582 lines
21 KiB
Plaintext
582 lines
21 KiB
Plaintext
/***************************************************************************************************
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* Copyright (c) 2025 - 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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* SPDX-License-Identifier: BSD-3-Clause
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*
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* Redistribution and use in source and binary forms, with or without
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* modification, are permitted provided that the following conditions are met:
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*
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* 1. Redistributions of source code must retain the above copyright notice, this
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* list of conditions and the following disclaimer.
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*
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* 2. Redistributions in binary form must reproduce the above copyright notice,
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* this list of conditions and the following disclaimer in the documentation
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* and/or other materials provided with the distribution.
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*
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* 3. Neither the name of the copyright holder nor the names of its
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* contributors may be used to endorse or promote products derived from
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* this software without specific prior written permission.
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*
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* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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* AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
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* DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
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* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
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* DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
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* SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
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* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
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* OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
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* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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*
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**************************************************************************************************/
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/*! \file
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\brief An FP8 blockwise scaled GEMM example for the NVIDIA Blackwell SM100 architecture using CUTLASS.
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*/
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#include <iostream>
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#include "cutlass/cutlass.h"
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#include "cute/tensor.hpp"
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#include "cutlass/tensor_ref.h"
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#include "cutlass/epilogue/thread/activation.h"
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#include "cutlass/gemm/dispatch_policy.hpp"
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#include "cutlass/gemm/collective/collective_builder.hpp"
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#include "cutlass/epilogue/dispatch_policy.hpp"
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#include "cutlass/epilogue/collective/collective_builder.hpp"
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#include "cutlass/gemm/device/gemm_universal_adapter.h"
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#include "cutlass/gemm/kernel/gemm_universal.hpp"
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#include "cutlass/gemm/kernel/tile_scheduler_params.h"
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#include "cutlass/util/command_line.h"
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#include "cutlass/util/distribution.h"
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#include "cutlass/util/host_tensor.h"
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#include "cutlass/util/packed_stride.hpp"
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#include "cutlass/util/tensor_view_io.h"
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#include "cutlass/util/reference/host/tensor_fill.h"
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#include "cutlass/util/reference/host/tensor_copy.h"
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#include "cutlass/util/reference/host/tensor_compare.h"
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#include "cutlass/util/reference/host/tensor_norm.h"
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#include "cutlass/util/reference/host/gett.hpp"
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#include "helper.h"
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using namespace cute;
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#if defined(CUTLASS_ARCH_MMA_SM100_SUPPORTED)
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/////////////////////////////////////////////////////////////////////////////////////////////////
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/// GEMM kernel configurations
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/////////////////////////////////////////////////////////////////////////////////////////////////
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// A matrix configuration
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using ElementA = cutlass::float_e4m3_t; // Element type for A matrix operand
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using LayoutA = cutlass::layout::RowMajor; // Layout type for A matrix operand
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constexpr int AlignmentA = 128 / cutlass::sizeof_bits<ElementA>::value; // Memory access granularity/alignment of A matrix in units of elements (up to 16 bytes)
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// B matrix configuration
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using ElementB = cutlass::float_e4m3_t; // Element type for B matrix operand
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using LayoutB = cutlass::layout::ColumnMajor; // Layout type for B matrix operand
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constexpr int AlignmentB = 128 / cutlass::sizeof_bits<ElementB>::value; // Memory access granularity/alignment of A matrix in units of elements (up to 16 bytes)
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// C/D matrix configuration
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using ElementC = cutlass::float_e4m3_t; // Element type for C and D matrix operands
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using LayoutC = cutlass::layout::ColumnMajor; // Layout type for C and D matrix operands
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constexpr int AlignmentC = 128 / cutlass::sizeof_bits<ElementC>::value; // Memory access granularity/alignment of A matrix in units of elements (up to 16 bytes)
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using ElementD = ElementC;
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using LayoutD = LayoutC;
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constexpr int AlignmentD = AlignmentC;
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// MMA type
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using ElementAccumulator = float; // Element Accumulator will also be our scale factor type
