### Description
<!-- Describe your changes. -->
Add Swift Package Manager (SPM) support for ORT based on #14621
- uses the existing objective-c bindings
- some re-organization of the directory structure was required but the
contents of the files are unchanged, apart from adjustments due to file
movements
Add tool for updating ORT native pod used in the SPM package
Update CIs to use ORT native pod from build, and build/test using SPM
### 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. -->
iOS developers are using SPM as much as cocoapods, so adding SPM means
both are catered for.
### Description
Bump ruff version in CI and fixed new lint errors.
- This change enables the flake8-implicit-str-concat rules which helps
detect unintended string concatenations:
https://beta.ruff.rs/docs/rules/#flake8-implicit-str-concat-isc
- Update gitignore to include common python files that we want to
exclude.
### Motivation and Context
Code quality
### Description
1. Disable XNNPack EP's tests in Windows CI pipeline
The EP code has a known problem(memory alignment), but the problem does
not impact the usages that we ship the code to. Now we only use XNNPack
EP in mobile apps and web usages. We have already pipelines to cover
these usages. We need to prioritize fixing the bugs found in these
pipelines, and there no resource to put on this Windows one. We can
re-enable the tests once we reached an agreement on how to fix the
memory alignment bug.
2. Delete anybuild.yml which was for an already deleted pipeline.
3. Move Windows CPU pipelines to AMD CPU machine pools which are
cheaper.
4. Disable some qdq/int8 model tests that will fail if the CPU doesn't
have Intel AVX512 8-bit instructions.
### Description
<!-- Describe your changes. -->
* Integrate TRT 8.6EA on relevant Linux/Windows/pkg pipelines
* Update onnx-tensorrt to 8.6
* Add new dockerfiles for TRT 8.6 and clean old ones
* Update
[CGManifest](https://github.com/microsoft/onnxruntime/tree/main/cgmanifests)
files and ort build deps version
* yml/script update
* Enable built-in TRT parser option on TRT related pipelines by default
* Exclude test TopKOperator.Top3ExplicitAxisInfinity out of TRT EP tests
(8.6-EA has issue with topk operator)
This change moves the DML CI pipeline to the A10 machines and fixes or
disables tests that were failing from this change.
- Max error rate threshold was increased for Image Tests
- Some failing batch tests were disabled
---------
Co-authored-by: Changming Sun <chasun@microsoft.com>
### Description
Recently Visual Studio and python started to provide native Windows
ARM64 packages. This PR is to provide better support for building on
Windows ARM64. You can do it as what you did for x64. Like:
```
python tools\ci_build\build.py --config Debug --update --skip_submodule_sync --build_dir b --cmake_generator "Visual Studio 17 2022"
```
You do not need to append the "--arm64" build arg, and do not need to
cross-compile protoc for a different arch as you are not cross-compiling.
**caveat:** it does not work with the latest cmake release(3.26.x). It
only works fine with cmake 3.25.x and below. Filed a bug to them:
https://gitlab.kitware.com/cmake/cmake/-/issues/24797
### Motivation and Context
Provide better support for building on Windows ARM64.
### Description
<!-- Describe your changes. -->
As title
### 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. -->
https://github.com/microsoft/onnxruntime/issues/15110
---------
Co-authored-by: rachguo <rachguo@rachguos-Mac-mini.local>
Co-authored-by: rachguo <rachguo@rachguos-Mini.attlocal.net>
Co-authored-by: Scott McKay <skottmckay@gmail.com>
### Description
add script to validate generated NPM packages and publish it to
artifacts, so that release pipeline can use it.
once this PR is merged, I will update the NPM package release pipeline.
### 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.
Add support for kMSInternalNHWCDomain and kPytorchAtenDomain op domains to op reduction script.
Make it an error if the op reduction script encounters unknown op domains.
### Description
1. Move it to a separated pool that use the same image as [the public
hosted
pool](https://learn.microsoft.com/en-us/azure/devops/pipelines/agents/hosted?view=azure-devops&tabs=yaml).
Also, create a beta pool which contains the next version image of the
hosted pool, and add jobs in our post merge pipeline to test if the next
version image will break our CI. So, usually we will have at least one
week to prepare.
2. Change the cmake generator in use in our pipelines from "Ninja" to
"MingW Makefile", because the latest version of cmake doesn't work with
the latest version of Ninja. People who prefer Ninja could still use
ninja in their local build by passing "--cmake_generator ninja" to
[build.py](https://github.com/microsoft/onnxruntime/blob/main/tools/ci_build/build.py).
3. Delete eager mode CI pipeline.
### Motivation and Context
I need to update the software we have in our CI build machines, and I
need to resolve this incompatibility issue. In more detail, the build
error I hit was:
em++: error:
CMakeFilesonnxruntime_mlas_test.dirC_a_work1sonnxruntimetestmlasunittesttest_activation.cpp.o:
No such file or directory
("CMakeFilesonnxruntime_mlas_test.dirC_a_work1sonnxruntimetestmlasunittesttest_activation.cpp.o"
was expected to be an input file, based on the commandline arguments
provided)
After this PR we will deprecate python 3.7 support. The eager mode CI
pipeline is the last one that still use python 3.7. Then we can rework
the PR #10953 made by [fs-eire](https://github.com/fs-eire) last year.
Fixed
[AB#14435](https://aiinfra.visualstudio.com/6a833879-cd9b-44a4-a9de-adc2d818f13c/_workitems/edit/14435)
Add workflow to update Objective-C API docs. Remove the Objective-C API doc generation step from the packaging pipeline.
There are similar workflows for automatically updating other language API docs. This change enables this for Objective-C too.
Ensure that we build with a known version of NDK and are not surprised when the default version on the build machine changes.
A similar change was made for other Android build pipelines previously, but this one was missed.
### Description
1. The protoc package on nuget.org contains binaries for
Windows_x86/Windows_x64/Linux_x86/Linux_x64/MacOS_x64, which can cover
most use cases. Though it doesn't have binaries for AMR64, they are only
needed when we cross-compile for Intel CPUs on ARM CPUs. It is rare.
When you have such a need, you always can build protoc from source by
yourself and pass it to build.py as "--path_to_protoc_exe". Or if you
have security concerns that you don't want to use prebuilt binaries from
outside, you can do the same thing.
2. Remove GoogleTestAdapter related thing. That part of code is out of
maintain.
### Motivation and Context
As a follow-up of PR #15190.
### Description
Update mimalloc dependency.
### Motivation and Context
The latest release contains important fixes including memory leaks and
used by customers.
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
Upgrade remainding python to 3.11 removing 3.7
### 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. -->
Change the default behavior to link against the nvonnxparser library
(onnx-tensorrt parser) that is included with the TensorRT package.
Previously, the default behavior was to build and statically link
against the OSS onnx-tensorrt parser.
Historically, we wanted to incorporate the latest commits/fixes from OSS
parser.
These days the OSS parser is not significantly different from the
included parser library so there is less reason to build against it by
default.
By linking with parser shared library from TensorRT library, the major
benefit is it's much easier to
build/link against a minor version update of TensorRT. And OnnxRuntime
can be updated with a new TensorRT minor version by simply replacing
TensorRT libraries with the newer version. (because the parser is no
longer statically linked into onnxruntime)
Added --use_tensorrt_oss_parser to build.py to support the previous
default behavior. (build + static link OSS parser)
### Description
Update python package pipeline to support 3.11
### 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
In merge branch, the run only reads the cache generated in main build.
As a result, each run in merge branch will not upload new cache except
at the first time.
### Motivation and Context
1.Reduce the cache storage.
If there's some big changes, devs should trigger the specific builds
manually in https://dev.azure.com/onnxruntime/onnxruntime/_build. It
still reads own branch cache.
Temporarily remove Azure build check to unblock PR(s).
We need to investigate the sudden build failure and reenable.
Co-authored-by: Randy Shuai <rashuai@microsoft.com>
rocm python packaging pipeline failed because manylinux version and
manylinux.patch update.
1. fix duplicate `epel-release` installation issue, ROCm pipeline
install it at the begin of the dockerfile to install rocm libs. remove
duplicate installation on install-runtime-packages.sh.
```
/var/tmp/yum-root-sMRl36/epel-release-latest-7.noarch.rpm: does not update installed package.
Error: Nothing to do
```
2. add python10 to fix error below.
```
+ /opt/python/cp310-cp310/bin/python -m venv /opt/_internal/tools
build_scripts/finalize.sh: line 40: /opt/python/cp310-cp310/bin/python: No such file or directory
```
3. add python10 to rocm pipeline.
pipeline link:
https://aiinfra.visualstudio.com/Lotus/_build/results?buildId=294776&view=results
### Description
<!-- Describe your changes. -->
Add required graph transformer to duplicate DQ nodes to ensure that QDQ
node units have unique DQ nodes. This condition is necessary for QDQ
node unit processing.
