### 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. -->
Add LeakyRelu to the list as support was added a while ago.
### 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 a CodeSign validation task before the binaries are published, to
make sure all DLL files are signed.
2. Auto-trigger the CUDA 12 pipeline's publishing job.
### Description
<!-- Describe your changes. -->
Check whether the min/max inputs are provided and use default values if not provided.
### 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
dft is updated in opset20. implement it in ort
### Motivation and Context
this is for ort 1.17.0 release
Fixes#17723
---------
Signed-off-by: Liqun Fu <liqfu@microsoft.com>
### Description
Fixes a failure in the ortmodule nightly 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. -->
### 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
The casing of Podfile is incorrect in the plugin. This causes issues
when building iOS on case-sensitive systems such as Linux.
### Motivation and Context
because cannot build ios on case sensitive systems
### 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. -->
Update deprecated TRT api:
1.
[setMaxWorkspaceSize](https://docs.nvidia.com/deeplearning/tensorrt/api/c_api/classnvinfer1_1_1_i_builder_config.html#a8209999988ab480c60c8a905dfd2654d)(max_workspace_size_)-------->setMemoryPoolLimit(nvinfer1::MemoryPoolType::kWORKSPACE,
max_workspace_size_)
2.
[kENABLE_TACTIC_HEURISTIC](https://docs.nvidia.com/deeplearning/tensorrt/api/c_api/namespacenvinfer1.html#abdc74c40fe7a0c3d05d2caeccfbc29c1a1215692ad24465e4d9e37a8a7fce1a38)-------->supersede
by trt builder optimization level 2
Perf & warning log comparison
<html xmlns:o="urn:schemas-microsoft-com:office:office"
xmlns:dt="uuid:C2F41010-65B3-11d1-A29F-00AA00C14882"
xmlns="http://www.w3.org/TR/REC-html40">
<head>
<meta name=ProgId content=OneNote.File>
<meta name=Generator content="Microsoft OneNote 15">
</head>
<body lang=en-US style='font-family:"Microsoft YaHei";font-size:12.0pt'>
<!--StartFragment-->
<div style='direction:ltr'>
TRT EP options | User will see corresponding warning logs: | Average
inference time cost (FRCNN on A100)
-- | -- | --
trt_build_heuristics_enable\|true | [TensorRT EP]
trt_build_heuristics_enable is deprecated on TRT 8.6 onwards. Please set
builder optimization level as 2 to enable builder heuristics. | ~300ms
trt_build_heuristics_enable\|true trt_builder_optimization_level\|2 |
[TensorRT EP] Builder heuristics are enabled automatically by builder
optimization level 2. trt_build_heuristics_enable is deprecated on TRT
8.6 onwards. | ~275ms
trt_builder_optimization_level\|2 | | ~275ms
</div>
<!--EndFragment-->
</body>
</html>
### 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. -->
Prepare for upcoming TRT 10
### Description
Change Nuget packaging pipeline's build TRT job to download CUDA SDK
on-the-fly, so that we do not need to put a CUDA SDK in the build
machine's image.
### Description
The changes in this PR includes:
1) Fix f16 errors in InstanceNormalization with NCHW format.
2) Use vec to further optimize the original algorithm.
3) (Removed) Don't do layout conversion for InstanceNormalization for
JSEP since InstanceNormalization itself is suitable for NCHW layout and
has better performance in our current implementation.
