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Author SHA1 Message Date
Yi Zhang
12f426c63f
update size limit check of training GPU wheel (#21762) 
### Description
<!-- Describe your changes. -->



### Motivation and Context
The training wheel size limit should be 400M
 2024-08-21 09:30:05 +08:00
Tianlei Wu
7c93d5ded1
Upgrade pytorch_lightning to 2.3.3 to fix orttraining_amd_gpu_ci_pipeline (#21789) 
### Description
Upgrade pytorch_lightning to fix orttraining_amd_gpu_ci_pipeline
```
#24 1.838 WARNING: Ignoring version 1.6.0 of pytorch_lightning since it has invalid metadata:
#24 1.838 Requested pytorch_lightning==1.6.0 from cee67f4849/pytorch_lightning-1.6.0-py3-none-any.whl has invalid metadata: .* suffix can only be used with `==` or `!=` operators
#24 1.838     torch (>=1.8.*)
#24 1.838            ~~~~~~^
#24 1.838 Please use pip<24.1 if you need to use this version.
#24 1.838 ERROR: Ignored the following versions that require a different python version: 1.14.0 Requires-Python >=3.10; 1.14.0rc1 Requires-Python >=3.10; 1.14.0rc2 Requires-Python >=3.10; 2.1.0 Requires-Python >=3.10; 2.1.0rc1 Requires-Python >=3.10
#24 1.838 ERROR: Could not find a version that satisfies the requirement pytorch_lightning==1.6.0 (from versions: 0.0.2, 0.2, 0.2.2, 0.2.3, 0.2.4, 0.2.4.1, 0.2.5, 0.2.5.1, 0.2.5.2, 0.2.6, 0.3, 0.3.1, 0.3.2, 0.3.3, 0.3.4, 0.3.4.1, 0.3.5, 0.3.6, 0.3.6.1, 0.3.6.3, 0.3.6.4, 0.3.6.5, 0.3.6.6, 0.3.6.7, 0.3.6.8, 0.3.6.9, 0.4.0, 0.4.1, 0.4.2, 0.4.3, 0.4.4, 0.4.5, 0.4.6, 0.4.7, 0.4.8, 0.4.9, 0.5.0, 0.5.1, 0.5.1.2, 0.5.1.3, 0.5.2, 0.5.2.1, 0.5.3, 0.5.3.1, 0.5.3.2, 0.5.3.3, 0.6.0, 0.7.1, 0.7.3, 0.7.5, 0.7.6, 0.8.1, 0.8.3, 0.8.4, 0.8.5, 0.9.0, 0.10.0, 1.0.0, 1.0.1, 1.0.2, 1.0.3, 1.0.4, 1.0.5, 1.0.6, 1.0.7, 1.0.8, 1.1.0, 1.1.1, 1.1.2, 1.1.3, 1.1.4, 1.1.5, 1.1.6, 1.1.7, 1.1.8, 1.2.0rc0, 1.2.0rc1, 1.2.0rc2, 1.2.0, 1.2.1, 1.2.2, 1.2.3, 1.2.4, 1.2.5, 1.2.6, 1.2.7, 1.2.8, 1.2.9, 1.2.10, 1.3.0rc1, 1.3.0rc2, 1.3.0rc3, 1.3.0, 1.3.1, 1.3.2, 1.3.3, 1.3.4, 1.3.5, 1.3.6, 1.3.7, 1.3.7.post0, 1.3.8, 1.4.0rc0, 1.4.0rc1, 1.4.0rc2, 1.4.0, 1.4.1, 1.4.2, 1.4.3, 1.4.4, 1.4.5, 1.4.6, 1.4.7, 1.4.8, 1.4.9, 1.5.0rc0, 1.5.0rc1, 1.5.0, 1.5.1, 1.5.2, 1.5.3, 1.5.4, 1.5.5, 1.5.6, 1.5.7, 1.5.8, 1.5.9, 1.5.10, 1.6.0rc0, 1.6.0rc1, 1.6.0, 1.6.1, 1.6.2, 1.6.3, 1.6.4, 1.6.5, 1.7.0rc0, 1.7.0rc1, 1.7.0, 1.7.1, 1.7.2, 1.7.3, 1.7.4, 1.7.5, 1.7.6, 1.7.7, 1.8.0rc0, 1.8.0rc1, 1.8.0rc2, 1.8.0, 1.8.0.post1, 1.8.1, 1.8.2, 1.8.3, 1.8.3.post0, 1.8.3.post1, 1.8.3.post2, 1.8.4, 1.8.4.post0, 1.8.5, 1.8.5.post0, 1.8.6, 1.9.0rc0, 1.9.0, 1.9.1, 1.9.2, 1.9.3, 1.9.4, 1.9.5, 2.0.0rc0, 2.0.0, 2.0.1, 2.0.1.post0, 2.0.2, 2.0.3, 2.0.4, 2.0.5, 2.0.6, 2.0.7, 2.0.8, 2.0.9, 2.0.9.post0, 2.1.0rc0, 2.1.0rc1, 2.1.0, 2.1.1, 2.1.2, 2.1.3, 2.1.4, 2.2.0rc0, 2.2.0, 2.2.0.post0, 2.2.1, 2.2.2, 2.2.3, 2.2.4, 2.2.5, 2.3.0, 2.3.1, 2.3.2, 2.3.3, 2.4.0)
#24 1.838 ERROR: No matching distribution found for pytorch_lightning==1.6.0
```
 2024-08-19 12:58:22 -07:00
jingyanwangms
c018ba43ef
[Running CI] [TensorRT EP] support TensorRT 10.3-GA (#21742) 
### Description
- TensorRT 10.2.0.19 -> 10.3.0.26

