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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. 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 `torch_dtype` attribute specified in the model config file. However, if the `torch_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. 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. max_seq_len_to_capture: Maximum sequence len covered by CUDA graphs. When a sequence has context length larger than this, we fall back to eager mode. Additionally for encoder-decoder models, if the sequence length of the encoder input is larger than this, we fall back to the eager mode. disable_custom_all_reduce: See [ParallelConfig][vllm.config.ParallelConfig]. disable_async_output_proc: Disable async output processing. This may result in lower performance. 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}`. override_pooler_config: Initialize non-default pooling config or override default pooling config for the pooling model. e.g. `PoolerConfig(pooling_type="mean", normalize=False)`. compilation_config: Either an integer or a dictionary. If it is an integer, it is used as the level of compilation optimization. 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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 prompts. 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. 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. rzLLM.generate() is only supported for generative models. 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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. beamrpcSs@d|ji}|jdur|j|d<|jdur|j|d<tdi|S)NrrrkrN)tokensrrkr0)rZtoken_prompt_kwargsrNrNrOcreate_tokens_prompt_from_beams   z7LLM.beam_search..create_tokens_prompt_from_beamrS)logprobs max_tokens temperaturerrkrpromptN)rrz Beam searchtokenF)descunitZ unit_scalezThe progress bar shows the upper bound on token steps and may finish early due to stopping conditions. It does not reflect instance-level progress.css|]}|jVqdSr)beamsrinstancerNrNrO sz"LLM.beam_search..rcs|]}t|jVqdSr)rrrrNrNrOr  csg|] }||jfqSrN)r)rrrrNrOrsz#LLM.beam_search..rrr)rrr cum_logprobrrkT)keyreverse) sequences)+ beam_widthrr ignore_eoslength_penaltyrrrZ eos_token_idrr0r=rr encodeappendrranger rrrsum itertools accumulaterrrrrrrrZlogprobrrk completedrrextenddecodetextr)+rrrrrrrrrrZ lora_requestsrWZsort_beams_keyZbeam_search_paramsZ instancesZlora_reqrZ mm_kwargsZ prompt_tokensZ token_iter_Z all_beamsposZinstance_start_and_endZ prompts_batchZlora_req_batchoutputstartendrZinstance_new_beamsiZ current_beamresultrZtoken_idZ logprob_objZnew_beamZ sorted_beamsrZsorted_completedZ best_beamsrrNrrO beam_searchws          ! zLLM.beam_searchmessages chat_templatechat_template_content_formatadd_generation_promptcontinue_final_messagetoolschat_template_kwargsc Cst|trtttt|} nttt|g} ||} |j}t|| || |d}t|||| d}| | p7ig}| D]H}t ||| |d\}}t | t rYt | fd|i|}ntd | ||d|}| j|dd}t|d}|d urx||d <| d ur| |d <||q=|j||||d S)a Generate responses for a chat conversation. The chat conversation is converted into a text prompt using the tokenizer and calls the [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. r)rrrr)Zcontent_formatr)rW conversationrF)Zadd_special_tokensrNrrkrrN)rFrr rrrZget_model_configr$r}updater#ryr@r"r!rr0rr)rrrrrrrrrrrrkZlist_of_messagesrWrZresolved_content_formatZ_chat_template_kwargsrZmsgsr Zmm_datarZ prompt_strrrNrNrOchatsx E       zLLM.chatr)rrr pooling_taskrpooling_paramsrrrcCrrrN)rrrrrrrrrNrNrOrz LLM.encodec CrrrN rrrrrrrrrrNrNrOrrc CrrrNrrNrNrOrrc CrrrNrrNrNrOrc CrrrNrrNrNrOrrc CrrrNrrNrNrOrs cCrrrrNrNrNrOrw rc Cs4|durd|jvr d}nd}td||jj} | j} | dkr#td||jvr1td|jd|durH|jtt t t t t f||d } n tt t tt f|} |durZt}t|trf||| n |D]} | || qh|durtt tf}t| j|||j| ||||d |j|d } |j| tS) aApply 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 prompts. 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. 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. Nembedra`LLM.encode` is currently using `pooling_task = %s`. Please use one of the more specific methods or set the task directly when using `LLM.encode`: - For embeddings, use `LLM.embed(...)