Implement CloudEP for hybrid inferencing.
The PR introduces zero new API, customers could configure session and
run options to do inferencing with Azure [triton
endpoint.](https://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-with-triton?tabs=azure-cli%2Cendpoint)
Sample configuration in python be like:
```
sess_opt.add_session_config_entry('cloud.endpoint_type', 'triton');
sess_opt.add_session_config_entry('cloud.uri', 'https://cloud.com');
sess_opt.add_session_config_entry('cloud.model_name', 'detection2');
sess_opt.add_session_config_entry('cloud.model_version', '7'); // optional, default 1
sess_opt.add_session_config_entry('cloud.verbose', '1'); // optional, default '0', meaning no verbose
...
run_opt.add_run_config_entry('use_cloud', '1') # 0 for local inferencing, 1 for cloud endpoint.
run_opt.add_run_config_entry('cloud.auth_key', '...')
...
sess.run(None, {'input':input_}, run_opt)
```
Co-authored-by: Randy Shuai <rashuai@microsoft.com>
## 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.
1. Update CK to its latest develop branch
2. `-mllvm -amdgpu-early-inline-all=true` is critical to CK's
performance, ensure it is properly configured.
- The flags are propagated from target `hip-lang::device`'s
`INTERFACE_COMPILE_OPTIONS`, we must not manually add the flags.
- Instead, we must ensure this target is properly configured by checking
_CMAKE_HIP_DEVICE_RUNTIME_TARGET is set.
TL,DR
`hip-lang::device` sometime will be not be properly configured if our
`CMAKE_PREFIX_PATH` is not configured carefully. In the CI docker, the
configuration is in good state, but on dev machine it is not, which then
silently result poor performance for kernels. We fixed it in this PR and
add a guard to avoid unsuccessful future editing and to prevent
convoluted debugging process.
`_CMAKE_HIP_DEVICE_RUNTIME_TARGET ` is shared in
`/opt/rocm/lib/cmake/hip-lang/hip-lang-config.cmake` and it is internal
to
[CMake](https://gitlab.kitware.com/cmake/cmake/-/merge_requests/6121/diffs),
the variable name will not be changed in the foreseeable future.
This PR enables ORT to execute graphs captured by TorchDynamo. Major compilation code is in `OrtBackend.compile` in ort_backend.py. `register_backend.py` is for plugging `OrtBackend` into TorchDynamo as a compiler.
`python setup.py develop` doesn't install PyTorch as a normal package in
site-packages anymore, and the user must stay at PyTorch's root
directory to call `import torch`. This will break LORT tests because
LORT tests contains `import torch` and are called outside PyTorch root
directory. To make PyTorch a normal package again, this PR build PyTorch
with `python setup.py install`.
**Description**: This PR adds Ascend CANN execution provider support.
**Motivation and Context**
- Why is this change required? What problem does it solve?
As the info shown in the issue. CANN is the API layer for Ascend
processor. Add CANN EP can allow user run onnx model on Ascend hardware
via onnxruntime
The detail change:
1. Added CANN EP framework.
2. Added the basic operators to support ResNet and VGG model.
3. Added C/C++、Python API support
- If it fixes an open issue, please link to the issue here.
https://github.com/microsoft/onnxruntime/issues/11477
Author:
lijiawei <lijiawei19@huawei.com>
wangxiyuan <wangxiyuan1007@gmail.com>
Co-authored-by: FFrog <ljw1101.vip@gmail.com>
**Description**: **Python API Bindings for on device training. **
**Motivation and Context**
- This PR contains api bindings so python users can perform a whole
training loop.
Co-authored-by: Adam Louly <adamlouly@microsoft.com@orttrainingdev7.d32nl1ml4oruzj4qz3bqlggovf.px.internal.cloudapp.net>
Co-authored-by: Baiju Meswani <bmeswani@microsoft.com>
* Make ORT as Pytorch JIT backend
LORT likely doesn't work with aten fallback so we only test LORT in its own CI.
* Revert changes to enable external CUDA allocator. Will add it later.
