Skip to content

GitLab

Commit 85ae70d3 authored by Chao Liu's avatar Chao Liu
Browse files

do more benchmark

parent 35269cf7
Show whitespace changes
Inline Side-by-side
Showing with 46 additions and 1845 deletions
driver/include/device_convolution_implicit_gemm_v4_nchw_kcyx_nkhw.hpp
View file @ 85ae70d3
......@@ -59,7 +59,7 @@ void device_convolution_implicit_gemm_v4_nchw_kcyx_nkhw(InDesc,
constexpr index_t B = (N * Ho * Wo) / (N1 * N2);
#if 0
#if 1
constexpr index_t BlockSize = 256;
constexpr index_t BPerBlock = 16;
......@@ -93,7 +93,7 @@ void device_convolution_implicit_gemm_v4_nchw_kcyx_nkhw(InDesc,
constexpr index_t WeiBlockCopySrcDataPerRead_E = 4;
constexpr index_t WeiBlockCopyDstDataPerWrite_K = 1;
#elif 1
#elif 0
constexpr index_t BlockSize = 256;
constexpr index_t BPerBlock = 16;
......
driver/src/driver.cpp
View file @ 85ae70d3
......@@ -595,9 +595,9 @@ int main(int argc, char* argv[])
constexpr index_t HPad = 0;
constexpr index_t WPad = 0;
#elif 1
#elif 0
// 1x1 filter, 8x8 image
// cuDNN 68%, miopen 34%
// cuDNN 68%, ck:nvidia: 72.6%, ck:amd 34%
constexpr index_t N = 64;
constexpr index_t C = 1536;
constexpr index_t HI = 8;
......@@ -611,9 +611,9 @@ int main(int argc, char* argv[])
constexpr index_t HPad = 0;
constexpr index_t WPad = 0;
#elif 1
#elif 0
// 1x1 filter, 8x8 image
// cuDNN 77%, miopen 47%
// cuDNN 77%, ck:nvidia 76.4%, ck:amd 47%
constexpr index_t N = 128;
constexpr index_t C = 2048;
constexpr index_t HI = 8;
......@@ -627,9 +627,9 @@ int main(int argc, char* argv[])
constexpr index_t HPad = 0;
constexpr index_t WPad = 0;
#elif 1
#elif 0
// 1x1 filter, 7x7 image
// cuDNN 82%, miopen 54%
// cuDNN 82%, ck:nvidia 76.6%, ck:amd 54%
constexpr index_t N = 128;
constexpr index_t C = 832;
constexpr index_t HI = 7;
......@@ -643,9 +643,9 @@ int main(int argc, char* argv[])
constexpr index_t HPad = 0;
constexpr index_t WPad = 0;
#elif 1
#elif 0
// 1x1 filter, 8x8 image
// cuDNN 83%, miopen 58%
// cuDNN 83%, ck:nvidia 75.4%, ck:amd 58%
constexpr index_t N = 128;
constexpr index_t C = 1280;
constexpr index_t HI = 8;
......@@ -659,9 +659,9 @@ int main(int argc, char* argv[])
constexpr index_t HPad = 0;
constexpr index_t WPad = 0;
#elif 1
#elif 0
// 1x1 filter, 14x14 image
// cuDNN 62%, miopen 44%
// cuDNN 62%, ck:nvidia 68.4%, ck:amd 44%
constexpr index_t N = 128;
constexpr index_t C = 512;
constexpr index_t HI = 14;
......@@ -675,9 +675,9 @@ int main(int argc, char* argv[])
constexpr index_t HPad = 0;
constexpr index_t WPad = 0;
#elif 1
#elif 0
// 1x1 filter, 8x8 image
// cuDNN 74%, miopen 52%
// cuDNN 74%, ck:nvidia 57.1%, ck:amd 52%
constexpr index_t N = 64;
constexpr index_t C = 1536;
constexpr index_t HI = 8;
......@@ -691,9 +691,9 @@ int main(int argc, char* argv[])
constexpr index_t HPad = 0;
constexpr index_t WPad = 0;
#elif 1
#elif 0
// 1x1 filter, 28x28 image
// cuDNN 86%, miopen 64%
// cuDNN 86%, ck:nvidia 84.6%, ck:amd 64%
constexpr index_t N = 128;
constexpr index_t C = 256;
constexpr index_t HI = 28;
......@@ -707,9 +707,9 @@ int main(int argc, char* argv[])
constexpr index_t HPad = 0;
constexpr index_t WPad = 0;
#elif 1
#elif 0
// 1x1 filter, 7x7 image
// cuDNN 71%, miopen 54%
// cuDNN 71%, ck:55.9%, ck:amd 54%
constexpr index_t N = 128;
constexpr index_t C = 832;
constexpr index_t HI = 7;
......@@ -723,9 +723,9 @@ int main(int argc, char* argv[])
constexpr index_t HPad = 0;
constexpr index_t WPad = 0;
#elif 1
#elif 0
// 3x3 filter, 2x2 stride, 35x35 input, 17x17 output
// cuDNN 90%, miopen 73%
// cuDNN 90%, ck:nvidia 93%, ck:amd 73%
constexpr index_t N = 128;
constexpr index_t C = 288;
constexpr index_t HI = 35;
......@@ -739,9 +739,9 @@ int main(int argc, char* argv[])
constexpr index_t HPad = 0;
constexpr index_t WPad = 0;
#elif 1
#elif 0
// 1x1 filter, 17x17 input
// cuDNN 81%, miopen 66%
