Fix mypy in utilities.distributed

pyproject.toml

@@ -67,6 +67,7 @@ module = [
     "pytorch_lightning.utilities.cloud_io",
     "pytorch_lightning.utilities.device_dtype_mixin",
     "pytorch_lightning.utilities.device_parser",
+    "pytorch_lightning.utilities.distributed",
     "pytorch_lightning.utilities.parsing",
 ]
 ignore_errors = "False"

pytorch_lightning/utilities/distributed.py

@@ -16,7 +16,7 @@
 import os
 from functools import wraps
 from platform import python_version
-from typing import Any, Optional, Union
+from typing import Any, Callable, List, Optional, Tuple, Type, Union
 
 import torch
 from torch.nn.parallel.distributed import DistributedDataParallel
@@ -31,21 +31,22 @@
 
 else:
 
-    class ReduceOp:
+    class ReduceOp:  # type: ignore # (see https://github.com/python/mypy/issues/1153)
         SUM = None
 
-    class group:
+    class group:  # type: ignore
         WORLD = None
 
 
 log = logging.getLogger(__name__)
 
 
-def rank_zero_only(fn):
+def rank_zero_only(fn: Callable) -> Callable:
     @wraps(fn)
-    def wrapped_fn(*args, **kwargs):
+    def wrapped_fn(*args: Any, **kwargs: Any) -> Optional[Any]:
         if rank_zero_only.rank == 0:
             return fn(*args, **kwargs)
+        return None
 
     return wrapped_fn
 
@@ -64,7 +65,7 @@ def _get_rank() -> int:
 rank_zero_only.rank = getattr(rank_zero_only, "rank", _get_rank())
 
 
-def rank_zero_warn(*args, stacklevel: int = 5, **kwargs):
+def rank_zero_warn(*args: Any, stacklevel: int = 5, **kwargs: Any) -> None:
     from pytorch_lightning.utilities.warnings import rank_zero_deprecation, rank_zero_warn
 
     rank_zero_deprecation(
@@ -74,7 +75,7 @@ def rank_zero_warn(*args, stacklevel: int = 5, **kwargs):
     return rank_zero_warn(*args, stacklevel=stacklevel, **kwargs)
 
 
-def rank_zero_deprecation(*args, stacklevel: int = 5, **kwargs):
+def rank_zero_deprecation(*args: Any, stacklevel: int = 5, **kwargs: Any) -> None:
     from pytorch_lightning.utilities.warnings import rank_zero_deprecation
 
     rank_zero_deprecation(
@@ -84,29 +85,29 @@ def rank_zero_deprecation(*args, stacklevel: int = 5, **kwargs):
     return rank_zero_deprecation(*args, stacklevel=stacklevel, **kwargs)
 
 
-def _info(*args, stacklevel: int = 2, **kwargs):
+def _info(*args: Any, stacklevel: int = 2, **kwargs: Any) -> None:
     if python_version() >= "3.8.0":
         kwargs["stacklevel"] = stacklevel
     log.info(*args, **kwargs)
 
 
-def _debug(*args, stacklevel: int = 2, **kwargs):
+def _debug(*args: Any, stacklevel: int = 2, **kwargs: Any) -> None:
     if python_version() >= "3.8.0":
         kwargs["stacklevel"] = stacklevel
     log.debug(*args, **kwargs)
 
 
 @rank_zero_only
-def rank_zero_debug(*args, stacklevel: int = 4, **kwargs):
+def rank_zero_debug(*args: Any, stacklevel: int = 4, **kwargs: Any) -> None:
     _debug(*args, stacklevel=stacklevel, **kwargs)
 
 
 @rank_zero_only
-def rank_zero_info(*args, stacklevel: int = 4, **kwargs):
+def rank_zero_info(*args: Any, stacklevel: int = 4, **kwargs: Any) -> None:
     _info(*args, stacklevel=stacklevel, **kwargs)
 
 
-def gather_all_tensors(result: Union[torch.Tensor], group: Optional[Any] = None):
+def gather_all_tensors(result: torch.Tensor, group: Optional[Any] = None) -> List[torch.Tensor]:
     """
     Function to gather all tensors from several ddp processes onto a list that
     is broadcasted to all processes
@@ -141,7 +142,7 @@ def distributed_available() -> bool:
 
 
 def sync_ddp_if_available(
-    result: Union[torch.Tensor], group: Optional[Any] = None, reduce_op: Optional[Union[ReduceOp, str]] = None
+    result: torch.Tensor, group: Optional[Any] = None, reduce_op: Optional[Union[ReduceOp, str]] = None
 ) -> torch.Tensor:
     """
     Function to reduce a tensor across worker processes during distributed training
@@ -160,7 +161,7 @@ def sync_ddp_if_available(
 
 
 def sync_ddp(
-    result: Union[torch.Tensor], group: Optional[Any] = None, reduce_op: Optional[Union[ReduceOp, str]] = None
+    result: torch.Tensor, group: Optional[Any] = None, reduce_op: Optional[Union[ReduceOp, str]] = None
 ) -> torch.Tensor:
     """
     Function to reduce the tensors from several ddp processes to one master process
@@ -196,7 +197,11 @@ def sync_ddp(
 
 class AllGatherGrad(torch.autograd.Function):
     @staticmethod
-    def forward(ctx, tensor, group=group.WORLD):
+    def forward(
+        ctx: Any,
+        tensor: torch.Tensor,
+        group: Optional["torch.distributed.ProcessGroup"] = group.WORLD,
+    ) -> torch.Tensor:
         ctx.group = group
 
         gathered_tensor = [torch.zeros_like(tensor) for _ in range(torch.distributed.get_world_size())]
@@ -207,7 +212,7 @@ def forward(ctx, tensor, group=group.WORLD):
         return gathered_tensor
 
     @staticmethod
-    def backward(ctx, *grad_output):
+    def backward(ctx: Any, *grad_output: torch.Tensor) -> Tuple[torch.Tensor, None]:
         grad_output = torch.cat(grad_output)
 
         torch.distributed.all_reduce(grad_output, op=torch.distributed.ReduceOp.SUM, async_op=False, group=ctx.group)
@@ -216,7 +221,7 @@ def backward(ctx, *grad_output):
 
 
 def all_gather_ddp_if_available(
-    tensor: Union[torch.Tensor], group: Optional[Any] = None, sync_grads: bool = False
+    tensor: torch.Tensor, group: Optional["torch.distributed.ProcessGroup"] = None, sync_grads: bool = False
 ) -> torch.Tensor:
     """
     Function to gather a tensor from several distributed processes
@@ -241,8 +246,8 @@ def all_gather_ddp_if_available(
 def register_ddp_comm_hook(
     model: DistributedDataParallel,
     ddp_comm_state: Optional[object] = None,
-    ddp_comm_hook: Optional[callable] = None,
-    ddp_comm_wrapper: Optional[callable] = None,
+    ddp_comm_hook: Optional[Callable] = None,
+    ddp_comm_wrapper: Optional[Callable] = None,
 ) -> None:
     """
     Function to register communication hook for DDP model
@@ -322,6 +327,9 @@ def register_ddp_comm_hook(
         return
     if ddp_comm_hook is None:
         return
+    # inform mypy that ddp_comm_hook is callable
+    ddp_comm_hook: Callable = ddp_comm_hook
+
     if ddp_comm_wrapper is not None:
         if not _TORCH_GREATER_EQUAL_1_9:
             rank_zero_warn("Not applying DDP comm wrapper. To use communication wrapper, please use pytorch>=1.9.0.")