import asyncio import time import weakref from functools import partial from typing import (Any, AsyncGenerator, Callable, Dict, Iterable, List, Optional, Set, Tuple, Type, Union) from weakref import ReferenceType from loguru import logger import aphrodite.common.envs as envs from aphrodite.common.config import (DecodingConfig, EngineConfig, LoRAConfig, ModelConfig, ParallelConfig, SchedulerConfig) from aphrodite.common.outputs import EmbeddingRequestOutput, RequestOutput from aphrodite.common.pooling_params import PoolingParams from aphrodite.common.sampling_params import SamplingParams from aphrodite.common.sequence import ExecuteModelRequest from aphrodite.common.utils import weak_bind from aphrodite.engine.aphrodite_engine import (AphroditeEngine, SchedulerOutputState) from aphrodite.engine.args_tools import AsyncEngineArgs from aphrodite.engine.async_timeout import asyncio_timeout from aphrodite.engine.metrics_types import StatLoggerBase from aphrodite.executor.executor_base import ExecutorAsyncBase from aphrodite.executor.ray_utils import initialize_ray_cluster from aphrodite.inputs import PromptType from aphrodite.lora.request import LoRARequest from aphrodite.modeling.layers.sampler import SamplerOutput from aphrodite.processing.scheduler import SchedulerOutputs from aphrodite.prompt_adapter.request import PromptAdapterRequest from aphrodite.transformers_utils.tokenizer import AnyTokenizer ENGINE_ITERATION_TIMEOUT_S = envs.APHRODITE_ENGINE_ITERATION_TIMEOUT_S class AsyncEngineDeadError(RuntimeError): pass def _log_task_completion(task: asyncio.Task, error_callback: Callable[[Exception], None]) -> None: """This function is only intended for the `engine.run_engine_loop()` task. In particular, that task runs a `while True` loop that can only exit if there is an exception. """ exception = None try: return_value = task.result() raise AssertionError( f"The engine background task should never finish without an " f"exception. {return_value}") except asyncio.exceptions.CancelledError: # We assume that if the task is cancelled, we are gracefully shutting # down. This should only happen on program exit. logger.info("Engine is gracefully shutting down.") except Exception as e: exception = e logger.error("Engine background task failed", exc_info=e) error_callback(exception) raise AsyncEngineDeadError( "Task finished unexpectedly. This should never happen! " "Please open an issue on Github. See stack trace above for the " "actual cause.") from e STOP_ITERATION = Exception() # Sentinel class AsyncStream: """A stream of RequestOutputs or EmbeddingRequestOutputs for a request that can be iterated over asynchronously via an async generator.""" def __init__(self, request_id: str, cancel: Callable[[str], None]) -> None: self.request_id = request_id self._cancel = cancel self._queue: asyncio.Queue = asyncio.Queue() self._finished = False def put(self, item: Union[RequestOutput, EmbeddingRequestOutput, Exception]) -> None: if not self._finished: self._queue.put_nowait(item) def finish( self, exception: Optional[Union[BaseException, Type[BaseException]]] = None, ) -> None: if not self._finished: self._finished = True self._queue.put_nowait( exception if self._is_raisable(exception) else STOP_ITERATION) @property def finished(self) -> bool: return self._finished async def generator( self ) -> AsyncGenerator[Union[RequestOutput, EmbeddingRequestOutput], None]: try: while True: result = await self._queue.get() if self._is_raisable(result): if result == STOP_ITERATION: return raise result yield result except GeneratorExit: self._cancel(self.request_id) raise asyncio.CancelledError from None @staticmethod def _is_raisable(value: Any): return isinstance(value, BaseException) or \ (isinstance(value, type) and \ issubclass(value, BaseException)) class RequestTracker: """Synchronous abstraction for tracking requests.""" def __init__(self) -> None: self._request_streams: Dict[str, AsyncStream] = {} self._aborted_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.abort_request(request_id, exception=exc) else: # NB: tuple() used here because self.abort_request pops the stream # out of self._request_streams, so we can't iterate on it directly for rid in tuple(self._request_streams.keys()): self.abort_request(rid, exception=exc) def process_request_output(self, request_output: Union[RequestOutput, EmbeddingRequestOutput], *, verbose: bool = False) -> None: """Process a request output