05fd39dca2
* Add SM103 grouped block-scaled GEMM kernel Implements sm103_grouped_blockscaled_gemm.py combining SM103 kernel internals (7-warp layout, MXF4/NVF4 ops, K=768, dedicated TMA SF warp) with SM100 grouped scheduling (StaticPersistentGroupTileScheduler, per-group tensormap updates via SMEM, 5 tensormaps). * [CuTeDSL] Port SM103 grouped block-scaled GEMM to v4.5 + fix scale correctness Rebases the SM103 grouped block-scaled GEMM onto the current tree, which reorganized the CuTeDSL examples (#3202) and changed the TMEM-storage API. Port: - Move kernel to cute/blackwell/kernel/blockscaled_grouped_gemm/ sm103_grouped_blockscaled_gemm.py (beside the SM100 grouped kernel); update the test import to the new package path. - Adopt the v4.5 TMEM storage API: struct field tmem_dealloc_mbar_ptr -> tmem_dealloc_mbar; pointers via storage.<field>.ptr; local tmem_holding_buf -> tmem_holding_buf_ptr. - Rename 3xFP4 -> FP4 Ultra terminology to match the v4.5 dense kernel. Correctness fix at scale: The AB/SF TMA producer wait tokens (try_acquire peek) were initialized once before the persistent tile loop. Each tile's final stage skips the next try_acquire(), so a stale token was carried into the next work tile, letting acquire_and_advance(True) overwrite a pipeline buffer stage before MMA released it -> wrong results once total output tiles exceeded ~1-2K (launch failure at the largest sizes), while small-shape tests passed. Refresh ab_producer / sf_producer at each work-tile boundary inside the loop, matching the SM103 dense and SM100 grouped kernels. Add a large persistent regression test (8 x 2048^3). Verified on NVIDIA GB300 (sm_103, CUTLASS DSL 4.5.2): pytest test/examples/CuTeDSL/sm_103/ --runtime-sm 103 => 23 passed; compute-sanitizer memcheck clean on 8 x 4096^3.
CUTLASS - Programming Examples
Important
⚠️ Not for Benchmarking! ⚠️
These examples are designed solely for demonstrating CUTLASS functionality and may NOT optimized for performance benchmarking.
For accurate performance measurements, please use the CUTLASS Profiler instead (recommended) or manually auto-tune the example, if unavailable via the profiler.
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launches a basic GEMM with single precision inputs and outputs
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demonstrates CUTLASS Utilities for allocating and initializing tensors
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debugging utilities for printing register and shared memory contents
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utility for visualizing all layout functions in CUTLASS
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example demonstrating an iterator over tiles in memory
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example demonstrating CUTLASS's batched strided GEMM operation
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example demonstrating CUTLASS's Split-K parallel reduction kernel
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example demonstrating mixed precision GEMM using Volta Tensor Cores
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example demonstrating integer GEMM using Turing Tensor Cores
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09_turing_tensorop_conv2dfprop
example demonstrating integer implicit GEMM convolution (forward propagation) using Turing Tensor Cores
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example demonstrating planar complex GEMM kernels
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example demonstrating planar complex kernels with batch-specific problem sizes
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example demonstrating GEMM fused with bias and relu
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example demonstrating two GEMMs or convolutions fused in one kernel
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example demonstrating FP32 GEMM with implicit TF32 conversion
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15_ampere_sparse_tensorop_gemm
example demonstrating usage of Sparse Tensor cores
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16_ampere_tensorop_conv2dfprop
example demonstrating forward convolution on tensors of layout NHWC
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example demonstrating convolution fused with per channel bias and relu
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18_ampere_fp64_tensorop_affine2_gemm
example demonstrating Affine-2 GEMM
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Canonical GEMM using tensor cores
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Canonical GEMM using SIMT
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example demonstrating Quaternion GEMM computations
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example demonstrating Quaternion convolution
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23_ampere_gemm_operand_reduction_fusion
example demonstrating how to reduce one of the operands of the GEMM along the k-dimension when computing GEMM
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example demonstrating batch of GEMM operations with distinct problem sizes
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25_ampere_fprop_mainloop_fusion
example demonstrating fusing activation's per channel scale+bias+relu into the fgrad mainloop
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26_ampere_wgrad_mainloop_fusion
example demonstrating fusing activation's per channel scale+bias+relu into the wgrad mainloop
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27_ampere_3xtf32_fast_accurate_tensorop_gemm
example demonstrating emulation of a fast accurate SGEMM with TF32 operations
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28_ampere_3xtf32_fast_accurate_tensorop_fprop
example demonstrating emulation of a fast accurate FP32 convolution with TF32 operation
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29_ampere_3xtf32_fast_accurate_tensorop_complex_gemm
example demonstrating emulation of a fast accurate CGEMM with TF32 operation
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example demonstrating how to compute conv2d gradient with respect to weight (wgrad) together with split-K
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example demonstrating Symmetric Rank-K update
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example demonstrating Triangular Matrix-Matrix multiplication
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33_ampere_3xtf32_tensorop_symm
example demonstrating Symmetric Matrix-Matrix multiplication with FP32 emulation
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example demonstrating how to compute 2d transposed convolution, also known as deconvolution, using CUTLASS conv2d Dgrad kernels
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example demonstrating GEMM fused with Softmax in mixed precision using Ampere Tensor Cores
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example demonstrating fuses gather before GEMM and scatter after GEMM into the same GEMM kernel
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example demonstrating fuses gemm->layernorm->gemm into one kernel.
