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|
from datetime import timedelta
from enum import Enum
from typing import Optional, List, Any, Tuple, overload, Union
from torch import Tensor
# This module is defined in torch/csrc/distributed/c10d/init.cpp
_DEFAULT_FIRST_BUCKET_BYTES: int
_DEFAULT_NO_TIMEOUT: timedelta
_DEFAULT_PG_TIMEOUT: timedelta
class BuiltinCommHookType(Enum):
ALLREDUCE = ...
FP16_COMPRESS = ...
def _register_comm_hook(reducer: Reducer, state: Any, comm_hook: Any): ...
def _register_builtin_comm_hook(
reducer: Reducer, comm_hook_type: BuiltinCommHookType
): ...
class GradBucket:
def index(self) -> int: ...
def buffer(self) -> Tensor: ...
def gradients(self) -> List[Tensor]: ...
def is_last(self) -> bool: ...
def set_buffer(self, tensor: Tensor) -> None: ...
def parameters(self) -> List[Tensor]: ...
class Reducer:
def __init__(
self,
params: List[Tensor],
bucket_indices: List[List[int]],
process_group: ProcessGroup,
expect_sparse_gradients: List[bool],
bucket_bytes_cap: int,
find_unused_parameters: bool,
gradient_as_bucket_view: bool,
): ...
...
class Logger:
def __init__(self, reducer: Reducer): ...
def set_construction_data_and_log(
self,
module_name: str,
device_ids: List[int],
output_device: int,
broadcast_buffers: bool,
has_sync_bn: bool,
): ...
...
def get_debug_level(): ...
def set_debug_level(): ...
def set_debug_level_from_env(): ...
class DebugLevel(Enum):
OFF = ...
INFO = ...
DETAIL = ...
class ReduceOp:
SUM = ...
PRODUCT = ...
MIN = ...
MAX = ...
BAND = ...
BOR = ...
BXOR = ...
PREMUL_SUM = ...
UNUSED = ...
class RedOpType(Enum): ...
class BroadcastOptions:
rootRank: int
rootTensor: int
timeout: timedelta
class AllreduceOptions:
reduceOp: ReduceOp
timeout: timedelta
class AllreduceCoalescedOptions(AllreduceOptions): ...
class ReduceOptions:
reduceOp: ReduceOp
rootRank: int
rootTensor: int
timeout: timedelta
class AllGatherOptions:
timeout: timedelta
class GatherOptions:
rootRank: int
timeout: timedelta
class ScatterOptions:
rootRank: int
timeout: timedelta
class ReduceScatterOptions:
reduceOp: ReduceOp
timeout: timedelta
class BarrierOptions:
device_ids: List[int]
timeout: timedelta
class AllToAllOptions:
timeout: timedelta
class Store:
def set(self, key: str, value: str): ...
def get(self, key: str) -> bytes: ...
def add(self, key: str, value: int) -> int: ...
def compare_set(self, key: str, expected_value: str, desired_value: str) -> bytes: ...
def delete_key(self, key: str) -> bool: ...
def num_keys(self) -> int: ...
def set_timeout(self, timeout: timedelta): ...
@overload
def wait(self, keys: List[str]): ...
@overload
def wait(self, keys: List[str], timeout: timedelta): ...
class FileStore(Store):
def __init__(self, path: str, numWorkers: int = ...): ...
class HashStore(Store):
def __init__(self): ...
class TCPStore(Store):
def __init__(
self,
host_name: str,
port: int,
world_size: Optional[int] = ...,
is_master: bool = ...,
timeout: timedelta = ...,
wait_for_workers: bool = ...,
multi_tenant: bool = ...
): ...
@property
def host(self) -> str: ...
@property
def port(self) -> int: ...
class PrefixStore(Store):
def __init__(self, prefix: str, store: Store): ...
@property
def underlying_store(self) -> Store: ...
class Work:
def is_completed(self) -> bool: ...
def is_success(self) -> bool: ...
def exception(self) -> Any: ...
def wait(self, timeout: timedelta = _DEFAULT_NO_TIMEOUT) -> bool: ...
def source_rank(self) -> int: ...
def _source_rank(self) -> int: ...
def result(self) -> List[Tensor]: ...
def synchronize(self): ...
...
class ProcessGroup:
class Options: ...
def __init__(self): ...
def rank(self) -> int: ...
def size(self) -> int: ...
@overload
def broadcast(
self,
tensors: List[Tensor],
opts=BroadcastOptions(),
) -> Work: ...
@overload
def broadcast(
self,
tensor: Tensor,
root: int,
) -> Work: ...
@overload
def allreduce(
self,
tensors: List[Tensor],
opts: AllreduceOptions = AllreduceOptions(),
) -> Work: ...
@overload
def allreduce(
self,
tensors: List[Tensor],
op=ReduceOp.SUM,
) -> Work: ...
@overload
def allreduce(
self,
tensor: Tensor,
op=ReduceOp.SUM,
) -> Work: ...
def allreduce_coalesced(
self,
tensors: List[Tensor],
opts=AllreduceCoalescedOptions(),
) -> Work: ...
