| Name | tglite JSON |
| Version |
0.0.4
JSON |
| download |
| home_page | |
| Summary | Temporal GNN Lightweight Framework |
| upload_time | 2024-03-19 10:10:39 |
| maintainer | |
| docs_url | None |
| author | |
| requires_python | <3.11,>=3.7 |
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| keywords |
machine learning
tgnn
|
| VCS |
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# TGLite - A Framework for Temporal GNNs
TGLite is a lightweight framework that provides core abstractions and building blocks for practitioners and researchers to implement efficient TGNN models. TGNNs, or Temporal Graph Neural Networks, learn node embeddings for graphs that dynamically change over time by jointly aggregating structural and temporal information from neighboring nodes. TGLite employs an abstraction called a _TBlock_ to represent the temporal graph dependencies when aggregating from neighbors, with explicit support for capturing temporal details like edge timestamps, as well as composable operators and optimizations. Compared to prior art, TGLite can outperform the [TGL][tgl] framework by [up to 3x](#publication) in terms of training time.
<div align="center">
<img src="https://raw.githubusercontent.com/ADAPT-uiuc/tglite/main/docs/source/img/train.png">
End-to-end training epoch time comparison on an Nvidia A100 GPU.
</div>
[tgl]: https://github.com/amazon-science/tgl
## Installation
See our [documentation][docs] for instructions on how to install the TGLite binaries, as well as examples and references for supported functionality. To install from source or for local development, go to the [Building from source](build_src) session, it also explains how to run [examples](exp).
[docs]: tglite.rtfd.io
[build_src]: docs/install/from_source.md
[exp]: https://github.com/ADAPT-uiuc/tglite/tree/main/examples
## Getting Started
TGLite is currently designed to be used with PyTorch as a training backend, typically with GPU devices. A TGNN model can be defined and trained in the usual way using PyTorch, with the computations constructed using a mix of PyTorch functions and operators/optimizations from TGLite. Below is a simple example (not a real network architecture, just for demonstration purposes):
```python
import torch
import tglite as tg
class TGNN(torch.nn.Module):
def __init__(self, ctx: tg.TContext, dim_node=100, dim_time=100):
super().__init__()
self.ctx = ctx
self.linear = torch.nn.Linear(dim_node + dim_time, dim_node)
self.sampler = tg.TSampler(num_nbrs=10, strategy='recent')
self.encoder = tg.nn.TimeEncode(dim_time)
def forward(self, batch: tg.TBatch):
blk = batch.block(self.ctx)
blk = tg.op.dedup(blk)
blk = self.sampler.sample(blk)
blk.srcdata['h'] = blk.srcfeat()
return tg.op.aggregate(blk, self.compute, key='h')
def compute(self, blk: tg.TBlock):
feats = self.encoder(blk.time_deltas())
feats = torch.cat([blk.srcdata['h'], feats], dim=1)
embeds = self.linear(feats)
embeds = tg.op.edge_reduce(blk, embeds, op='sum')
return torch.relu(embeds)
graph = tg.from_csv(...)
ctx = tg.TContext(graph)
model = TGNN(ctx)
train(model)
```
The example model is defined to first construct the graph dependencies for nodes in the current batch of edges. The `dedup()` optimization is applied before sampling for 10 recent neighbors. Node embeddings are computed by simply combining node and time features, applying a linear layer and summing across neighbors. More complex computations and aggregations, such as temporal self-attention often used with TGNNs, can be defined using the provided building blocks.
## Publication
* Yufeng Wang and Charith Mendis. 2024. [TGLite: A Lightweight Programming Framework for Continuous-Time Temporal Graph Neural Networks][tglite-paper]. In 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 2 (ASPLOS '24), April 2024, La Jolla, CA, USA.
* Yufeng Wang and Charith Mendis. 2023. [TGOpt: Redundancy-Aware Optimizations for Temporal Graph Attention Networks][tgopt-paper]. In Proceedings of the 28th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming (PPoPP '23), February 2023, Montreal, QC, Canada.
