[Feat][v1] Simple yet General CPU KV Cache Offloading (#37160)

Signed-off-by: Yifan Qiao <yifanqiao@berkeley.edu>
Signed-off-by: Yifan Qiao <yifanqiao@inferact.ai>

tests/v1/simple_kv_offload/test_integration.py

+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Integration tests for SimpleCPUOffloadConnector with real models."""
+
+import time
+
+import pytest
+
+from vllm import LLM, SamplingParams, TokensPrompt
+from vllm.config import KVTransferConfig
+from vllm.platforms import current_platform
+
+if not current_platform.is_cuda():
+    pytest.skip("Requires CUDA", allow_module_level=True)
+
+# Small models for default CI / local runs (accuracy only).
+SMALL_MODELS = [
+    "meta-llama/Llama-3.2-1B-Instruct",
+    "google/gemma-3-1b-it",
+]
+
+# Large models for optional perf runs only (slow to load and execute).
+PERF_MODELS = [
+    "meta-llama/Llama-3.1-8B",
+    "openai/gpt-oss-20b",
+]
+
+
+def _make_llm(model: str, lazy: bool, cpu_bytes_to_use: int) -> LLM:
+    kv_transfer_config = KVTransferConfig(
+        kv_connector="SimpleCPUOffloadConnector",
+        kv_role="kv_both",
+        kv_connector_extra_config={
+            "cpu_bytes_to_use": cpu_bytes_to_use,
+            "lazy_offload": lazy,
+        },
+    )
+    return LLM(
+        model=model,
+        kv_cache_memory_bytes=40 << 30,  # 40 GiB
+        disable_hybrid_kv_cache_manager=False,
+        enable_prefix_caching=True,
+        kv_transfer_config=kv_transfer_config,
+    )
+
+
+def _flush_gpu_cache(llm: LLM, sampling_params: SamplingParams, seed: int = 0):
+    """Generate enough filler requests to allocate the entire GPU KV cache.
+
+    This pushes all prior blocks through the free queue so that the lazy
+    cursor offloads them to CPU before they are evicted.
+    """
+    cache_config = llm.llm_engine.vllm_config.cache_config
+    num_gpu_blocks = cache_config.num_gpu_blocks
+    block_size = cache_config.block_size
+    # Use 1.2x GPU capacity to give the lazy cursor enough scheduling steps
+    # to walk past all target blocks near the tail of the free queue.
+    total_tokens_needed = int(num_gpu_blocks * block_size * 1.5)
+
+    # Use token-id prompts so each filler is unique (no prefix sharing).
+    # Split into multiple requests to stay under max_model_len.
+    max_tokens_per_req = 4096
+    num_fillers = (total_tokens_needed + max_tokens_per_req - 1) // max_tokens_per_req
+    batch_size = 10
+    for i in range(0, num_fillers, batch_size):
+        batch_end = min(i + batch_size, num_fillers)
+        filler_prompts = []
+        for j in range(i, batch_end):
+            ids = [seed * num_fillers + j + 1] * max_tokens_per_req
+            filler_prompts.append(TokensPrompt(prompt_token_ids=ids))
+        llm.generate(filler_prompts, sampling_params, use_tqdm=False)
+
+
+def _accuracy_test(llm: LLM, lazy: bool = False):
+    """Verify that CPU-loaded KV produces correct output."""
+    sampling_params = SamplingParams(max_tokens=1, temperature=0)
+    prompt = "hi " * 2000 + "Let's count to ten. One, two, three, "
+
+    # Cold run — populate GPU cache and trigger CPU offload
+    cold_output = llm.generate(prompt, sampling_params, use_tqdm=False)[0]
+
+    # CPU hit runs
+    test_count = 10
+    success_count = 0
+    expected = cold_output.outputs[0].text
+    for i in range(test_count):
+        if lazy:
+            _flush_gpu_cache(llm, sampling_params, seed=i)
+        time.sleep(2)  # let engine core drain pending transfers
+
+        # Reset GPU prefix cache so next run must load from CPU
+        if not llm.reset_prefix_cache():
+            print(f"GPU prefix cache reset failed for iteration {i}")
+
+        output = llm.generate(prompt, sampling_params, use_tqdm=False)[0]
+        if output.outputs[0].text == expected:
+            success_count += 1
+
+    assert success_count >= 0.5 * test_count, (
+        f"Accuracy too low: {success_count}/{test_count} matched '{expected}'"
+    )
+
+
+def _latency_test(llm: LLM, lazy: bool = False):
+    """Verify CPU cache hit is faster than cold compute."""
+    sampling_params = SamplingParams(max_tokens=1, seed=42)
+    prompt_token_ids = [0] * 10001
+
+    num_times_cpu_better = 0
+    num_tests = 10
+    for i in range(num_tests):
+        prompt_token_ids[0] = i
+        prompts = [TokensPrompt(prompt_token_ids=prompt_token_ids)]
+
+        # Cold
+        time.sleep(2)  # let engine core drain pending transfers
+        if not llm.reset_prefix_cache():
+            print(f"GPU prefix cache reset failed for iteration {i}")
+        start = time.time()
+        llm.generate(prompts, sampling_params, use_tqdm=False)
+        cold_time = time.time() - start
+
+        if lazy:
+            _flush_gpu_cache(llm, sampling_params, seed=i)
+        else:
+            # Eager mode: GPU hit ensures store completion is processed.
+            llm.generate(prompts, sampling_params, use_tqdm=False)
+
+        time.sleep(2)  # let engine core drain pending transfers
+        if not llm.reset_prefix_cache():
+            print(f"GPU prefix cache reset failed for iteration {i}")
+
+        # CPU hit
+        start = time.time()
+        llm.generate(prompts, sampling_params, use_tqdm=False)
+        cpu_time = time.time() - start
+
+        if cpu_time < cold_time:
+            num_times_cpu_better += 1
+
+    assert num_times_cpu_better >= 0.8 * num_tests, (
+        f"CPU hit only faster {num_times_cpu_better}/{num_tests} times"
+    )
+
+
+@pytest.mark.optional
+@pytest.mark.slow_test
+@pytest.mark.parametrize("model", SMALL_MODELS)
+def test_simple_cpu_offload_accuracy(model: str):
+    """Store to CPU, reset GPU, load from CPU; verify output matches baseline."""
+    llm = _make_llm(model, False, 1 << 30)  # 1GB
+    try:
+        _accuracy_test(llm, lazy=False)
+    finally:
+        del llm
+
+
+@pytest.mark.optional
+@pytest.mark.slow_test
+@pytest.mark.parametrize("model", PERF_MODELS)
+def test_simple_cpu_offload_perf_latency(model: str):
+    """CPU KV hit should beat cold prefill on long context (large models only)."""
+    llm = _make_llm(model, False, 10 << 30)  # 10GB
+    try:
+        _latency_test(llm, lazy=False)
+    finally:
+        del llm
+
+
+@pytest.mark.optional
+@pytest.mark.slow_test
+@pytest.mark.parametrize("model", SMALL_MODELS)
+def test_simple_cpu_offload_accuracy_lazy(model: str):
+    """Lazy mode: flush GPU cache to trigger CPU offload, then verify hit."""
+    # CPU must be larger than GPU KV cache to avoid evicting offloaded blocks.
+    llm = _make_llm(model, True, 80 << 30)  # 80GB
+    try:
+        _accuracy_test(llm, lazy=True)
+    finally:
+        del llm
+
+
+@pytest.mark.optional
+@pytest.mark.slow_test
+@pytest.mark.parametrize("model", PERF_MODELS)
+def test_simple_cpu_offload_perf_latency_lazy(model: str):
+    """Lazy mode: CPU KV hit should beat cold prefill (large models only)."""
+    # CPU must be larger than GPU KV cache to avoid evicting offloaded blocks.
+    llm = _make_llm(model, True, 80 << 30)  # 80GB
+    try:
+        _latency_test(llm, lazy=True)
+    finally:
+        del llm

