-`i9tddlZddlmZmZddlmZmZmZddlZddl m Z ddl m Z ddl mZddlmZddlmZmZmZmZddlmZmZmZmZmZmZdd lmZdd lm Z m!Z!m"Z"m#Z#m$Z$dd l%m&Z&dd l'm(Z(m)Z)dd l*m+Z+m,Z,m-Z-m.Z.m/Z/m0Z0ddl1m2Z2m3Z3ddl4m5Z5m6Z6m7Z7m8Z8m9Z9ddl:m;Z;ddlm?Z?ddl@mAZAddlBmCZCmDZDmEZEmFZFmGZGddlHmIZIddlJmKZKddlLmMZMmNZNmOZOddlPmQZQddlRmSZSddlTmUZUddlVmWZWddlXmYZYmZZZddl[m\Z\ddl]m^Z^ddl_m`Z`dd lambZberdd!lcmdZde=eeZfed"e#ZgGd$d%ZhdS)&N)CallableSequence) TYPE_CHECKINGAnycast)ValidationError)tqdm)TypeVar)BeamSearchInstanceBeamSearchOutputBeamSearchSequencecreate_sort_beams_key_function)AttentionConfigCompilationConfig PoolerConfigProfilerConfigStructuredOutputsConfig is_init_field)CompilationMode) ConvertOption HfOverrides ModelDType RunnerOption TokenizerMode) EngineArgs)ChatCompletionMessageParamChatTemplateContentFormatOption)ScoreContentPartParamScoreMultiModalParam_cosine_similarity_validate_score_input_lenscompress_token_type_idsget_score_prompt)_validate_truncation_sizelog_non_default_args) DataPrompt PromptTypeSingletonPrompt TextPrompt TokensPrompt)get_prompt_components) init_logger) LoRARequest)QuantizationMethods)ClassificationRequestOutputEmbeddingRequestOutputPoolingRequestOutput RequestOutputScoringRequestOutput)current_platform) PoolingParams)BeamSearchParamsRequestOutputKindSamplingParams) PoolingTask) TokenizerLike)MistralTokenizer) UsageContext)as_iter is_list_of)Counter)EngineCoreRequest) LLMEngine)LogitsProcessor)Metric_R)defaultcC eZdZdZdddddddddddddddd ddddddddddddddd d ed ed ededzdeezdededede edzde de de dzdedzdedzde de de de dededed eezdzd!edzd"eeefdzd#edzd$eeefezdzd%eeefezdzd&eeefezdzd'e dzd(e eeefzezdzd)e eeezdzd*ed+dfBd,Zd+efd-Zdzd.Zd+efd/Z d{d0ddd1d2eeezd3eeezdzd4ee d5e!fzd6e e"e"zdzd7e e dzd+e e#f d8Z$d2eeezd6e e"e"zdzfd9Z%d:ed6e"dzd;eeefdzfd<Z& d|d>ee d5e'fzd?e dzd@e(d*eeefdzd+e e'f dAZ)dBe e*j+ge'fd+e e'fdCZ,d6e e"e"zdzd2e e-e.zd+e e"dzfdDZ/ d}d2e e-e.zdEe0d6e e"e"zdzd4edFe dzd+e e1f dGZ2 d~dHe e3e e e3zdIedzdJe4dKedLedMe eeefdzdNeeefdzd"eeefdzd+e e.e-zfdOZ5 ddHe e3e e e3zd3ee ezdzd4ee d5e!fzd6e"dzdIedzdJe4dKedLedMe eeefdzdNeeefdzd"eeefdzd+e e#fdPZ6 d{dd0ddddQd2eeeze7zdRe8ee8zdzdSe dzd4ee d5e!fzd6e e"e"zdzdTe9dzdUeeefdzd+e e:fdVZ;dd0ddddWd2eeezdSe dzd4ee d5e!fzdRe8ee8zdzd6e e"e"zdzdUeeefdzd+e e<fdXZ=d0ddddYd2eeezd4ee d5e!fzdRe8ee8zdzd6e e"e"zdzdUeeefdzd+e e>f dZZ?dd0ddddWd2eeezdSe dzd4ee d5e!fzdRe8ee8zdzd6e e"e"zdzdUeeefdzd+e e:fd[Z@ dded\e ee.ze-zd]e ee.ze-zdSe dzd4ee d5e!fzdRe8dzd6e e"e"zdzdUeeefdzd+e eAfd^ZB dded_e ee eCzd`e ee eCzdSe dzd4ee d5e!fzdRe8dzd6e e"e"zdzdUeeefdzdaedzd+e eAfdbZDdd0ddddcd_eEeeEzeFzd`eEeeEzeFzdSe dzd4ee d5e!fzdRe8dzd6e e"e"zdzdIedzd+e eAfddZGdzdeZHdzdfZI ddgedhed+efdiZJddje fdkZKd{dle edzfdmZLd+e dnfdoZMd0dddpd2eeeze7zdEeeeze8zee8zd4ee d5e!fzd6ee"e"zdzd7e e dzdUeeefdzd+dfdqZNddrdsedtedEee8zd6e"dzd7e dUeeefdzd+e(eOeeefffduZP dd:edEee8zd6e"dzd7e dUeeefdzd+ef dvZQd0dwd4ee d5e!fzd+e e#e:zfdxZRd+efdyZSdS)LLMaAn LLM for generating texts from given prompts and sampling parameters. This class includes a tokenizer, a language model (possibly distributed across multiple GPUs), and GPU memory space allocated for intermediate states (aka KV cache). Given a batch of prompts and sampling parameters, this class generates texts from the model, using an intelligent batching mechanism and efficient memory management. Args: model: The name or path of a HuggingFace Transformers model. tokenizer: The name or path of a HuggingFace Transformers tokenizer. tokenizer_mode: The tokenizer mode. "auto" will use the fast tokenizer if available, and "slow" will always use the slow tokenizer. skip_tokenizer_init: If true, skip initialization of tokenizer and detokenizer. Expect valid prompt_token_ids and None for prompt from the