[refactor] Config resolution pipeline: full-stack review (10-PR series, review only) (#30137)

python/sglang/srt/layers/sampler.py

@@ -13,6 +13,7 @@ from sglang.srt.layers.dp_attention import (
 from sglang.srt.layers.logits_processor import LogitsProcessorOutput
 from sglang.srt.layers.utils.hash import murmur_hash32
 from sglang.srt.layers.utils.logprob import get_token_ids_logprobs, get_top_logprobs
+from sglang.srt.runtime_context import get_flags
 from sglang.srt.sampling.sampling_batch_info import SamplingBatchInfo
 from sglang.srt.sampling.sampling_params import TOP_K_ALL
 from sglang.srt.server_args import get_global_server_args
@@ -79,7 +80,7 @@ class Sampler(nn.Module):
         )
         # In RL on-policy mode, we use log_softmax to compute logprobs to match the trainer.
         self.use_log_softmax_logprob = self.rl_on_policy_target is not None
-        self.use_ascend_backend = get_global_server_args().sampling_backend == "ascend"
+        self.use_ascend_backend = get_flags().sampling_backend == "ascend"
 
     def _preprocess_logits(
         self, logits: torch.Tensor, sampling_info: SamplingBatchInfo
@@ -230,7 +231,7 @@ class Sampler(nn.Module):
                 positions=positions,
             )
         else:
-            backend = get_global_server_args().sampling_backend
+            backend = get_flags().sampling_backend
             if backend == "flashinfer":
                 assert (
                     sampling_info.sampling_seed is None