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android / platform / external / pytorch / 6ec3d65b0c / . / torch / csrc / distributed / c10d / ProcessGroupGloo.cpp
blob: 1d68523204c712ba0d5f76e24ce8f6611c84c648 [file] [log] [blame]
#include <c10/util/Exception.h>
#include <torch/csrc/distributed/c10d/ProcessGroupGloo.hpp>
#ifdef USE_C10D_GLOO
#include <torch/csrc/distributed/c10d/GlooDeviceFactory.hpp>
#include <torch/csrc/distributed/c10d/PrefixStore.hpp>
#include <chrono>
#include <exception>
#include <ratio>
#include <tuple>
#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 <sys/types.h>
#include <type_traits>
#include <gloo/allgather.h>
#include <gloo/allgatherv.h>
#include <gloo/allreduce.h>
#include <gloo/alltoall.h>
#include <gloo/alltoallv.h>
#include <gloo/barrier.h>
#include <gloo/broadcast.h>
#include <gloo/gather.h>
#include <gloo/reduce.h>
#include <gloo/scatter.h>
#include <ATen/SparseTensorUtils.h>
#include <ATen/ThreadLocalState.h>
#include <c10/util/StringUtil.h>
#include <c10/util/intrusive_ptr.h>
#include <c10/util/irange.h>
#include <gloo/config.h>
#include <gloo/rendezvous/context.h>
#include <gloo/rendezvous/prefix_store.h>
#ifdef _WIN32
#define GENERATE_ALL_TYPES(type, func, ...) \
switch (type) { \
case ::at::ScalarType::Float: \
func<float>(__VA_ARGS__); \
break; \
case ::at::ScalarType::Double: \
func<double>(__VA_ARGS__); \
break; \
case ::at::ScalarType::Half: \
func<gloo::float16>(__VA_ARGS__); \
break; \
case ::at::ScalarType::Char: \
func<int8_t>(__VA_ARGS__); \
break; \
case ::at::ScalarType::Byte: \
func<uint8_t>(__VA_ARGS__); \
break; \
case ::at::ScalarType::Int: \
func<int32_t>(__VA_ARGS__); \
break; \
case ::at::ScalarType::Long: \
func<int64_t>(__VA_ARGS__); \
break; \
default: \
TORCH_CHECK(false, "Invalid scalar type"); \
}
#define HOST_NAME_MAX 256
#else
#define GENERATE_ALL_TYPES(type, func, args...) \
switch (type) { \
case ::at::ScalarType::Float: \
func<float>(args); \
break; \
case ::at::ScalarType::Double: \
func<double>(args); \
break; \
case ::at::ScalarType::Half: \
func<gloo::float16>(args); \
break; \
case ::at::ScalarType::Char: \
func<int8_t>(args); \
break; \
case ::at::ScalarType::Byte: \
func<uint8_t>(args); \
break; \
case ::at::ScalarType::Int: \
func<int32_t>(args); \
break; \
case ::at::ScalarType::Long: \
func<int64_t>(args); \
break; \
default: \
TORCH_CHECK(false, "Invalid scalar type"); \
}
#endif
namespace c10d {
namespace {
constexpr int kBytes = 8;
using steady_clock_time_point =
std::chrono::time_point<std::chrono::steady_clock>;
std::chrono::milliseconds getRemainingTime(
steady_clock_time_point startTime,
const std::chrono::milliseconds& timeout,
bool waitAllRanks) {
if (waitAllRanks) {
// See Note in monitoredBarrier
return timeout;
}
auto elapsedTime = std::chrono::steady_clock::now() - startTime;
auto remainingMillis = timeout -
std::chrono::duration_cast<std::chrono::milliseconds>(elapsedTime);
// If no more remaining time, return -1 to indicate to caller.
if (remainingMillis.count() <= 0) {
return std::chrono::milliseconds(-1);
}
return remainingMillis;
}
// Emit a LOG(ERROR) and throws using TORCH_CHECK with the given messages.
void logAndThrow(
const std::string& logMessage,
const std::string& errorMessage) {
LOG(ERROR) << logMessage;
TORCH_CHECK(false, errorMessage);
}
// For monitoredBarrier, checks remaining time left to finish processing ranks
// and throws error if timeout.
void checkRemainingTime(
const std::chrono::milliseconds& monitoredBarrierTimeout,
const std::chrono::milliseconds& remainingTime,
const std::vector<int>& processedRanks,
int currentRank) {
const std::string kNoRemainingTimeError = c10::str(
"Rank ",
currentRank,
" timed out in monitoredBarrier after ",
monitoredBarrierTimeout.count(),
" ms.");
if (remainingTime.count() < 0) {
std::string rankInfo;
if (processedRanks.size() > 0) {
rankInfo = c10::str(
"Successfully processed ranks: ", c10::Join(", ", processedRanks));
} else {
rankInfo = "No ranks successfully processed in monitoredBarrier.";
}
auto error = c10::str(kNoRemainingTimeError, "\n", rankInfo);
logAndThrow(error, error);
}
}
typedef void (*ReduceFunc)(void*, const void*, const void*, size_t);
template <
typename T,
typename std::enable_if<!std::is_integral<T>::value, int>::type = 0>
ReduceFunc toFunction(const ReduceOp& r) {
switch (r) {
case ReduceOp::SUM:
return ReduceFunc(&::gloo::sum<T>);
case ReduceOp::PRODUCT:
return ReduceFunc(&::gloo::product<T>);
case ReduceOp::MIN:
return ReduceFunc(&::gloo::min<T>);
case ReduceOp::MAX:
return ReduceFunc(&::gloo::max<T>);
case ReduceOp::BAND:
TORCH_CHECK(false, "Cannot use ReduceOp.BAND with non-integral dtype");
break;
case ReduceOp::BOR:
TORCH_CHECK(false, "Cannot use ReduceOp.BOR with non-integral dtype");
break;
case ReduceOp::BXOR:
TORCH_CHECK(false, "Cannot use ReduceOp.BXOR with non-integral dtype");
break;
case ReduceOp::AVG:
TORCH_CHECK(false, "Cannot use ReduceOp.AVG with Gloo");
break;
case ReduceOp::PREMUL_SUM:
TORCH_CHECK(false, "Cannot use ReduceOp.PREMUL_SUM with Gloo");
break;
case ReduceOp::UNUSED:
break;
}
TORCH_CHECK(false, "Unhandled ReduceOp");
}
// Bitwise AND with SFINAE guard for integral types.
template <
typename T,
typename std::enable_if<std::is_integral<T>::value, int>::type = 0>
void band(void* c, const void* a, const void* b, size_t n) {
auto tc = static_cast<T*>(c);
auto ta = static_cast<const T*>(a);
auto tb = static_cast<const T*>(b);
for (const auto i : c10::irange(n)) {
tc[i] = ta[i] & tb[i];
}
}
// Bitwise OR with SFINAE guard for integral types.
template <
typename T,
typename std::enable_if<std::is_integral<T>::value, int>::type = 0>
void bor(void* c, const void* a, const void* b, size_t n) {
auto tc = static_cast<T*>(c);
auto ta = static_cast<const T*>(a);
auto tb = static_cast<const T*>(b);
for (const auto i : c10::irange(n)) {
tc[i] = ta[i] | tb[i];
}
}
// Bitwise XOR with SFINAE guard for integral types.
template <
typename T,
typename std::enable_if<std::is_integral<T>::value, int>::type = 0>
void bxor(void* c, const void* a, const void* b, size_t n) {
auto tc = static_cast<T*>(c);
auto ta = static_cast<const T*>(a);
auto tb = static_cast<const T*>(b);
for (const auto i : c10::irange(n)) {
tc[i] = ta[i] ^ tb[i];
}
}
template <
typename T,
typename std::enable_if<std::is_integral<T>::value, int>::type = 0>
ReduceFunc toFunction(const ReduceOp& r) {
switch (r) {
case ReduceOp::SUM:
return ReduceFunc(&::gloo::sum<T>);
case ReduceOp::PRODUCT:
return ReduceFunc(&::gloo::product<T>);
case ReduceOp::MIN:
return ReduceFunc(&::gloo::min<T>);
case ReduceOp::MAX:
return ReduceFunc(&::gloo::max<T>);
case ReduceOp::BAND:
return ReduceFunc(&band<T>);
case ReduceOp::BOR:
return ReduceFunc(&bor<T>);
case ReduceOp::BXOR:
return ReduceFunc(&bxor<T>);
case ReduceOp::AVG:
TORCH_CHECK(false, "Cannot use ReduceOp.AVG with Gloo");
break;
case ReduceOp::PREMUL_SUM:
TORCH_CHECK(false, "Cannot use ReduceOp.PREMUL_SUM with Gloo");
break;
case ReduceOp::UNUSED:
break;
}
TORCH_CHECK(false, "Unhandled ReduceOp");
}
template <typename T, typename O>
void setInputs(O& opts, std::vector<at::Tensor>& tensors) {
opts.setInputs(getDataPointers<T>(tensors), tensors[0].numel());
}
template <typename T, typename O>
void setInput(O& opts, at::Tensor& tensor) {
opts.setInput(getDataPointer<T>(tensor), tensor.numel());
}
template <typename T, typename O>
void setInput(O& opts, at::Tensor& tensor, std::vector<size_t>& counts) {
opts.setInput(getDataPointer<T>(tensor), counts);
}
template <typename T, typename O>
void setInput(O& opts, at::Tensor& tensor, std::vector<int64_t>& counts) {
opts.setInput(getDataPointer<T>(tensor), counts);
}
template <typename T, typename O>
void setOutputs(O& opts, std::vector<at::Tensor>& tensors) {
opts.setOutputs(getDataPointers<T>(tensors), tensors[0].numel());
}
template <typename T, typename O>
void setOutput(O& opts, at::Tensor& tensor) {
opts.setOutput(getDataPointer<T>(tensor), tensor.numel());
}
template <typename T, typename O>
void setOutput(O& opts, at::Tensor& tensor, std::vector<size_t>& counts) {
opts.setOutput(getDataPointer<T>(tensor), counts);
}
template <typename T, typename O>
void setOutput(O& opts, at::Tensor& tensor, std::vector<int64_t>& counts) {
opts.setOutput(getDataPointer<T>(tensor), counts);
}
at::Tensor pinnedLike(at::Tensor& tensor) {
auto* allocator = at::detail::getCUDAHooks().getPinnedMemoryAllocator();
auto storage = c10::Storage(
c10::Storage::use_byte_size_t(),
at::detail::computeStorageNbytes(
tensor.sizes(), tensor.strides(), tensor.dtype().itemsize()),
allocator,
/*resizable=*/false);
return at::empty({0}, tensor.options().device(at::kCPU))
.set_(storage, 0, tensor.sizes(), tensor.strides());
}
// This function initializes a vector of CUDA streams, one for every
// tensor in the input tensor vector, and ensures that these streams are
// synchronized with the current default streams. This is needed so
// that new work on the new streams is serialized w.r.t. all operations
// on the tensors.
void initializeStreamsEvents(
const std::vector<at::Tensor>& tensors,
std::vector<c10::Stream>& streams,
std::vector<c10::Event>& events) {
streams.reserve(tensors.size());
events.reserve(tensors.size());
for (const auto i : c10::irange(tensors.size())) {
c10::Device device = tensors[i].device();
c10::impl::VirtualGuardImpl impl(device.type());
// Record event on current stream
events.emplace_back(device.type());
events[i].record(impl.getStream(device));
// Get a non-default stream to execute asynchronous CUDA operations
// on for this device. This ensures that the default stream used
// by the caller is not occupied by c10d related operations.
streams.push_back(
impl.getStreamFromGlobalPool(device, /*isHighPriority=*/true));
// Ensure the new stream is synchronized with the current stream.
events[i].block(streams[i]);
// `tensors` are created on a different stream. Hence, they must record
// new streams in this Work to prevent being freed before the Work finishes.
if (tensors[i].is_sparse()) {
if (tensors[i].is_coalesced()) {
impl.recordDataPtrOnStream(
tensors[i].indices().storage().data_ptr(), streams[i]);
impl.recordDataPtrOnStream(
tensors[i].values().storage().data_ptr(), streams[i]);
} else {
// We will need to coalesce first, which means new tensors will
// be allocated on the streams we just allocated, and there
// is no need to record them separately.
