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.
**Description**: LayerNormalization is now part of the ONNX spec as of
opset 17.
We had a LayerNormalization contrib op, which (incorrectly) was
registered in the ONNX domain. Use that implementation for the ONNX
operator.
Update skip_layer_norm_fusion.cc. There are other optimizers that use
LayerNormalization that need updates as well.
**Motivation and Context**
#12916
**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>
**Description**:
Use full ORT package for onnxruntime-react-native.
Left the params required for the mobile build in comments so they're
easily discovered if we need to create onnxruntime-react-native-mobile
in the future.
**Motivation and Context**
Remove barrier to using ORT with react native as the mobile package that
was being used supports a limited range of opsets/operators/types, and
requires ORT format models. The full package will run any model.
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>
1. add node test data to current model tests
2. support opset version to filter tests.
3. remove old filter based on onnx version. To avoid confusion, ONLY
support opset version filter in onnxruntime_test_all
4. support read onnx test data from absolute path on Windows.
# Motivation
Currently, ORT minimal builds use kernel def hashes to map from nodes to
kernels to execute when loading the model. As the kernel def hashes must
be known ahead of time, this works for statically registered kernels.
This works well for the CPU EP.
For this approach to work, the kernel def hashes must also be known at
ORT format model conversion time, which means the EP with statically
registered kernels must also be enabled then. This is not an issue for
the always-available CPU EP. However, we do not want to require that any
EP which statically registers kernels is always available too.
Consequently, we explore another approach to match nodes to kernels that
does not rely on kernel def hashes. An added benefit of this is the
possibility of moving away from kernel def hashes completely, which
would eliminate the maintenance burden of keeping the hashes stable.
# Approach
In a full build, ORT uses some information from the ONNX op schema to
match a node to a kernel. We want to avoid including the ONNX op schema
in a minimal build to reduce binary size. Essentially, we take the
necessary information from the ONNX op schema and make it available in a
minimal build.
We decouple the ONNX op schema from the kernel matching logic. The
kernel matching logic instead relies on per-op information which can
either be obtained from the ONNX op schema or another source.
This per-op information must be available in a minimal build when there
are no ONNX op schemas. We put it in the ORT format model.
Existing uses of kernel def hashes to look up kernels are replaced
with the updated kernel matching logic. We no longer store
kernel def hashes in the ORT format model’s session state and runtime
optimization representations. We no longer keep the logic to
generate and ensure stability of kernel def hashes.
Recent change in CUDA EP #12814 makes hipify extremely slow and breaks the building. This PR fixes it by c
The onnxruntime/contrib_ops/rocm/bert/attention.h is checkout-ed from the version before #12814 and manually hipify-ed.
Slightly extend amd_hipify.py to allow wildcard file match and exclude all `tensorrt_fused_multihead_attention/*` files from hipify
1. Move the Linux ARM64 part of python packaging pipeline to a real ARM64 machine pool
2. Refactor the Linux CPU build jobs of python packaging pipeline to two parts: build and test. The test part will be exempted from Cyber EO compliance requirements as it won't affect the final bits we publish. This refactoring is to reduce dependencies in the build part. For example, this PR remove pytorch from the build dependencies.
3. Combine DML nuget packaging pipeline with "Zip-Nuget-Java-Nodejs Packaging Pipeline" as they all produce ORT nuget packages. Also, publish DML nuget packages and ORT GPU nuget packages to https://aiinfra.visualstudio.com/PublicPackages/_artifacts/feed/ORT-Nightly feed.
* Fix bug in pybind get_all_operator_schema due to premature reference dropping
* Add updated operator kernels markdown table
* Update build.py to include documentation generation for DML operators too
* Update GPU pipeline to include DML in the build to so operators can be generated.
* Use a separate pipeline stage, feedback from Changming and Scott
* Appease annoying Python linter
* Add onnxruntime_BUILD_UNIT_TESTS=OFF and remove stale --use_dml in cuda stage
* 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
* upgrade cuda version on ci pipelines
* keeping folder name same
* keeping folder name same
* setting manual seed for primitive test case
* resolving comments
* changing atol and rtrol only for test case
Co-authored-by: Adam Louly <adamlouly@microsoft.com@orttrainingdev7.d32nl1ml4oruzj4qz3bqlggovf.px.internal.cloudapp.net>
* moving training pipelines from cuda 11.5 to 11.6 and deprecating cuda 11.3
* change to cuda 11.6.2
* change pytorch's & torchvision's cuda version to 11.6
* specify deps version to 11.6.2
* update pytorch and torch text version
* torch 1.12.1
* change torchvision and torchtext version to be compatible with torch 1.12.1
* change cuda to 11.6 for cuda_home comaptibility
Co-authored-by: Adam Louly <adamlouly@microsoft.com@orttrainingdev7.d32nl1ml4oruzj4qz3bqlggovf.px.internal.cloudapp.net>
* Add asm statement to model.mm to force linker to link against CoreML.Framework.
Update targets.xml as per Rolf's suggestions
* Remove explicit numpy version from macos build. We don't specify it for other CIs and the version specified doesn't have a pre-built 3.10 wheel. This leads to the CI attempting to build numpy which fails.
