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Diffstat (limited to 'src/dynamic_fusion/sketch/gpu/template_writer/cl/ClTemplatePool2d.cpp')
| -rw-r--r-- | src/dynamic_fusion/sketch/gpu/template_writer/cl/ClTemplatePool2d.cpp | 472 |
1 files changed, 472 insertions, 0 deletions
diff --git a/src/dynamic_fusion/sketch/gpu/template_writer/cl/ClTemplatePool2d.cpp b/src/dynamic_fusion/sketch/gpu/template_writer/cl/ClTemplatePool2d.cpp new file mode 100644 index 0000000000..5df4438afe --- /dev/null +++ b/src/dynamic_fusion/sketch/gpu/template_writer/cl/ClTemplatePool2d.cpp @@ -0,0 +1,472 @@ +/* + * Copyright (c) 2023 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 "ClTemplatePool2d.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 +{ +namespace +{ +// Shape indexes for NHWC Datalayout +constexpr static int32_t batch_idx = 3; +constexpr static int32_t height_idx = 2; +constexpr static int32_t width_idx = 1; +constexpr static int32_t channel_idx = 0; +} +ClTemplatePool2d::ClTemplatePool2d(ComponentId id, + const ArgumentPack<ITensorInfo> &tensors, + const Attributes &attributes, + const Settings &settings) + : IGpuTemplateComponentWriter{ id, tensors }, + _src{}, + _dst{}, + _attributes{ attributes }, + _settings{ settings } +{ + _src = this->tensors().get_const_tensor(TensorType::ACL_SRC_0); + _dst = this->tensors().get_const_tensor(TensorType::ACL_DST_0); + ARM_COMPUTE_ERROR_ON_NULLPTR(_src, _dst); +} + +std::string ClTemplatePool2d::get_name() const +{ + return "pool2d"; +} + +std::string ClTemplatePool2d::get_component_code(const ComponentGroup &comp_group) const +{ + ARM_COMPUTE_UNUSED(comp_group); + + // Condition to use 2x2 optimized kernel + if(_attributes.pool_size() == Size2D(2, 2)) + { + return get_2x2_kernel_code(); + } + else + { + return get_MxN_kernel_code(); + } +} + +std::string ClTemplatePool2d::get_MxN_kernel_code() const +{ + const auto pool_type = _attributes.pool_type(); + const bool fp_mixed_precision = (_src->data_type() == DataType::F16) && _settings.mixed_precision() && pool_type != PoolingType::MAX; + + // Define pool op macro. + std::string pool_op = (pool_type == PoolingType::AVG) ? R"_(#define POOL_OP(x,y) ((x) + (y)))_" : R"_(#define POOL_OP(x,y) (fmax((x), (y))) )_"; + + // Kernel start + // Note: If C is not multiple of N0, we shift back of PARTIAL_N0 elements to compute the leftover elements for get_global_id(0) == 0 + // Note: If C is less than N0, N0 should be SHRINKED to the closest smaller N0. This operation is performed on the host side + std::string code = R"_( +//------------------ START KERNEL {{meta_kernel_id}} --------------------- +// IN_0(src) {{src}} +// OUT(dst, accum) {{dst}} + +{ + const int idx_out_c = g_ind_0; + const int idx_out_w = g_ind_1; +)_"; + + // Add macro for POOL_OP + code += "\n" + pool_op + "\n"; + + code += R"_( + const int idx_out_h = g_ind_2 % {{DST_HEIGHT}}; + const int idx_out_n = g_ind_2 / {{DST_HEIGHT}}; +)_"; + + // Define common variables. + code += R"_( + __global unsigned char *in_base_ptr = {{src}}_ptr + {{src}}_offset_first_element_in_bytes + idx_out_c * sizeof({{DATA_TYPE}}) + idx_out_n * {{src}}_stride_w; + + __global unsigned char *out_base_ptr = {{dst}}_ptr + {{dst}}_offset_first_element_in_bytes + idx_out_c * sizeof({{DATA_TYPE}}) + idx_out_w * {{dst}}_stride_y + idx_out_h * {{dst}}_stride_z + idx_out_n * {{dst}}_stride_w; + + VEC_DATA_TYPE({{ACC_DATA_TYPE}}, N0) + res0 = {{INITIAL_VALUE}}; + + const int idx_in_w = idx_out_w * {{STRIDE_X}} - {{PAD_X}}; + const int idx_in_h = idx_out_h * {{STRIDE_Y}} - {{PAD_Y}}; + + const int pool_x_s = max((int)0, -idx_in_w); + const int pool_x_e = min((int){{POOL_SIZE_X}}, (int){{SRC_WIDTH}} - idx_in_w); + const int pool_y_s = max((int)0, -idx_in_h); + const int pool_y_e = min((int){{POOL_SIZE_Y}}, (int){{SRC_HEIGHT}} - idx_in_h); +)_"; + + // Determine filter size depending on if padding is excluded or not + if(_attributes.exclude_padding()) + { + code += R"_( + const int filter_size = (pool_y_e - pool_y_s) * (pool_x_e - pool_x_s); +)_"; + } + else + { + code += R"_( + const int filter_size = {{POOL_SIZE_X}} * {{POOL_SIZE_Y}}; +)_"; + } + + // Loop through pool size + // if global pooling + if(_attributes.pool_size().x() == _src->dimension(width_idx) && _attributes.pool_size().y() == _src->dimension(height_idx)) + { + // Begin loop + code += R"_( + // Global pooling path + for(int y = 0; y < {{POOL_SIZE_Y}}; ++y) + { + #pragma unroll 8 + for(int x = 0; x < {{POOL_SIZE_X}}; ++x) + { + VEC_DATA_TYPE({{ACC_DATA_TYPE}}, N0) + data0; +)_"; + } + else // if local pooling size + { + code += R"_( + for(int y = pool_y_s; y < pool_y_e; ++y) + { + #pragma unroll 8 + for(int x = pool_x_s; x < pool_x_e; ++x) + { + VEC_DATA_TYPE({{ACC_DATA_TYPE}}, N0) + data0; +)_"; + } // end else + + // if condition inside loop - use 32bit acc if mixed_precision. + // End loop through pooling section. + if(fp_mixed_precision) + { + // In case of FP_MIXED_PRECISION, ACC_DATA_TYPE is != DATA_TYPE + code += R"_( + data0 = CONVERT(VLOAD(N0)(0, (__global {{DATA_TYPE}} *)(in_base_ptr + (x + idx_in_w) * {{src}}_stride_y + (y + idx_in_h) * {{src}}_stride_z)), VEC_DATA_TYPE({{ACC_DATA_TYPE}}, N0)); + res0 = POOL_OP(res0, data0); + } + } +)_"; + } + else // load data, compute result and end loop + { + code += R"_( + data0 = VLOAD(N0)(0, (__global {{DATA_TYPE}} *)(in_base_ptr + (x + idx_in_w) * {{src}}_stride_y + (y + idx_in_h) * {{src}}_stride_z)); + res0 = POOL_OP(res0, data0); + } + } +)_"; + } + + // For Pool AVG ONLY, divide pool output by filter size + if(pool_type == PoolingType::AVG) + { + code += R"_( + res0 /= (VEC_DATA_TYPE({{ACC_DATA_TYPE}}, N0))filter_size; +)_"; + } + + // If mixed precision convert datatype before storing. Then end kernel. + if(fp_mixed_precision) + { + code += R"_( + VEC_DATA_TYPE({{DATA_TYPE}}, N0) + res_converted0 = CONVERT(res0, VEC_DATA_TYPE({{DATA_TYPE}}, N0)); + STORE_VECTOR_SELECT(res_converted, {{DATA_TYPE}}, out_base_ptr, N0, PARTIAL_N0, (PARTIAL_N0 != 0) && g_ind_0 == 0); +)_"; + } + else + { + // Store data + code += R"_( + STORE_VECTOR_SELECT(res, {{DATA_TYPE}}, out_base_ptr, N0, PARTIAL_N0, (PARTIAL_N0 != 0) && g_ind_0 == 0); +)_"; + } + + code += R"_( +//------------------ END KERNEL {{meta_kernel_id}} --------------------- +} +)_"; + + return code; +} + +std::string ClTemplatePool2d::get_2x2_kernel_code() const +{ + const auto pool_type = _attributes.pool_type(); + const bool fp_mixed_precision = (_src->data_type() == DataType::F16) && _settings.mixed_precision() && pool_type != PoolingType::MAX; + std::string pool_op = (pool_type == PoolingType::AVG) ? R"_(#define POOL_OP(x,y) ((x) + (y)))_" : R"_(#define POOL_OP(x,y) (fmax((x), (y))) )_"; + + std::string code = R"_( +//------------------ START KERNEL {{meta_kernel_id}} --------------------- +// IN_0(src) {{src}} +// OUT(dst, accum) {{dst}} + +#define SELECT_TYPE SELECT_VEC_DATA_TYPE({{ACC_DATA_TYPE}}, N0) + +{ + const int idx_out_c = g_ind_0; + const int idx_out_w = g_ind_1; +)_"; + + // Add pool op macro + code += "\n" + pool_op + "\n"; + + // If batch size != 1, the batch size dimension is collapsed over the height dimension + code += R"_( + const int idx_out_h = g_ind_2 % {{DST_HEIGHT}}; + const int idx_out_n = g_ind_2 / {{DST_HEIGHT}}; +)_"; + + code += R"_( + const int idx_in_w = idx_out_w * {{STRIDE_X}} - {{PAD_X}}; + const int idx_in_h = idx_out_h * {{STRIDE_Y}} - {{PAD_Y}}; + + __global unsigned char *in_base_ptr = {{src}}_ptr + {{src}}_offset_first_element_in_bytes + idx_out_c * sizeof({{DATA_TYPE}}) + idx_out_n * {{src}}_stride_w; + __global unsigned char *out_base_ptr = {{dst}}_ptr + {{dst}}_offset_first_element_in_bytes + idx_out_c * sizeof({{DATA_TYPE}}) + idx_out_w * {{dst}}_stride_y + idx_out_h * {{dst}}_stride_z + idx_out_n * + {{dst}}_stride_w; + const int pool_x_s = max((int)0, -idx_in_w); + const int pool_x_e = min((int)2, (int){{SRC_WIDTH}} - idx_in_w); + const int pool_y_s = max((int)0, -idx_in_h); + const int pool_y_e = min((int)2, (int){{SRC_HEIGHT}} - idx_in_h); + + const int filter_size = (pool_x_e - pool_x_s) * (pool_y_e - pool_y_s); + const int x0 = pool_x_s + idx_in_w; + const int y0 = pool_y_s + idx_in_h; + const int x1 = pool_x_e - 1 + idx_in_w; + const int y1 = pool_y_e - 1 + idx_in_h; + + REPEAT_VAR_INIT_TO_CONST(4, VEC_DATA_TYPE({{ACC_DATA_TYPE}}, N0), data, 0); +)_"; + + if(fp_mixed_precision) + { + // In case of FP_MIXED_PRECISION, ACC_DATA_TYPE is != DATA_TYPE + code += R"_( + data0 = CONVERT(VLOAD(N0)(0, (__global {{DATA_TYPE}} *)(in_base_ptr + x0 * {{src}}_stride_y + y0 * {{src}}_stride_z)), VEC_DATA_TYPE({{ACC_DATA_TYPE}}, N0)); + data1 = CONVERT(VLOAD(N0)(0, (__global {{DATA_TYPE}} *)(in_base_ptr + x1 * {{src}}_stride_y + y0 * {{src}}_stride_z)), VEC_DATA_TYPE({{ACC_DATA_TYPE}}, N0)); + data2 = CONVERT(VLOAD(N0)(0, (__global {{DATA_TYPE}} *)(in_base_ptr + x0 * {{src}}_stride_y + y1 * {{src}}_stride_z)), VEC_DATA_TYPE({{ACC_DATA_TYPE}}, N0)); + data3 = CONVERT(VLOAD(N0)(0, (__global {{DATA_TYPE}} *)(in_base_ptr + x1 * {{src}}_stride_y + y1 * {{src}}_stride_z)), VEC_DATA_TYPE({{ACC_DATA_TYPE}}, N0)); +)_"; + } + else + { + code += R"_( + data0 = VLOAD(N0)(0, (__global {{DATA_TYPE}} *)(in_base_ptr + x0 * {{src}}_stride_y + y0 * {{src}}_stride_z)); + data1 = VLOAD(N0)(0, (__global {{DATA_TYPE}} *)(in_base_ptr + x1 * {{src}}_stride_y + y0 * {{src}}_stride_z)); + data2 = VLOAD(N0)(0, (__global {{DATA_TYPE}} *)(in_base_ptr + x0 * {{src}}_stride_y + y1 * {{src}}_stride_z)); + data3 = VLOAD(N0)(0, (__global {{DATA_TYPE}} *)(in_base_ptr + x1 * {{src}}_stride_y + y1 * {{src}}_stride_z)); +)_"; + } + + if(pool_type != PoolingType::MAX) + { + // Make invalid the values loaded if the x or y coordinate was clamped (out-of-bound) + code += R"_( + if(filter_size != 4) + { + SELECT_TYPE cond_w_s = (SELECT_TYPE)idx_in_w < (SELECT_TYPE)0; + SELECT_TYPE cond_w_e = (SELECT_TYPE)idx_in_w >= (SELECT_TYPE)({{SRC_WIDTH}} - 1); + SELECT_TYPE cond_h_s = (SELECT_TYPE)idx_in_h < (SELECT_TYPE)0; + SELECT_TYPE cond_h_e = (SELECT_TYPE)idx_in_h >= (SELECT_TYPE)({{SRC_HEIGHT}} - 1); + + data0 = select(data0, (VEC_DATA_TYPE({{ACC_DATA_TYPE}}, N0)){{INITIAL_VALUE}}, (SELECT_TYPE)(cond_w_s | cond_h_s)); + data1 = select(data1, (VEC_DATA_TYPE({{ACC_DATA_TYPE}}, N0)){{INITIAL_VALUE}}, (SELECT_TYPE)(cond_w_e | cond_h_s)); + data2 = select(data2, (VEC_DATA_TYPE({{ACC_DATA_TYPE}}, N0)){{INITIAL_VALUE}}, (SELECT_TYPE)(cond_w_s | cond_h_e)); + data3 = select(data3, (VEC_DATA_TYPE({{ACC_DATA_TYPE}}, N0)){{INITIAL_VALUE}}, (SELECT_TYPE)(cond_w_e | cond_h_e)); + } +)_"; + } + + code += R"_( + VEC_DATA_TYPE({{ACC_DATA_TYPE}}, N0) + res0 = data0; + res0 = POOL_OP(res0, data1); + res0 = POOL_OP(res0, data2); + res0 = POOL_OP(res0, data3); +)_"; + + if(pool_type == PoolingType::AVG) + { + // If avg pooling divide result accordingly. + if(_attributes.exclude_padding()) + { + code += R"_( + res0 /= (VEC_DATA_TYPE({{ACC_DATA_TYPE}}, N0))filter_size; +)_"; + } + else + { + code += R"_( + res0 /= (VEC_DATA_TYPE({{ACC_DATA_TYPE}}, N0))4; +)_"; + } + } + + // Store result + if(fp_mixed_precision) + { + code += R"_( + VEC_DATA_TYPE({{DATA_TYPE}}, N0) + res_converted0 = CONVERT(res0, VEC_DATA_TYPE({{DATA_TYPE}}, N0)); + STORE_VECTOR_SELECT(res_converted, {{DATA_TYPE}}, out_base_ptr, N0, PARTIAL_N0, (PARTIAL_N0 != 0) && g_ind_0 == 0); +)_"; + } + else + { + code += R"_( + STORE_VECTOR_SELECT(res, {{DATA_TYPE}}, out_base_ptr, N0, PARTIAL_N0, (PARTIAL_N0 != 0) && g_ind_0 == 0); +)_"; + } + + code += R"_( + //------------------ END KERNEL {{meta_kernel_id}} --------------------- +} +#undef SELECT_TYPE +)_"; + + return code; +} + +void ClTemplatePool2d::declare_variables(GpuKernelVariableTable &vtable, const ComponentGroup &comp_group) const +{ + vtable.declare_variable( + comp_group, + _src, + GpuKernelArgumentInfo(GpuKernelArgumentInfo::Type::Tensor_4D_t_Buffer), + "src"); + + vtable.declare_variable( + comp_group, + _dst, + GpuKernelArgumentInfo(GpuKernelArgumentInfo::Type::Tensor_4D_t_Buffer), + "dst"); +} + +TagLUT ClTemplatePool2d::get_tag_lut(const GpuKernelVariableTable &vtable, const ComponentGroup &comp_group) const +{ + ARM_COMPUTE_UNUSED(comp_group); + + TagLUT