[Blackwell] Add CUDNN_ATTN and FLASHINFER_ATTN backends for diffusion (auto-route) (#3079)

Signed-off-by: lishunyang <lishunyang12@163.com>
Signed-off-by: Zhang <jianmusings@gmail.com>
Signed-off-by: Zhang Jian <jianmusings@gmail.com>
Co-authored-by: frauttauteffasu <247219357+frauttauteffasu@users.noreply.github.com>
Co-authored-by: Zhang Jian <jianmusings@gmail.com>

vllm_omni/diffusion/diffusion_engine.py

@@ -691,6 +691,10 @@ class DiffusionEngine:
             dummy_audio = np.random.randn(audio_sr * 2).astype(np.float32)
             prompt.setdefault("multi_modal_data", {})["audio"] = dummy_audio
 
+        # Audio pipelines round audio token count from num_frames; the default
+        # of 1 yields seq_len=1 K/V which cuDNN SDPA refuses under torch.compile.
+        # 2 is the minimum that produces audio_num_frames > 1.
+        num_frames = 2 if supports_audio_input or supports_audio_output(self.od_config.model_class_name) else 1
         req = OmniDiffusionRequest(
             prompts=[prompt],
             request_ids=["dummy_req_id"],
@@ -698,6 +702,7 @@ class DiffusionEngine:
                 height=height,
                 width=width,
                 num_inference_steps=num_inference_steps,
+                num_frames=num_frames,
                 # Keep warmup path minimal and robust across text encoders.
                 # Some models may fail when warmup implicitly triggers
                 # classifier-free guidance with an empty negative prompt.