### Description
Use onnxruntime_fetchcontent_makeavailable cmake function for TRT. See
the comment for the reason.
### Motivation and Context
To support a newer TRT version. Previously they have a "BUILD_EXE" build
option to allow us to exclude such things from build. But in
https://github.com/onnx/onnx-tensorrt/pull/879 they deleted the build
option. It wouldn't be a problem if we continue to use git submodules as
before, because cmake's add_subdirectories function has an
"EXCLUDE_FROM_ALL" keyword. However, cmake's FetchContent module
doesn't. That's why I needed to create our own version of the macro.
Split copies data - we can add support for all data types without too much binary size impact by using data type size-based implementations. The DispatchStridedCopy() function used here does this.
### Description
To pass session_options to Xnnpack EP via
`XnnpackProviderFactoryCreator` for Initializing xnnpack's threadpool.
If you want to use different threadpool size or even disable xnnpack's
threadpool, just setting intra_threadpool to 1 by xnnpack EP's
provider_options.
### Motivation and Context
Co-authored-by: Guangyun Han <guangyunhan@microsoft.com>
Co-authored-by: Jicheng Wen <jicwen@microsoft.com>
Use updated ONNX license in ThirdPartyNotices.txt. It got changed to the Apache license.
Copied LICENSE file content from onnx submodule at cmake/external/onnx.
### Description
This PR fixes some typos in the training apis.
We need to add more tests and make sure they are all run on the CIs to
capture such issues. These changes are out of scope of this 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. -->
Co-authored-by: Ashwini Khade <askhade@microsoft.com@orttrainingdev8.d32nl1ml4oruzj4qz3bqlggovf.px.internal.cloudapp.net>
### Description
Replaced the previous build steps with the latest documentation in
onnxruntime.ai
### Motivation and Context
Removing duplicates in the documentation sources
### Description
- Adds a dockerfile for Ubuntu with TensorRT 8.5.1.1.
- Adds option to run EP Perf pipeline with TensorRT 8.5
### Motivation and Context
Necessary to benchmark models with TensorRT 8.5
### Description
The existing CUDA profiler is neither session-aware, nor thread-safe.
This PR ensures both.
### Motivation and Context
[PR 13549](https://github.com/microsoft/onnxruntime/pull/13549) brought
thread-safety and session-awareness to the ROCm profiler. This PR brings
the same goodness to the CUDA profiler as well.
Sample outputs of a profiling run from the StableDiffusion model (this
model was chosen because it requires orchestration of multiple sessions,
and verifies that the profilers are now indeed session-aware) on both
CUDA and ROCm EPs are attached, along with a script that checks that the
trace files generated by the profile are well-formed.
Update 11/29: Updated the profile outputs. The older profile outputs
exhibited an issue where some timestamps were wildly out of range,
leading to problems visualizing the traces. The bug has been fixed and
the profile outputs have been updated, along with an update to the check
script to ensure that timestamps are monotonically increasing.
[sd_profile_outputs_cuda.tar.gz](https://github.com/microsoft/onnxruntime/files/10118088/sd_profile_outputs_cuda.tar.gz)
[sd_profile_outputs_rocm.tar.gz](https://github.com/microsoft/onnxruntime/files/10118089/sd_profile_outputs_rocm.tar.gz)
[check_profile_output_well_formedness.zip](https://github.com/microsoft/onnxruntime/files/10118090/check_profile_output_well_formedness.zip)
Co-authored-by: Abhishek Udupa <abhishek.udupa@microsoft.com>
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Bumps [Newtonsoft.Json](https://github.com/JamesNK/Newtonsoft.Json) from
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<details>
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<p><em>Sourced from <a
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<blockquote>
<h2>13.0.2</h2>
<ul>
<li>New feature - Add support for DateOnly and TimeOnly</li>
<li>New feature - Add UnixDateTimeConverter.AllowPreEpoch property</li>
<li>New feature - Add copy constructor to JsonSerializerSettings</li>
<li>New feature - Add JsonCloneSettings with property to disable copying
annotations</li>
<li>Change - Add nullable annotation to JToken.ToObject(Type,
JsonSerializer)</li>
<li>Change - Reduced allocations by reusing boxed values</li>
<li>Fix - Fixed MaxDepth when used with ToObject inside of a
JsonConverter</li>
<li>Fix - Fixed deserializing mismatched JToken types in properties</li>
<li>Fix - Fixed merging enumerable content and validate content</li>
<li>Fix - Fixed using $type with arrays of more than two dimensions</li>
<li>Fix - Fixed rare race condition in name table when deserializing on
device with ARM processors</li>
<li>Fix - Fixed deserializing via constructor with ignored base type
properties</li>
<li>Fix - Fixed MaxDepth not being used with ISerializable
deserialization</li>
</ul>
</blockquote>
</details>
<details>
<summary>Commits</summary>
<ul>
<li><a
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Remove prerelease for 13.0.2</li>
<li><a
