How to Optimize ALBERT for Mobile Deployment with Hugging Face Transformers

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Let’s learn how to optimize ALBERT LLM for smaller mobile deployment.
 

Preparation

 
For our tutorial would require the Transformers and ONNX package. We can install them using the following code:

pip install transformers onnx

Additionally, you should install the PyTorch package by selecting the version that is suitable for your environment.

With the package installed, we will get into the next part.
 

Optimize ALBERT for Mobile Deployment

Large Deep Learning Models, such as Large Language Models (LLM), typically require higher performance, and not every device will run them smoothly, especially mobile devices. Mobile devices have limited resources compared to running your model on a desktop or machine, so optimizing our model for the mobile is beneficial. By optimizing the model, we can improve many aspects of running the model on mobile, including better computational performance, battery efficiency, and latency.

ALBERT is a pre-trained model based on BERT but with smaller memory consumption and improved training process time. It’s a language model suitable for mobile devices as it’s small and can be deployed nicely.

Even if ALBERT is small, we can optimize them further to improve the model efficiency in the mobile device.

Let’s start by downloading the ALBERT model.

import torch
from transformers import AlbertTokenizer, AlbertForSequenceClassification
model_name = "albert-base-v2"
tokenizer = AlbertTokenizer.from_pretrained(model_name)
model = AlbertForSequenceClassification.from_pretrained(model_name)

Next, we would trace the model for any subsequent activity.

class AlbertWrapper(torch.nn.Module):
    def __init__(self, model):
        super(AlbertWrapper, self).__init__()
        self.model = model

    def forward(self, input_ids, attention_mask=None, token_type_ids=None):
        outputs = self.model(input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids)
        return outputs.logits

wrapped_model = AlbertWrapper(model)
dummy_input = tokenizer("Hugging Face Transformers are great for optimization!", return_tensors="pt")
traced_model = torch.jit.trace(wrapped_model, (dummy_input['input_ids'], dummy_input['attention_mask']))

We wrap the model to override the original ALBERT output so it returns the logit output, which is the raw score.

Next, we would quantize the model. This would reduce the model’s weight precision, resulting in less model size and increased speed without significantly decreasing the accuracy.

quantized_model = torch.quantization.quantize_dynamic(
    traced_model, {torch.nn.Linear}, dtype=torch.qint8
)

quantized_model.save("quantized_albert.pt")

We would also prune the model to remove less important weights to reduce model size and increase speed.

from torch.nn.utils import prune

for name, module in quantized_model.named_modules():
    if isinstance(module, torch.nn.Linear):
        prune.l1_unstructured(module, name='weight', amount=0.2)
        prune.remove(module, 'weight')

Lastly, we would convert the model into ONNX (Open Neural Network Exchange) format. ONNX is an open-source format that allows the model to be used in different frameworks or tools optimized for inference. It’s a universal format that is great for deploying on mobile devices.

import torch.onnx

torch.onnx.export(
    quantized_model,
    (dummy_input['input_ids'], dummy_input['attention_mask']),
    "quantized_albert.onnx",
    export_params=True,
    opset_version=11,
    input_names=['input_ids', 'attention_mask'],
    output_names=['logits'],
    dynamic_axes={'input_ids': {0: 'batch_size'}, 'logits': {0: 'batch_size'}})

Master the optimization process to improve your model efficiency in the mobile device deployment.

Additional Resources

• Optimizing Your LLM for Performance and Scalability
• 7 Steps to Mastering Large Language Models (LLMs)
• 5 Essential Free Tools for Getting Started with LLMs

Cornellius Yudha Wijaya is a data science assistant manager and data writer. While working full-time at Allianz Indonesia, he loves to share Python and data tips via social media and writing media. Cornellius writes on a variety of AI and machine learning topics.