### 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)
### 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. -->
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
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
`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>
- 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
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. -->
### 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
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
### 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
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>
### 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>
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
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.
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
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
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>
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.
### 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.
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.
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.
`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. -->
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. -->
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
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.
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>
# 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.
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.
* 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>
* 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
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
* 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>
* 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
* 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>
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
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
* remove rocm42 CI
* update torch to v1.11.0
Co-authored-by: Ethan Tao <ettao@microsoft.com@orttrainingdev7.d32nl1ml4oruzj4qz3bqlggovf.px.internal.cloudapp.net>
* Enabling ov-ep for 2022.1 Release
->Added ov-ep 2022.1 flow
->Validated CPU Unit tests with OV
Master using onnxruntime_test_all unit
tests.
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Fix for output mismatch b/w OpenVINO and ONNX
Refer:
https://jira.devtools.intel.com/browse/CVS-60310
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Enabling Adobe ops
->Enable Resize op for iGPU
->Enable Add op for iGPU
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Removing irrelevant conditions
->Removing some conditions from
GetCapability() which are now not
required. (Removed conditions for
OV version support less than 2021.2)
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Enable upsample op
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Enable Adobe proxy-e model
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Removing any extra conditions for Opset13 ops
* Opset13 changes
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Exception handling for devices
* Added comments
* Implement GPU Throttling feature
*Added GPU Throttling feature for iGPU's.
when user enables it as a runtime option,
it helps in reducing overall CPU usage
of the application
*Added changes to exercise this option
using onnxruntime_perf_test application.
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Renaming the runtime config option
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Added the user to video and users group
* Handling_GPU.0_GPU.1
* Handling special conditions
->Handling corner cases for
device_type checks
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Modification to include new api 2.0 changes in the code
* Added opset13 changes
->Enabled Few ops
->Added Debug info for case 3b in getcapability()
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Enabling ov-ep for 2022.1 Release
->Added ov-ep 2022.1 flow
->Validated CPU Unit tests with OV
Master using onnxruntime_test_all unit
tests.
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Fix for output mismatch b/w OpenVINO and ONNX
Refer:
https://jira.devtools.intel.com/browse/CVS-60310
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Enabling Adobe ops
->Enable Resize op for iGPU
->Enable Add op for iGPU
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Removing irrelevant conditions
->Removing some conditions from
GetCapability() which are now not
required. (Removed conditions for
OV version support less than 2021.2)
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Enable upsample op
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Enable Adobe proxy-e model
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Removing any extra conditions for Opset13 ops
* Opset13 changes
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Exception handling for devices
* Added comments
* Implement GPU Throttling feature
*Added GPU Throttling feature for iGPU's.
when user enables it as a runtime option,
it helps in reducing overall CPU usage
of the application
*Added changes to exercise this option
using onnxruntime_perf_test application.
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Renaming the runtime config option
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Added the user to video and users group
* Handling_GPU.0_GPU.1
* Handling special conditions
->Handling corner cases for
device_type checks
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Added opset13 changes
->Enabled Few ops
->Added Debug info for case 3b in getcapability()
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Log comments updated
* Changes to enable 2.0 api
* Enabling ov-ep for 2022.1 Release
->Added ov-ep 2022.1 flow
->Validated CPU Unit tests with OV
Master using onnxruntime_test_all unit
tests.
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Fix for output mismatch b/w OpenVINO and ONNX
Refer:
https://jira.devtools.intel.com/browse/CVS-60310
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Enabling Adobe ops
->Enable Resize op for iGPU
->Enable Add op for iGPU
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Removing irrelevant conditions
->Removing some conditions from
GetCapability() which are now not
required. (Removed conditions for
OV version support less than 2021.2)
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Enable upsample op
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Enable Adobe proxy-e model
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Removing any extra conditions for Opset13 ops
* Opset13 changes
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Exception handling for devices
* Added comments
* Implement GPU Throttling feature
*Added GPU Throttling feature for iGPU's.
when user enables it as a runtime option,
it helps in reducing overall CPU usage
of the application
*Added changes to exercise this option
using onnxruntime_perf_test application.
