### 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. -->
---------
Co-authored-by: Changming Sun <chasun@microsoft.com>
Co-authored-by: kunal-vaishnavi <115581922+kunal-vaishnavi@users.noreply.github.com>
Co-authored-by: Sai Kishan Pampana <sai.kishan.pampana@intel.com>
Co-authored-by: rachguo <rachguo@rachguos-Mini.attlocal.net>
Co-authored-by: Jian Chen <cjian@microsoft.com>
Co-authored-by: Shubham Bhokare <32080845+shubhambhokare1@users.noreply.github.com>
### Description
This PR is a preview of cherry-picks for ort-web to `rel-1.17.3` based
on `rel-1.17.2`.
<details>
<summary>Changes of ort-web to cherry-pick</summary>
The following commits are from main branch.
`o` stands for pick, and `x` stands for skip.
```
o 2e0a388c36 [js/webgpu] Add HardSigmoid support (#19215)
o d226e40856 [js/webgpu] set query type in onRunStart (#19202)
o 61610ff986 [js/webgpu] Add FusedConv clip test case (#18900)
o a33b5bd1fa [JS/WebGPU] Added Uniforms to SkipLayerNorm. (#18788)
o 591f90c0b9 [js/webgpu] Fix issue of timestamp query (#19258)
o 7252c6e747 [WebNN EP] Support WebNN async API with Asyncify (#19145)
o 5b06505073 [js/webgpu] Fix Tanh explosion (#19201)
o 656ca66186 [js/webgpu] Support uniforms for conv, conv transpose, conv grouped (#18753)
o a3f0e2422b [js/webgpu] Support f16 uniform (#19098)
o 9e69606360 fix f16 for attention, enable slice and flatten for more types (#19262)
o 624b4e2063 [js/webgpu] Remove enableShapesUniforms (#19279)
o 90883a366a [js/webgpu] Add hardSigmoid activation for fusedConv (#19233)
o 85cef0af8c [js/webgpu] Support capture and replay for jsep (#18989)
o d73131cf0f [js/webgpu] Use DataType as uniform cpu type (#19281)
o dd1f6ccc45 [js/webgpu] resolve codescan alert (#19343)
o 3a2ab1963a [js/webgpu] Refactor createTensorShapeVariables (#18883)
o efc17e79de [js/webgpu] Fix the undefined push error (#19366)
x 50806a7dd5 [js/web] support external data in npm test (#19377)
o ccbe264a39 [js/webgpu] Add LeakyRelu activation for fusedConv (#19369)
o 5ff27ef02a [js/webgpu] support customop FastGelu (#19392)
x 03be65e064 [js/web] fix types exports in package.json (#19458)
o 06269a3952 [js/webgpu] allow uint8 tensors for webgpu (#19545)
o dfeda9019c [JS/WebGPU] Add MatMulNBits (#19446)
o 1b48054e1b [js/webgpu] Create Split indices helpers by rank, not by shape (#19554)
o 3fe2c137ee [js] small fix to workaround formatter (#19400)
x 70567a4b3a [js/web] use ApiTensor insteadof onnxjs Tensor in TensorResultValidator (#19358)
o 6e04e36e3f [js/common] upgrade tsc in common from 4.9.5 to 5.2.2 (#19317)
o 58f4921686 [js] changes to allow Float16Array if any polyfill is available (#19305)
o 57d6819212 [js/web] Fix fused-conv is not included in npm test (#19581)
o ebd220b073 Misspelling in README.md (#19433)
o 38c3432393 Bump ip from 1.1.8 to 1.1.9 in /js/react_native (#19582)
o fe82fccf1a [js/webgpu] Fix Conv2DTransposeMatMul f16 compilation failure (#19596)
o 76a2a487a1 Bump ip from 1.1.8 to 1.1.9 in /js/react_native/e2e (#19583)
o 29b1106033 [node] Switch to setImmediate to avoid starving the Node.js event loop (#19610)
o ae3d73c981 [JS/WebGPU] Fix Split and Where to handle corner cases. (#19613)
o aec2389ad0 [js/webgpu] allows a ProgramInfo's RunData to use zero sized output (#19614)
