// 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. #pragma once #include #include "paddle/phi/core/dense_tensor.h" #include "paddle/phi/kernels/empty_kernel.h" #include "paddle/phi/kernels/funcs/eigen/common.h" #include "paddle/phi/kernels/funcs/math_function.h" namespace phi { template void WarprnntGradKernel(const Context& dev_ctx, const DenseTensor& input UNUSED, const DenseTensor& input_lengths UNUSED, const DenseTensor& warprnntgrad, const DenseTensor& loss_grad, int blank UNUSED, float fastemit_lambda UNUSED, DenseTensor* input_grad) { dev_ctx.template Alloc(input_grad); int B = warprnntgrad.dims()[0]; // B int Tmax = warprnntgrad.dims()[1]; // Tmax int Umax = warprnntgrad.dims()[2]; // Umax int D = warprnntgrad.dims()[3]; // D // (B,) auto loss_grad_e = EigenTensor::From(loss_grad); // (B, T, U, D) auto warprnntgrad_e = EigenTensor::From(warprnntgrad); auto acts_grad_e = EigenTensor::From(*input_grad); Eigen::DSizes grad_shape(B, 1, 1, 1); Eigen::DSizes bcast(1, Tmax, Umax, D); auto acts_g = warprnntgrad_e * loss_grad_e.reshape(grad_shape).broadcast(bcast).eval(); auto* place = dev_ctx.eigen_device(); acts_grad_e.device(*place) = acts_g; } } // namespace phi