add github ci for gpu pt install check

Author: Gasoonjia

.ci/scripts/test-cuda-build.sh

@@ -0,0 +1,84 @@
+#!/bin/bash
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+set -exu
+
+# shellcheck source=/dev/null
+source "$(dirname "${BASH_SOURCE[0]}")/utils.sh"
+
+CUDA_VERSION=${1:-"12.6"}
+
+echo "=== Testing ExecutorTorch CUDA ${CUDA_VERSION} Build ==="
+
+# Function to build and test ExecutorTorch with CUDA support
+test_executorch_cuda_build() {
+    local cuda_version=$1
+
+    echo "Building ExecutorTorch with CUDA ${cuda_version} support..."
+    echo "ExecutorTorch will automatically detect CUDA and install appropriate PyTorch wheel"
+
+    # Set CMAKE_ARGS to enable CUDA build - ExecutorTorch will handle PyTorch installation automatically
+    export CMAKE_ARGS="-DEXECUTORCH_BUILD_CUDA=ON"
+
+    # Install ExecutorTorch with CUDA support - this will automatically:
+    # 1. Detect CUDA version using nvcc
+    # 2. Install appropriate PyTorch wheel for the detected CUDA version
+    # 3. Build ExecutorTorch with CUDA support
+    ./install_executorch.sh
+
+    echo "SUCCESS: ExecutorTorch CUDA build completed"
+
+    # Verify the installation
+    echo "=== Verifying ExecutorTorch CUDA Installation ==="
+
+    # Test that ExecutorTorch was built successfully
+    python -c "
+import executorch
+print('SUCCESS: ExecutorTorch imported successfully')
+"
+
+    # Test CUDA availability and show details
+    python -c "
+try:
+    import torch
+    print('INFO: PyTorch version:', torch.__version__)
+    print('INFO: CUDA available:', torch.cuda.is_available())
+
+    if torch.cuda.is_available():
+        print('SUCCESS: CUDA is available for ExecutorTorch')
+        print('INFO: CUDA version:', torch.version.cuda)
+        print('INFO: GPU device count:', torch.cuda.device_count())
+        print('INFO: Current GPU device:', torch.cuda.current_device())
+        print('INFO: GPU device name:', torch.cuda.get_device_name())
+
+        # Test basic CUDA tensor operation
+        device = torch.device('cuda')
+        x = torch.randn(10, 10).to(device)
+        y = torch.randn(10, 10).to(device)
+        z = torch.mm(x, y)
+        print('SUCCESS: CUDA tensor operation completed on device:', z.device)
+        print('INFO: Result tensor shape:', z.shape)
+
+        print('SUCCESS: ExecutorTorch CUDA integration verified')
+    else:
+        print('WARNING: CUDA not detected, but ExecutorTorch built successfully')
+        exit(1)
+except Exception as e:
+    print('ERROR: ExecutorTorch CUDA test failed:', e)
+    exit(1)
+"
+
+    echo "SUCCESS: ExecutorTorch CUDA ${cuda_version} build and verification completed successfully"
+}
+
+# Main execution
+echo "Current working directory: $(pwd)"
+echo "Directory contents:"
+ls -la
+
+# Run the CUDA build test
+test_executorch_cuda_build "${CUDA_VERSION}"