// Copyright (c) 2019 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 #include #include "paddle/fluid/framework/op_registry.h" #include "paddle/fluid/memory/malloc.h" #include "paddle/fluid/operators/math/bert_encoder_functor.h" #include "paddle/fluid/operators/math/blas.h" namespace paddle { namespace operators { template class SkipLayerNormKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext &context) const override { using Tensor = framework::Tensor; auto *X = context.Input("X"); auto *Y = context.Input("Y"); auto *scale = context.Input("Scale"); auto *bias = context.Input("Bias"); auto *X_d = X->data(); auto *Y_d = Y->data(); auto *scale_d = scale->data(); auto *bias_d = bias->data(); float epsilon = context.Attr("epsilon"); int begin_norm_axis = context.Attr("begin_norm_axis"); auto *out = context.Output("Out"); out->Resize(X->dims()); auto *output_d = out->mutable_data(context.GetPlace()); size_t num = 1; for (size_t i = 0; i < X->dims().size(); i++) { num *= X->dims()[i]; } int hidden = X->dims()[2]; auto &device_ctx = context.template device_context(); operators::math::SkipLayerNormFunctor skip_layer_norm_func; skip_layer_norm_func(num, hidden, X_d, Y_d, scale_d, bias_d, output_d, epsilon, device_ctx.stream()); } }; } // namespace operators } // namespace paddle namespace ops = paddle::operators; REGISTER_OP_CUDA_KERNEL( skip_layernorm, ops::SkipLayerNormKernel);