/
ProcessGroupHeter.cc
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/
ProcessGroupHeter.cc
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// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include "paddle/fluid/distributed/collective/ProcessGroupHeter.h"
#include <chrono>
#include "paddle/fluid/platform/device/gpu/nccl_helper.h"
#include "paddle/fluid/platform/place.h"
#include "paddle/phi/api/include/api.h"
#include "paddle/phi/common/place.h"
constexpr int64_t kWaitBlockTImeout = 10;
namespace paddle {
namespace distributed {
using Place = paddle::platform::Place;
int ProcessGroupHeter::send_count = 0;
int ProcessGroupHeter::recv_count = 0;
std::shared_ptr<ProcessGroupHeter::HeterTask> ProcessGroupHeter::CreateTask(
int rank, CommType comm_type, const std::vector<phi::DenseTensor>& inputs) {
return std::make_shared<ProcessGroupHeter::HeterTask>(rank, comm_type,
inputs);
}
ProcessGroupHeter::HeterTask::HeterTask(
int rank, CommType CommType, const std::vector<phi::DenseTensor>& inputs)
: Task(rank, inputs, CommType) {}
ProcessGroupHeter::HeterTask::~HeterTask() {}
bool ProcessGroupHeter::HeterTask::IsCompleted() { return true; }
// TODO(sheniang03): Add timeout for wait, now timeout unused
bool ProcessGroupHeter::HeterTask::Wait(std::chrono::milliseconds timeout) {
return true;
}
ProcessGroupHeter::ProcessGroupHeter(
const std::shared_ptr<Store>& store, int rank, int size,
const platform::Place& place, int gid, int local_rank, int local_size,
int gloo_rank, int gloo_size, bool with_switch, std::string switch_endpoint,
int src_rank, int dst_rank)
: ProcessGroup(rank, size, place, gid),
store_(store),
local_rank_(local_rank),
local_size_(local_size),
gloo_rank_(gloo_rank),
gloo_size_(gloo_size),
with_switch_(with_switch),
switch_endpoint_(switch_endpoint),
src_rank_(src_rank),
dst_rank_(dst_rank) {
return;
#if defined(PADDLE_WITH_NCCL)
inner_pg_ = std::make_shared<ProcessGroupNCCL>(store, local_rank, local_size,
place_, IGNORE_ID);
#elif defined(PADDLE_WITH_ASCEND_CL)
inner_pg_ = std::make_shared<ProcessGroupHCCL>(store, local_rank, local_size,
place_, IGNORE_ID);
#else
PADDLE_THROW(platform::errors::Fatal(
"ProcessGroupHeter only supports NCCL and HCCL now.");
#endif
if (local_rank_ == 0 && !with_switch_) {
auto opts = ProcessGroupGloo::GlooOptions::create();
opts->device = ProcessGroupGloo::createDefaultDevice();
inter_pg_ = std::make_shared<ProcessGroupGloo>(
store, gloo_rank_, gloo_size_, place_, IGNORE_ID, opts);
}
}
template <typename T>
static void _do_add(T* dst, T* src, size_t size) {
for (size_t i = 0; i < size; i++) {
*dst += *src;
dst++;
src++;
}
}
std::shared_ptr<ProcessGroup::Task> ProcessGroupHeter::AllReduce(
std::vector<phi::DenseTensor>& in_tensors,
std::vector<phi::DenseTensor>& out_tensors, const AllreduceOptions& opts) {
#if defined(PADDLE_WITH_NCCL)
PADDLE_ENFORCE_EQ(
CheckTensorsInCudaPlace(in_tensors), true,
platform::errors::InvalidArgument("All inputs should be in CudaPlace."));
PADDLE_ENFORCE_EQ(
CheckTensorsInCudaPlace(out_tensors), true,
platform::errors::InvalidArgument("All outputs should be in CudaPlace."));
#endif
// Step1: do allreduce in inner cluster
auto task = inner_pg_->AllReduce(in_tensors, in_tensors, opts);
task->Wait();
// Step2: copy tensors to CPU
if (local_rank_ == 0) {
std::vector<phi::DenseTensor> cpu_tensors;
cpu_tensors.reserve(in_tensors.size());
phi::DenseTensor cpu_tensor;
for (size_t i = 0; i < in_tensors.size(); i++) {
auto gpu_tensor = in_tensors[i];
cpu_tensor.Resize(gpu_tensor.dims());
framework::TensorCopySync(gpu_tensor, platform::CPUPlace(), &cpu_tensor);
cpu_tensors.push_back(cpu_tensor);
}
// Step3: do inter cluster allreduce
if (with_switch_) {
if (local_rank_ == 0) {
HeterClient* client_ =
HeterClient::GetInstance({switch_endpoint_}, {}, 0).get();
auto dense_cpu_tensor = cpu_tensors[0];
std::vector<int64_t> send_size;
send_size.push_back(dense_cpu_tensor.numel());
