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from typing import Dict, List, Any |
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import time |
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import torch |
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from transformers import AutoTokenizer, AutoModel |
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class EndpointHandler: |
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def __init__(self, path=''): |
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self.tokenizer = AutoTokenizer.from_pretrained(path) |
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self.model = AutoModel.from_pretrained(path, load_in_8bit=True) |
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def __call__(self, data: Dict[str, Any]) -> Dict[str, str]: |
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inputs = data.pop('inputs', data) |
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parameters = data.pop('parameters', {}) |
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starting_time = time.time() |
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tokenized = self.tokenizer(inputs, return_tensors='pt') |
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out = self.model.generate(tokenized.to('cuda'), **parameters).to('cpu') |
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detokenized = self.tokenizer.batch_decode(out) |
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ending_time = time.time() |
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return [{'generated_text': detokenized, 'generation_time': ending_time-starting_time}] |
