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Example for conv2d backward weight fp16 (#106)
* add wrw reference
* start device
* raw not split version
* run simple example
* start to use atomic add
* simple transform result correct
* first version that can run
* fix atomic and set operator choice
* add check split-k
* format
* change input parameter
* add pad for t total
* rename example index
Co-authored-by: ltqin <letaoqin@amd.com>
[ROCm/composable_kernel commit: 7a9b93f4b6]
This commit is contained in:
ltqin
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#ifndef DEVICE_CONV2D_WRW_XDL_C_SHUFFLE_NHWC_KYXC_NHWK_HPP
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#define DEVICE_CONV2D_WRW_XDL_C_SHUFFLE_NHWC_KYXC_NHWK_HPP
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#include <iostream>
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#include <sstream>
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#include "device.hpp"
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#include "device_base.hpp"
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#include "device_conv_backward_weight.hpp"
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#include "convolution_forward_specialization.hpp"
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#include "common_header.hpp"
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#include "tensor_layout.hpp"
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#include "tensor_descriptor.hpp"
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#include "tensor_descriptor_helper.hpp"
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#include "gridwise_gemm_xdlops_v2r4r2.hpp"
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namespace ck {
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namespace tensor_operation {
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namespace device {
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// out[N, Ho, Wo, K] = in[N, Hi, Wi, C] * wei[K, Y, X, C]
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template <typename InDataType,
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typename WeiDataType,
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typename OutDataType,
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typename AccDataType,
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typename InElementwiseOperation,
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typename WeiElementwiseOperation,
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typename OutElementwiseOperation,
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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 MPerXdl,
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ck::index_t NPerXdl,
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ck::index_t MXdlPerWave,
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ck::index_t NXdlPerWave,
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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 CShuffleMXdlPerWavePerShuffle,
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index_t CShuffleNXdlPerWavePerShuffle,
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typename CBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
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index_t CBlockTransferScalarPerVector_NWaveNPerXdl>
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struct DeviceConv2dWrWXdl_C_Shuffle_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_N_Ho_Wo_K
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: public DeviceConvWrw<InElementwiseOperation, WeiElementwiseOperation, OutElementwiseOperation>
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{
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using DeviceOp = DeviceConv2dWrWXdl_C_Shuffle_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_N_Ho_Wo_K;
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using ADataType = OutDataType;
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using BDataType = InDataType;
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using CDataType = WeiDataType;
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using AElementwiseOperation = OutElementwiseOperation;
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using BElementwiseOperation = InElementwiseOperation;
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using CElementwiseOperation = WeiElementwiseOperation;
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// TODO make A/B datatype different
