# SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project import itertools from collections.abc import Callable, Sequence from typing import TYPE_CHECKING, Any, cast import cloudpickle import torch.nn as nn from pydantic import ValidationError from tqdm.auto import tqdm from typing_extensions import TypeVar from vllm.beam_search import ( BeamSearchInstance, BeamSearchOutput, BeamSearchSequence, create_sort_beams_key_function, ) from vllm.config import ( AttentionConfig, CompilationConfig, PoolerConfig, ProfilerConfig, StructuredOutputsConfig, is_init_field, ) from vllm.config.compilation import CompilationMode from vllm.config.model import ( ConvertOption, HfOverrides, ModelDType, RunnerOption, TokenizerMode, ) from vllm.engine.arg_utils import EngineArgs from vllm.entrypoints.chat_utils import ( ChatCompletionMessageParam, ChatTemplateContentFormatOption, ) from vllm.entrypoints.pooling.score.utils import ( ScoreContentPartParam, ScoreMultiModalParam, _cosine_similarity, _validate_score_input_lens, compress_token_type_ids, get_score_prompt, ) from vllm.entrypoints.utils import _validate_truncation_size, log_non_default_args from vllm.inputs import ( DataPrompt, PromptType, SingletonPrompt, TextPrompt, TokensPrompt, ) from vllm.inputs.parse import get_prompt_components from vllm.logger import init_logger from vllm.lora.request import LoRARequest from vllm.model_executor.layers.quantization import QuantizationMethods from vllm.outputs import ( ClassificationRequestOutput, EmbeddingRequestOutput, PoolingRequestOutput, RequestOutput, ScoringRequestOutput, ) from vllm.platforms import current_platform from vllm.pooling_params import PoolingParams from vllm.sampling_params import BeamSearchParams, RequestOutputKind, SamplingParams from vllm.tasks import PoolingTask from vllm.tokenizers import TokenizerLike from vllm.tokenizers.mistral import MistralTokenizer from vllm.usage.usage_lib import UsageContext from vllm.utils.collection_utils import as_iter, is_list_of from vllm.utils.counter import Counter from vllm.v1.engine import EngineCoreRequest from vllm.v1.engine.llm_engine import LLMEngine from vllm.v1.sample.logits_processor import LogitsProcessor if TYPE_CHECKING: from vllm.v1.metrics.reader import Metric logger = init_logger(__name__) _R = TypeVar("_R", default=Any) class LLM: """An LLM for generating texts from given prompts and sampling parameters. This class includes a tokenizer, a language model (possibly distributed across multiple GPUs), and GPU memory space allocated for intermediate states (aka KV cache). Given a batch of prompts and sampling parameters, this class generates texts from the model, using an intelligent batching mechanism and efficient memory management. Args: model: The name or path of a HuggingFace Transformers model. tokenizer: The name or path of a HuggingFace Transformers tokenizer. tokenizer_mode: The tokenizer mode. "auto" will use the fast tokenizer if available, and "slow" will always use the slow tokenizer. skip_tokenizer_init: If true, skip initialization of tokenizer and detokenizer. Expect valid prompt_token_ids and None for prompt from the input. trust_remote_code: Trust remote code (e.g., from HuggingFace) when downloading the model and tokenizer. allowed_local_media_path: Allowing API requests to read local images or videos from directories specified by the server file system. This is a security risk. Should only be enabled in trusted environments. allowed_media_domains: If set, only media URLs that belong to this domain can be used for multi-modal inputs. tensor_parallel_size: The number of GPUs to use for distributed execution with tensor parallelism. dtype: The data type for the model weights and activations. Currently, we support `float32`, `float16`, and `bfloat16`. If `auto`, we use the `dtype` attribute of the Transformers model's config. However, if the `dtype` in the config is `float32`, we will use `float16` instead. quantization: The method used to quantize the model weights. Currently, we support "awq", "gptq", and "fp8" (experimental). If None, we first check the `quantization_config` attribute in the model config file. If that is None, we assume the model weights are not quantized and use `dtype` to determine the data type of the weights. revision: The specific model version to use. It can be a branch name, a tag name, or a commit id. tokenizer_revision: The specific tokenizer version to use. It can be a branch name, a tag name, or a commit id. seed: The seed to initialize the random number generator for sampling. gpu_memory_utilization: The ratio (between 0 and 1) of GPU memory to reserve for the model weights, activations, and KV cache. Higher values will increase the KV cache size and thus improve the model's throughput. However, if the value is too high, it may cause out-of- memory (OOM) errors. kv_cache_memory_bytes: Size of KV Cache per GPU in bytes. By default, this is set to None and vllm can automatically infer the kv cache size based on gpu_memory_utilization. However, users may want to manually specify the kv cache memory size. kv_cache_memory_bytes allows more fine-grain control of how much memory gets used when compared with using gpu_memory_utilization. Note that kv_cache_memory_bytes (when not-None) ignores gpu_memory_utilization swap_space: The size (GiB) of CPU memory per GPU to use as swap space. This can be used for temporarily storing the states of the requests when their `best_of` sampling parameters are larger than 1. If all requests will have `best_of=1`, you can safely set this to 0. Noting that `best_of` is only supported in V0. Otherwise, too small values may cause out-of-memory (OOM) errors. cpu_offload_gb: The size (GiB) of CPU memory to use for offloading the model weights. This virtually increases the GPU memory space you can use to hold the model weights, at the cost of CPU-GPU data transfer for every forward pass. enforce_eager: Whether to enforce eager execution. If True, we will disable CUDA graph and always execute the model in eager mode. If False, we will use CUDA graph and eager execution in hybrid. enable_return_routed_experts: Whether to return routed experts. disable_custom_all_reduce: See [ParallelConfig][vllm.config.ParallelConfig]. hf_token: The token to use as HTTP bearer authorization for remote files . If `True`, will use the token generated when running `huggingface-cli login` (stored in `~/.huggingface`). hf_overrides: If a dictionary, contains arguments to be forwarded to the HuggingFace config. If a callable, it is called to update the HuggingFace config. mm_processor_kwargs: Arguments to be forwarded to the model's processor for multi-modal data, e.g., image processor. Overrides for the multi-modal processor obtained from `AutoProcessor.from_pretrained`. The available overrides depend on the model that is being run. For example, for Phi-3-Vision: `{"num_crops": 4}`. pooler_config: Initialize