From 6aa18940935138753deefd71dae40e5fbb5c6f0a Mon Sep 17 00:00:00 2001 From: Faraz Khoubsirat <58580514+farazkh80@users.noreply.github.com> Date: Thu, 25 Sep 2025 00:32:11 +0000 Subject: [PATCH] Enable mxfp8-mxfp4 group gemm on cutlass Signed-off-by: Faraz Khoubsirat <58580514+farazkh80@users.noreply.github.com> --- .../gemm/collective/builders/sm1xx_common.inl | 2 + .../gemm/device/gemm_testbed_3x_ptr_array.hpp | 12 +- .../CMakeLists.txt | 1 + ...bs_gemm_mxf8_mxf4_f32_group_gemm_fusion.cu | 362 ++++++++++++++++++ 4 files changed, 373 insertions(+), 4 deletions(-) create mode 100644 test/unit/gemm/device/sm120_blockscaled_tensorop_gemm/sm120_bs_gemm_mxf8_mxf4_f32_group_gemm_fusion.cu diff --git a/include/cutlass/gemm/collective/builders/sm1xx_common.inl b/include/cutlass/gemm/collective/builders/sm1xx_common.inl index a6444e02..cc20297a 100644 --- a/include/cutlass/gemm/collective/builders/sm1xx_common.inl +++ b/include/cutlass/gemm/collective/builders/sm1xx_common.inl @@ -566,6 +566,8 @@ check_input_datatypes() { ((SfVectorSizeA == 32 && cute::is_same_v) || (SfVectorSizeA == 32 && cute::is_same_v) || (SfVectorSizeA == 32 && cute::is_same_v) + || (SfVectorSizeA == 32 && cute::is_same_v) + || (SfVectorSizeA == 32 && cute::is_same_v) || (SfVectorSizeA == 32 && cute::is_base_of_v) || (SfVectorSizeA == 32 && cute::is_base_of_v) || (SfVectorSizeA == 64 && cute::is_base_of_v) diff --git a/test/unit/gemm/device/gemm_testbed_3x_ptr_array.hpp b/test/unit/gemm/device/gemm_testbed_3x_ptr_array.hpp index d2badb8c..82928f6f 100644 --- a/test/unit/gemm/device/gemm_testbed_3x_ptr_array.hpp +++ b/test/unit/gemm/device/gemm_testbed_3x_ptr_array.hpp @@ -2256,12 +2256,16 @@ bool TestSmall(double alpha = 1.0, double beta = 1.0, using ElementA = typename Gemm::GemmKernel::ElementA; using ElementB = typename Gemm::GemmKernel::ElementB; using TiledMma = typename Gemm::GemmKernel::TiledMma; - int alignment_bits = 128; static constexpr bool IsF8F6F4 = cutlass::gemm::collective::detail::is_sm100_mma_f8f6f4(); - alignment_bits = cutlass::detail::get_input_alignment_bits(); - // For fp4 and fp6 kernels, the min alignment_input is 128 elements, so we don't need to add alignment_input in test problem sizes. - int alignment_input = (alignment_bits / cute::sizeof_bits::value == 128) ? 0 : (alignment_bits / cute::sizeof_bits::value); + // For fp4 and fp6 kernels, the min alignment_input is 128 elements, so we don't need to add alignment_input in test problem sizes. + int alignment_bits_a = cutlass::detail::get_input_alignment_bits(); + int alignment_input_a = (alignment_bits_a / cute::sizeof_bits::value == 128) ? 0 : (alignment_bits_a / cute::sizeof_bits::value); + + int alignment_bits_b = cutlass::detail::get_input_alignment_bits(); + int alignment_input_b = (alignment_bits_b / cute::sizeof_bits::value == 128) ? 0 : (alignment_bits_b / cute::sizeof_bits::value); + + int alignment_input = (alignment_input_a == 0 || alignment_input_b == 0) ? 