### Description
This PR enables onnxruntime to build with the most recent release of Arm
Compute Library
### Motivation and Context
The latest version of Arm Compute Library that onnxruntime can build is
20.02 which is more than 3 years old.
### Description
1. Remove Windows ARM32 from nuget packaging pipelines
2. Add missing component-governance-component-detection-steps.yml to
some build jobs.
### Motivation and Context
Stop supporting Windows ARM32 to align with [Windows's support
policy](https://learn.microsoft.com/en-us/windows/arm/arm32-to-arm64).
Users who need this feature still can build the DLLs from source.
However, later on we will remove that support too.
### Description
- Removes `--disable_ml_ops` build flag
- Automatically detects ORT version from VERSION file via
`templates/set-version-number-variables-step.yml`. We will no longer
need to create a commit to update ORT versions.
### Motivation and Context
- A new unit test caused failures in the QNN Nuget pipeline because it
did not enable ml ops.
- Automate ORT version specification
### Description
Change all macOS python packages to use universal2, to reduce the number
of packages we have.
### Motivation and Context
According to [wikipedia](https://en.wikipedia.org/wiki/MacOS_Big_Sur),
macOS 11 is the first macOS version that supports universal 2. And it is
the min macOS version we support. So we no longer need to maintain
separate binaries for different CPU archs.
### Description
- Add mutex to protect QNN API calls for executing a graph and
extracting the corresponding profile data.
- Ensures QNN EP's execute function does not store unnecessary state
(i.e., input and output buffer pointers do not need to be stored as
class members.)
### Motivation and Context
Allow calling `session.Run()` from multiple threads when using QNN EP.
### Description
1. Update donwload-artifacts to flex-downloadartifacts to make it eaiser
to debug.
2. Move the native files into Gpu.Windows and Gpu-linux packages.
Onnxruntime-Gpu has dependency on them.
3. update the package validation as well
4. Add 2 stages to run E2E test for GPU.Windows and GPU.Linux
for example:
### Motivation and Context
Single Onnxruntime.Gpu Package size has already excceded the Nuget size
limit.
We split the package into some smaller packages to make them can be
published.
For compatibility, the user can install or upgrade Onnxruntime.Gpu,
which will install Gpu.Windows and Gpu.Linux automatically.
And the user can only install Gpu.Windows and Gpu.Linux directly.
### Test Link
1. In ORT_NIGHTLY
2. Install the preview version in nuget-int. (nuget source:
https://apiint.nugettest.org/v3/index.json)
---------
Co-authored-by: Scott McKay <skottmckay@gmail.com>
Bumps [transformers](https://github.com/huggingface/transformers) from
4.30.0 to 4.36.0.
<details>
<summary>Release notes</summary>
<p><em>Sourced from <a
href="https://github.com/huggingface/transformers/releases">transformers's
releases</a>.</em></p>
<blockquote>
<h2>v4.36: Mixtral, Llava/BakLlava, SeamlessM4T v2, AMD ROCm, F.sdpa
wide-spread support</h2>
<h2>New model additions</h2>
<h3>Mixtral</h3>
<p>Mixtral is the new open-source model from Mistral AI announced by the
blogpost <a href="https://mistral.ai/news/mixtral-of-experts/">Mixtral
of Experts</a>. The model has been proven to have comparable
capabilities to Chat-GPT according to the benchmark results shared on
the release blogpost.</p>
<!-- raw HTML omitted -->
<p>The architecture is a sparse Mixture of Experts with Top-2 routing
strategy, similar as <code>NllbMoe</code> architecture in transformers.
