Back out "Convert directory fbcode/torchrec to use the Ruff Formatter"

Summary:
Original commit changeset: ee300de21222

Original Phabricator Diff: D66013071

bypass-github-export-checks

Reviewed By: aporialiao

Differential Revision: D66198773

fbshipit-source-id: 4a8e5a124937a8329d7ed39444d3fdc5e4f2d10c

Author: Thomas Polasek

benchmarks/ebc_benchmarks.py

@@ -163,6 +163,7 @@ def get_fused_ebc_uvm_time(
     location: EmbeddingLocation,
     epochs: int = 100,
 ) -> Tuple[float, float]:
+
     fused_ebc = FusedEmbeddingBagCollection(
         tables=embedding_bag_configs,
         optimizer_type=torch.optim.SGD,
@@ -194,6 +195,7 @@ def get_ebc_comparison(
     device: torch.device,
     epochs: int = 100,
 ) -> Tuple[float, float, float, float, float]:
+
     # Simple EBC module wrapping a list of nn.EmbeddingBag
     ebc = EmbeddingBagCollection(
         tables=embedding_bag_configs,

benchmarks/ebc_benchmarks_utils.py

@@ -26,6 +26,7 @@ def get_random_dataset(
     embedding_bag_configs: List[EmbeddingBagConfig],
     pooling_factors: Optional[Dict[str, int]] = None,
 ) -> IterableDataset[Batch]:
+
     if pooling_factors is None:
         pooling_factors = {}
 
@@ -56,6 +57,7 @@ def train_one_epoch(
     dataset: IterableDataset[Batch],
     device: torch.device,
 ) -> float:
+
     start_time = time.perf_counter()
 
     for data in dataset:
@@ -80,6 +82,7 @@ def train_one_epoch_fused_optimizer(
     dataset: IterableDataset[Batch],
     device: torch.device,
 ) -> float:
+
     start_time = time.perf_counter()
 
     for data in dataset:
@@ -103,6 +106,7 @@ def train(
     device: torch.device,
     epochs: int = 100,
 ) -> Tuple[float, float]:
+
     training_time = []
     for _ in range(epochs):
         if optimizer:

examples/bert4rec/bert4rec_main.py

@@ -35,6 +35,7 @@
 
 # OSS import
 try:
+
     # pyre-ignore[21]
     # @manual=//torchrec/github/examples/bert4rec:bert4rec_metrics
     from bert4rec_metrics import recalls_and_ndcgs_for_ks

examples/golden_training/train_dlrm_data_parallel.py

@@ -160,7 +160,7 @@ def train(
     )
 
     def dense_filter(
-        named_parameters: Iterator[Tuple[str, nn.Parameter]],
+        named_parameters: Iterator[Tuple[str, nn.Parameter]]
     ) -> Iterator[Tuple[str, nn.Parameter]]:
         for fqn, param in named_parameters:
             if "sparse" not in fqn:

examples/retrieval/two_tower_retrieval.py

@@ -27,6 +27,7 @@
 
 # OSS import
 try:
+
     # pyre-ignore[21]
     # @manual=//torchrec/github/examples/retrieval:knn_index
     from knn_index import get_index

tools/lint/black_linter.py

@@ -179,9 +179,7 @@ def main() -> None:
         level=(
             logging.NOTSET
             if args.verbose
-            else logging.DEBUG
-            if len(args.filenames) < 1000
-            else logging.INFO
+            else logging.DEBUG if len(args.filenames) < 1000 else logging.INFO
         ),
         stream=sys.stderr,
     )

torchrec/datasets/criteo.py

@@ -351,9 +351,8 @@ def get_file_row_ranges_and_remainder(
 
             # If the ranges overlap.
             if rank_left_g <= file_right_g and rank_right_g >= file_left_g:
-                overlap_left_g, overlap_right_g = (
-                    max(rank_left_g, file_left_g),
-                    min(rank_right_g, file_right_g),
+                overlap_left_g, overlap_right_g = max(rank_left_g, file_left_g), min(
+                    rank_right_g, file_right_g
                 )
 