o bb43a0f133 [js/webgpu] minor fixes to make tinyllama work (#19564)
o 0edb035808 [js/web] fix suite test list for zero sized tensor (#19638)
o 3cb81cdde2 [js/common] move 'env.wasm.trace' to 'env.trace' (#19617)
o e30618d055 [js/webgpu] use Headless for webgpu test by default (#19702)
o f06164ef8b [js/web] transfer input buffer back to caller thread (#19677)
x a788514027 [js/web] dump debug logs for karma for diagnose purpose (#19785)
o 24b72d2613 [JS/WebGPU] Preserve zero size input tensor dims. (#19737)
o 4538d31a8b [js/webgpu] expose a few properties in WebGPU API (#19857)
o 53de2d8cb0 [js/webgpu] Enable GroupedConvVectorize path (#19791)
o ed250b88c3 [JS/WebGPU] Optimize MatMulNBits (#19852)
x e771a763c3 [js/test] align web test runner flags with ort.env (#19790)
o 79e50aeef3 [js/web] rewrite backend resolve to allow multiple EPs (#19735)
o acb0df2280Fix#19931 broken Get Started link of "ONNX Runtime JavaScript API" page (#19932)
o b29849a287 [js/common] fix typedoc warnings (#19933)
o afdab62f53 Bump follow-redirects from 1.15.4 to 1.15.6 in /js/web (#19949)
o 28ad6c3955 Bump follow-redirects from 1.15.4 to 1.15.6 in /js/node (#19951)
o 7e0d424934 accumulate in fp32 for Reduce* (#19868)
o 4c6a6a37f7 [js/webgpu] Fix NAN caused by un-initialized buffer in instance-norm (#19387)
o 01c7aaf6aa [js/webgpu] allow setting env.webgpu.adapter (#19940)
o c45cff60cf [js/webgpu] fix maxpool / fp16 (#19981)
```
</details>
<details>
<summary>Cherry-pick commandlines</summary>
```sh
git cherry-pick 2e0a388c36
git cherry-pick d226e40856
git cherry-pick 61610ff986
git cherry-pick a33b5bd1fa
git cherry-pick 591f90c0b9
git cherry-pick 7252c6e747
git cherry-pick 5b06505073
git cherry-pick 656ca66186
git cherry-pick a3f0e2422b
git cherry-pick 9e69606360
git cherry-pick 624b4e2063
git cherry-pick 90883a366a
git cherry-pick 85cef0af8c #<<<<< Note: conflicts
git cherry-pick d73131cf0f
git cherry-pick dd1f6ccc45
git cherry-pick 3a2ab1963a
git cherry-pick efc17e79de
git cherry-pick ccbe264a39
git cherry-pick 5ff27ef02a
git cherry-pick 06269a3952
git cherry-pick dfeda9019c
git cherry-pick 1b48054e1b
git cherry-pick 3fe2c137ee
git cherry-pick 6e04e36e3f
git cherry-pick 58f4921686
git cherry-pick 57d6819212
git cherry-pick ebd220b073
git cherry-pick 38c3432393
git cherry-pick fe82fccf1a
git cherry-pick 76a2a487a1
git cherry-pick 29b1106033
git cherry-pick ae3d73c981
git cherry-pick aec2389ad0
git cherry-pick bb43a0f133
git cherry-pick 0edb035808
git cherry-pick 3cb81cdde2
git cherry-pick e30618d055
git cherry-pick f06164ef8b
git cherry-pick 24b72d2613
git cherry-pick 4538d31a8b
git cherry-pick 53de2d8cb0
git cherry-pick ed250b88c3
git cherry-pick 79e50aeef3
git cherry-pick acb0df2280
git cherry-pick b29849a287
git cherry-pick afdab62f53
git cherry-pick 28ad6c3955
git cherry-pick 7e0d424934
git cherry-pick 4c6a6a37f7
git cherry-pick 01c7aaf6aa
git cherry-pick c45cff60cf
```
</details>
<details>
<summary>Cherry-pick conflicts</summary>
- 85cef0af8c#18989
this change is for enabling graph capture feature for JSEP, and it is
done after ROCM EP enabled graph capture feature. However, the ROCM EP
graph capture feature is not cherry-picked in rel-1.17.2.
