o ۾i C@sddlZddlZddlmZmZddlmZmZmZddl Z ddl m Z ddl m Z ddlmZddlmZddlmZmZmZmZddlmZmZmZmZmZmZdd lmZdd l m!Z!m"Z"m#Z#m$Z$m%Z%dd l&m'Z'm(Z(dd l)m*Z*dd l+m,Z,m-Z-ddl.m/Z/m0Z0m1Z1m2Z2m3Z3m4Z4m5Z5ddl6m7Z7ddl8m9Z9m:Z:m;Z;mm?Z?ddl@mAZAddlBmCZCddlDmEZEmFZFmGZGmHZHmIZIddlJmKZKddlLmMZMddlNmOZOmPZPmQZQddlRmSZSmTZTddlUmVZVmWZWmXZXmYZYddlZm[Z[m\Z\m]Z]ddl^m_Z_ddl`maZaddlbmcZcddldmeZeddlfmgZgmhZhdd limjZjdd!lkmlZldd"lmmnZner1dd#lompZpe?eqZred$ed%ZsGd&d'd'ZtdS)(N)CallableSequence) TYPE_CHECKINGAnycast)ValidationError)tqdm)TypeVar)BeamSearchInstanceBeamSearchOutputBeamSearchSequencecreate_sort_beams_key_function)AttentionConfigCompilationConfig PoolerConfigProfilerConfigStructuredOutputsConfig is_init_field)CompilationMode) ConvertOption HfOverrides ModelDType RunnerOption TokenizerMode)WeightTransferInitRequestWeightTransferUpdateRequest) EngineArgs)ChatCompletionMessageParamChatTemplateContentFormatOption) ScoreDataScoreMultiModalParam_cosine_similaritycompress_token_type_idscompute_maxsim_scoreget_score_promptvalidate_score_input)log_non_default_args) DataPrompt PromptTypeSingletonPrompt TextPrompt TokensPrompt) init_logger) LoRARequest)QuantizationMethods)ClassificationRequestOutputEmbeddingRequestOutputPoolingRequestOutput RequestOutputScoringRequestOutput)current_platform) PoolingParams) ChatParamsTokenizeParams merge_kwargs) DictPrompt TokPrompt)conversation_to_seqextract_prompt_componentsparse_model_prompt prompt_to_seq)BeamSearchParamsRequestOutputKindSamplingParams) PoolingTask) TokenizerLike)MistralTokenizer) UsageContext)as_iter is_list_of)Counter) LLMEngine)LogitsProcessor)Metric_R)defaultcC@s eZdZdZdddddddddddddddd ddddddddddddddd d ed ed ededBdeeBdededede edBde de de dBdedBdedBde de de de dededed eeBdBd!edBd"eeefdBd#edBd$eeefeBdBd%eeefeBdBd&eeefeBdBd'e dBd(e eeefBeBdBd)e eeeBdBd*ed+dfBd,d-Zd+efd.d/Zdd1ed+e fd2d3Zdd4d5Zd+efd6d7Z dd0dddd8d9ee eBd:ee eBdBd;ee!de e dBd?eeefdBd+e e$fd@dAZ%d9e e&e'Bd=e e#e#BdBfdBdCZ(dDe&e'Bd=e#dBdEeeefdBfdFdGZ)  H ddIee!de e>BdBdke dBd;ee!de e>BdBd=e e#e#BdBd?eeefdBd+e eBfdpdqZCdd0dddrd9ee eBdje>e e>BdBd;ee!de e>BdBdke dBd;ee!ddBd=e e#e#BdBd?eeefd+e eHfdzd{ZIdxe eGdye eGd;ee!ddBd=e e#e#BdBd?eeefd+e eHfd|d}ZJdxe eGdye eGd;ee!ddBd=e e#e#BdBd?eeefd~edBd+e eHfddZKd0ddddddxeLe eLBeMBe eMBdyeLe eLBeMBe eMBd;ee!ddBd=e e#e#BdBd?eeefdBd^edBd+e eHfddZNdddZOdddZP ddeded+efddZQdde fddZRdde edBfddZSd+e dfddZTdSee>Be ee>BBde d+e ee>BfddZUd=e#dBe e#dBBde d+e e#dBfddZVd>e e dBde d+e e fddZWd0dddd8d9ee eBdSee>Be ee>BBd;ee!de e dBd?eeefdBf ddZXd0dddd0dddddd dfe e9e e e9BdSee>Be ee>BBd;ee!dBe ee>BBd;ee!de e dBd+dfddZZ   ddDee&Be'BdSee>Bd=e#dBd?eeefdBd>e d+ef ddZ[d0dd;ee!ds z6LLM.__init__.._make_config..rz) isinstancedictitems)rxryrzr~r _make_configs   z"LLM.