### Description
1. Keep symlink in the package.
2. keep the artifact package format
### 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
This PR enables execution of subgraphs in OVEP and currently, when OVEP
developers install the onnxruntime-openvino package on windows from
pypi, they would have to additionally download OpenVINO windows binaries
and run the setupvars.bat script which sets the environment PATH to
locate the OV dll's. Also this PR fixes issues of OVEP windows io buffer
sample.
### Motivation and Context
Fix: We want to make the user experience easy for OVEP Python developers
on windows platform.
This fix, introduces a function add_openvino_libs_to_path at the
location tools/python/util/add_openvino_win_libs.py.
The above function, can be called by OVEP python users in the
application code and that takes care of setting
the OpenVINO dll's to the path from the OpenVINO pypi packge (openvino)
which was installed.
This change also makes sure that add_openvino_libs_to_path() function is
added to onnxruntime python package
only when it is build for OpenVINO Execution Provider for ONNXRuntime
and not for default ORT python package builds.
New user experience for Python OVEP developers on windows platform:
step 1: pip install onnxruntime-openvino
step 2: pip install openvino
step 3: <Add these 2 lines in the application code>
import onnxruntime.tools.add_openvino_win_libs as utils
utils.add_openvino_libs_to_path()
---------
Signed-off-by: MaajidKhan <n.maajid.khan@intel.com>
Co-authored-by: MaajidKhan <n.maajid.khan@intel.com>
Co-authored-by: Suryaprakash Shanmugam <suryaprakash.shanmugam@intel.com>
### Description
The build pipeline runs on Azure NV12 machines that will be deprecated
soon because the SKU is too old. So this PR will move the pipeline to a
Windows machine with two A10 GPUs.
1. Enable xnnpack test
2. Change TSA database name from onnxruntime_master to onnxruntime_main.
This is a leftover of renaming the "master" branch to "main"
3. Add two static analysis jobs for WinML and DML
4. Rename the machine pool "aiinfra-dml-winbuild" to
"onnxruntime-Win2019-GPU-dml-A10", so that the internal and public ADO
instances use the same machine pool name.
5. Move Windows GPU CI build pipeline from "onnxruntime-Win2022-GPU-T4"
to "onnxruntime-Win2022-GPU-A10" machine pool, because we do not have
enough T4 GPUs.
### Description
<!-- Describe your changes. -->
Publish E2E test logs on build failure too.
### 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. -->
Get more information about intermittent test failures.
### Description
A few QDQ tests failed on XNNPACK EP.
The reason should be the range of input_data doesn't fit for scale and
zero_point.
### 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
Add an API for users to get version of current package. example usage:
```js
import { env } from 'onnxruntime-node';
console.log(env.versions.node); // output "1.16.0"
```
```js
import { env } from 'onnxruntime-web';
console.log(env.versions.web); // output "1.16.0"
console.log(env.versions.common); // output "1.16.0"
console.log(env.versions.node); // output "undefined"
```
#16156
### Description
1. Updated Mac package workflow for easily debugging.
2. Changed Archive type from tgz to zip since zip is supported by ESRP.
3. .../dylib.dSYM/Contents/Resources/DWARF/libonnxruntime.1.16.0.dylib
is a debug symbol file, so it couldn't be signed.
### Motivation and Context
It‘s required from VS code.
Mac binaries in nuget should be signed
### Description
Implement Objective-C binding for `ORTCheckPoint`. Additionally,
- Modify `onnxruntime_objectivec.cmake` to only include training header
and sources when training flag is enabled
- Enable objective-c binding for `orttraining-mac-ci-pipeline`
### Motivation and Context
This PR is part of implementing Objective-C bindings for training API.
It implements objective-c binding for ORTCheckPoint class. The
objective-C API closely resembles the C++ API.
**Note**: The test for saving checkpoint is skipped as it requires use
of training session. It will be added when the objective-c binding for
`ORTTrainingSession` is added.
- Fix flatbuffers flatc warning, unused-but-set-variable.
- Address `-Wshorten-64-to-32` warnings (fix in our code, allow in dependencies' code).
- Update CI builds to use Xcode 14.3.
- Update minimum iOS version to 12.0.
- Update Mac hosted agents to MacOS 13 where possible.
MIGraphX CI
- Change docker container user name to `onnxruntimedev`
ROCm CI
- Build docker image every job instead of using prebuild image.
