### Description
<!-- Describe your changes. -->
Test case failing sometimes and passing other times.
### Motivation and Context
Prevent unnecessary CI build failures requiring manually rerunning tests
### Description
Upgrade python from 3.9 to 3.10 in ROCm and MigraphX docker files and CI
pipelines. Upgrade ROCm version to 6.2.3 in most places except ROCm CI,
see comment below.
Some improvements/upgrades on ROCm/Migraphx docker or pipeline:
* rocm 6.0/6.1.3 => 6.2.3
* python 3.9 => 3.10
* Ubuntu 20.04 => 22.04
* Also upgrade ml_dtypes, numpy and scipy packages.
* Fix message "ROCm version from ..." with correct file path in
CMakeList.txt
* Exclude some NHWC tests since ROCm EP lacks support for NHWC
convolution.
#### ROCm CI Pipeline:
ROCm 6.1.3 is kept in the pipeline for now.
- Failed after upgrading to ROCm 6.2.3: `HIPBLAS_STATUS_INVALID_VALUE ;
GPU=0 ; hostname=76123b390aed ;
file=/onnxruntime_src/onnxruntime/core/providers/rocm/rocm_execution_provider.cc
; line=170 ; expr=hipblasSetStream(hipblas_handle_, stream);` . It need
further investigation.
- cupy issues:
(1) It currently supports numpy < 1.27, might not work with numpy 2.x.
So we locked numpy==1.26.4 for now.
(2) cupy support of ROCm 6.2 is still in progress:
https://github.com/cupy/cupy/issues/8606.
Note that miniconda issues: its libstdc++.so.6 and libgcc_s.so.1 might
have conflict with the system ones. So we created links to use the
system ones.
#### MigraphX CI pipeline
MigraphX CI does not use cupy, and we are able to use ROCm 6.2.3 and
numpy 2.x in the pipeline.
#### Other attempts
Other things that I've tried which might help in the future:
Attempt to use a single docker file for both ROCm and Migraphx:
https://github.com/microsoft/onnxruntime/pull/22478
Upgrade to ubuntu 24.04 and python 3.12, and use venv like
[this](27903e7ff1/tools/ci_build/github/linux/docker/rocm-ci-pipeline-env.Dockerfile).
### Motivation and Context
In 1.20 release, ROCm nuget packaging pipeline will use 6.2:
https://github.com/microsoft/onnxruntime/pull/22461.
This upgrades rocm to 6.2.3 in CI pipelines to be consistent.
### Description
Replace `hipMemcpy` with `hipMemcpyWithStream`
### Motivation and Context
`hipMemcpy` uses default stream, which may be out of synchronization
with the current stream when ORT_ENABLE_STREAM is defined.
### Description
<!-- Describe your changes. -->
**Changes applied to maven related signing:**
* Windows sha256 file encoded by utf8(no BOM)
* powershell script task used latest version, previous 5.1 version only
supports utf8 with BOM.
* Windows sha256 file content in format 'sha256value
*filename.extension'.
* Linux sha256 file content in format 'sha256value *filename.extension'.
**More information about powershell encoding:**
Windows powershell encoding reference: [about_Character_Encoding -
PowerShell | Microsoft
Learn](https://learn.microsoft.com/en-us/powershell/module/microsoft.powershell.core/about/about_character_encoding?view=powershell-7.4)
- for version 5.1, it only has 'UTF8 Uses UTF-8 (with BOM).'
- for version v7.1 and higher, it has:
utf8: Encodes in UTF-8 format (no BOM).
utf8BOM: Encodes in UTF-8 format with Byte Order Mark (BOM)
utf8NoBOM: Encodes in UTF-8 format without Byte Order Mark (BOM)
### Description
part of https://github.com/microsoft/onnxruntime/issues/21448
This change is intend to save CPU memory during model load for
inference.
Added session option save_prepacked_constant_initializers, with
save_prepacked_constant_initializers turn on:
1. optimize model with inference session, prepacked external initializer
will be saved into data file.
2. load optimized model and external data file with prepacked
initializer, no prepack is needed
3. run inference with optimized model and data file
Tested with model Phi-3-mini-instruct-onnx,
with ORT 1.12.0:
with this change:
Peak memory usage dropped from **5.438 GB to 2.726GB**.
This change takes advantage of ORT loads external initializer with mmap
on CPU. Prepack will use extra memory on heap, omit prepack process can
save this part of memory (roughly same size as external initializers).
next step:
Change all the kernels on CPU with PrePack method implemented and test
properly. Will do in next 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. -->
### 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. -->
This PR add dependency to the global-agent package, and use it in JSEP
scripts that download files from network (i.e. `js/scripts/utils.ts` and
`js/web/script/pull-prebuilt-wasm-artifacts.ts`), so that user can make
these script use network proxy by setting environment variable
GLOBAL_AGENT_HTTPS_PROXY.
### Description
<!-- Describe your changes. -->
Should make the binary size report more stable as changes < 4K can occur
when a padding boundary is crossed.
### 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 Expand input be a scalar
- Allows Reshape input be a scalar
- Allows Reshape to a scalar
Fixed#22215
---------
Co-authored-by: Dwayne Robinson <fdwr@hotmail.com>
### Description
Move Linux Github actions to a dedicated pool. Currently the
"orttraining-linux-ci-pipeline " is too slow.
### Motivation and Context
To speed up the running.
### Description
<!-- Describe your changes. -->
Add DoEsrp Check for Signature Verification
### 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. -->
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
This PR introduces support for registering external data inside WebNN
EP.
