Use cuda version PT when build with CUDA delegate

.ci/docker/ci_commit_pins/pytorch.txt

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

.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 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}"

.github/workflows/test-cuda-builds.yml

@@ -0,0 +1,63 @@
+# Test ExecuTorch CUDA Build Compatibility
+# This workflow tests whether ExecuTorch can be successfully built with CUDA support
+# across different CUDA versions (12.6, 12.8, 12.9) using the command:
+# CMAKE_ARGS="-DEXECUTORCH_BUILD_CUDA=ON" ./install_executorch.sh
+#
+# Note: ExecuTorch automatically detects the system CUDA version using nvcc and
+# installs the appropriate PyTorch wheel. No manual CUDA/PyTorch installation needed.
+
+name: Test CUDA Builds
+
+on:
+  pull_request:
+  push:
+    branches:
+      - main
+      - release/*
+
+concurrency:
+  group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.sha }}-${{ github.event_name == 'workflow_dispatch' }}-${{ github.event_name == 'schedule' }}
+  cancel-in-progress: false
+
+jobs:
+  test-cuda-builds:
+    strategy:
+      fail-fast: false
+      matrix:
+        cuda-version: ["12.6", "12.8", "12.9"]
+
+    name: test-executorch-cuda-build-${{ matrix.cuda-version }}
+    uses: pytorch/test-infra/.github/workflows/linux_job_v2.yml@main
+    permissions:
+      id-token: write
+      contents: read
+    with:
+      timeout: 90
+      runner: linux.g5.4xlarge.nvidia.gpu
+      gpu-arch-type: cuda
+      gpu-arch-version: ${{ matrix.cuda-version }}
+      use-custom-docker-registry: false
+      submodules: recursive
+      ref: ${{ github.event_name == 'pull_request' && github.event.pull_request.head.sha || github.sha }}
+      script: |
+        set -eux
+
+        # Test ExecuTorch CUDA build - ExecuTorch will automatically detect CUDA version
+        # and install the appropriate PyTorch wheel when CMAKE_ARGS="-DEXECUTORCH_BUILD_CUDA=ON"
+        source .ci/scripts/test-cuda-build.sh "${{ matrix.cuda-version }}"
+
+  # This job will fail if any of the CUDA versions fail
+  check-all-cuda-builds:
+    needs: test-cuda-builds
+    runs-on: ubuntu-latest
+    if: always()
+    steps:
+      - name: Check if all CUDA builds succeeded
+        run: |
+          if [[ "${{ needs.test-cuda-builds.result }}" != "success" ]]; then
+            echo "ERROR: One or more ExecuTorch CUDA builds failed!"
+            echo "CUDA build results: ${{ needs.test-cuda-builds.result }}"
+            exit 1
+          else
+            echo "SUCCESS: All ExecuTorch CUDA builds (12.6, 12.8, 12.9) completed successfully!"
+          fi

install_requirements.py

@@ -7,60 +7,22 @@
 
 import argparse
 import os
-import platform
-import re
 import subprocess
 import sys
 
-
-def python_is_compatible():
-    # Scrape the version range from pyproject.toml, which should be in the current directory.
-    version_specifier = None
-    with open("pyproject.toml", "r") as file:
-        for line in file:
-            if line.startswith("requires-python"):
-                match = re.search(r'"([^"]*)"', line)
-                if match:
-                    version_specifier = match.group(1)
-                    break
-
-    if not version_specifier:
-        print(
-            "WARNING: Skipping python version check: version range not found",
-            file=sys.stderr,
-        )
-        return False
-
-    # Install the packaging module if necessary.
-    try:
-        import packaging
-    except ImportError:
-        subprocess.run(
-            [sys.executable, "-m", "pip", "install", "packaging"], check=True
-        )
-    # Compare the current python version to the range in version_specifier. Exits
-    # with status 1 if the version is not compatible, or with status 0 if the
-    # version is compatible or the logic itself fails.
-    try:
-        import packaging.specifiers
-        import packaging.version
-
-        python_version = packaging.version.parse(platform.python_version())
-        version_range = packaging.specifiers.SpecifierSet(version_specifier)
-        if python_version not in version_range:
-            print(
-                f'ERROR: ExecuTorch does not support python version {python_version}: must satisfy "{version_specifier}"',
-                file=sys.stderr,
-            )
-            return False
-    except Exception as e:
-        print(f"WARNING: Skipping python version check: {e}", file=sys.stderr)
-    return True
-
+from install_utils import determine_torch_url, is_intel_mac_os, python_is_compatible
 
 # The pip repository that hosts nightly torch packages.
-TORCH_NIGHTLY_URL = "https://download.pytorch.org/whl/nightly/cpu"
+# This will be dynamically set based on CUDA availability and CUDA backend enabled/disabled.
+TORCH_NIGHTLY_URL_BASE = "https://download.pytorch.org/whl/nightly"
 
+# Supported CUDA versions - modify this to add/remove supported versions
+# Format: tuple of (major, minor) version numbers
+SUPPORTED_CUDA_VERSIONS = (
+    (12, 6),
+    (12, 8),
+    (12, 9),
+)
 
 # Since ExecuTorch often uses main-branch features of pytorch, only the nightly
 # pip versions will have the required features.
@@ -71,7 +33,10 @@ def python_is_compatible():
 #
 # NOTE: If you're changing, make the corresponding change in .ci/docker/ci_commit_pins/pytorch.txt
 # by picking the hash from the same date in https://hud.pytorch.org/hud/pytorch/pytorch/nightly/
-NIGHTLY_VERSION = "dev20250906"
+#
+# NOTE: If you're changing, make the corresponding supported CUDA versions in
+# SUPPORTED_CUDA_VERSIONS above if needed.
+NIGHTLY_VERSION = "dev20250915"
 
 
 def install_requirements(use_pytorch_nightly):
@@ -84,12 +49,15 @@ def install_requirements(use_pytorch_nightly):
         )
         sys.exit(1)
 
+    # Determine the appropriate PyTorch URL based on CUDA delegate status
+    torch_url = determine_torch_url(TORCH_NIGHTLY_URL_BASE, SUPPORTED_CUDA_VERSIONS)
+
     # pip packages needed by exir.
     TORCH_PACKAGE = [
         # Setting use_pytorch_nightly to false to test the pinned PyTorch commit. Note
         # that we don't need to set any version number there because they have already
         # been installed on CI before this step, so pip won't reinstall them
-        f"torch==2.9.0.{NIGHTLY_VERSION}" if use_pytorch_nightly else "torch",
+        f"torch==2.10.0.{NIGHTLY_VERSION}" if use_pytorch_nightly else "torch",
     ]
 
     # Install the requirements for core ExecuTorch package.
@@ -105,7 +73,7 @@ def install_requirements(use_pytorch_nightly):
             "requirements-dev.txt",
             *TORCH_PACKAGE,
             "--extra-index-url",
-            TORCH_NIGHTLY_URL,
+            torch_url,
         ],
         check=True,
     )
@@ -147,10 +115,13 @@ def install_requirements(use_pytorch_nightly):
 
 
 def install_optional_example_requirements(use_pytorch_nightly):
+    # Determine the appropriate PyTorch URL based on CUDA delegate status
+    torch_url = determine_torch_url(TORCH_NIGHTLY_URL_BASE, SUPPORTED_CUDA_VERSIONS)
+
     print("Installing torch domain libraries")
     DOMAIN_LIBRARIES = [
         (
-            f"torchvision==0.24.0.{NIGHTLY_VERSION}"
+            f"torchvision==0.25.0.{NIGHTLY_VERSION}"
             if use_pytorch_nightly
             else "torchvision"
         ),
@@ -165,7 +136,7 @@ def install_optional_example_requirements(use_pytorch_nightly):
             "install",
             *DOMAIN_LIBRARIES,
             "--extra-index-url",
-            TORCH_NIGHTLY_URL,
+            torch_url,
         ],
         check=True,
     )
@@ -180,25 +151,14 @@ def install_optional_example_requirements(use_pytorch_nightly):
             "-r",
             "requirements-examples.txt",
             "--extra-index-url",
-            TORCH_NIGHTLY_URL,
+            torch_url,
             "--upgrade-strategy",
             "only-if-needed",
         ],
         check=True,
     )
 
 
-# Prebuilt binaries for Intel-based macOS are no longer available on PyPI; users must compile from source.
-# PyTorch stopped building macOS x86_64 binaries since version 2.3.0 (January 2024).
-def is_intel_mac_os():
-    # Returns True if running on Intel macOS.
-    return platform.system().lower() == "darwin" and platform.machine().lower() in (
-        "x86",
-        "x86_64",
-        "i386",
-    )
-
-
 def main(args):
     parser = argparse.ArgumentParser()
     parser.add_argument(