### Description
Introduce `Float16/BFloat16` support for C# and C++ APIs.
User should be able to perform conversions from `float` to/from
`Float16/BFloat16`, compare values and tests for `NaN, Inifnity, and
whether the number is denormalized.`
### Motivation and Context
User filed issues such as:
https://github.com/microsoft/onnxruntime/issues/14303
### Description
Introduce an API that allows users to gain access to a string tensor
element buffer of requested length in bytes
so then can quickly load any utf8 data.
### Motivation and Context
Useful for testing an otherwise.
### Description
Expose `OrtValue` class API as first-class citizen.
Make it simular with C++ API.
Enable safe direct native memory access.
Make string tensor manipulation more efficient.
Avoid intermediate structures such as `NamedOnnxValue`,
`DisposableNamedOnnxvalue` and etc.
Provide more examples with `IOBinding`, although `OrtValue` API
potentially makes `IOBinding` redundant for most of scenarios, since
`OrtValue` can be created on top of any memory.
Run all the pre-trained models now with `OrtValue` API as well.
Obsolete `OrtExternalMemory class`. Obsolete IOBinding API that takes
`FixedBufferOnnxValue`.
### Motivation and Context
Make the API efficient and uniform with C++.
This aspires to address:
https://github.com/microsoft/onnxruntime/issues/14918https://github.com/microsoft/onnxruntime/issues/15381
Cc: @Craigacp
Microsoft.AI.MachineLearning NuGet fails to build on WinUI projects due
to the conflict between the ReferenceCopy of binaries that occurs with
managed applications, with the manual binplacement that occurs with
native appliactions.
With WinUI, both cases are triggered, and a duplicate binplace is
detected as an error.
Fix: Don't rely on the ReferenceCopy for WinUI applications, and
manually binplace the Microsoft.AI.MachineLearning dll.
### Description
The PR implements FloatE4M3FN, FloatE5M2, FloatE4MEFNUZ, FloatE5M2FNUZ
as described in PR https://github.com/onnx/onnx/pull/4805. It uses CUDA
API to cast float/half to float8 if CUDA>=11.8, a custom implementation
if CUDA<11.8.
* It implements, Cast, QuantizeLinear, DequantizeLinear for all types on
CPU, only for types FloatE4M3FN, FloatE5M2 on CUDA.
* It extends the supported types for control flow operator, Shape,
Reshape, Identity, If, Loop, Scan, Reshape
* It implements Equal(19).
* Cast, QuantizeLinear, DequantizeLinear operators now support a
parameter `saturate` only valid for float 8 types. It is true by
default. In that case, any value out of range is converted into the
maximum float 8 value. If false, it is infinite.
* QuantizeLinear, DequantizeLinear now supports multiple scales on CUDA
(and ROCm by extension), scale = 1D tensor with one scale per channel
### Motivation and Context
Supports latest onnx version.
Fixes
[AB#15395](https://aiinfra.visualstudio.com/6a833879-cd9b-44a4-a9de-adc2d818f13c/_workitems/edit/15395)
---------
Co-authored-by: Xavier Dupre <xadupre@microsoft.com@orttrainingdev8.d32nl1ml4oruzj4qz3bqlggovf.px.internal.cloudapp.net>
Co-authored-by: Randy Shuai <rashuai@microsoft.com>
Co-authored-by: Edward Chen <18449977+edgchen1@users.noreply.github.com>
Co-authored-by: Scott McKay <Scott.McKay@microsoft.com>
### Description
Remove NETSTANDARD1.1 moniker and NETSTD1.1 specific code. We no longer
target this platform.
### Motivation and Context
NETSTANDARD1.1 target constraints the development and the modern
libraries we would like to use in the code while it is apparently no
longer required by customers.
This PR mainly fixes building errors when trying to build nupkg for ROCm EP.
It also slighly improve the packaging logic so that devlopers can
produce the nupkg on linux natively.
### Description
This PR partially reverts changes introduced in
https://github.com/microsoft/onnxruntime/pull/15643
We make two API return std::string always in UTF-8.
We also move the entry points from OrtApiBase to OrtApi to make them
versioned.
### Motivation and Context
`GetVersionString` always returns x.y.z numbers that are not subject to
internationalization.
`GetBuildInfoString` can hold international chars, but UTF-8 should be
fine to contain those.
We prefix them with u8"" in case the compiler default charset is not
UTF-8.
Furthermore, creating platform dependent APIs is discouraged.
`ORTCHAR_T` is platform dependent and was created for paths only.
On non-unix platforms would still produce `std::string` that can only
contain UTF-8
The API was introduced after the latest release, and can still be
adjusted.
### Description
<!-- Describe your changes. -->
Various fixes to the CSharp setup
- fix warnings
- fix invalid tests
- update test sdk nuget package
- enables testing on linux
- fixes issue with some unit tests not running in CI
- run unit tests in linux pipeline using dotnet
### 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. -->
Unit tests weren't breaking in CIs for both Windows and Linux builds and
should have been.
### Description
1. Update VERSION_NUMBER for preparing the upcoming release. This PR's
commit will not be included in the 1.15 release branch
2. Delete package/rpm/onnxruntime.spec since it was not used in past
years.
### Motivation and Context
Preparing the release.
Fixed
[AB#15311](https://aiinfra.visualstudio.com/6a833879-cd9b-44a4-a9de-adc2d818f13c/_workitems/edit/15311)
### Description
This PR creates Nuget and Android for Training.
### Motivation and Context
These packages are intended to be released in ORT 1.15 to enable
On-Device Training Scenarios.
