* Enable selecting custom ops in onnxruntime-extensions.
* Move cmake_helper.py.
* Remove over-indented spaces.
* Add doc.
* Remove onnxruntime-extensions from git submodules, and user should pass path of onnxruntime-extensions for build.
* Modify doc.
* Remove argument --enable_onnxruntime_extensions and use --onnxruntime_extensions_path.
* Fix build error.
* Fix build error.
* Use onnxruntime_extensions_path.
* support both submodule and external source folders
* refinement
* Update cgmanifest.json
* Support building onnxruntime-extensions from either git submodule or pre-pulled path.
* Update doc.
* more standard name
* update docs
* add the copyright header
Co-authored-by: Zuwei Zhao <zuzhao@microsoft.com>
Co-authored-by: Wenbing Li <wenbingl@outlook.com>
Co-authored-by: Wenbing Li <10278425+wenbingl@users.noreply.github.com>
* adding support for tracing to sqldb instead of files
* use compiled statements
* script to pull tensors from db
* link sqlite3
* remove node info redundant with onnx graph
* addressing PR comments
* address PR comments and include program counter
* third party notice
* use find_pacakge
* add to cgmanifests.json
* address thread safety and add pid suffix
* build fi
* python script to select on devicetype
* remove unpopulated and redundant Shape and Type fields
* comment
* comment
* PR comments
* add graph execution counter to session state
* move increment to inference session
* std::endl to \n
* ifdef on graph execution counter
* add ifdef to inference session
* move DEBUG_NODE_INPUTS_OUTPUTS to CMakeLists.txt
This change adds a new pipeline for checking Python code. Currently this pipeline only runs flake8.
flake8 is also run as part of the CMake project builds, but we can switch over completely to the new pipeline later.
The .flake8 config file was also updated to make it easier to run standalone (flake8 --config ./.flake8) and some Python formatting issues were addressed in files that were not previously scanned.
* updates for picking pnnx commit
* add tests filter to c# tests
* plus test fixes
* fix versioning for contrib ops
* fix tests
* test filter for optional ops
* more versioning related updates
* fix test
* fix layernorm spec
* more updates
* update docs
* add more test filters
* more filters
* update binary size threshold
* update docs
* plus more fixes
* updates per review
* update to release commit
* add filters for optional type tests
* plus updates
1. Update SDLNativeRules from v2 to v3. The new one allows us setting excluded paths.
2. Update TSAUpload from v1 to v2. And add a config file ".gdn/.gdntsa" for it.
3. Fix some parentheses warnings
4. Update cmake to the latest.
5. Remove "--x86" build option from pipeline yaml files. Now we can auto-detect cpu architecture from python. So we don't need to ask user to specify it.
* Update submodule onnxruntime-extensions to latest.
* Add document for onnxruntime-extensions.
* Update cgmanifest.json for onnxruntime-extensions.
* Add example in JavaScript.
Co-authored-by: Zuwei Zhao <zuzhao@microsoft.com>
Pytorch cpuinfo library allows us to query current cpu features, micro-architecture and cache size, etc. These information is needed for targeted performance optimizations.
Unfortunately it does not work under Windows/ARM. We need to develop our own later
Switched the code to C++17. To build ONNX Runtime on old distros like CentOS 7, you need to install a newer GCC from additionary repos. If you build onnxruntime with the newer GCC, typically the result binary can't be distributed to other places because it depends on the new GCC's runtime libraries, something that the stock OS doesn't have. But on RHEL/CentOS, it can be better. We use Red Hat devtoolset 8/9/10 with CentOS7 building our code. The new library features(like std::filesystem) that not exists in the old C++ runtime will be statically linked into the applications with some restrictions:
1. GCC has dual ABI, but we can only use the old one. It means std::string is still copy-on-write and std::list::size() is still O(n). Also, if you build onnxruntime on CentOS 7 and link it with some binaries that were built on CentOS 8 or Ubuntu with the new ABI and export C++ symbols directly(instead of using a C API), the it won't work.
2. We still can't use std::optional. It is a limitation coming from macOS. We will solve it when we got macOS 11 build machines. It won't be too long.
3. Please avoid to use C++17 in CUDA files(*.cu). Also, the *.h files that they include(like core/framework/float16.h). This is Because CUDA 10.2 doesn't support C++17. You are welcome to use the new features in any *.cc files.
1. Update manylinux build scripts. This will add [PEP600](https://www.python.org/dev/peps/pep-0600/)(manylinux2 tags) support. numpy has adopted this new feature, we should do the same. The old build script files were copied from https://github.com/pypa/manylinux, but they has been deleted and replaced in the upstream repo. The manylinux repo doesn't have a manylinux2014 branch anymore. So I'm removing the obsolete code, sync the files with the latest master.
