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...
Post-Training Quantizationclay001 Imagination 软件工程师1 人赞同了该文章 常规精度一般是FP32,低精度有FP16,INT8等格式,混合精度是指在模型中混合使用FP32和FP16的做法。 目前工业界在训练时依然使用FP32,而在推理阶段则会转换为INT8,目前有两种方式,一种是把转换和还原的过程插入在特定算子的前后,一...
PTQ（Post Training Quantization）是模型量化过程，旨在以较低精度的参数减少模型的内存消耗和计算成本，同时保持相似的性能水平。在本文中，我们探讨PTQ中如何将量化信息集成到模型中，并进行保存。PTQ工作流程包括四个关键步骤：计算量化参数、确定阈值、保存输出阈值和包裹模拟层。在实现中，主要依赖于Imperat...
Post-training quantization (PTQ) is a technique in machine learning that reduces a trained model’s memory and computational footprint. In this playbook, you’ll learn how to apply PTQ to two Large Language Models (LLMs), Nemotron4-340B and Llama3-70B, enabling export to TRTLLM and deplo...
You may also find the NeMo Framework Post-Training Quantization (PTQ) playbook useful. It guides you through the whole deployment process using two example models: Llama 3 and Nemotron-340b. As for QAT, the entry point is the megatron_gpt_qat.py script and the corresponding pl...
Recent studies have leveraged post-training quantization (PTQ) to compress diffusion models. However, most of them only focus on unconditional models, leaving the quantization of widely-used pretrained text-to-image models, e.g., Stable Diffusion, largely unexplored. 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 ...
The code for the Post-training Quantization on Diffusion Models, which has been accepted to CVPR 2023. paperKey Obersevation: Studies on the activation distribution w.r.t. time-step. (Upper) Per (output) channel weight ranges of the first depthwise-separable layer in diffusion model on ...
Paper tables with annotated results for LLM-QBench: A Benchmark Towards the Best Practice for Post-training Quantization of Large Language Models