Add hf3fs support for hicache storage (based on #7704) (#7280)

Co-authored-by: Zhiqiang Xie <xiezhq@stanford.edu>

benchmark/hf3fs/bench.sh

+SGLANG_HICACHE_HF3FS_CONFIG_PATH=/sgl-workspace/sglang/benchmark/hf3fs/hf3fs.json \
+python3 benchmark/hf3fs/bench_storage.py
+
+####################################################################################################
+
+rm -rf nohup.out && \
+nohup python3 -m sglang.launch_server \
+    --model-path /code/models/Qwen3-32B/ \
+    --host 0.0.0.0 --port 33301 \
+    --page-size 64 \
+    --enable-hierarchical-cache \
+    --hicache-ratio 2 --hicache-size 0 \
+    --hicache-write-policy write_through \
+    --hicache-storage-backend hf3fs &
+
+rm -rf bench_multiturn.out && \
+nohup python3 benchmark/hicache/bench_multiturn.py \
+    --model-path /code/models/Qwen3-32B \
+    --dataset-path /code/models/ShareGPT_V3_unfiltered_cleaned_split.json \
+    --port 33301 \
+    --request-length 2048 --num-clients 512 --num-rounds 3 --max-parallel 8 \
+    > bench_multiturn.out &
+
+####################################################################################################
+
+rm -rf nohup.out && \
+nohup python3 -m sglang.launch_server \
+    --model-path /code/models/DeepSeek-R1/ \
+    --tp 16 --nnodes 2 --node-rank 0 \
+    --dist-init-addr 10.74.249.153:5000 \
+    --host 0.0.0.0 --port 33301 \
+    --page-size 64 \
+    --enable-hierarchical-cache \
+    --hicache-ratio 2 --hicache-size 60 \
+    --hicache-write-policy write_through \
+    --hicache-storage-backend hf3fs &
+
+rm -rf bench_multiturn.out && \
+nohup python3 benchmark/hicache/bench_multiturn.py \
+    --model-path /code/models/Qwen3-32B \
+    --dataset-path /code/models/ShareGPT_V3_unfiltered_cleaned_split.json \
+    --port 33301 \
+    --request-length 2048 --num-clients 1024 --num-rounds 3 --max-parallel 8 \
+    > bench_multiturn.out &
+
+####################################################################################################
+
+ps aux | grep "sglang.launch_server" | grep -v grep | awk '{print $2}' | xargs kill -9
+ps aux | grep "bench_multiturn.py" | grep -v grep | awk '{print $2}' | xargs kill -9

benchmark/hf3fs/bench_client.py

+import concurrent.futures
+import logging
+import random
+import time
+from typing import List
+
+import torch
+from tqdm import tqdm
+
+from sglang.srt.mem_cache.storage.hf3fs.client_hf3fs import Hf3fsClient
+
+
+def print_stats(x: List[int]):
+    x = sorted(x)
+    lenx = len(x)
+    print(
+        f"mean = {sum(x)/len(x):.2f}, "
+        f"min = {min(x):.2f}, "
+        f"p25 = {x[int(lenx*0.25)]:.2f}, "
+        f"p50 = {x[int(lenx*0.5)]:.2f}, "
+        f"p75 = {x[int(lenx*0.75)]:.2f}, "
+        f"max = {max(x):.2f}"
+    )
+
+
+def test():
+    # /path/to/hf3fs
+    file_path = "/data/bench.bin"
+    file_size = 1 << 40
+    bytes_per_page = 16 << 20
+    entries = 32
+    file_ops = Hf3fsClient(file_path, file_size, bytes_per_page, entries)
+
+    print("test batch_read / batch_write")
+    num_pages = 128
+    dtype = torch.bfloat16
+    numel = bytes_per_page // dtype.itemsize
+    offsets = list(range(file_size // bytes_per_page))
+    random.shuffle(offsets)
+    offsets = offsets[:num_pages]
+    offsets = [i * bytes_per_page for i in offsets]
+    tensor_writes = [
+        torch.randn(numel, dtype=dtype)
+        for _ in tqdm(range(num_pages), desc="prepare tensor")
+    ]
+    for i in tqdm(range(0, num_pages, file_ops.entries), desc="batch_write"):
+        results = file_ops.batch_write(
+            offsets[i : i + file_ops.entries], tensor_writes[i : i + file_ops.entries]
+        )
+        assert all([result == numel * dtype.itemsize for result in results])
+    tensor_reads = [
+        torch.empty(numel, dtype=dtype)
+        for _ in tqdm(range(num_pages), desc="prepare tensor")
+    ]
+    for i in tqdm(range(0, num_pages, file_ops.entries), desc="batch_read"):
+        results = file_ops.batch_read(
+            offsets[i : i + file_ops.entries], tensor_reads[i : i + file_ops.entries]
+        )
+        assert all([result == numel * dtype.itemsize for result in results])
+    assert all([torch.allclose(r, w) for r, w in zip(tensor_reads, tensor_writes)])
+
+    file_ops.close()
+    print("test done")
+
+
+def bench():
+    file_path = "/data/bench.bin"
+    file_size = 1 << 40
+    bytes_per_page = 16 << 20
+    entries = 8
+    numjobs = 16
+
+    dtype = torch.bfloat16
+    numel = bytes_per_page // dtype.itemsize
+
+    file_ops = [
+        Hf3fsClient(file_path, file_size, bytes_per_page, entries)
+        for _ in range(numjobs)
+    ]
+
+    num_page = entries
+
+    offsets = list(range(file_size // bytes_per_page))
+    tensors_write = [torch.randn(numel, dtype=dtype)] * num_page
+    tensors_read = [torch.empty(numel, dtype=dtype)] * num_page
+    random.shuffle(offsets)
+
+    warmup = 50
+    iteration = 100
+
+    executor = concurrent.futures.ThreadPoolExecutor(max_workers=numjobs)
+
+    w_bw = []
+    w_size = num_page * numjobs * bytes_per_page / (1 << 30)
+    for i in tqdm(range(warmup + iteration), desc="Benchmarking write (GB/s)"):
+        _offsets = [
+            [
+                offset * bytes_per_page
+                for offset in offsets[
+                    (i * numjobs + j) * num_page : (i * numjobs + j + 1) * num_page
+                ]
+            ]
+            for j in range(numjobs)
+        ]
+        tik = time.perf_counter()
+        futures = [
+            executor.submit(file_ops[j].batch_write, offset, tensors_write)
+            for j, offset in enumerate(_offsets)
+        ]
+        results = [future.result() for future in futures]
+        tok = time.perf_counter()
+        if i < warmup:
+            continue
+        w_bw.append(w_size / (tok - tik))
+        results = [
+            _result == bytes_per_page for result in results for _result in result
+        ]
+        assert all(results)
+    print_stats(w_bw)
+
+    r_bw = []
+    r_size = w_size
+    for i in tqdm(range(warmup + iteration), desc="Benchmarking read (GB/s)"):
+        _offsets = [
+            [
+                offset * bytes_per_page
+                for offset in offsets[
+                    (i * numjobs + j) * num_page : (i * numjobs + j + 1) * num_page
+                ]
+            ]
+            for j in range(numjobs)
+        ]
+        tik = time.perf_counter()
+        futures = [
+            executor.submit(file_ops[j].batch_read, offset, tensors_read)
+            for j, offset in enumerate(_offsets)
+        ]
+        results = [future.result() for future in futures]
+        tok = time.perf_counter()
+        if i < warmup:
+            continue
+        r_bw.append(r_size / (tok - tik))
+        results = [
+            _result == bytes_per_page for result in results for _result in result
+        ]
+        assert all(results)
+    print_stats(r_bw)
+
+    executor.shutdown(wait=True)
+    for _file_ops in file_ops:
+        _file_ops.close()
+    print("bench done")
+
+
+def main():
+    logging.basicConfig(level=logging.INFO)
+    test()
+    bench()
+
+
+if __name__ == "__main__":
+    main()