@overload
def reduce(
self,
tensors: List[Tensor],
opts=ReduceOptions(),
) -> Work: ...
@overload
def reduce(
self,
tensor: Tensor,
root: int,
op=ReduceOp.SUM,
) -> Work: ...
@overload
def allgather(
self,
output_tensors: List[List[Tensor]],
input_tensors: List[Tensor],
opts=AllGatherOptions(),
) -> Work: ...
@overload
def allgather(
self,
output_tensors: List[Tensor],
input_tensor: Tensor,
) -> Work: ...
def _allgather_base(
self,
output: Tensor,
input: Tensor,
opts = AllGatherOptions(),
) -> Work: ...
def allgather_coalesced(
self,
output_lists: List[List[Tensor]],
input_list: List[Tensor],
opts=AllGatherOptions(),
) -> Work: ...
@overload
def gather(
self,
output_tensors: List[List[Tensor]],
input_tensors: List[Tensor],
opts=GatherOptions(),
) -> Work: ...
@overload
def gather(
self,
output_tensors: List[Tensor],
input_tensor: Tensor,
root: int,
) -> Work: ...
@overload
def scatter(
self,
output_tensors: List[Tensor],
input_tensors: List[List[Tensor]],
opts=ScatterOptions(),
) -> Work: ...
@overload
def scatter(
self,
output_tensor: Tensor,
input_tensors: List[Tensor],
root: int,
) -> Work: ...
@overload
def reduce_scatter(
self,
output_tensors: List[Tensor],
input_tensors: List[List[Tensor]],
opts=ReduceScatterOptions(),
) -> Work: ...
@overload
def reduce_scatter(
self,
output_tensors: Tensor,
input_tensor: List[Tensor],
) -> Work: ...
def _reduce_scatter_base(
self,
outputTensor: Tensor,
inputTensor: Tensor,
) -> Work: ...
@overload
def alltoall_base(
self,
output_tensor: Tensor,
input_tensor: Tensor,
output_split_sizes: List[int],
input_split_sizes: List[int],
opts=AllToAllOptions(),
) -> Work: ...
@overload
def alltoall_base(
self,
output: Tensor,
input: Tensor,
output_split_sizes: List[int],
input_split_sizes: List[int],
) -> Work: ...
@overload
def alltoall(
self,
output_tensor: List[Tensor],
input_tensor: List[Tensor],
opts=AllToAllOptions(),
) -> Work: ...
@overload
def alltoall(
self,
output: List[Tensor],
input: List[Tensor],
) -> Work: ...
def send(
self,
tensors: List[Tensor],
dstRank: int,
tag: int,
) -> Work: ...
def recv(
self,
tensors: List[Tensor],
srcRank: int,
tag: int,
) -> Work: ...
def recv_anysource(self, tensors: List[Tensor], tag: int) -> Work: ...
def barrier(self, opts=BarrierOptions()) -> Work: ...
class ProcessGroupRoundRobin(ProcessGroup): ...
def _round_robin_process_groups(
process_groups: List[ProcessGroup],
) -> ProcessGroupRoundRobin: ...
class ProcessGroupGloo(ProcessGroup):
class Device: ...
class Options: ...
def __init__(
self,
store: Store,
rank: int,
size: int,
timeout: timedelta,
): ...
@staticmethod
def create_device(hostname=str(), interface=str()) -> Device: ...
...
@staticmethod
def create_default_device() -> Device: ...
...
class _ProcessGroupWrapper(ProcessGroup):
def __init__(
self,
pg: ProcessGroup,
gloo_pg: ProcessGroupGloo
): ...
wrapped_pg: ProcessGroup
class ProcessGroupNCCL(ProcessGroup):
class Options: ...
def __init__(
self,
store: Store,
rank: int,
size: int,
timeout: timedelta,
): ...
@staticmethod
def _group_start() -> None: ...
@staticmethod
def _group_end() -> None: ...
...
class ProcessGroupUCC(ProcessGroup):
def __init__(
self,
store: Store,
rank: int,
size: int,
timeout: timedelta,
): ...
class ProcessGroupMPI(ProcessGroup):
def __init__(
self,
rank: int,
size: int,
pgComm: int,
): ...
@staticmethod
def create(ranks: List[int]) -> ProcessGroupMPI: ...
def _compute_bucket_assignment_by_size(
tensors: List[Tensor],
bucket_size: int,
expect_sparse_gradient: List[bool],
tensor_indices: List[int],
) -> Tuple[List[List[int]], List[int]]: ...
def _broadcast_coalesced(
process_group: ProcessGroup,
tensors: List[Tensor],
buffer_size: int,
src: int,
): ...
def _test_python_store(store: Store): ...
def _verify_params_across_processes(
process_group: ProcessGroup,
params: List[Tensor],
logger: Optional[Logger],
): ...
def _make_nccl_premul_sum(factor: Union[float, List[Tensor]]) -> ReduceOp: ...
|
