[Generative Score API] Multi-Item scoring with custom attention mask. (#10979)

python/sglang/srt/layers/attention/flashinfer_backend.py

@@ -7,6 +7,7 @@ FlashInfer is faster and Triton is easier to customize.
 Each backend supports two operators: extend (i.e. prefill with cached prefix) and decode.
 """
 
+import logging
 import os
 from dataclasses import dataclass
 from enum import Enum, auto
@@ -16,11 +17,11 @@ from typing import TYPE_CHECKING, Callable, List, Optional, Union
 import torch
 
 if os.environ["SGLANG_ENABLE_TORCH_COMPILE"] == "1":
-    import logging
-
     torch._logging.set_logs(dynamo=logging.ERROR)
     torch._dynamo.config.suppress_errors = True
 
+logger = logging.getLogger(__name__)
+
 from sglang.global_config import global_config
 from sglang.srt.layers.attention.base_attn_backend import AttentionBackend
 from sglang.srt.layers.attention.utils import create_flashinfer_kv_indices_triton
@@ -58,6 +59,36 @@ class WrapperDispatch(Enum):
     CROSS_ATTENTION = auto()
 
 
+@dataclass
+class MultiItemScoringParams:
+    """Parameters for multi-item scoring in attention computation.
+
+    Used when processing sequences with multiple items separated by delimiters,
+    where each item needs specific attention patterns that respect item boundaries.
+
+    Attributes:
+        prefix_len_ptr: A uint32 1D tensor indicating the prefix length of each prompt.
+                       The tensor size is equal to the batch size.
+        token_pos_in_items_ptr: A uint16 1D tensor indicating the token position of each item
+                               starting from 0 (delimiter) for each item. For batch size > 1,
+                               sequences are concatenated with zero padding to ensure same length.
+        token_pos_in_items_len: Zero padding length for token_pos_in_items_ptr to handle
+                               batch_size > 1 case. Defines the padded length for each sequence.
+        max_item_len_ptr: A uint16 tensor containing the max token length of all items
+                         for each prompt in the batch.
+
+    """
+
+    prefix_len_ptr: Optional[torch.Tensor] = None
+    token_pos_in_items_ptr: Optional[torch.Tensor] = None
+    token_pos_in_items_len: int = 0
+    max_item_len_ptr: Optional[torch.Tensor] = None
+
+    def is_enabled(self) -> bool:
+        """Check if multi-item scoring is enabled."""
+        return self.prefix_len_ptr is not None
+
+
 @dataclass
 class DecodeMetadata:
     decode_wrappers: List[BatchDecodeWithPagedKVCacheWrapper]
@@ -68,6 +99,7 @@ class PrefillMetadata:
     prefill_wrappers: List[BatchPrefillWithPagedKVCacheWrapper]
     use_ragged: bool
     extend_no_prefix: bool
+    multi_item_params: Optional[MultiItemScoringParams] = None
 
 
 # Reuse this workspace buffer across all flashinfer wrappers
@@ -90,6 +122,11 @@ class FlashInferAttnBackend(AttentionBackend):
     ):
         super().__init__()
 
+        # Store multi-item scoring delimiter for efficient access
+        self.multi_item_scoring_delimiter = (
+            model_runner.server_args.multi_item_scoring_delimiter
+        )
+
         # Parse constants
         self.decode_use_tensor_cores = should_use_tensor_core(
             kv_cache_dtype=model_runner.kv_cache_dtype,
@@ -229,10 +266,133 @@ class FlashInferAttnBackend(AttentionBackend):
 
         # Other metadata
         self.forward_metadata: Union[PrefillMetadata, DecodeMetadata] = None
+
         self.decode_cuda_graph_metadata = {}
         self.prefill_cuda_graph_metadata = {}  # For verify
         self.draft_extend_cuda_graph_metadata = {}  # For draft extend
 
