[Misc] [Core] Implement RFC "Augment BaseExecutor interfaces to enable...

[Misc] [Core] Implement RFC "Augment BaseExecutor interfaces to enable hardware-agnostic speculative decoding" (#3837)

tests/core/block/e2e/test_correctness.py

@@ -16,7 +16,7 @@ from vllm import SamplingParams
 
         # Allow only 5 sequences of ~1024 tokens in worst case.
         "block_size": 16,
-        "forced_num_gpu_blocks": 5 * (64 + 1),
+        "num_gpu_blocks_override": 5 * (64 + 1),
     }])
 @pytest.mark.parametrize("per_test_common_llm_kwargs", [{}])
 @pytest.mark.parametrize("baseline_llm_kwargs", [{
@@ -162,14 +162,14 @@ def test_v1_v2_greedy_equality_with_cow(baseline_llm_generator,
 
             # Allow only 2 sequences of ~128 tokens in worst case.
             # Note 8 = 128/block_size
-            "forced_num_gpu_blocks": 2 * (8 + 1),
+            "num_gpu_blocks_override": 2 * (8 + 1),
         },
         {
             "block_size": 8,
 
             # Allow only 2 sequences of ~128 tokens in worst case.
             # Note 16 = 128/block_size
-            "forced_num_gpu_blocks": 2 * (16 + 1),
+            "num_gpu_blocks_override": 2 * (16 + 1),
         }
     ])
 @pytest.mark.parametrize("baseline_llm_kwargs", [{

tests/lora/test_worker.py

@@ -3,8 +3,8 @@ import random
 import tempfile
 from unittest.mock import patch
 
-from vllm.config import (DeviceConfig, LoRAConfig, ModelConfig, ParallelConfig,
-                         SchedulerConfig)
+from vllm.config import (CacheConfig, DeviceConfig, LoRAConfig, ModelConfig,
+                         ParallelConfig, SchedulerConfig)
 from vllm.lora.models import LoRAMapping
 from vllm.lora.request import LoRARequest
 from vllm.worker.worker import Worker
@@ -27,6 +27,10 @@ def test_worker_apply_lora(sql_lora_files):
         parallel_config=ParallelConfig(1, 1, False),
         scheduler_config=SchedulerConfig(32, 32, 32),
         device_config=DeviceConfig("cuda"),
+        cache_config=CacheConfig(block_size=16,
+                                 gpu_memory_utilization=1.,
+                                 swap_space=0,
+                                 cache_dtype="auto"),
         local_rank=0,
         rank=0,
         lora_config=LoRAConfig(max_lora_rank=8, max_cpu_loras=32,

tests/spec_decode/test_spec_decode_worker.py

@@ -512,8 +512,8 @@ def test_init_device():
 
 
 @torch.inference_mode()
-def test_init_cache_engine():
-    """Verify SpecDecodeWorker invokes init_cache_engine on proposer/scorer
+def test_initialize_cache():
+    """Verify SpecDecodeWorker invokes initialize_cache on proposer/scorer
     workers.
     """
     draft_worker = mock_worker(cls=MultiStepWorker)
@@ -525,12 +525,11 @@ def test_init_cache_engine():
     worker = SpecDecodeWorker(draft_worker, target_worker, rejection_sampler,
                               metrics_collector)
 
-    cache_config = MagicMock()
+    kwargs = {"num_gpu_blocks": 1024, "num_cpu_blocks": 1023}
+    worker.initialize_cache(**kwargs)
 
-    worker.init_cache_engine(cache_config)
-
-    draft_worker.init_cache_engine.assert_called_once_with(cache_config)
-    target_worker.init_cache_engine.assert_called_once_with(cache_config)
+    draft_worker.initialize_cache.assert_called_once_with(**kwargs)
+    target_worker.initialize_cache.assert_called_once_with(**kwargs)
 
 
 @pytest.mark.parametrize('available_gpu_blocks', [1, 1024])
@@ -538,10 +537,10 @@ def test_init_cache_engine():
 @pytest.mark.parametrize('target_cache_block_size_bytes', [2 * 2 * 4096])
 @pytest.mark.parametrize('draft_kv_size_bytes', [0, 2 * 2 * 768, 2 * 2 * 4096])
 @pytest.mark.skip_global_cleanup
-def test_profile_num_available_blocks(available_gpu_blocks: int,
-                                      available_cpu_blocks: int,
-                                      target_cache_block_size_bytes: int,
-                                      draft_kv_size_bytes: int):
+def test_determine_num_available_blocks(available_gpu_blocks: int,
+                                        available_cpu_blocks: int,
+                                        target_cache_block_size_bytes: int,
+                                        draft_kv_size_bytes: int):
     """Verify SpecDecodeWorker correctly profiles num available GPU blocks.
     Specifically, it should run profiling in the scorer worker, and then evenly
     split the blocks between proposer and scorer worker.
@@ -552,7 +551,7 @@ def test_profile_num_available_blocks(available_gpu_blocks: int,
     rejection_sampler.token_id_dtype = torch.int64
     metrics_collector = MagicMock(spec=AsyncMetricsCollector)
 
-    target_worker.profile_num_available_blocks.return_value = (
+    target_worker.determine_num_available_blocks.return_value = (
         available_gpu_blocks, available_cpu_blocks)
     target_worker.get_cache_block_size_bytes.return_value = (
         target_cache_block_size_bytes)
@@ -561,17 +560,9 @@ def test_profile_num_available_blocks(available_gpu_blocks: int,
     worker = SpecDecodeWorker(draft_worker, target_worker, rejection_sampler,
                               metrics_collector)
 
-    # These values do not directly impact the adjusted block size calculation,
-    # so they can be fixed.
-    gpu_memory_utilization = 0.9
-    cpu_swap_space = 100
-    block_size = 16
-
-    num_gpu_blocks, num_cpu_blocks = worker.profile_num_available_blocks(
-        block_size, gpu_memory_utilization, cpu_swap_space, cache_dtype="auto")
+    num_gpu_blocks, num_cpu_blocks = worker.determine_num_available_blocks()
 
-    target_worker.profile_num_available_blocks.assert_called_once_with(
-        block_size, gpu_memory_utilization, cpu_swap_space, "auto")
+    target_worker.determine_num_available_blocks.assert_called_once()
     assert num_cpu_blocks == available_cpu_blocks
 
     assert num_gpu_blocks == split_num_cache_blocks_evenly(

tests/spec_decode/utils.py

@@ -117,6 +117,7 @@ def create_worker(cls: type,
         parallel_config=engine_config.parallel_config,
         scheduler_config=engine_config.scheduler_config,
         device_config=engine_config.device_config,
+        cache_config=engine_config.cache_config,
         local_rank=0,
         rank=0,
         distributed_init_method=distributed_init_method,
@@ -128,8 +129,9 @@ def create_worker(cls: type,
 
     engine_config.cache_config.num_gpu_blocks = num_gpu_blocks
     engine_config.cache_config.num_cpu_blocks = 0
-    worker.init_cache_engine(engine_config.cache_config)
-    worker.warm_up_model()
+    worker.initialize_cache(
+        num_gpu_blocks=engine_config.cache_config.num_gpu_blocks,
+        num_cpu_blocks=engine_config.cache_config.num_cpu_blocks)
 
     return worker
 

tests/worker/test_swap.py

@@ -11,8 +11,8 @@ def test_swap() -> None:
                              dtype="half",
                              load_format="dummy")
     engine_config = engine_args.create_engine_config()
-    engine_config.cache_config.num_gpu_blocks = 100
-    engine_config.cache_config.num_cpu_blocks = 100
+    engine_config.cache_config.num_gpu_blocks = 1000
+    engine_config.cache_config.num_cpu_blocks = 1000
 
