Fix some linker errors that come up when integrating the onnxruntime-training-c pod into another Xcode project. The problematic configuration is a minimal build with training APIs enabled.
- training_op_defs.o had some unresolved references to ONNX functions. It should not be included at all in a minimal build.
- tree_ensemble_helper.o also had unresolved references to ONNX ParseData. The containing function is unused in a minimal build.
Added a test to cover this configuration.
### Description
<!-- Describe your changes. -->
Pre-link with `ld -r` to apply symbol visibility when the static library
is created to replicate XCode's Single Object Pre-link.
Current builds set the visibility flags but that doesn't get applied
until the static library is linked into something else, which can be too
late. Pre-linking fixes this.
The pre-link uses the .o files from the ORT static libraries and the .a
files from external libraries. This combination limits the symbols
included from the .a files to things required by the ORT .o files.
In order to minimize changes elsewhere in the build we extract the .o
files from the ORT static libraries using `ar -x`.
Re-ordered the pieces use to build the Apple framework to make it a
little more readable.
Fixed a couple of misc issues with missing symbols from the minimal
build that show up when pre-linking is applied.
### Motivation and Context
<!-- - Why is this change required? What problem does it solve?
- If it fixes an open issue, please link to the issue here. -->
Will hopefully address #17722
### Description
1. `onnxruntime_fetchcontent_makeavailable` works around unconditional
install commands so that can be used instead of `FetchContent_Populate`
2. This dependency is Windows specific, mark it as such.
### Motivation and Context
1. This simplifies `cmake/external/wil.cmake` not to do anything
specific wether WIL was fetched or found
2. Given it's specific to Windows, it might not be available on other OS
in specific air-gapped environment such as
[conan-center-index](https://github.com/conan-io/conan-center-index).
This allows downstream builds not to require specific patches for
something not required by the build in the first place.
**Description**:
Adds support for cmake find_package.
**Motivation and Context**
As mentioned in issue #7150 onnxruntime doesn't have support for CMake
find_package, this PR adds that and also adds the CMake package version
file. Now anyone can link onnxruntime like this:
```cmake
find_package(onnxruntime)
add_executable(test Source.cpp)
target_link_libraries(test PRIVATE onnxruntime::onnxruntime)
```
this also simplifies #3124
### Description
Enable creating dedicated build for on device training. With this PR we
can build a lean binary for on device training using flag
--enable_training_apis. This binary includes only the essentials like
training ops, optimizers etc and NOT features like Aten fallback,
strided tensors, gradient builders etc . This binary also removes all
the deprecated components like training::TrainingSession and OrtTrainer
etc
### Motivation and Context
This enables our partners to create a lean binary for on device
training.
### Description
Use target name for flatbuffers.
Add version range for flatbuffers. It is similar to #13870
### Motivation and Context
To fix a build error:
```
CMake Error at onnxruntime_graph.cmake:88 (add_dependencies):
The dependency target "flatbuffers" of target "onnxruntime_graph" does not
exist.
Call Stack (most recent call first):
CMakeLists.txt:1490 (include)
```
It happens when flatbuffers library is already installed. For example,
on Ubuntu people may get it from apt-get. But, the one provided by
Ubuntu 20.04 is not compatible with our code. The one in Ubuntu 22.04
works fine.
### Description
Fix usage of enable_training_ops and reduce ifdef complexity for
training builds.
### Motivation and Context
This is the second refactoring PR towards creating a dedicated build for
on device training. This PR aims to reduce some complexity. We can set
ENABLE_TRAINING_OPS in cmake when either ENABLE_TRAINING or
ENABLE_TRAINING_ON_DEVICE is selected, this way we dont have to use if
defined(ENABLE_TRAINING) || defined(ENABLE_TRAINING_ON_DEVICE )
everywhere in the code.
- If it fixes an open issue, please link to the issue here. -->
## Description
1. Convert some git submodules to cmake external projects
2. Update nsync from
[1.23.0](https://github.com/google/nsync/releases/tag/1.23.0) to
[1.25.0](https://github.com/google/nsync/releases/tag/1.25.0)
3. Update re2 from 2021-06-01 to 2022-06-01
4. Update wil from an old commit to 1.0.220914.1 tag
5. Update gtest to a newer commit so that it can optionally leverage
absl/re2 for parsing command line flags.
The following git submodules are deleted:
1. FP16
2. safeint
3. XNNPACK
4. cxxopts
5. dlpack
7. flatbuffers
8. googlebenchmark
9. json
10. mimalloc
11. mp11
12. pthreadpool
More will come.
