/* * Copyright (c) 2017-2020 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/CLGEMMMatrixMultiplyKernel.h" #include "arm_compute/core/AccessWindowStatic.h" #include "arm_compute/core/CL/CLHelpers.h" #include "arm_compute/core/CL/CLKernelLibrary.h" #include "arm_compute/core/CL/CLValidate.h" #include "arm_compute/core/CL/ICLTensor.h" #include "arm_compute/core/CL/OpenCL.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/Window.h" #include "arm_compute/core/utils/helpers/float_ops.h" #include "arm_compute/core/utils/misc/ShapeCalculator.h" #include "support/StringSupport.h" #include #include namespace arm_compute { using namespace arm_compute::misc::shape_calculator; namespace { using ElementsProcessed = Steps; inline Status validate_arguments(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output, float beta, bool is_interleaved_transposed, const GEMMReshapeInfo &reshape_info, bool fp_mixed_precision) { ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input0, input1, output); ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(input0); ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input0, 1, DataType::F16, DataType::F32); ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input0, input1); ARM_COMPUTE_RETURN_ERROR_ON_MSG((fp_mixed_precision && (input0->data_type() != DataType::F16)), "Mixed precision floating point is supported only for F16 data"); ARM_COMPUTE_RETURN_ERROR_ON_MSG(input0->num_dimensions() > 4, "The number of dimensions for the matrix A must be <= 4"); ARM_COMPUTE_RETURN_ERROR_ON_MSG(input1->num_dimensions() > 3, "The number of dimensions for the matrix B must be <= 3"); ARM_COMPUTE_RETURN_ERROR_ON_MSG(is_interleaved_transposed && reshape_info.reinterpret_input_as_3d(), "The input tensor cannot be reinterpreted as 3D if is_interleaved_transposed is true"); ARM_COMPUTE_RETURN_ERROR_ON_MSG(input1->num_dimensions() > 2 && reshape_info.reinterpret_input_as_3d(), "The input1 tensor cannot have more than 2 dimensions if input0 has to be reinterpreted as 3D"); ARM_COMPUTE_RETURN_ERROR_ON_MSG((reshape_info.reinterpret_input_as_3d() || reshape_info.depth_output_gemm3d() != 0) && (input2 != nullptr) && (!reshape_info.broadcast_bias()), "Bias addition only supported with broadcast mode in case the input or output has to be reinterpreted as 3D"); if(!is_interleaved_transposed) { ARM_COMPUTE_RETURN_ERROR_ON(input0->dimension(0) != input1->dimension(1)); if(input2 != nullptr && !(helpers::float_ops::is_zero(beta))) { const unsigned int m = reshape_info.reinterpret_input_as_3d() ? input0->dimension(1) * input0->dimension(2) : input0->dimension(1); const unsigned int n = input1->dimension(0); const unsigned int input2_dim0 = input2->dimension(0); const unsigned int input2_dim1 = input2->dimension(1); ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input2, input1); if(reshape_info.broadcast_bias()) { ARM_COMPUTE_RETURN_ERROR_ON_MSG((input2_dim1 != 1 || input2_dim0 != n), "Incorrect dimension of bias matrix which is to be broadcasted"); } else { ARM_COMPUTE_RETURN_ERROR_ON_MSG((input2_dim0 != n || input2_dim1 != m), "Incorrect dimension of bias matrix"); } } } else { GEMMRHSMatrixInfo rhs_info; GEMMLHSMatrixInfo lhs_info; const auto m = static_cast(reshape_info.m()); const auto n = static_cast(reshape_info.n()); const int k = reshape_info.k(); const int mult_transpose1xW_width = reshape_info.mult_transpose1xW_width(); const int mult_interleave4x4_height = reshape_info.mult_interleave4x4_height(); rhs_info.n0 = 16 / input1->element_size(); rhs_info.k0 = 1; rhs_info.h0 = mult_transpose1xW_width; rhs_info.interleave = false; rhs_info.transpose = false; lhs_info.m0 = 4; lhs_info.k0 = 4; lhs_info.v0 = mult_interleave4x4_height; lhs_info.interleave = true; lhs_info.transpose = true; TensorShape tensor_shape0{ input0->tensor_shape() }; tensor_shape0.set(0, k); tensor_shape0.set(1, m); TensorShape tensor_shape1{ input1->tensor_shape() }; tensor_shape1.set(0, n); tensor_shape1.set(1, k); const TensorInfo tensor_info0 = input0->clone()->set_tensor_shape(tensor_shape0); const TensorInfo tensor_info1 = input1->clone()->set_tensor_shape(tensor_shape1); const TensorInfo tensor_info_reshaped0 = input0->clone()->set_tensor_shape(compute_lhs_reshaped_shape(tensor_info0, lhs_info)); const TensorInfo tensor_info_reshaped1 = input1->clone()->set_tensor_shape(compute_rhs_reshaped_shape(tensor_info1, rhs_info)); ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input0, &tensor_info_reshaped0); ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input1, &tensor_info_reshaped1); if(input2 != nullptr && !