     # Create the worker.
     distributed_init_method = get_distributed_init_method(
@@ -22,6 +22,7 @@ def test_swap() -> None:
         parallel_config=engine_config.parallel_config,
         scheduler_config=engine_config.scheduler_config,
         device_config=engine_config.device_config,
+        cache_config=engine_config.cache_config,
         local_rank=0,
         rank=0,
         distributed_init_method=distributed_init_method,
@@ -31,8 +32,9 @@ def test_swap() -> None:
     # Initialize the worker.
     worker.init_device()
     worker.load_model()
-    worker.init_cache_engine(engine_config.cache_config)
-    worker.warm_up_model()
+    worker.initialize_cache(
+        num_gpu_blocks=engine_config.cache_config.num_gpu_blocks,
+        num_cpu_blocks=engine_config.cache_config.num_cpu_blocks)
 
     # Randomly initialize the cache.
     gpu_cache = worker.cache_engine.gpu_cache

vllm/config.py

@@ -334,7 +334,7 @@ class CacheConfig:
             vLLM execution.
         swap_space: Size of the CPU swap space per GPU (in GiB).
         cache_dtype: Data type for kv cache storage.
-        forced_num_gpu_blocks: Number of GPU blocks to use. This overrides the
+        num_gpu_blocks_override: Number of GPU blocks to use. This overrides the
             profiled num_gpu_blocks if specified. Does nothing if None.
     """
 
@@ -344,14 +344,14 @@ class CacheConfig:
         gpu_memory_utilization: float,
         swap_space: int,
         cache_dtype: str,
-        forced_num_gpu_blocks: Optional[int] = None,
+        num_gpu_blocks_override: Optional[int] = None,
         sliding_window: Optional[int] = None,
         enable_prefix_caching: bool = False,
     ) -> None:
         self.block_size = block_size
         self.gpu_memory_utilization = gpu_memory_utilization
         self.swap_space_bytes = swap_space * _GB
-        self.forced_num_gpu_blocks = forced_num_gpu_blocks
+        self.num_gpu_blocks_override = num_gpu_blocks_override
         self.cache_dtype = cache_dtype
         self.sliding_window = sliding_window
         self.enable_prefix_caching = enable_prefix_caching

vllm/engine/arg_utils.py

@@ -55,7 +55,7 @@ class EngineArgs:
     max_cpu_loras: Optional[int] = None
     device: str = 'auto'
     ray_workers_use_nsight: bool = False
-    forced_num_gpu_blocks: Optional[int] = None
+    num_gpu_blocks_override: Optional[int] = None
     num_lookahead_slots: int = 0
 
     # Related to Vision-language models such as llava
@@ -246,7 +246,7 @@ class EngineArgs:
             'the model executor, which can range from 0 to 1.'
             'If unspecified, will use the default value of 0.9.')
         parser.add_argument(
-            '--forced-num-gpu-blocks',
+            '--num-gpu-blocks-override',
             type=int,
             default=None,
             help='If specified, ignore GPU profiling result and use this number'
@@ -426,7 +426,7 @@ class EngineArgs:
         cache_config = CacheConfig(self.block_size,
                                    self.gpu_memory_utilization,
                                    self.swap_space, self.kv_cache_dtype,
-                                   self.forced_num_gpu_blocks,
+                                   self.num_gpu_blocks_override,
                                    model_config.get_sliding_window(),
                                    self.enable_prefix_caching)
         parallel_config = ParallelConfig(

vllm/engine/llm_engine.py

@@ -127,6 +127,8 @@ class LLMEngine:
             speculative_config=speculative_config,
         )
 
+        self._initialize_kv_caches()
+
         # If usage stat is enabled, collect relevant info.
         if is_usage_stats_enabled():
             from vllm.model_executor.model_loader import (
@@ -178,6 +180,26 @@ class LLMEngine:
                 labels=dict(model_name=model_config.model))
             self.stat_logger.info("cache_config", self.cache_config)
 
+    def _initialize_kv_caches(self) -> None:
+        """Initialize the KV cache in the worker(s).
+
+        The workers will determine the number of blocks in both the GPU cache
+        and the swap CPU cache.
+        """
+        num_gpu_blocks, num_cpu_blocks = (
+            self.model_executor.determine_num_available_blocks())
+
+        if self.cache_config.num_gpu_blocks_override is not None:
+            num_gpu_blocks_override = self.cache_config.num_gpu_blocks_override
+            logger.info(f"Overriding {num_gpu_blocks=} with "
+                        f"{num_gpu_blocks_override=}")
+            num_gpu_blocks = num_gpu_blocks_override
+
+        self.cache_config.num_gpu_blocks = num_gpu_blocks
+        self.cache_config.num_cpu_blocks = num_cpu_blocks
+
+        self.model_executor.initialize_cache(num_gpu_blocks, num_cpu_blocks)
+
     @classmethod
     def from_engine_args(
         cls,

vllm/executor/cpu_executor.py

@@ -35,7 +35,6 @@ class CPUExecutor(ExecutorBase):
 
         # Instantiate the worker and load the model to CPU.
         self._init_worker()
-        self._init_cache()
 
     def _init_worker(self):
         from vllm.worker.cpu_worker import CPUWorker
@@ -46,10 +45,11 @@ class CPUExecutor(ExecutorBase):
         distributed_init_method = get_distributed_init_method(
             get_ip(), get_open_port())
         self.driver_worker = CPUWorker(
-            self.model_config,
-            self.parallel_config,
-            self.scheduler_config,
-            self.device_config,
+            model_config=self.model_config,
+            parallel_config=self.parallel_config,
+            scheduler_config=self.scheduler_config,
+            device_config=self.device_config,
+            cache_config=self.cache_config,
             local_rank=0,
             rank=0,
             distributed_init_method=distributed_init_method,
@@ -60,35 +60,21 @@ class CPUExecutor(ExecutorBase):
         self.driver_worker.init_device()
         self.driver_worker.load_model()
 
