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#include <ATen/core/dispatch/Dispatcher.h>
#include <c10/util/intrusive_ptr.h>
#include <torch/csrc/distributed/c10d/ProcessGroup.hpp>
#include <torch/csrc/distributed/c10d/Types.hpp>
#include <torch/library.h>
namespace c10d {
namespace {
TORCH_LIBRARY(c10d, m) {
// The following ProcessGroup, Work, and ReduceOp definitions are more like
// declarations. They don't expose the details of the two classes into
// TorchScript.
m.class_<ProcessGroup>("ProcessGroup").def(torch::init<int64_t, int64_t>());
m.class_<Work>("Work")
.def(torch::init<>())
.def("wait", [](const c10::intrusive_ptr<Work>& self) { self->wait(); });
m.class_<ReduceOp>("ReduceOp").def(torch::init<>());
m.def(
"broadcast_(Tensor[] tensors, __torch__.torch.classes.c10d.ProcessGroup process_group, int root_rank, int root_tensor, bool asyncOp, int timeout) -> (Tensor[], __torch__.torch.classes.c10d.Work)");
m.def(
"allreduce_(Tensor[] tensors, __torch__.torch.classes.c10d.ProcessGroup process_group, __torch__.torch.classes.c10d.ReduceOp reduce_op, Tensor? sparse_indices, int timeout) -> (Tensor[], __torch__.torch.classes.c10d.Work)");
m.def(
"allreduce_coalesced_(Tensor[] tensors, __torch__.torch.classes.c10d.ProcessGroup process_group, __torch__.torch.classes.c10d.ReduceOp reduce_op, int timeout) -> __torch__.torch.classes.c10d.Work");
m.def(
"allgather_(Tensor[][] output_tensors, Tensor[] input_tensors, __torch__.torch.classes.c10d.ProcessGroup process_group, int timeout) -> (Tensor[][], __torch__.torch.classes.c10d.Work)");
m.def(
"_allgather_base_(Tensor output_tensor, Tensor input_tensor, __torch__.torch.classes.c10d.ProcessGroup process_group, bool asyncOp, int timeout) -> (Tensor, __torch__.torch.classes.c10d.Work)");
m.def(
"allgather_coalesced_(Tensor[][] output_lists, Tensor[] input_list, __torch__.torch.classes.c10d.ProcessGroup process_group) -> __torch__.torch.classes.c10d.Work");
m.def(
"allgather_into_tensor_coalesced_(Tensor[] outputs, Tensor[] inputs, __torch__.torch.classes.c10d.ProcessGroup process_group) -> __torch__.torch.classes.c10d.Work");
m.def(
"reduce_scatter_(Tensor[] output_tensors, Tensor[][] input_tensors, __torch__.torch.classes.c10d.ProcessGroup process_group, __torch__.torch.classes.c10d.ReduceOp reduce_op, int timeout) -> (Tensor[], __torch__.torch.classes.c10d.Work)");
m.def(
"_reduce_scatter_base_(Tensor output_tensor, Tensor input_tensor, __torch__.torch.classes.c10d.ProcessGroup process_group, __torch__.torch.classes.c10d.ReduceOp reduce_op, bool asyncOp, int timeout) -> (Tensor, __torch__.torch.classes.c10d.Work)");
m.def(
"reduce_scatter_tensor_coalesced_(Tensor[] outputs, Tensor[] inputs, __torch__.torch.classes.c10d.ProcessGroup process_group, __torch__.torch.classes.c10d.ReduceOp reduce_op, int timeout) -> __torch__.torch.classes.c10d.Work");
m.def(
"reduce_(Tensor[] tensors, __torch__.torch.classes.c10d.ProcessGroup process_group, __torch__.torch.classes.c10d.ReduceOp reduce_op, int root_rank, int root_tensor, int timeout) -> __torch__.torch.classes.c10d.Work");
m.def(
"gather_(Tensor[][] output_tensors, Tensor[] input_tensors, __torch__.torch.classes.c10d.ProcessGroup process_group, int root_rank, int timeout) -> __torch__.torch.classes.c10d.Work");
m.def(
"scatter_(Tensor[] output_tensors, Tensor[][] input_tensors, __torch__.torch.classes.c10d.ProcessGroup process_group, int root_rank, bool asyncOp, int timeout) -> (Tensor[], __torch__.torch.classes.c10d.Work)");
m.def(
"alltoall_(Tensor[] output_tensors, Tensor[] input_tensors, __torch__.torch.classes.c10d.ProcessGroup process_group, int timeout) -> (Tensor[], __torch__.torch.classes.c10d.Work)");
m.def(
