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Unverified Commit 690c75a7 authored by ltqin's avatar ltqin Committed by GitHub
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References for conv2d fwd bias relu and add (#75) 



* add reference

* clean up

* add reference for conv

* rename
Co-authored-by: default avatarltqin <letaoqin@amd.com>
Co-authored-by: default avatarChao Liu <chao.liu2@amd.com>
parent 6d92959a
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Showing with 706 additions and 209 deletions
example/4_conv2d_fwd_xdl/conv2d_fwd_xdl.cpp
View file @ 690c75a7
......@@ -11,8 +11,9 @@
#include "host_tensor_generator.hpp"
#include "device_tensor.hpp"
#include "tensor_layout.hpp"
#include "device_operation/include/device_conv2d_fwd_xdl_c_shuffle_nhwc_kyxc_nhwk.hpp"
#include "element_wise_operation.hpp"
#include "device_conv2d_fwd_xdl_c_shuffle_nhwc_kyxc_nhwk.hpp"
#include "reference_conv_fwd.hpp"
using InDataType = ck::half_t;
using WeiDataType = ck::half_t;
......@@ -33,65 +34,53 @@ using OutElementOp = ck::tensor_operation::element_wise::PassThrough;
static constexpr auto ConvFwdDefault =
ck::tensor_operation::device::ConvolutionForwardSpecialization_t::Default;
// clang-format off
using DeviceConvFwdInstance = ck::tensor_operation::device::
DeviceConv2dFwdXdl_C_Shuffle_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_N_Ho_Wo_K
// clang-format off
// | InData| WeiData| OutData| AccData| In| Wei| Out| ConvForward| Block| MPer| NPer| K0Per| K1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CBlockTransfer|
// | Type| Type| Type| Type| Elementwise| Elementwise| Elementwise| Specialization| Size| Block| Block| Block| | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| _MBlock_MXdlPerWave_MWaveMPerXdl| ScalarPerVector|
// | | | | | Operation| Operation| Operation| | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NXdlPerWave_NWaveNPerXdl| _NWaveNPerXdl|
// | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
<InDataType, WeiDataType, OutDataType, AccDataType, InElementOp, WeiElementOp, OutElementOp, ConvFwdDefault, 256, 128, 256, 4, 8, 32, 32, 2, 4, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 1, 1, S<1, 1, 32, 1, 1, 8>, 8>;
DeviceConv2dFwdXdl_C_Shuffle_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_N_Ho_Wo_K<
InDataType, // InDataType
WeiDataType, // WeiDataType
OutDataType, // OutDataType
AccDataType, // AccDataType
InElementOp, // InElementwiseOperation
WeiElementOp, // WeiElementwiseOperation
OutElementOp, // OutElementwiseOperation
ConvFwdDefault, // ConvForwardSpecialization
256, // BlockSize
128, // MPerBlock
256, // NPerBlock
4, // K0PerBlock
8, // K1
32, // MPerXdl
32, // NPerXdl
2, // MXdlPerWave
4, // NXdlPerWave
S<4, 64, 1>, // ABlockTransferThreadClusterLengths_K0_M_K1
S<1, 0, 2>, // ABlockTransferThreadClusterArrangeOrder
S<1, 0, 2>, // ABlockTransferSrcAccessOrder
2, // ABlockTransferSrcVectorDim
8, // ABlockTransferSrcScalarPerVector
8, // ABlockTransferDstScalarPerVector_K1
true, // ABlockLdsAddExtraM
S<4, 64, 1>, // BBlockTransferThreadClusterLengths_K0_N_K1
S<1, 0, 2>, // BBlockTransferThreadClusterArrangeOrder
S<1, 0, 2>, // BBlockTransferSrcAccessOrder
2, // BBlockTransferSrcVectorDim
8, // BBlockTransferSrcScalarPerVector
8, // BBlockTransferDstScalarPerVector_K1
true, // BBlockLdsAddExtraN
1, // CShuffleMXdlPerWavePerShuffle
1, // CShuffleNXdlPerWavePerShuffle
S<1, 1, 32, 1, 1, 8>, // CBlockTransferClusterLengths_MBlock_MXdlPerWave_MWaveMPerXdl_NBlock_NXdlPerWave_NWaveNPerXdl
8>; // CBlockTransferScalarPerVector_NWaveNPerXdl
// clang-format on
template <typename TIn,
typename TWei,
typename TOut,
typename InElementOp,
typename WeiElementOp,
typename OutElementOp>
void host_verify(const Tensor<TIn>& in,
