Patch Protobuf and ONNX's cmake files and enforce BinSkim check.
This PR has overlap with #13523 . I would prefer to get this one merged
first so that we can finished the BinSkim work, and I try to make this
PR as small as possible.
### Description
After this change, you will see GSL.natvis and wil.nativs files will be
added to every onnxruntime_xxx project.
Like this:
This is because in onnxruntime_common.cmake we have:
```cmake
if (MSVC)
set(ABSEIL_NATVIS_FILE "abseil-cpp.natvis")
target_sources(
onnxruntime_common
INTERFACE $<BUILD_INTERFACE:${PROJECT_SOURCE_DIR}/external/${ABSEIL_NATVIS_FILE}>)
endif()
```
It sets a property, INTERFACE_SOURCES, on the target
"onnxruntime_common".
Then if anyone else uses:
```
target_link_libraries(mytarget PRIVATE onnxruntime_common)
```
The nativis file will be added to `mytarget`.
However, in this project we don't use such things for the targets that
are static libraries. For example, onnxruntime_graph is a static
library.
Instead, we use the `onnxruntime_add_include_to_target ` function to
explicitly control what we want to propagate . The function was written
before we started to have nativis files. So it doesn't pass a source
file from one static library to another. Now we have the need. Probably
only for Windows.
### Motivation and Context
Add natvis files to every project.
### Description
Fix round 6
### 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 a DML registration for Shape to avoid copying back to the CPU just
to get the shape of a GPU tensor.
### Motivation and Context
When using free dimensions, many Transformers models extensively use the
`Shape` operator. This causes hundreds of GPU->CPU copy that should be
completely avoidable. Note that this change also uses the same
heuristics as other providers (e.g. CUDA) to force some tensors on the
CPU in certain situations.
Co-authored-by: Patrice Vignola <pavignol@microsoft.com>
### Description
In the TVM EP, this adds more entries to the conversion from
`ONNXTensorElementDataType` to `DLDataType`. Additionally, it removes an
unused function and updates the TVM revision to allow running models
from recent revisions of TVM.
### Motivation and Context
In the TVM EP, the mapping from `ONNXTensorElementDataType` to
`DLDataType` was incomplete and neglected several integer types (in
particular `ONNX_TENSOR_ELEMENT_DATA_TYPE_UINT8` and
`ONNX_TENSOR_ELEMENT_DATA_TYPE_INT8`) which prevented some models from
running.
Co-authored-by: Peter Salas <psalas@octoml.ai>
1. Remove the cmake option onnxruntime_DEV_MODE and replace it with
"--compile-no-warning-as-error"
2. Suppress some GSL warnings because now we treat nvcc diag warnings as
errors
### Description
Upgrade cmake version to 3.24 because I need to use a new feature that
is only provided in that version and later. Starting from cmake 3.24,
the
[FetchContent](https://cmake.org/cmake/help/latest/module/FetchContent.html#module:FetchContent)
module and the
[find_package()](https://cmake.org/cmake/help/latest/command/find_package.html#command:find_package)
command now support integration capabilities, which means calls to
"FetchContent" can be implicitly redirected to "find_package", and vice
versa. Users can use a cmake variable to control the behavior. So, we
don't need to provide such a build option. We can delete our
"onnxruntime_PREFER_SYSTEM_LIB" build option and let cmake handle it.
And it would be easier for who wants to use vcpkg.
### Motivation and Context
Provide a unified package management method, and get aligned with the
community. This change is split from #13523 for easier review.
### Description
This adds bfloat16 support to the oneDNN ep.
When using the oneDNN ep this enables bfloat16 support for the following
ops:
Exp, Sigmoid, Tanh, Relu, MatMul, Gelu, BiasGelu, Add, Sub,
Mul, Div, Div, Sqrt, Pow, ReduceMean, Abs, Cast, Equal, Exp,
FastGelu, FusedMatMul, Gemm, Greter, GreaterOrEqual, LeakyRelu,
Less, LessOrEqual, LRN, ReduceOps, Reshape, Squeeze, Transpose,
and Unsqueeze.
LayerNorm with some internal casting.
BatchNorm only enabled BFloat16 for input and output, scale and bias
still need fp32 input.
Added bfloat16 unit tests for all of the operators in question. When
possible we reused the already existing unit tests that were added by
CUDA and ROCM eps.
In many of the unit tests an unusual pattern will be seen
#if defined(USE_DNNL)
TEST(Test, bfloat16_test) {
#if defined(USE_DNNL)
// oneDNN ep specific code
#endif
//test code
}
#endif
Although it looks unusual this was purposely done if another ep
implements bfloat16 support for that operator they will be able to
enable the unit test by adding there execution provider to the first
line without needing to edit inside the test.
Example: `#if defined(USE_CUDA) || defined(USE_DNNL)` see the
MatMul_float16 test in matmul_test.cc for and example of how this is
useful.
