mirror of
https://github.com/ROCm/composable_kernel.git
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f5708882a3
* Add initial fp16_mem_128x128x32_2x2x1_32x32x16_NonPersistent test suite * Account for stride when computing K offsets for A and B tensor This change ensures that the correct stride is used when computing the K offsets into the A and B tensors in the Stream-K Kernel's operator() function. This ensures that the kernel executes correct regardless of whether A and B are row or column major. * Move helper code to test_gemm_streamk_util.hpp * Separate tests into smoke/regression/extended. Add bf16 datatype * Run clang-format * Refactor combinatorial macro expansion and naming * Adjust the initialization values to account for better tolerance on bf16 * Correct BF16 datatypes in comments * Move the extended tests under the REGRESSION_TESTS label * Apply suggestions from code review Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> --------- Co-authored-by: Emily Martins <emily.martins@amd.com> Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
270 lines
13 KiB
C++
270 lines
13 KiB
C++
// Copyright © Advanced Micro Devices, Inc., or its affiliates.
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// SPDX-License-Identifier: MIT
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#pragma once
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#include <gtest/gtest.h>
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#include <hip/hip_runtime.h>
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#include <iostream>
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#include <string>
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#include <tuple>
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#include "ck_tile/host.hpp"
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#include "ck_tile/ops/epilogue.hpp"
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#include "ck_tile/ops/gemm.hpp"
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#include "test_gemm_streamk_util.hpp"
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template <typename Tuple>
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class TestCkTileStreamK : public ::testing::Test
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{
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protected:
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using ALayout = std::tuple_element_t<0, Tuple>;
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using BLayout = std::tuple_element_t<1, Tuple>;
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using CLayout = std::tuple_element_t<2, Tuple>;
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using ADataType = std::tuple_element_t<3, Tuple>;
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using BDataType = std::tuple_element_t<4, Tuple>;
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using AccDataType = std::tuple_element_t<5, Tuple>;
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using CDataType = std::tuple_element_t<6, Tuple>;
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using DsLayout = ck_tile::tuple<>;
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using DsDataType = ck_tile::tuple<>;
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static constexpr ck_tile::index_t M_Tile = std::tuple_element_t<7, Tuple>::value;
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static constexpr ck_tile::index_t N_Tile = std::tuple_element_t<8, Tuple>::value;
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static constexpr ck_tile::index_t K_Tile = std::tuple_element_t<9, Tuple>::value;
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static constexpr ck_tile::index_t M_Warp = std::tuple_element_t<10, Tuple>::value;
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static constexpr ck_tile::index_t N_Warp = std::tuple_element_t<11, Tuple>::value;
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static constexpr ck_tile::index_t K_Warp = std::tuple_element_t<12, Tuple>::value;
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static constexpr ck_tile::index_t M_Warp_Tile = std::tuple_element_t<13, Tuple>::value;
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static constexpr ck_tile::index_t N_Warp_Tile = std::tuple_element_t<14, Tuple>::value;
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static constexpr ck_tile::index_t K_Warp_Tile = std::tuple_element_t<15, Tuple>::value;
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static constexpr GemmPipelineType PipelineType = std::tuple_element_t<16, Tuple>::value;
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static constexpr bool Persistent = std::tuple_element_t<17, Tuple>::value;
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template <ck_tile::StreamKReductionStrategy ReductionStrategy,
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bool PadM = true,
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bool PadN = true,
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bool PadK = true,
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bool Preshuffle = false,
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bool TransposeC = false>
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bool invoke_streamk(const ck_tile::StreamKHostArgs& args,
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const ck_tile::stream_config& s,
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int num_cu,
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int occupancy)
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{
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constexpr bool kPadM = PadM;
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constexpr bool kPadN = PadN;
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constexpr bool kPadK = PadK;
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constexpr bool preshuffle = Preshuffle;
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constexpr bool DoubleSmemBuffer = false;
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constexpr int kBlockPerCu = 1;
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constexpr bool StructuredSparsity = false;
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constexpr bool NumWaveGroup = 1;
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using GemmShape =
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ck_tile::TileGemmShape<ck_tile::sequence<M_Tile, N_Tile, K_Tile>,
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ck_tile::sequence<M_Warp, N_Warp, K_Warp>,
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ck_tile::sequence<M_Warp_Tile, N_Warp_Tile, K_Warp_Tile>>;
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using TilePartitioner = ck_tile::StreamKTilePartitioner<GemmShape, ReductionStrategy>;
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using GemmUniversalTraits = ck_tile::TileGemmUniversalTraits<kPadM,
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kPadN,
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kPadK,
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DoubleSmemBuffer,
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ALayout,
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BLayout,
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CLayout,
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TransposeC,
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StructuredSparsity,
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false,
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NumWaveGroup,
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preshuffle>;
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const auto Run = [&](const auto memory_operation_) {
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constexpr auto memory_operation = memory_operation_.value;
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constexpr auto scheduler = ck_tile::GemmPipelineScheduler::Intrawave;
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// We create the GEMM pipeline without specifying has_hot_loop or tail_num.