const Tensor<TWei>& wei,
Tensor<TOut>& out,
const std::vector<ck::index_t>& conv_strides,
const std::vector<ck::index_t>& conv_dilations,
const std::vector<ck::index_t>& in_left_pads,
const std::vector<ck::index_t>&,
const InElementOp& in_element_op,
const WeiElementOp& wei_element_op,
const OutElementOp& out_element_op)
{
auto f_nchw = [&](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 * conv_strides[0] + y * conv_dilations[0] - in_left_pads[0];
for(int x = 0; x < wei.mDesc.GetLengths()[3]; ++x)
{
int wi = wo * conv_strides[1] + x * conv_dilations[1] - in_left_pads[1];
if(hi >= 0 && hi < in.mDesc.GetLengths()[2] && wi >= 0 &&
wi < in.mDesc.GetLengths()[3])
{
v += in_element_op(static_cast<const double>(in(n, c, hi, wi))) *
wei_element_op(static_cast<const double>(wei(k, c, y, x)));
}
}
}
}
double v2 = out(n, k, ho, wo);
out_element_op(v2, v);
out(n, k, ho, wo) = v2;
};
make_ParallelTensorFunctor(f_nchw,
out.mDesc.GetLengths()[0],
out.mDesc.GetLengths()[1],
out.mDesc.GetLengths()[2],
out.mDesc.GetLengths()[3])(std::thread::hardware_concurrency());
}
using ReferenceConvFwdInstance = ck::tensor_operation::host::ReferenceConvFwd<InDataType,
WeiDataType,
OutDataType,
AccDataType,
InElementOp,
WeiElementOp,
OutElementOp>;
int main(int argc, char* argv[])
{
......@@ -265,16 +254,20 @@ int main(int argc, char* argv[])
if(do_verification)
{
host_verify(in_n_c_hi_wi,
wei_k_c_y_x,
out_n_k_ho_wo_host_result,
conv_filter_strides,
conv_filter_dilations,
input_left_pads,
input_right_pads,
InElementOp{},
WeiElementOp{},
OutElementOp{});
auto refConv = ReferenceConvFwdInstance{};
auto refInvoker = refConv.MakeInvoker();
auto refArgument = refConv.MakeArgument(in_n_c_hi_wi,
wei_k_c_y_x,
out_n_k_ho_wo_host_result,
conv_filter_strides,
conv_filter_dilations,
input_left_pads,
input_right_pads,
InElementOp{},
WeiElementOp{},
OutElementOp{});
refInvoker.Run(refArgument);
out_device_buf.FromDevice(out_n_k_ho_wo_device_result.mData.data());
......
example/5_conv2d_fwd_xdl_bias_relu/conv2d_fwd_xdl_bias_relu.cpp
View file @ 690c75a7
......@@ -11,8 +11,9 @@
#include "host_tensor_generator.hpp"
#include "device_tensor.hpp"
#include "tensor_layout.hpp"
#include "device_conv2d_fwd_xdl_c_shuffle_bias_activation_nhwc_kyxc_nhwk.hpp"
#include "element_wise_operation.hpp"
#include "device_conv2d_fwd_xdl_c_shuffle_bias_activation_nhwc_kyxc_nhwk.hpp"
#include "reference_conv_fwd_bias_activation.hpp"
using InDataType = ck::half_t;
using WeiDataType = ck::half_t;
......@@ -37,63 +38,53 @@ static constexpr auto ConvFwdDefault =
// clang-format off
using DeviceConvFwdInstance = ck::tensor_operation::device::
DeviceConv2dFwdXdl_C_Shuffle_Bias_Activation_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_N_Ho_Wo_K
// clang-format off
// | InData| WeiData| OutData| AccData| In| Wei| Out| Out| ConvForward| Block| MPer| NPer| K0Per| K1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CBlockTransfer|
// | Type| Type| Type| Type| Elementwise| Elementwise| Elementwise| GlobalMemory| Specialization| Size| Block| Block| Block| | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| _MBlock_MXdlPerWave_MWaveMPerXdl| ScalarPerVector|
// | | | | | Operation| Operation| Operation| DataOperation| | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NXdlPerWave_NWaveNPerXdl| _NWaveNPerXdl|
// | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
<InDataType, WeiDataType, OutDataType, AccDataType, InElementOp, WeiElementOp, OutElementOp, MemorySet, ConvFwdDefault, 256, 128, 256, 4, 8, 32, 32, 2, 4, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 1, 1, S<1, 1, 32, 1, 1, 8>, 8>;
DeviceConv2dFwdXdl_C_Shuffle_Bias_Activation_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_N_Ho_Wo_K<
InDataType, // InDataType
