v4.4 update. (#2979)
This commit is contained in:
Junkai-Wu
@@ -260,11 +260,11 @@ set(header_files_to_check
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)
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# for each header in _header_files:
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# create a .cu file with the same name as the header's path, except with / replaced with %
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# create a .cu file with the same name as the header's path, except with / replaced with #
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# have the .cu file include that header
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set(_gen_source_files "")
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foreach(header_file ${header_files_to_check})
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string(REPLACE "/" "%" header_file_esc ${header_file})
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string(REPLACE "/" "#" header_file_esc ${header_file})
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file(WRITE "${CMAKE_CURRENT_BINARY_DIR}/${header_file_esc}.cu"
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"#include <${header_file}>")
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@@ -0,0 +1,482 @@
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/***************************************************************************************************
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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;
|
||||
if constexpr (cute::is_same_v<typename Gemm::GemmKernel::TileSchedulerTag, cutlass::gemm::StreamKScheduler>) {
|
||||
scheduler_args = { static_cast<int>(splits), static_cast<int>(max_swizzle), raster_order, decomposition_mode };
|
||||
}
|
||||
else {
|
||||
scheduler_args = { static_cast<int>(max_swizzle), raster_order };
|
||||
}
|
||||
|
||||
auto arguments = typename Gemm::Arguments {
|
||||
cutlass::gemm::GemmUniversalMode::kGemm,
|
||||
problem_size,
|
||||
{
|
||||
tensor_A.device_data(), stride_a, tensor_A.device_data_imag(), stride_a,
|
||||
tensor_B.device_data(), stride_b, tensor_B.device_data_imag(), stride_b
|
||||
},
|
||||
{
|
||||
{alpha, beta},
|
||||
tensor_C.device_data(), stride_c, tensor_C.device_data_imag(), stride_c,
|
||||
tensor_D.device_data(), stride_d, tensor_D.device_data_imag(), stride_d
|
||||
},
|
||||
hw_info,
|
||||
scheduler_args
|
||||
};
|
||||
|
||||
Gemm gemm_op;
|
||||
|
||||
size_t workspace_size = Gemm::get_workspace_size(arguments);
|
||||
cutlass::device_memory::allocation<uint8_t> workspace(workspace_size);
|
||||
|
||||
cutlass::Status status = gemm_op.can_implement(arguments);
|
||||
|
||||
if (status != cutlass::Status::kSuccess) {
|
||||
cudaError_t error = cudaGetLastError();
|
||||
std::cerr << "This test is not supported: " << cudaGetErrorString(error) << "\n";
|
||||
return true;
|
||||
}
|
||||
|
||||
//
|
||||
// Run the GEMM
|
||||
//
|
||||
|
||||
cudaError_t result;
|
||||
status = gemm_op.initialize(arguments, workspace.get());
|
||||
status = gemm_op.run();
|
||||
result = cudaDeviceSynchronize();
|
||||
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
|
||||
@@ -0,0 +1,536 @@
|
||||
/***************************************************************************************************
|
||||
* Copyright (c) 2023 - 2026 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 Testbed for Ptr-Array and Grouped Planar Complex GEMM interface
|
||||
*/
|
||||
|
||||
|
||||
|
||||
#pragma once
|
||||
|
||||
#include <iostream>
|
||||
#include <fstream>
|
||||
#include <sstream>
|
||||
|
||||
#include "../../common/cutlass_unit_test.h"
|
||||
#include "gemm_testbed_3x.hpp"
|
||||
|
||||
#include "cutlass/util/host_tensor.h"
|
||||
#include "cutlass/util/host_tensor_planar_complex.h"
|
||||
#include "cutlass/util/tensor_view_io.h"
|
||||
#include "cutlass/util/distribution.h"
|
||||
#include "cutlass/util/packed_stride.hpp"
|
||||
#include "cutlass/util/reference/host/tensor_fill.h"
|
||||
#include "cutlass/util/reference/host/tensor_copy.h"
|
||||
#include "cutlass/util/reference/host/tensor_compare.h"
|
||||
#include "cutlass/util/reference/host/tensor_norm.h"
|
||||
#include "cutlass/util/reference/host/gemm_planar_complex.h"
|
||||
#include "cutlass/numeric_types.h"
|
||||
|
||||
#include "testbed_utils.h"
|
||||
|
||||
#include "cutlass/kernel_hardware_info.hpp"
|
||||
#include "cutlass/layout/matrix.h"
|
||||
#include "cutlass/matrix_coord.h"
|
||||
#include "cutlass/gemm/gemm.h"
|
||||
#include "cutlass/fast_math.h"
|
||||
#include "cutlass/platform/platform.h"
|
||||
|
||||
#include "cute/int_tuple.hpp"
|
||||
#include "cute/layout.hpp"
|
||||
|
||||
|
||||
namespace test {
|
||||
namespace gemm {
|
||||
namespace device {
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
template <typename Gemm>
|
||||
struct Testbed3xPlanarComplex {
|
||||
// Kernel data types
|
||||
using ElementA = typename Gemm::GemmKernel::ElementA;
|
||||
using StrideA = typename Gemm::GemmKernel::StrideA;
|
||||
using ElementB = typename Gemm::GemmKernel::ElementB;
|
||||
using StrideB = typename Gemm::GemmKernel::StrideB;
|
||||
using ElementC = std::conditional_t<std::is_void_v<typename Gemm::GemmKernel::ElementC>,
|
||||
typename Gemm::GemmKernel::ElementD,typename Gemm::GemmKernel::ElementC>;
|
||||
using StrideC = typename Gemm::GemmKernel::StrideC;
|
||||
using ElementD = typename Gemm::GemmKernel::ElementD;
|
||||
using StrideD = typename Gemm::GemmKernel::StrideD;
|
||||
using ElementAccumulator = typename Gemm::GemmKernel::ElementAccumulator;
|
||||
using ProblemShapeType = typename Gemm::GemmKernel::ProblemShape;
|
||||
using EpilogueOutputOp = typename Gemm::EpilogueOutputOp;
|
||||
using ClusterShapeType = typename Gemm::GemmKernel::CollectiveMainloop::DispatchPolicy::ClusterShape;
|
||||
/// For custom EVTs
|
||||
using ElementCompute = typename EpilogueOutputOp::ElementCompute;
|
||||
using ElementScalar = typename EpilogueOutputOp::ElementScalar;
|
||||
|
||||
static_assert(rank(StrideC{}) == 3, "StrideCD must be rank-3: [M, N, L]");
|
||||
static_assert(rank(StrideD{}) == 3, "StrideCD must be rank-3: [M, N, L]");
|
||||
|
||||
static constexpr uint32_t mma_promotion_interval = 4;
|
||||
|
||||
// Looks at Cute Stride to check Row / Column Major
|
||||
template<typename Stride>
|
||||
static constexpr bool is_row_or_col_major(){
|
||||
int stride_0 = int(cute::size<0>(Stride{}));
|
||||
int stride_1 = int(cute::size<1>(Stride{}));
|
||||
int depth = cute::depth(Stride{});
|
||||
return ((stride_0 == 1) || (stride_1 == 1)) && (depth == 1);
|
||||
}
|
||||
|
||||
// Note: this limitation comes from testbed / not the library
|
||||
static_assert(is_row_or_col_major<StrideA>(),
|
||||
"ERROR : A Layout is neither Row / Column Major)");
|
||||
static_assert(is_row_or_col_major<StrideB>(),
|
||||
"ERROR : B Layout is neither Row / Column Major)");
|
||||
static_assert(is_row_or_col_major<StrideC>(),
|
||||
"ERROR : C Layout is neither Row / Column Major)");
|
||||
static_assert(is_row_or_col_major<StrideD>(),
|
||||
"ERROR : D Layout is neither Row / Column Major)");
|
||||
|
||||
// Deduce Cutlass Layouts (RowMajor & ColumnMajor)
|
||||
using LayoutTagA = cutlass::detail::StrideToLayoutTagA_t<StrideA>;
|
||||
using LayoutTagB = cutlass::detail::StrideToLayoutTagB_t<StrideB>;
|
||||
using LayoutTagC = cutlass::detail::StrideToLayoutTagA_t<StrideC>;
|
||||
using LayoutTagD = cutlass::detail::StrideToLayoutTagA_t<StrideD>;
|
||||
|
||||
/// Initialization
|
||||
StrideA stride_a;
|
||||
StrideB stride_b;
|
||||
StrideC stride_c;
|
||||
StrideD stride_d;
|
||||
|
||||
typename LayoutTagA::Stride stride_factor_A;
|
||||
typename LayoutTagB::Stride stride_factor_B;
|
||||
typename LayoutTagC::Stride stride_factor_C;
|
||||
typename LayoutTagD::Stride stride_factor_D;
|
||||
|
||||
cutlass::Distribution::Kind init_A;
|
||||
cutlass::Distribution::Kind init_B;
|
||||
cutlass::Distribution::Kind init_C;
|
||||
uint64_t seed;
|
||||
static constexpr uint64_t kDefaultSeed = 4096;
|
||||
|
||||
// Data members
|
||||
std::vector<cutlass::HostTensorPlanarComplex<ElementA, LayoutTagA>> tensors_A;
|
||||
std::vector<cutlass::HostTensorPlanarComplex<ElementB, LayoutTagB>> tensors_B;
|
||||
std::vector<cutlass::HostTensorPlanarComplex<ElementC, LayoutTagC>> tensors_C;
|
||||
std::vector<cutlass::HostTensorPlanarComplex<ElementC, LayoutTagC>> tensors_D;
|
||||
std::vector<cutlass::HostTensorPlanarComplex<ElementC, LayoutTagC>> references_D;
|
||||
|
||||
cutlass::DeviceAllocation<const ElementA *> device_tensors_A_real;
|
||||
cutlass::DeviceAllocation<const ElementA *> device_tensors_A_imag;
|
||||
|
||||
cutlass::DeviceAllocation<const ElementB *> device_tensors_B_real;
|
||||
cutlass::DeviceAllocation<const ElementB *> device_tensors_B_imag;
|
||||
|
||||
cutlass::DeviceAllocation<const ElementC *> device_tensors_C_real;
|
||||
cutlass::DeviceAllocation<const ElementC *> device_tensors_C_imag;
|
||||
|
||||
cutlass::DeviceAllocation<ElementC *> device_tensors_D_real;
|
||||
cutlass::DeviceAllocation<ElementC *> device_tensors_D_imag;
|
||||
|
||||
uint32_t sm_count;
|
||||
|
||||
// Used to force multi-wave tests for persistent kernel schedules
|
||||
constexpr static int MaxSmCount = 16;
|
||||
using RasterOrderOptions = typename cutlass::gemm::kernel::detail::PersistentTileSchedulerSm90::RasterOrderOptions;
|
||||
using DecompositionMode = typename cutlass::gemm::kernel::detail::PersistentTileSchedulerSm90StreamKParams::DecompositionMode;
|
||||
|
||||
cutlass::ComplexTransform TransformA = Gemm::kTransformA;
|
||||
cutlass::ComplexTransform TransformB = Gemm::kTransformB;
|
||||
|
||||
//
|
||||
// Methods
|
||||
//
|
||||
Testbed3xPlanarComplex(
|
||||
cutlass::Distribution::Kind init_A_ = cutlass::Distribution::Uniform,
|
||||
cutlass::Distribution::Kind init_B_ = cutlass::Distribution::Uniform,
|
||||
cutlass::Distribution::Kind init_C_ = cutlass::Distribution::Uniform,
|
||||
uint64_t seed_ = kDefaultSeed
|
||||
):
|
||||
stride_factor_A(typename LayoutTagA::Stride()),
|
||||
stride_factor_B(typename LayoutTagB::Stride()),
|
||||
stride_factor_C(typename LayoutTagC::Stride()),
|
||||
stride_factor_D(typename LayoutTagD::Stride()),
|
||||
init_A(init_A_), init_B(init_B_), init_C(init_C_), seed(seed_) { }
|
||||
|
||||
/// Helper to initialize a tensor view
|
||||
template <typename Element, typename Layout>
|
||||
bool initialize_tensor(
|
||||
cutlass::TensorViewPlanarComplex<Element, Layout> view,
|
||||
cutlass::Distribution::Kind dist_kind,
|
||||
uint64_t seed) {
|
||||
|
||||
if (dist_kind == cutlass::Distribution::Uniform) {
|
||||
double scope_max, scope_min;
|
||||
int bits_input = cutlass::sizeof_bits<Element>::value;
|
||||
int bits_output = cutlass::sizeof_bits<ElementD>::value;
|
||||
|
||||
if (bits_input == 1) {
|
||||
scope_max = 2;
|
||||
scope_min = 0;
|
||||
}
|
||||
else if (bits_input <= 8) {
|
||||
scope_max = 2;
|
||||
scope_min = -2;
|
||||
}
|
||||
else if (bits_output == 16) {
|
||||
scope_max = 5;
|
||||
scope_min = -5;
|
||||
}
|
||||
else {
|
||||
scope_max = 8;
|
||||
scope_min = -8;
|
||||
}
|
||||
cutlass::reference::host::TensorFillRandomUniform(
|
||||
view, seed, scope_max, scope_min, 0);
|
||||
}
|
||||
|
||||
else if (dist_kind == cutlass::Distribution::Gaussian) {
|
||||
cutlass::reference::host::TensorFillRandomGaussian(view, seed, 0, 0.5);
|
||||
}
|
||||
|
||||
else if (dist_kind == cutlass::Distribution::AllOnes) {
|
||||
cutlass::reference::host::TensorFill(view, {Element(1), Element(0)});
|
||||
}
|
||||
else {
|
||||
EXPECT_TRUE(false) << "Not implemented";
|
||||
return false;
|
||||
}
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
/// Initializes data structures
|
||||
void initialize(ProblemShapeType problem_shapes) {
|
||||
//
|
||||
// Allocate the GEMM workspace
|
||||
//
|
||||
auto [M, N, K, L] = problem_shapes.get_host_problem_shape();
|
||||
|
||||
stride_a = cutlass::make_cute_packed_stride(StrideA{}, cute::make_shape(M, K, 1));
|
||||
stride_b = cutlass::make_cute_packed_stride(StrideB{}, cute::make_shape(N, K, 1));
|
||||
stride_c = cutlass::make_cute_packed_stride(StrideC{}, cute::make_shape(M, N, 1));
|
||||
stride_d = cutlass::make_cute_packed_stride(StrideD{}, cute::make_shape(M, N, 1));
|
||||
|
||||
auto a_coord = cutlass::make_Coord(M, K);
|
||||
auto c_coord = cutlass::make_Coord(M, N);
|
||||
// Cutlass has Row/Col major refers to MxK times KxN matrix product,
|
||||
// so the HostTensorB should be treated as KxN in "coord"'s view
|
||||
auto b_coord = cutlass::make_Coord(K, N);
|
||||
|
||||
tensors_A.clear();
|
||||
tensors_B.clear();
|
||||
tensors_C.clear();
|
||||
tensors_D.clear();
|
||||
references_D.clear();
|
||||
|
||||
for (int32_t i = 0; i < L; ++i) {
|
||||
tensors_A.push_back(cutlass::HostTensorPlanarComplex<ElementA, LayoutTagA>(a_coord, cutlass::layout::Affine2Layout_Factory<LayoutTagA>::layout_factory(a_coord, stride_factor_A)));
|
||||
tensors_B.push_back(cutlass::HostTensorPlanarComplex<ElementB, LayoutTagB>(b_coord, cutlass::layout::Affine2Layout_Factory<LayoutTagB>::layout_factory(b_coord, stride_factor_B)));
|
||||
tensors_C.push_back(cutlass::HostTensorPlanarComplex<ElementC, LayoutTagC>(c_coord, cutlass::layout::Affine2Layout_Factory<LayoutTagC>::layout_factory(c_coord, stride_factor_C)));
|
||||
tensors_D.push_back(cutlass::HostTensorPlanarComplex<ElementC, LayoutTagC>(c_coord, cutlass::layout::Affine2Layout_Factory<LayoutTagD>::layout_factory(c_coord, stride_factor_D)));
|
||||
references_D.push_back(cutlass::HostTensorPlanarComplex<ElementC, LayoutTagC>(c_coord, cutlass::layout::Affine2Layout_Factory<LayoutTagD>::layout_factory(c_coord, stride_factor_D), false));
|
||||
|
||||
EXPECT_TRUE(initialize_tensor(tensors_A[i].host_view(), init_A, seed + 2022 + i));
|
||||
EXPECT_TRUE(initialize_tensor(tensors_B[i].host_view(), init_B, seed + 2021 + i));
|
||||
EXPECT_TRUE(initialize_tensor(tensors_C[i].host_view(), init_B, seed + 2020 + i));
|
||||
|
||||
cutlass::reference::host::TensorFill(tensors_D[i].host_view(), cutlass::complex<ElementC>());
|
||||
cutlass::reference::host::TensorFill(references_D[i].host_view(), cutlass::complex<ElementC>());
|
||||
|
||||
tensors_A[i].sync_device();
|
||||
tensors_B[i].sync_device();
|
||||
tensors_C[i].sync_device();
|
||||
tensors_D[i].sync_device();
|
||||
}
|
||||
}
|
||||
|
||||
/// Verifies the result is a GEMM
|
||||
bool verify(
|
||||
ProblemShapeType problem_shapes,
|
||||
ElementScalar alpha,
|
||||
ElementScalar beta)
|
||||
{
|
||||
using namespace cute;
|
||||
auto [M, N, K, L] = problem_shapes.get_host_problem_shape();
|
||||
|
||||
#if 0
|
||||
std::cout << " M : " << M << " N : " << N << " K : " << K << " L : " << L << std::endl;
|
||||
#endif
|
||||
|
||||
//
|
||||
// Compute reference
|
||||
//
|
||||
bool passed = false;
|
||||
for(int batch = 0; batch < L; ++batch) {
|
||||
cutlass::reference::host::GemmPlanarComplex<
|
||||
ElementA, LayoutTagA,
|
||||
ElementB, LayoutTagB,
|
||||
ElementC, LayoutTagC,
|
||||
ElementAccumulator
|
||||
>(
|
||||
cutlass::gemm::GemmCoord(M,N,K),
|
||||
alpha,
|
||||
tensors_A[batch].host_ref(),
|
||||
TransformA,
|
||||
tensors_B[batch].host_ref(),
|
||||
TransformB,
|
||||
beta,
|
||||
tensors_C[batch].host_ref(),
|
||||
references_D[batch].host_ref()
|
||||
);
|
||||
|
||||
tensors_D[batch].sync_host();
|
||||
passed = cutlass::reference::host::TensorEquals(
|
||||
tensors_D[batch].host_view(),
|
||||
references_D[batch].host_view()
|
||||
);
|
||||
|
||||
EXPECT_TRUE(passed);
|
||||
|
||||
if (!passed) {
|
||||
std::stringstream fname;
|
||||
fname << "error_Planar_Complex_Gemm_device_"
|
||||
<< M << "x" << N << "x" << K << "x" << batch << "_"
|
||||
<< cute::get<0>(typename Gemm::GemmKernel::TileShape{}) << "_"
|
||||
<< cute::get<1>(typename Gemm::GemmKernel::TileShape{}) << "_"
|
||||
<< cute::get<2>(typename Gemm::GemmKernel::TileShape{}) << ".txt";
|
||||
|
||||
std::ofstream file(fname.str());
|
||||
file
|
||||
<< "problem: " << ' ' << M << "x" << N << "x" << K << ", Batch idx = " << batch
|
||||
<< ", alpha: " << alpha << ", beta: " << beta
|
||||
<< "\n\n";
|
||||
|
||||
file
|
||||
<< "A =\n" << tensors_A[batch].host_view()
|
||||
<< "\nB =\n" << tensors_B[batch].host_view()
|
||||
<< "\nC =\n" << tensors_C[batch].host_view()
|
||||
<< "\n\nReference =\n" << references_D[batch].host_view()
|
||||
<< "\n\nComputed =\n" << tensors_D[batch].host_view();
|
||||
}
|
||||
}
|
||||
return passed;
|
||||
}
|
||||
|
||||
/// Returns true if the CUDA device is sufficient to execute the kernel.
