Update on "Runner support for multiple ptd files"

Add `std::unordered_set<std::string>` to allow for multiple data files in the runner.

Differential Revision: [D82072385](https://our.internmc.facebook.com/intern/diff/D82072385/)

[ghstack-poisoned]

Author: lucylq

.ci/docker/build.sh

@@ -54,13 +54,13 @@ case "${IMAGE_NAME}" in
   executorch-ubuntu-22.04-mediatek-sdk)
     MEDIATEK_SDK=yes
     CLANG_VERSION=12
-    ANDROID_NDK_VERSION=r27b
+    ANDROID_NDK_VERSION=r28c
     ;;
   executorch-ubuntu-22.04-clang12-android)
     LINTRUNNER=""
     CLANG_VERSION=12
     # From https://developer.android.com/ndk/downloads
-    ANDROID_NDK_VERSION=r27b
+    ANDROID_NDK_VERSION=r28c
     ;;
   *)
     echo "Invalid image name ${IMAGE_NAME}"

.ci/docker/ci_commit_pins/optimum-executorch.txt

@@ -1 +1 @@
-40b02a2dc61bbf901a2df91719f47c98d65368ec
+bd06b54e627fbfd354a2cffa4c80fb21883209a9

.ci/docker/ci_commit_pins/pytorch.txt

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

.ci/scripts/setup-samsung-linux-deps.sh

@@ -11,7 +11,7 @@ set -ex
 
 download_ai_lite_core() {
   API_BASE="https://soc-developer.semiconductor.samsung.com/api/v1/resource/ai-litecore/download"
-  API_KEY="kn10SoSY3hkC-9Qny5TqD2mnqVrlupv3krnjLeBt5cY"
+  API_KEY=$SAMSUNG_AI_LITECORE_KEY
 
   VERSION="0.5"
   OS_NAME="Ubuntu 22.04"
@@ -52,17 +52,8 @@ download_ai_lite_core() {
 install_enn_backend() {
   NDK_INSTALLATION_DIR=/opt/ndk
   rm -rf "${NDK_INSTALLATION_DIR}" && sudo mkdir -p "${NDK_INSTALLATION_DIR}"
-  ANDROID_NDK_VERSION=r27b
+  ANDROID_NDK_VERSION=r28c
 
-  pushd .
-  cd /tmp
-  curl -Os --retry 3 "https://ossci-android.s3.amazonaws.com/android-ndk-${ANDROID_NDK_VERSION}-linux.zip"
-  unzip -qo "android-ndk-${ANDROID_NDK_VERSION}-linux.zip"
-
-  # Print the content for manual verification
-  ls -lah "android-ndk-${ANDROID_NDK_VERSION}"
-  sudo mv "android-ndk-${ANDROID_NDK_VERSION}"/* "${NDK_INSTALLATION_DIR}"
-  popd
   # build Exynos backend
   export ANDROID_NDK_ROOT=${ANDROID_NDK_ROOT:-/opt/ndk}
   bash backends/samsung/build.sh --build all

.ci/scripts/setup-windows.ps1

@@ -1,5 +1,5 @@
 param (
-    [string]$editable = $false
+    [string]$editable = "false"
 )
 
 conda create --yes --quiet -n et python=3.12

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

@@ -0,0 +1,95 @@
+#!/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
+
+CUDA_VERSION=${1:-"12.6"}
+
+echo "=== Testing ExecuTorch CUDA ${CUDA_VERSION} Build ==="
+
+# Function to build and test ExecuTorch with CUDA support
+test_executorch_cuda_build() {
+    local cuda_version=$1
+
+    echo "Building ExecuTorch with CUDA ${cuda_version} support..."
+    echo "ExecuTorch 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: $(nproc)"
+    echo "CUDA version check:"
+    nvcc --version || echo "nvcc not found"
+    nvidia-smi || echo "nvidia-smi not found"
+
+    # Set CMAKE_ARGS to enable CUDA build - ExecuTorch will handle PyTorch installation automatically
+    export CMAKE_ARGS="-DEXECUTORCH_BUILD_CUDA=ON"
+
+    echo "=== Starting ExecuTorch Installation ==="
+    # Install ExecuTorch with CUDA support with timeout and error handling
+    timeout 5400 ./install_executorch.sh || {
+        local exit_code=$?
+        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: ExecuTorch CUDA build completed"
+
+    # Verify the installation
+    echo "=== Verifying ExecuTorch CUDA Installation ==="
+
+    # Test that ExecuTorch was built successfully
+    python -c "
+import executorch
+print('SUCCESS: ExecuTorch 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 ExecuTorch')
+        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: ExecuTorch CUDA integration verified')
+    else:
+        print('WARNING: CUDA not detected, but ExecuTorch built successfully')
+        exit(1)
+except Exception as e:
+    print('ERROR: ExecuTorch CUDA test failed:', e)
+    exit(1)
+"
+
+    echo "SUCCESS: ExecuTorch 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_backend_linux.sh renamed to .ci/scripts/test_backend.sh

@@ -10,16 +10,26 @@ SUITE=$1
 FLOW=$2
 ARTIFACT_DIR=$3
 
-REPORT_FILE="$ARTIFACT_DIR/test-report-$FLOW-$SUITE.csv"
+REPORT_FILE="$ARTIFACT_DIR/test-report-$FLOW-$SUITE.json"
 
 echo "Running backend test job for suite $SUITE, flow $FLOW."
 echo "Saving job artifacts to $ARTIFACT_DIR."
 
-# The generic Linux job chooses to use base env, not the one setup by the image
 eval "$(conda shell.bash hook)"
 CONDA_ENV=$(conda env list --json | jq -r ".envs | .[-1]")
 conda activate "${CONDA_ENV}"
 
+if [[ "$(uname)" == "Darwin" ]]; then
+    bash .ci/scripts/setup-conda.sh
+    eval "$(conda shell.bash hook)"
+    CONDA_RUN_CMD="${CONDA_RUN} --no-capture-output"
+    ${CONDA_RUN_CMD} pip install awscli==1.37.21
+    IS_MACOS=1
+else
+    CONDA_RUN_CMD=""
+    IS_MACOS=0
+fi
+
 export PYTHON_EXECUTABLE=python
 
 # CMake options to use, in addition to the defaults.
@@ -39,17 +49,25 @@ if [[ "$FLOW" == *qnn* ]]; then
 fi
 
 if [[ "$FLOW" == *vulkan* ]]; then
-    # Setup swiftshader and Vulkan SDK which are required to build the Vulkan delegate
+    # Setup swiftshader and Vulkan SDK which are required to build the Vulkan delegate.
     source .ci/scripts/setup-vulkan-linux-deps.sh
 
     EXTRA_BUILD_ARGS+=" -DEXECUTORCH_BUILD_VULKAN=ON"
 fi
 
-# We need the runner to test the built library.
-PYTHON_EXECUTABLE=python CMAKE_ARGS="$EXTRA_BUILD_ARGS" .ci/scripts/setup-linux.sh --build-tool cmake --build-mode Release --editable true
+if [[ "$FLOW" == *arm* ]]; then
+    # Setup ARM deps.
+    .ci/scripts/setup-arm-baremetal-tools.sh
+fi
+
+if [[ $IS_MACOS -eq 1 ]]; then
+    SETUP_SCRIPT=.ci/scripts/setup-macos.sh
+else
+    SETUP_SCRIPT=.ci/scripts/setup-linux.sh
+fi
+CMAKE_ARGS="$EXTRA_BUILD_ARGS" ${CONDA_RUN_CMD} $SETUP_SCRIPT --build-tool cmake --build-mode Release --editable true
 
 EXIT_CODE=0
-python -m executorch.backends.test.suite.runner $SUITE --flow $FLOW --report "$REPORT_FILE" || EXIT_CODE=$?
-
+${CONDA_RUN_CMD} pytest -c /dev/nul -n auto backends/test/suite/$SUITE/ -m flow_$FLOW --json-report --json-report-file="$REPORT_FILE" || EXIT_CODE=$?
 # Generate markdown summary.
-python -m executorch.backends.test.suite.generate_markdown_summary "$REPORT_FILE" > ${GITHUB_STEP_SUMMARY:-"step_summary.md"} --exit-code $EXIT_CODE
+${CONDA_RUN_CMD} python -m executorch.backends.test.suite.generate_markdown_summary_json "$REPORT_FILE" > ${GITHUB_STEP_SUMMARY:-"step_summary.md"} --exit-code $EXIT_CODE