Tested on sd-vae-decoder-f16.onnx, it becomes 285 ms from 314 ms. The
aggregate gpu profiling data can be found as below (Note the data is
based change 3).):
Before:
<html>
<body>
<!--StartFragment--><span><span class="ui-provider ef bbg bbh bbi bbj
bbk bbl bbm bbn bbo bbp bbq bbr bbs bbt bbu bbv bbw bbx bby bbz bca bcb
bcc bcd bce bcf bcg bch bci bcj bck bcl bcm bcn" dir="ltr">
Kernel | Time (Ms) | Percentage (%)
-- | -- | --
Conv | 201.55 | 69.56
InstanceNormalization | 42.49 | 14.67
Transpose | 28.95 | 9.99
Mul | 5.69 | 1.96
Add | 3.82 | 1.32
MatMul | 3.27 | 1.13
Sigmoid | 2.24 | 0.77
Resize | 1.16 | 0.40
Softmax | 0.34 | 0.12
Cast | 0.24 | 0.08
Sum | 289.75
<br class="Apple-interchange-newline"><!--EndFragment-->
</body>
</html>
After:
<html>
<body>
<!--StartFragment--><span><span class="ui-provider ef bbg bbh bbi bbj
bbk bbl bbm bbn bbo bbp bbq bbr bbs bbt bbu bbv bbw bbx bby bbz bca bcb
bcc bcd bce bcf bcg bch bci bcj bck bcl bcm bcn" dir="ltr">
Kernel | Time (Ms) | Percentage (%)
-- | -- | --
Conv | 205.44 | 79.43
InstanceNormalization | 18.24 | 7.05
Transpose | 17.64 | 6.82
Mul | 5.69 | 2.20
Add | 3.81 | 1.47
MatMul | 3.56 | 1.38
Sigmoid | 2.24 | 0.86
Resize | 1.19 | 0.46
Softmax | 0.59 | 0.23
Cast | 0.24 | 0.09
Sum | 258.65 |
</span></span><!--EndFragment-->
</body>
</html>
From above table, we can see that two ops time are greatly reduced. One
is InstanceNormalization and the other is Transpose. The reason that the
transpose time is reduced is because each InstanceNormalization is
surrounded with two reshape ops in sd-vae-decoder-f16.onnx. Due to JSEP
is prefer NHWC and InstanceNormalization is layout sensitive op, so two
extra transpose ops are inserted dynamically when executing this model.
After this change, those inserted transpose ops are not needed anymore.
So the overall transpose time is reduced.
### Description
Disable mlas unit test in ARM64EC build because the program has some
link errors. We will fix the errors later.
This PR only impacts Windows ARM64EC build. It has no impact on the
existing build pipelines.
In the latest spec, the axes option of WebNN's argMax and argMin
requires the use of a sequence long type. Replace axis option (long
type) with axes (sequence long type) for argMax and argMin.
### Improve perf for stage3 training - first wave
Port existing PythonOp/PythonOpGrad python runner to C++, also introduce
an unsafe run mode (to skip inplace, save for backward, materrialized
grad detection on the fly).
This reduce the overhead from XX~XXX us to X ~ lower end of XX us . In
LLAMA2 7B training with 8x32GV100, we have observed 6.7% gains over
PyTorch. (1.59 v.s. 1.49it/s)
Peak memory also dropped from 31GB to 28GB.
### 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
Update absl and googletest to their latest version to include some cmake
changes:
1. A googletest's cmake change that will allow using external absl and
re2.
2. Nullability enhancements that will allow our clang-based static
analysis detecting many kinds of null pointer errors.
### Motivation and Context
To fix a C4744 link warning in our Windows pipelines.