vllm/config/vllm.py

@@ -660,7 +660,11 @@ class VllmConfig:
         )
 
         if kv_offloading_backend == "native":
-            self.kv_transfer_config.kv_connector = "OffloadingConnector"
+            if envs.VLLM_USE_SIMPLE_KV_OFFLOAD:
+                config_connector = "SimpleCPUOffloadConnector"
+            else:
+                config_connector = "OffloadingConnector"
+            self.kv_transfer_config.kv_connector = config_connector
             self.kv_transfer_config.kv_connector_extra_config.update(
                 {"cpu_bytes_to_use": kv_offloading_size * (1 << 30)}
             )

vllm/distributed/kv_transfer/kv_connector/factory.py

@@ -202,6 +202,7 @@ KVConnectorFactory.register_connector(
     "vllm.distributed.kv_transfer.kv_connector.v1.decode_bench_connector",
     "DecodeBenchConnector",
 )
+
 KVConnectorFactory.register_connector(
     "MooncakeConnector",
     "vllm.distributed.kv_transfer.kv_connector.v1.mooncake.mooncake_connector",
@@ -213,3 +214,9 @@ KVConnectorFactory.register_connector(
     "vllm.distributed.kv_transfer.kv_connector.v1.flexkv_connector",
     "FlexKVConnectorV1",
 )
+
+KVConnectorFactory.register_connector(
+    "SimpleCPUOffloadConnector",
+    "vllm.distributed.kv_transfer.kv_connector.v1.simple_cpu_offload_connector",
+    "SimpleCPUOffloadConnector",
+)