from the engine.""" request_id = request_output.request_id finished = request_output.finished if finished: stream = self._request_streams.pop(request_id, None) else: stream = self._request_streams.get(request_id) # Guard against a KeyError which can occur if the request was aborted # while the output was generated if stream is not None: stream.put(request_output) if finished: stream.finish() if verbose and finished: logger.info(f"Finished request {request_id}.") def process_exception(self, request_id: str, exception: BaseException, *, verbose: bool = False) -> None: """Propagate an exception from the engine.""" if verbose: logger.info(f"Finished request {request_id}.") self.abort_request(request_id, exception=exception) def add_request(self, request_id: str, *, verbose: bool = False, **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.") abort_request = partial(self.abort_request, verbose=verbose) stream = AsyncStream(request_id, abort_request) self._new_requests.put_nowait((stream, { "request_id": request_id, **engine_add_request_kwargs })) self.new_requests_event.set() if verbose: logger.info(f"Added request {request_id}.") return stream def abort_request(self, request_id: str, *, exception: Optional[Union[BaseException, Type[BaseException]]] = None, verbose: bool = False) -> None: """Abort a request during next background loop iteration.""" if verbose: logger.info(f"Aborted request {request_id}.") self._aborted_requests.put_nowait(request_id) stream = self._request_streams.pop(request_id, None) if stream is not None: stream.finish(exception=exception) def get_new_and_aborted_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._aborted_requests.empty(): request_id = self._aborted_requests.get_nowait() finished_requests.add(request_id) while not self._new_requests.empty(): stream, new_request = self._new_requests.get_nowait() request_id = stream.request_id if request_id in finished_requests: # The request has already been aborted. stream.finish(asyncio.CancelledError) finished_requests.discard(request_id) else: self._request_streams[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.""" def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) async def step_async( self, virtual_engine: int ) -> List[Union[RequestOutput, EmbeddingRequestOutput]]: """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. """ # these are cached outputs from previous iterations. None if on first # iteration cached_outputs = self.cached_scheduler_outputs[virtual_engine] seq_group_metadata_list = cached_outputs.seq_group_metadata_list scheduler_outputs = cached_outputs.scheduler_outputs allow_async_output_proc = cached_outputs.allow_async_output_proc ctx = self.scheduler_contexts[virtual_engine] # Clear outputs for each new scheduler iteration ctx.request_outputs.clear() # skip the scheduler if there are any remaining steps in the seq groups. # This ensures that the scheduler is only called again when the current # batch has completed. if not self._has_remaining_steps(seq_group_metadata_list): # Schedule iteration (seq_group_metadata_list, scheduler_outputs, allow_async_output_proc ) = self.scheduler[virtual_engine].schedule() ctx.seq_group_metadata_list = seq_group_metadata_list ctx.scheduler_outputs = scheduler_outputs # Maybe switch from async mode to sync mode if not allow_async_output_proc and len(ctx.output_queue) > 0: self._process_model_outputs(ctx=ctx) if (self.scheduler_config.is_multi_step and scheduler_outputs.num_lookahead_slots > 0): # cache the scheduler outputs for the next iteration if we have # lookahead slots self._cache_scheduler_outputs_for_multi_step( virtual_engine, seq_group_metadata_list, scheduler_outputs, allow_async_output_proc) assert seq_group_metadata_list is not None assert scheduler_outputs is not None if not scheduler_outputs.is_empty(): finished_requests_ids = self.scheduler[ virtual_engine].get_and_reset_finished_requests_ids() # Check if we have a cached last_output from the previous iteration. # For supporting PP this is probably the best way to pass the # sampled_token_ids, as a separate broadcast over all the PP stages # will cause one virtual engine's microbatch to block the pipeline. last_sampled_token_ids = \ self._get_last_sampled_token_ids(virtual_engine) execute_model_req = ExecuteModelRequest( seq_group_metadata_list=seq_group_metadata_list, blocks_to_swap_in=scheduler_outputs.blocks_to_swap_in, blocks_to_swap_out=scheduler_outputs.blocks_to_swap_out, blocks_to_copy=scheduler_outputs.blocks_to_copy, virtual_engine=virtual_engine, num_lookahead_slots=scheduler_outputs.num_lookahead_slots, running_queue_size=scheduler_outputs.running_queue_size, finished_requests_ids=finished_requests_ids, # We use ExecuteModelRequest to pass the last sampled_token_ids # to each of the non-last PP stages for in-place prepare_input. last_sampled_token_ids=last_sampled_token_ids) if allow_async_output_proc: execute_model_req.async_callback = self.async_callbacks[ virtual_engine] # Execute the model. outputs = await self.model_executor.execute_model_async( execute_model_req) # we need to do this here so that last step's sampled_token_ids can # be passed to the next iteration for PP. if self.scheduler_config.is_multi_step: self._update_cached_scheduler_output(virtual_engine, outputs) else: if len(ctx.output_queue) > 0: self._process_model_outputs(ctx=ctx) outputs = [] # Finish the current step for all the sequence groups. if self.scheduler_config.is_multi_step: for seq_group in seq_group_metadata_list: seq_group.finish_step() if not self._has_remaining_steps(seq_group_metadata_list): # Clear the cache if we have finished all the steps if self.scheduler_config.is_multi_step: self.cached_scheduler_outputs[ virtual_engine] = SchedulerOutputState() ctx.append_output(outputs=outputs, seq_group_metadata_list=seq_group_metadata_list, scheduler_outputs=scheduler_outputs, is_async=allow_async_output_proc, is_last_step=True) if outputs and allow_async_output_proc: assert len( outputs ) == 1, "Async postprocessor expects only a single output set" self._advance_to_next_step( outputs[0], seq_group_metadata_list, scheduler_outputs.scheduled_seq_groups) if not allow_async_output_proc: self._process_model_outputs(ctx=ctx) # Log stats. self.do_log_stats(scheduler_outputs, outputs) else: # Multi-step case return ctx.request_outputs if not self.has_unfinished_requests(): # Drain async postprocessor (if exists) if len(ctx.output_queue) > 0: self._process_model_outputs(ctx=ctx) assert len(ctx.output_queue) == 0 return ctx.request_outputs async def stop_remote_worker_execution_loop_async(self) -> None: """Stop the remote worker execution loop.""" await self.model_executor.stop_remote_worker_execution_loop_async() async def add_request_async( self, request_id: str, prompt: PromptType, params: Union[SamplingParams, PoolingParams], arrival_time: Optional[float] = None, lora_request: Optional[LoRARequest] = None, prompt_adapter_request: Optional[PromptAdapterRequest] = None, ) -> None: """Async version of :meth:`add_request`.""" 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() preprocessed_inputs = await self.input_preprocessor.preprocess_async( prompt, request_id=request_id, lora_request=lora_request, prompt_adapter_request=prompt_adapter_request, ) processed_inputs = self.input_processor(preprocessed_inputs) self._add_processed_request( request_id=request_id, processed_inputs=processed_inputs, params=params, arrival_time=arrival_time, lora_request=lora_request, prompt_adapter_request=prompt_adapter_request, ) async def check_health_async(self) -> None: if self.tokenizer: self.tokenizer.check_health() self.model_executor.check_health() class AsyncAphrodite: """An asynchronous wrapper for :class:`AphroditeEngine`. This class is used to wrap the :class:`AphroditeEngine` class to make it asynchronous. It uses asyncio to create a background loop that keeps processing incoming requests. The :class:`AphroditeEngine` is kicked by the generate method when there are requests in the waiting queue. The generate method yields the outputs from the :class:`AphroditeEngine` to the caller. Args:. log_requests: Whether to log the requests. start_engine_loop: If True, the background task to run the engine will be automatically started in the generate call. *args: Arguments for :class:`AphroditeEngine`. **kwargs: Arguments for :class:`AphroditeEngine`. """ _engine_class: Type[_AsyncAphrodite] = _AsyncAphrodite def __init__(self, *args, log_requests: bool = True, start_engine_loop: bool = True, **kwargs) -> None: self.log_requests = log_requests self.engine = self._engine_class(*args, **kwargs) # This ensures quick processing of request outputs # so the append to asyncio queues is not delayed, # especially for multi-step. self.use_process_request_outputs_callback = ( self.engine.model_config.use_async_output_proc) if self.use_process_request_outputs_callback: self.engine.process_request_outputs_callback = \ weak_bind(self.process_request_outputs) self.background_loop: Optional[asyncio.Future] = None # We need to keep a reference to unshielded # task as well to prevent it from being garbage # collected self._background_loop_unshielded: Optional[asyncio.Task] = None self.start_engine_loop = start_engine_loop self._errored_with: Optional[BaseException] = None # Lazy initialized fields self._request_tracker: RequestTracker def __del__(self): if rt := getattr(self, "request_tracker", None): # Wake up engine loop so that it will exit cleanly rt.new_requests_event.set() @classmethod def _get_executor_cls( cls, engine_config: EngineConfig) -> Type[ExecutorAsyncBase]: distributed_executor_backend = ( engine_config.parallel_config.distributed_executor_backend) if isinstance(distributed_executor_backend, type): if not issubclass(distributed_executor_backend, ExecutorAsyncBase): raise TypeError( "distributed_executor_backend must be a subclass of " f"ExecutorAsyncBase. Got {distributed_executor_backend}.") executor_class = distributed_executor_backend elif engine_config.device_config.device_type == "neuron": from aphrodite.executor.neuron_executor import NeuronExecutorAsync executor_class = NeuronExecutorAsync elif engine_config.device_config.device_type == "tpu": if distributed_executor_backend == "ray": from aphrodite.executor.ray_tpu_executor import ( RayTPUExecutorAsync) executor_class = RayTPUExecutorAsync else: assert distributed_executor_backend is None from aphrodite.executor.tpu_executor import TPUExecutorAsync executor_class = TPUExecutorAsync elif engine_config.device_config.device_type == "cpu": from aphrodite.executor.cpu_executor import CPUExecutorAsync executor_class = CPUExecutorAsync elif engine_config.device_config.device_type == "openvino": assert distributed_executor_backend is None, ( "Distributed execution is not supported with " "the OpenVINO backend.") from aphrodite.executor.openvino_executor import ( OpenVINOExecutorAsync) executor_class = OpenVINOExecutorAsync elif engine_config.device_config.device_type == "xpu": if distributed_executor_backend is None: from aphrodite.executor.xpu_executor import XPUExecutorAsync executor_class = XPUExecutorAsync elif distributed_executor_backend == "ray": from aphrodite.executor.ray_xpu_executor import ( RayXPUExecutorAsync) executor_class = RayXPUExecutorAsync elif distributed_executor_backend == "mp": from aphrodite.executor.multiproc_xpu_executor import ( MultiprocessingXPUExecutorAsync) executor_class = MultiprocessingXPUExecutorAsync else: raise RuntimeError( "Not supported distributed execution model on XPU device.") elif distributed_executor_backend == "ray": from aphrodite.executor.ray_gpu_executor import RayGPUExecutorAsync executor_class = RayGPUExecutorAsync elif distributed_executor_backend == "mp": from aphrodite.executor.multiproc_gpu_executor import ( MultiprocessingGPUExecutorAsync) executor_class = MultiprocessingGPUExecutorAsync else: from aphrodite.executor.gpu_executor import GPUExecutorAsync executor_class = GPUExecutorAsync return executor_class @classmethod def from_engine_args( cls, engine_args: AsyncEngineArgs, engine_config: Optional[EngineConfig] = None, start_engine_loop: bool = True, stat_loggers: Optional[Dict[str, StatLoggerBase]] = None, ) -> "AsyncAphrodite": """Creates an async LLM engine from the engine arguments.""" # Create the engine configs. if engine_config is None: engine_config = engine_args.create_engine_config() executor_class = cls._get_executor_cls(engine_config) if executor_class.uses_ray: initialize_ray_cluster(engine_config.parallel_config) # Create the async LLM engine. engine = cls( **engine_config.to_dict(), executor_class=executor_class, log_requests=not engine_args.disable_log_requests, log_stats=not engine_args.disable_log_stats, start_engine_loop=start_engine_loop, stat_loggers=stat_loggers, ) return engine @property def is_running(self) -> bool: return (self.background_loop is not None and self._background_loop_unshielded 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 is not None and self._background_loop_unshielded.done()) @property def errored(self) -> bool: return self._errored_with is not None @property def dead_error(self) -> BaseException: return 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).") 