"alltoall_base_(Tensor output, Tensor input, __torch__.torch.classes.c10d.ProcessGroup process_group, int[] output_split_sizes, int[] input_split_sizes, int timeout) -> __torch__.torch.classes.c10d.Work");
m.def(
"barrier(Tensor tensor, __torch__.torch.classes.c10d.ProcessGroup process_group, int[] device_ids, int timeout) -> __torch__.torch.classes.c10d.Work");
m.def(
"monitored_barrier_(Tensor tensor, __torch__.torch.classes.c10d.ProcessGroup process_group, int[] device_ids, int timeout, bool wait_all_ranks) -> ()");
m.def(
"send(Tensor[] tensors, __torch__.torch.classes.c10d.ProcessGroup process_group, int dst, int tag) -> __torch__.torch.classes.c10d.Work");
m.def(
"recv_(Tensor[] tensors, __torch__.torch.classes.c10d.ProcessGroup process_group, int src, int tag) -> __torch__.torch.classes.c10d.Work");
m.def(
"recv_any_source_(Tensor[] tensors, __torch__.torch.classes.c10d.ProcessGroup process_group, int tag) -> __torch__.torch.classes.c10d.Work");
}
} // namespace
namespace ops {
// Below are ProcessGroup's corresponding ops for each backend. Ops are but
// routed through the dispatcher to be dispatched to the appropriate backend.
// Currently a no-op as the process group does not have a list of backends.
namespace {
#define IMPL_SEND(DEV) \
c10::intrusive_ptr<Work> send##DEV( \
at::TensorList tensors, \
const c10::intrusive_ptr<ProcessGroup>& process_group, \
int64_t dstRank, \
int64_t tag) { \
auto tensor_vec = tensors.vec(); \
return process_group->getBackend(c10::DeviceType::DEV) \
->send(tensor_vec, static_cast<int>(dstRank), static_cast<int>(tag)); \
}
IMPL_SEND(CPU)
IMPL_SEND(CUDA)
IMPL_SEND(PrivateUse1)
#define IMPL_RECV(DEV) \
c10::intrusive_ptr<Work> recv_##DEV( \
at::TensorList tensors, \
const c10::intrusive_ptr<ProcessGroup>& process_group, \
int64_t srcRank, \
int64_t tag) { \
auto tensor_vec = tensors.vec(); \
return process_group->getBackend(c10::DeviceType::DEV) \
->recv(tensor_vec, static_cast<int>(srcRank), static_cast<int>(tag)); \
}
IMPL_RECV(CPU)
IMPL_RECV(CUDA)
IMPL_RECV(PrivateUse1)
#define IMPL_RECV_ANY_SOURCE(DEV) \
c10::intrusive_ptr<Work> recv_any_source_##DEV( \
at::TensorList tensors, \
const c10::intrusive_ptr<ProcessGroup>& process_group, \
int64_t tag) { \
auto tensor_vec = tensors.vec(); \
return process_group->getBackend(c10::DeviceType::DEV) \
->recvAnysource(tensor_vec, static_cast<int>(tag)); \
}
IMPL_RECV_ANY_SOURCE(CPU)
IMPL_RECV_ANY_SOURCE(CUDA)
IMPL_RECV_ANY_SOURCE(PrivateUse1)
#define IMPL_REDUCE(DEV) \
c10::intrusive_ptr<Work> reduce_##DEV( \
at::TensorList tensors, \
const c10::intrusive_ptr<ProcessGroup>& process_group, \
const c10::intrusive_ptr<ReduceOp>& reduce_op, \
int64_t root_rank, \
int64_t root_tensor, \
int64_t timeout) { \
auto tensor_vec = tensors.vec(); \
return process_group->getBackend(c10::DeviceType::DEV) \
->reduce( \
tensor_vec, \
ReduceOptions{ \
*reduce_op.get(), \
root_rank, \
root_tensor, \
std::chrono::milliseconds(timeout)}); \
}
IMPL_REDUCE(CPU)
IMPL_REDUCE(CUDA)
IMPL_REDUCE(PrivateUse1)
#define IMPL_BROADCAST(DEV) \
std::tuple<std::vector<at::Tensor>, c10::intrusive_ptr<Work>> \
broadcast_##DEV( \
at::TensorList tensors, \
const c10::intrusive_ptr<ProcessGroup>& process_group, \
int64_t root_rank, \
int64_t root_tensor, \
bool asyncOp, \
int64_t timeout) { \
auto tensor_vec = tensors.vec(); \
auto work = process_group->getBackend(c10::DeviceType::DEV) -> broadcast( \
tensor_vec, \
BroadcastOptions{ \
root_rank, \
root_tensor, \
std::chrono::milliseconds(timeout), \
asyncOp}); \
return std::tuple<std::vector<at::Tensor>, c10::intrusive_ptr<Work>>( \
std::move(tensor_vec), work); \
}
IMPL_BROADCAST(CPU)
IMPL_BROADCAST(CUDA)
IMPL_BROADCAST(PrivateUse1)
// Return input tensors as output tensors to make inplace allreduce look like
// a functional API, so that make_fx can correctly build the dependencies in
// the graph later.