|
||||
bool sufficient() {
|
||||
//
|
||||
// Determine SMEM requirements and waive if not satisfied
|
||||
//
|
||||
|
||||
int smem_size = Gemm::GemmKernel::SharedStorageSize;
|
||||
|
||||
int device_idx;
|
||||
cudaError_t result = cudaGetDevice(&device_idx);
|
||||
|
||||
if (result != cudaSuccess) {
|
||||
throw std::runtime_error("cudaGetDevice() API call failed.");
|
||||
}
|
||||
|
||||
cudaDeviceProp properties;
|
||||
result = cudaGetDeviceProperties(&properties, device_idx);
|
||||
this->sm_count = properties.multiProcessorCount;
|
||||
|
||||
if (result != cudaSuccess) {
|
||||
throw std::runtime_error("cudaGetDeviceProperties() failed");
|
||||
}
|
||||
|
||||
if (properties.sharedMemPerBlockOptin < size_t(smem_size)) {
|
||||
return false;
|
||||
}
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
bool run(
|
||||
ProblemShapeType problem_shapes,
|
||||
ElementScalar alpha = ElementScalar(1),
|
||||
ElementScalar beta = ElementScalar(0),
|
||||
RasterOrderOptions raster_order = RasterOrderOptions::Heuristic,
|
||||
detail::MaxSwizzleSize max_swizzle = detail::MaxSwizzleSize{},
|
||||
detail::Splits splits = detail::Splits{},
|
||||
DecompositionMode decomposition_mode = DecompositionMode::Heuristic,
|
||||
unsigned int cluster_m = 0,
|
||||
unsigned int cluster_n = 0,
|
||||
unsigned int cluster_m_fallback = 0,
|
||||
unsigned int cluster_n_fallback = 0
|
||||
) {
|
||||
// Waive test if insufficient CUDA device
|
||||
if (!sufficient()) {
|
||||
if (CUTLASS_TEST_UNIT_ENABLE_WARNINGS) {
|
||||
std::cerr << "Test waived due to insufficient CUDA device." << std::endl;
|
||||
}
|
||||
return true;
|
||||
}
|
||||
|
||||
this->initialize(problem_shapes);
|
||||
|
||||
//
|
||||
// Launch device kernel
|
||||
//
|
||||
|
||||
//
|
||||
// Initialize the GEMM operator
|
||||
//
|
||||
|
||||
cutlass::KernelHardwareInfo hw_info;
|
||||
hw_info.device_id = 0;
|
||||
|
||||
this->sm_count = cutlass::KernelHardwareInfo::query_device_multiprocessor_count(hw_info.device_id);
|
||||
hw_info.sm_count = this->sm_count;
|
||||
if (not cute::is_static_v<ClusterShapeType>) {
|
||||
// Runtime and preferred cluster setting
|
||||
hw_info.cluster_shape = {cluster_m, cluster_n, 1};
|
||||
hw_info.cluster_shape_fallback = {cluster_m_fallback, cluster_n_fallback, 1};
|
||||
}
|
||||
|
||||
typename Gemm::GemmKernel::TileScheduler::Arguments scheduler_args;
|
||||
if constexpr (cute::is_same_v<typename Gemm::GemmKernel::TileSchedulerTag, cutlass::gemm::StreamKScheduler>) {
|
||||
scheduler_args = { static_cast<int>(splits), static_cast<int>(max_swizzle), raster_order, decomposition_mode };
|
||||
}
|
||||
else {
|
||||
scheduler_args = { static_cast<int>(max_swizzle), raster_order };
|
||||
}
|
||||
|
||||
auto [M, N, K, L] = problem_shapes.get_host_problem_shape();
|
||||
std::vector<ElementA *> ptr_A_real_host(L);
|
||||
std::vector<ElementA *> ptr_A_imag_host(L);
|
||||
|
||||
std::vector<ElementB *> ptr_B_real_host(L);
|
||||
std::vector<ElementB *> ptr_B_imag_host(L);
|
||||
|
||||
std::vector<ElementC *> ptr_C_real_host(L);
|
||||
std::vector<ElementC *> ptr_C_imag_host(L);
|
||||
|
||||
std::vector<ElementC *> ptr_D_real_host(L);
|
||||
std::vector<ElementC *> ptr_D_imag_host(L);
|
||||
|
||||
for (int32_t i = 0; i < L; ++i) {
|
||||
ptr_A_real_host.at(i) = tensors_A[i].device_data();
|
||||
ptr_A_imag_host.at(i) = tensors_A[i].device_data_imag();
|
||||
|
||||
ptr_B_real_host.at(i) = tensors_B[i].device_data();
|
||||
ptr_B_imag_host.at(i) = tensors_B[i].device_data_imag();
|
||||
|
||||
ptr_C_real_host.at(i) = tensors_C[i].device_data();
|
||||
ptr_C_imag_host.at(i) = tensors_C[i].device_data_imag();
|
||||
|
||||
ptr_D_real_host.at(i) = tensors_D[i].device_data();
|
||||
ptr_D_imag_host.at(i) = tensors_D[i].device_data_imag();
|
||||
}
|
||||
|
||||
device_tensors_A_real.reset(L);
|
||||
device_tensors_A_real.copy_from_host(ptr_A_real_host.data());
|
||||
device_tensors_A_imag.reset(L);
|
||||
device_tensors_A_imag.copy_from_host(ptr_A_imag_host.data());
|
||||
|
||||
device_tensors_B_real.reset(L);
|
||||
device_tensors_B_real.copy_from_host(ptr_B_real_host.data());
|
||||
device_tensors_B_imag.reset(L);
|
||||
device_tensors_B_imag.copy_from_host(ptr_B_imag_host.data());
|
||||
|
||||
device_tensors_C_real.reset(L);
|
||||
device_tensors_C_real.copy_from_host(ptr_C_real_host.data());
|
||||
device_tensors_C_imag.reset(L);
|
||||
device_tensors_C_imag.copy_from_host(ptr_C_imag_host.data());
|
||||
|
||||
device_tensors_D_real.reset(L);
|
||||
device_tensors_D_real.copy_from_host(ptr_D_real_host.data());
|
||||
device_tensors_D_imag.reset(L);
|
||||
device_tensors_D_imag.copy_from_host(ptr_D_imag_host.data());
|
||||
|
||||
auto arguments = typename Gemm::Arguments {
|
||||
cutlass::gemm::GemmUniversalMode::kArray,
|
||||
problem_shapes,
|
||||
{
|
||||
device_tensors_A_real.get(), stride_a, device_tensors_A_imag.get(), stride_a,
|
||||
device_tensors_B_real.get(), stride_b, device_tensors_B_imag.get(), stride_b
|
||||
},
|
||||
{
|
||||
{alpha, beta},
|
||||
device_tensors_C_real.get(), stride_c, device_tensors_C_imag.get(), stride_c,
|
||||
device_tensors_D_real.get(), stride_d, device_tensors_D_imag.get(), stride_d
|
||||
},
|
||||
hw_info,
|
||||
scheduler_args
|
||||
};
|
||||
|
||||
Gemm gemm_op;
|
||||
|
||||
size_t workspace_size = Gemm::get_workspace_size(arguments);
|
||||
cutlass::device_memory::allocation<uint8_t> workspace(workspace_size);
|
||||
|
||||
cutlass::Status status = gemm_op.can_implement(arguments);
|
||||
|
||||
if (status != cutlass::Status::kSuccess) {
|
||||
cudaError_t error = cudaGetLastError();
|
||||
std::cerr << "This test is not supported: " << cudaGetErrorString(error) << "\n";
|
||||
return true;
|
||||
}
|
||||
|
||||
//
|
||||
// Run the GEMM
|
||||
//
|
||||
|
||||
cudaError_t result;
|
||||
status = gemm_op.initialize(arguments, workspace.get());
|
||||
status = gemm_op.run();
|
||||
result = cudaDeviceSynchronize();
|
||||
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_shapes, alpha, beta);
|
||||
|
||||
if (!passed) {
|
||||
std::cout << "Error : Failed : with alpha: " << alpha << ", beta: " << beta
|
||||
<< "\n";
|
||||
}
|
||||
|
||||
return passed;
|
||||
}
|
||||
};
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
} // namespace device
|
||||
} // namespace gemm
|
||||
} // namespace test
|
||||
@@ -0,0 +1,163 @@
|
||||
/***************************************************************************************************
|
||||
* Copyright (c) 2023 - 2026 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 GEMM interface
|
||||
*/
|
||||
|
||||
#include <iostream>
|
||||
|
||||
#include "cutlass/cutlass.h"
|
||||
#include "cute/tensor.hpp"
|
||||
#include "cute/atom/mma_atom.hpp"
|
||||
#include "cutlass/numeric_types.h"
|
||||
#include "cutlass/arch/mma_sm100.h"
|
||||
#include "cutlass/gemm/device/gemm_universal_adapter.h"
|
||||
#include "cutlass/gemm/kernel/gemm_universal.hpp"
|
||||
#include "cutlass/gemm/collective/collective_builder.hpp"
|
||||
#include "cutlass/epilogue/dispatch_policy.hpp"
|
||||
#include "cutlass/epilogue/collective/collective_builder.hpp"
|
||||
#include "cute/atom/mma_traits_sm100.hpp"
|
||||
#include "../../common/cutlass_unit_test.h"
|
||||
#include "gemm_testbed_3x_planar_complex.hpp"
|
||||
|
||||
using namespace cute;
|
||||
|
||||
#if defined(CUTLASS_ARCH_MMA_SM100_SUPPORTED)
|
||||
|
||||
///////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
///////////////////////////////////////////////////// 128x128x64 //////////////////////////////////////////////////////
|
||||
///////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
// TTT
|
||||
TEST(SM100_Device_Gemm_Planar_cbf16t_cbf16t_f32t_tensorop_1sm, 128x128x64_1x1x1) {
|
||||
using ElementA = cutlass::bfloat16_t;
|
||||
using TransformA = cute::identity;
|
||||
using ElementPairA = cute::tuple<ElementA, TransformA>;
|
||||
using LayoutA = cutlass::layout::RowMajor;
|
||||
|
||||
using ElementB = cutlass::bfloat16_t;
|
||||
using TransformB = cute::identity;
|
||||
using ElementPairB = cute::tuple<ElementB, TransformB>;
|
||||
using LayoutB = cutlass::layout::RowMajor;
|
||||
|
||||
using ElementAccumulator = float;
|
||||
using LayoutC = cutlass::layout::RowMajor;
|
||||
|
||||
using MmaTileShape = cute::Shape<_128,_128,_64>;
|
||||
using ClusterShape = Shape<_1,_1,_1>;
|
||||
|
||||
using CollectiveEpilogue = typename cutlass::epilogue::collective::CollectiveBuilder<
|
||||
cutlass::arch::Sm100, cutlass::arch::OpClassTensorOp,
|
||||
MmaTileShape, ClusterShape,
|
||||
cutlass::epilogue::collective::EpilogueTileAuto,
|
||||
float, float,
|
||||
cutlass::bfloat16_t, LayoutC, 8,
|
||||
cutlass::bfloat16_t, LayoutC, 8,
|
||||
cutlass::epilogue::PlanarComplexTmaWarpSpecialized1Sm
|
||||
>::CollectiveOp;
|
||||
|
||||
using CollectiveMainloop = typename cutlass::gemm::collective::CollectiveBuilder<
|
||||
cutlass::arch::Sm100, cutlass::arch::OpClassTensorOp,
|
||||
ElementPairA, LayoutA, 8,
|
||||
ElementPairB, LayoutB, 8,
|
||||
ElementAccumulator,
|
||||
MmaTileShape, ClusterShape,
|
||||
cutlass::gemm::collective::StageCountAutoCarveout<
|
||||
static_cast<int>(sizeof(typename CollectiveEpilogue::SharedStorage))>,
|
||||
cutlass::gemm::KernelTmaWarpSpecialized1SmPlanarComplexSm100
|
||||
>::CollectiveOp;
|
||||
|
||||
using GemmKernel = cutlass::gemm::kernel::GemmUniversal<
|
||||
Shape<int,int,int,int>,
|
||||
CollectiveMainloop,
|
||||
CollectiveEpilogue
|
||||
>;
|
||||
|
||||
using Gemm = cutlass::gemm::device::GemmUniversalAdapter<GemmKernel>;
|
||||
EXPECT_TRUE(test::gemm::device::TestPlanarComplexSmall<Gemm>());
|
||||
}
|
||||
|
||||
///////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
///////////////////////////////////////////////////// TMA MULTICAST ///////////////////////////////////////////////////
|
||||
///////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
// TNN
|
||||
TEST(SM100_Device_Gemm_Planar_cbf16t_cbf16n_f32n_tensorop_1sm, 512x256x64_4x4x1) {
|
||||
using ElementA = cutlass::bfloat16_t;
|
||||
using TransformA = cute::identity;
|
||||
using ElementPairA = cute::tuple<ElementA, TransformA>;
|
||||
using LayoutA = cutlass::layout::RowMajor;
|
||||
|
||||
using ElementB = cutlass::bfloat16_t;
|
||||
using TransformB = cute::identity;
|
||||
using ElementPairB = cute::tuple<ElementB, TransformB>;
|
||||
using LayoutB = cutlass::layout::ColumnMajor;
|
||||
|
||||
using ElementAccumulator = float;
|
||||
using LayoutC = cutlass::layout::ColumnMajor;
|
||||
|
||||
using MmaTileShape = cute::Shape<_128,_64,_64>;
|
||||
using ClusterShape = Shape<_4,_4,_1>;
|
||||
|
||||
using CollectiveEpilogue = typename cutlass::epilogue::collective::CollectiveBuilder<
|
||||
cutlass::arch::Sm100, cutlass::arch::OpClassTensorOp,
|
||||
MmaTileShape, ClusterShape,
|
||||
cutlass::epilogue::collective::EpilogueTileAuto,
|
||||
float, float,
|
||||
cutlass::bfloat16_t, LayoutC, 8,
|
||||
cutlass::bfloat16_t, LayoutC, 8,
|
||||
cutlass::epilogue::PlanarComplexTmaWarpSpecialized1Sm
|
||||
>::CollectiveOp;
|
||||
|
||||
using CollectiveMainloop = typename cutlass::gemm::collective::CollectiveBuilder<
|
||||
cutlass::arch::Sm100, cutlass::arch::OpClassTensorOp,
|
||||
ElementPairA, LayoutA, 8,
|
||||
ElementPairB, LayoutB, 8,
|
||||
ElementAccumulator,
|
||||
MmaTileShape, ClusterShape,
|
||||
cutlass::gemm::collective::StageCountAutoCarveout<
|
||||
