Move ORT Training pipeline to github actions and enable CodeQL scan for the code(including inference code).
We will move all pull request pipelines to Github Actions.
### Description
Add a temporary path to RN 0.69.3 to update the boost url
### Motivation and Context
Fix the React-native CI until we update the RN to 0.70.15 or 0.73.3+
versions
Makes the QNN provider option `offload_graph_io_quantization` enabled by
default. It was previously disabled by default.
Enabling this option significantly decreases inference latency for many
models.
### Description
* Fix `NumericLimits<float>` that used infinity as max, which is not
consistent with `std::numeric_limits<float>::max()`
In Windows, (float)(1e+300) is used for INFINITY, which causes compiler
error in Visual Studio 2022 v17.12 Preview 5.
* Rename `NumericLimits<T>::Min` to Lowest to be consistent with
std::numeric_limits
* Fix topk implementation: use `NumericLimits<CudaT>` instead of
`NumericLimits<T>` in kernel. That could avoid defining a confusing
defintion of `NumericLimits<MLFloat16>` that returns half instead of
MLFloat16.
* Use CUDART_MAX_NORMAL_FP16 if possible. It sets bits value directly,
which is faster than converting float to half.
Note that NumericLimits does not support __nv_bfloat16 and _nv_fp8_e4m3
and __nv_fp8_e5m2 right now.
### Motivation and Context
https://github.com/microsoft/onnxruntime/issues/22728
### Description
The Web CI pipeline uses three different Windows machine pools:
1. onnxruntime-Win2022-webgpu-A10
2. onnxruntime-Win2022-VS2022-webgpu-A10
3. onnxruntime-Win-CPU-2022-web
This PR merges them together to reduce ongoing maintenance cost.
### Description
<!-- Describe your changes. -->
All three PRs are cherry-picked in this round:
1. [Refactor SkipLayerNorm and handle beta properly (#22862)
](https://github.com/microsoft/onnxruntime/pull/22862)
2. [[TensorRT EP] Exclude DDS ops from running on TRT
(#22875)](https://github.com/microsoft/onnxruntime/pull/22875)
3. [[QNN EP] QNN SDK 2.28.2 (#22844)
](https://github.com/microsoft/onnxruntime/pull/22844)
### 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. -->
---------
Signed-off-by: Liqun Fu <liqfu@microsoft.com>
Signed-off-by: Liqun Fu <liqun.fu@microsoft.com>
Co-authored-by: Chi Lo <54722500+chilo-ms@users.noreply.github.com>
Co-authored-by: liqun Fu <liqfu@microsoft.com>
Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
Co-authored-by: Adrian Lizarraga <adlizarraga@microsoft.com>
### Description
<!-- Describe your changes. -->
Commits to be added:
https://github.com/microsoft/onnxruntime/pulls?q=is%3Apr+label%3Arelease%3A1.20.1+sort%3Aupdated-desc
### 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: Kyle <92152685+idiskyle@users.noreply.github.com>
Co-authored-by: Scott McKay <skottmckay@gmail.com>
Co-authored-by: Hector Li <hecli@microsoft.com>
Co-authored-by: Adrian Lizarraga <adlizarraga@microsoft.com>
Co-authored-by: amarin16 <91211819+amarin16@users.noreply.github.com>
### Description
Our nightly CPU python package's name is "ort-nightly" instead of
"onnxruntime". It was because of some historical reasons. Tensorflow was
like that.
Now we would prefer to make them the same.
Do this change for all nightly python packages, including CPU,
GPU(CUDA), and maybe others.
### 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. -->
* Add in missing operators for llama run
* Add simplified layer norm ops
### Description
<!-- Describe your changes. -->
Adding additional supported operators into MIGraphX EP that are
supported in MIGraphX
### 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. -->
Allows for more models to be run through MIGraphX EP
### Description
Today, stable diffusion stage failed due to there's a upgrade in timm.
controlnet_aux depends on it.
And its latest version limit the timm version less than 0.6.7.
So upgrading controlnet_aux can solve it.
And controlnet_aux uses opencv-python-headless, pin
opencv-python-headless to 4.8.0.74 too.
