| /* |
| * Copyright (c) 2022 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/ClGemmLowpMatrixMultiplyReshapedOnlyRhsMMULKernel.h" |
| |
| #include "arm_compute/core/CL/CLHelpers.h" |
| #include "arm_compute/core/CL/ICLTensor.h" |
| #include "arm_compute/core/TensorInfo.h" |
| #include "arm_compute/core/utils/misc/ShapeCalculator.h" |
| |
| #include "src/core/helpers/AutoConfiguration.h" |
| #include "src/core/helpers/WindowHelpers.h" |
| |
| #include "support/Cast.h" |
| |
| 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_MSG(!arm_matrix_multiply_supported(CLKernelLibrary::get().get_device()), "The extension cl_arm_matrix_multiply is not supported on the target platform"); |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src0, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src0, src1); |
| 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 != 4 || lhs_info.k0 != 4, "Only 4 is supported as value for k0"); |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(!(lhs_info.m0 == 1 || lhs_info.m0 == 2 || lhs_info.m0 == 4), "Only 1,2,4 are supported for m0"); |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(!(rhs_info.n0 == 1 || rhs_info.n0 == 4 || rhs_info.n0 == 8), "Only 1,4,8 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(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_output_as_3d = (gemm_info.depth_output_gemm3d != 0); |
| |
| Window win{}; |
| bool window_changed = false; |
| |
| constexpr unsigned int mmul_n0 = 4; |
| constexpr unsigned int mmul_m0 = 4; |
| constexpr unsigned int mmul_k0 = 16; |
| |
| 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 = 1; |
| num_elems_processed_per_iteration_y = 1; |
| |
| win = calculate_max_window(tmp_info, 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, 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, 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, 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 |
| const unsigned int dimension_to_collapse = std::min(static_cast<unsigned int>(dst->num_dimensions()), 2u); |
| Window collapsed = win.collapse(win, dimension_to_collapse); |
| |
| // Reconfigure window size, one arm_matrix_multiply kernel needs 16 threads to finish. |
| Window::Dimension x_dimension = collapsed.x(); |
| Window::Dimension y_dimension = collapsed.y(); |
| |
| // Make M and N multiple of M0 and N0 respectively |
| const unsigned int ceil_to_multiple_n_n0 = ceil_to_multiple(x_dimension.end(), gemm_info.rhs_info.n0); |
| const unsigned int ceil_to_multiple_m_m0 = ceil_to_multiple(y_dimension.end(), gemm_info.lhs_info.m0); |
| |
| // Divide M and N by M0 and N0 respectively |
| const unsigned int n_div_n0 = ceil_to_multiple_n_n0 / gemm_info.rhs_info.n0; |
| const unsigned int m_div_m0 = ceil_to_multiple_m_m0 / gemm_info.lhs_info.m0; |
| |
| // Make n_div_n0 and m_div_m0 multiple of mmul_n0 and mmul_k0 respectively |
| const unsigned int ceil_to_multiple_n_div_n0_mmul_n0 = ceil_to_multiple(n_div_n0, mmul_n0); |
| const unsigned int ceil_to_multiple_m_div_m0_mmul_m0 = ceil_to_multiple(m_div_m0, mmul_k0); |
| |
| // Ensure x_dimension is multiple of MMUL block size (mmul_n0 * mmul_m0) |
| x_dimension.set_end(ceil_to_multiple_n_div_n0_mmul_n0 * mmul_n0); |
| y_dimension.set_end(ceil_to_multiple_m_div_m0_mmul_m0 / mmul_m0); |
| |
| collapsed.set(Window::DimX, x_dimension); |
| collapsed.set(Window::DimY, y_dimension); |
| |
| Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{}; |
| return std::make_pair(err, collapsed); |
| } |
| } // namespace |
| |
| ClGemmLowpMatrixMultiplyReshapedOnlyRhsMMULKernel::ClGemmLowpMatrixMultiplyReshapedOnlyRhsMMULKernel() |
| { |
| _type = CLKernelType::GEMM; |
| } |
| |
| void ClGemmLowpMatrixMultiplyReshapedOnlyRhsMMULKernel::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; |
| constexpr int mmul_m0 = 4; |
| constexpr int mmul_n0 = 4; |
| constexpr int mmul_k0 = 16; |
| |
| _m = gemm_info.m; |
| _n = gemm_info.n; |
| _k = gemm_info.k; |
| |
| 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); |
| |
| const unsigned int m0_leftover = _m % lhs_info.m0; |
| const unsigned int n0_leftover = _n % rhs_info.n0; |
| |
