[Hardware][Intel] Add CPU inference backend (#3634)

Co-authored-by: Kunshang Ji <kunshang.ji@intel.com>
Co-authored-by: Yuan Zhou <yuan.zhou@intel.com>

vllm/executor/cpu_executor.py

@@ -0,0 +1,154 @@
+import os
+from typing import Dict, List, Optional
+
+import torch
+
+from vllm.config import (CacheConfig, DeviceConfig, LoRAConfig, ModelConfig,
+                         ParallelConfig, SchedulerConfig)
+from vllm.executor.executor_base import ExecutorBase
+from vllm.logger import init_logger
+from vllm.lora.request import LoRARequest
+from vllm.sequence import SamplerOutput, SequenceGroupMetadata
+from vllm.utils import get_distributed_init_method, get_ip, get_open_port
+
+logger = init_logger(__name__)
+
+
+class CPUExecutor(ExecutorBase):
+
+    def __init__(self, model_config: ModelConfig, cache_config: CacheConfig,
+                 parallel_config: ParallelConfig,
+                 scheduler_config: SchedulerConfig,
+                 device_config: DeviceConfig,
+                 lora_config: Optional[LoRAConfig], *args, **kwargs) -> None:
+        assert device_config.device_type == "cpu"
+        assert lora_config is None, "cpu backend doesn't support LoRA"
+        model_config = _verify_and_get_model_config(model_config)
+        cache_config = _verify_and_get_cache_config(cache_config)
+
+        self.model_config = model_config
+        self.cache_config = cache_config
+        self.lora_config = lora_config
+        self.parallel_config = parallel_config
+        self.scheduler_config = scheduler_config
+        self.device_config = device_config
+
+        # 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
+
+        assert self.parallel_config.world_size == 1, (
+            "CPUExecutor only supports single CPU socket currently.")
+
+        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,
+            local_rank=0,
+            rank=0,
+            distributed_init_method=distributed_init_method,
+            lora_config=self.lora_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:
+        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,
+        )
+
+        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)
+
+    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:
+        output = self.driver_worker.execute_model(
+            seq_group_metadata_list=seq_group_metadata_list,
+            blocks_to_swap_in=blocks_to_swap_in,
+            blocks_to_swap_out=blocks_to_swap_out,
+            blocks_to_copy=blocks_to_copy,
+        )
+        return output
+
+    def add_lora(self, lora_request: LoRARequest) -> bool:
+        raise NotImplementedError("LoRA is not implemented for cpu backend.")
+
+    def remove_lora(self, lora_id: int) -> bool:
+        raise NotImplementedError("LoRA is not implemented for cpu backend.")
+
+    def list_loras(self) -> List[int]:
+        raise NotImplementedError("LoRA is not implemented for cpu backend.")
+
+    def check_health(self) -> None:
+        # CPUExecutor will always be healthy as long as
+        # it's running.
+        return
+
+
+def _verify_and_get_model_config(config: ModelConfig) -> ModelConfig:
+    if config.dtype == torch.float16:
+        logger.warning("float16 is not supported on CPU, casting to bfloat16.")
+        config.dtype = torch.bfloat16
+    if not config.enforce_eager:
+        logger.warning(
+            "CUDA graph is not supported on CPU, fallback to the eager "
+            "mode.")
+        config.enforce_eager = True
+    return config
+
+
+def _verify_and_get_cache_config(config: CacheConfig) -> CacheConfig:
+    _GB = 1 << 30
+    if config.enable_prefix_caching:
+        logger.warning("Prefix caching is not supported on CPU, disable it.")
+        config.enable_prefix_caching = False
+
+    kv_cache_space_str = os.getenv("VLLM_CPU_KVCACHE_SPACE", "0")
+    kv_cache_space = int(kv_cache_space_str)
+
+    if kv_cache_space >= 0:
+        if kv_cache_space == 0:
+            config.cpu_kvcache_space_bytes = 4 * _GB  # type: ignore
+            logger.warning("Environment variable VLLM_CPU_KVCACHE_SPACE (GB) "
+                           "for CPU backend is not set, using 4 by default.")
+        else:
+            config.cpu_kvcache_space_bytes = kv_cache_space * _GB  # type: ignore
+    else:
+        raise RuntimeError(
+            "Invalid environment variable VLLM_CPU_KVCACHE_SPACE"
+            f" {kv_cache_space}, expect a positive integer value.")
+
+    return config