CUTLASS 3.4.0 (NVIDIA#1286)

* CUTLASS 3.4.0

* Update CHANGELOG.md

---------

Co-authored-by: Pradeep Ramani <prramani@nvidia.com>

CHANGELOG.md

@@ -1,4 +1,12 @@
 # NVIDIA CUTLASS Changelog
+## [3.4](https://github.com/NVIDIA/cutlass/releases/tag/v3.4) (2023-12-29)
+* 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).
+* NamedBarriers usability improvement and list of [ReservedNamedBarriers](/include/cutlass/arch/barrier.h) has been officially released.
+* Improved [CuTe TMA Tensor](/media/docs/cute/0z_tma_tensors.md) documentation.
+
 
 ## [3.3](https://github.com/NVIDIA/cutlass/releases/tag/v3.3) (2023-10-31)
 * [Mixed-input Hopper GEMMs](/examples/55_hopper_mixed_dtype_gemm) support covering 16-bit x 8-bit input operand types.

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 3.3.0 LANGUAGES CXX)
+project(CUTLASS VERSION 3.4.0 LANGUAGES CXX)
 include(${CMAKE_CURRENT_SOURCE_DIR}/CUDA.cmake)
 
 if (CUDA_VERSION VERSION_LESS 11.3)
@@ -681,6 +681,12 @@ endif()
 
 ################################################################################
 
+set(CUTLASS_DEFAULT_ACTIVE_TEST_SETS "default" CACHE STRING "Default
+  activated test sets. In `make test` mode, this string determines the
+  active set of tests. In `ctest` mode, this value can be overriden
+  with CUTLASS_TEST_SETS environment variable when running the ctest
+  executable.")
+
 set(CUTLASS_CTEST_TEMPLATE_FILE ${CMAKE_CURRENT_LIST_DIR}/cmake/CTestTestfile.configure.cmake)
 set(CUTLASS_CTEST_GENERATED_FILES "" CACHE INTERNAL "")
 
@@ -701,11 +707,12 @@ function(cutlass_add_executable_tests NAME TARGET)
 #   generating the full variable name to be referenced.
 # RESULT_CACHE_FILE: A file to be installed alongside the test executable with pre-computed
 #   test results to speed up test runtime.
+# TEST_SETS_SUPPORTED: A list of test set names these tests support. 
 # 
 
   set(options DISABLE_EXECUTABLE_INSTALL_RULE)
   set(oneValueArgs DISABLE_TESTS RESULT_CACHE_FILE TEST_COMMAND_OPTIONS_PREFIX)
-  set(multiValueArgs DEPENDS DEPENDEES TEST_COMMAND_OPTIONS)
+  set(multiValueArgs DEPENDS DEPENDEES TEST_COMMAND_OPTIONS TEST_SETS_SUPPORTED)
   cmake_parse_arguments(_ "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
 
   if (NOT DEFINED __DISABLE_TESTS)
@@ -715,6 +722,12 @@ function(cutlass_add_executable_tests NAME TARGET)
   set(TEST_EXE $<TARGET_FILE_NAME:${TARGET}>)
   set(TEST_EXE_WORKING_DIRECTORY ./${CMAKE_INSTALL_BINDIR})
 
+  if (NOT DEFINED __TEST_SETS_SUPPORTED)
+    set(__TEST_SETS_SUPPORTED ${CUTLASS_DEFAULT_ACTIVE_TEST_SETS})
+  endif()
+
+  set(TEST_SETS_SUPPORTED ${__TEST_SETS_SUPPORTED})
+
   if (__RESULT_CACHE_FILE)
 
     add_custom_command(
@@ -816,8 +829,6 @@ 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.${TEST_NAME}.cmake" @ONLY)
@@ -873,9 +884,9 @@ if (CUTLASS_INSTALL_TESTS)
   file(MAKE_DIRECTORY "${CMAKE_BINARY_DIR}/ctest")
 
   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" "cmake_policy(SET CMP0057 NEW) # Allow IN_LIST for if()\n\n")
+  file(APPEND "${CMAKE_BINARY_DIR}/ctest/CTestTestfile.cmake" "if (NOT DEFINED ENV{CUTLASS_TEST_SETS})\n")
+  file(APPEND "${CMAKE_BINARY_DIR}/ctest/CTestTestfile.cmake" "  set(ENV{CUTLASS_TEST_SETS} ${CUTLASS_DEFAULT_ACTIVE_TEST_SETS})\n")
   file(APPEND "${CMAKE_BINARY_DIR}/ctest/CTestTestfile.cmake" "endif()\n\n")
 
   foreach(GENERATED_FILE ${CUTLASS_CTEST_GENERATED_FILES})
@@ -897,9 +908,15 @@ write_basic_package_version_file(
   ${CMAKE_CURRENT_BINARY_DIR}/NvidiaCutlassConfigVersion.cmake 
   COMPATIBILITY AnyNewerVersion)
 