lut{}; + // Arguments and global shared variables + lut["src"] = vtable.get_variable(_src); + lut["dst"] = vtable.get_variable(_dst); + + // Local build options + lut["meta_kernel_id"] = id(); + + // Retrieve relevant data + const auto padding = _attributes.pad(); + const auto stride = _attributes.stride(); + const auto pool_size = _attributes.pool_size(); + const auto data_type = _src->data_type(); + const auto use_fp_mixed_precision = (_src->data_type() == DataType::F16) && _settings.mixed_precision() && _attributes.pool_type() != PoolingType::MAX; + + // pool specific + lut["STRIDE_X"] = stride.x(); + lut["STRIDE_Y"] = stride.y(); + lut["PAD_X"] = padding.left; + lut["PAD_Y"] = padding.top; + lut["POOL_SIZE_X"] = pool_size.width; + lut["POOL_SIZE_Y"] = pool_size.height; + + // Datatypes and variables + lut["ACC_DATA_TYPE"] = get_cl_type_from_data_type((use_fp_mixed_precision) ? (DataType::F32) : (data_type)); // Type of accumulators to use. + lut["DATA_TYPE"] = get_cl_type_from_data_type(data_type); + lut["SRC_WIDTH"] = _src->dimension(width_idx); + lut["SRC_HEIGHT"] = _src->dimension(height_idx); + lut["INITIAL_VALUE"] = (_attributes.pool_type() == PoolingType::MAX) ? float_to_string_with_full_precision(std::numeric_limits<float>::lowest()) : std::string("0"); + + // Tensor specific data + lut["DST_HEIGHT"] = _dst->dimension(height_idx); + + return lut; +} + +CLBuildOptions ClTemplatePool2d::get_build_options(const ComponentGroup &comp_group) const +{ + const auto root_window = comp_group.get_root_component()->template_writer()->get_window(); + const unsigned int n0 = root_window.x().step(); + const unsigned int partial_store_n0 = _dst->dimension(0) % n0; + + CLBuildOptions build_opts{}; + build_opts.add_option("-DN0=" + support::cpp11::to_string(n0)); + build_opts.add_option("-DPARTIAL_N0=" + support::cpp11::to_string(partial_store_n0)); + + return build_opts; +} + +std::string ClTemplatePool2d::get_config_id() const +{ + const DataType data_type = _src->data_type(); + const DataLayout data_layout = _src->data_layout(); + + std::string config_id{}; + config_id += "pooling_layer_2d_"; + config_id += lower_string(string_from_data_type(data_type)); + config_id += "_"; + config_id += lower_string(string_from_data_layout(data_layout)); + 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 += support::cpp11::to_string(_dst->dimension(channel_idx)); + + return config_id; +} + +std::set<std::string> ClTemplatePool2d::get_headers_list() const +{ + return std::set<std::string>{ "helpers.h", "tile_helpers.h", "repeat.h" }; +} + +Window ClTemplatePool2d::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 = adjust_vec_size(((_dst->data_type() == DataType::F32) ? 2 : 4), _dst->dimension(0)); + + // Create and configure kernel window + auto win = calculate_max_window(output_shape, Steps(vec_size)); + win = win.collapse_if_possible(win, Window::DimZ); // collapse window on batch size. + return win; +} + +} // namespace dynamic_fusion +} // namespace experimental +} // namespace arm_compute |