ROCm/composable_kernel
1
0
Fork 0
mirror of https://github.com/ROCm/composable_kernel.git synced 2026-05-11 17:00:18 +00:00
Code Issues Packages Projects Releases Wiki Activity

Merge branch 'master' into implicit_gemm_fp16

Browse Source
This commit is contained in:
Chao Liu
2019-03-09 13:46:47 -06:00
parent 8edbc659b8 f54cad7d4f
commit ce0182ce05
9 changed files with 182 additions and 182 deletions
Download Patch File Download Diff File Expand all files Collapse all files

120
driver/driver.hip.cpp
View File

@@ -9,9 +9,9 @@
#include "conv_common.hip.hpp"
#include "device_direct_convolution_1.hpp"
#include "device_direct_convolution_2.hpp"
#include "device_implicit_gemm_convolution_1_chwn_csrk_khwn.hpp"
#include "device_implicit_gemm_convolution_1_chwn_csrk_khwn_padded.hpp"
#include "device_implicit_gemm_convolution_2_chwn_csrk_khwn.hpp"
#include "device_implicit_gemm_convolution_1_chwn_cyxk_khwn.hpp"
#include "device_implicit_gemm_convolution_1_chwn_cyxk_khwn_padded.hpp"
#include "device_implicit_gemm_convolution_2_chwn_cyxk_khwn.hpp"
struct GeneratorTensor_1
{
@@ -108,7 +108,7 @@ auto make_TensorDescriptor(TConstTensorDesc)
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)
const Tensor<T>& in_nchw, const Tensor<T>& wei_kcyx, Tensor<T>& out, LowerPads, UpperPads)
{
unsigned h_pad_low = LowerPads{}.Get(Number<0>{});
unsigned w_pad_low = LowerPads{}.Get(Number<1>{});
@@ -118,18 +118,18 @@ void host_direct_convolution(
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 c = 0; c < wei_kcyx.mDesc.GetLengths()[1]; ++c)
{
for(int y = 0; y < wei_kcsr.mDesc.GetLengths()[2]; ++y)
for(int y = 0; y < wei_kcyx.mDesc.GetLengths()[2]; ++y)
{
int hi = ho + y - h_pad_low;
for(int x = 0; x < wei_kcsr.mDesc.GetLengths()[3]; ++x)
for(int x = 0; x < wei_kcyx.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);
v += in_nchw(n, c, hi, wi) * wei_kcyx(k, c, y, x);
}
}
}
@@ -148,7 +148,7 @@ void host_direct_convolution(
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)
const Tensor<T>& in_nchw, const Tensor<T>& wei_kcyx, Tensor<T>& out, LowerPads, UpperPads)
{
constexpr std::size_t HoPerTile = 2;
constexpr std::size_t WoPerTile = 2;
@@ -158,9 +158,9 @@ void host_winograd_3x3_convolution(
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 Y = wei_kcsr.mDesc.GetLengths()[2];
std::size_t X = wei_kcsr.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.mDesc.GetLengths()[2];
std::size_t WO = out.mDesc.GetLengths()[3];
@@ -259,49 +259,49 @@ void host_winograd_3x3_convolution(
};
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, 0) = wei_kcyx(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);
0.5 * wei_kcyx(k, c, 0, 0) + 0.5 * wei_kcyx(k, c, 0, 1) + 0.5 * wei_kcyx(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);
0.5 * wei_kcyx(k, c, 0, 0) - 0.5 * wei_kcyx(k, c, 0, 1) + 0.5 * wei_kcyx(k, c, 0, 2);
wei_transform(k, c, 0, 3) = wei_kcyx(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);
0.5 * wei_kcyx(k, c, 0, 0) + 0.5 * wei_kcyx(k, c, 1, 0) + 0.5 * wei_kcyx(k, c, 2, 0);
wei_transform(k, c, 1, 1) = 0.25 * wei_kcyx(k, c, 0, 0) + 0.25 * wei_kcyx(k, c, 0, 1) +
0.25 * wei_kcyx(k, c, 0, 2) + 0.25 * wei_kcyx(k, c, 1, 0) +
0.25 * wei_kcyx(k, c, 1, 1) + 0.25 * wei_kcyx(k, c, 1, 2) +
0.25 * wei_kcyx(k, c, 2, 0) + 0.25 * wei_kcyx(k, c, 2, 1) +
0.25 * wei_kcyx(k, c, 2, 2);
wei_transform(k, c, 1, 2) = 0.25 * wei_kcyx(k, c, 0, 0) - 0.25 * wei_kcyx(k, c, 0, 1) +
0.25 * wei_kcyx(k, c, 0, 2) + 0.25 * wei_kcyx(k, c, 1, 0) -
0.25 * wei_kcyx(k, c, 1, 1) + 0.25 * wei_kcyx(k, c, 1, 2) +
0.25 * wei_kcyx(k, c, 2, 0) - 0.25 * wei_kcyx(k, c, 2, 1) +
0.25 * wei_kcyx(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);
0.5 * wei_kcyx(k, c, 0, 2) + 0.5 * wei_kcyx(k, c, 1, 2) + 0.5 * wei_kcyx(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);