### Motivation and Context
### Description
For no, CoreML only support run mlmodels on CPU/ALL, However, sometimes
CPU_GPU would be faster a lot.
We support the option to select different hardware to boost performance
in this PR.
### 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: Edward Chen <18449977+edgchen1@users.noreply.github.com>
### Description
Change the hipify step to remove the -roc option to hipify-perl. This
will prefer hipblas over rocblas. rocblas can still be called directly
such as in TunableOp.
### Motivation and Context
hip interfaces are preferred over roc for porting from cuda to hip.
Calling roc interfaces is meant for ROCm-specific enhancements or
extensions.
- Added a microbenchmark for the `LayerNormalization` MLFloat16 support
added in https://github.com/microsoft/onnxruntime/pull/22063.
- Updated the `LayerNormalization` MLFloat16 implementation to improve
the latency.
```
----------------------------------------------------------------------------------------------
Original MLFloat16 support Time CPU Iterations
----------------------------------------------------------------------------------------------
BM_LayerNormalization<MLFloat16, float>/1/real_time 15599 us 15625 us 47
BM_LayerNormalization<MLFloat16, float>/1/real_time 14714 us 14824 us 39
BM_LayerNormalization<MLFloat16, float>/1/real_time 14634 us 14688 us 50
----------------------------------------------------------------------------------------------
Updated MLFloat16 support Time CPU Iterations
----------------------------------------------------------------------------------------------
BM_LayerNormalization<MLFloat16, float>/1/real_time 7276 us 7254 us 84
BM_LayerNormalization<MLFloat16, float>/1/real_time 6820 us 6720 us 93
BM_LayerNormalization<MLFloat16, float>/1/real_time 6840 us 6882 us 84
```
1. Add python 3.13 to our python packaging pipelines
2. Because numpy 2.0.0 doesn't support thread free python, this PR also
upgrades numpy to the latest
3. Delete some unused files.
### Description
<!-- Describe your changes. -->
This PR further optimizes matmulnbits specially for iGPUs. The phi3 demo
becomes ~12 tokens/second from ~8 tokens on iGPUs.
Some todos:
1. Make the optimization more general, Remove the blockSize = 32
limitation.
2. Tune the parameter, such as workgroupSize, components size (currently
only support components = 1), to see the performance change.
Bumps [cookie](https://github.com/jshttp/cookie) and
[socket.io](https://github.com/socketio/socket.io). These dependencies
needed to be updated together.
Updates `cookie` from 0.4.2 to 0.7.2
<details>
<summary>Release notes</summary>
<p><em>Sourced from <a
href="https://github.com/jshttp/cookie/releases">cookie's
releases</a>.</em></p>
<blockquote>
<h2>v0.7.2</h2>
<p><strong>Fixed</strong></p>
<ul>
<li>Fix object assignment of <code>hasOwnProperty</code> (<a
href="https://redirect.github.com/jshttp/cookie/issues/177">#177</a>)
bc38ffd</li>
</ul>
<p><a
href="https://github.com/jshttp/cookie/compare/v0.7.1...v0.7.2">https://github.com/jshttp/cookie/compare/v0.7.1...v0.7.2</a></p>
<h2>0.7.1</h2>
<p><strong>Fixed</strong></p>
<ul>
<li>Allow leading dot for domain (<a
href="https://redirect.github.com/jshttp/cookie/issues/174">#174</a>)
<ul>
<li>Although not permitted in the spec, some users expect this to work
and user agents ignore the leading dot according to spec</li>
</ul>
</li>
<li>Add fast path for <code>serialize</code> without options, use
<code>obj.hasOwnProperty</code> when parsing (<a
href="https://redirect.github.com/jshttp/cookie/issues/172">#172</a>)</li>
</ul>
<p><a
