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ProcessGroupXCCL.cpp
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#ifdef USE_C10D_XCCL
#include <comm/XPUGuard.h>
#include <torch/csrc/distributed/c10d/ParamCommsUtils.hpp>
#include <xccl/ProcessGroupXCCL.hpp>
namespace c10d {
namespace {
const std::map<c10d::ReduceOp, ccl::reduction> xcclOps = {
{ReduceOp::MIN, ccl::reduction::min},
{ReduceOp::MAX, ccl::reduction::max},
{ReduceOp::SUM, ccl::reduction::sum},
{ReduceOp::PRODUCT, ccl::reduction::prod},
};
const std::map<at::ScalarType, ccl::datatype> xcclDatatypes = {
{at::kByte, ccl::datatype::uint8},
{at::kChar, ccl::datatype::int8},
{at::kInt, ccl::datatype::int32},
{at::kLong, ccl::datatype::int64},
{at::kHalf, ccl::datatype::float16},
{at::kFloat, ccl::datatype::float32},
{at::kDouble, ccl::datatype::float64},
{at::kBFloat16, ccl::datatype::bfloat16},
{at::kBool, ccl::datatype::uint8},
// use for non-reducetion op like allgather
{at::kFloat8_e5m2, ccl::datatype::uint8},
{at::kFloat8_e4m3fn, ccl::datatype::uint8},
{at::kFloat8_e4m3fnuz, ccl::datatype::uint8},
{at::kFloat8_e5m2fnuz, ccl::datatype::uint8},
};
bool computeLengthsAndCheckAndGetFlat(
const std::vector<at::Tensor>& tensors,
std::vector<size_t>& lengths,
at::Tensor& flatTensor,
int64_t& flatLength) {
int64_t groupSize = tensors.size();
auto firstTensor = tensors[0];
int64_t totalSize = 0;
bool isFlat = true;
auto storage = firstTensor.storage();
int64_t firstStorageOffset = firstTensor.storage_offset();
for (int i = 0; i < groupSize; i++) {
auto& curTensor = tensors[i];
int64_t length = curTensor.numel();
lengths[i] = length;
totalSize += length;
if (isFlat &&
(!storage.is_alias_of(curTensor.storage()) ||
curTensor.storage_offset() !=
firstStorageOffset + totalSize - length)) {
isFlat = false;
}
}
flatLength = totalSize;
if (isFlat) {
flatTensor = firstTensor;
} else {
flatTensor = at::empty({totalSize}, firstTensor.options());
}
return isFlat;
}
bool checkSameSize(const std::vector<at::Tensor>& input_tensors) {
for (const auto& input_tensor : input_tensors) {
if (!input_tensors[0].is_same_size(input_tensor)) {
return false;
}
}
return true;
}
void checkSingleTensor(
const at::Tensor& tensor,
const bool p2p = false // whether operation is a P2P operation
) {
if (!tensor.is_xpu() || tensor.is_sparse() || tensor.is_complex()) {
C10_THROW_ERROR(
ValueError, "Tensors must be XPU and dense and non-complex");
// Skip the following requirements for P2P operations
if (!tensor.is_contiguous(tensor.suggest_memory_format())) {
if (p2p) {
TORCH_WARN_ONCE(
"Detected non-contiguous tensor in P2P operations. It is user "
"responsibility to guarantee that source and destination tensors have "
"the same contiguity format.");
} else {
C10_THROW_ERROR(ValueError, "Tensors must be contiguous");
}
}
}
}
int64_t checkTensorOnSameDevice(const std::vector<at::Tensor>& tensors) {
TORCH_CHECK_WITH(
ValueError, tensors.size() != 0, "Tensor list must be nonempty");
const auto& first = tensors.front();
int64_t total_numel = 0;
for (const auto& t : tensors) {
if (!t.is_xpu() || t.is_sparse() || t.is_complex()) {
C10_THROW_ERROR(
ValueError, "Tensors must be XPU and dense and non-complex");
}
if (t.scalar_type() != first.scalar_type()) {
C10_THROW_ERROR(TypeError, "Tensors must have identical type");
}
TORCH_CHECK_WITH(
ValueError,
t.get_device() == tensors[0].get_device(),
"Expected list of tensors on the same device");
total_numel += t.numel();
}
return total_numel;
}
ccl::datatype getXcclDataType(
at::ScalarType type,
bool is_reduction_op = false) {
if (is_reduction_op)
TORCH_CHECK(
!isFloat8Type(type),
"Float8 dtypes are not currenlty supported for XCCL reductions");
auto it = xcclDatatypes.find(type);
TORCH_CHECK_WITH(
TypeError,
it != xcclDatatypes.end(),
"Input tensor data type is not supported for XCCL process group: ",
type);
return it->second;
}
ccl::reduction getXcclReduceOp(const ReduceOp& reduceOp, at::Tensor& input) {
try {
if (input.scalar_type() == at::kBool && reduceOp == ReduceOp::SUM) {
// Map sum to max for bool tensors to avoid overflow issues with sum.