-    def _init_cache(self) -> None:
-        num_cpu_blocks = self.driver_worker.get_cpu_cache_block_num(
-            block_size=self.cache_config.block_size,
-            cache_space=self.cache_config.cpu_kvcache_space_bytes,
-            cache_dtype=self.cache_config.cache_dtype,
-        )
-
+    def determine_num_available_blocks(self) -> tuple[int, int]:
+        """Determine the number of available KV blocks by invoking the
+        underlying worker.
+        """
+        return self.driver_worker.determine_num_available_blocks()
+
+    def initialize_cache(self, num_gpu_blocks: int,
+                         num_cpu_blocks: int) -> None:
+        """Initialize the KV cache by invoking the underlying worker.
+        """
+        # NOTE: We log here to avoid multiple logs when number of workers is
+        # greater than one. We could log in the engine, but not all executors
+        # have GPUs.
         logger.info(f"# CPU blocks: {num_cpu_blocks}")
-        if num_cpu_blocks <= 0:
-            raise ValueError("No available memory for the cache blocks. "
-                             "Try increasing `VLLM_CPU_KVCACHE_SPACE` when "
-                             "initializing the engine.")
-
-        max_seq_len = self.cache_config.block_size * num_cpu_blocks
-        if self.model_config.max_model_len > max_seq_len:
-            raise ValueError(
-                f"The model's max seq len ({self.model_config.max_model_len}) "
-                "is larger than the maximum number of tokens that can be "
-                f"stored in KV cache ({max_seq_len}). Try increasing "
-                "`VLLM_CPU_KVCACHE_SPACE` or decreasing `max_model_len` when "
-                "initializing the engine.")
-
-        # Note: To reuse the cache management procedure,
-        # use cpu cache as 'gpu cache'.
-        self.cache_config.num_gpu_blocks = num_cpu_blocks  # type: ignore
-        self.cache_config.num_cpu_blocks = 0  # type: ignore
-
-        # Initialize the cache.
-        self.driver_worker.init_cache_engine(cache_config=self.cache_config)
+        self.driver_worker.initialize_cache(num_gpu_blocks, num_cpu_blocks)
 
     def execute_model(self,
                       seq_group_metadata_list: List[SequenceGroupMetadata],
@@ -104,13 +90,13 @@ class CPUExecutor(ExecutorBase):
         return output
 
     def add_lora(self, lora_request: LoRARequest) -> bool:
-        raise NotImplementedError("LoRA is not implemented for cpu backend.")
+        return self.driver_worker.add_lora(lora_request)
 
     def remove_lora(self, lora_id: int) -> bool:
-        raise NotImplementedError("LoRA is not implemented for cpu backend.")
+        return self.driver_worker.remove_lora(lora_id)
 
     def list_loras(self) -> List[int]:
-        raise NotImplementedError("LoRA is not implemented for cpu backend.")
+        return self.driver_worker.list_loras()
 
     def check_health(self) -> None:
         # CPUExecutor will always be healthy as long as

vllm/executor/executor_base.py

@@ -30,6 +30,29 @@ class ExecutorBase(ABC):
     ) -> None:
         raise NotImplementedError
 
+    @abstractmethod
+    def determine_num_available_blocks(self) -> tuple[int, int]:
+        """Determine the number of available blocks for the GPU KV cache and
+        swappable CPU KV cache.
+
+        Normally, this should simply delegate to the underlying Worker. Some
+        ExecutorBase may require modification of the result, e.g. to ensure the
+        selected cache sizes are compatible with all workers.
+
+        Returns a tuple[num_gpu_blocks, num_cpu_blocks], where num_gpu_blocks
+        are blocks that are "active" on the device and can be appended to.
+        num_cpu_blocks refers to "swapped" blocks in CPU memory and cannot be
+        appended to.
+        """
+        raise NotImplementedError
+
+    @abstractmethod
+    def initialize_cache(self, num_gpu_blocks: int,
+                         num_cpu_blocks: int) -> None:
+        """Initialize the KV cache with the given size in blocks.
+        """
+        raise NotImplementedError
+
     @abstractmethod
     def execute_model(self,
                       seq_group_metadata_list: List[SequenceGroupMetadata],

vllm/executor/gpu_executor.py

@@ -4,7 +4,6 @@ from vllm.config import (CacheConfig, DeviceConfig, LoRAConfig, ModelConfig,
                          ParallelConfig, SchedulerConfig, SpeculativeConfig,
                          VisionLanguageConfig)
 from vllm.executor.executor_base import ExecutorAsyncBase, ExecutorBase
-from vllm.executor.utils import check_block_size_valid
 from vllm.logger import init_logger
 from vllm.lora.request import LoRARequest
 from vllm.sequence import SamplerOutput, SequenceGroupMetadata
@@ -41,9 +40,6 @@ class GPUExecutor(ExecutorBase):
         # Instantiate the worker and load the model to GPU.
         self._init_worker()
 
-        # Profile the memory usage and initialize the cache.
-        self._init_cache()
-
     def _init_worker(self):
         # Lazy import the Worker to avoid importing torch.cuda/xformers
         # before CUDA_VISIBLE_DEVICES is set in the Worker
@@ -55,61 +51,37 @@ class GPUExecutor(ExecutorBase):
         distributed_init_method = get_distributed_init_method(
             get_ip(), get_open_port())
         self.driver_worker = Worker(
-            self.model_config,
-            self.parallel_config,
-            self.scheduler_config,
-            self.device_config,
+            model_config=self.model_config,
+            parallel_config=self.parallel_config,
+            scheduler_config=self.scheduler_config,
+            device_config=self.device_config,
+            cache_config=self.cache_config,
             local_rank=0,
             rank=0,
             distributed_init_method=distributed_init_method,
             lora_config=self.lora_config,
             vision_language_config=self.vision_language_config,
-            kv_cache_dtype=self.cache_config.cache_dtype,
             is_driver_worker=True,
         )
         self.driver_worker.init_device()
         self.driver_worker.load_model()
 
-    def _init_cache(self) -> None:
-        """Profiles the memory usage and initializes the KV cache.
-
-        The engine first profiles the existing memory usage.
-        Then, it allocates the remaining memory for KV blocks.
-
-        .. tip::
-            You may limit the usage of GPU memory
-            by adjusting the `gpu_memory_utilization` parameter.
+    def determine_num_available_blocks(self) -> tuple[int, int]:
+        """Determine the number of available KV blocks by invoking the
+        underlying worker.
         """
-        # Get the maximum number of blocks that can be allocated on GPU and CPU.
-        num_gpu_blocks, num_cpu_blocks = (
-            self.driver_worker.profile_num_available_blocks(
-                block_size=self.cache_config.block_size,
-                gpu_memory_utilization=self.cache_config.
-                gpu_memory_utilization,
-                cpu_swap_space=self.cache_config.swap_space_bytes,
-                cache_dtype=self.cache_config.cache_dtype,
-            ))
-
-        if self.cache_config.forced_num_gpu_blocks is not None:
-            forced_num_gpu_blocks = self.cache_config.forced_num_gpu_blocks
-            logger.info(f"Replacing profiled {num_gpu_blocks=} with "
-                        f"{forced_num_gpu_blocks=}")
-            num_gpu_blocks = forced_num_gpu_blocks
+        return self.driver_worker.determine_num_available_blocks()
 
+    def initialize_cache(self, num_gpu_blocks: int, num_cpu_blocks) -> None:
+        """Initialize the KV cache by invoking the underlying worker.
+        """
+        # NOTE: This is logged in the executor because there can be >1 worker
+        # with other executors. We could log in the engine level, but work
+        # remains to abstract away the device for non-GPU configurations.
         logger.info(f"# GPU blocks: {num_gpu_blocks}, "
                     f"# CPU blocks: {num_cpu_blocks}")
 