```
LINK : warning C4744: 'static char const absl::lts_20230802::base_internal::FastTypeTag<bool>::dummy_var' has different type in 'd:\a\_work\_temp\abseil_cpp\abseil-cpp-20230802.0\absl\flags\parse.cc' and 'd:\a\_work\1\b\relwithdebinfo\_deps\googletest-src\googletest\src\gtest-all.cc': 'signed char' and 'unsigned char' [D:\a\_work\1\b\RelWithDebInfo\onnxruntime_mlas_test.vcxproj]
LINK : warning C4744: 'static char const absl::lts_20230802::base_internal::FastTypeTag<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > >::dummy_var' has different type in 'd:\a\_work\_temp\abseil_cpp\abseil-cpp-20230802.0\absl\flags\parse.cc' and 'd:\a\_work\1\b\relwithdebinfo\_deps\googletest-src\googletest\src\gtest-all.cc': 'signed char' and 'unsigned char' [D:\a\_work\1\b\RelWithDebInfo\onnxruntime_mlas_test.vcxproj]
LINK : warning C4744: 'static char const absl::lts_20230802::base_internal::FastTypeTag<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > >::dummy_var' has different type in 'd:\a\_work\_temp\abseil_cpp\abseil-cpp-20230802.0\absl\flags\internal\usage.cc' and 'd:\a\_work\1\b\relwithdebinfo\_deps\googletest-src\googletest\src\gtest-all.cc': 'signed char' and 'unsigned char' [D:\a\_work\1\b\RelWithDebInfo\onnxruntime_mlas_test.vcxproj]
LINK : warning C4744: 'static char const absl::lts_20230802::base_internal::FastTypeTag<bool>::dummy_var' has different type in 'd:\a\_work\_temp\abseil_cpp\abseil-cpp-20230802.0\absl\flags\internal\flag.cc' and 'd:\a\_work\1\b\relwithdebinfo\_deps\googletest-src\googletest\src\gtest-all.cc': 'signed char' and 'unsigned char' [D:\a\_work\1\b\RelWithDebInfo\onnxruntime_mlas_test.vcxproj]
LINK : warning C4744: 'static char const absl::lts_20230802::base_internal::FastTypeTag<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > >::dummy_var' has different type in 'd:\a\_work\_temp\abseil_cpp\abseil-cpp-20230802.0\absl\flags\internal\flag.cc' and 'd:\a\_work\1\b\relwithdebinfo\_deps\googletest-src\googletest\src\gtest-all.cc': 'signed char' and 'unsigned char' [D:\a\_work\1\b\RelWithDebInfo\onnxruntime_mlas_test.vcxproj]
LINK : warning C4744: 'static char const absl::lts_20230802::base_internal::FastTypeTag<int>::dummy_var' has different type in 'd:\a\_work\_temp\abseil_cpp\abseil-cpp-20230802.0\absl\flags\internal\flag.cc' and 'd:\a\_work\1\b\relwithdebinfo\_deps\googletest-src\googletest\src\gtest-all.cc': 'signed char' and 'unsigned char' [D:\a\_work\1\b\RelWithDebInfo\onnxruntime_mlas_test.vcxproj]
```
### Description
<!-- Describe your changes. -->
1. Add a backward-compatible API for compiling model.
2. Run-time load vitisai-ep.dll
### 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: Yueqing Zhang <yueqingz@amd.com>
Co-authored-by: Zhenze Wang <zhenzew@xilinx.com>
WebNN will remove autoPad option, we need to use explicit padding
values.
Compute padding values of autopad(same-upper, same-lower) for Op Pool,
Conv and ConvTranspose.
### Description
ONNX model zoo changed their dir structure. So some our pipelines are
failing. In prevent such things happening again, we'd better to read the
test data for a cache from local disk instead of downloading it remotely
every time.
It's branched off from
https://github.com/microsoft/onnxruntime/pull/17751 but removes
KernelContext_SetOutput() API. It copies output allocation buffer to
kernel context.
---------
Co-authored-by: George Wu <jywu@microsoft.com>
### Description
<!-- Describe your changes. -->
### Motivation and Context
Now, the nightly Microsoft.ML.Onnxruntime.Managed Nuget Packag couldn't
be added in dotnet console program in VS2022 with target framework .NET
6.0.
I just restore it to previous setting to make it work.
- Add support for OpenVINO 2023.2
- num_of_threads provider option is mapped to the CPU device property
inference_num_threads of the CPU plugin, so users can control the
#threads used for inference by the CPU
- Logging in Debug mode now includes the runtime properties set for
devices
- Fix issue in using external weights through OpenVINO
---------
Co-authored-by: Preetha Veeramalai <preetha.veeramalai@intel.com>
### Description
<!-- Describe your changes. -->
Add macos build for objc pod.
### 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. -->
Follow up pr for #18550
---------
Co-authored-by: rachguo <rachguo@rachguos-Mini.attlocal.net>
### Description
<!-- Describe your changes. -->
TrainingSession has been deprecated for a while now, but the gradient
ops tests are still using training session. This PR updates these tests
to use inference session instead of training session.