return ccl::reduction::max;
}
// Use SUM emu AVG due to oneCCL not support AVG.
// oneCCL is expected to support avg in basekit 2025.2 release.
if (reduceOp == ReduceOp::AVG) {
return ccl::reduction::sum;
}
return xcclOps.at(reduceOp);
} catch (const std::out_of_range&) {
C10_THROW_ERROR(
ValueError,
"Cannot use ReduceOp." + reduceOpToString(reduceOp) + " with XCCL");
}
}
void syncStream(
at::Device& device,
at::xpu::XPUEvent& xcclEvent,
at::xpu::XPUStream& xcclStream) {
xcclEvent.record(at::xpu::getCurrentXPUStream(device.index()));
xcclEvent.block(xcclStream);
}
} // namespace
constexpr int64_t kSynchronizeBusyWaitMillis = 10;
thread_local uint64_t ProcessGroupXCCL::xcclActiveGroupCounter_ = 0;
ProcessGroupXCCL::WorkXCCL::WorkXCCL(
at::Device& device,
int rank,
OpType opType,
uint64_t seq,
const char* profilingTitle,
const std::optional<std::vector<at::Tensor>>& inputs)
: Work(rank, opType, profilingTitle, inputs),
device_(device),
workStartTime_(std::chrono::steady_clock::now()),
seq_(seq) {
xcclEndEvent_ = std::make_shared<at::xpu::XPUEvent>();
}
ProcessGroupXCCL::WorkXCCL::WorkXCCL(const WorkXCCL& w)
: Work(w.rank_, w.opType_),
device_(w.device_),
xcclEndEvent_(w.xcclEndEvent_),
blockingWait_(w.blockingWait_),
workStartTime_(w.workStartTime_),
seq_(w.seq_) {}
ProcessGroupXCCL::WorkXCCL::~WorkXCCL() = default;
bool ProcessGroupXCCL::WorkXCCL::isCompleted() {
if (xcclEndEvent_ && xcclEndEvent_->query()) {
return true;
}
return false;
}
void ProcessGroupXCCL::WorkXCCL::synchronize() {
synchronizeInternal(kNoTimeout);
}
void ProcessGroupXCCL::WorkXCCL::synchronizeInternal(
std::chrono::milliseconds timeout) {
auto currentStream = at::xpu::getCurrentXPUStream(device_.index());
xcclEndEvent_->block(currentStream);
if (blockingWait_) {
while (!isCompleted()) {
auto currentTimepoint = std::chrono::steady_clock::now();
auto timeElapsed = std::chrono::duration_cast<std::chrono::milliseconds>(
currentTimepoint - workStartTime_);
if (timeElapsed >= timeout) {
std::string exceptionMsg = c10::str(
"Work ran time out after ", timeElapsed.count(), " milliseconds.");
TORCH_CHECK(false, exceptionMsg)
}
std::this_thread::sleep_for(
std::chrono::milliseconds(kSynchronizeBusyWaitMillis));
}
}
if (barrierTensor_.defined()) {
auto currentStream = at::xpu::getCurrentXPUStream(device_.index());
currentStream.synchronize();
}
}
bool ProcessGroupXCCL::WorkXCCL::wait(std::chrono::milliseconds timeout) {
synchronizeInternal(timeout);
return true;
}
constexpr const char* MULTI_DEVICE_ERROR_MSG =
"Expecting one tensor only but got multiple";
ProcessGroupXCCL::ProcessGroupXCCL(
const c10::intrusive_ptr<Store>& store,
int rank,
int size)
: Backend(rank, size), store_(store), xcclCommCounter_(0) {
blockingWait_ = getCvarBool(TORCH_XCCL_BLOCKING_WAIT, false);
init();
}
ProcessGroupXCCL::~ProcessGroupXCCL() = default;
void ProcessGroupXCCL::setSequenceNumberForGroup() {}
