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c866773642cf7cf8386303b6167844d93e608547
composable_kernel/driver/conv.cu
Chao Liu c866773642 unroll some loop, register double buffer gemm
2019-02-06 23:44:21 -06:00

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#include <iostream>
#include <numeric>
#include <initializer_list>
#include <cstdlib>
#include "nvToolsExt.h"
#include "tensor.hpp"
#include "ConstantTensorDescriptor.cuh"
#include "conv_common.cuh"
#include "device_direct_convolution_1.cuh"
#include "device_direct_convolution_2.cuh"
#include "device_implicit_gemm_convolution_1_nchw_kcsr.cuh"
#include "device_implicit_gemm_convolution_1_nchw_srck_nkhw.cuh"
#include "device_implicit_gemm_convolution_1_chwn_csrk_khwn.cuh"
#include "device_implicit_gemm_convolution_1_chwn_csrk_khwn_padded.cuh"
#include "device_implicit_gemm_convolution_2_cnhw_srck_knhw.cuh"
#include "device_implicit_gemm_convolution_2_cnhw_csrk_knhw.cuh"
#include "device_implicit_gemm_convolution_2_cnhw_csrk_knhw_gemm_2.cuh"
//#include "device_winograd_convolution.cuh"
struct GeneratorTensor_1
{
template <class... Is>
double operator()(Is... is)
{
return 1;
}
};
struct GeneratorTensor_2
{
int min_value = 0;
int max_value = 1;
template <class... Is>
double operator()(Is...)
{
return (std::rand() % (max_value - min_value)) + min_value;
}
};
struct GeneratorTensor_3
{
template <class... Is>
double operator()(Is... is)
{
#if 0
std::initializer_list<std::size_t> ls = {static_cast<std::size_t>(is)...};
return std::accumulate(ls.begin(), ls.end(), std::size_t(0));
#elif 1
assert(sizeof...(Is) > 0);
std::initializer_list<std::size_t> ids = {static_cast<std::size_t>(is)...};
std::vector<std::size_t> lens(sizeof...(Is), 100);
std::vector<std::size_t> strides(sizeof...(Is), 1);
std::partial_sum(lens.rbegin(), lens.rbegin() + (sizeof...(Is) - 1), strides.rbegin() + 1);
return std::inner_product(ids.begin(), ids.end(), strides.begin(), std::size_t(0)) + 1;
#endif
}
};
struct GeneratorTensor_Checkboard
{
template <class... Ts>
double operator()(Ts... Xs) const
{
std::array<unsigned long, sizeof...(Ts)> dims = {{Xs...}};
return std::accumulate(dims.begin(),
dims.end(),
true,
[](bool init, unsigned long x) -> int { return init != (x % 2); })
? 1
: -1;
}
};
// this is ugly, only for 4d
template <class TConstTensorDesc>
void ostream_ConstantTensorDescriptor(TConstTensorDesc, std::ostream& os = std::cout)
{
static_assert(TConstTensorDesc::nDim == 4, "nDim is not 4");
constexpr auto I0 = Number<0>{};
constexpr auto I1 = Number<1>{};
constexpr auto I2 = Number<2>{};
constexpr auto I3 = Number<3>{};