Revert "Revert changes to enable external CUDA allocator. Will add it later."
This reverts commit d5487f2e193014c805505afae8fb577c53667658.
Fix external allocator
* Relax tolerance and remove commented code
* Print more information in CI
* Fix pointer
* Address comments.
1. Reuse ORT-eager mode's environment.
2. Remove unused ctor.
* Use Pytorch master branch as all PRs are merged
Fix
* Refine based on cpplint feedbacks
* Revert changes to allow custom CUDA allocator in public APIs
* Use torch.testing.assert_close
* Use unittest framework
* Switch docker repo
* Rename *.cpp to *.cc
* Address comments
* Add comment
* Use same pipeline file for eager and lort pipelines
* Address comments
* Add yaml comment
* Fix cmake files
* Address comments
* Rename flags, remove printing code, remove dead comment
* Rework the EP factory creation setup so we're not cut-and-pasting function declarations in multiple places.
Convert append EP for SNPE to be generic, and also use for XNNPACK.
Add XNNPACK to C# API
* Don't need stub for MIGraphX as it's using provider bridge.
* Remove old 'create' functions that aren't applicable now that the EPs are built as separate libraries.
* Only use EPs that require the layout transform if the opset is supported by the layout transformer.
* Update wasm registration of xnnpack.
* 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>
* Tweaks to the model utils
* Add handling for a dim_value of -1 when replacing the entire input shape. This occurs in models exported from PaddlePaddle
* make pytorch helpers accessible in package
* make QDQ helpers accessible in package
Add runtime optimization support to ONNX -> ORT format conversion script.
Replace `--optimization_level`, `--use_nnapi`, and `--use_coreml` with a new `--optimization_style` option.
* add support for bool type
* add TVM EP support for tests
* include TVM EP in python test pool
* fix pylint
* moved technical imports to a separate file
* clean up post build actions & move _ld_preload.py extension to CMake level
* add files for include TVM EP into CI
* implement custom logger for TVM
* replace TVM logging with ONNX RT logging
* update link for TVM EP tutorial
* clean up TVM EP cmake
* add pybind auto enabling for TVM EP
* fix blank spaces
* code review fixes
* replace print with comment
* add list of EP without TVM EP
* enable onnx tests
* disable contrib ops and ml ops
* reuse Dockerfile.ubuntu
* Move install_tvm_test_dependencies.sh out of Docker context dir, update build definition.
Co-authored-by: Edward Chen <18449977+edgchen1@users.noreply.github.com>
* clearing map for eager mode backends
* clearing map for eager mode backends manager
* making OrtBackendsManager an extern variable and trying to delete it
* cleaning backends manager when the python interpret exits
* adding ifdef for eager mode code
* disabling warning for pybind state file
* disabling warning for python module file
* running clang auto format and reducing redundancy
* remove new line
* moving declaration to a new header file
* adding the header file for eager mode for python module
* removing source files for eager mode
* add source file for python module in eager mode
* Update orttraining/orttraining/python/orttraining_python_module_eager.h
Co-authored-by: Thiago Crepaldi <thiago.crepaldi@microsoft.com>
Co-authored-by: Thiago Crepaldi <thiago.crepaldi@microsoft.com>
* squashed commit for standalone tvm execution provider
* critical fix for correct python build with stvm ep
* get tuning log file from ep options. It has priority over AUTOTVM_TUNING_LOG
* updates and fixes
* update parsing of stvm provider options
* add support of external data for onnx model
* add conditional dump of subgraphs
* remove unused code
* get input tensor shapes through provider options. get output shapes for fixed input ones by TVM API
* support AUTO_TVM tuning log file inside ORT. Selector for Ansor and Auto_TVM is provider option (tuning_type)
* add fp16
* add functionality of conversion of model layout to NHWC if need. Necessary parameter was added to STVM provider options
* fix license text in header. fix log format
* small fixes
* fix issues from flake8
* remove model proto construction from GetCapability
* reserve memory for vector of DLTensors
* add simple tutorial for STVM EP
* STVM docs
* jroesch/tvm -> apache/tvm
* remove dead code, unneccessary logs and comments
* fix in readme
* improve tutorial notebook
* tvm update
* update STVM_EP.md
* fix default value
* update STVM_EP.md
* some TODOs for the future development
* shorten long lines
* add hyperlink to STVM_EP.md
* fix Linux CI error
* fix error in csharp test
Co-authored-by: Jared Roesch <jroesch@octoml.ai>
Co-authored-by: Valery Chernov <valery.chernov@deelvin.com>
Co-authored-by: KJlaccHoeUM9l <wotpricol@mail.ru>
* explicit link with libtorch instead of use cmake var to avoid introduce mkl dependency
* use find_lib to get libtorch lib name
* temp fix
* add missing libraries
Co-authored-by: Cheng Tang <chenta@microsoft.com>
* libonnxruntime_providers_rocm.so and libonnxruntime_providers_shared.so are not included in python package.