If you find TGLite useful, please consider attributing to the following citation:
```bibtex
@inproceedings{wang2024tglite,
author = {Wang, Yufeng and Mendis, Charith},
title = {TGLite: A Lightweight Programming Framework for Continuous-Time Temporal Graph Neural Networks},
year = {2024},
booktitle = {Proceedings of the 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 2},
doi = {10.1145/3620665.3640414}
}
```
[tglite-paper]: https://doi.org/10.1145/3620665.3640414
[tgopt-paper]: https://doi.org/10.1145/3572848.3577490
Raw data
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"description": "# TGLite - A Framework for Temporal GNNs\n\nTGLite is a lightweight framework that provides core abstractions and building blocks for practitioners and researchers to implement efficient TGNN models. TGNNs, or Temporal Graph Neural Networks, learn node embeddings for graphs that dynamically change over time by jointly aggregating structural and temporal information from neighboring nodes. TGLite employs an abstraction called a _TBlock_ to represent the temporal graph dependencies when aggregating from neighbors, with explicit support for capturing temporal details like edge timestamps, as well as composable operators and optimizations. Compared to prior art, TGLite can outperform the [TGL][tgl] framework by [up to 3x](#publication) in terms of training time.\n\n<div align=\"center\">\n <img src=\"https://raw.githubusercontent.com/ADAPT-uiuc/tglite/main/docs/source/img/train.png\">\n End-to-end training epoch time comparison on an Nvidia A100 GPU.\n</div>\n\n[tgl]: https://github.com/amazon-science/tgl\n\n## Installation\n\nSee our [documentation][docs] for instructions on how to install the TGLite binaries, as well as examples and references for supported functionality. To install from source or for local development, go to the [Building from source](build_src) session, it also explains how to run [examples](exp).\n\n[docs]: tglite.rtfd.io\n[build_src]: docs/install/from_source.md\n[exp]: https://github.com/ADAPT-uiuc/tglite/tree/main/examples\n\n## Getting Started\n\nTGLite is currently designed to be used with PyTorch as a training backend, typically with GPU devices. A TGNN model can be defined and trained in the usual way using PyTorch, with the computations constructed using a mix of PyTorch functions and operators/optimizations from TGLite. Below is a simple example (not a real network architecture, just for demonstration purposes):\n\n```python\nimport torch\nimport tglite as tg\n\nclass TGNN(torch.nn.Module):\n def __init__(self, ctx: tg.TContext, dim_node=100, dim_time=100):\n super().__init__()\n self.ctx = ctx\n self.linear = torch.nn.Linear(dim_node + dim_time, dim_node)\n self.sampler = tg.TSampler(num_nbrs=10, strategy='recent')\n self.encoder = tg.nn.TimeEncode(dim_time)\n\n def forward(self, batch: tg.TBatch):\n blk = batch.block(self.ctx)\n blk = tg.op.dedup(blk)\n blk = self.sampler.sample(blk)\n blk.srcdata['h'] = blk.srcfeat()\n return tg.op.aggregate(blk, self.compute, key='h')\n\n def compute(self, blk: tg.TBlock):\n feats = self.encoder(blk.time_deltas())\n feats = torch.cat([blk.srcdata['h'], feats], dim=1)\n embeds = self.linear(feats)\n embeds = tg.op.edge_reduce(blk, embeds, op='sum')\n return torch.relu(embeds)\n\ngraph = tg.from_csv(...)\nctx = tg.TContext(graph)\nmodel = TGNN(ctx)\ntrain(model)\n```\n\nThe example model is defined to first construct the graph dependencies for nodes in the current batch of edges. The `dedup()` optimization is applied before sampling for 10 recent neighbors. Node embeddings are computed by simply combining node and time features, applying a linear layer and summing across neighbors. More complex computations and aggregations, such as temporal self-attention often used with TGNNs, can be defined using the provided building blocks.\n\n## Publication\n\n* Yufeng Wang and Charith Mendis. 2024. [TGLite: A Lightweight Programming Framework for Continuous-Time Temporal Graph Neural Networks][tglite-paper]. In 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 2 (ASPLOS '24), April 2024, La Jolla, CA, USA.\n\n* Yufeng Wang and Charith Mendis. 2023. [TGOpt: Redundancy-Aware Optimizations for Temporal Graph Attention Networks][tgopt-paper]. In Proceedings of the 28th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming (PPoPP '23), February 2023, Montreal, QC, Canada.\n\nIf you find TGLite useful, please consider attributing to the following citation:\n\n```bibtex\n@inproceedings{wang2024tglite,\n author = {Wang, Yufeng and Mendis, Charith},\n title = {TGLite: A Lightweight Programming Framework for Continuous-Time Temporal Graph Neural Networks},\n year = {2024},\n booktitle = {Proceedings of the 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 2},\n doi = {10.1145/3620665.3640414}\n}\n```\n\n[tglite-paper]: https://doi.org/10.1145/3620665.3640414\n[tgopt-paper]: https://doi.org/10.1145/3572848.3577490\n",
"bugtrack_url": null,
"license": "Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. Definitions. \"License\" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document. \"Licensor\" shall mean the copyright owner or entity authorized by the copyright owner that is granting the License. \"Legal Entity\" shall mean the union of the acting entity and all other entities that control, are controlled by, or are under common control with that entity. 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For the purposes of this License, Derivative Works shall not include works that remain separable from, or merely link (or bind by name) to the interfaces of, the Work and Derivative Works thereof. \"Contribution\" shall mean any work of authorship, including the original version of the Work and any modifications or additions to that Work or Derivative Works thereof, that is intentionally submitted to Licensor for inclusion in the Work by the copyright owner or by an individual or Legal Entity authorized to submit on behalf of the copyright owner. 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Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable copyright license to reproduce, prepare Derivative Works of, publicly display, publicly perform, sublicense, and distribute the Work and such Derivative Works in Source or Object form. 3. Grant of Patent License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable (except as stated in this section) patent license to make, have made, use, offer to sell, sell, import, and otherwise transfer the Work, where such license applies only to those patent claims licensable by such Contributor that are necessarily infringed by their Contribution(s) alone or by combination of their Contribution(s) with the Work to which such Contribution(s) was submitted. 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You may reproduce and distribute copies of the Work or Derivative Works thereof in any medium, with or without modifications, and in Source or Object form, provided that You meet the following conditions: (a) You must give any other recipients of the Work or Derivative Works a copy of this License; and (b) You must cause any modified files to carry prominent notices stating that You changed the files; and (c) You must retain, in the Source form of any Derivative Works that You distribute, all copyright, patent, trademark, and attribution notices from the Source form of the Work, excluding those notices that do not pertain to any part of the Derivative Works; and (d) If the Work includes a \"NOTICE\" text file as part of its distribution, then any Derivative Works that You distribute must include a readable copy of the attribution notices contained within such NOTICE file, excluding those notices that do not pertain to any part of the Derivative Works, in at least one of the following places: within a NOTICE text file distributed as part of the Derivative Works; within the Source form or documentation, if provided along with the Derivative Works; or, within a display generated by the Derivative Works, if and wherever such third-party notices normally appear. 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