+configure_file(
+  ${CMAKE_CURRENT_SOURCE_DIR}/cmake/NvidiaCutlassConfig.cmake.in
+  ${CMAKE_CURRENT_BINARY_DIR}/NvidiaCutlassConfig.cmake  
+  @ONLY
+  )
+
 install(
   FILES 
-    ${CMAKE_CURRENT_SOURCE_DIR}/cmake/NvidiaCutlassConfig.cmake
+    ${CMAKE_CURRENT_BINARY_DIR}/NvidiaCutlassConfig.cmake  
     ${CMAKE_CURRENT_BINARY_DIR}/NvidiaCutlassConfigVersion.cmake 
   DESTINATION ${CMAKE_INSTALL_LIBDIR}/cmake/NvidiaCutlass/
   )

CONTRIBUTORS.md

@@ -13,6 +13,7 @@ Cris Cecka<br />
 Aniket Shivam<br />
 Jack Kosaian<br />
 Mark Hoemmen<br />
+Richard Cai<br />
 Honghao Lu<br />
 Ethan Yan<br />
 Haicheng Wu<br />
@@ -21,6 +22,8 @@ Dustyn Blasig<br />
 Fengqi Qiao<br />
 Duane Merrill<br />
 Yujia Zhai<br />
+Rawn Henry<br />
+Sergey Klevtsov<br />
 Shang Zhang<br />
 Piotr Majcher<br />
 Paul Springer<br />
@@ -55,6 +58,7 @@ Alan Kaatz<br />
 Tina Li<br />
 Timmy Liu<br />
 Wei Liu<br />
+Tim Martin<br />
 Duane Merrill<br />
 Kevin Siu<br />
 Markus Tavenrath<br />

CUDA.cmake

@@ -248,11 +248,15 @@ function(cutlass_unify_source_files TARGET_ARGS_VAR)
     message(FATAL_ERROR "TARGET_ARGS_VAR parameter is required")
   endif()
 
+  if (NOT DEFINED __BATCH_SOURCES)
+    set(__BATCH_SOURCES ON)
+  endif()
+
   if (__BATCH_SOURCES AND NOT DEFINED __BATCH_SIZE)
     set(__BATCH_SIZE ${CUTLASS_UNITY_BUILD_BATCH_SIZE})
   endif()
 
-  if (CUTLASS_UNITY_BUILD_ENABLED AND DEFINED __BATCH_SIZE AND __BATCH_SIZE GREATER 1)
+  if (CUTLASS_UNITY_BUILD_ENABLED AND __BATCH_SOURCES AND __BATCH_SIZE GREATER 1)
 
     set(CUDA_FILE_ARGS)
     set(TARGET_SOURCE_ARGS)

README.md

@@ -1,8 +1,8 @@
 ![ALT](/media/images/gemm-hierarchy-with-epilogue-no-labels.png "Complete CUDA GEMM decomposition")
 
-# CUTLASS 3.3
+# CUTLASS 3.4
 
-_CUTLASS 3.3 - October 2023_
+_CUTLASS 3.4 - December 2023_
 
 CUTLASS is a collection of CUDA C++ template abstractions for implementing
 high-performance matrix-matrix multiplication (GEMM) and related computations at all levels 
@@ -41,17 +41,14 @@ 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 3.3
+# What's New in CUTLASS 3.4
 
-CUTLASS 3.3.0 is an update to CUTLASS adding:
+CUTLASS 3.4.0 is an update to CUTLASS adding:
 
