Fix vocab mask update bug (#1376)

python/sglang/srt/managers/schedule_batch.py

@@ -652,8 +652,6 @@ class ScheduleBatch:
             self.req_pool_indices, self.seq_lens - 1
         ] = self.out_cache_loc
 
-        self.sampling_info.update_regex_vocab_mask(self)
-
     def filter_batch(self, unfinished_indices: List[int]):
         if unfinished_indices is None or len(unfinished_indices) == 0:
             # Filter out all requests

python/sglang/srt/model_executor/forward_batch_info.py

@@ -195,7 +195,8 @@ class InputMetadata:
             top_logprobs_nums=batch.top_logprobs_nums,
         )
 
-        ret.sampling_info.prepare_penalties()
+        ret.sampling_info.update_penalties()
+        ret.sampling_info.update_regex_vocab_mask(batch)
 
         ret.compute_positions(batch)
 

python/sglang/srt/sampling/sampling_batch_info.py

@@ -34,6 +34,9 @@ class SamplingBatchInfo:
     linear_penalties: torch.Tensor = None
     scaling_penalties: torch.Tensor = None
 
+    def __len__(self):
+        return len(self.temperatures)
+
     def can_run_in_cuda_graph(self):
         # Vocab bias and min_ps are not supported in CUDA graph
         return (
@@ -118,11 +121,9 @@ class SamplingBatchInfo:
         # Handle logit bias but only allocate when needed
         ret.logit_bias = None
 
-        ret.update_regex_vocab_mask(batch)
-
         return ret
 
-    def prepare_penalties(self):
+    def update_penalties(self):
         self.scaling_penalties = None
         self.linear_penalties = None
 
@@ -174,6 +175,26 @@ class SamplingBatchInfo:
             if self_val is not None:  # logit_bias can be None
                 setattr(self, item, self_val[new_indices])
 
+    @staticmethod
+    def merge_bias_tensor(
+        lhs: torch.Tensor, rhs: torch.Tensor, bs1: int, bs2: int, default: int = 0
+    ):
+        # bias tensor can be None
+        if lhs is not None or rhs is not None:
+            shape, dtype = None, None
+            if lhs is not None:
+                shape, dtype = lhs.shape[1:], lhs.dtype
+            else:
+                shape, dtype = rhs.shape[1:], rhs.dtype
+            with torch.dtype(dtype):
+                if lhs is None:
+                    lhs = torch.empty((bs1, *shape), device="cuda").fill_(default)
+                if rhs is None:
+                    rhs = torch.empty((bs2, *shape), device="cuda").fill_(default)
+            return torch.cat([lhs, rhs])
+
+        return None
+
     def merge(self, other: "SamplingBatchInfo"):
         self.penalizer_orchestrator.merge(other.penalizer_orchestrator)
 
@@ -187,19 +208,6 @@ class SamplingBatchInfo:
             other_val = getattr(other, item, None)
             setattr(self, item, torch.concat([self_val, other_val]))
 
-        # logit_bias can be None
+        self.logit_bias = SamplingBatchInfo.merge_bias_tensor(
-        if self.logit_bias is not None or other.logit_bias is not None:
+            self.logit_bias, other.logit_bias, len(self), len(other)
-            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])