uint64_t ProcessGroupXCCL::getSequenceNumberForGroup() {
return seqCollective_;
}
c10::intrusive_ptr<ProcessGroupXCCL::WorkXCCL> ProcessGroupXCCL::initWork(
at::Device& device,
int rank,
OpType opType,
const char* profilingTitle,
const std::vector<at::Tensor>& inputs,
const std::vector<at::Tensor>& outputs) {
auto r = c10::make_intrusive<ProcessGroupXCCL::WorkXCCL>(
device,
rank,
opType,
seqCollective_,
profilingTitle,
std::optional<std::vector<at::Tensor>>(inputs));
return r;
}
std::shared_ptr<xcclComm_t> ProcessGroupXCCL::getXCCLComm(
const std::string& deviceKey,
at::Device& device,
OpType opType,
int p2pRank,
bool isSendRecvSelf) {
if (deviceKey.empty()) {
C10_THROW_ERROR(
DistBackendError,
"Not able to create/get the XCCL Communicator since "
"the devices are empty ");
}
usedDeviceIdxs_.insert(device.index());
{
std::lock_guard<std::mutex> lock(mutex_);
if (devXCCLCommMap_.find(deviceKey) != devXCCLCommMap_.end()) {
return devXCCLCommMap_[deviceKey];
}
}
std::shared_ptr<xcclComm_t> XCCLComm;
bool batchP2P = xcclActiveGroupCounter_ > 0;
bool singleP2POp = isP2POp(opType, batchP2P);
at::xpu::OptionalXPUGuard gpuGuard(device);
for (const auto i : c10::irange(xcclActiveGroupCounter_)) {
(void)i;
ccl::group_end();
}
int numRanks, rank;
if (!singleP2POp) {
numRanks = getSize();
rank = getRank();
} else if (isSendRecvSelf) {
numRanks = 1;
rank = 0;
} else {
numRanks = 2;
rank = p2pRank;
}
c10::impl::VirtualGuardImpl impl(device.type());
c10::Stream stream =
impl.getStreamFromGlobalPool(device, /*isHighPriority=*/false);
sycl::queue& q = c10::xpu::XPUStream(stream).queue();
auto ctx = ccl::create_context(q.get_context());
ccl::vector_class<ccl::pair_class<int, ccl::device>> devs_rank;
devs_rank.emplace_back(rank, ccl::create_device(q.get_device()));
auto xccl_kvs = get_kvs(rank_, *store_, singleP2POp, deviceKey, p2pRank);
auto comms = ccl::create_communicators(numRanks, devs_rank, ctx, xccl_kvs);
XCCLComm = std::make_shared<xcclComm_t>(std::move(comms[0]));
RECORD_PARAM_COMMS(
0, // seq
std::make_tuple(pg_uid_, pg_desc_), // PG name tuple
rank, // rank
"init", // collective name
0, // inNelems
0, // outNelems
at::kByte, // dType
std::vector<int64_t>(), // inSplitSizes
std::vector<int64_t>(), // outSplitSizes
-1, // globalRankStart
-1, // globalRankStride
size_); // worldSize
for (const auto i : c10::irange(xcclActiveGroupCounter_)) {
(void)i;
ccl::group_start();
}
// The oneCCL group API requires retaining the SYCL queue (xcclstream) object
// within the lifecycle of the communicator. If the XPU stream is created
// within the collective operation, it would be destroyed earlier than the
// communicator after the operation ends. Therefore, the XPU stream is stored
// in a map alongside the communicator. Similarly, oneCCLv2 also requires
// retaining the SYCL queue pointer for collective operations, so this change
// will be necessary in oneCCLv2 as well.