input. trust_remote_code: Trust remote code (e.g., from HuggingFace) when downloading the model and tokenizer. allowed_local_media_path: Allowing API requests to read local images or videos from directories specified by the server file system. This is a security risk. Should only be enabled in trusted environments. allowed_media_domains: If set, only media URLs that belong to this domain can be used for multi-modal inputs. tensor_parallel_size: The number of GPUs to use for distributed execution with tensor parallelism. dtype: The data type for the model weights and activations. Currently, we support `float32`, `float16`, and `bfloat16`. If `auto`, we use the `dtype` attribute of the Transformers model's config. However, if the `dtype` in the config is `float32`, we will use `float16` instead. quantization: The method used to quantize the model weights. Currently, we support "awq", "gptq", and "fp8" (experimental). If None, we first check the `quantization_config` attribute in the model config file. If that is None, we assume the model weights are not quantized and use `dtype` to determine the data type of the weights. revision: The specific model version to use. It can be a branch name, a tag name, or a commit id. tokenizer_revision: The specific tokenizer version to use. It can be a branch name, a tag name, or a commit id. seed: The seed to initialize the random number generator for sampling. gpu_memory_utilization: The ratio (between 0 and 1) of GPU memory to reserve for the model weights, activations, and KV cache. Higher values will increase the KV cache size and thus improve the model's throughput. However, if the value is too high, it may cause out-of- memory (OOM) errors. kv_cache_memory_bytes: Size of KV Cache per GPU in bytes. By default, this is set to None and vllm can automatically infer the kv cache size based on gpu_memory_utilization. However, users may want to manually specify the kv cache memory size. kv_cache_memory_bytes allows more fine-grain control of how much memory gets used when compared with using gpu_memory_utilization. Note that kv_cache_memory_bytes (when not-None) ignores gpu_memory_utilization swap_space: The size (GiB) of CPU memory per GPU to use as swap space. This can be used for temporarily storing the states of the requests when their `best_of` sampling parameters are larger than 1. If all requests will have `best_of=1`, you can safely set this to 0. Noting that `best_of` is only supported in V0. Otherwise, too small values may cause out-of-memory (OOM) errors. cpu_offload_gb: The size (GiB) of CPU memory to use for offloading the model weights. This virtually increases the GPU memory space you can use to hold the model weights, at the cost of CPU-GPU data transfer for every forward pass. enforce_eager: Whether to enforce eager execution. If True, we will disable CUDA graph and always execute the model in eager mode. If False, we will use CUDA graph and eager execution in hybrid. enable_return_routed_experts: Whether to return routed experts. disable_custom_all_reduce: See [ParallelConfig][vllm.config.ParallelConfig]. hf_token: The token to use as HTTP bearer authorization for remote files . If `True`, will use the token generated when running `huggingface-cli login` (stored in `~/.huggingface`). hf_overrides: If a dictionary, contains arguments to be forwarded to the HuggingFace config. If a callable, it is called to update the HuggingFace config. mm_processor_kwargs: Arguments to be forwarded to the model's processor for multi-modal data, e.g., image processor. Overrides for the multi-modal processor obtained from `AutoProcessor.from_pretrained`. The available overrides depend on the model