dmlc--dgl
e0e8736fa0
* * Added half_(), float_(), and double_() functions to DGLHeteroGraph, HeteroNodeDataView, and HeteroEdgeDataView, for converting floating-point tensor data to float16, float32, or float64 precision * * Extracted out private functions for floating-point type conversion, to reduce code duplication * * Added test for floating-point data conversion functions, half_(), float_(), and double_() * * Moved half_(), float_(), and double_() functions from HeteroNodeDataView and HeteroEdgeDataView to Frame class * * Updated test_float_cast() to use dgl.heterograph instead of dgl.graph * Added to CONTRIBUTORS.md * * Changed data type conversion to be deferred until the data is accessed, to avoid redundant conversions of data that isn't used. * * Addressed issues flagged by linter * * Worked around a bug in the old version of mxnet that's currently used for DGL testing * * Only defer Column data type conversion if there is a pending device transfer or index sampling to be done. This is expected to be the desired behaviour based on discussions of a few use cases, as described in the comments. * * Moved floating-point feature data conversion functions to dgl.transforms.functional * Changed them from in-place behaviour to shallow copy (out-of-place) behaviour * * Fixed linter issues * * Removed lines that unintentionally added to_half, to_float, and to_double to DGLHeteroGraph * Moved _init_api line to the end of the file again * * Removed one of the two leading underscores from Frame.__astype_float, making it not fully private Co-authored-by: nv-dlasalle <63612878+nv-dlasalle@users.noreply.github.com>
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Contributing to DGL
Contribution is always welcomed. A good starting place is the roadmap issue, where you can find our current milestones. All contributions must go through pull requests and be reviewed by the committers. See our contribution guide for more details.
Once your contribution is accepted and merged, congratulations, you are now a contributor to the DGL project. We will put your name in the list below.
Contributors
- Minjie Wang from AWS
- Da Zheng from AWS
- Quan Gan from AWS
- Mufei Li from AWS
- Jinjing Zhou from AWS
- Xiang Song from AWS
- Tianjun Xiao from AWS
- Tong He from AWS
- Jian Zhang from AWS
- Qipeng Guo from AWS
- Xiangkun Hu from AWS
- Ying Rui from AWS
- Israt Nisa from AWS
- Zheng Zhang from AWS
- Zihao Ye from University of Washington
- Chao Ma
- Qidong
- Lingfan Yu from New York University
- Yu Gai from University of California, Berkeyley
- Qi Huang from New York University
- Dominique LaSalle from Nvidia
- Pawel Piotrowcz from Intel
- Michal Szarmach from Intel
- Izabela Mazur from Intel
- Sanchit Misra from Intel
- Sheng Zha from AWS
- Yifei Ma from AWS
- Yizhi Liu from AWS
- Kay Liu from UIC
- Tianqi Zhang from SJTU
- Hengrui Zhang
- Seung Won Min from UIUC
- @hbsun2113: GraphSAGE in PyTorch
- Tianyi Zhang: SGC in PyTorch
- Jun Chen: GIN in PyTorch
- Aymen Waheb: APPNP in PyTorch
- Chengqiang Lu: MGCN, SchNet and MPNN in PyTorch
- Gongze Cao: Cluster GCN
- Yicheng Wu: RotatE in PyTorch
- Hao Xiong: DeepWalk in PyTorch
- Zhi Lin: Integrate FeatGraph into DGL
- Andrew Tsesis: Framework-Agnostic Graph Ops
- Brett Koonce
- @giuseppefutia
- @mori97
- @xnuohz
- Hao Jin from Amazon
- Xin Yao from Nvidia
- Abdurrahman Yasar from Nvidia
- Shaked Brody from Technion
- Jiahui Liu from Nvidia
- Neil Dickson from Nvidia