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[Enhancement] Support tvm (#1216)
* finish framework * add autotvm and auto-scheduler tuner * add python deploy api * add SDK net(WIP * add sdk support * support det, support vm * fix vm sdk * support two stage detector * add instance seg support * add docstring * update docs and ut * add quantize * update doc * update docs * synchronize stream * support dlpack * remove submodule * fix stride * add alignment * support dlpack * remove submodule * replace exclusive_scan * add backend check * add build script * fix comment * add ci * fix ci * ci fix2 * update build script * update ci * add pytest * update sed command * update sed again * add xgboost * remove tvm ut * update ansor runner * add stream sync * fix topk * sync default stream * fix tvm net * fix window
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‎.codespell_ignore.txt

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cann
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CANN
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nd

‎.github/workflows/backend-tvm.yml

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name: backend-tvm
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on:
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push:
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paths-ignore:
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- "demo/**"
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- "tools/**"
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pull_request:
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paths-ignore:
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- "demo/**"
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- "tools/**"
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- "docs/**"
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concurrency:
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group: ${{ github.workflow }}-${{ github.ref }}
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cancel-in-progress: true
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jobs:
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script_install:
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runs-on: ubuntu-20.04
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strategy:
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matrix:
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python-version: [3.7]
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steps:
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- name: Checkout repository
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uses: actions/checkout@v3
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with:
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submodules: 'recursive'
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- name: Set up Python ${{ matrix.python-version }}
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uses: actions/setup-python@v2
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with:
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python-version: ${{ matrix.python-version }}
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- name: Install mmdeploy
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run: |
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python3 tools/scripts/build_ubuntu_x64_tvm.py
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source ~/mmdeploy.env
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python3 -m pip install torch==1.8.2 torchvision==0.9.2 --extra-index-url https://download.pytorch.org/whl/lts/1.8/cpu
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python3 -m pip install mmcv-full==1.5.1 -f https://download.openmmlab.com/mmcv/dist/cpu/torch1.8.0/index.html
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python3 -m pip install decorator psutil scipy attrs tornado pytest
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python3 -c 'import mmdeploy.apis.tvm as tvm_api; assert tvm_api.is_available()'

‎.gitmodules

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[submodule "third_party/cub"]
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path = third_party/cub
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url = https://github.com/NVIDIA/cub.git
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path = third_party/cub
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url = https://github.com/NVIDIA/cub.git
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[submodule "third_party/pybind11"]
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path = third_party/pybind11
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url = https://github.com/pybind/pybind11.git
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path = third_party/pybind11
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url = https://github.com/pybind/pybind11.git
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[submodule "third_party/spdlog"]
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path = third_party/spdlog
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url = https://github.com/gabime/spdlog.git
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path = third_party/spdlog
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url = https://github.com/gabime/spdlog.git

‎README.md

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‎README_zh-CN.md

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‎cmake/modules/FindTVM.cmake

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# Copyright (c) OpenMMLab. All rights reserved.
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if (NOT DEFINED TVM_DIR)
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set(TVM_DIR $ENV{TVM_DIR})
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endif ()
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if (NOT TVM_DIR)
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message(FATAL_ERROR "Please set TVM_DIR with cmake -D option.")
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endif()
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find_path(
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TVM_INCLUDE_DIR tvm/runtime/c_runtime_api.h
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HINTS ${TVM_DIR}
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PATH_SUFFIXES include)
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find_path(
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DMLC_CORE_INCLUDE_DIR dmlc/io.h
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HINTS ${TVM_DIR}/3rdparty/dmlc-core
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PATH_SUFFIXES include)
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find_path(
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DLPACK_INCLUDE_DIR dlpack/dlpack.h
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HINTS ${TVM_DIR}/3rdparty/dlpack
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PATH_SUFFIXES include)
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find_library(
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TVM_LIBRARY_PATH tvm_runtime
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HINTS ${TVM_DIR}
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PATH_SUFFIXES build lib build/${CMAKE_BUILD_TYPE})
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if (NOT (TVM_INCLUDE_DIR AND DMLC_CORE_INCLUDE_DIR AND DLPACK_INCLUDE_DIR AND TVM_LIBRARY_PATH))
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message(FATAL_ERROR "Couldn't find tvm in TVM_DIR: "
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"${TVM_DIR}, please check if the path is correct.")
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endif()
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add_library(tvm_runtime SHARED IMPORTED)
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set_property(TARGET tvm_runtime APPEND PROPERTY IMPORTED_CONFIGURATIONS RELEASE)
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if (MSVC)
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set_target_properties(tvm_runtime PROPERTIES
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IMPORTED_IMPLIB_RELEASE ${TVM_LIBRARY_PATH}
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INTERFACE_INCLUDE_DIRECTORIES ${TVM_INCLUDE_DIR} ${DMLC_CORE_INCLUDE_DIR} ${DLPACK_INCLUDE_DIR}
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)
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else()
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set_target_properties(tvm_runtime PROPERTIES
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IMPORTED_LOCATION_RELEASE ${TVM_LIBRARY_PATH}
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INTERFACE_INCLUDE_DIRECTORIES ${TVM_INCLUDE_DIR} ${DMLC_CORE_INCLUDE_DIR} ${DLPACK_INCLUDE_DIR}
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)
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endif()

‎configs/_base_/backends/tvm.py

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backend_config = dict(type='tvm')

‎configs/mmcls/classification_tvm-ansor_static-224x224.py

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_base_ = ['./classification_static.py', '../_base_/backends/tvm.py']
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onnx_config = dict(input_shape=[224, 224])
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backend_config = dict(model_inputs=[
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dict(
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shape=dict(input=[1, 3, 224, 224]),
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dtype=dict(input='float32'),
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tuner=dict(
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type='AutoScheduleTuner',
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log_file='tvm_tune_log.log',
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num_measure_trials=2000))
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])

‎configs/mmcls/classification_tvm-autotvm-int8_static-224x224.py

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_base_ = ['./classification_tvm-autotvm_static-224x224.py']
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calib_config = dict(create_calib=True, calib_file='calib_data.h5')
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backend_config = dict(model_inputs=[
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dict(
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shape=dict(input=[1, 3, 224, 224]),
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dtype=dict(input='float32'),
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tuner=dict(
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type='AutoTVMTuner',
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log_file='tvm_tune_log.log',
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n_trial=1000,
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tuner=dict(type='XGBTuner'),
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),
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qconfig=dict(calibrate_mode='kl_divergence', weight_scale='max'),
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)
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])

‎configs/mmcls/classification_tvm-autotvm_static-224x224.py

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_base_ = ['./classification_static.py', '../_base_/backends/tvm.py']
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onnx_config = dict(input_shape=[224, 224])
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backend_config = dict(model_inputs=[
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dict(
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shape=dict(input=[1, 3, 224, 224]),
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dtype=dict(input='float32'),
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tuner=dict(
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type='AutoTVMTuner',
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log_file='tvm_tune_log.log',
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n_trial=1000,
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tuner=dict(type='XGBTuner')))
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])

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