that is being run. For example, for Phi-3-Vision: `{"num_crops": 4}`. pooler_config: Initialize non-default pooling config for the pooling model. e.g. `PoolerConfig(seq_pooling_type="MEAN", normalize=False)`. compilation_config: Either an integer or a dictionary. If it is an integer, it is used as the mode of compilation optimization. If it is a dictionary, it can specify the full compilation configuration. attention_config: Configuration for attention mechanisms. Can be a dictionary or an AttentionConfig instance. If a dictionary, it will be converted to an AttentionConfig. Allows specifying the attention backend and other attention-related settings. **kwargs: Arguments for [`EngineArgs`][vllm.EngineArgs]. Note: This class is intended to be used for offline inference. For online serving, use the [AsyncLLMEngine][vllm.AsyncLLMEngine] class instead. autoNFrg?)runnerconvert tokenizertokenizer_modeskip_tokenizer_inittrust_remote_codeallowed_local_media_pathallowed_media_domainstensor_parallel_sizedtype quantizationrevisiontokenizer_revisionseedgpu_memory_utilization swap_spacecpu_offload_gb enforce_eagerenable_return_routed_expertsdisable_custom_all_reducehf_token hf_overridesmm_processor_kwargs pooler_configstructured_outputs_configprofiler_configattention_configkv_cache_memory_bytescompilation_configlogits_processorsmodelrLrMrNrOrPrQrRrSrTrUrVrWrXrYrZr[r\r]r^r_r`rarbrcrdrerfrgrhrikwargsreturnc d| vrd| d<d| vr4| d}!t|!trtj|!| d<d| vrwt| dtr\ddlm}"| d}# |"d6i|#| d<nA#t$r4}$t d|#|$td|$|$d }$~$wwxYw|i}d td ttd tfd }%t|trtt|}&n|%|t}&|%|t }'|%|t"}(|%|t$})t| dd}*| d}+|*dkr,|+dks&t)jstd|*dt-d6id|d|d|d|d|d|d|d|d| d| d| d | d!| d"|d#|d$|d%|d&|d'|d(|d)|d*|d+|d,|d-|d.|d/|'d0|(d1|)d2|&d3|| },t/|,t1j|,t4j4|_t|j|_t=|_d |_ |j!}-t"d5|-|-|_#|jj$|_$|jj%|_%|jj&|_&d |_'d S)7zLLM constructor.disable_log_statsT worker_clskv_transfer_configr)KVTransferConfigz[Failed to convert 'kv_transfer_config' dict to KVTransferConfig object. Dict: %s. Error: %sz'Invalid 'kv_transfer_config' provided: Nvalueclsrlc| St|tr&difd|DS|S)z0Convert dict/None/instance to a config instance.Nc<i|]\}}t|||S)r).0kvrss h/home/jaya/work/projects/VOICE-AGENT/VIET/agent-env/lib/python3.11/site-packages/vllm/entrypoints/llm.py z6LLM.__init__.._make_config..s/VVVtq! cST@U@UVaVVVrv) isinstancedictitems)rrrss `rz _make_configz"LLM.__init__.._make_configs^}suu %&& XsWWVVVVu{{}}VVVWWWLr|)modedata_parallel_sizerJdistributed_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'.rjrLrMrNrOrPrQrRrSrTrUrVrWrXrYrZrgr[r\r]r^r_r`rarbrcrdrerfrhri) engine_args usage_contextzSupported tasks: %srv)(r}type cloudpickledumpsr~vllm.config.kv_transferrqrloggererror ValueErrorrrDintrrrrrgetr4is_tpurr%rAfrom_engine_argsr< LLM_CLASS llm_engine engine_classr?request_counterdefault_sampling_paramsget_supported_tasksinfosupported_tasks model_configinput_processor io_processor _cached_repr).selfrjrLrMrNrOrPrQrRrSrTrUrVrWrXrYrZr[r\r]r^r_r`rarbrcrdrerfrgrhrirkrorqraw_config_dictercompilation_config_instancestructured_outputs_instanceprofiler_config_instanceattention_config_instance_dp_size_distributed_executor_backendrrs. rz__init__z LLM.__init__sP f , ,*.F& ' 6 ! ! -J*d++ E'2'8'D'D|$ 6 ) )j ' ($/ / ) A @ @ @ @ @$%9:O W/?/?/R/R//R/R+,," W W W C# !!N1!N!NOOUVV W  L  $r( r     (# . . *;$%788+++ ' '+7,"$5++ ''3l %'>' ' #$0<#P#P $0L1A?$S$S!vzz"6::;;(. 3Q(R(R% qLL15HHH$+--IA(AAA !! ! ! %! 6! G!  i ! *> ! !4 3 ! 0/! &>%=! #8"7! "6!5! %! &! X!  21! ! $:#9!! "#8"7#! $"z%! &*>'! ((-)! **F)E+! ,'@&?-! .X/! 0&1! 2!4 33! 4(-5! 6'B&A7! 