feat: limit peak memory usage when computing logprobs (#6318)

Signed-off-by: Zhao Chen <zhaochen.zju@gmail.com>
Co-authored-by: 赵晨阳 <zhaochen20@outlook.com>

python/sglang/srt/environ.py

@@ -273,6 +273,10 @@ class Envs:
     # Sparse Embeddings
     SGLANG_EMBEDDINGS_SPARSE_HEAD = EnvStr(None)
 
+    # Logits processor
+    SGLANG_ENABLE_LOGITS_PROCESSER_CHUNK = EnvBool(False)
+    SGLANG_LOGITS_PROCESSER_CHUNK_SIZE = EnvInt(2048)
+
     # Tool-Call behavior
     SGLANG_TOOL_STRICT_LEVEL = EnvInt(ToolStrictLevel.OFF)
 

python/sglang/srt/layers/logits_processor.py

@@ -15,7 +15,7 @@
 
 import dataclasses
 import logging
-from typing import List, Optional, Union
+from typing import List, Optional, Tuple, Union
 
 import torch
 import triton
@@ -26,6 +26,7 @@ from sglang.srt.distributed import (
     get_tensor_model_parallel_world_size,
     tensor_model_parallel_all_gather,
 )
+from sglang.srt.environ import envs
 from sglang.srt.layers.dp_attention import (
     DpPaddingMode,
     attn_tp_all_gather,
@@ -53,6 +54,15 @@ logger = logging.getLogger(__name__)
 _is_npu = is_npu()
 
 
+@dataclasses.dataclass
+class InputLogprobsResult:
+    input_token_logprobs: torch.Tensor
+    input_top_logprobs_val: Optional[List] = None
+    input_top_logprobs_idx: Optional[List] = None
+    input_token_ids_logprobs_val: Optional[List] = None
+    input_token_ids_logprobs_idx: Optional[List] = None
+
+
 @dataclasses.dataclass
 class LogitsProcessorOutput:
     ## Part 1: This part will be assigned in python/sglang/srt/layers/logits_processor.py::LogitsProcessor
@@ -248,6 +258,11 @@ class LogitsProcessor(nn.Module):
         ):
             self.final_logit_softcapping = None
 
+        # enable chunked logprobs processing
+        self.enable_logprobs_chunk = envs.SGLANG_ENABLE_LOGITS_PROCESSER_CHUNK.value
+        # chunk size for logprobs processing
+        self.logprobs_chunk_size = envs.SGLANG_LOGITS_PROCESSER_CHUNK_SIZE.value
+
     def compute_logprobs_for_multi_item_scoring(
         self,
         input_ids,
@@ -405,19 +420,41 @@ class LogitsProcessor(nn.Module):
             sample_indices = None
             input_logprob_indices = None
         else:
-            # Input logprobs are required.
+            # Prefill with input logprobs.
-            # Find 3 different indices.
+            # Find 4 different indices.
             # 1. pruned_states: hidden states that we want logprobs from.
             # 2. sample_indices: Indices that have sampled tokens.
             # 3. input_logprob_indices: Indices that have input logprob tokens.
+            # 4. token_to_seq_idx: map each token to its sequence index
+            #
+            # Example
+            # -------
+            # Suppose a batch (flattened by sequence):
+            # [t00, t01, t02, t03, t10, t11, t12, t13, t14, t20, t21, t22, t23, t24, t25]
+            # extend_seq_lens_cpu           = [4, 5, 6]
+            # extend_logprob_start_lens_cpu = [0, 5, 3]
+            #
+            # Then, the indices are:
+            # pruned_states         -> [t00, t01, t02, t03, t14, t23, t24, t25]
+            # sample_indices        -> [3, 4, 7]
+            # input_logprob_indices -> [0, 1, 2, 3, 5, 6, 7]
+            # token_to_seq_idx      -> [0, 0, 0, 0, 1, 2, 2, 2]
+            #
+            # If chunk is enabled and chunk_size = 3, the chunks will be computed in a chunked manner:
+            # [t00, t01, t02], [t03, t14, t23], [t24, t25]
+
             sample_index_pt = -1
             sample_indices = []
             input_logprob_indices_pt = 0
             input_logprob_indices = []
             pt, pruned_states = 0, []
-            for extend_logprob_start_len, extend_len in zip(
+            token_to_seq_idx = []
-                logits_metadata.extend_logprob_start_lens_cpu,
+
-                logits_metadata.extend_seq_lens_cpu,
+            for idx, (extend_logprob_start_len, extend_len) in enumerate(
+                zip(
+                    logits_metadata.extend_logprob_start_lens_cpu,
+                    logits_metadata.extend_seq_lens_cpu,
+                )
             ):
                 # It can happen in chunked prefill. We still need to sample 1 token,
                 # But we don't want to include it in input logprob.
@@ -430,6 +467,9 @@ class LogitsProcessor(nn.Module):
                 # by a caller.
                 assert extend_len > start_len
                 pruned_states.append(hidden_states[pt + start_len : pt + extend_len])
+                # Map each token to its sequence index, for chunked computation
+                # of input logprobs
+                token_to_seq_idx.extend([idx] * (extend_len - start_len))
                 pt += extend_len
                 sample_index_pt += extend_len - start_len
                 sample_indices.append(sample_index_pt)
@@ -441,6 +481,8 @@ class LogitsProcessor(nn.Module):
                 )
                 input_logprob_indices_pt += extend_len - start_len
 
