Update CHANGELOG.md

* Ex77 forward kernel fix.

.github/workflows/blossom-ci.yml

@@ -0,0 +1,112 @@
+#################################################################################################
+#
+# Copyright (c) 2023 - 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: BSD-3-Clause
+#
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions are met:
+#
+# 1. Redistributions of source code must retain the above copyright notice, this
+# list of conditions and the following disclaimer.
+#
+# 2. Redistributions in binary form must reproduce the above copyright notice,
+# this list of conditions and the following disclaimer in the documentation
+# and/or other materials provided with the distribution.
+#
+# 3. Neither the name of the copyright holder nor the names of its
+# contributors may be used to endorse or promote products derived from
+# this software without specific prior written permission.
+#
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+#
+#################################################################################################
+
+# A workflow to trigger ci on hybrid infra (github + self hosted runner)
+name: Blossom-CI
+on:
+  issue_comment:
+    types: [created]
+  workflow_dispatch:
+      inputs:
+          platform:
+            description: 'runs-on argument'
+            required: false
+          args:
+            description: 'argument'
+            required: false
+
+jobs:
+  Authorization:
+    name: Authorization
+    runs-on: blossom
+    outputs:
+      args: ${{ env.args }}
+
+    # This job only runs for pull request comments
+    if: |
+        (startsWith(github.event.comment.body, '/bot run') ||
+        startsWith(github.event.comment.body, '/bot kill')) && contains(
+        fromJson('["zekunf-nv"]'),
+        github.actor)
+    steps:
+      - name: Check if comment is issued by authorized person
+        run: blossom-ci
+        env:
+          OPERATION: 'AUTH'
+          REPO_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+          REPO_KEY_DATA: ${{ secrets.BLOSSOM_KEY }}
+
+  Vulnerability-scan:
+    name: Vulnerability scan
+    needs: [Authorization]
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v2
+        with:
+          repository: ${{ fromJson(needs.Authorization.outputs.args).repo }}
+          ref: ${{ fromJson(needs.Authorization.outputs.args).ref }}
+          lfs: 'true'
+
+      - name: Run blossom action
+        uses: NVIDIA/blossom-action@main
+        env:
+          REPO_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+          REPO_KEY_DATA: ${{ secrets.BLOSSOM_KEY }}
+        with:
+          args1: ${{ fromJson(needs.Authorization.outputs.args).args1 }}
+          args2: ${{ fromJson(needs.Authorization.outputs.args).args2 }}
+          args3: ${{ fromJson(needs.Authorization.outputs.args).args3 }}
+
+  Job-trigger:
+    name: Start ci job
+    needs: [Vulnerability-scan]
+    runs-on: blossom
+    steps:
+      - name: Start ci job
+        run: blossom-ci
+        env:
+          OPERATION: 'START-CI-JOB'
+          CI_SERVER: ${{ secrets.CI_SERVER }}
+          REPO_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+
+  Upload-Log:
+    name: Upload log
+    runs-on: blossom
+    if : github.event_name == 'workflow_dispatch'
+    steps:
+      - name: Jenkins log for pull request ${{ fromJson(github.event.inputs.args).pr }} (click here)
+        run: blossom-ci
+        env:
+          OPERATION: 'POST-PROCESSING'
+          CI_SERVER: ${{ secrets.CI_SERVER }}
+          REPO_TOKEN: ${{ secrets.GITHUB_TOKEN }}

CHANGELOG.md

@@ -1,34 +1,185 @@
-# NVIDIA CUTLASS Changelog
+# Changelog
+
+# CUTLASS 4.x
+
+## [4.1.0](https://github.com/NVIDIA/cutlass/releases/tag/v4.1.0) (2025-07-16)
+
+### 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
+
+### CUTLASS C++
+* 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.
+* 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.
+* Various improvements and fixes from the community and CUTLASS team. Thanks to everyone who submitted PRs!
+* Optimal code generation with CUDA toolkit versions 12.9.
+
+## [4.0.0](https://github.com/NVIDIA/cutlass/releases/tag/v4.0.0) (2025-06-03)
+
+### CuTe DSL
+* CuTe DSL, a Python DSL centered around CuTe's abstractions
+    - [Core DSL implementation files](https://github.com/NVIDIA/cutlass/tree/main/python/CuTeDSL)
+    - [DSL quick start](https://docs.nvidia.com/cutlass/media/docs/pythonDSL/quick_start.html)
+    - [DSL Overview](https://docs.nvidia.com/cutlass/media/docs/pythonDSL/overview.html)
+* [Overhauled documentation with a new dedicated website](https://docs.nvidia.com/cutlass)
+* Set of examples demonstrating how to use CuTe DSL to write peak-performance kernels
+    - [Blackwell SM100 persistent dense GEMM with static scheduling](https://github.com/NVIDIA/cutlass/tree/main/examples/python/CuTeDSL/blackwell/dense_gemm_persistent.py)
+    - [Blackwell SM100 grouped GEMM](https://github.com/NVIDIA/cutlass/tree/main/examples/python/CuTeDSL/blackwell/grouped_gemm.py)
+    - [Blackwell SM100 fused multi-head attention forward pass](https://github.com/NVIDIA/cutlass/tree/main/examples/python/CuTeDSL/blackwell/fmha.py)
+    - [Hopper GEMM](https://github.com/NVIDIA/cutlass/tree/main/examples/python/CuTeDSL/hopper/dense_gemm.py)
+    - [Ampere GEMM](https://github.com/NVIDIA/cutlass/tree/main/examples/python/CuTeDSL/ampere/tensorop_gemm.py)
+    - [FlashAttention-2 implementation targeting Ampere and Ada class GPUs (SM80, SM86, SM89)](https://github.com/NVIDIA/cutlass/tree/main/examples/python/CuTeDSL/ampere/flash_attention_v2.py)
+    - [SmemAllocator to facilitate shared memory allocation and management](https://github.com/NVIDIA/cutlass/tree/main/examples/python/CuTeDSL/ampere/smem_allocator.py)
+    - [C-structure based customized interface between JIT function and user codes](https://github.com/NVIDIA/cutlass/tree/main/examples/python/CuTeDSL/cute/ffi/jit_argument.py)
+* [Educational notebooks for getting started with CuTe DSL](https://github.com/NVIDIA/cutlass/tree/main/examples/python/CuTeDSL/notebooks)
+* API updates
+    - Please refer to [FUNCTIONALITY.md](https://github.com/NVIDIA/cutlass/blob/main/FUNCTIONALITY.md) for details
+
+### CUTLASS C++
+* Support [Family Specific Architecture Features](https://developer.nvidia.com/blog/nvidia-blackwell-and-nvidia-cuda-12-9-introduce-family-specific-architecture-features/) which was introduced in CUDA 12.9
+  - 100f, 101f, 120f were added to support Family Specific Architecture Features which allows running the same binary on different chips belonging to the same Family (e.g. sm100) without recompiling.  Note 101a is supported since CUTLASS 3.9
+* Instruction shapes and redundant accumulation type have been removed from CUTLASS 3.x-style library kernel names to disambiguate kernels and shorten names.
+  - For example:
+    + `(old) cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_bf16_bf16_128x256x64_1x1x1_0_tnn_align8_warpspecialized_cooperative_epi_tma`
+    + `(new) cutlass3x_sm90_tensorop_gemm_bf16_bf16_f32_bf16_bf16_128x256x64_1x1x1_0_tnn_align8_warpspecialized_cooperative_epi_tma`
+   - If you are using the CUTLASS library kernel names directly (e.g. to compile a subset of the CUTLASS library with `-DCUTLASS_LIBRARY_KERNELS`, filter kernels in the CUTLASS profiler with `--kernels`), please update your uses accordingly, this is a breaking change.
+* Further improved [Blockwise](https://github.com/NVIDIA/cutlass/tree/main/examples/67_hopper_fp8_warp_specialized_gemm_with_blockwise_scaling/67_hopper_fp8_warp_specialized_gemm_with_blockwise_scaling.cu) and [Groupwise](https://github.com/NVIDIA/cutlass/tree/main/examples/67_hopper_fp8_warp_specialized_gemm_with_blockwise_scaling/67_hopper_fp8_warp_specialized_gemm_with_groupwise_scaling.cu) GEMMs on Hopper and Blackwell.
+  - Added non-power-of-two tile sizes.
+  - Improved performance for K-major scale factors.
+  - The argument `mma_promotion_interval` has been removed from non-grouped GEMM to align with the grouped and Blackwell SM100 versions.
+* Enhance Blackwell SM100 Attention kernels in [example 77](https://github.com/NVIDIA/cutlass/tree/main/examples/77_blackwell_fmha/).
+  - Support LSE output in FMHA Forward kernel.
+  - Enhance performance measurement: support of different warmup iterations; buffer rotation to keep L2 cold; separate testing of persistent and non-persistent.
+  - Enhance testing of variable sequence length.
+  - Disable B2B mode in MLA to simplify the sample.
+  - Clarify that `fmha_gen`  sample only supports head dim 128.
+  - Fixes for split-kv output in MLA.
+* Improve Blackwell and Hopper grouped GEMM performance, functionality, and profiler support.
+  - Enable runtime datatype for Blackwell SM100 grouped GEMM. Profiler support is also added.
+  - Enable kernel parameter exploration for Blackwell SM100 grouped GEMM - raster_order, swizzle.
+* Add [Blackwell SM100 implicit GEMM conv fprop/dgrad/wgrad unit tests](https://github.com/NVIDIA/cutlass/tree/main/test/unit/conv/device_3x/).
+* Add dynamic and preferred cluster support for convolution Blackwell SM100 kernels.
+* Fix profiler issues which cause no output or not supported error for some kernels.
+* Optimizations for Blackwell SM100 and SM120 block scaled kernels.
+* Support for Blackwell SM120 blockwise dense gemm in CUTLASS library and profiler.
+* New [Hopper SM90 FMHA example](https://github.com/NVIDIA/cutlass/tree/main/examples/88_hopper_fmha/), similar in design to the existing [Blackwell FMHA](https://github.com/NVIDIA/cutlass/tree/main/examples/77_blackwell_fmha/).
+* CuTe changes:
+    - Rework `cute::copy_if` so that the predicate tensor is also a true CuTe Tensor rather than a lambda and introduces transform-tensors to avoid any extra register or load/store overhead in using bool-tensors.
+    - New [CuTe tutorial](https://github.com/NVIDIA/cutlass/tree/main/examples/cute/tutorial/tiled_copy_if.cu) to show the usage of copy_if in tile copy.
+    - Add [CuTe C++ reduce op](https://github.com/NVIDIA/cutlass/tree/main/include/cute/algorithm/tensor_reduce.hpp).
+        - Add several [unit tests](https://github.com/NVIDIA/cutlass/tree/main/test/unit/cute/core/tensor_algs.cpp) for CuTe tensor algorithms.
+* Various improvements and fixes from the community and CUTLASS team. Thanks to everyone who submitted PRs!
+* Optimal code generation with CUDA toolkit versions 12.9.
+
+
+# CUTLASS 3.x
+
+## [3.9.2](https://github.com/NVIDIA/cutlass/releases/tag/v3.9.2) (2025-05-03)
+* Fixed [Blockwise](https://github.com/NVIDIA/cutlass/tree/main/examples/67_hopper_fp8_warp_specialized_gemm_with_blockwise_scaling/67_hopper_fp8_warp_specialized_gemm_with_blockwise_scaling.cu) and [Groupwise](https://github.com/NVIDIA/cutlass/tree/main/examples/67_hopper_fp8_warp_specialized_gemm_with_blockwise_scaling/67_hopper_fp8_warp_specialized_gemm_with_groupwise_scaling.cu) GEMM hang issue when problem size K is 128.
+* Optimal code generation with CUDA toolkit versions 12.9.
+
+## [3.9.1](https://github.com/NVIDIA/cutlass/releases/tag/v3.9.1) (2025-04-30)
+* Fixed Group Gemm hang issue in CUTLASS 3.x
+* Improved Hopper [Blockwise](https://github.com/NVIDIA/cutlass/tree/main/examples/67_hopper_fp8_warp_specialized_gemm_with_blockwise_scaling/67_hopper_fp8_warp_specialized_gemm_with_blockwise_scaling.cu) and [Groupwise](https://github.com/NVIDIA/cutlass/tree/main/examples/67_hopper_fp8_warp_specialized_gemm_with_blockwise_scaling/67_hopper_fp8_warp_specialized_gemm_with_groupwise_scaling.cu) GEMM performance.
+
+## [3.9.0](https://github.com/NVIDIA/cutlass/releases/tag/v3.9.0) (2025-04-24)
+
+* Support for Blackwell SM120 kernels for GeForce GPUs in CUTLASS 3.x API:
+  - Collective mainloops that target for:
+    * [Blockscaled datatypes with support for dense GEMM](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/gemm/collective/sm120_blockscaled_mma_tma.hpp)
+    * [Blockscaled datatypes with support for sparse GEMM](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/gemm/collective/sm120_blockscaled_sparse_mma_tma.hpp)
+  - New [GEMM](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/gemm/dispatch_policy.hpp) and [epilogue](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/epilogue/dispatch_policy.hpp) dispatch policies for collectives, kernel layers, and builders.
+  - [Blackwell SM120 epilogue](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/epilogue/fusion/sm120_visitor_store_tma_warpspecialized.hpp) and [full set of EVT fusions](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/epilogue/fusion/sm120_callbacks_tma_warpspecialized.hpp).
+* Set of examples that demonstrate the usage of the 3.x API for targeting Blackwell SM120 architecture:
+  - [Blockscaled GEMM with NVFP4 input datatype and BF16 output tensor](https://github.com/NVIDIA/cutlass/tree/main/examples/79_blackwell_geforce_gemm/79a_blackwell_geforce_nvfp4_bf16_gemm.cu).
+  - [Blockscaled GEMM with NVFP4 input datatype and NVFP4 output tensor with scale factor generation](https://github.com/NVIDIA/cutlass/tree/main/examples/79_blackwell_geforce_gemm/79b_blackwell_geforce_nvfp4_nvfp4_gemm.cu).
+  - [Blockscaled GEMM with mixed input datatype (MXFP8 and MXFP6) and BF16 output tensor](https://github.com/NVIDIA/cutlass/tree/main/examples/79_blackwell_geforce_gemm/79c_blackwell_geforce_mixed_mxfp8_mxfp6_bf16_gemm.cu).
+  - [Grouped GEMM with nvfp4 datatype](https://github.com/NVIDIA/cutlass/tree/main/examples/79_blackwell_geforce_gemm/79d_blackwell_geforce_nvfp4_grouped_gemm.cu).
+  - [Sparse Blockscaled GEMM with mxfp8 input datatype and BF16 output tensor](https://github.com/NVIDIA/cutlass/tree/main/examples/80_blackwell_geforce_sparse_gemm/80a_blackwell_geforce_mxfp8_bf16_sparse_gemm.cu).
+  - [Sparse Blockscaled GEMM with NVFP4 input datatype and NVFP4 output tensor](https://github.com/NVIDIA/cutlass/tree/main/examples/80_blackwell_geforce_sparse_gemm/80b_blackwell_geforce_nvfp4_nvfp4_sparse_gemm.cu).
+* Set of unit tests that demonstrate the usage of both [sparse](https://github.com/NVIDIA/cutlass/tree/main/test/unit/gemm/device/sm120_blockscaled_sparse_tensorop_gemm/) and [dense](https://github.com/NVIDIA/cutlass/tree/main/test/unit/gemm/device/sm120_blockscaled_tensorop_gemm/) Blackwell SM120 blockscaled GEMM.
+* Support for Blackwell SM100 Sparse kernels:
+  - Collective mainloop that target for
+    * [SM100 Sparse GEMM](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/gemm/collective/sm100_sparse_mma_warpspecialized.hpp)
+* Set of example that demonstrate the usage of the 3.x API for targeting Blackwell SM100 Sparse GEMM:
+  - [Sparse GEMM](https://github.com/NVIDIA/cutlass/tree/main/examples/83_blackwell_sparse_gemm/83_blackwell_sparse_gemm.cu)
+  - [Blockscaled Sparse GEMM with NVFP4 input data type](https://github.com/NVIDIA/cutlass/tree/main/examples/84_blackwell_narrow_precision_sparse_gemm/84a_blackwell_nvfp4_bf16_sparse_gemm.cu)
+  - [Blockscaled Sparse GEMM with mixed input data type (MXFP8 and MXFP4)](https://github.com/NVIDIA/cutlass/tree/main/examples/84_blackwell_narrow_precision_sparse_gemm/84b_blackwell_mixed_mxfp8_bf16_sparse_gemm.cu)
+* Set of unit tests that demonstrate the usage of [sparse](https://github.com/NVIDIA/cutlass/tree/main/test/unit/gemm/device/sm100_sparse_tensorop_gemm) and [blockscaled sparse](https://github.com/NVIDIA/cutlass/tree/main/test/unit/gemm/device/sm100_blockscaled_sparse_tensorop_gemm) Blackwell SM100 GEMM.
+* A new Multi-head Latent Attention (MLA) for SM100 Blackwell architecture in CUTLASS [example](https://github.com/NVIDIA/cutlass/tree/main/examples/77_blackwell_fmha/) covers the flashMLA-like weight-absorbed decoding use-case.
+* A new FMHA Backward kernel for SM100 Blackwell architecture extends CUTLASS [example](https://github.com/NVIDIA/cutlass/tree/main/examples/77_blackwell_fmha/) to show how the five backward pass MMAs can be fused into a single kernel to achieve high performance.
+* A new [distributed GEMM example](https://github.com/NVIDIA/cutlass/tree/main/examples/82_blackwell_distributed_gemm/82_blackwell_distributed_gemm.cu) for SM100 Blackwell architecture.
+* Enhancement and new support of block-wise and group-wise GEMM for Hopper and Blackwell architectures:
+  - Enhancement of [blockwise GEMM](https://github.com/NVIDIA/cutlass/tree/main/examples/67_hopper_fp8_warp_specialized_gemm_with_blockwise_scaling/67_hopper_fp8_warp_specialized_gemm_with_blockwise_scaling.cu) for Hopper architecture.
+  - Enhancement of [groupwise GEMM](https://github.com/NVIDIA/cutlass/tree/main/examples/67_hopper_fp8_warp_specialized_gemm_with_blockwise_scaling/67_hopper_fp8_warp_specialized_gemm_with_groupwise_scaling.cu) for Hopper architecture.
+  - Support for [grouped GEMM with blockwise and groupwise scaling](https://github.com/NVIDIA/cutlass/tree/main/examples/68_hopper_fp8_warp_specialized_grouped_gemm_with_blockwise_scaling/) for Hopper architecture.
+  - Support for [grouped-wise GEMM](https://github.com/NVIDIA/cutlass/tree/main/tools/profiler/src/blockwise_gemm_operation_profiler.cu) in CUTLASS profiler.
+  - Support for [blockwise GEMM](https://github.com/NVIDIA/cutlass/tree/main/examples/81_blackwell_gemm_blockwise/81_blackwell_gemm_blockwise.cu) for Blackwell architecture.
+  - Support for [groupwise GEMM](https://github.com/NVIDIA/cutlass/tree/main/examples/81_blackwell_gemm_blockwise/81_blackwell_gemm_groupwise.cu) for Blackwell architecture.
+  - Support for [grouped GEMM with blockwise](https://github.com/NVIDIA/cutlass/tree/main/examples/81_blackwell_gemm_blockwise/81_blackwell_grouped_gemm_blockwise.cu) and [groupwise scaling](https://github.com/NVIDIA/cutlass/tree/main/examples/81_blackwell_gemm_blockwise/81_blackwell_grouped_gemm_groupwise.cu) for Blackwell architecture.
+* Added support for enhanced kernel performance search (auto-tuning) in CUTLASS profiler:
+  - Sorting performance results by GFLOPs/second: Users can now sort the final performance report based on GFLOPs/second, making it easier to identify the most efficient kernels.
+  - Exhaustive search for best kernel performance in GFLOPs/second: The profiler now searches for the best-performing kernel across a range of problem sizes, swizzle sizes, rasterization orders, and dynamic cluster configurations to maximize performance.
+  - Performance search under a fixed GEMM shape: Enables exhaustive tuning within a fixed GEMM shape, exploring various kernel parameters to find the best configuration.
+  - More detailed introductions and examples to leverage this feature can be found in [profiler.md](https://docs.nvidia.com/cutlass/media/docs/cpp/profiler.html#exhaustive-search-mode-and-top-k-output-ranking-according-to-performance-in-gflopss).
+* Support `void` as the D element in sm100 kernel epilogues.
+* Various improvements and fixes from the community and CUTLASS team. Thanks to everyone who submitted PRs!
+* Optimal code generation with CUDA toolkit versions 12.8U1.
 
 ## [3.8.0](https://github.com/NVIDIA/cutlass/releases/tag/v3.8.0) (2025-01-25)
 
 * Support for new CuTe building blocks specifically for Blackwell SM100 architecture:
-  - [5th generation Blackwell Tensor Core instructions (TCGen05)](./include/cute/atom/mma_traits_sm100.hpp) via CuTe MMA atoms.
-  - Extensions to [Tensor Memory Accelerator](./include/cute/atom/copy_traits_sm100_tma.hpp) via CuTe Copy atoms.
-  - Exposure of Blackwell's new tensor memory (note: distinct from TMA) as [`tmem`](./include/cute/pointer.hpp) across CuTe as a first class data locale.
-  - Exposure of [`tmem->rmem`, `rmem->tmem` and `smem->tmem data movement instructions`](./include/cute/atom/copy_traits_sm100.hpp) as copy atoms in CuTe.
-  - [`make_tmem_copy()`](./include/cute/atom/copy_traits_sm100.hpp) utility method to ease creation of tiled copies for tmem copy atoms.
-  - Support for [new variants of LDSM on Blackwell](./include/cute/atom/copy_traits_sm100.hpp) via CuTe Copy atoms.
+  - [5th generation Blackwell Tensor Core instructions (TCGen05)](https://github.com/NVIDIA/cutlass/tree/main/include/cute/atom/mma_traits_sm100.hpp) via CuTe MMA atoms.
+  - Extensions to [Tensor Memory Accelerator](https://github.com/NVIDIA/cutlass/tree/main/include/cute/atom/copy_traits_sm100_tma.hpp) via CuTe Copy atoms.
+  - Exposure of Blackwell's new tensor memory (note: distinct from TMA) as [`tmem`](https://github.com/NVIDIA/cutlass/tree/main/include/cute/pointer.hpp) across CuTe as a first class data locale.
+  - Exposure of [`tmem->rmem`, `rmem->tmem` and `smem->tmem data movement instructions`](https://github.com/NVIDIA/cutlass/tree/main/include/cute/atom/copy_traits_sm100.hpp) as copy atoms in CuTe.
+  - [`make_tmem_copy()`](https://github.com/NVIDIA/cutlass/tree/main/include/cute/atom/copy_traits_sm100.hpp) utility method to ease creation of tiled copies for tmem copy atoms.
+  - Support for [new variants of LDSM on Blackwell](https://github.com/NVIDIA/cutlass/tree/main/include/cute/atom/copy_traits_sm100.hpp) via CuTe Copy atoms.
 * Support for new CUTLASS building blocks specifically for Blackwell SM100 architecture:
-  - Various narrow precision [FP4, FP6, and FP8](./include/cutlass/exmy_base.h) formats as well as their [block-scaled variants NVFP4, MXFP4, MXFP6, and MXFP8](./include/cutlass/float_subbyte.h)
-  - [Pipelines that implement Blackwell specific synchronization](./include/cutlass/pipeline/sm100_pipeline.hpp).
-  - [Cluster launch control API supporting preferred and fallback cluster shapes](./include/cutlass/cluster_launch.hpp).
+  - Various narrow precision [FP4, FP6, and FP8](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/exmy_base.h) formats as well as their [block-scaled variants NVFP4, MXFP4, MXFP6, and MXFP8](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/float_subbyte.h)
+  - [Pipelines that implement Blackwell specific synchronization](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/pipeline/sm100_pipeline.hpp).
+  - [Cluster launch control API supporting preferred and fallback cluster shapes](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/cluster_launch.hpp).
   - Data types including NVFP4, MXFP4, MXFP6, and MXFP8 and all their supported element and scale factor types.
-  - Tile schedulers using [Blackwell's Cluster Launch Control (CLC) feature](./media/docs/blackwell_cluster_launch_control.md) to implement dynamic persistence scheduling for [GEMMs](./include/cutlass/gemm/kernel/sm100_tile_scheduler.hpp), and [stream-K](./include/cutlass/gemm/kernel/sm100_tile_scheduler_stream_k.hpp).
+  - Tile schedulers using [Blackwell's Cluster Launch Control (CLC) feature](https://docs.nvidia.com/cutlass/media/docs/cpp/blackwell_cluster_launch_control.html) to implement dynamic persistence scheduling for [GEMMs](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/gemm/kernel/sm100_tile_scheduler.hpp), and [stream-K](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/gemm/kernel/sm100_tile_scheduler_stream_k.hpp).
   - Extensions to testbeds and reference check code for unit tests and CUTLASS profiler.
 * Full support for Blackwell SM100 kernels in CUTLASS 3.x API:
-  - [Blackwell specific kernel layers](./include/cutlass/gemm/kernel/sm100_gemm_tma_warpspecialized.hpp) that
+  - [Blackwell specific kernel layers](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/gemm/kernel/sm100_gemm_tma_warpspecialized.hpp) that
     + Implement a new warp-specialization recipe tuned specifically for Blackwell SM100 architecture.
     + Leverage all the new features such as CLC based tile scheduling, preferred cluster, and TMEM based double buffering of accumulators.
     + Support stream-K load balancing for all kernel types everywhere via composable scheduler support.
   - Blackwell collective mainloops that target the TCGen05 MMA instructions (both SS and TS) for
-    * [Non-block scaled data types without support for pointer array and grouped GEMM with TMA](./include/cutlass/gemm/collective/sm100_mma_warpspecialized.hpp)
-    * [Non-block scaled data types with support for pointer array and grouped GEMM with TMA](./include/cutlass/gemm/collective/sm100_mma_array_warpspecialized.hpp)
-    * [Block scaled data types without support for pointer array and grouped GEMM with TMA](./include/cutlass/gemm/collective/sm100_blockscaled_mma_warpspecialized.hpp)
-    * [Block scaled data types with support for pointer array and grouped GEMM with TMA](./include/cutlass/gemm/collective/sm100_blockscaled_mma_array_warpspecialized.hpp)
-  - Blackwell [collective mainloop for convolution kernels](./include/cutlass/conv/collective/sm100_implicit_gemm_umma_warpspecialized.hpp) supporting non-block scaled data types for fprop, dgrad, and wgrad.
-  - New [GEMM](./include/cutlass/gemm/dispatch_policy.hpp), [convolution](./include/cutlass/conv/dispatch_policy.hpp), and [epilogue](./include/cutlass/epilogue/dispatch_policy.hpp) dispatch policies for collectives, kernel layers, and builders.
-  - [Blackwell epilogue that supports loading accumulators from `tmem`](./include/cutlass/epilogue/collective/sm100_epilogue_tma_warpspecialized.hpp) and [full set of EVT fusions]().
+    * [Non-block scaled data types without support for pointer array and grouped GEMM with TMA](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/gemm/collective/sm100_mma_warpspecialized.hpp)
+    * [Non-block scaled data types with support for pointer array and grouped GEMM with TMA](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/gemm/collective/sm100_mma_array_warpspecialized.hpp)
+    * [Block scaled data types without support for pointer array and grouped GEMM with TMA](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/gemm/collective/sm100_blockscaled_mma_warpspecialized.hpp)
+    * [Block scaled data types with support for pointer array and grouped GEMM with TMA](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/gemm/collective/sm100_blockscaled_mma_array_warpspecialized.hpp)
+  - Blackwell [collective mainloop for convolution kernels](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/conv/collective/sm100_implicit_gemm_umma_warpspecialized.hpp) supporting non-block scaled data types for fprop, dgrad, and wgrad.
+  - New [GEMM](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/gemm/dispatch_policy.hpp), [convolution](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/conv/dispatch_policy.hpp), and [epilogue](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/epilogue/dispatch_policy.hpp) dispatch policies for collectives, kernel layers, and builders.
+  - [Blackwell epilogue that supports loading accumulators from `tmem`](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/epilogue/collective/sm100_epilogue_tma_warpspecialized.hpp) and full set of EVT fusions.
 * CUTLASS library and profiler integration for block scaled data types for kernel emission, profiling, and verification.
   - Support for preferred and fallback cluster shapes via profiler command line arguments parsing to set dynamic cluster shapes.
   - Support for dynamic datatypes by parsing profiler via profiler command line arguments parsing to set dynamic datatype setting in TCGen05 MMA instruction descriptors.
@@ -36,131 +187,131 @@
 * New CUTLASS profiler flag `use-cuda-graphs` to reduce overheads when benchmarking launch-bound kernels.
 * A new 3.x version of grouped GEMM to the CUTLASS library and generates kernels for Hopper and Blackwell. Now grouped GEMM support is enabled in the CUTLASS profiler (`./cutlass_profiler --operation=GroupedGemm --help` for details).
 * Set of examples that demonstrate the usage of the 3.x API for targeting Blackwell SM100 architecture:
-  - [Basic FP16 and FP8 GEMMs with minimal changes from Hopper examples](./examples/70_blackwell_gemm/), demonstrating ease of migration for off the shelf kernels using the 3.x collective builder API.
-  - GEMM with [opt-in collective builder schedules showcasing available recipes](./examples/71_blackwell_gemm_with_collective_builder/71_blackwell_gemm_with_collective_builder.cu) for Blackwell.
+  - [Basic FP16 and FP8 GEMMs with minimal changes from Hopper examples](https://github.com/NVIDIA/cutlass/tree/main/examples/70_blackwell_gemm/), demonstrating ease of migration for off the shelf kernels using the 3.x collective builder API.
+  - GEMM with [opt-in collective builder schedules showcasing available recipes](https://github.com/NVIDIA/cutlass/tree/main/examples/71_blackwell_gemm_with_collective_builder/71_blackwell_gemm_with_collective_builder.cu) for Blackwell.
   - Block scaled data type GEMMs targeting Blackwell's native block scaled Tensor Cores:
-    + [NVFP4 inputs with BF16 output](./examples/72_blackwell_narrow_precision_gemm/72a_blackwell_nvfp4_bf16_gemm.cu)
-    + [NVFP4 inputs with NVFP4 output](./examples/72_blackwell_narrow_precision_gemm/72b_blackwell_nvfp4_nvfp4_gemm.cu)
-    + [Mixed MXFP8 and MXFP6 inputs with BF16 output](./examples/72_blackwell_narrow_precision_gemm/72c_blackwell_mixed_mxfp8_bf16_gemm.cu)
-  - GEMM example demonstrating [Blackwell's new preferred cluster support via dynamic cluster shapes](./examples/73_blackwell_gemm_preferred_cluster/blackwell_gemm_preferred_cluster.cu) for increased occupancy.
-  - [GEMM with CLC based StreamK scheduler for load balancing](./examples/74_blackwell_gemm_streamk/blackwell_gemm_streamk.cu).
-  - Grouped GEMM for [vanilla FP8 data inputs](./examples/75_blackwell_grouped_gemm/75_blackwell_grouped_gemm.cu) and [NVFP4 block scaled inputs](./examples/75_blackwell_grouped_gemm/75_blackwell_grouped_gemm_block_scaled.cu).
-  - Convolution kernels for [fprop](./examples/76_blackwell_conv/76_blackwell_conv_fprop.cu), [dgrad](./examples/76_blackwell_conv/76_blackwell_conv_dgrad.cu), and [wgrad](./examples/76_blackwell_conv/76_blackwell_conv_wgrad.cu).
-  - [Fused multi-head attention fprop kernel](./examples/77_blackwell_fmha/77_blackwell_fmha.cu) supporting fp16/bf16/fp8 data types across head dims of 32,64, and 128.
-  - A new BF16x9 GEMM [kernel](./examples/78_blackwell_emulated_bf16x9_gemm/78_blackwell_emulated_bf16x9_gemm.cu) that emulates FP32 GEMM (SGEMM) using BF16 operations.
+    + [NVFP4 inputs with BF16 output](https://github.com/NVIDIA/cutlass/tree/main/examples/72_blackwell_narrow_precision_gemm/72a_blackwell_nvfp4_bf16_gemm.cu)
+    + [NVFP4 inputs with NVFP4 output](https://github.com/NVIDIA/cutlass/tree/main/examples/72_blackwell_narrow_precision_gemm/72b_blackwell_nvfp4_nvfp4_gemm.cu)
+    + [Mixed MXFP8 and MXFP6 inputs with BF16 output](https://github.com/NVIDIA/cutlass/tree/main/examples/72_blackwell_narrow_precision_gemm/72c_blackwell_mixed_mxfp8_bf16_gemm.cu)
+  - GEMM example demonstrating [Blackwell's new preferred cluster support via dynamic cluster shapes](https://github.com/NVIDIA/cutlass/tree/main/examples/73_blackwell_gemm_preferred_cluster/blackwell_gemm_preferred_cluster.cu) for increased occupancy.
+  - [GEMM with CLC based StreamK scheduler for load balancing](https://github.com/NVIDIA/cutlass/tree/main/examples/74_blackwell_gemm_streamk/blackwell_gemm_streamk.cu).
+  - Grouped GEMM for [vanilla FP8 data inputs](https://github.com/NVIDIA/cutlass/tree/main/examples/75_blackwell_grouped_gemm/75_blackwell_grouped_gemm.cu) and [NVFP4 block scaled inputs](https://github.com/NVIDIA/cutlass/tree/main/examples/75_blackwell_grouped_gemm/75_blackwell_grouped_gemm_block_scaled.cu).
+  - Convolution kernels for [fprop](https://github.com/NVIDIA/cutlass/tree/main/examples/76_blackwell_conv/76_blackwell_conv_fprop.cu), [dgrad](https://github.com/NVIDIA/cutlass/tree/main/examples/76_blackwell_conv/76_blackwell_conv_dgrad.cu), and [wgrad](https://github.com/NVIDIA/cutlass/tree/main/examples/76_blackwell_conv/76_blackwell_conv_wgrad.cu).
+  - [Fused multi-head attention fprop kernel](https://github.com/NVIDIA/cutlass/tree/main/examples/77_blackwell_fmha/77_blackwell_fmha.cu) supporting fp16/bf16/fp8 data types across head dims of 32,64, and 128.
+  - A new BF16x9 GEMM [kernel](https://github.com/NVIDIA/cutlass/tree/main/examples/78_blackwell_emulated_bf16x9_gemm/78_blackwell_emulated_bf16x9_gemm.cu) that emulates FP32 GEMM (SGEMM) using BF16 operations.
 * Set of examples that demonstrate the usage of the 3.x API for targeting Hopper architecture:
-  - A set of new [Hopper grouped GEMM kernels](./examples/69_hopper_mixed_dtype_grouped_gemm/) that support mixed A and B datatypes.
-  - A new [Hopper FP8 GEMM with groupwise scaling](./examples/67_hopper_fp8_warp_specialized_gemm_with_blockwise_scaling/67_hopper_fp8_warp_specialized_gemm_with_groupwise_scaling.cu).
+  - A set of new [Hopper grouped GEMM kernels](https://github.com/NVIDIA/cutlass/tree/main/examples/69_hopper_mixed_dtype_grouped_gemm/) that support mixed A and B datatypes.
+  - A new [Hopper FP8 GEMM with groupwise scaling](https://github.com/NVIDIA/cutlass/tree/main/examples/67_hopper_fp8_warp_specialized_gemm_with_blockwise_scaling/67_hopper_fp8_warp_specialized_gemm_with_groupwise_scaling.cu).
 * Documentation updates:
-  - [Quickstart - instantiating a Blackwell block-scaled GEMM](./media/docs/quickstart.md#instantiating-a-blackwell-gemm-kernel).
-  - Detailed [Blackwell block-scaled GEMM functionality documentation](./media/docs/blackwell_functionality.md)
-  - A new [functionality documentation](./media/docs/functionality.md) specifically for 3.x API comprehensively documenting all supported kernel types, data types, kernel features, minimum CUDA tookit support etc for 3.x supported architectures.
-  - Updates to [compatibility](./README.md#compatibility) section regarding supported compilers, operating systems, CUDA Toolkits, Hardware Architectures, and [Target Architecture](./README.md#Target-Architecture).
-  - Updates to [profiler documentation](./media/docs/profiler.md) for testing mixed input GEMM kernels on Hopper.
+  - [Quickstart - instantiating a Blackwell block-scaled GEMM](https://docs.nvidia.com/cutlass/media/docs/cpp/quickstart.html#instantiating-a-blackwell-sm100-gemm-kernel).
+  - Detailed [Blackwell block-scaled GEMM functionality documentation](https://docs.nvidia.com/cutlass/media/docs/cpp/blackwell_functionality.html)
+  - A new [functionality documentation](https://docs.nvidia.com/cutlass/media/docs/cpp/functionality.html) specifically for 3.x API comprehensively documenting all supported kernel types, data types, kernel features, minimum CUDA tookit support etc for 3.x supported architectures.
+  - Updates to [compatibility](https://docs.nvidia.com/cutlass/overview.html#compatibility) section regarding supported compilers, operating systems, CUDA Toolkits, Hardware Architectures, and [Target Architecture](https://docs.nvidia.com/cutlass/overview.html#target-architecture).
+  - Updates to [profiler documentation](https://docs.nvidia.com/cutlass/media/docs/cpp/profiler.html) for testing mixed input GEMM kernels on Hopper.
 
 ## [3.7.0](https://github.com/NVIDIA/cutlass/releases/tag/v3.7.0) (2025-01-11)
-- [Hopper blockwise scaling FP8 GEMM](./examples/67_hopper_fp8_warp_specialized_gemm_with_blockwise_scaling/67_hopper_fp8_warp_specialized_gemm_with_blockwise_scaling.cu) uses 2D scaling tensor, assigning one value per threadblock.  This allows a finer-grained scaling to be applied for each output tile per gemm-k iteration. The operands and scaling tensors are loaded from global memory to shared memory using TMA and cp_async, respectively. The scaling is applied inside the mainloop.  Details with figures are [here](https://github.com/NVIDIA/cutlass/pull/1932#issue-2645398439).
-- [Distributed GEMM](./examples/65_distributed_gemm/65_distributed_gemm.cu) is a new (experimental) API which can turn existing CUTLASS GEMM kernels into pipelined Tensor Parallel GEMMs that run efficiently on NVLink-based network of GPUs. Its pipelining schedules can hide most of the communication behind computation, and relies on point-to-point communication, which can simply use CUDA runtime's peer device access feature. It also utilizes remote TMA loads and memcopies with CUDA graphs to handle communication primarily through the Copy Engine, leaving all SMs free for Hopper's persistent kernels.  For more details you can refer to the [DistGEMM blog post](https://blog.shi-labs.com/distributed-gemm-88be6a481e2b).
-- Improved persistent grid launch for Hopper kernels with large cluster sizes (>= size of 4) using the new `make_kernel_hardware_info` API as shown in [example 48](./examples/48_hopper_warp_specialized_gemm/48_hopper_warp_specialized_gemm.cu).
+- [Hopper blockwise scaling FP8 GEMM](https://github.com/NVIDIA/cutlass/tree/main/examples/67_hopper_fp8_warp_specialized_gemm_with_blockwise_scaling/67_hopper_fp8_warp_specialized_gemm_with_blockwise_scaling.cu) uses 2D scaling tensor, assigning one value per threadblock.  This allows a finer-grained scaling to be applied for each output tile per gemm-k iteration. The operands and scaling tensors are loaded from global memory to shared memory using TMA and cp_async, respectively. The scaling is applied inside the mainloop.  Details with figures are [here](https://github.com/NVIDIA/cutlass/pull/1932#issue-2645398439).
+- [Distributed GEMM](https://github.com/NVIDIA/cutlass/tree/main/examples/65_distributed_gemm/65_distributed_gemm.cu) is a new (experimental) API which can turn existing CUTLASS GEMM kernels into pipelined Tensor Parallel GEMMs that run efficiently on NVLink-based network of GPUs. Its pipelining schedules can hide most of the communication behind computation, and relies on point-to-point communication, which can simply use CUDA runtime's peer device access feature. It also utilizes remote TMA loads and memcopies with CUDA graphs to handle communication primarily through the Copy Engine, leaving all SMs free for Hopper's persistent kernels.  For more details you can refer to the [DistGEMM blog post](https://blog.shi-labs.com/distributed-gemm-88be6a481e2b).
+- Improved persistent grid launch for Hopper kernels with large cluster sizes (>= size of 4) using the new `make_kernel_hardware_info` API as shown in [example 48](https://github.com/NVIDIA/cutlass/tree/main/examples/48_hopper_warp_specialized_gemm/48_hopper_warp_specialized_gemm.cu).
 - Enabled high precision accumulation for Hopper FP8 Sparse GEMM.
 - Potential API breaking changes:
   + Fix `cute::UniversalCopy` for type safety.
   + No longer implicitly select `cute::SM80_CP_ASYNC_*` based on input tensors. This avoids implicit downstream synchronization requirements. To use `SM80_CP_ASYNC`, users must explicitly select the appropriate CopyAtom.
   + Fix `cute::SM80_CP_ASYNC_CACHEALWAYS`, `cute::SM80_CP_ASYNC_CACHEGLOBAL`, `cute::SM80_CP_ASYNC_CACHEALWAYS_ZFILL`, `cute::SM80_CP_ASYNC_CACHEGLOBAL_ZFILL` to avoid implicitly selecting `ZFILL` behavior on predication.
   + Remove `cute::copy_vec<T>` in favor of `cute::copy_aligned` and `cute::copy(AutoVectorizingCopyWithAssumedAlignment<NumBits>,...)`.
-  + A refactor of default epilogue struct `DefaultEpilogue` [API](./include/cutlass/epilogue/collective/default_epilogue.hpp) to avoid reading non-void `ElementC` value for `ElementC = void` kernel.
-- New CUTLASS profiler flags: `profiling-duration`, `min-iterations`, and `kernels-file` documented in [profiler.md](./media/docs/profiler.md#cutlass-profiler).
+  + A refactor of default epilogue struct `DefaultEpilogue` [API](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/epilogue/collective/default_epilogue.hpp) to avoid reading non-void `ElementC` value for `ElementC = void` kernel.
+- New CUTLASS profiler flags: `profiling-duration`, `min-iterations`, and `kernels-file` documented in [profiler.md](https://docs.nvidia.com/cutlass/media/docs/cpp/profiler.html#cutlass-profiler).
 - Various improvements and fixes from the community and CUTLASS team. Thanks to everyone who submitted PRs!
 - Optimal code generation with CUDA toolkit versions 12.6.
 
 ## [3.6.0](https://github.com/NVIDIA/cutlass/releases/tag/v3.6.0) (2024-10-03)
 
-- [Hopper structured sparse GEMM](./examples/62_hopper_sparse_gemm/62_hopper_sparse_gemm.cu).
-  + [FP16](./test/unit/gemm/device/sm90_sparse_gemm_f16_f16_f32_tensor_op_f32.cu)
-  + [FP8](./test/unit/gemm/device/sm90_sparse_gemm_f8_f8_f32_tensor_op_f32.cu)
-  + [INT8](./test/unit/gemm/device/sm90_sparse_gemm_s8_s8_s32_tensor_op_s32.cu)
-  + [TF32](./test/unit/gemm/device/sm90_sparse_gemm_tf32_tf32_f32_tensor_op_f32.cu)
-- A refactor to the CUTLASS 3.x convolution `kernel::ConvUniversal` [API](./include/cutlass/conv/kernel/sm90_implicit_gemm_tma_warpspecialized.hpp) to bring it in line with `gemm::GemmUniversal`. Now the 3.x convolution API is no longer considered as a beta API.
-- [An improved mixed input GEMM](./examples/55_hopper_mixed_dtype_gemm/README.md) and a [lookup table implementation](./examples/55_hopper_mixed_dtype_gemm/55_hopper_int4_fp8_gemm.cu) for `INT4`x`FP8` scale-only mode.
-- [EVT nodes for Top-K selection and softmax](./include/cutlass/epilogue/fusion/sm90_visitor_topk_softmax.hpp) and [GEMM example using those](./examples/61_hopper_gemm_with_topk_and_softmax/61_hopper_gemm_with_topk_and_softmax.cu).
-- [Programmatic Dependent Launch](./include/cutlass/arch/grid_dependency_control.h) (PDL) that leverages a new Hopper feature to speedup two back-to-back kernels, and its corresponding [documentations](./media/docs/dependent_kernel_launch.md).
-- [A new debugging tool, synclog](./include/cutlass/arch/synclog.hpp), for dumping out all synchronization events from within a kernel to a file. Please see [synclog documentation](./media/docs/utilities.md#debugging-asynchronous-kernels-with-cutlasss-built-in-synclog-tool) for details.
-- A new TMA-enabled [epilogue](./include/cutlass/epilogue/collective/sm90_epilogue_array_tma_warpspecialized.hpp) for grouped GEMM that brings significant performance improvement, as well as its EVT support.
-- A SIMT-enabled pointer-array [epilogue](./include/cutlass/epilogue/collective/sm70_epilogue_vectorized_array.hpp).
-- A new [Ping-Pong kernel schedule for Grouped GEMM](./include/cutlass/gemm/kernel/sm90_gemm_array_tma_warpspecialized_pingpong.hpp) and some other optimizations.
-- [A new instantiation strategy for CUTLASS profiler kernels](./python/cutlass_library/sm90_shapes.py) along with [improved documentation for instantiation level in CUTLASS profiler](./media/docs/profiler.md#instantiating-more-kernels-with-hopper).
-- A new hardware support for comparisons and computations of [`cutlass::bfloat16_t`](./include/cutlass/bfloat16.h)
-- Fixed use of isnan on Windows for [`half_t`](./test/unit/core/functional.cu).
+- [Hopper structured sparse GEMM](https://github.com/NVIDIA/cutlass/tree/main/examples/62_hopper_sparse_gemm/62_hopper_sparse_gemm.cu).
+  + [FP16](https://github.com/NVIDIA/cutlass/tree/main/test/unit/gemm/device/sm90_sparse_gemm_f16_f16_f32_tensor_op_f32.cu)
+  + [FP8](https://github.com/NVIDIA/cutlass/tree/main/test/unit/gemm/device/sm90_sparse_gemm_f8_f8_f32_tensor_op_f32.cu)
+  + [INT8](https://github.com/NVIDIA/cutlass/tree/main/test/unit/gemm/device/sm90_sparse_gemm_s8_s8_s32_tensor_op_s32.cu)
+  + [TF32](https://github.com/NVIDIA/cutlass/tree/main/test/unit/gemm/device/sm90_sparse_gemm_tf32_tf32_f32_tensor_op_f32.cu)
+- A refactor to the CUTLASS 3.x convolution `kernel::ConvUniversal` [API](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/conv/kernel/sm90_implicit_gemm_tma_warpspecialized.hpp) to bring it in line with `gemm::GemmUniversal`. Now the 3.x convolution API is no longer considered as a beta API.
+- [An improved mixed input GEMM](https://github.com/NVIDIA/cutlass/tree/main/examples/55_hopper_mixed_dtype_gemm/README.md) and a [lookup table implementation](https://github.com/NVIDIA/cutlass/tree/main/examples/55_hopper_mixed_dtype_gemm/55_hopper_int4_fp8_gemm.cu) for `INT4`x`FP8` scale-only mode.
+- [EVT nodes for Top-K selection and softmax](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/epilogue/fusion/sm90_visitor_topk_softmax.hpp) and [GEMM example using those](https://github.com/NVIDIA/cutlass/tree/main/examples/61_hopper_gemm_with_topk_and_softmax/61_hopper_gemm_with_topk_and_softmax.cu).
+- [Programmatic Dependent Launch](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/arch/grid_dependency_control.h) (PDL) that leverages a new Hopper feature to speedup two back-to-back kernels, and its corresponding [documentations](https://docs.nvidia.com/cutlass/media/docs/cpp/dependent_kernel_launch.html).
+- [A new debugging tool, synclog](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/arch/synclog.hpp), for dumping out all synchronization events from within a kernel to a file. Please see [synclog documentation](https://docs.nvidia.com/cutlass/media/docs/cpp/utilities.html#debugging-asynchronous-kernels-with-cutlasss-built-in-synclog-tool) for details.
+- A new TMA-enabled [epilogue](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/epilogue/collective/sm90_epilogue_array_tma_warpspecialized.hpp) for grouped GEMM that brings significant performance improvement, as well as its EVT support.
+- A SIMT-enabled pointer-array [epilogue](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/epilogue/collective/sm70_epilogue_vectorized_array.hpp).
+- A new [Ping-Pong kernel schedule for Grouped GEMM](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/gemm/kernel/sm90_gemm_array_tma_warpspecialized_pingpong.hpp) and some other optimizations.
+- [A new instantiation strategy for CUTLASS profiler kernels](https://github.com/NVIDIA/cutlass/tree/main/python/cutlass_library/sm90_shapes.py) along with [improved documentation for instantiation level in CUTLASS profiler](https://docs.nvidia.com/cutlass/media/docs/cpp/profiler.html#instantiating-more-kernels-with-hopper).
+- A new hardware support for comparisons and computations of [`cutlass::bfloat16_t`](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/bfloat16.h)
+- Fixed use of isnan on Windows for [`half_t`](https://github.com/NVIDIA/cutlass/tree/main/test/unit/core/functional.cu).
 - Various improvements and fixes from the community and CUTLASS team. Thanks to everyone who submitted PRs!
 - Optimal code generation with CUDA toolkit versions 12.6.
 
 ## [3.5.1](https://github.com/NVIDIA/cutlass/releases/tag/v3.5.1) (2024-07-25)
 
-- [Minimal SM90 WGMMA + TMA GEMM example in 100 lines of code](./examples/cute/tutorial/wgmma_sm90.cu)
-- [Exposure of L2 `cache_hint`s in TMA copy atoms](./include/cute/arch/copy_sm90_tma.hpp#L48)
-- Exposure of raster order and tile swizzle extent in [CUTLASS library profiler](./media/docs/profiler.md#GEMM), and
-[example 48](./examples/48_hopper_warp_specialized_gemm/48_hopper_warp_specialized_gemm.cu).
-- [TMA store based and EVT supported epilogues](./include/cutlass/epilogue/collective/sm90_epilogue_array_tma_warpspecialized.hpp) for [Hopper pointer array batched kernels](./test/unit/gemm/device/sm90_gemm_f16_f16_f16_tensor_op_f32_ptr_array.cu).
-- A new [`GemmSparseUniversal` API for CUTLASS 2.x Ampere kernels](./include/cutlass/gemm/device/gemm_sparse_universal.h) to enable serial and parallel split-k for sparse tensor cores and new tiny tile sizes to better support LLM inferrence:
-  + [FP16 TN](./test/unit/gemm/device/gemm_f16t_f16n_f32t_tensor_op_f32_sparse_sm80.cu#L269-L393) and [NT](./test/unit/gemm/device/gemm_f16n_f16t_f32t_tensor_op_f32_sparse_sm80.cu#L269-L411).
-  + [int8 TN](./test/unit/gemm/device/gemm_s8t_s8n_s32t_tensor_op_s32_sparse_sm80.cu#L264-L452).
-  + [int4 TN](./test/unit/gemm/device/gemm_s4t_s4n_s32t_tensor_op_s32_sparse_sm80.cu#L264-L452).
-  + [FP32 TN](./test/unit/gemm/device/gemm_f32t_f32n_f32t_tensor_op_f32_sparse_sm80.cu#L427-L642) and [NT](./test/unit/gemm/device/gemm_f32n_f32t_f32t_tensor_op_f32_sparse_sm80.cu#L427-L456).
-- [CUDA host adapter](./include/cutlass/cuda_host_adapter.hpp) extensions to support TMA descriptor construction driver APIs.
-- Inclusion of more [Hopper fprop, dgrad, and wgrad convolution kernels in CUTLASS library and profiler](./python/cutlass_library/generator.py).
+- [Minimal SM90 WGMMA + TMA GEMM example in 100 lines of code](https://github.com/NVIDIA/cutlass/tree/main/examples/cute/tutorial/wgmma_sm90.cu)
+- [Exposure of L2 `cache_hint`s in TMA copy atoms](https://github.com/NVIDIA/cutlass/tree/main/include/cute/arch/copy_sm90_tma.hpp#L48)
+- Exposure of raster order and tile swizzle extent in [CUTLASS library profiler](./media/docs/cpp/profiler.md#gemm), and
+[example 48](https://github.com/NVIDIA/cutlass/tree/main/examples/48_hopper_warp_specialized_gemm/48_hopper_warp_specialized_gemm.cu).
+- [TMA store based and EVT supported epilogues](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/epilogue/collective/sm90_epilogue_array_tma_warpspecialized.hpp) for [Hopper pointer array batched kernels](https://github.com/NVIDIA/cutlass/tree/main/test/unit/gemm/device/sm90_gemm_f16_f16_f16_tensor_op_f32_ptr_array.cu).
+- A new [`GemmSparseUniversal` API for CUTLASS 2.x Ampere kernels](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/gemm/device/gemm_sparse_universal.h) to enable serial and parallel split-k for sparse tensor cores and new tiny tile sizes to better support LLM inferrence:
+  + [FP16 TN](https://github.com/NVIDIA/cutlass/tree/main/test/unit/gemm/device/gemm_f16t_f16n_f32t_tensor_op_f32_sparse_sm80.cu#L269-L393) and [NT](https://github.com/NVIDIA/cutlass/tree/main/test/unit/gemm/device/gemm_f16n_f16t_f32t_tensor_op_f32_sparse_sm80.cu#L269-L411).
+  + [int8 TN](https://github.com/NVIDIA/cutlass/tree/main/test/unit/gemm/device/gemm_s8t_s8n_s32t_tensor_op_s32_sparse_sm80.cu#L264-L452).
+  + [int4 TN](https://github.com/NVIDIA/cutlass/tree/main/test/unit/gemm/device/gemm_s4t_s4n_s32t_tensor_op_s32_sparse_sm80.cu#L264-L452).
+  + [FP32 TN](https://github.com/NVIDIA/cutlass/tree/main/test/unit/gemm/device/gemm_f32t_f32n_f32t_tensor_op_f32_sparse_sm80.cu#L427-L642) and [NT](https://github.com/NVIDIA/cutlass/tree/main/test/unit/gemm/device/gemm_f32n_f32t_f32t_tensor_op_f32_sparse_sm80.cu#L427-L456).
+- [CUDA host adapter](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/cuda_host_adapter.hpp) extensions to support TMA descriptor construction driver APIs.
+- Inclusion of more [Hopper fprop, dgrad, and wgrad convolution kernels in CUTLASS library and profiler](https://github.com/NVIDIA/cutlass/tree/main/python/cutlass_library/generator.py).
 - Support for residual add (beta != 0) in convolution kernels.
-- A new convolution [epilogue](./examples/16_ampere_tensorop_conv2dfprop/ampere_tensorop_conv2dfprop.cu#L269) for CUTLASS 2.x to support non-packed NHWC output.
-- A refactor of [include files throughout CUTLASS core directories](./include/cutlass/gemm/collective/collective_mma_decl.hpp) to reduce circular dependencies and [tests to guard against them](./test/self_contained_includes/CMakeLists.txt).
-- [A guide for setting up VSCode to work well with CUTLASS](./media/docs/ide_setup.md) and [expanded code style guide](./media/docs/programming_guidelines.md).
+- A new convolution [epilogue](https://github.com/NVIDIA/cutlass/tree/main/examples/16_ampere_tensorop_conv2dfprop/ampere_tensorop_conv2dfprop.cu#L269) for CUTLASS 2.x to support non-packed NHWC output.
+- A refactor of [include files throughout CUTLASS core directories](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/gemm/collective/collective_mma_decl.hpp) to reduce circular dependencies and [tests to guard against them](https://github.com/NVIDIA/cutlass/tree/main/test/self_contained_includes/CMakeLists.txt).
+- [A guide for setting up VSCode to work well with CUTLASS](https://docs.nvidia.com/cutlass/media/docs/cpp/ide_setup.html) and [expanded code style guide](https://docs.nvidia.com/cutlass/media/docs/cpp/programming_guidelines.html).
 - Better support for MSVC as a host compiler.
 - Many performance optimizations, improvements, and bug fixes including fixes for FlashAttention-2.
 - Optimal code generation with CUDA toolkit versions 12.4 and 12.5u1.
 
 ## [3.5.0](https://github.com/NVIDIA/cutlass/releases/tag/v3.5.0) (2024-04-09)
 
-- Implicit GEMM Convolutions targeting Hopper SM90A via WGMMA + [TMA im2col](./include/cute/atom/copy_traits_sm90_im2col.hpp)
-  + Native implementation in CUTLASS 3.x using CuTe, mirroring the [same design hierarchy as that of GEMMs](./media/docs/gemm_api_3x.md).
-  + Support for 1D, 2D, and 3D convolutions in a [rank-agnostic fashion](./include/cutlass/conv/convnd_problem_shape.hpp).
-  + Support for [Fprop](./test/unit/conv/device_3x/fprop/sm90_conv3d_fprop_implicit_gemm_s8_s8_s32_tensorop_s32.cu), [Dgrad](./test/unit/conv/device_3x/dgrad/sm90_conv2d_dgrad_implicit_gemm_f16_f16_f32_tensorop_f16.cu), and [Wgrad](./test/unit/conv/device_3x/wgrad/sm90_conv1d_wgrad_implicit_gemm_f16_f16_f32_tensorop_f16.cu) algorithms
-  + [CUTLASS profiler support](./python/cutlass_library/conv3x_emitter.py) for 2D and 3D convolutions implemented via the 3.x API.
+- Implicit GEMM Convolutions targeting Hopper SM90A via WGMMA + [TMA im2col](https://github.com/NVIDIA/cutlass/tree/main/include/cute/atom/copy_traits_sm90_im2col.hpp)
+  + Native implementation in CUTLASS 3.x using CuTe, mirroring the [same design hierarchy as that of GEMMs](https://docs.nvidia.com/cutlass/media/docs/cpp/gemm_api_3x.html).
+  + Support for 1D, 2D, and 3D convolutions in a [rank-agnostic fashion](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/conv/convnd_problem_shape.hpp).
+  + Support for [Fprop](https://github.com/NVIDIA/cutlass/tree/main/test/unit/conv/device_3x/fprop/sm90_conv3d_fprop_implicit_gemm_s8_s8_s32_tensorop_s32.cu), [Dgrad](https://github.com/NVIDIA/cutlass/tree/main/test/unit/conv/device_3x/dgrad/sm90_conv2d_dgrad_implicit_gemm_f16_f16_f32_tensorop_f16.cu), and [Wgrad](https://github.com/NVIDIA/cutlass/tree/main/test/unit/conv/device_3x/wgrad/sm90_conv1d_wgrad_implicit_gemm_f16_f16_f32_tensorop_f16.cu) algorithms
+  + [CUTLASS profiler support](https://github.com/NVIDIA/cutlass/tree/main/python/cutlass_library/conv3x_emitter.py) for 2D and 3D convolutions implemented via the 3.x API.
   + NOTE: this is a beta release. Further updates to CUTLASS will include major performance improvements, feature enablement, and possible breaking changes to the API until 3.7 release. Your feedback is welcome on the design!
-- Support for [Ada (SM89) FP8 tensor cores via the 2.x API](./examples/58_ada_fp8_gemm/ada_fp8_gemm.cu). Requires CUDA 12.4 or newer.
-- [Ampere gather/scatter convolution example](./examples/59_ampere_gather_scatter_conv/README.md) in CuTe and CUTLASS 3.x
+- Support for [Ada (SM89) FP8 tensor cores via the 2.x API](https://github.com/NVIDIA/cutlass/tree/main/examples/58_ada_fp8_gemm/ada_fp8_gemm.cu). Requires CUDA 12.4 or newer.
+- [Ampere gather/scatter convolution example](https://github.com/NVIDIA/cutlass/tree/main/examples/59_ampere_gather_scatter_conv/README.md) in CuTe and CUTLASS 3.x
   + Showcasing how custom kernels can be written and optimized using CUTLASS 3.x and CuTe and the general strategy for implementing convolutions as specializations of GETTs.
   + Implementation of a coarse grained sparse gather/scatter kernel achieving peak performance on Ampere class tensor cores.
 - 32x and 16x tile sizes are added to CUTLASS 2.x to improve the performance of narrow-tall and wide-short matrices.
-  + [Ampere FP16 TN](./test/unit/gemm/device/gemm_f16t_f16n_f16t_tensor_op_f32_sm80.cu) and [NT](./test/unit/gemm/device/gemm_f16n_f16t_f16t_tensor_op_f32_sm80.cu#L227-L301), [Ampere INT8 TN](./test/unit/gemm/device/gemm_s8t_s8n_s8t_tensor_op_s32_sm80.cu#L392-L1342), [Ampere INT4 TN](./test/unit/gemm/device/gemm_s4t_s4n_s4t_tensor_op_s32_sm80.cu#L372-L934).
-  + [Turing FP16 TN](./test/unit/gemm/device/gemm_f16t_f16n_f16t_tensor_op_f32_sm75.cu#L55-L394), [Turing INT8 TN](./test/unit/gemm/device/gemm_s8t_s8n_s8t_tensor_op_s32_sm75.cu#L166-L537), [Turing INT4 TN](./test/unit/gemm/device/gemm_s4t_s4n_s4t_tensor_op_s32_sm75.cu#L310-L564).
-- Updates to CuTe documentation for [`cute::Tensor<>`](./media/docs/cute/03_tensor.md), [MMA atoms](./media/docs/cute/0t_mma_atom.md), and an overhauled [CuTe GEMM tutorial series](./examples/cute/tutorial).
-- Extensions to CuTe to support [L2 prefetching](./include/cute/algorithm/prefetch.hpp) and [TMA store+reductions](./include/cute/arch/copy_sm90_tma.hpp#L1337).
+  + [Ampere FP16 TN](https://github.com/NVIDIA/cutlass/tree/main/test/unit/gemm/device/gemm_f16t_f16n_f16t_tensor_op_f32_sm80.cu) and [NT](https://github.com/NVIDIA/cutlass/tree/main/test/unit/gemm/device/gemm_f16n_f16t_f16t_tensor_op_f32_sm80.cu#L227-L301), [Ampere INT8 TN](https://github.com/NVIDIA/cutlass/tree/main/test/unit/gemm/device/gemm_s8t_s8n_s8t_tensor_op_s32_sm80.cu#L392-L1342), [Ampere INT4 TN](https://github.com/NVIDIA/cutlass/tree/main/test/unit/gemm/device/gemm_s4t_s4n_s4t_tensor_op_s32_sm80.cu#L372-L934).
+  + [Turing FP16 TN](https://github.com/NVIDIA/cutlass/tree/main/test/unit/gemm/device/gemm_f16t_f16n_f16t_tensor_op_f32_sm75.cu#L55-L394), [Turing INT8 TN](https://github.com/NVIDIA/cutlass/tree/main/test/unit/gemm/device/gemm_s8t_s8n_s8t_tensor_op_s32_sm75.cu#L166-L537), [Turing INT4 TN](https://github.com/NVIDIA/cutlass/tree/main/test/unit/gemm/device/gemm_s4t_s4n_s4t_tensor_op_s32_sm75.cu#L310-L564).
+- Updates to CuTe documentation for [`cute::Tensor<>`](./media/docs/cpp/cute/03_tensor.md), [MMA atoms](./media/docs/cpp/cute/0t_mma_atom.md), and an overhauled [CuTe GEMM tutorial series](https://github.com/NVIDIA/cutlass/tree/main/examples/cute/tutorial).
+- Extensions to CuTe to support [L2 prefetching](https://github.com/NVIDIA/cutlass/tree/main/include/cute/algorithm/prefetch.hpp) and [TMA store+reductions](https://github.com/NVIDIA/cutlass/tree/main/include/cute/arch/copy_sm90_tma.hpp#L1337).
 - Remove C++11 requirement on a few CUTLASS 2.x API header files. All CUTLASS files now require C++17.
 - Fixes to greatly reduce build warnings.
 - Updates and bugfixes from the community (thanks!)
 
 ## [3.4.1](https://github.com/NVIDIA/cutlass/releases/tag/v3.4.1) (2024-02-14)
 
-- Statically available [CUTLASS Version macros](./include/cutlass/version.h) that allow for handling API changes between CUTLASS releases on the users' side.
-- Improvements for Hopper [Group-GEMMs](./examples/57_hopper_grouped_gemm) and [Pointer-Array Batched GEMMs](./examples/56_hopper_ptr_array_batched_gemm).
+- Statically available [CUTLASS Version macros](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/version.h) that allow for handling API changes between CUTLASS releases on the users' side.
+- Improvements for Hopper [Group-GEMMs](https://github.com/NVIDIA/cutlass/tree/main/examples/57_hopper_grouped_gemm) and [Pointer-Array Batched GEMMs](https://github.com/NVIDIA/cutlass/tree/main/examples/56_hopper_ptr_array_batched_gemm).
 - Updates and bugfixes from the community (thanks!).
 
 ## [3.4.0](https://github.com/NVIDIA/cutlass/releases/tag/v3.4.0) (2024-01-12)
-* Expanded [Mixed-input Hopper GEMMs](./examples/55_hopper_mixed_dtype_gemm) support covering {16-bit, 8-bit} x {8-bit, 4-bit} input types with fast numerical converters and group scaling factors.
-* Performance improvements to [Mixed-input Hopper GEMMs](./examples/55_hopper_mixed_dtype_gemm)
-* Beta release of [Pointer-Array Batched GEMMs](./examples/56_hopper_ptr_array_batched_gemm) now available on Hopper GPUs utilizing TMA and WGMMA (requires CUDA 12.3 or above).
-* Beta release of [Group-GEMM](./examples/57_hopper_grouped_gemm) utilizing TMA and WGMMA (requires CUDA 12.3 or above).
-* [Ampere Sparse GEMM](./examples/15_ampere_sparse_tensorop_gemm/ampere_sparse_tensorop_gemm_with_visitor.cu) supports Epilogue Visitor Tree (EVT) now.
-* NamedBarriers usability improvement and list of [ReservedNamedBarriers](./include/cutlass/arch/barrier.h) has been officially released.
-* Improved [CuTe documentation](./media/docs/cute/) including improved clarity and depth of [Quickstart](./media/docs/cute/00_quickstart.md), [CuTe Layout](./media/docs/cute/01_layout.md), and [CuTe Layout Algebra](./media/docs/cute/02_layout_algebra.md). Associated code comments, post-conditions, and details in [CuTe Core Unit Tests](./test/unit/cute/core/) also improved.
+* Expanded [Mixed-input Hopper GEMMs](https://github.com/NVIDIA/cutlass/tree/main/examples/55_hopper_mixed_dtype_gemm) support covering {16-bit, 8-bit} x {8-bit, 4-bit} input types with fast numerical converters and group scaling factors.
+* Performance improvements to [Mixed-input Hopper GEMMs](https://github.com/NVIDIA/cutlass/tree/main/examples/55_hopper_mixed_dtype_gemm)
+* Beta release of [Pointer-Array Batched GEMMs](https://github.com/NVIDIA/cutlass/tree/main/examples/56_hopper_ptr_array_batched_gemm) now available on Hopper GPUs utilizing TMA and WGMMA (requires CUDA 12.3 or above).
+* Beta release of [Group-GEMM](https://github.com/NVIDIA/cutlass/tree/main/examples/57_hopper_grouped_gemm) utilizing TMA and WGMMA (requires CUDA 12.3 or above).
+* [Ampere Sparse GEMM](https://github.com/NVIDIA/cutlass/tree/main/examples/15_ampere_sparse_tensorop_gemm/ampere_sparse_tensorop_gemm_with_visitor.cu) supports Epilogue Visitor Tree (EVT) now.
+* NamedBarriers usability improvement and list of [ReservedNamedBarriers](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/arch/barrier.h) has been officially released.
+* Improved CuTe documentation including improved clarity and depth of [Quickstart](./media/docs/cpp/cute/00_quickstart.md), [CuTe Layout](./media/docs/cpp/cute/01_layout.md), and [CuTe Layout Algebra](./media/docs/cpp/cute/02_layout_algebra.md). Associated code comments, post-conditions, and details in [CuTe Core Unit Tests](./test/unit/cute/core/) also improved.
 
