[KV offload][5/N] Add CPUOffloadingSpec

docs/features/disagg_prefill.md

@@ -31,6 +31,12 @@ Now supports 5 types of connectors:
   --kv-transfer-config '{"kv_connector":"MultiConnector","kv_role":"kv_both","kv_connector_extra_config":{"connectors":[{"kv_connector":"NixlConnector","kv_role":"kv_both"},{"kv_connector":"SharedStorageConnector","kv_role":"kv_both","kv_connector_extra_config":{"shared_storage_path":"local_storage"}}]}}'
   ```
 
+- **OffloadingConnector**: enable offloading of KV data to CPU memory, customizing the CPU block size (in tokens) and number of blocks to allocate (per worker):
+
+  ```bash
+  --kv-transfer-config '{"kv_connector":"OffloadingConnector","kv_role":"kv_both","kv_connector_extra_config":{"block_size": 64, "num_cpu_blocks": 1000}}'
+  ```
+
 ## Benchmarks
 
 Please refer to <gh-file:benchmarks/disagg_benchmarks> for disaggregated prefilling benchmarks.

tests/v1/kv_offload/test_cpu_offloading.py

@@ -0,0 +1,62 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import time
+
+import pytest
+
+from vllm import LLM, SamplingParams
+from vllm.config import KVTransferConfig
+
+CPU_BLOCK_SIZES = [16, 48]
+
+
+@pytest.mark.parametrize("cpu_block_size", CPU_BLOCK_SIZES)
+def test_cpu_offloading(cpu_block_size: int) -> None:
+    """
+    Tests OffloadingConnector with CPUOffloadingSpec.
+    """
+
+    # configure OffloadingConnector (spec_name=CPUOffloadingSpec by default)
+    kv_transfer_config = KVTransferConfig(
+        kv_connector="OffloadingConnector",
+        kv_role="kv_both",
+        kv_connector_extra_config={
+            "num_cpu_blocks": 100,
+            "block_size": cpu_block_size
+        },
+    )
+
+    llm = LLM(
+        model="meta-llama/Llama-3.2-1B-Instruct",
+        gpu_memory_utilization=0.5,
+        kv_transfer_config=kv_transfer_config,
+    )
+
+    prompts = ["Hi " * 100]
+    sampling_params = SamplingParams(temperature=0, max_tokens=20)
+
+    # run generation - this should trigger saving KV cache
+    start_time = time.time()
+    llm.generate(prompts, sampling_params, use_tqdm=False)
+    cold_time = time.time() - start_time
+
+    # run generation again - should hit the GPU prefix cache
+    start_time = time.time()
+    llm.generate(prompts, sampling_params, use_tqdm=False)
+    gpu_hit_time = time.time() - start_time
+
+    # reset prefix cache to avoid GPU hit.
+    llm.reset_prefix_cache()
+
+    # sleep for a sec to make sure CPU finished storing
+    time.sleep(1)
+
+    # run generation again - this should trigger loading from CPU
+    start_time = time.time()
+    llm.generate(prompts, sampling_params, use_tqdm=False)
+    cpu_hit_time = time.time() - start_time
+
+    print("Generation times:")
+    print(f"    Cold: {cold_time * 1000:.2f}ms")
+    print(f"    GPU hit: {gpu_hit_time * 1000:.2f}ms")
+    print(f"    CPU hit: {cpu_hit_time * 1000:.2f}ms")

vllm/v1/kv_offload/cpu.py

@@ -0,0 +1,75 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from collections.abc import Iterator
+from typing import Optional
+
+import torch
+
+from vllm.config import VllmConfig, get_layers_from_vllm_config
+from vllm.model_executor.layers.attention_layer_base import AttentionLayerBase
+from vllm.platforms import current_platform
+from vllm.v1.kv_offload.abstract import LoadStoreSpec, OffloadingManager
+from vllm.v1.kv_offload.backends.cpu import CPUBackend
+from vllm.v1.kv_offload.lru_manager import LRUOffloadingManager
+from vllm.v1.kv_offload.mediums import CPULoadStoreSpec, GPULoadStoreSpec
+from vllm.v1.kv_offload.spec import OffloadingSpec
+from vllm.v1.kv_offload.worker.cpu_gpu import CpuGpuOffloadingHandler
+from vllm.v1.kv_offload.worker.worker import OffloadingHandler
+
+
+class CPUOffloadingSpec(OffloadingSpec):
+
+    def __init__(self, vllm_config: VllmConfig):
+        super().__init__(vllm_config)
+
+        num_cpu_blocks = self.extra_config.get("num_cpu_blocks")
+        if not num_cpu_blocks:
+            raise Exception("num_cpu_blocks must be specified "
+                            "in kv_connector_extra_config")
+        self.num_cpu_blocks: int = num_cpu_blocks
+
+        # scheduler-side
+        self._manager: Optional[OffloadingManager] = None
+
+        # worker-side
+        self._handler: Optional[OffloadingHandler] = None
+
+    def get_manager(self) -> OffloadingManager:
+        if not self._manager:
+            kv_events_config = self.vllm_config.kv_events_config
+            enable_events = (kv_events_config is not None
+                             and kv_events_config.enable_kv_cache_events)
+            self._manager = LRUOffloadingManager(CPUBackend(
+                block_size=self.offloaded_block_size,
+                num_blocks=self.num_cpu_blocks),
+                                                 enable_events=enable_events)
+        return self._manager
+
+    def get_handlers(
+        self, kv_caches: dict[str, torch.Tensor]
+    ) -> Iterator[tuple[type[LoadStoreSpec], type[LoadStoreSpec],
+                        OffloadingHandler]]:
+        if not self._handler:
+            if not current_platform.is_cuda():
+                raise Exception("CPU Offloading is currently only supported"
+                                " on CUDA GPUs")
+
+            layer_names = list(kv_caches.keys())
+            layers = get_layers_from_vllm_config(self.vllm_config,
+                                                 AttentionLayerBase,
+                                                 layer_names)
+            attn_backends = {
+                layer_name: layers[layer_name].get_attn_backend()
+                for layer_name in layer_names
+            }
+
+            self._handler = CpuGpuOffloadingHandler(
+                attn_backends=attn_backends,
+                gpu_block_size=self.gpu_block_size,
+                cpu_block_size=self.offloaded_block_size,
+                num_cpu_blocks=self.num_cpu_blocks,
+                gpu_caches=kv_caches)
+
+        assert self._handler is not None
+        yield GPULoadStoreSpec, CPULoadStoreSpec, self._handler
+        yield CPULoadStoreSpec, GPULoadStoreSpec, self._handler

vllm/v1/kv_offload/factory.py

@@ -51,3 +51,6 @@ def create_spec(
 
 
 # Register various specs here.
+OffloadingSpecFactory.register_spec("CPUOffloadingSpec",
+                                    "vllm.v1.kv_offload.cpu",
+                                    "CPUOffloadingSpec")