## Motivation and Context
There are 3 ways of integrating 3rd party C/C++ libraries into ONNX
Runtime:
1. Install them to a system location, then use cmake's find_package
module to locate them.
2. Use git submodules
6. Use cmake's external projects(externalproject_add).
At first when this project was just started, we considered both option 2
and option 3. We preferred option 2 because:
1. It's easier to handle authentication. At first this project was not
open source, and it had some other non-public dependencies. If we use
git submodule, ADO will handle authentication smoothly. Otherwise we
need to manually pass tokens around and be very careful on not exposing
them in build logs.
2. At that time, cmake fetched dependencies after "cmake" finished
generating vcprojects/makefiles. So it was very difficult to make cflags
consistent. Since cmake 3.11, it has a new command: FetchContent, which
fetches dependencies when it generates vcprojects/makefiles just before
add_subdirectories, so the parent project's variables/settings can be
easily passed to the child projects.
And when the project went on, we had some new concerns:
1. As we started to have more and more EPs and build configs, the number
of submodules grew quickly. For more developers, most ORT submodules are
not relevant to them. They shouldn't need to download all of them.
2. It is impossible to let two different build configs use two different
versions of the same dependency. For example, right now we have protobuf
3.18.3 in the submodules. Then every EP must use the same version.
Whenever we have a need to upgrade protobuf, we need to coordinate
across the whole team and many external developers. I can't manage it
anymore.
3. Some projects want to manage the dependencies in a different way,
either because of their preference or because of compliance
requirements. For example, some Microsoft teams want to use vcpkg, but
we don't want to force every user of onnxruntime using vcpkg.
7. Someone wants to dynamically link to protobuf, but our build script
only does static link.
8. Hard to handle security vulnerabilities. For example, whenever
protobuf has a security patch, we have a lot of things to do. But if we
allowed people to build ORT with a different version of protobuf without
changing ORT"s source code, the customer who build ORT from source will
be able to act on such things in a quicker way. They will not need to
wait ORT having a patch release.
9. Every time we do a release, github will also publish a source file
zip file and a source file tarball for us. But they are not usable,
because they miss submodules.
### New features
After this change, users will be able to:
1. Build the dependencies in the way they want, then install them to
somewhere(for example, /usr or a temp folder).
2. Or download the dependencies by using cmake commands from these
dependencies official website
3. Similar to the above, but use your private mirrors to migrate supply
chain risks.
4. Use different versions of the dependencies, as long as our source
code is compatible with them. For example, you may use you can't use
protobuf 3.20.x as they need code changes in ONNX Runtime.
6. Only download the things the current build needs.
10. Avoid building external dependencies again and again in every build.
### Breaking change
The onnxruntime_PREFER_SYSTEM_LIB build option is removed you could think from now
it is default ON. If you don't like the new behavior, you can set FETCHCONTENT_TRY_FIND_PACKAGE_MODE to NEVER.
Besides, for who relied on the onnxruntime_PREFER_SYSTEM_LIB build
option, please be aware that this PR will change find_package calls from
Module mode to Config mode. For example, in the past if you have
installed protobuf from apt-get from ubuntu 20.04's official repo,
find_package can find it and use it. But after this PR, it won't. This
is because that protobuf version provided by Ubuntu 20.04 is too old to
support the "config mode". It can be resolved by getting a newer version
of protobuf from somewhere.
* aten op for inference
* fix build error
* more some code to training only
* remove domain from operator name
* move aten_op_executor ext out from ortmodule
* add pipeline
* add exec mode
* fix script
* fix ut script
* fix test pipeline
* failure test
* rollback
* bugfix
* resolve comments
* enable aten for python build only
* fix win build
* use target_compile_definitions
* support io binding
* turn off aten by default
* fix ut
Co-authored-by: Vincent Wang <weicwang@microsoft.com>
Co-authored-by: zhijxu <zhijxu@microsoft.com>
* use the lightweight compile api as default; use dnnl ep for testing
* apply to tensorrt ep
* fix the missing files
* fix build
* fix the copy issue on linux
* migrate migraphx and openvino ep
* fix openvino build break
* fix linux build
* fix unused parameter
* fix coreml build
* use graph view's filtered initializers
* fix openvino break
* fix tvm compile api
* fix tvm / rknpu / vitisai ep build
* add IsInitializedTensor in graph_viewer; fix nuphar build
* use serializer directly as tvm ep is still static lib
* fix the type mismatch
* fix the type mismatch
* fix merge conflict
* add a comment
* fix minimal build
* fix the DML EP's legacy approach
* save type/shape in dnnl IR
* fix linux break
* fix tvm failure
* dnnl ep: move initializer referenced out of dnnl subgraph
* Revert "add IsInitializedTensor in graph_viewer; fix nuphar build"
This reverts commit 1cc3c7f08c16fee4fe3309a67209eb769d479587.