#define IMPL_ALLREDUCE(DEV) \
std::tuple<std::vector<at::Tensor>, c10::intrusive_ptr<Work>> \
allreduce_##DEV( \
at::TensorList tensors, \
const c10::intrusive_ptr<ProcessGroup>& process_group, \
const c10::intrusive_ptr<ReduceOp>& reduce_op, \
const std::optional<at::Tensor>& sparse_indices, \
int64_t timeout) { \
auto tensor_vec = tensors.vec(); \
auto work = process_group->getBackend(c10::DeviceType::DEV) -> allreduce( \
tensor_vec, \
AllreduceOptions{ \
*reduce_op.get(), std::chrono::milliseconds(timeout)}); \
return std::tuple<std::vector<at::Tensor>, c10::intrusive_ptr<Work>>( \
std::move(tensor_vec), work); \
}
IMPL_ALLREDUCE(CPU)
IMPL_ALLREDUCE(CUDA)
IMPL_ALLREDUCE(PrivateUse1)
#define IMPL_ALLREDUCE_COALESCED(DEV) \
c10::intrusive_ptr<Work> allreduce_coalesced_##DEV( \
at::TensorList tensors, \
const c10::intrusive_ptr<ProcessGroup>& process_group, \
const c10::intrusive_ptr<ReduceOp>& reduce_op, \
int64_t timeout) { \
auto tensor_vec = tensors.vec(); \
AllreduceCoalescedOptions opts = AllreduceCoalescedOptions{}; \
opts.reduceOp = *reduce_op.get(); \
opts.timeout = std::chrono::milliseconds(timeout); \
return process_group->getBackend(c10::DeviceType::DEV) \
->allreduce_coalesced(tensor_vec, opts); \
}
IMPL_ALLREDUCE_COALESCED(CPU)
IMPL_ALLREDUCE_COALESCED(CUDA)
IMPL_ALLREDUCE_COALESCED(PrivateUse1)
// Copy output tensors (not storage) so that this can be used in a functional
// manner
#define IMPL_ALLGATHER(DEV) \
std::tuple<std::vector<std::vector<at::Tensor>>, c10::intrusive_ptr<Work>> \
allgather_##DEV( \
const std::vector<std::vector<at::Tensor>>& output_tensors, \
at::TensorList input_tensors, \
const c10::intrusive_ptr<ProcessGroup>& process_group, \
int64_t timeout) { \
auto input_tensors_vec = input_tensors.vec(); \
auto work = process_group->getBackend(c10::DeviceType::DEV) -> allgather( \
const_cast<std::vector<std::vector<at::Tensor>>&>(output_tensors), \
input_tensors_vec, \
AllgatherOptions{std::chrono::milliseconds(timeout)}); \
return std:: \
tuple<std::vector<std::vector<at::Tensor>>, c10::intrusive_ptr<Work>>( \
output_tensors, work); \
}
// NOLINTBEGIN(cppcoreguidelines-pro-type-const-cast)
IMPL_ALLGATHER(CPU)
IMPL_ALLGATHER(CUDA)
IMPL_ALLGATHER(PrivateUse1)
#define IMPL__ALLGATHER_BASE(DEV) \
std::tuple<at::Tensor, c10::intrusive_ptr<Work>> _allgather_base_##DEV( \
at::Tensor& output_tensor, \
at::Tensor& input_tensor, \
const c10::intrusive_ptr<ProcessGroup>& process_group, \
bool asyncOp, \
int64_t timeout) { \
auto work = \
process_group->getBackend(c10::DeviceType::DEV) -> _allgather_base( \
output_tensor, \
input_tensor, \
AllgatherOptions{std::chrono::milliseconds(timeout), asyncOp}); \
return std::tuple<at::Tensor, c10::intrusive_ptr<Work>>( \
output_tensor, work); \
}
IMPL__ALLGATHER_BASE(CPU)
IMPL__ALLGATHER_BASE(CUDA)
IMPL__ALLGATHER_BASE(PrivateUse1)
#define IMPL_ALLGATHER_COALESCED(DEV) \
c10::intrusive_ptr<Work> allgather_coalesced_##DEV( \
const std::vector<std::vector<at::Tensor>>& output_lists, \
const at::TensorList& input_list, \
