lpai e2e & minimum inference runtime support

[haowhsu-quic]

Test plan

Tested with QNN version 2.41.0.251128

[Initialization]

• Build hexagon & aarch64 target

# e.g.
export HEXAGON_SDK_ROOT=$WORKSPACE/aisw-tools/avante-tools/prebuilt/dsp/hexagon-sdk-6.4.0
export HEXAGON_TOOLS_ROOT=$HEXAGON_SDK_ROOT/tools/HEXAGON_Tools/21.0.0
export HEXAGON_ARCH=v81
# QNN_SDK_ROOT & ANDROID_NDK_ROOT are required as well
cd path/to/executorch
backends/qualcomm/scripts/build.sh --enable_hexagon

[aarch64-android]

• Sign artifact

# e.g.
cd path/to/executorch
backends/qualcomm/scripts/lpai_utils.sh --lpai v6

• Run e2e test

cd path/to/executorch
mkdir lpai_artifacts
python backends/qualcomm/tests/test_qnn_delegate.py TestQNNQuantizedOperator.test_qnn_backend_lpai -b build-android -s f3c0531 -m SM8850 --backend lpai -a lpai_artifacts

[hexagon direct]

• Sign & push artifacts + execution

cd path/to/executorch
backends/qualcomm/scripts/lpai_utils.sh --direct --lpai v6 --hexagon v81 --workspace /data/local/tmp/lpai_direct --artifact lpai_artifacts/ --serial f3c0531

• Verify

cd path/to/executorch
python backends/qualcomm/scripts/sqnr_verifier.py -g lpai_artifacts/golden_0* -o lpai_artifacts/output_direct/output_0* -e lpai_artifacts/encoding.txt -d uint8
# should have SQNR number for each output

[pytorch-bot]

🔗 Helpful Links

🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/16421

• 📄 Preview Python docs built from this PR

Note: Links to docs will display an error until the docs builds have been completed.

❌ 10 New Failures, 2 Unrelated Failures

As of commit 1232bac with merge base a0ba28e ():

NEW FAILURES - The following jobs have failed:

• Lint / lintrunner / linux-job (gh)
  >>> Lint for examples/qualcomm/executor_runner/qnn_executor_runner.cpp:
• pull / test-llama-runner-qnn-linux (fp32, qnn_16a16w, qnn) / linux-job (gh)
  /pytorch/executorch/backends/qualcomm/runtime/backends/lpai/LpaiBackendCustomConfig.cpp:48:37: error: use of undeclared identifier 'QNN_LPAI_BACKEND_HW_VERSION_V7'
• pull / test-llama-runner-qnn-linux (fp32, qnn_8a8w, qnn) / linux-job (gh)
  /pytorch/executorch/backends/qualcomm/runtime/backends/lpai/LpaiBackendCustomConfig.cpp:48:37: error: use of undeclared identifier 'QNN_LPAI_BACKEND_HW_VERSION_V7'
• pull / test-static-llama-qnn-linux (stories_110m) / linux-job (gh)
  /pytorch/executorch/backends/qualcomm/runtime/backends/lpai/LpaiBackendCustomConfig.cpp:48:37: error: use of undeclared identifier 'QNN_LPAI_BACKEND_HW_VERSION_V7'
• pull / test-static-llama-qnn-linux (stories_260k_bc) / linux-job (gh)
  /pytorch/executorch/backends/qualcomm/runtime/backends/lpai/LpaiBackendCustomConfig.cpp:48:37: error: use of undeclared identifier 'QNN_LPAI_BACKEND_HW_VERSION_V7'
• pull / unittest / linux / linux-job (gh)
  RuntimeError: Command docker exec -t add9c7736c6090e970a7374c5754a95c266abee8f01a0044c6bc0f5affa73f06 /exec failed with exit code 8
• pull / unittest / macos / macos-job (gh)
  RuntimeError: Command bash /Users/ec2-user/runner/_work/_temp/exec_script failed with exit code 8
• pull / unittest-editable / linux / linux-job (gh)
  RuntimeError: Command docker exec -t 3a730983a7ac25bbe450f5b75f5581cfc85df8ec3e9b9c709ea6c1f086bb95af /exec failed with exit code 8
• pull / unittest-editable / macos / macos-job (gh)
  RuntimeError: Command bash /Users/ec2-user/runner/_work/_temp/exec_script failed with exit code 8
• Test CUDA Builds / check-all-cuda-builds (gh)
  Process completed with exit code 1.

FLAKY - The following job failed but was likely due to flakiness present on trunk:

• Test CUDA Builds / test-executorch-cuda-build-12.8 / linux-job (gh) (detected as infra flaky with no log or failing log classifier)

UNSTABLE - The following job is marked as unstable, possibly due to flakiness on trunk:

• pull / android / run-emulator (gh) (#16137)
  Timeout waiting for emulator to boot.

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[haowhsu-quic]

Hi @cccclai, this PoC is for LPAI backend & minimum inference runtime on hexagon scalar core. Currently only aDSP is supported with 2 models given from RL team are verified. We'll also work on general solution in parallel (e.g. support cDSP(HTP), backend awareness quantizer, etc.).

[cccclai]

tagging @mohankumarkumar to review