Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.

This is a compile-time flag for PyTorch that enables device-side assertions (DSA) when running CUDA code. Device-side assertions are a debugging tool that allows developers to check for errors that occur on the GPU during runtime.

To compile PyTorch with `TORCH_USE_CUDA_DSA` flag, you need to rebuild PyTorch from source. Here are the steps to do that:

1. Clone the PyTorch repository from GitHub:

git clone --recursive https://github.com/pytorch/pytorch

2. Checkout the branch or tag you want to build:

cd pytorch
git checkout <branch or tag>

3. Install the dependencies needed to build PyTorch. You can find the list of dependencies in the PyTorch documentation.

4. Set the environment variables needed to build PyTorch:

export CMAKE_PREFIX_PATH=/path/to/libtorch
export CUDA_HOME=/usr/local/cuda

`CMAKE_PREFIX_PATH` should point to the location where you installed the C++ API for PyTorch. `CUDA_HOME` should point to the location where CUDA is installed on your system.

5. Build PyTorch with the `TORCH_USE_CUDA_DSA` flag:

python setup.py build --cmake --define TORCH_USE_CUDA_DSA=ON

This will build PyTorch with device-side assertions enabled. Once the build is complete, you can install PyTorch by running:

python setup.py install

Note that enabling device-side assertions can significantly slow down your code, so it is recommended to only use them for debugging purposes.