WeiDataType, // WeiDataType
OutDataType, // OutDataType
AccDataType, // AccDataType
InElementOp, // InElementwiseOperation
WeiElementOp, // WeiElementwiseOperation
OutElementOp, // OutElementwiseOperation
MemorySet, // OutGlobalMemoryDataOperation
ConvFwdDefault, // ConvForwardSpecialization
256, // BlockSize
128, // MPerBlock
256, // NPerBlock
4, // K0PerBlock
8, // K1
32, // MPerXdl
32, // NPerXdl
2, // MXdlPerWave
4, // NXdlPerWave
S<4, 64, 1>, // ABlockTransferThreadClusterLengths_K0_M_K1
S<1, 0, 2>, // ABlockTransferThreadClusterArrangeOrder
S<1, 0, 2>, // ABlockTransferSrcAccessOrder
2, // ABlockTransferSrcVectorDim
8, // ABlockTransferSrcScalarPerVector
8, // ABlockTransferDstScalarPerVector_K1
true, // ABlockLdsAddExtraM
S<4, 64, 1>, // BBlockTransferThreadClusterLengths_K0_N_K1
S<1, 0, 2>, // BBlockTransferThreadClusterArrangeOrder
S<1, 0, 2>, // BBlockTransferSrcAccessOrder
2, // BBlockTransferSrcVectorDim
8, // BBlockTransferSrcScalarPerVector
8, // BBlockTransferDstScalarPerVector_K1
true, // BBlockLdsAddExtraN
1, // CShuffleMXdlPerWavePerShuffle
1, // CShuffleNXdlPerWavePerShuffle
S<1, 1, 32, 1, 1, 8>, // CBlockTransferClusterLengths_MBlock_MXdlPerWave_MWaveMPerXdl_NBlock_NXdlPerWave_NWaveNPerXdl
8>; // CBlockTransferScalarPerVector_NWaveNPerXdl
// clang-format on
template <typename TIn,
typename TWei,
typename TOut,
typename InElementOp,
typename WeiElementOp,
typename OutElementOp>
void host_reference_calculation(const Tensor<TIn>& in_n_c_hi_wi,
const Tensor<TWei>& wei_k_c_y_x,
Tensor<TOut>& out_n_k_ho_wo,
const Tensor<TOut>& bias_k,
const std::vector<ck::index_t>& conv_strides,
const std::vector<ck::index_t>& conv_dilations,
const std::vector<ck::index_t>& in_left_pads,
const std::vector<ck::index_t>& /* in_right_pads */,
const InElementOp& in_element_op,
const WeiElementOp& wei_element_op,
const OutElementOp& out_element_op)
{
auto f_nchw = [&](auto n, auto k, auto ho, auto wo) {
double v = 0;
for(int c = 0; c < wei_k_c_y_x.mDesc.GetLengths()[1]; ++c)
{
for(int y = 0; y < wei_k_c_y_x.mDesc.GetLengths()[2]; ++y)
{
int hi = ho * conv_strides[0] + y * conv_dilations[0] - in_left_pads[0];
for(int x = 0; x < wei_k_c_y_x.mDesc.GetLengths()[3]; ++x)
{
int wi = wo * conv_strides[1] + x * conv_dilations[1] - in_left_pads[1];
if(hi >= 0 && hi < in_n_c_hi_wi.mDesc.GetLengths()[2] && wi >= 0 &&
wi < in_n_c_hi_wi.mDesc.GetLengths()[3])
{
v += in_element_op(static_cast<const double>(in_n_c_hi_wi(n, c, hi, wi))) *
wei_element_op(static_cast<const double>(wei_k_c_y_x(k, c, y, x)));
}
}
}
}
out_n_k_ho_wo(n, k, ho, wo) = out_element_op(v, bias_k(k));
};
make_ParallelTensorFunctor(f_nchw,
out_n_k_ho_wo.mDesc.GetLengths()[0],
out_n_k_ho_wo.mDesc.GetLengths()[1],
out_n_k_ho_wo.mDesc.GetLengths()[2],
out_n_k_ho_wo.mDesc.GetLengths()[3])(
std::thread::hardware_concurrency());
}
using ReferenceConvFwdInstance =
ck::tensor_operation::host::ReferenceConvFwd_Bias_Activation<InDataType,
WeiDataType,
OutDataType,
AccDataType,
InElementOp,
WeiElementOp,
OutElementOp>;
int main(int argc, char* argv[])
{
......@@ -277,17 +268,21 @@ int main(int argc, char* argv[])
if(do_verification)
{
host_reference_calculation(in_n_c_hi_wi,
wei_k_c_y_x,
out_n_k_ho_wo_host_result,
bias_k,
conv_filter_strides,
conv_filter_dilations,
input_left_pads,
input_right_pads,
InElementOp{},
WeiElementOp{},
OutElementOp{});
auto refConv = ReferenceConvFwdInstance{};
auto refInvoker = refConv.MakeInvoker();
auto refArgument = refConv.MakeArgument(in_n_c_hi_wi,
wei_k_c_y_x,
out_n_k_ho_wo_host_result,
bias_k,
conv_filter_strides,
conv_filter_dilations,
input_left_pads,
input_right_pads,
InElementOp{},
WeiElementOp{},
OutElementOp{});
refInvoker.Run(refArgument);
out_device_buf.FromDevice(out_n_k_ho_wo_device_result.mData.data());
......
