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from typing import Dict, List, Any |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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import torch |
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class PreTrainedPipeline: |
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def __init__(self, path=""): |
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self.model = AutoModelForCausalLM.from_pretrained( |
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path, torch_dtype=torch.float16, device_map="auto", load_in_8bit=True |
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) |
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self.tokenizer = AutoTokenizer.from_pretrained(path) |
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def __call__(self, data: Any) -> List[List[Dict[str, float]]]: |
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""" |
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Args: |
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data (:obj:): |
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includes the input data and the parameters for the inference. |
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Return: |
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A :obj:`list`:. The list contains the embeddings of the inference inputs |
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""" |
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inputs = data.get("inputs", data) |
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parameters = data.get("parameters", {}) |
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input_ids = self.tokenizer(inputs, return_tensors="pt").input_ids.to(self.model.device) |
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logits = self.model.generate(input_ids, **parameters) |
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return {"generated_text": self.tokenizer.decode(logits[0].tolist())} |
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