}
} else {
impl.recordDataPtrOnStream(tensors[i].storage().data_ptr(), streams[i]);
}
}
}
// This function initializes a vector of CUDA streams, one per device,
// and ensures that these streams are synchronized with the current default
// streams. It is assumed that the tensors in the nested tensor vectors are
// on the same device.
void initializeStreamsEvents(
std::vector<std::vector<at::Tensor>>& tensors,
std::vector<c10::Stream>& streams,
std::vector<c10::Event>& events) {
// Ensure that the tensors in the nested tensor vectors are on the same
// device.
for (const auto& tensorgroup : tensors) {
const auto device_id = tensorgroup[0].device().index();
for (const auto& tensor : tensorgroup) {
if (tensor.device().index() != device_id) {
TORCH_CHECK(
false,
"tensors in the nested tensor vectors need to "
"be on the same device");
}
}
}
streams.reserve(tensors.size());
events.reserve(tensors.size());
for (const auto i : c10::irange(tensors.size())) {
c10::Device device = tensors[i][0].device();
c10::impl::VirtualGuardImpl impl(device.type());
// Record event on current stream
events.emplace_back(device.type());
events[i].record(impl.getStream(device));
// Get a non-default stream to execute asynchronous CUDA operations
// on for this output. This ensures that the default stream used
// by the caller is not occupied by c10d related operations.
streams.push_back(
impl.getStreamFromGlobalPool(device, /*isHighPriority=*/true));
// Ensure the new stream is synchronized with the current stream.
events[i].block(streams[i]);
for (at::Tensor& tensor : tensors[i]) {
// `tensors` are created on a different stream. Hence, they must record
// new streams in this Work to prevent being freed before the Work
// finishes.
impl.recordDataPtrOnStream(tensor.storage().data_ptr(), streams[i]);
}
}
}
const auto kLoopbackAddress = "127.0.0.1";
} // namespace
// static
void ProcessGroupGloo::AsyncWork::execute(c10::intrusive_ptr<AsyncWork> work) {
if (work->recordFunctionBeforeCallback_) {
work->recordFunctionBeforeCallback_();
}
try {
work->run();
} catch (...) {
work->finishWorkGlooError(std::current_exception());
return;
}
// FIXME: We need to call it here since Future completion requires all
// the work to be synchronized to CUDA.
work->synchronize();
work->finishWorkGloo();
}
std::vector<at::Tensor> ProcessGroupGloo::AsyncWork::result() {
TORCH_CHECK(
isCompleted(),
"Work needs to be completed before calling result(). "
"Should call wait() before result().");
TORCH_CHECK(
outputTensors_.size() <= 1,
"work result does not support list of lists, use .getFuture() and value()");
return outputTensors_.size() == 0 ? std::vector<at::Tensor>()
: outputTensors_.at(0);
}
c10::intrusive_ptr<c10::ivalue::Future> ProcessGroupGloo::AsyncWork::
getFuture() {
return future_;
}
namespace {
c10::intrusive_ptr<c10::ivalue::Future> createFutureAsOutput(
const std::vector<std::vector<at::Tensor>>& outputTensors) {
if (outputTensors.size() > 1) {
return c10::make_intrusive<c10::ivalue::Future>(
c10::ListType::create(c10::ListType::create(c10::TensorType::get())));
}
return c10::make_intrusive<c10::ivalue::Future>(
c10::ListType::create(c10::TensorType::get()));
}
void returnFutureWithOutput(
c10::intrusive_ptr<c10::ivalue::Future>& future,
const std::vector<std::vector<at::Tensor>>& outputTensors) {
if (outputTensors.size() == 0) {
future->markCompleted(c10::IValue(std::vector<at::Tensor>()));
return;
}
if (outputTensors.size() > 1) {
future->markCompleted(c10::IValue(outputTensors));
return;
}
future->markCompleted(c10::IValue(outputTensors[0]));
}
} // namespace
inline void ProcessGroupGloo::AsyncWork::recordAsyncWorkProfilingInfo(
const char* profilingTitle,
const c10::optional<std::vector<at::Tensor>>& inputTensors) {
auto recordingFunction =
std::make_shared<at::RecordFunction>(at::RecordScope::USER_SCOPE);
if (recordingFunction->isActive()) {
std::function<void()> before_handler =
[inputTensors, profilingTitle, recordingFunction]() {
// The work will be started and completed by different threads.
recordingFunction->_setAsync();
std::vector<c10::IValue> inputs;
if (inputTensors) {
inputs.reserve(inputTensors->size());
for (const auto& tensor : *inputTensors) {
inputs.emplace_back(tensor);
}
}
recordingFunction->before(
profilingTitle,
c10::ArrayRef<const c10::IValue>(inputs.data(), inputs.size()));
};
recordFunctionBeforeCallback_ = at::wrapPropagateTLSState(before_handler);
std::function<void()> end_handler = [recordingFunction]() {
recordingFunction->end();
};
recordFunctionEndCallback_ = at::wrapPropagateTLSState(end_handler);
}
}
ProcessGroupGloo::AsyncWork::AsyncWork(
std::vector<std::vector<at::Tensor>> outputTensors,
const char* profilingTitle,
const c10::optional<std::vector<at::Tensor>>& inputTensors)
// Profiler: Pass nullptr as profilingTitle to parent constructor to
// replace default profiler implementation with async version that reports
// correct timestamps for work that is asynchronously executed.
: Work(-1, OpType::UNKNOWN, nullptr, inputTensors),
outputTensors_(std::move(outputTensors)),
future_(createFutureAsOutput(outputTensors)) {
if (profilingTitle != nullptr) {
recordAsyncWorkProfilingInfo(profilingTitle, inputTensors);
}
}
void ProcessGroupGloo::AsyncWork::finishWorkGlooError(std::exception_ptr eptr) {
future_->setError(eptr);
finish(eptr);
}
void ProcessGroupGloo::AsyncWork::finishWorkGloo() {
returnFutureWithOutput(future_, outputTensors_);
finish();
}
ProcessGroupGloo::SendWork::SendWork(
at::Tensor& tensor,
std::unique_ptr<::gloo::transport::UnboundBuffer> buffer)
: Work(
-1,
OpType::SEND,
"gloo:send",
c10::optional<std::vector<at::Tensor>>({tensor})),
tensor_(tensor),
buffer_(std::move(buffer)) {}
bool ProcessGroupGloo::SendWork::wait(std::chrono::milliseconds timeout) {
bool sendCompleted = false;
std::exception_ptr exception{nullptr};
try {
if (timeout == kNoTimeout) {
sendCompleted = buffer_->waitSend();
} else {
sendCompleted = buffer_->waitSend(timeout);
}
} catch (...) {
exception = std::current_exception();
}
// Completes the Work object and throws the exception.
finishAndThrow(exception);
return sendCompleted;
}
void ProcessGroupGloo::SendWork::abort() {
buffer_->abortWaitSend();
}
ProcessGroupGloo::RecvWork::RecvWork(
at::Tensor& tensor,
std::unique_ptr<::gloo::transport::UnboundBuffer> buffer,
const char* profilingTitle)
: Work(
-1,
OpType::UNKNOWN,
profilingTitle,
c10::optional<std::vector<at::Tensor>>({tensor})),
tensor_(tensor),
buffer_(std::move(buffer)),
srcRank_(-1) {}
int ProcessGroupGloo::RecvWork::sourceRank() const {
std::lock_guard<std::mutex> lock(mutex_);
return srcRank_;
}
bool ProcessGroupGloo::RecvWork::wait(std::chrono::milliseconds timeout) {
bool recvCompleted = false;
std::exception_ptr exception{nullptr};
try {
if (timeout == kNoTimeout) {
recvCompleted = buffer_->waitRecv(&srcRank_);
} else {
recvCompleted = buffer_->waitRecv(&srcRank_, timeout);
}
} catch (...) {
exception = std::current_exception();
}
// Completes the Work object and throws the exception.
finishAndThrow(exception);
return recvCompleted;
}
void ProcessGroupGloo::RecvWork::abort() {
buffer_->abortWaitRecv();
}
ProcessGroupGloo::Options::Options(std::chrono::milliseconds timeout)
: Backend::Options(GLOO_BACKEND_NAME, timeout), threads(2) {}
namespace {
void socketInitialize() {
#ifdef _WIN32
::gloo::init_winsock();
#endif
}
// Gloo assumes that this machine's hostname can always be resolved
// to an address. If it doesn't it throws a runtime error saying
// that it can't be resolved. Instead of catching it, we choose
// to proactively check if an address can be resolved, so we can
// gracefully fall back to an alternative if it doesn't.
bool doesHostnameResolveToUsableAddress(const std::string& hostname) {
socketInitialize();
struct addrinfo hints {};
memset(&hints, 0, sizeof(hints));
hints.ai_family = AF_UNSPEC;
hints.ai_socktype = SOCK_STREAM;
struct addrinfo* result = nullptr;
auto rv = getaddrinfo(hostname.c_str(), nullptr, &hints, &result);
if (rv < 0) {
return false;
}
struct addrinfo* rp = nullptr;
for (rp = result; rp != nullptr; rp = rp->ai_next) {
auto fd = socket(rp->ai_family, rp->ai_socktype, rp->ai_protocol);
if (fd == -1) {
continue;
}
rv = bind(fd, rp->ai_addr, rp->ai_addrlen);
#ifdef _WIN32
closesocket(fd);
#else
close(fd);
#endif
if (rv == -1) {
continue;
}
break;
}
freeaddrinfo(result);
return rp != nullptr;
}
} // namespace
std::shared_ptr<::gloo::transport::Device> ProcessGroupGloo::
createDeviceForInterface(const std::string& interface_name) {
return ::c10d::GlooDeviceFactory::makeDeviceForInterface(interface_name);
}
std::shared_ptr<::gloo::transport::Device> ProcessGroupGloo::
createDeviceForHostname(const std::string& hostname) {
TORCH_CHECK(
doesHostnameResolveToUsableAddress(hostname),
"Cannot resolve ",
hostname,
" to a (local) address");
return ::c10d::GlooDeviceFactory::makeDeviceForHostname(hostname);
}
#if defined(__linux__) || defined(_WIN32)
std::shared_ptr<::gloo::transport::Device> ProcessGroupGloo::
createDefaultDevice() {
// Use the hostname to resolve the network address to
// use. Note: if the hostname does not resolve to an address (e.g.
// because of misconfigured /etc/hosts file), this will not work.
socketInitialize();
std::array<char, HOST_NAME_MAX> hostname{};
auto rv = gethostname(hostname.data(), HOST_NAME_MAX);
if (rv != 0) {
throw std::system_error(errno, std::system_category());
}
// Use this machine's hostname if it resolves to an address.
if (doesHostnameResolveToUsableAddress(hostname.data())) {
return ::c10d::GlooDeviceFactory::makeDeviceForHostname(hostname.data());
}
// Otherwise, use the loopback address.
TORCH_WARN_ONCE(
"Unable to resolve hostname to a (local) address. ",
"Using the loopback address as fallback. ",
"Manually set the network interface to bind to with GLOO_SOCKET_IFNAME.");
return createDeviceForHostname(kLoopbackAddress);
}
#endif
#ifdef __APPLE__
std::shared_ptr<::gloo::transport::Device> ProcessGroupGloo::
createDefaultDevice() {
// Use the hostname to resolve the network address to
// use. Note: if the hostname does not resolve to an address (e.g.