benchmark/hf3fs/bench_storage.py

+import json
+import logging
+import os
+import random
+import time
+from typing import List
+
+import torch
+from tqdm import tqdm
+
+from sglang.srt.mem_cache.storage.hf3fs.storage_hf3fs import HiCacheHF3FS
+
+
+def print_stats(x: List[int]):
+    x = sorted(x)
+    lenx = len(x)
+    print(
+        f"mean = {sum(x)/len(x):.2f}, "
+        f"min = {min(x):.2f}, "
+        f"p25 = {x[int(lenx*0.25)]:.2f}, "
+        f"p50 = {x[int(lenx*0.5)]:.2f}, "
+        f"p75 = {x[int(lenx*0.75)]:.2f}, "
+        f"max = {max(x):.2f}"
+    )
+
+
+def test():
+    # Qwen3-32B
+    layer_num = 64
+    head_num, head_dim = 8, 128
+    kv_lora_rank, qk_rope_head_dim = 0, 0
+    store_dtype = torch.bfloat16
+    tokens_per_page = 64
+
+    file_path_prefix = "/data/test"
+    file_size = 128 << 20
+    numjobs = 16
+    bytes_per_page = 16 << 20
+    entries = 2
+    dtype = store_dtype
+
+    config_path = os.getenv(HiCacheHF3FS.default_env_var)
+    assert config_path
+    try:
+        with open(config_path, "w") as f:
+            json.dump(
+                {
+                    "file_path_prefix": file_path_prefix,
+                    "file_size": file_size,
+                    "numjobs": numjobs,
+                    "entries": entries,
+                },
+                f,
+            )
+    except Exception as e:
+        raise RuntimeError(f"Failed to dump config to {config_path}: {str(e)}")
+
+    rank = 0
+    hicache_hf3fs = HiCacheHF3FS.from_env_config(rank, bytes_per_page, dtype)
+
+    numel = 2 * tokens_per_page * layer_num * head_num * head_dim
+    assert numel * dtype.itemsize == bytes_per_page
+
+    num_pages = 10
+    tensors = {}
+    for i in range(num_pages):
+        k = f"key_{i}"
+        v = torch.randn((numel,)).to(dtype=dtype)
+        ok = hicache_hf3fs.set(k, v)
+        assert ok, f"Failed to insert {k}"
+        tensors[k] = v
+    assert hicache_hf3fs.get("key_0") is None
+    assert hicache_hf3fs.get("key_1") is None
+
+    start = num_pages - hicache_hf3fs.num_pages
+    for i in range(start, start + hicache_hf3fs.num_pages):
+        k = f"key_{i}"
+        assert hicache_hf3fs.exists(k)
+        out = hicache_hf3fs.get(k)
+        assert out is not None
+        v = tensors[k]
+        assert torch.allclose(v, out, atol=1e-3), f"Tensor mismatch for {k}"
+
+    assert not hicache_hf3fs.exists("not_exists")
+
+    hicache_hf3fs.delete("key_9")
+    v2 = torch.randn((numel,)).to(dtype=dtype)
+    assert hicache_hf3fs.set("key_new", v2)
+    assert torch.allclose(hicache_hf3fs.get("key_new"), v2, atol=1e-3)
+
+    hicache_hf3fs.clear()
+    assert len(hicache_hf3fs.free_pages) == hicache_hf3fs.num_pages
+
+    # batch
+    num_pages = 10
+    tensors = {}
+    keys = []
+    values = []
+    for i in range(num_pages):
+        k = f"key_{i}"
+        keys.append(k)
+        v = torch.randn((numel,)).to(dtype=dtype)
+        values.append(v)
+
+    ok = hicache_hf3fs.batch_set(keys, values)
+    assert not ok
+    assert hicache_hf3fs.get("key_8") is None
+    assert hicache_hf3fs.get("key_9") is None
+
+    results = hicache_hf3fs.batch_get(keys[: hicache_hf3fs.num_pages])
+    for result, key, value in zip(
+        results, keys[: hicache_hf3fs.num_pages], values[: hicache_hf3fs.num_pages]
+    ):
+        assert torch.allclose(value, result, atol=1e-3), f"Tensor mismatch for {key}"
+
+    hicache_hf3fs.close()
+    os.remove(hicache_hf3fs.file_path)
+
+    print("All test cases passed.")
+
+
+def bench():
+    # Qwen3-32B
+    layer_num = 64
+    head_num, head_dim = 8, 128
+    kv_lora_rank, qk_rope_head_dim = 0, 0
+    store_dtype = torch.bfloat16
+    tokens_per_page = 64
+
+    file_path = "/data/test.bin"
+    file_size = 1 << 40
+    numjobs = 16
+    bytes_per_page = 16 << 20
+    entries = 8
+    dtype = store_dtype
+    hicache_hf3fs = HiCacheHF3FS(
+        file_path=file_path,
+        file_size=file_size,
+        numjobs=numjobs,
+        bytes_per_page=bytes_per_page,
+        entries=entries,
+        dtype=dtype,
+    )
+
+    numel = 2 * tokens_per_page * layer_num * head_num * head_dim
+    assert numel * dtype.itemsize == bytes_per_page
+
+    num_page = 128
+    values = [torch.randn((numel,)).to(dtype=dtype) for _ in tqdm(range(num_page))]
+
+    warmup = 50
+    iteration = 100
+
+    w_bw = []
+    w_size = num_page * bytes_per_page / (1 << 30)
+    for i in tqdm(range(warmup + iteration), desc="Benchmarking write (GB/s)"):
+        keys = [f"{j}" for j in range(i * num_page, (i + 1) * num_page)]
+        tik = time.perf_counter()
+        ok = hicache_hf3fs.batch_set(keys, values)
+        tok = time.perf_counter()
+        if i < warmup:
+            continue
+        w_bw.append(w_size / (tok - tik))
+        assert ok
+    print_stats(w_bw)
+
+    r_bw = []
+    r_size = num_page * bytes_per_page / (1 << 30)
+    for i in tqdm(range(warmup + iteration), desc="Benchmarking read (GB/s)"):
+        keys = random.sample(list(hicache_hf3fs.key_to_index.keys()), num_page)
+        tik = time.perf_counter()
+        results = hicache_hf3fs.batch_get(keys)
+        tok = time.perf_counter()
+        if i < warmup:
+            continue
+        r_bw.append(r_size / (tok - tik))
+        assert all([r is not None for r in results])
+    print_stats(r_bw)
+
+    hicache_hf3fs.close()
+
+
+def allclose():
+    # Qwen3-32B
+    layer_num = 64
+    head_num, head_dim = 8, 128
+    kv_lora_rank, qk_rope_head_dim = 0, 0
+    store_dtype = torch.bfloat16
+    tokens_per_page = 64
+
+    file_path = "/data/test.bin"
+    file_size = 1 << 40
+    numjobs = 16
+    bytes_per_page = 16 << 20
+    entries = 8
+    dtype = store_dtype
+    hicache_hf3fs = HiCacheHF3FS(
+        file_path=file_path,
+        file_size=file_size,
+        numjobs=numjobs,
+        bytes_per_page=bytes_per_page,
+        entries=entries,
+        dtype=dtype,
+    )
+
+    numel = 2 * tokens_per_page * layer_num * head_num * head_dim
+    assert numel * dtype.itemsize == bytes_per_page
+
+    num_page = 128
+    values = [torch.randn((numel,)).to(dtype=dtype) for _ in tqdm(range(num_page))]
+
+    iteration = 100
+
+    for i in tqdm(range(iteration), desc="Benchmarking write (GB/s)"):
+        keys = [f"{j}" for j in range(i * num_page, (i + 1) * num_page)]
+        ok = hicache_hf3fs.batch_set(keys, values)
+        assert ok
+
+    read_keys, read_results = [], []
+    for i in tqdm(range(iteration), desc="Benchmarking read (GB/s)"):
+        keys = random.sample(list(hicache_hf3fs.key_to_index.keys()), num_page)
+        results = hicache_hf3fs.batch_get(keys)
+        read_keys.extend(keys)
+        read_results.extend(results)
+        assert all([r is not None for r in results])
+
+    for key, result in tqdm(zip(read_keys, read_results)):
+        assert torch.allclose(values[int(key) % num_page], result, atol=1e-3)
+
+    hicache_hf3fs.close()
+
+
+def main():
+    logging.basicConfig(level=logging.INFO)
+    test()
+    bench()
+    allclose()
+
+
+if __name__ == "__main__":
+    main()

python/sglang/srt/managers/cache_controller.py

@@ -26,6 +26,7 @@ if TYPE_CHECKING:
     from sglang.srt.mem_cache.memory_pool_host import HostKVCache
 
 from sglang.srt.mem_cache.hicache_storage import HiCacheFile, get_hash_str
+from sglang.srt.mem_cache.storage.hf3fs.storage_hf3fs import HiCacheHF3FS
 
 logger = logging.getLogger(__name__)
 