int ret = client_->Send(
gid_, {dense_cpu_tensor.name()}, send_size, dense_cpu_tensor.data(),
dense_cpu_tensor.numel() *
framework::DataTypeSize(dense_cpu_tensor.dtype()));
PADDLE_ENFORCE_EQ(ret, 0, platform::errors::PreconditionNotMet(
"Send to the switch module error."));
phi::DenseTensor cpu_tensor2;
cpu_tensor2.AllocateFrom(
std::make_unique<paddle::experimental::DefaultAllocator>(
paddle::platform::CPUPlace())
.get(),
dense_cpu_tensor.dtype(), dense_cpu_tensor.numel());
ret = client_->Recv(
gid_, {dense_cpu_tensor.name()}, cpu_tensor2.data(),
cpu_tensor2.numel() * framework::DataTypeSize(cpu_tensor2.dtype()));
PADDLE_ENFORCE_EQ(ret, 0, platform::errors::PreconditionNotMet(
"Recv from the switch module error."));
switch (dense_cpu_tensor.dtype()) {
case DataType::FLOAT32:
_do_add<float>(reinterpret_cast<float*>(dense_cpu_tensor.data()),
reinterpret_cast<float*>(cpu_tensor2.data()),
dense_cpu_tensor.numel());
break;
case DataType::FLOAT64:
_do_add<double>(reinterpret_cast<double*>(dense_cpu_tensor.data()),
reinterpret_cast<double*>(cpu_tensor2.data()),
dense_cpu_tensor.numel());
break;
case DataType::INT32:
_do_add<int>(reinterpret_cast<int*>(dense_cpu_tensor.data()),
reinterpret_cast<int*>(cpu_tensor2.data()),
dense_cpu_tensor.numel());
break;
default:
PADDLE_THROW(platform::errors::PreconditionNotMet(
"Unsupported data type (%s) to do add.",
framework::DataType2String(dense_cpu_tensor.dtype())));
}
}
} else {
auto gloo_task = inter_pg_->AllReduce(cpu_tensors, cpu_tensors, opts);
gloo_task->Wait();
}
// Step4: copy cpu tensors to gpu
// copy cpu tensors to gpu
for (size_t i = 0; i < in_tensors.size(); i++) {
auto gpu_tensor = out_tensors[i];
auto cpu_tensor = cpu_tensors[i];
framework::TensorCopySync(cpu_tensor, cpu_tensor.place(), &gpu_tensor);
}
}
// Step5: broadcast among inner cluster
auto b_opts = BroadcastOptions();
b_opts.source_rank = 0;
auto broadcast_task = inner_pg_->Broadcast(out_tensors, out_tensors, b_opts);
broadcast_task->Wait();
return CreateTask(rank_, CommType::ALLREDUCE, in_tensors);
}
std::shared_ptr<ProcessGroup::Task> ProcessGroupHeter::Broadcast(
std::vector<phi::DenseTensor>& in_tensors,
std::vector<phi::DenseTensor>& out_tensors, const BroadcastOptions& opts) {
#if defined(PADDLE_WITH_NCCL)
PADDLE_ENFORCE_EQ(
CheckTensorsInCudaPlace(in_tensors), true,
platform::errors::InvalidArgument("All inputs should be in CudaPlace."));
PADDLE_ENFORCE_EQ(
CheckTensorsInCudaPlace(out_tensors), true,
platform::errors::InvalidArgument("All outputs should be in CudaPlace."));
#endif
// Step1: do broadcast in inner cluster
auto b_opts = BroadcastOptions();
b_opts.source_rank = 0;
inner_pg_->Broadcast(in_tensors, out_tensors, b_opts);
if (local_rank_ == 0) {
std::vector<phi::DenseTensor> cpu_tensors;
cpu_tensors.reserve(in_tensors.size());
for (size_t i = 0; i < in_tensors.size(); i++) {
auto gpu_tensor = in_tensors[i];
phi::DenseTensor cpu_tensor;
cpu_tensor.Resize(gpu_tensor.dims());
framework::TensorCopySync(gpu_tensor, platform::CPUPlace(), &cpu_tensor);
cpu_tensors.push_back(cpu_tensor);
}
if (with_switch_) {
if (local_rank_ == 0) {
HeterClient* client_ =
HeterClient::GetInstance({switch_endpoint_}, {}, 0).get();
auto dense_cpu_tensor = cpu_tensors[0];
if (gloo_rank_ == 0) {
std::vector<int64_t> send_size;
send_size.push_back(dense_cpu_tensor.numel());
int ret = client_->Send(
gid_, {dense_cpu_tensor.name()}, send_size,
dense_cpu_tensor.data(),
dense_cpu_tensor.numel() *
framework::DataTypeSize(dense_cpu_tensor.dtype()));
PADDLE_ENFORCE_EQ(ret, 0, platform::errors::PreconditionNotMet(
"Send to the switch module error."));
} else {
int ret = client_->Recv(
gid_, {dense_cpu_tensor.name()}, dense_cpu_tensor.data(),
dense_cpu_tensor.numel() *
framework::DataTypeSize(dense_cpu_tensor.dtype()));
PADDLE_ENFORCE_EQ(ret, 0,
platform::errors::PreconditionNotMet(
"Receive from the switch module error."));
}
}
} else {
auto gloo_task = inter_pg_->Broadcast(cpu_tensors, cpu_tensors, opts);
