# SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project import itertools from collections.abc import Callable, Iterable, Sequence from pathlib import Path from typing import TYPE_CHECKING, Any import cloudpickle import torch.nn as nn from pydantic import ValidationError from tqdm.auto import tqdm from typing_extensions import TypeVar, overload 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.distributed.weight_transfer.base import ( WeightTransferInitRequest, WeightTransferUpdateRequest, ) from vllm.engine.arg_utils import EngineArgs from vllm.entrypoints.chat_utils import ( ChatCompletionMessageParam, ChatTemplateConfig, ChatTemplateContentFormatOption, load_chat_template, ) from vllm.entrypoints.pooling.io_processor_factories import init_pooling_io_processors from vllm.entrypoints.pooling.score.utils import ( ScoreData, ScoreMultiModalParam, _cosine_similarity, compress_token_type_ids, compute_maxsim_score, get_score_prompt, score_data_to_prompts, validate_score_input, ) from vllm.entrypoints.utils import log_non_default_args from vllm.inputs.data import ( DataPrompt, ProcessorInputs, PromptType, SingletonPrompt, TextPrompt, TokensPrompt, ) 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.renderers import ChatParams, merge_kwargs from vllm.renderers.inputs.preprocess import ( conversation_to_seq, parse_model_prompt, prompt_to_seq, ) from vllm.sampling_params import BeamSearchParams, RequestOutputKind, SamplingParams from vllm.tasks import PoolingTask from vllm.tokenizers import TokenizerLike from vllm.usage.usage_lib import UsageContext from vllm.utils.counter import Counter from vllm.utils.mistral import is_mistral_tokenizer from vllm.utils.tqdm_utils import maybe_tqdm from vllm.v1.engine import PauseMode 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__) _O = TypeVar( "_O", bound=RequestOutput | PoolingRequestOutput, default=RequestOutput | PoolingRequestOutput, ) _P = TypeVar("_P", bound=SamplingParams | PoolingParams | None) _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. chat_template: The chat template to apply. 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 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. offload_group_size: Prefetch offloading: Group every N layers together. Offload last `offload_num_in_group` layers of each group. Default is 0 (disabled). offload_num_in_group: Prefetch offloading: Number of layers to offload per group. Default is 1. offload_prefetch_step: Prefetch offloading: Number of layers to prefetch ahead. Higher values hide more latency but use more GPU memory. Default is 1. offload_params: Prefetch offloading: Set of parameter name segments to selectively offload. Only parameters whose names contain one of these segments will be offloaded (e.g., {"gate_up_proj", "down_proj"} for MLP weights, or {"w13_weight", "w2_weight"} for MoE expert weights). If None or empty, all parameters are offloaded. 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 `hf auth login` (stored in `~/.cache/huggingface/token`). 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", use_activation=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, chat_template: Path | str | None = None, seed: int = 0, gpu_memory_utilization: float = 0.9, cpu_offload_gb: float = 0, offload_group_size: int = 0, offload_num_in_group: int = 1, offload_prefetch_step: int = 1, offload_params: set[str] | None = None, 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 "swap_space" in kwargs: kwargs.pop("swap_space") import warnings warnings.warn( "The 'swap_space' parameter is deprecated and ignored. " "It will be removed in a future version.", DeprecationWarning, stacklevel=2, ) 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, cpu_offload_gb=cpu_offload_gb, offload_group_size=offload_group_size, offload_num_in_group=offload_num_in_group, offload_prefetch_step=offload_prefetch_step, offload_params=offload_params or set(), 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.renderer = self.llm_engine.renderer self.chat_template = load_chat_template(chat_template) self.io_processor = self.llm_engine.io_processor