__init__.._make_config)modedata_parallel_sizerQdistributed_executor_backendexternal_launcherzLLM(data_parallel_size=z) 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'.rqrSrTrUrVrWrXrYrZr[r\r]r^r_r`rarnrbrcrdrerfrgrhrirjrkrlrmrorp) engine_args usage_contextzSupported tasks: %srz)(rtype cloudpickledumpsrvllm.config.kv_transferrwrloggererror ValueErrorrrLintrrrrrgetr4is_tpurr&rIfrom_engine_argsrE LLM_CLASS llm_engine engine_classrHrequest_counterdefault_sampling_paramsget_supported_tasksinfosupported_tasks model_configinput_processor io_processor _cached_repr).selfrqrSrTrUrVrWrXrYrZr[r\r]r^r_r`rarbrcrdrerfrgrhrirjrkrlrmrnrorprrrurwraw_config_dictercompilation_config_instancestructured_outputs_instanceprofiler_config_instanceattention_config_instance_dp_size_distributed_executor_backendrrrzrzr__init__s(              #       z LLM.__init__cC |jSN)r get_tokenizerrrzrzrrms zLLM.get_tokenizerT include_dpcCs|jjj}|r |jS|jS)aGet 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. )r vllm_configparallel_configworld_size_across_dp world_size)rrrrzrzrget_world_sizeps zLLM.get_world_sizecCs|j|jdSr)rclear_mm_cacherreset_mm_cacherrzrzrrs zLLM.reset_mm_cachecCs4|jdur |j|_|jrtjdi|jStS)Nrz)rrget_diff_sampling_paramrA from_optionalrrzrzrget_default_sampling_paramss  zLLM.get_default_sampling_params)use_tqdm lora_requestprioritytokenization_kwargspromptssampling_paramsr.rrrc CsP|j}|j}|dkrtd|dur|}|j||||||d} |j| tS)aGenerates 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. generatezLLM.generate() is only supported for generative models. Try passing `--runner generate` to use the model as a generative model.Nrparamsrrrr)r runner_typerr_run_completionrvalidate_outputsr2) rrrrrrrrroutputsrzrzrrs") z LLM.generatecsbjjjdusjjrrjdur|St|ts"|gt|n|}fddt ||DS)Ncs g|] \}}||jqSrz)_resolve_single_prompt_mm_loradefault_mm_loras)r{prompt opt_lora_req lora_configrrzr sz8LLM._get_modality_specific_lora_reqs..) rrrris_multimodal_modelrrrlenzip)rrroptional_lorasrzrr _get_modality_specific_lora_reqss   z$LLM._get_modality_specific_lora_reqsrrc Cs|r |dpi}s |St||}|s|St|dkr*td||S|}||}t| |d}|rI|j |krGtd|St |||S)Nmulti_modal_datarQzMultiple 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 skippedzA 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) rsetkeys intersectionrrwarningpopsortedindex lora_int_idr-) rrrrmm_datar modality_namemodality_lora_pathmodality_lora_idrzrzrrs>   z"LLM._resolve_single_prompt_mm_lorarzmethodtimeoutargscCs|j||||S)a 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. )rcollective_rpc)rrrrrrrzrzrr szLLM.collective_rpcfunccCs |j|S)a 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! )r apply_model)rrrzrzrr@s zLLM.apply_modelcCsTt|trt|t|krtd|dust|tr!|gt|Stdt|)z;Get the optional lora request corresponding to each prompt.z:Lora request list should be the same length as the promptsNzInvalid lora_request type )rrrrr- TypeErrorr)rrrrzrzr_get_beam_search_lora_requestsMsz"LLM._get_beam_search_lora_requestsrconcurrency_limitc. sV|j}|j}|j}|j} |j} |||} |} t| j| } |r,|dur,t dd}|dur4t |}dt dt fddtd|d |d d }g}t| |D]=\}}i}d |vr^|d |d <d |vrh|d |d <d|vrvtt |}|d}n| |d}|t|f|dd|qNtdt ||D]}||||}t|}|rt|dddd}t d|D]}ttdd|Dg}dgttdd|D}tt|dd|d d}t |dkrntfdd|D\}}|j||d|d}t||D]r\\}}} g}!t||D]U}"||"}#||"}$|$jdjdurc|$jdjd}%|%D]4\}&}'t |#j|&g|#j|%g|#j|#j |'j!|#j"|#j#d}(|&| jkr\| s\| j$|(q.|!|(q.qt%|!