### 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 will enable us to remove all the training session related
deprecated code from the repo.
### Description
The warning is:
```
C:\a\_work\1\s\onnxruntime\contrib_ops\cpu\math\sparse_dense_matmul.cc(88,54): warning C4244: 'argument': conversion from 'const __int64' to 'Eigen::EigenBase<Derived>::Index', possible loss of data [C:\a\_work\1\b\RelWithDebInfo\onnxruntime_providers.vcxproj]
2023-12-08T20:58:48.1812949Z with
2023-12-08T20:58:48.2144272Z [
2023-12-08T20:58:48.2145285Z Derived=Eigen::Map<const Eigen::SparseMatrix<uint64_t,1,int64_t>,0,Eigen::Stride<0,0>>
2023-12-08T20:58:48.2801935Z ]
2023-12-08T20:58:48.2804047Z C:\a\_work\1\s\onnxruntime\contrib_ops\cpu\math\sparse_dense_matmul.cc(82,8): message : while compiling class template member function 'void onnxruntime::contrib::`anonymous-namespace'::SparseToDenseCsr<uint64_t>::operator ()(const onnxruntime::contrib::`anonymous-namespace'::ComputeCtx &,const onnxruntime::SparseTensor &,const onnxruntime::Tensor &,onnxruntime::Tensor &) const' [C:\a\_work\1\b\RelWithDebInfo\onnxruntime_providers.vcxproj]
2023-12-08T20:58:48.2806197Z C:\a\_work\1\s\include\onnxruntime\core/framework/data_types_internal.h(302,27): message : see the first reference to 'onnxruntime::contrib::`anonymous-namespace'::SparseToDenseCsr<uint64_t>::operator ()' in 'onnxruntime::utils::mltype_dispatcher_internal::CallableDispatchableHelper::Invoke' (compiling source file C:\a\_work\1\s\onnxruntime\contrib_ops\cpu\math\sparse_dense_matmul.cc) [C:\a\_work\1\b\RelWithDebInfo\onnxruntime_providers.vcxproj]
2023-12-08T20:58:48.2871783Z C:\a\_work\1\s\include\onnxruntime\core/framework/data_types_internal.h(438,100): message : see reference to class template instantiation 'onnxruntime::contrib::`anonymous-namespace'::SparseToDenseCsr<uint64_t>' being compiled (compiling source file C:\a\_work\1\s\onnxruntime\contrib_ops\cpu\math\sparse_dense_matmul.cc) [C:\a\_work\1\b\RelWithDebInfo\onnxruntime_providers.vcxproj]
2023-12-08T20:58:48.2893010Z C:\a\_work\1\s\include\onnxruntime\core/framework/data_types_internal.h(414,5): message : see reference to function template instantiation 'void onnxruntime::utils::MLTypeCallDispatcher<float,double,int32_t,uint32_t,int64_t,uint64_t>::InvokeWithLeadingTemplateArgs<Fn,onnxruntime::TypeList<>,onnxruntime::contrib::`anonymous-namespace'::ComputeCtx&,const T&,const onnxruntime::Tensor&,onnxruntime::Tensor&>(onnxruntime::contrib::`anonymous-namespace'::ComputeCtx &,const T &,const onnxruntime::Tensor &,onnxruntime::Tensor &) const' being compiled [C:\a\_work\1\b\RelWithDebInfo\onnxruntime_providers.vcxproj]
2023-12-08T20:58:48.2894476Z with
2023-12-08T20:58:48.2911521Z [
2023-12-08T20:58:48.2912457Z Fn=onnxruntime::contrib::`anonymous-namespace'::SparseToDenseCsr,
2023-12-08T20:58:48.3067840Z T=onnxruntime::SparseTensor