0 : std::max(alignment_input_a, alignment_input_b); if constexpr (apply_alignment_offset) { diff --git a/test/unit/gemm/device/sm120_blockscaled_tensorop_gemm/CMakeLists.txt b/test/unit/gemm/device/sm120_blockscaled_tensorop_gemm/CMakeLists.txt index 27b5a00a..05a6d388 100644 --- a/test/unit/gemm/device/sm120_blockscaled_tensorop_gemm/CMakeLists.txt +++ b/test/unit/gemm/device/sm120_blockscaled_tensorop_gemm/CMakeLists.txt @@ -71,6 +71,7 @@ cutlass_test_unit_gemm_device_add_executable( cutlass_test_unit_gemm_device_add_executable( cutlass_test_unit_bs_grouped_gemm_device_tensorop_sm120 sm120_bs_gemm_nvf4_nvf4_f32_nvf4_group_gemm_fusion.cu + sm120_bs_gemm_mxf8_mxf4_f32_group_gemm_fusion.cu ) endif() diff --git a/test/unit/gemm/device/sm120_blockscaled_tensorop_gemm/sm120_bs_gemm_mxf8_mxf4_f32_group_gemm_fusion.cu b/test/unit/gemm/device/sm120_blockscaled_tensorop_gemm/sm120_bs_gemm_mxf8_mxf4_f32_group_gemm_fusion.cu new file mode 100644 index 00000000..34907fe4 --- /dev/null +++ b/test/unit/gemm/device/sm120_blockscaled_tensorop_gemm/sm120_bs_gemm_mxf8_mxf4_f32_group_gemm_fusion.cu @@ -0,0 +1,362 @@ +/*************************************************************************************************** + * Copyright (c) 2025 - 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 Tests for device-wide grouped GEMM interface +*/ + +#include + +#include "cutlass/cutlass.h" +#include "cute/tensor.hpp" +#include "cute/atom/mma_atom.hpp" + +#include "cutlass/numeric_types.h" + +#include "cutlass/gemm/device/gemm_universal_adapter.h" +#include "cutlass/gemm/kernel/gemm_universal.hpp" +#include "cutlass/epilogue/collective/collective_builder.hpp" +#include "cutlass/gemm/collective/collective_builder.hpp" +#include "cutlass/epilogue/collective/default_epilogue.hpp" +#include "cutlass/epilogue/thread/linear_combination.h" +#include "cutlass/gemm/dispatch_policy.hpp" +#include "cutlass/epilogue/thread/activation.h" + +#include "../../../common/cutlass_unit_test.h" +#include "../gemm_testbed_3x_ptr_array.hpp" + + +using namespace cute; + +#if defined(CUTLASS_ARCH_MMA_SM120_SUPPORTED) + +// Pingpong kernel schedule +TEST(SM120_Device_Gemm_e5m2t_e2m1n_e2m1t_tensorop_f32_epilogue_VS32_group_pingpong, row_sf) { + using ElementInputA = float_e5m2_t; + using ElementInputB = float_e2m1_t; + using ElementA = cutlass::mx_float8_t; + using ElementB = cutlass::mx_float4_t; + using ElementC = cutlass::half_t; + using ElementD = cutlass::float_e2m1_t; + using ElementCompute = float; + using ElementAccumulator = float; + using ElementSF = cutlass::float_ue8m0_t; + using ElementSFD = ElementSF; + using ElementAccumulator = float; + using GmemLayoutA = cutlass::layout::RowMajor; + using GmemLayoutB = cutlass::layout::ColumnMajor; + using GmemLayoutC = cutlass::layout::RowMajor; + constexpr int SFVectorSize = 32; + using TileShape_MNK = Shape<_128,_128,_128>; + using ClusterShape_MNK = Shape<_1,_1,_1>; + + constexpr int AlignmentA = 128 / cutlass::sizeof_bits::value; + constexpr int AlignmentB = 128; + constexpr int AlignmentC = 128 / cutlass::sizeof_bits::value; + constexpr int AlignmentD = 128 / cutlass::sizeof_bits::value; + + // + // Construct CollectiveEpilogue + // + + constexpr int OutputSFVectorSize = SFVectorSize; + // D = alpha * acc + beta * C + // With Row-major BlockScaleFactor generation. + using FusionOperation = cutlass::epilogue::fusion::LinCombBlockScaleFactor< + OutputSFVectorSize, + ElementD, + ElementCompute, + ElementSFD, GmemLayoutC, + ElementC>; + + using CollectiveEpilogue = typename cutlass::epilogue::collective::CollectiveBuilder< + cutlass::arch::Sm120, cutlass::arch::OpClassBlockScaledTensorOp, + TileShape_MNK, ClusterShape_MNK, + cutlass::epilogue::collective::EpilogueTileAuto, + ElementAccumulator, ElementCompute, + ElementC, GmemLayoutC *, AlignmentC, + ElementD, GmemLayoutC *, AlignmentD, + cutlass::epilogue::collective::EpilogueScheduleAuto, + FusionOperation + >::CollectiveOp; + + // + // Construct CollectiveMainloop + // + using CollectiveMainloop = typename cutlass::gemm::collective::CollectiveBuilder< + cutlass::arch::Sm120, cutlass::arch::OpClassBlockScaledTensorOp, + ElementA, GmemLayoutA *, AlignmentA, + ElementB, GmemLayoutB *, AlignmentB, + ElementAccumulator, + TileShape_MNK, ClusterShape_MNK, + cutlass::gemm::collective::StageCountAutoCarveout(sizeof(typename CollectiveEpilogue::SharedStorage))>, + cutlass::gemm::KernelPtrArrayTmaWarpSpecializedPingpong + >::CollectiveOp; + + using GemmKernel = cutlass::gemm::kernel::GemmUniversal< + cutlass::gemm::GroupProblemShape>, + CollectiveMainloop, + CollectiveEpilogue + >; + + using Gemm = cutlass::gemm::device::GemmUniversalAdapter; + auto pass = test::gemm::device::TestSmallFusion(1.0, 0.5); + EXPECT_TRUE(pass); +} + + + +TEST(SM120_Device_Gemm_e5m2t_e2m1n_e2m1t_tensorop_f32_epilogue_VS32_group_pingpong, silu_row_sf) { + using ElementInputA = float_e5m2_t; + using ElementInputB = float_e2m1_t; + using ElementA = cutlass::mx_float8_t; + using ElementB = cutlass::mx_float4_t; + using ElementC = cutlass::half_t; + using ElementD = cutlass::float_e2m1_t; + using ElementCompute = float; + using ElementAccumulator = float; + using ElementSF = cutlass::float_ue4m3_t; + using ElementSFD = ElementSF; + using ElementAccumulator = float; + using GmemLayoutA = cutlass::layout::RowMajor; + using GmemLayoutB = cutlass::layout::ColumnMajor; + using GmemLayoutC = cutlass::layout::RowMajor; + constexpr int SFVectorSize = 32; + using TileShape_MNK = Shape<_128,_128,_128>; + using ClusterShape_MNK = Shape<_1,_1,_1>; + + constexpr int AlignmentA = 128 / cutlass::sizeof_bits::value; + constexpr int AlignmentB = 128; + constexpr int AlignmentC = 128 / cutlass::sizeof_bits::value; + constexpr int AlignmentD = 128 / cutlass::sizeof_bits::value; + + // + // Construct CollectiveEpilogue + // + + constexpr int OutputSFVectorSize = SFVectorSize; + // D = SiLu(alpha * acc + beta * C) + // With Row-major BlockScaleFactor generation. + using FusionOperation = cutlass::epilogue::fusion::LinCombEltActBlockScaleFactor< + cutlass::epilogue::thread::SiLu, + OutputSFVectorSize, + ElementD, + ElementCompute, + ElementSFD, GmemLayoutC, + ElementC>; + + using CollectiveEpilogue = typename cutlass::epilogue::collective::CollectiveBuilder< + cutlass::arch::Sm120, cutlass::arch::OpClassBlockScaledTensorOp, + TileShape_MNK, ClusterShape_MNK, + cutlass::epilogue::collective::EpilogueTileAuto, + ElementAccumulator, ElementCompute, + ElementC, GmemLayoutC *, AlignmentC, + ElementD, GmemLayoutC *, AlignmentD, + cutlass::epilogue::collective::EpilogueScheduleAuto, + FusionOperation + >::CollectiveOp; + + // + // Construct CollectiveMainloop + // + using CollectiveMainloop = typename cutlass::gemm::collective::CollectiveBuilder< + cutlass::arch::Sm120, cutlass::arch::OpClassBlockScaledTensorOp, + ElementA, GmemLayoutA *, AlignmentA, + ElementB, GmemLayoutB *, AlignmentB, + ElementAccumulator, + TileShape_MNK, ClusterShape_MNK, + cutlass::gemm::collective::StageCountAutoCarveout(sizeof(typename CollectiveEpilogue::SharedStorage))>, + cutlass::gemm::KernelPtrArrayTmaWarpSpecializedPingpong + >::CollectiveOp; + + using GemmKernel = cutlass::gemm::kernel::GemmUniversal< + cutlass::gemm::GroupProblemShape>, + CollectiveMainloop, + CollectiveEpilogue + >; + + using Gemm = cutlass::gemm::device::GemmUniversalAdapter; + auto pass = test::gemm::device::TestSmallFusion(1.0, 0.5); + EXPECT_TRUE(pass); +} + + +// Cooperative kenel schedule +TEST(SM120_Device_Gemm_e5m2t_e2m1n_e2m1t_tensorop_f32_epilogue_VS32_group_cooperative, row_sf) { + using ElementInputA = float_e5m2_t; + using ElementInputB = float_e2m1_t; + using ElementA = cutlass::mx_float8_t; + using ElementB = cutlass::mx_float4_t; + using ElementC = cutlass::half_t; + using ElementD = cutlass::float_e2m1_t; + using ElementCompute = float; + using ElementAccumulator = float; + using ElementSF = cutlass::float_ue4m3_t; + using ElementSFD = ElementSF; + using ElementAccumulator = float; + using GmemLayoutA = cutlass::layout::RowMajor; + using GmemLayoutB = cutlass::layout::ColumnMajor; + using GmemLayoutC = cutlass::layout::RowMajor; + constexpr int SFVectorSize = 32; + using TileShape_MNK = Shape<_128,_128,_128>; + using ClusterShape_MNK = Shape<_1,_1,_1>; + + constexpr int AlignmentA = 128 / cutlass::sizeof_bits::value; + constexpr int AlignmentB = 128; + constexpr int AlignmentC = 128 / cutlass::sizeof_bits::value; + constexpr int AlignmentD = 128 / cutlass::sizeof_bits::value; + + // + // Construct CollectiveEpilogue + // + + constexpr int OutputSFVectorSize = SFVectorSize; + // D = alpha * acc + beta * C + // With Row-major BlockScaleFactor generation. + using FusionOperation = cutlass::epilogue::fusion::LinCombBlockScaleFactor< + OutputSFVectorSize, + ElementD, + ElementCompute, + ElementSFD, GmemLayoutC, + ElementC>; + + using CollectiveEpilogue = typename cutlass::epilogue::collective::CollectiveBuilder< + cutlass::arch::Sm120, cutlass::arch::OpClassBlockScaledTensorOp, + TileShape_MNK, ClusterShape_MNK, + cutlass::epilogue::collective::EpilogueTileAuto, + ElementAccumulator, ElementCompute, + ElementC, GmemLayoutC *, AlignmentC, + ElementD, GmemLayoutC *, AlignmentD, + cutlass::epilogue::collective::EpilogueScheduleAuto, + FusionOperation + >::CollectiveOp; + + // + // Construct CollectiveMainloop + // + using CollectiveMainloop = typename cutlass::gemm::collective::CollectiveBuilder< + cutlass::arch::Sm120, cutlass::arch::OpClassBlockScaledTensorOp, + ElementA, GmemLayoutA *, AlignmentA, + ElementB, GmemLayoutB *, AlignmentB, + ElementAccumulator, + TileShape_MNK, ClusterShape_MNK, + cutlass::gemm::collective::StageCountAutoCarveout(sizeof(typename