NVIDIA/cutlass v4.3.0

CUTLASS 4.3.0

Repository: NVIDIA/cutlass

Tag: v4.3.0

Published: 2025-11-24T22:24:16Z

Prerelease: no

Release notes:

CuTe DSL

• New features:
• Supported Apache TVM-FFI for further reduced host runtime overhead for JIT functions, better PyTorch and ML frameworks interopability
• Added fake tensor and stream to decouple compile jit function with "from_dlpack" flow. Now we no longer require users to have real tensor when compile jit function.
• Added FastDivmodDivisor with Python operator overloads, new APIs, Cute dialect integration, and optimized static tile scheduler performance for faster index mapping.
• Added l2 cache evict priority for tma related ops. Users could do fine-grain l2 cache control.
• Debuggability improvements:
• Supported source location tracking for DSL APIs (Allow tools like `nsight` profiling to correlate perf metrics with Python source code)
• Supported dumping PTX and CUBIN code: Hello World Example
• More examples and notebooks to get started with CuTe DSL:
• Improved performance of elementwise example:

+ Generalize code to handle list of input tensors + Generalize TV layout computation to handle different data types

• Improved Blackwell SM100 persistent dense GEMM with static scheduling:

+ To demonstrate usage of new Pipeline APIs PipelineProducer and PipelineConsumer to simplify code without explicit pipeline state management (Exiting APIs are still maintained) + Separated epilogue code for non-TMA and TMA implementation

• Tutorial for Blackwell GEMM: Basic Blackwell SM100 GEMM

+ Baseline Blackwell GEMM achieves 84% SOL performance with MNK 8K + More examples are coming for demo of optimization: Baseline + X

• Tutorial for Async Pipeline API
• Reworked elementwise add notebook with more details and detailed explanation about TV layout

+ Updated implementation to handle general data type and multiple inputs + Updated explanation for TV layout in simpler language + Added visualization of TV Layout with 3rd party utils

• Benchmark and autotune demonstration
• More examples of authorizing peak-performance kernels:
• Blackwell SM100 mixed-input GEMM
• Blackwell SM100 persistent blockwise dense GEMM
• Blackwell SM100 persistent blockwise contiguous grouped dense GEMM
• Blackwell SM100 persistent blockwise masked grouped dense GEMM
• Blackwell SM100 fmha bwd
• Blackwell SM100 mla
• Hopper SM90 persistent dense GEMM with static scheduling
• Blackwell GeForce batched dense GEMM
• Ampere HSTU Attention
• API updates:
• Please refer to DSL API changelog for details
• Bug fixings and improvements
• Add mma_tiler_n=64 and mma_tiler_n=192 support in Blackwell SM100 persistent dense blockscaled GEMM with static scheduling.
• Fixed `TensorSSA.reduce` to support static value as initial value
• Updated docstring for following APIs to be more concise and easier to understand:
• make_layout_tv
• is_static
• PipelineAsync
• SmemAllocator
• Fixed documentation for `pipeline, utils and cute.math
• Added overlapping accumulator optimization for block tile N = 256 case for better epilogue latency hiding in Blackwell SM100 persistent dense blockscaled GEMM with static scheduling.
• Fixed TensorSSA.__getitem__ indexing to match CuTe's indexing convention
• Fixed an issue with cutlass.max and cutlass.min
• Fixed an issue with mark_compact_shape_dynamic

CUTLASS C++

• Further enhance Blackwell SM100 Attention kernels in example 77.
• Add softmax skip correction.
• Fix a shared memory allocation bug where it needs to opt in maximum dynamics shared memory explicitly once it exceeds 48KB.
• Fix a dead hang issue caused by early return warp.
• Add support through cmdline argument lists for batch, no_verif, cluster_shape and cluster_shape_fallback in example 89.
• Add Ragged Contiguous Grouped gemm kernel in example 92.
• This kernel uses a TMA 3D load to load the weights matrix and use the tensormap update method to load activations.
• Add 256x128 tile size support for Hopper SM90 deepgemm in [example...