### Description
1. Renames all references of on device training to training apis. This
is to keep the naming general. Nothing really prevents us from using the
same apis on servers\non-edge devices.
2. Update ENABLE_TRAINING option: With this PR when this option is
enabled, training apis and torch interop is also enabled.
3. Refactoring for onnxruntime_ENABLE_TRAINING_TORCH_INTEROP option:
- Removed user facing option
- Setting onnxruntime_ENABLE_TRAINING_TORCH_INTEROP to ON when
onnxruntime_ENABLE_TRAINING is ON as we always build with torch interop.
Once this PR is merged when --enable_training is selected we will do a
"FULL Build" for training (with all the training entry points and
features).
Training entry points include:
1. ORTModule
2. Training APIs
Features include:
1. ATen Fallback
2. All Training OPs includes communication and collectives
3. Strided Tensor Support
4. Python Op (torch interop)
5. ONNXBlock (Front end tools for training artifacts prep when using
trianing apis)
### Motivation and Context
Intention is to simply the options for building training enabled builds.
This is part of the larger work item to create dedicated build for
learning on the edge scenarios with just training apis enabled.
### 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
<!-- Describe your changes. -->
ROCm developers always need to build onnxruntime *whl with
`--enable_rocm_profiling`.
Add a ROCm dev python package pipeline which product *.whl with build
args `--enable_rocm_profiling`.
The dev *whl need to upload to azure storage and can get from
https://download.onnxruntime.ai/onnxruntime_nightly_rocm53.profiling.html
### 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. -->
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
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.
