### Description
* Pass topk_scores to beam scorer in slow topk path.
* Add an env variable `ORT_BEAM_SEARCH_USE_FAST_TOPK` to enable/disable fast topk.
* Add a test case for slow topk path.
### Motivation and Context
This bug was introduced in
https://github.com/microsoft/onnxruntime/pull/16272
Beam search uses fast cuda kernel when number of beams <= 32. When beam
size is larger than that threshold, we use another code path (slower
cuda kernel) to get topk. In such `slow topk path`, topk_scores shall be
passed to beam scorer but it is not.
This bug will cause incorrect result when num_beams > 32. It was not
found previously since such large beam size is rarely used.
### Description
This change implements FlashAttention 2 for the webgpu EP for the MHA
operator.
Numbers from Alderlake device show a 2.2x speed up for prefill, which
considering that Attention is 50% of prefill phase (other 50% being
MatMul) implies 4x speed up for Attention with this implementation. This
is inline with the expected perf gain of 2-4x with FlashAttention over
regular attention.
```
Baseline
PS C:\onnxruntime> C:\model_benchmark\model_benchmark.exe -i C:\Phi-3.5-mini-instruct-onnx-web\Phi-3.5-mini-instruct-onnx-web\ -l 1000
Batch size: 1, prompt tokens: 1001, tokens to generate: 128
Prompt processing (time to first token):
avg (us): 9.54997e+06 <<<<<
avg (tokens/s): 104.817
p50 (us): 9.49218e+06
stddev (us): 251442
n: 5 * 1001 token(s)
------
With FlashAttention 2
PS C:\onnxruntime> C:\model_benchmark\model_benchmark.exe -i C:\Phi-3.5-mini-instruct-onnx-web\Phi-3.5-mini-instruct-onnx-web\ -l 1000
Batch size: 1, prompt tokens: 1001, tokens to generate: 128
Prompt processing (time to first token):
avg (us): 4.27937e+06 <<<<<
avg (tokens/s): 233.913
p50 (us): 4.27687e+06
stddev (us): 5344.1
n: 5 * 1001 token(s)
```
### Motivation and Context
On integrated GPUs memory bandwidth is premium, Flash attention makes
softmax computation (and therefore output attention vector computation)
a running operation instead of maintaining full QKt attention scores in
memory. As a result, we see significant improvements in prefill speed -
200% speed up measured here.
This change uses techniques from co-operative matrix multiply to use
registers from a subgroup for fast in register matrix multiply. Without
the co-operative matrix multiply technique ALD showed about 6.0s prefill
time.
Tested on ALD/TGL intel integrated and Nvidia 4070.
### Future Work
- Fine tuning and profiling optimizations.
- Current implement is for prefill only, a generation phase optimized
FA2 implementation is possible, however attention is a tiny part of the
generation phase.
### Description
These changes are done to ensure that weight sharing happens between two model using session context option ep_weight_sharing.
Key changes introduced in this feature are:
Creating a shared context between two models Extracting external constant initializers and re labelling them back as
inputs to the model to allow weight loading in the direct blob. Creating EP Context Nodes when Subgraph partitioning is happening.
### Motivation and Context
This change was required to ensure that LLM with prefill and kvcache models can use the same share
The change was also required to ensure EP Context nodes can be formed even when model is being subgraph partitioned.
---------
Co-authored-by: jatinwadhwa921 <jatin.wadhwa@intel.com>
Co-authored-by: jatinwadhwa921 <110383850+jatinwadhwa921@users.noreply.github.com>
Co-authored-by: saurabh <saurabh1.kale@intel.com>
Co-authored-by: TejalKhade28 <tejal.khade@intel.com>
Co-authored-by: sfatimar <sahar.fatima@intel.com>
Co-authored-by: Javier E. Martinez <javier.e.martinez@intel.com>
Co-authored-by: Preetha Veeramalai <preetha.veeramalai@intel.com>
Co-authored-by: Eric Crawford <eric.r.crawford@intel.com>
### Description
BeamSearchTest.DummyT5WithSequenceInputIds failed in Windows due to
early stopping triggered. The cause is state.early_stopping_ is
interpreted as true in cuda kernel at some point, however printf still
show its value is false. The root cause is unknown.
Update the code to use early_stopping as template parameter seems walk
around the issue.
Other changes:
* Add some debug code (will not be built into binary unless
DEBUG_GENERATION is fined) to assist debugging beam search scorer in
CUDA.
* Enable DummyT5WithSequenceInputIds test in CI. This test was not run
in Windows CUDA CI pipeline previously.
### Motivation and Context
Fix a unit test BeamSearchTest.DummyT5WithSequenceInputIds failure in
Windows.