* add IsInitializedTensor to graph viewer
* add the legacy code for nuphar build to temporarily make nuphar build work
* ignore internal test for nuphar
* remove the out of date tests
* keep the legacy API in EP for a while
* turn serializer into a static function
* update comments
* fix tvm build
* Update include/onnxruntime/core/framework/execution_provider.h
Co-authored-by: Pranav Sharma <prs@microsoft.com>
* Update include/onnxruntime/core/framework/execution_provider.h
Co-authored-by: Pranav Sharma <prs@microsoft.com>
* Update onnxruntime/core/framework/execution_provider.cc
Co-authored-by: Pranav Sharma <prs@microsoft.com>
* updatee comments; add warning message for legacy compil call
* add a flag to control out of scope arg in serialization
* fix trt build; improve the test
* resolve merege errors
* fix a typo
Co-authored-by: Cheng Tang <chenta@microsoft.com>
Co-authored-by: Cheng Tang <chenta@microsoft.com@orttrainingdev9.d32nl1ml4oruzj4qz3bqlggovf.px.internal.cloudapp.net>
Co-authored-by: Pranav Sharma <prs@microsoft.com>
* initial fix
* refactor the function handle
* update the implementation
* fix linux build break
* fix training build
* fix minmal build
* fix gradient checker
* deprecate the local function members in graph. host it in model
* fix changming's comments
* fix comments about inlined containers
* fix a missed inlined container
* fix training build
* avoid const for std string_view
Co-authored-by: Cheng Tang <chenta@microsoft.com>
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.
* atenop for inference
* assert if dtype mismatch
* atenop config in frontend
* fix orttrainer test
* gradient def not only for ATenOp
* bugfix
* fix gradient input shape and type issue
* fix after merge master
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.
* Add function body to SoftmaxGrad schema
* Add type context and cleanup
* Add test case with symbolic dimensions
* Add opset specification to function
* handle opset dependence
* Exclude from minimal build
* model building
* fix build
* winml adapter model building api
* model building
* make build
* make build again
* add model building with audio op
* inplace and inorder fft
* add ifft
* works!
* cleanup
* add comments
* switch to iterative rather than recursive and use parallelization
* batched parallelization
* fft->dft
* cleanup
* window functions
* add melweightmatrix op
* updates to make spectrogram test work
* push latest
* add onesided
* cleanup
* Clean up building apis and fix mel
* cleanup
* cleanup
* naive stft
* fix test output
* middle c complete
* 3 tones
* cleanup
* signal def new line
* Add save functionality
* Perf improvements, 10x improvement
* cleanup
* use bitreverse lookup table for performance
* implement constant initializers for tensors
* small changes
* add matmul tests
* merge issues
* support add attribute
* add tests for double data type windowfunctions and minor cleanup
* stft onesided/and not tests
* cleanup
* cleanup
* clean up
* cleanup
* remove threading attribute
* forward declare orttypeinfo
* warnings
* fwd declare
* fix warnings
* 1 more warning
* remove saving to e drive...
* cleanup and fix stft test
* add opset picker
* small additions
* add onnxruntime tests
* add signed/unsigned
* fix warning
* fix warning
* finish onnxruntime tests
* make windows namespace build succeed
* add experimental flag
* add experimental api into nuget package
* add experimental api build flag and add to windows ai nuget package
* turn experimental for tests
* add minimum opset version to new experimental domain
* api cleanup
* disable ms experimental ops test when --ms_experimental is not enabled
* add macro behind flag
* remove unused x
* pr feedback
Co-authored-by: Sheil Kumar <sheilk@microsoft.com>
* Prototype NCCL P2P
* Clean code
* Fix NCCL path and some minor bugs
* Add path
* Fix path
* Try fix path
* Add missed files
* Address some comments
* Clean code
* Rename files
* Add MPI path back and fix a path
* Put MPI path under USE_NCCL flag
* not to build Send and Recv when MPI is not installed
* 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.
* Next round of changes.
Remove inclusion of ONNX schema header
Exclude custom registry related things
Move IsConstantInitializer from graph_utils to Graph as it's needed in a minimal build and graph_utils is excluded.
* Initial set of changes to start disabling code in the minimal build. Breaking changes into multiple PRs so they're more easily reviewed. Focus on InferenceSession, Model and Graph here. SessionState will be next.
