import asyncio import os import time from functools import partial from typing import (Callable, Dict, Iterable, List, Optional, Set, Tuple, Type, Union, AsyncIterator) from loguru import logger from transformers import PreTrainedTokenizer from aphrodite.lora.request import LoRARequest from aphrodite.common.config import ModelConfig from aphrodite.engine.args_tools import AsyncEngineArgs from aphrodite.engine.aphrodite_engine import AphroditeEngine from aphrodite.engine.ray_tools import initialize_ray_cluster, ray from aphrodite.common.outputs import RequestOutput from aphrodite.common.sampling_params import SamplingParams from aphrodite.common.sequence import MultiModalData ENGINE_ITERATION_TIMEOUT_S = int( os.environ.get("APHRODITE_ENGINE_ITERATION_TIMEOUT_S", "120")) class AsyncEngineDeadError(RuntimeError): pass def _raise_exception_on_finish( task: asyncio.Task, error_callback: Callable[[Exception], None]) -> None: msg = ("Task finished unexpectedly. This should never happen! " "Please open an issue on Github. Include your full error " "log after killing the process with Ctrl+C.") exception = None try: task.result() # NOTE: This will be thrown if task exits normally (which it should not) raise AsyncEngineDeadError(msg) except asyncio.exceptions.CancelledError: pass except KeyboardInterrupt: raise except Exception as e: exception = e logger.error("Engine background task failed", exc_info=e) error_callback(exception) raise AsyncEngineDeadError( msg + " See stack trace above for the actual cause.") from e class AsyncStream: """A stream of RequestOutputs for a request that can be iterated over asynchronously.""" def __init__(self, request_id: str) -> None: self.request_id = request_id self._queue = asyncio.Queue() self._finished = False def put(self, item: Union[RequestOutput, Exception]) -> None: if self._finished: return self._queue.put_nowait(item) def finish(self) -> None: self._queue.put_nowait(StopAsyncIteration()) self._finished = True @property def finished(self) -> bool: return self._finished def __aiter__(self): return self async def __anext__(self) -> RequestOutput: result = await self._queue.get() if isinstance(result, Exception): raise result return result class RequestTracker: """Synchronous abstraction for tracking requests.""" def __init__(self) -> None: self._request_streams: Dict[str, AsyncStream] = {} self._finished_requests: asyncio.Queue[str] = asyncio.Queue() self._new_requests: asyncio.Queue[Tuple[AsyncStream, dict]] = asyncio.Queue() self.new_requests_event = asyncio.Event() def __contains__(self, item): return item in self._request_streams def __len__(self) -> int: return len(self._request_streams) def propagate_exception(self, exc: Exception, request_id: Optional[str] = None) -> None: """Propagate an exception to request streams (all if request_id is None).""" if request_id is not None: self._request_streams[request_id].put(exc) self.abort_request(request_id) else: for rid, stream in self._request_streams.items(): stream.put(exc) self.abort_request(rid) def process_request_output(self, request_output: RequestOutput, *, verbose: bool = False) -> None: """Process a request output from the engine.""" request_id = request_output.request_id self._request_streams[request_id].put(request_output) if request_output.finished: if verbose: logger.info(f"Finished request {request_id}.") self.abort_request(request_id) def process_exception(self, request_id: str, exception: Exception, *, verbose: bool = False) -> None: """Propagate an exception from the engine.""" self._request_streams[request_id].put(exception) if verbose: logger.info(f"Finished request {request_id}.") self.abort_request(request_id) def add_request(self, request_id: str, **engine_add_request_kwargs) -> AsyncStream: """Add a request to be sent to the engine on the next background loop iteration.""" if request_id in self._request_streams: raise KeyError(f"Request {request_id} already exists.") stream = AsyncStream(request_id) self._new_requests.put_nowait((stream, { "request_id": request_id, **engine_add_request_kwargs })) self.new_requests_event.set() return stream def abort_request(self, request_id: str, *, verbose: bool = False) -> None: """Abort a request