constexpr auto desc = TConstTensorDesc{};
os << "Lengths: {" << desc.GetLength(I0) << ", " << desc.GetLength(I1) << ", "
<< desc.GetLength(I2) << ", " << desc.GetLength(I3) << "}, "
<< "Strides: {" << desc.GetStride(I0) << ", " << desc.GetStride(I1) << ", "
<< desc.GetStride(I2) << ", " << desc.GetStride(I3) << "}" << std::endl;
}
// this is ugly, only for 4d
template <class TConstTensorDesc>
auto make_TensorDescriptor(TConstTensorDesc)
{
static_assert(TConstTensorDesc::nDim == 4, "nDim is not 4");
constexpr auto I0 = Number<0>{};
constexpr auto I1 = Number<1>{};
constexpr auto I2 = Number<2>{};
constexpr auto I3 = Number<3>{};
constexpr auto desc = TConstTensorDesc{};
std::initializer_list<unsigned> lengths = {
desc.GetLength(I0), desc.GetLength(I1), desc.GetLength(I2), desc.GetLength(I3)};
std::initializer_list<unsigned> strides = {
desc.GetStride(I0), desc.GetStride(I1), desc.GetStride(I2), desc.GetStride(I3)};
return TensorDescriptor(lengths, strides);
}
template <class T, class LowerPads, class UpperPads>
void host_direct_convolution(
const Tensor<T>& in_nchw, const Tensor<T>& wei_kcsr, Tensor<T>& out, LowerPads, UpperPads)
{
unsigned h_pad_low = LowerPads{}.Get(Number<0>{});
unsigned w_pad_low = LowerPads{}.Get(Number<1>{});
unsigned h_pad_up = UpperPads{}.Get(Number<0>{});
unsigned w_pad_up = UpperPads{}.Get(Number<1>{});
auto f = [&](auto n, auto k, auto ho, auto wo) {
double v = 0;
for(int c = 0; c < wei_kcsr.mDesc.GetLengths()[1]; ++c)
{
for(int y = 0; y < wei_kcsr.mDesc.GetLengths()[2]; ++y)
{
int hi = ho + y - h_pad_low;
for(int x = 0; x < wei_kcsr.mDesc.GetLengths()[3]; ++x)
{
int wi = wo + x - w_pad_low;
if(hi >= 0 && hi < in_nchw.mDesc.GetLengths()[2] && wi >= 0 &&
wi < in_nchw.mDesc.GetLengths()[3])
{
v += in_nchw(n, c, hi, wi) * wei_kcsr(k, c, y, x);
}
}
}
}
out(n, k, ho, wo) = v;
};
auto f_par = make_ParallelTensorFunctor(f,
out.mDesc.GetLengths()[0],
out.mDesc.GetLengths()[1],
out.mDesc.GetLengths()[2],
out.mDesc.GetLengths()[3]);
f_par(std::thread::hardware_concurrency());
}
template <class T, class LowerPads, class UpperPads>
void host_winograd_3x3_convolution(
const Tensor<T>& in_nchw, const Tensor<T>& wei_kcsr, Tensor<T>& out, LowerPads, UpperPads)
{
constexpr std::size_t OutTileSizeH = 2;
constexpr std::size_t OutTileSizeW = 2;
std::size_t N = in_nchw.mDesc.GetLengths()[0];
std::size_t C = in_nchw.mDesc.GetLengths()[1];
std::size_t HI = in_nchw.mDesc.GetLengths()[2];
std::size_t WI = in_nchw.mDesc.GetLengths()[3];
std::size_t K = wei_kcsr.mDesc.GetLengths()[0];
std::size_t S = wei_kcsr.mDesc.GetLengths()[2];
std::size_t R = wei_kcsr.mDesc.GetLengths()[3];
std::size_t HO = out.mDesc.GetLengths()[2];
std::size_t WO = out.mDesc.GetLengths()[3];
unsigned h_pad_low = LowerPads{}.Get(Number<0>{});
unsigned w_pad_low = LowerPads{}.Get(Number<1>{});
unsigned h_pad_up = UpperPads{}.Get(Number<0>{});
unsigned w_pad_up = UpperPads{}.Get(Number<1>{});
std::size_t InTileSizeH = OutTileSizeH + S - 1;
std::size_t InTileSizeW = OutTileSizeW + R - 1;
std::size_t Y = (HO + OutTileSizeH - 1) / OutTileSizeH;
std::size_t X = (WO + OutTileSizeW - 1) / OutTileSizeW;
Tensor<T> in_hold({N, C, Y, X, InTileSizeH, InTileSizeW});
Tensor<T> in_transform({N, C, Y, X, InTileSizeH, InTileSizeW});
Tensor<T> wei_transform({K, C, InTileSizeH, InTileSizeW});
Tensor<T> out_transform({N, K, Y, X, InTileSizeH, InTileSizeH});
Tensor<T> out_hold({N, K, Y, X, OutTileSizeH, OutTileSizeW});
auto f_in_hold = [&](auto n, auto c, auto y, auto x) {
for(int j = 0; j < InTileSizeH; ++j)
{
int hi = OutTileSizeH * y + j - h_pad_low;
for(int i = 0; i < InTileSizeW; ++i)
{
int wi = OutTileSizeW * x + i - w_pad_low;
if(hi >= 0 && hi < in_nchw.mDesc.GetLengths()[2] && wi >= 0 &&
wi < in_nchw.mDesc.GetLengths()[3])
{
in_hold(n, c, y, x, j, i) = in_nchw(n, c, hi, wi);
}
else
{
in_hold(n, c, y, x, j, i) = T(0);
}
}
}
};
auto f_in_transform = [&](auto n, auto c, auto y, auto x) {
in_transform(n, c, y, x, 0, 0) = in_hold(n, c, y, x, 0, 0) - in_hold(n, c, y, x, 0, 2) -
in_hold(n, c, y, x, 2, 0) + in_hold(n, c, y, x, 2, 2);
in_transform(n, c, y, x, 0, 1) = in_hold(n, c, y, x, 0, 1) + in_hold(n, c, y, x, 0, 2) -
in_hold(n, c, y, x, 2, 1) - in_hold(n, c, y, x, 2, 2);
in_transform(n, c, y, x, 0, 2) = -in_hold(n, c, y, x, 0, 1) + in_hold(n, c, y, x, 0, 2) +
in_hold(n, c, y, x, 2, 1) - in_hold(n, c, y, x, 2, 2);
in_transform(n, c, y, x, 0, 3) = in_hold(n, c, y, x, 0, 1) - in_hold(n, c, y, x, 0, 3) -
in_hold(n, c, y, x, 2, 1) + in_hold(n, c, y, x, 2, 3);
in_transform(n, c, y, x, 1, 0) = in_hold(n, c, y, x, 1, 0) - in_hold(n, c, y, x, 1, 2) +
in_hold(n, c, y, x, 2, 0) - in_hold(n, c, y, x, 2, 2);
in_transform(n, c, y, x, 1, 1) = in_hold(n, c, y, x, 1, 1) + in_hold(n, c, y, x, 1, 2) +
in_hold(n, c, y, x, 2, 1) + in_hold(n, c, y, x, 2, 2);
in_transform(n, c, y, x, 1, 2) = -in_hold(n, c, y, x, 1, 1) + in_hold(n, c, y, x, 1, 2) -
in_hold(n, c, y, x, 2, 1) + in_hold(n, c, y, x, 2, 2);
in_transform(n, c, y, x, 1, 3) = in_hold(n, c, y, x, 1, 1) - in_hold(n, c, y, x, 1, 3) +
in_hold(n, c, y, x, 2, 1) - in_hold(n, c, y, x, 2, 3);
in_transform(n, c, y, x, 2, 0) = -in_hold(n, c, y, x, 1, 0) + in_hold(n, c, y, x, 1, 2) +
in_hold(n, c, y, x, 2, 0) - in_hold(n, c, y, x, 2, 2);
in_transform(n, c, y, x, 2, 1) = -in_hold(n, c, y, x, 1, 1) - in_hold(n, c, y, x, 1, 2) +
in_hold(n, c, y, x, 2, 1) + in_hold(n, c, y, x, 2, 2);
in_transform(n, c, y, x, 2, 2) = in_hold(n, c, y, x, 1, 1) - in_hold(n, c, y, x, 1, 2) -
in_hold(n, c, y, x, 2, 1) + in_hold(n, c, y, x, 2, 2);
in_transform(n, c, y, x, 2, 3) = -in_hold(n, c, y, x, 1, 1) + in_hold(n, c, y, x, 1, 3) +
in_hold(n, c, y, x, 2, 1) - in_hold(n, c, y, x, 2, 3);
in_transform(n, c, y, x, 3, 0) = in_hold(n, c, y, x, 1, 0) - in_hold(n, c, y, x, 1, 2) -
in_hold(n, c, y, x, 3, 0) + in_hold(n, c, y, x, 3, 2);
in_transform(n, c, y, x, 3, 1) = in_hold(n, c, y, x, 1, 1) + in_hold(n, c, y, x, 1, 2) -
in_hold(n, c, y, x, 3, 1) - in_hold(n, c, y, x, 3, 2);
in_transform(n, c, y, x, 3, 2) = -in_hold(n, c, y, x, 1, 1) + in_hold(n, c, y, x, 1, 2) +
in_hold(n, c, y, x, 3, 1) - in_hold(n, c, y, x, 3, 2);
in_transform(n, c, y, x, 3, 3) = in_hold(n, c, y, x, 1, 1) - in_hold(n, c, y, x, 1, 3) -
in_hold(n, c, y, x, 3, 1) + in_hold(n, c, y, x, 3, 3);
};
auto f_wei_transform = [&](auto k, auto c) {
wei_transform(k, c, 0, 0) = wei_kcsr(k, c, 0, 0);
wei_transform(k, c, 0, 1) =
0.5 * wei_kcsr(k, c, 0, 0) + 0.5 * wei_kcsr(k, c, 0, 1) + 0.5 * wei_kcsr(k, c, 0, 2);
wei_transform(k, c, 0, 2) =
0.5 * wei_kcsr(k, c, 0, 0) - 0.5 * wei_kcsr(k, c, 0, 1) + 0.5 * wei_kcsr(k, c, 0, 2);
wei_transform(k, c, 0, 3) = wei_kcsr(k, c, 0, 2);
wei_transform(k, c, 1, 0) =
0.5 * wei_kcsr(k, c, 0, 0) + 0.5 * wei_kcsr(k, c, 1, 0) + 0.5 * wei_kcsr(k, c, 2, 0);
wei_transform(k, c, 1, 1) = 0.25 * wei_kcsr(k, c, 0, 0) + 0.25 * wei_kcsr(k, c, 0, 1) +
0.25 * wei_kcsr(k, c, 0, 2) + 0.25 * wei_kcsr(k, c, 1, 0) +
0.25 * wei_kcsr(k, c, 1, 1) + 0.25 * wei_kcsr(k, c, 1, 2) +
0.25 * wei_kcsr(k, c, 2, 0) + 0.25 * wei_kcsr(k, c, 2, 1) +
0.25 * wei_kcsr(k, c, 2, 2);
wei_transform(k, c, 1, 2) = 0.25 * wei_kcsr(k, c, 0, 0) - 0.25 * wei_kcsr(k, c, 0, 1) +
0.25 * wei_kcsr(k, c, 0, 2) + 0.25 * wei_kcsr(k, c, 1, 0) -
0.25 * wei_kcsr(k, c, 1, 1) + 0.25 * wei_kcsr(k, c, 1, 2) +
0.25 * wei_kcsr(k, c, 2, 0) - 0.25 * wei_kcsr(k, c, 2, 1) +
0.25 * wei_kcsr(k, c, 2, 2);
wei_transform(k, c, 1, 3) =
0.5 * wei_kcsr(k, c, 0, 2) + 0.5 * wei_kcsr(k, c, 1, 2) + 0.5 * wei_kcsr(k, c, 2, 2);
wei_transform(k, c, 2, 0) =
0.5 * wei_kcsr(k, c, 0, 0) - 0.5 * wei_kcsr(k, c, 1, 0) + 0.5 * wei_kcsr(k, c, 2, 0);
wei_transform(k, c, 2, 1) = 0.25 * wei_kcsr(k, c, 0, 0) + 0.25 * wei_kcsr(k, c, 0, 1) +
0.25 * wei_kcsr(k, c, 0, 2) - 0.25 * wei_kcsr(k, c, 1, 0) -
0.25 * wei_kcsr(k, c, 1, 1) - 0.25 * wei_kcsr(k, c, 1, 2) +
0.25 * wei_kcsr(k, c, 2, 0) + 0.25 * wei_kcsr(k, c, 2, 1) +
0.25 * wei_kcsr(k, c, 2, 2);
wei_transform(k, c, 2, 2) = 0.25 * wei_kcsr(k, c, 0, 0) - 0.25 * wei_kcsr(k, c, 0, 1) +
0.25 * wei_kcsr(k, c, 0, 2) - 0.25 * wei_kcsr(k, c, 1, 0) +
0.25 * wei_kcsr(k, c, 1, 1) - 0.25 * wei_kcsr(k, c, 1, 2) +
0.25 * wei_kcsr(k, c, 2, 0) - 0.25 * wei_kcsr(k, c, 2, 1) +
0.25 * wei_kcsr(k, c, 2, 2);
wei_transform(k, c, 2, 3) =
0.5 * wei_kcsr(k, c, 0, 2) - 0.5 * wei_kcsr(k, c, 1, 2) + 0.5 * wei_kcsr(k, c, 2, 2);
wei_transform(k, c, 3, 0) = wei_kcsr(k, c, 2, 0);
wei_transform(k, c, 3, 1) =
0.5 * wei_kcsr(k, c, 2, 0) + 0.5 * wei_kcsr(k, c, 2, 1) + 0.5 * wei_kcsr(k, c, 2, 2);
wei_transform(k, c, 3, 2) =
0.5 * wei_kcsr(k, c, 2, 0) - 0.5 * wei_kcsr(k, c, 2, 1) + 0.5 * wei_kcsr(k, c, 2, 2);
wei_transform(k, c, 3, 3) = wei_kcsr(k, c, 2, 2);
};
auto f_out_transform = [&](auto n, auto k, auto y, auto x) {
for(int j = 0; j < InTileSizeH; ++j)
{
for(int i = 0; i < InTileSizeW; ++i)
{
double v = 0;
for(int c = 0; c < C; ++c)
{
v += in_transform(n, c, y, x, j, i) * wei_transform(k, c, j, i);
}
out_transform(n, k, y, x, j, i) = v;
}
}
};
auto f_out_hold = [&](auto n, auto k, auto y, auto x) {
out_hold(n, k, y, x, 0, 0) =
out_transform(n, k, y, x, 0, 0) + out_transform(n, k, y, x, 0, 1) +
out_transform(n, k, y, x, 0, 2) + out_transform(n, k, y, x, 1, 0) +
out_transform(n, k, y, x, 1, 1) + out_transform(n, k, y, x, 1, 2) +
out_transform(n, k, y, x, 2, 0) + out_transform(n, k, y, x, 2, 1) +
out_transform(n, k, y, x, 2, 2);
out_hold(n, k, y, x, 0, 1) =
out_transform(n, k, y, x, 0, 1) - out_transform(n, k, y, x, 0, 2) -
out_transform(n, k, y, x, 0, 3) + out_transform(n, k, y, x, 1, 1) -
out_transform(n, k, y, x, 1, 2) - out_transform(n, k, y, x, 1, 3) +
out_transform(n, k, y, x, 2, 1) - out_transform(n, k, y, x, 2, 2) -
out_transform(n, k, y, x, 2, 3);
out_hold(n, k, y, x, 1, 0) =
out_transform(n, k, y, x, 1, 0) + out_transform(n, k, y, x, 1, 1) +
out_transform(n, k, y, x, 1, 2) - out_transform(n, k, y, x, 2, 0) -
out_transform(n, k, y, x, 2, 1) - out_transform(n, k, y, x, 2, 2) -
out_transform(n, k, y, x, 3, 0) - out_transform(n, k, y, x, 3, 1) -
out_transform(n, k, y, x, 3, 2);
out_hold(n, k, y, x, 1, 1) =
out_transform(n, k, y, x, 1, 1) - out_transform(n, k, y, x, 1, 2) -
out_transform(n, k, y, x, 1, 3) - out_transform(n, k, y, x, 2, 1) +
out_transform(n, k, y, x, 2, 2) + out_transform(n, k, y, x, 2, 3) -
out_transform(n, k, y, x, 3, 1) + out_transform(n, k, y, x, 3, 2) +
out_transform(n, k, y, x, 3, 3);
};
auto f_out = [&](auto n, auto k, auto y, auto x) {
for(int j = 0; j < OutTileSizeH; ++j)
{
std::size_t ho = OutTileSizeH * y + j;
for(int i = 0; i < OutTileSizeW; ++i)