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Renaming the runtime config option
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Added the user to video and users group
* Handling_GPU.0_GPU.1
* Handling special conditions
->Handling corner cases for
device_type checks
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Added opset13 changes
->Enabled Few ops
->Added Debug info for case 3b in getcapability()
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Fix build issue
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Fixes issues
*Fixes compiler warnings c4458 on windows.
*Fixes the bug in device_type check logic
*Adds print info for enable_opencl_throttling
option in onnxruntime_perf_test
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* commit to make openvino_2021.4 compatible
* Fixed IO Buffer Optimization
* Fix output names issue
* Fix 2021.3 branch
* Bug Fix for Multiple inputs/outputs
- Assigns the right output_name and
input_name for the graph when
returned by CompiledModel::inputs()
OV function.
- Also takex care of output mismatch
issue b/w openvino output and onnx
output
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Add comments for the changes made
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* IO Buffer Changes
* Commit for Disabling GPU Throttling for 2021.4
* Updated branch
* Fix windows build
->Fixed windows build in debug mode
->Disabled scatternd3_tensor_int64
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Fixed CPP Unit tests for CPU
-Fixed shrink, MVN, ReduceL2, Maxpool,
upsample, scatter, slice, reshape,
unsqueeze.
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Fixed first set of GPU Tests
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Fixed additional failing tests on GPU
->Added conditions to disable certain ops
under certain conditions
->Disabled certain tests
->Added some op supports for no_dimension
supported
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Added Expand op support for CPU
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Added condition for squeeze op
->Shape can't have empty axes attribute
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Add support for LessOrEqual op function
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* OV Interface wait for replaced by indefinite wait call
* use names from ONNX model to access OV tensors
This chnage is to use the input/output names
retrieved from original onnx model to access
OV tensors and to check if there's any input
or output names mismatch b/w ONNX naming
and OV naming.
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Fixes Myriad unit tests and other issues
->Fixes Myriad CPP unit tests
->Fixes output mismatch issue with models with
sub graph partitioning
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Fix segfault issue
->Fixed case 3b condition in get_capability()
which was causing the segfault issue
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Fixed build isuse with ov 2021.4 with I/O buffer
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Disables performance counters for I/O Buffer
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Fixed inputs/outputs mismatch for HDDL with 2022.1
Signed-off-by: Mohammad Amir Aqeel <mohammadx.amir.aqeel@intel.com>
* Fix to enable GPU FP16
* Enabled mlperf_ssd_mobilenet_300 model fully on CPU
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Added ov version specific dll packaging for nuget
* Fixed conditions for few ops
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Dockerfile updates
* Updated License Info
-Updated the copyrights License Info
-modified FP16 transformations with OV 2022.1
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Disabling mlperf_ssd_mobilenet_300 model
->Disabled this model for openvino. The
test is failing in Internal_CI pipelines.
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Disabling failing python CPU Tests
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Fixed flake8 python errors
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
Co-authored-by: hdgx <harinix.d.g@intel.com>
Co-authored-by: mayavijx <mayax.vijayan@intel.com>
Co-authored-by: sfatimar <sahar.fatima@intel.com>
Co-authored-by: mohsinmx <mohsinx.mohammad@intel.com>
Co-authored-by: Mohammad Amir Aqeel <mohammadx.amir.aqeel@intel.com>
* Update orttraining release pipelines to use torch 1.11.0
* Change requirements_torch...txt to requirements.txt
* Update cuda cmake architectures and clean up old files
* add support for bool type
* add TVM EP support for tests
* include TVM EP in python test pool
* fix pylint
* moved technical imports to a separate file
* clean up post build actions & move _ld_preload.py extension to CMake level
* add files for include TVM EP into CI
* implement custom logger for TVM
* replace TVM logging with ONNX RT logging
* update link for TVM EP tutorial
* clean up TVM EP cmake
* add pybind auto enabling for TVM EP
* fix blank spaces
* code review fixes
* replace print with comment
* add list of EP without TVM EP
* enable onnx tests
* disable contrib ops and ml ops
* reuse Dockerfile.ubuntu
* Move install_tvm_test_dependencies.sh out of Docker context dir, update build definition.
