| /* |
| * Copyright (c) 2022 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 "ClTemplateDirectConv2d.h" |
| |
| #include "src/dynamic_fusion/sketch/gpu/GpuKernelComponentGroup.h" |
| #include "src/dynamic_fusion/sketch/gpu/components/cl/ClComponentDirectConv2d.h" |
| |
| #include "arm_compute/core/utils/misc/ShapeCalculator.h" |
| #include "src/core/helpers/WindowHelpers.h" |
| |
| #include "support/StringSupport.h" |
| |
| namespace arm_compute |
| { |
| namespace experimental |
| { |
| namespace dynamic_fusion |
| { |
| ClTemplateDirectConv2d::ClTemplateDirectConv2d(ComponentId id, |
| const ArgumentPack<ITensorInfo> &tensors, |
| const Attributes &attributes, |
| const Settings &settings) |
| : IGpuTemplateComponentWriter{ id, tensors }, |
| _src{}, |
| _weight{}, |
| _bias{}, |
| _dst{}, |
| _attributes{ attributes }, |
| _settings{ settings } |
| { |
| _src = this->tensors().get_const_tensor(TensorType::ACL_SRC_0); |
| _weight = this->tensors().get_const_tensor(TensorType::ACL_SRC_1); |
| if(this->tensors().get_const_tensor(TensorType::ACL_SRC_2)) |
| { |
| _bias = 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, _weight, _dst); |
| } |
| |
| std::string ClTemplateDirectConv2d::get_name() const |
| { |
| return "direct_conv2d"; |
| } |
| |
| std::string ClTemplateDirectConv2d::get_component_code(const ComponentGroup &comp_group) const |
| { |
| ARM_COMPUTE_UNUSED(comp_group); |
| |
| const auto channel_idx = get_data_layout_dimension_index(_src->data_layout(), DataLayoutDimension::CHANNEL); |
| const auto k0 = adjust_vec_size(is_data_type_quantized(_src->data_type()) ? 16u : 8u, _src->dimension(channel_idx)); |
| const bool leftover_loop = (_src->dimension(channel_idx) % k0) != 0; |
| |
| std::string code = R"_( |
| //------------------ START KERNEL {{meta_kernel_id}} --------------------- |
| // IN_0(src) {{src}} |
| // IN_1(wei) {{weight}} |
| )_"; |
| if(_bias && _bias->has_valid_id()) |
| { |
| code += R"_( |
| // IN_1(bia) {{bias}} |
| )_"; |
| } |
| code += R"_( |
| // OUT(dst, accum) {{dst}} |
| |
| // Initialize the accumulators |
| TILE({{ACC_DATA_TYPE}}, M0, N0, {{dst}}); |
| { |
| // All the tensor dimensions are passed at compile time. |
| // In case of dynamic tensor support, the following dimensions should be passed as function argument. |
| #define _IWEI_WIDTH {{WEI_WIDTH}} |
| #define _IWEI_HEIGHT {{WEI_HEIGHT}} |
| #define _ISRC_WIDTH {{src}}_w |
| #define _ISRC_HEIGHT {{src}}_h |
| #define _ISRC_CHANNELS {{src}}_c |
| #define _IDST_WIDTH {{arg_dst}}_w |
| #define _IDST_HEIGHT {{arg_dst}}_h |
| #define _IDST_CHANNELS {{arg_dst}}_c |
| #define _IY_MULTIPLIER (_IWEI_WIDTH * _IWEI_HEIGHT) |
| |
| // .v = access the whole vector (OpenCL vector) |
| // .s[x] = access the vector element at position x (scalar access) |
| TILE(int, M0, 1, xi); |
| TILE(int, M0, 1, yi); |
| |
| // Convert the linear index to coordinate |
| LOOP_UNROLLING(int, i, 0, 1, M0, |
| { |
| xi[i].v = ((g_ind_1 + i) % _IDST_WIDTH) * {{STRIDE_X}}; |
| yi[i].v = ((g_ind_1 + i) / _IDST_WIDTH) * {{STRIDE_Y}}; |
| xi[i].v -= {{PAD_LEFT}}; |
| yi[i].v -= {{PAD_TOP}}; |
| }) |
| |
| LOOP_UNROLLING(int, i, 0, 1, M0, |
| { |
| {{dst}}[i].v = 0; |
