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

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)