Co-authored-by: Edward Chen <18449977+edgchen1@users.noreply.github.com>
Move binary size check(s) to a separate pipeline. In the future, other binary size-related builds can go here.
Add publishing of build artifacts for easier analysis.
Add optional build with debug info.
* migrate to 1ES Hosted Pool
* migrate to Kusto database
* refactor and organize ep names with ORT prefix
* standardize TRT benchmarking with save/load engine, input binding, and workspace
* Add TRT 8.2 to ep perf pipeline
* update model_list.json with full onnx zoo
* add anubis credentials
* add anubis credentials
* clarify trt variables
* get system info from docker image
* remove unwanted commenting
In a reduced ops build, some source files get updated. This change moves the updated files into the build directory. This way, it is easier to simultaneously manage different build directories (with possibly different reduced ops configurations) based on a single source directory.
* update base image from 11.4.0 to 11.4.2
* update Linux TRT GPU pipeline to TRT 8.2
* update onnx-tensorrt to 8.2-GA
* disable failing TensorRT 8.2 tests.
* update pad test.
* fix
* update win trt ci pipeline to trt 8.2
* test run with cuda 11.4 and cudnn 8.2
* increase timeout
* revert
* revert
* update packaging pipelines to use trt 8.2
* fix typo
* update trt gpu perf pipeline to trt 8.2
* increase timeout
* delete deprecated ci-perf-pipeline.yml
* bump timeout
* adjust timeout packaging
* update to torch 1.10
* update torchvision version
* update torchtext version
* remove deprecated option enable_onnx_checker
* add unit test to test gradient of GatherElements
* add ORTMODULE_ONNX_OPSET_VERSION in a docker file
* add ortmodule and eager mode test
* add ortmodule dependency
* fix eager pipeline
* skip tthe ortmodule test for windows due to win ci issue
* remove useless win ci change
* add torch
Co-authored-by: Abhishek Jindal <abjindal@microsoft.com>
* Changes to ensure openvino build go through in Windows
* Modified Hetero plugin Logic
*Modified Hetero Feature logic. In Hetero,
if the operator to be marked true in getcapability(),
it should be supported by either of the devices
specified with HETERO in the device_type.
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* OV updated to 2021.4.2 version
* OV updated to 2021.4.2 version
* Updated OV to 2021.4.2 version, mono download link and dotnet version
* Copying Managed nugets in openvino c# docker file
*Copying Managed nuget to nugets artifacts
directory
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
Co-authored-by: saharfraza <sfatima.3001@gmail.com>
Co-authored-by: mayavijx <mayax.vijayan@intel.com>
Co-authored-by: Aravind Gunda <aravindx.gunda@intel.com>
Adding ARM64 depthwise convolution kernel for symmetric quantization
Motivation and Context
Two improvements against current kernel code :
1. Signed int8 based instructions, no need to extend from 8b to 16b before multiplication.
2. Unrolled loop with manual software pipelining
Co-authored-by: Chen Fu <fuchen@microsoft.com>
ORT format model runtime optimization implementation is in progress.
This change adds a build.py option to disable the partial runtime optimization implementation, adds CI builds to test it, and disables runtime optimizations in mobile package builds.
Add Xamarin support to the ORT nuget packages.
