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da003d7b95
This is the result of applying the ruff `UP035` check. `Callable` is imported from `collections.abc` instead of `typing`. This PR is the follow-up of #164054. Pull Request resolved: https://github.com/pytorch/pytorch/pull/164104 Approved by: https://github.com/Skylion007
86 lines
2.8 KiB
Python
86 lines
2.8 KiB
Python
# mypy: allow-untyped-defs
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import enum
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from collections.abc import Callable
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from typing import Any, overload
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import torch
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from torch.distributed.algorithms.join import Joinable, JoinHook
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from torch.optim import Optimizer
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class _ZeROJoinHook(JoinHook):
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zero: Any = ...
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def __init__(self, zero: Any) -> None: ...
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def main_hook(self) -> None: ...
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class _DDPBucketAssignment:
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bucket_index: int
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parameters: list[torch.Tensor]
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offset: int
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device: torch.device
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tensor: torch.Tensor | None
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class _OverlapStatus(enum.IntEnum):
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UNINITIALIZED = ...
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DDP_HAS_REBUILT_BUCKETS = ...
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INITIALIZED = ...
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class _OverlapInfo:
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status: Any = ...
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params_per_bucket: Any = ...
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params_per_rank: Any = ...
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offsets: Any = ...
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broadcast_handles: Any = ...
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bucket_index_to_future: Any = ...
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bucket_index_to_bucket: Any = ...
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bucket_indices_seen: Any = ...
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assigned_ranks_per_bucket: list[set[int]] = ...
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total_size: int = ...
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shard_buckets: bool = ...
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def __init__(self) -> None: ...
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def wait_for_broadcasts(self) -> None: ...
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def clear_per_iter_info(self) -> None: ...
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class ZeroRedundancyOptimizer(Optimizer, Joinable):
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functional_optim_map: Any = ...
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initialized: bool = ...
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process_group: Any = ...
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world_size: int = ...
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rank: int = ...
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global_rank: int = ...
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parameters_as_bucket_view: bool = ...
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optim: Any = ...
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_device_to_device_index: dict[torch.device, int] = ...
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_overlap_with_ddp: bool = ...
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_overlap_info: _OverlapInfo = ...
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_buckets: list[list[torch.Tensor]] = ...
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_bucket_assignments_per_rank: list[dict[int, _DDPBucketAssignment]] = ...
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def __init__(
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self,
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params: Any,
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optimizer_class: type[Optimizer],
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process_group: Any | None = ...,
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parameters_as_bucket_view: bool = ...,
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overlap_with_ddp: bool = ...,
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**defaults: Any,
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) -> None: ...
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def add_param_group(self, param_group: dict[str, Any]) -> None: ...
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def consolidate_state_dict(self, to: int = ...) -> None: ...
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@overload
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def step(self, closure: None = None, **kwargs: Any) -> None: ...
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@overload
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def step(self, closure: Callable[[], float], **kwargs: Any) -> float: ...
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def load_state_dict(self, state_dict: dict[str, Any]) -> None: ...
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def state_dict(self) -> dict[str, Any]: ...
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def _local_step(
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self,
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gradients: list[torch.Tensor | None] | None = None,
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closure: Callable[[], float] | None = None,
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**kwargs: Any,
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) -> float | None: ...
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def _get_assigned_rank(self, bucket_index: int) -> int: ...
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def _init_zero_for_overlap(self) -> None: ...
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def join_hook(self, **kwargs): ...
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@property
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def join_device(self) -> torch.device: ...
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def join_process_group(self) -> Any: ...
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