+    def _process_multi_item_scoring(
+        self, forward_batch: ForwardBatch
+    ) -> MultiItemScoringParams:
+        """Process multi-item scoring tensors for FlashInfer attention.
+
+        This method handles sequences containing multiple "items" separated by delimiter tokens,
+        where each item needs specific attention patterns that respect item boundaries.
+
+        The method produces four key tensors for FlashInfer:
+        - prefix_len_ptr: uint32 tensor with prefix length for each prompt in batch
+        - token_pos_in_items_ptr: uint16 tensor with token positions starting from 0 at delimiters
+        - token_pos_in_items_len: padding length for batch processing
+        - max_item_len_ptr: uint16 tensor with max item length for each prompt
+
+        Args:
+            forward_batch: The forward batch containing input sequences and delimiter info
+
+        Returns:
+            MultiItemScoringParams: The processed multi-item scoring parameters
+
+        Examples:
+            Following FlashInfer definition: for 3 items of length 3, 2, 4 respectively:
+            token_pos_in_items_ptr = [0, 1, 2, 3, 0, 1, 2, 0, 1, 2, 3, 4, 0]
+
+            Case 1: Single sequence
+            Text: "What is the capital of France? <delim> London <delim> Paris <delim> Berlin <delim>"
+            Tokens: [What, is, the, capital, of, France, ?, <delim>, London, <delim>, Paris, <delim>, Berlin, <delim>]
+            Indices: [ 0,   1,  2,   3,      4,  5,     6,   7,     8,      9,     10,    11,    12,     13]
+            - prefix_len_ptr: [7] (query length before first delimiter)
+            - token_pos_in_items_ptr: [0, 1, 0, 1, 0, 1, 0] (delim=0, London=1, delim=0, Paris=1, delim=0, Berlin=1, delim=0)
+            - token_pos_in_items_len: 7 (actual length)
+            - max_item_len_ptr: [1] (max item length is 1 token - all options are single tokens)
+
+            Case 2: Batch processing (batch_size=2)
+            Sequence 1: 2 items of length 2, 1 → [0, 1, 2, 0, 1, 0] (6 elements)
+            Sequence 2: 3 items of length 1, 3, 2 → [0, 1, 0, 1, 2, 3, 0, 1, 2, 0] (10 elements)
+            After padding both to length 10:
+            - token_pos_in_items_ptr: [0, 1, 2, 0, 1, 0, 0, 0, 0, 0,    0, 1, 0, 1, 2, 3, 0, 1, 2, 0]
+            - token_pos_in_items_len: 10 (padded length for batch processing)
+            - max_item_len_ptr: [2, 3] (max lengths per sequence)
+        """
+
+        delimiter = self.multi_item_scoring_delimiter
+        if delimiter is None or forward_batch.forward_mode == ForwardMode.DECODE:
+            return MultiItemScoringParams()
+
+        delimiter_mask = forward_batch.input_ids == delimiter
+        prefix_cache_lens = getattr(forward_batch, "extend_prefix_lens", None)
+        extend_seq_lens = getattr(forward_batch, "extend_seq_lens", None)
+        prefix_len_ptr, token_pos_in_items_ptr = [], []
+        token_pos_in_items_len = 0
+
+        # If no extend_seq_lens, treat whole batch as one sequence
+        if extend_seq_lens is None or len(extend_seq_lens) <= 1:
+            extend_seq_lens = [forward_batch.input_ids.size(0)]
+
+        seq_start = 0
+        for i, seq_len in enumerate(extend_seq_lens):
+            seq_end = seq_start + seq_len
+            mask = delimiter_mask[seq_start:seq_end]
+            pos = forward_batch.positions[seq_start:seq_end]
+            delimiter_indices = torch.nonzero(mask, as_tuple=True)[0]
+
+            if len(delimiter_indices) > 0:
+                first_delim = delimiter_indices[0]
+                # Prefix length: store as scalar
+                prefix_len = first_delim + (
+                    prefix_cache_lens[i] if prefix_cache_lens is not None else 0
+                )
+                prefix_len_ptr.append(
+                    prefix_len.item() if torch.is_tensor(prefix_len) else prefix_len
+                )
+
+                # Compute relative positions within items after delimiters
+                diff = pos[first_delim:] - torch.cummax(mask[first_delim:], 0)[1]
+                token_pos = (diff - pos[first_delim]).to(torch.uint16)
+                token_pos_in_items_ptr.append(token_pos)
+
+                # Update forward_batch positions in-place
+                pos[first_delim:] = diff - 1
+                forward_batch.positions[seq_start:seq_end] = pos
+
+            seq_start = seq_end
+
+        # Pad token_pos_in_items_ptr for batch processing
+        if token_pos_in_items_ptr:
+            token_pos_in_items_len = max(t.numel() for t in token_pos_in_items_ptr)
+            device = forward_batch.input_ids.device
+            token_pos_in_items_ptr = [
+                torch.cat(
+                    [
+                        t,
+                        torch.zeros(
+                            token_pos_in_items_len - t.numel(),
+                            dtype=torch.uint16,
+                            device=device,
+                        ),
+                    ]
+                )
+                for t in token_pos_in_items_ptr
+            ]
+
+        if not prefix_len_ptr or not token_pos_in_items_ptr:
+            return MultiItemScoringParams()
+
+        # Build final params
+        device = forward_batch.input_ids.device
+        return MultiItemScoringParams(
+            prefix_len_ptr=torch.tensor(
+                prefix_len_ptr, dtype=torch.uint32, device=device
+            ),
+            token_pos_in_items_ptr=torch.cat(token_pos_in_items_ptr, dim=0),
+            token_pos_in_items_len=token_pos_in_items_len & 0xFFFFFFFF,
+            max_item_len_ptr=torch.stack(
+                [
+                    t.to(torch.int32).max().to(torch.uint16)
+                    for t in token_pos_in_items_ptr
+                ],
+                dim=0,
+            ),
+        )
+
     def init_forward_metadata(self, forward_batch: ForwardBatch):
         if forward_batch.forward_mode.is_decode_or_idle():
             self.indices_updater_decode.update(
@@ -280,13 +440,26 @@ class FlashInferAttnBackend(AttentionBackend):
         else:
             prefix_lens = forward_batch.extend_prefix_lens
 
-            if self.is_multimodal:
+            # Disable ragged wrapper and ensure prefix handling for multimodal and multi-item scoring
+            if self.is_multimodal or self.multi_item_scoring_delimiter is not None:
+                # use_ragged = False: Multi-item scoring requires the paged wrapper because:
+                # 1. Ragged wrapper doesn't support the specialized multi-item parameters
+                #    (prefix_len_ptr, token_pos_in_items_ptr, etc.)
+                # 2. Paged wrapper provides better control over attention masking needed
+                #    for respecting item boundaries in multi-item sequences
+                # 3. Custom masking logic conflicts with ragged wrapper's assumptions
                 use_ragged = False
                 extend_no_prefix = False
             else:
                 use_ragged = not self.enable_deterministic
                 extend_no_prefix = not any(forward_batch.extend_prefix_lens_cpu)
 
+            # Process multi-item scoring in attention backend instead of ForwardBatch
+            multi_item_params = MultiItemScoringParams()
+            if self.multi_item_scoring_delimiter is not None:
+                # Use new backend-specific implementation
+                multi_item_params = self._process_multi_item_scoring(forward_batch)
+
             self.indices_updater_prefill.update(
                 forward_batch.req_pool_indices,
                 forward_batch.seq_lens,
@@ -298,9 +471,13 @@ class FlashInferAttnBackend(AttentionBackend):
                 encoder_lens=forward_batch.encoder_lens,
                 spec_info=None,
                 fixed_split_size=self.prefill_split_tile_size,
+                multi_item_params=multi_item_params,
             )
             self.forward_metadata = PrefillMetadata(
-                self.prefill_wrappers_paged, use_ragged, extend_no_prefix
+                self.prefill_wrappers_paged,
+                use_ragged,
+                extend_no_prefix,
+                multi_item_params,
             )
 
     def init_cuda_graph_state(
@@ -531,7 +708,20 @@ class FlashInferAttnBackend(AttentionBackend):
                 forward_batch.token_to_kv_pool.get_kv_buffer(layer.layer_id),
                 causal=not layer.is_cross_attention,
                 sm_scale=layer.scaling,
-                window_left=layer.sliding_window_size,
+                # Disable sliding window attention for multi-item scoring:
+                # - Sliding window could cut across item boundaries, breaking semantic coherence
+                # - Multi-item sequences need full attention to properly handle delimiter tokens
+                # - Specialized multi-item parameters (prefix_len_ptr, token_pos_in_items_ptr)
+                #   provide more precise attention control than simple sliding windows
+                # - Item-aware masking takes precedence over window-based masking
+                window_left=(
+                    layer.sliding_window_size
+                    if not (
+                        self.forward_metadata.multi_item_params
+                        and self.forward_metadata.multi_item_params.is_enabled()
+                    )
+                    else -1
+                ),
                 logits_soft_cap=logits_soft_cap,
                 # Must use _float to avoid device-to-host copy that breaks cuda graph capture.
                 k_scale=layer.k_scale_float,
@@ -952,6 +1142,7 @@ class FlashInferIndicesUpdaterPrefill:
         encoder_lens: Optional[torch.Tensor],
         spec_info: Optional[SpecInput],
         fixed_split_size: Optional[int] = None,
+        multi_item_params: Optional[MultiItemScoringParams] = None,
     ):
         if use_ragged:
             # TODO: remove this device sync, we can use forward_batch.extend_prefix_lens_cpu
@@ -976,6 +1167,7 @@ class FlashInferIndicesUpdaterPrefill:
             use_ragged,
             spec_info,
             fixed_split_size=fixed_split_size,
+            multi_item_params=multi_item_params,
         )
 
     def update_sliding_window(
@@ -990,6 +1182,7 @@ class FlashInferIndicesUpdaterPrefill:
         encoder_lens: Optional[torch.Tensor],
         spec_info: Optional[SpecInput],
         fixed_split_size: Optional[int] = None,
+        multi_item_params: Optional[MultiItemScoringParams] = None,
     ):
         for wrapper_id in range(2):
             if wrapper_id == 0:
@@ -1023,6 +1216,7 @@ class FlashInferIndicesUpdaterPrefill:
                 use_ragged,
                 spec_info,
                 use_sliding_window_kv_pool=use_sliding_window_kv_pool,
+                multi_item_params=multi_item_params,
             )
 
     def update_cross_attention(
@@ -1037,6 +1231,7 @@ class FlashInferIndicesUpdaterPrefill:
         encoder_lens: Optional[torch.Tensor],
         spec_info: Optional[SpecInput],
         fixed_split_size: Optional[int] = None,
+        multi_item_params: Optional[MultiItemScoringParams] = None,
     ):
         for wrapper_id in range(2):
             if wrapper_id == 0:
@@ -1063,6 +1258,7 @@ class FlashInferIndicesUpdaterPrefill:
                 self.qo_indptr[wrapper_id],
                 use_ragged,
                 spec_info,
+                multi_item_params=multi_item_params,
             )
 
     def call_begin_forward(
@@ -1081,6 +1277,7 @@ class FlashInferIndicesUpdaterPrefill:
         spec_info: Optional[SpecInput],
         use_sliding_window_kv_pool: bool = False,
         fixed_split_size: Optional[int] = None,
+        multi_item_params: Optional[MultiItemScoringParams] = None,
     ):
         bs = len(seq_lens)
         if spec_info is None:
@@ -1136,6 +1333,22 @@ class FlashInferIndicesUpdaterPrefill:
             )
 
         # cached part
+        # Conditionally set multi-item parameters
+        if multi_item_params is not None and multi_item_params.is_enabled():
+            # Multi-item scoring is active - use specialized parameters and disable generic custom_mask
+            use_custom_mask = None
+            prefix_len_ptr = multi_item_params.prefix_len_ptr
+            token_pos_in_items_ptr = multi_item_params.token_pos_in_items_ptr
+            token_pos_in_items_len = multi_item_params.token_pos_in_items_len
+            max_item_len_ptr = multi_item_params.max_item_len_ptr
+        else:
+            # No multi-item scoring - use standard parameters
+            use_custom_mask = custom_mask
+            prefix_len_ptr = None
+            token_pos_in_items_ptr = None
+            token_pos_in_items_len = 0
+            max_item_len_ptr = None
+
         wrapper_paged.begin_forward(
             qo_indptr,
             kv_indptr,
@@ -1147,9 +1360,13 @@ class FlashInferIndicesUpdaterPrefill:
             1,
             q_data_type=self.q_data_type,
             kv_data_type=self.data_type,
-            custom_mask=custom_mask,
+            custom_mask=use_custom_mask,
             non_blocking=True,
             fixed_split_size=fixed_split_size,
+            prefix_len_ptr=prefix_len_ptr,
+            token_pos_in_items_ptr=token_pos_in_items_ptr,
+            token_pos_in_items_len=token_pos_in_items_len,
+            max_item_len_ptr=max_item_len_ptr,
         )
 
 

python/sglang/srt/layers/logits_processor.py

@@ -60,7 +60,8 @@ _is_npu = is_npu()
 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 @@ class LogitsMetadata:
             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 @@ class LogitsProcessor(nn.Module):
             "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 @@ class LogitsProcessor(nn.Module):
     ) -> 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 @@ class LogitsProcessor(nn.Module):
 
     @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 @@ class LogitsProcessor(nn.Module):
                 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 @@ GLOBAL_SERVER_ARGS_KEYS = [
     "enable_deterministic_inference",
     "nsa_prefill",
     "nsa_decode",
+    "multi_item_scoring_delimiter",
 ]
 