gloo_task->Wait();
}
for (size_t i = 0; i < in_tensors.size(); i++) {
auto gpu_tensor = out_tensors[i];
auto cpu_tensor = cpu_tensors[i];
framework::TensorCopySync(cpu_tensor, gpu_tensor.place(), &gpu_tensor);
}
}
auto broadcast_task = inner_pg_->Broadcast(out_tensors, out_tensors, b_opts);
broadcast_task->Wait();
return CreateTask(rank_, CommType::BROADCAST, in_tensors);
}
std::shared_ptr<ProcessGroup::Task> ProcessGroupHeter::Send(
std::vector<phi::DenseTensor>& in_tensors, int peer) {
PADDLE_ENFORCE_EQ(
in_tensors.size(), 1,
platform::errors::PreconditionNotMet(
"For each send operation, there can only be one tensor to send."));
// Copy Tensor to cpu
auto start = std::chrono::high_resolution_clock::now();
phi::DenseTensor cpu_tensor;
auto& gpu_tensor = in_tensors[0];
framework::TensorCopySync(gpu_tensor, platform::CPUPlace(), &cpu_tensor);
PADDLE_ENFORCE_EQ(with_switch_, true,
platform::errors::PreconditionNotMet(
"Gloo does not support the send operation."));
auto end = std::chrono::high_resolution_clock::now();
std::chrono::duration<double> diff = end - start;
VLOG(2) << "Time to copy tensor of dims(" << cpu_tensor.dims()
<< ") from gpu to cpu for send " << std::setw(9)
<< " is: " << diff.count() << " s" << std::endl;
// Send to switch
HeterClient* client_ =
HeterClient::GetInstance({switch_endpoint_}, {}, 0).get();
int64_t tensor_size =
cpu_tensor.numel() * framework::DataTypeSize(cpu_tensor.dtype());
std::vector<int64_t> send_size;
send_size.push_back(tensor_size);
auto id = src_rank_ * 10000 + dst_rank_;
std::string tensor_name = std::to_string(gid_) + "_id_" + std::to_string(id) +
std::string("_") + std::to_string(send_count++);
VLOG(2) << "tensor_name:" << tensor_name;
int ret = client_->Send(gid_, {tensor_name}, send_size, cpu_tensor.data(),
tensor_size);
PADDLE_ENFORCE_EQ(ret, 0, platform::errors::PreconditionNotMet(
"Send to the switch module error."));
return CreateTask(rank_, CommType::SEND, in_tensors);
}
std::shared_ptr<ProcessGroup::Task> ProcessGroupHeter::Recv(
std::vector<phi::DenseTensor>& out_tensors, int peer) {
PADDLE_ENFORCE_EQ(
out_tensors.size(), 1,
platform::errors::PreconditionNotMet(
"For each rece operation, there can only be one tensor to receive."));
// Copy Tensor to cpu
phi::DenseTensor cpu_tensor;
auto& gpu_tensor = out_tensors[0];
cpu_tensor.Resize(gpu_tensor.dims());
cpu_tensor.set_layout(gpu_tensor.layout());
cpu_tensor.mutable_data(platform::CPUPlace(), gpu_tensor.dtype());
PADDLE_ENFORCE_EQ(with_switch_, true,
platform::errors::PreconditionNotMet(
"Gloo does not support the send operation."));
// recv from switch
HeterClient* client_ =
HeterClient::GetInstance({switch_endpoint_}, {}, 0).get();
auto id = src_rank_ * 10000 + dst_rank_;
std::string tensor_name = std::to_string(gid_) + "_id_" + std::to_string(id) +
std::string("_") + std::to_string(recv_count++);
VLOG(2) << "tensor_name: " << tensor_name;
auto start = std::chrono::high_resolution_clock::now();
int ret = client_->Recv(
gid_, {tensor_name}, cpu_tensor.data(),
cpu_tensor.numel() * framework::DataTypeSize(cpu_tensor.dtype()));
PADDLE_ENFORCE_EQ(ret, 0, platform::errors::PreconditionNotMet(
"receive to the switch module error."));
auto end = std::chrono::high_resolution_clock::now();
std::chrono::duration<double> diff = end - start;
double goodput = cpu_tensor.numel() *
framework::DataTypeSize(cpu_tensor.dtype()) / diff.count();
VLOG(2) << "Goodput: " << goodput << "B/s" << std::endl;
start = std::chrono::high_resolution_clock::now();
framework::TensorCopySync(cpu_tensor, gpu_tensor.place(), &gpu_tensor);
end = std::chrono::high_resolution_clock::now();
diff = end - start;
VLOG(2) << "Time to copy tensor of dims(" << cpu_tensor.dims()
<< ") from cpu to gpu for recv " << std::setw(9)
<< " is: " << diff.count() << " s" << std::endl;
return CreateTask(rank_, CommType::RECV, out_tensors);
}
} // namespace distributed
} // namespace paddle