// cuDNN 81%, ck:nvidia 76.8%, ck:amd 66%
constexpr index_t N = 128;
constexpr index_t C = 768;
constexpr index_t HI = 17;
......@@ -757,7 +757,23 @@ int main(int argc, char* argv[])
constexpr index_t WPad = 0;
#elif 1
// 1x1 filter, 14x14 image
// cuDNN 73%, miopen 65%
// cuDNN 73%, ck:nvidia 72.7%, ck:amd 65%
constexpr index_t N = 128;
constexpr index_t C = 528;
constexpr index_t HI = 14;
constexpr index_t WI = 14;
constexpr index_t K = 128;
constexpr index_t Y = 1;
constexpr index_t X = 1;
using ConvStrides = Sequence<1, 1>;
using ConvDilations = Sequence<1, 1>;
constexpr index_t HPad = 0;
constexpr index_t WPad = 0;
#elif 0
// 1x1 filter, 14x14 image
// cuDNN 73%, ck:nvidia 72.7%, ck:amd 65%
constexpr index_t N = 128;
constexpr index_t C = 528;
constexpr index_t HI = 14;
......@@ -771,14 +787,15 @@ int main(int argc, char* argv[])
constexpr index_t HPad = 0;
constexpr index_t WPad = 0;
#elif 1
#elif 0
// 1x1 filter, 7x7 image
// cuDNN 49%, miopen 45%
// cuDNN 49%, ck:nvidia 52.8%, ck:amd 45%
constexpr index_t N = 128;
constexpr index_t C = 832;
constexpr index_t HI = 7;
constexpr index_t WI = 7;
constexpr index_t K = 128 constexpr index_t Y = 1;
constexpr index_t K = 128;
constexpr index_t Y = 1;
constexpr index_t X = 1;
using ConvStrides = Sequence<1, 1>;
......
driver/src/driver.cu deleted 100644 → 0
View file @ 35269cf7
#include <iostream>
#include <numeric>
#include <initializer_list>
#include <cstdlib>
#include <stdlib.h>
#include "config.hpp"
#include "ConstantTensorDescriptor.hpp"
#include "device.hpp"
#include "conv_common.hpp"
#include "device_convolution_direct_v2_nchw_kcyx_nkhw.hpp"
#include "device_convolution_implicit_gemm_v1_chwn_cyxk_khwn.hpp"
#include "device_convolution_implicit_gemm_v1_nchw_cyxk_nkhw.hpp"
#include "device_convolution_implicit_gemm_v2_chwn_cyxk_khwn.hpp"
#include "device_convolution_implicit_gemm_v3_nchw_cyxk_nkhw.hpp"
#include "device_convolution_implicit_gemm_v4_nchw_kcyx_nkhw.hpp"
using namespace ck;
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)
{
std::array<index_t, sizeof...(Is)> dims = {{static_cast<index_t>(is)...}};
auto f_acc = [](auto a, auto b) { return 100 * a + b; };
return std::accumulate(dims.begin(), dims.end(), index_t(0), f_acc);
}
};
struct GeneratorTensor_Checkboard
{
template <class... Ts>
double operator()(Ts... Xs) const
{
std::array<index_t, sizeof...(Ts)> dims = {{Xs...}};
return std::accumulate(dims.begin(),
dims.end(),
true,
[](bool init, index_t 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<index_t> lengths = {
desc.GetLength(I0), desc.GetLength(I1), desc.GetLength(I2), desc.GetLength(I3)};
std::initializer_list<index_t> strides = {
desc.GetStride(I0), desc.GetStride(I1), desc.GetStride(I2), desc.GetStride(I3)};
return TensorDescriptor(lengths, strides);
}
template <class TIn,
class TWei,
class TOut,
class ConvStrides,
class ConvDilations,
class LowerPads,
class UpperPads>
void host_direct_convolution(const Tensor<TIn>& in_nchw,
const Tensor<TWei>& wei_kcyx,
Tensor<TOut>& out_nkhw,
ConvStrides,
ConvDilations,
LowerPads,
UpperPads)
{
index_t h_pad_low = LowerPads{}.Get(Number<0>{});
index_t w_pad_low = LowerPads{}.Get(Number<1>{});
index_t h_pad_up = UpperPads{}.Get(Number<0>{});
index_t 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_kcyx.mDesc.GetLengths()[1]; ++c)
{
for(int y = 0; y < wei_kcyx.mDesc.GetLengths()[2]; ++y)
{
int hi = ho * ConvStrides{}[0] + y * ConvDilations{}[0] - h_pad_low;
for(int x = 0; x < wei_kcyx.mDesc.GetLengths()[3]; ++x)
{
int wi = wo * ConvStrides{}[1] + x * ConvDilations{}[1] - w_pad_low;
if(hi >= 0 && hi < in_nchw.mDesc.GetLengths()[2] && wi >= 0 &&
wi < in_nchw.mDesc.GetLengths()[3])
{
v += double(in_nchw(n, c, hi, wi)) * double(wei_kcyx(k, c, y, x));
}
}
}
}
out_nkhw(n, k, ho, wo) = v;
};
auto f_par = make_ParallelTensorFunctor(f,
