PTQ和QAT的区别: 和PTQ不同的是,QAT在训练过程中就开启了量化功能,原因在于量化的本质是将模型的高精度转换成低精度,很有可能导致模型性能变差,当出现这种情况的时候则需要考虑QAT进行量化:在训练过程中就开启量化的功能。 模型融合:将一些相邻模块进行融合,以提高计算效率 情形一:将conv和relu相融合 conv和relu均...
python ptq.py --weights ./weights/yolov5s.pt --cocodir /home/wyh/disk/coco/ --batch_size 5 --save_ptq True --eval_origin --eval_ptq --sensitive False 3.Start QAT Training python qat.py --weights ./weights/yolov5s.pt --cocodir /home/wyh/disk/coco/ --batch_size 5 --save_...
we've developed a QAT recipe that demonstrates significant accuracy improvements over traditional PTQ, recovering96% of the accuracy degradation on hellaswag and 68% of the perplexity degradation on wikitextfor Llama3 compared to post-training quantization (PTQ). And we've provided a full recipeher...
4 Experiments 4-1 与PTQ比较 4-2 与QAT比较 4-3 与TRT低于8比特时比较
to be compatible with most PTQ optimization algorithms like [hqq](https://mobiusml.github.io/hqq_blog/) or [AWQ](https://github.com/mit-han-lab/llm-awq). Moving forward, the plan is to integrate the most popular algorithms in the most seamless possible way. ## Contributing to 🤗...
3Branches0Tags Code Folders and files Name Last commit message Last commit date Latest commit Jermmy add QSigmoid Jun 18, 2023 704857d·Jun 18, 2023 History 32 Commits .gitignore add note for PTQ and QAT Jun 30, 2022 LICENSE Initial commit ...
SOTA low-bit LLM quantization (INT8/FP8/INT4/FP4/NF4) & sparsity; leading model compression techniques on TensorFlow, PyTorch, and ONNX Runtime - intel/neural-compressor
Brevitas currently offers quantized implementations of the most common PyTorch layers used in DNN underbrevitas.nn, such asQuantConv1d,QuantConv2d,QuantConvTranspose1d,QuantConvTranspose2d,QuantMultiheadAttention,QuantRNN,QuantLSTMetc., for adoption within PTQ and/or QAT. For each one of these layers...
https://pytorch.org/tutorials/prototype/pt2e_quant_ptq.html#prototype-pytorch-2-export-post-training-quantization https://pytorch.org/tutorials/prototype/pt2e_quant_qat.html#prototype-pytorch-2-export-quantization-aware-training-qat 👍 3 Contributor jerryzh168 commented Jul 16, 2024 yeah tutoria...
from torch.ao.quantization.experimental.observer import APoTObserver to: from torch.quantization.experimental.observer import APoTObserver in these file: % grep torch.ao.quantization test/quantization/core/experimental/*.py test/quantization/core/experimental/apot_fx_graph_mode_ptq.py:from torch.ao.qu...