42 lines
1.6 KiB
Python
42 lines
1.6 KiB
Python
from typing import Dict, List, Any
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import torch
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from accelerate import Accelerator
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from transformers import AutoTokenizer, AutoModelForCausalLM
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import numpy as np
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def softmax(x):
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z = x - max(x)
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numerator = np.exp(z)
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denominator = np.sum(numerator)
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softmax = numerator/denominator
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return softmax
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class EndpointHandler():
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def __init__(self, path=""):
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self.accelerator = Accelerator()
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self.device = self.accelerator.device
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self.model = AutoModelForCausalLM.from_pretrained(path, trust_remote_code=True, device_map="auto")
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self.model = self.accelerator.prepare(self.model)
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self.tokenizer = AutoTokenizer.from_pretrained(path)
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self.options_tokens = [self.tokenizer.encode(choice)[-1] for choice in ["A", "B", "C", "D"]]
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def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
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"""
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data args:
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inputs (:obj: `str` | `PIL.Image` | `np.array`)
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kwargss
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Return:
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A :obj:`list` | `dict`: will be serialized and returned
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"""
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with torch.no_grad():
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prompt = data.pop("prompt")
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inputs = self.tokenizer(prompt, return_tensors="pt").to(self.device)
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input_size = inputs['input_ids'].size(1)
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input_ids = inputs["input_ids"].to(self.device)
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outputs = self.model(**inputs)
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last_token_logits = outputs.logits[:, -1, :]
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options_tokens_logits = last_token_logits[:, self.options_tokens].detach().cpu().numpy()
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conf = softmax(options_tokens_logits[0])
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pred = np.argmax(options_tokens_logits[0])
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return [{"pred": pred, "conf":conf}] |