### 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
changelog</a>.</em></p>
<blockquote>
<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>
</ul>
</blockquote>
</details>
<details>
<summary>Commits</summary>
<ul>
<li><a
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>
</details>
<br />
[](https://docs.github.com/en/github/managing-security-vulnerabilities/about-dependabot-security-updates#about-compatibility-scores)
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
`@dependabot rebase`.
[//]: # (dependabot-automerge-start)
[//]: # (dependabot-automerge-end)
---
<details>
<summary>Dependabot commands and options</summary>
<br />
You can trigger Dependabot actions by commenting on this PR:
- `@dependabot rebase` will rebase this PR
- `@dependabot recreate` will recreate this PR, overwriting any edits
that have been made to it
- `@dependabot merge` will merge this PR after your CI passes on it
- `@dependabot squash and merge` will squash and merge this PR after
your CI passes on it
- `@dependabot cancel merge` will cancel a previously requested merge
and block automerging
- `@dependabot reopen` will reopen this PR if it is closed
- `@dependabot close` will close this PR and stop Dependabot recreating
it. You can achieve the same result by closing it manually
- `@dependabot show <dependency name> ignore conditions` will show all
of the ignore conditions of the specified dependency
- `@dependabot ignore this major version` will close this PR and stop
Dependabot creating any more for this major version (unless you reopen
the PR or upgrade to it yourself)
- `@dependabot ignore this minor version` will close this PR and stop
Dependabot creating any more for this minor version (unless you reopen
the PR or upgrade to it yourself)
- `@dependabot ignore this dependency` will close this PR and stop
Dependabot creating any more for this dependency (unless you reopen the
PR or upgrade to it yourself)
</details>
Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
### 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.