Co-authored-by: Weixing Zhang <wezhan@microsoft.com>
* optimize python overhead of _post_amp_backward
* overwrite apex amp's zero_grad for faster implementation
* move unscale_fp16_grads_into_fp32_grads into C++ impl
* improve the efficiency furthur, reducing 3.5ms to 1.7ms for unilm.
* unilm 1.7ms to 338us: 1). optimize python list <==> std::vector copy, 2). launch the kernels as long as num_elem reach thresh hold. This help reduce the CUDA idel time.
* refine the logic a bit after validating
Co-authored-by: Baiju Meswani <bmeswani@microsoft.com>
* re-hipify all rocm EP sources
* fix all other files affected by re-hipify
* add cuda_provider_factory.h to amd_hipify.py
* do not use cudnn_conv_algo_search in ROCm EP, missing reduce min registration
* Fix ReduceConsts template specialization introduced in #9101.
Fixes the error when building for ROCm 4.3.1:
error: too many template headers for onnxruntime::rocm::ReduceConsts<__half>::One (should be 0)
* fix flake8 error in amd_hipify.py
* speed up hipify with concurrent.futures
* flake8 fix in amd_hipify.py
* removing warnings which are causing errors from torch and changing flags for Windows
* adding MKL library resolution and comments
* cleaning up the code
* fixing onnxruntime_python file for windows build
* fix the include order to aovid the python_d.lib issue on win debug build
* changes for warnings, typos and other comments
* merge conflict
* adding fix for mkl library error
* Revert "adding fix for mkl library error"
This reverts commit 73b87c73c2.
* fix for dll path for windows
* typo for dll path
Co-authored-by: Cheng Tang <chenta@microsoft.com>
* Include pytorch_export_contrib_ops in inference builds
Rename / move it from tools/python/register_custom_ops_pytorch_exporter
to onnxruntime/python/tools/pytorch_export_contrib_ops.
Rationale for inclusion in inference builds:
This code is potentially useful for anyone using ORT, not just training.
Rationale for new name:
"Contrib op" is the nomenclature used within ORT to refer to the set of
ops that are not in the standard op set but are included by default with
ORT. This is more specific than "custom op", which is what the PyTorch
exporter uses to refer to any non-standard op.
Step 1 of addressing #8818. After this is merged I will update the docs.
* Enable test_pytorch_export_contrib_ops.py in CI
Fixes AB#1342330
* Use PROTOBUF_LIB instead of protobuf::libprotbuf
* Moved setdlopenflags to _pybind_state.py
* Copy the generated _pybind_state.py to required location for Windows.
* Expose symbols in onnx and protobuf namespaces in python when building with --enable_external_custom_op_schemas
* Add external onnx and protobuf files to wheel
* Added an example to demonstrate external custom ops use-case
* Added a Linux build pipeline to test external custom ops
* seperate the training python module; share the execution proivder instance
* fix build break
* fix cuda test crash; reorg the python module code base
* se correct env
* use provider customized hash func
* fixbuild break
* fix rocm break
* use const ref in argument
* rename the file
* move hash func to trainiing module
