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21c918162e81211d2b7ec3a555accef1235957eb
composable_kernel/driver/conv.cu
Chao Liu 21c918162e added blockwise tensor reorder operation
2019-01-04 14:48:57 -06:00

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#include <iostream>
#include <numeric>
#include <initializer_list>
#include <cstdlib>
#include "nvToolsExt.h"
#include "tensor.hpp"
#include "constant_tensor_descriptor.cuh"
#include "device_direct_convolution_1.cuh"
#include "device_direct_convolution_2.cuh"
//#include "device_implicit_gemm_convolution.cuh"
//#include "device_winograd_convolution.cuh"
struct GeneratorTensor_1
{
template <class... Is>
double operator()(Is... is)
{
#if 0
return double(std::rand()) / double(RAND_MAX);
#elif 1
return 1;
#elif 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));
#else
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_2
{
int min_value = 0;
int max_value = 1;
template <class... Is>
double operator()(Is...)
{
return (std::rand() % (max_value - min_value)) + min_value;
}
};
// 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>
void host_direct_convolution(const Tensor<T>& in, const Tensor<T>& wei, Tensor<T>& out)
{
auto f = [&](auto n, auto k, auto ho, auto wo) {
double v = 0;
for(int c = 0; c < wei.mDesc.GetLengths()[1]; ++c)
{
for(int y = 0; y < wei.mDesc.GetLengths()[2]; ++y)
{
int hi = ho + y;
for(int x = 0; x < wei.mDesc.GetLengths()[3]; ++x)
{
int wi = wo + x;
v += in(n, c, hi, wi) * wei(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>
void host_winograd_3x3_convolution(const Tensor<T>& in, const Tensor<T>& wei, Tensor<T>& out)
{
constexpr std::size_t OutTileSizeH = 2;
constexpr std::size_t OutTileSizeW = 2;
std::size_t N = in.mDesc.GetLengths()[0];
std::size_t C = in.mDesc.GetLengths()[1];
std::size_t HI = in.mDesc.GetLengths()[2];
std::size_t WI = in.mDesc.GetLengths()[3];
std::size_t K = wei.mDesc.GetLengths()[0];
std::size_t S = wei.mDesc.GetLengths()[2];
std::size_t R = wei.mDesc.GetLengths()[3];
std::size_t HO = out.mDesc.GetLengths()[2];
std::size_t WO = out.mDesc.GetLengths()[3];
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)
{
std::size_t hi = OutTileSizeH * y + j;
for(int i = 0; i < InTileSizeW; ++i)
{
std::size_t wi = OutTileSizeW * x + i;
in_hold(n, c, y, x, j, i) = in(n, c, hi, wi);
}
}
};
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(k, c, 0, 0);
wei_transform(k, c, 0, 1) =
0.5 * wei(k, c, 0, 0) + 0.5 * wei(k, c, 0, 1) + 0.5 * wei(k, c, 0, 2);
wei_transform(k, c, 0, 2) =
0.5 * wei(k, c, 0, 0) - 0.5 * wei(k, c, 0, 1) + 0.5 * wei(k, c, 0, 2);
wei_transform(k, c, 0, 3) = wei(k, c, 0, 2);
wei_transform(k, c, 1, 0) =
0.5 * wei(k, c, 0, 0) + 0.5 * wei(k, c, 1, 0) + 0.5 * wei(k, c, 2, 0);
wei_transform(k, c, 1, 1) =
0.25 * wei(k, c, 0, 0) + 0.25 * wei(k, c, 0, 1) + 0.25 * wei(k, c, 0, 2) +
0.25 * wei(k, c, 1, 0) + 0.25 * wei(k, c, 1, 1) + 0.25 * wei(k, c, 1, 2) +
0.25 * wei(k, c, 2, 0) + 0.25 * wei(k, c, 2, 1) + 0.25 * wei(k, c, 2, 2);
wei_transform(k, c, 1, 2) =
0.25 * wei(k, c, 0, 0) - 0.25 * wei(k, c, 0, 1) + 0.25 * wei(k, c, 0, 2) +
0.25 * wei(k, c, 1, 0) - 0.25 * wei(k, c, 1, 1) + 0.25 * wei(k, c, 1, 2) +
0.25 * wei(k, c, 2, 0) - 0.25 * wei(k, c, 2, 1) + 0.25 * wei(k, c, 2, 2);
wei_transform(k, c, 1, 3) =
0.5 * wei(k, c, 0, 2) + 0.5 * wei(k, c, 1, 2) + 0.5 * wei(k, c, 2, 2);
wei_transform(k, c, 2, 0) =
0.5 * wei(k, c, 0, 0) - 0.5 * wei(k, c, 1, 0) + 0.5 * wei(k, c, 2, 0);
wei_transform(k, c, 2, 1) =
