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import torch |
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from typing import Dict, List, Any |
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from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline |
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dtype = torch.bfloat16 if torch.cuda.get_device_capability()[0] == 8 else torch.float16 |
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class EndpointHandler: |
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def __init__(self, path=""): |
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tokenizer = AutoTokenizer.from_pretrained(path) |
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model = AutoModelForCausalLM.from_pretrained(path, device_map="auto",torch_dtype=dtype) |
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self.pipeline = pipeline("text-generation", model=model, tokenizer=tokenizer) |
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def __call__(self, data: Any) -> List[List[Dict[str, float]]]: |
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inputs = data.pop("inputs", data) |
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parameters = data.pop("parameters", None) |
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if parameters is not None: |
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prediction = self.pipeline(inputs, **parameters) |
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else: |
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prediction = self.pipeline(inputs) |
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return prediction |
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