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Add missing headers</li>
<li><a
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Update to 13.0.2-beta3</li>
<li><a
href="b13717a1c1"><code>b13717a</code></a>
Add JsonCloneSettings to disable copy annotations (<a
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<li><a
href="d0a328e8a4"><code>d0a328e</code></a>
Fix MaxDepth not being used with ISerializable deserialization (<a
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<li><a
href="aae9284e20"><code>aae9284</code></a>
Update SDK</li>
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Update to 13.0.2-beta2</li>
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Fix deserializing via constructor with ignored base type properties (<a
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<blockquote>
<h2>13.0.2</h2>
<ul>
<li>New feature - Add support for DateOnly and TimeOnly</li>
<li>New feature - Add UnixDateTimeConverter.AllowPreEpoch property</li>
<li>New feature - Add copy constructor to JsonSerializerSettings</li>
<li>New feature - Add JsonCloneSettings with property to disable copying
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<li>Change - Add nullable annotation to JToken.ToObject(Type,
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<li>Fix - Fixed MaxDepth when used with ToObject inside of a
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<li>Fix - Fixed merging enumerable content and validate content</li>
<li>Fix - Fixed using $type with arrays of more than two dimensions</li>
<li>Fix - Fixed rare race condition in name table when deserializing on
device with ARM processors</li>
<li>Fix - Fixed deserializing via constructor with ignored base type
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<li>Fix - Fixed MaxDepth not being used with ISerializable
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Fix deserializing via constructor with ignored base type properties (<a
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<li>New feature - Add copy constructor to JsonSerializerSettings</li>
<li>New feature - Add JsonCloneSettings with property to disable copying
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<li>Change - Add nullable annotation to JToken.ToObject(Type,
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<li>Fix - Fixed rare race condition in name table when deserializing on
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Fixing misspelling (<a
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Fix deserializing via constructor with ignored base type properties (<a
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### Description
Improve the handling of "/external:I". The
"onnxruntime_external_lib_include_dir" variable may be:
1. A simple file path
2. A cmake generator expression like "$<INSTALL_INTERFACE:include>",
"$<TARGET_PROPERTY:onnx_proto,INTERFACE_INCLUDE_DIRECTORIES>",
"$<BUILD_INTERFACE:xxxx>". It seems that we can't simply put them in to
the "target_compile_options" line. So this PR tries to parse the
expression and extract the part we need out.
### Motivation and Context
Resolve the Github issue: https://github.com/microsoft/onnxruntime/issues/13893
### Description
<!-- Describe your changes. -->
As title.
This pr is missing an un-updated index.android.gradle, which causing an
unstable e2e unit test run for React Native CI.
Revert the changes for now.
### 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. -->
To unblock Ort React Native CI pipeline failure.
To fix a build error:
/home/xxxxxxxxxxxxx/onnxruntime/build/Linux/Debug/tensorboard/compat/proto/cost_graph.pb.cc:17:8:
error:
‘PROTOBUF_INTERNAL_EXPORT_tensorboard_2fcompat_2fproto_2ftensor_5fshape_2eproto’
does not name a type
17 | extern
PROTOBUF_INTERNAL_EXPORT_tensorboard_2fcompat_2fproto_2ftensor_5fshape_2eproto
::PROTOBUF_NAMESPACE_ID::internal::SCCInfo<1>
scc_info_TensorShapeProto_tensorboard_2fcompat_2fproto_2ftensor_5fshape_2eproto;
### Description
Add cuda support to the on device training python bindings.
### Motivation and Context
Now users can set the execution provider (cpu or cuda) when using python
bindings for on device training apis.
### Description
Extend GEMM autotuning by including algorithms exposed by a ROCBLAS
extension API.
### Motivation and Context
Based on our request, the ROCm team has implemented extension APIs in
ROCBLAS that provides a list of application GEMM
algorithms/implementations for a given input size, along with an API
that actually performs the GEMM using the specified
implementation/algorithm. We have observed that the ROCBLAS
algorithm/implementation selection logic does not always pick the
optimal. This PR uses the extension APIs to integrate the exposed
ROCBLAS algorithms/implementations into the autotuning framework.
The feature is disabled by default (the ROCBlas extension APIs are
slated to be released with ROCm 5.5, and are not yet generally
available). To enable: build with `--cmake-extra-defines
USE_ROCBLAS_EXTENSION_API=1 CMAKE_HIP_FLAGS=-DUSE_ROCBLAS_EXTENSION_API`
and then enable tuning in the provider options.
