#!/bin/bash

# adapted from https://github.com/pytorch/pytorch/blob/main/.ci/docker/common/install_cuda.sh

set -ex

NCCL_VERSION=v2.25.1-1
CUDNN_VERSION=9.5.1.17

# Include Hopper support if using CUDA11.8 or above
NVCC_GENCODE="-gencode=arch=compute_80,code=sm_80 -gencode=arch=compute_90,code=sm_90 -gencode=arch=compute_90,code=compute_90"

function install_cusparselt_040 {
    # cuSparseLt license: https://docs.nvidia.com/cuda/cusparselt/license.html
    mkdir tmp_cusparselt && pushd tmp_cusparselt
    wget -q https://developer.download.nvidia.com/compute/cusparselt/redist/libcusparse_lt/linux-x86_64/libcusparse_lt-linux-x86_64-0.4.0.7-archive.tar.xz
    tar xf libcusparse_lt-linux-x86_64-0.4.0.7-archive.tar.xz
    cp -a libcusparse_lt-linux-x86_64-0.4.0.7-archive/include/* /usr/local/cuda/include/
    cp -a libcusparse_lt-linux-x86_64-0.4.0.7-archive/lib/* /usr/local/cuda/lib64/
    popd
    rm -rf tmp_cusparselt
}

function install_cusparselt_062 {
    # cuSparseLt license: https://docs.nvidia.com/cuda/cusparselt/license.html
    mkdir tmp_cusparselt && pushd tmp_cusparselt
    wget -q https://developer.download.nvidia.com/compute/cusparselt/redist/libcusparse_lt/linux-x86_64/libcusparse_lt-linux-x86_64-0.6.2.3-archive.tar.xz
    tar xf libcusparse_lt-linux-x86_64-0.6.2.3-archive.tar.xz
    cp -a libcusparse_lt-linux-x86_64-0.6.2.3-archive/include/* /usr/local/cuda/include/
    cp -a libcusparse_lt-linux-x86_64-0.6.2.3-archive/lib/* /usr/local/cuda/lib64/
    popd
    rm -rf tmp_cusparselt
}

function install_cusparselt_063 {
    # cuSparseLt license: https://docs.nvidia.com/cuda/cusparselt/license.html
    mkdir tmp_cusparselt && pushd tmp_cusparselt
    wget -q https://developer.download.nvidia.com/compute/cusparselt/redist/libcusparse_lt/linux-x86_64/libcusparse_lt-linux-x86_64-0.6.3.2-archive.tar.xz
    tar xf libcusparse_lt-linux-x86_64-0.6.3.2-archive.tar.xz
    cp -a libcusparse_lt-linux-x86_64-0.6.3.2-archive/include/* /usr/local/cuda/include/
    cp -a libcusparse_lt-linux-x86_64-0.6.3.2-archive/lib/* /usr/local/cuda/lib64/
    popd
    rm -rf tmp_cusparselt
}

function install_118 {
    CUDNN_VERSION=9.1.0.70
    NCCL_VERSION=v2.21.5-1
    echo "Installing CUDA 11.8 and cuDNN ${CUDNN_VERSION} and NCCL ${NCCL_VERSION} and cuSparseLt-0.4.0"
    rm -rf /usr/local/cuda-11.8 /usr/local/cuda
    # install CUDA 11.8.0 in the same container
    wget -q https://developer.download.nvidia.com/compute/cuda/11.8.0/local_installers/cuda_11.8.0_520.61.05_linux.run
    chmod +x cuda_11.8.0_520.61.05_linux.run
    ./cuda_11.8.0_520.61.05_linux.run --toolkit --silent
    rm -f cuda_11.8.0_520.61.05_linux.run
    rm -f /usr/local/cuda && ln -s /usr/local/cuda-11.8 /usr/local/cuda

