diff options
Diffstat (limited to 'src/dynamic_fusion/sketch/gpu/ckw_driver/components/GpuCkwDirectConv2d.cpp')
| -rw-r--r-- | src/dynamic_fusion/sketch/gpu/ckw_driver/components/GpuCkwDirectConv2d.cpp | 427 |
1 files changed, 427 insertions, 0 deletions
diff --git a/src/dynamic_fusion/sketch/gpu/ckw_driver/components/GpuCkwDirectConv2d.cpp b/src/dynamic_fusion/sketch/gpu/ckw_driver/components/GpuCkwDirectConv2d.cpp new file mode 100644 index 0000000000..eb4f644eb6 --- /dev/null +++ b/src/dynamic_fusion/sketch/gpu/ckw_driver/components/GpuCkwDirectConv2d.cpp @@ -0,0 +1,427 @@ +/* + * Copyright (c) 2023-2024 Arm Limited. + * + * SPDX-License-Identifier: MIT + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to + * deal in the Software without restriction, including without limitation the + * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or + * sell copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all + * copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + */ + +#include "src/dynamic_fusion/sketch/gpu/ckw_driver/components/GpuCkwDirectConv2d.h" + +#include "arm_compute/core/Error.h" +#include "arm_compute/core/utils/helpers/AdjustVecSize.h" +#include "arm_compute/core/Validate.h" + +#include "src/core/helpers/WindowHelpers.h" +#include "src/dynamic_fusion/sketch/gpu/ckw_driver/components/utils/CkwHelper.h" +#include "src/dynamic_fusion/sketch/gpu/ckw_driver/components/utils/type_converter/Common.h" +#include "src/dynamic_fusion/sketch/gpu/ckw_driver/GpuCkwComponentArgument.h" +#include "src/dynamic_fusion/sketch/gpu/ckw_driver/GpuCkwVariableTable.h" + +#include "compute_kernel_writer/include/ckw/KernelWriter.h" +#include <cstdint> +#include <string> +#include <vector> + +namespace arm_compute +{ +namespace experimental +{ +namespace dynamic_fusion +{ + +using TileContainer = std::vector<std::vector<int32_t>>; + +GpuCkwDirectConv2d::GpuCkwDirectConv2d(ComponentId id, + const ArgumentPack<ITensorInfo> &tensors, + const Attributes &attributes, + const Settings &settings) + : IGpuCkwComponentDriver{id, tensors}, _src{}, _wei{}, _bia{}, _dst{}, _attributes{attributes}, _settings{settings} +{ + _src = this->tensors().get_const_tensor(TensorType::ACL_SRC_0); + _wei = this->tensors().get_const_tensor(TensorType::ACL_SRC_1); + _bia = this->tensors().get_const_tensor(TensorType::ACL_SRC_2); + _dst = this->tensors().get_const_tensor(TensorType::ACL_DST_0); + ARM_COMPUTE_ERROR_ON_NULLPTR(_src, _wei, _dst); // Bias can be null +} + +void GpuCkwDirectConv2d::write_component_code(const ComponentGroup &comp_group, + GpuCkwVariableTable &vtable, + GpuCkwScopedKernelWriter writer) const +{ + const auto desc = _settings.direct_conv_descriptor(); + ARM_COMPUTE_ERROR_ON_MSG(desc.export_input_to_cl_image || desc.export_output_to_cl_image, + "Only the weights tensor can be exported to cl_image"); + + const uint32_t channel_idx = get_data_layout_dimension_index(_src->data_layout(), DataLayoutDimension::CHANNEL); + const uint32_t width_idx = get_data_layout_dimension_index(_wei->data_layout(), DataLayoutDimension::WIDTH); + const uint32_t height_idx = get_data_layout_dimension_index(_wei->data_layout(), DataLayoutDimension::HEIGHT); + + /******************************************************************************** + * 1 - Define tensors + ********************************************************************************/ + GpuCkwComponentArgument *src = vtable.declare_variable(comp_group, writer, _src, "src"); + GpuCkwComponentArgument *wei = vtable.declare_variable(comp_group, writer, _wei, "wei"); + GpuCkwComponentArgument *dst = vtable.declare_variable(comp_group, writer, _dst, "dst"); + GpuCkwComponentArgument *bia = nullptr; + + const bool using_bias = _bia != nullptr; + + if (using_bias) + { + bia = vtable.declare_variable(comp_group, writer, _bia, "bia"); + } + + /******************************************************************************** + * 2 - Define CKW constants + ********************************************************************************/ + const auto dst_dt = to_ckw(_dst->data_type()); + const auto kernel_height = static_cast<int32_t>(_wei->dimension(height_idx)); + const auto kernel_width = static_cast<int32_t>(_wei->dimension(width_idx)); + const auto src_c = static_cast<int32_t>(_src->dimension(channel_idx)); + const auto src_w = static_cast<int32_t>(_src->dimension(width_idx)); + const auto src_h = static_cast<int32_t>(_src->dimension(height_idx)); + const auto dst_w = static_cast<int32_t>(_dst->dimension(width_idx)); + const auto stride_x = static_cast<int32_t>(_attributes.stride().x()); + const auto stride_y = static_cast<int32_t>(_attributes.stride().y()); + const auto pad_x = static_cast<int32_t>(_attributes.pad().left); + const auto pad_y = static_cast<int32_t>(_attributes.pad().top); + const auto kernel_size = kernel_width * kernel_height; + const auto k0 = + static_cast<int32_t>(adjust_vec_size(_settings.direct_conv_descriptor().k0, _src->dimension(channel_idx))); + + // CKW constants + auto const_kernel_w_i32 = writer->declare_constant_tile(ckw::ConstantData({{kernel_width}}, ckw::DataType::Int32)); + auto const_kernel_size_i32 = + writer->declare_constant_tile(ckw::ConstantData({{kernel_size}}, ckw::DataType::Int32)); + auto const_src_c_i32 = writer->declare_constant_tile(ckw::ConstantData({{src_c}}, ckw::DataType::Int32)); + auto const_src_w_i32 = writer->declare_constant_tile(ckw::ConstantData({{src_w}}, ckw::DataType::Int32)); + auto const_src_h_i32 = writer->declare_constant_tile(ckw::ConstantData({{src_h}}, ckw::DataType::Int32)); + auto const_dst_w_i32 = writer->declare_constant_tile(ckw::ConstantData({{dst_w}}, ckw::DataType::Int32)); + auto const_stride_x_i32 = writer->declare_constant_tile(ckw::ConstantData({{stride_x}}, ckw::DataType::Int32)); + auto const_stride_y_i32 = writer->declare_constant_tile(ckw::ConstantData({{stride_y}}, ckw::DataType::Int32)); + auto const_pad_x_i32 = writer->declare_constant_tile(ckw::ConstantData({{pad_x}}, ckw::DataType::Int32)); + auto const_pad_y_i32 = writer->declare_constant_tile(ckw::ConstantData({{pad_y}}, ckw::DataType::Int32)); + auto const_k0_i32 = writer->declare_constant_tile(ckw::ConstantData({{k0}}, ckw::DataType::Int32)); + auto const_0_i32 = writer->declare_constant_tile(ckw::ConstantData({{0}}, ckw::DataType::Int32)); + auto const_pos_1_i32 = writer->declare_constant_tile(ckw::ConstantData({{1}}, ckw::DataType::Int32)); + auto const_neg_1_i32 = writer->declare_constant_tile(ckw::ConstantData({{-1}}, ckw::DataType::Int32)); + auto const_0_fp = writer->declare_constant_tile(ckw::ConstantData({{0.0f}}, dst_dt)); + auto const_src_c_i32_minus_k0_i32 = + writer->declare_constant_tile(ckw::ConstantData({{src_c - k0}}, ckw::DataType::Int32)); + + /******************************************************************************** + * 3 - Define the compute block parameters and destination tile (if not root component) + * Bind the tile to the tensor to share it among different components and + * initialize the compute block parameters + ********************************************************************************/ + // The compute block parameters depend on the employed tensor format + const auto