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using ABDataType = InDataType;
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static constexpr index_t NDimSpatial = 2;
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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 constexpr auto I4 = Number<4>{};
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static constexpr auto I5 = Number<5>{};
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static constexpr auto K1Number = Number<K1>{};
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static constexpr auto GemmK1Number = K1Number;
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static auto
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MakeABCGridDescriptor_A_K0_M_K1_B_K0_N_K1_C_M_N(ck::index_t N,
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ck::index_t K,
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ck::index_t C,
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std::vector<ck::index_t> input_spatial_lengths,
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std::vector<ck::index_t> filter_spatial_lengths,
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std::vector<ck::index_t> output_spatial_lengths,
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std::vector<ck::index_t> conv_filter_strides,
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std::vector<ck::index_t> conv_filter_dilations,
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std::vector<ck::index_t> input_left_pads,
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std::vector<ck::index_t> input_right_pads,
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ck::index_t batch_k)
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{
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using namespace ck;
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const index_t Hi = input_spatial_lengths[0];
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const index_t Wi = input_spatial_lengths[1];
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const index_t Ho = output_spatial_lengths[0];
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const index_t Wo = output_spatial_lengths[1];
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const index_t Y = filter_spatial_lengths[0];
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const index_t X = filter_spatial_lengths[1];
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const index_t ConvStrideH = conv_filter_strides[0];
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const index_t ConvStrideW = conv_filter_strides[1];
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const index_t ConvDilationH = conv_filter_dilations[0];
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const index_t ConvDilationW = conv_filter_dilations[1];
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const index_t InLeftPadH = input_left_pads[0];
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const index_t InLeftPadW = input_left_pads[1];
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const index_t InRightPadH = input_right_pads[0];
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const index_t InRightPadW = input_right_pads[1];
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const index_t GemmKTotal = N * Ho * Wo;
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const index_t GemmM = K;
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const index_t GemmN = C * X * Y;
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const index_t GemmKBatch = batch_k;
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const index_t GemmK0 =
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math::integer_divide_ceil(GemmKTotal, GemmK1Number * K0PerBlock * GemmKBatch) *
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K0PerBlock;
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const index_t GemmKPad = GemmKBatch * GemmK0 * GemmK1Number;
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const auto out_gemmktotal_gemmm_grid_desc =
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make_naive_tensor_descriptor_packed(make_tuple(N * Ho * Wo, K));
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const auto in_n_hi_wi_c_grid_desc =
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make_naive_tensor_descriptor_packed(make_tuple(N, Hi, Wi, C));
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// A: output tensor
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const auto out_gemmkpad_gemmm_grid_desc = transform_tensor_descriptor(
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out_gemmktotal_gemmm_grid_desc,
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make_tuple(make_right_pad_transform(GemmKTotal, GemmKPad - GemmKTotal),
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make_pass_through_transform(GemmM)),
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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 out_gemmkbatch_gemmk0_gemmm_gemmk1_grid_desc = transform_tensor_descriptor(
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out_gemmkpad_gemmm_grid_desc,
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make_tuple(make_unmerge_transform(make_tuple(GemmKBatch, GemmK0, GemmK1Number)),
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make_pass_through_transform(GemmM)),
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make_tuple(Sequence<0>{}, Sequence<1>{}),
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make_tuple(Sequence<0, 1, 3>{}, Sequence<2>{}));
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// B: input tensor
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const auto in_n_hip_wip_c_grid_desc = transform_tensor_descriptor(
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in_n_hi_wi_c_grid_desc,
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make_tuple(make_pass_through_transform(N),
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make_pad_transform(Hi, InLeftPadH, InRightPadH),
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make_pad_transform(Wi, InLeftPadW, InRightPadW),
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make_pass_through_transform(C)),
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make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
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make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}));
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const auto in_n_y_ho_x_wo_c_grid_desc = transform_tensor_descriptor(
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in_n_hip_wip_c_grid_desc,
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make_tuple(
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make_pass_through_transform(N),
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make_embed_transform(make_tuple(Y, Ho), make_tuple(ConvDilationH, ConvStrideH)),
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make_embed_transform(make_tuple(X, Wo), make_tuple(ConvDilationW, ConvStrideW)),
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make_pass_through_transform(C)),
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make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
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make_tuple(Sequence<0>{}, Sequence<1, 2>{}, Sequence<3, 4>{}, Sequence<5>{}));
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const auto in_gemmktotal_gemmn_grid_desc =
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transform_tensor_descriptor(in_n_y_ho_x_wo_c_grid_desc,
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make_tuple(make_merge_transform(make_tuple(Y, X, C)),
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make_merge_transform(make_tuple(N, Ho, Wo))),
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make_tuple(Sequence<1, 3, 5>{}, Sequence<0, 2, 4>{}),
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make_tuple(Sequence<1>{}, Sequence<0>{}));
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const auto in_gemmkpad_gemmn_grid_desc = transform_tensor_descriptor(
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in_gemmktotal_gemmn_grid_desc,
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make_tuple(make_right_pad_transform(GemmKTotal, GemmKPad - GemmKTotal),
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make_pass_through_transform(GemmN)),
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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 in_gemmkbatch_gemmk0_gemmn_gemmk1_grid_desc = transform_tensor_descriptor(
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in_gemmkpad_gemmn_grid_desc,
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make_tuple(make_unmerge_transform(make_tuple(GemmKBatch, GemmK0, GemmK1Number)),
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make_pass_through_transform(GemmN)),
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make_tuple(Sequence<0>{}, Sequence<1>{}),
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make_tuple(Sequence<0, 1, 3>{}, Sequence<2>{}));
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// C: weight tensor
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const auto wei_gemmm_gemmn_grid_desc =
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make_naive_tensor_descriptor_packed(make_tuple(K, Y * X * C));
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return make_tuple(out_gemmkbatch_gemmk0_gemmm_gemmk1_grid_desc,
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in_gemmkbatch_gemmk0_gemmn_gemmk1_grid_desc,
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wei_gemmm_gemmn_grid_desc);
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}
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using ABCGridDescs = decltype(MakeABCGridDescriptor_A_K0_M_K1_B_K0_N_K1_C_M_N(
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1, 1, 1, {1, 1}, {1, 1}, {1, 1}, {1, 1}, {1, 1}, {1, 1}, {1, 1}, 1));
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using AGridDesc_K0_M_K1 = remove_cvref_t<decltype(ABCGridDescs{}[I0])>;
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using BGridDesc_K0_N_K1 = remove_cvref_t<decltype(ABCGridDescs{}[I1])>;
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using CGridDesc_M_N = remove_cvref_t<decltype(ABCGridDescs{}[I2])>;
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// GridwiseGemm
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using GridwiseGemm = GridwiseGemm_bk0mk1_bk0nk1_mn_xdlops_v2r4r2<
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BlockSize,
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ADataType, // TODO: distinguish A/B datatype
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AccDataType,
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CDataType,
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InMemoryDataOperationEnum_t::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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MPerXdl,
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NPerXdl,
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K1,
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MXdlPerWave,
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NXdlPerWave,
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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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CShuffleMXdlPerWavePerShuffle,
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CShuffleNXdlPerWavePerShuffle,
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CBlockTransferScalarPerVector_NWaveNPerXdl,
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CBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock>;
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using GridwiseGemmAtomicAdd = GridwiseGemm_bk0mk1_bk0nk1_mn_xdlops_v2r4r2<
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BlockSize,
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ADataType, // TODO: distinguish A/B datatype
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AccDataType,
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CDataType,
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InMemoryDataOperationEnum_t::AtomicAdd,
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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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MPerXdl,
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NPerXdl,
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K1,
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MXdlPerWave,
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NXdlPerWave,
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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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CShuffleMXdlPerWavePerShuffle,
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CShuffleNXdlPerWavePerShuffle,
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CBlockTransferScalarPerVector_NWaveNPerXdl,
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CBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock>;
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// Argument
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using CGridDesc_MBlock_MPerBlock_NBlock_NPerBlock =
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decltype(GridwiseGemm::MakeCGridDesc_MBlock_MPerBlock_NBlock_NPerBlock(CGridDesc_M_N{}));
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using Block2CTileMap =
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decltype(GridwiseGemm::MakeCBlockClusterAdaptor(CGridDesc_M_N{}, 1, 1, 1));
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struct Argument : public BaseArgument
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{
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Argument(const InDataType* p_in_grid,
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WeiDataType* p_wei_grid,
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const OutDataType* p_out_grid,
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ck::index_t N,
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ck::index_t K,
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ck::index_t C,
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std::vector<ck::index_t> input_spatial_lengths,
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std::vector<ck::index_t> filter_spatial_lengths,
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std::vector<ck::index_t> output_spatial_lengths,
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std::vector<ck::index_t> conv_filter_strides,
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std::vector<ck::index_t> conv_filter_dilations,
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std::vector<ck::index_t> input_left_pads,
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std::vector<ck::index_t> input_right_pads,
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ck::index_t M01,
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ck::index_t N01,
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InElementwiseOperation in_element_op,
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WeiElementwiseOperation wei_element_op,
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OutElementwiseOperation out_element_op,
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ck::index_t split_k)
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: p_a_grid_{p_out_grid},
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p_b_grid_{p_in_grid},
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p_c_grid_{p_wei_grid},
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a_grid_desc_kbatch_k0_m_k1_{},
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b_grid_desc_kbatch_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_{out_element_op},
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b_element_op_{in_element_op},
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c_element_op_{wei_element_op},
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Conv_N_{N},
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Conv_K_{K},
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Conv_C_{C},
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output_spatial_lengths_{output_spatial_lengths},
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filter_spatial_lengths_{filter_spatial_lengths},
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conv_filter_strides_{conv_filter_strides},
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input_left_pads_{input_left_pads},
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input_right_pads_{input_right_pads},
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k_batch_{split_k}
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{
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const auto descs =
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DeviceOp::MakeABCGridDescriptor_A_K0_M_K1_B_K0_N_K1_C_M_N(N,
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K,
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C,
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input_spatial_lengths,
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filter_spatial_lengths,
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output_spatial_lengths,
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conv_filter_strides,
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conv_filter_dilations,
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input_left_pads,
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input_right_pads,
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k_batch_);
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a_grid_desc_kbatch_k0_m_k1_ = descs[I0];
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b_grid_desc_kbatch_k0_n_k1_ = descs[I1];
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c_grid_desc_m_n_ = descs[I2];
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if(GridwiseGemm::CheckValidity(a_grid_desc_kbatch_k0_m_k1_,
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b_grid_desc_kbatch_k0_n_k1_,
