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Commit 5771a040 authored by carlushuang's avatar carlushuang
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fix a bug in general index calculation

parent 5e6cca6f
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library/src/tensor_operation_instance/cpu/conv2d_fwd/device_conv2d_fwd_avx2_nhwc_kyxc_nhwk_instance.cpp
View file @ 5771a040
#include <stdlib.h> #include <stdlib.h>
#include "convolution_forward_specialization_cpu.hpp" #include "convolution_forward_specialization_cpu.hpp"
#include "config.hpp" #include "config.hpp"
#include "device_convnd_fwd_avx2_nhwc_kyxc_nhwk.hpp" #include "device_convnd_fwd_avx2_nhwc_kyxc_nhwk.hpp"
#include "element_wise_operation_cpu.hpp" #include "element_wise_operation_cpu.hpp"
#include "device_operation_instance.hpp" #include "device_operation_instance.hpp"
namespace ck { namespace ck {
namespace tensor_operation { namespace tensor_operation {
namespace cpu { namespace cpu {
namespace device { namespace device {
namespace device_conv2d_fwd_avx2_instance { namespace device_conv2d_fwd_avx2_instance {
using InType = float; using InType = float;
using WeiType = float; using WeiType = float;
using OutType = float; using OutType = float;
using AccType = float; using AccType = float;
using InLayout = ck::tensor_layout::gemm::RowMajor; // NHWC using InLayout = ck::tensor_layout::gemm::RowMajor; // NHWC
using WeiLayout = ck::tensor_layout::gemm::ColumnMajor; // KYXC using WeiLayout = ck::tensor_layout::gemm::ColumnMajor; // KYXC
static constexpr bool NonTemporalStore = false; static constexpr bool NonTemporalStore = false;
using PT = ck::tensor_operation::cpu::element_wise::PassThrough; using PT = ck::tensor_operation::cpu::element_wise::PassThrough;
using ThreadwiseGemmAvx2_MxN_4x24_Dispatch = using ThreadwiseGemmAvx2_MxN_4x24_Dispatch =
ck::cpu::ThreadwiseGemmAvx2_MxN_4x24_Dispatch<InType, ck::cpu::ThreadwiseGemmAvx2_MxN_4x24_Dispatch<InType,
WeiType, WeiType,
OutType, OutType,
InLayout, InLayout,
WeiLayout, WeiLayout,
NonTemporalStore>; NonTemporalStore>;
static constexpr auto ConvFwdDefault = static constexpr auto ConvFwdDefault =
ck::tensor_operation::cpu::device::ConvolutionForwardSpecialization_t::Default; ck::tensor_operation::cpu::device::ConvolutionForwardSpecialization_t::Default;
static constexpr auto ConvFwd1x1P0 = static constexpr auto ConvFwd1x1P0 =
ck::tensor_operation::cpu::device::ConvolutionForwardSpecialization_t::Filter1x1Pad0; ck::tensor_operation::cpu::device::ConvolutionForwardSpecialization_t::Filter1x1Pad0;
static constexpr auto ConvFwd1x1S1P0 = static constexpr auto ConvFwd1x1S1P0 =
ck::tensor_operation::cpu::device::ConvolutionForwardSpecialization_t::Filter1x1Stride1Pad0; ck::tensor_operation::cpu::device::ConvolutionForwardSpecialization_t::Filter1x1Stride1Pad0;
static constexpr auto DefaultGemmKLoop = static constexpr auto DefaultGemmKLoop =
ck::tensor_operation::cpu::device::ConvolutionForwardGemmKSpecialization_t::DefaultGemmKLoop; ck::tensor_operation::cpu::device::ConvolutionForwardGemmKSpecialization_t::DefaultGemmKLoop;