+            # Set the last token of the last sequence
+            token_to_seq_idx.append(len(logits_metadata.extend_seq_lens_cpu) - 1)
             pruned_states = torch.cat(pruned_states)
             sample_indices = torch.tensor(
                 sample_indices, device=pruned_states.device, dtype=torch.int64
@@ -449,12 +491,6 @@ class LogitsProcessor(nn.Module):
                 input_logprob_indices, device=pruned_states.device, dtype=torch.int64
             )
 
-        # Compute logits for both input and sampled tokens.
-        logits = self._get_logits(pruned_states, lm_head, logits_metadata)
-        sampled_logits = (
-            logits[sample_indices] if sample_indices is not None else logits
-        )
-
         hidden_states_to_store: Optional[torch.Tensor] = None
         if logits_metadata.capture_hidden_mode.need_capture():
             if logits_metadata.capture_hidden_mode.is_full():
@@ -482,67 +518,278 @@ class LogitsProcessor(nn.Module):
             else:
                 assert False, "Should never reach"
 
+        del hidden_states
+
         if not logits_metadata.extend_return_logprob:
+            # Compute logits for both input and sampled tokens.
+            logits = self._get_logits(pruned_states, lm_head, logits_metadata)
+            sampled_logits = (
+                logits[sample_indices] if sample_indices is not None else logits
+            )
+
             # Decode mode or extend mode without return_logprob.
             return LogitsProcessorOutput(
                 next_token_logits=sampled_logits,
                 hidden_states=hidden_states_to_store,
             )
-        else:
-            input_logprobs = logits[input_logprob_indices]
-            del hidden_states, logits
 