### Description
<!-- Describe your changes. -->
This PR is a follow-up to
https://github.com/microsoft/onnxruntime/pull/23488 and partially
improves upon https://github.com/microsoft/onnxruntime/issues/23403. It
does the following:
- Prevents unnecessary cache shader recompilation for 'nearest' resize
operation.
- Fixes precision (offset-by-one) errors with asymmetric coordinate
transform. When running the Kokoro TTS model, values for the
`/decoder/decoder/generator/f0_upsamp/Resize_output_0` results in
differences at the end bounds due to precision issues when dividing
21600 by 72 (should be 300, but seemingly results in 299.999, which
causes issues when flooring)
### Motivation and Context
<!-- - Why is this change required? What problem does it solve?
- If it fixes an open issue, please link to the issue here. -->
I did a deep dive over the weekend to try fix Kokoro TTS on WebGPU and
found that the above node had a large difference. Thinking this was a
major issue, I spent some time fixing it. Turns out, it only happens for
a small number of values, leading to high maximum error, but most values
are correct (as seen here).
BEFORE:
```
[/decoder/decoder/generator/f0_upsamp/Resize_output_0] atol: 78.6640682220459 | rtol: 24.13991587587724 | avgDiff: 0.009967932171121087 | medianDiff: 0.000030517578125
```
AFTER:
```
[/decoder/decoder/generator/f0_upsamp/Resize_output_0] atol: 0.0011138916015625 | rtol: 0.0020059924232260704 | avgDiff: 0.00008570214675873825 | medianDiff: 0.000030517578125
```
So, although it has a very small impact on the final output (waveform),
this bug could appear with other models in a more severe way.
BEFORE:
```
[waveform] atol: 0.04784199967980385 | rtol: 1366.0462001093495 | avgDiff: 0.0009544936942737713 | medianDiff: 0.00015346752479672432
```
AFTER:
```
[waveform] atol: 0.04775865003466606 | rtol: 1354.7002460360852 | avgDiff: 0.000954830244055033 | medianDiff: 0.00015274062752723694
```
### 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
The quantization calibrators have `execution_providers` attributes but
there is no way for a user to provide their own providers when using the
`quantize` or `quantize_static` functions. This PR adds a
`calibration_providers` parameter to allow users to specify the
execution providers to use during calibration. It is helpful when
quantizing large models which are slow to calibrate on the CPU.
- Chose `calibration_providers` as the name since there is the
docstrings refer to another `execution_provider`
169917b1e7/onnxruntime/python/tools/quantization/quantize.py (L204)169917b1e7/onnxruntime/python/tools/quantization/quantize.py (L415)
which are not present anywhere in the code.
- Can change the name to something else if needed like
calibrator_providers, and/or make it into a string instead of a
providers list.
Add session option to enable user loading model with external data from memory buffer. User want to set the folder path for the external data files.
### Description
For some cases user load the model from memory buffer, but they can't load the external files into memory. They need to have a way to set the folder path for the external data files so that Ort can figure out the external data location.
### Description
Convert output_padding attribute from 1D to 2D convtranspose
### 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. -->
https://github.com/microsoft/onnxruntime/issues/23403
Remove the auto-generated cgmanifest.json. Because now we can get the
same information from vcpkg.
Also, remote some outdated entries in the main cgmanifest.json file.
### Description
1. Enable VCPKG flag in Windows CPU CI build pipelines.
2. Increased the min supported cmake version from 3.26 to 3.28. Because
of it, drop the support for the old way of finding python by
"find_package(PythonLibs)". Therefore, in build.py we no longer set
"PYTHON_EXECUTABLE" cmake var when doing cmake configure.
3. Added "xnnpack-ep" as a feature for ORT's vcpkg config.
4. Added asset cache support for ORT's vcpkg build
5. Added VCPKG triplet files for Android build.
6. Set VCPKG triplet to "universal2-osx" if CMAKE_OSX_ARCHITECTURES was
found in cmake extra defines.
7. Removed a small piece of code in build.py, which was for support CUDA
version < 11.8.
8. Fixed an issue that CMAKE_OSX_ARCHITECTURES sometimes got specified
twice when build.py invoked cmake.
9. Added more model tests to Android build. After this change, we will
test all ONNX versions instead of just the latest one.
10. Fixed issues that are related to build.py's "--build_nuget"
parameter. Also, enable the flag in most Windows CPU CI build jobs.
11. Removed a restriction in build.py that disallowed cross-compiling
Windows ARM64 nuget package on Windows x86.
### Motivation and Context
Adopt vcpkg.
Bumps [lintrunner](https://github.com/suo/lintrunner) from 0.12.5 to
0.12.7.