ccl::stream xccl_stream = ccl::create_stream(q);
std::lock_guard<std::mutex> lock(mutex_);
devXCCLCommMap_.emplace(deviceKey, XCCLComm);
xcclStreamsMap_.emplace(
deviceKey,
std::make_pair(at::xpu::XPUStream(stream), std::move(xccl_stream)));
xcclEventsMap_.emplace(deviceKey, at::xpu::XPUEvent());
return XCCLComm;
}
void ProcessGroupXCCL::groupStart() {
ccl::group_start();
++xcclActiveGroupCounter_;
}
void ProcessGroupXCCL::groupEnd() {
ccl::group_end();
--xcclActiveGroupCounter_;
}
static constexpr int CoalActive = 0x01, CoalColl = 0x02, CoalP2P = 0x04;
void ProcessGroupXCCL::startCoalescing() {
if (coalescing_state_ & CoalP2P) {
seqP2P_++;
} else {
seqCollective_++;
}
coalescedDevice_.set_index(-1);
coalescedComm_ = nullptr;
coalescing_state_ |= CoalActive;
groupStart();
}
c10::intrusive_ptr<Work> ProcessGroupXCCL::endCoalescing(OpType optype) {
if (coalescedComm_ == nullptr) {
// There is no actual work being coalesced, return here
groupEnd();
coalescing_state_ = 0;
return nullptr;
}
TORCH_CHECK(
coalescedDevice_.index() >= 0,
"Somthing went wrong. Did you call end_coalescing before start_coalescing?");
auto comm = coalescedComm_;
auto device = coalescedDevice_;
const auto key = std::to_string(device.index());
auto stream = xcclStreamsMap_.at(key).first;
auto work = initWork(device, rank_, optype);
work->blockingWait_ = blockingWait_;
groupEnd();
work->xcclEndEvent_->record(stream);
coalescing_state_ = 0;
coalescedComm_ = nullptr;
return work;
}
c10::intrusive_ptr<Work> ProcessGroupXCCL::endCoalescing() {
// Default OpType to COALESCED if not specified
return endCoalescing(OpType::COALESCED);
}
template <typename Fn, typename PreProcess, typename PostProcess>
c10::intrusive_ptr<Work> ProcessGroupXCCL::collective(
std::vector<at::Tensor>& inputs,
std::vector<at::Tensor>& outputs,
Fn fn,
PreProcess pre,
PostProcess post,
OpType opType,
const char* profilingTitle) {
seqCollective_++;
auto device = inputs[0].device();
const auto key = std::to_string(device.index());
auto comm = getXCCLComm(key, device, opType);
if (coalescing_state_ & CoalActive) {
coalescing_state_ |= CoalColl;
if (coalescedDevice_.index() < 0) {
coalescedDevice_ = device;
} else {
TORCH_CHECK(
coalescedDevice_.index() == device.index(), MULTI_DEVICE_ERROR_MSG);
}
if (coalescedComm_ == nullptr) {
coalescedComm_ = comm;
} else {
TORCH_CHECK(coalescedComm_ == comm, MULTI_DEVICE_ERROR_MSG);
}
}
auto stream = xcclStreamsMap_.at(key).first;
auto cclstream = xcclStreamsMap_.at(key).second;
syncStream(device, xcclEventsMap_[key], stream);
c10::intrusive_ptr<ProcessGroupXCCL::WorkXCCL> work;
work = initWork(device, rank_, opType);
work->outputs_ = std::make_shared<std::vector<at::Tensor>>(outputs);
at::xpu::OptionalXPUGuard gpuGuard(device);
pre(stream, work);
for (const auto i : c10::irange(inputs.size())) {
c10::xpu::XPUCachingAllocator::recordStream(
inputs[i].storage().data_ptr(), stream);
fn(inputs[i], outputs[i], *comm, stream, cclstream);
}
post(stream, work);
if (!coalescing_state_) {
work->xcclEndEvent_->record(stream);
}
std::vector<c10::Stream> streams = {stream.unwrap()};
c10::MultiStreamGuard streamGuard(streams);
std::vector<at::Device> devices{device};
work->future_ = c10::make_intrusive<at::ivalue::Future>(
c10::ListType::create(c10::TensorType::get()), devices);
work->future_->markCompleted(at::IValue(*work->outputs_));
