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ProcessGroupGloo.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 <iostream>
#ifdef _WIN32
#include <gloo/common/win.h>
#include <winsock2.h>
#include <ws2tcpip.h>
#else
#include <netdb.h>
#include <sys/socket.h>
#include <unistd.h>
#endif
#include <gloo/broadcast.h>
#include <gloo/reduce.h>
#include <gloo/scatter.h>
#include "paddle/fluid/distributed/collective/Common.h"
#include "paddle/fluid/distributed/collective/ProcessGroupGloo.h"
#include "paddle/fluid/framework/fleet/gloo_wrapper.h"
#include "paddle/fluid/platform/enforce.h"
namespace paddle {
namespace distributed {
#ifdef _WIN32
#define GENERATE_FUNC(type, func, ...) \
switch (type) { \
case experimental::DataType::FLOAT32: \
func<float>(__VA_ARGS__); \
break; \
case experimental::DataType::FLOAT64: \
func<double>(__VA_ARGS__); \
break; \
case experimental::DataType::FLOAT16: \
func<gloo::float16>(__VA_ARGS__); \
break; \
case experimental::DataType::INT32: \
func<int32_t>(__VA_ARGS__); \
break; \
case experimental::DataType::INT64: \
func<int64_t>(__VA_ARGS__); \
break; \
default: \
VLOG(0) << "Error: Unknown DataType."; \
exit(-1); \
}
#define HOST_NAME_MAX 256
#else
#define GENERATE_FUNC(type, func, args...) \
switch (type) { \
case experimental::DataType::FLOAT32: \
func<float>(args); \
break; \
case experimental::DataType::FLOAT64: \
func<double>(args); \
break; \
case experimental::DataType::FLOAT16: \
func<gloo::float16>(args); \
break; \
case experimental::DataType::INT32: \
func<int32_t>(args); \
break; \
case experimental::DataType::INT64: \
func<int64_t>(args); \
break; \
default: \
VLOG(0) << "Error: Unknown DataType."; \
exit(-1); \
}
#endif
typedef void (*reduce_func)(void*, const void*, const void*, size_t);
template <typename T>
reduce_func get_function(const ReduceOp& r) {
switch (r) {
case ReduceOp::SUM:
return reduce_func(&::gloo::sum<T>);
case ReduceOp::PRODUCT:
return reduce_func(&::gloo::product<T>);
case ReduceOp::MIN:
return reduce_func(&::gloo::min<T>);
case ReduceOp::MAX:
return reduce_func(&::gloo::max<T>);
case ReduceOp::AVG:
VLOG(0) << "Error: Unsupported ReduceOp::AVG.";
exit(-1);
}
VLOG(0) << "Error: Unknown ReduceOp.";
exit(-1);
}
template <typename T>
T* get_data(phi::DenseTensor& tensor) { // NOLINT
return reinterpret_cast<T*>(tensor.data());
}
template <typename T>
std::vector<T*> get_multi_data(
std::vector<phi::DenseTensor>& tensors) { // NOLINT
std::vector<T*> ret;
ret.reserve(tensors.size());
for (size_t i = 0; i < tensors.size(); i++) {
ret.push_back(get_data<T>(tensors[i]));
}
return ret;
}
template <typename T, typename P>
void set_output(P& opts, phi::DenseTensor& tensor) { // NOLINT
opts.setOutput(get_data<T>(tensor), tensor.numel());
}
template <typename T, typename P>
void set_input(P& opts, phi::DenseTensor& tensor) { // NOLINT
opts.setInput(get_data<T>(tensor), tensor.numel());
}
template <typename T, typename P>
void set_outputs(P& opts, // NOLINT
std::vector<phi::DenseTensor>& tensors) { // NOLINT
opts.setOutputs(get_multi_data<T>(tensors), tensors[0].numel());
}
template <typename T, typename P>
void set_inputs(P& opts, // NOLINT
std::vector<phi::DenseTensor>& tensors) { // NOLINT
opts.setInputs(get_multi_data<T>(tensors), tensors[0].numel());
}
template <typename T, typename P>
void set_inputs_for_scatter(P& opts, // NOLINT
phi::DenseTensor& tensor, // NOLINT
int nranks) {
std::vector<T*> ret;
ret.reserve(nranks);
T* raw_pointer = reinterpret_cast<T*>(tensor.data());
size_t offset = 0;