root_window = comp_group.get_root_component()->ckw_component_driver()->get_window(); + + // Destination compute block size + const int32_t dst_n0 = root_window.x().step(); + const int32_t dst_m0 = root_window.y().step(); + + // Destination compute block size left-over + const int32_t dst_n0_partial = _dst->dimension(0) % dst_n0; + const int32_t dst_m0_partial = (_dst->dimension(1) * _dst->dimension(2)) % dst_m0; + + // Shift-back for the overlapping-min strategy + const int32_t dst_shift_back = (dst_n0 - dst_n0_partial) % dst_n0; + + ckw::TensorSampler sampler_dst; + sampler_dst.format(ckw::TensorSamplerFormat::Dim0_Dim1xDim2_1); + if (dst_n0_partial == 0) + { + sampler_dst.address_mode_x(ckw::TensorSamplerAddressModeX::None); + } + else + { + sampler_dst.address_mode_x(ckw::TensorSamplerAddressModeX::OverlappingMin); + } + + if (dst_m0_partial == 0) + { + sampler_dst.address_mode_y(ckw::TensorSamplerAddressModeY::None); + } + else + { + sampler_dst.address_mode_y(ckw::TensorSamplerAddressModeY::ClampToBorderMaxOnly); + } + + sampler_dst.address_mode_z(ckw::TensorSamplerAddressModeZ::None); + sampler_dst.storage(ckw::TensorStorageType::BufferUint8Ptr); + + // Declare destination tile + auto tile_dst = writer->declare_tile("dst", ckw::TileInfo(dst_dt, dst_m0, dst_n0)); + + // Initialize destination tile + writer->op_assign(tile_dst, const_0_fp); + + // Bind tile to the tensor + dst->init_virtual_tensor(tile_dst, sampler_dst); + + /******************************************************************************** + * 4 - Define the compute block parameters CKW constants + ********************************************************************************/ + // Only now we can declare the N0 and M0 as constant + auto const_dst_n0_i32 = writer->declare_constant_tile(ckw::ConstantData({{dst_n0}}, ckw::DataType::Int32)); + auto const_dst_m0_i32 = writer->declare_constant_tile(ckw::ConstantData({{dst_m0}}, ckw::DataType::Int32)); + auto const_shift_back_dst_n0_i32 = + writer->declare_constant_tile(ckw::ConstantData({{dst_shift_back}}, ckw::DataType::Int32)); + + /******************************************************************************** + * 5 - Define the samplers for the input tensors + ********************************************************************************/ + // Exporting the weights tensor to an OpenCL image object is currently only supported when: + // a) k0 is equal to 4 + // The current implementation expects to read a vector of 4 float values into the OpenCL image object. + // b) K is a multiple of 4 + // This is a limitation in the current interface due to the variable table being responsible for maintaining + // information about the TensorStorageType rather than the TensorTileSampler. As a result, TensorStorageType cannot + // be reassigned, and we cannot use a texture object for the weights tensor in cases where we expect to have an + // extra loop to compute the left-over elements. + const bool use_cl_image_for_weights = desc.export_weights_to_cl_image && (k0 == 4) && (src_c % 4 == 0); + + // SOURCE SAMPLER + // - We cannot have out-of-bounds reads in the X dimension (mapped to the IFMs) as we have an extra loop to + // compute left-over elements + // - We cannot have out-of-bounds reads when the kernel height is equal to 1. In all other cases, we need to ensure the + // indirection buffer mi does not contain negative values representing out-of-bounds reads. + auto address_mode_y_src = + kernel_height == 1 ? ckw::TensorSamplerAddressModeY::None : ckw::TensorSamplerAddressModeY::SkipLessThanZero; + ckw::TensorSampler