static_cast<int>(sizeof(typename CollectiveEpilogue::SharedStorage))>,
|
||||
cutlass::gemm::KernelTmaWarpSpecialized1SmPlanarComplexSm100
|
||||
>::CollectiveOp;
|
||||
|
||||
using GemmKernel = cutlass::gemm::kernel::GemmUniversal<
|
||||
Shape<int,int,int,int>,
|
||||
CollectiveMainloop,
|
||||
CollectiveEpilogue,
|
||||
cutlass::gemm::StreamKScheduler
|
||||
>;
|
||||
|
||||
using Gemm = cutlass::gemm::device::GemmUniversalAdapter<GemmKernel>;
|
||||
EXPECT_TRUE(test::gemm::device::TestPlanarComplexSmall<Gemm>());
|
||||
}
|
||||
|
||||
#endif // #if defined(CUTLASS_ARCH_MMA_SM100_SUPPORTED)
|
||||
@@ -0,0 +1,163 @@
|
||||
/***************************************************************************************************
|
||||
* Copyright (c) 2023 - 2026 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 GEMM interface
|
||||
*/
|
||||
|
||||
#include <iostream>
|
||||
|
||||
#include "cutlass/cutlass.h"
|
||||
#include "cute/tensor.hpp"
|
||||
#include "cute/atom/mma_atom.hpp"
|
||||
#include "cutlass/numeric_types.h"
|
||||
#include "cutlass/arch/mma_sm100.h"
|
||||
#include "cutlass/gemm/device/gemm_universal_adapter.h"
|
||||
#include "cutlass/gemm/kernel/gemm_universal.hpp"
|
||||
#include "cutlass/gemm/collective/collective_builder.hpp"
|
||||
#include "cutlass/epilogue/dispatch_policy.hpp"
|
||||
#include "cutlass/epilogue/collective/collective_builder.hpp"
|
||||
#include "cute/atom/mma_traits_sm100.hpp"
|
||||
#include "../../common/cutlass_unit_test.h"
|
||||
#include "gemm_testbed_3x_planar_complex.hpp"
|
||||
|
||||
using namespace cute;
|
||||
|
||||
#if defined(CUTLASS_ARCH_MMA_SM100_SUPPORTED)
|
||||
|
||||
///////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
///////////////////////////////////////////////////// 128x64x64 ///////////////////////////////////////////////////////
|
||||
///////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
// NNN
|
||||
TEST(SM100_Device_Gemm_Planar_cbf16n_cbf16n_f32n_tensorop_2sm, 128x64x64_2x1x1) {
|
||||
using ElementA = cutlass::bfloat16_t;
|
||||
using TransformA = cute::identity;
|
||||
using ElementPairA = cute::tuple<ElementA, TransformA>;
|
||||
using LayoutA = cutlass::layout::ColumnMajor;
|
||||
|
||||
using ElementB = cutlass::bfloat16_t;
|
||||
using TransformB = cute::identity;
|
||||
using ElementPairB = cute::tuple<ElementB, TransformB>;
|
||||
using LayoutB = cutlass::layout::ColumnMajor;
|
||||
|
||||
using ElementAccumulator = float;
|
||||
using LayoutC = cutlass::layout::ColumnMajor;
|
||||
|
||||
using MmaTileShape = cute::Shape<_128,_64,_64>;
|
||||
using ClusterShape = Shape<_2,_1,_1>;
|
||||
|
||||
using CollectiveEpilogue = typename cutlass::epilogue::collective::CollectiveBuilder<
|
||||
cutlass::arch::Sm100, cutlass::arch::OpClassTensorOp,
|
||||
MmaTileShape, ClusterShape,
|
||||
cutlass::epilogue::collective::EpilogueTileAuto,
|
||||
float, float,
|
||||
cutlass::bfloat16_t, LayoutC, 8,
|
||||
cutlass::bfloat16_t, LayoutC, 8,
|
||||
cutlass::epilogue::PlanarComplexTmaWarpSpecialized2Sm
|
||||
>::CollectiveOp;
|
||||
|
||||
using CollectiveMainloop = typename cutlass::gemm::collective::CollectiveBuilder<
|
||||
cutlass::arch::Sm100, cutlass::arch::OpClassTensorOp,
|
||||
ElementPairA, LayoutA, 8,
|
||||
ElementPairB, LayoutB, 8,
|
||||
ElementAccumulator,
|
||||
MmaTileShape, ClusterShape,
|
||||
cutlass::gemm::collective::StageCountAutoCarveout<
|
||||
static_cast<int>(sizeof(typename CollectiveEpilogue::SharedStorage))>,
|
||||
cutlass::gemm::KernelTmaWarpSpecialized2SmPlanarComplexSm100
|
||||
>::CollectiveOp;
|
||||
|
||||
using GemmKernel = cutlass::gemm::kernel::GemmUniversal<
|
||||
Shape<int,int,int,int>,
|
||||
CollectiveMainloop,
|
||||
CollectiveEpilogue
|
||||
>;
|
||||
|
||||
using Gemm = cutlass::gemm::device::GemmUniversalAdapter<GemmKernel>;
|
||||
EXPECT_TRUE(test::gemm::device::TestPlanarComplexSmall<Gemm>());
|
||||
}
|
||||
|
||||
///////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
///////////////////////////////////////////////////// TMA MULTICAST //////////////////////////////////////////////////////
|
||||
///////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
// NTT
|
||||
TEST(SM100_Device_Gemm_Planar_cbf16n_cbf16t_f32t_tensorop_2sm, 256x256x64_4x4x1) {
|
||||
using ElementA = cutlass::bfloat16_t;
|
||||
using TransformA = cute::identity;
|
||||
using ElementPairA = cute::tuple<ElementA, TransformA>;
|
||||
using LayoutA = cutlass::layout::ColumnMajor;
|
||||
|
||||
using ElementB = cutlass::bfloat16_t;
|
||||
using TransformB = cute::identity;
|
||||
using ElementPairB = cute::tuple<ElementB, TransformB>;
|
||||
using LayoutB = cutlass::layout::RowMajor;
|
||||
|
||||
using ElementAccumulator = float;
|
||||
using LayoutC = cutlass::layout::RowMajor;
|
||||
|
||||
using MmaTileShape = cute::Shape<_128,_64,_64>;
|
||||
using ClusterShape = Shape<_4,_4,_1>;
|
||||
|
||||
using CollectiveEpilogue = typename cutlass::epilogue::collective::CollectiveBuilder<
|
||||
cutlass::arch::Sm100, cutlass::arch::OpClassTensorOp,
|
||||
MmaTileShape, ClusterShape,
|
||||
cutlass::epilogue::collective::EpilogueTileAuto,
|
||||
float, float,
|
||||
cutlass::bfloat16_t, LayoutC, 8,
|
||||
cutlass::bfloat16_t, LayoutC, 8,
|
||||
cutlass::epilogue::PlanarComplexTmaWarpSpecialized2Sm
|
||||
>::CollectiveOp;
|
||||
|
||||
using CollectiveMainloop = typename cutlass::gemm::collective::CollectiveBuilder<
|
||||
cutlass::arch::Sm100, cutlass::arch::OpClassTensorOp,
|
||||
ElementPairA, LayoutA, 8,
|
||||
ElementPairB, LayoutB, 8,
|
||||
ElementAccumulator,
|
||||
MmaTileShape, ClusterShape,
|
||||
cutlass::gemm::collective::StageCountAutoCarveout<
|
||||
static_cast<int>(sizeof(typename CollectiveEpilogue::SharedStorage))>,
|
||||
cutlass::gemm::KernelTmaWarpSpecialized2SmPlanarComplexSm100
|
||||
>::CollectiveOp;
|
||||
|
||||
using GemmKernel = cutlass::gemm::kernel::GemmUniversal<
|
||||
Shape<int,int,int,int>,
|
||||
CollectiveMainloop,
|
||||
CollectiveEpilogue,
|
||||
cutlass::gemm::StreamKScheduler
|
||||
>;
|
||||
|
||||
using Gemm = cutlass::gemm::device::GemmUniversalAdapter<GemmKernel>;
|
||||
EXPECT_TRUE(test::gemm::device::TestPlanarComplexSmall<Gemm>());
|
||||
}
|
||||
|
||||
#endif // #if defined(CUTLASS_ARCH_MMA_SM100_SUPPORTED)
|
||||
+211
@@ -0,0 +1,211 @@
|
||||
/***************************************************************************************************
|
||||
* Copyright (c) 2023 - 2026 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 GEMM interface
|
||||
*/
|
||||
|
||||
#include <iostream>
|
||||
|
||||
#include "cutlass/cutlass.h"
|
||||
#include "cute/tensor.hpp"
|
||||
#include "cute/atom/mma_atom.hpp"
|
||||
#include "cutlass/numeric_types.h"
|
||||
#include "cutlass/arch/mma_sm100.h"
|
||||
#include "cutlass/gemm/device/gemm_universal_adapter.h"
|
||||
#include "cutlass/gemm/kernel/gemm_universal.hpp"
|
||||
#include "cutlass/gemm/collective/collective_builder.hpp"
|
||||
#include "cutlass/epilogue/dispatch_policy.hpp"
|
||||
#include "cutlass/epilogue/collective/collective_builder.hpp"
|
||||
#include "cute/atom/mma_traits_sm100.hpp"
|
||||
#include "../../common/cutlass_unit_test.h"
|
||||
#include "gemm_testbed_3x_planar_complex.hpp"
|
||||
|
||||
using namespace cute;
|
||||
|
||||
#if defined(CUTLASS_ARCH_MMA_SM100_SUPPORTED)
|
||||
|
||||
///////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
///////////////////////////////////////////////////// TCGEN05.1CTA ///////////////////////////////////////////////////
|
||||
///////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
// CN
|
||||
TEST(SM100_Device_Gemm_Planar_cbf16c_cbf16n_f32n_tensorop_1sm, 128x64x64_1x1x1) {
|
||||
using ElementA = cutlass::bfloat16_t;
|
||||
using TransformA = cute::conjugate;
|
||||
using ElementPairA = cute::tuple<ElementA, TransformA>;
|
||||
using LayoutA = cutlass::layout::RowMajor;
|
||||
|
||||
using ElementB = cutlass::bfloat16_t;
|
||||
using TransformB = cute::identity;
|
||||
using ElementPairB = cute::tuple<ElementB, TransformB>;
|
||||
using LayoutB = cutlass::layout::ColumnMajor;
|
||||
|
||||
using ElementAccumulator = float;
|
||||
using LayoutC = cutlass::layout::ColumnMajor;
|
||||
|
||||
using MmaTileShape = cute::Shape<_128,_64,_64>;
|
||||
using ClusterShape = Shape<_1,_1,_1>;
|
||||
|
||||
using CollectiveEpilogue = typename cutlass::epilogue::collective::CollectiveBuilder<
|
||||
cutlass::arch::Sm100, cutlass::arch::OpClassTensorOp,
|
||||
MmaTileShape, ClusterShape,
|
||||
cutlass::epilogue::collective::EpilogueTileAuto,
|
||||
float, float,
|
||||
cutlass::bfloat16_t, LayoutC, 8,
|
||||
cutlass::bfloat16_t, LayoutC, 8,
|
||||
cutlass::epilogue::PlanarComplexTmaWarpSpecialized1Sm
|
||||
>::CollectiveOp;
|
||||
|
||||
using CollectiveMainloop = typename cutlass::gemm::collective::CollectiveBuilder<
|
||||
cutlass::arch::Sm100, cutlass::arch::OpClassTensorOp,
|
||||
ElementPairA, LayoutA, 8,
|
||||
ElementPairB, LayoutB, 8,
|
||||
ElementAccumulator,
|
||||
MmaTileShape, ClusterShape,
|
||||
cutlass::gemm::collective::StageCountAutoCarveout<
|
||||
static_cast<int>(sizeof(typename CollectiveEpilogue::SharedStorage))>,
|
||||
cutlass::gemm::KernelTmaWarpSpecialized1SmPlanarComplexSm100
|
||||
>::CollectiveOp;
|
||||
|
||||
using GemmKernel = cutlass::gemm::kernel::GemmUniversal<
|
||||
Shape<int,int,int,int>,
|
||||
CollectiveMainloop,
|
||||
CollectiveEpilogue
|
||||
>;
|
||||
|
||||
using Gemm = cutlass::gemm::device::GemmUniversalAdapter<GemmKernel>;
|
||||
EXPECT_TRUE(test::gemm::device::TestPlanarComplexSmall<Gemm>());
|
||||
}
|
||||
|
||||
// NC
|
||||
TEST(SM100_Device_Gemm_Planar_cbf16n_cbf16c_f32n_tensorop_1sm, 128x128x64_1x1x1) {
|
||||
using ElementA = cutlass::bfloat16_t;
|
||||
using TransformA = cute::identity;
|
||||
using ElementPairA = cute::tuple<ElementA, TransformA>;
|
||||
using LayoutA = cutlass::layout::ColumnMajor;
|
||||
|
||||
using ElementB = cutlass::bfloat16_t;
|
||||
using TransformB = cute::conjugate;
|
||||
using ElementPairB = cute::tuple<ElementB, TransformB>;
|
||||
using LayoutB = cutlass::layout::RowMajor;
|
||||
|
||||
using ElementAccumulator = float;
|
||||
using LayoutC = cutlass::layout::ColumnMajor;
|
||||
|
||||
using MmaTileShape = cute::Shape<_128,_128,_64>;
|
||||
using ClusterShape = Shape<_1,_1,_1>;
|
||||
|
||||
using CollectiveEpilogue = typename cutlass::epilogue::collective::CollectiveBuilder<
|
||||
cutlass::arch::Sm100, cutlass::arch::OpClassTensorOp,
|
||||
MmaTileShape, ClusterShape,
|
||||
cutlass::epilogue::collective::EpilogueTileAuto,
|
||||
float, float,
|
||||
cutlass::bfloat16_t, LayoutC, 8,
|
||||
cutlass::bfloat16_t, LayoutC, 8,
|
||||
cutlass::epilogue::PlanarComplexTmaWarpSpecialized1Sm
|
||||
>::CollectiveOp;
|
||||
|
||||
using CollectiveMainloop = typename cutlass::gemm::collective::CollectiveBuilder<
|
||||
cutlass::arch::Sm100, cutlass::arch::OpClassTensorOp,
|
||||
ElementPairA, LayoutA, 8,
|
||||
ElementPairB, LayoutB, 8,
|
||||
ElementAccumulator,
|
||||
MmaTileShape, ClusterShape,
|
||||
cutlass::gemm::collective::StageCountAutoCarveout<
|
||||
static_cast<int>(sizeof(typename CollectiveEpilogue::SharedStorage))>,
|
||||
cutlass::gemm::KernelTmaWarpSpecialized1SmPlanarComplexSm100
|
||||
>::CollectiveOp;
|
||||
|
||||
using GemmKernel = cutlass::gemm::kernel::GemmUniversal<
|
||||
Shape<int,int,int,int>,
|
||||
CollectiveMainloop,
|
||||
CollectiveEpilogue
|
||||
>;
|
||||
|
||||
