From 71133a0426a331b182ec27294736468879ae21f4 Mon Sep 17 00:00:00 2001
From: Lianmin Zheng <lianminzheng@gmail.com>
Date: Tue, 9 Sep 2025 01:29:52 -0700
Subject: [PATCH] [Auto Sync] Update sampling_batch_info.py (20250909) (#10212)

Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
Co-authored-by: cctry <shiyang@x.ai>
---
 .../srt/sampling/sampling_batch_info.py       | 33 ++++++++++---------
 1 file changed, 18 insertions(+), 15 deletions(-)

diff --git a/python/sglang/srt/sampling/sampling_batch_info.py b/python/sglang/srt/sampling/sampling_batch_info.py
index ec649f479..6ba8a7777 100644
--- a/python/sglang/srt/sampling/sampling_batch_info.py
+++ b/python/sglang/srt/sampling/sampling_batch_info.py
@@ -67,28 +67,31 @@ class SamplingBatchInfo:
     logit_bias: Optional[torch.Tensor] = None
 
     @classmethod
-    def from_schedule_batch(cls, batch: ScheduleBatch, vocab_size: int):
+    def _get_global_server_args_dict(cls):
         from sglang.srt.managers.schedule_batch import global_server_args_dict
 
+        return global_server_args_dict
+
+    @classmethod
+    def from_schedule_batch(cls, batch: ScheduleBatch, vocab_size: int):
+        global_server_args_dict = cls._get_global_server_args_dict()
+
         reqs = batch.reqs
         device = batch.device
-        temperatures = (
-            torch.tensor(
-                [r.sampling_params.temperature for r in reqs],
-                dtype=torch.float,
-            )
-            .view(-1, 1)
-            .to(device, non_blocking=True)
-        )
+        temperatures = torch.tensor(
+            [r.sampling_params.temperature for r in reqs],
+            dtype=torch.float,
+            device=device,
+        ).view(-1, 1)
         top_ps = torch.tensor(
-            [r.sampling_params.top_p for r in reqs], dtype=torch.float
-        ).to(device, non_blocking=True)
+            [r.sampling_params.top_p for r in reqs], dtype=torch.float, device=device
+        )
         top_ks = torch.tensor(
-            [r.sampling_params.top_k for r in reqs], dtype=torch.int32
-        ).to(device, non_blocking=True)
+            [r.sampling_params.top_k for r in reqs], dtype=torch.int32, device=device
+        )
         min_ps = torch.tensor(
-            [r.sampling_params.min_p for r in reqs], dtype=torch.float
-        ).to(device, non_blocking=True)
+            [r.sampling_params.min_p for r in reqs], dtype=torch.float, device=device
+        )
 
         logit_bias = None
         if any(r.sampling_params.logit_bias is not None for r in reqs):