sampler_src; + sampler_src.format(ckw::TensorSamplerFormat::Dim0_Dim1xDim2_1); // 3rd dimension collapsed with 2nd dimension + sampler_src.address_mode_x(ckw::TensorSamplerAddressModeX::None); + sampler_src.address_mode_y(address_mode_y_src); + sampler_src.address_mode_z(ckw::TensorSamplerAddressModeZ::None); + sampler_src.storage(ckw::TensorStorageType::BufferUint8Ptr); + + // WEIGHTS SAMPLER + // We cannot have out-of-bounds accesses for the weights + ckw::TensorSampler sampler_wei; + sampler_wei.format(ckw::TensorSamplerFormat::Dim0_Dim1xDim2_1); // 3rd dimension collapsed with 2nd dimension + sampler_wei.address_mode_x(ckw::TensorSamplerAddressModeX::None); + sampler_wei.address_mode_y(ckw::TensorSamplerAddressModeY::None); + sampler_wei.address_mode_z(ckw::TensorSamplerAddressModeZ::None); + if (use_cl_image_for_weights) + { + sampler_wei.storage(ckw::TensorStorageType::Texture2dReadOnly); + } + else + { + sampler_wei.storage(ckw::TensorStorageType::BufferUint8Ptr); + } + + // BIAS SAMPLER + ckw::TensorSampler sampler_bia; + + if (using_bias) + { + sampler_bia.format(ckw::TensorSamplerFormat::Dim0_Dim1xDim2_1); + sampler_bia.address_mode_x(sampler_dst.address_mode_x()); + sampler_bia.address_mode_y(ckw::TensorSamplerAddressModeY::None); + sampler_bia.address_mode_z(ckw::TensorSamplerAddressModeZ::None); + sampler_bia.storage(ckw::TensorStorageType::BufferUint8Ptr); + } + + /******************************************************************************** + * 6 - Extra operations required before writing the main code (optional) + ********************************************************************************/ + + // Not required + + /******************************************************************************** + * 7 - Get the coordinates of the destination tile + ********************************************************************************/ + auto tile_gid_0 = writer->declare_tile("gid_0", ckw::TileInfo(ckw::DataType::Int32)); + auto tile_gid_1 = writer->declare_tile("gid_1", ckw::TileInfo(ckw::DataType::Int32)); + auto tile_gid_2 = writer->declare_tile("gid_2", ckw::TileInfo(ckw::DataType::Int32)); + + writer->op_get_global_id(tile_gid_0, 0); + writer->op_get_global_id(tile_gid_1, 1); + writer->op_get_global_id(tile_gid_2, 2); + + auto tile_cout = writer->declare_tile("cout", ckw::TileInfo(ckw::DataType::Int32)); // OFM + auto tile_mout = writer->declare_tile("mout", ckw::TileInfo(ckw::DataType::Int32)); // WIDTH x HEIGHT + auto tile_bout = writer->declare_tile("bout", ckw::TileInfo(ckw::DataType::Int32)); // BATCH SIZE IDX + + // Calculate coordinates + get_coordinate_from_gws_overlapping_min(writer, tile_cout, tile_gid_0, const_dst_n0_i32, + const_shift_back_dst_n0_i32, const_0_i32); + get_coordinate_from_gws(writer, tile_mout, tile_gid_1, const_dst_m0_i32); + get_coordinate_from_gws(writer, tile_bout, tile_gid_2, const_pos_1_i32); + + /******************************************************************************** + * 8 - Write the rest of the code + ********************************************************************************/ + // We create a 2d container of size (dst_m0, 1) to store the indices for iteration + TileContainer it; + for (int32_t m = 0; m < dst_m0; ++m) + { + std::vector<int32_t> idx{m}; + it.push_back({idx}); + } + + const auto &const_idxs = writer->declare_constant_tile(ckw::ConstantData(it, ckw::DataType::Int32)); + + auto tile_xi = writer->declare_tile("xi", ckw::TileInfo(ckw::DataType::Int32, dst_m0, 1)); + auto tile_yi = writer->declare_tile("yi", ckw::TileInfo(ckw::DataType::Int32, dst_m0, 1)); + + // Convert the linear