static constexpr auto GemmKLoopOverC = static constexpr auto GemmKLoopOverC =
ck::tensor_operation::cpu::device::ConvolutionForwardGemmKSpecialization_t::NHWC_GemmKLoopOverC; ck::tensor_operation::cpu::device::ConvolutionForwardGemmKSpecialization_t::NHWC_GemmKLoopOverC;
static constexpr auto LoopOver_MNK = ck::tensor_operation::cpu::device::LoopOver_MNK; static constexpr auto LoopOver_MNK = ck::tensor_operation::cpu::device::LoopOver_MNK;
static constexpr auto LoopOver_MKN = ck::tensor_operation::cpu::device::LoopOver_MKN; static constexpr auto LoopOver_MKN = ck::tensor_operation::cpu::device::LoopOver_MKN;
// clang-format off // clang-format off
#define DEVICE_CONV2D_FWD_AVX2_NHWC_KYXC_NHWK_F32(a_elem_op, b_elem_op, c_elem_op, m_per_block, n_per_block, k_per_block, m_per_thread, n_per_thread, a_local_buf, b_local_buf, c_local_buf) \ #define DEVICE_CONV2D_FWD_AVX2_NHWC_KYXC_NHWK_F32(a_elem_op, b_elem_op, c_elem_op, m_per_block, n_per_block, k_per_block, m_per_thread, n_per_thread, a_local_buf, b_local_buf, c_local_buf) \
DeviceConvNDFwdAvx2_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_N_Ho_Wo_K<float , float , float, a_elem_op, b_elem_op, c_elem_op, ConvFwdDefault, GemmKLoopOverC , LoopOver_MNK, 2, m_per_block, n_per_block, k_per_block, m_per_thread, n_per_thread, a_local_buf, b_local_buf, c_local_buf>, \ DeviceConvNDFwdAvx2_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_N_Ho_Wo_K<float , float , float, a_elem_op, b_elem_op, c_elem_op, ConvFwdDefault, GemmKLoopOverC , LoopOver_MNK, 2, m_per_block, n_per_block, k_per_block, m_per_thread, n_per_thread, a_local_buf, b_local_buf, c_local_buf>, \
DeviceConvNDFwdAvx2_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_N_Ho_Wo_K<float , float , float, a_elem_op, b_elem_op, c_elem_op, ConvFwd1x1P0, GemmKLoopOverC , LoopOver_MNK, 2, m_per_block, n_per_block, k_per_block, m_per_thread, n_per_thread, a_local_buf, b_local_buf, c_local_buf>, \ DeviceConvNDFwdAvx2_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_N_Ho_Wo_K<float , float , float, a_elem_op, b_elem_op, c_elem_op, ConvFwd1x1P0, GemmKLoopOverC , LoopOver_MNK, 2, m_per_block, n_per_block, k_per_block, m_per_thread, n_per_thread, a_local_buf, b_local_buf, c_local_buf>, \
DeviceConvNDFwdAvx2_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_N_Ho_Wo_K<float , float , float, a_elem_op, b_elem_op, c_elem_op, ConvFwdDefault, DefaultGemmKLoop, LoopOver_MNK, 2, m_per_block, n_per_block, k_per_block, m_per_thread, n_per_thread, a_local_buf, b_local_buf, c_local_buf>, \ DeviceConvNDFwdAvx2_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_N_Ho_Wo_K<float , float , float, a_elem_op, b_elem_op, c_elem_op, ConvFwdDefault, DefaultGemmKLoop, LoopOver_MNK, 2, m_per_block, n_per_block, k_per_block, m_per_thread, n_per_thread, a_local_buf, b_local_buf, c_local_buf>, \