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, lora_request: Optional[LoRARequest] = None, ) -> AnyTokenizer: return await (self.engine.get_tokenizer_group(). get_lora_tokenizer_async(lora_request)) 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(weakref.ref(self))) self._background_loop_unshielded.add_done_callback( partial(_log_task_completion, error_callback=self._error_callback)) self.background_loop = asyncio.shield(self._background_loop_unshielded) def shutdown_background_loop(self) -> None: """ Shut down the background loop. This method needs to be called during cleanup to remove references to `self` and properly GC the resources held by the async LLM engine (e.g., the executors as well as their resources). """ if self._background_loop_unshielded is not None: self._background_loop_unshielded.cancel() self._background_loop_unshielded = None self.background_loop = None async def engine_step(self, virtual_engine: int) -> bool: """Kick the engine to process the waiting requests. Returns True if there are in-progress requests.""" new_requests, aborted_requests = ( self._request_tracker.get_new_and_aborted_requests()) for new_request in new_requests: # Add the request into the Aphrodite engine's waiting queue. try: 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 aborted_requests: await self._engine_abort(aborted_requests) request_outputs = await self.engine.step_async(virtual_engine) # Put the outputs into the corresponding streams. # If used as a callback, then already invoked inside # LLMEngine's _process_model_outputs if not self.use_process_request_outputs_callback: all_finished = self.process_request_outputs(request_outputs) else: # For callback case, we only need to detect when all # requests are finished all_finished = all(request_output.finished for request_output in request_outputs) return not all_finished def process_request_outputs(self, request_outputs) -> bool: # Put the outputs into the corresponding streams. all_finished = True for request_output in request_outputs: self._request_tracker.process_request_output( request_output, verbose=self.log_requests) all_finished = all_finished and request_output.finished return all_finished async def _engine_abort(self, request_ids: Iterable[str]): self.engine.abort_request(request_ids) @staticmethod async def run_engine_loop(engine_ref: ReferenceType): """We use a weakref to the engine so that the running loop doesn't prevent the engine being garbage collected.""" engine: Optional["AsyncAphrodite"] = engine_ref() if not engine: return pipeline_parallel_size = \ engine.engine.parallel_config.pipeline_parallel_size has_requests_in_progress = [False] * pipeline_parallel_size while True: if not any(has_requests_in_progress): logger.debug("Waiting for new requests...") # Stop the execute model loop in parallel workers until there # are more requests to process. This avoids waiting # indefinitely in torch.distributed ops which may otherwise # timeout, and unblocks the RPC thread in the workers so that # they can process any other queued control plane messages, # such as add/remove lora adapters. await engine.engine.stop_remote_worker_execution_loop_async() request_tracker = engine._request_tracker # Allow engine to be garbage collected while # waiting for new requests del engine await asyncio.sleep(0) if engine_ref() is None: return await request_tracker.wait_for_new_requests() engine = engine_ref() if not engine: return logger.debug("Got new requests!") requests_in_progress = [ asyncio.create_task(engine.engine_step(ve)) for ve in range(pipeline_parallel_size) ] has_requests_in_progress = [True] * pipeline_parallel_size # Abort if iteration takes too long due to unrecoverable errors # (eg. NCCL timeouts). try: async with asyncio_timeout(ENGINE_ITERATION_TIMEOUT_S): done, _ = await asyncio.wait( requests_in_progress, return_when=asyncio.FIRST_COMPLETED) for _ in range(pipeline_parallel_size): await asyncio.sleep(0) for task in done: result = task.result() virtual_engine = requests_in_progress.index(task) has_unfinished_requests = ( engine.engine. has_unfinished_requests_for_virtual_engine( virtual_engine)) if result or has_unfinished_requests: requests_in_progress[virtual_engine] = ( asyncio.create_task( engine.engine_step(virtual_engine))) has_requests_in_progress[virtual_engine] = True else: has_requests_in_progress[virtual_engine] = False except asyncio.TimeoutError as exc: logger.error( "Engine iteration timed out. This should never happen!") engine.set_errored(exc) raise