- Every job create a container with only one GPU with command `docker
run -it --device=/dev/kfd --device=/dev/dri/renderDxxx`
- Remove tests that are unstable or use outdated interfaces.
- Enable training ortmodule test.
### Description
1. Avoid taking dependency on dl.fedoraproject.org
The website is not very stable. Our build pipelines often fail to fetch
packages from there.
2. Update manylinux to the latest version
### Description
1. Add a Memory Profiling build job
2. Remove no absl build job since the feature will be removed
3. Simplify post-merge-jobs.yml by unifying the pool names
### Motivation and Context
To catch build errors in #16124
### Description
The PR implements FloatE4M3FN, FloatE5M2, FloatE4MEFNUZ, FloatE5M2FNUZ
as described in PR https://github.com/onnx/onnx/pull/4805. It uses CUDA
API to cast float/half to float8 if CUDA>=11.8, a custom implementation
if CUDA<11.8.
* It implements, Cast, QuantizeLinear, DequantizeLinear for all types on
CPU, only for types FloatE4M3FN, FloatE5M2 on CUDA.
* It extends the supported types for control flow operator, Shape,
Reshape, Identity, If, Loop, Scan, Reshape
* It implements Equal(19).
* Cast, QuantizeLinear, DequantizeLinear operators now support a
parameter `saturate` only valid for float 8 types. It is true by
default. In that case, any value out of range is converted into the
maximum float 8 value. If false, it is infinite.
* QuantizeLinear, DequantizeLinear now supports multiple scales on CUDA
(and ROCm by extension), scale = 1D tensor with one scale per channel
### Motivation and Context
Supports latest onnx version.
Fixes
[AB#15395](https://aiinfra.visualstudio.com/6a833879-cd9b-44a4-a9de-adc2d818f13c/_workitems/edit/15395)
---------
Co-authored-by: Xavier Dupre <xadupre@microsoft.com@orttrainingdev8.d32nl1ml4oruzj4qz3bqlggovf.px.internal.cloudapp.net>
Co-authored-by: Randy Shuai <rashuai@microsoft.com>
Co-authored-by: Edward Chen <18449977+edgchen1@users.noreply.github.com>
Co-authored-by: Scott McKay <Scott.McKay@microsoft.com>
### Description
<!-- Describe your changes. -->
Add 2 new QNN CIs to tools/python/run_CIs_for_external_pr.py
### 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 tool so it runs all current CIs
1. Cherry-pick #16054 back to the main branch
2. Replace onnxruntime-gpu-winbuild-t4 with onnxruntime-Win2022-GPU-T4.
The later one has VS2022.
---------
Co-authored-by: Patrice Vignola <vignola.patrice@gmail.com>
### Description
This change is a follow-up to #15327. It adds Unary operators (Sqrt,
Reciprocal) and Reduce operators (ReduceSum, ReduceMean). I've tried to
follow existing patterns in the code :-)
### Motivation and Context
This reduces fragmentation across EPs when using CoreML on macOS,
thereby speeding up execution.
---------
Co-authored-by: Edward Chen <18449977+edgchen1@users.noreply.github.com>
### Description
Remove the "onnxruntime_BUILD_WEBASSEMBLY" cmake option. Use `if
(CMAKE_SYSTEM_NAME STREQUAL "Emscripten")` instead. It makes some code
look more nature.
For example,
```cmake
if (CMAKE_SYSTEM_NAME STREQUAL "iOS" OR CMAKE_SYSTEM_NAME STREQUAL "Android" OR onnxruntime_BUILD_WEBASSEMBLY)
```
becomes
```cmake
if (CMAKE_SYSTEM_NAME STREQUAL "iOS" OR CMAKE_SYSTEM_NAME STREQUAL "Android" OR CMAKE_SYSTEM_NAME STREQUAL "Emscripten")
```
### Description
Enable Qnn Context cache feature to save model initialization time
Provider options:
qnn_context_cache_enable|1 to enable the cache feature
qnn_context_cache_path to set the cache path. It is set to model_file.onnx.bin by default.
### Motivation and Context
Model initialization time takes long because the cost of conversion from Onnx model to Qnn model. Qnn have feature to serialize the Qnn context to file, then next time user can load it from the cache context and execute the graph to save the cost.
---------
Co-authored-by: Adrian Lizarraga <adlizarraga@microsoft.com>
Here's the motivating issue:
https://github.com/microsoft/azure-pipelines-tasks/issues/10331
Noticed some problems in other repos so also updating usages in ORT.