work->blockingWait_ = blockingWait_;
return work;
}
template <typename Fn>
c10::intrusive_ptr<Work> ProcessGroupXCCL::pointToPoint(
at::Tensor& tensor,
Fn fn,
int peer,
OpType opType,
const char* profilingTitle) {
auto device = tensor.device();
std::string key;
int p2pRank = 0, p2pTargetRank = 0;
bool isSendRecvSelf = false;
bool batchP2P = xcclActiveGroupCounter_ > 0;
if (batchP2P) {
key = std::to_string(device.index());
p2pRank = rank_;
p2pTargetRank = peer;
} else {
int lowRank = rank_ < peer ? rank_ : peer;
int highRank = rank_ < peer ? peer : rank_;
key = std::to_string(lowRank) + ":" + std::to_string(highRank);
p2pRank = rank_ <= peer ? 0 : 1;
isSendRecvSelf = rank_ == peer;
p2pTargetRank = isSendRecvSelf ? 0 : 1 - p2pRank;
if (!coalescing_state_) {
seqP2P_++;
}
}
auto comm = getXCCLComm(key, device, opType, p2pRank, isSendRecvSelf);
if (coalescing_state_ & CoalActive) {
coalescing_state_ |= CoalP2P;
if (coalescedDevice_.index() < 0) {
coalescedDevice_ = device;
} else {
TORCH_CHECK(
coalescedDevice_.index() == device.index(), MULTI_DEVICE_ERROR_MSG);
}
if (coalescedComm_ == nullptr) {
coalescedComm_ = comm;
} else {
TORCH_CHECK(coalescedComm_ == comm, MULTI_DEVICE_ERROR_MSG);
}
}
auto stream = xcclStreamsMap_.at(key).first;
auto cclstream = xcclStreamsMap_.at(key).second;
syncStream(device, xcclEventsMap_[key], stream);
if (!coalescing_state_) {
c10::intrusive_ptr<ProcessGroupXCCL::WorkXCCL> work;
work = initWork(device, rank_, opType);
work->outputs_ = std::make_shared<std::vector<at::Tensor>>();
work->outputs_->push_back(tensor);
at::xpu::OptionalXPUGuard gpuGuard(device);
c10::xpu::XPUCachingAllocator::recordStream(
tensor.storage().data_ptr(), stream);
fn(tensor, *comm, stream, cclstream, p2pTargetRank);
work->xcclEndEvent_->record(stream);
work->blockingWait_ = blockingWait_;
std::vector<c10::Stream> streams = {stream.unwrap()};
c10::MultiStreamGuard streamGuard(streams);
std::vector<at::Device> devices{device};
work->future_ = c10::make_intrusive<at::ivalue::Future>(
c10::ListType::create(c10::TensorType::get()), devices);
work->future_->markCompleted(at::IValue(*work->outputs_));
return work;
} else {
at::xpu::OptionalXPUGuard gpuGuard(device);
c10::xpu::XPUCachingAllocator::recordStream(
tensor.storage().data_ptr(), stream);
fn(tensor, *comm, stream, cclstream, p2pTargetRank);
return nullptr;
}
}
c10::intrusive_ptr<Work> ProcessGroupXCCL::send(
std::vector<at::Tensor>& tensors,
int dstRank,
int /* unused */) {
TORCH_CHECK(tensors.size() == 1, MULTI_DEVICE_ERROR_MSG);
// @lint-ignore CLANGTIDY
auto tensor = tensors.back();
checkSingleTensor(tensor, true);
RECORD_PARAM_COMMS_DATA(
static_cast<int>(
this->getSequenceNumberForGroup() + 1), // seq + 1 to match collective
std::make_tuple(pg_uid_, pg_desc_), // PG name tuple
tensors, // inputTensors
tensors, // outputTensors
dstRank, // dst rank
"send", // collective name
tensor.numel(), // inNelems
tensor.numel(), // outNelems
tensor.scalar_type(), // dType
std::vector<int64_t>(), // inSplitSizes
std::vector<int64_t>(), // outSplitSizes
-1, // globalRankStart
-1, // globalRankStride
this->getSize()); // worldSize
auto ret = pointToPoint(
tensor,
[&](at::Tensor& input,
xcclComm_t& comm,
at::xpu::XPUStream& stream,
ccl::stream& xcclStream,
int dst) {
auto xcclDataType = getXcclDataType(input.scalar_type());