 ## [3.3](https://github.com/NVIDIA/cutlass/releases/tag/v3.3.0) (2023-10-31)
-* [Mixed-input Hopper GEMMs](./examples/55_hopper_mixed_dtype_gemm) support covering 16-bit x 8-bit input operand types.
+* [Mixed-input Hopper GEMMs](https://github.com/NVIDIA/cutlass/tree/main/examples/55_hopper_mixed_dtype_gemm) support covering 16-bit x 8-bit input operand types.
 * [Mixed-input Ampere GEMMs](https://github.com/NVIDIA/cutlass/pull/1084) with support for canonical layouts (TN). The implementation supports upcast on operandB {fp16, bf16} x {s8, u8}, and upcast on operandA {s8, u8} x {fp16, bf16}.
-* [Copy Async based Hopper GEMMs](./test/unit/gemm/device/sm90_gemm_bf16_bf16_bf16_alignx_tensor_op_f32_warpspecialized_cooperative.cu) - which support lower than 16B aligned input tensors.
+* [Copy Async based Hopper GEMMs](https://github.com/NVIDIA/cutlass/tree/main/test/unit/gemm/device/sm90_gemm_bf16_bf16_bf16_alignx_tensor_op_f32_warpspecialized_cooperative.cu) - which support lower than 16B aligned input tensors.
 * Kernel schedules and Builder support for mixed precision and Copy Async GEMMs with < 16B aligned input tensors.
 * Profiler support for lower-aligned Hopper GEMMs.
-* Performance Improvements to [Scatter-Gather Hopper Example](./examples/52_hopper_gather_scatter_fusion).
+* Performance Improvements to [Scatter-Gather Hopper Example](https://github.com/NVIDIA/cutlass/tree/main/examples/52_hopper_gather_scatter_fusion).
 * Sub-Byte type fixes and improvements.
-* EVT Support for RELU with Aux bitmap tensor store (used in dRELU). See [SM90 EVT fusions](./include/cutlass/epilogue/fusion/sm90_visitor_compute_tma_warpspecialized.hpp) for details.
+* EVT Support for RELU with Aux bitmap tensor store (used in dRELU). See [SM90 EVT fusions](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/epilogue/fusion/sm90_visitor_compute_tma_warpspecialized.hpp) for details.
 * Fusion support for backprop fusions including drelu, dgelu, and dbias.
 * Support for void-C kernels and SM80 mixed-input GEMMs in the CUTLASS Python interface
 
@@ -172,7 +323,7 @@
 * SM80 EVT support in C++ and Python.
 * Other SM90 epilogue improvements.
 * Splitting CUTLASS library into smaller units based on operation, arch and datatypes. See [1105](https://github.com/NVIDIA/cutlass/discussions/1105) for details.
-* Making `tools/library/scripts` packageable - `tools/library/scripts` is now moving to `python/cutlass_library`. See the Python [README](./python/README.md) for details.
+* Making `tools/library/scripts` packageable - `tools/library/scripts` is now moving to `python/cutlass_library`. See the Python [README](https://github.com/NVIDIA/cutlass/tree/main/python/README.md) for details.
 * SM90 TF32 kernel improvements for all layouts.
 * SM90 rasterization direction support in the CUTLASS profiler.
 * Improvement for CUTLASS profiler build times.
@@ -180,65 +331,67 @@
 
 ## [3.2.0](https://github.com/NVIDIA/cutlass/releases/tag/v3.2.0) (2023-08-03)
 
-* New warp-specialized persistent FP8 GEMM kernel [kernel schedules](./include/cutlass/gemm/kernel/sm90_gemm_tma_warpspecialized_cooperative.hpp) and [mainloops](./include/cutlass/gemm/collective/sm90_mma_tma_gmma_ss_warpspecialized_fp8.hpp)  targeting Hopper architecture that achieve great performance with TMA, WGMMA, and threadblock clusters. An example showcasing [Hopper warp-specialized FP8 GEMMs](./examples/54_hopper_fp8_warp_specialized_gemm). FP8 GEMMs come with a fast accumulation mode. When enabled, problem execution might be faster but at the cost of lower accuracy because intermediate results will not periodically be promoted to a higher precision.
-* New [Epilogue Visitor Tree (EVT)](./examples/49_hopper_gemm_with_collective_builder/49_collective_builder.cu) support for Hopper TMA epilogues. EVTs allows for user-defined customized epilogue fusion patterns without having to write a new epilogue.
-* [Stream-K](./include/cutlass/gemm/kernel/sm90_tile_scheduler_stream_k.hpp) feature for Hopper. Note that this is only a functional implementation of stream-K, and should not be used for performance comparison. Optimizations are expected in a future release.
-* Improved CTA rasterization and support for CTA swizzling for Hopper kernels using the [Tile Scheduler](./include/cutlass/gemm/kernel/sm90_tile_scheduler.hpp).
-* Improved performance for [warp-specialized TensorFloat-32 (TF32) GEMM kernels](test/unit/gemm/device/sm90_gemm_tf32_tf32_f32_tensor_op_f32_gmma_rs_cluster_warpspecialized.cu) targeting Hopper TMA.
-* [Hopper GEMM+Permute](./examples/53_hopper_gemm_permute/53_hopper_gemm_permute.cu), an example of fusing tensor reordering (permutation) with GEMM mainloop or epilogue.
-* New CUTLASS 2D Convolution Python interface. New [example](./examples/python/03_basic_conv2d.ipynb) here.
+* New warp-specialized persistent FP8 GEMM kernel [kernel schedules](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/gemm/kernel/sm90_gemm_tma_warpspecialized_cooperative.hpp) and [mainloops](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/gemm/collective/sm90_mma_tma_gmma_ss_warpspecialized_fp8.hpp)  targeting Hopper architecture that achieve great performance with TMA, WGMMA, and threadblock clusters. An example showcasing [Hopper warp-specialized FP8 GEMMs](https://github.com/NVIDIA/cutlass/tree/main/examples/54_hopper_fp8_warp_specialized_gemm). FP8 GEMMs come with a fast accumulation mode. When enabled, problem execution might be faster but at the cost of lower accuracy because intermediate results will not periodically be promoted to a higher precision.
+* New [Epilogue Visitor Tree (EVT)](https://github.com/NVIDIA/cutlass/tree/main/examples/49_hopper_gemm_with_collective_builder/49_collective_builder.cu) support for Hopper TMA epilogues. EVTs allows for user-defined customized epilogue fusion patterns without having to write a new epilogue.
+* [Stream-K](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/gemm/kernel/sm90_tile_scheduler_stream_k.hpp) feature for Hopper. Note that this is only a functional implementation of stream-K, and should not be used for performance comparison. Optimizations are expected in a future release.
+* Improved CTA rasterization and support for CTA swizzling for Hopper kernels using the [Tile Scheduler](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/gemm/kernel/sm90_tile_scheduler.hpp).
+* Improved performance for [warp-specialized TensorFloat-32 (TF32) GEMM kernels](https://github.com/NVIDIA/cutlass/tree/main/test/unit/gemm/device/sm90_gemm_tf32_tf32_f32_tensor_op_f32_gmma_rs_cluster_warpspecialized.cu) targeting Hopper TMA.
+* [Hopper GEMM+Permute](https://github.com/NVIDIA/cutlass/tree/main/examples/53_hopper_gemm_permute/53_hopper_gemm_permute.cu), an example of fusing tensor reordering (permutation) with GEMM mainloop or epilogue.
+* New CUTLASS 2D Convolution Python interface. New [example](https://github.com/NVIDIA/cutlass/tree/main/examples/python/03_basic_conv2d.ipynb) here.
 * Support for Windows (MSVC) builds. Tested with Visual Studio 2019 v16.11.27 on Windows 10.0.
 * Optimal performance using [**CUDA 12.2u1**](https://developer.nvidia.com/cuda-downloads)
 * Updates and bugfixes from the community (thanks!)
 
 ## [3.1.0](https://github.com/NVIDIA/cutlass/releases/tag/v3.1.0) (2023-04-14)
-* New CUTLASS Python interface that aims to provide an ease-of-use interface for instantiating, emitting, compiling, and running CUTLASS kernels via Python. More details [here](./python/README.md) and new [examples](./examples/python).
-* New [efficient epilogues](test/unit/gemm/device/sm90_gemm_f16_f16_f16_tensor_op_f32_cluster_warpspecialized_cooperative.cu#L783) using TMA for Hopper.
-* Support for [fused epilogues](test/unit/gemm/device/sm90_gemm_f16_f16_f16_tensor_op_f32_cluster_warpspecialized_cooperative_bias_elementwise.cu), such Bias, ReLU and GELU, using the new efficient epilogues.
-* New [warp-specialized TensorFloat-32 (TF32) GEMM kernels](test/unit/gemm/device/sm90_gemm_tf32_tf32_f32_tensor_op_f32_gmma_rs_cluster_warpspecialized.cu) targeting Hopper TMA.
-* New [*warp-specialized persistent cooperative*](./include/cutlass/gemm/kernel/sm90_gemm_tma_warpspecialized_cooperative.hpp) kernel design that allows for larger tile sizes and improves performance on Hopper.
-* An [example](./examples/51_hopper_gett) showcasing GEMM-Like Tensor-Tensor Contraction (GETT) capability on Hopper.
-* Epilogue builders. Similar to mainloop builders (see [example 49](./examples/49_hopper_gemm_with_collective_builder/49_collective_builder.cu)), epilogue builders aim to generate the best-possible epilogue while exposing incremental opt-ins for greater customization.
+* New CUTLASS Python interface that aims to provide an ease-of-use interface for instantiating, emitting, compiling, and running CUTLASS kernels via Python. More details [here](https://github.com/NVIDIA/cutlass/tree/main/python/README.md) and new [examples](https://github.com/NVIDIA/cutlass/tree/main/examples/python).
+* New [efficient epilogues](https://github.com/NVIDIA/cutlass/tree/main/test/unit/gemm/device/sm90_gemm_f16_f16_f16_tensor_op_f32_cluster_warpspecialized_cooperative.cu#L783) using TMA for Hopper.
+* Support for [fused epilogues](https://github.com/NVIDIA/cutlass/tree/main/test/unit/gemm/device/sm90_gemm_f16_f16_f16_tensor_op_f32_cluster_warpspecialized_cooperative_bias_elementwise.cu), such Bias, ReLU and GELU, using the new efficient epilogues.
+* New [warp-specialized TensorFloat-32 (TF32) GEMM kernels](https://github.com/NVIDIA/cutlass/tree/main/test/unit/gemm/device/sm90_gemm_tf32_tf32_f32_tensor_op_f32_gmma_rs_cluster_warpspecialized.cu) targeting Hopper TMA.
+* New [*warp-specialized persistent cooperative*](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/gemm/kernel/sm90_gemm_tma_warpspecialized_cooperative.hpp) kernel design that allows for larger tile sizes and improves performance on Hopper.
+* An [example](https://github.com/NVIDIA/cutlass/tree/main/examples/51_hopper_gett) showcasing GEMM-Like Tensor-Tensor Contraction (GETT) capability on Hopper.
+* Epilogue builders. Similar to mainloop builders (see [example 49](https://github.com/NVIDIA/cutlass/tree/main/examples/49_hopper_gemm_with_collective_builder/49_collective_builder.cu)), epilogue builders aim to generate the best-possible epilogue while exposing incremental opt-ins for greater customization.
 * Profiler support for overriding kernel and epilogue builder auto schedules for 3.x API kernels, allowing specific policies to be run in the CUTLASS profiler.
-* Performance optimizations for the [*warp-specialized persistent ping-pong*](./include/cutlass/gemm/kernel/sm90_gemm_tma_warpspecialized_pingpong.hpp) kernel.
-* Changes to the [GEMM API 3.x](./media/docs/gemm_api_3x.md), involving the host-facing arguments and the underlying `Params` structs.
-* [FMHA Backward Pass](./examples/41_fused_multi_head_attention/fused_multi_head_attention_backward.cu) from Meta xFormers.
-* [Streamk GEMM with Broadcast](./examples/47_ampere_gemm_universal_streamk/ampere_gemm_universal_streamk_broadcast.cu) enables epilogue broadcast with StreamK GEMM.
-* [Batched B2B GEMM](./examples/13_two_tensor_op_fusion) now can run multiple Back-to-Back GEMM with the same problem size in parallel.
-* [Batched Strided GEMV](test/unit/gemm/device/gemv.cu) support both row major and column major input matrix.
-* [Permute + GEMM fusion](./examples/39_gemm_permute) can fuse Permute with following GEMM now.  Before, we only support fusing GEMM with Permute in the epilogue.
-* [Row Broadcast](./include/cutlass/epilogue/threadblock/predicated_tile_iterator_row_broadcast.h) can be fused in the epilogue.
+* Performance optimizations for the [*warp-specialized persistent ping-pong*](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/gemm/kernel/sm90_gemm_tma_warpspecialized_pingpong.hpp) kernel.
+* Changes to the [GEMM API 3.x](./media/docs/cpp/gemm_api_3x.md), involving the host-facing arguments and the underlying `Params` structs.
+* [FMHA Backward Pass](https://github.com/NVIDIA/cutlass/tree/main/examples/41_fused_multi_head_attention/fused_multi_head_attention_backward.cu) from Meta xFormers.
+* [Streamk GEMM with Broadcast](https://github.com/NVIDIA/cutlass/tree/main/examples/47_ampere_gemm_universal_streamk/ampere_gemm_universal_streamk_broadcast.cu) enables epilogue broadcast with StreamK GEMM.
+* [Batched B2B GEMM](https://github.com/NVIDIA/cutlass/tree/main/examples/13_two_tensor_op_fusion) now can run multiple Back-to-Back GEMM with the same problem size in parallel.
+* [Batched Strided GEMV](https://github.com/NVIDIA/cutlass/tree/main/test/unit/gemm/device/gemv.cu) support both row major and column major input matrix.
+* [Permute + GEMM fusion](https://github.com/NVIDIA/cutlass/tree/main/examples/39_gemm_permute) can fuse Permute with following GEMM now.  Before, we only support fusing GEMM with Permute in the epilogue.
+* [Row Broadcast](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/epilogue/threadblock/predicated_tile_iterator_row_broadcast.h) can be fused in the epilogue.
 * The GitHub branch is renamed from `master` to `main` in this release.
 * Optimal performance using [**CUDA 12.1**](https://developer.nvidia.com/cuda-downloads)
 * Updates and bugfixes from the community (thanks!)
 
 ## [3.0.0](https://github.com/NVIDIA/cutlass/releases/tag/v3.0.0) (2023-01-23)
-* [CuTe](./media/docs/cute/00_quickstart.md), a [new core library and backend](./include/cute) for CUTLASS 3.0 that defines a single Layout vocabulary type and an associated algebra of layouts for a much more expressive and composable abstraction for tensors, sets of parallel agents, and operations by said agents on tensors.
-* [A new conceptual operation hierarchy](./media/docs/cutlass_3x_design.md) that replaces the architecture-centric hierarchy of CUTLASS 2.x and [documentation for CUTLASS 3.0's GEMM API changes](./media/docs/gemm_api_3x.md).
-* Strict API backwards compatibility that exposes both 2.x and 3.x API kernels through the same [`device::GemmUniversalAdapter`](./include/cutlass/gemm/device/gemm_universal_adapter.h) and [`kernel::GemmUniversal`](./include/cutlass/gemm/kernel/gemm_universal.hpp) types, allowing users to include both APIs in the same translation units. More information can be found in the [3.x backwards compatibility section](./media/docs/cutlass_3x_backwards_compatibility.md).
-* Updates to [Functionality](./media/docs/functionality.md) which directs users on which kernels are supported via CUTLASS-2 and CUTLASS-3.
-* Updates to [Compatibility](./README.md#compatibility) Section regarding supported compilers, operating systems, CUDA Toolkits, Hardware Architectures and [Target Architecture](./README.md#Target-Architecture).
-* New warp-specialized GEMM [kernel schedules](./include/cutlass/gemm/kernel/sm90_gemm_tma_warpspecialized.hpp) and [mainloops](./include/cutlass/gemm/collective/sm90_mma_tma_gmma_ss_warpspecialized.hpp) targeting Hopper architecture that achieve great performance with TMA, WGMMA, and threadblock clusters.
+* [CuTe](./media/docs/cpp/cute/00_quickstart.md), a [new core library and backend](./include/cute) for CUTLASS 3.0 that defines a single Layout vocabulary type and an associated algebra of layouts for a much more expressive and composable abstraction for tensors, sets of parallel agents, and operations by said agents on tensors.
+* [A new conceptual operation hierarchy](./media/docs/cpp/cutlass_3x_design.md) that replaces the architecture-centric hierarchy of CUTLASS 2.x and [documentation for CUTLASS 3.0's GEMM API changes](./media/docs/cpp/gemm_api_3x.md).
+* Strict API backwards compatibility that exposes both 2.x and 3.x API kernels through the same [`device::GemmUniversalAdapter`](./include/cutlass/gemm/device/gemm_universal_adapter.h) and [`kernel::GemmUniversal`](./include/cutlass/gemm/kernel/gemm_universal.hpp) types, allowing users to include both APIs in the same translation units. More information can be found in the [3.x backwards compatibility section](./media/docs/cpp/cutlass_3x_backwards_compatibility.md).
+* Updates to [Functionality](./media/docs/cpp/functionality.md) which directs users on which kernels are supported via CUTLASS-2 and CUTLASS-3.
+* Updates to [Compatibility](./README.md#compatibility) Section regarding supported compilers, operating systems, CUDA Toolkits, Hardware Architectures and [Target Architecture](./README.md#target-architecture).
+* New warp-specialized GEMM [kernel schedules](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/gemm/kernel/sm90_gemm_tma_warpspecialized.hpp) and [mainloops](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/gemm/collective/sm90_mma_tma_gmma_ss_warpspecialized.hpp) targeting Hopper architecture that achieve great performance with TMA, WGMMA, and threadblock clusters.
 * Extensions to CUTLASS profiler to support threadblock cluster shapes in library and profiler tile configurations.
-* [CUTLASS library integration](./tools/library/src/gemm_operation_3x.hpp) for 3.x API kernels built through the new `CollectiveBuilder` API, enabling CUTLASS profiler.
-* Support for [Hopper GEMMs](./examples/48_hopper_warp_specialized_gemm) through the new 3.0 API with CuTe-based exposure of the Hopper [Tensor Memory Accelerator](https://docs.nvidia.com/cuda/parallel-thread-execution/index.html#data-movement-and-conversion-instructions-cp-async-bulk-tensor) and [WGMMA Tensor Core](https://docs.nvidia.com/cuda/parallel-thread-execution/index.html#asynchronous-warpgroup-level-matrix-instructions) features.
-* Set of examples that demonstrate the usage of the new 3.0 API to easily build GEMM kernels targeting Hopper: examples [48](./examples/48_hopper_warp_specialized_gemm), [49](./examples/49_hopper_gemm_schedules_with_collective_builder), and [50](./examples/50_hopper_gemm_with_epilogue_swizzle).
+* [CUTLASS library integration](https://github.com/NVIDIA/cutlass/tree/main/tools/library/src/gemm_operation_3x.hpp) for 3.x API kernels built through the new `CollectiveBuilder` API, enabling CUTLASS profiler.
+* Support for [Hopper GEMMs](https://github.com/NVIDIA/cutlass/tree/main/examples/48_hopper_warp_specialized_gemm) through the new 3.0 API with CuTe-based exposure of the Hopper [Tensor Memory Accelerator](https://docs.nvidia.com/cuda/parallel-thread-execution/index.html#data-movement-and-conversion-instructions-cp-async-bulk-tensor) and [WGMMA Tensor Core](https://docs.nvidia.com/cuda/parallel-thread-execution/index.html#asynchronous-warpgroup-level-matrix-instructions) features.
+* Set of examples that demonstrate the usage of the new 3.0 API to easily build GEMM kernels targeting Hopper: examples [48](https://github.com/NVIDIA/cutlass/tree/main/examples/48_hopper_warp_specialized_gemm), [49](https://github.com/NVIDIA/cutlass/tree/main/examples/49_hopper_gemm_schedules_with_collective_builder), and [50](https://github.com/NVIDIA/cutlass/tree/main/examples/50_hopper_gemm_with_epilogue_swizzle).
+
+# CUTLASS 2.x
 
 ## [2.11.0](https://github.com/NVIDIA/cutlass/releases/tag/v2.11.0) (2022-11-19)
-* [Stream-K](./examples/47_ampere_gemm_universal_streamk), which is a new general way to do split-K.  It can not only improve performance, but can also significantly reduce the number of tile sizes that need to be profiled to find the best one.
-* [Fused multi-head attention Kernel](./examples/41_fused_multi_head_attention).  It has two variants: one uses batched GEMM for the fixed sequence length, and the other one uses group GEMM for the variable sequence length.  Both versions just need one kernel.
-* [Dual GEMM](./examples/45_dual_gemm), which can fuse A x B and A x C into one kernel. Two GEMMs has no producer-consumer dependency.
-* Hopper improves [double precision matrix multiplication](./test/unit/gemm/device/gemm_f64n_f64t_f64t_tensor_op_f64_sm90.cu) by 2x compared to Ampere at iso-clocks. It is supported since CUDA 11.8.
-* [BLAS3](./test/unit/gemm/device/hemm_cf64_cf64_cf64_tensor_op_f64_sm90.cu) functions with Hoppers new double precision matrix multiplication instructions.
-* [ELL Block Sparse GEMM](./examples/43_ell_block_sparse_gemm), which uses an [ELL matrix](https://developer.nvidia.com/blog/accelerating-matrix-multiplication-with-block-sparse-format-and-nvidia-tensor-cores/) to describe the sparsity of A matrix.  B and output matrices are still dense. The block size can be arbitary.
-* Optimized [Group Conv](./examples/42_ampere_tensorop_group_conv) for SingleGroup mode, which requires that the output channel per group is a multiple of Threadblock tile N.
-* [Optimized DepthWise Conv](./examples/46_depthwise_simt_conv2dfprop/depthwise_simt_conv2dfprop.cu).  Two new modes are added
-  * [kOptimized](./test/unit/conv/device/depthwise_conv2d_fprop_direct_conv_f16nhwc_f16nhwc_f16nhwc_simt_f16_sm60.cu) - use direct conv to compute instead of implicit GEMM.
+* [Stream-K](https://github.com/NVIDIA/cutlass/tree/main/examples/47_ampere_gemm_universal_streamk), which is a new general way to do split-K.  It can not only improve performance, but can also significantly reduce the number of tile sizes that need to be profiled to find the best one.
+* [Fused multi-head attention Kernel](https://github.com/NVIDIA/cutlass/tree/main/examples/41_fused_multi_head_attention).  It has two variants: one uses batched GEMM for the fixed sequence length, and the other one uses group GEMM for the variable sequence length.  Both versions just need one kernel.
+* [Dual GEMM](https://github.com/NVIDIA/cutlass/tree/main/examples/45_dual_gemm), which can fuse A x B and A x C into one kernel. Two GEMMs has no producer-consumer dependency.
+* Hopper improves [double precision matrix multiplication](https://github.com/NVIDIA/cutlass/tree/main/test/unit/gemm/device/gemm_f64n_f64t_f64t_tensor_op_f64_sm90.cu) by 2x compared to Ampere at iso-clocks. It is supported since CUDA 11.8.
+* [BLAS3](https://github.com/NVIDIA/cutlass/tree/main/test/unit/gemm/device/hemm_cf64_cf64_cf64_tensor_op_f64_sm90.cu) functions with Hoppers new double precision matrix multiplication instructions.
+* [ELL Block Sparse GEMM](https://github.com/NVIDIA/cutlass/tree/main/examples/43_ell_block_sparse_gemm), which uses an [ELL matrix](https://developer.nvidia.com/blog/accelerating-matrix-multiplication-with-block-sparse-format-and-nvidia-tensor-cores/) to describe the sparsity of A matrix.  B and output matrices are still dense. The block size can be arbitary.
+* Optimized [Group Conv](https://github.com/NVIDIA/cutlass/tree/main/examples/42_ampere_tensorop_group_conv) for SingleGroup mode, which requires that the output channel per group is a multiple of Threadblock tile N.
+* [Optimized DepthWise Conv](https://github.com/NVIDIA/cutlass/tree/main/examples/46_depthwise_simt_conv2dfprop/depthwise_simt_conv2dfprop.cu).  Two new modes are added
+  * [kOptimized](https://github.com/NVIDIA/cutlass/tree/main/test/unit/conv/device/depthwise_conv2d_fprop_direct_conv_f16nhwc_f16nhwc_f16nhwc_simt_f16_sm60.cu) - use direct conv to compute instead of implicit GEMM.
     *  The restrictions are: 1) input ,output channel and group number should be multiple of (128 / sizeof(input element)). 2) The input filter size should be the same as the template parameter configuration.
-  * [kFixedStrideDilation](./test/unit/conv/device/depthwise_conv2d_fprop_direct_conv_fixed_stride_dilation_f16nhwc_f16nhwc_f16nhwc_simt_f16_sm60.cu) - which puts stride and dilation into templates to further improve the performance. In this mode, kernel persistents some inputs into register to squeeze more performance, so large filter/stride/dilation is not recommanded.
+  * [kFixedStrideDilation](https://github.com/NVIDIA/cutlass/tree/main/test/unit/conv/device/depthwise_conv2d_fprop_direct_conv_fixed_stride_dilation_f16nhwc_f16nhwc_f16nhwc_simt_f16_sm60.cu) - which puts stride and dilation into templates to further improve the performance. In this mode, kernel persistents some inputs into register to squeeze more performance, so large filter/stride/dilation is not recommanded.
     * The restrictions are: 1) input, output channel and group number should be multiple of (128 / sizeof(input element)). 2) input filter size, stride, dilation should same as the template parameter configuration.
-* [Scripts](./examples/44_multi_gemm_ir_and_codegen) to fuse multiple back-to-back GEMM.  Its implementation was discussed in a GTC'22 Spring [talk](https://www.nvidia.com/en-us/on-demand/session/gtcspring22-s41606/).
-* [FP8 data type definition](./include/cutlass/float8.h) and [conversion routines](./include/cutlass/numeric_conversion.h#L1274-2115).
+* [Scripts](https://github.com/NVIDIA/cutlass/tree/main/examples/44_multi_gemm_ir_and_codegen) to fuse multiple back-to-back GEMM.  Its implementation was discussed in a GTC'22 Spring [talk](https://www.nvidia.com/en-us/on-demand/session/gtcspring22-s41606/).
+* [FP8 data type definition](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/float8.h) and [conversion routines](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/numeric_conversion.h#L1274-2115).
 * Updates and bugfixes from the community (thanks!).  Big shout out to Meta's [xFormers](https://github.com/facebookresearch/xformers).
 
 * **Deprecation announcement:** CUTLASS plans to deprecate the following:
@@ -247,54 +400,54 @@
   * CUDA 10.2
 
 ## [2.10.0](https://github.com/NVIDIA/cutlass/releases/tag/v2.10.0) (2022-08-23)
-* [CUTLASS Python](./examples/40_cutlass_py) now supports GEMM, CONV, Group GEMM for different data types as well as different epilogue flavours.
-* Optimizations for CUTLASS's [Grouped GEMM](./examples/24_gemm_grouped/gemm_grouped.cu) kernel.  Threadblock scheduling part is improved.  Some computation can be moved to the host side if applicable.  [Grouped Syr2k](./examples/38_syr2k_grouped/syr2k_grouped.cu) kernels are added, too.
-* Optimizations for [GEMM+Softmax](./examples/35_gemm_softmax).  All the reduction computation is fused into the previous GEMM.  More template arguments are provided to fine tune the performance.
-* [Grouped GEMM for Multihead Attention](./examples/41_multi_head_attention).  This general group gemm based MHA does not require the sequence length of all GEMMs to be the same which makes it most useful for natural language processing.
-* [GEMM + Layer norm fusion for Ampere](./examples/37_gemm_layernorm_gemm_fusion/) splits the layernorm into two parts and both of them can be fused into the GEMMs before and after separately.  In addition to use square sum to compute variance of layernorm, [Shift-K](https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance#Computing_shifted_data) is provided if square sum raise numerical issues.
-* [GEMM Epilogue Permutation Fusion](./examples/39_gemm_permute) can apply user provided permutation layout mapping in the GEMM epilogue.
-* [Grouped convolution targeting implicit GEMM](test/unit/conv/device/group_conv2d_fprop_implicit_gemm_f16nhwc_f16nhwc_f16nhwc_tensor_op_f32_sm80.cu) introduces the first group convolution implementation to CUTLASS.  It is an Analytical implementation, not an Optimized.  The restrictions are: 1) input and output channel number should be multiple of group number. 2) split-K is not supported.  The implementation has 2 modes:
+* [CUTLASS Python](https://github.com/NVIDIA/cutlass/tree/main/examples/40_cutlass_py) now supports GEMM, CONV, Group GEMM for different data types as well as different epilogue flavours.
+* Optimizations for CUTLASS's [Grouped GEMM](https://github.com/NVIDIA/cutlass/tree/main/examples/24_gemm_grouped/gemm_grouped.cu) kernel.  Threadblock scheduling part is improved.  Some computation can be moved to the host side if applicable.  [Grouped Syr2k](https://github.com/NVIDIA/cutlass/tree/main/examples/38_syr2k_grouped/syr2k_grouped.cu) kernels are added, too.
+* Optimizations for [GEMM+Softmax](https://github.com/NVIDIA/cutlass/tree/main/examples/35_gemm_softmax).  All the reduction computation is fused into the previous GEMM.  More template arguments are provided to fine tune the performance.
+* [Grouped GEMM for Multihead Attention](https://github.com/NVIDIA/cutlass/tree/main/examples/41_multi_head_attention).  This general group gemm based MHA does not require the sequence length of all GEMMs to be the same which makes it most useful for natural language processing.
+* [GEMM + Layer norm fusion for Ampere](https://github.com/NVIDIA/cutlass/tree/main/examples/37_gemm_layernorm_gemm_fusion/) splits the layernorm into two parts and both of them can be fused into the GEMMs before and after separately.  In addition to use square sum to compute variance of layernorm, [Shift-K](https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance#Computing_shifted_data) is provided if square sum raise numerical issues.
+* [GEMM Epilogue Permutation Fusion](https://github.com/NVIDIA/cutlass/tree/main/examples/39_gemm_permute) can apply user provided permutation layout mapping in the GEMM epilogue.
+* [Grouped convolution targeting implicit GEMM](https://github.com/NVIDIA/cutlass/tree/main/test/unit/conv/device/group_conv2d_fprop_implicit_gemm_f16nhwc_f16nhwc_f16nhwc_tensor_op_f32_sm80.cu) introduces the first group convolution implementation to CUTLASS.  It is an Analytical implementation, not an Optimized.  The restrictions are: 1) input and output channel number should be multiple of group number. 2) split-K is not supported.  The implementation has 2 modes:
   * kSingleGroup: output channel per group is multiple of Threadblock tile N.
   * kMultipleGroup: Threadblock tile N is multiple of output channel per group.
-* [Depthwise separable convolution](test/unit/conv/device/depthwise_conv2d_fprop_implicit_gemm_f16nhwc_f16nhwc_f16nhwc_simt_f16_sm60.cu) introduces the first depthwise convolution which is also Analytical for now.  The restrictions are: 1) SIMT only 2) No split-K 3) input channel equals to output channel equals to group number.
-* Standalone [Layernorm](./tools/util/include/cutlass/util/device_layernorm.h) and [Pooling](./tools/util/include/cutlass/util/device_nhwc_pooling.h) kernels.
-* [Back-to-back GEMM/CONV](./examples/13_two_tensor_op_fusion) relaxes the requirement that the first GEMM K dimension needs to be the multiple of Threadblock Tile K dimension.
+* [Depthwise separable convolution](https://github.com/NVIDIA/cutlass/tree/main/test/unit/conv/device/depthwise_conv2d_fprop_implicit_gemm_f16nhwc_f16nhwc_f16nhwc_simt_f16_sm60.cu) introduces the first depthwise convolution which is also Analytical for now.  The restrictions are: 1) SIMT only 2) No split-K 3) input channel equals to output channel equals to group number.
+* Standalone [Layernorm](https://github.com/NVIDIA/cutlass/tree/main/tools/util/include/cutlass/util/device_layernorm.h) and [Pooling](https://github.com/NVIDIA/cutlass/tree/main/tools/util/include/cutlass/util/device_nhwc_pooling.h) kernels.
+* [Back-to-back GEMM/CONV](https://github.com/NVIDIA/cutlass/tree/main/examples/13_two_tensor_op_fusion) relaxes the requirement that the first GEMM K dimension needs to be the multiple of Threadblock Tile K dimension.
 * Optimal performance using [**CUDA 11.6u2**](https://developer.nvidia.com/cuda-downloads)
 * Updates and bugfixes from the community (thanks!)
 
 ## [2.9.0](https://github.com/NVIDIA/cutlass/releases/tag/v2.9.0) (2022-04-21)
 
-* [First layer Convolution kernels](./test/unit/conv/device/conv2d_fprop_fixed_channels_f16nhwc_f16nhwc_f16nhwc_tensor_op_f32_sm80.cu) specialized for small channel counts and reduced alignment
-  * [Few channels](./include/cutlass/conv/threadblock/conv2d_fprop_activation_tile_access_iterator_few_channels.h) specialization for reduced alignment capabilities
-  * [Fixed channels](./include/cutlass/conv/threadblock/conv2d_fprop_activation_tile_access_iterator_fixed_channels.h) further specialized when channel count perfectly matches the access vector size
-  * [Unit tests](./test/unit/conv/device/conv2d_fprop_few_channels_f16nhwc_f16nhwc_f16nhwc_tensor_op_f32_sm80.cu)
-  * [Python-based instance emitter](./python/cutlass_library/generator.py) in the CUTLASS Library and support in the Profiler
+* [First layer Convolution kernels](https://github.com/NVIDIA/cutlass/tree/main/test/unit/conv/device/conv2d_fprop_fixed_channels_f16nhwc_f16nhwc_f16nhwc_tensor_op_f32_sm80.cu) specialized for small channel counts and reduced alignment
+  * [Few channels](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/conv/threadblock/conv2d_fprop_activation_tile_access_iterator_few_channels.h) specialization for reduced alignment capabilities
+  * [Fixed channels](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/conv/threadblock/conv2d_fprop_activation_tile_access_iterator_fixed_channels.h) further specialized when channel count perfectly matches the access vector size
+  * [Unit tests](https://github.com/NVIDIA/cutlass/tree/main/test/unit/conv/device/conv2d_fprop_few_channels_f16nhwc_f16nhwc_f16nhwc_tensor_op_f32_sm80.cu)
+  * [Python-based instance emitter](https://github.com/NVIDIA/cutlass/tree/main/python/cutlass_library/generator.py) in the CUTLASS Library and support in the Profiler
 * [BLAS3](https://docs.nvidia.com/cuda/cublas/index.html#cublas-level-3-function-reference) operators accelerated by Tensor Cores
   * Supported types: f32, cf32, f64, cf64, tf32x3, complex tf32x3
-  * [HERK](./test/unit/gemm/device/her2k_cf32h_cf32n_tensor_op_fast_f32_sm80.cu) with [emitter](./python/cutlass_library/rank_k_operation.py)
-  * [SYRK](./test/unit/gemm/device/syrk_f32n_f32t_tensor_op_fast_f32_sm80.cu) with [emitter](./python/cutlass_library/rank_k_operation.py)
-  * [SYMM](./test/unit/gemm/device/symm_f32n_f32n_tensor_op_fast_f32_ls_sm80.cu) with [emitter](./python/cutlass_library/symm_operation.py)
-  * [TRMM](./test/unit/gemm/device/trmm_f32n_f32t_f32t_tensor_op_fast_f32_ls_sm80.cu) with [emitter](./python/cutlass_library/trmm_operation.py)
-  * [Unit tests](./test/unit/gemm/device/testbed_rank_k_universal.h)
-* [CUTLASS Python](./examples/40_cutlass_py) demonstrating JIT compilation of CUTLASS kernels and a Python-based runtime using [CUDA Python](https://developer.nvidia.com/cuda-python)
-  * [Python-based runtime](./tools/library/scripts/rt.py) interoperable with existing emitters
-* [GEMM + Softmax example](./examples/35_gemm_softmax)
-* [Gather and Scatter Fusion with GEMM](./examples/36_gather_scatter_fusion) can gather inputs and scatters outputs based on indices vectors in the same GEMM kernel.
+  * [HERK](https://github.com/NVIDIA/cutlass/tree/main/test/unit/gemm/device/her2k_cf32h_cf32n_tensor_op_fast_f32_sm80.cu) with [emitter](https://github.com/NVIDIA/cutlass/tree/main/python/cutlass_library/rank_k_operation.py)
+  * [SYRK](https://github.com/NVIDIA/cutlass/tree/main/test/unit/gemm/device/syrk_f32n_f32t_tensor_op_fast_f32_sm80.cu) with [emitter](https://github.com/NVIDIA/cutlass/tree/main/python/cutlass_library/rank_k_operation.py)
+  * [SYMM](https://github.com/NVIDIA/cutlass/tree/main/test/unit/gemm/device/symm_f32n_f32n_tensor_op_fast_f32_ls_sm80.cu) with [emitter](https://github.com/NVIDIA/cutlass/tree/main/python/cutlass_library/symm_operation.py)
+  * [TRMM](https://github.com/NVIDIA/cutlass/tree/main/test/unit/gemm/device/trmm_f32n_f32t_f32t_tensor_op_fast_f32_ls_sm80.cu) with [emitter](https://github.com/NVIDIA/cutlass/tree/main/python/cutlass_library/trmm_operation.py)
+  * [Unit tests](https://github.com/NVIDIA/cutlass/tree/main/test/unit/gemm/device/testbed_rank_k_universal.h)
+* [CUTLASS Python](https://github.com/NVIDIA/cutlass/tree/main/examples/40_cutlass_py) demonstrating JIT compilation of CUTLASS kernels and a Python-based runtime using [CUDA Python](https://developer.nvidia.com/cuda-python)
+  * [Python-based runtime](https://github.com/NVIDIA/cutlass/tree/main/tools/library/scripts/rt.py) interoperable with existing emitters
+* [GEMM + Softmax example](https://github.com/NVIDIA/cutlass/tree/main/examples/35_gemm_softmax)
+* [Gather and Scatter Fusion with GEMM](https://github.com/NVIDIA/cutlass/tree/main/examples/36_gather_scatter_fusion) can gather inputs and scatters outputs based on indices vectors in the same GEMM kernel.
   * It can select random rows in a row major matrix.
   * It can select random columns in a column major matrix.
-* [Back-to-back GEMM/CONV](./examples/13_two_tensor_op_fusion) fully supports buffering the first GEMM/CONV results in the shared memory for the latter one to use.  It can eliminate register spill when the tile size is big.  Additionally, bias vector add is supported in the first GEMM/CONV.
+* [Back-to-back GEMM/CONV](https://github.com/NVIDIA/cutlass/tree/main/examples/13_two_tensor_op_fusion) fully supports buffering the first GEMM/CONV results in the shared memory for the latter one to use.  It can eliminate register spill when the tile size is big.  Additionally, bias vector add is supported in the first GEMM/CONV.
   * Supported kernels: GEMM and CONV.
   * Supported types: fp16 and int8.
   * Supported architectures: Turing and Ampere.
-* [Transposed Convolution](./examples/34_transposed_conv2d) (a.k.a Deconvolution) support which reuses Dgrad implementation.
-* [Utility functions](./tools/util/include/cutlass/util) that can pad NHWC and convert between NCHW and NHWC.
+* [Transposed Convolution](https://github.com/NVIDIA/cutlass/tree/main/examples/34_transposed_conv2d) (a.k.a Deconvolution) support which reuses Dgrad implementation.
+* [Utility functions](https://github.com/NVIDIA/cutlass/tree/main/tools/util/include/cutlass/util) that can pad NHWC and convert between NCHW and NHWC.
 * [Small alignment implicit gemm](https://github.com/NVIDIA/cutlass/issues/242) support for Fprop/Dgrad/Wgrad so that padding is no longer mandated to use tensor cores in these kernels.
 * Epilogue enhancement:
   * Eliminate bank conflicts in int8 tensor core kernels.
   * Half2 usage if epilogue compute type is fp16.
   * More activation functions: Silu, Hardswish, Leaky Relu.
-  * New elementwise fusion pattern for [residual block](./include/cutlass/epilogue/thread/linear_combination_residual_block.h).
-* [Group GEMM](./examples/24_gemm_grouped) thread block number calculation fix which helps to launch the intended number of threadblocks to fully occupy the GPUs.
+  * New elementwise fusion pattern for [residual block](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/epilogue/thread/linear_combination_residual_block.h).
+* [Group GEMM](https://github.com/NVIDIA/cutlass/tree/main/examples/24_gemm_grouped) thread block number calculation fix which helps to launch the intended number of threadblocks to fully occupy the GPUs.
 * [Parallel GEMM splitk](https://github.com/NVIDIA/cutlass/pull/277) support in the CUTLASS profiler.
 * Optimal performance using [**CUDA 11.6u2**](https://developer.nvidia.com/cuda-downloads)
 * Updates and bugfixes from the community (thanks!)
@@ -304,17 +457,17 @@
 
 * **TF32x3:** emulated single-precision using Tensor Cores
   * 45+ TFLOPs on NVIDIA A100
-  * [GEMM SDK example](./examples/27_ampere_3xtf32_fast_accurate_tensorop_gemm/27_ampere_3xtf32_fast_accurate_tensorop_gemm.cu) (real)
-  * [COMPLEX GEMM SDK example](./examples/29_ampere_3xtf32_fast_accurate_tensorop_complex_gemm/29_3xtf32_complex_gemm.cu) (complex)
-  * [Implicit GEMM Convolution SDK example](./examples/28_ampere_3xtf32_fast_accurate_tensorop_fprop/ampere_3xtf32_fast_accurate_tensorop_fprop.cu)
+  * [GEMM SDK example](https://github.com/NVIDIA/cutlass/tree/main/examples/27_ampere_3xtf32_fast_accurate_tensorop_gemm/27_ampere_3xtf32_fast_accurate_tensorop_gemm.cu) (real)
+  * [COMPLEX GEMM SDK example](https://github.com/NVIDIA/cutlass/tree/main/examples/29_ampere_3xtf32_fast_accurate_tensorop_complex_gemm/29_3xtf32_complex_gemm.cu) (complex)
+  * [Implicit GEMM Convolution SDK example](https://github.com/NVIDIA/cutlass/tree/main/examples/28_ampere_3xtf32_fast_accurate_tensorop_fprop/ampere_3xtf32_fast_accurate_tensorop_fprop.cu)
 * **Mainloop fusion for Convolution:** convolution with fused per-channel scale-bias-relu
-  * [Conv Fprop SDK example](./examples/25_ampere_fprop_mainloop_fusion/ampere_fprop_mainloop_fusion.cu)
-  * [Conv WGrad SDK example](./examples/26_ampere_wgrad_mainloop_fusion/ampere_wgrad_mainloop_fusion.cu)
-  * [cutlass::conv::device::ImplicitGemmConvolutionFusion](./include/cutlass/conv/device/implicit_gemm_convolution_fusion.h)
+  * [Conv Fprop SDK example](https://github.com/NVIDIA/cutlass/tree/main/examples/25_ampere_fprop_mainloop_fusion/ampere_fprop_mainloop_fusion.cu)
+  * [Conv WGrad SDK example](https://github.com/NVIDIA/cutlass/tree/main/examples/26_ampere_wgrad_mainloop_fusion/ampere_wgrad_mainloop_fusion.cu)
+  * [cutlass::conv::device::ImplicitGemmConvolutionFusion](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/conv/device/implicit_gemm_convolution_fusion.h)
 * **Grouped GEMM:** similar to batched GEMM with distinct problem size per group
-  * [SDK example](./examples/24_gemm_grouped) with performance comparison with Batched Strided GEMM
-  * [cutlass::gemm::device::GemmGrouped](./include/cutlass/gemm/device/gemm_grouped.h)
-* [Implicit GEMM Convolution fusion](./examples/13_two_tensor_op_fusion/) supports staging 1st convolution's output accumulator in the shared memory on Turing. This allows more flexible warp tile sizes and less regsiter pressue.
+  * [SDK example](https://github.com/NVIDIA/cutlass/tree/main/examples/24_gemm_grouped) with performance comparison with Batched Strided GEMM
+  * [cutlass::gemm::device::GemmGrouped](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/gemm/device/gemm_grouped.h)
+* [Implicit GEMM Convolution fusion](https://github.com/NVIDIA/cutlass/tree/main/examples/13_two_tensor_op_fusion/) supports staging 1st convolution's output accumulator in the shared memory on Turing. This allows more flexible warp tile sizes and less regsiter pressue.
 * Optimal performance using [**CUDA 11.5**](https://developer.nvidia.com/cuda-downloads)
 * Updates from the community (thanks!)
 
@@ -324,13 +477,13 @@
   * CUDA 10.2
 
 ## [2.7.0](https://github.com/NVIDIA/cutlass/releases/tag/v2.7.0) (2021-09-24)
-  * Mainloop fusion for GEMM: [summation over A or B](./examples/23_ampere_gemm_operand_reduction_fusion/ampere_gemm_operand_reduction_fusion.cu)
-  * [Strided DGRAD (optimized iterators)](./include/cutlass/conv/kernel/default_conv2d_dgrad.h)
-  * [Half-precision GELU_taylor activation functions](./include/cutlass/epilogue/thread/activation.h#L196)
+  * Mainloop fusion for GEMM: [summation over A or B](https://github.com/NVIDIA/cutlass/tree/main/examples/23_ampere_gemm_operand_reduction_fusion/ampere_gemm_operand_reduction_fusion.cu)
+  * [Strided DGRAD (optimized iterators)](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/conv/kernel/default_conv2d_dgrad.h)
+  * [Half-precision GELU_taylor activation functions](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/epilogue/thread/activation.h#L196)
     * Use these when accumulation and epilogue compute types are all `cutlass::half_t`
-  * Tuning and bug fixes to [fused GEMM + GEMM example](./examples/13_two_tensor_op_fusion/)
-  * Support for smaller than 128b aligned Convolutions: [see examples](test/unit/conv/device/conv2d_fprop_implicit_gemm_f16nhwc_f16nhwc_f16nhwc_tensor_op_f16_sm80.cu#L272)
-  * Caching of results to accelerate Convolution [unit tests](test/unit/conv/device/cache_testbed_output.h)
+  * Tuning and bug fixes to [fused GEMM + GEMM example](https://github.com/NVIDIA/cutlass/tree/main/examples/13_two_tensor_op_fusion/)
+  * Support for smaller than 128b aligned Convolutions: [see examples](https://github.com/NVIDIA/cutlass/tree/main/test/unit/conv/device/conv2d_fprop_implicit_gemm_f16nhwc_f16nhwc_f16nhwc_tensor_op_f16_sm80.cu#L272)
+  * Caching of results to accelerate Convolution [unit tests](https://github.com/NVIDIA/cutlass/tree/main/test/unit/conv/device/cache_testbed_output.h)
     * Can be enabled or disabled by running `cmake .. -DCUTLASS_TEST_ENABLE_CACHED_RESULTS=OFF`
   * Corrections and bug fixes reported by the CUTLASS community
     * Thank you for filing these issues!
@@ -343,24 +496,24 @@
 
 ## [2.6.0](https://github.com/NVIDIA/cutlass/releases/tag/v2.6.0) (2021-07-22)
   * Optimal performance when compiled with the [CUDA 11.4 Toolkit](https://developer.nvidia.com/cuda-toolkit)
-    * Adopt the new L2 prefetch feature in [cp.async](./include/cutlass/arch/memory.h) and [global load](./include/cutlass/arch/memory_sm80.h)
+    * Adopt the new L2 prefetch feature in [cp.async](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/arch/memory.h) and [global load](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/arch/memory_sm80.h)
   * Fused operators with GEMM and Convolution
-    * [Fused broadcast in epilogue](test/unit/gemm/device/gemm_with_broadcast_f16n_f16n_f16n_tensorop_f32_sm75.cu)
-    * [Fused partial reduction in epilogue](./test/unit/gemm/device/gemm_with_reduction_f16n_f16n_f16n_tensorop_f32_sm75.cu)
+    * [Fused broadcast in epilogue](https://github.com/NVIDIA/cutlass/tree/main/test/unit/gemm/device/gemm_with_broadcast_f16n_f16n_f16n_tensorop_f32_sm75.cu)
+    * [Fused partial reduction in epilogue](https://github.com/NVIDIA/cutlass/tree/main/test/unit/gemm/device/gemm_with_reduction_f16n_f16n_f16n_tensorop_f32_sm75.cu)
   * 64b tensor strides and leading dimensions support for GEMMs
   * Affine rank=2 matrix layouts
-    * Row stride and column stride for matrices using [cutlass::layout::AffineRank2](./include/cutlass/layout/matrix.h)
-    * Support [FP64 tensor core](./examples/18_ampere_fp64_tensorop_affine2_gemm/ampere_fp64_tensorop_affine2_gemm.cu) and SIMT GEMM.
-  * [Batched GEMV](./test/unit/gemm/device/gemv.cu) preview implementation
-  * [New strided Dgrad](test/unit/conv/device/conv2d_strided_dgrad_implicit_gemm_f16nhwc_f16nhwc_f32nhwc_tensor_op_f32_sm80.cu) implementation
+    * Row stride and column stride for matrices using [cutlass::layout::AffineRank2](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/layout/matrix.h)
+    * Support [FP64 tensor core](https://github.com/NVIDIA/cutlass/tree/main/examples/18_ampere_fp64_tensorop_affine2_gemm/ampere_fp64_tensorop_affine2_gemm.cu) and SIMT GEMM.
+  * [Batched GEMV](https://github.com/NVIDIA/cutlass/tree/main/test/unit/gemm/device/gemv.cu) preview implementation
+  * [New strided Dgrad](https://github.com/NVIDIA/cutlass/tree/main/test/unit/conv/device/conv2d_strided_dgrad_implicit_gemm_f16nhwc_f16nhwc_f32nhwc_tensor_op_f32_sm80.cu) implementation
     * Accelerates over previous implementation by cutting down redundant math by 4x
     * Support using new `Dy` and `w` analytic iterators and existing `cutlass::conv::device::ImplicitGemmConvolution` interface
   * Quaternion-valued GEMM and Convolution in single- and double-precision (targeting CUDA Cores)
-    * Updates to [quaternion.h](./include/cutlass/quaternion.h) and [functional.h](./include/cutlass/functional.h)
-    * SDK Example for [GEMM](./examples/21_quaternion_gemm/quaternion_gemm.cu) and [Convolution](./examples/22_quaternion_conv/quaternion_conv.cu)
-    * [Unit tests for GEMM](./test/unit/gemm/device/simt_qgemm_nn_sm50.cu) and [Convolution](./test/unit/conv/device/conv2d_fprop_implicit_gemm_qf32nhwc_qf32nhwc_qf32nhwc_simt_f32_sm50.cu)
+    * Updates to [quaternion.h](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/quaternion.h) and [functional.h](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/functional.h)
+    * SDK Example for [GEMM](https://github.com/NVIDIA/cutlass/tree/main/examples/21_quaternion_gemm/quaternion_gemm.cu) and [Convolution](https://github.com/NVIDIA/cutlass/tree/main/examples/22_quaternion_conv/quaternion_conv.cu)
+    * [Unit tests for GEMM](https://github.com/NVIDIA/cutlass/tree/main/test/unit/gemm/device/simt_qgemm_nn_sm50.cu) and [Convolution](https://github.com/NVIDIA/cutlass/tree/main/test/unit/conv/device/conv2d_fprop_implicit_gemm_qf32nhwc_qf32nhwc_qf32nhwc_simt_f32_sm50.cu)
   * Many improvements to the epilogue.
-    * Provide an [option](./include/cutlass/epilogue/threadblock/epilogue.h) to not fully unroll the epilogue to reduce the code size and improve the performance when using complicated elementwise operations
+    * Provide an [option](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/epilogue/threadblock/epilogue.h) to not fully unroll the epilogue to reduce the code size and improve the performance when using complicated elementwise operations
     * Performance improvement for FP16 tensor core kernels
     * Bug fixes
   * Enhanced Clang support and the combination of Clang 13 and CUDA 11.4 can build and run kernels from Pascal and Ampere.
@@ -372,14 +525,14 @@
 ## [2.5.0](https://github.com/NVIDIA/cutlass/releases/tag/v2.5.0) (2021-02-26)
   * Tensor reductions
     * _m_-to-_n_ reductions of tensors with affine layout
-    * [Specializations](./test/unit/reduction/device/tensor_reduce_contiguous.cu) for reductions including contiguous dimension
-    * [Specializations](./test/unit/reduction/device/tensor_reduce_strided.cu) for reductions excluding contiguous dimension
+    * [Specializations](https://github.com/NVIDIA/cutlass/tree/main/test/unit/reduction/device/tensor_reduce_contiguous.cu) for reductions including contiguous dimension
+    * [Specializations](https://github.com/NVIDIA/cutlass/tree/main/test/unit/reduction/device/tensor_reduce_strided.cu) for reductions excluding contiguous dimension
     * Custom reduction functors such as `cutlass::logical_and`
     * Large tensor support, up to 2^63 elements (however, each dimension is limited to an extent of 2^31)
   * Optimizations for 3-D convolution
-    * [Optimized tile iterators](./include/cutlass/conv/threadblock/conv3d_fprop_activation_tile_access_iterator_optimized.h) using precomputed delta table for 3-D convolution
-    * Full coverage of [forward](test/unit/conv/device/conv3d_fprop_implicit_gemm_f16ndhwc_f16ndhwc_f32ndhwc_tensor_op_f32_sm80.cu) and [backwards](test/unit/conv/device/conv3d_dgrad_implicit_gemm_f16ndhwc_f16ndhwc_f32ndhwc_tensor_op_f32_sm80.cu) passes for 3D convolution
-  * [Fused Convolution+Convolution example](./examples/13_two_tensor_op_fusion/README.md)
+    * [Optimized tile iterators](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/conv/threadblock/conv3d_fprop_activation_tile_access_iterator_optimized.h) using precomputed delta table for 3-D convolution
+    * Full coverage of [forward](https://github.com/NVIDIA/cutlass/tree/main/test/unit/conv/device/conv3d_fprop_implicit_gemm_f16ndhwc_f16ndhwc_f32ndhwc_tensor_op_f32_sm80.cu) and [backwards](https://github.com/NVIDIA/cutlass/tree/main/test/unit/conv/device/conv3d_dgrad_implicit_gemm_f16ndhwc_f16ndhwc_f32ndhwc_tensor_op_f32_sm80.cu) passes for 3D convolution
+  * [Fused Convolution+Convolution example](https://github.com/NVIDIA/cutlass/tree/main/examples/13_two_tensor_op_fusion/README.md)
   * Corrections and bug fixes reported by the CUTLASS community
     * Thank you for filing these issues!
 
@@ -394,21 +547,21 @@
     * Global memory iterators supporting Fprop, Dgrad, and Wgrad
     * `MmaMultistage` for implicit GEMM convolution for NVIDIA Ampere architecture
     * `MmaPipeline` for implicit GEMM convolution for NVIDIA Volta and Turing architectures
-    * [Documentation](./media/docs/implicit_gemm_convolution.md) describing Implicit GEMM Convolution algorithm and implementation
+    * [Documentation](./media/docs/cpp/implicit_gemm_convolution.md) describing Implicit GEMM Convolution algorithm and implementation
 
 ## [2.3.0](https://github.com/NVIDIA/cutlass/releases/tag/v2.3.0) (2020-09-23)
  * [NVIDIA Ampere Architecture features](https://devblogs.nvidia.com/nvidia-ampere-architecture-in-depth/)
-   * [Sparse Tensor Core GEMM kernels](test/unit/gemm/device/gemm_f16n_f16n_f32t_tensor_op_f32_sparse_sm80.cu):
+   * [Sparse Tensor Core GEMM kernels](https://github.com/NVIDIA/cutlass/tree/main/test/unit/gemm/device/gemm_f16n_f16n_f32t_tensor_op_f32_sparse_sm80.cu):
      * Direct access to Sparse Tensor Cores and maximum performance via [`mma.sp.sync`](https://docs.nvidia.com/cuda/parallel-thread-execution/index.html#warp-level-matrix-instructions-mma-and-friends)
    * Fast SGEMM targeting GeForce RTX 30-series CUDA Cores
  * Minor Features:
-   * [Activation functions](./include/cutlass/epilogue/thread/activation.h) such as [GeLU](./include/cutlass/epilogue/thread/linear_combination_gelu.h) and [Sigmoid](./include/cutlass/epilogue/thread/linear_combination_sigmoid.h)
-   * Small [matrix](./include/cutlass/matrix.h) and [quaternion](./include/cutlass/quaternion.h) template classes in device code
-   * [Floating-point constants](./include/cutlass/constants.h)
+   * [Activation functions](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/epilogue/thread/activation.h) such as [GeLU](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/epilogue/thread/linear_combination_gelu.h) and [Sigmoid](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/epilogue/thread/linear_combination_sigmoid.h)
+   * Small [matrix](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/matrix.h) and [quaternion](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/quaternion.h) template classes in device code
+   * [Floating-point constants](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/constants.h)
  * NVIDIA Ampere GPU Architecture examples and documentation:
-   * [Tensor Float 32](./examples/14_ampere_tf32_tensorop_gemm/ampere_tf32_tensorop_gemm.cu) and
-   * [Sparse Tensor Cores](./examples/15_ampere_sparse_tensorop_gemm/ampere_sparse_tensorop_gemm.cu)
-   * Documentation added on CUTLASS [efficient row-major epilogue](./media/docs/gemm_api.md#efficient-epilogue)
+   * [Tensor Float 32](https://github.com/NVIDIA/cutlass/tree/main/examples/14_ampere_tf32_tensorop_gemm/ampere_tf32_tensorop_gemm.cu) and
+   * [Sparse Tensor Cores](https://github.com/NVIDIA/cutlass/tree/main/examples/15_ampere_sparse_tensorop_gemm/ampere_sparse_tensorop_gemm.cu)
+   * Documentation added on CUTLASS [efficient row-major epilogue](./media/docs/cpp/gemm_api.md#efficient-epilogue)
 
 ## [2.2.0](https://github.com/NVIDIA/cutlass/releases/tag/v2.2.0) (2020-06-08)
  * [NVIDIA Ampere Architecture features](https://devblogs.nvidia.com/nvidia-ampere-architecture-in-depth/)
@@ -428,11 +581,11 @@
    * Disabled F16C by default for compatibility - enable on cmake command line with `-DCUTLASS_ENABLE_F16C=ON`
 
 ## [2.1.0](https://github.com/NVIDIA/cutlass/releases/tag/v2.1.0) (2020-04-06)
- * BLAS-style host-side API added to [CUTLASS Library](./media/docs/quickstart.md#cutlass-library)
+ * BLAS-style host-side API added to [CUTLASS Library](./media/docs/cpp/quickstart.md#cutlass-library)
     * API to launch compiled kernel instances for GEMM and planar complex GEMM
  * Planar Complex GEMM kernels targeting Volta and Turing Tensor Cores
     * Computes complex matrix products on matrices stored as disjoint real and imaginary parts
-    * [SDK Examples of Planar Complex GEMMs](./examples/10_planar_complex/planar_complex.cu)
+    * [SDK Examples of Planar Complex GEMMs](https://github.com/NVIDIA/cutlass/tree/main/examples/10_planar_complex/planar_complex.cu)
  * Minor enhancements and bug fixes
 
 ## [2.0.0](https://github.com/NVIDIA/cutlass/releases/tag/v2.0.0) (2019-11-19)
@@ -442,10 +595,10 @@
     * Encapsulated functionality embodying modern C++11 programming techniques
     * Optimized containers and data types for efficient, generic, portable device code
   * Updates to:
-    * [Quick start guide](./media/docs/quickstart.md)
+    * [Quick start guide](./media/docs/cpp/quickstart.md)
     * [Documentation](./README.md#documentation)
-    * [Utilities](./media/docs/utilities.md)
-    * [CUTLASS Profiler](./media/docs/profiler.md)
+    * [Utilities](./media/docs/cpp/utilities.md)
+    * [CUTLASS Profiler](./media/docs/cpp/profiler.md)
  * Native Turing Tensor Cores
     * Efficient GEMM kernels targeting Turing Tensor Cores
     * Mixed-precision floating point, 8-bit integer, 4-bit integer, and binarized operands
@@ -538,4 +691,3 @@ SPDX-License-Identifier: BSD-3-Clause
   OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
   OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 ```
-

CMakeLists.txt

@@ -102,6 +102,8 @@ set(CMAKE_CUDA_STANDARD_REQUIRED ON)
 
 list(APPEND CUTLASS_CUDA_NVCC_FLAGS --expt-relaxed-constexpr)
 
+list(APPEND CUTLASS_CUDA_NVCC_FLAGS -ftemplate-backtrace-limit=0)
+
 if(CMAKE_INSTALL_PREFIX_INITIALIZED_TO_DEFAULT)
   set(CMAKE_INSTALL_PREFIX install CACHE PATH "Default installation location." FORCE)
 endif()
@@ -173,7 +175,13 @@ if (CUDA_VERSION VERSION_GREATER_EQUAL 12.0)
 endif()
 
 if (CUDA_VERSION VERSION_GREATER_EQUAL 12.8)
-  list(APPEND CUTLASS_NVCC_ARCHS_SUPPORTED 100 100a)
+  list(APPEND CUTLASS_NVCC_ARCHS_SUPPORTED 100 100a 120 120a)
+  list(APPEND CUTLASS_NVCC_ARCHS_SUPPORTED 101 101a)
+endif()
+
+if (CUDA_VERSION VERSION_GREATER_EQUAL 12.9)
+  list(APPEND CUTLASS_NVCC_ARCHS_SUPPORTED 100f 120f)
+  list(APPEND CUTLASS_NVCC_ARCHS_SUPPORTED 101f)
 endif()
 
 set(CUTLASS_NVCC_ARCHS ${CUTLASS_NVCC_ARCHS_SUPPORTED} CACHE STRING "The SM architectures requested.")
@@ -338,6 +346,10 @@ if(CUTLASS_ENABLE_SM90_EXTENDED_MMA_SHAPES)
   list(APPEND CUTLASS_CUDA_NVCC_FLAGS -DCUTE_SM90_EXTENDED_MMA_SHAPES_ENABLED)
 endif()
 
+if (CUTLASS_NVCC_ARCHS MATCHES 100f OR CUTLASS_NVCC_ARCHS MATCHES 101f)
+list(APPEND CUTLASS_CUDA_NVCC_FLAGS -DCUTLASS_SM100_FAMILY_ARCHS_ENABLED)
+endif()
+
 set(CUTLASS_SKIP_REDUCTION_INIT OFF CACHE BOOL "Disable init reduction workspace")
 
 #
@@ -382,7 +394,21 @@ endif()
 
 if (CUTLASS_ENABLE_GDC_FOR_SM90)
   message(STATUS "Grid Dependency Control (GDC) is enabled for SM90 kernels (required for programmatic dependent launches).")
-  list(APPEND CUTLASS_CUDA_NVCC_FLAGS -DCUTLASS_ENABLE_GDC_FOR_SM90=1)
+  list(APPEND CUTLASS_CUDA_FLAGS -DCUTLASS_ENABLE_GDC_FOR_SM90=1)
+endif()
+
+if (NOT DEFINED CUTLASS_ENABLE_GDC_FOR_SM100_DEFAULT)
+  set(CUTLASS_ENABLE_GDC_FOR_SM100_DEFAULT ON)
+endif()
+
+set(CUTLASS_ENABLE_GDC_FOR_SM100
+    ${CUTLASS_ENABLE_GDC_FOR_SM100_DEFAULT}
+    CACHE BOOL
+    "Enables Grid Dependency Control (GDC) for SM100 kernels (required for PDL).")
+
+if (CUTLASS_ENABLE_GDC_FOR_SM100)
+  message(STATUS "Grid Dependency Control (GDC) is enabled for SM100 kernels (required for programmatic dependent launches).")
+  list(APPEND CUTLASS_CUDA_FLAGS -DCUTLASS_ENABLE_GDC_FOR_SM100=1)
 endif()
 
 set(CUTLASS_ENABLE_SYNCLOG OFF CACHE BOOL "Enable synchronization event logging for race condition debugging. WARNING: This redefines __syncthreads() and __syncwarp() in all downstream code!")
@@ -427,7 +453,7 @@ if (NOT MSVC AND CUTLASS_NVCC_KEEP)
   # MSVC flow handles caching already, but for other generators we handle it here.
   set(CUTLASS_NVCC_KEEP_DIR ${CMAKE_CURRENT_BINARY_DIR}/tmp CACHE PATH "Location to store NVCC scratch files")
   file(MAKE_DIRECTORY ${CUTLASS_NVCC_KEEP_DIR})
-  list(APPEND CUTLASS_CUDA_NVCC_FLAGS --keep -v) # --keep-dir may not work with nvcc for some directories.
+  list(APPEND CUTLASS_CUDA_NVCC_FLAGS --keep -v -objtemp) # --keep-dir may not work with nvcc for some directories.
   list(APPEND CUTLASS_CUDA_CLANG_FLAGS -save-temps=${CUTLASS_NVCC_KEEP_DIR})
 endif()
 
@@ -454,6 +480,13 @@ if(UNIX)
   list(APPEND CUTLASS_CUDA_NVCC_FLAGS -Xcompiler=-fno-strict-aliasing)
 endif()
 
+# Known ctk11.4 issue (fixed later)
+# Also see https://stackoverflow.com/questions/64523302/cuda-missing-return-statement-at-end-of-non-void-function-in-constexpr-if-fun
+if (CUDA_VERSION VERSION_LESS 11.5.0)
+  list(APPEND CUTLASS_CUDA_NVCC_FLAGS -Xcudafe "--diag_suppress=implicit_return_from_non_void_function" )
+  message("CUDA_VERSION check pass ${CUDA_VERSION}")
+endif()
+
 # Don't leak lineinfo in release builds
 if (NOT CMAKE_BUILD_TYPE MATCHES "Release")
   list(APPEND CUTLASS_CUDA_CLANG_FLAGS -gmlt)
@@ -653,25 +686,6 @@ if (NOT CUTLASS_NAMESPACE STREQUAL "cutlass")
   target_compile_definitions(CUTLASS INTERFACE CUTLASS_NAMESPACE=${CUTLASS_NAMESPACE})
 endif()
 
-if (NOT DEFINED CUTLASS_REVISION)
-
-  find_package(Git QUIET)
-
-  execute_process(
-    COMMAND ${GIT_EXECUTABLE} rev-parse --short HEAD
-    RESULT_VARIABLE CUTLASS_REVISION_RESULT
-    OUTPUT_VARIABLE CUTLASS_REVISION
-    OUTPUT_STRIP_TRAILING_WHITESPACE
-  )
-
-  if (CUTLASS_REVISION_RESULT)
-    message(STATUS "CUTLASS Revision: Unable to detect, Git returned code ${CUTLASS_REVISION_RESULT}.")
-  else()
-    message(STATUS "CUTLASS Revision: ${CUTLASS_REVISION}")
-  endif()
-
-endif()
-
 configure_file(
   ${CMAKE_CURRENT_SOURCE_DIR}/cmake/version_extended.h.in
   ${CMAKE_CURRENT_BINARY_DIR}/include/cutlass/version_extended.h
@@ -690,6 +704,7 @@ target_include_directories(
   CUTLASS
   SYSTEM INTERFACE
   $<BUILD_INTERFACE:${CUDA_TOOLKIT_ROOT_DIR}/include>
+  $<BUILD_INTERFACE:${CUDA_TOOLKIT_ROOT_DIR}/include/cccl>
   )
 
 install(
@@ -1031,6 +1046,7 @@ function(cutlass_generate_profiler_tests NAME)
       string(REGEX REPLACE "_cluster_k_fallback=[0-9]+" "" TEST_NAME "${TEST_NAME}")
       string(REPLACE "runtime_input_datatype_a=" "" TEST_NAME "${TEST_NAME}")
       string(REPLACE "runtime_input_datatype_b=" "" TEST_NAME "${TEST_NAME}")
+      string(REPLACE "swizzle_size=" "" TEST_NAME "${TEST_NAME}")
       string(REGEX REPLACE "verification_enabled=(true|false)" "" TEST_NAME "${TEST_NAME}")
       string(REGEX REPLACE "warmup_iterations=[0-9]+" "" TEST_NAME "${TEST_NAME}")
       string(REGEX REPLACE "profiling_iterations=[0-9]+" "" TEST_NAME "${TEST_NAME}")

CONTRIBUTORS.md

@@ -2,7 +2,7 @@
 
 [README](./README.md#documentation) > **Contributors**
 
-# CUTLASS Developers **
+# CUTLASS C++ Developers **
 
 Andrew Kerr<br />
 Paul Springer<br />
@@ -70,8 +70,49 @@ Shreya Gaur<br />
 
 ** _The list is sorted in order of the author's first contribution to the CUTLASS project._
 
+# CUTLASS DSL Developers ***
 
-# CUTE Developers
+Albert Di<br />
+Albert Xu<br />
+Anakin Zheng<br />
+Arvin Jou<br />
+Brandon Sun<br />
+Chenyang Xu<br />
+Chunyu Wang<br />
+Cris Cecka<br />
+dePaul Miller<br />
+Edward Cao<br />
+Fung Xie<br />
+Guray Ozen<br />
+Hao Hu<br />
+Hong Wang<br />
+Jeremy Furtek<br />
+Jie Fang <br />
+JingZe Cui<br />
+Kihiro Bando<br />
+Linfeng Zheng<br />
+Longsheng Du<br />
+Mina Sun<br />
+Mindy Li<br />
+Pradeep Ramani<br />
+Questa Wang<br />
+Serif Yesil<br />
+Tao Xie<br />
+Tina Li<br />
+Vicki Wang<br />
+Vincent Zhang<br />
+Vijay Thakkar<br />
+Xiao Dong<br />
+Xiaolei Shi<br />
+Xinyu Wang<br />
+Yihan Chen<br />
+Yuhan Li<br />
+Zekun Fan<br />
+
+*** _Sorted in alphabetical order._
+
+
+# CuTe Developers
 
 Cris Cecka<br />
 Vijay Thakkar<br />
@@ -100,6 +141,9 @@ David Tanner<br />
 
 Tri Dao<br />
 Jay Shah<br />
+Mehdi Amini<br />
+Larry Wu<br />
+Justin Holewinski<br />
 Timothy Costa<br />
 Julien Demouth<br />
 Brian Fahs<br />
@@ -108,14 +152,11 @@ Michael Goldfarb<br />
 Mostafa Hagog<br />
 Fei Hu<br />
 Alan Kaatz<br />
-Tina Li<br />
 Wei Liu<br />
 Tim Martin<br />
 Kevin Siu<br />
 Markus Tavenrath<br />
 John Tran<br />
-Vicki Wang<br />
-Fung Xie<br />
 Yang Xu<br />
 Scott Yokim<br />
 Girish Bharambe<br />
@@ -128,3 +169,35 @@ Bryce Lelbach<br />
 Joel McCormack<br />
 Kyrylo Perelygin<br />
 Sean Treichler<br />
+
+# Copyright
+
+Copyright (c) 2017 - 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+SPDX-License-Identifier: BSD-3-Clause
+
+```
+  Redistribution and use in source and binary forms, with or without
+  modification, are permitted provided that the following conditions are met:
+
+  1. Redistributions of source code must retain the above copyright notice, this
+  list of conditions and the following disclaimer.
+
+  2. Redistributions in binary form must reproduce the above copyright notice,
+  this list of conditions and the following disclaimer in the documentation
+  and/or other materials provided with the distribution.
+
+  3. Neither the name of the copyright holder nor the names of its
+  contributors may be used to endorse or promote products derived from
+  this software without specific prior written permission.
+
+  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+  AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+  IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+  DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+  FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+  DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+  SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+  CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+  OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+  OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+```

CUDA.cmake

@@ -57,7 +57,7 @@ if (CMAKE_CUDA_COMPILER_ID MATCHES "(nvcc|[Nn][Vv][Ii][Dd][Ii][Aa])")
 elseif (CMAKE_CUDA_COMPILER_ID MATCHES "[Cc]lang")
   set(CUTLASS_CLANG_DEVICE_COMPILE ON CACHE BOOL "Using Clang tools for device compilation")
 else()
-  message(FATAL_ERROR "Uknown device-side compiler ${CMAKE_CUDA_COMPILER_ID} found. Set CMAKE_CUDA_COMPILER to either nvcc or clang++.")
+  message(FATAL_ERROR "Unknown device-side compiler ${CMAKE_CUDA_COMPILER_ID} found. Set CMAKE_CUDA_COMPILER to either nvcc or clang++.")
 endif()
 
 if (CUTLASS_CLANG_DEVICE_COMPILE AND CMAKE_VERSION VERSION_LESS_EQUAL "3.30")

EULA.txt

@@ -0,0 +1,188 @@
+NVIDIA Software License Agreement
+
+IMPORTANT NOTICE – PLEASE READ AND AGREE BEFORE USING THE SOFTWARE
+This software license agreement (“Agreement”) is a legal agreement between you, whether an individual or entity, (“you”) and NVIDIA Corporation (“NVIDIA”) and governs the use of the NVIDIA CUTLASS DSLs software and materials that NVIDIA delivers to you under this Agreement (“Software”).
+NVIDIA and you are each a “party” and collectively the “parties.”
+This Agreement can be accepted only by an adult of legal age of majority in the country in which the Software is used.
+If you don’t have the required age or authority to accept this Agreement, or if you don’t accept all the terms and conditions of this Agreement, do not use the Software.
+
+1. License Grants
+
+    1.1. License Grant to You. The Software made available by NVIDIA to you is licensed, not sold.
+    Subject to the terms of this Agreement, NVIDIA grants you a limited, non-exclusive, revocable, non-transferable, and non-sublicensable (except as expressly granted in this Agreement), license to:
+
+    a. install and use copies of the Software,
+    b. configure the Software using configuration files provided (if applicable),
+    c. modify and create derivative works of any sample or example source code NVIDIA delivers to you as part of the Software (“Derivatives”) (if applicable), and
+    d. distribute python files in the Software package in source format as incorporated into a software application subject to the following distribution requirements:
+
+       i. Your application must have material additional functionality, beyond the included portions of the Software.
+       ii. The distributable portions of the Software shall only be accessed by your application.
+       iii. The following notice shall be included in modifications and derivative works of sample source code distributed: “This software contains source code provided by NVIDIA Corporation.”
+       iv. Unless a developer tool is identified in this Agreement as distributable, it is delivered for your internal use only.
+       v. The terms under which you distribute your application must be consistent with the terms of this Agreement, including (without limitation) terms relating to the license grant and license restrictions and protection of NVIDIA’s intellectual property rights.
+       vi. Additionally, you agree that you will protect the privacy, security and legal rights of your application users.
+
+    The foregoing (a) through (d) are, collectively, the “Purpose”, and the developed applications are only for use in systems with NVIDIA GPUs.
+
+    1.2. License Grant to NVIDIA. Subject to the terms of this Agreement, you grant NVIDIA and its affiliates a non-exclusive, perpetual, irrevocable, sublicensable, worldwide, royalty-free, fully paid-up and transferable license, under your intellectual property rights, to publicly perform, publicly display, reproduce, use, make, have made, sell, offer for sale, distribute (through multiple tiers of distribution), import, create derivative works of and otherwise commercialize and exploit at NVIDIA’s discretion any Derivatives created by or for you.
+    You may, but are not required to, deliver any Derivatives to NVIDIA.
+
+2. License Restrictions
+
+    Your license to use the Software and Derivatives is restricted as stated in this Section 2 (“License Restrictions”).
+    You will cooperate with NVIDIA and, upon NVIDIA’s written request, you will confirm in writing and provide reasonably requested information to verify your compliance with the terms of this Agreement.
+    You may not:
+
+    2.1. Use the Software or Derivatives for any purpose other than the Purpose;
+
+    2.2. Sell, rent, sublicense, transfer, distribute or otherwise make available to others (except authorized users as stated in Section 3 (“Authorized Users”)) any portion of the Software or Derivatives, except as expressly granted in Section 1.1 (“License Grant to You”);
+
+    2.3. Reverse engineer, decompile, or disassemble the Software components provided in binary form, nor attempt in any other manner to obtain source code of such Software;
+
+    2.4. Modify or create derivative works of the Software, except as expressly granted in Section 1.1 (“License Grant to You”);
+
+    2.5. Change or remove copyright or other proprietary notices in the Software;
+
+    2.6. Bypass, disable, or circumvent any technical limitation, encryption, security, digital rights management or authentication mechanism in the Software;
+
+    2.7. Use the Software or Derivatives in any manner that would cause them to become subject to an open source software license, subject to the terms in Section 6 (“Components Under Other Licenses”);
+
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+
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+
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+
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+
+3. Authorized Users
+
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+
+4. Pre-Release
+
+    Software versions identified as alpha, beta, preview, early access or otherwise as pre-release (“Pre-Release”) may not be fully functional, may contain errors or design flaws, and may have reduced or different security, privacy, availability and reliability standards relative to NVIDIA commercial offerings.
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+    NVIDIA may choose not to make available a commercial version of Pre-Release Software.
+    NVIDIA may also choose to abandon development and terminate the availability of Pre-Release Software at any time without liability.
+
+5. Updates
+
+    NVIDIA may at any time and at its option, change, discontinue, or deprecate any part, or all, of the Software, or change or remove features or functionality, or make available patches, workarounds or other updates to the Software.
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+
+6. Components Under Other Licenses
+
+    The Software may include or be distributed with components provided with separate legal notices or terms that accompany the components, such as open source software licenses and other license terms (“Other Licenses”).
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+
+7. Ownership
+
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+    Except as expressly granted in this Agreement, (a) NVIDIA reserves all rights, interests and remedies in connection with the Software, and (b) no other license or right is granted to you by implication, estoppel or otherwise.
+
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+8. Feedback
+
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+
+9. Termination
+
+    9.1. Termination. This Agreement will automatically terminate without notice from NVIDIA if you fail to comply with any of the terms in this Agreement or if you commence or participate in any legal proceeding against NVIDIA with respect to the Software.
+    Additionally, either party may terminate this Agreement at any time with thirty (30) days’ advance written notice to the other party.
+
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+
+10. Disclaimer of Warranties
+
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+
+(v. May 8, 2025)

FUNCTIONALITY.md

@@ -0,0 +1,30 @@
+# Changelog for CuTe DSL API changes
+
+## [4.1.0](https://github.com/NVIDIA/cutlass/releases/tag/v4.1.0) (2025-07-16)
+
+  * for loop
+      - Python built-in ``range`` now always generates IR and executes at runtime
+      - ``cutlass.range`` is advanced ``range`` with IR level unrolling and pipelining control
+      - Deprecated ``cutlass.range_dynamic``, please replace with ``range`` or ``cutlass.range``
+      - **Experimental** Added ``pipelining`` control for compiler generated software pipeline code
+  * while/if
+      - ``while``/``if`` now by default generates IR and executes at runtime unless ``cutlass.const_expr`` is specified for the predicate
+      - Deprecated ``cutlass.dynamic_expr``, please remove it
+  * Rename mbarrier functions to reduce ambiguity
+  * Modify SyncObject API (`MbarrierArray`, `NamedBarrier`, `TmaStoreFence`) to match `std::barrier`
+  * Change pipeline `create` function to take only keyword arguments, and make `barrier_storage` optional.
+  * Introduce `cutlass.cute.arch.get_dyn_smem_size` api to get runtime dynamic shared memory size.
+  * Various API Support for SM100 BlockScaled Gemm
+    - Introduce BlockScaled MmaOps in [tcgen05/mma.py]([https://github.com/NVIDIA/cutlass/blob/main/python/CuTeDSL/cutlass/cute/nvgpu/tcgen05/mma.py]), and provide a `make_blockscaled_trivial_tiled_mma` function in [blackwell_helpers.py](https://github.com/NVIDIA/cutlass/blob/main/python/CuTeDSL/cutlass/utils/blackwell_helpers.py) to help construct a BlockScaled TiledMma.
+    - Introduce S2T CopyOps in [tcgen05/copy.py](https://github.com/NVIDIA/cutlass/blob/main/python/CuTeDSL/cutlass/cute/nvgpu/tcgen05/copy.py).
+    - Introduce BlockScaled layout utilities in [blockscaled_layout.py](https://github.com/NVIDIA/cutlass/blob/main/python/CuTeDSL/cutlass/utils/blockscaled_layout.py) for creating the required scale factor layouts in global memory, shared memory and tensor memory.
+  * `cutlass.cute.compile` now supports compilation options. Refer to [JIT compilation options](https://docs.nvidia.com/cutlass/media/docs/pythonDSL/cute_dsl_general/dsl_jit_compilation_options.html) for more details.
+  * `cutlass.cute.testing.assert_` now works for device JIT function. Specify `--enable-device-assertions` as compilation option to enable.
+  * `cutlass.cute.make_tiled_copy` is now deprecated. Please use `cutlass.cute.make_tiled_copy_tv` instead.
+  * Shared memory capacity query
+      - Introduce `cutlass.utils.get_smem_capacity_in_bytes` for querying the shared memory capacity.
+      - `<arch>_utils.SMEM_CAPACITY["<arch_str>"]` is now deprecated.
+
+## [4.0.0](https://github.com/NVIDIA/cutlass/releases/tag/v4.0.0) (2025-06-03)
+
+  * Fixed API mismatch in class ``cute.runtime.Pointer``: change ``element_type`` to ``dtype`` to match ``typing.Pointer``

LICENSE.txt

@@ -25,3 +25,10 @@ SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
 CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
 OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
 OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+Certain files within this repository are subject to separate licensing terms:
+
+- The files located in the `python/CuTeDSL` directory are licensed under the
+  NVIDIA End User License Agreement (EULA). Please refer to
+  https://docs.nvidia.com/cutlass/media/docs/pythonDSL/license.html
+  for the full terms.

