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from ..base_handler import ModelHandler
from transformers import AutoTokenizer
import torch
import time
import numpy as np
class MaskedLMHandler(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):
inputs = self.tokenizer(text, return_tensors='pt').to(self.device)
start_time = time.time()
with torch.no_grad():
outputs = model(**inputs)
end_time = time.time()
return outputs, inputs, end_time - start_time
def decode_output(self, outputs, inputs):
logits = outputs.logits
masked_index = torch.where(inputs['input_ids'] == self.tokenizer.mask_token_id)[1]
predicted_token_id = logits[0, masked_index].argmax(axis=-1)
return self.tokenizer.decode(predicted_token_id)
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 |