Files
composable_kernel/profiler/src/profile_grouped_conv_bwd_data.cpp
Bartłomiej Kocot 4094ad158a Integrate universal gemm with conv bwd data and add SplitK (#1315)
* Integrate universal gemm with conv bwd data

* Fix multi d kernel

* Add splitK support

* instances refactor

* instances refactor

* refactor

* fixeS

* fixes

* 16x16 instnaces

* Fixes

* Fix

* Fix

* Fix

* Fix

* Fix

* Fixes

* fix

* fix
2025-04-28 23:54:49 +02:00

253 lines
9.2 KiB
C++

// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2025, Advanced Micro Devices, Inc. All rights reserved.
#include <iostream>
#include <numeric>
#include <initializer_list>
#include <cstdlib>
#include "profiler/profile_grouped_conv_bwd_data_impl.hpp"
#include "profiler_operation_registry.hpp"
namespace {
enum struct ConvLayout
{
GNHWC_GKYXC_GNHWK, // 0
NHWGC_GKYXC_NHWGK, // 1
NGCHW_GKYXC_NGKHW, // 2
NGCHW_GKCYX_NGKHW, // 3
};
enum struct ConvDataType
{
F32_F32_F32, // 0
F16_F16_F16, // 1
BF16_BF16_BF16, // 2
};
#define OP_NAME "grouped_conv_bwd_data"
#define OP_DESC "Grouped Convolution Backward Data"
static void print_helper_msg()
{
std::cout
// clang-format off
<< "arg1: tensor operation (" OP_NAME ": " OP_DESC ")\n"
<< "arg2: data type (0: Output fp32, Weight fp32, Input fp32\n"
<< " 1: Output fp16, Weight fp16, Input fp16\n"
<< " 2: Output bf16, Weight bf16, Input bf16\n"
<< "arg3: tensor layout (0: Output[G, N, Ho, Wo, C], Weight[G, K, Y, X, C], Input[G, N, Hi, Wi, K]\n"
<< " 1: Output[N, Ho, Wo, G, C], Weight[G, K, Y, X, C], Input[N, Hi, Wi, G, K])\n"
<< " 2: Output[N, G, C, Ho, Wo], Weight[G, K, Y, X, C], Input[N, G, K, Hi, Wi])\n"
<< " 3: Output[N, G, C, Ho, Wo], Weight[G, K, C, Y, X], Input[N, G, K, Hi, Wi])\n"
<< "arg4: verification (0: no, 1: yes)\n"
<< "arg5: initialization (0: no init, 1: integer value, 2: decimal value)\n"
<< "arg6: print tensor value (0: no; 1: yes)\n"
<< "arg7: time kernel (0: no, 1: yes)\n"
<< ck::utils::conv::get_conv_param_parser_helper_msg() << std::endl;
// clang-format on
}
} // namespace
int profile_grouped_conv_bwd_data(int argc, char* argv[])
{
// 8 for control, 1 for num_dim_spatial
if(argc < 9)
{
print_helper_msg();
return 1;
}
const auto data_type = static_cast<ConvDataType>(std::stoi(argv[2]));
const auto layout = static_cast<ConvLayout>(std::stoi(argv[3]));
const bool do_verification = std::stoi(argv[4]);
const int init_method = std::stoi(argv[5]);
const bool do_log = std::stoi(argv[6]);
const bool time_kernel = std::stoi(argv[7]);
const int num_dim_spatial = std::stoi(argv[8]);
// 8 for control, 1 for num_dim_spatial, 4 for G/N/K/C, and 6 * num_dim_spatial, 1 for split-K
if(argc != 8 + 1 + 4 + 6 * num_dim_spatial + 1)
{
print_helper_msg();
return 1;
}
const auto params = ck::utils::conv::parse_conv_param(num_dim_spatial, 9, argv);
ck::index_t split_k = std::stoi(argv[8 + 1 + 4 + 6 * num_dim_spatial]);
using F32 = float;
using F16 = ck::half_t;
using BF16 = ck::bhalf_t;
using namespace ck::tensor_layout::convolution;
constexpr auto I2 = ck::Number<2>{};
constexpr auto I3 = ck::Number<3>{};
auto profile = [&](auto num_dim_spatial_tmp,
auto out_layout,
auto wei_layout,
auto in_layout,
auto wei_type,
auto out_type,
auto in_type) {
constexpr ck::index_t NDimSpatial = num_dim_spatial_tmp.value;
using OutLayout = decltype(out_layout);
using WeiLayout = decltype(wei_layout);
using InLayout = decltype(in_layout);
using OutDataType = decltype(out_type);
using WeiDataType = decltype(wei_type);
using InDataType = decltype(in_type);
bool pass = ck::profiler::profile_grouped_conv_bwd_data_impl<NDimSpatial,
OutLayout,
WeiLayout,
InLayout,
OutDataType,
WeiDataType,
InDataType>(