href="https://github.com/jshttp/cookie/compare/v0.7.0...v0.7.1">https://github.com/jshttp/cookie/compare/v0.7.0...v0.7.1</a></p>
<h2>0.7.0</h2>
<ul>
<li>perf: parse cookies ~10% faster (<a
href="https://redirect.github.com/jshttp/cookie/issues/144">#144</a> by
<a href="https://github.com/kurtextrem"><code>@kurtextrem</code></a>
and <a
href="https://redirect.github.com/jshttp/cookie/issues/170">#170</a>)</li>
<li>fix: narrow the validation of cookies to match RFC6265 (<a
href="https://redirect.github.com/jshttp/cookie/issues/167">#167</a> by
<a href="https://github.com/bewinsnw"><code>@bewinsnw</code></a>)</li>
<li>fix: add <code>main</code> to <code>package.json</code> for rspack
(<a href="https://redirect.github.com/jshttp/cookie/issues/166">#166</a>
by <a
href="https://github.com/proudparrot2"><code>@proudparrot2</code></a>)</li>
</ul>
<p><a
href="https://github.com/jshttp/cookie/compare/v0.6.0...v0.7.0">https://github.com/jshttp/cookie/compare/v0.6.0...v0.7.0</a></p>
<h2>0.6.0</h2>
<ul>
<li>Add <code>partitioned</code> option</li>
</ul>
<h2>0.5.0</h2>
<ul>
<li>Add <code>priority</code> option</li>
<li>Fix <code>expires</code> option to reject invalid dates</li>
<li>pref: improve default decode speed</li>
<li>pref: remove slow string split in parse</li>
</ul>
</blockquote>
</details>
<details>
<summary>Commits</summary>
<ul>
<li><a
href="d19eaa1a2b"><code>d19eaa1</code></a>
0.7.2</li>
<li><a
href="bc38ffd0ea"><code>bc38ffd</code></a>
Fix object assignment of <code>hasOwnProperty</code> (<a
href="https://redirect.github.com/jshttp/cookie/issues/177">#177</a>)</li>
<li><a
href="cf4658f492"><code>cf4658f</code></a>
0.7.1</li>
<li><a
href="6a8b8f5a49"><code>6a8b8f5</code></a>
Allow leading dot for domain (<a
href="https://redirect.github.com/jshttp/cookie/issues/174">#174</a>)</li>
<li><a
href="58015c0b93"><code>58015c0</code></a>
Remove more code and perf wins (<a
href="https://redirect.github.com/jshttp/cookie/issues/172">#172</a>)</li>
<li><a
href="ab057d6c06"><code>ab057d6</code></a>
0.7.0</li>
<li><a
href="5f02ca8768"><code>5f02ca8</code></a>
Migrate history to GitHub releases</li>
<li><a
href="a5d591ce84"><code>a5d591c</code></a>
Migrate history to GitHub releases</li>
<li><a
href="51968f94b5"><code>51968f9</code></a>
Skip isNaN</li>
<li><a
href="9e7ca51ade"><code>9e7ca51</code></a>
perf(parse): cache length, return early (<a
href="https://redirect.github.com/jshttp/cookie/issues/144">#144</a>)</li>
<li>Additional commits viewable in <a
href="https://github.com/jshttp/cookie/compare/v0.4.2...v0.7.2">compare
view</a></li>
</ul>
</details>
<details>
<summary>Maintainer changes</summary>
<p>This version was pushed to npm by <a
href="https://www.npmjs.com/~blakeembrey">blakeembrey</a>, a new
releaser for cookie since your current version.</p>
</details>
<br />
Updates `socket.io` from 4.7.5 to 4.8.0
<details>
<summary>Release notes</summary>
<p><em>Sourced from <a
href="https://github.com/socketio/socket.io/releases">socket.io's
releases</a>.</em></p>
<blockquote>
<h2>socket.io-client@4.8.0</h2>
<h3>Features</h3>
<h4>Custom transport implementations</h4>
<p>The <code>transports</code> option now accepts an array of transport
implementations:</p>
<pre lang="js"><code>import { io } from "socket.io-client";
import { XHR, WebSocket } from "engine.io-client";
<p>const socket = io({
transports: [XHR, WebSocket]
});
</code></pre></p>
<p>Here is the list of provided implementations:</p>
<table>
<thead>
<tr>
<th>Transport</th>
<th>Description</th>
</tr>
</thead>
<tbody>
<tr>
<td><code>Fetch</code></td>
<td>HTTP long-polling based on the built-in <code>fetch()</code>
method.</td>
</tr>