You can use it through <code>AutoModelForCausalLM</code> interface:</p>
<pre lang="py"><code>>>> import torch
>>> from transformers import AutoModelForCausalLM,
AutoTokenizer
<p>>>> model =
AutoModelForCausalLM.from_pretrained("mistralai/Mixtral-8x7B",
torch_dtype=torch.float16, device_map="auto")
>>> tokenizer =
AutoTokenizer.from_pretrained("mistralai/Mistral-8x7B")</p>
<p>>>> prompt = "My favourite condiment is"</p>
<p>>>> model_inputs = tokenizer([prompt],
return_tensors="pt").to(device)
>>> model.to(device)</p>
<p>>>> generated_ids = model.generate(**model_inputs,
max_new_tokens=100, do_sample=True)
>>> tokenizer.batch_decode(generated_ids)[0]
</code></pre></p>
<p>The model is compatible with existing optimisation tools such Flash
Attention 2, <code>bitsandbytes</code> and PEFT library. The checkpoints
are release under <a
href="https://huggingface.co/mistralai"><code>mistralai</code></a>
organisation on the Hugging Face Hub.</p>
<h3>Llava / BakLlava</h3>
<p>Llava is an open-source chatbot trained by fine-tuning LlamA/Vicuna
on GPT-generated multimodal instruction-following data. It is an
auto-regressive language model, based on the transformer architecture.
In other words, it is an multi-modal version of LLMs fine-tuned for chat
/ instructions.</p>
<!-- raw HTML omitted -->
<p>The Llava model was proposed in <a
href="https://arxiv.org/pdf/2310.03744">Improved Baselines with Visual
Instruction Tuning</a> by Haotian Liu, Chunyuan Li, Yuheng Li and Yong
Jae Lee.</p>
<ul>
<li>[<code>Llava</code>] Add Llava to transformers by <a
href="https://github.com/younesbelkada"><code>@younesbelkada</code></a>
in <a
href="https://redirect.github.com/huggingface/transformers/issues/27662">#27662</a></li>
<li>[LLaVa] Some improvements by <a
href="https://github.com/NielsRogge"><code>@NielsRogge</code></a> in <a
href="https://redirect.github.com/huggingface/transformers/issues/27895">#27895</a></li>
</ul>
<p>The integration also includes <a
href="https://github.com/SkunkworksAI/BakLLaVA"><code>BakLlava</code></a>
which is a Llava model trained with Mistral backbone.</p>
<p>The mode is compatible with <code>"image-to-text"</code>
pipeline:</p>
<pre lang="py"><code>from transformers import pipeline
from PIL import Image
import requests
<p>model_id = "llava-hf/llava-1.5-7b-hf"
</tr></table>
</code></pre></p>
</blockquote>
<p>... (truncated)</p>
</details>
<details>
<summary>Commits</summary>
<ul>
<li><a
href="14666775a2"><code>1466677</code></a>
Release: v4.36.0</li>
<li><a
href="accccdd008"><code>accccdd</code></a>
[<code>Add Mixtral</code>] Adds support for the Mixtral MoE (<a
href="https://redirect.github.com/huggingface/transformers/issues/27942">#27942</a>)</li>
<li><a
href="0676d992a5"><code>0676d99</code></a>
[<code>from_pretrained</code>] Make from_pretrained fast again (<a
href="https://redirect.github.com/huggingface/transformers/issues/27709">#27709</a>)</li>
<li><a
href="9f18cc6df0"><code>9f18cc6</code></a>
Fix SDPA dispatch & make SDPA CI compatible with torch<2.1.1 (<a
href="https://redirect.github.com/huggingface/transformers/issues/27940">#27940</a>)</li>
<li><a
href="7ea21f1f03"><code>7ea21f1</code></a>
[LLaVa] Some improvements (<a
href="https://redirect.github.com/huggingface/transformers/issues/27895">#27895</a>)</li>
<li><a
href="5e620a92cf"><code>5e620a9</code></a>
Fix <code>SeamlessM4Tv2ModelIntegrationTest</code> (<a
href="https://redirect.github.com/huggingface/transformers/issues/27911">#27911</a>)</li>
<li><a
href="e96c1de191"><code>e96c1de</code></a>
Skip <code>UnivNetModelTest::test_multi_gpu_data_parallel_forward</code>
(<a
href="https://redirect.github.com/huggingface/transformers/issues/27912">#27912</a>)</li>
<li><a
href="8d8970efdd"><code>8d8970e</code></a>
[BEiT] Fix test (<a
href="https://redirect.github.com/huggingface/transformers/issues/27934">#27934</a>)</li>
<li><a
href="235be08569"><code>235be08</code></a>
[DETA] fix backbone freeze/unfreeze function (<a
href="https://redirect.github.com/huggingface/transformers/issues/27843">#27843</a>)</li>
<li><a
href="df5c5c62ae"><code>df5c5c6</code></a>
Fix typo (<a
href="https://redirect.github.com/huggingface/transformers/issues/27918">#27918</a>)</li>
<li>Additional commits viewable in <a
href="https://github.com/huggingface/transformers/compare/v4.30.0...v4.36.0">compare
view</a></li>
</ul>
</details>
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alter it yourself. You can also trigger a rebase manually by commenting
`@dependabot rebase`.
