/* * 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/ClGemmMatrixMultiplyNativeKernel.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/CL/CLUtils.h" #include "src/core/experimental/PostOpUtils.h" #include "src/core/helpers/AutoConfiguration.h" #include "src/core/helpers/WindowHelpers.h" #include "src/core/utils/helpers/float_ops.h" #include "support/Cast.h" #include "support/StringSupport.h" namespace arm_compute { namespace opencl { namespace kernels { namespace { using ElementsProcessed = Steps; const auto post_op_utils = experimental::PostOpCLKernelUtils( { // PostOp sequence -> {Kernel Postfix, PostOp Slots} { {}, { "", {} } }, { { experimental::PostOpType::Activation }, { "", { 1 } } }, { { experimental::PostOpType::Eltwise_Add }, { "_post_act_eltwise_op_act", { 2 } } }, { { experimental::PostOpType::Eltwise_PRelu }, { "_post_act_eltwise_op_act", { 2 } } }, { { experimental::PostOpType::Activation, experimental::PostOpType::Eltwise_Add }, { "_post_act_eltwise_op_act", { 1, 2 } } }, { { experimental::PostOpType::Activation, experimental::PostOpType::Eltwise_PRelu }, { "_post_act_eltwise_op_act", { 1, 2 } } }, { { experimental::PostOpType::Eltwise_Add, experimental::PostOpType::Activation }, { "_post_act_eltwise_op_act", { 2, 3 } } }, { { experimental::PostOpType::Eltwise_PRelu, experimental::PostOpType::Activation }, { "_post_act_eltwise_op_act", { 2, 3 } } }, { { experimental::PostOpType::Activation, experimental::PostOpType::Eltwise_Add, experimental::PostOpType::Activation }, { "_post_act_eltwise_op_act", { 1, 2, 3 } } }, { { experimental::PostOpType::Activation, experimental::PostOpType::Eltwise_PRelu, experimental::PostOpType::Activation }, { "_post_act_eltwise_op_act", { 1, 2, 3 } } } }); Status validate_arguments(const ITensorInfo *src0, const ITensorInfo *src1, const ITensorInfo *src2, const ITensorInfo *dst, 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(src0, src1, dst); ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src0, 1, DataType::F32, DataType::F16); 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"); 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.k0 > 16); 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), "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) && (src2 != nullptr) && (!gemm_info.broadcast_bias), "Bias addition only supported with broadcast mode in case the input or dst has to be reinterpreted as 3D"); ARM_COMPUTE_RETURN_ERROR_ON_MSG(gemm_info.fp_mixed_precision, "Mixed precision not supported"); ARM_COMPUTE_RETURN_ERROR_ON_MSG(rhs_info.export_to_cl_image, "Export to CLImage not supported for GEMM native"); ARM_COMPUTE_RETURN_ERROR_ON_MSG(!post_op_utils.is_post_op_sequence_supported(gemm_info.post_ops), "The sequence of Post Ops is not supported"); const unsigned int m = gemm_info.m; const unsigned int n = gemm_info.n; const unsigned int k = gemm_info.k; ARM_COMPUTE_UNUSED(m); ARM_COMPUTE_UNUSED(n); ARM_COMPUTE_UNUSED(k); ARM_COMPUTE_RETURN_ERROR_ON(src0->dimension(0) != k); ARM_COMPUTE_RETURN_ERROR_ON(src1->dimension(0) != n); ARM_COMPUTE_RETURN_ERROR_ON(src1->dimension(1) != k); if(gemm_info.reinterpret_input_as_3d) { ARM_COMPUTE_RETURN_ERROR_ON(src0->dimension(1) * src0->dimension(2) != m); } else { ARM_COMPUTE_RETURN_ERROR_ON(src0->dimension(1) != m); } if(src2 != nullptr && !(helpers::float_ops::is_zero(beta))) { const unsigned int src2_dim0 = src2->dimension(0); const unsigned int src2_dim1 = src2->dimension(1); ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src2, src1); if(gemm_info.broadcast_bias) { ARM_COMPUTE_RETURN_ERROR_ON_MSG((src2_dim1 != 1 || src2_dim0 != n), "Incorrect dimension of bias matrix which is to be broadcasted"); } else { ARM_COMPUTE_RETURN_ERROR_ON_MSG((src2_dim0 != n || src2_dim1 != m), "Incorrect dimension of bias matrix"); } } if(dst->total_size() != 0) { const TensorInfo tensor_info_dst = dst->clone()->set_tensor_shape(misc::shape_calculator::compute_mm_shape(*src0, *src1, gemm_info)); ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(dst, &tensor_info_dst); ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src0, dst); ARM_COMPUTE_RETURN_ERROR_ON_MSG(!post_op_utils.are_post_op_shapes_compliant(dst, gemm_info.post_ops), "The