Wmma support for grouped convolution bwd weight

[EnricoDeg]

Proposed changes

Summary:

• Modify gridwise implementation to work with convolution (grid descriptors are not created internally but passed from the device level)
• Add device level implementation: DeviceGroupedConvBwdWeight_Wmma_CShuffleV3 , DeviceGroupedConvBwdWeightTwoStage_Wmma_CShuffleV3 and DeviceGroupedConvBwdWeightMultipleD_Wmma_CShuffleV3
• Add device implementation of batched gemm multiple Ds (needed for explicit gemm - conv bwd weight)
• Adapt existing device implementation of explicit gemm to work for both xdl and wmma implementations of batched gemm multiple Ds
• Add support for occupancy-based splitk for one stage and two stage implementations of grouped conv bwd weight
• Create instances
• Add examples
• Remove old instances (they don't support splitk)
• Add tests for bwd weight scale

The implementations are based on CShuffleV3 but the functionality is the same as xdl.

Checklist

Please put an x into the boxes that apply. You can also fill these out after creating the PR. If you're not sure, please don't hesitate to ask.

• [x] I have added tests relevant to the introduced functionality, and the unit tests are passing locally
• [x] I have added the test to REGRESSION_TESTS list defined at the top of CMakeLists.txt in tests/CMakeLists.txt, IF the test takes more than 30 seconds to run.
• [x] I have added inline documentation which enables the maintainers with understanding the motivation
• [x] I have removed the stale documentation which is no longer relevant after this pull request
• [ ] (If this change is user-facing) I have added release notes which provide the end users with a brief summary of the improvement from this pull request
• [x] I have run clang-format on all changed files
• [x] Any dependent changes have been merged

Discussion

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