# SPDX-License-Identifier: Apache-2.0 import queue import signal import threading import time from concurrent.futures import Future from multiprocessing.connection import Connection from typing import Any, List, Optional, Tuple, Type import psutil import zmq import zmq.asyncio from vllm.config import VllmConfig from vllm.logger import init_logger from vllm.lora.request import LoRARequest from vllm.transformers_utils.config import ( maybe_register_config_serialize_by_value) from vllm.utils import get_exception_traceback, zmq_socket_ctx from vllm.v1.core.kv_cache_utils import get_kv_cache_configs from vllm.v1.core.scheduler import Scheduler, SchedulerOutput from vllm.v1.engine import (EngineCoreOutputs, EngineCoreRequest, EngineCoreRequestType) from vllm.v1.engine.mm_input_cache import MMInputCacheServer from vllm.v1.executor.abstract import Executor from vllm.v1.outputs import ModelRunnerOutput from vllm.v1.request import Request, RequestStatus from vllm.v1.serial_utils import MsgpackDecoder, MsgpackEncoder from vllm.v1.spec_decode.ngram_proposer import NgramProposer from vllm.version import __version__ as VLLM_VERSION logger = init_logger(__name__) POLLING_TIMEOUT_S = 2.5 class EngineCore: """Inner loop of vLLM's Engine.""" def __init__( self, vllm_config: VllmConfig, executor_class: Type[Executor], log_stats: bool, ): assert vllm_config.model_config.runner_type != "pooling" logger.info("Initializing a V1 LLM engine (v%s) with config: %s", VLLM_VERSION, vllm_config) self.log_stats = log_stats # Setup Model. self.model_executor = executor_class(vllm_config) # Setup KV Caches and update CacheConfig after profiling. num_gpu_blocks, num_cpu_blocks = self._initialize_kv_caches( vllm_config) vllm_config.cache_config.num_gpu_blocks = num_gpu_blocks vllm_config.cache_config.num_cpu_blocks = num_cpu_blocks # Setup scheduler. self.scheduler = Scheduler( scheduler_config=vllm_config.scheduler_config, model_config=vllm_config.model_config, cache_config=vllm_config.cache_config, lora_config=vllm_config.lora_config, speculative_config=vllm_config.speculative_config, log_stats=self.log_stats, ) # Setup MM Input Mapper. self.mm_input_cache_server = MMInputCacheServer( vllm_config.model_config) # Setup batch queue for pipeline parallelism. # Batch queue for scheduled batches. This enables us to asynchronously # schedule and execute batches, and is required by pipeline parallelism # to eliminate pipeline bubbles. self.batch_queue_size = self.model_executor.max_concurrent_batches self.batch_queue: Optional[queue.Queue[Tuple[Future[ModelRunnerOutput], SchedulerOutput]]] = None if self.batch_queue_size > 1: logger.info("Batch queue is enabled with size %d", self.batch_queue_size) self.batch_queue = queue.Queue(self.batch_queue_size) # Setup speculative decode. # TODO: find a better way to check if we are using ngram. self.use_spec_decode = False if self.scheduler.speculative_config: assert self.scheduler.speculative_config.ngram_prompt_lookup_min \ , "Only ngram spec decode is supported in V1." self.proposer = NgramProposer() self.use_spec_decode = True def _initialize_kv_caches(self, vllm_config: VllmConfig) -> Tuple[int, int]: start = time.time() # Get all kv cache needed by the model kv_cache_specs = self.model_executor.get_kv_cache_specs() # Profiles the peak memory usage of the model to determine how much # memory can be allocated for kv cache. available_gpu_memory = self.model_executor.determine_available_memory() # Get the kv cache tensor size kv_cache_configs = get_kv_cache_configs(vllm_config, kv_cache_specs, available_gpu_memory) num_gpu_blocks_set = set(config.num_blocks for config in kv_cache_configs) assert len(num_gpu_blocks_set) == 1, ( f"num_gpu_blocks need to be the same across workers, " f"but they are different: {num_gpu_blocks_set}") num_gpu_blocks = num_gpu_blocks_set.pop() num_cpu_blocks = 0 # Initialize kv cache and warmup the execution self.model_executor.initialize(kv_cache_configs) elapsed = time.time() - start logger.info(("init engine (profile, create kv cache, " "warmup model) took %.2f seconds"), elapsed) return num_gpu_blocks, num_cpu_blocks def add_request(self, request: EngineCoreRequest): """Add request to the scheduler.""" if request.mm_hashes is not None: # Here, if hash exists for a multimodal input, then it will be # fetched from the cache, else it will be added to the cache. # Note that the cache here is mirrored with the client cache, so # anything that has a hash must have a HIT cache entry here # as well. assert request.mm_inputs is not None request.mm_inputs = self.mm_input_cache_server.get_and_update( request.mm_inputs, request.mm_hashes) req = Request.from_engine_core_request(request) self.scheduler.add_request(req) def abort_requests(self, request_ids: List[str]): """Abort requests from the scheduler.""" # TODO: The scheduler doesn't really need to know the # specific finish reason, TBD whether we propagate that # (i.e. client-aborted vs stop criteria met). self.scheduler.finish_requests(request_ids, RequestStatus.FINISHED_ABORTED) def step(self) -> EngineCoreOutputs: """Schedule, execute, and make output.""" if not self.scheduler.has_unfinished_requests(): return EngineCoreOutputs( outputs=[], scheduler_stats=self.scheduler.make_stats()) if self.use_spec_decode: self.propose_tokens() scheduler_output = self.scheduler.schedule() output = self.model_executor.execute_model(scheduler_output) engine_core_outputs = self.scheduler.update_from_output( scheduler_output, output) # type: ignore return engine_core_outputs def step_with_batch_queue(self) -> Optional[EngineCoreOutputs]: """Schedule and execute batches with the batch queue. Note that if nothing to output in this step, None is returned. The execution flow is as follows: 1. Try to schedule a new batch if there are unscheduled requests and the job queue is not full. If a new batch is scheduled, directly return an empty engine core output. In other words, we won't check and return model outputs before the batch queue is full. 2. If there is no new scheduled batch, meaning that the batch queue is full or no other requests can be scheduled, we block until the first batch in the job queue is finished. 3. Update the scheduler from the output. """ assert self.batch_queue is not None engine_core_outputs = None scheduler_output = None # If there are unscheduled requests and the job queue # is not full, schedule a new batch. Note that this is not blocking. if (self.scheduler.get_num_unscheduled_requests() > 0 and not self.batch_queue.full()): scheduler_output = self.scheduler.schedule() if scheduler_output.total_num_scheduled_tokens > 0: future = self.model_executor.execute_model(scheduler_output) self.batch_queue.put_nowait( (future, scheduler_output)) # type: ignore # If all requests are scheduled or the job queue is full, # block until the first batch in the job queue is finished. if (scheduler_output is None or scheduler_output.total_num_scheduled_tokens == 0): try: future, scheduler_output = self.batch_queue.get( timeout=POLLING_TIMEOUT_S) # Blocking until the first result is available. model_output = future.result() self.batch_queue.task_done() engine_core_outputs = self.scheduler.update_from_output( scheduler_output, model_output) except queue.Empty: # If the queue is empty (timeout at .get), return # an empty EngineCoreOutputs for logging. engine_core_outputs = EngineCoreOutputs( outputs=[], scheduler_stats=self.scheduler.make_stats()) return engine_core_outputs def shutdown(self): self.model_executor.shutdown() def profile(self, is_start: bool = True): self.model_executor.profile(is_start) def propose_tokens(self): assert self.scheduler.speculative_config is not None for req in self.scheduler.running: # Ignore requests that are doing chunked prefill. if req.num_computed_tokens < req.num_tokens - 1: continue # Ignore requests that already have spec tokens. if req.spec_token_ids: continue spec_tokens = self.proposer.propose( req.all_token_ids, self.scheduler.speculative_config.ngram_prompt_lookup_min, self.scheduler.speculative_config.num_speculative_tokens, ) if spec_tokens: req.append_spec_token_ids(spec_tokens) def reset_prefix_cache(self): self.scheduler.reset_prefix_cache() def add_lora(self, lora_request: LoRARequest) -> None: self.model_executor.add_lora(lora_request) class EngineCoreProc(EngineCore): """ZMQ-wrapper for running EngineCore in background process.""" def __init__( self, input_path: str, output_path: str, ready_pipe: Connection, vllm_config: VllmConfig, executor_class: Type[Executor], log_stats: bool, ): super().__init__(vllm_config, executor_class, log_stats) # Background Threads and Queues for IO. These enable us to # overlap ZMQ socket IO with GPU since they release the GIL, # and to overlap some serialization/deserialization with the # model forward pass. # Threads handle Socket <-> Queues and core_busy_loop uses Queue. self.input_queue: queue.Queue[Tuple[EngineCoreRequestType, Any]] = queue.Queue() self.output_queue: queue.Queue[EngineCoreOutputs] = queue.Queue() threading.Thread(target=self.process_input_socket, args=(input_path, ), daemon=True).start() threading.Thread(target=self.process_output_socket, args=(output_path, ), daemon=True).start() # Send Readiness signal to EngineClient. ready_pipe.send({"status": "READY"}) @staticmethod def run_engine_core(*args, **kwargs): """Launch EngineCore busy loop in background process.""" # Signal handler used for graceful termination. # SystemExit exception is only raised once to allow this and worker # processes to terminate without error shutdown_requested = False # Ensure we can serialize transformer config after spawning maybe_register_config_serialize_by_value() def signal_handler(signum, frame): nonlocal shutdown_requested if not shutdown_requested: shutdown_requested = True raise SystemExit() # Either SIGTERM or SIGINT will terminate the engine_core signal.signal(signal.SIGTERM, signal_handler) signal.signal(signal.SIGINT, signal_handler) parent_process = psutil.Process().parent() engine_core = None try: engine_core = EngineCoreProc(*args, **kwargs) engine_core.run_busy_loop() except SystemExit: logger.debug("EngineCore interrupted.") except Exception: traceback = get_exception_traceback() logger.error("EngineCore hit an exception: %s", traceback) parent_process.send_signal(signal.SIGUSR1) finally: if engine_core is not None: engine_core.shutdown() def run_busy_loop(self): """Core busy loop of the EngineCore.""" step_fn = (self.step if self.batch_queue is None else self.step_with_batch_queue) # Loop until process is sent a SIGINT or SIGTERM while True: # 1) Poll the input queue until there is work to do. if not self.scheduler.has_unfinished_requests(): while True: try: req = self.input_queue.get(timeout=POLLING_TIMEOUT_S) self._handle_client_request(*req) break except queue.Empty: logger.debug("EngineCore busy loop waiting.") # Break out the loop so we can log_stats in step(). if self.log_stats: break except BaseException: raise # 2) Handle any new client requests. while not self.input_queue.empty(): req = self.input_queue.get_nowait() self._handle_client_request(*req) # 3) Step the engine core. outputs = step_fn() # 4) Put EngineCoreOutputs into the output queue. if outputs is not None: self.output_queue.put_nowait(outputs) def _handle_client_request(self, request_type: EngineCoreRequestType, request: Any) -> None: """Dispatch request from client.""" if request_type == EngineCoreRequestType.ADD: self.add_request(request) elif request_type == EngineCoreRequestType.ABORT: self.abort_requests(request) elif request_type == EngineCoreRequestType.RESET_PREFIX_CACHE: self.reset_prefix_cache() elif request_type == EngineCoreRequestType.PROFILE: self.model_executor.profile(request) elif request_type == EngineCoreRequestType.ADD_LORA: self.model_executor.add_lora(request) def process_input_socket(self, input_path: str): """Input socket IO thread.""" # Msgpack serialization decoding. add_request_decoder = MsgpackDecoder(EngineCoreRequest) add_lora_decoder = MsgpackDecoder(LoRARequest) generic_decoder = MsgpackDecoder() with zmq_socket_ctx(input_path, zmq.constants.PULL) as socket: while True: # (RequestType, RequestData) type_frame, data_frame = socket.recv_multipart(copy=False) request_type = EngineCoreRequestType(bytes(type_frame.buffer)) # Deserialize the request data. decoder = None if request_type == EngineCoreRequestType.ADD: decoder = add_request_decoder elif request_type == EngineCoreRequestType.ADD_LORA: decoder = add_lora_decoder else: decoder = generic_decoder request = decoder.decode(data_frame.buffer) # Push to input queue for core busy loop. self.input_queue.put_nowait((request_type, request)) def process_output_socket(self, output_path: str): """Output socket IO thread.""" # Msgpack serialization encoding. encoder = MsgpackEncoder() # Reuse send buffer. buffer = bytearray() with zmq_socket_ctx(output_path, zmq.constants.PUSH) as socket: while True: outputs = self.output_queue.get() encoder.encode_into(outputs, buffer) socket.send_multipart((buffer, ), copy=False)