example/6_conv2d_fwd_xdl_bias_relu_add/conv2d_fwd_xdl_bias_relu_add.cpp
View file @ 690c75a7
......@@ -11,8 +11,9 @@
#include "host_tensor_generator.hpp"
#include "device_tensor.hpp"
#include "tensor_layout.hpp"
#include "device_conv2d_fwd_xdl_c_shuffle_bias_activation_add_nhwc_kyxc_nhwk.hpp"
#include "element_wise_operation.hpp"
#include "device_conv2d_fwd_xdl_c_shuffle_bias_activation_add_nhwc_kyxc_nhwk.hpp"
#include "reference_conv_fwd_bias_activation_add.hpp"
using InDataType = ck::half_t;
using WeiDataType = ck::half_t;
......@@ -35,70 +36,52 @@ static constexpr auto ConvFwdDefault =
// clang-format off
using DeviceConvFwdInstance = ck::tensor_operation::device::
DeviceConv2dFwdXdl_C_Shuffle_Bias_Activation_Add_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_N_Ho_Wo_K
// | InData| WeiData| OutData| AccData| In| Wei| Out| ConvForward| Block| MPer| NPer| K0Per| K1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CBlockTransfer|
// | Type| Type| Type| Type| Elementwise| Elementwise| Elementwise| Specialization| Size| Block| Block| Block| | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| _MBlock_MXdlPerWave_MWaveMPerXdl| ScalarPerVector|
// | | | | | Operation| Operation| Operation| | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NXdlPerWave_NWaveNPerXdl| _NWaveNPerXdl|
// | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
<InDataType, WeiDataType, OutDataType, AccDataType, InElementOp, WeiElementOp, OutElementOp, ConvFwdDefault, 256, 128, 256, 4, 8, 32, 32, 2, 4, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 1, 1, S<1, 1, 32, 1, 1, 8>, 8>;
DeviceConv2dFwdXdl_C_Shuffle_Bias_Activation_Add_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_N_Ho_Wo_K<
InDataType, // InDataType
WeiDataType, // WeiDataType
OutDataType, // OutDataType
AccDataType, // AccDataType
InElementOp, // InElementwiseOperation
WeiElementOp, // WeiElementwiseOperation
OutElementOp, // OutElementwiseOperation
ConvFwdDefault, // ConvForwardSpecialization
256, // BlockSize
128, // MPerBlock
256, // NPerBlock
4, // K0PerBlock
8, // K1
32, // MPerXdl
32, // NPerXdl
2, // MXdlPerWave
4, // NXdlPerWave
S<4, 64, 1>, // ABlockTransferThreadClusterLengths_K0_M_K1
S<1, 0, 2>, // ABlockTransferThreadClusterArrangeOrder
S<1, 0, 2>, // ABlockTransferSrcAccessOrder
2, // ABlockTransferSrcVectorDim
8, // ABlockTransferSrcScalarPerVector
8, // ABlockTransferDstScalarPerVector_K1
true, // ABlockLdsAddExtraM
S<4, 64, 1>, // BBlockTransferThreadClusterLengths_K0_N_K1
S<1, 0, 2>, // BBlockTransferThreadClusterArrangeOrder
S<1, 0, 2>, // BBlockTransferSrcAccessOrder
2, // BBlockTransferSrcVectorDim
8, // BBlockTransferSrcScalarPerVector
8, // BBlockTransferDstScalarPerVector_K1
true, // BBlockLdsAddExtraN
1, // CShuffleMXdlPerWavePerShuffle
1, // CShuffleNXdlPerWavePerShuffle
S<1, 1, 32, 1, 1, 8>, // CBlockTransferClusterLengths_MBlock_MXdlPerWave_MWaveMPerXdl_NBlock_NXdlPerWave_NWaveNPerXdl
8>; // CBlockTransferScalarPerVector_NWaveNPerXdl
// clang-format on
template <typename TIn,
typename TWei,
typename TOut,
typename InElementOp,
typename WeiElementOp,
typename OutElementOp>
void host_reference_calculation(const Tensor<TIn>& in_n_c_hi_wi,
const Tensor<TWei>& wei_k_c_y_x,
Tensor<TOut>& out_n_k_ho_wo,
const Tensor<TOut>& bias_k,
const Tensor<TOut>& resi_n_k_ho_wo,
const std::vector<ck::index_t>& conv_strides,
const std::vector<ck::index_t>& conv_dilations,
const std::vector<ck::index_t>& in_left_pads,
const std::vector<ck::index_t>& /* in_right_pads */,
const InElementOp& in_element_op,
const WeiElementOp& wei_element_op,
const OutElementOp& out_element_op)
{
auto f_nchw = [&](auto n, auto k, auto ho, auto wo) {
double v = 0;
for(int c = 0; c < wei_k_c_y_x.mDesc.GetLengths()[1]; ++c)
{
for(int y = 0; y < wei_k_c_y_x.mDesc.GetLengths()[2]; ++y)
{
int hi = ho * conv_strides[0] + y * conv_dilations[0] - in_left_pads[0];
for(int x = 0; x < wei_k_c_y_x.mDesc.GetLengths()[3]; ++x)
{
int wi = wo * conv_strides[1] + x * conv_dilations[1] - in_left_pads[1];
if(hi >= 0 && hi < in_n_c_hi_wi.mDesc.GetLengths()[2] && wi >= 0 &&
wi < in_n_c_hi_wi.mDesc.GetLengths()[3])
{
v += in_element_op(static_cast<const double>(in_n_c_hi_wi(n, c, hi, wi))) *
wei_element_op(static_cast<const double>(wei_k_c_y_x(k, c, y, x)));
}
}
}
}
double v2 = out_n_k_ho_wo(n, k, ho, wo);
out_element_op(v2,
v,
static_cast<const double>(bias_k(k)),
static_cast<const double>(resi_n_k_ho_wo(n, k, ho, wo)));
out_n_k_ho_wo(n, k, ho, wo) = v2;
};
make_ParallelTensorFunctor(f_nchw,
out_n_k_ho_wo.mDesc.GetLengths()[0],
out_n_k_ho_wo.mDesc.GetLengths()[1],
out_n_k_ho_wo.mDesc.GetLengths()[2],
out_n_k_ho_wo.mDesc.GetLengths()[3])(
std::thread::hardware_concurrency());
}
using ReferenceConvFwdInstance =
ck::tensor_operation::host::ReferenceConvFwd_Bias_Activation_Add<InDataType,
WeiDataType,
OutDataType,
AccDataType,
InElementOp,
WeiElementOp,
OutElementOp>;
int main(int argc, char* argv[])
{
......@@ -292,18 +275,22 @@ int main(int argc, char* argv[])
if(do_verification)
{
host_reference_calculation(in_n_c_hi_wi,
wei_k_c_y_x,
out_n_k_ho_wo_host_result,
bias_k,
resi_n_k_ho_wo,
conv_filter_strides,
conv_filter_dilations,
input_left_pads,
input_right_pads,
InElementOp{},
WeiElementOp{},
OutElementOp{});
auto refConv = ReferenceConvFwdInstance{};
auto refInvoker = refConv.MakeInvoker();
auto refArgument = refConv.MakeArgument(in_n_c_hi_wi,
wei_k_c_y_x,
out_n_k_ho_wo_host_result,
bias_k,
resi_n_k_ho_wo,
conv_filter_strides,
conv_filter_dilations,
input_left_pads,
input_right_pads,
InElementOp{},
WeiElementOp{},
OutElementOp{});
refInvoker.Run(refArgument);
out_device_buf.FromDevice(out_n_k_ho_wo_device_result.mData.data());
......
