/* * Copyright (c) 2019-2023 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/ClGemmMatrixMultiplyReshapedOnlyRhsKernel.h" #include "arm_compute/core/CL/ICLTensor.h" #include "arm_compute/core/utils/ActivationFunctionUtils.h" #include "arm_compute/core/utils/misc/ShapeCalculator.h" #include "arm_compute/core/utils/StringUtils.h" #include "src/core/CL/CLUtils.h" #include "src/core/CL/CLValidate.h" #include "src/core/helpers/AutoConfiguration.h" #include "src/core/helpers/WindowHelpers.h" #include "src/core/utils/helpers/float_ops.h" #include "src/gpu/cl/kernels/gemm/ClGemmHelpers.h" #include "support/Cast.h" #include "support/StringSupport.h" namespace arm_compute { namespace opencl { namespace kernels { namespace { using ElementsProcessed = Steps; 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_F16_UNSUPPORTED(src0); ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src0, 1, DataType::F16, DataType::F32); 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(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) && (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_ON_ERROR(gemm::validate_image2d_support_on_rhs(*src1, 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{src1->tensor_shape()}; tensor_shape1.set(0, n); tensor_shape1.set(1, k); 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, src0); 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"); } } const TensorInfo tensor_info1 = src1->clone()->set_tensor_shape(tensor_shape1); const TensorInfo tensor_info_reshaped1 = src1->clone()->set_tensor_shape(misc::shape_calculator::compute_rhs_reshaped_shape(tensor_info1, rhs_info)); ARM_COMPUTE_RETURN_ERROR_ON(src0->dimension(0) != 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); } ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(src1, &tensor_info_reshaped1); 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); } return Status{}; } Window 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) { ARM_COMPUTE_UNUSED(src0, src1, src2); 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; // 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. // This approach should only be used when the input/dst tensors have pad on the y direction if ((reinterpret_input_as_3d == reinterpret_output_as_3d) && gemm_info.has_pad_y) { reinterpret_output_as_3d = false; } 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; Window win = calculate_max_window(tmp_info, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y)); // 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(dst->num_dimensions()), 2u); Window collapsed = win.collapse(win, dimension_to_collapse); return collapsed; } } // namespace ClGemmMatrixMultiplyReshapedOnlyRhsKernel::ClGemmMatrixMultiplyReshapedOnlyRhsKernel() { _type = CLKernelType::GEMM; } void ClGemmMatrixMultiplyReshapedOnlyRhsKernel::configure(const CLCompileContext &compile_context, const ITensorInfo *src0, const ITensorInfo *src1, const 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)); _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; _export_to_cl_image = rhs_info.export_to_cl_image; _has_pad_y = gemm_info.has_pad_y; auto padding_info = get_padding_info({src0, src1, src2, dst}); // 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) && _has_pad_y) { _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 Window win = 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); ICLKernel::configure_internal(win); // 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); // These variables are used only if gemm_info.has_pad_y == true 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); // 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; _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(!_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_if(rhs_info.export_to_cl_image, "-DOPENCL_IMAGE_SUPPORT"); build_opts.add_option("-DRHS_HEIGHT=" + support::cpp11::to_string(src1->dimension(1))); 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)); if (_has_pad_y) { 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(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" : ""; // 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 += (_has_pad_y ? "" : "no_pad_y_"); _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); _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 ClGemmMatrixMultiplyReshapedOnlyRhsKernel::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) { ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(src0, src1, src2, dst, alpha, beta, lhs_info, rhs_info, gemm_info)); return Status{}; } void ClGemmMatrixMultiplyReshapedOnlyRhsKernel::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); } const size_t lhs_idx_batch_size = _reinterpret_input_as_3d && !_has_pad_y ? 3u : 2u; const size_t rhs_idx_batch_size = 2u; const size_t bia_idx_batch_size = 2u; const size_t out_idx_batch_size = _reinterpret_output_as_3d && !_has_pad_y ? 3u : 2u; 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)); // Get cross plane pads const unsigned int total_cross_plane_pad_lhs = src0->info()->padding().top + src0->info()->padding().bottom; const unsigned int total_cross_plane_pad_out = dst->info()->padding().top + dst->info()->padding().bottom; // The execution should fail if we try to run with has_pad_y = false but we have padding in either the LHS or DST tensor ARM_COMPUTE_ERROR_ON(!_has_pad_y && ((total_cross_plane_pad_lhs != 0) || (total_cross_plane_pad_out != 0))); cl::Image2D src1_image2d; if (_export_to_cl_image) { const TensorShape shape2d(src1->info()->dimension(0) / 4, src1->info()->dimension(1) * src1->info()->dimension(2)); const size_t image_row_pitch = src1->info()->strides_in_bytes()[1]; src1_image2d = create_image2d_from_buffer(CLKernelLibrary::get().context(), src1->cl_buffer(), shape2d, src1->info()->data_type(), image_row_pitch, CLImage2DType::ReadOnly); } 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, src0, slice); // RHS buffer or RHS OpenCL image (_export_to_cl_image == true) if (_export_to_cl_image) { _kernel.setArg(idx++, src1_image2d); } else { add_2D_tensor_argument(idx, src1, slice_b); } // Bias buffer (_add_bias == true) add_2D_tensor_argument_if(_add_bias, idx, src2, slice); // dst buffer add_2D_tensor_argument(idx, dst, slice); // LHS stride_z _kernel.setArg(idx++, static_cast(src0->info()->strides_in_bytes()[lhs_idx_batch_size])); // RHS stride_z (not used if _export_to_cl_image == true) _kernel.setArg(idx++, static_cast(src1->info()->strides_in_bytes()[rhs_idx_batch_size])); // Bias stride_z (if _add_bias == true) if (_add_bias) { _kernel.setArg(idx++, static_cast(src2->info()->strides_in_bytes()[bia_idx_batch_size])); } // dst stride_z _kernel.setArg(idx++, static_cast(dst->info()->strides_in_bytes()[out_idx_batch_size])); // Cross-plan padding (if _reinterpret_input_as_3d = true) if (_reinterpret_input_as_3d && _has_pad_y) { _kernel.setArg(idx++, static_cast(total_cross_plane_pad_lhs)); } // Cross-plan padding (if reinterpret_output_as_3d = true) if (_reinterpret_output_as_3d && _has_pad_y) { _kernel.setArg(idx++, static_cast(total_cross_plane_pad_out)); } // Pass m, n and k at runtime as signed ints, to ensure results of any subractions they could be operand in, would still be signed. _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