self.input_processor = self.llm_engine.input_processor self.chat_template_config = ChatTemplateConfig(chat_template=self.chat_template) self.pooling_io_processors = init_pooling_io_processors( supported_tasks=supported_tasks, model_config=self.model_config, renderer=self.renderer, chat_template_config=self.chat_template_config, ) # 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 get_world_size(self, include_dp: bool = True) -> int: """Get the world size from the parallel config. Args: include_dp: If True (default), returns the world size including data parallelism (TP * PP * DP). If False, returns the world size without data parallelism (TP * PP). Returns: The world size (tensor_parallel_size * pipeline_parallel_size), optionally multiplied by data_parallel_size if include_dp is True. """ parallel_config = self.llm_engine.vllm_config.parallel_config if include_dp: return parallel_config.world_size_across_dp return parallel_config.world_size def reset_mm_cache(self) -> None: self.renderer.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: Sequence[LoRARequest] | LoRARequest | None = None, priority: list[int] | None = None, tokenization_kwargs: dict[str, Any] | 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. tokenization_kwargs: Overrides for `tokenizer.encode`. Returns: A list of `RequestOutput` objects containing the generated completions in the same order as the input prompts. """ runner_type = self.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: sampling_params = self.get_default_sampling_params() return self._run_completion( prompts=prompts, params=sampling_params, output_type=RequestOutput, use_tqdm=use_tqdm, lora_request=lora_request, tokenization_kwargs=tokenization_kwargs, priority=priority, ) def enqueue( self, prompts: PromptType | Sequence[PromptType], sampling_params: SamplingParams | Sequence[SamplingParams] | None = None, lora_request: Sequence[LoRARequest] | LoRARequest | None = None, priority: list[int] | None = None, use_tqdm: bool | Callable[..., tqdm] = True, tokenization_kwargs: dict[str, Any] | None = None, ) -> list[str]: """Enqueue prompts for generation without waiting for completion. This method adds requests to the engine queue but does not start processing them. Use wait_for_completion() to process the queued requests and get results. Args: prompts: The prompts to the LLM. See generate() for details. sampling_params: The sampling parameters for text generation. lora_request: LoRA request to use for generation, if any. priority: The priority of the requests, if any. use_tqdm: If True, shows a tqdm progress bar while adding requests. tokenization_kwargs: Overrides for `tokenizer.encode`. Returns: A list of request IDs for the enqueued requests. """ runner_type = self.model_config.runner_type if runner_type != "generate": raise ValueError("LLM.enqueue() is only supported for generative models.") if sampling_params is None: sampling_params = self.get_default_sampling_params() return self._add_completion_requests( prompts=prompts, params=sampling_params, use_tqdm=use_tqdm, lora_request=lora_request, priority=priority, tokenization_kwargs=tokenization_kwargs, ) @overload def wait_for_completion( self, *, use_tqdm: bool | Callable[..., tqdm] = True, ) -> list[RequestOutput | PoolingRequestOutput]: ... @overload def wait_for_completion( self, output_type: type[_O] | tuple[type[_O], ...], *, use_tqdm: bool | Callable[..., tqdm] = True, ) -> list[_O]: ... def wait_for_completion( self, output_type: type[Any] | tuple[type[Any], ...] | None = None, *, use_tqdm: bool | Callable[..., tqdm] = True, ) -> list[Any]: """Wait for all enqueued requests to complete and return results. This method processes all requests currently in the engine queue and returns their outputs. Use after enqueue() to get results. Args: output_type: The expected output type, defaults to RequestOutput. use_tqdm: If True, shows a tqdm progress bar. Returns: A list of output objects for all completed requests. """ if output_type is None: output_type = (RequestOutput, PoolingRequestOutput) return self._run_engine(output_type, use_tqdm=use_tqdm) def _resolve_mm_lora( self, prompt: ProcessorInputs, lora_request: LoRARequest | None, ) -> LoRARequest | None: if prompt["type"] != "multimodal": return lora_request lora_config = self.llm_engine.vllm_config.lora_config default_mm_loras = None if lora_config is None else lora_config.default_mm_loras if not default_mm_loras: return lora_request prompt_modalities = prompt["mm_placeholders"].keys() intersection = set(prompt_modalities).