### 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. -->
 2024-08-18 13:26:41 -07:00
Edward Chen
63e8849992
build_aar_package.py - Check that executable is present before trying to copy it. (#21730) 
Check that executable is present before trying to copy it.

Accommodate builds where we skip building the test executables.
 2024-08-16 11:21:09 -07:00
Yi Zhang
8a59b4dc4b
Move Python Training CUDA 12.2 pipeline to another pool. (#21745) 
### Description
<!-- Describe your changes. -->



### Motivation and Context
[Python Training CUDA 12.2
pipeline](https://dev.azure.com/aiinfra/Lotus/_build?definitionId=1308&_a=summary)
has been always cancelled by remote provider since Aug 2nd.
But other workflows with the same pool haven't this issue.
 It looks like there're some weird things in Azure devops.
It works by using another pool. In fact, the SKU is smaller than the
old.

### Verification
https://dev.azure.com/aiinfra/Lotus/_build?definitionId=1308&_a=summary
 2024-08-15 17:31:56 +08:00
Satya Kumar Jandhyala
6d8de1f7b8
Upgrade emsdk from 3.1.59 to 3.1.62 (#21421) 
### Description
Upgrade EM SDK to 3.1.62.



### Motivation and Context
The changes are required to clear wasm64 errors.
 2024-08-14 12:38:52 -07:00
Prathik Rao
e32e3575d8
pin pytorch lightning version for training CI (#21731) 
### Description
<!-- Describe your changes. -->

Pins pytorch-lightning package to version 2.3.3 since version >=2.4.0
requires torch > 2.1.0 which is not compatible with cu118.


### 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. -->

ORT 1.19 Release Preparation
 2024-08-13 20:04:56 -07:00
Yi Zhang
6db3d63add
move the A100 stage to main build (#21722) 
### Description
<!-- Describe your changes. -->



### Motivation and Context
We couldn't get enough A100 agent time to finish the jobs since today.
The PR makes the A100 job only runs in main branch to unblock other PRs
if it's not recovered in a short time.
 2024-08-13 22:48:58 +08:00
George Wu
a8462ffb61
enable qnn python arm64ec packaging (#21575) 
create the x64 qnn python package as arm64ec so it can be published
publicly.
 2024-08-12 22:43:17 -07:00
Yulong Wang
6ae7e02d34
Web CI: make multi-browser test job optional (#21669) 
### Description

This job is a little bit unstable. Make it optional to avoid blocking
other PRs before we revise it.
 2024-08-09 23:53:26 -07:00
Scott McKay
410ae94e9e
Use zipped xcframework in nuget package (#21663) 
### Description
<!-- Describe your changes. -->
The xcframework now uses symlinks to have the correct structure
according to Apple requirements. Symlinks are not supported by nuget on
Windows.

In order to work around that we can store a zip of the xcframeworks in
the nuget package.

### 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 nuget packaging build break
 2024-08-09 17:38:18 +10:00
Tianlei Wu
a46e49b439
Unblock migraphx and linux GPU training ci pipelines (#21662) 
### Description
* Fix migraphx build error caused by
https://github.com/microsoft/onnxruntime/pull/21598:
Add a conditional compile on code block that depends on ROCm >= 6.2.
Note that the pipeline uses ROCm 6.0.