2. Update GPU CUDA version from 11.0 to 11.1(after a discussion with PMs).
3. Delete tools/ci_build/github/linux/docker/Dockerfile.manylinux2014_cuda10_2. (Merged the content to tools/ci_build/github/linux/docker/Dockerfile.manylinux2014_cuda11)
4. Modernize the cmake code of how to locate python devel files. It was suggested in https://github.com/onnx/onnx/pull/1631 .
5. Remove `onnxruntime_MSVC_STATIC_RUNTIME` and `onnxruntime_GCC_STATIC_CPP_RUNTIME` build options. Now cmake has builtin support for it. Starting from cmake 3.15, we can use `CMAKE_MSVC_RUNTIME_LIBRARY` cmake variable to choose which MSVC runtime library we want to use.
6. Update Ubuntu docker images that used in our CI build from Ubuntu 18.04 to Ubuntu 20.04.
7. Update GCC version in CUDA 11.1 pipelines from 8.x to 9.3.1
8. Split Linux GPU CI pipeline to two jobs: build the code on a CPU machine then run the tests on another GPU machines. In the past we didn't test our python packages. We only tested the pre-packed files. So we didn't catch the rpath issue in CI build.
9. Add a CentOS machine pool and test our Linux GPU build on real CentOS machines.
10. Rework ARM64 Linux GPU python packaging pipeline. Previously it uses cross-compiling therefore we must static link to C Runtime. But now have pluggable EP API and it doesn't support static link. So I changed to use qemu emulation instead. Now the build is 10x slower than before. But it is more extensible.
Co-authored-by: Chen Fu <fuchen@microsoft.com>
Description:
This change add google benchmark git repo as a submodule in onnxruntime repo.
Motivation and Context
Currently we have benchmarking code that depends on google benchmark. The version we are using has cross compilation issues for ARM CPUs. Recent changes in Google benchmark fixed these issues.
Another problem is that we now rely on ONNX to pull in Google benchmark, an indirect dependency. Updating ONNX involves complex steps and rightly so. However, updating Google benchmark dependency should not be hindered by these processes.
Changes include:
* Revert Event Pool changes
* Add copyright and revert unrelated changes
* Add DLPack as submodule and remove to_dlpack and from_dlpack from public API
* Update golden numbers for DHP Parallel tests
* Update ORTTrainer unit test numbers
* Rollback to DLPack v0.3
* Disable flaky test
* Update third party notices and CG manifest file
* Minor refactoring of ORTValue API
Add python 3.8/3.9 support for Windows GPU and Linux ARM64
Delete jemalloc from cgmanifest.json.
Add onnx node test to Nuphar pipeline.
Change $ANDROID_HOME/ndk-bundle to $ANDROID_NDK_HOME. The later one is more accurate.
Delete Java GPU packaging pipeline
Remove test data download step in Nuget Mac OS pipeline. Because these machines are out of control and out of our network, it's hard to make it reliable and the data secure.
Fix a doc problem in c-api-artifacts-package-and-publish-steps-windows.yml. It shouldn't copy C_API.md, because the file has been moved into a different branch.
Delete the CI build docker file for Ubuntu cuda 9.x and Ubuntu x86 32 bits
And, due to some internal restrictions, I need to rename some of the agent pools
* assert sequence tensor and remove skips
* update testdata json
* use ONNX 1.8 in cgmanifest.json
* use previous commit to workaround
* update ONNX commit ID in docker
* skip test_maxpool_2d_dilations test for now
* update function name
* Remove nGraph Execution Provider
Pursuant to nGraph deprecation notice: https://github.com/microsoft/onnxruntime/blob/master/docs/execution_providers/nGraph-ExecutionProvider.md#deprecation-notice
**Deprecation Notice**
| | |
| --- | --- |
| Deprecation Begins | June 1, 2020 |
| Removal Date | December 1, 2020 |
Starting with the OpenVINO™ toolkit 2020.2 release, all of the features
previously available through nGraph have been merged into the OpenVINO™
toolkit. As a result, all the features previously available through
ONNX RT Execution Provider for nGraph have been merged with ONNX RT
Execution Provider for OpenVINO™ toolkit.
Therefore, ONNX RT Execution Provider for **nGraph** will be deprecated
starting June 1, 2020 and will be completely removed on December 1,
2020. Users are recommended to migrate to the ONNX RT Execution Provider
for OpenVINO™ toolkit as the unified solution for all AI inferencing on
Intel® hardware.
* Remove nGraph Licence info from ThirdPartyNotices.txt
* Use simple Test.Run() for tests without EP exclusions
To be consistent with rest of test code.
* Remove nGraph EP functions from Java code
* Add iOS test pipeline and a sample app.
* clean up the unused code.
* clean up.
* revert the unknown change
* disable the shared library for iOS.
* add open source notice text.
* ignore the skipped test.
