/* * Copyright (c) 2019-2020 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/CLGEMMMatrixMultiplyReshapedOnlyRHSKernel.h" #include "arm_compute/core/AccessWindowStatic.h" #include "arm_compute/core/CL/CLValidate.h" #include "arm_compute/core/CL/ICLTensor.h" #include "arm_compute/core/CL/gemm/CLGEMMHelpers.h" #include "arm_compute/core/Error.h" #include "arm_compute/core/Helpers.h" #include "arm_compute/core/TensorInfo.h" #include "arm_compute/core/Utils.h" #include "arm_compute/core/utils/helpers/float_ops.h" #include "arm_compute/core/utils/misc/ShapeCalculator.h" #include "src/core/CL/CLUtils.h" #include "support/StringSupport.h" #include using namespace arm_compute::misc::shape_calculator; namespace arm_compute { namespace { using ElementsProcessed = Steps; Status validate_arguments(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output, float alpha, float beta, const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info, const GEMMKernelInfo &gemm_info) { ARM_COMPUTE_UNUSED(alpha); 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(input0->num_dimensions() > 4, "The number of dimensions for the LHS matrix must be <= 4"); ARM_COMPUTE_RETURN_ERROR_ON_MSG(input1->num_dimensions() > 3, "The number of dimensions for the RHS matrix must be <= 3"); ARM_COMPUTE_RETURN_ERROR_ON_MSG(lhs_info.m0 < 1 || lhs_info.m0 > 8, "Only 1,2,3,4,5,6,7,8 are supported for m0"); ARM_COMPUTE_RETURN_ERROR_ON(rhs_info.k0 > 16 || rhs_info.k0 < 2); ARM_COMPUTE_RETURN_ERROR_ON_MSG(((rhs_info.k0 & (rhs_info.k0 - 1)) && rhs_info.k0 != 3), "Only 2,3,4,8,16 are supported for k0"); ARM_COMPUTE_RETURN_ERROR_ON(rhs_info.n0 > 16 || rhs_info.n0 < 2); ARM_COMPUTE_RETURN_ERROR_ON_MSG(((rhs_info.n0 & (rhs_info.n0 - 1)) && rhs_info.n0 != 3), "Only 2,3,4,8,16 are supported for n0"); ARM_COMPUTE_RETURN_ERROR_ON_MSG((gemm_info.reinterpret_input_as_3d || gemm_info.depth_output_gemm3d != 0) && (input2 != nullptr) && (!gemm_info.broadcast_bias), "Bias addition only supported with broadcast mode in case the input or output has to be reinterpreted as 3D"); ARM_COMPUTE_RETURN_ERROR_ON_MSG(gemm_info.fp_mixed_precision, "Mixed precision not supported"); ARM_COMPUTE_RETURN_ON_ERROR(cl_gemm::validate_image2d_support_on_rhs(*input1, rhs_info)); const unsigned int m = gemm_info.m; const unsigned int n = gemm_info.n; const unsigned int k = gemm_info.k; TensorShape tensor_shape1{ input1->tensor_shape() }; tensor_shape1.set(0, n); tensor_shape1.set(1, k); 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, input0); if(gemm_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"); } } const TensorInfo tensor_info1 = input1->clone()->set_tensor_shape(tensor_shape1); const TensorInfo tensor_info_reshaped1 = input1->clone()->set_tensor_shape(compute_rhs_reshaped_shape(tensor_info1, rhs_info)); ARM_COMPUTE_RETURN_ERROR_ON(input0->dimension(0) != k); if(gemm_info.reinterpret_input_as_3d) { ARM_COMPUTE_RETURN_ERROR_ON(input0->dimension(1) * input0->dimension(2) != m); } else { ARM_COMPUTE_RETURN_ERROR_ON(input0->dimension(1) != m); } ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input1, &tensor_info_reshaped1); if(output->total_size() != 0) { const TensorInfo tensor_info_output = output->clone()->set_tensor_shape(compute_mm_shape(*input0, *input1, gemm_info)); ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(output, &tensor_info_output); ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input0, output); } return Status{}; } std::pair validate_and_configure_window(ITensorInfo *input0, ITensorInfo *input1, ITensorInfo *input2, ITensorInfo *output, const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info, const GEMMKernelInfo &gemm_info, ElementsProcessed &num_elements_processed) { 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 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_output_as_3d = false; } // Output tensor auto initialization if not yet initialized auto_init_if_empty(*output, input0->clone()->set_tensor_shape(compute_mm_shape(*input0, *input1, gemm_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); } // Configure