[Feature] Add EIC as sglang HiCache Storage backend (#10271)

Co-authored-by: mashisong <mashisong@bytedance.com>

python/sglang/srt/managers/cache_controller.py

@@ -310,7 +310,7 @@ class HiCacheController:
             self.page_get_func = self._generic_page_get
             self.page_set_func = self._generic_page_set
 
-            if self.storage_backend_type in ["hf3fs", "mooncake"]:
+            if self.storage_backend_type in ["hf3fs", "mooncake", "eic"]:
                 self.page_get_func = self._page_get_zero_copy
                 self.page_set_func = self._page_set_zero_copy
 

python/sglang/srt/mem_cache/storage/backend_factory.py

@@ -181,6 +181,8 @@ class StorageBackendFactory:
 
             dtype = mem_pool_host.dtype
             return backend_class.from_env_config(bytes_per_page, dtype, storage_config)
+        elif backend_name == "eic":
+            return backend_class(storage_config, mem_pool_host)
         else:
             raise ValueError(f"Unknown built-in backend: {backend_name}")
 
@@ -213,3 +215,9 @@ StorageBackendFactory.register_backend(
     "sglang.srt.mem_cache.storage.aibrix_kvcache.aibrix_kvcache_storage",
     "AibrixKVCacheStorage",
 )
+
+StorageBackendFactory.register_backend(
+    "eic",
+    "sglang.srt.mem_cache.storage.eic.eic_storage",
+    "EICStorage",
+)

python/sglang/srt/mem_cache/storage/eic/README.md

+# EIC as sglang HiCache Storage
+EIC(Elastic Instant Cache) is a distributed database designed for LLM KV Cache. It supports RDMA, GDR and has the capabilities of distributed disaster tolerance and expansion.
+You can understand the principles and architecture of EIC through these articles: https://mp.weixin.qq.com/s/tasDqXf0Gxr3o_WCJ2IJUQ https://mp.weixin.qq.com/s/b_4YhTa96Zeklh23lv8qBw
+
+
+## Deploy EIC
+You can visit the official link https://console.volcengine.com/eic and deploy EIC KVCache on your compute cluster with web UI.In addition, we provide particular image in volcano engine, which integrates various optimizations based on the official image.
+You may use test_unit.py to detect the connectivity of EIC.
+
+
+
+## Deploy Model With EIC
+You can enable EIC KVCache offload with the official interface, such as
+
+```bash
+python -m sglang.launch_server \
+    --model-path [model_path]
+    --enable-hierarchical-cache \
+    --hicache-storage-backend eic \
+    --hicache-write-policy 'write_through' \
+    --hicache-mem-layout 'page_first' \
+
+```
+For more details, you can see https://www.volcengine.com/docs/85848/1749188 .