### 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. -->
There is an existing Python utility that does this:
c7ced7a5e9/tools/python/util/qdq_helpers/qdq_model_utils.py (L77)
This PR implements it as a graph transformer so it is integrated into
ORT and does not require a separate step to update the model. There are
also tests to ensure that its effects are not undone by basic level
graph optimizations.
### Description
1. Make 2 cache tasks in one pipeline really works
2. Each building step has its own environment variable CCACHE_DIR
instead of job variables.
3. Extenal Protobuf compilation cache only updates with deps.txt. It
doesn't generate new cache in every commit.
### Motivation and Context
The simple workflow is as below
```
--------build with ccache-------
|
cache
|
{CCACHE_DIR}-----cache stat.
```
```
-------Cache@2------
|
download cache
|
{path}--------upload cache
```
1. {XXX} means environment variable or task input.
2. {CCACHE_DIR} must be consistent with {path}. Ccache produces caches
in {CCACHE_DIR} and Cache@2 download cache into {path} and tar {path}
and upload it.
3. Protobuf changes with deps.txt so that it would reduce the storage
size.
4. Next step, we may split the compilation into 2 steps, one for
external dependencies and another for ORT.
### Description
1. move the cache task definition into template
2. In debug mode, the compiler mtime is different in different machine.
So, change the CCACHE_COMPILERCHECK to content.
### Motivation and Context
1. Accelerate the CoreML pipeline.
Test run:
https://dev.azure.com/onnxruntime/onnxruntime/_build/results?buildId=938040&view=logs&j=1ac7588f-a5bd-5ff7-4a8a-a34869d50220
With Cache, the run can be finished in 12 minutes. Without cache, it
takes about 1 hour.
3. Make the cache function easy to use and maintain.
---------
Co-authored-by: Edward Chen <18449977+edgchen1@users.noreply.github.com>
### Description
This PR disables browser test temporarily. The test randomly fails and
we are investigating the issue. Disable the test to unblock others.
### Description
1. Remove Linux jobs for ORT-Extension combined build
2. Add a macOS build job for ORT-Extension combined build
3. Adjust the yaml file so that it can support two different ADO
instances.
### Motivation and Context
To test our code better. And it will enable us to run such tests for
every commit in the main branch. It would be easier for us to figure out
which change caused a build break.
See
[AB#13435](https://aiinfra.visualstudio.com/6a833879-cd9b-44a4-a9de-adc2d818f13c/_workitems/edit/13435)
### Description
windows update python3.11
### 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. -->
---------
Co-authored-by: Ubuntu <chasun@chasunlinux.lw3b1xzoyrkuzm34swpscft0ff.dx.internal.cloudapp.net>
### Description
1. Move Linux CPU pipelines to an AMD CPU pool which is cheaper
2. Enable CCache for orttraining pipeline
### Motivation and Context
Azure AMD CPU machines are generally much cheaper than Intel CPU
machines. However, they don't have local disks.
### Description
Pause caching the docker images in pipeline cache in Linux Aten
Pipeline.
### Motivation and Context
We need to work out a better way to reduce the storage.
### Description
`lintrunner` is a linter runner successfully used by pytorch, onnx and
onnx-script. It provides a uniform experience running linters locally
and in CI. It supports all major dev systems: Windows, Linux and MacOs.
The checks are enforced by the `Python format` workflow.
This PR adopts `lintrunner` to onnxruntime and fixed ~2000 flake8 errors
in Python code. `lintrunner` now runs all required python lints
including `ruff`(replacing `flake8`), `black` and `isort`. Future lints
like `clang-format` can be added.
Most errors are auto-fixed by `ruff` and the fixes should be considered
robust.
Lints that are more complicated to fix are applied `# noqa` for now and
should be fixed in follow up PRs.
### Notable changes
1. This PR **removed some suboptimal patterns**:
- `not xxx in` -> `xxx not in` membership checks
- bare excepts (`except:` -> `except Exception`)
- unused imports
The follow up PR will remove:
- `import *`
- mutable values as default in function definitions (`def func(a=[])`)
- more unused imports
- unused local variables
2. Use `ruff` to replace `flake8`. `ruff` is much (40x) faster than
flake8 and is more robust. We are using it successfully in onnx and
onnx-script. It also supports auto-fixing many flake8 errors.
3. Removed the legacy flake8 ci flow and updated docs.
4. The added workflow supports SARIF code scanning reports on github,
example snapshot:
5. Removed `onnxruntime-python-checks-ci-pipeline` as redundant
### 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. -->
Unified linting experience in CI and local.
Replacing https://github.com/microsoft/onnxruntime/pull/14306
---------
Signed-off-by: Justin Chu <justinchu@microsoft.com>
### Description
Use build.sourceversion in docker image cache key.
### Motivation and Context
We used filpath as the cache key in #14496.
In most cases, the docker base image tag is latest.
So, the hash of the files couldn't be aware of the change of base image.
As the result, the docker image restored, but the image will still be
rebuilt .
The maintenance cost would be huge if we pin image hash in docker file.
For example,
https://quay.io/repository/pypa/manylinux2014_x86_64?tab=tags&tag=latest,
it's updated almost every week.
So far, the build.sourceversion is the right way to keep cache is
updated and valid.
### Description
<!-- Describe your changes. -->
1. upgrade cutlass to 3.0 that containing attn_bias support.
2. extend Attention/MHA to use memory efficient attention when
rel_pos_bias with [1, num_head, s, s*] and 1d mask with [2 * batch_size
+ 1] are present.
new mask format introduction:
MASK_1D_KEY_SEQ_LEN_START,
[3 * batch_size + 2] with [key_len[0], ..., key_len[batch_size - 1],
query_start[0], ..., query_start[batch_size - 1], query_end[batch_size -
1], key_start[0], ..., key_start[batch_size - 1], key_end[batch_size -
1]]
e.g
2D mask with [[1, 1, 1, 0, 0, 0], [1, 1, 1, 1, 1, 0]] converts to this
1D mask is [3, 5, 0, 6, 12, 0, 6, 12]
### 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. -->
It potentially benefits tnlrv6 and t5(encoder)
---------
Co-authored-by: Ubuntu <wy@v100-2.0cdb2e52twzevn1i4fi45bylyg.jx.internal.cloudapp.net>
Co-authored-by: Kunal Vaishnavi <kvaishnavi@microsoft.com>
Co-authored-by: Kunal Vaishnavi <kvaishnavi@microsoft.com@orttrainingdev7.d32nl1ml4oruzj4qz3bqlggovf.px.internal.cloudapp.net>
### Description
Check the Mac x86_64 packages installation.
### Motivation and Context
To avoid installation error, add packages smoking test before release.
### Description
<!-- Describe your changes. -->
This fix macos packaging build on universal2 arch.
### 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. -->
Re-enable the react native e2e android unit test for react native CI as
recent change of specifying `default` instead of `google-apis` in
android emulator CI tests gives pretty stable result for now.
Upgrade the targetSDKversion for gradle test project in
react-native/android to meet minimum target api level requirement for
Google Play apps.
https://support.google.com/googleplay/android-developer/answer/11926878?hl=en
### 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. -->
React Native CI issue.
### Description
BUG FIX: the if...else in telemetry-steps.yml does not really work. It
always says "Telemetry is disabled." even through the pipeline doesn't
have the pipeline variable.
### Motivation and Context
For example, recently I setup a new pipeline in
https://dev.azure.com/onnxruntime/onnxruntime/_build without setting the
ADO variable, but the powershell code still thinks that we have enabled
telemetry.
See:
https://dev.azure.com/onnxruntime/onnxruntime/_build/results?buildId=910107&view=results
The reason it didn't work because when the pipeline
variable("TELEMETRYGUID") doesn't exist, the occurrence of
"$(TELEMETRYGUID)" would be not replace to anything. It will remain as
it is.
### Description
- Add QNN 2.8 SDK
- Make QNN SDK version a pipeline template parameter for QNN pipelines.
### Motivation and Context
Updates to latest QNN SDK version, and allows testing different QNN SDK
versions without modifying yaml files.
- Use java/gradlew directly in .github/workflows/publish-java-apidocs.yml.
- Remove use of deleted step from tools/ci_build/github/azure-pipelines/android-arm64-v8a-QNN-crosscompile-ci-pipeline.yml.
- Remove Gradle installations and PATH updates from Dockerfiles and scripts. Now Gradle wrapper is used so a system Gradle installation is not needed.
### Description
Make patch manylinux one single step.
### Motivation and Context
If we want to use hash of docker-related files as the cache key, the
files should keep consistent before and after docker build.
And changes in generated build_scripts should trigger rebuilding the
image as well.
### Description
tensorboard depends on rsa>=3.1.4, while rsa 4.5 has vuln issue, so pin
it to higher version as suggested
Fixed
[AB#7352](https://aiinfra.visualstudio.com/6a833879-cd9b-44a4-a9de-adc2d818f13c/_workitems/edit/7352)
### 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. -->
- 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
Current pipeline refers to an old image which is causing test failures.
Updating the image to the latest one.
### Motivation and Context
<!-- - Why is this change required? What problem does it solve?
