mirror of
https://github.com/ROCm/composable_kernel.git
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Gemm multiple d multiple r (#335)
* Imitate XXX_gemm_multiple_d, add XXX_gemm_multiple_d_multiple_r for gemm + reduction * Implement run of kernel * Add example * Fix parameter of typo * Rewrite the reduceMax example * Rewrite the reduceMean + reduceMeanSquare example * Refine naming * Refine folder name * refine naming * Rewrite the gemm + bias + relu + add + layernorm example * Rewrite the gemm + layernorm example * clang-format * Fix bug if sync lds * Fix compile error
This commit is contained in:
rocking5566
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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 "device_base.hpp"
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namespace ck {
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namespace tensor_operation {
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namespace device {
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// FIXME: DeviceGemmReduce type need to well define the problem
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template <typename ALayout,
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typename BLayout,
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typename DELayout,
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typename ADataType,
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typename BDataType,
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typename DsDataType,
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typename EDataType,
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typename RsDataType,
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typename AElementwiseOperation,
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typename BElementwiseOperation,
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typename CDEElementwiseOperation,
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typename QsElementwiseOperation,
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typename RsElementwiseOperation>
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struct DeviceGemmMultipleDMultipleR : public BaseOperator
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{
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static constexpr index_t NumDTensor = DsDataType::Size();
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static constexpr index_t NumRTensor = RsDataType::Size();
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virtual std::unique_ptr<BaseArgument>
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MakeArgumentPointer(const void* p_a,
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const void* p_b,
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std::array<const void*, NumDTensor> p_ds,
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void* p_e,
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std::array<void*, NumRTensor> p_rs,
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ck::index_t M,
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ck::index_t N,
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ck::index_t K,
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ck::index_t StrideA,
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ck::index_t StrideB,
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std::array<ck::index_t, NumDTensor> StrideDs,
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ck::index_t StrideE,
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AElementwiseOperation a_element_op,
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BElementwiseOperation b_element_op,
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CDEElementwiseOperation cde_element_op,
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QsElementwiseOperation qs_element_op,
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RsElementwiseOperation rs_element_op) = 0;
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virtual std::unique_ptr<BaseInvoker> MakeInvokerPointer() = 0;
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};
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template <typename ALayout,
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typename BLayout,
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typename DELayout,
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typename ADataType,
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typename BDataType,
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typename DsDataType,
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typename EDataType,
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typename RsDataType,
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typename AElementwiseOperation,
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typename BElementwiseOperation,
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typename CDEElementwiseOperation,
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typename QsElementwiseOperation,
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typename RsElementwiseOperation>
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using DeviceGemmMultipleDMultipleRPtr =
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std::unique_ptr<DeviceGemmMultipleDMultipleR<ALayout,
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BLayout,
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DELayout,
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ADataType,
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BDataType,
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DsDataType,
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EDataType,
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RsDataType,
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AElementwiseOperation,
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BElementwiseOperation,
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CDEElementwiseOperation,
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QsElementwiseOperation,
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RsElementwiseOperation>>;
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} // namespace device
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} // namespace tensor_operation
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} // namespace ck
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@@ -0,0 +1,873 @@
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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_multiple_d_multiple_r.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_multiple_d_multiple_r_xdl_cshuffle.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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template <typename GridwiseGemm,
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typename FloatAB,
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typename FloatDsPointer,
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typename FloatE,
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typename FloatRsPointer,
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typename AElementwiseOperation,
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typename BElementwiseOperation,
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typename CDEElementwiseOperation,
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typename QsElementwiseOperation,
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typename RsElementwiseOperation,
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typename AGridDesc_AK0_M_AK1,
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typename BGridDesc_BK0_N_BK1,
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typename DsGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock,
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typename EGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock,
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typename RsGridDescriptor_MBlock_MPerBlock,
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typename Block2ETileMap,
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bool HasMainKBlockLoop>
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__global__ void
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#if CK_USE_LAUNCH_BOUNDS
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__launch_bounds__(CK_MAX_THREAD_PER_BLOCK, CK_MIN_BLOCK_PER_CU)
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#endif
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kernel_gemm_multiple_d_multiple_r_xdl_cshuffle(
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const FloatAB* __restrict__ p_a_grid,
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const FloatAB* __restrict__ p_b_grid,
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FloatDsPointer p_ds_grid,
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FloatE* __restrict__ p_e_grid,
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FloatRsPointer p_rs_grid,
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const AElementwiseOperation a_element_op,
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const BElementwiseOperation b_element_op,
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const CDEElementwiseOperation cde_element_op,
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const QsElementwiseOperation qs_element_op,
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const RsElementwiseOperation rs_element_op,
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const AGridDesc_AK0_M_AK1 a_grid_desc_ak0_m_ak1,
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const BGridDesc_BK0_N_BK1 b_grid_desc_bk0_n_bk1,
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const DsGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock
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ds_grid_desc_mblock_mperblock_nblock_nperblock,
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const EGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock
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e_grid_desc_mblock_mperblock_nblock_nperblock,
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const RsGridDescriptor_MBlock_MPerBlock rs_grid_desc_mblock_mperblock,
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const Block2ETileMap block_2_etile_map)
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{
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#if(!defined(__HIP_DEVICE_COMPILE__) || defined(__gfx908__) || defined(__gfx90a__))
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__shared__ char p_shared[GridwiseGemm::GetSharedMemoryNumberOfByte()];
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GridwiseGemm::template Run<HasMainKBlockLoop>(p_a_grid,
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p_b_grid,
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p_ds_grid,
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p_e_grid,
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p_rs_grid,
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p_shared,
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a_element_op,
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b_element_op,
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cde_element_op,
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qs_element_op,
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rs_element_op,