<tr>
<td><code>NodeXHR</code></td>
<td>HTTP long-polling based on the <code>XMLHttpRequest</code> object
provided by the <code>xmlhttprequest-ssl</code> package.</td>
</tr>
<tr>
<td><code>XHR</code></td>
<td>HTTP long-polling based on the built-in <code>XMLHttpRequest</code>
object.</td>
</tr>
<tr>
<td><code>NodeWebSocket</code></td>
<td>WebSocket transport based on the <code>WebSocket</code> object
provided by the <code>ws</code> package.</td>
</tr>
<tr>
<td><code>WebSocket</code></td>
<td>WebSocket transport based on the built-in <code>WebSocket</code>
object.</td>
</tr>
<tr>
<td><code>WebTransport</code></td>
<td>WebTransport transport based on the built-in
<code>WebTransport</code> object.</td>
</tr>
</tbody>
</table>
<p>Usage:</p>
<table>
<thead>
<tr>
<th>Transport</th>
<th>browser</th>
<th>Node.js</th>
<th>Deno</th>
<th>Bun</th>
</tr>
</thead>
<tbody>
<tr>
<td><code>Fetch</code></td>
<td>✅</td>
<td>✅ (1)</td>
<td>✅</td>
<td>✅</td>
</tr>
<tr>
<td><code>NodeXHR</code></td>
<td></td>
<td>✅</td>
<td>✅</td>
<td>✅</td>
</tr>
<tr>
<td><code>XHR</code></td>
<td>✅</td>
<td></td>
<td></td>
<td></td>
</tr>
<tr>
<td><code>NodeWebSocket</code></td>
<td></td>
<td>✅</td>
<td>✅</td>
<td>✅</td>
</tr>
<tr>
<td><code>WebSocket</code></td>
<td>✅</td>
<td>✅ (2)</td>
<td>✅</td>
<td>✅</td>
</tr>
<tr>
<td><code>WebTransport</code></td>
<td>✅</td>
<td>✅</td>
<td></td>
<td></td>
</tr>
</tbody>
</table>
<p>(1) since <a
href="https://nodejs.org/api/globals.html#fetch">v18.0.0</a>
(2) since <a
href="https://nodejs.org/api/globals.html#websocket">v21.0.0</a></p>
<p>Added in <a
href="f4d898ee96">f4d898e</a>
and <a
href="b11763beec">b11763b</a>.</p>
<h4>Test each low-level transports</h4>
<p>When setting the <code>tryAllTransports</code> option to
<code>true</code>, if the first transport (usually, HTTP long-polling)
fails, then the other transports will be tested too:</p>
<pre lang="js"><code>import { io } from "socket.io-client";
</tr></table>
</code></pre>
</blockquote>
<p>... (truncated)</p>
</details>
<details>
<summary>Commits</summary>
<ul>
<li><a
href="d0fc720420"><code>d0fc720</code></a>
chore(release): socket.io@4.8.0</li>
<li><a
href="4a0555c671"><code>4a0555c</code></a>
chore(release): socket.io-client@4.8.0</li>
<li><a
href="2b60df18a8"><code>2b60df1</code></a>
chore(release): engine.io@6.6.1</li>
<li><a
href="d4cb375856"><code>d4cb375</code></a>
ci: ignore tests when publishing to npm</li>
<li><a
href="c251ae7ba7"><code>c251ae7</code></a>
chore(release): engine.io-client@6.6.1</li>
<li><a
href="8a2f5a3da0"><code>8a2f5a3</code></a>
fix(eio-client): move 'offline' event listener at the top</li>
<li><a
href="b04fa64365"><code>b04fa64</code></a>
fix(sio): allow to join a room in a middleware (uws)</li>
<li><a
href="7085f0e3e4"><code>7085f0e</code></a>
refactor(sio-client): mangle private attributes</li>
<li><a
href="4f66708210"><code>4f66708</code></a>
chore(sio-client): use babel loose mode when transpiling classes</li>
<li><a
href="1a95db2145"><code>1a95db2</code></a>
chore(sio-client): add a script to compute the bundle size</li>
<li>Additional commits viewable in <a
href="https://github.com/socketio/socket.io/compare/socket.io@4.7.5...socket.io@4.8.0">compare
view</a></li>
</ul>
</details>
<br />
Dependabot will resolve any conflicts with this PR as long as you don't
alter it yourself. You can also trigger a rebase manually by commenting
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### Description
<!-- Describe your changes. -->
With this optimization, 96 MultiHeadAttention|Transpose ops in phi3
disappear. Phi3 becomes 113 tokens from 107 tokens on my dGPUs.
