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

python/sglang/srt/utils.py

@@ -1917,14 +1917,11 @@ def configure_ipv6(dist_init_addr):
     return port, host
 
 
-def rank0_print(msg: str):
+def rank0_log(msg: str):
     from sglang.srt.distributed import get_tensor_model_parallel_rank
 
     if get_tensor_model_parallel_rank() == 0:
-        print(msg, flush=True)
-
-
-rank0_log = rank0_print
+        logger.info(msg)
 
 
 def get_cuda_version():
@@ -2344,45 +2341,6 @@ def require_mlp_sync(server_args):
     return server_args.enable_dp_attention or require_gathered_buffer(server_args)
 
 
-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])
-
-
 def find_local_repo_dir(repo_id: str, revision: Optional[str] = None) -> Optional[str]:
     import huggingface_hub as hf