[Enhancement] Custom Logit Processor Improvement (#2998)

Signed-off-by: Hongpeng Guo <hpguo@anyscale.com>

python/sglang/bench_one_batch.py

@@ -232,6 +232,7 @@ def extend(reqs, model_runner):
         model_config=model_runner.model_config,
         enable_overlap=False,
         spec_algorithm=SpeculativeAlgorithm.NONE,
+        enable_custom_logit_processor=False,
     )
     batch.prepare_for_extend()
     model_worker_batch = batch.get_model_worker_batch()

python/sglang/srt/layers/sampler.py

@@ -132,6 +132,11 @@ class Sampler(nn.Module):
         """Apply custom logit processors to the logits.
         This function will modify the logits in-place."""
 
+        assert logits.shape[0] == len(sampling_batch_info), (
+            f"The batch size of logits ({logits.shape[0]}) does not match the batch size of "
+            f"sampling_batch_info ({len(sampling_batch_info)})"
+        )
+
         for _, (
             processor,
             batch_mask,
@@ -139,6 +144,11 @@ class Sampler(nn.Module):
             # Get the batch indices that need to be processed
             batch_indices = batch_mask.nonzero(as_tuple=True)[0]
 
+            assert batch_mask.shape[0] == len(sampling_batch_info), (
+                f"The number of batch mask ({batch_mask.shape[0]}) does not match the number of "
+                f"sampling_batch_info ({len(sampling_batch_info)})"
+            )
+
             # Apply the processor to the logits
             logits[batch_mask] = processor(
                 logits[batch_mask],

python/sglang/srt/managers/schedule_batch.py

@@ -595,6 +595,9 @@ class ScheduleBatch:
     spec_algorithm: SpeculativeAlgorithm = None
     spec_info: Optional[SpecInfo] = None
 
+    # Enable custom logit processor
+    enable_custom_logit_processor: bool = False
+
     @classmethod
     def init_new(
         cls,
@@ -605,6 +608,7 @@ class ScheduleBatch:
         model_config: ModelConfig,
         enable_overlap: bool,
         spec_algorithm: SpeculativeAlgorithm,
+        enable_custom_logit_processor: bool,
     ):
         return cls(
             reqs=reqs,
@@ -618,6 +622,7 @@ class ScheduleBatch:
             has_grammar=any(req.grammar for req in reqs),
             device=req_to_token_pool.device,
             spec_algorithm=spec_algorithm,
+            enable_custom_logit_processor=enable_custom_logit_processor,
         )
 
     def batch_size(self):
@@ -1201,6 +1206,7 @@ class ScheduleBatch:
             return_logprob=self.return_logprob,
             decoding_reqs=self.decoding_reqs,
             spec_algorithm=self.spec_algorithm,
+            enable_custom_logit_processor=self.enable_custom_logit_processor,
         )
 
     def __str__(self):

python/sglang/srt/managers/scheduler.py

@@ -966,6 +966,7 @@ class Scheduler:
             self.model_config,
             self.enable_overlap,
             self.spec_algorithm,
+            self.server_args.enable_custom_logit_processor,
         )
         new_batch.prepare_for_extend()
 
@@ -1520,6 +1521,7 @@ class Scheduler:
             self.model_config,
             self.enable_overlap,
             self.spec_algorithm,
+            self.server_args.enable_custom_logit_processor,
         )
         idle_batch.prepare_for_idle()
         return idle_batch

python/sglang/srt/sampling/sampling_batch_info.py

@@ -89,7 +89,10 @@ class SamplingBatchInfo:
         ).to(device, non_blocking=True)
 
