Minor: improve sampler & remove unused fields from model_config.py (#11531)

python/sglang/srt/configs/model_config.py

@@ -25,7 +25,7 @@ from transformers import PretrainedConfig
 from sglang.srt.environ import envs
 from sglang.srt.layers.quantization import QUANTIZATION_METHODS
 from sglang.srt.server_args import ServerArgs
-from sglang.srt.utils import is_hip, retry
+from sglang.srt.utils import is_hip
 from sglang.srt.utils.hf_transformers_utils import (
     get_config,
     get_context_length,
@@ -86,11 +86,11 @@ class ModelConfig:
         dtype: str = "auto",
         quantization: Optional[str] = None,
         modelopt_quant: Optional[Union[str, Dict]] = None,
-        modelopt_checkpoint_restore_path: Optional[str] = None,
-        modelopt_checkpoint_save_path: Optional[str] = None,
         override_config_file: Optional[str] = None,
         is_draft_model: bool = False,
-        hybrid_kvcache_ratio: Optional[float] = None,
+        hybrid_kvcache_ratio: Optional[
+            float
+        ] = None,  # TODO: remove this, it is not a model config
         model_impl: Union[str, ModelImpl] = ModelImpl.AUTO,
         sampling_defaults: str = "openai",
     ) -> None:

python/sglang/srt/layers/sampler.py

@@ -92,6 +92,12 @@ class Sampler(nn.Module):
             if return_logprob:
                 logprobs = torch.nn.functional.log_softmax(logits, dim=-1)
         else:
+            can_sample_directly_from_probs = (
+                not sampling_info.need_top_p_sampling
+                and not sampling_info.need_top_k_sampling
+                and not sampling_info.need_min_p_sampling
+            )
+
             # If requested, cache probabilities from original logits before temperature scaling.
             if return_logprob and RETURN_ORIGINAL_LOGPROB:
                 probs_without_temp_scaling = torch.softmax(logits, dim=-1)
@@ -102,7 +108,14 @@ class Sampler(nn.Module):
             probs = logits
             del logits
 
-            if True:  # Keep this redundant check to simplify some internal code sync
+            if can_sample_directly_from_probs:
+                # when we don't need top-k, top-p, or min-p sampling, we can directly sample from the probs
+                batch_next_token_ids = sampling_from_probs_torch(
+                    probs,
+                    sampling_seed=sampling_info.sampling_seed,
+                    positions=positions,
+                )
+            else:
                 if get_global_server_args().sampling_backend == "flashinfer":
                     if sampling_info.need_min_p_sampling:
                         probs = top_k_renorm_prob(probs, sampling_info.top_ks)

python/sglang/srt/model_executor/model_runner.py

@@ -648,7 +648,8 @@ class ModelRunner:
                     // (self.tp_size // self.moe_ep_size)
                 ) % weight_block_size_n != 0:
                     raise ValueError(
-                        f"For qwen3-vl-fp8 models, please make sure ({text_config.moe_intermediate_size=} // ({self.tp_size=} // {self.moe_ep_size=})) % {weight_block_size_n=} == 0"
+                        f"For qwen3-vl-fp8 models, please make sure ({text_config.moe_intermediate_size=} // ({self.tp_size=} // {self.moe_ep_size=})) % {weight_block_size_n=} == 0. "
+                        f"You can fix this by using arguments such as `--tp-size 8 --ep-size 8`"
                     )
 
     def init_torch_distributed(self):

python/sglang/srt/sampling/sampling_params.py

@@ -17,8 +17,6 @@ import logging
 import sre_parse
 from typing import Any, Dict, List, Optional, Union
 
-from sglang.srt.utils import get_bool_env_var
-
 _SAMPLING_EPS = 1e-6
 TOP_K_ALL = 1 << 30