GitLab

* [Refactor] Consolidate GemmWarpPolicy Enum and Add Utility Method

- Move GemmWarpPolicy from copy.py and gemm.py to primitives/gemm/base.py
- Implement from_warp_partition class method to determine warp policy
- Add docstring with examples for policy determination
- Remove duplicate GemmWarpPolicy class definitions

* [Enhancement] Add TensorCore Intrinsic Matrix Multiplication Benchmarks

- Implement two new matrix multiplication benchmark scripts:
  1. `benchmark_matmul_intrinsic.py`: Uses TensorCore intrinsics with advanced configuration
  2. `benchmark_matmul.py`: Provides a more generic matrix multiplication benchmark

- Add support for roller-based configuration generation in both benchmarks
- Enhance MMA macro generator to handle 2D and 4D output buffer layouts
- Implement flexible autotuning configurations with multiple parameters
- Support different data types and accumulation modes
- Add command-line arguments for matrix dimensions and roller configuration

* lint fix

* Fix roller hints generation in get_roller_hints_from_func

- Simplify roller hints generation logic
- Ensure policy-based configuration is always emitted when a policy is available
- Remove redundant None check for roller hints

* Add shared memory for matrix multiplication in benchmark and quickstart examples

- Modify benchmark_matmul.py and quickstart.py to include C_shared allocation
- Change accumulation dtype from float16 to float in benchmark_matmul.py
- Update matrix multiplication kernels to use shared memory for result storage
- Enable CUDA kernel source printing in quickstart example