-- New [Mixed-input Hopper GEMMs](/examples/55_hopper_mixed_dtype_gemm) support covering 16-bit x 8-bit input types with optimal performance.
-- New [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}. 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-input GEMMs in the CUTLASS Python interface
+- Improved [Mixed-input Hopper GEMMs](/examples/55_hopper_mixed_dtype_gemm) supporting {16-bit, 8-bit} x {8-bit, 4-bit} input types with fast numerical converters and group scaling factors tuned for optimal performance on Hopper H100.
+- Beta release of [Pointer-Array Batched GEMMs](/examples/56_hopper_ptr_array_batched_gemm) utilizing TMA and Hopper H100 tensor cores now available. (Requires CUDA 12.3 or above)
+- Beta release of [Group-GEMM](/examples/57_hopper_grouped_gemm) - commonly used in optimization of Mixture-Of-Expert models, is now available on Hopper GPUs taking advantage of TMA and Hopper H100 tensor cores. (Requires CUDA 12.3 or above)
+- Impovements to NamedBarriers including details of [ReservedNamedBarriers](/include/cutlass/arch/barrier.h) used within the CUTLASS library.
 
 Minimum requirements:
 
@@ -95,7 +92,7 @@ as shown in the above figure.  Tensor Core operations are implemented using CUDA
 
 CUTLASS requires a C++17 host compiler and 
 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.
+It is also compatible with CUDA 11.4, CUDA 11.5, CUDA 11.6, CUDA 11.7, CUDA 11.8, CUDA 12.0, CUDA 12.1, CUDA 12.2.2 and CUDA 12.3.1
 
 ## Operating Systems
 We have tested the following environments.
@@ -107,6 +104,7 @@ We have tested the following environments.
 | Ubuntu 22.04 | GCC 11.2.0 |
 | Ubuntu 22.04 | Clang 10.0.0 |
 | Ubuntu 22.04 | Clang 14.0.6 |
+| Ubuntu 22.04 | Clang 17.0.6 |
 | Windows 10.0 | Visual Studio 2019 v16.11.27 |
 
 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.

cmake/CTestTestfile.configure.cmake

@@ -2,10 +2,16 @@
 
 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()
+if (NOT DEFINED ENV{CUTLASS_TEST_SETS})
+  set(ENV{CUTLASS_TEST_SETS} @CUTLASS_DEFAULT_ACTIVE_TEST_SETS@)
+endif()
+
+foreach(TEST_SET_REQUESTED IN ITEMS $ENV{CUTLASS_TEST_SETS})
+  if (NOT TEST_SET_REQUESTED IN_LIST TEST_SETS_SUPPORTED) 
+    message(STATUS "Skipping tests for @TEST_EXE_PATH@ as ${TEST_SET_REQUESTED} is not in the set of [${TEST_SETS_SUPPORTED}].")
+    return()
+  endif()
+endforeach()
 
 set(TEST_EXE_PATH @TEST_EXE_PATH@)
 set(TEST_EXE_WORKING_DIRECTORY @TEST_EXE_WORKING_DIRECTORY@)

cmake/NvidiaCutlassConfig.cmake → cmake/NvidiaCutlassConfig.cmake.in

@@ -7,6 +7,3 @@ if(TARGET nvidia::cutlass::CUTLASS)
 endif()
 
 include("${NvidiaCutlass_CMAKE_DIR}/NvidiaCutlassTargets.cmake")
-
-# For backward compatibility with the old name
-add_library(cutlass_lib ALIAS cutlass_library)

examples/08_turing_tensorop_gemm/turing_tensorop_gemm.cu

@@ -291,8 +291,8 @@ int run() {
                                    LayoutInputB,
                                    ElementOutput,
                                    LayoutOutput,
-                                   int32_t,
-                                   int32_t>
+                                   ElementComputeEpilogue,
+                                   ElementComputeEpilogue>
       gemm_device;
 
   // Launch device reference gemm kernel

examples/23_ampere_gemm_operand_reduction_fusion/ampere_gemm_operand_reduction_fusion.cu

@@ -279,7 +279,7 @@ struct Options {
   /// Prints the usage statement.
   std::ostream & print_usage(std::ostream &out) const {
 
-    out << "28_ampere_gemm_bias_fusion example\n\n"
+    out << "23_ampere_operand_gemm_reduction_fusion\n\n"
       << "Options:\n\n"
       << "  --help               If specified, displays this usage statement.\n\n"
       << "  --m=<int>            GEMM M\n"
@@ -297,7 +297,7 @@ struct Options {
       << "  --tag=<string>       String to replicate across the first column in the results table\n";
 
     out << "\n\nExamples:\n\n"
-      << "$ ./examples/23_ampere_gemm_bias_fusion_example/ampere_gemm_bias_fusion  --m=1024 --n=1024 --k=1024 \n\n";
+      << "$ ./examples/23_ampere_gemm_operand_reduction_fusion/23_ampere_gemm_operand_reduction_fusion  --m=1024 --n=1024 --k=1024 \n\n";
 
     return out;
   }

examples/30_wgrad_split_k/30_wgrad_split_k.cu

@@ -602,7 +602,7 @@ Result profile_convolution(Options const &options) {
 
     std::stringstream ss;
 