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 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 tokenizer = self.renderer.get_tokenizer() eos_token_id = tokenizer.eos_token_id sort_beams_key = create_sort_beams_key_function(eos_token_id, length_penalty) engine_prompts = self._preprocess_cmpl(prompts) lora_requests = self._lora_request_to_seq(lora_request, len(engine_prompts)) 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(engine_prompts) # 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 sampling_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, engine_prompts): if prompt["type"] == "embeds": raise NotImplementedError( "Embedding prompt not supported for beam search" ) instances.append( BeamSearchInstance( prompt, lora_request=lora_req, logprobs=None, ), ) for prompt_start in range(0, len(instances), 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 # only runs for one step # we don't need to use tqdm here output = self._render_and_run_requests( prompts=(beam.get_prompt() for beam in all_beams), params=self._params_to_seq(sampling_params, len(all_beams)), output_type=RequestOutput, lora_requests=[beam.lora_request for beam in all_beams], use_tqdm=False, ) 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( current_beam.orig_prompt, 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, ) if token_id == 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_cmpl( self, prompts: Sequence[PromptType], tokenization_kwargs: dict[str, Any] | None = None, ) -> Sequence[ProcessorInputs]: """ Convert prompt inputs from LLM APIs (other than [LLM.chat][]) into a format that can be passed to `_add_request`. Refer to [LLM.generate][] for a complete description of the arguments. Returns: A list of `ProcessorInputs` objects ready to be passed into LLMEngine. """ renderer = self.renderer model_config = self.model_config parsed_prompts = [ parse_model_prompt(model_config, prompt) for prompt in prompts ] tok_params = renderer.default_cmpl_tok_params.with_kwargs( **(tokenization_kwargs or {}) ) return renderer.render_cmpl(parsed_prompts, tok_params) def _preprocess_cmpl_one( self, prompt: PromptType, tokenization_kwargs: dict[str, Any] | None = None, ) -> ProcessorInputs: (engine_prompt,) = self._preprocess_cmpl([prompt], tokenization_kwargs) return engine_prompt def _preprocess_chat( self, conversations: Sequence[list[ChatCompletionMessageParam]], chat_template: str | None = None, chat_template_content_format: ChatTemplateContentFormatOption = "auto", chat_template_kwargs: dict[str, Any] | None = None, add_generation_prompt: bool = True, continue_final_message: bool = False, tools: list[dict[str, Any]] | None = None, tokenization_kwargs: dict[str, Any] | None = None, mm_processor_kwargs: dict[str, Any] | None = None, ) -> Sequence[ProcessorInputs]: """ Convert a list of conversations into prompts so that they can then be used as input for other LLM APIs. Refer to [LLM.chat][] for a complete description of the arguments. Returns: A list of `ProcessorInputs` objects ready to be passed into LLMEngine. """ renderer = self.renderer chat_params = ChatParams( chat_template=chat_template, chat_template_content_format=chat_template_content_format, chat_template_kwargs=merge_kwargs( chat_template_kwargs, dict( add_generation_prompt=add_generation_prompt, continue_final_message=continue_final_message, tools=tools, tokenize=is_mistral_tokenizer(renderer.tokenizer), ), ), ) tok_params = renderer.default_chat_tok_params.with_kwargs( **(tokenization_kwargs or {}) ) _, engine_prompts = renderer.render_chat( conversations, chat_params, tok_params, prompt_extras={"mm_processor_kwargs": mm_processor_kwargs}, ) return