-        check_block_size_valid(num_gpu_blocks, self.cache_config.block_size,
-                               self.model_config.max_model_len)
-
-        self.cache_config.num_gpu_blocks = num_gpu_blocks
-        self.cache_config.num_cpu_blocks = num_cpu_blocks
-
-        # Initialize the cache.
-        self.driver_worker.init_cache_engine(cache_config=self.cache_config)
-        # Warm up the model. This includes capturing the model into CUDA graph
-        # if enforce_eager is False.
-        self.driver_worker.warm_up_model()
+        self.driver_worker.initialize_cache(num_gpu_blocks, num_cpu_blocks)
 
     def execute_model(self,
                       seq_group_metadata_list: List[SequenceGroupMetadata],

vllm/executor/neuron_executor.py

@@ -25,7 +25,6 @@ class NeuronExecutor(ExecutorBase):
         speculative_config: Optional[SpeculativeConfig],
     ) -> None:
         self.model_config = model_config
-        self.cache_config = cache_config
         assert lora_config is None, "LoRA is not supported for Neuron backend."
         self.parallel_config = parallel_config
         self.scheduler_config = scheduler_config
@@ -33,12 +32,6 @@ class NeuronExecutor(ExecutorBase):
         assert (not speculative_config
                 ), "Speculative decoding not yet supported for Neuron backend."
 
-        # Set the number of GPU blocks to be the same as the maximum number of
-        # sequences that can be processed in a single batch. This is equivalent
-        # to schedule without PagedAttention.
-        self.cache_config.num_gpu_blocks = self.scheduler_config.max_num_seqs
-        self.cache_config.num_cpu_blocks = 0
-
         # Instantiate the worker and load the model to the device.
         self._init_worker()
 
@@ -54,6 +47,18 @@ class NeuronExecutor(ExecutorBase):
         self.driver_worker.init_device()
         self.driver_worker.load_model()
 
+    def determine_num_available_blocks(self) -> tuple[int, int]:
+        """Determine the number of available KV blocks by invoking the
+        underlying worker.
+        """
+        return self.driver_worker.determine_num_available_blocks()
+
+    def initialize_cache(self, num_gpu_blocks: int,
+                         num_cpu_blocks: int) -> None:
+        """Initialize the KV cache by invoking the underlying worker.
+        """
+        self.driver_worker.initialize_cache(num_gpu_blocks, num_cpu_blocks)
+
     def execute_model(self,
                       seq_group_metadata_list: List[SequenceGroupMetadata],
                       blocks_to_swap_in: Dict[int, int],
@@ -68,16 +73,13 @@ class NeuronExecutor(ExecutorBase):
         return output
 
     def add_lora(self, lora_request: LoRARequest) -> bool:
-        raise NotImplementedError(
-            "LoRA is not implemented for neuron backend.")
+        return self.driver_worker.add_lora(lora_request)
 
     def remove_lora(self, lora_id: int) -> bool:
-        raise NotImplementedError(
-            "LoRA is not implemented for neuron backend.")
+        return self.driver_worker.remove_lora(lora_id)
 
     def list_loras(self) -> List[int]:
-        raise NotImplementedError(
-            "LoRA is not implemented for neuron backend.")
+        return self.driver_worker.list_loras()
 
     def check_health(self) -> None:
         # NeuronExecutor will always be healthy as long as

vllm/executor/ray_gpu_executor.py

@@ -10,7 +10,6 @@ from vllm.config import (CacheConfig, DeviceConfig, LoRAConfig, ModelConfig,
                          VisionLanguageConfig)
 from vllm.engine.ray_utils import RayWorkerVllm, ray
 from vllm.executor.executor_base import ExecutorAsyncBase, ExecutorBase
-from vllm.executor.utils import check_block_size_valid
 from vllm.logger import init_logger
 from vllm.lora.request import LoRARequest
 from vllm.sequence import SamplerOutput, SequenceGroupMetadata
@@ -65,9 +64,6 @@ class RayGPUExecutor(ExecutorBase):
         # Create the parallel GPU workers.
         self._init_workers_ray(placement_group)
 
-        # Profile the memory usage and initialize the cache.
-        self._init_cache()
-
         self.forward_dag = None
         if USE_RAY_COMPILED_DAG:
             self.forward_dag = self._compiled_ray_dag()
@@ -154,8 +150,8 @@ class RayGPUExecutor(ExecutorBase):
         scheduler_config = copy.deepcopy(self.scheduler_config)
         device_config = copy.deepcopy(self.device_config)
         lora_config = copy.deepcopy(self.lora_config)
+        cache_config = copy.deepcopy(self.cache_config)
         vision_language_config = copy.deepcopy(self.vision_language_config)
-        kv_cache_dtype = self.cache_config.cache_dtype
 
         # Initialize the actual workers with the Worker class.
         for rank, (worker, (node_id, _)) in enumerate(
@@ -165,32 +161,32 @@ class RayGPUExecutor(ExecutorBase):
             local_rank = node_workers[node_id].index(rank)
             worker.init_worker.remote(
                 lambda rank=rank, local_rank=local_rank: Worker(
-                    model_config,
-                    parallel_config,
-                    scheduler_config,
-                    device_config,
-                    local_rank,
-                    rank,
-                    distributed_init_method,
+                    model_config=model_config,
+                    parallel_config=parallel_config,
+                    scheduler_config=scheduler_config,
+                    device_config=device_config,
+                    cache_config=cache_config,
+                    local_rank=local_rank,
+                    rank=rank,
+                    distributed_init_method=distributed_init_method,
                     lora_config=lora_config,
                     vision_language_config=vision_language_config,
-                    kv_cache_dtype=kv_cache_dtype,
                 ))
 
         # Initialize the driver worker with the Worker class.
         driver_rank = 0
         driver_local_rank = node_workers[driver_node_id].index(driver_rank)
         self.driver_worker = Worker(
-            self.model_config,
-            self.parallel_config,
-            self.scheduler_config,
-            self.device_config,
-            driver_local_rank,
-            driver_rank,
-            distributed_init_method,
+            model_config=self.model_config,
+            parallel_config=self.parallel_config,
+            scheduler_config=self.scheduler_config,
+            device_config=self.device_config,
+            cache_config=self.cache_config,
+            local_rank=driver_local_rank,
+            rank=driver_rank,
+            distributed_init_method=distributed_init_method,
             lora_config=self.lora_config,
             vision_language_config=self.vision_language_config,
-            kv_cache_dtype=kv_cache_dtype,
             is_driver_worker=True,
         )
 
@@ -201,35 +197,18 @@ class RayGPUExecutor(ExecutorBase):
             max_parallel_loading_workers,
         )
 
-    def _init_cache(self) -> None:
-        """Profiles the memory usage and initializes the KV cache.
-
-        The engine will first conduct a profiling of the existing memory usage.
-        Then, it calculate the maximum possible number of GPU and CPU blocks
-        that can be allocated with the remaining free memory.
-        More details can be found in the
-        :meth:`~vllm.worker.worker.Worker.profile_num_available_blocks` method
-        from class :class:`~vllm.worker.Worker`.
+    def determine_num_available_blocks(self) -> tuple[int, int]:
+        """Determine the number of available KV blocks.
 