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// This is because num_loop can vary (a) per WG and (b) per iteration of the Stream-K
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// while loop. Instead, has_hot_loop and tail_num are determined in the Stream-K
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// Kernel's RunGemm function. This is a similar pattern used by grouped GEMM.
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using UniversalGemmProblem = ck_tile::UniversalGemmPipelineProblem<ADataType,
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BDataType,
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AccDataType,
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GemmShape,
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GemmUniversalTraits,
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scheduler>;
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// For initial testing, we will just test with one pipeline.
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// More extensive testing is coming later and will test other pipelines.
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using GemmPipeline =
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typename GemmPipelineTypeSelector<PipelineType, UniversalGemmProblem>::pipeline;
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using GemmEpilogue = ck_tile::CShuffleEpilogue<
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ck_tile::CShuffleEpilogueProblem<ADataType,
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BDataType,
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ck_tile::tuple<>,
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AccDataType,
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CDataType,
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ck_tile::tuple<>,
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CLayout,
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ck_tile::element_wise::PassThrough,
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TilePartitioner::MPerBlock,
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TilePartitioner::NPerBlock,
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M_Warp,
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N_Warp,
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M_Warp_Tile,
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N_Warp_Tile,
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K_Warp_Tile,
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UniversalGemmProblem::TransposeC,
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memory_operation>>;
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using Kernel = ck_tile::StreamKKernel<TilePartitioner, GemmPipeline, GemmEpilogue>;
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auto kargs = Kernel::MakeKernelArgs(args, num_cu, occupancy);
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if(!Kernel::IsSupportedArgument(kargs))
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{
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return false;
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}
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dim3 grid_dims = Kernel::GridSize(kargs.tile_partitioner);
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dim3 block_dims = Kernel::BlockSize();
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ck_tile::launch_kernel(
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s, ck_tile::make_kernel<kBlockPerCu>(Kernel{}, grid_dims, block_dims, 0, kargs));
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return true;
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};
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return Run(ck_tile::integral_constant<ck_tile::memory_operation_enum,
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// Since we are doing stream K, in the case of
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// atomics, multiple workgroups may write to the same
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// output tile in the C tensor, so we must atomic add
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// the results (not set)
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ck_tile::memory_operation_enum::atomic_add>{});
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}
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public:
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// Since Stream-K is build on gfx9, the lower bound for CUs is 104. Thus, we default num_cu to
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// 104 and occupancy to 1 to ensure tests are reproducible on different architectures.
