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Commit dbffe05a authored by Chao Liu's avatar Chao Liu
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add host winograd 3x3 conv

parent a21b0d27
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Showing with 250 additions and 53 deletions
driver/conv.cu
View file @ dbffe05a
...@@ -8,27 +8,25 @@ ...@@ -8,27 +8,25 @@
#include "device_direct_convolution_1.cuh" #include "device_direct_convolution_1.cuh"
#include "device_direct_convolution_2.cuh" #include "device_direct_convolution_2.cuh"
template <class T>
struct GeneratorConstant struct GeneratorConstant
{ {
T value = 0; double value = 0;
template <class... Is> template <class... Is>
T operator()(Is... is) double operator()(Is...)
{ {
return value; return value;
} }
}; };
template <class T>
struct GeneratorTensor struct GeneratorTensor
{ {
template <class... Is> template <class... Is>
T operator()(Is... is) double operator()(Is... is)
{ {
#if 1 #if 1
return T(std::rand()) / T(RAND_MAX); return double(std::rand()) / double(RAND_MAX);
#elif 1 #elif 0
return 1; return 1;
#elif 0 #elif 0
std::initializer_list<std::size_t> ls = {static_cast<std::size_t>(is)...}; std::initializer_list<std::size_t> ls = {static_cast<std::size_t>(is)...};
...@@ -44,6 +42,18 @@ struct GeneratorTensor ...@@ -44,6 +42,18 @@ struct GeneratorTensor
} }
}; };
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 // this is ugly, only for 4d
template <class TConstTensorDesc> template <class TConstTensorDesc>
void ostream_ConstantTensorDescriptor(TConstTensorDesc, std::ostream& os = std::cout) void ostream_ConstantTensorDescriptor(TConstTensorDesc, std::ostream& os = std::cout)
...@@ -83,7 +93,7 @@ auto make_TensorDescriptor(TConstTensorDesc) ...@@ -83,7 +93,7 @@ auto make_TensorDescriptor(TConstTensorDesc)
} }
template <class T> template <class T>
void host_convolution(const Tensor<T>& in, const Tensor<T>& wei, Tensor<T>& out) 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) { auto f = [&](auto n, auto k, auto ho, auto wo) {
double v = 0; double v = 0;
...@@ -111,9 +121,217 @@ void host_convolution(const Tensor<T>& in, const Tensor<T>& wei, Tensor<T>& out) ...@@ -111,9 +121,217 @@ void host_convolution(const Tensor<T>& in, const Tensor<T>& wei, Tensor<T>& out)
f_par(std::thread::hardware_concurrency()); f_par(std::thread::hardware_concurrency());
} }
int main() 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 = 0;
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 #if 0
constexpr unsigned N = 1; constexpr unsigned N = 1;
constexpr unsigned C = 1; constexpr unsigned C = 1;
...@@ -139,13 +357,21 @@ int main() ...@@ -139,13 +357,21 @@ int main()
constexpr unsigned S = 3; constexpr unsigned S = 3;
constexpr unsigned R = 3; constexpr unsigned R = 3;
#elif 0 #elif 0
constexpr unsigned N = 2; constexpr unsigned N = 1;
constexpr unsigned C = 3; constexpr unsigned C = 1;
constexpr unsigned HI = 130; constexpr unsigned HI = 4;
constexpr unsigned WI = 130; constexpr unsigned WI = 4;
constexpr unsigned K = 5; constexpr unsigned K = 1;
constexpr unsigned S = 3; constexpr unsigned S = 3;
constexpr unsigned R = 3; constexpr unsigned R = 3;
#elif 0
constexpr unsigned N = 2;
constexpr unsigned C = 3;
constexpr unsigned HI = 130;
constexpr unsigned WI = 130;
constexpr unsigned K = 5;
constexpr unsigned S = 3;
constexpr unsigned R = 3;
#elif 0 #elif 0
constexpr unsigned N = 3; constexpr unsigned N = 3;
constexpr unsigned C = 16; constexpr unsigned C = 16;
...@@ -169,11 +395,10 @@ int main() ...@@ -169,11 +395,10 @@ int main()