-        Afterwards, as there may be multiple workers,
-        we take the minimum number of blocks across all workers
-        to ensure this can be applied to all of them.
+        This invokes `determine_num_available_blocks` on each worker and takes
+        the min of the results, guaranteeing that the selected cache sizes are
+        compatible with all workers.
 
-        Finally, the engine will initialize the KV cache
-        with the calculated number of blocks.
-
-        .. tip::
-            You may limit the usage of GPU memory
-            by adjusting the `gpu_memory_utilization` parameter.
+        Returns:
+            - tuple[num_gpu_blocks, num_cpu_blocks]
         """
         # Get the maximum number of blocks that can be allocated on GPU and CPU.
-        num_blocks = self._run_workers(
-            "profile_num_available_blocks",
-            block_size=self.cache_config.block_size,
-            gpu_memory_utilization=self.cache_config.gpu_memory_utilization,
-            cpu_swap_space=self.cache_config.swap_space_bytes,
-            cache_dtype=self.cache_config.cache_dtype,
-        )
+        num_blocks = self._run_workers("determine_num_available_blocks", )
 
         # Since we use a shared centralized controller, we take the minimum
         # number of blocks across all workers to make sure all the memory
@@ -237,26 +216,25 @@ class RayGPUExecutor(ExecutorBase):
         num_gpu_blocks = min(b[0] for b in num_blocks)
         num_cpu_blocks = min(b[1] for b in num_blocks)
 
-        if self.cache_config.forced_num_gpu_blocks is not None:
-            forced_num_gpu_blocks = self.cache_config.forced_num_gpu_blocks
-            logger.info(f"Replacing profiled {num_gpu_blocks=} with "
-                        f"{forced_num_gpu_blocks=}")
-            num_gpu_blocks = forced_num_gpu_blocks
+        return num_gpu_blocks, num_cpu_blocks
 
+    def initialize_cache(self, num_gpu_blocks: int,
+                         num_cpu_blocks: int) -> None:
+        """Initialize the KV cache in all workers.
+        """
+
+        # NOTE: We log here to avoid multiple logs when number of workers is
+        # greater than one. We could log in the engine, but not all executors
+        # have GPUs.
         logger.info(f"# GPU blocks: {num_gpu_blocks}, "
                     f"# CPU blocks: {num_cpu_blocks}")
 
-        check_block_size_valid(num_gpu_blocks, self.cache_config.block_size,
-                               self.model_config.max_model_len)
-
         self.cache_config.num_gpu_blocks = num_gpu_blocks
         self.cache_config.num_cpu_blocks = num_cpu_blocks
 
-        # Initialize the cache.
-        self._run_workers("init_cache_engine", cache_config=self.cache_config)
-        # Warm up the model. This includes capturing the model into CUDA graph
-        # if enforce_eager is False.
-        self._run_workers("warm_up_model")
+        self._run_workers("initialize_cache",
+                          num_gpu_blocks=num_gpu_blocks,
+                          num_cpu_blocks=num_cpu_blocks)
 
     def execute_model(self,
                       seq_group_metadata_list: List[SequenceGroupMetadata],

vllm/executor/utils.py

-def check_block_size_valid(num_gpu_blocks, block_size, max_model_len) -> None:
-    if num_gpu_blocks <= 0:
-        raise ValueError("No available memory for the cache blocks. "
-                         "Try increasing `gpu_memory_utilization` when "
-                         "initializing the engine.")
-    max_seq_len = block_size * num_gpu_blocks
-    if max_model_len > max_seq_len:
-        raise ValueError(
-            f"The model's max seq len ({max_model_len}) "
-            "is larger than the maximum number of tokens that can be "
-            f"stored in KV cache ({max_seq_len}). Try increasing "
-            "`gpu_memory_utilization` or decreasing `max_model_len` when "
-            "initializing the engine.")

vllm/spec_decode/spec_decode_worker.py

@@ -3,7 +3,6 @@ from typing import Dict, List, Optional, Tuple
 
 import torch
 
-from vllm.config import CacheConfig
 from vllm.model_executor.layers.rejection_sampler import RejectionSampler
 from vllm.sequence import (SamplerOutput, SequenceGroupMetadata,
                            SequenceGroupOutput, SequenceOutput)
@@ -15,9 +14,10 @@ from vllm.spec_decode.multi_step_worker import MultiStepWorker
 from vllm.spec_decode.util import (get_all_seq_ids, nvtx_range,
                                    split_batch_by_proposal_len)
 from vllm.worker.worker import Worker
+from vllm.worker.worker_base import LoraNotSupportedWorkerBase
 
 
-class SpecDecodeWorker:
+class SpecDecodeWorker(LoraNotSupportedWorkerBase):
     """Worker which implements speculative decoding.
 
     Speculative decoding reduces decoding per-token latency by using a proposal
@@ -94,10 +94,7 @@ class SpecDecodeWorker:
             device=self.device,
             vocab_size=self._vocab_size)
 
-    def profile_num_available_blocks(self, block_size: int,
-                                     gpu_memory_utilization: float,
-                                     cpu_swap_space: int,
-                                     cache_dtype: str) -> Tuple[int, int]:
+    def determine_num_available_blocks(self) -> Tuple[int, int]:
         """Determine the number of cache blocks to use.
 
         This is done by profiling the scorer model (which is typically the
@@ -106,27 +103,26 @@ class SpecDecodeWorker:
         such that the number of blocks is equal in both KV caches.
         """
         num_gpu_blocks, num_cpu_blocks = (
-            self.scorer_worker.profile_num_available_blocks(
-                block_size, gpu_memory_utilization, cpu_swap_space,
-                cache_dtype))
+            self.scorer_worker.determine_num_available_blocks())
 
         scorer_cache_block_size_bytes = (
-            self.scorer_worker.get_cache_block_size_bytes(
-                block_size, cache_dtype))
+            self.scorer_worker.get_cache_block_size_bytes())
         proposer_cache_block_size_bytes = (
-            self.proposer_worker.get_cache_block_size_bytes(
-                block_size, cache_dtype))
+            self.proposer_worker.get_cache_block_size_bytes())
 
         new_num_gpu_blocks = split_num_cache_blocks_evenly(
             scorer_cache_block_size_bytes, proposer_cache_block_size_bytes,
             num_gpu_blocks)
         return new_num_gpu_blocks, num_cpu_blocks
 
-    def init_cache_engine(self, cache_config: CacheConfig):
+    def initialize_cache(self, num_gpu_blocks: int,
+                         num_cpu_blocks: int) -> None:
         """Initialize the cache engine of the scorer and proposer workers.
         """
-        self.scorer_worker.init_cache_engine(cache_config)
-        self.proposer_worker.init_cache_engine(cache_config)
+        self.scorer_worker.initialize_cache(num_gpu_blocks=num_gpu_blocks,
+                                            num_cpu_blocks=num_cpu_blocks)
+        self.proposer_worker.initialize_cache(num_gpu_blocks=num_gpu_blocks,
+                                              num_cpu_blocks=num_cpu_blocks)
 