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using ElementCompute = float;
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// MMA and Cluster Tile Shapes
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// Shape of the tile computed by tcgen05 MMA, could be across 2 SMs if Cluster Shape %2 == 0
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using MmaTileShape_MNK = Shape<_128,_128,_128>;
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// Shape of the threadblocks in a cluster
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using ClusterShape_MNK = Shape<_1,_1,_1>;
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using ScaleConfig = decltype(cutlass::detail::sm100_trivial_blockwise_scale_config(MmaTileShape_MNK{}));
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using LayoutSFA = decltype(ScaleConfig::deduce_layoutSFA()); // Layout type for SFA matrix operand
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using LayoutSFB = decltype(ScaleConfig::deduce_layoutSFB()); // Layout type for SFB matrix operand
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using CollectiveEpilogue = typename cutlass::epilogue::collective::CollectiveBuilder<
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cutlass::arch::Sm100, cutlass::arch::OpClassTensorOp,
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MmaTileShape_MNK, ClusterShape_MNK,
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cutlass::epilogue::collective::EpilogueTileAuto,
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ElementAccumulator, ElementCompute,
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ElementC, LayoutC, AlignmentC,
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ElementD, LayoutC, AlignmentD,
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cutlass::epilogue::collective::EpilogueScheduleAuto
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>::CollectiveOp;
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using CollectiveMainloop = typename cutlass::gemm::collective::CollectiveBuilder<
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cutlass::arch::Sm100, cutlass::arch::OpClassTensorOp,
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ElementA, cute::tuple<LayoutA, LayoutSFA>, AlignmentA,
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ElementB, cute::tuple<LayoutB, LayoutSFB>, AlignmentB,
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ElementAccumulator,
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MmaTileShape_MNK, ClusterShape_MNK,
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cutlass::gemm::collective::StageCountAutoCarveout<static_cast<int>(sizeof(typename CollectiveEpilogue::SharedStorage))>,
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cutlass::gemm::KernelScheduleSm100Blockwise
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>::CollectiveOp;
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using GemmKernel = cutlass::gemm::kernel::GemmUniversal<
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Shape<int,int,int,int>,
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CollectiveMainloop,
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CollectiveEpilogue,
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void>; // Default to ClusterLaunchControl (CLC) based tile scheduler
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using Gemm = cutlass::gemm::device::GemmUniversalAdapter<GemmKernel>;
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using StrideA = typename Gemm::GemmKernel::StrideA;
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using StrideB = typename Gemm::GemmKernel::StrideB;
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using StrideC = typename Gemm::GemmKernel::StrideC;
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using StrideD = typename Gemm::GemmKernel::StrideD;
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/// Initialization
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StrideA stride_A;
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StrideB stride_B;
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StrideC stride_C;
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StrideD stride_D;
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// Strides just iterate over scalars and have no zeros
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LayoutSFA layout_SFA;
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LayoutSFB layout_SFB;
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// Layouts are tiled to the problem size and the strides have zeros
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uint64_t seed;
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cutlass::HostTensor<ElementA , LayoutA> tensor_A;
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cutlass::HostTensor<ElementAccumulator, cutlass::layout::PackedVectorLayout> tensor_SFA;
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cutlass::HostTensor<ElementB , LayoutB> tensor_B;
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cutlass::HostTensor<ElementAccumulator, cutlass::layout::PackedVectorLayout> tensor_SFB;
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cutlass::HostTensor<ElementC , LayoutC> tensor_C;
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cutlass::HostTensor<ElementD , LayoutD> tensor_D;
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cutlass::HostTensor<ElementD , LayoutD> tensor_ref_D;
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#endif // defined(CUTLASS_ARCH_MMA_SM100_SUPPORTED)
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/////////////////////////////////////////////////////////////////////////////////////////////////
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/// Testbed utility types
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/////////////////////////////////////////////////////////////////////////////////////////////////
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// Command line options parsing
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struct Options {
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bool help = false;
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bool skip_verification = false;
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float alpha = 1.f, beta = 0.f;
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int iterations = 1000;
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int m = 1024, n = 512, k = 1024, l = 1;
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// Parses the command line
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void parse(int argc, char const **args) {
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cutlass::CommandLine cmd(argc, args);
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if (cmd.check_cmd_line_flag("help")) {
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help = true;
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return;
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}
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if (cmd.check_cmd_line_flag("skip-verification")) {
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skip_verification = true;
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}
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cmd.get_cmd_line_argument("m", m);
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cmd.get_cmd_line_argument("n", n);
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cmd.get_cmd_line_argument("k", k);
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cmd.get_cmd_line_argument("l", l);
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cmd.get_cmd_line_argument("alpha", alpha, 1.f);
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cmd.get_cmd_line_argument("beta", beta, 0.f);
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cmd.get_cmd_line_argument("iterations", iterations);
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}
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/// Prints the usage statement.
