mirror of
https://github.com/ROCm/composable_kernel.git
synced 2026-05-03 13:11:25 +00:00
Fusion Conv+Bias+ReLU(+Add) (#62)
* fix relu * clean up * clean up * adding 1x1 conv * adding 1x1 conv * added 1x1 conv * refactor * refactor * refactor * added profiler for conv+bias+relu+add * clean up * adding conv+bias+relu * adding conv+bias+relu * added conv+bias+relu * Update README.md * update cpu verification * adding c shuffle * update static_tensor for dealing with invalid element * adding c shuffle * debugging * fix bug * convert to fp16 before shuffle * shuffle more than one M/NRepeat * clean up * remove coordinate step hack from GridwiseGemm_k0mk1_k0nk1_mn_xdlops_v3r1 * clean up * remove coordinate step hack from all gridwise gemm xdl * clean up coordinate step hack * clean up coordinate step hack * ThreadwiseTensorSliceTransfer_v3r2 support pointwise op on both src and dst * adding output shuffle in conv+bias+relu+add * update * added conv+bias+relu+add with c shuffle * added conv+bias+relu+add with c shuffle * fix forward_sweep bugs in threadwise copy * clean up * refactor * clean up * clean up * added conv_c_shuffle+bias_relu * clean up * added conv+bias+relu+atomic_add * clean up * clean up * clean up * clean up * clean up * clean up * misc fixes; add 1x1 specialization * clean up * delete unused device op * clean up * add support for odd C value
This commit is contained in:
Chao Liu
283
example/4_conv2d_fwd_xdl/conv2d_fwd_xdl.cpp
Normal file
283
example/4_conv2d_fwd_xdl/conv2d_fwd_xdl.cpp
Normal file
@@ -0,0 +1,283 @@
|
||||
#include <iostream>
|
||||
#include <numeric>
|
||||
#include <initializer_list>
|
||||
#include <cstdlib>
|
||||
#include <stdlib.h>
|
||||
#include <half.hpp>
|
||||
#include "config.hpp"
|
||||
#include "print.hpp"
|
||||
#include "device.hpp"
|
||||
#include "host_tensor.hpp"
|
||||
#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"
|
||||
|
||||
using InDataType = ck::half_t;
|
||||
using WeiDataType = ck::half_t;
|
||||
using OutDataType = ck::half_t;
|
||||
using AccDataType = float;
|
||||
|
||||
template <ck::index_t... Is>
|
||||
using S = ck::Sequence<Is...>;
|
||||
|
||||
using InLayout = ck::tensor_layout::convolution::NHWC;
|
||||
using WeiLayout = ck::tensor_layout::convolution::KYXC;
|
||||
using OutLayout = ck::tensor_layout::convolution::NHWK;
|
||||
|
||||
using InElementOp = ck::tensor_operation::element_wise::PassThrough;
|
||||
using WeiElementOp = ck::tensor_operation::element_wise::PassThrough;
|
||||
using OutElementOp = ck::tensor_operation::element_wise::PassThrough;
|
||||
|
||||
static constexpr auto ConvFwdDefault =
|
||||
ck::tensor_operation::device::ConvolutionForwardSpecialization_t::Default;
|
||||
|
||||
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>;
|
||||
// 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());
|
||||
}
|
||||
|
||||
int main(int argc, char* argv[])
|
||||
{
|
||||
bool do_verification = 0;
|
||||
int init_method = 0;
|
||||
int nrepeat = 5;
|
||||
|
||||
// Conv shape
|
||||
ck::index_t N = 128;
|
||||
ck::index_t K = 256;
|
||||
ck::index_t C = 192;
|
||||
ck::index_t Y = 3;
|
||||
ck::index_t X = 3;
|
||||
ck::index_t Hi = 71;
|
||||
ck::index_t Wi = 71;
|
||||
ck::index_t conv_stride_h = 2;
|
||||
ck::index_t conv_stride_w = 2;