{
std::size_t wo = OutTileSizeW * x + i;
out(n, k, ho, wo) = out_hold(n, k, y, x, j, i);
}
}
};
std::size_t num_thread = std::thread::hardware_concurrency();
make_ParallelTensorFunctor(f_in_hold, N, C, Y, X)(num_thread);
make_ParallelTensorFunctor(f_in_transform, N, C, Y, X)(num_thread);
make_ParallelTensorFunctor(f_wei_transform, K, C)(num_thread);
make_ParallelTensorFunctor(f_out_transform, N, K, Y, X)(num_thread);
make_ParallelTensorFunctor(f_out_hold, N, K, Y, X)(num_thread);
make_ParallelTensorFunctor(f_out, N, K, Y, X)(num_thread);
}
template <class T>
void check_error(const Tensor<T>& ref, const Tensor<T>& result)
{
float error = 0;
float max_diff = -1;
float ref_value = 0, result_value = 0;
for(int i = 0; i < ref.mData.size(); ++i)
{
error += std::abs(ref.mData[i] - result.mData[i]);
float diff = std::abs(ref.mData[i] - result.mData[i]);
if(max_diff < diff)
{
max_diff = diff;
ref_value = ref.mData[i];
result_value = result.mData[i];
}
}
std::cout << "error: " << error << std::endl;
std::cout << "max_diff: " << max_diff << ", " << ref_value << ", " << result_value << std::endl;
}
int main()
{
#if 0
constexpr unsigned N = 1;
constexpr unsigned C = 1;
constexpr unsigned HI = 28;
constexpr unsigned WI = 28;
constexpr unsigned K = 1;
constexpr unsigned S = 3;
constexpr unsigned R = 3;
constexpr unsigned HPad = 0;
constexpr unsigned WPad = 0;
#elif 0
// 3x3, 34x34
constexpr unsigned N = 64;
constexpr unsigned C = 256;
constexpr unsigned HI = 34;
constexpr unsigned WI = 34;
constexpr unsigned K = 64;
constexpr unsigned S = 3;
constexpr unsigned R = 3;
constexpr unsigned HPad = 0;
constexpr unsigned WPad = 0;
#elif 0
// 3x3, 56x56
constexpr unsigned N = 64;
constexpr unsigned C = 64;
constexpr unsigned HI = 56;
constexpr unsigned WI = 56;
constexpr unsigned K = 64;
constexpr unsigned S = 3;
constexpr unsigned R = 3;
#elif 0
// 3x3, 58x58
constexpr unsigned N = 64;
constexpr unsigned C = 64;
constexpr unsigned HI = 58;
constexpr unsigned WI = 58;
constexpr unsigned K = 64;
constexpr unsigned S = 3;
constexpr unsigned R = 3;
#elif 0
// 5x5, 36x36
constexpr unsigned N = 64;
constexpr unsigned C = 256;
constexpr unsigned HI = 36;
constexpr unsigned WI = 36;
constexpr unsigned K = 64;
constexpr unsigned S = 5;
constexpr unsigned R = 5;
#elif 0
// 7x7, 38x38
constexpr unsigned N = 64;
constexpr unsigned C = 256;
constexpr unsigned HI = 38;
constexpr unsigned WI = 38;
constexpr unsigned K = 64;
constexpr unsigned S = 7;
constexpr unsigned R = 7;
#elif 0
// 3x3, 58x58
constexpr unsigned N = 16;
constexpr unsigned C = 128;
constexpr unsigned HI = 58;
