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v4.2 release. (#2587)

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* Fix default cluster callback values to 1 to avoid profiler failure when these values are not set in command line.

* v4.2 release.
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Junkai-Wu
2025-08-23 06:11:24 +08:00
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![ALT](./media/images/gemm-hierarchy-with-epilogue-no-labels.png "Complete CUDA GEMM decomposition")
# Overview
# CUTLASS 4.1.0
# CUTLASS 4.2.0
_CUTLASS 4.1.0 - July 2025_
_CUTLASS 4.2.0 - Aug 2025_
CUTLASS is a collection of abstractions for implementing high-performance matrix-matrix multiplication (GEMM)
and related computations at all levels and scales within CUDA. It incorporates strategies for
@ -43,40 +43,52 @@ To get started quickly - please refer :
- [CUTLASS C++ Quick Start Guide](https://docs.nvidia.com/cutlass/media/docs/cpp/quickstart.html).
- [CuTe DSL Quick Start Guide](https://docs.nvidia.com/cutlass/media/docs/pythonDSL/quick_start.html).
# What's New in CUTLASS 4.1
# What's New in CUTLASS 4.2
## CuTe DSL
* Add aarch64 support, you can now pip install `nvidia-cutlass-dsl` on GB200 systems!
* More examples demonstrating how to use CuTe DSL to write peak-performance kernels
- [Blackwell Mamba2 SSD](https://github.com/NVIDIA/cutlass/tree/main/examples/python/CuTeDSL/blackwell/mamba2_ssd/mamba2_ssd.py)
- [Blackwell SM100 persistent dense blockscaled GEMM with static scheduling](https://github.com/NVIDIA/cutlass/tree/main/examples/python/CuTeDSL/blackwell/dense_blockscaled_gemm_persistent.py)
* API updates
- Please refer to [FUNCTIONALITY.md](https://github.com/NVIDIA/cutlass/blob/main/FUNCTIONALITY.md) for details
* We will likely be skipping 4.2.dev release and directly target 4.2.
* CuTeDSL version remains at 4.1.0 till then.
## CUTLASS C++
* Add K major scale factor support for Hopper SM90 blockwise kernels.
* Further enhance Blackwell SM100 Attention kernels in [example 77](https://github.com/NVIDIA/cutlass/tree/main/examples/77_blackwell_fmha/).
- Add variable sequence length support for FMHA Backward kernel.
- Add varlen test support to Backward runner.
- Codes support empty batch sequences.
* Replace `subbyte_iterator` with `cute::recast_ptr` when constructing logical iterators/arrays.
- Add fused reduction kernel support for cutlass MLA.
- Fix an issue where `get_unmasked_trip_count` may return a negative value.
- Fix an issue where mbarriers are initialized with a zero arrival count.
* Add Blackwell SM120 blockwise gemm kernel example: [example 87](https://github.com/NVIDIA/cutlass/tree/main/87_blackwell_geforce_gemm_blockwise/).
* Support for Blackwell SM100 cpasync kernel.
- Collective mainloop codes: [cpasync mainloop](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/gemm/collective/sm100_mma_cpasync_warpspecialized.hpp).
- Kernel codes: [cpasync kernel](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/gemm/kernel/sm100_gemm_cpasync_warpspecialized.hpp).
* Support for Blackwell SM121 kernels for DGX Spark GPUs.
- Share the major codes with Blackwell SM120 kernels.
* Support for Blackwell SM100 legacy mixed input GEMM kernels.
- Collective mainloop codes: [Mixed input mainloop](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/gemm/collective/sm100_mma_warpspecialized_mixed_input.hpp).
- Kernel codes: [Mixed input kernel](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/gemm/kernel/sm100_gemm_tma_warpspecialized_mixed_input_transform.hpp).
- Example codes: [example 86](https://github.com/NVIDIA/cutlass/tree/main/examples/86_blackwell_mixed_dtype_gemm/).
* Support for Blackwell SM100 fp4 gemv kernels.
- Kernel codes: [Gemv kernel](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/gemm/kernel/gemv_blockscaled.h).
- Example codes: [example 91](https://github.com/NVIDIA/cutlass/tree/main/examples/91_fp4_gemv/)
* From CUDA 13.0, the Blackwell SM101 for Thor GPUs is renamed to SM110.
- For CUDA toolkit version < 13.0, SM101 is still used for Thor GPUs.
- For CUDA toolkit version >= 13.0, SM110 is used for Thor GPUs and SM101 is no longer valid.
* CuTe changes:
- Rewrite ArithTuple and ScaledBasis for robustness and clarity.
- Remove buggy and kludgy `get_layoutA|B|C_MN` and friends from Atoms/TiledX.
- Factor out `print_latex` and friends and rewrite.
- Factor out `print_svg` and friends and rewrite.
* Support Blackwell SM100 SIMT packed fp32x2 kernels.
* Support residual add for implicit gemm kernels.
* Various fixes for CUTLASS C++ Python interface's EVT tracer:
- Add verifier for sm90 to report the invalid input.
- When adding an edge to the graph, if the edge already exists, add an identity compute node to avoid having multiple parallel edges.
- Register operations of tanh, sigmoid, exp, gelu to the python ast frontend.
- Replace the NotImplemented Error by packing all nodes into a single topological visitor node as a fallback.
* Fix profiler bugs in exhaustive perf search.
- Fix incorrect cluster shape output issue when doing exhaustive search.
- Fix a bug in profiler grouped GEMM for setting tile scheduler swizzles, cluster shapes, and raster orders.
* Fix some profiler issues.
- Complete the reference for Blackwell blockwise gemm kernels.
- Fix incorrect regex logic for L1 test.
- Fix inaccurate GridDim calculation under [CuTe tutorial](https://github.com/NVIDIA/cutlass/tree/main/examples/cute/tutorial/blackwell/).
- Add [movmatrix](https://docs.nvidia.com/cuda/parallel-thread-execution/index.html#warp-level-matrix-instructions-movmatrix) support.
- Fix smallest MMA-N allowed for Blackwell fp8 and fp16 gemm kernels.
- Support fp16 accmulator for sm89 fp8 mma.
- Shorten `nullspace` implementation.
- Isolate and comment on `cosize` hacks.
- Important documentation correction: `E<0,1> == 1@0@1`.
* Add support for heuristics-based kernel filtering and autotuning using `nvidia-matmul-heuristics`.
- Details please refer to [heuristics doc](https://github.com/NVIDIA/cutlass/tree/main/media/docs/cpp/heuristics.md).
* Rename legacy Python API package from `cutlass` to `cutlass_cppgen`.
* Fix some profiler issues:
- Modify default cluster callback values to none 0 to avoid profiler failure when these values are not set in command line.
- Fix some no output and timeout issues.
* Add following unit tests:
- [fp16 accmulator for sm89 fp8 mma](https://github.com/NVIDIA/cutlass/tree/main/test/unit/cute/ampere/cooperative_gemm.cu)
- [movmatrix test](https://github.com/NVIDIA/cutlass/tree/main/test/unit/cute/turing/movm.cu)
- [fp8 narrow mma n](https://github.com/NVIDIA/cutlass/tree/main/test/unit/gemm/device/sm100_tensorop_gemm/f16_f16_void_f32_narrow_mma_n.cu) and [fp16 narrow mma n](test/unit/gemm/device/sm100_tensorop_gemm/f8_f8_void_bf16_narrow_mma_n.cu)
Note: CUTLASS 4.x builds are known to be down on Windows platforms for all CUDA toolkits.
CUTLASS team is working on a fix.