mirror of
https://github.com/ROCm/composable_kernel.git
synced 2026-05-16 19:09:59 +00:00
456f5306df
* init StaticBufferV2
* clean
* adopt old output stage for staticBufferV2
* clean
* remove hack
* clean
* clean
* add parameters
* clean code
* move c_buffer alloc into blockwise gemm
* add adaptors for m/n_thread_data_on_grid
* tweak gemm
* adjust blockwise_gemm_xdlops
* tweak
* update conv
* update script
* adding bwd 1x1
* update script
* adding 1x1 bwd
* debugging bwd 1x1 failure
* update script
* update script
* test
* test v100
* add bf16_1k
* clang-format
* clean
* add bfp16 for gfx908
* add verification
* clean up
* clean code
* restore bfl16
* clean
* add bfp16 support into gemm_driver
* apply new generator to other drivers
* add int8 support
* cleanb
* clean
* clean
* clean
Co-authored-by: Chao Liu <chao.liu2@amd.com>
Co-authored-by: Chao Liu <lc.roy86@gmail.com>
Co-authored-by: root <root@hayabusa6111.amd.com>
[ROCm/composable_kernel commit: 3737bb039a]
531 lines
20 KiB
C++
531 lines
20 KiB
C++
#include <iostream>
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#include <numeric>
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#include <initializer_list>
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#include <cstdlib>
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#include <stdlib.h>
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#include <half.hpp>
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#include "config.hpp"
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#include "debug.hpp"
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#include "print.hpp"
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#include "device.hpp"
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#include "host_tensor.hpp"
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#include "host_tensor_generator.hpp"
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#include "conv_common.hpp"
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#include "device_tensor.hpp"
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#include "device_convolution_backward_weight_implicit_gemm_v4r4r2_xdlops_nchw_kcyx_nkhw.hpp"
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#include "device_convolution_backward_weight_implicit_gemm_v4r4r4_xdlops_nhwc_kyxc_nhwk.hpp"
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#include "device_convolution_backward_weight_implicit_gemm_v4r4r2_xdlops_atomic_nchw_kcyx_nkhw.hpp"
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#include "device_convolution_backward_weight_implicit_gemm_v4r4r4_xdlops_atomic_nhwc_kyxc_nhwk.hpp"
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#include "device_convolution_backward_weight_implicit_gemm_v4r4r5_xdlops_atomic_nhwc_kyxc_nhwk.hpp"
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enum ConvTensorLayout
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{
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NCHW,
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NHWC,
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CHWN,
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NCHWc,
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NHWCc
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};
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#define USE_DYNAMIC_MODE 1
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#define USE_CONV_WRW_V4R4R2_XDL_NCHW 0
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#define USE_CONV_WRW_V4R4R4_XDL_NHWC 0
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#define USE_CONV_WRW_V4R4R2_XDL_ATOMIC_NCHW 0
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#define USE_CONV_WRW_V4R4R4_XDL_ATOMIC_NHWC 0
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#define USE_CONV_WRW_V4R4R5_XDL_ATOMIC_NHWC 1
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enum ConvBackwardWeightAlgo
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{
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V4R4R2XDLNCHW, // 0
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V4R4R4XDLNHWC, // 1
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V4R4R2XDLATOMICNCHW, // 2
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V4R4R4XDLATOMICNHWC, // 3
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V4R4R5XDLATOMICNHWC, // 4
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};
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template <typename TOut,
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typename TIn,
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typename TWei,
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typename ConvStrides,
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typename ConvDilations,
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typename InLeftPads,
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typename InRightPads>
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void host_convolution_backward_weight(const Tensor<TOut>& out,
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const Tensor<TIn>& in,
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Tensor<TWei>& wei,
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const ConvStrides& conv_strides,
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const ConvDilations& conv_dilations,
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const InLeftPads& in_left_pads,
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const InRightPads&,
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const ConvTensorLayout layout = ConvTensorLayout::NCHW)
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{
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using namespace ck;
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constexpr auto I0 = Number<0>{};
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constexpr auto I1 = Number<1>{};
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auto f_kcyx = [&](auto k, auto c, auto y, auto x) {
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double v = 0;
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for(int n = 0; n < out.mDesc.GetLengths()[0]; ++n)
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{
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for(int ho = 0; ho < out.mDesc.GetLengths()[2]; ++ho)
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{
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int hi = ho * conv_strides[I0] + y * conv_dilations[I0] - in_left_pads[I0];
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for(int wo = 0; wo < out.mDesc.GetLengths()[3]; ++wo)
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{
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int wi = wo * conv_strides[I1] + x * conv_dilations[I1] - in_left_pads[I1];
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if(hi >= 0 && hi < in.mDesc.GetLengths()[2] && wi >= 0 &&
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wi < in.mDesc.GetLengths()[3])
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{
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v += static_cast<const double>(in(n, c, hi, wi)) *
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static_cast<const double>(out(n, k, ho, wo));
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}
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}
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}
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}
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wei(k, c, y, x) = v;
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};
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auto f_kyxc = [&](auto k, auto y, auto x, auto c) {
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double v = 0;
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for(int n = 0; n < out.mDesc.GetLengths()[0]; ++n)
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{
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for(int ho = 0; ho < out.mDesc.GetLengths()[1]; ++ho)
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{
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int hi = ho * conv_strides[I0] + y * conv_dilations[I0] - in_left_pads[I0];
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for(int wo = 0; wo < out.mDesc.GetLengths()[2]; ++wo)
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{
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int wi = wo * conv_strides[I1] + x * conv_dilations[I1] - in_left_pads[I1];
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if(hi >= 0 && hi < in.mDesc.GetLengths()[1] && wi >= 0 &&
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wi < in.mDesc.GetLengths()[2])
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{
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v += static_cast<const double>(in(n, hi, wi, c)) *
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static_cast<const double>(out(n, ho, wo, k));
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}
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}
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}
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}
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wei(k, y, x, c) = v;
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};
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if(layout == ConvTensorLayout::NCHW)
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{
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make_ParallelTensorFunctor(f_kcyx,
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wei.mDesc.GetLengths()[0],
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wei.mDesc.GetLengths()[1],
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wei.mDesc.GetLengths()[2],
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wei.mDesc.GetLengths()[3])(std::thread::hardware_concurrency());
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}
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else if(layout == ConvTensorLayout::NHWC)
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{
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make_ParallelTensorFunctor(f_kyxc,
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wei.mDesc.GetLengths()[0],
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wei.mDesc.GetLengths()[1],
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wei.mDesc.GetLengths()[2],
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wei.mDesc.GetLengths()[3])(std::thread::hardware_concurrency());
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}
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else
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{
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throw std::runtime_error("wrong! not supported layout");
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}
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}
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int main(int argc, char* argv[])
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{
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using namespace ck;
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constexpr auto I0 = Number<0>{};
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constexpr auto I1 = Number<1>{};
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constexpr auto I2 = Number<2>{};
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constexpr auto I3 = Number<3>{};
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constexpr auto I4 = Number<4>{};
