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Diffstat (limited to 'src/core/CL/kernels/CLGEMMLowpOffsetContributionOutputStageKernel.cpp')
| -rw-r--r-- | src/core/CL/kernels/CLGEMMLowpOffsetContributionOutputStageKernel.cpp | 301 |
1 files changed, 301 insertions, 0 deletions
diff --git a/src/core/CL/kernels/CLGEMMLowpOffsetContributionOutputStageKernel.cpp b/src/core/CL/kernels/CLGEMMLowpOffsetContributionOutputStageKernel.cpp new file mode 100644 index 0000000000..83af0c63eb --- /dev/null +++ b/src/core/CL/kernels/CLGEMMLowpOffsetContributionOutputStageKernel.cpp @@ -0,0 +1,301 @@ +/* + * Copyright (c) 2018 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 "arm_compute/core/CL/kernels/CLGEMMLowpOffsetContributionOutputStageKernel.h" + +#include "arm_compute/core/AccessWindowStatic.h" +#include "arm_compute/core/CL/ICLTensor.h" +#include "arm_compute/core/Error.h" +#include "arm_compute/core/Helpers.h" +#include "arm_compute/core/TensorInfo.h" +#include "arm_compute/core/Types.h" +#include "arm_compute/core/Utils.h" +#include "arm_compute/core/Validate.h" +#include "arm_compute/core/Window.h" +#include "support/ToolchainSupport.h" + +#include <cstddef> +#include <cstdint> + +using namespace arm_compute; + +namespace arm_compute +{ +class Coordinates; +} // namespace arm_compute + +namespace +{ +Status validate_arguments(const ITensorInfo *mm_result, const ITensorInfo *vector_sum_col, const ITensorInfo *vector_sum_row, const ITensorInfo *bias, const ITensorInfo *output, + int32_t a_offset, int32_t b_offset, const GEMMLowpOutputStageInfo &output_stage) +{ + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(mm_result, 1, DataType::S32); + ARM_COMPUTE_RETURN_ERROR_ON(output_stage.type == GEMMLowpOutputStageType::NONE); + ARM_COMPUTE_RETURN_ERROR_ON(bias == nullptr && a_offset == 0 && b_offset == 0); + ARM_COMPUTE_RETURN_ERROR_ON(output_stage.gemmlowp_max_bound > 255); + ARM_COMPUTE_RETURN_ERROR_ON(output_stage.gemmlowp_min_bound < 0 || output_stage.gemmlowp_min_bound > output_stage.gemmlowp_max_bound); + + if(bias != nullptr) + { + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(bias, 1, DataType::S32); + ARM_COMPUTE_RETURN_ERROR_ON(bias->num_dimensions() > 1); + ARM_COMPUTE_RETURN_ERROR_ON(mm_result->dimension(0) != bias->dimension(0)); + } + + // If a_offset == 0, vector_sum_col can be a nullptr + if(a_offset != 0) + { + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(vector_sum_col, 1, DataType::S32); + ARM_COMPUTE_RETURN_ERROR_ON(vector_sum_col->dimension(0) != mm_result->dimension(0)); + } + + // If b_offset == 0, vector_sum_row can be a nullptr + if(b_offset != 0) + { + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(vector_sum_row, 1, DataType::S32); + + // Check if input is a 3D reinterpretation + const bool reinterpret_as_3d = mm_result->num_dimensions() > 1 && mm_result->tensor_shape().y() != vector_sum_row->tensor_shape().x(); + + // Validate input + ARM_COMPUTE_RETURN_ERROR_ON(reinterpret_as_3d && vector_sum_row->dimension(0) != (mm_result->dimension(1) * mm_result->dimension(2))); + ARM_COMPUTE_RETURN_ERROR_ON(!reinterpret_as_3d && vector_sum_row->dimension(0) != mm_result->dimension(1)); + + TensorShape output_shape = mm_result->tensor_shape(); + if(output_shape.num_dimensions() > 1) + { + const unsigned int output_batch_idx = reinterpret_as_3d ? 3 : 2; + + TensorShape vector_sum_row_shape = vector_sum_row->tensor_shape(); + vector_sum_row_shape.collapse_from(1); + output_shape.collapse_from(output_batch_idx); + + ARM_COMPUTE_RETURN_ERROR_ON_MSG(vector_sum_row_shape[1] != output_shape[output_batch_idx], + "mm_result tensor must have the same number of batches of output tensor"); + + if(a_offset != 0) + { + TensorShape vector_sum_col_shape = vector_sum_col->tensor_shape(); + vector_sum_col_shape.collapse_from(1); + + ARM_COMPUTE_RETURN_ERROR_ON_MSG(vector_sum_col_shape[1] != 1 && vector_sum_col_shape[1] != vector_sum_row_shape[1], + "vector_sum_col tensor must have the same number of batches of vector_sum_row_shape or the number of batches must be set to 1"); + } + } + } + + if(output->total_size() != 0) + { + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::QASYMM8); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(mm_result, output); + } + + return Status{}; +} + +std::pair<Status, Window> validate_and_configure_window(ITensorInfo *mm_result, ITensorInfo *vector_sum_col, ITensorInfo *vector_sum_row, ITensorInfo *bias, ITensorInfo *output, + int32_t a_offset, int32_t b_offset) +{ + constexpr unsigned int num_elems_processed_per_iteration = 4; + bool window_changed = false; + + // Auto initialize the output + auto_init_if_empty(*output, mm_result->clone()->set_data_type(DataType::QASYMM8)); + + // Configure kernel window + Window win = calculate_max_window(*mm_result, Steps(num_elems_processed_per_iteration)); + + AccessWindowHorizontal mm_result_access(mm_result, 0, num_elems_processed_per_iteration); + window_changed = window_changed || update_window_and_padding(win, mm_result_access); + + AccessWindowHorizontal output_access(output, 0, num_elems_processed_per_iteration); + window_changed = window_changed || update_window_and_padding(win, output_access); + + if(a_offset != 0) + { + AccessWindowHorizontal vector_sum_col_access(vector_sum_col, 0, num_elems_processed_per_iteration); + window_changed = window_changed || update_window_and_padding(win, vector_sum_col_access); + } + if(b_offset != 0) + { + AccessWindowStatic vector_sum_row_access(vector_sum_row, 0, 0, vector_sum_row->dimension(0), 0); // NOLINT + window_changed = window_changed || update_window_and_padding(win, vector_sum_row_access); + } + + if(bias != nullptr) + { + AccessWindowStatic bias_access(bias, 0, 0, ceil_to_multiple(bias->dimension(0), num_elems_processed_per_iteration), bias->tensor_shape()[1]); + window_changed = window_changed || update_window_and_padding(win, bias_access); + } + + Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{}; + return std::make_pair(err, win); +} +} // namespace + +CLGEMMLowpOffsetContributionOutputStageKernel::CLGEMMLowpOffsetContributionOutputStageKernel() + : _mm_result(nullptr), _vector_sum_col(nullptr), _vector_sum_row(nullptr), _bias(nullptr), _output(nullptr) +{ +} + +void CLGEMMLowpOffsetContributionOutputStageKernel::configure(const ICLTensor *mm_result, const ICLTensor *vector_sum_col, const ICLTensor *vector_sum_row, const ICLTensor *bias, ICLTensor *output, + int32_t k, int32_t a_offset, int32_t b_offset, const GEMMLowpOutputStageInfo &output_stage) +{ + // Perform validate step + ARM_COMPUTE_ERROR_ON_NULLPTR(mm_result, output); + ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(mm_result->info(), + vector_sum_col != nullptr ? vector_sum_col->info() : nullptr, + vector_sum_row != nullptr ? vector_sum_row->info() : nullptr, + bias != nullptr ? bias->info() : nullptr, + output->info(), + a_offset, b_offset, output_stage)); // NOLINT + + const int min = output_stage.gemmlowp_min_bound; + const int max = output_stage.gemmlowp_max_bound; + + _vector_sum_col = vector_sum_col; + _vector_sum_row = vector_sum_row; + _mm_result = mm_result; + _bias = bias; + _output = output; + + // Check if input is a 3D reinterpretation + const bool reinterpret_as_3d = vector_sum_row != nullptr + && mm_result->info()->num_dimensions() > 1 + && mm_result->info()->tensor_shape().y() != vector_sum_row->info()->tensor_shape().x(); + + // Set the arguments to pass at compile time + CLBuildOptions build_opts; + + // If a_offset == 0, vector_sum_col can be a nullptr + if(a_offset != 0) + { + build_opts.add_option("-DA_OFFSET=" + support::cpp11::to_string(a_offset)); + build_opts.add_option_if(vector_sum_col->info()->tensor_shape().num_dimensions() > 1, "-DSUM_COL_HAS_BATCHES"); + } + // If b_offset == 0, vector_sum_row can be a nullptr + build_opts.add_option_if(b_offset != 0, "-DB_OFFSET=" + support::cpp11::to_string(b_offset)); + build_opts.add_option("-DK_OFFSET=" + support::cpp11::to_string(a_offset * b_offset * k)); + build_opts.add_option_if(reinterpret_as_3d, "-DHEIGHT_INPUT3D=" + support::cpp11::to_string(mm_result->info()->dimension(1))); + build_opts.add_option_if(reinterpret_as_3d, "-DDEPTH_INPUT3D=" + support::cpp11::to_string(mm_result->info()->dimension(2))); + build_opts.add_option_if(bias != nullptr, "-DADD_BIAS"); + build_opts.add_option("-DRESULT_OFFSET=" + support::cpp11::to_string(output_stage.gemmlowp_offset)); + build_opts.add_option("-DRESULT_MULTIPLIER=" + support::cpp11::to_string(output_stage.gemmlowp_multiplier)); + build_opts.add_option("-DRESULT_SHIFT=" + support::cpp11::to_string(output_stage.gemmlowp_shift)); + build_opts.add_option_if((min != 0) && (min != max), "-DMIN_BOUND=" + support::cpp11::to_string(min)); + build_opts.add_option_if((max != 255) && (min != max), "-DMAX_BOUND=" + support::cpp11::to_string(max)); + + std::string kernel_name("gemmlowp_offset_contribution"); + + // Fuse output stage + if(output_stage.type != GEMMLowpOutputStageType::NONE) + { + kernel_name += "_" + string_from_gemmlowp_output_stage(output_stage.type); + } + else + { + ARM_COMPUTE_ERROR("GEMMLowpOutputStage can not be NONE!"); + } + + // Create kernel + _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel(kernel_name, build_opts.options())); + + // Configure kernel window + auto win_config = validate_and_configure_window(mm_result->info(), + vector_sum_col != nullptr ? vector_sum_col->info() : nullptr, + vector_sum_row != nullptr ? vector_sum_row->info() : nullptr, + bias != nullptr ? bias->info() : nullptr, + output->info(), + a_offset, b_offset); // NOLINT + ARM_COMPUTE_ERROR_THROW_ON(win_config.first); + ICLKernel::configure_internal(win_config.second); + + // Set config_id for enabling LWS tuning + _config_id = kernel_name + "_"; + _config_id += support::cpp11::to_string(mm_result->info()->dimension(0)); + _config_id += "_"; + _config_id += support::cpp11::to_string(mm_result->info()->dimension(1)); + _config_id += "_"; + _config_id += support::cpp11::to_string(mm_result->info()->dimension(2)); +} + +Status CLGEMMLowpOffsetContributionOutputStageKernel::validate(const ITensorInfo *mm_result, const ITensorInfo *vector_sum_col, const ITensorInfo *vector_sum_row, const ITensorInfo *bias, + const ITensorInfo *output, + int32_t a_offset, int32_t b_offset, const GEMMLowpOutputStageInfo &output_stage) +{ + ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(mm_result, vector_sum_col, vector_sum_row, bias, output, a_offset, b_offset, output_stage)); + ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(mm_result->clone().get(), + vector_sum_col != nullptr ? vector_sum_col->clone().get() : nullptr, + vector_sum_row != nullptr ? vector_sum_row->clone().get() : nullptr, + bias != nullptr ? bias->clone().get() : nullptr, + output->clone().get(), + a_offset, b_offset) + .first); // NOLINT + + return Status{}; +} + +void CLGEMMLowpOffsetContributionOutputStageKernel::run(const Window &window, cl::CommandQueue &queue) +{ + ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); + ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICLKernel::window(), window); + + Window collapsed = window.collapse_if_possible(ICLKernel::window(), Window::DimZ); + Window slice = collapsed.first_slice_window_3D(); + + // Set window for vector_sum_col + Window win_vector_sum_col = slice; + win_vector_sum_col.set(Window::DimY, Window::Dimension(0, 0, 0)); + win_vector_sum_col.set(Window::DimZ, Window::Dimension(0, 0, 0)); + + // Set window for vector_sum_row + Window win_vector_sum_row = slice; + win_vector_sum_row.set(Window::DimX, Window::Dimension(0, 0, 0)); + win_vector_sum_row.set(Window::DimY, Window::Dimension(0, 0, 0)); + win_vector_sum_col.set(Window::DimZ, Window::Dimension(0, 0, 0)); + + Window biases_slice = slice; + biases_slice.set(Window::DimY, Window::Dimension(0, 1, 1)); + biases_slice.set(Window::DimZ, Window::Dimension(0, 1, 1)); + + do + { + unsigned int idx = 0; + add_3D_tensor_argument(idx, _mm_result, slice); + if(_vector_sum_col != nullptr) + { + add_2D_tensor_argument(idx, _vector_sum_col, win_vector_sum_col); + } + if(_vector_sum_row != nullptr) + { + add_2D_tensor_argument(idx, _vector_sum_row, win_vector_sum_row); + } + if(_bias != nullptr) + { + add_1D_tensor_argument(idx, _bias, biases_slice); + } + add_3D_tensor_argument(idx, _output, slice); + enqueue(queue, *this, slice, lws_hint()); + } + while(collapsed.slide_window_slice_3D(slice)); +} |