Quantization Aware Training

Making your model work with low-precision inference easier

Quantization Aware Training (QAT) is a technique used to prepare deep learning models for efficient deployment on hardware that supports lower precision (e.g., 8-bit integers) by simulating the effects of quantization during the training process itself.

While not exclusively a distributed training technique, QAT can be highly beneficial in distributed settings where models are often large and inference efficiency is a concern. Standard training typically uses 32-bit floating-point (FP32) numbers (or BF16). Quantization involves mapping these FP32 values to a smaller set of discrete lower-precision values (e.g., 8-bit integers, or INT8). Naively quantizing a model post-training can lead to significant accuracy degradation. QAT addresses this by incorporating "fake" quantization operations into the forward and backward passes of the training graph. These operations simulate the rounding and clamping effects of quantization, allowing the model to adapt its weights to the expected precision loss. The optimizer still updates the weights in FP32, but the model learns to be robust to the quantization noise.

This results in a model that, after training, can be quantized to lower precision with minimal loss in accuracy, leading to smaller model sizes, faster inference, and reduced memory bandwidth requirements. QAT is often used as a final fine-tuning step after a model has been pre-trained using standard FP32 precision.

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