* 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
* Update to handle multiline declarations for the kernels which are typical these days.
* Update to new path for the cpu contrib_op kernel registrations.
* Update tools/python/find_optimizer_opset_version_updates_required.py
Co-authored-by: Justin Chu <justinchuby@users.noreply.github.com>
* [ROCm] Add InstanceNormalization Op
* Enable InstanceNormBatch1_fp16 and InstanceNormBatch2_fp16 for ROCm
* [ROCm] Add BatchNormalization for fp32 and fp16
* Enable BatchNormTest for ROCm
* [ROCm] Add LRN Op
* [ROCM] replace miCompat functions with Helper functions
Losen the following test timeout:
1. "Test Web Multi-Browsers" stage in "ONNX Runtime Web CI Pipeline": 30min -> 60min
2. Node.js binding default per-case timeout: 30 sec -> 90 sec
1. Delete the build scripts that were copied from manylinux project. Use "git checkout" instead.
2. Update manylinux version to get python 3.11. Related issue: Python 3.11 support #12343
3. Change the cuda version of linux gpu build job of nuget packaging pipeline from cuda 11.4 to cuda 11.6 to match the TRT job within the same pipeline.. (A lot other places need be updated as well, but I'd prefer to put them in another PR)
4. Make dockerfile names static. For example, replace tools/ci_build/github/linux/docker/$(DockerFile) to tools/ci_build/github/linux/docker/Dockerfile.manylinux2014_cpu . The former one relies on a runtime variable $(DockerFile), Template Parameters are expanded early in processing a pipeline run when most variables are not available. It like C++ macros vs variables.
* update to 2022
* Update the VS version
* Rolling back to gcc 10
* Rolling back
* Update cuda home
* remove "CMAKE_CUDA_ARCHITECTURES=52"
* update cuda Architure to 70
* Delete cuda 10.2 training pipeline
* rolling back a mistake
* Update win-gpu-reduce-op-ci-pipeline.yml
* Update win-gpu-reduce-op-ci-pipeline.yml
* Update win-gpu-reduce-op-ci-pipeline.yml
* Delete tools/ci_build/github/linux/docker/scripts/training/ortmodule/stage1/requirements_torch1.10.0_cu10.2 directory
* Delete tools/ci_build/github/linux/docker/scripts/training/ortmodule/stage1/requirements_torch1.11.0_cu10.2 directory
Current builds use a NDK version that happens to be on the build machine. The build machine environment may change in ways that are outside of our control.
This change installs a specific version of NDK (the current LTS version 25.0.8775105) and uses it.
* refactor test for model with undefined shapes
* add test for TVMso EP
* update build script for TVM EP tests
* fix pylint
* disable test for Windows
* fix black
* fix python format
* fix pylint
* fix python format
* replace Path.resolve with os.path.join
* fix python path issue
Co-authored-by: Valery Chernov <valery.chernov@deelvin.com>
Fix bug where onnxruntime_USE_NCCL flag would default to ON, causing ORT to not build properly. New functionality: flag is ON when training is enabled and NCCL is not disabled. Flag is OFF otherwise
* [UPDATE] update ci to rocm5.2 + torch1.11
* [Revert] disable ort module test
* [DELETE] delete Rocm5.1.1 ci test result
* [UPDATE] update the comments
* 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>
* Add tests for all uniary aten ops supported in eager mode
* fixing the PR draft
* fixing the merge
* changing eval to be at compile time
* adding requirements for eager
* 1.adding function to {ops}_out
2.cleaning the code
and adding comments
* editing the code according to code review
Co-authored-by: root <root@AHA-LIRONKESE-1>
* First attempt for half2 vectorized memory access in SkipLayerNorm
* Add some functions for debugging
* Clean up the code
* Clean up the code
* Generalize the vectorized kernels with aligned_vector and remove cudaDeviceProp
* Add a unit test for a larger input size
* Fix some Lint C++ warnings
* Use ILP = 4 for the vectorized kernels
* Rewrite the vectorized kernel and templatize ComputeSkipLayerNorm
* Use conditional operator for input_v
* Refactor LaunchSkipLayerNormKernel and replace the original SkipLayerNormKernelSmall with the vectorized kernel
* Clean some comments and rename the layernorm function
* Use ComputeSkipLayerNorm to replace LaunchSkipLayerNormKernel
* Resolve a Lint C++ warning
* Fix SkipLayerNormBatch1_Float16_vec output data
* Add hipified code of bert SkipLayerNorm for ROCmEP
* Resolve some Lint C++ warnings
* Resolve some Lint C++ warnings
* Resolve some Lint C++ warnings
* Resolve Python formatting issue
* Add net6 targets.
Remove maccatalyst as we don't have a native build targetting that.
* Set platform in macos targets
* Add targetFramework entries
* Move NativeLib.DllName definition and set using preprocessor values for simplicity. Couldn't get it to build with the preprocessor based setup when it was in a separate file.