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constexpr auto I5 = Number<5>{};
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constexpr auto I6 = Number<6>{};
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#if USE_DYNAMIC_MODE
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// dynamic mode
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if(argc != 23)
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{
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printf("arg1 to 6: layout, algo, do_verification, init_method, do_log, nrepeat\n");
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printf("rest: N, K, C, Y, X, Hi, Wi, Sy, Sx, Dy, Dx, LeftPy, LeftPx, RightPy, RightPx\n");
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printf("additional: desired_grid_size\n");
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exit(1);
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}
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const ConvTensorLayout layout = static_cast<ConvTensorLayout>(std::stoi(argv[1]));
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const ConvBackwardWeightAlgo algo = static_cast<ConvBackwardWeightAlgo>(std::stoi(argv[2]));
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const bool do_verification = std::stoi(argv[3]);
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const int init_method = std::stoi(argv[4]);
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const bool do_log = std::stoi(argv[5]);
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const int nrepeat = std::stoi(argv[6]);
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const index_t N = std::stoi(argv[7]);
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const index_t K = std::stoi(argv[8]);
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const index_t C = std::stoi(argv[9]);
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const index_t Y = std::stoi(argv[10]);
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const index_t X = std::stoi(argv[11]);
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const index_t Hi = std::stoi(argv[12]);
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const index_t Wi = std::stoi(argv[13]);
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const index_t conv_stride_h = std::stoi(argv[14]);
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const index_t conv_stride_w = std::stoi(argv[15]);
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const index_t conv_dilation_h = std::stoi(argv[16]);
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const index_t conv_dilation_w = std::stoi(argv[17]);
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const index_t in_left_pad_h = std::stoi(argv[18]);
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const index_t in_left_pad_w = std::stoi(argv[19]);
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const index_t in_right_pad_h = std::stoi(argv[20]);
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const index_t in_right_pad_w = std::stoi(argv[21]);
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const index_t desired_grid_size = std::stoi(argv[22]);
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const index_t YEff = (Y - 1) * conv_dilation_h + 1;
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const index_t XEff = (X - 1) * conv_dilation_w + 1;
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const index_t Ho = (Hi + in_left_pad_h + in_right_pad_h - YEff) / conv_stride_h + 1;
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const index_t Wo = (Wi + in_left_pad_w + in_right_pad_w - XEff) / conv_stride_w + 1;
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#else
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// static mode
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if(argc < 7)
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{
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printf("arg1 to 6: layout, algo, do_verification, init_method, do_log, nrepeat\n");
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exit(1);
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}
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const ConvTensorLayout layout = static_cast<ConvTensorLayout>(std::stoi(argv[1]));
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const ConvBackwardWeightAlgo algo = static_cast<ConvBackwardWeightAlgo>(std::stoi(argv[2]));
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const bool do_verification = std::stoi(argv[3]);
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const int init_method = std::stoi(argv[4]);
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const bool do_log = std::stoi(argv[5]);
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const int nrepeat = std::stoi(argv[6]);
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constexpr auto N = Number<128>{};
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constexpr auto C = Number<128>{};
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constexpr auto Hi = Number<14>{};
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constexpr auto Wi = Number<14>{};
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constexpr auto K = Number<256>{};
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constexpr auto Y = Number<3>{};
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constexpr auto X = Number<3>{};
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constexpr auto conv_stride_h = I1;
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constexpr auto conv_stride_w = I1;
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constexpr auto conv_dilation_h = I1;
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constexpr auto conv_dilation_w = I1;
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constexpr auto in_left_pad_h = I1;
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constexpr auto in_left_pad_w = I1;
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constexpr auto in_right_pad_h = I1;
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constexpr auto in_right_pad_w = I1;
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constexpr auto YEff = (Y - I1) * conv_dilation_h + I1;
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constexpr auto XEff = (X - I1) * conv_dilation_w + I1;
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constexpr auto Ho = (Hi + in_left_pad_h + in_right_pad_h - YEff) / conv_stride_h + I1;
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constexpr auto Wo = (Wi + in_left_pad_w + in_right_pad_w - XEff) / conv_stride_w + I1;
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#endif
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#if 0
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using in_data_t = float;
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using wei_data_t = float;
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using acc_data_t = float;
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using out_data_t = float;
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#elif 1
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using in_data_t = half_t;
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using out_data_t = half_t;
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using acc_data_t = float;
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using wei_data_t = float;
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#elif 1
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using in_data_t = int8_t;
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using out_data_t = int8_t;
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using acc_data_t = int32_t;
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using wei_data_t = int8_t;
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#endif
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std::vector<std::size_t> in_lengths_host(4), wei_lengths_host(4), out_lengths_host(4);
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if(layout == ConvTensorLayout::NCHW)
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{
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in_lengths_host[0] = static_cast<std::size_t>(N);
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in_lengths_host[1] = static_cast<std::size_t>(C);
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in_lengths_host[2] = static_cast<std::size_t>(Hi);
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in_lengths_host[3] = static_cast<std::size_t>(Wi);
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wei_lengths_host[0] = static_cast<std::size_t>(K);
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wei_lengths_host[1] = static_cast<std::size_t>(C);
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wei_lengths_host[2] = static_cast<std::size_t>(Y);
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wei_lengths_host[3] = static_cast<std::size_t>(X);
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out_lengths_host[0] = static_cast<std::size_t>(N);
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out_lengths_host[1] = static_cast<std::size_t>(K);
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out_lengths_host[2] = static_cast<std::size_t>(Ho);
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out_lengths_host[3] = static_cast<std::size_t>(Wo);
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}
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else if(layout == ConvTensorLayout::NHWC)
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{
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in_lengths_host[0] = static_cast<std::size_t>(N);
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in_lengths_host[1] = static_cast<std::size_t>(Hi);
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in_lengths_host[2] = static_cast<std::size_t>(Wi);
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in_lengths_host[3] = static_cast<std::size_t>(C);
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wei_lengths_host[0] = static_cast<std::size_t>(K);
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wei_lengths_host[1] = static_cast<std::size_t>(Y);
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wei_lengths_host[2] = static_cast<std::size_t>(X);
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wei_lengths_host[3] = static_cast<std::size_t>(C);
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out_lengths_host[0] = static_cast<std::size_t>(N);
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out_lengths_host[1] = static_cast<std::size_t>(Ho);
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out_lengths_host[2] = static_cast<std::size_t>(Wo);
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out_lengths_host[3] = static_cast<std::size_t>(K);
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}
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else
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{
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std::runtime_error("wrong! not implemented");
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}
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Tensor<in_data_t> in(in_lengths_host);
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Tensor<wei_data_t> wei_device(wei_lengths_host);
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Tensor<wei_data_t> wei_host(wei_lengths_host);
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Tensor<out_data_t> out(out_lengths_host);
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std::cout << "layout: " << layout << std::endl;
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ostream_HostTensorDescriptor(in.mDesc, std::cout << "in: ");
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ostream_HostTensorDescriptor(wei_host.mDesc, std::cout << "wei: ");
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ostream_HostTensorDescriptor(out.mDesc, std::cout << "out: ");
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print_array("InLeftPads", make_tuple(in_left_pad_h, in_left_pad_w));
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print_array("InRightPads", make_tuple(in_right_pad_h, in_right_pad_w));
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print_array("ConvStrides", make_tuple(conv_stride_h, conv_stride_w));
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print_array("ConvDilations", make_tuple(conv_dilation_h, conv_dilation_w));
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std::size_t num_thread = std::thread::hardware_concurrency();
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switch(init_method)
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{
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case 0:
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// no initialization
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break;
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case 1:
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in.GenerateTensorValue(GeneratorTensor_1<in_data_t>{}, num_thread);
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out.GenerateTensorValue(GeneratorTensor_1<out_data_t>{}, num_thread);
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break;
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case 2:
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in.GenerateTensorValue(GeneratorTensor_1<in_data_t>{}, num_thread);
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out.GenerateTensorValue(GeneratorTensor_2<out_data_t>{-5, 5}, num_thread);
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break;
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case 3:
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in.GenerateTensorValue(GeneratorTensor_2<in_data_t>{-5, 5}, num_thread);
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out.GenerateTensorValue(GeneratorTensor_1<out_data_t>{}, num_thread);
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break;
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case 4:
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in.GenerateTensorValue(GeneratorTensor_2<in_data_t>{-5, 5}, num_thread);
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out.GenerateTensorValue(GeneratorTensor_2<out_data_t>{-5, 5}, num_thread);
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break;
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case 5:
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in.GenerateTensorValue(GeneratorTensor_3<in_data_t>{-0.1, 0.1}, num_thread);
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out.GenerateTensorValue(GeneratorTensor_3<out_data_t>{-0.1, 0.1}, num_thread);
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break;
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default:
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in.GenerateTensorValue(GeneratorTensor_2<in_data_t>{1, 5}, num_thread);
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auto gen_out = [](auto... is) {
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return GeneratorTensor_2<out_data_t>{1, 5}(is...) * GeneratorTensor_Checkboard{}(is...);
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};
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out.GenerateTensorValue(gen_out, num_thread);
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}
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auto f_make_for_device_nchw = [&]() {
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const auto in_lengths_dev = make_tuple(N, C, Hi, Wi);
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const auto wei_lengths_dev = make_tuple(K, C, Y, X);
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const auto out_lengths_dev = make_tuple(N, K, Ho, Wo);
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const auto conv_strides_dev = make_tuple(conv_stride_h, conv_stride_w);
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const auto conv_dilations_dev = make_tuple(conv_dilation_h, conv_dilation_w);
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const auto in_left_pads_dev = make_tuple(in_left_pad_h, in_left_pad_w);
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const auto in_right_pads_dev = make_tuple(in_right_pad_h, in_right_pad_w);
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return make_tuple(in_lengths_dev,
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wei_lengths_dev,
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out_lengths_dev,
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conv_strides_dev,
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conv_dilations_dev,
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in_left_pads_dev,
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in_right_pads_dev);
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};
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auto f_make_for_device_nhwc = [&]() {
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const auto in_lengths_dev = make_tuple(N, Hi, Wi, C);
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const auto wei_lengths_dev = make_tuple(K, Y, X, C);
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const auto out_lengths_dev = make_tuple(N, Ho, Wo, K);