@@ -250,17 +251,33 @@ class HiCacheController:
             self.tp_world_size = torch.distributed.get_world_size(group=tp_group)
             if self.tp_world_size > 1:
                 group_ranks = torch.distributed.get_process_group_ranks(tp_group)
-                self.tp_group = torch.distributed.new_group(group_ranks, backend="gloo")
+                self.prefetch_tp_group = torch.distributed.new_group(
+                    group_ranks, backend="gloo"
+                )
+                self.backup_tp_group = torch.distributed.new_group(
+                    group_ranks, backend="gloo"
+                )
 
             if storage_backend == "file":
                 self.storage_backend = HiCacheFile()
-                self.enable_storage = True
-                # todo: threshold policy for prefetching
-                self.prefetch_threshold = max(prefetch_threshold, self.page_size)
+            elif storage_backend == "hf3fs":
+                from sglang.srt.distributed import get_tensor_model_parallel_rank
+
+                rank = get_tensor_model_parallel_rank()
+                bytes_per_page = (
+                    mem_pool_host.get_size_per_token() * mem_pool_host.page_size
+                )
+                dtype = mem_pool_host.dtype
+                self.storage_backend = HiCacheHF3FS.from_env_config(
+                    rank, bytes_per_page, dtype
+                )
             else:
                 raise NotImplementedError(
                     f"Unsupported storage backend: {storage_backend}"
                 )
+            self.enable_storage = True
+            # todo: threshold policy for prefetching
+            self.prefetch_threshold = max(prefetch_threshold, self.page_size)
 
         self.load_cache_event = load_cache_event
         self.layer_done_counter = LayerDoneCounter(self.mem_pool_device.layer_num)
@@ -522,8 +539,8 @@ class HiCacheController:
         while not self.stop_event.is_set():
             try:
                 operation = self.prefetch_buffer.get(block=True, timeout=1)
-                for h in operation.hash_value:
-                    page_data = self.storage_backend.get(h)
+                page_datas = self.storage_backend.batch_get(operation.hash_value)
+                for h, page_data in zip(operation.hash_value, page_datas):
                     if page_data is None:
                         logger.warning(
                             f"Prefetch operation {operation.request_id} failed to retrieve page {h}."
@@ -531,7 +548,9 @@ class HiCacheController:
                         break
                     if operation.increment(self.page_size):
                         self.mem_pool_host.set_from_flat_data_page(
-                            operation.host_indices[operation.completed_tokens],
+                            operation.host_indices[
+                                operation.completed_tokens - self.page_size
+                            ],
                             page_data,
                         )
                     else:
@@ -583,7 +602,7 @@ class HiCacheController:
                     torch.distributed.all_reduce(
                         storage_hit_count_tensor,
                         op=torch.distributed.ReduceOp.MIN,
-                        group=self.tp_group,
+                        group=self.prefetch_tp_group,
                     )
                     storage_hit_count = storage_hit_count_tensor.item()
 
@@ -635,21 +654,23 @@ class HiCacheController:
                 last_hash = operation.last_hash
                 tokens_to_backup = operation.token_ids
 
+                last_hashes, data_pages = [], []
                 for i in range(0, len(tokens_to_backup), self.page_size):
                     last_hash = get_hash_str(
                         tokens_to_backup[i : i + self.page_size], last_hash
                     )
-                    success = self.storage_backend.set(
-                        last_hash,
-                        self.mem_pool_host.get_flat_data_page(
-                            operation.host_indices[i]
-                        ),
+                    data_page = self.mem_pool_host.get_flat_data_page(
+                        operation.host_indices[i]
                     )
-                    if not success:
-                        logger.warning(f"Failed to write page {last_hash} to storage.")
-                        break
-                    operation.completed_tokens += self.page_size
-                    operation.hash_value.append(last_hash)
+                    last_hashes.append(last_hash)
+                    data_pages.append(data_page)
+
+                success = self.storage_backend.batch_set(last_hashes, data_pages)
+                if not success:
+                    logger.warning(f"Failed to write page {last_hashes} to storage.")
+                else:
+                    operation.completed_tokens += len(tokens_to_backup)
+                    operation.hash_value.extend(last_hashes)
 
                 min_completed_tokens = operation.completed_tokens
                 if self.tp_world_size > 1:
@@ -659,7 +680,7 @@ class HiCacheController:
                     torch.distributed.all_reduce(
                         completed_tokens_tensor,
                         op=torch.distributed.ReduceOp.MIN,
-                        group=self.tp_group,
+                        group=self.backup_tp_group,
                     )
                     min_completed_tokens = completed_tokens_tensor.item()
 

python/sglang/srt/mem_cache/hiradix_cache.py

@@ -79,7 +79,9 @@ class HiRadixCache(RadixCache):
         self.write_through_threshold = (
             1 if hicache_write_policy == "write_through" else 3
         )
-        self.write_through_threshold_storage = 3
+        self.write_through_threshold_storage = (
+            1 if hicache_write_policy == "write_through" else 3
+        )
         self.load_back_threshold = 10
         super().__init__(
             req_to_token_pool, token_to_kv_pool_allocator, page_size, disable=False
@@ -388,10 +390,14 @@ class HiRadixCache(RadixCache):
                 self.cache_controller.ack_backup_queue.get()
             )
             host_node = self.ongoing_backup[ack_id]
-            if completed_tokens < len(host_node.key):
+            if completed_tokens == 0:
+                host_node.hash_value = None
+            elif completed_tokens < len(host_node.key):
                 # backup is only partially successful, split the node
                 new_node = self._split_node(host_node.key, host_node, completed_tokens)
                 new_node.hash_value = hash_value
+            else:
+                host_node.hash_value = hash_value
             host_node.release_host()
             del self.ongoing_backup[ack_id]
 