Fixes pipeline failure:
https://dev.azure.com/onnxruntime/onnxruntime/_build?definitionId=198
- If it fixes an open issue, please link to the issue here. -->
### Description
Split up the ORT build step in the Linux QNN CI Pipeline.
### Motivation and Context
Build errors were not being immediately reported at the end of the build
step. The build step currently concatenates multiple shell commands, and
the return code for the last (mkdir) was being reported. This PR ensures
that the return code of the `python build.py ...` command is reported
for the build step.
### Description
To reduce CUDA package's size a little bit. 37 is for Tesla K80. Azure's
NC-series uses it, but in most cases CUDA can dynamic generate device
code .
### Description
1. Remove Python 3.7 from the python packaging pipeline. It is planned
for the next release and approved by the PMs. Also we will add 3.11, but
it will be addressed in another PR.
2. Stop generating python packages based on Ubuntu 18.04 which will
reach EOL next month. We will either replace them with Ubuntu 20.04 or a
CentOS 8 variant.
### Description
<!-- Describe your changes. -->
Consume ONNX 1.13.1 in ONNX Runtime. (ONNX 1.13.0 to ONNX 1.13.1)
### 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. -->
ONNX 1.13.1 patch was just released yesterday. This PR is making ORT's
ONNX submodule consistent with the latest released ONNX. Not sure
whether this PR is really needed, but let me make it ready. Previous PR
for testing ONNX 1.13.1rc2 :
https://github.com/microsoft/onnxruntime/pull/14634.
Fixed
[AB#13174](https://aiinfra.visualstudio.com/6a833879-cd9b-44a4-a9de-adc2d818f13c/_workitems/edit/13174)
.
### Description
<!-- Describe your changes. -->
Changes to support standalone custom ops in a minimal build. Also
incorporates changes from #14492 (needed to test builds prior to that
being checked in).
We first need to save the schema info from the operators used by the
standalone op invoker in the ORT format model. Add mechanism for that.
Merge the kernel lookup logic so the same is used in full and minimal
build. NOTE: the version matching is now consistent with all other
kernel lookups, and the call to CreateOp MUST use the exact version for
the operator. Previously matching wasn't as strict, but this can lead to
the incorrect kernel being chosen.
Add tests.
NOTE: There is currently no way to detect the ops/types/opsets used
inside these custom ops as they don't exist until we create kernels,
which is after model loading completes (which is the point the ORT
format model is saved). Due to that they have to be manually added to
the configuration used to do the reduced ops build. That shouldn't be
too hard for the custom op author to add given the custom op
implementation is specifying the op, opset and type constraints (i.e.
they have the info and it's just a case of capturing/formatting it
correctly).
### 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. -->
Enable usage of the standalone op invoker by custom ops in a minimal
build.
---------
Co-authored-by: Edward Chen <18449977+edgchen1@users.noreply.github.com>
### Description
Make GPU job depends on all CPU jobs
### Motivation and Context
GPU resources are very limited in packaging pipeline.
And GPU test job is very time consuming.
Only one CPU job fails, the workflow fails, so the GPU job is
meaningless.
To utilize GPU resources more efficiently, run GPU job only after all
CPU jobs succeed.
###test pipeline
https://dev.azure.com/aiinfra/Lotus/_build/results?buildId=280905&view=results
### Description
allow onnxruntime_test_all to run in browser for WebAssembly build (use
flag `--wasm_run_tests_in_browser`).
To output the logs from stdout correctly, this test needs to be build
with `--enable_wasm_threads`.
### Description
<!-- Describe your changes. -->
Disable e2e android react native test temporarily to unblock the CI
failure with no easy fix.
### 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. -->
Temp solution to unblock CI failure.
### Description
<!-- Describe your changes. -->
PR a change made to 1.14.1 into Main branch as well.
### 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. -->
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. -->
Merging extensions from Git submodule to cmake FetchContent
### 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. -->
---------
Co-authored-by: Changming Sun <chasun@microsoft.com>
Co-authored-by: Jian Chen <jchen351@MacBook-Pro.local>
### 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`.
Add the ability to get and set tuning results of an inference session.
Also add tool to manipulate onnx file to embed the results into the
model file and automatically load it on session initialization.
### Description
Fixes the DML release build for 1.14.1. This was initially fixed by
https://github.com/microsoft/onnxruntime/pull/13417 for 1.13.1, but the
changes didn't make their way back to the main branch.
### Description
Introduce collective ops into onnxruntime inference build, including
1) AllReduce and AllGather schema in contrib op, controlled by USE_MPI
flag
2) AllReduce and AllGather kernel in cuda EP, controlled by ORT_USE_NCCL
flag
### Motivation and Context
Enable the collective ops in onnxruntime inference build so we have the
ability to run distributed inference with multiple GPUs.
The original ncclAllReduce ops in training build require quite complex
configurations, which is not suitable for inference case, and it already
broken. so we introduce a new implementation.
---------
Co-authored-by: Cheng Tang <chenta@microsoft.com@orttrainingdev9.d32nl1ml4oruzj4qz3bqlggovf.px.internal.cloudapp.net>
Making basic porting effort to run Sampling UT on ROCm ep, based on the
commits:
https://github.com/microsoft/onnxruntime/pull/13426https://github.com/microsoft/onnxruntime/pull/14218
1. enabling EmbedLayerNorm op
2. enabling Sampling op
3. enabling helpers to copy data from CPU->GPU for subgraph
This task is the first checkpoint. There could be other missing ops when
testing a real model.
We will migrate more code onto ROCm as needed.
Co-authored-by: Ubuntu <ettao@ettao-amd-dev1.zvflicr54joexhdgnhvmxrxygg.phxx.internal.cloudapp.net>
### Only link mpi when either use_mpi or use_nccl enabled
To fix the issue https://github.com/microsoft/onnxruntime/issues/14278.
Talked with @askhade, we think if users want to enable NCCL/MPi but MPI
is not found, it should be failure instead of warning.
So this PR made the change. As a result, to make CIs pass, we need
disable NCCL/MPI explicitly in the build command. This PR take an
alternative approach, e.g. since NCCL and MPi are not used for
customers, disable NCCL by default if "--disable_nccl" not specified,
disable MPI by default if "--use_mpi" not specified.
### 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. -->
* 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
### Description
upgrade protobuf to 3.20.2, same as onnx 1.13.0
### Motivation and Context
Per component governance requirement and Fixes#14060
unused-parameter error occurs in 2 conditions.
1. compile protolbuf
`onnxruntime_src/cmake/external/protobuf/src/google/protobuf/repeated_ptr_field.h:752:66:
error: unused parameter ‘prototype’ [-Werror=unused-parameter]`
2. include onnx_pb.h
```
2023-01-28T10:20:15.0410853Z FAILED: CMakeFiles/onnxruntime_pybind11_state.dir/onnxruntime_src/onnxruntime/python/onnxruntime_pybind_iobinding.cc.o
......
2023-01-28T10:20:15.0466024Z from /build/Debug/_deps/onnx-src/onnx/onnx_pb.h:51,
2023-01-28T10:20:15.0466958Z from /onnxruntime_src/include/onnxruntime/core/framework/to_tensor_proto_element_type.h:10,
....
2023-01-28T10:20:15.0609678Z /build/Debug/_deps/onnx-build/onnx/onnx-operators-ml.pb.h:1178:25: required from here
2023-01-28T10:20:15.0610895Z /onnxruntime_src/cmake/external/protobuf/src/google/protobuf/repeated_ptr_field.h:752:66: error: unused parameter ‘prototype’ [-Werror=unused-parameter]
2023-01-28T10:20:15.0611707Z cc1plus: all warnings being treated as errors
```
https://dev.azure.com/onnxruntime/2a773b67-e88b-4c7f-9fc0-87d31fea8ef2/_apis/build/builds/874605/logs/22
PyTorch skipped version 1.14 and jumped to 2.0, while the image for the
onnxruntime-CI-nightly-ort-pipeline is still using
nightly-ubuntu2004-cu116-py38-torch1140dev. Switch to the new torch
version image to fix the failure of the pipeline.
A tool to convert ONNX model to tfevents so that we can use tensorboard
to open it for visualization. This is especially useful for debugging
when the ONNX model is too large to open by Netron.
usage: onnx2tfevents.py [-h] [--logdir LOGDIR] [--model MODEL]
### Description
Updated DirectML version to 1.10.1
(https://www.nuget.org/packages/Microsoft.AI.DirectML/1.10.1)
### 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. -->
Update Android package custom build script.
- Use later version of various dependencies (CMake, JDK, Android command line tools, Android NDK, Ubuntu). The CMake version was too old for the current ORT code.
- Do in-container build in a directory that is not shared with the host. Resolves some file permission issues and speeds up file access.
Add a nightly build to make sure the script works with the latest ORT.
…ckaging_CPU_x86_default (#14332)"
This reverts commit a491f33f54.
### Description
### Motivation and Context
It looks an ADO issue.
Now, it's recovered.