example/CMakeLists.txt
View file @ 690c75a7
......@@ -2,6 +2,7 @@ include_directories(BEFORE
${PROJECT_SOURCE_DIR}
${PROJECT_SOURCE_DIR}/host/host_tensor/include
${PROJECT_SOURCE_DIR}/host/device/include
${PROJECT_SOURCE_DIR}/host/include
${PROJECT_SOURCE_DIR}/device_operation/include
${PROJECT_SOURCE_DIR}/composable_kernel/include
${PROJECT_SOURCE_DIR}/composable_kernel/include/utility
......
host/include/reference_conv_fwd.hpp 0 → 100644
View file @ 690c75a7
#ifndef REFERENCE_CONV_FWD_HPP
#define REFERENCE_CONV_FWD_HPP
#include <iostream>
#include <sstream>
#include "device_base.hpp"
#include "host_tensor.hpp"
namespace ck {
namespace tensor_operation {
namespace host {
// out[N, K, Ho, Wo] = in[N, C, Hi, Wi] * wei[K, C, Y, X]
template <typename InDataType,
typename WeiDataType,
typename OutDataType,
typename AccDataType,
typename InElementwiseOperation,
typename WeiElementwiseOperation,
typename OutElementwiseOperation>
struct ReferenceConvFwd : public device::BaseOperator
{
// Argument
struct Argument : public device::BaseArgument
{
Argument(const Tensor<InDataType>& in_n_c_hi_wi,
const Tensor<WeiDataType>& wei_k_c_y_x,
Tensor<OutDataType>& out_n_k_ho_wo,
std::vector<ck::index_t> conv_filter_strides,
std::vector<ck::index_t> conv_filter_dilations,
std::vector<ck::index_t> input_left_pads,
std::vector<ck::index_t> input_right_pads,
InElementwiseOperation in_element_op,
WeiElementwiseOperation wei_element_op,
OutElementwiseOperation out_element_op)
: in_n_c_hi_wi_{in_n_c_hi_wi},
wei_k_c_y_x_{wei_k_c_y_x},
out_n_k_ho_wo_{out_n_k_ho_wo},
conv_strides_{conv_filter_strides},
conv_dilations_{conv_filter_dilations},
in_left_pads_{input_left_pads},
in_right_pads_{input_right_pads},
in_element_op_{in_element_op},
wei_element_op_{wei_element_op},
out_element_op_{out_element_op}
{
}
const Tensor<InDataType>& in_n_c_hi_wi_;
const Tensor<WeiDataType>& wei_k_c_y_x_;
Tensor<OutDataType>& out_n_k_ho_wo_;
std::vector<index_t> conv_strides_;
std::vector<index_t> conv_dilations_;
std::vector<index_t> in_left_pads_;
std::vector<index_t> in_right_pads_;
InElementwiseOperation in_element_op_;
WeiElementwiseOperation wei_element_op_;
OutElementwiseOperation out_element_op_;
};
// Invoker
struct Invoker : public device::BaseInvoker
{
using Argument = ReferenceConvFwd::Argument;
float Run(const Argument& arg)
{
auto f_nchw = [&](auto n, auto k, auto ho, auto wo) {
float v = 0;
for(int c = 0; c < arg.wei_k_c_y_x_.mDesc.GetLengths()[1]; ++c)
{
for(int y = 0; y < arg.wei_k_c_y_x_.mDesc.GetLengths()[2]; ++y)
{
int hi = ho * arg.conv_strides_[0] + y * arg.conv_dilations_[0] -
arg.in_left_pads_[0];
for(int x = 0; x < arg.wei_k_c_y_x_.mDesc.GetLengths()[3]; ++x)
{
int wi = wo * arg.conv_strides_[1] + x * arg.conv_dilations_[1] -
arg.in_left_pads_[1];
if(hi >= 0 && hi < arg.in_n_c_hi_wi_.mDesc.GetLengths()[2] && wi >= 0 &&
wi < arg.in_n_c_hi_wi_.mDesc.GetLengths()[3])
{
v += arg.in_element_op_(
ck::type_convert<float>(arg.in_n_c_hi_wi_(n, c, hi, wi))) *
arg.wei_element_op_(
ck::type_convert<float>(arg.wei_k_c_y_x_(k, c, y, x)));
}
}
}
}
arg.out_n_k_ho_wo_(n, k, ho, wo) =
ck::type_convert<OutDataType>(arg.out_element_op_(v));
};
make_ParallelTensorFunctor(f_nchw,
arg.out_n_k_ho_wo_.mDesc.GetLengths()[0],
arg.out_n_k_ho_wo_.mDesc.GetLengths()[1],
arg.out_n_k_ho_wo_.mDesc.GetLengths()[2],
arg.out_n_k_ho_wo_.mDesc.GetLengths()[3])(
std::thread::hardware_concurrency());
return 0;
}
float Run(const device::BaseArgument* p_arg, int) override
{
return Run(*dynamic_cast<const Argument*>(p_arg));
}
};
static constexpr bool IsValidCompilationParameter()
{
// TODO: properly implement this check
