### Description
This PR enables onnxruntime to build with the most recent release of Arm
Compute Library
### Motivation and Context
The latest version of Arm Compute Library that onnxruntime can build is
20.02 which is more than 3 years old.
Build onnxruntime.dll as arm64x
Added a .cmake file to generate a link repro of the onnxruntime.dll
during arm64 build. This provides us a directory containing all the
arm64 objs, def file and libs to link to when it is time to building
arm64x onnxruntime.dll during the arm64ec build by passing the
/machine:arm64x flag to the linker along with the arm64 artifacts.
If other dlls wanted to be built as x, setting the ARM64X_TARGETS
variable in the toplevel cmakelists.txt to include these other targets
is all that will be needed.
Added build_arm64x.bat as a wrapper for the multiple (rm64, then
arm64ec) cmake calls needed to build as arm64x.
AB#22533
Add ACL as the DNNL runtime option for aarch64 platforms. Update
makefile and the python wheel build script.
### Description
<!-- Describe your changes. -->
Add ACL as the DNNL runtime option for aarch64 platforms. Update
makefile and the python wheel build script.
### 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 is to enable the optimized ACL gemm kernels for dnnl execution
provider on aarch64 platform.
Reverts microsoft/onnxruntime#18413
there's a timing issue here. we eventually want to get this change
merged in but we need to update OSS onnx-tensorrt first.
### Description
<!-- Describe your changes. -->
As title.
1. Add macos build as an optionally enabled arch for pod and changes to
exsiting build_ios_framework/assemble_c_pod scripts.
2. Enable macos build arch in ios packaging pipeline (currently for
variants other than Mobile) and check the output artifacts are correct.
3. Write MacOS Test Target scheme in the test app and integrate into ios
packaging CI testing pipeline.
Currently the changes only apply to onnxruntime-c pod. as the original
request was from ORT SPM which consumes the onnxruntime-c pod only as
the binary target. TODO: could look into adding macos platform to objc
pod as well.
### 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. -->
Enable macos platform support in cocoapods. and also potentially produce
binary target for enabling macos platform in SPM as well.
Replace https://github.com/microsoft/onnxruntime/pull/18334
---------
Co-authored-by: rachguo <rachguo@rachguos-Mac-mini.local>
Co-authored-by: rachguo <rachguo@rachguos-Mini.attlocal.net>
Co-authored-by: Edward Chen <18449977+edgchen1@users.noreply.github.com>
### Description
PR #16051 introduced operator GemmFloat8 but the flags
DISABLE_FLOAT8_TYPES was missing in a couple of places. The PR addresses
that issue. That would allows the compilation on CUDA < 11.8.
### Description
Add CI changes for #18287
Install onnx explicitly to pass windows GPU+dml stage.
### Motivation and Context
'eigen-3.4' was refering to a branch, not to a tag. There is now an
Eigen 3.4.1 on that branch, and thus the hash has changed.
See
https://github.com/microsoft/onnxruntime/issues/18286#issuecomment-1793683416
### Description
Update the C# nuget build infrastructure to make building a test nuget
package more user friendly and to simplify
- Remove usage of dotnet and msbuild in CIs
- was temporary requirement until .net 6 MAUI was added to the released
Visual Studio
- remove SelectedTargets property and its usage
- Add property for excluding mobile targets
- generally we exclude based on the nuget package name
- can now specify `/p:IncludeMobileTargets=false` on the command line to
force exclusion
- support building test package using build.py `--build_nuget` better
- limit inclusion of xamarin targets as building with them requires a
lot more infrastructure
- use msbuild directly if xamarin targets are included. use dotnet
otherwise.
- remove quoting of property values as it doesn't appear to be necessary
and breaks when msbuild is being used
- add infrastructure to be able to pack the nuget package on linux with
`dotnet pack`
- `nuget pack` is not user friendly as-per comments in changes
- requires stub csproj to provide the nuspec path
- Remove netstandard1.0 targets from nuspec
- we removed support from the actual bindings previously
- Remove usage of nuget-staging directory when creating nuget package on
linux
- the nuspec file element has a fully qualified path for a source file
so there is no obvious benefit to copying to a staging directory prior
to packing
### Motivation and Context
Address issues with 1P users trying to create test nuget packages
locally.
Long overdue cleanup of CI complexity.
### Description
Integration to OpenVINO 2023.1
### Motivation and Context
- Alignment with latest OpenVINO Version.
- Device name change from VPUX to NPU and Remove from supported list
until official public support is available.