PUBLICATIONS.md

@@ -2,10 +2,16 @@
 
 ## 2025
 
+- ["Comet: Fine-grained Computation-communication Overlapping for Mixture-of-Experts"](https://arxiv.org/abs/2502.19811). Shulai Zhang, Ningxin Zheng, Haibin Lin, Ziheng Jiang, Wenlei Bao, Chengquan Jiang, Qi Hou, Weihao Cui, Size Zheng, Li-Wen Chang, Quan Chen, Xin Liu. _arXiv_, February 2025.
+
 - ["ParetoQ: Scaling Laws in Extremely Low-bit LLM Quantization"](https://arxiv.org/abs/2502.02631). Zechun Liu, Changsheng Zhao, Hanxian Huang, Sijia Chen, Jing Zhang, Jiawei Zhao, Scott Roy, Lisa Jin, Yunyang Xiong, Yangyang Shi, Lin Xiao, Yuandong Tian, Bilge Soran, Raghuraman Krishnamoorthi, Tijmen Blankevoort, Vikas Chandra. _arXiv_, February 2025.
 
+- ["Generalized Neighborhood Attention: Multi-dimensional Sparse Attention at the Speed of Light"](https://arxiv.org/abs/2504.16922). Ali Hassani, Fengzhe Zhou, Aditya Kane, Jiannan Huang, Chieh-Yun Chen, Min Shi, Steven Walton, Markus Hoehnerbach, Vijay Thakkar, Michael Isaev, Qinsheng Zhang, Bing Xu, Haicheng Wu, Wen-mei Hwu, Ming-Yu Liu, Humphrey Shi. _arXiv_, April 2025.
+
 ## 2024
 
+- ["DeepSeek-V3 Technical Report"](https://arxiv.org/abs/2412.19437). DeepSeek-AI. _arXiv_, December 2024.
+
 - ["ShadowKV: KV Cache in Shadows for High-Throughput Long-Context LLM Inference"](https://arxiv.org/abs/2410.21465). Hanshi Sun, Li-Wen Chang, Wenlei Bao, Size Zheng, Ningxin Zheng, Xin Liu, Harry Dong, Yuejie Chi, Beidi Chen. _arXiv_, October 2024.
 
 - ["FLUX: Fast Software-based Communication Overlap On GPUs Through Kernel Fusion"](https://arxiv.org/abs/2406.06858). Li-Wen Chang, Wenlei Bao, Qi Hou, Chengquan Jiang, Ningxin Zheng, Yinmin Zhong, Xuanrun Zhang, Zuquan Song, Chengji Yao, Ziheng Jiang, Haibin Lin, Xin Jin, Xin Liu. _arXiv_, June 2024.
@@ -32,9 +38,9 @@
 
 - ["Graphene: An IR for Optimized Tensor Computations on GPUs"](https://dl.acm.org/doi/pdf/10.1145/3582016.3582018). Hagedorn, Bastian, Bin Fan, Hanfeng Chen, Cris Cecka, Michael Garland, Vinod Grover. _Proceedings of the 28th ACM International Conference on Architectural Support for Programming Languages and Operating Systems_, March 2023.
 
-- ["Mixed Precision Post Training Quantization of Neural Networks with Sensitivity Guided Search"](https://arxiv.org/abs/2302.01382). Clemens JS Schaefer, Elfie Guo, Caitlin Stanton, Xiaofan Zhang, Tom Jablin, Navid Lambert-Shirzad, Jian Li, Chiachen Chou, Siddharth Joshi, Yu Emma Wang. _arXiv_, Feburary 2023.
+- ["Mixed Precision Post Training Quantization of Neural Networks with Sensitivity Guided Search"](https://arxiv.org/abs/2302.01382). Clemens JS Schaefer, Elfie Guo, Caitlin Stanton, Xiaofan Zhang, Tom Jablin, Navid Lambert-Shirzad, Jian Li, Chiachen Chou, Siddharth Joshi, Yu Emma Wang. _arXiv_, February 2023.
 
-- ["Dynamic N:M Fine-Grained Structured Sparse Attention Mechanism"](https://dl.acm.org/doi/abs/10.1145/3572848.3577500). Zhaodong Chen, Zheng Qu, Yuying Quan, Liu Liu, Yufei Ding, Yuan Xie. _Proceedings of the 28th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming_, Feburary 2023.
+- ["Dynamic N:M Fine-Grained Structured Sparse Attention Mechanism"](https://dl.acm.org/doi/abs/10.1145/3572848.3577500). Zhaodong Chen, Zheng Qu, Yuying Quan, Liu Liu, Yufei Ding, Yuan Xie. _Proceedings of the 28th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming_, February 2023.
 
 - ["Stream-K: Work-centric Parallel Decomposition for Dense Matrix-Matrix Multiplication on the GPU"](https://arxiv.org/abs/2301.03598). Muhammad Osama, Duane Merrill, Cris Cecka, Michael Garland, John D. Owens. _arXiv_, January 2023.
 
@@ -64,3 +70,35 @@
 "](https://arxiv.org/abs/2008.13006). Cong Guo, Bo Yang Hsueh, Jingwen Leng, Yuxian Qiu, Yue Guan, Zehuan Wang, Xiaoying Jia, Xipeng Li, Minyi Guo, Yuhao Zhu. _Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis_, November 2020.
 
 - ["Strassen's Algorithm Reloaded on GPUs"](https://dl.acm.org/doi/10.1145/3372419). Jianyu Huang, Chenhan D. Yu, Robert A. van de Geijn. _ACM Transactions on Mathematical Software_, March 2020.
+
+## Copyright
+
+Copyright (c) 2017 - 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+SPDX-License-Identifier: BSD-3-Clause
+
+```
+  Redistribution and use in source and binary forms, with or without
+  modification, are permitted provided that the following conditions are met:
+
+  1. Redistributions of source code must retain the above copyright notice, this
+  list of conditions and the following disclaimer.
+
+  2. Redistributions in binary form must reproduce the above copyright notice,
+  this list of conditions and the following disclaimer in the documentation
+  and/or other materials provided with the distribution.
+
+  3. Neither the name of the copyright holder nor the names of its
+  contributors may be used to endorse or promote products derived from
+  this software without specific prior written permission.
+
+  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+  AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+  IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+  DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+  FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+  DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+  SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+  CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+  OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+  OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+```

README.md

@@ -1,127 +1,107 @@
 ![ALT](./media/images/gemm-hierarchy-with-epilogue-no-labels.png "Complete CUDA GEMM decomposition")
+# Overview
 
-# CUTLASS 3.8.0
+# CUTLASS 4.1.0
 
-_CUTLASS 3.8.0 - January 2025_
+_CUTLASS 4.1.0 - July 2025_
 
-CUTLASS is a collection of CUDA C++ template abstractions for implementing
-high-performance matrix-matrix multiplication (GEMM) and related computations at all levels 
-and scales within CUDA. It incorporates strategies for hierarchical decomposition and 
-data movement similar to those used to implement cuBLAS and cuDNN.  CUTLASS decomposes 
-these "moving parts" into reusable, modular software components abstracted by C++ template 
-classes.  Primitives for different levels of a conceptual parallelization hierarchy
-can be specialized and tuned via custom tiling sizes, data types,
-and other algorithmic policy. The resulting flexibility simplifies their use
-as building blocks within custom kernels and applications.
+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
+hierarchical decomposition and data movement. CUTLASS decomposes these "moving parts" into reusable, modular
+software components and abstractions.
 
-To support a wide variety of applications, CUTLASS provides extensive support for
-mixed-precision computations, providing specialized data-movement and
+Primitives for different levels of a conceptual parallelization hierarchy can be specialized and tuned
+via custom tiling sizes, data types, and other algorithmic policy. The resulting flexibility simplifies
+their use as building blocks within custom kernels and applications.
+
+CUTLASS has been providing CUDA C++ template abstractions for high-performance linear algebra since 2017 and
+these abstractions provide extensive support for a wide range of computations including
+mixed-precision computations, specialized data-movement (async copy) and
 multiply-accumulate abstractions for FP64, FP32, TF32, FP16, BF16,
-[FP32 emulation via tensor core instruction](./examples/27_ampere_3xtf32_fast_accurate_tensorop_gemm), 
+[FP32 emulation via tensor core instruction](https://github.com/NVIDIA/cutlass/tree/main/examples/27_ampere_3xtf32_fast_accurate_tensorop_gemm),
  8b floating point types (e5m2 and e4m3),
  block scaled data types (NVIDIA NVFP4 and OCP standard MXFP4, MXFP6, MXFP8),
  narrow integer types (4 and 8b signed and unsigned integers),
  and binary 1b data types (where architectures allow for the
-native support of such data types).
-CUTLASS demonstrates optimal matrix multiply operations
+native support of such data types) across NVIDIA's Volta, Turing, Ampere, Ada, Hopper, and Blackwell architectures.
+
+To this rich ecosystem of C++ based kernel programming abstractions, CUTLASS 4 adds CUTLASS DSLs. These are Python native interfaces for writing high-performance CUDA kernels based on core CUTLASS and CuTe concepts without any performance compromises. This allows for a much smoother learning curve, orders of magnitude faster compile times, native integration with DL frameworks without writing glue code, and much more intuitive metaprogramming that does not require deep C++ expertise.
+
+Overall we envision CUTLASS DSLs as a family of domain-specific languages (DSLs). With the release of 4.0, we are releasing the first of these in CuTe DSL. This is a low level programming model that is fully consistent with CuTe C++ abstractions — exposing core concepts such as layouts, tensors, hardware atoms, and full control over the hardware thread and data hierarchy.
+
+CuTe DSL demonstrates optimal matrix multiply and other linear algebra operations
 targeting the programmable, high-throughput _Tensor Cores_ implemented by
-NVIDIA's Volta, Turing, Ampere, Ada, Hopper, and Blackwell architectures.
+NVIDIA's Ampere, Hopper, and Blackwell architectures.
 
-In addition to GEMMs, CUTLASS implements high-performance convolution via
-the implicit GEMM algorithm. Implicit GEMM is the formulation of a convolution
-operation as a GEMM thereby taking advantage of CUTLASS's modular GEMM pipeline.
-This allows CUTLASS to build convolutions by reusing highly-optimized GEMM components.
+We believe it will become an indispensable tool for students, researchers, and performance
+engineers alike — flattening the learning curve of GPU programming, rapidly prototyping kernel
+designs, and bringing optimized solutions into production.
 
-See the [Quick Start Guide](./media/docs/quickstart.md) to get started quickly.
+CuTe DSL is currently in public beta and will graduate out of beta by end of summer 2025.
 
-See the [functionality docs](./media/docs/functionality.md) for a more comprehensive
-list of kernel level features, data types, instructions, and minimum supported by CUTLASS on each GPU
-architecture.
+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 3.8
+# What's New in CUTLASS 4.1
 
-CUTLASS 3.8 is the first release that supports the NVIDIA Blackwell SM100 architecture.
-For a background on Blackwell's new features, please consult the PTX documentation for CUDA 12.8.
+## 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
 
-* Support for new CuTe building blocks specifically for Blackwell SM100 architecture:
-  - [5th generation Blackwell Tensor Core instructions (TCGen05)](./include/cute/atom/mma_traits_sm100.hpp) via CuTe MMA atoms.
-  - Extensions to [Tensor Memory Accelerator](./include/cute/atom/copy_traits_sm100_tma.hpp) via CuTe Copy atoms.
-  - Exposure of Blackwell's new tensor memory (note: distinct from TMA) as [`tmem`](./include/cute/pointer.hpp) across CuTe as a first class data locale.
-  - Exposure of [`tmem->rmem`, `rmem->tmem` and `smem->tmem data movement instructions`](./include/cute/atom/copy_traits_sm100.hpp) as copy atoms in CuTe.
-  - [`make_tmem_copy()`](./include/cute/atom/copy_traits_sm100.hpp) utility method to ease creation of tiled copies for tmem copy atoms.
-  - Support for [new variants of LDSM on Blackwell](./include/cute/atom/copy_traits_sm100.hpp) via CuTe Copy atoms.
-* Support for new CUTLASS building blocks specifically for Blackwell SM100 architecture:
-  - Various narrow precision [FP4, FP6, and FP8](./include/cutlass/exmy_base.h) formats as well as their [block-scaled variants NVFP4, MXFP4, MXFP6, and MXFP8](./include/cutlass/float_subbyte.h)
-  - [Pipelines that implement Blackwell specific synchronization](./include/cutlass/pipeline/sm100_pipeline.hpp).
-  - [Cluster launch control API supporting preferred and fallback cluster shapes](./include/cutlass/cluster_launch.hpp).
-  - Data types including NVFP4, MXFP4, MXFP6, and MXFP8 and all their supported element and scale factor types.
-  - Tile schedulers using [Blackwell's Cluster Launch Control (CLC) feature](./media/docs/blackwell_cluster_launch_control.md) to implement dynamic persistence scheduling for [GEMMs](./include/cutlass/gemm/kernel/sm100_tile_scheduler.hpp), and [stream-K](./include/cutlass/gemm/kernel/sm100_tile_scheduler_stream_k.hpp).
-  - Extensions to testbeds and reference check code for unit tests and CUTLASS profiler.
-* Full support for Blackwell SM100 kernels in CUTLASS 3.x API:
-  - [Blackwell specific kernel layers](./include/cutlass/gemm/kernel/sm100_gemm_tma_warpspecialized.hpp) that
-    + Implement a new warp-specialization recipe tuned specifically for Blackwell SM100 architecture.
-    + Leverage all the new features such as CLC based tile scheduling, preferred cluster, and TMEM based double buffering of accumulators.
-    + Support stream-K load balancing for all kernel types everywhere via composable scheduler support.
-  - Blackwell collective mainloops that target the TCGen05 MMA instructions (both SS and TS) for
-    * [Non-block scaled data types without support for pointer array and grouped GEMM with TMA](./include/cutlass/gemm/collective/sm100_mma_warpspecialized.hpp)
-    * [Non-block scaled data types with support for pointer array and grouped GEMM with TMA](./include/cutlass/gemm/collective/sm100_mma_array_warpspecialized.hpp)
-    * [Block scaled data types without support for pointer array and grouped GEMM with TMA](./include/cutlass/gemm/collective/sm100_blockscaled_mma_warpspecialized.hpp)
-    * [Block scaled data types with support for pointer array and grouped GEMM with TMA](./include/cutlass/gemm/collective/sm100_blockscaled_mma_array_warpspecialized.hpp)
-  - Blackwell [collective mainloop for convolution kernels](./include/cutlass/conv/collective/sm100_implicit_gemm_umma_warpspecialized.hpp) supporting non-block scaled data types for fprop, dgrad, and wgrad.
-  - New [GEMM](./include/cutlass/gemm/dispatch_policy.hpp), [convolution](./include/cutlass/conv/dispatch_policy.hpp), and [epilogue](./include/cutlass/epilogue/dispatch_policy.hpp) dispatch policies for collectives, kernel layers, and builders.
-  - [Blackwell epilogue that supports loading accumulators from `tmem`](./include/cutlass/epilogue/collective/sm100_epilogue_tma_warpspecialized.hpp) and [full set of EVT fusions]().
-* CUTLASS library and profiler integration for block scaled data types for kernel emission, profiling, and verification.
-  - Support for preferred and fallback cluster shapes via profiler command line arguments parsing to set dynamic cluster shapes.
-  - Support for dynamic datatypes by parsing profiler via profiler command line arguments parsing to set dynamic datatype setting in TCGen05 MMA instruction descriptors.
-  - Support for mixed input GEMM kernels on Hopper in the profiler.
-* New CUTLASS profiler flag `use-cuda-graphs` to reduce overheads when benchmarking launch-bound kernels.
-* A new 3.x version of grouped GEMM to the CUTLASS library and generates kernels for Hopper and Blackwell. Now grouped GEMM support is enabled in the CUTLASS profiler (`./cutlass_profiler --operation=GroupedGemm --help` for details).
-* Set of examples that demonstrate the usage of the 3.x API for targeting Blackwell SM100 architecture:
-  - [Basic FP16 and FP8 GEMMs with minimal changes from Hopper examples](./examples/70_blackwell_gemm/), demonstrating ease of migration for off the shelf kernels using the 3.x collective builder API.
-  - GEMM with [opt-in collective builder schedules showcasing available recipes](./examples/71_blackwell_gemm_with_collective_builder/71_blackwell_gemm_with_collective_builder.cu) for Blackwell.
-  - Block scaled data type GEMMs targeting Blackwell's native block scaled Tensor Cores:
-    + [NVFP4 inputs with BF16 output](./examples/72_blackwell_narrow_precision_gemm/72a_blackwell_nvfp4_bf16_gemm.cu)
-    + [NVFP4 inputs with NVFP4 output](./examples/72_blackwell_narrow_precision_gemm/72b_blackwell_nvfp4_nvfp4_gemm.cu)
-    + [Mixed MXFP8 and MXFP6 inputs with BF16 output](./examples/72_blackwell_narrow_precision_gemm/72c_blackwell_mixed_mxfp8_bf16_gemm.cu)
-  - GEMM example demonstrating [Blackwell's new preferred cluster support via dynamic cluster shapes](./examples/73_blackwell_gemm_preferred_cluster/blackwell_gemm_preferred_cluster.cu) for increased occupancy.
-  - [GEMM with CLC based StreamK scheduler for load balancing](./examples/74_blackwell_gemm_streamk/blackwell_gemm_streamk.cu).
-  - Grouped GEMM for [vanilla FP8 data inputs](./examples/75_blackwell_grouped_gemm/75_blackwell_grouped_gemm.cu) and [NVFP4 block scaled inputs](./examples/75_blackwell_grouped_gemm/75_blackwell_grouped_gemm_block_scaled.cu).
-  - Convolution kernels for [fprop](./examples/76_blackwell_conv/76_blackwell_conv_fprop.cu), [dgrad](./examples/76_blackwell_conv/76_blackwell_conv_dgrad.cu), and [wgrad](./examples/76_blackwell_conv/76_blackwell_conv_wgrad.cu).
-  - [Fused multi-head attention fprop kernel](./examples/77_blackwell_fmha/77_blackwell_fmha.cu) supporting fp16/bf16/fp8 data types across head dims of 32,64, and 128.
-  - A new BF16x9 GEMM [kernel](./examples/78_blackwell_emulated_bf16x9_gemm/78_blackwell_emulated_bf16x9_gemm.cu) that emulates FP32 GEMM (SGEMM) using BF16 operations.
-* Set of examples that demonstrate the usage of the 3.x API for targeting Hopper architecture:
-  - A set of new [Hopper grouped GEMM kernels](./examples/69_hopper_mixed_dtype_grouped_gemm/) that support mixed A and B datatypes.
-  - A new [Hopper FP8 GEMM with groupwise scaling](./examples/67_hopper_fp8_warp_specialized_gemm_with_blockwise_scaling/67_hopper_fp8_warp_specialized_gemm_with_groupwise_scaling.cu).
-* Documentation updates:
-  - [Quickstart - instantiating a Blackwell block-scaled GEMM](./media/docs/quickstart.md#instantiating-a-blackwell-gemm-kernel).
-  - Detailed [Blackwell block-scaled GEMM functionality documentation](./media/docs/blackwell_functionality.md)
-  - A new [functionality documentation](./media/docs/functionality.md) specifically for 3.x API comprehensively documenting all supported kernel types, data types, kernel features, minimum CUDA tookit support etc for 3.x supported architectures.
-  - Updates to [compatibility](./README.md#compatibility) section regarding supported compilers, operating systems, CUDA Toolkits, Hardware Architectures, and [Target Architecture](./README.md#Target-Architecture).
+## CUTLASS C++
+* 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.
+* 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.
 
-Note: CUTLASS 3.x builds are known to be down on Windows platforms for all CUDA toolkits.
+Note: CUTLASS 4.x builds are known to be down on Windows platforms for all CUDA toolkits.
 CUTLASS team is working on a fix.
 
-**See the [CHANGELOG](CHANGELOG.md) for details of all past releases and updates.**
+**See the [CHANGELOG](https://docs.nvidia.com/cutlass/CHANGELOG.html) for details of all past releases and updates.**
 
 # Performance
 
 CUTLASS primitives are very efficient.  When used to construct device-wide GEMM kernels,
 they exhibit nearly optimal utilization of peak theoretical throughput. The figure below
-shows CUTLASS 3.8's performance as a % of theoretical peak utilization 
+shows CUTLASS 3.8's performance as a % of theoretical peak utilization
 on various input and output data types when run on NVIDIA Blackwell SM100 architecture GPU.
 
-<p align="center"><img src=media/images/cutlass-3.8-blackwell-gemm-peak-performance.svg></p>
+![ALT](media/images/cutlass-3.8-blackwell-gemm-peak-performance.svg "")
 
-The two figures below show the continual CUTLASS performance improvements 
+The two figures below show the continual CUTLASS performance improvements
 on an [NVIDIA H100](https://www.nvidia.com/en-us/data-center/h100/) (NVIDIA Hopper architecture) since
 CUTLASS 3.1.
-CUTLASS 3.5.1 was compiled with the [CUDA 12.5u1 Toolkit](https://developer.nvidia.com/cuda-downloads). 
-Tensor Core operations are implemented using CUDA's 
+CUTLASS 3.5.1 was compiled with the [CUDA 12.5u1 Toolkit](https://developer.nvidia.com/cuda-downloads).
+Tensor Core operations are implemented using CUDA's
 [mma](https://docs.nvidia.com/cuda/parallel-thread-execution/index.html#warp-level-matrix-instructions-mma) and
 [wgmma](https://docs.nvidia.com/cuda/parallel-thread-execution/index.html#asynchronous-warpgroup-level-matrix-instructions) instructions.
 
-<p align="center"><img src=media/images/cutlass-3.5.1-gemm-peak-performance.png></p>
-<p align="center"><img src=media/images/cutlass-3.5.1-gemm-peak-performance-fp8.png></p>
+![ALT](media/images/cutlass-3.5.1-gemm-peak-performance.png "")
+![ALT](media/images/cutlass-3.5.1-gemm-peak-performance-fp8.png "")
 
 # CuTe
 
@@ -143,7 +123,7 @@ Layouts can also be combined and manipulated via functional composition, on whic
 CUTLASS 3.0 and beyond adopts CuTe throughout the GEMM hierarchy in its templates.
 This greatly simplifies the design and improves code composability and readability.
 More documentation specific to CuTe can be found in its
-[dedicated documentation directory](./media/docs/cute/00_quickstart.md).
+[dedicated documentation directory](https://docs.nvidia.com/cutlass/media/docs/cpp/cute/00_quickstart.html).
 
 # Compatibility
 
@@ -153,7 +133,7 @@ Minimum requirements:
 - Compiler: Must support at least C++17
 - CUDA Toolkit version: 11.4
 
-CUTLASS requires a C++17 host compiler and 
+CUTLASS requires a C++17 host compiler and
 performs best when built with the [**CUDA 12.8 Toolkit**](https://developer.nvidia.com/cuda-downloads).
 It is also compatible with CUDA 11.4, CUDA 11.5, CUDA 11.6, CUDA 11.7, CUDA 11.8, and all other CUDA 12.x versions.
 
@@ -190,6 +170,7 @@ CUTLASS runs successfully on the following NVIDIA GPUs, and it is expected to be
 |NVIDIA H100 Tensor Core GPU            |9.0|11.8|
 |NVIDIA H200 Tensor Core GPU            |9.0|11.8|
 |NVIDIA B200 Tensor Core GPU            |10.0|12.8|
+|NVIDIA GeForce RTX 50x0 series         |10.0|12.8|
 
 ## Target Architecture
 
@@ -213,19 +194,19 @@ the kernel is expected to fail with a runtime error.
 ```
 cmake .. -DCUTLASS_NVCC_ARCHS="90a"
 ```
-Or 
+Or
 
 ```
-cmake .. -DCUTLASS_NVCC_ARCHS="100a" 
+cmake .. -DCUTLASS_NVCC_ARCHS="100a"
 ```
 
-Note: The NVIDIA Blackwell SM100 architecture used in the datacenter 
-products has a different compute capability than the one underpinning 
-NVIDIA Blackwell GeForce RTX 50 series GPUs. As a result, kernels 
-compiled for Blackwell SM100 architecture with arch conditional features 
-(using `sm100a`) are not compatible with RTX 50 series GPUs. 
+Note: The NVIDIA Blackwell SM100 architecture used in the datacenter
+products has a different compute capability than the one underpinning
+NVIDIA Blackwell GeForce RTX 50 series GPUs. As a result, kernels
+compiled for Blackwell SM100 architecture with arch conditional features
+(using `sm100a`) are not compatible with RTX 50 series GPUs.
 
-Please refer to the [functionality documentation](./media/docs/functionality.md)
+Please refer to the [functionality documentation](https://docs.nvidia.com/cutlass/media/docs/cpp/functionality.html)
 for details on which kernels require which target architectures.
 
 # Documentation
@@ -233,22 +214,22 @@ for details on which kernels require which target architectures.
 CUTLASS is described in the following documents and the accompanying
 [Doxygen documentation](https://nvidia.github.io/cutlass).
 
-- [Quick Start Guide](./media/docs/quickstart.md) - basics of building and running CUTLASS
-- [Functionality](./media/docs/functionality.md) - summarizes functionality available in CUTLASS
-- [Efficient GEMM in CUDA](./media/docs/efficient_gemm.md) - describes how GEMM kernels may be implemented efficiently in CUDA
-- [CUTLASS 3.x Design](./media/docs/cutlass_3x_design.md) - describes the CUTLASS 3.x design, its benefits, and how CuTe enables us to write much more composable components
-- [GEMM API 3.x](./media/docs/gemm_api_3x.md) - describes the CUTLASS 3.x GEMM model and C++ template concepts
-- [GEMM API 2.x](./media/docs/gemm_api.md) - describes the CUTLASS 2.x GEMM model and C++ template concepts
-- [Implicit GEMM Convolution](./media/docs/implicit_gemm_convolution.md) - describes 2-D and 3-D convolution in CUTLASS
-- [Code Organization](./media/docs/code_organization.md) - describes the organization and contents of the CUTLASS project
-- [Terminology](./media/docs/terminology.md) - describes terms used in the code
-- [Programming Guidelines](./media/docs/programming_guidelines.md) - guidelines for writing efficient modern CUDA C++
-- [Fundamental types](./media/docs/fundamental_types.md) - describes basic C++ classes used in CUTLASS to represent numeric quantities and arrays
-- [Layouts](./media/docs/layout.md) - describes layouts of matrices and tensors in memory
-- [Tile Iterators](./media/docs/tile_iterator_concept.md) - describes C++ concepts for iterating over tiles of matrices in memory
-- [CUTLASS Profiler](./media/docs/profiler.md) - command-line driven profiling application
-- [CUTLASS Utilities](./media/docs/utilities.md) - additional templates used to facilitate rapid development
-- [Dependent kernel launch](./media/docs/dependent_kernel_launch.md) - describes a new feature in Hopper which allows overlapping dependent 
+- [Quick Start Guide](https://docs.nvidia.com/cutlass/media/docs/cpp/quickstart.html) - basics of building and running CUTLASS
+- [Functionality](https://docs.nvidia.com/cutlass/media/docs/cpp/functionality.html) - summarizes functionality available in CUTLASS
+- [Efficient GEMM in CUDA](https://docs.nvidia.com/cutlass/media/docs/cpp/efficient_gemm.html) - describes how GEMM kernels may be implemented efficiently in CUDA
+- [CUTLASS 3.x Design](https://docs.nvidia.com/cutlass/media/docs/cpp/cutlass_3x_design.html) - describes the CUTLASS 3.x design, its benefits, and how CuTe enables us to write much more composable components
+- [GEMM API 3.x](https://docs.nvidia.com/cutlass/media/docs/cpp/gemm_api_3x.html) - describes the CUTLASS 3.x GEMM model and C++ template concepts
+- [GEMM API 2.x](https://docs.nvidia.com/cutlass/media/docs/cpp/gemm_api.html) - describes the CUTLASS 2.x GEMM model and C++ template concepts
+- [Implicit GEMM Convolution](https://docs.nvidia.com/cutlass/media/docs/cpp/implicit_gemm_convolution.html) - describes 2-D and 3-D convolution in CUTLASS
+- [Code Organization](https://docs.nvidia.com/cutlass/media/docs/cpp/code_organization.html) - describes the organization and contents of the CUTLASS project
+- [Terminology](https://docs.nvidia.com/cutlass/media/docs/cpp/terminology.html) - describes terms used in the code
+- [Programming Guidelines](https://docs.nvidia.com/cutlass/media/docs/cpp/programming_guidelines.html) - guidelines for writing efficient modern CUDA C++
+- [Fundamental types](https://docs.nvidia.com/cutlass/media/docs/cpp/fundamental_types.html) - describes basic C++ classes used in CUTLASS to represent numeric quantities and arrays
+- [Layouts](https://docs.nvidia.com/cutlass/media/docs/cpp/layout.html) - describes layouts of matrices and tensors in memory
+- [Tile Iterators](https://docs.nvidia.com/cutlass/media/docs/cpp/tile_iterator_concept.html) - describes C++ concepts for iterating over tiles of matrices in memory
+- [CUTLASS Profiler](https://docs.nvidia.com/cutlass/media/docs/cpp/profiler.html) - command-line driven profiling application
+- [CUTLASS Utilities](https://docs.nvidia.com/cutlass/media/docs/cpp/utilities.html) - additional templates used to facilitate rapid development
+- [Dependent kernel launch](https://docs.nvidia.com/cutlass/media/docs/cpp/dependent_kernel_launch.html) - describes a new feature in Hopper which allows overlapping dependent
 kernels in the same stream, and how it is used in CUTLASS.
 
 # Resources
@@ -268,7 +249,7 @@ projects. Client applications should target CUTLASS's `include/` directory in th
 paths.
 
 CUTLASS unit tests, examples, and utilities can be build with CMake.
-The minimum version of CMake is given in the [Quickstart guide](./media/docs/quickstart.md).
+The minimum version of CMake is given in the [Quickstart guide](https://docs.nvidia.com/cutlass/media/docs/cpp/quickstart.html).
 Make sure the `CUDACXX` environment  variable points to NVCC in the CUDA Toolkit installed
 on your system.
 
@@ -308,12 +289,12 @@ All tests should pass on supported platforms, though the exact number of tests m
 
 # Project Structure
 
-CUTLASS is arranged as a header-only library along with Utilities, Tools, Examples, and unit tests. 
-[Doxygen documentation](https://nvidia.github.io/cutlass) provides a complete list of files, classes, 
+CUTLASS is arranged as a header-only library along with Utilities, Tools, Examples, and unit tests.
+[Doxygen documentation](https://nvidia.github.io/cutlass) provides a complete list of files, classes,
 and template concepts defined in the CUTLASS project.
 
-A detailed explanation of the source code organization may be found in the 
-[CUTLASS documentation](./media/docs/code_organization.md), but several main components are summarized below.
+A detailed explanation of the source code organization may be found in the
+[CUTLASS documentation](https://docs.nvidia.com/cutlass/media/docs/cpp/code_organization.html), but several main components are summarized below.
 
 ## CUTLASS Template Library
 
@@ -337,7 +318,7 @@ include/                     # client applications should target this directory
     reduction/               # bandwidth-limited reduction kernels that do not fit the "gemm" model
 
     thread/                  # simt code that can be performed within a CUDA thread
-    
+
     transform/               # code specialized for layout, type, and domain transformations
 
     *                        # core vocabulary types, containers, and basic numeric operations
@@ -362,7 +343,7 @@ include/                     # client applications should target this directory
 
 ### CUTLASS SDK Examples
 
-[CUTLASS SDK examples](./examples) apply CUTLASS templates to implement basic computations.
+[CUTLASS SDK examples](https://github.com/NVIDIA/cutlass/tree/main/examples) apply CUTLASS templates to implement basic computations.
 
 ### Tools
 
@@ -375,9 +356,9 @@ tools/
 
   profiler/                  # CUTLASS Profiler         - command-line utility for executing operations in the
                              #                            CUTLASS Library
-  
+
   util/                      # CUTLASS Utilities        - contains numerous helper classes for
-    include/                 #                            manging tensors in device memory, reference
+    include/                 #                            managing tensors in device memory, reference
       cutlass/               #                            implementations for GEMM, random initialization
         util/                #                            of tensors, and I/O.
 ```
@@ -387,7 +368,7 @@ tools/
 The `test/unit/` directory consist of unit tests implemented with Google Test that demonstrate
 basic usage of Core API components and complete tests of the CUTLASS GEMM computations.
 
-Instructions for building and running the Unit tests are described in the [Quickstart guide](./media/docs/quickstart.md).
+Instructions for building and running the Unit tests are described in the [Quickstart guide](https://docs.nvidia.com/cutlass/media/docs/cpp/quickstart.html).
 
 # Performance Profiling
 
@@ -401,7 +382,7 @@ $ make cutlass_profiler -j16
 
 By default, only one tile size is instantiated for each data type, math instruction, and layout.
 To instantiate all, set the following environment variable when running CMake from an empty `build/` directory.
-Beware, this results in *tens of thousands* of kernels and long build times. 
+Beware, this results in *tens of thousands* of kernels and long build times.
 This would also result in a large binary size and on some platforms linker to fail on building the library.
 Therefore, it's highly recommended to generate only a subset of kernels as demonstrated in the sub-section below.
 ```bash
@@ -412,13 +393,13 @@ $ make cutlass_profiler -j16
 
 ## Building a subset of GEMM and Convolution kernels (_reduced_ build times)
 
-To compile strictly one kernel or a small set of kernels, a comma-delimited list of kernel names with 
+To compile strictly one kernel or a small set of kernels, a comma-delimited list of kernel names with
 wildcard characters may be used to reduce the set of kernels. The following examples show building exactly one
 or a subset of kernels for NVIDIA Ampere and Turing architecture:
 
 ### Building a subset Tensor Core GEMM kernels
 
-To compile a subset of Tensor Core GEMM kernels with FP32 accumulation and FP16 input targeting NVIDIA Ampere and Turing architecture, 
+To compile a subset of Tensor Core GEMM kernels with FP32 accumulation and FP16 input targeting NVIDIA Ampere and Turing architecture,
 use the below cmake command line:
 ```bash
 $ cmake .. -DCUTLASS_NVCC_ARCHS='75;80' -DCUTLASS_LIBRARY_KERNELS=cutlass_tensorop_s*gemm_f16_*_nt_align8
@@ -507,7 +488,7 @@ $ ./tools/profiler/cutlass_profiler --kernels=sgemm --m=3456 --n=4096 --k=4096
 
 ### Building a subset of Tensor Core Convolution kernels
 
-To compile a subset of Tensor core convolution kernels implementing forward propagation (fprop) with FP32 accumulation 
+To compile a subset of Tensor core convolution kernels implementing forward propagation (fprop) with FP32 accumulation
 and FP16 input targeting NVIDIA Ampere and Turing architecture, use the below cmake command line:
 ```bash
 $ cmake .. -DCUTLASS_NVCC_ARCHS='75;80' -DCUTLASS_LIBRARY_KERNELS=cutlass_tensorop_s*fprop_optimized_f16
@@ -555,7 +536,7 @@ reference_device: Passed
 
 ### Building one Convolution CUDA kernel
 
-To compile and run one CUDA Core convolution kernel implementing forward propagation (fprop) with F32 accumulation 
+To compile and run one CUDA Core convolution kernel implementing forward propagation (fprop) with F32 accumulation
 and FP32 input targeting NVIDIA Ampere and Turing architecture, use the below cmake command line:
 ```bash
 $ cmake .. -DCUTLASS_NVCC_ARCHS='75;80' -DCUTLASS_LIBRARY_KERNELS=cutlass_simt_sfprop_optimized_128x128_8x2_nhwc
@@ -603,14 +584,14 @@ reference_device: Passed
 
 ## More Details on Compiling CUTLASS Kernels and CUTLASS Profiler
 - Please follow the links for more CMake examples on selectively compiling CUTLASS kernels:
-  - [GEMM CMake Examples](./media/docs/quickstart.md#gemm-cmake-examples) 
-  - [Implicit GEMM convolution CMake Examples](./media/docs/quickstart.md#convolution-cmake-examples)
-- [Further details about the CUTLASS Profiler are described here.](./media/docs/profiler.md)
+  - [GEMM CMake Examples](https://docs.nvidia.com/cutlass/media/docs/cpp/quickstart.html#gemm-cmake-examples)
+  - [Implicit GEMM convolution CMake Examples](https://docs.nvidia.com/cutlass/media/docs/cpp/quickstart.html#convolution-cmake-examples)
+- [Further details about the CUTLASS Profiler are described here.](https://docs.nvidia.com/cutlass/media/docs/cpp/profiler.html)
 
 
 # About
 
-CUTLASS is released by NVIDIA Corporation as Open Source software under the 
+CUTLASS is released by NVIDIA Corporation as Open Source software under the
 [3-clause "New" BSD license](LICENSE.txt).
 
 # Contributors

customConfigs.cmake

@@ -36,7 +36,7 @@ set(CUTLASS_PROFILER_REGRESSION_TEST_LEVEL  ${CUTLASS_TEST_LEVEL} CACHE STRING "
 
 find_package(Python3 3.5 COMPONENTS Interpreter REQUIRED)
 
-function(cutlass_generate_kernel_filter_and_testlists_files)
+function(cutlass_generate_kernel_filter_and_testlist_files)
 
   set(options)
   set(oneValueArgs TEST_SET_NAME)
@@ -59,30 +59,30 @@ function(cutlass_generate_kernel_filter_and_testlists_files)
   )
 
   if(NOT cutlass_FILTER_GENERATION_RESULT EQUAL 0)
-    message(FATAL_ERROR "Error generating kernel filters and testlists files. See ${CMAKE_CURRENT_BINARY_DIR}/library_filter_generation.log")
+    message(FATAL_ERROR "Error generating kernel filters and testlist files. See ${CMAKE_CURRENT_BINARY_DIR}/library_filter_generation.log")
   endif()
 endfunction()
 
 if(CUTLASS_BUILD_FOR_PROFILER_REGRESSIONS)
 
-    set(PROFILER_ARCH_LIST 100a)
+    set(PROFILER_ARCH_LIST 100a 100f 101a 101f 120a 120f)
     foreach(ARCH IN LISTS CUTLASS_NVCC_ARCHS)
       if(NOT (ARCH IN_LIST PROFILER_ARCH_LIST))
-        message(FATAL_ERROR "Only SM100a compute capability is supported with profiler-based unit tests")
+        message(FATAL_ERROR "Only SM${PROFILER_ARCH_LIST} compute capabilities are supported with profiler-based unit tests")
       endif()
     endforeach()
 
     if(CUTLASS_PROFILER_REGRESSION_TEST_LEVEL  EQUAL 0)
 
       message(STATUS "Building for L0 profiler-based functional regressions")
-      cutlass_generate_kernel_filter_and_testlists_files(TEST_SET_NAME kernel_testlist_l0)
+      cutlass_generate_kernel_filter_and_testlist_files(TEST_SET_NAME kernel_testlist_l0)
       set(KERNEL_FILTER_FILE ${CMAKE_CURRENT_BINARY_DIR}/FK_functional_L0_testlist_SM${CUTLASS_NVCC_ARCHS}_cutlass3x_gemm_kernel_filter.list CACHE STRING "Kernel set")
       set(CUTLASS_PROFILER_REGRESSION_LIST_FILE ${CMAKE_CURRENT_BINARY_DIR}/FK_functional_L0_testlist_SM${CUTLASS_NVCC_ARCHS}_cutlass3x_gemm.csv CACHE STRING "Regression set")
 
     elseif (CUTLASS_PROFILER_REGRESSION_TEST_LEVEL  EQUAL 1)
       
       message(STATUS "Building for L1 profiler-based functional regressions")
-      cutlass_generate_kernel_filter_and_testlists_files(TEST_SET_NAME kernel_testlist_l1)
+      cutlass_generate_kernel_filter_and_testlist_files(TEST_SET_NAME kernel_testlist_l1)
       set(KERNEL_FILTER_FILE ${CMAKE_CURRENT_BINARY_DIR}/FK_functional_L1_testlist_SM${CUTLASS_NVCC_ARCHS}_cutlass3x_gemm_kernel_filter.list CACHE STRING "Kernel set")
       set(CUTLASS_PROFILER_REGRESSION_LIST_FILE ${CMAKE_CURRENT_BINARY_DIR}/FK_functional_L1_testlist_SM${CUTLASS_NVCC_ARCHS}_cutlass3x_gemm.csv CACHE STRING "Regression set")
 

examples/04_tile_iterator/tile_iterator.cu

@@ -34,7 +34,7 @@
   addressable memory, and then store it back into addressable memory.
 
   TileIterator is a core concept in CUTLASS that enables efficient loading and storing of data to
-  and from addressable memory. The PredicateTileIterator accepts a ThreadMap type, which defines
+  and from addressable memory. The PredicatedTileIterator accepts a ThreadMap type, which defines
   the mapping of threads to a "tile" in memory. This separation of concerns enables user-defined
   thread mappings to be specified. 
 
@@ -124,7 +124,7 @@ __global__ void copy(
 
 cudaError_t TestTileIterator(int M, int K) {
 
-    // For this example, we chose a <64, 4> tile shape. The PredicateTileIterator expects
+    // For this example, we chose a <64, 4> tile shape. The PredicatedTileIterator expects
     // PitchLinearShape and PitchLinear layout.
     using Shape = cutlass::layout::PitchLinearShape<64, 4>;
     using Layout = cutlass::layout::PitchLinear;
@@ -136,7 +136,7 @@ cudaError_t TestTileIterator(int M, int K) {
     // dimension then along the strided dimension.
     using ThreadMap = cutlass::transform::PitchLinearStripminedThreadMap<Shape, kThreads>;
 
-    // Define the PredicateTileIterator, using TileShape, Element, Layout, and ThreadMap types
+    // Define the PredicatedTileIterator, using TileShape, Element, Layout, and ThreadMap types
     using Iterator = cutlass::transform::threadblock::PredicatedTileIterator<
         Shape, Element, Layout, 1, ThreadMap>;
 

examples/07_volta_tensorop_gemm/volta_tensorop_gemm.cu

@@ -64,7 +64,7 @@ ElementAccumulator (float), ElementComputeEpilogue (float), ElementInputA (cutla
 ElementInputB (cutlass::half_t), ElementOutput (float). Communicating just the data type is not
 enough. As the data is laid out linearly in memory, we have to convey the layout of matrices. We do
 that by initializing template variable LayoutInputA to column major cutlass variable, LayoutInputB
-to row major and LayoutOutput to row major. Next, we setup rules to comptue alpha * X + beta * C
+to row major and LayoutOutput to row major. Next, we setup rules to compute alpha * X + beta * C
 which is called epilogue of the kernel. We initialize template variable EpilogueOp, which takes the
 data type of output ElementOutput (int32_t), the number of elements per vector memory access (16),
 data type of accumulator (int32_t) and data type of computation of linear combination (alpha * X +

examples/08_turing_tensorop_gemm/turing_tensorop_gemm.cu

@@ -64,7 +64,7 @@ ElementComputeEpilogue (int32_t), ElementInputA (int8_t), ElementInputB (int8_t)
 (int32_t). Communicating just the data type is not enough. As the data is laid out linearly in
 memory, we have to convey the layout of matrices. We do that by initializing template variable
 LayoutInputA to column major cutlass variable, LayoutInputB to row major and LayoutOutput to row
-major. Next, we setup rules to comptue alpha * X + beta * C which is called epilogue of the kernel.
+major. Next, we setup rules to compute alpha * X + beta * C which is called epilogue of the kernel.
 We initialize template variable EpilogueOp, which takes the data type of output ElementOutput
 (int32_t), the number of elements per vector memory access (16), data type of accumulator (int32_t)
 and data type of computation of linear combination (alpha * X + beta * C).

examples/09_turing_tensorop_conv2dfprop/turing_tensorop_conv2dfprop.cu

@@ -66,7 +66,7 @@ ElementComputeEpilogue (float), ElementInputA (cutlass::int4b_t), ElementInputB
 ElementOutput (int32_t). Communicating just the data type is not enough. As the data is laid out 
 linearly in memory, we have to convey the layout of tensors. We do that by initializing template
 variables LayoutInputA, LayoutInputB and LayoutOutput to TensorNHWC cutlass variable. Next, we setup
-rules to comptue alpha * X + beta * C which is called epilogue of the kernel. We initialize template
+rules to compute alpha * X + beta * C which is called epilogue of the kernel. We initialize template
 variable EpilogueOp, which takes the data type of output ElementOutput (int32_t), the number of
 elements per vector memory access (32), data type of accumulator (int32_t) and data type of
 computation of linear combination (alpha * X + beta * C).

examples/13_two_tensor_op_fusion/README.md

@@ -115,4 +115,3 @@ SPDX-License-Identifier: BSD-3-Clause
   OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
   OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 ```
-

examples/13_two_tensor_op_fusion/device/b2b_implicit_gemm_convolution.h

@@ -177,7 +177,7 @@ public:
     if(args.split_k_mode == SplitKMode::kParallel) {
 
       // Split-K parallel: CTAs in k-dimension write the partial results in a temporary workspace.
-      // The user needs to call a reduction operator to optain the final output tensor
+      // The user needs to call a reduction operator to obtain the final output tensor
       workspace_bytes = 
         sizeof(ElementAccumulator) *
         size_t(cutlass::conv::implicit_gemm_tensor_c_size(kConvolutionalOperator, args.problem_size_0)) *

examples/13_two_tensor_op_fusion/fused_two_gemms_grouped_f16_sm80_rf.cu

@@ -153,7 +153,7 @@ struct Options {
 
     out << "13_fused_two_gemms_grouped_f16_sm80_rf\n\n"
       << "  This example runs a grouped back-to-back GEMM kernel. A group of independent back-to-back GEMMs are\n"
-      << "  run in a single kernel. Each indivdual problem in the group is subject to the same constraints that non-grouped\n"
+      << "  run in a single kernel. Each individual problem in the group is subject to the same constraints that non-grouped\n"
       << "  back-to-back GEMMs are subject to.s"
       << "Options:\n\n"
       << "  --help                           If specified, displays this usage statement.\n\n"

examples/13_two_tensor_op_fusion/kernel/b2b_gemm.h

@@ -248,7 +248,7 @@ struct B2bGemm {
     typename Epilogue::OutputTileIterator::TensorRef* ref_C1;
     typename Epilogue::OutputTileIterator::TensorRef* ref_D1;
 
-    // Epilogue params remain constant across all problmes in the group. Thus,
+    // Epilogue params remain constant across all problems in the group. Thus,
     // the parameter here is not a pointer.
     typename OutputOp0::Params epilogue0;
     typename OutputOp1::Params epilogue1;
@@ -402,7 +402,7 @@ struct B2bGemm {
     typename Epilogue::OutputTileIterator::TensorRef* ref_C1;
     typename Epilogue::OutputTileIterator::TensorRef* ref_D1;
 
-    // Epilogue params remain constant across all problmes in the group. Thus,
+    // Epilogue params remain constant across all problems in the group. Thus,
     // the parameter here is not a pointer.
     typename OutputOp0::Params output_op_0;
     typename OutputOp1::Params output_op_1;
@@ -434,7 +434,7 @@ struct B2bGemm {
       // Only row-major outputs are currently supported, so no transpose is performed
     }
 
-    /// Returns non-grouped paramaters to be used as input to the kernel-level
+    /// Returns non-grouped parameters to be used as input to the kernel-level
     /// operator for the problem indicated by problem_visitor.
     CUTLASS_HOST_DEVICE
     Params to_single_params(const ProblemVisitor& problem_visitor) const {

examples/13_two_tensor_op_fusion/kernel/default_b2b_conv2d_fprop_sm80.h

@@ -560,7 +560,7 @@ struct DefaultB2bConv2dFprop <
 
 /////////////////////////////////////////////////////////////////////////////////////////////////
 
-/// Defines a kernel for Conv2dFprop specialization for Optimzed IteratorAlgorithm and 
+/// Defines a kernel for Conv2dFprop specialization for Optimized IteratorAlgorithm and 
 // multistage pipeline with interleaved layout.
 template <
   typename ElementA,

examples/13_two_tensor_op_fusion/kernel/default_b2b_conv2d_fprop_smem_accumulator_sm80.h

@@ -606,7 +606,7 @@ struct DefaultB2bConv2dFprop <
 
 /////////////////////////////////////////////////////////////////////////////////////////////////
 
-/// Defines a kernel for Conv2dFprop specialization for Optimzed IteratorAlgorithm and 
+/// Defines a kernel for Conv2dFprop specialization for Optimized IteratorAlgorithm and 
 // multistage pipeline with interleaved layout.
 /// Accumulator will be staged in shared memory.
 template <

examples/13_two_tensor_op_fusion/threadblock/b2b_mma_pipelined.h

@@ -277,7 +277,7 @@ public:
     IteratorAccumulatorScaleBias iterator_A1_scale,    ///< iterator over A1 operand scale vectors in global memory
     IteratorAccumulatorScaleBias iterator_A1_bias,     ///< iterator over A1 operand bias vectors in global memory
     IteratorB1 iterator_B1,                              ///< iterator over B1 operand in global memory  
-    FragmentC0 const &src_accum,                         ///< source accumualtor tile
+    FragmentC0 const &src_accum,                         ///< source accumulator tile
     OutputOp output_op_0,                                ///< epilogue operation after 1st Gemm
     TransformA0 transform_A0 = TransformA0(),            ///< transformation applied to A0 fragment
     TransformB0 transform_B0 = TransformB0(),            ///< transformation applied to B0 fragment

examples/13_two_tensor_op_fusion/threadblock/b2b_mma_pipelined_smem_accumulator.h

@@ -298,7 +298,7 @@ public:
     IteratorAccumulatorScaleBias iterator_accum0_scale,  ///< iterator over D0 scale vector in global memory
     IteratorAccumulatorScaleBias iterator_accum0_bias,   ///< iterator over D0 bias vector in global memory
     IteratorB1 iterator_B1,                              ///< iterator over B1 operand in global memory  
-    FragmentC0 const &src_accum,                         ///< source accumualtor tile
+    FragmentC0 const &src_accum,                         ///< source accumulator tile
     OutputOp output_op_0,                                ///< epilogue operation after 1st Gemm
     TransformA0 transform_A0 = TransformA0(),            ///< transformation applied to A0 fragment
     TransformB0 transform_B0 = TransformB0(),            ///< transformation applied to B0 fragment

examples/13_two_tensor_op_fusion/threadblock/default_b2b_mma.h

@@ -93,7 +93,7 @@ template <
     typename InstructionShape_,
     /// Number of stages used in the pipelined mainloop
     int Stages,
-    /// Operation perfomed by GEMM
+    /// Operation performed by GEMM
     typename Operator,
     /// Epilogue output operator
     typename EpilogueOutputOp,

examples/16_ampere_tensorop_conv2dfprop/ampere_tensorop_conv2dfprop.cu

@@ -203,7 +203,7 @@ requires any memory for scratch space.
 If yes, we reserve scratch space and pass it along
 with other arguments to initialize the CUTLASS kernel.
 
-After lauching the CUTLASS kernel, this example runs
+After launching the CUTLASS kernel, this example runs
 a reference convolution kernel (from CUTLASS utilities)
 to check correctness.
 */

examples/18_ampere_fp64_tensorop_affine2_gemm/ampere_fp64_tensorop_affine2_gemm.cu

@@ -144,7 +144,7 @@ int run() {
   // Construct Gemm ProblemSize with user defined output size
   cutlass::gemm::GemmCoord problem_size = {1024, 512, 1024};
 
-  // Stride factor shows the distance between two elements in the differnet dimensions.  The
+  // Stride factor shows the distance between two elements in the different dimensions.  The
   // first data is the logical distance between two rows, the second is between two columns.
   // CUTLASS has a utility tool cutlass::layout::Affine2Layout_Factory<Layout>::layout_factory
   // to help to convert stride_factor to the two strides.

examples/19_tensorop_canonical/tensorop_canonical.cu

@@ -55,7 +55,7 @@
 
 ///////////////////////////////////////////////////////////////////////////////////////////////////
 
-// Define the overal warp-level problem shape
+// Define the overall warp-level problem shape
 int const kM = 27;
 int const kN = 31;
 int const kK = 17;

examples/20_simt_canonical/simt_canonical.cu

@@ -59,7 +59,7 @@
 
 ///////////////////////////////////////////////////////////////////////////////////////////////////
 
-// Define the overal warp-level problem shape
+// Define the overall warp-level problem shape
 int const kM = 14;
 int const kN = 27;
 int const kK = 17;

examples/36_gather_scatter_fusion/gather_scatter_fusion.cu

@@ -30,7 +30,7 @@
  **************************************************************************************************/
 
 // This example fuses gather before GEMM and scatter after GEMM into the same
-// GEMM kernel.  Gather and scatter operation is controled by an index vector
+// GEMM kernel.  Gather and scatter operation is controlled by an index vector
 // to select rows or columns from A, B, C or D matrices.
 //
 // Suppose, all matrices are column major.  The pseudo code of the fused kernel

examples/37_gemm_layernorm_gemm_fusion/gemm_with_layernorm.h

@@ -87,7 +87,7 @@ public:
   using ElementLayernormCompute = ElementLayernormCompute_;
   using ThreadblockShape = ThreadblockShape_;
 
-  // Pre-processing has ensured the layout equivelent to RowMajor
+  // Pre-processing has ensured the layout equivalent to RowMajor
   using Layout = cutlass::layout::RowMajor;
 
   using TensorVariance = TensorRef<ElementVariance, Layout>;

examples/40_cutlass_py/README.md

@@ -2,3 +2,35 @@
 
 This directory contains deprecated examples for PyCUTLASS, a precursor to the CUTLASS Python interface.
 For examples of using CUTLASS's actively-maintained Pythonic interface, see the [examples/python](/examples/python) directory.
+
+# Copyright
+
+Copyright (c) 2017 - 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+SPDX-License-Identifier: BSD-3-Clause
+
+```
+  Redistribution and use in source and binary forms, with or without
+  modification, are permitted provided that the following conditions are met:
+
+  1. Redistributions of source code must retain the above copyright notice, this
+  list of conditions and the following disclaimer.
+
+  2. Redistributions in binary form must reproduce the above copyright notice,
+  this list of conditions and the following disclaimer in the documentation
+  and/or other materials provided with the distribution.
+
+  3. Neither the name of the copyright holder nor the names of its
+  contributors may be used to endorse or promote products derived from
+  this software without specific prior written permission.
+
+  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+  AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+  IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+  DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+  FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+  DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+  SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+  CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+  OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+  OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+```

examples/40_cutlass_py/customizable/README.md

@@ -165,3 +165,35 @@ Example 7: GELU
 ```python
 python gemm.py -i 16 8 16 -ta bfloat16 -tb bfloat16 -tc float32 -tacc float32 -m multiply_add -op TensorOp -b 64 128 64 -s 3 -w 2 2 1 -cc 80 -la ColumnMajor -aa 8 -lb ColumnMajor -ab 8 -lc RowMajor -ac 4 -te float32 -ep LinearCombination -sw IdentitySwizzle2 -p 512 256 128 -alpha 0.0 -beta 0.5 -gm GemmSplitKParallel -k 5 -bias -activ gelu
 ```
+
+# Copyright
+
+Copyright (c) 2017 - 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+SPDX-License-Identifier: BSD-3-Clause
+
+```
+  Redistribution and use in source and binary forms, with or without
+  modification, are permitted provided that the following conditions are met:
+
+  1. Redistributions of source code must retain the above copyright notice, this
+  list of conditions and the following disclaimer.
+
+  2. Redistributions in binary form must reproduce the above copyright notice,
+  this list of conditions and the following disclaimer in the documentation
+  and/or other materials provided with the distribution.
+
+  3. Neither the name of the copyright holder nor the names of its
+  contributors may be used to endorse or promote products derived from
+  this software without specific prior written permission.
+
+  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+  AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+  IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+  DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+  FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+  DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+  SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+  CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+  OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+  OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+```

examples/40_cutlass_py/customizable/conv2d.py

@@ -87,7 +87,7 @@ parser.add_argument('-la', "--layout_a", default="TensorNHWC", type=str, choices
                     "TensorNHWC", "TensorNC32HW32"], 
                     help="Memory layout of input tensor A")
 parser.add_argument('-aa', '--alignment_a', default=1,
-                    type=int, help="Memory alignement of input tensor A")
+                    type=int, help="Memory alignment of input tensor A")
 # B
 parser.add_argument('-lb', "--layout_b", default="TensorNHWC", type=str, choices=[
                     "TensorNHWC", "TensorC32RSK32"], 

examples/40_cutlass_py/customizable/gemm.py

@@ -86,7 +86,7 @@ parser.add_argument('-la', "--layout_a", default="RowMajor", type=str, choices=[
                     "RowMajor", "ColumnMajor", "RowMajorInterleaved32", "ColumnMajorInterleaved32"], 
                     help="Memory layout of input tensor A")
 parser.add_argument('-aa', '--alignment_a', default=1,
-                    type=int, help="Memory alignement of input tensor A")
+                    type=int, help="Memory alignment of input tensor A")
 # B
 parser.add_argument('-lb', "--layout_b", default="RowMajor", type=str, choices=[
                     "RowMajor", "ColumnMajor", "RowMajorInterleaved32", "ColumnMajorInterleaved32"], 

examples/41_fused_multi_head_attention/fused_multihead_attention_fixed_seqlen.cu

@@ -55,7 +55,7 @@
       ```
 
       In practice, and for numerical stability reasons,
-      we also substract the maximum so far (`mi`) before doing
+      we also subtract the maximum so far (`mi`) before doing
       the exponential. When we encounter new keys, the maximum
       used to compute O so far (`m_prime`) can differ from the
       current maximum, so we update O before accumulating with

examples/41_fused_multi_head_attention/fused_multihead_attention_variable_seqlen.cu

@@ -55,7 +55,7 @@
       ```
 
       In practice, and for numerical stability reasons,
-      we also substract the maximum so far (`mi`) before doing
+      we also subtract the maximum so far (`mi`) before doing
       the exponential. When we encounter new keys, the maximum
       used to compute O so far (`m_prime`) can differ from the
       current maximum, so we update O before accumulating with

examples/41_fused_multi_head_attention/gemm/find_default_mma.h

@@ -31,7 +31,7 @@
 
 /*! \file
     \brief Cutlass provides helper template functions to figure out the right
-   datastructures to instanciate to run a GEMM with various parameters (see
+   datastructures to instantiate to run a GEMM with various parameters (see
    `cutlass/gemm/threadblock/default_mma.h`). However, due to template
    instantiation priority rules, it will only create an MmaMultiStage with
    kStages=3 (otherwise creates an MmePipelined - which is not compatible with
@@ -83,7 +83,7 @@ template <
     typename InstructionShape,
     /// Number of stages used in the pipelined mainloop
     int Stages,
-    /// Operation perfomed by GEMM
+    /// Operation performed by GEMM
     typename Operator,
     typename Enable_ = void>
 struct FindDefaultMma {

examples/41_fused_multi_head_attention/gemm/mma_from_smem.h

@@ -522,7 +522,7 @@ class MmaPipelinedFromSharedMemory : public MmaBaseFromSharedMemory<
 
   // For API compatibility with MmaMultistageFromSharedMemory
   // but not supported as it worsens perf: older gpus < sm80 don't
-  // support async tranfers and have to waste registers
+  // support async transfers and have to waste registers
   CUTLASS_DEVICE
   void set_prologue_done(bool value) {}
   CUTLASS_DEVICE

examples/41_fused_multi_head_attention/iterators/default_warp_iterator_from_smem.h

@@ -29,7 +29,7 @@
  *
  **************************************************************************************************/
 /*! \file
-    \brief Instanciates the right WarpIterator to read from shared memory
+    \brief Instantiates the right WarpIterator to read from shared memory
     The class `DefaultWarpIteratorAFromSharedMemory` is useful when reading
         data dumped with `B2bGemm::accumToSmem`.
 */

examples/41_fused_multi_head_attention/iterators/predicated_tile_iterator_residual_last.h

@@ -86,7 +86,7 @@ namespace threadblock {
 /// To be efficient, this assumes the iterator will be dereferenced and advanced
 /// at least once outside any looping structure to minimize integer arithmetic.
 ///
-/// Acceses out of bounds are safe so long as `clear_mask()` is called prior to
+/// Accesses out of bounds are safe so long as `clear_mask()` is called prior to
 /// dereferencing the iterator.
 ///
 ///

examples/43_ell_block_sparse_gemm/ell_block_sparse_gemm.cu

@@ -49,7 +49,7 @@
     Description of parameters and tensors used to represent the Blocked-Ellpack (ELL) format
     for this example:
       a_rows              - Rows in the sparse matrix.
-      a_cols              - Colums in the sparse matrix.
+      a_cols              - Columns in the sparse matrix.
       a_ell_blocksize     - Size of the ELL-Blocks.
       a_ell_num_columns   - Number of columns in the Blocked-Ellpack format (ellValue columns)
       tensor_a            - ellValue matrix, whose size is (a_rows * a_ell_num_columns)

examples/44_multi_gemm_ir_and_codegen/ir_gen/gen_device.py

@@ -153,7 +153,7 @@ class gen_device:
 
         warp_M_tile = 32
 
-        # Determine maxmimum N_tile
+        # Determine maximum N_tile
         Max_Ntile = 0
         for layer in self.fuse_gemm_info:
             n_tile = layer['mnk'][1]

examples/44_multi_gemm_ir_and_codegen/ir_gen/gen_verify.py

@@ -76,9 +76,9 @@ class gen_verify:
             )
 
 
-    def get_params(self, declartion = True):
+    def get_params(self, declaration = True):
         code = ""
-        if declartion:
+        if declaration:
             for param in self.params:
                 code += param[0] + " " + param[1] + ";\n"
 

examples/44_multi_gemm_ir_and_codegen/ir_gen/helper.py

@@ -64,8 +64,8 @@ def write_2_headfile(filename, file_dir, string):
     with open(file_dir + filename, 'w') as f:
         f.write("/* Auto Generated code - Do not edit.*/\n\n\n#pragma once\n" + string)
 
-def var_idx(varaiable, index):
-    return varaiable + str(index)
+def var_idx(variable, index):
+    return variable + str(index)
 
 
 def list_2_string(input_list, ):

examples/49_hopper_gemm_with_collective_builder/49_collective_builder.cu

@@ -78,7 +78,7 @@
     a single default value.
 
     CUTLASS 3.x provides builders for both collective mainloops and epilogues. The particular implementation of
-    the collective is specified via the schedule tags that corresond to the underlying collective's
+    the collective is specified via the schedule tags that correspond to the underlying collective's
     dispatch policy. `gemm::collective::KernelScheduleAuto` and `epilogue::collective::EpilogueScheduleAuto`
     are special cases of these schedules that allow the builder to also decide the dispatch policy for you,
     therefore letting the builder pick the collective specialization.

examples/50_hopper_gemm_with_epilogue_swizzle/50_hopper_gemm_with_epilogue_swizzle.cu

@@ -425,7 +425,7 @@ int main(int argc, char const **args) {
   // Pipeline Depth to be used i.e number of A, B buffers in shared memory
   constexpr int PipelineStages = 8;
 
-  // Let's choose a Warp-Specialized Mainloop implemention which uses TMA
+  // Let's choose a Warp-Specialized Mainloop implementation which uses TMA
   // Note : This requires / assumes the tensors to be 16B aligned
   using DispatchPolicy = cutlass::gemm::MainloopSm90TmaGmmaWarpSpecialized<PipelineStages, ClusterShape,
                            cutlass::gemm::KernelTmaWarpSpecialized>;

examples/52_hopper_gather_scatter_fusion/52_hopper_gather_scatter_fusion.cu

@@ -32,7 +32,7 @@
   \brief Example of a Hopper gather+GEMM+scatter kernel fusion.
 
   This example fuses gather before GEMM and scatter after GEMM into the same
-  GEMM kernel. Gather and scatter operation is controled by an index vector
+  GEMM kernel. Gather and scatter operation is controlled by an index vector
   to select rows or columns from A, B, C or D matrices.
 
   Gather/scatter operations are always performed along a strided dimension 

examples/53_hopper_gemm_permute/53_hopper_gemm_permute.cu

@@ -65,7 +65,7 @@
     The approach relies on two things:
     - The ability of CUTLASS 3 to naturally perform general tensor contractions (GETT) owing to the
     flexibility of CuTe's hierarchical layouts (see example 51_hopper_gett for more details).
-    - The harware capabilities of Hopper TMA units that allow for loading multidimensional tensors with
+    - The hardware capabilities of Hopper TMA units that allow for loading multidimensional tensors with
     (almost) arbitrary strides, which can be used to represent a permuted view of the data.
 
     In this example we reuse the permutation classes of examples 39_gemm_permute as operation tags.

examples/55_hopper_mixed_dtype_gemm/55_hopper_int4_bf16_gemm.cu

@@ -402,7 +402,7 @@ struct Options : MixedDtypeOptions{
 void initialize(Options const& options) {
 
   auto shape_B = cute::make_shape(options.n, options.k, options.l);
-  int const scale_k = (options.k + options.g - 1) / options.g;
+  int const scale_k = cutlass::ceil_div(options.k, options.g);
   stride_A = cutlass::make_cute_packed_stride(StrideA{}, cute::make_shape(options.m, options.k, options.l));
   stride_B = cutlass::make_cute_packed_stride(StrideB{}, shape_B);
   // Reverse stride here due to swap and transpose
@@ -429,7 +429,7 @@ void initialize(Options const& options) {
   block_zero.reset(scale_k * options.l * options.n);
 
   initialize_tensor(block_A, seed + 2022);
-  initialize_quant_tensor(block_B, seed + 2021);
+  initialize_tensor(block_B, seed + 2021);
   initialize_tensor(block_C, seed + 2020);
   initialize_scale(block_scale, options);
   initialize_zero(block_zero, options);

examples/55_hopper_mixed_dtype_gemm/55_hopper_int4_fp8_gemm.cu

@@ -318,7 +318,7 @@ struct Options : MixedDtypeOptions {
 void initialize(Options const& options) {
 
   auto shape_B = cute::make_shape(options.n, options.k, options.l);
-  int const scale_k = (options.k + options.g - 1) / options.g;
+  int const scale_k = cutlass::ceil_div(options.k, options.g);
   stride_A = cutlass::make_cute_packed_stride(StrideA{}, cute::make_shape(options.m, options.k, options.l));
   stride_B = cutlass::make_cute_packed_stride(StrideB{}, shape_B);
   // Reverse stride here due to swap and transpose
@@ -347,7 +347,7 @@ void initialize(Options const& options) {
   block_zero.reset(scale_k * options.l * options.n);
 
   initialize_tensor(block_A, seed + 2022);
-  initialize_quant_tensor(block_B, seed + 2021);
+  initialize_tensor(block_B, seed + 2021);
   cutlass::unified_encode_int4b(block_B.get(), block_B_modified.get(), block_B.size());
   initialize_tensor(block_C, seed + 2020);
   initialize_scale(block_scale, options);

examples/55_hopper_mixed_dtype_gemm/55_hopper_mixed_dtype_gemm.cu

@@ -288,7 +288,7 @@ cutlass::DeviceAllocation<typename GemmScaleWithZeroPoint::EpilogueOutputOp::Ele
 void initialize(MixedDtypeOptions const& options) {
 
   auto shape_b = cute::make_shape(options.n, options.k, options.l);
-  int const scale_k = (options.k + options.g - 1) / options.g;
+  int const scale_k = cutlass::ceil_div(options.k, options.g);
   stride_A = cutlass::make_cute_packed_stride(StrideA{}, cute::make_shape(options.m, options.k, options.l));
   stride_B = cutlass::make_cute_packed_stride(StrideB{}, shape_b);
   // Reverse stride here due to swap and transpose
@@ -313,7 +313,7 @@ void initialize(MixedDtypeOptions const& options) {
   block_zero.reset(scale_k * options.l * options.n);
 
   initialize_tensor(block_A, seed + 2022);
-  initialize_quant_tensor(block_B, seed + 2021);
+  initialize_tensor(block_B, seed + 2021);
   initialize_tensor(block_C, seed + 2020);
   initialize_scale(block_scale, options);
   initialize_zero(block_zero, options);

examples/55_hopper_mixed_dtype_gemm/README.md

@@ -41,3 +41,35 @@ We are currently optimizing the following cases:
 * Optimizations for memory bound cases.
 
 * Optimizations for scale and zero-point loading when the group size is not equal to the threadblock-k size.
+
+## Copyright
+
+Copyright (c) 2017 - 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+SPDX-License-Identifier: BSD-3-Clause
+
+```
+  Redistribution and use in source and binary forms, with or without
+  modification, are permitted provided that the following conditions are met:
+
+  1. Redistributions of source code must retain the above copyright notice, this
+  list of conditions and the following disclaimer.
+
+  2. Redistributions in binary form must reproduce the above copyright notice,
+  this list of conditions and the following disclaimer in the documentation
+  and/or other materials provided with the distribution.
+
+  3. Neither the name of the copyright holder nor the names of its
+  contributors may be used to endorse or promote products derived from
+  this software without specific prior written permission.
+
+  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+  AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+  IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+  DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+  FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+  DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+  SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+  CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+  OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+  OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+```

examples/55_hopper_mixed_dtype_gemm/mixed_dtype_utils.hpp

@@ -208,20 +208,6 @@ bool initialize_tensor(
   return true;
 }
 
-template <typename Element>
-bool initialize_quant_tensor(
-  cutlass::DeviceAllocation<Element>& block,
-  uint64_t seed = 2023) {
-  
-  float scope_min = float(cutlass::platform::numeric_limits<Element>::lowest());
-  float scope_max = float(cutlass::platform::numeric_limits<Element>::max());
-
-  cutlass::reference::device::BlockFillRandomUniform(
-    block.get(), block.size(), seed, Element(scope_max), Element(scope_min));
-
-  return true;
-}
-
 template <class Element>
 bool initialize_scale(
   cutlass::DeviceAllocation<Element>& block, 
@@ -232,10 +218,8 @@ bool initialize_scale(
   float scope_max = 1.0f, scope_min = 1.0f;
   if (options.mode != MixedDtypeGemmMode::ConvertOnly) {
     float elt_max_f = float(cutlass::platform::numeric_limits<Element>::max());
-    const float max_dequant_val = 4.f;
-    const float min_dequant_val = 0.5f;
-    scope_max = max_dequant_val / elt_max_f;
-    scope_min = min_dequant_val / elt_max_f;
+    scope_max = 2.f;
+    scope_min = 0.1f;
   }
   cutlass::reference::device::BlockFillRandomUniform(
     block.get(), block.size(), seed, Element(scope_max), Element(scope_min));

examples/56_hopper_ptr_array_batched_gemm/56_hopper_ptr_array_batched_gemm.cu

@@ -489,7 +489,7 @@ int run(Options &options)
     std::cout << "  Batches     : " << options.l  << std::endl;
     std::cout << "  Alpha, Beta : " << options.alpha << ',' << options.beta << std::endl;
     std::cout << "  Avg runtime : " << result.avg_runtime_ms << " ms" << std::endl;
-    std::cout << "  GFLOPS      : " << result.gflops << std::endl;
+    std::cout << "  TFLOPS      : " << result.gflops / 1000.0 << std::endl;
   }
 
   return 0;

examples/57_hopper_grouped_gemm/57_hopper_grouped_gemm.cu

@@ -124,7 +124,7 @@ struct CooperativeConfig {
   using KernelSchedule = cutlass::gemm::KernelPtrArrayTmaWarpSpecializedCooperativeFP8FastAccum;
   using EpilogueSchedule = cutlass::epilogue::PtrArrayTmaWarpSpecializedCooperative;
   using TileShape           = Shape<_256,_128,_128>;
-  using ClusterShape        = Shape<_2,_2,_1>;
+  using ClusterShape        = Shape<_1,_2,_1>;
 };
 
 struct PingpongConfig {
@@ -296,14 +296,14 @@ struct Options {
       int m = cmd_line_m;
       int n = cmd_line_n;
       int k = cmd_line_k;
-      if (m < 1) {
-        m = alignment * ((rand() % 64) + 1);
+      if (m < 0) {
+        m = alignment * ((rand() % 64));
       }
-      if (n < 1) {
-        n = alignment * ((rand() % 64) + 1);
+      if (n < 0) {
+        n = alignment * ((rand() % 64));
       }
-      if (k < 1) {
-        k = alignment * ((rand() % 64) + 1);
+      if (k < 0) {
+        k = alignment * ((rand() % 64));
       }
       problem_sizes_host.push_back({m, n, k});
     }
@@ -333,19 +333,9 @@ struct Options {
       cutlass::CommandLine::tokenize(tokens, extent_str, 'x');
 
       for (int i = 0; i < int(tokens.size()); ++i) {
-        int x = std::atoi(tokens.at(i).c_str());
-
-        // round up
-        if (x % alignment) {
-          x += (alignment - (x % alignment));
-        }
-
-        extent.at(i) = x;
-      }
-
-      if (extent.product()) {
-        problem_sizes_host.push_back({extent.m(), extent.n(), extent.k()});
+        extent.at(i) = std::atoi(tokens.at(i).c_str());
       }
+      problem_sizes_host.push_back({extent.m(), extent.n(), extent.k()});
     }
     groups = static_cast<int>(problem_sizes_host.size());
 
@@ -500,10 +490,27 @@ void initialize(const Options &options) {
   std::vector<ElementAccumulator *> ptr_beta_host(options.groups);
 
   for (int32_t i = 0; i < options.groups; ++i) {
-    ptr_A_host.at(i) = block_A.get() + offset_A.at(i);
-    ptr_B_host.at(i) = block_B.get() + offset_B.at(i);
-    ptr_C_host.at(i) = block_C.get() + offset_C.at(i);
-    ptr_D_host.at(i) = block_D.get() + offset_D.at(i);
+    // If the current group's matrix has size 0, set the pointer to nullptr
+    if (i < options.groups - 1 && offset_A.at(i) == offset_A.at(i + 1)) {
+      ptr_A_host.at(i) = nullptr;
+    } else {
+      ptr_A_host.at(i) = block_A.get() + offset_A.at(i);
+    }
+    if (i < options.groups - 1 && offset_B.at(i) == offset_B.at(i + 1)) {
+      ptr_B_host.at(i) = nullptr;
+    } else {
+      ptr_B_host.at(i) = block_B.get() + offset_B.at(i);
+    }
+    if (i < options.groups - 1 && offset_C.at(i) == offset_C.at(i + 1)) {
+      ptr_C_host.at(i) = nullptr;
+    } else {
+      ptr_C_host.at(i) = block_C.get() + offset_C.at(i);
+    }
+    if (i < options.groups - 1 && offset_D.at(i) == offset_D.at(i + 1)) {
+      ptr_D_host.at(i) = nullptr;
+    } else {
+      ptr_D_host.at(i) = block_D.get() + offset_D.at(i);
+    }
     alpha_host.push_back((options.alpha == FLT_MAX) ? static_cast<ElementAccumulator>((rand() % 5) + 1) : options.alpha);
     beta_host.push_back((options.beta == FLT_MAX) ? static_cast<ElementAccumulator>(rand() % 5) : options.beta);
     ptr_alpha_host.at(i) = block_alpha.get() + i;
@@ -539,9 +546,10 @@ void initialize(const Options &options) {
   beta_device.reset(options.groups);
   beta_device.copy_from_host(ptr_beta_host.data());
 
-  initialize_block(block_A, seed + 2023);
+  initialize_block(block_A, seed + 2021);
   initialize_block(block_B, seed + 2022);
-  initialize_block(block_C, seed + 2021);
+  initialize_block(block_C, seed + 2023);
+  initialize_block(block_D, seed + 2024);
   block_alpha.copy_from_host(alpha_host.data());
   block_beta.copy_from_host(beta_host.data());
 }
@@ -653,6 +661,13 @@ int run(Options &options, bool host_problem_shapes_available = true)
   allocate(options);
   initialize(options);
 
+  std::cout << "  Problem Sizes, Alpha, Beta " << std::endl;
+  for (int32_t i = 0; i < options.groups; ++i) {
+    std::cout << "    " << options.problem_sizes_host.at(i);
+    std::cout << ", "   << alpha_host.at(i) << ", " << beta_host.at(i) << std::endl;
+  }
+  std::cout << "  Groups      : " << options.groups  << std::endl;
+
   // Instantiate CUTLASS kernel depending on templates
   GemmT gemm;
 
@@ -700,14 +715,8 @@ int run(Options &options, bool host_problem_shapes_available = true)
     result.avg_runtime_ms  = double(elapsed_ms) / double(options.iterations);
     result.gflops          = options.gflops(result.avg_runtime_ms / 1000.0, options.problem_sizes_host);
 
-    std::cout << "  Problem Sizes, Alpha, Beta " << std::endl;
-    for (int32_t i = 0; i < options.groups; ++i) {
-      std::cout << "    " << options.problem_sizes_host.at(i);
-      std::cout << ", " << alpha_host.at(i) << ", " << beta_host.at(i) << std::endl;
-    }
-    std::cout << "  Groups      : " << options.groups  << std::endl;
     std::cout << "  Avg runtime : " << result.avg_runtime_ms << " ms" << std::endl;
-    std::cout << "  GFLOPS      : " << result.gflops << std::endl;
+    std::cout << "  TFLOPS      : " << result.gflops / 1000.0 << std::endl;
   }
 
   return 0;

examples/58_ada_fp8_gemm/ada_fp8_gemm.cu

@@ -770,9 +770,6 @@ int main(int argc, char const** argv) {
 
   bool satisfied;
   if (props.major < 10) {
-    // Pre-Blackwell 
-    satisfied =  (__CUDACC_VER_MAJOR__ > 12) || (__CUDACC_VER_MAJOR__ == 12 && __CUDACC_VER_MINOR__ >= 4);
-    satisfied &= (props.major > 8) || (props.major == 8 && props.minor == 9); 
   }
   else {
     satisfied = (__CUDACC_VER_MAJOR__ > 12) || (__CUDACC_VER_MAJOR__ == 12 && __CUDACC_VER_MINOR__ >= 8);
@@ -786,7 +783,6 @@ int main(int argc, char const** argv) {
     std::cout
       << "CUTLASS's FP8 SM89 example requires an NVIDIA GPU with compute capability 8.9 or greater "
       << "and CUDA toolkit version 12.4 or later"
-      << " (12.8 or later needed for SM100+)" 
       << std::endl;
 
     return 0;

examples/59_ampere_gather_scatter_conv/README.md

@@ -188,7 +188,7 @@ Running this example on an RTX 3080Ti prints the following performance numbers (
 