ccl::send(
input.data_ptr(),
(size_t)input.numel(),
xcclDataType,
dst,
comm,
xcclStream);
return;
},
dstRank,
OpType::SEND,
c10::str("xccl:send ", rank_, "->", dstRank).c_str());
return ret;
}
c10::intrusive_ptr<Work> ProcessGroupXCCL::recv(
std::vector<at::Tensor>& tensors,
int srcRank,
int /* unused */) {
TORCH_CHECK(tensors.size() == 1, MULTI_DEVICE_ERROR_MSG);
// @lint-ignore CLANGTIDY
auto tensor = tensors.back();
checkSingleTensor(tensor, true);
RECORD_PARAM_COMMS_DATA(
static_cast<int>(
this->getSequenceNumberForGroup() + 1), // seq + 1 to match collective
std::make_tuple(pg_uid_, pg_desc_), // PG name tuple
tensors, // inputTensors
tensors, // outputTensors
srcRank, // src rank
"recv", // collective name
tensor.numel(), // inNelems
tensor.numel(), // outNelems
tensor.scalar_type(), // dType
std::vector<int64_t>(), // inSplitSizes
std::vector<int64_t>(), // outSplitSizes
-1, // globalRankStart
-1, // globalRankStride
this->getSize()); // worldSize
auto ret = pointToPoint(
tensor,
[&](at::Tensor& output,
xcclComm_t& comm,
at::xpu::XPUStream& stream,
ccl::stream& xcclStream,
int src) {
auto xcclDataType = getXcclDataType(output.scalar_type());
ccl::recv(
output.data_ptr(),
(size_t)output.numel(),
xcclDataType,
src,
comm,
xcclStream);
return;
},
srcRank,
OpType::RECV,
c10::str("xccl:recv ", rank_, "<-", srcRank).c_str());
return ret;
}
c10::intrusive_ptr<Work> ProcessGroupXCCL::gather(
std::vector<std::vector<at::Tensor>>& outputTensors,
std::vector<at::Tensor>& inputTensors,
const GatherOptions& opts) {
static auto invalidArgument = [](const std::string& msg) {
C10_THROW_ERROR(ValueError, "ProcessGroupXCCL::gather: " + msg);
};
assertRootRank(invalidArgument, opts.rootRank, size_);
TORCH_CHECK(inputTensors.size() == 1, MULTI_DEVICE_ERROR_MSG);
// @lint-ignore CLANGTIDY
auto inputTensor = inputTensors.back();
std::vector<at::Tensor> outputs;
if (getRank() == opts.rootRank) {
if (outputTensors.size() != 1) {
std::stringstream ss;
ss << "requires a single-element output list containing a list with "
<< getSize() << " tensors.";
invalidArgument(ss.str());
} else if (outputTensors[0].size() != static_cast<size_t>(getSize())) {
std::stringstream ss;
ss << "Incorrect output list size " << outputTensors[0].size()
<< ". Output list size should be " << getSize()
<< ", same as size of the process group.";
invalidArgument(ss.str());
}
const auto& options = inputTensor.options();
const auto& sizes = inputTensor.sizes();
assertTypeAndSizesMatch(invalidArgument, outputTensors[0], options, sizes);
outputs = outputTensors[0];
} else {
// if not in the root rank, initialize outputs as empty list
if (outputTensors.size() != 0) {
invalidArgument("requires empty output on non-root");
}
outputs = {};
// append a empty tensor to the list, we don't use it but the
// `collective` template function requires it to invoke its function
outputs.emplace_back();
}
RECORD_PARAM_COMMS_DATA(
static_cast<int>(
this->getSequenceNumberForGroup() + 1), // seq + 1 to match collective
std::make_tuple(pg_uid_, pg_desc_), // PG name tuple
inputTensors, // inputTensors
outputTensors, // outputTensors
opts.rootRank, // root rank
"gather", // collective name
inputTensor.numel(), // inNelems
inputTensor.numel() * this->getSize(), // outNelems
inputTensor.scalar_type(), // dType
std::vector<int64_t>(), // inSplitSizes