do_verification, init_method, do_log, time_kernel, params, split_k);
return pass ? 0 : 1;
};
if(num_dim_spatial == 2)
{
if(layout == ConvLayout::GNHWC_GKYXC_GNHWK)
{
if(data_type == ConvDataType::F32_F32_F32)
{
return profile(I2, GNHWK{}, GKYXC{}, GNHWC{}, F32{}, F32{}, F32{});
}
else if(data_type == ConvDataType::F16_F16_F16)
{
return profile(I2, GNHWK{}, GKYXC{}, GNHWC{}, F16{}, F16{}, F16{});
}
else if(data_type == ConvDataType::BF16_BF16_BF16)
{
return profile(I2, GNHWK{}, GKYXC{}, GNHWC{}, BF16{}, BF16{}, BF16{});
}
}
else if(layout == ConvLayout::NHWGC_GKYXC_NHWGK)
{
if(data_type == ConvDataType::F32_F32_F32)
{
return profile(I2, NHWGK{}, GKYXC{}, NHWGC{}, F32{}, F32{}, F32{});
}
else if(data_type == ConvDataType::F16_F16_F16)
{
return profile(I2, NHWGK{}, GKYXC{}, NHWGC{}, F16{}, F16{}, F16{});
}
else if(data_type == ConvDataType::BF16_BF16_BF16)
{
return profile(I2, NHWGK{}, GKYXC{}, NHWGC{}, BF16{}, BF16{}, BF16{});
}
}
else if(layout == ConvLayout::NGCHW_GKYXC_NGKHW)
{
if(data_type == ConvDataType::F32_F32_F32)
{
return profile(I2, NGKHW{}, GKYXC{}, NGCHW{}, F32{}, F32{}, F32{});
}
else if(data_type == ConvDataType::F16_F16_F16)
{
return profile(I2, NGKHW{}, GKYXC{}, NGCHW{}, F16{}, F16{}, F16{});
}
else if(data_type == ConvDataType::BF16_BF16_BF16)
{
return profile(I2, NGKHW{}, GKYXC{}, NGCHW{}, BF16{}, BF16{}, BF16{});
}
}
else if(layout == ConvLayout::NGCHW_GKCYX_NGKHW)
{
if(data_type == ConvDataType::F32_F32_F32)
{
return profile(I2, NGKHW{}, GKCYX{}, NGCHW{}, F32{}, F32{}, F32{});
}
else if(data_type == ConvDataType::F16_F16_F16)
{
return profile(I2, NGKHW{}, GKCYX{}, NGCHW{}, F16{}, F16{}, F16{});
}
else if(data_type == ConvDataType::BF16_BF16_BF16)
{
return profile(I2, NGKHW{}, GKCYX{}, NGCHW{}, BF16{}, BF16{}, BF16{});
}
}
}
else if(num_dim_spatial == 3)
{
if(layout == ConvLayout::GNHWC_GKYXC_GNHWK)
{
if(data_type == ConvDataType::F32_F32_F32)
{
return profile(I3, GNDHWK{}, GKZYXC{}, GNDHWC{}, F32{}, F32{}, F32{});
}
else if(data_type == ConvDataType::F16_F16_F16)
{
return profile(I3, GNDHWK{}, GKZYXC{}, GNDHWC{}, F16{}, F16{}, F16{});
}
else if(data_type == ConvDataType::BF16_BF16_BF16)
{
return profile(I3, GNDHWK{}, GKZYXC{}, GNDHWC{}, BF16{}, BF16{}, BF16{});
}
}
else if(layout == ConvLayout::NHWGC_GKYXC_NHWGK)
{
if(data_type == ConvDataType::F32_F32_F32)
{
return profile(I3, NDHWGK{}, GKZYXC{}, NDHWGC{}, F32{}, F32{}, F32{});
}
else if(data_type == ConvDataType::F16_F16_F16)
{
return profile(I3, NDHWGK{}, GKZYXC{}, NDHWGC{}, F16{}, F16{}, F16{});
}
else if(data_type == ConvDataType::BF16_BF16_BF16)
{
return profile(I3, NDHWGK{}, GKZYXC{}, NDHWGC{}, BF16{}, BF16{}, BF16{});
}
}
else if(layout == ConvLayout::NGCHW_GKYXC_NGKHW)
{
if(data_type == ConvDataType::F32_F32_F32)
{
return profile(I3, NGKDHW{}, GKZYXC{}, NGCDHW{}, F32{}, F32{}, F32{});
}
else if(data_type == ConvDataType::F16_F16_F16)
{
return profile(I3, NGKDHW{}, GKZYXC{}, NGCDHW{}, F16{}, F16{}, F16{});
}
else if(data_type == ConvDataType::BF16_BF16_BF16)
{
return profile(I3, NGKDHW{}, GKZYXC{}, NGCDHW{}, BF16{}, BF16{}, BF16{});
}
}
else if(layout == ConvLayout::NGCHW_GKYXC_NGKHW)
{
if(data_type == ConvDataType::F32_F32_F32)
{
return profile(I3, NGKDHW{}, GKCZYX{}, NGCDHW{}, F32{}, F32{}, F32{});
}
else if(data_type == ConvDataType::F16_F16_F16)
{
return profile(I3, NGKDHW{}, GKCZYX{}, NGCDHW{}, F16{}, F16{}, F16{});
}
else if(data_type == ConvDataType::BF16_BF16_BF16)
{
return profile(I3, NGKDHW{}, GKCZYX{}, NGCDHW{}, BF16{}, BF16{}, BF16{});
}
}
}
std::cout << "this data_type & layout is not implemented" << std::endl;
return 1;
}
REGISTER_PROFILER_OPERATION(OP_NAME, OP_DESC, profile_grouped_conv_bwd_data);