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[Navi3x-LWPCK-545] Block-wise GEMM + Real GEMM_WMMA_FP16 (#541)
* wmma_op + unit test * add arch limitation to wmma test * change arch limitation * Refactor + Add all type unit test(int4 compile failed) * Add f32_16x16x16_bf16 unit test * tempsave * tempsave * tempsave * runtime bug, cannot find symbol * workaround for incorrect HIP warpSize return value * debugging * tempsave * Correctness OK, waiting for optimization * Tidy up + format * temp save * temp save, reproduce the v_bfi_b32 issue * add inline asm for wmmaop test * tidy up * clean some debug purpose code * discard some codes * clang format * clang format * compiler issue fixed + increase tile size
This commit is contained in:
Haocong WANG
571
include/ck/tensor_operation/gpu/device/impl/device_gemm_wmma.hpp
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571
include/ck/tensor_operation/gpu/device/impl/device_gemm_wmma.hpp
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@@ -0,0 +1,571 @@
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// SPDX-License-Identifier: MIT
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// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
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#pragma once
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#include <iostream>
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#include <sstream>
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#include "ck/utility/common_header.hpp"
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#include "ck/tensor_description/tensor_descriptor.hpp"
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#include "ck/tensor_description/tensor_descriptor_helper.hpp"
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#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
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#include "ck/tensor_operation/gpu/device/device_gemm.hpp"
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#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
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#include "ck/tensor_operation/gpu/grid/gridwise_gemm_wmma.hpp"
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#include "ck/host_utility/device_prop.hpp"
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#include "ck/host_utility/kernel_launch.hpp"
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namespace ck {
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namespace tensor_operation {
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namespace device {
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template <typename ALayout,
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typename BLayout,
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typename CLayout,
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typename ADataType,
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typename BDataType,
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typename CDataType,
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typename AccDataType,
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typename CShuffleDataType,
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typename AElementwiseOperation,
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typename BElementwiseOperation,
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typename CElementwiseOperation,
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GemmSpecialization GemmSpec,
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ck::index_t BlockSize,
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ck::index_t MPerBlock,
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ck::index_t NPerBlock,
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ck::index_t K0PerBlock,
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ck::index_t K1,
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ck::index_t MPerWMMA,
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ck::index_t NPerWMMA,
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ck::index_t MRepeat,
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ck::index_t NRepeat,
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typename ABlockTransferThreadClusterLengths_K0_M_K1,
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typename ABlockTransferThreadClusterArrangeOrder,
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typename ABlockTransferSrcAccessOrder,
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ck::index_t ABlockTransferSrcVectorDim,
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ck::index_t ABlockTransferSrcScalarPerVector,
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ck::index_t ABlockTransferDstScalarPerVector_K1,
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bool ABlockLdsAddExtraM,
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typename BBlockTransferThreadClusterLengths_K0_N_K1,
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typename BBlockTransferThreadClusterArrangeOrder,
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typename BBlockTransferSrcAccessOrder,
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ck::index_t BBlockTransferSrcVectorDim,
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ck::index_t BBlockTransferSrcScalarPerVector,
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ck::index_t BBlockTransferDstScalarPerVector_K1,
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bool BBlockLdsAddExtraN,
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index_t CShuffleMRepeatPerShuffle,
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index_t CShuffleNRepeatPerShuffle,
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typename CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
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index_t CShuffleBlockTransferScalarPerVector_NPerBlock,
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ck::index_t NumPrefetch = 1,
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ck::LoopScheduler LoopSched = make_default_loop_scheduler(),
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ck::PipelineVersion PipelineVer = ck::PipelineVersion::v1>
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struct DeviceGemmWmma_CShuffle : public DeviceGemm<ALayout,
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BLayout,
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CLayout,
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ADataType,
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BDataType,
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CDataType,
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AElementwiseOperation,
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BElementwiseOperation,
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CElementwiseOperation>
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{
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static constexpr auto I0 = Number<0>{};
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static constexpr auto I1 = Number<1>{};
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static constexpr auto I2 = Number<2>{};
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// K1 = Max Vector Access Pixels
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static constexpr auto K1Number = Number<K1>{};
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static auto MakeAGridDescriptor_K0_M_K1(index_t M, index_t K, index_t StrideA)
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{
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assert(K % K1 == 0);
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const index_t K0 = K / K1;
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const auto a_grid_desc_m_k = [&]() {
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if constexpr(is_same<tensor_layout::gemm::RowMajor, ALayout>::value)
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{