@@ -431,6 +437,8 @@ class HiRadixCache(RadixCache):
             written_indices,
             hash_value[:min_completed_tokens],
         )
+        if len(written_indices):
+            self.cache_controller.mem_pool_host.update_prefetch(written_indices)
 
         self.cache_controller.mem_pool_host.free(host_indices[:matched_length])
         self.cache_controller.mem_pool_host.free(

python/sglang/srt/mem_cache/memory_pool_host.py

@@ -25,7 +25,6 @@ def synchronized(debug_only=False):
         @wraps(func)
         def wrapper(self, *args, **kwargs):
             if (not debug_only) or self.debug:
-                return func(self, *args, **kwargs)
                 with self.lock:
                     return func(self, *args, **kwargs)
             else:
@@ -181,6 +180,15 @@ class HostKVCache(abc.ABC):
             )
         self.mem_state[indices] = MemoryStateInt.BACKUP
 
+    @synchronized(debug_only=True)
+    def update_prefetch(self, indices: torch.Tensor):
+        if not self.is_reserved(indices):
+            raise ValueError(
+                f"The host memory slots should be in RESERVED state before turning into BACKUP. "
+                f"Current state: {self.get_state(indices)}"
+            )
+        self.mem_state[indices] = MemoryStateInt.BACKUP
+
     @synchronized(debug_only=True)
     def update_synced(self, indices: torch.Tensor):
         self.mem_state[indices] = MemoryStateInt.SYNCED

python/sglang/srt/mem_cache/storage/hf3fs/README.md

+# HiCacheHF3FS Setup
+
+## Build & Package
+### Source Code
+https://github.com/deepseek-ai/3FS/blob/main/README.md#check-out-source-code
+```sh
+git clone https://github.com/deepseek-ai/3fs
+
+cd 3fs
+git submodule update --init --recursive
+./patches/apply.sh
+```
+
+### Build Dev Container
+https://github.com/deepseek-ai/3FS/blob/main/dockerfile/dev.dockerfile
+```sh
+cd 3fs/dockerfile
+docker build -t hf3fs:dev -f dev.dockerfile .
+```
+
+### Generate Python Wheel
+```sh
+docker run -it hf3fs:dev bash
+
+# Inside the development container
+git clone https://github.com/deepseek-ai/3fs
+
+cd 3fs
+git submodule update --init --recursive
+./patches/apply.sh
+
+apt-get update \
+&& apt-get install -y --no-install-recommends \
+python3 python3-pip \
+&& apt-get clean \
+&& rm -rf /var/lib/apt/lists/*
+
+# Generated wheel location: dist/hf3fs_py_usrbio-1.2.9+2db69ce-cp310-cp310-linux_x86_64.whl
+python3 setup.py bdist_wheel
+```
+
+## Installation
+```sh
+# Install Dependencies
+# https://github.com/deepseek-ai/3FS/blob/main/dockerfile/dev.dockerfile
+apt update && apt install -y                            \
+  libaio-dev                                            \
+  libboost-all-dev                                      \
+  libdouble-conversion-dev                              \
+  libdwarf-dev                                          \
+  libgflags-dev                                         \
+  libgmock-dev                                          \
+  libgoogle-glog-dev                                    \
+  libgoogle-perftools-dev                               \
+  libgtest-dev                                          \
+  liblz4-dev                                            \
+  liblzma-dev                                           \
+  libssl-dev                                            \
+  libunwind-dev                                         \
+  libuv1-dev
+
+# Install Python Package
+pip install hf3fs_py_usrbio-1.2.9+2db69ce-cp310-cp310-linux_x86_64.whl
+export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/lib/python3.10/dist-packages
+```