8549! :76;! < ;:=! >0/A! ! F [)))#4#<3I   !11&yy>B$/==?? )?;;;. O8#> O8)-s0 A<< B:/B55B:c4|jSN)r get_tokenizerrs rzrzLLM.get_tokenizeras,,...r|cj|j|jdSr)rclear_mm_cacherreset_mm_cachers rzrzLLM.reset_mm_cacheds1 ++--- &&(((((r|c|j|j|_|jrtjdi|jStS)Nrv)rrget_diff_sampling_paramr8 from_optionalrs rzget_default_sampling_paramszLLM.get_default_sampling_paramshsT  ' /+/+<+T+T+V+VD (  ' P!/OO$2NOO Or|T)use_tqdm lora_requestprioritypromptssampling_paramsr.rrc@|j}|j}|dkrtd||}|||}||||||||}|j|tS)adGenerates the completions for the input prompts. This class automatically batches the given prompts, considering the memory constraint. For the best performance, put all of your prompts into a single list and pass it to this method. Args: prompts: The prompts to the LLM. You may pass a sequence of prompts for batch inference. See [PromptType][vllm.inputs.PromptType] for more details about the format of each prompt. sampling_params: The sampling parameters for text generation. If None, we use the default sampling parameters. When it is a single value, it is applied to every prompt. When it is a list, the list must have the same length as the prompts and it is paired one by one with the prompt. use_tqdm: If `True`, shows a tqdm progress bar. If a callable (e.g., `functools.partial(tqdm, leave=False)`), it is used to create the progress bar. If `False`, no progress bar is created. lora_request: LoRA request to use for generation, if any. priority: The priority of the requests, if any. Only applicable when priority scheduling policy is enabled. If provided, must be a list of integers matching the length of `prompts`, where each priority value corresponds to the prompt at the same index. Returns: A list of `RequestOutput` objects containing the generated completions in the same order as the input prompts. Note: Using `prompts` and `prompt_token_ids` as keyword parameters is considered legacy and may be deprecated in the future. You should instead pass them via the `inputs` parameter. generatezLLM.generate() is only supported for generative models. Try passing `--runner generate` to use the model as a generative model.N)rparamsrrrr) r runner_typerr _get_modality_specific_lora_reqs_validate_and_add_requests _run_enginervalidate_outputsr2) rrrrrrrroutputss rzrz LLM.generateosX( ". * $ $$   "">>@@O<z8LLM._get_modality_specific_lora_reqs..sK   %  / /,     r|) r vllm_configrris_multimodal_modelrr}rstrlenzip)rrroptional_lorasrs` @rzrz$LLM._get_modality_specific_lora_reqsso1=  $8  + < D '8,, 7C0H0H iGlH55 \NS\\ ) )       ),G^(D(D     r|rrcP|r.t|tr|dpix}s|St||}|s|St |dkrtd||S| }||}t| |dz}|r'|j |krtd|St|||S)Nmulti_modal_datarJzMultiple 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)r}r~rsetkeys intersectionrrwarningpopsortedindex lora_int_idr-) rrrrmm_datar modality_namemodality_lora_pathmodality_lora_ids rzrz"LLM._resolve_single_prompt_mm_lorasa! fd++  &zz*<==CCG   LLNN  ,',,.. / /    |  q NN#      %((** -m<!"23399-HH1L  '+;;;O        r|rvmethodtimeoutargsc<|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)rrrrrks rzrzLLM.collective_rpcs <--fgtVLLLr|funcc6|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)rrs rzrzLLM.apply_model/s**4000r|ct|tr/t|t|krtd|t|tr|gt|zSt dt |)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 )r}rrrr- TypeErrorr)rrrs rz_get_beam_search_lora_requestsz"LLM._get_beam_search_lora_requests<s lH - - #l2C2Cs7||2S2SL   :lK#H#H  >CLL0 0IT,5G5GIIJJJr|rconcurrency_limitc .|j}|j}|j}|j} |j} |||} |} t| j| } |r|t dd}|t|}dtdtfd.td|zd|d }g}t| |D]\}}i}d |vr |d |d <d |vr |d |d <d |vrt!t|}|d }n| |d}|t'|f|dd|t)dt||D]D}||||z}t)|}|r-t+|ddd}t d|D]}t-t/d|Dg}dgt-t1jd|Dz}t-t|dd|dd}t|dkrnXt.fd|D\}}|||d|}t||D]\\}}} g}!t)||D]}"||"}#||"}$|$jdj|$jdjd}%|%D]\}&}'t|#j|&gz|#j|%gz|#j|#j |'j!z|#j"|#j#}(|&| jkr| s| j$|(p|!|(tK|!| d })|)d|| _&Fg}*|D]} | j$'| j&tK| j$| d }+|+d|},|,D]!