We may be fine now without it, but this change adds some safeguard against future additions of 'set -x' for debugging.
### Description
Change CUDA pipelines to download CUDA SDK in every build job
### 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. Set gtest output while ctest is set to empty.
2. onnx_src in _deps shouldn't be removed because
onnx_test_pytorch_converted and onnx_test_pytorch_converted need to read
data from onnx/backend/test/data/..
### Motivation and Context
Test result report is important to find the flaky tests.
### To do
Tests are not inconsistent.
If ctest_path is empty, onnx_test_pytorch_converted and
onnx_test_pytorch_converted will not be executed, if it's not,
onnxruntime_mlas_test will not be executed.
270c09a37f/tools/ci_build/build.py (L1743-L1753)
### Description
After this PR there are following pool need to be updated.
old|new|note
---|---|---
onnxruntime-Win2019-GPU-dml-A10|tbd|
onnxruntime-Win2019-GPU-T4|onnxruntime-Win2022-GPU-T4|
onnxruntime-Win2019-GPU-training-T4|onnxruntime-Win2022-GPU-T4|ame as
the above because we do not have many T4 GPUs
onnxruntime-tensorrt8-winbuild-T4|tbd|
aiinfra-dml-winbuild|tbd|
### 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
In some scenarios, the triton written kernels are more performant than
CK or other handwritten kernels, so we implement a framework that
onnxruntime can use these triton written kernels.
This PR is to integrate triton into ort, so that ort can use kernels
that written and compiled by triton.
The main change focus on two part:
1. a build part to compile triton written kernel and combine these
kernels into libonnxruntime_providers_rocm.so
2. a loader and launcher in c++, for loading and launch triton written
kernels.
#### Build
To compile triton written kernel, add a script
`tools/ci_build/compile_triton.py`. This script will dynamic load all
kernel files, compile them, and generate `triton_kernel_infos.a` and
`triton_kernel_infos.h`.
`triton_kernel_infos.a` contains all compiled kernel instructions, this
file will be combined into libonnxruntime_providers_rocm.so, using
--whole-archive flag.
`triton_kernel_infos.h` defines a const array that contains all the
metadata for each compiled kernel. These metadata will be used for load
and launch. So this header file is included by 'triton_kernel.cu' which
defines load and launch functions.
Add a build flag in build.py and CMakeList.txt, when building rocm
provider, it will call triton_kernel build command, and generate all
necessary files.
#### C++ Load and Launch
On c++ part, we implement load and launch functions in triton_kernel.cu
and triton_kernel.h.
These two files located in `providers/cuda`, and when compiling rocm,
they will be hipified. so this part supports both cuda and rocm. But
currently we only call triton kernel in rocm.
We also implement a softmax triton op for example. Because there will
generate many kernels for different input shape of softmax, we use
TunableOp to select the best one.
### 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. -->
Old pool | New pool | Notes
-- | -- | --
onnxruntime-Win-CPU-2019 | onnxruntime-Win-CPU-2022 |
onnxruntime-Win2019-CPU-training | onnxruntime-Win2022-CPU-training-AMD
|
onnxruntime-Win2019-CPU-training-AMD |
onnxruntime-Win2022-CPU-training-AMD | Same as the above
onnxruntime-Win2019-GPU-dml-A10 | Need be created | You need to create a
new image for it first
onnxruntime-Win2019-GPU-T4 | onnxruntime-Win2022-GPU-T4 |
onnxruntime-Win2019-GPU-training-T4 | onnxruntime-Win2022-GPU-T4 | Same
as the above because we do not have many T4 GPUs
onnxruntime-tensorrt8-winbuild-T4| TBD|TBD
Win-CPU-2021|onnxruntime-Win-CPU-2022| will do it in next PR
Win-CPU-2019|onnxruntime-Win2022-Intel-CPU'| Intel CPU needed for
win-ci-pipeline.yml -> `stage: x64_release_dnnl`
<br class="Apple-interchange-newline">
### Motivation and Context
With vs2022 we can take the advantage of 64bit compiler. It also with
better c++20 support
Set default value for parameters in nuget-zip pipeline, and only apply
the configurations when they are not "NONE".
---------
Co-authored-by: Randy Shuai <rashuai@microsoft.com>
This PR mainly fixes building errors when trying to build nupkg for ROCm EP.
It also slighly improve the packaging logic so that devlopers can
produce the nupkg on linux natively.
### 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. -->