kernel window num_elems_processed_per_iteration_x = rhs_info.n0; num_elems_processed_per_iteration_y = 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(*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, input1->dimension(0), input1->dimension(1)); AccessWindowStatic output_access(output, 0, 0, output->dimension(0), output->dimension(1)); if(input2 != nullptr) { const int bias_processed_per_iteration_x = num_elems_processed_per_iteration_x; AccessWindowStatic input2_access(input2, 0, 0, ceil_to_multiple(input2->dimension(0), bias_processed_per_iteration_x), input2->dimension(1)); 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(), 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 CLGEMMMatrixMultiplyReshapedOnlyRHSKernel::CLGEMMMatrixMultiplyReshapedOnlyRHSKernel() : _input0(nullptr), _input1(nullptr), _input2(nullptr), _output(nullptr), _slide_matrix_b(true), _reinterpret_input_as_3d(false), _reinterpret_output_as_3d(false), _use_dummy_work_items(false), _add_bias(false), _broadcast_bias(false), _export_to_cl_image(false) { } void CLGEMMMatrixMultiplyReshapedOnlyRHSKernel::configure(const ICLTensor *input0, const ICLTensor *input1, const ICLTensor *input2, ICLTensor *output, float alpha, float beta, const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info, const GEMMKernelInfo &gemm_info) { configure(CLKernelLibrary::get().get_compile_context(), input0, input1, input2, output, alpha, beta, lhs_info, rhs_info, gemm_info); } void CLGEMMMatrixMultiplyReshapedOnlyRHSKernel::configure(const CLCompileContext &compile_context, const ICLTensor *input0, const ICLTensor *input1, const ICLTensor *input2, ICLTensor *output, float alpha, float beta, const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info, const GEMMKernelInfo &gemm_info) { ARM_COMPUTE_ERROR_ON_NULLPTR(input0, input1, output); ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input0->info(), input1->info(), (input2 != nullptr ? input2->info() : nullptr), output->info(), alpha, beta, lhs_info, rhs_info, gemm_info)); _input0 = input0; _input1 = input1; _input2 = helpers::float_ops::is_zero(beta) ? nullptr : input2; _output = output; _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()); _add_bias = _input2 != nullptr; _broadcast_bias = gemm_info.broadcast_bias; _export_to_cl_image = rhs_info.export_to_cl_image; // 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 = _input0->info()->num_dimensions(); _slide_matrix_b = (_input1->info()->num_dimensions() >= num_dimensions_input0); 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(), lhs_info, rhs_info, gemm_info, 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 output->info()->dimension(1) and not by gemm_info.m const unsigned int internal_m = _reinterpret_output_as_3d ? gemm_info.m : output->info()->dimension(1); const unsigned int h_gemm_3d = _reinterpret_output_as_3d ? output->info()->dimension(1) : input0->info()->dimension(1); const unsigned int d_gemm_3d = _reinterpret_output_as_3d ? output->info()->dimension(2) : input0->info()->dimension(2); // 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 % lhs_info.m0; const unsigned int partial_store_n0 = gemm_info.n % rhs_info.n0; // 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); // Create build options CLBuildOptions build_opts; build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(input0->info()->data_type())); 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(_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(gemm_info.broadcast_bias, "-DBROADCAST_BIAS"); build_opts.add_option_if(_reinterpret_input_as_3d || _reinterpret_output_as_3d, "-DHEIGHT_GEMM3D=" + support::cpp11::to_string(h_gemm_3d)); build_opts.add_option_if(_reinterpret_input_as_3d || _reinterpret_output_as_3d, "-DDEPTH_GEMM3D=" + support::cpp11::to_string(d_gemm_3d)); build_opts.add_option_if(!