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
* drop nuphar code and configs
* refactor test case
* format python
* remove nuphar from training test
* remove commented nuphar logics
* restore llvm setting
* drop nuphar ci
* fix compile err
* fix compile err
Co-authored-by: Randy Shuai <rashuai@microsoft.com>
* upgrade emsdk to 3.1.19
* fix build break
* ignore '-Wunused-but-set-variable' in eigen
* add malloc and free in exported functions
* EXPORTED_FUNCTIONS
* Add first pass of rocm kernel profiler
* Clean up rocm_profiler. Format args. Demangle kernel names.
Add Api EventRecords
* Remove debug output
* Temporarily disable profiling unit test 'api record check' for cupti
* Fix compile error for non-gpu builds
* Use common file for demangle and pid/tid. Namespace ThreadUtil. Fix gpu buffer clearing.
* Merge demangle into profiler_common
* Merge demangle into profiler_common part 2
* Style cleanup
* Resolve linking issues via ProviderHost interface
* Demangle cuda kernel names
* Clean up comments
* Fix formatting
* Fix anal retentive formatting
* Make ORT as Pytorch JIT backend
LORT likely doesn't work with aten fallback so we only test LORT in its own CI.
* Revert changes to enable external CUDA allocator. Will add it later.
Revert "Revert changes to enable external CUDA allocator. Will add it later."
This reverts commit d5487f2e193014c805505afae8fb577c53667658.
Fix external allocator
* Relax tolerance and remove commented code
* Print more information in CI
* Fix pointer
* Address comments.
1. Reuse ORT-eager mode's environment.
2. Remove unused ctor.
* Use Pytorch master branch as all PRs are merged
Fix
* Refine based on cpplint feedbacks
* Revert changes to allow custom CUDA allocator in public APIs
* Use torch.testing.assert_close
* Use unittest framework
* Switch docker repo
* Rename *.cpp to *.cc
* Address comments
* Add comment
* Use same pipeline file for eager and lort pipelines
* Address comments
* Add yaml comment
* Fix cmake files
* Address comments
* Rename flags, remove printing code, remove dead comment
* Add build option to link prebuilt TensorRT parser
* Test without the build option to link prebuilt TRTParser
* Minor: update name of build option
* Minor: update name of build option
* [ROCm] enable NGramRepeatBlock Op
* [ROCm] Enable testing ROCm in NGramRepeatBlockTest.NGramSize_3
Also link onnxruntime_test_all with amdhip64 when USE_ROCM=1
* [ROCm] add LongformerAttention Op
* [ROCm] Enable LongformerAttentionTest
* [ROCm] Add DecoderAttention Op
* Enable DecoderAttention Test for ROCm.
* [ROCM] Updates according to reviews
* Split GemmBase RocBlasGemm
* Add composable kernel GEMM baseline
* Make linter happy
* Address review comment
* Update bert cases with batchsize
* Adjust includes to fix IWYU lint
* Only builds and links used ck kernels to improve building time
* Remove warmup run on SelectImpl
* Add comment to utility function
* Mute cpplint
* Make RocBlasGemm<T>::SelectImpl semantically correct
* Add reduced basic test cases for ck gemm
* More robust gemm testing
* Fix warnings
* Fix grammar
With recent versions of NDK (since 23), the `-O` optimization level compile flag is not being passed when building in the "Release" configuration.
More details here: https://github.com/android/ndk/issues/1740
Our "Release" Android builds have been built without the optimization flag since we upgraded from NDK 21.
This change is a workaround to manually add `-O3` for "Release" Android builds.
* add scripts
* update docker scripts
* update build script
* create run script
* add test script
* add log 3 flags
* use the right build function
* build navi
* add clean script
* add pytorch like soln
* only build gfx 1030
* use HOST side var
* ignore logs
* update scripts
* GPU_WARP_SIZE_HOST
* update scripts
* remove scripts/amd
* match main
* add GPU_WARP_SIZE_HOST on cuda side
* match main
* correct gfx1030
* remove print
* move gfx add to rocm5.0
* remove inline
* make constexpr on cuda side
* add description of build ORT+TVM EP on Windows
* fix cmake error related to symlink creation on Windows
* add llvm config path to build flags for correct build on Windows
* update TVM_EP.md for llvm_config build arg
* fix warnings skipping during build on Windows
* fix using string or wstring for model path to correct build on Windows (MSVC error)
* fix error in custom logger for correct build on Windows
* implement glob algorithm for Windows
* additional build fixes
* update TVM with export of VM symbols for dll
* description of nasm issue and workaround
* update TVM with export of Executable from VM symbols for dll
* description of installation of ipp-crypto dependencies on Windows
* cmake key for ipp-crypto build
* fix wstring for TVMso EP
* fix ipp-crypto build
* cmake key onnxruntime_TVM_USE_HASH switch off not specific methods, but full hash functionality
* fix absolute path to compiled lib
* update TVM_EP.md, fix lint warnings
* update TVM_EP.md
* small fixes after review
* switch on handshake functionality for Linux workflow
Co-authored-by: Valery Chernov <valery.chernov@deelvin.com>
Co-authored-by: KJlaccHoeUM9l <wotpricol@mail.ru>
