efficient inferenceCUDATensorRTIn the recent years, there has been a significant growth of interest in real-world systems based on deep neural networks (DNNs). These systems typically incorporate multiple DNNs running simultaneously. In this paper we propose a novel approach of multi-DNN execution ...
简介:量化理解(Google量化白皮书《Quantizing deep convolutional networks for efficient inference: A whitepaper》) 简介:量化理解(Google量化白皮书《Quantizing deep convolutional networks for efficient inference: A whitepaper》) 可以说这篇博客是对Google量化白皮书的完整解读,篇幅较长,可以收藏慢慢阅读。笔者在翻译...
TensorRT工具箱使用KL散度来最小化量化前模型的数据编码分布和量化后模型的数据编码分布的差异,通过最小化两种数据分布的差异,即找到使得KL散度最小的点其实就能找到一个比较好裁剪范围 以resnet-152为例,通过KL散度可以估计出裁剪范围如下图竖线表示 8-bit-Inference-with-TensorRT 现在常用的做法是使用MSE去找到裁剪...
vLLM is a fast and easy-to-use library for LLM inference and serving. Originally developed in theSky Computing Labat UC Berkeley, vLLM has evolved into a community-driven project with contributions from both academia and industry. vLLM is fast with: ...
NVIDIA® TensorRT™ is an SDK for high-performance deep learning inference on NVIDIA GPUs. This repository contains the open source components of TensorRT. - TensorRT/plugin/efficientNMSPlugin/README.md at release/10.1 · NVIDIA/TensorRT
该分析不包含一些流行的相关项目,包括1)用于其他硬件的专用解决方案(例如,PopTransformer[17]、CTranslate2[8]、lammap.cpp和ggml[14])和2)建立在其他系统之上的部署解决方案,如OpenLLM[26](vLLM)、xinference[30](ggml+vLLM+xFormers)、LMDeploy[20](FasterTransformer)、gpt-fast[15](PyTorch),DeepSpeed ...
memory efficient attentions to optimize the stable diffusion pipeline released by Hugging Face. This snippet of code is not yet compatible with TensorRT, but we are currently working on making this possible. These modifications allowed us to double the speed on the Nvidia A10G inference GPU...
The native visual attention model as the backbone network is not well suited for common dense prediction tasks such as object detection and semantic segmentation. In addition, compared with convolutional neural networks, ViT usually requires more computation and slower inference speed, which is not con...
8-bit Inference with TensorRT [Szymon Migacz, 2017] Optimal Clipping and Magnitude-aware Differentiation for Improved Quantization-aware Training [Sakr et al., ICML 2022] XNOR-Net: ImageNet Classification using Binary Convolutional Neural Networks [Rastegari et al., ECCV 2016] ...
Fast model execution with CUDA/HIP graph Quantization: GPTQ, AWQ, SqueezeLLM, FP8 KV Cache Optimized CUDA kernels Performance benchmark: We include a performance benchmark that compares the performance of vllm against other LLM serving engines (TensorRT-LLM, text-generation-inference and lmdeploy...