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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 tofast_layernorm
, the model will employ the layernorm we have developed; otherwise, the naive PyTorch layernorm will be adopted. The kernels will be compiled whenfast_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_ATTTENTION=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
bypip
, you can set environment variableCUTLASS_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.