## Packaging Story for Learning On The Edge Release
### Nuget Packages:
1. New Native package -> **Microsoft.ML.OnnxRuntime.Training** (Native
package will contain binaries for: win-x86, win-x64, win-arm, win-arm64,
linux-x64, linux-arm64, android)
2. C# bindings will be added to existing package ->
**Microsoft.ML.OnnxRuntime.Managed**
### Android Package published to Maven:
1. New package for training (full build) ->
**onnxruntime-training-android-full-aar**
### Python Package published to PyPi:
1. Python bindings and offline tooling will be added to the existing ort
training package -> **onnxruntime-training**
Symbol visibility from DllImport is inconsistent across platforms resulting in the symbol not necessarily being visible to ORT native code that tries to look it up by name.
Best solution is to use DllImport to load the library and to call the registration function directly. That requires the native SessionOptions handle and OrtApiBase struct. We could either make those public, or provide a helper where the user passes in a delegate from their DllImport. Can add when needed.
### Description
<!-- Describe your changes. -->
Disable new test that is failing on linux. Not required for this
release. Will fix in the next week.
Marshal.Prelink can be used on Windows to make the symbol available but
Linux appears to work differently.
Also need to update the pre-checkin tests so this is tested early as
it's only failing in the E2E tests run in the packaging pipeline.
### 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. -->
Fix packaging pipeline error.
### Description
<!-- Describe your changes. -->
Add infrastructure so it's easy for a user to add the ORT extensions
nuget package and register the custom ops for C# apps.
### 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. -->
Need to be able to use extensions on mobile platforms with Xamarin/MAUI
The test limits GPU's running memory requirements to 20MB. It might be
enough in the past, but it seems not enough now when we upgrade CUDA to
a newer version or add more kernels/graph transformers to our code.
Therefore we need to increase it. Our test log shows sometimes the model
needs 128MB memory. So I set the limit to 256MB.
### Description
Reduce a number of auxillary objects created to reduce GC pressure.
Eliminate GCHandle type of memory pinning in most of the places.
Improve string marshalling by allocating unmanaged memory that does not
require pinning. Change native methods from `IntPtr` to `byte[]`
(marshalling pinning is more efficient).
Allocate input/output UTF-8 names in unmanaged heap for the lifetime of
InferenceSession. So we do not keep converting them and pinning on every
Run.
Introduce a new native API that allows to allocate and convert/copy
strings directly into a native tensor.
The PR delivers around 50% latency improvements and less GC pauses.
Inspired by: https://github.com/microsoft/onnxruntime/pull/15520
### Motivation and Context
Client experience GC pressure and performance degradation when dealing
with string tensors.
Co-Authored-By: @tannergooding
### Description
Run clang-format in CI. Formatted all c/c++, objective-c/c++ files.
Excluded
```
'onnxruntime/core/mlas/**',
'onnxruntime/contrib_ops/cuda/bert/tensorrt_fused_multihead_attention/**',
```
because they contain assembly or is data heavy
### Motivation and Context
Coding style consistency
### Description
Implement Optional Type metadata support in the library.
Implement optional support in C# API along with metadata.
Implement Sequence, Map, Optional test data support
and test execution.
Prune tests and provide more details for failing tests in C# code.
Note, this PR does not enable running onnx test models in C++.
### Motivation and Context
Opset18 optional type support.
WindowsAI build failing due to deprecated .NET5 SDK missing in build
image
.NET5 was deprecated last year, and recently the build machine images
have been updated to not include this SDK.
Unblock failing builds by force insalling .NET5 SDK as part of the build
pipeline.
### Description
Add sbyte specific test case support.
### Motivation and Context
C# Test Data loading code and comparators are missing sbyte (Int8)
specializations.
This fails a test
### Description
<!-- Describe your changes. -->
I fixed some broken links in the C API documentation, but then did a
quick pass over all of the links I could find and then fixed those.
### 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. -->
I got some 404's when exploring the documentation and wanted to fix it.
### Description
Reenable disabled opset18 tests
### 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. -->
### Description
Re-work `OrtApi::GetAvailableProviders` in a way that the data is
returned in a single allocation.
Fix exception safety issues and fix `Release` function.
Remove warning suppressions.
Fix exception safety issue in C++ API.
Fix exception safety issue in C# API.
Move EP name length enforcement to the implementation.
### Motivation and Context
The original motivation comes from
https://github.com/microsoft/onnxruntime/issues/14378.
However, the API is already implemented.
Cc: @prabhat00155
### Description
<!-- Describe your changes. -->
Remove exclusions for ONNX model tests that now pass due to kernels
being implemented.
Update ONNX update doc to point to correct location for tests.
### 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. -->
Run as many tests as possible.
Co-authored-by: Edward Chen <18449977+edgchen1@users.noreply.github.com>
### Description
To Implement Resize 18.
This PR depends on https://github.com/microsoft/onnxruntime/pull/13765.
### 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. -->
### Description
<!-- Describe your changes. -->
Skip tests for opset18 models that we haven't implemented kernels for
yet.
Slice was checked in today so those failures should go away.
Resize: #13890 (all resize failures are fixed by this PR as confirmed in
output
[here](https://dev.azure.com/aiinfra/530acbc4-21bc-487d-8cd8-348ff451d2ff/_apis/build/builds/264725/logs/729))
Col2Im: #12311
ScatterND and ScatterElement: #14224
Pad (should also fix CenterCropPad failures): #14219 Bitwise ops: #14197
Optional: Unknown if we're intending to support this in 1.14
Not sure about SoftPlus as that is failing due to `Could not find an
implementation for Exp(1)`. ORT supports Exp from opset 6 and on, and it
seems incorrect for the test model created for opset 18 to be using a
version of Exp that is so old. Would have expected it to use the latest
- Exp(13). @liqunfu is this something that requires a fix to the ONNX
model?
### 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. -->
Fix pipeline
### 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
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.