Unblock orttraining-linux-gpu-ci-pipeline and
orttraining-ortmodule-distributed and orttraining-amd-gpu-ci-pipeline
pipelines:
* Disable a model test in linux GPU training ci pipelines caused by
https://github.com/microsoft/onnxruntime/pull/19470:
Sometime, cudnn frontend throws exception that cudnn graph does not
support a Conv node of keras_lotus_resnet3D model on V100 GPU.
Note that same test does not throw exception in other GPU pipelines. The
failure might be related to cudnn 8.9 and V100 GPU used in the pipeline
(Amper GPUs and cuDNN 9.x do not have the issue).
The actual fix requires fallback logic, which will take time to
implement, so we temporarily disable the test in training pipelines.
* Force install torch for cuda 11.8. (The docker has torch 2.4.0 for
cuda 12.1 to build torch extension, which it is not compatible cuda
11.8). Note that this is temporary walkround. More elegant fix is to
make sure right torch version in docker build step, that might need
update install_python_deps.sh and corresponding requirements.txt.
* Skip test_gradient_correctness_conv1d since it causes segment fault.
Root cause need more investigation (maybe due to cudnn frontend as
well).
* Skip test_aten_attention since it causes assert failure. Root cause
need more investigation (maybe due to torch version).
* Skip orttraining_ortmodule_distributed_tests.py since it has error
that compiler for torch extension does not support c++17. One possible
fix it to set the following compile argument inside setup.py of
extension fused_adam: extra_compile_args['cxx'] = ['-std=c++17'].
However, due to the urgency of unblocking the pipelines, just disable
the test for now.
* skip test_softmax_bf16_large. For some reason,
torch.cuda.is_bf16_supported() returns True in V100 with torch 2.3.1, so
the test was run in CI, but V100 does not support bf16 natively.
* Fix typo of deterministic

### 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. -->
 2024-08-08 19:44:15 -07:00
Scott McKay
d616025884
Match changes in gh-pages PR (#21628) 
### Description
<!-- Describe your changes. -->
Update to match #21627 and make the info for Split consistent.

As a Split that doesn't split anything is a no-op it doesn't seem
meaningful to call that limitation out in the docs.


### 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. -->
 2024-08-08 10:29:15 +10:00
Adrian Lizarraga
0acefc7988
[QNN EP] Update QNN SDK to 2.25 (#21623) 
### Description
- Update pipelines to use QNN SDK 2.25 by default
- Update ifdef condition to apply workaround for QNN LayerNorm
validation bug to QNN SDK 2.25 (as well as 2.24)



### Motivation and Context
Use the latest QNN SDK
 2024-08-06 09:08:48 -07:00
Yi Zhang
0d1da41ca8
Fix docker image layer caching to avoid redundant docker building and transient connection exceptions. (#21612) 
### Description
Improve docker commands to make docker image layer caching works.
It can make docker building faster and more stable.
So far, A100 pool's system disk is too small to use docker cache.
We won't use pipeline cache for docker image and remove some legacy
code.

### Motivation and Context
There are often an exception of
```
64.58 + curl https://nodejs.org/dist/v18.17.1/node-v18.17.1-linux-x64.tar.gz -sSL --retry 5 --retry-delay 30 --create-dirs -o /tmp/src/node-v18.17.1-linux-x64.tar.gz --fail
286.4 curl: (92) HTTP/2 stream 0 was not closed cleanly: INTERNAL_ERROR (err 2)
```
Because Onnxruntime pipeline have been sending too many requests to
download Nodejs in docker building.
Which is the major reason of pipeline failing now

In fact, docker image layer caching never works.
We can always see the scrips are still running
```
#9 [3/5] RUN cd /tmp/scripts && /tmp/scripts/install_centos.sh && /tmp/scripts/install_deps.sh && rm -rf /tmp/scripts
#9 0.234 /bin/sh: warning: setlocale: LC_ALL: cannot change locale (en_US.UTF-8)
#9 0.235 /bin/sh: warning: setlocale: LC_ALL: cannot change locale (en_US.UTF-8)
#9 0.235 /tmp/scripts/install_centos.sh: line 1: !/bin/bash: No such file or directory
#9 0.235 ++ '[' '!' -f /etc/yum.repos.d/microsoft-prod.repo ']'
#9 0.236 +++ tr -dc 0-9.
#9 0.236 +++ cut -d . -f1
#9 0.238 ++ os_major_version=8
....
#9 60.41 + curl https://nodejs.org/dist/v18.17.1/node-v18.17.1-linux-x64.tar.gz -sSL --retry 5 --retry-delay 30 --create-dirs -o /tmp/src/node-v18.17.1-linux-x64.tar.gz --fail
#9 60.59 + return 0
...
```

This PR is improving the docker command to make image layer caching
work.
Thus, CI won't send so many redundant request of downloading NodeJS.
```
#9 [2/5] ADD scripts /tmp/scripts
#9 CACHED

#10 [3/5] RUN cd /tmp/scripts && /tmp/scripts/install_centos.sh && /tmp/scripts/install_deps.sh && rm -rf /tmp/scripts
#10 CACHED

#11 [4/5] RUN adduser --uid 1000 onnxruntimedev
#11 CACHED

#12 [5/5] WORKDIR /home/onnxruntimedev
#12 CACHED
```