         # Check if any request has custom logit processor
-        has_custom_logit_processor = any(r.custom_logit_processor for r in reqs)
+        has_custom_logit_processor = (
+            batch.enable_custom_logit_processor  # check the flag first.
+            and any(r.custom_logit_processor for r in reqs)  # then check the requests.
+        )
 
         if has_custom_logit_processor:
             # Merge the same type of custom logit processors together
@@ -247,8 +250,7 @@ class SamplingBatchInfo:
         self, unfinished_indices: List[int], new_indices: torch.Tensor
     ):
         """Filter the custom logit processor and custom params"""
-        if not self.custom_logit_processor:
-            return
+
         self.custom_logit_processor = {
             k: (p, mask[new_indices])
             for k, (p, mask) in self.custom_logit_processor.items()
@@ -258,7 +260,9 @@ class SamplingBatchInfo:
         }
         self.custom_params = [self.custom_params[i] for i in unfinished_indices]
 
-        if len(self) == 0:
+        # If the custom logit processor is an empty dict, set the flag to False,
+        # and set the custom logit processor and custom params to None.
+        if len(self.custom_logit_processor) == 0:
             self.custom_logit_processor = None
             self.custom_params = None
             self.has_custom_logit_processor = False
@@ -290,8 +294,8 @@ class SamplingBatchInfo:
 
     @staticmethod
     def merge_custom_logit_processor(
-        lhs: Optional[Dict[str, torch.Tensor]],
-        rhs: Optional[Dict[str, torch.Tensor]],
+        lhs: Optional[Dict[int, Tuple[CustomLogitProcessor, torch.Tensor]]],
+        rhs: Optional[Dict[int, Tuple[CustomLogitProcessor, torch.Tensor]]],
         bs1: int,
         bs2: int,
         device: str,
@@ -319,27 +323,22 @@ class SamplingBatchInfo:
             )
             merged_dict[k] = (processor, torch.cat([left_mask, right_mask]))
 
+            assert merged_dict[k][1].shape[0] == bs1 + bs2, (
+                f"The batch size of merged mask ({merged_dict[k][1].shape[0]}) does not match "
+                f"the sum of the batch sizes of the two masks ({bs1 + bs2})"
+                f"\n{left_mask=}\n{right_mask=}\n{bs1=}\n{bs2=}"
+                f"\n{lhs=}\n{rhs=}"
+            )
+
         return merged_dict
 
     def merge_batch(self, other: "SamplingBatchInfo"):
         self.penalizer_orchestrator.merge(other.penalizer_orchestrator)
 
-        for item in [
-            "temperatures",
-            "top_ps",
-            "top_ks",
-            "min_ps",
-        ]:
-            self_val = getattr(self, item, None)
-            other_val = getattr(other, item, None)
-            setattr(self, item, torch.concat([self_val, other_val]))
-
-        self.is_all_greedy = self.is_all_greedy and other.is_all_greedy
+        # Merge the logit bias tensor
         self.logit_bias = SamplingBatchInfo.merge_bias_tensor(
             self.logit_bias, other.logit_bias, len(self), len(other), self.device
         )
-        self.need_min_p_sampling = self.need_min_p_sampling or other.need_min_p_sampling
-
         # Merge the custom logit processors and custom params lists
         if self.has_custom_logit_processor or other.has_custom_logit_processor:
             # Merge the custom logit processors
@@ -360,6 +359,22 @@ class SamplingBatchInfo:
             # Set the flag to True if any of the two has custom logit processor
             self.has_custom_logit_processor = True
 
+        # Note: becasue the __len()__ operator is defined on the temperatures tensor,
+        # please make sure any merge operation with len(self) or len(other) is done before
+        # the merge operation of the temperatures tensor below.
+        for item in [
+            "temperatures",
+            "top_ps",
+            "top_ks",
+            "min_ps",
+        ]:
+            self_val = getattr(self, item, None)
+            other_val = getattr(other, item, None)
+            setattr(self, item, torch.concat([self_val, other_val]))
+
+        self.is_all_greedy = self.is_all_greedy and other.is_all_greedy
+        self.need_min_p_sampling = self.need_min_p_sampling or other.need_min_p_sampling
+
     def apply_logits_bias(self, logits: torch.Tensor):
         # Apply logit_bias
         if self.logit_bias is not None: