How to quantize existing inference model?

[l03126]

I have a finetuned inference model. I would like to try to reduce the time it takes to perform OCR on images. From the documentation, it mentions that quantization is done in training itself (Quant-aware training). Are there other ways around it or other methods for my use case?

[GreatV]

To quantize your existing fine-tuned inference model for OCR and reduce inference time, there are alternatives to quantization-aware training (QAT). Based on the provided documentation, here are the key steps and methods you can explore:

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1. Quantization Options

Quantization is a technique to reduce the precision of a model's data (e.g., from FP32 to INT8), which can improve inference speed and reduce model size. PaddleOCR supports the following types of quantization:

• Offline Quantization: This method does not require retraining the model. It applies quantization to a pre-trained model by analyzing its weights and activations. It is a good option if you do not want to retrain your model.

• Quantization-Aware Training (QAT): This is a more effective method that involves retraining the model with quantization in mind. While it gives better performance, it requires access to the training pipeline and data.

Since you want to avoid retraining (QAT), offline quantization would be a suitable alternative.

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2. Steps for Offline Quantization

You can perform offline quantization using PaddleSlim, which is a library provided by PaddleOCR for model compression. Here’s how you can proceed:

Step 1: Install PaddleSlim

Make sure you have PaddleSlim installed:

pip3 install paddleslim==2.3.2

Step 2: Prepare the Trained Model

Ensure that you have your fine-tuned model ready. If necessary, you can download pre-trained models from the PaddleOCR model list.

Step 3: Apply Offline Quantization

Offline quantization can be done using PaddleSlim's quantization APIs. The process does not require retraining but involves analyzing the model’s weights and activations to quantize them.

Here’s an example of how to apply offline quantization:

from paddleslim.dygraph.quant import quant_post_static
from paddle.jit import save

# Load your trained model
model = YourTrainedModel()  # Replace with your model definition
model.load_state_dict(paddle.load('path_to_your_model.pdparams'))

# Apply quantization
quantized_model = quant_post_static(
    model,
    save_path='output/quant_model',  # Directory to save quantized model
    data_loader=your_data_loader,   # Data loader for calibration data
    batch_size=16                  # Batch size for calibration
)

# Save quantized inference model
save(quantized_model, "output/quant_inference_model")

Step 4: Optimize for Deployment

Once the model is quantized, you can convert it for deployment on mobile or embedded devices using PaddleLite’s opt tool. This further prepares the model for efficient inference.

paddle_lite_opt --model_dir=output/quant_inference_model --valid_targets=arm --optimize_out=optimized_model

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3. Comparison of Methods

• Offline Quantization: Easier to implement, no retraining required, suitable for deploying existing models.
• Quantization-Aware Training (QAT): Requires retraining but offers better performance in terms of accuracy and speed.

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4. Additional Considerations

• Ensure you have enough calibration data for offline quantization. This data is used to simulate inference and determine appropriate quantization scales.
• If inference speed is still not sufficient, consider combining quantization with other techniques like model pruning or distillation.

For further details, refer to the PaddleSlim Quantization Documentation.

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By following the offline quantization method, you should be able to compress your existing model and accelerate OCR inference without retraining.

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