* Add dataclass example: passing pointers via frozen dataclass
Demonstrates passing pointers from tensor arguments in @cute.jit to
@cute.kernel using @dataclass(frozen=True). Shows the pattern of
extracting pointers with tensor.iterator, bundling into a dataclass,
and reconstructing tensors in the kernel.
Uses fake tensors for compilation and TVM-FFI for runtime dispatch.
Co-authored-by: Cursor <cursoragent@cursor.com>
* Add dataclass example: passing tensors via frozen dataclass
Demonstrates passing tensors from @cute.jit to @cute.kernel using
@dataclass(frozen=True). Shows the pattern of bundling tensors into
a dataclass with static configuration.
Uses fake tensors for compilation and TVM-FFI for runtime dispatch.
Includes reference check against PyTorch implementation.
Co-authored-by: Cursor <cursoragent@cursor.com>
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Co-authored-by: Cursor <cursoragent@cursor.com>
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
The notebook uses float16 tensors but the vectorized kernel documentation
incorrectly describes elements as 32-bit and uses 4-element vectorization.
Updated to correctly state 16-bit elements with 8-element vectorization
for proper 128-bit loads/stores.
Signed-off-by: Blake Ledden <bledden@users.noreply.github.com>
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
- Running without this generates a lot of kernels
- Clarified CMake configuration for selecting GEMM kernels and added details on kernel generation granularity.
* Add rmsnorm example
* Address reviewer comments. (1) use the cute.runtime definition directly. (2) use the nvvm_wrapper's warp reduce directly
* Separate out reduce.py
* Change copyright notice years
* init commit for distirbuted examples
* better OOB protection
* and try import to nvshmem for better error message and a READMME.md to introduce nvshmem and multimem instructions
* add some lamport explanation
* enhance f8 output and warn that f8 output can have nan in it
* tell user why we need complicate data conversions in ref check part
* tell user we don't support nvshmem device function
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Co-authored-by: bangyus <bangyus@nvidia.com>