| }) |
| |
| for(int i = 0; i < (_IWEI_WIDTH * _IWEI_HEIGHT); ++i) |
| { |
| int ck = 0; |
| int xk = i % _IWEI_WIDTH; |
| int yk = i / _IWEI_WIDTH; |
| |
| int k = 0; |
| for(; k <= (_ISRC_CHANNELS - K0); k += K0) |
| { |
| TILE({{SRC_DATA_TYPE}}, M0, K0, a); |
| TILE({{WEI_DATA_TYPE}}, N0, K0, b); |
| |
| // Initialize tiles |
| LOOP_UNROLLING(int, i, 0, 1, M0, |
| { |
| a[i].v = {{ZERO_VALUE}}; |
| }) |
| |
| LOOP_UNROLLING(int, i, 0, 1, N0, |
| { |
| b[i].v = {{ZERO_VALUE}}; |
| }) |
| |
| // Load tile from the src tensor |
| T_LOAD_NHWC_INDIRECT({{SRC_DATA_TYPE}}, M0, K0, {{SRC_TENSOR_TYPE}}, {{src}}, g_ind_2, yk, xk, ck, _ISRC_WIDTH, _ISRC_HEIGHT, {{src}}_stride_y, xi, yi, a); |
| |
| // Load tile from the weights tensor |
| T_LOAD({{WEI_DATA_TYPE}}, N0, K0, {{WEI_TENSOR_TYPE}}, {{weight}}, ck, g_ind_0 * _IY_MULTIPLIER + i, _IY_MULTIPLIER, {{weight}}_stride_y, b); |
| |
| // Compute the matrix multiplication between two tiles |
| T_MMUL({{SRC_DATA_TYPE}}, {{WEI_DATA_TYPE}}, {{ACC_DATA_TYPE}}, M0, N0, K0, NT, T, a, b, {{dst}}); |
| |
| ck += K0; |
| } |
| |
| // We voluntarily use SRC_CHANNELS rather than _DSRC_CHANNELS |
| // This #if directive should be removed in case of dynamic tensor support |
| )_"; |
| |
| if(leftover_loop) |
| { |
| code += R"_( |
| // Left-over accumulations |
| for(; k < _ISRC_CHANNELS; ++k) |
| { |
| TILE({{SRC_DATA_TYPE}}, M0, 1, a); |
| TILE({{WEI_DATA_TYPE}}, N0, 1, b); |
| |
| // Initialize tiles |
| LOOP_UNROLLING(int, i, 0, 1, M0, |
| { |
| a[i].v = {{ZERO_VALUE}}; |
| }) |
| |
| LOOP_UNROLLING(int, i, 0, 1, N0, |
| { |
| b[i].v = {{ZERO_VALUE}}; |
| }) |
| |
| // Load tile from the src tensor |
| T_LOAD_NHWC_INDIRECT({{SRC_DATA_TYPE}}, M0, 1, {{SRC_TENSOR_TYPE}}, {{src}}, g_ind_2, yk, xk, ck, _ISRC_WIDTH, _ISRC_HEIGHT, {{src}}_stride_y, xi, yi, a); |
| |
| // Load tile from the weights tensor |
| // The T_LOAD for the left-over elements can only use BUFFER because we load one element per iteration |
| T_LOAD({{WEI_DATA_TYPE}}, N0, 1, BUFFER, {{weight}}, ck, g_ind_0 * _IY_MULTIPLIER + i, _IY_MULTIPLIER, {{weight}}_stride_y, b); |
| |
| // Compute the matrix multiplication between two tiles |
| T_MMUL({{SRC_DATA_TYPE}}, {{WEI_DATA_TYPE}}, {{ACC_DATA_TYPE}}, M0, N0, 1, NT, T, a, b, {{dst}}); |
| |
| ++ck; |
| } |
| )_"; |
| } |
| |
| code += R"_( |
| #undef _I_WEI_WIDTH |
| #undef _I_WEI_HEIGHT |
| #undef _ISRC_WIDTH |
| #undef _ISRC_HEIGHT |
| #undef _ISRC_CHANNELS |
| #undef _IDST_WIDTH |
| #undef _IDST_HEIGHT |
| #undef _IDST_CHANNELS |
| #undef _IY_MULTIPLIER |
| |
| } |
| )_"; |
| |
| if(_bias && _bias->has_valid_id()) |
| { |
| code += R"_( |
| TILE({{BIA_DATA_TYPE}}, 1, N0, bias0); |
| |
| T_LOAD({{BIA_DATA_TYPE}}, 1, N0, BUFFER, {{bias}}, g_ind_0, 0, 1, 0, bias0); |
| |
| // c = c + bias[broadcasted] |
| T_ELTWISE_BROADCAST_ADD_X({{ACC_DATA_TYPE}}, M0, N0, {{dst}}, bias0, {{dst}}); |
| )_"; |
| } |
| |
| code += R"_( |
| } |
| //------------------ END KERNEL {{meta_kernel_id}} --------------------- |
| )_"; |
| return code; |
| } |
| |
| void ClTemplateDirectConv2d::declare_variables(GpuKernelVariableTable &vtable, const ComponentGroup &comp_group) const |
| { |
| vtable.declare_variable( |