     @torch.inference_mode()
     def execute_model(
@@ -351,6 +347,16 @@ class SpecDecodeWorker:
     def device(self):
         return self.scorer_worker.device
 
+    def get_cache_block_size_bytes(self):
+        """Return the size of a cache block in bytes.
+        
+        This function is only used to compose workers within a SpecDecodeWorker.
+        We leave composing a SpecDecodeWorker within a SpecDecodeWorker
+        undefined for now, although it could be implemented in the future.
+        See https://arxiv.org/abs/2308.04623.
+        """
+        raise NotImplementedError
+
 
 def split_num_cache_blocks_evenly(scorer_cache_block_size_bytes: int,
                                   proposer_cache_block_size_bytes: int,

vllm/worker/cache_engine.py

@@ -82,8 +82,7 @@ class CacheEngine:
 
     @staticmethod
     def get_cache_block_size(
-        block_size: int,
-        cache_dtype: str,
+        cache_config: CacheConfig,
         model_config: ModelConfig,
         parallel_config: ParallelConfig,
     ) -> int:
@@ -91,13 +90,13 @@ class CacheEngine:
         num_heads = model_config.get_num_kv_heads(parallel_config)
         num_layers = model_config.get_num_layers(parallel_config)
 
-        key_cache_block = block_size * num_heads * head_size
+        key_cache_block = cache_config.block_size * num_heads * head_size
         value_cache_block = key_cache_block
         total = num_layers * (key_cache_block + value_cache_block)
-        if cache_dtype == "auto":
+        if cache_config.cache_dtype == "auto":
             dtype = model_config.dtype
         else:
-            dtype = STR_DTYPE_TO_TORCH_DTYPE[cache_dtype]
+            dtype = STR_DTYPE_TO_TORCH_DTYPE[cache_config.cache_dtype]
         dtype_size = _get_dtype_size(dtype)
         return dtype_size * total
 

vllm/worker/cpu_worker.py

@@ -17,6 +17,7 @@ from vllm.model_executor.parallel_utils.parallel_state import (
 from vllm.sequence import SamplerOutput, SequenceGroupMetadata
 from vllm.utils import STR_DTYPE_TO_TORCH_DTYPE
 from vllm.worker.model_runner import ModelRunner
+from vllm.worker.worker_base import LoraNotSupportedWorkerBase
 
 logger = init_logger(__name__)
 
@@ -112,7 +113,7 @@ class CPUCacheEngine:
         return dtype_size * total
 
 
-class CPUWorker:
+class CPUWorker(LoraNotSupportedWorkerBase):
     """A worker class that executes (a partition of) the model on a CPU socket.
 
     Each worker is associated with a single CPU socket. The worker is 
@@ -127,6 +128,7 @@ class CPUWorker:
         parallel_config: ParallelConfig,
         scheduler_config: SchedulerConfig,
         device_config: DeviceConfig,
+        cache_config: CacheConfig,
         local_rank: int,
         rank: int,
         distributed_init_method: str,
@@ -138,6 +140,7 @@ class CPUWorker:
         self.parallel_config = parallel_config
         self.scheduler_config = scheduler_config
         self.device_config = device_config
+        self.cache_config = cache_config
         self.local_rank = local_rank
         self.rank = rank
         self.distributed_init_method = distributed_init_method
@@ -154,8 +157,7 @@ class CPUWorker:
                                            kv_cache_dtype=kv_cache_dtype,
                                            is_driver_worker=is_driver_worker)
         # Uninitialized cache engine. Will be initialized by
-        # self.init_cache_engine().
-        self.cache_config = None
+        # initialize_cache.
         self.cache_engine = None
         self.cpu_cache = None
 
@@ -167,28 +169,70 @@ class CPUWorker:
     def load_model(self):
         self.model_runner.load_model()
 
-    def get_cpu_cache_block_num(
-        self,
-        block_size: int,
-        cache_space: int,
-        cache_dtype: str,
-    ) -> int:
-        """
-        Args:
-            block_size: The size of the cache block.
-            cache_space: The size of the CPU KV cache space in bytes.
+    def determine_num_available_blocks(self) -> tuple[int, int]:
+        """Determine the number of blocks available for the KV cache.
+
+        This determines how many KV blocks can fit into the configured CPU
+        KV cache space.
+
+        Note that since vLLM assumes a block resides on GPU if it can be
+        modified, we return num_gpu_blocks=num_cpu_blocks and num_cpu_blocks=0.
+        This allows us to reuse the scheduler of vLLM without generalizing it
+        to different devices.
         """
         # For CPU device, the block number will be calculated based on the
         # cpu_kvcache_space.
-        cache_block_size = CPUCacheEngine.get_cache_block_size(
-            block_size, cache_dtype, self.model_config, self.parallel_config)
-        num_cpu_blocks = int(cache_space // cache_block_size)
+        cache_block_size = self.get_cache_block_size_bytes()
+        num_cpu_blocks = int(self.cache_config.cpu_kvcache_space_bytes //
+                             cache_block_size)
         num_cpu_blocks = max(num_cpu_blocks, 0)
 
-        return num_cpu_blocks
+        # Note: To reuse the cache management procedure,
+        # use cpu cache as 'gpu cache'.
+        num_gpu_blocks = num_cpu_blocks
+        num_cpu_blocks = 0
+        return num_gpu_blocks, num_cpu_blocks
 
-    def init_cache_engine(self, cache_config: CacheConfig) -> None:
-        self.cache_config = cache_config
+    def initialize_cache(self, num_gpu_blocks: int,
+                         num_cpu_blocks: int) -> None:
+        """Initialize the KV cache. Currently, swappable CPU memory is not
+        supported.
+
+        Since this worker does not support GPUs, we use the num_gpu_blocks to
+        determine how many non-swappable CPU blocks to allocate.
+        """
+        assert (num_cpu_blocks == 0
+                ), f"{type(self)} does not support swappable cache"
+
+        # Note: To reuse the cache management procedure,
+        # use cpu cache as 'gpu cache'.
+        num_cpu_blocks = num_gpu_blocks
+
+        self._validate_num_cpu_blocks(num_cpu_blocks)
+        self.cache_config.num_gpu_blocks = num_cpu_blocks
+        self.cache_config.num_cpu_blocks = 0
+
+        # Initialize the cache.
+        self._init_cache_engine()
+
+    def _validate_num_cpu_blocks(self, num_cpu_blocks: int) -> None:
+        """Raise errors if the num_cpu_blocks is invalid.
+        """
+        if num_cpu_blocks <= 0:
+            raise ValueError("No available memory for the cache blocks. "
+                             "Try increasing `VLLM_CPU_KVCACHE_SPACE` when "
+                             "initializing the engine.")
+
+        max_seq_len = self.cache_config.block_size * num_cpu_blocks
+        if self.model_config.max_model_len > max_seq_len:
+            raise ValueError(
+                f"The model's max seq len ({self.model_config.max_model_len}) "
+                "is larger than the maximum number of tokens that can be "
+                f"stored in KV cache ({max_seq_len}). Try increasing "
+                "`VLLM_CPU_KVCACHE_SPACE` or decreasing `max_model_len` when "
+                "initializing the engine.")
+
+    def _init_cache_engine(self) -> None:
         self.cache_engine = CPUCacheEngine(self.cache_config,
                                            self.model_config,
                                            self.parallel_config,
@@ -264,3 +308,10 @@ class CPUWorker:
         ensure_model_parallel_initialized(
             parallel_config.tensor_parallel_size,
             parallel_config.pipeline_parallel_size)
+
+    def get_cache_block_size_bytes(self) -> int:
+        """Return the size in bytes of a single KV cache block.
+        """
+        return CPUCacheEngine.get_cache_block_size(
+            self.cache_config.block_size, self.cache_config.cache_dtype,
+            self.model_config, self.parallel_config)

