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NHWC conv 2d: bwd fp32/fp16/bfp16/int8, Device level tuning and host API (#92)
* start conv2d bwd api * kernel running * add bwd reference * change to no shuffle * fix bwd reference * pass verification * add Filter1x1Stride1Pad0 and start testing * change some tuning parameter * fix test error * add fp16 tuning parameter * add bf16 tuning parameter * add int8 tuning parameters * change fp32 tuning parameter * add bwd to profiler * fix bug for bwd profiler * fix ckProfiler bug * change conv2d_bwd_xdl to fp16 * fix bug in comments * fix precompile id * fix enum conv name * chage _bwd_ to _bwd_data_ * change conv2d_bwd example id * bwd to bwd data * fix prehead * fix MakeDefaultBlock2CTileMap ,import form merge develop * format bwd instance * bwd to bwd data * change name bwd to bwd data * change name bwd to bwd data in example * formate code * change conv2d bwd data id in example * rewrite readme for example * fix CalculateMagicNumbers about div zero * add workaround CK_WORKAROUND_SWDEV_325164 * change test_conf2d_bwd_data show info * format * fix bug for workaround:CK_WORKAROUND_SWDEV_325164 * formate tuning parameters * formate tuning parameters again * formate tuning parameters 3 * formate tuning parameters 4 * remove add function template * format * update comment Co-authored-by: ltqin <letaoqin@amd.com> Co-authored-by: Chao Liu <chao.liu2@amd.com>
This commit is contained in:
ltqin
@@ -0,0 +1,17 @@
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#ifndef CONVOLUTION_BACKWARD_DATA_SPECIALIZATION
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#define CONVOLUTION_BACKWARD_DATA_SPECIALIZATION
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namespace ck {
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namespace tensor_operation {
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namespace device {
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enum ConvolutionBackwardDataSpecialization_t
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{
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Default,
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Filter1x1Stride1Pad0,
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};
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} // namespace device
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} // namespace tensor_operation
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} // namespace ck
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#endif
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@@ -0,0 +1,814 @@
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#ifndef DEVICE_CONV2D_BWD_DATA_XDL_NHWC_KYXC_NHWK_HPP
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#define DEVICE_CONV2D_BWD_DATA_XDL_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_bwd_data.hpp"
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#include "convolution_backward_data_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_v2r3.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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ConvolutionBackwardDataSpecialization_t ConvBackwardDataSpecialization,
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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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ck::index_t CThreadTransferSrcDstVectorDim,
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ck::index_t CThreadTransferDstScalarPerVector>
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struct DeviceConv2dBwdDataXdl_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_N_Ho_Wo_K
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: public DeviceConvBwdData<InElementwiseOperation,
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WeiElementwiseOperation,
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OutElementwiseOperation>
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{
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using DeviceOp = DeviceConv2dBwdDataXdl_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 = WeiDataType;
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using CDataType = InDataType;