index to coordinate + // xi = ((mout + i) % dst_w) * stride_x - pad_x + // yi = ((mout + i) / dst_w) * stride_y - pad_y + writer->op_binary(tile_xi, ckw::BinaryOp::Add, tile_mout, const_idxs); + writer->op_binary(tile_yi, ckw::BinaryOp::Add, tile_mout, const_idxs); + writer->op_binary(tile_xi, ckw::BinaryOp::Mod, tile_xi, const_dst_w_i32); + writer->op_binary(tile_yi, ckw::BinaryOp::Div, tile_yi, const_dst_w_i32); + writer->op_binary(tile_xi, ckw::BinaryOp::Mul, tile_xi, const_stride_x_i32); + writer->op_binary(tile_yi, ckw::BinaryOp::Mul, tile_yi, const_stride_y_i32); + writer->op_binary(tile_xi, ckw::BinaryOp::Sub, tile_xi, const_pad_x_i32); + writer->op_binary(tile_yi, ckw::BinaryOp::Sub, tile_yi, const_pad_y_i32); + + auto tile_y_b = writer->declare_tile("y_b", ckw::TileInfo(ckw::DataType::Int32)); + writer->op_binary(tile_y_b, ckw::BinaryOp::Mul, tile_cout, const_kernel_size_i32); + + auto tile_i = writer->declare_tile("i", ckw::TileInfo(ckw::DataType::Int32)); + writer->op_assign(tile_i, const_0_i32); + + // clang-format off + writer->op_for_loop(tile_i, ckw::BinaryOp::Less, const_kernel_size_i32, tile_i, ckw::AssignmentOp::Increment, const_pos_1_i32, [&]() + { + auto tile_x_k = writer->declare_tile("x_k", ckw::TileInfo(ckw::DataType::Int32)); + auto tile_y_k = writer->declare_tile("y_k", ckw::TileInfo(ckw::DataType::Int32)); + + writer->op_binary(tile_x_k, ckw::BinaryOp::Mod, tile_i, const_kernel_w_i32); + writer->op_binary(tile_y_k, ckw::BinaryOp::Div, tile_i, const_kernel_w_i32); + + auto tile_ck = writer->declare_tile("ck", ckw::TileInfo(ckw::DataType::Int32)); + writer->op_assign(tile_ck, const_0_i32); + + // Construct an indirection buffer containing the precalculated addresses of elements in the source tensor + // x_s = xi + x_k + // y_s = yi + y_k + // mi = x_s + y_s * width; + // mi = select(-1, mi, x_s >= 0); + // mi = select(-1, mi, x_s < width); + // mi = select(-1, mi, y_s >= 0); + // mi = select(-1, mi, y_s < height); + auto tile_xs = writer->declare_tile("xs", ckw::TileInfo(ckw::DataType::Int32, dst_m0, 1)); + auto tile_ys = writer->declare_tile("ys", ckw::TileInfo(ckw::DataType::Int32, dst_m0, 1)); + auto tile_mi = writer->declare_tile("mi", ckw::TileInfo(ckw::DataType::Int32, dst_m0, 1)); + + auto tile_xs_gte_0 = writer->declare_tile("xs_gte_0", ckw::TileInfo(ckw::DataType::Int32, dst_m0, 1)); + auto tile_ys_gte_0 = writer->declare_tile("ys_gte_0", ckw::TileInfo(ckw::DataType::Int32, dst_m0, 1)); + auto tile_xs_lt_w = writer->declare_tile("xs_lt_w", ckw::TileInfo(ckw::DataType::Int32, dst_m0, 1)); + auto tile_ys_lt_h = writer->declare_tile("ys_lt_h", ckw::TileInfo(ckw::DataType::Int32, dst_m0, 1)); + + writer->op_binary(tile_xs, ckw::BinaryOp::Add, tile_xi, tile_x_k); + writer->op_binary(tile_ys, ckw::BinaryOp::Add, tile_yi, tile_y_k); + writer->op_binary(tile_mi, ckw::BinaryOp::Mul, tile_ys, const_src_w_i32); + writer->op_binary(tile_mi, ckw::BinaryOp::Add, tile_mi, tile_xs); + writer->op_binary(tile_xs_gte_0, ckw::BinaryOp::GreaterEqual, tile_xs, const_0_i32); + writer->op_binary(tile_ys_gte_0, ckw::BinaryOp::GreaterEqual, tile_ys, const_0_i32); + writer->op_binary(tile_xs_lt_w, ckw::BinaryOp::Less, tile_xs, const_src_w_i32); + writer->op_binary(tile_ys_lt_h, ckw::BinaryOp::Less, tile_ys, const_src_h_i32); + writer->op_ternary(tile_mi, ckw::TernaryOp::Select, const_neg_1_i32, tile_mi, tile_xs_gte_0); + writer->op_ternary(tile_mi, ckw::TernaryOp::Select, const_neg_1_i32, tile_mi, tile_ys_gte_0); + writer->op_ternary(tile_mi, ckw::TernaryOp::Select, const_neg_1_i32, tile_mi, tile_xs_lt_w); + writer->op_ternary(tile_mi, ckw::TernaryOp::Select, const_neg_1_i32, tile_mi, tile_ys_lt_h); + + writer->op_for_loop(tile_ck, ckw::BinaryOp::LessEqual, const_src_c_i32_minus_k0_i32, tile_ck, ckw::AssignmentOp::Increment, const_k0_i32, [&]() + { + auto