during next background loop iteration.""" if verbose: logger.info(f"Aborted request {request_id}.") self._finished_requests.put_nowait(request_id) if request_id not in self._request_streams or self._request_streams[ request_id].finished: # The request has already finished or been aborted. return self._request_streams[request_id].finish() def get_new_and_finished_requests(self) -> Tuple[List[Dict], Set[str]]: """Get the new requests and finished requests to be sent to the engine.""" new_requests: List[Dict] = [] finished_requests: Set[str] = set() while not self._finished_requests.empty(): request_id = self._finished_requests.get_nowait() finished_requests.add(request_id) self._request_streams.pop(request_id, None) while not self._new_requests.empty(): stream, new_request = self._new_requests.get_nowait() if stream.request_id in finished_requests: # The request has already been aborted. stream.finish() continue self._request_streams[stream.request_id] = stream new_requests.append(new_request) return new_requests, finished_requests async def wait_for_new_requests(self): if not self.has_new_requests(): await self.new_requests_event.wait() self.new_requests_event.clear() def has_new_requests(self): return not self._new_requests.empty() class _AsyncAphrodite(AphroditeEngine): """Extension of AphroditeEngine to add async methods.""" async def step_async(self) -> List[RequestOutput]: """Performs one decoding iteration and returns newly generated results. The workers are ran asynchronously if possible. This function performs one decoding iteration of the engine. It first schedules the sequences to be executed in the next iteration and the token blocks to be swapped in/out/copy. Then, it executes the model and updates the scheduler with the model outputs. Finally, it decodes the sequences and returns the newly generated results. """ seq_group_metadata_list, scheduler_outputs = self.scheduler.schedule() if not scheduler_outputs.is_empty(): # Execute the model. output = await self.model_executor.execute_model_async( seq_group_metadata_list, scheduler_outputs.blocks_to_swap_in, scheduler_outputs.blocks_to_swap_out, scheduler_outputs.blocks_to_copy) else: output = [] return self._process_model_outputs(output, scheduler_outputs) async def encode_request_async( self, request_id: str, # pylint: disable=unused-argument prompt: Optional[str], prompt_token_ids: Optional[List[int]] = None, lora_request: Optional[LoRARequest] = None, ): if prompt_token_ids is None: assert prompt is not None prompt_token_ids = await self.tokenizer.encode_async( request_id=request_id, prompt=prompt, lora_request=lora_request) return prompt_token_ids async def add_request_async( self, request_id: str, prompt: Optional[str], sampling_params: SamplingParams, prompt_token_ids: Optional[List[int]] = None, arrival_time: Optional[float] = None, lora_request: Optional[LoRARequest] = None, multi_modal_data: Optional[MultiModalData] = None, ) -> None: if lora_request is not None and not self.lora_config: raise ValueError(f"Got lora_request {lora_request} but LoRA is " "not enabled!") if arrival_time is None: arrival_time = time.time() prompt_token_ids = await self.encode_request_async( request_id=request_id, prompt=prompt, prompt_token_ids=prompt_token_ids, lora_request=lora_request) return self.add_request(request_id, prompt=prompt, prompt_token_ids=prompt_token_ids, sampling_params=sampling_params, arrival_time=arrival_time, lora_request=lora_request, multi_modal_data=multi_modal_data) async def check_health_async(self) -> None: self.model_executor.check_health() class AsyncAphrodite: """An asynchronous wrapper for AphroditeEngine. This class is used to wrap the AphroditeEngine class to make it asynchronous. It uses asyncio to create a background loop that keeps processing incoming requests. The AphroditeEngine is kicked by the generate method when there are requests in the waiting queue. The generate method yields the outputs from the AphroditeEngine to the caller. NOTE: For the comprehensive list of arguments, see `AphroditeEngine`. Args: worker_use_ray: Whether to use Ray for model workers. Required for distributed execution. Should be the same as `parallel_config.worker_use_ray`. engine_use_ray: Whether to make AphroditeEngine a Ray actor. If so, the async frontend will be executed in a separate process as the model workers. log_requests: Whether to log the requests. max_log_len: Maximum number of prompt characters or prompt ID numbers being printed in log. start_engine_loop: If True, the background task to run the engine will be automatically started in the generate call. *args: Arguments for AphroditeEngine. *kwargs: Arguments for AphroditeEngine. """ _engine_class: Type[_AsyncAphrodite] = _AsyncAphrodite def __init__(self, worker_use_ray: bool, engine_use_ray: bool, *args, log_requests: bool = True, max_log_len: int = 0, start_engine_loop: bool = True, **kwargs) -> None: self.worker_use_ray = worker_use_ray self.engine_use_ray = engine_use_ray self.log_requests = log_requests self.max_log_len = max_log_len self.engine = self._init_engine(*args, **kwargs) self.background_loop = None # We need to keep a reference to unshielded # task as well to prevent it from being garbage # collected self._background_loop_unshielded = None self.start_engine_loop = start_engine_loop self._request_tracker: Optional[RequestTracker] = None self._errored_with: Optional[BaseException] = None @classmethod def from_engine_args(cls, engine_args: AsyncEngineArgs, start_engine_loop: bool = True) -> "AsyncAphrodite": """Creates an async LLM engine from the engine arguments.""" # Create the engine configs. engine_config = engine_args.create_engine_config() if engine_config.device_config.device_type == "neuron": raise NotImplementedError("Neuron is not supported for " "async engine yet.") elif engine_config.device_config.device_type == "cpu": from aphrodite.executor.cpu_executor import CPUExecutor executor_class = CPUExecutor elif (engine_config.parallel_config.worker_use_ray or engine_args.engine_use_ray): initialize_ray_cluster(engine_config.parallel_config) from aphrodite.executor.ray_gpu_executor import RayGPUExecutorAsync executor_class = RayGPUExecutorAsync else: assert engine_config.parallel_config.world_size == 1, ( "Ray is required if parallel_config.world_size > 1.") from aphrodite.executor.gpu_executor import GPUExecutorAsync executor_class = GPUExecutorAsync # Create the async LLM engine. engine = cls(engine_config.parallel_config.worker_use_ray, engine_args.engine_use_ray, **engine_config.to_dict(), executor_class=executor_class, log_requests=not engine_args.disable_log_requests, log_stats=not engine_args.disable_log_stats, max_log_len=engine_args.max_log_len, start_engine_loop=start_engine_loop) return engine @property def is_running(self) -> bool: return (self.background_loop is not None and not self._background_loop_unshielded.done()) @property def is_stopped(self) -> bool: return self.errored or (self.background_loop is not None and self._background_loop_unshielded.done()) @property def errored(self) -> bool: return self._errored_with is not None def set_errored(self, exc: Exception) -> None: self._errored_with = exc def _error_callback(self, exc: Exception) -> None: self.set_errored(exc) self._request_tracker.propagate_exception(exc) async def get_tokenizer(self) -> "PreTrainedTokenizer": if self.engine_use_ray: return await self.engine.get_tokenizer.remote() else: return self.engine.get_tokenizer() def start_background_loop(self) -> None: """Start the background loop.""" if self.errored: raise AsyncEngineDeadError( "Background loop has errored already.") from self._errored_with if self.is_running: raise RuntimeError("Background loop is already running.") # Initialize the RequestTracker here so it uses the right event loop. self._request_tracker = RequestTracker() self._background_loop_unshielded = asyncio.get_event_loop( ).create_task(self.run_engine_loop()) self._background_loop_unshielded.add_done_callback( partial(_raise_exception_on_finish, error_callback=self._error_callback)) self.background_loop = asyncio.shield(self._background_loop_unshielded) def _init_engine(self, *args, **kwargs) -> Union[_AsyncAphrodite, "ray.ObjectRef"]: if not self.engine_use_ray: engine_class = self._engine_class elif self.worker_use_ray: engine_class = ray.remote(num_cpus=0)(self._engine_class).remote else: # FIXME: This is a bit hacky. Be