for (int i = 0; i < nranks; i++) {
ret.push_back(raw_pointer + offset);
offset += tensor.numel() / nranks;
}
opts.setInputs(ret, tensor.numel() / nranks);
}
ProcessGroupGloo::GlooTask::GlooTask(
int rank, const std::vector<phi::DenseTensor>& inputs, CommType comm_type)
: ProcessGroup::Task(rank, inputs, comm_type) {}
ProcessGroupGloo::ProcessGroupGloo(
const std::shared_ptr<distributed::Store>& store, int rank, int world_size,
const platform::Place& place, int gid,
const std::shared_ptr<GlooOptions> options)
: ProcessGroup(rank, world_size, place, gid),
_tag(0),
_store(new GlooStore(store)) {
_context = std::make_shared<gloo::rendezvous::Context>(rank, world_size);
auto prefix_store =
::gloo::rendezvous::PrefixStore(std::to_string(gid), *_store);
_context->connectFullMesh(prefix_store, options->device);
}
class BroadcastGlooTask : public ProcessGroupGloo::GlooTask {
public:
BroadcastGlooTask(const std::shared_ptr<gloo::Context>& context,
std::vector<phi::DenseTensor>& inputs, // NOLINT
std::vector<phi::DenseTensor>& outputs, // NOLINT
int rank, int root, uint32_t tag)
: ProcessGroupGloo::GlooTask(rank, inputs, CommType::BROADCAST),
_context(context),
_root(root),
_inputs(inputs),
_outputs(outputs),
_tag(tag) {}
void Run() override { _do_broadcast(_inputs[0], _outputs[0]); }
private:
std::shared_ptr<gloo::Context> _context;
const int _root;
std::vector<phi::DenseTensor> _inputs{};
std::vector<phi::DenseTensor> _outputs{};
const uint32_t _tag;
void _do_broadcast(phi::DenseTensor& in, phi::DenseTensor& out) { // NOLINT
gloo::BroadcastOptions opts(_context);
const auto& dtype = in.dtype();
if (rank_ == _root) {
GENERATE_FUNC(dtype, set_input, opts, in);
}
GENERATE_FUNC(dtype, set_output, opts, out);
opts.setRoot(_root);
opts.setTag(_tag);
gloo::broadcast(opts);
}
};
std::shared_ptr<ProcessGroup::Task> ProcessGroupGloo::Broadcast(
std::vector<phi::DenseTensor>& inputs,
std::vector<phi::DenseTensor>& outputs, const BroadcastOptions& opts) {
auto root = opts.source_rank;
std::unique_ptr<BroadcastGlooTask> task;
auto tag = next_tag();
auto context = get_context();
task = std::make_unique<BroadcastGlooTask>(context, inputs, outputs, rank_,
root, tag);
task->Run();
return task;
}
class AllreduceGlooTask : public ProcessGroupGloo::GlooTask {
public:
AllreduceGlooTask(int rank, const std::shared_ptr<gloo::Context>& context,
std::vector<phi::DenseTensor>& inputs, // NOLINT
std::vector<phi::DenseTensor>& outputs, // NOLINT
ReduceOp reduce_op, uint32_t tag)
: ProcessGroupGloo::GlooTask(rank, inputs, CommType::ALLREDUCE),
_context(context),
_inputs(inputs),
_outputs(outputs),
_reduce_op(reduce_op),
_tag(tag) {}
void Run() override { _do_allreduce(_inputs, _outputs); }
private:
std::shared_ptr<gloo::Context> _context;
std::vector<phi::DenseTensor> _inputs;
std::vector<phi::DenseTensor> _outputs;
const ReduceOp _reduce_op;
uint32_t _tag;
gloo::AllreduceOptions::Func _get_function(const experimental::DataType type,
const ReduceOp op) {
gloo::AllreduceOptions::Func fn;
GENERATE_FUNC(type, _get_function_impl, fn, op);
return fn;
}
template <typename T>
void _get_function_impl(gloo::AllreduceOptions::Func& fn, // NOLINT
const ReduceOp op) {
fn = get_function<T>(op);
}
void _do_allreduce(std::vector<phi::DenseTensor>& ins, // NOLINT
std::vector<phi::DenseTensor>& outs) { // NOLINT
const auto& dtype = ins[0].dtype();
gloo::AllreduceOptions opts(_context);
GENERATE_FUNC(dtype, set_inputs, opts, ins);
GENERATE_FUNC(dtype, set_outputs, opts, outs);
opts.setReduceFunction(_get_function(dtype, _reduce_op));
