[Feature] Add sampler custom logits processor (#2396)

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

python/sglang/srt/sampling/custom_logit_processor.py

@@ -0,0 +1,38 @@
+import json
+from abc import ABC, abstractmethod
+from functools import lru_cache
+from typing import Any, Dict, List, Optional
+
+import dill
+import torch
+
+
+@lru_cache(maxsize=None)
+def _cache_from_str(json_str: str):
+    """Deserialize a json string to a Callable object.
+    This function is cached to avoid redundant deserialization.
+    """
+    data = json.loads(json_str)
+    return dill.loads(bytes.fromhex(data["callable"]))
+
+
+class CustomLogitProcessor(ABC):
+    """Abstract base class for callable functions."""
+
+    @abstractmethod
+    def __call__(
+        self,
+        logits: torch.Tensor,
+        custom_param_list: Optional[List[Dict[str, Any]]] = None,
+    ) -> torch.Tensor:
+        """Define the callable behavior."""
+        raise NotImplementedError
+
+    def to_str(self) -> str:
+        """Serialize the callable function to a JSON-compatible string."""
+        return json.dumps({"callable": dill.dumps(self).hex()})
+
+    @classmethod
+    def from_str(cls, json_str: str):
+        """Deserialize a callable function from a JSON string."""
+        return _cache_from_str(json_str)

python/sglang/srt/sampling/sampling_batch_info.py

@@ -3,7 +3,7 @@ from __future__ import annotations
 import dataclasses
 import logging
 import threading
-from typing import TYPE_CHECKING, Callable, List, Optional
+from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Tuple
 
 import torch
 
@@ -14,6 +14,7 @@ if is_cuda:
     from sgl_kernel import sampling_scaling_penalties
 
 import sglang.srt.sampling.penaltylib as penaltylib
+from sglang.srt.sampling.custom_logit_processor import CustomLogitProcessor
 
 logger = logging.getLogger(__name__)
 
@@ -36,6 +37,9 @@ class SamplingBatchInfo:
     # Dispatch in CUDA graph
     need_min_p_sampling: bool
 
+    # Whether any request has custom logit processor
+    has_custom_logit_processor: bool
+
     # Bias Tensors
     vocab_size: int
     grammars: Optional[List] = None
@@ -52,6 +56,14 @@ class SamplingBatchInfo:
     # Device
     device: str = "cuda"
 
+    # Custom Parameters
+    custom_params: Optional[List[Optional[Dict[str, Any]]]] = None
+
+    # Custom Logit Processor
+    custom_logit_processor: Optional[
+        Dict[int, Tuple[CustomLogitProcessor, torch.Tensor]]
+    ] = None
+
     @classmethod
     def from_schedule_batch(
         cls, batch: ScheduleBatch, vocab_size: int, enable_overlap_schedule: bool
@@ -76,6 +88,36 @@ class SamplingBatchInfo:
             [r.sampling_params.min_p for r in reqs], dtype=torch.float
         ).to(device, non_blocking=True)
 
+        # Check if any request has custom logit processor
+        has_custom_logit_processor = any(r.custom_logit_processor for r in reqs)
+
+        if has_custom_logit_processor:
+            # Merge the same type of custom logit processors together
+            processor_dict = {}
+            for i, r in enumerate(reqs):
+                if r.custom_logit_processor is None:
+                    continue
+                processor_str = r.custom_logit_processor
+                if processor_str not in processor_dict:
+                    processor_dict[processor_str] = []
+                processor_dict[processor_str].append(i)
+
+            merged_custom_logit_processor = {
+                hash(processor_str): (
+                    # The deserialized custom logit processor object
+                    CustomLogitProcessor.from_str(processor_str),
+                    # The mask tensor for the requests that use this custom logit processor
+                    torch.zeros(len(reqs), dtype=torch.bool)
+                    .scatter_(0, torch.tensor(true_indices), True)
+                    .to(device, non_blocking=True),
+                )
+                for processor_str, true_indices in processor_dict.items()
+            }
+            custom_params = [r.sampling_params.custom_params for r in reqs]
+        else:
+            merged_custom_logit_processor = None
+            custom_params = None
+
         ret = cls(
             temperatures=temperatures,
             top_ps=top_ps,
@@ -83,8 +125,11 @@ class SamplingBatchInfo:
             min_ps=min_ps,
             need_min_p_sampling=any(r.sampling_params.min_p > 0 for r in reqs),
             is_all_greedy=all(r.sampling_params.top_k <= 1 for r in reqs),
+            has_custom_logit_processor=has_custom_logit_processor,
             vocab_size=vocab_size,
             device=device,
+            custom_params=custom_params,
+            custom_logit_processor=merged_custom_logit_processor,
         )
         # TODO (lianmin): `need_min_p_sampling` needs to be updated in filter and merge.
 