std::vector<int64_t>(), // outSplitSize
-1, // globalRankStart
-1, // globalRankStride
this->getSize()); // worldSize
auto inputs = std::vector<at::Tensor>{inputTensor};
return collective(
inputs,
outputs, // just to fit the collective interface
[&](at::Tensor& /* unused */,
at::Tensor& /* unused */,
xcclComm_t& comm,
at::xpu::XPUStream& stream,
ccl::stream& xcclStream) {
const auto root = opts.rootRank;
if (getRank() == root) {
for (auto output : outputs) {
c10::xpu::XPUCachingAllocator::recordStream(
output.storage().data_ptr(), stream);
}
}
{
auto xcclDataType = getXcclDataType(inputTensor.scalar_type());
if (rank_ == root) {
for (const auto r : c10::irange(size_)) {
if (r != root) {
// do receive
ccl::recv(
outputs[r].data_ptr(),
(size_t)inputTensor.numel(),
xcclDataType,
r,
comm,
xcclStream);
} else {
// on its own rank, simply copy from the input
outputs[r].copy_(inputTensor);
}
}
} else {
// do send
ccl::send(
inputTensor.data_ptr(),
(size_t)inputTensor.numel(),
xcclDataType,
root,
comm,
xcclStream);
}
return;
}
},
OpType::GATHER);
}
c10::intrusive_ptr<Work> ProcessGroupXCCL::scatter(
std::vector<at::Tensor>& outputTensors,
std::vector<std::vector<at::Tensor>>& inputTensors,
const ScatterOptions& opts) {
static auto invalidArgument = [](const std::string& msg) {
C10_THROW_ERROR(ValueError, "ProcessGroupXCCL::scatter: " + msg);
};
assertRootRank(invalidArgument, opts.rootRank, size_);
TORCH_CHECK(outputTensors.size() == 1, MULTI_DEVICE_ERROR_MSG);
auto outputTensor = outputTensors.back();
std::vector<at::Tensor> inputs;
if (getRank() == opts.rootRank) {
if (inputTensors.size() != 1) {
std::stringstream ss;
ss << "requires a single-element input list containing a list with "
<< getSize() << " tensors.";
invalidArgument(ss.str());
} else if (inputTensors[0].size() != static_cast<size_t>(getSize())) {
std::stringstream ss;
ss << "Incorrect input list size " << inputTensors[0].size()
<< ". Input list size should be " << getSize()
<< ", same as size of the process group.";
invalidArgument(ss.str());
}
const auto& options = outputTensor.options();
const auto& sizes = outputTensor.sizes();
assertTypeAndSizesMatch(invalidArgument, inputTensors[0], options, sizes);
inputs = inputTensors[0];
} else {
// if not in the root rank, initialize inputTensors as empty place holder
// with an empty list
if (inputTensors.size() != 0) {
invalidArgument("requires empty input on non-root");
}
inputs = {};
// append a empty tensor to the list, we don't use it but the
// `collective` template function requires it to invoke its function
inputs.emplace_back();
}
RECORD_PARAM_COMMS_DATA(
static_cast<int>(
this->getSequenceNumberForGroup() + 1), // seq + 1 to match collective
std::make_tuple(pg_uid_, pg_desc_), // PG name tuple
inputTensors, // inputTensors
outputTensors, // outputTensors
opts.rootRank, // root rank
"scatter", // collective name
outputTensor.numel() * this->getSize(), // inNelems
outputTensor.numel(), // outNelems
outputTensor.scalar_type(), // dType
std::vector<int64_t>(), // inSplitSizes
std::vector<int64_t>(), // outSplitSize
-1, // globalRankStart
-1, // globalRankStride
this->getSize()); // worldSize
const auto root = opts.rootRank;
auto outputs = std::vector<at::Tensor>{outputTensor};
return collective(
outputs,
inputs, // just to fit the collective interface
[&](at::Tensor& /* unused */,