// because of misconfigured /etc/hosts file), this will not work.
const auto hostNameMax = sysconf(_SC_HOST_NAME_MAX);
auto hostname = std::unique_ptr<char[]>(new char[hostNameMax]);
auto rv = gethostname(hostname.get(), hostNameMax);
if (rv != 0) {
throw std::system_error(errno, std::system_category());
}
// Use this machine's hostname if it resolves to an address.
if (doesHostnameResolveToUsableAddress(hostname.get())) {
return ::c10d::GlooDeviceFactory::makeDeviceForHostname(hostname.get());
}
// Otherwise, use the loopback address.
TORCH_WARN_ONCE(
"Unable to resolve hostname to a (local) address. ",
"Using the loopback address as fallback. ",
"Manually set the network interface to bind to with GLOO_SOCKET_IFNAME.");
return createDeviceForHostname(kLoopbackAddress);
}
#endif
ProcessGroupGloo::ProcessGroupGloo(
const c10::intrusive_ptr<Store>& store,
int rank,
int size,
c10::intrusive_ptr<Options> options)
: Backend(rank, size),
store_(new GlooStore(store)),
options_(options),
stop_(false),
collectiveCounter_(0) {
auto& devices = options->devices;
if (devices.empty()) {
TORCH_CHECK(false, "No device(s) specified");
}
// Create and connect a context for every device.
//
// Note that the same device can be specified multiple times, either
// the same object, or the same logical device as different objects.
// Either mode is fine and only has performance implications.
//
// Using the same object multiple times means all contexts share a
// single I/O thread. If you use different objects for the same
// logical device they will have independent I/O threads. The latter
// option is needed if you have a fast NIC that cannot be saturated
// by a single I/O thread.
//
contexts_.reserve(options->devices.size());
for (const auto i : c10::irange(options->devices.size())) {
auto context = std::make_shared<::gloo::rendezvous::Context>(rank_, size_);
auto store = ::gloo::rendezvous::PrefixStore(std::to_string(i), *store_);
context->setTimeout(options->timeout);
context->connectFullMesh(store, options->devices[i]);
contexts_.push_back(std::move(context));
}
// Every worker thread stores the AsyncWork object it's currently
// working on in the workInProgress_ vector. It must have size equal
// to the number of workers such that they can simply index into it
// using the worker index they are started with.
workInProgress_.resize(options->threads);
threads_.resize(options->threads);
for (const auto i : c10::irange(threads_.size())) {
threads_[i] = std::thread(&ProcessGroupGloo::runLoop, this, i);
}
init();
}
ProcessGroupGloo::~ProcessGroupGloo() {
std::unique_lock<std::mutex> lock(workMutex_);
workConsumeCV_.wait(lock, [&] { return workQueue_.empty(); });
// Queue is empty, signal stop
stop_ = true;
// Release lock to allow threads to terminate
lock.unlock();
workProduceCV_.notify_all();
// Wait for worker threads to terminate
for (auto& thread : threads_) {
thread.join();
}
}
uint32_t ProcessGroupGloo::nextTag() {
return collectiveCounter_++;
}
std::shared_ptr<::gloo::Context> ProcessGroupGloo::getContext(uint32_t tag) {
return contexts_[tag % contexts_.size()];
}
void ProcessGroupGloo::runLoop(int workerIndex) {
std::unique_lock<std::mutex> lock(workMutex_);
while (!stop_) {
if (workQueue_.empty()) {
workProduceCV_.wait(lock);
continue;
}
auto work = std::move(workQueue_.front());
workQueue_.pop_front();
workInProgress_[workerIndex] = work;
lock.unlock();
// Notify after releasing the lock so that the waiter
// does not immediately block.
workConsumeCV_.notify_one();
AsyncWork::execute(std::move(work));
lock.lock();
workInProgress_[workerIndex].reset();
}
}
void ProcessGroupGloo::enqueue(c10::intrusive_ptr<AsyncWork> work) {
std::unique_lock<std::mutex> lock(workMutex_);
// Bump collective counter
if (sequenceNum_) {
sequenceNum_->increment();
}
workQueue_.push_back(std::move(work));
lock.unlock();
// Notify after releasing the lock so that the waiter
// does not immediately block.
workProduceCV_.notify_one();
}
namespace {
class AsyncBroadcastWork : public ProcessGroupGloo::AsyncWork {
public:
AsyncBroadcastWork(
const std::shared_ptr<gloo::Context>& context,
std::vector<at::Tensor>& inputs,
int rootRank,
int rootTensor,
uint32_t tag)
: ProcessGroupGloo::AsyncWork({inputs}, "gloo:broadcast", inputs),
context(context),
inputs(inputs),
rootRank(rootRank),
rootTensor(rootTensor),
tag(tag) {}
std::shared_ptr<gloo::Context> context;
std::vector<at::Tensor> inputs;
const int rootRank;
const int rootTensor;
const uint32_t tag;
void broadcast(at::Tensor& tensor) {
const auto& scalarType = tensor.scalar_type();
gloo::BroadcastOptions opts(context);
opts.setRoot(rootRank);
opts.setTag(tag);
GENERATE_ALL_TYPES(scalarType, setOutput, opts, tensor);
gloo::broadcast(opts);
}
void run() override {
broadcast(inputs[rootTensor]);
// Copy to non-root tensors
for (const auto i : c10::irange(inputs.size())) {
if (i == static_cast<size_t>(rootTensor)) {
continue;
}
inputs[i].copy_(inputs[rootTensor]);
}
}
};
class AsyncBroadcastCUDAWork : public AsyncBroadcastWork {
public:
AsyncBroadcastCUDAWork(
const std::shared_ptr<gloo::Context>& context,
std::vector<at::Tensor>& inputs,
int rootRank,
int rootTensor,
uint32_t tag)
: AsyncBroadcastWork(context, inputs, rootRank, rootTensor, tag) {
initializeStreamsEvents(inputs, streams, events);
// Create pinned host side tensors.
tmp = pinnedLike(inputs[rootTensor]);
c10::OptionalStreamGuard guard;
if (context->rank == rootRank) {
guard.reset_stream(streams[rootTensor]);
tmp.copy_(inputs[rootTensor], /* non_blocking */ true);
}
}
void run() override {
// Synchronize with copy operation if applicable.
if (context->rank == rootRank) {
streams[rootTensor].synchronize();
}
// Run broadcast on host side tensors.
broadcast(tmp);
// Kick off copy back to the CUDA tensors.
c10::OptionalStreamGuard guard;
for (const auto i : c10::irange(inputs.size())) {
guard.reset_stream(streams[i]);
inputs[i].copy_(tmp, /* non_blocking */ true);
events[i].record(streams[i]);
}
}
void synchronize() override {
// Synchronize with the copy back to CUDA tensors.
for (const auto i : c10::irange(inputs.size())) {
c10::Device device = inputs[i].device();
events[i].block(
c10::impl::VirtualGuardImpl(device.type()).getStream(device));
}
}
at::Tensor tmp;
std::vector<c10::Stream> streams;
std::vector<c10::Event> events;
};
} // namespace
c10::intrusive_ptr<Work> ProcessGroupGloo::broadcast(
std::vector<at::Tensor>& inputs,
const BroadcastOptions& opts) {
static auto invalidArgument = [](const std::string& msg) {
TORCH_CHECK(false, "ProcessGroupGloo::broadcast: " + msg);
};
assertRootRank(invalidArgument, opts.rootRank, size_);
assertRootTensor(invalidArgument, opts.rootTensor, inputs.size());
assertDense(invalidArgument, inputs);
assertTypeAndSizesMatch(invalidArgument, inputs);
const auto& device = inputs[0].device();
switch (device.type()) {
case at::kCPU:
break;
case at::kCUDA:
// If the user gave us a CUDA tensor then CUDA must be loaded.
TORCH_INTERNAL_ASSERT(at::hasCUDA());
break;
default:
invalidArgument(c10::str("unsupported device type ", device.type()));
}
c10::intrusive_ptr<AsyncBroadcastWork> work;
auto tag = nextTag();
auto context = getContext(tag);
if (device.type() == at::kCPU) {
work = c10::make_intrusive<AsyncBroadcastWork>(
std::move(context), inputs, opts.rootRank, opts.rootTensor, tag);
} else if (device.type() == at::kCUDA) {
work = c10::make_intrusive<AsyncBroadcastCUDAWork>(
std::move(context), inputs, opts.rootRank, opts.rootTensor, tag);
} else {
TORCH_CHECK(false, "Invalid backend");
}
enqueue(work);
return work;
}
namespace {
class AsyncAllreduceWork : public ProcessGroupGloo::AsyncWork {
public:
AsyncAllreduceWork(
const std::shared_ptr<gloo::Context>& context,
std::vector<at::Tensor>& inputs,
ReduceOp reduceOp,
uint32_t tag)
: ProcessGroupGloo::AsyncWork({inputs}, "gloo:all_reduce", inputs),
context(context),
inputs(inputs),
reduceOp(reduceOp),
tag(tag) {}
std::shared_ptr<gloo::Context> context;
std::vector<at::Tensor> inputs;
const ReduceOp reduceOp;
const uint32_t tag;
void allreduce(std::vector<at::Tensor>& tensors) {
const auto& scalarType = tensors[0].scalar_type();
gloo::AllreduceOptions opts(context);
opts.setReduceFunction(getFunction(scalarType, reduceOp));
opts.setTag(tag);
GENERATE_ALL_TYPES(scalarType, setOutputs, opts, tensors);
gloo::allreduce(opts);
}
void run() override {
allreduce(inputs);
}
template <typename T>
void getFunction(gloo::AllreduceOptions::Func& fn, const ReduceOp op) {
fn = toFunction<T>(op);
}
gloo::AllreduceOptions::Func getFunction(
const at::ScalarType& dtype,
const ReduceOp op) {
gloo::AllreduceOptions::Func fn;
GENERATE_ALL_TYPES(dtype, getFunction, fn, op);
return fn;
}
};
class AsyncAllreduceCoalescedWork : public AsyncAllreduceWork {
public:
AsyncAllreduceCoalescedWork(
const std::shared_ptr<gloo::Context>& context,
std::vector<at::Tensor>& inputs,
ReduceOp reduceOp,
uint32_t tag)
: AsyncAllreduceWork(context, inputs, reduceOp, tag) {}
void run() override {
allreduceCoalesced(inputs);
}
private:
void allreduceCoalesced(std::vector<at::Tensor>& tensors) {
// reduce coalesced, flattened tensors.
at::Tensor coalescedTensor = flattenDenseTensors(tensors);
std::vector<at::Tensor> allreduceInput = {coalescedTensor};
allreduce(allreduceInput);
// separate and reshape tensors.
size_t offset = 0;
for (at::Tensor& tensor : tensors) {
const int64_t tensorNumel = tensor.numel();
const c10::IntArrayRef tensorShape = tensor.sizes();
tensor.copy_(coalescedTensor.slice(0, offset, offset + tensorNumel)
.view(tensorShape));
offset += tensorNumel;
}
}
};
class AsyncSparseAllreduceWork : public ProcessGroupGloo::AsyncWork {
public:
AsyncSparseAllreduceWork(
const std::shared_ptr<gloo::Context>& context,
std::vector<at::Tensor>& inputs,
uint32_t tag)
: ProcessGroupGloo::AsyncWork({inputs}, "gloo:sparse_all_reduce", inputs),
context(context),
inputs(inputs),
tag(tag) {}
std::shared_ptr<gloo::Context> context;
std::vector<at::Tensor> inputs;
const uint32_t tag;
// We share dimensionality about the sparse tensors before collecting
// their contents. We assume here that the maximum number of sparse
// and dense dimensions is 4. This is stored in a contiguous piece of
// memory so that we can easily run allgather on it.