python/sglang/srt/mem_cache/storage/eic/eic_storage.py

+import json
+import logging
+import os
+import time
+import uuid
+from dataclasses import dataclass
+from typing import Any, Dict, List, Optional, Tuple
+
+import eic
+import torch
+import yaml
+
+from sglang.srt.layers.dp_attention import get_attention_tp_rank, get_attention_tp_size
+from sglang.srt.mem_cache.hicache_storage import (
+    HiCacheStorage,
+    HiCacheStorageConfig,
+    HiCacheStorageExtraInfo,
+)
+from sglang.srt.mem_cache.memory_pool_host import HostKVCache, MLATokenToKVPoolHost
+
+logger = logging.getLogger(__name__)
+
+
+TensorPoolSize = 2048
+
+REMOTE_EIC_YAML_ENV_VAR = "REMOTE_EIC_YAML"
+
+# gpu direct rdma for kv set
+G_EnableKVSetGPUDirect = False
+
+# gpu direct rdma for kv get
+G_EnableKVGetGPUDirect = False
+
+# gpu nic affinity
+G_EnableGPUNicAffinity = False
+
+# default H20 gpu nic affinity
+GPUNicAffinity = {
+    "cuda:0": "eth1",
+    "cuda:1": "eth1",
+    "cuda:2": "eth2",
+    "cuda:3": "eth2",
+    "cuda:4": "eth3",
+    "cuda:5": "eth3",
+    "cuda:6": "eth4",
+    "cuda:7": "eth4",
+}
+
+# default H20 cpu nic affinity
+CPUNicAffinity = {
+    "cuda:0": "cpu",
+    "cuda:1": "cpu",
+    "cuda:2": "cpu",
+    "cuda:3": "cpu",
+    "cuda:4": "cpu",
+    "cuda:5": "cpu",
+    "cuda:6": "cpu",
+    "cuda:7": "cpu",
+}
+
+
+def get_eic_config_file_path():
+    if os.environ.get(REMOTE_EIC_YAML_ENV_VAR) is not None:
+        logger.info(f"eic init with env var {REMOTE_EIC_YAML_ENV_VAR}")
+        config_file = os.environ.get(REMOTE_EIC_YAML_ENV_VAR)
+    else:
+        config_file = "/sgl-workspace/config/remote-eic.yaml"
+        logger.info(f"eic init with default config, config_file {config_file}")
+    return config_file
+
+
+class FlexibleKVCacheMemoryPool:
+    def __init__(self, conn, kvcache_shape, kvcache_dtype, device):
+        self.connection = conn
+
+        if device.startswith("cpu") and G_EnableGPUNicAffinity:
+            gpu_id = torch.cuda.current_device()
+            self.device = CPUNicAffinity["cuda:" + str(gpu_id)]
+            # current memory pool size is 5 times of CPU TensorPoolSize
+            mempool_size = TensorPoolSize * 5
+        else:
+            self.device = device
+            mempool_size = TensorPoolSize
+
+        self.kvcache_shape = kvcache_shape
+        self.kvcache_dtype = kvcache_dtype
+
+        self.kv_cache_numel = 1
+        for i in self.kvcache_shape:
+            self.kv_cache_numel *= i
+
+        self.free_data_addr = set()
+        self.data_ptr_to_index = dict()
+
+        if self.device.startswith("cpu"):
+            self.kvcache_mempool = torch.zeros(
+                (mempool_size,) + kvcache_shape,
+                dtype=kvcache_dtype,
+                device=self.device,
+                pin_memory=True,
+            )
+        else:
+            self.kvcache_mempool = torch.zeros(
+                (mempool_size,) + kvcache_shape, dtype=kvcache_dtype, device=self.device
+            )
+
+        for i in range(mempool_size):
+            self.free_data_addr.add(i)
+            self.data_ptr_to_index[self.kvcache_mempool[i].data_ptr()] = i
+
+        meminfo = eic.MemoryInfo()
+        meminfo.type = eic.MemoryType.MEMORY_CUDA
+        meminfo.cuda_id = 0
+        vals = eic.IOBuffers()
+        vals.append(
+            self.kvcache_mempool.data_ptr(),
+            self.kvcache_mempool.numel() * self.kvcache_mempool.element_size(),
+            True,
+        )
+        self.connection.register_memory(vals, meminfo)
+        logger.info(
+            f"allocate memory pool, size {self.kvcache_mempool.numel() * self.kvcache_mempool.element_size()}, device {self.device}"
+        )
+
+    def try_allocate_kv_cache(self, shape, dtype, count=1):
+        if len(self.free_data_addr) < count:
+            return None
+
+        numel = 1
+        for i in shape:
+            numel *= i
+        if numel != self.kv_cache_numel or dtype != self.kvcache_dtype:
+            logger.error(
+                f"allocate from mempool failed, self.kvcache_shape {self.kvcache_shape}, dtype {self.kvcache_dtype}, require shape {shape}, dtype {dtype}"
+            )
+            return None
+
+        ret = []
+        for _ in range(count):
+            free_index = self.free_data_addr.pop()
+            ret.append(self.kvcache_mempool[free_index])
+        return ret
+
+    def free_to_mempool(self, data_ptr):
+        if data_ptr not in self.data_ptr_to_index:
+            logger.error(
+                f"free_to_mempool failed, data_ptr {data_ptr} not in allocated_data_addr"
+            )
+            return
+        self.free_data_addr.add(self.data_ptr_to_index[data_ptr])
+
+    def check_data_ptr_allocated(self, data_ptr):
+        return data_ptr in self.data_ptr_to_index
+
+    def left_count(self):
+        return len(self.free_data_addr)
+
+
+class EICStorage(HiCacheStorage):