| d d})|)d|| _&qqqg}*|D]-} | j$'| j&t%| j$| d d}+|+d|},|,D] }-| (|-j|-_)q|*t*|,dq{|*S) a 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. NzSProgress bar is not supported when using concurrency_limit. Disabling progress bar.FbeamrscSs@d|ji}|jdur|j|d<|jdur|j|d<tdi|S)Nprompt_token_idsrrirz)tokensrrir+)rtoken_prompt_kwargsrzrzrcreate_tokens_prompt_from_beams     z7LLM.beam_search..create_tokens_prompt_from_beamrQT)logprobs max_tokens temperature skip_clonerrirr)rrrz Beam searchtoken)descunit 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)beamsr{instancerzrzr sz"LLM.beam_search..cs|]}t|jVqdSr)rrrrzrzrr  csg|] }||jfqSrz)r)r{rrrzrrs z#LLM.beam_search..)rrr)rrr cum_logprobrri)keyreverse) sequences)+ beam_widthrr ignore_eoslength_penaltyrrr eos_token_idrrrr r+rArrencodeappendr rangerlistsum itertools accumulaterrrrrrrlogprobrri completedrrextenddecodetextr ).rrrrrrrrrrr lora_requestsrUsort_beams_keybeam_search_params instanceslora_reqr mm_kwargs prompt_tokens prompt_startinstances_batch token_iter_ all_beamsposinstance_start_and_end prompts_batchlora_req_batchoutputstartendrinstance_new_beamsi current_beamresultrtoken_id logprob_objnew_beam sorted_beamsrsorted_completed best_beamsrrzrr beam_search]s               % zLLM.beam_searchcCs2|j}|jpi}t|j|dd|j d|SN do_lower_caseF)max_total_tokensr3add_special_tokens)rencoder_configr7 max_model_lenris_encoder_decoder with_kwargsrrrr6rzrzr_get_cmpl_tok_paramss  zLLM._get_cmpl_tok_paramscs6|jj}|jfdd|D}||}|||S)a 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 `TokensPrompts` objects containing the tokenized prompt after chat template interpolation, and the raw multi-modal inputs. csg|]}t|qSrz)r=)r{rrrzrr6s z.LLM._preprocess_completion..)rrendererrr; render_cmpl)rrrr=parsed_prompts tok_paramsrzr<r_preprocess_completion$s   zLLM._preprocess_completioncCs.|j}|jpi}t|j|dddd|Sr2)rr6r7r7rr9r:rzrzr_get_chat_tok_params=s  zLLM._get_chat_tok_params conversations chat_templatechat_template_content_formatchat_template_kwargsadd_generation_promptcontinue_final_messagetoolsc  CsX|jj} t||t|t|||t| jtdd} ||} | j || | d| id\} }|S)a| 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 `TokensPrompts` objects containing the tokenized prompt after chat template interpolation, and the raw multi-modal inputs. )rGrHrItokenize)rDrErFri) prompt_extras) rr=r6r8rrrUrDrB render_chat)rrCrDrErFrGrHrIrrir= chat_paramsr@rengine_promptsrzrzr_preprocess_chatGs,   zLLM._preprocess_chatmessagesc Cs\|j} | j}|dkrtd|dur|}|j||||||| ||| | | d }|j|tS)a 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. rz{LLM.chat() is only supported for generative models. Try passing `--runner generate` to use the model as a generative model.N) rPrrrrDrErFrGrHrIrri)rrrr _run_chatrrr2)rrPrrrrDrErGrHrIrFrrirrrrzrzrchatws.BzLLM.chat)truncate_prompt_tokensrr pooling_taskrpooling_paramsrSrTcCs|durtd|j}|j} | dkrtd|dur*tjdtddt|t|d}d } t|trMd |vrMd } |j dur@td |j |} |j j | d }| r}|j dusVJt |t rpg} t|D] } | |j | qa| }n t|trwJ|j |}|durt }t|D]} | jdur|| _q| j|krd| jd|d}t|q|j|||||d}|j|t}| r|j dusJ|j j|d}ttd|t|ddgd dgS|S)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 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. Napooling_task required for `LLM.encode` Please use one of the more specific methods or set the pooling_task when using `LLM.encode`: - For embeddings, use `LLM.embed(...)