await asyncio.sleep(0) # This method does not need to be async, but kept that way # for backwards compatibility. async def add_request( self, request_id: str, prompt: PromptType, params: Union[SamplingParams, PoolingParams], arrival_time: Optional[float] = None, lora_request: Optional[LoRARequest] = None, prompt_adapter_request: Optional[PromptAdapterRequest] = None ) -> AsyncGenerator[Union[RequestOutput, EmbeddingRequestOutput], None]: 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).") stream = self._request_tracker.add_request( request_id, verbose=self.log_requests, prompt=prompt, params=params, arrival_time=arrival_time or time.time(), lora_request=lora_request, prompt_adapter_request=prompt_adapter_request) return stream.generator() async def generate( self, prompt: PromptType, sampling_params: SamplingParams, request_id: str, lora_request: Optional[LoRARequest] = None, prompt_adapter_request: Optional[PromptAdapterRequest] = None ) -> AsyncGenerator[RequestOutput, None]: """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 to the LLM. See :class:`~aphrodite.inputs.PromptType` sampling_params: The sampling parameters of the request. request_id: The unique id of the request. lora_request: LoRA request to use for generation, if any. prompt_adapter_request: Prompt Adapter 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 >>> ... """ async for output in await self.add_request( request_id, prompt, sampling_params, lora_request=lora_request, prompt_adapter_request=prompt_adapter_request, ): yield AphroditeEngine.validate_output(output, RequestOutput) async def encode( self, prompt: PromptType, pooling_params: PoolingParams, request_id: str, lora_request: Optional[LoRARequest] = None, ) -> AsyncGenerator[EmbeddingRequestOutput, None]: """Generate outputs for a request from an embedding model. 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 to the LLM. See :class:`~aphrodite.inputs.PromptType` for more details about the format of each input. pooling_params: The pooling parameters of the request. request_id: The unique id of the request. lora_request: LoRA request to use for generation, if any. Yields: The output `EmbeddingRequestOutput` objects from the AphroditeEngine for the request. Details: - If the engine is not running, start the background loop, which iteratively invokes :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 endpoints/api_server.py for >>> # the complete example. >>> >>> # initialize the engine and the example input >>> engine = AsyncAphrodite.from_engine_args(engine_args) >>> example_input = { >>> "input": "What is LLM?", >>> "request_id": 0, >>> } >>> >>> # start the generation >>> results_generator = engine.encode( >>> example_input["input"], >>> PoolingParams(), >>> 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 >>> ... """ async for output in await self.add_request( request_id, prompt, pooling_params, lora_request=lora_request, ): yield AphroditeEngine.validate_output(output, EmbeddingRequestOutput) 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, exception=asyncio.CancelledError, verbose=self.log_requests) async def get_model_config(self) -> ModelConfig: """Get the model configuration of the Aphrodite engine.""" return self.engine.get_model_config() async def get_parallel_config(self) -> ParallelConfig: """Get the parallel configuration of the Aphrodite engine.""" return self.engine.get_parallel_config() async def get_decoding_config(self) -> DecodingConfig: """Get the decoding configuration of the Aphrodite engine.""" return self.engine.get_decoding_config() async def get_scheduler_config(self) -> SchedulerConfig: """Get the scheduling configuration of the Aphrodite engine.""" return self.engine.get_scheduler_config() async def get_lora_config(self) -> LoRAConfig: """Get the lora configuration of the Aphrodite engine.""" return self.engine.get_lora_config() async def do_log_stats( self, scheduler_outputs: Optional[SchedulerOutputs] = None, model_output: Optional[List[SamplerOutput]] = None) -> None: 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.") await self.engine.check_health_async() logger.debug(f"Health check took {time.perf_counter() - t}s") def add_logger(self, logger_name: str, logger: StatLoggerBase) -> None: self.engine.add_logger(logger_name=logger_name, logger=logger) def remove_logger(self, logger_name: str) -> None: self.engine.remove_logger(logger_name=logger_name)