-    ss << "26_ampere_fused_wgrad_batch_normalization_"
+    ss << "30_wgrad_split_k_"
        << options.input_size.n() << "x" << options.input_size.h() << "x" << options.input_size.w() << "x" << options.input_size.c()
        << "_"
        << options.filter_size.n() << "x" << options.filter_size.h() << "x" << options.filter_size.w() << "x" << options.filter_size.c()

examples/34_transposed_conv2d/34_transposed_conv2d.cu

@@ -251,7 +251,7 @@ struct Options {
 	<< "  --tag=<string>       String to replicate across the first column in the results table\n";
 
     out << "\n\nExamples:\n\n"
-	<< "$ ./examples/31_transposed_conv2d/31_transposed_conv2d --n=8 --h=32 --w=32 --c=16 --k=32 --r=3 --s=3\n\n";
+	<< "$ ./examples/34_transposed_conv2d/34_transposed_conv2d --n=8 --h=32 --w=32 --c=16 --k=32 --r=3 --s=3\n\n";
 
     return out;
   }

examples/38_syr2k_grouped/syr2k_grouped.cu

@@ -398,7 +398,7 @@ struct Options {
       << "$ ./examples/38_syr2k_grouped/38_syr2k_grouped --benchmark=problems.txt\n\n"
 
       << "# Execute Grouped SYR2K and profile with NSight\n"
-      << "$ nv-nsight-cu-cli ./examples/24_gemm_grouped/24_gemm_grouped --n=256 --k=256 --verbose=true --iterations=1 --reference-check=false\n\n";
+      << "$ nv-nsight-cu-cli ./examples/38_syr2k_grouped/38_syr2k_grouped --n=256 --k=256 --verbose=true --iterations=1 --reference-check=false\n\n";
 
     return out;
   }

examples/46_depthwise_simt_conv2dfprop/depthwise_simt_conv2dfprop.cu

@@ -306,7 +306,7 @@ struct Options {
 
   /// Prints the usage statement.
   std::ostream &print_usage(std::ostream &out) const {
-    out << "41_depthwise_gemm_fprop example\n\n"
+    out << "46_depthwise_gemm_fprop example\n\n"
         << "  This example uses Ampere's Tensor Core operators on F16 data types to compute\n"
         << "  forward convolution on tensors of layout NHWC.\n\n"
         << "Options:\n\n"
@@ -554,7 +554,7 @@ Result profile_convolution(Options const &options) {
   if (options.save_workspace) {
     std::stringstream ss;
 
-    ss << "45_depthwise_simt_conv2dfprop" << options.input_size.n() << "x" << options.input_size.h()
+    ss << "46_depthwise_simt_conv2dfprop" << options.input_size.n() << "x" << options.input_size.h()
        << "x" << options.input_size.w() << "x" << options.input_size.c() << "_"
        << options.filter_size.n() << "x" << options.filter_size.h() << "x"
        << options.filter_size.w() << "x" << options.filter_size.c() << ".dat";

examples/49_hopper_gemm_with_collective_builder/49_collective_builder.cu

@@ -291,7 +291,7 @@ struct ExampleRunner {
   using CustomEVT =  // alpha * acc + beta * C
     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::Sm90SrcFetch<ElementC>, // C
       cutlass::epilogue::fusion::Sm90EVT<cutlass::epilogue::fusion::Sm90Compute<cutlass::multiplies, ElementCompute, ElementCompute, RoundStyle>, // alpha * acc
         cutlass::epilogue::fusion::Sm90ScalarBroadcast<ElementScalar>, // alpha
         cutlass::epilogue::fusion::Sm90AccFetch // acc
@@ -302,7 +302,7 @@ struct ExampleRunner {
   // Users can select one of these operations by passing one of the tags defined in include/cutlass/epilogue/fusion/operations.hpp
   // to the CollectiveBuilder. This frees the user from having to compute additional parameters such as stage counts and copy atoms/layouts.
   // These tags also provide additional metadata that can be queried at compile time.
-  using DefaultOperation = cutlass::epilogue::fusion::LinearCombination<ElementD, ElementCompute, ElementScalar, RoundStyle>;
+  using DefaultOperation = cutlass::epilogue::fusion::LinearCombination<ElementD, ElementCompute, ElementC, ElementScalar, RoundStyle>;
 