` or `pooling_task="embed"`. - For classification logits, use `LLM.classify(...)` or `pooling_task="classify"`. - For similarity scores, use `LLM.score(...)`. - For rewards, use `LLM.reward(...)` or `pooling_task="token_classify"` - For token classification, use `pooling_task="token_classify"` - For multi-vector retrieval, use `pooling_task="token_embed"`poolingzvLLM.encode() is only supported for pooling models. Try passing `--runner pooling` to use the model as a pooling model.zThe `truncate_prompt_tokens` parameter in `LLM.encode()` is deprecated and will be removed in v0.16. Please pass it via `tokenization_kwargs` instead.r stacklevelrSFdataTzNo IOProcessor plugin installed. Please refer to the documentation and to the 'prithvi_geospatial_mae_io_processor' offline inference example for more details.)rz You cannot overwrite param.task=z with pooling_task=!rrrrr) model_outputrPnum_cached_tokensr) request_idrr^rfinished)rrrwarningswarnDeprecationWarningr8rrr parse_request pre_processrGr5rFr validate_or_generate_paramsrtaskrrrr1 post_processrgetattr)rrrUrSrrrTrrrio_processor_promptvalidated_promptvalidated_pooling_paramsparammsgr model_outputsprocessed_outputsrzrzrrs%         z LLM.encode)rSrrUrrcCsNd|jvr td|durt|t|d}|j||||d|d}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 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. embedz_Embedding API is not supported by this model. Try converting the model using `--convert embed`.NrYrrUrrTrcSg|]}t|qSrz)r0 from_baser{itemrzrzrrzLLM.embed..)rrr8rr)rrrSrrUrrrrzrzrrqos$ " z LLM.embed)rUrrrcCs6d|jvr td|j||||d|d}dd|DS)a 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. classifyzgClassification API is not supported by this model. Try converting the model using `--convert classify`.rrcSrsrz)r/rtrurzrzrrrwz LLM.classify..)rrr)rrrUrrrrrzrzrrxs ! z LLM.classify)rUrSrrrc Cs|j|||||d|dS)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 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. token_classify)rrrUrSrTr)r)rrrUrSrrrrzrzrrewards z LLM.rewarddata_1data_2cCs|}g}||D]} t| tstd|| q |j||||d|d} | dt|} | t|d} t| dkrB| t| } t|| | d} |j | t }dd|DS) Nz;Embedding scores currently do not support multimodal input.rqrrrUrTrrrQ)rUembed_1embed_2cSrsrzr3rtrurzrzrr.rwz(LLM._embedding_score..) rrstrNotImplementedErrorr rrr!rrr1)rr{r|rrUrrrU input_textsrencoded_outputencoded_output_1encoded_output_2scoresrrzrzr_embedding_scores6       zLLM._embedding_scorec Csdddlm}|}g} |D]} t| tstd| | qg} |D]} t| ts.td| | q#|j| | |||d|d} | dt| } | t| d}t| dkr]| t|} g}g}|j }durk|g}t | |D]3\}}|j j }|j j }t ||}|j||j}|t|jd|j||d ||j|jd d qp|j|t}d d |DS)z 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. r) PoolingOutputzBLate interaction scores currently do not support multimodal