    # cuDNN license: https://developer.nvidia.com/cudnn/license_agreement
    mkdir tmp_cudnn && cd tmp_cudnn
    wget -q https://developer.download.nvidia.com/compute/cudnn/redist/cudnn/linux-x86_64/cudnn-linux-x86_64-${CUDNN_VERSION}_cuda11-archive.tar.xz -O cudnn-linux-x86_64-${CUDNN_VERSION}_cuda11-archive.tar.xz
    tar xf cudnn-linux-x86_64-${CUDNN_VERSION}_cuda11-archive.tar.xz
    cp -a cudnn-linux-x86_64-${CUDNN_VERSION}_cuda11-archive/include/* /usr/local/cuda/include/
    cp -a cudnn-linux-x86_64-${CUDNN_VERSION}_cuda11-archive/lib/* /usr/local/cuda/lib64/
    cd ..
    rm -rf tmp_cudnn

    # NCCL license: https://docs.nvidia.com/deeplearning/nccl/#licenses
    # Follow build: https://github.com/NVIDIA/nccl/tree/master?tab=readme-ov-file#build
    git clone -b $NCCL_VERSION --depth 1 https://github.com/NVIDIA/nccl.git
    cd nccl && make -j src.build NVCC_GENCODE="${NVCC_GENCODE}"
    cp -a build/include/* /usr/local/cuda/include/
    cp -a build/lib/* /usr/local/cuda/lib64/
    cd ..
    rm -rf nccl

    install_cusparselt_040

    ldconfig
}

function install_124 {
  CUDNN_VERSION=9.1.0.70
  echo "Installing CUDA 12.4.1 and cuDNN ${CUDNN_VERSION} and NCCL ${NCCL_VERSION} and cuSparseLt-0.6.2"
  rm -rf /usr/local/cuda-12.4 /usr/local/cuda
  # install CUDA 12.4.1 in the same container
  wget -q https://developer.download.nvidia.com/compute/cuda/12.4.1/local_installers/cuda_12.4.1_550.54.15_linux.run
  chmod +x cuda_12.4.1_550.54.15_linux.run
  ./cuda_12.4.1_550.54.15_linux.run --toolkit --silent
  rm -f cuda_12.4.1_550.54.15_linux.run
  rm -f /usr/local/cuda && ln -s /usr/local/cuda-12.4 /usr/local/cuda

  # cuDNN license: https://developer.nvidia.com/cudnn/license_agreement
  mkdir tmp_cudnn && cd tmp_cudnn
  wget -q https://developer.download.nvidia.com/compute/cudnn/redist/cudnn/linux-x86_64/cudnn-linux-x86_64-${CUDNN_VERSION}_cuda12-archive.tar.xz -O cudnn-linux-x86_64-${CUDNN_VERSION}_cuda12-archive.tar.xz
  tar xf cudnn-linux-x86_64-${CUDNN_VERSION}_cuda12-archive.tar.xz
  cp -a cudnn-linux-x86_64-${CUDNN_VERSION}_cuda12-archive/include/* /usr/local/cuda/include/
  cp -a cudnn-linux-x86_64-${CUDNN_VERSION}_cuda12-archive/lib/* /usr/local/cuda/lib64/
  cd ..
  rm -rf tmp_cudnn

  # NCCL license: https://docs.nvidia.com/deeplearning/nccl/#licenses
  # Follow build: https://github.com/NVIDIA/nccl/tree/master?tab=readme-ov-file#build
  git clone -b $NCCL_VERSION --depth 1 https://github.com/NVIDIA/nccl.git
  cd nccl && make -j src.build NVCC_GENCODE="${NVCC_GENCODE}"
  cp -a build/include/* /usr/local/cuda/include/
  cp -a build/lib/* /usr/local/cuda/lib64/
  cd ..
  rm -rf nccl

  install_cusparselt_062

  ldconfig
}

function install_126 {
  echo "Installing CUDA 12.6.3 and cuDNN ${CUDNN_VERSION} and NCCL ${NCCL_VERSION} and cuSparseLt-0.6.3"
  rm -rf /usr/local/cuda-12.6 /usr/local/cuda
  # install CUDA 12.6.3 in the same container
  wget -q https://developer.download.nvidia.com/compute/cuda/12.6.3/local_installers/cuda_12.6.3_560.35.05_linux.run
  chmod +x cuda_12.6.3_560.35.05_linux.run
  ./cuda_12.6.3_560.35.05_linux.run --toolkit --silent
  rm -f cuda_12.6.3_560.35.05_linux.run
  rm -f /usr/local/cuda && ln -s /usr/local/cuda-12.6 /usr/local/cuda