It could be reenabled.
### Description
Remove intermedia obj files and reenable cache
### Motivation and Context
Recently, training_debug_x64 pipeline often failed due to not enough
space.
It could free nearly 8G space by deleting obj files.
So, the compilation cache can be reenabled
- Fix debug node inputs outputs nullptr dereference with ONNX optional types.
- Fix model test memory leak.
- Convert jobs to stages in post-merge-jobs.yml to allow a subset of builds to be enabled when running manually.
- Fix buffer overrun in CumSum op exposed by Mimalloc build.
### Description
disable cache to save disk space for training_x64_debug
### Motivation and Context
To mitigate not enough disk space in training_x64_debug first.
Two modifications:
- After [TRT 8.5](https://github.com/microsoft/onnxruntime/pull/13867)
being merged, we can manually set timeout and make TRT EP only run small
portion of unit tests
(`onnxruntime_SKIP_AND_PERFORM_FILTERED_TENSORRT_TESTS=ON`) due to
additional TRT kernel overhead introduced by TRT 8.5 which increases
test time a lot. This PR modifies the checking condition and make
TensorRT CIs (can enable builder placeholder) still run most of the unit
tests.
- Exclude TRT EP from [Resize Opset
18](https://github.com/microsoft/onnxruntime/pull/13890) unit tests
since TensorRT 8.5 supports operators up to Opset 17.
### Description
Allows the PostAnalysis@2 task for windows CI jobs to continue even if
an error is encountered.
### Motivation and Context
This is a temporary workaround that enables the
`Windows_Packaging_CPU_x86_default` job within the Zip-Nuget-Java-NodeJS
packaging pipeline to finish. A recent update to dotnet 6 has broken the
PostAnalysis task for this job.
This task was originally added by
https://github.com/microsoft/onnxruntime/pull/13694
### Description
Add compilation cache in Linux CPU Aten Pipeline.
The pipeline could be completed in 6 minutes at best.
### Motivation and Context
1. Accelerate the pipeline.
2. It's the shortest pipeline with docker image. I'll use it to try
moving the storage of linux docker image from ACR to ADO pipeline cache.
### Description
Add a new install_shared_deps.sh
### Motivation and Context
Azcopy, Ninja, Node.js and CCache are all needed, but they are copied
everywhere.
### Description
Use pytest-xdist to distribute tests across multiple CPUs to speed up
test execution.
Use pytest-rerunfailures to rerun failed test in case of pytest-xdist
crash.
`pytest -n 16` can reduce pytest time from 80 minutes to 20 minutes.
### Motivation and Context
Now kernel explorer pytest of ROCm CI takes nearly 1 hour 20 minutes. It
will take longer time when we add more tunableOp in the future.
### Description
<!-- Describe your changes. -->
Use dlsym/GetProcAddress to lookup a custom ops registration function by
name and call it.
This will be better on mobile platforms where the custom ops library is
linked against, and there isn't necessarily a filesystem that a library
path can be loaded from.
Alternative is to wire up passing in the address of the function, but
that has multiple complications which differ by platform.
### 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. -->
Enable using ort and ort-ext packages on mobile platforms.
Co-authored-by: Edward Chen <18449977+edgchen1@users.noreply.github.com>
### Description
Changes to incorporate OpenVINO EP 2022.3
### Motivation and Context
This change is required to incorportate OpenVINO EP 2022.3
- If it fixes an open issue, please link to the issue here. -->
Co-authored-by: mohsinmx <mohsinx.mohammad@intel.com>
Co-authored-by: Preetha Veeramalai <preetha.veeramalai@intel.com>
Co-authored-by: Aravind <aravindx.gunda@intel.com>
Co-authored-by: mayavijx <mayax.vijayan@intel.com>
Co-authored-by: flexci <mohsinmx>
Fix https://github.com/microsoft/onnxruntime/issues/14017.
Before: shape_value = np.asarray([0, 0, np.array([4]), np.array([8])],
dtype=np.int64) raise Error in numpy 1.24.
After: shape_value = np.asarray([0, 0, 4, 8)], dtype=np.int64) is good
in numpy 1.24.
Update test environment to use numpy 1.24.
### 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
Update the MIGraphX version used in ORT to rocm-5.4.0
### Motivation and Context
The previous branch migraphx_for_ort has stopped updating, it is too far
away from the MIgraphX latest release branch. More discussion here:
https://github.com/microsoft/onnxruntime/issues/14126#issuecomment-1373201049
Co-authored-by: peixuanzuo <peixuanzuo@linmif39a000004.zvflicr54joexhdgnhvmxrxygg.phxx.internal.cloudapp.net>
### Description
1. Set the WithCache default value as false in Mac OS CI workflow too.
2. Add date of today in cache key to avoid cache size keep increasing
too.
WithCache, the pipeline duration reduced from 70 more minutes to 10 more
minutes
### Description
Add date value of today into the cache key.
### Motivation and Context
Microsoft-host agent has only 10GB for build.
To limit cache size, pipeline only use cache generated today.
### 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.
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
Deprecate one step beam search since it lacks maintenance (some tests
failed) and its performance is not optimal.
For users who still need this feature, please use older version
(<=1.13.1) of onnxruntime to export one step beam search model, and the
model can run in latest onnxruntime.
It is recommend to use
[convert_generation.py](https://github.com/microsoft/onnxruntime/blob/main/onnxruntime/python/tools/transformers/convert_generation.py)
to generate beam search onnx model for better performance.
### Description
Update CUDA ArgMin/ArgMax op kernels to have end version 11 since opset
12+ is not supported yet.
With the way these kernels are currently registered, the documentation
shows support for opset 11+. This is not accurate.
### Motivation and Context
Fix#13781
### Description
Update absl to a new version
### Motivation and Context
The new version contains fixes that are needed for Nvidia GPU build.
Once we update it to that version, we don't need to maintain our private
patches for Nvidia GPU build.
### Description
update versions of a few build dependencies for onnxruntime NPM
packages.
update nodejs version to v16.x in linux CI. v12 is too out-of-dated. see
[nodejs release
schedule](https://github.com/nodejs/release#release-schedule)
### Motivation and Context
- upgrade to latest webpack allows using of latest Node.js LTS version.
previous version of webpack does not work on Node.js v18 and it is fixed
in latest version
- upgrade to latest typescript, ts-loader and other dev deps to
accelerate the build and bundling.
- upgrade also helps to resolve security warnings that may be vulnerable
in out-of-dated version
### Description
Add the ability to run graph
### Motivation and Context
A brief description is as follows:
1) If the whole graph is supported, then will be processed by the graph
engine, directly.
2) If the whole graph is not supported, the whole graph will be divided
into subgraphs and single operators; The sub-graphs will be run on graph
engine, and the single operators will fallback to the traditional mode.
### Description
<!-- Describe your changes. -->
### 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
For compilation in container, ADO Cache task doesn't work directly.
The workaround is to mount the cache directory to the container, and let
CCache in container to read/write cache data.
In short, we just leverage ADO API to download/upload cache data.
The Post-jobs works in stack-mode, So the PostBuildCleanUp Tasks should
be defined first.
Thus, The PostBuildCleanUp would be executed lastly.
Else, Cache Task would fail to upload cache because the Agent Directory
is cleaned.
**Description**: This PR including following works:
1. provide stream and related synchronization abstractions in
onnxruntime.
2. enhance onnxruntime's execution planner / executor / memory arena to
support execute multiple streams in parallel.
3. deprecate the parallel executor for cpu.
4. deprecate the Fence mechanism.
5. update the cuda / tensorrt EP to support the stream mechanism,
support running different request in different cuda stream.
**Motivation and Context**
- Why is this change required?
currently, the execution plan is just a linear list of those primitives,
ort will execute them step by step. For any given graph, ORT will
serialize it to a fixed execution order. This sequential execution
design simplifies most scenarios, but it has the following limitations:
1. it is difficult to enable inter-node parallelization, we have a
half-baked parallel executor but it is very difficult to make it work
with GPU.
2. The fence mechanism can work with single gpu stream + cpu thread
case, but when extend to multiple stream, it is difficult to manage the
cross GPU stream synchronizations.
3. our cuda EP rely on the BFCArena to make the memory management work
with the GPU async kernels, but current BFCArena is not aware of the
streams, so it doesn't behavior correctly when run with multiple
streams.
This PR enhance our existing execution plan and executor to support
multiple stream execution. we use an unified algorithm to mange both
single stream and multiple stream scenarios.
This PR mainly focus on the infrastructure support for multiple stream
execution, that is said, given a valid stream assignment, onnxruntime
can execute it correctly. How to generate a good stream assignment for a
given model will be in the future PR.
Co-authored-by: Cheng Tang <chenta@microsoft.com@orttrainingdev9.d32nl1ml4oruzj4qz3bqlggovf.px.internal.cloudapp.net>
Co-authored-by: Cheng Tang <chenta@microsoft.com>
Co-authored-by: RandySheriffH <48490400+RandySheriffH@users.noreply.github.com>
Co-authored-by: Randy Shuai <rashuai@microsoft.com>
Co-authored-by: cao lei <jslhcl@gmail.com>
Co-authored-by: Lei Cao <leca@microsoft.com>
### Description
Fix a problem: macOS CI pipeline doesn't run tests. It is due a code
refactoring I recently made.
