onnxruntime/java
RandySheriffH 62226d030f
Cherry-pick tagged commits to 1.12.0 release candidate (#12097)
* Update ONNX to 1.12 (#11924)

Follow-ups that need to happen after this and before the next ORT release:
* Support SequenceMap with https://github.com/microsoft/onnxruntime/pull/11731
* Support signal ops with https://github.com/microsoft/onnxruntime/pull/11778

Follow-ups that need to happen after this but don't necessarily need to happen before the release:
* Implement LayerNormalization kernel for opset version 17: https://github.com/microsoft/onnxruntime/issues/11916

Fixes #11640

* Dll version fix ovep4.1 (#11953)

* Setting default version values for ovep dlls as well

* Update backend_manager.cc

Co-authored-by: mayavijx <mayax.vijayan@intel.com>
Co-authored-by: mohsin <mohsinx.mohammad@intel.com>

* Optimize t5 encoder in beam search (#11926)

* ooptimize t5 encoder

* update

* update

* update

* refactor expand impl

* cuda tests passed

* update

* alignment

* more alignments

* review comments

* Allow saving on CPU usage for infrequent inference requests by reducing thread spinning (#11841)

Introduce Start/Stop threadpool spinning switch
Add a session config option to force spinning stop at the end of the Run()

* Restructure function inliner (#11731)

* Add nested function call tests

* Add overload for Specialize

* Pass symboltable to onnx shape inference

* Avoid renaming empty names

* Enable sequence_map tests which failed before this change

* Deprecate APIs returning raw ptrs and provide replacements (#11922)

Provider better documentation

* register signal ops for opset 17 (#11778)

* Register signal ops for op set 17

Note code is mostly being moved, not added. These ops were previously
only registered as Microsoft contrib ops and only built if
`BUILD_MS_EXPERIMENTAL_OPS=1`. They've been added to the ai.onnx
standard op set in version 17.

Main components of this change:

* Move the kernels from the conrib_ops directory to the
  core directory.
* Add function bodies for ms experimental ops. This will allow
  old models that use the contrib ops to continue to function.
  All the function bodies consist of a single op (the
  new standard op), so performance overhead should be minimal.

Minor clean-up also in this change:

* De-duplicate get_scalar_value_from_tensor: put it in a new utils.h.
* Fix some bugs that caused compilation errors with the experimental
  ops. Tested with `build.sh --ms_experimental`
* Fix some spelling errors and lint violations.
* Replace a couple of switch statements with `MLTypeCallDispatcher`.
* Use `InlineVector` instead of `std::vector`.

Unblocks https://github.com/microsoft/onnxruntime/issues/11640

* Include opset 15 in Conv+BatchNormalization fusion (#11960)

* Fix WinML Tests are still targetting deprecated (deleted) experimental signal op definitions (#12006)

* fix winml tests

* remove legacy test

* switch idft -> dft+inverse attr

* upgrade opset 13->17 for signal ops tests

* [C# Tests] Add support for double tensor output in TestPreTrainedModels. (#12008)

Add support for double tensor output in TestPreTrainedModels.

* DML EP ResNet50 opset 15 fails in ONNX checker for FusedBatchNormalization lacking training_mode attribute (#12010)

FusedBatchNormalization include training_mode attribute

* Generalize native op creation (#11539)

* create op from ep

* read input count from context

* create holder to host nodes

* fix typo

* cast type before comparison

* throw error on API fail

* silence warning from minimal build

* switch to unique_ptr with deleter to host nodes

* fix typo

* fix build err for minimal

* fix build err for minimal

* add UT for conv

* enable test on CUDA

* add comment

* fix typo

* use gsl::span and string view for Node constructor

* Added two APIs - CopyKernelInfo and ReleaseKernelInfo

* pass gsl::span by value

* switch to span<NodeArg* const> to allow for reference to const containers

* fix typo

* fix reduced build err

* fix reduced build err

* refactoring node construction logic

* rename exceptions

* add input and output count as arguments for op creation

* refactor static member

* use ORT_CATCH instead of catch

* cancel try catch

* add static value name map

* format input definition and set err code

* fix comments

* fix typo

* [DML EP] Pad operator: Handle negative pad counts (#11974)

* Pad fallback to CPU

* Added queryPad in operatorRegistration.cpp

* Acknowledged PR comments

* Used any_of

* used none_of instead of any_of

Co-authored-by: Sumit Agarwal <sumitagarwal@microsoft.com>

* Add warning about future computation change for ConvTranspose with auto_pad (#11984)

* Add warning about future computation change for Convtranspose with auto_pad

* improve msg

* update TODO to make lint happy

* update more contents for warning and add if

* valid was not infected

* move it into kernel registration

* parse auto_pad myself

* try to use conv_transpose_attrs_.auto_pad directly

* update roialign cuda impl to onnx opset16 (#12036)

* roialign opset16

* fix

* fix

* Fix windows eager build break by pinning to torch version 1.11.0 (#12033)

Fix windows and linux eager build to torch 1.11.0.