-            # Normalize the logprob w/o temperature, top-p
+        # Start to process input logprobs
-            pruned_lens = torch.tensor(
+        # Normalize the logprob w/o temperature, top-p
-                logits_metadata.extend_logprob_pruned_lens_cpu,
+        pruned_lens = torch.tensor(
-                device=input_logprobs.device,
+            logits_metadata.extend_logprob_pruned_lens_cpu,
+            device=pruned_states.device,
+        )
+        if logits_metadata.temp_scaled_logprobs:
+            logits_metadata.temperature = torch.repeat_interleave(
+                logits_metadata.temperature.view(-1),
+                pruned_lens,
+            ).view(-1, 1)
+        if logits_metadata.top_p_normalized_logprobs:
+            logits_metadata.top_p = torch.repeat_interleave(
+                logits_metadata.top_p,
+                pruned_lens,
             )
-            if logits_metadata.temp_scaled_logprobs:
+
-                logits_metadata.temperature = torch.repeat_interleave(
+        # Determine whether to use chunked or non-chunked logits processing.
-                    logits_metadata.temperature.view(-1),
+        # Skip chunking if:
-                    pruned_lens,
+        # 1. Chunking is disabled
-                ).view(-1, 1)
+        # 2. Total count is below chunk size threshold
-            if logits_metadata.top_p_normalized_logprobs:
+        # 3. DP attention all-gather is enabled (can use "enable_dp_lm_head" to enable chunking)
-                logits_metadata.top_p = torch.repeat_interleave(
+        should_skip_chunking = (
-                    logits_metadata.top_p,
+            not self.enable_logprobs_chunk
-                    pruned_lens,
+            or pruned_states.shape[0] <= self.logprobs_chunk_size
-                )
+            or self.do_tensor_parallel_all_gather_dp_attn
-            input_logprobs = self.compute_temp_top_p_normalized_logprobs(
+        )
+
+        if should_skip_chunking:
+            # Compute logits for both input and sampled tokens.
+            logits = self._get_logits(pruned_states, lm_head, logits_metadata)
+            sampled_logits = (
+                logits[sample_indices] if sample_indices is not None else logits
+            )
+
+            input_logprobs = logits[input_logprob_indices]
+            del logits
+
+            logprobs_result = self._process_input_logprobs(
                 input_logprobs, logits_metadata
             )
+        else:
+            (logprobs_result, sampled_logits) = self._process_input_logprobs_by_chunk(
+                pruned_states,
+                sample_indices,
+                input_logprob_indices,
+                token_to_seq_idx,
+                lm_head,
+                logits_metadata,
+            )
+
+        return LogitsProcessorOutput(
+            next_token_logits=sampled_logits,
+            hidden_states=hidden_states_to_store,
+            input_token_logprobs=logprobs_result.input_token_logprobs,
+            input_top_logprobs_val=logprobs_result.input_top_logprobs_val,
+            input_top_logprobs_idx=logprobs_result.input_top_logprobs_idx,
+            input_token_ids_logprobs_val=logprobs_result.input_token_ids_logprobs_val,
+            input_token_ids_logprobs_idx=logprobs_result.input_token_ids_logprobs_idx,
+        )
+
+    def _process_input_logprobs(self, input_logprobs, logits_metadata):
+        input_logprobs = self.compute_temp_top_p_normalized_logprobs(
+            input_logprobs, logits_metadata
+        )
+
+        # 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(input_logprobs, logits_metadata)
+        else:
+            input_top_logprobs_val = input_top_logprobs_idx = None
+
+        # Get the logprob of given token id
+        if logits_metadata.extend_token_ids_logprob:
+            (
+                input_token_ids_logprobs_val,
+                input_token_ids_logprobs_idx,
+            ) = self.get_token_ids_logprobs(input_logprobs, logits_metadata)
+        else:
+            input_token_ids_logprobs_val = input_token_ids_logprobs_idx = None
+
+        input_token_logprobs = input_logprobs[
+            torch.arange(input_logprobs.shape[0], device=input_logprobs.device),
+            logits_metadata.extend_input_logprob_token_ids_gpu,
+        ]
+
+        return InputLogprobsResult(
+            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 _process_input_logprobs_by_chunk(
+        self,
+        pruned_states: torch.Tensor,
+        sample_indices: torch.Tensor,
+        input_logprob_indices: torch.Tensor,
+        token_to_seq_idx: list[int],
+        lm_head: VocabParallelEmbedding,
+        logits_metadata: LogitsMetadata,
+    ) -> Tuple[InputLogprobsResult, torch.Tensor]:
+        """
+        compute logprobs for the output token from the hidden states.
+        To avoid using too much memory, we split pruned_states into chunks of
+        rows to compute input_logprobs separately, then concatenate the results.
+
+        Returns:
+            InputLogprobsResult: logprobs result
+            torch.Tensor: sampled logits
+        """
+
+        # The peak memory usage is proportional to the chunk size.
+        chunk_size = self.logprobs_chunk_size
+        total_size = pruned_states.shape[0]
+        num_chunks = (total_size + chunk_size - 1) // chunk_size
+
+        input_token_logprobs = []
+        if logits_metadata.extend_return_top_logprob:
+            input_top_logprobs_val = []
+            input_top_logprobs_idx = []
+        else:
+            input_top_logprobs_val = None
+            input_top_logprobs_idx = None
+        if logits_metadata.extend_token_ids_logprob:
+            input_token_ids_logprobs_val = []
+            input_token_ids_logprobs_idx = []
+        else:
+            input_token_ids_logprobs_val = None
+            input_token_ids_logprobs_idx = None
+
+        # If a single sequence is split into multiple chunks, we need to keep track
+        # of the pruned length of the sequences in the previous chunks.
+        split_len_topk = 0
+        split_len_token_ids = 0
+
+        for i in range(num_chunks):
+            start_idx = i * chunk_size
+            end_idx = min((i + 1) * chunk_size, total_size)
+
+            # Get indices for this chunk
+            chunk_mask = (input_logprob_indices >= start_idx) & (
+                input_logprob_indices < end_idx
+            )
+            global_indices = input_logprob_indices[chunk_mask]
+            chunk_indices = global_indices - start_idx
+            # Get the positions in the original array where chunk_mask is True
+            # This is needed to correctly index into extend_input_logprob_token_ids_gpu
+            mask_indices = torch.nonzero(chunk_mask, as_tuple=True)[0]
+
+            # Get the logits for this chunk
+            chunk_states = pruned_states[start_idx:end_idx]
+            chunk_logits = self._get_logits(chunk_states, lm_head, logits_metadata)
+
+            # Initialize sampled_logits on first chunk
+            if i == 0:
+                sampled_logits = torch.empty(
+                    (sample_indices.shape[0], chunk_logits.shape[1]),
+                    dtype=chunk_logits.dtype,
+                    device=chunk_logits.device,
+                )
+
+            # Handle sampled logits for the chunk if needed
+            # This must be done before the continue statement to ensure all sampled_logits are filled
+            chunk_sample_mask = (sample_indices >= start_idx) & (
+                sample_indices < end_idx
+            )
+            if chunk_sample_mask.any():
+                chunk_sample_indices = sample_indices[chunk_sample_mask] - start_idx
+                sampled_logits[chunk_sample_mask] = chunk_logits[chunk_sample_indices]
+
+            # If there are no input logprobs in this chunk, skip the rest
+            if chunk_indices.numel() == 0:
+                continue
+
+            # Compute the logprobs of the chunk
+            chunk_input_logprobs = chunk_logits[chunk_indices]
+            chunk_temperature = (
+                logits_metadata.temperature[global_indices]
+                if logits_metadata.temperature is not None
+                else None
+            )
+            chunk_top_p = (
+                logits_metadata.top_p[global_indices]
+                if logits_metadata.top_p is not None
+                else None
+            )
+            chunk_input_logprobs = self.compute_temp_top_p_normalized_logprobs(
+                chunk_input_logprobs,
+                logits_metadata,
+                chunk_top_p,
+                chunk_temperature,
+            )
+
+            # For each chunk, we need to get the slice of the token_to_seq_idx
+            chunk_slice = slice(
+                token_to_seq_idx[start_idx], token_to_seq_idx[end_idx] + 1
+            )
 
