o *jl@sZddlZddlmZmZmZmZmZddlZddlm Z ddl m Z ddl m Z ddlmZddlmZmZmZddlmZmZdd lmZdd lmZdd lmZdd lmZmZdd l m!Z!m"Z"ddl#m$Z$ddl%m&Z&ddl'm(Z(e$Z)gdZ*ej+ej,e j,dGdddee&Z-ej+ej.e j/dej+ej.e j0dej+ej.e j1dej+ej.e j.dGddde-Z2ej+ej3ddGdddeZ4ej+ej3ddGdddeZ5ej+ej3ddGdd d eZ6ej+ej,d!dGd"d#d#eZ7ej+ej,d$dGd%d&d&eZ8ej+ej,e j9dGd'd(d(e-Z:ej+ej3e j;dGd)d*d*eZ>> from modelscope.pipelines import pipeline >>> pipeline_ins = pipeline(task='text-generation', >>> model='damo/nlp_palm2.0_text-generation_chinese-base') >>> sentence1 = '本文总结了十个可穿戴产品的设计原则，而这些原则，同样也是笔者认为是这个行业最吸引人的地方：' >>> '1.为人们解决重复性问题；2.从人开始，而不是从机器开始；3.要引起注意，但不要刻意；4.提升用户能力，而不是取代' >>> print(pipeline_ins(sentence1)) >>> # Or use the dict input: >>> print(pipeline_ins({'sentence': sentence1})) To view other examples please check tests/pipelines/test_text_generation.py. compileFcompile_options)r%r&r'r( auto_collater)r*z,please check whether model config exists in Nfirst_sequence postprocessor model_dirdecode)super__init__pop isinstancer%r rZ CONFIGURATIONrfrom_pretrainedr.r&evalr-hasattrrget has_logged) selfr%r&r'r(r+r,kwargscfg __class__r0r/var/www/html/Deteccion_Ine/venv/lib/python3.10/site-packages/modelscope/pipelines/nlp/text_generation_pipeline.pyr2's<        zTextGenerationPipeline.__init__cK i|ifSNr0r:Zpipeline_parametersr0r0r?_sanitize_parameters^ z+TextGenerationPipeline._sanitize_parametersinputsreturnc KstGz|jj|fi|WWdStyJ}z'|js.td|dd|_|jjdi||WYd}~WdSd}~ww1sNwYdS)Nz.When inputs are passed directly, the error is z,, which can be ignored if it runs correctly.Tr0)torchno_gradr%generateAttributeErrorr9loggerwarning)r:rEforward_paramser0r0r?forwardas"  zTextGenerationPipeline.forwardcCs|jj|ddS)NTskip_special_tokensr&r/tolistr:rEr0r0r?r/oszTextGenerationPipeline.decodecCs|j|SrArRrTr0r0r?sentence_piecessz%TextGenerationPipeline.sentence_piececCs,|j|}|ddddddS)Nzz. zz)r&r/rSreplace)r:rEdecodedr0r0r?robertavs zTextGenerationPipeline.robertacKsXt|ttfr |d}t|tst|jdkr|d}t||j|}t|}t j |iS)zprocess the prediction results Args: inputs (Dict[str, Any]): _description_ Returns: Dict[str, str]: the prediction results sequencesr) r4dictr listlenshapegetattrr-rr TEXT)r:rEZpostprocess_paramsrXtextr0r0r? 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Args: model (str or Model): Supply either a local model dir which supported the text generation task, or a model id from the model hub, or a torch model instance. preprocessor (Preprocessor): An optional preprocessor instance, please make sure the preprocessor fits for the model if supplied. kwargs (dict, `optional`): Extra kwargs passed into the preprocessor's constructor. Examples: >>> from modelscope.utils.constant import Tasks >>> import torch >>> from modelscope.pipelines import pipeline >>> from modelscope import snapshot_download, Model >>> model_dir = snapshot_download("modelscope/Llama-2-13b-chat-ms", >>> ignore_file_pattern = [r'\w+\.safetensors']) >>> pipe = pipeline(task=Tasks.text_generation, model=model_dir, device_map='auto', >>> torch_dtype=torch.float16) >>> inputs="咖啡的作用是什么？" >>> result = pipe(inputs,max_length=200, do_sample=True, top_p=0.85, >>> temperature=1.0, repetition_penalty=1., eos_token_id=2, bos_token_id=1, pad_token_id=0) >>> print(result['text']) To view other examples please check tests/pipelines/test_llama2_text_generation_pipeline.py. rrrrLlama2Tokenizerr%Nr0) r r5rGZfloat16r%modelscope.models.nlp.llama2rrr1r2)r:r%r&r'r(r+r;rr=r0r?r2s    zLlama2TaskPipeline.