### Description
The Java `TensorInfo` object which is used to describe a tensor's shape,
along with the input and output placeholders for a model couldn't show
any symbolic/named dimensions in that tensor. Now this information is
stored in Java strings on construction and included in the toString.
### Motivation and Context
Setting symbolic dimensions required external information in Java, the
names were not discoverable from within the API.
### Description
The `OnnxValue` and `OrtProviderOptions` implementations now check to
see if they've been closed before accessing the native pointer, and also
before close is called.
### Motivation and Context
Before they could be closed twice which SIGSEGV'd the JVM. Fixes#19125.
### Description
Adds a method to access the backing direct byte buffer from a Java
`OnnxTensor` object, assuming it is backed by a direct byte buffer
(tensors created by ORT's run call or ones created in Java from
multidimensional arrays are not). Also adds a method to check if the
backing byte buffer was copied from the user's buffer supplied on
creation (this could be tested via a pointer comparison from the output
of `getBufferRef` and the user's input buffer, so I'm not sure if it's
necessary).
### Motivation and Context
This is the first part of changes necessary to support output pinning in
Java OrtSession.run/OrtTrainingSession.run calls. I split it out from
the rest of the work as it's useful by itself (e.g. to allow users to
keep a single input tensor and rewrite it each time with new inputs
rather than allocate a fresh one) and the other change will be much more
involved so splitting it makes it easier to review.
cc @yuslepukhin
Two major modifications of this PR:
1. Refactor OrtTensorRTProviderOptions initialization and make it easy
to add new field.
2. Make Python API capable of using TensorRT plugins by adding new
Python binding api `register_tensorrt_plugins_as_custom_ops`. (It needs
to register ep's custom op domain before model load. For C++ API, it's
slightly different, when calling
SessionOptionsAppendExecutionProvider_TensorRT_XX, it appends cutom op
domain to session option. Later ORT can register custom op domain from
session option before model loading)
### Description
Reduces precision on the CoreML provider test as it returns slightly
different answers than the other tested providers. Checked on a 2020 13"
M1 MBP.
### Motivation and Context
Fixes Java CoreML test failure after #16763.
### Description
Adds javadoc for all protected and public members, methods and classes.
### Motivation and Context
The javadoc warnings were annoying me when running the builds. Also,
those types should have been documented.
---------
Co-authored-by: Scott McKay <Scott.McKay@microsoft.com>
### Description
This PR splits out the FP16 conversions into a separate package we can
override in the android build with a version which works on old versions
of Android.
I'm not sure the android build system changes are correct as I haven't
got an android build environment configured on my workstation.
@YUNQIUGUO if the CI build fails we should follow up offline to get my
environment configured so I can iterate on it.
### Motivation and Context
Fixes the CI failure after #16703.
### Description
The Java API currently only supports fp16 output tensors which it
automatically casts to floats on the way out. This PR adds support for
creating fp16 and bf16 tensors (from `java.nio.Buffer` objects or as the
output of models, creation from Java short arrays is not supported),
along with efficient methods for casting `FloatBuffer` into
`ShortBuffer` filled with fp16 or bf16 values and vice versa.
The fp16 conversions use a trick to pull in the efficient conversion
methods added to Java 20, falling back to ports of the MLAS methods
otherwise. The Java 20 methods can be special cased by the C2 JIT
compiler to emit the single instruction on x86 and ARM which converts
fp32<->fp16, or the vectorized versions thereof, so they should be quite
a bit faster than the MLAS ported one.
### Motivation and Context
fp16 and bf16 are increasingly popular formats and we've had several
requests for this functionality. Fixes#7003.
cc @yuslepukhin @cassiebreviu
---------
Co-authored-by: Scott McKay <Scott.McKay@microsoft.com>
### Description
Adds support for adding external initializers or overriding initializers
to a session options from Java.
### Motivation and Context
We want to instantiate large models from Java without filesystem access.
cc @yuslepukhin
### Description
Unlike most ORT classes `SessionOptions` and `RunOptions` don't trigger
native library loading of the JNI binding and ORT when the classes are
initialized (after class loading). This was initially because I thought
that loading an inner class would trigger the static initialization of
the outer class, but this is not true. So if you create a
`SessionOptions` instance before referencing `OrtEnvironment` then you
won't trigger library loading and you'll get an error saying it couldn't
link the native method that creates a `SessionOptions` object.