### Motivation and Context
Add the tests back.
Integrate TensorRT 8.5
- Update TensorRT EP to support TensorRT 8.5
- Update relevant CI pipelines
- Disable known non-supported ops for TensorRT
- Make timeout configurable.
We observe more than [20
hours](https://aiinfra.visualstudio.com/Lotus/_build/results?buildId=256729&view=logs&j=71ce39d8-054f-502a-dcd0-e89fa9931f40)
of running unit tests with TensorRT 8.5 in package pipelines. Because we
can't use placeholder to significantly reduce testing time (c-api
application test will deadlock) in package pipelines, we only run
subsets of model tests and unit tests that are related to TRT (add new
build flag--test_all_timeout and set it to 72000 seconds by package
pipelines). Just to remember, we still run all the tests in TensorRT CI
pipelines to have full test coverage.
- include https://github.com/microsoft/onnxruntime/pull/13918 to fix
onnx-tensorrt compile error.
Co-authored-by: George Wu <jywu@microsoft.com>
### Description
<!-- Describe your changes. -->
Update protobuf version to 3.18.3 in
tools/ci_build/github/linux/docker/scripts/requirements.txt.
### Motivation and Context
Address component governance alert CVE-2022-1941
### Description
- Adds a dockerfile for Ubuntu with TensorRT 8.5.1.1.
- Adds option to run EP Perf pipeline with TensorRT 8.5
### Motivation and Context
Necessary to benchmark models with TensorRT 8.5
### Description
<!-- Describe your changes. -->
1. Remove ROCm5.3 pipeline because it has rocblas bug, we don't need it.
2. We removed the dependency on centos docker image provided by
AMD(https://hub.docker.com/r/rocm/dev-centos-7) and build ROCm centos
base image by ourselves. The reference
dockerfile(https://github.com/RadeonOpenCompute/ROCm-docker/blob/master/dev/Dockerfile-centos-7)
is very redundant for our need. We simplified the ROCm manylinux
dockerfile.
3. Different versions of rocm use the same dockerfile
`Dockerfile.manylinux2014_rocm`.
### 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. -->
Co-authored-by: peixuanzuo <peixuanzuo@linmif39a000004.zvflicr54joexhdgnhvmxrxygg.phxx.internal.cloudapp.net>
### 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. -->
## 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.
### Description
1. Move C/C++ deps' URLs to deps.txt, and download the dependencies from
Azure Devops Artifacts instead of github.
2. Add "EXCLUDE_FROM_ALL" keyword to the cmake external projects, so
that we only build the parts we need and avoid installing the 3rd-party
dependencies when people run `make install` in ORT's build directory.
However, at this moment cmake itself doesn't have the feature. So I
copied their code to cmake/external/helper_functions.cmake and modified
it.
This PR is split from #13523, to make that one smaller.
### Motivation and Context
1. Secure the supply chain
2. Make it be possible to automatically detect if ORT has an old
dependency that hasn't been updated from a long time.
Revert TRT EP Linux CI to old behavior that code build and unit tests
are both executing in container. So that we don't have to update the VM
image for native Ubuntu to include latest TRT libraries every time newer
version of TRT is introduced.
fix for https://github.com/microsoft/onnxruntime/issues/13383,
https://github.com/microsoft/onnxruntime/issues/13408
Currently ort-web doesn't catch exceptions because turning on exception
catching increases the binary size by 3MB (~30%).
But ort can throw (ie onnx errors or ORT_ENFORCE) and there is no
useable error message.
Turning on exception catching just for top level api released file will
fix the error messages at minimal increase of binary size.
Right now we fix the warnings in an ad-hoc way. We run static analysis
in nightly builds, then create work items for the finding it found. Our
CI build pipelines run the same scan but do not break the build. So,
this PR will fix the remaining findings in the CPU EP(including the
training part) and enforce the check. Later on we can continue to expand
the scope.
We still have some warnings left in the JNI part. I will try to address
them later in the next month.
### Description
Add '-DCMAKE_OSX_ARCHITECTURES=x86_64;arm64' when build protobuf from
source on MacOS. Because later on we will the built library with the
other parts of onnxruntime to generate libonnxruntime.dylib, and if the
target CPU ARCH of libonnxruntime.dylib is not x86_64, it will fail.
### Motivation and Context
To fix a packaging pipeline failure, which was introduced from #13694
### Description
<!-- Describe your changes. -->
1. Build ROCm CI with Release config to save time.
2. use 32 threads to build, we have 256 threads on new CI machine.
3. enable ROCm kernel explorer test.
### 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. -->
Co-authored-by: peixuanzuo <peixuanzuo@linmif39a000004.zvflicr54joexhdgnhvmxrxygg.phxx.internal.cloudapp.net>
Patch Protobuf and ONNX's cmake files and enforce BinSkim check.
This PR has overlap with #13523 . I would prefer to get this one merged
first so that we can finished the BinSkim work, and I try to make this
PR as small as possible.
### Description
Update protobuf-java to version 3.21.7. This change only impact tests.
### Motivation and Context
The current version exhibits CVE-2022-3509
### Description
<!-- Describe your changes. -->
Add ROCm5.3.2 to python package pipeline
we build rocm/dev-centos-7:x.x.x stage by ourselves to avoid dependence
on AMD's release.
### 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. -->
Co-authored-by: peixuanzuo <peixuanzuo@linmif39a000004.zvflicr54joexhdgnhvmxrxygg.phxx.internal.cloudapp.net>
### Description
<!-- Describe your changes. -->
The default python upgrades to 3.11 in Mac, but 3.11 hasn't been
supported yet.
So Use python3.8 instead.
### 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 MacOS CI in Zip-Nuget-Java-Nodejs Packaging Pipeline
### Test Run
https://dev.azure.com/aiinfra/Lotus/_build/results?buildId=249020&view=logs&j=ded01483-6627-58ac-64dc-d4a232827e5d
Pytorch was added to inference pipelines in PR #8027. But, actually
these pipelines do not use PyTorch. PyTorch is huge, here we need to
install it for 4 different Python versions. If we remove PyTorch, we
will significantly reduce the image size. And, now downloading a pytorch
package often takes more than 1 hour. If we do it 4 times, it may take 4
hours.
Valgrind was added by me long time back, and it was not used too. Now we
run Linux tests outside of docker containers. So, when we have the need,
we could install it through apt-get on Ubuntu instead of doing it in the
CentOS container.
1. Remove the cmake option onnxruntime_DEV_MODE and replace it with
"--compile-no-warning-as-error"
2. Suppress some GSL warnings because now we treat nvcc diag warnings as
errors
### Description
Upgrade cmake version to 3.24 because I need to use a new feature that
is only provided in that version and later. Starting from cmake 3.24,
the
[FetchContent](https://cmake.org/cmake/help/latest/module/FetchContent.html#module:FetchContent)
module and the
[find_package()](https://cmake.org/cmake/help/latest/command/find_package.html#command:find_package)
command now support integration capabilities, which means calls to
"FetchContent" can be implicitly redirected to "find_package", and vice
versa. Users can use a cmake variable to control the behavior. So, we
don't need to provide such a build option. We can delete our
"onnxruntime_PREFER_SYSTEM_LIB" build option and let cmake handle it.
And it would be easier for who wants to use vcpkg.
### Motivation and Context
Provide a unified package management method, and get aligned with the
community. This change is split from #13523 for easier review.
### Description
It missed a space there.
### Motivation and Context
Right now the pipeline is failing because GSL was just converted from a
submodule to a cmake external project.
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.
1. Move DML packaging pipelines to aiinfra-dml-winbuild machine pool
2. Delete
tools/ci_build/github/azure-pipelines/templates/windowsai-nuget-build.yml
because the pipeline has been migrated to Onebranch. I monitored it for
months, it worked well.
### Description
support building xnnpack for IOS
### 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. -->
Fix document generation CI. It's not currently updating the docs as
we're skipping the tests, which is the invocation of build.py that would
have generated the documentation.
Setup specific task to generate documentation for greater clarity.
### 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. -->
Operator kernel documentation is not getting updated and is now out of
date.
### Description
Updates naming scheme for docker images built by the EP Perf pipeline.
Specifically, the docker image name is no longer based on the branch
name.
### Motivation and Context
The docker image name used by EP Perf pipeline is built from the branch
name. This makes the pipeline fail for branches with uppercase letters
because docker image names can only contain lower-case letters.
### Description
enabling on device training apis in the packaging pipelines.