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return make_naive_tensor_descriptor(make_tuple(M, K), make_tuple(StrideA, I1));
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}
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#ifdef ENABLE_COLMAJOR
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else if constexpr(is_same<tensor_layout::gemm::ColumnMajor, ALayout>::value)
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{
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return make_naive_tensor_descriptor(make_tuple(M, K), make_tuple(I1, StrideA));
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}
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#endif
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}();
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if constexpr(GemmSpec == GemmSpecialization::MNPadding)
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{
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const auto PadM = (MPerBlock - M % MPerBlock) % MPerBlock;
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return transform_tensor_descriptor(
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a_grid_desc_m_k,
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make_tuple(make_unmerge_transform(make_tuple(K0, K1Number)),
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make_right_pad_transform(M, PadM)),
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make_tuple(Sequence<1>{}, Sequence<0>{}),
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make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
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}
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else
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{
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return transform_tensor_descriptor(
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a_grid_desc_m_k,
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make_tuple(make_unmerge_transform(make_tuple(K0, K1Number)),
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make_pass_through_transform(M)),
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make_tuple(Sequence<1>{}, Sequence<0>{}),
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make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
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}
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}
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static auto MakeBGridDescriptor_K0_N_K1(index_t K, index_t N, index_t StrideB)
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{
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assert(K % K1 == 0);
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const index_t K0 = K / K1;
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const auto b_grid_desc_k_n = [&]() {
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if constexpr(is_same<tensor_layout::gemm::RowMajor, BLayout>::value)
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{
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return make_naive_tensor_descriptor(make_tuple(K, N), make_tuple(StrideB, I1));
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}
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else if constexpr(is_same<tensor_layout::gemm::ColumnMajor, BLayout>::value)
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{
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return make_naive_tensor_descriptor(make_tuple(K, N), make_tuple(I1, StrideB));
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}
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}();
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if constexpr(GemmSpec == GemmSpecialization::MNPadding)
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{
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const auto PadN = (NPerBlock - N % NPerBlock) % NPerBlock;
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return transform_tensor_descriptor(
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b_grid_desc_k_n,
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make_tuple(make_unmerge_transform(make_tuple(K0, K1Number)),
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make_right_pad_transform(N, PadN)),
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make_tuple(Sequence<0>{}, Sequence<1>{}),
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make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
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}
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else
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{
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return transform_tensor_descriptor(
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b_grid_desc_k_n,
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make_tuple(make_unmerge_transform(make_tuple(K0, K1Number)),
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make_pass_through_transform(N)),
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make_tuple(Sequence<0>{}, Sequence<1>{}),
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make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
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}
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}
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static auto MakeCGridDescriptor_M_N(index_t M, index_t N, index_t StrideC)
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{
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const auto c_grid_desc_m_n = [&]() {
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if constexpr(is_same<tensor_layout::gemm::RowMajor, CLayout>::value)
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{
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return make_naive_tensor_descriptor(make_tuple(M, N), make_tuple(StrideC, I1));
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}
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else if constexpr(is_same<tensor_layout::gemm::ColumnMajor, CLayout>::value)
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{
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return make_naive_tensor_descriptor(make_tuple(M, N), make_tuple(I1, StrideC));
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}
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}();
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if constexpr(GemmSpec == GemmSpecialization::MNPadding)
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{
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const auto PadM = (MPerBlock - M % MPerBlock) % MPerBlock;
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const auto PadN = (NPerBlock - N % NPerBlock) % NPerBlock;
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return transform_tensor_descriptor(
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c_grid_desc_m_n,
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make_tuple(make_right_pad_transform(M, PadM), make_right_pad_transform(N, PadN)),
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make_tuple(Sequence<0>{}, Sequence<1>{}),
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make_tuple(Sequence<0>{}, Sequence<1>{}));
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}
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else
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{
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return transform_tensor_descriptor(
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c_grid_desc_m_n,
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make_tuple(make_pass_through_transform(M), make_pass_through_transform(N)),
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make_tuple(Sequence<0>{}, Sequence<1>{}),
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make_tuple(Sequence<0>{}, Sequence<1>{}));
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}
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}
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// Gridwise descriptor, mapping to whole given provblem.