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std::ostream & print_usage(std::ostream &out) const {
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out << "81_blackwell_gemm_blockwise\n\n"
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<< " Blackwell FP8 GEMM with Blockwise Scaling using a Warp Specialized kernel.\n\n"
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<< "Options:\n\n"
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<< " --help If specified, displays this usage statement\n\n"
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<< " --m=<int> Sets the M extent of the GEMM\n"
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<< " --n=<int> Sets the N extent of the GEMM\n"
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<< " --k=<int> Sets the K extent of the GEMM\n"
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<< " --l=<int> Sets the l extent (batch) of the GEMM\n"
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<< " --alpha=<f32> Epilogue scalar alpha\n"
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<< " --beta=<f32> Epilogue scalar beta\n"
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<< " --iterations=<int> Number of profiling iterations to perform.\n\n"
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<< " --skip-verification Skip verification.\n\n";
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out
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<< "\n\nExamples:\n\n"
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<< "$ " << "112_blackwell_gemm_blockwise" << " --m=1024 --n=512 --k=1024 --alpha=2 --beta=0.707 \n\n";
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return out;
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}
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/// Compute performance in GFLOP/s
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double gflops(double runtime_s) const {
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// Two flops per multiply-add
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uint64_t flop = uint64_t(2) * m * n * k;
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double gflop = double(flop) / double(1.0e9);
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return gflop / runtime_s;
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}
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};
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/// Result structure
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struct Result {
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double avg_runtime_ms;
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double gflops;
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cutlass::Status status;
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cudaError_t error;
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bool passed;
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Result(
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double avg_runtime_ms = 0,
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double gflops = 0,
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cutlass::Status status = cutlass::Status::kSuccess,
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cudaError_t error = cudaSuccess)
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:
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avg_runtime_ms(avg_runtime_ms), gflops(gflops), status(status), error(error), passed(false)
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{}
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};
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#if defined(CUTLASS_ARCH_MMA_SM100_SUPPORTED)
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/////////////////////////////////////////////////////////////////////////////////////////////////
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/// GEMM setup and evaluation
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/////////////////////////////////////////////////////////////////////////////////////////////////
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/// Helper to initialize a block of device data
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template <typename Element, typename Layout>
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bool initialize_tensor(
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cutlass::TensorView<Element, Layout> view,
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cutlass::Distribution::Kind dist_kind,
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uint64_t seed) {
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if (dist_kind == cutlass::Distribution::Uniform) {
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double scope_max, scope_min;
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int bits_input = cutlass::sizeof_bits<Element>::value;
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int bits_output = cutlass::sizeof_bits<Element>::value;
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if (bits_input == 1) {
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scope_max = 2;
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scope_min = 0;
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}
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else if (bits_input <= 8) {
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scope_max = 2;
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scope_min = -2;
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}
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else if (bits_output == 16) {
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scope_max = 5;
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scope_min = -5;
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}
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else {
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scope_max = 8;
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scope_min = -8;
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}
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cutlass::reference::host::TensorFillRandomUniform(
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view, seed, scope_max, scope_min, 0);
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}
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else if (dist_kind == cutlass::Distribution::AllZeros) {
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cutlass::reference::host::TensorFill(view);
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}
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else if (dist_kind == cutlass::Distribution::Identity) {
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cutlass::reference::host::TensorFillIdentity(view);
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}
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else if (dist_kind == cutlass::Distribution::Gaussian) {
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cutlass::reference::host::TensorFillRandomGaussian(view, seed, 0, 0.5);
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}
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else if (dist_kind == cutlass::Distribution::Sequential) {
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cutlass::reference::host::BlockFillSequential(view.data(), view.capacity());
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}
