_t i = 0; i < nccl_ids->size(); ++i) {
VLOG(3) << "trainer: " << endpoint
<< " receiving comm_id from trainer 0, nccl_comm_no: " << i;
RecvCommID(client, &(*nccl_ids)[i]);
}
VLOG(3) << "receiving completed...";
CloseSocket(client);
}
} // namespace operators
/// template instantiation
#define INSTANT_TEMPLATE(Type) \
template void SendBroadCastCommID<Type>(std::vector<std::string> servers, \
std::vector<Type> * nccl_ids); \
template void RecvBroadCastCommID<Type>(std::string endpoint, \
std::vector<Type> * nccl_ids);
#ifdef PADDLE_WITH_NCCL
INSTANT_TEMPLATE(ncclUniqueId)
#endif
#ifdef PADDLE_WITH_XPU_BKCL
INSTANT_TEMPLATE(bkclUniqueId)
#endif
} // namespace platform
} // namespace paddle
#endif
paddle/fluid/operators/collective/gen_nccl_id_op_helper.h → paddle/fluid/platform/gen_comm_id_helper.h
浏览文件 @ 572c466d
......@@ -14,35 +14,31 @@ limitations under the License. */
#pragma once
#ifdef PADDLE_WITH_NCCL
#include <functional>
#include <string>
#include <vector>
namespace paddle {
namespace framework {
class Scope;
} // namespace framework
} // namespace paddle
namespace paddle {
namespace operators {
namespace platform {
int CreateListenSocket(const std::string& ep);
void CloseSocket(int fd);
void SendBroadCastNCCLID(std::vector<std::string> servers, int nccl_comm_num,
std::function<std::string(size_t)> func,
const framework::Scope& scope);
template <typename CommUniqueId>
void SendBroadCastCommID(std::vector<std::string> servers,
std::vector<CommUniqueId>* nccl_ids);
// server listen on endpoint, then recv nccl id
void RecvBroadCastNCCLID(std::string endpoint, int nccl_comm_num,
std::function<std::string(size_t)> func,
const framework::Scope& scope);
template <typename CommUniqueId>
void RecvBroadCastCommID(std::string endpoint,
std::vector<CommUniqueId>* nccl_ids);
// recv nccl id from socket
void RecvBroadCastNCCLID(int server_fd, std::string endpoint, int nccl_comm_num,
std::function<std::string(size_t)> func,
const framework::Scope& scope);
} // namespace operators
template <typename CommUniqueId>
void RecvBroadCastCommID(int server_fd, std::string endpoint,
std::vector<CommUniqueId>* nccl_ids);
} // namespace platform
} // namespace paddle
#endif
paddle/fluid/platform/nccl_helper.h
浏览文件 @ 572c466d
......@@ -12,9 +12,9 @@
// See the License for the specific language governing permissions and
// limitations under the License.
#ifdef PADDLE_WITH_NCCL
#pragma once
#ifdef PADDLE_WITH_NCCL
#include <stdio.h>
#include <memory>
#include <string>
......
python/paddle/fluid/tests/unittests/test_gen_nccl_id_op.py
浏览文件 @ 572c466d
......@@ -14,10 +14,11 @@
import unittest
import os
import copy
from launch_function_helper import wait, _find_free_port
from multiprocessing import Pool, Process
from threading import Thread
os.environ['GLOG_vmodule'] = str("gen_nccl_id_op*=10")
os.environ['GLOG_vmodule'] = str("gen_nccl_id_op*=10,gen_comm_id*=10")
import paddle
from paddle.fluid import core
......@@ -29,8 +30,8 @@ def run_gen_ncc_id(attr):
nccl_comm_num = attr['nccl_comm_num']
use_hallreduce = attr['use_hierarchical_allreduce']
startup_program = paddle.static.default_startup_program()
main_program = paddle.static.default_main_program()
startup_program = paddle.static.Program()
main_program = paddle.static.Program()
with paddle.static.program_guard(main_program, startup_program):
nccl_id_var = startup_program.global_block().create_var(
......@@ -60,8 +61,9 @@ def run_gen_ncc_id(attr):
attrs=attr)
place = paddle.CPUPlace()
exe = paddle.static.Executor(place)
scope = paddle.static.Scope()
with paddle.static.scope_guard(scope):
exe.run(startup_program)
......@@ -97,16 +99,19 @@ class TestGenNcclIdOp(unittest.TestCase):
procs = []
for i in range(nranks):
attr['trainer_id'] = i
p = Process(target=run_gen_ncc_id, args=(attr, ))
# NOTE. multiprocessing cannot be covered by coverage
p = Thread(target=run_gen_ncc_id, args=(copy.copy(attr), ))
p.start()
procs.append(p)
wait(procs, timeout=120)
for p in procs:
p.join()
def test_flat(self):
print(">>> test gen flat nccl id")
self.gen_nccl_id(2)
print("<<< end test gen flat nccl id")
print()
def test_hierarchical(self):
print(">>> test gen hierarchical nccl id")
......
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