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using AGridDesc_K0_M_K1 = decltype(MakeAGridDescriptor_K0_M_K1(1, 1, 1));
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using BGridDesc_K0_N_K1 = decltype(MakeBGridDescriptor_K0_N_K1(1, 1, 1));
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using CGridDesc_M_N = decltype(MakeCGridDescriptor_M_N(1, 1, 1));
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// GridwiseGemm
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using GridwiseGemm = GridwiseGemm_k0mk1_k0nk1_mn_wmma<
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BlockSize,
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ADataType,
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BDataType,
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AccDataType,
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CShuffleDataType,
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CDataType,
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InMemoryDataOperationEnum::Set,
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AGridDesc_K0_M_K1,
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BGridDesc_K0_N_K1,
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CGridDesc_M_N,
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AElementwiseOperation,
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BElementwiseOperation,
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CElementwiseOperation,
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MPerBlock,
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NPerBlock,
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K0PerBlock,
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MPerWMMA,
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NPerWMMA,
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K1,
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MRepeat,
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NRepeat,
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ABlockTransferThreadClusterLengths_K0_M_K1,
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ABlockTransferThreadClusterArrangeOrder,
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ABlockTransferSrcAccessOrder,
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ABlockTransferSrcVectorDim,
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ABlockTransferSrcScalarPerVector,
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ABlockTransferDstScalarPerVector_K1,
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false, // AThreadTransferSrcResetCoordinateAfterRun,
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ABlockLdsAddExtraM,
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BBlockTransferThreadClusterLengths_K0_N_K1,
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BBlockTransferThreadClusterArrangeOrder,
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BBlockTransferSrcAccessOrder,
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BBlockTransferSrcVectorDim,
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BBlockTransferSrcScalarPerVector,
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BBlockTransferDstScalarPerVector_K1,
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false, // BThreadTransferSrcResetCoordinateAfterRun,
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BBlockLdsAddExtraN,
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CShuffleMRepeatPerShuffle,
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CShuffleNRepeatPerShuffle,
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CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
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CShuffleBlockTransferScalarPerVector_NPerBlock,
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NumPrefetch,
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LoopSched,
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PipelineVer>;
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// Argument
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struct Argument : public BaseArgument
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{
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Argument(const ADataType* p_a_grid,
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const BDataType* p_b_grid,
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CDataType* p_c_grid,
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index_t M,
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index_t N,
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index_t K,
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index_t StrideA,
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index_t StrideB,
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index_t StrideC,
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index_t M01,
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index_t N01,
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AElementwiseOperation a_element_op,