}-| (|-j|-_)"|*tU|,|*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.Fbeamrlcpd|ji}|j |j|d<|j |j|d<tdi|S)Nprompt_token_idsrrbrv)tokensrrbr*)rtoken_prompt_kwargss rzcreate_tokens_prompt_from_beamz7LLM.beam_search..create_tokens_prompt_from_beamzsT1CT[0Q $0:>:O#$67'3=A=U#$9:66"566 6r|rJT)logprobs max_tokens temperature skip_clonerrbrr)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.c3$K|] }|jV dSr)beamsrwinstances rz z"LLM.beam_search..s$HHHHHHHHHr|c3>K|]}t|jVdSr)rrrs rzrz"LLM.beam_search..s=))08HN++))))))r|c4g|]}||jfSrv)r)rwrrs rzrz#LLM.beam_search..s< 87==t?PQr|rrr)rrr cum_logprobrrb)keyreverse) sequences)+ beam_widthrr ignore_eoslength_penaltyrrr eos_token_idrrrr r*r8rrencodeappendr ranger listsum itertools accumulaterrrrrrrlogprobrrb completedrrextenddecodetextr )/rrrrrrrrrrr  lora_requestsrNsort_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_beamsrrs/ @rz beam_searchzLLM.beam_searchLs,& & ( & .;;L'RR &&(( 7  "     )5 NN*   H  $ #G  71C 7  7 7 7 7,^#     /1 #M7 ; ;   HfI!V++067I0J ,-$..39:O3P /0!V++lF33 &'9 : ) 0 01A B B   "!!)!     "!S\\3DEEQ ?Q ?L' |FW7W(WXOz**J !]U 7  D ?D ?6:HHHHH"MM77 cD())>Q&&E14$-1- ~!$6"!/ '/2*O//#?#?*LUC(*,&"5#..HH'0| !'!>!,5A (.~a'8'A!'DH9A9I9IHH 5++=+7+>(+K-9-BhZ-O1=1J0<0H&1&91:5A5R8D8X,",","%- 0F$F$F,6%G%-$6$=$=h$G$G$G$G$6$=$=h$G$G$G$G#)*$$$L&2+:+%>HNNG#?J! C CH   % %hn 5 5 5%"    *+:+6J" : :%,,T[99 NN+jAAA B B B Br|messages chat_templatechat_template_content_formatadd_generation_promptcontinue_final_messagetoolschat_template_kwargsc t|tr,tttt|} n!ttt|g} |jj} ||||d|pi}tt tz} | D]1} | j| fd|i|\} }|||d<| |2| S)a Generate prompt for a chat conversation. The pre-processed prompt can then be used as input for the other LLM methods. Refer to `chat` for a complete description of the arguments. Returns: A list of `TokensPrompts` objects containing the tokenized prompt after chat template interpolation, and the pre-processed multi-modal inputs. )r6r8r9r:r7Nrb) r>rrrrrendererr)r*render_messagesr )rr5r6r7r8r9r:r;rblist_of_messagesr=rmsgsr!rs rzpreprocess_chatzLLM.preprocess_chats 2 h % % R#D.H)I$JHUU  !%T*D%Ex P PQ ?++%:&<   $)r  zL0133$ # #D10-I'IAv #.0C,- NN6 " " " "r|c n||||||| | | } || |||S)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 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. )r5r6r7r8r9r:r;rbr)rAr) rr5rrrr6r7r8r9r:r;rbrs rzchatzLLM.chatBs`D&&')E"7#9!5 3'   }} +%    r|)truncate_prompt_tokensrr pooling_tasktokenization_kwargspooling_paramsrDrErFc |d}|t||j} | j} | dkrtdd} t|trQd|vrMd} |jtd|j|} |j| }| r|jJt|trDg} t|D]/}| |j |0| }nBt|trJ|j |}n|t}||jvrtd |jd t|D]!}||| |||_"|||||| || }|j|t*}| rS|jJ|j|}t+t.d|t1|ddgdgS|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 tokenization_kwargs set in pooling_params Returns: A list of `PoolingRequestOutput` objects containing the pooled hidden states in the same order as the input prompts. Note: Using `prompts` and `prompt_token_ids` as keyword parameters is considered legacy and may be deprecated in the future. You should instead pass them via the `inputs` parameter. apooling_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"`NpoolingzvLLM.encode() is only supported for pooling models. Try passing `--runner pooling` to use the model as a pooling model.FdataTzNo IOProcessor plugin installed. Please refer to the documentation and to the 'prithvi_geospatial_mae_io_processor' offline inference example for more details.)rzpooling_task must be one of .)rrrrrFr) model_outputrInum_cached_tokensr) request_idrrMrfinished)rrrr}r~r parse_request pre_processr>r5r=r validate_or_generate_paramsrrverifyrDrrrrr1 post_processrgetattr)rrrGrDrrrErF error_strrrio_processor_promptvalidated_promptvalidated_pooling_paramsparamr model_outputsprocessed_outputss rzr z LLM.encodesX O   Y'' '( ". ) # #!  $ gt $ $ M7):):"&  ( B $0>>wGG '33;K3LLG  1$000.-88 @B($^44E,33)EEeLL":%nh?????!%!2!N!N"""%!. t3 3 3SD >*!?!!  %S)!-&-)+>''&(!  ! r|)rDrrGrrFc d|jvrtd||||||d|}d|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. 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`.)rDrrGrrErFc6g|]}tj|Srv)r0 from_baserwitems rzrzLLM.embed..^s$III4&066IIIr|rrr )rrrDrrGrrFrs rzr^z LLM.embed-sqB $. . .D   #9)% 3  JI5IIIIr|)rrGrrFcd|jvrtd|||||d|}d|DS)aY Generate class logits for each prompt. This class automatically batches the given prompts, considering the memory constraint. For the best performance, put all of your prompts into a single list and pass it to this method. Args: prompts: The prompts to the LLM. You may pass a sequence of prompts for batch inference. See [PromptType][vllm.inputs.PromptType] for more details about the format of each prompt. use_tqdm: If `True`, shows a tqdm progress bar. If a callable (e.g., `functools.partial(tqdm, leave=False)`), it is used to create the progress bar. If `False`, no progress bar is created. lora_request: LoRA request to use for generation, if any. pooling_params: The pooling parameters for pooling. If None, we use the default pooling parameters. Returns: A list of `ClassificationRequestOutput` objects containing the embedding vectors in the same order as the input prompts. classifyzgClassification API is not supported by this model. Try converting the model using `--convert classify`.)rrGrrErFc6g|]}tj|Srv)r/r`ras rzrz LLM.classify..s$NNN+5d;;NNNr|rc)rrrrGrrFrs rzrez LLM.classify`sm> T1 1 1G   )%# 3   ONNNNNr|c :||||||d|S)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. 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. token_classify)rrrGrDrErF)r )rrrDrrGrrFs rzrewardz LLM.rewards4<{{ %)#9) 3   r|text_1text_2c b|||z||||d|} | dt|} | t|d} t| dkr| t| z} t|| | } |j| t } d| DS)Nr^)rDrrrGrErFrrJ)rNembed_1embed_2c6g|]}tj|Srvr3r`ras rzrz(LLM._embedding_score..$GGG$.t44GGGr|)r rr rrr1)rrNrjrkrDrrGrrFencoded_outputencoded_output_1encoded_output_2scoresrs rz_embedding_scorezLLM._embedding_scores6:[[ VO#9%) 36A6 6 8Fa#f++o7V7Ec&kkmm7T  A % %/#6F2G2GG #)9CS   !226;OPPGGGGGGr|data_1data_2score_templatec |j} t|trtdt |dkr|t |z}|t d}|d| tt } |pi} | }t| j ||tt} dt||D}|D]\}}t| ||||| \}}|ddx}rB|}t!|}d|i|_| |n| || ||| | || || }|j|t.}d |DS) Nz0Score API is not supported for Mistral tokenizerrJscore)taskcg|] \}}||f Srvrv)rwt1t2s rzrz-LLM._cross_encoding_score..s BBBFBBxBBBr|)rrwrxrNrFrytoken_type_idscompressed_token_type_ids)rrrrrc6g|]}tj|Srvrpras rzrz-LLM._cross_encoding_score..rqr|)rr}r;rrr5rSrcopyr$ max_model_lenr'rr#rcloner" extra_kwargsr rrrrr1)rrNrwrxrDrrGrrFryrpooling_params_list local_kwargsr input_pairsqdr! engine_promptrr compressedrrs rz_cross_encoding_scorezLLM._cross_encoding_scores-( i!1 2 2 QOPP P v;;!  c&kk)F  !*888Ng|444"=133*0b *//11!  &(>@S   z"$$BBc&&.A.ABBB  * *DAq/)#$7-     A}"/!2!23CT!J!JJ~ ;'--//4^DD 'BJ&O##**62222#**>::: NN= ) ) ) ) ''&% (   ""H"55!227` or ``. The inputs can be `1 -> 1`, `1 -> N` or `N -> N`. In the `1 - N` case the `data_1` input will be replicated `N` times to pair with the `data_2` inputs. The input pairs are used to build a list of prompts for the cross encoder model. This class automatically batches the prompts, considering the memory constraint. For the best performance, put all of your inputs into a single list and pass it to this method. Supports both text and multi-modal data (images, etc.) when used with appropriate multi-modal models. For multi-modal inputs, ensure the prompt structure matches the model's expected input format. Args: data_1: Can be a single prompt, a list of prompts or `ScoreMultiModalParam`, which can