* extract the common ortenv setup
* Add minimal build option to build.py
Group some of the build settings so binary size reduction options are all together
Make some cmake variable naming more consistent
Replace usage of std::hash with murmurhash3 for kernel. std::hash is implementation dependent so can't be used.
Add initial doco and ONNX to ORT model conversion script
Misc cleanups of minimal build breaks.
* correct some errors in the flatbuffers schema, move flatbuffers submodule to cmake/external
* update the ort flatbuffers schema to use less namespace
* minor update
Co-authored-by: gwang0000 <62914304+gwang0000@users.noreply.github.com>
* update onnx to latest master
* implement per-channel for quantizelinear and dequantizelinear
* refine the unit test
* exclude sequence_insert tests
* refine onnx cmake
* add failure tests to broken_tests
* move qdq common code to a seperate function
* refine code
* add flatbuffers submodule
* test version of flat buffer schema
* test version of flat buffer schema
* minor updates
* add serialization of the value info, group defs in different namespace
* update comments
* update cgmanifest.json
* update namespace, changed typeinfovalue to use union, added root_type and file_identifier
* add new container type, add max_node_index to graph
* add serializing session state
* addressed review comments
* minor updates
Co-authored-by: gwang0000 <62914304+gwang0000@users.noreply.github.com>
1. Publish the image ACR, instead of building it every time for every PR
2. Make USE_MKLML and USE_OPENMP be able to co-exist. Currently both of them are enabled in our Linux CI build but indeed only one of them is taking effect.
3. Split nuphar and DNNL to separated pipelines.
4. Fix two warnings in onnxruntime/core/optimizer/matmul_scale_fusion.cc and onnxruntime/test/tvm/tvm_basic_test.cc.
5. Update the manylinux2010_x86_64 image to the latest.
* add training dockerfile tested for examples repo
* forgot pytorch patch for build from source
* make apt-get update -y adjacent apt-get install -y due to Docker caching rules
* comment for mellanox libraries
* mpi4py comment as I forgot where it came from
* apparently curl not included anymore
* grr.. nvidia change nccl location
* dont need findnccl.patch after nvidia changed nccl location
* pr comment /opt/ompi4 => /opt/openmpi-xxx
* switch to pip install pytorch
* use Release instead of RelWithDebInfo
* comment wording
* wordin
* missed RelWithDebInfo => Release
* replace Mellanox with libibverbs
* stale comment
* ordering
* no more ninja
* add / at end of copy
* update cgmanifest.json
* pr comments
Co-authored-by: suffian khan <sukha@OrtTrainingDev1.af05slrtruoetgaxwwjv5nsq5e.px.internal.cloudapp.net>
* Move nnapi dnnlib to subfolder
* dnnlib compile settings
* add nnapi buildin build.py
* add onnxruntime_USE_NNAPI_BUILTIN
* compile using onnxruntime_USE_NNAPI_BUILTIN
* remove dnnlib from built in code
* Group onnxruntime_USE_NNAPI_BUILTIN sources
* add file stubs
* java 32bit compile error
* built in nnapi support 5-26
* init working version
* initializer support
* fix crash on free execution
* add dynamic input support
* bug fixes for dynamic input shape, add mul support, working on conv and batchnorm
* Add batchnormalization, add overflow check for int64 attributes
* add global average/max pool and reshape
* minor changes
* minor changes
* add skip relu and options to use different type of memory
* small bug fix for in operator relu
* bug fix for nnapi
* add transpose support, minor bug fix
* Add transpose support
* minor bug fixes, depthwise conv weight fix
* fixed the bug where the onnx model input has mismatch order than the nnapi model input
* add helper to add scalar operand
* add separated opbuilder to handle single operator
* add cast operator
* fixed reshape, moved some logs to verbose
* Add softmax and identity support, change shaper calling signature, and add support for int32 output
* changed the way to execute the NNAPI
* move NNMemory and InputOutputInfo into Model class
* add limited support for input dynamic shape
* add gemm support, fixed crash when allocating big array on stack
* add abs/exp/floor/log/sigmoid/neg/sin/sqrt/tanh support
* better dynamic input shape support;
* add more check for IsOpSupportedImpl, refactored some code
* some code style fix, switch to safeint
* Move opbuilders to a map with single instance, minor bug fixes
* add GetUniqueName for new temp tensors
* change from throw std to ort_throw
* build settings change and 3rd party notice update
* add readme for nnapi_lib, move to ort log, add comments to public functions, clean the code
* add android log sink and more logging changes, add new string for NnApiErrorDescription
* add nnapi execution options/fp16 relax
* fix a dnnlibrary build break
* addressed review comments
* address review comments, changed adding output for subgraph in NnapiExecutionProvider::GetCapability, minor issue fixes
* formatting in build.py
* more formatting fix in build.py, return fail status instead of throw in compute_func
* moved android_log_sink to platform folder, minor coding style changes
* addressed review comments