input. token_embedr}NrQr)rZT)r_rrr^r`cSrsrzrrurzrzrrrwz/LLM._late_interaction_score..) vllm.outputsrrrrrr rr pad_token_idrrrZr#rr1r_r^rr)rr{r|rrUrrrrUtext_1rtext_2rrrrpaddingremb_1emb_2q_embd_emb maxsim_scorerrrzrzr_late_interaction_score0s`           zLLM._late_interaction_scorescore_templatec Cs"|j}|} t| trtdt|dkr|t|}|dur&tdd}n|jdur.d|_tt} tt } ddt ||D} | D]5\} }t || || ||d\}}| dd}ro| }t|}d |i|_| |n| || |qD|j| | ||d }|j|t}d d|DS) Nz0Score API is not supported for Mistral tokenizerrQscore)rgcSsg|]\}}||fqSrzrz)r{t1t2rzrzrrsz-LLM._cross_encoding_score..)rr{r|rUrrtoken_type_idscompressed_token_type_ids)rrrrcSrsrzrrurzrzrrrw)rrrrDrrr5rgr r(rr$rcloner" extra_kwargsr rrrr1)rr{r|rrUrrrrrUpooling_params_listr input_pairsqdr engine_promptrr compressedrrrzrzr_cross_encoding_scoresJ               zLLM._cross_encoding_score)rrUrrrDc s|j}|j} | dkrtd|j|j} | s%tfdddDr%td|jr5t|jddd kr5td |js@|d ur@td |j } |j } t ||| | d \} }| |}| }|jrh|j| ||||||dS| ru|j| |||||dS|j| |||||dS)a]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. rVzuLLM.score() is only supported for pooling models. Try passing `--runner pooling` to use the model as a pooling model.c3s|]}|vVqdSrrz)r{trrzrrs zLLM.score..)rqrxzsScore API is not supported by this model. Try converting the model using `--convert embed` or `--convert classify`. num_labelsrrQz.Score API is only enabled for num_labels == 1.Nz9chat_template is only supported for cross-encoder models.)r architecture)rrUrrr)rrUrr)rrrris_late_interactionallis_cross_encoderri hf_configrrr%r;get_encode_kwargsrrr)rr{r|rrUrrrDrrrrr score_data_1 score_data_2r@ encode_kwargsrzrrrsv8   z LLM.scorecC|jdSr)r start_profilerrzrzrrBzLLM.start_profilecCrr)r stop_profilerrzrzrrErzLLM.stop_profilereset_running_requestsreset_connectorcCs|j||Sr)rreset_prefix_cache)rrrrzrzrrHszLLM.reset_prefix_cachelevelcCs||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. )rN)rrsleep)rrrzrzrrOsz LLM.sleeptagscCs|j|dS)a 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. N)rwake_up)rrrzrzrres z LLM.wake_uprKcCr)aReturn 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. )r get_metricsrrzrzrrss zLLM.get_metrics num_requestscCs>t|trt||krtd|dt|d|S|g|S)NThe lengths of prompts (z) and params () must be the same.rrrr)rrrrzrzr_params_to_seqs   zLLM._params_to_seqcCs>t|trt||krtd|dt|d|S|g|S)Nrz) and lora_request (rr)rrrrzrzr_lora_request_to_seqs   zLLM._lora_request_to_seqcCs<|durt||krtd|dt|d|Sdg|S)Nrz) and priority (rr)rr)rrrrzrzr_priority_to_seqs  zLLM._priority_to_seqc s~t|}|t|}tdd|Dr#fddt||D} nj|d} j| ||| ||dj|dS)Ncss|]}|jduVqdSrrY)r{rmrzrzrrsz&LLM._run_completion..c s8g|]\}}j|gtt|jddD]}|qqS)rYr)rAr8rrS)r{rrmrrrrzrrs z'LLM._run_completion..rrr) r>rranyrrA_validate_and_add_requestsr _run_engine) rrrrrrr seq_prompts seq_paramsrNrzrrrs*    zLLM._run_completion) rrrDrErGrHrIrFrric  CsH|jt|||| ||| | | d } |j| |||| || d|j|dS)N)rDrErFrGrHrIrrir\r)rOr;rrr)rrPrrrrDrErGrHrIrFrrirNrzrzrrQs*  z LLM._run_chat)rrrc Cst|}|||}|||} |||} |D] } t| tr#tj| _q|} |r6t |r.