[Minor] clean up multimodal processor and tokenizer manager (#7624)

python/sglang/srt/sampling/sampling_batch_info.py

@@ -10,7 +10,6 @@ import torch
 import sglang.srt.sampling.penaltylib as penaltylib
 from sglang.srt.sampling.custom_logit_processor import CustomLogitProcessor
 from sglang.srt.sampling.sampling_params import TOP_K_ALL
-from sglang.srt.utils import merge_bias_tensor
 
 if TYPE_CHECKING:
     from sglang.srt.managers.schedule_batch import ScheduleBatch
@@ -345,3 +344,42 @@ class SamplingBatchInfo:
         self.logit_bias = merge_bias_tensor(
             self.logit_bias, other.logit_bias, len(self), len(other), self.device, 0.0
         )
+
+
+def merge_bias_tensor(
+    lhs: Optional[torch.Tensor],
+    rhs: Optional[torch.Tensor],
+    bs1: int,
+    bs2: int,
+    device: str,
+    default: float,
+):
+    """Merge two bias tensors for batch merging.
+
+    Args:
+        lhs: Left-hand side tensor
+        rhs: Right-hand side tensor
+        bs1: Batch size of left-hand side tensor
+        bs2: Batch size of right-hand side tensor
+        device: Device to place the merged tensor on
+        default: Default value for missing tensor elements
+
+    Returns:
+        Merged tensor or None if both inputs are None
+    """
+    if lhs is None and rhs is None:
+        return None
+
+    if lhs is not None and rhs is not None:
+        return torch.cat([lhs, rhs])
+    else:
+        if lhs is not None:
+            shape, dtype = lhs.shape[1:], lhs.dtype
+        else:
+            shape, dtype = rhs.shape[1:], rhs.dtype
+
+        if lhs is None:
+            lhs = torch.empty((bs1, *shape), device=device, dtype=dtype).fill_(default)
+        if rhs is None:
+            rhs = torch.empty((bs2, *shape), device=device, dtype=dtype).fill_(default)
+        return torch.cat([lhs, rhs])