using Gemm = cutlass::gemm::device::GemmUniversalAdapter<GemmKernel>;
|
||||
EXPECT_TRUE(test::gemm::device::TestPlanarComplexSmall<Gemm>());
|
||||
}
|
||||
|
||||
///////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
///////////////////////////////////////////////////// TCGEN05.2CTA ////////////////////////////////////////////////////
|
||||
///////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
// TC
|
||||
TEST(SM100_Device_Gemm_Planar_cbf16t_cbf16c_f32t_tensorop_2sm, 128x64x64_2x1x1) {
|
||||
using ElementA = cutlass::bfloat16_t;
|
||||
using TransformA = cute::identity;
|
||||
using ElementPairA = cute::tuple<ElementA, TransformA>;
|
||||
using LayoutA = cutlass::layout::RowMajor;
|
||||
|
||||
using ElementB = cutlass::bfloat16_t;
|
||||
using TransformB = cute::conjugate;
|
||||
using ElementPairB = cute::tuple<ElementB, TransformB>;
|
||||
using LayoutB = cutlass::layout::RowMajor;
|
||||
|
||||
using ElementAccumulator = float;
|
||||
using LayoutC = cutlass::layout::RowMajor;
|
||||
|
||||
using MmaTileShape = cute::Shape<_128,_64,_64>;
|
||||
using ClusterShape = Shape<_2,_1,_1>;
|
||||
|
||||
using CollectiveEpilogue = typename cutlass::epilogue::collective::CollectiveBuilder<
|
||||
cutlass::arch::Sm100, cutlass::arch::OpClassTensorOp,
|
||||
MmaTileShape, ClusterShape,
|
||||
cutlass::epilogue::collective::EpilogueTileAuto,
|
||||
float, float,
|
||||
cutlass::bfloat16_t, LayoutC, 8,
|
||||
cutlass::bfloat16_t, LayoutC, 8,
|
||||
cutlass::epilogue::PlanarComplexTmaWarpSpecialized2Sm
|
||||
>::CollectiveOp;
|
||||
|
||||
using CollectiveMainloop = typename cutlass::gemm::collective::CollectiveBuilder<
|
||||
cutlass::arch::Sm100, cutlass::arch::OpClassTensorOp,
|
||||
ElementPairA, LayoutA, 8,
|
||||
ElementPairB, LayoutB, 8,
|
||||
ElementAccumulator,
|
||||
MmaTileShape, ClusterShape,
|
||||
cutlass::gemm::collective::StageCountAutoCarveout<
|
||||
static_cast<int>(sizeof(typename CollectiveEpilogue::SharedStorage))>,
|
||||
cutlass::gemm::KernelTmaWarpSpecialized2SmPlanarComplexSm100
|
||||
>::CollectiveOp;
|
||||
|
||||
using GemmKernel = cutlass::gemm::kernel::GemmUniversal<
|
||||
Shape<int,int,int,int>,
|
||||
CollectiveMainloop,
|
||||
CollectiveEpilogue
|
||||
>;
|
||||
|
||||
using Gemm = cutlass::gemm::device::GemmUniversalAdapter<GemmKernel>;
|
||||
EXPECT_TRUE(test::gemm::device::TestPlanarComplexSmall<Gemm>());
|
||||
}
|
||||
|
||||
#endif
|
||||
+165
@@ -0,0 +1,165 @@
|
||||
/***************************************************************************************************
|
||||
* Copyright (c) 2023 - 2026 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 GEMM interface
|
||||
*/
|
||||
|
||||
#include <iostream>
|
||||
|
||||
#include "cutlass/cutlass.h"
|
||||
#include "cute/tensor.hpp"
|
||||
#include "cute/atom/mma_atom.hpp"
|
||||
#include "cutlass/numeric_types.h"
|
||||
#include "cutlass/arch/mma_sm100.h"
|
||||
#include "cutlass/gemm/device/gemm_universal_adapter.h"
|
||||
#include "cutlass/gemm/kernel/gemm_universal.hpp"
|
||||
#include "cutlass/gemm/collective/collective_builder.hpp"
|
||||
#include "cutlass/epilogue/dispatch_policy.hpp"
|
||||
#include "cutlass/epilogue/collective/collective_builder.hpp"
|
||||
#include "cute/atom/mma_traits_sm100.hpp"
|
||||
#include "../../common/cutlass_unit_test.h"
|
||||
#include "gemm_testbed_3x_planar_complex.hpp"
|
||||
|
||||
using namespace cute;
|
||||
|
||||
#if defined(CUTLASS_ARCH_MMA_SM100_SUPPORTED)
|
||||
|
||||
///////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
////////////////////////////////////////////// 64x128x64_0x0x1 TCGEN05.1CTA ///////////////////////////////////////////
|
||||
///////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
// TTT
|
||||
TEST(SM100_Device_Gemm_Planar_cbf16t_cbf16t_f32t_tensorop_1sm_preferred_cluster, 64x128x64_0x0x1) {
|
||||
using ElementA = cutlass::bfloat16_t;
|
||||
using TransformA = cute::identity;
|
||||
using ElementPairA = cute::tuple<ElementA, TransformA>;
|
||||
using LayoutA = cutlass::layout::RowMajor;
|
||||
|
||||
using ElementB = cutlass::bfloat16_t;
|
||||
using TransformB = cute::identity;
|
||||
using ElementPairB = cute::tuple<ElementB, TransformB>;
|
||||
using LayoutB = cutlass::layout::RowMajor;
|
||||
|
||||
using ElementAccumulator = float;
|
||||
using LayoutC = cutlass::layout::RowMajor;
|
||||
|
||||
using MmaTileShape = cute::Shape<_64,_128,_64>;
|
||||
using ClusterShape = cute::Shape<int,int,_1>; // Runtime cluster shape
|
||||
|
||||
using CollectiveEpilogue = typename cutlass::epilogue::collective::CollectiveBuilder<
|
||||
cutlass::arch::Sm100, cutlass::arch::OpClassTensorOp,
|
||||
MmaTileShape, ClusterShape,
|
||||
cutlass::epilogue::collective::EpilogueTileAuto,
|
||||
float, float,
|
||||
cutlass::bfloat16_t, LayoutC, 8,
|
||||
cutlass::bfloat16_t, LayoutC, 8,
|
||||
cutlass::epilogue::PlanarComplexTmaWarpSpecialized1Sm
|
||||
>::CollectiveOp;
|
||||
|
||||
using CollectiveMainloop = typename cutlass::gemm::collective::CollectiveBuilder<
|
||||
cutlass::arch::Sm100, cutlass::arch::OpClassTensorOp,
|
||||
ElementPairA, LayoutA, 8,
|
||||
ElementPairB, LayoutB, 8,
|
||||
ElementAccumulator,
|
||||
MmaTileShape, ClusterShape,
|
||||
cutlass::gemm::collective::StageCountAutoCarveout<
|
||||
static_cast<int>(sizeof(typename CollectiveEpilogue::SharedStorage))>,
|
||||
cutlass::gemm::KernelTmaWarpSpecialized1SmPlanarComplexSm100
|
||||
>::CollectiveOp;
|
||||
|
||||
using GemmKernel = cutlass::gemm::kernel::GemmUniversal<
|
||||
Shape<int,int,int,int>,
|
||||
CollectiveMainloop,
|
||||
CollectiveEpilogue
|
||||
>;
|
||||
|
||||
using Gemm = cutlass::gemm::device::GemmUniversalAdapter<GemmKernel>;
|
||||
EXPECT_TRUE(test::gemm::device::TestPlanarComplexPreferredClusterSmall<Gemm>());
|
||||
}
|
||||
|
||||
///////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
////////////////////////////////////////////// 128x128x64_0x0x1 TCGEN05.2CTA //////////////////////////////////////////
|
||||
///////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
// NNN
|
||||
TEST(SM100_Device_Gemm_Planar_cbf16n_cbf16n_f32n_tensorop_2sm_preferred_cluster, 128x128x64_0x0x1) {
|
||||
using ElementA = cutlass::bfloat16_t;
|
||||
using TransformA = cute::identity;
|
||||
using ElementPairA = cute::tuple<ElementA, TransformA>;
|
||||
using LayoutA = cutlass::layout::ColumnMajor;
|
||||
|
||||
using ElementB = cutlass::bfloat16_t;
|
||||
using TransformB = cute::identity;
|
||||
using ElementPairB = cute::tuple<ElementB, TransformB>;
|
||||
using LayoutB = cutlass::layout::ColumnMajor;
|
||||
|
||||
using ElementAccumulator = float;
|
||||
using LayoutC = cutlass::layout::ColumnMajor;
|
||||
|
||||
using MmaTileShape = cute::Shape<_128,_128,_64>;
|
||||
using ClusterShape = cute::Shape<int,int,_1>; // Runtime cluster shape
|
||||
|
||||
using CollectiveEpilogue = typename cutlass::epilogue::collective::CollectiveBuilder<
|
||||
cutlass::arch::Sm100, cutlass::arch::OpClassTensorOp,
|
||||
MmaTileShape, ClusterShape,
|
||||
cutlass::epilogue::collective::EpilogueTileAuto,
|
||||
float, float,
|
||||
cutlass::bfloat16_t, LayoutC, 8,
|
||||
cutlass::bfloat16_t, LayoutC, 8,
|
||||
cutlass::epilogue::PlanarComplexTmaWarpSpecialized2Sm
|
||||
>::CollectiveOp;
|
||||
|
||||
using CollectiveMainloop = typename cutlass::gemm::collective::CollectiveBuilder<
|
||||
cutlass::arch::Sm100, cutlass::arch::OpClassTensorOp,
|
||||
ElementPairA, LayoutA, 8,
|
||||
ElementPairB, LayoutB, 8,
|
||||
ElementAccumulator,
|
||||
MmaTileShape, ClusterShape,
|
||||
cutlass::gemm::collective::StageCountAutoCarveout<
|
||||
static_cast<int>(sizeof(typename CollectiveEpilogue::SharedStorage))>,
|
||||
// 2Sm preferred cluster kernel should directly use 2Sm KernelSchedule for now
|
||||
cutlass::gemm::KernelTmaWarpSpecialized2SmPlanarComplexSm100
|
||||
>::CollectiveOp;
|
||||
|
||||
using GemmKernel = cutlass::gemm::kernel::GemmUniversal<
|
||||
Shape<int,int,int,int>,
|
||||
CollectiveMainloop,
|
||||
CollectiveEpilogue
|
||||
>;
|
||||
|
||||
using Gemm = cutlass::gemm::device::GemmUniversalAdapter<GemmKernel>;
|
||||
EXPECT_TRUE(test::gemm::device::TestPlanarComplexPreferredClusterSmall<Gemm>());
|
||||
}
|
||||
|
||||
#endif // #if defined(CUTLASS_ARCH_MMA_SM100_SUPPORTED)
|
||||
+282
@@ -0,0 +1,282 @@
|
||||
/***************************************************************************************************
|
||||
* Copyright (c) 2023 - 2026 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 Ptr-Array GEMM interface
|
||||
*/
|
||||
|
||||
|
||||
|
||||
#include <iostream>
|
||||
|
||||
#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/gemm/kernel/tile_scheduler.hpp"
|
||||
#include "cutlass/gemm/collective/collective_builder.hpp"
|
||||
#include "cutlass/epilogue/collective/collective_builder.hpp"
|
||||
#include "cutlass/epilogue/collective/sm70_epilogue_vectorized.hpp"
|
||||
#include "cutlass/epilogue/collective/default_epilogue.hpp"
|
||||
#include "cutlass/epilogue/thread/linear_combination.h"
|
||||
|
||||
#include "../../common/cutlass_unit_test.h"
|
||||
|
||||
#include "gemm_testbed_3x_ptr_array_planar_complex.hpp"
|
||||
|
||||
using namespace cute;
|
||||
|
||||
#if defined(CUTLASS_ARCH_MMA_SM100_SUPPORTED)
|
||||
|
||||
///////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
/////////// CTA_TILE_SIZE : 128x64x64, CLUSTER_SIZE : 1x1x1, CLUSTER_TILE_SIZE : 128x64x64, TCGEN05.1CTA //////////////
|
||||
///////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
// TT
|
||||
TEST(SM100_Device_Gemm_Planar_cbf16t_cbf16t_f32n_tensorop_ptr_array_1sm, 128x64x64_1x1x1) {
|
||||
using ElementA = cutlass::bfloat16_t;
|
||||
using TransformA = cute::identity;
|
||||
using ElementPairA = cute::tuple<ElementA, TransformA>;
|
||||
using LayoutA = cutlass::layout::RowMajor;
|
||||
|
||||
using ElementB = cutlass::bfloat16_t;
|
||||
using TransformB = cute::identity;
|
||||
using ElementPairB = cute::tuple<ElementB, TransformB>;
|
||||
using LayoutB = cutlass::layout::RowMajor;
|
||||
|
||||
using ElementAccumulator = float;
|
||||
using LayoutC = cutlass::layout::ColumnMajor;
|
||||
|
||||
using MmaTileShape = cute::Shape<_128,_64,_64>;
|
||||
using ClusterShape = Shape<_1,_1,_1>;
|
||||
|
||||
using MainloopSchedule = cutlass::gemm::KernelPtrArrayTmaWarpSpecialized1SmPlanarComplexSm100; // Kernel to launch
|
||||
using EpilogueSchedule = cutlass::epilogue::PtrArrayPlanarComplexTmaWarpSpecialized1Sm; // Epilogue to launch
|
||||
|
||||
using CollectiveEpilogue = typename cutlass::epilogue::collective::CollectiveBuilder<
|
||||
cutlass::arch::Sm100, cutlass::arch::OpClassTensorOp,
|
||||
MmaTileShape, ClusterShape,
|
||||
cutlass::epilogue::collective::EpilogueTileAuto,
|
||||
float, float,
|
||||
cutlass::bfloat16_t, LayoutC, 8,
|
||||
cutlass::bfloat16_t, LayoutC, 8,
|
||||
EpilogueSchedule
|
||||
>::CollectiveOp;
|
||||
|
||||
using CollectiveMainloop = typename cutlass::gemm::collective::CollectiveBuilder<
|
||||
cutlass::arch::Sm100, cutlass::arch::OpClassTensorOp,
|
||||
ElementPairA, LayoutA, 8,
|
||||
ElementPairB, LayoutB, 8,
|
||||
ElementAccumulator,
|
||||
MmaTileShape, ClusterShape,
|
||||
cutlass::gemm::collective::StageCountAutoCarveout<
|
||||
static_cast<int>(sizeof(typename CollectiveEpilogue::SharedStorage))>,
|
||||
MainloopSchedule
|
||||
>::CollectiveOp;
|
||||
|
||||
using GemmKernel = cutlass::gemm::kernel::GemmUniversal<
|
||||
cutlass::gemm::ArrayProblemShape<Shape<int,int,int,int>>,
|
