// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. // // Licensed under the Apache License, Version 2.0 (the "License"); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an "AS IS" BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. #include #include "paddle/phi/core/compat/op_utils.h" #include "paddle/utils/small_vector.h" namespace phi { KernelSignature AdamOpArgumentMapping(const ArgumentMappingContext& ctx) { paddle::small_vector in_names = {"Param", "Grad", "LearningRate", "Moment1", "Moment2", "Beta1Pow", "Beta2Pow", "MasterParam", "SkipUpdate"}; paddle::small_vector out_names = {"ParamOut", "Moment1Out", "Moment2Out", "Beta1PowOut", "Beta2PowOut", "MasterParamOut"}; paddle::small_vector attr_names; attr_names.emplace_back(ctx.HasInput("Beta1Tensor") ? "Beta1Tensor" : "beta1"); attr_names.emplace_back(ctx.HasInput("Beta2Tensor") ? "Beta2Tensor" : "beta2"); attr_names.emplace_back(ctx.HasInput("EpsilonTensor") ? "EpsilonTensor" : "epsilon"); attr_names.emplace_back("lazy_mode"); attr_names.emplace_back("min_row_size_to_use_multithread"); attr_names.emplace_back("multi_precision"); attr_names.emplace_back("use_global_beta_pow"); if (ctx.IsSelectedRowsInput("Grad")) { return KernelSignature("adam_dense_param_sparse_grad", std::move(in_names), std::move(attr_names), std::move(out_names)); } else if (ctx.IsDenseTensorInput("Grad")) { return KernelSignature("adam", std::move(in_names), std::move(attr_names), std::move(out_names)); } else { return KernelSignature("unregistered", {}, {}, {}); } } } // namespace phi PD_REGISTER_ARG_MAPPING_FN(adam, phi::AdamOpArgumentMapping);