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Move QNN EP provider options to session options
### Description
Need to use session option to support multi-partition for context cache feature. To smooth the transaction, move the provider options to session options first.
This is the first step for PR:
PR https://github.com/microsoft/onnxruntime/pull/18865
### Description
<!-- Describe your changes. -->
Add LeakyRelu to the list as support was added a while ago.
### 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. Add a CodeSign validation task before the binaries are published, to
make sure all DLL files are signed.
2. Auto-trigger the CUDA 12 pipeline's publishing job.
### Description
Fixes a failure in the ortmodule nightly pipeline.
### 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. -->
### 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
Change Nuget packaging pipeline's build TRT job to download CUDA SDK
on-the-fly, so that we do not need to put a CUDA SDK in the build
machine's image.
### Description
Update absl and googletest to their latest version to include some cmake
changes:
1. A googletest's cmake change that will allow using external absl and
re2.
2. Nullability enhancements that will allow our clang-based static
analysis detecting many kinds of null pointer errors.
### Motivation and Context
To fix a C4744 link warning in our Windows pipelines.
```
LINK : warning C4744: 'static char const absl::lts_20230802::base_internal::FastTypeTag<bool>::dummy_var' has different type in 'd:\a\_work\_temp\abseil_cpp\abseil-cpp-20230802.0\absl\flags\parse.cc' and 'd:\a\_work\1\b\relwithdebinfo\_deps\googletest-src\googletest\src\gtest-all.cc': 'signed char' and 'unsigned char' [D:\a\_work\1\b\RelWithDebInfo\onnxruntime_mlas_test.vcxproj]
LINK : warning C4744: 'static char const absl::lts_20230802::base_internal::FastTypeTag<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > >::dummy_var' has different type in 'd:\a\_work\_temp\abseil_cpp\abseil-cpp-20230802.0\absl\flags\parse.cc' and 'd:\a\_work\1\b\relwithdebinfo\_deps\googletest-src\googletest\src\gtest-all.cc': 'signed char' and 'unsigned char' [D:\a\_work\1\b\RelWithDebInfo\onnxruntime_mlas_test.vcxproj]
LINK : warning C4744: 'static char const absl::lts_20230802::base_internal::FastTypeTag<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > >::dummy_var' has different type in 'd:\a\_work\_temp\abseil_cpp\abseil-cpp-20230802.0\absl\flags\internal\usage.cc' and 'd:\a\_work\1\b\relwithdebinfo\_deps\googletest-src\googletest\src\gtest-all.cc': 'signed char' and 'unsigned char' [D:\a\_work\1\b\RelWithDebInfo\onnxruntime_mlas_test.vcxproj]
LINK : warning C4744: 'static char const absl::lts_20230802::base_internal::FastTypeTag<bool>::dummy_var' has different type in 'd:\a\_work\_temp\abseil_cpp\abseil-cpp-20230802.0\absl\flags\internal\flag.cc' and 'd:\a\_work\1\b\relwithdebinfo\_deps\googletest-src\googletest\src\gtest-all.cc': 'signed char' and 'unsigned char' [D:\a\_work\1\b\RelWithDebInfo\onnxruntime_mlas_test.vcxproj]
LINK : warning C4744: 'static char const absl::lts_20230802::base_internal::FastTypeTag<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > >::dummy_var' has different type in 'd:\a\_work\_temp\abseil_cpp\abseil-cpp-20230802.0\absl\flags\internal\flag.cc' and 'd:\a\_work\1\b\relwithdebinfo\_deps\googletest-src\googletest\src\gtest-all.cc': 'signed char' and 'unsigned char' [D:\a\_work\1\b\RelWithDebInfo\onnxruntime_mlas_test.vcxproj]
LINK : warning C4744: 'static char const absl::lts_20230802::base_internal::FastTypeTag<int>::dummy_var' has different type in 'd:\a\_work\_temp\abseil_cpp\abseil-cpp-20230802.0\absl\flags\internal\flag.cc' and 'd:\a\_work\1\b\relwithdebinfo\_deps\googletest-src\googletest\src\gtest-all.cc': 'signed char' and 'unsigned char' [D:\a\_work\1\b\RelWithDebInfo\onnxruntime_mlas_test.vcxproj]
```
### Description
ONNX model zoo changed their dir structure. So some our pipelines are
failing. In prevent such things happening again, we'd better to read the
test data for a cache from local disk instead of downloading it remotely
every time.