(helpers::float_ops::is_zero(beta))) { const unsigned int input2_dim0 = input2->dimension(0); const unsigned int input2_dim1 = input2->dimension(1); ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input2, input1); if(reshape_info.broadcast_bias()) { ARM_COMPUTE_RETURN_ERROR_ON_MSG((input2_dim1 != 1 || input2_dim0 != n), "Incorrect dimension of bias matrix which is to be broadcasted"); } else { ARM_COMPUTE_RETURN_ERROR_ON_MSG((input2_dim0 != n || input2_dim1 != m), "Incorrect dimension of bias matrix"); } } } if(output->total_size() != 0) { const TensorInfo tensor_info_output = output->clone()->set_tensor_shape(compute_mm_shape(*input0, *input1, is_interleaved_transposed, reshape_info)); ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(output, &tensor_info_output); ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input0, output); } return Status{}; } inline std::pair validate_and_configure_window(ITensorInfo *input0, ITensorInfo *input1, ITensorInfo *input2, ITensorInfo *output, float beta, bool is_interleaved_transposed, const GEMMReshapeInfo &reshape_info, GPUTarget gpu_target, ElementsProcessed &num_elements_processed) { ARM_COMPUTE_UNUSED(beta); bool window_changed = false; Window win{}; Window win_out{}; const DataType data_type = input0->data_type(); 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 = reshape_info.reinterpret_input_as_3d(); bool reinterpret_output_as_3d = (reshape_info.depth_output_gemm3d() != 0); // In case both input and output 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; } // Output tensor auto inizialitation if not yet initialized auto_init_if_empty(*output, input0->clone()->set_tensor_shape(compute_mm_shape(*input0, *input1, is_interleaved_transposed, reshape_info))); TensorInfo tmp_info(*output); if(reinterpret_output_as_3d) { // Since the output 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(output->tensor_shape()); tmp_shape.collapse(2U, 1U); tmp_info.set_tensor_shape(tmp_shape); } if(is_interleaved_transposed) { // reinterpret_input_as_3d is not supported if is_interleaved_transposed is set ARM_COMPUTE_ERROR_ON(reshape_info.reinterpret_input_as_3d()); // Configure kernel window num_elems_processed_per_iteration_x = max_cl_vector_width / data_size_from_type(data_type); num_elems_processed_per_iteration_y = 4; // Note: bottom paddings are calculated manually as the output can be reinterpreted as 3D tensor // The only way to set properly the paddings, it is to set those explicitly through the AccessWindowStatic const int m = reshape_info.m(); const int bottom_pad = (num_elems_processed_per_iteration_y - (m % num_elems_processed_per_iteration_y)) % num_elems_processed_per_iteration_y; win = calculate_max_window(tmp_info, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y)); win_out = calculate_max_window(*output, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y)); AccessWindowStatic input0_access(input0, 0, 0, input0->dimension(0), input0->dimension(1)); AccessWindowStatic input1_access(input1, 0, 0, ceil_to_multiple(input1->dimension(0), num_elems_processed_per_iteration_x), ceil_to_multiple(input1->dimension(1), num_elems_processed_per_iteration_y)); AccessWindowStatic output_access(output, 0, 0, ceil_to_multiple(output->dimension(0), num_elems_processed_per_iteration_x), output->dimension(1) + bottom_pad); if(input2 != nullptr) { const int bias_processed_per_iteration_x = num_elems_processed_per_iteration_x; const int bias_processed_per_iteration_y = reshape_info.broadcast_bias() ? 1 : num_elems_processed_per_iteration_y; AccessWindowStatic input2_access(input2, 0, 0, ceil_to_multiple(input2->dimension(0), bias_processed_per_iteration_x), ceil_to_multiple(input2->dimension(1), bias_processed_per_iteration_y)); window_changed = update_window_and_padding(win, input0_access, input1_access, input2_access) || // window used by the execute_window_loop update_window_and_padding(win_out, output_access); // window used to update the padding requirements of output tensor } else { window_changed = update_window_and_padding(win, input0_access, input1_access) || // window used by the execute_window_loop update_window_and_padding(win_out, output_access); // window