0.25 * wei(k, c, 0, 0) + 0.25 * wei(k, c, 0, 1) + 0.25 * wei(k, c, 0, 2) -
0.25 * wei(k, c, 1, 0) - 0.25 * wei(k, c, 1, 1) - 0.25 * wei(k, c, 1, 2) +
0.25 * wei(k, c, 2, 0) + 0.25 * wei(k, c, 2, 1) + 0.25 * wei(k, c, 2, 2);
wei_transform(k, c, 2, 2) =
0.25 * wei(k, c, 0, 0) - 0.25 * wei(k, c, 0, 1) + 0.25 * wei(k, c, 0, 2) -
0.25 * wei(k, c, 1, 0) + 0.25 * wei(k, c, 1, 1) - 0.25 * wei(k, c, 1, 2) +
0.25 * wei(k, c, 2, 0) - 0.25 * wei(k, c, 2, 1) + 0.25 * wei(k, c, 2, 2);
wei_transform(k, c, 2, 3) =
0.5 * wei(k, c, 0, 2) - 0.5 * wei(k, c, 1, 2) + 0.5 * wei(k, c, 2, 2);
wei_transform(k, c, 3, 0) = wei(k, c, 2, 0);
wei_transform(k, c, 3, 1) =
0.5 * wei(k, c, 2, 0) + 0.5 * wei(k, c, 2, 1) + 0.5 * wei(k, c, 2, 2);
wei_transform(k, c, 3, 2) =
0.5 * wei(k, c, 2, 0) - 0.5 * wei(k, c, 2, 1) + 0.5 * wei(k, c, 2, 2);
wei_transform(k, c, 3, 3) = wei(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 = 4;
constexpr unsigned WI = 4;
constexpr unsigned K = 1;
constexpr unsigned S = 3;
constexpr unsigned R = 3;
#elif 0
constexpr unsigned N = 1;
constexpr unsigned C = 1;
constexpr unsigned HI = 34;
constexpr unsigned WI = 34;
constexpr unsigned K = 1;
constexpr unsigned S = 3;
constexpr unsigned R = 3;
#elif 1
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;
#elif 0
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
constexpr unsigned N = 64;
constexpr unsigned C = 64;
constexpr unsigned HI = 66;
constexpr unsigned WI = 66;
constexpr unsigned K = 64;
constexpr unsigned S = 3;
constexpr unsigned R = 3;
#endif
auto in_desc = make_ConstantTensorDescriptor(Sequence<N, C, HI, WI>{});
auto wei_desc = make_ConstantTensorDescriptor(Sequence<K, C, S, R>{});
auto out_desc = get_convolution_output_4d_tensor_descriptor(in_desc, wei_desc);
ostream_ConstantTensorDescriptor(in_desc, std::cout << "in_desc: ");
ostream_ConstantTensorDescriptor(wei_desc, std::cout << "wei_desc: ");
ostream_ConstantTensorDescriptor(out_desc, std::cout << "out_desc: ");
Tensor<float> in(make_TensorDescriptor(in_desc));
Tensor<float> wei(make_TensorDescriptor(wei_desc));
Tensor<float> out_host(make_TensorDescriptor(out_desc));
Tensor<float> out_device(make_TensorDescriptor(out_desc));
#if 0
std::size_t num_thread = std::thread::hardware_concurrency();
in.GenerateTensorValue(GeneratorTensor_1{}, num_thread);
wei.GenerateTensorValue(GeneratorTensor_1{}, num_thread);
#elif 1
std::size_t num_thread = std::thread::hardware_concurrency();
in.GenerateTensorValue(GeneratorTensor_2{-5, 5}, num_thread);
wei.GenerateTensorValue(GeneratorTensor_2{-5, 5}, num_thread);
#endif
for(int i = 0; i < 40; ++i)
{
#if 0
device_direct_convolution_1(in_desc, in, wei_desc, wei, out_desc, out_device);
#elif 1
device_direct_convolution_2(in_desc, in, wei_desc, wei, out_desc, out_device);
#elif 0
device_implicit_gemm_convolution(in_desc, in, wei_desc, wei, out_desc, out_device);
#elif 0
device_winograd_convolution(in_desc, in, wei_desc, wei, out_desc, out_device);
#endif
}
#if 1
host_winograd_3x3_convolution(in, wei, out_host);
check_error(out_host, out_device);
#elif 0
host_direct_convolution(in, wei, out_host);
check_error(out_host, out_device);
#endif
#if 0
LogRange(std::cout << "in : ", in.mData, ",") << std::endl;
LogRange(std::cout << "wei: ", wei.mData, ",") << std::endl;
LogRange(std::cout << "out_host : ", out_host.mData, ",") << std::endl;
LogRange(std::cout << "out_device: ", out_device.mData, ",") << std::endl;
#endif
}
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