tile_lhs = writer->declare_tile("lhs", ckw::TileInfo(to_ckw(_src->data_type()), dst_m0, k0)); + auto tile_rhs = writer->declare_tile("rhs", ckw::TileInfo(to_ckw(_wei->data_type()), dst_n0, k0)); + writer->op_assign(tile_lhs, const_0_fp); + writer->op_assign(tile_rhs, const_0_fp); + + writer->op_load_indirect(tile_lhs, src->tensor(), sampler_src, tile_ck, tile_mi, const_0_i32, tile_bout); + writer->op_load_dilated(tile_rhs, wei->tensor(), sampler_wei, tile_ck, tile_y_b, const_0_i32, const_0_i32, const_pos_1_i32, const_kernel_size_i32); + + writer->op_binary(tile_dst, ckw::BinaryOp::MatMul_Nt_T, tile_lhs, tile_rhs); + }); + + // Left-over accumulations for when K is not a multiple of k0 + if(((src_c % k0) != 0)) + { + writer->op_for_loop(tile_ck, ckw::BinaryOp::Less, const_src_c_i32, tile_ck, ckw::AssignmentOp::Increment, const_pos_1_i32, [&]() + { + auto tile_lhs = writer->declare_tile("lhs_leftover", ckw::TileInfo(to_ckw(_src->data_type()), dst_m0, 1)); + auto tile_rhs = writer->declare_tile("rhs_leftover", ckw::TileInfo(to_ckw(_wei->data_type()), dst_n0, 1)); + writer->op_assign(tile_lhs, const_0_fp); + writer->op_assign(tile_rhs, const_0_fp); + + writer->op_load_indirect(tile_lhs, src->tensor(), sampler_src, tile_ck, tile_mi, const_0_i32, tile_bout); + writer->op_load_dilated(tile_rhs, wei->tensor(), sampler_wei, tile_ck, tile_y_b, const_0_i32, const_0_i32, const_pos_1_i32, const_kernel_size_i32); + + writer->op_binary(tile_dst, ckw::BinaryOp::MatMul_Nt_T, tile_lhs, tile_rhs); + }); + } + + writer->op_binary(tile_y_b, ckw::BinaryOp::Add, tile_y_b, const_pos_1_i32); + }); + // clang-format on + + // NOTE: The bias addition will be removed from this kernel as the interface is standardized. The intended way of + // performing bias addition is to fuse this convolution kernel with a following elementwise addition kernel. + if (using_bias) + { + if (!bia->has_tile()) + { + auto tile_bia = writer->declare_tile("bia", ckw::TileInfo(to_ckw(_src->data_type()), 1, dst_n0)); + writer->op_load(tile_bia, bia->tensor(), sampler_bia, tile_cout, const_0_i32, const_0_i32, const_0_i32); + bia->init_virtual_tensor(tile_bia, sampler_bia); + } + auto &tile_bia = bia->tile(); + + writer->op_binary(tile_dst, ckw::BinaryOp::Add, tile_dst, tile_bia); + } + + ARM_COMPUTE_ERROR_ON_MSG(dst->has_tile() == false, "You must bind a tile before appending another component"); +} + +Window GpuCkwDirectConv2d::get_window() const +{ + ARM_COMPUTE_ERROR_ON_MSG(_dst->tensor_shape().total_size() == 0U, "Destination tensor is not initialized"); + + const auto dst_shape = _dst->tensor_shape(); + const auto desc = _settings.direct_conv_descriptor(); + + const uint32_t dst_n0 = adjust_vec_size(desc.n0, dst_shape[0]); + const uint32_t dst_m0 = adjust_vec_size(desc.m0, dst_shape[1] * dst_shape[2]); + + Window win = calculate_max_window(dst_shape, Steps(dst_n0, dst_m0)); + + const size_t dim_y_collapsed = ceil_to_multiple(dst_shape[1] * dst_shape[2], dst_m0); + win.set(Window::DimY, Window::Dimension(0, dim_y_collapsed, dst_m0)); + win.set(Window::DimZ, Window::Dimension(0, dst_shape.total_size_upper(3), 1)); + + return win; +} + +std::string GpuCkwDirectConv2d::get_name(const ComponentGroup &comp_group) const +{ + ARM_COMPUTE_UNUSED(comp_group); + + return "direct_conv2d"; +} + +} // namespace dynamic_fusion +} // namespace experimental +} // namespace arm_compute |