Implement grouped GEMM (C_g = A_g x B_g for g groups) on Hopper using
CuTe DSL, extending the dense persistent GEMM with per-group TMA
descriptor management.
Kernel design (grouped_gemm.py):
- Warp-specialized pipeline: DMA warp group handles TMA loads and
per-group tensormap updates; MMA warp group runs WGMMA and stores C
- StaticPersistentGroupTileScheduler for cross-group tile scheduling
- Per-group TMA descriptor updates via GMEM or SMEM mode
- Supports fp16, fp8 (E4M3FN/E5M2), int8 with mixed A/B dtypes
- Configurable tile shapes (128x128, 128x256) and cluster shapes
- Fix base TensorMapManager: hoist uniform_smem_ptrs outside predicated
block to avoid illegal @P0 R2UR on sm_90a
Tests (test/examples/CuTeDSL/hopper/test_grouped_gemm.py):
- L0 compile and L1 correctness pytest suite covering tile shapes,
dtypes, major modes, cluster shapes, group counts, and mixed sizes
- Move to test/examples/CuTeDSL/hopper/ following sm_100a convention
- Fix deprecated startdir arg in test_sharding.py pytest hook
* v4.3 update.
* Update the cute_dsl_api changelog's doc link
* Update version to 4.3.0
* Update the example link
* Update doc to encourage user to install DSL from requirements.txt
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Co-authored-by: Larry Wu <larwu@nvidia.com>
