[Misc] Use `apply_rotary_emb` from vllm_flash_attn for Qwen2-VL vision RoPE (#17726)

Signed-off-by: Isotr0py <2037008807@qq.com>

vllm/model_executor/models/qwen2_5_vl.py

@@ -297,13 +297,8 @@ class Qwen2_5_VisionAttention(nn.Module):
         q, k, v = (rearrange(x, "s b ... -> b s ...").contiguous()
                    for x in (q, k, v))
         if rotary_pos_emb is not None:
-            use_flash_attn = self.attn_backend == _Backend.FLASH_ATTN
-            q = apply_rotary_pos_emb_vision(q,
-                                            rotary_pos_emb,
-                                            use_flash_attn=use_flash_attn)
-            k = apply_rotary_pos_emb_vision(k,
-                                            rotary_pos_emb,
-                                            use_flash_attn=use_flash_attn)
+            q = apply_rotary_pos_emb_vision(q, rotary_pos_emb)
+            k = apply_rotary_pos_emb_vision(k, rotary_pos_emb)
 
         if self.attn_backend == _Backend.FLASH_ATTN:
             # from vllm_flash_attn.flash_attn_interface import (

vllm/model_executor/models/qwen2_vl.py

@@ -64,7 +64,7 @@ from vllm.multimodal.processing import (BaseMultiModalProcessor,
                                         BaseProcessingInfo, PromptReplacement,
                                         PromptUpdate)
 from vllm.multimodal.profiling import BaseDummyInputsBuilder
-from vllm.platforms import _Backend
+from vllm.platforms import _Backend, current_platform
 from vllm.sequence import IntermediateTensors
 from vllm.transformers_utils.config import uses_mrope
 from vllm.transformers_utils.processor import (
@@ -230,14 +230,13 @@ def apply_rotary_emb_torch(x: torch.Tensor,
 
 
 def apply_rotary_pos_emb_vision(t: torch.Tensor,
-                                freqs: torch.Tensor,
-                                use_flash_attn=False) -> torch.Tensor:
+                                freqs: torch.Tensor) -> torch.Tensor:
     t_ = t.float()
     cos = freqs.cos()
     sin = freqs.sin()
     apply_rotary_emb = apply_rotary_emb_torch
-    if use_flash_attn:
-        from flash_attn.layers.rotary import apply_rotary_emb
+    if current_platform.is_cuda():
+        from vllm.vllm_flash_attn.layers.rotary import apply_rotary_emb
     output = apply_rotary_emb(t_, cos, sin).type_as(t)
     return output