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# Madlad Optimized Inference Script
import torch
import onnxruntime as ort
from transformers import T5Tokenizer
import numpy as np
class MadladOptimizedInference:
def __init__(self, model_dir):
self.tokenizer = T5Tokenizer.from_pretrained(model_dir)
# Load model components
self.encoder_session = ort.InferenceSession(f"{model_dir}/madlad_encoder.onnx")
self.decoder_session = ort.InferenceSession(f"{model_dir}/madlad_decoder.onnx")
# If embed/lm_head separated successfully
# self.embed_session = ort.InferenceSession(f"{model_dir}/madlad_embed_and_lm_head.onnx")
def translate(self, text, max_length=128):
# Tokenize input
inputs = self.tokenizer(text, return_tensors="np")
# Run encoder
encoder_outputs = self.encoder_session.run(None, {
"input_ids": inputs["input_ids"],
"attention_mask": inputs["attention_mask"]
})
# Simplified generation loop (would need KV-cache for full optimization)
# This is a basic version - full implementation would follow NLLB pattern
generated_ids = []
# Implementation details would go here...
return self.tokenizer.decode(generated_ids, skip_special_tokens=True)
# Usage example:
# inference = MadladOptimizedInference("madlad_optimized")
# result = inference.translate("<2pt> I love pizza!")
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