---------
Co-authored-by: Sahar Fatima <sfatima.3001@gmail.com>
Co-authored-by: Saurabh Kale <saurabh1.kale@intel.com>
Co-authored-by: Suryaprakash Shanmugam <suryaprakash.shanmugam@intel.com>
Co-authored-by: sfatimar <sahar.fatima@intel.com>
### Description
This PR:
(1) Fixes AMD builds after #17200 broke them (Need to remember to run
AMD builds while trying to merge external CUDA PRs next time)
(2) Turn on the NHWC CUDA feature in the Linux GPU CI. The extra time
spent in building a few more files and running a few more tests will not
be much.
Test Linux GPU CI run :
https://dev.azure.com/onnxruntime/onnxruntime/_build/results?buildId=1170770
### Motivation and Context
Keep the NHWC CUDA ops tested
(https://github.com/microsoft/onnxruntime/pull/17200) and guard against
regressions
Bump ruff version and remove pylint from the linter list. Fix any new
error detected by ruff.
### Motivation and Context
Ruff covers many of the pylint rules. Since pylint is not enabled in
this repo and runs slow, we remove it from the linters
@fdwr This is the part 2 of the pybind work that was started earlier.
This adds the following features to the python IO binding
implementation:
- Use a bucketized allocator in order to reduce the number of resource
allocations
- Implement the following functions: `ortvalue_from_numpy`,
`update_inplace`, `ortvalue_from_shape_and_type` and `numpy`
- Modify the `onnxruntime_test_python_iobinding` tests to also run on
DML
---------
Co-authored-by: Jeff Bloomfield <jeffbloo@microsoft.com>
### Description
<!-- Describe your changes. -->
Remove onnxruntime_test_all from emulator once tests have finished as
it's 1.2GB and takes up too much space given the 2GB maximum partition
size for the emulator.
Side issue is the java build isn't able to strip the binaries in the
java apk which causes that to be 800MB (exceeding the 2GB max). That may
require an Android/Gradle fix as I don't think we can hardcode an NDK
version into our build files.
https://issuetracker.google.com/issues/237187538?pli=1
### 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 Android CI build failures for
### Description
There are 8 cu files under [flash
attention](https://github.com/microsoft/onnxruntime/tree/main/onnxruntime/contrib_ops/cuda/bert/flash_attention)
and 4 cu files under [cutlass
fmha](https://github.com/microsoft/onnxruntime/tree/main/onnxruntime/contrib_ops/cuda/bert/cutlass_fmha)
need a lot of memory to compile.
Previously, the default value is same as parallel - number of CPU cores.
Standard_NC4as_T4_v3 has 4 CPUs and 28 GB memory, and we launched 16
nvcc threads in total (4 parallel jobs, and 4 nvcc threads per job).
Each thread might take 4 GB on average (peak is around 6GB, but threads
are not started at same time). OOM happens since 16 threads might need
close to 64 GB in worst case. When build machine has 64GB or larger
memory, OOM is rare.
Here we set a proper nvcc --threads based on available memory to avoid
OOM.
### Motivation and Context
Fix `Python Packaging Pipeline (Training Cuda 11.8)`
### Description
<!-- Describe your changes. -->
As title.
### 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. -->
Now we have multiple data types that we want to disable for minimal
build and to reduce binary size. may be worth adding an argument in the
build script for specifying that.
Also for fp16 type stuff, it may be too restrict to disable that for all
minimal build.
---------
Co-authored-by: rachguo <rachguo@rachguos-Mac-mini.local>
### Description
* Integrate `trt_multi_gpu` test stage in ORT post merge CI (Win-2xA10
vm)
* Deprecate Linux MultiGPU TRT CI (This vm will be deprecated soon)
* Add multi gpu support to existing C# test cases
* Deprecate unfunctional flag `--enable_multi_device_tests`
### Motivation and Context
* Two contexts of replacing Linux MultiGPU TRT CI:
* Flag `--enable_multi_device_tests` is not functional, which cannot
detect issues like #17036
* The Linux-2xM60 VM of this CI pool is about to be deprecated 9/6/23.
Need to enable this test in other dualGPU vm pool.
### Description
This change upgrade emsdk to 3.1.44.
Because backend is upgraded to LLVM 16, so need to fix a lot of build
failures caused by "-Wshorten-64-to-32".
most of the build failures comes from generated `onnx.pb.h`, and this
can be fixed by including "core/graph/onnx_protobuf.h", which detects
and ignore shorten-64-to-32 warnings.
### Description
1. Add "--windows_sdk_version" argument to build.py
2. Fix Windows Static Analysis build pipeline. It is failing because it
picks up a different Windows SDK version after a build machine image
update. If we can explicitly specify Windows SDK version, we can avoid
such things happening again.