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a_grid_desc_ak0_m_ak1,
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b_grid_desc_bk0_n_bk1,
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ds_grid_desc_mblock_mperblock_nblock_nperblock,
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e_grid_desc_mblock_mperblock_nblock_nperblock,
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rs_grid_desc_mblock_mperblock,
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block_2_etile_map);
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#else
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ignore = p_a_grid;
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ignore = p_b_grid;
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ignore = p_ds_grid;
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ignore = p_e_grid;
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ignore = p_rs_grid;
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ignore = a_element_op;
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ignore = b_element_op;
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ignore = cde_element_op;
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ignore = qs_element_op;
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ignore = rs_element_op;
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ignore = a_grid_desc_ak0_m_ak1;
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ignore = b_grid_desc_bk0_n_bk1;
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ignore = ds_grid_desc_mblock_mperblock_nblock_nperblock;
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ignore = e_grid_desc_mblock_mperblock_nblock_nperblock;
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ignore = rs_grid_desc_mblock_mperblock;
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ignore = block_2_etile_map;
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#endif
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}
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} // namespace ck
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namespace ck {
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namespace tensor_operation {
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namespace device {
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// GEMM:
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// input : A[AK0, M, AK1]
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// input : B[AK0, N, AK1]
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// input : D0[M, N], D1[M, N], ...
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// output : E[M, N]
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// output : R0[M], R1[M], ...
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// C = a_op(A) * b_op(B)
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// E = cde_op(C, D0, D1, ...)
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// Q0 = reduce0(q_op0(E)), Q1 = reduce1(q_op0(E)), ...
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// R0 = r_op0(Q0), R1 = r_op1(Q1), ...
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// Assume:
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// D0, D1, ... and E have the same layout
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template <typename ALayout,
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typename BLayout,
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typename DELayout,
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typename ADataType,
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typename BDataType,
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typename GemmAccDataType,
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typename CShuffleDataType,
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typename DsDataType,
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typename EDataType,
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typename ReduceAccDataType,
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typename RsDataType,
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typename AElementwiseOperation,
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typename BElementwiseOperation,
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typename CDEElementwiseOperation,
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typename QsElementwiseOperation,
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typename RsElementwiseOperation,
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typename ThreadReduceOperations,
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typename RsGlobalMemoryDataOperation,
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GemmSpecialization GemmSpec,
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index_t NumGemmKPrefetchStage,
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index_t BlockSize,
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index_t MPerBlock,
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index_t NPerBlock,
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index_t KPerBlock,
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index_t AK1,
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index_t BK1,
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index_t MPerXDL,
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index_t NPerXDL,
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index_t MXdlPerWave,
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index_t NXdlPerWave,
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typename ABlockTransferThreadClusterLengths_AK0_M_AK1,
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typename ABlockTransferThreadClusterArrangeOrder,
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typename ABlockTransferSrcAccessOrder,
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index_t ABlockTransferSrcVectorDim,
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index_t ABlockTransferSrcScalarPerVector,
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index_t ABlockTransferDstScalarPerVector_AK1,
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bool ABlockLdsExtraM,
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typename BBlockTransferThreadClusterLengths_BK0_N_BK1,
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typename BBlockTransferThreadClusterArrangeOrder,
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typename BBlockTransferSrcAccessOrder,
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index_t BBlockTransferSrcVectorDim,
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index_t BBlockTransferSrcScalarPerVector,
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index_t BBlockTransferDstScalarPerVector_BK1,
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bool BBlockLdsExtraN,
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index_t CShuffleMXdlPerWavePerShuffle,
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index_t CShuffleNXdlPerWavePerShuffle,
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typename CDRThreadTransferClusterLengths_MPerBlock_NPerBlock,
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index_t CDEReduceThreadTransferScalarPerVector_NPerBlock,
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index_t RThreadTransferDstScalarPerVector_MPerBlock,
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LoopScheduler LoopSched = make_default_loop_scheduler()>
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struct DeviceGemmMultipleDMultipleR_Xdl_CShuffle
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: public DeviceGemmMultipleDMultipleR<ALayout,
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BLayout,
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DELayout,
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ADataType,
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BDataType,
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DsDataType,
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EDataType,
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RsDataType,
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AElementwiseOperation,
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BElementwiseOperation,
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CDEElementwiseOperation,
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QsElementwiseOperation,
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RsElementwiseOperation>
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{
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using DeviceOp = DeviceGemmMultipleDMultipleR_Xdl_CShuffle;
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static constexpr index_t NumDTensor = DsDataType::Size();
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static constexpr index_t NumRTensor = RsDataType::Size();
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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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static constexpr auto I3 = Number<3>{};
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static auto MakeAGridDescriptor_AK0_M_AK1(index_t MRaw, index_t KRaw, index_t StrideA)
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{
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const auto a_grid_desc_mraw_kraw = [&]() {
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if constexpr(is_same_v<tensor_layout::gemm::RowMajor, ALayout>)
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{
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return make_naive_tensor_descriptor(make_tuple(MRaw, KRaw),
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make_tuple(StrideA, I1));
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}
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else if constexpr(is_same_v<tensor_layout::gemm::ColumnMajor, ALayout>)
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{
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return make_naive_tensor_descriptor(make_tuple(MRaw, KRaw),
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make_tuple(I1, StrideA));
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}
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}();
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const auto M = math::integer_divide_ceil(MRaw, MPerBlock) * MPerBlock;
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const auto K = math::integer_divide_ceil(KRaw, KPerBlock) * KPerBlock;
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const auto MPad = M - MRaw;
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const auto KPad = K - KRaw;
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if constexpr(GemmSpec == GemmSpecialization::MKPadding ||
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GemmSpec == GemmSpecialization::MNKPadding)
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{
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// pad both M and K
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assert(K % AK1 == 0);
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const auto AK0 = K / AK1;
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const auto a_grid_desc_m_k =
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transform_tensor_descriptor(a_grid_desc_mraw_kraw,
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make_tuple(make_right_pad_transform(MRaw, MPad),
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make_right_pad_transform(KRaw, KPad)),
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make_tuple(Sequence<0>{}, Sequence<1>{}),
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make_tuple(Sequence<0>{}, Sequence<1>{}));
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const auto a_grid_desc_ak0_m_ak1 =
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transform_tensor_descriptor(a_grid_desc_m_k,
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make_tuple(make_unmerge_transform(make_tuple(AK0, AK1)),
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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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return a_grid_desc_ak0_m_ak1;
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}
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else if constexpr(GemmSpec == GemmSpecialization::MPadding ||
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GemmSpec == GemmSpecialization::MNPadding)
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{
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// pad M, but not K
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assert(KRaw % AK1 == 0);
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const auto AK0 = KRaw / AK1;
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const auto a_grid_desc_ak0_m_ak1 =
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transform_tensor_descriptor(a_grid_desc_mraw_kraw,
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make_tuple(make_unmerge_transform(make_tuple(AK0, AK1)),
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make_right_pad_transform(MRaw, MPad)),
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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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return a_grid_desc_ak0_m_ak1;
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}
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else if constexpr(GemmSpec == GemmSpecialization::KPadding ||
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GemmSpec == GemmSpecialization::NKPadding)
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{
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// pad K, but not M
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assert(K % AK1 == 0);
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const auto AK0 = K / AK1;
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const auto a_grid_desc_m_k = transform_tensor_descriptor(
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a_grid_desc_mraw_kraw,
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make_tuple(make_pass_through_transform(MRaw), make_right_pad_transform(KRaw, KPad)),
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make_tuple(Sequence<0>{}, Sequence<1>{}),
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make_tuple(Sequence<0>{}, Sequence<1>{}));
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const auto a_grid_desc_ak0_m_ak1 =
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transform_tensor_descriptor(a_grid_desc_m_k,
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make_tuple(make_unmerge_transform(make_tuple(AK0, AK1)),
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make_pass_through_transform(MRaw)),
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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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return a_grid_desc_ak0_m_ak1;
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}
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else
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{
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// not pad M or K
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assert(KRaw % AK1 == 0);
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const auto AK0 = KRaw / AK1;
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const auto a_grid_desc_ak0_m_ak1 =
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transform_tensor_descriptor(a_grid_desc_mraw_kraw,
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make_tuple(make_unmerge_transform(make_tuple(AK0, AK1)),
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make_pass_through_transform(MRaw)),
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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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return a_grid_desc_ak0_m_ak1;
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}
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}
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static auto MakeBGridDescriptor_BK0_N_BK1(index_t KRaw, index_t NRaw, index_t StrideB)
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{
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const auto b_grid_desc_nraw_kraw = [&]() {
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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(NRaw, KRaw),
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make_tuple(I1, StrideB));
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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(NRaw, KRaw),
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make_tuple(StrideB, I1));
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}
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}();
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const auto N = math::integer_divide_ceil(NRaw, NPerBlock) * NPerBlock;
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const auto K = math::integer_divide_ceil(KRaw, KPerBlock) * KPerBlock;
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const auto NPad = N - NRaw;
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const auto KPad = K - KRaw;
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if constexpr(GemmSpec == GemmSpecialization::NKPadding ||
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GemmSpec == GemmSpecialization::MNKPadding)
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{
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// pad both N and K
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assert(K % BK1 == 0);
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const auto BK0 = K / BK1;
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const auto b_grid_desc_n_k =
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transform_tensor_descriptor(b_grid_desc_nraw_kraw,
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make_tuple(make_right_pad_transform(NRaw, NPad),
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make_right_pad_transform(KRaw, KPad)),
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make_tuple(Sequence<0>{}, Sequence<1>{}),
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make_tuple(Sequence<0>{}, Sequence<1>{}));
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const auto b_grid_desc_bk0_n_bk1 =
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transform_tensor_descriptor(b_grid_desc_n_k,
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make_tuple(make_unmerge_transform(make_tuple(BK0, BK1)),
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make_pass_through_transform(N)),
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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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return b_grid_desc_bk0_n_bk1;
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}
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else if constexpr(GemmSpec == GemmSpecialization::NPadding ||
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GemmSpec == GemmSpecialization::MNPadding)
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{
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// pad N, but not K
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assert(KRaw % BK1 == 0);
|
||||
|
||||
const auto BK0 = KRaw / BK1;
|
||||
|
||||
const auto b_grid_desc_bk0_n_bk1 =
|
||||
transform_tensor_descriptor(b_grid_desc_nraw_kraw,
|
||||
make_tuple(make_unmerge_transform(make_tuple(BK0, BK1)),
|
||||
make_right_pad_transform(NRaw, NPad)),
|
||||
make_tuple(Sequence<1>{}, Sequence<0>{}),
|
||||
make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
|
||||
|
||||
return b_grid_desc_bk0_n_bk1;
|
||||
}
|
||||
else if constexpr(GemmSpec == GemmSpecialization::KPadding ||
|
||||
GemmSpec == GemmSpecialization::MKPadding)
|
||||
{
|
||||
// pad K, but not N
|
||||
assert(K % BK1 == 0);
|
||||
|
||||
const auto BK0 = K / BK1;
|
||||
|
||||
const auto b_grid_desc_n_k = transform_tensor_descriptor(
|
||||
b_grid_desc_nraw_kraw,
|
||||
make_tuple(make_pass_through_transform(NRaw), make_right_pad_transform(KRaw, KPad)),
|
||||
make_tuple(Sequence<0>{}, Sequence<1>{}),
|
||||
make_tuple(Sequence<0>{}, Sequence<1>{}));
|
||||
|
||||
const auto b_grid_desc_bk0_n_bk1 =
|
||||
transform_tensor_descriptor(b_grid_desc_n_k,
|
||||
make_tuple(make_unmerge_transform(make_tuple(BK0, BK1)),
|
||||
make_pass_through_transform(NRaw)),
|
||||
make_tuple(Sequence<1>{}, Sequence<0>{}),
|
||||
make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
|
||||
|
||||
return b_grid_desc_bk0_n_bk1;
|
||||
}
|
||||
else
|
||||
{
|
||||
// not pad N or K
|
||||
assert(KRaw % BK1 == 0);
|
||||
|
||||
const auto BK0 = KRaw / BK1;
|
||||
|
||||
const auto b_grid_desc_bk0_n_bk1 =
|
||||
transform_tensor_descriptor(b_grid_desc_nraw_kraw,
|
||||
make_tuple(make_unmerge_transform(make_tuple(BK0, BK1)),
|
||||
make_pass_through_transform(NRaw)),
|
||||
make_tuple(Sequence<1>{}, Sequence<0>{}),
|
||||
make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
|
||||
|
||||
return b_grid_desc_bk0_n_bk1;
|
||||
}
|
||||
}
|
||||
|
||||
static auto MakeEGridDescriptor_M_N(index_t MRaw, index_t NRaw, index_t StrideE)
|
||||
{
|
||||
const auto e_grid_desc_mraw_nraw = [&]() {
|
||||
if constexpr(is_same<tensor_layout::gemm::RowMajor, DELayout>::value)
|
||||
{
|
||||
return make_naive_tensor_descriptor(make_tuple(MRaw, NRaw),
|
||||
make_tuple(StrideE, I1));
|
||||
}
|
||||
else if constexpr(is_same<tensor_layout::gemm::ColumnMajor, DELayout>::value)
|
||||
{
|
||||
return make_naive_tensor_descriptor(make_tuple(MRaw, NRaw),
|
||||
make_tuple(I1, StrideE));
|
||||
}
|
||||
}();
|
||||
|
||||
const auto M = math::integer_divide_ceil(MRaw, MPerBlock) * MPerBlock;
|
||||
const auto N = math::integer_divide_ceil(NRaw, NPerBlock) * NPerBlock;
|
||||
|
||||
const auto MPad = M - MRaw;
|
||||
const auto NPad = N - NRaw;
|
||||
|
||||
if constexpr(GemmSpec == GemmSpecialization::MNPadding ||
|
||||
GemmSpec == GemmSpecialization::MNKPadding)
|
||||
{
|
||||
// pad M and N
|
||||
return transform_tensor_descriptor(e_grid_desc_mraw_nraw,
|
||||
make_tuple(make_right_pad_transform(MRaw, MPad),
|
||||
make_right_pad_transform(NRaw, NPad)),
|
||||
make_tuple(Sequence<0>{}, Sequence<1>{}),
|
||||
make_tuple(Sequence<0>{}, Sequence<1>{}));
|
||||
}
|
||||
else if constexpr(GemmSpec == GemmSpecialization::MPadding ||
|
||||
GemmSpec == GemmSpecialization::MKPadding)
|
||||
{
|
||||
// pad M, but not N
|
||||
return transform_tensor_descriptor(
|
||||
e_grid_desc_mraw_nraw,
|
||||
make_tuple(make_right_pad_transform(MRaw, MPad), make_pass_through_transform(NRaw)),
|
||||
make_tuple(Sequence<0>{}, Sequence<1>{}),
|
||||
make_tuple(Sequence<0>{}, Sequence<1>{}));
|
||||
}
|
||||
else if constexpr(GemmSpec == GemmSpecialization::NPadding ||
|
||||
GemmSpec == GemmSpecialization::NKPadding)
|
||||
{
|
||||
// pad N, but not M
|
||||