||||
CollectiveMainloop,
|
||||
CollectiveEpilogue
|
||||
>;
|
||||
|
||||
using Gemm = cutlass::gemm::device::GemmUniversalAdapter<GemmKernel>;
|
||||
EXPECT_TRUE(test::gemm::device::TestPlanarComplexSmall<Gemm>());
|
||||
}
|
||||
|
||||
///////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
/////////// CTA_TILE_SIZE : 64x128x64, CLUSTER_SIZE : 4x4x1, CLUSTER_TILE_SIZE : 256x256x64, TCGEN05.1CTA /////////////
|
||||
///////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
// NN
|
||||
TEST(SM100_Device_Gemm_Planar_cbf16n_cbf16n_f32n_tensorop_ptr_array_1sm, 256x512x64_4x4x1) {
|
||||
using ElementA = cutlass::bfloat16_t;
|
||||
using TransformA = cute::identity;
|
||||
using ElementPairA = cute::tuple<ElementA, TransformA>;
|
||||
using LayoutA = cutlass::layout::ColumnMajor;
|
||||
|
||||
using ElementB = cutlass::bfloat16_t;
|
||||
using TransformB = cute::identity;
|
||||
using ElementPairB = cute::tuple<ElementB, TransformB>;
|
||||
using LayoutB = cutlass::layout::ColumnMajor;
|
||||
|
||||
using ElementAccumulator = float;
|
||||
using LayoutC = cutlass::layout::ColumnMajor;
|
||||
|
||||
using MmaTileShape = cute::Shape<_64,_128,_64>;
|
||||
using ClusterShape = Shape<_4,_4,_1>;
|
||||
|
||||
using MainloopSchedule = cutlass::gemm::KernelPtrArrayTmaWarpSpecialized1SmPlanarComplexSm100; // Kernel to launch
|
||||
using EpilogueSchedule = cutlass::epilogue::PtrArrayPlanarComplexTmaWarpSpecialized1Sm; // Epilogue to launch
|
||||
|
||||
using CollectiveEpilogue = typename cutlass::epilogue::collective::CollectiveBuilder<
|
||||
cutlass::arch::Sm100, cutlass::arch::OpClassTensorOp,
|
||||
MmaTileShape, ClusterShape,
|
||||
cutlass::epilogue::collective::EpilogueTileAuto,
|
||||
float, float,
|
||||
cutlass::bfloat16_t, LayoutC, 8,
|
||||
cutlass::bfloat16_t, LayoutC, 8,
|
||||
EpilogueSchedule
|
||||
>::CollectiveOp;
|
||||
|
||||
using CollectiveMainloop = typename cutlass::gemm::collective::CollectiveBuilder<
|
||||
cutlass::arch::Sm100, cutlass::arch::OpClassTensorOp,
|
||||
ElementPairA, LayoutA, 8,
|
||||
ElementPairB, LayoutB, 8,
|
||||
ElementAccumulator,
|
||||
MmaTileShape, ClusterShape,
|
||||
cutlass::gemm::collective::StageCountAutoCarveout<
|
||||
static_cast<int>(sizeof(typename CollectiveEpilogue::SharedStorage))>,
|
||||
MainloopSchedule
|
||||
>::CollectiveOp;
|
||||
|
||||
using GemmKernel = cutlass::gemm::kernel::GemmUniversal<
|
||||
cutlass::gemm::ArrayProblemShape<Shape<int,int,int,int>>,
|
||||
CollectiveMainloop,
|
||||
CollectiveEpilogue
|
||||
>;
|
||||
|
||||
using Gemm = cutlass::gemm::device::GemmUniversalAdapter<GemmKernel>;
|
||||
EXPECT_TRUE(test::gemm::device::TestPlanarComplexSmall<Gemm>());
|
||||
}
|
||||
|
||||
///////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
/////////// CTA_TILE_SIZE : 64x128x64, CLUSTER_SIZE : 2x1x1, CLUSTER_TILE_SIZE : 128x128x64, TCGEN05.1CTA /////////////
|
||||
///////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
// TN
|
||||
TEST(SM100_Device_Gemm_Planar_cbf16t_cbf16n_f32n_tensorop_ptr_array_2sm, 128x128x64_2x1x1) {
|
||||
using ElementA = cutlass::bfloat16_t;
|
||||
using TransformA = cute::identity;
|
||||
using ElementPairA = cute::tuple<ElementA, TransformA>;
|
||||
using LayoutA = cutlass::layout::RowMajor;
|
||||
|
||||
using ElementB = cutlass::bfloat16_t;
|
||||
using TransformB = cute::identity;
|
||||
using ElementPairB = cute::tuple<ElementB, TransformB>;
|
||||
using LayoutB = cutlass::layout::ColumnMajor;
|
||||
|
||||
using ElementAccumulator = float;
|
||||
using LayoutC = cutlass::layout::ColumnMajor;
|
||||
|
||||
using MmaTileShape = cute::Shape<_128,_128,_64>;
|
||||
using ClusterShape = Shape<_2,_1,_1>;
|
||||
|
||||
using MainloopSchedule = cutlass::gemm::KernelPtrArrayTmaWarpSpecialized2SmPlanarComplexSm100; // Kernel to launch
|
||||
using EpilogueSchedule = cutlass::epilogue::PtrArrayPlanarComplexTmaWarpSpecialized2Sm; // Epilogue to launch
|
||||
|
||||
using CollectiveEpilogue = typename cutlass::epilogue::collective::CollectiveBuilder<
|
||||
cutlass::arch::Sm100, cutlass::arch::OpClassTensorOp,
|
||||
MmaTileShape, ClusterShape,
|
||||
cutlass::epilogue::collective::EpilogueTileAuto,
|
||||
float, float,
|
||||
cutlass::bfloat16_t, LayoutC, 8,
|
||||
cutlass::bfloat16_t, LayoutC, 8,
|
||||
EpilogueSchedule
|
||||
>::CollectiveOp;
|
||||
|
||||
using CollectiveMainloop = typename cutlass::gemm::collective::CollectiveBuilder<
|
||||
cutlass::arch::Sm100, cutlass::arch::OpClassTensorOp,
|
||||
ElementPairA, LayoutA, 8,
|
||||
ElementPairB, LayoutB, 8,
|
||||
ElementAccumulator,
|
||||
MmaTileShape, ClusterShape,
|
||||
cutlass::gemm::collective::StageCountAutoCarveout<
|
||||
static_cast<int>(sizeof(typename CollectiveEpilogue::SharedStorage))>,
|
||||
MainloopSchedule
|
||||
>::CollectiveOp;
|
||||
|
||||
using GemmKernel = cutlass::gemm::kernel::GemmUniversal<
|
||||
cutlass::gemm::ArrayProblemShape<Shape<int,int,int,int>>,
|
||||
CollectiveMainloop,
|
||||
CollectiveEpilogue
|
||||
>;
|
||||
|
||||
using Gemm = cutlass::gemm::device::GemmUniversalAdapter<GemmKernel>;
|
||||
EXPECT_TRUE(test::gemm::device::TestPlanarComplexSmall<Gemm>());
|
||||
}
|
||||
|
||||
///////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
/////////// CTA_TILE_SIZE : 128x64x64, CLUSTER_SIZE : 4x4x1, CLUSTER_TILE_SIZE : 512x256x64, TCGEN05.1CTA /////////////
|
||||
///////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
// NT
|
||||
TEST(SM100_Device_Gemm_Planar_cbf16n_cbf16t_f32n_tensorop_ptr_array_2sm, 512x256x64_4x4x1) {
|
||||
using ElementA = cutlass::bfloat16_t;
|
||||
using TransformA = cute::identity;
|
||||
using ElementPairA = cute::tuple<ElementA, TransformA>;
|
||||
using LayoutA = cutlass::layout::ColumnMajor;
|
||||
|
||||
using ElementB = cutlass::bfloat16_t;
|
||||
using TransformB = cute::identity;
|
||||
using ElementPairB = cute::tuple<ElementB, TransformB>;
|
||||
using LayoutB = cutlass::layout::RowMajor;
|
||||
|
||||
using ElementAccumulator = float;
|
||||
using LayoutC = cutlass::layout::ColumnMajor;
|
||||
|
||||
using MmaTileShape = cute::Shape<_256,_64,_64>;
|
||||
using ClusterShape = Shape<_4,_4,_1>;
|
||||
|
||||
using MainloopSchedule = cutlass::gemm::KernelPtrArrayTmaWarpSpecialized2SmPlanarComplexSm100; // Kernel to launch
|
||||
using EpilogueSchedule = cutlass::epilogue::PtrArrayPlanarComplexTmaWarpSpecialized2Sm; // Epilogue to launch
|
||||
|
||||
using CollectiveEpilogue = typename cutlass::epilogue::collective::CollectiveBuilder<
|
||||
cutlass::arch::Sm100, cutlass::arch::OpClassTensorOp,
|
||||
MmaTileShape, ClusterShape,
|
||||
cutlass::epilogue::collective::EpilogueTileAuto,
|
||||
float, float,
|
||||
cutlass::bfloat16_t, LayoutC, 8,
|
||||
cutlass::bfloat16_t, LayoutC, 8,
|
||||
EpilogueSchedule
|
||||
>::CollectiveOp;
|
||||
|
||||
using CollectiveMainloop = typename cutlass::gemm::collective::CollectiveBuilder<
|
||||
cutlass::arch::Sm100, cutlass::arch::OpClassTensorOp,
|
||||
ElementPairA, LayoutA, 8,
|
||||
ElementPairB, LayoutB, 8,
|
||||
ElementAccumulator,
|
||||
MmaTileShape, ClusterShape,
|
||||
cutlass::gemm::collective::StageCountAutoCarveout<
|
||||
static_cast<int>(sizeof(typename CollectiveEpilogue::SharedStorage))>,
|
||||
MainloopSchedule
|
||||
>::CollectiveOp;
|
||||
|
||||
using GemmKernel = cutlass::gemm::kernel::GemmUniversal<
|
||||
cutlass::gemm::ArrayProblemShape<Shape<int,int,int,int>>,
|
||||
CollectiveMainloop,
|
||||
CollectiveEpilogue
|
||||
>;
|
||||
|
||||
using Gemm = cutlass::gemm::device::GemmUniversalAdapter<GemmKernel>;
|
||||
EXPECT_TRUE(test::gemm::device::TestPlanarComplexSmall<Gemm>());
|
||||
}
|
||||
|
||||
#endif // #if defined(CUTLASS_ARCH_MMA_SM100_SUPPORTED)
|
||||
@@ -0,0 +1,163 @@
|
||||
/***************************************************************************************************
|
||||
* Copyright (c) 2023 - 2026 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 GEMM interface
|
||||
*/
|
||||
|
||||
#include <iostream>
|
||||
|
||||
#include "cutlass/cutlass.h"
|
||||
#include "cute/tensor.hpp"
|
||||
#include "cute/atom/mma_atom.hpp"
|
||||
#include "cutlass/numeric_types.h"
|
||||
#include "cutlass/arch/mma_sm100.h"
|
||||
#include "cutlass/gemm/device/gemm_universal_adapter.h"
|
||||
#include "cutlass/gemm/kernel/gemm_universal.hpp"
|
||||
#include "cutlass/gemm/collective/collective_builder.hpp"
|
||||
#include "cutlass/epilogue/dispatch_policy.hpp"
|
||||
#include "cutlass/epilogue/collective/collective_builder.hpp"
|
||||
#include "cute/atom/mma_traits_sm100.hpp"
|
||||
#include "../../common/cutlass_unit_test.h"
|
||||
#include "gemm_testbed_3x_planar_complex.hpp"
|
||||
|
||||
using namespace cute;
|
||||
|
||||
#if defined(CUTLASS_ARCH_MMA_SM100_SUPPORTED)
|
||||
|
||||
///////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
////////////////////////////////////////////////////// 64x64x64 ///////////////////////////////////////////////////////
|
||||
///////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
// NNN
|
||||
TEST(SM100_Device_Gemm_Planar_cf16n_cf16n_f32n_tensorop_1sm, 64x64x64_1x1x1) {
|
||||
using ElementA = cutlass::half_t;
|
||||
using TransformA = cute::identity;
|
||||
using ElementPairA = cute::tuple<ElementA, TransformA>;
|
||||
using LayoutA = cutlass::layout::ColumnMajor;
|
||||
|
||||
using ElementB = cutlass::half_t;
|
||||
using TransformB = cute::identity;
|
||||
using ElementPairB = cute::tuple<ElementB, TransformB>;
|
||||
using LayoutB = cutlass::layout::ColumnMajor;
|
||||
|
||||
using ElementAccumulator = float;
|
||||
using LayoutC = cutlass::layout::ColumnMajor;
|
||||
|
||||
using MmaTileShape = cute::Shape<_64,_64,_64>;
|
||||
using ClusterShape = Shape<_1,_1,_1>;
|
||||
|
||||
using CollectiveEpilogue = typename cutlass::epilogue::collective::CollectiveBuilder<
|
||||
cutlass::arch::Sm100, cutlass::arch::OpClassTensorOp,
|
||||
MmaTileShape, ClusterShape,
|
||||
cutlass::epilogue::collective::EpilogueTileAuto,
|
||||
float, float,
|
||||
cutlass::half_t, LayoutC, 8,
|
||||
cutlass::half_t, LayoutC, 8,
|
||||
cutlass::epilogue::PlanarComplexTmaWarpSpecialized1Sm
|
||||
>::CollectiveOp;
|
||||
|
||||
using CollectiveMainloop = typename cutlass::gemm::collective::CollectiveBuilder<
|
||||
cutlass::arch::Sm100, cutlass::arch::OpClassTensorOp,
|
||||
ElementPairA, LayoutA, 8,
|
||||
ElementPairB, LayoutB, 8,
|
||||
ElementAccumulator,
|
||||
MmaTileShape, ClusterShape,
|
||||
cutlass::gemm::collective::StageCountAutoCarveout<
|
||||
static_cast<int>(sizeof(typename CollectiveEpilogue::SharedStorage))>,
|
||||
cutlass::gemm::KernelTmaWarpSpecialized1SmPlanarComplexSm100
|
||||
>::CollectiveOp;
|
||||
|
||||
using GemmKernel = cutlass::gemm::kernel::GemmUniversal<
|
||||
Shape<int,int,int,int>,
|
||||
CollectiveMainloop,
|
||||
CollectiveEpilogue
|
||||
>;
|
||||
|
||||
using Gemm = cutlass::gemm::device::GemmUniversalAdapter<GemmKernel>;
|
||||
EXPECT_TRUE(test::gemm::device::TestPlanarComplexSmall<Gemm>());
|
||||
}
|
||||
|
||||
///////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
///////////////////////////////////////////////////// TMA MULTICAST ///////////////////////////////////////////////////
|
||||
///////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
// NTT
|
||||
TEST(SM100_Device_Gemm_Planar_cf16n_cf16t_f32t_tensorop_1sm, 512x512x64_4x4x1) {
|
||||
using ElementA = cutlass::half_t;
|
||||
using TransformA = cute::identity;
|
||||