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else {
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throw std::runtime_error("Not implementated.");
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}
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return true;
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}
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/// Helper to initialize a block of device data (scale_tensors)
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template <typename Element, typename Layout>
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bool initialize_scale_tensor(
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cutlass::TensorView<Element, Layout> view,
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cutlass::Distribution::Kind dist_kind,
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uint64_t seed) {
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if (dist_kind == cutlass::Distribution::Uniform) {
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double scope_max, scope_min;
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scope_min = -8;
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scope_max = 8;
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cutlass::reference::host::TensorFillRandomUniform(
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view, seed, scope_max, scope_min, 0);
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}
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else if (dist_kind == cutlass::Distribution::AllZeros) {
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cutlass::reference::host::TensorFill(view);
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}
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else if (dist_kind == cutlass::Distribution::Identity) {
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cutlass::reference::host::TensorFillIdentity(view);
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}
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else if (dist_kind == cutlass::Distribution::Gaussian) {
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cutlass::reference::host::TensorFillRandomGaussian(view, seed, 0, 0.5);
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}
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else if (dist_kind == cutlass::Distribution::Sequential) {
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cutlass::reference::host::BlockFillSequential(view.data(), view.capacity());
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}
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else {
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throw std::runtime_error("Not implementated.");
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}
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return true;
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}
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/// Initialize operands to be used in the GEMM and reference GEMM
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void initialize(const Options &options) {
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using namespace cute;
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auto gemm_problem_shape = cute::make_shape(options.m, options.n, options.k);
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stride_A = cutlass::make_cute_packed_stride(StrideA{}, cute::make_shape(options.m, options.k, options.l));
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stride_B = cutlass::make_cute_packed_stride(StrideB{}, cute::make_shape(options.n, options.k, options.l));
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stride_C = cutlass::make_cute_packed_stride(StrideC{}, cute::make_shape(options.m, options.n, options.l));
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stride_D = cutlass::make_cute_packed_stride(StrideD{}, cute::make_shape(options.m, options.n, options.l));
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layout_SFA = ScaleConfig::tile_atom_to_shape_SFA(make_shape(options.m, options.n, options.k, options.l));
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layout_SFB = ScaleConfig::tile_atom_to_shape_SFB(make_shape(options.m, options.n, options.k, options.l));
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auto a_coord = cutlass::make_Coord(options.m * options.l, options.k);
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auto c_coord = cutlass::make_Coord(options.m * options.l, options.n);
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auto b_coord = cutlass::make_Coord(options.k, options.n * options.l);
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auto blockscale_a_coord = cutlass::make_Coord(size(filter_zeros(layout_SFA)));
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auto blockscale_b_coord = cutlass::make_Coord(size(filter_zeros(layout_SFB)));
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tensor_A.resize(a_coord);
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tensor_B.resize(b_coord);
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tensor_C.resize(c_coord);
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tensor_D.resize(c_coord);
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tensor_ref_D.resize(c_coord);
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tensor_SFA.resize(blockscale_a_coord);
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tensor_SFB.resize(blockscale_b_coord);
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initialize_tensor(tensor_A.host_view(), cutlass::Distribution::Uniform, seed + 2022);
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initialize_tensor(tensor_B.host_view(), cutlass::Distribution::Uniform, seed + 2023);
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initialize_tensor(tensor_C.host_view(), cutlass::Distribution::Uniform, seed + 2024);
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initialize_scale_tensor(tensor_SFA.host_view(), cutlass::Distribution::Uniform, seed + 2025);
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initialize_scale_tensor(tensor_SFB.host_view(), cutlass::Distribution::Uniform, seed + 2026);
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tensor_A.sync_device();
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tensor_B.sync_device();
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tensor_C.sync_device();
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tensor_D.sync_device();
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tensor_SFA.sync_device();
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tensor_SFB.sync_device();
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}
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/// Populates a Gemm::Arguments structure from the given commandline options
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typename Gemm::Arguments args_from_options(const Options &options) {
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typename Gemm::Arguments arguments{
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cutlass::gemm::GemmUniversalMode::kGemm,
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{options.m, options.n, options.k, options.l},
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{tensor_A.device_data(), stride_A,
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tensor_B.device_data(), stride_B,
