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...
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,一种是全网络都直接使用INT8的输入输出。 训练后量化(PTQ)就是在高帧率,高实时性的...
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...
'\ptq.run.model_train_name=nemotron_340b\ptq.run.time_limit=45\ptq.run.results_dir=/results\ptq.quantization.algorithm=fp8\ptq.export.decoder_type=gptnext\ptq.export.inference_tensor_parallel=${INFER_TP}\ptq.export.inference_pipeline_parallel=1\ptq.trainer.precision=bf16\ptq.model.restore_...
PTQ is a natural extension of the NeMo LLM building and customizing capabilities for seamless and efficient deployment paths using NVIDIA TensorRT Model Optimizer and NVIDIA TensorRT-LLM. As an example, NVIDIA NIM benefits from the PTQ workflow in NeMo. From a technical perspective, qu...
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 ...
1、quantization: quantization-aware-training(QAT), High-Bit(>2b)(DoReFa/Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference)、Low-Bit(≤2b)/Ternary and Binary(TWN/BNN/XNOR-Net); post-training-quantization(PTQ), 8-bit(tensorrt); 2、 pruning: normal、reg...
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...
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 into multiple modules, we minimize the reconstruction error incurred by quantization for each ...
PTQ4DM: Post-training Quantization on Diffusion ModelsYuzhang Shang*, Zhihang Yuan*, Bin Xie, Bingzhe Wu, and Yan Yan (* denote equal contribution)The code for the Post-training Quantization on Diffusion Models, which has been accepted to CVPR 2023. paperKey...