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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_assert((K1 % ABlockTransferThreadClusterLengths_K0_M_K1{}[I2]) %
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ABlockTransferSrcScalarPerVector ==
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0);
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static_assert((NPerBlock / BBlockTransferThreadClusterLengths_K0_N_K1{}[I1]) %
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BBlockTransferSrcScalarPerVector ==
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0);
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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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index_t i_ytilda,
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index_t i_xtilda)
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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 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 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 auto K0 = K / K1;
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const auto out_n_ho_wo_k_grid_desc =
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make_naive_tensor_descriptor_packed(make_tuple(N, Ho, Wo, K));
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const auto wei_k_y_x_c_grid_desc =
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make_naive_tensor_descriptor_packed(make_tuple(K, Y, X, C));
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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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if constexpr(ConvBackwardDataSpecialization ==
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ConvolutionBackwardDataSpecialization_t::Filter1x1Stride1Pad0)
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{
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// A: output tensor
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const auto out_gemmk0_gemmm_gemmk1_grid_desc = transform_tensor_descriptor(
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make_naive_tensor_descriptor_packed(make_tuple(N * Ho * Wo, K)),
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make_tuple(make_pass_through_transform(N * Ho * Wo),
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make_unmerge_transform(make_tuple(K0, K1))),
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make_tuple(Sequence<0>{}, Sequence<1>{}),
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make_tuple(Sequence<1>{}, Sequence<0, 2>{}));
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// B: weight tensor
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const auto wei_gemmk0_gemmn_gemmk1_grid_desc =
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transform_tensor_descriptor(make_naive_tensor_descriptor_packed(make_tuple(K, C)),
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make_tuple(make_unmerge_transform(make_tuple(K0, K1)),
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make_pass_through_transform(C)),
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make_tuple(Sequence<0>{}, Sequence<1>{}),
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make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
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// C: input tensor
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const auto in_n_y_ho_x_wo_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_embed_transform(make_tuple(I1, Ho), make_tuple(I1, ConvStrideH)),
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make_embed_transform(make_tuple(I1, Wo), make_tuple(I1, 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_gemmm_gemmn_grid_desc = transform_tensor_descriptor(
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in_n_y_ho_x_wo_c_grid_desc,
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make_tuple(make_freeze_transform(I0),
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make_freeze_transform(I0),
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make_merge_transform(make_tuple(N, Ho, Wo)),
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make_pass_through_transform(C)),
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make_tuple(Sequence<1>{}, Sequence<3>{}, Sequence<0, 2, 4>{}, Sequence<5>{}),
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make_tuple(Sequence<>{}, Sequence<>{}, Sequence<0>{}, Sequence<1>{}));
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return make_tuple(out_gemmk0_gemmm_gemmk1_grid_desc,
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wei_gemmk0_gemmn_gemmk1_grid_desc,
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in_gemmm_gemmn_grid_desc);
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}
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else
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{
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const auto GcdStrideDilationH = math::gcd(ConvStrideH, ConvDilationH);
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const auto GcdStrideDilationW = math::gcd(ConvStrideW, ConvDilationW);
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const auto YTilda = ConvStrideH / GcdStrideDilationH;
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const auto XTilda = ConvStrideW / GcdStrideDilationW;
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const auto YDot = math::integer_divide_ceil(Y, YTilda);
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const auto XDot = math::integer_divide_ceil(X, XTilda);
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const auto HTilda =
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Ho + math::integer_divide_ceil(ConvDilationH * (Y - I1), ConvStrideH);
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const auto WTilda =
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Wo + math::integer_divide_ceil(ConvDilationW * (X - I1), ConvStrideW);
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// only work on HTilda and WTilda that contribute to non-padding area of input tensor
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const auto IHTildaSliceBegin = math::integer_divide_floor(
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math::max(I0, InLeftPadH - ConvDilationH * (YTilda - I1)), ConvStrideH);
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const auto IWTildaSliceBegin = math::integer_divide_floor(
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math::max(I0, InLeftPadW - ConvDilationW * (XTilda - I1)), ConvStrideW);
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const auto IHTildaSliceEnd = math::min(
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HTilda, math::integer_divide_ceil(InLeftPadH + Hi - I1, ConvStrideH) + I1);
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const auto IWTildaSliceEnd = math::min(
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WTilda, math::integer_divide_ceil(InLeftPadW + Wi - I1, ConvStrideW) + I1);
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const auto HTildaSlice = IHTildaSliceEnd - IHTildaSliceBegin;
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const auto WTildaSlice = IWTildaSliceEnd - IWTildaSliceBegin;
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// GemmK is different for each GEMM
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const auto YDotSlice = math::integer_divide_ceil(Y - i_ytilda, YTilda);
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const auto XDotSlice = math::integer_divide_ceil(X - i_xtilda, XTilda);
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// A: output tensor
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const auto out_n_hop_wop_k_grid_desc = transform_tensor_descriptor(
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out_n_ho_wo_k_grid_desc,
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make_tuple(make_pass_through_transform(N),
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make_pad_transform(Ho, I0, I0),
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make_pad_transform(Wo, I0, I0),
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make_pass_through_transform(K)),
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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 out_n_ydot_htilda_xdot_wtilda_k_grid_desc = transform_tensor_descriptor(
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out_n_hop_wop_k_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(YDot, HTilda),
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make_tuple(-ConvDilationH / GcdStrideDilationH, I1)),
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make_embed_transform(make_tuple(XDot, WTilda),
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make_tuple(-ConvDilationW / GcdStrideDilationW, I1)),
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make_pass_through_transform(K)),
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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 out_n_ydotslice_htildaslice_xdotslice_wtildaslice_k0_k1_grid_desc =
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transform_tensor_descriptor(
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out_n_ydot_htilda_xdot_wtilda_k_grid_desc,
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make_tuple(make_pass_through_transform(N),
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make_slice_transform(YDot, I0, YDotSlice),
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make_slice_transform(HTilda, IHTildaSliceBegin, HTildaSlice),
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make_slice_transform(XDot, I0, XDotSlice),
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make_slice_transform(WTilda, IWTildaSliceBegin, WTildaSlice),
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make_unmerge_transform(make_tuple(K0, K1))),
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make_tuple(Sequence<0>{},
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Sequence<1>{},
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Sequence<2>{},
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Sequence<3>{},
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Sequence<4>{},
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Sequence<5>{}),
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make_tuple(Sequence<0>{},
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Sequence<1>{},
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Sequence<2>{},
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Sequence<3>{},
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Sequence<4>{},
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Sequence<5, 6>{}));
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const auto out_gemmk0_gemmm_gemmk1_grid_desc = transform_tensor_descriptor(
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out_n_ydotslice_htildaslice_xdotslice_wtildaslice_k0_k1_grid_desc,
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make_tuple(make_merge_transform(make_tuple(YDotSlice, XDotSlice, K0)),
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make_merge_transform(make_tuple(N, HTildaSlice, WTildaSlice)),
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make_pass_through_transform(K1)),
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make_tuple(Sequence<1, 3, 5>{}, Sequence<0, 2, 4>{}, Sequence<6>{}),
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make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}));
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// B weight tensor
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const auto wei_k_ydot_ytilda_xdot_xtilda_c_grid_desc = transform_tensor_descriptor(
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wei_k_y_x_c_grid_desc,
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make_tuple(make_pass_through_transform(K),
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make_embed_transform(make_tuple(YDot, YTilda),
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make_tuple(ConvStrideH / GcdStrideDilationH, I1)),
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make_embed_transform(make_tuple(XDot, XTilda),
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make_tuple(ConvStrideW / GcdStrideDilationW, I1)),
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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 wei_k0_k1_ydotslice_xdotslice_c_grid_desc =
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transform_tensor_descriptor(wei_k_ydot_ytilda_xdot_xtilda_c_grid_desc,
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make_tuple(make_unmerge_transform(make_tuple(K0, K1)),
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make_slice_transform(YDot, I0, YDotSlice),
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make_slice_transform(XDot, I0, XDotSlice),
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make_freeze_transform(i_ytilda),
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make_freeze_transform(i_xtilda),
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make_pass_through_transform(C)),
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make_tuple(Sequence<0>{},
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Sequence<1>{},
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Sequence<3>{},
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Sequence<2>{},
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Sequence<4>{},
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Sequence<5>{}),
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make_tuple(Sequence<0, 1>{},
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Sequence<2>{},
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Sequence<3>{},
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Sequence<>{},
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Sequence<>{},
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Sequence<4>{}));
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const auto wei_gemmk0_gemmn_gemmk1_grid_desc = transform_tensor_descriptor(
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wei_k0_k1_ydotslice_xdotslice_c_grid_desc,
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make_tuple(make_merge_transform(make_tuple(YDotSlice, XDotSlice, K0)),
|
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make_pass_through_transform(C),
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make_pass_through_transform(K1)),
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make_tuple(Sequence<2, 3, 0>{}, Sequence<4>{}, Sequence<1>{}),
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make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}));
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// C: 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_ytilda_htilda_xtilda_wtilda_c_grid_desc = transform_tensor_descriptor(
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in_n_hip_wip_c_grid_desc,
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make_tuple(make_pass_through_transform(N),
|
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make_embed_transform(make_tuple(YTilda, HTilda),
|
||||
make_tuple(ConvDilationH, ConvStrideH)),
|
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make_embed_transform(make_tuple(XTilda, WTilda),
|
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make_tuple(ConvDilationW, ConvStrideW)),
|
||||
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_n_htildaslice_wtildaslice_c_grid_desc = transform_tensor_descriptor(
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in_n_ytilda_htilda_xtilda_wtilda_c_grid_desc,
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make_tuple(make_pass_through_transform(N),
|
||||
make_freeze_transform(i_ytilda),
|
||||
make_slice_transform(HTilda, IHTildaSliceBegin, HTildaSlice),
|
||||
make_freeze_transform(i_xtilda),
|
||||
make_slice_transform(WTilda, IWTildaSliceBegin, WTildaSlice),
|
||||
make_pass_through_transform(C)),
|
||||
make_tuple(Sequence<0>{},
|
||||
Sequence<1>{},
|
||||
Sequence<2>{},
|
||||
Sequence<3>{},
|
||||
Sequence<4>{},
|
||||
Sequence<5>{}),
|
||||
make_tuple(Sequence<0>{},
|
||||
Sequence<>{},
|
||||
Sequence<1>{},
|
||||
Sequence<>{},
|
||||
Sequence<2>{},
|
||||
Sequence<3>{}));
|
||||
|
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const auto in_gemmm_gemmn_grid_desc = transform_tensor_descriptor(
|
||||
in_n_htildaslice_wtildaslice_c_grid_desc,
|
||||
make_tuple(make_merge_transform(make_tuple(N, HTildaSlice, WTildaSlice)),
|
||||
make_pass_through_transform(C)),
|
||||
make_tuple(Sequence<0, 1, 2>{}, Sequence<3>{}),
|
||||
make_tuple(Sequence<0>{}, Sequence<1>{}));
|
||||
|
||||
return make_tuple(out_gemmk0_gemmm_gemmk1_grid_desc,
|
||||
wei_gemmk0_gemmn_gemmk1_grid_desc,
|
||||
in_gemmm_gemmn_grid_desc);
|
||||
}
|
||||
|
||||
} // function end
|
||||
|
||||
using ABCGridDescs = decltype(MakeABCGridDescriptor_A_K0_M_K1_B_K0_N_K1_C_M_N(
|
||||
1, 1, 1, {1, 1}, {1, 1}, {1, 1}, {1, 1}, {1, 1}, {1, 1}, {1, 1}, 0, 0));