  # cuDNN license: https://developer.nvidia.com/cudnn/license_agreement
  mkdir tmp_cudnn && cd tmp_cudnn
  wget -q https://developer.download.nvidia.com/compute/cudnn/redist/cudnn/linux-x86_64/cudnn-linux-x86_64-${CUDNN_VERSION}_cuda12-archive.tar.xz -O cudnn-linux-x86_64-${CUDNN_VERSION}_cuda12-archive.tar.xz
  tar xf cudnn-linux-x86_64-${CUDNN_VERSION}_cuda12-archive.tar.xz
  cp -a cudnn-linux-x86_64-${CUDNN_VERSION}_cuda12-archive/include/* /usr/local/cuda/include/
  cp -a cudnn-linux-x86_64-${CUDNN_VERSION}_cuda12-archive/lib/* /usr/local/cuda/lib64/
  cd ..
  rm -rf tmp_cudnn

  # NCCL license: https://docs.nvidia.com/deeplearning/nccl/#licenses
  # Follow build: https://github.com/NVIDIA/nccl/tree/master?tab=readme-ov-file#build
  git clone -b $NCCL_VERSION --depth 1 https://github.com/NVIDIA/nccl.git
  cd nccl && make -j src.build NVCC_GENCODE="${NVCC_GENCODE}"
  cp -a build/include/* /usr/local/cuda/include/
  cp -a build/lib/* /usr/local/cuda/lib64/
  cd ..
  rm -rf nccl

  install_cusparselt_063

  ldconfig
}

function prune_118 {
    echo "Pruning CUDA 11.8 and cuDNN"
    #####################################################################################
    # CUDA 11.8 prune static libs
    #####################################################################################
    export NVPRUNE="/usr/local/cuda-11.8/bin/nvprune"
    export CUDA_LIB_DIR="/usr/local/cuda-11.8/lib64"

    export GENCODE="-gencode arch=compute_35,code=sm_35 -gencode arch=compute_50,code=sm_50 -gencode arch=compute_60,code=sm_60 -gencode arch=compute_70,code=sm_70 -gencode arch=compute_75,code=sm_75 -gencode arch=compute_80,code=sm_80 -gencode arch=compute_86,code=sm_86 -gencode arch=compute_90,code=sm_90"
    export GENCODE_CUDNN="-gencode arch=compute_35,code=sm_35 -gencode arch=compute_37,code=sm_37 -gencode arch=compute_50,code=sm_50 -gencode arch=compute_60,code=sm_60 -gencode arch=compute_61,code=sm_61 -gencode arch=compute_70,code=sm_70 -gencode arch=compute_75,code=sm_75 -gencode arch=compute_80,code=sm_80 -gencode arch=compute_86,code=sm_86 -gencode arch=compute_90,code=sm_90"

    if [[ -n "$OVERRIDE_GENCODE" ]]; then
        export GENCODE=$OVERRIDE_GENCODE
    fi

    # all CUDA libs except CuDNN and CuBLAS (cudnn and cublas need arch 3.7 included)
    ls $CUDA_LIB_DIR/ | grep "\.a" | grep -v "culibos" | grep -v "cudart" | grep -v "cudnn" | grep -v "cublas" | grep -v "metis"  \
      | xargs -I {} bash -c \
                "echo {} && $NVPRUNE $GENCODE $CUDA_LIB_DIR/{} -o $CUDA_LIB_DIR/{}"

    # prune CuDNN and CuBLAS
    $NVPRUNE $GENCODE_CUDNN $CUDA_LIB_DIR/libcublas_static.a -o $CUDA_LIB_DIR/libcublas_static.a
    $NVPRUNE $GENCODE_CUDNN $CUDA_LIB_DIR/libcublasLt_static.a -o $CUDA_LIB_DIR/libcublasLt_static.a