 ```
 $> ./examples/59_ampere_gather_scatter_conv/59_ampere_gather_scatter_conv --n=131072 --i=128 --no-check
-Ampere convolution forward propogation kernel supporting both affine and gather/scatter tensors.
+Ampere convolution forward propagation kernel supporting both affine and gather/scatter tensors.
 
 Allocating tensors ... done.
 Initializing data ... done.
@@ -207,3 +207,35 @@ With this in mind, this example kernel has the following limitations:
 - This example kernel only supports dynamic image count, all other conv problem shape must be defined as `cute::Constant<>`s
 - Problem shapes (including dynamic image count `N`) must be evenly divisible by the tile shape
 - It does not perform fp32->tf32 numeric conversion, gmem inputs must be rounded to tf32 already
+
+## Copyright
+
+Copyright (c) 2017 - 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+SPDX-License-Identifier: BSD-3-Clause
+
+```
+  Redistribution and use in source and binary forms, with or without
+  modification, are permitted provided that the following conditions are met:
+
+  1. Redistributions of source code must retain the above copyright notice, this
+  list of conditions and the following disclaimer.
+
+  2. Redistributions in binary form must reproduce the above copyright notice,
+  this list of conditions and the following disclaimer in the documentation
+  and/or other materials provided with the distribution.
+
+  3. Neither the name of the copyright holder nor the names of its
+  contributors may be used to endorse or promote products derived from
+  this software without specific prior written permission.
+
+  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+  AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+  IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+  DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+  FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+  DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+  SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+  CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+  OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+  OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+```

examples/59_ampere_gather_scatter_conv/ampere_gather_scatter_conv.cu

@@ -29,7 +29,7 @@
  *
  **************************************************************************************************/
 /*! \file
-  \brief Example demonstrating CuTe and CUTLASS 3.x based Ampere convolution forward propogation kernel
+  \brief Example demonstrating CuTe and CUTLASS 3.x based Ampere convolution forward propagation kernel
       capable of operating on both affine and gather/scatter tensors.
 
   This example demonstartes a few super cool features of CUTLASS and CuTe. It shows off
@@ -284,7 +284,7 @@ int ampere_gather_scatter_conv_fprop(
 int
 main(int argc, char const** argv) {
   cutlass::CommandLine cmd(argc, argv);
-  std::cout << "Ampere convolution forward propogation kernel supporting both affine and gather/scatter tensors.\n\n";
+  std::cout << "Ampere convolution forward propagation kernel supporting both affine and gather/scatter tensors.\n\n";
   if (cmd.check_cmd_line_flag("help")) {
     std::cout
       << "Options:\n"

examples/63_hopper_gemm_with_weight_prefetch/CMakeLists.txt

@@ -26,11 +26,13 @@
 # OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
 # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 
-include_directories(
-  .
-)
+set(TEST_PREFETCH_CASE --m=8192 --n=64 --k=8192 --iterations=0) 
 
 cutlass_example_add_executable(
   63_hopper_gemm_with_weight_prefetch
   63_hopper_gemm_with_weight_prefetch.cu
-  )
+  TEST_COMMAND_OPTIONS
+  TEST_PREFETCH_CASE
+)
+
+target_include_directories(63_hopper_gemm_with_weight_prefetch PUBLIC .)

examples/63_hopper_gemm_with_weight_prefetch/README.md

@@ -74,9 +74,40 @@ echo "Overlap ratio of 0.8, prefetch ratio of 0.7"
 However, note that the example still runs a single GEMM, and most of the performance improvement
 is expected in end to end applications.
 
-
 ## Limitations
 * The parameter defaults are typically not good choices, especially `prefetch_ratio`. 
   When `prefetch_ratio` is unspecified (set to `-1.0`), the prefetch warp will `try_wait` on a 
   memory barrier before issuing every single TMA load, and in many cases this will slow down 
   prefetching to the point of being almost ineffective.
+
+## Copyright
+
+Copyright (c) 2017 - 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+SPDX-License-Identifier: BSD-3-Clause
+
+```
+  Redistribution and use in source and binary forms, with or without
+  modification, are permitted provided that the following conditions are met:
+
+  1. Redistributions of source code must retain the above copyright notice, this
+  list of conditions and the following disclaimer.
+
+  2. Redistributions in binary form must reproduce the above copyright notice,
+  this list of conditions and the following disclaimer in the documentation
+  and/or other materials provided with the distribution.
+
+  3. Neither the name of the copyright holder nor the names of its
+  contributors may be used to endorse or promote products derived from
+  this software without specific prior written permission.
+
+  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+  AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+  IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+  DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+  FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+  DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+  SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+  CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+  OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+  OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+```

examples/63_hopper_gemm_with_weight_prefetch/collective/sm90_mma_tma_gmma_ss_warpspecialized_with_prefetch.hpp

@@ -42,7 +42,6 @@
 #include "cute/algorithm/functional.hpp"
 #include "cute/atom/mma_atom.hpp"
 #include "cute/algorithm/gemm.hpp"
-#include "cute/tensor_predicate.hpp"
 #include "cute/numeric/arithmetic_tuple.hpp"
 #include "cutlass/arch/grid_dependency_control.h"
 
@@ -288,7 +287,7 @@ struct CollectiveMma<
     constexpr int tma_alignment_bits = 128;
     auto problem_shape_MNKL = append<4>(problem_shape, 1);
     auto [M,N,K,L] = problem_shape_MNKL;
-    
+
     constexpr int min_tma_aligned_elements_A = tma_alignment_bits / cutlass::sizeof_bits<ElementA>::value;
     bool implementable = cutlass::detail::check_alignment<min_tma_aligned_elements_A>(cute::make_shape(M,K,L), StrideA{});
     constexpr int min_tma_aligned_elements_B = tma_alignment_bits / cutlass::sizeof_bits<ElementB>::value;
@@ -445,7 +444,7 @@ struct CollectiveMma<
         copy(mainloop_params.tma_load_b.with(*tma_barrier, mcast_mask_b, cute::TMA::CacheHintSm90::EVICT_LAST), tBgB(_,_,_,*k_tile_iter), tBsB(_,_,_,write_stage));
         ++k_tile_iter;
 
-        if (!disable_gdc && cnt >= launch_dep_grids_threshold && !launch_dep_grids) { 
+        if (!disable_gdc && cnt >= launch_dep_grids_threshold && !launch_dep_grids) {
           launch_dep_grids = true;
           cutlass::arch::launch_dependent_grids();
         }
@@ -453,7 +452,7 @@ struct CollectiveMma<
         // Advance smem_pipe_write
         ++smem_pipe_write;
       }
-      if (!disable_gdc && !launch_dep_grids) { 
+      if (!disable_gdc && !launch_dep_grids) {
         cutlass::arch::launch_dependent_grids();
       }
     }
@@ -533,7 +532,7 @@ struct CollectiveMma<
         copy(mainloop_params.tma_load_a.with(*tma_barrier, mcast_mask_a, cute::TMA::CacheHintSm90::EVICT_FIRST), tAgA(_,_,_,*k_tile_iter), tAsA(_,_,_,write_stage));
         ++k_tile_iter;
 
-        if (!disable_gdc && cnt >= launch_dep_grids_threshold && !launch_dep_grids) { 
+        if (!disable_gdc && cnt >= launch_dep_grids_threshold && !launch_dep_grids) {
           launch_dep_grids = true;
           cutlass::arch::launch_dependent_grids();
         }
@@ -541,7 +540,7 @@ struct CollectiveMma<
         // Advance smem_pipe_write
         ++smem_pipe_write;
       }
-      if (!disable_gdc && !launch_dep_grids) { 
+      if (!disable_gdc && !launch_dep_grids) {
         cutlass::arch::launch_dependent_grids();
       }
     }
@@ -634,9 +633,9 @@ struct CollectiveMma<
     // Issue the epilogue waits
     if (lane_predicate) {
       /* This helps avoid early exit of blocks in Cluster
-       * Waits for all stages to either be released (all 
+       * Waits for all stages to either be released (all
        * Consumer UNLOCKs), or if the stage was never used
-       * then would just be acquired since the phase was 
+       * then would just be acquired since the phase was
        * still inverted from make_producer_start_state
        */
       pipeline.producer_tail(smem_pipe_write);
@@ -854,7 +853,7 @@ struct CollectiveMma<
     k_tile_count -= prologue_mma_count;
 
     smem_pipe_release.advance(k_tile_count);
-    
+
     // Wait on all GMMAs to complete
     warpgroup_wait<0>();
 

examples/63_hopper_gemm_with_weight_prefetch/kernel/sm90_gemm_tma_warpspecialized_with_prefetch.hpp

@@ -362,11 +362,11 @@ public:
           using ClusterSyncWithPrefetchBarrier = typename cutlass::arch::NamedBarrier;
           auto prefetcher_arrive_barrier = ClusterSyncWithPrefetchBarrier(
               blockDim.x * blockDim.y * blockDim.z,
-              /*reserved_named_barriers_*/ 14);
+              /*id*/ 0);
           // Prefetcher warp doesn't arrive on this barrier.
           auto cluster_arrive_barrier = ClusterSyncWithPrefetchBarrier(
               blockDim.x * blockDim.y * blockDim.z - NumThreadsPerWarp,
-              /*reserved_named_barriers_*/ 15);
+              /*id*/ 1);
 
           if (warp_group_role == WarpGroupRole::Producer && producer_warp_role == ProducerWarpRole::PrefetchMK) {
             __syncwarp();

examples/64_ada_fp8_gemm_grouped/ada_fp8_gemm_grouped.cu

@@ -291,7 +291,7 @@ struct Options {
     // Post-process the problem sizes
     bin_problems();
 
-    // Initalize alpha array
+    // Initialize alpha array
     randomize_alpha_ptr_array(cmd);
   }
 

examples/65_distributed_gemm/65_distributed_gemm.cu

@@ -120,8 +120,7 @@
 #include "helper.h"
 
 // Distributed GEMM helpers
-#include "util/benchmark.h"
-#include "util/device_copy.h"
+#include "dist_gemm_helpers.h"
 
 using namespace cute;
 
@@ -133,8 +132,8 @@ using namespace cute;
 using TP = _8;
 static constexpr int TP_ = TP{};
 
-#if defined(CUTLASS_ARCH_MMA_SM90_SUPPORTED) && \
-  (__CUDACC_VER_MAJOR__ > 12 || (__CUDACC_VER_MAJOR__ == 12 && __CUDACC_VER_MINOR__ >= 4))
+#if defined(CUTLASS_ARCH_MMA_SM90A_ENABLED) && \
+  (__CUDACC_VER_MAJOR__ > 12 || (__CUDACC_VER_MAJOR__ == 12 && __CUDACC_VER_MINOR__ >= 6))
 
 // Distributed GEMM tiling/sharding schedule
 // Choices:
@@ -253,7 +252,8 @@ HostTensorB tensor_B_arr[TP_];
 HostTensorD tensor_C_arr[TP_];
 HostTensorD tensor_D_arr[TP_];
 
-#endif // (defined(CUTLASS_ARCH_MMA_SM90_SUPPORTED) && (__CUDACC_VER_MAJOR__ >= 12) && (__CUDACC_VER_MINOR__ >= 4))
+#endif // (defined(CUTLASS_ARCH_MMA_SM90A_ENABLED) &&
+       // (__CUDACC_VER_MAJOR__ > 12 || (__CUDACC_VER_MAJOR__ == 12 && __CUDACC_VER_MINOR__ >= 6))
 
 /////////////////////////////////////////////////////////////////////////////////////////////////
 /// Testbed utility types
@@ -345,8 +345,8 @@ struct Result {
 
 };
 
-#if defined(CUTLASS_ARCH_MMA_SM90_SUPPORTED) && \
-  (__CUDACC_VER_MAJOR__ > 12 || (__CUDACC_VER_MAJOR__ == 12 && __CUDACC_VER_MINOR__ >= 4))
+#if defined(CUTLASS_ARCH_MMA_SM90A_ENABLED) && \
+  (__CUDACC_VER_MAJOR__ > 12 || (__CUDACC_VER_MAJOR__ == 12 && __CUDACC_VER_MINOR__ >= 6))
 
 /////////////////////////////////////////////////////////////////////////////////////////////////
 /// GEMM setup and evaluation
@@ -804,17 +804,18 @@ int run(Options &options) {
   return 0;
 }
 
-#endif // (defined(CUTLASS_ARCH_MMA_SM90_SUPPORTED) && (__CUDACC_VER_MAJOR__ >= 12) && (__CUDACC_VER_MINOR__ >= 4))
+#endif // (defined(CUTLASS_ARCH_MMA_SM90A_ENABLED) &&
+       // (__CUDACC_VER_MAJOR__ > 12 || (__CUDACC_VER_MAJOR__ == 12 && __CUDACC_VER_MINOR__ >= 6))
 
 ///////////////////////////////////////////////////////////////////////////////////////////////////
 
 int main(int argc, char const **args) {
 
-  // CUTLASS must be compiled with CUDA Toolkit 12.4 or newer to run this example
+  // CUTLASS must be compiled with CUDA Toolkit 12.6 or newer to run this example
   // and must have compute capability at least 90.
-  // Some necessary cuda graph APIs were only introduced in CUDA 12.4.
-  if (__CUDACC_VER_MAJOR__ < 12 || (__CUDACC_VER_MAJOR__ == 12 && __CUDACC_VER_MINOR__ < 4)) {
-    std::cerr << "This example requires CUDA 12.4 or newer." << std::endl;
+  // Some necessary cuda graph APIs were only introduced in CUDA 12.6.
+  if (__CUDACC_VER_MAJOR__ < 12 || (__CUDACC_VER_MAJOR__ == 12 && __CUDACC_VER_MINOR__ < 6)) {
+    std::cerr << "This example requires CUDA 12.6 or newer." << std::endl;
     // Returning zero so this test passes on older Toolkits. Its actions are no-op.
     return 0;
   }
@@ -834,10 +835,10 @@ int main(int argc, char const **args) {
   CUDA_CHECK(cudaGetDevice(&current_device_id));
   CUDA_CHECK(cudaGetDeviceProperties(&props, current_device_id));
   cudaError_t error = cudaGetDeviceProperties(&props, 0);
-  if (props.major < 9) {
+  if (props.major != 9 || props.minor != 0) {
     std::cerr
-      << "This example requires a GPU of NVIDIA's Hopper Architecture or "
-      << "later (compute capability 90 or greater)." << std::endl;
+      << "This example requires a GPU of NVIDIA's Hopper Architecture "
+      << "(compute capability 90)." << std::endl;
     return 0;
   }
 
@@ -858,8 +859,12 @@ int main(int argc, char const **args) {
   // Evaluate CUTLASS kernels
   //
 
-#if (defined(CUTLASS_ARCH_MMA_SM90_SUPPORTED) && (__CUDACC_VER_MAJOR__ >= 12) && (__CUDACC_VER_MINOR__ >= 4))
+#if (defined(CUTLASS_ARCH_MMA_SM90A_ENABLED) && (__CUDACC_VER_MAJOR__ > 12 || (__CUDACC_VER_MAJOR__ == 12 && __CUDACC_VER_MINOR__ >= 6)))
   run(options);
+#else
+    std::cerr
+      << "This example must be compiled with `sm90a` and CUDA Toolkit 12.6 or later." << std::endl;
+    return 0;
 #endif
 
   return 0;

examples/65_distributed_gemm/README.md

@@ -62,3 +62,40 @@ procedure is the same, simply modify the following line in the example:
 ```cpp
 using TP = _8;
 ```
+
+## References
+* [Distributed GEMM Blog](https://blog.shi-labs.com/distributed-gemm-88be6a481e2b)
+* [Distributed GEMM Talk on CUDA Mode](https://www.youtube.com/watch?v=NHRTCQBZokg)
+
+## Copyright
+
+Copyright (c) 2017 - 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+SPDX-License-Identifier: BSD-3-Clause
+
+```
+  Redistribution and use in source and binary forms, with or without
+  modification, are permitted provided that the following conditions are met:
+
+  1. Redistributions of source code must retain the above copyright notice, this
+  list of conditions and the following disclaimer.
+
+  2. Redistributions in binary form must reproduce the above copyright notice,
+  this list of conditions and the following disclaimer in the documentation
+  and/or other materials provided with the distribution.
+
+  3. Neither the name of the copyright holder nor the names of its
+  contributors may be used to endorse or promote products derived from
+  this software without specific prior written permission.
+
+  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+  AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+  IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+  DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+  FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+  DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+  SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+  CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+  OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+  OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+```
+

examples/65_distributed_gemm/REQUIREMENTS.md

@@ -17,6 +17,8 @@ Like all other CUTLASS examples, the NVIDIA driver, runtime, and CUDA Toolkit ar
 This example specifically requires CUDA Toolkit 12.6 or newer, due to some of the necessary
 CUDA graph APIs.
 
+The minimum CUDA driver version for running this example is [560.28.03](https://docs.nvidia.com/cuda/archive/12.6.0/cuda-toolkit-release-notes/index.html#id5).
+
 ### Hardware / driver settings
 
