feat: basic support for server-level multimodal cache

python/sglang/srt/managers/mm_utils.py

@@ -17,7 +17,7 @@
     MultimodalDataItem,
     MultimodalInputs,
 )
-from sglang.srt.mem_cache.multimodal_cache import MultiModalCache
+from sglang.srt.mem_cache.multimodal_cache import MultiModalStaticCache
 from sglang.srt.model_executor.forward_batch_info import ForwardBatch
 from sglang.srt.server_args import get_global_server_args
 from sglang.srt.utils import flatten_nested_list, is_npu, print_warning_once
@@ -283,17 +283,12 @@ def pad_input_tokens(
         return ret_input_ids
 
 
-embedding_cache: Optional[MultiModalCache] = None
+embedding_cache: Optional[MultiModalStaticCache] = None
 
 
-def init_embedding_cache(max_size: int = 0):
+def init_mm_embedding_cache(max_size: int = 0):
     global embedding_cache
-    embedding_cache = MultiModalCache(max_size)
-
-
-def get_embedding_hash(embedding_items: List[MultimodalDataItem]) -> int:
-    hash_list = [item.hash for item in embedding_items]
-    return hash(tuple(hash_list))
+    embedding_cache = MultiModalStaticCache(max_size)
 
 
 def get_embedding_chunk(
@@ -380,14 +375,15 @@ def _get_chunked_prefill_embedding(
         embedding_items_per_req = embedding_items[items_size[i] : items_size[i + 1]]
         items_offset = items_offset_list[i]
         assert items_offset is not None, items_offset
-        embedding_items_hash = get_embedding_hash(embedding_items_per_req)
         # if all items has been prefixed, we do not need to calculate embedding
         if all([offset_end < prefix_length[i] for _, offset_end in items_offset]):
             continue
-        embedding_per_req = embedding_cache.get(embedding_items_hash)
+        item_hashes = [item.hash for item in embedding_items]
+        embedding_items_hash = MultiModalStaticCache.combine_hashes(item_hashes)
+        embedding_per_req = embedding_cache.get(item_hashes)
         if embedding_per_req is None:
             embedding_per_req = data_embedding_func(embedding_items_per_req)
-            if not embedding_cache.put(embedding_items_hash, embedding_per_req):
+            if not embedding_cache.set(embedding_items_hash, embedding_per_req):
                 print_warning_once(
                     "Multimodal embedding cache is full. This typically occurs when a single "
                     "embedding exceeds the cache size limit. Consider increasing the "

python/sglang/srt/managers/scheduler.py

@@ -112,7 +112,7 @@
     UpdateWeightsFromIPCReqInput,
     UpdateWeightsFromTensorReqInput,
 )
-from sglang.srt.managers.mm_utils import init_embedding_cache
+from sglang.srt.managers.mm_utils import init_mm_embedding_cache
 from sglang.srt.managers.overlap_utils import FutureMap
 from sglang.srt.managers.schedule_batch import (
     FINISH_ABORT,
@@ -846,7 +846,7 @@ def init_memory_pool_and_cache(self):
         )
 
         embedding_cache_size = int(os.environ.get("SGLANG_VLM_CACHE_SIZE_MB", "100"))
-        init_embedding_cache(embedding_cache_size * 1024 * 1024)
+        init_mm_embedding_cache(embedding_cache_size * 1024 * 1024)
 
     def init_disaggregation(self):
         self.transfer_backend = TransferBackend(

python/sglang/srt/mem_cache/multimodal_cache.py

@@ -1,67 +1,133 @@
-import logging
+import abc
 from collections import OrderedDict
+from typing import List, Optional
 
 import torch
 
-# Set up logging for cache behavior
-logger = logging.getLogger(__name__)
+from sglang.srt.mem_cache.allocator import BaseTokenToKVPoolAllocator
 
 
-class MultiModalCache:
-    """MultiModalCache is used to store vlm encoder results with LRU eviction"""
+class MultimodalCache(abc.ABC):
+    @abc.abstractmethod
+    def __init__(
+        self,
+    ): ...
+
+    @staticmethod
+    def combine_hashes(mm_hashes: List[int]) -> Optional[int]:
+        """
+        Get a combined hash from individual mm item hashes
+        """
+        if not mm_hashes:
+            return None
+        return hash(tuple(mm_hashes))
+
+    @abc.abstractmethod
+    def get(
+        self, mm_hashes: List[int], combined_hash: Optional[int] = None
+    ) -> Optional[torch.Tensor]:
+        """
+        Extract the embedding with the hash-ids of the queried items. Try combined hash first, if missed, fallback to individual hashes
+        The returned tensor may not be contiguous
+        """
+        raise NotImplementedError()
+
+    @abc.abstractmethod
+    def set(
+        self,
+        mm_hash: int,
+        embedding: torch.Tensor,
+        mm_embedding_allocator: BaseTokenToKVPoolAllocator,
+    ) -> bool:
+        """
+        Set the embedding to the pre-allocated locations with a hash id
+        """
+        raise NotImplementedError()
+
+    @abc.abstractmethod
+    def has(self, mm_hash: int) -> bool:
+        raise NotImplementedError()
+
+    @abc.abstractmethod
+    def free(
+        self, mm_hash: int, mm_embedding_allocator: BaseTokenToKVPoolAllocator
+    ) -> bool:
+        raise NotImplementedError()
+
+    @abc.abstractmethod
+    def clear(self):
+        raise NotImplementedError()
+
+    @abc.abstractmethod
+    def available_size(self):
+        raise NotImplementedError()
+
+
+def _get_tensor_size(embedding: torch.Tensor):
+    return embedding.element_size() * embedding.numel()
+
+
+class MultiModalStaticCache(MultimodalCache):
+    """
+    A server-level cache for multimodal embedding.
+    Embeddings are computed prior, and this cache does not really pre-alloc
+    """
 