0.5 * wei_kcyx(k, c, 0, 0) - 0.5 * wei_kcyx(k, c, 1, 0) + 0.5 * wei_kcyx(k, c, 2, 0);
wei_transform(k, c, 2, 1) = 0.25 * wei_kcyx(k, c, 0, 0) + 0.25 * wei_kcyx(k, c, 0, 1) +
0.25 * wei_kcyx(k, c, 0, 2) - 0.25 * wei_kcyx(k, c, 1, 0) -
0.25 * wei_kcyx(k, c, 1, 1) - 0.25 * wei_kcyx(k, c, 1, 2) +
0.25 * wei_kcyx(k, c, 2, 0) + 0.25 * wei_kcyx(k, c, 2, 1) +
0.25 * wei_kcyx(k, c, 2, 2);
wei_transform(k, c, 2, 2) = 0.25 * wei_kcyx(k, c, 0, 0) - 0.25 * wei_kcyx(k, c, 0, 1) +
0.25 * wei_kcyx(k, c, 0, 2) - 0.25 * wei_kcyx(k, c, 1, 0) +
0.25 * wei_kcyx(k, c, 1, 1) - 0.25 * wei_kcyx(k, c, 1, 2) +
0.25 * wei_kcyx(k, c, 2, 0) - 0.25 * wei_kcyx(k, c, 2, 1) +
0.25 * wei_kcyx(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);
0.5 * wei_kcyx(k, c, 0, 2) - 0.5 * wei_kcyx(k, c, 1, 2) + 0.5 * wei_kcyx(k, c, 2, 2);
wei_transform(k, c, 3, 0) = wei_kcsr(k, c, 2, 0);
wei_transform(k, c, 3, 0) = wei_kcyx(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);
0.5 * wei_kcyx(k, c, 2, 0) + 0.5 * wei_kcyx(k, c, 2, 1) + 0.5 * wei_kcyx(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);
0.5 * wei_kcyx(k, c, 2, 0) - 0.5 * wei_kcyx(k, c, 2, 1) + 0.5 * wei_kcyx(k, c, 2, 2);
wei_transform(k, c, 3, 3) = wei_kcyx(k, c, 2, 2);
};
auto f_out_transform = [&](auto n, auto k, auto htile, auto wtile) {
@@ -569,16 +569,16 @@ int main(int argc, char* argv[])
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, Y, X>{});
auto wei_kcyx_desc = make_ConstantTensorDescriptor(Sequence<K, C, Y, X>{});
auto out_nkhw_desc = get_convolution_with_padding_output_default_4d_tensor_descriptor(
in_nchw_desc, wei_kcsr_desc, lower_pads, upper_pads);
in_nchw_desc, wei_kcyx_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(wei_kcyx_desc, std::cout << "wei_kcyx_desc: ");
ostream_ConstantTensorDescriptor(out_nkhw_desc, std::cout << "out_nkhw_desc: ");
Tensor<half> in_nchw(make_TensorDescriptor(in_nchw_desc));
Tensor<half> wei_kcsr(make_TensorDescriptor(wei_kcsr_desc));
Tensor<half> wei_kcyx(make_TensorDescriptor(wei_kcyx_desc));
Tensor<half> out_nkhw_host(make_TensorDescriptor(out_nkhw_desc));
Tensor<half> out_nkhw_device(make_TensorDescriptor(out_nkhw_desc));
@@ -597,13 +597,13 @@ int main(int argc, char* argv[])
{
#if 0
in_nchw.GenerateTensorValue(GeneratorTensor_1{}, num_thread);
wei_kcsr.GenerateTensorValue(GeneratorTensor_1{}, num_thread);
wei_kcyx.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);
wei_kcyx.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);
wei_kcyx.GenerateTensorValue(GeneratorTensor_1{}, num_thread);
#endif
}
@@ -613,17 +613,17 @@ int main(int argc, char* argv[])
#elif 0
device_direct_convolution_2
#elif 1
device_implicit_gemm_convolution_1_chwn_csrk_khwn
device_implicit_gemm_convolution_1_chwn_cyxk_khwn
#elif 0
device_implicit_gemm_convolution_2_chwn_csrk_khwn
device_implicit_gemm_convolution_2_chwn_cyxk_khwn
#endif
(in_nchw_desc, in_nchw, wei_kcsr_desc, wei_kcsr, out_nkhw_desc, out_nkhw_device, nrepeat);
(in_nchw_desc, in_nchw, wei_kcyx_desc, wei_kcyx, out_nkhw_desc, out_nkhw_device, nrepeat);
#elif 1
device_implicit_gemm_convolution_1_chwn_csrk_khwn_padded(in_nchw_desc,
device_implicit_gemm_convolution_1_chwn_cyxk_khwn_padded(in_nchw_desc,
in_nchw,
wei_kcsr_desc,
wei_kcsr,
wei_kcyx_desc,
wei_kcyx,
out_nkhw_desc,
out_nkhw_device,
lower_pads,
@@ -636,18 +636,18 @@ int main(int argc, char* argv[])
#if 0
if(Y == 3 && X == 3)
{
host_winograd_3x3_convolution(in_nchw, wei_kcsr, out_nkhw_host, lower_pads, upper_pads);
host_winograd_3x3_convolution(in_nchw, wei_kcyx, out_nkhw_host, lower_pads, upper_pads);
}
else
{
host_direct_convolution(in_nchw, wei_kcsr, out_nkhw_host, lower_pads, upper_pads);
host_direct_convolution(in_nchw, wei_kcyx, 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 << "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