fix: 修复 expanded sampling metadata 对 numpy/array-like 输入不兼容导致崩溃

  - repeat_counts/CPU 元数据为 numpy/array-like 时会在 repeat_interleave/.to() 崩溃
  - 统一转换为 CPU torch.Tensor 后再扩展并拷到 GPU

vllm/v1/worker/gpu_input_batch.py

@@ -735,21 +735,34 @@ class InputBatch:
         self, repeat_counts: torch.Tensor
     ) -> SamplingMetadata:
         num_reqs = self.num_reqs
-        repeat_counts_cpu = repeat_counts
+        # `repeat_counts` is expected to be a CPU torch tensor, but some
+        # call sites may pass a NumPy array (or other array-likes). Normalize
+        # to a CPU tensor to keep downstream ops (e.g. repeat_interleave)
+        # consistent and avoid hard crashes.
+        if isinstance(repeat_counts, torch.Tensor):
+            repeat_counts_cpu = repeat_counts.to(device="cpu")
+        else:
+            repeat_counts_cpu = torch.as_tensor(repeat_counts, device="cpu")
         all_greedy = self.all_greedy
         all_random = self.all_random
         # For reject-sampling optimization, force greedy sampling to keep
         # rejection sampler assumptions (per-request shapes) intact.
 
         def _expand_cpu_to_gpu(
-            t: Optional[torch.Tensor],
+            t: Optional[object],
             *,
             dtype: Optional[torch.dtype] = None,
         ) -> Optional[torch.Tensor]:
             if t is None:
                 return None
+            # `t` should be a CPU torch tensor, but can be a NumPy array view
+            # (e.g. created via `tensor.numpy()`). Convert if needed.
+            if isinstance(t, torch.Tensor):
                 base = t[:num_reqs]
-            if repeat_counts_cpu is not None:
+            elif isinstance(t, np.ndarray):
+                base = torch.from_numpy(t[:num_reqs])
+            else:
+                base = torch.as_tensor(t, device="cpu")[:num_reqs]
             base = base.repeat_interleave(repeat_counts_cpu, dim=0)
             return base.to(device=self.device,
                            dtype=dtype if dtype is not None else None,