const c10::intrusive_ptr<ProcessGroup>& process_group) { \
auto input_list_vec = input_list.vec(); \
return process_group->getBackend(c10::DeviceType::DEV) \
->allgather_coalesced( \
const_cast<std::vector<std::vector<at::Tensor>>&>(output_lists), \
input_list_vec); \
}
IMPL_ALLGATHER_COALESCED(CPU)
IMPL_ALLGATHER_COALESCED(CUDA)
IMPL_ALLGATHER_COALESCED(PrivateUse1)
#define IMPL_ALLGATHER_INTO_TENSOR_COALESCED(DEV) \
c10::intrusive_ptr<c10d::Work> allgather_into_tensor_coalesced_##DEV( \
at::TensorList outputs, \
at::TensorList inputs, \
const c10::intrusive_ptr<ProcessGroup>& process_group) { \
auto output_vec = outputs.vec(); \
auto input_vec = inputs.vec(); \
return process_group->getBackend(c10::DeviceType::DEV) \
->allgather_into_tensor_coalesced(output_vec, input_vec); \
}
IMPL_ALLGATHER_INTO_TENSOR_COALESCED(CPU)
IMPL_ALLGATHER_INTO_TENSOR_COALESCED(CUDA)
IMPL_ALLGATHER_INTO_TENSOR_COALESCED(PrivateUse1)
#define IMPL_REDUCE_SCATTER(DEV) \
std::tuple<std::vector<at::Tensor>, c10::intrusive_ptr<Work>> \
reduce_scatter_##DEV( \
const at::TensorList& output_tensors, \
const std::vector<std::vector<at::Tensor>>& input_tensors, \
const c10::intrusive_ptr<ProcessGroup>& process_group, \
const c10::intrusive_ptr<ReduceOp>& reduce_op, \
int64_t timeout) { \
auto output_tensors_vec = output_tensors.vec(); \
auto work = \
process_group->getBackend(c10::DeviceType::DEV) -> reduce_scatter( \
output_tensors_vec, \
const_cast<std::vector<std::vector<at::Tensor>>&>(input_tensors), \
ReduceScatterOptions{ \
*reduce_op.get(), std::chrono::milliseconds(timeout)}); \
return std::tuple<std::vector<at::Tensor>, c10::intrusive_ptr<Work>>( \
output_tensors_vec, work); \
}
IMPL_REDUCE_SCATTER(CPU)
IMPL_REDUCE_SCATTER(CUDA)
IMPL_REDUCE_SCATTER(PrivateUse1)
#define IMPL__REDUCE_SCATTER_BASE(DEV) \
std::tuple<at::Tensor, c10::intrusive_ptr<Work>> _reduce_scatter_base_##DEV( \
at::Tensor& output_tensor, \
at::Tensor& input_tensor, \
const c10::intrusive_ptr<ProcessGroup>& process_group, \
const c10::intrusive_ptr<ReduceOp>& reduce_op, \
bool asyncOp, \
int64_t timeout) { \
auto work = process_group->getBackend(c10::DeviceType::DEV) \
-> _reduce_scatter_base( \
output_tensor, \
input_tensor, \
ReduceScatterOptions{ \
*reduce_op.get(), \
std::chrono::milliseconds(timeout), \
asyncOp}); \
return std::tuple<at::Tensor, c10::intrusive_ptr<Work>>( \
output_tensor, work); \
}
IMPL__REDUCE_SCATTER_BASE(CPU)
IMPL__REDUCE_SCATTER_BASE(CUDA)
IMPL__REDUCE_SCATTER_BASE(PrivateUse1)
#define IMPL_REDUCE_SCATTER_TENSOR_COALESCED(DEV) \
c10::intrusive_ptr<c10d::Work> reduce_scatter_tensor_coalesced_##DEV( \
at::TensorList outputs, \
at::TensorList inputs, \
const c10::intrusive_ptr<ProcessGroup>& process_group, \
const c10::intrusive_ptr<ReduceOp>& reduce_op, \
int64_t timeout) { \
auto output_vec = outputs.vec(); \
auto input_vec = inputs.vec(); \
return process_group->getBackend(c10::DeviceType::DEV) \
->reduce_scatter_tensor_coalesced( \
output_vec, \
input_vec, \
ReduceScatterOptions{ \