### Motivation and Context
adding on device training flag so we can enable the on-device training
apis for Federated learning scenarios
Co-authored-by: Adam Louly <adamlouly@microsoft.com@orttrainingdev7.d32nl1ml4oruzj4qz3bqlggovf.px.internal.cloudapp.net>
### Description
Today, ORT+DML NuGet package does not validate the existence of the DML
EP header files and DML dlls. This change extends the existing python
script to verify the existence of DML EP related headers.
For DML as a dependent package, we will be using another task and it
will a separate PR.
### Motivation and Context
- Why is this change required? What problem does it solve?
Pro-actively verifies the ORT+DML release candidate rather than a
customer raise an issue after it gets published to NuGet.
- If it fixes an open issue, please link to the issue here. N/A
Co-authored-by: Sumit Agarwal <sumitagarwal@microsoft.com>
### Description
Currently, hipify happens before cmake is configured and then cmake glob
the directories. This get rids of thoes customized python threading
logic and opt for build system itself to generate the files.
This also supersede the half baked branch
[sukha/hipify-with-cmake](https://github.com/microsoft/onnxruntime/tree/sukha/hipify-with-cmake)
### Description
<!-- Describe your changes. -->
Use SAS Token to fix error` failed to perform copy command due to error:
no SAS token or OAuth token is present and the resource is not public`
Generate SAS Token of target data, add it into Key vault, and use it as
Pipeline Variable.
### 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. -->
Co-authored-by: peixuanzuo <peixuanzuo@linmif39a000004.zvflicr54joexhdgnhvmxrxygg.phxx.internal.cloudapp.net>
### Description
<!-- Describe your changes. -->
ROCm CI build step takes more than one hour. Set parallel=16 when build
on ROCm CI to reduce build time.
### 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. -->
Co-authored-by: peixuanzuo <peixuanzuo@linmif39a000004.zvflicr54joexhdgnhvmxrxygg.phxx.internal.cloudapp.net>
Updates EP perf benchmarking scripts to upload new data with an improved table schema. In order to preserve compatibility with the current benchmarking pipeline, we still upload data that uses the old schema as well. These changes are required in order to improve data filtering capabilities and general UX in dashboards that visualize this data.
Details:
- EP names no longer hardcoded as columns for tables that store inference latency, session creation times, memory usage, and model/EP status.
- Add explicit branch, commit ID, and commit date columns to all tables
- Improvements to the docker image building scripts (simplify docker image build; support installing binary TensorRT packages)
- Remove use of deprecated DataFrame.append in favor of pandas.concat.
Record more info from the React Native CI E2E test. In particular, log the view hierarchy when exiting the test and dump logs from Android emulator to the build output.
### Description
updating the ptca image used in the nightly pipeline
Co-authored-by: Adam Louly <adamlouly@microsoft.com@orttrainingdev7.d32nl1ml4oruzj4qz3bqlggovf.px.internal.cloudapp.net>
### Description
<!-- Describe your changes. -->
ROCm developers always need to build onnxruntime *whl with
`--enable_rocm_profiling`.
Add a ROCm dev python package pipeline which product *.whl with build
args `--enable_rocm_profiling`.
The dev *whl need to upload to azure storage and can get from
https://download.onnxruntime.ai/onnxruntime_nightly_rocm53.profiling.html
### 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. -->
`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
<!-- Describe your changes. -->
Unit test with ROCm5.3 slower than ROCm5.2.3. Revert to ROCm5.2.3.
We will update to ROCm5.3 when the issue resloved by AMD.
### 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
supplement of #13248
Add PR trigger
https://learn.microsoft.com/en-us/azure/devops/pipelines/repos/github?view=azure-devops&tabs=yaml#pr-triggers
fix: master -> main
Testted with #13289#13292
NB:
the real pipeline is always triggered if the workflow yaml changed even
it's added in the path filter.
### 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. -->
Make sure the real pipeline not run in the backend.
### Description
<!-- Describe your changes. -->
1. Remove ROCm5.1.1 and ROCm5.2 from ROCm python package pipeline
2. Add ROCm5.3 to ROCm python package pipeline
pipeline:
https://aiinfra.visualstudio.com/Lotus/_build/results?buildId=237172&view=results
### 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 warnings and enable dev mode for ROCm CI:
* Fix ROCm headers complaining "This file is deprecated. Use the header file from ..."
* Disable warning signed and unsigned compare for kernel explorer
* Fix unused and nondiscard warnings
* Enable dev mode for ROCm CI
* Walkaround error "unknown warning option '-Wno-nonnull-compare'" in kernel explorer by using '-Wno-unknown-warning-option' to ignore the unknown option
* Fix error "unused parameter 'mask'"
* Fix warning "instantiation of variable 'onnxruntime::rocm::Consts<float>::One' required here, but no definition is available", etc. Fixed by using C++17's inline (implied by constexpr) static initialization.
* Remove unused variable
* Add the missing `override` specifier
Update for ROCm CI before reland tunable GEMM #12853. This PR also update
composable kernel to use CMakes's HIP language support so that we can
mix C/C++ compiler with HIP compiler instead of locking to hip-clang
**Description**: This PR adds support for "XNNPACK EP" in ORTWeb and
changes the behavior of how ORTWeb deals with "backends", or "EPs" in
API.
**Background**: Term "backend" is introduced in ONNX.js to representing
a TypeScript type which implements a "backend" interface, which is a
similar but different concept to ORT's EP (execution provider). There
was 3 backends in ONNX.js: "cpu", "wasm" and "webgl".
When ORT Web is launched, the concept is derived to help users to
integrate smoothly. Technically, when "wasm" backend is used, users need
to also specify "EP" in the session options. Considering it may get
complicated and confused for users to figure out the difference between
"backend" and "EP", the JS API hide the "backend" concept and made a
mapping between names, backends and EPs:
"webgl" (Name) <==> "onnxjsBackend" (Backend)
"wasm" (Name) <==> "wasmBackend" (Backend) <==> "CPU" (EP)
**Details**:
The following changes are applied in this PR:
1. allow multi-registration for backends using the same name. This is
for use scenarios where both "onnxruntime-node" and "onnxruntime-web"
are consumed in a Node.js App ( so "cpu" will be registered twice in
this scenario. )
2. re-assign priority values to backends. I give 100 as base to "cpu"
for node and react_native, and 10 as base to "cpu" in web.
3. add "cpu", "xnnpack" as new names of backends.
4. update onnxruntime wasm exported functions to support EP
registration.
5. update implementations in ort web to handle execution providers in
session options.
6. add '--use_xnnpack' as default build flag for ort-web
### Description
fix XNNPACK on WebAssembly SIMD.
Flag "-msimd128" need to be applied to every source file when compiling
WASM SIMD. Currently only a part of the source files are compiled with
this flag so we get inconsistent result for
`sizeof(xnn_f32_minmax_params)` because the type definition include a
`#ifdef` for `__wasm_simd128__`. The inconsistency causes writing
garbage data to a stack variable and eventually cause the crash.
XNNPACK libraries are C libraries so need to apply the build flags not
only to `CMAKE_CXX_FLAGS` but also to `CMAKE_C_FLAGS`.
**Description**:
Use the onnx headers to find the latest opset for each operator. This
allows the script to detect optimizers with
`graph_utils::IsSupportedOptypeVersionAndDomain` calls that need
updating when run during the update of the onnx commit id. Without this
change issues are not detected until a new kernel is registered.
**Motivation and Context**
Detect optimizers that need updates as part of the ONNX update process.
### Description
<!-- Describe your changes. -->
fix migraphx ci pipeline failed problem.
Disabled MIGraphX pipeline now. It will be Enabled when this PR merge.
### 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
We fix iGPU Unit and Python tests with this PR
We add packaging pip pkg to build Many Linux DockerFile
### Motivation and Context
This change is required to make sure iGPU Unit Test/Python Tests with OV
are fixed
- If it fixes an open issue, please link to the issue here. -->
Co-authored-by: shamaksx <shamax.kshirsagar@intel.com>
Co-authored-by: mayavijx <mayax.vijayan@intel.com>
Co-authored-by: pratiksha <pratikshax.bapusaheb.vanse@intel.com>
Co-authored-by: pratiksha <mohsinx.mohammad@intel.com>
Co-authored-by: Sahar Fatima <sfatima.3001@gmail.com>
Co-authored-by: Preetha Veeramalai <preetha.veeramalai@intel.com>
Co-authored-by: nmaajidk <n.maajid.khan@intel.com>
Co-authored-by: Mateusz Tabaka <mateusz.tabaka@intel.com>
1. Update CUDA version from 11.4 to 11.6.
2. Update Manylinux version
3. Upgrade GCC version from 10 to 11 for most x86_64 pipelines. CentOS 7 ARM64 doesn't have GCC 11 yet.
4. Refactor python packaging pipeline:
a. Split Linux GPU build job to two parts, build and test, so that the
build part doesn't need to use a GPU machine
b. Make the Linux GPU build job and Linux CPU build job more similar: share the same bash script and yaml file.
5. Temporarily disable Attention_Mask1D_Fp16_B2_FusedNoPadding because it is causing one of our packaging pipeline to fail. I have created an ADO task for this.
