### Description
Updates the default QNN SDK version to 2.19.2.240210.
### Motivation and Context
Build and test the latest version of QNN SDK in our pipelines.
### Description
Some test thresholds that previously worked in T4 GPU does not work
anymore. The reason is current pipeline uses A10, and TF32 is enabled by
default.
Disable TF32 in Linux GPU CI Pipeline in testing to avoid such random
test failure.
### Motivation and Context
Linux Test has random failure at tests:
ProviderOptionsTest > testCUDAOptions() FAILED
org.opentest4j.AssertionFailedError: array contents differ at index
[446], expected: <0.0419757> but was: <0.041948937>
at
app//org.junit.jupiter.api.AssertionFailureBuilder.build(AssertionFailureBuilder.java:151)
at
app//org.junit.jupiter.api.AssertionFailureBuilder.buildAndThrow(AssertionFailureBuilder.java:132)
at
app//org.junit.jupiter.api.AssertArrayEquals.failArraysNotEqual(AssertArrayEquals.java:440)
at
app//org.junit.jupiter.api.AssertArrayEquals.assertArrayEquals(AssertArrayEquals.java:290)
at
app//org.junit.jupiter.api.AssertArrayEquals.assertArrayEquals(AssertArrayEquals.java:123)
at
app//org.junit.jupiter.api.AssertArrayEquals.assertArrayEquals(AssertArrayEquals.java:119)
at
app//org.junit.jupiter.api.Assertions.assertArrayEquals(Assertions.java:1360)
at
app//ai.onnxruntime.providers.ProviderOptionsTest.runProvider(ProviderOptionsTest.java:99)
at
app//ai.onnxruntime.providers.ProviderOptionsTest.testCUDAOptions(ProviderOptionsTest.java:43)
org.opentest4j.AssertionFailedError: array contents differ at index [6],
expected: <0.0225981> but was: <0.022587791>
at
app//org.junit.jupiter.api.AssertionFailureBuilder.build(AssertionFailureBuilder.java:151)
at
app//org.junit.jupiter.api.AssertionFailureBuilder.buildAndThrow(AssertionFailureBuilder.java:132)
at
app//org.junit.jupiter.api.AssertArrayEquals.failArraysNotEqual(AssertArrayEquals.java:440)
at
app//org.junit.jupiter.api.AssertArrayEquals.assertArrayEquals(AssertArrayEquals.java:290)
at
app//org.junit.jupiter.api.AssertArrayEquals.assertArrayEquals(AssertArrayEquals.java:123)
at
app//org.junit.jupiter.api.AssertArrayEquals.assertArrayEquals(AssertArrayEquals.java:119)
at
app//org.junit.jupiter.api.Assertions.assertArrayEquals(Assertions.java:1360)
at app//ai.onnxruntime.InferenceTest.runProvider(InferenceTest.java:676)
at app//ai.onnxruntime.InferenceTest.testCUDA(InferenceTest.java:615)
### Description
<!-- Describe your changes. -->
ROCm CI pipeline issue.
```
Downloading and preparing dataset wikitext/wikitext-2-raw-v1 (download: 4.50 MiB, generated: 12.91 MiB, post-processed: Unknown size, total: 17.41 MiB) to /home/onnxruntimedev/.cache/huggingface/datasets/wikitext/wikitext-2-raw-v1/1.0.0/aa5e094000ec7afeb74c3be92c88313cd6f132d564c7effd961c10fd47c76f20...