<details>
<summary>Changelog</summary>
<p><em>Sourced from <a
href="https://github.com/suo/lintrunner/blob/main/CHANGELOG.md">lintrunner's
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<h2>[0.12.7] - 2024-12-05</h2>
<h3>Bug Fixes</h3>
<ul>
<li>Build x86_64 wheels for Windows (<a
href="a4d6b74693">a4d6b74</a>)</li>
<li>Fix <a href="https://doc.rust-lang.org/clippy/">Clippy</a>
violatoins (<a
href="05ff6431bb">05ff643</a>)</li>
<li>Fetch all commit history to fix MacOS builds (<a
href="3770be65ee">3770be6</a>)</li>
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<summary>Commits</summary>
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href="1b70da01a6"><code>1b70da0</code></a>
chore(release): prep for 0.12.7</li>
<li><a
href="3770be65ee"><code>3770be6</code></a>
[CI] Fetch full commit history (<a
href="https://redirect.github.com/suo/lintrunner/issues/81">#81</a>)</li>
<li><a
href="b2482aff48"><code>b2482af</code></a>
[CI] Use <code>actions/checkout@v4</code> (<a
href="https://redirect.github.com/suo/lintrunner/issues/80">#80</a>)</li>
<li><a
href="05ff6431bb"><code>05ff643</code></a>
Fix clippy violations (<a
href="https://redirect.github.com/suo/lintrunner/issues/79">#79</a>)</li>
<li><a
href="1be20c6b8f"><code>1be20c6</code></a>
chore(release): prep for 0.12.6</li>
<li><a
href="a4d6b74693"><code>a4d6b74</code></a>
fix(build): build x86_64 wheels for Windows (<a
href="https://redirect.github.com/suo/lintrunner/issues/73">#73</a>)</li>
<li>See full diff in <a
href="https://github.com/suo/lintrunner/compare/v0.12.5...v0.12.7">compare
view</a></li>
</ul>
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### Description
The Node JS Samples included in the repository have outdated package
references that are broken, which are fixed in this PR.
### Motivation and Context
The samples included in this repository should just work, but sadly do
not. The reason is that they are using very outdated references for the
npm modules. This fix updates the dependencies to the current
onnxruntime-node, which fixes the samples. Also adds a small update to
the .gitignore to exclude the node_modules directories in the samples
directory, which keeps the local repo changelist cleaner.
### Description
Remove MSVC warnings 4244, 4267 from the list of disabled warnings in
cmake.
Fix the code that generates the warnings so that it no longer does.
### Motivation and Context
This makes onnxruntime_providers_openvino.dll pass BinSkim analysis.
Without this change BinSkim complains about the disabled warnings.
## Description
Follow-up for #23383 and #23474
* Adds android BrowserStack test back in
* Modifies MAUI csproj file to build into an APK
### Motivation and Context
There were 2 issues with the previous PRs:
1. The updated MAUI .csproj file configuration failed when building to
iOS and MacCatalyst. This caused problems in the packaging pipeline
because we build all C# projects in the .soln file in the packaging
pipeline. Removed the Mac & iOS build targets for now
3. The previous MAUI .csproj file configuration did not build into an
APK. It was missing the `<OutputType>` XAML tag and the Android package
type XAML tag.
### Description
(1) Remove `if (CMAKE_CUDA_COMPILER_VERSION VERSION_GREATER_EQUAL 11)`
since build requires cuda >= 11.4.
(2) Add sm_86 and sm_89 since we generate SASS code for specified cuda
architectures only. This change could support popular consumer GPUs
(like RTX 30X0 and RTX 40X0).
(3) Add sm_120 to support Blackwell GPUs (like RTX 50X0 etc).
(4) Add `-Xfatbin=-compress-all` to reduce wheel size. When
CMAKE_CUDA_ARCHITECTURES is not specified, the linux wheel size built by
CUDA 12.8 is reduced 8% (from 324MB to 299MB).
### Motivation and Context
To support popular consumer GPUs (RTX 30x0, 40x0, 50x0) in the default
setting. Reduce binary size.
Note that the default sm settings does not impact official released
binary. ORT official released binary are built with augmentation like
CMAKE_CUDA_ARCHITECTURES=75;80;90, which has both SASS (real) and PTX
(virtual) by default. See
https://cmake.org/cmake/help/latest/prop_tgt/CUDA_ARCHITECTURES.html for
more info.
### Description
Makes the QNN provider option `offload_graph_io_quantization` enabled by
default. It was previously disabled by default.
### Motivation and Context
Enabling this option significantly decreases inference latency for many
models.
### Description
* Update rocm to 6.3.2;
* Remove dependency on cupy (which does not support rocm 6.3 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. -->
…-andriod-e2e-test-job.yml
### 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. -->