Additionally two new ISA checks (AVX512_BF16 and AMX-BF16) were added to
the cpuid_info code in. This was important to detecting is bfloat16
operations are supported by the CPU.
### Motivation and Context
This expands the capabilities of the oneDNN execution provider to
support models containing bfloat16 operations.
Signed-off-by: George Nash <george.nash@intel.com>
Signed-off-by: Ruihan-Yin <ruihan.yin@intel.com>
1. Update CK to its latest develop branch
2. `-mllvm -amdgpu-early-inline-all=true` is critical to CK's
performance, ensure it is properly configured.
- The flags are propagated from target `hip-lang::device`'s
`INTERFACE_COMPILE_OPTIONS`, we must not manually add the flags.
- Instead, we must ensure this target is properly configured by checking
_CMAKE_HIP_DEVICE_RUNTIME_TARGET is set.
TL,DR
`hip-lang::device` sometime will be not be properly configured if our
`CMAKE_PREFIX_PATH` is not configured carefully. In the CI docker, the
configuration is in good state, but on dev machine it is not, which then
silently result poor performance for kernels. We fixed it in this PR and
add a guard to avoid unsuccessful future editing and to prevent
convoluted debugging process.
`_CMAKE_HIP_DEVICE_RUNTIME_TARGET ` is shared in
`/opt/rocm/lib/cmake/hip-lang/hip-lang-config.cmake` and it is internal
to
[CMake](https://gitlab.kitware.com/cmake/cmake/-/merge_requests/6121/diffs),
the variable name will not be changed in the foreseeable future.
The oneDNN 2.7.1 release includes multiple functional and performance
improvements.
Signed-off-by: George Nash <george.nash@intel.com>
### Description
Update the oneDNN library from 2.7.0 to 2.7.1. This contains multiple
functional and performance improvements.
### Motivation and Context
This is a minor point release from the oneDNN library that gives
performance and functional fixes that were found in the oneDNN 2.7
library shortly after release.
Signed-off-by: George Nash <george.nash@intel.com>
This PR enables ORT to execute graphs captured by TorchDynamo. Major compilation code is in `OrtBackend.compile` in ort_backend.py. `register_backend.py` is for plugging `OrtBackend` into TorchDynamo as a compiler.
### Description
support building xnnpack for IOS
### 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
Currently, hipify happens before cmake is configured and then cmake glob
the directories. This get rids of thoes customized python threading
logic and opt for build system itself to generate the files.
This also supersede the half baked branch
[sukha/hipify-with-cmake](https://github.com/microsoft/onnxruntime/tree/sukha/hipify-with-cmake)
**Description**: Changes to the MIGraphx execution provider code to
allow for stream synchronization on the gpu side
**Motivation and Context**
Performance boost by removing redundant host to device synchronizations
The current implementation of the execution provider continuously calls
hipDeviceSynchronize() between computations which adds overhead and an
idle wait between the GPU's computations. This is noticeable during
device
This change leverages new functionality that's been added to MIGraphX to
allow for GPU side synchronization which avoids the need for
host->device waits.
To maintain backwards compatibility with older MIGraphX versions, the
compile time define MIGRAPHX_STREAM_SYNC has been added to the API to
allow for older version operate with newer builds of onnxruntime without
loss of functionality to the current feature set as of (08/09/22)
Co-authored-by: Ted Themistokleous <tthemist@amd.com>
`python setup.py develop` doesn't install PyTorch as a normal package in
site-packages anymore, and the user must stay at PyTorch's root
directory to call `import torch`. This will break LORT tests because
LORT tests contains `import torch` and are called outside PyTorch root
directory. To make PyTorch a normal package again, this PR build PyTorch
with `python setup.py install`.
Fix warnings and enable dev mode for ROCm CI:
* Fix ROCm headers complaining "This file is deprecated. Use the header file from ..."
* Disable warning signed and unsigned compare for kernel explorer
* Fix unused and nondiscard warnings
* Enable dev mode for ROCm CI
* Walkaround error "unknown warning option '-Wno-nonnull-compare'" in kernel explorer by using '-Wno-unknown-warning-option' to ignore the unknown option
* Fix error "unused parameter 'mask'"
* Fix warning "instantiation of variable 'onnxruntime::rocm::Consts<float>::One' required here, but no definition is available", etc. Fixed by using C++17's inline (implied by constexpr) static initialization.
* Remove unused variable
* Add the missing `override` specifier
Update for ROCm CI before reland tunable GEMM #12853. This PR also update
composable kernel to use CMakes's HIP language support so that we can
mix C/C++ compiler with HIP compiler instead of locking to hip-clang
### Description
fix XNNPACK on WebAssembly SIMD.
Flag "-msimd128" need to be applied to every source file when compiling
WASM SIMD. Currently only a part of the source files are compiled with
this flag so we get inconsistent result for
`sizeof(xnn_f32_minmax_params)` because the type definition include a
`#ifdef` for `__wasm_simd128__`. The inconsistency causes writing
garbage data to a stack variable and eventually cause the crash.
