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zonglinpengzonglinpeng
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fix hifi and g3 build errors, fix cpu build errors
Differential Revision: D66672887 Pull Request resolved: #7162
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‎backends/cadence/build_cadence_fusionG3.sh

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#!/bin/bash
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# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the BSD-style license found in the
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# LICENSE file in the root directory of this source tree.
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set -euo pipefail
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unset CMAKE_PREFIX_PATH
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unset XTENSA_CORE
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export XTENSA_CORE=FCV_FG3GP
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git submodule sync
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git submodule update --init
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./install_requirements.sh
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rm -rf cmake-out
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STEPWISE_BUILD=false
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if $STEPWISE_BUILD; then
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echo "Building ExecuTorch"
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cmake -DCMAKE_INSTALL_PREFIX=cmake-out \
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-DCMAKE_TOOLCHAIN_FILE=./backends/cadence/cadence.cmake \
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-DCMAKE_BUILD_TYPE=Release \
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-DEXECUTORCH_ENABLE_EVENT_TRACER=OFF \
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-DEXECUTORCH_BUILD_EXTENSION_RUNNER_UTIL=ON \
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-DEXECUTORCH_BUILD_EXECUTOR_RUNNER=ON \
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-DEXECUTORCH_BUILD_PTHREADPOOL=OFF \
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-DEXECUTORCH_BUILD_CPUINFO=OFF \
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-DEXECUTORCH_ENABLE_LOGGING=ON \
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-DEXECUTORCH_USE_DL=OFF \
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-DEXECUTORCH_BUILD_CADENCE=OFF \
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-DFLATC_EXECUTABLE="$(which flatc)" \
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-DHAVE_FNMATCH_H=OFF \
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-Bcmake-out .
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echo "Building any Cadence-specific binaries on top"
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cmake -DBUCK2="$BUCK" \
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-DCMAKE_TOOLCHAIN_FILE=/home/zonglinpeng/ws/zonglinpeng/executorch/backends/cadence/cadence.cmake \
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-DCMAKE_INSTALL_PREFIX=cmake-out \
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-DCMAKE_BUILD_TYPE=Release \
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-DEXECUTORCH_BUILD_HOST_TARGETS=ON \
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-DEXECUTORCH_BUILD_EXECUTOR_RUNNER=ON \
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-DEXECUTORCH_BUILD_PTHREADPOOL=OFF \
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-DEXECUTORCH_BUILD_CADENCE=ON \
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-DFLATC_EXECUTABLE="$(which flatc)" \
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-DEXECUTORCH_ENABLE_LOGGING=ON \
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-DEXECUTORCH_ENABLE_PROGRAM_VERIFICATION=ON \
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-DEXECUTORCH_USE_DL=OFF \
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-DBUILD_EXECUTORCH_PORTABLE_OPS=ON \
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-DEXECUTORCH_BUILD_KERNELS_CUSTOM=OFF \
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-DPYTHON_EXECUTABLE=python3 \
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-DEXECUTORCH_FUSION_G3_OPT=ON \
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-DEXECUTORCH_BUILD_GFLAGS=ON \
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-DHAVE_FNMATCH_H=OFF \
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-Bcmake-out/backends/cadence \
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backends/cadence
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cmake --build cmake-out/backends/cadence -j8
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else
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echo "Building Cadence toolchain with ExecuTorch packages"
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cmake_prefix_path="${PWD}/cmake-out/lib/cmake/ExecuTorch;${PWD}/cmake-out/third-party/gflags"
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cmake -DBUCK2="$BUCK" \
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-DCMAKE_PREFIX_PATH="${cmake_prefix_path}" \
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-DHAVE_SYS_STAT_H=ON \
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-DCMAKE_TOOLCHAIN_FILE=./backends/cadence/cadence.cmake \
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-DCMAKE_INSTALL_PREFIX=cmake-out \
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-DCMAKE_BUILD_TYPE=Release \
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-DEXECUTORCH_BUILD_HOST_TARGETS=ON \
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-DEXECUTORCH_BUILD_EXECUTOR_RUNNER=ON \
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-DEXECUTORCH_BUILD_PTHREADPOOL=OFF \
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-DEXECUTORCH_BUILD_CPUINFO=OFF \
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-DEXECUTORCH_BUILD_FLATC=OFF \
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-DEXECUTORCH_BUILD_CADENCE=ON \
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-DFLATC_EXECUTABLE="$(which flatc)" \
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-DEXECUTORCH_BUILD_EXTENSION_RUNNER_UTIL=ON \
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-DEXECUTORCH_ENABLE_LOGGING=ON \
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-DEXECUTORCH_ENABLE_PROGRAM_VERIFICATION=ON \
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-DEXECUTORCH_USE_DL=OFF \
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-DBUILD_EXECUTORCH_PORTABLE_OPS=ON \
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-DEXECUTORCH_BUILD_KERNELS_CUSTOM=OFF \
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-DPYTHON_EXECUTABLE=python3 \
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-DEXECUTORCH_FUSION_G3_OPT=ON \
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-DHAVE_FNMATCH_H=OFF \
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-Bcmake-out
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cmake --build cmake-out --target install --config Release -j8
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fi
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echo "Run simple model to verify cmake build"
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python3 -m examples.portable.scripts.export --model_name="add"
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xt-run --turbo cmake-out/executor_runner --model_path=add.pte

‎backends/cadence/build_cadence_xtensa.sh renamed to ‎backends/cadence/build_cadence_hifi4.sh

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set -euo pipefail
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unset CMAKE_PREFIX_PATH
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unset XTENSA_CORE
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export XTENSA_CORE=nxp_rt600_RI23_11_newlib
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git submodule sync
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git submodule update --init
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./install_requirements.sh
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-DHAVE_FNMATCH_H=OFF \
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-Bcmake-out/backends/cadence \
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backends/cadence
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cmake --build cmake-out/backends/cadence -j16
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cmake --build cmake-out/backends/cadence -j8
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else
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echo "Building Cadence toolchain with ExecuTorch packages"
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cmake_prefix_path="${PWD}/cmake-out/lib/cmake/ExecuTorch;${PWD}/cmake-out/third-party/gflags"
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-DEXECUTORCH_NNLIB_OPT=ON \
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-DHAVE_FNMATCH_H=OFF \
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-Bcmake-out
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cmake --build cmake-out --target install --config Release -j16
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cmake --build cmake-out --target install --config Release -j8
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fi
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echo "Run simple model to verify cmake build"

‎backends/cadence/hifi/operators/op_mean.cpp

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ET_SWITCH_REALHB_TYPES(in.scalar_type(), ctx, "mean.out", CTYPE_IN, [&] {
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ET_SWITCH_FLOATH_TYPES(out.scalar_type(), ctx, "mean.out", CTYPE_OUT, [&] {
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CTYPE_OUT* out_data = out.mutable_data_ptr<CTYPE_OUT>();
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const size_t num =
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torch::executor::exeget_reduced_dim_product(in, dim_list);
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const size_t num = torch::executor::get_reduced_dim_product(in, dim_list);
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for (size_t out_ix = 0; out_ix < out.numel(); ++out_ix) {
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CTYPE_OUT sum = 0;
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if (in.numel() > 0) {

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