DeviceConvNDFwdAvx2_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_N_Ho_Wo_K<float , float , float, a_elem_op, b_elem_op, c_elem_op, ConvFwd1x1P0, DefaultGemmKLoop, LoopOver_MNK, 2, m_per_block, n_per_block, k_per_block, m_per_thread, n_per_thread, a_local_buf, b_local_buf, c_local_buf>, \ DeviceConvNDFwdAvx2_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_N_Ho_Wo_K<float , float , float, a_elem_op, b_elem_op, c_elem_op, ConvFwd1x1P0, DefaultGemmKLoop, LoopOver_MNK, 2, m_per_block, n_per_block, k_per_block, m_per_thread, n_per_thread, a_local_buf, b_local_buf, c_local_buf>, \
DeviceConvNDFwdAvx2_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_N_Ho_Wo_K<float , float , float, a_elem_op, b_elem_op, c_elem_op, ConvFwdDefault, GemmKLoopOverC , LoopOver_MKN, 2, m_per_block, n_per_block, k_per_block, m_per_thread, n_per_thread, a_local_buf, b_local_buf, c_local_buf>, \ DeviceConvNDFwdAvx2_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_N_Ho_Wo_K<float , float , float, a_elem_op, b_elem_op, c_elem_op, ConvFwdDefault, GemmKLoopOverC , LoopOver_MKN, 2, m_per_block, n_per_block, k_per_block, m_per_thread, n_per_thread, a_local_buf, b_local_buf, c_local_buf>, \
DeviceConvNDFwdAvx2_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_N_Ho_Wo_K<float , float , float, a_elem_op, b_elem_op, c_elem_op, ConvFwd1x1P0, GemmKLoopOverC , LoopOver_MKN, 2, m_per_block, n_per_block, k_per_block, m_per_thread, n_per_thread, a_local_buf, b_local_buf, c_local_buf>, \ DeviceConvNDFwdAvx2_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_N_Ho_Wo_K<float , float , float, a_elem_op, b_elem_op, c_elem_op, ConvFwd1x1P0, GemmKLoopOverC , LoopOver_MKN, 2, m_per_block, n_per_block, k_per_block, m_per_thread, n_per_thread, a_local_buf, b_local_buf, c_local_buf>, \
DeviceConvNDFwdAvx2_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_N_Ho_Wo_K<float , float , float, a_elem_op, b_elem_op, c_elem_op, ConvFwdDefault, DefaultGemmKLoop, LoopOver_MKN, 2, m_per_block, n_per_block, k_per_block, m_per_thread, n_per_thread, a_local_buf, b_local_buf, c_local_buf>, \ DeviceConvNDFwdAvx2_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_N_Ho_Wo_K<float , float , float, a_elem_op, b_elem_op, c_elem_op, ConvFwdDefault, DefaultGemmKLoop, LoopOver_MKN, 2, m_per_block, n_per_block, k_per_block, m_per_thread, n_per_thread, a_local_buf, b_local_buf, c_local_buf>, \
DeviceConvNDFwdAvx2_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_N_Ho_Wo_K<float , float , float, a_elem_op, b_elem_op, c_elem_op, ConvFwd1x1P0, DefaultGemmKLoop, LoopOver_MKN, 2, m_per_block, n_per_block, k_per_block, m_per_thread, n_per_thread, a_local_buf, b_local_buf, c_local_buf> DeviceConvNDFwdAvx2_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_N_Ho_Wo_K<float , float , float, a_elem_op, b_elem_op, c_elem_op, ConvFwd1x1P0, DefaultGemmKLoop, LoopOver_MKN, 2, m_per_block, n_per_block, k_per_block, m_per_thread, n_per_thread, a_local_buf, b_local_buf, c_local_buf>
// clang-format on // clang-format on
using device_conv2d_fwd_avx2_nhwc_kyxc_nhwk_f32_instances = std::tuple<