**Description**: LayerNormalization is now part of the ONNX spec as of
opset 17.
We had a LayerNormalization contrib op, which (incorrectly) was
registered in the ONNX domain. Use that implementation for the ONNX
operator.
Update skip_layer_norm_fusion.cc. There are other optimizers that use
LayerNormalization that need updates as well.
**Motivation and Context**
#12916
**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**:
Use full ORT package for onnxruntime-react-native.
Left the params required for the mobile build in comments so they're
easily discovered if we need to create onnxruntime-react-native-mobile
in the future.
**Motivation and Context**
Remove barrier to using ORT with react native as the mobile package that
was being used supports a limited range of opsets/operators/types, and
requires ORT format models. The full package will run any model.
This changes are to align OV 2022.2 Release with ORT . Changes
CPU FP16 Support, dGPU Support, RHEL Dockerfile, Ubuntu 20 Dockerfile
**Motivation and Context**
- This change is required to ensure ORT-OpenVINO Execution Provider is
aligned with latest changes.
- If it fixes an open issue, please link to the issue here.
Co-authored-by: mayavijx <mayax.vijayan@intel.com>
Co-authored-by: shamaksx <shamax.kshirsagar@intel.com>
Co-authored-by: pratiksha <pratikshax.bapusaheb.vanse@intel.com>
Co-authored-by: pratiksha <mohsinx.mohammad@intel.com>
Co-authored-by: Sahar Fatima <sfatima.3001@gmail.com>
Co-authored-by: Preetha Veeramalai <preetha.veeramalai@intel.com>
Co-authored-by: nmaajidk <n.maajid.khan@intel.com>
Co-authored-by: Mateusz Tabaka <mateusz.tabaka@intel.com>
Co-authored-by: intel <intel@iotgecsp-nuc04.iind.intel.com>
1. add node test data to current model tests
2. support opset version to filter tests.
3. remove old filter based on onnx version. To avoid confusion, ONLY
support opset version filter in onnxruntime_test_all
4. support read onnx test data from absolute path on Windows.
# 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.
Recent change in CUDA EP #12814 makes hipify extremely slow and breaks the building. This PR fixes it by c
The onnxruntime/contrib_ops/rocm/bert/attention.h is checkout-ed from the version before #12814 and manually hipify-ed.
Slightly extend amd_hipify.py to allow wildcard file match and exclude all `tensorrt_fused_multihead_attention/*` files from hipify
1. Move the Linux ARM64 part of python packaging pipeline to a real ARM64 machine pool
2. Refactor the Linux CPU build jobs of python packaging pipeline to two parts: build and test. The test part will be exempted from Cyber EO compliance requirements as it won't affect the final bits we publish. This refactoring is to reduce dependencies in the build part. For example, this PR remove pytorch from the build dependencies.
3. Combine DML nuget packaging pipeline with "Zip-Nuget-Java-Nodejs Packaging Pipeline" as they all produce ORT nuget packages. Also, publish DML nuget packages and ORT GPU nuget packages to https://aiinfra.visualstudio.com/PublicPackages/_artifacts/feed/ORT-Nightly feed.
* Fix bug in pybind get_all_operator_schema due to premature reference dropping
* Add updated operator kernels markdown table
* Update build.py to include documentation generation for DML operators too
* Update GPU pipeline to include DML in the build to so operators can be generated.
* Use a separate pipeline stage, feedback from Changming and Scott
* Appease annoying Python linter
* Add onnxruntime_BUILD_UNIT_TESTS=OFF and remove stale --use_dml in cuda stage
* drop nuphar code and configs
* refactor test case
* format python
* remove nuphar from training test
* remove commented nuphar logics
* restore llvm setting
* drop nuphar ci
* fix compile err
* fix compile err
Co-authored-by: Randy Shuai <rashuai@microsoft.com>
* upgrade emsdk to 3.1.19
* fix build break
* ignore '-Wunused-but-set-variable' in eigen
* add malloc and free in exported functions
* EXPORTED_FUNCTIONS
* Add first pass of rocm kernel profiler
* Clean up rocm_profiler. Format args. Demangle kernel names.
Add Api EventRecords
* Remove debug output
* Temporarily disable profiling unit test 'api record check' for cupti
* Fix compile error for non-gpu builds
* Use common file for demangle and pid/tid. Namespace ThreadUtil. Fix gpu buffer clearing.
* Merge demangle into profiler_common
* Merge demangle into profiler_common part 2
* Style cleanup
* Resolve linking issues via ProviderHost interface
* Demangle cuda kernel names
* Clean up comments
* Fix formatting
* Fix anal retentive formatting
* upgrade cuda version on ci pipelines
* keeping folder name same
* keeping folder name same
* setting manual seed for primitive test case
* resolving comments
* changing atol and rtrol only for test case
Co-authored-by: Adam Louly <adamlouly@microsoft.com@orttrainingdev7.d32nl1ml4oruzj4qz3bqlggovf.px.internal.cloudapp.net>
* moving training pipelines from cuda 11.5 to 11.6 and deprecating cuda 11.3
* change to cuda 11.6.2
* change pytorch's & torchvision's cuda version to 11.6
* specify deps version to 11.6.2
* update pytorch and torch text version
* torch 1.12.1
* change torchvision and torchtext version to be compatible with torch 1.12.1
* change cuda to 11.6 for cuda_home comaptibility
Co-authored-by: Adam Louly <adamlouly@microsoft.com@orttrainingdev7.d32nl1ml4oruzj4qz3bqlggovf.px.internal.cloudapp.net>
* Add asm statement to model.mm to force linker to link against CoreML.Framework.
Update targets.xml as per Rolf's suggestions
* Remove explicit numpy version from macos build. We don't specify it for other CIs and the version specified doesn't have a pre-built 3.10 wheel. This leads to the CI attempting to build numpy which fails.
* 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
* Add build option to link prebuilt TensorRT parser
* Test without the build option to link prebuilt TRTParser
* Minor: update name of build option
* Minor: update name of build option
* [ROCm] enable NGramRepeatBlock Op
* [ROCm] Enable testing ROCm in NGramRepeatBlockTest.NGramSize_3
Also link onnxruntime_test_all with amdhip64 when USE_ROCM=1
* [ROCm] add LongformerAttention Op
* [ROCm] Enable LongformerAttentionTest
* [ROCm] Add DecoderAttention Op
* Enable DecoderAttention Test for ROCm.
* [ROCM] Updates according to reviews
* Update to handle multiline declarations for the kernels which are typical these days.
* Update to new path for the cpu contrib_op kernel registrations.
* Update tools/python/find_optimizer_opset_version_updates_required.py
Co-authored-by: Justin Chu <justinchuby@users.noreply.github.com>
* [ROCm] Add InstanceNormalization Op
* Enable InstanceNormBatch1_fp16 and InstanceNormBatch2_fp16 for ROCm
* [ROCm] Add BatchNormalization for fp32 and fp16
* Enable BatchNormTest for ROCm
* [ROCm] Add LRN Op
* [ROCM] replace miCompat functions with Helper functions
Losen the following test timeout:
1. "Test Web Multi-Browsers" stage in "ONNX Runtime Web CI Pipeline": 30min -> 60min
2. Node.js binding default per-case timeout: 30 sec -> 90 sec
1. Delete the build scripts that were copied from manylinux project. Use "git checkout" instead.
2. Update manylinux version to get python 3.11. Related issue: Python 3.11 support #12343
3. Change the cuda version of linux gpu build job of nuget packaging pipeline from cuda 11.4 to cuda 11.6 to match the TRT job within the same pipeline.. (A lot other places need be updated as well, but I'd prefer to put them in another PR)
4. Make dockerfile names static. For example, replace tools/ci_build/github/linux/docker/$(DockerFile) to tools/ci_build/github/linux/docker/Dockerfile.manylinux2014_cpu . The former one relies on a runtime variable $(DockerFile), Template Parameters are expanded early in processing a pipeline run when most variables are not available. It like C++ macros vs variables.
* update to 2022
* Update the VS version
* Rolling back to gcc 10
* Rolling back
* Update cuda home
* remove "CMAKE_CUDA_ARCHITECTURES=52"
* update cuda Architure to 70
* Delete cuda 10.2 training pipeline
* rolling back a mistake
* Update win-gpu-reduce-op-ci-pipeline.yml
* Update win-gpu-reduce-op-ci-pipeline.yml
* Update win-gpu-reduce-op-ci-pipeline.yml
* Delete tools/ci_build/github/linux/docker/scripts/training/ortmodule/stage1/requirements_torch1.10.0_cu10.2 directory
* Delete tools/ci_build/github/linux/docker/scripts/training/ortmodule/stage1/requirements_torch1.11.0_cu10.2 directory
Current builds use a NDK version that happens to be on the build machine. The build machine environment may change in ways that are outside of our control.
This change installs a specific version of NDK (the current LTS version 25.0.8775105) and uses it.