 This example requires Hopper GPUs with NVLink network.
@@ -84,3 +86,35 @@ GPU5   OK      OK      OK      OK      OK      X       OK      OK
 GPU6   OK      OK      OK      OK      OK      OK      X       OK
 GPU7   OK      OK      OK      OK      OK      OK      OK      X
 ```
+
+## Copyright
+
+Copyright (c) 2017 - 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+SPDX-License-Identifier: BSD-3-Clause
+
+```
+  Redistribution and use in source and binary forms, with or without
+  modification, are permitted provided that the following conditions are met:
+
+  1. Redistributions of source code must retain the above copyright notice, this
+  list of conditions and the following disclaimer.
+
+  2. Redistributions in binary form must reproduce the above copyright notice,
+  this list of conditions and the following disclaimer in the documentation
+  and/or other materials provided with the distribution.
+
+  3. Neither the name of the copyright holder nor the names of its
+  contributors may be used to endorse or promote products derived from
+  this software without specific prior written permission.
+
+  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+  AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+  IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+  DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+  FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+  DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+  SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+  CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+  OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+  OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+```

examples/67_hopper_fp8_warp_specialized_gemm_with_blockwise_scaling/67_hopper_fp8_warp_specialized_gemm_with_blockwise_scaling.cu

@@ -75,11 +75,11 @@
 #include "cutlass/util/reference/host/tensor_copy.h"
 #include "cutlass/util/reference/host/tensor_compare.h"
 #include "cutlass/util/reference/host/tensor_norm.h"
+#include "cutlass/util/reference/host/gett.hpp"
 
 // Includes from examples directory
 #include "helper.h"
 #include "hopper_fp8_commandline.hpp"
-#include "reference/host/gemm_with_blockwise_scaling.h"
 
 using namespace cute;
 
@@ -100,7 +100,7 @@ using         LayoutB     = cutlass::layout::ColumnMajor;                   // L
 constexpr int AlignmentB  = 128 / cutlass::sizeof_bits<ElementB>::value;    // Memory access granularity/alignment of B matrix in units of elements (up to 16 bytes)
 
 // C matrix configuration
-using         ElementC    = cutlass::float_e4m3_t;                          // Element type for C and D matrix operands
+using         ElementC    = float;                          // Element type for C and D matrix operands
 using         LayoutC     = cutlass::layout::ColumnMajor;                   // Layout type for C and D matrix operands
 constexpr int AlignmentC  = 128 / cutlass::sizeof_bits<ElementC>::value;    // Memory access granularity/alignment of C matrix in units of elements (up to 16 bytes)
 
@@ -123,7 +123,13 @@ using ArchTag             = cutlass::arch::Sm90;                            // T
 using OperatorClass       = cutlass::arch::OpClassTensorOp;                 // Operator class tag
 using TileShape           = Shape<_128,_128,_128>;                           // Threadblock-level tile size
 using ClusterShape        = Shape<_1,_2,_1>;                                // Shape of the threadblocks in a cluster
-using KernelSchedule      = cutlass::gemm::KernelTmaWarpSpecializedCooperativeFP8BlockScaledAccum<>;
+
+using ScaleConfig = decltype(cutlass::detail::sm90_trivial_blockwise_scale_config(TileShape{}));
+
+using LayoutSFA             = decltype(ScaleConfig::deduce_layoutSFA());                     // Layout type for SFA matrix operand
+using LayoutSFB             = decltype(ScaleConfig::deduce_layoutSFB());                     // Layout type for SFB matrix operand
+
+using KernelSchedule      = cutlass::gemm::KernelTmaWarpSpecializedCooperativeFP8BlockScaledAccum; 
 using EpilogueSchedule    = cutlass::epilogue::TmaWarpSpecializedCooperative;
 
 using EpilogueTileType    = cutlass::epilogue::collective::EpilogueTileAuto;
@@ -143,8 +149,8 @@ using CollectiveEpilogue = typename cutlass::epilogue::collective::CollectiveBui
 
 using CollectiveMainloopWithBlockWiseScaling = typename cutlass::gemm::collective::CollectiveBuilder<
     ArchTag, OperatorClass,
-    ElementA, LayoutA, AlignmentA,
-    ElementB, LayoutB, AlignmentB,
+    ElementA, cute::tuple<LayoutA, LayoutSFA>, AlignmentA,
+    ElementB, cute::tuple<LayoutB, LayoutSFB>, AlignmentB,
     ElementAccumulator,
     TileShape, ClusterShape,
     cutlass::gemm::collective::StageCountAutoCarveout<
@@ -190,20 +196,21 @@ StrideB stride_B;
 StrideC stride_C;
 StrideD stride_D;
 StrideAux stride_aux;
+LayoutSFA layout_SFA;
+LayoutSFB layout_SFB;
 uint64_t seed;
 
+using LayoutScalar = cutlass::layout::PackedVectorLayout;
 cutlass::HostTensor<ElementA  , LayoutA  > tensor_A;
 cutlass::HostTensor<ElementB  , LayoutB  > tensor_B;
 cutlass::HostTensor<ElementC  , LayoutC  > tensor_C;
 cutlass::HostTensor<ElementD  , LayoutD  > tensor_D;
-uint32_t mma_promotion_interval;
-cutlass::HostTensor<ElementBlockScale, LayoutA> blockscale_tensor_A;
-cutlass::HostTensor<ElementBlockScale, LayoutB> blockscale_tensor_B;
+cutlass::HostTensor<ElementBlockScale, LayoutScalar> blockscale_tensor_A;
+cutlass::HostTensor<ElementBlockScale, LayoutScalar> blockscale_tensor_B;
 cutlass::HostTensor<ElementD  , LayoutD  > tensor_ref_D;
 cutlass::HostTensor<ElementAux, LayoutAux> tensor_aux;
 cutlass::HostTensor<ElementAux, LayoutAux> tensor_ref_aux;
 
-using LayoutScalar = cutlass::layout::PackedVectorLayout;
 cutlass::HostTensor<ElementScalar, LayoutScalar> scalar_alpha;
 cutlass::HostTensor<ElementScalar, LayoutScalar> scalar_beta;
 cutlass::HostTensor<ElementScalar, LayoutScalar> scale_A;
@@ -251,117 +258,116 @@ struct Result
 /////////////////////////////////////////////////////////////////////////////////////////////////
 
 /// Helper to initialize a block of device data
-  template <typename Element, typename Layout>
-  bool initialize_tensor(
-    cutlass::TensorView<Element, Layout> view,
-    cutlass::Distribution::Kind dist_kind,
-    uint64_t seed) {
+template <typename Element, typename Layout>
+bool initialize_tensor(
+  cutlass::TensorView<Element, Layout> view,
+  cutlass::Distribution::Kind dist_kind,
+  uint64_t seed) {
 
-    if (dist_kind == cutlass::Distribution::Uniform) {
+  if (dist_kind == cutlass::Distribution::Uniform) {
 
-      double scope_max, scope_min;
-      int bits_input = cutlass::sizeof_bits<Element>::value;
-      int bits_output = cutlass::sizeof_bits<Element>::value;
+    double scope_max, scope_min;
+    int bits_input = cutlass::sizeof_bits<Element>::value;
+    int bits_output = cutlass::sizeof_bits<Element>::value;
 
-      if (bits_input == 1) {
-        scope_max = 2;
-        scope_min = 0;
-      } else if (bits_input <= 8) {
-        scope_max = 2;
-        scope_min = -2;
-      } else if (bits_output == 16) {
-        scope_max = 5;
-        scope_min = -5;
-      } else {
-        scope_max = 8;
-        scope_min = -8;
-      }
-
-      cutlass::reference::host::TensorFillRandomUniform(
-        view, seed, scope_max, scope_min, 0);
-    }
-    else if (dist_kind == cutlass::Distribution::AllZeros) {
-      cutlass::reference::host::TensorFill(view);
-    }
-    else if (dist_kind == cutlass::Distribution::Identity) {
-
-      cutlass::reference::host::TensorFillIdentity(view);
-    }
-    else if (dist_kind == cutlass::Distribution::Gaussian) {
-
-      cutlass::reference::host::TensorFillRandomGaussian(view, seed, 0, 0.5);
-    }
-    else if (dist_kind == cutlass::Distribution::Sequential) {
-      cutlass::reference::host::BlockFillSequential(view.data(), view.capacity());
-    }
-    else {
-      throw std::runtime_error("Not implementated.");
+    if (bits_input == 1) {
+      scope_max = 2;
+      scope_min = 0;
+    } else if (bits_input <= 8) {
+      scope_max = 2;
+      scope_min = -2;
+    } else if (bits_output == 16) {
+      scope_max = 5;
+      scope_min = -5;
+    } else {
+      scope_max = 8;
+      scope_min = -8;
     }
 
-    return true;
+    cutlass::reference::host::TensorFillRandomUniform(
+      view, seed, scope_max, scope_min, bits_input);
   }
+  else if (dist_kind == cutlass::Distribution::AllZeros) {
+    cutlass::reference::host::TensorFill(view);
+  }
+  else if (dist_kind == cutlass::Distribution::Identity) {
+
+    cutlass::reference::host::TensorFillIdentity(view);
+  }
+  else if (dist_kind == cutlass::Distribution::Gaussian) {
+
+    cutlass::reference::host::TensorFillRandomGaussian(view, seed, 0, 0.5);
+  }
+  else if (dist_kind == cutlass::Distribution::Sequential) {
+    cutlass::reference::host::BlockFillSequential(view.data(), view.capacity());
+  }
+  else {
+    throw std::runtime_error("Not implementated.");
+  }
+
+  return true;
+}
 
 /// Helper to initialize a block of device data (scale_tensors)
-  template <typename Element, typename Layout>
-  bool initialize_scale_tensor(
-    cutlass::TensorView<Element, Layout> view,
-    cutlass::Distribution::Kind dist_kind,
-    uint64_t seed) {
+template <typename Element, typename Layout>
+bool initialize_scale_tensor(
+  cutlass::TensorView<Element, Layout> view,
+  cutlass::Distribution::Kind dist_kind,
+  uint64_t seed) {
 
-    if (dist_kind == cutlass::Distribution::Uniform) {
+  if (dist_kind == cutlass::Distribution::Uniform) {
 
-      double scope_max, scope_min;
+    double scope_max, scope_min;
 
-      scope_min = -1;
-      scope_max = 1;
+    scope_min = -1;
+    scope_max = 1;
 
-      cutlass::reference::host::TensorFillRandomUniform(
-        view, seed, scope_max, scope_min, 0);
-    }
-    else if (dist_kind == cutlass::Distribution::AllZeros) {
-      cutlass::reference::host::TensorFill(view);
-    }
-    else if (dist_kind == cutlass::Distribution::Identity) {
-
-      cutlass::reference::host::TensorFillIdentity(view);
-    }
-    else if (dist_kind == cutlass::Distribution::Gaussian) {
-
-      cutlass::reference::host::TensorFillRandomGaussian(view, seed, 0, 0.5);
-    }
-    else if (dist_kind == cutlass::Distribution::Sequential) {
-      cutlass::reference::host::BlockFillSequential(view.data(), view.capacity());
-    }
-    else {
-      throw std::runtime_error("Not implementated.");
-    }
-
-    return true;
+    cutlass::reference::host::TensorFillRandomUniform(
+      view, seed, scope_max, scope_min);
   }
+  else if (dist_kind == cutlass::Distribution::AllZeros) {
+    cutlass::reference::host::TensorFill(view);
+  }
+  else if (dist_kind == cutlass::Distribution::Identity) {
+
+    cutlass::reference::host::TensorFillIdentity(view);
+  }
+  else if (dist_kind == cutlass::Distribution::Gaussian) {
+
+    cutlass::reference::host::TensorFillRandomGaussian(view, seed, 0, 0.5);
+  }
+  else if (dist_kind == cutlass::Distribution::Sequential) {
+    cutlass::reference::host::BlockFillSequential(view.data(), view.capacity());
+  }
+  else {
+    throw std::runtime_error("Not implementated.");
+  }
+
+  return true;
+}
 
 /// Initialize operands to be used in the GEMM and reference GEMM
 void initialize(const Options<RasterOrderOptions> &options) {
 
-  // Find Block Scaling tensor shapes based on problem shape and TileShape
-  auto gemm_problem_shape = cute::make_shape(options.m, options.n, options.k);
-  auto blockscale_shape = shape(get<1>(cute::zipped_divide(cute::make_layout(gemm_problem_shape), TileShape{})));
-  auto blockscale_m = cute::get<0>(blockscale_shape);
-  auto blockscale_n = cute::get<1>(blockscale_shape);
-  auto blockscale_k = cute::get<2>(blockscale_shape);
-
   stride_A = cutlass::make_cute_packed_stride(StrideA{}, cute::make_shape(options.m, options.k, options.l));
   stride_B = cutlass::make_cute_packed_stride(StrideB{}, cute::make_shape(options.n, options.k, options.l));
   stride_C = cutlass::make_cute_packed_stride(StrideC{}, cute::make_shape(options.m, options.n, options.l));
   stride_D = cutlass::make_cute_packed_stride(StrideD{}, cute::make_shape(options.m, options.n, options.l));
   stride_aux = stride_D;
 
+  // Layout SFA and SFB represent logically broadcasting data in CuTe.
+  // E.g., if Layout SFA has shape ((ScaleGranularityM, M / ScaleGranularityM), (ScaleGraunularityK, K / ScaleGranularityK))
+  // and strides ((0, 1), (0, M / ScaleGraunuarlityM)), then each collection of ScaleGranularityM x ScaleGranularityK
+  // indices in the tensor map to the same offset.
 
+  layout_SFA = ScaleConfig::tile_atom_to_shape_SFA(make_shape(options.m, options.n, options.k, options.l));
+  layout_SFB = ScaleConfig::tile_atom_to_shape_SFB(make_shape(options.m, options.n, options.k, options.l));
 
   auto a_coord = cutlass::make_Coord(options.m * options.l, options.k);
   auto c_coord = cutlass::make_Coord(options.m * options.l, options.n);
   auto b_coord = cutlass::make_Coord(options.k, options.n * options.l);
-  auto blockscale_a_coord = cutlass::make_Coord(blockscale_m * options.l, blockscale_k);
-  auto blockscale_b_coord = cutlass::make_Coord(blockscale_k, blockscale_n * options.l);
+  auto blockscale_a_coord = cutlass::make_Coord(size(filter_zeros(layout_SFA)));
+  auto blockscale_b_coord = cutlass::make_Coord(size(filter_zeros(layout_SFB)));
 
   tensor_A.resize(a_coord);
   blockscale_tensor_A.resize(blockscale_a_coord);
@@ -398,8 +404,6 @@ void initialize(const Options<RasterOrderOptions> &options) {
   blockscale_tensor_A.sync_device();
   blockscale_tensor_B.sync_device();
 
-  mma_promotion_interval = 4;
-
   if (options.save_aux) {
     tensor_aux.resize(c_coord);
     tensor_aux.sync_device();
@@ -434,14 +438,18 @@ void initialize(const Options<RasterOrderOptions> &options) {
 
   if (IsDFp8 && options.save_amax) {
     abs_max_D.resize(cutlass::make_Coord(1));
+    initialize_tensor(abs_max_D.host_view(), cutlass::Distribution::AllZeros, 0);
     abs_max_D.sync_device();
     reference_abs_max_D.resize(cutlass::make_Coord(1));
+    initialize_tensor(reference_abs_max_D.host_view(), cutlass::Distribution::AllZeros, 0);
   }
 
   if (IsAuxFp8 && options.save_aux && options.save_amax) {
     abs_max_aux.resize(cutlass::make_Coord(1));
+    initialize_tensor(abs_max_aux.host_view(), cutlass::Distribution::AllZeros, 0);
     abs_max_aux.sync_device();
     reference_abs_max_aux.resize(cutlass::make_Coord(1));
+    initialize_tensor(reference_abs_max_aux.host_view(), cutlass::Distribution::AllZeros, 0);
   }
 }
 
@@ -455,9 +463,10 @@ typename Gemm::Arguments args_from_options(const Options<RasterOrderOptions> &op
      stride_A,
      tensor_B.device_data(),
      stride_B,
-     mma_promotion_interval,
      blockscale_tensor_A.device_data(),
-     blockscale_tensor_B.device_data()
+     layout_SFA,
+     blockscale_tensor_B.device_data(),
+     layout_SFB
      },
     {
       {}, // epilogue.thread
@@ -511,13 +520,6 @@ bool verify(const Options<RasterOrderOptions> &options) {
   // Compute reference output
   //
 
-  // Block scaling tensors shapes based CTA Block (TileShape) and GEMM Problem shape
-  auto gemm_problem_shape = cute::make_shape(options.m, options.n, options.k);
-  auto blockscale_shape = shape(get<1>(cute::zipped_divide(cute::make_layout(gemm_problem_shape), TileShape{})));
-  auto blockscale_m = cute::get<0>(blockscale_shape);
-  auto blockscale_n = cute::get<1>(blockscale_shape);
-  auto blockscale_k = cute::get<2>(blockscale_shape);
-
   // Create instantiation for device reference gemm kernel
   auto A = cute::make_tensor(tensor_A.host_data(),
                              cute::make_layout(
@@ -550,28 +552,18 @@ bool verify(const Options<RasterOrderOptions> &options) {
                                 )
                               );
 
-  auto blockscale_A = cute::make_tensor(blockscale_tensor_A.host_data(),
-                                        cute::make_layout(
-                                          cute::make_shape(blockscale_m, blockscale_k, options.l),
-                                          cute::make_stride(blockscale_k, 1, blockscale_m * blockscale_k)
-                                        )
-                                      );
-  auto blockscale_B = cute::make_tensor(blockscale_tensor_B.host_data(),
-                                        cute::make_layout(
-                                          cute::make_shape(blockscale_n, blockscale_k, options.l),
-                                          cute::make_stride(blockscale_k, 1, blockscale_n * blockscale_k)
-                                        )
-                                      );
+  auto SFA = cute::make_tensor(blockscale_tensor_A.host_data(), layout_SFA);
+  auto SFB = cute::make_tensor(blockscale_tensor_B.host_data(), layout_SFB);
 
   using unused_t = decltype(D);
 
-  cutlass::reference::host::GettMainloopParams<ElementAccumulator,
-                                               decltype(A), decltype(B),
-                                               decltype(blockscale_A), decltype(blockscale_B),
-                                               TileShape> mainloop_params{
-                                               A, B,                         // Operand Tensors
-                                               blockscale_A, blockscale_B    // Blockwise scaling Tensors
-                                              };
+  cutlass::reference::host::GettBlockScalingMainloopParams<
+      ElementAccumulator,
+      decltype(A),
+      decltype(SFA),
+      decltype(B),
+      decltype(SFB)
+    > mainloop_params{A, SFA, B, SFB};
 
   cutlass::reference::host::GettEpilogueParams<
       ElementScalar,
@@ -604,29 +596,40 @@ bool verify(const Options<RasterOrderOptions> &options) {
   cutlass::reference::host::Gemm3x(mainloop_params, epilogue_params);
 
   // compare_reference
+  bool passed = true;
   tensor_D.sync_host();
-  bool passed = cutlass::reference::host::TensorEquals(tensor_ref_D.host_view(), tensor_D.host_view());
+  passed &= cutlass::reference::host::TensorRelativelyEquals(tensor_D.host_view(), tensor_ref_D.host_view(), ElementAux(options.epsilon), ElementAux(options.non_zero_floor));
+  double mse = cutlass::reference::host::TensorMSE(tensor_D.host_view(), tensor_ref_D.host_view());
+  double mre = cutlass::reference::host::TensorMRE(tensor_D.host_view(), tensor_ref_D.host_view());
+  double max_error = cutlass::reference::host::TensorGreatestError(tensor_D.host_view(), tensor_ref_D.host_view());
+  std::cout << "  Result MSE: " << mse << ", MRE: " << mre << ", greatest error: " << max_error << std::endl;
 
-  if (false) {
-    std::cout << "tensor_ref_D.host_view() {" << std::endl
-              << tensor_ref_D.host_view() << std::endl
-              << "}"  << std::endl;
-    std::cout << "tensor_D.host_view() {" << std::endl
-              << tensor_D.host_view() << std::endl
-              << "}"  << std::endl;
-  }
+#if 0
+  std::cout << "tensor_ref_D.host_view() {" << std::endl
+            << tensor_ref_D.host_view() << std::endl
+            << "}"  << std::endl;
+  std::cout << "tensor_D.host_view() {" << std::endl
+            << tensor_D.host_view() << std::endl
+            << "}"  << std::endl;
+#endif
 
   if (IsDFp8 && options.save_amax) {
     abs_max_D.sync_host();
-    passed &= abs_max_D.at(cutlass::make_Coord(0)) == reference_abs_max_D.at(cutlass::make_Coord(0));
+    std::cout << "  Abs max D: " << abs_max_D.at(cutlass::make_Coord(0)) << ", reference: " << reference_abs_max_D.at(cutlass::make_Coord(0)) << std::endl;
+    passed &= cutlass::relatively_equal(abs_max_D.at(cutlass::make_Coord(0)), reference_abs_max_D.at(cutlass::make_Coord(0)), ElementScalar(options.epsilon), ElementScalar(options.non_zero_floor));
   }
 
   if (options.save_aux) {
     tensor_aux.sync_host();
-    passed &= cutlass::reference::host::TensorEquals(tensor_ref_aux.host_view(), tensor_aux.host_view());
+    passed &= cutlass::reference::host::TensorRelativelyEquals(tensor_aux.host_view(), tensor_ref_aux.host_view(), ElementAux(options.epsilon), ElementAux(options.non_zero_floor));
+    mse = cutlass::reference::host::TensorMSE(tensor_aux.host_view(), tensor_ref_aux.host_view());
+    mre = cutlass::reference::host::TensorMRE(tensor_aux.host_view(), tensor_ref_aux.host_view());
+    max_error = cutlass::reference::host::TensorGreatestError(tensor_aux.host_view(), tensor_ref_aux.host_view());
+    std::cout << "  Aux MSE: " << mse << ", MRE: " << mre << ", greatest error: " << max_error << std::endl;
     if (IsAuxFp8 && options.save_amax) {
       abs_max_aux.sync_host();
-      passed &= abs_max_aux.at(cutlass::make_Coord(0)) == reference_abs_max_aux.at(cutlass::make_Coord(0));
+      std::cout << "  Abs max aux: " << abs_max_aux.at(cutlass::make_Coord(0)) << ", reference: " << reference_abs_max_aux.at(cutlass::make_Coord(0)) << std::endl;
+      passed &= cutlass::relatively_equal(abs_max_aux.at(cutlass::make_Coord(0)), reference_abs_max_aux.at(cutlass::make_Coord(0)), ElementScalar(options.epsilon), ElementScalar(options.non_zero_floor));
     }
   }
 
@@ -662,20 +665,22 @@ int run(Options<RasterOrderOptions> &options)
 
   // Check if output from CUTLASS kernel and reference kernel are equal or not
   Result result;
-  result.passed = verify(options);
+  if (options.verify) {
+    result.passed = verify(options);
 
-  std::cout << "  Disposition: " << (result.passed ? "Passed" : "Failed") << std::endl;
-
-  // if (!result.passed) {
-  //  exit(-1);
-  // }
+    std::cout << "  Disposition: " << (result.passed ? "Passed" : "Failed") << std::endl;
+  }
+  else {
+    result.passed = true;
+  }
 
   // Run profiling loop
   if (options.iterations > 0)
   {
     GpuTimer timer;
-    timer.start();
-    for (int iter = 0; iter < options.iterations; ++iter) {
+    for (int iter = 0; iter < options.warmup + options.iterations; ++iter) {
+      if (iter == options.warmup)
+        timer.start();
       CUTLASS_CHECK(gemm.run());
     }
     timer.stop();
@@ -700,7 +705,7 @@ int run(Options<RasterOrderOptions> &options)
     std::cout << "  GFLOPS: " << result.gflops << std::endl;
   }
 
-  return 0;
+  return result.passed;
 }
 
 #endif // defined(CUTLASS_ARCH_MMA_SM90_SUPPORTED)
@@ -746,7 +751,9 @@ int main(int argc, char const **args) {
   //
 
 #if defined(CUTLASS_ARCH_MMA_SM90_SUPPORTED)
-  run<Gemm>(options);
+  bool passed = run<Gemm>(options);
+  if (!passed)
+    return -1;
 #endif
 
   return 0;

examples/67_hopper_fp8_warp_specialized_gemm_with_blockwise_scaling/67_hopper_fp8_warp_specialized_gemm_with_groupwise_scaling.cu

@@ -75,11 +75,11 @@
 #include "cutlass/util/reference/host/tensor_copy.h"
 #include "cutlass/util/reference/host/tensor_compare.h"
 #include "cutlass/util/reference/host/tensor_norm.h"
+#include "cutlass/util/reference/host/gett.hpp"
 
 // Includes from examples directory
 #include "helper.h"
 #include "hopper_fp8_commandline.hpp"
-#include "reference/host/gemm_with_groupwise_scaling.h"
 
 using namespace cute;
 
@@ -100,7 +100,7 @@ using         LayoutB     = cutlass::layout::ColumnMajor;                   // L
 constexpr int AlignmentB  = 128 / cutlass::sizeof_bits<ElementB>::value;    // Memory access granularity/alignment of B matrix in units of elements (up to 16 bytes)
 
 // C matrix configuration
-using         ElementC    = cutlass::float_e4m3_t;                          // Element type for C and D matrix operands
+using         ElementC    = float;                          // Element type for C and D matrix operands
 using         LayoutC     = cutlass::layout::ColumnMajor;                   // Layout type for C and D matrix operands
 constexpr int AlignmentC  = 128 / cutlass::sizeof_bits<ElementC>::value;    // Memory access granularity/alignment of C matrix in units of elements (up to 16 bytes)
 
@@ -120,55 +120,30 @@ using ElementAccumulator  = float;                                          // E
 using ElementBlockScale   = float;                                          // Element type for blockscaling during accumulation
 using ElementCompute      = float;                                          // Element type for epilogue computation
 
-using TileShape_  = Shape<_128,_128,_128>;  // This one is just to make the compiler happy with verify()...
+using ArchTag       = cutlass::arch::Sm90;                          // Tag indicating the minimum SM that supports the intended feature
+using OperatorClass = cutlass::arch::OpClassTensorOp;               // Operator class tag
+using TileShape     = Shape<_128,_128,_128>;                        // Threadblock-level tile size
+using ClusterShape  = Shape<_1,_2,_1>;                              // Shape of the threadblocks in a cluster
 
-// ScaleGranularity{M,N}: number of {rows in A}/{columns in B} that share the same scaling factor
-// Given TileShape = Shape<_128,_128,_128>:
-//   ScaleGranularityM == 128 and ScaleGranularityN == 128 --> 2Dx2D (the shape of the scaling factor)
-//   ScaleGranularityM == 1   and ScaleGranularityN == 128 --> 1Dx2D scaling
-//   ScaleGranularityM == 128 and ScaleGranularityN == 1   --> 2Dx1D scaling
-//   ScaleGranularityM == 1   and ScaleGranularityN == 1   --> 1Dx1D scaling
-template <int ScaleGranularityM_, int ScaleGranularityN_>
-struct GroupScaleConfig {
-  using ArchTag       = cutlass::arch::Sm90;                          // Tag indicating the minimum SM that supports the intended feature
-  using OperatorClass = cutlass::arch::OpClassTensorOp;               // Operator class tag
-  using TileShape     = Shape<_128,_128,_128>;                        // Threadblock-level tile size
-  using ClusterShape  = Shape<_1,_2,_1>;                              // Shape of the threadblocks in a cluster
+constexpr int ScaleGranularityM = 1;
+constexpr int ScaleGranularityN = 128;
+constexpr int ScaleGranularityK = 128;
 
-  static constexpr int ScaleGranularityM = ScaleGranularityM_;
-  static constexpr int ScaleGranularityN = ScaleGranularityN_;
-  static constexpr int ScaleMsPerTile = size<0>(TileShape{}) / ScaleGranularityM;
-  static constexpr int ScaleNsPerTile = size<1>(TileShape{}) / ScaleGranularityN;
+constexpr int ScaleMsPerTile = size<0>(TileShape{}) / ScaleGranularityM;
+constexpr int ScaleNsPerTile = size<1>(TileShape{}) / ScaleGranularityN;
 
-  static_assert(size<0>(TileShape{}) == ScaleGranularityM * ScaleMsPerTile,
-              "FP8 scaling granularity must evenly divide tile shape along M.");
-  static_assert(size<1>(TileShape{}) == ScaleGranularityN * ScaleNsPerTile,
-              "FP8 scaling granularity must evenly divide tile shape along N.");
+using ScaleConfig   = cutlass::detail::Sm90BlockwiseScaleConfig<ScaleGranularityM, ScaleGranularityN, ScaleGranularityK>;
 
-  using KernelSchedule    = cutlass::gemm::KernelTmaWarpSpecializedCooperativeFP8BlockScaledAccum<ScaleGranularityM_, ScaleGranularityN_>;
-  using EpilogueSchedule  = cutlass::epilogue::TmaWarpSpecializedCooperative;
-  using EpilogueTileType  = cutlass::epilogue::collective::EpilogueTileAuto;
-  using FusionOperation   = cutlass::epilogue::fusion::ScaledLinCombPerRowBiasEltActAmaxAux<
+using LayoutSFA     = decltype(ScaleConfig::deduce_layoutSFA());    // Layout type for SFA matrix operand
+using LayoutSFB     = decltype(ScaleConfig::deduce_layoutSFB());    // Layout type for SFB matrix operand
+
+using KernelSchedule    = cutlass::gemm::KernelTmaWarpSpecializedCooperativeFP8BlockScaledAccum;
+using EpilogueSchedule  = cutlass::epilogue::TmaWarpSpecializedCooperative;
+using EpilogueTileType  = cutlass::epilogue::collective::EpilogueTileAuto;
+using FusionOperation   = cutlass::epilogue::fusion::ScaledLinCombPerRowBiasEltActAmaxAux<
     LayoutAux, cutlass::epilogue::thread::ReLU, ElementD, ElementCompute, ElementAux, ElementAmax, ElementBias, ElementC>;
-};
 
-using GroupScale1D1DConfig = GroupScaleConfig<                    1,                     1>;
-using GroupScale1D2DConfig = GroupScaleConfig<                    1, size<1>(TileShape_{})>;
-using GroupScale2D1DConfig = GroupScaleConfig<size<0>(TileShape_{}),                     1>;
-using GroupScale2D2DConfig = GroupScaleConfig<size<0>(TileShape_{}), size<1>(TileShape_{})>;
-
-template <typename ScheduleConfig>
-struct GroupScaleGemm {
-  using ArchTag           = typename ScheduleConfig::ArchTag;
-  using OperatorClass     = typename ScheduleConfig::OperatorClass;
-  using TileShape         = typename ScheduleConfig::TileShape;
-  using ClusterShape      = typename ScheduleConfig::ClusterShape;
-  using KernelSchedule    = typename ScheduleConfig::KernelSchedule;
-  using EpilogueSchedule  = typename ScheduleConfig::EpilogueSchedule;
-  using EpilogueTileType  = typename ScheduleConfig::EpilogueTileType;
-  using FusionOperation   = typename ScheduleConfig::FusionOperation;
-
-  using CollectiveEpilogue = typename cutlass::epilogue::collective::CollectiveBuilder<
+using CollectiveEpilogue = typename cutlass::epilogue::collective::CollectiveBuilder<
     ArchTag, OperatorClass,
     TileShape, ClusterShape,
     EpilogueTileType,
@@ -179,10 +154,10 @@ struct GroupScaleGemm {
     FusionOperation
   >::CollectiveOp;
 
-  using CollectiveMainloopWithGroupWiseScaling = typename cutlass::gemm::collective::CollectiveBuilder<
+using CollectiveMainloop = typename cutlass::gemm::collective::CollectiveBuilder<
     ArchTag, OperatorClass,
-    ElementA, LayoutA, AlignmentA,
-    ElementB, LayoutB, AlignmentB,
+    ElementA, cute::tuple<LayoutA, LayoutSFA>, AlignmentA,
+    ElementB, cute::tuple<LayoutB, LayoutSFB>, AlignmentB,
     ElementAccumulator,
     TileShape, ClusterShape,
     cutlass::gemm::collective::StageCountAutoCarveout<
@@ -191,38 +166,26 @@ struct GroupScaleGemm {
     KernelSchedule
   >::CollectiveOp;
 
-  using GemmKernelDefault = cutlass::gemm::kernel::GemmUniversal<
-      Shape<int,int,int,int>,
-      CollectiveMainloopWithGroupWiseScaling,
-      CollectiveEpilogue
+
+using GemmKernel = cutlass::gemm::kernel::GemmUniversal<
+    Shape<int,int,int,int>,
+    CollectiveMainloop,
+    CollectiveEpilogue,
+    cutlass::gemm::StreamKScheduler
   >;
 
-  using GemmKernelStreamK = cutlass::gemm::kernel::GemmUniversal<
-      Shape<int,int,int,int>,
-      CollectiveMainloopWithGroupWiseScaling,
-      CollectiveEpilogue,
-      cutlass::gemm::StreamKScheduler
-  >;
-
-  using GemmDefault = cutlass::gemm::device::GemmUniversalAdapter<GemmKernelDefault>;
-  using GemmStreamK = cutlass::gemm::device::GemmUniversalAdapter<GemmKernelStreamK>;
-};
-
-using GroupScale1D1DGemm = GroupScaleGemm<GroupScale1D1DConfig>;
-using GroupScale1D2DGemm = GroupScaleGemm<GroupScale1D2DConfig>;
-using GroupScale2D1DGemm = GroupScaleGemm<GroupScale2D1DConfig>;
-using GroupScale2D2DGemm = GroupScaleGemm<GroupScale2D2DConfig>;
+using Gemm = cutlass::gemm::device::GemmUniversalAdapter<GemmKernel>;
 
 // Extract information from Gemm kernel.
-using EpilogueOutputOp  = typename GroupScale1D1DGemm::GemmDefault::EpilogueOutputOp;
+using EpilogueOutputOp  = typename Gemm::EpilogueOutputOp;
 using ElementScalar     = typename EpilogueOutputOp::ElementScalar;
 using ElementAmax       = typename EpilogueOutputOp::ElementAmax;
 using ActivationFunctor = typename EpilogueOutputOp::ActivationFn;
 
-using StrideA = typename GroupScale1D1DGemm::GemmDefault::GemmKernel::StrideA;
-using StrideB = typename GroupScale1D1DGemm::GemmDefault::GemmKernel::StrideB;
-using StrideC = typename GroupScale1D1DGemm::GemmDefault::GemmKernel::StrideC;
-using StrideD = typename GroupScale1D1DGemm::GemmDefault::GemmKernel::StrideD;
+using StrideA = typename Gemm::GemmKernel::StrideA;
+using StrideB = typename Gemm::GemmKernel::StrideB;
+using StrideC = typename Gemm::GemmKernel::StrideC;
+using StrideD = typename Gemm::GemmKernel::StrideD;
 using StrideAux = StrideD;
 
 constexpr bool IsDFp8 =
@@ -242,20 +205,22 @@ StrideB stride_B;
 StrideC stride_C;
 StrideD stride_D;
 StrideAux stride_aux;
+LayoutSFA layout_SFA;
+LayoutSFB layout_SFB;
 uint64_t seed;
 
+using LayoutScalar = cutlass::layout::PackedVectorLayout;
+
 cutlass::HostTensor<ElementA  , LayoutA  > tensor_A;
 cutlass::HostTensor<ElementB  , LayoutB  > tensor_B;
 cutlass::HostTensor<ElementC  , LayoutC  > tensor_C;
 cutlass::HostTensor<ElementD  , LayoutD  > tensor_D;
-uint32_t mma_promotion_interval;
-cutlass::HostTensor<ElementBlockScale, LayoutA> blockscale_tensor_A;
-cutlass::HostTensor<ElementBlockScale, LayoutB> blockscale_tensor_B;
+cutlass::HostTensor<ElementBlockScale, LayoutScalar> blockscale_tensor_A;
+cutlass::HostTensor<ElementBlockScale, LayoutScalar> blockscale_tensor_B;
 cutlass::HostTensor<ElementD  , LayoutD  > tensor_ref_D;
 cutlass::HostTensor<ElementAux, LayoutAux> tensor_aux;
 cutlass::HostTensor<ElementAux, LayoutAux> tensor_ref_aux;
 
-using LayoutScalar = cutlass::layout::PackedVectorLayout;
 cutlass::HostTensor<ElementScalar, LayoutScalar> scalar_alpha;
 cutlass::HostTensor<ElementScalar, LayoutScalar> scalar_beta;
 cutlass::HostTensor<ElementScalar, LayoutScalar> scale_A;
@@ -303,120 +268,114 @@ struct Result
 /////////////////////////////////////////////////////////////////////////////////////////////////
 
 /// Helper to initialize a block of device data
-  template <typename Element, typename Layout>
-  bool initialize_tensor(
-    cutlass::TensorView<Element, Layout> view,
-    cutlass::Distribution::Kind dist_kind,
-    uint64_t seed) {
+template <typename Element, typename Layout>
+bool initialize_tensor(
+  cutlass::TensorView<Element, Layout> view,
+  cutlass::Distribution::Kind dist_kind,
+  uint64_t seed) {
 
-    if (dist_kind == cutlass::Distribution::Uniform) {
+  if (dist_kind == cutlass::Distribution::Uniform) {
 
-      double scope_max, scope_min;
-      int bits_input = cutlass::sizeof_bits<Element>::value;
-      int bits_output = cutlass::sizeof_bits<Element>::value;
+    double scope_max, scope_min;
+    int bits_input = cutlass::sizeof_bits<Element>::value;
+    int bits_output = cutlass::sizeof_bits<Element>::value;
 
-      if (bits_input == 1) {
-        scope_max = 2;
-        scope_min = 0;
-      } else if (bits_input <= 8) {
-        scope_max = 2;
-        scope_min = -2;
-      } else if (bits_output == 16) {
-        scope_max = 5;
-        scope_min = -5;
-      } else {
-        scope_max = 8;
-        scope_min = -8;
-      }
-
-      cutlass::reference::host::TensorFillRandomUniform(
-        view, seed, scope_max, scope_min, 0);
-    }
-    else if (dist_kind == cutlass::Distribution::AllZeros) {
-      cutlass::reference::host::TensorFill(view);
-    }
-    else if (dist_kind == cutlass::Distribution::Identity) {
-
-      cutlass::reference::host::TensorFillIdentity(view);
-    }
-    else if (dist_kind == cutlass::Distribution::Gaussian) {
-
-      cutlass::reference::host::TensorFillRandomGaussian(view, seed, 0, 0.5);
-    }
-    else if (dist_kind == cutlass::Distribution::Sequential) {
-      cutlass::reference::host::BlockFillSequential(view.data(), view.capacity());
-    }
-    else {
-      throw std::runtime_error("Not implementated.");
+    if (bits_input == 1) {
+      scope_max = 2;
+      scope_min = 0;
+    } else if (bits_input <= 8) {
+      scope_max = 2;
+      scope_min = -2;
+    } else if (bits_output == 16) {
+      scope_max = 5;
+      scope_min = -5;
+    } else {
+      scope_max = 8;
+      scope_min = -8;
     }
 
-    return true;
+    cutlass::reference::host::TensorFillRandomUniform(
+      view, seed, scope_max, scope_min, bits_input);
   }
+  else if (dist_kind == cutlass::Distribution::AllZeros) {
+    cutlass::reference::host::TensorFill(view);
+  }
+  else if (dist_kind == cutlass::Distribution::Identity) {
+
+    cutlass::reference::host::TensorFillIdentity(view);
+  }
+  else if (dist_kind == cutlass::Distribution::Gaussian) {
+
+    cutlass::reference::host::TensorFillRandomGaussian(view, seed, 0, 0.5);
+  }
+  else if (dist_kind == cutlass::Distribution::Sequential) {
+    cutlass::reference::host::BlockFillSequential(view.data(), view.capacity());
+  }
+  else {
+    throw std::runtime_error("Not implementated.");
+  }
+
+  return true;
+}
 
 /// Helper to initialize a block of device data (scale_tensors)
-  template <typename Element, typename Layout>
-  bool initialize_scale_tensor(
-    cutlass::TensorView<Element, Layout> view,
-    cutlass::Distribution::Kind dist_kind,
-    uint64_t seed) {
+template <typename Element, typename Layout>
+bool initialize_scale_tensor(
+  cutlass::TensorView<Element, Layout> view,
+  cutlass::Distribution::Kind dist_kind,
+  uint64_t seed) {
 
-    if (dist_kind == cutlass::Distribution::Uniform) {
+  if (dist_kind == cutlass::Distribution::Uniform) {
 
-      double scope_max, scope_min;
+    double scope_max, scope_min;
 
-      scope_min = -1;
-      scope_max = 1;
+    scope_min = -1;
+    scope_max = 1;
 
-      cutlass::reference::host::TensorFillRandomUniform(
-        view, seed, scope_max, scope_min, 0);
-    }
-    else if (dist_kind == cutlass::Distribution::AllZeros) {
-      cutlass::reference::host::TensorFill(view);
-    }
-    else if (dist_kind == cutlass::Distribution::Identity) {
+    cutlass::reference::host::TensorFillRandomUniform(
+      view, seed, scope_max, scope_min);
+  }
+  else if (dist_kind == cutlass::Distribution::AllZeros) {
+    cutlass::reference::host::TensorFill(view);
+  }
+  else if (dist_kind == cutlass::Distribution::Identity) {
 
-      cutlass::reference::host::TensorFillIdentity(view);
-    }
-    else if (dist_kind == cutlass::Distribution::Gaussian) {
+    cutlass::reference::host::TensorFillIdentity(view);
+  }
+  else if (dist_kind == cutlass::Distribution::Gaussian) {
 
-      cutlass::reference::host::TensorFillRandomGaussian(view, seed, 0, 0.5);
-    }
-    else if (dist_kind == cutlass::Distribution::Sequential) {
-      cutlass::reference::host::BlockFillSequential(view.data(), view.capacity());
-    }
-    else {
-      throw std::runtime_error("Not implementated.");
-    }
-
-    return true;
+    cutlass::reference::host::TensorFillRandomGaussian(view, seed, 0, 0.5);
+  }
+  else if (dist_kind == cutlass::Distribution::Sequential) {
+    cutlass::reference::host::BlockFillSequential(view.data(), view.capacity());
+  }
+  else {
+    throw std::runtime_error("Not implementated.");
   }
 
+  return true;
+}
+
 /// Initialize operands to be used in the GEMM and reference GEMM
-template <typename GroupScaleConfig>
 void initialize(const Options<RasterOrderOptions> &options) {
 
-  using TileShape = typename GroupScaleConfig::TileShape;
-  const int ScaleMsPerTile = GroupScaleConfig::ScaleMsPerTile;
-  const int ScaleNsPerTile = GroupScaleConfig::ScaleNsPerTile;
-
-  // Find Group Scaling tensor shapes based on `ScaleGranularityM`, problem shape, and TileShape
-  auto gemm_problem_shape = cute::make_shape(options.m, options.n, options.k);
-  auto blockscale_shape = shape(get<1>(cute::zipped_divide(cute::make_layout(gemm_problem_shape), TileShape{})));
-  auto groupscale_m = cute::get<0>(blockscale_shape) * ScaleMsPerTile; // We need to pad along M in scale tensor of A to prevent illegal memory access.
-  auto groupscale_n = cute::get<1>(blockscale_shape) * ScaleNsPerTile; // We need to pad along N in scale tensor of A to prevent illegal memory access.
-  auto blockscale_k = cute::get<2>(blockscale_shape);
+  assert(options.m % ScaleGranularityM == 0);
+  assert(options.n % ScaleGranularityN == 0);
 
   stride_A = cutlass::make_cute_packed_stride(StrideA{}, cute::make_shape(options.m, options.k, options.l));
   stride_B = cutlass::make_cute_packed_stride(StrideB{}, cute::make_shape(options.n, options.k, options.l));
   stride_C = cutlass::make_cute_packed_stride(StrideC{}, cute::make_shape(options.m, options.n, options.l));
   stride_D = cutlass::make_cute_packed_stride(StrideD{}, cute::make_shape(options.m, options.n, options.l));
   stride_aux = stride_D;
+  layout_SFA = ScaleConfig::tile_atom_to_shape_SFA(make_shape(options.m, options.n, options.k, options.l));
+  layout_SFB = ScaleConfig::tile_atom_to_shape_SFB(make_shape(options.m, options.n, options.k, options.l));
+
 
   auto a_coord = cutlass::make_Coord(options.m * options.l, options.k);
   auto c_coord = cutlass::make_Coord(options.m * options.l, options.n);
   auto b_coord = cutlass::make_Coord(options.k, options.n * options.l);
-  auto groupscale_a_coord = cutlass::make_Coord(groupscale_m * options.l, blockscale_k);
-  auto groupscale_b_coord = cutlass::make_Coord(groupscale_n * options.l, blockscale_k);
+  auto groupscale_a_coord = cutlass::make_Coord(size(filter_zeros(layout_SFA)));
+  auto groupscale_b_coord = cutlass::make_Coord(size(filter_zeros(layout_SFB)));
 
   tensor_A.resize(a_coord);
   tensor_B.resize(b_coord);
@@ -453,8 +412,6 @@ void initialize(const Options<RasterOrderOptions> &options) {
   blockscale_tensor_A.sync_device();
   blockscale_tensor_B.sync_device();
 
-  mma_promotion_interval = 4;
-
   if (options.save_aux) {
     tensor_aux.resize(c_coord);
     tensor_aux.sync_device();
@@ -515,9 +472,10 @@ GemmArguments args_from_options(const Options<RasterOrderOptions> &options)
      stride_A,
      tensor_B.device_data(),
      stride_B,
-     mma_promotion_interval,
      blockscale_tensor_A.device_data(),
-     blockscale_tensor_B.device_data()
+     layout_SFA,
+     blockscale_tensor_B.device_data(),
+     layout_SFB
      },
     {
       {}, // epilogue.thread
@@ -567,18 +525,11 @@ GemmArguments args_from_options(const Options<RasterOrderOptions> &options)
 }
 
 /// Don't know why the compiler does not like verify() being templated...
-bool verify(const Options<RasterOrderOptions> &options, const int ScaleMsPerTile, const int ScaleNsPerTile) {
+bool verify(const Options<RasterOrderOptions> &options) {
   //
   // Compute reference output
   //
 
-  // Group scaling tensors shapes based `ScaleGranularityM`, CTA Block (TileShape) and GEMM Problem shape
-  auto gemm_problem_shape = cute::make_shape(options.m, options.n, options.k);
-  auto blockscale_shape = shape(get<1>(cute::zipped_divide(cute::make_layout(gemm_problem_shape), TileShape_{})));
-  auto blockscale_m = cute::get<0>(blockscale_shape);
-  auto blockscale_n = cute::get<1>(blockscale_shape);
-  auto blockscale_k = cute::get<2>(blockscale_shape);
-
   // Create instantiation for device reference gemm kernel
   auto A = cute::make_tensor(tensor_A.host_data(),
                              cute::make_layout(
@@ -611,28 +562,18 @@ bool verify(const Options<RasterOrderOptions> &options, const int ScaleMsPerTile
                                 )
                               );
 
-  auto blockscale_A = cute::make_tensor(blockscale_tensor_A.host_data(),
-                                        cute::make_layout(
-                                          cute::make_shape(blockscale_m, ScaleMsPerTile, blockscale_k, options.l),
-                                          cute::make_stride(blockscale_k * ScaleMsPerTile, 1, ScaleMsPerTile, blockscale_m * blockscale_k * ScaleMsPerTile)
-                                        )
-                                      );
-  auto blockscale_B = cute::make_tensor(blockscale_tensor_B.host_data(),
-                                        cute::make_layout(
-                                          cute::make_shape(blockscale_n, ScaleNsPerTile, blockscale_k, options.l),
-                                          cute::make_stride(blockscale_k * ScaleNsPerTile, 1, ScaleNsPerTile, blockscale_n * blockscale_k * ScaleNsPerTile)
-                                        )
-                                      );
+  auto SFA = cute::make_tensor(blockscale_tensor_A.host_data(), layout_SFA);
+  auto SFB = cute::make_tensor(blockscale_tensor_B.host_data(), layout_SFB);
 
   using unused_t = decltype(D);
 
-  cutlass::reference::host::GettMainloopParams<ElementAccumulator,
-                                               decltype(A), decltype(B),
-                                               decltype(blockscale_A), decltype(blockscale_B),
-                                               TileShape_> mainloop_params{
-                                               A, B,                         // Operand Tensors
-                                               blockscale_A, blockscale_B    // Groupwise scaling Tensors
-                                              };
+  cutlass::reference::host::GettBlockScalingMainloopParams<
+      ElementAccumulator,
+      decltype(A), 
+      decltype(SFA), 
+      decltype(B),
+      decltype(SFB)
+    > mainloop_params{A, SFA, B, SFB};
 
   cutlass::reference::host::GettEpilogueParams<
       ElementScalar,
@@ -665,29 +606,40 @@ bool verify(const Options<RasterOrderOptions> &options, const int ScaleMsPerTile
   cutlass::reference::host::Gemm3x(mainloop_params, epilogue_params);
 
   // compare_reference
+  bool passed = true;
   tensor_D.sync_host();
-  bool passed = cutlass::reference::host::TensorEquals(tensor_ref_D.host_view(), tensor_D.host_view());
+  passed &= cutlass::reference::host::TensorRelativelyEquals(tensor_D.host_view(), tensor_ref_D.host_view(), ElementAux(options.epsilon), ElementAux(options.non_zero_floor));
+  double mse = cutlass::reference::host::TensorMSE(tensor_D.host_view(), tensor_ref_D.host_view());
+  double mre = cutlass::reference::host::TensorMRE(tensor_D.host_view(), tensor_ref_D.host_view());
+  double max_error = cutlass::reference::host::TensorGreatestError(tensor_D.host_view(), tensor_ref_D.host_view());
+  std::cout << "  Result MSE: " << mse << ", MRE: " << mre << ", greatest error: " << max_error << std::endl;
 
-  if (false) {
-    std::cout << "tensor_ref_D.host_view() {" << std::endl
-              << tensor_ref_D.host_view() << std::endl
-              << "}"  << std::endl;
-    std::cout << "tensor_D.host_view() {" << std::endl
-              << tensor_D.host_view() << std::endl
-              << "}"  << std::endl;
-  }
+#if 0
+  std::cout << "tensor_ref_D.host_view() {" << std::endl
+            << tensor_ref_D.host_view() << std::endl
+            << "}"  << std::endl;
+  std::cout << "tensor_D.host_view() {" << std::endl
+            << tensor_D.host_view() << std::endl
+            << "}"  << std::endl;
+#endif
 
   if (IsDFp8 && options.save_amax) {
     abs_max_D.sync_host();
-    passed &= abs_max_D.at(cutlass::make_Coord(0)) == reference_abs_max_D.at(cutlass::make_Coord(0));
+    std::cout << "  Abs max D: " << abs_max_D.at(cutlass::make_Coord(0)) << ", reference: " << reference_abs_max_D.at(cutlass::make_Coord(0)) << std::endl;
+    passed &= cutlass::relatively_equal(abs_max_D.at(cutlass::make_Coord(0)), reference_abs_max_D.at(cutlass::make_Coord(0)), ElementScalar(options.epsilon), ElementScalar(options.non_zero_floor));
   }
 
   if (options.save_aux) {
     tensor_aux.sync_host();
-    passed &= cutlass::reference::host::TensorEquals(tensor_ref_aux.host_view(), tensor_aux.host_view());
+    passed &= cutlass::reference::host::TensorRelativelyEquals(tensor_aux.host_view(), tensor_ref_aux.host_view(), ElementAux(options.epsilon), ElementAux(options.non_zero_floor));
+    mse = cutlass::reference::host::TensorMSE(tensor_aux.host_view(), tensor_ref_aux.host_view());
+    mre = cutlass::reference::host::TensorMRE(tensor_aux.host_view(), tensor_ref_aux.host_view());
+    max_error = cutlass::reference::host::TensorGreatestError(tensor_aux.host_view(), tensor_ref_aux.host_view());
+    std::cout << "  Aux MSE: " << mse << ", MRE: " << mre << ", greatest error: " << max_error << std::endl;
     if (IsAuxFp8 && options.save_amax) {
       abs_max_aux.sync_host();
-      passed &= abs_max_aux.at(cutlass::make_Coord(0)) == reference_abs_max_aux.at(cutlass::make_Coord(0));
+      std::cout << "  Abs max aux: " << abs_max_aux.at(cutlass::make_Coord(0)) << ", reference: " << reference_abs_max_aux.at(cutlass::make_Coord(0)) << std::endl;
+      passed &= cutlass::relatively_equal(abs_max_aux.at(cutlass::make_Coord(0)), reference_abs_max_aux.at(cutlass::make_Coord(0)), ElementScalar(options.epsilon), ElementScalar(options.non_zero_floor));
     }
   }
 
@@ -695,16 +647,34 @@ bool verify(const Options<RasterOrderOptions> &options, const int ScaleMsPerTile
 }
 
 /// Execute a given example GEMM computation
-template <typename GroupScaleConfig, typename Gemm>
-int run(Options<RasterOrderOptions> &options)
-{
-  using TileShape = typename GroupScaleConfig::TileShape;
-  const int ScaleGranularityM = GroupScaleConfig::ScaleGranularityM;
-  const int ScaleGranularityN = GroupScaleConfig::ScaleGranularityN;
-  const int ScaleMsPerTile    = GroupScaleConfig::ScaleMsPerTile;
-  const int ScaleNsPerTile    = GroupScaleConfig::ScaleNsPerTile;
+int run(Options<RasterOrderOptions> &options) {
 
-  initialize<GroupScaleConfig>(options);
+  bool skip = false;
+  std::cout << "  Problem Size: " << options.m << 'x' << options.n << 'x' << options.k << 'x' << options.l << std::endl;
+  std::cout << "  Tile shape (M, N, K): " << size<0>(TileShape{}) << ", " << size<1>(TileShape{}) << ", " << size<2>(TileShape{}) << std::endl;
+  std::cout << "  ScaleGranularityM: " << ScaleGranularityM << " (ScaleMsPerTile: " << ScaleMsPerTile << ")" << std::endl;
+  std::cout << "  ScaleGranularityN: " << ScaleGranularityN << " (ScaleNsPerTile: " << ScaleNsPerTile << ")" << std::endl;
+
+
+  if (options.m < ScaleGranularityM) {
+    std::cout << "  Skippig (m size: " << options.m << " less than ScaleGranularityM: " << ScaleGranularityM << "):" << std::endl;
+    skip = true;
+  }
+
+  if (options.n < ScaleGranularityN) {
+    std::cout << "  Skippig (n size: " << options.n << " less than ScaleGranularityN: " << ScaleGranularityN << "):" << std::endl;
+    skip = true;
+  }
+
+  if (options.k < size<2>(TileShape{})) {
+    std::cout << "  Skippig (k size: " << options.k << " less than TileShape[2]: " << size<2>(TileShape{}) << "):" << std::endl;
+    skip = true;
+  }
+
+  if (!skip) std::cout << "  Running... " << std::endl;
+  else return -1;
+
+  initialize(options);
 
   // Instantiate CUTLASS kernel depending on templates
   Gemm gemm;
@@ -729,20 +699,22 @@ int run(Options<RasterOrderOptions> &options)
 
   // Check if output from CUTLASS kernel and reference kernel are equal or not
   Result result;
-  result.passed = verify(options, ScaleMsPerTile, ScaleNsPerTile);
+  if (options.verify) {
+    result.passed = verify(options);
 
-  std::cout << "  Disposition: " << (result.passed ? "Passed" : "Failed") << std::endl;
-
-  // if (!result.passed) {
-  //  exit(-1);
-  // }
+    std::cout << "  Disposition: " << (result.passed ? "Passed" : "Failed") << std::endl;
+  }
+  else {
+    result.passed = true;
+  }
 
   // Run profiling loop
   if (options.iterations > 0)
   {
     GpuTimer timer;
-    timer.start();
-    for (int iter = 0; iter < options.iterations; ++iter) {
+    for (int iter = 0; iter < options.warmup + options.iterations; ++iter) {
+      if (iter == options.warmup)
+        timer.start();
       CUTLASS_CHECK(gemm.initialize(arguments, workspace.get()));
       CUTLASS_CHECK(gemm.run());
     }
@@ -762,17 +734,13 @@ int run(Options<RasterOrderOptions> &options)
       raster = "Along M";
     }
 
-    std::cout << "  Problem Size: " << options.m << 'x' << options.n << 'x' << options.k << 'x' << options.l << std::endl;
-    std::cout << "  Tile shape (M, N, K): " << size<0>(TileShape{}) << ", " << size<1>(TileShape{}) << ", " << size<2>(TileShape{}) << std::endl;
-    std::cout << "  ScaleGranularityM: " << ScaleGranularityM << " (ScaleMsPerTile: " << ScaleMsPerTile << ")" << std::endl;
-    std::cout << "  ScaleGranularityN: " << ScaleGranularityN << " (ScaleNsPerTile: " << ScaleNsPerTile << ")" << std::endl;
     std::cout << "  Rasterization: " << raster << " with a maximum CTA swizzle of " << options.swizzle << std::endl;
     std::cout << "  Avg runtime: " << result.avg_runtime_ms << " ms" << std::endl;
     std::cout << "  GFLOPS: " << result.gflops << std::endl;
     fflush(stdout);
   }
 
-  return 0;
+  return result.passed;
 }
 
 #endif // defined(CUTLASS_ARCH_MMA_SM90_SUPPORTED)
@@ -818,27 +786,10 @@ int main(int argc, char const **args) {
   //
 
 #if defined(CUTLASS_ARCH_MMA_SM90_SUPPORTED)
-  std::cout << "Basic split-K GEMM kernel" << std::endl;
-  run<GroupScale1D1DConfig, GroupScale1D1DGemm::GemmDefault>(options);
-  std::cout << std::endl;
-  run<GroupScale1D2DConfig, GroupScale1D2DGemm::GemmDefault>(options);
-  std::cout << std::endl;
-  run<GroupScale2D1DConfig, GroupScale2D1DGemm::GemmDefault>(options);
-  std::cout << std::endl;
-  run<GroupScale2D2DConfig, GroupScale2D2DGemm::GemmDefault>(options);
-  std::cout << std::endl;
-
-  std::cout << std::endl;
-
-  std::cout << "StreamK GEMM kernel" << std::endl;
-  run<GroupScale1D1DConfig, GroupScale1D1DGemm::GemmStreamK>(options);
-  std::cout << std::endl;
-  run<GroupScale1D2DConfig, GroupScale1D2DGemm::GemmStreamK>(options);
-  std::cout << std::endl;
-  run<GroupScale2D1DConfig, GroupScale2D1DGemm::GemmStreamK>(options);
-  std::cout << std::endl;
-  run<GroupScale2D2DConfig, GroupScale2D2DGemm::GemmStreamK>(options);
-  std::cout << std::endl;
+  bool passed = true;
+  passed = run(options);
+  if (!passed)
+    return -1;
 #endif
 
   return 0;

examples/67_hopper_fp8_warp_specialized_gemm_with_blockwise_scaling/hopper_fp8_commandline.hpp

@@ -34,6 +34,7 @@ template<typename RasterOrderOptions>
 struct Options {
 
   bool help = false;
+  bool verify = true;
 
   float alpha = 1.f, beta = 0.f;
   float scale_a = 1.f, scale_b = 1.f, scale_c = 1.f, scale_d = 1.f, scale_aux = 1.f;
@@ -41,9 +42,12 @@ struct Options {
   bool save_aux = true;
   bool save_amax = true;
   int iterations = 1000;
+  int warmup = 1000;
   int m = 1024, n = 512, k = 1024, l = 1;
   RasterOrderOptions raster;
   int swizzle;
+  float epsilon = 0.02f;
+  float non_zero_floor = 1.f;
 
   // Parses the command line
   void parse(int argc, char const **args) {
@@ -68,7 +72,11 @@ struct Options {
     cmd.get_cmd_line_argument("device_scale", device_scale, false);
     cmd.get_cmd_line_argument("save_aux", save_aux, true);
     cmd.get_cmd_line_argument("save_amax", save_amax, true);
+    cmd.get_cmd_line_argument("warmup", warmup);
     cmd.get_cmd_line_argument("iterations", iterations);
+    cmd.get_cmd_line_argument("verify", verify);
+    cmd.get_cmd_line_argument("epsilon", epsilon);
+    cmd.get_cmd_line_argument("non-zero-floor", non_zero_floor);
 
     char raster_char;
     cmd.get_cmd_line_argument("raster", raster_char);
@@ -89,8 +97,8 @@ struct Options {
   /// Prints the usage statement.
   std::ostream & print_usage(std::ostream &out) const {
 
-    out << "54_fp8_hopper_warp_specialized_gemm\n\n"
-      << "  Hopper FP8 GEMM using a Warp Specialized kernel.\n\n"
+    out << "67_hopper_fp8_warp_specialized_gemm_with_blockwise_scaling\n\n"
+      << "  Hopper FP8 GEMM using a Warp Specialized kernel with Blockwise Scaling.\n\n"
       << "Options:\n\n"
       << "  --help                      If specified, displays this usage statement\n\n"
       << "  --m=<int>                   Sets the M extent of the GEMM\n"
@@ -109,11 +117,14 @@ struct Options {
       << "  --save_amax=<bool>          Save the pre-scaled max absolute value of any fp8 outputs (aux and/or D) (default: true)\n"
       << "  --raster=<char>             CTA Rasterization direction (N for along N, M for along M, and H for heuristic)\n\n"
       << "  --swizzle=<int>             CTA Rasterization swizzle\n\n"
-      << "  --iterations=<int>          Number of profiling iterations to perform.\n\n";
+      << "  --iterations=<int>          Number of profiling iterations to perform.\n\n"
+      << "  --verify=<bool>             Verify the results.\n\n"
+      << "  --epsilon=<float>           The epsilon value for comparing the results.\n\n"
+      << "  --non-zero-floor=<float>    The none zero floor for comparing the results.\n\n";
 
     out
       << "\n\nExamples:\n\n"
-      << "$ " << "54_fp8_hopper_warp_specialized_gemm" << " --m=1024 --n=512 --k=1024 --alpha=2 --beta=0.707 \n\n";
+      << "$ " << "67_hopper_fp8_warp_specialized_gemm_with_blockwise_scaling" << " --m=1024 --n=512 --k=1024 --alpha=2 --beta=0.707 \n\n";
 
     return out;
   }

examples/67_hopper_fp8_warp_specialized_gemm_with_blockwise_scaling/reference/host/gemm_with_blockwise_scaling.h

@@ -1,504 +0,0 @@
-/***************************************************************************************************
- * Copyright (c) 2023 - 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
- * SPDX-License-Identifier: BSD-3-Clause
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *
- * 1. Redistributions of source code must retain the above copyright notice, this
- * list of conditions and the following disclaimer.
- *
- * 2. Redistributions in binary form must reproduce the above copyright notice,
- * this list of conditions and the following disclaimer in the documentation
- * and/or other materials provided with the distribution.
- *
- * 3. Neither the name of the copyright holder nor the names of its
- * contributors may be used to endorse or promote products derived from
- * this software without specific prior written permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
- * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
- * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
- * FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
- * DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
- * SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
- * CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
- * OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
- * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- **************************************************************************************************/
-/*! \file
-    \brief Reference implementation for GETT in host-side code.
-*/
-
-#pragma once
-
-/////////////////////////////////////////////////////////////////////////////////////////////////
-#include "cutlass/gemm/gemm.h"
-#include "cutlass/complex.h"
-#include "cutlass/numeric_conversion.h"
-#include "cutlass/epilogue/thread/activation.h"
-#include "cutlass/relatively_equal.h"
-#include <iostream>
-#include "cute/tensor.hpp"
-
-/////////////////////////////////////////////////////////////////////////////////////////////////
-
-namespace cutlass::reference::host {
-
-template<class T, class = void>
-struct ElementTraits {
-  using type = T;
-};
-
-template<class T>
-struct ElementTraits<T, std::enable_if_t<!std::is_same_v<decltype(std::declval<T>().get()), void> > >  {
-  using type = decltype(std::declval<T>().get());
-};
-
-/////////////////////////////////////////////////////////////////////////////////////////////////
-
-template<
-  class ElementAccumulator_,
-  class TensorA_,                                                                                         // (M, K, L)
-  class TensorB_,                                                                                         // (N, K, L)
-  class TensorScaleA_,                                                                                    // (m, k, L)
-  class TensorScaleB_,                                                                                    // (n, k, L)
-  class TileShape_
->
-struct GettMainloopParams {
-  using ElementAccumulator = ElementAccumulator_;
-  using TensorA = TensorA_;
-  using TensorB = TensorB_;
-  using EngineA = typename TensorA::engine_type;
-  using LayoutA = typename TensorA::layout_type;
-  using EngineB = typename TensorB::engine_type;
-  using LayoutB = typename TensorB::layout_type;
-
-  using TensorScaleA = TensorScaleA_;
-  using TensorScaleB = TensorScaleB_;
-  using TileShape = TileShape_;
-  using EngineScaleA = typename TensorScaleA::engine_type;
-  using EngineScaleB = typename TensorScaleB::engine_type;
-
-  TensorA A{};
-  TensorB B{};
-  TensorScaleA ScaleA{};
-  TensorScaleB ScaleB{};  
-};
-
-/////////////////////////////////////////////////////////////////////////////////////////////////
-template<
-  class ElementScalar_,
-  class ElementScalingFactor_,
-  class ElementAccumulator_,
-  class ElementCompute_,
-  class TensorC_,                                                                                          // (M, N, L)
-  class TensorD_,                                                                                          // (M, N, L)
-  class VectorBias_ = TensorD_,                                                                            //    (M, 1)
-  class TensorAux_ = TensorD_,                                                                             // (M, N, L)
-  class VectorAlpha_ = TensorD_,                                                                           //    (M, 1)
-  class VectorBeta_ = VectorAlpha_,                                                                        //    (M, 1)
-  class ActivationFunctor_ = cutlass::epilogue::thread::Identity<ElementCompute_>,
-  class BiasBinaryOp_ = cutlass::plus<ElementCompute_>,
-  bool PerColumnBias_ = false
->
-struct GettEpilogueParams {
-  using ElementScalar = ElementScalar_;
-  using ElementScalingFactor = ElementScalingFactor_;
-  using ElementAccumulator = ElementAccumulator_;
-  using ElementCompute = ElementCompute_;
-  using TensorC = TensorC_;
-  using TensorD = TensorD_;
-  using TensorAux = TensorAux_;
-  using VectorBias = VectorBias_;
-  using VectorAlpha = VectorAlpha_;
-  using VectorBeta = VectorBeta_;
-  using ActivationFunctor = ActivationFunctor_;
-  using BiasBinaryOp = BiasBinaryOp_;
-
-  using EngineC = typename TensorC::engine_type;
-  using LayoutC = typename TensorC::layout_type;
-  using EngineD =  typename TensorD::engine_type;
-  using LayoutD = typename TensorD::layout_type;
-  static constexpr bool PerColumnBias = PerColumnBias_;
-  ElementScalar alpha = ElementScalar(1);
-  ElementScalar beta = ElementScalar(0);
-
-  TensorC C{};
-  TensorD D{};
-  VectorBias Bias{};
-  TensorAux Aux{};
-  VectorAlpha Valpha{};
-  VectorBeta Vbeta{};
-  ElementCompute st = ElementCompute(1);
-
-  ElementAccumulator* abs_max_D = nullptr;
-  ElementAccumulator* abs_max_Aux = nullptr;
-
-  ElementScalingFactor scale_a = ElementScalingFactor(1);
-  ElementScalingFactor scale_b = ElementScalingFactor(1);
-  ElementScalingFactor scale_c = ElementScalingFactor(1);
-  ElementScalingFactor scale_d = ElementScalingFactor(1);
-  ElementScalingFactor scale_aux = ElementScalingFactor(1);
-
-  bool beta_per_channel_scaling = false;
-};
-
-/////////////////////////////////////////////////////////////////////////////////////////////////
-
-/// GETT - General Tensor-Tensor contraction reference kernel with Blockwise scaling
-template <
-  class MainloopParams,
-  class EpilogueParams
->
-void Gett(
-    MainloopParams const& mainloop_params,
-    EpilogueParams const& epilogue_params)
-{
-
-  static int constexpr kBlockM = cute::get<0>(typename MainloopParams::TileShape{});
-  static int constexpr kBlockN = cute::get<1>(typename MainloopParams::TileShape{});
-  // printf("mainloop_params.ScaleA.layout()"); cute::print(mainloop_params.ScaleA.layout()); printf("\n");
-  // printf("mainloop_params.ScaleB.layout()"); cute::print(mainloop_params.ScaleB.layout()); printf("\n");
-
-#if defined(_OPENMP)
-  #pragma omp parallel for collapse(3)
-#endif
-  for (int64_t l = 0; l < cute::size<2>(mainloop_params.A.layout()); ++l) {
-    for (int64_t m = 0; m < cute::size<0>(mainloop_params.A.layout()); m += kBlockM) {
-      for (int64_t n = 0; n < cute::size<0>(mainloop_params.B.layout()); n += kBlockN) {
-        typename MainloopParams::ElementAccumulator acc[kBlockM][kBlockN];
-        gett_mainloop(mainloop_params, m, n, l, acc);
-        gett_epilogue(epilogue_params, m, n, l, acc);
-      }
-    }
-  }
-}
-
-/////////////////////////////////////////////////////////////////////////////////////////////////
-
-/// GETT - Mainloop
-template <class MainloopParams, class ElementAccumulator, int kBlockM, int kBlockN>
-void gett_mainloop(
-    MainloopParams const& mainloop_params,
-    int64_t m,
-    int64_t n,
-    int64_t l,
-    ElementAccumulator (&acc)[kBlockM][kBlockN])
-{
-
-  static_assert(cute::rank(typename MainloopParams::LayoutA{}) == 3, "M, K, B");
-  static_assert(cute::rank(typename MainloopParams::LayoutB{}) == 3, "N, K, B");
-
-  using cute::raw_pointer_cast;
-
-  using ElementA = typename ElementTraits<typename MainloopParams::EngineA::value_type>::type;
-  using ElementB = typename ElementTraits<typename MainloopParams::EngineB::value_type>::type;
-  using ElementBlockScaleA = typename ElementTraits<typename MainloopParams::EngineScaleA::value_type>::type;
-  using ElementBlockScaleB = typename ElementTraits<typename MainloopParams::EngineScaleB::value_type>::type;
-
-  using RingOp = multiply_add<ElementAccumulator, ElementAccumulator, ElementAccumulator>;
-  RingOp fma_op;
-
-  multiplies<ElementAccumulator> scale_op;
-
-  static int constexpr kBlockK = cute::get<2>(typename MainloopParams::TileShape{});;
-
-  // Tempo accumulators to seperate blockwise accumulation
-  typename MainloopParams::ElementAccumulator acc_temp[kBlockM][kBlockN];
-
-  // Zero out accumulators
-  for (int m_b = 0; m_b < kBlockM; ++m_b) {
-    for (int n_b = 0; n_b < kBlockN; ++n_b) {
-      acc[m_b][n_b] = ElementAccumulator(0); // RingOp::AdditionIdentity
-      acc_temp[m_b][n_b] = ElementAccumulator(0);
-    }
-  }
-
-  int64_t block_m = m / kBlockM;
-  int64_t block_n = n / kBlockN;
-  cute::Tensor blockscale_A = mainloop_params.ScaleA(block_m, _, l);
-  cute::Tensor blockscale_B = mainloop_params.ScaleB(block_n, _, l);
-
-  // Compute on this k-block
-  for (int64_t k = 0; k < cute::size<1>(mainloop_params.A.layout()); ++k) {
-
-    // Load Blockwise scaling factor from blockscale Tensors for A and B
-    int64_t block_k = k / kBlockK;
-    ElementBlockScaleA scale_a = blockscale_A[block_k];
-    ElementBlockScaleB scale_b = blockscale_B[block_k];
-
-    // Load A
-    ElementAccumulator a_frag[kBlockM];
-    for (int m_b = 0; m_b < kBlockM; ++m_b) {
-      if (m + m_b < cute::size<0>(mainloop_params.A.layout())) {
-        // Perform reference GEMM calculations at the accumulator's precision. Cast A value to accumulator type.
-        a_frag[m_b] = static_cast<ElementAccumulator>(ElementA(mainloop_params.A(m + m_b, k, l)));
-      } else {
-        a_frag[m_b] = ElementAccumulator(0); // RingOp::AdditionIdentity
-      }
-    }
-
-    // Load B
-    ElementAccumulator b_frag[kBlockN];
-    for (int n_b = 0; n_b < kBlockN; ++n_b) {
-      if (n + n_b < cute::size<0>(mainloop_params.B.layout())) {
-        // Perform reference GEMM calculations at the accumulator's precision. Cast A value to accumulator type.
-        b_frag[n_b] = static_cast<ElementAccumulator>(ElementB(mainloop_params.B(n + n_b, k, l)));
-      } else {
-        b_frag[n_b] = ElementAccumulator(0); // RingOp::AdditionIdentity
-      }
-    }
-
-    // do compute
-    for (int m_b = 0; m_b < kBlockM; ++m_b) {
-      for (int n_b = 0; n_b < kBlockN; ++n_b) {
-        acc_temp[m_b][n_b] = fma_op(a_frag[m_b], b_frag[n_b], acc_temp[m_b][n_b]);
-      }
-    }
-
-    // Apply Blockwise-scaling at kBlockK boundary
-    // (a) Apply block scaling factors on the partial accumulated results (acc_temp) at the kBlocK boundary 
-    // (b) Zero-out partial temporary (acc_temp),
-    // (c) Update permanent (accu)
-    if ((k+1) % kBlockK == 0) {
-      for (int m_b = 0; m_b < kBlockM; ++m_b) {
-        for (int n_b = 0; n_b < kBlockN; ++n_b) {
-          ElementAccumulator blockwise_scaled_accum = acc_temp[m_b][n_b] * scale_a * scale_b;
-          acc[m_b][n_b] = blockwise_scaled_accum + acc[m_b][n_b];
-          acc_temp[m_b][n_b] = ElementAccumulator(0); 
-        }
-      }
-    }
-
-  }
-}
-
-/////////////////////////////////////////////////////////////////////////////////////////////////
-
-/// GETT - Epilogue
-template <class EpilogueParams, class ElementAccumulator, int kBlockM, int kBlockN>
-void gett_epilogue(
-    EpilogueParams const& epilogue_params,
-    int64_t m,
-    int64_t n,
-    int64_t l,
-    ElementAccumulator (&acc)[kBlockM][kBlockN])
-{
-  static_assert(cute::rank(typename EpilogueParams::LayoutC{}) == 3, "M, K, B");
-  static_assert(cute::rank(typename EpilogueParams::LayoutD{}) == 3, "N, K, B");
-
-  using cute::raw_pointer_cast;
-
-  using ElementCompute = typename EpilogueParams::ElementCompute;
-  using ElementC = typename EpilogueParams::TensorC::value_type;
-  using ElementD = typename EpilogueParams::TensorD::value_type;
-  using ElementAux = typename EpilogueParams::TensorAux::value_type;
-  using ElementBias = typename EpilogueParams::VectorBias::value_type;
-  using ElementScalar = typename EpilogueParams::ElementScalar;
-  using ElementScalingFactor = typename EpilogueParams::ElementScalingFactor;
-  using ActivationFunctor = typename EpilogueParams::ActivationFunctor;
-  using BiasBinaryOp = typename EpilogueParams::BiasBinaryOp;
-
-  constexpr bool PerColBias = EpilogueParams::PerColumnBias;
-  constexpr bool IsScalingAndAmaxOutputNeeded = 
-      cute::is_same_v<ElementD, cutlass::float_e4m3_t> or
-      cute::is_same_v<ElementD, cutlass::float_e5m2_t>;
-
-  constexpr bool IsScalingAndAmaxAuxOutputNeeded =
-      cute::is_same_v<ElementAux, cutlass::float_e4m3_t> or
-      cute::is_same_v<ElementAux, cutlass::float_e5m2_t>;
-
-  constexpr bool IsReLUAuxNeeded =
-      (cute::is_same_v<ActivationFunctor, cutlass::epilogue::thread::ReLu<ElementCompute>> or
-       cute::is_same_v<ActivationFunctor, cutlass::epilogue::thread::Clamp<ElementCompute>>) and 
-      cute::is_same_v<ElementAux, cutlass::uint1b_t>;
-  constexpr bool IsClamp =
-      cute::is_same_v<ActivationFunctor, cutlass::epilogue::thread::Clamp<ElementCompute>>;
-
-  constexpr bool IsBackpropFusion =
-      cute::is_same_v<ActivationFunctor, cutlass::epilogue::thread::dGELU<ElementCompute>> or
-      cute::is_same_v<ActivationFunctor, cutlass::epilogue::thread::dReLU<ElementCompute>>;
-
-  // Input related converter
-  NumericConverter<ElementCompute, ElementAccumulator> accumulator_converter;
-  NumericConverter<ElementCompute, ElementC> source_converter;
-  NumericConverter<ElementCompute, ElementBias> bias_converter;
-  [[maybe_unused]] NumericConverter<ElementCompute, ElementAux> aux_source_converter;
-
-  // Scale related converter
-  NumericConverter<ElementCompute, ElementScalar> scale_converter;
-  NumericConverter<ElementCompute, ElementScalingFactor> scaling_factor_converter;
-
-  // Abs max converter
-  [[maybe_unused]] NumericConverter<ElementAccumulator, ElementCompute> abs_max_output_converter;
-
-  // Output related converter
-  NumericConverter<ElementD, ElementCompute> destination_converter;
-  [[maybe_unused]] NumericConverter<ElementAux, ElementCompute> aux_destination_converter;
-  NumericConverter<ElementBias, ElementCompute> dBias_converter;
-
-  // Epilogue operations
-  multiply_add<ElementCompute, ElementCompute, ElementCompute> epilogue_fma;
-  multiplies<ElementCompute> mul;
-  plus<ElementCompute> add;
-
-  // Activation operation
-  ActivationFunctor activation;
-
-  // Bias binary operation
-  BiasBinaryOp bias_op;
-
-  // Do conversion
-  ElementCompute converted_alpha = scale_converter(epilogue_params.alpha);
-  ElementCompute converted_beta = scale_converter(epilogue_params.beta);
-  ElementCompute converted_scale_a = scaling_factor_converter(epilogue_params.scale_a);
-  ElementCompute converted_scale_b = scaling_factor_converter(epilogue_params.scale_b);
-  ElementCompute converted_scale_c = scaling_factor_converter(epilogue_params.scale_c);
-  ElementCompute converted_scale_d = scaling_factor_converter(epilogue_params.scale_d);
-  ElementCompute converted_scale_aux = scaling_factor_converter(epilogue_params.scale_aux);
-
-  // Init local var
-  [[maybe_unused]] ElementCompute local_abs_max_output = ElementCompute(0);
-  [[maybe_unused]] ElementCompute local_abs_max_aux_output = ElementCompute(0);
-
-  converted_alpha = mul(converted_alpha, mul(converted_scale_a, converted_scale_b));
-  converted_beta = mul(converted_beta, converted_scale_c);
-
-  ElementCompute inter_accum[kBlockM][kBlockN];
-
-  for (int m_b = 0; m_b < kBlockM; ++m_b) {
-    ElementCompute local_dBias = ElementCompute(0);
-
-    for (int n_b = 0; n_b < kBlockN; ++n_b) {
-      if (m + m_b < cute::size<0>(epilogue_params.D.layout()) && n + n_b < cute::size<1>(epilogue_params.D.layout())) {
-        // Convert every type to ElementCompute first, do compute, convert to output type, write it out
-        ElementCompute converted_acc = accumulator_converter(acc[m_b][n_b]);
-        // per-row alpha
-        if (raw_pointer_cast(epilogue_params.Valpha.data())) {
-          converted_alpha = scale_converter(epilogue_params.Valpha(m + m_b));
-        }
-        ElementCompute output = mul(converted_alpha, converted_acc);
