| /* |
| * Copyright (c) 2018-2020 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/CL/CLHelpers.h" |
| #include "arm_compute/core/CL/ICLTensor.h" |
| #include "arm_compute/core/Helpers.h" |
| #include "arm_compute/core/TensorInfo.h" |
| #include "arm_compute/core/Utils.h" |
| #include "arm_compute/core/Validate.h" |
| #include "src/core/AccessWindowStatic.h" |
| #include "src/core/helpers/AutoConfiguration.h" |
| #include "src/core/helpers/WindowHelpers.h" |
| #include "support/StringSupport.h" |
| |
| 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, const ITensorInfo *output_multipliers, const ITensorInfo *output_shifts) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(mm_result, 1, DataType::S32); |
| |
| 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)); |
| } |
| |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output_multipliers, 1, DataType::S32); |
| ARM_COMPUTE_RETURN_ERROR_ON(output_multipliers->num_dimensions() > 1); |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output_shifts, 1, DataType::S32); |
| ARM_COMPUTE_RETURN_ERROR_ON(output_shifts->num_dimensions() > 1); |
| if(output_stage.is_quantized_per_channel) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON(mm_result->dimension(0) != output_shifts->dimension(0)); |
| ARM_COMPUTE_RETURN_ERROR_ON(mm_result->dimension(0) != output_multipliers->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"); |
| } |
| } |
| } |
| |
| ARM_COMPUTE_RETURN_ERROR_ON(output_stage.type == GEMMLowpOutputStageType::NONE); |
| // Checks performed when output is configured |
| if((output != nullptr) && (output->total_size() != 0)) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON(output_stage.output_data_type != output->data_type()); |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(mm_result, output); |
| } |
| |
| ARM_COMPUTE_RETURN_ERROR_ON(output_stage.gemmlowp_min_bound > output_stage.gemmlowp_max_bound); |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(output_stage.gemmlowp_multipliers.size() != output_stage.gemmlowp_shifts.size(), "per channel quantization info is incorrect"); |
| |
| return Status{}; |
| } |
| } // namespace |
| |
| CLGEMMLowpOffsetContributionOutputStageKernel::CLGEMMLowpOffsetContributionOutputStageKernel() |
| : _mm_result(nullptr), |
| _vector_sum_col(nullptr), |
| _vector_sum_row(nullptr), |
| _bias(nullptr), |
| _output(nullptr), |
| _output_multipliers(nullptr), |
| _output_shifts(nullptr), |
| _is_quantized_per_channel(false) |
| { |
| } |
| |
| 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, |
| const ICLTensor *output_multipliers, const ICLTensor *output_shifts) |
| { |
| configure(CLKernelLibrary::get().get_compile_context(), mm_result, vector_sum_col, vector_sum_row, bias, output, k, a_offset, b_offset, output_stage, output_multipliers, output_shifts); |
| } |
| |
| void CLGEMMLowpOffsetContributionOutputStageKernel::configure(const CLCompileContext &compile_context, 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, |
| const ICLTensor *output_multipliers, const ICLTensor *output_shifts) |
| { |
| // Perform validate step |
| ARM_COMPUTE_ERROR_ON_NULLPTR(mm_result, output, output_multipliers, output_shifts); |
| 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, |
| output_multipliers->info(), output_shifts->info())); // NOLINT |
| |
| auto padding_info = get_padding_info({ mm_result, vector_sum_col, vector_sum_row, bias, output, output_multipliers, output_shifts }); |
| |
| 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; |
| _output_multipliers = output_multipliers; |
| _output_shifts = output_shifts; |
| _is_quantized_per_channel = output_stage.is_quantized_per_channel; |
| |
| // 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(); |
| |
| // Auto initialize the output |
| auto_init_if_empty(*output->info(), mm_result->info()->clone()->set_data_type(output_stage.output_data_type)); |
| |
| const unsigned int num_elems_processed_per_iteration = adjust_vec_size(4, mm_result->info()->dimension(0)); |
| |
| // Set the arguments to pass at compile time |
| CLBuildOptions build_opts; |
| build_opts.add_option("-DVEC_SIZE=" + support::cpp11::to_string(num_elems_processed_per_iteration)); |
| build_opts.add_option("-DVEC_SIZE_LEFTOVER=" + support::cpp11::to_string(mm_result->info()->dimension(0) % num_elems_processed_per_iteration)); |
| |
| // 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_multipliers[0])); |
| build_opts.add_option("-DRESULT_SHIFT=" + support::cpp11::to_string(output_stage.gemmlowp_shifts[0])); |
| build_opts.add_option_if(_is_quantized_per_channel, "-DPER_CHANNEL_QUANTIZATION"); |
| build_opts.add_option("-DOUTPUT_DATA_TYPE=" + get_cl_type_from_data_type(output->info()->data_type())); |
| |
| PixelValue min_val{}; |
| PixelValue max_val{}; |
| std::tie(min_val, max_val) = get_min_max(output->info()->data_type()); |
| build_opts.add_option_if((min > min_val.get<int32_t>()), "-DMIN_BOUND=" + support::cpp11::to_string(min)); |
| build_opts.add_option_if((max < max_val.get<int32_t>()), "-DMAX_BOUND=" + support::cpp11::to_string(max)); |
| |
| std::string kernel_name("gemmlowp_offset_contribution"); |
| kernel_name += "_" + string_from_gemmlowp_output_stage(output_stage.type); |
| |
| // Create kernel |
| _kernel = create_kernel(compile_context, kernel_name, build_opts.options()); |
| |
| // Configure kernel window |
| Window win = calculate_max_window(*mm_result->info(), Steps(num_elems_processed_per_iteration)); |
| ICLKernel::configure_internal(win); |
| |
| // 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)); |
| |
| ARM_COMPUTE_ERROR_ON(has_padding_changed(padding_info)); |
| } |
| |
| 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, |
| const ITensorInfo *output_multipliers, const ITensorInfo *output_shifts) |
| { |
| ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(mm_result, vector_sum_col, vector_sum_row, bias, output, a_offset, b_offset, output_stage, output_multipliers, output_shifts)); |
| 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); |
| add_2D_tensor_argument_if((_vector_sum_col != nullptr), idx, _vector_sum_col, win_vector_sum_col); |
| add_2D_tensor_argument_if((_vector_sum_row != nullptr), idx, _vector_sum_row, win_vector_sum_row); |
| add_1D_tensor_argument_if((_bias != nullptr), idx, _bias, biases_slice); |
| add_3D_tensor_argument(idx, _output, slice); |
| add_1D_tensor_argument_if(_is_quantized_per_channel, idx, _output_multipliers, biases_slice); |
| add_1D_tensor_argument_if(_is_quantized_per_channel, idx, _output_shifts, biases_slice); |
| enqueue(queue, *this, slice, lws_hint()); |
| } |
| while(collapsed.slide_window_slice_3D(slice)); |
| } |
| } // namespace arm_compute |