import time from collections import deque from contextlib import contextmanager from dataclasses import dataclass from functools import partial from typing import (TYPE_CHECKING, Any, Callable, ClassVar, Deque, Dict, Iterable, List, NamedTuple, Optional) from typing import Sequence as GenericSequence from typing import Set, Type, Union import torch from loguru import logger from typing_extensions import TypeVar import aphrodite.common.envs as envs from aphrodite.common.config import (CacheConfig, DecodingConfig, DeviceConfig, EngineConfig, LoadConfig, LoRAConfig, ModelConfig, ParallelConfig, PromptAdapterConfig, SchedulerConfig, SpeculativeConfig) from aphrodite.common.logger import setup_logger from aphrodite.common.outputs import (EmbeddingRequestOutput, RequestOutput, RequestOutputFactory) from aphrodite.common.pooling_params import PoolingParams from aphrodite.common.sampling_params import RequestOutputKind, SamplingParams from aphrodite.common.sequence import (EmbeddingSequenceGroupOutput, ExecuteModelRequest, Sequence, SequenceGroup, SequenceGroupMetadata, SequenceStatus) from aphrodite.common.utils import Counter, Device, weak_bind from aphrodite.engine.args_tools import EngineArgs from aphrodite.engine.metrics_types import StatLoggerBase, Stats from aphrodite.engine.output_processor.interfaces import ( SequenceGroupOutputProcessor) from aphrodite.engine.output_processor.stop_checker import StopChecker from aphrodite.engine.output_processor.util import ( create_output_by_sequence_group) from aphrodite.executor.executor_base import ExecutorBase from aphrodite.executor.ray_utils import initialize_ray_cluster from aphrodite.inputs import (INPUT_REGISTRY, EncoderDecoderLLMInputs, InputRegistry, LLMInputs, PromptType) from aphrodite.inputs.preprocess import InputPreprocessor from aphrodite.lora.request import LoRARequest from aphrodite.modeling.layers.sampler import SamplerOutput from aphrodite.processing.scheduler import (ScheduledSequenceGroup, Scheduler, SchedulerOutputs) from aphrodite.prompt_adapter.request import PromptAdapterRequest from aphrodite.transformers_utils.config import try_get_generation_config from aphrodite.transformers_utils.detokenizer import Detokenizer from aphrodite.transformers_utils.tokenizer import AnyTokenizer from aphrodite.transformers_utils.tokenizer_group import ( BaseTokenizerGroup, init_tokenizer_from_configs) from aphrodite.version import __version__ as APHRODITE_VERSION _LOCAL_LOGGING_INTERVAL_SEC = 5 APHRODITE_USE_RAY_SPMD_WORKER = envs.APHRODITE_USE_RAY_SPMD_WORKER def _load_generation_config_dict(model_config: ModelConfig) -> Dict[str, Any]: config = try_get_generation_config( model_config.model, trust_remote_code=model_config.trust_remote_code, revision=model_config.revision, ) if config is None: return {} return config.to_diff_dict() _G = TypeVar("_G", bound=BaseTokenizerGroup, default=BaseTokenizerGroup) _O = TypeVar("_O", RequestOutput, EmbeddingRequestOutput) @dataclass class SchedulerOutputState: """Caches the scheduler outputs for a virtual engine. Used for Multi-Step""" seq_group_metadata_list: Optional[List[SequenceGroupMetadata]] = None scheduler_outputs: Optional[SchedulerOutputs] = None allow_async_output_proc: bool = False last_output: Optional[SamplerOutput] = None class OutputData(NamedTuple): outputs: List[SamplerOutput] seq_group_metadata_list: List[SequenceGroupMetadata] scheduler_outputs: SchedulerOutputs is_async: bool is_last_step: bool skip: List[int] class SchedulerContext: def __init__(self): self.output_queue: Deque[OutputData] = deque() self.request_outputs: List[Union[RequestOutput, EmbeddingRequestOutput]] = [] self.seq_group_metadata_list: Optional[ List[SequenceGroupMetadata]] = None self.scheduler_outputs: Optional[SchedulerOutputs] = None def append_output(self, outputs: List[SamplerOutput], seq_group_metadata_list: List[SequenceGroupMetadata], scheduler_outputs: SchedulerOutputs, is_async: bool, is_last_step: bool): self.output_queue.append( OutputData(outputs=outputs, seq_group_metadata_list=seq_group_metadata_list, scheduler_outputs=scheduler_outputs, is_async=is_async, is_last_step=is_last_step, skip=[])) class AphroditeEngine: """An LLM engine that receives requests and generates texts. This is the main class for the Aphrodite engine. It receives requests from clients and generates texts from the LLM. It includes a tokenizer, a language model (possibly distributed across multiple GPUs), and GPU memory space allocated for intermediate states (aka KV cache). This class utilizes iteration-level scheduling and efficient memory management to maximize the serving throughput. The `LLM` class wraps this class for offline batched inference and the `AsyncAphrodite` class wraps this class for online serving. NOTE: The config arguments are derived from the `EngineArgs` class. For the comprehensive list of arguments, see `EngineArgs`. Args: model_config: The configuration related to the LLM model. cache_config: The configuration related to the KV cache memory management. parallel_config: The configuration related to distributed execution. scheduler_config: The configuration related to the request scheduler. device_config: The configuration related to the device. lora_config (Optional): The configuration related to serving multi-LoRA. speculative_config (Optional): The configuration related to speculative decoding. executor_class: The model executor class for managing distributed execution. prompt_adapter_config (Optional): The configuration related to serving prompt adapters. log_stats: Whether to log statistics. """ DO_VALIDATE_OUTPUT: ClassVar[bool] = False """A flag to toggle whether to validate the type of request output.""" @classmethod @contextmanager def enable_output_validation(cls): cls.DO_VALIDATE_OUTPUT = True yield cls.DO_VALIDATE_OUTPUT = False @classmethod def validate_output( cls, output: object, output_type: Type[_O], ) -> _O: do_validate = cls.DO_VALIDATE_OUTPUT if ((TYPE_CHECKING or do_validate) and not isinstance(output, output_type)): raise TypeError(f"Expected output of type {output_type}, " f"but found type {type(output)}") return output @classmethod def validate_outputs( cls, outputs: GenericSequence[object], output_type: Type[_O], ) -> List[_O]: do_validate = cls.DO_VALIDATE_OUTPUT outputs_: List[_O] if TYPE_CHECKING or do_validate: outputs_ = [] for output in outputs: if not isinstance(output, output_type): raise TypeError(f"Expected output of type {output_type}, " f"but found type {type(output)}") outputs_.append(output) else: outputs_ = outputs return outputs_ tokenizer: Optional[BaseTokenizerGroup] def __init__( self, model_config: ModelConfig, cache_config: CacheConfig, parallel_config: ParallelConfig, scheduler_config: SchedulerConfig, device_config: DeviceConfig, load_config: LoadConfig, lora_config: Optional[LoRAConfig], speculative_config: Optional[SpeculativeConfig], decoding_config: Optional[DecodingConfig], prompt_adapter_config: Optional[PromptAdapterConfig], executor_class: Type[ExecutorBase], log_stats: bool, stat_loggers: Optional[Dict[str, StatLoggerBase]] = None, input_registry: InputRegistry = INPUT_REGISTRY, ) -> None: try: import aphrodite.commit_id commit_id = True except ImportError: commit_id = False config_dict = { "Model": model_config.model, "Speculative Config": speculative_config, "DataType": model_config.dtype, "Model Load Format": load_config.load_format, "Tensor Parallel Size": parallel_config.tensor_parallel_size, "Pipeline Parallel Size": parallel_config.pipeline_parallel_size, "Disable Custom All-Reduce": parallel_config.disable_custom_all_reduce, "Quantization Format": model_config.quantization, "Context Length": model_config.max_model_len, "Enforce Eager Mode": model_config.enforce_eager, "Prefix Caching": cache_config.enable_prefix_caching, "KV Cache DataType": cache_config.cache_dtype, "Device": device_config.device, "Rope Scaling": model_config.rope_scaling, "Guided Decoding Backend": decoding_config, "Scheduler Steps": scheduler_config.num_scheduler_steps, "Async Output Processing": model_config.use_async_output_proc, } logger.info("-" * 85) if not commit_id: logger.info( f"Initializing Aphrodite Engine (v{APHRODITE_VERSION}) " "with the following config:") else: logger.info(f"Initializing Aphrodite Engine (v{APHRODITE_VERSION} " f"commit {aphrodite.__short_commit__}) with the " "following config:") for key, value in config_dict.items(): if value is not None and not ((key == "Model Load Format" or key ==\ "KV Cache DataType") and value == \ "auto"): logger.info(f"{key} = {value!r}") logger.info("-" * 85) # TODO: Print more configs in debug mode. from aphrodite.plugins import load_general_plugins load_general_plugins() 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 self.speculative_config = speculative_config self.load_config = load_config self.decoding_config = decoding_config or DecodingConfig() self.prompt_adapter_config = prompt_adapter_config self.log_stats = log_stats if not self.model_config.skip_tokenizer_init: self.tokenizer = self._init_tokenizer() self.detokenizer = Detokenizer(self.tokenizer) tokenizer_group = self.get_tokenizer_group() else: self.tokenizer = None self.detokenizer = None tokenizer_group = None # Ensure that the function doesn't contain a reference to self, # to avoid engine GC issues def get_tokenizer_for_seq(sequence: Sequence) -> AnyTokenizer: assert tokenizer_group, ("tokenizer_group cannot be None, " "make sure skip_tokenizer_init is False") return tokenizer_group.get_lora_tokenizer(sequence.lora_request) self.seq_counter = Counter() self.generation_config_fields = _load_generation_config_dict( model_config) self.input_preprocessor = InputPreprocessor(model_config, self.tokenizer) self.input_registry = input_registry self.input_processor = input_registry.create_input_processor( model_config) self.model_executor = executor_class( model_config=model_config, cache_config=cache_config, parallel_config=parallel_config, scheduler_config=scheduler_config, device_config=device_config, lora_config=lora_config, speculative_config=speculative_config, load_config=load_config, prompt_adapter_config=prompt_adapter_config, ) if not self.model_config.embedding_mode: self._initialize_kv_caches() if self.tokenizer: # Ping the tokenizer to ensure liveness if it runs in a # different process. self.tokenizer.ping() self.cached_scheduler_outputs = [ SchedulerOutputState() for _ in range(self.parallel_config.pipeline_parallel_size) ] self.scheduler_contexts = [ SchedulerContext() for _ in range(self.parallel_config.pipeline_parallel_size) ] if model_config.use_async_output_proc: process_model_outputs = weak_bind(self._process_model_outputs) self.async_callbacks = [ partial(process_model_outputs, ctx=self.scheduler_contexts[v_id]) for v_id in range(self.parallel_config.pipeline_parallel_size) ] else: self.async_callbacks = [] # Currently used by AsyncLLMEngine to ensure quick append # of request outputs to asyncio queues self.process_request_outputs_callback: Optional[Callable] = None # Create the scheduler. # NOTE: the cache_config here have been updated with the numbers of # GPU and CPU blocks, which are profiled in the distributed executor. self.scheduler = [ Scheduler( scheduler_config, cache_config, lora_config, parallel_config.pipeline_parallel_size, self.async_callbacks[v_id] if model_config.use_async_output_proc else None) for v_id in range(parallel_config.pipeline_parallel_size) ] # Metric Logging. if self.log_stats: if stat_loggers is not None: self.stat_loggers = stat_loggers else: # Lazy import for prometheus multiprocessing. # We need to set PROMETHEUS_MULTIPROC_DIR environment variable # before prometheus_client is imported. # See https://prometheus.github.io/client_python/multiprocess/ from aphrodite.engine.metrics import (LoggingStatLogger, PrometheusStatLogger) self.stat_loggers = { "logging": LoggingStatLogger( local_interval=_LOCAL_LOGGING_INTERVAL_SEC), "prometheus": PrometheusStatLogger( local_interval=_LOCAL_LOGGING_INTERVAL_SEC, labels=dict(model_name=model_config.served_model_name), max_model_len=self.model_config.max_model_len), } self.stat_loggers["prometheus"].info("cache_config", self.cache_config) # Create sequence output processor, e.g. for beam search or # speculative decoding. self.output_processor = ( SequenceGroupOutputProcessor.create_output_processor( self.scheduler_config, self.detokenizer, self.scheduler, self.seq_counter, get_tokenizer_for_seq, stop_checker=StopChecker( self.scheduler_config.max_model_len, get_tokenizer_for_seq, ), )) def _initialize_kv_caches(self) -> None: """Initialize the KV cache in the worker(s). The workers will determine the number of blocks in both the GPU cache and the swap CPU cache. """ num_gpu_blocks, num_cpu_blocks = ( self.model_executor.determine_num_available_blocks()) if self.cache_config.num_gpu_blocks_override is not None: num_gpu_blocks_override = self.cache_config.num_gpu_blocks_override logger.info(f"Overriding {num_gpu_blocks=} with " f"{num_gpu_blocks_override=}") num_gpu_blocks = num_gpu_blocks_override self.cache_config.num_gpu_blocks = num_gpu_blocks self.cache_config.num_cpu_blocks = num_cpu_blocks self.model_executor.initialize_cache(num_gpu_blocks, num_cpu_blocks) @classmethod def _get_executor_cls(cls, engine_config: EngineConfig) -> Type[ExecutorBase]: distributed_executor_backend = ( engine_config.parallel_config.distributed_executor_backend) # Initialize the cluster and specify the executor class. if isinstance(distributed_executor_backend, type): if not issubclass(distributed_executor_backend, ExecutorBase): raise TypeError( "distributed_executor_backend must be a subclass of " f"ExecutorBase. Got {distributed_executor_backend}.") if distributed_executor_backend.uses_ray: # type: ignore initialize_ray_cluster(engine_config.parallel_config) executor_class = distributed_executor_backend elif engine_config.device_config.device_type == "neuron": from aphrodite.executor.neuron_executor import NeuronExecutor executor_class = NeuronExecutor elif engine_config.device_config.device_type == "tpu": if distributed_executor_backend == "ray": initialize_ray_cluster(engine_config.parallel_config) from aphrodite.executor.ray_tpu_executor import RayTPUExecutor executor_class = RayTPUExecutor else: assert distributed_executor_backend is None from aphrodite.executor.tpu_executor import TPUExecutor executor_class = TPUExecutor elif engine_config.device_config.device_type == "cpu": from aphrodite.executor.cpu_executor import CPUExecutor executor_class = CPUExecutor elif engine_config.device_config.device_type == "openvino": from aphrodite.executor.openvino_executor import OpenVINOExecutor executor_class = OpenVINOExecutor elif engine_config.device_config.device_type == "xpu": if distributed_executor_backend == "ray": initialize_ray_cluster(engine_config.parallel_config) from aphrodite.executor.ray_xpu_executor import RayXPUExecutor executor_class = RayXPUExecutor elif distributed_executor_backend == "mp": logger.error( "Both start methods (spawn and fork) have issues " "on XPU if you use mp backend, Please try ray instead.") else: from aphrodite.executor.xpu_executor import XPUExecutor executor_class = XPUExecutor elif distributed_executor_backend == "ray": initialize_ray_cluster(engine_config.parallel_config) from aphrodite.executor.ray_gpu_executor import RayGPUExecutor executor_class = RayGPUExecutor elif distributed_executor_backend == "mp": from aphrodite.executor.multiproc_gpu_executor import ( MultiprocessingGPUExecutor) assert not envs.APHRODITE_USE_RAY_SPMD_WORKER, ( "multiprocessing distributed executor backend does not " "support APHRODITE_USE_RAY_SPMD_WORKER=1") executor_class = MultiprocessingGPUExecutor else: from aphrodite.executor.gpu_executor import GPUExecutor executor_class = GPUExecutor return executor_class @classmethod def from_engine_args( cls, engine_args: EngineArgs, stat_loggers: Optional[Dict[str, StatLoggerBase]] = None, ) -> "AphroditeEngine": """Creates an Aphrodite engine from the engine arguments.""" # Create the engine configs. engine_config = engine_args.create_engine_config() executor_class = cls._get_executor_cls(engine_config) # Create the LLM engine. engine = cls( **engine_config.to_dict(), executor_class=executor_class, log_stats=not engine_args.disable_log_stats, stat_loggers=stat_loggers, ) return engine def __reduce__(self): # This is to ensure that the AphroditeEngine is not referenced in # the closure used to initialize Ray worker actors raise RuntimeError("AphroditeEngine should not be pickled!") def __del__(self): # Shutdown model executor when engine is garbage collected # Use getattr since __init__ can fail before the field is set if model_executor := getattr(self, "model_executor", None): model_executor.shutdown() def get_tokenizer_group( self, group_type: Type[_G] = BaseTokenizerGroup, ) -> _G: tokenizer_group = self.tokenizer if tokenizer_group is None: raise ValueError("Unable to get tokenizer because " "skip_tokenizer_init is True") if not isinstance(tokenizer_group, group_type): raise TypeError("Invalid type of tokenizer group. " f"Expected type: {group_type}, but " f"found type: {type(tokenizer_group)}") return tokenizer_group def get_tokenizer( self, lora_request: Optional[LoRARequest] = None, ) -> AnyTokenizer: return self.get_tokenizer_group().get_lora_tokenizer(lora_request) def _init_tokenizer(self) -> BaseTokenizerGroup: return init_tokenizer_from_configs( model_config=self.model_config, scheduler_config=self.scheduler_config, parallel_config=self.parallel_config, enable_lora=bool(self.lora_config)) def _verify_args(self) -> None: self.model_config.verify_with_parallel_config(self.parallel_config) self.cache_config.verify_with_parallel_config(self.parallel_config) if self.lora_config: self.lora_config.verify_with_model_config(self.model_config) self.lora_config.verify_with_scheduler_config( self.scheduler_config) if self.prompt_adapter_config: self.prompt_adapter_config.verify_with_model_config( self.model_config) def _add_processed_request( self, request_id: str, processed_inputs: Union[LLMInputs, EncoderDecoderLLMInputs], params: Union[SamplingParams, PoolingParams], arrival_time: float, lora_request: Optional[LoRARequest], prompt_adapter_request: Optional[PromptAdapterRequest], ) -> None: self._validate_model_inputs(processed_inputs) # Create the sequences. block_size = self.cache_config.block_size seq_id = next(self.seq_counter) eos_token_id = self.input_preprocessor.get_eos_token_id(lora_request) seq = Sequence(seq_id, processed_inputs, block_size, eos_token_id, lora_request, prompt_adapter_request) encoder_seq = None if 'encoder_prompt_token_ids' in processed_inputs: encoder_seq = Sequence(seq_id, processed_inputs, block_size, eos_token_id, lora_request, prompt_adapter_request, from_decoder_prompt=False) # Create a SequenceGroup based on SamplingParams or PoolingParams if isinstance(params, SamplingParams): seq_group = self._create_sequence_group_with_sampling( request_id, seq, params, arrival_time=arrival_time, lora_request=lora_request, prompt_adapter_request=prompt_adapter_request, encoder_seq=encoder_seq) elif isinstance(params, PoolingParams): seq_group = self._create_sequence_group_with_pooling( request_id, seq, params, arrival_time=arrival_time, lora_request=lora_request, prompt_adapter_request=prompt_adapter_request, encoder_seq=encoder_seq) else: raise ValueError( "Either SamplingParams or PoolingParams must be provided.") # Add the sequence group to the scheduler with least unfinished seqs. costs = [ scheduler.get_num_unfinished_seq_groups() for scheduler in self.scheduler ] min_cost_scheduler = self.scheduler[costs.index(min(costs))] min_cost_scheduler.add_seq_group(seq_group) def stop_remote_worker_execution_loop(self) -> None: self.model_executor.stop_remote_worker_execution_loop() 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, ) -> None: """Add a request to the engine's request pool. The request is added to the request pool and will be processed by the scheduler as `engine.step()` is called. The exact scheduling policy is determined by the scheduler. Args: request_id: The unique ID of the request. prompt: The prompt to the LLM. See :class:`~aphrodite.common.inputs.PromptType` for more details about the format of each input. params: Parameters for sampling or pooling. SamplingParams for text generation. PoolingParams for pooling. prompt_token_ids: The token IDs of the prompt. If None, we use the tokenizer to convert the prompts to token IDs. arrival_time: The arrival time of the request. If None, we use the current monotonic time. Details: - Set arrival_time to the current time if it is None. - Set prompt_token_ids to the encoded prompt if it is None. - Create `best_of` number of :class:`~aphrodite.common.sequence` objects. - Create a :class:`~aphrodite.common.sequenceGroup` object from the list of :class:`~aphrodite.common.sequence`. - Add the :class:`~aphrodite.common.sequenceGroup` object to the scheduler. Example: >>> # initialize engine >>> engine = AphroditeEngine.from_engine_args(engine_args) >>> # set request arguments >>> example_prompt = "Who is the president of the United States?" >>> sampling_params = SamplingParams(temperature=0.0) >>> request_id = 0 >>> >>> # add the request to the engine >>> engine.add_request( >>> str(request_id), >>> example_prompt, >>> SamplingParams(temperature=0.0)) >>> # continue the request processing >>> ... """ 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 = self.input_preprocessor.preprocess( 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, ) def _create_sequence_group_with_sampling( self, request_id: str, seq: Sequence, sampling_params: SamplingParams, arrival_time: float, lora_request: Optional[LoRARequest], prompt_adapter_request: Optional[PromptAdapterRequest] = None, encoder_seq: Optional[Sequence] = None, ) -> SequenceGroup: """Creates a SequenceGroup with SamplingParams.""" max_logprobs = self.get_model_config().max_logprobs if (sampling_params.logprobs and sampling_params.logprobs > max_logprobs) or ( sampling_params.prompt_logprobs and sampling_params.prompt_logprobs > max_logprobs): raise ValueError(f"Cannot request more than " f"{max_logprobs} logprobs.") # Defensive copy of SamplingParams, which are used by the sampler, # this doesn't deep-copy LogitsProcessor objects sampling_params = sampling_params.clone() sampling_params.update_from_generation_config( self.generation_config_fields, seq.eos_token_id) sampling_params._verify_with_scheduler_config(self.scheduler_config) # Create the sequence group. seq_group = SequenceGroup( request_id=request_id, seqs=[seq], arrival_time=arrival_time, sampling_params=sampling_params, lora_request=lora_request, prompt_adapter_request=prompt_adapter_request, encoder_seq=encoder_seq) return seq_group def _create_sequence_group_with_pooling( self, request_id: str, seq: Sequence, pooling_params: PoolingParams, arrival_time: float, lora_request: Optional[LoRARequest], prompt_adapter_request: Optional[PromptAdapterRequest], encoder_seq: Optional[Sequence] = None, ) -> SequenceGroup: """Creates a SequenceGroup with PoolingParams.""" # Defensive copy of PoolingParams, which are used by the pooler pooling_params = pooling_params.clone() # Create the sequence group. seq_group = SequenceGroup( request_id=request_id, seqs=[seq], arrival_time=arrival_time, lora_request=lora_request, pooling_params=pooling_params, prompt_adapter_request=prompt_adapter_request, encoder_seq=encoder_seq) return seq_group def abort_request(self, request_id: Union[str, Iterable[str]]) -> None: """Aborts a request(s) with the given ID. Args: request_id: The ID(s) of the request to abort. Details: - Refer to the :meth:`~aphrodite.processing.scheduler.Scheduler.abort_seq_group` from class :class:`~aphrodite.processing.scheduler.Scheduler`. Example: >>> # initialize engine and add a request with request_id >>> request_id = str(0) >>> # abort the request >>> engine.abort_request(request_id) """ for scheduler in self.scheduler: scheduler.abort_seq_group(request_id) def get_model_config(self) -> ModelConfig: """Gets the model configuration.""" return self.model_config def get_parallel_config(self) -> ParallelConfig: """Gets the parallel configuration.""" return self.parallel_config def get_decoding_config(self) -> DecodingConfig: """Gets the decoding configuration.""" return self.decoding_config def get_scheduler_config(self) -> SchedulerConfig: """Gets the scheduler configuration.""" return self.scheduler_config def get_lora_config(self) -> LoRAConfig: """Gets the LoRA configuration.""" return self.lora_config def get_num_unfinished_requests(self) -> int: """Gets the number of unfinished requests.""" return sum(scheduler.get_num_unfinished_seq_groups() for scheduler in self.scheduler) def has_unfinished_requests(self) -> bool: """Returns True if there are unfinished requests.""" return any(scheduler.has_unfinished_seqs() for scheduler in self.scheduler) def has_unfinished_requests_for_virtual_engine( self, virtual_engine: int) -> bool: """ Returns True if there are unfinished requests for the virtual engine. """ return self.scheduler[virtual_engine].has_unfinished_seqs() @staticmethod def _process_sequence_group_outputs( seq_group: SequenceGroup, outputs: List[EmbeddingSequenceGroupOutput], ) -> None: seq_group.embeddings = outputs[0].embeddings for seq in seq_group.get_seqs(): seq.status = SequenceStatus.FINISHED_STOPPED return def _process_model_outputs(self, ctx: SchedulerContext, request_id: Optional[str] = None) -> None: """Apply the model output to the sequences in the scheduled seq groups and return responses. ctx: The virtual engine context to work on request_id: If provided, then only this request is going to be processed """ now = time.time() if len(ctx.output_queue) == 0: return None # Get pending async postprocessor if request_id: # When we process only one request, no pop is required # (since later we will process all of the rest) (outputs, seq_group_metadata_list, scheduler_outputs, is_async, is_last_step, skip) = ctx.output_queue[0] else: (outputs, seq_group_metadata_list, scheduler_outputs, is_async, is_last_step, skip) = ctx.output_queue.popleft() # Sanity check assert len(seq_group_metadata_list) == len( scheduler_outputs.scheduled_seq_groups) # Organize outputs by [step][sequence group] instead of # [sequence group][step]. if len(outputs) > 1: outputs_by_sequence_group = create_output_by_sequence_group( outputs, num_seq_groups=len(seq_group_metadata_list)) else: outputs_by_sequence_group = outputs # Determine the requests we need to operate on if request_id: indices = [] for i, seq_group_meta in enumerate(seq_group_metadata_list): if seq_group_meta.request_id == request_id: assert i not in skip # Cannot be called twice indices.append(i) break # If the request_id was not found, then it means that # this is a new request that has no pending async # postprocessor if not indices: return else: indices = range(len(seq_group_metadata_list)) # type: ignore finished_before: List[int] = [] finished_now: List[int] = [] for i in indices: if i in skip: continue seq_group_meta = seq_group_metadata_list[i] scheduled_seq_group = scheduler_outputs.scheduled_seq_groups[i] seq_group = scheduled_seq_group.seq_group if seq_group.is_finished(): finished_before.append(i) continue if len(outputs) > 1: output = outputs_by_sequence_group[i] else: output = [outputs_by_sequence_group[0][i]] if not is_async: seq_group.update_num_computed_tokens( scheduled_seq_group.token_chunk_size) if self.model_config.embedding_mode: self._process_sequence_group_outputs(seq_group, output) else: self.output_processor.process_prompt_logprob(seq_group, output) if seq_group_meta.do_sample: self.output_processor.process_outputs( seq_group, output, is_async) if seq_group.is_finished(): finished_now.append(i) # Generate outputs for the requests that finished this iteration for i in finished_now: scheduled_seq_group = scheduler_outputs.scheduled_seq_groups[i] seq_group = scheduled_seq_group.seq_group seq_group.maybe_set_first_token_time(now) request_output = RequestOutputFactory.create(seq_group) if request_output: ctx.request_outputs.append(request_output) # When we process a single request, we skip it for the next time, # and invoke the request output callback (if there was final output) if request_id: assert len(indices) == 1 skip.append(indices[0]) if (finished_now and self.process_request_outputs_callback is not None): self.process_request_outputs_callback(ctx.request_outputs) ctx.request_outputs.clear() return # Free currently finished requests if finished_now: for scheduler in self.scheduler: scheduler.free_finished_seq_groups() # For multi-step, do not create outputs each iteration if not is_last_step: # Immediately process request outputs here (if callback is given) if (finished_now and self.process_request_outputs_callback is not