using ElementPairA = cute::tuple<ElementA, TransformA>;
|
||||
using LayoutA = cutlass::layout::ColumnMajor;
|
||||
|
||||
using ElementB = cutlass::half_t;
|
||||
using TransformB = cute::identity;
|
||||
using ElementPairB = cute::tuple<ElementB, TransformB>;
|
||||
using LayoutB = cutlass::layout::RowMajor;
|
||||
|
||||
using ElementAccumulator = float;
|
||||
using LayoutC = cutlass::layout::RowMajor;
|
||||
|
||||
using MmaTileShape = cute::Shape<_128,_128,_64>;
|
||||
using ClusterShape = Shape<_4,_4,_1>;
|
||||
|
||||
using CollectiveEpilogue = typename cutlass::epilogue::collective::CollectiveBuilder<
|
||||
cutlass::arch::Sm100, cutlass::arch::OpClassTensorOp,
|
||||
MmaTileShape, ClusterShape,
|
||||
cutlass::epilogue::collective::EpilogueTileAuto,
|
||||
float, float,
|
||||
cutlass::half_t, LayoutC, 8,
|
||||
cutlass::half_t, LayoutC, 8,
|
||||
cutlass::epilogue::PlanarComplexTmaWarpSpecialized1Sm
|
||||
>::CollectiveOp;
|
||||
|
||||
using CollectiveMainloop = typename cutlass::gemm::collective::CollectiveBuilder<
|
||||
cutlass::arch::Sm100, cutlass::arch::OpClassTensorOp,
|
||||
ElementPairA, LayoutA, 8,
|
||||
ElementPairB, LayoutB, 8,
|
||||
ElementAccumulator,
|
||||
MmaTileShape, ClusterShape,
|
||||
cutlass::gemm::collective::StageCountAutoCarveout<
|
||||
static_cast<int>(sizeof(typename CollectiveEpilogue::SharedStorage))>,
|
||||
cutlass::gemm::KernelTmaWarpSpecialized1SmPlanarComplexSm100
|
||||
>::CollectiveOp;
|
||||
|
||||
using GemmKernel = cutlass::gemm::kernel::GemmUniversal<
|
||||
Shape<int,int,int,int>,
|
||||
CollectiveMainloop,
|
||||
CollectiveEpilogue,
|
||||
cutlass::gemm::StreamKScheduler
|
||||
>;
|
||||
|
||||
using Gemm = cutlass::gemm::device::GemmUniversalAdapter<GemmKernel>;
|
||||
EXPECT_TRUE(test::gemm::device::TestPlanarComplexSmall<Gemm>());
|
||||
}
|
||||
|
||||
#endif // #if defined(CUTLASS_ARCH_MMA_SM100_SUPPORTED)
|
||||
@@ -0,0 +1,163 @@
|
||||
/***************************************************************************************************
|
||||
* Copyright (c) 2023 - 2026 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 GEMM interface
|
||||
*/
|
||||
|
||||
#include <iostream>
|
||||
|
||||
#include "cutlass/cutlass.h"
|
||||
#include "cute/tensor.hpp"
|
||||
#include "cute/atom/mma_atom.hpp"
|
||||
#include "cutlass/numeric_types.h"
|
||||
#include "cutlass/arch/mma_sm100.h"
|
||||
#include "cutlass/gemm/device/gemm_universal_adapter.h"
|
||||
#include "cutlass/gemm/kernel/gemm_universal.hpp"
|
||||
#include "cutlass/gemm/collective/collective_builder.hpp"
|
||||
#include "cutlass/epilogue/dispatch_policy.hpp"
|
||||
#include "cutlass/epilogue/collective/collective_builder.hpp"
|
||||
#include "cute/atom/mma_traits_sm100.hpp"
|
||||
#include "../../common/cutlass_unit_test.h"
|
||||
#include "gemm_testbed_3x_planar_complex.hpp"
|
||||
|
||||
using namespace cute;
|
||||
|
||||
#if defined(CUTLASS_ARCH_MMA_SM100_SUPPORTED)
|
||||
|
||||
///////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
///////////////////////////////////////////////////// 256x64x64 //////////////////////////////////////////////////////
|
||||
///////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
// TTT
|
||||
TEST(SM100_Device_Gemm_Planar_cf16t_cf16t_f32t_tensorop_2sm, 256x64x64_2x1x1) {
|
||||
using ElementA = cutlass::half_t;
|
||||
using LayoutA = cutlass::layout::RowMajor;
|
||||
using TransformA = cute::identity;
|
||||
using ElementPairA = cute::tuple<ElementA, TransformA>;
|
||||
|
||||
using ElementB = cutlass::half_t;
|
||||
using LayoutB = cutlass::layout::RowMajor;
|
||||
using TransformB = cute::identity;
|
||||
using ElementPairB = cute::tuple<ElementB, TransformB>;
|
||||
|
||||
using ElementAccumulator = float;
|
||||
using LayoutC = cutlass::layout::RowMajor;
|
||||
|
||||
using MmaTileShape = cute::Shape<_256,_64,_64>;
|
||||
using ClusterShape = Shape<_2,_1,_1>;
|
||||
|
||||
using CollectiveEpilogue = typename cutlass::epilogue::collective::CollectiveBuilder<
|
||||
cutlass::arch::Sm100, cutlass::arch::OpClassTensorOp,
|
||||
MmaTileShape, ClusterShape,
|
||||
cutlass::epilogue::collective::EpilogueTileAuto,
|
||||
float, float,
|
||||
cutlass::half_t, LayoutC, 8,
|
||||
cutlass::half_t, LayoutC, 8,
|
||||
cutlass::epilogue::PlanarComplexTmaWarpSpecialized2Sm
|
||||
>::CollectiveOp;
|
||||
|
||||
using CollectiveMainloop = typename cutlass::gemm::collective::CollectiveBuilder<
|
||||
cutlass::arch::Sm100, cutlass::arch::OpClassTensorOp,
|
||||
ElementPairA, LayoutA, 8,
|
||||
ElementPairB, LayoutB, 8,
|
||||
ElementAccumulator,
|
||||
MmaTileShape, ClusterShape,
|
||||
cutlass::gemm::collective::StageCountAutoCarveout<
|
||||
static_cast<int>(sizeof(typename CollectiveEpilogue::SharedStorage))>,
|
||||
cutlass::gemm::KernelTmaWarpSpecialized2SmPlanarComplexSm100
|
||||
>::CollectiveOp;
|
||||
|
||||
using GemmKernel = cutlass::gemm::kernel::GemmUniversal<
|
||||
Shape<int,int,int,int>,
|
||||
CollectiveMainloop,
|
||||
CollectiveEpilogue
|
||||
>;
|
||||
|
||||
using Gemm = cutlass::gemm::device::GemmUniversalAdapter<GemmKernel>;
|
||||
EXPECT_TRUE(test::gemm::device::TestPlanarComplexSmall<Gemm>());
|
||||
}
|
||||
|
||||
///////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
///////////////////////////////////////////////////// TMA MULTICAST //////////////////////////////////////////////////////
|
||||
///////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
// TNN
|
||||
TEST(SM100_Device_Gemm_Planar_cf16t_cf16n_f32n_tensorop_2sm, 256x512x64_4x4x1) {
|
||||
using ElementA = cutlass::half_t;
|
||||
using TransformA = cute::identity;
|
||||
using ElementPairA = cute::tuple<ElementA, TransformA>;
|
||||
using LayoutA = cutlass::layout::RowMajor;
|
||||
|
||||
using ElementB = cutlass::half_t;
|
||||
using TransformB = cute::identity;
|
||||
using ElementPairB = cute::tuple<ElementB, TransformB>;
|
||||
using LayoutB = cutlass::layout::ColumnMajor;
|
||||
|
||||
using ElementAccumulator = float;
|
||||
using LayoutC = cutlass::layout::ColumnMajor;
|
||||
|
||||
using MmaTileShape = cute::Shape<_128,_128,_64>;
|
||||
using ClusterShape = Shape<_4,_4,_1>;
|
||||
|
||||
using CollectiveEpilogue = typename cutlass::epilogue::collective::CollectiveBuilder<
|
||||
cutlass::arch::Sm100, cutlass::arch::OpClassTensorOp,
|
||||
MmaTileShape, ClusterShape,
|
||||
cutlass::epilogue::collective::EpilogueTileAuto,
|
||||
float, float,
|
||||
cutlass::half_t, LayoutC, 8,
|
||||
cutlass::half_t, LayoutC, 8,
|
||||
cutlass::epilogue::PlanarComplexTmaWarpSpecialized2Sm
|
||||
>::CollectiveOp;
|
||||
|
||||
using CollectiveMainloop = typename cutlass::gemm::collective::CollectiveBuilder<
|
||||
cutlass::arch::Sm100, cutlass::arch::OpClassTensorOp,
|
||||
ElementPairA, LayoutA, 8,
|
||||
ElementPairB, LayoutB, 8,
|
||||
ElementAccumulator,
|
||||
MmaTileShape, ClusterShape,
|
||||
cutlass::gemm::collective::StageCountAutoCarveout<
|
||||
static_cast<int>(sizeof(typename CollectiveEpilogue::SharedStorage))>,
|
||||
cutlass::gemm::KernelTmaWarpSpecialized2SmPlanarComplexSm100
|
||||
>::CollectiveOp;
|
||||
|
||||
using GemmKernel = cutlass::gemm::kernel::GemmUniversal<
|
||||
Shape<int,int,int,int>,
|
||||
CollectiveMainloop,
|
||||
CollectiveEpilogue,
|
||||
cutlass::gemm::StreamKScheduler
|
||||
>;
|
||||
|
||||
using Gemm = cutlass::gemm::device::GemmUniversalAdapter<GemmKernel>;
|
||||
EXPECT_TRUE(test::gemm::device::TestPlanarComplexSmall<Gemm>());
|
||||
}
|
||||
|
||||
#endif // #if defined(CUTLASS_ARCH_MMA_SM100_SUPPORTED)
|
||||
+212
@@ -0,0 +1,212 @@
|
||||
/***************************************************************************************************
|
||||
* Copyright (c) 2023 - 2026 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 GEMM interface
|
||||
*/
|
||||
|
||||
#include <iostream>
|
||||
|
||||
#include "cutlass/cutlass.h"
|
||||
#include "cute/tensor.hpp"
|
||||
#include "cute/atom/mma_atom.hpp"
|
||||
#include "cutlass/numeric_types.h"
|
||||
#include "cutlass/arch/mma_sm100.h"
|
||||
#include "cutlass/gemm/device/gemm_universal_adapter.h"
|
||||
#include "cutlass/gemm/kernel/gemm_universal.hpp"
|
||||
#include "cutlass/gemm/collective/collective_builder.hpp"
|
||||
#include "cutlass/epilogue/dispatch_policy.hpp"
|
||||
#include "cutlass/epilogue/collective/collective_builder.hpp"
|
||||
#include "cute/atom/mma_traits_sm100.hpp"
|
||||
#include "../../common/cutlass_unit_test.h"
|
||||
#include "gemm_testbed_3x_planar_complex.hpp"
|
||||
|
||||
using namespace cute;
|
||||
|
||||
#if defined(CUTLASS_ARCH_MMA_SM100_SUPPORTED)
|
||||
|
||||
///////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
///////////////////////////////////////////////////// TCGEN05.1CTA ////////////////////////////////////////////////////
|
||||
///////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
// NC
|
||||
TEST(SM100_Device_Gemm_Planar_cf16n_cf16c_f32n_tensorop_1sm, 128x128x64_1x1x1) {
|
||||
using ElementA = cutlass::half_t;
|
||||
using TransformA = cute::identity;
|
||||
using ElementPairA = cute::tuple<ElementA, TransformA>;
|
||||
using LayoutA = cutlass::layout::ColumnMajor;
|
||||
|
||||
using ElementB = cutlass::half_t;
|
||||
using TransformB = cute::conjugate;
|
||||
using ElementPairB = cute::tuple<ElementB, TransformB>;
|
||||
using LayoutB = cutlass::layout::RowMajor;
|
||||
|
||||
using ElementAccumulator = float;
|
||||
using LayoutC = cutlass::layout::ColumnMajor;
|
||||
|
||||
using MmaTileShape = cute::Shape<_128,_128,_64>;
|
||||
using ClusterShape = Shape<_1,_1,_1>;
|
||||
|
||||
using CollectiveEpilogue = typename cutlass::epilogue::collective::CollectiveBuilder<
|
||||
cutlass::arch::Sm100, cutlass::arch::OpClassTensorOp,
|
||||
MmaTileShape, ClusterShape,
|
||||
cutlass::epilogue::collective::EpilogueTileAuto,
|
||||
float, float,
|
||||
cutlass::half_t, LayoutC, 8,
|
||||
cutlass::half_t, LayoutC, 8,
|
||||
cutlass::epilogue::PlanarComplexTmaWarpSpecialized1Sm
|
||||
>::CollectiveOp;
|
||||
|
||||
using CollectiveMainloop = typename cutlass::gemm::collective::CollectiveBuilder<
|
||||
cutlass::arch::Sm100, cutlass::arch::OpClassTensorOp,
|
||||
ElementPairA, LayoutA, 8,
|
||||
ElementPairB, LayoutB, 8,
|
||||
ElementAccumulator,
|
||||
MmaTileShape, ClusterShape,
|
||||
cutlass::gemm::collective::StageCountAutoCarveout<
|
||||
static_cast<int>(sizeof(typename CollectiveEpilogue::SharedStorage))>,
|
||||
cutlass::gemm::KernelTmaWarpSpecialized1SmPlanarComplexSm100
|
||||
>::CollectiveOp;
|
||||
|
||||
using GemmKernel = cutlass::gemm::kernel::GemmUniversal<
|
||||
Shape<int,int,int,int>,
|
||||
CollectiveMainloop,
|
||||
CollectiveEpilogue
|
||||
>;
|
||||
|
||||
using Gemm = cutlass::gemm::device::GemmUniversalAdapter<GemmKernel>;
|
||||
EXPECT_TRUE(test::gemm::device::TestPlanarComplexSmall<Gemm>());
|
||||
}
|
||||
|
||||
// CT
|
||||
TEST(SM100_Device_Gemm_Planar_cf16c_cf16t_f32n_tensorop_1sm, 64x64x64_1x1x1) {
|
||||
using ElementA = cutlass::half_t;
|
||||
using TransformA = cute::conjugate;
|
||||
using ElementPairA = cute::tuple<ElementA, TransformA>;
|
||||
using LayoutA = cutlass::layout::RowMajor;
|
||||
|
||||
using ElementB = cutlass::half_t;
|
||||
using TransformB = cute::identity;
|
||||
using ElementPairB = cute::tuple<ElementB, TransformB>;
|
||||
using LayoutB = cutlass::layout::RowMajor;
|
||||
|
||||
using ElementAccumulator = float;
|
||||
using LayoutC = cutlass::layout::ColumnMajor;
|
||||
|
||||
using MmaTileShape = cute::Shape<_64,_64,_64>;