using device_conv2d_fwd_avx2_nhwc_kyxc_nhwk_f32_instances = std::tuple< // clang-format off
// clang-format off DEVICE_CONV2D_FWD_AVX2_NHWC_KYXC_NHWK_F32(PT, PT, PT, 256, 120, 64, 4, 24, true, true, false),
DEVICE_CONV2D_FWD_AVX2_NHWC_KYXC_NHWK_F32(PT, PT, PT, 256, 120, 64, 4, 24, true, true, false), DEVICE_CONV2D_FWD_AVX2_NHWC_KYXC_NHWK_F32(PT, PT, PT, 512, 144, 128, 4, 24, true, true, false),
DEVICE_CONV2D_FWD_AVX2_NHWC_KYXC_NHWK_F32(PT, PT, PT, 512, 144, 128, 4, 24, true, true, false), DEVICE_CONV2D_FWD_AVX2_NHWC_KYXC_NHWK_F32(PT, PT, PT, 512, 240, 128, 4, 24, true, true, false),
DEVICE_CONV2D_FWD_AVX2_NHWC_KYXC_NHWK_F32(PT, PT, PT, 512, 240, 128, 4, 24, true, true, false), // DEVICE_CONV2D_FWD_AVX2_NHWC_KYXC_NHWK_F32(PT, PT, PT, 768, 192, 128, 4, 24, true, true, false),
// DEVICE_CONV2D_FWD_AVX2_NHWC_KYXC_NHWK_F32(PT, PT, PT, 768, 192, 128, 4, 24, true, true, false), DEVICE_CONV2D_FWD_AVX2_NHWC_KYXC_NHWK_F32(PT, PT, PT, 768, 288, 128, 4, 24, true, true, false)>;
DEVICE_CONV2D_FWD_AVX2_NHWC_KYXC_NHWK_F32(PT, PT, PT, 768, 288, 128, 4, 24, true, true, false)>; // clang-format on
// clang-format on
void add_device_conv2d_fwd_avx2_nhwc_kyxc_nhwk(std::vector<DeviceConvFwdPtr<PT, PT, PT>>& instances)
void add_device_conv2d_fwd_avx2_nhwc_kyxc_nhwk(std::vector<DeviceConvFwdPtr<PT, PT, PT>>& instances) {
{ ck::tensor_operation::device::add_device_operation_instances(
ck::tensor_operation::device::add_device_operation_instances( instances, device_conv2d_fwd_avx2_nhwc_kyxc_nhwk_f32_instances{});
instances, device_conv2d_fwd_avx2_nhwc_kyxc_nhwk_f32_instances{}); }
}
} // namespace device_conv2d_fwd_avx2_instance
} // namespace device_conv2d_fwd_avx2_instance } // namespace device
} // namespace device } // namespace cpu
} // namespace cpu } // namespace tensor_operation
} // namespace tensor_operation } // namespace ck
} // namespace ck
test/convnd_fwd_cpu/conv2d_fwd_cpu.cpp
View file @ 5771a040
...@@ -37,26 +37,53 @@ using WeiElementOp = ck::tensor_operation::cpu::element_wise::PassThrough; ...@@ -37,26 +37,53 @@ using WeiElementOp = ck::tensor_operation::cpu::element_wise::PassThrough;
using OutElementOp = ck::tensor_operation::cpu::element_wise::PassThrough; using OutElementOp = ck::tensor_operation::cpu::element_wise::PassThrough;
template <typename T> template <typename T>
static bool check_out(const Tensor<T>& ref, const Tensor<T>& result) static bool
check_out(const Tensor<T>& ref, const Tensor<T>& result, double nrms, int per_pixel_check = 0)
{ {
int error_count = 0; int error_count = 0;
float max_diff = 1e-6; float max_diff = 1e-5;
double square_difference = .0;
double mag1 = .0;
double mag2 = .0;
for(int i = 0; i < ref.mData.size(); ++i) for(int i = 0; i < ref.mData.size(); ++i)
{ {
float diff = std::abs(double(ref.mData[i]) - double(result.mData[i])); double ri = (double)ref.mData[i];
if(max_diff < diff) double pi = (double)result.mData[i];
double d = ri - pi;
if(per_pixel_check)
{ {
error_count++; if(max_diff < std::abs(d))
printf("idx:%3d, ref:%f, res:%f (diff:%f)\n", {
i, error_count++;