###Reference
https://docs.docker.com/build/drivers/

---------

Co-authored-by: Yi Zhang <your@email.com>
 2024-08-06 21:37:09 +08:00
Edward Chen
a5ce65d87a
Clean up some mobile package related files and their usages. (#21606) 
The mobile packages have been removed.
 2024-08-05 16:38:20 -07:00
Scott McKay
bcc01ac123
Updates to apple packaging (#21611) 
### Description
<!-- Describe your changes. -->
Add ability to test packaging without rebuilding every time.
Add ability to comment out some platforms/architectures without the
scripts to assemble the c/obj-c packages breaking.
Update a couple of commands to preserve symlinks.


### 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. -->
Make debugging packaging issues faster.
Creates correct package for mac-catalyst and doesn't require setting
symlinks via bash script.
 2024-08-06 08:50:56 +10:00
vraspar
88c811b638
Restructure MacOS framework package to fix malformed Framework errors (#21536) 
### Description

Refactor framework directory structure for MacOS packages

### 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. -->
Apple started enforcing specific [framework
structure](https://developer.apple.com/library/archive/documentation/MacOSX/Conceptual/BPFrameworks/Concepts/FrameworkAnatomy.html)
for MacOS packages. We need to change how we package for MacOS to follow
the guidelines

Fixes following issue: [Malformed
Framework](https://github.com/microsoft/onnxruntime-swift-package-manager/issues/19
)
 2024-08-04 12:47:16 -07:00
Julius Tischbein
1391354265
Adding CUDNN Frontend and use for CUDA NN Convolution (#19470) 
### Description
Added CUDNN Frontend and used it for NHWC convolutions, and optionally
fuse activation.

#### Backward compatible 
- For model existed with FusedConv, model can still run. 
- If ORT is built with cuDNN 8, cuDNN frontend will not be built into
binary. Old kernels (using cudnn backend APIs) are used.

#### Major Changes
- For cuDNN 9, we will enable cudnn frontend to fuse convolution and
bias when a provider option `fuse_conv_bias=1`.
- Remove the fusion of FusedConv from graph transformer for CUDA
provider, so there will not be FusedConv be added to graph for CUDA EP
in the future.
- Update cmake files regarding to cudnn settings. The search order of
CUDNN installation in build are like the following:
  * environment variable `CUDNN_PATH`
* `onnxruntime_CUDNN_HOME` cmake extra defines. If a build starts from
build.py/build.sh, user can pass it through `--cudnn_home` parameter, or
by environment variable `CUDNN_HOME` if `--cudnn_home` not used.
* cudnn python package installation directory like
python3.xx/site-packages/nvidia/cudnn
  * CUDA installation path

#### Potential Issues

- If ORT is built with cuDNN 8, FusedConv fusion is no longer done
automatically, so some model might have performance regression. If user
still wants FusedConv operator for performance reason, they can still
have multiple ways to walkaround: like use older version of onnxruntime;
or use older version of ORT to save optimized onnx, then run with latest
version of ORT. We believe that majority users have moved to cudnn 9
when 1.20 release (since the default in ORT and PyTorch is cudnn 9 for 3
months when 1.20 release), so the impact is small.
- cuDNN graph uses TF32 by default, and user cannot disable TF32 through
the use_tf32 cuda provider option. If user encounters accuracy issue
(like in testing), user has to set environment variable
`NVIDIA_TF32_OVERRIDE=0` to disable TF32. Need update the document of
use_tf32 later.

#### Follow ups
This is one of PRs that target to enable NHWC convolution in CUDA EP by
default if device supports it. There are other changes will follow up to
make it possible.
(1) Enable `prefer_nhwc` by default for device with sm >= 70. 
(2) Change `fuse_conv_bias=1` by default after more testing.
(3) Add other NHWC operators (like Resize or UpSample).

### Motivation and Context

The new CUDNN Frontend library provides the functionality to fuse
operations and provides new heuristics for kernel selection. Here it
fuses the convolution with the pointwise bias operation. On the [NVIDIA
ResNet50](https://pytorch.org/hub/nvidia_deeplearningexamples_resnet50/)
we get a performance boost from 49.1144 ms to 42.4643 ms per inference
on a 2560x1440 input (`onnxruntime_perf_test -e cuda -I -q -r 100-d 1 -i
'prefer_nhwc|1' resnet50.onnx`).

