#!/bin/bash

set -e

# Parameters with defaults
FLASH_ATTN_VERSION=$1
PYTHON_VERSION=$2
TORCH_VERSION=$3
CUDA_VERSION=$4

echo "Building Flash Attention with parameters:"
echo "  Flash-Attention: $FLASH_ATTN_VERSION"
echo "  Python: $PYTHON_VERSION"
echo "  PyTorch: $TORCH_VERSION"
echo "  CUDA: $CUDA_VERSION"

# Set CUDA and PyTorch versions
MATRIX_CUDA_VERSION=$(echo $CUDA_VERSION | awk -F \. {'print $1 $2'})
MATRIX_TORCH_VERSION=$(echo $TORCH_VERSION | awk -F \. {'print $1 "." $2'})

echo "Derived versions:"
echo "  CUDA Matrix: $MATRIX_CUDA_VERSION"
echo "  Torch Matrix: $MATRIX_TORCH_VERSION"

# Install PyTorch
TORCH_CUDA_VERSION=$(python get_torch_cuda_version.py $MATRIX_CUDA_VERSION $MATRIX_TORCH_VERSION)

echo "Installing PyTorch $TORCH_VERSION+cu$TORCH_CUDA_VERSION..."
if [[ $TORCH_VERSION == *"dev"* ]]; then
  pip install --force-reinstall --no-cache-dir --pre torch==$TORCH_VERSION --index-url https://download.pytorch.org/whl/nightly/cu${TORCH_CUDA_VERSION}
else
  pip install --force-reinstall --no-cache-dir torch==$TORCH_VERSION --index-url https://download.pytorch.org/whl/cu${TORCH_CUDA_VERSION}
fi

# Verify installation
echo "Verifying installations..."
nvcc --version
python -V
python -c "import torch; print('PyTorch:', torch.__version__)"
python -c "import torch; print('CUDA:', torch.version.cuda)"
python -c "from torch.utils import cpp_extension; print(cpp_extension.CUDA_HOME)"

# Checkout flash-attn
echo "Checking out flash-attention v$FLASH_ATTN_VERSION..."
git clone https://github.com/Dao-AILab/flash-attention.git -b "v$FLASH_ATTN_VERSION"

# Determine MAX_JOBS and NVCC_THREADS based on system resources
NUM_THREADS=$(nproc)
RAM_GB=$(free -g | awk '/^Mem:/{print $2}')
echo "System resources:"
echo "  CPU threads: $NUM_THREADS"
echo "  RAM: ${RAM_GB}GB"

# Determine MAX_JOBS and NVCC_THREADS based on system resources
if [[ -z "${MAX_JOBS:-}" && -z "${NVCC_THREADS:-}" ]]; then
  # Calculate max product based on following constraints:
  # - MAX_JOBS x NVCC_THREADS(<= 4) <= NUM_THREADS
  # - 2.8GB x MAX_JOBS x NVCC_THREADS(<= 4) <= RAM_GB

  # Set MAX_PRODUCT from RAM
  MAX_PRODUCT_CPU=$NUM_THREADS
  MAX_PRODUCT_RAM=$(awk -v ram="$RAM_GB" 'BEGIN {print int(ram / 2.8)}')
  MAX_PRODUCT=$((MAX_PRODUCT_CPU < MAX_PRODUCT_RAM ? MAX_PRODUCT_CPU : MAX_PRODUCT_RAM))

  # Set MAX_JOBS and NVCC_THREADS so that MAX_JOBS x NVCC_THREADS ≈ MAX_PRODUCT with NVCC_THREADS <= 4
  BASE_THREADS=$(awk -v max="$MAX_PRODUCT" 'BEGIN {print int(sqrt(max))}')

  if awk "BEGIN {exit !($RAM_GB <= 16)}"; then
    # If RAM is less than 16GB, set NVCC_THREADS to 1 and MAX_JOBS to 2
    NVCC_THREADS=1
    MAX_JOBS=2
  elif (( BASE_THREADS <= 4 )); then
    NVCC_THREADS=$BASE_THREADS
    MAX_JOBS=$BASE_THREADS
  else
    NVCC_THREADS=4
    MAX_JOBS=$((MAX_PRODUCT / NVCC_THREADS))
  fi

  # Ensure minimum values of 1
  MAX_JOBS=$((MAX_JOBS < 1 ? 1 : MAX_JOBS))
  NVCC_THREADS=$((NVCC_THREADS < 1 ? 1 : NVCC_THREADS))
fi

echo "Build parallelism settings:"
echo "  MAX_JOBS: $MAX_JOBS"
echo "  NVCC_THREADS: $NVCC_THREADS"

# Build wheels
echo "Building wheels..."
cd flash-attention
LOCAL_VERSION_LABEL="cu${MATRIX_CUDA_VERSION}torch${MATRIX_TORCH_VERSION}"
NVCC_THREADS=$NVCC_THREADS MAX_JOBS=$MAX_JOBS \
  FLASH_ATTENTION_FORCE_BUILD=TRUE FLASH_ATTN_LOCAL_VERSION=${LOCAL_VERSION_LABEL} \
  NVCC_APPEND_FLAGS="--allow-unsupported-compiler" \
  time python setup.py bdist_wheel --dist-dir=dist
wheel_name=$(basename $(ls dist/*.whl | head -n 1))
echo "Built wheel: $wheel_name"