python/sglang/srt/mem_cache/storage/hf3fs/client_hf3fs.py

+import logging
+import multiprocessing
+import os
+import threading
+from functools import wraps
+from pathlib import Path
+from typing import List
+
+import torch
+from torch.utils.cpp_extension import load
+
+root = Path(__file__).parent.resolve()
+hf3fs_utils = load(name="hf3fs_utils", sources=[f"{root}/hf3fs_utils.cpp"])
+
+logger = logging.getLogger(__name__)
+
+try:
+    from hf3fs_fuse.io import (
+        deregister_fd,
+        extract_mount_point,
+        make_ioring,
+        make_iovec,
+        register_fd,
+    )
+except ImportError as e:
+    logger.warning(f"hf3fs_fuse.io is not available: {e}")
+
+
+def rsynchronized():
+    def _decorator(func):
+        @wraps(func)
+        def wrapper(self, *args, **kwargs):
+            with self.rlock:
+                return func(self, *args, **kwargs)
+
+        return wrapper
+
+    return _decorator
+
+
+def wsynchronized():
+    def _decorator(func):
+        @wraps(func)
+        def wrapper(self, *args, **kwargs):
+            with self.wlock:
+                return func(self, *args, **kwargs)
+
+        return wrapper
+
+    return _decorator
+
+
+class Hf3fsClient:
+    def __init__(self, path: str, size: int, bytes_per_page: int, entries: int):
+        self.path = path
+        self.size = size
+        self.bytes_per_page = bytes_per_page
+        self.entries = entries
+
+        self.file = os.open(self.path, os.O_RDWR | os.O_CREAT)
+        os.ftruncate(self.file, size)
+        register_fd(self.file)
+
+        self.hf3fs_mount_point = extract_mount_point(path)
+        self.bs = self.bytes_per_page
+        self.shm_r = multiprocessing.shared_memory.SharedMemory(
+            size=self.bs * self.entries, create=True
+        )
+        self.shm_w = multiprocessing.shared_memory.SharedMemory(
+            size=self.bs * self.entries, create=True
+        )
+
+        self.shm_r_tensor = torch.frombuffer(self.shm_r.buf, dtype=torch.uint8)
+        self.shm_w_tensor = torch.frombuffer(self.shm_w.buf, dtype=torch.uint8)
+
+        self.numa = -1
+        self.ior_r = make_ioring(
+            self.hf3fs_mount_point,
+            self.entries,
+            for_read=True,
+            timeout=1,
+            numa=self.numa,
+        )
+        self.ior_w = make_ioring(
+            self.hf3fs_mount_point,
+            self.entries,
+            for_read=False,
+            timeout=1,
+            numa=self.numa,
+        )
+        self.iov_r = make_iovec(self.shm_r, self.hf3fs_mount_point)
+        self.iov_w = make_iovec(self.shm_w, self.hf3fs_mount_point)
+
+        self.rlock = threading.RLock()
+        self.wlock = threading.RLock()
+
+    @rsynchronized()
+    def batch_read(self, offsets: List[int], tensors: List[torch.Tensor]) -> List[int]:
+        self.check(offsets, tensors)
+
+        # prepare
+        current = 0
+        for offset, tensor in zip(offsets, tensors):
+            size = tensor.numel() * tensor.itemsize
+            self.ior_r.prepare(
+                self.iov_r[current : current + size], True, self.file, offset
+            )
+            current += size
+
+        # submit
+        ionum = len(offsets)
+        resv = self.ior_r.submit().wait(min_results=ionum)
+
+        # results
+        hf3fs_utils.read_shm(self.shm_r_tensor, tensors)
+        results = [res.result for res in resv]
+
+        return results
+
+    @wsynchronized()
+    def batch_write(self, offsets: List[int], tensors: List[torch.Tensor]) -> List[int]:
+        self.check(offsets, tensors)
+
+        # prepare
+        hf3fs_utils.write_shm(tensors, self.shm_w_tensor)
+        current = 0
+        for offset, tensor in zip(offsets, tensors):
+            size = tensor.numel() * tensor.itemsize
+            self.ior_w.prepare(
+                self.iov_w[current : current + size], False, self.file, offset
+            )
+            current += size
+
+        # submit
+        ionum = len(offsets)
+        resv = self.ior_w.submit().wait(min_results=ionum)
+
+        # results
+        results = [res.result for res in resv]
+
+        return results
+
+    def check(self, offsets: List[int], tensors: List[torch.Tensor]) -> None:
+        sizes = [t.numel() * t.itemsize for t in tensors]
+        if any(
+            [
+                len(offsets) > self.entries,
+                len(offsets) != len(sizes),
+                all(
+                    [
+                        offset < 0 or offset + size > self.size
+                        for offset, size in zip(offsets, sizes)
+                    ]
+                ),
+                all([size > self.bytes_per_page for size in sizes]),
+            ]
+        ):
+            self.close()
+            raise ValueError(f"Hf3fsClient.check: {offsets=}, {sizes=}")
+
+    def get_size(self) -> int:
+        return self.size
+
+    def close(self) -> None:
+        deregister_fd(self.file)
+        os.close(self.file)
+        del self.ior_r
+        del self.ior_w
+        del self.iov_r
+        del self.iov_w
+        self.shm_r.close()
+        self.shm_w.close()
+        self.shm_r.unlink()
+        self.shm_w.unlink()
+
+    def flush(self) -> None:
+        os.fsync(self.file)

python/sglang/srt/mem_cache/storage/hf3fs/hf3fs_utils.cpp

+#include <torch/extension.h>
+
+#include <cstring>
+#include <vector>
+
+void read_shm(const torch::Tensor &shm, std::vector<torch::Tensor> dst) {
+  py::gil_scoped_release release;
+  char *src_ptr = static_cast<char *>(shm.data_ptr());
+  size_t current = 0;
+  for (size_t i = 0; i < dst.size(); ++i) {
+    auto &t = dst[i];
+    size_t t_bytes = t.numel() * t.element_size();
+    char *dst_ptr = static_cast<char *>(t.data_ptr());
+    std::memcpy(dst_ptr, src_ptr + current, t_bytes);
+    current += t_bytes;
+  }
+}
+
+void write_shm(const std::vector<torch::Tensor> src, torch::Tensor &shm) {
+  py::gil_scoped_release release;
+  char *dst_ptr = static_cast<char *>(shm.data_ptr());
+  size_t current = 0;
+  for (size_t i = 0; i < src.size(); ++i) {
+    auto &t = src[i];
+    size_t t_bytes = t.numel() * t.element_size();
+    char *src_ptr = static_cast<char *>(t.data_ptr());
+    std::memcpy(dst_ptr + current, src_ptr, t_bytes);
+    current += t_bytes;
+  }
+}
+
+PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
+  m.def("read_shm", &read_shm, "Read tensors from shared memory");
+  m.def("write_shm", &write_shm, "Write tensors to shared memory");
+}