Post Op shapes are not compliant"); } return Status{}; } std::pair validate_and_configure_window(ITensorInfo *src0, ITensorInfo *src1, ITensorInfo *src2, ITensorInfo *dst, 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 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 auto_init_if_empty(*dst, src0->clone()->set_tensor_shape(misc::shape_calculator::compute_mm_shape(*src0, *src1, gemm_info))); 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 = 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(*dst, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y)); AccessWindowStatic src0_access(src0, 0, 0, src0->dimension(0), src0->dimension(1)); AccessWindowStatic src1_access(src1, 0, 0, ceil_to_multiple(src1->dimension(0), num_elems_processed_per_iteration_x), src1->dimension(1)); AccessWindowStatic dst_access(dst, 0, 0, dst->dimension(0), dst->dimension(1)); if(src2 != nullptr) { const int bias_processed_per_iteration_x = num_elems_processed_per_iteration_x; AccessWindowStatic src2_access(src2, 0, 0, ceil_to_multiple(src2->dimension(0), bias_processed_per_iteration_x), src2->dimension(1)); window_changed = update_window_and_padding(win, src0_access, src1_access, src2_access) || // window used by the execute_window_loop update_window_and_padding(win_out, dst_access); // window used to update the padding requirements of dst tensor } else { window_changed = update_window_and_padding(win, src0_access, src1_access) || // window used by the execute_window_loop update_window_and_padding(win_out, dst_access); // window used to update the padding requirements of dst tensor } // Collapse along the Z direction // This collapse needs to be here in order to tune the Z dimension of LWS Window collapsed = win; const unsigned int dimension_to_collapse = std::min(static_cast(dst->num_dimensions()), 2u); collapsed = win.collapse(win, dimension_to_collapse); Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{}; return std::make_pair(err, collapsed); } } // namespace ClGemmMatrixMultiplyNativeKernel::ClGemmMatrixMultiplyNativeKernel() { _type = CLKernelType::GEMM; } void ClGemmMatrixMultiplyNativeKernel::configure(const CLCompileContext &compile_context, ITensorInfo *src0, ITensorInfo *src1, ITensorInfo *src2, ITensorInfo *dst, float alpha, float beta, const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info, const GEMMKernelInfo &gemm_info) { ARM_COMPUTE_ERROR_ON_NULLPTR(src0, src1, dst); // dst tensor auto initialization if not yet initialized auto_init_if_empty(*dst, src0->clone()->set_tensor_shape(misc::shape_calculator::compute_mm_shape(*src0, *src1, gemm_info))); ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(src0, src1, src2, dst, alpha, beta, lhs_info, rhs_info, gemm_info)); auto padding_info = get_padding_info({ src0, dst }); _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 = src2 != nullptr; _num_post_op_args = gemm_info.post_ops.total_num_arguments(); // 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, src2 != nullptr ? src2 : nullptr, dst, 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 dst->dimension(1) and not by gemm_info.m const unsigned int internal_m = _reinterpret_output_as_3d ? gemm_info.m : dst->dimension(1); const unsigned int h_gemm_3d = _reinterpret_output_as_3d ? dst->dimension(1) : src0->dimension(1); const unsigned int d_gemm_3d = _reinterpret_output_as_3d ? dst->dimension(2) : src0->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); _m = internal_m; _n = gemm_info.n; _k = gemm_info.k; // 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_if(!(helpers::float_ops::is_one(alpha)), "-DALPHA=" + float_to_string_with_full_precision(alpha)); build_opts.add_option_if(src2 != 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(gemm_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(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(src1->dimension(2))); build_opts.add_option_if(_use_dummy_work_items, "-DDUMMY_WORK_ITEMS"); 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("-DPARTIAL_STORE_M0=" + support::cpp11::to_string(partial_store_m0)); build_opts.add_option("-DPARTIAL_STORE_N0=" + support::cpp11::to_string(partial_store_n0)); // If post_ops are used, then we disable the use of gemm_info.activation_info if(gemm_info.post_ops.size() > 0) { post_op_utils.set_post_ops_cl_build_options(build_opts, gemm_info.post_ops); } else { 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_native"); post_op_utils.set_post_ops_cl_kernel_name(kernel_name, gemm_info.post_ops); // 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 += (_add_bias ? "add_bias_" : ""); _config_id += (gemm_info.