2023-12-08T20:58:48.3068863Z ] (compiling source file C:\a\_work\1\s\onnxruntime\contrib_ops\cpu\math\sparse_dense_matmul.cc)
2023-12-08T20:58:48.3195854Z C:\a\_work\1\s\onnxruntime\contrib_ops\cpu\math\sparse_dense_matmul.cc(198,11): message : see reference to function template instantiation 'void onnxruntime::utils::MLTypeCallDispatcher<float,double,int32_t,uint32_t,int64_t,uint64_t>::Invoke<onnxruntime::contrib::`anonymous-namespace'::SparseToDenseCsr,onnxruntime::contrib::`anonymous-namespace'::ComputeCtx&,const T&,const onnxruntime::Tensor&,onnxruntime::Tensor&>(onnxruntime::contrib::`anonymous-namespace'::ComputeCtx &,const T &,const onnxruntime::Tensor &,onnxruntime::Tensor &) const' being compiled [C:\a\_work\1\b\RelWithDebInfo\onnxruntime_providers.vcxproj]
2023-12-08T20:58:48.3197946Z with
2023-12-08T20:58:48.3198565Z [
2023-12-08T20:58:48.3199093Z T=onnxruntime::SparseTensor
2023-12-08T20:58:48.3905678Z ]
2023-12-08T20:58:48.3907275Z C:\a\_work\1\s\onnxruntime\contrib_ops\cpu\math\sparse_dense_matmul.cc(198,36): message : see the first reference to 'onnxruntime::utils::MLTypeCallDispatcher<float,double,int32_t,uint32_t,int64_t,uint64_t>::Invoke' in 'onnxruntime::contrib::SparseToDenseMatMul::Compute' [C:\a\_work\1\b\RelWithDebInfo\onnxruntime_providers.vcxproj]
2023-12-08T20:58:48.3910999Z ##[warning]onnxruntime\contrib_ops\cpu\math\sparse_dense_matmul.cc(88,43): Warning C4244: 'argument': conversion from 'const __int64' to 'Eigen::EigenBase<Derived>::Index', possible loss of data
2023-12-08T20:58:48.3912734Z 182>C:\a\_work\1\s\onnxruntime\contrib_ops\cpu\math\sparse_dense_matmul.cc(88,43): warning C4244: 'argument': conversion from 'const __int64' to 'Eigen::EigenBase<Derived>::Index', possible loss of data [C:\a\_work\1\b\RelWithDebInfo\onnxruntime_providers.vcxproj]
2023-12-08T20:58:48.3913414Z with
2023-12-08T20:58:48.3913660Z [
2023-12-08T20:58:48.3914001Z Derived=Eigen::Map<const Eigen::SparseMatrix<uint64_t,1,int64_t>,0,Eigen::Stride<0,0>>
2023-12-08T20:58:48.3914499Z ]
2023-12-08T20:58:48.3914743Z qlinear_concat.cc
2023-12-08T20:58:48.3917082Z ##[warning]onnxruntime\contrib_ops\cpu\math\sparse_dense_matmul.cc(92,74): Warning C4244: 'argument': conversion from 'const __int64' to 'Eigen::EigenBase<Derived>::Index', possible loss of data
2023-12-08T20:58:48.3918624Z 182>C:\a\_work\1\s\onnxruntime\contrib_ops\cpu\math\sparse_dense_matmul.cc(92,74): warning C4244: 'argument': conversion from 'const __int64' to 'Eigen::EigenBase<Derived>::Index', possible loss of data [C:\a\_work\1\b\RelWithDebInfo\onnxruntime_providers.vcxproj]
2023-12-08T20:58:48.5534583Z with
2023-12-08T20:58:48.5541266Z [
2023-12-08T20:58:48.5542401Z Derived=Eigen::Map<const Eigen::Matrix<uint64_t,-1,-1,1,-1,-1>,0,Eigen::Stride<0,0>>
2023-12-08T20:58:48.5544914Z ]
2023-12-08T20:58:48.5548670Z ##[warning]onnxruntime\contrib_ops\cpu\math\sparse_dense_matmul.cc(92,63): Warning C4244: 'argument': conversion from 'const __int64' to 'Eigen::EigenBase<Derived>::Index', possible loss of data