python/sglang/srt/mem_cache/storage/hf3fs/storage_hf3fs.py

+import atexit
+import concurrent.futures
+import json
+import logging
+import os
+import signal
+import threading
+from collections import OrderedDict
+from functools import wraps
+from typing import List, Optional
+
+import torch
+
+from sglang.srt.mem_cache.hicache_storage import HiCacheStorage
+from sglang.srt.mem_cache.storage.hf3fs.client_hf3fs import Hf3fsClient
+
+logger = logging.getLogger(__name__)
+
+
+class AtomicCounter:
+    def __init__(self, n: int):
+        assert n > 0
+        self.n = n
+        self._value = 0
+        self._lock = threading.Lock()
+
+    def next(self) -> int:
+        with self._lock:
+            current = self._value
+            self._value = (current + 1) % self.n
+            return current
+
+
+def synchronized():
+    def _decorator(func):
+        @wraps(func)
+        def wrapper(self, *args, **kwargs):
+            with self.lock:
+                return func(self, *args, **kwargs)
+
+        return wrapper
+
+    return _decorator
+
+
+class HiCacheHF3FS(HiCacheStorage):
+    default_env_var: str = "SGLANG_HICACHE_HF3FS_CONFIG_PATH"
+
+    def __init__(
+        self,
+        file_path: str,
+        file_size: int,
+        numjobs: int,
+        bytes_per_page: int,
+        entries: int,
+        dtype: torch.dtype,
+    ):
+        self.file_path = file_path
+        self.file_size = file_size
+        self.numjobs = numjobs
+        self.bytes_per_page = bytes_per_page
+        self.entries = entries
+        self.dtype = dtype
+
+        self.numel = self.bytes_per_page // self.dtype.itemsize
+
+        self.num_pages = self.file_size // self.bytes_per_page
+
+        logger.info(
+            "HiCacheHF3FS "
+            f"file_path = {self.file_path}, "
+            f"file_size = {self.file_size/(2**30):.2f} GB, "
+            f"numjobs = {self.numjobs}, "
+            f"bytes_per_page = {self.bytes_per_page/(2**20):.2f} MB, "
+            f"entries = {self.entries}, "
+            f"num_pages = {self.num_pages}"
+        )
+
+        self.ac = AtomicCounter(self.numjobs)
+        self.clients = [
+            Hf3fsClient(
+                self.file_path, self.file_size, self.bytes_per_page, self.entries
+            )
+            for _ in range(numjobs)
+        ]
+        self.executor = concurrent.futures.ThreadPoolExecutor(
+            max_workers=self.numjobs, thread_name_prefix="HiCacheHF3FS"
+        )
+
+        # Implemented a preliminary single-file page_hash -> file_offset index as interim storage.
+        # Future iterations may adopt a global KVCache manager to coordinate external cache instances
+        # through centralized metadata orchestration.
+        self.lock = threading.RLock()
+        self.free_pages = list(range(self.num_pages))
+        self.key_to_index = OrderedDict()
+
+        atexit.register(self.close)
+
+        signal.signal(signal.SIGINT, lambda sig, frame: self.close())
+        signal.signal(signal.SIGTERM, lambda sig, frame: self.close())
+        signal.signal(signal.SIGQUIT, lambda sig, frame: self.close())
+
+    @staticmethod
+    def from_env_config(
+        rank: int, bytes_per_page: int, dtype: torch.dtype
+    ) -> "HiCacheHF3FS":
+        config_path = os.getenv(HiCacheHF3FS.default_env_var)
+        if not config_path:
+            return HiCacheHF3FS(
+                file_path=f"/data/hicache.{rank}.bin",
+                file_size=1 << 40,
+                numjobs=16,
+                bytes_per_page=bytes_per_page,
+                entries=8,
+                dtype=dtype,
+            )
+
+        try:
+            with open(config_path, "r") as f:
+                config = json.load(f)
+        except Exception as e:
+            raise RuntimeError(f"Failed to load config from {config_path}: {str(e)}")
+
+        required_keys = {
+            "file_path_prefix",
+            "file_size",
+            "numjobs",
+            "entries",
+        }
+        missing_keys = required_keys - set(config.keys())
+        if missing_keys:
+            raise ValueError(f"Missing required keys in config: {missing_keys}")
+
+        return HiCacheHF3FS(
+            file_path=f"{config['file_path_prefix']}.{rank}.bin",
+            file_size=int(config["file_size"]),
+            numjobs=int(config["numjobs"]),
+            bytes_per_page=bytes_per_page,
+            entries=int(config["entries"]),
+            dtype=dtype,
+        )
+
+    def get(
+        self, key: str, target_location: Optional[torch.Tensor] = None
+    ) -> torch.Tensor | None:
+        return self.batch_get([key], target_location)[0]
+
+    @synchronized()
+    def batch_get(
+        self,
+        keys: List[str],
+        target_locations: Optional[List[torch.Tensor]] = None,