             # Get the logprob of top-k tokens
             if logits_metadata.extend_return_top_logprob:
-                (
+                top_k_nums = logits_metadata.top_logprobs_nums[chunk_slice]
+                pruned_lens = logits_metadata.extend_logprob_pruned_lens_cpu[
+                    chunk_slice
+                ]
+                split_len_topk = self.get_top_logprobs_chunk(
+                    chunk_input_logprobs,
+                    logits_metadata,
+                    top_k_nums,
+                    pruned_lens,
                     input_top_logprobs_val,
                     input_top_logprobs_idx,
-                ) = self.get_top_logprobs(input_logprobs, logits_metadata)
+                    split_len_topk,
-            else:
+                )
-                input_top_logprobs_val = input_top_logprobs_idx = None
 
             # Get the logprob of given token id
             if logits_metadata.extend_token_ids_logprob:
-                (
+                token_ids_logprobs = logits_metadata.token_ids_logprobs[chunk_slice]
+                pruned_lens = logits_metadata.extend_logprob_pruned_lens_cpu[
+                    chunk_slice
+                ]
+                split_len_token_ids = self.get_token_ids_logprobs_chunk(
+                    chunk_input_logprobs,
+                    logits_metadata,
+                    token_ids_logprobs,
+                    pruned_lens,
                     input_token_ids_logprobs_val,
                     input_token_ids_logprobs_idx,
-                ) = self.get_token_ids_logprobs(input_logprobs, logits_metadata)
+                    split_len_token_ids,
-            else:
+                )
-                input_token_ids_logprobs_val = input_token_ids_logprobs_idx = None
 
-            input_token_logprobs = input_logprobs[
+            # Get the logprob of the requested token ids
-                torch.arange(input_logprobs.shape[0], device=input_logprobs.device),
+            chunk_input_token_logprobs = chunk_input_logprobs[
-                logits_metadata.extend_input_logprob_token_ids_gpu,
+                torch.arange(
+                    chunk_input_logprobs.shape[0], device=chunk_input_logprobs.device
+                ),
+                logits_metadata.extend_input_logprob_token_ids_gpu[mask_indices],
             ]
+            input_token_logprobs.append(chunk_input_token_logprobs)
 
-            return LogitsProcessorOutput(
+        # Concatenate the results
-                next_token_logits=sampled_logits,
+        input_token_logprobs = torch.cat(input_token_logprobs, dim=0)
+
+        return (
+            InputLogprobsResult(
                 input_token_logprobs=input_token_logprobs,
                 input_top_logprobs_val=input_top_logprobs_val,
                 input_top_logprobs_idx=input_top_logprobs_idx,
-                hidden_states=hidden_states_to_store,
                 input_token_ids_logprobs_val=input_token_ids_logprobs_val,
                 input_token_ids_logprobs_idx=input_token_ids_logprobs_idx,
-            )
+            ),
+            sampled_logits,
+        )
 
     def _get_logits(
         self,
@@ -691,6 +938,80 @@ class LogitsProcessor(nn.Module):
 
         return input_top_logprobs_val, input_top_logprobs_idx
 
+    @staticmethod
+    def get_top_logprobs_chunk(
+        logprobs: torch.Tensor,
+        logits_metadata: LogitsMetadata,
+        top_k_nums: List[int],
+        pruned_lens: List[int],
+        input_top_logprobs_val: List,
+        input_top_logprobs_idx: List,
+        split_pruned_len: int,
+    ) -> int:
+        """Get top-k logprobs for each sequence in the chunk.
+
+        Args:
+            logprobs: Log probabilities tensor of shape [seq_len, vocab_size]
+            logits_metadata: Metadata containing top-k and pruned length info
+            top_k_nums: List of top-k numbers for each sequence
+            pruned_lens: List of pruned lengths for each sequence
+            input_top_logprobs_val: List to store top-k logprob values
+            input_top_logprobs_idx: List to store top-k token indices
+            split_pruned_len: Length of pruned tokens from previous chunk
+
+        Returns:
+            int: Number of remaining tokens to process in next chunk
+        """
+        # No sequences in the chunk
+        if logprobs.shape[0] == 0:
+            return 0
+
+        max_k = max(logits_metadata.top_logprobs_nums)
+        ret = logprobs.topk(max_k, dim=1)
+        values = ret.values.tolist()
+        indices = ret.indices.tolist()
+
+        pt = 0
+        next_split_pruned_len = 0
+        for n, (k, pruned_len) in enumerate(zip(top_k_nums, pruned_lens)):
+            if n == 0:
+                # For the first sequence, adjust the pruned length
+                pruned_len -= split_pruned_len
+            else:
+                # After the first sequence, no split in the middle
+                split_pruned_len = 0
+
+            if pruned_len <= 0:
+                # if pruned length is less than or equal to 0,
+                # there is no top-k logprobs to process
+                input_top_logprobs_val.append([])
+                input_top_logprobs_idx.append([])
+                continue
+
+            # Get the top-k logprobs
+            val = []
+            idx = []
+            for j in range(pruned_len):
+                # Handle remaining tokens in next chunk if any
+                if pt + j >= len(values):
+                    next_split_pruned_len = split_pruned_len + j
+                    break
+                # Append the top-k logprobs
+                val.append(values[pt + j][:k])
+                idx.append(indices[pt + j][:k])
+
+            # Append or extend based on whether the sequence was split across chunks
+            if len(val) > 0:
+                if split_pruned_len > 0:
+                    input_top_logprobs_val[-1].extend(val)
+                    input_top_logprobs_idx[-1].extend(idx)
+                else:
+                    input_top_logprobs_val.append(val)
+                    input_top_logprobs_idx.append(idx)
+
+            pt += pruned_len
+        return next_split_pruned_len
+
     @staticmethod
     def get_token_ids_logprobs(
         all_logprobs: torch.Tensor,
@@ -724,9 +1045,86 @@ class LogitsProcessor(nn.Module):
 
