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CUTLASS 3.0.0 (#786)

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* CUTLASS 3.0.0
This commit is contained in:
Vijay Thakkar
2023-01-23 20:55:28 -05:00
committed by GitHub
parent 66d9cddc83
commit 277bd6e537
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tools/library/scripts/pycutlass/README.md
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## Installation
### Using Docker
You can run the PyCUTLASS on NGC PyTorch container.
We recommend using one of our provided Docker images for using PyCUTLASS.
**To run CUTLASS 3 GEMM kernels targetting the NVIDIA Hopper architecture via PyCUTLASS,** you can use an included [Dockerfile](docker/Dockerfile-cuda12.0) based on the NGC CUDA 12.0 container:
```shell
docker run --gpus all -it --rm nvcr.io/nvidia/pytorch:22.09-py3
docker build -t pycutlass-cuda12.0:latest -f docker/Dockerfile-cuda12.0 .
docker run --gpus all -it --rm pycutlass-cuda12.0:latest
```
Note that this Docker container does not include CuPy or PyTorch, and, thus, will not be able to run PyCUTLASS examples that
leverage these packages.
**To run CUTLASS 2.x kernels targetting pre-SM90 architectures via PyCUTLASS,** you can use an included [Dockerfile](docker/Dockerfile-cuda11.8-pytorch) based on an NGC PyTorch container:
```shell
docker build -t pycutlass-cuda11.8-pytorch:latest -f docker/Dockerfile-cuda11.8-pytorch .
docker run --gpus all -it --rm pycutlass-cuda11.8-pytorch:latest
```
### Environment variables
PyCUTLASSS requires two environment variables:
PyCUTLASS requires two environment variables:
* `CUTLASS_PATH`: the root directory of CUTLASS. You can set this from the location at which you cloned CUTLASS via: `export CUTLASS_PATH=$(pwd)`.
* `CUDA_INSTALL_PATH`: the directory where cuda toolkit is installed. If running in bash with `nvcc` installed under a CUDA toolkit, you can set this to the location of your `nvcc` installation via: `export CUDA_INSTALL_PATH=$(which nvcc | awk -F'/bin/nvcc' '{print $1}')`