-
-        if (raw_pointer_cast(epilogue_params.Bias.data()) && not IsBackpropFusion) {
-          ElementCompute converted_bias = bias_converter(epilogue_params.Bias(PerColBias ? n + n_b : m + m_b));
-          output = bias_op(output, converted_bias);
-        }
-
-        if (raw_pointer_cast(epilogue_params.C.data())) {
-          ElementCompute converted_src = source_converter(epilogue_params.C(m + m_b, n + n_b, l));
-          // per-row beta
-          if (epilogue_params.Vbeta.data()) {
-            converted_beta = scale_converter(epilogue_params.Vbeta(m + m_b));
-          }
-          output = epilogue_fma(converted_beta, converted_src, output);
-        }
-
-        if constexpr (IsBackpropFusion) {
-          ElementAux aux_input = ElementAux(0);
-          if (raw_pointer_cast(epilogue_params.Aux.data())) {
-            aux_input = epilogue_params.Aux(m + m_b, n + n_b, l);
-          }
-
-          output = activation(output, aux_source_converter(aux_input));
-          local_dBias = add(local_dBias, output);
-        }
-        else {
-          if (raw_pointer_cast(epilogue_params.Aux.data())) {
-            auto aux_output = output;
-            if constexpr (IsScalingAndAmaxAuxOutputNeeded) {
-              maximum_absolute_value_reduction<ElementCompute, true> amax_op;
-              local_abs_max_aux_output = amax_op(local_abs_max_aux_output, aux_output);
-              aux_output = epilogue_fma(converted_scale_aux, aux_output, ElementCompute(0));
-            }
-
-            if constexpr (IsReLUAuxNeeded) {
-              epilogue_params.Aux(m + m_b, n + n_b, l) = not (aux_output < 0) ? uint1b_t(1) : uint1b_t(0);
-            } else {
-              epilogue_params.Aux(m + m_b, n + n_b, l) = aux_destination_converter(aux_output);
-            }
-          }
-
-          if constexpr (IsClamp) { // Treat Clamp as ReLU
-            output = activation(output, {0, std::numeric_limits<ElementCompute>::max()});
-          }
-          else {
-            output = activation(output);
-          }
-        }
-
-        if constexpr (IsScalingAndAmaxOutputNeeded) {
-          maximum_absolute_value_reduction<ElementCompute, true> amax_op;
-          local_abs_max_output = amax_op(local_abs_max_output, output);
-          output = epilogue_fma(converted_scale_d, output, ElementCompute(0));
-        }
-
-        inter_accum[m_b][n_b] = ElementCompute(output);
-      }
-    } // n_b
-
-    if (m + m_b < cute::size<0>(epilogue_params.D.layout()) && n < cute::size<1>(epilogue_params.D.layout())) {
-      if (raw_pointer_cast(epilogue_params.Bias.data()) && IsBackpropFusion) {
-        ElementCompute converted_dBias = bias_converter(epilogue_params.Bias(m + m_b));
-        local_dBias = add(local_dBias, converted_dBias);
-        epilogue_params.Bias(m + m_b) = dBias_converter(local_dBias);
-      }
-    }
-  } // m_b
-  for (int m_b = 0; m_b < kBlockM; ++m_b) {
-    for (int n_b = 0; n_b < kBlockN; ++n_b) {
-      if (m + m_b < cute::size<0>(epilogue_params.D.layout()) && n + n_b < cute::size<1>(epilogue_params.D.layout())) {
-        epilogue_params.D(m + m_b, n + n_b, l) = destination_converter(inter_accum[m_b][n_b]);
-      }
-    }
-  }
-
-#if defined(_OPENMP)
-  #pragma omp critical(Abs_Max_Data_Update)
-#endif
-  {
-    if constexpr (IsScalingAndAmaxOutputNeeded) {
-      if (epilogue_params.abs_max_D) {
-        *epilogue_params.abs_max_D = maximum_with_nan_propogation<ElementAccumulator>{}(
-          *epilogue_params.abs_max_D, abs_max_output_converter(local_abs_max_output));
-      }
-    }
-
-    if constexpr (IsScalingAndAmaxAuxOutputNeeded) {
-      if (epilogue_params.abs_max_Aux) {
-        *epilogue_params.abs_max_Aux = maximum_with_nan_propogation<ElementAccumulator>{}(
-            *epilogue_params.abs_max_Aux, abs_max_output_converter(local_abs_max_aux_output));
-      }
-    }
-  }
-}
-
-/////////////////////////////////////////////////////////////////////////////////////////////////
-
-/// GEMM - General Matrix-Matrix contraction without conjugation options
-template <
-  class MainloopParams,
-  class EpilogueParams
->
-void Gemm3x(
-    MainloopParams const& mainloop_params,
-    EpilogueParams const& epilogue_params)
-{
-  using namespace cute;
-
-  static_assert(cute::rank(typename MainloopParams::LayoutA{}) == cute::rank(typename MainloopParams::LayoutB{}));
-  static_assert(cute::rank(typename EpilogueParams::LayoutC{}) == cute::rank(typename EpilogueParams::LayoutD{}));
-  static_assert(cute::rank(typename MainloopParams::LayoutA{}) == cute::rank(typename EpilogueParams::LayoutC{}));
-  static_assert(cute::rank(typename MainloopParams::LayoutA{}) == 3, "Only Rank3 Tensors (M, K, Batch_Count) "
-                                                                     "with Batchmode are supported");
-  // Lower the Matrix-Multiplication with Blockwise scaling (Gemm3x) to a Tensor Contraction (Gett).
-  Gett(mainloop_params, epilogue_params);
-}
-
-/////////////////////////////////////////////////////////////////////////////////////////////////
-
-} // cutlass::reference::host
-
-/////////////////////////////////////////////////////////////////////////////////////////////////

examples/67_hopper_fp8_warp_specialized_gemm_with_blockwise_scaling/reference/host/gemm_with_groupwise_scaling.h

@@ -1,511 +0,0 @@
-/***************************************************************************************************
- * Copyright (c) 2023 - 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
- * SPDX-License-Identifier: BSD-3-Clause
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *
- * 1. Redistributions of source code must retain the above copyright notice, this
- * list of conditions and the following disclaimer.
- *
- * 2. Redistributions in binary form must reproduce the above copyright notice,
- * this list of conditions and the following disclaimer in the documentation
- * and/or other materials provided with the distribution.
- *
- * 3. Neither the name of the copyright holder nor the names of its
- * contributors may be used to endorse or promote products derived from
- * this software without specific prior written permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
- * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
- * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
- * FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
- * DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
- * SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
- * CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
- * OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
- * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- **************************************************************************************************/
-/*! \file
-    \brief Reference implementation for GETT in host-side code.
-*/
-
-#pragma once
-
-/////////////////////////////////////////////////////////////////////////////////////////////////
-#include "cutlass/gemm/gemm.h"
-#include "cutlass/complex.h"
-#include "cutlass/numeric_conversion.h"
-#include "cutlass/epilogue/thread/activation.h"
-#include "cutlass/relatively_equal.h"
-#include <iostream>
-#include "cute/tensor.hpp"
-
-/////////////////////////////////////////////////////////////////////////////////////////////////
-
-namespace cutlass::reference::host {
-
-template<class T, class = void>
-struct ElementTraits {
-  using type = T;
-};
-
-template<class T>
-struct ElementTraits<T, std::enable_if_t<!std::is_same_v<decltype(std::declval<T>().get()), void> > >  {
-  using type = decltype(std::declval<T>().get());
-};
-
-/////////////////////////////////////////////////////////////////////////////////////////////////
-
-template<
-  class ElementAccumulator_,
-  class TensorA_,                                                                                         // (M, K, L)
-  class TensorB_,                                                                                         // (N, K, L)
-  class TensorScaleA_,                                                                                    // (m, k, L)
-  class TensorScaleB_,                                                                                    // (n, k, L)
-  class TileShape_
->
-struct GettMainloopParams {
-  using ElementAccumulator = ElementAccumulator_;
-  using TensorA = TensorA_;
-  using TensorB = TensorB_;
-  using EngineA = typename TensorA::engine_type;
-  using LayoutA = typename TensorA::layout_type;
-  using EngineB = typename TensorB::engine_type;
-  using LayoutB = typename TensorB::layout_type;
-
-  using TensorScaleA = TensorScaleA_;
-  using TensorScaleB = TensorScaleB_;
-  using TileShape = TileShape_;
-  using EngineScaleA = typename TensorScaleA::engine_type;
-  using EngineScaleB = typename TensorScaleB::engine_type;
-
-  TensorA A{};
-  TensorB B{};
-  TensorScaleA ScaleA{};
-  TensorScaleB ScaleB{};  
-};
-
-/////////////////////////////////////////////////////////////////////////////////////////////////
-template<
-  class ElementScalar_,
-  class ElementScalingFactor_,
-  class ElementAccumulator_,
-  class ElementCompute_,
-  class TensorC_,                                                                                          // (M, N, L)
-  class TensorD_,                                                                                          // (M, N, L)
-  class VectorBias_ = TensorD_,                                                                            //    (M, 1)
-  class TensorAux_ = TensorD_,                                                                             // (M, N, L)
-  class VectorAlpha_ = TensorD_,                                                                           //    (M, 1)
-  class VectorBeta_ = VectorAlpha_,                                                                        //    (M, 1)
-  class ActivationFunctor_ = cutlass::epilogue::thread::Identity<ElementCompute_>,
-  class BiasBinaryOp_ = cutlass::plus<ElementCompute_>,
-  bool PerColumnBias_ = false
->
-struct GettEpilogueParams {
-  using ElementScalar = ElementScalar_;
-  using ElementScalingFactor = ElementScalingFactor_;
-  using ElementAccumulator = ElementAccumulator_;
-  using ElementCompute = ElementCompute_;
-  using TensorC = TensorC_;
-  using TensorD = TensorD_;
-  using TensorAux = TensorAux_;
-  using VectorBias = VectorBias_;
-  using VectorAlpha = VectorAlpha_;
-  using VectorBeta = VectorBeta_;
-  using ActivationFunctor = ActivationFunctor_;
-  using BiasBinaryOp = BiasBinaryOp_;
-
-  using EngineC = typename TensorC::engine_type;
-  using LayoutC = typename TensorC::layout_type;
-  using EngineD =  typename TensorD::engine_type;
-  using LayoutD = typename TensorD::layout_type;
-  static constexpr bool PerColumnBias = PerColumnBias_;
-  ElementScalar alpha = ElementScalar(1);
-  ElementScalar beta = ElementScalar(0);
-
-  TensorC C{};
-  TensorD D{};
-  VectorBias Bias{};
-  TensorAux Aux{};
-  VectorAlpha Valpha{};
-  VectorBeta Vbeta{};
-  ElementCompute st = ElementCompute(1);
-
-  ElementAccumulator* abs_max_D = nullptr;
-  ElementAccumulator* abs_max_Aux = nullptr;
-
-  ElementScalingFactor scale_a = ElementScalingFactor(1);
-  ElementScalingFactor scale_b = ElementScalingFactor(1);
-  ElementScalingFactor scale_c = ElementScalingFactor(1);
-  ElementScalingFactor scale_d = ElementScalingFactor(1);
-  ElementScalingFactor scale_aux = ElementScalingFactor(1);
-
-  bool beta_per_channel_scaling = false;
-};
-
-/////////////////////////////////////////////////////////////////////////////////////////////////
-
-/// GETT - General Tensor-Tensor contraction reference kernel with Groupwise scaling
-template <
-  class MainloopParams,
-  class EpilogueParams
->
-void Gett(
-    MainloopParams const& mainloop_params,
-    EpilogueParams const& epilogue_params)
-{
-
-  static int constexpr kBlockM = cute::get<0>(typename MainloopParams::TileShape{});
-  static int constexpr kBlockN = cute::get<1>(typename MainloopParams::TileShape{});
-  // printf("mainloop_params.ScaleA.layout()"); cute::print(mainloop_params.ScaleA.layout()); printf("\n");
-  // printf("mainloop_params.ScaleB.layout()"); cute::print(mainloop_params.ScaleB.layout()); printf("\n");
-
-#if defined(_OPENMP)
-  #pragma omp parallel for collapse(3)
-#endif
-  for (int64_t l = 0; l < cute::size<2>(mainloop_params.A.layout()); ++l) {
-    for (int64_t m = 0; m < cute::size<0>(mainloop_params.A.layout()); m += kBlockM) {
-      for (int64_t n = 0; n < cute::size<0>(mainloop_params.B.layout()); n += kBlockN) {
-        typename MainloopParams::ElementAccumulator acc[kBlockM][kBlockN];
-        gett_mainloop(mainloop_params, m, n, l, acc);
-        gett_epilogue(epilogue_params, m, n, l, acc);
-      }
-    }
-  }
-}
-
-/////////////////////////////////////////////////////////////////////////////////////////////////
-
-/// GETT - Mainloop
-template <class MainloopParams, class ElementAccumulator, int kBlockM, int kBlockN>
-void gett_mainloop(
-    MainloopParams const& mainloop_params,
-    int64_t m,
-    int64_t n,
-    int64_t l,
-    ElementAccumulator (&acc)[kBlockM][kBlockN])
-{
-
-  static_assert(cute::rank(typename MainloopParams::LayoutA{}) == 3, "M, K, B");
-  static_assert(cute::rank(typename MainloopParams::LayoutB{}) == 3, "N, K, B");
-
-  using cute::raw_pointer_cast;
-
-  using ElementA = typename ElementTraits<typename MainloopParams::EngineA::value_type>::type;
-  using ElementB = typename ElementTraits<typename MainloopParams::EngineB::value_type>::type;
-  using ElementBlockScaleA = typename ElementTraits<typename MainloopParams::EngineScaleA::value_type>::type;
-  using ElementBlockScaleB = typename ElementTraits<typename MainloopParams::EngineScaleB::value_type>::type;
-
-  using RingOp = multiply_add<ElementAccumulator, ElementAccumulator, ElementAccumulator>;
-  RingOp fma_op;
-
-  multiplies<ElementAccumulator> scale_op;
-
-  static int constexpr kBlockK = cute::get<2>(typename MainloopParams::TileShape{});;
-
-  // Tempo accumulators to seperate blockwise accumulation
-  typename MainloopParams::ElementAccumulator acc_temp[kBlockM][kBlockN];
-
-  // Zero out accumulators
-  for (int m_b = 0; m_b < kBlockM; ++m_b) {
-    for (int n_b = 0; n_b < kBlockN; ++n_b) {
-      acc[m_b][n_b] = ElementAccumulator(0); // RingOp::AdditionIdentity
-      acc_temp[m_b][n_b] = ElementAccumulator(0);
-    }
-  }
-
-  int64_t block_m = m / kBlockM;
-  int64_t block_n = n / kBlockN;
-  cute::Tensor blockscale_A = mainloop_params.ScaleA(block_m, _, _, l);
-  cute::Tensor blockscale_B = mainloop_params.ScaleB(block_n, _, _, l);
-
-  const int ScaleGranularityM = cute::size<0>(typename MainloopParams::TileShape{}) / cute::size<1>(mainloop_params.ScaleA.shape());
-  const int ScaleGranularityN = cute::size<1>(typename MainloopParams::TileShape{}) / cute::size<1>(mainloop_params.ScaleB.shape());
-  assert(cute::size<0>(typename MainloopParams::TileShape{}) == ScaleGranularityM * cute::size<1>(mainloop_params.ScaleA.shape()));
-  assert(cute::size<1>(typename MainloopParams::TileShape{}) == ScaleGranularityN * cute::size<1>(mainloop_params.ScaleB.shape()));
-
-  // Compute on this k-block
-  for (int64_t k = 0; k < cute::size<1>(mainloop_params.A.layout()); ++k) {
-
-    // Load Blockwise scaling factor from blockscale Tensors for B
-    int64_t block_k = k / kBlockK;
-    cute::Tensor scale_a = blockscale_A(_, block_k);
-    cute::Tensor scale_b = blockscale_B(_, block_k);
-
-    // Load A
-    ElementAccumulator a_frag[kBlockM];
-    for (int m_b = 0; m_b < kBlockM; ++m_b) {
-      if (m + m_b < cute::size<0>(mainloop_params.A.layout())) {
-        // Perform reference GEMM calculations at the accumulator's precision. Cast A value to accumulator type.
-        a_frag[m_b] = static_cast<ElementAccumulator>(ElementA(mainloop_params.A(m + m_b, k, l)));
-      } else {
-        a_frag[m_b] = ElementAccumulator(0); // RingOp::AdditionIdentity
-      }
-    }
-
-    // Load B
-    ElementAccumulator b_frag[kBlockN];
-    for (int n_b = 0; n_b < kBlockN; ++n_b) {
-      if (n + n_b < cute::size<0>(mainloop_params.B.layout())) {
-        // Perform reference GEMM calculations at the accumulator's precision. Cast A value to accumulator type.
-        b_frag[n_b] = static_cast<ElementAccumulator>(ElementB(mainloop_params.B(n + n_b, k, l)));
-      } else {
-        b_frag[n_b] = ElementAccumulator(0); // RingOp::AdditionIdentity
-      }
-    }
-
-    // do compute
-    for (int m_b = 0; m_b < kBlockM; ++m_b) {
-      for (int n_b = 0; n_b < kBlockN; ++n_b) {
-        acc_temp[m_b][n_b] = fma_op(a_frag[m_b], b_frag[n_b], acc_temp[m_b][n_b]);
-      }
-    }
-
-    // Apply Groupwise-scaling at kBlockK boundary
-    // (a) Apply group and block scaling factors on the partial accumulated results (acc_temp) at the kBlocK boundary 
-    // (b) Zero-out partial temporary (acc_temp),
-    // (c) Update permanent (accu)
-    if ((k+1) % kBlockK == 0) {
-      for (int m_b = 0; m_b < kBlockM; ++m_b) {
-        auto scale_a_m_b = scale_a[m_b / ScaleGranularityM];
-        for (int n_b = 0; n_b < kBlockN; ++n_b) {
-          auto scale_b_n_b = scale_b[n_b / ScaleGranularityN];
-          ElementAccumulator blockwise_scaled_accum = acc_temp[m_b][n_b] * scale_a_m_b * scale_b_n_b;
-          acc[m_b][n_b] = blockwise_scaled_accum + acc[m_b][n_b];
-          acc_temp[m_b][n_b] = ElementAccumulator(0); 
-        }
-      }
-    }
-
-  }
-}
-
-/////////////////////////////////////////////////////////////////////////////////////////////////
-
-/// GETT - Epilogue
-template <class EpilogueParams, class ElementAccumulator, int kBlockM, int kBlockN>
-void gett_epilogue(
-    EpilogueParams const& epilogue_params,
-    int64_t m,
-    int64_t n,
-    int64_t l,
-    ElementAccumulator (&acc)[kBlockM][kBlockN])
-{
-  static_assert(cute::rank(typename EpilogueParams::LayoutC{}) == 3, "M, K, B");
-  static_assert(cute::rank(typename EpilogueParams::LayoutD{}) == 3, "N, K, B");
-
-  using cute::raw_pointer_cast;
-
-  using ElementCompute = typename EpilogueParams::ElementCompute;
-  using ElementC = typename EpilogueParams::TensorC::value_type;
-  using ElementD = typename EpilogueParams::TensorD::value_type;
-  using ElementAux = typename EpilogueParams::TensorAux::value_type;
-  using ElementBias = typename EpilogueParams::VectorBias::value_type;
-  using ElementScalar = typename EpilogueParams::ElementScalar;
-  using ElementScalingFactor = typename EpilogueParams::ElementScalingFactor;
-  using ActivationFunctor = typename EpilogueParams::ActivationFunctor;
-  using BiasBinaryOp = typename EpilogueParams::BiasBinaryOp;
-
-  constexpr bool PerColBias = EpilogueParams::PerColumnBias;
-  constexpr bool IsScalingAndAmaxOutputNeeded = 
-      cute::is_same_v<ElementD, cutlass::float_e4m3_t> or
-      cute::is_same_v<ElementD, cutlass::float_e5m2_t>;
-
-  constexpr bool IsScalingAndAmaxAuxOutputNeeded =
-      cute::is_same_v<ElementAux, cutlass::float_e4m3_t> or
-      cute::is_same_v<ElementAux, cutlass::float_e5m2_t>;
-
-  constexpr bool IsReLUAuxNeeded =
-      (cute::is_same_v<ActivationFunctor, cutlass::epilogue::thread::ReLu<ElementCompute>> or
-       cute::is_same_v<ActivationFunctor, cutlass::epilogue::thread::Clamp<ElementCompute>>) and 
-      cute::is_same_v<ElementAux, cutlass::uint1b_t>;
-  constexpr bool IsClamp =
-      cute::is_same_v<ActivationFunctor, cutlass::epilogue::thread::Clamp<ElementCompute>>;
-
-  constexpr bool IsBackpropFusion =
-      cute::is_same_v<ActivationFunctor, cutlass::epilogue::thread::dGELU<ElementCompute>> or
-      cute::is_same_v<ActivationFunctor, cutlass::epilogue::thread::dReLU<ElementCompute>>;
-
-  // Input related converter
-  NumericConverter<ElementCompute, ElementAccumulator> accumulator_converter;
-  NumericConverter<ElementCompute, ElementC> source_converter;
-  NumericConverter<ElementCompute, ElementBias> bias_converter;
-  [[maybe_unused]] NumericConverter<ElementCompute, ElementAux> aux_source_converter;
-
-  // Scale related converter
-  NumericConverter<ElementCompute, ElementScalar> scale_converter;
-  NumericConverter<ElementCompute, ElementScalingFactor> scaling_factor_converter;
-
-  // Abs max converter
-  [[maybe_unused]] NumericConverter<ElementAccumulator, ElementCompute> abs_max_output_converter;
-
-  // Output related converter
-  NumericConverter<ElementD, ElementCompute> destination_converter;
-  [[maybe_unused]] NumericConverter<ElementAux, ElementCompute> aux_destination_converter;
-  NumericConverter<ElementBias, ElementCompute> dBias_converter;
-
-  // Epilogue operations
-  multiply_add<ElementCompute, ElementCompute, ElementCompute> epilogue_fma;
-  multiplies<ElementCompute> mul;
-  plus<ElementCompute> add;
-
-  // Activation operation
-  ActivationFunctor activation;
-
-  // Bias binary operation
-  BiasBinaryOp bias_op;
-
-  // Do conversion
-  ElementCompute converted_alpha = scale_converter(epilogue_params.alpha);
-  ElementCompute converted_beta = scale_converter(epilogue_params.beta);
-  ElementCompute converted_scale_a = scaling_factor_converter(epilogue_params.scale_a);
-  ElementCompute converted_scale_b = scaling_factor_converter(epilogue_params.scale_b);
-  ElementCompute converted_scale_c = scaling_factor_converter(epilogue_params.scale_c);
-  ElementCompute converted_scale_d = scaling_factor_converter(epilogue_params.scale_d);
-  ElementCompute converted_scale_aux = scaling_factor_converter(epilogue_params.scale_aux);
-
-  // Init local var
-  [[maybe_unused]] ElementCompute local_abs_max_output = ElementCompute(0);
-  [[maybe_unused]] ElementCompute local_abs_max_aux_output = ElementCompute(0);
-
-  converted_alpha = mul(converted_alpha, mul(converted_scale_a, converted_scale_b));
-  converted_beta = mul(converted_beta, converted_scale_c);
-
-  ElementCompute inter_accum[kBlockM][kBlockN];
-
-  for (int m_b = 0; m_b < kBlockM; ++m_b) {
-    ElementCompute local_dBias = ElementCompute(0);
-
-    for (int n_b = 0; n_b < kBlockN; ++n_b) {
-      if (m + m_b < cute::size<0>(epilogue_params.D.layout()) && n + n_b < cute::size<1>(epilogue_params.D.layout())) {
-        // Convert every type to ElementCompute first, do compute, convert to output type, write it out
-        ElementCompute converted_acc = accumulator_converter(acc[m_b][n_b]);
-        // per-row alpha
-        if (raw_pointer_cast(epilogue_params.Valpha.data())) {
-          converted_alpha = scale_converter(epilogue_params.Valpha(m + m_b));
-        }
-        ElementCompute output = mul(converted_alpha, converted_acc);
-
-        if (raw_pointer_cast(epilogue_params.Bias.data()) && not IsBackpropFusion) {
-          ElementCompute converted_bias = bias_converter(epilogue_params.Bias(PerColBias ? n + n_b : m + m_b));
-          output = bias_op(output, converted_bias);
-        }
-
-        if (raw_pointer_cast(epilogue_params.C.data())) {
-          ElementCompute converted_src = source_converter(epilogue_params.C(m + m_b, n + n_b, l));
-          // per-row beta
-          if (epilogue_params.Vbeta.data()) {
-            converted_beta = scale_converter(epilogue_params.Vbeta(m + m_b));
-          }
-          output = epilogue_fma(converted_beta, converted_src, output);
-        }
-
-        if constexpr (IsBackpropFusion) {
-          ElementAux aux_input = ElementAux(0);
-          if (raw_pointer_cast(epilogue_params.Aux.data())) {
-            aux_input = epilogue_params.Aux(m + m_b, n + n_b, l);
-          }
-
-          output = activation(output, aux_source_converter(aux_input));
-          local_dBias = add(local_dBias, output);
-        }
-        else {
-          if (raw_pointer_cast(epilogue_params.Aux.data())) {
-            auto aux_output = output;
-            if constexpr (IsScalingAndAmaxAuxOutputNeeded) {
-              maximum_absolute_value_reduction<ElementCompute, true> amax_op;
-              local_abs_max_aux_output = amax_op(local_abs_max_aux_output, aux_output);
-              aux_output = epilogue_fma(converted_scale_aux, aux_output, ElementCompute(0));
-            }
-
-            if constexpr (IsReLUAuxNeeded) {
-              epilogue_params.Aux(m + m_b, n + n_b, l) = not (aux_output < 0) ? uint1b_t(1) : uint1b_t(0);
-            } else {
-              epilogue_params.Aux(m + m_b, n + n_b, l) = aux_destination_converter(aux_output);
-            }
-          }
-
-          if constexpr (IsClamp) { // Treat Clamp as ReLU
-            output = activation(output, {0, std::numeric_limits<ElementCompute>::max()});
-          }
-          else {
-            output = activation(output);
-          }
-        }
-
-        if constexpr (IsScalingAndAmaxOutputNeeded) {
-          maximum_absolute_value_reduction<ElementCompute, true> amax_op;
-          local_abs_max_output = amax_op(local_abs_max_output, output);
-          output = epilogue_fma(converted_scale_d, output, ElementCompute(0));
-        }
-
-        inter_accum[m_b][n_b] = ElementCompute(output);
-      }
-    } // n_b
-
-    if (m + m_b < cute::size<0>(epilogue_params.D.layout()) && n < cute::size<1>(epilogue_params.D.layout())) {
-      if (raw_pointer_cast(epilogue_params.Bias.data()) && IsBackpropFusion) {
-        ElementCompute converted_dBias = bias_converter(epilogue_params.Bias(m + m_b));
-        local_dBias = add(local_dBias, converted_dBias);
-        epilogue_params.Bias(m + m_b) = dBias_converter(local_dBias);
-      }
-    }
-  } // m_b
-  for (int m_b = 0; m_b < kBlockM; ++m_b) {
-    for (int n_b = 0; n_b < kBlockN; ++n_b) {
-      if (m + m_b < cute::size<0>(epilogue_params.D.layout()) && n + n_b < cute::size<1>(epilogue_params.D.layout())) {
-        epilogue_params.D(m + m_b, n + n_b, l) = destination_converter(inter_accum[m_b][n_b]);
-      }
-    }
-  }
-
-#if defined(_OPENMP)
-  #pragma omp critical(Abs_Max_Data_Update)
-#endif
-  {
-    if constexpr (IsScalingAndAmaxOutputNeeded) {
-      if (epilogue_params.abs_max_D) {
-        *epilogue_params.abs_max_D = maximum_with_nan_propogation<ElementAccumulator>{}(
-          *epilogue_params.abs_max_D, abs_max_output_converter(local_abs_max_output));
-      }
-    }
-
-    if constexpr (IsScalingAndAmaxAuxOutputNeeded) {
-      if (epilogue_params.abs_max_Aux) {
-        *epilogue_params.abs_max_Aux = maximum_with_nan_propogation<ElementAccumulator>{}(
-            *epilogue_params.abs_max_Aux, abs_max_output_converter(local_abs_max_aux_output));
-      }
-    }
-  }
-}
-
-/////////////////////////////////////////////////////////////////////////////////////////////////
-
-/// GEMM - General Matrix-Matrix contraction without conjugation options
-template <
-  class MainloopParams,
-  class EpilogueParams
->
-void Gemm3x(
-    MainloopParams const& mainloop_params,
-    EpilogueParams const& epilogue_params)
-{
-  using namespace cute;
-
-  static_assert(cute::rank(typename MainloopParams::LayoutA{}) == cute::rank(typename MainloopParams::LayoutB{}));
-  static_assert(cute::rank(typename EpilogueParams::LayoutC{}) == cute::rank(typename EpilogueParams::LayoutD{}));
-  static_assert(cute::rank(typename MainloopParams::LayoutA{}) == cute::rank(typename EpilogueParams::LayoutC{}));
-  static_assert(cute::rank(typename MainloopParams::LayoutA{}) == 3, "Only Rank3 Tensors (M, K, Batch_Count) "
-                                                                     "with Batchmode are supported");
-  // Lower the Matrix-Multiplication with Groupwise scaling (Gemm3x) to a Tensor Contraction (Gett).
-  Gett(mainloop_params, epilogue_params);
-}
-
-/////////////////////////////////////////////////////////////////////////////////////////////////
-
-} // cutlass::reference::host
-
-/////////////////////////////////////////////////////////////////////////////////////////////////

examples/68_hopper_fp8_warp_specialized_grouped_gemm_with_blockwise_scaling/68_hopper_fp8_warp_specialized_grouped_gemm_with_blockwise_scaling.cu

@@ -0,0 +1,771 @@
+/***************************************************************************************************
+ * Copyright (c) 2023 - 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+ * SPDX-License-Identifier: BSD-3-Clause
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions are met:
+ *
+ * 1. Redistributions of source code must retain the above copyright notice, this
+ * list of conditions and the following disclaimer.
+ *
+ * 2. Redistributions in binary form must reproduce the above copyright notice,
+ * this list of conditions and the following disclaimer in the documentation
+ * and/or other materials provided with the distribution.
+ *
+ * 3. Neither the name of the copyright holder nor the names of its
+ * contributors may be used to endorse or promote products derived from
+ * this software without specific prior written permission.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+ * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+ * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+ * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+ * FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+ * DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+ * SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+ * CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+ * OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+ * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *
+ **************************************************************************************************/
+
+/*! \file
+    \brief Grouped scale Hopper FP8 Grouped GEMM example using CUTLASS 3.0 APIs for NVIDIA Hopper architecture
+    This example demonstrates a grouped scaled FP8 Grouped GEMM using the new CUTLASS 3.0.
+    APIs on NVIDIA Hopper architecture. New features that will be showcased in this example are as follows:
+    1. NVIDIA Hopper architecture introduces a new series of tensor core instructions (GMMA)
+    which are more efficient than the Ampere tensor core instructions.
+    2. NVIDIA Hopper architecture includes new Tensor Memory Accelerator (TMA) unit to transfer large
+    blocks of data efficiently between global memory and shared memory. TMA also supports asynchronous
+    copies between thread blocks in a cluster. This example also showcases on-the-fly modification of TMA
+    descriptors to move between groups/problem_count (represented by groups).
+    3. This example uses the Warp Specialized kernel design (see /media/docs/efficient_gemm.md for details).
+    4. A simple way to tune the CTA rasterization direction and swizzle pattern of Hopper kernels. Both the
+    CTA rasterization direction and swizzle pattern impact cross-CTA locality of accesses. By tuning we can
+    improve performance.
+    Examples:
+      $ ./examples/68_hopper_fp8_warp_specialized_grouped_gemm_with_blockwise_scaling/68_hopper_fp8_warp_specialized_grouped_gemm_with_blockwise_scaling  \
+        --m=2816 --n=3072 --k=16384 --save_aux=false --save_amax=false \
+        --raster=h --swizzle=2 --benchmark=./test_benchmark.txt
+
+      Where the test_benchmark.txt may look as such:
+        0 256x512x128
+        1 256x512x512
+        2 512x256x128
+        3 256x256x128
+        4 256x512x1024
+        5 1024x512x128 and so on
+*/
+
+#include <iostream>
+#include <optional>
+#include <fstream>
+#include <sstream>
+#include <vector>
+#include <cfloat>
+
+#include "cutlass/cutlass.h"
+#include "cutlass/numeric_types.h"
+
+#include "cute/tensor.hpp"
+#include "cutlass/tensor_ref.h"
+#include "cutlass/gemm/dispatch_policy.hpp"
+#include "cutlass/gemm/collective/collective_builder.hpp"
+#include "cutlass/gemm/device/gemm_universal_adapter.h"
+#include "cutlass/gemm/kernel/gemm_universal.hpp"
+#include "cutlass/gemm/kernel/tile_scheduler_params.h"
+#include "cutlass/epilogue/dispatch_policy.hpp"
+#include "cutlass/epilogue/collective/collective_builder.hpp"
+
+#include "cutlass/util/command_line.h"
+#include "cutlass/util/distribution.h"
+#include "cutlass/util/host_tensor.h"
+#include "cutlass/util/packed_stride.hpp"
+#include "cutlass/util/tensor_view_io.h"
+#include "cutlass/util/reference/host/tensor_fill.h"
+#include "cutlass/util/reference/host/tensor_copy.h"
+#include "cutlass/util/reference/host/tensor_compare.h"
+#include "cutlass/util/reference/host/tensor_norm.h"
+#include "cutlass/util/reference/device/tensor_fill.h"
+#include "cutlass/util/reference/host/gett.hpp"
+
+// Includes from examples directory
+#include "helper.h"
+#include "hopper_fp8_commandline.hpp"
+
+using namespace cute;
+
+using ProblemShape = cutlass::gemm::GroupProblemShape<Shape<int,int,int>>; // <M,N,K> per group
+
+#if defined(CUTLASS_ARCH_MMA_SM90_SUPPORTED) && defined(CUTLASS_ARCH_MMA_MODIFIABLE_TMA_SM90_SUPPORTED)
+
+/////////////////////////////////////////////////////////////////////////////////////////////////
+/// GEMM kernel configurations
+/////////////////////////////////////////////////////////////////////////////////////////////////
+
+// A matrix configuration
+using         ElementA    = cutlass::float_e4m3_t;                          // Element type for A matrix operand
+using         LayoutA     = cutlass::layout::RowMajor;                      // Layout type for A matrix operand
+constexpr int AlignmentA  = 128 / cutlass::sizeof_bits<ElementA>::value;    // Memory access granularity/alignment of A matrix in units of elements (up to 16 bytes)
+
+// B matrix configuration
+using         ElementB    = cutlass::float_e4m3_t;                          // Element type for B matrix operand
+using         LayoutB     = cutlass::layout::ColumnMajor;                   // Layout type for B matrix operand
+constexpr int AlignmentB  = 128 / cutlass::sizeof_bits<ElementB>::value;    // Memory access granularity/alignment of B matrix in units of elements (up to 16 bytes)
+
+// C matrix configuration
+using         ElementC    = cutlass::float_e4m3_t;                          // Element type for C and D matrix operands
+using         LayoutC     = cutlass::layout::ColumnMajor;                   // Layout type for C and D matrix operands
+constexpr int AlignmentC  = 128 / cutlass::sizeof_bits<ElementC>::value;    // Memory access granularity/alignment of C matrix in units of elements (up to 16 bytes)
+
+// D matrix configuration
+using         ElementD    = ElementC;
+using         LayoutD     = LayoutC;
+constexpr int AlignmentD  = AlignmentC;
+
+// Core kernel configurations
+using ElementAccumulator  = float;                                          // Element type for internal accumulation
+using ElementBlockScale   = float;                                          // Element type for blockscaling during accumulation
+using ElementCompute      = float;                                          // Element type for epilogue computation
+
+using ArchTag       = cutlass::arch::Sm90;                          // Tag indicating the minimum SM that supports the intended feature
+using OperatorClass = cutlass::arch::OpClassTensorOp;               // Operator class tag
+using TileShape     = Shape<_128,_128,_128>;                        // Threadblock-level tile size
+using ClusterShape  = Shape<_1,_2,_1>;                              // Shape of the threadblocks in a cluster
+
+constexpr int ScaleGranularityM = 1;
+constexpr int ScaleGranularityN = 128;
+constexpr int ScaleGranularityK = 128;
+
+constexpr int ScaleMsPerTile = size<0>(TileShape{}) / ScaleGranularityM;
+constexpr int ScaleNsPerTile = size<1>(TileShape{}) / ScaleGranularityN;
+
+using ScaleConfig   = cutlass::detail::Sm90BlockwiseScaleConfig<ScaleGranularityM, ScaleGranularityN, ScaleGranularityK>;
+
+using LayoutSFA     = decltype(ScaleConfig::deduce_layoutSFA());    // Layout type for SFA matrix operand
+using LayoutSFB     = decltype(ScaleConfig::deduce_layoutSFB());    // Layout type for SFB matrix operand
+
+using KernelSchedule    = cutlass::gemm::KernelPtrArrayTmaWarpSpecializedCooperativeFP8BlockScaledAccum;
+using EpilogueSchedule  = cutlass::epilogue::PtrArrayTmaWarpSpecializedCooperative;
+using EpilogueTileType  = cutlass::epilogue::collective::EpilogueTileAuto;
+using FusionOperation   = cutlass::epilogue::fusion::LinearCombination<ElementC, ElementAccumulator>;
+
+using CollectiveEpilogue = typename cutlass::epilogue::collective::CollectiveBuilder<
+    ArchTag, OperatorClass,
+    TileShape, ClusterShape,
+    EpilogueTileType,
+    ElementAccumulator, ElementCompute,
+    ElementC, LayoutC *, AlignmentC,
+    ElementD, LayoutD *, AlignmentD,
+    EpilogueSchedule,
+    FusionOperation
+  >::CollectiveOp;
+
+using CollectiveMainloopWithGroupWiseScaling = typename cutlass::gemm::collective::CollectiveBuilder<
+    ArchTag, OperatorClass,
+    ElementA, cute::tuple<LayoutA *, LayoutSFA *>, AlignmentA,
+    ElementB, cute::tuple<LayoutB *, LayoutSFB *>, AlignmentB,
+    ElementAccumulator,
+    TileShape, ClusterShape,
+    cutlass::gemm::collective::StageCountAutoCarveout<
+      static_cast<int>(sizeof(typename CollectiveEpilogue::SharedStorage))
+    >,
+    KernelSchedule
+  >::CollectiveOp;
+
+using GemmKernel = cutlass::gemm::kernel::GemmUniversal<
+    ProblemShape,
+    CollectiveMainloopWithGroupWiseScaling,
+    CollectiveEpilogue
+  >;
+
+using Gemm = cutlass::gemm::device::GemmUniversalAdapter<GemmKernel>;
+
+
+// Extract information from Gemm kernel.
+using EpilogueOutputOp  = typename Gemm::EpilogueOutputOp;
+using ElementScalar     = typename EpilogueOutputOp::ElementScalar;
+
+using StrideA = typename Gemm::GemmKernel::InternalStrideA;
+using StrideB = typename Gemm::GemmKernel::InternalStrideB;
+using StrideC = typename Gemm::GemmKernel::InternalStrideC;
+using StrideD = typename Gemm::GemmKernel::InternalStrideD;
+
+static_assert(cute::is_same_v<ElementAccumulator, ElementBlockScale>,
+             "ElementAccumulator and ElementBlockScale should be same datatype");
+
+/// Initialization
+
+cutlass::DeviceAllocation<typename ProblemShape::UnderlyingProblemShape> problem_sizes;
+
+std::vector<int64_t> offset_A;
+std::vector<int64_t> offset_B;
+std::vector<int64_t> offset_C;
+std::vector<int64_t> offset_D;
+std::vector<int64_t> offset_blockscale_A;
+std::vector<int64_t> offset_blockscale_B;
+
+std::vector<StrideA> stride_A_host;
+std::vector<StrideB> stride_B_host;
+std::vector<StrideC> stride_C_host;
+std::vector<StrideD> stride_D_host;
+std::vector<LayoutSFA> layout_SFA_host;
+std::vector<LayoutSFB> layout_SFB_host;
+
+std::vector<ElementAccumulator> alpha_host;
+std::vector<ElementAccumulator> beta_host;
+
+uint64_t seed;
+
+cutlass::DeviceAllocation<ElementA> block_A;
+cutlass::DeviceAllocation<ElementB> block_B;
+cutlass::DeviceAllocation<ElementC> block_C;
+cutlass::DeviceAllocation<ElementD> block_D;
+cutlass::DeviceAllocation<ElementBlockScale> blockscale_block_A;
+cutlass::DeviceAllocation<ElementBlockScale> blockscale_block_B;
+
+cutlass::DeviceAllocation<const ElementA *> ptr_A;
+cutlass::DeviceAllocation<const ElementB *> ptr_B;
+cutlass::DeviceAllocation<const ElementC *> ptr_C;
+cutlass::DeviceAllocation<ElementD *> ptr_D;
+cutlass::DeviceAllocation<ElementD *> ptr_ref_D;
+cutlass::DeviceAllocation<const ElementBlockScale *> ptr_blockscale_A;
+cutlass::DeviceAllocation<const ElementBlockScale *> ptr_blockscale_B;
+
+cutlass::DeviceAllocation<StrideA> stride_A;
+cutlass::DeviceAllocation<StrideB> stride_B;
+cutlass::DeviceAllocation<StrideC> stride_C;
+cutlass::DeviceAllocation<StrideD> stride_D;
+cutlass::DeviceAllocation<LayoutSFA> layout_SFA;
+cutlass::DeviceAllocation<LayoutSFB> layout_SFB;
+
+cutlass::DeviceAllocation<ElementAccumulator*> alpha_device;
+cutlass::DeviceAllocation<ElementAccumulator*> beta_device;
+cutlass::DeviceAllocation<ElementAccumulator> block_alpha;
+cutlass::DeviceAllocation<ElementAccumulator> block_beta;
+
+#endif // defined(CUTLASS_ARCH_MMA_SM90_SUPPORTED) && defined(CUTLASS_ARCH_MMA_MODIFIABLE_TMA_SM90_SUPPORTED) 
+
+/////////////////////////////////////////////////////////////////////////////////////////////////
+/// Testbed utility types
+/////////////////////////////////////////////////////////////////////////////////////////////////
+
+/// Result structure
+struct Result
+{
+  double avg_runtime_ms;
+  double gflops;
+  cutlass::Status status;
+  cudaError_t error;
+  bool passed;
+
+  Result(
+    double avg_runtime_ms = 0,
+    double gflops = 0,
+    cutlass::Status status = cutlass::Status::kSuccess,
+    cudaError_t error = cudaSuccess)
+  :
+    avg_runtime_ms(avg_runtime_ms), gflops(gflops), status(status), error(error), passed(false)
+  {}
+
+};
+
+#if defined(CUTLASS_ARCH_MMA_SM90_SUPPORTED) && defined(CUTLASS_ARCH_MMA_MODIFIABLE_TMA_SM90_SUPPORTED)
+
+/////////////////////////////////////////////////////////////////////////////////////////////////
+/// GEMM setup and evaluation
+/////////////////////////////////////////////////////////////////////////////////////////////////
+
+/// Helper to initialize a block of device data
+template <class Element, class ScopeMin = std::nullopt_t, class ScopeMax = std::nullopt_t>
+bool initialize_block(
+  cutlass::DeviceAllocation<Element>& block,
+  uint64_t seed=2023,
+  ScopeMin scope_min = std::nullopt, ScopeMax scope_max = std::nullopt) {
+
+  double _scope_max, _scope_min;
+  int bits_input = cutlass::sizeof_bits<Element>::value;
+  if (bits_input == 1) {
+    _scope_max = 2;
+    _scope_min = 0;
+  } else if (bits_input <= 8) {
+    _scope_max = 2;
+    _scope_min = -2;
+  } else if (bits_input == 16) {
+    _scope_max = 5;
+    _scope_min = -5;
+  } else {
+    _scope_max = 8;
+    _scope_min = -8;
+  }
+  if constexpr (!std::is_same_v<ScopeMax, std::nullopt_t>) {
+    _scope_max = scope_max;
+  }
+  if constexpr (!std::is_same_v<ScopeMin, std::nullopt_t>) {
+    _scope_min = scope_min;
+  }
+  cutlass::reference::device::BlockFillRandomUniform(
+    block.get(), block.size(), seed, (Element) _scope_max, (Element) _scope_min, 0);
+
+  return true;
+}
+
+/// Allocates device-side data
+template <typename OptionType>
+void allocate(const OptionType &options) {
+
+  int64_t total_elements_A = 0;
+  int64_t total_elements_B = 0;
+  int64_t total_elements_C = 0;
+  int64_t total_elements_D = 0;
+  int64_t total_elements_blockscale_A = 0;
+  int64_t total_elements_blockscale_B = 0;
+
+  offset_A.clear();
+  offset_B.clear();
+  offset_C.clear();
+  offset_D.clear();
+  offset_blockscale_A.clear();
+  offset_blockscale_B.clear();
+  stride_A_host.clear();
+  stride_B_host.clear();
+  stride_C_host.clear();
+  stride_D_host.clear();
+  
+  for (int32_t i = 0; i < options.groups; ++i) {
+
+    auto problem = options.problem_sizes_host.at(i);
+    auto M = get<0>(problem);
+    auto N = get<1>(problem);
+    auto K = get<2>(problem);
+
+    auto group_layout_SFA = ScaleConfig::tile_atom_to_shape_SFA(make_shape(M, N, K, 1));
+    auto group_layout_SFB = ScaleConfig::tile_atom_to_shape_SFB(make_shape(M, N, K, 1));
+
+    offset_A.push_back(total_elements_A);
+    offset_B.push_back(total_elements_B);
+    offset_C.push_back(total_elements_C);
+    offset_D.push_back(total_elements_D);
+    offset_blockscale_A.push_back(total_elements_blockscale_A);
+    offset_blockscale_B.push_back(total_elements_blockscale_B);
+
+    int64_t elements_A = M * K;
+    int64_t elements_B = K * N;
+    int64_t elements_C = M * N;
+    int64_t elements_D = M * N;
+    int64_t elements_blockscale_A = size(filter_zeros(group_layout_SFA));
+    int64_t elements_blockscale_B = size(filter_zeros(group_layout_SFB));
+
+    total_elements_A += elements_A;
+    total_elements_B += elements_B;
+    total_elements_C += elements_C;
+    total_elements_D += elements_D;
+    total_elements_blockscale_A += elements_blockscale_A;
+    total_elements_blockscale_B += elements_blockscale_B;
+
+    stride_A_host.push_back(cutlass::make_cute_packed_stride(StrideA{}, {M, K, 1}));
+    stride_B_host.push_back(cutlass::make_cute_packed_stride(StrideB{}, {N, K, 1}));
+    stride_C_host.push_back(cutlass::make_cute_packed_stride(StrideC{}, {M, N, 1}));
+    stride_D_host.push_back(cutlass::make_cute_packed_stride(StrideD{}, {M, N, 1}));
+    layout_SFA_host.push_back(group_layout_SFA);
+    layout_SFB_host.push_back(group_layout_SFB);
+
+  }
+
+  block_A.reset(total_elements_A);
+  block_B.reset(total_elements_B);
+  block_C.reset(total_elements_C);
+  block_D.reset(total_elements_D);
+  block_alpha.reset(options.groups);
+  block_beta.reset(options.groups);
+  blockscale_block_A.reset(total_elements_blockscale_A);
+  blockscale_block_B.reset(total_elements_blockscale_B);
+}
+
+/// Initialize operands to be used in the GEMM and reference GEMM
+template <typename OptionType>
+void initialize(const OptionType &options) {
+
+  problem_sizes.reset(options.groups);
+  problem_sizes.copy_from_host(options.problem_sizes_host.data());
+
+  std::vector<ElementA *> ptr_A_host(options.groups);
+  std::vector<ElementB *> ptr_B_host(options.groups);
+  std::vector<ElementC *> ptr_C_host(options.groups);
+  std::vector<ElementD *> ptr_D_host(options.groups);
+  std::vector<ElementAccumulator *> ptr_alpha_host(options.groups);
+  std::vector<ElementAccumulator *> ptr_beta_host(options.groups);
+  std::vector<ElementBlockScale *> ptr_blockscale_A_host(options.groups);
+  std::vector<ElementBlockScale *> ptr_blockscale_B_host(options.groups);
+
+  alpha_host.clear();
+  beta_host.clear();
+
+  for (int i = 0; i < options.groups; i++) {
+    ptr_A_host.at(i) = block_A.get() + offset_A.at(i);
+    ptr_B_host.at(i) = block_B.get() + offset_B.at(i);
+    ptr_C_host.at(i) = block_C.get() + offset_C.at(i);
+    ptr_D_host.at(i) = block_D.get() + offset_D.at(i);
+    ptr_blockscale_A_host.at(i) = blockscale_block_A.get() + offset_blockscale_A.at(i);
+    ptr_blockscale_B_host.at(i) = blockscale_block_B.get() + offset_blockscale_B.at(i);
+    alpha_host.push_back((options.alpha == FLT_MAX) ? static_cast<ElementAccumulator>((rand() % 5) + 1) : options.alpha);
+    beta_host.push_back((options.beta == FLT_MAX) ? static_cast<ElementAccumulator>(rand() % 5) : options.beta);
+    ptr_alpha_host.at(i) = block_alpha.get() + i;
+    ptr_beta_host.at(i) = block_beta.get() + i;
+  }
+
+  ptr_A.reset(options.groups);
+  ptr_A.copy_from_host(ptr_A_host.data());
+
+  ptr_B.reset(options.groups);
+  ptr_B.copy_from_host(ptr_B_host.data());
+
+  ptr_C.reset(options.groups);
+  ptr_C.copy_from_host(ptr_C_host.data());
+
+  ptr_D.reset(options.groups);
+  ptr_D.copy_from_host(ptr_D_host.data());
+
+  ptr_blockscale_A.reset(options.groups);
+  ptr_blockscale_A.copy_from_host(ptr_blockscale_A_host.data());
+
+  ptr_blockscale_B.reset(options.groups);
+  ptr_blockscale_B.copy_from_host(ptr_blockscale_B_host.data());
+
+  stride_A.reset(options.groups);
+  stride_A.copy_from_host(stride_A_host.data());
+
+  stride_B.reset(options.groups);
+  stride_B.copy_from_host(stride_B_host.data());
+
+  stride_C.reset(options.groups);
+  stride_C.copy_from_host(stride_C_host.data());
+
+  stride_D.reset(options.groups);
+  stride_D.copy_from_host(stride_D_host.data());
+
+  layout_SFA.reset(options.groups);
+  layout_SFA.copy_from_host(layout_SFA_host.data());
+
+  layout_SFB.reset(options.groups);
+  layout_SFB.copy_from_host(layout_SFB_host.data());
+
+  alpha_device.reset(options.groups);
+  alpha_device.copy_from_host(ptr_alpha_host.data());
+  beta_device.reset(options.groups);
+  beta_device.copy_from_host(ptr_beta_host.data());
+
+  initialize_block(block_A, seed + 2022);
+  initialize_block(block_B, seed + 2023);
+  initialize_block(block_C, seed + 2024);
+  initialize_block(blockscale_block_A, seed + 2025, -1, 1);
+  initialize_block(blockscale_block_B, seed + 2026, -1, 1);
+
+  block_alpha.copy_from_host(alpha_host.data());
+  block_beta.copy_from_host(beta_host.data());
+
+}
+
+/// Populates a Gemm::Arguments structure from the given commandline options
+template<typename GemmArguments, typename OptionType>
+GemmArguments args_from_options(const OptionType &options, bool host_problem_shapes_available = true)
+{
+  // Change device_id to another value if you are running on a machine with multiple GPUs and wish
+  // to use a GPU other than that with device ID 0.
+  int device_id = 0;
+  cutlass::KernelHardwareInfo kernel_hw_info = cutlass::KernelHardwareInfo::make_kernel_hardware_info<typename Gemm::GemmKernel>(device_id);
+
+  GemmArguments arguments{
+    cutlass::gemm::GemmUniversalMode::kGrouped,
+    {options.groups, problem_sizes.get(), host_problem_shapes_available ? options.problem_sizes_host.data() : (decltype(options.problem_sizes_host.data())) nullptr},
+    {ptr_A.get(), stride_A.get(), ptr_B.get(), stride_B.get(),
+     ptr_blockscale_A.get(), layout_SFA.get(),
+     ptr_blockscale_B.get(), layout_SFB.get()
+    },
+    {
+      {}, // epilogue.thread
+      ptr_C.get(), stride_C.get(),
+      ptr_D.get(), stride_D.get()
+    },
+    kernel_hw_info
+  };
+
+  auto &fusion_args = arguments.epilogue.thread;
+  if (options.alpha != FLT_MAX && options.beta != FLT_MAX) {
+    // If both alpha/beta are provided (via cmd line args) and are scalar, i.e., same alpha/beta applies to all batches.
+    fusion_args.alpha = options.alpha;
+    fusion_args.beta = options.beta;
+    fusion_args.alpha_ptr = nullptr;
+    fusion_args.beta_ptr = nullptr;
+    fusion_args.alpha_ptr_array = nullptr;
+    fusion_args.beta_ptr_array = nullptr;
+    // Single alpha and beta for all groups
+    fusion_args.dAlpha = {cute::_0{}, cute::_0{}, 0};
+    fusion_args.dBeta = {cute::_0{}, cute::_0{}, 0};
+  }
+  else {
+    // If pointers to alpha/beta are provided, i.e., alpha/beta can differ between batches/groups.
+    fusion_args.alpha = 0;
+    fusion_args.beta = 0;
+    fusion_args.alpha_ptr = nullptr;
+    fusion_args.beta_ptr = nullptr;
+    fusion_args.alpha_ptr_array = alpha_device.get();
+    fusion_args.beta_ptr_array = beta_device.get();
+    // One alpha and beta per each group
+    fusion_args.dAlpha = {cute::_0{}, cute::_0{}, 1};
+    fusion_args.dBeta = {cute::_0{}, cute::_0{}, 1};
+  }
+
+  arguments.scheduler.raster_order = options.raster_order;
+  // The tile scheduler will swizzle up to 8 and with the nearest multiple of 2 (i.e., 1, 2, 4, and 8)
+  arguments.scheduler.max_swizzle_size = options.swizzle;
+
+  return arguments;
+}
+
+template <typename OptionType>
+bool verify(const OptionType &options) {
+
+  //
+  // Compute reference output
+  //
+
+  std::vector<ElementA> block_A_host(block_A.size());
+  std::vector<ElementB> block_B_host(block_B.size());
+  std::vector<ElementC> block_C_host(block_C.size());
+  std::vector<ElementD> block_D_host_kernel(block_D.size());
+  std::vector<ElementD> block_D_host_ref(block_D.size());
+  std::vector<ElementBlockScale> blockscale_block_A_host(blockscale_block_A.size());
+  std::vector<ElementBlockScale> blockscale_block_B_host(blockscale_block_B.size());
+
+  block_A.copy_to_host(block_A_host.data());
+  block_B.copy_to_host(block_B_host.data());
+  block_C.copy_to_host(block_C_host.data());
+  block_D.copy_to_host(block_D_host_kernel.data());
+  blockscale_block_A.copy_to_host(blockscale_block_A_host.data());
+  blockscale_block_B.copy_to_host(blockscale_block_B_host.data());
+
+  bool passed = true;
+  for (int group_idx = 0; group_idx < options.groups; group_idx++) {
+    // Group scaling tensors shapes based `ScaleGranularityM`, CTA Block (TileShape) and GEMM Problem shape
+    auto [m, n, k] = options.problem_sizes_host.at(group_idx);
+    auto gemm_problem_shape = cute::make_shape(m, n, k);
+
+    // Create instantiation for device reference gemm kernel
+    auto A = cute::make_tensor(block_A_host.data() + offset_A.at(group_idx),
+                              cute::make_layout(
+                                  cute::make_shape(m, k, 1),
+                                  stride_A_host.at(group_idx)
+                                )
+                              );
+    auto B = cute::make_tensor(block_B_host.data() + offset_B.at(group_idx),
+                              cute::make_layout(
+                                cute::make_shape(n, k, 1),
+                                stride_B_host.at(group_idx)
+                                )
+                              );
+    auto C = cute::make_tensor(block_C_host.data() + offset_C.at(group_idx),
+                              cute::make_layout(
+                                  cute::make_shape(m, n, 1),
+                                  stride_C_host.at(group_idx)
+                                )
+                              );
+    auto D = cute::make_tensor(block_D_host_ref.data() + offset_D.at(group_idx),
+                              cute::make_layout(
+                                  cute::make_shape(m, n, 1),
+                                  stride_D_host.at(group_idx)
+                                )
+                              );
+
+    auto SFA = cute::make_tensor(blockscale_block_A_host.data() + offset_blockscale_A.at(group_idx),
+                                 layout_SFA_host.at(group_idx));
+    auto SFB = cute::make_tensor(blockscale_block_B_host.data() + offset_blockscale_B.at(group_idx),
+                                 layout_SFB_host.at(group_idx));
+
+    using unused_t = decltype(D);
+
+    cutlass::reference::host::GettBlockScalingMainloopParams<
+      ElementAccumulator,
+      decltype(A), 
+      decltype(SFA), 
+      decltype(B),
+      decltype(SFB)
+    > mainloop_params{A, SFA, B, SFB};
+
+    cutlass::reference::host::GettEpilogueParams<
+        ElementScalar,
+        ElementScalar,
+        ElementAccumulator,
+        ElementCompute,
+        decltype(C),
+        decltype(D),
+        unused_t, // bias
+        unused_t, // Aux
+        unused_t, // valpha
+        unused_t  // vbeta
+    > epilogue_params;
+
+    epilogue_params.C = C;
+    epilogue_params.D = D;
+    epilogue_params.alpha = alpha_host.at(group_idx);
+    epilogue_params.beta = beta_host.at(group_idx);
+
+    // get reference result
+    cutlass::reference::host::Gemm3x(mainloop_params, epilogue_params);
+
+    // Check if output from CUTLASS kernel and reference kernel are equal or not
+    auto this_group_passed = std::equal(
+      // std::execution::par_unseq,
+      block_D_host_ref.data() + offset_D.at(group_idx),
+      block_D_host_ref.data() + offset_D.at(group_idx) + m * n,
+      block_D_host_kernel.data() + offset_D.at(group_idx)
+    );
+    
+    passed &= this_group_passed;
+
+#if 0
+    std::cout << "Group: " << group_idx << " M: " << m << " N: " << n << " K: " << k << " Status: " << this_group_passed << std::endl;
+#endif
+
+  }
+
+  return passed;
+}
+
+/// Execute a given example GEMM computation
+template <typename OptionType>
+int run(OptionType &options, bool host_problem_shapes_available = true)
+{
+  allocate(options);
+  initialize(options);
+
+  // Instantiate CUTLASS kernel depending on templates
+  Gemm gemm;
+
+  // Create a structure of gemm kernel arguments suitable for invoking an instance of Gemm
+  auto arguments = args_from_options<typename Gemm::Arguments>(options, host_problem_shapes_available);
+
+  // Using the arguments, query for extra workspace required for matrix multiplication computation
+  size_t workspace_size = Gemm::get_workspace_size(arguments);
+
+  // Allocate workspace memory
+  cutlass::device_memory::allocation<uint8_t> workspace(workspace_size);
+
+  // Check if the problem size is supported or not
+  CUTLASS_CHECK(gemm.can_implement(arguments));
+
+  // Initialize CUTLASS kernel with arguments and workspace pointer
+  CUTLASS_CHECK(gemm.initialize(arguments, workspace.get()));
+
+  // Correctness / Warmup iteration
+  CUTLASS_CHECK(gemm.run());
+
+  // Check if output from CUTLASS kernel and reference kernel are equal or not
+  Result result;
+  result.passed = verify(options);
+
+  std::cout << "  Disposition: " << (result.passed ? "Passed" : "Failed") << std::endl;
+
+  if (!result.passed) {
+   exit(-1);
+  }
+
+  // Run profiling loop
+  if (options.iterations > 0)
+  {
+    GpuTimer timer;
+    timer.start();
+    for (int iter = 0; iter < options.iterations; ++iter) {
+      CUTLASS_CHECK(gemm.initialize(arguments, workspace.get()));
+      CUTLASS_CHECK(gemm.run());
+    }
+    timer.stop();
+
+    // Compute average runtime and GFLOPs.
+    float elapsed_ms = timer.elapsed_millis();
+    result.avg_runtime_ms = double(elapsed_ms) / double(options.iterations);
+    result.gflops = options.gflops(result.avg_runtime_ms / 1000.0);
+
+    std::string raster = "Heuristic";
+
+    if (options.raster_order == RasterOrderOptions::AlongN) {
+      raster = "Along N";
+    }
+    else if (options.raster_order == RasterOrderOptions::AlongM) {
+      raster = "Along M";
+    }
+
+    std::cout << "  Problem Sizes, Alpha, Beta " << std::endl;
+    for (int32_t i = 0; i < options.groups; ++i) {
+      std::cout << "    " << options.problem_sizes_host.at(i);
+      std::cout << ", " << alpha_host.at(i) << ", " << beta_host.at(i) << std::endl;
+    }
+    std::cout << "  Groups      : " << options.groups  << std::endl;
+    std::cout << "  Tile shape (M, N, K): " << size<0>(TileShape{}) << ", " << size<1>(TileShape{}) << ", " << size<2>(TileShape{}) << std::endl;
+    std::cout << "  ScaleGranularityM: " << ScaleGranularityM << " (ScaleMsPerTile: " << ScaleMsPerTile << ")" << std::endl;
+    std::cout << "  ScaleGranularityN: " << ScaleGranularityN << " (ScaleNsPerTile: " << ScaleNsPerTile << ")" << std::endl;
+    std::cout << "  Rasterization: " << raster << " with a maximum CTA swizzle of " << options.swizzle << std::endl;
+    std::cout << "  Avg runtime: " << result.avg_runtime_ms << " ms" << std::endl;
+    std::cout << "  GFLOPS: " << result.gflops << std::endl;
+    fflush(stdout);
+  }
+
+  return 0;
+}
+
+#endif // defined(CUTLASS_ARCH_MMA_SM90_SUPPORTED) && defined(CUTLASS_ARCH_MMA_MODIFIABLE_TMA_SM90_SUPPORTED)
+
+///////////////////////////////////////////////////////////////////////////////////////////////////
+
+int main(int argc, char const **args) {
+
+  // CUTLASS must be compiled with CUDA 12.0 Toolkit to run this example
+  // and must have compute capability at least 90.
+  if (__CUDACC_VER_MAJOR__ < 12 || (__CUDACC_VER_MAJOR__ == 12 && __CUDACC_VER_MINOR__ < 3)) {
+    std::cerr << "This example requires CUDA 12.3 or newer.\n";
+    // Returning zero so this test passes on older Toolkits. Its actions are no-op.
+    return 0;
+  }
+
+  cudaDeviceProp props;
+  int current_device_id;
+  CUDA_CHECK(cudaGetDevice(&current_device_id));
+  CUDA_CHECK(cudaGetDeviceProperties(&props, current_device_id));
+  cudaError_t error = cudaGetDeviceProperties(&props, 0);
+  if (props.major != 9) {
+    std::cerr
+      << "This example requires a GPU of NVIDIA's Hopper Architecture or "
+      << "later (compute capability 90 or greater).\n";
+    return 0;
+  }
+
+#if defined(CUTLASS_ARCH_MMA_SM90_SUPPORTED) && defined(CUTLASS_ARCH_MMA_MODIFIABLE_TMA_SM90_SUPPORTED)
+
+  //
+  // Parse options
+  //
+
+  Options<ProblemShape> options;
+
+  options.parse(argc, args);
+
+  if (options.help) {
+    options.print_usage(std::cout) << std::endl;
+    return 0;
+  }
+
+  //
+  // Evaluate CUTLASS kernels
+  //
+
+  std::cout << "Running tests with host problem shapes:" << std::endl;
+  run(options, true);
+  std::cout << "Running tests without host problem shapes:" << std::endl;
+  run(options, false);
+
+#endif
+
+  return 0;
+}
+
+/////////////////////////////////////////////////////////////////////////////////////////////////

examples/68_hopper_fp8_warp_specialized_grouped_gemm_with_blockwise_scaling/68_hopper_fp8_warp_specialized_grouped_gemm_with_blockwise_scaling_with_sparse_groups.cu

@@ -0,0 +1,779 @@
+/***************************************************************************************************
+ * Copyright (c) 2023 - 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+ * SPDX-License-Identifier: BSD-3-Clause
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions are met:
+ *
+ * 1. Redistributions of source code must retain the above copyright notice, this
+ * list of conditions and the following disclaimer.
+ *
+ * 2. Redistributions in binary form must reproduce the above copyright notice,
+ * this list of conditions and the following disclaimer in the documentation
+ * and/or other materials provided with the distribution.
+ *
+ * 3. Neither the name of the copyright holder nor the names of its
+ * contributors may be used to endorse or promote products derived from
+ * this software without specific prior written permission.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+ * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+ * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+ * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+ * FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+ * DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+ * SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+ * CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+ * OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+ * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *
+ **************************************************************************************************/
+
+/*! \file
+    \brief Grouped scale Hopper FP8 Grouped GEMM example using CUTLASS 3.0 APIs for NVIDIA Hopper architecture
+    This example demonstrates a grouped scaled FP8 Grouped GEMM using the new CUTLASS 3.0.
+    APIs on NVIDIA Hopper architecture. New features that will be showcased in this example are as follows:
+    1. NVIDIA Hopper architecture introduces a new series of tensor core instructions (GMMA)
+    which are more efficient than the Ampere tensor core instructions.
+    2. NVIDIA Hopper architecture includes new Tensor Memory Accelerator (TMA) unit to transfer large
+    blocks of data efficiently between global memory and shared memory. TMA also supports asynchronous
+    copies between thread blocks in a cluster. This example also showcases on-the-fly modification of TMA
+    descriptors to move between groups/problem_count (represented by groups).
+    3. This example uses the Warp Specialized kernel design (see /media/docs/efficient_gemm.md for details).
+    4. A simple way to tune the CTA rasterization direction and swizzle pattern of Hopper kernels. Both the
+    CTA rasterization direction and swizzle pattern impact cross-CTA locality of accesses. By tuning we can
+    improve performance.
+    5. This example is tuned specifically for the sparse groups case, where the number of active groups (groups
+    with non-zero problem count) is much smaller than the total number of groups.
+    Examples:
+      $ ./examples/68_hopper_fp8_warp_specialized_grouped_gemm_with_blockwise_scaling/68_hopper_fp8_warp_specialized_grouped_gemm_with_blockwise_scaling_with_sparse_groups  \
+        --m=2816 --n=3072 --k=16384 --save_aux=false --save_amax=false \
+        --raster=h --swizzle=2 --benchmark=./test_benchmark.txt
+
+      Where the test_benchmark.txt may look as such:
+        0 256x512x128
+        1 256x512x512
+        2 512x256x128
+        3 256x256x128
+        4 256x512x1024
+        5 1024x512x128 and so on
+*/
+
+#include <iostream>
+#include <optional>
+#include <fstream>
+#include <sstream>
+#include <vector>
+#include <cfloat>
+
+#include "cutlass/cutlass.h"
+#include "cutlass/numeric_types.h"
+
+#include "cute/tensor.hpp"
+#include "cutlass/tensor_ref.h"
+#include "cutlass/gemm/dispatch_policy.hpp"
+#include "cutlass/gemm/collective/collective_builder.hpp"
+#include "cutlass/gemm/device/gemm_universal_adapter.h"
+#include "cutlass/gemm/kernel/gemm_universal.hpp"
+#include "cutlass/gemm/kernel/tile_scheduler_params.h"
+#include "cutlass/epilogue/dispatch_policy.hpp"
+#include "cutlass/epilogue/collective/collective_builder.hpp"
+
+#include "cutlass/util/command_line.h"
+#include "cutlass/util/distribution.h"
+#include "cutlass/util/host_tensor.h"
+#include "cutlass/util/packed_stride.hpp"
+#include "cutlass/util/tensor_view_io.h"
+#include "cutlass/util/reference/host/tensor_fill.h"
+#include "cutlass/util/reference/host/tensor_copy.h"
+#include "cutlass/util/reference/host/tensor_compare.h"
+#include "cutlass/util/reference/host/tensor_norm.h"
+#include "cutlass/util/reference/device/tensor_fill.h"
+#include "cutlass/util/reference/host/gett.hpp"
+
+// Includes from examples directory
+#include "helper.h"
+#include "hopper_fp8_commandline.hpp"
+
+using namespace cute;
+
+using ProblemShape = cutlass::gemm::GroupProblemShape<Shape<int,int,int>>; // <M,N,K> per group
+
+#if defined(CUTLASS_ARCH_MMA_SM90_SUPPORTED) && defined(CUTLASS_ARCH_MMA_MODIFIABLE_TMA_SM90_SUPPORTED)
+
+/////////////////////////////////////////////////////////////////////////////////////////////////
+/// GEMM kernel configurations
+/////////////////////////////////////////////////////////////////////////////////////////////////
+
+// A matrix configuration
+using         ElementA    = cutlass::float_e4m3_t;                          // Element type for A matrix operand
+using         LayoutA     = cutlass::layout::RowMajor;                      // Layout type for A matrix operand
+constexpr int AlignmentA  = 128 / cutlass::sizeof_bits<ElementA>::value;    // Memory access granularity/alignment of A matrix in units of elements (up to 16 bytes)
+
+// B matrix configuration
+using         ElementB    = cutlass::float_e4m3_t;                          // Element type for B matrix operand
+using         LayoutB     = cutlass::layout::ColumnMajor;                   // Layout type for B matrix operand
+constexpr int AlignmentB  = 128 / cutlass::sizeof_bits<ElementB>::value;    // Memory access granularity/alignment of B matrix in units of elements (up to 16 bytes)
+
+// C matrix configuration
+using         ElementC    = cutlass::float_e4m3_t;                          // Element type for C and D matrix operands
+using         LayoutC     = cutlass::layout::ColumnMajor;                   // Layout type for C and D matrix operands
+constexpr int AlignmentC  = 128 / cutlass::sizeof_bits<ElementC>::value;    // Memory access granularity/alignment of C matrix in units of elements (up to 16 bytes)
+
+// D matrix configuration
+using         ElementD    = ElementC;
+using         LayoutD     = LayoutC;
+constexpr int AlignmentD  = AlignmentC;
+
+// Core kernel configurations
+using ElementAccumulator  = float;                                          // Element type for internal accumulation
+using ElementBlockScale   = float;                                          // Element type for blockscaling during accumulation
+using ElementCompute      = float;                                          // Element type for epilogue computation
+
+using ArchTag       = cutlass::arch::Sm90;                          // Tag indicating the minimum SM that supports the intended feature
+using OperatorClass = cutlass::arch::OpClassTensorOp;               // Operator class tag
+
+using TileShape  = Shape<_128,_128,_128>;  // This one is just to make the compiler happy with verify()...
+using ClusterShape  = Shape<_1,_1,_1>;                              // Shape of the threadblocks in a cluster
+
+static constexpr int ScaleGranularityM = 1;
+static constexpr int ScaleGranularityN = 128;
+static constexpr int ScaleGranularityK = 128;
+static constexpr int ScaleMsPerTile = size<0>(TileShape{}) / ScaleGranularityM;
+static constexpr int ScaleNsPerTile = size<1>(TileShape{}) / ScaleGranularityN;
+
+using ScaleConfig   = cutlass::detail::Sm90BlockwiseScaleConfig<ScaleGranularityM, ScaleGranularityN, ScaleGranularityK>;
+
+using LayoutSFA     = decltype(ScaleConfig::deduce_layoutSFA());    // Layout type for SFA matrix operand
+using LayoutSFB     = decltype(ScaleConfig::deduce_layoutSFB());    // Layout type for SFB matrix operand
+
+
+using KernelSchedule = cutlass::gemm::KernelPtrArrayTmaWarpSpecializedPingpongFP8BlockScaledAccum;
+using EpilogueSchedule = cutlass::epilogue::PtrArrayTmaWarpSpecializedPingpong;
+using EpilogueTileType  = cutlass::epilogue::collective::EpilogueTileAuto;
+using FusionOperation   = cutlass::epilogue::fusion::LinearCombination<ElementC, ElementAccumulator>;
+
+using CollectiveEpilogue = typename cutlass::epilogue::collective::CollectiveBuilder<
+  ArchTag, OperatorClass,
+  TileShape, ClusterShape,
+  EpilogueTileType,
+  ElementAccumulator, ElementCompute,
+  ElementC, LayoutC *, AlignmentC,
+  ElementD, LayoutD *, AlignmentD,
+  EpilogueSchedule,
+  FusionOperation
+>::CollectiveOp;
+
+using CollectiveMainloopWithGroupWiseScaling = typename cutlass::gemm::collective::CollectiveBuilder<
+  ArchTag, OperatorClass,
+  ElementA, cute::tuple<LayoutA *, LayoutSFA *>, AlignmentA,
+  ElementB, cute::tuple<LayoutB *, LayoutSFB *>, AlignmentB,
+  ElementAccumulator,
+  TileShape, ClusterShape,
+  cutlass::gemm::collective::StageCountAutoCarveout<
+    static_cast<int>(sizeof(typename CollectiveEpilogue::SharedStorage))
+  >,
+  KernelSchedule
+>::CollectiveOp;
+
+using GemmKernel = cutlass::gemm::kernel::GemmUniversal<
+    ProblemShape,
+    CollectiveMainloopWithGroupWiseScaling,
+    CollectiveEpilogue
+>;
+
+using Gemm = cutlass::gemm::device::GemmUniversalAdapter<GemmKernel>;
+
+// Extract information from Gemm kernel.
+using EpilogueOutputOp  = typename Gemm::EpilogueOutputOp;
+using ElementScalar     = typename EpilogueOutputOp::ElementScalar;
+using ActivationFunctor = typename EpilogueOutputOp::ActivationFn;
+
+using StrideA = typename Gemm::GemmKernel::InternalStrideA;
+using StrideB = typename Gemm::GemmKernel::InternalStrideB;
+using StrideC = typename Gemm::GemmKernel::InternalStrideC;
+using StrideD = typename Gemm::GemmKernel::InternalStrideD;
+
+static_assert(cute::is_same_v<ElementAccumulator, ElementBlockScale>,
+             "ElementAccumulator and ElementBlockScale should be same datatype");
+
+/// Initialization
+
+cutlass::DeviceAllocation<typename ProblemShape::UnderlyingProblemShape> problem_sizes;
+
+std::vector<int64_t> offset_A;
+std::vector<int64_t> offset_B;
+std::vector<int64_t> offset_C;
+std::vector<int64_t> offset_D;
+std::vector<int64_t> offset_blockscale_A;
+std::vector<int64_t> offset_blockscale_B;
+
+std::vector<StrideA> stride_A_host;
+std::vector<StrideB> stride_B_host;
+std::vector<StrideC> stride_C_host;
+std::vector<StrideD> stride_D_host;
+std::vector<LayoutSFA> layout_SFA_host;
+std::vector<LayoutSFB> layout_SFB_host;
+
+std::vector<ElementAccumulator> alpha_host;
+std::vector<ElementAccumulator> beta_host;
+
+uint64_t seed;
+
+cutlass::DeviceAllocation<ElementA> block_A;
+cutlass::DeviceAllocation<ElementB> block_B;
+cutlass::DeviceAllocation<ElementC> block_C;
+cutlass::DeviceAllocation<ElementD> block_D;
+cutlass::DeviceAllocation<ElementBlockScale> blockscale_block_A;
+cutlass::DeviceAllocation<ElementBlockScale> blockscale_block_B;
+
+cutlass::DeviceAllocation<const ElementA *> ptr_A;
+cutlass::DeviceAllocation<const ElementB *> ptr_B;
+cutlass::DeviceAllocation<const ElementC *> ptr_C;
+cutlass::DeviceAllocation<ElementD *> ptr_D;
+cutlass::DeviceAllocation<ElementD *> ptr_ref_D;
+cutlass::DeviceAllocation<const ElementBlockScale *> ptr_blockscale_A;
+cutlass::DeviceAllocation<const ElementBlockScale *> ptr_blockscale_B;
+
+cutlass::DeviceAllocation<StrideA> stride_A;
+cutlass::DeviceAllocation<StrideB> stride_B;
+cutlass::DeviceAllocation<StrideC> stride_C;
+cutlass::DeviceAllocation<StrideD> stride_D;
+cutlass::DeviceAllocation<LayoutSFA> layout_SFA;
+cutlass::DeviceAllocation<LayoutSFB> layout_SFB;
+
+cutlass::DeviceAllocation<ElementAccumulator*> alpha_device;
+cutlass::DeviceAllocation<ElementAccumulator*> beta_device;
+cutlass::DeviceAllocation<ElementAccumulator> block_alpha;
+cutlass::DeviceAllocation<ElementAccumulator> block_beta;
+
+#endif // defined(CUTLASS_ARCH_MMA_SM90_SUPPORTED) && defined(CUTLASS_ARCH_MMA_MODIFIABLE_TMA_SM90_SUPPORTED) 
+
+/////////////////////////////////////////////////////////////////////////////////////////////////
+/// Testbed utility types
+/////////////////////////////////////////////////////////////////////////////////////////////////
+
+/// Result structure
+struct Result
+{
+  double avg_runtime_ms;
+  double gflops;
+  double gbps;
+  cutlass::Status status;
+  cudaError_t error;
+  bool passed;
+
+  Result(
+    double avg_runtime_ms = 0,
+    double gflops = 0,
+    double gbps = 0,
+    cutlass::Status status = cutlass::Status::kSuccess,
+    cudaError_t error = cudaSuccess)
+  :
+    avg_runtime_ms(avg_runtime_ms), gflops(gflops), gbps(gbps), status(status), error(error), passed(false)
+  {}
+
+};
+
+#if defined(CUTLASS_ARCH_MMA_SM90_SUPPORTED) && defined(CUTLASS_ARCH_MMA_MODIFIABLE_TMA_SM90_SUPPORTED)
+
+/////////////////////////////////////////////////////////////////////////////////////////////////
+/// GEMM setup and evaluation
+/////////////////////////////////////////////////////////////////////////////////////////////////
+
+/// Helper to initialize a block of device data