                 # Convert overlap in global numbers to (local) numbers specific to the

torchrec/datasets/random.py

@@ -33,6 +33,7 @@ def __init__(
         *,
         min_ids_per_features: Optional[List[int]] = None,
     ) -> None:
+
         self.keys = keys
         self.keys_length: int = len(keys)
         self.batch_size = batch_size
@@ -75,6 +76,7 @@ def __next__(self) -> Batch:
         return batch
 
     def _generate_batch(self) -> Batch:
+
         values = []
         lengths = []
         for key_idx, _ in enumerate(self.keys):

torchrec/datasets/test_utils/criteo_test_utils.py

@@ -103,6 +103,7 @@ def _create_dataset_npys(
         labels: Optional[np.ndarray] = None,
     ) -> Generator[Tuple[str, ...], None, None]:
         with tempfile.TemporaryDirectory() as tmpdir:
+
             if filenames is None:
                 filenames = [filename]
 

torchrec/distributed/batched_embedding_kernel.py

@@ -785,7 +785,9 @@ def purge(self) -> None:
     def named_split_embedding_weights(
         self, prefix: str = "", recurse: bool = True, remove_duplicate: bool = True
     ) -> Iterator[Tuple[str, torch.Tensor]]:
-        assert remove_duplicate, "remove_duplicate=False not supported in BaseBatchedEmbedding.named_split_embedding_weights"
+        assert (
+            remove_duplicate
+        ), "remove_duplicate=False not supported in BaseBatchedEmbedding.named_split_embedding_weights"
         for config, param in zip(
             self._config.embedding_tables,
             self.emb_module.split_embedding_weights(),
@@ -897,7 +899,9 @@ def named_parameters(
     def named_split_embedding_weights(
         self, prefix: str = "", recurse: bool = True, remove_duplicate: bool = True
     ) -> Iterator[Tuple[str, torch.Tensor]]:
-        assert remove_duplicate, "remove_duplicate=False not supported in BaseBatchedEmbedding.named_split_embedding_weights"
+        assert (
+            remove_duplicate
+        ), "remove_duplicate=False not supported in BaseBatchedEmbedding.named_split_embedding_weights"
         for config, tensor in zip(
             self._config.embedding_tables,
             self.split_embedding_weights(),
@@ -1078,9 +1082,8 @@ def named_parameters(
         combined_key = "/".join(
             [config.name for config in self._config.embedding_tables]
         )
-        yield (
-            append_prefix(prefix, f"{combined_key}.weight"),
-            cast(nn.Parameter, self._emb_module.weights),
+        yield append_prefix(prefix, f"{combined_key}.weight"), cast(
+            nn.Parameter, self._emb_module.weights
         )
 
 
@@ -1098,8 +1101,7 @@ def __init__(
         self._pg = pg
 
         self._pooling: PoolingMode = pooling_type_to_pooling_mode(
-            config.pooling,
-            sharding_type,  # pyre-ignore[6]
+            config.pooling, sharding_type  # pyre-ignore[6]
         )
 
         self._local_rows: List[int] = []
@@ -1218,7 +1220,9 @@ def purge(self) -> None:
     def named_split_embedding_weights(
         self, prefix: str = "", recurse: bool = True, remove_duplicate: bool = True
     ) -> Iterator[Tuple[str, torch.Tensor]]:
-        assert remove_duplicate, "remove_duplicate=False not supported in BaseBatchedEmbedding.named_split_embedding_weights"
+        assert (
+            remove_duplicate
+        ), "remove_duplicate=False not supported in BaseBatchedEmbedding.named_split_embedding_weights"
         for config, tensor in zip(
             self._config.embedding_tables,
             self.emb_module.split_embedding_weights(),
@@ -1358,7 +1362,9 @@ def named_parameters(
     def named_split_embedding_weights(
         self, prefix: str = "", recurse: bool = True, remove_duplicate: bool = True
     ) -> Iterator[Tuple[str, PartiallyMaterializedTensor]]:
-        assert remove_duplicate, "remove_duplicate=False not supported in BaseBatchedEmbedding.named_split_embedding_weights"
+        assert (
+            remove_duplicate
+        ), "remove_duplicate=False not supported in BaseBatchedEmbedding.named_split_embedding_weights"
         for config, tensor in zip(
             self._config.embedding_tables,
             self.split_embedding_weights(),
@@ -1561,7 +1567,6 @@ def named_parameters(
         combined_key = "/".join(
             [config.name for config in self._config.embedding_tables]
         )
-        yield (
-            append_prefix(prefix, f"{combined_key}.weight"),
-            cast(nn.Parameter, self._emb_module.weights),
+        yield append_prefix(prefix, f"{combined_key}.weight"), cast(
+            nn.Parameter, self._emb_module.weights
         )