[Generative Score API] Scoring(Prefill-only) optimizations. (#9748)

python/sglang/srt/managers/schedule_batch.py

@@ -561,7 +561,10 @@ class Req:
             # shape: (bs, k)
             self.output_top_logprobs_val = []
             self.output_top_logprobs_idx = []
-            self.output_token_ids_logprobs_val = []
+            # Can contain either lists or GPU tensors (delayed copy optimization for prefill-only scoring)
+            self.output_token_ids_logprobs_val: List[
+                Union[List[float], torch.Tensor]
+            ] = []
             self.output_token_ids_logprobs_idx = []
         else:
             self.output_token_logprobs_val = self.output_token_logprobs_idx = (
@@ -619,6 +622,11 @@ class Req:
     def seqlen(self):
         return len(self.origin_input_ids) + len(self.output_ids)
 
+    @property
+    def is_prefill_only(self) -> bool:
+        """Check if this request is prefill-only (no token generation needed)."""
+        return self.sampling_params.max_new_tokens == 0
+
     def extend_image_inputs(self, image_inputs):
         if self.multimodal_inputs is None:
             self.multimodal_inputs = image_inputs
@@ -950,9 +958,7 @@ class ScheduleBatch(ScheduleBatchDisaggregationDecodeMixin):
             device=req_to_token_pool.device,
             spec_algorithm=spec_algorithm,
             return_hidden_states=any(req.return_hidden_states for req in reqs),
-            is_prefill_only=all(
-                req.sampling_params.max_new_tokens == 0 for req in reqs
-            ),
+            is_prefill_only=all(req.is_prefill_only for req in reqs),
             chunked_req=chunked_req,
         )
 
@@ -1210,13 +1216,36 @@ class ScheduleBatch(ScheduleBatchDisaggregationDecodeMixin):
             req.is_retracted = False
 
             # Compute the relative logprob_start_len in an extend batch
+            #
+            # Key variables:
+            # - logprob_start_len: Absolute position in full sequence where logprob computation begins
+            # - extend_logprob_start_len: Relative position within current extend batch where logprob computation begins
+            # - extend_input_len: Number of tokens that need to be processed in this extend batch
+            #   (= len(fill_ids) - len(prefix_indices), where fill_ids = origin_input_ids + output_ids
+            #    and prefix_indices are the cached/shared prefix tokens)
+            #
             if req.logprob_start_len >= pre_len:
-                req.extend_logprob_start_len = min(
-                    req.logprob_start_len - pre_len,
-                    req.extend_input_len,
-                    req.seqlen - 1,
-                )
+                # Optimization for prefill-only requests: When we only need logprobs at
+                # positions beyond the input sequence (to score next-token likelihood), skip all
+                # input logprob computation during prefill since no generation will occur.
+                if self.is_prefill_only and req.logprob_start_len == len(
+                    req.origin_input_ids
+                ):
+                    # Skip ALL input logprobs: set extend_logprob_start_len = extend_input_len
+                    req.extend_logprob_start_len = req.extend_input_len
+                else:
+                    # Convert absolute logprob_start_len to relative extend_logprob_start_len
+                    #
+                    # Example: origin_input_ids=[1,2,3,4,5] (5 tokens, positions 0-4), logprob_start_len=3
+                    # Regular logic: min(3-0, 5, 5-1) = min(3,5,4) = 3
+                    # This means: "compute logprobs from position 3 onwards in extend batch"
+                    req.extend_logprob_start_len = min(
+                        req.logprob_start_len - pre_len,
+                        req.extend_input_len,
+                        req.seqlen - 1,
+                    )
             else:
+                # logprob_start_len is before the current extend batch, so start from beginning
                 req.extend_logprob_start_len = 0
 
             if self.return_logprob:
@@ -1763,6 +1792,7 @@ class ScheduleBatch(ScheduleBatchDisaggregationDecodeMixin):
             ),
             extend_input_logprob_token_ids=self.extend_input_logprob_token_ids,
             launch_done=self.launch_done,
+            is_prefill_only=self.is_prefill_only,
         )
 
     def copy(self):
@@ -1905,6 +1935,9 @@ class ModelWorkerBatch:
     # Overlap event
     launch_done: Optional[threading.Event] = None
 
+    # Whether this batch is prefill-only (no token generation needed)
+    is_prefill_only: bool = False
+
 
 @triton.jit
 def write_req_to_token_pool_triton(