   using CollectiveEpilogue = typename cutlass::epilogue::collective::CollectiveBuilder<
       cutlass::arch::Sm90, cutlass::arch::OpClassTensorOp,

examples/52_hopper_gather_scatter_fusion/52_hopper_gather_scatter_fusion.cu

@@ -568,7 +568,7 @@ struct ExampleRunner
     if (options.reference_check) {
       if (!verify()) {
         std::cout << "Failed validation" << std::endl;
-#if 1
+#if 0
         debug_output(std::cout);
 #endif
         return false;

examples/52_hopper_gather_scatter_fusion/gather_gemm.hpp

@@ -122,7 +122,7 @@ class GemmGather
   static_assert(cute::size(GmemTiledCopyA{}) == cute::size(GmemTiledCopyB{}), "Number of threads in A/B tiled copies must be the same.");
 
   static constexpr uint32_t NumLoadWarpGroups = cute::size(GmemTiledCopyA{}) / NumThreadsPerWarpGroup;
-  static constexpr uint32_t NumMmaWarpGroups = cute::size(TiledMma{}) / NumThreadsPerWarpGroup;
+  static constexpr uint32_t NumMmaWarpGroups = CUTE_STATIC_V(cute::size(TiledMma{})) / NumThreadsPerWarpGroup;
   static constexpr uint32_t NumWarpGroups = NumLoadWarpGroups + NumMmaWarpGroups;
   static_assert(NumWarpGroups == 2 || NumWarpGroups == 3, "Number of warp groups must be 2 or 3 for good performance.");
 

examples/53_hopper_gemm_permute/53_hopper_gemm_permute.cu

@@ -109,6 +109,8 @@
 namespace example
 {
 
+#if defined(CUTLASS_ARCH_MMA_SM90_SUPPORTED)
+
 struct Options {
 
   bool help;
@@ -724,6 +726,7 @@ private:
     return true;
   }
 };
+#endif // defined(CUTLASS_ARCH_MMA_SM90_SUPPORTED)
 
 } // namespace example
 
@@ -749,7 +752,7 @@ int main(int argc, char const **argv)
   if (notSupported) {
     return EXIT_SUCCESS; // Do not fail CI checks on unsupported systems
   }
-
+#if defined(CUTLASS_ARCH_MMA_SM90_SUPPORTED)
   example::Options options;
   options.parse(argc, argv);
 
@@ -970,6 +973,6 @@ int main(int argc, char const **argv)
     result &= runner.run(options);
   }
 #endif
-
   return result ? EXIT_SUCCESS : EXIT_FAILURE;
+#endif // defined(CUTLASS_ARCH_MMA_SM90_SUPPORTED)
 }

examples/54_hopper_fp8_warp_specialized_gemm/54_hopper_fp8_warp_specialized_gemm.cu

@@ -109,9 +109,11 @@ using         ElementD    = ElementC;
 using         LayoutD     = LayoutC;
 constexpr int AlignmentD  = AlignmentC;
 
-// Auxiliary matrix configuration
+// Auxiliary matrix configuration and other fusion types
 using         ElementAux   = ElementC;
 using         LayoutAux    = LayoutC;
+using         ElementAmax  = float;
+using         ElementBias  = float;
 
 // Core kernel configurations
 using ElementAccumulator  = float;                                          // Element type for internal accumulation
@@ -124,7 +126,7 @@ using KernelSchedule      = cutlass::gemm::KernelTmaWarpSpecialized;
 using EpilogueSchedule    = cutlass::epilogue::TmaWarpSpecialized;
 using EpilogueTileType    = cutlass::epilogue::collective::EpilogueTileAuto;
 using FusionOperation     = cutlass::epilogue::fusion::ScaledLinCombPerRowBiasEltActAmaxAux<
-    LayoutAux, cutlass::epilogue::thread::ReLU, ElementD, ElementCompute, ElementAux>;
+    LayoutAux, cutlass::epilogue::thread::ReLU, ElementD, ElementCompute, ElementAux, ElementAmax, ElementBias, ElementC>;
 
 using CollectiveEpilogue = typename cutlass::epilogue::collective::CollectiveBuilder<
     ArchTag, OperatorClass,