vllm/worker/neuron_worker.py

@@ -4,14 +4,15 @@ from typing import List, Optional
 import torch
 import torch.distributed
 
-from vllm.config import (DeviceConfig, ModelConfig, ParallelConfig,
-                         SchedulerConfig)
+from vllm.config import (CacheConfig, DeviceConfig, ModelConfig,
+                         ParallelConfig, SchedulerConfig)
 from vllm.model_executor import set_random_seed
 from vllm.sequence import SamplerOutput, SequenceGroupMetadata
 from vllm.worker.neuron_model_runner import NeuronModelRunner
+from vllm.worker.worker_base import LoraNotSupportedWorkerBase
 
 
-class NeuronWorker:
+class NeuronWorker(LoraNotSupportedWorkerBase):
     """A worker class that executes the model on a group of neuron cores.
     """
 
@@ -21,11 +22,13 @@ class NeuronWorker:
         parallel_config: ParallelConfig,
         scheduler_config: SchedulerConfig,
         device_config: DeviceConfig,
+        cache_config: CacheConfig,
     ) -> None:
         self.model_config = model_config
         self.parallel_config = parallel_config
         self.scheduler_config = scheduler_config
         self.device_config = device_config
+        self.cache_config = cache_config
 
         self.model_runner = NeuronModelRunner(model_config, parallel_config,
                                               scheduler_config, device_config)
@@ -37,6 +40,35 @@ class NeuronWorker:
     def load_model(self):
         self.model_runner.load_model()
 
+    def determine_num_available_blocks(self) -> tuple[int, int]:
+        """Determine the number of available KV blocks.
+
+        Swapping is not yet supported, so always return num_cpu_blocks=0.
+
+        We configure num_gpu_blocks to be equal to max_num_seqs.
+        """
+        # Set the number of GPU blocks to be the same as the maximum number of
+        # sequences that can be processed in a single batch. This is equivalent
+        # to schedule without PagedAttention.
+        num_gpu_blocks = self.scheduler_config.max_num_seqs
+
+        # Swap not yet supported with Neuron backend.
+        num_cpu_blocks = 0
+
+        return num_gpu_blocks, num_cpu_blocks
+
+    def initialize_cache(self, num_gpu_blocks: int,
+                         num_cpu_blocks: int) -> None:
+        """Initialize the KV cache.
+        """
+
+        # Different values are not tested.
+        assert num_cpu_blocks == 0
+        assert num_gpu_blocks == self.scheduler_config.max_num_seqs
+
+        self.cache_config.num_gpu_blocks = num_gpu_blocks
+        self.cache_config.num_cpu_blocks = num_cpu_blocks
+
     @torch.inference_mode()
     def execute_model(
         self,
@@ -50,3 +82,10 @@ class NeuronWorker:
 
         output = self.model_runner.execute_model(seq_group_metadata_list)
         return output
+
+    def get_cache_block_size_bytes(self) -> int:
+        """Determine the size in bytes of a cache block.
+
+        This is required for speculative decoding; it is not yet implemented.
+        """
+        raise NotImplementedError

vllm/worker/worker.py

@@ -19,9 +19,10 @@ from vllm.model_executor.parallel_utils.parallel_state import (
 from vllm.sequence import SamplerOutput, SequenceGroupMetadata
 from vllm.worker.cache_engine import CacheEngine
 from vllm.worker.model_runner import ModelRunner
+from vllm.worker.worker_base import WorkerBase
 
 
-class Worker:
+class Worker(WorkerBase):
     """A worker class that executes (a partition of) the model on a GPU.
 
     Each worker is associated with a single GPU. The worker is responsible for
@@ -35,18 +36,19 @@ class Worker:
         parallel_config: ParallelConfig,
         scheduler_config: SchedulerConfig,
         device_config: DeviceConfig,
+        cache_config: CacheConfig,
         local_rank: int,
         rank: int,
         distributed_init_method: str,
         lora_config: Optional[LoRAConfig] = None,
         vision_language_config: Optional[VisionLanguageConfig] = None,
-        kv_cache_dtype: Optional[str] = "auto",
         is_driver_worker: bool = False,
     ) -> None:
         self.model_config = model_config
         self.parallel_config = parallel_config
         self.scheduler_config = scheduler_config
         self.device_config = device_config
+        self.cache_config = cache_config
         self.local_rank = local_rank
         self.rank = rank
         self.distributed_init_method = distributed_init_method
@@ -66,12 +68,11 @@ class Worker:
             scheduler_config,
             device_config,
             lora_config=self.lora_config,
-            kv_cache_dtype=kv_cache_dtype,
+            kv_cache_dtype=self.cache_config.cache_dtype,
             is_driver_worker=is_driver_worker,
             vision_language_config=vision_language_config)
         # Uninitialized cache engine. Will be initialized by
-        # self.init_cache_engine().
-        self.cache_config = None
+        # initialize_cache.
         self.cache_engine = None
         self.gpu_cache = None
 
@@ -107,20 +108,17 @@ class Worker:
         self.model_runner.load_model()
 
     @torch.inference_mode()
-    def profile_num_available_blocks(
-        self,
-        block_size: int,
-        gpu_memory_utilization: float,
-        cpu_swap_space: int,
-        cache_dtype: str,
-    ) -> Tuple[int, int]:
-        """Profiles the peak memory usage of the model and returns the maximum
-        number of GPU and CPU cache blocks that can be allocated.
-
-        Args:
-            block_size: The size of the cache block.
-            gpu_memory_utilization: The fraction of the total GPU memory to use.
-            cpu_swap_space: The size of the CPU swap space in bytes.
+    def determine_num_available_blocks(self) -> Tuple[int, int]:
+        """Profiles the peak memory usage of the model to determine how many
+        KV blocks may be allocated without OOMs.
+
+        The engine will first conduct a profiling of the existing memory usage.
+        Then, it calculate the maximum possible number of GPU and CPU blocks
+        that can be allocated with the remaining free memory.
+
+        .. tip::
+            You may limit the usage of GPU memory
+            by adjusting the `gpu_memory_utilization` parameter.
         """
         # Profile the memory usage of the model and get the maximum number of
         # cache blocks that can be allocated with the remaining free memory.
@@ -141,12 +139,12 @@ class Worker:
             "Error in memory profiling. This happens when the GPU memory was "
             "not properly cleaned up before initializing the vLLM instance.")
 
