Eliminate 2 gpu ops during sampling when logit_bias is zero (#338)

Co-authored-by: hnyls2002 <hnyls2002@gmail.com>

python/sglang/srt/managers/router/infer_batch.py

@@ -251,10 +251,14 @@ class Batch:
             ] = out_cache_loc[pt : pt + extend_lens[i]]
             pt += extend_lens[i]
 
-        # Handle logit bias
-        logit_bias = torch.zeros((bs, vocab_size), dtype=torch.float32, device=device)
+        # Handle logit bias but only allocate when needed
+        logit_bias = None
         for i in range(bs):
             if reqs[i].sampling_params.dtype == "int":
+                if logit_bias is None:
+                    logit_bias = torch.zeros(
+                        (bs, vocab_size), dtype=torch.float32, device=device
+                    )
                 logit_bias[i] = int_token_logit_bias
 
         # Set fields
@@ -433,9 +437,12 @@ class Batch:
             "presence_penalties",
             "logit_bias",
         ]:
-            setattr(self, item, getattr(self, item)[new_indices])
+            self_val = getattr(self, item, None)
+            # logit_bias can be None
+            if self_val is not None:
+                setattr(self, item, self_val[new_indices])
 
-    def merge(self, other):
+    def merge(self, other: "Batch"):
         self.reqs.extend(other.reqs)
 
         self.req_pool_indices = torch.concat(
@@ -456,16 +463,33 @@ class Batch:
             "top_ks",
             "frequency_penalties",
             "presence_penalties",
-            "logit_bias",
         ]:
-            setattr(
-                self, item, torch.concat([getattr(self, item), getattr(other, item)])
+            self_val = getattr(self, item, None)
+            other_val = getattr(other, item, None)
+            setattr(self, item, torch.concat([self_val, other_val]))
+
+        # logit_bias can be None
+        if self.logit_bias is not None or other.logit_bias is not None:
+            vocab_size = (
+                self.logit_bias.shape[1]
+                if self.logit_bias is not None
+                else other.logit_bias.shape[1]
+            )
+            if self.logit_bias is None:
+                self.logit_bias = torch.zeros(
+                    (len(self.reqs), vocab_size), dtype=torch.float32, device="cuda"
+                )
+            if other.logit_bias is None:
+                other.logit_bias = torch.zeros(
+                    (len(other.reqs), vocab_size), dtype=torch.float32, device="cuda"
                 )
+            self.logit_bias = torch.concat([self.logit_bias, other.logit_bias])
 
     def sample(self, logits: torch.Tensor):
         # Post process logits
         logits = logits.contiguous()
         logits.div_(self.temperatures)
+        if self.logit_bias is not None:
             logits.add_(self.logit_bias)
 
         has_regex = any(req.regex_fsm is not None for req in self.reqs)