| _src, |
| GpuKernelArgumentInfo(GpuKernelArgumentInfo::Type::Tensor_4D_t_Buffer), |
| comp_group.is_intermediate_tensor(_src), |
| "src"); |
| |
| const GpuKernelArgumentInfo::Type weight_type = _settings.export_to_cl_image() ? GpuKernelArgumentInfo::Type::Tensor_4D_t_Image : GpuKernelArgumentInfo::Type::Tensor_4D_t_Buffer; |
| vtable.declare_variable( |
| _weight, |
| GpuKernelArgumentInfo(weight_type), |
| comp_group.is_intermediate_tensor(_weight), |
| "weight"); |
| |
| if(_bias && _bias->has_valid_id()) // optional bias |
| { |
| vtable.declare_variable( |
| _bias, |
| GpuKernelArgumentInfo(GpuKernelArgumentInfo::Type::Vector), |
| comp_group.is_intermediate_tensor(_bias), |
| "bias"); |
| } |
| vtable.declare_variable( |
| _dst, |
| GpuKernelArgumentInfo(GpuKernelArgumentInfo::Type::Tensor_4D_t_Buffer), |
| comp_group.is_intermediate_tensor(_dst), |
| "dst"); |
| } |
| |
| TagLUT ClTemplateDirectConv2d::get_tag_lut(const GpuKernelVariableTable &vtable, const ComponentGroup &comp_group) const |
| { |
| TagLUT lut{}; |
| // Arguments and global shared variables |
| lut["src"] = vtable.get_variable(_src); |
| lut["weight"] = vtable.get_variable(_weight); |
| |
| if(_bias && _bias->has_valid_id()) // optional bias |
| { |
| lut["bias"] = vtable.get_variable(_bias); |
| lut["BIA_DATA_TYPE"] = get_cl_type_from_data_type(_bias->data_type()); |
| } |
| lut["dst"] = vtable.get_variable(_dst); |
| |
| const auto dst_argument = vtable.get_variable(comp_group.get_dst_tensors()[0]); |
| lut["arg_dst"] = dst_argument.uniq_name; |
| |
| // Local build options |
| lut["meta_kernel_id"] = id(); |
| lut["ACC_DATA_TYPE"] = _src->data_type(); |
| lut["SRC_DATA_TYPE"] = _src->data_type(); |
| lut["WEI_DATA_TYPE"] = _weight->data_type(); |
| |
| lut["SRC_TENSOR_TYPE"] = "BUFFER"; |
| switch(vtable.get_variable(_weight).kernel_argument_info.type) |
| { |
| case GpuKernelArgumentInfo::Type::Image_Export_To_ClImage2D: |
| case GpuKernelArgumentInfo::Type::Image_3D_Export_To_ClImage2D: |
| case GpuKernelArgumentInfo::Type::Tensor_4D_t_Image: |
| { |
| lut["WEI_TENSOR_TYPE"] = "IMAGE"; |
| break; |
| } |
| default: |
| { |
| lut["WEI_TENSOR_TYPE"] = "BUFFER"; |
| break; |
| } |
| } |
| const auto width_idx = 1; |
| const auto height_idx = 2; |
| lut["WEI_WIDTH"] = _weight->dimension(width_idx); |
| lut["WEI_HEIGHT"] = _weight->dimension(height_idx); |
| |
| lut["STRIDE_X"] = _attributes.stride().x(); |
| lut["STRIDE_Y"] = _attributes.stride().y(); |
| |
| lut["PAD_LEFT"] = _attributes.pad().left; |
| lut["PAD_TOP"] = _attributes.pad().top; |
| |
| lut["ZERO_VALUE"] = 0; |
| |
| return lut; |
| } |
| |
| CLBuildOptions ClTemplateDirectConv2d::get_build_options(const ComponentGroup &comp_group) const |
| { |
| const unsigned int channel_idx = get_data_layout_dimension_index(_src->data_layout(), DataLayoutDimension::CHANNEL); |
| const DataType data_type = _src->data_type(); |
| |
| /// NOTE: For now tile sizes (n0, m0, n0) are set by the execution window. This may change in the future |
| const auto root_window = comp_group.get_root_component()->template_writer()->get_window(); |
| const unsigned int n0 = root_window.x().step(); |
| const unsigned int m0 = root_window.y().step(); |