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c_grid_desc_m_n_,
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M01_,
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N01_))
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{
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c_grid_desc_mblock_mperblock_nblock_nperblock_ =
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GridwiseGemm::MakeCGridDesc_MBlock_MPerBlock_NBlock_NPerBlock(c_grid_desc_m_n_);
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block_2_ctile_map_ =
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GridwiseGemm::MakeCBlockClusterAdaptor(c_grid_desc_m_n_, M01, N01, k_batch_);
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}
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}
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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_kbatch_k0_m_k1_;
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BGridDesc_K0_N_K1 b_grid_desc_kbatch_k0_n_k1_;
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CGridDesc_M_N c_grid_desc_m_n_;
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CGridDesc_MBlock_MPerBlock_NBlock_NPerBlock c_grid_desc_mblock_mperblock_nblock_nperblock_;
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Block2CTileMap block_2_ctile_map_;
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index_t M01_;
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index_t N01_;
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InElementwiseOperation a_element_op_;
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OutElementwiseOperation b_element_op_;
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WeiElementwiseOperation c_element_op_;
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// for checking IsSupportedArgument()
|
||||
index_t Conv_N_;
|
||||
index_t Conv_K_;
|
||||
index_t Conv_C_;
|
||||
std::vector<index_t> output_spatial_lengths_;
|
||||
std::vector<index_t> filter_spatial_lengths_;
|
||||
std::vector<index_t> conv_filter_strides_;
|
||||
std::vector<index_t> input_left_pads_;
|
||||
std::vector<index_t> input_right_pads_;
|
||||
index_t k_batch_;
|
||||
};
|
||||
|
||||
// Invoker
|
||||
struct Invoker : public BaseInvoker
|
||||
{
|
||||
using Argument = DeviceOp::Argument;
|
||||
|
||||
void ShowInfo(const Argument& arg)
|
||||
{
|
||||
std::cout << "arg.a_grid_desc_kbatch_k0_m_k1_{"
|
||||
<< arg.a_grid_desc_kbatch_k0_m_k1_.GetLength(I0) << ", "
|
||||
<< arg.a_grid_desc_kbatch_k0_m_k1_.GetLength(I1) << ", "
|
||||
<< arg.a_grid_desc_kbatch_k0_m_k1_.GetLength(I2) << ", "
|
||||
<< arg.a_grid_desc_kbatch_k0_m_k1_.GetLength(I3) << "}" << std::endl;
|
||||
|
||||
std::cout << "arg.b_grid_desc_kbatch_k0_n_k1_{"
|
||||
<< arg.b_grid_desc_kbatch_k0_n_k1_.GetLength(I0) << ", "
|
||||
<< arg.b_grid_desc_kbatch_k0_n_k1_.GetLength(I1) << ", "
|
||||
<< arg.b_grid_desc_kbatch_k0_n_k1_.GetLength(I2) << ", "
|
||||
<< arg.b_grid_desc_kbatch_k0_n_k1_.GetLength(I3) << "}" << std::endl;
|
||||
|
||||
std::cout << "arg.c_grid_desc_m_n_{ " << arg.c_grid_desc_m_n_.GetLength(I0) << ", "
|
||||
<< arg.c_grid_desc_m_n_.GetLength(I1) << "}" << std::endl;
|
||||
}
|
||||
|
||||
float Run(const Argument& arg, int nrepeat = 1)
|
||||
{
|
||||
ShowInfo(arg);
|
||||
if(!GridwiseGemm::CheckValidity(arg.a_grid_desc_kbatch_k0_m_k1_,
|
||||
arg.b_grid_desc_kbatch_k0_n_k1_,
|
||||
arg.c_grid_desc_m_n_,
|
||||
arg.M01_,
|
||||
arg.N01_))
|
||||
{
|
||||
throw std::runtime_error(
|
||||
"wrong! GridwiseGemm_km_kn_m0m1n0n1_xdlops_v3r1 has invalid setting");
|
||||
}
|
||||
const auto kbatch = arg.a_grid_desc_kbatch_k0_m_k1_.GetLength(I0);
|
||||
const index_t grid_size = GridwiseGemm::CalculateGridSize(arg.c_grid_desc_m_n_, kbatch);
|
||||
|
||||
const auto K0 = arg.a_grid_desc_kbatch_k0_m_k1_.GetLength(I1);
|
||||
|
||||
const bool has_main_k0_block_loop = GridwiseGemm::CalculateHasMainK0BlockLoop(K0);
|
||||
|
||||
float ave_time = 0;
|
||||
|
||||
const auto Run = [&](const auto& kernel) {
|
||||
if(nrepeat > 0)
|
||||
{
|
||||
ave_time =
|
||||
launch_and_time_kernel(kernel,
|
||||
nrepeat,
|
||||
dim3(grid_size),
|
||||
dim3(BlockSize),
|
||||
0,
|
||||
arg.p_a_grid_,
|
||||
arg.p_b_grid_,
|
||||
arg.p_c_grid_,
|
||||
arg.a_grid_desc_kbatch_k0_m_k1_,
|
||||
arg.b_grid_desc_kbatch_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_);
|
||||
}
|
||||
|
||||
if(kbatch > 1 || nrepeat <= 0)
|
||||
{
|
||||
hipGetErrorString(hipMemset(
|
||||
arg.p_c_grid_,
|
||||
0,
|
||||