| // Create build options |
| CLBuildOptions build_opts; |
| build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(src0->data_type())); |
| build_opts.add_option("-DVEC_TYPE=" + get_cl_type_from_data_type(src0->data_type()) + "4"); |
| build_opts.add_option("-DACC_DATA_TYPE=int"); |
| build_opts.add_option("-DOUT_DATA_TYPE=" + get_cl_type_from_data_type(dst->data_type())); |
| build_opts.add_option("-DM0=" + support::cpp11::to_string(lhs_info.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("-DM0_LEFTOVER=" + support::cpp11::to_string(m0_leftover)); |
| build_opts.add_option("-DN0_LEFTOVER=" + support::cpp11::to_string(n0_leftover)); |
| build_opts.add_option("-DMMUL_M0=" + support::cpp11::to_string(mmul_m0)); |
| build_opts.add_option("-DMMUL_N0=" + support::cpp11::to_string(mmul_n0)); |
| build_opts.add_option("-DMMUL_K0=" + support::cpp11::to_string(mmul_k0)); |
| build_opts.add_option("-DACTIVATION_TYPE=" + lower_string(string_from_activation_func(gemm_info.activation_info.activation()))); |
| build_opts.add_option("-DA_VAL=" + float_to_string_with_full_precision(gemm_info.activation_info.a())); |
| build_opts.add_option("-DB_VAL=" + float_to_string_with_full_precision(gemm_info.activation_info.b())); |
| |
| std::string kernel_name("gemmlowp_mm_reshaped_only_rhs_mmul"); |
| |
| if(output_stage.type == GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT) |
| { |
| build_opts.add_option("-DFUSED_OUTPUT_STAGE_FIXED_POINT"); |
| _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_if(gemm_info.broadcast_bias == true, "-DBROADCAST_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])); |
| |
| 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)); |
| } |
| |
| // 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 += (bias != nullptr ? "add_bias_" : ""); |
| _config_id += (gemm_info.broadcast_bias ? "broadcast_bias_" : ""); |
| _config_id += (gemm_info.activation_info.enabled() ? "fused_activation_" : ""); |
| _config_id += lower_string(string_from_data_type(src0->data_type())); |
| _config_id += "_"; |
| _config_id += support::cpp11::to_string(_m); |
| _config_id += "_"; |
| _config_id += support::cpp11::to_string(_n); |
| _config_id += "_"; |
| _config_id += support::cpp11::to_string(_k); |
| _config_id += "_"; |
| _config_id += support::cpp11::to_string(lhs_info.m0); |
| _config_id += "_"; |
| _config_id += support::cpp11::to_string(rhs_info.n0); |
| |
| ARM_COMPUTE_ERROR_ON(has_padding_changed(padding_info)); |
| } |
| |
| Status ClGemmLowpMatrixMultiplyReshapedOnlyRhsMMULKernel::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 ClGemmLowpMatrixMultiplyReshapedOnlyRhsMMULKernel::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 src2 = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_2)); |
| 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)); |
| auto dst = utils::cast::polymorphic_downcast<ICLTensor *>(tensors.get_tensor(TensorType::ACL_DST)); |
| |
| ARM_COMPUTE_ERROR_ON_NULLPTR(src0, src1, 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); |
| } |
| |
| cl::Image2D src1_image2d; |
| |
| Window slice = window.first_slice_window_3D(); |
| |
| do |
| { |
| unsigned int idx = 0; |
| |
| add_3d_tensor_nhw_argument(idx, src0); |
| add_3d_tensor_nhw_argument(idx, src1); |
| |
| // Bias buffer (_add_bias == true) |
| if(src2 != nullptr) |
| { |
| add_3d_tensor_nhw_argument(idx, src2); |
| } |
| // dst buffer |
| add_3d_tensor_nhw_argument(idx, dst); |
| |
| // Pass m, n and k at runtime as signed ints, to ensure results of any subtraction they could be operand in, would still be signed. |
| _kernel.setArg<cl_int>(idx++, _m); |
| _kernel.setArg<cl_int>(idx++, _n); |
| _kernel.setArg<cl_int>(idx++, _k); |
| |
| if(_fuse_output_stage) |
| { |
| if(vector_sum_col != nullptr) |
| { |
| add_3d_tensor_nhw_argument(idx, vector_sum_col); |
| } |
| if(vector_sum_row != nullptr) |
| { |
| add_3d_tensor_nhw_argument(idx, vector_sum_row); |
| } |
| } |
| |
| enqueue(queue, *this, slice, cl::NDRange(32, 2), false); |
| } |
| while(window.slide_window_slice_3D(slice)); |
| } |
| } // namespace kernels |
| } // namespace opencl |
| } // namespace arm_compute |