483 lines
17 KiB
C++
483 lines
17 KiB
C++
/***************************************************************************************************
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* Copyright (c) 2023 - 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 Tests for device-wide Planar Complex GEMM interface
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*/
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#pragma once
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#include <iostream>
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#include <fstream>
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#include <sstream>
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#include "../../common/cutlass_unit_test.h"
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#include "gemm_testbed_3x.hpp"
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#include "cutlass/util/host_tensor.h"
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#include "cutlass/util/host_tensor_planar_complex.h"
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#include "cutlass/util/tensor_view_io.h"
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#include "cutlass/util/distribution.h"
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#include "cutlass/util/packed_stride.hpp"
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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/gemm_planar_complex.h"
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#include "cutlass/numeric_types.h"
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#include "testbed_utils.h"
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#include "cutlass/kernel_hardware_info.hpp"
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#include "cutlass/layout/matrix.h"
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#include "cutlass/matrix_coord.h"
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#include "cutlass/gemm/gemm.h"
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#include "cutlass/fast_math.h"
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#include "cutlass/platform/platform.h"
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#include "cute/int_tuple.hpp"
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#include "cute/layout.hpp"
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namespace test {
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namespace gemm {
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namespace device {
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/////////////////////////////////////////////////////////////////////////////////////////////////
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template <typename Gemm>
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struct Testbed3xPlanarComplex {
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// Kernel data types
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using ElementA = typename Gemm::GemmKernel::ElementA;
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using StrideA = typename Gemm::GemmKernel::StrideA;
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using ElementB = typename Gemm::GemmKernel::ElementB;
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using StrideB = typename Gemm::GemmKernel::StrideB;
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using ElementC = std::conditional_t<std::is_void_v<typename Gemm::GemmKernel::ElementC>,
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typename Gemm::GemmKernel::ElementD,typename Gemm::GemmKernel::ElementC>;
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using StrideC = typename Gemm::GemmKernel::StrideC;
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using ElementD = typename Gemm::GemmKernel::ElementD;
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using StrideD = typename Gemm::GemmKernel::StrideD;
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using ElementAccumulator = typename Gemm::GemmKernel::ElementAccumulator;
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using ProblemShapeType = typename Gemm::GemmKernel::ProblemShape;
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using EpilogueOutputOp = typename Gemm::EpilogueOutputOp;
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using ClusterShapeType = typename Gemm::GemmKernel::CollectiveMainloop::DispatchPolicy::ClusterShape;
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/// For custom EVTs
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using ElementCompute = typename EpilogueOutputOp::ElementCompute;
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using ElementScalar = typename EpilogueOutputOp::ElementScalar;
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static_assert(rank(StrideC{}) == 3, "StrideCD must be rank-3: [M, N, L]");
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static_assert(rank(StrideD{}) == 3, "StrideCD must be rank-3: [M, N, L]");
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static constexpr uint32_t mma_promotion_interval = 4;
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// Looks at Cute Stride to check Row / Column Major
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template<typename Stride>
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static constexpr bool is_row_or_col_major(){
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int stride_0 = int(cute::size<0>(Stride{}));
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int stride_1 = int(cute::size<1>(Stride{}));
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int depth = cute::depth(Stride{});
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return ((stride_0 == 1) || (stride_1 == 1)) && (depth == 1);
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}
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// Note: this limitation comes from testbed / not the library
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static_assert(is_row_or_col_major<StrideA>(),
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"ERROR : A Layout is neither Row / Column Major)");
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static_assert(is_row_or_col_major<StrideB>(),
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"ERROR : B Layout is neither Row / Column Major)");
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static_assert(is_row_or_col_major<StrideC>(),
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"ERROR : C Layout is neither Row / Column Major)");
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static_assert(is_row_or_col_major<StrideD>(),
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"ERROR : D Layout is neither Row / Column Major)");
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// Deduce Cutlass Layouts (RowMajor & ColumnMajor)
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using LayoutTagA = cutlass::detail::StrideToLayoutTagA_t<StrideA>;
