41 lines
1.2 KiB
Python
41 lines
1.2 KiB
Python
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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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# get dtype
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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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# load the model
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tokenizer = AutoTokenizer.from_pretrained(
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path,
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trust_remote_code=True
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)
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model = AutoModelForCausalLM.from_pretrained(
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path,
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device_map="auto",
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torch_dtype=dtype,
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trust_remote_code=True,
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revision="main"
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)
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# create inference pipeline
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self.pipeline = pipeline(
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"text-generation",
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model=model,
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tokenizer=tokenizer,
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trust_remote_code=True
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)
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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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# pass inputs with all kwargs in data
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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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# postprocess the prediction
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return prediction
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