/* * Copyright (c) 2019-2023 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 "src/gpu/cl/kernels/ClGemmLowpMatrixMultiplyReshapedOnlyRhsKernel.h" #include "arm_compute/core/CL/CLHelpers.h" #include "arm_compute/core/CL/CLKernelLibrary.h" #include "arm_compute/core/CL/ICLTensor.h" #include "arm_compute/core/CL/OpenCL.h" #include "arm_compute/core/Helpers.h" #include "arm_compute/core/TensorInfo.h" #include "arm_compute/core/utils/misc/ShapeCalculator.h" #include "arm_compute/core/utils/StringUtils.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/Cast.h" #include "support/StringSupport.h" #include namespace arm_compute { namespace opencl { namespace kernels { using namespace misc::shape_calculator; namespace { using ElementsProcessed = Steps; Status validate_arguments(const ITensorInfo *src0, const ITensorInfo *src1, const ITensorInfo *dst, const GEMMKernelInfo &gemm_info, const ITensorInfo *vector_sum_col, const ITensorInfo *vector_sum_row, const ITensorInfo *bias, const ITensorInfo *output_multipliers, const ITensorInfo *output_shifts) { ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(src0, src1, dst); ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src0, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED); if (src0->data_type() == DataType::QASYMM8) { ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src0, src1); } else { ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src1, 1, DataType::QASYMM8, DataType::QSYMM8, DataType::QASYMM8_SIGNED, DataType::QSYMM8_PER_CHANNEL); } ARM_COMPUTE_RETURN_ERROR_ON_MSG(src0->num_dimensions() > 4, "The number of dimensions for the LHS matrix must be <= 4"); ARM_COMPUTE_RETURN_ERROR_ON_MSG(src1->num_dimensions() > 3, "The number of dimensions for the RHS matrix must be <= 3"); const GEMMRHSMatrixInfo rhs_info = gemm_info.rhs_info; const GEMMLHSMatrixInfo lhs_info = gemm_info.lhs_info; const GEMMLowpOutputStageInfo output_stage = gemm_info.output_stage; ARM_COMPUTE_RETURN_ERROR_ON_MSG((((rhs_info.k0 & (rhs_info.k0 - 1)) && rhs_info.k0 != 3) || (rhs_info.k0 > 16)), "Only 2,3,4,8,16 are supported for k0"); ARM_COMPUTE_RETURN_ERROR_ON(lhs_info.m0 < 1 || lhs_info.m0 > 8); ARM_COMPUTE_RETURN_ERROR_ON_MSG((((rhs_info.n0 & (rhs_info.n0 - 1)) && rhs_info.n0 != 3) || rhs_info.n0 > 16), "Only 2,3,4,8,16 are supported for n0"); ARM_COMPUTE_RETURN_ERROR_ON_MSG(rhs_info.export_to_cl_image, "Export to CLImage not supported for quantized GEMM"); const int m = gemm_info.m; const int n = gemm_info.n; const int k = gemm_info.k; TensorShape tensor_shape1{src1->tensor_shape()}; tensor_shape1.set(0, n); tensor_shape1.set(1, k); const TensorInfo tensor_info1 = src1->clone()->set_tensor_shape(tensor_shape1); const TensorInfo tensor_info_reshaped1 = src1->clone()->set_tensor_shape(compute_rhs_reshaped_shape(tensor_info1, rhs_info)); ARM_COMPUTE_RETURN_ERROR_ON(src0->dimension(0) != static_cast(k)); if (gemm_info.reinterpret_input_as_3d) { ARM_COMPUTE_RETURN_ERROR_ON(src0->dimension(1) * src0->dimension(2) != static_cast(m)); } else { ARM_COMPUTE_RETURN_ERROR_ON(src0->dimension(1) != static_cast(m)); } ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(src1, &tensor_info_reshaped1); const TensorShape expected_dst_shape = compute_mm_shape(*src0, *src1, gemm_info); if (dst->total_size() != 0) { const TensorInfo tensor_info_dst = dst->clone()->set_tensor_shape(expected_dst_shape); ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(dst, &tensor_info_dst); if (output_stage.type == GEMMLowpOutputStageType::NONE) { ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(dst, 1, DataType::S32); } else { ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src0, dst); } } 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(expected_dst_shape[0] != bias->dimension(0)); } ARM_COMPUTE_RETURN_ERROR_ON_MSG((output_stage.type == GEMMLowpOutputStageType::QUANTIZE_DOWN) || (output_stage.type == GEMMLowpOutputStageType::QUANTIZE_DOWN_FLOAT), "Only GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT is supported"); // Checks performed if the dst stage needs to be fused if (output_stage.type == GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT) { // If a_offset == 0, vector_sum_col can be a nullptr if (gemm_info.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) != expected_dst_shape[0]); } // If b_offset == 0, vector_sum_row can be a nullptr if (gemm_info.b_offset != 0) { ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(vector_sum_row, 1, DataType::S32); // Check if mm result is a 3D reinterpretation const bool reinterpret_as_3d = expected_dst_shape.num_dimensions() > 1 && expected_dst_shape.y() != vector_sum_row->tensor_shape().x(); // Validate input ARM_COMPUTE_RETURN_ERROR_ON(reinterpret_as_3d && vector_sum_row->dimension(0) != (expected_dst_shape[1] * expected_dst_shape[2])); ARM_COMPUTE_RETURN_ERROR_ON(!reinterpret_as_3d && vector_sum_row->dimension(0) != expected_dst_shape[1]); if (expected_dst_shape.num_dimensions() > 1) { const unsigned int dst_batch_idx = reinterpret_as_3d ? 3 : 2; TensorShape vector_sum_row_shape = vector_sum_row->tensor_shape(); vector_sum_row_shape.collapse_from(1); TensorShape collapsed_dst_shape(expected_dst_shape); collapsed_dst_shape.collapse_from(dst_batch_idx); ARM_COMPUTE_RETURN_ERROR_ON_MSG(vector_sum_row_shape[1] != collapsed_dst_shape[dst_batch_idx], "vector_sum_row must have the same number of batches of dst tensor"); if (gemm_info.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 (dst->total_size() != 0) { ARM_COMPUTE_RETURN_ERROR_ON(output_stage.output_data_type != dst->data_type()); } ARM_COMPUTE_RETURN_ERROR_ON(output_stage.gemmlowp_min_bound > output_stage.gemmlowp_max_bound); if (output_multipliers != nullptr && output_shifts != nullptr) { 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(expected_dst_shape[0] != output_shifts->dimension(0)); ARM_COMPUTE_RETURN_ERROR_ON(expected_dst_shape[0] != output_multipliers->dimension(0)); } } } return Status{}; } std::pair validate_and_configure_window(const ITensorInfo *src0, const ITensorInfo *src1, ITensorInfo *dst, const GEMMKernelInfo &gemm_info, ITensorInfo *vector_sum_col, const ITensorInfo *vector_sum_row, ITensorInfo *bias, ITensorInfo *output_multipliers, ITensorInfo *output_shifts, ElementsProcessed &num_elements_processed) { const GEMMLowpOutputStageInfo output_stage = gemm_info.output_stage; unsigned int &num_elems_processed_per_iteration_x = num_elements_processed[0]; unsigned int &num_elems_processed_per_iteration_y = num_elements_processed[1]; bool reinterpret_input_as_3d = gemm_info.reinterpret_input_as_3d; bool reinterpret_output_as_3d = (gemm_info.depth_output_gemm3d != 0); Window win{}; Window win_out{}; bool window_changed = false; // In case both input and dst have to be reinterpreted as 3D tensors, // force reinterpret_input_as_3d and reinterpret_output_as_3d to be false. if (reinterpret_input_as_3d == reinterpret_output_as_3d) { reinterpret_output_as_3d = false; } // dst tensor auto initialization if not yet initialized const TensorShape expected_dst_shape = compute_mm_shape(*src0, *src1, gemm_info); if (output_stage.type != GEMMLowpOutputStageType::NONE) { auto_init_if_empty( *dst, src0->clone()->set_tensor_shape(expected_dst_shape).set_data_type(output_stage.output_data_type)); } else { auto_init_if_empty(*dst, src0->clone()->set_tensor_shape(expected_dst_shape).set_data_type(DataType::S32)); } TensorInfo tmp_info(*dst); if (reinterpret_output_as_3d) { // Since the dst tensor has to be reinterpreted as 3D and the execute window is based on a 2D GEMM, // the window needs to be constructed on the 2D collapsed version of the tensor TensorShape tmp_shape(dst->tensor_shape()); tmp_shape.collapse(2U, 1U); tmp_info.set_tensor_shape(tmp_shape); } // Configure kernel window num_elems_processed_per_iteration_x = gemm_info.rhs_info.n0; num_elems_processed_per_iteration_y = gemm_info.lhs_info.m0; win = calculate_max_window(tmp_info, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y)); win_out = calculate_max_window(*dst, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y)); if (output_stage.type == GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT) { if (gemm_info.a_offset != 0) { AccessWindowHorizontal vector_sum_col_access(vector_sum_col, 0, num_elems_processed_per_iteration_x); window_changed = window_changed || update_window_and_padding(win_out, vector_sum_col_access); } // No access window needed for vector_sum_row ARM_COMPUTE_UNUSED(vector_sum_row); if (bias != nullptr) { AccessWindowHorizontal bias_access(bias, 0, num_elems_processed_per_iteration_x); window_changed = window_changed || update_window_and_padding(win_out, bias_access); } if (output_multipliers != nullptr && output_stage.is_quantized_per_channel) { AccessWindowHorizontal output_multipliers_access(output_multipliers, 0, num_elems_processed_per_iteration_x); AccessWindowHorizontal output_shifts_access(output_shifts, 0, num_elems_processed_per_iteration_x); window_changed = window_changed || update_window_and_padding(win_out, output_multipliers_access, output_shifts_access); } } // Collapse along the Z direction // This collapse needs to be here in order to tune the Z dimension of LWS Window collapsed = win; const unsigned int dimension_to_collapse = std::min(static_cast(dst->num_dimensions()), 2u); collapsed = win.collapse(win, dimension_to_collapse); Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{}; return std::make_pair(err, collapsed); } } // namespace ClGemmLowpMatrixMultiplyReshapedOnlyRhsKernel::ClGemmLowpMatrixMultiplyReshapedOnlyRhsKernel() { _type = CLKernelType::GEMM; } void ClGemmLowpMatrixMultiplyReshapedOnlyRhsKernel::configure(const CLCompileContext &compile_context, const ITensorInfo *src0, const ITensorInfo *src1, ITensorInfo *dst, const GEMMKernelInfo &gemm_info, ITensorInfo *vector_sum_col, const ITensorInfo *vector_sum_row, ITensorInfo *bias, ITensorInfo *output_multipliers, ITensorInfo *output_shifts) { ARM_COMPUTE_ERROR_ON_NULLPTR(src0, src1, dst); ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(src0, src1, dst, gemm_info, vector_sum_col, vector_sum_row, bias, output_multipliers, output_shifts)); auto padding_info = get_padding_info({src0, src1, dst, vector_sum_row}); const GEMMRHSMatrixInfo rhs_info = gemm_info.rhs_info; const GEMMLHSMatrixInfo lhs_info = gemm_info.lhs_info; const GEMMLowpOutputStageInfo output_stage = gemm_info.output_stage; const int32_t a_offset = gemm_info.a_offset; const int32_t b_offset = gemm_info.b_offset; _reinterpret_input_as_3d = gemm_info.reinterpret_input_as_3d; _reinterpret_output_as_3d = (gemm_info.depth_output_gemm3d != 0); _use_dummy_work_items = preferred_dummy_work_items_support(CLKernelLibrary::get().get_device()); _is_quantized_per_channel = output_stage.is_quantized_per_channel; // In case both input and dst have to be reinterpreted as 3D tensors, // force reinterpret_input_as_3d and reinterpret_output_as_3d to be false. if (_reinterpret_input_as_3d == _reinterpret_output_as_3d) { _reinterpret_input_as_3d = false; _reinterpret_output_as_3d = false; } // Check if we need to slide the matrix B const unsigned int num_dimensions_src0 = src0->num_dimensions(); _slide_matrix_b = (src1->num_dimensions() >= num_dimensions_src0); ElementsProcessed num_elements_processed{}; // Configure kernel window auto win_config = validate_and_configure_window(src0, src1, dst, gemm_info, vector_sum_col, vector_sum_row, bias, output_multipliers, output_shifts, num_elements_processed); ARM_COMPUTE_ERROR_THROW_ON(win_config.first); ICLKernel::configure_internal(win_config.second); // If _reinterpret_input_as_3d = _reinterpret_output_as_3d = true, // we will dispatch a batched-GEMM to reduce the complexity of the address calculation within the OpenCL kernel. // This means that the actual m used by the kernel is given by dst->dimension(1) and not by gemm_info.m const unsigned int internal_m = _reinterpret_output_as_3d ? gemm_info.m : dst->dimension(1); // Shrink M0 to be always <= M (internal_m) to prevent out-of-bounds reads. // NOTE: This might have implications on heuristics and performance const unsigned int internal_m0 = std::min(internal_m, lhs_info.m0); // Calculate partial (store instead of load) M0 and partial N0 for the partial blocks at the end of a row/column if any. This is to avoid padding. const unsigned int partial_store_m0 = internal_m % internal_m0; const unsigned int partial_store_n0 = gemm_info.n % rhs_info.n0; // Create build options CLBuildOptions build_opts; build_opts.add_option_if(_reinterpret_input_as_3d, "-DREINTERPRET_INPUT_AS_3D"); build_opts.add_option_if(_reinterpret_output_as_3d, "-DREINTERPRET_OUTPUT_AS_3D"); build_opts.add_option_if(_reinterpret_input_as_3d || _reinterpret_output_as_3d, "-DHEIGHT_GEMM3D=" + support::cpp11::to_string(dst->dimension(1))); build_opts.add_option_if(_reinterpret_input_as_3d || _reinterpret_output_as_3d, "-DDEPTH_GEMM3D=" + support::cpp11::to_string(dst->dimension(2))); build_opts.add_option_if(!_slide_matrix_b, "-DMATRIX_B_DEPTH=" + support::cpp11::to_string(src1->dimension(2))); build_opts.add_option_if(rhs_info.interleave, "-DRHS_INTERLEAVE"); build_opts.add_option_if(_use_dummy_work_items, "-DDUMMY_WORK_ITEMS"); build_opts.add_option("-DM=" + support::cpp11::to_string(internal_m)); build_opts.add_option("-DN=" + support::cpp11::to_string(gemm_info.n)); build_opts.add_option("-DK=" + support::cpp11::to_string(gemm_info.k)); build_opts.add_option("-DM0=" + support::cpp11::to_string(internal_m0)); build_opts.add_option("-DN0=" + support::cpp11::to_string(rhs_info.n0)); build_opts.add_option("-DK0=" + support::cpp11::to_string(rhs_info.k0)); build_opts.add_option("-DH0=" + support::cpp11::to_string(rhs_info.h0)); build_opts.add_option("-DPARTIAL_STORE_M0=" + support::cpp11::to_string(partial_store_m0)); build_opts.add_option("-DPARTIAL_STORE_N0=" + support::cpp11::to_string(partial_store_n0)); build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(src0->data_type())); build_opts.add_option("-DACC_DATA_TYPE=" + get_cl_dot8_acc_type_from_data_type(src0->data_type())); std::string kernel_name("gemmlowp_mm_reshaped_only_rhs_"); kernel_name += rhs_info.transpose ? "t" : "nt"; if (output_stage.type == GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT) { kernel_name += "_fused_output_stage_fixedpoint"; _fuse_output_stage = true; // If a_offset == 0, vector_sum_col can be a nullptr if (a_offset != 0 && vector_sum_col != nullptr) { build_opts.add_option("-DA_OFFSET=" + support::cpp11::to_string(a_offset)); build_opts.add_option_if(vector_sum_col->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 * src0->dimension(0))); build_opts.add_option_if(bias != nullptr, "-DADD_BIAS"); build_opts.add_option("-DRESULT_OFFSET=" + support::cpp11::to_string(output_stage.gemmlowp_offset)); // In case of _is_quantized_per_channel, RESULT_MULTIPLIER and RESULT_SHIFT are not utilized, but they are passed as a part of T_QUANTIZE8 macro. if (!_is_quantized_per_channel) { 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])); } else { build_opts.add_option("-DRESULT_MULTIPLIER=0"); build_opts.add_option("-DRESULT_SHIFT=0"); } build_opts.add_option_if(_is_quantized_per_channel, "-DPER_CHANNEL_QUANTIZATION"); const int min = output_stage.gemmlowp_min_bound; const int max = output_stage.gemmlowp_max_bound; PixelValue min_val{}; PixelValue max_val{}; std::tie(min_val, max_val) = get_min_max(dst->data_type()); build_opts.add_option_if(min != min_val.get(), "-DMIN_BOUND=" + support::cpp11::to_string(min)); build_opts.add_option_if(max != max_val.get(), "-DMAX_BOUND=" + support::cpp11::to_string(max)); } // A macro guard to compile ONLY the kernel of interest build_opts.add_option("-D" + upper_string(kernel_name)); // Create kernel _kernel = create_kernel(compile_context, kernel_name, build_opts.options()); // Set config_id for enabling LWS tuning _config_id = kernel_name; _config_id += "_"; _config_id += dot8_supported(CLKernelLibrary::get().get_device()) ? "_dot8" : ""; _config_id += "_"; _config_id += (_reinterpret_input_as_3d ? "3di_" : ""); _config_id += (_reinterpret_output_as_3d ? "3do_" : ""); _config_id += support::cpp11::to_string(dst->dimension(1)); _config_id += "_"; _config_id += support::cpp11::to_string(dst->dimension(0)); _config_id += "_"; _config_id += support::cpp11::to_string(gemm_info.k); _config_id += "_"; _config_id += support::cpp11::to_string(dst->dimension(2)); _config_id += "_"; _config_id += support::cpp11::to_string(lhs_info.m0); _config_id += "_"; _config_id += support::cpp11::to_string(rhs_info.n0); _config_id += "_"; _config_id += support::cpp11::to_string(rhs_info.k0); _config_id += "_"; _config_id += support::cpp11::to_string(rhs_info.h0); _config_id += "_"; _config_id += support::cpp11::to_string(rhs_info.interleave); ARM_COMPUTE_ERROR_ON(has_padding_changed(padding_info)); } Status ClGemmLowpMatrixMultiplyReshapedOnlyRhsKernel::validate(const ITensorInfo *src0, const ITensorInfo *src1, const ITensorInfo *dst, const GEMMKernelInfo &gemm_info, const ITensorInfo *vector_sum_col, const ITensorInfo *vector_sum_row, const ITensorInfo *bias, const ITensorInfo *output_multipliers, const ITensorInfo *output_shifts) { ElementsProcessed num_elements_processed{}; ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(src0, src1, dst, gemm_info, vector_sum_col, vector_sum_row, bias, output_multipliers, output_shifts)); ARM_COMPUTE_RETURN_ON_ERROR( validate_and_configure_window(src0->clone().get(), src1->clone().get(), dst->clone().get(), gemm_info, 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_multipliers != nullptr ? output_multipliers->clone().get() : nullptr, output_shifts != nullptr ? output_shifts->clone().get() : nullptr, num_elements_processed) .first); return Status{}; } void ClGemmLowpMatrixMultiplyReshapedOnlyRhsKernel::run_op(ITensorPack &tensors, const Window &window, cl::CommandQueue &queue) { ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICLKernel::window(), window); const auto src0 = utils::cast::polymorphic_downcast(tensors.get_const_tensor(TensorType::ACL_SRC_0)); const auto src1 = utils::cast::polymorphic_downcast(tensors.get_const_tensor(TensorType::ACL_SRC_1)); const auto bias = utils::cast::polymorphic_downcast(tensors.get_const_tensor(TensorType::ACL_BIAS)); const auto vector_sum_col = utils::cast::polymorphic_downcast(tensors.get_const_tensor(TensorType::ACL_VEC_COL_SUM)); const auto vector_sum_row = utils::cast::polymorphic_downcast(tensors.get_const_tensor(TensorType::ACL_VEC_ROW_SUM)); const auto output_shifts = utils::cast::polymorphic_downcast(tensors.get_const_tensor(TensorType::ACL_SHIFTS)); const auto output_multipliers = utils::cast::polymorphic_downcast(tensors.get_const_tensor(TensorType::ACL_MULTIPLIERS)); auto dst = utils::cast::polymorphic_downcast(tensors.get_tensor(TensorType::ACL_DST)); if (src1->info()->num_dimensions() < 3) { // The stride_z for matrix B must be zero if we do not slice ARM_COMPUTE_ERROR_ON(src1->info()->strides_in_bytes()[3] != 0); } Window slice = window.first_slice_window_3D(); Window slice_matrix_b = slice; slice_matrix_b.set(Window::DimX, Window::Dimension(0, 1, 1)); slice_matrix_b.set(Window::DimY, Window::Dimension(0, 1, 1)); if (_reinterpret_input_as_3d) { // Pass bottom paddings to the kernel if the input has to be reinterpreted as 3D tensor const unsigned int idx0 = 3 * num_arguments_per_2D_tensor() + 3; const unsigned int total_cross_plane_pad = src0->info()->padding().top + src0->info()->padding().bottom; _kernel.setArg(idx0, static_cast(total_cross_plane_pad)); } if (_reinterpret_output_as_3d) { // Pass bottom paddings to the kernel if the dst has to be reinterpreted as 3D tensor const unsigned int idx0 = 3 * num_arguments_per_2D_tensor() + 3 + (_reinterpret_input_as_3d ? 1 : 0); const unsigned int total_cross_plane_pad = dst->info()->padding().top + dst->info()->padding().bottom; _kernel.setArg(idx0, static_cast(total_cross_plane_pad)); } // 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 { Window slice_b = slice; // Don't slice matrix B along the z dimension if matrix B has just 2 dimensions and matrix A more than 2 // This scenario can happen when the matrix multiplication is used to perform a convolution operation if (!_slide_matrix_b) { slice_b = slice_matrix_b; } unsigned int idx = 0; add_2D_tensor_argument(idx, src0, slice); add_2D_tensor_argument(idx, src1, slice_b); add_2D_tensor_argument(idx, dst, slice); _kernel.setArg(idx++, static_cast(src0->info()->strides_in_bytes()[2])); _kernel.setArg(idx++, static_cast(src1->info()->strides_in_bytes()[2])); _kernel.setArg(idx++, static_cast(dst->info()->strides_in_bytes()[2])); if (_reinterpret_input_as_3d) { // Pass bottom paddings to the kernel if the input has to be reinterpreted as 3D tensor idx++; } if (_reinterpret_output_as_3d) { // Pass bottom paddings to the kernel if the dst has to be reinterpreted as 3D tensor idx++; } if (_fuse_output_stage) { 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_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(), _use_dummy_work_items); } while (window.slide_window_slice_3D(slice)); } } // namespace kernels } // namespace opencl } // namespace arm_compute