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from ..base_handler import ModelHandler
from transformers import AutoTokenizer
import torch
import time
import numpy as np
class MultipleChoiceHandler(ModelHandler):
def __init__(self, model_name, model_class, quantization_type, test_text):
super().__init__(model_name, model_class, quantization_type, test_text)
self.tokenizer = AutoTokenizer.from_pretrained(model_name)
def run_inference(self, model, text):
choices = [text.split(f"({chr(65 + i)})")[1].strip() for i in range(4)]
inputs = self.tokenizer(choices, return_tensors='pt', padding=True).to(self.device)
start_time = time.time()
with torch.no_grad():
outputs = model(**inputs)
end_time = time.time()
return outputs, end_time - start_time
def decode_output(self, outputs):
logits = outputs.logits
predicted_choice = chr(65 + logits.argmax().item())
return f"Predicted choice: {predicted_choice}"
def compare_outputs(self, original_outputs, quantized_outputs):
if original_outputs is None or quantized_outputs is None:
return None
original_logits = original_outputs.logits.detach().cpu().numpy()
quantized_logits = quantized_outputs.logits.detach().cpu().numpy()
metrics = {
'mse': ((original_logits - quantized_logits) ** 2).mean(),
'top_1_accuracy': np.mean(
np.argmax(original_logits, axis=-1) == np.argmax(quantized_logits, axis=-1)
),
}
return metrics |