|
||||
ck::index_t conv_dilation_h = 1;
|
||||
ck::index_t conv_dilation_w = 1;
|
||||
ck::index_t in_left_pad_h = 1;
|
||||
ck::index_t in_left_pad_w = 1;
|
||||
ck::index_t in_right_pad_h = 1;
|
||||
ck::index_t in_right_pad_w = 1;
|
||||
|
||||
if(argc == 4)
|
||||
{
|
||||
do_verification = std::stoi(argv[1]);
|
||||
init_method = std::stoi(argv[2]);
|
||||
nrepeat = std::stoi(argv[3]);
|
||||
}
|
||||
else if(argc == 19)
|
||||
{
|
||||
do_verification = std::stoi(argv[1]);
|
||||
init_method = std::stoi(argv[2]);
|
||||
nrepeat = std::stoi(argv[3]);
|
||||
|
||||
N = std::stoi(argv[4]);
|
||||
K = std::stoi(argv[5]);
|
||||
C = std::stoi(argv[6]);
|
||||
Y = std::stoi(argv[7]);
|
||||
X = std::stoi(argv[8]);
|
||||
Hi = std::stoi(argv[9]);
|
||||
Wi = std::stoi(argv[10]);
|
||||
conv_stride_h = std::stoi(argv[11]);
|
||||
conv_stride_w = std::stoi(argv[12]);
|
||||
conv_dilation_h = std::stoi(argv[13]);
|
||||
conv_dilation_w = std::stoi(argv[14]);
|
||||
in_left_pad_h = std::stoi(argv[15]);
|
||||
in_left_pad_w = std::stoi(argv[16]);
|
||||
in_right_pad_h = std::stoi(argv[17]);
|
||||
in_right_pad_w = std::stoi(argv[18]);
|
||||
}
|
||||
else
|
||||
{
|
||||
printf("arg1: verification (0=no, 1=yes)\n");
|
||||
printf("arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n");
|
||||
printf("arg3: run kernel # of times (>1)\n");
|
||||
printf("arg4 to 18: N, K, C, Y, X, Hi, Wi, Sy, Sx, Dy, Dx, LeftPy, LeftPx, RightPy, "
|
||||
"RightPx\n");
|
||||
exit(0);
|
||||
}
|
||||
|
||||
const ck::index_t YEff = (Y - 1) * conv_dilation_h + 1;
|
||||
const ck::index_t XEff = (X - 1) * conv_dilation_w + 1;
|
||||
|
||||
const ck::index_t Ho = (Hi + in_left_pad_h + in_right_pad_h - YEff) / conv_stride_h + 1;
|
||||
const ck::index_t Wo = (Wi + in_left_pad_w + in_right_pad_w - XEff) / conv_stride_w + 1;
|
||||
|
||||
const std::vector<ck::index_t> conv_filter_strides{{conv_stride_h, conv_stride_w}};
|
||||
const std::vector<ck::index_t> conv_filter_dilations{{conv_dilation_h, conv_dilation_w}};
|
||||
const std::vector<ck::index_t> input_left_pads{{in_left_pad_h, in_left_pad_w}};
|
||||
const std::vector<ck::index_t> input_right_pads{{in_right_pad_h, in_right_pad_w}};
|
||||
|
||||
// tensor layout
|
||||
auto f_host_tensor_descriptor = [](std::size_t N_,
|
||||
std::size_t C_,
|
||||
std::size_t H,
|
||||
std::size_t W,
|
||||
auto layout) {
|
||||
if constexpr(ck::is_same<decltype(layout), ck::tensor_layout::convolution::NCHW>::value ||
|
||||
ck::is_same<decltype(layout), ck::tensor_layout::convolution::KCYX>::value ||
|
||||
ck::is_same<decltype(layout), ck::tensor_layout::convolution::NKHW>::value)
|
||||
{
|
||||
return HostTensorDescriptor(std::vector<std::size_t>({N_, C_, H, W}),
|
||||
std::vector<std::size_t>({C_ * H * W, H * W, W, 1}));
|
||||
}
|
||||
else if constexpr(ck::is_same<decltype(layout),
|
||||
ck::tensor_layout::convolution::NHWC>::value ||
|
||||
ck::is_same<decltype(layout),
|
||||
ck::tensor_layout::convolution::KYXC>::value ||
|
||||
ck::is_same<decltype(layout),
|
||||
ck::tensor_layout::convolution::NHWK>::value)
|
||||
{
|
||||
return HostTensorDescriptor(std::vector<std::size_t>({N_, C_, H, W}),
|
||||
std::vector<std::size_t>({C_ * H * W, 1, W * C_, C_}));
|
||||
}
|
||||
};
|
||||
|
||||