+    def __init__(
+        self, hicache_config: HiCacheStorageConfig, memory_pool_host: HostKVCache
+    ):
+        global G_EnableKVSetGPUDirect, G_EnableKVGetGPUDirect
+        global GPUNicAffinity, CPUNicAffinity, G_EnableGPUNicAffinity
+
+        config_file = get_eic_config_file_path()
+        if os.path.exists(config_file) is False:
+            logger.error(f"config file {config_file} not exists")
+            raise RuntimeError(f"eic config file {config_file} not exists")
+
+        with open(config_file, "r") as fin:
+            config = yaml.safe_load(fin)
+
+        remote_url = config.get("remote_url", None)
+        if remote_url is None:
+            AssertionError("remote_url is None")
+
+        endpoint = remote_url[len("eic://") :]
+
+        logger.info(f"eic remote_url:" + remote_url + " endpoint: " + endpoint)
+
+        eic_instance_id = config.get("eic_instance_id", None)
+        logger.info(f"eic instance_id: {eic_instance_id}")
+
+        eic_thread_num = config.get("eic_thread_num", 1)
+        logger.info(f"eic thread_num: {eic_thread_num}")
+
+        eic_log_dir = config.get("eic_log_dir", None)
+        logger.info(f"eic log_dir: {eic_log_dir}")
+
+        eic_log_level = config.get("eic_log_level", 2)
+        logger.info(f"eic log_level: {eic_log_level}")
+
+        eic_trans_type = config.get("eic_trans_type", 3)
+        logger.info(f"eic trans_type: {eic_trans_type}")
+
+        eic_flag_file = config.get("eic_flag_file", None)
+        logger.info(f"eic flag_file: {eic_flag_file}")
+
+        # GDR now is not used
+        G_EnableKVSetGPUDirect = (
+            config.get("enable_kvset_gpu_direct", False) and torch.cuda.is_available()
+        )
+        logger.debug(f"eic enable_kvset_gpu_direct: {G_EnableKVSetGPUDirect}")
+
+        G_EnableKVGetGPUDirect = (
+            config.get("enable_kvget_gpu_direct", False) and torch.cuda.is_available()
+        )
+        logger.debug(f"eic enable_kvget_gpu_direct: {G_EnableKVGetGPUDirect}")
+
+        self.model_name = hicache_config.model_name
+
+        # rdma
+        enable_kv_set_direct = config.get("enable_kvset_direct", True)
+        logger.info(f"eic enable_kv_set_direct: {enable_kv_set_direct}")
+        self.enable_kv_set_direct = enable_kv_set_direct
+
+        enable_kv_get_direct = config.get("enable_kvget_direct", True)
+        logger.info(f"eic enable_kv_get_direct: {enable_kv_get_direct}")
+        self.enable_kv_get_direct = enable_kv_get_direct
+
+        # gpu nic affinity
+        G_EnableGPUNicAffinity = config.get("enable_gpu_nic_affinity", False)
+        logger.info(f"eic enable_gpu_nic_affinity: {G_EnableGPUNicAffinity}")
+        self.enable_gpu_nic_affinity = G_EnableGPUNicAffinity
+
+        if G_EnableGPUNicAffinity:
+            if "gpu_nic_affinity_config" in config:
+                GPUNicAffinity = json.loads(config["gpu_nic_affinity_config"])
+            if "cpu_nic_affinity_config" in config:
+                CPUNicAffinity = json.loads(config["cpu_nic_affinity_config"])
+            logger.info(f"eic gpu nic affinity {GPUNicAffinity}")
+            logger.info(f"eic cpu nic affinity {CPUNicAffinity}")
+
+        eic_namespace = config.get("eic_namespace", "")
+        logger.info(f"eic namespace: {eic_namespace}")
+        self.eic_namespace = eic_namespace
+
+        if not os.path.exists(eic_log_dir) and not os.path.isdir(eic_log_dir):
+            os.makedirs(eic_log_dir, exist_ok=True)
+
+        self.connection = eic.Client()
+        init_option = eic.InitOption()
+        init_option.log_dir = eic_log_dir
+        init_option.log_level = eic.LogLevel(eic_log_level)
+        init_option.transport_type = eic.TransportType(eic_trans_type)
+        init_option.flag_file = eic_flag_file
+
+        if G_EnableGPUNicAffinity:
+            gpu_id = torch.cuda.current_device()
+            init_option.multi_net_local_interface_names = GPUNicAffinity[
+                "cuda:" + str(gpu_id)
+            ]
+            logger.info(
+                f"gpu {gpu_id} set gpu nic affinity to {init_option.multi_net_local_interface_names}"
+            )
+
+        ret = self.connection.init(eic_instance_id, endpoint, init_option)
+        if ret != 0:
+            logger.error(f"fail to init eic client, ret: {ret}")
+            raise RuntimeError("EIC Client Init Failed.")
+        self.warmup()
+
+        self.memory_pool_host = memory_pool_host
+        self.host_kvcache_layout = self.memory_pool_host.layout
+        self.trans_type = eic.TransportType(eic_trans_type)
+        self.kv_cache_dtype = self.memory_pool_host.dtype
+        self.is_mla_model = hicache_config.is_mla_model
+        self.rank = hicache_config.tp_rank
+        self.world_size = hicache_config.tp_size
+        self.page_size = self.memory_pool_host.page_size
+        self.use_zero_copy = self.memory_pool_host.layout == "page_first"
