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composable_kernel/host/driver_online/conv_fwd_driver_online.cpp
Chao Liu d09ea4f4e5 Update develop (#5) 
* refactor
2021-08-06 16:11:15 -05:00

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#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 "conv_common.hpp"
#include "host_conv.hpp"
#include "device_tensor.hpp"
#include "handle.hpp"
#include "hipCheck.hpp"
#include "online_device_dynamic_convolution_forward_implicit_gemm_v4r4_dlops_nchw_kcyx_nkhw.hpp"
#include "online_device_dynamic_convolution_forward_implicit_gemm_v6r1_dlops_nchw_kcyx_nkhw.hpp"
#include "online_device_dynamic_convolution_forward_implicit_gemm_v4r4_xdlops_nchw_kcyx_nkhw.hpp"
#include "online_device_dynamic_convolution_forward_implicit_gemm_v4r4_xdlops_nhwc_kyxc_nhwk.hpp"
#define USE_CONV_FWD_V4R4_NCHW 1
#define USE_CONV_FWD_V6R1_NCHW 1
#define USE_CONV_FWD_V4R4_XDLOPS_NCHW 1
#define USE_CONV_FWD_V4R4_XDLOPS_NHWC 1
enum ConvForwardAlgo
{
V4R4NCHW, // 0
V6R1NCHW, // 1
V4R4XDLNCHW, // 2
V4R4XDLNHWC // 3
};
int main(int argc, char* argv[])
{
using namespace ck;
using namespace ck_driver;
using size_t = std::size_t;
hipStream_t stream;
online_compile::Handle* handle;
MY_HIP_CHECK(hipStreamCreate(&stream));
handle = new online_compile::Handle(stream);
constexpr auto I0 = Number<0>{};
constexpr auto I1 = Number<1>{};
constexpr auto I2 = Number<2>{};
constexpr auto I3 = Number<3>{};
constexpr auto I4 = Number<4>{};
constexpr auto I5 = Number<5>{};
constexpr auto I6 = Number<6>{};
if(argc != 22)
{
printf("arg1 to 5: layout, algo, do_verification, init_method, do_log, nrepeat\n");
printf("rest: N, K, C, Y, X, Hi, Wi, Sy, Sx, Dy, Dx, LeftPy, LeftPx, RightPy, RightPx\n");
exit(1);
}
const ConvTensorLayout layout = static_cast<ConvTensorLayout>(atoi(argv[1]));
const ConvForwardAlgo algo = static_cast<ConvForwardAlgo>(atoi(argv[2]));
const bool do_verification = atoi(argv[3]);
const int init_method = atoi(argv[4]);
const bool do_log = atoi(argv[5]);
const int nrepeat = atoi(argv[6]);
const index_t N = atoi(argv[7]);
const index_t K = atoi(argv[8]);
const index_t C = atoi(argv[9]);
const index_t Y = atoi(argv[10]);
const index_t X = atoi(argv[11]);
const index_t Hi = atoi(argv[12]);
const index_t Wi = atoi(argv[13]);
const index_t conv_stride_h = atoi(argv[14]);
const index_t conv_stride_w = atoi(argv[15]);
const index_t conv_dilation_h = atoi(argv[16]);
const index_t conv_dilation_w = atoi(argv[17]);
const index_t in_left_pad_h = atoi(argv[18]);
const index_t in_left_pad_w = atoi(argv[19]);
const index_t in_right_pad_h = atoi(argv[20]);
const index_t in_right_pad_w = atoi(argv[21]);
const index_t YEff = (Y - 1) * conv_dilation_h + 1;
const index_t XEff = (X - 1) * conv_dilation_w + 1;
const index_t Ho = (Hi + in_left_pad_h + in_right_pad_h - YEff) / conv_stride_h + 1;