The optimization mainly skips the transpose op if one of the transposed
dims is 1. Reshape is enough.
### Description
Request and create DML EP and its data transfer.
Use to copy on device.
The PR includes changes to fix issues in DML provider.
### Motivation and Context
This enables Lora users to run it with DML which is important for GenAI.
Co-authored-by: @PatriceVignola
---------
Co-authored-by: Patrice Vignola <vignola.patrice@gmail.com>
### Description
Support OV2024.4
Refactor tensor initialization check for external weights
Support loading OV Config
OVEP: Tensor Caching fix, Fix accuracy issues
Refactor device memory implementation to make it more generic
### Motivation and Context
The changes are required to fix accuracy issues, support loading of OV
config, support OV2024.4
---------
Co-authored-by: Eric Crawford <eric.r.crawford@intel.com>
Co-authored-by: saurabhkale17 <saurabh1.kale@intel.com>
Co-authored-by: Javier E. Martinez <javier.e.martinez@intel.com>
Co-authored-by: sfatimar <sahar.fatima@intel.com>
Co-authored-by: ankitm3k <ankit.maheshkar@intel.com>
Co-authored-by: Preetha Veeramalai <preetha.veeramalai@intel.com>
Co-authored-by: n1harika <niharika.sathish@intel.com>
Co-authored-by: jatinwadhwa921 <110383850+jatinwadhwa921@users.noreply.github.com>
- Work around Xcode 16 iOS test build issue: `error: Multiple commands produce '.../PlugIns'`.
- Fix link error in iOS static framework test.
- Update build.py to check for the right kind of build before running iOS tests on the simulator.
- Update Xcode 16 build images to 'macos-15' because that's the only image that will have Xcode 16 soon. See https://github.com/actions/runner-images/issues/10703.
### Description
Add a new pipeline to publish ROCM package to ADO
### 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. -->
### Test Link
https://dev.azure.com/aiinfra/Lotus/_build?definitionId=1615
### Description
This PR fixes a bug when the ONNX checker is called while exporting
Whisper for FP16 CUDA with optional flags.
### Motivation and Context
Sometimes, the ONNX checker raises an error depending on the optional
flags passed. By wrapping the ONNX checker in a try-except, the
conversion can continue even if the checker fails.
### Description
Add additonal gfx targets for AMD GPU support
### Motivation and Context
Required to integrate mainline onnxruntime support for AMD GPUs
---------
Co-authored-by: Stefan Sokolovic <stsokolo@amd.com>
Co-authored-by: Jeff Daily <jeff.daily@amd.com>
[TensorRT-Model-Optimizer](https://github.com/NVIDIA/TensorRT-Model-Optimizer)
have a implementation for INT4 AWQ. Adding the support in onnxruntime
tools to quantized the models with TensorRT-Model-Optimizer
### Description
* Add digital signature to dll files in jar files.
* Jar file names: onnxruntime-{version}.jar,
onnxruntime_gpu-{version}.jar
### Motivation and Context
#19204
### Description
<!-- Describe your changes. -->
flatbuffers always write data in LE and it is automatically traslated
to/from BE as needed,
but only if we use proper accessors. This would work for shape.
However, we store parameters as bytes, so we need to swap bytes as
needed for BE.
### Motivation and Context
Address https://github.com/microsoft/onnxruntime/issues/22364
### Description
Aallows alpha, beta and rc version releases to Maven for Android
artifacts.
### Motivation and Context
Helpful to release rc versions or test artifacts to Maven for testing.
For example, a new QNN android package is being released and it will be
nice to test the RC version for dependencies before release
## Future Work
Allow RC version for all Maven artifacts.