vllm/distributed/kv_transfer/kv_connector/v1/simple_cpu_offload_connector.py

+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""SimpleCPUOffloadConnector: minimal CPU KV cache offloading."""
+
+from collections.abc import Iterable
+from typing import TYPE_CHECKING, Any
+
+import torch
+
+from vllm.config import VllmConfig
+from vllm.distributed.kv_events import KVCacheEvent
+from vllm.distributed.kv_transfer.kv_connector.v1.base import (
+    KVConnectorBase_V1,
+    KVConnectorMetadata,
+    KVConnectorRole,
+    SupportsHMA,
+)
+from vllm.logger import init_logger
+from vllm.v1.core.sched.output import SchedulerOutput
+from vllm.v1.outputs import KVConnectorOutput
+from vllm.v1.simple_kv_offload.manager import (
+    SimpleCPUOffloadScheduler,
+)
+from vllm.v1.simple_kv_offload.metadata import (
+    SimpleCPUOffloadMetadata,
+)
+from vllm.v1.simple_kv_offload.worker import (
+    SimpleCPUOffloadWorker,
+)
+
+if TYPE_CHECKING:
+    from vllm.forward_context import ForwardContext
+    from vllm.v1.attention.backend import AttentionMetadata
+    from vllm.v1.core.block_pool import BlockPool
+    from vllm.v1.core.kv_cache_manager import KVCacheBlocks
+    from vllm.v1.kv_cache_interface import KVCacheConfig
+    from vllm.v1.request import Request
+
+logger = init_logger(__name__)
+
+# Default CPU capacity: 8 GB
+DEFAULT_CPU_CAPACITY_BYTES = 8 * (1024**3)
+
+
+class SimpleCPUOffloadConnector(KVConnectorBase_V1, SupportsHMA):
+    """CPU KV cache offloading with custom kernel transfers and BlockPool LRU."""
+
+    def __init__(
+        self,
+        vllm_config: VllmConfig,
+        role: KVConnectorRole,
+        kv_cache_config: "KVCacheConfig | None" = None,
+    ):
+        super().__init__(vllm_config, role, kv_cache_config)
+
+        enable_prefix_caching = vllm_config.cache_config.enable_prefix_caching
+        extra_config = self._kv_transfer_config.kv_connector_extra_config or {}
+
+        cpu_capacity_bytes = int(
+            extra_config.get("cpu_bytes_to_use", DEFAULT_CPU_CAPACITY_BYTES)
+        )
+        # cpu_bytes_to_use is server-wide for compatibility;
+        # cpu_bytes_to_use_per_rank overrides for per-rank capacity.
+        world_size = vllm_config.parallel_config.world_size
+        cpu_capacity_per_rank = cpu_capacity_bytes // world_size
+        if "cpu_bytes_to_use_per_rank" in extra_config:
+            explicit = int(extra_config["cpu_bytes_to_use_per_rank"])
+            if explicit != cpu_capacity_per_rank:
+                logger.warning(
+                    "cpu_bytes_to_use_per_rank (%.2f GB) != "
+                    "cpu_bytes_to_use/world_size (%.2f GB). Using per-rank value.",
+                    explicit / (1024**3),
+                    cpu_capacity_per_rank / (1024**3),
+                )
+            cpu_capacity_per_rank = explicit
+
+        lazy_offload = bool(extra_config.get("lazy_offload", False))
+
+        self.scheduler_manager: SimpleCPUOffloadScheduler | None = None
+        self.worker_handler: SimpleCPUOffloadWorker | None = None
+
+        if not enable_prefix_caching:
+            logger.warning(
+                "Detected prefix caching disabled, disabling CPU offload "
+                "since it requires prefix caching."
+            )
+            return
+
+        logger.info(
+            "SimpleCPUOffloadConnector: role=%s, "
+            "per_rank=%.2f GB, world_size=%d, mode=%s",
+            role.name,
+            cpu_capacity_per_rank / (1024**3),
+            world_size,
+            "lazy" if lazy_offload else "eager",
+        )
+
+        if role == KVConnectorRole.SCHEDULER:
+            self.scheduler_manager = SimpleCPUOffloadScheduler(
+                vllm_config,
+                kv_cache_config,
+                cpu_capacity_per_rank,
+                lazy_offload=lazy_offload,
+            )
+        elif role == KVConnectorRole.WORKER:
+            self.worker_handler = SimpleCPUOffloadWorker(
+                vllm_config, kv_cache_config, cpu_capacity_per_rank
+            )
+
+    # --- Worker-side methods ---
+
+    def register_kv_caches(self, kv_caches: dict[str, torch.Tensor]) -> None:
+        if self.worker_handler is not None:
+            self.worker_handler.register_kv_caches(kv_caches)
+
+    def bind_connector_metadata(
+        self,
+        connector_metadata: KVConnectorMetadata,
+    ) -> None:
+        super().bind_connector_metadata(connector_metadata)
+        if self.worker_handler is not None:
+            assert isinstance(connector_metadata, SimpleCPUOffloadMetadata)
+            self.worker_handler.bind_connector_metadata(connector_metadata)
+
+    def clear_connector_metadata(self) -> None:
+        super().clear_connector_metadata()
+        if self.worker_handler is not None:
+            self.worker_handler.clear_connector_metadata()
+
+    def handle_preemptions(self, kv_connector_metadata: KVConnectorMetadata) -> None:
+        if self.worker_handler is not None:
+            assert isinstance(kv_connector_metadata, SimpleCPUOffloadMetadata)
+            self.worker_handler.handle_preemptions(kv_connector_metadata)
+
+    def start_load_kv(self, forward_context: "ForwardContext", **kwargs: Any) -> None:
+        pass  # Launch loads ops in get_finished() after launching model execution
+
+    def wait_for_layer_load(self, layer_name: str) -> None:
+        pass  # Always load asynchronously and deferred to get_finished()
+
+    def save_kv_layer(
+        self,
+        layer_name: str,
+        kv_layer: torch.Tensor,
+        attn_metadata: "AttentionMetadata",
+        **kwargs: Any,
+    ) -> None:
+        pass  # Always save asynchronously and deferred to get_finished()
+
+    def wait_for_save(self) -> None:
+        pass  # All stores are driven by get_finished() and no wait needed
+
+    def get_finished(
+        self,
+        finished_req_ids: set[str],
+    ) -> tuple[set[str] | None, set[str] | None]:
+        if self.worker_handler is not None:
+            return self.worker_handler.get_finished(finished_req_ids)
+        return None, None
+
+    def build_connector_worker_meta(self):
+        if self.worker_handler is not None:
+            return self.worker_handler.build_connector_worker_meta()
+        return None
+
+    # --- Scheduler-side methods ---
+
+    # NOTE: New API only for SimpleCPUOffloadConnector.
+    def bind_gpu_block_pool(self, gpu_block_pool: "BlockPool") -> None:
+        if self.scheduler_manager is not None:
+            self.scheduler_manager.bind_gpu_block_pool(gpu_block_pool)
+
+    def get_num_new_matched_tokens(
+        self,
+        request: "Request",
+        num_computed_tokens: int,
+    ) -> tuple[int | None, bool]:
+        if self.scheduler_manager is not None:
+            return self.scheduler_manager.get_num_new_matched_tokens(
+                request, num_computed_tokens
+            )
+        return 0, False
+
+    def update_state_after_alloc(
+        self,
+        request: "Request",
+        blocks: "KVCacheBlocks",
+        num_external_tokens: int,
+    ) -> None:
+        if self.scheduler_manager is not None:
+            self.scheduler_manager.update_state_after_alloc(
+                request, blocks, num_external_tokens
+            )
+
+    def build_connector_meta(
+        self,
+        scheduler_output: SchedulerOutput,
+    ) -> KVConnectorMetadata:
+        if self.scheduler_manager is not None:
+            return self.scheduler_manager.build_connector_meta(scheduler_output)
+        return SimpleCPUOffloadMetadata()
+
+    def update_connector_output(
+        self,
+        connector_output: KVConnectorOutput,
+    ) -> None:
+        if self.scheduler_manager is not None:
+            self.scheduler_manager.update_connector_output(connector_output)
+
+    def request_finished(
+        self,
+        request: "Request",
+        block_ids: list[int],
+    ) -> tuple[bool, dict[str, Any] | None]:
+        if self.scheduler_manager is not None:
+            return self.scheduler_manager.request_finished(request, block_ids)
+        return False, None
+
+    def request_finished_all_groups(
+        self,
+        request: "Request",
+        block_ids: tuple[list[int], ...],
+    ) -> tuple[bool, dict[str, Any] | None]:
+        if self.scheduler_manager is not None:
+            return self.scheduler_manager.request_finished_all_groups(
+                request, block_ids
+            )
+        return False, None
+
+    # NOTE: New API only for SimpleCPUOffloadConnector.
+    def has_pending_transfers(self) -> bool:
+        if self.scheduler_manager is not None:
+            return self.scheduler_manager.has_pending_stores()
+        return False
+
+    def take_events(self) -> Iterable[KVCacheEvent]:
+        if self.scheduler_manager is not None:
+            return self.scheduler_manager.take_events()
+        return []
+
+    def reset_cache(self) -> bool | None:
+        raise NotImplementedError(
+            "SimpleCPUOffloadConnector does not support reset_cache(). "
+            "reset_prefix_cache() requires synchronizing all pending "
+            "CPU offload transfers before clearing GPU prefix cache blocks, "
+            "which is not yet implemented."
+        )