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using LayoutTagB = cutlass::detail::StrideToLayoutTagB_t<StrideB>;
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using LayoutTagC = cutlass::detail::StrideToLayoutTagA_t<StrideC>;
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using LayoutTagD = cutlass::detail::StrideToLayoutTagA_t<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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typename LayoutTagA::Stride stride_factor_A;
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typename LayoutTagB::Stride stride_factor_B;
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typename LayoutTagC::Stride stride_factor_C;
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typename LayoutTagD::Stride stride_factor_D;
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cutlass::Distribution::Kind init_A;
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cutlass::Distribution::Kind init_B;
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cutlass::Distribution::Kind init_C;
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uint64_t seed;
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static constexpr uint64_t kDefaultSeed = 4096;
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// Data members
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cutlass::HostTensorPlanarComplex<ElementA, LayoutTagA> tensor_A;
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cutlass::HostTensorPlanarComplex<ElementB, LayoutTagB> tensor_B;
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cutlass::HostTensorPlanarComplex<ElementC, LayoutTagC> tensor_C;
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cutlass::HostTensorPlanarComplex<ElementC, LayoutTagC> tensor_D;
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cutlass::HostTensorPlanarComplex<ElementC, LayoutTagC> reference_D;
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uint32_t sm_count;
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// Used to force multi-wave tests for persistent kernel schedules
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constexpr static int MaxSmCount = 16;
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using RasterOrderOptions = typename cutlass::gemm::kernel::detail::PersistentTileSchedulerSm90::RasterOrderOptions;
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using DecompositionMode = typename cutlass::gemm::kernel::detail::PersistentTileSchedulerSm90StreamKParams::DecompositionMode;
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cutlass::ComplexTransform TransformA = Gemm::kTransformA;
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cutlass::ComplexTransform TransformB = Gemm::kTransformB;
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//
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// Methods
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//
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Testbed3xPlanarComplex(
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cutlass::Distribution::Kind init_A_ = cutlass::Distribution::Uniform,
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cutlass::Distribution::Kind init_B_ = cutlass::Distribution::Uniform,
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cutlass::Distribution::Kind init_C_ = cutlass::Distribution::Uniform,
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uint64_t seed_ = kDefaultSeed
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):
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stride_factor_A(typename LayoutTagA::Stride()),
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stride_factor_B(typename LayoutTagB::Stride()),
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stride_factor_C(typename LayoutTagC::Stride()),
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stride_factor_D(typename LayoutTagD::Stride()),
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init_A(init_A_), init_B(init_B_), init_C(init_C_), seed(seed_) { }
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/// Helper to initialize a tensor view
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template <typename Element, typename Layout>
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bool initialize_tensor(
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cutlass::TensorViewPlanarComplex<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<ElementD>::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::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::AllOnes) {
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cutlass::reference::host::TensorFill(view, {Element(1), Element(0)});
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}
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else {
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EXPECT_TRUE(false) << "Not implemented";
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return false;
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}
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return true;
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}
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/// Initializes data structures
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void initialize(ProblemShapeType problem_size) {
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//
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// Allocate the GEMM workspace
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//
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auto problem_shape_MNKL = cute::append<4>(problem_size, 1);
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auto M = cute::size<0>(problem_shape_MNKL);
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auto N = cute::size<1>(problem_shape_MNKL);
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auto K = cute::size<2>(problem_shape_MNKL);
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auto L = cute::size<3>(problem_shape_MNKL);
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stride_a = cutlass::make_cute_packed_stride(StrideA{}, cute::make_shape(M, K, L));
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stride_b = cutlass::make_cute_packed_stride(StrideB{}, cute::make_shape(N, K, L));
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stride_c = cutlass::make_cute_packed_stride(StrideC{}, cute::make_shape(M, N, L));
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stride_d = cutlass::make_cute_packed_stride(StrideD{}, cute::make_shape(M, N, L));
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// 2.x host tensor does not natively contain a batch stride or coord, so we spoof if by folding it into the outer mode
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auto a_coord = cutlass::make_Coord(M * L, K);
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auto c_coord = cutlass::make_Coord(M * L, N);
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// Cutlass has Row/Col major refers to MxK times KxN matrix product,