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(src0->data_type())); _config_id += "_"; _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); ARM_COMPUTE_ERROR_ON(has_padding_changed(padding_info)); } Status ClGemmMatrixMultiplyNativeKernel::validate(const ITensorInfo *src0, const ITensorInfo *src1, const ITensorInfo *src2, const ITensorInfo *dst, 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(src0, src1, src2, dst, alpha, beta, lhs_info, rhs_info, gemm_info)); ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(src0->clone().get(), src1->clone().get(), src2 != nullptr ? src2->clone().get() : nullptr, dst->clone().get(), lhs_info, rhs_info, gemm_info, num_elements_processed) .first); return Status{}; } void ClGemmMatrixMultiplyNativeKernel::run_op(ITensorPack &tensors, const Window &window, cl::CommandQueue &queue) { ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICLKernel::window(), window); const auto src0 = utils::cast::polymorphic_downcast(tensors.get_const_tensor(TensorType::ACL_SRC_0)); const auto src1 = utils::cast::polymorphic_downcast(tensors.get_const_tensor(TensorType::ACL_SRC_1)); const auto src2 = utils::cast::polymorphic_downcast(tensors.get_const_tensor(TensorType::ACL_SRC_2)); auto dst = utils::cast::polymorphic_downcast(tensors.get_tensor(TensorType::ACL_DST)); ARM_COMPUTE_ERROR_ON_NULLPTR(src0, src1, dst); ARM_COMPUTE_ERROR_ON(_add_bias && src2 == nullptr); 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 unsigned int idx0; if(_add_bias) { idx0 = (4 + _num_post_op_args) * num_arguments_per_2D_tensor() + (7 + _num_post_op_args); } else { idx0 = (3 + _num_post_op_args) * num_arguments_per_2D_tensor() + (6 + _num_post_op_args); } const unsigned int total_cross_plane_pad = src0->info()->padding().top + src0->info()->padding().bottom; _kernel.setArg(idx0, static_cast(total_cross_plane_pad)); } if(_reinterpret_output_as_3d) { // Pass bottom paddings to the kernel if the dst has to be reinterpreted as 3D tensor unsigned int idx0; if(_add_bias) { idx0 = (4 + _num_post_op_args) * num_arguments_per_2D_tensor() + 7 + (_reinterpret_input_as_3d ? 1 : 0) + _num_post_op_args; } else { idx0 = (3 + _num_post_op_args) * num_arguments_per_2D_tensor() + 6 + (_reinterpret_input_as_3d ? 1 : 0) + _num_post_op_args; } const unsigned int total_cross_plane_pad = dst->info()->padding().top + dst->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, src0, slice); add_2D_tensor_argument(idx, src1, slice_b); if(_add_bias) { add_2D_tensor_argument(idx, src2, slice); } add_2D_tensor_argument(idx, dst, slice); // post op argument buffers for(size_t i = 0; i < _num_post_op_args; ++i) { const auto post_op_arg = utils::cast::polymorphic_downcast(tensors.get_const_tensor(experimental::get_post_op_arg_type(i))); add_2D_tensor_argument(idx, post_op_arg, slice); } _kernel.setArg(idx++, static_cast(src0->info()->strides_in_bytes()[2])); _kernel.setArg(idx++, static_cast(src1->info()->strides_in_bytes()[2])); if(_add_bias) { _kernel.setArg(idx++, static_cast(src2->info()->strides_in_bytes()[2])); } _kernel.setArg(idx++, static_cast(dst->info()->strides_in_bytes()[2])); // post op argument stride_z for(size_t i = 0; i < _num_post_op_args; ++i) { const auto post_op_arg = utils::cast::polymorphic_downcast(tensors.get_const_tensor(experimental::get_post_op_arg_type(i))); _kernel.setArg(idx++, static_cast(post_op_arg->info()->strides_in_bytes()[2])); } // Pass m, n and k at runtime _kernel.setArg(idx++, _m); _kernel.setArg(idx++, _n); _kernel.setArg(idx++, _k); enqueue(queue, *this, slice, lws_hint(), _use_dummy_work_items); } while(window.slide_window_slice_3D(slice)); } } // namespace kernels } // namespace opencl } // namespace arm_compute