const index_t Wo = (Wi + in_left_pad_w + in_right_pad_w - XEff) / conv_stride_w + 1;
#if 1
using in_data_t = float;
using acc_data_t = float;
using out_data_t = float;
#elif 0
using in_data_t = half_t;
using acc_data_t = float;
using out_data_t = half_t;
#elif 1
using in_data_t = int8_t;
using acc_data_t = int32_t;
using out_data_t = int8_t;
#endif
std::vector<std::size_t> in_lengths_host(4), wei_lengths_host(4), out_lengths_host(4);
switch(layout)
{
case ConvTensorLayout::NCHW:
// NCHW
in_lengths_host[0] = static_cast<std::size_t>(N);
in_lengths_host[1] = static_cast<std::size_t>(C);
in_lengths_host[2] = static_cast<std::size_t>(Hi);
in_lengths_host[3] = static_cast<std::size_t>(Wi);
wei_lengths_host[0] = static_cast<std::size_t>(K);
wei_lengths_host[1] = static_cast<std::size_t>(C);
wei_lengths_host[2] = static_cast<std::size_t>(Y);
wei_lengths_host[3] = static_cast<std::size_t>(X);
out_lengths_host[0] = static_cast<std::size_t>(N);
out_lengths_host[1] = static_cast<std::size_t>(K);
out_lengths_host[2] = static_cast<std::size_t>(Ho);
out_lengths_host[3] = static_cast<std::size_t>(Wo);
break;
case ConvTensorLayout::NHWC:
// NHWC
in_lengths_host[0] = static_cast<std::size_t>(N);
in_lengths_host[1] = static_cast<std::size_t>(Hi);
in_lengths_host[2] = static_cast<std::size_t>(Wi);
in_lengths_host[3] = static_cast<std::size_t>(C);
wei_lengths_host[0] = static_cast<std::size_t>(K);
wei_lengths_host[1] = static_cast<std::size_t>(Y);
wei_lengths_host[2] = static_cast<std::size_t>(X);
wei_lengths_host[3] = static_cast<std::size_t>(C);
out_lengths_host[0] = static_cast<std::size_t>(N);
out_lengths_host[1] = static_cast<std::size_t>(Ho);
out_lengths_host[2] = static_cast<std::size_t>(Wo);
out_lengths_host[3] = static_cast<std::size_t>(K);
break;
default: throw std::runtime_error("wrong! not implemented");
}
Tensor<in_data_t> in(in_lengths_host);
Tensor<in_data_t> wei(wei_lengths_host);
Tensor<out_data_t> out_host(out_lengths_host);
Tensor<out_data_t> out_device(out_lengths_host);
std::cout << "layout: " << layout << std::endl;
ostream_HostTensorDescriptor(in.mDesc, std::cout << "in: ");
ostream_HostTensorDescriptor(wei.mDesc, std::cout << "wei: ");
ostream_HostTensorDescriptor(out_host.mDesc, std::cout << "out: ");
print_array("InLeftPads", make_tuple(in_left_pad_h, in_left_pad_w));
print_array("InRightPads", make_tuple(in_right_pad_h, in_right_pad_w));
print_array("ConvStrides", make_tuple(conv_stride_h, conv_stride_w));
print_array("ConvDilations", make_tuple(conv_dilation_h, conv_dilation_w));
std::size_t num_thread = std::thread::hardware_concurrency();
switch(init_method)
{
case 0:
// no initialization
break;
case 1:
in.GenerateTensorValue(GeneratorTensor_1{}, num_thread);
wei.GenerateTensorValue(GeneratorTensor_1{}, num_thread);
break;
case 2:
in.GenerateTensorValue(GeneratorTensor_1{}, num_thread);
wei.GenerateTensorValue(GeneratorTensor_2{-5, 5}, num_thread);