return transform_tensor_descriptor(
|
||||
e_grid_desc_mraw_nraw,
|
||||
make_tuple(make_pass_through_transform(MRaw), make_right_pad_transform(NRaw, NPad)),
|
||||
make_tuple(Sequence<0>{}, Sequence<1>{}),
|
||||
make_tuple(Sequence<0>{}, Sequence<1>{}));
|
||||
}
|
||||
else
|
||||
{
|
||||
// not pad M or N
|
||||
return e_grid_desc_mraw_nraw;
|
||||
}
|
||||
}
|
||||
|
||||
// assume D is packed tensor
|
||||
static auto MakeRGridDescriptor_M(index_t MRaw)
|
||||
{
|
||||
const auto r_grid_desc_mraw = make_naive_tensor_descriptor_packed(make_tuple(MRaw));
|
||||
|
||||
const auto M = math::integer_divide_ceil(MRaw, MPerBlock) * MPerBlock;
|
||||
const auto MPad = M - MRaw;
|
||||
|
||||
if constexpr(GemmSpec == GemmSpecialization::MPadding ||
|
||||
GemmSpec == GemmSpecialization::MNPadding ||
|
||||
GemmSpec == GemmSpecialization::MKPadding ||
|
||||
GemmSpec == GemmSpecialization::MNKPadding)
|
||||
{
|
||||
// pad M
|
||||
return transform_tensor_descriptor(r_grid_desc_mraw,
|
||||
make_tuple(make_right_pad_transform(MRaw, MPad)),
|
||||
make_tuple(Sequence<0>{}),
|
||||
make_tuple(Sequence<0>{}));
|
||||
}
|
||||
else
|
||||
{
|
||||
// not pad M
|
||||
return r_grid_desc_mraw;
|
||||
}
|
||||
}
|
||||
|
||||
using AGridDesc_AK0_M_AK1 = decltype(MakeAGridDescriptor_AK0_M_AK1(1, 1, 1));
|
||||
using BGridDesc_BK0_N_BK1 = decltype(MakeBGridDescriptor_BK0_N_BK1(1, 1, 1));
|
||||
using EGridDesc_M_N = decltype(MakeEGridDescriptor_M_N(1, 1, 1));
|
||||
using RGridDesc_M = decltype(MakeRGridDescriptor_M(1));
|
||||
|
||||
// GridwiseGemm
|
||||
using GridwiseGemm = GridwiseGemmMultipleDMultipleR_k0mk1_k0nk1_mn_xdl_cshuffle_v1<
|
||||
ADataType, // TODO: distinguish A/B datatype
|
||||
GemmAccDataType,
|
||||
CShuffleDataType,
|
||||
DsDataType,
|
||||
EDataType,
|
||||
ReduceAccDataType,
|
||||
RsDataType,
|
||||
AElementwiseOperation,
|
||||
BElementwiseOperation,
|
||||
CDEElementwiseOperation,
|
||||
QsElementwiseOperation,
|
||||
RsElementwiseOperation,
|
||||
ThreadReduceOperations,
|
||||
InMemoryDataOperationEnum::Set,
|
||||
RsGlobalMemoryDataOperation,
|
||||
AGridDesc_AK0_M_AK1,
|
||||
BGridDesc_BK0_N_BK1,
|
||||
EGridDesc_M_N,
|
||||
RGridDesc_M,
|
||||
NumGemmKPrefetchStage,
|
||||
BlockSize,
|
||||
MPerBlock,
|
||||
NPerBlock,
|
||||
KPerBlock,
|
||||
AK1,
|
||||
BK1,
|
||||
MPerXDL,
|
||||
NPerXDL,
|
||||
MXdlPerWave,
|
||||
NXdlPerWave,
|
||||
ABlockTransferThreadClusterLengths_AK0_M_AK1,
|
||||
ABlockTransferThreadClusterArrangeOrder,
|
||||
ABlockTransferSrcAccessOrder,
|
||||
ABlockTransferSrcVectorDim,
|
||||
ABlockTransferSrcScalarPerVector,
|
||||
ABlockTransferDstScalarPerVector_AK1,
|
||||
false,
|
||||
ABlockLdsExtraM,
|
||||
BBlockTransferThreadClusterLengths_BK0_N_BK1,
|
||||
BBlockTransferThreadClusterArrangeOrder,
|
||||
BBlockTransferSrcAccessOrder,
|
||||
BBlockTransferSrcVectorDim,
|
||||
BBlockTransferSrcScalarPerVector,
|
||||
BBlockTransferDstScalarPerVector_BK1,
|
||||
false,
|
||||
BBlockLdsExtraN,
|
||||
CShuffleMXdlPerWavePerShuffle,
|
||||
CShuffleNXdlPerWavePerShuffle,
|
||||
CDRThreadTransferClusterLengths_MPerBlock_NPerBlock,
|
||||
CDEReduceThreadTransferScalarPerVector_NPerBlock,
|
||||
RThreadTransferDstScalarPerVector_MPerBlock,
|
||||
LoopSched>;
|
||||
|
||||
// Argument
|
||||
struct Argument : public BaseArgument
|
||||
{
|
||||
Argument(const void* p_a_grid,
|
||||
const void* p_b_grid,
|
||||
std::array<const void*, NumDTensor> p_ds_grid,
|
||||
void* p_e_grid,
|
||||
std::array<void*, NumRTensor> p_rs_grid,
|
||||
index_t MRaw,
|
||||
index_t NRaw,
|
||||
index_t KRaw,
|
||||
index_t StrideA,
|
||||
index_t StrideB,
|
||||
std::array<index_t, NumDTensor> StrideDs,
|
||||
index_t StrideE,
|
||||
AElementwiseOperation a_element_op,
|
||||
BElementwiseOperation b_element_op,
|
||||
CDEElementwiseOperation cde_element_op,
|
||||
QsElementwiseOperation qs_element_op,
|
||||
RsElementwiseOperation rs_element_op)
|
||||
: p_a_grid_{static_cast<const ADataType*>(p_a_grid)},
|
||||
p_b_grid_{static_cast<const BDataType*>(p_b_grid)},
|
||||
p_ds_grid_{}, // FIXME
|
||||
p_e_grid_{static_cast<EDataType*>(p_e_grid)},
|
||||
p_rs_grid_{}, // FIXME
|
||||
a_grid_desc_ak0_m_ak1_{DeviceOp::MakeAGridDescriptor_AK0_M_AK1(MRaw, KRaw, StrideA)},
|
||||
b_grid_desc_bk0_n_bk1_{DeviceOp::MakeBGridDescriptor_BK0_N_BK1(KRaw, NRaw, StrideB)},
|
||||
ds_grid_desc_mblock_mperblock_nblock_nperblock_{},
|
||||
e_grid_desc_m_n_{DeviceOp::MakeEGridDescriptor_M_N(MRaw, NRaw, StrideE)},
|
||||
e_grid_desc_mblock_mperblock_nblock_nperblock_{},
|
||||
r_grid_desc_m_{DeviceOp::MakeRGridDescriptor_M(MRaw)},
|
||||
rs_grid_desc_mblock_mperblock_{},
|
||||
block_2_etile_map_{GridwiseGemm::MakeDefaultBlock2ETileMap(e_grid_desc_m_n_)},
|
||||
a_element_op_{a_element_op},
|
||||
b_element_op_{b_element_op},
|
||||
cde_element_op_{cde_element_op},
|
||||
qs_element_op_{qs_element_op},
|
||||
rs_element_op_{rs_element_op}
|
||||
{
|
||||
if(GridwiseGemm::CheckValidity(a_grid_desc_ak0_m_ak1_,
|
||||
b_grid_desc_bk0_n_bk1_,
|
||||
e_grid_desc_m_n_,
|
||||
r_grid_desc_m_,
|
||||
block_2_etile_map_))
|
||||
{
|
||||
e_grid_desc_mblock_mperblock_nblock_nperblock_ =
|
||||
GridwiseGemm::MakeEGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock(
|
||||
e_grid_desc_m_n_);
|
||||
|
||||
static_for<0, NumDTensor, 1>{}([&](auto i) {
|
||||
using DDataType = remove_cvref_t<tuple_element_t<i.value, DsDataType>>;
|
||||
|
||||
p_ds_grid_(i) = static_cast<const DDataType*>(p_ds_grid[i]);
|
||||
|
||||
const auto d_grid_desc_m_n =
|
||||
DeviceOp::MakeEGridDescriptor_M_N(MRaw, NRaw, StrideDs[i]);
|
||||
|
||||
ds_grid_desc_mblock_mperblock_nblock_nperblock_(i) =
|
||||
GridwiseGemm::MakeEGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock(
|
||||
d_grid_desc_m_n);
|
||||
});
|
||||
|
||||
static_for<0, NumRTensor, 1>{}([&](auto i) {
|
||||
using RDataType = remove_cvref_t<tuple_element_t<i.value, RsDataType>>;
|
||||
|
||||
p_rs_grid_(i) = static_cast<RDataType*>(p_rs_grid[i]);
|
||||
|
||||
rs_grid_desc_mblock_mperblock_(i) =
|
||||
GridwiseGemm::MakeRGridDescriptor_MBlock_MPerBlock(r_grid_desc_m_);
|
||||
});
|
||||
}
|
||||
}
|
||||
|
||||
// private:
|
||||
// pointers
|
||||
const ADataType* p_a_grid_;
|
||||
const BDataType* p_b_grid_;
|
||||
typename GridwiseGemm::DsGridPointer p_ds_grid_;
|
||||
EDataType* p_e_grid_;
|
||||
typename GridwiseGemm::RsGridPointer p_rs_grid_;
|
||||
|
||||
// tensor descriptors
|
||||
AGridDesc_AK0_M_AK1 a_grid_desc_ak0_m_ak1_;
|
||||
BGridDesc_BK0_N_BK1 b_grid_desc_bk0_n_bk1_;
|
||||
StaticallyIndexedArray<
|
||||
typename GridwiseGemm::EGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock,
|
||||
NumDTensor>
|
||||
ds_grid_desc_mblock_mperblock_nblock_nperblock_; // FIXME: Ds desc may be of different
|
||||
// type from E
|
||||
EGridDesc_M_N e_grid_desc_m_n_;
|
||||
typename GridwiseGemm::EGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock
|
||||
e_grid_desc_mblock_mperblock_nblock_nperblock_;
|
||||
|
||||
RGridDesc_M r_grid_desc_m_;
|
||||
StaticallyIndexedArray<typename GridwiseGemm::RGridDescriptor_MBlock_MPerBlock, NumRTensor>
|
||||
rs_grid_desc_mblock_mperblock_;
|
||||
|
||||
// block-to-e-tile map
|
||||
typename GridwiseGemm::DefaultBlock2ETileMap block_2_etile_map_;
|
||||
|
||||
// element-wise op
|
||||
AElementwiseOperation a_element_op_;
|
||||
BElementwiseOperation b_element_op_;
|
||||
CDEElementwiseOperation cde_element_op_;
|
||||
QsElementwiseOperation qs_element_op_;
|
||||
RsElementwiseOperation rs_element_op_;
|
||||
};
|
||||
|
||||
// Invoker
|
||||
struct Invoker : public BaseInvoker
|
||||
{
|
||||
using Argument = DeviceOp::Argument;
|
||||
|
||||
float Run(const Argument& arg, const StreamConfig& stream_config = StreamConfig{})
|
||||
{
|
||||
if(!GridwiseGemm::CheckValidity(arg.a_grid_desc_ak0_m_ak1_,
|
||||
arg.b_grid_desc_bk0_n_bk1_,
|
||||
arg.e_grid_desc_m_n_,
|
||||
arg.r_grid_desc_m_,
|
||||
arg.block_2_etile_map_))
|
||||
{
|
||||
throw std::runtime_error("wrong! GridwiseGemm has invalid setting");
|
||||
}
|
||||
|
||||
const index_t grid_size =
|
||||
arg.block_2_etile_map_.CalculateGridSize(arg.e_grid_desc_m_n_);
|
||||
|
||||
const auto K =
|
||||
arg.a_grid_desc_ak0_m_ak1_.GetLength(I0) * arg.a_grid_desc_ak0_m_ak1_.GetLength(I2);
|
||||
|
||||
auto launch_kernel = [&](auto has_main_k_block_loop) {
|
||||
constexpr bool has_main_loop = has_main_k_block_loop.value;
|
||||
|
||||
const auto kernel = kernel_gemm_multiple_d_multiple_r_xdl_cshuffle<
|
||||
GridwiseGemm,
|
||||
ADataType, // TODO: distiguish A/B datatype
|
||||
typename GridwiseGemm::DsGridPointer,
|
||||
EDataType,
|
||||
typename GridwiseGemm::RsGridPointer,
|
||||
AElementwiseOperation,
|
||||
BElementwiseOperation,
|
||||
CDEElementwiseOperation,
|
||||
QsElementwiseOperation,
|
||||
RsElementwiseOperation,
|
||||
DeviceOp::AGridDesc_AK0_M_AK1,
|
||||
DeviceOp::BGridDesc_BK0_N_BK1,
|
||||
ck::StaticallyIndexedArray<
|
||||
typename GridwiseGemm::EGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock,
|
||||
NumDTensor>,
|
||||
typename GridwiseGemm::EGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock,
|
||||
ck::StaticallyIndexedArray<
|
||||
typename GridwiseGemm::RGridDescriptor_MBlock_MPerBlock,
|
||||
NumRTensor>,
|
||||
typename GridwiseGemm::DefaultBlock2ETileMap,
|
||||
has_main_loop>;
|
||||
|
||||
return launch_and_time_kernel(stream_config,
|
||||
kernel,
|
||||
dim3(grid_size),
|
||||
dim3(BlockSize),
|
||||
0,
|
||||
arg.p_a_grid_,
|
||||
arg.p_b_grid_,
|
||||
arg.p_ds_grid_,
|
||||
arg.p_e_grid_,
|
||||
arg.p_rs_grid_,
|
||||
arg.a_element_op_,
|
||||
arg.b_element_op_,
|
||||
arg.cde_element_op_,
|
||||
arg.qs_element_op_,
|
||||
arg.rs_element_op_,
|
||||
arg.a_grid_desc_ak0_m_ak1_,
|
||||
arg.b_grid_desc_bk0_n_bk1_,
|
||||
arg.ds_grid_desc_mblock_mperblock_nblock_nperblock_,
|
||||
arg.e_grid_desc_mblock_mperblock_nblock_nperblock_,
|
||||
arg.rs_grid_desc_mblock_mperblock_,
|
||||
arg.block_2_etile_map_);
|
||||
};
|
||||
|
||||
float ave_time = 0;
|
||||
|
||||
if(GridwiseGemm::CalculateHasMainKBlockLoop(K))
|
||||
{
|
||||
ave_time = launch_kernel(integral_constant<bool, true>{});
|
||||
}
|
||||
else
|
||||
{
|
||||
ave_time = launch_kernel(integral_constant<bool, false>{});
|
||||
}
|
||||
|
||||
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 bool IsSupportedArgument(const Argument& arg)
|
||||
{
|
||||
if(!(ck::get_device_name() == "gfx908" || ck::get_device_name() == "gfx90a"))
|
||||
{
|
||||
return false;
|
||||
}
|
||||
|
||||
return GridwiseGemm::CheckValidity(arg.a_grid_desc_ak0_m_ak1_,
|
||||
arg.b_grid_desc_bk0_n_bk1_,
|
||||
arg.e_grid_desc_m_n_,
|
||||
arg.r_grid_desc_m_,
|
||||
arg.block_2_etile_map_);
|
||||
}
|
||||
|
||||
// polymorphic
|
||||
bool IsSupportedArgument(const BaseArgument* p_arg) override
|
||||
{
|
||||
return IsSupportedArgument(*dynamic_cast<const Argument*>(p_arg));
|
||||
}
|
||||
|
||||
static auto MakeArgument(const void* p_a,
|
||||
const void* p_b,
|
||||
std::array<const void*, NumDTensor> p_ds,
|
||||
void* p_e,
|
||||
std::array<void*, NumRTensor> p_rs,
|
||||
index_t MRaw,
|
||||
index_t NRaw,
|
||||
index_t KRaw,
|
||||
index_t StrideA,
|
||||
index_t StrideB,
|
||||
std::array<index_t, NumDTensor> StrideDs,
|
||||
index_t StrideE,
|
||||
AElementwiseOperation a_element_op,
|
||||
BElementwiseOperation b_element_op,
|
||||
CDEElementwiseOperation cde_element_op,
|
||||
QsElementwiseOperation qs_element_op,
|
||||
RsElementwiseOperation rs_element_op)
|
||||
{
|
||||
return Argument{p_a,
|
||||
p_b,
|
||||
p_ds,
|
||||
p_e,
|
||||
p_rs,
|
||||
MRaw,
|
||||
NRaw,
|
||||
KRaw,
|
||||
StrideA,
|
||||
StrideB,
|
||||
StrideDs,
|
||||
StrideE,
|
||||
a_element_op,
|
||||
b_element_op,
|
||||
cde_element_op,
|
||||
qs_element_op,
|
||||
rs_element_op};
|
||||
}
|
||||
|
||||
static auto MakeInvoker() { return Invoker{}; }
|
||||
|
||||
// polymorphic
|
||||
std::unique_ptr<BaseArgument> MakeArgumentPointer(const void* p_a,
|
||||
const void* p_b,
|
||||
std::array<const void*, NumDTensor> p_ds,
|
||||
void* p_e,
|
||||
std::array<void*, NumRTensor> p_rs,
|
||||
index_t MRaw,
|
||||
index_t NRaw,
|
||||
index_t KRaw,
|
||||
index_t StrideA,
|
||||
index_t StrideB,
|
||||
std::array<index_t, NumDTensor> StrideDs,
|
||||
index_t StrideE,
|
||||
AElementwiseOperation a_element_op,
|
||||
BElementwiseOperation b_element_op,
|
||||
CDEElementwiseOperation cde_element_op,
|
||||
QsElementwiseOperation qs_element_op,
|
||||
RsElementwiseOperation rs_element_op) override
|
||||
{
|
||||
return std::make_unique<Argument>(p_a,
|
||||
p_b,
|
||||
p_ds,
|
||||
p_e,
|
||||
p_rs,
|
||||
MRaw,
|
||||
NRaw,
|
||||
KRaw,
|
||||
StrideA,
|
||||
StrideB,
|
||||
StrideDs,
|
||||
StrideE,
|
||||
a_element_op,
|
||||
b_element_op,
|
||||
cde_element_op,
|
||||
qs_element_op,
|
||||
rs_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();
|
||||
|
||||
// clang-format off
|
||||
str << "DeviceGemmMultipleDMultipleR_Xdl_CShuffle"
|
||||
<< "<"
|
||||
<< BlockSize << ", "
|
||||
<< MPerBlock << ", "
|
||||
<< NPerBlock << ", "
|
||||
<< KPerBlock << ", "
|
||||
<< AK1 << ", "
|
||||
<< BK1 << ", "
|
||||
<< getGemmSpecializationString(GemmSpec)
|
||||
<< ">";
|
||||
// clang-format on
|
||||
|
||||
return str.str();
|
||||
}
|
||||
};
|
||||
|
||||
} // namespace device
|
||||
} // namespace tensor_operation
|
||||
} // namespace ck
|
||||
@@ -130,6 +130,35 @@ struct AddHardswishAdd
|
||||
}
|
||||
};
|
||||
|
||||
// C = A * B
|
||||
// E = C + D0 + D1
|
||||
struct AddAdd
|
||||
{
|
||||
template <typename E, typename C, typename D0, typename D1>
|
||||
__host__ __device__ void operator()(E& e, const C& c, const D0& d0, const D1& d1) const
|
||||
{
|
||||
// Only support floating so far
|
||||
static_assert(is_same<E, half_t>::value || is_same<E, float>::value ||
|
||||
is_same<E, double>::value,
|
||||
"Data type is not supported by this operation!");
|
||||
|
||||
static_assert(is_same<C, half_t>::value || is_same<C, float>::value ||
|
||||
is_same<C, double>::value,
|
||||
"Data type is not supported by this operation!");
|
||||
|
||||
static_assert(is_same<D0, half_t>::value || is_same<D0, float>::value ||
|
||||
is_same<D0, double>::value,
|
||||
"Data type is not supported by this operation!");
|
||||
|
||||
static_assert(is_same<D1, half_t>::value || is_same<D1, float>::value ||
|
||||
is_same<D1, double>::value,
|
||||
"Data type is not supported by this operation!");
|
||||
|
||||
const C y = c + type_convert<C>(d0) + type_convert<C>(d1);
|
||||
e = type_convert<E>(y);
|
||||
}
|
||||
};
|
||||
|
||||
// C = A * B
|
||||
// E = FastGelu(C + D0 + D1)
|
||||
struct AddAddFastGelu
|
||||
|
||||
@@ -0,0 +1,901 @@
|
||||
// SPDX-License-Identifier: MIT
|
||||
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
|
||||
|
||||
#pragma once
|
||||
|
||||
#include "ck/utility/common_header.hpp"
|
||||
#include "ck/tensor_description/multi_index_transform_helper.hpp"
|
||||
#include "ck/tensor_description/tensor_descriptor.hpp"
|
||||
#include "ck/tensor_description/tensor_descriptor_helper.hpp"
|
||||
#include "ck/tensor_operation/gpu/grid/block_to_ctile_map.hpp"
|
||||
#include "ck/tensor_operation/gpu/grid/gridwise_gemm_pipeline_v1.hpp"
|
||||
#include "ck/tensor_operation/gpu/block/blockwise_gemm_xdlops.hpp"
|
||||
#include "ck/tensor_operation/gpu/block/thread_group_tensor_slice_transfer_v4r1.hpp"
|
||||
#include "ck/tensor_operation/gpu/block/thread_group_tensor_slice_transfer_v6r1.hpp"
|
||||
#include "ck/tensor_operation/gpu/thread/threadwise_tensor_slice_transfer.hpp"
|
||||
#include "ck/tensor_operation/gpu/thread/reduction_functions_threadwise.hpp"
|
||||
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
|
||||
|
||||
namespace ck {
|
||||
template <typename FloatAB,
|
||||
typename FloatGemmAcc,
|
||||
typename FloatCShuffle,
|
||||
typename DsDataType,
|
||||
typename FloatE,
|
||||
typename FloatReduceAcc,
|
||||
typename RsDataType,
|
||||
typename AElementwiseOperation,
|
||||
typename BElementwiseOperation,
|
||||
typename CDEElementwiseOperation,
|
||||
typename QsElementwiseOperation,
|
||||
typename RsElementwiseOperation,
|
||||
typename ThreadReduceOperations,
|
||||
InMemoryDataOperationEnum EGlobalMemoryDataOperation,
|
||||
typename RsGlobalMemoryDataOperation,
|
||||
typename AGridDesc_AK0_M_AK1,
|
||||
typename BGridDesc_BK0_N_BK1,
|
||||
typename EGridDesc_M_N,
|
||||
typename RGridDesc_M,
|
||||
index_t NumGemmKPrefetchStage,
|
||||
index_t BlockSize,
|
||||
index_t MPerBlock,
|
||||
index_t NPerBlock,
|
||||
index_t KPerBlock,
|
||||
index_t AK1Value,
|
||||
index_t BK1Value,
|
||||
index_t MPerXdl,
|
||||
index_t NPerXdl,
|
||||
index_t MXdlPerWave,
|
||||
index_t NXdlPerWave,
|
||||
typename ABlockTransferThreadClusterLengths_AK0_M_AK1,
|
||||
typename ABlockTransferThreadClusterArrangeOrder,
|
||||
typename ABlockTransferSrcAccessOrder,
|
||||
index_t ABlockTransferSrcVectorDim,
|
||||
index_t ABlockTransferSrcScalarPerVector,
|
||||
index_t ABlockTransferDstScalarPerVector_AK1,
|
||||
bool AThreadTransferSrcResetCoordinateAfterRun,
|
||||
index_t ABlockLdsExtraM,
|
||||
typename BBlockTransferThreadClusterLengths_BK0_N_BK1,
|
||||
typename BBlockTransferThreadClusterArrangeOrder,
|
||||
typename BBlockTransferSrcAccessOrder,
|
||||
index_t BBlockTransferSrcVectorDim,
|
||||
index_t BBlockTransferSrcScalarPerVector,
|
||||
index_t BBlockTransferDstScalarPerVector_BK1,
|
||||
bool BThreadTransferSrcResetCoordinateAfterRun,
|
||||
index_t BBlockLdsExtraN,
|
||||
index_t CShuffleMXdlPerWavePerShuffle,
|
||||
index_t CShuffleNXdlPerWavePerShuffle,
|
||||
typename CDRThreadTransferClusterLengths_MPerBlock_NPerBlock,
|
||||
index_t CDEReduceThreadTransferScalarPerVector_NPerBlock,
|
||||
index_t RThreadTransferDstScalarPerVector_MPerBlock,
|
||||
LoopScheduler LoopSched>
|
||||
struct GridwiseGemmMultipleDMultipleR_k0mk1_k0nk1_mn_xdl_cshuffle_v1
|
||||
{
|
||||
static constexpr index_t NumDTensor = DsDataType::Size();
|
||||
static constexpr index_t NumRTensor = RsDataType::Size();
|
||||
|
||||
static constexpr auto I0 = Number<0>{};
|
||||
static constexpr auto I1 = Number<1>{};
|
||||
static constexpr auto I2 = Number<2>{};
|
||||
static constexpr auto I3 = Number<3>{};
|
||||
static constexpr auto I4 = Number<4>{};
|
||||
static constexpr auto I5 = Number<5>{};
|
||||
static constexpr auto I6 = Number<6>{};
|
||||
static constexpr auto I7 = Number<7>{};
|
||||
|
||||
// K1 should be Number<...>
|
||||
static constexpr auto AK0 = Number<KPerBlock / AK1Value>{};
|
||||
static constexpr auto BK0 = Number<KPerBlock / BK1Value>{};
|
||||
static constexpr auto AK1 = Number<AK1Value>{};
|
||||
static constexpr auto BK1 = Number<BK1Value>{};
|
||||
|
||||
using ThisThreadBlock = ThisThreadBlock<BlockSize>;
|
||||
|
||||
using GridwiseGemmPipe = GridwiseGemmPipeline_v1<NumGemmKPrefetchStage>;
|
||||
|
||||
__host__ __device__ static constexpr auto GetABlockDescriptor_AK0PerBlock_MPerBlock_AK1()
|
||||
{
|
||||
// A matrix in LDS memory, dst of blockwise copy
|
||||
return make_naive_tensor_descriptor(
|
||||
make_tuple(AK0, Number<MPerBlock>{}, AK1),
|
||||
make_tuple(Number<MPerBlock + ABlockLdsExtraM>{} * AK1, AK1, I1));
|
||||
}
|
||||
|
||||
__host__ __device__ static constexpr auto GetBBlockDescriptor_BK0PerBlock_NPerBlock_BK1()
|
||||
{
|
||||
// B matrix in LDS memory, dst of blockwise copy
|
||||
return make_naive_tensor_descriptor(
|
||||
make_tuple(BK0, Number<NPerBlock>{}, BK1),
|
||||
make_tuple(Number<NPerBlock + BBlockLdsExtraN>{} * BK1, BK1, I1));
|
||||
}
|
||||
|
||||
__host__ __device__ static constexpr auto
|
||||
GetCShuffleBlockDescriptor_MBlock_MPerBlock_NBlock_NPerBlock()
|
||||
{
|
||||
constexpr index_t MWave = MPerBlock / (MXdlPerWave * MPerXdl);
|
||||
constexpr index_t NWave = NPerBlock / (NXdlPerWave * NPerXdl);
|
||||
|
||||
constexpr auto c_shuffle_block_desc_mblock_mperblock_nblock_nperblock =
|
||||
make_naive_tensor_descriptor_packed(
|
||||
make_tuple(I1,
|
||||
Number<CShuffleMXdlPerWavePerShuffle * MWave * MPerXdl>{},
|
||||
I1,
|
||||
Number<CShuffleNXdlPerWavePerShuffle * NWave * NPerXdl>{}));
|
||||
|
||||
return c_shuffle_block_desc_mblock_mperblock_nblock_nperblock;
|
||||
}
|
||||
|
||||
// ck::Tuple<const T0DataType*, const T1DataType*, ...>
|
||||
template <typename Ts, bool isConst = true>
|
||||
static constexpr auto MakeTsGridPointer()
|
||||
{
|
||||
return generate_tuple(
|
||||
[&](auto i) {
|
||||
using T = remove_cvref_t<tuple_element_t<i.value, Ts>>;
|
||||
if constexpr(isConst)
|
||||
return static_cast<const T*>(nullptr);
|
||||
else
|
||||
return static_cast<T*>(nullptr);
|
||||
},
|
||||
Number<Ts::Size()>{});
|
||||
}
|
||||
|
||||
__host__ __device__ static constexpr index_t GetSharedMemoryNumberOfByte()
|
||||
{
|
||||
// LDS allocation for A and B: be careful of alignment
|
||||
constexpr auto a_block_desc_ak0_m_ak1 = GetABlockDescriptor_AK0PerBlock_MPerBlock_AK1();
|
||||
constexpr auto b_block_desc_bk0_n_bk1 = GetBBlockDescriptor_BK0PerBlock_NPerBlock_BK1();
|
||||
|
||||
// lds max alignment
|
||||
constexpr auto max_lds_align = math::lcm(AK1, BK1);
|
||||
|
||||
constexpr auto a_block_space_size_aligned = math::integer_least_multiple(
|
||||
a_block_desc_ak0_m_ak1.GetElementSpaceSize(), max_lds_align);
|
||||
|
||||
constexpr auto b_block_space_size_aligned = math::integer_least_multiple(
|
||||
b_block_desc_bk0_n_bk1.GetElementSpaceSize(), max_lds_align);
|
||||
|
||||
// LDS allocation for C shuffle in LDS
|
||||
constexpr auto c_shuffle_block_desc_mblock_mperblock_nblock_nperblock =
|
||||
GetCShuffleBlockDescriptor_MBlock_MPerBlock_NBlock_NPerBlock();
|
||||
|
||||
constexpr auto c_block_size =
|
||||
c_shuffle_block_desc_mblock_mperblock_nblock_nperblock.GetElementSpaceSize();
|
||||
|
||||
return math::max((a_block_space_size_aligned + b_block_space_size_aligned) *
|
||||
sizeof(FloatAB),
|
||||
c_block_size * sizeof(FloatCShuffle));
|
||||
}
|
||||
|
||||
// block_id to matrix tile idx (m0, n0) mapping are controlled by {M01, N01}
|
||||
template <typename Block2ETileMap>
|
||||
__host__ __device__ static constexpr bool
|
||||
CheckValidity(const AGridDesc_AK0_M_AK1& a_grid_desc_ak0_m_ak1,
|
||||
const BGridDesc_BK0_N_BK1& b_grid_desc_bk0_n_bk1,
|
||||
const EGridDesc_M_N& e_grid_desc_m_n,
|
||||
const RGridDesc_M& r_grid_desc_m,
|
||||
const Block2ETileMap& block_2_etile_map)
|
||||
{
|
||||
static_assert((MPerBlock % (MPerXdl * MXdlPerWave) == 0) &&
|
||||
(NPerBlock % (NXdlPerWave * NPerXdl)) == 0,
|
||||
"Invalid tuning param!");
|
||||
|
||||
const auto M = a_grid_desc_ak0_m_ak1.GetLength(I1);
|
||||
const auto N = b_grid_desc_bk0_n_bk1.GetLength(I1);
|
||||
const auto K = a_grid_desc_ak0_m_ak1.GetLength(I0) * a_grid_desc_ak0_m_ak1.GetLength(I2);
|
||||
|
||||
if(!(M == e_grid_desc_m_n.GetLength(I0) && N == e_grid_desc_m_n.GetLength(I1)))
|
||||
return false;
|
||||
|
||||
if(!(M % MPerBlock == 0 && N % NPerBlock == 0 && K % KPerBlock == 0))
|
||||
return false;
|
||||
|
||||
if(M != r_grid_desc_m.GetLength(I0))
|
||||
return false;
|
||||
|
||||
