diff --git a/requirements-cuda.txt b/requirements-cuda.txt
index 5109f173..35361798 100644
--- a/requirements-cuda.txt
+++ b/requirements-cuda.txt
@@ -6,4 +6,4 @@ ray >= 2.9
 nvidia-ml-py # for pynvml package
 torch == 2.3.0
 xformers == 0.0.26.post1  # Requires PyTorch 2.3.0
-vllm-flash-attn == 2.5.8.post2  # Requires PyTorch 2.3.0
+vllm-flash-attn == 2.5.9  # Requires PyTorch 2.3.0
diff --git a/vllm/attention/backends/flash_attn.py b/vllm/attention/backends/flash_attn.py
index 0b9d6283..070c074e 100644
--- a/vllm/attention/backends/flash_attn.py
+++ b/vllm/attention/backends/flash_attn.py
@@ -317,7 +317,7 @@ class FlashAttentionImpl(AttentionImpl):
                 # normal attention
                 # When block_tables are not filled, it means q and k are the
                 # prompt, and they have the same length.
-                out = flash_attn_varlen_func(
+                flash_attn_varlen_func(
                     q=query,
                     k=key,
                     v=value,
@@ -329,14 +329,13 @@ class FlashAttentionImpl(AttentionImpl):
                     causal=True,
                     window_size=self.sliding_window,
                     alibi_slopes=self.alibi_slopes,
+                    out=output[:num_prefill_tokens],
                 )
-                assert output[:num_prefill_tokens].shape == out.shape
-                output[:num_prefill_tokens] = out
             else:
                 # prefix-enabled attention
                 assert prefill_meta.seq_lens is not None
                 max_seq_len = max(prefill_meta.seq_lens)
-                output[:num_prefill_tokens] = flash_attn_varlen_func(
+                flash_attn_varlen_func(
                     q=query,
                     k=key_cache,
                     v=value_cache,
@@ -348,11 +347,12 @@ class FlashAttentionImpl(AttentionImpl):
                     causal=True,
                     alibi_slopes=self.alibi_slopes,
                     block_table=prefill_meta.block_tables,
+                    out=output[:num_prefill_tokens],
                 )
 
         if decode_meta := attn_metadata.decode_metadata:
             # Decoding run.
-            output[num_prefill_tokens:] = flash_attn_with_kvcache(
+            flash_attn_with_kvcache(
                 decode_query.unsqueeze(1),
                 key_cache,
                 value_cache,
@@ -361,7 +361,8 @@ class FlashAttentionImpl(AttentionImpl):
                 softmax_scale=self.scale,
                 causal=True,
                 alibi_slopes=self.alibi_slopes,
-            ).squeeze(1)
+                out=output[num_prefill_tokens:].unsqueeze(1),
+            )
 
         # Reshape the output tensor.
         return output.view(num_tokens, hidden_size)