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from ..base_handler import ModelHandler
from transformers import AutoTokenizer
import torch
import time
import numpy as np
class Seq2SeqLMHandler(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.generate(**inputs, max_length=50)
end_time = time.time()
return outputs, end_time - start_time
def decode_output(self, outputs):
return self.tokenizer.decode(outputs[0], skip_special_tokens=True)
def compare_outputs(self, original_outputs, quantized_outputs):
if original_outputs is None or quantized_outputs is None:
return None
original_tokens = original_outputs[0].cpu().numpy()
quantized_tokens = quantized_outputs[0].cpu().numpy()
metrics = {
'sequence_similarity': np.mean(original_tokens == quantized_tokens),
'sequence_length_diff': abs(len(original_tokens) - len(quantized_tokens)),
}
return metrics |