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BElementwiseOperation b_element_op,
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CElementwiseOperation c_element_op)
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: p_a_grid_{p_a_grid},
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p_b_grid_{p_b_grid},
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p_c_grid_{p_c_grid},
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a_grid_desc_k0_m_k1_{},
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b_grid_desc_k0_n_k1_{},
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c_grid_desc_m_n_{},
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c_grid_desc_mblock_mperblock_nblock_nperblock{},
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block_2_ctile_map_{},
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M01_{M01},
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N01_{N01},
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a_element_op_{a_element_op},
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b_element_op_{b_element_op},
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c_element_op_{c_element_op}
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{
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a_grid_desc_k0_m_k1_ =
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DeviceGemmWmma_CShuffle::MakeAGridDescriptor_K0_M_K1(M, K, StrideA);
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b_grid_desc_k0_n_k1_ =
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DeviceGemmWmma_CShuffle::MakeBGridDescriptor_K0_N_K1(K, N, StrideB);
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c_grid_desc_m_n_ = DeviceGemmWmma_CShuffle::MakeCGridDescriptor_M_N(M, N, StrideC);
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block_2_ctile_map_ =
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GridwiseGemm::MakeDefaultBlock2CTileMap(c_grid_desc_m_n_, M01, N01);
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if(GridwiseGemm::CheckValidity(a_grid_desc_k0_m_k1_,
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b_grid_desc_k0_n_k1_,
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c_grid_desc_m_n_,
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block_2_ctile_map_))
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{
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c_grid_desc_mblock_mperblock_nblock_nperblock =
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GridwiseGemm::MakeCGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock(
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c_grid_desc_m_n_);
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}
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}
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// private:
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const ADataType* p_a_grid_;
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const BDataType* p_b_grid_;
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CDataType* p_c_grid_;
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AGridDesc_K0_M_K1 a_grid_desc_k0_m_k1_;
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BGridDesc_K0_N_K1 b_grid_desc_k0_n_k1_;
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CGridDesc_M_N c_grid_desc_m_n_;
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typename GridwiseGemm::CGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock
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c_grid_desc_mblock_mperblock_nblock_nperblock;
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typename GridwiseGemm::DefaultBlock2CTileMap block_2_ctile_map_;
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index_t M01_;
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index_t N01_;
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AElementwiseOperation a_element_op_;
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BElementwiseOperation b_element_op_;
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CElementwiseOperation c_element_op_;
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};
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// Invoker
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struct Invoker : public BaseInvoker
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{
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using Argument = DeviceGemmWmma_CShuffle::Argument;
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float Run(const Argument& arg, const StreamConfig& stream_config = StreamConfig{})
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{
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#if 0
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{
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std::cout << "arg.a_grid_desc_k0_m_k1_{" << arg.a_grid_desc_k0_m_k1_.GetLength(I0)
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<< ", " << arg.a_grid_desc_k0_m_k1_.GetLength(I1) << ", "