careful when changing the # order of the arguments. cache_config = args[1] parallel_config = args[2] if parallel_config.tensor_parallel_size == 1: num_gpus = cache_config.gpu_memory_utilization else: num_gpus = 1 engine_class = ray.remote(num_gpus=num_gpus)( self._engine_class).remote return engine_class(*args, **kwargs) async def engine_step(self) -> bool: """Kick the engine to process the waiting requests. Returns True if there are in-progress requests.""" new_requests, finished_requests = ( self._request_tracker.get_new_and_finished_requests()) for new_request in new_requests: # Add the request into the Aphrodite engine's waiting queue. # TODO: Maybe add add_request_batch to reduce Ray overhead try: if self.engine_use_ray: await self.engine.add_request.remote(**new_request) else: await self.engine.add_request_async(**new_request) except ValueError as e: # TODO: use an Aphrodite specific error for failed validation self._request_tracker.process_exception( new_request["request_id"], e, verbose=self.log_requests, ) if finished_requests: await self._engine_abort(finished_requests) if self.engine_use_ray: request_outputs = await self.engine.step.remote() else: request_outputs = await self.engine.step_async() # Put the outputs into the corresponding streams. for request_output in request_outputs: self._request_tracker.process_request_output( request_output, verbose=self.log_requests) return len(request_outputs) > 0 async def _engine_abort(self, request_ids: Iterable[str]): if self.engine_use_ray: await self.engine.abort_request.remote(request_ids) else: self.engine.abort_request(request_ids) async def run_engine_loop(self): has_requests_in_progress = False try: while True: if not has_requests_in_progress: logger.debug("Waiting for new requests...") await self._request_tracker.wait_for_new_requests() logger.debug("Got new requests!") # Abort if iteration takes too long due to unrecoverable errors # (eg. NCCL timeouts). try: has_requests_in_progress = await asyncio.wait_for( self.engine_step(), ENGINE_ITERATION_TIMEOUT_S) except asyncio.TimeoutError as exc: logger.error( "Engine iteration timed out. This should never happen!" ) self.set_errored(exc) raise await asyncio.sleep(0) except KeyboardInterrupt: logger.info("Engine loop interrupted. Exiting gracefully.") async def add_request( self, request_id: str, prompt: Optional[str], sampling_params: SamplingParams, prompt_token_ids: Optional[List[int]] = None, arrival_time: Optional[float] = None, lora_request: Optional[LoRARequest] = None, ) -> AsyncStream: if self.log_requests: shortened_prompt = prompt shortened_token_ids = prompt_token_ids if self.max_log_len is not None: if shortened_prompt is not None: shortened_prompt = shortened_prompt[:self.max_log_len] if shortened_token_ids is not None: shortened_token_ids = shortened_token_ids[:self. max_log_len] logger.info(f"Received request {request_id}: " f"prompt: {shortened_prompt!r}, " f"sampling_params: {sampling_params}, " f"lora_request: {lora_request}.") if not self.is_running: if self.start_engine_loop: self.start_background_loop() else: raise AsyncEngineDeadError( "Background loop is not running. If it was running, " "inspect the output to find the stacktrace of the " "error that caused the background loop to stop " "(AsyncEngineDeadError).") if arrival_time is None: arrival_time = time.time() if self.engine_use_ray: prompt_token_ids = await self.engine.encode_request_async.remote( request_id=request_id, prompt=prompt, prompt_token_ids=prompt_token_ids, lora_request=lora_request) else: prompt_token_ids = await self.engine.encode_request_async( request_id=request_id, prompt=prompt, prompt_token_ids=prompt_token_ids, lora_request=lora_request) stream = self._request_tracker.add_request( request_id, prompt=prompt, sampling_params=sampling_params, prompt_token_ids=prompt_token_ids, arrival_time=arrival_time, lora_request=lora_request) return stream async def generate( self, prompt: Optional[str], sampling_params: SamplingParams, request_id: str, prompt_token_ids: Optional[List[int]] = None, lora_request: Optional[LoRARequest] = None, ) -> AsyncIterator[RequestOutput]: """Generate outputs for a request. Generate outputs for a request. This