| const unsigned int k0 = adjust_vec_size(is_data_type_quantized(data_type) ? 16u : 8u, _src->dimension(channel_idx)); |
| const unsigned int partial_store_n0 = _dst->dimension(0) % n0; |
| |
| CLBuildOptions build_opts{}; |
| if(_settings.fast_relaxed_math()) |
| { |
| build_opts.add_option("-cl-fast-relaxed-math"); |
| } |
| else |
| { |
| // -cl-fast-relaxed-math also sets -cl-finite-math-only and -cl-unsafe-math-optimizations |
| // to disable -cl-finite-math-only, we only include -cl-unsafe-math-optimizations |
| build_opts.add_option("-cl-unsafe-math-optimizations"); |
| } |
| build_opts.add_option("-DIS_TILED"); |
| build_opts.add_option("-DN0=" + support::cpp11::to_string(n0)); |
| build_opts.add_option("-DM0=" + support::cpp11::to_string(m0)); |
| build_opts.add_option("-DK0=" + support::cpp11::to_string(k0)); |
| build_opts.add_option("-DPARTIAL_N0=" + support::cpp11::to_string(partial_store_n0)); |
| |
| return build_opts; |
| } |
| |
| std::string ClTemplateDirectConv2d::get_config_id() const |
| { |
| const DataType data_type = _src->data_type(); |
| const DataLayout data_layout = _src->data_layout(); |
| |
| const unsigned int width_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH); |
| const unsigned int height_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT); |
| |
| const unsigned int kernel_size = _weight->dimension(width_idx); |
| |
| std::string config_id{}; |
| config_id += lower_string(string_from_data_type(data_type)); |
| config_id += "_"; |
| config_id += support::cpp11::to_string(kernel_size); |
| config_id += "_"; |
| config_id += support::cpp11::to_string(_attributes.stride().x()); |
| config_id += "_"; |
| config_id += support::cpp11::to_string(_attributes.stride().y()); |
| config_id += "_"; |
| config_id += support::cpp11::to_string(_dst->dimension(width_idx)); |
| config_id += "_"; |
| config_id += support::cpp11::to_string(_dst->dimension(height_idx)); |
| config_id += "_"; |
| config_id += lower_string(string_from_data_layout(data_layout)); |
| return config_id; |
| } |
| |
| std::set<std::string> ClTemplateDirectConv2d::get_headers_list() const |
| { |
| return std::set<std::string>{ "helpers.h", "tile_helpers.h" }; |
| } |
| |
| Window ClTemplateDirectConv2d::get_window() const |
| { |
| ARM_COMPUTE_ERROR_ON_MSG(_dst->tensor_shape().total_size() == 0U, "Destination tensor is not initialized"); |
| |
| const auto output_shape = _dst->tensor_shape(); |
| |
| const unsigned int vec_size = std::min(static_cast<unsigned int>(output_shape[0]), 4u); |
| const unsigned int num_rows = (_dst->tensor_shape()[0] > 16) ? ((_src->data_type() == DataType::F32) ? 2U : 4U) : 1U; |
| |
| // Create and configure kernel window |
| Window win = calculate_max_window(output_shape, Steps(vec_size, num_rows)); |
| |
| const size_t dim_y_collapsed = ceil_to_multiple(output_shape[1] * output_shape[2], num_rows); |
| win.set(Window::DimY, Window::Dimension(0, dim_y_collapsed, num_rows)); |
| win.set(Window::DimZ, Window::Dimension(0, output_shape.total_size_upper(3), 1)); |
| |
| return win; |
| } |
| |
| } // namespace dynamic_fusion |
| } // namespace experimental |
| } // namespace arm_compute |