</details>
---------
Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: Jiajia Qin <jiajia.qin@intel.com>
Co-authored-by: Xu Xing <xing.xu@intel.com>
Co-authored-by: satyajandhyala <satya.k.jandhyala@gmail.com>
Co-authored-by: Yang Gu <yang.gu@intel.com>
Co-authored-by: Wanming Lin <wanming.lin@intel.com>
Co-authored-by: Jiajie Hu <jiajie.hu@intel.com>
Co-authored-by: Guenther Schmuelling <guschmue@microsoft.com>
Co-authored-by: Matttttt <18152455+martholomew@users.noreply.github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
Co-authored-by: Segev Finer <segev208@gmail.com>
Co-authored-by: Belem Zhang <belem.zhang@intel.com>
### Description
<!-- Describe your changes. -->
Web prs are not included yet.
### 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: Yufeng Li <liyufeng1987@gmail.com>
Co-authored-by: Maximilian Müller <44298237+gedoensmax@users.noreply.github.com>
Co-authored-by: Yi Zhang <zhanyi@microsoft.com>
Co-authored-by: Your Name <your@email.com>
Co-authored-by: Edward Chen <18449977+edgchen1@users.noreply.github.com>
Co-authored-by: enximi <70036307+enximi@users.noreply.github.com>
Co-authored-by: George Wu <jywu@microsoft.com>
Co-authored-by: Markus Tavenrath <mtavenrath@users.noreply.github.com>
Co-authored-by: Tianlei Wu <tlwu@microsoft.com>
Co-authored-by: rachguo <rachguo@rachguos-Mini.attlocal.net>
Co-authored-by: Adam Pocock <adam.pocock@oracle.com>
Co-authored-by: aciddelgado <139922440+aciddelgado@users.noreply.github.com>
Co-authored-by: kunal-vaishnavi <115581922+kunal-vaishnavi@users.noreply.github.com>
Co-authored-by: Changming Sun <chasun@microsoft.com>
Co-authored-by: Chi Lo <54722500+chilo-ms@users.noreply.github.com>
### Description
<!-- Describe your changes. -->
As title. Follow up pr for source code release 1.17.2
### 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: rachguo <rachguo@rachguos-Mini.attlocal.net>
Co-authored-by: Changming Sun <chasun@microsoft.com>
### Description
<!-- Describe your changes. -->
Cherry-pick Final Round
### 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: Adrian Lizarraga <adlizarraga@microsoft.com>
Co-authored-by: Changming Sun <chasun@microsoft.com>
Co-authored-by: Chi Lo <54722500+chilo-ms@users.noreply.github.com>
Co-authored-by: rachguo <rachguo@rachguos-Mini.attlocal.net>
Co-authored-by: Edward Chen <18449977+edgchen1@users.noreply.github.com>
Co-authored-by: kunal-vaishnavi <115581922+kunal-vaishnavi@users.noreply.github.com>
Co-authored-by: aciddelgado <139922440+aciddelgado@users.noreply.github.com>
Co-authored-by: Yufeng Li <liyufeng1987@gmail.com>
### Description
<!-- Describe your changes. -->
[ORT 1.17.0 Release] Cherry pick 1st round
PR authors please take a look, and let me know if there are any
questions about the changes or approve accordingly.
### 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: wejoncy <wejoncy@163.com>
Co-authored-by: Xavier Dupré <xadupre@users.noreply.github.com>
Co-authored-by: Yulong Wang <7679871+fs-eire@users.noreply.github.com>
Co-authored-by: Hector Li <hecli@microsoft.com>
Co-authored-by: luoyu-intel <yu.luo@intel.com>
Co-authored-by: kunal-vaishnavi <115581922+kunal-vaishnavi@users.noreply.github.com>
Co-authored-by: Chi Lo <54722500+chilo-ms@users.noreply.github.com>
Co-authored-by: Ye Wang <52801275+wangyems@users.noreply.github.com>
Co-authored-by: Adrian Lizarraga <adlizarraga@microsoft.com>
Co-authored-by: snadampal <87143774+snadampal@users.noreply.github.com>
Co-authored-by: Tianlei Wu <tlwu@microsoft.com>
Co-authored-by: Heflin Stephen Raj <heflinstephen03@gmail.com>
Co-authored-by: Yifan Li <109183385+yf711@users.noreply.github.com>
Co-authored-by: Yufeng Li <liyufeng1987@gmail.com>
Co-authored-by: Changming Sun <chasun@microsoft.com>
### 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
Update DML version to 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. -->
### Description
<!-- Describe your changes. -->
### Motivation and Context
Linux_GPU_x64 job in the pipeline has been canceled due to timeout since
0112.