         return input_token_ids_logprobs_val, input_token_ids_logprobs_idx
 
+    @staticmethod
+    def get_token_ids_logprobs_chunk(
+        logprobs: torch.Tensor,
+        logits_metadata: LogitsMetadata,
+        token_ids_logprobs: List[int],
+        pruned_lens: List[int],
+        input_token_ids_logprobs_val: List,
+        input_token_ids_logprobs_idx: List,
+        split_pruned_len: int = 0,
+    ):
+        """Get token_ids logprobs for each sequence in the chunk.
+
+        Args:
+            logprobs: Log probabilities tensor of shape [seq_len, vocab_size]
+            logits_metadata: Metadata containing token IDs and pruned length info
+            token_ids_logprobs: List of token IDs for each sequence
+            pruned_lens: List of pruned lengths for each sequence
+            input_token_ids_logprobs_val: List to store token logprob values
+            input_token_ids_logprobs_idx: List to store token indices
+            split_pruned_len: Length of pruned tokens from previous chunk
+
+        Returns:
+            int: Number of remaining tokens to process in next chunk
+        """
+
+        # No sequences in the chunk
+        if logprobs.shape[0] == 0:
+            return 0
+
+        pt = 0
+        next_split_pruned_len = 0
+        for n, (token_ids, pruned_len) in enumerate(
+            zip(
+                token_ids_logprobs,
+                pruned_lens,
+            )
+        ):
+            # Adjust pruned length for first sequence
+            if n == 0:
+                pruned_len -= split_pruned_len
+            else:
+                split_pruned_len = 0
+
+            if pruned_len <= 0:
+                # if pruned length is less than or equal to 0,
+                # there is no token ids logprobs to process
+                input_token_ids_logprobs_val.append([])
+                input_token_ids_logprobs_idx.append([])
+                continue
+
+            # Get the token ids logprobs
+            val = []
+            idx = []
+            for j in range(pruned_len):
+                # Handle remaining tokens in next chunk if any
+                if pt + j >= logprobs.shape[0]:
+                    next_split_pruned_len = split_pruned_len + j
+                    break
+                if token_ids is not None:
+                    val.append(logprobs[pt + j, token_ids].tolist())
+                    idx.append(token_ids)
+
+            # Append or extend based on whether the sequence was split across chunks
+            if len(val) > 0:
+                if split_pruned_len > 0:
+                    input_token_ids_logprobs_val[-1].extend(val)
+                    input_token_ids_logprobs_idx[-1].extend(idx)
+                else:
+                    input_token_ids_logprobs_val.append(val)
+                    input_token_ids_logprobs_idx.append(idx)
+
+            pt += pruned_len
+        return next_split_pruned_len
+
     @staticmethod
     def compute_temp_top_p_normalized_logprobs(
-        last_logits: torch.Tensor, logits_metadata: LogitsMetadata
+        last_logits: torch.Tensor,
+        logits_metadata: LogitsMetadata,
+        top_p: Optional[torch.Tensor] = None,
+        temperature: Optional[torch.Tensor] = None,
     ) -> torch.Tensor:
         """
         compute logprobs for the output token from the given logits.
@@ -734,21 +1132,23 @@ class LogitsProcessor(nn.Module):
         Returns:
             torch.Tensor: logprobs from logits
         """
+        if top_p is None:
+            top_p = logits_metadata.top_p
+        if temperature is None:
+            temperature = logits_metadata.temperature
+
         # Scale logits if temperature scaling is enabled
         if logits_metadata.temp_scaled_logprobs:
-            last_logits = last_logits / logits_metadata.temperature
+            last_logits = last_logits / temperature
 
         # Normalize logprobs if top_p normalization is enabled
         # NOTE: only normalize logprobs when top_p is set and not equal to 1.0
-        if (
+        if logits_metadata.top_p_normalized_logprobs and (top_p != 1.0).any():
-            logits_metadata.top_p_normalized_logprobs
-            and (logits_metadata.top_p != 1.0).any()
-        ):
             from sglang.srt.layers.sampler import top_p_normalize_probs_torch
 
             probs = torch.softmax(last_logits, dim=-1)
             del last_logits
-            probs = top_p_normalize_probs_torch(probs, logits_metadata.top_p)
+            probs = top_p_normalize_probs_torch(probs, top_p)
             return torch.log(probs)
         else:
             return torch.nn.functional.log_softmax(last_logits, dim=-1)

test/srt/test_logprobs.py

@@ -85,6 +85,16 @@ MAX_LEN = 20000
 DEFAULT_BASELINE_PKL = "sglang_baseline_local.pkl"
 DEFAULT_META_JSON = "baseline_meta_preview.json"
 