### Description
<!-- Describe your changes. -->
Add macos build for objc pod.
### 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. -->
Follow up pr for #18550
---------
Co-authored-by: rachguo <rachguo@rachguos-Mini.attlocal.net>
### Description
The warning is:
```
C:\a\_work\1\s\onnxruntime\contrib_ops\cpu\math\sparse_dense_matmul.cc(88,54): warning C4244: 'argument': conversion from 'const __int64' to 'Eigen::EigenBase<Derived>::Index', possible loss of data [C:\a\_work\1\b\RelWithDebInfo\onnxruntime_providers.vcxproj]
2023-12-08T20:58:48.1812949Z with
2023-12-08T20:58:48.2144272Z [
2023-12-08T20:58:48.2145285Z Derived=Eigen::Map<const Eigen::SparseMatrix<uint64_t,1,int64_t>,0,Eigen::Stride<0,0>>
2023-12-08T20:58:48.2801935Z ]
2023-12-08T20:58:48.2804047Z C:\a\_work\1\s\onnxruntime\contrib_ops\cpu\math\sparse_dense_matmul.cc(82,8): message : while compiling class template member function 'void onnxruntime::contrib::`anonymous-namespace'::SparseToDenseCsr<uint64_t>::operator ()(const onnxruntime::contrib::`anonymous-namespace'::ComputeCtx &,const onnxruntime::SparseTensor &,const onnxruntime::Tensor &,onnxruntime::Tensor &) const' [C:\a\_work\1\b\RelWithDebInfo\onnxruntime_providers.vcxproj]
2023-12-08T20:58:48.2806197Z C:\a\_work\1\s\include\onnxruntime\core/framework/data_types_internal.h(302,27): message : see the first reference to 'onnxruntime::contrib::`anonymous-namespace'::SparseToDenseCsr<uint64_t>::operator ()' in 'onnxruntime::utils::mltype_dispatcher_internal::CallableDispatchableHelper::Invoke' (compiling source file C:\a\_work\1\s\onnxruntime\contrib_ops\cpu\math\sparse_dense_matmul.cc) [C:\a\_work\1\b\RelWithDebInfo\onnxruntime_providers.vcxproj]
2023-12-08T20:58:48.2871783Z C:\a\_work\1\s\include\onnxruntime\core/framework/data_types_internal.h(438,100): message : see reference to class template instantiation 'onnxruntime::contrib::`anonymous-namespace'::SparseToDenseCsr<uint64_t>' being compiled (compiling source file C:\a\_work\1\s\onnxruntime\contrib_ops\cpu\math\sparse_dense_matmul.cc) [C:\a\_work\1\b\RelWithDebInfo\onnxruntime_providers.vcxproj]
2023-12-08T20:58:48.2893010Z C:\a\_work\1\s\include\onnxruntime\core/framework/data_types_internal.h(414,5): message : see reference to function template instantiation 'void onnxruntime::utils::MLTypeCallDispatcher<float,double,int32_t,uint32_t,int64_t,uint64_t>::InvokeWithLeadingTemplateArgs<Fn,onnxruntime::TypeList<>,onnxruntime::contrib::`anonymous-namespace'::ComputeCtx&,const T&,const onnxruntime::Tensor&,onnxruntime::Tensor&>(onnxruntime::contrib::`anonymous-namespace'::ComputeCtx &,const T &,const onnxruntime::Tensor &,onnxruntime::Tensor &) const' being compiled [C:\a\_work\1\b\RelWithDebInfo\onnxruntime_providers.vcxproj]
