Convert FBCODE to use the Ruff Formatter

Summary:
Converts the directory specified to use the Ruff formatter. This is the last big diff to convert all of Fbcode to Ruff.

pomsky_fix_bugs

drop-conflicts
bypass-github-export-checks
allow-large-files

Reviewed By: amyreese

Differential Revision: D66886610

fbshipit-source-id: 8276a7f6164efec189ca0b87e535543ed5bc3615

Author: Thomas Polasek

tests/test_manifest.py

@@ -725,7 +725,7 @@ def test_replicated_entries_only_on_rank_0(rank: int) -> None:
 
 
 def _update_local_manifest_with_merged_entries(
-    local_manifest: Dict[str, Entry]
+    local_manifest: Dict[str, Entry],
 ) -> None:
     """
     Update the expected local manifest with manually merged ShardedTensorEntries

torchsnapshot/asyncio_utils.py

@@ -47,7 +47,9 @@ def _run_once(self):
         timeout = (
             0
             if ready or self._stopping
-            else min(max(scheduled[0]._when - now, 0), 86400) if scheduled else None
+            else min(max(scheduled[0]._when - now, 0), 86400)
+            if scheduled
+            else None
         )
         event_list = self._selector.select(timeout)
         self._process_events(event_list)

torchsnapshot/io_preparers/tensor.py

@@ -199,7 +199,7 @@ def can_load_inplace(
 
     @staticmethod
     def empty_tensor_from_entry(
-        entry: Union[TensorEntry, ChunkedTensorEntry]
+        entry: Union[TensorEntry, ChunkedTensorEntry],
     ) -> torch.Tensor:
         if entry.dtype in SUPPORTED_QUANTIZED_DTYPES:
             # TODO: we can't allocate empty quantized tensors because we don't
@@ -394,11 +394,15 @@ def tensor_copy(dst: torch.Tensor, src: torch.Tensor) -> None:
     # a region of the larger tensor's storage contain data that does not match
     # the larger tensor's qscheme.
 
-    if src.is_quantized and (
-        not dst.is_quantized  # Copying from quantized Tensor to non-quantized Tensor is not allowed
-        or dst.qscheme() != src.qscheme()  # Quantized copy only works with same qscheme
-        or dst.dtype != src.dtype  # Quantized copy requires matching dtypes
-        or (dst._is_view() and not _q_params_equal(dst, src))  # See the top comment
+    if (
+        src.is_quantized
+        and (
+            not dst.is_quantized  # Copying from quantized Tensor to non-quantized Tensor is not allowed
+            or dst.qscheme()
+            != src.qscheme()  # Quantized copy only works with same qscheme
+            or dst.dtype != src.dtype  # Quantized copy requires matching dtypes
+            or (dst._is_view() and not _q_params_equal(dst, src))  # See the top comment
+        )
     ):
         # TODO: tile the dequantize -> copy to reduce memory footprint
         src = _tensor_dequantize(src)

torchsnapshot/manifest_ops.py

@@ -109,7 +109,7 @@ def _get_rank_to_manifest(metadata: SnapshotMetadata) -> List[Dict[str, Entry]]:
 
 
 def _get_merged_sharded_tensor_entries(
-    rank_to_manifest: List[Dict[str, Entry]]
+    rank_to_manifest: List[Dict[str, Entry]],
 ) -> Dict[str, Entry]:
     groups = defaultdict(list)
     for manifest in rank_to_manifest:
@@ -130,7 +130,7 @@ def _get_merged_sharded_tensor_entries(
 
 
 def _get_merged_dtensor_entries(
-    rank_to_manifest: List[Dict[str, Entry]]
+    rank_to_manifest: List[Dict[str, Entry]],
 ) -> Dict[str, Entry]:
     """
     Merge all DTensor entries across ranks if sharded

torchsnapshot/partitioner.py

@@ -283,7 +283,7 @@ def partition_write_reqs(
 
 
 def _consolidate_replicated_chunked_tensor_entries(
-    rank_to_entries: List[Dict[str, Entry]]
+    rank_to_entries: List[Dict[str, Entry]],
 ) -> List[Dict[str, Entry]]:
     groups: Dict[str, List[ChunkedTensorEntry]] = defaultdict(list)
 

torchsnapshot/serialization.py

@@ -245,8 +245,7 @@ def contiguous_view_as_untyped_storage(tensor: torch.Tensor) -> UntypedStorage:
     else:
         untyped_storage = tensor.storage().untyped()
     return untyped_storage[
-        tensor.storage_offset()
-        * tensor.element_size() : tensor.storage_offset()
+        tensor.storage_offset() * tensor.element_size() : tensor.storage_offset()
         * tensor.element_size()
         + tensor.nelement() * tensor.element_size()
     ]

torchsnapshot/snapshot.py

@@ -863,7 +863,6 @@ def _coalesce_path_and_replicated(
         app_state: AppState,
         replicated: List[str],
     ) -> Tuple[str, Set[str]]:
-
         rank = pg_wrapper.get_rank()
 
         # coalesce path