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<< arg.a_grid_desc_k0_m_k1_.GetLength(I2) << "}" << std::endl;
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std::cout << "arg.b_grid_desc_k0_n_k1_{" << arg.b_grid_desc_k0_n_k1_.GetLength(I0)
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<< ", " << arg.b_grid_desc_k0_n_k1_.GetLength(I1) << ", "
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<< arg.b_grid_desc_k0_n_k1_.GetLength(I2) << "}" << std::endl;
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std::cout << "arg.c_grid_desc_m_n_{ " << arg.c_grid_desc_m_n_.GetLength(I0)
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<< ", " << arg.c_grid_desc_m_n_.GetLength(I1) << ", "
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<< arg.c_grid_desc_m_n_.GetLength(I2) << "}" << std::endl;
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}
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#endif
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if(!GridwiseGemm::CheckValidity(arg.a_grid_desc_k0_m_k1_,
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arg.b_grid_desc_k0_n_k1_,
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arg.c_grid_desc_m_n_,
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arg.block_2_ctile_map_))
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{
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throw std::runtime_error(
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"wrong! GridwiseGemm_k0mk1_k0nk1_m0nm1_wmma_v1r1 has invalid setting");
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}
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const index_t grid_size =
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arg.block_2_ctile_map_.CalculateGridSize(arg.c_grid_desc_m_n_);
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const auto K =
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arg.a_grid_desc_k0_m_k1_.GetLength(I0) * arg.a_grid_desc_k0_m_k1_.GetLength(I2);
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float ave_time = 0;
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if(GridwiseGemm::CalculateHasMainKBlockLoop(K))
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{
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const auto kernel = kernel_gemm_wmma<
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GridwiseGemm,
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ADataType,
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BDataType,
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CDataType,
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remove_reference_t<DeviceGemmWmma_CShuffle::AGridDesc_K0_M_K1>,
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remove_reference_t<DeviceGemmWmma_CShuffle::BGridDesc_K0_N_K1>,
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remove_reference_t<
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typename GridwiseGemm::CGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock>,
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AElementwiseOperation,
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BElementwiseOperation,
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CElementwiseOperation,
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remove_reference_t<typename GridwiseGemm::DefaultBlock2CTileMap>,
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true>; // Last Option is W/O
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ave_time = launch_and_time_kernel(stream_config,
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kernel,
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dim3(grid_size),
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dim3(BlockSize),
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0,
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arg.p_a_grid_,
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arg.p_b_grid_,
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arg.p_c_grid_,
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arg.a_grid_desc_k0_m_k1_,
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arg.b_grid_desc_k0_n_k1_,
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arg.c_grid_desc_mblock_mperblock_nblock_nperblock,
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arg.a_element_op_,
|
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arg.b_element_op_,
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arg.c_element_op_,
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arg.block_2_ctile_map_);
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}
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else
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{
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const auto kernel = kernel_gemm_wmma<
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GridwiseGemm,
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ADataType,
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||||
BDataType,
|
||||
