### Description
1. Renames all references of on device training to training apis. This
is to keep the naming general. Nothing really prevents us from using the
same apis on servers\non-edge devices.
2. Update ENABLE_TRAINING option: With this PR when this option is
enabled, training apis and torch interop is also enabled.
3. Refactoring for onnxruntime_ENABLE_TRAINING_TORCH_INTEROP option:
- Removed user facing option
- Setting onnxruntime_ENABLE_TRAINING_TORCH_INTEROP to ON when
onnxruntime_ENABLE_TRAINING is ON as we always build with torch interop.
Once this PR is merged when --enable_training is selected we will do a
"FULL Build" for training (with all the training entry points and
features).
Training entry points include:
1. ORTModule
2. Training APIs
Features include:
1. ATen Fallback
2. All Training OPs includes communication and collectives
3. Strided Tensor Support
4. Python Op (torch interop)
5. ONNXBlock (Front end tools for training artifacts prep when using
trianing apis)
### Motivation and Context
Intention is to simply the options for building training enabled builds.
This is part of the larger work item to create dedicated build for
learning on the edge scenarios with just training apis enabled.
### Description
This PR enables building nuget packages locally for on device training
using --build_nuget arg.
This PR also enables the C# bindings by default in the managed package.
If a user triggers any training apis when the native binary is not built
for training, an exception with message "Training is disabled in the
current build. Please build ONNXRuntime from source with the build flags
enable_training and enable_training_on_device. " is thrown.
Build command for creating nuget packes for on device training:
build.bat --enable_training --enable_training_on_device --build_nuget
2 Nuget packages are built
1. Microsoft.ML.OnnxRuntime.Managed
2. Microsoft.ML.OnnxRuntime.Training OR
Microsoft.ML.OnnxRuntime.Training.Gpu
### 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. -->
* 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.
* 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>
Add Xamarin support to the ORT nuget packages.
- Update C# code to support Xamarin builds for iOS and Android
- refactor some things to split out common code
- include iOS and Android ORT native shared library in native nuget package
Fix C# add EP bindings.
Add stubs to ORT so that if EP is not included in the build we return a graceful error message.
Move declaration of stubs into C API and out for EP so they're in one place and are easier to use (no extra header required in the C/C++ world and consistent with the CUDA EP setup).
Fix inconsistency in ROCM EP.
Cleanup a few other things.
* 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 amd migraphx execution provider to onnx runtime
* rename MiGraphX to MIGraphX
* remove unnecessary changes in migraphx_execution_provider.cc
* add migraphx EP to tests
* add input requests of the batchnorm operator
* add to support an onnx operator PRelu
* update migrapx dockerfile and removed one unused line
* sync submodules with mater branch
* fixed a small bug
* fix various bugs to run msft real models correctly
* some code cleanup
* fix python file format
* fixed a code style issue
* add default provider for migraphx execution provider
Co-authored-by: Shucai Xiao <Shucai.Xiao@amd.com>
1. refactor the pipeline, remove some duplicated code
2. Move Windows_py_GPU_Wheels job to Win-GPU-CUDA10. We'll deprecated the "Win-GPU" pool
3. Delete cpu-nocontribops-esrp-pipeline.yml and cpu-nocontribops-pipeline.yml
4. In Linux nuget jobs, run "make install" before creating the package. So that extra RPAH info will be removed