out_nkhw.mDesc.GetLengths()[0],
out_nkhw.mDesc.GetLengths()[1],
out_nkhw.mDesc.GetLengths()[2],
out_nkhw.mDesc.GetLengths()[3]);
f_par(std::thread::hardware_concurrency());
}
template <class TIn, class TWei, class TOut, class LowerPads, class UpperPads>
void host_winograd_3x3_convolution(const Tensor<TIn>& in_nchw,
const Tensor<TWei>& wei_kcyx,
Tensor<TOut>& out_nkhw,
LowerPads,
UpperPads)
{
constexpr std::size_t HoPerTile = 2;
constexpr std::size_t WoPerTile = 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_kcyx.mDesc.GetLengths()[0];
std::size_t Y = wei_kcyx.mDesc.GetLengths()[2];
std::size_t X = wei_kcyx.mDesc.GetLengths()[3];
std::size_t HO = out_nkhw.mDesc.GetLengths()[2];
std::size_t WO = out_nkhw.mDesc.GetLengths()[3];
index_t h_pad_low = LowerPads{}.Get(Number<0>{});
index_t w_pad_low = LowerPads{}.Get(Number<1>{});
index_t h_pad_up = UpperPads{}.Get(Number<0>{});
index_t w_pad_up = UpperPads{}.Get(Number<1>{});
std::size_t HiPerTile = HoPerTile + Y - 1;
std::size_t WiPerTile = WoPerTile + X - 1;
std::size_t HTile = (HO + HoPerTile - 1) / HoPerTile;
std::size_t WTile = (WO + WoPerTile - 1) / WoPerTile;
Tensor<double> in_hold({N, C, HTile, WTile, HiPerTile, WiPerTile});
Tensor<double> in_transform({N, C, HTile, WTile, HiPerTile, WiPerTile});
Tensor<double> wei_transform({K, C, HiPerTile, WiPerTile});
Tensor<double> out_transform({N, K, HTile, WTile, HiPerTile, HiPerTile});
Tensor<double> out_hold({N, K, HTile, WTile, HoPerTile, WoPerTile});
auto f_in_hold = [&](auto n, auto c, auto htile, auto wtile) {
for(int j = 0; j < HiPerTile; ++j)
{
int hi = HoPerTile * htile + j - h_pad_low;
for(int i = 0; i < WiPerTile; ++i)
{
int wi = WoPerTile * wtile + 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, htile, wtile, j, i) = in_nchw(n, c, hi, wi);
}
else
{
in_hold(n, c, htile, wtile, j, i) = TIn(0);
}
}
}
};
auto f_in_transform = [&](auto n, auto c, auto htile, auto wtile) {
in_transform(n, c, htile, wtile, 0, 0) =
in_hold(n, c, htile, wtile, 0, 0) - in_hold(n, c, htile, wtile, 0, 2) -
in_hold(n, c, htile, wtile, 2, 0) + in_hold(n, c, htile, wtile, 2, 2);
in_transform(n, c, htile, wtile, 0, 1) =
in_hold(n, c, htile, wtile, 0, 1) + in_hold(n, c, htile, wtile, 0, 2) -
in_hold(n, c, htile, wtile, 2, 1) - in_hold(n, c, htile, wtile, 2, 2);
in_transform(n, c, htile, wtile, 0, 2) =
-in_hold(n, c, htile, wtile, 0, 1) + in_hold(n, c, htile, wtile, 0, 2) +
in_hold(n, c, htile, wtile, 2, 1) - in_hold(n, c, htile, wtile, 2, 2);
in_transform(n, c, htile, wtile, 0, 3) =
in_hold(n, c, htile, wtile, 0, 1) - in_hold(n, c, htile, wtile, 0, 3) -
in_hold(n, c, htile, wtile, 2, 1) + in_hold(n, c, htile, wtile, 2, 3);
in_transform(n, c, htile, wtile, 1, 0) =
in_hold(n, c, htile, wtile, 1, 0) - in_hold(n, c, htile, wtile, 1, 2) +
in_hold(n, c, htile, wtile, 2, 0) - in_hold(n, c, htile, wtile, 2, 2);
in_transform(n, c, htile, wtile, 1, 1) =
in_hold(n, c, htile, wtile, 1, 1) + in_hold(n, c, htile, wtile, 1, 2) +
in_hold(n, c, htile, wtile, 2, 1) + in_hold(n, c, htile, wtile, 2, 2);
in_transform(n, c, htile, wtile, 1, 2) =
-in_hold(n, c, htile, wtile, 1, 1) + in_hold(n, c, htile, wtile, 1, 2) -
in_hold(n, c, htile, wtile, 2, 1) + in_hold(n, c, htile, wtile, 2, 2);
in_transform(n, c, htile, wtile, 1, 3) =
in_hold(n, c, htile, wtile, 1, 1) - in_hold(n, c, htile, wtile, 1, 3) +
in_hold(n, c, htile, wtile, 2, 1) - in_hold(n, c, htile, wtile, 2, 3);
in_transform(n, c, htile, wtile, 2, 0) =
-in_hold(n, c, htile, wtile, 1, 0) + in_hold(n, c, htile, wtile, 1, 2) +
in_hold(n, c, htile, wtile, 2, 0) - in_hold(n, c, htile, wtile, 2, 2);
in_transform(n, c, htile, wtile, 2, 1) =
-in_hold(n, c, htile, wtile, 1, 1) - in_hold(n, c, htile, wtile, 1, 2) +
in_hold(n, c, htile, wtile, 2, 1) + in_hold(n, c, htile, wtile, 2, 2);
in_transform(n, c, htile, wtile, 2, 2) =