opts.setTag(_tag);
gloo::allreduce(opts);
}
};
std::shared_ptr<ProcessGroup::Task> ProcessGroupGloo::AllReduce(
std::vector<phi::DenseTensor>& inputs,
std::vector<phi::DenseTensor>& outputs, const AllreduceOptions& opts) {
auto tag = next_tag();
std::shared_ptr<GlooTask> task;
auto context = get_context();
task = std::make_shared<AllreduceGlooTask>(rank_, context, inputs, outputs,
opts.reduce_op, tag);
task->Run();
return task;
}
class BarrierGlooTask : public ProcessGroupGloo::GlooTask {
public:
BarrierGlooTask(int rank, const std::shared_ptr<gloo::Context>& context)
: ProcessGroupGloo::GlooTask(rank, std::vector<phi::DenseTensor>{},
CommType::BARRIER),
_context(context) {}
void Run() override { _do_barrier(); }
private:
std::shared_ptr<gloo::Context> _context;
void _do_barrier() {
gloo::BarrierOptions opts(_context);
gloo::barrier(opts);
}
};
std::shared_ptr<ProcessGroup::Task> ProcessGroupGloo::Barrier(
const BarrierOptions& opts) {
std::shared_ptr<BarrierGlooTask> task;
auto context = get_context();
task = std::make_shared<BarrierGlooTask>(rank_, context);
task->Run();
return task;
}
class AllgatherGlooTask : public ProcessGroupGloo::GlooTask {
public:
AllgatherGlooTask(int rank, const std::shared_ptr<gloo::Context>& context,
std::vector<phi::DenseTensor>& inputs, // NOLINT
std::vector<phi::DenseTensor>& outputs, // NOLINT
uint32_t tag)
: ProcessGroupGloo::GlooTask(rank, inputs, CommType::ALLGATHER),
_context(context),
_inputs(inputs),
_outputs(outputs),
_tag(tag) {}
void Run() override { _do_allgather(_inputs, _outputs); }
private:
std::shared_ptr<gloo::Context> _context;
std::vector<phi::DenseTensor> _inputs;
std::vector<phi::DenseTensor> _outputs;
uint32_t _tag;
void _do_allgather(std::vector<phi::DenseTensor>& in, // NOLINT
std::vector<phi::DenseTensor>& out) { // NOLINT
const auto& dtype = in[0].dtype();
gloo::AllgatherOptions opts(_context);
GENERATE_FUNC(dtype, set_input, opts, in[0]);
GENERATE_FUNC(dtype, set_output, opts, out[0]);
opts.setTag(_tag);
gloo::allgather(opts);
}
};
std::shared_ptr<ProcessGroup::Task> ProcessGroupGloo::AllGather(
std::vector<phi::DenseTensor>& in_tensors,
std::vector<phi::DenseTensor>& out_tensors) {
std::shared_ptr<AllgatherGlooTask> task;
auto tag = next_tag();
auto context = get_context();
task = std::make_shared<AllgatherGlooTask>(rank_, context, in_tensors,
out_tensors, tag);
task->Run();
return task;
}
class ReduceGlooTask : public ProcessGroupGloo::GlooTask {
public:
ReduceGlooTask(int rank, const std::shared_ptr<gloo::Context>& context,
std::vector<phi::DenseTensor>& inputs, // NOLINT
std::vector<phi::DenseTensor>& outputs, // NOLINT
ReduceOp reduce_op, int dst, uint32_t tag)
: ProcessGroupGloo::GlooTask(rank, inputs, CommType::REDUCE),
_context(context),
_inputs(inputs),
_outputs(outputs),
_reduce_op(reduce_op),
_dst(dst),
_tag(tag) {}
void Run() override { _do_reduce(_inputs, _outputs, _dst); }
private:
std::shared_ptr<gloo::Context> _context;
std::vector<phi::DenseTensor> _inputs;
std::vector<phi::DenseTensor> _outputs;
const ReduceOp _reduce_op;
int _dst;
uint32_t _tag;
gloo::ReduceOptions::Func _get_function(const experimental::DataType type,
const ReduceOp op) {
gloo::ReduceOptions::Func fn;
GENERATE_FUNC(type, _get_function_impl, fn, op);
return fn;
}
template <typename T>
void _get_function_impl(gloo::ReduceOptions::Func& fn, // NOLINT
const ReduceOp op) {
fn = get_function<T>(op);
}
void _do_reduce(std::vector<phi::DenseTensor>& inputs, // NOLINT
std::vector<phi::DenseTensor>& outputs, // NOLINT
