NVIDIA/cutlass v4.2.0

CUTLASS 4.2.0

Repository: NVIDIA/cutlass

Tag: v4.2.0

Published: 2025-09-18T03:32:52Z

Prerelease: no

Release notes:

CuTe DSL

• More Python versions are now supported for both x86-64 and aarch64, including
• Python 3.10, 3.11, 3.12, and 3.13
• Added new example and updated notebook to get started with CuTe DSL
• Call kernels with dlpack bypassed
• Updates on TensorSSA demonstration

+ Added a section for introducing the broadcast

• API updates
• Please refer to DSL API changelog for details
• Bug fixings and improvements
• Fixed `cute.print_tensor` for coordinate tensor
• Fixed cute.print for tuple of layouts
• Fixed frozen object is not properly updated after fully assigned in dynamic control flow
• Fixed assign tuple/list element in a dynamic control flow may cause compilation failure
• Improved error message when CUDA context is not initialized
• Improved docstring of congruent and weakly_congruent

CUTLASS C++

• Support for Blackwell SM103 kernels for B300 GPUs.
• Collective mainloop codes: Blockscaled datatypes with support for dense GEMM mainloop
• New GEMM and epilogue dispatch policies for collectives, kernel layers, and builders.
• Kernel codes: Blockscaled datatypes with support for dense GEMM kernel.
• Set of examples that demonstrate the usage of the 3.x API for targeting Blackwell SM103 architecture:
• Blockscaled ultra fp4 dense GEMM.
• Blockscaled ultra fp4 dense grouped GEMM.
• Set of unit tests that demonstrate the usage of Blackwell SM103 blockscaled GEMM
• Unit test files with prefix name of sm103_ under GEMM device unit tests.
• Support for Blackwell SM121 kernels for DGX Spark GPUs.
• Share the major codes with Blackwell SM120 kernels.
• Add support for heuristics-based kernel filtering and autotuning using nvidia-matmul-heuristics to find the best kernels for a given scenario.
• Details please refer to heuristics doc.
• Further enhance Blackwell SM100 Attention kernels in example 77.
• Add fused reduction kernel support for cutlass MLA.
• Add softmax skip correction.
• Support for GQA in FMHA backward kernel.
• 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.
• Fix a corner case issue where the sequence length of q is not a multiple of tile_q.
• Remove tma padding for forward kernel inputs.
• Add Blackwell SM100 kernels for MoEs (focusing on Low-Latency inference performance): example 92. It uses TMA (for weights) and CPASYNC (for tokens) to load input matrices and allow only one problem dimension to vary across groups/experts, unlike general Grouped GEMMs. Note: further API simplifications and kernel improvements are upcoming. Any feedback on API is welcome.
• Further enhance blockwise and groupwise GEMMs on Hopper and Blackwell
• On Blackwell SM120, a blockwise gemm kernel is added: example 87.
• On Hopper, add K major scale factor support for SM90 blockwise kernels.
• On Hopper, relax the restriction that the k dimension of the problem size has to be the multiple of the k dimension of the tile size.
• On Hopper, grouped version supports the case when k = 0.
• Support for Blackwell SM100 fp4 gemv kernels.
• Kernel codes: Gemv kernel.
• Example codes: example 91
• Support for Blackwell SM100 legacy mixed input GEMM kernels.
• Collective mainloop codes: Mixed input mainloop.
• Kernel codes: Mixed input kernel.
• Example codes: example 86.
• Support for Blackwell SM100 cpasync kernel.
• Collective mainloop codes: cpasync mainloop.
• Kernel codes: cpasync kernel.
• Support Blackwell SM120 mixed input blockscaled grouped GEMM.
• Instantiating more Blackwell kernels in profiler.
• Blackwell SM100 and SM103 kernels support CUTLASS_LIBRARY_INSTANTIATION_LEVEL to instantiate all possible combinations.
• To use this feature, CUTLASS_LIBRARY_KERNELS must be non-empty. Profiler will combine CUTLASS_LIBRARY_KERNELS and CUTLASS_LIBRARY_INSTANTIATION_LEVEL to instantiate specific kernels.
• Details please check Profiler Doc.
• 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.
• Fix Pingpong Blockwise Hopper library generation.
• From CUDA 13.0, the Blackwell SM101 for Thor GPUs is renamed to SM110.
• For CUDA toolkit version = 13.0, SM110 is used for Thor GPUs and SM101...