+template <class Element, class ScopeMin = std::nullopt_t, class ScopeMax = std::nullopt_t>
+bool initialize_block(
+  cutlass::DeviceAllocation<Element>& block,
+  uint64_t seed=2023,
+  ScopeMin scope_min = std::nullopt, ScopeMax scope_max = std::nullopt) {
+
+  double _scope_max, _scope_min;
+  int bits_input = cutlass::sizeof_bits<Element>::value;
+  if (bits_input == 1) {
+    _scope_max = 2;
+    _scope_min = 0;
+  } else if (bits_input <= 8) {
+    _scope_max = 2;
+    _scope_min = -2;
+  } else if (bits_input == 16) {
+    _scope_max = 5;
+    _scope_min = -5;
+  } else {
+    _scope_max = 8;
+    _scope_min = -8;
+  }
+  if constexpr (!std::is_same_v<ScopeMax, std::nullopt_t>) {
+    _scope_max = scope_max;
+  }
+  if constexpr (!std::is_same_v<ScopeMin, std::nullopt_t>) {
+    _scope_min = scope_min;
+  }
+  cutlass::reference::device::BlockFillRandomUniform(
+    block.get(), block.size(), seed, (Element) _scope_max, (Element) _scope_min, 0);
+
+  return true;
+}
+
+/// Allocates device-side data
+template <typename OptionType>
+void allocate(const OptionType &options) {
+
+  int64_t total_elements_A = 0;
+  int64_t total_elements_B = 0;
+  int64_t total_elements_C = 0;
+  int64_t total_elements_D = 0;
+  int64_t total_elements_blockscale_A = 0;
+  int64_t total_elements_blockscale_B = 0;
+
+  offset_A.clear();
+  offset_B.clear();
+  offset_C.clear();
+  offset_D.clear();
+  offset_blockscale_A.clear();
+  offset_blockscale_B.clear();
+  stride_A_host.clear();
+  stride_B_host.clear();
+  stride_C_host.clear();
+  stride_D_host.clear();
+  
+  for (int32_t i = 0; i < options.groups; ++i) {
+
+    auto problem = options.problem_sizes_after_alignment_host.at(i);
+    auto M = get<0>(problem);
+    auto N = get<1>(problem);
+    auto K = get<2>(problem);
+
+    auto group_layout_SFA = ScaleConfig::tile_atom_to_shape_SFA(make_shape(M, N, K, 1));
+    auto group_layout_SFB = ScaleConfig::tile_atom_to_shape_SFB(make_shape(M, N, K, 1));
+
+    offset_A.push_back(total_elements_A);
+    offset_B.push_back(total_elements_B);
+    offset_C.push_back(total_elements_C);
+    offset_D.push_back(total_elements_D);
+    offset_blockscale_A.push_back(total_elements_blockscale_A);
+    offset_blockscale_B.push_back(total_elements_blockscale_B);
+
+    int64_t elements_A = M * K;
+    int64_t elements_B = K * N;
+    int64_t elements_C = M * N;
+    int64_t elements_D = M * N;
+    int64_t elements_blockscale_A = size(filter_zeros(group_layout_SFA));
+    int64_t elements_blockscale_B = size(filter_zeros(group_layout_SFB));
+
+    total_elements_A += elements_A;
+    total_elements_B += elements_B;
+    total_elements_C += elements_C;
+    total_elements_D += elements_D;
+    total_elements_blockscale_A += elements_blockscale_A;
+    total_elements_blockscale_B += elements_blockscale_B;
+
+    stride_A_host.push_back(cutlass::make_cute_packed_stride(StrideA{}, {M, K, 1}));
+    stride_B_host.push_back(cutlass::make_cute_packed_stride(StrideB{}, {N, K, 1}));
+    stride_C_host.push_back(cutlass::make_cute_packed_stride(StrideC{}, {M, N, 1}));
+    stride_D_host.push_back(cutlass::make_cute_packed_stride(StrideD{}, {M, N, 1}));
+    layout_SFA_host.push_back(group_layout_SFA);
+    layout_SFB_host.push_back(group_layout_SFB);
+
+  }
+
+  block_A.reset(total_elements_A);
+  block_B.reset(total_elements_B);
+  block_C.reset(total_elements_C);
+  block_D.reset(total_elements_D);
+  block_alpha.reset(options.groups);
+  block_beta.reset(options.groups);
+  blockscale_block_A.reset(total_elements_blockscale_A);
+  blockscale_block_B.reset(total_elements_blockscale_B);
+}
+
+/// Initialize operands to be used in the GEMM and reference GEMM
+template <typename OptionType>
+void initialize(const OptionType &options) {
+
+  problem_sizes.reset(options.groups);
+  problem_sizes.copy_from_host(options.problem_sizes_after_alignment_host.data());
+
+  std::vector<ElementA *> ptr_A_host(options.groups);
+  std::vector<ElementB *> ptr_B_host(options.groups);
+  std::vector<ElementC *> ptr_C_host(options.groups);
+  std::vector<ElementD *> ptr_D_host(options.groups);
+  std::vector<ElementAccumulator *> ptr_alpha_host(options.groups);
+  std::vector<ElementAccumulator *> ptr_beta_host(options.groups);
+  std::vector<ElementBlockScale *> ptr_blockscale_A_host(options.groups);
+  std::vector<ElementBlockScale *> ptr_blockscale_B_host(options.groups);
+
+  alpha_host.clear();
+  beta_host.clear();
+
+  for (int i = 0; i < options.groups; i++) {
+    ptr_A_host.at(i) = block_A.get() + offset_A.at(i);
+    ptr_B_host.at(i) = block_B.get() + offset_B.at(i);
+    ptr_C_host.at(i) = block_C.get() + offset_C.at(i);
+    ptr_D_host.at(i) = block_D.get() + offset_D.at(i);
+    ptr_blockscale_A_host.at(i) = blockscale_block_A.get() + offset_blockscale_A.at(i);
+    ptr_blockscale_B_host.at(i) = blockscale_block_B.get() + offset_blockscale_B.at(i);
+    alpha_host.push_back((options.alpha == FLT_MAX) ? static_cast<ElementAccumulator>((rand() % 5) + 1) : options.alpha);
+    beta_host.push_back((options.beta == FLT_MAX) ? static_cast<ElementAccumulator>(rand() % 5) : options.beta);
+    ptr_alpha_host.at(i) = block_alpha.get() + i;
+    ptr_beta_host.at(i) = block_beta.get() + i;
+  }
+
+  ptr_A.reset(options.groups);
+  ptr_A.copy_from_host(ptr_A_host.data());
+
+  ptr_B.reset(options.groups);
+  ptr_B.copy_from_host(ptr_B_host.data());
+
+  ptr_C.reset(options.groups);
+  ptr_C.copy_from_host(ptr_C_host.data());
+
+  ptr_D.reset(options.groups);
+  ptr_D.copy_from_host(ptr_D_host.data());
+
+  ptr_blockscale_A.reset(options.groups);
+  ptr_blockscale_A.copy_from_host(ptr_blockscale_A_host.data());
+
+  ptr_blockscale_B.reset(options.groups);
+  ptr_blockscale_B.copy_from_host(ptr_blockscale_B_host.data());
+
+  stride_A.reset(options.groups);
+  stride_A.copy_from_host(stride_A_host.data());
+
+  stride_B.reset(options.groups);
+  stride_B.copy_from_host(stride_B_host.data());
+
+  stride_C.reset(options.groups);
+  stride_C.copy_from_host(stride_C_host.data());
+
+  stride_D.reset(options.groups);
+  stride_D.copy_from_host(stride_D_host.data());
+
+  layout_SFA.reset(options.groups);
+  layout_SFA.copy_from_host(layout_SFA_host.data());
+
+  layout_SFB.reset(options.groups);
+  layout_SFB.copy_from_host(layout_SFB_host.data());
+
+  alpha_device.reset(options.groups);
+  alpha_device.copy_from_host(ptr_alpha_host.data());
+  beta_device.reset(options.groups);
+  beta_device.copy_from_host(ptr_beta_host.data());
+
+  initialize_block(block_A, seed + 2022);
+  initialize_block(block_B, seed + 2023);
+  initialize_block(block_C, seed + 2024);
+  initialize_block(blockscale_block_A, seed + 2025, -1, 1);
+  initialize_block(blockscale_block_B, seed + 2026, -1, 1);
+
+  block_alpha.copy_from_host(alpha_host.data());
+  block_beta.copy_from_host(beta_host.data());
+
+}
+
+/// Populates a Gemm::Arguments structure from the given commandline options
+template<typename GemmArguments, typename OptionType>
+GemmArguments args_from_options(const OptionType &options, bool host_problem_shapes_available = true)
+{
+  // Change device_id to another value if you are running on a machine with multiple GPUs and wish
+  // to use a GPU other than that with device ID 0.
+  int device_id = 0;
+  cutlass::KernelHardwareInfo kernel_hw_info = cutlass::KernelHardwareInfo::make_kernel_hardware_info<typename Gemm::GemmKernel>(device_id);
+
+  GemmArguments arguments{
+    cutlass::gemm::GemmUniversalMode::kGrouped,
+    {options.groups, problem_sizes.get(), host_problem_shapes_available ? options.problem_sizes_after_alignment_host.data() : (decltype(options.problem_sizes_after_alignment_host.data())) nullptr},
+    {ptr_A.get(), stride_A.get(), ptr_B.get(), stride_B.get(),
+     ptr_blockscale_A.get(), layout_SFA.get(),
+     ptr_blockscale_B.get(), layout_SFB.get()
+    },
+    {
+      {}, // epilogue.thread
+      ptr_C.get(), stride_C.get(),
+      ptr_D.get(), stride_D.get()
+    },
+    kernel_hw_info
+  };
+
+  auto &fusion_args = arguments.epilogue.thread;
+  if (options.alpha != FLT_MAX && options.beta != FLT_MAX) {
+    // If both alpha/beta are provided (via cmd line args) and are scalar, i.e., same alpha/beta applies to all batches.
+    fusion_args.alpha = options.alpha;
+    fusion_args.beta = options.beta;
+    fusion_args.alpha_ptr = nullptr;
+    fusion_args.beta_ptr = nullptr;
+    fusion_args.alpha_ptr_array = nullptr;
+    fusion_args.beta_ptr_array = nullptr;
+    // Single alpha and beta for all groups
+    fusion_args.dAlpha = {cute::_0{}, cute::_0{}, 0};
+    fusion_args.dBeta = {cute::_0{}, cute::_0{}, 0};
+  }
+  else {
+    // If pointers to alpha/beta are provided, i.e., alpha/beta can differ between batches/groups.
+    fusion_args.alpha = 0;
+    fusion_args.beta = 0;
+    fusion_args.alpha_ptr = nullptr;
+    fusion_args.beta_ptr = nullptr;
+    fusion_args.alpha_ptr_array = alpha_device.get();
+    fusion_args.beta_ptr_array = beta_device.get();
+    // One alpha and beta per each group
+    fusion_args.dAlpha = {cute::_0{}, cute::_0{}, 1};
+    fusion_args.dBeta = {cute::_0{}, cute::_0{}, 1};
+  }
+
+  arguments.scheduler.raster_order = options.raster_order;
+  // The tile scheduler will swizzle up to 8 and with the nearest multiple of 2 (i.e., 1, 2, 4, and 8)
+  arguments.scheduler.max_swizzle_size = options.swizzle;
+
+  return arguments;
+}
+
+template <typename OptionType>
+bool verify(const OptionType &options) {
+
+  //
+  // Compute reference output
+  //
+
+  std::vector<ElementA> block_A_host(block_A.size());
+  std::vector<ElementB> block_B_host(block_B.size());
+  std::vector<ElementC> block_C_host(block_C.size());
+  std::vector<ElementD> block_D_host_kernel(block_D.size());
+  std::vector<ElementD> block_D_host_ref(block_D.size());
+  std::vector<ElementBlockScale> blockscale_block_A_host(blockscale_block_A.size());
+  std::vector<ElementBlockScale> blockscale_block_B_host(blockscale_block_B.size());
+
+  block_A.copy_to_host(block_A_host.data());
+  block_B.copy_to_host(block_B_host.data());
+  block_C.copy_to_host(block_C_host.data());
+  block_D.copy_to_host(block_D_host_kernel.data());
+  blockscale_block_A.copy_to_host(blockscale_block_A_host.data());
+  blockscale_block_B.copy_to_host(blockscale_block_B_host.data());
+
+  bool passed = true;
+  for (int group_idx = 0; group_idx < options.groups; group_idx++) {
+    // Group scaling tensors shapes based `ScaleGranularityM`, CTA Block (TileShape) and GEMM Problem shape
+    auto [m, n, k] = options.problem_sizes_after_alignment_host.at(group_idx);
+    auto gemm_problem_shape = cute::make_shape(m, n, k);
+
+    // Create instantiation for device reference gemm kernel
+    auto A = cute::make_tensor(block_A_host.data() + offset_A.at(group_idx),
+                              cute::make_layout(
+                                  cute::make_shape(m, k, 1),
+                                  stride_A_host.at(group_idx)
+                                )
+                              );
+    auto B = cute::make_tensor(block_B_host.data() + offset_B.at(group_idx),
+                              cute::make_layout(
+                                cute::make_shape(n, k, 1),
+                                stride_B_host.at(group_idx)
+                                )
+                              );
+    auto C = cute::make_tensor(block_C_host.data() + offset_C.at(group_idx),
+                              cute::make_layout(
+                                  cute::make_shape(m, n, 1),
+                                  stride_C_host.at(group_idx)
+                                )
+                              );
+    auto D = cute::make_tensor(block_D_host_ref.data() + offset_D.at(group_idx),
+                              cute::make_layout(
+                                  cute::make_shape(m, n, 1),
+                                  stride_D_host.at(group_idx)
+                                )
+                              );
+
+    auto SFA = cute::make_tensor(blockscale_block_A_host.data() + offset_blockscale_A.at(group_idx),
+                                 layout_SFA_host.at(group_idx));
+    auto SFB = cute::make_tensor(blockscale_block_B_host.data() + offset_blockscale_B.at(group_idx),
+                                 layout_SFB_host.at(group_idx));
+
+    using unused_t = decltype(D);
+
+    cutlass::reference::host::GettBlockScalingMainloopParams<
+      ElementAccumulator,
+      decltype(A), 
+      decltype(SFA), 
+      decltype(B),
+      decltype(SFB)
+    > mainloop_params{A, SFA, B, SFB};
+
+    cutlass::reference::host::GettEpilogueParams<
+        ElementScalar,
+        ElementScalar,
+        ElementAccumulator,
+        ElementCompute,
+        decltype(C),
+        decltype(D)
+    > epilogue_params;
+
+    epilogue_params.C = C;
+    epilogue_params.D = D;
+    epilogue_params.alpha = alpha_host.at(group_idx);
+    epilogue_params.beta = beta_host.at(group_idx);
+
+    // get reference result
+    cutlass::reference::host::Gemm3x(mainloop_params, epilogue_params);
+
+    // Check if output from CUTLASS kernel and reference kernel are equal or not
+    auto this_group_passed = std::equal(
+      // std::execution::par_unseq,
+      block_D_host_ref.data() + offset_D.at(group_idx),
+      block_D_host_ref.data() + offset_D.at(group_idx) + m * n,
+      block_D_host_kernel.data() + offset_D.at(group_idx)
+    );
+    
+    passed &= this_group_passed;
+
+#if 0
+    std::cout << "Group: " << group_idx << " M: " << m << " N: " << n << " K: " << k << " Status: " << this_group_passed << std::endl;
+#endif
+
+  }
+
+  return passed;
+}
+
+/// Execute a given example GEMM computation
+template <typename OptionType>
+int run(OptionType &options, bool host_problem_shapes_available = true)
+{
+
+  allocate(options);
+  initialize(options);
+
+  // Instantiate CUTLASS kernel depending on templates
+  Gemm gemm;
+
+  // Create a structure of gemm kernel arguments suitable for invoking an instance of Gemm
+  auto arguments = args_from_options<typename Gemm::Arguments>(options, host_problem_shapes_available);
+
+  // Using the arguments, query for extra workspace required for matrix multiplication computation
+  size_t workspace_size = Gemm::get_workspace_size(arguments);
+
+  // Allocate workspace memory
+  cutlass::device_memory::allocation<uint8_t> workspace(workspace_size);
+
+  // Check if the problem size is supported or not
+  CUTLASS_CHECK(gemm.can_implement(arguments));
+
+  // Initialize CUTLASS kernel with arguments and workspace pointer
+  CUTLASS_CHECK(gemm.initialize(arguments, workspace.get()));
+
+  // Correctness / Warmup iteration
+  CUTLASS_CHECK(gemm.run());
+
+  // Check if output from CUTLASS kernel and reference kernel are equal or not
+  Result result;
+  result.passed = verify(options);
+
+  std::cout << "  Disposition: " << (result.passed ? "Passed" : "Failed") << std::endl;
+
+  if (!result.passed) {
+   exit(-1);
+  }
+
+  // Run profiling loop
+  if (options.iterations > 0) {
+    GpuTimer timer;
+    timer.start();
+    for (int iter = 0; iter < options.iterations; ++iter) {
+      CUTLASS_CHECK(gemm.initialize(arguments, workspace.get()));
+      CUTLASS_CHECK(gemm.run());
+    }
+    timer.stop();
+
+    // Compute average runtime and GFLOPs.
+    float elapsed_ms = timer.elapsed_millis();
+    result.avg_runtime_ms = double(elapsed_ms) / double(options.iterations);
+    result.gflops = options.gflops(result.avg_runtime_ms / 1000.0);
+    result.gbps = options.template gbps<ElementA, 
+                                        ElementB, 
+                                        ElementC, 
+                                        ElementD, 
+                                        ElementBlockScale, 
+                                        TileShape, 
+                                        ScaleMsPerTile, 
+                                        ScaleNsPerTile>(result.avg_runtime_ms / 1000.0);
+
+    std::string raster = "Heuristic";
+
+    if (options.raster_order == RasterOrderOptions::AlongN) {
+      raster = "Along N";
+    }
+    else if (options.raster_order == RasterOrderOptions::AlongM) {
+      raster = "Along M";
+    }
+
+    std::cout << "  Problem Sizes, Alpha, Beta " << std::endl;
+    for (int32_t i = 0; i < options.groups; ++i) {
+      std::cout << "    " << options.problem_sizes_host.at(i);
+      std::cout << ", " << alpha_host.at(i) << ", " << beta_host.at(i) << std::endl;
+    }
+    std::cout << "  Groups      : " << options.groups  << std::endl;
+    std::cout << "  Tile shape (M, N, K): " << size<0>(TileShape{}) << ", " << size<1>(TileShape{}) << ", " << size<2>(TileShape{}) << std::endl;
+    std::cout << "  ScaleGranularityM: " << ScaleGranularityM << " (ScaleMsPerTile: " << ScaleMsPerTile << ")" << std::endl;
+    std::cout << "  ScaleGranularityN: " << ScaleGranularityN << " (ScaleNsPerTile: " << ScaleNsPerTile << ")" << std::endl;
+    std::cout << "  Rasterization: " << raster << " with a maximum CTA swizzle of " << options.swizzle << std::endl;
+    std::cout << "  Avg runtime: " << result.avg_runtime_ms << " ms" << std::endl;
+    std::cout << "  GFLOPS: " << result.gflops << std::endl;
+  }
+
+  return 0;
+}
+
+#endif // defined(CUTLASS_ARCH_MMA_SM90_SUPPORTED) && defined(CUTLASS_ARCH_MMA_MODIFIABLE_TMA_SM90_SUPPORTED)
+
+///////////////////////////////////////////////////////////////////////////////////////////////////
+
+int main(int argc, char const **args) {
+
+  // CUTLASS must be compiled with CUDA 12.0 Toolkit to run this example
+  // and must have compute capability at least 90.
+  if (__CUDACC_VER_MAJOR__ < 12 || (__CUDACC_VER_MAJOR__ == 12 && __CUDACC_VER_MINOR__ < 3)) {
+    std::cerr << "This example requires CUDA 12.3 or newer.\n";
+    // Returning zero so this test passes on older Toolkits. Its actions are no-op.
+    return 0;
+  }
+
+  cudaDeviceProp props;
+  int current_device_id;
+  CUDA_CHECK(cudaGetDevice(&current_device_id));
+  CUDA_CHECK(cudaGetDeviceProperties(&props, current_device_id));
+  cudaError_t error = cudaGetDeviceProperties(&props, 0);
+  if (props.major != 9) {
+    std::cerr
+      << "This example requires a GPU of NVIDIA's Hopper Architecture or "
+      << "later (compute capability 90 or greater).\n";
+    return 0;
+  }
+
+#if defined(CUTLASS_ARCH_MMA_SM90_SUPPORTED) && defined(CUTLASS_ARCH_MMA_MODIFIABLE_TMA_SM90_SUPPORTED)
+
+  //
+  // Parse options
+  //
+
+  Options<ProblemShape> options;
+
+  options.parse(argc, args);
+
+  if (options.help) {
+    options.print_usage(std::cout) << std::endl;
+    return 0;
+  }
+
+  //
+  // Evaluate CUTLASS kernels
+  //
+
+  run(options, true);
+
+  std::cout << "Running tests without host problem shapes:" << std::endl;
+  run(options, false);
+
+#endif
+
+  return 0;
+}
+
+/////////////////////////////////////////////////////////////////////////////////////////////////

examples/68_hopper_fp8_warp_specialized_grouped_gemm_with_blockwise_scaling/CMakeLists.txt

@@ -0,0 +1,84 @@
+# Copyright (c) 2023 - 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: BSD-3-Clause
+#
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions are met:
+#
+# 1. Redistributions of source code must retain the above copyright notice, this
+# list of conditions and the following disclaimer.
+#
+# 2. Redistributions in binary form must reproduce the above copyright notice,
+# this list of conditions and the following disclaimer in the documentation
+# and/or other materials provided with the distribution.
+#
+# 3. Neither the name of the copyright holder nor the names of its
+# contributors may be used to endorse or promote products derived from
+# this software without specific prior written permission.
+#
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+# Note that we set --iterations=0 for all tests below to disable the performance benchmarking.
+# Only the correctness check will be run by these commands.
+
+set(TEST_RANDOM --iterations=0)                                                     # Random problem sizes
+set(TEST_RANDOM_LARGE_GROUP --groups=500 --iterations=0)                            # Random problem sizes
+
+set(TEST_EPILOGUE --alpha=0.5 --beta=0.5 --iterations=0)                            # Random problem sizes
+set(TEST_EPILOGUE_LARGE_GROUP --alpha=1.5 --beta=2.0 --groups=500 --iterations=0)   # Random problem sizes
+
+set(TEST_EPILOGUE_OP --beta=0.5 --iterations=0)                                     # Random problem sizes
+set(TEST_EPILOGUE_OP_LARGE_GROUP --alpha=1.5 --iterations=0)                        # Random problem sizes
+
+set(TEST_FIXED --m=2048 --n=5120 --k=512 --groups=50 --iterations=0)                # Fixed problem sizes
+set(TEST_FIXED_LARGE_GROUP --m=2048 --n=512 --k=512 --groups=512 --iterations=0)    # Fixed problem sizes
+
+set(TEST_SMALL --m=256 --n=128 --iterations=0)                                      # Small problem sizes
+set(TEST_SMALL_LARGE_GROUP --m=128 --n=128 --groups=500 --iterations=0)             # Small problem sizes
+
+cutlass_example_add_executable(
+  68_hopper_fp8_warp_specialized_grouped_gemm_with_blockwise_scaling
+  68_hopper_fp8_warp_specialized_grouped_gemm_with_blockwise_scaling.cu
+  TEST_COMMAND_OPTIONS
+  TEST_RANDOM
+  TEST_RANDOM_LARGE_GROUP
+  TEST_EPILOGUE
+  TEST_EPILOGUE_LARGE_GROUP
+  TEST_EPILOGUE_OP
+  TEST_EPILOGUE_OP_LARGE_GROUP
+  TEST_FIXED
+  TEST_FIXED_LARGE_GROUP
+  TEST_SMALL
+  TEST_SMALL_LARGE_GROUP
+  )
+
+# MSVC will fail to compile this example with the following error:
+# fatal error C1083: Cannot open source file: <Some Mojibake>: No such file or directory [...\examples\68_hopper_fp8_warp_specialized_grouped_gemm_with_blockwise_scaling\68_hopper_fp8_warp_specialized_grouped_gemm_with_blockwise_scaling_with_sparse_groups.vcxproj]
+# This is a known issue and we are working on a fix.
+if (NOT MSVC)
+
+cutlass_example_add_executable(
+  68_hopper_fp8_warp_specialized_grouped_gemm_with_blockwise_scaling_with_sparse_groups
+  68_hopper_fp8_warp_specialized_grouped_gemm_with_blockwise_scaling_with_sparse_groups.cu
+  TEST_COMMAND_OPTIONS
+  TEST_RANDOM
+  TEST_RANDOM_LARGE_GROUP
+  TEST_EPILOGUE
+  TEST_EPILOGUE_LARGE_GROUP
+  TEST_EPILOGUE_OP
+  TEST_EPILOGUE_OP_LARGE_GROUP
+  TEST_FIXED
+  TEST_FIXED_LARGE_GROUP
+  TEST_SMALL
+  TEST_SMALL_LARGE_GROUP
+  )
+
+endif()

examples/68_hopper_fp8_warp_specialized_grouped_gemm_with_blockwise_scaling/hopper_fp8_commandline.hpp

@@ -0,0 +1,270 @@
+/***************************************************************************************************
+ * Copyright (c) 2023 - 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+ * SPDX-License-Identifier: BSD-3-Clause
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions are met:
+ *
+ * 1. Redistributions of source code must retain the above copyright notice, this
+ * list of conditions and the following disclaimer.
+ *
+ * 2. Redistributions in binary form must reproduce the above copyright notice,
+ * this list of conditions and the following disclaimer in the documentation
+ * and/or other materials provided with the distribution.
+ *
+ * 3. Neither the name of the copyright holder nor the names of its
+ * contributors may be used to endorse or promote products derived from
+ * this software without specific prior written permission.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+ * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+ * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+ * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+ * FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+ * DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+ * SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+ * CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+ * OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+ * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *
+ **************************************************************************************************/
+
+// Command line options parsing
+using RasterOrderOptions = cutlass::gemm::kernel::detail::RasterOrderOptions;
+template<typename _ProblemShape>
+struct Options {
+  using ProblemShape = _ProblemShape;
+
+  bool help = false;
+
+  float alpha = 1.f, beta = 0.f;
+  int iterations = 1000;
+  int m = 1024, n = 512, k = 1024, groups = 10;
+  std::string benchmark_path;
+  std::vector<typename ProblemShape::UnderlyingProblemShape> problem_sizes_after_alignment_host;
+  std::vector<typename ProblemShape::UnderlyingProblemShape> problem_sizes_host;
+  int const tma_alignment_bits = 128;
+  int const alignment = tma_alignment_bits / cutlass::sizeof_bits<cutlass::float_e4m3_t>::value;
+  int const k_alignment = 128;
+  int const m_alignment = 128;
+  int const n_alignment = 128;
+
+  RasterOrderOptions raster_order;
+  int swizzle;
+
+  // Parses the command line
+  void parse(int argc, char const **args) {
+    cutlass::CommandLine cmd(argc, args);
+
+    if (cmd.check_cmd_line_flag("help")) {
+      help = true;
+      return;
+    }
+
+    cmd.get_cmd_line_argument("m", m);
+    cmd.get_cmd_line_argument("n", n);
+    cmd.get_cmd_line_argument("k", k);
+    cmd.get_cmd_line_argument("groups", groups);
+    cmd.get_cmd_line_argument("alpha", alpha, 1.f);
+    cmd.get_cmd_line_argument("beta", beta, 0.f);
+    cmd.get_cmd_line_argument("iterations", iterations);
+
+    char raster_char;
+    cmd.get_cmd_line_argument("raster", raster_char);
+
+    if (raster_char == 'N' || raster_char == 'n') {
+      raster_order = RasterOrderOptions::AlongN;
+    }
+    else if (raster_char == 'M' || raster_char == 'm') {
+      raster_order = RasterOrderOptions::AlongM;
+    }
+    else if (raster_char == 'H' || raster_char == 'h') {
+      raster_order = RasterOrderOptions::Heuristic;
+    }
+
+    cmd.get_cmd_line_argument("swizzle", swizzle, 1);
+    cmd.get_cmd_line_argument("benchmark", benchmark_path);
+
+    // Decide how to initialize the problems
+    if (!benchmark_path.empty()) {
+      if (!benchmark_problems()) {
+        problem_sizes_after_alignment_host.clear();
+        problem_sizes_host.clear();
+        return;
+      }
+    }
+    else {
+      randomize_problems(cmd);
+    }
+
+  }
+
+  void randomize_problems(cutlass::CommandLine &cmd) {
+    int cmd_line_m = -1, cmd_line_n = -1, cmd_line_k = -1;
+    cmd.get_cmd_line_argument("m", cmd_line_m);
+    cmd.get_cmd_line_argument("n", cmd_line_n);
+    cmd.get_cmd_line_argument("k", cmd_line_k);
+
+    problem_sizes_after_alignment_host.reserve(groups);
+    problem_sizes_host.reserve(groups);
+    for (int i = groups; i > 0; i--) {
+      int m = cmd_line_m;
+      int n = cmd_line_n;
+      int k = cmd_line_k;
+      if (m < 1) {
+        m = m_alignment * ((rand() % (64 * alignment / m_alignment)) + 1);
+      }
+      if (n < 1) {
+        n = n_alignment * ((rand() % (64 * alignment / n_alignment)) + 1);
+      }
+      if (k < 1) {
+        k = k_alignment * ((rand() % (32 * alignment / k_alignment)) + 1);
+      }
+      problem_sizes_after_alignment_host.push_back({m, n, k});
+      problem_sizes_host.push_back({m, n, k});
+    }
+  }
+
+  /// Load a benchmark
+  bool benchmark_problems() {
+    std::ifstream file(benchmark_path);
+    if (!file.good()) {
+      return false;
+    }
+
+    while (file.good()) {
+
+      int idx = -1;
+      std::string extent_str;
+
+      file >> idx >> extent_str;
+
+      if (idx < 0 || extent_str.empty()) {
+        break;
+      }
+
+      cutlass::gemm::GemmCoord extent_after_alignment, extent;
+      std::vector<std::string> tokens;
+
+      cutlass::CommandLine::tokenize(tokens, extent_str, 'x');
+
+      for (int i = 0; i < int(tokens.size()); ++i) {
+        int x = std::atoi(tokens.at(i).c_str());
+
+        extent.at(i) = x;
+        // round up
+        if (x % alignment) {
+          x += (alignment - (x % alignment));
+        }
+
+        extent_after_alignment.at(i) = x;
+      }
+
+      problem_sizes_after_alignment_host.push_back({extent_after_alignment.m(), extent_after_alignment.n(), extent_after_alignment.k()});
+      problem_sizes_host.push_back({extent.m(), extent.n(), extent.k()});
+    }
+    groups = static_cast<int>(problem_sizes_after_alignment_host.size());
+
+    return true;
+  }
+
+  /// Calculate memory bandwidth statistics
+  template <class ElementA, 
+            class ElementB,
+            class ElementC,
+            class ElementD,
+            class ElementBlockScale,
+            class TileShape,
+            int ScaleMsPerTile,
+            int ScaleNsPerTile>
+  auto gbps(double runtime_s) const {
+    double total_read_bytes = 0;
+    double total_write_bytes = 0;
+    
+    // Calculate bytes read and written for each problem
+    for (int i = 0; i < groups; ++i) {
+      auto problem = problem_sizes_host.at(i);
+      auto M = cute::get<0>(problem);
+      auto N = cute::get<1>(problem);
+      auto K = cute::get<2>(problem);
+      
+      if (M > 0) {  // Only count active problems
+        // Matrix A: M*K elements read
+        total_read_bytes += M * K * sizeof(ElementA);
+        
+        // Matrix B: K*N elements read
+        total_read_bytes += K * N * sizeof(ElementB);
+        
+        // Matrix C: M*N elements read (for beta operation)
+        total_read_bytes += M * N * sizeof(ElementC);
+        
+        // Block scales for A and B
+        auto blockscale_shape = cute::shape(cute::get<1>(cute::zipped_divide(cute::make_layout(problem), TileShape{})));
+        auto blockscale_m = cute::get<0>(blockscale_shape);
+        auto blockscale_n = cute::get<1>(blockscale_shape);
+        auto blockscale_k = cute::get<2>(blockscale_shape);
+        auto groupscale_m = blockscale_m * ScaleMsPerTile;
+        auto groupscale_n = blockscale_n * ScaleNsPerTile;
+        
+        total_read_bytes += groupscale_m * blockscale_k * sizeof(ElementBlockScale);  // A scales
+        total_read_bytes += groupscale_n * blockscale_k * sizeof(ElementBlockScale);  // B scales
+        
+        // Matrix D: M*N elements written
+        total_write_bytes += M * N * sizeof(ElementD);
+      }
+    }
+
+    return (total_read_bytes + total_write_bytes) / 1.0e9 / runtime_s;
+  }
+
+  double bandwidth_util(double eff_bandwidth) const {
+    int memoryClockRate;
+    int memoryBusWidth;
+    cudaDeviceGetAttribute(&memoryClockRate, cudaDevAttrMemoryClockRate, 0);
+    cudaDeviceGetAttribute(&memoryBusWidth, cudaDevAttrGlobalMemoryBusWidth , 0);
+    double bw = 2.0 * memoryClockRate * (memoryBusWidth / 8) / 1.0e6;
+    return eff_bandwidth / bw * 100.0;
+  }
+
+  /// Prints the usage statement.
+  std::ostream & print_usage(std::ostream &out) const {
+
+    out << "68_hopper_fp8_warp_specialized_grouped_gemm_with_blockwise_scaling\n\n"
+      << "  Hopper FP8 Grouped GEMM using a Warp Specialized kernel with Blockwise Scaling.\n\n"
+      << "Options:\n\n"
+      << "  --help                      If specified, displays this usage statement\n\n"
+      << "  --m=<int>                   Sets the M extent of the GEMM\n"
+      << "  --n=<int>                   Sets the N extent of the GEMM\n"
+      << "  --k=<int>                   Sets the K extent of the GEMM\n"
+      << "  --groups=<int>              Sets the number of individual GEMM problems for Grouped GEMM\n"
+      << "  --alpha=<f32>               Epilogue scalar alpha\n"
+      << "  --beta=<f32>                Epilogue scalar beta\n"
+      << "  --raster=<char>             CTA Rasterization direction (N for along N, M for along M, and H for heuristic)\n\n"
+      << "  --swizzle=<int>             CTA Rasterization swizzle\n\n"
+      << "  --benchmark=<str>           Executes a benchmark problem size.\n\n"
+      << "  --iterations=<int>          Number of profiling iterations to perform.\n\n";
+
+    out
+      << "\n\nExamples:\n\n"
+      << "$ " << "68_hopper_fp8_warp_specialized_grouped_gemm_with_blockwise_scaling" << " --m=1024 --n=512 --k=1024 --groups=10 --alpha=2 --beta=0.707 \n\n";
+
+    return out;
+  }
+
+  /// Compute performance in GFLOP/s
+  double gflops(double runtime_s) const
+  {
+    // Number of real-valued multiply-adds
+    uint64_t fmas = 0ull;
+
+    for (auto const [m, n, k] : problem_sizes_host) {
+      fmas += static_cast<uint64_t>(m) *
+              static_cast<uint64_t>(n) *
+              static_cast<uint64_t>(k);
+    }
+    // Two flops per multiply-add
+    uint64_t flop = uint64_t(2) * uint64_t(fmas);
+    double gflop = double(flop) / double(1.0e9);
+    return gflop / runtime_s;
+  }
+};

examples/69_hopper_mixed_dtype_grouped_gemm/69_hopper_int4_bf16_grouped_gemm.cu

@@ -374,7 +374,7 @@ void allocate(Options const& options) {
     auto N = get<1>(problem);
     auto K = get<2>(problem);
 
-    const int scale_k = 1;
+  int const scale_k = cutlass::ceil_div(options.k, options.c);
 
     offset_A.push_back(total_elements_A);
     offset_B.push_back(total_elements_B * cutlass::sizeof_bits<QuantType>::value / 8);
@@ -510,7 +510,7 @@ void initialize(Options &options) {
   beta_device.copy_from_host(ptr_beta_host.data());
 
   initialize_tensor(block_A, seed + 2023);
-  initialize_quant_tensor(block_B, seed + 2022);
+  initialize_tensor(block_B, seed + 2022);
   initialize_tensor(block_C, seed + 2021);
   initialize_scale(block_scale, options);
   initialize_zero(block_zero, options);
@@ -519,13 +519,13 @@ void initialize(Options &options) {
 
   
   for (int32_t i = 0; i < options.groups; ++i) {
-    const int scale_k = 1;
+    int const scale_k = cutlass::ceil_div(options.k, options.c);
     auto shape_B = cute::make_shape(cute::get<1>(options.problem_sizes_host[i]), cute::get<2>(options.problem_sizes_host[i]), Int<1>{});
     auto shape_scale = cute::make_shape(cute::get<1>(options.problem_sizes_host[i]), scale_k, Int<1>{});
     auto layout_B = make_layout(shape_B, stride_B_host.at(i));
     auto layout_scale = make_layout(shape_scale, stride_S_host_ref.at(i));
     cudaStream_t stream = cudaStreamDefault;
-    cutlass::dequantize(block_B_dq.get() + offset_B_dq.at(i), block_B.get() + offset_B.at(i), layout_B, block_scale.get() + offset_scale.at(i), block_zero.get() + offset_zero.at(i), layout_scale, options.k, stream);
+    cutlass::dequantize(block_B_dq.get() + offset_B_dq.at(i), block_B.get() + offset_B.at(i), layout_B, block_scale.get() + offset_scale.at(i), block_zero.get() + offset_zero.at(i), layout_scale, options.c, stream);
   }
 
   problem_sizes.reset(options.groups);
@@ -619,7 +619,7 @@ typename Gemm::Arguments args_from_options(Options const& options, bool host_pro
     arguments = Args {
       cutlass::gemm::GemmUniversalMode::kGrouped,
       {options.groups, problem_sizes.get(), nullptr},
-      {ptr_B.get(), dB, ptr_A.get(), stride_A.get(), ptr_scale.get(), stride_S.get(), options.k},
+      {ptr_B.get(), dB, ptr_A.get(), stride_A.get(), ptr_scale.get(), stride_S.get(), options.c},
       {fusion_args, ptr_C.get(), stride_C.get(), ptr_D.get(), stride_D.get()},
       hw_info
     };
@@ -676,6 +676,7 @@ bool verify(Options const& options) {
 
   for (int32_t i = 0; i < options.groups; ++i) {
     auto problem = options.problem_sizes_host.at(i);
+    // we don't swap and transpose in the verify so revert the problem shape.
     auto N = get<0>(problem);
     auto M = get<1>(problem);
     auto K = get<2>(problem);
@@ -712,7 +713,7 @@ bool verify(Options const& options) {
       CUDA_CHECK(cudaDeviceSynchronize());
 
       passed &= cutlass::reference::device::BlockCompareRelativelyEqual(block_ref_D.get() + offset_D.at(i), block_D.get() + offset_D.at(i), M * N, epsilon, non_zero_floor);
-      std::cout << "Group: " << i << " Status: " << passed << std::endl;
+      std::cout << "Group " << i << ": " << options.problem_sizes_host[i] << ", alpha: " << alpha_host[i] << ", beta: " << beta_host[i] << " Status: " << passed << std::endl;
     }
   }
   return passed;

examples/69_hopper_mixed_dtype_grouped_gemm/69_hopper_int4_fp8_grouped_gemm.cu

@@ -341,7 +341,7 @@ void allocate(Options const& options) {
     auto N = get<1>(problem);
     auto K = get<2>(problem);
 
-    const int scale_k = 1;
+    int const scale_k = cutlass::ceil_div(options.k, options.c);
 
     offset_A.push_back(total_elements_A);
     offset_B.push_back(total_elements_B * cutlass::sizeof_bits<QuantType>::value / 8);
@@ -479,7 +479,7 @@ void initialize(Options& options) {
   beta_device.copy_from_host(ptr_beta_host.data());
 
   initialize_tensor(block_A, seed + 2023);
-  initialize_quant_tensor(block_B, seed + 2022);
+  initialize_tensor(block_B, seed + 2022);
   cutlass::unified_encode_int4b(block_B.get(), block_B_modified.get(), block_B.size());
   initialize_tensor(block_C, seed + 2021);
   initialize_scale(block_scale, options);
@@ -565,7 +565,7 @@ typename Gemm::Arguments args_from_options(Options const& options, bool host_pro
   arguments = Args {
     cutlass::gemm::GemmUniversalMode::kGrouped,
     {options.groups, problem_sizes.get(), nullptr},
-    {ptr_B.get(), dB, ptr_A.get(), stride_A.get(), ptr_scale_packed.get(), stride_S.get(), options.k},
+    {ptr_B.get(), dB, ptr_A.get(), stride_A.get(), ptr_scale_packed.get(), stride_S.get(), options.c},
     {fusion_args, ptr_C.get(), stride_C.get(), ptr_D.get(), stride_D.get()},
     hw_info
   };
@@ -617,6 +617,7 @@ bool verify(Options const& options) {
 
   for (int32_t i = 0; i < options.groups; ++i) {
     auto problem = options.problem_sizes_host.at(i);
+    // we don't swap and transpose in the verify so revert the problem shape.
     auto N = get<0>(problem);
     auto M = get<1>(problem);
     auto K = get<2>(problem);
@@ -630,11 +631,11 @@ bool verify(Options const& options) {
       stride_A_verif = cutlass::make_cute_packed_stride(StrideA_verif{}, cute::make_shape(M, K, 1));
       stride_B_verif = cutlass::make_cute_packed_stride(StrideB_verif{}, cute::make_shape(N, K, 1));
 
-      const int scale_k = 1;
+      int const scale_k = cutlass::ceil_div(options.k, options.c);
       auto layout_B = make_layout(cute::make_shape(N, K, Int<1>{}), stride_B_host.at(i));
       auto layout_scale_zero = make_layout(cute::make_shape(N, scale_k, Int<1>{}), stride_S_host_ref.at(i));
       cudaStream_t stream = cudaStreamDefault;
-      cutlass::dequantize(block_B_dq.get() + offset_B_dq.at(i), block_B.get() + offset_B.at(i), layout_B, block_scale.get() + offset_scale.at(i), block_zero.get() + offset_zero.at(i), layout_scale_zero, options.k, stream);
+      cutlass::dequantize(block_B_dq.get() + offset_B_dq.at(i), block_B.get() + offset_B.at(i), layout_B, block_scale.get() + offset_scale.at(i), block_zero.get() + offset_zero.at(i), layout_scale_zero, options.c, stream);
 
       //
       // Compute reference output
@@ -659,7 +660,7 @@ bool verify(Options const& options) {
       CUDA_CHECK(cudaDeviceSynchronize());
 
       passed &= cutlass::reference::device::BlockCompareRelativelyEqual(block_ref_D.get() + offset_D.at(i), block_D.get() + offset_D.at(i), M * N, epsilon, non_zero_floor);
-      std::cout << "Group: " << i << " Status: " << passed << std::endl;
+      std::cout << "Group " << i << ": " << options.problem_sizes_host[i] << ", alpha: " << alpha_host[i] << ", beta: " << beta_host[i] << " Status: " << passed << std::endl;
     }
   }
   return passed;

examples/69_hopper_mixed_dtype_grouped_gemm/69_hopper_mixed_dtype_grouped_gemm.cu

@@ -282,7 +282,7 @@ void allocate(Options const& options) {
     auto N = get<1>(problem);
     auto K = get<2>(problem);
 
-    const int scale_k = 1;
+    int const scale_k = cutlass::ceil_div(options.k, options.c);
 
     offset_A.push_back(total_elements_A);
     offset_B.push_back(total_elements_B * cutlass::sizeof_bits<QuantType>::value / 8);
@@ -418,7 +418,7 @@ void initialize(Options &options) {
   beta_device.copy_from_host(ptr_beta_host.data());
 
   initialize_tensor(block_A, seed + 2023);
-  initialize_quant_tensor(block_B, seed + 2022);
+  initialize_tensor(block_B, seed + 2022);
   initialize_tensor(block_C, seed + 2021);
   initialize_scale(block_scale, options);
   initialize_zero(block_zero, options);
@@ -485,7 +485,7 @@ typename Gemm::Arguments args_from_options(Options const& options, bool host_pro
     arguments = typename Gemm::Arguments {
       cutlass::gemm::GemmUniversalMode::kGrouped,
       {options.groups, problem_sizes.get(), nullptr},
-      {ptr_B.get(), stride_B.get(), ptr_A.get(), stride_A.get(), ptr_scale.get(), stride_S.get(), options.k},
+      {ptr_B.get(), stride_B.get(), ptr_A.get(), stride_A.get(), ptr_scale.get(), stride_S.get(), options.c},
       {fusion_args, ptr_C.get(), stride_C.get(), ptr_D.get(), stride_D.get()},
       hw_info
     };
@@ -542,6 +542,7 @@ bool verify(Options const& options) {
 
   for (int32_t i = 0; i < options.groups; ++i) {
     auto problem = options.problem_sizes_host.at(i);
+    // we don't swap and transpose in the verify so revert the problem shape.
     auto N = get<0>(problem);
     auto M = get<1>(problem);
     auto K = get<2>(problem);
@@ -555,11 +556,11 @@ bool verify(Options const& options) {
       stride_A_verif = cutlass::make_cute_packed_stride(StrideA_verif{}, cute::make_shape(M, K, 1));
       stride_B_verif = cutlass::make_cute_packed_stride(StrideB_verif{}, cute::make_shape(N, K, 1));
 
-      const int scale_k = 1;
+      int const scale_k = cutlass::ceil_div(options.k, options.c);
       auto layout_B = make_layout(cute::make_shape(N, K, Int<1>{}), stride_B_host.at(i));
       auto layout_scale_zero = make_layout(cute::make_shape(N, scale_k, Int<1>{}), stride_S_host_ref.at(i));
       cudaStream_t stream = cudaStreamDefault;
-      cutlass::dequantize(block_B_dq.get() + offset_B_dq.at(i), block_B.get() + offset_B.at(i), layout_B, block_scale.get() + offset_scale.at(i), block_zero.get() + offset_zero.at(i), layout_scale_zero, options.k, stream);
+      cutlass::dequantize(block_B_dq.get() + offset_B_dq.at(i), block_B.get() + offset_B.at(i), layout_B, block_scale.get() + offset_scale.at(i), block_zero.get() + offset_zero.at(i), layout_scale_zero, options.c, stream);
 
       //
       // Compute reference output
@@ -584,7 +585,7 @@ bool verify(Options const& options) {
       CUDA_CHECK(cudaDeviceSynchronize());
 
       passed &= cutlass::reference::device::BlockCompareRelativelyEqual(block_ref_D.get() + offset_D.at(i), block_D.get() + offset_D.at(i), M * N, epsilon, non_zero_floor);
-      std::cout << "Group: " << i << " Status: " << passed << std::endl;
+      std::cout << "Group " << i << ": " << options.problem_sizes_host[i] << ", alpha: " << alpha_host[i] << ", beta: " << beta_host[i] << " Status: " << passed << std::endl;
     }
   }
   return passed;

examples/69_hopper_mixed_dtype_grouped_gemm/CMakeLists.txt

@@ -50,6 +50,7 @@ set(TEST_RANDOM_PERF_LARGE_GROUP --groups=100 --iterations=10)
 set(TEST_DIRECT_BATCHED --m=2048 --n=5120 --k=8192 --mode=0 --iterations=0)              # Direct conversion
 
 set(TEST_SCALE_PERCOL --m=4096 --n=5120 --k=8192 --c=8192 --mode=1 --iterations=0)       # Per Column scaling
+set(TEST_SCALE_GROUP --m=2048 --n=5120 --k=8192 --c=512 --mode=1 --iterations=0)         # Group-wise scaling
 
 cutlass_example_add_executable(
   69_hopper_mixed_dtype_grouped_gemm
@@ -69,6 +70,7 @@ cutlass_example_add_executable(
   TEST_RANDOM_PERF_LARGE_GROUP
   TEST_DIRECT_BATCHED
   TEST_SCALE_PERCOL
+  TEST_SCALE_GROUP
 )
 
 cutlass_example_add_executable(
@@ -89,6 +91,7 @@ cutlass_example_add_executable(
   TEST_RANDOM_PERF_LARGE_GROUP
   TEST_DIRECT_BATCHED
   TEST_SCALE_PERCOL
+  TEST_SCALE_GROUP
 )
 
 cutlass_example_add_executable(
@@ -109,4 +112,5 @@ cutlass_example_add_executable(
   TEST_RANDOM_PERF_LARGE_GROUP
   TEST_DIRECT_BATCHED
   TEST_SCALE_PERCOL
+  TEST_SCALE_GROUP
 )

examples/69_hopper_mixed_dtype_grouped_gemm/README.md

@@ -7,8 +7,40 @@ This example shows how to perform Grouped GEMMs on Hopper when A and B have diff
 - in the arguments, pass the group size, array of the problem sizes, and the array of strides for matrix A and B.
 - if scales and zero-points are included, also pass the array of their strides in the arguments.
 
-Note that in Example 55, the argument `--g` is used to determine the block scale size. It is important not to confuse this with the `--groups` argument in this example, which specifies the number of GEMMs.
+Note that in Example 55, the argument `--g` is used to determine the group size of scaling. To avoid confusion with the `--groups` argument in this example, which defines the number of GEMMs, `--c` is used here to represent the group size for scaling.
 
 ## Upcoming features
 
-Currently, the Mixed-input Grouped GEMM only supports row-wise scaling. Please contact us if zero-points or block-wise scaling are needed.
+Currently, the Mixed-input Grouped GEMM only supports row-wise scaling, and group-wise scaling for identical problem shapes across all groups. Please contact us if zero-points or block-wise scaling are needed.
+
+## Copyright
+
+Copyright (c) 2017 - 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+SPDX-License-Identifier: BSD-3-Clause
+
+```
+  Redistribution and use in source and binary forms, with or without
+  modification, are permitted provided that the following conditions are met:
+
+  1. Redistributions of source code must retain the above copyright notice, this
+  list of conditions and the following disclaimer.
+
+  2. Redistributions in binary form must reproduce the above copyright notice,
+  this list of conditions and the following disclaimer in the documentation
+  and/or other materials provided with the distribution.
+
+  3. Neither the name of the copyright holder nor the names of its
+  contributors may be used to endorse or promote products derived from
+  this software without specific prior written permission.
+
+  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+  AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+  IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+  DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+  FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+  DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+  SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+  CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+  OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+  OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+```

examples/69_hopper_mixed_dtype_grouped_gemm/grouped_mixed_dtype_utils.hpp

@@ -58,6 +58,7 @@ public:
     void parse(int argc, char const **args) {
         cutlass::CommandLine cmd(argc, args);
         cmd.get_cmd_line_argument("groups", groups);
+        cmd.get_cmd_line_argument("benchmark", benchmark_path);
         cmd.get_cmd_line_argument("c", c);
         MixedDtypeOptions::parse(argc, args);
 
@@ -71,6 +72,7 @@ public:
             << "  --m=<int>                   Sets the M extent of the GEMM for all groups\n"
             << "  --n=<int>                   Sets the N extent of the GEMM for all groups\n"
             << "  --k=<int>                   Sets the K extent of the GEMM for all groups\n"
+            << "  --c=<int>                   Sets the chunk size for scaling the quantized weights\n"
             << "  --groups=<int>              Sets the number of individual GEMM problems\n"
             << "  --mode=<int>                The mode to run the gemm\n"
             << "  --alpha=<f32>               Epilogue scalar alpha\n"
@@ -183,11 +185,6 @@ void grouped_mixed_dtype_profiling(
 
     result.avg_runtime_ms = std::accumulate(runtimes.begin(), runtimes.end(), 0.0f) / runtimes.size();
     result.gflops = options.gflops(result.avg_runtime_ms / 1000.0);
-
-    std::cout << "  Problem Sizes, Alpha, Beta\n";
-    for (int32_t i = 0; i < options.groups; ++i) {
-        std::cout << "    " << options.problem_sizes_host[i] << ", " << alpha_host[i] << ", " << beta_host[i] << '\n';
-    }
     std::cout << "  Groups      : " << options.groups << '\n'
               << "  Avg runtime : " << result.avg_runtime_ms << " ms\n"
               << "  GFLOPS      : " << result.gflops << '\n';

examples/70_blackwell_gemm/70_blackwell_fp16_gemm.cu

@@ -194,12 +194,14 @@ struct Options {
   float alpha, beta;
   int iterations;
   int m, n, k;
+  int swizzle;
 
   Options():
     help(false),
     m(8192), n(8192), k(8192),
     alpha(1.f), beta(0.f),
-    iterations(10)
+    iterations(10),
+    swizzle(0)
   { }
 
   // Parses the command line
@@ -217,6 +219,7 @@ struct Options {
     cmd.get_cmd_line_argument("alpha", alpha, 1.f);
     cmd.get_cmd_line_argument("beta", beta, 0.f);
     cmd.get_cmd_line_argument("iterations", iterations);
+    cmd.get_cmd_line_argument("swizzle", swizzle);
   }
 
   /// Prints the usage statement.
@@ -231,6 +234,7 @@ struct Options {
       << "  --k=<int>                   Sets the K extent of the GEMM\n"
       << "  --alpha=<f32>               Epilogue scalar alpha\n"
       << "  --beta=<f32>                Epilogue scalar beta\n\n"
+      << "  --swizzle=<int>             Cluster rasterization swizzle\n\n"
       << "  --iterations=<int>          Number of profiling iterations to perform.\n\n";
 
     out
@@ -331,6 +335,8 @@ typename Gemm::Arguments args_from_options(const Options &options)
     {{options.alpha, options.beta}, block_C.get(), stride_C, block_D.get(), stride_D}
   };
 
+  arguments.scheduler.max_swizzle_size = options.swizzle;
+
   return arguments;
 }
 

examples/70_blackwell_gemm/70_blackwell_fp8_gemm.cu

@@ -231,6 +231,7 @@ struct Options {
   bool save_amax = true;
   int iterations = 1000;
   int m = 1024, n = 512, k = 1024, l = 1;
+  int swizzle = 0;
 
   // Parses the command line
   void parse(int argc, char const **args) {
@@ -256,6 +257,7 @@ struct Options {
     cmd.get_cmd_line_argument("save_aux", save_aux, true);
     cmd.get_cmd_line_argument("save_amax", save_amax, true);
     cmd.get_cmd_line_argument("iterations", iterations);
+    cmd.get_cmd_line_argument("swizzle", swizzle);
   }
 
   /// Prints the usage statement.
@@ -271,6 +273,7 @@ struct Options {
       << "  --l=<int>                   Sets the l extent (batch) of the GEMM\n"
       << "  --alpha=<f32>               Epilogue scalar alpha\n"
       << "  --beta=<f32>                Epilogue scalar beta\n"
+      << "  --swizzle=<int>             Cluster rasterization swizzle\n"
       << "  --scale_a=<f32>             Scaling factor for A\n"
       << "  --scale_b=<f32>             Scaling factor for B\n"
       << "  --scale_c=<f32>             Scaling factor for C\n"
@@ -476,6 +479,8 @@ typename Gemm::Arguments args_from_options(const Options &options)
     fusion_args.amax_D_ptr = abs_max_D.device_data();
   }
 
+  arguments.scheduler.max_swizzle_size = options.swizzle;
+
   return arguments;
 }
 

examples/70_blackwell_gemm/CMakeLists.txt

@@ -28,14 +28,29 @@
 # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 
 
-if (CUTLASS_NVCC_ARCHS MATCHES 100a)
+set(TEST_SWIZZLE_1 --swizzle=1)
+set(TEST_SWIZZLE_2 --swizzle=2)
+set(TEST_SWIZZLE_5 --swizzle=5)
+set(TEST_SWIZZLE_5_UNEVEN --swizzle=5 --m=4096 --n=16384)
+
+if(NOT CUTLASS_NVCC_ARCHS STREQUAL "100")
 cutlass_example_add_executable(
   70_blackwell_fp16_gemm
   70_blackwell_fp16_gemm.cu
-)  
+  TEST_COMMAND_OPTIONS
+  TEST_SWIZZLE_1
+  TEST_SWIZZLE_2
+  TEST_SWIZZLE_5
+  TEST_SWIZZLE_5_UNEVEN
+)
 
 cutlass_example_add_executable(
   70_blackwell_fp8_gemm
   70_blackwell_fp8_gemm.cu
+  TEST_COMMAND_OPTIONS
+  TEST_SWIZZLE_1
+  TEST_SWIZZLE_2
+  TEST_SWIZZLE_5
+  TEST_SWIZZLE_5_UNEVEN
 )
 endif()

examples/71_blackwell_gemm_with_collective_builder/71_blackwell_gemm_with_collective_builder.cu

@@ -74,12 +74,14 @@ struct Options {
 
   int m, n, k, l;
   float alpha, beta;
+  int swizzle;
 
   Options():
     help(false),
     error(false),
     m(2048), n(2048), k(2048), l(1),
-    alpha(1.f), beta(0.f)
+    alpha(1.f), beta(0.f),
+    swizzle(0)
   { }
 
   // Parses the command line
@@ -97,6 +99,7 @@ struct Options {
     cmd.get_cmd_line_argument("l", l, 1);
     cmd.get_cmd_line_argument("alpha", alpha, 1.f);
     cmd.get_cmd_line_argument("beta", beta, 0.f);
+    cmd.get_cmd_line_argument("swizzle", swizzle);
   }
 
   /// Prints the usage statement.
@@ -112,7 +115,8 @@ struct Options {
       << "  --k=<int>                   Sets the K extent of the GEMM\n"
       << "  --l=<int>                   Sets the L extent (batch count) of the GEMM\n"
       << "  --alpha=<f32>               Epilogue scalar alpha\n"
-      << "  --beta=<f32>                Epilogue scalar beta\n\n";
+      << "  --beta=<f32>                Epilogue scalar beta\n"
+      << "  --swizzle=<int>             Cluster rasterization swizzle\n\n";
 
     return out;
   }
@@ -352,6 +356,8 @@ struct ExampleRunner {
       hw_info
     };
 
+    arguments.scheduler.max_swizzle_size = options.swizzle;
+
     // See example 48 for details on custom EVT construction
     if constexpr (UseCustomEVT) {
       arguments.epilogue.thread =

examples/72_blackwell_narrow_precision_gemm/72a_blackwell_nvfp4_bf16_gemm.cu

@@ -40,10 +40,10 @@
 
     Similar to 70_blackwell_gemm, this kernel leverages:
     1. Per-SM memory called Tensor Memory (TMEM)  (Please refer to CUDA 12.8 docs on https://docs.nvidia.com/cuda/).
-    
-    2. The extended warp-specialized kernel design introduced in Hopper enabled by use of TMEM 
-    which allows us to decouple the execution of MMA and epilogue into separate warps. 
-    
+
+    2. The extended warp-specialized kernel design introduced in Hopper enabled by use of TMEM
+    which allows us to decouple the execution of MMA and epilogue into separate warps.
+
     3. A new SW controlled dynamic scheduler based on cluster launch control (See https://docs.nvidia.com/cuda/parallel-thread-execution).
 
     Usage:
@@ -119,7 +119,7 @@ using MmaTileShape        = Shape<_256,_256,_256>;                          // M
 using ClusterShape        = Shape<_4,_4,_1>;                                // Shape of the threadblocks in a cluster
 
 using CollectiveEpilogue = typename cutlass::epilogue::collective::CollectiveBuilder<
-    ArchTag, OperatorClass,                      
+    ArchTag, OperatorClass,
     MmaTileShape, ClusterShape,
     cutlass::epilogue::collective::EpilogueTileAuto,
     ElementAccumulator, ElementAccumulator,
@@ -190,13 +190,7 @@ cutlass::HostTensor<ElementD, cutlass::layout::PackedVectorLayout> block_referen
 
 template <typename T>
 auto make_iterator(T* ptr) {
-  using namespace cute;
-  if constexpr (cute::is_subbyte_v<T>) {
-    return subbyte_iterator<T>(ptr);
-  }
-  else {
-    return ptr;
-  }
+  return cute::recast_ptr<T>(ptr);
 }
 
 /////////////////////////////////////////////////////////////////////////////////////////////////
@@ -211,12 +205,14 @@ struct Options {
   float alpha, beta;
   int iterations;
   int m, n, k;
+  int swizzle = 0;
 
   Options():
     help(false),
     m(1024), n(1024), k(1024),
     alpha(1.f), beta(0.f),
-    iterations(10)
+    iterations(10),
+    swizzle(0)
   { }
 
   // Parses the command line
@@ -234,6 +230,7 @@ struct Options {
     cmd.get_cmd_line_argument("alpha", alpha, 1.f);
     cmd.get_cmd_line_argument("beta", beta, 0.f);
     cmd.get_cmd_line_argument("iterations", iterations);
+    cmd.get_cmd_line_argument("swizzle", swizzle);
   }
 
   /// Prints the usage statement.
@@ -247,7 +244,8 @@ struct Options {
       << "  --n=<int>                   Sets the N extent of the GEMM\n"
       << "  --k=<int>                   Sets the K extent of the GEMM\n"
       << "  --alpha=<f32>               Epilogue scalar alpha\n"
-      << "  --beta=<f32>                Epilogue scalar beta\n\n"
+      << "  --beta=<f32>                Epilogue scalar beta\n"
+      << "  --swizzle=<int>             Cluster rasterization swizzle\n"
       << "  --iterations=<int>          Number of profiling iterations to perform.\n\n";
 
     out << "\n\nExamples:\n\n"
@@ -325,7 +323,7 @@ bool initialize_block(
   }
   cutlass::reference::host::TensorFillRandomUniform(
     view, seed, scope_max, scope_min, 0);
-  
+
   return true;
 }
 
@@ -333,7 +331,7 @@ bool initialize_block(
 void initialize(const Options &options) {
   using namespace cute;
   // For SFA and SFB tensors layouts
-  using Sm100BlkScaledConfig =  typename Gemm::GemmKernel::CollectiveMainloop::Sm100BlkScaledConfig;
+  using Sm1xxBlkScaledConfig =  typename Gemm::GemmKernel::CollectiveMainloop::Sm1xxBlkScaledConfig;
 
   stride_A = cutlass::make_cute_packed_stride(StrideA{}, {options.m, options.k, 1});
   stride_B = cutlass::make_cute_packed_stride(StrideB{}, {options.n, options.k, 1});
@@ -344,8 +342,8 @@ void initialize(const Options &options) {
   layout_B = make_layout(make_shape(options.n, options.k, 1), stride_B);
   layout_C = make_layout(make_shape(options.m, options.n, 1), stride_C);
   layout_D = make_layout(make_shape(options.m, options.n, 1), stride_D);
-  layout_SFA = Sm100BlkScaledConfig::tile_atom_to_shape_SFA(cute::make_shape(options.m, options.n, options.k, 1));
-  layout_SFB = Sm100BlkScaledConfig::tile_atom_to_shape_SFB(cute::make_shape(options.m, options.n, options.k, 1));
+  layout_SFA = Sm1xxBlkScaledConfig::tile_atom_to_shape_SFA(cute::make_shape(options.m, options.n, options.k, 1));
+  layout_SFB = Sm1xxBlkScaledConfig::tile_atom_to_shape_SFB(cute::make_shape(options.m, options.n, options.k, 1));
 
   block_A.reset(cutlass::make_Coord(size(layout_A)));
   block_B.reset(cutlass::make_Coord(size(layout_B)));
@@ -387,6 +385,7 @@ typename Gemm::Arguments args_from_options(const Options &options)
     }
   };
 
+  arguments.scheduler.max_swizzle_size = options.swizzle;
   return arguments;
 }
 
@@ -408,7 +407,7 @@ bool verify(const Options &options) {
 
   auto tensor_C = cute::make_tensor(make_iterator(block_C.host_data()), layout_C);
   auto tensor_D = cute::make_tensor(make_iterator(block_reference_D.host_data()), layout_D);
- 
+
   cutlass::reference::host::GettBlockScalingEpilogueParams<
       ElementAccumulator,                   // ElementScalar
       ElementAccumulator,                   // ElementAccumulator
@@ -509,9 +508,9 @@ int main(int argc, char const **args) {
   cudaDeviceProp props;
   int current_device_id;
   CUDA_CHECK(cudaGetDevice(&current_device_id));
-  
+
   CUDA_CHECK(cudaGetDeviceProperties(&props, current_device_id));
-  
+
   if (!(props.major == 10 && props.minor == 0)) {
     std::cerr << "This example requires a GPU of NVIDIA's Blackwell architecture (compute capability 100)." << std::endl;
     return 0;

examples/72_blackwell_narrow_precision_gemm/72b_blackwell_nvfp4_nvfp4_gemm.cu

@@ -39,10 +39,10 @@
     1. Blockscaled tcgen05.mma instructions.
 
     2. Per-SM memory called Tensor Memory (TMEM)
-    
-    3. The extended warp-specialized kernel design introduced in Hopper enabled by use of TMEM 
-    which allows us to decouple the execution of MMA and epilogue into separate warps. 
-    
+
+    3. The extended warp-specialized kernel design introduced in Hopper enabled by use of TMEM
+    which allows us to decouple the execution of MMA and epilogue into separate warps.
+
     4. A new SW controlled dynamic scheduler based on cluster launch control (See https://docs.nvidia.com/cuda/parallel-thread-execution).
 
     Usage:
@@ -129,13 +129,13 @@ constexpr int OutputSFVectorSize = InputSFVectorSize;
 //      With BlockScaleFactor generation.
 using FusionOperation = cutlass::epilogue::fusion::LinCombBlockScaleFactor<
     OutputSFVectorSize,
-    ElementD, 
-    ElementCompute, 
+    ElementD,
+    ElementCompute,
     ElementSFD, LayoutSFDTag,
     ElementC>;
 
 using CollectiveEpilogue = typename cutlass::epilogue::collective::CollectiveBuilder<
-    ArchTag, OperatorClass,                     
+    ArchTag, OperatorClass,
     MmaTileShape, ClusterShape,
     cutlass::epilogue::collective::EpilogueTileAuto,
     ElementAccumulator, ElementAccumulator,
@@ -177,7 +177,7 @@ using LayoutD   = decltype(cute::make_layout(make_shape(0,0,0), StrideD{}));
 
 using FusionOp = typename Gemm::EpilogueOutputOp;
 constexpr bool IsBlockScaleSupported = FusionOp::IsBlockScaleSupported;
-using SfdOutputCfg = cutlass::detail::Sm100BlockScaledOutputConfig<OutputSFVectorSize>;
+using SfdOutputCfg = cutlass::detail::Sm1xxBlockScaledOutputConfig<OutputSFVectorSize>;
 using LayoutSFD = typename SfdOutputCfg::LayoutSF;
 
 //
@@ -219,13 +219,7 @@ cutlass::HostTensor<ElementCompute, cutlass::layout::PackedVectorLayout> block_N
 
 template <typename T>
 auto make_iterator(T* ptr) {
-  using namespace cute;
-  if constexpr (cute::is_subbyte_v<T>) {
-    return subbyte_iterator<T>(ptr);
-  }
-  else {
-    return ptr;
-  }
+  return cute::recast_ptr<T>(ptr);
 }
 
 /////////////////////////////////////////////////////////////////////////////////////////////////
@@ -240,12 +234,14 @@ struct Options {
   float alpha, beta;
   int iterations;
   int m, n, k;
+  int swizzle = 0;
 
   Options():
     help(false),
     m(1024), n(1024), k(1024),
     alpha(1.f), beta(0.f),
-    iterations(10)
+    iterations(10),
+    swizzle(0)
   { }
 
   // Parses the command line
@@ -263,6 +259,7 @@ struct Options {
     cmd.get_cmd_line_argument("alpha", alpha, 1.f);
     cmd.get_cmd_line_argument("beta", beta, 0.f);
     cmd.get_cmd_line_argument("iterations", iterations);
+    cmd.get_cmd_line_argument("swizzle", swizzle);
   }
 
   /// Prints the usage statement.
@@ -276,7 +273,8 @@ struct Options {
       << "  --n=<int>                   Sets the N extent of the GEMM\n"
       << "  --k=<int>                   Sets the K extent of the GEMM\n"
       << "  --alpha=<f32>               Epilogue scalar alpha\n"
-      << "  --beta=<f32>                Epilogue scalar beta\n\n"
+      << "  --beta=<f32>                Epilogue scalar beta\n"
+      << "  --swizzle=<int>             Cluster rasterization swizzle\n"
       << "  --iterations=<int>          Number of profiling iterations to perform.\n\n";
 
     out << "\n\nExamples:\n\n"
@@ -354,7 +352,7 @@ bool initialize_block(
   }
   cutlass::reference::host::TensorFillRandomUniform(
     view, seed, scope_max, scope_min, 0);
-  
+
   return true;
 }
 
@@ -362,9 +360,9 @@ bool initialize_block(
 void initialize(const Options &options) {
   using namespace cute;
   // For SFA and SFB tensors layouts
-  using Sm100BlkScaledConfig =  typename Gemm::GemmKernel::CollectiveMainloop::Sm100BlkScaledConfig;
+  using Sm1xxBlkScaledConfig =  typename Gemm::GemmKernel::CollectiveMainloop::Sm1xxBlkScaledConfig;
   // For SFD tensor layout
-  using Sm100BlockScaledOutputConfig =  typename Gemm::GemmKernel::CollectiveMainloop::Sm100BlkScaledConfig;
+  using Sm1xxBlockScaledOutputConfig=  typename Gemm::GemmKernel::CollectiveMainloop::Sm1xxBlkScaledConfig;
 
   stride_A = cutlass::make_cute_packed_stride(StrideA{}, {options.m, options.k, 1});
   stride_B = cutlass::make_cute_packed_stride(StrideB{}, {options.n, options.k, 1});
@@ -375,8 +373,8 @@ void initialize(const Options &options) {
   layout_B = make_layout(make_shape(options.n, options.k, 1), stride_B);
   layout_C = make_layout(make_shape(options.m, options.n, 1), stride_C);
   layout_D = make_layout(make_shape(options.m, options.n, 1), stride_D);
-  layout_SFA = Sm100BlkScaledConfig::tile_atom_to_shape_SFA(cute::make_shape(options.m, options.n, options.k, 1));
-  layout_SFB = Sm100BlkScaledConfig::tile_atom_to_shape_SFB(cute::make_shape(options.m, options.n, options.k, 1));
+  layout_SFA = Sm1xxBlkScaledConfig::tile_atom_to_shape_SFA(cute::make_shape(options.m, options.n, options.k, 1));
+  layout_SFB = Sm1xxBlkScaledConfig::tile_atom_to_shape_SFB(cute::make_shape(options.m, options.n, options.k, 1));
   layout_SFD = SfdOutputCfg::tile_atom_to_shape_SFD(cute::make_shape(options.m, options.n, options.k, 1));
 
   block_A.reset(cutlass::make_Coord(size(layout_A)));
@@ -432,6 +430,7 @@ typename Gemm::Arguments args_from_options(const Options &options)
     arguments.epilogue.thread.norm_constant_ptr      = block_Normconst.device_data();
   }
 
+  arguments.scheduler.max_swizzle_size = options.swizzle;
   return arguments;
 }
 
@@ -451,7 +450,7 @@ bool verify(const Options &options) {
       decltype(tensor_B),                   // TensorB
       decltype(tensor_SFB)                  // TensorSfB
     > mainloop_params{tensor_A, tensor_SFA, tensor_B, tensor_SFB};
-  
+
   Tensor tensor_C = cute::make_tensor(make_iterator(block_C.host_data()), layout_C);
   Tensor tensor_D = cute::make_tensor(make_iterator(block_reference_D.host_data()), layout_D);
   Tensor tensor_SFD = make_tensor(block_reference_SFD.host_data(), layout_SFD);
@@ -475,7 +474,12 @@ bool verify(const Options &options) {
   passed &= (cutlass::reference::host::TensorNorm(block_reference_D.host_view()) > 0);
   passed &= (cutlass::reference::host::TensorNorm(block_D.host_view()) > 0);
 
-  return passed;
+  block_SFD.sync_host();
+  bool passed_sfd = cutlass::reference::host::TensorEquals(block_reference_SFD.host_view(), block_SFD.host_view());
+  passed_sfd &= (cutlass::reference::host::TensorNorm(block_reference_SFD.host_view()) > 0);
+  passed_sfd &= (cutlass::reference::host::TensorNorm(block_SFD.host_view()) > 0);
+
+  return passed && passed_sfd;
 }
 
 /// Execute a given example GEMM computation
@@ -559,9 +563,9 @@ int main(int argc, char const **args) {
   cudaDeviceProp props;
   int current_device_id;
   CUDA_CHECK(cudaGetDevice(&current_device_id));
-  
+
   CUDA_CHECK(cudaGetDeviceProperties(&props, current_device_id));
-  
+
   if (!(props.major == 10 && props.minor == 0)) {
     std::cerr << "This example requires a GPU of NVIDIA's Blackwell architecture (compute capability 100)." << std::endl;
     return 0;

examples/72_blackwell_narrow_precision_gemm/72c_blackwell_mixed_mxfp8_bf16_gemm.cu

@@ -41,10 +41,10 @@
     1. Blockscaled tcgen05.mma instructions.
 
     2. Per-SM memory called Tensor Memory (TMEM) (Please refer to CUDA 12.8 docs on https://docs.nvidia.com/cuda/).
-    
-    3. The extended warp-specialized kernel design introduced in Hopper enabled by use of TMEM 
-    which allows us to decouple the execution of MMA and epilogue into separate warps. 
-    
+
+    3. The extended warp-specialized kernel design introduced in Hopper enabled by use of TMEM
+    which allows us to decouple the execution of MMA and epilogue into separate warps.
+
     4. A new SW controlled dynamic scheduler based on cluster launch control (See https://docs.nvidia.com/cuda/parallel-thread-execution).
 
     Usage:
@@ -120,7 +120,7 @@ using MmaTileShape        = Shape<_256,_256,_256>;                          // M
 using ClusterShape        = Shape<_4,_4,_1>;                                // Shape of the threadblocks in a cluster
 
 using CollectiveEpilogue = typename cutlass::epilogue::collective::CollectiveBuilder<
-    ArchTag, OperatorClass,                      
+    ArchTag, OperatorClass,
     MmaTileShape, ClusterShape,
     cutlass::epilogue::collective::EpilogueTileAuto,
     ElementAccumulator, ElementAccumulator,
@@ -191,13 +191,7 @@ cutlass::HostTensor<ElementD, cutlass::layout::PackedVectorLayout> block_referen
 
 template <typename T>
 auto make_iterator(T* ptr) {
-  using namespace cute;
-  if constexpr (cute::is_subbyte_v<T>) {
-    return subbyte_iterator<T>(ptr);
-  }
-  else {
-    return ptr;
-  }
+  return cute::recast_ptr<T>(ptr);
 }
 
 /////////////////////////////////////////////////////////////////////////////////////////////////
@@ -212,12 +206,14 @@ struct Options {
   float alpha, beta;
   int iterations;
   int m, n, k;
+  int swizzle = 0;
 
   Options():
     help(false),
     m(1024), n(1024), k(1024),
     alpha(1.f), beta(0.f),
-    iterations(10)
+    iterations(10),
+    swizzle(0)
   { }
 
   // Parses the command line
@@ -235,6 +231,7 @@ struct Options {
     cmd.get_cmd_line_argument("alpha", alpha, 1.f);
     cmd.get_cmd_line_argument("beta", beta, 0.f);
     cmd.get_cmd_line_argument("iterations", iterations);
+    cmd.get_cmd_line_argument("swizzle", swizzle);
   }
 
   /// Prints the usage statement.
@@ -248,7 +245,8 @@ struct Options {
       << "  --n=<int>                   Sets the N extent of the GEMM\n"
       << "  --k=<int>                   Sets the K extent of the GEMM\n"
       << "  --alpha=<f32>               Epilogue scalar alpha\n"
-      << "  --beta=<f32>                Epilogue scalar beta\n\n"
+      << "  --beta=<f32>                Epilogue scalar beta\n"
+      << "  --swizzle=<int>             Cluster rasterization swizzle\n"
       << "  --iterations=<int>          Number of profiling iterations to perform.\n\n";
 
     out << "\n\nExamples:\n\n"
@@ -326,7 +324,7 @@ bool initialize_block(
   }
   cutlass::reference::host::TensorFillRandomUniform(
     view, seed, scope_max, scope_min, 0);
-  
+
   return true;
 }
 
@@ -334,7 +332,7 @@ bool initialize_block(
 void initialize(const Options &options) {
   using namespace cute;
   // For SFA and SFB tensors layouts
-  using Sm100BlkScaledConfig =  typename Gemm::GemmKernel::CollectiveMainloop::Sm100BlkScaledConfig;
+  using Sm1xxBlkScaledConfig =  typename Gemm::GemmKernel::CollectiveMainloop::Sm1xxBlkScaledConfig;
 
   stride_A = cutlass::make_cute_packed_stride(StrideA{}, {options.m, options.k, 1});
   stride_B = cutlass::make_cute_packed_stride(StrideB{}, {options.n, options.k, 1});
@@ -345,8 +343,8 @@ void initialize(const Options &options) {
   layout_B = make_layout(make_shape(options.n, options.k, 1), stride_B);
   layout_C = make_layout(make_shape(options.m, options.n, 1), stride_C);
   layout_D = make_layout(make_shape(options.m, options.n, 1), stride_D);
-  layout_SFA = Sm100BlkScaledConfig::tile_atom_to_shape_SFA(cute::make_shape(options.m, options.n, options.k, 1));
-  layout_SFB = Sm100BlkScaledConfig::tile_atom_to_shape_SFB(cute::make_shape(options.m, options.n, options.k, 1));
+  layout_SFA = Sm1xxBlkScaledConfig::tile_atom_to_shape_SFA(cute::make_shape(options.m, options.n, options.k, 1));
+  layout_SFB = Sm1xxBlkScaledConfig::tile_atom_to_shape_SFB(cute::make_shape(options.m, options.n, options.k, 1));
 
   block_A.reset(cutlass::make_Coord(size(layout_A)));
   block_B.reset(cutlass::make_Coord(size(layout_B)));
@@ -388,6 +386,7 @@ typename Gemm::Arguments args_from_options(const Options &options)
     }
   };
 
+  arguments.scheduler.max_swizzle_size = options.swizzle;
   return arguments;
 }
 
@@ -409,7 +408,7 @@ bool verify(const Options &options) {
 
   auto tensor_C = cute::make_tensor(make_iterator(block_C.host_data()), layout_C);
   auto tensor_D = cute::make_tensor(make_iterator(block_reference_D.host_data()), layout_D);
- 
+
   cutlass::reference::host::GettBlockScalingEpilogueParams<
       ElementAccumulator,                   // ElementScalar
       ElementAccumulator,                   // ElementAccumulator
@@ -510,14 +509,14 @@ int main(int argc, char const **args) {
   cudaDeviceProp props;
   int current_device_id;
   CUDA_CHECK(cudaGetDevice(&current_device_id));
-  
+
   CUDA_CHECK(cudaGetDeviceProperties(&props, current_device_id));
-  
+
   if (!(props.major == 10 && props.minor == 0)) {
     std::cerr << "This example requires a GPU of NVIDIA's Blackwell architecture (compute capability 100)." << std::endl;
     return 0;
   }
-  
+
   //
   // Parse options
   //

examples/73_blackwell_gemm_preferred_cluster/blackwell_gemm_preferred_cluster.cu

@@ -214,7 +214,8 @@ struct Options {
   int iterations;
   int m, n, k;
   int preferred_cluster_m, preferred_cluster_n, fallback_cluster_m, fallback_cluster_n;
-  
+  int swizzle = 0;
+
   Options():
     help(false),
     m(4096), n(4096), k(4096),
@@ -223,7 +224,8 @@ struct Options {
     preferred_cluster_m(4),
     preferred_cluster_n(4),
     fallback_cluster_m(2),
-    fallback_cluster_n(1)
+    fallback_cluster_n(1),
+    swizzle(0)
   { }
 
   // Parses the command line
@@ -245,6 +247,7 @@ struct Options {
     cmd.get_cmd_line_argument("preferred_cluster_n", preferred_cluster_n, 4);
     cmd.get_cmd_line_argument("fallback_cluster_m", fallback_cluster_m, 2);
     cmd.get_cmd_line_argument("fallback_cluster_n", fallback_cluster_n, 1);
+    cmd.get_cmd_line_argument("swizzle", swizzle);
 
     if (!validate_cluster_shape()){
       std::cout << "--Invalid cluster shapes" << std::endl;
@@ -265,6 +268,7 @@ struct Options {
       << "  --k=<int>                   Sets the K extent of the GEMM\n"
       << "  --alpha=<f32>               Epilogue scalar alpha\n"
       << "  --beta=<f32>                Epilogue scalar beta\n"
+      << "  --swizzle=<int>             Cluster rasterization swizzle\n"
       << "  --preferred_cluster_m=<str> Sets the M extent of preferred cluster shape\n"
       << "  --preferred_cluster_n=<str> Sets the N extent of preferred cluster shape\n"
       << "  --fallback_cluster_m=<str>  Sets the M extent of fallback cluster shape\n"
@@ -384,7 +388,8 @@ typename Gemm::Arguments args_from_options(const Options &options) {
 
   arguments.hw_info.cluster_shape = dim3(options.preferred_cluster_m, options.preferred_cluster_n,1);
   arguments.hw_info.cluster_shape_fallback = dim3(options.fallback_cluster_m, options.fallback_cluster_n,1);
-  
+
+  arguments.scheduler.max_swizzle_size = options.swizzle;
   return arguments;
 }
 

examples/74_blackwell_gemm_streamk/blackwell_gemm_streamk.cu

@@ -61,7 +61,7 @@
        # Heuristic mode with deterministic reduction
       ./74_blackwell_gemm_streamk" --m=256 --n=256 --k=16384 --decomposition=Heuristic --reduction=Deterministic
 
-      # Stream-K mode with determinsitic reduction
+      # Stream-K mode with deterministic reduction
       ./74_blackwell_gemm_streamk" --m=256 --n=256 --k=16384 --decomposition=StreamK --reduction=Deterministic
 
       # Split-K mode with a splitting factor of 2 and deterministic reduction
@@ -543,7 +543,7 @@ int run(Options &options) {
 
 int main(int argc, char const **args) {
 
-  // CUTLASS must be compiled with CUDA 12.8 Toolkit to run this example
+  // CUTLASS must be compiled with CUDA 12.8 Toolkit or newer to run this example
   // and must have compute capability at least 100.
   if (__CUDACC_VER_MAJOR__ < 12 || (__CUDACC_VER_MAJOR__ == 12 && __CUDACC_VER_MINOR__ < 8)) {
     std::cerr << "This example requires CUDA 12.8 or newer." << std::endl;
@@ -560,7 +560,6 @@ int main(int argc, char const **args) {
     std::cerr << "This example requires a GPU of NVIDIA's Blackwell architecture (compute capability 100 or 101)." << std::endl;
     return 0;
   }
-
   //
   // Parse options
   //

examples/75_blackwell_grouped_gemm/75_blackwell_grouped_gemm.cu

@@ -237,11 +237,12 @@ cutlass::DeviceAllocation<ElementAccumulator> block_beta;
 /// Testbed utility types
 /////////////////////////////////////////////////////////////////////////////////////////////////
 
-using RasterOrderOptions = typename cutlass::gemm::kernel::detail::PersistentTileSchedulerSm100GroupParams<typename ProblemShape::UnderlyingProblemShape>::RasterOrderOptions;
+using RasterOrderOptions = cutlass::gemm::kernel::detail::RasterOrderOptions;
 // Command line options parsing
 struct Options {
 
   bool help = false;
+  bool use_pdl = false;
 
   float alpha = FLT_MAX;
   float beta  = FLT_MAX;
@@ -264,6 +265,9 @@ struct Options {
       help = true;
       return;
     }
+    if (cmd.check_cmd_line_flag("use_pdl")) {
+      use_pdl = true;
+    }
 
     cmd.get_cmd_line_argument("m", m);
     cmd.get_cmd_line_argument("n", n);
@@ -350,19 +354,9 @@ struct Options {
       cutlass::CommandLine::tokenize(tokens, extent_str, 'x');
 
       for (int i = 0; i < int(tokens.size()); ++i) {
-        int x = std::atoi(tokens.at(i).c_str());
-
-        // round up
-        if (x % alignment) {
-          x += (alignment - (x % alignment));
-        }
-
-        extent.at(i) = x;
-      }
-
-      if (extent.product()) {
-        problem_sizes_host.push_back({extent.m(), extent.n(), extent.k()});
+        extent.at(i) = std::atoi(tokens.at(i).c_str());
       }
+      problem_sizes_host.push_back({extent.m(), extent.n(), extent.k()});
     }
     groups = static_cast<int>(problem_sizes_host.size());
 
@@ -387,7 +381,8 @@ struct Options {
       << "  --raster=<char>                                              CTA Rasterization direction (N for along N, M for along M)\n\n"
       << "  --iterations=<int>                                           Number of profiling iterations to perform\n\n"
       << "  --benchmark=<str>                                            Executes a benchmark problem size\n"
-      << "  --max_sm_count=<int>                                         Run kernels using only these number of SMs\n";
+      << "  --max_sm_count=<int>                                         Run kernels using only these number of SMs\n"
+      << "  --use_pdl                                                    Launch kernel with PDL (Programmatic Dependent Launch) enabled\n";
                                                                                              
     out
       << "\n\nExamples:\n\n"
@@ -711,7 +706,7 @@ int run(Options &options, bool host_problem_shapes_available = true)
   CUTLASS_CHECK(gemm.initialize(arguments, workspace.get()));
 
   // Correctness / Warmup iteration
-  CUTLASS_CHECK(gemm.run());
+  CUTLASS_CHECK(gemm.run(/* stream = */ nullptr, /* cuda_adapter = */ nullptr, /* launch_with_pdl = */ options.use_pdl));
 
   // Check if output from CUTLASS kernel and reference kernel are equal or not
   Result result;
@@ -730,7 +725,7 @@ int run(Options &options, bool host_problem_shapes_available = true)
     timer.start();
     for (int iter = 0; iter < options.iterations; ++iter) {
       CUTLASS_CHECK(gemm.initialize(arguments, workspace.get()));
-      CUTLASS_CHECK(gemm.run());
+      CUTLASS_CHECK(gemm.run(/* stream = */ nullptr, /* cuda_adapter = */ nullptr, /* launch_with_pdl = */ options.use_pdl));
     }
     timer.stop();
 
@@ -740,7 +735,7 @@ int run(Options &options, bool host_problem_shapes_available = true)
     result.gflops          = options.gflops(result.avg_runtime_ms / 1000.0, options.problem_sizes_host);
 
     std::cout << "  Avg runtime : " << result.avg_runtime_ms << " ms" << std::endl;
-    std::cout << "  GFLOPS      : " << result.gflops << std::endl;
+    std::cout << "  TFLOPS      : " << result.gflops / 1000.0 << std::endl;
   }
 
   return 0;

examples/75_blackwell_grouped_gemm/75_blackwell_grouped_gemm_block_scaled.cu

@@ -124,13 +124,14 @@ constexpr int AlignmentD  = 128 / cutlass::sizeof_bits<ElementD>::value;    // A
 using ElementAccumulator  = float;                                          // Element type for internal accumulation
 
 // using ElementD = cutlass::float_e2m1_t; // Enable for SF Output          // Element type for D matrix operands
+
 using ElementSFD  = cutlass::float_ue4m3_t;                                 // Element type for SF Output operands
 constexpr int OutputSFVectorSize = 16;
 using FusionOperation = cutlass::epilogue::fusion::LinCombEltActBlockScaleFactor<
     cutlass::epilogue::thread::SiLu,
     OutputSFVectorSize,
-    ElementD, 
-    ElementAccumulator, 
+    ElementD,
+    ElementAccumulator,
     ElementSFD,
     LayoutC,
     ElementC>;
@@ -219,14 +220,14 @@ using StrideD = typename Gemm::GemmKernel::InternalStrideD;
 
 using LayoutSFA = typename Gemm::GemmKernel::CollectiveMainloop::InternalLayoutSFA;
 using LayoutSFB = typename Gemm::GemmKernel::CollectiveMainloop::InternalLayoutSFB;
-using Sm100BlkScaledConfig =  typename Gemm::GemmKernel::CollectiveMainloop::Sm100BlkScaledConfig;
-using Sm100BlockScaledOutputConfig = cutlass::detail::Sm100BlockScaledOutputConfig<
-                                        OutputSFVectorSize, 
+using Sm1xxBlkScaledConfig =  typename Gemm::GemmKernel::CollectiveMainloop::Sm1xxBlkScaledConfig;
+using Sm1xxBlockScaledOutputConfig= cutlass::detail::Sm1xxBlockScaledOutputConfig<
+                                        OutputSFVectorSize,
                                         cute::is_same_v<typename FusionOperation::GmemLayoutTagScalefactor,
                                             cutlass::layout::RowMajor> ? cute::UMMA::Major::K : cute::UMMA::Major::MN
                                      >;
-using OutputSFAtom = typename Sm100BlockScaledOutputConfig::SfAtom;
-using LayoutSFD = typename Sm100BlockScaledOutputConfig::LayoutSF;
+using OutputSFAtom = typename Sm1xxBlockScaledOutputConfig::SfAtom;
+using LayoutSFD = typename Sm1xxBlockScaledOutputConfig::LayoutSF;
 
 // Host-side allocations
 std::vector<StrideA> stride_A_host;
@@ -286,25 +287,20 @@ cutlass::DeviceAllocation<ElementAccumulator> norm_constant_device;
 
 template <typename T>
 auto make_iterator(T* ptr) {
-  using namespace cute;
-  if constexpr (cute::is_subbyte_v<T>) {
-    return subbyte_iterator<T>(ptr);
-  }
-  else {
-    return ptr;
-  }
+  return cute::recast_ptr<T>(ptr);
 }
 
 /////////////////////////////////////////////////////////////////////////////////////////////////
 /// Testbed utility types
 /////////////////////////////////////////////////////////////////////////////////////////////////
 
-using RasterOrderOptions = typename cutlass::gemm::kernel::detail::PersistentTileSchedulerSm100GroupParams<typename ProblemShape::UnderlyingProblemShape>::RasterOrderOptions;
+using RasterOrderOptions = cutlass::gemm::kernel::detail::RasterOrderOptions;
 // Command line options parsing
 struct Options {
 
   bool help = false;
   bool verification = true;
+  bool use_pdl = false;
 
   float alpha = FLT_MAX;
   float beta  = FLT_MAX;
@@ -328,9 +324,12 @@ struct Options {
       help = true;
       return;
     }
-    if (cmd.check_cmd_line_flag("no-verif")) {
+    if (cmd.check_cmd_line_flag("no_verif")) {
       verification = false;
     }
+    if (cmd.check_cmd_line_flag("use_pdl")) {
+      use_pdl = true;
+    }
 
     cmd.get_cmd_line_argument("m", m);
     cmd.get_cmd_line_argument("n", n);
@@ -418,19 +417,9 @@ struct Options {
       cutlass::CommandLine::tokenize(tokens, extent_str, 'x');
 
       for (int i = 0; i < int(tokens.size()); ++i) {
-        int x = std::atoi(tokens.at(i).c_str());
-
-        // round up
-        if (x % alignment) {
-          x += (alignment - (x % alignment));
-        }
-
-        extent.at(i) = x;
-      }
-
-      if (extent.product()) {
-        problem_sizes_host.push_back({extent.m(), extent.n(), extent.k()});
+        extent.at(i) = std::atoi(tokens.at(i).c_str());
       }
+      problem_sizes_host.push_back({extent.m(), extent.n(), extent.k()});
     }
     groups = static_cast<int>(problem_sizes_host.size());
 
@@ -457,7 +446,8 @@ struct Options {
       << "  --iterations=<int>                                           Number of profiling iterations to perform\n\n"
       << "  --benchmark=<str>                                            Executes a benchmark problem size\n"
       << "  --max_sm_count=<int>                                         Run kernels using only these number of SMs\n"
-      << "  --no-verif                                                   Do not run (host-side) verification kernels\n";
+      << "  --no_verif                                                   Do not run (host-side) verification kernels\n"
+      << "  --use_pdl                                                    Launch kernel with PDL (Programmatic Dependent Launch) enabled\n";
 
     out
       << "\n\nExamples:\n\n"
@@ -533,7 +523,7 @@ bool initialize_block(
   }
   cutlass::reference::host::TensorFillRandomUniform(
     view, seed, scope_max, scope_min, 0);
-  
+
   return true;
 }
 
@@ -554,9 +544,9 @@ void allocate(const Options &options) {
     auto layout_B = make_layout(make_shape(N, K, 1), stride_B);
     auto layout_C = make_layout(make_shape(M, N, 1), stride_C);
     auto layout_D = make_layout(make_shape(M, N, 1), stride_D);
-    auto layout_SFA = Sm100BlkScaledConfig::tile_atom_to_shape_SFA(cute::make_shape(M, N, K, 1));
-    auto layout_SFB = Sm100BlkScaledConfig::tile_atom_to_shape_SFB(cute::make_shape(M, N, K, 1));
-    auto layout_SFD = Sm100BlockScaledOutputConfig::tile_atom_to_shape_SFD(cute::make_shape(M, N, K, 1));
+    auto layout_SFA = Sm1xxBlkScaledConfig::tile_atom_to_shape_SFA(cute::make_shape(M, N, K, 1));
+    auto layout_SFB = Sm1xxBlkScaledConfig::tile_atom_to_shape_SFB(cute::make_shape(M, N, K, 1));
+    auto layout_SFD = Sm1xxBlockScaledOutputConfig::tile_atom_to_shape_SFD(cute::make_shape(M, N, K, 1));
 
     stride_A_host.push_back(stride_A);
     stride_B_host.push_back(stride_B);
@@ -775,9 +765,9 @@ bool verify(const Options &options) {
     auto layout_B = make_layout(make_shape(N, K, 1), stride_B);
     auto layout_C = make_layout(make_shape(M, N, 1), stride_C);
     auto layout_D = make_layout(make_shape(M, N, 1), stride_D);
-    auto layout_SFA = Sm100BlkScaledConfig::tile_atom_to_shape_SFA(cute::make_shape(M, N, K, 1));
-    auto layout_SFB = Sm100BlkScaledConfig::tile_atom_to_shape_SFB(cute::make_shape(M, N, K, 1));
-    auto layout_SFD = Sm100BlockScaledOutputConfig::tile_atom_to_shape_SFD(cute::make_shape(M, N, K, 1));
+    auto layout_SFA = Sm1xxBlkScaledConfig::tile_atom_to_shape_SFA(cute::make_shape(M, N, K, 1));
+    auto layout_SFB = Sm1xxBlkScaledConfig::tile_atom_to_shape_SFB(cute::make_shape(M, N, K, 1));
+    auto layout_SFD = Sm1xxBlockScaledOutputConfig::tile_atom_to_shape_SFD(cute::make_shape(M, N, K, 1));
 
     // Create the arguments for host reference implementation
     Tensor tensor_A = make_tensor(make_iterator(block_A.at(i).host_data()), layout_A);
@@ -789,9 +779,9 @@ bool verify(const Options &options) {
         decltype(tensor_SFA),
         decltype(tensor_B),
         decltype(tensor_SFB)
-      > 
+      >
     mainloop_params{tensor_A, tensor_SFA, tensor_B, tensor_SFB};
-  
+
     auto tensor_C = cute::make_tensor(make_iterator(block_C.at(i).host_data()), layout_C);
     auto tensor_ref_D = cute::make_tensor(make_iterator(block_ref_D.at(i).host_data()), layout_D);
 
@@ -845,7 +835,7 @@ int run(Options &options, bool host_problem_shapes_available = true)
   CUTLASS_CHECK(gemm.initialize(arguments, workspace.get()));
 
   // Correctness / Warmup iteration
-  CUTLASS_CHECK(gemm.run());
+  CUTLASS_CHECK(gemm.run(/* stream = */ nullptr, /* cuda_adapter = */ nullptr, /* launch_with_pdl = */ options.use_pdl));
 
   cudaDeviceSynchronize();
 
@@ -860,8 +850,8 @@ int run(Options &options, bool host_problem_shapes_available = true)
     }
   }
   else {
-    std::cout << "  Verfication is turned off for this run." << std::endl;
-  } 
+    std::cout << "  Verification is turned off for this run." << std::endl;
+  }
 
   // Run profiling loop
   if (options.iterations > 0)
@@ -870,7 +860,7 @@ int run(Options &options, bool host_problem_shapes_available = true)
     timer.start();
     for (int iter = 0; iter < options.iterations; ++iter) {
       CUTLASS_CHECK(gemm.initialize(arguments, workspace.get()));
-      CUTLASS_CHECK(gemm.run());
+      CUTLASS_CHECK(gemm.run(/* stream = */ nullptr, /* cuda_adapter = */ nullptr, /* launch_with_pdl = */ options.use_pdl));
     }
     timer.stop();
 
@@ -880,7 +870,7 @@ int run(Options &options, bool host_problem_shapes_available = true)
     result.gflops          = options.gflops(result.avg_runtime_ms / 1000.0, options.problem_sizes_host);
 
     std::cout << "  Avg runtime : " << result.avg_runtime_ms << " ms" << std::endl;
-    std::cout << "  GFLOPS      : " << result.gflops << std::endl;
+    std::cout << "  TFLOPS      : " << result.gflops / 1000.0 << std::endl;
   }
 
   return 0;
@@ -937,7 +927,7 @@ int main(int argc, char const **args) {
   std::cout << "Running kernel with 1SM MMA config:" << std::endl;
   run<Gemm1SM>(options, false /*host_problem_shapes_available*/);
   std::cout << "Running kernel with 2SM MMA config:" << std::endl;
-  run<Gemm2SM>(options, false /*host_problem_shapes_available*/); 
+  run<Gemm2SM>(options, false /*host_problem_shapes_available*/);
 #endif
 
   return 0;

examples/76_blackwell_conv/76_blackwell_conv_dgrad.cu

@@ -36,7 +36,7 @@
     APIs on NVIDIA Blackwell SM100 architecture.
 
     The basic computation logic of dgrad convolution kernel is, take 3D convolution as an example:
-        Xformed Actication (NZPQK) * Weight/Filter (KTRSC) = Activation (NDHWC)
+        Xformed Activation (NZPQK) * Weight/Filter (KTRSC) = Activation (NDHWC)
 
     where in terms of GEMM perspective,
         Matrix A = Xformed Activation, Matrix B = Weight/Filter, Matrix C = Activation
@@ -490,7 +490,7 @@ int run(Options &options)
 
 int main(int argc, char const **args) {
 
-  // CUTLASS must be compiled with CUDA 12.0 Toolkit to run this example
+  // CUTLASS must be compiled with CUDA 12.8 Toolkit to run this example
   // and must have compute capability at least 90.
   if (__CUDACC_VER_MAJOR__ < 12 || (__CUDACC_VER_MAJOR__ == 12 && __CUDACC_VER_MINOR__ < 8)) {
     std::cerr << "This example requires CUDA 12.8 or newer." << std::endl;
@@ -503,11 +503,11 @@ int main(int argc, char const **args) {
   CUDA_CHECK(cudaGetDevice(&current_device_id));
   CUDA_CHECK(cudaGetDeviceProperties(&props, current_device_id));
   cudaError_t error = cudaGetDeviceProperties(&props, 0);
+
   if (props.major != 10 && (props.minor != 0 || props.minor != 1)) {
     std::cerr << "This example requires a GPU of NVIDIA's Blackwell architecture (compute capability 100 or 101)." << std::endl;
     return 0;
-  } 
-  
+  }
   //
   // Parse options
   //

examples/76_blackwell_conv/76_blackwell_conv_fprop.cu

@@ -36,7 +36,7 @@
     APIs on NVIDIA Blackwell SM100 architecture.
 
     The basic computation logic of fprop convolution kernel is, take 3D convolution as an example:
-        Activation (NDHWC) * Weight/Filter (KTRSC) = Xformed Actication (NZPQK)
+        Activation (NDHWC) * Weight/Filter (KTRSC) = Xformed Activation (NZPQK)
 
     where in terms of GEMM perspective,
         Matrix A = Activation, Matrix B = Weight/Filter, Matrix C = Xformed Activation
@@ -490,7 +490,7 @@ int run(Options &options)
 
 int main(int argc, char const **args) {
 
-  // CUTLASS must be compiled with CUDA 12.0 Toolkit to run this example
+  // CUTLASS must be compiled with CUDA 12.8 Toolkit to run this example
   // and must have compute capability at least 90.
   if (__CUDACC_VER_MAJOR__ < 12 || (__CUDACC_VER_MAJOR__ == 12 && __CUDACC_VER_MINOR__ < 8)) {
     std::cerr << "This example requires CUDA 12.8 or newer." << std::endl;
@@ -503,11 +503,11 @@ int main(int argc, char const **args) {
   CUDA_CHECK(cudaGetDevice(&current_device_id));
   CUDA_CHECK(cudaGetDeviceProperties(&props, current_device_id));
   cudaError_t error = cudaGetDeviceProperties(&props, 0);
+  
   if (props.major != 10 && (props.minor != 0 || props.minor != 1)) {
     std::cerr << "This example requires a GPU of NVIDIA's Blackwell architecture (compute capability 100 or 101)." << std::endl;
     return 0;
-  } 
-  
+  }
   //
   // Parse options
   //

examples/76_blackwell_conv/76_blackwell_conv_wgrad.cu

@@ -36,7 +36,7 @@
     APIs on NVIDIA Blackwell SM100 architecture.
 
     The basic computation logic of wgrad convolution kernel is, take 3D convolution as an example:
-        Xformed Actication (NZPQK) * Activation (NDHWC) = Weight/Filter (KTRSC)
+        Xformed Activation (NZPQK) * Activation (NDHWC) = Weight/Filter (KTRSC)
 
     where in terms of GEMM perspective,
         Matrix A = Xformed Activation, Matrix B = Activation, Matrix C = Weight/Filter
@@ -499,11 +499,11 @@ int main(int argc, char const **args) {
   CUDA_CHECK(cudaGetDevice(&current_device_id));
   CUDA_CHECK(cudaGetDeviceProperties(&props, current_device_id));
   cudaError_t error = cudaGetDeviceProperties(&props, 0);
+  
   if (props.major != 10 && (props.minor != 0 || props.minor != 1)) {
     std::cerr << "This example requires a GPU of NVIDIA's Blackwell architecture (compute capability 100 or 101)." << std::endl;
     return 0;
   } 
-  
   //
   // Parse options
   //

examples/77_blackwell_fmha/77_blackwell_fmha.cu

@@ -67,9 +67,6 @@
             --b=2048 --h=2048 --d=2048 --q=2048 --k=2048
 */
 
-#define DSHOW(x) print(#x ": "); print(x); print("\n");
-#define DSHOWT(x) print(#x ": "); print_tensor(x); print("\n");
-
 #include <iostream>
 #include <random>
 #include <regex>
@@ -119,16 +116,20 @@ struct Options {
   int h_k = 1;
   int q = 256;
   int k = 256;
+  std::vector<int> varlen_q;
+  std::vector<int> varlen_k;
   int d = 128;
+  int warmup_iterations = 1;
   int iterations = 3;
+  int tensor_ring_buffers = 1;
   bool verify = false;
   bool verbose = false;
 
   bool causal = false;
   bool residual = false;
   bool varlen = false;
+  bool persistent = false;
   int sm_count = 0;
-
   std::string kernel_filter;
 
   InitStyle init_style_q = InitStyle::kRandom;
@@ -182,20 +183,87 @@ struct Options {
     cmd.get_cmd_line_argument("h_k", h_k, -1);
     if (h_k == -1) h_k = h;
 
+    varlen = cmd.check_cmd_line_flag("varlen");
+
     cmd.get_cmd_line_argument("q", q, -1);
     cmd.get_cmd_line_argument("k", k, -1);
+    cmd.get_cmd_line_argument("b", b, -1);
+
+    std::string varlen_q_str;
+    cmd.get_cmd_line_argument("varlen-q", varlen_q_str);
+    std::string varlen_k_str;
+    cmd.get_cmd_line_argument("varlen-k", varlen_k_str);
+
+    if (varlen && ! varlen_q_str.empty()) {
+      varlen_q.clear();
+      while (! varlen_q_str.empty()) {
+        size_t pos = varlen_q_str.find(':');
+        varlen_q.push_back(std::stoi(varlen_q_str.substr(0, pos)));
+        if (pos == std::string::npos) {
+          break;
+        }
+        varlen_q_str = varlen_q_str.substr(pos + 1);
+      }
+      if (b == -1) {
+        b = static_cast<int>(varlen_q.size());
+      }
+      if (b != static_cast<int>(varlen_q.size())) {
+        std::cout << "Error: Invalid --varlen-q length\n";
+        std::exit(-1);
+      }
+      int new_q = 0;
+      for (auto elem : varlen_q) {
+        new_q += elem;
+      }
+      if (q != -1) {
+        std::cout << "Error: Can't provide --q and --varlen-q\n";
+        std::exit(-1);
+      }
+      q = new_q;
+    }
+
+    if (varlen && ! varlen_k_str.empty()) {
+      varlen_k.clear();
+      while (! varlen_k_str.empty()) {
+        size_t pos = varlen_k_str.find(':');
+        varlen_k.push_back(std::stoi(varlen_k_str.substr(0, pos)));
+        if (pos == std::string::npos) {
+          break;
+        }
+        varlen_k_str = varlen_k_str.substr(pos + 1);
+      }
+      if (b == -1) {
+        b = static_cast<int>(varlen_k.size());
+      }
+      if (b != static_cast<int>(varlen_k.size())) {
+        std::cout << " Error: Invalid --varlen-k length\n";
+        std::exit(-1);
+      }
+      int new_k = 0;
+      for (auto elem : varlen_k) {
+        new_k += elem;
+      }
+      if (k != -1) {
+        std::cout << "Error: Can't provide --k and --varlen-k\n";
+        std::exit(-1);
+      }
+      k = new_k;
+    }
+
     if (q == -1) q = k;
     if (k == -1) k = q;
     if (q == -1 && k == -1) q = k = defaults.q;
-
-    cmd.get_cmd_line_argument("b", b, -1);
     if (b == -1) b = 16384 / k;
     if (b == 0) b = 1;
 
+    cmd.get_cmd_line_argument("warmup_iterations", warmup_iterations, defaults.warmup_iterations);
     cmd.get_cmd_line_argument("iterations", iterations, defaults.iterations);
+    cmd.get_cmd_line_argument("tensor_ring_buffers", tensor_ring_buffers, defaults.tensor_ring_buffers);
+
     verify = cmd.check_cmd_line_flag("verify");
     verbose = cmd.check_cmd_line_flag("verbose");
-    varlen = cmd.check_cmd_line_flag("varlen");
+    persistent = cmd.check_cmd_line_flag("persistent");
+
     std::string mask;
     cmd.get_cmd_line_argument<std::string>("mask", mask, "");
     if (mask == "no" || mask == "") {
@@ -213,7 +281,6 @@ struct Options {
       causal = false;
     }
     cmd.get_cmd_line_argument("sm-count", sm_count, defaults.sm_count);
-    
     get_init_style_argument(cmd, "init-style", init_style_q, defaults.init_style_q);
     get_init_style_argument(cmd, "init-style", init_style_k, defaults.init_style_q);
     get_init_style_argument(cmd, "init-style", init_style_v, defaults.init_style_q);
@@ -237,18 +304,23 @@ struct Options {
       << "  --h_k=<int>                 Sets the H_K/V extent (for GQA/MQA)\n"
       << "  --q=<int>                   Sets the Q extent\n"
       << "  --k=<int>                   Sets the K extent\n"
-      << "  --d=<int>                   Sets the D extentn"
+      << "  --varlen-q=<int>:<int...>   Sets the variable Q extent per batch (colon separated)\n"
+      << "  --varlen-k=<int>:<int...>   Sets the variable K extent per batch (colon separated)\n"
+      << "  --d=<int>                   Sets the D extent\n"
+      << "  --tensor_ring_buffers=<int> Sets the number of tensor ring buffers\n"
+      << "  --warmup_iterations=<int>   Sets the warmup iterations\n"
       << "  --iterations=<int>          Benchmarking iterations\n"
       << "  --verify                    Verify results\n"
       << "  --verbose                   Print smem and execution time per kernel\n"
       << "  --mask=<no|residual|causal> Enables masking\n"
+      << "  --persistent                Enables persistent scheduler\n"
       << "  --varlen                    Enables variable sequence length\n"
       << "                              B*Q and B*K become the total sequence length\n"
       << "                              and are split B-ways, alternatingly +10% and -10%\n"
       << "                              with the last batch sized to make it fit\n"
       << "                              implies at least residual masking for correctness\n"
-      << " --sm-count                   Sets SM count rather than querying it\n"
-      << " --kernel-filter=<filter>     Sets regexp to match kernel against\n"
+      << "  --sm-count                  Sets SM count rather than querying it\n"
+      << "  --kernel-filter=<filter>    Sets regexp to match kernel against\n"
       << "\n";
 
     return out;
@@ -382,44 +454,63 @@ struct FwdRunner {
   StrideLSE stride_LSE;
   uint64_t seed = 0;
 
-  DeviceAllocation<Element> block_Q;
-  DeviceAllocation<Element> block_K;
-  DeviceAllocation<Element> block_V;
-  DeviceAllocation<ElementOut> block_O;
-  DeviceAllocation<ElementAccumulatorPV> block_LSE;
-  DeviceAllocation<ElementOut> block_ref_O;
-  DeviceAllocation<ElementAccumulatorPV> block_ref_LSE;
+  struct DeviceBuffer {
+    DeviceAllocation<Element> block_Q;
+    DeviceAllocation<Element> block_K;
+    DeviceAllocation<Element> block_V;
+    DeviceAllocation<ElementOut> block_O;
+    DeviceAllocation<ElementAccumulatorPV> block_LSE;
+    DeviceAllocation<ElementOut> block_ref_O;
+    DeviceAllocation<ElementAccumulatorPV> block_ref_LSE;
+    DeviceAllocation<int> device_cumulative_seqlen_q;
+    DeviceAllocation<int> device_cumulative_seqlen_kv;
+
+    DeviceBuffer() = default;
+    DeviceBuffer(const DeviceBuffer&) = delete;
+    DeviceBuffer& operator=(const DeviceBuffer&) = delete;
+
+    size_t get_storage_size() const {
+      return block_Q.get_storage_size() + block_K.get_storage_size() + block_V.get_storage_size()
+          + block_O.get_storage_size() + block_LSE.get_storage_size() + block_ref_O.get_storage_size()
+          + block_ref_LSE.get_storage_size() + device_cumulative_seqlen_q.get_storage_size()
+          + device_cumulative_seqlen_kv.get_storage_size();
+    }
+  };
+
+  std::vector<std::unique_ptr<DeviceBuffer>> buffers;
 
   std::vector<int> cumulative_seqlen_q;
   std::vector<int> cumulative_seqlen_kv;
-  DeviceAllocation<int> device_cumulative_seqlen_q;
-  DeviceAllocation<int> device_cumulative_seqlen_kv;
 
   //
   // Methods
   //
-  bool verify(const ProblemShapeType& problem_shape) {
-    Tensor mQ = make_tensor(make_gmem_ptr(block_Q.get()),
+  bool verify(const ProblemShapeType& problem_shape, DeviceBuffer& buffer) {
+    Tensor mQ = make_tensor(make_gmem_ptr(buffer.block_Q.get()),
       select<0,2,3>(problem_shape),
       stride_Q);
 
-    Tensor mK = make_tensor(make_gmem_ptr(block_K.get()),
+    Tensor mK = make_tensor(make_gmem_ptr(buffer.block_K.get()),
       select<1,2,3>(problem_shape),
       stride_K);
 
-    Tensor mV = make_tensor(make_gmem_ptr(block_V.get()),
+    Tensor mV = make_tensor(make_gmem_ptr(buffer.block_V.get()),
       select<1,2,3>(problem_shape),
       stride_V);
 
-    Tensor mO = make_tensor(make_gmem_ptr(block_ref_O.get()),
+    Tensor mO = make_tensor(make_gmem_ptr(buffer.block_ref_O.get()),
       select<0,2,3>(problem_shape),
       stride_O);
 
-    Tensor mLSE = make_tensor(make_gmem_ptr(block_ref_LSE.get()),
+    Tensor mLSE = make_tensor(make_gmem_ptr(buffer.block_ref_LSE.get()),
       select<0,3>(problem_shape),
       stride_LSE);
+    
+    auto [Q, K, D, HB] = problem_shape;
 
-    fmha_reference(problem_shape, mQ, mK, mV, mO, mLSE, ActiveMask{});
+    auto problem_shape_ref = cute::make_tuple(Q, K, D, D, HB);
+
+    fmha_reference(problem_shape_ref, mQ, mK, mV, mO, mLSE, ActiveMask{});
 
     cudaError_t result = cudaDeviceSynchronize();
     if (result != cudaSuccess) {
@@ -434,7 +525,7 @@ struct FwdRunner {
     // Check if output from CUTLASS kernel and reference kernel are equal or not
     double max_diff = 0;
     double mean_diff = 0;
-    reference_abs_diff(block_O, block_ref_O, max_diff, mean_diff);
+    reference_abs_diff(buffer.block_O, buffer.block_ref_O, max_diff, mean_diff);
 
     bool passed_O = (max_diff < kMaxDiffThresh) && (mean_diff < kMeanDiffThresh);
     if (! passed_O) {
@@ -442,20 +533,22 @@ struct FwdRunner {
                 << " mean " << mean_diff << std::endl;
     }
 
-    // reference_abs_diff(block_LSE, block_ref_LSE, max_diff, mean_diff);
+    reference_abs_diff(buffer.block_LSE, buffer.block_ref_LSE, max_diff, mean_diff);
 
-    bool passed_LSE = true;  // future work
-    // bool passed_LSE = (max_diff < kMaxDiffThresh) && (mean_diff < kMeanDiffThresh);
-    // if ( ! passed_LSE) {
-    //   std::cerr << "failed LSE: max diff " << max_diff 
-    //             << " mean " << mean_diff << std::endl;
-    // }
+    bool passed_LSE = (max_diff < kMaxDiffThresh) && (mean_diff < kMeanDiffThresh);
+    if ( ! passed_LSE) {
+      std::cerr << "failed LSE: max diff " << max_diff 
+                << " mean " << mean_diff << std::endl;
+    }
 
     return passed_O && passed_LSE;
   }
 
   template<class ProblemShape>
-  auto initialize_varlen(const ProblemShape& problem_size, const bool kVarlenSame = true) {
+  auto initialize_varlen(
+      const Options& options, const ProblemShape& problem_size,
+      const bool kVarlenSame = true) {
+
     int num_batches = get<3,1>(problem_size);
 
     // generate Q as --b times
@@ -483,8 +576,12 @@ struct FwdRunner {
     int max_seqlen_kv = 0;
 
     for (int i = 0; i < num_batches; i++) {
-      int seqlen_q = kVarlenSame ? get<0>(problem_size) : generate_positive_int(dist_q, rng);
-      int seqlen_kv = kVarlenSame ? get<1>(problem_size) : generate_positive_int(dist_kv, rng);
+      int seqlen_q = (! options.varlen_q.empty()) ? options.varlen_q.at(i) : 
+              kVarlenSame ? get<0>(problem_size) :
+              generate_positive_int(dist_q, rng);
+      int seqlen_kv = (! options.varlen_k.empty()) ? options.varlen_k.at(i) :
+              kVarlenSame ? get<1>(problem_size) :
+              generate_positive_int(dist_kv, rng);
 
       total_seqlen_q += seqlen_q;
       total_seqlen_kv += seqlen_kv;
@@ -525,7 +622,7 @@ struct FwdRunner {
     decltype(problem_shape_in) problem_size;
 
     if constexpr (kIsVarlen) {
-      auto [problem_shape_init, problem_shape_launch] = initialize_varlen(problem_shape_in);
+      auto [problem_shape_init, problem_shape_launch] = initialize_varlen(options, problem_shape_in);
       problem_shape = problem_shape_launch;
       problem_size = problem_shape_init;
     }
@@ -562,50 +659,72 @@ struct FwdRunner {
       get<1,1>(stride_LSE) = 0;
     }
 
-    block_Q.reset(size(shape_QO), kIsVarlen ? D*SQ*H : 0);
-    block_K.reset(size(shape_KV), kIsVarlen ? D*SK*H_K : 0);
-    block_V.reset(size(shape_KV), kIsVarlen ? D*SK*H_K : 0);
-    block_O.reset(size(shape_QO), kIsVarlen ? D*SQ*H : 0);
-    block_LSE.reset(size(shape_LSE));
-    block_ref_O.reset(size(shape_QO));
-    block_ref_LSE.reset(size(shape_LSE));
+    auto buffer_init_fn = [&](auto& buffer) {
+      buffer.block_Q.reset(size(shape_QO), kIsVarlen ? D*SQ*H : 0);
+      buffer.block_K.reset(size(shape_KV), kIsVarlen ? D*SK*H_K : 0);
+      buffer.block_V.reset(size(shape_KV), kIsVarlen ? D*SK*H_K : 0);
+      buffer.block_O.reset(size(shape_QO), kIsVarlen ? D*SQ*H : 0);
+      buffer.block_LSE.reset(size(shape_LSE));
+      buffer.block_ref_O.reset(size(shape_QO), kIsVarlen ? D*SQ*H : 0);
+      buffer.block_ref_LSE.reset(size(shape_LSE));
 
-    initialize_block(block_Q, seed + 2023, options.init_style_q);
-    initialize_block(block_K, seed + 2022, options.init_style_k);
-    initialize_block(block_V, seed + 2021, options.init_style_v);
+      initialize_block(buffer.block_Q, seed + 2023, options.init_style_q);
+      initialize_block(buffer.block_K, seed + 2022, options.init_style_k);
+      initialize_block(buffer.block_V, seed + 2021, options.init_style_v);
 
-    if ( ! cumulative_seqlen_q.empty()) {
-      device_cumulative_seqlen_q.reset(cumulative_seqlen_q.size());
-      device_cumulative_seqlen_q.copy_from_host(
-        cumulative_seqlen_q.data(), cumulative_seqlen_q.size());
-    }
-    if ( ! cumulative_seqlen_kv.empty()) {
-      device_cumulative_seqlen_kv.reset(cumulative_seqlen_kv.size());
-      device_cumulative_seqlen_kv.copy_from_host(
-        cumulative_seqlen_kv.data(), cumulative_seqlen_kv.size());
+      if ( ! cumulative_seqlen_q.empty()) {
+        buffer.device_cumulative_seqlen_q.reset(cumulative_seqlen_q.size());
+        buffer.device_cumulative_seqlen_q.copy_from_host(
+          cumulative_seqlen_q.data(), cumulative_seqlen_q.size());
+      }
+      if ( ! cumulative_seqlen_kv.empty()) {
+        buffer.device_cumulative_seqlen_kv.reset(cumulative_seqlen_kv.size());
+        buffer.device_cumulative_seqlen_kv.copy_from_host(
+          cumulative_seqlen_kv.data(), cumulative_seqlen_kv.size());
+      }   
+    };
+
+    buffers.push_back(std::make_unique<DeviceBuffer>());
+    buffer_init_fn(*buffers.back());
+
+    int tensor_ring_buffers = options.tensor_ring_buffers;
+    for (int i = 1; i < tensor_ring_buffers; i++) {
+      buffers.push_back(std::make_unique<DeviceBuffer>());
+      buffer_init_fn(*buffers.back());
     }
 
     if constexpr (kIsVarlen) {
-      get<0>(problem_shape).cumulative_length = device_cumulative_seqlen_q.get();
-      get<1>(problem_shape).cumulative_length = device_cumulative_seqlen_kv.get();
+      get<0>(problem_shape).cumulative_length = buffers[0]->device_cumulative_seqlen_q.get();
+      get<1>(problem_shape).cumulative_length = buffers[0]->device_cumulative_seqlen_kv.get();
     }
 
     return problem_shape;
   }
 
+  auto get_arguments(const ProblemShapeType& problem_shape, const cutlass::KernelHardwareInfo& hw_info, int buffer_index) {
+    auto problem_shape_ = problem_shape;
+    if constexpr (kIsVarlen) {
+      get<0>(problem_shape_).cumulative_length = buffers[buffer_index]->device_cumulative_seqlen_q.get();
+      get<1>(problem_shape_).cumulative_length = buffers[buffer_index]->device_cumulative_seqlen_kv.get();
+    }
+    typename Operation::Arguments arguments{
+      problem_shape_,
+      { buffers[buffer_index]->block_Q.get(), stride_Q,
+        buffers[buffer_index]->block_K.get(), stride_K,
+        buffers[buffer_index]->block_V.get(), stride_V },
+      { buffers[buffer_index]->block_O.get(), stride_O,
+        buffers[buffer_index]->block_LSE.get(), stride_LSE },
+      hw_info
+    };
+    return arguments;
+  }
+
   ExampleResult run(const Options& options, const cutlass::KernelHardwareInfo& hw_info) {
 
     ProblemShapeType problem_shape = initialize(options);
 
-    typename Operation::Arguments arguments{
-      problem_shape,
-      { block_Q.get(), stride_Q,
-        block_K.get(), stride_K,
-        block_V.get(), stride_V },
-      { block_O.get(), stride_O,
-      block_LSE.get(), stride_LSE },
-      hw_info
-    };
+    int buffer_index = 0;
+    typename Operation::Arguments arguments = get_arguments(problem_shape, hw_info, buffer_index);
 
     Operation op;
 
@@ -633,11 +752,21 @@ struct FwdRunner {
     }
 
     // Run
-    status = op.run();
-    if (status != cutlass::Status::kSuccess) {
-      std::cerr << "Failed to launch the CUTLASS kernel. Last CUDA error is: "
-                << cudaGetErrorString(cudaGetLastError()) << std::endl;
-      return example_result;
+    for (int i = 0; i < options.warmup_iterations; i++) {
+      status = op.run();
+      if (status != cutlass::Status::kSuccess) {
+        std::cerr << "Failed to launch the CUTLASS kernel. Last CUDA error is: "
+                  << cudaGetErrorString(cudaGetLastError()) << std::endl;
+        return example_result;
+      }
+      buffer_index = (buffer_index + 1) % buffers.size();
+      arguments = get_arguments(problem_shape, hw_info, buffer_index);
+      status = op.update(arguments, workspace.get());
+      if (status != cutlass::Status::kSuccess) {
+        std::cerr << "Failed to update the CUTLASS kernel's parameters. Last CUDA error is: "
+                  << std::endl;
+        return example_result;
+      }
     }
 
     cudaError_t result = cudaDeviceSynchronize();
@@ -675,6 +804,14 @@ struct FwdRunner {
                   << cudaGetErrorString(cudaGetLastError()) << std::endl;
         return example_result;
       }
+      buffer_index = (buffer_index + 1) % buffers.size();
+      arguments = get_arguments(problem_shape, hw_info, buffer_index);
+      status = op.update(arguments, workspace.get());
+      if (status != cutlass::Status::kSuccess) {
+        std::cerr << "Failed to update the CUTLASS kernel's parameters. Last CUDA error is: "
+                  << std::endl;
+        return example_result;
+      }
     }
 
     //
@@ -720,7 +857,7 @@ struct FwdRunner {
       flops *= static_cast<double>(size<1>(problem_shape));
       flops *= static_cast<double>(size<3,1>(problem_shape));
     }
-    flops *= 4.0 * (std::is_same_v<ActiveMask, CausalMask> ? 0.5 : 1.0);
+    flops *= 4.0 * (std::is_same_v<ActiveMask, CausalMask<true>> || std::is_same_v<ActiveMask, CausalMask<false>> ? 0.5 : 1.0);
     flops *= static_cast<double>(size<2>(problem_shape));
     flops *= static_cast<double>(size<3,0>(problem_shape));
     double tflops_s = flops * 1e-12 /*tera*/ / (runtime_ms * 1e-3 /*ms*/);
@@ -737,10 +874,10 @@ struct FwdRunner {
     // Verify that the result is correct
     bool passed = true;
     if (options.verify) {
-      passed = verify(problem_shape);
+      passed = verify(problem_shape, *buffers[0]);
       if (passed) example_result.verified = true;
     }
-    
+
     if (!passed) {
       std::cerr << "Reference check failed" << std::endl;
       return example_result;
@@ -755,11 +892,18 @@ struct FwdRunner {
 
 ///////////////////////////////////////////////////////////////////////////////////////////////////
 
+int main_result = 0;
+
+///////////////////////////////////////////////////////////////////////////////////////////////////
+
 /// Helper to print a description of the example run and its result
 void print_result(const std::string& description, ExampleResult result, bool verbose) {
   std::ios fmt(nullptr);
   fmt.copyfmt(std::cout);
   std::cout << (result.passed ? (result.verified ? " [OK]  " : " [--] ") : "[FAIL] ");
+  if (! result.passed) {
+    main_result = -1;
+  }
   std::cout << std::setw(32) << std::left << description;
   std::cout.copyfmt(fmt);
   std::cout << " : " << result.tflops_tc_s << " TFLOPS/s" << std::endl;
@@ -792,10 +936,14 @@ void run_fwd_128(Mask fusion, Options const & options, cutlass::KernelHardwareIn
 
   using HeadDim = _128;
 
-  // Persistent Tile Scheduler
-  run(Shape<_256, _128, HeadDim>{}, "tma ws 256x128 acc fp32 persistent", Option<Tag::kIsPersistent, true_type>{});
-  // Individual Tile Scheduler
-  run(Shape<_256, _128, HeadDim>{}, "tma ws 256x128 acc fp32 individual", Option<Tag::kIsPersistent, false_type>{});
+  if (options.persistent) {
+    // Persistent Tile Scheduler
+    run(Shape<_256, _128, HeadDim>{}, "tma ws 256x128 acc fp32 persistent", Option<Tag::kIsPersistent, true_type>{});
+  }
+  else {
+    // Individual Tile Scheduler
+    run(Shape<_256, _128, HeadDim>{}, "tma ws 256x128 acc fp32 individual", Option<Tag::kIsPersistent, false_type>{});
+  }
 }
 
 ///////////////////////////////////////////////////////////////////////////////////////////////////
@@ -821,10 +969,14 @@ void run_fwd_64(Mask fusion, Options const & options, cutlass::KernelHardwareInf
 
   using HeadDim = _64;
 
-  // Persistent Tile Scheduler
-  run(Shape<_256, _128, HeadDim>{}, "tma ws 256x128 acc fp32 persistent", Option<Tag::kIsPersistent, true_type>{});
-  // Individual Tile Scheduler
-  run(Shape<_256, _128, HeadDim>{}, "tma ws 256x128 acc fp32 individual", Option<Tag::kIsPersistent, false_type>{});
+  if (options.persistent) {
+    // Persistent Tile Scheduler
+    run(Shape<_256, _128, HeadDim>{}, "tma ws 256x128 acc fp32 persistent", Option<Tag::kIsPersistent, true_type>{});
+  }
+  else {
+    // Individual Tile Scheduler
+    run(Shape<_256, _128, HeadDim>{}, "tma ws 256x128 acc fp32 individual", Option<Tag::kIsPersistent, false_type>{});
+  }
 }
 
 
@@ -848,10 +1000,14 @@ void run_fwd_32(Mask fusion, Options const & options, cutlass::KernelHardwareInf
   using HeadDim = _32;
 
 #ifdef FP8
-  // Persistent Tile Scheduler
-  run(Shape<_256, _128, HeadDim>{}, "tma ws 256x128 acc fp32 persistent", Option<Tag::kIsPersistent, true_type>{});
-  // Individual Tile Scheduler
-  run(Shape<_256, _128, HeadDim>{}, "tma ws 256x128 acc fp32 individual", Option<Tag::kIsPersistent, false_type>{});
+  if (options.persistent) {
+    // Persistent Tile Scheduler
+    run(Shape<_256, _128, HeadDim>{}, "tma ws 256x128 acc fp32 persistent", Option<Tag::kIsPersistent, true_type>{});
+  }
+  else {
+    // Individual Tile Scheduler
+    run(Shape<_256, _128, HeadDim>{}, "tma ws 256x128 acc fp32 individual", Option<Tag::kIsPersistent, false_type>{});
+  }
 #endif
 }
 
@@ -948,7 +1104,7 @@ int main_single(int argc, char const **args) {
   });
 #endif
 
-  return 0;
+  return main_result;
 }
 
 /////////////////////////////////////////////////////////////////////////////////////////////////
@@ -956,8 +1112,6 @@ int main_single(int argc, char const **args) {
 int main(int argc, char const **args) {
   std::vector<std::string> full_arguments(args, args + argc);
 
-  int result = 0;
-
   bool recursed = false;
   for (size_t i = 1; i < full_arguments.size(); i++) {
     if (full_arguments[i].find(',') != std::string::npos) {
@@ -984,7 +1138,7 @@ int main(int argc, char const **args) {
     main_single(argc, args);
   }
 
-  return result;
+  return main_result;
 }
 
 /////////////////////////////////////////////////////////////////////////////////////////////////

examples/77_blackwell_fmha/77_blackwell_fmha_gen.cu

@@ -689,11 +689,18 @@ struct ExampleRunner {
 
 ///////////////////////////////////////////////////////////////////////////////////////////////////
 
+int main_result = 0;
+
+///////////////////////////////////////////////////////////////////////////////////////////////////
+
 /// Helper to print a description of the example run and its result
 void print_result(const std::string& description, ExampleResult result, bool verbose) {
   std::ios fmt(nullptr);
   fmt.copyfmt(std::cout);
   std::cout << (result.supported ? (result.passed ? (result.verified ? " [OK]  " : " [--] ") : "[FAIL] ") : "[NSUP] ");
+  if (result.supported && ! result.passed) {
+    main_result = -1;
+  }
   std::cout << std::setw(32) << std::left << description;
   std::cout.copyfmt(fmt);
   std::cout << " : " << result.tbytes_s << " TB/s" << std::endl;
@@ -781,12 +788,17 @@ int main_single(int argc, char const **args) {
       std::integral_constant<KernelType, KernelType::MODE>{}, Shape<_##m, _##n, _##k>{}, Shape<_##tm, _##tn, _##tk>{} \
     )
 
-  RUN(UMMA_I, 128, 64, 128, 1, 1, 1);
-  RUN(UMMA_I, 128, 128, 128, 1, 1, 1);
-  RUN(UMMA_I, 128, 256, 128, 1, 1, 1);
-  RUN(UMMA_P, 128, 64, 128, 1, 1, 1);
-  RUN(UMMA_P, 128, 128, 128, 1, 1, 1);
-  RUN(UMMA_P, 128, 256, 128, 1, 1, 1);
+  if (options.d == 128) {
+    RUN(UMMA_I, 128, 64, 128, 1, 1, 1);
+    RUN(UMMA_I, 128, 128, 128, 1, 1, 1);
+    RUN(UMMA_I, 128, 256, 128, 1, 1, 1);
+    RUN(UMMA_P, 128, 64, 128, 1, 1, 1);
+    RUN(UMMA_P, 128, 128, 128, 1, 1, 1);
+    RUN(UMMA_P, 128, 256, 128, 1, 1, 1);
+  }
+  else {
+    std::cout << "Head Dimension != 128 is not supported for the fmha_gen example\n";
+  }
 #endif
 
   return 0;
@@ -797,8 +809,6 @@ int main_single(int argc, char const **args) {
 int main(int argc, char const **args) {
   std::vector<std::string> full_arguments(args, args + argc);
 
-  int result = 0;
-
   bool recursed = false;
   for (size_t i = 1; i < full_arguments.size(); i++) {
     if (full_arguments[i].find(',') != std::string::npos) {
@@ -825,7 +835,7 @@ int main(int argc, char const **args) {
     main_single(argc, args);
   }
 
-  return result;
+  return main_result;
 }
 
 /////////////////////////////////////////////////////////////////////////////////////////////////

examples/77_blackwell_fmha/77_blackwell_mla.cu

@@ -0,0 +1,839 @@
+/***************************************************************************************************
+ * Copyright (c) 2024 - 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+ * SPDX-License-Identifier: BSD-3-Clause
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions are met:
+ *
+ * 1. Redistributions of source code must retain the above copyright notice, this
+ * list of conditions and the following disclaimer.
+ *
+ * 2. Redistributions in binary form must reproduce the above copyright notice,
+ * this list of conditions and the following disclaimer in the documentation
+ * and/or other materials provided with the distribution.
+ *
+ * 3. Neither the name of the copyright holder nor the names of its
+ * contributors may be used to endorse or promote products derived from
+ * this software without specific prior written permission.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+ * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+ * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+ * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+ * FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+ * DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+ * SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+ * CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+ * OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+ * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *
+ **************************************************************************************************/
+/*! \file A MLA (Multi-Head Latent Attention) inference kernel sample for the
+          NVIDIA Blackwell Architecture.
+*/
+
+#include <iostream>
+#include <random>
+#include <regex>
+#include <cmath>
+
+#include "cute/tensor.hpp"
+
+#include "cutlass/cutlass.h"
+#include "cutlass/kernel_hardware_info.h"
+
+#include "cutlass/util/command_line.h"
+#include "cutlass/util/distribution.h"
+#include "cutlass/util/reference/device/tensor_fill.h"
+#include "reference/fmha_mla_reference.hpp"
+#include "reference/reference_abs_error.hpp"
+
+#include "device/sm100_mla.hpp"
+#include "kernel/sm100_mla_tile_scheduler.hpp"
+
+///////////////////////////////////////////////////////////////////////////////////////////////////
+
+using namespace cute;
+using namespace cutlass::fmha::kernel;
+
+///////////////////////////////////////////////////////////////////////////////////////////////////
+
+enum class InitStyle {
+  kOne, kLinearStride128, kLinearStride1, kRandom, kRandomLarge, kNone
+};
+
+///////////////////////////////////////////////////////////////////////////////////////////////////
+
+/// Command line options parsing
+struct Options {
+
+  bool help = false;
+  bool error = false;
+
+  int b = 1;
+  int k = 256;
+  int split_kv = -1; // number of split along k dim.
+  bool is_var_split_kv = false;
+  int max_split_kv = 16;
+  int page = -1;
+  float spread = 0.2f;
+  int iterations = 3;
+  bool verify = false;
+  bool verbose = false;
+
+  int sm_count = 0;
+
+  std::string kernel_filter;
+
+  InitStyle init_style_q = InitStyle::kRandom;
+  InitStyle init_style_c = InitStyle::kRandom;
+
+  static void get_init_style_argument(cutlass::CommandLine& cmd, const char* name, InitStyle& dst, InitStyle const& src) {
+    std::string s;
+    cmd.get_cmd_line_argument(name, s, s);
+    if (s.empty()) {
+      dst = src;
+    }
+    else {
+      if (s == "r") {
+        dst = InitStyle::kRandom;
+      }
+      else if (s == "l") {
+        dst = InitStyle::kRandomLarge;
+      }
+      else if (s == "1") {
+        dst = InitStyle::kOne;
+      }
+      else if (s == "d") {
+        dst = InitStyle::kLinearStride1;
+      }
+      else if (s == "s") {
+        dst = InitStyle::kLinearStride128;
+      }
+      else if (s == "n") {
+        dst = InitStyle::kNone;
+      }
+      else {
+        std::cout << "Error: " << s << " is not a valid input type.\n";
+        std::exit(-1);
+      }
+    }
+  }
+
+  // Parses the command line
+  void parse(int argc, char const **args) {
+    cutlass::CommandLine cmd(argc, args);
+
+    Options defaults;
+
+    if (cmd.check_cmd_line_flag("help")) {
+      help = true;
+      return;
+    }
+
+    cmd.get_cmd_line_argument("k", k, -1);
+    if (k == -1) k = defaults.k;
+
+    cmd.get_cmd_line_argument("b", b, -1);
+    if (b == -1) b = 16384 / k;
+    if (b == 0) b = 1;
+
+    cmd.get_cmd_line_argument("split_kv", split_kv, defaults.split_kv);
+    cmd.get_cmd_line_argument("page", page, defaults.page);
+    cmd.get_cmd_line_argument("spread", spread, defaults.spread);
+    cmd.get_cmd_line_argument("is_var_split_kv", is_var_split_kv, false);
+    if (page == -1) {
+      is_var_split_kv = false;
+    }
+    cmd.get_cmd_line_argument("max_split_kv", max_split_kv, defaults.max_split_kv);
+    if (is_var_split_kv == true) {
+      split_kv = max_split_kv;
+    }
+    cmd.get_cmd_line_argument("iterations", iterations, defaults.iterations);
+    verify = cmd.check_cmd_line_flag("verify");
+    verbose = cmd.check_cmd_line_flag("verbose");
+    cmd.get_cmd_line_argument("sm-count", sm_count, defaults.sm_count);
+    
+    get_init_style_argument(cmd, "init-style", init_style_q, defaults.init_style_q);
+    get_init_style_argument(cmd, "init-style", init_style_c, defaults.init_style_c);
+    get_init_style_argument(cmd, "init-style-q", init_style_q, init_style_q);
+    get_init_style_argument(cmd, "init-style-c", init_style_c, init_style_c);
+
+    cmd.get_cmd_line_argument("kernel-filter", kernel_filter, defaults.kernel_filter);
+  }
+
+  /// Prints the usage statement.
+  std::ostream & print_usage(std::ostream &out) const {
+
+    out << "77_blackwell_mla\n\n"
+      << "  This example showcases the use of CUTLASS for fused multi-head latent\n"
+      << "  attention kernels targeting NVIDIA's Blackwell architecture.\n\n"
+      << "Options:\n\n"
+      << "  --help                      If specified, displays this usage statement\n\n"
+      << "  --b=<int>                   Sets the B extent\n"
+      << "  --k=<int>                   Sets the K extent\n"
+      << "  --page=<int>                Enables paging and sets the page size\n"
+      << "  --iterations=<int>          Benchmarking iterations\n"
+      << "  --spread=<float>            Relative spread away from K for paging\n"
+      << "  --split_kv=<int>            Split KV factor\n"
+      << "  --verify                    Verify results\n"
+      << "  --verbose                   Print smem and execution time per kernel\n"
+      << " --sm-count                   Sets SM count rather than querying it\n"
+      << " --kernel-filter=<filter>     Sets regexp to match kernel against\n"
+      << "\n";
+
+    return out;
+  }
+};
+
+
+///////////////////////////////////////////////////////////////////////////////////////////////////
+
+/// Helper to initialize a block of device data
+template <class Element>
+void initialize_block(
+    DeviceAllocation<Element>& block,
+    uint64_t seed=2023, InitStyle init_style = InitStyle::kRandom) {
+
+  switch (init_style) {
+    case InitStyle::kOne: {
+      cutlass::reference::device::BlockFillRandomUniform(
+        block.get(), block.size(), seed, (Element) 1, (Element) 1);
+      break;
+    }
+    case InitStyle::kRandom: {
+      cutlass::reference::device::BlockFillRandomGaussian(
+        block.get(), block.size(), seed, (Element) -1, (Element) 1);
+      break;
+    }
+    case InitStyle::kRandomLarge: {
+      cutlass::reference::device::BlockFillRandomGaussian(
+        block.get(), block.size(), seed, (Element) -1, (Element) 100);
+      break;
+    }
+    case InitStyle::kLinearStride1: {
+      std::vector<Element> data(block.size());
+      for (size_t i = 0; i < block.size() / 128; i ++) {
+        for (int j = 0; j < 128; j++) {
+          data[j + 128*i] = static_cast<Element>((double) (j % 4));
+        }
+      }
+      block.copy_from_host(data.data(), data.size());
+      break;
+    }
+    case InitStyle::kLinearStride128: {
+      std::vector<Element> data(block.size());
+      for (size_t i = 0; i < block.size() / 64; i ++) {
+        for (int j = 0; j < 64; j++) {
+          data[j + 64*i] = static_cast<Element>((double) (i % 9));
+        }
+      }
+      block.copy_from_host(data.data(), data.size());
+      break;
+    }
+    case InitStyle::kNone: {
+      break;
+    }
+  }
+}
+
+///////////////////////////////////////////////////////////////////////////////////////////////////
+
+struct ExampleResult {
+  bool passed = false;
+  bool verified = false;
+  float runtime_ms = 0;
+  double tflops_tc_s = 0;
+  double tbytes_s = 0;
+  size_t smem_size = 0;
+};
+
+///////////////////////////////////////////////////////////////////////////////////////////////////
+
+#if defined(CUTLASS_ARCH_MMA_SM100_SUPPORTED)
+
+///////////////////////////////////////////////////////////////////////////////////////////////////
+
+template<bool v>
+struct IsPersistent {
+  static const bool value = v;
+};
+
+///////////////////////////////////////////////////////////////////////////////////////////////////
+
+template<
+  class TileShape,
+  class PersistenceOption = IsPersistent<true>
+>
+struct Runner {
+
+#ifdef FP8
+  using Element = cutlass::float_e4m3_t;
+#elif FP16
+  using Element = cutlass::half_t;
+#else
+  #error "Must either define FP8 or FP16"
+#endif
+
+  using ElementAcc = float;
+  using ElementOut = cutlass::half_t;
+
+  using TileShapeH = cute::tuple_element_t<0, TileShape>;
+  using TileShapeD = cute::tuple_element_t<2, TileShape>;
+
+  // H K (D_latent D_rope) B
+  using ProblemShape = cute::tuple<TileShapeH, int, TileShapeD, int>;
+  
+  using StrideQ = cute::tuple<int64_t, _1, int64_t>;  // H D B
+  using StrideK = cute::tuple<int64_t, _1, int64_t>;  // K D B
+  using StrideO = StrideK;                            // H D B
+  using StrideLSE = cute::tuple<_1, int>;             // H B
+
+  using TileScheduler = std::conditional_t<
+      PersistenceOption::value,
+      Sm100MlaPersistentTileScheduler,
+      Sm100MlaIndividualTileScheduler
+  >;
+
+  using Kernel = cutlass::fmha::kernel::Sm100FmhaMlaKernelTmaWarpspecialized<
+    TileShape, Element, ElementAcc, ElementOut, ElementAcc, TileScheduler
+  >;
+  using Operation = cutlass::fmha::device::MLA<Kernel>;
+
+  //
+  // Data members
+  //
+
+  /// Initialization
+  StrideQ stride_Q_latent;
+  StrideK stride_C_latent;
+  StrideQ stride_Q_rope;
+  StrideK stride_K_rope;
+  StrideO stride_O;
+  StrideLSE stride_LSE;
+  StrideLSE stride_PT;
+  
+  uint64_t seed = 0;
+
+  int page_size = -1;
+  int page_count = -1;
+
+  // We allocate Q and C as first latent, then rope
+  // This means that we offset the pointer by HeadDim_latent to get the rope
+  // portion
+  DeviceAllocation<Element> block_Q;
+  DeviceAllocation<Element> block_C;
+  DeviceAllocation<ElementOut> block_O;
+  DeviceAllocation<int> block_seq;
+  DeviceAllocation<int> block_PT;
+  DeviceAllocation<int> block_split_kv;
+  DeviceAllocation<int> block_accum_split_len; 
+  DeviceAllocation<ElementAcc> block_LSE;
+  DeviceAllocation<ElementOut> block_ref_O;
+  DeviceAllocation<ElementAcc> block_ref_LSE;
+   
+  ElementAcc scale;
+
+  //
+  // Methods
+  //
+
+  bool verify(const ProblemShape& problem_shape) {
+    auto [H, K, D, B] = problem_shape;
+    auto [D_latent, D_rope] = D;
+
+    int page_K = K;
+    int page_B = B;
+    if (block_PT.get() != nullptr) {
+      page_K = page_size;
+      page_B = page_count;
+    }
+
+    Tensor mQ_latent = make_tensor(make_gmem_ptr(block_Q.get()),
+      cute::make_tuple(H, D_latent, B),
+      stride_Q_latent);
+
+    Tensor mQ_rope = make_tensor(make_gmem_ptr(block_Q.get() + D_latent),
+      cute::make_tuple(H, D_rope, B),
+      stride_Q_rope);
+
+    Tensor mC_latent = make_tensor(make_gmem_ptr(block_C.get()),
+      cute::make_tuple(page_K, D_latent, page_B),
+      stride_C_latent);
+
+    Tensor mK_rope = make_tensor(make_gmem_ptr(block_C.get() + D_latent),
+      cute::make_tuple(page_K, D_rope, page_B),
+      stride_K_rope);
+
+    Tensor mO = make_tensor(make_gmem_ptr(block_ref_O.get()),
+      cute::make_tuple(H, D_latent, B),
+      stride_O);
+
+    Tensor mLSE = make_tensor(make_gmem_ptr(block_ref_LSE.get()),
+      cute::make_tuple(H, B),
+      stride_LSE);
+
+    Tensor mSeq = make_tensor(make_gmem_ptr(static_cast<int*>(block_seq.get())), make_shape(B));
+    Tensor mPT = make_tensor(make_gmem_ptr(static_cast<int*>(block_PT.get())), make_shape(ceil_div(K, page_size), B), stride_PT);
+
+    fmha_mla_reference(problem_shape, mSeq, mPT, mQ_latent, mQ_rope, mC_latent, mK_rope, mO, mLSE, scale);
+
+    cudaError_t result = cudaDeviceSynchronize();
+    if (result != cudaSuccess) {
+      std::cerr << "Reference kernel failed. Last CUDA error: "
+                << cudaGetErrorString(result) << std::endl;
+      return false;
+    }
+
+    const double kMaxDiffThresh = sizeof(Element) == 1 ? 1e-1 : 1e-2;
+    const double kMeanDiffThresh = sizeof(Element) == 1 ? 1e-1 : 1e-3;
+
+    // Check if output from CUTLASS kernel and reference kernel are equal or not
+    double max_diff = 0;
+    double mean_diff = 0;
+    reference_abs_diff(block_O, block_ref_O, max_diff, mean_diff);
+
+    bool passed_O = (max_diff < kMaxDiffThresh) && (mean_diff < kMeanDiffThresh);
+    if (! passed_O) {
+      std::cerr << "failed O: max diff " << max_diff 
+                << " mean " << mean_diff << std::endl;
+    }
+
+    bool passed_LSE = true;
+    reference_abs_diff(block_LSE, block_ref_LSE, max_diff, mean_diff);
+
+    passed_LSE = (max_diff < kMaxDiffThresh) && (mean_diff < kMeanDiffThresh);
+    if ( ! passed_LSE) {
+      std::cerr << "failed LSE: max diff " << max_diff 
+                << " mean " << mean_diff << std::endl;
+    }
+
+    return passed_O && passed_LSE;
+  }
+
+  ProblemShape initialize(const Options& options) {
+    auto problem_shape = cute::make_tuple(TileShapeH{}, options.k, TileShapeD{}, options.b);
+
+    auto [H, K, D, B] = problem_shape;
+    auto [D_latent, D_rope] = D;
+
+    // the scale is based on the non-absorbed sizes, change as appropriate
+    // we can't determine this parameter from the info we have, it's an input
+    int D_non_latent = 128;
+    scale = static_cast<decltype(scale)>(1.0 / sqrt(1.0 * (D_non_latent + D_rope)));
+    // Shape (H, D, B)
+    stride_Q_latent = cute::make_tuple(static_cast<int64_t>(0 + D_latent + D_rope), _1{}, static_cast<int64_t>(H * (0 + D_latent + D_rope)));
+    stride_Q_rope = stride_Q_latent;
+    stride_O = cute::make_tuple(static_cast<int64_t>(0 + D_latent), _1{}, static_cast<int64_t>(0 + H * D_latent));
+    stride_LSE = cute::make_tuple(_1{}, 0 + H);
+
+    block_Q.reset(static_cast<size_t>(options.b) * H * (D_latent + D_rope));
+    block_O.reset(static_cast<size_t>(options.b) * H * D_latent);
+    block_LSE.reset(static_cast<size_t>(options.b) * H);
+    block_ref_O.reset(static_cast<size_t>(options.b) * H * D_latent);
+    block_ref_LSE.reset(static_cast<size_t>(options.b) * H);
+
+    if (options.page == -1) {
+
+      stride_C_latent = cute::make_tuple(static_cast<int64_t>(0 + D_latent + D_rope), _1{}, static_cast<int64_t>(options.k) * (D_latent + D_rope));
+      stride_K_rope = stride_C_latent;
+
+      block_C.reset(static_cast<size_t>(options.b) * options.k * (D_latent + D_rope));
+
+    }
+    else {
+      
+      float spread = options.spread;
+      int max_K = static_cast<int>((1 + spread) * K);
+      int min_K = static_cast<int>((1 - spread) * K);
+      page_size = options.page;
+      page_count = B * ceil_div(max_K, page_size);
+      stride_PT = cute::make_stride(_1{}, page_count);
+
+      std::vector<int> host_seq(B);
+      std::vector<int> host_PT(page_count * B);
+
+      for (int i = 0; i < B; i++) {
+        int seq = min_K + rand() % (max_K - min_K + 1);
+        host_seq[i] = seq;
+        for (int j = 0; j < ceil_div(seq, page_size); j++) {
+          host_PT[page_count * i + j] = i + j * B;
+        }
+      }
+
+      block_seq.reset(host_seq.size());
+      block_seq.copy_from_host(host_seq.data(), host_seq.size());
+      block_PT.reset(host_PT.size());
+      block_PT.copy_from_host(host_PT.data(), host_PT.size());
+
+      get<1>(problem_shape) = max_K;
+
+      stride_C_latent = cute::make_tuple(static_cast<int64_t>(0 + D_latent + D_rope), _1{}, page_size * static_cast<int64_t>((D_latent + D_rope)));
+      stride_K_rope = stride_C_latent;
+
+      block_C.reset(page_count * page_size * static_cast<int64_t>((D_latent + D_rope)));
+
+      if (options.is_var_split_kv == true) {
+        std::vector<int> host_split_kv(B);
+        for(int i = 0; i < B; ++i) {
+          auto len = host_seq[i];
+	  int split = ceil_div(options.max_split_kv, ceil_div(max_K, len));
+	  host_split_kv[i] = split;
+        }
+	block_split_kv.reset(B);
+        block_split_kv.copy_from_host(host_split_kv.data(), host_split_kv.size());
+      } 
+    }
+
+    initialize_block(block_Q, seed + 2023, options.init_style_q);
+    initialize_block(block_C, seed + 2022, options.init_style_c);
+
+    return problem_shape;
+  }
+
+  ExampleResult run(const Options& options, const cutlass::KernelHardwareInfo& hw_info) {
+
+    ProblemShape problem_shape = initialize(options);
+
+    auto [H, K, D, B] = problem_shape;
+    auto [D_latent, D_rope] = D;
+
+    typename Operation::Arguments arguments{
+      problem_shape,
+      { scale,
+        block_Q.get(), stride_Q_latent,
+        block_Q.get() + D_latent, stride_Q_rope,
+        block_C.get(), stride_C_latent,
+        block_C.get() + D_latent, stride_K_rope,
+        block_seq.get(),
+        block_PT.get(), stride_PT,
+        page_count, page_size},
+      { block_O.get(), 
+        stride_O,
+        block_LSE.get(),
+        stride_LSE}, 
+      hw_info,
+      options.split_kv,
+      options.is_var_split_kv ? block_split_kv.get() : nullptr
+    };
+    if (options.split_kv < 0 && !options.is_var_split_kv) {
+      Operation::set_split_kv(arguments);
+    }
+
+    Operation op;
+
+    ExampleResult example_result;
+
+    example_result.smem_size = Operation::Kernel::SharedStorageSize;
+
+    size_t workspace_size = 0;
+    workspace_size = Operation::get_workspace_size(arguments);
+    DeviceAllocation<uint8_t> workspace(workspace_size);
+
+    cutlass::Status status = cutlass::Status::kSuccess;
+    status = op.can_implement(arguments);
+    if (status != cutlass::Status::kSuccess) {
+      std::cerr << "This kernel is not supported. Last CUDA error is: "
+                << cudaGetErrorString(cudaGetLastError()) << std::endl;
+      return example_result;
+    }
+
+    status = op.initialize(arguments, workspace.get());
+    if (status != cutlass::Status::kSuccess) {
+      std::cerr << "Failed to initialize the CUTLASS kernel. Last CUDA error is: "
+                << cudaGetErrorString(cudaGetLastError()) << std::endl;
+      return example_result;
+    }
+    // Run
+    status = op.run();
+    if (status != cutlass::Status::kSuccess) {
+      std::cerr << "Failed to launch the CUTLASS kernel. Last CUDA error is: "
+                << cudaGetErrorString(cudaGetLastError()) << std::endl;
+      return example_result;
+    }
+
+    cudaError_t result = cudaDeviceSynchronize();
+    if (result != cudaSuccess) {
+      std::cerr << "Error running the CUTLASS kernel. Last CUDA error is: "
+                << cudaGetErrorString(result) << std::endl;
+      return example_result;
+    }
+
+    //
+    // Construct events
+    //
+
+    cudaEvent_t events[2];
+
+    for (auto & event : events) {
+      result = cudaEventCreate(&event);
+      if (result != cudaSuccess) {
+        std::cerr << "cudaEventCreate() failed: " << cudaGetErrorString(result) << std::endl;
+        return example_result;
+      }
+    }
+
+    // Record an event at the start of a series of GEMMs
+    result = cudaEventRecord(events[0]);
+    if (result != cudaSuccess) {
+      std::cerr << "cudaEventRecord() failed: " << cudaGetErrorString(result) << std::endl;
+      return example_result;
+    }
+
+    for (int i = 0; i < options.iterations; i++) {
+      status = op.run();
+      if (status != cutlass::Status::kSuccess) {
+        std::cerr << "Failed to launch the CUTLASS kernel. Last CUDA error is: " 
+                  << cudaGetErrorString(cudaGetLastError()) << std::endl;
+        return example_result;
+      } 
+    }
+
+    //
+    // Stop profiling loop
+    //
+
+    // Record an event when the GEMMs are complete
+    result = cudaEventRecord(events[1]);
+    if (result != cudaSuccess) {
+      std::cerr << "cudaEventRecord() failed: " << cudaGetErrorString(result) << std::endl;
+      return example_result;
+    }
+
+    // Wait for work on the device to complete.
+    result = cudaEventSynchronize(events[1]);
+    if (result != cudaSuccess) {
+      std::cerr << "cudaEventSynchronize() failed: " << cudaGetErrorString(result) << std::endl;
+      return example_result;
+    }
+
+    // Measure elapsed runtime
+    float runtime_ms = 0;
+    result = cudaEventElapsedTime(&runtime_ms, events[0], events[1]);
+    if (result != cudaSuccess) {
+      std::cerr << "cudaEventElapsed() failed: " << cudaGetErrorString(result) << std::endl;
+      return example_result;
+    }
+
+    runtime_ms /= static_cast<float>(options.iterations);
+
+    double flops = 1.0;
+    flops *= B;
+    flops *= K;
+    flops *= H;
+    flops *= 2.0;
+    flops *= (2.0 * D_latent + D_rope);
+
+    double bytes_q = sizeof(Element);
+    bytes_q *= B;
+    bytes_q *= H;
+    bytes_q *= (D_latent + D_rope);
+    double bytes_c = sizeof(Element);
+    bytes_c *= B;
+    bytes_c *= options.k;  // K may be max_K here
+    bytes_c *= (D_latent + D_rope);
+    double bytes_o = sizeof(ElementOut);
+    bytes_o *= B;
+    bytes_o *= H;
+    bytes_o *= D_latent;
+    double bytes = bytes_q + bytes_c + bytes_o;
+
+    double tflops_s = flops * 1e-12 /*tera*/ / (runtime_ms * 1e-3 /*ms*/);
+    double tbytes_s = bytes * 1e-12 /*tera*/ / (runtime_ms * 1e-3 /*ms*/);
+    example_result.tflops_tc_s = tflops_s;
+    example_result.tbytes_s = tbytes_s;
+    example_result.runtime_ms = runtime_ms;
+
+    result = cudaDeviceSynchronize();
+    if (result != cudaSuccess) {
+      std::cerr << "Error running the CUTLASS kernel. Last CUDA error is: "
+                << cudaGetErrorString(result) << std::endl;
+      return example_result;
+    }
+
+    // Verify that the result is correct
+    bool passed = true;
+    if (options.verify) {
+      passed = verify(problem_shape);
+      if (passed) example_result.verified = true;
+    }
+    
+    if (!passed) {
+      std::cerr << "Reference check failed" << std::endl;
+      return example_result;
+    }
+
+    example_result.passed = true;
+
+    return example_result;
+  }
+
+};
+
+///////////////////////////////////////////////////////////////////////////////////////////////////
+
+int main_result = 0;
+
+///////////////////////////////////////////////////////////////////////////////////////////////////
+
+/// Helper to print a description of the example run and its result
+void print_result(const std::string& description, ExampleResult result, bool verbose) {
+  std::ios fmt(nullptr);
+  fmt.copyfmt(std::cout);
+  std::cout << (result.passed ? (result.verified ? " [OK]  " : " [--] ") : "[FAIL] ");
+  if (! result.passed) {
+    main_result = -1;
+  }
+  std::cout << std::setw(32) << std::left << description;
+  std::cout.copyfmt(fmt);
+  std::cout << " : " << result.tflops_tc_s << " TFLOPS/s " << result.tbytes_s << " TB/s" << std::endl;
+  if (verbose) {
+    std::cout << "       t=" << result.runtime_ms * 1e3 << " us, "
+        "smem=" << result.smem_size << "b" << std::endl;
+  }
+}
+
+///////////////////////////////////////////////////////////////////////////////////////////////////
+
+void run_mla(Options const & options, cutlass::KernelHardwareInfo const& hw_info) {
+  auto run = [&](auto shape, const char* name, auto... kernel_options) {
+    if ((! options.kernel_filter.empty()) && (! std::regex_search(name, std::basic_regex(options.kernel_filter)))) {
+        return;
+    }
+    Runner<decltype(shape), decltype(kernel_options)...> runner;
+    auto result = runner.run(options, hw_info);
+    print_result(name, result, options.verbose);
+  };
+
+  using NumHeads = _128;
+  using HeadDimLatent = _512;
+  using HeadDim = Shape<HeadDimLatent, _64>;
+
+  std::cout << "###### B " << options.b << " MLA H " << 0 + NumHeads{} << " ";
+  std::cout << "D_rope " << 0 + get<1>(HeadDim{}) << " D_latent " << 0 + get<0>(HeadDim{}) << " ";
+  std::cout << "Q 1 K " << options.k << " Gen None ";
+  std::cout << "Split " << options.split_kv << " Gen None ";
+  std::cout << "#SM " << hw_info.sm_count << std::endl;
+
+  using Blocking = _128;
+  std::string name = std::to_string((int) NumHeads{}) + "x" + std::to_string((int) Blocking{});
+  std::string individual = " individual";
+  std::string persistent = " persistent";
+#if FP8
+  name += " fp8";
+  // Persistent Tile Scheduler
+  run(Shape<NumHeads, Blocking, HeadDim>{}, (name + persistent).c_str(), IsPersistent<true>{});
+  // Individual Tile Scheduler
+  run(Shape<NumHeads, Blocking, HeadDim>{}, (name + individual).c_str(), IsPersistent<false>{});
+#elif FP16
+  name += " fp16";
+  // Persistent Tile Scheduler
+  run(Shape<NumHeads, Blocking, HeadDim>{}, (name + persistent).c_str(), IsPersistent<true>{});
+  // Individual Tile Scheduler
+  run(Shape<NumHeads, Blocking, HeadDim>{}, (name + individual).c_str(), IsPersistent<false>{});
+#endif
+}
+
+///////////////////////////////////////////////////////////////////////////////////////////////////
+
+#endif // defined(CUTLASS_ARCH_MMA_SM100_SUPPORTED)
+
+///////////////////////////////////////////////////////////////////////////////////////////////////
+
+
+int main_single(int argc, char const **args) {
+
+  cudaDeviceProp props;
+
+  cudaError_t error = cudaGetDeviceProperties(&props, 0);
+  if (error != cudaSuccess) {
+    std::cerr << "cudaGetDeviceProperties() returned an error: " << cudaGetErrorString(error) << std::endl;
+    return -1;
+  }
+
+  if (__CUDACC_VER_MAJOR__ < 12 || props.major != 10) {
+    std::cout
+      << "This example requires a GPU of NVIDIA's Blackwell Architecture "
+      << "(compute capability major 10) and CUDA 12.8 or greater.\n";
+    return 0;
+  }
+
+  //
+  // Parse options
+  //
+
+  Options options;
+
+  options.parse(argc, args);
+
+  if (options.help) {
+    options.print_usage(std::cout) << std::endl;
+    return 0;
+  }
+
+  if (options.error) {
+    std::cerr << "Aborting execution." << std::endl;
+    return -1;
+  }
+
+#if defined(CUTLASS_ARCH_MMA_SM100_SUPPORTED)
+
+  //
+  // Run examples
+  //
+
+  // The KernelHardwareInfo struct holds the number of SMs on the GPU with a given device ID. This
+  // information is used by the underlying kernel.
+  cutlass::KernelHardwareInfo hw_info;
+
+  // Change device_id to another value if you are running on a machine with multiple GPUs and wish
+  // to use a GPU other than that with device ID 0.
+  hw_info.device_id = 0;
+  if (options.sm_count == 0) {
+    hw_info.sm_count = cutlass::KernelHardwareInfo::query_device_multiprocessor_count(hw_info.device_id);
+  }
+  else {
+    hw_info.sm_count = options.sm_count;
+  }
+
+  run_mla(options, hw_info);
+#endif
+
+  return 0;
+}
+
+/////////////////////////////////////////////////////////////////////////////////////////////////
+
+int main(int argc, char const **args) {
+  std::vector<std::string> full_arguments(args, args + argc);
+
+  bool recursed = false;
+  for (size_t i = 1; i < full_arguments.size(); i++) {
+    if (full_arguments[i].find(',') != std::string::npos) {
+      auto arg = full_arguments[i];
+      size_t eq_pos = arg.find('=');
+      std::string prefix = eq_pos == std::string::npos ? "" : arg.substr(0, eq_pos+1);
+      std::string rest = eq_pos == std::string::npos ? arg : arg.substr(eq_pos+1);
+      for (;;) {
+        size_t comma_pos = rest.find(',');
+        std::string current = rest.substr(0, comma_pos);
+        full_arguments[i] = prefix + current;
+        std::vector<const char*> next_args;
+        for (auto& elem : full_arguments) { next_args.push_back(elem.data()); }
+        main(argc, next_args.data());
+        if (comma_pos == std::string::npos) break;
+        rest = rest.substr(comma_pos+1);
+      }
+      recursed = true;
+      break;
+    }
+  }
+
+  if (! recursed) {
+    main_single(argc, args);
+  }
+
+  return main_result;
+}
+
+/////////////////////////////////////////////////////////////////////////////////////////////////