---------

Co-authored-by: Tianlei Wu <tlwu@microsoft.com>
Co-authored-by: Maximilian Mueller <maximilianm@nvidia.com>
 2024-08-02 15:16:42 -07:00
dependabot[bot]
3b73ef2bf7
Bump torch from 1.13.1 to 2.2.0 in /tools/ci_build/github/windows/eager (#21505) 
Bumps [torch](https://github.com/pytorch/pytorch) from 1.13.1 to 2.2.0.
<details>
<summary>Release notes</summary>
<p><em>Sourced from <a
href="https://github.com/pytorch/pytorch/releases">torch's
releases</a>.</em></p>
<blockquote>
<h2>PyTorch 2.2: FlashAttention-v2, AOTInductor</h2>
<h1>PyTorch 2.2 Release Notes</h1>
<ul>
<li>Highlights</li>
<li>Backwards Incompatible Changes</li>
<li>Deprecations</li>
<li>New Features</li>
<li>Improvements</li>
<li>Bug fixes</li>
<li>Performance</li>
<li>Documentation</li>
</ul>
<h1>Highlights</h1>
<p>We are excited to announce the release of PyTorch® 2.2! PyTorch 2.2
offers ~2x performance improvements to
<code>scaled_dot_product_attention</code> via FlashAttention-v2
integration, as well as AOTInductor, a new ahead-of-time compilation and
deployment tool built for non-python server-side deployments.</p>
<p>This release also includes improved torch.compile support for
Optimizers, a number of new inductor optimizations, and a new logging
mechanism called TORCH_LOGS.</p>
<p><strong>Please note that we are <a
href="https://redirect.github.com/pytorch/pytorch/issues/114602">deprecating
macOS x86 support</a>, and PyTorch 2.2.x will be the last version that
supports macOS x64.</strong></p>
<p>Along with 2.2, we are also releasing a series of updates to the
PyTorch domain libraries. More details can be found in the library
updates blog.</p>
<p>This release is composed of 3,628 commits and 521 contributors since
PyTorch 2.1. We want to sincerely thank our dedicated community for your
contributions. As always, we encourage you to try these out and report
any issues as we improve 2.2. More information about how to get started
with the PyTorch 2-series can be found at our <a
href="https://pytorch.org/get-started/pytorch-2.0/">Getting Started</a>
page.</p>
<p>Summary:</p>
<ul>
<li><code>scaled_dot_product_attention</code> (SDPA) now supports
FlashAttention-2, yielding around 2x speedups compared to previous
versions.</li>
<li>PyTorch 2.2 introduces a new ahead-of-time extension of
TorchInductor called AOTInductor, designed to compile and deploy PyTorch
programs for non-python server-side.</li>
<li><code>torch.distributed</code> supports a new abstraction for
initializing and representing ProcessGroups called device_mesh.</li>
<li>PyTorch 2.2 ships a standardized, configurable logging mechanism
called TORCH_LOGS.</li>
<li>A number of torch.compile improvements are included in PyTorch 2.2,
including improved support for compiling Optimizers and improved
TorchInductor fusion and layout optimizations.</li>
<li>Please note that we are deprecating macOS x86 support, and PyTorch
2.2.x will be the last version that supports macOS x64.</li>
<li><code>torch.ao.quantization</code> now offers a prototype
<code>torch.export</code> based flow</li>
</ul>
<!-- raw HTML omitted -->
</blockquote>
<p>... (truncated)</p>
</details>
<details>
<summary>Commits</summary>
<ul>
<li><a
href="8ac9b20d4b"><code>8ac9b20</code></a>
Run docker release build on final tag (<a
href="https://redirect.github.com/pytorch/pytorch/issues/117131">#117131</a>)
(<a
href="https://redirect.github.com/pytorch/pytorch/issues/117182">#117182</a>)</li>
<li><a
href="2490352430"><code>2490352</code></a>
Fix cuInit test on Windows (<a
href="https://redirect.github.com/pytorch/pytorch/issues/117095">#117095</a>)</li>
<li><a
href="3a44bb713f"><code>3a44bb7</code></a>
[CI] Test that cuInit is not called during import (<a
href="https://redirect.github.com/pytorch/pytorch/issues/117043">#117043</a>)</li>
<li><a
href="1c8ba3847d"><code>1c8ba38</code></a>
[CI] Use jemalloc for CUDA builds (<a
href="https://redirect.github.com/pytorch/pytorch/issues/116900">#116900</a>)
(<a
href="https://redirect.github.com/pytorch/pytorch/issues/116988">#116988</a>)</li>
<li><a
href="96d2ddbafe"><code>96d2ddb</code></a>
Store user model to simplify
ONNXProgram.{adapt_torch_*,<strong>call</strong>} APIs (<a
href="https://redirect.github.com/pytorch/pytorch/issues/1152">#1152</a>...</li>
<li><a
href="738b4a560a"><code>738b4a5</code></a>
Update ONNX's IO Adapter to support FakeTensor with ExportedProgram (<a
href="https://redirect.github.com/pytorch/pytorch/issues/114407">#114407</a>)...</li>
<li><a
href="4cf10bf4dc"><code>4cf10bf</code></a>
[Cherry-pick] [Quant] [PT2] Enable batchnorm in
_move_exported_model_to_eval ...</li>
<li><a
href="7e97e4b4b6"><code>7e97e4b</code></a>
[AARCH64] Fall back to GEMM if mkldnn_matmul fails (<a
href="https://redirect.github.com/pytorch/pytorch/issues/115936">#115936</a>)
(<a
href="https://redirect.github.com/pytorch/pytorch/issues/116666">#116666</a>)</li>
<li><a
href="1a3e3c7cff"><code>1a3e3c7</code></a>
[CUDA] baddmm should fall back to addmm for batch=1 (<a
href="https://redirect.github.com/pytorch/pytorch/issues/114992">#114992</a>)
(<a
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 2024-08-01 04:28:43 -07:00
vraspar
07d3be5b0e
CoreML: Add ML Program Split Op (#21456) 
### Description