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void Run(ck_tile::index_t M,
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ck_tile::index_t N,
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ck_tile::index_t K,
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uint32_t num_sk_blocks = 0xffffffff,
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ck_tile::StreamKReductionStrategy reduction_strategy =
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ck_tile::StreamKReductionStrategy::Atomic,
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int occupancy = 1,
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int num_cu = 104,
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ck_tile::index_t stride_A = 0,
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ck_tile::index_t stride_B = 0,
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ck_tile::index_t stride_C = 0)
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{
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using namespace ck_tile::literals;
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if(reduction_strategy == ck_tile::StreamKReductionStrategy::Reduction)
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{
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throw std::runtime_error("Reduction Strategy is current unsupported!\n");
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}
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auto f_host_tensor_descriptor = [](std::size_t row,
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std::size_t col,
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std::size_t stride,
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auto layout) {
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if constexpr(std::is_same_v<decltype(layout), ck_tile::tensor_layout::gemm::RowMajor>)
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{
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return ck_tile::HostTensorDescriptor({row, col}, {stride, 1_uz});
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}
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else
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{
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return ck_tile::HostTensorDescriptor({row, col}, {1_uz, stride});
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}
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};
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auto f_get_default_stride =
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[](std::size_t row, std::size_t col, std::size_t stride, auto layout) {
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if(stride == 0)
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{
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if constexpr(std::is_same_v<decltype(layout),
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ck_tile::tensor_layout::gemm::RowMajor>)
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{
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return col;
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}
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else
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{
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return row;
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}
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}
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else
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return stride;
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};
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stride_A = f_get_default_stride(M, K, stride_A, ALayout{});
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stride_B = f_get_default_stride(K, N, stride_B, BLayout{});
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stride_C = f_get_default_stride(M, N, stride_C, CLayout{});
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ck_tile::HostTensor<ADataType> a_m_k(f_host_tensor_descriptor(M, K, stride_A, ALayout{}));
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ck_tile::HostTensor<BDataType> b_k_n(f_host_tensor_descriptor(K, N, stride_B, BLayout{}));
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ck_tile::HostTensor<CDataType> c_m_n_dev_result(
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f_host_tensor_descriptor(M, N, stride_C, CLayout{}));
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// TODO: Add randomized number generation ranges for different datatypes
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ck_tile::FillUniformDistributionIntegerValue<ADataType>{-3, 3, /*seed*/ 11939}(a_m_k);
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ck_tile::FillUniformDistributionIntegerValue<BDataType>{-3, 3, /*seed*/ 11940}(b_k_n);
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ck_tile::DeviceMem a_m_k_dev_buf(a_m_k.get_element_space_size_in_bytes());
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ck_tile::DeviceMem b_k_n_dev_buf(b_k_n.get_element_space_size_in_bytes());
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ck_tile::DeviceMem c_m_n_dev_buf(c_m_n_dev_result.get_element_space_size_in_bytes());
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a_m_k_dev_buf.ToDevice(a_m_k.data());
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b_k_n_dev_buf.ToDevice(b_k_n.data());
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c_m_n_dev_buf.SetZero();
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c_m_n_dev_result.SetZero();
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ck_tile::StreamKHostArgs args{a_m_k_dev_buf.GetDeviceBuffer(),
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b_k_n_dev_buf.GetDeviceBuffer(),
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c_m_n_dev_buf.GetDeviceBuffer(),
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M,
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N,
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K,
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stride_A,
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stride_B,
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stride_C,
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reduction_strategy,
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num_sk_blocks};
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if(!invoke_streamk<ck_tile::StreamKReductionStrategy::Atomic>(
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args, ck_tile::stream_config{nullptr, false, 0, 0, 1}, num_cu, occupancy))
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{
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GTEST_SKIP() << "Skipping this test: The kernel cannot solve the problem\n";
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}
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c_m_n_dev_buf.FromDevice(c_m_n_dev_result.data());
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ck_tile::HostTensor<CDataType> c_m_n_host_ref(
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f_host_tensor_descriptor(M, N, stride_C, CLayout{}));
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c_m_n_host_ref.SetZero();
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ck_tile::reference_gemm<ADataType, BDataType, AccDataType, CDataType>(
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a_m_k, b_k_n, c_m_n_host_ref);
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const float max_accumulated_value =
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*std::max_element(c_m_n_host_ref.mData.begin(), c_m_n_host_ref.mData.end());
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const auto rtol_atol = calculate_rtol_atol<ADataType, BDataType, AccDataType, CDataType>(
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K, /*kbatch*/ 1, max_accumulated_value);
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bool pass = ck_tile::check_err(c_m_n_dev_result,
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c_m_n_host_ref,
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"Error: Incorrect results!",
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rtol_atol.at(ck_tile::number<0>{}),
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rtol_atol.at(ck_tile::number<1>{}));
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EXPECT_TRUE(pass);
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};
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};
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