non-default pooling config for the pooling model. e.g. `PoolerConfig(seq_pooling_type="MEAN", normalize=False)`. compilation_config: Either an integer or a dictionary. If it is an integer, it is used as the mode of compilation optimization. If it is a dictionary, it can specify the full compilation configuration. attention_config: Configuration for attention mechanisms. Can be a dictionary or an AttentionConfig instance. If a dictionary, it will be converted to an AttentionConfig. Allows specifying the attention backend and other attention-related settings. **kwargs: Arguments for [`EngineArgs`][vllm.EngineArgs]. Note: This class is intended to be used for offline inference. For online serving, use the [AsyncLLMEngine][vllm.AsyncLLMEngine] class instead. """ def __init__( self, model: str, *, runner: RunnerOption = "auto", convert: ConvertOption = "auto", tokenizer: str | None = None, tokenizer_mode: TokenizerMode | str = "auto", skip_tokenizer_init: bool = False, trust_remote_code: bool = False, allowed_local_media_path: str = "", allowed_media_domains: list[str] | None = None, tensor_parallel_size: int = 1, dtype: ModelDType = "auto", quantization: QuantizationMethods | None = None, revision: str | None = None, tokenizer_revision: str | None = None, seed: int = 0, gpu_memory_utilization: float = 0.9, swap_space: float = 4, cpu_offload_gb: float = 0, enforce_eager: bool = False, enable_return_routed_experts: bool = False, disable_custom_all_reduce: bool = False, hf_token: bool | str | None = None, hf_overrides: HfOverrides | None = None, mm_processor_kwargs: dict[str, Any] | None = None, pooler_config: PoolerConfig | None = None, structured_outputs_config: dict[str, Any] | StructuredOutputsConfig | None = None, profiler_config: dict[str, Any] | ProfilerConfig | None = None, attention_config: dict[str, Any] | AttentionConfig | None = None, kv_cache_memory_bytes: int | None = None, compilation_config: int | dict[str, Any] | CompilationConfig | None = None, logits_processors: list[str | type[LogitsProcessor]] | None = None, **kwargs: Any, ) -> None: """LLM constructor.""" if "disable_log_stats" not in kwargs: kwargs["disable_log_stats"] = True if "worker_cls" in kwargs: worker_cls = kwargs["worker_cls"] # if the worker_cls is not qualified string name, # we serialize it using cloudpickle to avoid pickling issues if isinstance(worker_cls, type): kwargs["worker_cls"] = cloudpickle.dumps(worker_cls) if "kv_transfer_config" in kwargs and isinstance( kwargs["kv_transfer_config"], dict ): from vllm.config.kv_transfer import KVTransferConfig raw_config_dict = kwargs["kv_transfer_config"] try: kwargs["kv_transfer_config"] = KVTransferConfig(**raw_config_dict) except ValidationError as e: logger.error( "Failed to convert 'kv_transfer_config' dict to " "KVTransferConfig object. Dict: %s. Error: %s", raw_config_dict, e, ) # Consider re-raising a more specific vLLM error or ValueError # to provide better context to the user. raise ValueError(f"Invalid 'kv_transfer_config' provided: {e}") from e if hf_overrides is None: hf_overrides = {} def _make_config(value: Any, cls: type[_R]) -> _R: """Convert dict/None/instance to a config instance.""" if value is None: return cls() if isinstance(value, dict): return cls(**{k: v for k, v in value.items() if is_init_field(cls, k)}) # type: ignore[arg-type] return value if isinstance(compilation_config, int): compilation_config_instance = CompilationConfig( mode=CompilationMode(compilation_config) ) else: compilation_config_instance = _make_config( compilation_config, CompilationConfig ) structured_outputs_instance = _make_config( structured_outputs_config, StructuredOutputsConfig ) profiler_config_instance = _make_config(profiler_config, ProfilerConfig) attention_config_instance = _make_config(attention_config, AttentionConfig) # warn about single-process data parallel usage. _dp_size = int(kwargs.get("data_parallel_size", 1)) _distributed_executor_backend = kwargs.get("distributed_executor_backend") if ( _dp_size > 1 and not _distributed_executor_backend == "external_launcher" and not current_platform.is_tpu() ): raise ValueError( f"LLM(data_parallel_size={_dp_size}) is not supported for single-" "process usage and may hang. Please use " "the explicit multi-process data-parallel example at " "'examples/offline_inference/data_parallel.py'." ) engine_args = EngineArgs( model=model, runner=runner, convert=convert, tokenizer=tokenizer, tokenizer_mode=tokenizer_mode, skip_tokenizer_init=skip_tokenizer_init, trust_remote_code=trust_remote_code, allowed_local_media_path=allowed_local_media_path, allowed_media_domains=allowed_media_domains, tensor_parallel_size=tensor_parallel_size, dtype=dtype, quantization=quantization, revision=revision, tokenizer_revision=tokenizer_revision, seed=seed, gpu_memory_utilization=gpu_memory_utilization, kv_cache_memory_bytes=kv_cache_memory_bytes, swap_space=swap_space, cpu_offload_gb=cpu_offload_gb, enforce_eager=enforce_eager, enable_return_routed_experts=enable_return_routed_experts, disable_custom_all_reduce=disable_custom_all_reduce, hf_token=hf_token, hf_overrides=hf_overrides, mm_processor_kwargs=mm_processor_kwargs, pooler_config=pooler_config, structured_outputs_config=structured_outputs_instance, profiler_config=profiler_config_instance, attention_config=attention_config_instance, compilation_config=compilation_config_instance, logits_processors=logits_processors, **kwargs, ) log_non_default_args(engine_args) self.llm_engine = LLMEngine.from_engine_args( engine_args=engine_args, usage_context=UsageContext.LLM_CLASS ) self.engine_class = type(self.llm_engine) self.request_counter = Counter() self.default_sampling_params: dict[str, Any] | None = None supported_tasks = self.llm_engine.get_supported_tasks() logger.info("Supported tasks: %s", supported_tasks) self.supported_tasks = supported_tasks self.model_config = self.llm_engine.model_config self.input_processor = self.llm_engine.input_processor self.io_processor = self.llm_engine.io_processor # Cache for __repr__ to avoid repeated collective_rpc calls self._cached_repr: str | None = None def get_tokenizer(self) -> TokenizerLike: return self.llm_engine.get_tokenizer() def reset_mm_cache(self) -> None: self.input_processor.clear_mm_cache() self.llm_engine.reset_mm_cache() def get_default_sampling_params(self) -> SamplingParams: if self.default_sampling_params is None: self.default_sampling_params = self.model_config.get_diff_sampling_param() if self.default_sampling_params: return SamplingParams.from_optional(**self.default_sampling_params) return SamplingParams() def generate( self, prompts: PromptType | Sequence[PromptType], sampling_params: SamplingParams | Sequence[SamplingParams] | None = None, *, use_tqdm: bool | Callable[..., tqdm] = True, lora_request: list[LoRARequest] | LoRARequest | None = None, priority: list[int] | None = None, ) -> list[RequestOutput]: """Generates the