used to update the padding requirements of output tensor } output_access.set_valid_region(win_out, ValidRegion(Coordinates(0, 0), output->tensor_shape())); } else // The input tensors have not been reshaped { // Special case for 1xN, 2xN, 3xN and 4xN input0 tensor. num_elems_processed_per_iteration_x is set up for the default case. num_elems_processed_per_iteration_x = max_cl_vector_width / data_size_from_type(data_type); num_elems_processed_per_iteration_y = std::min(static_cast(output->dimension(1)), 4); // Note: bottom paddings are calculated manually as the output can be reinterpreted as 3D tensor // The only way to set properly the paddings, it is to set those explicitly through the AccessWindowStatic const int m = reinterpret_input_as_3d ? input0->tensor_shape()[1] * input0->tensor_shape()[2] : input0->tensor_shape()[1]; const int bottom_pad = (num_elems_processed_per_iteration_y - (m % num_elems_processed_per_iteration_y)) % num_elems_processed_per_iteration_y; // Create kernels according to the architecture, data type and input size. GPUTarget arch_target = get_arch_from_target(gpu_target); if(arch_target == GPUTarget::BIFROST && data_type == DataType::F32) { num_elems_processed_per_iteration_x = (input1->dimension(0) <= 1000 && input0->num_dimensions() == 1) ? 2 : 4; } // Configure window win = calculate_max_window(tmp_info, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y)); win_out = calculate_max_window(*output, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y)); AccessWindowStatic input0_access(input0, 0, 0, input0->dimension(0), input0->dimension(1) + bottom_pad); AccessWindowStatic input1_access(input1, 0, 0, ceil_to_multiple(input1->dimension(0), num_elems_processed_per_iteration_x), input1->dimension(1)); AccessWindowStatic output_access(output, 0, 0, ceil_to_multiple(output->dimension(0), num_elems_processed_per_iteration_x), output->dimension(1) + bottom_pad); if(input2 != nullptr) { const int bias_processed_per_iteration_x = num_elems_processed_per_iteration_x; const int bias_processed_per_iteration_y = reshape_info.broadcast_bias() ? 1 : num_elems_processed_per_iteration_y; AccessWindowStatic input2_access(input2, 0, 0, ceil_to_multiple(input2->dimension(0), bias_processed_per_iteration_x), ceil_to_multiple(input2->dimension(1), bias_processed_per_iteration_y)); window_changed = update_window_and_padding(win, input0_access, input1_access, input2_access) || // window used by the execute_window_loop update_window_and_padding(win_out, output_access); // window used to update the padding requirements of output tensor } else { window_changed = update_window_and_padding(win, input0_access, input1_access) || // window used by the execute_window_loop update_window_and_padding(win_out, output_access); // window used to update the padding requirements of output tensor } Coordinates coord; coord.set_num_dimensions(output->num_dimensions()); output_access.set_valid_region(win_out, ValidRegion(coord, output->tensor_shape())); } // 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(output->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 CLGEMMMatrixMultiplyKernel::CLGEMMMatrixMultiplyKernel() : _input0(nullptr), _input1(nullptr), _input2(nullptr), _output(nullptr), _slide_matrix_b(true), _reinterpret_input_as_3d(false), _reinterpret_output_as_3d(false), _add_bias(false), _broadcast_bias(false) { } void CLGEMMMatrixMultiplyKernel::configure(const ICLTensor *input0, const ICLTensor *input1, const ICLTensor *input2, ICLTensor *output, float alpha, float beta, bool is_interleaved_transposed, const GEMMReshapeInfo &reshape_info, bool fp_mixed_precision, const ActivationLayerInfo &activation_info) { configure(CLKernelLibrary::get().get_compile_context(), input0, input1, input2, output, alpha, beta, is_interleaved_transposed, reshape_info, fp_mixed_precision, activation_info); } void CLGEMMMatrixMultiplyKernel::configure(const CLCompileContext &compile_context, const ICLTensor *input0, const ICLTensor *input1, const ICLTensor *input2, ICLTensor *output, float alpha, float beta, bool is_interleaved_transposed, const GEMMReshapeInfo &reshape_info, bool fp_mixed_precision, const ActivationLayerInfo &activation_info) { ARM_COMPUTE_ERROR_ON_NULLPTR(input0, input1, output); // Perform validate step ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input0->info(), input1->info(), (input2 != nullptr) ? input2->info() : nullptr, output->info(), beta, is_interleaved_transposed, reshape_info, fp_mixed_precision)); _input0 = input0; _input1 = input1; _input2 = helpers::float_ops::is_zero(beta) ? nullptr : input2; _output = output; _reinterpret_input_as_3d = reshape_info.reinterpret_input_as_3d(); _reinterpret_output_as_3d = (reshape_info.depth_output_gemm3d() != 0); _add_bias = _input2 != nullptr; _broadcast_bias = reshape_info.broadcast_bias(); // In case both input and output 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_input0 = _reinterpret_input_as_3d ? _input0->info()->num_dimensions() - 1 : _input0->info()->num_dimensions(); _slide_matrix_b = (_input1->info()->num_dimensions() >= num_dimensions_input0); const DataType data_type = input0->info()->data_type(); // Get target architecture GPUTarget gpu_target = get_target(); ElementsProcessed num_elements_processed{}; // Configure kernel window auto win_config = validate_and_configure_window(input0->info(), input1->info(), (input2 != nullptr) ? input2->info() : nullptr, output->info(), beta, is_interleaved_transposed, reshape_info, gpu_target, num_elements_processed); ARM_COMPUTE_ERROR_THROW_ON(win_config.first); ICLKernel::configure_internal(win_config.second); // Create build options CLBuildOptions build_opts; build_opts.add_option_if(!(helpers::float_ops::is_one(alpha)), "-DALPHA=" + float_to_string_with_full_precision(alpha)); build_opts.add_option_if(_input2 != nullptr, "-DBETA=" + float_to_string_with_full_precision(beta)); build_opts.add_option_if(helpers::float_ops::is_one(beta), "-DUNIT_BETA"); build_opts.add_option_if(reshape_info.broadcast_bias(), "-DBROADCAST_BIAS"); 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(output->info()->dimension(1))); build_opts.add_option_if(_reinterpret_input_as_3d || _reinterpret_output_as_3d, "-DDEPTH_GEMM3D=" + support::cpp11::to_string(output->info()->dimension(2))); build_opts.add_option_if(!_slide_matrix_b, "-DMATRIX_B_DEPTH=" + support::cpp11::to_string(input1->info()->dimension(2))); build_opts.add_option_if(activation_info.enabled(), "-DACTIVATION_TYPE=" + lower_string(string_from_activation_func(activation_info.activation()))); build_opts.add_option_if(activation_info.enabled(), "-DA_VAL=" + float_to_string_with_full_precision(activation_info.a())); build_opts.add_option_if(activation_info.enabled(), "-DB_VAL=" + float_to_string_with_full_precision(activation_info.b())); const bool is_bifrost = get_arch_from_target(gpu_target) == GPUTarget::BIFROST; std::string kernel_name; if(is_interleaved_transposed) { const int mult_transpose1xW_width = reshape_info.mult_transpose1xW_width(); const int mult_interleave4x4_height = reshape_info.mult_interleave4x4_height(); build_opts.add_option("-DCOLS_B=" + support::cpp11::to_string(input1->info()->dimension(0))); build_opts.add_option("-DMULT_TRANSPOSE1XW_WIDTH=" + support::cpp11::to_string(mult_transpose1xW_width)); build_opts.add_option("-DMULT_INTERLEAVE4X4_HEIGHT=" + support::cpp11::to_string(mult_interleave4x4_height)); if(is_data_type_float(data_type) && is_bifrost) { kernel_name = "gemm_mm_interleaved_transposed_" + lower_string(string_from_data_type(data_type)) + "_bifrost"; } else { kernel_name = "gemm_mm_interleaved_transposed_" + lower_string(string_from_data_type(data_type)); if(fp_mixed_precision && data_type == DataType::F16) { // currently wider accumulator is only supported for fp16 kernels. kernel_name += "_acc32"; } } } else // The input tensors have not been reshaped { build_opts.add_option("-DCOLS_A=" + support::cpp11::to_string(input0->info()->dimension(0))); build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(data_type)); // Create kernels according to the architecture, data type and input size. if(is_data_type_float(data_type) && is_bifrost) { kernel_name = "gemm_mm_floating_point"; if(input0->info()->num_dimensions() != 1) { kernel_name += "_" + lower_string(string_from_data_type(data_type)) + "_bifrost"; if(fp_mixed_precision && data_type == DataType::F16) { // currently wider accumulator is only supported for fp16 kernels. kernel_name += "_acc32"; } } else if(input1->info()->dimension(0) <= 1000 && data_type == DataType::F32) { // The first kernel is optimized for the case of 1000 or less output elements (e.g. FC8 of AlexNet and VGG-16, and // FC1 of Inception v3). The second kernel is optimized for the case of greater