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tensor_SFA.device_data(), layout_SFA,
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tensor_SFB.device_data(), layout_SFB},
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{
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{}, // epilogue.thread
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tensor_C.device_data(), stride_C,
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tensor_D.device_data(), stride_D
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}
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};
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auto &fusion_args = arguments.epilogue.thread;
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fusion_args.alpha = options.alpha;
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fusion_args.beta = options.beta;
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return arguments;
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}
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bool verify(const Options &options) {
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//
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// Compute reference output
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//
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// Create instantiation for device reference gemm kernel
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auto A = cute::make_tensor(tensor_A.host_data(),
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cute::make_layout(cute::make_shape(options.m, options.k, options.l), stride_A));
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auto B = cute::make_tensor(tensor_B.host_data(),
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cute::make_layout(cute::make_shape(options.n, options.k, options.l), stride_B));
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auto C = cute::make_tensor(tensor_C.host_data(),
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cute::make_layout(cute::make_shape(options.m, options.n, options.l), stride_C));
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auto D = cute::make_tensor(tensor_ref_D.host_data(),
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cute::make_layout(cute::make_shape(options.m, options.n, options.l), stride_D));
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auto SFA = cute::make_tensor(tensor_SFA.host_data(), layout_SFA);
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auto SFB = cute::make_tensor(tensor_SFB.host_data(), layout_SFB);
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using unused_t = decltype(D);
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cutlass::reference::host::GettBlockScalingMainloopParams<
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ElementAccumulator,
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decltype(A),
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decltype(SFA),
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decltype(B),
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decltype(SFB)
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> mainloop_params{A, SFA, B, SFB};
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cutlass::reference::host::GettEpilogueParams<
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ElementAccumulator,
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ElementAccumulator,
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ElementAccumulator,
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ElementCompute,
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decltype(C),
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decltype(D)
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> epilogue_params;
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epilogue_params.C = C;
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epilogue_params.D = D;
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epilogue_params.alpha = options.alpha;
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epilogue_params.beta = options.beta;
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// get reference result
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cutlass::reference::host::Gemm3x(mainloop_params, epilogue_params);
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// compare_reference
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tensor_D.sync_host();
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bool passed = cutlass::reference::host::TensorEquals(tensor_ref_D.host_view(), tensor_D.host_view());
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return passed;
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}
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|
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/// Execute a given example GEMM computation
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template <typename Gemm>
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int run(Options &options) {
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initialize(options);
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|
|
|
|
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// Instantiate CUTLASS kernel depending on templates
|
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Gemm gemm;
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|
|
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// Create a structure of gemm kernel arguments suitable for invoking an instance of Gemm
|
|
auto arguments = args_from_options(options);
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|
|
|
// Using the arguments, query for extra workspace required for matrix multiplication computation
|
|
size_t workspace_size = Gemm::get_workspace_size(arguments);
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|
|
|
// Allocate workspace memory
|
|
cutlass::device_memory::allocation<uint8_t> workspace(workspace_size);
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|
|
|
|
|
// 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());
|
|
|
|
Result result;
|
|
if (!options.skip_verification) {
|
|
// Check if output from CUTLASS kernel and reference kernel are equal or not
|
|
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.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::cout << " Problem Size: " << options.m << 'x' << options.n << 'x' << options.k << 'x' << options.l << 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_SM100_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 sm100a.
|
|
|
|
if (__CUDACC_VER_MAJOR__ < 12) {
|
|
std::cerr << "This example requires CUDA 12 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(¤t_device_id));
|
|
CUDA_CHECK(cudaGetDeviceProperties(&props, current_device_id));
|
|
cudaError_t error = cudaGetDeviceProperties(&props, 0);
|
|
if (props.major != 10 || props.minor != 0) {
|
|
std::cerr << "This example requires a GPU with compute capability 100a)." << std::endl;
|
|
return 0;
|
|
}
|
|
|
|
|
|
//
|
|
// Parse options
|
|
//
|
|
|
|
Options options;
|
|
|
|
options.parse(argc, args);
|
|
|
|
if (options.help) {
|
|
options.print_usage(std::cout) << std::endl;
|
|
return 0;
|
|
}
|
|
|
|
//
|
|
// Run
|
|
//
|
|
#if defined(CUTLASS_ARCH_MMA_SM100_SUPPORTED)
|
|
run<Gemm>(options);
|
|
#endif // defined(CUTLASS_ARCH_MMA_SM100_SUPPORTED)
|
|
|
|
return 0;
|
|
}
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|