// check gridwise gemm pipeline
|
||||
const auto num_k_loop = K / KPerBlock;
|
||||
|
||||
if(!GridwiseGemmPipe::IsSupported(num_k_loop))
|
||||
{
|
||||
return false;
|
||||
}
|
||||
|
||||
if(!block_2_etile_map.CheckValidity(e_grid_desc_m_n))
|
||||
{
|
||||
return false;
|
||||
}
|
||||
|
||||
// TODO: also check validity of all components (blockwise-copy, threadwise-copy, etc)
|
||||
return true;
|
||||
}
|
||||
|
||||
__host__ __device__ static constexpr bool CalculateHasMainKBlockLoop(index_t K)
|
||||
{
|
||||
const index_t num_loop = K / KPerBlock;
|
||||
|
||||
return GridwiseGemmPipe::CalculateHasMainLoop(num_loop);
|
||||
}
|
||||
|
||||
__host__ __device__ static constexpr auto
|
||||
MakeEGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock(const EGridDesc_M_N& e_grid_desc_m_n)
|
||||
{
|
||||
const auto M = e_grid_desc_m_n.GetLength(I0);
|
||||
const auto N = e_grid_desc_m_n.GetLength(I1);
|
||||
|
||||
const auto MBlock = M / MPerBlock;
|
||||
const auto NBlock = N / NPerBlock;
|
||||
|
||||
const auto e_grid_desc_mblock_mperblock_nblock_nperblock = transform_tensor_descriptor(
|
||||
e_grid_desc_m_n,
|
||||
make_tuple(make_unmerge_transform(make_tuple(MBlock, Number<MPerBlock>{})),
|
||||
make_unmerge_transform(make_tuple(NBlock, Number<NPerBlock>{}))),
|
||||
make_tuple(Sequence<0>{}, Sequence<1>{}),
|
||||
make_tuple(Sequence<0, 1>{}, Sequence<2, 3>{}));
|
||||
|
||||
return e_grid_desc_mblock_mperblock_nblock_nperblock;
|
||||
}
|
||||
|
||||
__host__ __device__ static constexpr auto
|
||||
MakeRGridDescriptor_MBlock_MPerBlock(const RGridDesc_M& r_grid_desc_m)
|
||||
{
|
||||
const auto M = r_grid_desc_m.GetLength(I0);
|
||||
const auto MBlock = M / MPerBlock;
|
||||
|
||||
const auto r_grid_desc_mblock_mperblock = transform_tensor_descriptor(
|
||||
r_grid_desc_m,
|
||||
make_tuple(make_unmerge_transform(make_tuple(MBlock, Number<MPerBlock>{}))),
|
||||
make_tuple(Sequence<0>{}),
|
||||
make_tuple(Sequence<0, 1>{}));
|
||||
|
||||
return r_grid_desc_mblock_mperblock;
|
||||
}
|
||||
|
||||
// return block_id to E matrix tile idx (m0, n0) mapping
|
||||
__host__ __device__ static constexpr auto
|
||||
MakeDefaultBlock2ETileMap(const EGridDesc_M_N& e_grid_desc_m_n)
|
||||
{
|
||||
return BlockToCTileMap_M00_N0_M01Adapt<MPerBlock, NPerBlock, EGridDesc_M_N>(
|
||||
e_grid_desc_m_n);
|
||||
}
|
||||
|
||||
using EGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock = remove_cvref_t<decltype(
|
||||
MakeEGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock(EGridDesc_M_N{}))>;
|
||||
|
||||
// Support 2 dimension in the future. Not only M
|
||||
using RGridDescriptor_MBlock_MPerBlock =
|
||||
remove_cvref_t<decltype(MakeRGridDescriptor_MBlock_MPerBlock(RGridDesc_M{}))>;
|
||||
|
||||
using DefaultBlock2ETileMap =
|
||||
remove_cvref_t<decltype(MakeDefaultBlock2ETileMap(EGridDesc_M_N{}))>;
|
||||
|
||||
using DsGridPointer = decltype(MakeTsGridPointer<DsDataType, true>());
|
||||
using RsGridPointer = decltype(MakeTsGridPointer<RsDataType, false>());
|
||||
|
||||
template <bool HasMainKBlockLoop, typename Block2ETileMap>
|
||||
__device__ static void
|
||||
Run(const FloatAB* __restrict__ p_a_grid,
|
||||
const FloatAB* __restrict__ p_b_grid,
|
||||
DsGridPointer p_ds_grid,
|
||||
FloatE* __restrict__ p_e_grid,
|
||||
RsGridPointer p_rs_grid,
|
||||
void* __restrict__ p_shared,
|
||||
const AElementwiseOperation& a_element_op,
|
||||
const BElementwiseOperation& b_element_op,
|
||||
const CDEElementwiseOperation& cde_element_op,
|
||||
const QsElementwiseOperation& qs_element_op,
|
||||
const RsElementwiseOperation& rs_element_op,
|
||||
const AGridDesc_AK0_M_AK1& a_grid_desc_ak0_m_ak1,
|
||||
const BGridDesc_BK0_N_BK1& b_grid_desc_bk0_n_bk1,
|
||||
const StaticallyIndexedArray<EGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock,
|
||||
NumDTensor>&
|
||||
ds_grid_desc_mblock_mperblock_nblock_nperblock, // FIXME: Ds desc may be of different
|
||||
const EGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock&
|
||||
e_grid_desc_mblock_mperblock_nblock_nperblock,
|
||||
const StaticallyIndexedArray<RGridDescriptor_MBlock_MPerBlock,
|
||||
NumRTensor>&
|
||||
rs_grid_desc_mblock_mperblock, // FIXME: Rs desc may be of different
|
||||
const Block2ETileMap& block_2_etile_map)
|
||||
{
|
||||
// FIXME - Share code with other gemm kernel
|
||||
const auto a_grid_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
|
||||
p_a_grid, a_grid_desc_ak0_m_ak1.GetElementSpaceSize());
|
||||
|
||||
const auto b_grid_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
|
||||
p_b_grid, b_grid_desc_bk0_n_bk1.GetElementSpaceSize());
|
||||
|
||||
const auto ds_grid_buf = generate_tuple(
|
||||
[&](auto i) {
|
||||
return make_dynamic_buffer<AddressSpaceEnum::Global>(
|
||||
p_ds_grid[i],
|
||||
ds_grid_desc_mblock_mperblock_nblock_nperblock[i].GetElementSpaceSize());
|
||||
},
|
||||
Number<NumDTensor>{});
|
||||
|
||||
auto e_grid_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
|
||||
p_e_grid, e_grid_desc_mblock_mperblock_nblock_nperblock.GetElementSpaceSize());
|
||||
|
||||
auto rs_grid_buf = generate_tuple(
|
||||
[&](auto i) {
|
||||
return make_dynamic_buffer<AddressSpaceEnum::Global>(
|
||||
p_rs_grid(i), rs_grid_desc_mblock_mperblock[i].GetElementSpaceSize());
|
||||
},
|
||||
Number<NumRTensor>{});
|
||||
|
||||
// divide block work by [M, N]
|
||||
const auto block_work_idx =
|
||||
block_2_etile_map.CalculateBottomIndex(make_multi_index(get_block_1d_id()));
|
||||
|
||||
if(!block_2_etile_map.ValidCTileIndex(
|
||||
block_work_idx,
|
||||
make_tuple(e_grid_desc_mblock_mperblock_nblock_nperblock.GetLength(I0),
|
||||
e_grid_desc_mblock_mperblock_nblock_nperblock.GetLength(I2))))
|
||||
{
|
||||
return;
|
||||
}
|
||||
|
||||
// HACK: this force m/n_block_data_idx_on_grid into SGPR
|
||||
const index_t m_block_data_idx_on_grid =
|
||||
__builtin_amdgcn_readfirstlane(block_work_idx[I0] * MPerBlock);
|
||||
|
||||
const index_t n_block_data_idx_on_grid =
|
||||
__builtin_amdgcn_readfirstlane(block_work_idx[I1] * NPerBlock);
|
||||
|
||||
// lds max alignment
|
||||
constexpr auto max_lds_align = math::lcm(AK1, BK1);
|
||||
|
||||
// A matrix in LDS memory, dst of blockwise copy
|
||||
constexpr auto a_block_desc_ak0_m_ak1 = GetABlockDescriptor_AK0PerBlock_MPerBlock_AK1();
|
||||
|
||||
// B matrix in LDS memory, dst of blockwise copy
|
||||
constexpr auto b_block_desc_bk0_n_bk1 = GetBBlockDescriptor_BK0PerBlock_NPerBlock_BK1();
|
||||
|
||||
// A matrix blockwise copy
|
||||
auto a_blockwise_copy =
|
||||
ThreadGroupTensorSliceTransfer_v4r1<ThisThreadBlock,
|
||||
AElementwiseOperation,
|
||||
ck::tensor_operation::element_wise::PassThrough,
|
||||
InMemoryDataOperationEnum::Set,
|
||||
Sequence<AK0, MPerBlock, AK1>,
|
||||
ABlockTransferThreadClusterLengths_AK0_M_AK1,
|
||||
ABlockTransferThreadClusterArrangeOrder,
|
||||
FloatAB,
|
||||
FloatAB,
|
||||
decltype(a_grid_desc_ak0_m_ak1),
|
||||
decltype(a_block_desc_ak0_m_ak1),
|
||||
ABlockTransferSrcAccessOrder,
|
||||
Sequence<1, 0, 2>,
|
||||
ABlockTransferSrcVectorDim,
|
||||
2,
|
||||
ABlockTransferSrcScalarPerVector,
|
||||
ABlockTransferDstScalarPerVector_AK1,
|
||||
1,
|
||||
1,
|
||||
AThreadTransferSrcResetCoordinateAfterRun,
|
||||
true,
|
||||
NumGemmKPrefetchStage>(
|
||||
a_grid_desc_ak0_m_ak1,
|
||||
make_multi_index(0, m_block_data_idx_on_grid, 0),
|
||||
a_element_op,
|
||||
a_block_desc_ak0_m_ak1,
|
||||
make_multi_index(0, 0, 0),
|
||||
ck::tensor_operation::element_wise::PassThrough{});
|
||||
|
||||
// B matrix blockwise copy
|
||||
auto b_blockwise_copy =
|
||||
ThreadGroupTensorSliceTransfer_v4r1<ThisThreadBlock,
|
||||
BElementwiseOperation,
|
||||
ck::tensor_operation::element_wise::PassThrough,
|
||||
InMemoryDataOperationEnum::Set,
|
||||
Sequence<BK0, NPerBlock, BK1>,
|
||||
BBlockTransferThreadClusterLengths_BK0_N_BK1,
|
||||
BBlockTransferThreadClusterArrangeOrder,
|
||||
FloatAB,
|
||||
FloatAB,
|
||||
decltype(b_grid_desc_bk0_n_bk1),
|
||||
decltype(b_block_desc_bk0_n_bk1),
|
||||
BBlockTransferSrcAccessOrder,
|
||||
Sequence<1, 0, 2>,
|
||||
BBlockTransferSrcVectorDim,
|
||||
2,
|
||||
BBlockTransferSrcScalarPerVector,
|
||||
BBlockTransferDstScalarPerVector_BK1,
|
||||
1,
|
||||
1,
|
||||
BThreadTransferSrcResetCoordinateAfterRun,
|
||||
true,
|
||||
NumGemmKPrefetchStage>(
|
||||
b_grid_desc_bk0_n_bk1,
|
||||
make_multi_index(0, n_block_data_idx_on_grid, 0),
|
||||
b_element_op,
|
||||
b_block_desc_bk0_n_bk1,
|
||||
make_multi_index(0, 0, 0),
|
||||
ck::tensor_operation::element_wise::PassThrough{});
|
||||
|
||||
// GEMM definition
|
||||
// c_mtx += transpose(a_mtx) * b_mtx
|
||||
// a_mtx[K0PerBlock, MPerBlock] is in LDS
|
||||
// b_mtx[K0PerBlock, NPerBlock] is in LDS
|
||||
// c_mtx[MPerBlock, NPerBlock] is distributed among threads, and saved in
|
||||
// register
|
||||
// sanity check
|
||||
constexpr index_t KPack = math::max(
|
||||
math::lcm(AK1, BK1), MfmaSelector<FloatAB, MPerXdl, NPerXdl>::selected_mfma.k_per_blk);
|
||||
|
||||
auto blockwise_gemm = BlockwiseGemmXdlops_k0mk1_k0nk1_m0n0m1n1m2m3m4n2_Selector<
|
||||
BlockSize,
|
||||
FloatAB,
|
||||
FloatGemmAcc,
|
||||
decltype(a_block_desc_ak0_m_ak1),
|
||||
decltype(b_block_desc_bk0_n_bk1),
|
||||
MPerXdl,
|
||||
NPerXdl,
|
||||
MXdlPerWave,
|
||||
NXdlPerWave,
|
||||
KPack,
|
||||
LoopSched>();
|
||||
|
||||
auto c_thread_buf = blockwise_gemm.GetCThreadBuffer();
|
||||
|
||||
// LDS allocation for A and B: be careful of alignment
|
||||
constexpr auto a_block_space_size_aligned = math::integer_least_multiple(
|
||||
a_block_desc_ak0_m_ak1.GetElementSpaceSize(), max_lds_align);
|
||||
|
||||
auto a_block_buf = make_dynamic_buffer<AddressSpaceEnum::Lds>(
|
||||
static_cast<FloatAB*>(p_shared), a_block_desc_ak0_m_ak1.GetElementSpaceSize());
|
||||
|
||||
auto b_block_buf = make_dynamic_buffer<AddressSpaceEnum::Lds>(
|
||||
static_cast<FloatAB*>(p_shared) + a_block_space_size_aligned,
|
||||
b_block_desc_bk0_n_bk1.GetElementSpaceSize());
|
||||
|
||||
constexpr auto a_block_slice_copy_step = make_multi_index(KPerBlock / AK1, 0, 0);
|
||||
constexpr auto b_block_slice_copy_step = make_multi_index(KPerBlock / BK1, 0, 0);
|
||||
|
||||
// gridwise GEMM pipeline
|
||||
const auto gridwise_gemm_pipeline =
|
||||
GridwiseGemmPipeline_v1_Selector<NumGemmKPrefetchStage, LoopSched>();
|
||||
|
||||
const index_t num_k_block_main_loop = __builtin_amdgcn_readfirstlane(
|
||||
(a_grid_desc_ak0_m_ak1.GetLength(I0) * a_grid_desc_ak0_m_ak1.GetLength(I2)) /
|
||||
KPerBlock);
|
||||
|
||||
gridwise_gemm_pipeline.template Run<HasMainKBlockLoop>(a_grid_desc_ak0_m_ak1,
|
||||
a_block_desc_ak0_m_ak1,
|
||||
a_blockwise_copy,
|
||||
a_grid_buf,
|
||||
a_block_buf,
|
||||
a_block_slice_copy_step,
|
||||
b_grid_desc_bk0_n_bk1,
|
||||
b_block_desc_bk0_n_bk1,
|
||||
b_blockwise_copy,
|
||||
b_grid_buf,
|
||||
b_block_buf,
|
||||
b_block_slice_copy_step,
|
||||
blockwise_gemm,
|
||||
c_thread_buf,
|
||||
num_k_block_main_loop);
|
||||
|
||||
// shuffle C + Ds + reduction + write out
|
||||
{
|
||||
static_assert(MXdlPerWave % CShuffleMXdlPerWavePerShuffle == 0 &&
|
||||
NXdlPerWave % CShuffleNXdlPerWavePerShuffle == 0,
|
||||
"wrong!");
|
||||
|
||||
constexpr index_t MWave = MPerBlock / (MXdlPerWave * MPerXdl);
|
||||
constexpr index_t NWave = NPerBlock / (NXdlPerWave * NPerXdl);
|
||||
|
||||
// TODO: hacky, fix it!
|
||||
constexpr auto c_thread_desc_m0_n0_m1_n1_m2_m3_m4_n2 =
|
||||
blockwise_gemm.GetCThreadDescriptor_M0_N0_M1_N1_M2_M3_M4_N2();
|
||||
|
||||
// TODO: hacky, fix it!