|nt } | | dd} g}z!t | D]\}}|j |||| ||| |d}| |q=WdStyq}z |rk|jj|dd|d}~ww)NzAdding requests)r)rrrT)internal)rrrrrrAr@ FINAL_ONLY output_kindcallabler enumerate _add_requestr  Exceptionr abort_request)rrrrrrrrrseq_lora_requests seq_priorityspit tqdm_funcadded_request_idsr(rr_rrzrzrr s<       zLLM._validate_and_add_requestsc Cst|j|\}}}tt|j}|jdur/t|j} tj d| dt ddt |t |jd}| |} | }|jj|||||||jd} |jj|| |||||d| jS)Nz+The `truncate_prompt_tokens` parameter in `z_` is deprecated and will be removed in v0.16. Please pass it via `tokenization_kwargs` instead.rrWrY)rrrr)rrr prompt_text)r<rrnextrrSr__name__rarbrcr8rr;rrprocess_inputsrr add_requestr_) rrrrrrrrr_ params_typer@engine_requestrzrzrr:sD       zLLM._add_requestrc CsL|r |j}t|r |nt}||dddddddddd}g}d}d}|jr|j}|D]`} | jr|| |rt| t rt | j } | j dusOJ|t | j | 7}||j d } |td d | j D7}||j d } d| dd| dd|_|| n|d |j|kr|q2|js+|r|t|d ddS)NzProcessed promptsTzest. speed input: rz.2fz toks/s, output: z toks/s)totalr dynamic_ncolspostfixelapsedcsrr)r token_ids)r{stprzrzrrrz"LLM._run_engine..rQcSs t|jSr)rr_)xrzrzrs z!LLM._run_engine..)r)rget_num_unfinished_requestsrrhas_unfinished_requestsstepr`r rr2rrr format_dictr rupdatenrefreshcloser) rrrrpbarr total_in_tokstotal_out_toks step_outputsr$rin_spdout_spdrzrzrrlsP             zLLM._run_enginerequestcC0t|tr |dn|j}|jjdd|iddS)z Initialize weight transfer for RL training. Args: request: Weight transfer initialization request with backend-specific info init_infoinit_weight_transfer_enginerrN)rrrrr)rrinit_info_dictrzrzrrs   zLLM.init_weight_transfer_enginecCr)z Update the weights of the model. Args: request: Weight update request with backend-specific update info update_infoupdate_weightsrN)rrrrr)rrupdate_info_dictrzrzrrs  zLLM.update_weightscCsB|jdur|jd}|r|d|_|jSd|jjd|_|jS)z;Return a transformers-style hierarchical view of the model.Nget_model_inspectionrz LLM(model=))rrrrrq)rresultsrzrzr__repr__s   z LLM.__repr__)T)rsNr)NrzN)NFN)NrONTFNNN) NTNNrOTFNNNN)FF)rQ)NNr)br __module__ __qualname____doc__rrrrboolr rrr.floatrrrrrrrrrrJrrCrrrrArr(rrrr-r2rr9r:rrrLtuplernnModulerr+r*rr?r r1r;rArBrrrOrRr'r5rBr1rr0rqr/rxrzrr3rrrr)r rrrrrrrrrrrrQrrrrrrrrrzrzrzrrNes|e        !"#$% '     @  ! 6 "       ;       4       b    =  7   *  ,  Q   J                  9        2     1  3  3 rN)ur racollections.abcrrtypingrrrrtorch.nnr pydanticr tqdm.autortyping_extensionsr vllm.beam_searchr r r r vllm.configrrrrrrvllm.config.compilationrvllm.config.modelrrrrr%vllm.distributed.weight_transfer.baserrvllm.engine.arg_utilsrvllm.entrypoints.chat_utilsrr$vllm.entrypoints.pooling.score.utilsrr r!r"r#r$r%vllm.entrypoints.utilsr&vllm.inputs.datar'r(r)r*r+ vllm.loggerr,vllm.lora.requestr-'vllm.model_executor.layers.quantizationr.rr/r0r1r2r3vllm.platformsr4vllm.pooling_paramsr5vllm.renderersr6r7r8vllm.renderers.inputsr9r: vllm.renderers.inputs.preprocessr;r<r=r>vllm.sampling_paramsr?r@rA vllm.tasksrBvllm.tokenizersrCvllm.tokenizers.mistralrDvllm.usage.usage_librEvllm.utils.collection_utilsrFrGvllm.utils.counterrHvllm.v1.engine.llm_enginerIvllm.v1.sample.logits_processorrJvllm.v1.metrics.readerrKrrrLrNrzrzrzrsT       $