3. Remove --enable_training from Windows Static Analysis build pipeline
because PR #16993 makes it incompatible with "no_rtti".
AB#18315
Add script to get iOS simulator device info so we don't need to use hardcoded specifiers which may or may not refer to a valid simulator device.
Add use-xcode-version step to a packaging pipeline so it uses a consistent version of Xcode.
### Description
Remove VS 2019 code.
### 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. -->
Current TRT EP can support model which has nested control flow ops
(multiple level subgraphs). But it fails at a case where the subgraph
has outer scope value that is defined several levels up in the top-level
graph, in this case, the outer scope value is the input of the top-level
graph. The outer scope values are not properly handled during TRT EP's
subgraph reconstruction stage and fails at `graph.resolve()`.
The way ORT gets capability from EPs is a bottom-up approach meaning
inner most subgraph gets handled first. TRT EP reconstructs each
subgraph level by level and following modifications are made to fix the
outer scope values issue:
- `SetGraphOuterScopeValuesAndInputs()` and `SetAllGraphInputs()` are
added to handle outer scope values and add those values as graph inputs
if needed in order to make `graph.resolve()` happy.
- Change to use `GetNodeArgIncludingParentGraphs` so that when creating
the fused TRT node for some subgraphs in`
Graph::CreateFusedSubGraphNode()`, it can get the NodeArgs for outer
scope values from top-level graph.
This PR fixes https://github.com/microsoft/onnxruntime/issues/16217
### Description
<!-- Describe your changes. -->
Add ort_value.h to session_options.h so OrtValue is defined.
Update a unit test binary to add required include paths. Adding
ort_value.h pulls in more data type headers.
### 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. -->
#16193
- Fix some warnings from Xcode build (`-Wshorten-64-to-32`).
- Enable `-Wshorten-64-to-32` warning if available. Currently it's not fully enabled for `onnxruntime_test_all` and `onnxruntime_providers_xnnpack` yet.
- Some clean up in build.py including setting CMake generator more consistently.
- 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.
### 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
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
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
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
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**:
This PR intends to enable WebNN EP in ONNX Runtime Web. It translates
the ONNX nodes by [WebNN
API](https://webmachinelearning.github.io/webnn/), which is implemented
in C++ and uses Emscripten [Embind
API](https://emscripten.org/docs/porting/connecting_cpp_and_javascript/embind.html#).
Temporarily using preferred layout **NHWC** for WebNN graph partitions
since the restriction in WebNN XNNPack backend implementation and the
ongoing
[discussion](https://github.com/webmachinelearning/webnn/issues/324) in
WebNN spec that whether WebNN should support both 'NHWC' and 'NCHW'
layouts. No WebNN native EP, only for Web.
**Motivation and Context**:
Allow ONNXRuntime Web developers to access WebNN API to benefit from
hardware acceleration.
**WebNN API Implementation Status in Chromium**:
- Tracked in Chromium issue:
[#1273291](https://bugs.chromium.org/p/chromium/issues/detail?id=1273291)
- **CPU device**: based on XNNPack backend, and had been available on
Chrome Canary M112 behind "#enable-experimental-web-platform-features"
flag for Windows and Linux platforms. Further implementation for more
ops is ongoing.
- **GPU device**: based on DML, implementation is ongoing.
**Open**:
- GitHub CI: WebNN currently is only available on Chrome Canary/Dev with
XNNPack backend for Linux and Windows. This is an open to reviewers to
help identify which GitHub CI should involved the WebNN EP and guide me
to enable it. Thanks!
### Description
latest emsdk generated multi-thread version sometimes crash with unknown
reason ( error: memory access out of bounds ).
we don't want to break existing ort-web users, so revert emsdk back to
3.1.19 (same to what ort v1.14.0 uses)
### Description
Download protoc from Github Release instead of Nuget to avoid having
dependency on nuget.exe on Linux
### Motivation and Context
To avoid having dependency on nuget.exe on Linux. Many users' build
environment do not have nuget or dotnet.
### Description
This change will allow us building CUDA EP without installing CUDA SDK
on Windows.
### Motivation and Context
Nvidia's CUDA installer comes with a VS extension. In the past, we
require installing the extension. It is a little bit inconvenient since:
1. Visual Studio must be installed before CUDA SDK. CUDA's installer
will not install the extension if your machine doesn't have Visual
Studio.
2. We need to install CUDA SDK on our build machines, instead of just
downloading it and using it.
After this change, we will not need to install CUDA SDK on our build
machines. So it will be easier to add a support for a different CUDA
version.
Also, fix two PreFast warnings.