     def __init__(
         self,
         max_size: int,
     ):
+        super().__init__()
         self.max_size = max_size
         self.mm_cache: OrderedDict[int, torch.Tensor] = OrderedDict()
         self.current_size = 0
 
-    def _allocate(self, embedding_size: int) -> bool:
-        """Allocate space by evicting least recently used entries"""
-        evictions = 0
-        while self.current_size + embedding_size > self.max_size and self.mm_cache:
-            _, old_embedding = self.mm_cache.popitem(last=False)
-            evicted_size = self._get_tensor_size(old_embedding)
-            self.current_size -= evicted_size
-            evictions += evicted_size
-
-        if evictions > 0:
-            logger.debug(
-                f"Cache eviction: evicted {evictions} bytes, remaining size: {self.current_size}/{self.max_size} bytes"
-            )
-
-        if self.current_size + embedding_size > self.max_size:
-            return False
-        return True
+    def get(
+        self, mm_hashes: List[int], combined_hash: Optional[int] = None
+    ) -> Optional[torch.Tensor]:
+        combined_hash = self.combine_hashes(mm_hashes)
+        # MultiModalStaticCache does not fallback to individual item lookup
+
+        embedding = self.mm_cache.get(combined_hash)
+        if embedding is not None:
+            self.mm_cache.move_to_end(combined_hash)
+        return embedding
+
+    def set(
+        self, mm_hash: int, embedding: torch.Tensor, loc: Optional[torch.Tensor] = None
+    ) -> bool:
+        if mm_hash in self.mm_cache:
+            self.mm_cache.move_to_end(mm_hash)
+            return True
+        data_size = _get_tensor_size(embedding)
+        while self.current_size + data_size > self.max_size:
+            if not self.mm_cache:
+                return False
+            lru_hash, lru_embedding = self.mm_cache.popitem(last=False)
+            self.current_size -= _get_tensor_size(lru_embedding)
 
-    def put(self, mm_hash: int, embedding: torch.Tensor) -> bool:
-        data_size = self._get_tensor_size(embedding)
-        # Lazy free cache if not enough space
-        if not self._allocate(data_size):
-            return False
         self.mm_cache[mm_hash] = embedding
         self.current_size += data_size
         return True
 
     def has(self, mm_hash: int) -> bool:
         return mm_hash in self.mm_cache
 
-    def get(self, mm_hash: int) -> torch.Tensor:
-        """Get embedding and update LRU order"""
-        if mm_hash in self.mm_cache:
-            # Move to end (most recently used)
-            self.mm_cache.move_to_end(mm_hash)
-            return self.mm_cache[mm_hash]
-        return None
+    def free(
+        self, mm_hash: int, mm_embedding_allocator: BaseTokenToKVPoolAllocator
+    ) -> bool:
+        if mm_hash not in self.mm_cache:
+            return False
+        old_embedding = self.mm_cache.pop(mm_hash)
+        self.current_size -= _get_tensor_size(old_embedding)
+        return True
 
     def clear(self):
         self.mm_cache.clear()
         self.current_size = 0
 
-    def _get_tensor_size(self, embedding: torch.Tensor):
-        return embedding.element_size() * embedding.numel()
-
     def __len__(self):
         return len(self.mm_cache)
+
+    def available_size(self):
+        return self.__len__()

python/sglang/utils.py

@@ -13,13 +13,11 @@
 import traceback
 import urllib.request
 import weakref
-from concurrent.futures import ThreadPoolExecutor
 from functools import wraps
 from io import BytesIO
 from json import dumps
 from typing import Any, Callable, List, Optional, Tuple, Type, Union
 
-import numpy as np
 import pybase64
 import requests
 from IPython.display import HTML, display

test/srt/test_vlm_accuracy.py

@@ -14,7 +14,7 @@
 
 from sglang.srt.configs.model_config import ModelConfig
 from sglang.srt.entrypoints.openai.protocol import ChatCompletionRequest
-from sglang.srt.managers.mm_utils import embed_mm_inputs, init_embedding_cache
+from sglang.srt.managers.mm_utils import embed_mm_inputs, init_mm_embedding_cache
 from sglang.srt.managers.schedule_batch import (
     Modality,
     MultimodalDataItem,
@@ -182,7 +182,7 @@ def setUpClass(cls):
             .eval()
             .to(cls.device)
         )
-        init_embedding_cache()
+        init_mm_embedding_cache()
 
     async def test_vlm_embedding_output(self):
         """
@@ -288,7 +288,7 @@ def setUpClass(cls):
             .eval()
             .to(cls.device)
         )
-        init_embedding_cache()
+        init_mm_embedding_cache()
 
     async def test_vlm_embedding_output(self):
         """