 # Put some global args for easy access
@@ -666,9 +667,11 @@ class Req:
     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):

python/sglang/srt/managers/scheduler_output_processor_mixin.py

@@ -104,7 +104,10 @@ class SchedulerOutputProcessorMixin:
                         assert extend_input_len_per_req is not None
                         extend_logprob_start_len = extend_logprob_start_len_per_req[i]
                         extend_input_len = extend_input_len_per_req[i]
-                        num_input_logprobs = extend_input_len - extend_logprob_start_len
+
+                        num_input_logprobs = self._calculate_num_input_logprobs(
+                            req, extend_input_len, extend_logprob_start_len
+                        )
 
                         if req.return_logprob:
                             self.add_logprob_return_values(
@@ -159,8 +162,8 @@ class SchedulerOutputProcessorMixin:
                         extend_input_len = extend_input_len_per_req[i]
                         if extend_logprob_start_len < extend_input_len:
                             # Update input logprobs.
-                            num_input_logprobs = (
-                                extend_input_len - extend_logprob_start_len
+                            num_input_logprobs = self._calculate_num_input_logprobs(
+                                req, extend_input_len, extend_logprob_start_len
                             )
                             if req.return_logprob:
                                 self.add_input_logprob_return_values(
@@ -303,6 +306,153 @@ class SchedulerOutputProcessorMixin:
         ):
             self.log_decode_stats(can_run_cuda_graph, running_batch=batch)
 
+    def _process_input_token_logprobs(
+        self, req: Req, input_token_logprobs: List
+    ) -> None:
+        """Process input token logprobs values and indices."""
+        is_multi_item_scoring = self._is_multi_item_scoring(req)
+
+        # Process logprob values - handle multi-item scoring vs regular requests
+        if is_multi_item_scoring:
+            # Multi-item scoring: use all logprobs as-is
+            req.input_token_logprobs_val = input_token_logprobs
+        else:
+            # Regular request: add None at start, remove last (sampling token)
+            req.input_token_logprobs_val = [None] + input_token_logprobs[:-1]
+
+        # Process logprob indices based on scoring type
+        if is_multi_item_scoring:
+            # Multi-item scoring: only include delimiter token positions
+            relevant_tokens = req.origin_input_ids[req.logprob_start_len :]
+            input_token_logprobs_idx = [
+                token_id
+                for token_id in relevant_tokens
+                if token_id == self.server_args.multi_item_scoring_delimiter
+            ]
+        else:
+            # Regular request: include all tokens from logprob_start_len onwards
+            input_token_logprobs_idx = req.origin_input_ids[req.logprob_start_len :]
+
+        # Clip padded hash values from image tokens to prevent detokenization errors
+        req.input_token_logprobs_idx = [
+            x if x < self.model_config.vocab_size - 1 else 0
+            for x in input_token_logprobs_idx
+        ]
+
+    def _process_input_top_logprobs(self, req: Req) -> None:
+        """Process input top logprobs."""
+        if req.top_logprobs_num <= 0:
+            return
+
+        is_multi_item_scoring = self._is_multi_item_scoring(req)
+
+        # Initialize arrays - multi-item scoring starts empty, others start with None
+        req.input_top_logprobs_val = [] if is_multi_item_scoring else [None]
+        req.input_top_logprobs_idx = [] if is_multi_item_scoring else [None]
+
+        # Extend arrays with temp values
+        for val, idx in zip(
+            req.temp_input_top_logprobs_val,
+            req.temp_input_top_logprobs_idx,
+            strict=True,
+        ):
+            req.input_top_logprobs_val.extend(val)
+            req.input_top_logprobs_idx.extend(idx)
+
+        # Remove last token (sampling token) for non multi-item scoring requests
+        if not is_multi_item_scoring:
+            req.input_top_logprobs_val.pop()
+            req.input_top_logprobs_idx.pop()
+
+        # Clean up temp storage
+        req.temp_input_top_logprobs_idx = None
+        req.temp_input_top_logprobs_val = None
+
+    def _process_input_token_ids_logprobs(self, req: Req) -> None:
+        """Process input token IDs logprobs."""
+        if req.token_ids_logprob is None:
+            return
+
+        is_multi_item_scoring = self._is_multi_item_scoring(req)
+
+        # Initialize arrays - multi-item scoring starts empty, others start with None
+        req.input_token_ids_logprobs_val = [] if is_multi_item_scoring else [None]
+        req.input_token_ids_logprobs_idx = [] if is_multi_item_scoring else [None]
+
+        # Process temp values - convert tensors to lists and extend arrays
+        for val, idx in zip(
+            req.temp_input_token_ids_logprobs_val,
+            req.temp_input_token_ids_logprobs_idx,
+            strict=True,
+        ):
+            val_list = val.tolist() if isinstance(val, torch.Tensor) else val
+            req.input_token_ids_logprobs_val.extend(
+                val_list if isinstance(val_list, list) else [val_list]
+            )
+            req.input_token_ids_logprobs_idx.extend(idx)
+
+        # Remove last token (sampling token) for non multi-item scoring requests
+        if not is_multi_item_scoring:
+            req.input_token_ids_logprobs_val.pop()
+            req.input_token_ids_logprobs_idx.pop()
+
+        # Clean up temp storage
+        req.temp_input_token_ids_logprobs_idx = None
+        req.temp_input_token_ids_logprobs_val = None
+
+    def _calculate_relevant_tokens_len(self, req: Req) -> int:
+        """Calculate the expected length of logprob arrays based on whether multi-item scoring is enabled.
+
+        For multi-item scoring, only delimiter positions have logprobs.
+        For regular requests, all positions from logprob_start_len onwards have logprobs.
+        """
+        is_multi_item_scoring = self._is_multi_item_scoring(req)
+
+        if is_multi_item_scoring:
+            # Multi-item scoring: count delimiter tokens from logprob_start_len onwards
+            relevant_tokens = req.origin_input_ids[req.logprob_start_len :]
+            return sum(
+                1
+                for token_id in relevant_tokens
+                if token_id == self.server_args.multi_item_scoring_delimiter
+            )
+        else:
+            # Regular request: all tokens from logprob_start_len onwards
+            return len(req.origin_input_ids) - req.logprob_start_len
+
+    def _calculate_num_input_logprobs(
+        self, req: Req, extend_input_len: int, extend_logprob_start_len: int
+    ) -> int:
+        """Calculate the number of input logprobs based on whether multi-item scoring is enabled.
+
+        For multi-item scoring, only delimiter positions have logprobs.
+        For regular requests, all positions in the range have logprobs.
+        """
+        is_multi_item_scoring = self._is_multi_item_scoring(req)
+
+        if is_multi_item_scoring:
+            # Multi-item scoring: count delimiter tokens in the relevant portion
+            relevant_tokens = req.origin_input_ids[
+                extend_logprob_start_len:extend_input_len
+            ]
+            return sum(
+                1
+                for token_id in relevant_tokens
+                if token_id == self.server_args.multi_item_scoring_delimiter
+            )
+        else:
+            # Regular request: all tokens in the range
+            return extend_input_len - extend_logprob_start_len
+
+    def _is_multi_item_scoring(self, req: Req) -> bool:
+        """Check if request uses multi-item scoring.
+
+        Multi-item scoring applies to prefill-only requests when a delimiter
+        token is configured. In this mode, only positions containing the
+        delimiter token receive logprobs.
+        """
+        return req.is_prefill_only and self.server_args.multi_item_scoring_delimiter
+
     def add_input_logprob_return_values(
         self: Scheduler,
         i: int,
@@ -371,63 +521,14 @@ class SchedulerOutputProcessorMixin:
             assert req.input_top_logprobs_val is None
             assert req.input_top_logprobs_idx is None
 