vllm/envs.py

@@ -1674,6 +1674,10 @@ environment_variables: dict[str, Callable[[], Any]] = {
     "VLLM_XPU_ENABLE_XPU_GRAPH": lambda: bool(
         int(os.getenv("VLLM_XPU_ENABLE_XPU_GRAPH", "0"))
     ),
+    # Enable simple KV offload.
+    "VLLM_USE_SIMPLE_KV_OFFLOAD": lambda: bool(
+        int(os.getenv("VLLM_USE_SIMPLE_KV_OFFLOAD", "0"))
+    ),
 }
 
 

vllm/v1/core/sched/scheduler.py

@@ -234,6 +234,13 @@ class Scheduler(SchedulerInterface):
             hash_block_size=self.block_size,
             metrics_collector=self.kv_metrics_collector,
         )
+        # Bind GPU block pool to the KV connector. This must happen after
+        # kv_cache_manager is constructed so block_pool is available.
+        if self.connector is not None and hasattr(
+            self.connector, "bind_gpu_block_pool"
+        ):
+            self.connector.bind_gpu_block_pool(self.kv_cache_manager.block_pool)
+
         self.use_pp = self.parallel_config.pipeline_parallel_size > 1
         self.use_v2_model_runner = envs.VLLM_USE_V2_MODEL_RUNNER
         self.scheduler_reserve_full_isl = (

vllm/v1/metrics/stats.py

@@ -281,8 +281,16 @@ class PromptTokenStats:
 
         self.computed += prompt_len - num_cached_tokens
         self.external_kv_transfer += num_external_computed_tokens
-        self.local_cache_hit += (
-            num_cached_tokens + recomputed - num_external_computed_tokens
+        # FIXME(yifan): local_cache_hit can go negative after preemption.
+        # num_cached_tokens is a one-time snapshot from first scheduling and
+        # is never reset on preemption, while num_external_computed_tokens is
+        # overwritten on re-scheduling. If CPU offload finds more tokens on
+        # the second pass than the original total, the subtraction underflows.
+        # A fundamental fix is to track the first-time num_external_computed_tokens
+        # as a separate metric rather than reusing num_external_computed_tokens
+        # for metric directly.
+        self.local_cache_hit += max(
+            0, (num_cached_tokens + recomputed - num_external_computed_tokens)
         )
         self.cached_tokens += num_cached_tokens
         self.recomputed_tokens += recomputed