break;
case 3:
in.GenerateTensorValue(GeneratorTensor_2{-5, 5}, num_thread);
wei.GenerateTensorValue(GeneratorTensor_1{}, num_thread);
break;
case 4:
in.GenerateTensorValue(GeneratorTensor_2{-5, 5}, num_thread);
wei.GenerateTensorValue(GeneratorTensor_2{-5, 5}, num_thread);
break;
case 5:
in.GenerateTensorValue(GeneratorTensor_3<float>{0.0, 1.0}, num_thread);
wei.GenerateTensorValue(GeneratorTensor_3<float>{-0.5, 0.5}, num_thread);
break;
default:
in.GenerateTensorValue(GeneratorTensor_2{1, 5}, num_thread);
auto gen_wei = [](auto... is) {
return GeneratorTensor_2{1, 5}(is...) * GeneratorTensor_Checkboard{}(is...);
};
wei.GenerateTensorValue(gen_wei, num_thread);
}
auto f_make_for_device_nchw = [&]() {
const auto in_lengths_dev = make_tuple(N, C, Hi, Wi);
const auto wei_lengths_dev = make_tuple(K, C, Y, X);
const auto out_lengths_dev = make_tuple(N, K, Ho, Wo);
return make_tuple(in_lengths_dev, wei_lengths_dev, out_lengths_dev);
};
auto f_make_for_device_nhwc = [&]() {
const auto in_lengths_dev = make_tuple(N, Hi, Wi, C);
const auto wei_lengths_dev = make_tuple(K, Y, X, C);
const auto out_lengths_dev = make_tuple(N, Ho, Wo, K);
return make_tuple(in_lengths_dev, wei_lengths_dev, out_lengths_dev);
};
const auto conv_strides = make_tuple(conv_stride_h, conv_stride_w);
const auto conv_dilations = make_tuple(conv_dilation_h, conv_dilation_w);
const auto in_left_pads = make_tuple(in_left_pad_h, in_left_pad_w);
const auto in_right_pads = make_tuple(in_right_pad_h, in_right_pad_w);
#if USE_CONV_FWD_V4R4_NCHW
if(algo == ConvForwardAlgo::V4R4NCHW)
{
if(layout != ConvTensorLayout::NCHW)
{
throw std::runtime_error("wrong! layout");
}
const auto tmp = f_make_for_device_nchw();
tunable_dyn_conv_fwd_v4r4_dlops_nchw_kcyx_nkhw* tunable =
&default_tunable_dyn_conv_fwd_v4r4_dlops_nchw_kcyx_nkhw;
online_device_dynamic_convolution_forward_implicit_gemm_v4r4_dlops_nchw_kcyx_nkhw<
in_data_t,
acc_data_t,
out_data_t>(handle,
tmp[I0],
tmp[I1],
tmp[I2],
conv_strides,
conv_dilations,
in_left_pads,
in_right_pads,
in,
wei,
out_device,
tunable,
nrepeat);
}
#endif
#if USE_CONV_FWD_V6R1_NCHW
if(algo == ConvForwardAlgo::V6R1NCHW)
{
if(layout != ConvTensorLayout::NCHW)
{
throw std::runtime_error("wrong! layout");
}
const auto tmp = f_make_for_device_nchw();
#if 1
const CompileParameterConvIgemmFwdV6r1DlopsNchwKcyxNkhw compile_param = {
get_datatype_enum_from_type<in_data_t>::value,
get_datatype_enum_from_type<acc_data_t>::value,
get_datatype_enum_from_type<out_data_t>::value,
256,
4,
1,
128,
32,
8,
4,
4,
1,
{8, 2},
{8, 2},
{4, 1, 1, 1, 1},
{2, 1, 1, 128, 1},
{4, 1, 1, 1, 1},
{1, 1, 1, 1, 1},
{1, 4, 1, 1, 1},
{8, 1, 1, 32, 1},
{1, 1, 1, 1, 1},
{1, 1, 1, 1, 1},
4,
true,
true};
#elif 0
const CompileParameterConvIgemmFwdV6r1DlopsNchwKcyxNkhw compile_param = {
get_datatype_enum_from_type<in_data_t>::value,
get_datatype_enum_from_type<acc_data_t>::value,
