How to Export and Use a RoBERTa Adapter with ONNX

Dec 31, 2022 | Educational

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In this blog post, we will guide you through the steps required to effectively export and use the RoBERTa Adapter from AdapterHub for question-answering tasks. This will be particularly focused on utilizing ONNX (Open Neural Network Exchange) to ensure model interoperability and hardware optimization.

Step 1: Setting Up Your Environment

Before diving into the process, ensure you have the necessary libraries installed in your Python environment. You’ll need the following:

Hugging Face Transformers
ONNX
NumPy
ONNX Runtime

Step 2: Exporting the Adapter

To get started with the export, you can directly download the model using the Hugging Face Hub. Here’s a sample code snippet:

python
onnx_path = hf_hub_download(repo_id='UKP-SQuARE/roberta-base-pf-squad-onnx', filename='model.onnx')  # or model_quant.onnx for quantization

This line of code fetches the ONNX model from the specified repository.

Step 3: Loading the Model

Next, we will load the model using ONNX Runtime. This is crucial to ensure that your model is prepared for inference:

python
onnx_model = InferenceSession(onnx_path, providers=[CPUExecutionProvider])

Step 4: Processing Input Data

Before feeding input data into the model, you will need to prepare the question and context. Here’s how to do that:

python
question = "What are advantages of ONNX?"
tokenizer = AutoTokenizer.from_pretrained('UKP-SQuARE/roberta-base-pf-squad-onnx')
inputs = tokenizer(question, context, padding=True, truncation=True, return_tensors='np')

# Convert inputs to dtype int64
inputs_int64 = {key: np.array(inputs[key], dtype=np.int64) for key in inputs}

In the above code, we define the question and use the tokenizer to prepare the input in a format compatible with ONNX.

Step 5: Running Inference

Finally, to get the model’s predictions, execute the following code:

python
outputs = onnx_model.run(input_feed=dict(inputs_int64), output_names=None)

Analogy: Think of it Like Cooking!

Imagine the entire process is similar to cooking a gourmet dish:

The ingredients are your model and data—the quality and preparation of these will determine the dish’s success (your model’s performance).
The recipe is your code, guiding you through the steps to prepare a delicious meal (complete a question-answering task).
Finally, using the oven (ONNX Runtime) correctly is crucial for the cooking process—in this case, ensuring that the adaptation and inference are performed smoothly.

Troubleshooting Tips

If you face issues during any of these steps, consider the following tips:

Check Library Versions: Make sure you are using compatible versions of the libraries.
Validate Model Path: Double-check that your model path is correct when using hf_hub_download.
Resource Availability: If encountering memory errors, try decreasing batch sizes or optimize memory usage.
ONNX Runtime Errors: Ensure that your ONNX Runtime supports the specific Operators used in your model.

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