engine_prompts def _preprocess_chat_one( self, conversation: list[ChatCompletionMessageParam], chat_template: str | None = None, chat_template_content_format: ChatTemplateContentFormatOption = "auto", chat_template_kwargs: dict[str, Any] | None = None, add_generation_prompt: bool = True, continue_final_message: bool = False, tools: list[dict[str, Any]] | None = None, tokenization_kwargs: dict[str, Any] | None = None, mm_processor_kwargs: dict[str, Any] | None = None, ) -> ProcessorInputs: (engine_prompt,) = self._preprocess_chat( [conversation], chat_template=chat_template, chat_template_content_format=chat_template_content_format, chat_template_kwargs=chat_template_kwargs, add_generation_prompt=add_generation_prompt, continue_final_message=continue_final_message, tools=tools, tokenization_kwargs=tokenization_kwargs, mm_processor_kwargs=mm_processor_kwargs, ) return engine_prompt def chat( self, messages: list[ChatCompletionMessageParam] | Sequence[list[ChatCompletionMessageParam]], sampling_params: SamplingParams | Sequence[SamplingParams] | None = None, use_tqdm: bool | Callable[..., tqdm] = True, lora_request: Sequence[LoRARequest] | 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, tokenization_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 sequence 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. tokenization_kwargs: Overrides for `tokenizer.encode`. mm_processor_kwargs: Overrides for `processor.__call__`. Returns: A list of `RequestOutput` objects containing the generated responses in the same order as the input messages. """ model_config = self.model_config runner_type = model_config.runner_type if runner_type != "generate": raise ValueError( "LLM.chat() is only supported for generative models. " "Try passing `--runner generate` to use the model as a " "generative model." ) if sampling_params is None: sampling_params = self.get_default_sampling_params() return self._run_chat( messages=messages, params=sampling_params, output_type=RequestOutput, use_tqdm=use_tqdm, lora_request=lora_request, chat_template=chat_template, chat_template_content_format=chat_template_content_format, chat_template_kwargs=chat_template_kwargs, add_generation_prompt=add_generation_prompt, continue_final_message=continue_final_message, tools=tools, tokenization_kwargs=tokenization_kwargs, mm_processor_kwargs=mm_processor_kwargs, ) def encode( self, prompts: PromptType | Sequence[PromptType] | DataPrompt, pooling_params: PoolingParams | Sequence[PoolingParams] | 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 for `tokenizer.encode`. Returns: A list of `PoolingRequestOutput` objects containing the pooled hidden states in the same order as the input prompts. """ if pooling_task is None: raise ValueError( "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"`' ) 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." ) if isinstance(prompts, dict) and "data" in prompts: 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 prompt_data = prompts.get("data") if prompt_data is None: raise ValueError( "The 'data' field of the prompt is expected to contain " "the prompt data and it cannot be None. " "Refer to the documentation of the IOProcessor " "in use for more details." ) validated_prompt = self.io_processor.parse_data(prompt_data) # obtain the actual model prompts from the pre-processor prompts = self.io_processor.pre_process(prompt=validated_prompt) prompts_seq = prompt_to_seq(prompts) params_seq: Sequence[PoolingParams] = [ self.io_processor.merge_pooling_params(param) for param in self._params_to_seq( pooling_params, len(prompts_seq), ) ] for p in params_seq: if p.task is None: p.task = "plugin" outputs = self._run_completion( prompts=prompts_seq, params=params_seq, output_type=PoolingRequestOutput, use_tqdm=use_tqdm, lora_request=lora_request, tokenization_kwargs=tokenization_kwargs, ) # get the post-processed model outputs assert self.io_processor is not None processed_outputs = self.io_processor.post_process(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: if pooling_params is None: # Use default pooling