CDataType,
|
||||
remove_reference_t<DeviceGemmWmma_CShuffle::AGridDesc_K0_M_K1>,
|
||||
remove_reference_t<DeviceGemmWmma_CShuffle::BGridDesc_K0_N_K1>,
|
||||
remove_reference_t<
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typename GridwiseGemm::CGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock>,
|
||||
AElementwiseOperation,
|
||||
BElementwiseOperation,
|
||||
CElementwiseOperation,
|
||||
remove_reference_t<typename GridwiseGemm::DefaultBlock2CTileMap>,
|
||||
false>;
|
||||
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||||
ave_time = launch_and_time_kernel(stream_config,
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||||
kernel,
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||||
dim3(grid_size),
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||||
dim3(BlockSize),
|
||||
0,
|
||||
arg.p_a_grid_,
|
||||
arg.p_b_grid_,
|
||||
arg.p_c_grid_,
|
||||
arg.a_grid_desc_k0_m_k1_,
|
||||
arg.b_grid_desc_k0_n_k1_,
|
||||
arg.c_grid_desc_mblock_mperblock_nblock_nperblock,
|
||||
arg.a_element_op_,
|
||||
arg.b_element_op_,
|
||||
arg.c_element_op_,
|
||||
arg.block_2_ctile_map_);
|
||||
}
|
||||
|
||||
return ave_time;
|
||||
}
|
||||
|
||||
// polymorphic
|
||||
float Run(const BaseArgument* p_arg,
|
||||
const StreamConfig& stream_config = StreamConfig{}) override
|
||||
{
|
||||
return Run(*dynamic_cast<const Argument*>(p_arg), stream_config);
|
||||
}
|
||||
};
|
||||
|
||||
static constexpr bool IsValidCompilationParameter()
|
||||
{
|
||||
// TODO: properly implement this check
|
||||
return true;
|
||||
}
|
||||
|
||||
static bool IsSupportedArgument(const Argument& arg)
|
||||
{
|
||||
if(ck::get_device_name() == "gfx1100")
|
||||
{
|
||||
if constexpr(!(is_same_v<AccDataType, float> || is_same_v<AccDataType, int32_t>))
|
||||
{
|
||||
return false;
|
||||
}
|
||||
}
|
||||
else
|
||||
{
|
||||
return false;
|
||||
}
|
||||
|
||||
return GridwiseGemm::CheckValidity(arg.a_grid_desc_k0_m_k1_,
|
||||
arg.b_grid_desc_k0_n_k1_,
|
||||
arg.c_grid_desc_m_n_,
|
||||
arg.block_2_ctile_map_);
|
||||
}
|
||||
|
||||
// polymorphic
|
||||
bool IsSupportedArgument(const BaseArgument* p_arg) override
|
||||
{
|
||||
return IsSupportedArgument(*dynamic_cast<const Argument*>(p_arg));
|
||||
}
|
||||
|
||||
static auto MakeArgument(const ADataType* p_a,
|
||||
const BDataType* p_b,
|
||||
CDataType* p_c,
|
||||
index_t M,
|
||||
index_t N,
|
||||
index_t K,
|
||||
index_t StrideA,
|
||||
index_t StrideB,
|
||||
index_t StrideC,
|
||||
AElementwiseOperation a_element_op,
|
||||
BElementwiseOperation b_element_op,
|
||||
CElementwiseOperation c_element_op)
|
||||
{
|
||||
return Argument{p_a,
|
||||
p_b,
|
||||
p_c,
|
||||
M,
|
||||
N,
|
||||
K,
|
||||
StrideA,
|
||||
StrideB,
|
||||
StrideC,
|
||||
1,
|
||||
1,
|
||||
a_element_op,
|
||||
b_element_op,
|
||||
c_element_op};
|
||||
}
|
||||
|
||||
static auto MakeInvoker() { return Invoker{}; }
|
||||
|
||||
// polymorphic
|
||||
std::unique_ptr<BaseArgument> MakeArgumentPointer(const void* p_a,
|
||||
const void* p_b,
|
||||
void* p_c,
|
||||
index_t M,
|
||||
index_t N,
|
||||
index_t K,
|
||||
index_t StrideA,
|
||||
index_t StrideB,
|
||||
index_t StrideC,
|
||||
AElementwiseOperation a_element_op,
|
||||
BElementwiseOperation b_element_op,
|
||||
CElementwiseOperation c_element_op) override
|
||||
{
|
||||
return std::make_unique<Argument>(static_cast<const ADataType*>(p_a),
|
||||
static_cast<const BDataType*>(p_b),
|
||||
static_cast<CDataType*>(p_c),
|
||||
M,
|
||||
N,
|
||||
K,
|
||||
StrideA,
|
||||
StrideB,
|
||||
StrideC,
|
||||
1,
|
||||
1,
|
||||
a_element_op,
|
||||
b_element_op,
|
||||
c_element_op);
|
||||
}
|
||||
|
||||
// polymorphic
|
||||
std::unique_ptr<BaseInvoker> MakeInvokerPointer() override
|
||||
{
|
||||
return std::make_unique<Invoker>(Invoker{});
|
||||
}
|
||||
|
||||
// polymorphic
|
||||
std::string GetTypeString() const override
|
||||
{
|
||||
auto str = std::stringstream();
|
||||
|
||||
std::map<LoopScheduler, std::string> LoopSchedToString{
|
||||
{LoopScheduler::Default, "Default"}, {LoopScheduler::Interwave, "Interwave"}};
|
||||
|
||||
std::map<PipelineVersion, std::string> PipelineVersionToString{{PipelineVersion::v1, "v1"},
|
||||
{PipelineVersion::v2, "v2"}};
|
||||
|
||||
// clang-format off
|
||||
str << "DeviceGemmWmma_CShuffle"
|
||||
<< "<"
|
||||
<< BlockSize << ", "
|
||||
<< MPerBlock << ", "
|
||||
<< NPerBlock << ", "
|
||||
<< K0PerBlock << ", "
|
||||
<< K1 << ", "
|
||||
<< MPerWMMA << ", "
|
||||
<< NPerWMMA << ", "
|
||||
<< MRepeat << ", "
|
||||
<< NRepeat
|
||||
<< ">"
|
||||
<< " NumPrefetch: "
|
||||
<< NumPrefetch << ", "
|
||||
<< "LoopScheduler: "
|
||||
<< LoopSchedToString[LoopSched] << ", "
|
||||
<< "PipelineVersion: "
|
||||
<< PipelineVersionToString[PipelineVer];
|
||||
// clang-format on
|
||||
|
||||
return str.str();
|
||||
}
|
||||
};
|
||||
|
||||
} // namespace device
|
||||
} // namespace tensor_operation
|
||||
} // namespace ck
|
||||
Reference in New Issue
Block a user