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#pragma once
#include <ATen/ATen.h>
#include <ATen/cuda/CUDAContext.h>
#include <cstddef>
#include <optional>
#include <vector>
// NCCL BFloat16 is enabled only for CUDA 11+ and NCCL versions 2.10+, or for
// HIP 3.1+
#if defined(__CUDA_BF16_TYPES_EXIST__)
#define HAS_NCCL_BF16_DATATYPE \
((NCCL_MAJOR > 2) || (NCCL_MAJOR == 2) && (NCCL_MINOR >= 10))
#elif defined(USE_ROCM) && (TORCH_HIP_VERSION >= 301)
#define HAS_NCCL_BF16_DATATYPE 1
#else
#define HAS_NCCL_BF16_DATATYPE 0
#endif
namespace torch::cuda::nccl {
/* The following are copied from <nccl.h> and redefined in torch::cuda::nccl
* namespace */
/* pytorch should only use the following definition within pytorch scope */
/* Opaque handle to communicator to ncclComm*, this will reinterpret as ncclComm
* in nccl.cpp */
typedef void* ncclComm_t;
/** redefine nccl unique ID in torch scope. this should be identical to native
* nccl impp. */
#define NCCL_UNIQUE_ID_BYTES 128
typedef struct {
// NOLINTNEXTLINE(*array*)
char internal[NCCL_UNIQUE_ID_BYTES];
} ncclUniqueId;
/* Error type */
enum class ncclResult {
Success = 0,
UnhandledCudaError = 1,
SystemError = 2,
InternalError = 3,
InvalidArgument = 4,
InvalidUsage = 5,
RemoteError = 6,
InProgress = 7,
NumResults = 8
};
/* Reduction operation selector */
enum class ncclRedOp { Sum = 0, Prod = 1, Max = 2, Min = 3, NumOps = 4 };
/* Data types */
enum class ncclDataType {
Int8 = 0,
Char = 0,
Uint8 = 1,
Int32 = 2,
Int = 2,
Uint32 = 3,
Int64 = 4,
Uint64 = 5,
Float16 = 6,
Half = 6,
Float32 = 7,
Float = 7,
Float64 = 8,
Double = 8,
Bfloat16 = 9,
NumTypes = 10
};
// RAII helper class to manage NCCL group API and CUDA free mutex.
// The destructor is allowed to throw since this helper class only
// manages group and lock lifetimes.
struct TORCH_CUDA_CPP_API AutoNcclGroup {
AutoNcclGroup();
AutoNcclGroup(ncclComm_t comm, bool comm_nonblocking);
~AutoNcclGroup() noexcept(false);
ncclComm_t comm_;
bool comm_nonblocking_;
};
// NOTE: this is exposed only so that python_nccl.cpp can some of these helpers.
// Don't use them outside of these files.
namespace detail {
TORCH_CUDA_CPP_API void throw_nccl_error(ncclResult status);
inline void NCCL_CHECK(ncclResult status) {
if (status != ncclResult::Success) {
throw_nccl_error(status);
}
}
TORCH_CUDA_CPP_API at::ArrayRef<ncclComm_t> get_communicators(
at::TensorList inputs);
TORCH_CUDA_CPP_API void check_inputs(
at::TensorList inputs,
at::TensorList outputs,
size_t input_multiplier,
size_t output_multiplier);
TORCH_CUDA_CPP_API void check_inputs(
at::TensorList inputs,
const at::Tensor& output,
int root,
size_t input_multiplier,
size_t output_multiplier);
} // namespace detail
using comm_list = std::vector<ncclComm_t>;
using stream_list = std::vector<std::optional<at::cuda::CUDAStream>>;
TORCH_CUDA_CPP_API std::uint64_t version();
TORCH_CUDA_CPP_API const char* version_suffix();
bool is_available(at::TensorList tensors);
TORCH_CUDA_CPP_API void get_unique_id(ncclUniqueId& id);
TORCH_CUDA_CPP_API ncclComm_t
comm_init_rank(int nranks, const ncclUniqueId& comm_id, int rank);
TORCH_CUDA_CPP_API void comm_destroy(ncclComm_t comm);
TORCH_CUDA_CPP_API void broadcast(
at::TensorList tensors,
const stream_list& streams = {},
const comm_list& user_comms = {});
size_t get_max_count();
TORCH_CUDA_CPP_API void reduce(
const std::vector<at::Tensor>& inputs,
at::Tensor& output,
int32_t root = 0,
int32_t op = static_cast<int>(ncclRedOp::Sum),
const stream_list& streams = {},
const comm_list& user_comms = {});
TORCH_CUDA_CPP_API void reduce(
std::vector<at::Tensor>& inputs,
int32_t root = 0,
int32_t op = static_cast<int>(ncclRedOp::Sum),
const stream_list& streams = {},
const comm_list& user_comms = {});
TORCH_CUDA_CPP_API void all_reduce(
const std::vector<at::Tensor>& inputs,
std::vector<at::Tensor>& outputs,
int32_t op = static_cast<int>(ncclRedOp::Sum),
const stream_list& streams = {},
const comm_list& user_comms = {});
TORCH_CUDA_CPP_API void reduce_scatter(
const std::vector<at::Tensor>& inputs,
std::vector<at::Tensor>& outputs,
int32_t op = static_cast<int>(ncclRedOp::Sum),
const stream_list& streams = {},
const comm_list& user_comms = {});
TORCH_CUDA_CPP_API void scatter(
const std::vector<at::Tensor>& inputs,
at::Tensor& outputs,
ncclComm_t comm,
at::cuda::CUDAStream& stream,
int32_t root = 0);
TORCH_CUDA_CPP_API void all_gather(
const std::vector<at::Tensor>& inputs,
std::vector<at::Tensor>& outputs,
const stream_list& streams = {},
const comm_list& user_comms = {});
TORCH_CUDA_CPP_API void gather(
const at::Tensor& inputs,
std::vector<at::Tensor>& outputs,
ncclComm_t comm,
at::cuda::CUDAStream& stream,
int32_t root = 0);
TORCH_CUDA_CPP_API void all2all_single_equal_split(
at::Tensor& input,
at::Tensor& output,
int size,
ncclComm_t comm,
at::cuda::CUDAStream& stream);
TORCH_CUDA_CPP_API void all2all_single_unequal_split(
void* sendbuff,
const size_t* sendcounts,
const size_t* senddispls,
void* recvbuff,
const size_t* recvcounts,
const size_t* recvdispls,
size_t size,
c10::ScalarType type,
ncclComm_t comm,
at::cuda::CUDAStream& stream);
TORCH_CUDA_CPP_API void all2all(
std::vector<at::Tensor>& outputTensors,
std::vector<at::Tensor>& inputTensors,
ncclComm_t _comm,
at::cuda::CUDAStream& stream);
TORCH_CUDA_CPP_API void send(
const at::Tensor& input,
ncclComm_t comm,
at::cuda::CUDAStream stream,
int dst);
TORCH_CUDA_CPP_API void recv(
at::Tensor& output,
ncclComm_t comm,
at::cuda::CUDAStream stream,
int src);
} // namespace torch::cuda::nccl
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