in_hold(n, c, htile, wtile, 1, 1) - in_hold(n, c, htile, wtile, 1, 2) -
in_hold(n, c, htile, wtile, 2, 1) + in_hold(n, c, htile, wtile, 2, 2);
in_transform(n, c, htile, wtile, 2, 3) =
-in_hold(n, c, htile, wtile, 1, 1) + in_hold(n, c, htile, wtile, 1, 3) +
in_hold(n, c, htile, wtile, 2, 1) - in_hold(n, c, htile, wtile, 2, 3);
in_transform(n, c, htile, wtile, 3, 0) =
in_hold(n, c, htile, wtile, 1, 0) - in_hold(n, c, htile, wtile, 1, 2) -
in_hold(n, c, htile, wtile, 3, 0) + in_hold(n, c, htile, wtile, 3, 2);
in_transform(n, c, htile, wtile, 3, 1) =
in_hold(n, c, htile, wtile, 1, 1) + in_hold(n, c, htile, wtile, 1, 2) -
in_hold(n, c, htile, wtile, 3, 1) - in_hold(n, c, htile, wtile, 3, 2);
in_transform(n, c, htile, wtile, 3, 2) =
-in_hold(n, c, htile, wtile, 1, 1) + in_hold(n, c, htile, wtile, 1, 2) +
in_hold(n, c, htile, wtile, 3, 1) - in_hold(n, c, htile, wtile, 3, 2);
in_transform(n, c, htile, wtile, 3, 3) =
in_hold(n, c, htile, wtile, 1, 1) - in_hold(n, c, htile, wtile, 1, 3) -
in_hold(n, c, htile, wtile, 3, 1) + in_hold(n, c, htile, wtile, 3, 3);
};
auto f_wei_transform = [&](auto k, auto c) {
wei_transform(k, c, 0, 0) = double(wei_kcyx(k, c, 0, 0));
wei_transform(k, c, 0, 1) = 0.5 * double(wei_kcyx(k, c, 0, 0)) +
0.5 * double(wei_kcyx(k, c, 0, 1)) +
0.5 * double(wei_kcyx(k, c, 0, 2));
wei_transform(k, c, 0, 2) = 0.5 * double(wei_kcyx(k, c, 0, 0)) -
0.5 * double(wei_kcyx(k, c, 0, 1)) +
0.5 * double(wei_kcyx(k, c, 0, 2));
wei_transform(k, c, 0, 3) = double(wei_kcyx(k, c, 0, 2));
wei_transform(k, c, 1, 0) = 0.5 * double(wei_kcyx(k, c, 0, 0)) +
0.5 * double(wei_kcyx(k, c, 1, 0)) +
0.5 * double(wei_kcyx(k, c, 2, 0));
wei_transform(k, c, 1, 1) =
0.25 * double(wei_kcyx(k, c, 0, 0)) + 0.25 * double(wei_kcyx(k, c, 0, 1)) +
0.25 * double(wei_kcyx(k, c, 0, 2)) + 0.25 * double(wei_kcyx(k, c, 1, 0)) +
0.25 * double(wei_kcyx(k, c, 1, 1)) + 0.25 * double(wei_kcyx(k, c, 1, 2)) +
0.25 * double(wei_kcyx(k, c, 2, 0)) + 0.25 * double(wei_kcyx(k, c, 2, 1)) +
0.25 * double(wei_kcyx(k, c, 2, 2));
wei_transform(k, c, 1, 2) =
0.25 * double(wei_kcyx(k, c, 0, 0)) - 0.25 * double(wei_kcyx(k, c, 0, 1)) +
0.25 * double(wei_kcyx(k, c, 0, 2)) + 0.25 * double(wei_kcyx(k, c, 1, 0)) -
0.25 * double(wei_kcyx(k, c, 1, 1)) + 0.25 * double(wei_kcyx(k, c, 1, 2)) +
0.25 * double(wei_kcyx(k, c, 2, 0)) - 0.25 * double(wei_kcyx(k, c, 2, 1)) +
0.25 * double(wei_kcyx(k, c, 2, 2));
wei_transform(k, c, 1, 3) = 0.5 * double(wei_kcyx(k, c, 0, 2)) +
0.5 * double(wei_kcyx(k, c, 1, 2)) +
0.5 * double(wei_kcyx(k, c, 2, 2));
wei_transform(k, c, 2, 0) = 0.5 * double(wei_kcyx(k, c, 0, 0)) -
0.5 * double(wei_kcyx(k, c, 1, 0)) +
0.5 * double(wei_kcyx(k, c, 2, 0));
wei_transform(k, c, 2, 1) =
0.25 * double(wei_kcyx(k, c, 0, 0)) + 0.25 * double(wei_kcyx(k, c, 0, 1)) +
0.25 * double(wei_kcyx(k, c, 0, 2)) - 0.25 * double(wei_kcyx(k, c, 1, 0)) -
0.25 * double(wei_kcyx(k, c, 1, 1)) - 0.25 * double(wei_kcyx(k, c, 1, 2)) +
0.25 * double(wei_kcyx(k, c, 2, 0)) + 0.25 * double(wei_kcyx(k, c, 2, 1)) +
0.25 * double(wei_kcyx(k, c, 2, 2));
wei_transform(k, c, 2, 2) =
0.25 * double(wei_kcyx(k, c, 0, 0)) - 0.25 * double(wei_kcyx(k, c, 0, 1)) +
0.25 * double(wei_kcyx(k, c, 0, 2)) - 0.25 * double(wei_kcyx(k, c, 1, 0)) +
0.25 * double(wei_kcyx(k, c, 1, 1)) - 0.25 * double(wei_kcyx(k, c, 1, 2)) +
0.25 * double(wei_kcyx(k, c, 2, 0)) - 0.25 * double(wei_kcyx(k, c, 2, 1)) +
0.25 * double(wei_kcyx(k, c, 2, 2));
wei_transform(k, c, 2, 3) = 0.5 * double(wei_kcyx(k, c, 0, 2)) -
0.5 * double(wei_kcyx(k, c, 1, 2)) +
0.5 * double(wei_kcyx(k, c, 2, 2));
wei_transform(k, c, 3, 0) = double(wei_kcyx(k, c, 2, 0));
wei_transform(k, c, 3, 1) = 0.5 * double(wei_kcyx(k, c, 2, 0)) +
0.5 * double(wei_kcyx(k, c, 2, 1)) +
0.5 * double(wei_kcyx(k, c, 2, 2));
wei_transform(k, c, 3, 2) = 0.5 * double(wei_kcyx(k, c, 2, 0)) -
0.5 * double(wei_kcyx(k, c, 2, 1)) +
