[Reland take-2] Add JIT graph fuser for oneDNN Graph API (v0.5)
Re-landing #68111/#74596
## Description
v0.5 PR of this [RFC](https://github.com/pytorch/pytorch/issues/49444).
On the basis of #50256, the below improvements are included:
* The [v0.5 release branch](https://github.com/oneapi-src/oneDNN/releases/tag/graph-v0.5) of the oneDNN Graph API is used
* The fuser now works with the profiling graph executor. We have inserted type check nodes to guard the profiled tensor properties.
### User API:
The optimization pass is disabled by default. Users could enable it by:
```
torch.jit.enable_onednn_fusion(True)
```
`torch.jit.freeze` should be used after tracing (recommended) or scripting a model.
### Performance:
[pytorch/benchmark](https://github.com/pytorch/benchmark) tool is used to compare the performance:
* SkyLake 8180 (1 socket of 28 cores):
* SkyLake 8180 (single thread):
* By mapping hardswish to oneDNN Graph, it’s 8% faster than PyTorch JIT (NNC + OFI)
** We expect performance gain after mapping transpose, contiguous & view to oneDNN graph ops
### Directory structure of the integration code
Fuser-related code is placed under:
```
torch/csrc/jit/codegen/onednn/
```
Optimization pass registration is done in:
```
torch/csrc/jit/passes/onednn_graph_fuser.h
```
CMake for the integration code is in:
```
caffe2/CMakeLists.txt
cmake/public/mkldnn.cmake
cmake/Modules/FindMKLDNN.cmake
```
## Limitations
* In this PR, we only support Pytorch-oneDNN-Graph integration on Linux platform. Support on Windows and MacOS will be enabled as a next step.
* We have only optimized the inference use-case.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/76622
Approved by: https://github.com/eellison
