[cleanup] fix pre-commit mypy issues (#87)

benchmarks/oss.py

@@ -5,7 +5,7 @@
 import math
 import os
 import time
-from typing import Any, List, Union, cast
+from typing import Any, List, cast
 
 import torch
 import torch.distributed as dist
@@ -62,7 +62,7 @@ def collate(inputs: List[Any]):
     torch.cuda.reset_peak_memory_stats(rank)
 
     # Shard the optimizer
-    optimizer: Union[OSS, OPTIM] = OSS(
+    optimizer: torch.optim.Optimizer = OSS(
         params=model.parameters(), optim=OPTIM, lr=1e-4, momentum=0.9
     ) if use_oss else OPTIM(model.parameters(), lr=1e-4, momentum=0.9)
 

docs/source/conf.py

@@ -1,3 +1,4 @@
+# type: ignore
 # Configuration file for the Sphinx documentation builder.
 #
 # This file only contains a selection of the most common options. For a full
@@ -73,10 +74,7 @@
 def setup(app):
     app.add_config_value(
         "recommonmark_config",
-        {
-            "url_resolver": lambda url: github_doc_root + url,
-            "auto_toc_tree_section": "Contents",
-        },
+        {"url_resolver": lambda url: github_doc_root + url, "auto_toc_tree_section": "Contents"},
         True,
     )
     app.add_transform(AutoStructify)

stubs/torch/cuda/__init__.pyi

@@ -39,6 +39,7 @@ def manual_seed(seed: int) -> None: ...
 def memory_allocated(device: Optional[_device_t]=...) -> int: ...
 def max_memory_allocated(device: Optional[_device_t]=...) -> int: ...
 def reset_max_memory_allocated(device: Optional[_device_t]=...) -> None: ...
+def reset_peak_memory_stats(device: Union[_device_t, int] = None) -> None: ...
 def memory_cached(device: Optional[_device_t]=...) -> int: ...
 def max_memory_cached(device: Optional[_device_t]=...) -> int: ...
 def reset_max_memory_cached(device: Optional[_device_t]=...) -> None: ...

stubs/torch/utils/data/dataset.pyi

@@ -8,7 +8,7 @@ T = TypeVar('T')
 class Dataset(Generic[T_co]):
     def __getitem__(self, index: int) -> T_co: ...
     def __len__(self) -> int: ...
-    def __add__(self, other: T_co) -> 'ConcatDataset[T_co]': ...
+    def __add__(self, other: 'Dataset[T_co]') -> 'ConcatDataset[T_co]': ...
 
 class IterableDataset(Dataset[T_co]):
     def __iter__(self) -> Iterable[T_co]: ...

stubs/torch/utils/data/distributed.pyi

@@ -6,6 +6,6 @@ from . import Sampler, Dataset
 T_co = TypeVar('T_co', covariant=True)
 class DistributedSampler(Sampler[T_co]):
     def __init__(self, dataset: Dataset, num_replicas: Optional[int]=..., rank: Optional[int]=...): ...
-    def __iter__(self) -> Iterator[int]: ...
+    def __iter__(self) -> Iterator[T_co]: ...
     def __len__(self) -> int: ...
     def set_epoch(self, epoch: int) -> None: ...