-            # Compute input_token_logprobs_val
-            # Always pad the first one with None.
-            req.input_token_logprobs_val = [None]
-            req.input_token_logprobs_val.extend(input_token_logprobs)
-            # The last input logprob is for sampling, so just pop it out.
-            req.input_token_logprobs_val.pop()
+            # Process all input logprob types using helper functions
+            self._process_input_token_logprobs(req, input_token_logprobs)
+            self._process_input_top_logprobs(req)
 
-            # Compute input_token_logprobs_idx
-            input_token_logprobs_idx = req.origin_input_ids[req.logprob_start_len :]
-            # Clip the padded hash values from image tokens.
-            # Otherwise, it will lead to detokenization errors.
-            input_token_logprobs_idx = [
-                x if x < self.model_config.vocab_size - 1 else 0
-                for x in input_token_logprobs_idx
-            ]
-            req.input_token_logprobs_idx = input_token_logprobs_idx
-
-            if req.top_logprobs_num > 0:
-                req.input_top_logprobs_val = [None]
-                req.input_top_logprobs_idx = [None]
-                assert len(req.temp_input_token_ids_logprobs_val) == len(
-                    req.temp_input_token_ids_logprobs_idx
-                )
-                for val, idx in zip(
-                    req.temp_input_top_logprobs_val,
-                    req.temp_input_top_logprobs_idx,
-                    strict=True,
-                ):
-                    req.input_top_logprobs_val.extend(val)
-                    req.input_top_logprobs_idx.extend(idx)
-
-                # Last token is a sample token.
-                req.input_top_logprobs_val.pop()
-                req.input_top_logprobs_idx.pop()
-                req.temp_input_top_logprobs_idx = None
-                req.temp_input_top_logprobs_val = None
-
-            if req.token_ids_logprob is not None:
-                req.input_token_ids_logprobs_val = [None]
-                req.input_token_ids_logprobs_idx = [None]
-
-                for val, idx in zip(
-                    req.temp_input_token_ids_logprobs_val,
-                    req.temp_input_token_ids_logprobs_idx,
-                    strict=True,
-                ):
-                    req.input_token_ids_logprobs_val.extend(val)
-                    req.input_token_ids_logprobs_idx.extend(idx)
-
-                # Last token is a sample token.
-                req.input_token_ids_logprobs_val.pop()
-                req.input_token_ids_logprobs_idx.pop()
-                req.temp_input_token_ids_logprobs_idx = None
-                req.temp_input_token_ids_logprobs_val = None
+            self._process_input_token_ids_logprobs(req)
 
             if req.return_logprob:
-                relevant_tokens_len = len(req.origin_input_ids) - req.logprob_start_len
+                relevant_tokens_len = self._calculate_relevant_tokens_len(req)
                 assert len(req.input_token_logprobs_val) == relevant_tokens_len
                 assert len(req.input_token_logprobs_idx) == relevant_tokens_len
                 if req.top_logprobs_num > 0:

python/sglang/srt/managers/tokenizer_manager.py

@@ -182,6 +182,8 @@ class TokenizerManager(TokenizerCommunicatorMixin):
             if speculative_algorithm.is_none()
             else server_args.speculative_num_draft_tokens
         )
+        # Initialize delimiter text for multi-item scoring (will be set after tokenizer is loaded)
+        self.multi_item_delimiter_text = None
 
         if self.model_config.is_multimodal:
             import_processors("sglang.srt.multimodal.processors")
@@ -223,6 +225,7 @@ class TokenizerManager(TokenizerCommunicatorMixin):
                 self.processor = _processor
                 self.tokenizer = get_tokenizer_from_processor(self.processor)
                 os.environ["TOKENIZERS_PARALLELISM"] = "false"
+                self._initialize_multi_item_delimiter_text()
         else:
             self.mm_processor = self.processor = None
 
@@ -235,6 +238,7 @@ class TokenizerManager(TokenizerCommunicatorMixin):
                     trust_remote_code=server_args.trust_remote_code,
                     revision=server_args.revision,
                 )
+                self._initialize_multi_item_delimiter_text()
         # Initialize async dynamic batch tokenizer if enabled (common for both multimodal and non-multimodal)
         if (
             server_args.enable_dynamic_batch_tokenizer
@@ -1678,6 +1682,201 @@ class TokenizerManager(TokenizerCommunicatorMixin):
             if len(self.model_update_tmp) == self.server_args.dp_size:
                 self.model_update_result.set_result(self.model_update_tmp)
 