*reduce_op.get(), std::chrono::milliseconds(timeout)}); \
}
IMPL_REDUCE_SCATTER_TENSOR_COALESCED(CPU)
IMPL_REDUCE_SCATTER_TENSOR_COALESCED(CUDA)
IMPL_REDUCE_SCATTER_TENSOR_COALESCED(PrivateUse1)
#define IMPL_GATHER(DEV) \
c10::intrusive_ptr<Work> gather_##DEV( \
const std::vector<std::vector<at::Tensor>>& output_tensors, \
const at::TensorList& input_tensors, \
const c10::intrusive_ptr<ProcessGroup>& process_group, \
int64_t root_rank, \
int64_t timeout) { \
auto input_tensors_vec = input_tensors.vec(); \
return process_group->getBackend(c10::DeviceType::DEV) \
->gather( \
const_cast<std::vector<std::vector<at::Tensor>>&>(output_tensors), \
input_tensors_vec, \
GatherOptions{root_rank, std::chrono::milliseconds(timeout)}); \
}
IMPL_GATHER(CPU)
IMPL_GATHER(CUDA)
IMPL_GATHER(PrivateUse1)
#define IMPL_SCATTER(DEV) \
std::tuple<std::vector<at::Tensor>, c10::intrusive_ptr<Work>> scatter_##DEV( \
const at::TensorList& output_tensors, \
const std::vector<std::vector<at::Tensor>>& input_tensors, \
const c10::intrusive_ptr<ProcessGroup>& process_group, \
int64_t root_rank, \
bool asyncOp, \
int64_t timeout) { \
auto output_tensors_vec = output_tensors.vec(); \
auto work = process_group->getBackend(c10::DeviceType::DEV) -> scatter( \
output_tensors_vec, \
const_cast<std::vector<std::vector<at::Tensor>>&>(input_tensors), \
ScatterOptions{ \
root_rank, std::chrono::milliseconds(timeout), asyncOp}); \
return std::tuple<std::vector<at::Tensor>, c10::intrusive_ptr<Work>>( \
std::move(output_tensors_vec), work); \
}
IMPL_SCATTER(CPU)
IMPL_SCATTER(CUDA)
IMPL_SCATTER(PrivateUse1)
#define IMPL_ALLTOALL(DEV) \
std::tuple<std::vector<at::Tensor>, c10::intrusive_ptr<Work>> \
alltoall_##DEV( \
const at::TensorList& output_tensors, \
const at::TensorList& input_tensors, \
const c10::intrusive_ptr<ProcessGroup>& process_group, \
int64_t timeout) { \
auto output_tensors_vec = output_tensors.vec(); \
auto input_tensors_vec = input_tensors.vec(); \
auto work = process_group->getBackend(c10::DeviceType::DEV) -> alltoall( \
output_tensors_vec, \
input_tensors_vec, \
AllToAllOptions{std::chrono::milliseconds(timeout)}); \
return std::tuple<std::vector<at::Tensor>, c10::intrusive_ptr<Work>>( \
std::move(output_tensors_vec), work); \
}
IMPL_ALLTOALL(CPU)
IMPL_ALLTOALL(CUDA)
IMPL_ALLTOALL(PrivateUse1)
#define IMPL_ALLTOALL_BASE(DEV) \
c10::intrusive_ptr<Work> alltoall_base_##DEV( \
at::Tensor& output, \
at::Tensor& input, \
const c10::intrusive_ptr<ProcessGroup>& process_group, \
std::vector<int64_t> output_split_sizes, \
std::vector<int64_t> input_split_sizes, \
int64_t timeout) { \
return process_group->getBackend(c10::DeviceType::DEV) \
->alltoall_base( \
output, \
input, \
output_split_sizes, \
input_split_sizes, \
AllToAllOptions{std::chrono::milliseconds(timeout)}); \
}
IMPL_ALLTOALL_BASE(CPU)
IMPL_ALLTOALL_BASE(CUDA)
IMPL_ALLTOALL_BASE(PrivateUse1)
// NOLINTBEGIN(performance-unnecessary-value-param)
#define IMPL_BARRIER(DEV) \
c10::intrusive_ptr<Work> barrier##DEV( \
at::Tensor /* unused */, \