2023-12-08T20:58:48.2894476Z with
2023-12-08T20:58:48.2911521Z [
2023-12-08T20:58:48.2912457Z Fn=onnxruntime::contrib::`anonymous-namespace'::SparseToDenseCsr,
2023-12-08T20:58:48.3067840Z T=onnxruntime::SparseTensor
2023-12-08T20:58:48.3068863Z ] (compiling source file C:\a\_work\1\s\onnxruntime\contrib_ops\cpu\math\sparse_dense_matmul.cc)
2023-12-08T20:58:48.3195854Z C:\a\_work\1\s\onnxruntime\contrib_ops\cpu\math\sparse_dense_matmul.cc(198,11): message : see reference to function template instantiation 'void onnxruntime::utils::MLTypeCallDispatcher<float,double,int32_t,uint32_t,int64_t,uint64_t>::Invoke<onnxruntime::contrib::`anonymous-namespace'::SparseToDenseCsr,onnxruntime::contrib::`anonymous-namespace'::ComputeCtx&,const T&,const onnxruntime::Tensor&,onnxruntime::Tensor&>(onnxruntime::contrib::`anonymous-namespace'::ComputeCtx &,const T &,const onnxruntime::Tensor &,onnxruntime::Tensor &) const' being compiled [C:\a\_work\1\b\RelWithDebInfo\onnxruntime_providers.vcxproj]
2023-12-08T20:58:48.3197946Z with
2023-12-08T20:58:48.3198565Z [
2023-12-08T20:58:48.3199093Z T=onnxruntime::SparseTensor
2023-12-08T20:58:48.3905678Z ]
2023-12-08T20:58:48.3907275Z C:\a\_work\1\s\onnxruntime\contrib_ops\cpu\math\sparse_dense_matmul.cc(198,36): message : see the first reference to 'onnxruntime::utils::MLTypeCallDispatcher<float,double,int32_t,uint32_t,int64_t,uint64_t>::Invoke' in 'onnxruntime::contrib::SparseToDenseMatMul::Compute' [C:\a\_work\1\b\RelWithDebInfo\onnxruntime_providers.vcxproj]
2023-12-08T20:58:48.3910999Z ##[warning]onnxruntime\contrib_ops\cpu\math\sparse_dense_matmul.cc(88,43): Warning C4244: 'argument': conversion from 'const __int64' to 'Eigen::EigenBase<Derived>::Index', possible loss of data
2023-12-08T20:58:48.3912734Z 182>C:\a\_work\1\s\onnxruntime\contrib_ops\cpu\math\sparse_dense_matmul.cc(88,43): warning C4244: 'argument': conversion from 'const __int64' to 'Eigen::EigenBase<Derived>::Index', possible loss of data [C:\a\_work\1\b\RelWithDebInfo\onnxruntime_providers.vcxproj]
2023-12-08T20:58:48.3913414Z with
2023-12-08T20:58:48.3913660Z [
2023-12-08T20:58:48.3914001Z Derived=Eigen::Map<const Eigen::SparseMatrix<uint64_t,1,int64_t>,0,Eigen::Stride<0,0>>
2023-12-08T20:58:48.3914499Z ]
2023-12-08T20:58:48.3914743Z qlinear_concat.cc
2023-12-08T20:58:48.3917082Z ##[warning]onnxruntime\contrib_ops\cpu\math\sparse_dense_matmul.cc(92,74): Warning C4244: 'argument': conversion from 'const __int64' to 'Eigen::EigenBase<Derived>::Index', possible loss of data
