Update vLLM precompiled wheel version and refactor various components for improved functionality and error handling. Changes include modifying the get_name method in DiffusionQuantizationConfig to use class-level attributes, enhancing metadata handling in StageEngineCoreClient, and adjusting the AsyncOmni class constructor for better parameter management. Additionally, improved error handling in omni_snapshot_download and optimized initial chunk size computation in talker2code2wav_async_chunk for dynamic load adaptation.

Signed-off-by: tzhouam <tzhouam@connect.ust.hk>

vllm_omni/model_executor/models/qwen3_omni/qwen3_omni_moe_thinker.py

@@ -155,8 +155,7 @@ class Qwen3Omni_VisionTransformer(_Qwen3Omni_VisionTransformer):
             cu_seqlens = F.pad(cu_seqlens, (1, 0), value=0)
         except RuntimeError:
             logger.warning(
-                "torch.repeat_interleave not executable, "
-                "switching to vectorized searchsorted implementation."
+                "torch.repeat_interleave not executable, switching to vectorized searchsorted implementation."
             )
             repeat_counts = grid_thw_tensor[:, 0]
             values = grid_thw_tensor[:, 1] * grid_thw_tensor[:, 2]
@@ -179,9 +178,9 @@ class Qwen3Omni_VisionTransformer(_Qwen3Omni_VisionTransformer):
         max_seqlen = self.compute_attn_mask_seqlen(cu_seqlens)
 
         grid_thw_np = grid_thw_tensor.cpu().numpy().astype(np.int32)
-        cu_seqlens_np = np.repeat(
-            grid_thw_np[:, 1] * grid_thw_np[:, 2], grid_thw_np[:, 0]
-        ).cumsum(axis=0, dtype=np.int32)
+        cu_seqlens_np = np.repeat(grid_thw_np[:, 1] * grid_thw_np[:, 2], grid_thw_np[:, 0]).cumsum(
+            axis=0, dtype=np.int32
+        )
         cu_seqlens_np = np.concatenate([np.zeros(1, dtype=np.int32), cu_seqlens_np])
         sequence_lengths = MMEncoderAttention.maybe_compute_seq_lens(
             self.attn_backend,