    #####################################################################################
    # CUDA 11.8 prune visual tools
    #####################################################################################
    export CUDA_BASE="/usr/local/cuda-11.8/"
    rm -rf $CUDA_BASE/libnvvp $CUDA_BASE/nsightee_plugins $CUDA_BASE/nsight-compute-2022.3.0 $CUDA_BASE/nsight-systems-2022.4.2/
}

function prune_124 {
  echo "Pruning CUDA 12.4"
  #####################################################################################
  # CUDA 12.4 prune static libs
  #####################################################################################
  export NVPRUNE="/usr/local/cuda-12.4/bin/nvprune"
  export CUDA_LIB_DIR="/usr/local/cuda-12.4/lib64"

  export GENCODE="-gencode arch=compute_50,code=sm_50 -gencode arch=compute_60,code=sm_60 -gencode arch=compute_70,code=sm_70 -gencode arch=compute_75,code=sm_75 -gencode arch=compute_80,code=sm_80 -gencode arch=compute_86,code=sm_86 -gencode arch=compute_90,code=sm_90"
  export GENCODE_CUDNN="-gencode arch=compute_50,code=sm_50 -gencode arch=compute_60,code=sm_60 -gencode arch=compute_61,code=sm_61 -gencode arch=compute_70,code=sm_70 -gencode arch=compute_75,code=sm_75 -gencode arch=compute_80,code=sm_80 -gencode arch=compute_86,code=sm_86 -gencode arch=compute_90,code=sm_90"

  if [[ -n "$OVERRIDE_GENCODE" ]]; then
      export GENCODE=$OVERRIDE_GENCODE
  fi
  if [[ -n "$OVERRIDE_GENCODE_CUDNN" ]]; then
      export GENCODE_CUDNN=$OVERRIDE_GENCODE_CUDNN
  fi

  # all CUDA libs except CuDNN and CuBLAS
  ls $CUDA_LIB_DIR/ | grep "\.a" | grep -v "culibos" | grep -v "cudart" | grep -v "cudnn" | grep -v "cublas" | grep -v "metis"  \
      | xargs -I {} bash -c \
                "echo {} && $NVPRUNE $GENCODE $CUDA_LIB_DIR/{} -o $CUDA_LIB_DIR/{}"

  # prune CuDNN and CuBLAS
  $NVPRUNE $GENCODE_CUDNN $CUDA_LIB_DIR/libcublas_static.a -o $CUDA_LIB_DIR/libcublas_static.a
  $NVPRUNE $GENCODE_CUDNN $CUDA_LIB_DIR/libcublasLt_static.a -o $CUDA_LIB_DIR/libcublasLt_static.a

  #####################################################################################
  # CUDA 12.4 prune visual tools
  #####################################################################################
  export CUDA_BASE="/usr/local/cuda-12.4/"
  rm -rf $CUDA_BASE/libnvvp $CUDA_BASE/nsightee_plugins $CUDA_BASE/nsight-compute-2024.1.0 $CUDA_BASE/nsight-systems-2023.4.4/
}

function prune_126 {
  echo "Pruning CUDA 12.6"
  #####################################################################################
  # CUDA 12.6 prune static libs
  #####################################################################################
  export NVPRUNE="/usr/local/cuda-12.6/bin/nvprune"
  export CUDA_LIB_DIR="/usr/local/cuda-12.6/lib64"

  export GENCODE="-gencode arch=compute_50,code=sm_50 -gencode arch=compute_60,code=sm_60 -gencode arch=compute_70,code=sm_70 -gencode arch=compute_75,code=sm_75 -gencode arch=compute_80,code=sm_80 -gencode arch=compute_86,code=sm_86 -gencode arch=compute_90,code=sm_90"
  export GENCODE_CUDNN="-gencode arch=compute_50,code=sm_50 -gencode arch=compute_60,code=sm_60 -gencode arch=compute_61,code=sm_61 -gencode arch=compute_70,code=sm_70 -gencode arch=compute_75,code=sm_75 -gencode arch=compute_80,code=sm_80 -gencode arch=compute_86,code=sm_86 -gencode arch=compute_90,code=sm_90"

  if [[ -n "$OVERRIDE_GENCODE" ]]; then
      export GENCODE=$OVERRIDE_GENCODE
  fi
  if [[ -n "$OVERRIDE_GENCODE_CUDNN" ]]; then
      export GENCODE_CUDNN=$OVERRIDE_GENCODE_CUDNN
  fi