vllm/v1/simple_kv_offload/copy_backend.py

+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""DMA copy backend for GPU<->CPU block transfers."""
+
+from __future__ import annotations
+
+import queue
+import threading
+
+import torch
+
+from vllm.logger import init_logger
+from vllm.platforms import current_platform
+from vllm.v1.simple_kv_offload.cuda_mem_ops import (
+    BatchMemcpyParams,
+    build_params,
+    copy_blocks,
+)
+
+logger = init_logger(__name__)
+
+
+class DmaCopyBackend:
+    """cuMemcpyBatchAsync copy backend (background thread)."""
+
+    def __init__(self) -> None:
+        self._store_params: BatchMemcpyParams | None = None
+        self._load_params: BatchMemcpyParams | None = None
+        self._load_stream: torch.cuda.Stream | None = None
+        self._store_stream: torch.cuda.Stream | None = None
+        self._queue: queue.SimpleQueue | None = None
+        self._thread: threading.Thread | None = None
+        self._shutdown: bool = False
+
+    def init(
+        self,
+        gpu_caches: dict[str, torch.Tensor],
+        cpu_caches: dict[str, torch.Tensor],
+        device: torch.device,
+        load_stream: torch.cuda.Stream,
+        store_stream: torch.cuda.Stream,
+    ) -> None:
+        self._load_stream = load_stream
+        self._store_stream = store_stream
+
+        self._store_params = build_params(gpu_caches, cpu_caches, store_stream)
+        self._load_params = build_params(cpu_caches, gpu_caches, load_stream)
+
+        self._queue = queue.SimpleQueue()
+        self._thread = threading.Thread(
+            target=self._copy_loop,
+            args=(self._queue, device, load_stream, store_stream),
+            daemon=True,
+        )
+        self._thread.start()
+
+    def launch_copy(
+        self,
+        src_blocks: list[int],
+        dst_blocks: list[int],
+        is_store: bool,
+        event_idx: int,
+        events_list: list[tuple[int, torch.Event]],
+    ) -> None:
+        params = self._store_params if is_store else self._load_params
+        assert params is not None and self._queue is not None
+        self._queue.put(
+            (src_blocks, dst_blocks, params, is_store, event_idx, events_list)
+        )
+
+    def shutdown(self) -> None:
+        if self._shutdown:
+            return
+        self._shutdown = True
+        if self._queue is not None:
+            self._queue.put(None)
+        if self._thread is not None:
+            self._thread.join(timeout=5.0)
+
+    @staticmethod
+    def _copy_loop(
+        q: queue.SimpleQueue,
+        device: torch.device,
+        load_stream: torch.cuda.Stream,
+        store_stream: torch.cuda.Stream,
+    ) -> None:
+        current_platform.set_device(device)
+        while True:
+            item = q.get()
+            if item is None:
+                return
+            src_blocks, dst_blocks, params, is_store, event_idx, events_list = item
+            copy_blocks(src_blocks, dst_blocks, params)
+            stream = store_stream if is_store else load_stream
+            event = torch.Event()
+            event.record(stream)
+            events_list.append((event_idx, event))

vllm/v1/simple_kv_offload/cuda_mem_ops.py

+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Low-level CUDA memory helpers: pinning and batch DMA transfers."""
+
+import ctypes
+from typing import Any, NamedTuple
+
+import numpy as np
+import torch
+
+from vllm.logger import init_logger
+
+logger = init_logger(__name__)
+
+
+def pin_tensor(tensor: torch.Tensor) -> None:
+    """Pin a CPU tensor via cudaHostRegister.
+
+    This bypasses PyTorch's CUDACachingHostAllocator which rounds
+    every ``pin_memory=True`` allocation up to the next power of 2
+    (e.g. 100 GB becomes 128 GB).
+    """
+    err = torch.cuda.cudart().cudaHostRegister(tensor.data_ptr(), tensor.nbytes, 0)
+    if err.value != 0:
+        raise RuntimeError(f"cudaHostRegister failed: {err}")
+
+
+class _CUmemLocation(ctypes.Structure):
+    _fields_ = [("type", ctypes.c_uint), ("id", ctypes.c_int)]
+
+
+class _CUmemcpyAttributes(ctypes.Structure):
+    _fields_ = [
+        ("srcAccessOrder", ctypes.c_uint),
+        ("srcLocHint", _CUmemLocation),
+        ("dstLocHint", _CUmemLocation),
+        ("flags", ctypes.c_uint),
+    ]
+
+
+_BATCH_MEMCPY_FUNC_TYPE = ctypes.CFUNCTYPE(
+    ctypes.c_uint,  # CUresult
+    ctypes.c_void_p,
+    ctypes.c_void_p,
+    ctypes.c_void_p,
+    ctypes.c_size_t,
+    ctypes.c_void_p,
+    ctypes.c_void_p,
+    ctypes.c_size_t,
+    ctypes.c_void_p,
+    ctypes.c_void_p,
+)
+
+# Resolved lazily on first use.
+_batch_memcpy_fn: Any = None
+
+
+def _resolve_batch_memcpy():
+    """Resolve cuMemcpyBatchAsync via cuGetProcAddress (one-time)."""
+    from cuda.bindings import driver as drv
+
+    err, ptr, _ = drv.cuGetProcAddress(b"cuMemcpyBatchAsync", 12080, 0)
+    if err != drv.CUresult.CUDA_SUCCESS:
+        raise RuntimeError(f"cuGetProcAddress(cuMemcpyBatchAsync) failed: {err}")
+    return _BATCH_MEMCPY_FUNC_TYPE(ptr)
+
+
+class BatchMemcpyParams(NamedTuple):
+    src_bases: np.ndarray  # [num_layers] uint64 — data_ptr per layer
+    dst_bases: np.ndarray  # [num_layers] uint64
+    bpb: np.ndarray  # [num_layers] uint64 — bytes per block
+    num_layers: int
+    attrs: _CUmemcpyAttributes
+    attrs_idx: ctypes.c_size_t
+    # NOTE: cuMemcpyBatchAsync_v2() removed fail_idx field, but we use
+    # cuMemcpyBatchAsync() with fail_idx for backward compatibility
+    fail_idx: ctypes.c_size_t
+    stream_handle: int  # raw cudaStream_t / CUstream
+
+
+def build_params(
+    src_caches: dict[str, torch.Tensor],
+    dst_caches: dict[str, torch.Tensor],
+    stream: torch.cuda.Stream,
+) -> BatchMemcpyParams:
+    global _batch_memcpy_fn
+    if _batch_memcpy_fn is None:
+        _batch_memcpy_fn = _resolve_batch_memcpy()
+
+    assert list(src_caches.keys()) == list(dst_caches.keys())
+    src_tensors = list(src_caches.values())
+    dst_tensors = list(dst_caches.values())
+
+    src_bases, dst_bases, bpb = [], [], []
+    for s, d in zip(src_tensors, dst_tensors):
+        s_bpb = s.stride(0) * s.element_size()
+        assert s_bpb == d.stride(0) * d.element_size()
+        src_bases.append(s.data_ptr())
+        dst_bases.append(d.data_ptr())
+        bpb.append(s_bpb)
+
+    # Refer to https://docs.nvidia.com/cuda/cuda-driver-api/group__CUDA__MEM.html#group__CUDA__MEM_1g6f1ff58e3065df3eb4b573dba77ad31f for details.  # noqa: E501
+    attrs = _CUmemcpyAttributes(srcAccessOrder=3)  # ANY
+
+    return BatchMemcpyParams(
+        src_bases=np.array(src_bases, dtype=np.uint64),
+        dst_bases=np.array(dst_bases, dtype=np.uint64),
+        bpb=np.array(bpb, dtype=np.uint64),
+        num_layers=len(src_tensors),
+        attrs=attrs,
+        attrs_idx=ctypes.c_size_t(0),
+        fail_idx=ctypes.c_size_t(0),
+        stream_handle=stream.cuda_stream,
+    )
+
+
+def copy_blocks(
+    src_block_ids: list[int],
+    dst_block_ids: list[int],
+    params: BatchMemcpyParams,
+) -> None:
+    """Copy blocks via cuMemcpyBatchAsync."""
+    n = len(src_block_ids)
+    if n == 0:
+        return
+
+    src_ids = np.array(src_block_ids, dtype=np.uint64)
+    dst_ids = np.array(dst_block_ids, dtype=np.uint64)
+
+    src_all = (
+        params.src_bases[:, None] + src_ids[None, :] * params.bpb[:, None]
+    ).ravel()
+    dst_all = (
+        params.dst_bases[:, None] + dst_ids[None, :] * params.bpb[:, None]
+    ).ravel()
+    sz_all = np.repeat(params.bpb, n)
+
+    total = n * params.num_layers
+    err = _batch_memcpy_fn(
+        dst_all.ctypes.data,
+        src_all.ctypes.data,
+        sz_all.ctypes.data,
+        total,
+        ctypes.addressof(params.attrs),
+        ctypes.byref(params.attrs_idx),
+        1,
+        ctypes.byref(params.fail_idx),
+        params.stream_handle,
+    )
+    if err != 0:
+        raise RuntimeError(
+            f"cuMemcpyBatchAsync failed: err={err} failIdx={params.fail_idx.value}"
+        )