+# Default engine configuration
+DEFAULT_ENGINE_CONFIG = {
+    "model_path": DENSE_MODEL_NAME,
+    "random_seed": 42,
+    "skip_tokenizer_init": True,
+    "mem_fraction_static": 0.8,
+    "enable_deterministic_inference": True,
+    "attention_backend": "flashinfer",
+}
+
 
 def generate_baseline(
     baseline_file=DEFAULT_BASELINE_PKL,
@@ -213,14 +223,7 @@ class TestLogprobsDense(unittest.TestCase):
     def setUpClass(cls):
         """Set up the test class - initialize the engine once for all tests."""
         print(f"Launching SGLang Engine with {DENSE_MODEL_NAME}...")
-        cls.engine = sgl.Engine(
+        cls.engine = sgl.Engine(**DEFAULT_ENGINE_CONFIG)
-            model_path=DENSE_MODEL_NAME,
-            random_seed=42,
-            attention_backend="flashinfer",
-            enable_deterministic_inference=True,
-            skip_tokenizer_init=True,
-            mem_fraction_static=0.80,
-        )
 
     @classmethod
     def tearDownClass(cls):
@@ -228,6 +231,26 @@ class TestLogprobsDense(unittest.TestCase):
         cls.engine.shutdown()
         torch.cuda.empty_cache()
 
+    @classmethod
+    def restart_engine_with_config(cls, **kwargs):
+        """Create engine with custom configuration"""
+        # Safely shutdown existing engine
+        cls.engine.shutdown()
+        torch.cuda.empty_cache()
+
+        # Set chunk size
+        chunk_size = kwargs.pop("chunk_size", None)
+        if chunk_size is not None:
+            print(f"Setting chunk size to {chunk_size}")
+            os.environ["SGLANG_ENABLE_LOGITS_PROCESSER_CHUNK"] = "True"
+            os.environ["SGLANG_LOGITS_PROCESSER_CHUNK_SIZE"] = str(chunk_size)
+        else:
+            os.environ["SGLANG_ENABLE_LOGITS_PROCESSER_CHUNK"] = "False"
+
+        # Create engine with merged configuration
+        engine_config = {**DEFAULT_ENGINE_CONFIG, **kwargs}
+        cls.engine = sgl.Engine(**engine_config)
+
     def load_test_data(self, baseline_file=None):
         """Load test data from local baseline file. In test mode, only local baseline is supported."""
         if not baseline_file:
@@ -281,140 +304,175 @@ class TestLogprobsDense(unittest.TestCase):
         # Load test data with retry mechanism
         records = self.load_test_data(baseline_file)
 
-        with self.subTest(
+        # Fast configs for CI
-            config={
+        test_configs = [
+            {"num_samples": NUM_SAMPLES},
+            {"num_samples": 42, "chunk_size": 1, "max_running_requests": 16},
+            {"num_samples": 42, "chunk_size": 2, "max_running_requests": 16},
+            {"num_samples": 42, "chunk_size": 3, "max_running_requests": 16},
+            {"num_samples": NUM_SAMPLES, "chunk_size": 16, "max_running_requests": 128},
+            {"num_samples": NUM_SAMPLES, "chunk_size": 128, "max_running_requests": 16},
+            {"num_samples": NUM_SAMPLES, "chunk_size": 128, "max_running_requests": 8},
+            {"num_samples": NUM_SAMPLES, "chunk_size": 128, "max_running_requests": 32},
+            {
                 "num_samples": NUM_SAMPLES,
-                "logprob_sample_ratio": LOGPROB_SAMPLE_RATIO,
+                "chunk_size": 128,
-                "temperature": TEMPERATURE,
+                "max_running_requests": 128,
-            }
+            },
-        ):
+            {"num_samples": NUM_SAMPLES, "chunk_size": 256, "max_running_requests": 8},
+            {"num_samples": NUM_SAMPLES, "chunk_size": 256, "max_running_requests": 32},
+            {
+                "num_samples": NUM_SAMPLES,
+                "chunk_size": 256,
+                "max_running_requests": 128,
+            },
+        ]
 
-            # Sample records for this config
+        # Run tests
-            test_records = random.sample(records, k=min(NUM_SAMPLES, len(records)))
+        for config in test_configs:
-            random.shuffle(test_records)
+            with self.subTest(config=config):
+                print(f"Testing with config: {config}")
 
-            # Calculate how many samples should return logprobs
+                # Sample records for this config
-            logprob_count = int(len(test_records) * LOGPROB_SAMPLE_RATIO)
+                test_records = random.sample(records, k=min(NUM_SAMPLES, len(records)))
-            print(
+                random.shuffle(test_records)
-                f"Testing with {len(test_records)} samples, temperature={TEMPERATURE}"
-            )
-            print(
-                f"Will return logprobs for {logprob_count} samples (ratio: {LOGPROB_SAMPLE_RATIO})"
-            )
 