+    ) -> List[torch.Tensor | None]:
+        batch_indices, file_offsets = [], []
+        for i, key in enumerate(keys):
+            if key not in self.key_to_index:
+                continue
+            batch_indices.append(i)
+            file_offsets.append(self.key_to_index[key] * self.bytes_per_page)
+            self.key_to_index.move_to_end(key)
+        # TODO: target_locations
+        file_results = [
+            torch.empty(self.numel, dtype=self.dtype) for _ in range(len(batch_indices))
+        ]
+
+        futures = [
+            self.executor.submit(
+                self.clients[self.ac.next()].batch_read,
+                file_offsets[i : i + self.entries],
+                file_results[i : i + self.entries],
+            )
+            for i in range(0, len(batch_indices), self.entries)
+        ]
+        read_results = [result for future in futures for result in future.result()]
+
+        results = [None] * len(keys)
+        for batch_index, file_result, read_result in zip(
+            batch_indices, file_results, read_results
+        ):
+            if read_result == self.bytes_per_page:
+                results[batch_index] = file_result
+            else:
+                logger.error(f"HiCacheHF3FS get {keys[batch_index]} failed")
+
+        return results
+
+    def set(self, key: str, value: torch.Tensor) -> bool:
+        return self.batch_set([key], [value])
+
+    def batch_set(self, keys: List[str], values: List[torch.Tensor]) -> bool:
+        indices = self.get_batch_set_indices(keys)
+        batch_indices, file_offsets, file_values = [], [], []
+        for i, (value, (is_written, index)) in enumerate(zip(values, indices)):
+            if is_written or index == -1:
+                continue
+            batch_indices.append(i)
+            file_offsets.append(index * self.bytes_per_page)
+            file_values.append(value.contiguous())
+
+        futures = [
+            self.executor.submit(
+                self.clients[self.ac.next()].batch_write,
+                file_offsets[i : i + self.entries],
+                file_values[i : i + self.entries],
+            )
+            for i in range(0, len(batch_indices), self.entries)
+        ]
+        write_results = [
+            result == self.bytes_per_page
+            for future in futures
+            for result in future.result()
+        ]
+
+        results = [index[0] for index in indices]
+        for batch_index, write_result in zip(batch_indices, write_results):
+            key = keys[batch_index]
+            index = indices[batch_index][1]
+            if write_result:
+                self.key_to_index[key] = index
+                self.key_to_index.move_to_end(key)
+            else:
+                logger.error(f"HiCacheHF3FS set {key} failed")
+                self.free_pages.append(index)
+            results[batch_index] = write_result
+        return all(results)
+
+    @synchronized()
+    def get_batch_set_indices(self, keys: List[str]) -> list:
+        ionum = len(keys)
+        # results: tuples of (is_written: bool, page_idx: int)
+        # - is_written: True = hit (no I/O), False = write (miss)
+        # - page_idx: page storing data
+        results = [None] * min(ionum, self.num_pages)
+        if ionum > self.num_pages:
+            results.extend([(False, -1)] * (ionum - self.num_pages))
+
+        new_keys = []
+        for batch_index, key in enumerate(keys[: self.num_pages]):
+            if key in self.key_to_index:
+                results[batch_index] = (True, self.key_to_index[key])
+                self.key_to_index.move_to_end(key)
+            else:
+                new_keys.append((batch_index, key))
+
+        for batch_index, _ in new_keys:
+            index = (
+                self.free_pages.pop()
+                if len(self.free_pages) > 0
+                else self.key_to_index.popitem(last=False)[1]
+            )
+            results[batch_index] = (False, index)
+
+        return results
+
+    @synchronized()
+    def delete(self, key: str) -> None:
+        if key not in self.key_to_index:
+            return
+        index = self.key_to_index.pop(key)
+        self.free_pages.append(index)
+
+    @synchronized()
+    def exists(self, key: str) -> bool:
+        return key in self.key_to_index
+
+    @synchronized()
+    def clear(self) -> None:
+        self.free_pages = list(range(self.num_pages))
+        self.key_to_index.clear()
+
+    def close(self) -> None:
+        try:
+            for c in self.clients:
+                c.close()
+            self.executor.shutdown(wait=True)
+        except Exception as e:
+            logger.error(f"close HiCacheHF3FS: {e}")
+        logger.info("close HiCacheHF3FS")

