[NFC] polish colossalai/zero/sharded_model/reduce_scatter.py code style (#1554)

colossalai/zero/sharded_model/reduce_scatter.py

@@ -20,6 +20,7 @@ else:
 
 
 class Bucket:
+
     def __init__(self, shard_size: int, dtype: torch.dtype, device: torch.device, group: ProcessGroup):
         self.buffer = torch.zeros((group.size(), shard_size), dtype=dtype, device=device)
         self.group = group
@@ -34,18 +35,18 @@ class Bucket:
             return
         # reduce-scatter bucket
         if hasattr(dist, "_reduce_scatter_base") and enable_nccl_base_collectives:
-            dist._reduce_scatter_base(
-                self.output_shard[: self.offset], self.buffer[:, : self.offset].contiguous(), group=self.group
-            )
+            dist._reduce_scatter_base(self.output_shard[:self.offset],
+                                      self.buffer[:, :self.offset].contiguous(),
+                                      group=self.group)
         else:
-            dist.reduce_scatter(
-                self.output_shard[: self.offset], list(self.buffer[:, : self.offset].unbind(0)), group=self.group
-            )
+            dist.reduce_scatter(self.output_shard[:self.offset],
+                                list(self.buffer[:, :self.offset].unbind(0)),
+                                group=self.group)
         # execute post-reduction callbacks
         for callback_fn in self.callbacks:
             callback_fn()
         # reuse input bucket but allocate a fresh output shard
-        self.buffer[:, : self.offset].zero_()
+        self.buffer[:, :self.offset].zero_()
         self.offset = 0
         self.callbacks.clear()
         self.output_shard = torch.zeros_like(self.buffer[0])
@@ -73,12 +74,12 @@ class Bucket:
         tensor_size = tensor_list[0].numel()
         stacked_input = torch.stack(tensor_list).view(self.group.size(), tensor_size)
         offset = self.offset
-        self.buffer[:, offset: offset + tensor_size].copy_(stacked_input)
+        self.buffer[:, offset:offset + tensor_size].copy_(stacked_input)
         self.offset += tensor_size
 
         # callback will be given the reduced result
         if callback_fn is not None:
-            result_view = self.output_shard[offset: offset + tensor_size].view_as(tensor_list[0])
+            result_view = self.output_shard[offset:offset + tensor_size].view_as(tensor_list[0])
             self.callbacks.append(functools.partial(callback_fn, result_view))
 
 
@@ -141,9 +142,8 @@ class ReduceScatterBucketer:
         """
         world_size = group.size()
 
-        assert (
-            len(input_list) == world_size
-        ), f"reduce_scatter received {len(input_list)} inputs, expected group.size() ({world_size})"
+        assert (len(input_list) == world_size
+               ), f"reduce_scatter received {len(input_list)} inputs, expected group.size() ({world_size})"
 
         first_input = input_list[0]
         first_input_size = first_input.numel()
@@ -183,7 +183,7 @@ class ReduceScatterBucketer:
 
     @functools.lru_cache()
     def _get_shard_size(self, element_size: int, num_shards: int) -> int:
-        if self.bucket_size_mb <= 0:  # Values <= 0 disable bucketing.
+        if self.bucket_size_mb <= 0:    # Values <= 0 disable bucketing.
             return 0
         MB = 1024 * 1024
         bucket_size = self.bucket_size_mb * MB / element_size