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Update base for Update on "Introduce data schema to store raw tensors"
Differential Revision: [D65156641](https://our.internmc.facebook.com/intern/diff/D65156641) [ghstack-poisoned]
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.ci/docker/common/install_clang.sh

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apt-get install -y --no-install-recommends clang-"$CLANG_VERSION"
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apt-get install -y --no-install-recommends llvm-"$CLANG_VERSION"
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# Also require LLD linker from llvm and libomp to build PyTorch from source
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apt-get install -y lld "libomp-${CLANG_VERSION}-dev"
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apt-get install -y lld "libomp-${CLANG_VERSION}-dev" "libc++-${CLANG_VERSION}-dev"
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# Use update-alternatives to make this version the default
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update-alternatives --install /usr/bin/clang clang /usr/bin/clang-"$CLANG_VERSION" 50

.ci/docker/requirements-ci.txt

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mpmath==1.3.0
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numpy==1.22.0; python_version == '3.10'
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numpy==1.21.3; python_version == '3.10'
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numpy==1.23.2; python_version == '3.11'
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numpy; python_version >= '3.12'
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PyYAML==6.0.1

.ci/scripts/build-qnn-sdk.sh

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# Workaround for fbs files in exir/_serialize
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cp schema/program.fbs exir/_serialize/program.fbs
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cp schema/scalar_type.fbs exir/_serialize/scalar_type.fbs
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cp schema/common.fbs exir/_serialize/common.fbs
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}
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build_qnn_backend

.ci/scripts/setup-linux.sh

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fi
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# As Linux job is running inside a Docker container, all of its dependencies
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# have already been installed
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install_executorch
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# have already been installed, so we use PyTorch build from source here instead
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# of nightly. This allows CI to test against latest commits from PyTorch
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install_executorch "use-pt-pinned-commit"
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build_executorch_runner "${BUILD_TOOL}"

.ci/scripts/setup-qnn-deps.sh

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}
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setup_libc++() {
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clang_version=$1
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sudo apt-get update
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pkgs_to_check=('libc++-dev')
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pkgs_to_check=("libc++-${clang_version}-dev")
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j=0
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while [ $j -lt ${#pkgs_to_check[*]} ]; do
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install_status=$(verify_pkg_installed ${pkgs_to_check[$j]})
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done
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}
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setup_libc++
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# This needs to match with the clang version from the Docker image
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setup_libc++ 12
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install_qnn

.ci/scripts/test_llama.sh

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export LD_LIBRARY_PATH="${QNN_SDK_ROOT}/lib/x86_64-linux-clang"
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export PYTHONPATH=".."
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cp schema/program.fbs exir/_serialize/program.fbs
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cp schema/scalar_type.fbs exir/_serialize/scalar_type.fbs
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cp schema/common.fbs exir/_serialize/common.fbs
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cp -f build-x86/backends/qualcomm/PyQnnManagerAdaptor.cpython-310-x86_64-linux-gnu.so backends/qualcomm/python
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cp -f build-x86/backends/qualcomm/PyQnnWrapperAdaptor.cpython-310-x86_64-linux-gnu.so backends/qualcomm/python
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.ci/scripts/test_llama_runner_eager.sh

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-d fp32 \
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--max_seq_length 32 \
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--temperature 0 \
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--show_tokens \
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--prompt "Once upon a time," > result.txt
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# Verify result.txt
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RESULT=$(cat result.txt)
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EXPECTED_RESULT="there was a little girl"
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EXPECTED_RESULT="727, 471, 263, 2217, 7826, 4257, 365, 2354, 29889, 2296, 18012, 304, 1708, 5377, 297, 278, 6575, 845, 457, 29889, 3118, 2462, 29892, 1183, 4446, 263"
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if [[ "${RESULT}" == *"${EXPECTED_RESULT}"* ]]; then
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echo "Actual result: ${RESULT}"
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echo "Success"

.ci/scripts/utils.sh

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which pip
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# Install executorch, this assumes that Executorch is checked out in the
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# current directory.
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# TODO(T199538337): clean up install scripts to use install_requirements.sh
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./install_requirements.sh --pybind xnnpack
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if [[ "${1:-}" == "use-pt-pinned-commit" ]]; then
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./install_requirements.sh --pybind xnnpack --use-pt-pinned-commit
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else
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./install_requirements.sh --pybind xnnpack
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fi
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# Just print out the list of packages for debugging
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pip list
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}

.github/pull_request_template.md

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### Summary
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[PLEASE REMOVE] See [CONTRIBUTING.md's Pull Requests](https://github.com/pytorch/executorch/blob/main/CONTRIBUTING.md#pull-requests) for ExecuTorch PR guidelines.
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[PLEASE REMOVE] If this PR closes an issue, please add a `Fixes #<issue-id>` line.
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[PLEASE REMOVE] If this PR introduces a fix or feature that should be the upcoming release notes, please add a "Release notes: <area>" label. For a list of available release notes labels, check out [CONTRIBUTING.md's Pull Requests](https://github.com/pytorch/executorch/blob/main/CONTRIBUTING.md#pull-requests).
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### Test plan
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[PLEASE REMOVE] How did you test this PR? Please write down any manual commands you used and note down tests that you have written if applicable.

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