CollectiveEpilogue::SharedStorage))>, + cutlass::gemm::collective::KernelScheduleAuto + >::CollectiveOp; + + using GemmKernel = cutlass::gemm::kernel::GemmUniversal< + cutlass::gemm::GroupProblemShape>, + CollectiveMainloop, + CollectiveEpilogue + >; + + using Gemm = cutlass::gemm::device::GemmUniversalAdapter; + auto pass = test::gemm::device::TestSmallFusion(1.0, 0.5); + EXPECT_TRUE(pass); +} + + + +TEST(SM120_Device_Gemm_e5m2t_e2m1n_e2m1t_tensorop_f32_epilogue_VS32_group_cooperative, silu_row_sf) { + using ElementInputA = float_e5m2_t; + using ElementInputB = float_e2m1_t; + using ElementA = cutlass::mx_float8_t; + using ElementB = cutlass::mx_float4_t; + using ElementC = cutlass::half_t; + using ElementD = cutlass::float_e2m1_t; + using ElementCompute = float; + using ElementAccumulator = float; + using ElementSF = cutlass::float_ue4m3_t; + using ElementSFD = ElementSF; + using ElementAccumulator = float; + using GmemLayoutA = cutlass::layout::RowMajor; + using GmemLayoutB = cutlass::layout::ColumnMajor; + using GmemLayoutC = cutlass::layout::RowMajor; + constexpr int SFVectorSize = 32; + using TileShape_MNK = Shape<_128,_128,_128>; + using ClusterShape_MNK = Shape<_1,_1,_1>; + + constexpr int AlignmentA = 128 / cutlass::sizeof_bits::value; + constexpr int AlignmentB = 128; + constexpr int AlignmentC = 128 / cutlass::sizeof_bits::value; + constexpr int AlignmentD = 128 / cutlass::sizeof_bits::value; + + // + // Construct CollectiveEpilogue + // + + constexpr int OutputSFVectorSize = SFVectorSize; + // D = SiLu(alpha * acc + beta * C) + // With Row-major BlockScaleFactor generation. + using FusionOperation = cutlass::epilogue::fusion::LinCombEltActBlockScaleFactor< + cutlass::epilogue::thread::SiLu, + OutputSFVectorSize, + ElementD, + ElementCompute, + ElementSFD, GmemLayoutC, + ElementC>; + + using CollectiveEpilogue = typename cutlass::epilogue::collective::CollectiveBuilder< + cutlass::arch::Sm120, cutlass::arch::OpClassBlockScaledTensorOp, + TileShape_MNK, ClusterShape_MNK, + cutlass::epilogue::collective::EpilogueTileAuto, + ElementAccumulator, ElementCompute, + ElementC, GmemLayoutC *, AlignmentC, + ElementD, GmemLayoutC *, AlignmentD, + cutlass::epilogue::collective::EpilogueScheduleAuto, + FusionOperation + >::CollectiveOp; + + // + // Construct CollectiveMainloop + // + using CollectiveMainloop = typename cutlass::gemm::collective::CollectiveBuilder< + cutlass::arch::Sm120, cutlass::arch::OpClassBlockScaledTensorOp, + ElementA, GmemLayoutA *, AlignmentA, + ElementB, GmemLayoutB *, AlignmentB, + ElementAccumulator, + TileShape_MNK, ClusterShape_MNK, + cutlass::gemm::collective::StageCountAutoCarveout(sizeof(typename CollectiveEpilogue::SharedStorage))>, + cutlass::gemm::collective::KernelScheduleAuto + >::CollectiveOp; + + using GemmKernel = cutlass::gemm::kernel::GemmUniversal< + cutlass::gemm::GroupProblemShape>, + CollectiveMainloop, + CollectiveEpilogue + >; + + using Gemm = cutlass::gemm::device::GemmUniversalAdapter; + auto pass = test::gemm::device::TestSmallFusion(1.0, 0.5); + EXPECT_TRUE(pass); +} +#endif // #if defined(CUTLASS_ARCH_MMA_SM120_SUPPORTED)