Update docstring

flash_mla/flash_mla_interface.py

@@ -16,7 +16,7 @@ def get_mla_metadata(
         num_heads_per_head_k: Equals to seq_len_q * num_heads_q // num_heads_k.
         num_heads_k: num_heads_k.
 
-    Return:
+    Returns:
         tile_scheduler_metadata: (num_sm_parts, TileSchedulerMetaDataSize), dtype torch.int32.
         num_splits: (batch_size + 1), dtype torch.int32.
     """
@@ -40,13 +40,13 @@ def flash_mla_with_kvcache(
         k_cache: (num_blocks, page_block_size, num_heads_k, head_dim).
         block_table: (batch_size, max_num_blocks_per_seq), torch.int32.
         cache_seqlens: (batch_size), torch.int32.
-        head_dim_v: Head_dim of v.
-        tile_scheduler_metadata: (num_sm_parts, TileSchedulerMetaDataSize), torch.int32, return by get_mla_metadata.
-        num_splits: (batch_size + 1), torch.int32, return by get_mla_metadata.
-        softmax_scale: float. The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim).
+        head_dim_v: Head dimension of v.
+        tile_scheduler_metadata: (num_sm_parts, TileSchedulerMetaDataSize), torch.int32, returned by get_mla_metadata.
+        num_splits: (batch_size + 1), torch.int32, returned by get_mla_metadata.
+        softmax_scale: float. The scale of QK^T before applying softmax. Default to 1 / sqrt(head_dim).
         causal: bool. Whether to apply causal attention mask.
 
-    Return:
+    Returns:
         out: (batch_size, seq_len_q, num_heads_q, head_dim_v).
         softmax_lse: (batch_size, num_heads_q, seq_len_q), torch.float32.
     """