main()
File "/stage/huggingface-transformers/examples/pytorch/language-modeling/run_mlm.py", line 242, in main
datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name, cache_dir=model_args.cache_dir)
File "/opt/miniconda/envs/rocm-ci/lib/python3.9/site-packages/datasets/load.py", line 856, in load_dataset
builder_instance.download_and_prepare(
File "/opt/miniconda/envs/rocm-ci/lib/python3.9/site-packages/datasets/builder.py", line 583, in download_and_prepare
self._download_and_prepare(
File "/opt/miniconda/envs/rocm-ci/lib/python3.9/site-packages/datasets/builder.py", line 639, in _download_and_prepare
split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
File "/home/onnxruntimedev/.cache/huggingface/modules/datasets_modules/datasets/wikitext/aa5e094000ec7afeb74c3be92c88313cd6f132d564c7effd961c10fd47c76f20/wikitext.py", line 138, in _split_generators
data_file = dl_manager.download_and_extract(self.config.data_url)
File "/opt/miniconda/envs/rocm-ci/lib/python3.9/site-packages/datasets/utils/download_manager.py", line 289, in download_and_extract
return self.extract(self.download(url_or_urls))
File "/opt/miniconda/envs/rocm-ci/lib/python3.9/site-packages/datasets/utils/download_manager.py", line 197, in download
downloaded_path_or_paths = map_nested(
File "/opt/miniconda/envs/rocm-ci/lib/python3.9/site-packages/datasets/utils/py_utils.py", line 195, in map_nested
return function(data_struct)
File "/opt/miniconda/envs/rocm-ci/lib/python3.9/site-packages/datasets/utils/download_manager.py", line 220, in _download
return cached_path(url_or_filename, download_config=download_config)
File "/opt/miniconda/envs/rocm-ci/lib/python3.9/site-packages/datasets/utils/file_utils.py", line 281, in cached_path
output_path = get_from_cache(
File "/opt/miniconda/envs/rocm-ci/lib/python3.9/site-packages/datasets/utils/file_utils.py", line 634, in get_from_cache
raise ConnectionError("Couldn't reach {}".format(url))
ConnectionError: Couldn't reach https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-2-raw-v1.zip
```
### 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. -->
Update the `datasets` pipeline to latest version `2.17.0`.
### Description
<!-- Describe your changes. -->
This PR upgrades ORTModule's default opset from 15 to 17. Opset 17 is
the final opset supported by torchscript exporter
(https://github.com/pytorch/pytorch/pull/107829)
### 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. -->
Engineering excellence contribution for ORT Training DRI.
---------
Co-authored-by: Prathik Rao <prathikrao@microsoft.com@orttrainingdev8.d32nl1ml4oruzj4qz3bqlggovf.px.internal.cloudapp.net>
### Description
Adds a job to the python packaging pipeline that builds x64 python
wheels for QNN EP.
### Motivation and Context
Necessary to create a cached QNN model on Windows x64, which is done by
creating a properly configured onnxruntime session with QNN EP.
### Description
This pull request includes a small change to the
`Dockerfile.manylinux2_28_cuda` file in the
`tools/ci_build/github/linux/docker` directory. The change corrects the
`PREPEND_PATH` argument from `/usr/local/cuda/binet` to
`/usr/local/cuda/bin`, ensuring the correct path to CUDA binaries is
set.
This pull request includes modifications to the `c-api-cpu.yml` Azure
Pipelines configuration file. The changes mainly revolve around the
Node.js packaging stage and the handling of Node.js artifacts. The most
significant changes include renaming the Node.js packaging stage, adding
a new dependency to the stage, changing artifact names, adding a new
script to list Node.js artifacts, and updating the source folder for
copying NuGet binaries.
Changes in Node.js packaging:
*
[`tools/ci_build/github/azure-pipelines/templates/c-api-cpu.yml`](diffhunk://#diff-00815920cc190d10fdebceac0c3a4b8a59e408684ae38177dfe7f96cae276c59L503-R508):
Renamed the Node.js packaging stage from `Nodejs_Packaging_CPU` to
`Nodejs_Packaging` and added `Windows_CI_GPU_DML_Dev` as a new
dependency to the stage.
Changes in handling of Node.js artifacts:
*
[`tools/ci_build/github/azure-pipelines/templates/c-api-cpu.yml`](diffhunk://#diff-00815920cc190d10fdebceac0c3a4b8a59e408684ae38177dfe7f96cae276c59L568-R569):
Changed the artifact name from `drop-onnxruntime-nodejs-win-x64` to
`drop-onnxruntime-nodejs-win-x64-dml` in the task to download pipeline
artifacts for Windows x64.
