### Description
Update DML runtime binary to 1.15.1
### Motivation and Context
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- If it fixes an open issue, please link to the issue here. -->
### Description
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### Motivation and Context
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I am prefiring this change to pre-run the non-dml checks, and also to
give folks the time to review it before DML gets released. When DML 1.14
officially releases, we'll only need to run the DML pipeline to
automatically pick up the nuget package. This should save us some
valuable time.
Note that DML 1.14 is the release needed for ORT 1.17.4, and DML 1.15
will come soon after.
### Description
Update DML version to 1.13.1
### Motivation and Context
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### Description
- Update DML version to 1.11.0
- Disable Gemm+Softmax fusion
### Motivation and Context
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### Description
1. The protoc package on nuget.org contains binaries for
Windows_x86/Windows_x64/Linux_x86/Linux_x64/MacOS_x64, which can cover
most use cases. Though it doesn't have binaries for AMR64, they are only
needed when we cross-compile for Intel CPUs on ARM CPUs. It is rare.
When you have such a need, you always can build protoc from source by
yourself and pass it to build.py as "--path_to_protoc_exe". Or if you
have security concerns that you don't want to use prebuilt binaries from
outside, you can do the same thing.
2. Remove GoogleTestAdapter related thing. That part of code is out of
maintain.
### Motivation and Context
As a follow-up of PR #15190.
### Description
Updated DirectML version to 1.10.1
(https://www.nuget.org/packages/Microsoft.AI.DirectML/1.10.1)
### Motivation and Context
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* Add suspend handler with new telemetry event
* Fix build warning
* Use cppwinrt from nuget
* Restore nuget packages
* add dependencies
* Add nuget_helpers
* Cleaned up
* Clean up
* Comment
* Add dependencies for the rest
* Remove unused line
* Update activation string
* PR comment to remove ALL
Transitions from the ORT-only DML NuGet (hosted on the onnxruntime_public feed) to the new unified DirectML NuGet (Microsoft.AI.DirectML) on nuget.org. In addition, the Microsoft.AI.MachineLearning (WinML) and Microsoft.ML.OnnxRuntime.DirectML packages now take a dependency on the Microsoft.AI.DirectML package. This means we can remove the extra copy of DML binaries in these packages since they will be installed by the DML package.
* Merged PR 4616739: Update QLinear Ops fix 1D support layout
Update QLinear Ops fix 1D support layout
Related work items: #26011523
* Merged PR 4617257: Gather operator DML EP fails with scalar indices and 1D inputs
Fix gather with scalar value.
The ONNX conformance test case is in another PR:
// 0D, axis 1, rank 0 indices tensor
{
"op_type": "Gather",
"axis": 0,
"data": [1,2,3],
"indices": 0,
"output": 1,
"T": "float32"
}
* Merged PR 4632178: Re-enable ORT onnx_test_runner test case (DirectML ConvTranspose validation needs to be loosened to comply with ONNX definition of output_padding)
Re-enable 1D convolution tests.
Related work items: #23499747
* Merged PR 4656672: Make DML EP use Direct queue
While a Compute queue has benefits, Direct is consistent with Winml.
Related work items: #26324112
* Update DML nuget version
* Merged PR 4662079: Update DmlDev branch again from github master
Include Sheil's changes to fix namespace and header file include paths. Without this, the ONNX conformance tests all fail with E_NOTIMPL.
* Increment DML nuget version
Co-authored-by: Nick Feeney <nickfe@microsoft.com>
Co-authored-by: Dwayne Robinson <dwayner@microsoft.com>
1. Enable warning "4503" # Decorated name length exceeded.
2. Enable warning "4146" # unary minus operator applied to unsigned type.
3. Enable float64 support for the Softmax operator
4. Enable compliance checks for Windows x86 32bits build
5. Use TryBatchParallelFor to replace some fallback code in mlas pooling.cc
6. Fix Android CI pipeline.
This change adds a new execution provider powered by [DirectML](https://aka.ms/DirectML).
DirectML is a high-performance, hardware-accelerated DirectX 12 library for machine learning on Windows. DirectML provides GPU acceleration for common machine learning tasks across a broad range of supported hardware and drivers.
The DirectML execution provider is capable of greatly improving evaluation time of models using commodity GPU hardware, without sacrificing broad hardware support or requiring vendor-specific extensions to be installed.
**Note** that the DML EP code was moved verbatim from the existing WindowsAI project, which is why it doesn't yet conform to the onnxruntime coding style. This is something that can be fixed later; we would like to keep formatting/whitespace changes to a minimum for the time being to make it easier to port fixes from WindowsAI to ORT during this transition.
Summary of changes:
* Initial commit of DML EP files under onnxruntime/core/providers/dml
* Add cmake entries for building the DML EP and for pulling down the DirectML redist using nuget
* Add a submodule dependency on the Windows Implementation Library (WIL)
* Add docs under docs/execution_providers/DirectML-ExecutionProvider.md
* Add support for DML EP to provider tests and perf tests
* Add support for DML EP to fns_candy_style_transfer sample
* Add entries to the C ABI for instantiating the DML EP
Sumit Agarwal
Sheil Kumar
Patrice Vignola
Jeff Bloomfield
Changming Sun
Dwayne Robinson
Dwayne Robinson
Tiago Koji Castro Shibata
Adrian Tsai
Ori Levari
Ryan Lai
Justin Stoecker
Jeff Bloomfield