GitLab

update protobuf version

Bumps [tensorflow](https://github.com/tensorflow/tensorflow) from 2.6.4 to 2.7.2.
- [Release notes](https://github.com/tensorflow/tensorflow/releases)
- [Changelog](https://github.com/tensorflow/tensorflow/blob/master/RELEASE.md)
- [Commits](https://github.com/tensorflow/tensorflow/compare/v2.6.4...v2.7.2

)

---
updated-dependencies:
- dependency-name: tensorflow
  dependency-type: direct:production
...
Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
Co-authored-by: Chris Austen <causten@users.noreply.github.com>

Following up on issue #1166 and PR #1220. Using the same approach as in #1220 for parallelizing the eval calls, we can significantly reduce the time spent on eliminate_contiguous pass.

Addressing issue #1166 - propagate_constant pass currently uses a recursive approach to find all instructions in a module that can be evaluated to a literal and performs the replacement in the same call.

New approach:

Perform single pass though instructions in the module to determine which instructions can be evaluated
Evaluate selected instructions in parallel
Replace the selected instructions with the corresponding literal

* Upgrade to cppcheck 2.8

raw is the download for the file, blob is the url for the github page.

Bumps [tensorflow](https://github.com/tensorflow/tensorflow) from 2.5.3 to 2.6.4.
- [Release notes](https://github.com/tensorflow/tensorflow/releases)
- [Changelog](https://github.com/tensorflow/tensorflow/blob/master/RELEASE.md)
- [Commits](https://github.com/tensorflow/tensorflow/compare/v2.5.3...v2.6.4

)

---
updated-dependencies:
- dependency-name: tensorflow
  dependency-type: direct:production
...
Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
Co-authored-by: Chris Austen <causten@users.noreply.github.com>

* Improve applicable batched gemms for bert

Remove std references in runtime compilation since these are not available when using hiprtc and the headers may not be available on the system

* Fuse gemm add with pointwise fusions

As described in #1196, the ONNX mean parser does not work correctly for integral types. This update fixes the issue by handling integral types separately, where summation is performed before division. Additional test cases have also been added for handling integral types.