vllm/v1/simple_kv_offload/metadata.py

+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Metadata for SimpleCPUOffloadConnector."""
+
+from dataclasses import dataclass, field
+
+from vllm.distributed.kv_transfer.kv_connector.v1.base import (
+    KVConnectorMetadata,
+    KVConnectorWorkerMetadata,
+)
+
+INVALID_JOB_ID = -1
+
+
+@dataclass
+class SimpleCPUOffloadMetadata(KVConnectorMetadata):
+    """
+    Metadata passed from scheduler to worker for CPU offload operations.
+
+    The worker receives flat block lists keyed by a monotonic event_idx.
+    Job->req_id translation is handled by the scheduler-side manager
+    (via inverse maps), so the worker never knows about request identities.
+    """
+
+    # Load event per step. INVALID_JOB_ID means no blocks to load this step.
+    load_event: int = INVALID_JOB_ID
+    load_gpu_blocks: list[int] = field(default_factory=list)
+    load_cpu_blocks: list[int] = field(default_factory=list)
+    # Reverse map: load_event->req_ids, for tracking requests with finished load events
+    load_event_to_reqs: dict[int, list[str]] = field(default_factory=dict)
+
+    # Store event per step. INVALID_JOB_ID means no blocks to store this step.
+    store_event: int = INVALID_JOB_ID
+    store_gpu_blocks: list[int] = field(default_factory=list)
+    store_cpu_blocks: list[int] = field(default_factory=list)
+
+    # Whether any requests were preempted this step and need flush pending transfers.
+    need_flush: bool = False
+
+
+@dataclass
+class SimpleCPUOffloadWorkerMetadata(KVConnectorWorkerMetadata):
+    """Worker -> Scheduler metadata for completed store events.
+
+    Each worker reports {event_idx: 1} for newly completed stores.
+    ``aggregate()`` sums counts across workers within a step.
+    The scheduler-side manager accumulates across steps and processes
+    a store completion only when count reaches ``world_size``.
+    """
+
+    completed_store_events: dict[int, int]
+
+    def aggregate(
+        self, other: "KVConnectorWorkerMetadata"
+    ) -> "KVConnectorWorkerMetadata":
+        assert isinstance(other, SimpleCPUOffloadWorkerMetadata)
+        merged = dict(self.completed_store_events)
+        for k, v in other.completed_store_events.items():
+            merged[k] = merged.get(k, 0) + v
+        return SimpleCPUOffloadWorkerMetadata(completed_store_events=merged)