-            all_max, all_mean = [], []
+                # Calculate how many samples should return logprobs
-            logprob_returned_count = 0
+                logprob_count = int(len(test_records) * LOGPROB_SAMPLE_RATIO)
-
+                print(
-            # Process all records at once
+                    f"Testing with {len(test_records)} samples, temperature={TEMPERATURE}"
-            input_ids = [rec["ids"] for rec in test_records]
-            logprob_start_lens = [rec["start_pos"] for rec in test_records]
-
-            # Determine which samples should return logprobs (randomly selected)
-            logprob_indices = set(
-                random.sample(range(len(test_records)), logprob_count)
-            )
-            return_logprob_array = [
-                sample_idx in logprob_indices for sample_idx in range(len(test_records))
-            ]
-
-            # Sampling param per request
-            sampling_params = [
-                {
-                    "temperature": TEMPERATURE,
-                    "top_p": 1.0,
-                    "top_k": TOP_K,
-                    "max_new_tokens": 1,
-                }
-                for _ in test_records
-            ]
-
-            outputs = self.engine.generate(
-                input_ids=input_ids,
-                sampling_params=sampling_params,
-                return_logprob=return_logprob_array,
-                logprob_start_len=logprob_start_lens,
-                top_logprobs_num=TOP_K,
-            )
-
-            for sample_idx, (rec, output) in enumerate(zip(test_records, outputs)):
-                # Only compare logprobs for samples that should have them
-                if sample_idx in logprob_indices:
-                    # Safe access to meta_info and input_top_logprobs
-                    meta_info = output.get("meta_info")
-                    input_top_logprobs = (
-                        meta_info.get("input_top_logprobs") if meta_info else None
-                    )
-
-                    self.assertIsNotNone(
-                        input_top_logprobs,
-                        f"return_logprob enabled on this sample, but input_top_logprobs is None (length: {len(input_top_logprobs) if input_top_logprobs is not None else 'N/A'})",
-                    )
-                    baseline_meta = rec["meta"]
-                    sglang_meta = meta_info
-
-                    max_diff, mean_diff = self.compare_meta(baseline_meta, sglang_meta)
-                    all_max.append(max_diff)
-                    all_mean.append(mean_diff)
-                    logprob_returned_count += 1
-                else:
-                    # Verify that logprobs were not returned for this sample
-                    meta_info = output.get("meta_info")
-                    input_top_logprobs = (
-                        meta_info.get("input_top_logprobs") if meta_info else None
-                    )
-                    output_token_ids_logprobs = (
-                        meta_info.get("output_token_ids_logprobs")
-                        if meta_info
-                        else None
-                    )
-
-                    self.assertFalse(
-                        input_top_logprobs,
-                        f"return_logprob is disabled on this sample, Sample {sample_idx} should not have logprobs, content: {output_token_ids_logprobs}",
-                    )
-
-            max_of_max = max(all_max) if all_max else 0.0
-            mean_of_mean = np.mean(all_mean) if all_mean else 0.0
-
-            print(f"max Δ={max_of_max:.6g}")
-            print(f"mean Δ={mean_of_mean:.6g}")
-            print(
-                f"logprobs returned for {logprob_returned_count} samples (expected: {logprob_count})"
-            )
-
-            # Verify correct number of logprobs returned
-            self.assertEqual(
-                logprob_returned_count,
-                logprob_count,
-                f"Expected {logprob_count} samples with logprobs, got {logprob_returned_count}",
-            )
-
-            # Basic validation
-            self.assertIsInstance(all_max, list)
-            self.assertIsInstance(all_mean, list)
-            self.assertGreater(
-                len(all_max),
-                0,
-                f"No test samples processed for config {{'num_samples': {NUM_SAMPLES}, 'logprob_sample_ratio': {LOGPROB_SAMPLE_RATIO}, 'temperature': {TEMPERATURE}}}",
-            )
-
-            # Tolerance checks with clear error messages
-            failed_samples = []
-            for sample_idx, (max_diff, mean_diff) in enumerate(zip(all_max, all_mean)):
-                if max_diff > DENSE_TOLERANCE_MAX_DIFF:
-                    failed_samples.append(
-                        f"Sample {sample_idx}: max_diff={max_diff:.6g} > {DENSE_TOLERANCE_MAX_DIFF}"
-                    )
-                if mean_diff > DENSE_TOLERANCE_MEAN_DIFF:
-                    failed_samples.append(
-                        f"Sample {sample_idx}: mean_diff={mean_diff:.6g} > {DENSE_TOLERANCE_MEAN_DIFF}"
-                    )
-
-            if failed_samples:
-                self.fail(
-                    f"Config {{'num_samples': {NUM_SAMPLES}, 'logprob_sample_ratio': {LOGPROB_SAMPLE_RATIO}, 'temperature': {TEMPERATURE}}} - Tolerance exceeded in {len(failed_samples)} samples:\n"
-                    + "\n".join(failed_samples[:5])
                 )
+                print(
+                    f"Will return logprobs for {logprob_count} samples (ratio: {LOGPROB_SAMPLE_RATIO})"
+                )
+
+                all_max, all_mean = [], []
+                logprob_returned_count = 0
+
+                # Process all records at once
+                input_ids = [rec["ids"] for rec in test_records]
+                logprob_start_lens = [rec["start_pos"] for rec in test_records]
+