params. pooling_params = PoolingParams() prompts_seq = prompt_to_seq(prompts) params_seq = self._params_to_seq(pooling_params, len(prompts_seq)) for param in params_seq: if param.task is None: param.task = pooling_task elif param.task != pooling_task: msg = ( f"You cannot overwrite {param.task=!r} with {pooling_task=!r}!" ) raise ValueError(msg) if pooling_task in self.pooling_io_processors: io_processor = self.pooling_io_processors[pooling_task] processor_inputs = io_processor.pre_process_offline( prompts_seq, tokenization_kwargs ) seq_lora_requests = self._lora_request_to_seq( lora_request, len(prompts_seq) ) seq_priority = self._priority_to_seq(None, len(prompts)) self._render_and_add_requests( prompts=processor_inputs, params=params_seq, lora_requests=seq_lora_requests, priorities=seq_priority, ) outputs = self._run_engine( use_tqdm=use_tqdm, output_type=PoolingRequestOutput ) outputs = io_processor.post_process_offline(outputs) else: outputs = self._run_completion( prompts=prompts_seq, params=params_seq, output_type=PoolingRequestOutput, use_tqdm=use_tqdm, lora_request=lora_request, tokenization_kwargs=tokenization_kwargs, ) return outputs def embed( 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[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. tokenization_kwargs: Overrides for `tokenizer.encode`. 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, 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], *, pooling_params: PoolingParams | Sequence[PoolingParams] | None = None, use_tqdm: bool | Callable[..., tqdm] = True, 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. 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. tokenization_kwargs: Overrides for `tokenizer.encode`. 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], /, *, pooling_params: PoolingParams | Sequence[PoolingParams] | None = None, use_tqdm: bool | Callable[..., tqdm] = True, 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. 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. tokenization_kwargs: Overrides for `tokenizer.encode`. 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, pooling_task="token_classify", tokenization_kwargs=tokenization_kwargs, ) def _embedding_score( self, data_1: list[ScoreData], data_2: list[ScoreData], *, use_tqdm: bool | Callable[..., tqdm], pooling_params: PoolingParams | None, lora_request: list[LoRARequest] | LoRARequest | None, tokenization_kwargs: dict[str, Any], ) -> list[ScoringRequestOutput]: tokenizer = self.get_tokenizer() input_texts: list[str] = [] for text in data_1 + data_2: if not isinstance(text, str): raise NotImplementedError( "Embedding scores currently do not support multimodal input." ) input_texts.append(text) encoded_output = self.encode( input_texts, use_tqdm=use_tqdm, lora_request=lora_request, pooling_params=pooling_params, pooling_task="embed", tokenization_kwargs=tokenization_kwargs, ) encoded_output_1 = encoded_output[0 : len(data_1)] encoded_output_2 = encoded_output[len(data_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, ) return [ScoringRequestOutput.from_base(item) for item in scores] def _late_interaction_score( self, data_1: list[ScoreData], data_2: list[ScoreData], *, use_tqdm: bool | Callable[..., tqdm], pooling_params: PoolingParams | None, lora_request: list[LoRARequest] | LoRARequest | None, tokenization_kwargs: dict[str, Any], ) -> list[ScoringRequestOutput]: """ Late interaction scoring (ColBERT MaxSim). Encodes queries and documents into per-token embeddings, then computes MaxSim: sum over query tokens of max similarity to any document token. """ from vllm.outputs import PoolingOutput tokenizer = self.get_tokenizer() # Convert ScoreData to PromptType (handles both text and multimodal) model_config = self.model_config prompts_1 = score_data_to_prompts(data_1, "query", model_config) prompts_2 = score_data_to_prompts(data_2, "document", model_config) encoded_output: list[PoolingRequestOutput] = self.encode( prompts_1 + prompts_2, use_tqdm=use_tqdm, lora_request=lora_request, pooling_params=pooling_params, pooling_task="token_embed", tokenization_kwargs=tokenization_kwargs, ) encoded_output_1: list[PoolingRequestOutput] = encoded_output[: len(prompts_1)] encoded_output_2: list[PoolingRequestOutput] = encoded_output[len(prompts_1) :] if len(encoded_output_1) == 1: encoded_output_1 = encoded_output_1 * len(encoded_output_2) # Compute MaxSim scores scores: list[PoolingRequestOutput] = [] padding: list[int] = [] if (pad_token_id := tokenizer.pad_token_id) is not None: padding = [pad_token_id] for emb_1, emb_2 in zip(encoded_output_1, encoded_output_2): # emb_1.outputs.data: [query_len, dim] # emb_2.outputs.data: [doc_len, dim] q_emb = emb_1.outputs.data d_emb = emb_2.outputs.data maxsim_score = compute_maxsim_score(q_emb, d_emb) tokens = emb_1.prompt_token_ids + padding + emb_2.prompt_token_ids scores.append( PoolingRequestOutput( request_id=f"{emb_1.request_id}_{emb_2.request_id}", outputs=PoolingOutput(data=maxsim_score), prompt_token_ids=tokens, num_cached_tokens=emb_1.num_cached_tokens + emb_2.num_cached_tokens, finished=True, ) ) return [ScoringRequestOutput.from_base(item) for item in scores] def _cross_encoding_score( self, data_1: list[ScoreData], data_2: list[ScoreData], *, use_tqdm: bool | Callable[..., tqdm], pooling_params: PoolingParams | None, lora_request: list[LoRARequest] | LoRARequest | None, tokenization_kwargs: dict[str, Any], score_template: str | None, ) -> list[ScoringRequestOutput]: model_config = self.model_config tokenizer = self.get_tokenizer() if is_mistral_tokenizer(tokenizer): 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") elif pooling_params.task is None: pooling_params.task = "score" pooling_params_list = list[PoolingParams]() 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) outputs = self._run_completion( prompts=prompts, params=pooling_params_list, output_type=PoolingRequestOutput, use_tqdm=use_tqdm, lora_request=lora_request, ) return [ScoringRequestOutput.from_base(item) for item in outputs] def score( self, data_1: SingletonPrompt | Sequence[SingletonPrompt] | ScoreMultiModalParam | list[ScoreMultiModalParam], data_2: SingletonPrompt | Sequence[SingletonPrompt] | ScoreMultiModalParam | list[ScoreMultiModalParam], /, *, 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, 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. 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. chat_template: The chat template to use for the scoring. If None, we use the model's default chat template. tokenization_kwargs: Overrides for `tokenizer.encode`. 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 score_type = self.model_config.score_type is_late_interaction = score_type == "late-interaction" is_cross_encoder = score_type == "cross-encoder" # Late interaction models (e.g., ColBERT) use token_embed for scoring if not is_late_interaction and 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 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 is_cross_encoder and chat_template is not None: raise ValueError( "chat_template is only supported for cross-encoder models." ) is_multimodal_model = model_config.is_multimodal_model architecture = model_config.architecture score_data_1, score_data_2 = validate_score_input( data_1, # type: ignore[arg-type] data_2, # type: ignore[arg-type] is_multimodal_model=is_multimodal_model, architecture=architecture, ) renderer = self.renderer tok_params = renderer.default_cmpl_tok_params.with_kwargs( **(tokenization_kwargs or {}) ) encode_kwargs = tok_params.get_encode_kwargs() if is_cross_encoder: return self._cross_encoding_score( score_data_1, score_data_2, use_tqdm=use_tqdm, pooling_params=pooling_params, lora_request=lora_request, tokenization_kwargs=encode_kwargs, score_template=chat_template, ) elif is_late_interaction: return self._late_interaction_score( score_data_1, score_data_2, use_tqdm=use_tqdm, pooling_params=pooling_params, lora_request=lora_request, tokenization_kwargs=encode_kwargs, ) else: return self._embedding_score( score_data_1, score_data_2, use_tqdm=use_tqdm, pooling_params=pooling_params, lora_request=lora_request, tokenization_kwargs=encode_kwargs, ) def start_profile(self, profile_prefix: str | None = None) -> None: """Start profiling with optional custom trace prefix. Args: profile_prefix: Optional prefix for the trace file names. If provided, trace files will be named as "_dp_pp_tp". If not provided, default naming will be used. """ self.llm_engine.start_profile(profile_prefix) 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, mode: PauseMode = "abort"): """ 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 0: Pause scheduling but continue accepting requests. Requests are queued but not processed. - Level 1: Offload model weights to CPU, discard KV cache. The content of kv cache is forgotten. Good for sleeping and waking up the engine to run the same model again. Please make sure there's enough CPU memory to store the model weights. - Level 2: Discard all GPU memory (weights + KV cache). 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. mode: How to handle any existing requests, can be "abort", "wait", or "keep". """ self.llm_engine.sleep(level=level, mode=mode) 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", "scheduling")`. If None, all memory is reallocated. wake_up should be called with all tags (or None) before the engine is used again. Use tags=["scheduling"] to resume from level 0 sleep. """ 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 _params_to_seq( self, params: _P | Sequence[_P], num_requests: int, ) -> Sequence[_P]: if isinstance(params, Sequence): if len(params) != num_requests: raise ValueError( f"The lengths of prompts ({params}) " f"and params ({len(params)}) must be the same." ) return params return [params] * num_requests def _lora_request_to_seq( self, lora_request: LoRARequest | None | Sequence[LoRARequest | None], num_requests: int, ) -> Sequence[LoRARequest | None]: if isinstance(lora_request, Sequence): if len(lora_request) != num_requests: raise ValueError( f"The lengths of prompts ({num_requests}) " f"and lora_request ({len(lora_request)}) must be the same." ) return lora_request return [lora_request] * num_requests def _priority_to_seq( self, priority: list[int] | None, num_requests: int, ) -> Sequence[int]: if priority is not None: if len(priority) != num_requests: raise ValueError( f"The lengths of prompts ({num_requests}) " f"and priority ({len(priority)}) must be the same." ) return priority return [0] * num_requests def _add_completion_requests( self, prompts: PromptType | Sequence[PromptType], params: SamplingParams | PoolingParams | Sequence[SamplingParams | PoolingParams], *, use_tqdm: bool | Callable[..., tqdm] = True, lora_request: Sequence[LoRARequest] | LoRARequest | None = None, priority: list[int] | None = None, tokenization_kwargs: dict[str, Any] | None = None, ) -> list[str]: seq_prompts = prompt_to_seq(prompts) seq_params = self._params_to_seq(params, len(seq_prompts)) seq_lora_requests = self._lora_request_to_seq(lora_request, len(seq_prompts)) seq_priority = self._priority_to_seq(priority, len(prompts)) return self._render_and_add_requests( prompts=( self._preprocess_cmpl_one(prompt, tokenization_kwargs) for prompt in maybe_tqdm( seq_prompts, use_tqdm=use_tqdm, desc="Rendering prompts", ) ), params=seq_params, lora_requests=seq_lora_requests, priorities=seq_priority, ) def _run_completion( self, prompts: PromptType | Sequence[PromptType], params: SamplingParams | PoolingParams | Sequence[SamplingParams | PoolingParams], output_type: type[_O], *, use_tqdm: bool | Callable[..., tqdm] = True, lora_request: Sequence[LoRARequest] | LoRARequest | None = None, priority: list[int] | None = None, tokenization_kwargs: dict[str, Any] | None = None, ): self._add_completion_requests( prompts=prompts, params=params, use_tqdm=use_tqdm, lora_request=lora_request, priority=priority, tokenization_kwargs=tokenization_kwargs, ) return self._run_engine(use_tqdm=use_tqdm, output_type=output_type) def _run_chat( self, messages: list[ChatCompletionMessageParam] | Sequence[list[ChatCompletionMessageParam]], params: SamplingParams | PoolingParams | Sequence[SamplingParams | PoolingParams], output_type: type[_O], *, use_tqdm: bool | Callable[..., tqdm] = True, lora_request: Sequence[LoRARequest] | 