+        if not self.use_zero_copy:
+            self.kv_cache_shape = self.memory_pool_host.get_data_page(
+                0, flat=True
+            ).shape
+            if self.enable_kv_set_direct:
+                self.kv_cache_write_mem_pool = FlexibleKVCacheMemoryPool(
+                    self.connection, self.kv_cache_shape, self.kv_cache_dtype, "cpu"
+                )
+            if self.enable_kv_get_direct:
+                self.kv_cache_get_mem_pool = FlexibleKVCacheMemoryPool(
+                    self.connection, self.kv_cache_shape, self.kv_cache_dtype, "cpu"
+                )
+        self._init_eic_prefix()
+
+    def warmup(self):
+        logger.info("begin warm up eic client")
+        start_time = time.perf_counter()
+        num_warmup = 1024
+        preheat_keys = ["warmup_key_" + str(i) for i in range(num_warmup)]
+        batch_size = 32
+        for i in range(0, num_warmup, batch_size):
+            keys_vec = eic.StringVector()
+            for key in preheat_keys[i : i + batch_size]:
+                keys_vec.append(key)
+            exist_option = eic.ExistOption()
+            _, _ = self.connection.mexist(keys_vec, exist_option)
+        logger.info(
+            f"finish eic client warm up, warm up cost {time.perf_counter() - start_time:.2f} seconds"
+        )
+
+    def register_mem_pool_host(self, memory_pool_host: HostKVCache) -> None:
+        # no need judge meminfo type, cuda_id, etc.
+        meminfo = eic.MemoryInfo()
+        meminfo.type = eic.MemoryType.MEMORY_CUDA
+        meminfo.cuda_id = 0
+        vals = eic.IOBuffers()
+        buffer = memory_pool_host.kv_buffer
+        vals.append(
+            buffer.data_ptr(),
+            buffer.numel() * buffer.element_size(),
+            True,
+        )
+        self.connection.register_memory(vals, meminfo)
+
+    def _init_eic_prefix(self):
+        if self.is_mla_model:
+            self.eic_prefix = (
+                f"{self.model_name}_mla_att_{self.host_kvcache_layout}@sglang"
+            )
+        else:
+            self.eic_prefix = f"{self.model_name}_mha_attn_{self.host_kvcache_layout}_{self.rank}_{self.world_size}_@sglang"
+
+    def _get_eic_key(self, keys: List[str]) -> str:
+        return [f"{self.eic_prefix}_{key}" for key in keys]
+
+    def set(
+        self,
+        key: str,
+        value: Optional[Any] = None,
+        target_location: Optional[Any] = None,
+        target_size: Optional[Any] = None,
+    ) -> bool:
+        # now is not used
+        if self.use_zero_copy:
+            return self.zero_copy_batch_set([key], [target_location])
+        else:
+            return self.generic_batch_set([key], [value])
+
+    # target_locations and target_sizes are not used for now
+    def batch_set(
+        self,
+        keys: List[str],
+        values: Optional[Any] = None,
+        target_locations: Optional[Any] = None,
+        target_sizes: Optional[Any] = None,
+    ) -> bool:
+        if len(keys) == 0:
+            return True
+        if self.use_zero_copy:
+            return self.zero_copy_batch_set(keys, values)
+        else:
+            return self.generic_batch_set(keys, values)
+
+    def get(
+        self,
+        key,
+        target_location: Optional[Any] = None,
+        target_size: Optional[Any] = None,
+    ) -> torch.Tensor | None:
+        # now is not used
+        if self.use_zero_copy:
+            return self.zero_copy_batch_get([key], [target_location])
+        else:
+            return self.generic_batch_get([key], [target_location])
+
+    # use for v1 interface, and shound not be called directly
+    def batch_get(
+        self,
+        keys: List[str],
+        target_locations: Optional[Any] = None,
+        target_sizes: Optional[Any] = None,
+    ) -> List[torch.Tensor | None]:
+        assert len(keys) == len(target_locations)
+        if len(keys) == 0:
+            return None
+        if self.use_zero_copy:
+            return self.zero_copy_batch_get(keys, target_locations)
+        else:
+            return self.generic_batch_get(keys, target_locations)
+
+    def _batch_exists_impl(self, keys) -> List[bool]:
+        if len(keys) == 0:
+            return 0
+        eic_keys = self._get_eic_key(keys)
+        logger.debug(f"eic exists {len(keys)}")
+        result = []
+        exist_bs = 1024
+        for i in range(0, len(eic_keys), exist_bs):
+            batch_keys = eic_keys[i : i + exist_bs]
+            keys_vec = eic.StringVector()
+            for key in batch_keys:
+                keys_vec.append(key)
+            exist_option = eic.ExistOption()
+            exist_option.ns = self.eic_namespace
+            status_code, exist_outcome = self.connection.mexist(keys_vec, exist_option)
+            if status_code != eic.StatusCode.SUCCESS:
+                logger.error(
+                    f"eic exists {len(keys)} failed, status_code {status_code}"