arg.c_grid_desc_mblock_mperblock_nblock_nperblock_.GetElementSpaceSize() *
|
||||
sizeof(CDataType)));
|
||||
|
||||
launch_kernel(kernel,
|
||||
dim3(grid_size),
|
||||
dim3(BlockSize),
|
||||
0,
|
||||
arg.p_a_grid_,
|
||||
arg.p_b_grid_,
|
||||
arg.p_c_grid_,
|
||||
arg.a_grid_desc_kbatch_k0_m_k1_,
|
||||
arg.b_grid_desc_kbatch_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_);
|
||||
}
|
||||
};
|
||||
|
||||
if(has_main_k0_block_loop)
|
||||
{
|
||||
if(kbatch == 1)
|
||||
{
|
||||
const auto kernel = kernel_gemm_xdlops_v2r4r2<
|
||||
GridwiseGemm,
|
||||
ADataType, // TODO: distiguish A/B datatype
|
||||
CDataType,
|
||||
remove_reference_t<DeviceOp::AGridDesc_K0_M_K1>,
|
||||
remove_reference_t<DeviceOp::BGridDesc_K0_N_K1>,
|
||||
remove_reference_t<DeviceOp::CGridDesc_MBlock_MPerBlock_NBlock_NPerBlock>,
|
||||
OutElementwiseOperation,
|
||||
InElementwiseOperation,
|
||||
WeiElementwiseOperation,
|
||||
remove_reference_t<DeviceOp::Block2CTileMap>,
|
||||
true>;
|
||||
|
||||
Run(kernel);
|
||||
}
|
||||
else
|
||||
{
|
||||
const auto kernel = kernel_gemm_xdlops_v2r4r2<
|
||||
GridwiseGemmAtomicAdd,
|
||||
ADataType, // TODO: distiguish A/B datatype
|
||||
CDataType,
|
||||
remove_reference_t<DeviceOp::AGridDesc_K0_M_K1>,
|
||||
remove_reference_t<DeviceOp::BGridDesc_K0_N_K1>,
|
||||
remove_reference_t<DeviceOp::CGridDesc_MBlock_MPerBlock_NBlock_NPerBlock>,
|
||||
OutElementwiseOperation,
|
||||
InElementwiseOperation,
|
||||
WeiElementwiseOperation,
|
||||
remove_reference_t<DeviceOp::Block2CTileMap>,
|
||||
true>;
|
||||
|
||||
Run(kernel);
|
||||
}
|
||||
}
|
||||
else
|
||||
{
|
||||
if(kbatch == 1)
|
||||
{
|
||||
const auto kernel = kernel_gemm_xdlops_v2r4r2<
|
||||
GridwiseGemm,
|
||||
ADataType, // TODO: distiguish A/B datatype
|
||||
CDataType,
|
||||
remove_reference_t<DeviceOp::AGridDesc_K0_M_K1>,
|
||||
remove_reference_t<DeviceOp::BGridDesc_K0_N_K1>,
|
||||
remove_reference_t<DeviceOp::CGridDesc_MBlock_MPerBlock_NBlock_NPerBlock>,
|
||||
OutElementwiseOperation,
|
||||
InElementwiseOperation,
|
||||
WeiElementwiseOperation,
|
||||
remove_reference_t<DeviceOp::Block2CTileMap>,
|
||||
false>;
|
||||
|
||||
Run(kernel);
|
||||
}
|
||||
else
|
||||
{
|
||||
const auto kernel = kernel_gemm_xdlops_v2r4r2<
|
||||
GridwiseGemmAtomicAdd,
|
||||
ADataType, // TODO: distiguish A/B datatype
|
||||
CDataType,
|
||||
remove_reference_t<DeviceOp::AGridDesc_K0_M_K1>,
|
||||
remove_reference_t<DeviceOp::BGridDesc_K0_N_K1>,
|
||||
remove_reference_t<DeviceOp::CGridDesc_MBlock_MPerBlock_NBlock_NPerBlock>,
|
||||
OutElementwiseOperation,
|
||||
InElementwiseOperation,
|
||||
WeiElementwiseOperation,
|
||||
remove_reference_t<DeviceOp::Block2CTileMap>,
|
||||
false>;
|
||||
|
||||
Run(kernel);
|
||||
}
|
||||
}
|
||||
|
||||
return ave_time;
|
||||
}
|
||||
|
||||
float Run(const BaseArgument* p_arg, int nrepeat = 1) override
|
||||
{
|
||||
return Run(*dynamic_cast<const Argument*>(p_arg), nrepeat);
|
||||
}
|
||||
};
|
||||
|
||||
static constexpr bool IsValidCompilationParameter()
|
||||
{
|
||||
// TODO: properly implement this check
|
||||
return true;
|
||||
}
|
||||
|
||||
static bool IsSupportedArgument(const Argument& arg)
|
||||
{
|
||||
// vector load A/B matrix from global memory
|
||||
if(!(ABlockTransferSrcVectorDim == 2 && BBlockTransferSrcVectorDim == 2 &&
|
||||
arg.Conv_K_ % ABlockTransferSrcScalarPerVector == 0 &&
|
||||
arg.Conv_C_ % BBlockTransferSrcScalarPerVector == 0))
|
||||
{
|
||||
return false;
|
||||
}
|
||||
|
||||
// vector store C matrix into global memory
|
||||
if(!(arg.Conv_C_ % CBlockTransferScalarPerVector_NWaveNPerXdl == 0))
|
||||
{
|
||||
return false;
|
||||
}
|
||||
|
||||
// Gridwise GEMM size
|
||||
return GridwiseGemm::CheckValidity(arg.a_grid_desc_kbatch_k0_m_k1_,
|
||||
arg.b_grid_desc_kbatch_k0_n_k1_,
|
||||
arg.c_grid_desc_m_n_,
|
||||
arg.M01_,
|
||||
arg.N01_);
|
||||
}
|
||||
|
||||
bool IsSupportedArgument(const BaseArgument* p_arg) override
|
||||
{
|
||||
return IsSupportedArgument(*dynamic_cast<const Argument*>(p_arg));
|
||||
}
|
||||
|
||||
static auto MakeArgument(const InDataType* p_in_grid,
|
||||
WeiDataType* p_wei_grid,
|
||||
const OutDataType* p_out_grid,
|
||||
ck::index_t N,
|
||||
ck::index_t K,
|
||||
ck::index_t C,
|
||||
std::vector<ck::index_t> input_spatial_lengths,
|
||||
std::vector<ck::index_t> filter_spatial_lengths,
|
||||
std::vector<ck::index_t> output_spatial_lengths,
|
||||
std::vector<ck::index_t> conv_filter_strides,
|
||||
std::vector<ck::index_t> conv_filter_dilations,
|
||||
std::vector<ck::index_t> input_left_pads,
|
||||