-        cache_block_size = self.get_cache_block_size_bytes(
-            block_size, cache_dtype)
+        cache_block_size = self.get_cache_block_size_bytes()
         num_gpu_blocks = int(
-            (total_gpu_memory * gpu_memory_utilization - peak_memory) //
-            cache_block_size)
-        num_cpu_blocks = int(cpu_swap_space // cache_block_size)
+            (total_gpu_memory * self.cache_config.gpu_memory_utilization -
+             peak_memory) // cache_block_size)
+        num_cpu_blocks = int(self.cache_config.swap_space_bytes //
+                             cache_block_size)
         num_gpu_blocks = max(num_gpu_blocks, 0)
         num_cpu_blocks = max(num_cpu_blocks, 0)
         if self.model_runner.lora_manager:
@@ -155,14 +153,30 @@ class Worker:
         torch.cuda.empty_cache()
         return num_gpu_blocks, num_cpu_blocks
 
-    def init_cache_engine(self, cache_config: CacheConfig) -> None:
-        self.cache_config = cache_config
+    def initialize_cache(self, num_gpu_blocks: int,
+                         num_cpu_blocks: int) -> None:
+        """Allocate GPU and CPU KV cache with the specified number of blocks.
+
+        This also warms up the model, which may record CUDA graphs.
+        """
+        raise_if_cache_size_invalid(num_gpu_blocks,
+                                    self.cache_config.block_size,
+                                    self.model_config.max_model_len)
+
+        self.cache_config.num_gpu_blocks = num_gpu_blocks
+        self.cache_config.num_cpu_blocks = num_cpu_blocks
+
+        self._init_cache_engine()
+        self._warm_up_model()
+
+    def _init_cache_engine(self):
+        assert self.cache_config.num_gpu_blocks is not None
         self.cache_engine = CacheEngine(self.cache_config, self.model_config,
                                         self.parallel_config)
         self.gpu_cache = self.cache_engine.gpu_cache
         self.model_runner.set_block_size(self.cache_engine.block_size)
 
-    def warm_up_model(self) -> None:
+    def _warm_up_model(self) -> None:
         if not self.model_config.enforce_eager:
             self.model_runner.capture_model(self.gpu_cache)
         # Reset the seed to ensure that the random state is not affected by
@@ -239,11 +253,10 @@ class Worker:
     def vocab_size(self) -> int:
         return self.model_runner.vocab_size
 
-    def get_cache_block_size_bytes(self, block_size: int,
-                                   cache_dtype: str) -> int:
+    def get_cache_block_size_bytes(self) -> int:
         """Get the size of the KV cache block size in bytes.
         """
-        return CacheEngine.get_cache_block_size(block_size, cache_dtype,
+        return CacheEngine.get_cache_block_size(self.cache_config,
                                                 self.model_config,
                                                 self.parallel_config)
 
@@ -300,3 +313,19 @@ def _check_if_gpu_supports_dtype(torch_dtype: torch.dtype):
                 f"{compute_capability[0]}.{compute_capability[1]}. "
                 "You can use float16 instead by explicitly setting the"
                 "`dtype` flag in CLI, for example: --dtype=half.")
+
+
+def raise_if_cache_size_invalid(num_gpu_blocks, block_size,
+                                max_model_len) -> None:
+    if num_gpu_blocks <= 0:
+        raise ValueError("No available memory for the cache blocks. "
+                         "Try increasing `gpu_memory_utilization` when "
+                         "initializing the engine.")
+    max_seq_len = block_size * num_gpu_blocks
+    if max_model_len > max_seq_len:
+        raise ValueError(
+            f"The model's max seq len ({max_model_len}) "
+            "is larger than the maximum number of tokens that can be "
+            f"stored in KV cache ({max_seq_len}). Try increasing "
+            "`gpu_memory_utilization` or decreasing `max_model_len` when "
+            "initializing the engine.")

vllm/worker/worker_base.py

+from abc import ABC, abstractmethod
+from typing import Dict, List
+
+from vllm.lora.request import LoRARequest
+from vllm.sequence import SamplerOutput, SequenceGroupMetadata
+
+
+class WorkerBase(ABC):
+    """Worker interface that allows vLLM to cleanly separate implementations for
+    different hardware.
+    """
+
+    @abstractmethod
+    def init_device(self) -> None:
+        """Initialize device state, such as loading the model or other on-device
+        memory allocations.
+        """
+        raise NotImplementedError
+
+    @abstractmethod
+    def determine_num_available_blocks(self) -> tuple[int, int]:
+        """Determine the number of available blocks for the GPU KV cache and
+        swappable CPU KV cache.
+
+        The implementation may run profiling or other heuristics to determine
+        the size of caches.
+
+        Returns a tuple[num_gpu_blocks, num_cpu_blocks], where num_gpu_blocks
+        are blocks that are "active" on the device and can be appended to.
+        num_cpu_blocks refers to "swapped" blocks in CPU memory and cannot be
+        appended to.
+        """
+        raise NotImplementedError
+
+    @abstractmethod
+    def initialize_cache(self, num_gpu_blocks: int,
+                         num_cpu_blocks: int) -> None:
+        """Initialize the KV cache with the given size in blocks.
+        """
+        raise NotImplementedError
+
+    @abstractmethod
+    def execute_model(self,
+                      seq_group_metadata_list: List[SequenceGroupMetadata],
+                      blocks_to_swap_in: Dict[int, int],
+                      blocks_to_swap_out: Dict[int, int],
+                      blocks_to_copy: Dict[int, List[int]]) -> SamplerOutput:
+        """Executes one model step on the given sequences."""
+        raise NotImplementedError
+
+    @abstractmethod
+    def get_cache_block_size_bytes() -> int:
+        """Return the size of a single cache block, in bytes. Used in
+        speculative decoding.
+        """
+        raise NotImplementedError
+
+    @abstractmethod
+    def add_lora(self, lora_request: LoRARequest) -> bool:
+        raise NotImplementedError
+
+    @abstractmethod
+    def remove_lora(self, lora_id: int) -> bool:
+        raise NotImplementedError
+
+    @abstractmethod
+    def list_loras(self) -> List[int]:
+        raise NotImplementedError
+
+
+class LoraNotSupportedWorkerBase(WorkerBase):
+    """Partial implementation of WorkerBase that raises exceptions when LoRA
+    methods are invoked.
+    """
+
+    def add_lora(self, lora_request: LoRARequest) -> bool:
+        raise ValueError(f"{type(self)} does not support LoRA")
+
+    def remove_lora(self, lora_id: int) -> bool:
+        raise ValueError(f"{type(self)} does not support LoRA")
+
+    def list_loras(self) -> List[int]:
+        raise ValueError(f"{type(self)} does not support LoRA")