2023-12-08T20:58:48.3918624Z 182>C:\a\_work\1\s\onnxruntime\contrib_ops\cpu\math\sparse_dense_matmul.cc(92,74): warning C4244: 'argument': conversion from 'const __int64' to 'Eigen::EigenBase<Derived>::Index', possible loss of data [C:\a\_work\1\b\RelWithDebInfo\onnxruntime_providers.vcxproj]
2023-12-08T20:58:48.5534583Z with
2023-12-08T20:58:48.5541266Z [
2023-12-08T20:58:48.5542401Z Derived=Eigen::Map<const Eigen::Matrix<uint64_t,-1,-1,1,-1,-1>,0,Eigen::Stride<0,0>>
2023-12-08T20:58:48.5544914Z ]
2023-12-08T20:58:48.5548670Z ##[warning]onnxruntime\contrib_ops\cpu\math\sparse_dense_matmul.cc(92,63): Warning C4244: 'argument': conversion from 'const __int64' to 'Eigen::EigenBase<Derived>::Index', possible loss of data
2023-12-08T20:58:48.5552099Z 182>C:\a\_work\1\s\onnxruntime\contrib_ops\cpu\math\sparse_dense_matmul.cc(92,63): warning C4244: 'argument': conversion from 'const __int64' to 'Eigen::EigenBase<Derived>::Index', possible loss of data [C:\a\_work\1\b\RelWithDebInfo\onnxruntime_providers.vcxproj]
2023-12-08T20:58:48.5553712Z with
2023-12-08T20:58:48.5555569Z [
2023-12-08T20:58:48.5556779Z Derived=Eigen::Map<const Eigen::Matrix<uint64_t,-1,-1,1,-1,-1>,0,Eigen::Stride<0,0>>
2023-12-08T20:58:48.5558707Z ]
2023-12-08T20:58:48.5561428Z ##[warning]onnxruntime\contrib_ops\cpu\math\sparse_dense_matmul.cc(93,90): Warning C4244: 'argument': conversion from 'const __int64' to 'Eigen::EigenBase<Derived>::Index', possible loss of data
2023-12-08T20:58:48.5565624Z 182>C:\a\_work\1\s\onnxruntime\contrib_ops\cpu\math\sparse_dense_matmul.cc(93,90): warning C4244: 'argument': conversion from 'const __int64' to 'Eigen::EigenBase<Derived>::Index', possible loss of data [C:\a\_work\1\b\RelWithDebInfo\onnxruntime_providers.vcxproj]
2023-12-08T20:58:48.5566354Z with
2023-12-08T20:58:48.5568185Z [
2023-12-08T20:58:48.5569305Z Derived=Eigen::Map<Eigen::Matrix<uint64_t,-1,-1,1,-1,-1>,0,Eigen::Stride<0,0>>
2023-12-08T20:58:48.5571339Z ]
2023-12-08T20:58:48.5574864Z ##[warning]onnxruntime\contrib_ops\cpu\math\sparse_dense_matmul.cc(93,77): Warning C4244: 'argument': conversion from 'const __int64' to 'Eigen::EigenBase<Derived>::Index', possible loss of data
2023-12-08T20:58:48.5577866Z 182>C:\a\_work\1\s\onnxruntime\contrib_ops\cpu\math\sparse_dense_matmul.cc(93,77): warning C4244: 'argument': conversion from 'const __int64' to 'Eigen::EigenBase<Derived>::Index', possible loss of data [C:\a\_work\1\b\RelWithDebInfo\onnxruntime_providers.vcxproj]
2023-12-08T20:58:48.5578562Z with
2023-12-08T20:58:48.5580399Z [
2023-12-08T20:58:48.5581503Z Derived=Eigen::Map<Eigen::Matrix<uint64_t,-1,-1,1,-1,-1>,0,Eigen::Stride<0,0>>
2023-12-08T20:58:48.5583465Z ]