+    def _initialize_multi_item_delimiter_text(self):
+        """Initialize multi-item delimiter text from token ID after tokenizer is loaded."""
+        if (
+            hasattr(self.server_args, "multi_item_scoring_delimiter")
+            and self.server_args.multi_item_scoring_delimiter is not None
+            and self.tokenizer is not None
+        ):
+            try:
+                self.multi_item_delimiter_text = self.tokenizer.decode(
+                    [self.server_args.multi_item_scoring_delimiter],
+                    skip_special_tokens=False,
+                )
+            except Exception as e:
+                logger.warning(
+                    f"Failed to decode delimiter token {self.server_args.multi_item_scoring_delimiter}: {e}"
+                )
+                self.multi_item_delimiter_text = None
+
+    def _build_multi_item_token_sequence(
+        self, query: List[int], items: List[List[int]], delimiter_token_id: int
+    ) -> List[int]:
+        """
+        Build a single token sequence for multi-item scoring.
+        Format: query<delimiter>item1<delimiter>item2<delimiter>item3<delimiter>
+
+        Args:
+            query: Query token IDs
+            items: List of item token ID sequences
+            delimiter_token_id: Token ID to use as delimiter
+
+        Returns:
+            Combined token sequence
+        """
+        combined_sequence = query[:]  # Start with query
+
+        for item in items:
+            combined_sequence.append(delimiter_token_id)  # Add delimiter
+            combined_sequence.extend(item)  # Add item tokens
+
+        # Add final delimiter after the last item for logprob extraction
+        combined_sequence.append(delimiter_token_id)
+
+        return combined_sequence
+
+    def _extract_logprobs_for_tokens(
+        self, logprobs_data: List, label_token_ids: List[int]
+    ) -> Dict[int, float]:
+        """
+        Extract logprobs for specified token IDs from logprobs data.
+
+        Args:
+            logprobs_data: List of (logprob, token_id, text) tuples
+            label_token_ids: Token IDs to extract logprobs for
+
+        Returns:
+            Dictionary mapping token_id to logprob
+        """
+        logprobs = {}
+        if logprobs_data:
+            for logprob, token_id, _ in logprobs_data:
+                if token_id in label_token_ids:
+                    logprobs[token_id] = logprob
+        return logprobs
+
+    def _convert_logprobs_to_scores(
+        self,
+        logprobs: Dict[int, float],
+        label_token_ids: List[int],
+        apply_softmax: bool,
+    ) -> List[float]:
+        """
+        Convert logprobs dictionary to ordered score list.
+
+        Args:
+            logprobs: Dictionary mapping token_id to logprob
+            label_token_ids: Token IDs in desired order
+            apply_softmax: Whether to apply softmax normalization
+
+        Returns:
+            List of scores in the same order as label_token_ids
+        """
+        score_list = [
+            logprobs.get(token_id, float("-inf")) for token_id in label_token_ids
+        ]
+
+        if apply_softmax:
+            score_list = torch.softmax(torch.tensor(score_list), dim=0).tolist()
+        else:
+            # Convert logprobs to probabilities if not using softmax
+            score_list = [
+                math.exp(x) if x != float("-inf") else 0.0 for x in score_list
+            ]
+
+        return score_list
+
+    def _process_multi_item_scoring_results(
+        self,
+        results: Any,
+        items: List,
+        label_token_ids: List[int],
+        apply_softmax: bool,
+        batch_request=None,
+    ) -> List[List[float]]:
+        """
+        Process results from multi-item scoring request.
+        Extracts logprobs at delimiter positions from input_token_ids_logprobs.
+
+        Args:
+            results: Results from generate_request
+            items: List of items being scored
+            label_token_ids: Token IDs to extract scores for
+            apply_softmax: Whether to apply softmax normalization
+            batch_request: The original batch request containing input sequence
+
+        Returns:
+            List of score lists, one for each item
+        """
+        single_result = results[0] if isinstance(results, list) else results
+
+        # For multi-item scoring, logprobs are in input_token_ids_logprobs
+        input_logprobs = single_result["meta_info"].get("input_token_ids_logprobs", [])
+
+        if not input_logprobs:
+            raise RuntimeError(
+                f"input_token_ids_logprobs is empty for multi-item scoring request {single_result['meta_info'].get('id', '<unknown>')}. "
+                "This indicates token_ids_logprobs were not computed properly for Mutil Item Scoring."
+            )
+
+        scores = []
+        num_items = len(items) if isinstance(items, list) else 1
+
+        # Check if we have the expected number of logprobs
+        expected_logprobs_count = num_items + 1
+        if len(input_logprobs) != expected_logprobs_count:
+            raise RuntimeError(
+                f"Expected {expected_logprobs_count} input_token_ids_logprobs for multi-item scoring "
+                f"with {num_items} items, but got {len(input_logprobs)}. "
+                f"Request ID: {single_result['meta_info'].get('id', '<unknown>')}"
+            )
+
+        # Skip the first delimiter (between query and first item) and process remaining delimiter positions
+        # We want to exclude the first one since it represents the boundary between query and first item, not an item boundary
+        start_idx = 1 if len(input_logprobs) > 1 else 0
+
+        # Process logprobs for each item position (excluding first delimiter)
+        for item_idx in range(num_items):
+            logprob_idx = start_idx + item_idx
+            item_logprobs_data = input_logprobs[logprob_idx]
+            logprobs = self._extract_logprobs_for_tokens(
+                item_logprobs_data, label_token_ids
+            )
+            score_list = self._convert_logprobs_to_scores(
+                logprobs, label_token_ids, apply_softmax
+            )
+            scores.append(score_list)
+
+        return scores
+
+    def _process_single_item_scoring_results(
+        self, results: Any, label_token_ids: List[int], apply_softmax: bool
+    ) -> List[List[float]]:
+        """
+        Process results from single-item scoring request.
+        Single-item scoring results are stored in output_token_ids_logprobs.
+
+        Args:
+            results: Results from generate_request
+            label_token_ids: Token IDs to extract scores for
+            apply_softmax: Whether to apply softmax normalization
+
+        Returns:
+            List of score lists, one for each result
+        """
+        scores = []
+
+        for result in results:
+            # For single-item scoring, logprobs are in output_token_ids_logprobs
+            output_logprobs = result["meta_info"].get("output_token_ids_logprobs", [])
+
+            if not output_logprobs or len(output_logprobs) == 0:
+                raise RuntimeError(
+                    f"output_logprobs is empty for request {result['meta_info'].get('id', '<unknown>')}."
+                )
+
+            # Extract logprobs for the first (and only) position
+            logprobs = self._extract_logprobs_for_tokens(
+                output_logprobs[0], label_token_ids
+            )
+            score_list = self._convert_logprobs_to_scores(
+                logprobs, label_token_ids, apply_softmax
+            )
+            scores.append(score_list)
+
+        return scores
+
     async def score_request(
         self,
         query: Optional[Union[str, List[int]]] = None,
@@ -1688,7 +1887,29 @@ class TokenizerManager(TokenizerCommunicatorMixin):
         request: Optional[Any] = None,
     ) -> List[List[float]]:
         """
-        See Engine.score() for more details.
+        Score the probability of specified token IDs appearing after the given (query + item) pair.
+
+        This method supports two scoring approaches:
+        1. Single-Item scoring (default): Process each query+item pair independently
+        2. Multi-Item scoring: When multi_item_scoring_delimiter is set, combine query and
+           multiple items into a single sequence using delimiter for efficient processing.
+           Note: item_first parameter is ignored in multi-item scoring mode since it uses
+           a fixed format: query<delimiter>item1<delimiter>item2<delimiter>item3<delimiter>
+
+           Multi-item scoring works with both text and pre-tokenized inputs:
+           - Text: query<delimiter_text>item1<delimiter_text>item2<delimiter_text>item3<delimiter_text>
+           - Tokens: query<delimiter_token_id>item1<delimiter_token_id>item2<delimiter_token_id>item3<delimiter_token_id>
+
+        Args:
+            query: The query text or pre-tokenized query token IDs
+            items: The item text(s) or pre-tokenized item token IDs
+            label_token_ids: List of token IDs to compute probabilities for
+            apply_softmax: Whether to normalize probabilities using softmax
+            item_first: If True, prepend items to query. Ignored for multi-item scoring.
+            request: Optional FastAPI request object
+
+        Returns:
+            List of lists containing probabilities for each item and each label token
         """
         if label_token_ids is None:
             raise ValueError("label_token_ids must be provided")
@@ -1701,9 +1922,17 @@ class TokenizerManager(TokenizerCommunicatorMixin):
                         f"Token ID {token_id} is out of vocabulary (vocab size: {vocab_size})"
                     )
 