Add support for Split Op


### Motivation and Context
Address operator gaps in high priority model.

---------

Co-authored-by: Scott McKay <skottmckay@gmail.com>
Co-authored-by: Edward Chen <18449977+edgchen1@users.noreply.github.com>
 2024-07-30 14:04:47 +10:00
Yifan Li
5d78b9a17b
[TensorRT EP] Update TRT OSS Parser to 10.2 (#21552) 
### Description
<!-- Describe your changes. -->
Update TRT OSS Parser to [latest 10.2-GA
branch](f161f95883)


### 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. -->
 2024-07-29 17:27:38 -07:00
Jian Chen
79537d0523
Remove tools/ci_build/github/android/run_nnapi_code_coverage.sh (#21371) 
### Description
Remove tools/ci_build/github/android/run_nnapi_code_coverage.sh

### Motivation and Context
This file is no longer needed
 2024-07-29 10:00:52 -07:00
Jian Chen
bc3713206d
Update QNN pipeline pool (#21482) 
### Description
Update QNN pipeline pool 



### Motivation and Context
Let all our pipelines are using the latest NDK version
 2024-07-29 10:00:21 -07:00
Yi Zhang
05cef469e8
Move on-device training packages publish step (#21539) 
### Description
Since the onedevice training cpu packaging has been a separated
pipeline, it's nuget package publishing step must be moved as well.

### Motivation and Context
Fixes the exception in Nuget Publishing Packaging Pipeline caused by
#21485
 2024-07-29 09:59:46 -07:00
Jian Chen
7e23212de9
Delete tools/ci_build/github/azure-pipelines/win-gpu-ci-pipeline.yml (#21529) 
### Description
Delete tools/ci_build/github/azure-pipelines/win-gpu-ci-pipeline.yml


### Motivation and Context
This CI pipeline has been divided into 4 different pipeline.
 2024-07-27 15:58:12 -07:00
maggie1059
10b4a3b90b
Fix conda failure for onnxruntime-directml (#21526) 
The change in #21005 works for directly building wheels with `build.py`,
but ort-nightly-directml wheels, as well as the 1.18.1 release of the
onnxruntime-directml python wheel, still do not work with conda since
they're built from the `py-win-gpu.yml` pipeline, which uses
`install_third_party_deps.ps1` to set compile flags.
 2024-07-26 22:26:38 -07:00
Scott McKay
5af423c7c0
Set version and other info in the C# dll (#21517) 
### Description
<!-- Describe your changes. -->
Set version and other info in the Microsoft.ML.OnnxRuntime C# dll by
setting GenerateAssemblyInfo to true and passing in ORT version in the
CI.

Minor re-org of the order of properties so related things are grouped a
little better.

### 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. -->
#21475
 2024-07-27 13:22:57 +10:00
Jian Chen
7db7c4e5c8
Separating all GPU stages into different Pipelines (#21521) 
### Description
Separating all GPU stages into different Pipelines
 2024-07-26 14:54:45 -07:00
Prathik Rao
278f0f5cd2
disables qnn in ort training cpu pipeline (#21510) 
### Description
<!-- Describe your changes. -->

`enable_windows_arm64_qnn` and `enable_windows_x64_qnn` are true by
default but unnecessary for training. This change explicitly sets these
parameters to false for training 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. -->

ORT 1.19 Release Preparation
 2024-07-26 17:23:35 +08:00
Scott McKay
b0e1f7f798
CoreML: Aggregated changes to add all required ops for priority model (#21472) 
### Description
<!-- Describe your changes. -->
Add these changes to one PR to simplify checkin
- Add Concat (#21423)
- Add DepthToSpace (#21426)
- Add LeakyRelu (#21453)
- Add test scripts (#21427)
- Add ability to set coreml flags from python (#21434)


Other changes
- updated partitioning utils to support dropping constant initializers
from a ComputeCapability's inputs.
- noticed that the list of inputs to the coreml model was unexpectedly
long due to this
- we copy constant initializers to a CoreML model so don't need the
originals, and if they remain as inputs ORT can't free them as they
appear to be in use.