+                )
+                result.extend([False] * len(batch_keys))
+            for err_code in exist_outcome.status_codes:
+                result.append(err_code == eic.StatusCode.SUCCESS)
+        return result
+
+    def exists(self, key) -> bool:
+        exist_num = self.batch_exists([key])
+        return exist_num == 1
+
+    def batch_exists(self, keys) -> int:
+        if len(keys) == 0:
+            return 0
+        if self.use_zero_copy and not self.is_mla_model:
+            keys = self._get_mha_zero_copy_keys(keys)
+        exist_mask = self._batch_exists_impl(keys)
+        prefix_success = 0
+        for exist in exist_mask:
+            if exist:
+                prefix_success += 1
+            else:
+                break
+        if not self.is_mla_model and self.use_zero_copy:
+            prefix_success = prefix_success // 2
+        return prefix_success
+
+    def delete(self, key) -> None:
+        eic_keys = self._get_eic_key([key])
+        keys_vec = eic.StringVector()
+        for eic_key in eic_keys:
+            keys_vec.append(eic_key)
+        del_option = eic.DelOption()
+        self.connection.mdel(keys_vec, del_option)
+
+    def clear(self) -> None:
+        return
+
+    # Not used for now
+    def _filter_kv_cache(self, total_len) -> Tuple[int, int]:
+        mean_len = total_len // self.world_size
+        remainder = total_len % self.world_size
+        tp_keys_len = mean_len + (1 if self.rank < remainder else 0)
+        start = self.rank * mean_len + min(self.rank, remainder)
+        end = start + tp_keys_len
+        logger.debug(f"start: {start}, end: {end}, tp_keys_len: {tp_keys_len}")
+        return start, end
+
+    def zero_copy_batch_set(self, keys: List[str], values: List[torch.Tensor]) -> bool:
+        logger.debug(f"eic zero copy set {len(keys)} keys")
+        if len(keys) == 0:
+            return True
+        eic_keys = self._get_eic_key(keys)
+        keys_vec = eic.StringVector()
+        vals_vec = eic.IOBuffers()
+        # set data key & value
+        for i, key in enumerate(eic_keys):
+            # set data key & value
+            keys_vec.append(key)
+            vals_vec.append(
+                values[i].data_ptr(),
+                values[i].element_size() * values[i].numel(),
+                True,
+            )
+        # set options
+        set_option = eic.SetOption()
+        set_option.ns = self.eic_namespace
+        set_option.ttl_second = -1
+        status_code, set_outcome = self.connection.mset(keys_vec, vals_vec, set_option)
+        if status_code != eic.StatusCode.SUCCESS:
+            logger.error(f"eic mset {len(keys)} failed, status_code {status_code}")
+            return [False] * len(keys)
+        else:
+            logger.debug(f"eic zero copy mset {len(keys)} success")
+        return [True] * len(keys)
+
+    def zero_copy_batch_get(
+        self, keys: List[str], values: List[torch.Tensor]
+    ) -> List[bool]:
+        logger.debug(f"eic zero copy get {len(keys)} keys")
+        # Get Data: generate data keys and vals
+        get_data_start_time = time.perf_counter()
+        eic_keys = self._get_eic_key(keys)
+        data_keys = eic.StringVector()
+        data_vals = eic.IOBuffers()
+        success_mask = [True] * len(keys)
+        count = len(keys)
+        for i, key in enumerate(eic_keys):
+            data_keys.append(key)
+            data_vals.append(
+                values[i].data_ptr(),
+                values[i].element_size() * values[i].numel(),
+                True,
+            )
+
+        # Get data: recv data buffer tensor
+        get_option = eic.GetOption()
+        get_option.ns = self.eic_namespace
+        status_code, data_vals, get_outcome = self.connection.mget(
+            data_keys, get_option, data_vals
+        )
+
+        if status_code != eic.StatusCode.SUCCESS:
+            if status_code == eic.StatusCode.PARTIAL_FAILED:
+                for i, err_code in enumerate(get_outcome.status_codes):
+                    success = err_code == eic.StatusCode.SUCCESS
+                    if success:
+                        logger.debug(f"eic get data {eic_keys[i]} success")
+                    else:
+                        logger.error(
+                            f"eic get data {eic_keys[i]} failed, err_code {err_code}"
+                        )
+                        success_mask[i] = False
+            else:
+                logger.error(
+                    f"eic mget {len(eic_keys)} keys failed, status_code {status_code}"
+                )
+                success_mask = [False] * len(keys)
+                return success_mask
+
+        get_data_end_time = time.perf_counter()
+        get_data_execution_time = (get_data_end_time - get_data_start_time) * 1e6
+        logger.debug(f"eic get {count} keys data cost %.2f us", get_data_execution_time)