` or `pooling_task="embed"`. - For classification logits, use `LLM.classify(...)` or `pooling_task="classify"`. - For rewards, use `LLM.reward(...)` or `pooling_task="reward"` - For similarity scores, use `LLM.score(...)`.poolingzvLLM.encode() is only supported for pooling models. Try passing `--runner pooling` to use the model as a pooling model.zpooling_task must be one of .r)rrrrrr)rrZ warning_oncerrrrrr rr rrr-rr:ryverifyr}rr+rrrrrr7)rrrrrrrrrrrrZ pooling_paramrrNrNrOr sb/       )rrrrcCs6d|jvr td|j|||||dd}dd|DS)a] 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 prompts. 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. rz_Embedding API is not supported by this model. Try converting the model using `--convert embed`.)rrrrrcSg|]}t|qSrN)r6 from_baseritemrNrNrOrzLLM.embed..rrr)rrrrrrrrNrNrOr|s " z LLM.embed)rrrcCs4d|jvr td|j||||dd}dd|DS)aZ 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 prompts. 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. classifyzgClassification API is not supported by this model. Try converting the model using `--convert classify`.)rrrrcSrrN)r5rrrNrNrOrrz LLM.classify..r)rrrrrrrNrNrOrs z LLM.classifycCs|j|||||ddS)a 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 prompts. 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. r)rrrrr)r)rrrrrrrNrNrOrewardsz LLM.rewardtext_1text_2c Cs||j||||||dd}|dt|} |t|d} t| dkr)| t| } t|| | d} |j| t} dd| DS)Nr)rrrrrrrS)rWZembed_1Zembed_2cSrrNr9rrrNrNrOr&rz(LLM._embedding_score..)rrr'rrr7) rrWrr rrrrZencoded_outputZencoded_output_1Zencoded_output_2ZscoresrrNrNrO_embedding_scores0     zLLM._embedding_scoredata_1data_2cCsB|jj}t|tr tdt|dkr|t|}|dur"tdd}|jj}|d|tt} i} t |j || g} ddt ||D} |jj}| D]7\} }t || ||| d\}}t jry|dd}ry|}t|}d |i|_| |n| || |qL|j| | ||d |j|d }|j|t}d d|DS) Nz0Score API is not supported for Mistral tokenizerrSscore)taskcSsg|]\}}||fqSrNrN)rt1t2rNrNrOrIsz-LLM._cross_encoding_score..)rr#r$rWrtoken_type_idsZcompressed_token_type_ids)rrrrrcSrrNr!rrNrNrOrlr)rrryr@rrr:rrr+rrr*rrrcloner) extra_kwargsrrrrrr7)rrWr#r$rrrrrZpooling_params_listrrZ input_pairsqdrZ engine_promptr)r compressedrrrNrNrO_cross_encoding_score(s^             zLLM._cross_encoding_scorec s|jjj}|dkrtd|jtfdddDr!tdjr1tjddd kr1td | j sd t t t t tfffd d }||||dt ffdd t|ttfrc|g}fdd|D}t|ttfrv|g}fdd|D}t|trd|vr|d}nt|tr|g}t|trd|vr|d}nt|tr|g}t||jr|||||||S|||||||S)aGenerate 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. Returns: A list of `ScoringRequestOutput` objects containing the generated scores in the same order as the input prompts. rzuLLM.score() is only supported for pooling models. Try passing `--runner pooling` to use the model as a pooling model.c3s|]}|vVqdSrrNrt)rrNrOrszLLM.score..)rrzsScore API is not supported by this model. Try converting the model using `--convert embed` or `--convert classify`.Z num_labelsrrSz.Score API is only enabled for num_labels == 1.datacs*t|trd|vrtdjdSdS)Ncontentz*ScoreMultiModalParam is not supported for )ryr}r architecture)r2r rNrOcheck_data_types z"LLM.score..check_data_typercsbt|tr'd|vr tdd|vrtt|d}n d|vr'tt|d}t|tus/J|S)Nrz/Multi-modal prompt is not supported for scoringrr) ryr}rrr r0r/rzrr)rWrNrO ensure_strs  zLLM.score..ensure_strcg|]}|qSrNrNr0r7rNrOrzLLM.score..cr8rNrNr0r9rNrOrr:r3)rrrrrallZis_cross_encodergetattrZ hf_configrrr r.rr&ryrr}getr(r/r") rr#r$rrrrrr5rN)r7rrrWrOr%nsn0       z LLM.scorecC|jdSr)r start_profilerrNrNrOr?zLLM.start_profilecCr>r)r stop_profilerrNrNrOrAr@zLLM.stop_profiledevicecCs |j|Sr)rreset_prefix_cache)rrBrNrNrOrCs zLLM.reset_prefix_cachertcCs||jj|ddS)a' 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. rsN)rCrsleep)rrtrNrNrOrDsz LLM.sleeptagscCs|j|dS)a Wake up the engine from sleep mode. See the [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. N)rwake_up)rrErNrNrOrFs z LLM.wake_uprHcCs&ddlm}t|j|sJ|jS)a!Return a snapshot of aggregated metrics from Prometheus. Returns: A ``MetricSnapshot`` instance capturing the current state of all aggregated metrics from Prometheus. 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