### 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. -->
 2024-07-26 08:29:33 +10:00
Scott McKay
3cdf4b917b
Fix Android CI Pipeline code coverage failure (#21504) 
### Description
<!-- Describe your changes. -->
Current failure is due to a version mismatch.

Use llvm-cov from the Android NDK instead of the system gcov so that the
version is correct.

Also comment out publishing to the Azure dashboard to simplify the
setup. The CI prints out the stats for review by developers.

### 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 CI pipeline
 2024-07-26 07:36:23 +10:00
Changming Sun
4167b68abf
Split ondevice training cpu packaging pipeline to a separated pipeline (#21485) 
### Description
Right now our "Zip-Nuget-Java-Nodejs Packaging Pipeline" is too big.
This OnDevice training part is independent of the others, so it can be
split out. Then our NPM Packaging pipeline will not depends on this
training stuff.

### Motivation and Context
Similar to #21235 

Also, this PR fixed a problem that: "NuGet_Test_Linux_Training_CPU" job
downloads artifacts from "onnxruntime-linux-x64" for getting customop
shared libs, but the job forget to declare it depends on the
"Linux_C_API_Packaging_CPU_x64" which produces the artifact. Such
problems can be hard to find when a pipeline goes big.
 2024-07-25 10:58:34 -07:00
Yifan Li
ebcb7075eb
Set CUDA12 as default in GPU packages (#21438) 
### Description
* Swap cuda version 11.8/12.2 in GPU CIs
* Set CUDA12 as default version in yamls of publishing nuget/python/java
GPU packages
* Suppress warnings as errors of flash_api.cc during ort win-build
 2024-07-25 10:17:16 -07:00
Adrian Lizarraga
eb9b377306
[QNN EP] Update to QNN SDK 2.24.0 (#21463) 
### Description
- Update pipelines to use QNN SDK 2.24 by default
- Update QNN_Nuget_Windows pipeline to build csharp solution without
mobile projects (fixes errors).
- Implement workaround for QNN 2.24 validation bug for LayerNorm ops
without an explicit bias input.
- Enable Relu unit test, which now passes due to the fact Relu is no
longer fused into QuantizeLinear for QNN EP.
- Fix bug where a negative quantization axis is not properly normalized
for per-channel int4 conv.



### Motivation and Context
Update QNN SDk.
 2024-07-24 10:17:12 -07:00
Changming Sun
b04adcc381
Update copy_strip_binary.sh: use "make install" instead (#21464) 
### Description
Before this change, copy_strip_binary.sh manually copies each file from
onnx runtime's build folder to an artifact folder. It can be hard when
dealing with symbolic link for shared libraries.
This PR will change the packaging pipelines to run "make install" first,
before packaging shared libs .


### Motivation and Context

Recently because of feature request #21281 , we changed
libonnxruntime.so's SONAME. Now every package that contains this shared
library must also contains libonnxruntime.so.1. Therefore we need to
change the packaging scripts to include this file. Instead of manually
construct the symlink layout, using `make install` is much easier and
will make things more consistent because it is a standard way of making
packages.

**Breaking change:**
After this change, our **inference** tarballs that are published to our
Github release pages will be not contain ORT **training** headers.
 2024-07-24 10:02:00 -07:00
Scott McKay
2580d935cb
CoreML: Add ML Program ConvTranspose (#21416) 
### Description
<!-- Describe your changes. -->
Add ML Program ConvTranspose
- some limitations to simplify the implementation for now
- some limitations due to flaky CoreML output

Added support for non-contiguous MLMultiArray output as we see that with
some unit tests when the CPU-only flag is not set (e.g. innermost dim
has min size of 16 but test output only has 8 values).
- support only one non-contiguous dim to keep it simple
- manually tested as we don't have a setup that can test objective-c
code
- test code is in model.mm and can be enabled via ifdef if we need to
validate any future 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. -->
Address operator gaps in high priority model.

---------

Co-authored-by: Edward Chen <18449977+edgchen1@users.noreply.github.com>
 2024-07-24 16:08:20 +10:00
Scott McKay
1df9aa2f08
CoreML: Add GridSample ML Program support (#21431) 
### Description
<!-- Describe your changes. -->
Add GridSample ML Program support

One combination of inputs has diffs between the pytorch generated unit
tests data and CoreML. Disabling until needed as investigation may take
a while.