### Motivation and Context
- The WebNN EP needs to register the initializers at graph compilation
stage, for initializers from external data, it can't leverage the
general external data loader framework because the graph compilation of
WebNN EP is executed before external data loader called.
- Exposes the `utils::GetExternalDataInfo`, it is useful for WebNN EP to
read the external tensor's infomation.
- Define a new `registerMLConstant` in JSEP to create WebNN constants
from external data in WebNN backend, with the info of tensor as
parameters, as well as the `Module.MountedFiles`, which holds all
preloaded external files.
### 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
Fix crash with extra checks ResetQnnLogLevel.
From the dump it looks like during ETW callbacks, while the provider is stopping, we attempt to reset the QNN log level.
While the QNN BackEndMgr (this) is alive logger_ is not valid
### Motivation and Context
ORT should not crash
### Description
Update list of CI pipelines to trigger for external PRs.
### Motivation and Context
The pipelines triggered for external PRs are not consistent with
internal PRs.
### Description
<!-- Describe your changes. -->
Current API docs workflows are scheduled to run monthly, but artifacts
expire after 30 days, which could create issues for 31-day months.
Updating to regenerate artifacts every 2 weeks.
### 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) Upgrade opencv
(2) Add some comments about onnxruntime-gpu installation
### Motivation and Context
opencv-python was locked to an older version, which has security
vulnerabilities: see https://github.com/microsoft/onnxruntime/pull/22445
for more info
### Description
relate to #22282. Let Vitis ai ep handles dynamic_options
### Motivation and Context
---------
Co-authored-by: genmingz <genmingz@amd.com>
### Description
1. Remove the onnxruntime::OrtMutex class and replace it with
~absl::Mutex~ std::mutex.
2. After this change, most source files will not include <Windows.h>
indirectly.
### Motivation and Context
To reduce the number of deps we have, and address some Github issues
that are related to build ONNX Runtime from source.
In PR #3000 , I added a custom implementation of std::mutex . It was
mainly because at that time std::mutex's default constructor was not
trivial on Windows. If you had such a mutex as a global var, it could
not be initialized at compile time. Then VC++ team fixed this issue.
Therefore we don't need this custom implementation anymore.
This PR also removes nsync. I ran several models tests on Linux. I
didn't see any perf difference.
This PR also reverts PR #21005 , which is no longer needed since conda
has updated its msvc runtime DLL.
This PR unblocks #22173 and resolves#22092 . We have a lot of open
issues with nsync. This PR can resolve all of them.
### Description
Updates the ROCm EP opsets to match the current CUDA EP opsets. Also
enable the test CApiTest.basic_cuda_graph_with_annotation.
Note that some changes are whitespace-only. These changes were made to
improve the comparison of corresponding ROCm and CUDA EP source files
when using a side by side diff tool.
### Motivation and Context
The ROCm EP derives from the CUDA EP. Many source files are shared
between the EPs and "hipified" during the ROCm EP build, however quite a
few files within the ROCm EP are under source control after their
initial hipification. Over time these ROCm EP files get stale relative
to their CUDA EP counterparts. It becomes necessary to re-hipify these
otherwise static files in order to pick up important changes such as
opset differences.
Update the python wrapper script to support weight sharing case
### Description
update the script to support json file that from QNN converter or the one extracted from QNN context binary file for the weight sharing scenario
The ONNX Runtime Release Roadmap on our website is not very easy to find
right now, so I'm adding a link here to make it more accessible.
### 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: Tianlei Wu <tlwu@microsoft.com>
- Allow specification of iOS simulator runtime version to use.
- Pick simulator runtime version (iphonesimulator 16.4) that is supported by the Xcode version (14.3.1) that we use.
- Disable CoreML EP's DepthToSpace op support for CoreML version less than 7, with DCR mode, and FP16 input. It doesn't produce the correct output in this case.
- Some cleanup of iOS test infrastructure.
### Description
This change enables caching `MLTensor`s between inferences runs. This is
done by keeping a reference to `MLTensor`s alive after they have been
released. `MLTensor`s are only destroyed once the sessions goes out of
scope.
### Motivation and Context
Creating and destroying `MTensor`s on every run has a non-trivial
performance penalty. This performance penalty materializes when using
`ort.Tensors`[location=cpu] for inputs/outputs or when using the CPU EP
as a fallback EP for unsupported operators. The former could be
mitigated by developer using `ort.Tensors`[location=ml-tensor]. The
latter cannot be mitigated by developers.
### Description
The recent PR #22223 introduced 2 bugs in implementation of CPU
LayerNorm f16:
- possible access to nullptr for bias
`const TensorShape& bias_shape = bias->Shape();` will crash when `bias`
does not exist. (amazingly seems this one is not coverred by any test
case)
- fix: guard with pointer check
- a racing condition inside ComputeJob
`ComputeJob()` is dispatched to threadpool and it internally tries to
modify `LayerNormImpl::scale_fp32_` and `LayerNormImpl::bias_fp32_`,
which are `std::unique_ptr`s and are not thread-safe.
- fix: move the modification of `LayerNormImpl::scale_fp32_` and
`LayerNormImpl::bias_fp32_` out of `ComputeJob()` and put into
`LayerNormImpl::ComputeWithoutContext()`. It may still have racing
condition because `ConcurrentRunSupported` is set to `true` for CPU EP.
Added an OrtMutex.
This should fixes the recent flaky tests as well.