0.5 * double(wei_kcyx(k, c, 2, 2));
wei_transform(k, c, 3, 3) = double(wei_kcyx(k, c, 2, 2));
};
auto f_out_transform = [&](auto n, auto k, auto htile, auto wtile) {
for(int j = 0; j < HiPerTile; ++j)
{
for(int i = 0; i < WiPerTile; ++i)
{
double v = 0;
for(int c = 0; c < C; ++c)
{
v += in_transform(n, c, htile, wtile, j, i) * wei_transform(k, c, j, i);
}
out_transform(n, k, htile, wtile, j, i) = v;
}
}
};
auto f_out_hold = [&](auto n, auto k, auto htile, auto wtile) {
out_hold(n, k, htile, wtile, 0, 0) =
out_transform(n, k, htile, wtile, 0, 0) + out_transform(n, k, htile, wtile, 0, 1) +
out_transform(n, k, htile, wtile, 0, 2) + out_transform(n, k, htile, wtile, 1, 0) +
out_transform(n, k, htile, wtile, 1, 1) + out_transform(n, k, htile, wtile, 1, 2) +
out_transform(n, k, htile, wtile, 2, 0) + out_transform(n, k, htile, wtile, 2, 1) +
out_transform(n, k, htile, wtile, 2, 2);
out_hold(n, k, htile, wtile, 0, 1) =
out_transform(n, k, htile, wtile, 0, 1) - out_transform(n, k, htile, wtile, 0, 2) -
out_transform(n, k, htile, wtile, 0, 3) + out_transform(n, k, htile, wtile, 1, 1) -
out_transform(n, k, htile, wtile, 1, 2) - out_transform(n, k, htile, wtile, 1, 3) +
out_transform(n, k, htile, wtile, 2, 1) - out_transform(n, k, htile, wtile, 2, 2) -
out_transform(n, k, htile, wtile, 2, 3);
out_hold(n, k, htile, wtile, 1, 0) =
out_transform(n, k, htile, wtile, 1, 0) + out_transform(n, k, htile, wtile, 1, 1) +
out_transform(n, k, htile, wtile, 1, 2) - out_transform(n, k, htile, wtile, 2, 0) -
out_transform(n, k, htile, wtile, 2, 1) - out_transform(n, k, htile, wtile, 2, 2) -
out_transform(n, k, htile, wtile, 3, 0) - out_transform(n, k, htile, wtile, 3, 1) -
out_transform(n, k, htile, wtile, 3, 2);
out_hold(n, k, htile, wtile, 1, 1) =
out_transform(n, k, htile, wtile, 1, 1) - out_transform(n, k, htile, wtile, 1, 2) -
out_transform(n, k, htile, wtile, 1, 3) - out_transform(n, k, htile, wtile, 2, 1) +
out_transform(n, k, htile, wtile, 2, 2) + out_transform(n, k, htile, wtile, 2, 3) -
out_transform(n, k, htile, wtile, 3, 1) + out_transform(n, k, htile, wtile, 3, 2) +
out_transform(n, k, htile, wtile, 3, 3);
};
auto f_out = [&](auto n, auto k, auto htile, auto wtile) {
for(int j = 0; j < HoPerTile; ++j)
{
std::size_t ho = HoPerTile * htile + j;
for(int i = 0; i < WoPerTile; ++i)
{
std::size_t wo = WoPerTile * wtile + i;
out_nkhw(n, k, ho, wo) = out_hold(n, k, htile, wtile, j, i);
}
}
};
std::size_t num_thread = std::thread::hardware_concurrency();
make_ParallelTensorFunctor(f_in_hold, N, C, HTile, WTile)(num_thread);
make_ParallelTensorFunctor(f_in_transform, N, C, HTile, WTile)(num_thread);
make_ParallelTensorFunctor(f_wei_transform, K, C)(num_thread);
make_ParallelTensorFunctor(f_out_transform, N, K, HTile, WTile)(num_thread);
make_ParallelTensorFunctor(f_out_hold, N, K, HTile, WTile)(num_thread);
make_ParallelTensorFunctor(f_out, N, K, HTile, WTile)(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(double(ref.mData[i]) - double(result.mData[i]));
float diff = std::abs(double(ref.mData[i]) - double(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(int argc, char* argv[])
{
#if 0
constexpr index_t N = 8;
constexpr index_t C = 16;
constexpr index_t HI = 3;
constexpr index_t WI = 18;
constexpr index_t K = 128;
constexpr index_t Y = 3;
constexpr index_t X = 3;
constexpr index_t HPad = 0;
constexpr index_t WPad = 0;
#elif 0
// 3x3, 34x34
constexpr index_t N = 128;
constexpr index_t C = 256;
constexpr index_t HI = 34;
constexpr index_t WI = 34;
constexpr index_t K = 128;
constexpr index_t Y = 3;
constexpr index_t X = 3;
using ConvStrides = Sequence<2, 2>;
using ConvDilations = Sequence<1, 1>;
constexpr index_t HPad = 0;
constexpr index_t WPad = 0;
#elif 0
// 3x3, 56x56
constexpr index_t N = 64;
constexpr index_t C = 64;
constexpr index_t HI = 56;
