core.py

# 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.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,
            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)

    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())

        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 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)