//
// The layout of this memory is as follows:
//
// - [0:4]: sparse dims
// - [4:8]: dense dims
// - [8]: nnz
//
class SparseTensorMetadata {
public:
static constexpr auto dim = 9;
// Construct from an existing metadata tensor to facilitate structured
// access to metadata from peers, after gathering it.
explicit SparseTensorMetadata(at::Tensor metadata)
: metadata_(metadata), data_(metadata_.data_ptr<int64_t>()) {
AT_ASSERT(metadata.scalar_type() == at::kLong);
AT_ASSERT(metadata.dim() == 1);
AT_ASSERT(metadata.size(0) == dim);
}
// Populate the metadata.
void populate_from_sparse_tensor(const at::Tensor& tensor) {
const auto sparse_dim = tensor.sparse_dim();
AT_ASSERT(sparse_dim <= 4);
for (const auto i : c10::irange(4)) {
if (i < sparse_dim) {
data_[i] = tensor.size(i);
}
}
const auto dense_dim = tensor.dense_dim();
AT_ASSERT(dense_dim <= 4);
for (const auto i : c10::irange(4)) {
if (i < dense_dim) {
data_[i + 4] = tensor.size(sparse_dim + i);
}
}
data_[8] = tensor._nnz();
}
std::vector<int64_t> sizes() const {
std::vector<int64_t> sizes;
// Sparse sizes
for (const auto i : c10::irange(4)) {
if (data_[i] <= 0) {
break;
}
sizes.push_back(data_[i]);
}
// Dense sizes
for (const auto i : c10::irange(4, 8)) {
if (data_[i] <= 0) {
break;
}
sizes.push_back(data_[i]);
}
return sizes;
}
int64_t nnz() const {
return data_[8];
}
protected:
at::Tensor metadata_;
int64_t* data_;
};
// Sparse allreduce is implemented with allgather on indices and values.
// Every process then sums the resulting sparse tensors locally.
// The nnz for sparse tensors may be different across processes, so first
// we run allgather on the nnz, and then allgather with max(nnz).
at::Tensor allreduce(std::vector<at::Tensor>& tensors) {
// TODO: This is a massive hack! There is some confusion about
// Variable/Tensor inside the body of this function. Turning off
// grad smooths over the confusion for now. This fixes
// test/test_c10d_gloo.py ProcessGroupGlooTest.test_sparse_allreduce_basics
//
// The correct fix is to stop allocating tensors that are not variables,
// but to conveniently do this c10d must depend on torch not ATen
at::AutoDispatchBelowAutograd guard;
auto input = tensors[0];
// Perform local reduction if we have multiple inputs.
for (const auto i : c10::irange(1, tensors.size())) {
input += tensors[i];
}
// Need to coalesce before we can access indices and values.
input = input.coalesce();
// Gather metadata information from all ranks.
auto metadata = allgather_metadata(input);
// Sanity check dimensionality across ranks.
{
const auto expected = metadata[context->rank].sizes();
for (const auto i : c10::irange(context->size)) {
if (i == context->rank) {
continue;
}
const auto actual = metadata[i].sizes();
TORCH_CHECK(actual == expected, "Sparse dimensions do not match");
}
}
// Gather all indices and all values.
auto indices = allgather_indices(input, metadata);
auto values = allgather_values(input, metadata);
// Perform global reduction.
AT_ASSERT(static_cast<int>(indices.size()) == context->size);
AT_ASSERT(static_cast<int>(values.size()) == context->size);
auto output = at::sparse_coo_tensor(
indices[0], values[0], input.sizes(), input.options());
for (const auto i : c10::irange(1, context->size)) {
output += at::sparse_coo_tensor(
indices[i], values[i], input.sizes(), input.options());
}
// Coalesce for good measure.
return output.coalesce();
}
void run() override {
auto output = allreduce(inputs);
// This copy is needed when we run a multi-gpu version of reduce (multiple
// inputs per rank).
for (const auto i : c10::irange(inputs.size())) {
inputs[i].copy_(output);
}
}
private:
std::vector<SparseTensorMetadata> allgather_metadata(
const at::Tensor& tensor) {
auto buffer =
at::zeros({context->size, SparseTensorMetadata::dim}, at::kLong);
// Prepare metadata vector (1 entry per rank)
std::vector<SparseTensorMetadata> metadata;
metadata.reserve(context->size);
for (const auto i : c10::irange(context->size)) {
metadata.emplace_back(buffer.select(0, i));
}
// Populate data for this rank
metadata[context->rank].populate_from_sparse_tensor(tensor);
// Allgather metadata
gloo::AllgatherOptions opts(context);
opts.setOutput(buffer.data_ptr<int64_t>(), buffer.numel());
opts.setTag(tag);
gloo::allgather(opts);
return metadata;
}
std::vector<at::Tensor> allgather_indices(
const at::Tensor& tensor,
const std::vector<SparseTensorMetadata>& metadata) {
const auto sparseDim = tensor.sparse_dim();
std::vector<size_t> counts(context->size);
int64_t totalSize = 0;
for (const auto i : c10::irange(metadata.size())) {
counts[i] = metadata[i].nnz() * sparseDim;
totalSize += counts[i];
}
auto output = at::empty({totalSize}, at::kLong);
// tensors copied from cuda may not be contiguous, get a contiguous
// tensor before use its data_ptr
auto input = tensor.indices().contiguous();
// Allgatherv indices.
gloo::AllgathervOptions opts(context);
opts.setInput(input.data_ptr<int64_t>(), input.numel());
opts.setOutput(output.data_ptr<int64_t>(), counts);
opts.setTag(tag);
gloo::allgatherv(opts);
// Compile indices tensor per rank.
std::vector<at::Tensor> indices;
indices.reserve(metadata.size());
size_t offset = 0;
for (const auto& i : metadata) {
const auto nnz = i.nnz();
const auto numel = sparseDim * nnz;
indices.push_back(
output.narrow(0, offset, numel).reshape({sparseDim, nnz}));
offset += numel;
}
return indices;
}
std::vector<at::Tensor> allgather_values(
const at::Tensor& tensor,
const std::vector<SparseTensorMetadata>& metadata) {
// There are nnz #dense_dim()-dimensional tensors per rank.
const auto valueShape = tensor.sizes().slice(tensor.sparse_dim());
size_t denseNumel = 1;
for (auto dim : valueShape) {
denseNumel *= dim;
}
std::vector<size_t> counts(context->size);
int64_t totalSize = 0;
for (const auto i : c10::irange(metadata.size())) {
counts[i] = metadata[i].nnz() * denseNumel;
totalSize += counts[i];
}
auto output = at::empty({totalSize}, tensor.scalar_type());
// Allgatherv indices.
gloo::AllgathervOptions opts(context);
// tensors copied from cuda may not be contiguous, get a contiguous
// tensor before use its data_ptr
at::Tensor valueTensor = tensor.values().contiguous();
GENERATE_ALL_TYPES(valueTensor.scalar_type(), setInput, opts, valueTensor);
GENERATE_ALL_TYPES(
valueTensor.scalar_type(), setOutput, opts, output, counts);
opts.setTag(tag);
gloo::allgatherv(opts);
// Compile values tensor per rank.
std::vector<at::Tensor> values;
values.reserve(metadata.size());
size_t offset = 0;
for (const auto& i : metadata) {
const auto nnz = i.nnz();
const auto numel = denseNumel * nnz;
auto tensorShape = std::vector<int64_t>({(int64_t)nnz});
std::copy(
valueShape.begin(),
valueShape.end(),
std::back_inserter(tensorShape));
values.push_back(output.narrow(0, offset, numel).reshape(tensorShape));
offset += numel;
}
return values;
}
};
class AsyncAllreduceCUDAWork : public AsyncAllreduceWork {
public:
AsyncAllreduceCUDAWork(
const std::shared_ptr<gloo::Context>& context,
std::vector<at::Tensor>& inputs,
ReduceOp reduceOp,
uint32_t tag)
: AsyncAllreduceWork(context, inputs, reduceOp, tag) {
initializeStreamsEvents(inputs, streams, events);
// Kick off copy from CUDA tensors to pinned CPU tensors.
tmp.reserve(inputs.size());
c10::OptionalStreamGuard guard;
for (const auto i : c10::irange(inputs.size())) {
guard.reset_stream(streams[i]);
tmp.push_back(pinnedLike(inputs[i]).copy_(inputs[i], true));
}
}
void run() override {
// Synchronize with copy operations.
for (const auto i : c10::irange(inputs.size())) {
streams[i].synchronize();
}
// Run allreduce on host side tensors.
allreduce(tmp);
c10::OptionalStreamGuard guard;
for (const auto i : c10::irange(inputs.size())) {
guard.reset_stream(streams[i]);
inputs[i].copy_(tmp[i], /* non_blocking */ true);
events[i].record(streams[i]);
}
}
void synchronize() override {
// Synchronize with the copy back to CUDA tensors.
for (const auto i : c10::irange(inputs.size())) {
c10::Device device = inputs[i].device();
events[i].block(
c10::impl::VirtualGuardImpl(device.type()).getStream(device));
}
}
std::vector<at::Tensor> tmp;
std::vector<c10::Stream> streams;
std::vector<c10::Event> events;
};
class AsyncSparseAllreduceCUDAWork : public AsyncSparseAllreduceWork {
public:
AsyncSparseAllreduceCUDAWork(
const std::shared_ptr<gloo::Context>& context,
std::vector<at::Tensor>& inputs,
uint32_t tag)
: AsyncSparseAllreduceWork(context, inputs, tag) {
initializeStreamsEvents(inputs, streams, events);
// Kick off copy from CUDA tensors to CPU tensors.
// Note that both coalescing the sparse tensor and copying it to CPU
// memory must be performed asynchronously, or we block the caller.
tmp.reserve(inputs.size());
c10::OptionalStreamGuard guard;
for (const auto i : c10::irange(inputs.size())) {
guard.reset_stream(streams[i]);
tmp.push_back(
inputs[i].coalesce().to(at::DeviceType::CPU, /*non_blocking=*/true));
}
}
void run() override {
// Synchronize with copy operations.
for (const auto i : c10::irange(inputs.size())) {
streams[i].synchronize();
}
// Run allreduce on host side tensors.
auto output = allreduce(tmp);
// Kick off copy back to the CUDA tensors.
c10::OptionalStreamGuard guard;
for (const auto i : c10::irange(inputs.size())) {
guard.reset_stream(streams[i]);
inputs[i].copy_(output, /*non_blocking=*/true);
events[i].record(streams[i]);
}
}
void synchronize() override {
// Synchronize with the copy back to CUDA tensors.
for (const auto i : c10::irange(inputs.size())) {
c10::Device device = inputs[i].device();
events[i].block(
c10::impl::VirtualGuardImpl(device.type()).getStream(device));
}
}
std::vector<at::Tensor> tmp;
std::vector<c10::Stream> streams;
std::vector<c10::Event> events;
};
} // namespace
c10::intrusive_ptr<Work> ProcessGroupGloo::allreduce(
std::vector<at::Tensor>& inputs,
const AllreduceOptions& opts) {
static auto invalidArgument = [](const std::string& msg) {
TORCH_CHECK(false, "ProcessGroupGloo::allreduce: " + msg);
};
assertNonEmpty(invalidArgument, inputs);
assertLayoutMatch(invalidArgument, inputs);
assertTypeAndSizesMatch(invalidArgument, inputs);
const auto& device = inputs[0].device();
switch (device.type()) {
case at::kCPU:
break;
case at::kCUDA:
// If the user gave us a CUDA tensor then CUDA must be loaded.