2023-12-08T20:58:48.5587661Z ##[warning]onnxruntime\contrib_ops\cpu\math\sparse_dense_matmul.cc(88,54): Warning C4244: 'argument': conversion from 'const __int64' to 'Eigen::EigenBase<Derived>::Index', possible loss of data
2023-12-08T20:58:48.5590705Z 182>C:\a\_work\1\s\onnxruntime\contrib_ops\cpu\math\sparse_dense_matmul.cc(88,54): warning C4244: 'argument': conversion from 'const __int64' to 'Eigen::EigenBase<Derived>::Index', possible loss of data [C:\a\_work\1\b\RelWithDebInfo\onnxruntime_providers.vcxproj]
2023-12-08T20:58:48.5591396Z with
2023-12-08T20:58:48.5593220Z [
2023-12-08T20:58:48.5593693Z Derived=Eigen::Map<const Eigen::SparseMatrix<int64_t,1,int64_t>,0,Eigen::Stride<0,0>>
2023-12-08T20:58:48.5595955Z ]
```
And the warning in #18195
### Motivation and Context
AB#22894
---------
Co-authored-by: Dmitri Smirnov <yuslepukhin@users.noreply.github.com>
### Description
Move NuGet nightly package publishing job to a separated pipeline.
Before this change, it runs at the end of 'Zip-Nuget-Java-Nodejs
Packaging Pipeline'. This PR moves it to a separate pipeline so that we
can manually trigger this step for any branch(e.g. release branches).
### 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
<!-- 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
Updating transformers package in test pipeline to fix a security
vulnerability.
### 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
Update absl and gtest to fix an ARM64EC build error
### Motivation and Context
We need to get an important fix into ORT.
The fix is:
8028a87c96
### Description
reuse EO pool in NPM pipeline.
### Motivation and Context
build_web_debug failed in onnxruntime-Win-CPU-2022 but it works in EO
pool.
Reuse EO pool to make the pipeline work now.
When I'm free, I'll try upgrading the chrome in the custom image.
Build onnxruntime.dll as arm64x
Added a .cmake file to generate a link repro of the onnxruntime.dll
during arm64 build. This provides us a directory containing all the
arm64 objs, def file and libs to link to when it is time to building
arm64x onnxruntime.dll during the arm64ec build by passing the
/machine:arm64x flag to the linker along with the arm64 artifacts.
If other dlls wanted to be built as x, setting the ARM64X_TARGETS
variable in the toplevel cmakelists.txt to include these other targets
is all that will be needed.
Added build_arm64x.bat as a wrapper for the multiple (rm64, then
arm64ec) cmake calls needed to build as arm64x.
AB#22533
### Description
<!-- Describe your changes. -->
As title.
### Motivation and Context
<!-- - Why is this change required? What problem does it solve?
- If it fixes an open issue, please link to the issue here. -->
yolo-v8 model missing operator support.