method is a coroutine. It adds the request into the waiting queue of the AphroditeEngine and streams the outputs from the AphroditeEngine to the caller. Args: prompt: The prompt string. Can be None if prompt_token_ids is provided. sampling_params: The sampling parameters of the request. request_id: The unique id of the request. prompt_token_ids: The token IDs of the prompt. If None, we use the tokenizer to convert the prompts to token IDs. lora_request: LoRA request to use for generation, if any. Yields: The output `RequestOutput` objects from the AphroditeEngine for the request. Details: - If the engine is not running, start the background loop, which iteratively invokes # pylint: disable=line-too-long :meth:`~aphrodite.engine.async_aphrodite.AsyncAphrodite.engine_step` to process the waiting requests. - Add the request to the engine's `RequestTracker`. On the next background loop, this request will be sent to the underlying engine. Also, a corresponding `AsyncStream` will be created. - Wait for the request outputs from `AsyncStream` and yield them. Example: >>> # Please refer to entrypoints/api_server.py for >>> # the complete example. >>> >>> # initialize the engine and the example input >>> engine = AsyncAphrodite.from_engine_args(engine_args) >>> example_input = { >>> "prompt": "What is LLM?", >>> "stream": False, # assume the non-streaming case >>> "temperature": 0.0, >>> "request_id": 0, >>> } >>> >>> # start the generation >>> results_generator = engine.generate( >>> example_input["prompt"], >>> SamplingParams(temperature=example_input["temperature"]), >>> example_input["request_id"]) >>> >>> # get the results >>> final_output = None >>> async for request_output in results_generator: >>> if await request.is_disconnected(): >>> # Abort the request if the client disconnects. >>> await engine.abort(request_id) >>> # Return or raise an error >>> ... >>> final_output = request_output >>> >>> # Process and return the final output >>> ... """ # Preprocess the request. # This should not be used for logging, as it is monotonic time. arrival_time = time.monotonic() try: stream = await self.add_request( request_id, prompt, sampling_params, prompt_token_ids=prompt_token_ids, arrival_time=arrival_time, lora_request=lora_request, ) async for request_output in stream: yield request_output except asyncio.exceptions.CancelledError: logger.info(f"Request {request_id} cancelled.") self._abort(request_id) raise except (Exception, asyncio.CancelledError) as e: # If there is an exception or coroutine is cancelled, abort the # request. self._abort(request_id) raise e async def abort(self, request_id: str) -> None: """Abort a request. Abort a submitted request. If the request is finished or not found, this method will be a no-op. Args: request_id: The unique id of the request. """ if not self.is_running: raise AsyncEngineDeadError( "Background loop is not running. If it was running, " "inspect the output to find the stacktrace of the " "error that caused the background loop to stop " "(AsyncEngineDeadError).") return self._abort(request_id) def _abort(self, request_id: str) -> None: """Abort a request. Abort a submitted request. If the request is finished or not found, this method will be a no-op. Args: request_id: The unique id of the request. """ self._request_tracker.abort_request(request_id, verbose=self.log_requests) async def get_model_config(self) -> ModelConfig: """Get the model configuration of the Aphrodite engine.""" if self.engine_use_ray: return await self.engine.get_model_config.remote() else: return self.engine.get_model_config() async def do_log_stats(self) -> None: if self.engine_use_ray: await self.engine.do_log_stats.remote() else: self.engine.do_log_stats() async def check_health(self) -> None: """Raises an error if engine is unhealthy.""" t = time.perf_counter() logger.debug("Starting health check...") if self.is_stopped: raise AsyncEngineDeadError("Background loop is stopped.") if self.engine_use_ray: try: await self.engine.check_health.remote() except ray.exceptions.RayActorError as e: raise RuntimeError("Engine is dead.") from e else: await self.engine.check_health_async() logger.debug(f"Health check took {time.perf_counter()-t}s")