|
||||
using ClusterShape = Shape<_1,_1,_1>;
|
||||
|
||||
using CollectiveEpilogue = typename cutlass::epilogue::collective::CollectiveBuilder<
|
||||
cutlass::arch::Sm100, cutlass::arch::OpClassTensorOp,
|
||||
MmaTileShape, ClusterShape,
|
||||
cutlass::epilogue::collective::EpilogueTileAuto,
|
||||
float, float,
|
||||
cutlass::half_t, LayoutC, 8,
|
||||
cutlass::half_t, LayoutC, 8,
|
||||
cutlass::epilogue::PlanarComplexTmaWarpSpecialized1Sm
|
||||
>::CollectiveOp;
|
||||
|
||||
using CollectiveMainloop = typename cutlass::gemm::collective::CollectiveBuilder<
|
||||
cutlass::arch::Sm100, cutlass::arch::OpClassTensorOp,
|
||||
ElementPairA, LayoutA, 8,
|
||||
ElementPairB, LayoutB, 8,
|
||||
ElementAccumulator,
|
||||
MmaTileShape, ClusterShape,
|
||||
cutlass::gemm::collective::StageCountAutoCarveout<
|
||||
static_cast<int>(sizeof(typename CollectiveEpilogue::SharedStorage))>,
|
||||
cutlass::gemm::KernelTmaWarpSpecialized1SmPlanarComplexSm100
|
||||
>::CollectiveOp;
|
||||
|
||||
using GemmKernel = cutlass::gemm::kernel::GemmUniversal<
|
||||
Shape<int,int,int,int>,
|
||||
CollectiveMainloop,
|
||||
CollectiveEpilogue
|
||||
>;
|
||||
|
||||
using Gemm = cutlass::gemm::device::GemmUniversalAdapter<GemmKernel>;
|
||||
EXPECT_TRUE(test::gemm::device::TestPlanarComplexSmall<Gemm>());
|
||||
}
|
||||
|
||||
///////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
///////////////////////////////////////////////////// TCGEN05.2CTA ////////////////////////////////////////////////////
|
||||
///////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
// CC
|
||||
TEST(SM100_Device_Gemm_Planar_cf16c_cf16c_f32t_tensorop_2sm, 256x64x64_2x1x1) {
|
||||
using ElementA = cutlass::half_t;
|
||||
using TransformA = cute::conjugate;
|
||||
using ElementPairA = cute::tuple<ElementA, TransformA>;
|
||||
using LayoutA = cutlass::layout::RowMajor;
|
||||
|
||||
using ElementB = cutlass::half_t;
|
||||
using TransformB = cute::conjugate;
|
||||
using ElementPairB = cute::tuple<ElementB, TransformB>;
|
||||
using LayoutB = cutlass::layout::RowMajor;
|
||||
|
||||
using ElementAccumulator = float;
|
||||
using LayoutC = cutlass::layout::RowMajor;
|
||||
|
||||
using MmaTileShape = cute::Shape<_256,_64,_64>;
|
||||
using ClusterShape = Shape<_2,_1,_1>;
|
||||
|
||||
using CollectiveEpilogue = typename cutlass::epilogue::collective::CollectiveBuilder<
|
||||
cutlass::arch::Sm100, cutlass::arch::OpClassTensorOp,
|
||||
MmaTileShape, ClusterShape,
|
||||
cutlass::epilogue::collective::EpilogueTileAuto,
|
||||
float, float,
|
||||
cutlass::half_t, LayoutC, 8,
|
||||
cutlass::half_t, LayoutC, 8,
|
||||
cutlass::epilogue::PlanarComplexTmaWarpSpecialized2Sm
|
||||
>::CollectiveOp;
|
||||
|
||||
using CollectiveMainloop = typename cutlass::gemm::collective::CollectiveBuilder<
|
||||
cutlass::arch::Sm100, cutlass::arch::OpClassTensorOp,
|
||||
ElementPairA, LayoutA, 8,
|
||||
ElementPairB, LayoutB, 8,
|
||||
ElementAccumulator,
|
||||
MmaTileShape, ClusterShape,
|
||||
cutlass::gemm::collective::StageCountAutoCarveout<
|
||||
static_cast<int>(sizeof(typename CollectiveEpilogue::SharedStorage))>,
|
||||
cutlass::gemm::KernelTmaWarpSpecialized2SmPlanarComplexSm100
|
||||
>::CollectiveOp;
|
||||
|
||||
using GemmKernel = cutlass::gemm::kernel::GemmUniversal<
|
||||
Shape<int,int,int,int>,
|
||||
CollectiveMainloop,
|
||||
CollectiveEpilogue,
|
||||
cutlass::gemm::StreamKScheduler
|
||||
>;
|
||||
|
||||
using Gemm = cutlass::gemm::device::GemmUniversalAdapter<GemmKernel>;
|
||||
EXPECT_TRUE(test::gemm::device::TestPlanarComplexSmall<Gemm>());
|
||||
}
|
||||
|
||||
#endif
|
||||
+165
@@ -0,0 +1,165 @@
|
||||
/***************************************************************************************************
|
||||
* Copyright (c) 2023 - 2026 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 GEMM interface
|
||||
*/
|
||||
|
||||
#include <iostream>
|
||||
|
||||
#include "cutlass/cutlass.h"
|
||||
#include "cute/tensor.hpp"
|
||||
#include "cute/atom/mma_atom.hpp"
|
||||
#include "cutlass/numeric_types.h"
|
||||
#include "cutlass/arch/mma_sm100.h"
|
||||
#include "cutlass/gemm/device/gemm_universal_adapter.h"
|
||||
#include "cutlass/gemm/kernel/gemm_universal.hpp"
|
||||
#include "cutlass/gemm/collective/collective_builder.hpp"
|
||||
#include "cutlass/epilogue/dispatch_policy.hpp"
|
||||
#include "cutlass/epilogue/collective/collective_builder.hpp"
|
||||
#include "cute/atom/mma_traits_sm100.hpp"
|
||||
#include "../../common/cutlass_unit_test.h"
|
||||
#include "gemm_testbed_3x_planar_complex.hpp"
|
||||
|
||||
using namespace cute;
|
||||
|
||||
#if defined(CUTLASS_ARCH_MMA_SM100_SUPPORTED)
|
||||
|
||||
///////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
////////////////////////////////////// 128x128x64_0x0x1 TCGEN05.1CTA //////////////////////////////////////////////////
|
||||
///////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
// TNT
|
||||
TEST(SM100_Device_Gemm_Planar_cf16t_cf16n_f32t_tensorop_1sm_preferred_cluster, 128x128x64_0x0x1) {
|
||||
using ElementA = cutlass::half_t;
|
||||
using TransformA = cute::identity;
|
||||
using ElementPairA = cute::tuple<ElementA, TransformA>;
|
||||
using LayoutA = cutlass::layout::RowMajor;
|
||||
|
||||
using ElementB = cutlass::half_t;
|
||||
using TransformB = cute::identity;
|
||||
using ElementPairB = cute::tuple<ElementB, TransformB>;
|
||||
using LayoutB = cutlass::layout::ColumnMajor;
|
||||
|
||||
using ElementAccumulator = float;
|
||||
using LayoutC = cutlass::layout::RowMajor;
|
||||
|
||||
using MmaTileShape = cute::Shape<_128,_128,_64>;
|
||||
using ClusterShape = cute::Shape<int,int,_1>; // Runtime cluster shape
|
||||
|
||||
using CollectiveEpilogue = typename cutlass::epilogue::collective::CollectiveBuilder<
|
||||
cutlass::arch::Sm100, cutlass::arch::OpClassTensorOp,
|
||||
MmaTileShape, ClusterShape,
|
||||
cutlass::epilogue::collective::EpilogueTileAuto,
|
||||
float, float,
|
||||
cutlass::half_t, LayoutC, 8,
|
||||
cutlass::half_t, LayoutC, 8,
|
||||
cutlass::epilogue::PlanarComplexTmaWarpSpecialized1Sm
|
||||
>::CollectiveOp;
|
||||
|
||||
using CollectiveMainloop = typename cutlass::gemm::collective::CollectiveBuilder<
|
||||
cutlass::arch::Sm100, cutlass::arch::OpClassTensorOp,
|
||||
ElementPairA, LayoutA, 8,
|
||||
ElementPairB, LayoutB, 8,
|
||||
ElementAccumulator,
|
||||
MmaTileShape, ClusterShape,
|
||||
cutlass::gemm::collective::StageCountAutoCarveout<
|
||||
static_cast<int>(sizeof(typename CollectiveEpilogue::SharedStorage))>,
|
||||
cutlass::gemm::KernelTmaWarpSpecialized1SmPlanarComplexSm100
|
||||
>::CollectiveOp;
|
||||
|
||||
using GemmKernel = cutlass::gemm::kernel::GemmUniversal<
|
||||
Shape<int,int,int,int>,
|
||||
CollectiveMainloop,
|
||||
CollectiveEpilogue
|
||||
>;
|
||||
|
||||
using Gemm = cutlass::gemm::device::GemmUniversalAdapter<GemmKernel>;
|
||||
EXPECT_TRUE(test::gemm::device::TestPlanarComplexPreferredClusterSmall<Gemm>());
|
||||
}
|
||||
|
||||
///////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
////////////////////////////////////// 256x128x64_0x0x1 MMA.2SM ///////////////////////////////////////////////////////
|
||||
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
// NTN
|
||||
TEST(SM100_Device_Gemm_Planar_cf16n_cf16t_f32n_tensorop_2sm_preferred_cluster, 256x128x64_0x0x1) {
|
||||
using ElementA = cutlass::half_t;
|
||||
using TransformA = cute::identity;
|
||||
using ElementPairA = cute::tuple<ElementA, TransformA>;
|
||||
using LayoutA = cutlass::layout::ColumnMajor;
|
||||
|
||||
using ElementB = cutlass::half_t;
|
||||
using TransformB = cute::identity;
|
||||
using ElementPairB = cute::tuple<ElementB, TransformB>;
|
||||
using LayoutB = cutlass::layout::RowMajor;
|
||||
|
||||
using ElementAccumulator = float;
|
||||
using LayoutC = cutlass::layout::ColumnMajor;
|
||||
|
||||
using MmaTileShape = cute::Shape<_256,_128,_64>;
|
||||
using ClusterShape = cute::Shape<int,int,_1>; // Runtime cluster shape
|
||||
|
||||
using CollectiveEpilogue = typename cutlass::epilogue::collective::CollectiveBuilder<
|
||||
cutlass::arch::Sm100, cutlass::arch::OpClassTensorOp,
|
||||
MmaTileShape, ClusterShape,
|
||||
cutlass::epilogue::collective::EpilogueTileAuto,
|
||||
float, float,
|
||||
cutlass::half_t, LayoutC, 8,
|
||||
cutlass::half_t, LayoutC, 8,
|
||||
cutlass::epilogue::PlanarComplexTmaWarpSpecialized2Sm
|
||||
>::CollectiveOp;
|
||||
|
||||
using CollectiveMainloop = typename cutlass::gemm::collective::CollectiveBuilder<
|
||||
cutlass::arch::Sm100, cutlass::arch::OpClassTensorOp,
|
||||
ElementPairA, LayoutA, 8,
|
||||
ElementPairB, LayoutB, 8,
|
||||
ElementAccumulator,
|
||||
MmaTileShape, ClusterShape,
|
||||
cutlass::gemm::collective::StageCountAutoCarveout<
|
||||
static_cast<int>(sizeof(typename CollectiveEpilogue::SharedStorage))>,
|
||||
// 2Sm preferred cluster kernel should directly use 2Sm KernelSchedule for now
|
||||
cutlass::gemm::KernelTmaWarpSpecialized2SmPlanarComplexSm100
|
||||
>::CollectiveOp;
|
||||
|
||||
using GemmKernel = cutlass::gemm::kernel::GemmUniversal<
|
||||
Shape<int,int,int,int>,
|
||||
CollectiveMainloop,
|
||||
CollectiveEpilogue
|
||||
>;
|
||||
|
||||
using Gemm = cutlass::gemm::device::GemmUniversalAdapter<GemmKernel>;
|
||||
EXPECT_TRUE(test::gemm::device::TestPlanarComplexPreferredClusterSmall<Gemm>());
|
||||
}
|
||||
|
||||
#endif // #if defined(CUTLASS_ARCH_MMA_SM100_SUPPORTED)
|
||||
@@ -0,0 +1,282 @@
|
||||
/***************************************************************************************************
|
||||
* Copyright (c) 2023 - 2026 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 Ptr-Array GEMM interface
|
||||
*/
|
||||
|
||||
|
||||
|
||||
#include <iostream>
|
||||
|
||||
#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/gemm/kernel/tile_scheduler.hpp"
|
||||
#include "cutlass/gemm/collective/collective_builder.hpp"
|
||||
#include "cutlass/epilogue/collective/collective_builder.hpp"
|
||||
#include "cutlass/epilogue/collective/sm70_epilogue_vectorized.hpp"
|
||||
#include "cutlass/epilogue/collective/default_epilogue.hpp"
|
||||
#include "cutlass/epilogue/thread/linear_combination.h"
|
||||
|
||||
#include "../../common/cutlass_unit_test.h"
|
||||
|
||||
#include "gemm_testbed_3x_ptr_array_planar_complex.hpp"
|
||||
|
||||
using namespace cute;
|
||||
|
||||
#if defined(CUTLASS_ARCH_MMA_SM100_SUPPORTED)
|
||||
|
||||
///////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
////////// CTA_TILE_SIZE : 128x128x64, CLUSTER_SIZE : 1x1x1, CLUSTER_TILE_SIZE : 128x128x64, TCGEN05.1CTA /////////////
|
||||
///////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
// TN
|
||||
TEST(SM100_Device_Gemm_Planar_cf16t_cf16n_f32n_tensorop_ptr_array_1sm, 128x128x64_1x1x1) {
|
||||
using ElementA = cutlass::half_t;
|
||||
using TransformA = cute::identity;
|
||||
using ElementPairA = cute::tuple<ElementA, TransformA>;
|
||||
using LayoutA = cutlass::layout::RowMajor;
|
||||
|
||||
using ElementB = cutlass::half_t;
|
||||
using TransformB = cute::identity;
|
||||
using ElementPairB = cute::tuple<ElementB, TransformB>;
|
||||
using LayoutB = cutlass::layout::ColumnMajor;
|
||||
|
||||
using ElementAccumulator = float;
|
||||
using LayoutC = cutlass::layout::ColumnMajor;
|
||||
|
||||
using MmaTileShape = cute::Shape<_128,_128,_64>;
|
||||
using ClusterShape = Shape<_1,_1,_1>;
|
||||
|
||||
