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from torch import cuda |
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import transformers |
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from accelerate import dispatch_model, infer_auto_device_map |
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from accelerate.utils import get_balanced_memory |
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from transformers import BitsAndBytesConfig, StoppingCriteria, StoppingCriteriaList |
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from typing import Dict, List, Any |
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class PreTrainedPipeline(): |
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def __init__(self, path=""): |
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path = "oleksandrfluxon/mpt-7b-instruct-evaluate" |
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print("===> path", path) |
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device = f'cuda:{cuda.current_device()}' if cuda.is_available() else 'cpu' |
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print("===> device", device) |
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model = transformers.AutoModelForCausalLM.from_pretrained( |
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'oleksandrfluxon/mpt-7b-instruct-evaluate', |
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trust_remote_code=True, |
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load_in_8bit=True, |
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max_seq_len=8192, |
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init_device=device |
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) |
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model.eval() |
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print(f"===> Model loaded on {device}") |
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tokenizer = transformers.AutoTokenizer.from_pretrained("mosaicml/mpt-7b") |
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self.pipeline = transformers.pipeline('text-generation', model=model, tokenizer=tokenizer) |
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print("===> init finished") |
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def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]: |
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""" |
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data args: |
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inputs (:obj: `str`) |
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parameters (:obj: `str`) |
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Return: |
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A :obj:`str`: todo |
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""" |
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inputs = data.pop("inputs",data) |
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parameters = data.pop("parameters", {}) |
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date = data.pop("date", None) |
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print("===> inputs", inputs) |
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print("===> parameters", parameters) |
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result = self.pipeline(inputs, **parameters) |
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print("===> result", result) |
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return result |