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, tokenization_kwargs: dict[str, Any] | None = None, mm_processor_kwargs: dict[str, Any] | None = None, ): seq_convs = conversation_to_seq(messages) seq_params = self._params_to_seq(params, len(seq_convs)) seq_lora_requests = self._lora_request_to_seq(lora_request, len(seq_convs)) return self._render_and_run_requests( prompts=( self._preprocess_chat_one( conversation, chat_template=chat_template, chat_template_content_format=chat_template_content_format, chat_template_kwargs=chat_template_kwargs, add_generation_prompt=add_generation_prompt, continue_final_message=continue_final_message, tools=tools, tokenization_kwargs=tokenization_kwargs, mm_processor_kwargs=mm_processor_kwargs, ) for conversation in maybe_tqdm( seq_convs, use_tqdm=use_tqdm, desc="Rendering conversations", ) ), params=seq_params, output_type=output_type, lora_requests=seq_lora_requests, use_tqdm=use_tqdm, ) def _render_and_run_requests( self, prompts: Iterable[ProcessorInputs], params: Sequence[SamplingParams | PoolingParams], output_type: type[_O], *, lora_requests: Sequence[LoRARequest | None] | None = None, priorities: Sequence[int] | None = None, use_tqdm: bool | Callable[..., tqdm] = True, ): if isinstance(prompts, (list, tuple)): logger.warning_once( "Rendering all prompts before adding them to the engine " "is less efficient than performing both on the same prompt " "before processing the next prompt. You should instead pass " "a generator that renders one prompt per iteration, as that allows " "engine execution to begin for the first prompt while processing " "the next prompt." ) self._render_and_add_requests( prompts=prompts, params=params, lora_requests=lora_requests, priorities=priorities, ) return self._run_engine(output_type, use_tqdm=use_tqdm) def _render_and_add_requests( self, prompts: Iterable[ProcessorInputs], params: Sequence[SamplingParams | PoolingParams], *, lora_requests: Sequence[LoRARequest | None] | None = None, priorities: Sequence[int] | None = None, ) -> list[str]: added_request_ids: list[str] = [] try: for i, prompt in enumerate(prompts): request_id = self._add_request( prompt, params[i], lora_request=self._resolve_mm_lora( prompt, None if lora_requests is None else lora_requests[i], ), priority=0 if priorities is None else priorities[i], ) 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 return added_request_ids def _add_request( self, prompt: ProcessorInputs, params: SamplingParams | PoolingParams, lora_request: LoRARequest | None = None, priority: int = 0, ) -> str: if isinstance(params, SamplingParams): # We only care about the final output params.output_kind = RequestOutputKind.FINAL_ONLY request_id = str(next(self.request_counter)) return self.llm_engine.add_request( request_id, prompt, params, lora_request=lora_request, priority=priority, ) def _run_engine( self, output_type: type[_O] | tuple[type[_O], ...], *, use_tqdm: bool | Callable[..., tqdm] = True, ) -> list[_O]: # 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[_O] = [] 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: assert isinstance(output, output_type) if output.finished: outputs.append(output) # type: ignore[arg-type] 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 init_weight_transfer_engine( self, request: WeightTransferInitRequest | dict ) -> None: """ Initialize weight transfer for RL training. Args: request: Weight transfer initialization request with backend-specific info """ init_info_dict = ( request["init_info"] if isinstance(request, dict) else request.init_info ) self.llm_engine.collective_rpc( "init_weight_transfer_engine", kwargs={"init_info": init_info_dict} ) def update_weights(self, request: WeightTransferUpdateRequest | dict) -> None: """ Update the weights of the model. Args: request: Weight update request with backend-specific update info """ update_info_dict = ( request["update_info"] if isinstance(request, dict) else request.update_info ) self.llm_engine.collective_rpc( "update_weights", kwargs={"update_info": update_info_dict} ) 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