XNNPACK libraries are C libraries so need to apply the build flags not
only to `CMAKE_CXX_FLAGS` but also to `CMAKE_C_FLAGS`.
### Description
This updates the oneDNN library used by oneDNN ep from version 2.6 to
version 2.7
### Motivation and Context
This brings in the many improvements incorporated into the oneDNN
library to the oneDNN execution provider.
Signed-off-by: George Nash <george.nash@intel.com>
Change ROCm to use tunable GEMM. It is not enabled in this PR. This will drastically improve GEMM performance in some shapes and dtypes configuration. This will benefit the overall performance for BERT inference and hopefully, training, when enabled.
### Description
<!-- Describe your changes. -->
As title
-Split long OpBuilder and OpSupportChecker files into individual
operator files.
-Add OpBuilder/SupportChecker registry factories.
-Combine the functionality of op_builder and op_support_checker into one
op_builder.
### 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. -->
The NNAPI OPBuilder was splitted into OPBuilder (For EP::Compile) and
OPSupportChecker (for EP::GetCapability)
At the time it was reasonable choice, but OPBuilder/OPSupportChecker
share some logic and has to use addition helper.
Clean up now to make NNAPI OPBuilder/OPSupportChecker into single
OPBuilder (similar to what CoreML EP has)
Fixesmicrosoft/onnxruntime#12969
### Motivation and Context
Build is broken, can't find cudnn.lib with nvidia official install of
cuDNN
Alternative method is to use `IF(EXISTS
${onnxruntime_CUDNN_HOME}/lib/x64/cudnn.lib)` to test for legacy
location and only add the legacy dir to the path, else add the current
official `lib/` dir.
1. Update CUDA version from 11.4 to 11.6.
2. Update Manylinux version
3. Upgrade GCC version from 10 to 11 for most x86_64 pipelines. CentOS 7 ARM64 doesn't have GCC 11 yet.
4. Refactor python packaging pipeline:
a. Split Linux GPU build job to two parts, build and test, so that the
build part doesn't need to use a GPU machine
b. Make the Linux GPU build job and Linux CPU build job more similar: share the same bash script and yaml file.
5. Temporarily disable Attention_Mask1D_Fp16_B2_FusedNoPadding because it is causing one of our packaging pipeline to fail. I have created an ADO task for this.
Some Ops in EP directory instead of contrib_ops directory will
require TunableOp. We will also need to add EP level session tuning
options for it. So move those code all at once.
Also remove duplicated utility functions.
**Description**: This PR adds Ascend CANN execution provider support.
**Motivation and Context**
- Why is this change required? What problem does it solve?
As the info shown in the issue. CANN is the API layer for Ascend
processor. Add CANN EP can allow user run onnx model on Ascend hardware
via onnxruntime
The detail change:
1. Added CANN EP framework.
2. Added the basic operators to support ResNet and VGG model.
3. Added C/C++、Python API support
- If it fixes an open issue, please link to the issue here.
https://github.com/microsoft/onnxruntime/issues/11477
Author:
lijiawei <lijiawei19@huawei.com>
wangxiyuan <wangxiyuan1007@gmail.com>
Co-authored-by: FFrog <ljw1101.vip@gmail.com>
This changes are to align OV 2022.2 Release with ORT . Changes
CPU FP16 Support, dGPU Support, RHEL Dockerfile, Ubuntu 20 Dockerfile
**Motivation and Context**
- This change is required to ensure ORT-OpenVINO Execution Provider is
aligned with latest changes.
- If it fixes an open issue, please link to the issue here.
Co-authored-by: mayavijx <mayax.vijayan@intel.com>
Co-authored-by: shamaksx <shamax.kshirsagar@intel.com>
Co-authored-by: pratiksha <pratikshax.bapusaheb.vanse@intel.com>
Co-authored-by: pratiksha <mohsinx.mohammad@intel.com>
Co-authored-by: Sahar Fatima <sfatima.3001@gmail.com>
Co-authored-by: Preetha Veeramalai <preetha.veeramalai@intel.com>
Co-authored-by: nmaajidk <n.maajid.khan@intel.com>
Co-authored-by: Mateusz Tabaka <mateusz.tabaka@intel.com>
Co-authored-by: intel <intel@iotgecsp-nuc04.iind.intel.com>
**Description**: **Python API Bindings for on device training. **
**Motivation and Context**
- This PR contains api bindings so python users can perform a whole
training loop.
Co-authored-by: Adam Louly <adamlouly@microsoft.com@orttrainingdev7.d32nl1ml4oruzj4qz3bqlggovf.px.internal.cloudapp.net>
Co-authored-by: Baiju Meswani <bmeswani@microsoft.com>
* consume ONNX 1.12.1 to prevent vulnerability issue while loading external tensors
* update ONNX 1.12.1
* test updated PR
* use official rel-1.12.1 commit
