diff options
Diffstat (limited to 'src/dynamic_fusion/sketch/gpu/template_writer/cl/ClTemplateDirectConv2d.cpp')
| -rw-r--r-- | src/dynamic_fusion/sketch/gpu/template_writer/cl/ClTemplateDirectConv2d.cpp | 400 |
1 files changed, 400 insertions, 0 deletions
diff --git a/src/dynamic_fusion/sketch/gpu/template_writer/cl/ClTemplateDirectConv2d.cpp b/src/dynamic_fusion/sketch/gpu/template_writer/cl/ClTemplateDirectConv2d.cpp new file mode 100644 index 0000000000..870de64eb8 --- /dev/null +++ b/src/dynamic_fusion/sketch/gpu/template_writer/cl/ClTemplateDirectConv2d.cpp @@ -0,0 +1,400 @@ +/* + * 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 |