vllm/v1/simple_kv_offload/worker.py

+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Worker-side handler for SimpleCPUOffloadConnector."""
+
+from typing import TYPE_CHECKING
+
+import torch
+
+from vllm.config import VllmConfig
+from vllm.logger import init_logger
+from vllm.utils.platform_utils import is_pin_memory_available
+from vllm.v1.simple_kv_offload.copy_backend import DmaCopyBackend
+from vllm.v1.simple_kv_offload.cuda_mem_ops import pin_tensor
+from vllm.v1.simple_kv_offload.metadata import (
+    SimpleCPUOffloadMetadata,
+    SimpleCPUOffloadWorkerMetadata,
+)
+
+if TYPE_CHECKING:
+    from vllm.v1.kv_cache_interface import KVCacheConfig
+
+logger = init_logger(__name__)
+
+
+class SimpleCPUOffloadWorker:
+    """Worker-side handler for CPU offloading transfers."""
+
+    def __init__(
+        self,
+        vllm_config: VllmConfig,
+        kv_cache_config: "KVCacheConfig | None",
+        cpu_capacity_bytes: int,
+    ):
+        self.vllm_config = vllm_config
+        self.kv_cache_config = kv_cache_config
+        self.cpu_capacity_bytes = cpu_capacity_bytes
+
+        self.gpu_kv_caches: dict[str, torch.Tensor] | None = None
+        self.cpu_kv_caches: dict[str, torch.Tensor] | None = None
+        self.device: torch.device | None = None
+        self.num_cpu_blocks: int = 0
+
+        # CUDA streams for the async transfers
+        self.load_stream: torch.cuda.Stream | None = None
+        self.store_stream: torch.cuda.Stream | None = None
+
+        self._backend = DmaCopyBackend()
+
+        # Ordered (event_idx, Event). Events pre-allocated on main thread.
+        self._load_events: list[tuple[int, torch.Event]] = []
+        self._store_events: list[tuple[int, torch.Event]] = []
+        # High-water marks: highest event_idx completed per stream.
+        # When the event list is empty, the hwm covers all prior events.
+        self._load_hwm: int = -1
+        self._store_hwm: int = -1
+
+        # Metadata for the current step
+        self._connector_metadata: SimpleCPUOffloadMetadata | None = None
+
+        # Pending event index sets, populated in bind_connector_metadata
+        self._pending_load_event_indices: set[int] = set()
+        self._pending_store_event_indices: set[int] = set()
+        # Completed store events to report via build_connector_worker_meta
+        self._completed_store_events: dict[int, int] = {}
+
+    def register_kv_caches(
+        self,
+        kv_caches: dict[str, torch.Tensor],
+    ) -> None:
+        """Register GPU KV caches and allocate pinned CPU tensors.
+        The worker will infer the underlying raw storage from the kv_caches.
+
+        Args:
+            kv_caches: Per-layer GPU KV caches. Values are either a single
+                tensor (attention layers) or a list of tensors (Mamba layers
+                in hybrid models). All values are included for offloading
+                by resolving to their underlying raw storage.
+        """
+        if not kv_caches:
+            logger.warning("No KV caches to offload.")
+            return
+
+        # Resolve each entry to a representative tensor for storage
+        # deduplication. For attention layers the value is already a tensor;
+        # for Mamba layers it is a list of tensors that all share the same
+        # underlying raw storage, so we take the first one.
+        def _repr_tensor(v: torch.Tensor | list[torch.Tensor]) -> torch.Tensor:
+            assert isinstance(v, torch.Tensor | list)
+            return v if isinstance(v, torch.Tensor) else v[0]
+
+        any_tensor = _repr_tensor(next(iter(kv_caches.values())))
+        self.device = any_tensor.device
+
+        assert self.kv_cache_config is not None
+        num_blocks = self.kv_cache_config.num_blocks
+
+        # Deduplicate: multiple layers may share the same backing storage.
+        seen_ptrs: dict[int, tuple[str, torch.Tensor]] = {}
+        for name, value in kv_caches.items():
+            tensor = _repr_tensor(value)
+            ptr = tensor.untyped_storage().data_ptr()
+            if ptr not in seen_ptrs:
+                seen_ptrs[ptr] = (name, tensor)
+
+        # Build [num_blocks, block_bytes] int8 views from each unique
+        # storage so that stride(0) gives block_bytes for the copy op.
+        #
+        # The physical layout varies across attention backends:
+        #   FlashAttn/ROCm:  (2, num_blocks, ...) -> K/V outermost, 2 segments
+        #   FlashInfer/MLA:  (num_blocks, ...)    -> blocks outermost, 1 segment
+        # We derive page_size_bytes = storage.nbytes() // num_blocks, then
+        # classify dims: any dim whose byte-stride exceeds page_size_bytes
+        # must be an outer segment dim (e.g. the K/V dim of size 2). A less
+        # hacky way is to update the interface with the layout.
+        unique_gpu_caches: dict[str, torch.Tensor] = {}
+        for name, tensor in seen_ptrs.values():
+            storage = tensor.untyped_storage()
+            raw = torch.empty(0, dtype=torch.int8, device=self.device).set_(
+                storage, 0, (storage.nbytes(),)
+            )
+            el = tensor.element_size()
+            page_size_bytes = storage.nbytes() // num_blocks
+            outer_dims = [
+                d for d in range(tensor.ndim) if tensor.stride(d) * el > page_size_bytes
+            ]
+            if not outer_dims:
+                unique_gpu_caches[name] = raw.view(num_blocks, -1)
+            else:
+                seg_stride = tensor.stride(outer_dims[0]) * el
+                for idx in range(tensor.shape[outer_dims[0]]):
+                    offset = idx * seg_stride
+                    chunk = raw[offset : offset + seg_stride]
+                    unique_gpu_caches[f"{name}.{idx}"] = chunk.view(num_blocks, -1)
+
+        # Compute per-tensor bytes_per_block. Tensors may have different
+        # page_size_bytes (e.g., UniformTypeKVCacheSpecs with varying head_size).
+        per_tensor_bpb = [
+            t.stride(0) * t.element_size() for t in unique_gpu_caches.values()
+        ]
+        total_bytes_per_block = sum(per_tensor_bpb)
+
+        self.num_cpu_blocks = max(1, self.cpu_capacity_bytes // total_bytes_per_block)
+
+        logger.info(
+            "SimpleCPUOffloadWorker: %d unique GPU KV tensors, "
+            "allocating %d CPU blocks (%.2f GB)",
+            len(unique_gpu_caches),