Note this doesn't prevent users from creating a `SessionOptions` and
modifying it before the `OrtEnvironment` is created, which can still
cause issues. It would be a breaking API change to modify the
`SessionOptions` constructor to take an environment, and it wouldn't
mirror the way it works in the C API which requires this by convention
rather than API design, but we can discuss making that modification
later.
### Motivation and Context
Reduces the occurrence of mysterious Java library loading errors. Helps
with #16434.
🛠️ __Changes in this pull request:__
This pull request introduces two significant changes to the project:
- Changing on device training checkpoint format: The current
implementation stores the on device training checkpoint as a sequence of
tensors in multiple files inside a checkpoint folder, which can be
inefficient in terms of storage and performance. In this PR, I have
modified the checkpoint format to utilize the flatbuffer table to save
the checkpoint to a single file, providing a more compact and efficient
representation. The changes around this are twofold:
- Add the checkpoint flatbuffer schema that will generate the necessary
checkpoint source files.
- Update the checkpoint saving and loading functionality to use the new
format.
- Adding support for onnxruntime minimal build: To support scenarios
where binary size is a constraint, I made changes to ensure that the
training build can work well with the minimal build.
🔍 __Open Issues:__
- In order to extract the optimizer type, the existing implementation
re-loaded the onnx optimizer model and parsed it. This is no longer
possible, since the model format can either be onnx or ort. One idea is
to do the same for ort format optimizer model. This needs some
investigation.
- Changes to the offline tooling to generate ort format training
artifacts.
- End-to-end training example showcasing the use of the minimal training
build.
- Add support for export model for inferencing in a minimal build.
### Description
The name of the flag we set when compiling the JNI binding to enable the CoreML EP changed at some point in the past. This PR fixes it by updating the flag in the JNI. I also added a quick smoke test for the CoreML provider to make sure it doesn't crash and can be enabled.
### Motivation and Context
All the EPs should work as expected in Java. Fixes#16230.
### Description
The tensor creation code now allows the creation of boolean tensors from
non-direct `ByteBuffer` instances. It previously only allowed them from
arrays and direct `ByteBuffer` instances and this fixes that
inconsistency. The boolean tensor test has been updated to cover all
three cases.
### Motivation and Context
Fixes#15509.
### 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
Removing C4090 warning suppression after windows pipelines adapt vs2022
### 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
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
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**
### Description
Updating the build option for enabling training in java builds from
ENABLE_TRAINING -> ENABLE_TRAINING_APIS.
In the native codebase ENABLE_TRAINING is used for enabling full
training and ENABLE_TRAINING_APIS is used for creating the lte builds
with training apis. Making the change to sync the naming convention
across all the language bindings.
It was a bit confusing to see ENABLE_TRAINING when debugging the android
build failures for training. Making this change just to improve
readability of logs during debugging.
### 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
Allows the creation of zero length tensors via the buffer path (the
array path with zero length arrays still throws as the validation logic
to check it's not ragged would require more intrusive revision), and
allows the `tensor.getValue()` method to return a Java multidimensional
array with a zero dimension. Also added a test for the creation and
extraction behaviour.
### Motivation and Context
The Python interface can return zero length tensors (e.g. if object
detection doesn't find any objects), and before this PR in Java calling
`tensor.getValue()` throws an exception with a confusing error message.
Fixes#7270 & #15107.
- Update Gradle version used in most places from 6.8.3 to 8.0.1. Update Android Gradle Plugin version where applicable.
Not updated in this change: React Native Android projects (under `js/react_native/`). That can be done later along with updating the React Native projects.
- Add Gradle wrapper in `java/` to make it easier to consistently use a specific Gradle version.
### 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
<!-- Describe your changes. -->
Update java/build.gradle to not use deprecated features that were
removed in gradle 8.0.
Also move gradle wrapper setup from a script into a step template.
### 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 builds which use hosted Mac agents and gradle.
Recently the system version of gradle got upgraded to 8.0. Even though
we use an older gradle wrapper version, java/build.gradle is still
processed with gradle 8.0 in the initial call to `gradle wrapper`.
* Added the OrtDnnlProviderOptions structure to expose configuration
options to the user
* The number of threads can be defined by the user with the -i flag on
the perftest
* Number of threads can also be configured via the OMP_NUM_THREADS
environment variable
* The number of threads defined in the OrtDnnlProviderOptions is
prioritized over the environment variable
### Description
Avoids thread oversubscription caused by OpenMP allocating the maximum
number of threads possible for oneDNN EP. Added support for the
OrtDnnlProviderOptions, this will allow for more EP customization
capabilities, and allows for user defined number of threads.
### Motivation and Context
- Improves performances and allows for user to fine tune the number of
threads
Fix C6011, C6385, C6386 found by Visual Studio. Basically, I set the
maximum number of options for every EP to 128. To my knowledge, 128 is
big enough to support all EPs.
For support arbitrary number of EP options, we probably need #13999 and
create a "std::vector"-like struct in C language.
Description
Add bindings for Android and iOS.
Motivation and Context
Enable mobile app linking against ort-extensions library and registering the custom ops with ORT.
**Description**:
Adds support for creating and receiving sparse tensors in the ORT Java
API.
CSRC and COO tensors as inputs are tested, but there is no op which
accepts a block sparse tensor to test. COO tensors are tested as
outputs, but there is no op which emits a CSRC or block sparse tensor to
test.
**Motivation and Context**
- Why is this change required? What problem does it solve? Request to
expose ORT sparse tensor support in Java.
cc @yuslepukhin
### Description
Update protobuf-java to version 3.21.7. This change only impact tests.
### Motivation and Context
The current version exhibits CVE-2022-3509
Previously OnnxSequence would flatten out a list of tensors into a
single output array assuming they were all scalar values. This doesn't
accurately represent the semantics of an ONNX sequence, but was what the
semantics appeared to be years ago when I first wrote that class. This
PR changes it so that the `getValue` method on `OnnxSequence` unwraps
the sequence and returns `List<? extends OnnxValue>` allowing the user
to process the individual ONNX values separately. It's done this way
rather than returning a multidimensional array for a tensor and a Java
map for a map as multidimensional arrays are very inefficient in Java
and best practice when operating with a OnnxTensor in Java is to use a
`java.nio.ByteBuffer`. So allowing users to access each `OnnxTensor`s
individually allows them to control how the data is materialised on the
Java heap.
# Motivation
Currently, ORT minimal builds use kernel def hashes to map from nodes to
kernels to execute when loading the model. As the kernel def hashes must
be known ahead of time, this works for statically registered kernels.
This works well for the CPU EP.
For this approach to work, the kernel def hashes must also be known at
ORT format model conversion time, which means the EP with statically
registered kernels must also be enabled then. This is not an issue for
the always-available CPU EP. However, we do not want to require that any
EP which statically registers kernels is always available too.
Consequently, we explore another approach to match nodes to kernels that
does not rely on kernel def hashes. An added benefit of this is the
possibility of moving away from kernel def hashes completely, which
would eliminate the maintenance burden of keeping the hashes stable.
# Approach
In a full build, ORT uses some information from the ONNX op schema to
match a node to a kernel. We want to avoid including the ONNX op schema
in a minimal build to reduce binary size. Essentially, we take the
necessary information from the ONNX op schema and make it available in a
minimal build.
We decouple the ONNX op schema from the kernel matching logic. The
kernel matching logic instead relies on per-op information which can
either be obtained from the ONNX op schema or another source.
This per-op information must be available in a minimal build when there
are no ONNX op schemas. We put it in the ORT format model.
Existing uses of kernel def hashes to look up kernels are replaced
with the updated kernel matching logic. We no longer store
kernel def hashes in the ORT format model’s session state and runtime
optimization representations. We no longer keep the logic to
generate and ensure stability of kernel def hashes.
Working on JNI refactor for OnnxTensor.
Simplifying the error handling logic in createTensor.
Collapsing casting branches and migrating to ONNX element type enum.
Disable cpplint for JNI C files.
**Description**: This fixes error handling in the JNI code in OnnxMap, OnnxSequence, OnnxRuntime, RunOptions. SessionOptions and OrtEnvironment are correct as is.
The bulk of the work will be in rewriting OnnxTensor, OnnxSparseTensor (after the merge of #10653) and OrtSession, along with the helper methods in OrtJniUtil. I plan to tackle those in separate PRs to reduce the amount of code to review.
**Motivation and Context**
- Why is this change required? What problem does it solve? The current native interop code doesn't return control to Java immediately on throwing an exception from an ORT error code, which can cause incorrect interactions with native ORT, and issues with exception propagation on the Java side.
- If it fixes an open issue, please link to the issue here. Partial work towards solving #11451.