Tensor<float> out_host(make_TensorDescriptor(out_desc)); Tensor<float> out_host(make_TensorDescriptor(out_desc));
Tensor<float> out_device(make_TensorDescriptor(out_desc)); Tensor<float> out_device(make_TensorDescriptor(out_desc));
int num_thread = std::thread::hardware_concurrency(); #if 1
std::size_t num_thread = std::thread::hardware_concurrency();
#if 0 in.GenerateTensorValue(GeneratorTensor_2{-5, 5}, num_thread);
in.GenerateTensorValue(GeneratorTensor<float>{}, num_thread); wei.GenerateTensorValue(GeneratorTensor_2{-5, 5}, num_thread);
wei.GenerateTensorValue(GeneratorTensor<float>{}, num_thread);
#endif #endif
for(int i = 0; i < 20; ++i) for(int i = 0; i < 20; ++i)
...@@ -182,31 +407,17 @@ int main() ...@@ -182,31 +407,17 @@ int main()
} }
#if 0 #if 0
host_convolution(in, wei, out_host); host_direct_convolution(in, wei, out_host);
#else
float error = 0; host_winograd_3x3_convolution(in, wei, out_host);
float max_diff = 0;
float host_value = 0, device_value = 0;
for(int i = 0; i < out_host.mData.size(); ++i)
{
error += std::abs(out_host.mData[i] - out_device.mData[i]);
float diff = std::abs(out_host.mData[i] - out_device.mData[i]);
if(max_diff < diff)
{
max_diff = diff;
host_value = out_host.mData[i];
device_value = out_device.mData[i];
}
}
std::cout << "error: " << error << std::endl;
std::cout << "max_diff: " << max_diff << ", " << host_value << ", " << device_value
<< std::endl;
#endif #endif
check_error(out_host, out_device);
#if 0 #if 0
LogRange(std::cout << "in : ", in.mData, ",") << std::endl; LogRange(std::cout << "in : ", in.mData, ",") << std::endl;
LogRange(std::cout << "wei: ", wei.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_host : ", out_host.mData, ",") << std::endl;
LogRange(std::cout << "out_device: ", out_device.mData, ",") << std::endl; LogRange(std::cout << "out_device: ", out_device.mData, ",") << std::endl;
#endif #endif
} }
\ No newline at end of file
src/include/gridwise_direct_convolution_2.cuh
View file @ dbffe05a
...@@ -176,13 +176,6 @@ __global__ void gridwise_direct_convolution_2(InGlobalDesc, ...@@ -176,13 +176,6 @@ __global__ void gridwise_direct_convolution_2(InGlobalDesc,
for(unsigned c_block_data_offset = 0; c_block_data_offset < in_global_desc.GetLength(I1); for(unsigned c_block_data_offset = 0; c_block_data_offset < in_global_desc.GetLength(I1);
c_block_data_offset += CPerBlock, __syncthreads()) c_block_data_offset += CPerBlock, __syncthreads())
{ {
#if 0
if(threadIdx.x == 0)
{
printf("c_block_data_offset: %u\n", c_block_data_offset);
}
#endif
// copy input tensor to LDS // copy input tensor to LDS
blockwise_4d_tensor_op_binary<TFloat, blockwise_4d_tensor_op_binary<TFloat,
decltype(in_block_global_desc), decltype(in_block_global_desc),
...@@ -224,13 +217,6 @@ __global__ void gridwise_direct_convolution_2(InGlobalDesc, ...@@ -224,13 +217,6 @@ __global__ void gridwise_direct_convolution_2(InGlobalDesc,
for(unsigned c_thread_data_offset = 0; c_thread_data_offset < CPerBlock; for(unsigned c_thread_data_offset = 0; c_thread_data_offset < CPerBlock;
c_thread_data_offset += CPerThread) c_thread_data_offset += CPerThread)
{ {
#if 0
if(threadIdx.x == 0)
{
printf("c_thread_data_offset: %u\n", c_thread_data_offset);
}
#endif
// copy input tensor into register // copy input tensor into register
threadwise_4d_tensor_op_binary<TFloat, threadwise_4d_tensor_op_binary<TFloat,
decltype(in_thread_block_desc), decltype(in_thread_block_desc),
......
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