completions for the input prompts. This class automatically batches the given prompts, considering the memory constraint. For the best performance, put all of your prompts into a single list and pass it to this method. Args: prompts: The prompts to the LLM. You may pass a sequence of prompts for batch inference. See [PromptType][vllm.inputs.PromptType] for more details about the format of each prompt. sampling_params: The sampling parameters for text generation. If None, we use the default sampling parameters. When it is a single value, it is applied to every prompt. When it is a list, the list must have the same length as the prompts and it is paired one by one with the prompt. use_tqdm: If `True`, shows a tqdm progress bar. If a callable (e.g., `functools.partial(tqdm, leave=False)`), it is used to create the progress bar. If `False`, no progress bar is created. lora_request: LoRA request to use for generation, if any. priority: The priority of the requests, if any. Only applicable when priority scheduling policy is enabled. If provided, must be a list of integers matching the length of `prompts`, where each priority value corresponds to the prompt at the same index. Returns: A list of `RequestOutput` objects containing the generated completions in the same order as the input prompts. Note: Using `prompts` and `prompt_token_ids` as keyword parameters is considered legacy and may be deprecated in the future. You should instead pass them via the `inputs` parameter. """ model_config = self.model_config runner_type = model_config.runner_type if runner_type != "generate": raise ValueError( "LLM.generate() is only supported for generative models. " "Try passing `--runner generate` to use the model as a " "generative model." ) if sampling_params is None: # Use default sampling params. sampling_params = self.get_default_sampling_params() # Add any modality specific loras to the corresponding prompts lora_request = self._get_modality_specific_lora_reqs(prompts, lora_request) self._validate_and_add_requests( prompts=prompts, params=sampling_params, use_tqdm=use_tqdm, lora_request=lora_request, priority=priority, ) outputs = self._run_engine(use_tqdm=use_tqdm) return self.engine_class.validate_outputs(outputs, RequestOutput) def _get_modality_specific_lora_reqs( self, prompts: PromptType | Sequence[PromptType], lora_request: list[LoRARequest] | LoRARequest | None, ): # Grab the lora config off the vllm config on the engine, # since this is the same for both v0 & v1. lora_config = self.llm_engine.vllm_config.lora_config # If there's no lora config / default_mm_loras, or the model # isn't multimodal, leave the lora as is. if ( lora_config is None or not self.model_config.is_multimodal_model or (lora_config and lora_config.default_mm_loras is None) ): return lora_request if not isinstance(prompts, Sequence) or isinstance(prompts, str): prompts = [prompts] optional_loras = ( [lora_request] * len(prompts) if not isinstance(lora_request, Sequence) else lora_request ) return [ self._resolve_single_prompt_mm_lora( prompt, opt_lora_req, lora_config.default_mm_loras, ) for prompt, opt_lora_req in zip(prompts, optional_loras) ] def _resolve_single_prompt_mm_lora( self, prompt: PromptType, lora_request: LoRARequest | None, default_mm_loras: dict[str, str] | None, ): if ( not default_mm_loras or not isinstance(prompt, dict) or not (mm_data := prompt.get("multi_modal_data") or {}) ): return lora_request intersection = set( mm_data.keys() # type: ignore ).intersection(default_mm_loras.keys()) if not intersection: return lora_request if len(intersection) > 1: # TODO: Would be nice to be able to have multiple loras per prompt logger.warning( "Multiple modality specific loras were registered and would be" " used by a single prompt consuming several modalities; " " currently we only support one lora per request; as such," " lora(s) registered with modalities: %s" " will be skipped", intersection, ) return lora_request # Build the LoRA request; the ID of the default mm lora is the # index of the modality name sorted alphabetically + 1. modality_name = intersection.pop() modality_lora_path = default_mm_loras[modality_name] modality_lora_id = sorted(default_mm_loras).index(modality_name) + 1 # If we have a collision, warn if there is a collision, # but always send the explicitly provided request. if lora_request: if lora_request.lora_int_id != modality_lora_id: logger.warning( "A modality with a registered lora and a lora_request " "with a different ID were provided; falling back to the " "lora_request as we only apply one LoRARequest per prompt" ) return lora_request return LoRARequest( modality_name, modality_lora_id, modality_lora_path, ) def collective_rpc( self, method: str | Callable[..., _R], timeout: float | None = None, args: tuple = (), kwargs: dict[str, Any] | None = None, ) -> list[_R]: """ Execute an RPC call on all workers. Args: method: Name of the worker method to execute, or a callable that is serialized and sent to all workers to execute. If the method is a callable, it should accept an additional `self` argument, in addition to the arguments passed in `args` and `kwargs`. The `self` argument will be the worker object. timeout: Maximum time in seconds to wait for execution. Raises a [`TimeoutError`][] on timeout. `None` means wait indefinitely. args: Positional arguments to pass to the worker method. kwargs: Keyword arguments to pass to the worker method. Returns: A list containing the results from each worker. Note: It is recommended to use this API to only pass control messages, and set up data-plane communication to pass data. """ return self.llm_engine.collective_rpc(method, timeout, args, kwargs) def apply_model(self, func: Callable[[nn.Module], _R]) -> list[_R]: """ Run a function directly on the model inside each worker, returning the result for each of them. !!! warning To reduce the overhead of data transfer, avoid returning large arrays or tensors from this method. If you must return them, make sure you move them to CPU first to avoid taking up additional VRAM! """ return self.llm_engine.apply_model(func) def _get_beam_search_lora_requests( self, lora_request: list[LoRARequest] | LoRARequest | None, prompts: list[TokensPrompt | TextPrompt], ) -> list[LoRARequest | None]: """Get the optional lora request corresponding to each prompt.""" if isinstance(lora_request, Sequence) and len(lora_request) != len(prompts): raise ValueError( "Lora request list should be the same length as the prompts" ) if lora_request is None or isinstance(lora_request, LoRARequest): return [lora_request] * len(prompts) raise TypeError(f"Invalid lora_request type {type(lora_request)}") def beam_search( self, prompts: list[TokensPrompt | TextPrompt], params: BeamSearchParams, lora_request: list[LoRARequest] | LoRARequest | None = None, use_tqdm: bool = False, concurrency_limit: int | None = None, ) -> list[BeamSearchOutput]: """ Generate sequences using beam search. Args: prompts: A list of prompts. Each prompt can be a string or a list of token IDs. params: The beam search parameters. lora_request: LoRA request to use for generation, if any. use_tqdm: Whether to use tqdm to display the progress bar. concurrency_limit: The maximum number of concurrent requests. If None, the number of concurrent requests is unlimited. """ # TODO: how does beam search work together with length penalty, # frequency, penalty, and stopping criteria, etc.? beam_width = params.beam_width max_tokens = params.max_tokens temperature = params.temperature ignore_eos = params.ignore_eos length_penalty = params.length_penalty lora_requests = self._get_beam_search_lora_requests(lora_request, prompts) tokenizer = self.get_tokenizer() sort_beams_key = create_sort_beams_key_function( tokenizer.eos_token_id, length_penalty, ) if use_tqdm and concurrency_limit is not None: logger.warning( "Progress bar is not supported when using concurrency_limit. " "Disabling progress bar." ) use_tqdm = False if concurrency_limit is None: concurrency_limit = len(prompts) def create_tokens_prompt_from_beam(beam: BeamSearchSequence) -> TokensPrompt: token_prompt_kwargs: TokensPrompt = {"prompt_token_ids": beam.tokens} if beam.multi_modal_data is not None: token_prompt_kwargs["multi_modal_data"] = beam.multi_modal_data if beam.mm_processor_kwargs is not None: token_prompt_kwargs["mm_processor_kwargs"] = beam.mm_processor_kwargs return TokensPrompt(**token_prompt_kwargs) # generate 2 * beam_width candidates at each step # following the huggingface transformers implementation # at https://github.com/huggingface/transformers/blob/e15687fffe5c9d20598a19aeab721ae0a7580f8a/src/transformers/generation/beam_search.py#L534 # noqa beam_search_params = SamplingParams( logprobs=2 * beam_width, max_tokens=1, temperature=temperature, skip_clone=True, # Internal beam search, safe to skip clone ) instances: list[BeamSearchInstance] = [] for lora_req, prompt in zip(lora_requests, prompts): # Add multimodal processor kwargs & data mm_kwargs = {} if "multi_modal_data" in prompt: mm_kwargs["multi_modal_data"] = prompt["multi_modal_data"] if "mm_processor_kwargs" in prompt: mm_kwargs["mm_processor_kwargs"] = prompt["mm_processor_kwargs"] if "prompt_token_ids" in prompt: prompt = cast(TokensPrompt, prompt) # Needed for mypy prompt_tokens = prompt["prompt_token_ids"] else: prompt_tokens = tokenizer.encode(prompt["prompt"]) instances.append( BeamSearchInstance( prompt_tokens, lora_request=lora_req, logprobs=None, **mm_kwargs, ), ) for prompt_start in range(0, len(prompts), concurrency_limit): instances_batch = instances[prompt_start : prompt_start + concurrency_limit] token_iter = range(max_tokens) if use_tqdm: token_iter = tqdm( token_iter, desc="Beam search", unit="token", unit_scale=False ) logger.warning( "The progress bar shows the upper bound on token steps and " "may finish early due to stopping conditions. It does not " "reflect instance-level progress." ) for _ in token_iter: all_beams: list[BeamSearchSequence] = list( sum((instance.beams for instance in instances_batch), []) ) pos = [0] + list( itertools.accumulate( len(instance.beams) for instance in instances_batch ) ) instance_start_and_end: list[tuple[int, int]] = list( zip(pos[:-1], pos[1:]) ) if len(all_beams) == 0: break # create corresponding batch entries for prompt & optional lora prompts_batch, lora_req_batch = zip( *[ (create_tokens_prompt_from_beam(beam), beam.lora_request) for beam in all_beams ] ) # only runs for one step # we don't need to use tqdm here output = self.generate( prompts_batch, sampling_params=beam_search_params, use_tqdm=False, lora_request=lora_req_batch, ) for (start, end), instance in zip( instance_start_and_end, instances_batch ): instance_new_beams = [] for i in range(start, end): current_beam = all_beams[i] result = output[i] if result.outputs[0].logprobs is not None: # if `result.outputs[0].logprobs` is None, it means # the sequence is completed because of the # max-model-len or abortion. we don't need to add # it to the new beams. logprobs = result.outputs[0].logprobs[0] for token_id, logprob_obj in logprobs.items(): new_beam = BeamSearchSequence( tokens=current_beam.tokens + [token_id], logprobs=current_beam.logprobs + [logprobs], lora_request=current_beam.lora_request, cum_logprob=current_beam.cum_logprob + logprob_obj.logprob, multi_modal_data=current_beam.multi_modal_data, mm_processor_kwargs=current_beam.mm_processor_kwargs, ) if ( token_id == tokenizer.eos_token_id and not ignore_eos ): instance.completed.append(new_beam) else: instance_new_beams.append(new_beam) sorted_beams = sorted( instance_new_beams, key=sort_beams_key, reverse=True ) instance.beams = sorted_beams[:beam_width] outputs = [] for instance in instances: instance.completed.extend(instance.beams) sorted_completed = sorted( instance.completed, key=sort_beams_key, reverse=True ) best_beams = sorted_completed[:beam_width] for beam in best_beams: beam.text = tokenizer.decode(beam.tokens) outputs.append(BeamSearchOutput(sequences=best_beams)) return outputs def preprocess_chat( self, messages: list[ChatCompletionMessageParam] | list[list[ChatCompletionMessageParam]], chat_template: str | None = None, chat_template_content_format: ChatTemplateContentFormatOption = "auto", add_generation_prompt: bool = True, continue_final_message: bool = False, tools: list[dict[str, Any]] | None = None, chat_template_kwargs: dict[str, Any] | None = None, mm_processor_kwargs: dict[str, Any] | None = None, ) -> list[TextPrompt | TokensPrompt]: """ Generate prompt for a chat conversation. The pre-processed prompt can then be used as input for the other LLM methods. Refer to `chat` for a complete description of the arguments. Returns: A list of `TokensPrompts` objects containing the tokenized prompt after chat template interpolation, and the pre-processed multi-modal inputs. """ list_of_messages: list[list[ChatCompletionMessageParam]] # Handle multi and single conversations if is_list_of(messages, list): # messages is list[list[...]] list_of_messages = cast(list[list[ChatCompletionMessageParam]], messages) else: # messages is list[...] list_of_messages = [cast(list[ChatCompletionMessageParam], messages)] renderer = self.llm_engine.renderer chat_template_kwargs = { "chat_template": chat_template, "add_generation_prompt": add_generation_prompt, "continue_final_message": continue_final_message, "tools": tools, **(chat_template_kwargs or {}), } prompts = list[TextPrompt | TokensPrompt]() for msgs in list_of_messages: # NOTE: renderer.render_messages() currently doesn't # handle mm_processor_kwargs, since there is no implementation in # the chat message parsing for it. _, prompt = renderer.render_messages( msgs, chat_template_content_format=chat_template_content_format, **chat_template_kwargs, ) if mm_processor_kwargs is not None: prompt["mm_processor_kwargs"] = mm_processor_kwargs