* refactor test for model with undefined shapes
* add test for TVMso EP
* update build script for TVM EP tests
* fix pylint
* disable test for Windows
* fix black
* fix python format
* fix pylint
* fix python format
* replace Path.resolve with os.path.join
* fix python path issue
Co-authored-by: Valery Chernov <valery.chernov@deelvin.com>
Fix bug where onnxruntime_USE_NCCL flag would default to ON, causing ORT to not build properly. New functionality: flag is ON when training is enabled and NCCL is not disabled. Flag is OFF otherwise
* [UPDATE] update ci to rocm5.2 + torch1.11
* [Revert] disable ort module test
* [DELETE] delete Rocm5.1.1 ci test result
* [UPDATE] update the comments
* add description of build ORT+TVM EP on Windows
* fix cmake error related to symlink creation on Windows
* add llvm config path to build flags for correct build on Windows
* update TVM_EP.md for llvm_config build arg
* fix warnings skipping during build on Windows
* fix using string or wstring for model path to correct build on Windows (MSVC error)
* fix error in custom logger for correct build on Windows
* implement glob algorithm for Windows
* additional build fixes
* update TVM with export of VM symbols for dll
* description of nasm issue and workaround
* update TVM with export of Executable from VM symbols for dll
* description of installation of ipp-crypto dependencies on Windows
* cmake key for ipp-crypto build
* fix wstring for TVMso EP
* fix ipp-crypto build
* cmake key onnxruntime_TVM_USE_HASH switch off not specific methods, but full hash functionality
* fix absolute path to compiled lib
* update TVM_EP.md, fix lint warnings
* update TVM_EP.md
* small fixes after review
* switch on handshake functionality for Linux workflow
Co-authored-by: Valery Chernov <valery.chernov@deelvin.com>
Co-authored-by: KJlaccHoeUM9l <wotpricol@mail.ru>
* Add tests for all uniary aten ops supported in eager mode
* fixing the PR draft
* fixing the merge
* changing eval to be at compile time
* adding requirements for eager
* 1.adding function to {ops}_out
2.cleaning the code
and adding comments
* editing the code according to code review
Co-authored-by: root <root@AHA-LIRONKESE-1>
* First attempt for half2 vectorized memory access in SkipLayerNorm
* Add some functions for debugging
* Clean up the code
* Clean up the code
* Generalize the vectorized kernels with aligned_vector and remove cudaDeviceProp
* Add a unit test for a larger input size
* Fix some Lint C++ warnings
* Use ILP = 4 for the vectorized kernels
* Rewrite the vectorized kernel and templatize ComputeSkipLayerNorm
* Use conditional operator for input_v
* Refactor LaunchSkipLayerNormKernel and replace the original SkipLayerNormKernelSmall with the vectorized kernel
* Clean some comments and rename the layernorm function
* Use ComputeSkipLayerNorm to replace LaunchSkipLayerNormKernel
* Resolve a Lint C++ warning
* Fix SkipLayerNormBatch1_Float16_vec output data
* Add hipified code of bert SkipLayerNorm for ROCmEP
* Resolve some Lint C++ warnings
* Resolve some Lint C++ warnings
* Resolve some Lint C++ warnings
* Resolve Python formatting issue
* 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
* Using vectorized loads (float2) for fp16 to improve performance
* Fix a few warnings from cpplint
* Fix a few warnings from cpplint
* Use __float2half2_rn and fix some cpplint warnings
* Move some computaions to LaunchFastGeluKernel
* Fix some Lint C++ warning
* Using vectorized loads (float4) for fp16 to improve performance
* Switch whether to optimize FastGelu with float4 vectorization
* Switch to float4 memory access based on input_length in FastGelu
* Comment how to set the threshold of float2 and float4 vectorized kernels
* Add FastGelu fp16 unit tests for bias_length = 2 and 8
* Make vectorized kernels generic with aligned_vector
* Unify the vectorized kernels with/without bias
* Refactor the code to suppress cpplint warnings
* Solve formatting issues
* Remove cudaDeviceProp from FastGeluKernel and LaunchFastGeluKernel
* Move fast_gelu_impl.h to rocm/bert
* Fix some Lint C++ warnings and code alignment
* Add .net6 support to the C# nuget package.
Currently requires jumping through a lot of hoops due to .net 6 only being supported in the preview release of VS 2022.
Build existing targets using msbuild.
Add .net6 targets and build using dotnet.
Create nuget package with combined targets.
A few misc automated changes from VS to spacing and adding a couple of properties.
* Try manually installing trt8.4 in multi-gpu pipeline
* Remove stmts that clean up cmake, ctest. Update tensorrt repository name passed to get_docker_image.py
* Update trt and cudnn home
* Don't install trtexec cli tool.
* Increase job timeout
* Revert timeout change and use trt placeholder builder build option
* update trt 8.4ga
* trt 8.4 linux ci pipeline
* fix cmake
* placeholder_builder
* trt 8.4 windows pipeline
* gpu package pipeline
* trt 8.4.1.5 , packaging pipeline updates
* python packaging
* ctest timeout
* python packaging test
* bump timeout
* python format
* format
* revert
* newline
* enable trt python tests
* typo
* python format
* disable on windows
* 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>
* update TVM
* get alignment constant from TVM
* update TVM_VM_SetInputs to upstream with TVM API
* fix CI issue: update TVM EP dependencies
* add sudo
* revert changes needed to install missing package
* add package for TVM EP CI
Co-authored-by: Valery Chernov <valery.chernov@deelvin.com>
Co-authored-by: KJlaccHoeUM9l <wotpricol@mail.ru>
* Initiate Ort SNPE EP
* fix snpe ep windows build which is caused by the utility method (ToUTF8String) name change on master
* correct the source path for libonnxruntime.so while building for andorid package
* add AdditionalDependencies for amr64
* On MS-Windows, the patchfile must be a text file, i.e. CR-LF must be used as line endings. A file with LF may give the error: "Assertion failed, hunk, file patch.c, line 343," unless the option '--binary' is given.
* fix build failure if snpe is not enabled
* update doc for contrib op
* separate out snpe ep settings to onnxruntime_snpe_provider.cmake
* renaming according review comments
* update according review comments
* Implement XNNPACK support via an EP.
* Layout transform uses the GraphPartitioner infrastructure.
* Node fusion is supported.
* Conv and MaxPool implementations were ported from Changming's PR.
* Added optional mutex in InferenceSession::Run as we only want to allow sequential calls if xnnpack is enabled
* [UPDATE] update amd ci pipeline 2 rocm5.1.1
* [FIX] json format error
* [ERROR] disable unit tests
* [FIX] ucx error
* [FIX] cmake version
* [FIX] units test
Description:
Add the extra param to match gelu in PyTorch in the contrib symbolic function
Motivation and Context
Why is this change required? What problem does it solve?
The symbolic function in /onnxruntime/python/tools/pytorch_export_contrib_ops.py is missing a recently added parameter approximate. We add this parameter and use the exporter defined gelu if approximate is "tanh".
* move all logic for ubuntu dockerfiles
* pass in trt version
* update trt 8.0 file
* downgrade protobuf
* uncomment
* and
* change to 8.0
* update dockerfiles
* checkout protobuf based on version
* adding last dockerfile:
:
* checkout 3.10 protobuf
* fix checkout version
* update to 8.2
* keep only one submodule sync
* cleanup
* Delete Dockerfile.custom-trt-perf
* create checkout submodules script
* properly compare decimals in bin/sh
* combine build ort paths
* deprecate TRT 7.2
* only checkout protobuf if we checkout older onnx-tensorrt
* only pull nvidia container if true, update image
* downgrade protobuf only if we checkout onnx-trt
* Update linux-gpu-tensorrt-daily-perf-pipeline.yml for Azure Pipelines
* Update linux-gpu-tensorrt-daily-perf-pipeline.yml for Azure Pipelines
* Add quotes to avoid path splitting
* address shellcheck
* use shellcheck suggestions
* Create new pipeline to sign ov ep binaries
* make codesign available
* make codesign available
* Update sign_ov_ep_binaries.yml for Azure Pipelines
* Update sign_ov_ep_binaries.yml for Azure Pipelines
* add codesign task
* Update sign_ov_ep_binaries.yml for Azure Pipelines
* Update sign_ov_ep_binaries.yml for Azure Pipelines
* windows
* reduce timeout to 15 minutes
Description: Set black's target version to be py37 - py310
Motivation and Context
Black by default targets its format for py3.10. Since our project supports python 3.7, we need to target version to all the python versions supported.
Re-ran black. 13 files reformatted.
Description: Format all python files under onnxruntime with black and isort.
After checking in, we can use .git-blame-ignore-revs to ignore the formatting PR in git blame.
#11315, #11316
* increase timeout
* show mac agent info
* Revert "show mac agent info"
This reverts commit a646ebefff8940a3044f1984107856db33319eb8.
* increase timeout in PR test
TODO: Someone should investigate why the AARCH64 build takes 3+ hours and reduce it if possible. Assuming it's using an emulator given the x64 build with the same arguments takes 13 minutes.
