* 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>
* 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
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
* 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>
* 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>
Description:
Add the extra param to match gelu in PyTorch in the contrib symbolic function
Motivation and Context
Why is this change required? What problem does it solve?
The symbolic function in /onnxruntime/python/tools/pytorch_export_contrib_ops.py is missing a recently added parameter approximate. We add this parameter and use the exporter defined gelu if approximate is "tanh".
* remove rocm42 CI
* update torch to v1.11.0
Co-authored-by: Ethan Tao <ettao@microsoft.com@orttrainingdev7.d32nl1ml4oruzj4qz3bqlggovf.px.internal.cloudapp.net>
* Update orttraining release pipelines to use torch 1.11.0
* Change requirements_torch...txt to requirements.txt
* Update cuda cmake architectures and clean up old files
* update to torch 1.10
* update torchvision version
* update torchtext version
* remove deprecated option enable_onnx_checker
* add unit test to test gradient of GatherElements
* add ORTMODULE_ONNX_OPSET_VERSION in a docker file
* add ortmodule and eager mode test
* add ortmodule dependency
* fix eager pipeline
* skip tthe ortmodule test for windows due to win ci issue
* remove useless win ci change
* add torch
Co-authored-by: Abhishek Jindal <abjindal@microsoft.com>
