-
Topic: Post-Training Quantization vs. Quantization-Aware Training
-
For PTQ, particularly static PTQ with calibration, I typically aim for <1% absolute drop in accuracy or F1-score. If it’s more than that, or if the model already has low accuracy to begin with, PTQ often introduces too much noise. Dynamic PTQ might offer more robustness but comes with a runtime overhead, which you mentioned you want to avoid for sub-20ms latency.
-
For QAT with PyTorch, the biggest architectural hurdle is usually inserting the
torch.quantization.FakeQuantizemodules correctly across all quantizable layers (convs, linear, element-wise ops). You also need to ensure that you usefuse_modelfor certain operations (e.g., Conv-ReLU, Linear-ReLU) before QAT, as fused operations quantize much more efficiently. Neglecting fusion often leads to worse QAT results. The Straight-Through Estimator (STE) in PyTorch handles the gradient approximation, but careful setup is key. -
We used QAT for an image segmentation model on a custom FPGA. PTQ introduced severe artifacts, particularly at object boundaries. We had to invest 3 extra weeks for QAT, but the ~3% accuracy gain compared to PTQ was critical for our use case, directly impacting the product’s performance metrics. The hardware vendor’s SDK had good QAT support, which was a huge plus.
-
-
0 Replies
Sorry, there were no replies found.
Log in to reply.