[Misc] [Core] Implement RFC "Augment BaseExecutor interfaces to enabl…

…e hardware-agnostic speculative decoding" (vllm-project#3837)

tests/core/block/e2e/test_correctness.py

@@ -16,7 +16,7 @@
 
         # 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 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)
@@ -525,23 +525,22 @@ 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])
 @pytest.mark.parametrize('available_cpu_blocks', [500])
 @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

@@ -366,7 +366,7 @@ 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.
     """
 
@@ -376,14 +376,14 @@ def __init__(
         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

@@ -59,7 +59,7 @@ 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
@@ -250,7 +250,7 @@ def add_cli_args(
             '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'
@@ -454,7 +454,7 @@ def create_engine_config(self, ) -> EngineConfig:
         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

@@ -129,6 +129,8 @@ def __init__(
             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 (
@@ -180,6 +182,26 @@ def __init__(
                 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 @@ def __init__(self, model_config: ModelConfig, cache_config: CacheConfig,
 
         # 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 @@ def _init_worker(self):
         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 @@ def _init_worker(self):
         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 @@ def execute_model(self,
         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 @@ def __init__(
     ) -> 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],