std::vector<ck::index_t> input_right_pads,
|
||||
InElementwiseOperation in_element_op,
|
||||
WeiElementwiseOperation wei_element_op,
|
||||
OutElementwiseOperation out_element_op,
|
||||
ck::index_t split_k)
|
||||
{
|
||||
return Argument{p_in_grid,
|
||||
p_wei_grid,
|
||||
p_out_grid,
|
||||
N,
|
||||
K,
|
||||
C,
|
||||
input_spatial_lengths,
|
||||
filter_spatial_lengths,
|
||||
output_spatial_lengths,
|
||||
conv_filter_strides,
|
||||
conv_filter_dilations,
|
||||
input_left_pads,
|
||||
input_right_pads,
|
||||
1,
|
||||
1,
|
||||
in_element_op,
|
||||
wei_element_op,
|
||||
out_element_op,
|
||||
split_k};
|
||||
}
|
||||
|
||||
static auto MakeInvoker() { return Invoker{}; }
|
||||
|
||||
std::unique_ptr<BaseArgument>
|
||||
MakeArgumentPointer(const void* p_in_grid,
|
||||
void* p_wei_grid,
|
||||
const void* p_out_grid,
|
||||
ck::index_t N,
|
||||
ck::index_t K,
|
||||
ck::index_t C,
|
||||
std::vector<ck::index_t> input_spatial_lengths,
|
||||
std::vector<ck::index_t> filter_spatial_lengths,
|
||||
std::vector<ck::index_t> output_spatial_lengths,
|
||||
std::vector<ck::index_t> conv_filter_strides,
|
||||
std::vector<ck::index_t> conv_filter_dilations,
|
||||
std::vector<ck::index_t> input_left_pads,
|
||||
std::vector<ck::index_t> input_right_pads,
|
||||
InElementwiseOperation in_element_op,
|
||||
WeiElementwiseOperation wei_element_op,
|
||||
OutElementwiseOperation out_element_op,
|
||||
ck::index_t split_k) override
|
||||
{
|
||||
return std::make_unique<Argument>(static_cast<const InDataType*>(p_in_grid),
|
||||
static_cast<WeiDataType*>(p_wei_grid),
|
||||
static_cast<const OutDataType*>(p_out_grid),
|
||||
N,
|
||||
K,
|
||||
C,
|
||||
input_spatial_lengths,
|
||||
filter_spatial_lengths,
|
||||
output_spatial_lengths,
|
||||
conv_filter_strides,
|
||||
conv_filter_dilations,
|
||||
input_left_pads,
|
||||
input_right_pads,
|
||||
1,
|
||||
1,
|
||||
in_element_op,
|
||||
wei_element_op,
|
||||
out_element_op,
|
||||
split_k);
|
||||
}
|
||||
|
||||
std::unique_ptr<BaseInvoker> MakeInvokerPointer() override
|
||||
{
|
||||
return std::make_unique<Invoker>(Invoker{});
|
||||
}
|
||||
|
||||
std::string GetTypeString() const override
|
||||
{
|
||||
auto str = std::stringstream();
|
||||
|
||||
// clang-format off
|
||||
str << "DeviceConv2dWrWXdl_C_Shuffle_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_N_Ho_Wo_K"
|
||||
<< "<"
|
||||
<< BlockSize << ", "
|
||||
<< MPerBlock << ", "
|
||||
<< NPerBlock << ", "
|
||||
<< K0PerBlock
|
||||
<< ">";
|
||||
// clang-format on
|
||||
|
||||
return str.str();
|
||||
}
|
||||
};
|
||||
|
||||
} // namespace device
|
||||
} // namespace tensor_operation
|
||||
} // namespace ck
|
||||
#endif
|
||||
47
device_operation/include/device_conv_backward_weight.hpp
Normal file
47
device_operation/include/device_conv_backward_weight.hpp
Normal file
@@ -0,0 +1,47 @@
|
||||
#ifndef DEVICE_CONV_WRW_HPP
|
||||
#define DEVICE_CONV_WRW_HPP
|
||||
|
||||
#include <iostream>
|
||||
#include "device_base.hpp"
|
||||
|
||||
namespace ck {
|
||||
namespace tensor_operation {
|
||||
namespace device {
|
||||
|
||||
template <typename InElementwiseOperation,
|
||||
typename WeiElementwiseOperation,
|
||||
typename OutElementwiseOperation>
|
||||
struct DeviceConvWrw : public BaseOperator
|
||||
{
|
||||
virtual std::unique_ptr<BaseArgument>
|
||||
MakeArgumentPointer(const void* p_in,
|
||||
void* p_wei,
|
||||
const void* p_out,
|
||||
ck::index_t N,
|
||||
ck::index_t K,
|
||||
ck::index_t C,
|
||||
std::vector<ck::index_t> input_spatial_lengths,
|
||||
std::vector<ck::index_t> filter_spatial_lengths,
|
||||
std::vector<ck::index_t> output_spatial_lengths,
|
||||
std::vector<ck::index_t> conv_filter_strides,
|
||||
std::vector<ck::index_t> conv_filter_dilations,
|
||||
std::vector<ck::index_t> input_left_pads,
|
||||
std::vector<ck::index_t> input_right_pads,
|
||||
InElementwiseOperation in_element_op,
|
||||
WeiElementwiseOperation wei_element_op,
|
||||
OutElementwiseOperation out_element_op,
|
||||
ck::index_t split_k) = 0;
|
||||
|
||||
virtual std::unique_ptr<BaseInvoker> MakeInvokerPointer() = 0;
|
||||
};
|
||||
|
||||
template <typename InElementwiseOperation,
|
||||
typename WeiElementwiseOperation,
|
||||
typename OutElementwiseOperation>
|
||||
using DeviceConvWrwPtr = std::unique_ptr<
|
||||
DeviceConvWrw<InElementwiseOperation, WeiElementwiseOperation, OutElementwiseOperation>>;
|
||||
|
||||
} // namespace device
|
||||
} // namespace tensor_operation
|
||||
} // namespace ck
|
||||
#endif
|
||||
Reference in New Issue
Block a user