return true;
}
bool IsSupportedArgument(const device::BaseArgument*) override { return true; }
static auto MakeArgument(const Tensor<InDataType>& in_n_c_hi_wi,
const Tensor<WeiDataType>& wei_k_c_y_x,
Tensor<OutDataType>& out_n_k_ho_wo,
std::vector<ck::index_t> conv_filter_strides,
std::vector<ck::index_t> conv_filter_dilations,
std::vector<ck::index_t> input_left_pads,
std::vector<ck::index_t> input_right_pads,
InElementwiseOperation in_element_op,
WeiElementwiseOperation wei_element_op,
OutElementwiseOperation out_element_op)
{
return Argument{in_n_c_hi_wi,
wei_k_c_y_x,
out_n_k_ho_wo,
conv_filter_strides,
conv_filter_dilations,
input_left_pads,
input_right_pads,
in_element_op,
wei_element_op,
out_element_op};
}
static auto MakeInvoker() { return Invoker{}; }
virtual std::unique_ptr<device::BaseInvoker> MakeInvokerPointer()
{
return std::make_unique<Invoker>(Invoker{});
}
std::string GetTypeString() const override
{
auto str = std::stringstream();
// clang-format off
str << "ReferenceConvFwd"
<< std::endl;
// clang-format on
return str.str();
}
};
} // namespace host
} // namespace tensor_operation
} // namespace ck
#endif
host/include/reference_conv_fwd_bias_activation.hpp 0 → 100644
View file @ 690c75a7
#ifndef REFERENCE_CONV_FWD_BIAS_ACTIVATION_HPP
#define REFERENCE_CONV_FWD_BIAS_ACTIVATION_HPP
#include <iostream>
#include <sstream>
#include "device_base.hpp"
#include "host_tensor.hpp"
namespace ck {
namespace tensor_operation {
namespace host {
// out[N, Ho, Wo, K] =
// activate(in[N, Hi, Wi, C] * wei[K, Y, X, C] + bias[K])
template <typename InDataType,
typename WeiDataType,
typename OutDataType,
typename AccDataType,
typename InElementwiseOperation,
typename WeiElementwiseOperation,
typename OutElementwiseOperation>
struct ReferenceConvFwd_Bias_Activation : public device::BaseOperator
{
// Argument
struct Argument : public device::BaseArgument
{
Argument(const Tensor<InDataType>& in_n_c_hi_wi,
const Tensor<WeiDataType>& wei_k_c_y_x,
Tensor<OutDataType>& out_n_k_ho_wo,
const Tensor<OutDataType>& bias_k,
std::vector<ck::index_t> conv_filter_strides,
std::vector<ck::index_t> conv_filter_dilations,
std::vector<ck::index_t> input_left_pads,
std::vector<ck::index_t> input_right_pads,
InElementwiseOperation in_element_op,
WeiElementwiseOperation wei_element_op,
OutElementwiseOperation out_element_op)
: in_n_c_hi_wi_{in_n_c_hi_wi},
wei_k_c_y_x_{wei_k_c_y_x},
out_n_k_ho_wo_{out_n_k_ho_wo},
bias_k_{bias_k},
conv_strides_{conv_filter_strides},
conv_dilations_{conv_filter_dilations},
in_left_pads_{input_left_pads},
in_right_pads_{input_right_pads},
in_element_op_{in_element_op},
wei_element_op_{wei_element_op},
out_element_op_{out_element_op}
{
}
const Tensor<InDataType>& in_n_c_hi_wi_;
const Tensor<WeiDataType>& wei_k_c_y_x_;
Tensor<OutDataType>& out_n_k_ho_wo_;
const Tensor<OutDataType>& bias_k_;
std::vector<index_t> conv_strides_;
std::vector<index_t> conv_dilations_;
std::vector<index_t> in_left_pads_;
std::vector<index_t> in_right_pads_;
InElementwiseOperation in_element_op_;
WeiElementwiseOperation wei_element_op_;
OutElementwiseOperation out_element_op_;
};
// Invoker
struct Invoker : public device::BaseInvoker
{
using Argument = ReferenceConvFwd_Bias_Activation::Argument;
float Run(const Argument& arg)
{
auto f_nchw = [&](auto n, auto k, auto ho, auto wo) {
float v = 0;
for(int c = 0; c < arg.wei_k_c_y_x_.mDesc.GetLengths()[1]; ++c)
{
for(int y = 0; y < arg.wei_k_c_y_x_.mDesc.GetLengths()[2]; ++y)
{
int hi = ho * arg.conv_strides_[0] + y * arg.conv_dilations_[0] -
arg.in_left_pads_[0];
for(int x = 0; x < arg.wei_k_c_y_x_.mDesc.GetLengths()[3]; ++x)