+        return success_mask
+
+    def generic_batch_set(
+        self,
+        keys: List[str],
+        values: List[torch.Tensor],
+    ) -> List[bool]:
+        assert len(keys) == len(values)
+        logger.debug(f"eic generic set {len(keys)} keys")
+        if len(keys) == 0:
+            return True
+        eic_keys = self._get_eic_key(keys)
+        keys_vec = eic.StringVector()
+        vals_vec = eic.IOBuffers()
+        count = len(keys)
+        registered = False
+        items = []
+        if self.enable_kv_set_direct:
+            values_data_ptrs = []
+            items = self.kv_cache_write_mem_pool.try_allocate_kv_cache(
+                self.kv_cache_shape, self.kv_cache_dtype, count
+            )
+            if items is None:
+                logger.warning("can not allocate tensor from pool")
+                for i, value in enumerate(values):
+                    values_data_ptrs.append(
+                        (value.data_ptr(), value.element_size() * value.numel(), False)
+                    )
+            else:
+                objs = items
+                registered = True
+                for i, key in enumerate(eic_keys):
+                    temp = objs[i].reshape(values[i].shape).contiguous()
+                    temp.copy_(values[i])
+                    if temp.data_ptr() != objs[i].data_ptr():
+                        registered = False
+                        temp = temp.cpu()
+                    values_data_ptrs.append(
+                        (
+                            temp.data_ptr(),
+                            temp.element_size() * temp.numel(),
+                            registered,
+                        )
+                    )
+
+            for i, key in enumerate(eic_keys):
+                keys_vec.append(key)
+                data_ptr, data_size, registered = values_data_ptrs[i]
+                vals_vec.append(data_ptr, data_size, registered)
+        else:
+            # use tensor direct
+            for i, key in enumerate(eic_keys):
+                keys_vec.append(key)
+                vals_vec.append(
+                    values[i].data_ptr(),
+                    values[i].element_size() * values[i].numel(),
+                    False,
+                )
+
+        # set options
+        set_option = eic.SetOption()
+        set_option.ns = self.eic_namespace
+        set_option.ttl_second = -1
+        status_code, set_outcome = self.connection.mset(keys_vec, vals_vec, set_option)
+        if status_code != eic.StatusCode.SUCCESS:
+            logger.error(f"eic mset {len(eic_keys)} failed, status_code {status_code}")
+        else:
+            logger.debug(f"eic mset {len(eic_keys)} success")
+
+        if self.enable_kv_set_direct and items is not None:
+            for item in items:
+                self.kv_cache_write_mem_pool.free_to_mempool(item.data_ptr())
+
+        err_code = set_outcome.status_codes[0]
+        if err_code != eic.StatusCode.SUCCESS:
+            logger.error(f"set data key {len(eic_keys)} failed, err_code {err_code}")
+            return [False] * len(keys)
+
+        logger.debug(f"set data key {len(eic_keys)} success")
+        return [True] * len(keys)
+
+    def generic_batch_get(
+        self, keys: List[str], buffers: List[torch.Tensor]
+    ) -> List[bool]:
+        # all success or all fail
+        logger.debug(f"eic generic get {len(keys)} keys")
+        eic_keys = self._get_eic_key(keys)
+        get_data_start_time = time.perf_counter()
+        data_keys = eic.StringVector()
+        data_vals = eic.IOBuffers()
+        count = len(eic_keys)
+        registered = False
+        items = []
+        success_mask = [True] * len(keys)
+        if self.enable_kv_get_direct:
+            items = self.kv_cache_get_mem_pool.try_allocate_kv_cache(
+                self.kv_cache_shape, self.kv_cache_dtype, count
+            )
+            if items is None:
+                logger.warning("can not allocate tensor from pool")
+                for i, key in enumerate(eic_keys):
+                    data_keys.append(key)
+                    data_vals.append(
+                        buffers[i].data_ptr(),
+                        buffers[i].element_size() * buffers[i].numel(),
+                        False,
+                    )
+            else:
+                registered = True
+                for i, key in enumerate(eic_keys):
+                    data_keys.append(key)
+                    data_vals.append(
+                        items[i].data_ptr(),
+                        items[i].element_size() * items[i].numel(),
+                        registered,
+                    )
+
+        else:
+            for i, key in enumerate(eic_keys):
+                data_keys.append(key)
+                data_vals.append(
+                    buffers[i].data_ptr(),