2023-12-08T20:58:48.5552099Z 182>C:\a\_work\1\s\onnxruntime\contrib_ops\cpu\math\sparse_dense_matmul.cc(92,63): warning C4244: 'argument': conversion from 'const __int64' to 'Eigen::EigenBase<Derived>::Index', possible loss of data [C:\a\_work\1\b\RelWithDebInfo\onnxruntime_providers.vcxproj]
2023-12-08T20:58:48.5553712Z with
2023-12-08T20:58:48.5555569Z [
2023-12-08T20:58:48.5556779Z Derived=Eigen::Map<const Eigen::Matrix<uint64_t,-1,-1,1,-1,-1>,0,Eigen::Stride<0,0>>
2023-12-08T20:58:48.5558707Z ]
2023-12-08T20:58:48.5561428Z ##[warning]onnxruntime\contrib_ops\cpu\math\sparse_dense_matmul.cc(93,90): Warning C4244: 'argument': conversion from 'const __int64' to 'Eigen::EigenBase<Derived>::Index', possible loss of data
2023-12-08T20:58:48.5565624Z 182>C:\a\_work\1\s\onnxruntime\contrib_ops\cpu\math\sparse_dense_matmul.cc(93,90): warning C4244: 'argument': conversion from 'const __int64' to 'Eigen::EigenBase<Derived>::Index', possible loss of data [C:\a\_work\1\b\RelWithDebInfo\onnxruntime_providers.vcxproj]
2023-12-08T20:58:48.5566354Z with
2023-12-08T20:58:48.5568185Z [
2023-12-08T20:58:48.5569305Z Derived=Eigen::Map<Eigen::Matrix<uint64_t,-1,-1,1,-1,-1>,0,Eigen::Stride<0,0>>
2023-12-08T20:58:48.5571339Z ]
2023-12-08T20:58:48.5574864Z ##[warning]onnxruntime\contrib_ops\cpu\math\sparse_dense_matmul.cc(93,77): Warning C4244: 'argument': conversion from 'const __int64' to 'Eigen::EigenBase<Derived>::Index', possible loss of data
2023-12-08T20:58:48.5577866Z 182>C:\a\_work\1\s\onnxruntime\contrib_ops\cpu\math\sparse_dense_matmul.cc(93,77): warning C4244: 'argument': conversion from 'const __int64' to 'Eigen::EigenBase<Derived>::Index', possible loss of data [C:\a\_work\1\b\RelWithDebInfo\onnxruntime_providers.vcxproj]
2023-12-08T20:58:48.5578562Z with
2023-12-08T20:58:48.5580399Z [
2023-12-08T20:58:48.5581503Z Derived=Eigen::Map<Eigen::Matrix<uint64_t,-1,-1,1,-1,-1>,0,Eigen::Stride<0,0>>
2023-12-08T20:58:48.5583465Z ]
2023-12-08T20:58:48.5587661Z ##[warning]onnxruntime\contrib_ops\cpu\math\sparse_dense_matmul.cc(88,54): Warning C4244: 'argument': conversion from 'const __int64' to 'Eigen::EigenBase<Derived>::Index', possible loss of data
2023-12-08T20:58:48.5590705Z 182>C:\a\_work\1\s\onnxruntime\contrib_ops\cpu\math\sparse_dense_matmul.cc(88,54): warning C4244: 'argument': conversion from 'const __int64' to 'Eigen::EigenBase<Derived>::Index', possible loss of data [C:\a\_work\1\b\RelWithDebInfo\onnxruntime_providers.vcxproj]
2023-12-08T20:58:48.5591396Z with
2023-12-08T20:58:48.5593220Z [
2023-12-08T20:58:48.5593693Z Derived=Eigen::Map<const Eigen::SparseMatrix<int64_t,1,int64_t>,0,Eigen::Stride<0,0>>
2023-12-08T20:58:48.5595955Z ]
```
And the warning in #18195
### Motivation and Context
AB#22894
---------
Co-authored-by: Dmitri Smirnov <yuslepukhin@users.noreply.github.com>
### Description
Move NuGet nightly package publishing job to a separated pipeline.