@@ -184,6 +229,8 @@ class SamplingBatchInfo:
 
     def filter_batch(self, unfinished_indices: List[int], new_indices: torch.Tensor):
         self.penalizer_orchestrator.filter(unfinished_indices, new_indices)
+        if self.has_custom_logit_processor:
+            self._filter_batch_custom_logit_processor(unfinished_indices, new_indices)
 
         for item in [
             "temperatures",
@@ -196,6 +243,26 @@ class SamplingBatchInfo:
             if value is not None:  # logit_bias can be None
                 setattr(self, item, value[new_indices])
 
+    def _filter_batch_custom_logit_processor(
+        self, unfinished_indices: List[int], new_indices: torch.Tensor
+    ):
+        """Filter the custom logit processor and custom params"""
+        if not self.custom_logit_processor:
+            return
+        self.custom_logit_processor = {
+            k: (p, mask[new_indices])
+            for k, (p, mask) in self.custom_logit_processor.items()
+            if any(
+                mask[new_indices]
+            )  # ignore the custom logit processor whose mask is all False
+        }
+        self.custom_params = [self.custom_params[i] for i in unfinished_indices]
+
+        if len(self) == 0:
+            self.custom_logit_processor = None
+            self.custom_params = None
+            self.has_custom_logit_processor = False
+
     @staticmethod
     def merge_bias_tensor(
         lhs: torch.Tensor,
@@ -221,6 +288,39 @@ class SamplingBatchInfo:
 
         return None
 
+    @staticmethod
+    def merge_custom_logit_processor(
+        lhs: Optional[Dict[str, torch.Tensor]],
+        rhs: Optional[Dict[str, torch.Tensor]],
+        bs1: int,
+        bs2: int,
+        device: str,
+    ):
+        if lhs is None and rhs is None:
+            return None
+        lhs, rhs = lhs or {}, rhs or {}
+
+        keys = set(lhs.keys()).union(set(rhs.keys()))
+        merged_dict = {}
+
+        for k in keys:
+            # Get the logit processor object
+            processor = lhs[k][0] if k in lhs else rhs[k][0]
+            # Get and merge the mask tensors from the two dicts
+            left_mask = (
+                lhs[k][1]
+                if k in lhs
+                else torch.zeros(bs1, dtype=torch.bool, device=device)
+            )
+            right_mask = (
+                rhs[k][1]
+                if k in rhs
+                else torch.zeros(bs2, dtype=torch.bool, device=device)
+            )
+            merged_dict[k] = (processor, torch.cat([left_mask, right_mask]))
+
+        return merged_dict
+
     def merge_batch(self, other: "SamplingBatchInfo"):
         self.penalizer_orchestrator.merge(other.penalizer_orchestrator)
 
@@ -240,6 +340,26 @@ class SamplingBatchInfo:
         )
         self.need_min_p_sampling = self.need_min_p_sampling or other.need_min_p_sampling
 
+        # Merge the custom logit processors and custom params lists
+        if self.has_custom_logit_processor or other.has_custom_logit_processor:
+            # Merge the custom logit processors
+            self.custom_logit_processor = (
+                SamplingBatchInfo.merge_custom_logit_processor(
+                    self.custom_logit_processor,
+                    other.custom_logit_processor,
+                    len(self),
+                    len(other),
+                    self.device,
+                )
+            )
+            # Merge the custom params lists
+            self.custom_params = self.custom_params or [None] * len(self)
+            other.custom_params = other.custom_params or [None] * len(other)
+            self.custom_params.extend(other.custom_params)
+
+            # Set the flag to True if any of the two has custom logit processor
+            self.has_custom_logit_processor = True
+
     def apply_logits_bias(self, logits: torch.Tensor):
         # Apply logit_bias
         if self.logit_bias is not None:

python/sglang/srt/sampling/sampling_params.py

@@ -13,7 +13,7 @@
 # ==============================================================================
 """Sampling parameters for text generation."""
 
-from typing import List, Optional, Union
+from typing import Any, Dict, List, Optional, Union
 
 _SAMPLING_EPS = 1e-6
 
@@ -48,6 +48,7 @@ class SamplingParams:
         no_stop_trim: bool = False,
         ignore_eos: bool = False,
         skip_special_tokens: bool = True,
+        custom_params: Optional[Dict[str, Any]] = None,
     ) -> None:
         self.temperature = temperature
         self.top_p = top_p
@@ -71,6 +72,7 @@ class SamplingParams:
         self.json_schema = json_schema
         self.ebnf = ebnf
         self.no_stop_trim = no_stop_trim
+        self.custom_params = custom_params
 
         # Process some special cases
         if self.temperature < _SAMPLING_EPS: