CUTLASS 3.3.0

.github/ISSUE_TEMPLATE/config.yml

@@ -2,4 +2,4 @@ blank_issues_enabled: true
 contact_links:
   - name: CUTLASS Discord
     url: https://discord.gg/nvidiadeveloper
-    about: Come chat about using and contributing to CUTLASS! 
+    about: Come chat about using and contributing to CUTLASS!

CHANGELOG.md

@@ -1,5 +1,20 @@
 # NVIDIA CUTLASS Changelog
 
+## [3.3](https://github.com/NVIDIA/cutlass/releases/tag/v3.3) (2023-10-31)
+* [Mixed Precision Hopper GEMMs](/examples/55_hopper_mixed_dtype_gemm) support covering 16-bit x 8-bit input operand types.
+* [Mixed Precision Ampere GEMMs](https://github.com/NVIDIA/cutlass/commit/7d8317a63e0a978a8dbb3c1fb7af4dbe4f286616) with support for canonical layouts (TN) and {fp16, bf16} x {s8/u8}.
+* [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.
+* 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)
+* 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.
+* Fusion support for backprop fusions including drelu, dgelu, and dbias.
+* Support for void-C kernels and SM80 mixed-precision GEMMs in the CUTLASS Python interface
+
+## [3.2.2](https://github.com/NVIDIA/cutlass/releases/tag/v3.2.1) (2023-10-25)
+* Minor patch for issue/1138
+
 ## [3.2.1](https://github.com/NVIDIA/cutlass/releases/tag/v3.2.1) (2023-09-22)
 * Python support SM90 Epilogue Visitor Tree (EVT) on top of the C++ support released in 3.2.0.
 * SM80 EVT support in C++ and Python.

CMakeLists.txt

@@ -40,7 +40,7 @@ endif()
 message(STATUS "CMake Version: ${CMAKE_VERSION}")
 set(IMPLICIT_CMAKE_CXX_STANDARD OFF CACHE BOOL "Do not explicitly specify -std=c++11 if set")
 
-project(CUTLASS VERSION 3.2.1 LANGUAGES CXX)
+project(CUTLASS VERSION 3.3.0 LANGUAGES CXX)
 include(${CMAKE_CURRENT_SOURCE_DIR}/CUDA.cmake)
 
 if (CUDA_VERSION VERSION_LESS 11.3)
@@ -87,19 +87,6 @@ set(CUTLASS_TEST_LEVEL "0" CACHE STRING "Level of tests to compile.")
 
 find_package(Python3 3.5 COMPONENTS Interpreter REQUIRED)
 
-# Install cutlass_library Python package
-execute_process(
-  WORKING_DIRECTORY ${CUTLASS_DIR}/python
-  COMMAND ${Python3_EXECUTABLE} ${CUTLASS_DIR}/python/setup_library.py develop --user
-  RESULT_VARIABLE cutlass_lib_GENERATOR_INSTALL_RESULT
-  OUTPUT_FILE ${CMAKE_CURRENT_BINARY_DIR}/cutlass_library_installation.log
-  ERROR_FILE ${CMAKE_CURRENT_BINARY_DIR}/cutlass_library_installation.log
-)
-
-if(NOT cutlass_lib_GENERATOR_INSTALL_RESULT EQUAL 0)
-  message(FATAL_ERROR "Error installing cutlass_library package. See ${CMAKE_CURRENT_BINARY_DIR}/cutlass_library_installation.log")
-endif()
-
 ################################################################################
 set(CUTLASS_ENABLE_HEADERS_ONLY OFF CACHE BOOL "Enable only the header library")
 
@@ -829,6 +816,8 @@ function(cutlass_add_executable_tests NAME TARGET)
   set(TEST_GEN_DIR ${CMAKE_CURRENT_BINARY_DIR}/ctest/${TEST_NAME})
   file(MAKE_DIRECTORY ${TEST_GEN_DIR})
 
+  set(TEST_SETS_SUPPORTED default)
+
   set(TEST_EXE_PATH $<TARGET_FILE:${TARGET}>)
   set(TEST_USE_EXTENDED_FORMAT ON)
   configure_file("${CUTLASS_CTEST_TEMPLATE_FILE}" "${TEST_GEN_DIR}/CTestTestfile.${TEST_NAME}.cmake" @ONLY)
@@ -883,7 +872,12 @@ if (CUTLASS_INSTALL_TESTS)
 
   file(MAKE_DIRECTORY "${CMAKE_BINARY_DIR}/ctest")
 
-  file(WRITE "${CMAKE_BINARY_DIR}/ctest/CTestTestfile.cmake" "# Generated File\n")
+  file(WRITE "${CMAKE_BINARY_DIR}/ctest/CTestTestfile.cmake" "# Generated File\n\n")
+
+  file(APPEND "${CMAKE_BINARY_DIR}/ctest/CTestTestfile.cmake" "if (NOT DEFINED ENV{CUTLASS_TEST_SET})\n")
+  file(APPEND "${CMAKE_BINARY_DIR}/ctest/CTestTestfile.cmake" "  set(ENV{CUTLASS_TEST_SET} \"default\")\n")
+  file(APPEND "${CMAKE_BINARY_DIR}/ctest/CTestTestfile.cmake" "endif()\n\n")
+
   foreach(GENERATED_FILE ${CUTLASS_CTEST_GENERATED_FILES})
     file(APPEND "${CMAKE_BINARY_DIR}/ctest/CTestTestfile.cmake" "include(${GENERATED_FILE})\n")
   endforeach()

README.md

@@ -1,8 +1,8 @@
 ![ALT](/media/images/gemm-hierarchy-with-epilogue-no-labels.png "Complete CUDA GEMM decomposition")
 
-# CUTLASS 3.2
+# CUTLASS 3.3
 
-_CUTLASS 3.2 - August 2023_
+_CUTLASS 3.3 - October 2023_
 
 CUTLASS is a collection of CUDA C++ template abstractions for implementing
 high-performance matrix-matrix multiplication (GEMM) and related computations at all levels 
@@ -41,26 +41,17 @@ and improves code composability and readability. More documentation specific to
 
 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.
 
-# What's New in CUTLASS 3.2
-
-CUTLASS 3.2.0 is an update to CUTLASS adding:
-- 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).
-- 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.
-- Support for Windows (MSVC) builds.
-
-CUTLASS 3.2.1 is an update to CUTLASS adding:
-- Python support SM90 Epilogue Visitor Tree (EVT) on top of the C++ support released in 3.2.0.
-- SM80 EVT support in C++ and Python.
-- 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.
-- SM90 TF32 kernel improvements for all layouts.
-- SM90 rasterization direction support in the CUTLASS profiler.
-- Improvement for CUTLASS profiler build times.
+# What's New in CUTLASS 3.3
+
+CUTLASS 3.3.0 is an update to CUTLASS adding:
+
+- New [Mixed Precision Hopper GEMMs](/examples/55_hopper_mixed_dtype_gemm) support covering 16-bit x 8-bit input types with optimal performance.
+- New [Mixed Precision Ampere GEMMs](https://github.com/NVIDIA/cutlass/commit/7d8317a63e0a978a8dbb3c1fb7af4dbe4f286616) with support for canonical layouts (TN) and {fp16, bf16} x {s8/u8}. They also include fast numeric conversion recipes and warp level shuffles to achieve optimal performance.
+- New [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 (across s8/fp8/fp16/bf16/tf32 types) with optimal performance. As a part of this, new kernel schedules, and Copy Ops [SM80\_CP\_ASYNC\_CACHE\_\*](/include/cute/arch/copy_sm80.hpp) were also added.
+- 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.
+- Various subbyte enhancements like tagged device ptrs, support for vectorized copy, various operators to treat subbyte iterators as pointers, and full-fledged CuTe Tensor support.
+- Support for Clang as a host compiler 
+- Support for void-C kernels and SM80 mixed-precision GEMMs in the CUTLASS Python interface
 
 Minimum requirements:
 
@@ -103,7 +94,7 @@ as shown in the above figure.  Tensor Core operations are implemented using CUDA
 # Compatibility
 
 CUTLASS requires a C++17 host compiler and 
-performs best when built with the [**CUDA 12.2 Toolkit**](https://developer.nvidia.com/cuda-toolkit).
+performs best when built with the [**CUDA 12.2.2 Toolkit**](https://developer.nvidia.com/cuda-toolkit-archive).
 It is also compatible with CUDA 11.4, CUDA 11.5, CUDA 11.6, CUDA 11.7, CUDA 11.8, CUDA 12.0 and CUDA 12.1.
 
 ## Operating Systems
@@ -114,9 +105,10 @@ We have tested the following environments.
 | Ubuntu 18.04 | GCC 7.5.0  |
 | Ubuntu 20.04 | GCC 10.3.0 |
 | Ubuntu 22.04 | GCC 11.2.0 |
+| Ubuntu 22.04 | Clang 10.0.0 |
+| Ubuntu 22.04 | Clang 14.0.6 |
 | Windows 10.0 | Visual Studio 2019 v16.11.27 |
 
-Note: We plan to add Clang compiler support soon.
 Note: GCC 8.5.0 has known regressions regarding fold expressions and overloaded operators. Using GCC 7.5.0 or (preferred) GCC >= 9 is recommended.
 
 ## Hardware

bin2hex.cmake

@@ -6,7 +6,7 @@ function(FILE_TO_C_STRING FILENAME VARIABLE_NAME OUTPUT_STRING ZERO_TERMINATED)
   endif()
 
   string(REGEX REPLACE "(....)" "\\1\n" HEX_OUTPUT ${HEX_INPUT})
-  string(REGEX REPLACE "([0-9a-f][0-9a-f])" "0x\\1," HEX_OUTPUT ${HEX_OUTPUT})
+  string(REGEX REPLACE "([0-9a-f][0-9a-f])" "char(0x\\1)," HEX_OUTPUT ${HEX_OUTPUT})
 
   set(HEX_OUTPUT "static char const ${VARIABLE_NAME}[] = {\n  ${HEX_OUTPUT}\n};\n")
 

cmake/CTestTestfile.configure.cmake

@@ -1,5 +1,12 @@
 # Generated file
 
+set(TEST_SETS_SUPPORTED @TEST_SETS_SUPPORTED@)
+
+#? if (DEFINED ENV{CUTLASS_TEST_SET} AND NOT ENV{CUTLASS_TEST_SET} IN_LIST TEST_SETS_SUPPORTED)
+#?   message(STATUS "Skipping tests for @TEST_EXE_PATH@ as $ENV{CUTLASS_TEST_SET} is not in the set of ${TEST_SETS_SUPPORTED}.")
+#?   return()
+#? endif()
+
 set(TEST_EXE_PATH @TEST_EXE_PATH@)
 set(TEST_EXE_WORKING_DIRECTORY @TEST_EXE_WORKING_DIRECTORY@)
 set(CUTLASS_USE_EXTENDED_ADD_TEST_FORMAT @TEST_USE_EXTENDED_FORMAT@)
@@ -11,4 +18,3 @@ else()
 endif()
 
 @_INLINE_PER_TEST_CODE@
-

examples/47_ampere_gemm_universal_streamk/CMakeLists.txt

@@ -33,7 +33,13 @@ cutlass_example_add_executable(
   ampere_gemm_universal_streamk.cu
   )
 
+# Deliberately test non-square sizes to ensure that internal transpose is
+# not triggered when using SM80 EVT
+set(TEST_COMMAND_00 --m=512 --n=768 --k=1152)
+
 cutlass_example_add_executable(
   47_ampere_gemm_universal_streamk_broadcast
   ampere_gemm_universal_streamk_broadcast.cu
+  TEST_COMMAND_OPTIONS
+  TEST_COMMAND_00
   )

examples/48_hopper_warp_specialized_gemm/48_hopper_warp_specialized_gemm.cu

@@ -107,16 +107,6 @@ using ClusterShape        = Shape<_1,_2,_1>;                                // S
 using StageCountType = cutlass::gemm::collective::StageCountAuto;           // Stage count maximized based on the tile size
 using KernelSchedule = cutlass::gemm::collective::KernelScheduleAuto;       // Kernel to launch based on the default setting in the Collective Builder 
 
-using CollectiveMainloop = typename cutlass::gemm::collective::CollectiveBuilder<
-    ArchTag, OperatorClass,
-    ElementA, LayoutA, AlignmentA,
-    ElementB, LayoutB, AlignmentB,
-    ElementAccumulator,
-    TileShape, ClusterShape,
-    cutlass::gemm::collective::StageCountAuto,
-    cutlass::gemm::collective::KernelScheduleAuto
-  >::CollectiveOp;
-
 using CollectiveEpilogue = typename cutlass::epilogue::collective::CollectiveBuilder<
     cutlass::arch::Sm90, cutlass::arch::OpClassTensorOp,
     TileShape, ClusterShape,
@@ -127,6 +117,17 @@ using CollectiveEpilogue = typename cutlass::epilogue::collective::CollectiveBui
     cutlass::epilogue::collective::EpilogueScheduleAuto
   >::CollectiveOp;
 
+using CollectiveMainloop = typename cutlass::gemm::collective::CollectiveBuilder<
+    ArchTag, OperatorClass,
+    ElementA, LayoutA, AlignmentA,
+    ElementB, LayoutB, AlignmentB,
+    ElementAccumulator,
+    TileShape, ClusterShape,
+    cutlass::gemm::collective::StageCountAutoCarveout<
+      static_cast<int>(sizeof(typename CollectiveEpilogue::SharedStorage))>,
+    cutlass::gemm::collective::KernelScheduleAuto
+  >::CollectiveOp;
+
 using GemmKernel = cutlass::gemm::kernel::GemmUniversal<
     Shape<int,int,int>, // Indicates ProblemShape
     CollectiveMainloop,
@@ -397,6 +398,7 @@ int run(Options &options)
     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();

examples/49_hopper_gemm_with_collective_builder/49_collective_builder.cu

@@ -86,7 +86,7 @@
     CUTLASS builders make an attempt to pick the best schedule when `Auto` is provided such that the
     assembled collectives have the best performance, but this is not a guarantee. A user relying on `Auto`
     may get a free performance upgrade with newer CUTLASS releases in case we can provide more optimized
-    implementations that the builder can transparently assemble for `Auto`. But a user should not rely on 
+    implementations that the builder can transparently assemble for `Auto`. But a user should not rely on
     `Auto` if they require a specific scheduling policy and/or stage count to be used.
 
     If a user decides to let the builders pick the collective specialization via `Auto` schedules,
@@ -289,7 +289,7 @@ struct ExampleRunner {
   // EVTs can be constructed by composing the fundamental load/store/compute visitor operations defined in include/cutlass/epilogue/fusion
   // For more complex examples of EVT construction please refer to include/cutlass/epilogue/fusion/sm90_callbacks_tma_warpspecialized.hpp
   using CustomEVT =  // alpha * acc + beta * C
-    cutlass::epilogue::fusion::Sm90EVT<cutlass::epilogue::fusion::Sm90Compute<cutlass::multiply_add, ElementD, ElementCompute, RoundStyle>, // beta * C + (alpha * acc)
+    cutlass::epilogue::fusion::Sm90EVT<cutlass::epilogue::fusion::Sm90Compute<cutlass::homogeneous_multiply_add, ElementD, ElementCompute, RoundStyle>, // beta * C + (alpha * acc)
       cutlass::epilogue::fusion::Sm90ScalarBroadcast<ElementScalar>, // beta
       cutlass::epilogue::fusion::Sm90SrcFetch, // C
       cutlass::epilogue::fusion::Sm90EVT<cutlass::epilogue::fusion::Sm90Compute<cutlass::multiplies, ElementCompute, ElementCompute, RoundStyle>, // alpha * acc
@@ -322,7 +322,7 @@ struct ExampleRunner {
       ElementAccumulator,
       Shape<_128,_128,_64>, Shape<_2,_1,_1>,
       cute::conditional_t<cute::is_same_v<StageCountType, cutlass::gemm::collective::StageCountAuto>,
-          cutlass::gemm::collective::StageCountAutoCarveout<(int)sizeof(typename CollectiveEpilogue::SharedStorage)>,
+          cutlass::gemm::collective::StageCountAutoCarveout<static_cast<int>(sizeof(typename CollectiveEpilogue::SharedStorage))>,
           StageCountType>,
       MainloopScheduleType
     >::CollectiveOp;

examples/52_hopper_gather_scatter_fusion/52_hopper_gather_scatter_fusion.cu

@@ -93,7 +93,7 @@ struct Options {
   int mode = 1; // N-mode gather/scatter by default
 
   float alpha = 1.0f;
-  float beta = 1.0f;
+  float beta  = 0.0f;
 
   bool reference_check = true;
   int iterations = 20;
@@ -179,19 +179,27 @@ struct ExampleRunner
 {
   // Useful aliases
 