None): self.process_request_outputs_callback(ctx.request_outputs) ctx.request_outputs.clear() return # Create the outputs for i in indices: if i in skip or i in finished_before or i in finished_now: continue # Avoids double processing scheduled_seq_group = scheduler_outputs.scheduled_seq_groups[i] seq_group = scheduled_seq_group.seq_group seq_group.maybe_set_first_token_time(now) request_output = RequestOutputFactory.create(seq_group) if request_output: ctx.request_outputs.append(request_output) for seq_group in scheduler_outputs.ignored_seq_groups: params = seq_group.sampling_params if params is not None and params.output_kind == ( RequestOutputKind.DELTA) and not seq_group.is_finished(): continue request_output = RequestOutputFactory.create(seq_group) if request_output: ctx.request_outputs.append(request_output) # Immediately process request outputs here (if callback is given) if (ctx.request_outputs and self.process_request_outputs_callback is not None): self.process_request_outputs_callback(ctx.request_outputs) ctx.request_outputs.clear() # For async case, we need to record the stats here. # For non-async case, the stats are done in the # LLMEngine/AsyncLLMEngine directly if is_async: # Log stats. self.do_log_stats(scheduler_outputs, outputs, finished_before, skip) return None def _advance_to_next_step( self, output: List[SamplerOutput], seq_group_metadata_list: List[SequenceGroupMetadata], scheduled_seq_groups: List[ScheduledSequenceGroup]) -> None: """Given model output from a single run, append the tokens to the sequences. This is normally done inside output processor, but it is required if the worker is to perform async forward pass to next step. """ for seq_group_metadata, sequence_group_outputs, scheduled_seq_group in \ zip(seq_group_metadata_list, output, scheduled_seq_groups): seq_group = scheduled_seq_group.seq_group if seq_group.is_finished(): continue seq_group.update_num_computed_tokens( seq_group_metadata.token_chunk_size) if seq_group_metadata.do_sample: assert len(sequence_group_outputs.samples) == 1, ( "Async output processor expects a single sample" " (i.e sampling_params.n == 1 and no " "sampling_params.best_of > 1)") sample = sequence_group_outputs.samples[0] assert len(seq_group.seqs) == 1 seq = seq_group.seqs[0] seq.append_token_id(sample.output_token, sample.logprobs) def step(self) -> List[Union[RequestOutput, EmbeddingRequestOutput]]: """Performs one decoding iteration and returns newly generated results. .. figure:: https://i.imgur.com/sv2HssD.png :alt: Overview of the step function :align: center Overview of the step function. Details: - Step 1: Schedules the sequences to be executed in the next iteration and the token blocks to be swapped in/out/copy. - Depending on the scheduling policy, sequences may be `preempted/reordered`. - A Sequence Group (SG) refer to a group of sequences that are generated from the same prompt. - Step 2: Calls the distributed executor to execute the model. - Step 3: Processes the model output. This mainly includes: - Decodes the relevant outputs. - Updates the scheduled sequence groups with model outputs based on its `sampling parameters` (`use_beam_search` or not). - Frees the finished sequence groups. - Finally, it creates and returns the newly generated results. Example: >>> # Please see the example/ folder for more detailed examples. >>> >>> # initialize engine and request arguments >>> engine = AphroditeEngine.from_engine_args(engine_args) >>> example_inputs = [(0, "What is LLM?", >>> SamplingParams(temperature=0.0))] >>> >>> # Start the engine with an event loop >>> while True: >>> if example_inputs: >>> req_id, prompt, sampling_params = example_inputs.pop(0) >>> engine.add_request(str(req_id),prompt,sampling_params) >>> >>> # continue the request processing >>> request_outputs = engine.step() >>> for request_output in request_outputs: >>> if request_output.finished: >>> # return or show the request output >>> >>> if not (engine.has_unfinished_requests() or example_inputs): >>> break """ if self.parallel_config.pipeline_parallel_size > 1: raise NotImplementedError( "Pipeline parallelism is only supported through AsyncAphrodite " "as performance will be severely degraded otherwise.") # For llm_engine, there is no pipeline parallel support, so the engine # used is always 0 virtual_engine = 0 # 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, 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] outputs = self.model_executor.execute_model( execute_model_req=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: # Nothing scheduled => If there is pending async postprocessor, # then finish it here. if len(ctx.output_queue) > 0: self._process_model_outputs(ctx=ctx) # No outputs in this case 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[0] = SchedulerOutputState() # Add results to the output_queue 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) # Check if need to run the usual non-async path 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 # 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. logger.debug("Stopping remote worker execution loop.") self.model_executor.stop_remote_worker_execution_loop() return ctx.request_outputs def _has_remaining_steps( self, seq_group_metadata_list: Optional[List[SequenceGroupMetadata]] ) -> bool: if (not self.scheduler_config.is_multi_step or not seq_group_metadata_list): return False # TODO: this is a sanity check for nowto make sure that all the # seqs are on the same steps. Eventually we will want to do some sort of # dynamic scheduling when doing multi-step decoding. ref_remaining_steps = seq_group_metadata_list[0].state.remaining_steps if any([ seq_group.state.remaining_steps != ref_remaining_steps for seq_group in seq_group_metadata_list[1:] ]): raise AssertionError(("All running sequence groups should " "have the same remaining steps.")) return ref_remaining_steps > 0 def _cache_scheduler_outputs_for_multi_step( self, virtual_engine: int, seq_group_metadata_list: Optional[List[SequenceGroupMetadata]], scheduler_outputs: SchedulerOutputs, allow_async_output_proc: bool) -> None: co = self.cached_scheduler_outputs[virtual_engine] co.seq_group_metadata_list = seq_group_metadata_list co.scheduler_outputs = scheduler_outputs co.allow_async_output_proc = allow_async_output_proc co.last_output = None def _update_cached_scheduler_output( self, virtual_engine: int, output: List[Optional[SamplerOutput]]) -> None: if (self.parallel_config.pipeline_parallel_size > 1 and len(output) > 0 and output[0] is not None): last_output = output[-1] assert last_output is not None assert last_output.sampled_token_ids_cpu is not None assert last_output.sampled_token_ids is None assert last_output.sampled_token_probs is None self.cached_scheduler_outputs[ virtual_engine].last_output = last_output def _get_last_sampled_token_ids( self, virtual_engine: int) -> Optional[torch.Tensor]: cached_last_output = self.cached_scheduler_outputs[ virtual_engine].last_output if (self.scheduler_config.is_multi_step and self.parallel_config.pipeline_parallel_size > 1 and cached_last_output is not None and cached_last_output.sampled_token_ids_cpu is not None): return cached_last_output.sampled_token_ids_cpu return None def add_logger(self, logger_name: str, logger: StatLoggerBase) -> None: if not self.log_stats: raise RuntimeError( "Stat logging is disabled. Set `disable_log_stats=False` " "argument to enable.") if logger_name in self.stat_loggers: raise KeyError(f"Logger with name {logger_name} already exists.") self.stat_loggers[logger_name] = logger def remove_logger(self, logger_name: str) -> None: if not self.log_stats: raise RuntimeError( "Stat logging is disabled. Set `disable_log_stats=False` " "argument to enable.") if logger_name not in self.stat_loggers: raise KeyError(f"Logger with name {logger_name} does not exist.") del self.stat_loggers[logger_name] def do_log_stats(self, scheduler_outputs: Optional[SchedulerOutputs] = None, model_output: Optional[List[SamplerOutput]] = None, finished_before: Optional[List[int]] = None, skip: Optional[List[int]] = None) -> None: """Forced log when no requests active.""" if self.log_stats: stats = self._get_stats(scheduler_outputs, model_output, finished_before, skip) for loggers in self.stat_loggers.values(): loggers.log(stats) def _get_stats(self, scheduler_outputs: Optional[SchedulerOutputs], model_output: Optional[List[SamplerOutput]] = None, finished_before: Optional[List[int]] = None, skip: Optional[List[int]] = None) -> Stats: """Get Stats to be Logged to Prometheus. Args: scheduler_outputs: Optional, used to populate metrics related to the scheduled batch, model_output: Optional, used to emit speculative decoding metrics which are created by the workers. finished_before: Optional, indices of sequences that were finished before. These sequences will be ignored. skip: Optional, indices of sequences that were preempted. These sequences will be ignored. """ now = time.time() # System State # Scheduler State num_running_sys = sum( len(scheduler.running) for scheduler in self.scheduler) num_swapped_sys = sum( len(scheduler.swapped) for scheduler in self.scheduler) num_waiting_sys = sum( len(scheduler.waiting) for scheduler in self.scheduler) # KV Cache Usage in % num_total_gpu = self.cache_config.num_gpu_blocks gpu_cache_usage_sys = 0. if num_total_gpu: # Guard against both None and 0 num_free_gpu = sum( scheduler.block_manager.get_num_free_gpu_blocks() for scheduler in self.scheduler) gpu_cache_usage_sys = 1.0 - (num_free_gpu / num_total_gpu) num_total_cpu = self.cache_config.num_cpu_blocks cpu_cache_usage_sys = 0. if num_total_cpu: # Guard against both None and 0 num_free_cpu = sum( scheduler.block_manager.get_num_free_cpu_blocks() for scheduler in self.scheduler) cpu_cache_usage_sys = 1.0 - (num_free_cpu / num_total_cpu) # Prefix Cache Hit Rate. Note that we always use # the cache hit rate of the first virtual engine. cpu_prefix_cache_hit_rate = self.scheduler[ 0].get_prefix_cache_hit_rate(Device.CPU) gpu_prefix_cache_hit_rate = self.scheduler[ 0].get_prefix_cache_hit_rate(Device.GPU) # Iteration stats num_prompt_tokens_iter = 0 num_generation_tokens_iter = 0 time_to_first_tokens_iter: List[float] = [] time_per_output_tokens_iter: List[float] = [] num_preemption_iter = (0 if scheduler_outputs is None else scheduler_outputs.preempted) # Request stats # Latency time_e2e_requests: List[float] = [] # Metadata num_prompt_tokens_requests: List[int] = [] num_generation_tokens_requests: List[int] = [] best_of_requests: List[int] = [] n_requests: List[int] = [] finished_reason_requests: List[str] = [] # NOTE: This loop assumes prefill seq_groups are before # decode seq_groups in scheduled_seq_groups. if scheduler_outputs is not None: # For async postprocessor, already finished sequences need to be # not counted (to avoid double counting) actual_num_batched_tokens = scheduler_outputs.num_batched_tokens # type: ignore num_generation_tokens_from_prefill_groups = 0. # NOTE: if scheduler_outputs.num_prefill_groups > 0 and # the len of scheduler_outputs.scheduled_seq_groups is != # scheduler_outputs.num_prefill_groups, this means that # chunked prefills have been detected. for idx, scheduled_seq_group in enumerate( scheduler_outputs.scheduled_seq_groups): # Skip double logging when using async output proc if finished_before and idx in finished_before: actual_num_batched_tokens -= 1 continue # Currently, skip == preempted sequences, so we need to skip # their log stats if skip and idx in skip: continue group_was_prefill = idx < scheduler_outputs.num_prefill_groups seq_group = scheduled_seq_group.seq_group # NOTE: a seq_group that completed all of its prefill tokens # in the last iteration will have seq_group.is_prefill() = False # with group_was_prefill = True if group_was_prefill: # Number of prompt tokens. num_prompt_tokens_iter += ( scheduled_seq_group.token_chunk_size) # If the seq_group just finished the prefill state # get TTFT. if not seq_group.is_prefill(): latency = seq_group.get_last_latency(now) time_to_first_tokens_iter.append(latency) # One generation token per finished prefill. num_generation_tokens_from_prefill_groups += ( seq_group.num_seqs()) else: # TPOTs. latency = seq_group.get_last_latency(now) time_per_output_tokens_iter.append(latency) # Because of chunked prefill, we can have a single sequence # group that does multiple prompt_runs. To prevent logging # the same metadata more than once per request, we standardize # on logging request level information for finished requests, # which can only happen once. if seq_group.is_finished(): # Latency timings time_e2e_requests.append(now - seq_group.metrics.arrival_time) # Metadata num_prompt_tokens_requests.append( len(seq_group.prompt_token_ids)) num_generation_tokens_requests.extend([ seq.get_output_len() for seq in seq_group.get_finished_seqs() ]) if seq_group.sampling_params is not None: best_of_requests.append( seq_group.sampling_params.best_of) n_requests.append(seq_group.sampling_params.n) finished_reason_requests.extend([ SequenceStatus.get_finished_reason(seq.status) for seq in seq_group.get_finished_seqs() ]) # Number of generation tokens. # num_batched_tokens equals the number of prompt_tokens plus the # number of decode_tokens in a single iteration. So, # num_generation_tokens = num_batched_tokens - num_prompt_tokens # + num_generation_tokens_from_prefill_groups (since we generate # one token on prefills on iters where the prefill finishes). num_generation_tokens_iter = ( actual_num_batched_tokens - num_prompt_tokens_iter + num_generation_tokens_from_prefill_groups) # Spec decode, if enabled, emits specialized metrics from the worker in # sampler output. if model_output and (model_output[0].spec_decode_worker_metrics is not None): spec_decode_metrics = model_output[0].spec_decode_worker_metrics else: spec_decode_metrics = None return Stats( now=now, # System stats # Scheduler State num_running_sys=num_running_sys, num_swapped_sys=num_swapped_sys, num_waiting_sys=num_waiting_sys, # KV Cache Usage in % gpu_cache_usage_sys=gpu_cache_usage_sys, cpu_cache_usage_sys=cpu_cache_usage_sys, # Prefix Cache Hit Rate cpu_prefix_cache_hit_rate=cpu_prefix_cache_hit_rate, gpu_prefix_cache_hit_rate=gpu_prefix_cache_hit_rate, # Iteration stats num_prompt_tokens_iter=num_prompt_tokens_iter, num_generation_tokens_iter=num_generation_tokens_iter, time_to_first_tokens_iter=time_to_first_tokens_iter, time_per_output_tokens_iter=time_per_output_tokens_iter, spec_decode_metrics=spec_decode_metrics, num_preemption_iter=num_preemption_iter, # Request stats # Latency time_e2e_requests=time_e2e_requests, # Metadata num_prompt_tokens_requests=num_prompt_tokens_requests, num_generation_tokens_requests=num_generation_tokens_requests, best_of_requests=best_of_requests, n_requests=n_requests, finished_reason_requests=finished_reason_requests, ) def add_lora(self, lora_request: LoRARequest) -> bool: return self.model_executor.add_lora(lora_request) def remove_lora(self, lora_id: int) -> bool: return self.model_executor.remove_lora(lora_id) def list_loras(self) -> Set[int]: return self.model_executor.list_loras() def pin_lora(self, lora_id: int) -> bool: return self.model_executor.pin_lora(lora_id) def add_prompt_adapter( self, prompt_adapter_request: PromptAdapterRequest) -> bool: return self.model_executor.add_prompt_adapter(prompt_adapter_request) def remove_prompt_adapter(self, prompt_adapter_id: int) -> bool: return self.model_executor.remove_prompt_adapter(prompt_adapter_id) def list_prompt_adapters(self) -> List[int]: return self.model_executor.list_prompt_adapters() def check_health(self) -> None: if self.tokenizer: self.tokenizer.check_health() self.model_executor.check_health() def shutdown(self) -> None: self.model_executor.stop_remote_worker_execution_loop() if hasattr(self, 'tokenizer') and self.tokenizer is not None: self.tokenizer = None if hasattr(self, 'scheduler'): self.scheduler.clear() if hasattr(self, 'cached_scheduler_outputs'): self.cached_scheduler_outputs.clear() if hasattr(self, 'scheduler_contexts'): self.scheduler_contexts.clear() if hasattr(self, 'stat_loggers'): self.stat_loggers.clear() if hasattr(self, 'model_executor'): self.model_executor.shutdown() def is_encoder_decoder_model(self): return self.input_preprocessor.is_encoder_decoder_model() def is_embedding_model(self): return self.model_config.is_embedding_model def _validate_model_inputs(self, inputs: Union[LLMInputs, EncoderDecoderLLMInputs]): if self.is_encoder_decoder_model(): prompt_ids = inputs.get("encoder_prompt_token_ids") else: prompt_ids = inputs.get("prompt_token_ids") if prompt_ids is None or len(prompt_ids) == 0: raise ValueError("Prompt cannot be empty") if self.model_config.is_multimodal_model: max_prompt_len = self.model_config.max_model_len if len(prompt_ids) > max_prompt_len: raise ValueError( f"The prompt (total length {len(prompt_ids)}) is too long " f"to fit into the model (context length {max_prompt_len}). " "Make sure that `max_model_len` is no smaller than the " "number of text tokens plus multimodal tokens. For image " "inputs, the number of image tokens depends on the number " "of images, and possibly their aspect ratios as well.") # TODO: Find out how many placeholder tokens are there so we can # check that chunked prefill does not truncate them # max_batch_len = self.scheduler_config.max_num_batched_tokens setup_logger()