{
int wi = wo * arg.conv_strides_[1] + x * arg.conv_dilations_[1] -
arg.in_left_pads_[1];
if(hi >= 0 && hi < arg.in_n_c_hi_wi_.mDesc.GetLengths()[2] && wi >= 0 &&
wi < arg.in_n_c_hi_wi_.mDesc.GetLengths()[3])
{
v += arg.in_element_op_(
ck::type_convert<float>(arg.in_n_c_hi_wi_(n, c, hi, wi))) *
arg.wei_element_op_(
ck::type_convert<float>(arg.wei_k_c_y_x_(k, c, y, x)));
}
}
}
}
arg.out_n_k_ho_wo_(n, k, ho, wo) =
ck::type_convert<OutDataType>(arg.out_element_op_(v, arg.bias_k_(k)));
};
make_ParallelTensorFunctor(f_nchw,
arg.out_n_k_ho_wo_.mDesc.GetLengths()[0],
arg.out_n_k_ho_wo_.mDesc.GetLengths()[1],
arg.out_n_k_ho_wo_.mDesc.GetLengths()[2],
arg.out_n_k_ho_wo_.mDesc.GetLengths()[3])(
std::thread::hardware_concurrency());
return 0;
}
float Run(const device::BaseArgument* p_arg, int) override
{
return Run(*dynamic_cast<const Argument*>(p_arg));
}
};
static constexpr bool IsValidCompilationParameter()
{
// TODO: properly implement this check
return true;
}
bool IsSupportedArgument(const device::BaseArgument*) override { return true; }
static auto MakeArgument(const Tensor<InDataType>& in_n_c_hi_wi,
const Tensor<WeiDataType>& wei_k_c_y_x,
Tensor<OutDataType>& out_n_k_ho_wo,
const Tensor<OutDataType>& bias_k,
std::vector<ck::index_t> conv_filter_strides,
std::vector<ck::index_t> conv_filter_dilations,
std::vector<ck::index_t> input_left_pads,
std::vector<ck::index_t> input_right_pads,
InElementwiseOperation in_element_op,
WeiElementwiseOperation wei_element_op,
OutElementwiseOperation out_element_op)
{
return Argument{in_n_c_hi_wi,
wei_k_c_y_x,
out_n_k_ho_wo,
bias_k,
conv_filter_strides,
conv_filter_dilations,
input_left_pads,
input_right_pads,
in_element_op,
wei_element_op,
out_element_op};
}
static auto MakeInvoker() { return Invoker{}; }
virtual std::unique_ptr<device::BaseInvoker> MakeInvokerPointer()
{
return std::make_unique<Invoker>(Invoker{});
}
std::string GetTypeString() const override
{
auto str = std::stringstream();
// clang-format off
str << "ReferenceConvFwd_Bias_Activation"
<< std::endl;
// clang-format on
return str.str();
}
};
} // namespace host
} // namespace tensor_operation
} // namespace ck
#endif
host/include/reference_conv_fwd_bias_activation_add.hpp 0 → 100644
View file @ 690c75a7
#ifndef REFERENCE_CONV2D_FWD_BIAS_ACTIVATION_ADD_HPP
#define REFERENCE_CONV2D_FWD_BIAS_ACTIVATION_ADD_HPP
#include <iostream>
#include <sstream>
#include "device_base.hpp"
#include "host_tensor.hpp"
namespace ck {
namespace tensor_operation {
namespace host {
// out[N, Ho, Wo, K] =
// activate(in[N, Hi, Wi, C] * wei[K, Y, X, C] + bias[K]) + residual[N, Ho, Wo, K]
template <typename InDataType,
typename WeiDataType,
typename OutDataType,
typename AccDataType,
typename InElementwiseOperation,
typename WeiElementwiseOperation,
typename OutElementwiseOperation>
struct ReferenceConvFwd_Bias_Activation_Add : public device::BaseOperator
{
// Argument
struct Argument : public device::BaseArgument
{
Argument(const Tensor<InDataType>& in_n_c_hi_wi,
const Tensor<WeiDataType>& wei_k_c_y_x,
Tensor<OutDataType>& out_n_k_ho_wo,
const Tensor<OutDataType>& bias_k,
const Tensor<OutDataType>& resi_n_k_ho_wo,
std::vector<ck::index_t> conv_filter_strides,
std::vector<ck::index_t> conv_filter_dilations,
std::vector<ck::index_t> input_left_pads,
std::vector<ck::index_t> input_right_pads,
InElementwiseOperation in_element_op,
WeiElementwiseOperation wei_element_op,
OutElementwiseOperation out_element_op)
: in_n_c_hi_wi_{in_n_c_hi_wi},
wei_k_c_y_x_{wei_k_c_y_x},
out_n_k_ho_wo_{out_n_k_ho_wo},
bias_k_{bias_k},
resi_n_k_ho_wo_{resi_n_k_ho_wo},
conv_strides_{conv_filter_strides},