### Description
This way, we will not need to update the windows images constantly and
allow more flexibility to choose the cuda version in the future.
### Description
Set default flags nvcc and do not set the flags for ROCM EP.
### Motivation and Context
1. To meet a BinSkim requirement for CUDA EP.
https://github.com/microsoft/binskim/blob/main/docs/BinSkimRules.md#rule-BA2024EnableSpectreMitigations
2. The ROCM EP's pipeline is broken since PR #19073 . Unit tests failed
to load the EP with the following error message:
Failed to load library libonnxruntime_providers_rocm.so with error:
/build/Release/libonnxruntime_providers_rocm.so: undefined symbol:
vtable for onnxruntime::InsertMaxPoolOutput .
This PR is a hot fix to bring the pipeline back. So far I don't know why
the error happened. The symbol "InsertMaxPoolOutput" is in
onnxruntime_optimizers. I don't see any EP code references it directly.
### Description
Disable ccache for all the jobs in in Windows CPU CI pipeline.
Before disabling it, the build has a warning that:
"MSIL .netmodule or module compiled with /GL found; restarting link with
/LTCG; add /LTCG to the link command line to improve linker performance"
After disabling it, the warning is gone and the build doesn't use /GL or
/LTCG.
Cache itself should not cause this difference.
### Motivation and Context
### 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
1. Add two build jobs for enabling Address Sanitizer in CI. One for
Windows CPU, One for Linux CPU.
2. Set default compiler flags/linker flags in build.py for normal
Windows/Linux/MacOS build. This can help control compiler flags in a
more centralized way.
3. All Windows binaries in our official packages will be built with
"/PROFILE" flag. Symbols of onnxruntime.dll can be found at [Microsoft
public symbol
server](https://learn.microsoft.com/en-us/windows-hardware/drivers/debugger/microsoft-public-symbols).
Limitations:
1. On Linux Address Sanitizer ignores RPATH settings in ELF binaries.
Therefore once Address Sanitizer is enabled, before running tests we
need to manually set LD_LIBRARY_PATH properly otherwise
libonnxruntime.so may not be able to find custom ops and shared EPs.
4. On Linux we also need to set LD_PRELOAD before running some tests(if
the main executable, like python, is not built with address sanitizer.
On Windows we do not need to.
5. On Windows before running python tests we should manually copy
address sanitizer DLL to the onnxruntime/capi directory, because python
3.8 and above has enabled "Safe DLL Search Mode" that wouldn't use the
information provided by PATH env.
6. On Linux Address Sanitizer found a lot of memory leaks from our
python binding code. Therefore right now we cannot enable Address
Sanitizer when building ONNX Runtime with python binding.
7. Address Sanitizer itself uses a lot of memory address space and
delays memory deallocations, which is easy to cause OOM issues in 32-bit
applications. We cannot run all the tests in onnxruntime_test_all in
32-bit mode with Address Sanitizer due to this reason. However, we still
can run individual tests in such a way. We just cannot run all of them
in one process.
### Motivation and Context
To catch memory issues.
### Description
Set pythonInterpreter in set-python-manylinux-variables-step.yml. To fix
a build error:
```
Starting: Set Python manylinux variables
==============================================================================
Task : Python script
Description : Run a Python file or inline script
Version : 0.231.1
Author : Microsoft Corporation
Help : https://docs.microsoft.com/azure/devops/pipelines/tasks/utility/python-script
==============================================================================
##[error]Parameter 'toolPath' cannot be null or empty.
Finishing: Set Python manylinux variables
```
The error was because today I deleted a bunch of software from the VM
image. The task might fail if no Python versions are found in
$(Agent.ToolsDirectory).
### 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. -->
---------
Co-authored-by: Yi Zhang <zhanyi@microsoft.com>
### Description
Adding python3.12 support to ORT
### 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
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>
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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
