What are the limitations of PyTorch quantization?

What are the limitations of PyTorch quantization?

PyTorch quantization can lead to reduced model accuracy, especially for complex models. It may also require careful calibration.

Can you explain how quantization affects model accuracy?

Quantization reduces the precision of weights and activations, which can introduce errors, particularly in sensitive layers.

Are there specific models that are more affected by quantization?

Yes, models with a lot of fine-grained details, like deep convolutional networks, often suffer more from quantization.

What techniques can mitigate the limitations of quantization?

Techniques like quantization-aware training and post-training quantization can help maintain accuracy while reducing model size.

Is there a trade-off between performance and model size?

Absolutely, quantization can significantly reduce model size and improve inference speed, but it may come at the cost of accuracy.