| /* |
| * Copyright (c) 2019-2021 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.h" |
| #include "arm_compute/core/Validate.h" |
| #include "arm_compute/core/utils/misc/ShapeCalculator.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 <tuple> |
| |
| 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<unsigned int>(k)); |
| if(gemm_info.reinterpret_input_as_3d) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON(src0->dimension(1) * src0->dimension(2) != static_cast<unsigned int>(m)); |
| } |
| else |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON(src0->dimension(1) != static_cast<unsigned int>(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<Status, Window> 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<unsigned int>(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)); |
| 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"); |
| |
| 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<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)); |
| } |
| |
| // 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<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_0)); |
| const auto src1 = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_1)); |
| const auto bias = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_BIAS)); |
| const auto vector_sum_col = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_VEC_COL_SUM)); |
| const auto vector_sum_row = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_VEC_ROW_SUM)); |
| const auto output_shifts = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SHIFTS)); |
| const auto output_multipliers = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_MULTIPLIERS)); |
| auto dst = utils::cast::polymorphic_downcast<ICLTensor *>(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<cl_uint>(idx0, static_cast<unsigned int>(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<cl_uint>(idx0, static_cast<unsigned int>(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<cl_uint>(idx++, static_cast<unsigned int>(src0->info()->strides_in_bytes()[2])); |
| _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(src1->info()->strides_in_bytes()[2])); |
| _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(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 |