[Generative Score API] Multi-Item scoring with custom attention mask.

python/sglang/srt/layers/logits_processor.py

@@ -60,7 +60,8 @@
 class LogitsProcessorOutput:
     ## Part 1: This part will be assigned in python/sglang/srt/layers/logits_processor.py::LogitsProcessor
     # The logits of the next tokens.       shape: [#seq, vocab_size]
-    next_token_logits: torch.Tensor
+    # Can be None for certain prefill-only requests (e.g., multi-item scoring) that don't need next token generation
+    next_token_logits: Optional[torch.Tensor]
     # Used by speculative decoding (EAGLE)
     # The last hidden layers
     hidden_states: Optional[torch.Tensor] = None
@@ -85,7 +86,10 @@ class LogitsProcessorOutput:
     input_top_logprobs_val: List = None
     input_top_logprobs_idx: List = None
     # The logprobs and ids of the requested token ids in input positions. shape: [#seq, n] (n is the number of requested token ids)
-    input_token_ids_logprobs_val: Optional[List] = None
+    # Can contain either lists or GPU tensors (for delayed GPU-to-CPU transfer optimization)
+    input_token_ids_logprobs_val: Optional[List[Union[List[float], torch.Tensor]]] = (
+        None
+    )
     input_token_ids_logprobs_idx: Optional[List] = None
 
 
@@ -127,6 +131,9 @@ class LogitsMetadata:
     # for padding
     padded_static_len: int = -1
 
+    # Whether this batch is prefill-only (no token generation needed)
+    is_prefill_only: bool = False
+
     @classmethod
     def from_forward_batch(cls, forward_batch: ForwardBatch):
         if (
@@ -169,6 +176,7 @@ def from_forward_batch(cls, forward_batch: ForwardBatch):
             token_ids_logprobs=forward_batch.token_ids_logprobs,
             extend_input_logprob_token_ids_gpu=forward_batch.extend_input_logprob_token_ids_gpu,
             padded_static_len=forward_batch.padded_static_len,
+            is_prefill_only=forward_batch.is_prefill_only,
             global_num_tokens_gpu=forward_batch.global_num_tokens_gpu,
             dp_local_start_pos=forward_batch.dp_local_start_pos,
             dp_local_num_tokens=forward_batch.dp_local_num_tokens,
@@ -247,6 +255,108 @@ def __init__(
             "debug_tensor_dump_output_folder", None
         )
 