Tensor<InDataType> in_n_c_hi_wi(f_host_tensor_descriptor(N, C, Hi, Wi, InLayout{}));
|
||||
Tensor<WeiDataType> wei_k_c_y_x(f_host_tensor_descriptor(K, C, Y, X, WeiLayout{}));
|
||||
Tensor<OutDataType> out_n_k_ho_wo_host_result(
|
||||
f_host_tensor_descriptor(N, K, Ho, Wo, OutLayout{}));
|
||||
Tensor<OutDataType> out_n_k_ho_wo_device_result(
|
||||
f_host_tensor_descriptor(N, K, Ho, Wo, OutLayout{}));
|
||||
|
||||
std::cout << "in_n_c_hi_wi: " << in_n_c_hi_wi.mDesc << std::endl;
|
||||
std::cout << "wei_k_c_y_x: " << wei_k_c_y_x.mDesc << std::endl;
|
||||
std::cout << "out_n_k_ho_wo: " << out_n_k_ho_wo_host_result.mDesc << std::endl;
|
||||
|
||||
switch(init_method)
|
||||
{
|
||||
case 0: break;
|
||||
case 1:
|
||||
in_n_c_hi_wi.GenerateTensorValue(GeneratorTensor_2<InDataType>{-5, 5});
|
||||
wei_k_c_y_x.GenerateTensorValue(GeneratorTensor_2<WeiDataType>{-5, 5});
|
||||
break;
|
||||
default:
|
||||
in_n_c_hi_wi.GenerateTensorValue(GeneratorTensor_3<InDataType>{0.0, 1.0});
|
||||
wei_k_c_y_x.GenerateTensorValue(GeneratorTensor_3<WeiDataType>{-0.5, 0.5});
|
||||
}
|
||||
|
||||
DeviceMem in_device_buf(sizeof(InDataType) * in_n_c_hi_wi.mDesc.GetElementSpace());
|
||||
DeviceMem wei_device_buf(sizeof(WeiDataType) * wei_k_c_y_x.mDesc.GetElementSpace());
|
||||
DeviceMem out_device_buf(sizeof(OutDataType) *
|
||||
out_n_k_ho_wo_device_result.mDesc.GetElementSpace());
|
||||
|
||||
in_device_buf.ToDevice(in_n_c_hi_wi.mData.data());
|
||||
wei_device_buf.ToDevice(wei_k_c_y_x.mData.data());
|
||||
|
||||
// do GEMM
|
||||
auto conv = DeviceConvFwdInstance{};
|
||||
auto invoker = conv.MakeInvoker();
|
||||
auto argument = conv.MakeArgument(static_cast<InDataType*>(in_device_buf.GetDeviceBuffer()),
|
||||
static_cast<WeiDataType*>(wei_device_buf.GetDeviceBuffer()),
|
||||
static_cast<OutDataType*>(out_device_buf.GetDeviceBuffer()),
|
||||
N,
|
||||
K,
|
||||
C,
|
||||
std::vector<ck::index_t>{{Hi, Wi}},
|
||||
std::vector<ck::index_t>{{Y, X}},
|
||||
std::vector<ck::index_t>{{Ho, Wo}},
|
||||
conv_filter_strides,
|
||||
conv_filter_dilations,
|
||||
input_left_pads,
|
||||
input_right_pads,
|
||||
InElementOp{},
|
||||
WeiElementOp{},
|
||||
OutElementOp{});
|
||||
|
||||
if(!conv.IsSupportedArgument(argument))
|
||||
{
|
||||
throw std::runtime_error(
|
||||
"wrong! device_conv with the specified compilation parameters does "
|
||||
"not support this Conv problem");
|
||||
}
|
||||
|
||||
float ave_time = invoker.Run(argument, nrepeat);
|
||||
|
||||
std::size_t flop = std::size_t(2) * N * K * Ho * Wo * C * Y * X;
|
||||
|
||||
std::size_t num_btype = sizeof(InDataType) * (N * C * Hi * Wi) +
|
||||
sizeof(WeiDataType) * (K * C * Y * X) +
|
||||
sizeof(OutDataType) * (N * K * Ho * Wo);
|
||||
|
||||
float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
|
||||
|
||||
float gb_per_sec = num_btype / 1.E6 / ave_time;
|
||||
|
||||
std::cout << "Perf: " << ave_time << " ms, " << tflops << " TFlops, " << gb_per_sec << " GB/s"
|
||||
<< std::endl;
|
||||
|
||||
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{});
|
||||
|
||||
out_device_buf.FromDevice(out_n_k_ho_wo_device_result.mData.data());
|
||||
|
||||
check_error(out_n_k_ho_wo_host_result, out_n_k_ho_wo_device_result);
|
||||
}
|
||||
}
|
||||
Reference in New Issue
Block a user