  # all CUDA libs except CuDNN and CuBLAS
  ls $CUDA_LIB_DIR/ | grep "\.a" | grep -v "culibos" | grep -v "cudart" | grep -v "cudnn" | grep -v "cublas" | grep -v "metis"  \
      | xargs -I {} bash -c \
                "echo {} && $NVPRUNE $GENCODE $CUDA_LIB_DIR/{} -o $CUDA_LIB_DIR/{}"

  # prune CuDNN and CuBLAS
  $NVPRUNE $GENCODE_CUDNN $CUDA_LIB_DIR/libcublas_static.a -o $CUDA_LIB_DIR/libcublas_static.a
  $NVPRUNE $GENCODE_CUDNN $CUDA_LIB_DIR/libcublasLt_static.a -o $CUDA_LIB_DIR/libcublasLt_static.a

  #####################################################################################
  # CUDA 12.6 prune visual tools
  #####################################################################################
  export CUDA_BASE="/usr/local/cuda-12.6/"
  rm -rf $CUDA_BASE/libnvvp $CUDA_BASE/nsightee_plugins $CUDA_BASE/nsight-compute-2024.3.2 $CUDA_BASE/nsight-systems-2024.5.1/
}

function install_128 {
  CUDNN_VERSION=9.7.1.26
  # Include Blackwell support if using CUDA12.8 or above
  NVCC_GENCODE="-gencode=arch=compute_80,code=sm_80 -gencode=arch=compute_90,code=sm_90 -gencode=arch=compute_100,code=sm_100 -gencode=arch=compute_120,code=sm_120 -gencode=arch=compute_120,code=compute_120"
  echo "Installing CUDA 12.8.0 and cuDNN ${CUDNN_VERSION} and NCCL ${NCCL_VERSION} and cuSparseLt-0.6.3"
  rm -rf /usr/local/cuda-12.8 /usr/local/cuda
  # install CUDA 12.8.0 in the same container
  wget -q https://developer.download.nvidia.com/compute/cuda/12.8.0/local_installers/cuda_12.8.0_570.86.10_linux.run
  chmod +x cuda_12.8.0_570.86.10_linux.run
  ./cuda_12.8.0_570.86.10_linux.run --toolkit --silent
  rm -f cuda_12.8.0_570.86.10_linux.run
  rm -f /usr/local/cuda && ln -s /usr/local/cuda-12.8 /usr/local/cuda

  # cuDNN license: https://developer.nvidia.com/cudnn/license_agreement
  mkdir tmp_cudnn && cd tmp_cudnn
  wget -q https://developer.download.nvidia.com/compute/cudnn/redist/cudnn/linux-x86_64/cudnn-linux-x86_64-${CUDNN_VERSION}_cuda12-archive.tar.xz -O cudnn-linux-x86_64-${CUDNN_VERSION}_cuda12-archive.tar.xz
  tar xf cudnn-linux-x86_64-${CUDNN_VERSION}_cuda12-archive.tar.xz
  cp -a cudnn-linux-x86_64-${CUDNN_VERSION}_cuda12-archive/include/* /usr/local/cuda/include/
  cp -a cudnn-linux-x86_64-${CUDNN_VERSION}_cuda12-archive/lib/* /usr/local/cuda/lib64/
  cd ..
  rm -rf tmp_cudnn

  # NCCL license: https://docs.nvidia.com/deeplearning/nccl/#licenses
  # Follow build: https://github.com/NVIDIA/nccl/tree/master?tab=readme-ov-file#build
  git clone -b $NCCL_VERSION --depth 1 https://github.com/NVIDIA/nccl.git
  cd nccl && make -j src.build NVCC_GENCODE="${NVCC_GENCODE}"
  cp -a build/include/* /usr/local/cuda/include/
  cp -a build/lib/* /usr/local/cuda/lib64/
  cd ..
  rm -rf nccl

  install_cusparselt_063

  ldconfig
}

# idiomatic parameter and option handling in sh
while test $# -gt 0
do
    case "$1" in
    11.8) install_118; prune_118
        ;;
    12.4) install_124; prune_124
        ;;
    12.6) install_126; prune_126
        ;;
    12.8) install_128;
        ;;
    *) echo "bad argument $1"; exit 1
        ;;
    esac
    shift
done