TORCH_INTERNAL_ASSERT(at::hasCUDA());
break;
default:
invalidArgument(c10::str("unsupported device type ", device.type()));
}
const auto& layout = inputs[0].layout();
if (layout == c10::kSparse && opts.reduceOp != ReduceOp::SUM) {
invalidArgument(
"unsupported reduction operation "
"(allreduce of sparse tensors only works with ReduceOp.SUM)");
}
c10::intrusive_ptr<AsyncWork> work;
auto tag = nextTag();
auto context = getContext(tag);
if (device.type() == at::kCPU) {
if (layout == c10::kStrided) {
work = c10::make_intrusive<AsyncAllreduceWork>(
std::move(context), inputs, opts.reduceOp, tag);
} else if (layout == c10::kSparse) {
work = c10::make_intrusive<AsyncSparseAllreduceWork>(
std::move(context), inputs, tag);
} else {
invalidArgument("unsupported layout");
}
} else if (device.type() == at::kCUDA) {
if (layout == c10::kStrided) {
work = c10::make_intrusive<AsyncAllreduceCUDAWork>(
std::move(context), inputs, opts.reduceOp, tag);
} else if (layout == c10::kSparse) {
work = c10::make_intrusive<AsyncSparseAllreduceCUDAWork>(
std::move(context), inputs, tag);
} else {
invalidArgument("unsupported layout");
}
} else {
TORCH_CHECK(false, "Invalid backend");
}
enqueue(work);
return work;
}
c10::intrusive_ptr<Work> ProcessGroupGloo::allreduce_coalesced(
std::vector<at::Tensor>& tensors,
const AllreduceCoalescedOptions& opts) {
static auto invalidArgument = [](const std::string& msg) {
TORCH_CHECK(false, "ProcessGroupGloo::allreduce_coalesced: " + msg);
};
assertNonEmpty(invalidArgument, tensors);
// tensors will be flattened and concatenated (coalesced). This means that
// input
// tensors must have the same device, layout and type.
assertLayoutMatch(invalidArgument, tensors);
if (!std::all_of(tensors.begin(), tensors.end(), [&](at::Tensor& t) {
return t.options().type_equal(tensors[0].options());
})) {
invalidArgument("tensors must all have the same type");
}
if (!std::all_of(tensors.begin(), tensors.end(), [&](at::Tensor& t) {
return t.device() == tensors[0].device();
})) {
invalidArgument("tensors must all be on the same device");
}
const c10::Device& device = tensors[0].device();
const c10::Layout& layout = tensors[0].layout();
// invalid arguments are detected early here before any calls to nextTag()
// which result in the collectiveCounter_ being incremented.
switch (device.type()) {
case c10::kCPU:
break;
default:
invalidArgument(c10::str("unsupported device type ", device.type()));
}
switch (layout) {
case c10::kStrided:
break;
default:
invalidArgument("unsupported layout");
}
c10::intrusive_ptr<AsyncWork> work;
const uint32_t tag = nextTag();
std::shared_ptr<gloo::Context> context = getContext(tag);
if (device.type() == c10::kCPU) {
if (layout == c10::kStrided) {
work = c10::make_intrusive<AsyncAllreduceCoalescedWork>(
std::move(context), tensors, opts.reduceOp, tag);
} else {
invalidArgument("unsupported layout");
}
} else {
TORCH_CHECK(false, "Invalid backend");
}
enqueue(work);
return work;
}
namespace {
class AsyncReduceWork : public ProcessGroupGloo::AsyncWork {
public:
AsyncReduceWork(
const std::shared_ptr<gloo::Context>& context,
std::vector<at::Tensor>& inputs,
int rootRank,
int rootTensor,
ReduceOp reduceOp,
uint32_t tag)
: ProcessGroupGloo::AsyncWork({inputs}, "gloo:reduce", inputs),
context(context),
inputs(inputs),
rootRank(rootRank),
rootTensor(rootTensor),
reduceOp(reduceOp),
tag(tag) {}
std::shared_ptr<gloo::Context> context;
std::vector<at::Tensor> inputs;
const int rootRank;
const int rootTensor;
const ReduceOp reduceOp;
const uint32_t tag;
void reduce(std::vector<at::Tensor>& tensors) {
const auto& scalarType = tensors[0].scalar_type();
gloo::ReduceOptions opts(context);
opts.setRoot(rootRank);
opts.setTag(tag);
opts.setReduceFunction(getFunction(scalarType, reduceOp));
GENERATE_ALL_TYPES(scalarType, setOutput, opts, tensors[0]);
gloo::reduce(opts);
}
void run() override {
reduce(inputs);
}
protected:
template <typename T>
void getFunction(gloo::ReduceOptions::Func& fn, const ReduceOp op) {
fn = toFunction<T>(op);
}
gloo::ReduceOptions::Func getFunction(
const at::ScalarType& dtype,
const ReduceOp op) {
gloo::ReduceOptions::Func fn;
GENERATE_ALL_TYPES(dtype, getFunction, fn, op);
return fn;
}
};
class AsyncReduceCUDAWork : public AsyncReduceWork {
public:
AsyncReduceCUDAWork(
const std::shared_ptr<gloo::Context>& context,
std::vector<at::Tensor>& inputs,
int rootRank,
int rootTensor,
ReduceOp reduceOp,
uint32_t tag)
: AsyncReduceWork(context, inputs, rootRank, rootTensor, reduceOp, tag) {
initializeStreamsEvents(inputs, streams, events);
// Kick off copy from CUDA tensors to pinned CPU tensors.
tmp.reserve(inputs.size());
c10::OptionalStreamGuard guard;
for (const auto i : c10::irange(inputs.size())) {
guard.reset_stream(streams[i]);
tmp.push_back(pinnedLike(inputs[i]).copy_(inputs[i], true));
}
}
void run() override {
// Synchronize with copy operations.
for (const auto i : c10::irange(inputs.size())) {
streams[i].synchronize();
}
// Run reduce on host side tensors.
reduce(tmp);
// Kick off copy back to the CUDA tensors.
c10::OptionalStreamGuard guard;
for (const auto i : c10::irange(inputs.size())) {
guard.reset_stream(streams[i]);
inputs[i].copy_(tmp[i], /* non_blocking */ true);
events[i].record(streams[i]);
}
}
void synchronize() override {
// Synchronize with the copy back to CUDA tensors.
for (const auto i : c10::irange(inputs.size())) {
c10::Device device = inputs[i].device();
events[i].block(
c10::impl::VirtualGuardImpl(device.type()).getStream(device));
}
}
std::vector<at::Tensor> tmp;
std::vector<c10::Stream> streams;
std::vector<c10::Event> events;
};
} // namespace
c10::intrusive_ptr<Work> ProcessGroupGloo::reduce(
std::vector<at::Tensor>& inputs,
const ReduceOptions& opts) {
static auto invalidArgument = [](const std::string& msg) {
TORCH_CHECK(false, "ProcessGroupGloo::reduce: " + msg);
};
assertRootRank(invalidArgument, opts.rootRank, size_);
assertRootTensor(invalidArgument, opts.rootTensor, inputs.size());
assertSingleElement(invalidArgument, inputs);
assertDense(invalidArgument, inputs);
const auto& device = inputs[0].device();
switch (device.type()) {
case at::kCPU:
break;
case at::kCUDA:
// If the user gave us a CUDA tensor then CUDA must be loaded.
TORCH_INTERNAL_ASSERT(at::hasCUDA());
break;
default:
invalidArgument(c10::str("unsupported device type ", device.type()));
}
c10::intrusive_ptr<AsyncReduceWork> work;
auto tag = nextTag();
auto context = getContext(tag);
if (device.type() == at::kCPU) {
work = c10::make_intrusive<AsyncReduceWork>(
std::move(context),
inputs,
opts.rootRank,
opts.rootTensor,
opts.reduceOp,
tag);
} else if (device.type() == at::kCUDA) {
work = c10::make_intrusive<AsyncReduceCUDAWork>(
std::move(context),
inputs,
opts.rootRank,
opts.rootTensor,
opts.reduceOp,
tag);
} else {
TORCH_CHECK(false, "Invalid backend");
}
enqueue(work);
return work;
}
namespace {
class AsyncAllgatherWork : public ProcessGroupGloo::AsyncWork {
public:
AsyncAllgatherWork(
const std::shared_ptr<gloo::Context>& context,
std::vector<std::vector<at::Tensor>>& outputs,
std::vector<at::Tensor>& inputs,
uint32_t tag)
: ProcessGroupGloo::AsyncWork(outputs, "gloo:all_gather", inputs),
context(context),
outputs(outputs),
inputs(inputs),
tag(tag) {}
std::shared_ptr<gloo::Context> context;
std::vector<std::vector<at::Tensor>> outputs;
std::vector<at::Tensor> inputs;
const uint32_t tag;
void allgather(
std::vector<std::vector<at::Tensor>>& outputs,
std::vector<at::Tensor>& inputs) {
const auto& scalarType = inputs[0].scalar_type();
gloo::AllgatherOptions opts(context);
opts.setTag(tag);
// Use single flattened input tensor.
at::Tensor flatInputTensor = flattenDenseTensors(inputs);
GENERATE_ALL_TYPES(scalarType, setInput, opts, flatInputTensor);
// Use single flat output tensor.
// The first dimension corresponds to the index into outputs[N],
// so copying into the actual output later is easy.
at::Tensor flatOutputTensor = newLikeFlat(outputs[0]);
GENERATE_ALL_TYPES(scalarType, setOutput, opts, flatOutputTensor);
gloo::allgather(opts);
// Unflatten into output tensors.
for (auto& outputgroup : outputs) {
for (const auto j : c10::irange(outputgroup.size())) {
outputgroup[j].copy_(flatOutputTensor[j]);
}
}
}
void run() override {
allgather(outputs, inputs);
}
};
// Note: current CUDA implementation holds the assumption that the
// tensors in the nested output tensor vectors are on the same device.
class AsyncAllgatherCUDAWork : public AsyncAllgatherWork {
public:
AsyncAllgatherCUDAWork(
const std::shared_ptr<gloo::Context>& context,
std::vector<std::vector<at::Tensor>>& outputs,
std::vector<at::Tensor>& inputs,
uint32_t tag)
: AsyncAllgatherWork(context, outputs, inputs, tag) {
initializeStreamsEvents(inputs, inputStreams, inputEvents);
initializeStreamsEvents(outputs, outputStreams, outputEvents);
// Kick off copy from CUDA tensors to pinned CPU tensors.
tmpInputs.reserve(inputs.size());
c10::OptionalStreamGuard guard;
for (const auto i : c10::irange(inputs.size())) {
guard.reset_stream(inputStreams[i]);
tmpInputs.push_back(pinnedLike(inputs[i]).copy_(inputs[i], true));
}
tmpOutputs.resize(outputs.size());
for (const auto i : c10::irange(outputs.size())) {
tmpOutputs[i].reserve(outputs[i].size());
for (const auto j : c10::irange(outputs[i].size())) {
tmpOutputs[i].push_back(pinnedLike(outputs[i][j]));
}
}
}
void run() override {
// Synchronize with copy operations.
for (const auto i : c10::irange(inputs.size())) {
inputStreams[i].synchronize();
}
for (const auto i : c10::irange(outputs.size())) {
outputStreams[i].synchronize();
}
// Run allgather on host side tensors.
allgather(tmpOutputs, tmpInputs);
// Kick off copy back to the CUDA tensors.
c10::OptionalStreamGuard guard;
for (const auto i : c10::irange(outputs.size())) {
guard.reset_stream(outputStreams[i]);
for (const auto j : c10::irange(outputs[i].size())) {
outputs[i][j].copy_(tmpOutputs[i][j], /* non_blocking */ true);
}
outputEvents[i].record(outputStreams[i]);
}
}
void synchronize() override {
// Synchronize with the copy back to CUDA tensors.
