Add AOTI backend skeleton code

.ci/docker/ci_commit_pins/pytorch.txt

@@ -1 +1 @@
-4d4abec80f03cd8fdefe1d9cb3a60d3690cd777e
+53a2908a10f414a2f85caa06703a26a40e873869

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

@@ -0,0 +1,98 @@
+#!/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"
+
+    # Check available resources before starting
+    echo "=== System Information ==="
+    echo "Available memory: $(free -h | grep Mem | awk '{print $2}')"
+    echo "Available disk space: $(df -h . | tail -1 | awk '{print $4}')"
+    echo "CPU cores: $(nproc)"
+    echo "CUDA version check:"
+    nvcc --version || echo "nvcc not found"
+    nvidia-smi || echo "nvidia-smi not found"
+
+    # Set CMAKE_ARGS to enable CUDA build - ExecutorTorch will handle PyTorch installation automatically
+    export CMAKE_ARGS="-DEXECUTORCH_BUILD_CUDA=ON"
+
+    echo "=== Starting ExecutorTorch Installation ==="
+    # Install ExecutorTorch with CUDA support with timeout and error handling
+    timeout 5400 ./install_executorch.sh || {
+        local exit_code=$?
+        echo "ERROR: install_executorch.sh failed with exit code: $exit_code"
+        if [ $exit_code -eq 124 ]; then
+            echo "ERROR: Installation timed out after 90 minutes"
+        fi
+        exit $exit_code
+    }
+
+    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}"

.ci/scripts/test-cuda-export-aoti.sh

@@ -0,0 +1,105 @@
+#!/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 AOTI Export ${CUDA_VERSION} ==="
+
+# Function to test CUDA AOTI export functionality
+test_cuda_aoti_export() {
+    local cuda_version=$1
+
+    echo "Testing CUDA AOTI export with CUDA ${cuda_version} support..."
+
+    # Check available resources before starting
+    echo "=== System Information ==="
+    echo "Available memory: $(free -h | grep Mem | awk '{print $2}')"
+    echo "Available disk space: $(df -h . | tail -1 | awk '{print $4}')"
+    echo "CPU cores: $(nproc)"
+    echo "CUDA version check:"
+    nvcc --version || echo "nvcc not found"
+    nvidia-smi || echo "nvidia-smi not found"
+
+    # Set up environment for CUDA builds
+    export CMAKE_ARGS="-DEXECUTORCH_BUILD_CUDA=ON"
+
+    echo "=== Installing ExecutorTorch with CUDA support ==="
+    # Install ExecutorTorch with CUDA support with timeout and error handling
+    timeout 5400 ./install_executorch.sh || {
+        local exit_code=$?
+        echo "ERROR: install_executorch.sh failed with exit code: $exit_code"
+        if [ $exit_code -eq 124 ]; then
+            echo "ERROR: Installation timed out after 90 minutes"
+        fi
+        exit $exit_code
+    }
+
+    echo "SUCCESS: ExecutorTorch CUDA installation 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 "=== Running CUDA AOTI Export Tests ==="
+    # Run the CUDA AOTI export tests using the Python script
+    python .ci/scripts/test_cuda_export_aoti.py \
+        --models linear conv2d add resnet18 \
+        --export-mode export_aoti_only \
+        --timeout 600 \
+        --cleanup
+
+    echo "SUCCESS: ExecutorTorch CUDA AOTI export ${cuda_version} tests completed successfully"
+}
+
+# Main execution
+echo "Current working directory: $(pwd)"
+echo "Directory contents:"
+ls -la
+
+# Run the CUDA AOTI export test
+test_cuda_aoti_export "${CUDA_VERSION}"