prompts.append(prompt) return prompts def chat( self, messages: list[ChatCompletionMessageParam] | list[list[ChatCompletionMessageParam]], sampling_params: SamplingParams | list[SamplingParams] | None = None, use_tqdm: bool | Callable[..., tqdm] = True, lora_request: LoRARequest | None = None, chat_template: str | None = None, chat_template_content_format: ChatTemplateContentFormatOption = "auto", add_generation_prompt: bool = True, continue_final_message: bool = False, tools: list[dict[str, Any]] | None = None, chat_template_kwargs: dict[str, Any] | None = None, mm_processor_kwargs: dict[str, Any] | None = None, ) -> list[RequestOutput]: """ Generate responses for a chat conversation. The chat conversation is converted into a text prompt using the tokenizer and calls the [generate][vllm.LLM.generate] method to generate the responses. Multi-modal inputs can be passed in the same way you would pass them to the OpenAI API. Args: messages: A list of conversations or a single conversation. - Each conversation is represented as a list of messages. - Each message is a dictionary with 'role' and 'content' keys. sampling_params: The sampling parameters for text generation. If None, we use the default sampling parameters. When it is a single value, it is applied to every prompt. When it is a list, the list must have the same length as the prompts and it is paired one by one with the prompt. use_tqdm: If `True`, shows a tqdm progress bar. If a callable (e.g., `functools.partial(tqdm, leave=False)`), it is used to create the progress bar. If `False`, no progress bar is created. lora_request: LoRA request to use for generation, if any. chat_template: The template to use for structuring the chat. If not provided, the model's default chat template will be used. chat_template_content_format: The format to render message content. - "string" will render the content as a string. Example: `"Who are you?"` - "openai" will render the content as a list of dictionaries, similar to OpenAI schema. Example: `[{"type": "text", "text": "Who are you?"}]` add_generation_prompt: If True, adds a generation template to each message. continue_final_message: If True, continues the final message in the conversation instead of starting a new one. Cannot be `True` if `add_generation_prompt` is also `True`. chat_template_kwargs: Additional kwargs to pass to the chat template. mm_processor_kwargs: Multimodal processor kwarg overrides for this chat request. Only used for offline requests. Returns: A list of `RequestOutput` objects containing the generated responses in the same order as the input messages. """ prompts = self.preprocess_chat( messages=messages, chat_template=chat_template, chat_template_content_format=chat_template_content_format, add_generation_prompt=add_generation_prompt, continue_final_message=continue_final_message, tools=tools, chat_template_kwargs=chat_template_kwargs, mm_processor_kwargs=mm_processor_kwargs, ) return self.generate( prompts, sampling_params=sampling_params, use_tqdm=use_tqdm, lora_request=lora_request, ) def encode( self, prompts: PromptType | Sequence[PromptType] | DataPrompt, pooling_params: PoolingParams | Sequence[PoolingParams] | None = None, *, truncate_prompt_tokens: int | None = None, use_tqdm: bool | Callable[..., tqdm] = True, lora_request: list[LoRARequest] | LoRARequest | None = None, pooling_task: PoolingTask | None = None, tokenization_kwargs: dict[str, Any] | None = None, ) -> list[PoolingRequestOutput]: """Apply pooling to the hidden states corresponding to the input prompts. This class automatically batches the given prompts, considering the memory constraint. For the best performance, put all of your prompts into a single list and pass it to this method. Args: prompts: The prompts to the LLM. You may pass a sequence of prompts for batch inference. See [PromptType][vllm.inputs.PromptType] for more details about the format of each prompt. pooling_params: The pooling parameters for pooling. If None, we use the default pooling parameters. use_tqdm: If `True`, shows a tqdm progress bar. If a callable (e.g., `functools.partial(tqdm, leave=False)`), it is used to create the progress bar. If `False`, no progress bar is created. lora_request: LoRA request to use for generation, if any. pooling_task: Override the pooling task to use. tokenization_kwargs: overrides tokenization_kwargs set in pooling_params Returns: A list of `PoolingRequestOutput` objects containing the pooled hidden states in the same order as the input prompts. Note: Using `prompts` and `prompt_token_ids` as keyword parameters is considered legacy and may be deprecated in the future. You should instead pass them via the `inputs` parameter. """ error_str = ( "pooling_task required for `LLM.encode`\n" "Please use one of the more specific methods or set the " "pooling_task when using `LLM.encode`:\n" " - For embeddings, use `LLM.embed(...)` " 'or `pooling_task="embed"`.\n' " - For classification logits, use `LLM.classify(...)` " 'or `pooling_task="classify"`.\n' " - For similarity scores, use `LLM.score(...)`.\n" " - For rewards, use `LLM.reward(...)` " 'or `pooling_task="token_classify"`\n' " - For token classification, " 'use `pooling_task="token_classify"`\n' ' - For multi-vector retrieval, use `pooling_task="token_embed"`' ) if pooling_task is None: raise ValueError(error_str) model_config = self.model_config runner_type = model_config.runner_type if runner_type != "pooling": raise ValueError( "LLM.encode() is only supported for pooling models. " "Try passing `--runner pooling` to use the model as a " "pooling model." ) io_processor_prompt = False if isinstance(prompts, dict) and "data" in prompts: io_processor_prompt = True if self.io_processor is None: raise ValueError( "No IOProcessor plugin installed. Please refer " "to the documentation and to the " "'prithvi_geospatial_mae_io_processor' " "offline inference example for more details." ) # Validate the request data is valid for the loaded plugin validated_prompt = self.io_processor.parse_request(prompts) # obtain the actual model prompts from the pre-processor prompts = self.io_processor.pre_process(prompt=validated_prompt) if io_processor_prompt: assert self.io_processor is not None if is_list_of(pooling_params, PoolingParams): validated_pooling_params: list[PoolingParams] = [] for param in as_iter(pooling_params): validated_pooling_params.append( self.io_processor.validate_or_generate_params(param) ) pooling_params = validated_pooling_params else: assert not isinstance(pooling_params, Sequence) pooling_params = self.io_processor.validate_or_generate_params( pooling_params ) else: if pooling_params is None: # Use default pooling params. pooling_params = PoolingParams() if pooling_task not in self.supported_tasks: raise ValueError(f"pooling_task must be one of {self.supported_tasks}.") for param in as_iter(pooling_params): param.verify(pooling_task, model_config) # for