- Update C# code to support Xamarin builds for iOS and Android
- refactor some things to split out common code
- include iOS and Android ORT native shared library in native nuget package
* make work for both rocm 4.2 and rocm 4.3.1
* fix rocm 4.3.1 docker image reference
* fix CUDA_VERSION to ROCM_VERSION
* fix ReduceConsts conflict def
* add ifdef to miopen_common.h as well
* trailing ws
* 2021.4.1 Docker and ci changes
* OV version change
* Removing Imagescaler op from the op's list
Reverting this change which was added in last
PR. Imagescaler is now deprecated. so removing
it from the supported list. Also this
op is causing regression in the performance
of the FP16 models.
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
* Re-writing the help message for num_of_threads
Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>
Co-authored-by: Aravind Gunda <aravindx.gunda@intel.com>
* install protobuf from source
* fix rm command in Dockerfile
* fix options on rm command
* fix cd into protobuf source directory
* try again
* remove strip step
* debug list the files
* ls on /usr
* more debug
* more debug
* adjust LD_LIBRARY_PATH
* try remove protobuf before ORT build
* updates for picking pnnx commit
* add tests filter to c# tests
* plus test fixes
* fix versioning for contrib ops
* fix tests
* test filter for optional ops
* more versioning related updates
* fix test
* fix layernorm spec
* more updates
* update docs
* add more test filters
* more filters
* update binary size threshold
* update docs
* draft - enable model local function
* enable model local functions in ORT
* update to latest rel onnx commit
* plus tests
* plus more updates
* plus updates
* test updates
* Fix for nested functions + shape inference
* plus bug fix and updates per review
* plus fixes per review
* plus test updates
* plus updates per review
* plus fixes
* fix a test
* copy changes from trt_and_mem
* second edits
* Update linux-gpu-tensorrt-ci-perf-pipeline.yml for Azure Pipelines
* Update linux-gpu-tensorrt-ci-perf-pipeline.yml for Azure Pipelines
* Update linux-gpu-tensorrt-ci-perf-pipeline.yml for Azure Pipelines
* change to cuda 11.4
* build with cuda 11.4
* Update Dockerfile.ubuntu_cuda11_1_tensorrt7_2
* add cmake extra defines
* cmake architectures
* fix cmake arch
* Delete ubuntu-18.04.Dockerfile
* Rename Dockerfile.ubuntu_cuda11_1_tensorrt7_2 to Dockerfile.ubuntu_cuda11_4_tensorrt7_2
* Update linux-gpu-tensorrt-ci-perf-pipeline.yml
* Update linux-gpu-tensorrt-ci-perf-pipeline.yml for Azure Pipelines
* removing previous ort args
* rename to cuda 11.4
* remove cuda 10_2
* delete trt 7.1
* remove 7.1
* Passing in cuda architecture to reduce build time
* always add submodule sync due to recursive cloning
* fix run command
* add and
* take away unused arms and share python installation script
* Update linux-gpu-tensorrt-ci-perf-pipeline.yml
* Update Dockerfile.tensorrt
* cleanup file
* install python directly on dockerfile - move to scripts in future
* Update Dockerfile.custom-trt-perf
* adding cuda 11.1 for missing Libnvrtc.so.11.1
* Delete install_python.sh
* modify for test
* modify for test
* modify for test
* modify for test
* modify for test
* modify for test
* prepare for PR
* Rename cuda directory to gpu directory in tarball
* Fix gpu java package
* fix bug
* fix small bug
* Add onnxruntime_providers_shared.dll into gpu nuget package
* Modify for test
* Temporarily remove for test
* Modify for test
* Modify for test
* Test packging Windows combined GPU
* Test packging Windows combined GPU
* Test packging Windows combined GPU
* Test packging Windows combined GPU
* modify for test
* modify for test
* fix bug
* Modify for test
* Modify for test
* Modify for test
* Modify for test
* Modify for test