+                    buffers[i].element_size() * buffers[i].numel(),
+                    False,
+                )
+
+        # Get data: recv data buffer tensor
+        get_option = eic.GetOption()
+        get_option.ns = self.eic_namespace
+        status_code, data_vals, get_outcome = self.connection.mget(
+            data_keys, get_option, data_vals
+        )
+
+        if status_code != eic.StatusCode.SUCCESS:
+            if status_code == eic.StatusCode.PARTIAL_FAILED:
+                for i, err_code in enumerate(get_outcome.status_codes):
+                    success = err_code == eic.StatusCode.SUCCESS
+                    if success:
+                        logger.debug(f"eic get data {eic_keys[i]} success")
+                    else:
+                        logger.error(
+                            f"eic get data {eic_keys[i]} failed, err_code {err_code}"
+                        )
+                        success_mask[i] = False
+            else:
+                logger.error(
+                    f"eic mget {len(eic_keys)} keys failed, status_code {status_code}"
+                )
+                success_mask = [False] * len(keys)
+
+        if registered:
+            for i, item in enumerate(items):
+                if success_mask[i]:
+                    buffers[i].copy_(item)
+                self.kv_cache_get_mem_pool.free_to_mempool(item.data_ptr())
+
+        get_data_end_time = time.perf_counter()
+        get_data_execution_time = (get_data_end_time - get_data_start_time) * 1e6
+        logger.debug(f"eic get {count} keys data cost %.2f us", get_data_execution_time)
+        return success_mask
+
+    def _get_mha_zero_copy_keys(self, keys: List[str]) -> List[str]:
+        new_keys = []
+        for k in keys:
+            new_keys.append(f"{k}_k")
+            new_keys.append(f"{k}_v")
+        return new_keys
+
+    def _get_mha_zero_copy_values(
+        self, values: List[torch.Tensor]
+    ) -> List[torch.Tensor]:
+        new_values = []
+        for value in values:
+            new_values.append(value[0])
+            new_values.append(value[1])
+        return new_values
+
+    def _batch_get_preprocess(self, keys, host_indices):
+        page_num = len(host_indices) // self.page_size
+        # use memory pool directly or dummy page
+        values = (
+            [
+                self.memory_pool_host.get_data_page(
+                    host_indices[i * self.page_size], flat=False
+                )
+                for i in range(page_num)
+            ]
+            if self.use_zero_copy
+            else [
+                self.memory_pool_host.get_dummy_flat_data_page()
+                for _ in range(page_num)
+            ]
+        )
+
+        if self.use_zero_copy and not self.is_mla_model:
+            keys = self._get_mha_zero_copy_keys(keys)
+            values = self._get_mha_zero_copy_values(values)
+
+        return keys, values
+
+    def _batch_get_postprocess(self, host_indices, values, results):
+        page_num = len(host_indices) // self.page_size
+
+        if self.use_zero_copy:
+            if not self.is_mla_model:
+                results = [
+                    (results[2 * i] and results[2 * i + 1]) for i in range(page_num)
+                ]
+                results = results[:page_num]
+            return results
+
+        # dummy page copy to host memory pool
+        for i in range(page_num):
+            if not results[i]:
+                break
+            self.memory_pool_host.set_from_flat_data_page(
+                host_indices[i * self.memory_pool_host.page_size], values[i]
+            )
+
+        return results
+
+    def batch_get_v1(
+        self,
+        keys: List[str],
+        host_indices: torch.Tensor,
+        extra_info: Optional[HiCacheStorageExtraInfo] = None,
+    ) -> List[bool]:
+        keys, values = self._batch_get_preprocess(keys, host_indices)
+        results = self.batch_get(keys, values)
+        return self._batch_get_postprocess(host_indices, values, results)
+
+    def _batch_set_preprocess(self, keys, host_indices):
+        page_num = len(host_indices) // self.page_size
+        flat = not self.use_zero_copy
+        values = [
+            self.memory_pool_host.get_data_page(
+                host_indices[i * self.page_size], flat=flat
+            )
+            for i in range(page_num)
+        ]
+
+        if self.use_zero_copy and not self.is_mla_model:
+            keys = self._get_mha_zero_copy_keys(keys)
+            values = self._get_mha_zero_copy_values(values)
+
+        return keys, values
+
+    def batch_set_v1(
+        self,
+        keys: List[str],
+        host_indices: torch.Tensor,
+        extra_info: Optional[HiCacheStorageExtraInfo] = None,
+    ) -> List[bool]:
+        keys, values = self._batch_set_preprocess(keys, host_indices)
+        results = self.batch_set(keys, values)
+        return results