get_datatype_enum_from_type<out_data_t>::value,
256,
4,
2,
128,
32,
8,
4,
4,
1,
{8, 2},
{8, 2},
{4, 1, 1, 1, 2},
{2, 1, 1, 128, 1},
{4, 1, 1, 1, 1},
{1, 1, 1, 1, 1},
{1, 4, 1, 1, 2},
{8, 1, 1, 32, 1},
{1, 1, 1, 1, 1},
{1, 1, 1, 1, 1},
4,
true,
true};
#elif 1
const CompileParameterConvIgemmFwdV6r1DlopsNchwKcyxNkhw compile_param = {
get_datatype_enum_from_type<in_data_t>::value,
get_datatype_enum_from_type<acc_data_t>::value,
get_datatype_enum_from_type<out_data_t>::value,
256,
4,
4,
128,
32,
8,
4,
4,
1,
{8, 2},
{8, 2},
{4, 1, 1, 1, 4},
{2, 1, 1, 128, 1},
{4, 1, 1, 1, 1},
{1, 1, 1, 1, 1},
{1, 4, 1, 1, 4},
{8, 1, 1, 32, 1},
{1, 1, 1, 1, 1},
{1, 1, 1, 1, 1},
4,
true,
true};
#endif
online_device_dynamic_convolution_forward_implicit_gemm_v6r1_dlops_nchw_kcyx_nkhw<
in_data_t,
acc_data_t,
out_data_t>(handle,
tmp[I0],
tmp[I1],
tmp[I2],
conv_strides,
conv_dilations,
in_left_pads,
in_right_pads,
in,
wei,
out_device,
compile_param,
nrepeat);
}
#endif
#if USE_CONV_FWD_V4R4_XDLOPS_NCHW
if(algo == ConvForwardAlgo::V4R4XDLNCHW)
{
if(layout != ConvTensorLayout::NCHW)
{
throw std::runtime_error("wrong! layout");
}
const auto tmp = f_make_for_device_nchw();
tunable_dyn_conv_fwd_v4r4_xdlops_nchw_kcyx_nkhw* tunable =
&default_tunable_dyn_conv_fwd_v4r4_xdlops_nchw_kcyx_nkhw;
online_device_dynamic_convolution_forward_implicit_gemm_v4r4_xdlops_nchw_kcyx_nkhw<
in_data_t,
acc_data_t,
out_data_t>(handle,
tmp[I0],
tmp[I1],
tmp[I2],
conv_strides,
conv_dilations,
in_left_pads,
in_right_pads,
in,
wei,
out_device,
tunable,
nrepeat);
}
#endif
#if USE_CONV_FWD_V4R4_XDLOPS_NHWC
if(algo == ConvForwardAlgo::V4R4XDLNHWC)
{
if(layout != ConvTensorLayout::NHWC)
{
throw std::runtime_error("wrong! layout");
}
const auto tmp = f_make_for_device_nhwc();
tunable_dyn_conv_fwd_v4r4_xdlops_nhwc_kyxc_nhwk* tunable =
&default_tunable_dyn_conv_fwd_v4r4_xdlops_nhwc_kyxc_nhwk;
online_device_dynamic_convolution_forward_implicit_gemm_v4r4_xdlops_nhwc_kyxc_nhwk<
in_data_t,
acc_data_t,
out_data_t>(handle,
tmp[I0],
tmp[I1],
tmp[I2],
conv_strides,
conv_dilations,
in_left_pads,
in_right_pads,
in,
wei,
out_device,
tunable,
nrepeat);
}
#endif
if(do_verification)
{
host_direct_convolution(in,
wei,
out_host,
make_tuple(conv_stride_h, conv_stride_w),
make_tuple(conv_dilation_h, conv_dilation_w),
make_tuple(in_left_pad_h, in_left_pad_w),
make_tuple(in_right_pad_h, in_right_pad_w),
layout);
check_error(out_host, out_device);
#if 0
if(do_log)
{
LogRangeAsType<float>(std::cout << "in : ", in.mData, ",") << std::endl;
LogRangeAsType<float>(std::cout << "wei: ", wei.mData, ",") << std::endl;
LogRangeAsType<float>(std::cout << "out_host : ", out_host.mData, ",") << std::endl;
LogRangeAsType<float>(std::cout << "out_device: ", out_device.mData, ",") << std::endl;
}
#endif
}
delete handle;
MY_HIP_CHECK(hipStreamDestroy(stream));
}
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