using MainloopSchedule = cutlass::gemm::KernelPtrArrayTmaWarpSpecialized1SmPlanarComplexSm100; // Kernel to launch
|
||||
using EpilogueSchedule = cutlass::epilogue::PtrArrayPlanarComplexTmaWarpSpecialized1Sm; // Epilogue to launch
|
||||
|
||||
using CollectiveEpilogue = typename cutlass::epilogue::collective::CollectiveBuilder<
|
||||
cutlass::arch::Sm100, cutlass::arch::OpClassTensorOp,
|
||||
MmaTileShape, ClusterShape,
|
||||
cutlass::epilogue::collective::EpilogueTileAuto,
|
||||
float, float,
|
||||
cutlass::half_t, LayoutC, 8,
|
||||
cutlass::half_t, LayoutC, 8,
|
||||
EpilogueSchedule
|
||||
>::CollectiveOp;
|
||||
|
||||
using CollectiveMainloop = typename cutlass::gemm::collective::CollectiveBuilder<
|
||||
cutlass::arch::Sm100, cutlass::arch::OpClassTensorOp,
|
||||
ElementPairA, LayoutA, 8,
|
||||
ElementPairB, LayoutB, 8,
|
||||
ElementAccumulator,
|
||||
MmaTileShape, ClusterShape,
|
||||
cutlass::gemm::collective::StageCountAutoCarveout<
|
||||
static_cast<int>(sizeof(typename CollectiveEpilogue::SharedStorage))>,
|
||||
MainloopSchedule
|
||||
>::CollectiveOp;
|
||||
|
||||
using GemmKernel = cutlass::gemm::kernel::GemmUniversal<
|
||||
cutlass::gemm::ArrayProblemShape<Shape<int,int,int,int>>,
|
||||
CollectiveMainloop,
|
||||
CollectiveEpilogue
|
||||
>;
|
||||
|
||||
using Gemm = cutlass::gemm::device::GemmUniversalAdapter<GemmKernel>;
|
||||
EXPECT_TRUE(test::gemm::device::TestPlanarComplexSmall<Gemm>());
|
||||
}
|
||||
|
||||
///////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
/////////// CTA_TILE_SIZE : 64x64x64, CLUSTER_SIZE : 2x2x1, CLUSTER_TILE_SIZE : 128x128x64, TCGEN05.1CTA //////////////
|
||||
///////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
// NT
|
||||
TEST(SM100_Device_Gemm_Planar_cf16n_cf16t_f32n_tensorop_ptr_array_1sm, 128x128x64_2x2x1) {
|
||||
using ElementA = cutlass::half_t;
|
||||
using TransformA = cute::identity;
|
||||
using ElementPairA = cute::tuple<ElementA, TransformA>;
|
||||
using LayoutA = cutlass::layout::ColumnMajor;
|
||||
|
||||
using ElementB = cutlass::half_t;
|
||||
using TransformB = cute::identity;
|
||||
using ElementPairB = cute::tuple<ElementB, TransformB>;
|
||||
using LayoutB = cutlass::layout::RowMajor;
|
||||
|
||||
using ElementAccumulator = float;
|
||||
using LayoutC = cutlass::layout::ColumnMajor;
|
||||
|
||||
using MmaTileShape = cute::Shape<_64,_64,_64>;
|
||||
using ClusterShape = Shape<_2,_2,_1>;
|
||||
|
||||
using MainloopSchedule = cutlass::gemm::KernelPtrArrayTmaWarpSpecialized1SmPlanarComplexSm100; // Kernel to launch
|
||||
using EpilogueSchedule = cutlass::epilogue::PtrArrayPlanarComplexTmaWarpSpecialized1Sm; // Epilogue to launch
|
||||
|
||||
using CollectiveEpilogue = typename cutlass::epilogue::collective::CollectiveBuilder<
|
||||
cutlass::arch::Sm100, cutlass::arch::OpClassTensorOp,
|
||||
MmaTileShape, ClusterShape,
|
||||
cutlass::epilogue::collective::EpilogueTileAuto,
|
||||
float, float,
|
||||
cutlass::half_t, LayoutC, 8,
|
||||
cutlass::half_t, LayoutC, 8,
|
||||
EpilogueSchedule
|
||||
>::CollectiveOp;
|
||||
|
||||
using CollectiveMainloop = typename cutlass::gemm::collective::CollectiveBuilder<
|
||||
cutlass::arch::Sm100, cutlass::arch::OpClassTensorOp,
|
||||
ElementPairA, LayoutA, 8,
|
||||
ElementPairB, LayoutB, 8,
|
||||
ElementAccumulator,
|
||||
MmaTileShape, ClusterShape,
|
||||
cutlass::gemm::collective::StageCountAutoCarveout<
|
||||
static_cast<int>(sizeof(typename CollectiveEpilogue::SharedStorage))>,
|
||||
MainloopSchedule
|
||||
>::CollectiveOp;
|
||||
|
||||
using GemmKernel = cutlass::gemm::kernel::GemmUniversal<
|
||||
cutlass::gemm::ArrayProblemShape<Shape<int,int,int,int>>,
|
||||
CollectiveMainloop,
|
||||
CollectiveEpilogue
|
||||
>;
|
||||
|
||||
using Gemm = cutlass::gemm::device::GemmUniversalAdapter<GemmKernel>;
|
||||
EXPECT_TRUE(test::gemm::device::TestPlanarComplexSmall<Gemm>());
|
||||
}
|
||||
|
||||
///////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
/////////// CTA_TILE_SIZE : 64x64x64, CLUSTER_SIZE : 2x1x1, CLUSTER_TILE_SIZE : 128x64x64, TCGEN05.2CTA ///////////////
|
||||
///////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
// TT
|
||||
TEST(SM100_Device_Gemm_Planar_cf16t_cf16t_f32n_tensorop_ptr_array_2sm, 128x64x64_2x1x1) {
|
||||
using ElementA = cutlass::half_t;
|
||||
using TransformA = cute::identity;
|
||||
using ElementPairA = cute::tuple<ElementA, TransformA>;
|
||||
using LayoutA = cutlass::layout::RowMajor;
|
||||
|
||||
using ElementB = cutlass::half_t;
|
||||
using TransformB = cute::identity;
|
||||
using ElementPairB = cute::tuple<ElementB, TransformB>;
|
||||
using LayoutB = cutlass::layout::RowMajor;
|
||||
|
||||
using ElementAccumulator = float;
|
||||
using LayoutC = cutlass::layout::ColumnMajor;
|
||||
|
||||
using MmaTileShape = cute::Shape<_128,_64,_64>;
|
||||
using ClusterShape = Shape<_2,_1,_1>;
|
||||
|
||||
using MainloopSchedule = cutlass::gemm::KernelPtrArrayTmaWarpSpecialized2SmPlanarComplexSm100; // Kernel to launch
|
||||
using EpilogueSchedule = cutlass::epilogue::PtrArrayPlanarComplexTmaWarpSpecialized2Sm; // Epilogue to launch
|
||||
|
||||
using CollectiveEpilogue = typename cutlass::epilogue::collective::CollectiveBuilder<
|
||||
cutlass::arch::Sm100, cutlass::arch::OpClassTensorOp,
|
||||
MmaTileShape, ClusterShape,
|
||||
cutlass::epilogue::collective::EpilogueTileAuto,
|
||||
float, float,
|
||||
cutlass::half_t, LayoutC, 8,
|
||||
cutlass::half_t, LayoutC, 8,
|
||||
EpilogueSchedule
|
||||
>::CollectiveOp;
|
||||
|
||||
using CollectiveMainloop = typename cutlass::gemm::collective::CollectiveBuilder<
|
||||
cutlass::arch::Sm100, cutlass::arch::OpClassTensorOp,
|
||||
ElementPairA, LayoutA, 8,
|
||||
ElementPairB, LayoutB, 8,
|
||||
ElementAccumulator,
|
||||
MmaTileShape, ClusterShape,
|
||||
cutlass::gemm::collective::StageCountAutoCarveout<
|
||||
static_cast<int>(sizeof(typename CollectiveEpilogue::SharedStorage))>,
|
||||
MainloopSchedule
|
||||
>::CollectiveOp;
|
||||
|
||||
using GemmKernel = cutlass::gemm::kernel::GemmUniversal<
|
||||
cutlass::gemm::ArrayProblemShape<Shape<int,int,int,int>>,
|
||||
CollectiveMainloop,
|
||||
CollectiveEpilogue
|
||||
>;
|
||||
|
||||
using Gemm = cutlass::gemm::device::GemmUniversalAdapter<GemmKernel>;
|
||||
EXPECT_TRUE(test::gemm::device::TestPlanarComplexSmall<Gemm>());
|
||||
}
|
||||
|
||||
///////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
/////////// CTA_TILE_SIZE : 128x128x64, CLUSTER_SIZE : 4x4x1, CLUSTER_TILE_SIZE : 512x512x64, TCGEN05.2CTA ////////////
|
||||
///////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
// NN
|
||||
TEST(SM100_Device_Gemm_Planar_cf16n_cf16n_f32n_tensorop_ptr_array_2sm, 512x512x64_4x4x1) {
|
||||
using ElementA = cutlass::half_t;
|
||||
using TransformA = cute::identity;
|
||||
using ElementPairA = cute::tuple<ElementA, TransformA>;
|
||||
using LayoutA = cutlass::layout::ColumnMajor;
|
||||
|
||||
using ElementB = cutlass::half_t;
|
||||
using TransformB = cute::identity;
|
||||
using ElementPairB = cute::tuple<ElementB, TransformB>;
|
||||
using LayoutB = cutlass::layout::ColumnMajor;
|
||||
|
||||
using ElementAccumulator = float;
|
||||
using LayoutC = cutlass::layout::ColumnMajor;
|
||||
|
||||
using MmaTileShape = cute::Shape<_256,_128,_64>;
|
||||
using ClusterShape = Shape<_4,_4,_1>;
|
||||
|
||||
using MainloopSchedule = cutlass::gemm::KernelPtrArrayTmaWarpSpecialized2SmPlanarComplexSm100; // Kernel to launch
|
||||
using EpilogueSchedule = cutlass::epilogue::PtrArrayPlanarComplexTmaWarpSpecialized2Sm; // Epilogue to launch
|
||||
|
||||
using CollectiveEpilogue = typename cutlass::epilogue::collective::CollectiveBuilder<
|
||||
cutlass::arch::Sm100, cutlass::arch::OpClassTensorOp,
|
||||
MmaTileShape, ClusterShape,
|
||||
cutlass::epilogue::collective::EpilogueTileAuto,
|
||||
float, float,
|
||||
cutlass::half_t, LayoutC, 8,
|
||||
cutlass::half_t, LayoutC, 8,
|
||||
EpilogueSchedule
|
||||
>::CollectiveOp;
|
||||
|
||||
using CollectiveMainloop = typename cutlass::gemm::collective::CollectiveBuilder<
|
||||
cutlass::arch::Sm100, cutlass::arch::OpClassTensorOp,
|
||||
ElementPairA, LayoutA, 8,
|
||||
ElementPairB, LayoutB, 8,
|
||||
ElementAccumulator,
|
||||
MmaTileShape, ClusterShape,
|
||||
cutlass::gemm::collective::StageCountAutoCarveout<
|
||||
static_cast<int>(sizeof(typename CollectiveEpilogue::SharedStorage))>,
|
||||
MainloopSchedule
|
||||
>::CollectiveOp;
|
||||
|
||||
using GemmKernel = cutlass::gemm::kernel::GemmUniversal<
|
||||
cutlass::gemm::ArrayProblemShape<Shape<int,int,int,int>>,
|
||||
CollectiveMainloop,
|
||||
CollectiveEpilogue
|
||||
>;
|
||||
|
||||
using Gemm = cutlass::gemm::device::GemmUniversalAdapter<GemmKernel>;
|
||||
EXPECT_TRUE(test::gemm::device::TestPlanarComplexSmall<Gemm>());
|
||||
}
|
||||
|
||||
#endif // #if defined(CUTLASS_ARCH_MMA_SM100_SUPPORTED)
|
||||
@@ -306,14 +306,14 @@ public:
|
||||
|
||||
compressor_utility.structure_sparse_zero_mask_fill(datas.tensor_A.host_data(), seed + 6);
|
||||
|
||||
// Check for failed devide
|
||||
// Check for failed device
|
||||
CUDA_CHECK_FALSE(cudaGetLastError());
|
||||
|
||||
datas.tensor_A.sync_device();
|
||||
datas.tensor_A_Comp.sync_device();
|
||||
datas.tensor_E.sync_device();
|
||||
|
||||
// Check for failed devide
|
||||
// Check for failed device
|
||||
CUDA_CHECK_FALSE(cudaGetLastError());
|
||||
|
||||
return true;
|
||||
@@ -376,18 +376,6 @@ public:
|
||||
datas.tensor_A_Comp.sync_host();
|
||||
datas.tensor_E.sync_host();
|
||||
|
||||
#if 0
|
||||
{
|
||||
printf("\n--> DEVICE OUTPUT\n");
|
||||
printf("datas.tensor_A\n");
|
||||
std::cout << datas.tensor_A.host_view() << std::endl << std::endl;
|
||||
printf("datas.tensor_A_Comp\n");
|
||||
std::cout << datas.tensor_A_Comp.host_view() << std::endl << std::endl;
|
||||
printf("datas.tensor_E\n");
|
||||
std::cout << datas.tensor_E.host_view() << std::endl << std::endl;
|
||||
}
|
||||
#endif
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
@@ -816,9 +804,11 @@ public:
|
||||
printf("compare_reference() DEVICE <-> LEGACY HOST fail\n");
|
||||
return false;
|
||||
}
|
||||
// else {
|
||||
// printf("DEVICE <-> HOST PASS\n");
|
||||
// }
|
||||
#if 0
|
||||
else {
|
||||
printf("DEVICE <-> HOST PASS\n");
|
||||
}
|
||||
#endif
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
@@ -1,59 +0,0 @@
|
||||
# Copyright (c) 2025 - 2026 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.
|
||||
|
||||
|
||||
def _get_device_compute_capability():
|
||||
try:
|
||||
import cuda.bindings.driver as drv
|
||||
from cuda.bindings.driver import CUdevice_attribute as dev_attr
|
||||
|
||||
def drv_api(api_name, *args):
|
||||
ret_code, *result = getattr(drv, api_name)(*args)
|
||||
if ret_code:
|
||||
raise ValueError(f"CUDA error: {ret_code}")
|
||||
return result[0] if len(result) == 1 else result
|
||||
|
||||
drv_api("cuInit", 0)
|
||||
device = drv_api("cuDeviceGet", 0)
|
||||
major = drv_api(
|
||||
"cuDeviceGetAttribute",
|
||||
dev_attr.CU_DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR,
|
||||
device,
|
||||
)
|
||||
minor = drv_api(
|
||||
"cuDeviceGetAttribute",
|
||||
dev_attr.CU_DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR,
|
||||
device,
|
||||
)
|
||||
return f"{major}{minor}"
|
||||
except Exception as e:
|
||||
print(f"Failed to get CUDA compute capability: {e}")
|
||||
return None
|
||||
|
||||
|
||||
compute_capability = _get_device_compute_capability()
|
||||
Reference in New Issue
Block a user