+    def compute_logprobs_for_multi_item_scoring(
+        self,
+        input_ids,
+        hidden_states,
+        lm_head: VocabParallelEmbedding,
+        logits_metadata: Union[LogitsMetadata, ForwardBatch],
+        delimiter_token: int,
+    ):
+        """
+        Compute logprobs for multi-item scoring using delimiter-based token extraction.
+
+        This method is designed for scenarios where you want to score multiple items/candidates
+        against a single query by combining them into one sequence separated by delimiters.
+
+        Sequence format: Query<delimiter>Item1<delimiter>Item2<delimiter>...
+        Scoring positions: Extracts logprobs at positions before each <delimiter>
+
+        Args:
+            input_ids (torch.Tensor): Input token IDs containing query and items separated by delimiters.
+                Shape: [total_sequence_length] for single request or [batch_total_length] for batch.
+            hidden_states (torch.Tensor): Hidden states from the model.
+                Shape: [sequence_length, hidden_dim].
+            lm_head (VocabParallelEmbedding): Language model head for computing logits.
+            logits_metadata (Union[LogitsMetadata, ForwardBatch]): Metadata containing batch info
+                and token ID specifications for logprob extraction.
+            delimiter_token (int): Token ID used as delimiter between query and items.
+
+        Returns:
+            LogitsProcessorOutput: Contains:
+                - next_token_logits: None (not needed for scoring-only requests)
+                - input_token_logprobs: Logprobs of delimiter tokens at scoring positions
+                - input_top_logprobs_val: Top-k logprobs at delimiter positions (if requested)
+                - input_top_logprobs_idx: Top-k token indices at delimiter positions (if requested)
+                - input_token_ids_logprobs_val: Logprobs for user-requested token IDs (if any)
+                - input_token_ids_logprobs_idx: Indices for user-requested token IDs (if any)
+        """
+        multi_item_indices = (input_ids == delimiter_token).nonzero(as_tuple=True)[
+            0
+        ] - 1
+        # Extract hidden states at delimiter positions for multi-item scoring
+        sliced_hidden = hidden_states[multi_item_indices]
+
+        sliced_logits = self._get_logits(sliced_hidden, lm_head, logits_metadata)
+        sliced_logprobs = torch.nn.functional.log_softmax(sliced_logits, dim=-1)
+
+        # Initialize return values
+        input_token_ids_logprobs_val = []
+        input_token_ids_logprobs_idx = []
+        input_top_logprobs_val = None
+        input_top_logprobs_idx = None
+
+        # Recalculate extend_logprob_pruned_lens_cpu to match delimiter counts per request
+        # Original contains sequence lengths, but we need delimiter counts for sliced_logprobs
+        if (
+            logits_metadata.token_ids_logprobs
+            or logits_metadata.extend_return_top_logprob
+        ):
+            logits_metadata.extend_logprob_pruned_lens_cpu = []
+
+            if logits_metadata.extend_seq_lens_cpu is not None:
+                # Multi-request batch: count delimiters per request
+                input_pt = 0
+                for req_seq_len in logits_metadata.extend_seq_lens_cpu:
+                    req_input_ids = input_ids[input_pt : input_pt + req_seq_len]
+                    delimiter_count = (req_input_ids == delimiter_token).sum().item()
+                    logits_metadata.extend_logprob_pruned_lens_cpu.append(
+                        delimiter_count
+                    )
+                    input_pt += req_seq_len
+            else:
+                # Single request case: one request gets all delimiters
+                total_delimiters = (input_ids == delimiter_token).sum().item()
+                logits_metadata.extend_logprob_pruned_lens_cpu = [total_delimiters]
+
+        # Get the logprobs of specified token ids
+        if logits_metadata.extend_token_ids_logprob:
+            (
+                input_token_ids_logprobs_val,
+                input_token_ids_logprobs_idx,
+            ) = self.get_token_ids_logprobs(
+                sliced_logprobs, logits_metadata, delay_cpu_copy=True
+            )
+
+        # Get the logprob of top-k tokens
+        if logits_metadata.extend_return_top_logprob:
+            (
+                input_top_logprobs_val,
+                input_top_logprobs_idx,
+            ) = self.get_top_logprobs(sliced_logprobs, logits_metadata)
+
+        # For input_token_logprobs, use delimiter token logprobs
+        input_token_logprobs = sliced_logprobs[:, delimiter_token]
+
+        return LogitsProcessorOutput(
+            next_token_logits=None,  # Multi-item scoring doesn't need next token logits
+            input_token_logprobs=input_token_logprobs,
+            input_top_logprobs_val=input_top_logprobs_val,
+            input_top_logprobs_idx=input_top_logprobs_idx,
+            input_token_ids_logprobs_val=input_token_ids_logprobs_val,
+            input_token_ids_logprobs_idx=input_token_ids_logprobs_idx,
+        )
+
     def forward(
         self,
         input_ids,
@@ -257,6 +367,16 @@ def forward(
     ) -> LogitsProcessorOutput:
         if isinstance(logits_metadata, ForwardBatch):
             logits_metadata = LogitsMetadata.from_forward_batch(logits_metadata)
+
+        # Check if multi-item scoring is enabled via server args (only for prefill-only requests)
+        multi_item_delimiter = global_server_args_dict.get(
+            "multi_item_scoring_delimiter"
+        )
+        if multi_item_delimiter is not None and logits_metadata.is_prefill_only:
+            return self.compute_logprobs_for_multi_item_scoring(
+                input_ids, hidden_states, lm_head, logits_metadata, multi_item_delimiter
+            )
+
         # Get the last hidden states and last logits for the next token prediction
         if (
             logits_metadata.forward_mode.is_decode_or_idle()
@@ -584,7 +704,9 @@ def get_top_logprobs(all_logprobs: torch.Tensor, logits_metadata: LogitsMetadata
 
     @staticmethod
     def get_token_ids_logprobs(
-        all_logprobs: torch.Tensor, logits_metadata: LogitsMetadata
+        all_logprobs: torch.Tensor,
+        logits_metadata: LogitsMetadata,
+        delay_cpu_copy: bool = False,
     ):
         input_token_ids_logprobs_val, input_token_ids_logprobs_idx = [], []
         pt = 0
@@ -597,9 +719,17 @@ def get_token_ids_logprobs(
                 input_token_ids_logprobs_idx.append([])
                 continue
 
-            input_token_ids_logprobs_val.append(
-                [all_logprobs[pt + j, token_ids].tolist() for j in range(pruned_len)]
-            )
+            position_logprobs = all_logprobs[
+                pt : pt + pruned_len, token_ids
+            ]  # Shape: [pruned_len, num_tokens]
+
+            if delay_cpu_copy:
+                # Keep as tensor to delay GPU-to-CPU transfer
+                input_token_ids_logprobs_val.append(position_logprobs)
+            else:
+                # Convert to list immediately (default behavior)
+                input_token_ids_logprobs_val.append(position_logprobs.tolist())
+
             input_token_ids_logprobs_idx.append([token_ids for _ in range(pruned_len)])
             pt += pruned_len
 

python/sglang/srt/managers/schedule_batch.py

@@ -114,6 +114,7 @@
     "enable_deterministic_inference",
     "nsa_prefill",
     "nsa_decode",
+    "multi_item_scoring_delimiter",
 ]
 
 # Put some global args for easy access
@@ -666,9 +667,11 @@ def seqlen(self):
     def is_prefill_only(self) -> bool:
         """Check if this request is prefill-only (no token generation needed)."""
         # NOTE: when spec is enabled, prefill_only optimizations are disabled
-        return (
-            self.sampling_params.max_new_tokens == 0
-            and global_server_args_dict["speculative_algorithm"] is None
+        from sglang.srt.speculative.spec_info import SpeculativeAlgorithm
+
+        spec_alg = global_server_args_dict["speculative_algorithm"]
+        return self.sampling_params.max_new_tokens == 0 and (
+            spec_alg is None or spec_alg == SpeculativeAlgorithm.NONE
         )
 
     def add_latency(self, stage: RequestStage):