*
[`tools/ci_build/github/azure-pipelines/templates/c-api-cpu.yml`](diffhunk://#diff-00815920cc190d10fdebceac0c3a4b8a59e408684ae38177dfe7f96cae276c59R595-R598):
Added a new script to list Node.js artifacts from the directory
`$(Build.BinariesDirectory)/nodejs-artifacts/win32/x64/`.
*
[`tools/ci_build/github/azure-pipelines/templates/c-api-cpu.yml`](diffhunk://#diff-00815920cc190d10fdebceac0c3a4b8a59e408684ae38177dfe7f96cae276c59L635-R640):
Updated the source folder from
`$(Build.BinariesDirectory)\RelWithDebInfo\RelWithDebInfo\nuget-artifacts\onnxruntime-win-x64\lib`
to `$(Build.BinariesDirectory)\nodejs-artifacts\win32\x64` in the task
to copy NuGet binaries to the directory
`$(Build.SourcesDirectory)\js\node\bin\napi-v3\win32\x64`.
---------
Co-authored-by: Yulong Wang <7679871+fs-eire@users.noreply.github.com>
### Description
1. make parity_check use local model to avoid using hf token
2. del the model didn't work because it tried to del the object define
out of the function scope.
So it caused out of memory in A10.
3. In fact, 16G GPU memory (one T4) is enough. But the conversion
process always be killed in T4 and it works on A10/24G.
Standard_NC4as_T4_v3 has 28G CPU memory
Standard_NV36ads_A10_v5 has 440G memory.
It looks that the model conversion needs very huge memory.
### Motivation and Context
Last time, I came across some issues in convert_to_onnx.py so I use the
onnx model in https://github.com/microsoft/Llama-2-Onnx for testing.
Now, these issues could be fixed. So I use onnx model generated by this
repo and the CI can cover the model conversion.
Fix pytest version to 7.4.4, higher version will cause error
`from onnxruntime.capi import onnxruntime_validation
ModuleNotFoundError: No module named 'onnxruntime.capi'`
### Description
<!-- Describe your changes. -->
Setup usage of coremltools via dependencies instead of copying files.
Pull in some changes from
https://github.com/microsoft/onnxruntime/pull/19347 in preparation for
supporting ML Program and enabling building the ML Model on all
platforms to make development and testing of CoreML EP code easier.
- Update to coremltools 7.1
- Add patch for changes required for cross platform build of ML Program
related code
- Generate coreml proto files on all platforms
- mainly to test these changes work everywhere, as the proto files will
be used on all platforms when #19347 is checked in
- rename onnxruntime_coreml_proto target to coreml_proto as it contains
purely coreml protobuf code with no ORT related chagnes
### 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. -->
Improve setup.
### Description
1. save the model to pipeline cache
2. lower the similarly bar to 97
3. publish the generated image that we can check it once the test fails
### Motivation and Context
Reduce model downloads
### Description
<!-- Describe your changes. -->
Updates to only include ios archs framework in artifacts included in
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. -->
Related issue:
https://github.com/microsoft/onnxruntime/issues/19295#issuecomment-1914143256
---------
Co-authored-by: rachguo <rachguo@rachguos-Mini.attlocal.net>
Co-authored-by: Edward Chen <18449977+edgchen1@users.noreply.github.com>
### Description
In PR #19073 I mistunderstood the value of "--parallel". Instead of
testing if args.parallel is None or not , I should test the returned
value of number_of_parallel_jobs function.
If build.py was invoked without --parallel, then args.parallel equals to
1. Because it is the default value. Then we should not add "/MP".
However, the current code adds it. Because if `args.paralllel` is
evaluated to `if 1` , which is True.
If build.py was invoked with --parallel with additional numbers, then
args.parallel equals to 0. Because it is unspecified. Then we should add
"/MP". However, the current code does not add it. Because `if
args.paralllel` is evaluated to `if 0` , which is False.
This also adds a new build flag: use_binskim_compliant_compile_flags, which is intended to be only used in ONNX Runtime team's build pipelines for compliance reasons.
### Motivation and Context
### Description
1. Add visual parity test based on openai clip model
2. Add trigger rules
### Motivation and Context
1. check generated image is expected
2. reduce unnecessary triggers
### Description
Fix two issues:
(1) We can only use single quote inside `bash -c "..."`. Current
pipeline job stopped at `python3 demo_txt2img.py astronaut` and skip the
following commands. In this change, we remove the remaining commands to
get same effect (otherwise, the pipeline runtime might be 2 hours
instead of 15 minutes).