__init__rFcKrrAr0rr0r0r?rxrzLlama2TaskPipeline.preprocesscKr@rAr0rBr0r0r?rCrDz'Llama2TaskPipeline._sanitize_parametersF?333333??rr[rrErkrtop_p temperaturerepetition_penalty eos_token_id bos_token_id pad_token_idc  Ksdi} |j|ddd}|jj|jdf|||||||| d| } |jj| dddd} | | d <| S) NFr)Zadd_special_tokensrr|)rkrrrrrrrT)rQZclean_up_tokenization_spacesrrb)rr%rIrrr)r:rErkrrrrrrrrMoutputZ generate_idsoutr0r0r?rOs6    zLlama2TaskPipeline.forwardcKrrAr0rr0r0r?rc7rzLlama2TaskPipeline.postprocessNNr#T)rFrrrrr[r)rdrerfrr rgrr2rrrxrCrrfloatrOrcrhr0r0r=r?r!s\ &  !r!cseZdZdZ    d)deeefdededed ed e ee ff fd d Z d e ee ffd dZ ddZ dddddddddgf dedededededed ed!ed"ed#ed$ed e ee ffd%d&Zd e ee ffd'd(ZZS)*Llama2chatTaskPipelineaUse `model` and `preprocessor` to create a generation pipeline for prediction. Args: model (str or Model): Supply either a local model dir which supported the text generation task, or a model id from the model hub, or a torch model instance. preprocessor (Preprocessor): An optional preprocessor instance, please make sure the preprocessor fits for the model if supplied. kwargs (dict, `optional`): Extra kwargs passed into the preprocessor's constructor. Examples: >>> from modelscope.utils.constant import Tasks >>> import torch >>> from modelscope.pipelines import pipeline >>> from modelscope import Model >>> pipe = pipeline(task=Tasks.chat, model="modelscope/Llama-2-7b-chat-ms", device_map='auto', >>> torch_dtype=torch.float16, ignore_file_pattern = [r'.+\.bin$'], model_revision='v1.0.5') >>> inputs = 'Where is the capital of Zhejiang?' >>> result = pipe(inputs,max_length=512, do_sample=False, top_p=0.9, >>> temperature=0.6, repetition_penalty=1., eos_token_id=2, bos_token_id=1, pad_token_id=0) >>> print(result['response']) >>> inputs = 'What are the interesting places there?' >>> result = pipe(inputs,max_length=512, do_sample=False, top_p=0.9, >>> temperature=0.6, repetition_penalty=1., eos_token_id=2, bos_token_id=1, >>> pad_token_id=0, history=result['history']) >>> print(result['response']) >>> inputs = 'What are the company there?' >>> history_demo = [('Where is the capital of Zhejiang?', >>> 'Thank you for asking! The capital of Zhejiang Province is Hangzhou.')] >>> result = pipe(inputs,max_length=512, do_sample=False, top_p=0.9, >>> temperature=0.6, repetition_penalty=1., eos_token_id=2, bos_token_id=1, >>> pad_token_id=0, history=history_demo) >>> print(result['response']) To view other examples please check tests/pipelines/test_llama2_text_generation_pipeline.py. 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