constexpr unsigned WI = 58;
constexpr unsigned K = 256;
constexpr unsigned S = 3;
constexpr unsigned R = 3;
#elif 0
// 3x3 filter, 58x58 image, 0x0 padding
constexpr unsigned N = 16;
constexpr unsigned C = 128;
constexpr unsigned HI = 58;
constexpr unsigned WI = 58;
constexpr unsigned K = 256;
constexpr unsigned S = 3;
constexpr unsigned R = 3;
constexpr unsigned HPad = 0;
constexpr unsigned WPad = 0;
#elif 0
// 3x3 filter, 56x56 image, 1x1 padding
constexpr unsigned N = 16;
constexpr unsigned C = 128;
constexpr unsigned HI = 56;
constexpr unsigned WI = 56;
constexpr unsigned K = 256;
constexpr unsigned S = 3;
constexpr unsigned R = 3;
constexpr unsigned HPad = 1;
constexpr unsigned WPad = 1;
#elif 0
// 3x3 filter, 28x28 image, 1x1 padding
constexpr unsigned N = 16;
constexpr unsigned C = 256;
constexpr unsigned HI = 28;
constexpr unsigned WI = 28;
constexpr unsigned K = 512;
constexpr unsigned S = 3;
constexpr unsigned R = 3;
constexpr unsigned HPad = 1;
constexpr unsigned WPad = 1;
#elif 1
// 1x1 filter, 28x28 image
constexpr unsigned N = 16;
constexpr unsigned C = 256;
constexpr unsigned HI = 28;
constexpr unsigned WI = 28;
constexpr unsigned K = 512;
constexpr unsigned S = 1;
constexpr unsigned R = 1;
constexpr unsigned HPad = 0;
constexpr unsigned WPad = 0;
#elif 0
// 3x3 filter, 20x84 image, 1x1 padding
constexpr unsigned N = 16;
constexpr unsigned C = 256;
constexpr unsigned HI = 20;
constexpr unsigned WI = 84;
constexpr unsigned K = 256;
constexpr unsigned S = 3;
constexpr unsigned R = 3;
constexpr unsigned HPad = 1;
constexpr unsigned WPad = 1;
#elif 0
// 3x3 filter, 112x112 image, 1x1 padding
constexpr unsigned N = 16;
constexpr unsigned C = 64;
constexpr unsigned HI = 112;
constexpr unsigned WI = 112;
constexpr unsigned K = 128;
constexpr unsigned S = 3;
constexpr unsigned R = 3;
constexpr unsigned HPad = 1;
constexpr unsigned WPad = 1;
#elif 0
// 5x5 filter, 20x86 image, 1x1 padding
constexpr unsigned N = 16;
constexpr unsigned C = 256;
constexpr unsigned HI = 20;
constexpr unsigned WI = 86;
constexpr unsigned K = 512;
constexpr unsigned S = 5;
constexpr unsigned R = 5;
constexpr unsigned HPad = 1;
constexpr unsigned WPad = 1;
#elif 0
// 5x5 filter, 28x28 image, 2x2 padding
constexpr unsigned N = 16;
constexpr unsigned C = 192;
constexpr unsigned HI = 28;
constexpr unsigned WI = 28;
constexpr unsigned K = 32;
constexpr unsigned S = 5;
constexpr unsigned R = 5;
constexpr unsigned HPad = 2;
constexpr unsigned WPad = 2;
#endif
auto lower_pads = Sequence<HPad, WPad>{};
auto upper_pads = Sequence<HPad, WPad>{};
auto in_nchw_desc = make_ConstantTensorDescriptor(Sequence<N, C, HI, WI>{});