+        # Check if multi-item scoring is enabled by presence of delimiter
+        use_multi_item_scoring = (
+            self.server_args.multi_item_scoring_delimiter is not None
+            and self.multi_item_delimiter_text is not None
+        )
+
         batch_request = GenerateReqInput(
             token_ids_logprob=label_token_ids,
             return_logprob=True,
+            # Set logprob_start_len=0 for multi-item scoring since we want logprobs at all delimiter positions
+            logprob_start_len=0 if use_multi_item_scoring else -1,
             stream=False,
             sampling_params={"max_new_tokens": 0},
         )
@@ -1715,12 +1944,23 @@ class TokenizerManager(TokenizerCommunicatorMixin):
         ):
             # Both query and items are text
             items_list = [items] if isinstance(items, str) else items
-            if item_first:
-                prompts = [f"{item}{query}" for item in items_list]
-            else:
-                prompts = [f"{query}{item}" for item in items_list]
 
-            batch_request.text = prompts
+            if use_multi_item_scoring:
+                # Multi-item scoring: create single prompt with delimiter text
+                # Always use format: query<delimiter>item1<delimiter>item2<delimiter>item3<delimiter>
+                # (item_first is ignored for multi-item scoring)
+                delimiter = self.multi_item_delimiter_text
+                combined_items = delimiter.join(items_list)
+                # Add final delimiter after the last item for logprob extraction
+                single_prompt = f"{query}{delimiter}{combined_items}{delimiter}"
+                batch_request.text = [single_prompt]
+            else:
+                # Single-item scoring: create separate prompts for each item
+                if item_first:
+                    prompts = [f"{item}{query}" for item in items_list]
+                else:
+                    prompts = [f"{query}{item}" for item in items_list]
+                batch_request.text = prompts
 
         elif (
             isinstance(query, list)
@@ -1729,61 +1969,38 @@ class TokenizerManager(TokenizerCommunicatorMixin):
             and isinstance(items[0], list)
         ):
             # Both query and items are token IDs
-            if item_first:
-                input_ids_list = [item + query for item in items]
+            if use_multi_item_scoring:
+                # Multi-item scoring: concatenate with delimiter token ID
+                # Format: query<delimiter_token_id>item1<delimiter_token_id>item2<delimiter_token_id>item3<delimiter_token_id>
+                delimiter_token_id = self.server_args.multi_item_scoring_delimiter
+                combined_input_ids = self._build_multi_item_token_sequence(
+                    query, items, delimiter_token_id
+                )
+                batch_request.input_ids = [combined_input_ids]
             else:
-                input_ids_list = [query + item for item in items]
-
-            batch_request.input_ids = input_ids_list
+                # Single-item scoring: process each item separately
+                if item_first:
+                    input_ids_list = [item + query for item in items]
+                else:
+                    input_ids_list = [query + item for item in items]
+                batch_request.input_ids = input_ids_list
         else:
             raise ValueError(
                 "Invalid combination of query/items types for score_request."
             )
 
         results = await self.generate_request(batch_request, request).__anext__()
-        scores = []
 
