High-performance GPU kernels for Ads and Recsys model training, independently implemented and optimized for real-world workloads and model-specific input characteristics.
Python
57
22 commits
updated Oct 15, 2026
High-performance GPU kernels for Meta Ads Recommendation Systems, developed by Meta Ads AI. This library provides optimized GPU kernel implementations that have been published.
| Project | Description | Architecture | Path | Blog |
|---|---|---|---|---|
| GDPA | Generalized Dot Product Attention kernels | Blackwell (SM100) | gdpa/ | PyTorch Blog |
| TLX Block Attention | Triton TLX block attention kernels | Blackwell (SM100) | block_attention/ | PyTorch Blog |
| TLX Multi-CTA Norm Fusion | Triton TLX fused matmul with RMSNorm and LayerNorm kernels | Blackwell (SM100) | multi_cta_norm_fusion/ | PyTorch Blog |
| TLX GDPA Megakernel | Triton TLX generalized dot product attention megakernel | Blackwell (SM100) | gdpa_megakernel/ | PyTorch Blog |
| TLX Jagged Flash Attention | Triton TLX jagged flash attention kernel for variable-length sequences | Blackwell (SM100) | tlx_jfa/ | Coming soon |
| LP-FA4 | Low-precision FlashAttention-4 kernels with BF16 and MXFP8 APIs | Blackwell (SM10x) | lp_fa4/ | Coming soon |
fbtriton==3.6.1 for TLX packagesFor CuteDSL GDPA:
pip install nvidia-cutlass-dsl>=4.1.0 torch einops
For TLX packages, see the project-specific environment.yml files or install the matching Triton build:
pip install fbtriton==3.6.1
See individual project READMEs for detailed usage:
Meta Ads AI: Jiaqi Xu, Hongtao Yu, Dev Shanker, Junqing (Jacky) Zhou, Han Xu, Jake Siso, Xiaoyi Liu, Huayu Li, Markus Hoehnerbach, Manman Ren, Chao Chen, Hao Yan, Weinan Song
More contributors will be added as we publish more kernels.
Apache License 2.0. See LICENSE for details.
585 followers · starred May 2026
133 followers · starred May 2026
High-performance GPU kernels for Ads and Recsys model training, independently implemented and optimized for real-world workloads and model-specific input characteristics.
Python
57
22 commits
updated Oct 15, 2026
High-performance GPU kernels for Meta Ads Recommendation Systems, developed by Meta Ads AI. This library provides optimized GPU kernel implementations that have been published.
| Project | Description | Architecture | Path | Blog |
|---|---|---|---|---|
| GDPA | Generalized Dot Product Attention kernels | Blackwell (SM100) | gdpa/ | PyTorch Blog |
| TLX Block Attention | Triton TLX block attention kernels | Blackwell (SM100) | block_attention/ | PyTorch Blog |
| TLX Multi-CTA Norm Fusion | Triton TLX fused matmul with RMSNorm and LayerNorm kernels | Blackwell (SM100) | multi_cta_norm_fusion/ | PyTorch Blog |
| TLX GDPA Megakernel | Triton TLX generalized dot product attention megakernel | Blackwell (SM100) | gdpa_megakernel/ | PyTorch Blog |
| TLX Jagged Flash Attention | Triton TLX jagged flash attention kernel for variable-length sequences | Blackwell (SM100) | tlx_jfa/ | Coming soon |
| LP-FA4 | Low-precision FlashAttention-4 kernels with BF16 and MXFP8 APIs | Blackwell (SM10x) | lp_fa4/ | Coming soon |
fbtriton==3.6.1 for TLX packagesFor CuteDSL GDPA:
pip install nvidia-cutlass-dsl>=4.1.0 torch einops
For TLX packages, see the project-specific environment.yml files or install the matching Triton build:
pip install fbtriton==3.6.1
See individual project READMEs for detailed usage:
Meta Ads AI: Jiaqi Xu, Hongtao Yu, Dev Shanker, Junqing (Jacky) Zhou, Han Xu, Jake Siso, Xiaoyi Liu, Huayu Li, Markus Hoehnerbach, Manman Ren, Chao Chen, Hao Yan, Weinan Song
More contributors will be added as we publish more kernels.
Apache License 2.0. See LICENSE for details.
585 followers · starred May 2026
133 followers · starred May 2026