wehub-resource-sync
131 lines
4.6 KiB
C++
131 lines
4.6 KiB
C++
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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#pragma once
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#include "paddle/phi/backends/cpu/cpu_context.h"
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#include "paddle/phi/common/amp_type_traits.h"
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#include "paddle/phi/kernels/funcs/common_shape.h"
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#include "paddle/phi/kernels/funcs/eigen/common.h"
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namespace phi {
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template <typename Context, typename T, size_t D>
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static void LerpFunction(const Context& dev_ctx,
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const DenseTensor& x,
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const DenseTensor& y,
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const DenseTensor& weight,
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DenseTensor* out) {
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dev_ctx.template Alloc<T>(out);
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const auto& out_dims = out->dims();
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auto x_dims = funcs::ExtendDims2Rank(x.dims(), D);
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auto y_dims = funcs::ExtendDims2Rank(y.dims(), D);
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auto w_dims = funcs::ExtendDims2Rank(weight.dims(), D);
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Eigen::DSizes<int, D> x_bcast_dims;
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Eigen::DSizes<int, D> y_bcast_dims;
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Eigen::DSizes<int, D> w_bcast_dims;
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funcs::GetBroadcastDims<D>(x_dims, out_dims, &x_bcast_dims);
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funcs::GetBroadcastDims<D>(y_dims, out_dims, &y_bcast_dims);
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funcs::GetBroadcastDims<D>(w_dims, out_dims, &w_bcast_dims);
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auto eigen_x = EigenTensor<T, D>::From(x, x_dims);
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auto eigen_y = EigenTensor<T, D>::From(y, y_dims);
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auto eigen_w = EigenTensor<T, D>::From(weight, w_dims);
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auto eigen_out = EigenTensor<T, D>::From(*out);
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using MPType = typename MPTypeTrait<T>::Type;
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auto& place = *dev_ctx.eigen_device();
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eigen_out.device(place) =
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(eigen_x.broadcast(x_bcast_dims).template cast<MPType>() +
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eigen_w.broadcast(w_bcast_dims).template cast<MPType>() *
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(eigen_y.broadcast(y_bcast_dims).template cast<MPType>() -
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eigen_x.broadcast(x_bcast_dims).template cast<MPType>()))
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.template cast<T>();
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}
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template <typename Context, typename T>
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static void LerpFunctionZero(const Context& dev_ctx,
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const DenseTensor& x,
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const DenseTensor& y,
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const DenseTensor& weight,
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DenseTensor* out) {
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dev_ctx.template Alloc<T>(out);
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auto dim = make_ddim(std::vector<int64_t>(1, 1));
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auto eigen_x = EigenTensor<T, 1>::From(x, dim);
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auto eigen_y = EigenTensor<T, 1>::From(y, dim);
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auto eigen_w = EigenTensor<T, 1>::From(weight, dim);
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auto eigen_out = EigenTensor<T, 1>::From(*out, dim);
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using MPType = typename MPTypeTrait<T>::Type;
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auto& place = *dev_ctx.eigen_device();
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eigen_out.device(place) =
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(eigen_x.template cast<MPType>() +
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eigen_w.template cast<MPType>() *
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(eigen_y.template cast<MPType>() - eigen_x.template cast<MPType>()))
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.template cast<T>();
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}
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template <typename T, typename Context>
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void LerpKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const DenseTensor& y,
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const DenseTensor& weight,
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DenseTensor* out) {
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if (out && out->numel() == 0) {
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dev_ctx.template Alloc<T>(out);
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return;
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}
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int rank = out->dims().size();
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PADDLE_ENFORCE_GE(
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rank,
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0,
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common::errors::InvalidArgument(
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"The number of dimensions for LerpOp must be "
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"greater than or equal to 0, but the value received is %d.",
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rank));
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PADDLE_ENFORCE_LE(
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rank,
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6,
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common::errors::InvalidArgument(
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"The number of dimensions for LerpOp must be "
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"less than or equal to 6, but the value received is %d.",
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rank));
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switch (rank) {
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case 0:
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LerpFunctionZero<Context, T>(dev_ctx, x, y, weight, out);
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break;
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case 1:
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LerpFunction<Context, T, 1>(dev_ctx, x, y, weight, out);
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break;
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case 2:
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LerpFunction<Context, T, 2>(dev_ctx, x, y, weight, out);
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break;
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case 3:
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LerpFunction<Context, T, 3>(dev_ctx, x, y, weight, out);
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break;
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case 4:
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LerpFunction<Context, T, 4>(dev_ctx, x, y, weight, out);
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break;
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case 5:
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LerpFunction<Context, T, 5>(dev_ctx, x, y, weight, out);
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break;
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case 6:
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LerpFunction<Context, T, 6>(dev_ctx, x, y, weight, out);
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break;
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}
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}
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} // namespace phi
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