---------
Co-authored-by: rachguo <rachguo@rachguos-Mini.attlocal.net>
Co-authored-by: Edward Chen <18449977+edgchen1@users.noreply.github.com>
### Description
- Update QNN CI Pipelines to use QNN SDK version 2.17.0
- **Print warning if unit test requires adjusted tolerance to pass**
- **Temporarily disable unloading QnnCpu.dll for windows x64 due to
crash when calling FreeLibrary**
- Enable fixed HTP tests
- QnnHTPBackendTests.LayerNorm1D_LastAxis_DynamicScale
- QnnHTPBackendTests.GlobalMaxPool_LargeInput2_u8
- QnnHTPBackendTests.ReduceSumS8Opset13_Rank5
- QnnHTPBackendTests.ReduceSumU8Opset13_Rank5_LastAxis
- QnnHTPBackendTests.WhereLargeDataBroadcastU8
- QnnHTPBackendTests.WhereLargeDataBroadcastTransformedU8
- Enabled fixed CPU tests
- QnnCPUBackendTests.Resize_DownSample_Linear_AlignCorners_scales
- Increased tolerance for HTP tests that are less accurate on QNN SDK
2.17.0
- QnnHTPBackendTests.AveragePool_CountIncludePad_HTP_u8
- QnnHTPBackendTests.AveragePool_AutopadSameUpper_HTP_u8
- QnnHTPBackendTests.AveragePool_AutopadSameLower_HTP_u8
- QnnHTPBackendTests.ConvU8U8S32_bias_dynamic_input
- QnnHTPBackendTests.ConvU8U8S32_bias_initializer
- QnnHTPBackendTests.ConvU8U8S32_large_input1_padding_bias_initializer
- QnnHTPBackendTests.LRNSize3
- QnnHTPBackendTests.LRNSize5
- QnnHTPBackendTests.MaxPool_Large_Input_HTP_u8
- QnnHTPBackendTests.MaxPool_LargeInput_1Pads
- QnnHTPBackendTests.Resize_DownSample_Linear_HalfPixel
- QnnHTPBackendTests.ResizeU8_2xLinearPytorchHalfPixel
- QnnHTPBackendTests.ResizeU8_2xLinearHalfPixel
- QnnHTPBackendTests.ResizeU8_2xLinearAlignCorners
- QnnHTPBackendTests.ResizeU8_2xLinearAsymmetric
- Disabled ONNX model tests
- averagepool_2d_ceil: Accuracy issues **only on Windows x64
QnnCpu.dll**
- Disabled QDQ model tests (onnx_test_runner)
- facedetection_op8_qdq: Accuracy issues
- Disabled CPU EP tests (these use QnnCpu.dll)
- ActivationOpTest.Relu: QNN SDK 2.17 Relu treats inf as FLT_MAX
- GemmOpTypedTests/0.TestGemmBroadcast: Inaccuracy when weight is
initializer and bias is not
- MathOpTest.MatMulFloatType "test padding and broadcast B > A":
Inaccuracy (**only linux**)
- Fix Gemm translation bugs in QNN EP:
- Do not skip processing of inputs that need to be transposed.
### Motivation and Context
- Allow testing with newest QNN SDK version
- Take advantage of improvements to enable new models.
### Description
To make the code more consistent. Now some TRT pipelines download TRT
binaries on-the-fly, while other TRT pipelines use a preinstalled
version. This PR make them the same.
### Description
<!-- Describe your changes. -->
Remove developement id and force codesign not required in the test macos
target.
### 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 failure happened in iOS_Full_xcframwork stage in
Zip-Nuget-Java-NodeJS packaging pipeline.
---------
Co-authored-by: rachguo <rachguo@rachguos-Mac-mini.local>
Add ACL as the DNNL runtime option for aarch64 platforms. Update
makefile and the python wheel build script.
### Description
<!-- Describe your changes. -->
Add ACL as the DNNL runtime option for aarch64 platforms. Update
makefile and the python wheel build script.
### 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. -->
This is to enable the optimized ACL gemm kernels for dnnl execution
provider on aarch64 platform.
### Description
1. Add a new stage to download java tools from https://oss.sonatype.org
and publish them to pipeline artifact
2. Remove downloads in other jobs, they get the java tools from pipeline
artifact
3. consolidate final_java_testing stages.
### Motivation and Context
Reduce downloads to reduce the connection error like below.
```
--2023-11-28 07:16:31-- https://oss.sonatype.org/service/local/repositories/releases/content/org/junit/platform/junit-platform-console-standalone/1.6.2/junit-platform-console-standalone-1.6.2.jar
Resolving oss.sonatype.org (oss.sonatype.org)... 3.227.40.198, 3.229.50.23
Connecting to oss.sonatype.org (oss.sonatype.org)|3.227.40.198|:443... connected.
HTTP request sent, awaiting response... 502 Bad Gateway
2023-11-28 07:16:32 ERROR 502: Bad Gateway.
```
Reverts microsoft/onnxruntime#18413
there's a timing issue here. we eventually want to get this change
merged in but we need to update OSS onnx-tensorrt first.
### Description
Currently, the `drop-nuget` artifact only contains protoc.exe which is
also part of the `drop-extra` artifact.
### 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. -->
