### 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