double(ref.mData[i]), printf("idx:%3d, ref:%f, res:%f (diff:%f)\n",
double(result.mData[i]), i,
diff); double(ref.mData[i]),
double(result.mData[i]),
d);
}
} }
square_difference += d * d;
if(std::abs(mag1) < std::abs(ri))
mag1 = ri;
if(std::abs(mag2) < std::abs(pi))
mag2 = pi;
} }
return error_count == 0; double mag = std::max({std::fabs(mag1), std::fabs(mag2), std::numeric_limits<double>::min()});
double computed_nrms = std::sqrt(square_difference) / (std::sqrt(ref.mData.size()) * mag);
if(computed_nrms >= nrms)
printf("nrms:%lf, mag1:%lf, mag2:%lf, expected_nrms is %1f\n",
computed_nrms,
mag1,
mag2,
nrms);
return computed_nrms < nrms && error_count == 0;
} }
float calculate_gflops() {} float calculate_gflops() {}
...@@ -171,20 +198,28 @@ int main(int argc, char* argv[]) ...@@ -171,20 +198,28 @@ int main(int argc, char* argv[])
<< ", Dilation(H, W):" << conv_dilation_h << ", " << conv_dilation_w << ", Dilation(H, W):" << conv_dilation_h << ", " << conv_dilation_w
<< ", Threads:" << omp_get_max_threads() << std::endl; << ", Threads:" << omp_get_max_threads() << std::endl;
int per_pixel_check = 0;
switch(init_method) switch(init_method)
{ {
case 0: break; case 0:
in_n_c_hi_wi.GenerateTensorValue(GeneratorTensor_1<InDataType>{});
wei_k_c_y_x.GenerateTensorValue(GeneratorTensor_1<WeiDataType>{});
per_pixel_check = 1;
break;
case 1: case 1:
in_n_c_hi_wi.GenerateTensorValue(GeneratorTensor_2<InDataType>{-5, 5}); in_n_c_hi_wi.GenerateTensorValue(GeneratorTensor_2<InDataType>{-5, 5});
// in_n_c_hi_wi.GenerateTensorValue(GeneratorTensor_1<InDataType>{}); // in_n_c_hi_wi.GenerateTensorValue(GeneratorTensor_1<InDataType>{});
wei_k_c_y_x.GenerateTensorValue(GeneratorTensor_2<WeiDataType>{-5, 5}); wei_k_c_y_x.GenerateTensorValue(GeneratorTensor_2<WeiDataType>{-5, 5});
// wei_k_c_y_x.GenerateTensorValue(GeneratorTensor_1<WeiDataType>{}); // wei_k_c_y_x.GenerateTensorValue(GeneratorTensor_1<WeiDataType>{});
per_pixel_check = 1;
break; break;
case 2: case 2:
in_n_c_hi_wi.GenerateTensorValue(GeneratorTensor_1<InDataType>{}); in_n_c_hi_wi.GenerateTensorValue(GeneratorTensor_3<InDataType>{0.0, 1.0});
wei_k_c_y_x.GenerateTensorValue(GeneratorTensor_1<WeiDataType>{}); wei_k_c_y_x.GenerateTensorValue(GeneratorTensor_3<WeiDataType>{-0.5, 0.5});
break; break;
case 3: case 3:
#define PACK_32(v24, v16, v8, v0) \ #define PACK_32(v24, v16, v8, v0) \
...@@ -310,7 +345,10 @@ int main(int argc, char* argv[]) ...@@ -310,7 +345,10 @@ int main(int argc, char* argv[])
out_device_buf.FromDevice(out_n_k_ho_wo_device_result.mData.data()); out_device_buf.FromDevice(out_n_k_ho_wo_device_result.mData.data());
if(!check_out(out_n_k_ho_wo_host_result, out_n_k_ho_wo_device_result)) if(!check_out(out_n_k_ho_wo_host_result,
out_n_k_ho_wo_device_result,
1e-6,
per_pixel_check))
{ {
std::cout << "Fail Info: " << conv_ptr->GetTypeString() << std::endl; std::cout << "Fail Info: " << conv_ptr->GetTypeString() << std::endl;
success = false; success = false;
......
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