* Skip Constant Folding for ops producing an optional type output (#11839)

* Disable sequence-type tests since C# infra doesn't support well (#12037)

* Extend lifetime of KernelDef when creating a standalone op (#12057)

place tmp kernel def as local variable to cover the lifetime of kernel creation

* Add targets files for new .net6 frameworks (#12016)

* Add net6 targets.
Remove maccatalyst as we don't have a native build targetting that.

* Set platform in macos targets

* Add targetFramework entries

* Move NativeLib.DllName definition and set using preprocessor values for simplicity. Couldn't get it to build with the preprocessor based setup when it was in a separate file.

Update the nuspec generation to set platform version for .net6 targets. TODO: Validate versions. I copied them from the managed nuget package the packaging pipeline generated prior to adding targets. Possibly w could/should lower some of the versions.

Hopefully the need to specify a version goes away when the release version of VS2022 supports .net6.

* Try android 31.1 as https://github.com/actions/virtual-environments/blob/main/images/win/Windows2022-Readme.md suggests that should be available on the CI machines

* Fix patch version mismatch
Add some extra debug info in case it helps

* Debug nuget location in CI

* Add workspace entry back in

* Add steps

* One more attempt with hardcoded nuget.exe path and original android31.0 version

* Better fix - found explicit nuget download and updated version there.

* flake8 fixes

* Fix black complaints.

* Exit Microsoft_ML_OnnxRuntime_CheckPrerequisites for net6 iOS.

* Removed outdated comment

* Fix DML custom operators which set descriptor heap to command list (#12059)

* Make C# runtest.sh automatically set latest opset (#12039)

* Update C# runtest.sh for opset 17

Should have been part of https://github.com/microsoft/onnxruntime/pull/11924

* get appropriate opset version from onnx doc

* use absolute rather than relative path

* fix typo in var name

* Disable DML command list reuse for Xbox (#12063)

disable cl reuse for xbox

* Add data type check in ConvAddRelu fusion (#12058)

* Add undocumented attribute to disable generation of Java bindings from the Android AAR. (#12075)

The generated bindings causes C# build errors that require workaround code. Disabling generation should avoid the need for any workarounds.

As the user has the C# ORT package with the C# to C bindings there's no need for binding generation that calls the ORT Java API (which is C# -> Java ->C).

* enable the extensions custom build for java and android (#11823)

* generate quantization parameter for outputs (#12089)

* DML EP Update to DML 1.9 (#12090)

* Update to DML 1.9

* Appease obnoxious Python formatting tool

* Fix orttraining-linux-ci-pipeline - Symbolic shape infer (#11965)

fix symbolic shape error due to upgraded numpy + legacy sympy

* check consumers of dq node before swap dq and transpose (#12099)