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// so the HostTensorB should be treated as KxN in "coord"'s view
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auto b_coord = cutlass::make_Coord(K, N * L);
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tensor_A.resize(a_coord, cutlass::layout::Affine2Layout_Factory<LayoutTagA>::layout_factory(a_coord, stride_factor_A));
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tensor_B.resize(b_coord, cutlass::layout::Affine2Layout_Factory<LayoutTagB>::layout_factory(b_coord, stride_factor_B));
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tensor_C.resize(c_coord, cutlass::layout::Affine2Layout_Factory<LayoutTagC>::layout_factory(c_coord, stride_factor_C));
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tensor_D.resize(c_coord, cutlass::layout::Affine2Layout_Factory<LayoutTagD>::layout_factory(c_coord, stride_factor_D));
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reference_D.resize(c_coord, cutlass::layout::Affine2Layout_Factory<LayoutTagD>::layout_factory(c_coord, stride_factor_D), false);
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EXPECT_TRUE(initialize_tensor(tensor_A.host_view(), init_A, seed + 2022));
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EXPECT_TRUE(initialize_tensor(tensor_B.host_view(), init_B, seed + 2021));
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EXPECT_TRUE(initialize_tensor(tensor_C.host_view(), init_C, seed + 2020));
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cutlass::reference::host::TensorFill(tensor_D.host_view(), cutlass::complex<ElementC>());
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cutlass::reference::host::TensorFill(reference_D.host_view(), cutlass::complex<ElementC>());
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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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}
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/// Verifies the result is a GEMM
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bool verify(
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ProblemShapeType problem_size,
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ElementScalar alpha,
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ElementScalar beta)
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{
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auto problem_shape_MNKL = cute::append<4>(problem_size, 1);
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auto M = cute::size<0>(problem_shape_MNKL);
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auto N = cute::size<1>(problem_shape_MNKL);
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auto K = cute::size<2>(problem_shape_MNKL);
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auto L = cute::size<3>(problem_shape_MNKL);
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#if 0
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std::cout << " M : " << M << " N : " << N << " K : " << K << " L : " << L << std::endl;
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#endif
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//
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// Compute reference
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//
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cutlass::reference::host::GemmPlanarComplex<
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ElementA, LayoutTagA,
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ElementB, LayoutTagB,
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ElementC, LayoutTagC,
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ElementAccumulator
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>(
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cutlass::gemm::GemmCoord(M,N,K),
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alpha,
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tensor_A.host_ref(),
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TransformA,
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tensor_B.host_ref(),
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TransformB,
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beta,
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tensor_C.host_ref(),
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reference_D.host_ref()
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);
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bool passed = false;
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tensor_D.sync_host();
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passed = cutlass::reference::host::TensorEquals(
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tensor_D.host_view(),
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reference_D.host_view()
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);
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EXPECT_TRUE(passed);
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if (!passed) {
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std::stringstream fname;
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fname << "error_Planar_Complex_Gemm_device_"
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<< M << "x" << N << "x" << K << "x" << L << "_"
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<< cute::get<0>(typename Gemm::GemmKernel::TileShape{}) << "_"
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<< cute::get<1>(typename Gemm::GemmKernel::TileShape{}) << "_"
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<< cute::get<2>(typename Gemm::GemmKernel::TileShape{}) << ".txt";
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std::ofstream file(fname.str());
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file
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<< "problem: " << ' ' << M << "x" << N << "x" << K << ", Batch count = " << L
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<< ", alpha: " << alpha << ", beta: " << beta
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<< "\n\n";
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file
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<< "A =\n" << tensor_A.host_view()
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<< "\nB =\n" << tensor_B.host_view()
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<< "\nC =\n" << tensor_C.host_view()
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<< "\n\nReference =\n" << reference_D.host_view()
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<< "\n\nComputed =\n" << tensor_D.host_view();
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}
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return passed;
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}
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/// Returns true if the CUDA device is sufficient to execute the kernel.