auto wei_kcsr_desc = make_ConstantTensorDescriptor(Sequence<K, C, S, R>{});
auto out_nkhw_desc = get_convolution_with_padding_output_default_4d_tensor_descriptor(
in_nchw_desc, wei_kcsr_desc, lower_pads, upper_pads);
ostream_ConstantTensorDescriptor(in_nchw_desc, std::cout << "in_nchw_desc: ");
ostream_ConstantTensorDescriptor(wei_kcsr_desc, std::cout << "wei_kcsr_desc: ");
ostream_ConstantTensorDescriptor(out_nkhw_desc, std::cout << "out_nkhw_desc: ");
Tensor<float> in_nchw(make_TensorDescriptor(in_nchw_desc));
Tensor<float> wei_kcsr(make_TensorDescriptor(wei_kcsr_desc));
Tensor<float> out_nkhw_host(make_TensorDescriptor(out_nkhw_desc));
Tensor<float> out_nkhw_device(make_TensorDescriptor(out_nkhw_desc));
std::size_t num_thread = std::thread::hardware_concurrency();
#if 0
in_nchw.GenerateTensorValue(GeneratorTensor_1{}, num_thread);
wei_kcsr.GenerateTensorValue(GeneratorTensor_1{}, num_thread);
#elif 1
in_nchw.GenerateTensorValue(GeneratorTensor_2{-5, 5}, num_thread);
wei_kcsr.GenerateTensorValue(GeneratorTensor_2{-5, 5}, num_thread);
#elif 1
in_nchw.GenerateTensorValue(GeneratorTensor_2{-2, 2}, num_thread);
wei_kcsr.GenerateTensorValue(GeneratorTensor_1{}, num_thread);
#endif
unsigned nrepeat = 100;
#if 1
#if 0
device_direct_convolution_1
#elif 0
device_direct_convolution_2
#elif 0
device_implicit_gemm_convolution_1_nchw_kcsr
#elif 0
device_implicit_gemm_convolution_1_nchw_srck_nkhw
#elif 0
device_implicit_gemm_convolution_1_chwn_csrk_khwn
#elif 0
device_implicit_gemm_convolution_2_cnhw_srck_knhw
#elif 0
device_implicit_gemm_convolution_2_cnhw_csrk_knhw
#elif 1
device_implicit_gemm_convolution_2_cnhw_csrk_knhw_gemm_2
#endif
(in_nchw_desc, in_nchw, wei_kcsr_desc, wei_kcsr, out_nkhw_desc, out_nkhw_device, nrepeat);
#elif 1
device_implicit_gemm_convolution_1_chwn_csrk_khwn_padded(in_nchw_desc,
in_nchw,
wei_kcsr_desc,
wei_kcsr,
out_nkhw_desc,
out_nkhw_device,
lower_pads,
upper_pads,
nrepeat);
#endif
#if 1
if(S == 3 && R == 3)
{
host_winograd_3x3_convolution(in_nchw, wei_kcsr, out_nkhw_host, lower_pads, upper_pads);
}
else
{
host_direct_convolution(in_nchw, wei_kcsr, out_nkhw_host, lower_pads, upper_pads);
}
check_error(out_nkhw_host, out_nkhw_device);
#endif
#if 0
LogRange(std::cout << "in_nchw : ", in_nchw.mData, ",") << std::endl;
LogRange(std::cout << "wei_kcsr: ", wei_kcsr.mData, ",") << std::endl;
LogRange(std::cout << "out_nkhw_host : ", out_nkhw_host.mData, ",") << std::endl;
LogRange(std::cout << "out_nkhw_device: ", out_nkhw_device.mData, ",") << std::endl;
#endif
}
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