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const auto conv_strides_dev = make_tuple(conv_stride_h, conv_stride_w);
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const auto conv_dilations_dev = make_tuple(conv_dilation_h, conv_dilation_w);
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const auto in_left_pads_dev = make_tuple(in_left_pad_h, in_left_pad_w);
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const auto in_right_pads_dev = make_tuple(in_right_pad_h, in_right_pad_w);
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return make_tuple(in_lengths_dev,
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wei_lengths_dev,
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out_lengths_dev,
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conv_strides_dev,
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conv_dilations_dev,
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in_left_pads_dev,
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in_right_pads_dev);
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};
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// set zero to wei_device
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wei_device.GenerateTensorValue(GeneratorTensor_0{}, num_thread);
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#if USE_CONV_WRW_V4R4R2_XDL_NCHW
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if(algo == ConvBackwardWeightAlgo::V4R4R2XDLNCHW)
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{
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if(layout != ConvTensorLayout::NCHW)
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{
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throw std::runtime_error("wrong! layout");
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}
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const auto tmp = f_make_for_device_nchw();
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device_convolution_backward_weight_implicit_gemm_v4r4r2_xdlops_nchw_kcyx_nkhw<in_data_t,
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wei_data_t,
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acc_data_t,
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out_data_t>(
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tmp[I0],
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tmp[I1],
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tmp[I2],
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tmp[I3],
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tmp[I4],
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tmp[I5],
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|
tmp[I6],
|
|
in,
|
|
wei_device,
|
|
out,
|
|
nrepeat);
|
|
}
|
|
#endif
|
|
|
|
#if USE_CONV_WRW_V4R4R4_XDL_NHWC
|
|
if(algo == ConvBackwardWeightAlgo::V4R4R4XDLNHWC)
|
|
{
|
|
if(layout != ConvTensorLayout::NHWC)
|
|
{
|
|
throw std::runtime_error("wrong! layout");
|
|
}
|
|
|
|
const auto tmp = f_make_for_device_nhwc();
|
|
|
|
device_convolution_backward_weight_implicit_gemm_v4r4r4_xdlops_nhwc_kyxc_nhwk<in_data_t,
|
|
wei_data_t,
|
|
acc_data_t,
|
|
out_data_t>(
|
|
tmp[I0],
|
|
tmp[I1],
|
|
tmp[I2],
|
|
tmp[I3],
|
|
tmp[I4],
|
|
tmp[I5],
|
|
tmp[I6],
|
|
in,
|
|
wei_device,
|
|
out,
|
|
nrepeat);
|
|
}
|
|
#endif
|
|
|
|
#if USE_CONV_WRW_V4R4R2_XDL_ATOMIC_NCHW
|
|
if(algo == ConvBackwardWeightAlgo::V4R4R2XDLATOMICNCHW)
|
|
{
|
|
if(layout != ConvTensorLayout::NCHW)
|
|
{
|
|
throw std::runtime_error("wrong! layout");
|
|
}
|
|
|
|
const auto tmp = f_make_for_device_nchw();
|
|
|
|
device_convolution_backward_weight_implicit_gemm_v4r4r2_xdlops_atomic_nchw_kcyx_nkhw<
|
|
in_data_t,
|
|
wei_data_t,
|
|
acc_data_t,
|
|
out_data_t>(tmp[I0],
|
|
tmp[I1],
|
|
tmp[I2],
|
|
tmp[I3],
|
|
tmp[I4],
|
|
tmp[I5],
|
|
tmp[I6],
|
|
in,
|
|
wei_device,
|
|
out,
|
|
desired_grid_size,
|
|
nrepeat);
|
|
}
|
|
#endif
|
|
|
|
#if USE_CONV_WRW_V4R4R4_XDL_ATOMIC_NHWC
|
|
if(algo == ConvBackwardWeightAlgo::V4R4R4XDLATOMICNHWC)
|
|
{
|
|
if(layout != ConvTensorLayout::NHWC)
|
|
{
|
|
throw std::runtime_error("wrong! layout");
|
|
}
|
|
|
|
const auto tmp = f_make_for_device_nhwc();
|
|
|
|
device_convolution_backward_weight_implicit_gemm_v4r4r4_xdlops_atomic_nhwc_kyxc_nhwk<
|
|
in_data_t,
|
|
wei_data_t,
|
|
acc_data_t,
|
|
out_data_t>(tmp[I0],
|
|
tmp[I1],
|
|
tmp[I2],
|
|
tmp[I3],
|
|
tmp[I4],
|
|
tmp[I5],
|
|
tmp[I6],
|
|
in,
|
|
wei_device,
|
|
out,
|
|
desired_grid_size,
|
|
nrepeat);
|
|
}
|
|
#endif
|
|
|
|
#if USE_CONV_WRW_V4R4R5_XDL_ATOMIC_NHWC
|
|
if(algo == ConvBackwardWeightAlgo::V4R4R5XDLATOMICNHWC)
|
|
{
|
|
if(layout != ConvTensorLayout::NHWC)
|
|
{
|
|
throw std::runtime_error("wrong! layout");
|
|
}
|
|
|
|
const auto tmp = f_make_for_device_nhwc();
|
|
|
|
device_convolution_backward_weight_implicit_gemm_v4r4r5_xdlops_atomic_nhwc_kyxc_nhwk<
|
|
in_data_t,
|
|
wei_data_t,
|
|
acc_data_t,
|
|
out_data_t>(tmp[I0],
|
|
tmp[I1],
|
|
tmp[I2],
|
|
tmp[I3],
|
|
tmp[I4],
|
|
tmp[I5],
|
|
tmp[I6],
|
|
in,
|
|
wei_device,
|
|
out,
|
|
desired_grid_size,
|
|
nrepeat);
|
|
}
|
|
#endif
|
|
|
|
if(do_verification)
|
|
{
|
|
host_convolution_backward_weight(out,
|
|
in,
|
|
wei_host,
|
|
make_tuple(conv_stride_h, conv_stride_w),
|
|
make_tuple(conv_dilation_h, conv_dilation_w),
|
|
make_tuple(in_left_pad_h, in_left_pad_w),
|
|
make_tuple(in_right_pad_h, in_right_pad_w),
|
|
layout);
|
|
|
|
check_error(wei_host, wei_device);
|
|
|
|
if(do_log)
|
|
{
|
|
LogRangeAsType<float>(std::cout << "out: ", out.mData, ",") << std::endl;
|
|
LogRangeAsType<float>(std::cout << "in : ", in.mData, ",") << std::endl;
|
|
LogRangeAsType<float>(std::cout << "wei_device: ", wei_device.mData, ",") << std::endl;
|
|
LogRangeAsType<float>(std::cout << "wei_host : ", wei_host.mData, ",") << std::endl;
|
|
}
|
|
}
|
|
}
|