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[CK TILE] Grouped Convolution Forward Kernel (#2188)
* [CK TILE] Grouped Convolution Forward Kernel * custom vector size * fixes * refactor * rebase fixes * fixes * fixes
This commit is contained in:
Bartłomiej Kocot
165
include/ck_tile/host/reference/reference_grouped_conv_fwd.hpp
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165
include/ck_tile/host/reference/reference_grouped_conv_fwd.hpp
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// SPDX-License-Identifier: MIT
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// Copyright (c) 2025, Advanced Micro Devices, Inc. All rights reserved.
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#pragma once
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#include <cstdlib>
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#include <thread>
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#include "ck_tile/core.hpp"
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#include "ck_tile/host/host_tensor.hpp"
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namespace ck_tile {
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template <ck_tile::index_t NDimSpatial,
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typename InDataType,
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typename WeiDataType,
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typename OutDataType>
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CK_TILE_HOST void reference_grouped_conv_fwd(const HostTensor<InDataType>& input,
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const HostTensor<WeiDataType>& weight,
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HostTensor<OutDataType>& output,
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std::vector<ck_tile::long_index_t> conv_strides,
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std::vector<ck_tile::long_index_t> conv_dilations,
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std::vector<ck_tile::long_index_t> in_left_pads,
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std::vector<ck_tile::long_index_t>)
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{
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if(!(input.get_num_of_dimension() == NDimSpatial + 3 &&
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weight.get_num_of_dimension() == NDimSpatial + 3 &&
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output.get_num_of_dimension() == NDimSpatial + 3))
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{
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throw std::runtime_error("wrong! inconsistent dimension");
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}
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if constexpr(NDimSpatial == 1)
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{
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auto func = [&](auto g, auto n, auto k, auto wo) {
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float v_acc = 0;
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for(std::size_t c = 0; c < weight.get_lengths()[2]; ++c)
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{
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for(std::size_t x = 0; x < weight.get_lengths()[3]; ++x)
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{
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auto wi = static_cast<ck_tile::long_index_t>(wo * conv_strides[0]) +
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static_cast<ck_tile::long_index_t>(x * conv_dilations[0]) -
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static_cast<ck_tile::long_index_t>(in_left_pads[0]);
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if(wi >= 0 && ck_tile::type_convert<std::size_t>(wi) < input.get_lengths()[3])
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{
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InDataType v_in = input(g, n, c, wi);
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WeiDataType v_wei = weight(g, k, c, x);
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v_acc += ck_tile::type_convert<float>(v_in) *
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ck_tile::type_convert<float>(v_wei);
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}
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}
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}
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OutDataType v_acc_converted = ck_tile::type_convert<OutDataType>(v_acc);
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output(g, n, k, wo) = v_acc_converted;
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};
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make_ParallelTensorFunctor(func,
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output.get_lengths()[0],
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output.get_lengths()[1],
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output.get_lengths()[2],
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output.get_lengths()[3])(std::thread::hardware_concurrency());
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}
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else if constexpr(NDimSpatial == 2)
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{
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auto func = [&](auto g, auto n, auto k, auto ho, auto wo) {
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float v_acc = 0;
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for(std::size_t c = 0; c < weight.get_lengths()[2]; ++c)
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{
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for(std::size_t y = 0; y < weight.get_lengths()[3]; ++y)
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{
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auto hi = static_cast<ck_tile::long_index_t>(ho * conv_strides[0]) +
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static_cast<ck_tile::long_index_t>(y * conv_dilations[0]) -
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static_cast<ck_tile::long_index_t>(in_left_pads[0]);
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for(std::size_t x = 0; x < weight.get_lengths()[4]; ++x)
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{
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auto wi = static_cast<ck_tile::long_index_t>(wo * conv_strides[1]) +
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static_cast<ck_tile::long_index_t>(x * conv_dilations[1]) -
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static_cast<ck_tile::long_index_t>(in_left_pads[1]);
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if(hi >= 0 &&
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ck_tile::type_convert<std::size_t>(hi) < input.get_lengths()[3] &&
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wi >= 0 &&
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ck_tile::type_convert<std::size_t>(wi) < input.get_lengths()[4])
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{
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InDataType v_in = input(g, n, c, hi, wi);
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WeiDataType v_wei = weight(g, k, c, y, x);
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v_acc += ck_tile::type_convert<float>(v_in) *
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ck_tile::type_convert<float>(v_wei);
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}
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}
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}
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}
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OutDataType v_acc_converted = ck_tile::type_convert<OutDataType>(v_acc);
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output(g, n, k, ho, wo) = v_acc_converted;
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};
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make_ParallelTensorFunctor(func,
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output.get_lengths()[0],
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output.get_lengths()[1],
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output.get_lengths()[2],
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output.get_lengths()[3],
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output.get_lengths()[4])(std::thread::hardware_concurrency());
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}
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else if constexpr(NDimSpatial == 3)
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{
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auto func = [&](auto g, auto n, auto k, auto d_o, auto ho, auto wo) {
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float v_acc = 0;
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for(std::size_t c = 0; c < weight.get_lengths()[2]; ++c)
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{
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for(std::size_t z = 0; z < weight.get_lengths()[3]; ++z)
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{
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auto di = static_cast<ck_tile::long_index_t>(d_o * conv_strides[0]) +
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static_cast<ck_tile::long_index_t>(z * conv_dilations[0]) -
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static_cast<ck_tile::long_index_t>(in_left_pads[0]);
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for(std::size_t y = 0; y < weight.get_lengths()[4]; ++y)
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{
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auto hi = static_cast<ck_tile::long_index_t>(ho * conv_strides[1]) +
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static_cast<ck_tile::long_index_t>(y * conv_dilations[1]) -
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static_cast<ck_tile::long_index_t>(in_left_pads[1]);
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for(std::size_t x = 0; x < weight.get_lengths()[5]; ++x)
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{
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auto wi = static_cast<ck_tile::long_index_t>(wo * conv_strides[2]) +
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static_cast<ck_tile::long_index_t>(x * conv_dilations[2]) -
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static_cast<ck_tile::long_index_t>(in_left_pads[2]);
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if(di >= 0 &&
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ck_tile::type_convert<std::size_t>(di) < input.get_lengths()[3] &&
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hi >= 0 &&
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ck_tile::type_convert<std::size_t>(hi) < input.get_lengths()[4] &&
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wi >= 0 &&
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ck_tile::type_convert<std::size_t>(wi) < input.get_lengths()[5])
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{
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InDataType v_in = input(g, n, c, di, hi, wi);
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WeiDataType v_wei = weight(g, k, c, z, y, x);
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v_acc += ck_tile::type_convert<float>(v_in) *
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ck_tile::type_convert<float>(v_wei);
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}
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}
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}
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}
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}
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OutDataType v_acc_converted = ck_tile::type_convert<OutDataType>(v_acc);
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output(g, n, k, d_o, ho, wo) = v_acc_converted;
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};
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make_ParallelTensorFunctor(func,
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output.get_lengths()[0],
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output.get_lengths()[1],
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output.get_lengths()[2],
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output.get_lengths()[3],
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output.get_lengths()[4],
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output.get_lengths()[5])(std::thread::hardware_concurrency());
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}
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else
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{
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throw std::runtime_error("Ref_Conv_fwd: number of dimensions must be between 1 and 3.");
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}
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}
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} // namespace ck_tile
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