backwards compatibility if truncate_prompt_tokens is not None: param.truncate_prompt_tokens = truncate_prompt_tokens self._validate_and_add_requests( prompts=prompts, params=pooling_params, use_tqdm=use_tqdm, lora_request=lora_request, tokenization_kwargs=tokenization_kwargs, ) outputs = self._run_engine(use_tqdm=use_tqdm) model_outputs = self.engine_class.validate_outputs( outputs, PoolingRequestOutput ) if io_processor_prompt: # get the post-processed model outputs assert self.io_processor is not None processed_outputs = self.io_processor.post_process( model_output=model_outputs ) return [ PoolingRequestOutput[Any]( request_id="", outputs=processed_outputs, num_cached_tokens=getattr( processed_outputs, "num_cached_tokens", 0 ), prompt_token_ids=[], finished=True, ) ] else: return model_outputs def embed( self, prompts: PromptType | Sequence[PromptType], *, truncate_prompt_tokens: int | None = None, use_tqdm: bool | Callable[..., tqdm] = True, pooling_params: PoolingParams | Sequence[PoolingParams] | None = None, lora_request: list[LoRARequest] | LoRARequest | None = None, tokenization_kwargs: dict[str, Any] | None = None, ) -> list[EmbeddingRequestOutput]: """ Generate an embedding vector for each prompt. This class automatically batches the given prompts, considering the memory constraint. For the best performance, put all of your prompts into a single list and pass it to this method. Args: prompts: The prompts to the LLM. You may pass a sequence of prompts for batch inference. See [PromptType][vllm.inputs.PromptType] for more details about the format of each prompt. pooling_params: The pooling parameters for pooling. If None, we use the default pooling parameters. use_tqdm: If `True`, shows a tqdm progress bar. If a callable (e.g., `functools.partial(tqdm, leave=False)`), it is used to create the progress bar. If `False`, no progress bar is created. lora_request: LoRA request to use for generation, if any. Returns: A list of `EmbeddingRequestOutput` objects containing the embedding vectors in the same order as the input prompts. """ if "embed" not in self.supported_tasks: raise ValueError( "Embedding API is not supported by this model. " "Try converting the model using `--convert embed`." ) items = self.encode( prompts, truncate_prompt_tokens=truncate_prompt_tokens, use_tqdm=use_tqdm, pooling_params=pooling_params, lora_request=lora_request, pooling_task="embed", tokenization_kwargs=tokenization_kwargs, ) return [EmbeddingRequestOutput.from_base(item) for item in items] def classify( self, prompts: PromptType | Sequence[PromptType], *, use_tqdm: bool | Callable[..., tqdm] = True, pooling_params: PoolingParams | Sequence[PoolingParams] | None = None, lora_request: list[LoRARequest] | LoRARequest | None = None, tokenization_kwargs: dict[str, Any] | None = None, ) -> list[ClassificationRequestOutput]: """ Generate class logits for each prompt. This class automatically batches the given prompts, considering the memory constraint. For the best performance, put all of your prompts into a single list and pass it to this method. Args: prompts: The prompts to the LLM. You may pass a sequence of prompts for batch inference. See [PromptType][vllm.inputs.PromptType] for more details about the format of each prompt. use_tqdm: If `True`, shows a tqdm progress bar. If a callable (e.g., `functools.partial(tqdm, leave=False)`), it is used to create the progress bar. If `False`, no progress bar is created. lora_request: LoRA request to use for generation, if any. pooling_params: The pooling parameters for pooling. If None, we use the default pooling parameters. Returns: A list of `ClassificationRequestOutput` objects containing the embedding vectors in the same order as the input prompts. """ if "classify" not in self.supported_tasks: raise ValueError( "Classification API is not supported by this model. " "Try converting the model using `--convert classify`." ) items = self.encode( prompts, use_tqdm=use_tqdm, pooling_params=pooling_params, lora_request=lora_request, pooling_task="classify", tokenization_kwargs=tokenization_kwargs, ) return [ClassificationRequestOutput.from_base(item) for item in items] def reward( self, prompts: PromptType | Sequence[PromptType], /, *, truncate_prompt_tokens: int | None = None, use_tqdm: bool | Callable[..., tqdm] = True, pooling_params: PoolingParams | Sequence[PoolingParams] | None = None, lora_request: list[LoRARequest] | LoRARequest | None = None, tokenization_kwargs: dict[str, Any] | None = None, ) -> list[PoolingRequestOutput]: """ Generate rewards for each prompt. Args: prompts: The prompts to the LLM. You may pass a sequence of prompts for batch inference. See [PromptType][vllm.inputs.PromptType] for more details about the format of each prompt. use_tqdm: If `True`, shows a tqdm progress bar. If a callable (e.g., `functools.partial(tqdm, leave=False)`), it is used to create the progress bar. If `False`, no progress bar is created. lora_request: LoRA request to use for generation, if any. pooling_params: The pooling parameters for pooling. If None, we use the default pooling parameters. Returns: A list of `PoolingRequestOutput` objects containing the pooled hidden states in the same order as the input prompts. """ return self.encode( prompts, use_tqdm=use_tqdm, lora_request=lora_request, pooling_params=pooling_params, truncate_prompt_tokens=truncate_prompt_tokens, pooling_task="token_classify", tokenization_kwargs=tokenization_kwargs, ) def _embedding_score( self, tokenizer: TokenizerLike, text_1: list[str | TextPrompt | TokensPrompt], text_2: list[str | TextPrompt | TokensPrompt], truncate_prompt_tokens: int | None = None, use_tqdm: bool | Callable[..., tqdm] = True, pooling_params: PoolingParams | None = None, lora_request: list[LoRARequest] | LoRARequest | None = None, tokenization_kwargs: dict[str, Any] | None = None, ) -> list[ScoringRequestOutput]: encoded_output: list[PoolingRequestOutput] = self.encode( text_1 + text_2, truncate_prompt_tokens=truncate_prompt_tokens, use_tqdm=use_tqdm, lora_request=lora_request, pooling_params=pooling_params, pooling_task="embed", tokenization_kwargs=tokenization_kwargs, ) encoded_output_1: list[PoolingRequestOutput] = encoded_output[0 : len(text_1)] encoded_output_2: list[PoolingRequestOutput] = encoded_output[len(text_1) :] if len(encoded_output_1) == 1: encoded_output_1 = encoded_output_1 * len(encoded_output_2) scores = _cosine_similarity( tokenizer=tokenizer, embed_1=encoded_output_1, embed_2=encoded_output_2 ) items = self.engine_class.validate_outputs(scores, PoolingRequestOutput) return [ScoringRequestOutput.from_base(item) for item in items] def _cross_encoding_score( self, tokenizer: TokenizerLike, data_1: list[str] | list[ScoreContentPartParam], data_2: list[str] | list[ScoreContentPartParam], truncate_prompt_tokens: int | None = None, use_tqdm: bool | Callable[..., tqdm] = True, pooling_params: PoolingParams | None = None, lora_request: list[LoRARequest] | LoRARequest | None = None, tokenization_kwargs: dict[str, Any] | None = None, score_template: str | None = None, ) -> list[ScoringRequestOutput]: model_config = self.model_config if isinstance(tokenizer, MistralTokenizer): raise ValueError("Score API is not supported for Mistral tokenizer") if len(data_1) == 1: data_1 = data_1 * len(data_2) if pooling_params is None: pooling_params = PoolingParams(task="score") pooling_params.verify("score", model_config) pooling_params_list = list[PoolingParams]() local_kwargs = tokenization_kwargs or {} tokenization_kwargs = local_kwargs.copy() _validate_truncation_size( model_config.max_model_len, truncate_prompt_tokens, tokenization_kwargs ) prompts = list[PromptType]() input_pairs = [(t1, t2) for t1, t2 in zip(data_1, data_2)] for q, d in input_pairs: _, engine_prompt = get_score_prompt( model_config=model_config, data_1=q, data_2=d, tokenizer=tokenizer, tokenization_kwargs=tokenization_kwargs, score_template=score_template, ) if token_type_ids := engine_prompt.pop("token_type_ids", None): params = pooling_params.clone() compressed = compress_token_type_ids(token_type_ids) params.extra_kwargs = {"compressed_token_type_ids": compressed} pooling_params_list.append(params) else: pooling_params_list.append(pooling_params) prompts.append(engine_prompt) self._validate_and_add_requests( prompts=prompts, params=pooling_params_list, use_tqdm=use_tqdm, lora_request=lora_request, ) outputs = self._run_engine(use_tqdm=use_tqdm) items = self.engine_class.validate_outputs(outputs, PoolingRequestOutput) return [ScoringRequestOutput.from_base(item) for item in items] def score( self, data_1: SingletonPrompt | Sequence[SingletonPrompt] | ScoreMultiModalParam, data_2: SingletonPrompt | Sequence[SingletonPrompt] | ScoreMultiModalParam, /, *, truncate_prompt_tokens: int | None = None, use_tqdm: bool | Callable[..., tqdm] = True, pooling_params: PoolingParams | None = None, lora_request: list[LoRARequest] | LoRARequest | None = None, chat_template: str | None = None, ) -> list[ScoringRequestOutput]: """Generate similarity scores for all pairs `` or ``. The inputs can be `1 -> 1`, `1 -> N` or `N -> N`. In the `1 - N` case the `data_1` input will be replicated `N` times to pair with the `data_2` inputs. The input pairs are used to build a list of prompts for the cross encoder model. This class automatically batches the prompts, considering the memory constraint. For the best performance, put all of your inputs into a single list and pass it to this method. Supports both text and multi-modal data (images, etc.) when used with appropriate multi-modal models. For multi-modal inputs, ensure the prompt structure matches the model's expected input format. Args: data_1: Can be a single prompt, a list of prompts or `ScoreMultiModalParam`, which can contain either text or multi-modal data. When a list, it must have the same length as the `data_2` list. data_2: The data to pair with the query to form the input to the LLM. Can be text or multi-modal data. See [PromptType] [vllm.inputs.PromptType] for more details about the format of each prompt. use_tqdm: If `True`, shows a tqdm progress bar. If a callable (e.g., `functools.partial(tqdm, leave=False)`), it is used to create the progress bar. If `False`, no progress bar is created. lora_request: LoRA request to use for generation, if any. pooling_params: The pooling parameters for pooling. If None, we use the default pooling parameters. chat_template: The chat template to use for the scoring. If None, we use the model's default chat template. Returns: A list of `ScoringRequestOutput` objects containing the generated scores in the same order as the input prompts. """ model_config = self.model_config runner_type = model_config.runner_type if runner_type != "pooling": raise ValueError( "LLM.score() is only supported for pooling models. " "Try passing `--runner pooling` to use the model as a " "pooling model." ) supported_tasks = self.supported_tasks if all(t not in supported_tasks for t in ("embed", "classify")): raise ValueError( "Score API is not supported by this model. " "Try converting the model using " "`--convert embed` or `--convert classify`." ) if ( model_config.is_cross_encoder and getattr(model_config.hf_config, "num_labels", 0) != 1 ): raise ValueError("Score API is only enabled for num_labels == 1.") if not model_config.is_cross_encoder and chat_template is not None: raise ValueError( "chat_template is only supported for cross-encoder models." ) # the tokenizer for models such as # "cross-encoder/ms-marco-MiniLM-L-6-v2" doesn't support passing # lists of tokens to the `text` and `text_pair` kwargs tokenizer = self.get_tokenizer() if not model_config.is_multimodal_model: def check_data_type( data: SingletonPrompt | Sequence[SingletonPrompt] | ScoreMultiModalParam, ): if isinstance(data, dict) and "content" in data: raise ValueError( "ScoreMultiModalParam is not supported " f"for {model_config.architecture}" ) check_data_type(data_1) check_data_type(data_2) def ensure_str(prompt: SingletonPrompt): if isinstance(prompt, dict): if "multi_modal_data" in prompt: raise ValueError( "Multi-modal prompt is not supported for scoring" ) elif "prompt_token_ids" in prompt: prompt = tokenizer.decode( cast(TokensPrompt, prompt)["prompt_token_ids"] ) elif "prompt" in prompt: prompt = cast(TextPrompt, prompt)["prompt"] assert type(prompt) is str return prompt if isinstance(data_1, (str, dict)): # Convert a single prompt to a list. data_1 = [data_1] # type: ignore[list-item] data_1 = [ensure_str(t) for t in data_1] if isinstance(data_2, (str, dict)): # Convert a single prompt to a list. data_2 = [data_2] # type: ignore[list-item] data_2 = [ensure_str(t) for t in data_2] if isinstance(data_1, dict) and "content" in data_1: data_1 = data_1.get("content") # type: ignore[assignment] elif isinstance(data_1, str): data_1 = [data_1] if isinstance(data_2, dict) and "content" in data_2: data_2 = data_2.get("content") # type: ignore[assignment] elif isinstance(data_2, str): data_2 = [data_2] _validate_score_input_lens(data_1, data_2) # type: ignore[arg-type] if model_config.is_cross_encoder: return self._cross_encoding_score( tokenizer, data_1, # type: ignore[arg-type] data_2, # type: ignore[arg-type] truncate_prompt_tokens, use_tqdm, pooling_params, lora_request, score_template=chat_template, ) else: return self._embedding_score( tokenizer, data_1, # type: ignore[arg-type] data_2, # type: ignore[arg-type] truncate_prompt_tokens, use_tqdm, pooling_params, lora_request, ) def start_profile(self) -> None: self.llm_engine.start_profile() def stop_profile(self) -> None: self.llm_engine.stop_profile() def reset_prefix_cache( self, reset_running_requests: bool = False, reset_connector: bool = False ) -> bool: return self.llm_engine.reset_prefix_cache( reset_running_requests, reset_connector ) def