(2) Fix a typo of Stable.
### Description
Update abseil to a release tag and register neural_speed to CG.
### Motivation and Context
Now we are using a non-relesed version of abseil. Using a tag is better.
### Description
1. Update Linux GPU machine from T4 to A10, sm=8.6
2. update the tolerance
### Motivation and Context
1. Free more T4 and test with higher compute capability.
2. ORT enables TF32 in GEMM for A10/100. TF32 will cause precsion loss
and fail this test
```
2024-01-19T13:27:18.8302842Z [ RUN ] ModelTests/ModelTest.Run/cuda__models_zoo_opset12_SSD_ssd12
2024-01-19T13:27:25.8438153Z /onnxruntime_src/onnxruntime/test/providers/cpu/model_tests.cc:347: Failure
2024-01-19T13:27:25.8438641Z Expected equality of these values:
2024-01-19T13:27:25.8438841Z COMPARE_RESULT::SUCCESS
2024-01-19T13:27:25.8439276Z Which is: 4-byte object <00-00 00-00>
2024-01-19T13:27:25.8439464Z ret.first
2024-01-19T13:27:25.8445514Z Which is: 4-byte object <01-00 00-00>
2024-01-19T13:27:25.8445962Z expected 0.145984 (3e157cc1), got 0.975133 (3f79a24b), diff: 0.829149, tol=0.0114598 idx=375. 20 of 388 differ
2024-01-19T13:27:25.8446198Z
2024-01-19T13:27:25.8555736Z [ FAILED ] ModelTests/ModelTest.Run/cuda__models_zoo_opset12_SSD_ssd12, where GetParam() = "cuda_../models/zoo/opset12/SSD/ssd-12.onnx" (7025 ms)
2024-01-19T13:27:25.8556077Z [ RUN ] ModelTests/ModelTest.Run/cuda__models_zoo_opset12_YOLOv312_yolov312
2024-01-19T13:27:29.3174318Z /onnxruntime_src/onnxruntime/test/providers/cpu/model_tests.cc:347: Failure
2024-01-19T13:27:29.3175144Z Expected equality of these values:
2024-01-19T13:27:29.3175389Z COMPARE_RESULT::SUCCESS
2024-01-19T13:27:29.3175812Z Which is: 4-byte object <00-00 00-00>
2024-01-19T13:27:29.3176080Z ret.first
2024-01-19T13:27:29.3176322Z Which is: 4-byte object <01-00 00-00>
2024-01-19T13:27:29.3178431Z expected 4.34958 (408b2fb8), got 4.51324 (40906c80), diff: 0.16367, tol=0.0534958 idx=9929. 22 of 42588 differ
```
3. some other test like SSD throw other exception, so skip them
'''
2024-01-22T09:07:40.8446910Z [ RUN ]
ModelTests/ModelTest.Run/cuda__models_zoo_opset12_SSD_ssd12
2024-01-22T09:07:51.5587571Z
/onnxruntime_src/onnxruntime/test/providers/cpu/model_tests.cc:358:
Failure
2024-01-22T09:07:51.5588512Z Expected equality of these values:
2024-01-22T09:07:51.5588870Z COMPARE_RESULT::SUCCESS
2024-01-22T09:07:51.5589467Z Which is: 4-byte object <00-00 00-00>
2024-01-22T09:07:51.5589953Z ret.first
2024-01-22T09:07:51.5590462Z Which is: 4-byte object <01-00 00-00>
2024-01-22T09:07:51.5590841Z expected 1, got 63
'''
### Description
Adds a job to create a nightly python package for ORT/QNN on Windows
ARM64.
Must build onnxruntime-qnn with python 3.11 and numpy 1.25.
**Note: pipeline run may take up to 3 hrs**
### Motivation and Context
Make it possible to get a nightly python package with the latest updates
to QNN EP.
Issue #19161
### 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
<!-- 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
<!-- 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
<!-- 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
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>