Before this change, it runs at the end of 'Zip-Nuget-Java-Nodejs
Packaging Pipeline'. This PR moves it to a separate pipeline so that we
can manually trigger this step for any branch(e.g. release branches).
### Description
This PR provided a vectorized matmul algorithm. In most situations, we
still go to the workgroup memory optimized matmul. But for some
situations, like N and K are very small, using workgroup optimized
matmul can't fully utilize the underlying hardware due to the 32x32 tile
size. So for very small N/K, we switch to the naive vectorized matmul
algorithm to improve the hardware execution unit usage.
With this PR, matmul with input0: [1, 36864, 3], input1: [1, 3, 3],
input2: [3] becomes less than 1 ms from 4.34 ms on Intel Gen9 GPUs.
Supported added in MIGraphX. should be in operator list
### Description
Simple change to add support to EP for DynamicQuantizeLinear
### Motivation and Context
Changes added in MIGraphX. Should also be available in the EP to run
models that are int8 quantized. Currently we fail and fallback ops to
ROCm->CPU EPs
Co-authored-by: Ted Themistokleous <tedthemistokleous@amd.com>
### Disable test_bert_result_with_layerwise_recompute
<!-- 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. -->
Previously, we use `ck::tensor_layout::gemm::RowMajor` or `ColumnMajor`
to tag the template for correct dispatch. This is cumbersome in the case
of CK is disabled.
Switch to use the ORT BlasOp to tag the template and use
`CKBlasOpAdaptor` to adapt between ORT BlasOp enum and ck's Col/Row.
Just like what we have done for ORT datatype and ck datatype with
`CKDataTypeAdaptor`.
### Description
<!-- Describe your changes. -->
Added uniforms to Reduce op
### 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 perforamnce.
### Description
- Adds graph fusions to preprocessing step that can be called before
creating a QDQ model for QNN EP.
- Fuse Erf sequence to Gelu (adapted from
[optimizer.py](https://github.com/microsoft/onnxruntime/blob/main/onnxruntime/python/tools/transformers/fusion_gelu.py)).
Required by QNN EP.
- Fuse ReduceMean sequence to LayerNormaliation (adapted from
[optimizer.py](https://github.com/microsoft/onnxruntime/blob/main/onnxruntime/python/tools/transformers/fusion_layernorm.py)).
Not required by QNN EP.
- Fuse ReduceL2 sequence to LpNormalization (new, specific to QNN EP).
Required by QNN EP.
Example use:
```python3
from quantization.execution_providers.qnn import get_qnn_qdq_config, qnn_preprocess_model
# Added by this PR:
model_updated = qnn_preprocess_model("model.fp32.onnx", "model.fp32.preprocessed.onnx", fuse_layernorm=True)
model_to_quantize = "model.fp32.preprocessed.onnx" if model_updated else "model.fp32.onnx"
# Quantize model ...
qnn_config = get_qnn_qdq_config(model_to_quantize, data_reader, activation_type=QuantType.QUInt16)
quantize(model_to_quantize, "model.qdq.onnx", qnn_config)
```
### Motivation and Context
Allow more models to be quantized for use with QNN EP
---------
Signed-off-by: adrianlizarraga <adlizarraga@microsoft.com>
This fix resolves the build error “error: invalid parameter combination
for AltiVec intrinsic ‘__builtin_vec_vsx_st’” which is coming up with
the commit dea425e7c1.
### Description
<!-- Describe your changes. -->
Added uniforms to Tile and Where Ops
### 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 performance.
### Description
Logs out ORT session options as INFO if LogSeverityLevel is set high
enough. Also log out ORT session options on Windows if the provider is
enabled. The events are not Telemetry are will be emitted for local
analysis (if enabled).
[Microsoft.ML.ONNXRuntime](https://github.com/microsoft/onnxruntime/blob/main/onnxruntime/core/platform/windows/telemetry.cc#L47)
- 3a26b1ff-7484-7484-7484-15261f42614d
### Motivation and Context
ORT session options are key to understanding ORT behavior. This allows
better diagnosability to see what the options are set to.