sleep(self, level: int = 1): """ Put the engine to sleep. The engine should not process any requests. The caller should guarantee that no requests are being processed during the sleep period, before `wake_up` is called. Args: level: The sleep level. Level 1 sleep will offload the model weights and discard the kv cache. The content of kv cache is forgotten. Level 1 sleep is good for sleeping and waking up the engine to run the same model again. The model weights are backed up in CPU memory. Please make sure there's enough CPU memory to store the model weights. Level 2 sleep will discard both the model weights and the kv cache. The content of both the model weights and kv cache is forgotten. Level 2 sleep is good for sleeping and waking up the engine to run a different model or update the model, where previous model weights are not needed. It reduces CPU memory pressure. """ self.reset_prefix_cache() self.llm_engine.sleep(level=level) def wake_up(self, tags: list[str] | None = None): """ Wake up the engine from sleep mode. See the [sleep][vllm.LLM.sleep] method for more details. Args: tags: An optional list of tags to reallocate the engine memory for specific memory allocations. Values must be in `("weights", "kv_cache")`. If None, all memory is reallocated. wake_up should be called with all tags (or None) before the engine is used again. """ self.llm_engine.wake_up(tags) def get_metrics(self) -> list["Metric"]: """Return a snapshot of aggregated metrics from Prometheus. Returns: A `MetricSnapshot` instance capturing the current state of all aggregated metrics from Prometheus. Note: This method is only available with the V1 LLM engine. """ return self.llm_engine.get_metrics() def _validate_and_add_requests( self, prompts: PromptType | Sequence[PromptType] | DataPrompt, params: SamplingParams | Sequence[SamplingParams] | PoolingParams | Sequence[PoolingParams], *, use_tqdm: bool | Callable[..., tqdm] = True, lora_request: Sequence[LoRARequest] | LoRARequest | None, priority: list[int] | None = None, tokenization_kwargs: dict[str, Any] | None = None, ) -> None: if isinstance(prompts, (str, dict)): # Convert a single prompt to a list. prompts = [prompts] # type: ignore[list-item] num_requests = len(prompts) if isinstance(params, Sequence) and len(params) != num_requests: raise ValueError("The lengths of prompts and params must be the same.") if isinstance(lora_request, Sequence) and len(lora_request) != num_requests: raise ValueError( "The lengths of prompts and lora_request must be the same." ) if priority is not None and len(priority) != num_requests: raise ValueError( "The lengths of prompts " f"({num_requests}) and priority ({len(priority)}) " "must be the same." ) for sp in params if isinstance(params, Sequence) else (params,): if isinstance(sp, SamplingParams): # We only care about the final output sp.output_kind = RequestOutputKind.FINAL_ONLY # Add requests to the engine. it = prompts if use_tqdm: tqdm_func = use_tqdm if callable(use_tqdm) else tqdm it = tqdm_func(it, desc="Adding requests") added_request_ids: list[str] = [] try: for i, prompt in enumerate(it): request_id = self._add_request( prompt, params[i] if isinstance(params, Sequence) else params, lora_request=lora_request[i] if isinstance(lora_request, Sequence) else lora_request, priority=priority[i] if priority else 0, tokenization_kwargs=tokenization_kwargs, ) added_request_ids.append(request_id) except Exception as e: if added_request_ids: self.llm_engine.abort_request(added_request_ids, internal=True) raise e def _process_inputs( self, request_id: str, engine_prompt: PromptType, params: SamplingParams | PoolingParams, *, lora_request: LoRARequest | None, priority: int, tokenization_kwargs: dict[str, Any] | None = None, ) -> tuple[EngineCoreRequest, dict[str, Any]]: """Use the Processor to process inputs for LLMEngine.""" local_kwargs = tokenization_kwargs or {} tokenization_kwargs = local_kwargs.copy() _validate_truncation_size( self.model_config.max_model_len, params.truncate_prompt_tokens, tokenization_kwargs, ) engine_request = self.input_processor.process_inputs( request_id, engine_prompt, params, lora_request=lora_request, tokenization_kwargs=tokenization_kwargs, priority=priority, ) return engine_request, tokenization_kwargs def _add_request( self, prompt: PromptType, params: SamplingParams | PoolingParams, lora_request: LoRARequest | None = None, priority: int = 0, tokenization_kwargs: dict[str, Any] | None = None, ) -> str: prompt_text, _, _ = get_prompt_components(prompt) request_id = str(next(self.request_counter)) engine_request, tokenization_kwargs = self._process_inputs( request_id, prompt, params, lora_request=lora_request, priority=priority, tokenization_kwargs=tokenization_kwargs, ) self.llm_engine.add_request( request_id, engine_request, params, lora_request=lora_request, tokenization_kwargs=tokenization_kwargs, priority=priority, prompt_text=prompt_text, ) return engine_request.request_id def _run_engine( self, *, use_tqdm: bool | Callable[..., tqdm] = True ) -> list[RequestOutput | PoolingRequestOutput]: # Initialize tqdm. if use_tqdm: num_requests = self.llm_engine.get_num_unfinished_requests() tqdm_func = use_tqdm if callable(use_tqdm) else tqdm pbar = tqdm_func( total=num_requests, desc="Processed prompts", dynamic_ncols=True, postfix=(f"est. speed input: {0:.2f} toks/s, output: {0:.2f} toks/s"), ) # Run the engine. outputs: list[RequestOutput | PoolingRequestOutput] = [] total_in_toks = 0 total_out_toks = 0 while self.llm_engine.has_unfinished_requests(): step_outputs = self.llm_engine.step() for output in step_outputs: if output.finished: outputs.append(output) if use_tqdm: if isinstance(output, RequestOutput): # Calculate tokens only for RequestOutput n = len(output.outputs) assert output.prompt_token_ids is not None total_in_toks += len(output.prompt_token_ids) * n in_spd = total_in_toks / pbar.format_dict["elapsed"] total_out_toks += sum( len(stp.token_ids) for stp in output.outputs ) out_spd = total_out_toks / pbar.format_dict["elapsed"] pbar.postfix = ( f"est. speed input: {in_spd:.2f} toks/s, " f"output: {out_spd:.2f} toks/s" ) pbar.update(n) else: pbar.update(1) if pbar.n == num_requests: pbar.refresh() if use_tqdm: pbar.close() # Sort the outputs by request ID. # This is necessary because some requests may be finished earlier than # its previous requests. return sorted(outputs, key=lambda x: int(x.request_id)) def __repr__(self) -> str: """Return a transformers-style hierarchical view of the model.""" # Cache the result to avoid repeated collective_rpc calls if self._cached_repr is None: results = self.llm_engine.collective_rpc("get_model_inspection") # In distributed settings, we get results from all workers # Just return the first one (they should all be the same) if results: self._cached_repr = results[0] else: self._cached_repr = f"LLM(model={self.model_config.model!r})" return self._cached_repr