for (const auto i : c10::irange(outputs.size())) {
c10::Device device = outputs[i][0].device();
outputEvents[i].block(
c10::impl::VirtualGuardImpl(device.type()).getStream(device));
}
}
std::vector<at::Tensor> tmpInputs;
std::vector<c10::Stream> inputStreams;
std::vector<c10::Event> inputEvents;
std::vector<std::vector<at::Tensor>> tmpOutputs;
std::vector<c10::Stream> outputStreams;
std::vector<c10::Event> outputEvents;
};
} // namespace
// Note: current CUDA implementation holds the assumption that the
// tensors in the nested output tensor vectors are on the same device.
c10::intrusive_ptr<Work> ProcessGroupGloo::allgather(
std::vector<std::vector<at::Tensor>>& outputs,
std::vector<at::Tensor>& inputs,
const AllgatherOptions& opts) {
static auto invalidArgument = [](const std::string& msg) {
TORCH_CHECK(false, "ProcessGroupGloo::allgather: " + msg);
};
if (inputs.size() == 0) {
invalidArgument("requires non-empty input tensor list");
}
if (inputs.size() != outputs.size()) {
invalidArgument(
"requires input/output tensor lists to have the same length");
}
for (const auto i : c10::irange(outputs.size())) {
const auto expected = inputs.size() * getSize();
const auto actual = outputs[i].size();
if (actual != expected) {
invalidArgument(
"invalid output tensor list at index " + std::to_string(i) +
" (expected length " + std::to_string(expected) + ", got " +
std::to_string(actual) + ")");
}
}
assertDense(invalidArgument, inputs);
// Expect all input/output tensors to have the same type and sizes
const auto& options = inputs[0].options();
const auto& sizes = inputs[0].sizes();
assertTypeAndSizesMatch(invalidArgument, inputs, options, sizes);
for (const auto& output : outputs) {
assertTypeAndSizesMatch(invalidArgument, output, options, sizes);
}
const auto& device = inputs[0].device();
switch (device.type()) {
case at::kCPU:
break;
case at::kCUDA:
// If the user gave us a CUDA tensor then CUDA must be loaded.
TORCH_INTERNAL_ASSERT(at::hasCUDA());
break;
default:
invalidArgument(c10::str("unsupported device type ", device.type()));
}
c10::intrusive_ptr<AsyncAllgatherWork> work;
auto tag = nextTag();
auto context = getContext(tag);
if (device.type() == at::kCPU) {
work = c10::make_intrusive<AsyncAllgatherWork>(
std::move(context), outputs, inputs, tag);
} else if (device.type() == at::kCUDA) {
work = c10::make_intrusive<AsyncAllgatherCUDAWork>(
std::move(context), outputs, inputs, tag);
} else {
TORCH_CHECK(false, "Invalid backend");
}
enqueue(work);
return work;
}
namespace {
class AsyncAllgatherCoalescedWork : public ProcessGroupGloo::AsyncWork {
public:
AsyncAllgatherCoalescedWork(
const std::shared_ptr<gloo::Context>& context,
std::vector<std::vector<at::Tensor>>& output_lists,
std::vector<at::Tensor>& input_list,
uint32_t tag)
: ProcessGroupGloo::AsyncWork(
output_lists,
"gloo:all_gather",
input_list),
context(context),
output_lists(output_lists),
input_list(input_list),
tag(tag) {}
std::shared_ptr<gloo::Context> context;
std::vector<std::vector<at::Tensor>> output_lists;
std::vector<at::Tensor> input_list;
const uint32_t tag;
void allgather_coalesced() {
assert(!output_lists.empty());
assert(!output_lists[0].empty());
assert(!input_list.empty());
const auto& scalarType = input_list[0].scalar_type();
gloo::AllgatherOptions opts(context);
opts.setTag(tag);
// Use single flattened input tensor.
at::Tensor flatInputTensor = flattenDenseTensors(input_list);
GENERATE_ALL_TYPES(scalarType, setInput, opts, flatInputTensor);
// Compute total number of elements we need to allocate for all tensors
// requested.
int64_t output_numel = 0;
for (const auto& t : output_lists[0]) {
output_numel += t.numel();
}
output_numel *= output_lists.size();
// Use single flat output tensor.
at::Tensor flatOutputTensor =
at::empty({output_numel}, output_lists[0][0].options());
GENERATE_ALL_TYPES(scalarType, setOutput, opts, flatOutputTensor);
gloo::allgather(opts);
int64_t current_element = 0;
for (auto& output_list : output_lists) {
for (auto& output_tensor : output_list) {
output_tensor.copy_(
flatOutputTensor.narrow(0, current_element, output_tensor.numel())
.reshape(output_tensor.sizes()),
true);
current_element += output_tensor.numel();
}
}
}
void run() override {
allgather_coalesced();
}
};
} // namespace
c10::intrusive_ptr<Work> ProcessGroupGloo::allgather_coalesced(
std::vector<std::vector<at::Tensor>>& output_lists,
std::vector<at::Tensor>& input_list,
const AllgatherOptions& /* unused */) {
static auto invalidArgument = [](const std::string& msg) {
TORCH_CHECK(false, "ProcessGroupGloo::allgather_coalesced: " + msg);
};
if (input_list.empty()) {
invalidArgument("requires non-empty input tensor list");
}
if (output_lists.size() != getSize()) {
invalidArgument("output lists should be equal to world size");
}
assertSameDevice(invalidArgument, input_list);
// Expect i'th tensor of each list from 'output_lists' match i'th tensor
// from 'input_list' in type and size.
for (const auto& output_list : output_lists) {
if (output_list.size() != input_list.size()) {
invalidArgument(
"invalid output size: (expected length " +
std::to_string(input_list.size()) + ", got " +
std::to_string(output_list.size()) + ")");
}
for (const auto i : c10::irange(output_list.size())) {
const auto expected = input_list[i].sizes();
const auto actual = output_list[i].sizes();
if (actual != expected) {
invalidArgument(
"invalid size of output tensor at index " + std::to_string(i) +
" (expected length " + toString(expected) + ", got " +
toString(actual) + ")");
}
if (!input_list[i].options().type_equal(output_list[i].options())) {
invalidArgument(
"invalid tensor type at index " + std::to_string(i) +
" (expected " + input_list[i].toString() + ", got " +
output_list[i].toString() + ")");
}
}
}
assertDense(invalidArgument, input_list);
auto tag = nextTag();
auto context = getContext(tag);
auto work = c10::make_intrusive<AsyncAllgatherCoalescedWork>(
std::move(context), output_lists, input_list, tag);
enqueue(work);
return work;
}
c10::intrusive_ptr<Work> ProcessGroupGloo::_allgather_base(
at::Tensor& /*unused */,
at::Tensor& /*unused */,
const AllgatherOptions& /*unused */) {
TORCH_CHECK(false, "no support for _allgather_base in Gloo process group");
}
namespace {
class AsyncGatherWork : public ProcessGroupGloo::AsyncWork {
public:
AsyncGatherWork(
const std::shared_ptr<gloo::Context>& context,
std::vector<std::vector<at::Tensor>>& outputs,
std::vector<at::Tensor>& inputs,
int root,
uint32_t tag)
: ProcessGroupGloo::AsyncWork(outputs, "gloo:gather", inputs),
context(context),
outputs(outputs),
inputs(inputs),
root(root),
tag(tag) {}
std::shared_ptr<gloo::Context> context;
std::vector<std::vector<at::Tensor>> outputs;
std::vector<at::Tensor> inputs;
const int root;
const uint32_t tag;
void gather(
std::vector<std::vector<at::Tensor>>& outputs,
std::vector<at::Tensor>& inputs) {
const auto scalarType = inputs[0].scalar_type();
gloo::GatherOptions opts(context);
opts.setRoot(root);
opts.setTag(tag);
// Set single temporary tensor on root process.
// This is later scattered to the separate output tensors.
at::Tensor flatOutputTensor;
if (context->rank == root) {
flatOutputTensor = newLikeFlat(outputs[0]);
GENERATE_ALL_TYPES(scalarType, setOutput, opts, flatOutputTensor);
}
// Set single input tensor on all processes.
GENERATE_ALL_TYPES(scalarType, setInput, opts, inputs[0]);
gloo::gather(opts);
// Unflatten into output tensors on root process.
if (context->rank == root) {
for (const auto i : c10::irange(outputs[0].size())) {
outputs[0][i].copy_(flatOutputTensor[i]);
}
}
}
void run() override {
gather(outputs, inputs);
}
};
// Note: current CUDA implementation holds the assumptions:
// - inputs.size() is 1
// - outputs.size() is 1
// - the size of the nested output tensors is world size, i.e.,
// outputs[0].size, is world size
class AsyncGatherCUDAWork : public AsyncGatherWork {
public:
AsyncGatherCUDAWork(
const std::shared_ptr<gloo::Context>& context,
std::vector<std::vector<at::Tensor>>& outputs,
std::vector<at::Tensor>& inputs,
int root,
uint32_t tag)
: AsyncGatherWork(context, outputs, inputs, root, tag) {
initializeStreamsEvents(inputs, inputStreams, inputEvents);
initializeStreamsEvents(outputs, outputStreams, outputEvents);
// Kick off copy from CUDA tensors to pinned CPU tensors.
tmpInputs.reserve(inputs.size());
c10::OptionalStreamGuard guard;
for (const auto i : c10::irange(inputs.size())) {
guard.reset_stream(inputStreams[i]);
tmpInputs.push_back(pinnedLike(inputs[i]).copy_(inputs[i], true));
}
tmpOutputs.resize(outputs.size());
for (const auto i : c10::irange(outputs.size())) {
tmpOutputs[i].reserve(outputs[i].size());
for (const auto j : c10::irange(outputs[i].size())) {
tmpOutputs[i].push_back(pinnedLike(outputs[i][j]));
}
}
}
void run() override {
// Synchronize with copy operations.
for (const auto i : c10::irange(inputs.size())) {
inputStreams[i].synchronize();
}
for (const auto i : c10::irange(outputs.size())) {
outputStreams[i].synchronize();
}
// Run gather on host side tensors.
gather(tmpOutputs, tmpInputs);
// Kick off copy back to the CUDA tensors.
c10::OptionalStreamGuard guard;
for (const auto i : c10::irange(outputs.size())) {
guard.reset_stream(outputStreams[i]);
for (const auto j : c10::irange(outputs[i].size())) {
outputs[i][j].copy_(tmpOutputs[i][j], /* non_blocking */ true);
}
outputEvents[i].record(outputStreams[i]);
}
}
void synchronize() override {
// Synchronize with the copy back to CUDA tensors.
for (const auto i : c10::irange(outputs.size())) {
c10::Device device = outputs[i][0].device();
outputEvents[i].block(
c10::impl::VirtualGuardImpl(device.type()).getStream(device));
}
}
std::vector<at::Tensor> tmpInputs;
std::vector<c10::Stream> inputStreams;
std::vector<c10::Event> inputEvents;
std::vector<std::vector<at::Tensor>> tmpOutputs;
std::vector<c10::Stream> outputStreams;
std::vector<c10::Event> outputEvents;
};
} // namespace
c10::intrusive_ptr<Work> ProcessGroupGloo::gather(
std::vector<std::vector<at::Tensor>>& outputs,
std::vector<at::Tensor>& inputs,
const GatherOptions& opts) {
static auto invalidArgument = [](const std::string& msg) {
TORCH_CHECK(false, "ProcessGroupGloo::gather: " + msg);
};
assertRootRank(invalidArgument, opts.rootRank, size_);
assertSingleElementInput(invalidArgument, inputs);
assertDense(invalidArgument, inputs);
if (getRank() == opts.rootRank) {
if (outputs.size() != 1) {
std::stringstream ss;
ss << "requires a single-element output list containing a list with "
<< getSize() << " tensors.";
invalidArgument(ss.str());
} else if (outputs[0].size() != static_cast<size_t>(getSize())) {
std::stringstream ss;
ss << "Incorrect output list size " << outputs[0].size()
<< ". Output list size should be " << getSize()
<< ", same as size of the process group.";
invalidArgument(ss.str());
}
const auto& options = inputs[0].options();
const auto& sizes = inputs[0].sizes();
assertTypeAndSizesMatch(invalidArgument, outputs[0], options, sizes);
} else {
if (outputs.size() != 0) {
invalidArgument("requires empty output on non-root");
}
}
const auto& device = inputs[0].device();
switch (device.type()) {
case at::kCPU:
break;
case at::kCUDA:
// If the user gave us a CUDA tensor then CUDA must be loaded.