constexpr index_t WI = 56;
constexpr index_t K = 128;
constexpr index_t Y = 3;
constexpr index_t X = 3;
constexpr index_t HPad = 0;
constexpr index_t WPad = 0;
#elif 0
// 3x3 filter, 28x28 image
constexpr index_t N = 128;
constexpr index_t C = 256;
constexpr index_t HI = 28;
constexpr index_t WI = 28;
constexpr index_t K = 128;
constexpr index_t Y = 3;
constexpr index_t X = 3;
using ConvStrides = Sequence<1, 1>;
using ConvDilations = Sequence<1, 1>;
constexpr index_t HPad = 0;
constexpr index_t WPad = 0;
#elif 0
// 1x1 filter, 28x28 image
constexpr index_t N = 128;
constexpr index_t C = 512;
constexpr index_t HI = 28;
constexpr index_t WI = 28;
constexpr index_t K = 512;
constexpr index_t Y = 1;
constexpr index_t X = 1;
using ConvStrides = Sequence<1, 1>;
using ConvDilations = Sequence<1, 1>;
constexpr index_t HPad = 0;
constexpr index_t WPad = 0;
#elif 0
// 3x3 filter, 20x84 image, 1x1 padding
constexpr index_t N = 16;
constexpr index_t C = 256;
constexpr index_t HI = 20;
constexpr index_t WI = 84;
constexpr index_t K = 256;
constexpr index_t Y = 3;
constexpr index_t X = 3;
constexpr index_t HPad = 1;
constexpr index_t WPad = 1;
#elif 0
// 3x3 filter, 112x112 image, 1x1 padding
constexpr index_t N = 16;
constexpr index_t C = 64;
constexpr index_t HI = 112;
constexpr index_t WI = 112;
constexpr index_t K = 128;
constexpr index_t Y = 3;
constexpr index_t X = 3;
constexpr index_t HPad = 1;
constexpr index_t WPad = 1;
#elif 0
// 5x5 filter, 20x86 image
constexpr index_t N = 16;
constexpr index_t C = 256;
constexpr index_t HI = 20;
constexpr index_t WI = 86;
constexpr index_t K = 512;
constexpr index_t Y = 5;
constexpr index_t X = 5;
constexpr index_t HPad = 0;
constexpr index_t WPad = 0;
#elif 0
// 5x5 filter, 20x86 image, 1x1 padding
constexpr index_t N = 16;
constexpr index_t C = 256;
constexpr index_t HI = 20;
constexpr index_t WI = 86;
constexpr index_t K = 512;
constexpr index_t Y = 5;
constexpr index_t X = 5;
constexpr index_t HPad = 1;
constexpr index_t WPad = 1;
#elif 0
// 5x5 filter, 28x28 image, 2x2 padding
constexpr index_t N = 16;
constexpr index_t C = 192;
constexpr index_t HI = 28;
constexpr index_t WI = 28;
constexpr index_t K = 32;
constexpr index_t Y = 5;
constexpr index_t X = 5;
constexpr index_t HPad = 2;
constexpr index_t WPad = 2;
#elif 0
// 3x3 filter, 14x14 image
constexpr index_t N = 128;
constexpr index_t C = 256;
constexpr index_t HI = 14;
constexpr index_t WI = 14;
constexpr index_t K = 128;
constexpr index_t Y = 3;
constexpr index_t X = 3;
constexpr index_t HPad = 0;
constexpr index_t WPad = 0;
#elif 0
// 1x1 filter, 14x14 image
constexpr index_t N = 128;
constexpr index_t C = 512;
constexpr index_t HI = 14;
constexpr index_t WI = 14;
constexpr index_t K = 512;
constexpr index_t Y = 1;
constexpr index_t X = 1;
using ConvStrides = Sequence<1, 1>;
using ConvDilations = Sequence<1, 1>;
constexpr index_t HPad = 0;
constexpr index_t WPad = 0;
#elif 0
// 1x1 filter, 7x7 image
constexpr index_t N = 128;
constexpr index_t C = 512;
constexpr index_t HI = 7;
constexpr index_t WI = 7;
constexpr index_t K = 2048;
constexpr index_t Y = 1;
constexpr index_t X = 1;
constexpr index_t HPad = 0;
constexpr index_t WPad = 0;
#elif 0
// 1x1 filter, 73x73 image
constexpr index_t N = 128;
constexpr index_t C = 512;
constexpr index_t HI = 73;
constexpr index_t WI = 73;
constexpr index_t K = 128;
constexpr index_t Y = 1;
constexpr index_t X = 1;
constexpr index_t HPad = 0;
constexpr index_t WPad = 0;
#elif 1
// 1x1 filter, 8x8 image
// cuDNN 68%, miopen 34%
constexpr index_t N = 64;
constexpr index_t C = 1536;
constexpr index_t HI = 8;
constexpr index_t WI = 8;
constexpr index_t K = 256;
constexpr index_t Y = 1;
constexpr index_t X = 1;
using ConvStrides = Sequence<1, 1>;
using ConvDilations = Sequence<1, 1>;
constexpr index_t HPad = 0;
