Post-training quantization(PTQ) 工作流理解 目前神经网络在许多前沿领域的应用取得了较大进展,但经常会带来很高的计算成本,对内存带宽和算力要求高。另外降低神经网络的功率和时延在现代网络集成到边缘设备时也极其关键,在这些场景中模型推理具有严格的功率和计算要求。神经网络量化是解决上述问题有效方法之一,但是模型量化...
自动化所的一篇文章,文章地址,代码地址 文章主要有两个创新点 将量化后的weigths进行split, 然后优化每一位bit, 使得目标函数最小, 得到每一位的bit后再将所有位的bit进行stitching 在保证计算效率的前提下, 可以对Activation使用per-channel的量化, 论文中叫Error Compensated Activation Quantization(ECAQ) 下面针对...
The well-established uniform scheme for post-training quantization achieves satisfactory results by converting neural networks from full-precision to 8-bit fixed-point integers. However, it suffers from significant performance degradation when quantizing to lower bit-widths. In this paper, we propose a...
Post-training quantization (PTQ) converts a pre-trained full-precision (FP) model into a quantized model in a training-free manner. Determining suitable quantization parameters, such as scaling factors and zero points, is the primary strategy for mitigating the impact of quantization noise (calibrat...
Therefore, they suffer from slow training, large memory overhead, and data security issues. In this paper, we study post-training quantization~(PTQ) of PLMs, and propose module-wise quantization error minimization~(MREM), an efficient solution to mitigate these issues. By partitioning the PLM ...
In this paper, we introduce Vector Post-Training Quantization (VPTQ) for extremely low-bit quantization of LLMs. We use Second-Order Optimization to formulate the LLM VQ problem and guide our quantization algorithm design by solving the optimization. We further refine th...
指南:https://www.tensorflow.org/performance/model_optimizationpost-trainingquantization的工作原理 在底层,我们...机器学习模型,这可以实现最多4倍的压缩和3倍的执行速度提升。 通过量化模型,开发人员还将获得降低功耗的额外好处。这对于将模型部署到手机之外的终端设备是非常有用的。 启用post-training ...
Paper tables with annotated results for LLM-QBench: A Benchmark Towards the Best Practice for Post-training Quantization of Large Language Models
ECCV2022 Paper - Fine-grained Data Distribution Alignment for Post-Training Quantizationpaper Requirements Python >= 3.7.10 Pytorch >= 1.7.0 Torchvision >= 0.4.0 Reproduce the Experiment Results The pre-trained model will be downloaded automatically. If the download process fails, please use the ...
DEFAULT] # for post-training quantization converter.representative_dataset = calibration_gen # for full-integer quantization converter._experimental_new_quantizer = True # already here! with quantize.quantize_scope(): # is this right place for opening quantize_scope? tflite_model = converter.convert...