than 1000 output elements (e.g. // FC6 and FC7 of AlexNet and VGG-16). kernel_name += "_" + lower_string(string_from_data_type(data_type)) + "_bifrost_1000"; } // The work-group size equal to the Bifrost quad size has been proved to be optimal for these kernels // via exhaustive autotuning over a range of representative layer configurations. set_lws_hint(cl::NDRange(4)); } else // (MIDGARD and F32) or (F16) { kernel_name = "gemm_mm_floating_point"; } build_opts.add_option("-DNUM_ELEMS_PROCESSED_PER_THREAD_Y=" + support::cpp11::to_string(num_elements_processed.y())); build_opts.add_option("-DNUM_ELEMS_PROCESSED_PER_THREAD_X=" + support::cpp11::to_string(num_elements_processed.x())); } // Create kernel _kernel = create_kernel(compile_context, kernel_name, build_opts.options()); // Set config_id for enabling LWS tuning _config_id = "gemm_"; _config_id += (is_interleaved_transposed ? "reshaped_" : ""); _config_id += (_add_bias ? "add_bias_" : ""); _config_id += (_broadcast_bias ? "broadcast_bias_" : ""); _config_id += (fp_mixed_precision ? "fp_mixed_" : ""); _config_id += (_reinterpret_input_as_3d ? "3di_" : ""); _config_id += (_reinterpret_output_as_3d ? "3do_" : ""); _config_id += lower_string(string_from_data_type(input0->info()->data_type())); _config_id += "_"; _config_id += support::cpp11::to_string(output->info()->dimension(1)); _config_id += "_"; _config_id += support::cpp11::to_string(output->info()->dimension(0)); _config_id += "_"; _config_id += support::cpp11::to_string(output->info()->dimension(2)); _config_id += "_"; _config_id += support::cpp11::to_string(output->info()->dimension(3)); _config_id += "_"; _config_id += (is_interleaved_transposed ? support::cpp11::to_string(input1->info()->dimension(0)) : support::cpp11::to_string(input1->info()->dimension(1))); } Status CLGEMMMatrixMultiplyKernel::validate(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output, float alpha, float beta, bool is_interleaved_transposed, const GEMMReshapeInfo &reshape_info, GPUTarget gpu_target, bool fp_mixed_precision, const ActivationLayerInfo &activation_info) { // Note: num_elements_processed will be set in validate_and_configure_window() ElementsProcessed num_elements_processed{}; ARM_COMPUTE_UNUSED(alpha); ARM_COMPUTE_UNUSED(activation_info); ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input0, input1, input2, output, beta, is_interleaved_transposed, reshape_info, fp_mixed_precision)); ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input0->clone().get(), input1->clone().get(), (input2 != nullptr) ? input2->clone().get() : nullptr, output->clone().get(), beta, is_interleaved_transposed, reshape_info, gpu_target, num_elements_processed) .first); return Status{}; } void CLGEMMMatrixMultiplyKernel::run(const Window &window, cl::CommandQueue &queue) { ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICLKernel::window(), window); if(_input1->info()->num_dimensions() < 3) { // The stride_z for matrix B must be zero if we do not slice ARM_COMPUTE_ERROR_ON(_input1->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)); const unsigned int num_arguments_bias = _add_bias ? num_arguments_per_2D_tensor() + 1 : 0; 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 + num_arguments_bias; const unsigned int total_cross_plane_pad = _input0->info()->padding().top + _input0->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 output has to be reinterpreted as 3D tensor const unsigned int idx0 = 3 * num_arguments_per_2D_tensor() + 3 + (_reinterpret_input_as_3d ? 1 : 0) + num_arguments_bias; const unsigned int total_cross_plane_pad = _output->info()->padding().top + _output->info()->padding().bottom; _kernel.setArg(idx0, static_cast(total_cross_plane_pad)); } 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, _input0, slice); add_2D_tensor_argument(idx, _input1, slice_b); if(_add_bias) { add_2D_tensor_argument(idx, _input2, slice); } add_2D_tensor_argument(idx, _output, slice); _kernel.setArg(idx++, static_cast(_input0->info()->strides_in_bytes()[2])); _kernel.setArg(idx++, static_cast(_input1->info()->strides_in_bytes()[2])); if(_add_bias) { _kernel.setArg(idx++, static_cast(_input2->info()->strides_in_bytes()[2])); } _kernel.setArg(idx++, static_cast(_output->info()->strides_in_bytes()[2])); enqueue(queue, *this, slice, lws_hint()); } while(window.slide_window_slice_3D(slice)); } } // namespace arm_compute