| Name | transformer-lm-gan JSON |
| Version |
0.0.3
JSON |
| download |
| home_page | None |
| Summary | Explorations into Transformer Language Model with Adversarial Loss |
| upload_time | 2025-02-22 17:32:27 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.9 |
| license | MIT License
Copyright (c) 2025 Phil Wang
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE. |
| keywords |
adversarial training
artificial intelligence
autoregressive transformer
deep learning
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
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|
## Language model with adversarial loss
Explorations into adversarial losses on top of autoregressive loss for language modeling
I have tried this in the past, when GANs were still dominant. But at the time I was either too inexperienced or the research not there. Either way could not get it working. Will give it another shot in the next few weeks, mainly to see if an adversarial system could benefit [world modeling](https://github.com/lucidrains/improving-transformers-world-model-for-rl)
## Usage
```python
import torch
from transformer_lm_gan import (
LanguageModelGenerator,
Discriminator,
GAN,
)
gan = GAN(
strategy = 'gumbel_one_hot', # or 'rotate' for rotation trick, may try combination of two if both fails in experiments
generator = dict(
num_tokens = 256,
dim = 512,
depth = 6,
dim_head = 64,
heads = 8,
max_seq_len = 1024
),
discriminator = dict(
num_tokens = 256,
dim = 512,
depth = 2,
dim_head = 64,
heads = 9,
max_seq_len = 1024
)
).cuda()
seq = torch.randint(0, 256, (2, 1024)).cuda()
discr_loss = gan.discriminate_forward(seq)
discr_loss.backward()
gen_loss = gan.generate_forward(seq)
gen_loss.backward()
```
## Citations
```bibtex
@inproceedings{Huang2025TheGI,
title = {The GAN is dead; long live the GAN! A Modern GAN Baseline},
author = {Yiwen Huang and Aaron Gokaslan and Volodymyr Kuleshov and James Tompkin},
year = {2025},
url = {https://api.semanticscholar.org/CorpusID:275405495}
}
```
```bibtex
@article{Fifty2024Restructuring,
title = {Restructuring Vector Quantization with the Rotation Trick},
author = {Christopher Fifty, Ronald G. Junkins, Dennis Duan, Aniketh Iyengar, Jerry W. Liu, Ehsan Amid, Sebastian Thrun, Christopher Ré},
journal = {ArXiv},
year = {2024},
volume = {abs/2410.06424},
url = {https://api.semanticscholar.org/CorpusID:273229218}
}
```
Raw data
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"description": "\n## Language model with adversarial loss\n\nExplorations into adversarial losses on top of autoregressive loss for language modeling\n\nI have tried this in the past, when GANs were still dominant. But at the time I was either too inexperienced or the research not there. Either way could not get it working. Will give it another shot in the next few weeks, mainly to see if an adversarial system could benefit [world modeling](https://github.com/lucidrains/improving-transformers-world-model-for-rl)\n\n## Usage\n\n```python\nimport torch\n\nfrom transformer_lm_gan import (\n LanguageModelGenerator,\n Discriminator,\n GAN,\n)\n\ngan = GAN(\n strategy = 'gumbel_one_hot', # or 'rotate' for rotation trick, may try combination of two if both fails in experiments\n generator = dict(\n num_tokens = 256,\n dim = 512,\n depth = 6,\n dim_head = 64,\n heads = 8,\n max_seq_len = 1024\n ),\n discriminator = dict(\n num_tokens = 256,\n dim = 512,\n depth = 2,\n dim_head = 64,\n heads = 9,\n max_seq_len = 1024\n )\n).cuda()\n\nseq = torch.randint(0, 256, (2, 1024)).cuda()\n\ndiscr_loss = gan.discriminate_forward(seq)\ndiscr_loss.backward()\n\ngen_loss = gan.generate_forward(seq)\ngen_loss.backward()\n```\n\n## Citations\n\n```bibtex\n@inproceedings{Huang2025TheGI,\n title = {The GAN is dead; long live the GAN! A Modern GAN Baseline},\n author = {Yiwen Huang and Aaron Gokaslan and Volodymyr Kuleshov and James Tompkin},\n year = {2025},\n url = {https://api.semanticscholar.org/CorpusID:275405495}\n}\n```\n\n```bibtex\n@article{Fifty2024Restructuring,\n title = {Restructuring Vector Quantization with the Rotation Trick},\n author = {Christopher Fifty, Ronald G. Junkins, Dennis Duan, Aniketh Iyengar, Jerry W. Liu, Ehsan Amid, Sebastian Thrun, Christopher R\u00e9},\n journal = {ArXiv},\n year = {2024},\n volume = {abs/2410.06424},\n url = {https://api.semanticscholar.org/CorpusID:273229218}\n}\n```\n",
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