52 lines
1.7 KiB
Python
52 lines
1.7 KiB
Python
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from typing import Any, Dict
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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from peft import PeftConfig, PeftModel
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class EndpointHandler:
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def __init__(self, path=""):
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# load model and processor from path
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self.tokenizer = AutoTokenizer.from_pretrained(path)
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try:
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config = PeftConfig.from_pretrained(path)
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model = AutoModelForCausalLM.from_pretrained(
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config.base_model_name_or_path,
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return_dict=True,
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load_in_8bit=True,
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device_map="auto",
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torch_dtype=torch.float16,
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)
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model.resize_token_embeddings(len(self.tokenizer))
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model = PeftModel.from_pretrained(model, path)
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except Exception:
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model = AutoModelForCausalLM.from_pretrained(
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path,
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device_map="auto",
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load_in_8bit=True,
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torch_dtype=torch.float16,
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)
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self.model = model
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self.device = "cuda" if torch.cuda.is_available() else "cpu"
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def __call__(self, data: Dict[str, Any]) -> Dict[str, str]:
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# process input
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inputs = data.pop("inputs", data)
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parameters = data.pop("parameters", None)
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# preprocess
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inputs = self.tokenizer(inputs, return_tensors="pt").to(self.device)
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# pass inputs with all kwargs in data
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if parameters is not None:
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outputs = self.model.generate(**inputs, **parameters)
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else:
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outputs = self.model.generate(**inputs)
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# postprocess the prediction
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prediction = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
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return [{"generated_text": prediction}]
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