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example demonstrating a batch of SYR2K operations with distinct problem sizes
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example demonstrating batched GEMM operations with output results permuted as reshaped tensors
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example demonstrating CUTLASS with Python interface
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example demonstrating attention example with non-fixed sequence length input
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example demonstrating how to run group convolution kernels using functions and data structures provided by CUTLASS using tensor cores
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example demonstrating a Block-Ell sparse gemm
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example demonstrating fused multihead attention (fixed & variable) using shared memory
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example demonstrating how to fuse two GEMMs sharing the same left input matrix into one kernel
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example demonstrating depthwise 2d convolution kernels using functions and data structures provided by CUTLASS using SIMT instruction
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47_ampere_gemm_universal_streamk
example contrasting the Stream-K parallel decomposition for GEMM threadblocks versus the "classic data-parallel" and "Split-K" decompositions.
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48_hopper_warp_specialized_gemm
Simple tensorop GEMM example using CUTLASS 3.0 APIs targeting NVIDIA Hopper architecture
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49_hopper_gemm_schedules_with_collective_builder
Hopper GEMM example leveraging collective operation builders to showcase the builder API and the various kernel scheduled supported in CUTLASS 3.0 such as warp specialized persistent mainloops.
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50_hopper_gemm_with_epilogue_swizzle
Hopper GEMM example to create a GEMM kernel with custom a collective mainloop and a custom vectorized epilogue.
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Hopper GETT example illustrating the ease with which GETTs can be run due to CUTLASS 3.0's unified micro-kernels and CuTe's hierarchical layouts.
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52_hopper_gather_scatter_fusion
Hopper example that fuses gather before GEMM and scatter after GEMM into the same kernel
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Hopper example demonstrating the fusion of tensor permutation operations with a GEMM kernel
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54_hopper_fp8_warp_specialized_gemm
Hopper example of instantiating and running an FP8 GEMM kernel
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Hopper GEMM example with different A and B data types using CUTLASS 3.x APIs for DL kernels with fused dequantization.
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56_hopper_ptr_array_batched_gemm
Hopper Ptr-Array Batched GEMM example using CUTLASS 3.x API.
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Hopper Grouped GEMM using CUTLASS 3.x API.
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Ada GEMM kernel targetting Ada FP8 tensor cores via the CUTLASS 2.x API.
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CuTe and CUTLASS 3.x based Ampere convolution fprop kernel capable of operating on both affine and gather/scatter tensors, showing how kernel authors can re-use CUTLASS 3.x collectives in their custom kernels.
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61_hopper_gemm_with_topk_and_softmax
Hopper GEMM kernel with Top-K and softmax epilogue fusion.
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Simple dense GEMM example targeting the NVIDIA Blackwell SM100 Tensor Core MMA using CUTLASS 3.x APIs.
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71_blackwell_gemm_with_collective_builder
Blackwell SM100 GEMM example demonstrating compatible mainloop+epilogue builder schedules and epilogue visitor tree (EVT) construction
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72_blackwell_narrow_precision_gemm
Block-scaled dense GEMM example targeting the NVIDIA Blackwell SM100 Tensor Core MMA using CUTLASS 3.x APIs.
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73_blackwell_gemm_preferred_cluster
Blackwell SM100 GEMM kernel with preferred cluster feature.
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Blackwell SM100 GEMM kernel using the Stream-K scheduler
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Blackwell SM100 grouped GEMM kernel
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Simple convolution(fprop/dgrad/wgrad) example targeting NVIDIA Blackwell SM100 Tensor Core MMA using CUTLASS 3.x APIs.
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Blackwell SM100 FMHA kernel
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78_blackwell_emulated_bf16x9_gemm
Blackwell SM100 FastFP32 (using BF16 to emulate SGEMM) kernel
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Blackwell SM120 MMA kernel targeting GeForce RTX 50 series CUDA Cores
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80_blackwell_geforce_sparse_gemm
Blackwell SM120 sparse MMA kernel targeting GeForce RTX 50 series CUDA Cores
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Blackwell SM100 Sparse Gemm kernel
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84_blackwell_narrow_precision_sparse_gemm
Blackwell Block Scaled SM100 Sparse Gemm kernel
CuTe - Programming Examples
Examples that do not rely on CUTLASS and directly showcase the features of CuTe are located in cutlass/examples/cute.
Additionally, CuTe's core layout and layout algebra have their own test cases within cutlass/test/unit/cute/core/ that users might find useful as examples of CuTe.
Python Interface Examples
Examples leveraging CUTLASS's Python interface are located in cutlass/examples/python.
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