Needs to be integrated with de/serialization code before being testable so changes are all off by default.
Changes are limited to
- #ifdef'ing out code
- moving some things around so there are fewer #ifdef statements
- moving definition of some one-line methods into the header so we don't need to #ifdef out in a .cc as well
- exclude some things in the cmake setup
* Update session state and a few other places.
The core code builds if ORT_MINIMAL_BUILD is specified.
* ORT on CUDA 11
1. Seperate HOROVOD and MPI
2. Seperate NCCL from HOROVOD in CMakeLists.txt
2. Remove dependency on external cub
3. cudnnSetRNNDescriptor is changed in cuDNN 8.0
* polish the code about MPI/NCCL in CMakeLists.txt and build.py
* check CUDA version
* ${MPI_INCLUDE_DIRS} should be PUBLIC
* sm30, sm50 are deprecated in CUDA 11 Toolkit
* update change based on code review feedback.
* add sm_52
* improve MPI/NCCL build path
Co-authored-by: Weixing Zhang <wezhan@microsoft.com>
Advance commit to 4df80d5865a9d4e97f6d0b9304d4316115a04d9e
Add generated code for the commit before editing.
Import more featurizers.
Rename Automl ops domain to mlfeaturizers.
Rename conditional compilation macro.
Move and rename files getting rid of automl
Rename --use_automl build switch to --use_featurizers
Rename CMake option accordingly. Rename automl CMake targets.
Adjust CI and packaging pipeline switches.
Rename namespace automl to featurizers.
This change adds a new execution provider powered by [DirectML](https://aka.ms/DirectML).
DirectML is a high-performance, hardware-accelerated DirectX 12 library for machine learning on Windows. DirectML provides GPU acceleration for common machine learning tasks across a broad range of supported hardware and drivers.
The DirectML execution provider is capable of greatly improving evaluation time of models using commodity GPU hardware, without sacrificing broad hardware support or requiring vendor-specific extensions to be installed.
**Note** that the DML EP code was moved verbatim from the existing WindowsAI project, which is why it doesn't yet conform to the onnxruntime coding style. This is something that can be fixed later; we would like to keep formatting/whitespace changes to a minimum for the time being to make it easier to port fixes from WindowsAI to ORT during this transition.
Summary of changes:
* Initial commit of DML EP files under onnxruntime/core/providers/dml
* Add cmake entries for building the DML EP and for pulling down the DirectML redist using nuget
* Add a submodule dependency on the Windows Implementation Library (WIL)
* Add docs under docs/execution_providers/DirectML-ExecutionProvider.md
* Add support for DML EP to provider tests and perf tests
* Add support for DML EP to fns_candy_style_transfer sample
* Add entries to the C ABI for instantiating the DML EP
Remove gsl subodule and replace with a local copy of gsl-lite
Refactor for onnxruntime::make_unique
gsl::span size and index are now size_t
Remove lambda auto argument type detection.
Remove constexpr from fail_fast in gsl due to Linux not being happy.
Comment out std::stream support due to MacOS std lib broken.
Move make_unique into include/core/common so it is accessible for server builds.
Relax requirements for onnxruntime/test/providers/cpu/ml/write_scores_test.cc
due to x86 build.
Add ONNXRUNTIME_ROOT to Server Lib includes so gsl is recognized
Added Sample Featurizer and Infrastructure
Make featurizers and unit tests compile and run with GTest.
Create definitions for the first featurizer kernel.
Add new operator domain.
Create datetime_transformer kernel and build.
Move OPAQUE types definitions for featurizers kerneles out to a separate cc.
Register them with the type system.
Provide unit tests for new AutoML DateTimeTransformer kernel.
Make necessary adjustments to the test infrastructure to make it run
with new types.
* Test protobuf-lite
* Test protobuf-lite
* Test protobuf-lite
* Optimize protobuf usage for LITE_RUNTIME to reduce the binary size of
onnxruntime.dll. More details can be found here https://developers.google.com/protocol-buffers/docs/proto.
The reduction is significant. For commit id: 4873b452151bafe49da332aaeab639ef0318fc1ca28d728, the size
reduced by ~700K; from 4873728 to 4172800.
* Add LITE_RUNTIME flag in in.proto files
* Fix merge conflict.
* Address PR comments
* Forgot to add 2 files + fix linux and gpu build errors.
* Fix build errors + test failures
* Fix cuda tests
* Fix tensor rt build
* Use full protobuf for trt
* Address PR comments
* Print tensor shape proto as text string for easier debugging