+                # Determine which samples should return logprobs (randomly selected)
+                logprob_indices = set(
+                    random.sample(range(len(test_records)), logprob_count)
+                )
+                return_logprob_array = [
+                    sample_idx in logprob_indices
+                    for sample_idx in range(len(test_records))
+                ]
+
+                # Sampling param per request
+                sampling_params = [
+                    {
+                        "temperature": TEMPERATURE,
+                        "top_p": 1.0,
+                        "top_k": TOP_K,
+                        "max_new_tokens": 1,
+                    }
+                    for _ in test_records
+                ]
+
+                # Some configs must restart the engine to take effect
+                chunk_size = config.get("chunk_size", None)
+                max_running_requests = config.get("max_running_requests", None)
+                if chunk_size is not None or max_running_requests is not None:
+                    self.restart_engine_with_config(
+                        chunk_size=chunk_size,
+                        max_running_requests=max_running_requests,
+                    )
+
+                outputs = self.engine.generate(
+                    input_ids=input_ids,
+                    sampling_params=sampling_params,
+                    return_logprob=return_logprob_array,
+                    logprob_start_len=logprob_start_lens,
+                    top_logprobs_num=TOP_K,
+                )
+
+                for sample_idx, (rec, output) in enumerate(zip(test_records, outputs)):
+                    # Only compare logprobs for samples that should have them
+                    if sample_idx in logprob_indices:
+                        # Safe access to meta_info and input_top_logprobs
+                        meta_info = output.get("meta_info")
+                        input_top_logprobs = (
+                            meta_info.get("input_top_logprobs") if meta_info else None
+                        )
+
+                        self.assertIsNotNone(
+                            input_top_logprobs,
+                            f"return_logprob enabled on this sample, but input_top_logprobs is None (length: {len(input_top_logprobs) if input_top_logprobs is not None else 'N/A'})",
+                        )
+                        baseline_meta = rec["meta"]
+                        sglang_meta = meta_info
+
+                        max_diff, mean_diff = self.compare_meta(
+                            baseline_meta, sglang_meta
+                        )
+                        all_max.append(max_diff)
+                        all_mean.append(mean_diff)
+                        logprob_returned_count += 1
+                    else:
+                        # Verify that logprobs were not returned for this sample
+                        meta_info = output.get("meta_info")
+                        input_top_logprobs = (
+                            meta_info.get("input_top_logprobs") if meta_info else None
+                        )
+                        output_token_ids_logprobs = (
+                            meta_info.get("output_token_ids_logprobs")
+                            if meta_info
+                            else None
+                        )
+
+                        self.assertFalse(
+                            input_top_logprobs,
+                            f"return_logprob is disabled on this sample, Sample {sample_idx} should not have logprobs, content: {output_token_ids_logprobs}",
+                        )
+
+                max_of_max = max(all_max) if all_max else 0.0
+                mean_of_mean = np.mean(all_mean) if all_mean else 0.0
+
+                print(f"max Δ={max_of_max:.6g}")
+                print(f"mean Δ={mean_of_mean:.6g}")
+                print(
+                    f"logprobs returned for {logprob_returned_count} samples (expected: {logprob_count})"
+                )
+
+                # Verify correct number of logprobs returned
+                self.assertEqual(
+                    logprob_returned_count,
+                    logprob_count,
+                    f"Expected {logprob_count} samples with logprobs, got {logprob_returned_count}",
+                )
+
+                # Basic validation
+                self.assertIsInstance(all_max, list)
+                self.assertIsInstance(all_mean, list)
+                self.assertGreater(
+                    len(all_max),
+                    0,
+                    f"No test samples processed for config {{'num_samples': {NUM_SAMPLES}, 'logprob_sample_ratio': {LOGPROB_SAMPLE_RATIO}, 'temperature': {TEMPERATURE}}}",
+                )
+
+                # Tolerance checks with clear error messages
+                failed_samples = []
+                for sample_idx, (max_diff, mean_diff) in enumerate(
+                    zip(all_max, all_mean)
+                ):
+                    if max_diff > DENSE_TOLERANCE_MAX_DIFF:
+                        failed_samples.append(
+                            f"Sample {sample_idx}: max_diff={max_diff:.6g} > {DENSE_TOLERANCE_MAX_DIFF}"
+                        )
+                    if mean_diff > DENSE_TOLERANCE_MEAN_DIFF:
+                        failed_samples.append(
+                            f"Sample {sample_idx}: mean_diff={mean_diff:.6g} > {DENSE_TOLERANCE_MEAN_DIFF}"
+                        )
+
+                if failed_samples:
+                    self.fail(
+                        f"Config {{'num_samples': {NUM_SAMPLES}, 'logprob_sample_ratio': {LOGPROB_SAMPLE_RATIO}, 'temperature': {TEMPERATURE}}} - Tolerance exceeded in {len(failed_samples)} samples:\n"
+                        + "\n".join(failed_samples[:5])
+                    )
 
 
 def main():