const c10::intrusive_ptr<ProcessGroup>& process_group, \
const std::vector<int64_t>& device_ids, \
int64_t timeout) { \
return process_group->getBackend(c10::DeviceType::DEV) \
->barrier( \
BarrierOptions{device_ids, std::chrono::milliseconds(timeout)}); \
}
IMPL_BARRIER(CPU)
IMPL_BARRIER(CUDA)
IMPL_BARRIER(PrivateUse1)
// NOLINTEND(performance-unnecessary-value-param)
// NOLINTEND(cppcoreguidelines-pro-type-const-cast)
void monitored_barrier_CPU(
// NOLINTNEXTLINE(performance-unnecessary-value-param)
at::Tensor /* unused */,
const c10::intrusive_ptr<::c10d::ProcessGroup>& process_group,
const std::vector<int64_t>& device_ids,
int64_t timeout,
bool wait_all_ranks) {
process_group->getBackend(c10::DeviceType::CPU)
->monitoredBarrier(
BarrierOptions{device_ids, std::chrono::milliseconds(timeout)},
wait_all_ranks);
}
std::tuple<std::vector<at::Tensor>, c10::intrusive_ptr<Work>>
allreduce_sparse_cuda_(
at::TensorList tensors,
const c10::intrusive_ptr<ProcessGroup>& process_group,
const c10::intrusive_ptr<ReduceOp>& reduce_op,
const std::optional<at::Tensor>& sparse_indices,
int64_t timeout) {
auto tensor_vec = tensors.vec();
auto work = process_group->getBackend(c10::DeviceType::CUDA)
->allreduce_sparse(
tensor_vec,
AllreduceOptions{
*reduce_op,
std::chrono::milliseconds(timeout),
sparse_indices});
return std::tuple<std::vector<at::Tensor>, c10::intrusive_ptr<Work>>(
std::move(tensor_vec), work);
}
} // namespace
// register functions to dispatcher
namespace {
// 2nd level expansion
// FUNC: op name
// DEV: device
#define REGISTER_C10D_OP1(FUNC, DEV) \
TORCH_LIBRARY_IMPL(c10d, DEV, m) { \
m.impl(#FUNC, FUNC##DEV); \
}
// 1st level expansion
#define REGISTER_C10D_OP(FUNC) \
REGISTER_C10D_OP1(FUNC, CPU) \
REGISTER_C10D_OP1(FUNC, CUDA) \
REGISTER_C10D_OP1(FUNC, PrivateUse1)
// Now we start to register ops with the three device keys
REGISTER_C10D_OP(send)
REGISTER_C10D_OP(recv_)
REGISTER_C10D_OP(recv_any_source_)
REGISTER_C10D_OP(reduce_)
REGISTER_C10D_OP(broadcast_)
REGISTER_C10D_OP(allreduce_)
REGISTER_C10D_OP(allreduce_coalesced_)
REGISTER_C10D_OP(allgather_)
REGISTER_C10D_OP(_allgather_base_)
REGISTER_C10D_OP(allgather_coalesced_)
REGISTER_C10D_OP(allgather_into_tensor_coalesced_)
REGISTER_C10D_OP(reduce_scatter_)
REGISTER_C10D_OP(_reduce_scatter_base_)
REGISTER_C10D_OP(reduce_scatter_tensor_coalesced_)
REGISTER_C10D_OP(gather_)
REGISTER_C10D_OP(scatter_)
REGISTER_C10D_OP(alltoall_)
REGISTER_C10D_OP(alltoall_base_)
REGISTER_C10D_OP(barrier)
// The following ops are specialized, register them separately
TORCH_LIBRARY_IMPL(c10d, CPU, m) {
m.impl("monitored_barrier_", monitored_barrier_CPU);
}
// TODO: The SparseCPU/SparseCUDA dispatched methods are only used to support
// sparse all_reduce in the Gloo backend
TORCH_LIBRARY_IMPL(c10d, SparseCPU, m) {
m.impl("allreduce_", allreduce_CPU);
}
TORCH_LIBRARY_IMPL(c10d, SparseCUDA, m) {
m.impl("allreduce_", allreduce_sparse_cuda_);
}
} // namespace
} // namespace ops
} // namespace c10d
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