* check consumers of dq node before swap dq and transpose

* add unit test

Co-authored-by: Gary Miguel <garymiguel@microsoft.com>
Co-authored-by: Preetha Veeramalai <preetha.veeramalai@intel.com>
Co-authored-by: mayavijx <mayax.vijayan@intel.com>
Co-authored-by: mohsin <mohsinx.mohammad@intel.com>
Co-authored-by: Ye Wang <52801275+wangyems@users.noreply.github.com>
Co-authored-by: Dmitri Smirnov <yuslepukhin@users.noreply.github.com>
Co-authored-by: G. Ramalingam <grama@microsoft.com>
Co-authored-by: Dwayne Robinson <dwayner@microsoft.com>
Co-authored-by: Sheil Kumar <smk2007@gmail.com>
Co-authored-by: Edward Chen <18449977+edgchen1@users.noreply.github.com>
Co-authored-by: sumitsays <sumitagarwal330@gmail.com>
Co-authored-by: Sumit Agarwal <sumitagarwal@microsoft.com>
Co-authored-by: Chun-Wei Chen <jacky82226@gmail.com>
Co-authored-by: George Wu <jywu@microsoft.com>
Co-authored-by: Wil Brady <25513670+WilBrady@users.noreply.github.com>
Co-authored-by: Hariharan Seshadri <shariharan91@gmail.com>
Co-authored-by: Wei-Sheng Chin <wschin@outlook.com>
Co-authored-by: Scott McKay <skottmckay@gmail.com>
Co-authored-by: Jeff Bloomfield <38966965+jeffbloo@users.noreply.github.com>
Co-authored-by: Justin Stoecker <justoeck@microsoft.com>
Co-authored-by: Wenbing Li <10278425+wenbingl@users.noreply.github.com>
Co-authored-by: Yufeng Li <liyufeng1987@gmail.com>
Co-authored-by: pengwa <pengwa@microsoft.com>
2022-07-06 21:35:19 -07:00
..
src Cherry-pick tagged commits to 1.12.0 release candidate (#12097) 2022-07-06 21:35:19 -07:00
testdata [Java] Tidying up the sample MNIST code (#3824) 2020-05-05 14:34:13 -07:00
build-android.gradle Update protobuf-java to 3.20.1 (#10420) 2022-05-11 07:52:12 -07:00
build.gradle Update protobuf-java to 3.20.1 (#10420) 2022-05-11 07:52:12 -07:00
README.md Fix broken Java API link (#6826) 2021-03-08 11:28:41 -08:00
settings-android.gradle Make Java API available on Android (#3030) 2020-02-27 08:23:50 -08:00
settings.gradle

ONNX Runtime Java API

This directory contains the Java language binding for the ONNX runtime. Java Native Interface (JNI) is used to allow for seamless calls to ONNX runtime from Java.

Usage

This document pertains to developing, building, running, and testing the API itself in your local environment. For general purpose usage of the publicly distributed API, please see the general Java API documentation.

Building

Use the main project's build instructions with the --build_java option.

Requirements

JDK version 8 or later is required. The Gradle build system is required and used here to manage the Java project's dependency management, compilation, testing, and assembly. You may use your system Gradle installation installed on your PATH. Version 6 or newer is recommended. Optionally, you may use your own Gradle wrapper which will be locked to a version specified in the build.gradle configuration. This can be done once by using system Gradle installation to invoke the wrapper task in the java project's directory: cd REPO_ROOT/java && gradle wrapper Any installed wrapper is gitignored.

Build Output

The build will generate output in $REPO_ROOT/build/$OS/$CONFIGURATION/java/build:

  • docs/javadoc/ - HTML javadoc
  • reports/ - detailed test results and other reports
  • libs/onnxruntime-VERSION.jar - JAR with compiled classes, platform-specific JNI shared library, and platform-specific onnxruntime shared library.

Build System Overview

The main CMake build system delegates building and testing to Gradle. This allows the CMake system to ensure all of the C/C++ compilation is achieved prior to the Java build. The Java build depends on C/C++ onnxruntime shared library and a C JNI shared library (source located in the src/main/native directory). The JNI shared library is the glue that allows for Java to call functions in onnxruntime shared library. Given the fact that CMake injects native dependencies during CMake builds, some gradle tasks (primarily, build, test, and check) may fail.

When running the build script, CMake will compile the onnxruntime target and the JNI glue onnxruntime4j_jni target and expose the resulting libraries in a place where Gradle can ingest them. Upon successful compilation of those targets, a special Gradle task to build will be executed. The results will be placed in the output directory stated above.

Advanced Loading

The default behavior is to load the shared libraries using classpath resources. If your use case requires custom loading of the shared libraries, please consult the javadoc in the package-info.java or OnnxRuntime.java files.

Development

Code Formatting

Spotless is used to keep the code properly formatted. Gradle's spotlessCheck task will show any misformatted code. Gradle's spotlessApply task will try to fix the formatting. Misformatted code will raise failures when checks are ran during test run.

JNI Headers

When adding or updating native methods in the Java files, it may be necessary to examine the relevant JNI headers in build/headers/ai_onnxruntime*.h. These files can be manually generated using Gradle's compileJava task which will compile the Java and update the header files accordingly. Then the corresponding C files in ./src/main/native/ai_onnxruntime*.c may be updated and the build can be ran.

Dependencies

The Java API does not have any runtime or compile dependencies currently.