+  using ProblemShape = Shape<int,int,int,int>;
+
+  using StrideA = cutlass::gemm::TagToStrideA_t<LayoutA>;
+  using StrideB = cutlass::gemm::TagToStrideB_t<LayoutB>;
+  using StrideC = cutlass::gemm::TagToStrideC_t<LayoutC>;
+  using StrideD = cutlass::gemm::TagToStrideC_t<LayoutD>;
+
   // Alias to for the epilogue type that supports gather/scatter
-  using Epilogue = cutlass::epilogue::collective::EpilogueGatherScatter<
-    cutlass::gemm::TagToStrideC_t<LayoutC>,
-    cutlass::gemm::TagToStrideC_t<LayoutD>,
-    cutlass::epilogue::thread::LinearCombination<
-      ElementD, 1,
-      ElementAccumulator, ElementComputeEpilogue,
-      cutlass::epilogue::thread::ScaleType::Default,
-      cutlass::FloatRoundStyle::round_to_nearest, ElementC
-    >,
-    cutlass::gemm::EpilogueDefault,
-    GatherC,
-    ScatterD
+  using Epilogue = cutlass::epilogue::collective::detail::Sm90TmaWarpSpecializedAdapter<
+    cutlass::epilogue::collective::EpilogueGatherScatter<
+      StrideC, StrideD,
+      cutlass::epilogue::thread::LinearCombination<
+        ElementD, 1,
+        ElementAccumulator, ElementComputeEpilogue,
+        cutlass::epilogue::thread::ScaleType::Default,
+        cutlass::FloatRoundStyle::round_to_nearest, ElementC
+      >,
+      cutlass::gemm::EpilogueDefault,
+      GatherC,
+      ScatterD
+    >
   >;
 
   // Alias to for the mainloop type
@@ -202,27 +210,21 @@ struct ExampleRunner
     ElementAccumulator,
     Shape<_128,_128,_64>,
     Shape<_1,_1,_1>,
-    cutlass::gemm::collective::StageCount<5>,
-    cutlass::gemm::KernelMultistage
+    cutlass::gemm::collective::StageCountAuto,
+    cutlass::gemm::KernelCpAsyncWarpSpecialized
   >::CollectiveOp;
 
-  using ProblemShape = Shape<int,int,int,int>;
-
   using Kernel = cutlass::gemm::kernel::GemmGather<
     ProblemShape,
     Mainloop,
     Epilogue,
+    void,
     GatherA,
     GatherB
   >;
 
   using Gemm = cutlass::gemm::device::GemmUniversalAdapter<Kernel>;
 
-  using StrideA = typename Kernel::StrideA;
-  using StrideB = typename Kernel::StrideB;
-  using StrideC = typename Kernel::StrideC;
-  using StrideD = typename Kernel::StrideD;
-
   static constexpr bool DoGatherA  = not cutlass::platform::is_same<GatherA,  NoGather>::value;
   static constexpr bool DoGatherB  = not cutlass::platform::is_same<GatherB,  NoGather>::value;
   static constexpr bool DoGatherC  = not cutlass::platform::is_same<GatherC,  NoGather>::value;
@@ -250,16 +252,19 @@ struct ExampleRunner
 
   using MainloopRef = Mainloop;
 
-  using EpilogueRef = typename cutlass::epilogue::collective::DefaultEpilogue<
-    StrideC, StrideD,
-    typename Epilogue::ThreadEpilogueOp,
-    typename Epilogue::EpilogueSchedule
+  using EpilogueRef = cutlass::epilogue::collective::detail::Sm90TmaWarpSpecializedAdapter<
+    cutlass::epilogue::collective::DefaultEpilogue<
+      StrideC, StrideD,
+      typename Epilogue::ThreadEpilogueOp,
+      typename Epilogue::EpilogueSchedule
+    >
   >;
 
   using KernelRef = cutlass::gemm::kernel::GemmUniversal<
-      ProblemShape,
-      MainloopRef,
-      EpilogueRef
+    ProblemShape,
+    MainloopRef,
+    EpilogueRef,
+    void
   >;
 
   using GemmRef = cutlass::gemm::device::GemmUniversalAdapter<KernelRef>;
@@ -289,9 +294,10 @@ struct ExampleRunner
   >::CollectiveOp;
 
   using KernelOpt = cutlass::gemm::kernel::GemmUniversal<
-      ProblemShape,
-      MainloopOpt,
-      EpilogueOpt
+    ProblemShape,
+    MainloopOpt,
+    EpilogueOpt,
+    void
   >;
 
   using GemmOpt = cutlass::gemm::device::GemmUniversalAdapter<KernelOpt>;
@@ -404,6 +410,7 @@ struct ExampleRunner
         typename Epilogue::ScatterD{gather_indices.get()}
       },
       hw_info,
+      {},
       typename Kernel::GatherA{gather_indices.get()},
       typename Kernel::GatherB{gather_indices.get()}
     },