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bool sufficient() {
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//
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// Determine SMEM requirements and waive if not satisfied
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//
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int smem_size = Gemm::GemmKernel::SharedStorageSize;
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int device_idx;
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cudaError_t result = cudaGetDevice(&device_idx);
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if (result != cudaSuccess) {
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throw std::runtime_error("cudaGetDevice() API call failed.");
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}
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cudaDeviceProp properties;
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result = cudaGetDeviceProperties(&properties, device_idx);
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this->sm_count = properties.multiProcessorCount;
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if (result != cudaSuccess) {
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throw std::runtime_error("cudaGetDeviceProperties() failed");
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}
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if (properties.sharedMemPerBlockOptin < size_t(smem_size)) {
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return false;
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}
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return true;
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}
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bool run(
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ProblemShapeType problem_size,
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ElementScalar alpha = ElementScalar(1),
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ElementScalar beta = ElementScalar(0),
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RasterOrderOptions raster_order = RasterOrderOptions::Heuristic,
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detail::MaxSwizzleSize max_swizzle = detail::MaxSwizzleSize{},
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detail::Splits splits = detail::Splits{},
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DecompositionMode decomposition_mode = DecompositionMode::Heuristic,
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unsigned int cluster_m = 0,
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unsigned int cluster_n = 0,
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unsigned int cluster_m_fallback = 0,
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unsigned int cluster_n_fallback = 0
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) {
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// Waive test if insufficient CUDA device
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if (!sufficient()) {
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if (CUTLASS_TEST_UNIT_ENABLE_WARNINGS) {
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std::cerr << "Test waived due to insufficient CUDA device." << std::endl;
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}
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return true;
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}
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this->initialize(problem_size);
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//
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// Launch device kernel
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//
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//
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// Initialize the GEMM operator
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//
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cutlass::KernelHardwareInfo hw_info;
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hw_info.device_id = 0;
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if (cute::is_static_v<ClusterShapeType>) {
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this->sm_count = cutlass::platform::min(MaxSmCount, cutlass::KernelHardwareInfo::query_device_multiprocessor_count(hw_info.device_id));
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hw_info.sm_count = this->sm_count;
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}
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else {
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this->sm_count = cutlass::KernelHardwareInfo::query_device_multiprocessor_count(hw_info.device_id);
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hw_info.sm_count = this->sm_count;
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// Runtime and preferred cluster setting
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hw_info.cluster_shape = {cluster_m, cluster_n, 1};
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hw_info.cluster_shape_fallback = {cluster_m_fallback, cluster_n_fallback, 1};
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}
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typename Gemm::GemmKernel::TileScheduler::Arguments scheduler_args;
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if constexpr (cute::is_same_v<typename Gemm::GemmKernel::TileSchedulerTag, cutlass::gemm::StreamKScheduler>) {
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scheduler_args = { static_cast<int>(splits), static_cast<int>(max_swizzle), raster_order, decomposition_mode };
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}
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else {
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scheduler_args = { static_cast<int>(max_swizzle), raster_order };
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}
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auto arguments = typename Gemm::Arguments {
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cutlass::gemm::GemmUniversalMode::kGemm,
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problem_size,
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{
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tensor_A.device_data(), stride_a, tensor_A.device_data_imag(), stride_a,
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tensor_B.device_data(), stride_b, tensor_B.device_data_imag(), stride_b
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},
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{
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{alpha, beta},
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tensor_C.device_data(), stride_c, tensor_C.device_data_imag(), stride_c,
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tensor_D.device_data(), stride_d, tensor_D.device_data_imag(), stride_d
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},
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hw_info,
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scheduler_args
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};
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Gemm gemm_op;
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size_t workspace_size = Gemm::get_workspace_size(arguments);
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cutlass::device_memory::allocation<uint8_t> workspace(workspace_size);
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cutlass::Status status = gemm_op.can_implement(arguments);
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if (status != cutlass::Status::kSuccess) {
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cudaError_t error = cudaGetLastError();
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std::cerr << "This test is not supported: " << cudaGetErrorString(error) << "\n";
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return true;
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}
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//
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// Run the GEMM
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//
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cudaError_t result;
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status = gemm_op.initialize(arguments, workspace.get());
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status = gemm_op.run();
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result = cudaDeviceSynchronize();
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if (result != cudaSuccess) {
|
|
EXPECT_EQ(result, cudaSuccess) << "Error at Kernel Sync.";
|
|
return false;
|
|
}
|
|
|
|
EXPECT_TRUE(status == cutlass::Status::kSuccess) << to_string(status);
|
|
//
|
|
// Verify
|
|
//
|
|
bool passed = this->verify(problem_size, alpha, beta);
|
|
|
|
if (!passed) {
|
|
std::cout << "Error : Failed : with alpha: " << alpha << ", beta: " << beta
|
|
<< "\n";
|
|
}
|
|
|
|
return passed;
|
|
}
|
|
};
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
} // namespace device
|
|
} // namespace gemm
|
|
} // namespace test
|