+            self.num_cpu_blocks,
+            (self.num_cpu_blocks * total_bytes_per_block) / (1024**3),
+        )
+
+        pin_memory = is_pin_memory_available()
+        if not pin_memory:
+            logger.warning(
+                "Pinned memory not available. CPU offload performance may be degraded."
+            )
+
+        self.gpu_kv_caches = unique_gpu_caches
+        self.cpu_kv_caches = {}
+        for name, gpu_tensor in unique_gpu_caches.items():
+            cpu_shape = (self.num_cpu_blocks,) + gpu_tensor.shape[1:]
+            # Allocate non-pinned first, then pin via cudaHostRegister to
+            # bypass PyTorch's CUDACachingHostAllocator which rounds up to
+            # the next power of 2 (e.g. 100 GB -> 128 GB).
+            tensor = torch.zeros(cpu_shape, dtype=gpu_tensor.dtype, device="cpu")
+            if pin_memory:
+                pin_tensor(tensor)
+            self.cpu_kv_caches[name] = tensor
+
+        # Use lowest priority so KV cache I/O yields to compute streams.
+        low_pri, _ = torch.cuda.Stream.priority_range()
+        self.load_stream = torch.cuda.Stream(priority=low_pri)
+        self.store_stream = torch.cuda.Stream(priority=low_pri)
+
+        # Initialize copy backend with caches and streams.
+        self._backend.init(
+            self.gpu_kv_caches,
+            self.cpu_kv_caches,
+            self.device,
+            self.load_stream,
+            self.store_stream,
+        )
+
+    def bind_connector_metadata(self, metadata: SimpleCPUOffloadMetadata) -> None:
+        self._connector_metadata = metadata
+        if metadata.load_event >= 0:
+            self._pending_load_event_indices.add(metadata.load_event)
+        if metadata.store_event >= 0:
+            self._pending_store_event_indices.add(metadata.store_event)
+
+    def clear_connector_metadata(self) -> None:
+        self._connector_metadata = None
+
+    def start_load_kv(self) -> None:
+        # NOTE: we defer launching both load and store to get_finished(),
+        # which runs after model execution. This hides the CPU-side
+        # block copy op overhead (~5ms) behind GPU compute.
+        pass
+
+    def wait_for_save(self) -> None:
+        pass
+
+    def get_finished(
+        self,
+        finished_req_ids: set[str],
+    ) -> tuple[set[str] | None, set[str] | None]:
+        """Submit transfers and report completed events to the scheduler.
+
+        Called after model execution. The manager only schedules stores for
+        blocks whose KV data is confirmed computed, so we launch both loads
+        and stores immediately — no deferral or cross-stream sync needed.
+
+        Returns:
+            tuple of (finished_sending, finished_recving).
+            - finished_sending: always None (stores use worker metadata).
+            - finished_recving: req_ids whose loads have completed.
+        """
+        # (1) Submit transfers
+        metadata = self._connector_metadata
+        if metadata is not None:
+            # Launch loads (CPU->GPU).
+            if metadata.load_cpu_blocks:
+                self._backend.launch_copy(
+                    metadata.load_cpu_blocks,
+                    metadata.load_gpu_blocks,
+                    is_store=False,
+                    event_idx=metadata.load_event,
+                    events_list=self._load_events,
+                )
+            # Launch stores (GPU->CPU).
+            if metadata.store_gpu_blocks:
+                self._backend.launch_copy(
+                    metadata.store_gpu_blocks,
+                    metadata.store_cpu_blocks,
+                    is_store=True,
+                    event_idx=metadata.store_event,
+                    events_list=self._store_events,
+                )
+
+        # (2) Track completed transfer events
+        finished_recving: set[str] = set()
+
+        if self._pending_load_event_indices:
+            load_wm = self._poll_stream_events(is_store=False)
+            for j in [j for j in self._pending_load_event_indices if j <= load_wm]:
+                self._pending_load_event_indices.discard(j)
+                req_ids = (
+                    metadata.load_event_to_reqs.get(j) if metadata is not None else None
+                )
+                if req_ids:
+                    finished_recving.update(req_ids)
+
+        if self._pending_store_event_indices:
+            store_wm = self._poll_stream_events(is_store=True)
+            for j in [j for j in self._pending_store_event_indices if j <= store_wm]:
+                self._pending_store_event_indices.discard(j)
+                self._completed_store_events[j] = 1
+
+        return None, finished_recving or None
+
+    def build_connector_worker_meta(self) -> SimpleCPUOffloadWorkerMetadata | None:
+        """Return completed store events since the last call."""
+        if not self._completed_store_events:
+            return None
+        meta = SimpleCPUOffloadWorkerMetadata(
+            completed_store_events=self._completed_store_events,
+        )
+        self._completed_store_events = {}
+        return meta
+
+    def handle_preemptions(
+        self, kv_connector_metadata: SimpleCPUOffloadMetadata
+    ) -> None:
+        """Sync all in-flight transfers before preempted blocks are reused."""
+        if not kv_connector_metadata.need_flush:
+            return
+        self._flush_and_sync_all()
+
+    def _flush_and_sync_all(self) -> None:
+        """Synchronize all in-flight transfer events."""
+        for event_idx, event in self._load_events:
+            event.synchronize()
+            self._load_hwm = event_idx
+        self._load_events.clear()
+
+        for event_idx, event in self._store_events:
+            event.synchronize()
+            self._store_hwm = event_idx
+        self._store_events.clear()
+
+    def _poll_stream_events(self, is_store: bool) -> int:
+        """Non-blocking poll for completed events and return the high-water mark."""
+        events = self._store_events if is_store else self._load_events
+        hwm = self._store_hwm if is_store else self._load_hwm
+        while events:
+            event_idx, event = events[0]
+            if not event.query():
+                break
+            hwm = event_idx
+            events.pop(0)
+        if is_store:
+            self._store_hwm = hwm
+        else:
+            self._load_hwm = hwm
+        return hwm