|
||||
// c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp is only used to get lengths
|
||||
constexpr auto c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp =
|
||||
blockwise_gemm.GetCBlockDescriptor_M0_N0_M1_N1_M2_M3_M4_N2();
|
||||
|
||||
constexpr auto M0 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp.GetLength(I0);
|
||||
constexpr auto N0 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp.GetLength(I1);
|
||||
constexpr auto M1 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp.GetLength(I2);
|
||||
constexpr auto N1 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp.GetLength(I3);
|
||||
constexpr auto M2 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp.GetLength(I4);
|
||||
constexpr auto M3 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp.GetLength(I5);
|
||||
constexpr auto M4 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp.GetLength(I6);
|
||||
constexpr auto N2 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp.GetLength(I7);
|
||||
|
||||
constexpr auto c_shuffle_block_desc_mblock_mperblock_nblock_nperblock =
|
||||
GetCShuffleBlockDescriptor_MBlock_MPerBlock_NBlock_NPerBlock();
|
||||
|
||||
auto c_shuffle_block_buf = make_dynamic_buffer<AddressSpaceEnum::Lds>(
|
||||
static_cast<FloatCShuffle*>(p_shared),
|
||||
c_shuffle_block_desc_mblock_mperblock_nblock_nperblock.GetElementSpaceSize());
|
||||
|
||||
constexpr auto c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2 = transform_tensor_descriptor(
|
||||
c_shuffle_block_desc_mblock_mperblock_nblock_nperblock,
|
||||
make_tuple(
|
||||
make_freeze_transform(I0),
|
||||
make_unmerge_transform(make_tuple(
|
||||
Number<CShuffleMXdlPerWavePerShuffle>{}, // M0 (MXdlPerWave) per shuffle
|
||||
M1, // M1 = MWave
|
||||
M2, // M2 * M3 * M4 = MPerXdl
|
||||
M3,
|
||||
M4)),
|
||||
make_freeze_transform(I0),
|
||||
make_unmerge_transform(make_tuple(
|
||||
Number<CShuffleNXdlPerWavePerShuffle>{}, // N0 (NXdlPerWave) per shuffle
|
||||
N1, // N1 = NWave
|
||||
N2))), // N2 = NPerXdl
|
||||
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
|
||||
make_tuple(
|
||||
Sequence<>{}, Sequence<0, 2, 4, 5, 6>{}, Sequence<>{}, Sequence<1, 3, 7>{}));
|
||||
|
||||
// calculate origin of thread output tensor on global memory
|
||||
// blockwise GEMM c matrix starting index
|
||||
const auto c_thread_mtx_on_block =
|
||||
blockwise_gemm.CalculateCThreadOriginDataIndex(I0, I0, I0, I0);
|
||||
|
||||
const index_t m_thread_data_on_block = c_thread_mtx_on_block[I0];
|
||||
const index_t n_thread_data_on_block = c_thread_mtx_on_block[I1];
|
||||
|
||||
const auto m_thread_data_on_block_to_m0_m1_m2_m3_m4_adaptor =
|
||||
make_single_stage_tensor_adaptor(
|
||||
make_tuple(make_merge_transform(make_tuple(M0, M1, M2, M3, M4))),
|
||||
make_tuple(Sequence<0, 1, 2, 3, 4>{}),
|
||||
make_tuple(Sequence<0>{}));
|
||||
|
||||
const auto m_thread_data_on_block_idx =
|
||||
m_thread_data_on_block_to_m0_m1_m2_m3_m4_adaptor.CalculateBottomIndex(
|
||||
make_multi_index(m_thread_data_on_block));
|
||||
|
||||
const auto n_thread_data_on_block_to_n0_n1_n2_adaptor =
|
||||
make_single_stage_tensor_adaptor(
|
||||
make_tuple(make_merge_transform(make_tuple(N0, N1, N2))),
|
||||
make_tuple(Sequence<0, 1, 2>{}),
|
||||
make_tuple(Sequence<0>{}));
|
||||
|
||||
const auto n_thread_data_on_block_idx =
|
||||
n_thread_data_on_block_to_n0_n1_n2_adaptor.CalculateBottomIndex(
|
||||
make_multi_index(n_thread_data_on_block));
|
||||
|
||||
// shuffle: threadwise copy C from VGPR to LDS
|
||||
auto c_thread_copy_vgpr_to_lds =
|
||||
ThreadwiseTensorSliceTransfer_v1r3<FloatGemmAcc,
|
||||
FloatCShuffle,
|
||||
decltype(c_thread_desc_m0_n0_m1_n1_m2_m3_m4_n2),
|
||||
decltype(c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2),
|
||||
ck::tensor_operation::element_wise::PassThrough,
|
||||
Sequence<CShuffleMXdlPerWavePerShuffle,
|
||||
CShuffleNXdlPerWavePerShuffle,
|
||||
I1,
|
||||
I1,
|
||||
M2,
|
||||
I1,
|
||||
M4,
|
||||
I1>,
|
||||
Sequence<0, 1, 2, 3, 4, 5, 6, 7>,
|
||||
7,
|
||||
1,
|
||||
InMemoryDataOperationEnum::Set,
|
||||
1,
|
||||
true>{
|
||||
c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2,
|
||||
make_multi_index(0,
|
||||
0,
|
||||
m_thread_data_on_block_idx[I1],
|
||||
n_thread_data_on_block_idx[I1],
|
||||
m_thread_data_on_block_idx[I2],
|
||||
m_thread_data_on_block_idx[I3],
|
||||
m_thread_data_on_block_idx[I4],
|
||||
n_thread_data_on_block_idx[I2]),
|
||||
ck::tensor_operation::element_wise::PassThrough{}};
|
||||
|
||||
// space filling curve for threadwise C in VGPR
|
||||
constexpr auto sfc_c_vgpr =
|
||||
SpaceFillingCurve<Sequence<MXdlPerWave, NXdlPerWave, 1, 1, M2, 1, M4, 1>,
|
||||
Sequence<0, 1, 2, 3, 4, 5, 6, 7>,
|
||||
Sequence<CShuffleMXdlPerWavePerShuffle,
|
||||
CShuffleNXdlPerWavePerShuffle,
|
||||
1,
|
||||
1,
|
||||
M2,
|
||||
1,
|
||||
M4,
|
||||
1>>{};
|
||||
|
||||
// space filling curve for shuffled blockwise C in global mem
|
||||
constexpr auto sfc_der_global =
|
||||
SpaceFillingCurve<Sequence<1, MPerBlock, 1, NPerBlock>,
|
||||
Sequence<0, 2, 1, 3>,
|
||||
Sequence<1,
|
||||
CShuffleMXdlPerWavePerShuffle * MWave * MPerXdl,
|
||||
1,
|
||||
CShuffleNXdlPerWavePerShuffle * NWave * NPerXdl>>{};
|
||||
|
||||
// TODO: this should be implemented as a blockwise reduction
|
||||
// LDS c_reduce_block_desc_mperblock_nperblock
|
||||
constexpr auto c_reduce_block_desc_mperblock_nperblock = transform_tensor_descriptor(
|
||||
c_shuffle_block_desc_mblock_mperblock_nblock_nperblock,
|
||||
make_tuple(
|
||||
make_freeze_transform(I0),
|
||||
make_pass_through_transform(
|
||||
c_shuffle_block_desc_mblock_mperblock_nblock_nperblock.GetLength(I1)),
|
||||
make_freeze_transform(I0),
|
||||
make_pass_through_transform(
|
||||
c_shuffle_block_desc_mblock_mperblock_nblock_nperblock.GetLength(I3))),
|
||||
make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
|
||||
make_tuple(Sequence<>{}, Sequence<0>{}, Sequence<>{}, Sequence<1>{}));
|
||||
|
||||
static_assert(CDRThreadTransferClusterLengths_MPerBlock_NPerBlock::At(I0) *
|
||||
CDRThreadTransferClusterLengths_MPerBlock_NPerBlock::At(I1) ==
|
||||
BlockSize,
|
||||
"wrong!");
|
||||
|
||||
static_assert((CShuffleMXdlPerWavePerShuffle * MWave * MPerXdl) %
|
||||
CDRThreadTransferClusterLengths_MPerBlock_NPerBlock::At(I0) ==
|
||||
0 &&
|
||||
(CShuffleNXdlPerWavePerShuffle * NWave * NPerXdl) %
|
||||
CDRThreadTransferClusterLengths_MPerBlock_NPerBlock::At(I1) ==
|
||||
0,
|
||||
"wrong!");
|
||||
|
||||
constexpr index_t mreduce_per_thread =
|
||||
(CShuffleMXdlPerWavePerShuffle * MWave * MPerXdl) /
|
||||
CDRThreadTransferClusterLengths_MPerBlock_NPerBlock::At(I0);
|
||||
|
||||
constexpr index_t nreduce_per_thread =
|
||||
(CShuffleNXdlPerWavePerShuffle * NWave * NPerXdl) /
|
||||
CDRThreadTransferClusterLengths_MPerBlock_NPerBlock::At(I1);
|
||||
|
||||
constexpr auto c_reduce_thread_lengths_mperblock_nperblock =
|
||||
Sequence<mreduce_per_thread, nreduce_per_thread>{};
|
||||
|
||||
// VGPR cde_reduce_thread_desc_mperblock_nperblock
|
||||
constexpr auto cde_reduce_thread_desc_mperblock_nperblock =
|
||||
make_naive_tensor_descriptor_packed(
|
||||
make_tuple(Number<mreduce_per_thread>{}, Number<nreduce_per_thread>{}));
|
||||
|
||||
constexpr auto r_thread_desc_mperblock =
|
||||
make_naive_tensor_descriptor_packed(make_tuple(Number<mreduce_per_thread>{}));
|
||||
|
||||
constexpr auto r_thread_desc_mblock_mperblock =
|
||||
make_naive_tensor_descriptor_packed(make_tuple(I1, Number<mreduce_per_thread>{}));
|
||||
|
||||
auto e_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, FloatReduceAcc>(
|
||||
cde_reduce_thread_desc_mperblock_nperblock.GetElementSpaceSize());
|
||||
|
||||
// reduce: threadwise copy from LDS to VGPR
|
||||
constexpr auto c_reduce_thread_cluster_desc = make_cluster_descriptor(
|
||||
CDRThreadTransferClusterLengths_MPerBlock_NPerBlock{}, Sequence<1, 0>{});
|
||||
|
||||
const auto c_reduce_thread_cluster_idx =
|
||||
c_reduce_thread_cluster_desc.CalculateBottomIndex(
|
||||
make_multi_index(get_thread_local_1d_id()));
|
||||
|
||||
const auto c_reduce_thread_data_idx_begin =
|
||||
c_reduce_thread_cluster_idx * c_reduce_thread_lengths_mperblock_nperblock;
|
||||
|
||||
// To apply D0, D1, ... and reduction.
