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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>
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ltqin
192
reference_operation/include/reference_conv_bwd_data.hpp
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192
reference_operation/include/reference_conv_bwd_data.hpp
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#ifndef REFERENCE_CONV_BWD_DATA_HPP
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#define REFERENCE_CONV_BWD_DATA_HPP
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#include <iostream>
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#include <sstream>
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#include "device_base.hpp"
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#include "host_tensor.hpp"
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namespace ck {
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namespace tensor_operation {
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namespace host {
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// out[N, K, Ho, Wo] = in[N, C, Hi, Wi] * wei[K, C, Y, X]
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template <typename InDataType,
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typename WeiDataType,
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typename OutDataType,
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typename InElementwiseOperation,
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typename WeiElementwiseOperation,
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typename OutElementwiseOperation>
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struct ReferenceConvBwdData : public device::BaseOperator
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{
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// Argument
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struct Argument : public device::BaseArgument
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{
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Argument(Tensor<InDataType>& in_n_c_hi_wi,
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const Tensor<WeiDataType>& wei_k_c_y_x,
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const Tensor<OutDataType>& out_n_k_ho_wo,
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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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InElementwiseOperation in_element_op,
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WeiElementwiseOperation wei_element_op,
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OutElementwiseOperation out_element_op)
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: in_n_c_hi_wi_{in_n_c_hi_wi},
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wei_k_c_y_x_{wei_k_c_y_x},
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out_n_k_ho_wo_{out_n_k_ho_wo},
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conv_strides_{conv_filter_strides},
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conv_dilations_{conv_filter_dilations},
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in_left_pads_{input_left_pads},
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in_right_pads_{input_right_pads},
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in_element_op_{in_element_op},
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wei_element_op_{wei_element_op},
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out_element_op_{out_element_op}
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{
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}
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Tensor<InDataType>& in_n_c_hi_wi_;
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const Tensor<WeiDataType>& wei_k_c_y_x_;
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const Tensor<OutDataType>& out_n_k_ho_wo_;
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std::vector<index_t> conv_strides_;
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std::vector<index_t> conv_dilations_;
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std::vector<index_t> in_left_pads_;
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std::vector<index_t> in_right_pads_;
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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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};
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// Invoker
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struct Invoker : public device::BaseInvoker
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{
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using Argument = ReferenceConvBwdData::Argument;
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float Run(const Argument& arg)
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{
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auto f_nchw = [&](auto n, auto c, auto hi, auto wi) {
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std::size_t K = arg.wei_k_c_y_x_.mDesc.GetLengths()[0];
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std::size_t Y = arg.wei_k_c_y_x_.mDesc.GetLengths()[2];
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std::size_t X = arg.wei_k_c_y_x_.mDesc.GetLengths()[3];
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std::size_t Ho = arg.out_n_k_ho_wo_.mDesc.GetLengths()[2];
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std::size_t Wo = arg.out_n_k_ho_wo_.mDesc.GetLengths()[3];
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float v_acc = 0;
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for(int y = 0; y < Y; ++y)
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{
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int h_tmp = hi + arg.in_left_pads_[0] - y * arg.conv_dilations_[0];
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if(h_tmp % arg.conv_strides_[0] == 0)
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{
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int ho = h_tmp / arg.conv_strides_[0];
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if(ho >= 0 && ho < Ho)
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{
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for(int x = 0; x < X; ++x)
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{
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int w_tmp = wi + arg.in_left_pads_[1] - x * arg.conv_dilations_[1];
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if(w_tmp % arg.conv_strides_[1] == 0)
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{
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int wo = w_tmp / arg.conv_strides_[1];
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if(wo >= 0 && wo < Wo)
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{
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for(int k = 0; k < K; ++k)
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{
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float v_out = 0;
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float v_wei = 0;
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arg.out_element_op_(
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v_out,
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ck::type_convert<float>(
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arg.out_n_k_ho_wo_(n, k, ho, wo)));
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arg.wei_element_op_(v_wei,
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ck::type_convert<float>(
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arg.wei_k_c_y_x_(k, c, y, x)));
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v_acc += v_out * v_wei;
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}
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}
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}
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}
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}
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}
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}
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float v_in;
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arg.in_element_op_(v_in, v_acc);
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arg.in_n_c_hi_wi_(n, c, hi, wi) = ck::type_convert<InDataType>(v_in);
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};
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make_ParallelTensorFunctor(f_nchw,
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arg.in_n_c_hi_wi_.mDesc.GetLengths()[0],
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arg.in_n_c_hi_wi_.mDesc.GetLengths()[1],
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arg.in_n_c_hi_wi_.mDesc.GetLengths()[2],
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arg.in_n_c_hi_wi_.mDesc.GetLengths()[3])(
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std::thread::hardware_concurrency());
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return 0;
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}
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float Run(const device::BaseArgument* p_arg, int) override
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{
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return Run(*dynamic_cast<const Argument*>(p_arg));
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}
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};
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static constexpr bool IsValidCompilationParameter()
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{
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// TODO: properly implement this check
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return true;
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}
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bool IsSupportedArgument(const device::BaseArgument*) override { return true; }
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static auto MakeArgument(Tensor<InDataType>& in_n_c_hi_wi,
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const Tensor<WeiDataType>& wei_k_c_y_x,
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const Tensor<OutDataType>& out_n_k_ho_wo,
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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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InElementwiseOperation in_element_op,
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WeiElementwiseOperation wei_element_op,
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OutElementwiseOperation out_element_op)
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{
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return Argument{in_n_c_hi_wi,
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wei_k_c_y_x,
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out_n_k_ho_wo,
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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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in_element_op,
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wei_element_op,
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out_element_op};
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}
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static auto MakeInvoker() { return Invoker{}; }
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virtual std::unique_ptr<device::BaseInvoker> MakeInvokerPointer()
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{
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return std::make_unique<Invoker>(Invoker{});
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}
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std::string GetTypeString() const override
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{
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auto str = std::stringstream();
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// clang-format off
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str << "ReferenceConvBwdData"
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<< std::endl;
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// clang-format on
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return str.str();
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}
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};
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} // namespace host
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} // namespace tensor_operation
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} // namespace ck
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#endif
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