* Modify for test
* Modify for test
* Modify for test
* Prepare for PR
* Prepare for PR
* Code refactor
* Rename proper Artifact name
* Rename intermediate Artifact names
* Revert Artifact Names
* Rename Artifact Names
* Modify Artifact name
* Modify Artifact name
* Modify Artifact name
* Update Java package
* Update Java package
* fix bug to change artifact name
* Fix bug for the wrong file path
* Fix no fetching correct artifact and test
* temporarily modify for test
* undo the change for test
* fix build - python.h not found
* disable --build_shared_lib for ortmodule tests
* fix
* fix the build flag
* disable --build_shared_lib for training path (not only for ortmodule)
* fix missing test model files
* disable test CApiTest.test_custom_op_library when ENABLE_TRAINING_TORCH_INTEROP is ON
* enable custom_op_library build
* fix build
* fix
* merge master and fix build failure
* build onnx_test_runner when onnxruntime_ENABLE_TRAINING_TORCH_INTEROP is ON
* resolve comments
* use --enable_training_torch_interop to replace "onnxruntime_ENABLE_TRAINING_TORCH_INTEROP=ON"
* initial update from 11.1 to 11.4
* change 11.4.1 to 11.4.0
* adjusting to match nvidia/cuda image tags
* adjusting to match nvidia/cuda image tags centos7
* correction to 11.4.0
* correction to 11.4.0
* update to cuda 11.4
* change training back to 11.1
* change training back to 11.1
* point to correct nvcr.io/nvidia/cuda 11.4.1 image
* change centos8 to centos7
* correct cudnn path
* Update linux-gpu-ci-pipeline.yml for Azure Pipelines
* Update c-api-noopenmp-packaging-pipelines.yml
* need to resolve centos images but remove space and change to 11.4
* Update linux-gpu-ci-pipeline.yml
* add cudnn to docker image
* bump devtoolset to 10
* revert cuda 11.4 change to setup_env_trt
* orttraining back to 11.1
* use nvcr.io
* Fix previous change back to cuda 11.1
* update cudnn path
* use cudnn image (revert if failure)
Add IsSparseTensor
Add CreateSparseTensor
Add utilities and test fully sparse instantiation
Fully sparse blocksparse
Add test and docs for fully sparse tensor instantiation
Rework creation API
Use API
Non string API
Retrofit of existing String API
Add tests
Add documentation
Address build issues (Winml pending)
Add inference test
Bump binary size
Add ifdef DISABLE CONTRIB
* updates for picking pnnx commit
* add tests filter to c# tests
* plus test fixes
* fix versioning for contrib ops
* fix tests
* test filter for optional ops
* more versioning related updates
* fix test
* fix layernorm spec
* more updates
* update docs
* add more test filters
* more filters
* update binary size threshold
* update docs
* plus more fixes
* updates per review
* update to release commit
* add filters for optional type tests
* plus updates
* update onnx-tensorrt parser to master
* disable unsupported tests
* add cuda sm 75 for T4
* update tensorrt pipeline
* update trt pipelines
* update trt pipelines
* Update linux-gpu-tensorrt-ci-pipeline.yml
* update trt cid pipeline
* Update linux-gpu-tensorrt-ci-pipeline.yml
* Update Tensorrt Windows build pool and TensorRT/CUDA/CuDNN version
* update to cuda11.4 in trt ci pipeline
* update base image to cuda11.4
* update packaging pipeline to cuda11.4
* clean up
* remove cuda11.1 and cuda11.3 docker file
* disable unsupported tensorrt tests at runtime
* Update linux-multi-gpu-tensorrt-ci-pipeline.yml
1. Update SDLNativeRules from v2 to v3. The new one allows us setting excluded paths.
2. Update TSAUpload from v1 to v2. And add a config file ".gdn/.gdntsa" for it.
3. Fix some parentheses warnings
4. Update cmake to the latest.
5. Remove "--x86" build option from pipeline yaml files. Now we can auto-detect cpu architecture from python. So we don't need to ask user to specify it.