int dst) {
const auto& dtype = inputs[0].dtype();
gloo::ReduceOptions opts(_context);
GENERATE_FUNC(dtype, set_input, opts, inputs[0]);
GENERATE_FUNC(dtype, set_output, opts, outputs[0]);
opts.setReduceFunction(_get_function(dtype, _reduce_op));
opts.setTag(_tag);
opts.setRoot(dst);
gloo::reduce(opts);
}
};
std::shared_ptr<ProcessGroup::Task> ProcessGroupGloo::Reduce(
std::vector<phi::DenseTensor>& inputs,
std::vector<phi::DenseTensor>& outputs, const ReduceOptions& opts) {
std::shared_ptr<ReduceGlooTask> task;
auto tag = next_tag();
auto context = get_context();
task = std::make_shared<ReduceGlooTask>(rank_, context, inputs, outputs,
opts.reduce_op, opts.root_rank, tag);
task->Run();
return task;
}
class ScatterGlooTask : public ProcessGroupGloo::GlooTask {
public:
ScatterGlooTask(int rank, const std::shared_ptr<gloo::Context>& context,
std::vector<phi::DenseTensor>& inputs, // NOLINT
std::vector<phi::DenseTensor>& outputs, // NOLINT
int src, int size, uint32_t tag)
: ProcessGroupGloo::GlooTask(rank, inputs, CommType::SCATTER),
_context(context),
_inputs(inputs),
_outputs(outputs),
_src(src),
_size(size),
_tag(tag) {}
void Run() override { _do_scatter(_inputs, _outputs, _src); }
private:
std::shared_ptr<gloo::Context> _context;
std::vector<phi::DenseTensor> _inputs;
std::vector<phi::DenseTensor> _outputs;
int _src;
int _size;
uint32_t _tag;
void _do_scatter(std::vector<phi::DenseTensor>& in, // NOLINT
std::vector<phi::DenseTensor>& out, // NOLINT
int src) {
const auto& dtype = in[0].dtype();
gloo::ScatterOptions opts(_context);
if (rank_ == src) {
GENERATE_FUNC(dtype, set_inputs_for_scatter, opts, in[0], _size);
}
GENERATE_FUNC(dtype, set_output, opts, out[0]);
opts.setRoot(src);
opts.setTag(_tag);
gloo::scatter(opts);
}
};
std::shared_ptr<ProcessGroup::Task> ProcessGroupGloo::Scatter(
std::vector<phi::DenseTensor>& in_tensors,
std::vector<phi::DenseTensor>& out_tensors, const ScatterOptions& opts) {
std::shared_ptr<ScatterGlooTask> task;
auto tag = next_tag();
auto context = get_context();
task = std::make_shared<ScatterGlooTask>(
rank_, context, in_tensors, out_tensors, opts.root_rank, size_, tag);
task->Run();
return task;
}
std::shared_ptr<::gloo::transport::Device>
ProcessGroupGloo::createDeviceForInterface(const std::string& ifname) {
::gloo::transport::tcp::attr attr;
attr.iface = ifname;
return ::gloo::transport::tcp::CreateDevice(attr);
}
std::shared_ptr<::gloo::transport::Device>
ProcessGroupGloo::createDeviceForHostname(const std::string& hostname) {
::gloo::transport::tcp::attr attr;
attr.hostname = hostname;
return ::gloo::transport::tcp::CreateDevice(attr);
}
std::shared_ptr<::gloo::transport::Device>
ProcessGroupGloo::createDefaultDevice() {
std::array<char, HOST_NAME_MAX> hostname{};
auto ret = ::gethostname(hostname.data(), HOST_NAME_MAX);
PADDLE_ENFORCE_EQ(ret, 0, platform::errors::Fatal(
"Get hostname error for createDefaultDevice."));
::addrinfo* result;
result = tcputils::get_addr_info(hostname.data(), "", 0, AF_UNSPEC);
::addrinfo* cur;
for (cur = result; cur != nullptr; cur = cur->ai_next) {
SocketType socket =
::socket(cur->ai_family, cur->ai_socktype, cur->ai_protocol);
if (socket == -1) {
continue;
}
ret = ::bind(socket, cur->ai_addr, cur->ai_addrlen);
#ifdef _WIN32
closesocket(socket);
#else
close(socket);
#endif
if (ret == -1) {
continue;
}
break;
}
freeaddrinfo(result);
if (cur != nullptr) {
return createDeviceForHostname(hostname.data());
}
return createDeviceForHostname("127.0.0.1");
}
} // namespace distributed
} // namespace paddle