_slide_matrix_b, "-DMATRIX_B_DEPTH=" + support::cpp11::to_string(input1->info()->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_if(rhs_info.export_to_cl_image, "-DOPENCL_IMAGE_SUPPORT"); build_opts.add_option("-DRHS_HEIGHT=" + support::cpp11::to_string(input1->info()->dimension(1))); 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_if(gemm_info.activation_info.enabled(), "-DACTIVATION_TYPE=" + lower_string(string_from_activation_func(gemm_info.activation_info.activation()))); build_opts.add_option_if(gemm_info.activation_info.enabled(), "-DA_VAL=" + float_to_string_with_full_precision(gemm_info.activation_info.a())); build_opts.add_option_if(gemm_info.activation_info.enabled(), "-DB_VAL=" + float_to_string_with_full_precision(gemm_info.activation_info.b())); std::string kernel_name("gemm_mm_reshaped_only_rhs_"); kernel_name += rhs_info.transpose ? "t" : "nt"; kernel_name += rhs_info.export_to_cl_image ? "_texture" : ""; // 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 += (_add_bias ? "add_bias_" : ""); _config_id += (_broadcast_bias ? "broadcast_bias_" : ""); _config_id += (_reinterpret_input_as_3d ? "3di_" : ""); _config_id += (_reinterpret_output_as_3d ? "3do_" : ""); _config_id += (gemm_info.activation_info.enabled() ? "fused_activation_" : ""); _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(gemm_info.k); _config_id += "_"; _config_id += support::cpp11::to_string(output->info()->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); } Status CLGEMMMatrixMultiplyReshapedOnlyRHSKernel::validate(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output, float alpha, float beta, const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info, const GEMMKernelInfo &gemm_info) { ElementsProcessed num_elements_processed{}; ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input0, input1, input2, output, alpha, beta, lhs_info, rhs_info, gemm_info)); ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input0->clone().get(), input1->clone().get(), input2 != nullptr ? input2->clone().get() : nullptr, output->clone().get(), lhs_info, rhs_info, gemm_info, num_elements_processed) .first); return Status{}; } void CLGEMMMatrixMultiplyReshapedOnlyRHSKernel::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 total_cross_plane_pad_lhs = _input0->info()->padding().top + _input0->info()->padding().bottom; const unsigned int total_cross_plane_pad_out = _output->info()->padding().top + _output->info()->padding().bottom; cl::Image2D input1_image2d; if(_export_to_cl_image) { const TensorShape shape2d(_input1->info()->dimension(0) / 4, _input1->info()->dimension(1) * _input1->info()->dimension(2)); const size_t image_row_pitch = _input1->info()->strides_in_bytes()[1]; input1_image2d = create_image2d_from_buffer(CLKernelLibrary::get().context(), _input1->cl_buffer(), shape2d, CL_FLOAT, image_row_pitch); } 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; // LHS buffer add_2D_tensor_argument(idx, _input0, slice); // RHS buffer or RHS OpenCL image (_export_to_cl_image == true) if(_export_to_cl_image) { _kernel.setArg(idx++, input1_image2d); } else { add_2D_tensor_argument(idx, _input1, slice_b); } // Bias buffer (_add_bias == true) add_2D_tensor_argument_if(_add_bias, idx, _input2, slice); // Output buffer add_2D_tensor_argument(idx, _output, slice); // LHS stride_z _kernel.setArg(idx++, static_cast(_input0->info()->strides_in_bytes()[2])); // RHS stride_z (not used if _export_to_cl_image == true) _kernel.setArg(idx++, static_cast(_input1->info()->strides_in_bytes()[2])); // Bias stride_z (if _add_bias == true) if(_add_bias) { _kernel.setArg(idx++, static_cast(_input2->info()->strides_in_bytes()[2])); } // Output stride_z _kernel.setArg(idx++, static_cast(_output->info()->strides_in_bytes()[2])); // Cross-plan padding (if _reinterpret_input_as_3d = true) if(_reinterpret_input_as_3d) { _kernel.setArg(idx++, static_cast(total_cross_plane_pad_lhs)); } // Cross-plan padding (if _reinterpret_output_as_3d = true) if(_reinterpret_output_as_3d) { _kernel.setArg(idx++, static_cast(total_cross_plane_pad_out)); } enqueue(queue, *this, slice, lws_hint(), _use_dummy_work_items); } while(window.slide_window_slice_3D(slice)); } } // namespace arm_compute