|
||||
|
||||
using AGridDesc_K0_M_K1 = remove_cvref_t<decltype(ABCGridDescs{}[I0])>;
|
||||
using BGridDesc_K0_N_K1 = remove_cvref_t<decltype(ABCGridDescs{}[I1])>;
|
||||
using CGridDesc_M_N = remove_cvref_t<decltype(ABCGridDescs{}[I2])>;
|
||||
|
||||
// GridwiseGemm
|
||||
using GridwiseGemm = GridwiseGemm_k0mk1_k0nk1_mn_xdlops_v2r3<
|
||||
BlockSize,
|
||||
ABDataType, // TODO: distinguish A/B datatype
|
||||
AccDataType,
|
||||
CDataType,
|
||||
InMemoryDataOperationEnum_t::Set,
|
||||
AGridDesc_K0_M_K1,
|
||||
BGridDesc_K0_N_K1,
|
||||
CGridDesc_M_N,
|
||||
InElementwiseOperation,
|
||||
WeiElementwiseOperation,
|
||||
OutElementwiseOperation,
|
||||
MPerBlock,
|
||||
NPerBlock,
|
||||
K0PerBlock,
|
||||
MPerXdl,
|
||||
NPerXdl,
|
||||
K1,
|
||||
MXdlPerWave,
|
||||
NXdlPerWave,
|
||||
ABlockTransferThreadClusterLengths_K0_M_K1,
|
||||
ABlockTransferThreadClusterArrangeOrder,
|
||||
ABlockTransferSrcAccessOrder,
|
||||
ABlockTransferSrcVectorDim,
|
||||
ABlockTransferSrcScalarPerVector,
|
||||
ABlockTransferDstScalarPerVector_K1,
|
||||
false, // AThreadTransferSrcResetCoordinateAfterRun,
|
||||
ABlockLdsAddExtraM,
|
||||
BBlockTransferThreadClusterLengths_K0_N_K1,
|
||||
BBlockTransferThreadClusterArrangeOrder,
|
||||
BBlockTransferSrcAccessOrder,
|
||||
BBlockTransferSrcVectorDim,
|
||||
BBlockTransferSrcScalarPerVector,
|
||||
BBlockTransferDstScalarPerVector_K1,
|
||||
false, // BThreadTransferSrcResetCoordinateAfterRun,
|
||||
BBlockLdsAddExtraN,
|
||||
Sequence<2, 3, 0, 1, 7, 5, 4, 6>, // CThreadTransferSrcDstAccessOrder,
|
||||
7, // CThreadTransferSrcDstVectorDim,
|
||||
CThreadTransferDstScalarPerVector>;
|
||||
|
||||
// Argument
|
||||
struct Argument : public BaseArgument
|
||||
{
|
||||
Argument(InDataType* p_in_grid,
|
||||
const 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,
|
||||
ck::index_t M01,
|
||||
ck::index_t N01,
|
||||
InElementwiseOperation in_element_op,
|
||||
WeiElementwiseOperation wei_element_op,
|
||||
OutElementwiseOperation out_element_op)
|
||||
: p_a_grid_{p_out_grid},
|
||||
p_b_grid_{p_wei_grid},
|
||||
p_c_grid_{p_in_grid},
|
||||
M01_{M01},
|
||||
N01_{N01},
|
||||
a_element_op_{out_element_op},
|
||||
b_element_op_{wei_element_op},
|
||||
c_element_op_{in_element_op},
|
||||
Conv_N_{N},
|
||||
Conv_K_{K},
|
||||
Conv_C_{C},
|
||||
input_spatial_lengths_{input_spatial_lengths},
|
||||
filter_spatial_lengths_{filter_spatial_lengths},
|
||||
output_spatial_lengths_{output_spatial_lengths},
|
||||
conv_filter_strides_{conv_filter_strides},
|
||||
conv_filter_dilations_{conv_filter_dilations},
|
||||
input_left_pads_{input_left_pads},
|
||||
input_right_pads_{input_right_pads}
|
||||
{
|
||||
const index_t ConvStrideH = conv_filter_strides[0];
|
||||
const index_t ConvStrideW = conv_filter_strides[1];
|
||||
|
||||
const index_t ConvDilationH = conv_filter_dilations[0];
|
||||
const index_t ConvDilationW = conv_filter_dilations[1];
|
||||
|
||||
const auto GcdStrideDilationH = math::gcd(ConvStrideH, ConvDilationH);
|
||||
const auto GcdStrideDilationW = math::gcd(ConvStrideW, ConvDilationW);
|
||||
|
||||
const auto YTilda = ConvStrideH / GcdStrideDilationH;
|
||||
const auto XTilda = ConvStrideW / GcdStrideDilationW;
|
||||
|
||||
for(index_t i_ytilda = 0; i_ytilda < YTilda; ++i_ytilda)
|
||||
{
|
||||
for(index_t i_xtilda = 0; i_xtilda < XTilda; ++i_xtilda)
|
||||
{
|
||||
const auto descs = DeviceOp::MakeABCGridDescriptor_A_K0_M_K1_B_K0_N_K1_C_M_N(
|
||||
N,
|
||||
K,
|
||||
C,
|
||||
input_spatial_lengths,
|
||||
filter_spatial_lengths,
|
||||
output_spatial_lengths,
|
||||
conv_filter_strides,
|
||||
conv_filter_dilations,
|
||||
input_left_pads,
|
||||
input_right_pads,
|
||||
i_ytilda,
|
||||
i_xtilda);
|
||||
a_grid_desc_k0_m_k1_container_.push_back(descs[I0]);
|
||||
b_grid_desc_k0_n_k1_container_.push_back(descs[I1]);
|
||||
c_grid_desc_m_n_container_.push_back(descs[I2]);
|
||||
|
||||
if(GridwiseGemm::CheckValidity(descs[I0], descs[I1], descs[I2], M01_, N01_))
|
||||
{
|
||||
c_grid_desc_m0_n0_m1_n1_m2_m3_m4_n2_container_.push_back(
|
||||
GridwiseGemm::MakeCGridDescriptor_M0_N0_M1_N1_M2_M3_M4_N2(descs[I2]));
|
||||
|
||||
block_2_ctile_map_container_.push_back(
|
||||
GridwiseGemm::MakeDefaultBlock2CTileMap(descs[I2], M01, N01));
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
const ADataType* p_a_grid_;
|
||||
const BDataType* p_b_grid_;
|
||||
CDataType* p_c_grid_;
|
||||
std::vector<AGridDesc_K0_M_K1> a_grid_desc_k0_m_k1_container_;
|
||||
std::vector<BGridDesc_K0_N_K1> b_grid_desc_k0_n_k1_container_;
|
||||
std::vector<CGridDesc_M_N> c_grid_desc_m_n_container_;
|
||||
std::vector<typename GridwiseGemm::CGridDesc_M0_N0_M1_N1_M2_M3_M4_N2>
|
||||
c_grid_desc_m0_n0_m1_n1_m2_m3_m4_n2_container_;
|
||||
std::vector<typename GridwiseGemm::DefaultBlock2CTileMap> block_2_ctile_map_container_;
|
||||
index_t M01_;
|
||||
index_t N01_;
|
||||
OutElementwiseOperation a_element_op_;
|
||||
WeiElementwiseOperation b_element_op_;
|
||||
InElementwiseOperation c_element_op_;
|
||||
// for checking IsSupportedArgument()
|
||||
index_t Conv_N_;
|
||||
index_t Conv_K_;
|
||||
index_t Conv_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_;
|
||||
};
|
||||
|
||||
// Invoker
|
||||
struct Invoker : public BaseInvoker
|
||||
{
|
||||
using Argument = DeviceOp::Argument;
|
||||
|
||||
float Run(const Argument& arg, int nrepeat = 1)
|
||||
{
|
||||
nrepeat = 1;
|
||||
float ave_time = 0;
|
||||
for(size_t i = 0; i < arg.a_grid_desc_k0_m_k1_container_.size(); i++)
|
||||
{
|
||||
{
|
||||
std::cout << "arg.a_grid_desc_k0_m_k1_container_{"
|
||||
<< arg.a_grid_desc_k0_m_k1_container_[i].GetLength(I0) << ", "
|
||||
<< arg.a_grid_desc_k0_m_k1_container_[i].GetLength(I1) << ", "
|
||||
<< arg.a_grid_desc_k0_m_k1_container_[i].GetLength(I2) << "}"
|
||||
<< std::endl;
|
||||
|
||||
std::cout << "arg.b_grid_desc_k0_n_k1_container_{"
|
||||
<< arg.b_grid_desc_k0_n_k1_container_[i].GetLength(I0) << ", "
|
||||
<< arg.b_grid_desc_k0_n_k1_container_[i].GetLength(I1) << ", "
|
||||
<< arg.b_grid_desc_k0_n_k1_container_[i].GetLength(I2) << "}"
|
||||
<< std::endl;
|
||||
|
||||
std::cout << "arg.c_grid_desc_m_n_container_{ "
|
||||
<< arg.c_grid_desc_m_n_container_[i].GetLength(I0) << ", "
|
||||
<< arg.c_grid_desc_m_n_container_[i].GetLength(I1) << "}"
|
||||
<< std::endl;
|
||||
|
||||
std::cout << "arg.c_grid_desc_m0_n0_m1_n1_m2_m3_m4_n2_container_( "
|
||||
<< arg.c_grid_desc_m0_n0_m1_n1_m2_m3_m4_n2_container_[i].GetLength(I0)
|
||||
<< ", "
|
||||
<< arg.c_grid_desc_m0_n0_m1_n1_m2_m3_m4_n2_container_[i].GetLength(I1)
|
||||
<< ", "
|
||||
<< arg.c_grid_desc_m0_n0_m1_n1_m2_m3_m4_n2_container_[i].GetLength(I2)
|
||||
<< ", "
|
||||
<< arg.c_grid_desc_m0_n0_m1_n1_m2_m3_m4_n2_container_[i].GetLength(I3)
|
||||
<< ", "
|
||||
<< arg.c_grid_desc_m0_n0_m1_n1_m2_m3_m4_n2_container_[i].GetLength(I4)
|
||||
<< ", "
|
||||
<< arg.c_grid_desc_m0_n0_m1_n1_m2_m3_m4_n2_container_[i].GetLength(I5)
|
||||
<< " ) " << std::endl;
|
||||
}
|
||||
|
||||
if(!GridwiseGemm::CheckValidity(arg.a_grid_desc_k0_m_k1_container_[i],
|
||||
arg.b_grid_desc_k0_n_k1_container_[i],
|
||||
arg.c_grid_desc_m_n_container_[i],
|
||||
arg.M01_,
|
||||
arg.N01_))
|
||||
{
|
||||
throw std::runtime_error(
|
||||
"wrong! GridwiseGemm_km_kn_m0m1n0n1_xdlops_v3r1 has invalid setting");
|
||||
}
|
||||
|
||||
const index_t grid_size =
|
||||
GridwiseGemm::CalculateGridSize(arg.c_grid_desc_m_n_container_[i]);
|
||||
|
||||
const auto K0 = arg.a_grid_desc_k0_m_k1_container_[i].GetLength(I0);
|
||||
|
||||
const bool has_main_k0_block_loop = GridwiseGemm::CalculateHasMainK0BlockLoop(K0);
|
||||
|
||||
if(has_main_k0_block_loop)
|
||||
{
|
||||
const auto kernel = kernel_gemm_xdlops_v2r3<
|
||||
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<
|
||||
typename GridwiseGemm::CGridDesc_M0_N0_M1_N1_M2_M3_M4_N2>,
|
||||
OutElementwiseOperation,
|
||||
WeiElementwiseOperation,
|
||||
InElementwiseOperation,
|
||||
remove_reference_t<typename GridwiseGemm::DefaultBlock2CTileMap>,
|
||||
true>;
|
||||
|
||||
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_k0_m_k1_container_[i],
|
||||
arg.b_grid_desc_k0_n_k1_container_[i],
|
||||
arg.c_grid_desc_m0_n0_m1_n1_m2_m3_m4_n2_container_[i],
|
||||
arg.a_element_op_,
|
||||
arg.b_element_op_,
|
||||
arg.c_element_op_,
|
||||
arg.block_2_ctile_map_container_[i]);
|
||||
}
|
||||
else
|
||||
{
|
||||
const auto kernel = kernel_gemm_xdlops_v2r3<
|
||||
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<
|
||||
typename GridwiseGemm::CGridDesc_M0_N0_M1_N1_M2_M3_M4_N2>,
|
||||
OutElementwiseOperation,
|
||||
WeiElementwiseOperation,
|
||||
InElementwiseOperation,
|
||||
remove_reference_t<typename GridwiseGemm::DefaultBlock2CTileMap>,
|
||||
false>;
|
||||
|
||||
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_k0_m_k1_container_[i],
|
||||
arg.b_grid_desc_k0_n_k1_container_[i],
|
||||
arg.c_grid_desc_m0_n0_m1_n1_m2_m3_m4_n2_container_[i],
|
||||
arg.a_element_op_,
|
||||
arg.b_element_op_,
|
||||
arg.c_element_op_,
|
||||
arg.block_2_ctile_map_container_[i]);
|
||||
}
|
||||
}
|
||||
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)
|
||||
{
|
||||
if constexpr(ConvBackwardDataSpecialization ==
|
||||
ConvolutionBackwardDataSpecialization_t::Filter1x1Stride1Pad0)
|
||||
{
|
||||
// check if it's 1x1, stride=1 pad = 0 conv
|
||||
if(!(arg.filter_spatial_lengths_[0] == 1 && arg.filter_spatial_lengths_[1] == 1 &&
|
||||
arg.conv_filter_strides_[0] == 1 && arg.conv_filter_strides_[1] == 1 &&
|
||||
arg.input_left_pads_[0] == 0 && arg.input_left_pads_[1] == 0 &&
|
||||
arg.input_right_pads_[0] == 0 && arg.input_right_pads_[1] == 0))
|
||||
{
|
||||
return false;
|
||||
}
|
||||
}
|
||||
|
||||
// vector load A/B matrix from global memory
|
||||
if(!(ABlockTransferSrcVectorDim == 2 && BBlockTransferSrcVectorDim == 1 &&
|
||||
arg.Conv_K_ % ABlockTransferSrcScalarPerVector == 0 &&
|
||||
arg.Conv_C_ % BBlockTransferSrcScalarPerVector == 0))
|
||||
{
|
||||
return false;
|
||||
}
|
||||
|
||||
// vector store C matrix into global memory
|
||||
if(!(arg.Conv_C_ % CThreadTransferDstScalarPerVector == 0))
|
||||
{
|
||||
return false;
|
||||
}
|
||||
|
||||
// Gridwise GEMM size
|
||||
for(int i = 0; i < arg.a_grid_desc_k0_m_k1_container_.size(); i++)
|
||||
{
|
||||
if(!GridwiseGemm::CheckValidity(arg.a_grid_desc_k0_m_k1_container_[i],
|
||||
arg.b_grid_desc_k0_n_k1_container_[i],
|
||||
arg.c_grid_desc_m_n_container_[i],
|
||||
arg.M01_,
|
||||
arg.N01_))
|
||||
{
|
||||
return false;
|
||||
}
|
||||
}
|
||||
return true;
|
||||
}
|
||||
|
||||
bool IsSupportedArgument(const BaseArgument* p_arg) override
|
||||
{
|
||||
return IsSupportedArgument(*dynamic_cast<const Argument*>(p_arg));
|
||||
}
|
||||
|
||||
static auto MakeArgument(InDataType* p_in_grid,
|
||||
const 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)
|
||||
{
|
||||
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};
|
||||
}
|
||||
|
||||
static auto MakeInvoker() { return Invoker{}; }
|
||||
|
||||
std::unique_ptr<BaseArgument>
|
||||
MakeArgumentPointer(void* p_in_grid,
|
||||
const 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) override
|
||||
{
|
||||
return std::make_unique<Argument>(static_cast<InDataType*>(p_in_grid),
|
||||
static_cast<const 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);
|
||||
}
|
||||
|
||||
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 << "DeviceConv2dBwdDataXdl_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_bwd_data.hpp
Normal file
47
device_operation/include/device_conv_bwd_data.hpp
Normal file
@@ -0,0 +1,47 @@
|
||||
#ifndef DEVICE_CONV_BWD_DATA_HPP
|
||||
#define DEVICE_CONV_BWD_DATA_HPP
|
||||
|
||||
#include <iostream>
|
||||
#include "device_base.hpp"
|
||||
#include "element_wise_operation.hpp"
|
||||
|
||||
namespace ck {
|
||||
namespace tensor_operation {
|
||||
namespace device {
|
||||
|
||||
template <typename InElementwiseOperation,
|
||||
typename WeiElementwiseOperation,
|
||||
typename OutElementwiseOperation>
|
||||
struct DeviceConvBwdData : public BaseOperator
|
||||
{
|
||||
virtual std::unique_ptr<BaseArgument>
|
||||
MakeArgumentPointer(void* p_in,
|
||||
const 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) = 0;
|
||||
|
||||
virtual std::unique_ptr<BaseInvoker> MakeInvokerPointer() = 0;
|
||||
};
|
||||
|
||||
template <typename InElementwiseOperation,
|
||||
typename WeiElementwiseOperation,
|
||||
typename OutElementwiseOperation>
|
||||
using DeviceConvBwdDataPtr = std::unique_ptr<
|
||||
DeviceConvBwdData<InElementwiseOperation, WeiElementwiseOperation, OutElementwiseOperation>>;
|
||||
|
||||
} // namespace device
|
||||
} // namespace tensor_operation
|
||||
} // namespace ck
|
||||
#endif
|
||||
Reference in New Issue
Block a user