Revert "Eliminate 2 gpu ops during sampling when logit_bias is zero" (#345)

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

@@ -251,14 +251,10 @@ class Batch:
             ] = out_cache_loc[pt : pt + extend_lens[i]]
             pt += extend_lens[i]
 
-        # Handle logit bias but only allocate when needed
-        logit_bias = None
+        # Handle logit bias
+        logit_bias = torch.zeros((bs, vocab_size), dtype=torch.float32, device=device)
         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
@@ -437,12 +433,9 @@ class Batch:
             "presence_penalties",
             "logit_bias",
         ]:
-            self_val = getattr(self, item, None)
-            # logit_bias can be None
-            if self_val is not None:
-                setattr(self, item, self_val[new_indices])
+            setattr(self, item, getattr(self, item)[new_indices])
 
-    def merge(self, other: "Batch"):
+    def merge(self, other):
         self.reqs.extend(other.reqs)
 
         self.req_pool_indices = torch.concat(
@@ -463,34 +456,17 @@ class Batch:
             "top_ks",
             "frequency_penalties",
             "presence_penalties",
+            "logit_bias",
         ]:
-            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]
+            setattr(
+                self, item, torch.concat([getattr(self, item), getattr(other, item)])
             )
-            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)
+        logits.add_(self.logit_bias)
 
         has_regex = any(req.regex_fsm is not None for req in self.reqs)
         if has_regex: