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---
Language: Cpp
# BasedOnStyle: Google
AccessModifierOffset: -1
AlignAfterOpenBracket: Align
AlignConsecutiveAssignments: false
AlignConsecutiveDeclarations: false
AlignEscapedNewlines: Left
AlignOperands: true
AlignTrailingComments: true
AllowAllParametersOfDeclarationOnNextLine: true
AllowShortBlocksOnASingleLine: false
AllowShortCaseLabelsOnASingleLine: false
AllowShortFunctionsOnASingleLine: All
AllowShortIfStatementsOnASingleLine: true
AllowShortLoopsOnASingleLine: true
AlwaysBreakAfterDefinitionReturnType: None
AlwaysBreakAfterReturnType: None
AlwaysBreakBeforeMultilineStrings: true
AlwaysBreakTemplateDeclarations: Yes
BinPackArguments: true
BinPackParameters: true
BraceWrapping:
AfterClass: false
AfterControlStatement: false
AfterEnum: false
AfterFunction: false
AfterNamespace: false
AfterObjCDeclaration: false
AfterStruct: false
AfterUnion: false
AfterExternBlock: false
BeforeCatch: false
BeforeElse: false
IndentBraces: false
SplitEmptyFunction: true
SplitEmptyRecord: true
SplitEmptyNamespace: true
BreakBeforeBinaryOperators: None
BreakBeforeBraces: Attach
BreakBeforeInheritanceComma: false
BreakInheritanceList: BeforeColon
BreakBeforeTernaryOperators: true
BreakConstructorInitializersBeforeComma: false
BreakConstructorInitializers: BeforeColon
BreakAfterJavaFieldAnnotations: false
BreakStringLiterals: true
ColumnLimit: 120
CommentPragmas: '^ IWYU pragma:'
CompactNamespaces: false
ConstructorInitializerAllOnOneLineOrOnePerLine: true
ConstructorInitializerIndentWidth: 4
ContinuationIndentWidth: 2
Cpp11BracedListStyle: true
DerivePointerAlignment: true
DisableFormat: false
ExperimentalAutoDetectBinPacking: false
FixNamespaceComments: true
ForEachMacros:
# - foreach
- Q_FOREACH
- BOOST_FOREACH
IncludeBlocks: Preserve
IncludeCategories:
- Regex: '^<ext/.*\.h>'
Priority: 2
- Regex: '^<.*\.h>'
Priority: 1
- Regex: '^<.*'
Priority: 2
- Regex: '.*'
Priority: 3
IncludeIsMainRegex: '([-_](test|unittest))?$'
IndentCaseLabels: true
IndentPPDirectives: None
IndentWidth: 2
IndentWrappedFunctionNames: false
JavaScriptQuotes: Leave
JavaScriptWrapImports: true
KeepEmptyLinesAtTheStartOfBlocks: false
MacroBlockBegin: ''
MacroBlockEnd: ''
MaxEmptyLinesToKeep: 1
NamespaceIndentation: None
ObjCBinPackProtocolList: Never
ObjCBlockIndentWidth: 2
ObjCSpaceAfterProperty: false
ObjCSpaceBeforeProtocolList: true
PenaltyBreakAssignment: 2
PenaltyBreakBeforeFirstCallParameter: 1
PenaltyBreakComment: 300
PenaltyBreakFirstLessLess: 120
PenaltyBreakString: 1000
PenaltyBreakTemplateDeclaration: 10
PenaltyExcessCharacter: 1000000
PenaltyReturnTypeOnItsOwnLine: 200
PointerAlignment: Left
RawStringFormats:
- Language: Cpp
Delimiters:
- cc
- CC
- cpp
- Cpp
- CPP
- 'c++'
- 'C++'
CanonicalDelimiter: ''
BasedOnStyle: google
- Language: TextProto
Delimiters:
- pb
- PB
- proto
- PROTO
EnclosingFunctions:
- EqualsProto
- EquivToProto
- PARSE_PARTIAL_TEXT_PROTO
- PARSE_TEST_PROTO
- PARSE_TEXT_PROTO
- ParseTextOrDie
- ParseTextProtoOrDie
CanonicalDelimiter: ''
BasedOnStyle: google
ReflowComments: true
SortIncludes: true
SortUsingDeclarations: true
SpaceAfterCStyleCast: false
SpaceAfterTemplateKeyword: true
SpaceBeforeAssignmentOperators: true
SpaceBeforeCpp11BracedList: false
SpaceBeforeCtorInitializerColon: true
SpaceBeforeInheritanceColon: true
SpaceBeforeParens: ControlStatements
SpaceBeforeRangeBasedForLoopColon: true
SpaceInEmptyParentheses: false
SpacesBeforeTrailingComments: 2
SpacesInAngles: false
SpacesInContainerLiterals: true
SpacesInCStyleCastParentheses: false
SpacesInParentheses: false
SpacesInSquareBrackets: false
Standard: Auto
StatementMacros:
- Q_UNUSED
- QT_REQUIRE_VERSION
TabWidth: 2
UseTab: Never
SortIncludes: false
...
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# MindSpore
build/
mindspore/lib
output
*.ir
# Cmake files
CMakeFiles/
cmake_install.cmake
CMakeCache.txt
Makefile
cmake-build-debug
# Dynamic libraries
*.so
*.so.*
*.dylib
# Static libraries
*.la
*.lai
*.a
*.lib
# Protocol buffers
*_pb2.py
*.pb.h
*.pb.cc
# Object files
*.o
# Editor
.vscode
.idea/
# Cquery
.cquery_cached_index/
compile_commands.json
# Ctags and cscope
tags
TAGS
CTAGS
GTAGS
GRTAGS
GSYMS
GPATH
cscope.*
# Python files
*__pycache__*
.pytest_cache
# Mac files
*.DS_Store
# Test results
test_temp_summary_event_file/
*.dot
*.dat
*.svg
*.perf
*.info
*.ckpt
*.shp
*.pkl
.clangd
mindspore/version.py
mindspore/default_config.py
mindspore/.commit_id
onnx.proto
mindspore/ccsrc/onnx.proto
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[submodule "third_party/flatbuffers"]
path = third_party/flatbuffers
url = https://github.com/google/flatbuffers.git
[submodule "third_party/googletest"]
path = third_party/googletest
url = https://github.com/google/googletest.git
[submodule "third_party/incubator-tvm"]
path = third_party/incubator-tvm
url = https://github.com/apache/incubator-tvm.git
[submodule "third_party/protobuf"]
path = third_party/protobuf
url = https://github.com/protocolbuffers/protobuf.git
[submodule "graphengine"]
path = graphengine
url = https://gitee.com/mindspore/graphengine.git
CMakeLists.txt 0 → 100644
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cmake_minimum_required(VERSION 3.14)
project (MindSpore)
include(${CMAKE_SOURCE_DIR}/cmake/options.cmake)
set(CMAKE_MODULE_PATH ${CMAKE_MODULE_PATH} "${CMAKE_SOURCE_DIR}/cmake/modules/")
set(CMAKE_CXX_FLAGS_DEBUG "$ENV{CXXFLAGS} -O0 -g2 -ggdb -fno-inline-functions -fno-omit-frame-pointer -Wl,--allow-shlib-undefined -D_LIBCPP_INLINE_VISIBILITY='' -D'_LIBCPP_EXTERN_TEMPLATE(...)=' -DHALF_ENABLE_CPP11_USER_LITERALS=0 -D_FORTIFY_SOURCE=2 -Wno-cpp")
set(CMAKE_CXX_FLAGS_RELEASE "$ENV{CXXFLAGS} -O2 -Wl,--allow-shlib-undefined -DHALF_ENABLE_CPP11_USER_LITERALS=0 -D_FORTIFY_SOURCE=2")
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -I/usr/local/include -std=c++17 -Werror -Wall -Wno-deprecated-declarations -fPIC")
set(CMAKE_EXPORT_COMPILE_COMMANDS ON)
set(PYBIND11_CPP_STANDARD -std=c++17)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${OPTION_CXX_FLAGS}")
find_package(Threads)
include(${CMAKE_SOURCE_DIR}/cmake/mind_expression.cmake)
include_directories(${CMAKE_CURRENT_SOURCE_DIR})
include_directories(${CMAKE_CURRENT_SOURCE_DIR}/third_party/flatbuffers/include)
include_directories(${CMAKE_CURRENT_SOURCE_DIR}/third_party/flatbuffers/include/flatbuffers)
include(${CMAKE_SOURCE_DIR}/cmake/dependency_utils.cmake)
find_package(Python3 COMPONENTS Interpreter Development)
if(Python3_FOUND)
set(PYTHON_INCLUDE_DIRS "${Python3_INCLUDE_DIRS}")
set(PYTHON_LIBRARIES "${Python3_LIBRARIES}")
else()
find_python_package(py_inc py_lib)
set(PYTHON_INCLUDE_DIRS "${py_inc}")
set(PYTHON_LIBRARIES "${py_lib}")
endif()
message("PYTHON_INCLUDE_DIRS = ${PYTHON_INCLUDE_DIRS}")
message("PYTHON_LIBRARIES = ${PYTHON_LIBRARIES}")
include_directories(${PYTHON_INCLUDE_DIRS})
set(MS_CCSRC_PATH ${CMAKE_SOURCE_DIR}/mindspore/ccsrc)
set(MS_CCSRC_BUILD_PATH ${BUILD_PATH}/mindspore/mindspore/ccsrc)
if (ENABLE_GE)
link_directories(${CMAKE_SOURCE_DIR}/third_party/ge/lib)
else()
include(${CMAKE_SOURCE_DIR}/cmake/dependency_graphengine.cmake)
endif()
include_directories(${CMAKE_CURRENT_SOURCE_DIR}/graphengine/inc)
include_directories(${CMAKE_CURRENT_SOURCE_DIR}/graphengine/inc/external)
include_directories(${CMAKE_CURRENT_SOURCE_DIR}/graphengine/inc/framework)
include_directories(${CMAKE_CURRENT_SOURCE_DIR}/graphengine/third_party/fwkacllib/inc)
include_directories(${CMAKE_CURRENT_SOURCE_DIR}/graphengine/third_party/fwkacllib/inc/toolchain)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fvisibility=hidden")
add_subdirectory(mindspore/ccsrc)
if (ENABLE_TESTCASES)
add_subdirectory(tests)
endif()
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# MindSpore contributing guidelines
<!-- TOC -->
- [MindSpore contributing guidelines](#mindspore-contributing-guidelines)
- [Contributor License Agreement](#contributor-license-agreement)
- [Getting Started](#getting-started)
- [Contribution workflow](#contribution-workflow)
- [Code style](#code-style)
- [Fork-Pull development model](#fork-pull-development-model)
- [Report issues](#report-issues)
- [Propose PRs](#propose-prs)
<!-- /TOC -->
## Contributor License Agreement
It's required to sign CLA before your first code submission to MindSpore community.
For individual contributor, please refer to [ICLA online document](https://www.mindspore.cn/icla) for the detailed information.
## Getting Started
- Fork the repository on [Github](https://github.com/mindspore-ai/mindspore) or [Gitee](https://gitee.com/mindspore/mindspore).
- Read the [README.md](README.md) and [install page](https://www.mindspore.cn/install/en) for project information and build instructions.
## Contribution Workflow
### Code style
Please follow this style to make MindSpore easy to review, maintain and develop.
* Coding guidelines
The *Python* coding style suggested by [Python PEP 8 Coding Style](https://pep8.org/) and *C++* coding style suggested by [Google C++ Coding Guidelines](http://google.github.io/styleguide/cppguide.html) are used in MindSpore community.
* Unittest guidelines
The *Python* unittest style suggested by [pytest](http://www.pytest.org/en/latest/) and *C++* unittest style suggested by [Googletest Primer](https://github.com/google/googletest/blob/master/googletest/docs/primer.md) are used in MindSpore community.
### Fork-Pull development model
* Fork MindSpore repository
Before submitting code to MindSpore project, please make sure that this project have been forked to your own repository. It means that there will be parallel development between MindSpore repository and your own repository, so be careful to avoid the inconsistency between them.
* Clone the remote repository
If you want to download the code to the local machine, `git` is the best way:
```shell
# For GitHub
git clone https://github.com/{insert_your_forked_repo}/mindspore.git
git remote add upstream https://github.com/mindspore-ai/mindspore.git
# For Gitee
git clone https://gitee.com/{insert_your_forked_repo}/mindspore.git
git remote add upstream https://gitee.com/mindspore/mindspore.git
```
* Develop code locally
To avoid inconsistency between multiple branches, checking out to a new branch is `SUGGESTED`:
```shell
git checkout -b {new_branch_name} origin/master
```
Then you can change the code arbitrarily.
* Push the code to the remote repository
After updating the code, you should push the update in the formal way:
```shell
git add .
git status # Check the update status
git commit -m "Your commit title"
git commit -s --amend #Add the concrete description of your commit
git push origin {new_branch_name}
```
* Pull a request to MindSpore repository
In the last step, your need to pull a compare request between your new branch and MindSpore `master` branch. After finishing the pull request, the Jekins CI will be automatically set up for building test.
### Report issues
A great way to contribute to the project is to send a detailed report when you encounter an issue. We always appreciate a well-written, thorough bug report, and will thank you for it!
When reporting issues, refer to this format:
- What version of env (mindspore, os, python etc) are you using?
- Is this a BUG REPORT or FEATURE REQUEST?
- What happened?
- What you expected to happen?
- How to reproduce it?(as minimally and precisely as possible)
- Special notes for your reviewers?
**Issues advisory:**
- **If you find an unclosed issue, which is exactly what you are going to solve,** please put some comments on that issue to tell others you would be in charge of it.
- **If an issue is opened for a while,** it's recommended for contributors to precheck before working on solving that issue.
- **If you resolve an issue which is reported by yourself,** it's also required to let others know before closing that issue.
### Propose PRs
* Raise your idea as an *issue* on [GitHub](https://github.com/mindspore-ai/mindspore/issues) or [Gitee](https://gitee.com/mindspore/mindspore/issues)
* If it is a new feature that needs lots of design details, a design proposal should also be submitted.
* After reaching consensus in the issue discussions and design proposal reviews, complete the development on the forked repo and submit a PR.
* None of PRs is not permitted until it receives **2+ LGTM** from approvers. Please NOTICE that approver is NOT allowed to add *LGTM* on his own PR.
* After PR is sufficiently discussed, it will get merged, abondoned or rejected depending on the outcome of the discussion.
**PRs advisory:**
- Any irrelevant changes should be avoided.
- Make sure your commit history being ordered.
- Always keep your branch up with the master branch.
- For bug-fix PRs, make sure all related issues being linked.
LICENSE 0 → 100644
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Apache License
Version 2.0, January 2004
http://www.apache.org/licenses/
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NOTICE 0 → 100644
浏览文件 @ 930a1fb0
MindSpore
Copyright 2019-2020 Huawei Technologies Co., Ltd
README.md 0 → 100644
浏览文件 @ 930a1fb0
![MindSpore Logo](docs/MindSpore-logo.png "MindSpore logo")
============================================================
- [What is MindSpore?](#what-is-MindSpore)
- [Automatic Differentiation](#automatic-differentiation)
- [Automatic Parallel](#automatic-parallel)
- [Installation](#installation)
- [Binaries](#binaries)
- [From Source](#from-source)
- [Quickstart](#quickstart)
- [Docs](#docs)
- [Community](#community)
- [Governance](#governance)
- [Communication](#communication)
- [Contributing](#contributing)
- [Release Notes](#release-notes)
- [License](#license)
## What Is MindSpore
MindSpore is a new open source deep learning training/inference framework that
could be used for mobile, edge and cloud scenarios. MindSpore is designed to
provide development experience with friendly design and efficient execution for
the data scientists and algorithmic engineers, native support for Ascend AI
processor, and software hardware co-optimization. At the meantime MindSpore as
a global AI open source community, aims to further advance the development and
enrichment of the AI software/hardware application ecosystem.
<img src="docs/MindSpore-architecture.png" alt="MindSpore Architecture" width="600"/>
For more details please check out our [Architecture Guide](https://www.mindspore.cn/docs/en/0.1.0-alpha/architecture.html).
### Automatic Differentiation
There are currently three automatic differentiation techniques in mainstream deep learning frameworks:
- **Conversion based on static compute graph**: Convert the network into a static data flow graph at compile time, then turn the chain rule into a data flow graph to implement automatic differentiation.
- **Conversion based on dynamic compute graph**: Record the operation trajectory of the network during forward execution in an operator overloaded manner, then apply the chain rule to the dynamically generated data flow graph to implement automatic differentiation.
- **Conversion based on source code**: This technology is evolving from the functional programming framework and performs automatic differential transformation on the intermediate expression (the expression form of the program during the compilation process) in the form of just-in-time compilation (JIT), supporting complex control flow scenarios, higher-order functions and closures.
TensorFlow adopted static calculation diagrams in the early days, whereas PyTorch used dynamic calculation diagrams. Static maps can utilize static compilation technology to optimize network performance, however, building a network or debugging it is very complicated. The use of dynamic graphics is very convenient, but it is difficult to achieve extreme optimization in performance.
But MindSpore finds another way, automatic differentiation based on source code conversion. On the one hand, it supports automatic differentiation of automatic control flow, so it is quite convenient to build models like PyTorch. On the other hand, MindSpore can perform static compilation optimization on neural networks to achieve great performance.
<img src="docs/Automatic-differentiation.png" alt="Automatic Differentiation" width="600"/>
The implementation of MindSpore automatic differentiation can be understood as the symbolic differentiation of the program itself. Because MindSpore IR is a functional intermediate expression, it has an intuitive correspondence with the composite function in basic algebra. The derivation formula of the composite function composed of arbitrary basic functions can be derived. Each primitive operation in MindSpore IR can correspond to the basic functions in basic algebra, which can build more complex flow control.
### Automatic Parallel
The goal of MindSpore automatic parallel is to build a training method that combines data parallelism, model parallelism, and hybrid parallelism. It can automatically select a least cost model splitting strategy to achieve automatic distributed parallel training.
<img src="docs/Automatic-parallel.png" alt="Automatic Parallel" width="600"/>
At present, MindSpore uses a fine-grained parallel strategy of splitting operators, that is, each operator in the figure is splited into a cluster to complete parallel operations. The splitting strategy during this period may be very complicated, but as a developer advocating Pythonic, you don't need to care about the underlying implementation, as long as the top-level API compute is efficient.
## Installation
### Binaries
MindSpore offers build options across multiple backends:
| Hardware Platform | Operating System | Status |
| :---------------- | :--------------- | :----- |
| Ascend910 | Ubuntu-x86 | ✔️ |
| | EulerOS-x86 | ✔️ |
| | EulerOS-aarch64 | ✔️ |
| GPU CUDA 9.2 | Ubuntu-x86 | ✔️ |
| GPU CUDA 10.1 | Ubuntu-x86 | ✔️ |
| CPU | Ubuntu-x86 | ✔️ |
For installation using pip, take `Ubuntu-x86` and `CPU` build version as an example:
1. Download whl from [MindSpore website](https://www.mindspore.cn/), and install the package.
```
pip install https://ms-release.obs.cn-north-4.myhuaweicloud.com/0.1.0-alpha/MindSpore/cpu/ubuntu-x86/mindspore-0.1.0-cp37-cp37m-linux_x86_64.whl
```
2. Run the following command to verify the install.
```
python -c 'import mindspore'
```
### From Source
[Install MindSpore](https://www.mindspore.cn/install/en).
## Quickstart
See the [Quick Start](https://www.mindspore.cn/tutorial/en/0.1.0-alpha/quick_start/quick_start.html)
to implement the image classification.
## Docs
More details about installation guide, tutorials and APIs, please see the
[User Documentation](https://gitee.com/mindspore/docs).
## Community
### Governance
Check out how MindSpore Open Governance [works](https://gitee.com/mindspore/community/blob/master/governance.md).
### Communication
- [MindSpore Slack](https://join.slack.com/t/mindspore/shared_invite/enQtOTcwMTIxMDI3NjM0LTNkMWM2MzI5NjIyZWU5ZWQ5M2EwMTQ5MWNiYzMxOGM4OWFhZjI4M2E5OGI2YTg3ODU1ODE2Njg1MThiNWI3YmQ) - Communication platform for developers.
- IRC channel at `#mindspore` (only for meeting minutes logging purpose)
- Video Conferencing: meet.jit.si
- Mailing-list: https://mailweb.mindspore.cn/postorius/lists
## Contributing
Welcome contributions. See our [Contributor Wiki](CONTRIBUTING.md) for
more details.
## Release Notes
The release notes, see our [RELEASE](RELEASE.md).
## License
[Apache License 2.0](LICENSE)
RELEASE.md 0 → 100644
浏览文件 @ 930a1fb0
# Release 0.1.0-alpha
## Main Features
### Ascend 910 Training and Inference Framework
* Recommended OS: Ubuntu 16.04 (or later) or EulerOS 2.0
* Python version: 3.7.5
* Preset models
* ResNet-50: residual structure-based convolutional neural network (CNN) for image classification, which is widely used.
* AlexNet: classic CNN for image classification, achieving historical results in ImageNet LSVRC-2012.
* LeNet: classic CNN for image classification, which was proposed by Yann LeCun.
* VGG16: classic CNN for image classification, which was proposed by Oxford Visual Geometry Group.
* YoloV3: real-time object detection network.
* NEZHA: BERT-based Chinese pre-training network produced by Huawei Noah's Ark Laboratory.
* Execution modes
* Graph mode: provides graph optimization methods such as memory overcommitment, IR fusion, and buffer fusion to achieve optimal execution performance.
* PyNative mode: single-step execution mode, facilitating process debugging.
* Debugging capability and methods
* Save CheckPoints and Summary data during training.
* Support asynchronous printing.
* Dump the computing data.
* Support profiling analysis of the execution process performance.
* Distributed execution
* Support AllReduce, AllGather, and BroadCast collective communication.
* AllReduce data parallel: Each device obtains different training data, which accelerates the overall training process.
* Collective communication-based layerwise parallel: Models are divided and allocated to different devices to solve the problem of insufficient memory for large model processing and improve the training speed.
* Automatic parallel mode: The better data and model parallel mode can be predicted based on the cost model. It is recommended that this mode be used on ResNet series networks.
* Automatic differentiation
* Implement automatic differentiation based on Source to Source.
* Support distributed scenarios and automatic insertion of reverse communication operators.
* Data processing, augmentation, and save format
* Load common datasets such as ImageNet, MNIST, CIFAR-10, and CIFAR-100.
* Support common data loading pipeline operations, such as shuffle, repeat, batch, map, and sampler.
* Provide basic operator libraries to cover common CV scenarios.
* Support users to customize Python data augmentation operators through the Pyfunc mechanism.
* Support the access of user-defined datasets through the GeneratorDataset mechanism.
* Provide the MindSpore data format, data aggregation and storage, random access example, data partition, efficient parallel read, user-defined index, and dataset search.
* Convert user datasets to the MindSpore data format.
* After data processing and augmentation, provide training applications in feed and graph modes.
* FP32/16 mixed precision computation, supporting automatic and manual configuration
* Provide common operators such as nn, math, and array, which can be customized.
### Inference Deployment
* Deploy models in MindSpore format on the Ascend 310 platform for inference.
* Save models in ONNX format.
* Support saving models in LITE format and running models based on the lightweight inference framework.
* Recommended OS: Android 4.3 or later
* Supported network type: LeNet
* Provide the generalization operators generated by TVM and operators generated after specific networks are tuned.
### Other Hardware Support
* GPU platform training
* Recommended OS: Ubuntu 16.04
* CUDA version: 9.2 or 10.1
* CuDNN version: 7.6 or later
* Python version: 3.7.5
* NCCL version: 2.4.8-1
* OpenMPI version: 3.1.5
* Supported models: AlexNet, LeNet, and LSTM
* Supported datasets: MNIST and CIFAR-10
* Support data parallel.
* CPU platform training
* Recommended OS: Ubuntu 16.04
* Python version: 3.7.5
* Supported model: LeNet
* Supported dataset: MNIST
* Provide only the stand-alone operation version.
## Peripherals and Tools
* [MindSpore Official Website] (https://www.mindspore.cn/)
* [MindInsight Visualization Debugging and Optimization] (https://gitee.com/mindspore/mindinsight)
* [MindArmour Model Security Hardening Package] (https://gitee.com/mindspore/mindarmour)
* [GraphEngine Computational Graph Engine] (https://gitee.com/mindspore/graphengine)
\ No newline at end of file
SECURITY.md 0 → 100644
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# Security Risk Description
1. When MindSpore is used for AI model training, if the user-defined computational graph structure (for example, Python code for generating the MindSpore computational graph) is provided by an untrusted third party, malicious code may exist and will be loaded and executed to attack the system.
2. Model files are stored in binary mode. When MindSpore is used to optimize or infer AI models and the model files are loaded in deserialization mode, once malicious code is written into the model files, the code are loaded and executed, causing attacks on the system.
3. MindSpore performs only model training and inference based on the data provided by users. Users need to protect data security to avoid privacy leakage.
4. MindSpore is a distributed training platform. When MindSpore is used for distributed training, if an Ascend chip is used for training, a device provides a secure transmission protocol for gradient fusion. If GPUs or other clusters are used for training, identity authentication and secure transmission are not provided.
# Security Usage Suggestions
1. Run MindSpore in the sandbox.
2. Run MindSpore as a non-root user.
3. Ensure that the source of a computational graph structure is trustworthy. Do not write code irrelevant to model training in the network structure definition.
4. Ensure that the source of a network model is trustworthy or enter secure network model parameters to prevent model parameters from being tampered with.
5. Ensure that GPU distributed training is performed on an isolated cluster network.
Third_Party_Open_Source_Software_Notice 0 → 100644
浏览文件 @ 930a1fb0
因为 它太大了无法显示 source diff 。你可以改为 查看blob。
autogen.sh 0 → 100755
浏览文件 @ 930a1fb0
#!/bin/bash
# Copyright 2019 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
git submodule update --init --recursive
build.sh 0 → 100755
浏览文件 @ 930a1fb0
#!/bin/bash
# Copyright 2019 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
set -e
BASEPATH=$(cd "$(dirname $0)"; pwd)
PROJECT_PATH="${BASEPATH}"
CUDA_PATH=""
CUDNN_PATH=""
export BUILD_PATH="${BASEPATH}/build/"
# print usage message
usage()
{
echo "Usage:"
echo "bash build.sh [-d] [-r] [-v] [-c on|off] [-t on|off] [-g on|off] [-h] [-s] [-b ge|cpu] [-m infer|train] \\"
echo " [-a on|off] [-g on|off] [-p on|off] [-i] [-L] [-R] [-D on|off] [-j[n]] [-e gpu|d|cpu] \\"
echo " [-P on|off] [-z] [-M on|off] [-V 9.2|10.1] [-I] [-K]"
echo ""
echo "Options:"
echo " -d Debug mode"
echo " -r Release mode, default mode"
echo " -v Display build command"
echo " -c Enable code coverage switch, default off"
echo " -t Run testcases switch, default on"
echo " -g Use glog to output log, default on"
echo " -h Print usage"
echo " -s Install or setup"
echo " -b Select other backend, available: \\"
echo " ge:graph engine, cpu"
echo " -m Select mode, available: infer, train, default is infer "
echo " -a Enable ASAN, default off"
echo " -p Enable pipeline profile, default off"
echo " -i Enable increment building, default off"
echo " -L Enable load ANF-IR as input of 'infer', default off"
echo " -R Enable the time_line record, default off"
echo " -j[n] Set the threads when building (Default: -j8)"
echo " -e Use gpu, d or cpu"
echo " -P Enable dump anf graph to file in ProtoBuffer format, default on"
echo " -Q Enable dump end to end, default off"
echo " -D Enable dumping of function graph ir, default on"
echo " -z Compile dataset & mindrecord, default off"
echo " -M Enable MPI and NCCL for GPU training, default off"
echo " -V Specify the minimum required cuda version, default CUDA 9.2"
echo " -I Compile predict, default off"
echo " -K Compile with AKG, default off"
}
# check value of input is 'on' or 'off'
# usage: check_on_off arg_value arg_name
check_on_off()
{
if [[ "X$1" != "Xon" && "X$1" != "Xoff" ]]; then
echo "Invalid value $1 for option -$2"
usage
exit 1
fi
}
# check and set options
checkopts()
{
# Init default values of build options
THREAD_NUM=8
DEBUG_MODE="off"
VERBOSE=""
ENABLE_COVERAGE="off"
RUN_TESTCASES="off"
EXECUTE_SETUP="off"
ENABLE_BACKEND=""
TRAIN_MODE="INFER"
ENABLE_ASAN="off"
ENABLE_PROFILE="off"
INC_BUILD="off"
ENABLE_LOAD_IR="off"
ENABLE_TIMELINE="off"
ENABLE_DUMP2PROTO="on"
ENABLE_DUMPE2E="off"
ENABLE_DUMP_IR="on"
COMPILE_MINDDATA="off"
ENABLE_MPI="off"
CUDA_VERSION="9.2"
COMPILE_PREDICT="off"
USE_GLOG="on"
PREDICT_PLATFORM=""
ENABLE_AKG="off"
# Process the options
while getopts 'drvj:c:t:hsb:a:g:p:ie:m:I:LRP:Q:D:zM:V:K' opt
do
OPTARG=$(echo ${OPTARG} | tr '[A-Z]' '[a-z]')
case "${opt}" in
d)
DEBUG_MODE="on"
;;
r)
DEBUG_MODE="off"
;;
v)
VERBOSE="VERBOSE=1"
;;
j)
THREAD_NUM=$OPTARG
;;
c)
check_on_off $OPTARG c
ENABLE_COVERAGE="$OPTARG"
;;
t)
check_on_off $OPTARG t
RUN_TESTCASES="$OPTARG"
;;
g)
check_on_off $OPTARG g
USE_GLOG="$OPTARG"
;;
h)
usage
exit 0
;;
s)
EXECUTE_SETUP="on"
;;
b)
if [[ "X$OPTARG" != "Xge" && "X$OPTARG" != "Xcpu" ]]; then
echo "Invalid value ${OPTARG} for option -b"
usage
exit 1
fi
ENABLE_BACKEND=$(echo "$OPTARG" | tr '[a-z]' '[A-Z]')
if [[ "X$ENABLE_BACKEND" == "XGE" ]]; then
ENABLE_GE="on"
fi
if [[ "X$ENABLE_BACKEND" != "XCPU" ]]; then
ENABLE_CPU="on"
fi
;;
a)
check_on_off $OPTARG a
ENABLE_ASAN="$OPTARG"
;;
p)
check_on_off $OPTARG p
ENABLE_PROFILE="$OPTARG"
;;
i)
INC_BUILD="on"
;;
m)
if [[ "X$OPTARG" != "Xinfer" && "X$OPTARG" != "Xtrain" ]]; then
echo "Invalid value ${OPTARG} for option -m"
usage
exit 1
fi
TRAIN_MODE=$(echo "$OPTARG" | tr '[a-z]' '[A-Z]')
;;
L)
ENABLE_LOAD_IR="on"
echo "build with enable load anf ir"
;;
R)
ENABLE_TIMELINE="on"
echo "enable time_line record"
;;
e)
if [[ "X$OPTARG" == "Xgpu" ]]; then
ENABLE_GPU="on"
ENABLE_CPU="on"
elif [[ "X$OPTARG" == "Xd" ]]; then
ENABLE_D="on"
ENABLE_CPU="on"
elif [[ "X$OPTARG" == "Xcpu" ]]; then
ENABLE_CPU="on"
else
echo "Invalid value ${OPTARG} for option -e"
usage
exit 1
fi
;;
M)
check_on_off $OPTARG M
ENABLE_MPI="$OPTARG"
;;
V)
if [[ "X$OPTARG" != "X9.2" && "X$OPTARG" != "X10.1" ]]; then
echo "Invalid value ${OPTARG} for option -V"
usage
exit 1
fi
CUDA_VERSION="$OPTARG"
;;
P)
check_on_off $OPTARG p
ENABLE_DUMP2PROTO="$OPTARG"
echo "enable dump anf graph to proto file"
;;
Q)
check_on_off $OPTARG Q
ENABLE_DUMPE2E="$OPTARG"
echo "enable dump end to end"
;;
D)
check_on_off $OPTARG D
ENABLE_DUMP_IR="$OPTARG"
echo "enable dump function graph ir"
;;
z)
COMPILE_MINDDATA="on"
;;
I)
COMPILE_PREDICT="on"
if [[ "$OPTARG" == "arm64" ]]; then
PREDICT_PLATFORM="arm64"
elif [[ "$OPTARG" == "x86_64" ]]; then
PREDICT_PLATFORM="x86_64"
else
echo "-I parameter must be arm64 or x86_64"
exit 1
fi
;;
K)
ENABLE_AKG="on"
echo "enable compile with akg"
;;
*)
echo "Unknown option ${opt}!"
usage
exit 1
esac
done
}
checkopts "$@"
echo "---------------- mindspore: build start ----------------"
mkdir -pv "${BUILD_PATH}/package/mindspore/lib"
git submodule update --init graphengine
build_exit()
{
echo "$@" >&2
stty echo
exit 1
}
# Create building path
build_mindspore()
{
echo "start build mindspore project."
mkdir -pv "${BUILD_PATH}/mindspore"
cd "${BUILD_PATH}/mindspore"
CMAKE_ARGS="-DDEBUG_MODE=$DEBUG_MODE -DBUILD_PATH=$BUILD_PATH"
CMAKE_ARGS="${CMAKE_ARGS} -DENABLE_LOAD_ANF_IR=$ENABLE_LOAD_IR"
if [[ "X$ENABLE_COVERAGE" = "Xon" ]]; then
CMAKE_ARGS="${CMAKE_ARGS} -DENABLE_COVERAGE=ON"
fi
if [[ "X$RUN_TESTCASES" = "Xon" ]]; then
CMAKE_ARGS="${CMAKE_ARGS} -DENABLE_TESTCASES=ON"
fi
if [[ -n "$ENABLE_BACKEND" ]]; then
CMAKE_ARGS="${CMAKE_ARGS} -DENABLE_${ENABLE_BACKEND}=ON"
fi
if [[ -n "$TRAIN_MODE" ]]; then
CMAKE_ARGS="${CMAKE_ARGS} -DENABLE_${TRAIN_MODE}=ON"
fi
if [[ "X$ENABLE_ASAN" = "Xon" ]]; then
CMAKE_ARGS="${CMAKE_ARGS} -DENABLE_ASAN=ON"
fi
if [[ "X$ENABLE_PROFILE" = "Xon" ]]; then
CMAKE_ARGS="${CMAKE_ARGS} -DENABLE_PROFILE=ON"
fi
if [[ "X$ENABLE_TIMELINE" = "Xon" ]]; then
CMAKE_ARGS="${CMAKE_ARGS} -DENABLE_TIMELINE=ON"
fi
if [[ "X$ENABLE_DUMP2PROTO" = "Xon" ]]; then
CMAKE_ARGS="${CMAKE_ARGS} -DENABLE_DUMP_PROTO=ON"
fi
if [[ "X$ENABLE_DUMPE2E" = "Xon" ]]; then
CMAKE_ARGS="${CMAKE_ARGS} -DENABLE_DUMP_E2E=ON"
fi
CMAKE_ARGS="${CMAKE_ARGS} -DENABLE_DUMP_IR=${ENABLE_DUMP_IR^^}"
if [[ "X$ENABLE_MPI" = "Xon" ]]; then
CMAKE_ARGS="${CMAKE_ARGS} -DENABLE_MPI=ON"
fi
if [[ "X$ENABLE_D" = "Xon" ]]; then
CMAKE_ARGS="${CMAKE_ARGS} -DENABLE_D=ON"
fi
if [[ "X$ENABLE_GPU" = "Xon" ]]; then
CMAKE_ARGS="${CMAKE_ARGS} -DENABLE_GPU=ON -DCUDA_PATH=$CUDA_PATH -DCUDNN_PATH=$CUDNN_PATH -DMS_REQUIRE_CUDA_VERSION=${CUDA_VERSION}"
fi
if [[ "X$ENABLE_CPU" = "Xon" ]]; then
CMAKE_ARGS="${CMAKE_ARGS} -DENABLE_CPU=ON"
fi
if [[ "X$COMPILE_MINDDATA" = "Xon" ]]; then
CMAKE_ARGS="${CMAKE_ARGS} -DENABLE_MINDDATA=ON"
fi
if [[ "X$USE_GLOG" = "Xon" ]]; then
CMAKE_ARGS="${CMAKE_ARGS} -DUSE_GLOG=ON"
fi
if [[ "X$ENABLE_AKG" = "Xon" ]]; then
CMAKE_ARGS="${CMAKE_ARGS} -DENABLE_AKG=ON"
fi
echo "${CMAKE_ARGS}"
if [[ "X$INC_BUILD" = "Xoff" ]]; then
cmake ${CMAKE_ARGS} ../..
fi
make ${VERBOSE} -j$THREAD_NUM
if [[ "X$EXECUTE_SETUP" = "Xon" ]]; then
make install
fi
echo "success to build mindspore project!"
}
build_predict()
{
git submodule update --init --recursive third_party/incubator-tvm
echo "start build predict project"
git submodule update --init --recursive third_party/flatbuffers
git submodule update --init --recursive third_party/googletest
git submodule update --init --recursive third_party/protobuf
rm -rf "${BASEPATH}/predict/build"
mkdir -pv "${BASEPATH}/predict/build"
rm -rf "${BASEPATH}/predict/output"
mkdir -pv "${BASEPATH}/predict/output"
if [[ "$PREDICT_PLATFORM" == "arm64" ]]; then
if [ "${ANDROID_NDK}" ]; then
echo -e "\e[31mANDROID_NDK_PATH=$ANDROID_NDK \e[0m"
else
echo -e "\e[31mplease set ANDROID_NDK_PATH in environment variable for example: export ANDROID_NDK=/root/usr/android-ndk-r16b/ \e[0m"
exit 1
fi
fi
#build flatbuf
cd "${BASEPATH}/third_party/flatbuffers"
rm -rf build && mkdir -p build && cd build && cmake .. && make -j$THREAD_NUM
FLATC="${BASEPATH}"/third_party/flatbuffers/build/flatc
cd "${BASEPATH}"/predict/schema && mkdir -p "${BASEPATH}"/predict/schema/inner
find . -name "*.fbs" -print0 | xargs -0 "${FLATC}" -c -b
find . -name "*.fbs" -print0 | xargs -0 "${FLATC}" -c -b --reflect-types --gen-mutable --reflect-names --gen-object-api -o ${BASEPATH}/predict/schema/inner
# check LLVM_PATH
if [ "${LLVM_PATH}" == "" ]; then
echo "Please set LLVM_PATH in env for example export LLVM_PATH=/xxxx/bin/llvm-config"
exit
fi
#build tvm
tvm_open_source="${BASEPATH}/third_party/incubator-tvm"
tvm_kernel_build="${BASEPATH}/predict/module/tvm_kernel"
if [ ! -f "${tvm_kernel_build}"/incubator-tvm/build/libtvm.so ]; then
rm -fr "${tvm_kernel_build}"/incubator-tvm
cp -fr "${tvm_open_source}" "${tvm_kernel_build}"
mkdir -p "${tvm_kernel_build}"/incubator-tvm/build
patch -d "${tvm_kernel_build}"/incubator-tvm -p1 < "${BASEPATH}"/third_party/patch/predict/0001-RetBugFix-CustomRuntime_v06.patch
cp "${tvm_kernel_build}"/lite/src/codegen/llvm/lite_rtfunc_reset.cc "${tvm_kernel_build}"/incubator-tvm/src/codegen/llvm/
cp "${tvm_open_source}"/cmake/config.cmake "${tvm_kernel_build}"/incubator-tvm
if [ "${LLVM_PATH}" ]; then
sed -i "s#set(USE_LLVM .*)#set(USE_LLVM \"${LLVM_PATH}\")#g" "${tvm_kernel_build}"/incubator-tvm/config.cmake
else
echo "need set LLVM_PATH in env for example export LLVM_PATH=/xxxx/bin/llvm-config"
fi
cd "${tvm_kernel_build}"/incubator-tvm/build
cmake ..
make -j$THREAD_NUM
else
cd "${tvm_kernel_build}"/incubator-tvm/build
make -j$THREAD_NUM
fi
#gen op
predict_tvm_op_lib_path="${BASEPATH}/predict/module/tvm_kernel/build/lib_x86"
predict_platform="x86"
if [[ "$PREDICT_PLATFORM" == "arm64" ]]; then
predict_tvm_op_lib_path="${BASEPATH}/predict/module/tvm_kernel/build/lib_arm64"
predict_platform="arm64"
fi
need_get_libs=true
if [ -d "${predict_tvm_op_lib_path}" ]; then
file_list=$(ls "${predict_tvm_op_lib_path}")
if [ -n "${file_list}" ]; then
libstime=$(stat -c %Y "${predict_tvm_op_lib_path}"/* | sort -u | tail -n1)
pythontime=$(find "${BASEPATH}"/predict/module/tvm_kernel/lite/python/ -name "*.py" -exec stat -c %Y {} \; |
sort -u | tail -n1)
if [ "${libstime}" -ge "${pythontime}" ]; then
need_get_libs=false
else
rm -fr "${predict_tvm_op_lib_path}"
fi
fi
fi
if $need_get_libs; then
PYTHONPATH_OLD=${PYTHONPATH}
export PYTHONPATH="${tvm_kernel_build}/incubator-tvm/python:${tvm_kernel_build}/incubator-tvm/topi/python:${tvm_kernel_build}/incubator-tvm/nnvm/python:${tvm_kernel_build}/lite/python:"
cd "${BASEPATH}"/predict/module/tvm_kernel/lite/python/at_ops
python3 at_gen_strip.py ${predict_platform}
export PYTHONPATH=${PYTHONPATH_OLD}
fi
cd "${BASEPATH}/predict/build"
if [[ "$PREDICT_PLATFORM" == "arm64" ]]; then
cmake -DCMAKE_TOOLCHAIN_FILE="${ANDROID_NDK}/build/cmake/android.toolchain.cmake" \
-DANDROID_NATIVE_API_LEVEL=android-19 -DANDROID_NDK="${ANDROID_NDK}" \
-DANDROID_TOOLCHAIN_NAME="aarch64-linux-android-clang" -DANDROID_STL="c++_shared" \
-DANDROID_ABI="arm64-v8a" -DENABLE_PREDICT_ARM64=ON -DANDROID_ALLOW_UNDEFINED_SYMBOLS=TRUE ..
elif [[ "$PREDICT_PLATFORM" == "x86_64" ]]; then
cmake ..
fi
make ${VERBOSE} -j$THREAD_NUM
if [[ "$PREDICT_PLATFORM" == "x86_64" ]]; then
cd "${BASEPATH}/predict/build/test" && ./run_tests.sh
fi
# copy securec include files
mkdir -p "${BASEPATH}/predict/output/include/securec/include"
cp "${BASEPATH}"/third_party/securec/include/* "${BASEPATH}"/predict/output/include/securec/include
cd "${BASEPATH}/predict/output/"
if [[ "$PREDICT_PLATFORM" == "x86_64" ]]; then
tar -cf MSPredict-0.1.0-linux_x86_64.tar.gz include/ lib/ --warning=no-file-changed
elif [[ "$PREDICT_PLATFORM" == "arm64" ]]; then
tar -cf MSPredict-0.1.0-linux_aarch64.tar.gz include/ lib/ --warning=no-file-changed
fi
echo "success to build predict project!"
}
if [[ "X$COMPILE_PREDICT" = "Xon" ]]; then
build_predict
echo "---------------- mindspore: build end ----------------"
exit
else
build_mindspore
fi
if [[ "X$INC_BUILD" = "Xoff" ]]; then
if [[ "X$ENABLE_GE" = "Xon" ]]; then
bash "${PROJECT_PATH}/package.sh" ge
elif [[ "X$ENABLE_GPU" = "Xon" ]]; then
bash "${PROJECT_PATH}/package.sh" ms gpu
elif [[ "X$ENABLE_D" = "Xon" ]] || [[ "X$ENABLE_CPU" = "Xon" ]]; then
bash "${PROJECT_PATH}/package.sh" ms
else
bash "${PROJECT_PATH}/package.sh" debug
fi
fi
cp -rf ${BUILD_PATH}/package/mindspore/lib ${BUILD_PATH}/../mindspore
cp -rf ${BUILD_PATH}/package/mindspore/*.so ${BUILD_PATH}/../mindspore
if [[ -d "${BUILD_PATH}/package/build" ]]; then
rm -rf "${BUILD_PATH}/package/build"
fi
echo "---------------- mindspore: build end ----------------"
cmake/dependency_graphengine.cmake 0 → 100644
浏览文件 @ 930a1fb0
message(STATUS "ENABLE_GE set to FALSE, compiling GraphEngine")
set(GE_SOURCE_DIR ${CMAKE_SOURCE_DIR}/graphengine)
message(STATUS "ge dir: ${GE_SOURCE_DIR}")
# download json headers, rather than whole repository
include(${GE_SOURCE_DIR}/cmake/ge_utils.cmake)
include(${GE_SOURCE_DIR}/cmake/external_libs/json.cmake)
include(${GE_SOURCE_DIR}/cmake/external_libs/eigen.cmake)
include(${GE_SOURCE_DIR}/cmake/external_libs/gtest.cmake)
include(${GE_SOURCE_DIR}/cmake/external_libs/protobuf.cmake)
include(${GE_SOURCE_DIR}/cmake/external_libs/onnx.cmake)
# for CPU/GPU mode, find c_sec and slog from local prebuild
if (NOT ENABLE_D)
set(GE_PREBUILD_PATH ${GE_SOURCE_DIR}/third_party/prebuild/${CMAKE_HOST_SYSTEM_PROCESSOR})
find_library(c_sec libc_sec.so ${GE_PREBUILD_PATH})
find_library(slog libslog.so ${GE_PREBUILD_PATH})
elseif (DEFINED ENV{D_LINK_PATH})
set(GE_LIB_PATH $ENV{D_LINK_PATH})
set(GE_SYS_ARCH "")
if(CMAKE_HOST_SYSTEM_PROCESSOR MATCHES "x86_64")
# x86 ubuntu
set(GE_SYS_ARCH "x86_64")
elseif(CMAKE_HOST_SYSTEM_PROCESSOR MATCHES "aarch64")
# arm euleros
set(GE_SYS_ARCH "aarch64")
else()
message(FATAL_ERROR "Running on a unsupported architecture: ${SYSTEM_TYPE}, build terminated")
endif()
set(GE_LIB_PATH ${GE_LIB_PATH}/${GE_SYS_ARCH})
find_library(c_sec libc_sec.so ${GE_LIB_PATH})
find_library(slog libslog.so ${GE_LIB_PATH})
find_library(mmpa libmmpa.so ${GE_LIB_PATH})
find_library(runtime libruntime.so ${GE_LIB_PATH})
find_library(msprof libmsprof.so ${GE_LIB_PATH})
find_library(register libregister.so ${GE_LIB_PATH})
find_library(hccl libhccl.so ${GE_LIB_PATH})
find_library(cce libcce.so ${GE_LIB_PATH})
find_library(resource libresource.so ${GE_LIB_PATH})
else()
set(HIAI_INSTALLED_DIR /usr/local/HiAI)
set(HIAI_DRIVER_DIR ${HIAI_INSTALLED_DIR}/driver/lib64)
set(HIAI_RUNTIME_DIR ${HIAI_INSTALLED_DIR}/runtime/lib64)
find_library(c_sec libc_sec.so ${HIAI_DRIVER_DIR})
find_library(slog libslog.so ${HIAI_DRIVER_DIR})
find_library(mmpa libmmpa.so ${HIAI_DRIVER_DIR})
find_library(cce libcce.so ${HIAI_RUNTIME_DIR})
find_library(hccl libhccl.so ${HIAI_RUNTIME_DIR})
find_library(runtime libruntime.so ${HIAI_RUNTIME_DIR})
find_library(msprof libmsprof.so ${HIAI_RUNTIME_DIR})
find_library(register libregister.so ${HIAI_RUNTIME_DIR})
find_library(resource libresource.so ${HIAI_RUNTIME_DIR})
endif()
# compile libraries from following directories
# this cmake file is called only when NOT ENABLE_GE is set
set(_ge_tmp_CMAKE_CXX_FLAGS ${CMAKE_CXX_FLAGS})
string(REPLACE " -Wall" "" CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS}")
string(REPLACE " -Werror" "" CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS}")
# force __FILE__ to show relative path of file, from source directory
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -D__FILE__='\"$(subst ${CMAKE_SOURCE_DIR}/,,$(abspath $<))\"' -Wno-builtin-macro-redefined")
add_subdirectory(${GE_SOURCE_DIR}/src/common/graph)
if(ENABLE_D)
add_subdirectory(${GE_SOURCE_DIR}/src/ge/common)
add_subdirectory(${GE_SOURCE_DIR}/src/ge/ge_runtime)
endif()
set(CMAKE_CXX_FLAGS ${_ge_tmp_CMAKE_CXX_FLAGS})
cmake/dependency_gtest.cmake 0 → 100644
浏览文件 @ 930a1fb0
# googletest library
#
#
# GTest_LIBRARY
#
if (NOT TARGET gtest)
set(BUILD_TESTING OFF CACHE BOOL "Disable glog test")
set(_ms_tmp_CMAKE_POSITION_INDEPENDENT_CODE ${CMAKE_POSITION_INDEPENDENT_CODE})
set(_ms_tmp_CMAKE_CXX_FLAGS ${CMAKE_CXX_FLAGS})
set(_ms_tmp_BUILD_SHARED_LIBS ${BUILD_SHARED_LIBS})
set(_ms_tmp_CMAKE_MACOSX_RPATH ${CMAKE_MACOSX_RPATH})
set(CMAKE_POSITION_INDEPENDENT_CODE ON)
set(BUILD_SHARED_LIBS ON)
set(CMAKE_MACOSX_RPATH TRUE)
set(CMAKE_CXX_FLAGS "${SECURE_CXX_FLAGS}")
if (CMAKE_COMPILER_IS_GNUCXX AND CMAKE_CXX_COMPILER_VERSION VERSION_GREATER "5.0" AND CMAKE_HOST_SYSTEM_PROCESSOR MATCHES "x86_64" AND SYSTEM_TYPE MATCHES "euleros")
# -D_GLIBCXX_USE_CXX11_ABI=0 added for the ABI incompatible for libtsdclient.so
# set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -D_GLIBCXX_USE_CXX11_ABI=0")
endif()
add_subdirectory(${CMAKE_CURRENT_LIST_DIR}/../third_party/googletest ${CMAKE_BINARY_DIR}/googletest)
set(CMAKE_POSITION_INDEPENDENT_CODE ${_ms_tmp_CMAKE_POSITION_INDEPENDENT_CODE})
set(CMAKE_CXX_FLAGS ${_ms_tmp_CMAKE_CXX_FLAGS})
set(BUILD_SHARED_LIBS ${_ms_tmp_BUILD_SHARED_LIBS})
set(CMAKE_MACOSX_RPATH ${_ms_tmp_CMAKE_MACOSX_RPATH})
endif()
include_directories(${CMAKE_CURRENT_LIST_DIR}/../third_party/googletest/googletest/include)
set(GTEST_LIBRARY gtest)
cmake/dependency_protobuf.cmake 0 → 100644
浏览文件 @ 930a1fb0
# Protobuf
#
#
# PROTOBUF_LIBRARY - Link this to use protobuf
#
if (NOT TARGET protobuf::libprotobuf)
set(protobuf_BUILD_TESTS OFF CACHE BOOL "Disable protobuf test")
set(protobuf_BUILD_SHARED_LIBS OFF CACHE BOOL "Gen shared library")
set(_ms_tmp_CMAKE_CXX_FLAGS ${CMAKE_CXX_FLAGS})
string(REPLACE " -Wall" "" CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS}")
string(REPLACE " -Werror" "" CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS}")
add_subdirectory(${CMAKE_CURRENT_LIST_DIR}/../third_party/protobuf/cmake ${CMAKE_BINARY_DIR}/protobuf)
set(CMAKE_CXX_FLAGS ${_ms_tmp_CMAKE_CXX_FLAGS})
endif ()
include_directories(${CMAKE_CURRENT_LIST_DIR}/../third_party/protobuf/src)
set(PROTOBUF_LIBRARY protobuf::libprotobuf)
set(PROTOC_EXECUTABLE $<TARGET_FILE:protobuf::protoc>)
function(ms_protobuf_generate c_var h_var)
if(NOT ARGN)
message(SEND_ERROR "Error: ms_protobuf_generate() called without any proto files")
return()
endif()
set(${c_var})
set(${h_var})
foreach(file ${ARGN})
get_filename_component(abs_file ${file} ABSOLUTE)
get_filename_component(file_name ${file} NAME_WE)
get_filename_component(file_dir ${abs_file} PATH)
file(RELATIVE_PATH rel_path ${CMAKE_CURRENT_SOURCE_DIR} ${file_dir})
list(APPEND ${c_var} "${CMAKE_BINARY_DIR}/${rel_path}/${file_name}.pb.cc")
list(APPEND ${h_var} "${CMAKE_BINARY_DIR}/${rel_path}/${file_name}.pb.h")
add_custom_command(
OUTPUT "${CMAKE_BINARY_DIR}/${rel_path}/${file_name}.pb.cc"
"${CMAKE_BINARY_DIR}/${rel_path}/${file_name}.pb.h"
WORKING_DIRECTORY ${PROJECT_SOURCE_DIR}
COMMAND ${CMAKE_COMMAND} -E make_directory "${CMAKE_BINARY_DIR}/${rel_path}"
COMMAND protobuf::protoc -I${file_dir} --cpp_out=${CMAKE_BINARY_DIR}/${rel_path} ${abs_file}
DEPENDS protobuf::protoc ${abs_file}
COMMENT "Running C++ protocol buffer compiler on ${file}" VERBATIM )
endforeach()
set_source_files_properties(${${c_var}} ${${h_var}} PROPERTIES GENERATED TRUE)
set(${c_var} ${${c_var}} PARENT_SCOPE)
set(${h_var} ${${h_var}} PARENT_SCOPE)
endfunction()
function(ms_protobuf_generate_py c_var h_var py_var)
if(NOT ARGN)
message(SEND_ERROR "Error: ms_protobuf_generate() called without any proto files")
return()
endif()
set(${c_var})
set(${h_var})
set(${py_var})
foreach(file ${ARGN})
get_filename_component(abs_file ${file} ABSOLUTE)
get_filename_component(file_name ${file} NAME_WE)
get_filename_component(file_dir ${abs_file} PATH)
file(RELATIVE_PATH rel_path ${CMAKE_CURRENT_SOURCE_DIR} ${file_dir})
list(APPEND ${c_var} "${CMAKE_BINARY_DIR}/${rel_path}/${file_name}.pb.cc")
list(APPEND ${h_var} "${CMAKE_BINARY_DIR}/${rel_path}/${file_name}.pb.h")
list(APPEND ${py_var} "${CMAKE_BINARY_DIR}/${rel_path}/${file_name}_pb2.py")
add_custom_command(
OUTPUT "${CMAKE_BINARY_DIR}/${rel_path}/${file_name}.pb.cc"
"${CMAKE_BINARY_DIR}/${rel_path}/${file_name}.pb.h"
"${CMAKE_BINARY_DIR}/${rel_path}/${file_name}_pb2.py"
WORKING_DIRECTORY ${PROJECT_SOURCE_DIR}
COMMAND ${CMAKE_COMMAND} -E make_directory "${CMAKE_BINARY_DIR}/${rel_path}"
COMMAND protobuf::protoc -I${file_dir} --cpp_out=${CMAKE_BINARY_DIR}/${rel_path} ${abs_file}
COMMAND protobuf::protoc -I${file_dir} --python_out=${CMAKE_BINARY_DIR}/${rel_path} ${abs_file}
COMMAND protobuf::protoc -I${file_dir} --python_out=${CMAKE_BINARY_DIR}/${rel_path} ${abs_file}
COMMAND perl -pi -e "s/import (.+_pb2.*)/from . import \\1/" "${CMAKE_BINARY_DIR}/${rel_path}/${file_name}_pb2.py"
COMMAND cp "${CMAKE_BINARY_DIR}/${rel_path}/${file_name}_pb2.py" "${PROJECT_SOURCE_DIR}/mindspore/train/"
DEPENDS protobuf::protoc ${abs_file}
COMMENT "Running C++ protocol buffer compiler on ${file}" VERBATIM )
endforeach()
set_source_files_properties(${${c_var}} ${${h_var}} ${${py_var}} PROPERTIES GENERATED TRUE)
set(${c_var} ${${c_var}} PARENT_SCOPE)
set(${h_var} ${${h_var}} PARENT_SCOPE)
set(${py_var} ${${py_var}} PARENT_SCOPE)
endfunction()
cmake/dependency_securec.cmake 0 → 100644
浏览文件 @ 930a1fb0
# securec library
#
#
# SECUREC_LIBRARY
#
if (NOT TARGET securec)
set(_ms_tmp_CMAKE_POSITION_INDEPENDENT_CODE ${CMAKE_POSITION_INDEPENDENT_CODE})
set(_ms_tmp_CMAKE_C_FLAGS ${CMAKE_C_FLAGS})
set(CMAKE_C_FLAGS "${SECURE_CXX_FLAGS}")
add_subdirectory(${CMAKE_CURRENT_LIST_DIR}/../third_party/securec ${CMAKE_BINARY_DIR}/securec)
set(CMAKE_POSITION_INDEPENDENT_CODE ${_ms_tmp_CMAKE_POSITION_INDEPENDENT_CODE})
set(CMAKE_C_FLAGS ${_ms_tmp_CMAKE_C_FLAGS})
endif()
include_directories(${CMAKE_CURRENT_LIST_DIR}/../third_party/securec/include)
set(SECUREC_LIBRARY securec)
cmake/dependency_utils.cmake 0 → 100644
浏览文件 @ 930a1fb0
# MS Utils
#
function(find_python_package out_inc out_lib)
# Use PYTHON_EXECUTABLE if it is defined, otherwise default to python
if ("${PYTHON_EXECUTABLE}" STREQUAL "")
set(PYTHON_EXECUTABLE "python3")
else()
set(PYTHON_EXECUTABLE "${PYTHON_EXECUTABLE}")
endif()
execute_process(
COMMAND "${PYTHON_EXECUTABLE}" -c "from distutils.sysconfig import get_python_inc; print(get_python_inc())"
RESULT_VARIABLE result
OUTPUT_VARIABLE inc)
string(STRIP "${inc}" inc)
set(${out_inc} ${inc} PARENT_SCOPE)
execute_process(
COMMAND "${PYTHON_EXECUTABLE}" -c "import distutils.sysconfig as sysconfig; import os; print(os.path.join(sysconfig.get_config_var('LIBDIR'), sysconfig.get_config_var('LDLIBRARY')))"
RESULT_VARIABLE result
OUTPUT_VARIABLE lib)
string(STRIP "${lib}" lib)
set(${out_lib} ${lib} PARENT_SCOPE)
endfunction()
cmake/external_libs/dlpack.cmake 0 → 100644
浏览文件 @ 930a1fb0
mindspore_add_pkg(dlpack
VER 0.2
HEAD_ONLY ./
URL https://github.com/dmlc/dlpack/archive/0acb731e0e43d15deee27b66f10e4c5b4e667913.zip
MD5 6b8093f17ad4e830d3c63eb3171c4b45)
cmake/external_libs/dmlc_core.cmake 0 → 100644
浏览文件 @ 930a1fb0
mindspore_add_pkg(dmlc_core
VER 0.3
HEAD_ONLY ./
URL https://github.com/dmlc/dmlc-core/archive/808f485387f9a03f78fa9f1159f387d0d91b7a28.zip
MD5 ea36f94c57752bf40fb02dfc362f1ed9)
cmake/external_libs/eigen.cmake 0 → 100644
浏览文件 @ 930a1fb0
set(Eigen3_CXXFLAGS "-D_FORTIFY_SOURCE=2 -O2")
set(Eigen3_CFLAGS "-D_FORTIFY_SOURCE=2 -O2")
mindspore_add_pkg(Eigen3
VER 3.3.7
URL https://gitlab.com/libeigen/eigen/-/archive/3.3.7/eigen-3.3.7.tar.gz
MD5 9e30f67e8531477de4117506fe44669b
CMAKE_OPTION -DBUILD_TESTING=OFF)
find_package(Eigen3 3.3.7 REQUIRED ${MS_FIND_NO_DEFAULT_PATH})
include_directories(${Eigen3_INC})
include_directories(${EIGEN3_INCLUDE_DIR})
set_property(TARGET Eigen3::Eigen PROPERTY IMPORTED_GLOBAL TRUE)
add_library(mindspore::eigen ALIAS Eigen3::Eigen)
cmake/external_libs/flatbuffers.cmake 0 → 100644
浏览文件 @ 930a1fb0
set(flatbuffers_CXXFLAGS "-D_FORTIFY_SOURCE=2 -O2")
set(flatbuffers_CFLAGS "-D_FORTIFY_SOURCE=2 -O2")
mindspore_add_pkg(flatbuffers
VER 1.11.0
LIBS flatbuffers
EXE flatc
URL https://github.com/google/flatbuffers/archive/v1.11.0.tar.gz
MD5 02c64880acb89dbd57eebacfd67200d8
CMAKE_OPTION -DFLATBUFFERS_BUILD_TESTS=OFF )
include_directories(${flatbuffers_INC})
add_library(mindspore::flatbuffers ALIAS flatbuffers::flatbuffers)
add_executable(mindspore::flatc ALIAS flatbuffers::flatc)
include_directories(${flatbuffers_INC})
function(ms_build_flatbuffers source_schema_files
source_schema_dirs
custom_target_name
generated_output_dir)
set(total_schema_dirs "")
set(total_generated_files "")
set(FLATC mindspore::flatc)
foreach (schema_dir ${source_schema_dirs})
set(total_schema_dirs -I ${schema_dir} ${total_schema_dirs})
endforeach()
foreach(schema ${source_schema_files})
get_filename_component(filename ${schema} NAME_WE)
if (NOT ${generated_output_dir} STREQUAL "")
set(generated_file ${generated_output_dir}/${filename}_generated.h)
add_custom_command(
OUTPUT ${generated_file}
COMMAND ${FLATC} --gen-mutable
--reflect-names --gen-object-api -o ${generated_output_dir}
${total_schema_dirs}
-c -b --reflect-types ${schema}
DEPENDS ${FLATC} ${schema}
WORKING_DIRECTORY "${CMAKE_CURRENT_SOURCE_DIR}"
COMMENT "Running C++ flatbuffers compiler on ${schema}" VERBATIM)
list(APPEND total_generated_files ${generated_file})
endif()
endforeach()
add_custom_target(${custom_target_name} ALL
DEPENDS ${total_generated_files})
if (NOT ${generated_output_dir} STREQUAL "")
include_directories(${generated_output_dir})
set_property(TARGET ${custom_target_name}
PROPERTY GENERATED_OUTPUT_DIR
${generated_output_dir})
endif()
endfunction()
cmake/external_libs/glog.cmake 0 → 100644
浏览文件 @ 930a1fb0
set(glog_CXXFLAGS "-D_FORTIFY_SOURCE=2 -O2 ${SECURE_CXX_FLAGS}")
set(glog_CFLAGS "-D_FORTIFY_SOURCE=2 -O2")
mindspore_add_pkg(glog
VER 0.4.0
LIBS glog
URL https://github.com/google/glog/archive/v0.4.0.tar.gz
MD5 0daea8785e6df922d7887755c3d100d0
CMAKE_OPTION -DBUILD_TESTING=OFF -DCMAKE_POSITION_INDEPENDENT_CODE=ON -DBUILD_SHARED_LIBS=ON)
include_directories(${glog_INC})
add_library(mindspore::glog ALIAS glog::glog)
cmake/external_libs/gtest.cmake 0 → 100644
浏览文件 @ 930a1fb0
set(gtest_CXXFLAGS "-D_FORTIFY_SOURCE=2 -O2")
set(gtest_CFLAGS "-D_FORTIFY_SOURCE=2 -O2")
mindspore_add_pkg(gtest
VER 1.8.0
LIBS gtest
URL https://github.com/google/googletest/archive/release-1.8.0.tar.gz
MD5 16877098823401d1bf2ed7891d7dce36
CMAKE_OPTION -DBUILD_TESTING=OFF -DCMAKE_POSITION_INDEPENDENT_CODE=ON -DBUILD_SHARED_LIBS=ON
-DCMAKE_MACOSX_RPATH=TRUE -Dgtest_disable_pthreads=ON)
include_directories(${gtest_INC})
add_library(mindspore::gtest ALIAS gtest::gtest)
file(COPY ${gtest_LIBPATH}/libgtest.so DESTINATION ${CMAKE_BINARY_DIR}/googletest/googlemock/gtest)
file(COPY ${gtest_LIBPATH}/libgtest_main.so DESTINATION ${CMAKE_BINARY_DIR}/googletest/googlemock/gtest)
cmake/external_libs/jpeg_turbo.cmake 0 → 100644
浏览文件 @ 930a1fb0
set(jpeg_turbo_USE_STATIC_LIBS ON)
set(jpeg_turbo_CFLAGS "-fstack-protector-all -Wno-maybe-uninitialized -Wno-unused-parameter -fPIC -D_FORTIFY_SOURCE=2 -O2")
set(jpeg_turbo_LDFLAGS "-Wl,-z,relro,-z,now,-z,noexecstack")
mindspore_add_pkg(jpeg_turbo
VER 2.0.4
LIBS jpeg
URL https://github.com/libjpeg-turbo/libjpeg-turbo/archive/2.0.4.tar.gz
MD5 44c43e4a9fb352f47090804529317c88
CMAKE_OPTION -DCMAKE_BUILD_TYPE=Release -DCMAKE_SKIP_RPATH=TRUE
)
include_directories(${jpeg_turbo_INC})
add_library(mindspore::jpeg_turbo ALIAS jpeg_turbo::jpeg)
cmake/external_libs/json.cmake 0 → 100644
浏览文件 @ 930a1fb0
set(nlohmann_json_CXXFLAGS "-D_FORTIFY_SOURCE=2 -O2")
set(nlohmann_json_CFLAGS "-D_FORTIFY_SOURCE=2 -O2")
mindspore_add_pkg(nlohmann_json
VER 3.6.1
HEAD_ONLY ./
URL https://github.com/nlohmann/json/releases/download/v3.6.1/include.zip
MD5 0dc903888211db3a0f170304cd9f3a89)
include_directories(${nlohmann_json_INC})
add_library(mindspore::json ALIAS nlohmann_json)
\ No newline at end of file
cmake/external_libs/libtiff.cmake 0 → 100644
浏览文件 @ 930a1fb0
set(tiff_CXXFLAGS "-fstack-protector-all -Wno-maybe-uninitialized -Wno-unused-parameter -Wno-unused-result \
-Wno-unused-but-set-variable -fPIC -D_FORTIFY_SOURCE=2 -O2")
set(tiff_CFLAGS "-fstack-protector-all -Wno-maybe-uninitialized -Wno-unused-parameter -Wno-unused-result \
-Wno-unused-but-set-variable -fPIC -D_FORTIFY_SOURCE=2 -O2")
set(tiff_LDFLAGS "-Wl,-z,relro,-z,now,-z,noexecstack")
mindspore_add_pkg(tiff
VER 4.1.0
LIBS tiff
URL https://gitlab.com/libtiff/libtiff/-/archive/v4.1.0/libtiff-v4.1.0.tar.gz
MD5 21de8d35c1b21ac82663fa9f56d3350d
CMAKE_OPTION -DCMAKE_BUILD_TYPE=Release -Djbig=OFF -Dlzma=OFF -Djpeg12=OFF -Dzstd=OFF -Dpixarlog=OFF
-Dold-jpeg=OFF -Dwebp=OFF -DBUILD_SHARED_LIBS=OFF)
message("tiff include = ${tiff_INC}")
message("tiff lib = ${tiff_LIB}")
cmake/external_libs/mkl_dnn.cmake 0 → 100644
浏览文件 @ 930a1fb0
set(mkl_dnn_CXXFLAGS "-D_FORTIFY_SOURCE=2 -O2")
set(mkl_dnn_CFLAGS "-D_FORTIFY_SOURCE=2 -O2")
mindspore_add_pkg(mkl_dnn
VER 1.1.1
LIBS dnnl mkldnn
URL https://github.com/intel/mkl-dnn/archive/v1.1.1.tar.gz
MD5 d6a422b00459600bdc22242590953f38
CMAKE_OPTION -DDNNL_ARCH_OPT_FLAGS='' -DDNNL_CPU_RUNTIME='SEQ' -DDNNL_BUILD_EXAMPLES=OFF -DDNNL_BUILD_TESTS=OFF)
include_directories(${mkl_dnn_INC})
add_library(mindspore::dnnl ALIAS mkl_dnn::dnnl)
add_library(mindspore::mkldnn ALIAS mkl_dnn::mkldnn)
cmake/external_libs/nccl.cmake 0 → 100644
浏览文件 @ 930a1fb0
set(nccl_CFLAGS "-D_FORTIFY_SOURCE=2 -O2")
mindspore_add_pkg(nccl
VER 2.4.8-1
LIBS nccl
URL https://github.com/NVIDIA/nccl/archive/v2.4.8-1.tar.gz
MD5 f14b37d6af1c79db5f57cb029a753727
BUILD_OPTION src.build NVCC_GENCODE="-gencode=arch=compute_70,code=sm_70"
INSTALL_INCS build/include/*
INSTALL_LIBS build/lib/*)
include_directories(${nccl_INC})
add_library(mindspore::nccl ALIAS nccl::nccl)
\ No newline at end of file
cmake/external_libs/ompi.cmake 0 → 100644
浏览文件 @ 930a1fb0
set(ompi_CXXFLAGS "-D_FORTIFY_SOURCE=2 -O2")
mindspore_add_pkg(ompi
VER 3.1.5
LIBS mpi
URL https://github.com/open-mpi/ompi/archive/v3.1.5.tar.gz
MD5 f7f220b26532c11a2efbc0bb73af3282
PRE_CONFIGURE_COMMAND ./autogen.pl
CONFIGURE_COMMAND ./configure)
include_directories(${ompi_INC})
add_library(mindspore::ompi ALIAS ompi::mpi)
\ No newline at end of file
cmake/external_libs/onnx.cmake 0 → 100644
浏览文件 @ 930a1fb0
mindspore_add_pkg(ms_onnx
VER 1.6.0
HEAD_ONLY ./
URL https://github.com/onnx/onnx/releases/download/v1.6.0/onnx-1.6.0.tar.gz
MD5 512f2779d6215d4a36f366b6b9acdf1e)
cmake/external_libs/opencv.cmake 0 → 100644
浏览文件 @ 930a1fb0
set(opencv_CXXFLAGS "-fstack-protector-all -Wno-maybe-uninitialized -Wno-unused-parameter -D_FORTIFY_SOURCE=2 -O2")
set(opencv_CFLAGS "-fstack-protector-all -Wno-maybe-uninitialized -Wno-unused-parameter -D_FORTIFY_SOURCE=2 -O2")
set(opencv_LDFLAGS "-Wl,-z,relro,-z,now,-z,noexecstack")
mindspore_add_pkg(opencv
VER 4.2.0
LIBS opencv_core opencv_imgcodecs opencv_imgproc
URL https://github.com/opencv/opencv/archive/4.2.0.tar.gz
MD5 e8cb208ce2723481408b604b480183b6
CMAKE_OPTION -DCMAKE_BUILD_TYPE=Release -DWITH_PROTOBUF=OFF -DWITH_WEBP=OFF -DWITH_IPP=OFF -DWITH_ADE=OFF
-DBUILD_ZLIB=ON
-DBUILD_JPEG=ON
-DBUILD_PNG=ON
-DBUILD_OPENEXR=ON
-DBUILD_TESTS=OFF
-DBUILD_PERF_TESTS=OFF
-DBUILD_opencv_apps=OFF
-DCMAKE_SKIP_RPATH=TRUE
-DBUILD_opencv_python3=OFF
-DWITH_FFMPEG=OFF
-DWITH_TIFF=ON
-DBUILD_TIFF=OFF
-DWITH_JASPER=OFF
-DBUILD_JASPER=OFF
-DTIFF_INCLUDE_DIR=${tiff_INC}
-DTIFF_LIBRARY=${tiff_LIB})
include_directories(${opencv_INC}/opencv4)
add_library(mindspore::opencv_core ALIAS opencv::opencv_core)
add_library(mindspore::opencv_imgcodecs ALIAS opencv::opencv_imgcodecs)
add_library(mindspore::opencv_imgproc ALIAS opencv::opencv_imgproc)
cmake/external_libs/protobuf.cmake 0 → 100644
浏览文件 @ 930a1fb0
mindspore_add_pkg(protobuf
VER 3.8.0
HEAD_ONLY ./
URL https://github.com/protocolbuffers/protobuf/archive/v3.8.0.tar.gz
MD5 3d9e32700639618a4d2d342c99d4507a)
set(protobuf_BUILD_TESTS OFF CACHE BOOL "Disable protobuf test")
set(protobuf_BUILD_SHARED_LIBS OFF CACHE BOOL "Gen shared library")
set(_ms_tmp_CMAKE_CXX_FLAGS ${CMAKE_CXX_FLAGS})
string(REPLACE " -Wall" "" CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS}")
string(REPLACE " -Werror" "" CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS}")
add_subdirectory(${protobuf_DIRPATH}/cmake ${protobuf_DIRPATH}/build)
set(CMAKE_CXX_FLAGS ${_ms_tmp_CMAKE_CXX_FLAGS})
set(PROTOBUF_LIBRARY protobuf::libprotobuf)
include_directories(${protobuf_DIRPATH}/src)
add_library(mindspore::protobuf ALIAS libprotobuf)
function(ms_protobuf_generate c_var h_var)
if(NOT ARGN)
message(SEND_ERROR "Error: ms_protobuf_generate() called without any proto files")
return()
endif()
set(${c_var})
set(${h_var})
foreach(file ${ARGN})
get_filename_component(abs_file ${file} ABSOLUTE)
get_filename_component(file_name ${file} NAME_WE)
get_filename_component(file_dir ${abs_file} PATH)
file(RELATIVE_PATH rel_path ${CMAKE_CURRENT_SOURCE_DIR} ${file_dir})
list(APPEND ${c_var} "${CMAKE_BINARY_DIR}/${rel_path}/${file_name}.pb.cc")
list(APPEND ${h_var} "${CMAKE_BINARY_DIR}/${rel_path}/${file_name}.pb.h")
add_custom_command(
OUTPUT "${CMAKE_BINARY_DIR}/${rel_path}/${file_name}.pb.cc"
"${CMAKE_BINARY_DIR}/${rel_path}/${file_name}.pb.h"
WORKING_DIRECTORY ${PROJECT_SOURCE_DIR}
COMMAND ${CMAKE_COMMAND} -E make_directory "${CMAKE_BINARY_DIR}/${rel_path}"
COMMAND protobuf::protoc -I${file_dir} --cpp_out=${CMAKE_BINARY_DIR}/${rel_path} ${abs_file}
DEPENDS protobuf::protoc ${abs_file}
COMMENT "Running C++ protocol buffer compiler on ${file}" VERBATIM )
endforeach()
set_source_files_properties(${${c_var}} ${${h_var}} PROPERTIES GENERATED TRUE)
set(${c_var} ${${c_var}} PARENT_SCOPE)
set(${h_var} ${${h_var}} PARENT_SCOPE)
endfunction()
function(ms_protobuf_generate_py c_var h_var py_var)
if(NOT ARGN)
message(SEND_ERROR "Error: ms_protobuf_generate() called without any proto files")
return()
endif()
set(${c_var})
set(${h_var})
set(${py_var})
foreach(file ${ARGN})
get_filename_component(abs_file ${file} ABSOLUTE)
get_filename_component(file_name ${file} NAME_WE)
get_filename_component(file_dir ${abs_file} PATH)
file(RELATIVE_PATH rel_path ${CMAKE_CURRENT_SOURCE_DIR} ${file_dir})
list(APPEND ${c_var} "${CMAKE_BINARY_DIR}/${rel_path}/${file_name}.pb.cc")
list(APPEND ${h_var} "${CMAKE_BINARY_DIR}/${rel_path}/${file_name}.pb.h")
list(APPEND ${py_var} "${CMAKE_BINARY_DIR}/${rel_path}/${file_name}_pb2.py")
add_custom_command(
OUTPUT "${CMAKE_BINARY_DIR}/${rel_path}/${file_name}.pb.cc"
"${CMAKE_BINARY_DIR}/${rel_path}/${file_name}.pb.h"
"${CMAKE_BINARY_DIR}/${rel_path}/${file_name}_pb2.py"
WORKING_DIRECTORY ${PROJECT_SOURCE_DIR}
COMMAND ${CMAKE_COMMAND} -E make_directory "${CMAKE_BINARY_DIR}/${rel_path}"
COMMAND protobuf::protoc -I${file_dir} --cpp_out=${CMAKE_BINARY_DIR}/${rel_path} ${abs_file}
COMMAND protobuf::protoc -I${file_dir} --python_out=${CMAKE_BINARY_DIR}/${rel_path} ${abs_file}
COMMAND protobuf::protoc -I${file_dir} --python_out=${CMAKE_BINARY_DIR}/${rel_path} ${abs_file}
COMMAND perl -pi -e "s/import (.+_pb2.*)/from . import \\1/" "${CMAKE_BINARY_DIR}/${rel_path}/${file_name}_pb2.py"
COMMAND cp "${CMAKE_BINARY_DIR}/${rel_path}/${file_name}_pb2.py" "${PROJECT_SOURCE_DIR}/mindspore/train/"
DEPENDS protobuf::protoc ${abs_file}
COMMENT "Running C++ protocol buffer compiler on ${file}" VERBATIM )
endforeach()
set_source_files_properties(${${c_var}} ${${h_var}} ${${py_var}} PROPERTIES GENERATED TRUE)
set(${c_var} ${${c_var}} PARENT_SCOPE)
set(${h_var} ${${h_var}} PARENT_SCOPE)
set(${py_var} ${${py_var}} PARENT_SCOPE)
endfunction()
cmake/external_libs/pybind11.cmake 0 → 100644
浏览文件 @ 930a1fb0
set(pybind11_CXXFLAGS "-D_FORTIFY_SOURCE=2 -O2")
set(pybind11_CFLAGS "-D_FORTIFY_SOURCE=2 -O2")
mindspore_add_pkg(pybind11
VER 2.4.3
URL https://github.com/pybind/pybind11/archive/v2.4.3.tar.gz
MD5 62254c40f89925bb894be421fe4cdef2
CMAKE_OPTION -DPYBIND11_TEST=OFF -DPYBIND11_LTO_CXX_FLAGS=FALSE
)
include_directories(${pybind11_INC})
find_package(pybind11 REQUIRED)
set_property(TARGET pybind11::module PROPERTY IMPORTED_GLOBAL TRUE)
add_library(mindspore::pybind11_module ALIAS pybind11::module)
cmake/external_libs/rang.cmake 0 → 100644
浏览文件 @ 930a1fb0
mindspore_add_pkg(rang
VER 3.1.0
HEAD_ONLY ./
URL https://github.com/agauniyal/rang/archive/cabe04d6d6b05356fa8f9741704924788f0dd762.zip
MD5 0c5c9b251fea9ee7ce32f188655be0ea)
cmake/external_libs/sqlite.cmake 0 → 100644
浏览文件 @ 930a1fb0
set(sqlite_USE_STATIC_LIBS ON)
set(sqlite_CXXFLAGS)
set(sqlite_CFLAGS "-fstack-protector-all -Wno-maybe-uninitialized -Wno-unused-parameter -fPIC -D_FORTIFY_SOURCE=2 -O2")
set(sqlite_LDFLAGS "-Wl,-z,relro,-z,now,-z,noexecstack")
mindspore_add_pkg(sqlite
VER 3.31.1
LIBS sqlite3
URL https://github.com/sqlite/sqlite/archive/version-3.31.1.tar.gz
MD5 5f4e7b4016c15f4fb5855615279819da
PATCHES ${CMAKE_SOURCE_DIR}/third_party/patch/sqlite/sqlite.patch001
CONFIGURE_COMMAND ./configure --enable-shared=no --disable-tcl --disable-editline --enable-json1)
include_directories(${sqlite_INC})
add_library(mindspore::sqlite ALIAS sqlite::sqlite3)
\ No newline at end of file
cmake/external_libs/tvm_gpu.cmake 0 → 100644
浏览文件 @ 930a1fb0
set(incubator_tvm_gpu_CXXFLAGS "-D_FORTIFY_SOURCE=2 -O2")
set(incubator_tvm_gpu_CFLAGS "-D_FORTIFY_SOURCE=2 -O2")
mindspore_add_pkg(incubator_tvm_gpu
VER 0.6.0
HEAD_ONLY ./
URL https://github.com/apache/incubator-tvm/archive/v0.6.0.tar.gz
MD5 9cbbd32545a776023acabbba270449fe)
cmake/external_libs/tvm_predict.cmake 0 → 100644
浏览文件 @ 930a1fb0
set(incubator_tvm_predict_CXXFLAGS "-D_FORTIFY_SOURCE=2 -O2")
set(incubator_tvm_predict_CFLAGS "-D_FORTIFY_SOURCE=2 -O2")
mindspore_add_pkg(incubator_tvm_predict
VER 0.6.0
HEAD_ONLY ./
URL https://github.com/apache/incubator-tvm/release/download/v0.6.0/apache-tvm-src-v0.6.0-incubating.tar.gz
MD5 2d77a005f0046d937b99c67de82f6438
PATCHES ${CMAKE_SOURCE_DIR}/third_party/patch/predict/0001-RetBugFix-CustomRuntime_v06.patch)
include_directories(${incubator_tvm_predict_INC})
add_library(mindspore::incubator_tvm_predict ALIAS incubator_tvm_predict)
cmake/mind_expression.cmake 0 → 100644
浏览文件 @ 930a1fb0
set(SECURE_CXX_FLAGS "")
if ("${CMAKE_CXX_COMPILER_ID}" STREQUAL "GNU")
set(SECURE_CXX_FLAGS "-fstack-protector-all -Wl,-z,relro,-z,now,-z,noexecstack")
endif()
set(_ms_tmp_CMAKE_CXX_FLAGS_F ${CMAKE_CXX_FLAGS})
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fvisibility=hidden")
include(cmake/utils.cmake)
include(${CMAKE_SOURCE_DIR}/cmake/external_libs/eigen.cmake)
include(${CMAKE_SOURCE_DIR}/cmake/external_libs/json.cmake)
include(${CMAKE_SOURCE_DIR}/cmake/dependency_securec.cmake)
include(${CMAKE_SOURCE_DIR}/cmake/external_libs/protobuf.cmake)
include(${CMAKE_SOURCE_DIR}/cmake/external_libs/pybind11.cmake)
MESSAGE("go to link flatbuffers")
include(${CMAKE_SOURCE_DIR}/cmake/external_libs/flatbuffers.cmake)
if(USE_GLOG)
include(${CMAKE_SOURCE_DIR}/cmake/external_libs/glog.cmake)
endif()
find_package(Python3)
include_directories(${Python3_INCLUDE_DIRS})
include_directories(${CMAKE_SOURCE_DIR}/third_party)
if (ENABLE_CPU)
include(${CMAKE_SOURCE_DIR}/cmake/external_libs/mkl_dnn.cmake)
endif()
if (ENABLE_GPU)
include(${CMAKE_SOURCE_DIR}/cmake/external_libs/dlpack.cmake)
include(${CMAKE_SOURCE_DIR}/cmake/external_libs/dmlc_core.cmake)
include(${CMAKE_SOURCE_DIR}/cmake/external_libs/rang.cmake)
include(${CMAKE_SOURCE_DIR}/cmake/external_libs/tvm_gpu.cmake)
endif()
if (ENABLE_MPI)
include(${CMAKE_SOURCE_DIR}/cmake/external_libs/nccl.cmake)
include(${CMAKE_SOURCE_DIR}/cmake/external_libs/ompi.cmake)
endif()
if (ENABLE_GE)
include_directories(${CMAKE_SOURCE_DIR}/third_party/ge/include)
include_directories(${CMAKE_SOURCE_DIR}/third_party/ge/include/external)
include_directories(${CMAKE_SOURCE_DIR}/third_party/ge/include/external/graph)
else()
include_directories(${CMAKE_SOURCE_DIR}/graphengine/inc)
include_directories(${CMAKE_SOURCE_DIR}/graphengine/inc/ops)
include_directories(${CMAKE_SOURCE_DIR}/graphengine/inc/external)
include_directories(${CMAKE_SOURCE_DIR}/graphengine/inc/external/graph)
endif()
if (ENABLE_MINDDATA)
include(${CMAKE_SOURCE_DIR}/cmake/external_libs/jpeg_turbo.cmake)
include(${CMAKE_SOURCE_DIR}/cmake/external_libs/libtiff.cmake)
include(${CMAKE_SOURCE_DIR}/cmake/external_libs/opencv.cmake)
include(${CMAKE_SOURCE_DIR}/cmake/external_libs/sqlite.cmake)
endif()
include(${CMAKE_SOURCE_DIR}/cmake/external_libs/gtest.cmake)
include(${CMAKE_SOURCE_DIR}/cmake/external_libs/onnx.cmake)
set(CMAKE_CXX_FLAGS ${_ms_tmp_CMAKE_CXX_FLAGS_F})
cmake/options.cmake 0 → 100644
浏览文件 @ 930a1fb0
option(ENABLE_D "Enable d" OFF)
option(ENABLE_GPU "Enable gpu" OFF)
option(ENABLE_CPU "Enable cpu" OFF)
option(ENABLE_GE "Enable graph engine as backend to execute" OFF)
option(ENABLE_MINDDATA "Enable minddata compile" OFF)
option(ENABLE_TRAIN "Enable ge train, default off(only infer)" OFF)
option(ENABLE_TESTCASES "Run testcases switch, default off" OFF)
option(DEBUG_MODE "Debug mode, default off" OFF)
option(ENABLE_ASAN "Enable Google Sanitizer to find memory bugs")
option(ENABLE_LOAD_ANF_IR "Enable load ANF-IR as input of 'infer' stage of pipeline" OFF)
option(ENABLE_COVERAGE "Enable code coverage report" OFF)
option(USE_GLOG "Use glog to output log" OFF)
option(ENABLE_PROFILE "Enable pipeline profile, default off" OFF)
option(ENABLE_TIMELINE "Enable time line record" OFF)
option(ENABLE_DUMP_PROTO "Enable dump anf graph to file in ProtoBuffer format, default on" ON)
option(ENABLE_DUMP_E2E "Enable dump e2e file, default on" OFF)
option(ENABLE_DUMP_IR "Enable dump funciton graph ir, default on" ON)
option(ENABLE_MPI "enable mpi" OFF)
option(ENABLE_AKG "enable akg" OFF)
if (CMAKE_CXX_COMPILER_ID STREQUAL "GNU")
set(OPTION_CXX_FLAGS "${OPTION_CXX_FLAGS} -fstack-protector-all -Wl,-z,relro,-z,now,-z,noexecstack")
endif()
if (CMAKE_SYSTEM_NAME MATCHES "Darwin")
set(OPTION_CXX_FLAGS "${OPTION_CXX_FLAGS} -Wsign-compare")
endif()
if (ENABLE_COVERAGE)
set(COVERAGE_COMPILER_FLAGS "-g --coverage -fprofile-arcs -ftest-coverage")
set(OPTION_CXX_FLAGS "${OPTION_CXX_FLAGS} ${COVERAGE_COMPILER_FLAGS}")
endif()
if (ENABLE_ASAN)
if (CMAKE_CXX_COMPILER_ID STREQUAL "GNU")
set(OPTION_CXX_FLAGS "${OPTION_CXX_FLAGS} -fsanitize=address -fno-omit-frame-pointer -static-libasan -fsanitize=undefined")
else()
set(OPTION_CXX_FLAGS "${OPTION_CXX_FLAGS} -fsanitize=address -fno-omit-frame-pointer -static-libsan -fsanitize=undefined")
endif()
endif()
if (DEBUG_MODE)
set(CMAKE_BUILD_TYPE "Debug")
else()
add_compile_definitions(MEM_REUSE_DEBUG)
set(CMAKE_BUILD_TYPE "Release")
endif()
if ((CMAKE_SYSTEM_PROCESSOR MATCHES "aarch64") OR (CMAKE_BUILD_TYPE STREQUAL Release))
set(PYBIND11_LTO_CXX_FLAGS FALSE)
endif()
if (NOT BUILD_PATH)
set(BUILD_PATH "${CMAKE_SOURCE_DIR}/build")
endif()
if (ENABLE_GE OR ENABLE_D)
set(ENABLE_TDTQUE ON)
endif()
if (ENABLE_GPU)
set(ENABLE_GPUQUE ON)
endif()
if (ENABLE_GE)
add_compile_definitions(ENABLE_GE)
add_compile_definitions(CUSTOM_OP)
endif()
if(ENABLE_TRAIN)
add_compile_definitions(ENABLE_TRAIN=1)
else()
add_compile_definitions(ENABLE_TRAIN=0)
endif()
if(USE_GLOG)
add_compile_definitions(USE_GLOG)
endif()
if (ENABLE_PROFILE)
add_compile_definitions(ENABLE_PROFILE)
endif()
if (ENABLE_TIMELINE)
add_compile_definitions(ENABLE_TIMELINE)
endif()
if (ENABLE_LOAD_ANF_IR)
add_compile_definitions(ENABLE_LOAD_ANF_IR)
endif()
if (ENABLE_TESTCASES OR (NOT ENABLE_D AND NOT ENABLE_GE))
add_compile_definitions(NO_DLIB=1)
endif()
if(ENABLE_DUMP_IR)
add_compile_definitions(ENABLE_DUMP_IR)
endif(ENABLE_DUMP_IR)
if(ENABLE_MINDDATA)
add_compile_definitions(ENABLE_MINDDATA)
if (ENABLE_TDTQUE)
add_compile_definitions(ENABLE_TDTQUE)
endif()
endif()
if(ENABLE_DUMP_E2E)
add_compile_definitions(ENABLE_DUMP_E2E)
endif()
\ No newline at end of file
cmake/utils.cmake 0 → 100644
浏览文件 @ 930a1fb0
include(FetchContent)
set(FETCHCONTENT_QUIET OFF)
function(mindspore_add_submodule_obj des_submodule_objs sub_dir submodule_name_obj)
add_subdirectory(${sub_dir})
if(NOT TARGET ${submodule_name_obj})
message(FATAL_ERROR "Can not find submodule '${submodule_name_obj}'. in ${CMAKE_CURRENT_LIST_FILE}")
endif()
if("$<TARGET_OBJECTS:${submodule_name_obj}>" IN_LIST ${des_submodule_objs})
message(FATAL_ERROR "submodule '${submodule_name_obj}' added more than once. in ${CMAKE_CURRENT_LIST_FILE}")
endif()
set(${des_submodule_objs} ${${des_submodule_objs}} $<TARGET_OBJECTS:${submodule_name_obj}> PARENT_SCOPE)
endfunction()
get_filename_component(_MS_LIB_CACHE ~/.mslib REALPATH)
if (NOT EXISTS ${_MS_LIB_CACHE})
file(MAKE_DIRECTORY ${_MS_LIB_CACHE})
endif ()
# set(FETCHCONTENT_BASE_DIR ${_MS_LIB_CACHE})
# set(CMAKE_PREFIX_PATH ${_MS_LIB_CACHE})
if (DEFINED ENV{MSLIBS_SERVER})
set(LOCAL_LIBS_SERVER $ENV{MSLIBS_SERVER})
message("LOCAL_LIBS_SERVER: ${LOCAL_LIBS_SERVER}")
endif ()
if(LOCAL_LIBS_SERVER)
if (NOT ENV{no_proxy})
set(ENV{no_proxy} "${LOCAL_LIBS_SERVER}")
else()
string(FIND $ENV{no_proxy} ${LOCAL_LIBS_SERVER} IP_POS)
if (${IP_POS} EQUAL -1)
set(ENV{no_proxy} "$ENV{no_proxy},${LOCAL_LIBS_SERVER}")
endif ()
endif ()
endif()
function(__download_pkg pkg_name pkg_url pkg_md5)
if(LOCAL_LIBS_SERVER)
get_filename_component(_URL_FILE_NAME ${pkg_url} NAME)
set(pkg_url "http://${LOCAL_LIBS_SERVER}:8081/libs/${pkg_name}/${_URL_FILE_NAME}" ${pkg_url})
endif()
FetchContent_Declare(
${pkg_name}
URL ${pkg_url}
URL_HASH MD5=${pkg_md5}
)
FetchContent_GetProperties(${pkg_name})
message("download: ${${pkg_name}_SOURCE_DIR} , ${pkg_name} , ${pkg_url}")
if(NOT ${pkg_name}_POPULATED)
FetchContent_Populate(${pkg_name})
set(${pkg_name}_SOURCE_DIR ${${pkg_name}_SOURCE_DIR} PARENT_SCOPE)
endif()
endfunction()
function(__download_pkg_with_git pkg_name pkg_url pkg_git_commit pkg_md5)
if(LOCAL_LIBS_SERVER)
set(pkg_url "http://${LOCAL_LIBS_SERVER}:8081/libs/${pkg_name}/${pkg_git_commit}")
FetchContent_Declare(
${pkg_name}
URL ${pkg_url}
URL_HASH MD5=${pkg_md5}
)
else()
FetchContent_Declare(
${pkg_name}
GIT_REPOSITORY ${pkg_url}
GIT_TAG ${pkg_git_commit})
endif()
FetchContent_GetProperties(${pkg_name})
message("download: ${${pkg_name}_SOURCE_DIR} , ${pkg_name} , ${pkg_url}")
if(NOT ${pkg_name}_POPULATED)
FetchContent_Populate(${pkg_name})
set(${pkg_name}_SOURCE_DIR ${${pkg_name}_SOURCE_DIR} PARENT_SCOPE)
endif()
endfunction()
function(__find_pkg_then_add_target pkg_name pkg_exe)
unset(${pkg_name}_LIBS)
message("_FIND:${${pkg_name}_BASE_DIR}")
if(pkg_exe)
find_program(${pkg_exe}_EXE ${pkg_exe} PATHS ${${pkg_name}_BASE_DIR}/bin NO_DEFAULT_PATH)
if(NOT ${pkg_exe}_EXE)
return()
endif()
add_executable(${pkg_name}::${pkg_exe} IMPORTED GLOBAL)
set_target_properties(${pkg_name}::${pkg_exe} PROPERTIES
IMPORTED_LOCATION ${${pkg_exe}_EXE}
)
message("found ${${pkg_exe}_EXE}")
endif()
foreach(_LIB_NAME ${ARGN})
set(_LIB_SEARCH_NAME ${_LIB_NAME})
set(_LIB_TYPE SHARED)
if (${pkg_name}_USE_STATIC_LIBS)
set(_LIB_SEARCH_NAME "${CMAKE_STATIC_LIBRARY_PREFIX}${_LIB_NAME}${CMAKE_STATIC_LIBRARY_SUFFIX}")
set(_LIB_TYPE STATIC)
endif ()
set(${_LIB_NAME}_LIB ${_LIB_NAME}_LIB-NOTFOUND)
find_library(${_LIB_NAME}_LIB ${_LIB_SEARCH_NAME} PATHS ${${pkg_name}_BASE_DIR}/lib NO_DEFAULT_PATH)
if(NOT ${_LIB_NAME}_LIB)
return()
endif()
add_library(${pkg_name}::${_LIB_NAME} ${_LIB_TYPE} IMPORTED GLOBAL)
set_target_properties(${pkg_name}::${_LIB_NAME} PROPERTIES
INTERFACE_INCLUDE_DIRECTORIES "${${pkg_name}_BASE_DIR}/include"
IMPORTED_LOCATION ${${_LIB_NAME}_LIB}
)
list(APPEND ${pkg_name}_LIBS ${pkg_name}::${_LIB_NAME})
message("found ${${_LIB_NAME}_LIB}")
STRING( REGEX REPLACE "(.+)/(.+)" "\\1" LIBPATH ${${_LIB_NAME}_LIB})
set(${pkg_name}_LIBPATH ${LIBPATH} CACHE STRING INTERNAL)
endforeach(_LIB_NAME)
set(${pkg_name}_LIBS ${${pkg_name}_LIBS} PARENT_SCOPE)
endfunction()
function(__exec_cmd)
set(options )
set(oneValueArgs WORKING_DIRECTORY)
set(multiValueArgs COMMAND)
cmake_parse_arguments(EXEC "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN} )
execute_process(COMMAND ${EXEC_COMMAND}
WORKING_DIRECTORY ${EXEC_WORKING_DIRECTORY}
RESULT_VARIABLE RESULT)
if(NOT RESULT EQUAL "0")
message(FATAL_ERROR "error! when ${EXEC_COMMAND} in ${EXEC_WORKING_DIRECTORY}")
endif()
endfunction()
function(__check_patches pkg_patches)
# check patches
if (PKG_PATCHES)
file(TOUCH ${_MS_LIB_CACHE}/${pkg_name}_patch.md5)
file(READ ${_MS_LIB_CACHE}/${pkg_name}_patch.md5 ${pkg_name}_PATCHES_MD5)
message("patches md5:${${pkg_name}_PATCHES_MD5}")
set(${pkg_name}_PATCHES_NEW_MD5 )
foreach(_PATCH ${PKG_PATCHES})
file(MD5 ${_PATCH} _PF_MD5)
set(${pkg_name}_PATCHES_NEW_MD5 "${${pkg_name}_PATCHES_NEW_MD5},${_PF_MD5}")
endforeach(_PATCH)
if (NOT ${pkg_name}_PATCHES_MD5 STREQUAL ${pkg_name}_PATCHES_NEW_MD5)
set(${pkg_name}_PATCHES ${PKG_PATCHES})
file(REMOVE_RECURSE "${_MS_LIB_CACHE}/${pkg_name}-subbuild")
file(WRITE ${_MS_LIB_CACHE}/${pkg_name}_patch.md5 ${${pkg_name}_PATCHES_NEW_MD5})
message("patches changed : ${${pkg_name}_PATCHES_NEW_MD5}")
endif ()
endif ()
endfunction()
set(MS_FIND_NO_DEFAULT_PATH NO_CMAKE_PATH NO_CMAKE_ENVIRONMENT_PATH NO_SYSTEM_ENVIRONMENT_PATH
NO_CMAKE_BUILDS_PATH NO_CMAKE_PACKAGE_REGISTRY NO_CMAKE_SYSTEM_PATH
NO_CMAKE_SYSTEM_PACKAGE_REGISTRY)
set(MS_FIND_NO_DEFAULT_PATH ${MS_FIND_NO_DEFAULT_PATH} PARENT_SCOPE)
function(mindspore_add_pkg pkg_name )
set(options )
set(oneValueArgs URL MD5 GIT_REPOSITORY GIT_TAG VER EXE DIR HEAD_ONLY)
set(multiValueArgs CMAKE_OPTION LIBS PRE_CONFIGURE_COMMAND CONFIGURE_COMMAND BUILD_OPTION INSTALL_INCS INSTALL_LIBS PATCHES)
cmake_parse_arguments(PKG "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN} )
set(__FIND_PKG_NAME ${pkg_name})
string(TOLOWER ${pkg_name} pkg_name)
message("pkg name:${__FIND_PKG_NAME},${pkg_name}")
set(${pkg_name}_PATCHES_HASH )
foreach(_PATCH ${PKG_PATCHES})
file(MD5 ${_PATCH} _PF_MD5)
set(${pkg_name}_PATCHES_HASH "${${pkg_name}_PATCHES_HASH},${_PF_MD5}")
endforeach(_PATCH)
# check options
set(${pkg_name}_CONFIG_TXT
"${CMAKE_CXX_COMPILER_VERSION}-${CMAKE_C_COMPILER_VERSION}
${ARGN} - ${${pkg_name}_USE_STATIC_LIBS}- ${${pkg_name}_PATCHES_HASH}
${${pkg_name}_CXXFLAGS}--${${pkg_name}_CFLAGS}--${${pkg_name}_LDFLAGS}")
string(REPLACE ";" "-" ${pkg_name}_CONFIG_TXT ${${pkg_name}_CONFIG_TXT})
string(MD5 ${pkg_name}_CONFIG_HASH ${${pkg_name}_CONFIG_TXT})
message("${pkg_name} config hash: ${${pkg_name}_CONFIG_HASH}")
set(${pkg_name}_BASE_DIR ${_MS_LIB_CACHE}/${pkg_name}_${${pkg_name}_CONFIG_HASH})
set(${pkg_name}_DIRPATH ${${pkg_name}_BASE_DIR} CACHE STRING INTERNAL)
if(EXISTS ${${pkg_name}_BASE_DIR}/options.txt AND PKG_HEAD_ONLY)
set(${pkg_name}_INC ${${pkg_name}_BASE_DIR}/${PKG_HEAD_ONLY} PARENT_SCOPE)
add_library(${pkg_name} INTERFACE)
target_include_directories(${pkg_name} INTERFACE ${${pkg_name}_INC})
return()
endif ()
if(NOT PKG_EXE)
set(PKG_EXE 0)
endif()
set(${__FIND_PKG_NAME}_ROOT ${${pkg_name}_BASE_DIR})
set(${__FIND_PKG_NAME}_ROOT ${${pkg_name}_BASE_DIR} PARENT_SCOPE)
if (PKG_LIBS)
__find_pkg_then_add_target(${pkg_name} ${PKG_EXE} ${PKG_LIBS})
if(${pkg_name}_LIBS)
set(${pkg_name}_INC ${${pkg_name}_BASE_DIR}/include PARENT_SCOPE)
message("Found libs: ${${pkg_name}_LIBS}")
return()
endif()
elseif(NOT PKG_HEAD_ONLY)
find_package(${__FIND_PKG_NAME} ${PKG_VER} ${MS_FIND_NO_DEFAULT_PATH})
if (${__FIND_PKG_NAME}_FOUND)
set(${pkg_name}_INC ${${pkg_name}_BASE_DIR}/include PARENT_SCOPE)
message("Found pkg: ${__FIND_PKG_NAME}")
return()
endif ()
endif ()
if (NOT PKG_DIR)
if (PKG_GIT_REPOSITORY)
__download_pkg_with_git(${pkg_name} ${PKG_GIT_REPOSITORY} ${PKG_GIT_TAG} ${PKG_MD5})
else()
__download_pkg(${pkg_name} ${PKG_URL} ${PKG_MD5})
endif()
else()
set(${pkg_name}_SOURCE_DIR ${PKG_DIR})
endif ()
file(WRITE ${${pkg_name}_BASE_DIR}/options.txt ${${pkg_name}_CONFIG_TXT})
message("${pkg_name}_SOURCE_DIR : ${${pkg_name}_SOURCE_DIR}")
foreach(_PATCH_FILE ${PKG_PATCHES})
message("patching ${${pkg_name}_SOURCE_DIR} -p1 < ${_PATCH_FILE}")
execute_process(COMMAND patch -p1 INPUT_FILE ${_PATCH_FILE}
WORKING_DIRECTORY ${${pkg_name}_SOURCE_DIR}
RESULT_VARIABLE Result)
if(NOT Result EQUAL "0")
message(FATAL_ERROR "Failed patch: ${_PATCH_FILE}")
endif()
endforeach(_PATCH_FILE)
file(LOCK ${${pkg_name}_BASE_DIR} DIRECTORY GUARD FUNCTION RESULT_VARIABLE ${pkg_name}_LOCK_RET TIMEOUT 600)
if(NOT ${pkg_name}_LOCK_RET EQUAL "0")
message(FATAL_ERROR "error! when try lock ${${pkg_name}_BASE_DIR} : ${${pkg_name}_LOCK_RET}")
endif()
if(${pkg_name}_SOURCE_DIR)
if (PKG_HEAD_ONLY)
file(GLOB ${pkg_name}_SOURCE_SUBDIRS ${${pkg_name}_SOURCE_DIR}/*)
file(COPY ${${pkg_name}_SOURCE_SUBDIRS} DESTINATION ${${pkg_name}_BASE_DIR})
set(${pkg_name}_INC ${${pkg_name}_BASE_DIR}/${PKG_HEAD_ONLY} PARENT_SCOPE)
add_library(${pkg_name} INTERFACE)
target_include_directories(${pkg_name} INTERFACE ${${pkg_name}_INC})
elseif (PKG_CMAKE_OPTION)
# in cmake
file(MAKE_DIRECTORY ${${pkg_name}_SOURCE_DIR}/_build)
if (${pkg_name}_CFLAGS)
set(${pkg_name}_CMAKE_CFLAGS "-DCMAKE_C_FLAGS=${${pkg_name}_CFLAGS}")
endif ()
if (${pkg_name}_CXXFLAGS)
set(${pkg_name}_CMAKE_CXXFLAGS "-DCMAKE_CXX_FLAGS=${${pkg_name}_CXXFLAGS}")
endif ()
if (${pkg_name}_LDFLAGS)
if (${pkg_name}_USE_STATIC_LIBS)
#set(${pkg_name}_CMAKE_LDFLAGS "-DCMAKE_STATIC_LINKER_FLAGS=${${pkg_name}_LDFLAGS}")
else()
set(${pkg_name}_CMAKE_LDFLAGS "-DCMAKE_SHARED_LINKER_FLAGS=${${pkg_name}_LDFLAGS}")
endif ()
endif ()
__exec_cmd(COMMAND ${CMAKE_COMMAND} ${PKG_CMAKE_OPTION} -G ${CMAKE_GENERATOR}
${${pkg_name}_CMAKE_CFLAGS} ${${pkg_name}_CMAKE_CXXFLAGS} ${${pkg_name}_CMAKE_LDFLAGS}
-DCMAKE_INSTALL_PREFIX=${${pkg_name}_BASE_DIR} ..
WORKING_DIRECTORY ${${pkg_name}_SOURCE_DIR}/_build)
__exec_cmd(COMMAND ${CMAKE_COMMAND} --build . --target install -- -j8
WORKING_DIRECTORY ${${pkg_name}_SOURCE_DIR}/_build)
else()
if (${pkg_name}_CFLAGS)
set(${pkg_name}_MAKE_CFLAGS "CFLAGS=${${pkg_name}_CFLAGS}")
endif ()
if (${pkg_name}_CXXFLAGS)
set(${pkg_name}_MAKE_CXXFLAGS "CXXFLAGS=${${pkg_name}_CXXFLAGS}")
endif ()
if (${pkg_name}_LDFLAGS)
set(${pkg_name}_MAKE_LDFLAGS "LDFLAGS=${${pkg_name}_LDFLAGS}")
endif ()
# in configure && make
if (PKG_PRE_CONFIGURE_COMMAND)
__exec_cmd(COMMAND ${PKG_PRE_CONFIGURE_COMMAND}
WORKING_DIRECTORY ${${pkg_name}_SOURCE_DIR})
endif ()
if (PKG_CONFIGURE_COMMAND)
__exec_cmd(COMMAND ${PKG_CONFIGURE_COMMAND}
${${pkg_name}_MAKE_CFLAGS} ${${pkg_name}_MAKE_CXXFLAGS} ${${pkg_name}_MAKE_LDFLAGS}
--prefix=${${pkg_name}_BASE_DIR}
WORKING_DIRECTORY ${${pkg_name}_SOURCE_DIR})
endif ()
set(${pkg_name}_BUILD_OPTION ${PKG_BUILD_OPTION})
if (NOT PKG_CONFIGURE_COMMAND)
set(${pkg_name}_BUILD_OPTION ${${pkg_name}_BUILD_OPTION}
${${pkg_name}_MAKE_CFLAGS} ${${pkg_name}_MAKE_CXXFLAGS} ${${pkg_name}_MAKE_LDFLAGS})
endif ()
# build
__exec_cmd(COMMAND ${CMAKE_MAKE_PROGRAM} ${${pkg_name}_BUILD_OPTION} -j8
WORKING_DIRECTORY ${${pkg_name}_SOURCE_DIR})
if (PKG_INSTALL_INCS OR PKG_INSTALL_LIBS)
file(GLOB ${pkg_name}_INSTALL_INCS ${${pkg_name}_SOURCE_DIR}/${PKG_INSTALL_INCS})
file(GLOB ${pkg_name}_INSTALL_LIBS ${${pkg_name}_SOURCE_DIR}/${PKG_INSTALL_LIBS})
file(COPY ${${pkg_name}_INSTALL_INCS} DESTINATION ${${pkg_name}_BASE_DIR}/include)
file(COPY ${${pkg_name}_INSTALL_LIBS} DESTINATION ${${pkg_name}_BASE_DIR}/lib)
else()
__exec_cmd(COMMAND ${CMAKE_MAKE_PROGRAM} install WORKING_DIRECTORY ${${pkg_name}_SOURCE_DIR})
endif ()
endif ()
endif()
if (PKG_LIBS)
__find_pkg_then_add_target(${pkg_name} ${PKG_EXE} ${PKG_LIBS})
set(${pkg_name}_INC ${${pkg_name}_BASE_DIR}/include PARENT_SCOPE)
if(NOT ${pkg_name}_LIBS)
message(FATAL_ERROR "Can not find pkg: ${pkg_name}")
endif()
else()
find_package(${__FIND_PKG_NAME} ${PKG_VER} QUIET ${MS_FIND_NO_DEFAULT_PATH})
if (${__FIND_PKG_NAME}_FOUND)
set(${pkg_name}_INC ${${pkg_name}_BASE_DIR}/include PARENT_SCOPE)
message("Found pkg: ${${__FIND_PKG_NAME}_LIBRARIES}")
return()
endif ()
endif ()
endfunction()
config/e2e_dump_config.json 0 → 100644
浏览文件 @ 930a1fb0
{
"DumpSettings": {
"enable": false,
"trans_flag": false,
"path": "/tmp/net/",
"net_name": "ResNet50",
"mode": 0,
"iteration": 0,
"kernels": ["TensorAdd"]
},
"DumpSettingsSpec": {
"enable": "true: dump enable false: dump disable",
"trans_flag": "true: trans to host format,false: not trans format",
"path": "the dump file folder",
"net_name": "net name eg:ResNet50",
"mode": "0: dump all kernels 1: dump kernels in kernels list",
"iteration": "0: all iteration others: specified iteration ",
"kernels": "kernel name list need to be dump"
},
"other": {}
}
\ No newline at end of file
config/e2e_dump_config_0.json 0 → 100644
浏览文件 @ 930a1fb0
{
"DumpSettings": {
"enable": false,
"trans_flag": false,
"path": "/tmp/hccllog/0",
"net_name": "ResNet50",
"mode": 0,
"iteration": 0,
"kernels": ["AllReduce","BiasAddGrad","Conv2DBackpropFilter","SparseSoftmaxCrossEntropyWithLogits"]
},
"DumpSettingsSpec": {
"enable": "true: dump enable false: dump disable",
"trans_flag": "true: trans to host format,false: not trans format",
"path": "the dump file folder",
"net_name": "net name eg:ResNet50",
"mode": "0: dump all kernels 1: dump kernels in kernels list",
"iteration": "0: all iteration others: specified iteration ",
"kernels": "kernel name list need to be dump"
},
"other": {}
}
config/e2e_dump_config_1.json 0 → 100644
浏览文件 @ 930a1fb0
{
"DumpSettings": {
"enable": false,
"trans_flag": false,
"path": "/tmp/hccllog/1",
"net_name": "ResNet50",
"mode": 0,
"iteration": 0,
"kernels": ["AllReduce","BiasAddGrad","Conv2DBackpropFilter","SparseSoftmaxCrossEntropyWithLogits"]
},
"DumpSettingsSpec": {
"enable": "true: dump enable false: dump disable",
"trans_flag": "true: trans to host format,false: not trans format",
"path": "the dump file folder",
"net_name": "net name eg:ResNet50",
"mode": "0: dump all kernels 1: dump kernels in kernels list",
"iteration": "0: all iteration others: specified iteration ",
"kernels": "kernel name list need to be dump"
},
"other": {}
}
config/hccl_evb_multi_rank.json 0 → 100644
浏览文件 @ 930a1fb0
{
"board_id": "0x3000",
"chip_info": "910",
"deploy_mode": "lab",
"group_count": "1",
"group_list": [
{
"device_num": "2",
"server_num": "1",
"group_name": "",
"instance_count": "2",
"instance_list": [
{
"devices": [
{
"device_id": "0",
"device_ip": "[device_ip]"
}
],
"rank_id": "0",
"server_id": "[sever_id]"
},
{
"devices": [
{
"device_id": "1",
"device_ip": "[device_ip]"
}
],
"rank_id": "1",
"server_id": "[sever_id]"
}
]
}
],
"para_plane_nic_location": "device",
"para_plane_nic_name": [
"eth0",
"eth1"
],
"para_plane_nic_num": "2",
"status": "completed"
}
config/hccl_multi_machine_multi_rank.json 0 → 100644
浏览文件 @ 930a1fb0
{
"board_id": "0x0000",
"chip_info": "910",
"deploy_mode": "lab",
"group_count": "1",
"group_list": [{
"device_num": "16",
"server_num": "2",
"group_name": "",
"instance_count": "16",
"instance_list": [{
"devices": [{
"device_id": "0",
"device_ip": "[A_device_ip_0]"
}],
"rank_id": "0",
"server_id": "[server_id_A]"
},
{
"devices": [{
"device_id": "1",
"device_ip": "[A_device_ip_1]"
}],
"rank_id": "1",
"server_id": "[server_id_A]"
},
{
"devices": [{
"device_id": "2",
"device_ip": "[A_device_ip_2]"
}],
"rank_id": "2",
"server_id": "[server_id_A]"
},
{
"devices": [{
"device_id": "3",
"device_ip": "[A_device_ip_3]"
}],
"rank_id": "3",
"server_id": "[server_id_A]"
},
{
"devices": [{
"device_id": "4",
"device_ip": "[A_device_ip_4]"
}],
"rank_id": "4",
"server_id": "[server_id_A]"
},
{
"devices": [{
"device_id": "5",
"device_ip": "[A_device_ip_5]"
}],
"rank_id": "5",
"server_id": "[server_id_A]"
},
{
"devices": [{
"device_id": "6",
"device_ip": "[A_device_ip_6]"
}],
"rank_id": "6",
"server_id": "[server_id_A]"
},
{
"devices": [{
"device_id": "7",
"device_ip": "[A_device_ip_7]"
}],
"rank_id": "7",
"server_id": "[server_id_A]"
},
{
"devices": [{
"device_id": "0",
"device_ip": "[B_device_ip_0]"
}],
"rank_id": "8",
"server_id": "[server_id_B]"
},
{
"devices": [{
"device_id": "1",
"device_ip": "[B_device_ip_1]"
}],
"rank_id": "9",
"server_id": "[server_id_B]"
},
{
"devices": [{
"device_id": "2",
"device_ip": "[B_device_ip_2]"
}],
"rank_id": "10",
"server_id": "[server_id_B]"
},
{
"devices": [{
"device_id": "3",
"device_ip": "[B_device_ip_3]"
}],
"rank_id": "11",
"server_id": "[server_id_B]"
},
{
"devices": [{
"device_id": "4",
"device_ip": "[B_device_ip_4]"
}],
"rank_id": "12",
"server_id": "[server_id_B]"
},
{
"devices": [{
"device_id": "5",
"device_ip": "[B_device_ip_5]"
}],
"rank_id": "13",
"server_id": "[server_id_B]"
},
{
"devices": [{
"device_id": "6",
"device_ip": "[B_device_ip_6]"
}],
"rank_id": "14",
"server_id": "[server_id_B]"
},
{
"devices": [{
"device_id": "7",
"device_ip": "[B_device_ip_7]"
}],
"rank_id": "15",
"server_id": "[server_id_B]"
}
]
}],
"para_plane_nic_location": "device",
"para_plane_nic_name": [
"eth0",
"eth1",
"eth2",
"eth3",
"eth4",
"eth5",
"eth6",
"eth7"
],
"para_plane_nic_num": "8",
"status": "completed",
"hccl_config_json_spec": {
"board_id": "board id, current support x0000 or 0x3000",
"chip_info": "chip info, current is 910",
"deploy_mode": "current use lab",
"group_count": "number of groups used",
"group_list": "detailed group information",
"device_num": "number of devices used, the value is the nth power of 2",
"server_num": "number of multiple machines, single machine is 1",
"group_name": "default is hccl_world_group or specified",
"instance_count": "number of instance used, generally equal to device_num",
"instance_list": "detailed instance information",
"device_id": "designated davinic device id to use, values start from 0, but no more than single machine total device num.if server_num greater than 1, the id can be restart from 0",
"device_ip": "ip corresponding to device_id",
"rank_id": "the first device must be 0 and then increase in order",
"server_id": "can be specified as the machine's ip address",
"para_plane_nic_location": "current use device",
"para_plane_nic_name": "network card corresponding to device ip",
"para_plane_nic_num": "number of network cards used",
"status": "current use completed"
}
}
\ No newline at end of file
config/hccl_single_machine_multi_rank.json 0 → 100644
浏览文件 @ 930a1fb0
{
"board_id": "0x0000",
"chip_info": "910",
"deploy_mode": "lab",
"group_count": "1",
"group_list": [{
"device_num": "8",
"server_num": "1",
"group_name": "",
"instance_count": "8",
"instance_list": [{
"devices": [{
"device_id": "0",
"device_ip": "[device_ip_0]"
}],
"rank_id": "0",
"server_id": "[server_id]"
},
{
"devices": [{
"device_id": "1",
"device_ip": "[device_ip_1]"
}],
"rank_id": "1",
"server_id": "[server_id]"
},
{
"devices": [{
"device_id": "2",
"device_ip": "[device_ip_2]"
}],
"rank_id": "2",
"server_id": "[server_id]"
},
{
"devices": [{
"device_id": "3",
"device_ip": "[device_ip_3]"
}],
"rank_id": "3",
"server_id": "[server_id]"
},
{
"devices": [{
"device_id": "4",
"device_ip": "[device_ip_4]"
}],
"rank_id": "4",
"server_id": "[server_id]"
},
{
"devices": [{
"device_id": "5",
"device_ip": "[device_ip_5]"
}],
"rank_id": "5",
"server_id": "[server_id]"
},
{
"devices": [{
"device_id": "6",
"device_ip": "[device_ip_6]"
}],
"rank_id": "6",
"server_id": "[server_id]"
},
{
"devices": [{
"device_id": "7",
"device_ip": "[device_ip_7]"
}],
"rank_id": "7",
"server_id": "[server_id]"
}
]
}],
"para_plane_nic_location": "device",
"para_plane_nic_name": [
"eth0",
"eth1",
"eth2",
"eth3",
"eth4",
"eth5",
"eth6",
"eth7"
],
"para_plane_nic_num": "8",
"status": "completed",
"hccl_config_json_spec": {
"board_id": "board id, current support x0000 or 0x3000",
"chip_info": "chip info, current is 910",
"deploy_mode": "current use lab",
"group_count": "number of groups used",
"group_list": "detailed group information",
"device_num": "number of devices used, the value is the nth power of 2",
"server_num": "single machine is 1",
"group_name": "default is hccl_world_group or specified",
"instance_count": "number of instance used, generally equal to device_num",
"instance_list": "detailed instance information",
"device_id": "designated davinic device id to use, values start from 0, but no more than single machine total device num",
"device_ip": "ip corresponding to device_id",
"rank_id": "the first device must be 0 and then increase in order",
"server_id": "can be specified as the machine's ip address",
"para_plane_nic_location": "current use device",
"para_plane_nic_name": "network card corresponding to device ip",
"para_plane_nic_num": "number of network cards used",
"status": "current use completed"
}
}
\ No newline at end of file
dbg_dump_parser.sh 0 → 100755
浏览文件 @ 930a1fb0
#!/bin/bash
# Copyright 2019 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
set -x
set -e
export SAVE_GRAPHS=YES
# print usage message
function usage()
{
echo "Usage:"
echo "bash $0 [-g] [-d] [-a] [-h] [-f file]"
echo "e.g. $0 -f 3_specialize.dat"
echo ""
echo "Options:"
echo " -g Generate ir file for debug"
echo " -d Debug dumped ir"
echo " -a Execute all steps, default"
echo " -f File to be parse"
echo " -h Print usage"
}
# check and set options
function checkopts()
{
# init variable
MODE_GEN=0
MODE_DBG=1
MODE_ALL=2
FILE_NAME="3_optimize.dat"
mode="${MODE_ALL}" # default execute all steps
# Process the options
while getopts 'gdaf:h' opt
do
case "${opt}" in
g)
mode="${MODE_GEN}"
;;
d)
mode="${MODE_DBG}"
;;
a)
mode="${MODE_ALL}"
;;
f)
FILE_NAME="$OPTARG"
if ! [ -f "${FILE_NAME}" ]; then
echo "File $FILE_NAME does not exist"
usage
exit 1
fi
;;
h)
usage
exit 0
;;
*)
echo "Unknown option ${opt}!"
usage
exit 1
esac
done
}
# init variable
# check options
checkopts "$@"
cd build/mindspore/
make -j8
cp -v mindspore/ccsrc/_c_expression.cpython-*.so ../../mindspore/
cd -
UT_NAME="./tests/ut/python/model/test_lenet.py::test_lenet5_train_sens"
#UT_NAME="./tests/python/ops/test_math_ops.py::test_matmul_grad"
#UT_NAME="./tests/python/exec/resnet_example.py::test_compile"
#UT_NAME="./tests/perf_test/test_bert_train.py::test_bert_train"
if [[ "${mode}" == "${MODE_GEN}" || "${mode}" == "${MODE_ALL}" ]]; then
rm -rf pkl_objs
mkdir -p pkl_objs
echo "MS_IR_PATH=$(pwd)/pkl_objs pytest -s ${UT_NAME}"
MS_IR_PATH=$(pwd)/pkl_objs/ pytest -s "${UT_NAME}"
#pytest -s $UT_NAME
# 1_resolve.dat
# 3_specialize.dat
# 4_simplify_data_structures.dat
# 5_opt.dat
# 6_opt2.dat
# 7_opt_ge_adaptor_special.dat
# 8_cconv.dat
# 9_validate.dat
cp "${FILE_NAME}" anf_ir_file.dbg
rm -rf pkl_objs.dbg
cp -rf pkl_objs pkl_objs.dbg
fi
if [[ "${mode}" == "${MODE_DBG}" || "${mode}" == "${MODE_ALL}" ]]; then
echo "MS_IR_FILE=$(pwd)/anf_ir_file.dbg MS_IR_PATH=$(pwd)/pkl_objs.dbg/ pytest -s ${UT_NAME}"
MS_IR_FILE=$(pwd)/anf_ir_file.dbg MS_IR_PATH=$(pwd)/pkl_objs.dbg/ pytest -s "${UT_NAME}"
fi
docs/README.md 0 → 100644
浏览文件 @ 930a1fb0
# MindSpore Documentation
The MindSpore documentation is in the [MindSpore Docs](https://gitee.com/mindspore/docs) repository.
example/Bert_NEZHA/config.py 0 → 100644
浏览文件 @ 930a1fb0
# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""
network config setting, will be used in main.py
"""
from easydict import EasyDict as edict
import mindspore.common.dtype as mstype
from mindspore.model_zoo.Bert_NEZHA import BertConfig
bert_cfg = edict({
'epoch_size': 10,
'num_warmup_steps': 0,
'start_learning_rate': 1e-4,
'end_learning_rate': 1,
'decay_steps': 1000,
'power': 10.0,
'save_checkpoint_steps': 2000,
'keep_checkpoint_max': 10,
'checkpoint_prefix': "checkpoint_bert",
'DATA_DIR' = "/your/path/examples.tfrecord"
'SCHEMA_DIR' = "/your/path/datasetSchema.json"
'bert_config': BertConfig(
batch_size=16,
seq_length=128,
vocab_size=21136,
hidden_size=1024,
num_hidden_layers=24,
num_attention_heads=16,
intermediate_size=4096,
hidden_act="gelu",
hidden_dropout_prob=0.0,
attention_probs_dropout_prob=0.0,
max_position_embeddings=512,
type_vocab_size=2,
initializer_range=0.02,
use_relative_positions=True,
input_mask_from_dataset=True,
token_type_ids_from_dataset=True,
dtype=mstype.float32,
compute_type=mstype.float16,
)
})
example/Bert_NEZHA/main.py 0 → 100644
浏览文件 @ 930a1fb0
# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""
NEZHA (NEural contextualiZed representation for CHinese lAnguage understanding) is the Chinese pretrained language model currently based on BERT developed by Huawei.
1. Prepare data
Following the data preparation as in BERT, run command as below to get dataset for training:
python ./create_pretraining_data.py \
--input_file=./sample_text.txt \
--output_file=./examples.tfrecord \
--vocab_file=./your/path/vocab.txt \
--do_lower_case=True \
--max_seq_length=128 \
--max_predictions_per_seq=20 \
--masked_lm_prob=0.15 \
--random_seed=12345 \
--dupe_factor=5
2. Pretrain
First, prepare the distributed training environment, then adjust configurations in config.py, finally run main.py.
"""
import os
import pytest
import numpy as np
from numpy import allclose
from config import bert_cfg as cfg
import mindspore.common.dtype as mstype
import mindspore.dataset.engine.datasets as de
import mindspore._c_dataengine as deMap
from mindspore import context
from mindspore.common.tensor import Tensor
from mindspore.train.model import Model
from mindspore.train.callback import Callback
from mindspore.model_zoo.Bert_NEZHA import BertConfig, BertNetworkWithLoss, BertTrainOneStepCell
from mindspore.nn.optim import Lamb
from mindspore import log as logger
_current_dir = os.path.dirname(os.path.realpath(__file__))
DATA_DIR = [cfg.DATA_DIR]
SCHEMA_DIR = cfg.SCHEMA_DIR
def me_de_train_dataset(batch_size):
"""test me de train dataset"""
# apply repeat operations
repeat_count = cfg.epoch_size
ds = de.StorageDataset(DATA_DIR, SCHEMA_DIR, columns_list=["input_ids", "input_mask", "segment_ids",
"next_sentence_labels", "masked_lm_positions",
"masked_lm_ids", "masked_lm_weights"])
type_cast_op = deMap.TypeCastOp("int32")
ds = ds.map(input_columns="masked_lm_ids", operations=type_cast_op)
ds = ds.map(input_columns="masked_lm_positions", operations=type_cast_op)
ds = ds.map(input_columns="next_sentence_labels", operations=type_cast_op)
ds = ds.map(input_columns="segment_ids", operations=type_cast_op)
ds = ds.map(input_columns="input_mask", operations=type_cast_op)
ds = ds.map(input_columns="input_ids", operations=type_cast_op)
# apply batch operations
ds = ds.batch(batch_size, drop_remainder=True)
ds = ds.repeat(repeat_count)
return ds
def weight_variable(shape):
"""weight variable"""
np.random.seed(1)
ones = np.random.uniform(-0.1, 0.1, size=shape).astype(np.float32)
return Tensor(ones)
class ModelCallback(Callback):
def __init__(self):
super(ModelCallback, self).__init__()
self.loss_list = []
def step_end(self, run_context):
cb_params = run_context.original_args()
self.loss_list.append(cb_params.net_outputs.asnumpy()[0])
logger.info("epoch: {}, outputs are {}".format(cb_params.cur_epoch_num, str(cb_params.net_outputs)))
def test_bert_tdt():
"""test bert tdt"""
context.set_context(mode=context.GRAPH_MODE)
context.set_context(device_target="Ascend")
context.set_context(enable_task_sink=True)
context.set_context(enable_loop_sink=True)
context.set_context(enable_mem_reuse=True)
parallel_callback = ModelCallback()
ds = me_de_train_dataset(cfg.bert_config.batch_size)
config = cfg.bert_config
netwithloss = BertNetworkWithLoss(config, True)
optimizer = Lamb(netwithloss.trainable_params(), decay_steps=cfg.decay_steps, start_learning_rate=cfg.start_learning_rate,
end_learning_rate=cfg.end_learning_rate, power=cfg.power, warmup_steps=cfg.num_warmup_steps, decay_filter=lambda x: False)
netwithgrads = BertTrainOneStepCell(netwithloss, optimizer=optimizer)
netwithgrads.set_train(True)
model = Model(netwithgrads)
config_ck = CheckpointConfig(save_checkpoint_steps=cfg.save_checkpoint_steps, keep_checkpoint_max=cfg.keep_checkpoint_max)
ckpoint_cb = ModelCheckpoint(prefix=cfg.checkpoint_prefix, config=config_ck)
model.train(ds.get_repeat_count(), ds, callbacks=[parallel_callback, ckpoint_cb], dataset_sink_mode=False)
if __name__ == '__main__':
test_bert_tdt()
example/lenet/config.py 0 → 100644
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# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""
network config setting, will be used in main.py
"""
from easydict import EasyDict as edict
mnist_cfg = edict({
'num_classes': 10,
'lr': 0.01,
'momentum': 0.9,
'epoch_size': 1,
'batch_size': 32,
'repeat_size': 1,
'buffer_size': 1000,
'image_height': 32,
'image_width': 32,
'save_checkpoint_steps': 1875,
'keep_checkpoint_max': 10,
})
example/lenet/main.py 0 → 100644
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# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""
######################## train and test lenet example ########################
1. train lenet and get network model files(.ckpt) :
python main.py --data_path /home/workspace/mindspore_dataset/Tutorial_Network/Lenet/MNIST_Data
2. test lenet according to model file:
python main.py --data_path /home/workspace/mindspore_dataset/Tutorial_Network/Lenet/MNIST_Data
--mode test --ckpt_path checkpoint_lenet_1-1_1875.ckpt
"""
import os
import argparse
from config import mnist_cfg as cfg
import mindspore.dataengine as de
import mindspore.nn as nn
from mindspore.model_zoo.lenet import LeNet5
from mindspore import context, Tensor
from mindspore.train.serialization import load_checkpoint, load_param_into_net
from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor
from mindspore.train import Model
import mindspore.ops.operations as P
import mindspore.transforms.c_transforms as C
from mindspore.transforms import Inter
from mindspore.nn.metrics import Accuracy
from mindspore.ops import functional as F
from mindspore.common import dtype as mstype
class CrossEntropyLoss(nn.Cell):
"""
Define loss for network
"""
def __init__(self):
super(CrossEntropyLoss, self).__init__()
self.cross_entropy = P.SoftmaxCrossEntropyWithLogits()
self.mean = P.ReduceMean()
self.one_hot = P.OneHot()
self.on_value = Tensor(1.0, mstype.float32)
self.off_value = Tensor(0.0, mstype.float32)
def construct(self, logits, label):
label = self.one_hot(label, F.shape(logits)[1], self.on_value, self.off_value)
loss = self.cross_entropy(logits, label)[0]
loss = self.mean(loss, (-1,))
return loss
def create_dataset(data_path, batch_size=32, repeat_size=1,
num_parallel_workers=1):
"""
create dataset for train or test
"""
# define dataset
ds1 = de.MnistDataset(data_path)
# apply map operations on images
ds1 = ds1.map(input_columns="label", operations=C.TypeCast(mstype.int32))
ds1 = ds1.map(input_columns="image", operations=C.Resize((cfg.image_height, cfg.image_width),
interpolation=Inter.LINEAR),
num_parallel_workers=num_parallel_workers)
ds1 = ds1.map(input_columns="image", operations=C.Rescale(1 / 0.3081, -1 * 0.1307 / 0.3081),
num_parallel_workers=num_parallel_workers)
ds1 = ds1.map(input_columns="image", operations=C.Rescale(1.0 / 255.0, 0.0),
num_parallel_workers=num_parallel_workers)
ds1 = ds1.map(input_columns="image", operations=C.HWC2CHW(), num_parallel_workers=num_parallel_workers)
# apply DatasetOps
ds1 = ds1.shuffle(buffer_size=cfg.buffer_size) # 10000 as in LeNet train script
ds1 = ds1.batch(batch_size, drop_remainder=True)
ds1 = ds1.repeat(repeat_size)
return ds1
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='MindSpore MNIST Example')
parser.add_argument('--device_target', type=str, default="Ascend", choices=['Ascend', 'GPU', 'CPU'],
help='device where the code will be implemented (default: Ascend)')
parser.add_argument('--mode', type=str, default="train", choices=['train', 'test'],
help='implement phase, set to train or test')
parser.add_argument('--data_path', type=str, default="./MNIST_Data",
help='path where the dataset is saved')
parser.add_argument('--ckpt_path', type=str, default="", help='if mode is test, must provide\
path where the trained ckpt file')
args = parser.parse_args()
context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target)
network = LeNet5(cfg.num_classes)
network.set_train()
# net_loss = nn.SoftmaxCrossEntropyWithLogits() # support this loss soon
net_loss = CrossEntropyLoss()
net_opt = nn.Momentum(network.trainable_params(), cfg.lr, cfg.momentum)
config_ck = CheckpointConfig(save_checkpoint_steps=cfg.save_checkpoint_steps,
keep_checkpoint_max=cfg.keep_checkpoint_max)
ckpoint_cb = ModelCheckpoint(prefix="checkpoint_lenet", config=config_ck)
model = Model(network, net_loss, net_opt, metrics={"Accuracy": Accuracy()})
if args.mode == 'train': # train
ds = create_dataset(os.path.join(args.data_path, args.mode), batch_size=cfg.batch_size,
repeat_size=cfg.epoch_size)
print("============== Starting Training ==============")
model.train(cfg['epoch_size'], ds, callbacks=[ckpoint_cb, LossMonitor()], dataset_sink_mode=False)
elif args.mode == 'test': # test
print("============== Starting Testing ==============")
param_dict = load_checkpoint(args.ckpt_path)
load_param_into_net(network, param_dict)
ds_eval = create_dataset(os.path.join(args.data_path, "test"), 32, 1)
acc = model.eval(ds_eval, dataset_sink_mode=False)
print("============== Accuracy:{} ==============".format(acc))
else:
raise RuntimeError('mode should be train or test, rather than {}'.format(args.mode))
example/resnet50_cifar10/config.py 0 → 100755
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# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""
network config setting, will be used in train.py and eval.py
"""
from easydict import EasyDict as ed
config = ed({
"class_num": 10,
"batch_size": 32,
"loss_scale": 1024,
"momentum": 0.9,
"weight_decay": 1e-4,
"epoch_size": 90,
"buffer_size": 100,
"image_height": 224,
"image_width": 224,
"save_checkpoint": True,
"save_checkpoint_steps": 195,
"keep_checkpoint_max": 10,
"save_checkpoint_path": "./",
"lr_init": 0.01,
"lr_end": 0.00001,
"lr_max": 0.1,
"warmup_epochs": 5,
"lr_decay_mode": "poly"
})
example/resnet50_cifar10/dataset.py 0 → 100755
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# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""
create train or eval dataset.
"""
import os
import mindspore.common.dtype as mstype
import mindspore.dataset.engine as de
import mindspore.dataset.transforms.vision.c_transforms as C
import mindspore.dataset.transforms.c_transforms as C2
from config import config
def create_dataset(dataset_path, do_train, repeat_num=1, batch_size=32):
"""
create a train or eval dataset
Args:
dataset_path(string): the path of dataset.
do_train(bool): whether dataset is used for train or eval.
repeat_num(int): the repeat times of dataset. Default: 1
batch_size(int): the batch size of dataset. Default: 32
Returns:
dataset
"""
device_num = int(os.getenv("DEVICE_NUM"))
rank_id = int(os.getenv("RANK_ID"))
if device_num == 1:
ds = de.Cifar10Dataset(dataset_path, num_parallel_workers=4, shuffle=True)
else:
ds = de.Cifar10Dataset(dataset_path, num_parallel_workers=4, shuffle=True,
num_shards=device_num, shard_id=rank_id)
resize_height = config.image_height
resize_width = config.image_width
rescale = 1.0 / 255.0
shift = 0.0
# define map operations
random_crop_op = C.RandomCrop((32, 32), (4, 4, 4, 4))
random_horizontal_flip_op = C.RandomHorizontalFlip(rank_id / (rank_id + 1))
resize_op = C.Resize((resize_height, resize_width))
rescale_op = C.Rescale(rescale, shift)
normalize_op = C.Normalize([0.4914, 0.4822, 0.4465], [0.2023, 0.1994, 0.2010])
change_swap_op = C.HWC2CHW()
trans = []
if do_train:
trans += [random_crop_op, random_horizontal_flip_op]
trans += [resize_op, rescale_op, normalize_op, change_swap_op]
type_cast_op = C2.TypeCast(mstype.int32)
ds = ds.map(input_columns="label", operations=type_cast_op)
ds = ds.map(input_columns="image", operations=trans)
# apply shuffle operations
ds = ds.shuffle(buffer_size=config.buffer_size)
# apply batch operations
ds = ds.batch(batch_size, drop_remainder=True)
# apply dataset repeat operation
ds = ds.repeat(repeat_num)
return ds
example/resnet50_cifar10/eval.py 0 → 100755
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# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""
eval.
"""
import os
import argparse
import random
import numpy as np
from dataset import create_dataset
from config import config
from mindspore import context
from mindspore.model_zoo.resnet import resnet50
from mindspore.parallel._auto_parallel_context import auto_parallel_context
from mindspore.nn.loss import SoftmaxCrossEntropyWithLogits
from mindspore.train.model import Model, ParallelMode
from mindspore.train.serialization import load_checkpoint, load_param_into_net
import mindspore.dataset.engine as de
from mindspore.communication.management import init
random.seed(1)
np.random.seed(1)
de.config.set_seed(1)
parser = argparse.ArgumentParser(description='Image classification')
parser.add_argument('--run_distribute', type=bool, default=False, help='Run distribute')
parser.add_argument('--device_num', type=int, default=1, help='Device num.')
parser.add_argument('--do_train', type=bool, default=False, help='Do train or not.')
parser.add_argument('--do_eval', type=bool, default=True, help='Do eval or not.')
parser.add_argument('--checkpoint_path', type=str, default=None, help='Checkpoint file path')
parser.add_argument('--dataset_path', type=str, default=None, help='Dataset path')
args_opt = parser.parse_args()
device_id = int(os.getenv('DEVICE_ID'))
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", save_graphs=False)
context.set_context(enable_task_sink=True, device_id=device_id)
context.set_context(enable_loop_sink=True)
context.set_context(enable_mem_reuse=True)
if __name__ == '__main__':
if args_opt.do_eval:
context.set_context(enable_hccl=False)
else:
if args_opt.run_distribute:
context.set_context(enable_hccl=True)
context.set_auto_parallel_context(device_num=args_opt.device_num, parallel_mode=ParallelMode.DATA_PARALLEL,
mirror_mean=True)
auto_parallel_context().set_all_reduce_fusion_split_indices([140])
init()
else:
context.set_context(enable_hccl=False)
epoch_size = config.epoch_size
net = resnet50(class_num=config.class_num)
loss = SoftmaxCrossEntropyWithLogits(sparse=True)
if args_opt.do_eval:
dataset = create_dataset(dataset_path=args_opt.dataset_path, do_train=False, batch_size=config.batch_size)
step_size = dataset.get_dataset_size()
if args_opt.checkpoint_path:
param_dict = load_checkpoint(args_opt.checkpoint_path)
load_param_into_net(net, param_dict)
net.set_train(False)
model = Model(net, loss_fn=loss, metrics={'acc'})
res = model.eval(dataset)
print("result:", res, "ckpt=", args_opt.checkpoint_path)
example/resnet50_cifar10/lr_generator.py 0 → 100755
浏览文件 @ 930a1fb0
# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""learning rate generator"""
import numpy as np
def get_lr(global_step, lr_init, lr_end, lr_max, warmup_epochs, total_epochs, steps_per_epoch, lr_decay_mode):
"""
generate learning rate array
Args:
global_step(int): total steps of the training
lr_init(float): init learning rate
lr_end(float): end learning rate
lr_max(float): max learning rate
warmup_epochs(int): number of warmup epochs
total_epochs(int): total epoch of training
steps_per_epoch(int): steps of one epoch
lr_decay_mode(string): learning rate decay mode, including steps, poly or default
Returns:
np.array, learning rate array
"""
lr_each_step = []
total_steps = steps_per_epoch * total_epochs
warmup_steps = steps_per_epoch * warmup_epochs
if lr_decay_mode == 'steps':
decay_epoch_index = [0.3 * total_steps, 0.6 * total_steps, 0.8 * total_steps]
for i in range(total_steps):
if i < decay_epoch_index[0]:
lr = lr_max
elif i < decay_epoch_index[1]:
lr = lr_max * 0.1
elif i < decay_epoch_index[2]:
lr = lr_max * 0.01
else:
lr = lr_max * 0.001
lr_each_step.append(lr)
elif lr_decay_mode == 'poly':
if warmup_steps != 0:
inc_each_step = (float(lr_max) - float(lr_init)) / float(warmup_steps)
else:
inc_each_step = 0
for i in range(total_steps):
if i < warmup_steps:
lr = float(lr_init) + inc_each_step * float(i)
else:
base = (1.0 - (float(i) - float(warmup_steps)) / (float(total_steps) - float(warmup_steps)))
lr = float(lr_max) * base * base
if lr < 0.0:
lr = 0.0
lr_each_step.append(lr)
else:
for i in range(total_steps):
if i < warmup_steps:
lr = lr_init + (lr_max - lr_init) * i / warmup_steps
else:
lr = lr_max - (lr_max - lr_end) * (i - warmup_steps) / (total_steps - warmup_steps)
lr_each_step.append(lr)
current_step = global_step
lr_each_step = np.array(lr_each_step).astype(np.float32)
learning_rate = lr_each_step[current_step:]
return learning_rate
example/resnet50_cifar10/run_distribute_train.sh 0 → 100755
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#!/bin/bash
# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
if [ $# != 2 ]
then
echo "Usage: sh run_distribute_train.sh [MINDSPORE_HCCL_CONFIG_PATH] [DATASET_PATH]"
exit 1
fi
if [ ! -f $1 ]
then
echo "error: DMINDSPORE_HCCL_CONFIG_PATH=$1 is not a file"
exit 1
fi
if [ ! -d $2 ]
then
echo "error: DATASET_PATH=$2 is not a directory"
exit 1
fi
ulimit -u unlimited
export DEVICE_NUM=8
export RANK_SIZE=8
export MINDSPORE_HCCL_CONFIG_PATH=$1
for((i=0; i<${DEVICE_NUM}; i++))
do
export DEVICE_ID=$i
export RANK_ID=$i
rm -rf ./train_parallel$i
mkdir ./train_parallel$i
cp *.py ./train_parallel$i
cp *.sh ./train_parallel$i
cd ./train_parallel$i || exit
echo "start training for rank $RANK_ID, device $DEVICE_ID"
env > env.log
python train.py --do_train=True --run_distribute=True --device_num=$DEVICE_NUM --dataset_path=$2 &> log &
cd ..
done
example/resnet50_cifar10/run_infer.sh 0 → 100755
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#!/bin/bash
# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
if [ $# != 2 ]
then
echo "Usage: sh run_infer.sh [DATASET_PATH] [CHECKPOINT_PATH]"
exit 1
fi
if [ ! -d $1 ]
then
echo "error: DATASET_PATH=$1 is not a directory"
exit 1
fi
if [ ! -f $2 ]
then
echo "error: CHECKPOINT_PATH=$2 is not a file"
exit 1
fi
ulimit -u unlimited
export DEVICE_NUM=1
export DEVICE_ID=0
export RANK_SIZE=$DEVICE_NUM
export RANK_ID=0
if [ -d "infer" ];
then
rm -rf ./infer
fi
mkdir ./infer
cp *.py ./infer
cp *.sh ./infer
cd ./infer || exit
env > env.log
echo "start infering for device $DEVICE_ID"
python eval.py --do_eval=True --dataset_path=$1 --checkpoint_path=$2 &> log &
cd ..
example/resnet50_cifar10/run_standalone_train.sh 0 → 100755
浏览文件 @ 930a1fb0
#!/bin/bash
# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
if [ $# != 1 ]
then
echo "Usage: sh run_standalone_train.sh [DATASET_PATH]"
exit 1
fi
if [ ! -d $1 ]
then
echo "error: DATASET_PATH=$1 is not a directory"
exit 1
fi
ulimit -u unlimited
export DEVICE_NUM=1
export DEVICE_ID=0
export RANK_ID=0
if [ -d "train" ];
then
rm -rf ./train
fi
mkdir ./train
cp *.py ./train
cp *.sh ./train
cd ./train || exit
echo "start training for device $DEVICE_ID"
env > env.log
python train.py --do_train=True --dataset_path=$1 &> log &
cd ..
example/resnet50_cifar10/train.py 0 → 100755
浏览文件 @ 930a1fb0
# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""train_imagenet."""
import os
import argparse
import random
import numpy as np
from dataset import create_dataset
from lr_generator import get_lr
from config import config
from mindspore import context
from mindspore import Tensor
from mindspore.model_zoo.resnet import resnet50
from mindspore.parallel._auto_parallel_context import auto_parallel_context
from mindspore.nn.optim.momentum import Momentum
from mindspore.nn.loss import SoftmaxCrossEntropyWithLogits
from mindspore.train.model import Model, ParallelMode
from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor, TimeMonitor
from mindspore.train.loss_scale_manager import FixedLossScaleManager
import mindspore.dataset.engine as de
from mindspore.communication.management import init
random.seed(1)
np.random.seed(1)
de.config.set_seed(1)
parser = argparse.ArgumentParser(description='Image classification')
parser.add_argument('--run_distribute', type=bool, default=False, help='Run distribute')
parser.add_argument('--device_num', type=int, default=1, help='Device num.')
parser.add_argument('--do_train', type=bool, default=True, help='Do train or not.')
parser.add_argument('--do_eval', type=bool, default=False, help='Do eval or not.')
parser.add_argument('--dataset_path', type=str, default=None, help='Dataset path')
args_opt = parser.parse_args()
device_id = int(os.getenv('DEVICE_ID'))
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", save_graphs=False)
context.set_context(enable_task_sink=True, device_id=device_id)
context.set_context(enable_loop_sink=True)
context.set_context(enable_mem_reuse=True)
if __name__ == '__main__':
if args_opt.do_eval:
context.set_context(enable_hccl=False)
else:
if args_opt.run_distribute:
context.set_context(enable_hccl=True)
context.set_auto_parallel_context(device_num=args_opt.device_num, parallel_mode=ParallelMode.DATA_PARALLEL,
mirror_mean=True)
auto_parallel_context().set_all_reduce_fusion_split_indices([140])
init()
else:
context.set_context(enable_hccl=False)
epoch_size = config.epoch_size
net = resnet50(class_num=config.class_num)
loss = SoftmaxCrossEntropyWithLogits(sparse=True)
if args_opt.do_train:
dataset = create_dataset(dataset_path=args_opt.dataset_path, do_train=True,
repeat_num=epoch_size, batch_size=config.batch_size)
step_size = dataset.get_dataset_size()
loss_scale = FixedLossScaleManager(config.loss_scale, drop_overflow_update=False)
lr = Tensor(get_lr(global_step=0, lr_init=config.lr_init, lr_end=config.lr_end, lr_max=config.lr_max,
warmup_epochs=config.warmup_epochs, total_epochs=epoch_size, steps_per_epoch=step_size,
lr_decay_mode='poly'))
opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), lr, config.momentum,
config.weight_decay, config.loss_scale)
model = Model(net, loss_fn=loss, optimizer=opt, loss_scale_manager=loss_scale, metrics={'acc'})
time_cb = TimeMonitor(data_size=step_size)
loss_cb = LossMonitor()
cb = [time_cb, loss_cb]
if config.save_checkpoint:
config_ck = CheckpointConfig(save_checkpoint_steps=config.save_checkpoint_steps,
keep_checkpoint_max=config.keep_checkpoint_max)
ckpt_cb = ModelCheckpoint(prefix="resnet", directory=config.save_checkpoint_path, config=config_ck)
cb += [ckpt_cb]
model.train(epoch_size, dataset, callbacks=cb)
example/yolov3_coco2017/config.py 0 → 100644
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# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""Config parameters for YOLOv3 models."""
class ConfigYOLOV3ResNet18:
"""
Config parameters for YOLOv3.
Examples:
ConfigYoloV3ResNet18.
"""
img_shape = [352, 640]
feature_shape = [32, 3, 352, 640]
num_classes = 80
backbone_input_shape = [64, 64, 128, 256]
backbone_shape = [64, 128, 256, 512]
backbone_layers = [2, 2, 2, 2]
backbone_stride = [1, 2, 2, 2]
ignore_threshold = 0.5
anchor_scales = [(10, 13),
(16, 30),
(33, 23),
(30, 61),
(62, 45),
(59, 119),
(116, 90),
(156, 198),
(163, 326)]
out_channel = int(len(anchor_scales) / 3 * (num_classes + 5))
example/yolov3_coco2017/dataset.py 0 → 100644
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# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""YOLOv3 dataset"""
from __future__ import division
import abc
import io
import os
import math
import json
import numpy as np
from PIL import Image
from matplotlib.colors import rgb_to_hsv, hsv_to_rgb
import mindspore.dataset as de
import mindspore.dataset.transforms.vision.py_transforms as P
from config import ConfigYOLOV3ResNet18
iter_cnt = 0
_NUM_BOXES = 50
def preprocess_fn(image, box, is_training):
"""Preprocess function for dataset."""
config_anchors = [10, 13, 16, 30, 33, 23, 30, 61, 62, 45, 59, 119, 116, 90, 156, 198, 163, 326]
anchors = np.array([float(x) for x in config_anchors]).reshape(-1, 2)
do_hsv = False
max_boxes = 20
num_classes = ConfigYOLOV3ResNet18.num_classes
def _rand(a=0., b=1.):
return np.random.rand() * (b - a) + a
def _preprocess_true_boxes(true_boxes, anchors, in_shape=None):
"""Get true boxes."""
num_layers = anchors.shape[0] // 3
anchor_mask = [[6, 7, 8], [3, 4, 5], [0, 1, 2]]
true_boxes = np.array(true_boxes, dtype='float32')
# input_shape = np.array([in_shape, in_shape], dtype='int32')
input_shape = np.array(in_shape, dtype='int32')
boxes_xy = (true_boxes[..., 0:2] + true_boxes[..., 2:4]) // 2.
boxes_wh = true_boxes[..., 2:4] - true_boxes[..., 0:2]
true_boxes[..., 0:2] = boxes_xy / input_shape[::-1]
true_boxes[..., 2:4] = boxes_wh / input_shape[::-1]
grid_shapes = [input_shape // 32, input_shape // 16, input_shape // 8]
y_true = [np.zeros((grid_shapes[l][0], grid_shapes[l][1], len(anchor_mask[l]),
5 + num_classes), dtype='float32') for l in range(num_layers)]
anchors = np.expand_dims(anchors, 0)
anchors_max = anchors / 2.
anchors_min = -anchors_max
valid_mask = boxes_wh[..., 0] >= 1
wh = boxes_wh[valid_mask]
if len(wh) >= 1:
wh = np.expand_dims(wh, -2)
boxes_max = wh / 2.
boxes_min = -boxes_max
intersect_min = np.maximum(boxes_min, anchors_min)
intersect_max = np.minimum(boxes_max, anchors_max)
intersect_wh = np.maximum(intersect_max - intersect_min, 0.)
intersect_area = intersect_wh[..., 0] * intersect_wh[..., 1]
box_area = wh[..., 0] * wh[..., 1]
anchor_area = anchors[..., 0] * anchors[..., 1]
iou = intersect_area / (box_area + anchor_area - intersect_area)
best_anchor = np.argmax(iou, axis=-1)
for t, n in enumerate(best_anchor):
for l in range(num_layers):
if n in anchor_mask[l]:
i = np.floor(true_boxes[t, 0] * grid_shapes[l][1]).astype('int32')
j = np.floor(true_boxes[t, 1] * grid_shapes[l][0]).astype('int32')
k = anchor_mask[l].index(n)
c = true_boxes[t, 4].astype('int32')
y_true[l][j, i, k, 0:4] = true_boxes[t, 0:4]
y_true[l][j, i, k, 4] = 1.
y_true[l][j, i, k, 5 + c] = 1.
pad_gt_box0 = np.zeros(shape=[50, 4], dtype=np.float32)
pad_gt_box1 = np.zeros(shape=[50, 4], dtype=np.float32)
pad_gt_box2 = np.zeros(shape=[50, 4], dtype=np.float32)
mask0 = np.reshape(y_true[0][..., 4:5], [-1])
gt_box0 = np.reshape(y_true[0][..., 0:4], [-1, 4])
gt_box0 = gt_box0[mask0 == 1]
pad_gt_box0[:gt_box0.shape[0]] = gt_box0
mask1 = np.reshape(y_true[1][..., 4:5], [-1])
gt_box1 = np.reshape(y_true[1][..., 0:4], [-1, 4])
gt_box1 = gt_box1[mask1 == 1]
pad_gt_box1[:gt_box1.shape[0]] = gt_box1
mask2 = np.reshape(y_true[2][..., 4:5], [-1])
gt_box2 = np.reshape(y_true[2][..., 0:4], [-1, 4])
gt_box2 = gt_box2[mask2 == 1]
pad_gt_box2[:gt_box2.shape[0]] = gt_box2
return y_true[0], y_true[1], y_true[2], pad_gt_box0, pad_gt_box1, pad_gt_box2
def _data_aug(image, box, is_training, jitter=0.3, hue=0.1, sat=1.5, val=1.5, image_size=(352, 640)):
"""Data augmentation function."""
if not isinstance(image, Image.Image):
image = Image.fromarray(image)
iw, ih = image.size
ori_image_shape = np.array([ih, iw], np.int32)
h, w = image_size
if not is_training:
image = image.resize((w, h), Image.BICUBIC)
image_data = np.array(image) / 255.
if len(image_data.shape) == 2:
image_data = np.expand_dims(image_data, axis=-1)
image_data = np.concatenate([image_data, image_data, image_data], axis=-1)
image_data = image_data.astype(np.float32)
# correct boxes
box_data = np.zeros((max_boxes, 5))
if len(box) >= 1:
np.random.shuffle(box)
if len(box) > max_boxes:
box = box[:max_boxes]
# xmin ymin xmax ymax
box[:, [0, 2]] = box[:, [0, 2]] * float(w) / float(iw)
box[:, [1, 3]] = box[:, [1, 3]] * float(h) / float(ih)
box_data[:len(box)] = box
else:
image_data, box_data = None, None
# preprocess bounding boxes
bbox_true_1, bbox_true_2, bbox_true_3, gt_box1, gt_box2, gt_box3 = \
_preprocess_true_boxes(box_data, anchors, image_size)
return image_data, bbox_true_1, bbox_true_2, bbox_true_3, \
ori_image_shape, gt_box1, gt_box2, gt_box3
flip = _rand() < .5
# correct boxes
box_data = np.zeros((max_boxes, 5))
while True:
# Prevent the situation that all boxes are eliminated
new_ar = float(w) / float(h) * _rand(1 - jitter, 1 + jitter) / \
_rand(1 - jitter, 1 + jitter)
scale = _rand(0.25, 2)
if new_ar < 1:
nh = int(scale * h)
nw = int(nh * new_ar)
else:
nw = int(scale * w)
nh = int(nw / new_ar)
dx = int(_rand(0, w - nw))
dy = int(_rand(0, h - nh))
if len(box) >= 1:
t_box = box.copy()
np.random.shuffle(t_box)
t_box[:, [0, 2]] = t_box[:, [0, 2]] * float(nw) / float(iw) + dx
t_box[:, [1, 3]] = t_box[:, [1, 3]] * float(nh) / float(ih) + dy
if flip:
t_box[:, [0, 2]] = w - t_box[:, [2, 0]]
t_box[:, 0:2][t_box[:, 0:2] < 0] = 0
t_box[:, 2][t_box[:, 2] > w] = w
t_box[:, 3][t_box[:, 3] > h] = h
box_w = t_box[:, 2] - t_box[:, 0]
box_h = t_box[:, 3] - t_box[:, 1]
t_box = t_box[np.logical_and(box_w > 1, box_h > 1)] # discard invalid box
if len(t_box) >= 1:
box = t_box
break
box_data[:len(box)] = box
# resize image
image = image.resize((nw, nh), Image.BICUBIC)
# place image
new_image = Image.new('RGB', (w, h), (128, 128, 128))
new_image.paste(image, (dx, dy))
image = new_image
# flip image or not
if flip:
image = image.transpose(Image.FLIP_LEFT_RIGHT)
# convert image to gray or not
gray = _rand() < .25
if gray:
image = image.convert('L').convert('RGB')
# when the channels of image is 1
image = np.array(image)
if len(image.shape) == 2:
image = np.expand_dims(image, axis=-1)
image = np.concatenate([image, image, image], axis=-1)
# distort image
hue = _rand(-hue, hue)
sat = _rand(1, sat) if _rand() < .5 else 1 / _rand(1, sat)
val = _rand(1, val) if _rand() < .5 else 1 / _rand(1, val)
image_data = image / 255.
if do_hsv:
x = rgb_to_hsv(image_data)
x[..., 0] += hue
x[..., 0][x[..., 0] > 1] -= 1
x[..., 0][x[..., 0] < 0] += 1
x[..., 1] *= sat
x[..., 2] *= val
x[x > 1] = 1
x[x < 0] = 0
image_data = hsv_to_rgb(x) # numpy array, 0 to 1
image_data = image_data.astype(np.float32)
# preprocess bounding boxes
bbox_true_1, bbox_true_2, bbox_true_3, gt_box1, gt_box2, gt_box3 = \
_preprocess_true_boxes(box_data, anchors, image_size)
return image_data, bbox_true_1, bbox_true_2, bbox_true_3, \
ori_image_shape, gt_box1, gt_box2, gt_box3
images, bbox_1, bbox_2, bbox_3, _, gt_box1, gt_box2, gt_box3 = _data_aug(image, box, is_training)
return images, bbox_1, bbox_2, bbox_3, gt_box1, gt_box2, gt_box3
def anno_parser(annos_str):
"""Annotation parser."""
annos = []
for anno_str in annos_str:
anno = list(map(int, anno_str.strip().split(',')))
annos.append(anno)
return annos
def expand_path(path):
"""Get file list from path."""
files = []
if os.path.isdir(path):
for file in os.listdir(path):
if os.path.isfile(os.path.join(path, file)):
files.append(file)
else:
raise RuntimeError("Path given is not valid.")
return files
def read_image(img_path):
"""Read image with PIL."""
with open(img_path, "rb") as f:
img = f.read()
data = io.BytesIO(img)
img = Image.open(data)
return np.array(img)
class BaseDataset():
"""BaseDataset for GeneratorDataset iterator."""
def __init__(self, image_dir, anno_path):
self.image_dir = image_dir
self.anno_path = anno_path
self.cur_index = 0
self.samples = []
self.image_anno_dict = {}
self._load_samples()
def __getitem__(self, item):
sample = self.samples[item]
return self._next_data(sample, self.image_dir, self.image_anno_dict)
def __len__(self):
return len(self.samples)
@staticmethod
def _next_data(sample, image_dir, image_anno_dict):
"""Get next data."""
image = read_image(os.path.join(image_dir, sample))
annos = image_anno_dict[sample]
return [np.array(image), np.array(annos)]
@abc.abstractmethod
def _load_samples(self):
"""Base load samples."""
class YoloDataset(BaseDataset):
"""YoloDataset for GeneratorDataset iterator."""
def _load_samples(self):
"""Load samples."""
image_files_raw = expand_path(self.image_dir)
self.samples = self._filter_valid_data(self.anno_path, image_files_raw)
self.dataset_size = len(self.samples)
if self.dataset_size == 0:
raise RuntimeError("Valid dataset is none!")
def _filter_valid_data(self, anno_path, image_files_raw):
"""Filter valid data."""
image_files = []
anno_dict = {}
print("Start filter valid data.")
with open(anno_path, "rb") as f:
lines = f.readlines()
for line in lines:
line_str = line.decode("utf-8")
line_split = str(line_str).split(' ')
anno_dict[line_split[0].split("/")[-1]] = line_split[1:]
anno_set = set(anno_dict.keys())
image_set = set(image_files_raw)
for image_file in (anno_set & image_set):
image_files.append(image_file)
self.image_anno_dict[image_file] = anno_parser(anno_dict[image_file])
image_files.sort()
print("Filter valid data done!")
return image_files
class DistributedSampler():
"""DistributedSampler for YOLOv3"""
def __init__(self, dataset_size, batch_size, num_replicas=None, rank=None, shuffle=True):
if num_replicas is None:
num_replicas = 1
if rank is None:
rank = 0
self.dataset_size = dataset_size
self.num_replicas = num_replicas
self.rank = rank % num_replicas
self.epoch = 0
self.num_samples = max(batch_size, int(math.ceil(dataset_size * 1.0 / self.num_replicas)))
self.total_size = self.num_samples * self.num_replicas
self.shuffle = shuffle
def __iter__(self):
# deterministically shuffle based on epoch
if self.shuffle:
indices = np.random.RandomState(seed=self.epoch).permutation(self.dataset_size)
indices = indices.tolist()
else:
indices = list(range(self.dataset_size))
# add extra samples to make it evenly divisible
indices += indices[:(self.total_size - len(indices))]
assert len(indices) == self.total_size
# subsample
indices = indices[self.rank:self.total_size:self.num_replicas]
assert len(indices) == self.num_samples
return iter(indices)
def __len__(self):
return self.num_samples
def set_epoch(self, epoch):
self.epoch = epoch
def create_yolo_dataset(image_dir, anno_path, batch_size=32, repeat_num=10, device_num=1, rank=0,
is_training=True, num_parallel_workers=8):
"""Creatr YOLOv3 dataset with GeneratorDataset."""
yolo_dataset = YoloDataset(image_dir=image_dir, anno_path=anno_path)
distributed_sampler = DistributedSampler(yolo_dataset.dataset_size, batch_size, device_num, rank)
ds = de.GeneratorDataset(yolo_dataset, column_names=["image", "annotation"], sampler=distributed_sampler)
ds.set_dataset_size(len(distributed_sampler))
compose_map_func = (lambda image, annotation: preprocess_fn(image, annotation, is_training))
hwc_to_chw = P.HWC2CHW()
ds = ds.map(input_columns=["image", "annotation"],
output_columns=["image", "bbox_1", "bbox_2", "bbox_3", "gt_box1", "gt_box2", "gt_box3"],
columns_order=["image", "bbox_1", "bbox_2", "bbox_3", "gt_box1", "gt_box2", "gt_box3"],
operations=compose_map_func, num_parallel_workers=num_parallel_workers)
ds = ds.map(input_columns=["image"], operations=hwc_to_chw, num_parallel_workers=num_parallel_workers)
ds = ds.shuffle(buffer_size=256)
ds = ds.batch(batch_size, drop_remainder=True)
ds = ds.repeat(repeat_num)
return ds
example/yolov3_coco2017/run_distribute_train.sh 0 → 100644
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#!/bin/bash
# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
echo "Please run the scipt as: "
echo "sh run_distribute_train.sh DEVICE_NUM EPOCH_SIZE IMAGE_DIR ANNO_PATH MINDSPORE_HCCL_CONFIG_PATH"
echo "for example: sh run_distribute_train.sh 8 100 ./dataset/coco/train2017 ./dataset/train.txt　./hccl.json"
echo "After running the scipt, the network runs in the background. The log will be generated in LOGx/log.txt"
export RANK_SIZE=$1
EPOCH_SIZE=$2
IMAGE_DIR=$3
ANNO_PATH=$4
export MINDSPORE_HCCL_CONFIG_PATH=$5
for((i=0;i<RANK_SIZE;i++))
do
export DEVICE_ID=$i
rm -rf LOG$i
mkdir ./LOG$i
cp *.py ./LOG$i
cd ./LOG$i || exit
export RANK_ID=$i
echo "start training for rank $i, device $DEVICE_ID"
env > env.log
python ../train.py \
--distribute=1 \
--device_num=$RANK_SIZE \
--device_id=$DEVICE_ID \
--image_dir=$IMAGE_DIR \
--epoch_size=$EPOCH_SIZE \
--anno_path=$ANNO_PATH > log.txt 2>&1 &
cd ../
done
example/yolov3_coco2017/run_standalone_train.sh 0 → 100644
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#!/bin/bash
# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
echo "Please run the scipt as: "
echo "sh run_standalone_train.sh DEVICE_ID EPOCH_SIZE IMAGE_DIR ANNO_PATH"
echo "for example: sh run_standalone_train.sh 0 50 ./dataset/coco/train2017 ./dataset/train.txt"
python train.py --device_id=$1 --epoch_size=$2 --image_dir=$3 --anno_path=$4
example/yolov3_coco2017/train.py 0 → 100644
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# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# less required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""
######################## train YOLOv3 example ########################
train YOLOv3 and get network model files(.ckpt) :
python train.py --image_dir dataset/coco/coco/train2017 --anno_path dataset/coco/train_coco.txt
"""
import argparse
import numpy as np
import mindspore.nn as nn
from mindspore import context, Tensor
from mindspore.common.initializer import initializer
from mindspore.communication.management import init
from mindspore.train.callback import CheckpointConfig, ModelCheckpoint, LossMonitor, TimeMonitor
from mindspore.train import Model, ParallelMode
from mindspore.train.serialization import load_checkpoint, load_param_into_net
from mindspore.model_zoo.yolov3 import yolov3_resnet18, YoloWithLossCell, TrainingWrapper
from dataset import create_yolo_dataset
from config import ConfigYOLOV3ResNet18
def get_lr(learning_rate, start_step, global_step, decay_step, decay_rate, steps=False):
"""Set learning rate"""
lr_each_step = []
lr = learning_rate
for i in range(global_step):
if steps:
lr_each_step.append(lr * (decay_rate ** (i // decay_step)))
else:
lr_each_step.append(lr * (decay_rate ** (i / decay_step)))
lr_each_step = np.array(lr_each_step).astype(np.float32)
lr_each_step = lr_each_step[start_step:]
return lr_each_step
def init_net_param(net, init='ones'):
"""Init the parameters in net."""
params = net.trainable_params()
for p in params:
if isinstance(p.data, Tensor) and 'beta' not in p.name and 'gamma' not in p.name and 'bias' not in p.name:
p.set_parameter_data(initializer(init, p.data.shape(), p.data.dtype()))
if __name__ == '__main__':
parser = argparse.ArgumentParser(description="YOLOv3")
parser.add_argument("--distribute", type=bool, default=False, help="Run distribute, default is false.")
parser.add_argument("--device_id", type=int, default=0, help="Device id, default is 0.")
parser.add_argument("--device_num", type=int, default=1, help="Use device nums, default is 1.")
parser.add_argument("--mode", type=str, default="graph", help="Run graph mode or feed mode, default is graph")
parser.add_argument("--epoch_size", type=int, default=10, help="Epoch size, default is 10")
parser.add_argument("--batch_size", type=int, default=32, help="Batch size, default is 32.")
parser.add_argument("--checkpoint_path", type=str, default="", help="Checkpoint file path")
parser.add_argument("--save_checkpoint_epochs", type=int, default=5, help="Save checkpoint epochs, default is 5.")
parser.add_argument("--loss_scale", type=int, default=1024, help="Loss scale, default is 1024.")
parser.add_argument("--image_dir", type=str, required=True, help="Dataset image dir.")
parser.add_argument("--anno_path", type=str, required=True, help="Dataset anno path.")
args_opt = parser.parse_args()
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", device_id=args_opt.device_id)
context.set_context(enable_task_sink=True, enable_loop_sink=True, enable_mem_reuse=True)
if args_opt.distribute:
device_num = args_opt.device_num
context.reset_auto_parallel_context()
context.set_context(enable_hccl=True)
context.set_auto_parallel_context(parallel_mode=ParallelMode.DATA_PARALLEL, mirror_mean=True,
device_num=device_num)
init()
rank = args_opt.device_id
else:
context.set_context(enable_hccl=False)
rank = 0
device_num = 1
loss_scale = float(args_opt.loss_scale)
dataset = create_yolo_dataset(args_opt.image_dir, args_opt.anno_path, repeat_num=args_opt.epoch_size,
batch_size=args_opt.batch_size, device_num=device_num, rank=rank)
dataset_size = dataset.get_dataset_size()
net = yolov3_resnet18(ConfigYOLOV3ResNet18())
net = YoloWithLossCell(net, ConfigYOLOV3ResNet18())
init_net_param(net, "XavierUniform")
# checkpoint
ckpt_config = CheckpointConfig(save_checkpoint_steps=dataset_size * args_opt.save_checkpoint_epochs)
ckpoint_cb = ModelCheckpoint(prefix="yolov3", directory=None, config=ckpt_config)
if args_opt.checkpoint_path != "":
param_dict = load_checkpoint(args_opt.checkpoint_path)
load_param_into_net(net, param_dict)
lr = Tensor(get_lr(learning_rate=0.001, start_step=0, global_step=args_opt.epoch_size * dataset_size,
decay_step=1000, decay_rate=0.95))
opt = nn.Adam(filter(lambda x: x.requires_grad, net.get_parameters()), lr, loss_scale=loss_scale)
net = TrainingWrapper(net, opt, loss_scale)
callback = [TimeMonitor(data_size=dataset_size), LossMonitor(), ckpoint_cb]
model = Model(net)
dataset_sink_mode = False
if args_opt.mode == "graph":
dataset_sink_mode = True
print("Start train YOLOv3.")
model.train(args_opt.epoch_size, dataset, callbacks=callback, dataset_sink_mode=dataset_sink_mode)
graphengine @ 5f763679
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mindspore/__init__.py 0 → 100755
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# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""MindSpore package."""
from . import common, train
from .common import *
from .ops import _op_impl
from .train import *
from .log import *
from .version import __version__
__all__ = []
__all__.extend(__version__)
__all__.extend(common.__all__)
__all__.extend(train.__all__)
__all__.extend(log.__all__)
mindspore/_checkparam.py 0 → 100644
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mindspore/_extends/__init__.py 0 → 100644
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# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""
Extension functions.
Python functions that will be called in the c++ parts of MindSpore.
"""
from .utils import cell_attr_register
__all__ = ["cell_attr_register"]
mindspore/_extends/builtin_operations.py 0 → 100644
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# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""builtin_operations"""
import functools
import numpy as np
from mindspore.common.tensor import Tensor
from mindspore.common.dtype import dtype_to_nptype, get_py_obj_dtype
def scalar_add(x, y):
"""Implement `scalar_add`."""
return x + y
def scalar_mul(x, y):
"""Implement `scalar_mul`."""
return x * y
def scalar_mod(x, y):
"""Implement `scalar_mul`."""
return x % y
def scalar_sub(x, y):
"""Implement `scalar_sub`."""
return x - y
def scalar_usub(x):
"""Implement `scalar_usub`."""
return -x
def tuple_getitem(x, index):
"""Implement `tuple_getitem`."""
if isinstance(x, Tensor):
x = x.asnumpy()
y = x[index]
return Tensor(y)
return x[index]
def scalar_gt(x, y):
"""Implement `scalar_gt`."""
return x > y
def scalar_ne(x, y):
"""Implement `scalar_ne`."""
return x != y
def scalar_eq(x, y):
"""Implement `scalar_eq`."""
return x == y
def scalar_le(x, y):
"""Implement `scalar_le`."""
return x <= y
def scalar_lt(x, y):
"""Implement `scalar_lt`."""
return x < y
def identity(x):
"""Implement `identity`."""
return x
def zeros_like_tensor(x):
"""Implement `zeros_like_tensor`."""
x = x.asnumpy()
value = Tensor(np.zeros(x.shape))
return value
def switch(c, x, y):
"""Implement `switch`."""
return x if c else y
def list_getitem(data, item):
"""Implement `list_getitem`."""
return data[item]
def bool_not(x):
"""Implement `bool_not`."""
return not x
def bool_and(x, y):
"""Implement `bool_and`."""
return x and y
def bool_or(x, y):
"""Implement `bool_or`."""
return x or y
def make_list(*xs):
"""Implement `make_list`."""
return list(xs)
def list_len(x):
"""Implement `list_len`."""
return len(x)
# only used in PyNative modes
def partial(*args):
"""Implement `partial`."""
func = args[0].__call__
partial_func = functools.partial(func, *args[1:])
return partial_func
# only used in PyNative modes
def depend(value, expr):
return value
def scalar_cast(x, t):
"""Implement scalar_cast."""
np_type = dtype_to_nptype(t)
value = np_type(x)
cast_value = np.ndarray.item(value)
return cast_value
def typeof(x):
"""Implement typeof."""
return get_py_obj_dtype(x)
def tuple_to_array(x):
"""Implement `tuple_to_array`."""
return Tensor(np.array(x))
def stop_gradient(x):
"""Implement `stop_gradient`."""
return x
mindspore/_extends/parallel_compile/__init__.py 0 → 100644
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# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""
Extension functions.
Python functions that will be called in the c++ parts of MindSpore.
"""
mindspore/_extends/parallel_compile/multi_compiler.py 0 → 100644
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# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""Providing multi process compile with json"""
import json
import math
import os
import subprocess
import sys
from multiprocessing import Pool
def _compiletask(platform, *jsons):
"""
compile func called in single process
Parameters:
platform: str. AKG platform or TBE platform
*jsons: str. json str contain kernel info, suitable for json compile
api
"""
if platform == "AKG":
p = __import__("akg", globals(), locals(), ['ms'], 0)
func = getattr(p.ms, "compilewithjson")
for json_item in jsons:
res = func(json_item)
if not res:
raise ValueError("Compile error")
if platform == "TBE":
tbe_compiler = os.path.join(os.path.split(os.path.realpath(__file__))[0], "tbe_compiler", "compiler.py")
for json_item in jsons:
res = subprocess.run([sys.executable, tbe_compiler], input=json_item, text=True)
if res.returncode != 0:
raise ValueError("Tbe compile error")
def compilekernelparallel(jsons, process, waitime):
"""
compile kernel use multi processes
Parameters:
jsons: list. json str list contain kernel info
process: int. processes num
waittime: int. max time the function blocked
"""
if not isinstance(jsons, list):
raise ValueError("jsons must be a list")
if not isinstance(process, int):
raise ValueError("process must be a num")
if not isinstance(waitime, int):
raise ValueError("waittime must be a num")
jsons_akg = []
jsons_tbe = []
for json_ in jsons:
j = json.loads(json_)
if j["platform"] == "TBE":
jsons_tbe.append(json_)
continue
if j["platform"] == "AKG":
jsons_akg.append(json_)
continue
raise RuntimeError(
"not support this platform {0}".format(j["platform"]))
if jsons_akg:
process_akg = math.floor(len(jsons)/len(jsons_akg)*process)
else:
process_akg = 0
if process_akg == 0 and jsons_akg:
process_akg = 1
process_tbe = process-process_akg
if process_tbe == 0 and jsons_tbe:
process_tbe = 1
raise RuntimeWarning("we add a process for compile more operator")
args = [[] for _ in range(process_akg+process_tbe)]
args_lens = len(args)
for p in range(args_lens):
if p < process_tbe:
args[p].append("TBE")
else:
args[p].append("AKG")
jsons_tbe_lens = len(jsons_tbe)
for p in range(jsons_tbe_lens):
args[p % process_tbe].append(jsons_tbe[p])
jsons_akg_lens = len(jsons_akg)
for p in range(jsons_akg_lens):
args[process-p % process_akg-1].append(jsons_akg[p])
for p in range(args_lens):
args[p] = tuple(args[p])
with Pool(processes=process) as pool:
res = pool.starmap_async(_compiletask, args)
res.get(timeout=waitime)
return True
mindspore/_extends/parallel_compile/tbe_compiler/__init__.py 0 → 100644
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# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
mindspore/_extends/parallel_compile/tbe_compiler/common.py 0 → 100644
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# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""tbe common"""
import json
import os
class TBEException(Exception):
"""tbe exception class"""
def __init__(self, err_msg):
super().__init__(self)
self.__error_msg = err_msg
def __str__(self):
return self.__error_msg
def get_ddk_version():
"""get ddk version"""
ddk_version = os.environ.get("DDK_VERSION")
if ddk_version is None:
default_ddk_info_file = '/usr/local/HiAI/runtime/ddk_info'
backup_ddk_info_file = '/usr/local/Ascend/fwkacllib/ddk_info'
if os.path.exists(default_ddk_info_file):
with open(default_ddk_info_file, "r") as fp:
ddk_version = json.load(fp)["VERSION"]
elif os.path.exists(backup_ddk_info_file):
with open(backup_ddk_info_file, "r") as fp:
ddk_version = json.load(fp)["VERSION"]
else:
ddk_version = "1.60.T17.B830"
return ddk_version
def get_build_in_impl_path():
"""get build-in tbe implement path"""
tbe_impl_path = os.environ.get("TBE_IMPL_PATH")
if tbe_impl_path is None:
default_install_path = '/usr/local/HiAI/runtime/ops/op_impl/built-in/ai_core/tbe/'
backup_install_path = '/usr/local/Ascend/Ascend/opp/op_impl/built-in/ai_core/tbe/'
if os.path.exists(default_install_path):
tbe_impl_path = default_install_path
elif os.path.exists(backup_install_path):
tbe_impl_path = backup_install_path
if not tbe_impl_path:
raise ValueError("Can not find the env TBE_IMPL_PATH")
return tbe_impl_path
def _check_arg_info(item):
"""
Check parameter Validity.
Args:
item (dict): A dict, to be checked.
Raises:
Exception: If specific keyword is not found.
"""
if 'shape' not in item:
raise ValueError("Json string Errors, key:shape not found.")
if 'ori_shape' not in item:
raise ValueError("Json string Errors, key:ori_shape not found.")
if 'format' not in item or not item['format']:
raise ValueError("Json string Errors, key:format not found.")
if 'ori_format' not in item or not item['ori_format']:
raise ValueError("Json string Errors, key:ori_format not found.")
if 'dtype' not in item or not item['dtype']:
raise ValueError("Json string Errors, key:dtype not found.")
def get_args(op_info, arg_type):
"""
Parse args.
Args:
op_info (dict): Op info dict.
arg_type (str): arg, to be parsed.
Raises:
Exception: If specific keyword is not found.
"""
if arg_type not in op_info:
raise ValueError("Json string Errors, key:{} not found.".format(arg_type))
args = []
if not op_info[arg_type]:
return args
if arg_type in ['inputs', 'outputs']:
for item in op_info[arg_type]:
arg = []
for info in item:
if 'valid' not in info:
raise ValueError("Json string Errors, key:valid not found.")
if info['valid']:
_check_arg_info(info)
del info['valid']
del info['name']
if len(item) > 1:
arg.append(info)
else:
args.append(info)
else:
if len(item) > 1:
arg.append(None)
else:
args.append(None)
if len(item) > 1:
args.append(arg)
elif arg_type == 'attrs':
for item in op_info[arg_type]:
if 'value' not in item:
raise ValueError("Json string Errors, attr key:value not found.")
if item["name"] != "isRef":
args.append(item['value'])
return args
def check_kernel_info(kernel_info):
if 'op_info' not in kernel_info or not kernel_info['op_info']:
raise ValueError("Json string Errors, key:op_info not found.")
if 'name' not in kernel_info['op_info'] or not kernel_info['op_info']['name']:
raise ValueError("Json string Errors, key:name not found.")
if 'kernel_name' not in kernel_info['op_info'] or not kernel_info['op_info']['kernel_name']:
raise ValueError("Json string Errors, key:kernel_name not found.")
mindspore/_extends/parse/__init__.py 0 → 100644
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# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""
Interfaces for parser module in c++.
"""
from .parser import (Parser, create_obj_instance, generate_scope,
get_bprop_method_of_class, get_class_instance_type,
get_class_member_namespace_symbol,
get_dataclass_attributes, get_dataclass_methods,
get_module_namespace, get_obj_type, get_object_key,
get_parse_method_of_class, get_scope_name,
is_class_member, parse_cb, resolve_symbol)
from .serialize import *
__all__ = ['parse_cb', 'get_parse_method_of_class', 'get_bprop_method_of_class', 'resolve_symbol',
'get_object_key', 'get_class_instance_type', 'is_class_member', 'get_obj_type',
'create_obj_instance', 'get_module_namespace', 'get_class_member_namespace_symbol',
'Parser', 'get_dataclass_attributes', 'get_dataclass_methods', 'dump_obj', 'load_obj',
'get_dataclass_methods', 'get_scope_name']
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mindspore/akg/gpu/__init__.py 0 → 100644
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# Copyright 2019 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""__init__"""
from .equal import Equal
from .equal import gpu_schedule_Equal
from .tile import Tile
from .tile import gpu_schedule_Tile
from .cast import Cast
from .cast import gpu_schedule_Cast
from .relu6 import ReLU6, gpu_schedule_ReLU6
from .relu6_grad import ReLU6Grad, gpu_schedule_ReLU6Grad
from .squeeze import Squeeze, gpu_schedule_Squeeze
from .squeeze_grad import SqueezeGrad, gpu_schedule_SqueezeGrad
from .mean import SimpleMean, gpu_schedule_SimpleMean
from .mean_grad import SimpleMeanGrad, gpu_schedule_SimpleMeanGrad
from .mul import Mul, gpu_schedule_Mul
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