|
||||
// Copy c shuffle from LDS back to VGPR
|
||||
auto c_reduce_thread_copy_lds_to_vgpr = ThreadwiseTensorSliceTransfer_v2<
|
||||
FloatCShuffle,
|
||||
FloatReduceAcc,
|
||||
decltype(c_reduce_block_desc_mperblock_nperblock),
|
||||
decltype(cde_reduce_thread_desc_mperblock_nperblock),
|
||||
decltype(c_reduce_thread_lengths_mperblock_nperblock),
|
||||
Sequence<0, 1>,
|
||||
1,
|
||||
CDEReduceThreadTransferScalarPerVector_NPerBlock,
|
||||
1,
|
||||
true>{c_reduce_block_desc_mperblock_nperblock, c_reduce_thread_data_idx_begin};
|
||||
|
||||
// Copy result of reduction back from VGPR to global
|
||||
auto reduce_tuple_thread_copy_vgpr_to_global = generate_tuple(
|
||||
[&](auto I) {
|
||||
auto p_r_grid = p_rs_grid[I];
|
||||
auto r_element_op = rs_element_op[I];
|
||||
auto r_grid_desc_mblock_mperblock = rs_grid_desc_mblock_mperblock[I];
|
||||
|
||||
return ThreadwiseTensorSliceTransfer_v1r3<
|
||||
FloatReduceAcc,
|
||||
remove_pointer_t<decltype(p_r_grid)>,
|
||||
decltype(r_thread_desc_mblock_mperblock),
|
||||
decltype(r_grid_desc_mblock_mperblock),
|
||||
decltype(r_element_op),
|
||||
Sequence<1, mreduce_per_thread>,
|
||||
Sequence<0, 1>,
|
||||
1,
|
||||
RThreadTransferDstScalarPerVector_MPerBlock,
|
||||
RsGlobalMemoryDataOperation::At(I),
|
||||
1,
|
||||
false>{r_grid_desc_mblock_mperblock,
|
||||
make_multi_index(block_work_idx[I0], // mblock
|
||||
c_reduce_thread_data_idx_begin[I0]), // mperblock
|
||||
r_element_op};
|
||||
},
|
||||
Number<NumRTensor>{});
|
||||
|
||||
// D0, D1, ..., Dn
|
||||
constexpr auto cde_reduce_thread_desc_I1_mperblock_I1_nperblock =
|
||||
make_naive_tensor_descriptor_packed(
|
||||
make_tuple(I1, Number<mreduce_per_thread>{}, I1, Number<nreduce_per_thread>{}));
|
||||
|
||||
// FIXME: Decrease usage of VGPR
|
||||
// Apply pointwise lambda function from multi-source (Global and LDS) into VGPR
|
||||
auto ds_thread_buf = generate_tuple(
|
||||
[&](auto) {
|
||||
return make_static_buffer<AddressSpaceEnum::Vgpr, FloatReduceAcc>(
|
||||
cde_reduce_thread_desc_I1_mperblock_I1_nperblock.GetElementSpaceSize());
|
||||
},
|
||||
Number<NumDTensor>{});
|
||||
|
||||
// Copy D0, D1, ..., Dn from global to VGPR
|
||||
auto ds_thread_copy_global_to_vgpr = generate_tuple(
|
||||
[&](auto I) {
|
||||
using DDataType = remove_cvref_t<tuple_element_t<I.value, DsDataType>>;
|
||||
return ThreadwiseTensorSliceTransfer_v2<
|
||||
DDataType,
|
||||
FloatReduceAcc,
|
||||
decltype(ds_grid_desc_mblock_mperblock_nblock_nperblock[I]),
|
||||
decltype(cde_reduce_thread_desc_I1_mperblock_I1_nperblock),
|
||||
Sequence<I1, mreduce_per_thread, I1, nreduce_per_thread>,
|
||||
Sequence<0, 1, 2, 3>,
|
||||
3,
|
||||
CDEReduceThreadTransferScalarPerVector_NPerBlock,
|
||||
1,
|
||||
true>(ds_grid_desc_mblock_mperblock_nblock_nperblock[I],
|
||||
make_multi_index(
|
||||
I0,
|
||||
m_block_data_idx_on_grid + c_reduce_thread_data_idx_begin[I0],
|
||||
I0,
|
||||
n_block_data_idx_on_grid + c_reduce_thread_data_idx_begin[I1]));
|
||||
},
|
||||
Number<NumDTensor>{});
|
||||
|
||||
auto e_thread_copy_vgpr_to_global = ThreadwiseTensorSliceTransfer_v1r3<
|
||||
FloatReduceAcc,
|
||||
FloatE,
|
||||
decltype(cde_reduce_thread_desc_I1_mperblock_I1_nperblock),
|
||||
decltype(e_grid_desc_mblock_mperblock_nblock_nperblock),
|
||||
tensor_operation::element_wise::PassThrough,
|
||||
Sequence<I1, mreduce_per_thread, I1, nreduce_per_thread>, // SliceLengths
|
||||
Sequence<0, 1, 2, 3>, // DimAccessOrder
|
||||
3, // DstVectorDim
|
||||
CDEReduceThreadTransferScalarPerVector_NPerBlock,
|
||||
InMemoryDataOperationEnum::Set,
|
||||
1,
|
||||
true>{
|
||||
e_grid_desc_mblock_mperblock_nblock_nperblock,
|
||||
make_multi_index(I0,
|
||||
m_block_data_idx_on_grid + c_reduce_thread_data_idx_begin[I0],
|
||||
I0,
|
||||
n_block_data_idx_on_grid + c_reduce_thread_data_idx_begin[I1]),
|
||||
tensor_operation::element_wise::PassThrough{}};
|
||||
|
||||
constexpr index_t num_access = sfc_c_vgpr.GetNumOfAccess();
|
||||
|
||||
static_assert(num_access == sfc_der_global.GetNumOfAccess(), "wrong!");
|
||||
|
||||
static_for<0, num_access, 1>{}([&](auto access_id) {
|
||||
// make sure it's safe to read from LDS
|
||||
if constexpr(access_id > 0)
|
||||
block_sync_lds();
|
||||
|
||||
// each thread shuffle data from VGPR to LDS
|
||||
c_thread_copy_vgpr_to_lds.Run(c_thread_desc_m0_n0_m1_n1_m2_m3_m4_n2,
|
||||
sfc_c_vgpr.GetIndexTupleOfNumber(access_id),
|
||||
c_thread_buf,
|
||||
c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2,
|
||||
c_shuffle_block_buf);
|
||||
|
||||
// make sure it's safe to write to LDS
|
||||
block_sync_lds();
|
||||
|
||||
// Get shuffle data from LDS to VGPR
|
||||
c_reduce_thread_copy_lds_to_vgpr.Run(c_reduce_block_desc_mperblock_nperblock,
|
||||
c_shuffle_block_buf,
|
||||
cde_reduce_thread_desc_mperblock_nperblock,
|
||||
make_tuple(I0, I0),
|
||||
e_thread_buf);
|
||||
|
||||
// Global read D0, D1, ...
|
||||
static_for<0, NumDTensor, 1>{}([&](auto Id) {
|
||||
auto& d_thread_copy_global_to_vgpr = ds_thread_copy_global_to_vgpr(Id);
|
||||
d_thread_copy_global_to_vgpr.Run(
|
||||
ds_grid_desc_mblock_mperblock_nblock_nperblock[Id],
|
||||
ds_grid_buf[Id],
|
||||
cde_reduce_thread_desc_I1_mperblock_I1_nperblock,
|
||||
make_tuple(I0, I0, I0, I0),
|
||||
ds_thread_buf(Id));
|
||||
|
||||
if constexpr(access_id < num_access - 1)
|
||||
{
|
||||
// move on D0, D1, ...
|
||||
constexpr auto de_global_step = sfc_der_global.GetForwardStep(access_id);
|
||||
d_thread_copy_global_to_vgpr.MoveSrcSliceWindow(
|
||||
ds_grid_desc_mblock_mperblock_nblock_nperblock[Id], de_global_step);
|
||||
}
|
||||
});
|
||||
|
||||
// cde_element_op(e, c, d0, d1, ...);
|
||||
static_for<0, cde_reduce_thread_desc_mperblock_nperblock.GetElementSize(), 1>{}(
|
||||
[&](auto i) {
|
||||
const auto c_ds_src_data_refs = concat_tuple_of_reference(
|
||||
tie(e_thread_buf[i]),
|
||||
generate_tie(
|
||||
[&](auto Id) -> const auto& { return ds_thread_buf[Id][i]; },
|
||||
Number<NumDTensor>{}));
|
||||
auto e_dst_data_refs = tie(e_thread_buf(i));
|
||||
unpack2(cde_element_op, e_dst_data_refs, c_ds_src_data_refs);
|
||||
});
|
||||
|
||||
// Global write E
|
||||
e_thread_copy_vgpr_to_global.Run(cde_reduce_thread_desc_I1_mperblock_I1_nperblock,
|
||||
make_tuple(I0, I0, I0, I0),
|
||||
e_thread_buf,
|
||||
e_grid_desc_mblock_mperblock_nblock_nperblock,
|
||||
e_grid_buf);
|
||||
|
||||
if constexpr(access_id < num_access - 1)
|
||||
{
|
||||
// move on E
|
||||
constexpr auto de_global_step = sfc_der_global.GetForwardStep(access_id);
|
||||
e_thread_copy_vgpr_to_global.MoveDstSliceWindow(
|
||||
e_grid_desc_mblock_mperblock_nblock_nperblock, de_global_step);
|
||||
}
|
||||
|
||||
// reduction
|
||||
static_for<0, NumRTensor, 1>{}([&](auto Ir) {
|
||||
auto r_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, FloatReduceAcc>(
|
||||
r_thread_desc_mperblock.GetElementSpaceSize());
|
||||
|
||||
auto& reduce_thread_copy_vgpr_to_global =
|
||||
reduce_tuple_thread_copy_vgpr_to_global(Ir);
|
||||
|
||||
using ThreadReduceOperation =
|
||||
remove_cvref_t<decltype(ThreadReduceOperations{}[Ir])>;
|
||||
|
||||
using ThreadwiseReduce =
|
||||
ThreadwiseReduction<FloatReduceAcc,
|
||||
decltype(cde_reduce_thread_desc_mperblock_nperblock),
|
||||
decltype(r_thread_desc_mperblock),
|
||||
ThreadReduceOperation,
|
||||
false>;
|
||||
|
||||
// threadwise reduction
|
||||
const auto reduce_identityVal =
|
||||
ThreadReduceOperation::template GetIdentityValue<FloatReduceAcc>();
|
||||
static_for<0, mreduce_per_thread, 1>{}(
|
||||
[&](auto I) { r_thread_buf(I) = reduce_identityVal; });
|
||||
static_for<0, mreduce_per_thread, 1>{}([&](auto im) {
|
||||
static_for<0, nreduce_per_thread, 1>{}([&](auto in) {
|
||||
constexpr auto offset =
|
||||
Number<cde_reduce_thread_desc_mperblock_nperblock.CalculateOffset(
|
||||
make_tuple(im, in))>{};
|
||||
|
||||
qs_element_op[Ir](e_thread_buf(offset), e_thread_buf(offset));
|
||||
});
|
||||
});
|
||||
ThreadwiseReduce::Reduce(e_thread_buf, r_thread_buf);
|
||||
|
||||
// gridwise reduction
|
||||
reduce_thread_copy_vgpr_to_global.Run(r_thread_desc_mblock_mperblock,
|
||||
make_tuple(I0, I0),
|
||||
r_thread_buf,
|
||||
rs_grid_desc_mblock_mperblock[Ir],
|
||||
rs_grid_buf(Ir));
|
||||
|
||||
if constexpr(access_id < num_access - 1)
|
||||
{
|
||||
// move on R0, R1, ...
|
||||
constexpr auto de_global_step = sfc_der_global.GetForwardStep(access_id);
|
||||
reduce_thread_copy_vgpr_to_global.MoveDstSliceWindow(
|
||||
rs_grid_desc_mblock_mperblock[Ir],
|
||||
make_tuple(de_global_step[I0], de_global_step[I1]));
|
||||
}
|
||||
});
|
||||
}); // copy c, d, e + reduction
|
||||
|
||||
} // shuffle C + Ds + reduction + write out
|
||||
} // Run
|
||||
};
|
||||
|
||||
} // namespace ck
|
||||
Reference in New Issue
Block a user