Fuse top_k and top_k in the sampler (#1457)

docs/en/sampling_params.md

@@ -23,6 +23,7 @@ class GenerateReqInput:
     # Whether to return logprobs.
     return_logprob: Optional[Union[List[bool], bool]] = None
     # The start location of the prompt for return_logprob.
+    # By default, this value is "-1", which means it will only return logprobs for output tokens.
     logprob_start_len: Optional[Union[List[int], int]] = None
     # The number of top logprobs to return.
     top_logprobs_num: Optional[Union[List[int], int]] = None

python/sglang/srt/layers/sampler.py

@@ -31,8 +31,11 @@ class Sampler(nn.Module):
             logits = logits.next_token_logits
 
         # Post process logits
+        logits = logits.contiguous()
         logits.div_(sampling_info.temperatures)
-        probs = logits[:] = torch.softmax(logits, dim=-1)
+        probs = torch.softmax(logits, dim=-1)
+        logits = None
+        del logits
 
         if torch.any(torch.isnan(probs)):
             logger.warning("Detected errors during sampling! NaN in the probability.")
@@ -53,7 +56,11 @@ class Sampler(nn.Module):
                 )
             else:
                 batch_next_token_ids, success = top_k_top_p_sampling_from_probs(
-                    probs, uniform_samples, sampling_info.top_ks, sampling_info.top_ps
+                    probs,
+                    uniform_samples,
+                    sampling_info.top_ks,
+                    sampling_info.top_ps,
+                    filter_apply_order="joint",
                 )
 
             if not torch.all(success):

python/sglang/srt/model_executor/model_runner.py

@@ -400,8 +400,8 @@ class ModelRunner:
             )
 
         self.req_to_token_pool = ReqToTokenPool(
-            max_num_reqs,
-            self.model_config.context_len + 8,
+            max_num_reqs + 1,
+            self.model_config.context_len + 4,
         )
         if (
             self.model_config.attention_arch == AttentionArch.MLA