Update the nuspec generation to set platform version for .net6 targets. TODO: Validate versions. I copied them from the managed nuget package the packaging pipeline generated prior to adding targets. Possibly w could/should lower some of the versions.
Hopefully the need to specify a version goes away when the release version of VS2022 supports .net6.
* Try android 31.1 as https://github.com/actions/virtual-environments/blob/main/images/win/Windows2022-Readme.md suggests that should be available on the CI machines
* Fix patch version mismatch
Add some extra debug info in case it helps
* Debug nuget location in CI
* Add workspace entry back in
* Add steps
* One more attempt with hardcoded nuget.exe path and original android31.0 version
* Better fix - found explicit nuget download and updated version there.
* flake8 fixes
* Fix black complaints.
* Exit Microsoft_ML_OnnxRuntime_CheckPrerequisites for net6 iOS.
* Removed outdated comment
* Using vectorized loads (float2) for fp16 to improve performance
* Fix a few warnings from cpplint
* Fix a few warnings from cpplint
* Use __float2half2_rn and fix some cpplint warnings
* Move some computaions to LaunchFastGeluKernel
* Fix some Lint C++ warning
* Using vectorized loads (float4) for fp16 to improve performance
* Switch whether to optimize FastGelu with float4 vectorization
* Switch to float4 memory access based on input_length in FastGelu
* Comment how to set the threshold of float2 and float4 vectorized kernels
* Add FastGelu fp16 unit tests for bias_length = 2 and 8
* Make vectorized kernels generic with aligned_vector
* Unify the vectorized kernels with/without bias
* Refactor the code to suppress cpplint warnings
* Solve formatting issues
* Remove cudaDeviceProp from FastGeluKernel and LaunchFastGeluKernel
* Move fast_gelu_impl.h to rocm/bert
* Fix some Lint C++ warnings and code alignment
* Add .net6 support to the C# nuget package.
Currently requires jumping through a lot of hoops due to .net 6 only being supported in the preview release of VS 2022.
Build existing targets using msbuild.
Add .net6 targets and build using dotnet.
Create nuget package with combined targets.
A few misc automated changes from VS to spacing and adding a couple of properties.
* Try manually installing trt8.4 in multi-gpu pipeline
* Remove stmts that clean up cmake, ctest. Update tensorrt repository name passed to get_docker_image.py
* Update trt and cudnn home
* Don't install trtexec cli tool.
* Increase job timeout
* Revert timeout change and use trt placeholder builder build option
* update trt 8.4ga
* trt 8.4 linux ci pipeline
* fix cmake
* placeholder_builder
* trt 8.4 windows pipeline
* gpu package pipeline
* trt 8.4.1.5 , packaging pipeline updates
* python packaging
* ctest timeout
* python packaging test
* bump timeout
* python format
* format
* revert
* newline
* enable trt python tests
* typo
* python format
* disable on windows
* Rework the EP factory creation setup so we're not cut-and-pasting function declarations in multiple places.
Convert append EP for SNPE to be generic, and also use for XNNPACK.
Add XNNPACK to C# API
* Don't need stub for MIGraphX as it's using provider bridge.
* Remove old 'create' functions that aren't applicable now that the EPs are built as separate libraries.
* Only use EPs that require the layout transform if the opset is supported by the layout transformer.
* Update wasm registration of xnnpack.
* aten op for inference
* fix build error
* more some code to training only
* remove domain from operator name
* move aten_op_executor ext out from ortmodule
* add pipeline
* add exec mode
* fix script
* fix ut script
* fix test pipeline
* failure test
* rollback
* bugfix
* resolve comments
* enable aten for python build only
* fix win build
* use target_compile_definitions
* support io binding
* turn off aten by default
* fix ut
Co-authored-by: Vincent Wang <weicwang@microsoft.com>
Co-authored-by: zhijxu <zhijxu@microsoft.com>
* update TVM
* get alignment constant from TVM
* update TVM_VM_SetInputs to upstream with TVM API
* fix CI issue: update TVM EP dependencies
* add sudo
* revert changes needed to install missing package
* add package for TVM EP CI
Co-authored-by: Valery Chernov <valery.chernov@deelvin.com>
Co-authored-by: KJlaccHoeUM9l <wotpricol@mail.ru>
* Initiate Ort SNPE EP
* fix snpe ep windows build which is caused by the utility method (ToUTF8String) name change on master
* correct the source path for libonnxruntime.so while building for andorid package
* add AdditionalDependencies for amr64
* On MS-Windows, the patchfile must be a text file, i.e. CR-LF must be used as line endings. A file with LF may give the error: "Assertion failed, hunk, file patch.c, line 343," unless the option '--binary' is given.
* fix build failure if snpe is not enabled
* update doc for contrib op
* separate out snpe ep settings to onnxruntime_snpe_provider.cmake
* renaming according review comments
* update according review comments
* Implement XNNPACK support via an EP.
* Layout transform uses the GraphPartitioner infrastructure.
* Node fusion is supported.
* Conv and MaxPool implementations were ported from Changming's PR.
* Added optional mutex in InferenceSession::Run as we only want to allow sequential calls if xnnpack is enabled