Co-authored-by: Abhishek Udupa <abhishek.udupa@microsoft.com>
### Description
<!-- Describe your changes. -->
1. Update FastGelu conditions for supported parameters, avoid redundant
configurations participating in tuning。
2. Add kernel explorer test for FastGeluStaticSelection
### 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: peixuanzuo <peixuanzuo@linmif39a000004.zvflicr54joexhdgnhvmxrxygg.phxx.internal.cloudapp.net>
### Description
<!-- Describe your changes. -->
1. Remove ROCm5.3 pipeline because it has rocblas bug, we don't need it.
2. We removed the dependency on centos docker image provided by
AMD(https://hub.docker.com/r/rocm/dev-centos-7) and build ROCm centos
base image by ourselves. The reference
dockerfile(https://github.com/RadeonOpenCompute/ROCm-docker/blob/master/dev/Dockerfile-centos-7)
is very redundant for our need. We simplified the ROCm manylinux
dockerfile.
3. Different versions of rocm use the same dockerfile
`Dockerfile.manylinux2014_rocm`.
### 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: peixuanzuo <peixuanzuo@linmif39a000004.zvflicr54joexhdgnhvmxrxygg.phxx.internal.cloudapp.net>
### Description
- Use same data type as input for mask_index tensor which is used as DML
GEMM API's C parameter.
- Remove gsl header include as it is already gets included transitively.
### Motivation and Context
- Why is this change required? What problem does it solve?
Bug found in internal conformance testing.
- If it fixes an open issue, please link to the issue here.
N/A
Bumps [minimatch](https://github.com/isaacs/minimatch) from 3.0.4 to
3.0.5.
<details>
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<ul>
<li><a
href="707e1b231d"><code>707e1b2</code></a>
3.0.5</li>
<li><a
href="a8763f4388"><code>a8763f4</code></a>
Improve redos protection, add many tests</li>
<li><a
href="bafa295617"><code>bafa295</code></a>
Use master branch for travis badge</li>
<li><a
href="013d64dc24"><code>013d64d</code></a>
update travis</li>
<li>See full diff in <a
href="https://github.com/isaacs/minimatch/compare/v3.0.4...v3.0.5">compare
view</a></li>
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### Description
Decouple strided tensor support from ENABLE_TRAINING
### Motivation and Context
This is step 1 for creating a dedicated build for on device training.
Intention is
1. We can set ENABLE_STRIDED_TENSORS in cmake when either
ENABLE_TRAINING or ENABLE_TRAINING_ON_DEVICE is selected, this way we
dont have to use if defined(ENABLE_TRAINING) ||
defined(ENABLE_TRAINING_ON_DEVICE ) everywhere in the code.
2. This also paves the way to easily enable strided tensor support for
inference in future (if required).
This reverts commit 87d5703b14.
### Motivation and Context
There was a bug in Linux CUDA installation.
The OS image is updated.
The TestCUDAProviderOptions could be reenabled.
### 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
This PR enables building nuget packages locally for on device training
using --build_nuget arg.
This PR also enables the C# bindings by default in the managed package.
If a user triggers any training apis when the native binary is not built
for training, an exception with message "Training is disabled in the
current build. Please build ONNXRuntime from source with the build flags
enable_training and enable_training_on_device. " is thrown.
Build command for creating nuget packes for on device training:
build.bat --enable_training --enable_training_on_device --build_nuget
2 Nuget packages are built
1. Microsoft.ML.OnnxRuntime.Managed
2. Microsoft.ML.OnnxRuntime.Training OR
Microsoft.ML.OnnxRuntime.Training.Gpu
### 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
GetInputMemoryType was introduced in ver 13 in [this
PR](https://github.com/microsoft/onnxruntime/pull/10879). The ver check
introduced in this PR allows custom ops compiled using older versions to
work with newer versions (> 12) of the ORT binary.
### Motivation and Context
Fixes binary compatibility.
### Description
Add the NonZero op for DML
### Motivation and Context
NonZero is used in a few transformer models, so having a DML
implementation will stop large tensors from being transferred to the CPU
and back to the GPU
### Description
This PR implements the **Concat Operator** for the **OneDNN Execution
Provider**.
### Motivation and Context
- As part of evaluating ORT performance on ARM based targets such as
Graviton3, we discovered that the OneDNN EP had some gaps on operator
coverage.
- The Concat Operator is fairly common and used in models such as
Yolov5, MobileNet, DistillBert and GPT2
- For Yolov5 specifically, this improves average inference time over 100
runs on Graviton3 from 180.2ms to 115.5ms when using OneDNN + ARM
Compute Library.
Co-authored-by: Gaz Iqbal <giqbal@octoml.ai>