conv_dilations_{conv_filter_dilations},
in_left_pads_{input_left_pads},
in_right_pads_{input_right_pads},
in_element_op_{in_element_op},
wei_element_op_{wei_element_op},
out_element_op_{out_element_op}
{
}
const Tensor<InDataType>& in_n_c_hi_wi_;
const Tensor<WeiDataType>& wei_k_c_y_x_;
Tensor<OutDataType>& out_n_k_ho_wo_;
const Tensor<OutDataType>& bias_k_;
const Tensor<OutDataType>& resi_n_k_ho_wo_;
std::vector<index_t> conv_strides_;
std::vector<index_t> conv_dilations_;
std::vector<index_t> in_left_pads_;
std::vector<index_t> in_right_pads_;
InElementwiseOperation in_element_op_;
WeiElementwiseOperation wei_element_op_;
OutElementwiseOperation out_element_op_;
};
// Invoker
struct Invoker : public device::BaseInvoker
{
using Argument = ReferenceConvFwd_Bias_Activation_Add::Argument;
float Run(const Argument& arg)
{
auto f_nchw = [&](auto n, auto k, auto ho, auto wo) {
float v = 0;
for(int c = 0; c < arg.wei_k_c_y_x_.mDesc.GetLengths()[1]; ++c)
{
for(int y = 0; y < arg.wei_k_c_y_x_.mDesc.GetLengths()[2]; ++y)
{
int hi = ho * arg.conv_strides_[0] + y * arg.conv_dilations_[0] -
arg.in_left_pads_[0];
for(int x = 0; x < arg.wei_k_c_y_x_.mDesc.GetLengths()[3]; ++x)
{
int wi = wo * arg.conv_strides_[1] + x * arg.conv_dilations_[1] -
arg.in_left_pads_[1];
if(hi >= 0 && hi < arg.in_n_c_hi_wi_.mDesc.GetLengths()[2] && wi >= 0 &&
wi < arg.in_n_c_hi_wi_.mDesc.GetLengths()[3])
{
v += arg.in_element_op_(
ck::type_convert<float>(arg.in_n_c_hi_wi_(n, c, hi, wi))) *
arg.wei_element_op_(
ck::type_convert<float>(arg.wei_k_c_y_x_(k, c, y, x)));
}
}
}
}
float v2 = ck::type_convert<float>(arg.out_n_k_ho_wo_(n, k, ho, wo));
arg.out_element_op_(v2,
v,
ck::type_convert<float>(arg.bias_k_(k)),
ck::type_convert<float>(arg.resi_n_k_ho_wo_(n, k, ho, wo)));
arg.out_n_k_ho_wo_(n, k, ho, wo) = ck::type_convert<OutDataType>(v2);
};
make_ParallelTensorFunctor(f_nchw,
arg.out_n_k_ho_wo_.mDesc.GetLengths()[0],
arg.out_n_k_ho_wo_.mDesc.GetLengths()[1],
arg.out_n_k_ho_wo_.mDesc.GetLengths()[2],
arg.out_n_k_ho_wo_.mDesc.GetLengths()[3])(
std::thread::hardware_concurrency());
return 0;
}
float Run(const device::BaseArgument* p_arg, int) override
{
return Run(*dynamic_cast<const Argument*>(p_arg));
}
};
static constexpr bool IsValidCompilationParameter()
{
// TODO: properly implement this check
return true;
}
bool IsSupportedArgument(const device::BaseArgument*) override { return true; }
static auto MakeArgument(const Tensor<InDataType>& in_n_c_hi_wi,
const Tensor<WeiDataType>& wei_k_c_y_x,
Tensor<OutDataType>& out_n_k_ho_wo,
const Tensor<OutDataType>& bias_k,
const Tensor<OutDataType>& resi_n_k_ho_wo,
std::vector<ck::index_t> conv_filter_strides,
std::vector<ck::index_t> conv_filter_dilations,
std::vector<ck::index_t> input_left_pads,
std::vector<ck::index_t> input_right_pads,
InElementwiseOperation in_element_op,
WeiElementwiseOperation wei_element_op,
OutElementwiseOperation out_element_op)
{
return Argument{in_n_c_hi_wi,
wei_k_c_y_x,
out_n_k_ho_wo,
bias_k,
resi_n_k_ho_wo,
conv_filter_strides,
conv_filter_dilations,
input_left_pads,
input_right_pads,
in_element_op,
wei_element_op,
out_element_op};
}
static auto MakeInvoker() { return Invoker{}; }
virtual std::unique_ptr<device::BaseInvoker> MakeInvokerPointer()
{
return std::make_unique<Invoker>(Invoker{});
}
std::string GetTypeString() const override
{
auto str = std::stringstream();
// clang-format off
str << "ReferenceConvFwd_Bias_Activation_Add"
<< std::endl;
// clang-format on
return str.str();
}
};
} // namespace host
} // namespace tensor_operation
} // namespace ck
#endif
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