constexpr index_t WPad = 0;
#elif 1
// 1x1 filter, 8x8 image
// cuDNN 77%, miopen 47%
constexpr index_t N = 128;
constexpr index_t C = 2048;
constexpr index_t HI = 8;
constexpr index_t WI = 8;
constexpr index_t K = 384;
constexpr index_t Y = 1;
constexpr index_t X = 1;
using ConvStrides = Sequence<1, 1>;
using ConvDilations = Sequence<1, 1>;
constexpr index_t HPad = 0;
constexpr index_t WPad = 0;
#elif 1
// 1x1 filter, 7x7 image
// cuDNN 82%, miopen 54%
constexpr index_t N = 128;
constexpr index_t C = 832;
constexpr index_t HI = 7;
constexpr index_t WI = 7;
constexpr index_t K = 384;
constexpr index_t Y = 1;
constexpr index_t X = 1;
using ConvStrides = Sequence<1, 1>;
using ConvDilations = Sequence<1, 1>;
constexpr index_t HPad = 0;
constexpr index_t WPad = 0;
#elif 1
// 1x1 filter, 8x8 image
// cuDNN 83%, miopen 58%
constexpr index_t N = 128;
constexpr index_t C = 1280;
constexpr index_t HI = 8;
constexpr index_t WI = 8;
constexpr index_t K = 384;
constexpr index_t Y = 1;
constexpr index_t X = 1;
using ConvStrides = Sequence<1, 1>;
using ConvDilations = Sequence<1, 1>;
constexpr index_t HPad = 0;
constexpr index_t WPad = 0;
#elif 1
// 1x1 filter, 14x14 image
// cuDNN 62%, miopen 44%
constexpr index_t N = 128;
constexpr index_t C = 512;
constexpr index_t HI = 14;
constexpr index_t WI = 14;
constexpr index_t K = 128;
constexpr index_t Y = 1;
constexpr index_t X = 1;
using ConvStrides = Sequence<1, 1>;
using ConvDilations = Sequence<1, 1>;
constexpr index_t HPad = 0;
constexpr index_t WPad = 0;
#elif 1
// 1x1 filter, 8x8 image
// cuDNN 74%, miopen 52%
constexpr index_t N = 64;
constexpr index_t C = 1536;
constexpr index_t HI = 8;
constexpr index_t WI = 8;
constexpr index_t K = 384;
constexpr index_t Y = 1;
constexpr index_t X = 1;
using ConvStrides = Sequence<1, 1>;
using ConvDilations = Sequence<1, 1>;
constexpr index_t HPad = 0;
constexpr index_t WPad = 0;
#elif 1
// 1x1 filter, 28x28 image
// cuDNN 86%, miopen 64%
constexpr index_t N = 128;
constexpr index_t C = 256;
constexpr index_t HI = 28;
constexpr index_t WI = 28;
constexpr index_t K = 128;
constexpr index_t Y = 1;
constexpr index_t X = 1;
using ConvStrides = Sequence<1, 1>;
using ConvDilations = Sequence<1, 1>;
constexpr index_t HPad = 0;
constexpr index_t WPad = 0;
#elif 1
// 1x1 filter, 7x7 image
// cuDNN 71%, miopen 54%
constexpr index_t N = 128;
constexpr index_t C = 832;
constexpr index_t HI = 7;
constexpr index_t WI = 7;
constexpr index_t K = 256;
constexpr index_t Y = 1;
constexpr index_t X = 1;
using ConvStrides = Sequence<1, 1>;
using ConvDilations = Sequence<1, 1>;
constexpr index_t HPad = 0;
constexpr index_t WPad = 0;
#elif 1
// 3x3 filter, 2x2 stride, 35x35 input, 17x17 output
// cuDNN 90%, miopen 73%
constexpr index_t N = 128;
constexpr index_t C = 288;
constexpr index_t HI = 35;
constexpr index_t WI = 35;
constexpr index_t K = 384;
constexpr index_t Y = 3;
constexpr index_t X = 3;
using ConvStrides = Sequence<2, 2>;
using ConvDilations = Sequence<1, 1>;
constexpr index_t HPad = 0;
constexpr index_t WPad = 0;
#elif 1
// 1x1 filter, 17x17 input
// cuDNN 81%, miopen 66%
constexpr index_t N = 128;
constexpr index_t C = 768;
constexpr index_t HI = 17;
constexpr index_t WI = 17;
constexpr index_t K = 128;
constexpr index_t Y = 1;
constexpr index_t X = 1;
using ConvStrides = Sequence<1, 1>;
using ConvDilations = Sequence<1, 1>;
constexpr index_t HPad = 0;
constexpr index_t WPad = 0;
#elif 1
// 1x1 filter, 14x14 image
// cuDNN 73%, miopen 65%
constexpr index_t N = 128;
constexpr index_t C = 528;
constexpr index_t HI = 14;
constexpr index_t WI = 14;
constexpr index_t K = 256;
constexpr index_t Y = 1;
constexpr index_t X = 1;
using ConvStrides = Sequence<1, 1>;
using ConvDilations = Sequence<1, 1>;
constexpr index_t HPad = 0;
constexpr index_t WPad = 0;
#elif 1
// 1x1 filter, 7x7 image