at::Tensor& /* unused */,
xcclComm_t& comm,
at::xpu::XPUStream& stream,
ccl::stream& xcclStream) {
if (getRank() == root) {
for (auto input : inputs) {
c10::xpu::XPUCachingAllocator::recordStream(
input.storage().data_ptr(), stream);
}
}
{
if (rank_ == root) {
for (const auto r : c10::irange(size_)) {
if (r != root) {
// do send
size_t send_count = inputs[r].numel();
auto send_type = getXcclDataType(inputs[r].scalar_type());
ccl::send(
inputs[r].data_ptr(),
send_count,
send_type,
r,
comm,
xcclStream);
} else {
// on its own rank, simply copy from the input
outputTensor.copy_(inputs[r]);
}
}
} else {
// do receive
size_t recv_count = outputTensor.numel();
auto recv_type = getXcclDataType(outputTensor.scalar_type());
ccl::recv(
outputTensor.data_ptr(),
recv_count,
recv_type,
root,
comm,
xcclStream);
}
return;
}
},
OpType::SCATTER);
}
c10::intrusive_ptr<Work> ProcessGroupXCCL::allreduce_impl(
at::Tensor& tensor,
const AllreduceOptions& opts) {
return collective(
tensor,
tensor,
[&](at::Tensor& input,
at::Tensor& output,
xcclComm_t& comm,
at::xpu::XPUStream& stream,
ccl::stream& xcclStream) {
auto xcclDataType = getXcclDataType(input.scalar_type(), true);
auto xcclReduceOp = getXcclReduceOp(opts.reduceOp, input);
ccl::allreduce(
input.data_ptr(),
output.data_ptr(),
(size_t)input.numel(),
xcclDataType,
xcclReduceOp,
comm,
xcclStream);
// Use SUM emu AVG due to oneCCL not support AVG
// oneCCL is expected to support avg in basekit 2025.2 release.
if (opts.reduceOp == ReduceOp::AVG) {
auto divisor = getSize();
output.div_(divisor);
}
return;
},
OpType::ALLREDUCE,
"xccl:all_reduce");
}
c10::intrusive_ptr<Work> ProcessGroupXCCL::allreduce(
std::vector<at::Tensor>& tensors,
const AllreduceOptions& opts) {
TORCH_CHECK(tensors.size() == 1, MULTI_DEVICE_ERROR_MSG);
auto tensor = tensors.back();
checkSingleTensor(tensor);
// @lint-ignore CLANGTIDY
RECORD_PARAM_COMMS_DATA(
static_cast<int>(
this->getSequenceNumberForGroup() + 1), // seq + 1 to match collective
std::make_tuple(pg_uid_, pg_desc_), // PG name tuple
tensors, // inputTensors
tensors, // outputTensors
rank_, // rank
"allreduce", // collective name
tensor.numel(), // inNelems
tensor.numel(), // outNelems
tensor.scalar_type(), // dType
std::vector<int64_t>(), // inSplitSizes
std::vector<int64_t>(), // outSplitSizes
-1, // globalRankStart
-1, // globalRankStride
size_); // worldSize
return collective(
tensor,
tensor,
[&](at::Tensor& input,
at::Tensor& output,
xcclComm_t& comm,
at::xpu::XPUStream& stream,
ccl::stream& xcclStream) {
auto xcclDataType = getXcclDataType(input.scalar_type(), true);
auto xcclReduceOp = getXcclReduceOp(opts.reduceOp, input);
ccl::allreduce(
input.data_ptr(),
output.data_ptr(),
(size_t)input.numel(),
xcclDataType,
xcclReduceOp,
comm,
xcclStream);
// Use SUM emu AVG due to oneCCL not support AVG
// oneCCL is expected to support avg in basekit 2025.2 release.
if (opts.reduceOp == ReduceOp::AVG) {
auto divisor = getSize();
c10::StreamGuard guard(stream);
c10::xpu::XPUCachingAllocator::recordStream(
output.storage().data_ptr(), stream);
output.div_(divisor);
}
return;
},
OpType::ALLREDUCE,
"xccl:all_reduce");
}
c10::intrusive_ptr<Work> ProcessGroupXCCL::allreduce_coalesced(
std::vector<at::Tensor>& tensors,
const AllreduceCoalescedOptions& opts) {
auto total_numel = checkTensorOnSameDevice(tensors);