python/sglang/srt/mem_cache/storage/hf3fs/test_hf3fs_utils.py

+import multiprocessing.shared_memory
+from pathlib import Path
+
+import pytest
+import torch
+from torch.utils.cpp_extension import load
+from tqdm import tqdm
+
+root = Path(__file__).parent.resolve()
+hf3fs_utils = load(
+    name="hf3fs_utils", sources=[f"{root}/hf3fs_utils.cpp"], verbose=True
+)
+
+
+def test_rw_shm():
+    numel = 8 << 20
+    dtype = torch.bfloat16
+    page_num = 128
+    page_bytes = numel * dtype.itemsize
+    shm = multiprocessing.shared_memory.SharedMemory(
+        size=page_num * page_bytes, create=True
+    )
+    tshm = torch.frombuffer(shm.buf, dtype=torch.uint8)
+    a = [
+        torch.randn(numel, dtype=dtype)
+        for _ in tqdm(range(page_num), desc="prepare input")
+    ]
+    b = [
+        torch.empty(numel, dtype=dtype)
+        for _ in tqdm(range(page_num), desc="prepare output")
+    ]
+    hf3fs_utils.write_shm(a, tshm)
+    hf3fs_utils.read_shm(tshm, b)
+    for _a, _b in tqdm(zip(a, b), desc="assert_close"):
+        torch.testing.assert_close(_a, _b)
+
+    del tshm
+    shm.close()
+    shm.unlink()
+
+
+if __name__ == "__main__":
+    pytest.main([__file__])

python/sglang/srt/server_args.py

@@ -1476,7 +1476,7 @@ class ServerArgs:
         parser.add_argument(
             "--hicache-storage-backend",
             type=str,
-            choices=["file"],  # todo, mooncake
+            choices=["file", "hf3fs"],  # todo, mooncake
             default=ServerArgs.hicache_storage_backend,
             help="The storage backend for hierarchical KV cache.",
         )