TORCH_INTERNAL_ASSERT(at::hasCUDA());
break;
default:
invalidArgument(c10::str("unsupported device type ", device.type()));
}
c10::intrusive_ptr<AsyncGatherWork> work;
auto tag = nextTag();
auto context = getContext(tag);
if (device.type() == at::kCPU) {
work = c10::make_intrusive<AsyncGatherWork>(
std::move(context), outputs, inputs, opts.rootRank, tag);
} else if (device.type() == at::kCUDA) {
work = c10::make_intrusive<AsyncGatherCUDAWork>(
std::move(context), outputs, inputs, opts.rootRank, tag);
} else {
TORCH_CHECK(false, "Invalid backend");
}
enqueue(work);
return work;
}
namespace {
class AsyncScatterWork : public ProcessGroupGloo::AsyncWork {
public:
AsyncScatterWork(
const std::shared_ptr<gloo::Context>& context,
std::vector<at::Tensor>& outputs,
std::vector<std::vector<at::Tensor>>& inputs,
int root,
uint32_t tag)
: ProcessGroupGloo::AsyncWork(
{outputs},
"gloo:scatter",
inputs.size() > 0
? c10::optional<std::vector<at::Tensor>>(inputs[0])
: c10::nullopt),
context(context),
outputs(outputs),
inputs(inputs),
root(root),
tag(tag) {}
std::shared_ptr<gloo::Context> context;
std::vector<at::Tensor> outputs;
std::vector<std::vector<at::Tensor>> inputs;
const int root;
const uint32_t tag;
void scatter(
std::vector<at::Tensor>& outputs,
std::vector<std::vector<at::Tensor>>& inputs) {
const auto scalarType = outputs[0].scalar_type();
gloo::ScatterOptions opts(context);
opts.setRoot(root);
opts.setTag(tag);
// Set list of input tensors on root process
if (context->rank == root) {
GENERATE_ALL_TYPES(scalarType, setInputs, opts, inputs[0]);
}
// Set single output tensor on all processes
GENERATE_ALL_TYPES(scalarType, setOutput, opts, outputs[0]);
gloo::scatter(opts);
}
void run() override {
scatter(outputs, inputs);
}
};
class AsyncScatterCUDAWork : public AsyncScatterWork {
public:
AsyncScatterCUDAWork(
const std::shared_ptr<gloo::Context>& context,
std::vector<at::Tensor>& outputs,
std::vector<std::vector<at::Tensor>>& inputs,
int root,
uint32_t tag)
: AsyncScatterWork(context, outputs, inputs, root, tag) {
initializeStreamsEvents(inputs, inputStreams, inputEvents);
initializeStreamsEvents(outputs, outputStreams, outputEvents);
// Kick off copy from CUDA tensors to pinned CPU tensors.
tmpInputs.resize(inputs.size());
c10::OptionalStreamGuard guard;
for (const auto i : c10::irange(inputs.size())) {
guard.reset_stream(inputStreams[i]);
tmpInputs[i].reserve(inputs[i].size());
for (const auto j : c10::irange(inputs[i].size())) {
tmpInputs[i].push_back(
pinnedLike(inputs[i][j]).copy_(inputs[i][j], true));
}
}
tmpOutputs.reserve(outputs.size());
for (auto& output : outputs) {
tmpOutputs.push_back(pinnedLike(output));
}
}
void run() override {
// Synchronize with copy operations.
for (const auto i : c10::irange(inputs.size())) {
inputStreams[i].synchronize();
}
for (const auto i : c10::irange(outputs.size())) {
outputStreams[i].synchronize();
}
// Run scatter on host side tensors.
scatter(tmpOutputs, tmpInputs);
// Kick off copy back to the CUDA tensors.
c10::OptionalStreamGuard guard;
for (const auto i : c10::irange(outputs.size())) {
guard.reset_stream(outputStreams[i]);
outputs[i].copy_(tmpOutputs[i], /* non_blocking */ true);
outputEvents[i].record(outputStreams[i]);
}
}
void synchronize() override {
// Synchronize with the copy back to CUDA tensors.
for (const auto i : c10::irange(outputs.size())) {
c10::Device device = outputs[i].device();
outputEvents[i].block(
c10::impl::VirtualGuardImpl(device.type()).getStream(device));
}
}
std::vector<at::Tensor> tmpOutputs;
std::vector<c10::Stream> outputStreams;
std::vector<c10::Event> outputEvents;
std::vector<std::vector<at::Tensor>> tmpInputs;
std::vector<c10::Stream> inputStreams;
std::vector<c10::Event> inputEvents;
};
} // namespace
c10::intrusive_ptr<Work> ProcessGroupGloo::scatter(
std::vector<at::Tensor>& outputs,
std::vector<std::vector<at::Tensor>>& inputs,
const ScatterOptions& opts) {
static auto invalidArgument = [](const std::string& msg) {
TORCH_CHECK(false, "ProcessGroupGloo::scatter: " + msg);
};
assertRootRank(invalidArgument, opts.rootRank, size_);
assertSingleElementOutput(invalidArgument, outputs);
assertDense(invalidArgument, outputs);
if (getRank() == opts.rootRank) {
if (inputs.size() != 1) {
std::stringstream ss;
ss << "requires a single-element input list containing a list with "
<< getSize() << " tensors";
invalidArgument(ss.str());
} else if (inputs[0].size() != static_cast<size_t>(getSize())) {
std::stringstream ss;
ss << "Incorrect input list size " << inputs[0].size()
<< ". Input list size should be " << getSize()
<< ", same as size of the process group.";
invalidArgument(ss.str());
}
const auto& options = outputs[0].options();
const auto& sizes = outputs[0].sizes();
assertTypeAndSizesMatch(invalidArgument, inputs[0], options, sizes);
} else {
if (inputs.size() != 0) {
invalidArgument("requires empty input on non-root");
}
}
const auto& device = outputs[0].device();
switch (device.type()) {
case at::kCPU:
break;
case at::kCUDA:
// If the user gave us a CUDA tensor then CUDA must be loaded.
TORCH_INTERNAL_ASSERT(at::hasCUDA());
break;
default:
invalidArgument(c10::str("unsupported device type ", device.type()));
}
c10::intrusive_ptr<AsyncScatterWork> work;
auto tag = nextTag();
auto context = getContext(tag);
if (device.type() == at::kCPU) {
work = c10::make_intrusive<AsyncScatterWork>(
std::move(context), outputs, inputs, opts.rootRank, tag);
} else if (device.type() == at::kCUDA) {
work = c10::make_intrusive<AsyncScatterCUDAWork>(
std::move(context), outputs, inputs, opts.rootRank, tag);
} else {
TORCH_CHECK(false, "Invalid backend");
}
enqueue(work);
return work;
}
c10::intrusive_ptr<Work> ProcessGroupGloo::reduce_scatter(
std::vector<at::Tensor>& outputs,
std::vector<std::vector<at::Tensor>>& inputs,
const ReduceScatterOptions& opts) {
TORCH_CHECK(false, "ProcessGroupGloo does not support reduce_scatter");
}
namespace {
class AsyncAlltoallWork : public ProcessGroupGloo::AsyncWork {
public:
AsyncAlltoallWork(
const std::shared_ptr<gloo::Context>& context,
at::Tensor& outputTensor,
at::Tensor& inputTensor,
std::vector<int64_t>& outputCounts,
std::vector<int64_t>& inputCounts,
uint32_t tag)
: ProcessGroupGloo::AsyncWork(
{{outputTensor}},
"gloo:all_to_all",
c10::optional<std::vector<at::Tensor>>({inputTensor})),
context(context),
outputTensor(outputTensor),
inputTensor(inputTensor),
outputCounts(std::move(outputCounts)),
inputCounts(std::move(inputCounts)),
tag(tag) {}
std::shared_ptr<gloo::Context> context;
at::Tensor outputTensor;
at::Tensor inputTensor;
std::vector<int64_t> outputCounts;
std::vector<int64_t> inputCounts;
const uint32_t tag;
void alltoall(at::Tensor& outputTensor, at::Tensor& inputTensor) {
const auto scalarType = outputTensor.scalar_type();
if (outputCounts.size() == 0 && inputCounts.size() == 0) {
// Gloo alltoall
gloo::AlltoallOptions opts(context);
opts.setTag(tag);
GENERATE_ALL_TYPES(scalarType, setInput, opts, inputTensor);
GENERATE_ALL_TYPES(scalarType, setOutput, opts, outputTensor);
gloo::alltoall(opts);
} else {
// Gloo alltoallv
c10d::checkSplitSizes(inputCounts, inputTensor, context->size);
c10d::checkSplitSizes(outputCounts, outputTensor, context->size);
std::vector<int64_t> sendCounts(context->size);
std::vector<int64_t> recvCounts(context->size);
std::vector<int64_t> sendOffsets(context->size);
std::vector<int64_t> recvOffsets(context->size);
c10d::computeLengthsAndOffsets(
inputCounts, inputTensor, &sendCounts, &sendOffsets);
c10d::computeLengthsAndOffsets(
outputCounts, outputTensor, &recvCounts, &recvOffsets);
gloo::AlltoallvOptions opts(context);
opts.setTag(tag);
GENERATE_ALL_TYPES(scalarType, setInput, opts, inputTensor, sendCounts);
GENERATE_ALL_TYPES(scalarType, setOutput, opts, outputTensor, recvCounts);
gloo::alltoallv(opts);
}
}
void run() override {
alltoall(outputTensor, inputTensor);
}
};
class AsyncAlltoallCUDAWork : public AsyncAlltoallWork {
public:
AsyncAlltoallCUDAWork(
const std::shared_ptr<gloo::Context>& context,
at::Tensor& outputTensor,
at::Tensor& inputTensor,
std::vector<int64_t>& outputCounts,
std::vector<int64_t>& inputCounts,
uint32_t tag)
: AsyncAlltoallWork(
context,
outputTensor,
inputTensor,
outputCounts,
inputCounts,
tag) {
initializeStreamsEvents({inputTensor}, inputStreams, inputEvents);
initializeStreamsEvents({outputTensor}, outputStreams, outputEvents);
// Kick off copy from CUDA tensors to pinned CPU tensors.
c10::OptionalStreamGuard guard;
guard.reset_stream(inputStreams.front());
cpuInput = pinnedLike(inputTensor).copy_(inputTensor, true);
guard.reset_stream(outputStreams.front());
cpuOutput = pinnedLike(outputTensor);
}
void run() override {
// Synchronize with copy operations.
inputStreams.front().synchronize();
outputStreams.front().synchronize();
// Run alltoall on host side tensors.
alltoall(cpuOutput, cpuInput);
// Kick off copy back to the CUDA tensors.