contain either text or multi-modal data. When a list, it must have the same length as the `data_2` list. data_2: The data to pair with the query to form the input to the LLM. Can be text or multi-modal data. See [PromptType] [vllm.inputs.PromptType] for more details about the format of each prompt. use_tqdm: If `True`, shows a tqdm progress bar. If a callable (e.g., `functools.partial(tqdm, leave=False)`), it is used to create the progress bar. If `False`, no progress bar is created. lora_request: LoRA request to use for generation, if any. pooling_params: The pooling parameters for pooling. If None, we use the default pooling parameters. chat_template: The chat template to use for the scoring. If None, we use the model's default chat template. Returns: A list of `ScoringRequestOutput` objects containing the generated scores in the same order as the input prompts. rIzuLLM.score() is only supported for pooling models. Try passing `--runner pooling` to use the model as a pooling model.c3 K|]}|vV dSrrv)rwtrs rzrzLLM.score..Zs(GGAq'GGGGGGr|)r^rezsScore API is not supported by this model. Try converting the model using `--convert embed` or `--convert classify`. num_labelsrrJz.Score API is only enabled for num_labels == 1.Nz9chat_template is only supported for cross-encoder models.rJclt|trd|vrtdjdSdS)Ncontentz*ScoreMultiModalParam is not supported for )r}r~r architecture)rJrs rzcheck_data_typez"LLM.score..check_data_typessX dD))i4.?.?$;+8;;.?.?r|rc,t|tred|vrtdd|vr/t t |d}nd|vrt t |d}t|tusJ|S)Nrz/Multi-modal prompt is not supported for scoringrr) r}r~rrrr*r)rr)rrNs rz ensure_strzLLM.score..ensure_strsfd++ D)V33(M,v55!*!1!1 v667IJ"""V++!%j&!9!9(!CF||s**** r|c&g|] }|Srvrvrwrrs rzrzLLM.score..!444jjmm444r|c&g|] }|Srvrvrs rzrzLLM.score..rr|r)ry)rrrrallis_cross_encoderrU hf_configrrr(rrr}rr~rr!rrv)rrwrxrDrrGrr6rrrrrrNs @@@@rzr{z LLM.scoresb( ". ) # #!  . GGGG1FGGG G G =   ) O . a@@AEEMNN N, 1JK &&(( /) 5 %?+,&'       OF # # # OF # # # ?      &3+.. " 4444V444F&3+.. " 4444V444F fd # #  V(;(;ZZ **FF  $ $ XF fd # #  V(;(;ZZ **FF  $ $ XF"66222  ( --&,.   ((& r|c8|jdSr)r start_profilers rzrzLLM.start_profiles %%'''''r|c8|jdSr)r stop_profilers rzrzLLM.stop_profiles $$&&&&&r|reset_running_requestsreset_connectorc8|j||Sr)rreset_prefix_cache)rrrs rzrzLLM.reset_prefix_caches#11 "O   r|levelcd||j|dS)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)rrs rzrz LLM.sleeps5& !!! E*****r|tagsc:|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)rrs rzrz LLM.wake_ups  %%%%%r|rCc4|jS)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_metricsrs rzrzLLM.get_metricss**,,,r|)rrrFc t|ttfr|g}t|}t|tr"t||krt dt|tr"t||krt d|6t||kr#t d|dt|dt|tr|n|fD](}t|t rtj|_ )|} |r%t|r|nt} | | d} g} t| D]x\} } | | t|tr|| n|t|tr|| n||r|| nd| }| |ydS#t$r%}| r|j| d |d}~wwxYw) Nz3The lengths of prompts and params must be the same.z9The lengths of prompts and lora_request must be the same.zThe lengths of prompts (z) and priority (z) must be the same.zAdding requests)rrrrrFT)internal)r}rr~rrrr8r7 FINAL_ONLY output_kindcallabler enumerate _add_requestr  Exceptionr abort_request)rrrrrrrF num_requestsspit tqdm_funcadded_request_idsr+rrNrs rzrzLLM._validate_and_add_requestssa gT{ + + iG7|| fh ' ' TCKK<,G,GRSS S lH - - #l2C2C|2S2SK   CMM\$A$A$ $$25h--$$$  'vx88G&&vi > >B"n-- >!2!=  7$,X$6$6@DI2$5666B') &r]] 5 5 6!..!+FH!=!=IF1II6!,99"&a%,4;Xa[[!(;/ "((4444 5 5     P--.?$-OOOG sBG G= G88G=)rFrNrc|pi}|}t|jj|j||j||||||}||fS)z2Use the Processor to process inputs for LLMEngine.)rrFr)rr$rrrDrprocess_inputs) rrNrrrrrFrengine_requests rz_process_inputszLLM._process_inputs8s+0b *//11!   +  )    -<<   % 3 =  222r|c t|\}}}tt|j}|||||||\} }|j|| |||||| jS)Nr)rrFr prompt_text)r+rnextrrr add_requestrN) rrrrrrFrr!rNrs rzrzLLM._add_requestVs2&99 Qd23344 .2.B.B   % 3 /C/ / ++ ##   % 3# $   ((r|rc |rI|j}t|r|nt}||ddddddddd}g}d}d}|jr?|j}|D]} | jr|| |rt| trt| j } | j J|t| j | zz }||j d z } |td | j Dz }||j d z } d| dd| dd|_|| n|d |j|kr| |j?|r|t)|d S)NzProcessed promptsTzest. speed input: rz.2fz toks/s, output: z toks/s)totalr dynamic_ncolspostfixelapsedc3>K|]}t|jVdSr)r token_ids)rwstps rzrz"LLM._run_engine..s=227:CM 2 2222222r|rJc*t|jSr)rrN)xs rzz!LLM._run_engine..sS->->r|)r)rget_num_unfinished_requestsrr has_unfinished_requestssteprOr r}r2rrr format_dictrrupdatenrefreshcloser) rrrrpbarr total_in_tokstotal_out_toks step_outputsr'rin_spdout_spds rzrzLLM._run_engineus=  ?FFHHL$,X$6$6@DI9"("TaTTTaTTTT D?A o5577 +?//11L& + +?+NN6***+%fm<<+ #FN 3 3A#)#:#F#F#F)S1H-I-IA-MMM%2T5Ei5P%PF*c22>Dn222//N'5t7G 7R&RG!@V J!@!@+2 ?!@!@!@!L!KKNNNN KKNNN6\11 LLNNN1o5577 +4   JJLLLg#>#>????r|c|j?|jd}|r|d|_nd|jjd|_|jS)z;Return a transformers-style hierarchical view of the model.Nget_model_inspectionrz LLM(model=))rrrrrj)rresultss rz__repr__z LLM.__repr__s_   $o445KLLG N$+AJ!!$M1B1H$M$M$M!  r|)rlNr)NrvN)NFN)NrHTFNNN) NTNNrHTFNNN)NTNNN)NTNNNN)FF)rJ)NrN)T__name__ __module__ __qualname____doc__rrrrboolrrrr.floatrr~rrrrrrrrBrr:rrr8rr'rrr r-r2rrrrDtuplernnModulerr*r)rr6r r4rrrArCr&r5r9r1r r0r^r/rerir3rvrrr(rr{rrrrrrrr@rrrrrvr|rzrGrGYs+__J &!' $.4$)"'(*26$%"37#)-(+ !#-2*/&*+/59-1BFDH,0NRFJGd-d-d-d- d-  d- : d-&+d-"d- d-#&d- $Cy4/d-"d-d-*D0d-*d-  $J!d-"#d-$!&%d-&'d-()d-*+d-,'+-d-.$(/d-0*t#1d-2"D(3d-4"#s(^d25d-6$d*7d-8$(S> !$" $9d->c3h.84??d-@sCx.?:TAAd-B #TzCd-D $sCx.03DDtKEd-F d?&; ;*BBTIEJ #t),, EJ ;'+5< EJs)d"EJ m EJEJEJEJN" hz22" ;'+5<" " " " H3 3 "D(3 sCx.4/ 3 3 3 3 p!%(, MMhsBw''MM M S#X% M b MMMM@ 1")b 9 1d2h 1 1 1 1K;'+5<KlZ/0K kD ! KKKK(@D(, xxlZ/0x!x;'+5< x  x : x  xxxx|%)HN&*',-16:59::12 t./ 01:Tz : 'F : $ :!%:DcN#d*:#38nt3:"#s(^d2: j<' (::::@IM/3+/$(HN&*',-16:59R R 12 t./ 01R ($~*>>E R #t),, R "D( R TzR 'FR  $R !%R DcN#d*R #38nt3R "#s(^d2R  m R R R R nJNU! .2/3?C+/59U!U!U!hz22Z?U!&(??$FU! !$d U! #t),, U!;'+5<U!"D(U!"#s(^d2U! " #U!U!U!U!v.2/3IM?C591J1J1Jhz221J!$d 1J #t),, 1J &(??$F 1J;'+5<1J"#s(^d21J $ %1J1J1J1Jn04IM?C59.O.O.Ohz22.O#t),, .O &(??$F .O ;'+5< .O"#s(^d2.O ) *.O.O.O.Oj.2/3IM?C59& & & hz22& !$d & #t),, & &(??$F& ;'+5<& "#s(^d2&  " #& & & & Z.2/3/3?C59 H H  HS:% 45 HS:% 45 H !$d H #t),, H&, H;'+5< H"#s(^d2 H " # H H H HN.2/3/3?C59%)CHCH CHS D!677CHS D!677 CH !$d CH #t),, CH&,CH;'+5<CH"#s(^d2CHd CH " #CHCHCHCHV.2/3/3?C$(]]](?";;>RR] (?";;>RR] !$d ]#t),,]&,];'+5<]Tz] " #]]]]~((((''''MR  &* EI     ++3++++, & &DI, & & & & -T(^ - - - -(04%)59;;;hz22Z?; > "#  = !";#t),,;{+k9D@;s)d";"#s(^d2; ;;;;J6:3333"3. 3 "D( 33"#s(^d23  $sCx.0 13333D,059 ))).)"D( )  ) "#s(^d2 ) ))))@9=1@1@1@(39"551@ m22 31@1@1@1@f !# ! ! ! ! ! !r|rG)ircollections.abcrrtypingrrrrtorch.nnrpydanticr tqdm.autor typing_extensionsr vllm.beam_searchr r r r vllm.configrrrrrrvllm.config.compilationrvllm.config.modelrrrrrvllm.engine.arg_utilsrvllm.entrypoints.chat_utilsrr$vllm.entrypoints.pooling.score.utilsrrr r!r"r#vllm.entrypoints.utilsr$r% vllm.inputsr&r'r(r)r*vllm.inputs.parser+ vllm.loggerr,vllm.lora.requestr-'vllm.model_executor.layers.quantizationr. vllm.outputsr/r0r1r2r3vllm.platformsr4vllm.pooling_paramsr5vllm.sampling_paramsr6r7r8 vllm.tasksr9vllm.tokenizersr:vllm.tokenizers.mistralr;vllm.usage.usage_libr<vllm.utils.collection_utilsr=r>vllm.utils.counterr?vllm.v1.enginer@vllm.v1.engine.llm_enginerAvllm.v1.sample.logits_processorrBvllm.v1.metrics.readerrCrrrDrGrvr|rzrse........++++++++++$$$$$$%%%%%% 433333-,,,,,SRRRRRRR433333######))))))GGGGGG,+++++------TTTTTTTTTT""""""))))))444444------;;;;;;;;&&&&&&,,,,,,//////;;;;;;.------ X   WT3Z!Z!Z!Z!Z!Z!Z!Z!Z!Z!r|