Datasets:

Yimingbear
/

protenix

Custom CUDA layernorm kernels modified from FastFold and Oneflow accelerate about 30%-50% during different training stages. To use this feature, run the following command:
```
export LAYERNORM_TYPE=fast_layernorm
```
If the environment variable LAYERNORM_TYPE is set to fast_layernorm, the model will employ the layernorm we have developed; otherwise, the naive PyTorch layernorm will be adopted. The kernels will be compiled when fast_layernorm is called for the first time.
DeepSpeed DS4Sci_EvoformerAttention kernel is a memory-efficient attention kernel developed as part of a collaboration between OpenFold and the DeepSpeed4Science initiative. To use this feature, run the following command:
```
export USE_DEEPSPEED_EVO_ATTENTION=true
```
DS4Sci_EvoformerAttention is implemented based on CUTLASS. If you use this feature, You need to clone the CUTLASS repository and specify the path to it in the environment variable CUTLASS_PATH. The Dockerfile has already include this setting:
```
RUN git clone -b v3.5.1 https://github.com/NVIDIA/cutlass.git  /opt/cutlass
ENV CUTLASS_PATH=/opt/cutlass
```
If you set up Protenix by pip, you can set environment variable CUTLASS_PATH as follows:
```
git clone -b v3.5.1 https://github.com/NVIDIA/cutlass.git  /path/to/cutlass
export CUTLASS_PATH=/path/to/cutlass
```
The kernels will be compiled when DS4Sci_EvoformerAttention is called for the first time.