c10::OptionalStreamGuard guard;
guard.reset_stream(outputStreams.front());
outputTensor.copy_(cpuOutput, /* non_blocking */ true);
outputEvents.front().record(outputStreams.front());
}
void synchronize() override {
// Synchronize with the copy back to CUDA tensors.
c10::Device device = outputTensor.device();
outputEvents.front().block(
c10::impl::VirtualGuardImpl(device.type()).getStream(device));
}
at::Tensor cpuOutput;
std::vector<c10::Stream> outputStreams;
std::vector<c10::Event> outputEvents;
at::Tensor cpuInput;
std::vector<c10::Stream> inputStreams;
std::vector<c10::Event> inputEvents;
};
} // namespace
c10::intrusive_ptr<Work> ProcessGroupGloo::alltoall_base(
at::Tensor& outputTensor,
at::Tensor& inputTensor,
std::vector<int64_t>& outputCounts,
std::vector<int64_t>& inputCounts,
const AllToAllOptions& /* unused */) {
static auto invalidArgument = [](const std::string& msg) {
TORCH_CHECK(false, "ProcessGroupGloo::alltoall_base: " + msg);
};
TORCH_CHECK(
outputTensor.device() == inputTensor.device(),
"output tensor and input tensor must be on the same type of device");
assertDense(invalidArgument, {outputTensor});
assertDense(invalidArgument, {inputTensor});
const auto& device = outputTensor.device();
c10::intrusive_ptr<AsyncAlltoallWork> work;
auto tag = nextTag();
auto context = getContext(tag);
if (device.type() == at::kCPU) {
work = c10::make_intrusive<AsyncAlltoallWork>(
std::move(context),
outputTensor,
inputTensor,
outputCounts,
inputCounts,
tag);
} else if (device.type() == at::kCUDA) {
work = c10::make_intrusive<AsyncAlltoallCUDAWork>(
std::move(context),
outputTensor,
inputTensor,
outputCounts,
inputCounts,
tag);
} else {
invalidArgument(c10::str("unsupported device type ", device.type()));
}
enqueue(work);
return work;
}
at::Tensor& checkSingleTensor(std::vector<at::Tensor>& tensors) {
if (tensors.size() != 1) {
TORCH_CHECK(false, "ProcessGroupGloo::send takes a single tensor");
}
auto& tensor = tensors[0];
if (!tensor.is_contiguous()) {
TORCH_CHECK(false, "input tensor has to be contiguous");
}
if (tensor.is_sparse()) {
TORCH_CHECK(false, "input tensor has to be dense");
}
return tensor;
}
uint32_t checkTag(int32_t tag) {
TORCH_CHECK(tag >= 0, "Tag must be nonnegative");
return (uint32_t)tag;
}
c10::intrusive_ptr<Work> ProcessGroupGloo::send(
std::vector<at::Tensor>& tensors,
int dstRank,
int tag) {
auto& tensor = checkSingleTensor(tensors);
auto utag = checkTag(tag);
auto ptr = tensor.data_ptr();
auto size = tensor.numel() * tensor.element_size();
// Construct unbound buffer.
auto context = getContext(tag);
auto buf = context->createUnboundBuffer(ptr, size);
buf->send(dstRank, utag);
// The work captures the tensor to prevent it being deallocated and
// the unbound buffer to synchronize on completion of the send.
return c10::make_intrusive<SendWork>(tensor, std::move(buf));
}
c10::intrusive_ptr<Work> ProcessGroupGloo::recv(
std::vector<at::Tensor>& tensors,
int srcRank,
int tag) {
auto& tensor = checkSingleTensor(tensors);
auto utag = checkTag(tag);
auto ptr = tensor.data_ptr();
auto size = tensor.numel() * tensor.element_size();
// Construct unbound buffer.
auto context = getContext(tag);
auto buf = context->createUnboundBuffer(ptr, size);
buf->recv(srcRank, utag);
// The work captures the tensor to prevent it being deallocated and
// the unbound buffer to synchronize on completion of the recv.
return c10::make_intrusive<RecvWork>(tensor, std::move(buf), "gloo:recv");
}
c10::intrusive_ptr<Work> ProcessGroupGloo::recvAnysource(
std::vector<at::Tensor>& tensors,
int tag) {
auto& tensor = checkSingleTensor(tensors);
auto utag = checkTag(tag);
auto ptr = tensor.data_ptr();
auto size = tensor.numel() * tensor.element_size();
// Construct unbound buffer.
auto context = getContext(tag);
auto buf = context->createUnboundBuffer(ptr, size);
// Build list of ranks that this operation can recv from. In these
// bindings we don't differentiate between ranks and can receive
// from any other process in the group.
std::vector<int> srcRanks;
srcRanks.resize(size_);
for (const auto i : c10::irange(size_)) {
srcRanks.push_back(i);
}
buf->recv(srcRanks, utag);
// The work captures the tensor to prevent it being deallocated and
// the unbound buffer to synchronize on completion of the recv.
return c10::make_intrusive<RecvWork>(
tensor, std::move(buf), "gloo:recvAnySource");
}
namespace {
class AsyncBarrierWork : public ProcessGroupGloo::AsyncWork {
public:
AsyncBarrierWork(
const std::shared_ptr<gloo::Context>& context,
std::vector<c10::weak_intrusive_ptr<AsyncWork>> priorWork,
uint32_t tag)
: ProcessGroupGloo::AsyncWork({}, "gloo:barrier", c10::nullopt),
context(context),
priorWork(std::move(priorWork)),
tag(tag) {}
std::shared_ptr<gloo::Context> context;
std::vector<c10::weak_intrusive_ptr<AsyncWork>> priorWork;
const uint32_t tag;
void run() override {
// Wait on prior work to complete
for (auto& weakWork : priorWork) {
auto work = weakWork.lock();
if (work) {
work->wait();
}
}
gloo::BarrierOptions opts(context);
opts.setTag(tag);
gloo::barrier(opts);
}
};
} // namespace
c10::intrusive_ptr<Work> ProcessGroupGloo::barrier(const BarrierOptions& opts) {
std::vector<c10::weak_intrusive_ptr<AsyncWork>> priorWork;
// Snapshot all in progress and pending work as weak_ptr.
// When executing a barrier, we need to ensure that all prior work
// has completed before completing itself.
{
std::unique_lock<std::mutex> lock(workMutex_);
priorWork.insert(
priorWork.end(), workInProgress_.begin(), workInProgress_.end());
priorWork.insert(priorWork.end(), workQueue_.begin(), workQueue_.end());
}
auto tag = nextTag();
auto context = getContext(tag);
auto work = c10::make_intrusive<AsyncBarrierWork>(
std::move(context), std::move(priorWork), tag);
enqueue(work);
return work;
}
void ProcessGroupGloo::monitoredBarrier(
const BarrierOptions& opts,
bool waitAllRanks) {
C10_LOG_API_USAGE_ONCE("torch.distributed.monitored_barrier");
// Use default timeout if no timeout was specified.
auto monitoredBarrierTimeout =
(opts.timeout == kUnsetTimeout) ? this->options_->timeout : opts.timeout;
auto rank = this->getRank();
auto t1 = nextTag();
auto t2 = nextTag();
std::vector<at::Tensor> commTensor = {at::tensor({rank})};
// only enforce timeout on rank 0. This is so that other ranks aren't timed
// out first, bringing down the job without reporting which rank timed out.
if (rank != 0) {
auto sendWork = send(commTensor, 0, t1);
auto recvWork = recv(commTensor, 0, t2);
try {
sendWork->wait();
recvWork->wait();
} catch (const std::exception& e) {
const std::string error = c10::str(
"Rank ",
rank,
" successfully reached monitoredBarrier, but received errors while waiting",
" for send/recv from rank 0. Please check rank 0 logs for faulty rank.");
logAndThrow(
error, c10::str(error, "\n Original exception: \n", e.what()));
}
return;
}
auto startTime = std::chrono::steady_clock::now();
auto worldSize = this->getSize();
// Mappings of rank to recvWork/sendWork respectively.
std::map<int, c10::intrusive_ptr<Work>> recvWorkMap;
std::map<int, c10::intrusive_ptr<Work>> sendWorkMap;
// Kick off recvWork and wait to unblock sendWork->wait() from non-zero ranks.
// Failed/hanging ranks will not ack this call, letting rank 0 know about the
// failure.
for (const auto dstRank : c10::irange(1, worldSize)) {
recvWorkMap.insert({dstRank, recv(commTensor, dstRank, t1)});
}
auto waitLoop = [&](const std::map<int, c10::intrusive_ptr<Work>>& works) {
std::vector<int> processedRanks;
for (auto& work : works) {
bool rankResponded = false;
try {
// Note: if waitAllRanks=false, we recompute the time remaining in
// barrier and use this recomputed time in wait(). However, if
// waitAllRanks=true, we use the original timeout, since if we use
// up the entire timeout waiting for response from rank n, then we
// won't have any timeout left to query ranks beginning with n + 1.
auto remainingTime =
getRemainingTime(startTime, monitoredBarrierTimeout, waitAllRanks);
if (!waitAllRanks) {
checkRemainingTime(
monitoredBarrierTimeout, remainingTime, processedRanks, rank);
}
work.second->wait(remainingTime);
rankResponded = true;
} catch (const std::exception& e) {
const std::string error = c10::str(
"[Rank 0]: Rank ",
work.first,
" failed to pass monitoredBarrier in ",
monitoredBarrierTimeout.count(),
" ms");
if (waitAllRanks) {
LOG(ERROR) << error;
} else {
logAndThrow(
error, c10::str(error, "\n Original exception: \n", e.what()));
}
}
if (rankResponded) {
processedRanks.push_back(work.first);
}
}
// If we are collecting all failed ranks, check if we need to throw if
// some ranks have not responded.
// Ensure all ranks from 1, ... WORLD_SIZE -1 have been successfully
// processed.
auto rankFailure = (processedRanks.size() != size_ - 1);
if (waitAllRanks && rankFailure) {
std::vector<int> failedRanks;
for (const auto i : c10::irange(1, size_)) {
if (std::find(processedRanks.begin(), processedRanks.end(), i) ==
processedRanks.end()) {
failedRanks.push_back(i);
}
}
TORCH_INTERNAL_ASSERT(!failedRanks.empty());
const std::string ranksStr = c10::Join(", ", failedRanks);
const std::string error = c10::str(
"[Rank 0]: Ranks ",
ranksStr,
" failed to pass monitoredBarrier in ",
monitoredBarrierTimeout.count(),
" ms");
logAndThrow(error, error);
}
};
waitLoop(recvWorkMap);
// If we've reached here successfully, this means all ranks have acked in
// monitoredBarrier. Unblock all ranks now by responding to their recv(). This
// ensures that this is a true barrier in that all ranks exit it successfully
// or none of them do.
for (const auto dstRank : c10::irange(1, worldSize)) {
sendWorkMap.insert({dstRank, send(commTensor, dstRank, t2)});
}
waitLoop(sendWorkMap);
}
void ProcessGroupGloo::setSequenceNumberForGroup() {
if (rank_ == 0) {
// Create and broadcast sequence number
auto seq = 1 + rand();
sequenceNum_ = c10d::SequenceNum(seq);
std::vector<char> values = c10d::toVec<char>(seq, kBytes);
store_->set(kSeqNumStoreKey, values);
} else {
// Read rank 0's sequence number from store.
sequenceNum_ = c10d::SequenceNum();
store_->wait({kSeqNumStoreKey}, options_->timeout);
std::vector<char> values = store_->get(kSeqNumStoreKey);
uint64_t num = c10d::fromVec<char>(values);
sequenceNum_->set(num);
}
}
uint64_t ProcessGroupGloo::getSequenceNumberForGroup() {
if (sequenceNum_ == c10::nullopt) {
return 0;
}
return sequenceNum_->get();
}
} // namespace c10d
#endif // USE_C10D_GLOO