-        for result in results:
-            # Get logprobs for each token
-            logprobs = {}
-
-            # For scoring requests, we read from output_token_ids_logprobs since we want
-            # the logprobs for specific tokens mentioned in the label_token_ids at
-            # the next position after the last token in the prompt
-            output_logprobs = result["meta_info"].get("output_token_ids_logprobs", [])
-
-            # Check if output_logprobs is properly populated
-            if (
-                output_logprobs is None
-                or not output_logprobs
-                or len(output_logprobs) == 0
-            ):
-                raise RuntimeError(
-                    f"output_logprobs is empty for request {result['meta_info'].get('id', '<unknown>')}. "
-                    "This indicates token_ids_logprobs were not computed properly for the scoring request."
-                )
-
-            for logprob, token_id, _ in output_logprobs[0]:
-                if token_id in label_token_ids:
-                    logprobs[token_id] = logprob
-
-            # Get scores in order of label_token_ids
-            score_list = [
-                logprobs.get(token_id, float("-inf")) for token_id in label_token_ids
-            ]
-
-            # Apply softmax to logprobs if needed
-            if apply_softmax:
-                score_list = torch.softmax(torch.tensor(score_list), dim=0).tolist()
-            else:
-                # Convert logprobs to probabilities if not using softmax
-                score_list = [
-                    math.exp(x) if x != float("-inf") else 0.0 for x in score_list
-                ]
-
-            scores.append(score_list)
-
-        return scores
+        if use_multi_item_scoring:
+            # Multi-item scoring: extract scores from input_token_ids_logprobs
+            return self._process_multi_item_scoring_results(
+                results, items, label_token_ids, apply_softmax, batch_request
+            )
+        else:
+            # Single-item scoring: process each result separately
+            return self._process_single_item_scoring_results(
+                results, label_token_ids, apply_softmax
+            )
 
     async def watch_load_thread(self):
         # Only for dp_controller when dp_size > 1

python/sglang/srt/managers/tp_worker.py

@@ -266,10 +266,16 @@ class TpModelWorker:
 
             if model_worker_batch.is_prefill_only:
                 # For prefill-only requests, create dummy token IDs on CPU
-                batch_result.next_token_ids = torch.zeros_like(
-                    model_worker_batch.input_ids, dtype=torch.long
+                # The size should match the batch size (number of sequences), not total tokens
+                batch_result.next_token_ids = torch.zeros(
+                    len(model_worker_batch.seq_lens),
+                    dtype=torch.long,
+                    device=model_worker_batch.input_ids.device,
                 )
-                if model_worker_batch.return_logprob:
+                if (
+                    model_worker_batch.return_logprob
+                    and logits_output.next_token_logits is not None
+                ):
                     # NOTE: Compute logprobs without full sampling
                     self.model_runner.compute_logprobs_only(
                         logits_output, model_worker_batch

python/sglang/srt/model_executor/forward_batch_info.py

@@ -278,6 +278,9 @@ class ForwardBatch:
     can_run_dp_cuda_graph: bool = False
     global_forward_mode: Optional[ForwardMode] = None
 
+    # Whether this batch is prefill-only (no token generation needed)
+    is_prefill_only: bool = False
+
     # Speculative decoding
     spec_info: Optional[SpecInput] = None
     spec_algorithm: SpeculativeAlgorithm = None
@@ -325,6 +328,7 @@ class ForwardBatch:
             is_extend_in_batch=batch.is_extend_in_batch,
             can_run_dp_cuda_graph=batch.can_run_dp_cuda_graph,
             global_forward_mode=batch.global_forward_mode,
+            is_prefill_only=batch.is_prefill_only,
             lora_ids=batch.lora_ids,
             sampling_info=batch.sampling_info,
             req_to_token_pool=model_runner.req_to_token_pool,

python/sglang/srt/server_args.py

@@ -382,6 +382,12 @@ class ServerArgs:
     offload_prefetch_step: int = 1
     offload_mode: str = "cpu"
 
+    # Scoring configuration
+    # Delimiter token ID used to combine Query and Items into a single sequence for multi-item scoring.
+    # Format: Query<delimiter>Item1<delimiter>Item2<delimiter>...
+    # This enables efficient batch processing of multiple items against a single query.
+    multi_item_scoring_delimiter: Optional[Union[int]] = None
+
     # Optimization/debug options
     disable_radix_cache: bool = False
     cuda_graph_max_bs: Optional[int] = None
@@ -2334,7 +2340,13 @@ class ServerArgs:
             choices=["float32", "bfloat16"],
             help="The data type of the SSM states in mamba cache.",
         )
-
+        # Args for multi-item-scoring
+        parser.add_argument(
+            "--multi-item-scoring-delimiter",
+            type=int,
+            default=ServerArgs.multi_item_scoring_delimiter,
+            help="Delimiter token ID for multi-item scoring. Used to combine Query and Items into a single sequence: Query<delimiter>Item1<delimiter>Item2<delimiter>... This enables efficient batch processing of multiple items against a single query.",
+        )
         # Hierarchical cache
         parser.add_argument(
             "--enable-hierarchical-cache",
@@ -3004,6 +3016,17 @@ class ServerArgs:
                 "lof",
             ], f"To use priority scheduling, schedule_policy must be 'fcfs' or 'lof'. '{self.schedule_policy}' is not supported."
 
+        # Check multi-item scoring
+        if self.multi_item_scoring_delimiter is not None:
+            assert self.disable_radix_cache, (
+                "Multi-item scoring requires radix cache to be disabled. "
+                "Please set --disable-radix-cache when using --multi-item-scoring-delimiter."
+            )
+            assert self.chunked_prefill_size == -1, (
+                "Multi-item scoring requires chunked prefill to be disabled. "
+                "Please set --chunked-prefill-size -1 when using --multi-item-scoring-delimiter."
+            )
+
     def check_lora_server_args(self):
         assert self.max_loras_per_batch > 0, "max_loras_per_batch must be positive"
 

python/sglang/srt/two_batch_overlap.py

@@ -667,6 +667,7 @@ class TboForwardBatchPreparer:
             "can_run_dp_cuda_graph",
             "dp_padding_mode",
             "global_forward_mode",
+            "is_prefill_only",
             "spec_algorithm",
             "capture_hidden_mode",
             "padded_static_len",