### 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. -->
High priorities models
 2024-07-24 11:04:48 +10:00
George Wu
c65afcea55
fix python qnn pipelines issues (#21462) 
build_py_params wasn't plumbed through for python qnn pipelines. 
incorporate fixes for deprecated numpy version option from
https://github.com/microsoft/onnxruntime/pull/21459
 2024-07-23 15:54:44 -07:00
Changming Sun
f70215d4e6
Update C++ dependencies (#21410) 
1. Update google benchmark from 1.8.3 to 1.8.5
2. Update google test from commit in main branch to tag 1.15.0 
3. Update pybind11 from 2.12.0 to 2.13.1
4. Update pytorch cpuinfo to include the support for Arm Neoverse V2,
Cortex X4, A720 and A520.
5. Update re2 from 2024-05-01 to 2024-07-02
6. Update cmake to 3.30.1
7. Update Linux docker images
8. Fix a warning in test/perftest/ort_test_session.cc:826:37: error:
implicit conversion loses integer precision: 'streamoff' (aka 'long
long') to 'const std::streamsize' (aka 'const long')
[-Werror,-Wshorten-64-to-32]
 2024-07-23 10:00:36 -07:00
Scott McKay
0f1f3b7705
CoreML: ML Program Slice (#21433) 
### Description
<!-- Describe your changes. -->
Add support for Slice


### 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. -->
High priority models.
 2024-07-23 20:21:55 +10:00
mindest
5b9369e93c
Fix typos according to reviewdog report. (#21335) 
### Description
Fix typos based on reviewdog report but with some
exceptions/corrections.
 2024-07-22 13:37:32 -07:00
Jian Chen
4e75605eec
Replace inline pip install with pip install from requirements*.txt (#21106) 
### Description
Replace inline pip install with pip install from requirements*.txt



### Motivation and Context
so that CG can recognize

### Dependency

- [x] https://github.com/microsoft/onnxruntime/pull/21085
 2024-07-22 12:39:10 -07:00
Scott McKay
34cd2e8ed8
Add CoreML ML Program Resize (#21370) 
### Description
<!-- Describe your changes. -->
Add CoreML ML Program Resize
- refactor existing logic to try and simplify and share between
NeuralNetwork and MLProgram checks
- add handling for some new attributes
- antialias and axes - should have been done when setting the CoreML EP
max opset to 21

### 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. -->
Support priority models
 2024-07-20 09:35:05 +10:00
Changming Sun
9140d9b1ff
Update azure-kusto-data and azure-kusto-ingest (#21409) 
A vulnerability has been found in the Kusto SDK. We need to update it to
latest to address a security alert.
 2024-07-18 14:26:26 -07:00
Yifan Li
bb76ead96c
[TensorRT EP] support TensorRT 10.2-GA (#21395) 
### Description
<!-- Describe your changes. -->
* promote trt version to 10.2.0.19
* EP_Perf CI: clean config of legacy TRT<8.6, promote test env to
trt10.2-cu118/cu125
* skip two tests as Float8/BF16 are supported by TRT>10.0 but TRT CIs
are not hardware-compatible on these:
 ```
 1: [  FAILED  ] 2 tests, listed below:
 1: [  FAILED  ] IsInfTest.test_isinf_bfloat16
 1: [  FAILED  ] IsInfTest.test_Float8E4M3FN
 ```

### 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. -->
 2024-07-18 12:11:52 -07:00
kailums
1b38c05544
change ci docker image to rocm6.1 (#21296) 
### Description
<!-- Describe your changes. -->
There is a bug for kernel running on rocm6.0, so change ci docker image
to rocm6.1

For the torch installed in the docker image, change to rocm repo when it
is not 6.0 version.

### 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. -->
 2024-07-18 14:50:01 +08:00
vraspar
fa287042ca
Add ML Program support for transpose op (#21364) 
### Description
Add support for transpose op



### Motivation and Context
Enable support for Autodesk model
 2024-07-16 16:34:58 -07:00
vraspar
218301403d
Add ML Program support for basic activation ops (#21326) 
### Description

Add support for:

- Sigmoid
- Relu
- Tanh

### Motivation and Context
Enable support for Autodesk model
 2024-07-15 22:30:20 -07:00
George Wu
4005d12ed4
add vitisai ep build stage to Windows CPU Pipeline (#21361) 
We need to prevent VitisAI EP build breaks, add a stage in Windows CPU
CI Pipeline to build Vitis AI EP on Windows.
There are no external dependencies for builds. Tests have to be disabled
though as the EP has external SW/HW dependencies.
This will at least allow us to prevent build breaks which has happened
on multiple occasions recently.

tested
https://dev.azure.com/onnxruntime/onnxruntime/_build/results?buildId=1432346&view=results
and it seems to run fine.
 2024-07-15 19:34:08 -07:00