python/sglang/srt/mem_cache/storage/eic/test_unit.py

+import argparse
+import os
+
+import eic
+import torch
+import yaml
+
+
+def pase_args():
+    parser = argparse.ArgumentParser(description="EIC Storage Unit Test")
+    parser.add_argument(
+        "--config",
+        "-c",
+        type=str,
+        default="/sgl-workspace/config/remote-eic.yaml",
+        help="EIC yaml config",
+    )
+    args, _ = parser.parse_known_args()
+    return args
+
+
+def init_eic_client():
+    args = pase_args()
+    config_path = os.path.abspath(args.config)
+    if not os.path.exists(config_path):
+        raise FileNotFoundError(f"Config file not found: {config_path}")
+    with open(config_path, "r") as fin:
+        config = yaml.safe_load(fin)
+
+    remote_url = config.get("remote_url", None)
+    if remote_url is None:
+        AssertionError("remote_url is None")
+    endpoint = remote_url[len("eic://") :]
+    eic_instance_id = config.get("eic_instance_id", None)
+    eic_log_dir = config.get("eic_log_dir", None)
+    eic_log_level = config.get("eic_log_level", 2)
+    eic_trans_type = config.get("eic_trans_type", 3)
+    eic_flag_file = config.get("eic_flag_file", None)
+
+    if not os.path.exists(eic_log_dir):
+        os.makedirs(eic_log_dir, exist_ok=True)
+    eic_client = eic.Client()
+    init_option = eic.InitOption()
+    init_option.log_dir = eic_log_dir
+    init_option.log_level = eic.LogLevel(eic_log_level)
+    init_option.transport_type = eic.TransportType(eic_trans_type)
+    init_option.flag_file = eic_flag_file
+    ret = eic_client.init(eic_instance_id, endpoint, init_option)
+    if ret != 0:
+        raise RuntimeError(f"EIC Client init failed with error code: {ret}")
+    return eic_client
+
+
+def test_set(eic_client):
+    test_key = ["test_key_" + str(i) for i in range(16)]
+    tensors = [
+        torch.ones([12, 6, 1, 512], dtype=torch.bfloat16, device="cpu")
+        for _ in range(16)
+    ]
+    data_keys = eic.StringVector()
+    data_vals = eic.IOBuffers()
+    for i in range(16):
+        data_keys.append(test_key[i])
+        data_vals.append(
+            tensors[i].data_ptr(), tensors[i].numel() * tensors[i].element_size(), False
+        )
+    set_opt = eic.SetOption()
+    set_opt.ttl_second = 3
+    status_code, set_outcome = eic_client.mset(data_keys, data_vals, set_opt)
+    assert (
+        status_code == eic.StatusCode.SUCCESS
+    ), f"Set failed with status code: {status_code}"
+
+
+def test_get(eic_client):
+    test_key = ["test_key_" + str(i) for i in range(16)]
+    tensors = [
+        torch.zeros([12, 6, 1, 512], dtype=torch.bfloat16, device="cpu")
+        for _ in range(16)
+    ]
+    data_keys = eic.StringVector()
+    data_vals = eic.IOBuffers()
+    for i in range(16):
+        data_keys.append(test_key[i])
+        data_vals.append(
+            tensors[i].data_ptr(), tensors[i].numel() * tensors[i].element_size(), False
+        )
+    get_opt = eic.GetOption()
+    status_code, data_vals, get_outcome = eic_client.mget(data_keys, get_opt, data_vals)
+    assert (
+        status_code == eic.StatusCode.SUCCESS
+    ), f"Get failed with status code: {status_code}"
+
+
+def test_exists(eic_client):
+    test_key = ["test_key_" + str(i) for i in range(16)]
+    data_keys = eic.StringVector()
+    for key in test_key:
+        data_keys.append(key)
+    exists_opt = eic.ExistOption()
+    status_code, exists_outcome = eic_client.mexist(data_keys, exists_opt)
+    assert (
+        status_code == eic.StatusCode.SUCCESS
+    ), f"Exists failed with status code: {status_code}"
+
+
+def main():
+    eic_client = init_eic_client()
+    test_set(eic_client)
+    test_exists(eic_client)
+    test_get(eic_client)
+
+
+if __name__ == "__main__":
+    main()

python/sglang/srt/server_args.py

@@ -2260,7 +2260,7 @@ class ServerArgs:
         parser.add_argument(
             "--hicache-storage-backend",
             type=str,
-            choices=["file", "mooncake", "hf3fs", "nixl", "aibrix", "dynamic"],
+            choices=["file", "mooncake", "hf3fs", "nixl", "aibrix", "dynamic", "eic"],
             default=ServerArgs.hicache_storage_backend,
             help="The storage backend for hierarchical KV cache. "
             "Built-in backends: file, mooncake, hf3fs, nixl, aibrix. "