Torch.fx: Practical Program Capture and Transformation for Deep Learning in Python

15 December 2021

Papers citing "Torch.fx: Practical Program Capture and Transformation for Deep Learning in Python"

8 / 8 papers shown

Title
Collapsing Taylor Mode Automatic Differentiation Felix Dangel Tim Siebert Marius Zeinhofer Andrea Walther 17 0 0 19 May 2025
Allo: A Programming Model for Composable Accelerator Design Hongzheng Chen Niansong Zhang Shaojie Xiang Zhichen Zeng Mengjia Dai Zhiru Zhang 56 14 0 07 Apr 2024
HAP: SPMD DNN Training on Heterogeneous GPU Clusters with Automated Program Synthesis Shiwei Zhang Lansong Diao Chuan Wu Zongyan Cao Siyu Wang Wei Lin 43 12 0 11 Jan 2024
Relax: Composable Abstractions for End-to-End Dynamic Machine Learning Ruihang Lai Junru Shao Siyuan Feng Steven Lyubomirsky Bohan Hou ... Sunghyun Park Prakalp Srivastava Jared Roesch T. Mowry Tianqi Chen 47 9 0 01 Nov 2023
Efficient ConvBN Blocks for Transfer Learning and Beyond Kaichao You Guo Qin Anchang Bao Mengsi Cao Ping Huang Jiulong Shan Mingsheng Long 31 1 0 19 May 2023
Colossal-Auto: Unified Automation of Parallelization and Activation Checkpoint for Large-scale Models Yuliang Liu Shenggui Li Jiarui Fang Yan Shao Boyuan Yao Yang You OffRL 27 7 0 06 Feb 2023
Merak: An Efficient Distributed DNN Training Framework with Automated 3D Parallelism for Giant Foundation Models Zhiquan Lai Shengwei Li Xudong Tang Ke-shi Ge Weijie Liu Yabo Duan Linbo Qiao Dongsheng Li 35 41 0 10 Jun 2022
FBGEMM: Enabling High-Performance Low-Precision Deep Learning Inference D. Khudia Jianyu Huang Protonu Basu Summer Deng Haixin Liu Jongsoo Park M. Smelyanskiy FedML MQ 54 46 0 13 Jan 2021