// cuDNN 49%, miopen 45%
constexpr index_t N = 128;
constexpr index_t C = 832;
constexpr index_t HI = 7;
constexpr index_t WI = 7;
constexpr index_t K = 128 constexpr index_t Y = 1;
constexpr index_t X = 1;
using ConvStrides = Sequence<1, 1>;
using ConvDilations = Sequence<1, 1>;
constexpr index_t HPad = 0;
constexpr index_t WPad = 0;
#endif
auto lower_pads = Sequence<HPad, WPad>{};
auto upper_pads = Sequence<HPad, WPad>{};
auto in_nchw_desc = make_ConstantTensorDescriptor_packed(Sequence<N, C, HI, WI>{});
auto wei_kcyx_desc = make_ConstantTensorDescriptor_packed(Sequence<K, C, Y, X>{});
auto out_nkhw_desc = get_convolution_with_padding_output_default_4d_tensor_descriptor(
in_nchw_desc, wei_kcyx_desc, ConvStrides{}, ConvDilations{}, lower_pads, upper_pads);
ostream_ConstantTensorDescriptor(in_nchw_desc, std::cout << "in_nchw_desc: ");
ostream_ConstantTensorDescriptor(wei_kcyx_desc, std::cout << "wei_kcyx_desc: ");
ostream_ConstantTensorDescriptor(out_nkhw_desc, std::cout << "out_nkhw_desc: ");
using in_data_t = float;
using out_data_t = float;
Tensor<in_data_t> in_nchw(make_TensorDescriptor(in_nchw_desc));
Tensor<in_data_t> wei_kcyx(make_TensorDescriptor(wei_kcyx_desc));
Tensor<out_data_t> out_nkhw_host(make_TensorDescriptor(out_nkhw_desc));
Tensor<out_data_t> out_nkhw_device(make_TensorDescriptor(out_nkhw_desc));
std::size_t num_thread = std::thread::hardware_concurrency();
if(argc != 3)
{
printf("arg1: do_verification, arg2: nrepeat\n");
exit(1);
}
bool do_verification = atoi(argv[1]);
index_t nrepeat = atoi(argv[2]);
if(do_verification)
{
#if 0
in_nchw.GenerateTensorValue(GeneratorTensor_1{}, num_thread);
wei_kcyx.GenerateTensorValue(GeneratorTensor_1{}, num_thread);
#elif 0
in_nchw.GenerateTensorValue(GeneratorTensor_1{}, num_thread);
wei_kcyx.GenerateTensorValue(GeneratorTensor_2{-5, 5}, num_thread);
#elif 0
in_nchw.GenerateTensorValue(GeneratorTensor_3{}, num_thread);
wei_kcyx.GenerateTensorValue(GeneratorTensor_1{}, num_thread);
#elif 1
in_nchw.GenerateTensorValue(GeneratorTensor_2{-5, 5}, num_thread);
wei_kcyx.GenerateTensorValue(GeneratorTensor_2{-5, 5}, num_thread);
#elif 0
in_nchw.GenerateTensorValue(GeneratorTensor_2{1, 5}, num_thread);
auto gen_wei = [](auto... is) {
return GeneratorTensor_2{1, 5}(is...) * GeneratorTensor_Checkboard{}(is...);
};
wei_kcyx.GenerateTensorValue(gen_wei, num_thread);
#endif
}
#if 1
#if 0
device_convolution_direct_v2_nchw_kcyx_nkhw
#elif 0
device_convolution_implicit_gemm_v1_chwn_cyxk_khwn
#elif 0
device_convolution_implicit_gemm_v1_nchw_cyxk_nkhw
#elif 0
device_convolution_implicit_gemm_v2_chwn_cyxk_khwn
#elif 0
device_convolution_implicit_gemm_v3_nchw_cyxk_nkhw
#elif 1
device_convolution_implicit_gemm_v4_nchw_kcyx_nkhw
#endif
(in_nchw_desc,
in_nchw,
wei_kcyx_desc,
wei_kcyx,
out_nkhw_desc,
out_nkhw_device,
ConvStrides{},
ConvDilations{},
nrepeat);
#elif 0
device_implicit_gemm_convolution_1_chwn_cyxk_khwn_padded(in_nchw_desc,
in_nchw,
wei_kcyx_desc,
wei_kcyx,
out_nkhw_desc,
out_nkhw_device,
lower_pads,
upper_pads,
nrepeat);
#endif
if(do_verification)
{
#if 1
if(Y == 3 && X == 3 && ConvStrides{}[0] == 1 && ConvStrides{}[1] == 1 &&
ConvDilations{}[0] == 1 && ConvDilations{}[1] == 1)
{
host_winograd_3x3_convolution(in_nchw, wei_kcyx, out_nkhw_host, lower_pads, upper_pads);
}
else
#endif
{
host_direct_convolution(in_nchw,
wei_kcyx,
out_nkhw_host,
ConvStrides{},
ConvDilations{},
lower_pads,
upper_pads);
}
check_error(out_nkhw_host, out_nkhw_device);
#if 0
LogRange(std::cout << "in_nchw : ", in_nchw.mData, ",") << std::endl;
LogRange(std::cout << "wei_kcyx: ", wei_kcyx.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
}
}
driver/src/driver.cu 0 → 120000
View file @ 85ae70d3
driver.cpp
\ No newline at end of file
Markdown is supported
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment