chore: import upstream snapshot with attribution
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wehub-resource-sync
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// 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 <type_traits>
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#include <vector>
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#include "paddle/phi/core/tensor_utils.h"
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#include "paddle/phi/kernels/cast_kernel.h"
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#include "paddle/phi/kernels/full_kernel.h"
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#include "paddle/phi/kernels/funcs/eigen/common.h"
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#include "paddle/phi/kernels/funcs/eigen/eigen_function.h"
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#include "paddle/phi/kernels/tile_grad_kernel.h"
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namespace phi {
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template <typename Context, typename T, int Dims>
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void TileBackward(const Context& dev_ctx,
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const DenseTensor& out_grad,
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const std::vector<int64_t>& reshape_dims_vec,
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const std::vector<int64_t>& reduce_dims_vec,
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DenseTensor* x_grad) {
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size_t reshape_size = reshape_dims_vec.size();
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size_t reduce_size = reduce_dims_vec.size();
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dev_ctx.template Alloc<T>(x_grad);
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if constexpr (std::is_same_v<T, dtype::float16> ||
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std::is_same_v<T, dtype::bfloat16>) {
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const DenseTensor out_grad_fp32 =
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Cast<T, Context>(dev_ctx, out_grad, DataType::FLOAT32);
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DenseTensor x_grad_fp32;
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x_grad_fp32.Resize(x_grad->dims());
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dev_ctx.template Alloc<float>(&x_grad_fp32);
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auto eigen_x_grad = EigenVector<float>::Flatten(x_grad_fp32);
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Eigen::DSizes<int64_t, Dims * 2> reshape_dims;
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for (size_t i = 0; i < reshape_size; ++i) {
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reshape_dims[i] = reshape_dims_vec[i];
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}
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Eigen::DSizes<int64_t, Dims> reduce_dims;
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for (size_t i = 0; i < reduce_size; ++i) {
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reduce_dims[i] = reduce_dims_vec[i];
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}
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const auto eigen_out_grad_fp32 = EigenVector<float>::Flatten(out_grad_fp32);
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auto& place = *dev_ctx.eigen_device();
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funcs::EigenBroadcastGrad<std::decay_t<decltype(place)>, float, Dims>::Eval(
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place, eigen_x_grad, eigen_out_grad_fp32, reduce_dims, reshape_dims);
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if constexpr (std::is_same_v<T, dtype::float16>) {
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CastKernel<float, Context>(
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dev_ctx, x_grad_fp32, DataType::FLOAT16, x_grad);
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} else {
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CastKernel<float, Context>(
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dev_ctx, x_grad_fp32, DataType::BFLOAT16, x_grad);
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}
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} else {
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auto eigen_x_grad = EigenVector<T>::Flatten(*x_grad);
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Eigen::DSizes<int64_t, Dims * 2> reshape_dims;
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for (size_t i = 0; i < reshape_size; ++i) {
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reshape_dims[i] = reshape_dims_vec[i];
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}
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Eigen::DSizes<int64_t, Dims> reduce_dims;
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for (size_t i = 0; i < reduce_size; ++i) {
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reduce_dims[i] = reduce_dims_vec[i];
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}
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auto eigen_out_grad = EigenVector<T>::Flatten(out_grad);
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auto& place = *dev_ctx.eigen_device();
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funcs::EigenBroadcastGrad<std::decay_t<decltype(place)>, T, Dims>::Eval(
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place, eigen_x_grad, eigen_out_grad, reduce_dims, reshape_dims);
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}
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}
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template <typename T, typename Context>
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void TileGradKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const DenseTensor& out_grad,
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const IntArray& repeat_times,
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DenseTensor* x_grad) {
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// x_grad->numel() may be not 0.
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if (out_grad.numel() == 0) {
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Full<T, Context>(dev_ctx, x_grad->dims(), 0, x_grad);
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return;
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}
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auto x_dims = x.dims();
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auto vec_x_dims = vectorize<int64_t>(x_dims);
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auto repeat_times_data = repeat_times.GetData();
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if (repeat_times_data.size() < vec_x_dims.size()) {
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int diff = vec_x_dims.size() - repeat_times_data.size();
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repeat_times_data.insert(repeat_times_data.begin(), diff, 1);
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} else {
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int diff = repeat_times_data.size() - vec_x_dims.size();
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vec_x_dims.insert(vec_x_dims.begin(), diff, 1);
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}
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// 1. reshape_dims_vec is the broadcast parameter.
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// 2. reduce_dims_vec is the dimension parameter to compute gradients. For
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// each dimension expanded, the gradients should be summed to original
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// size.
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std::vector<int64_t> reshape_dims_vec;
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std::vector<int64_t> reduce_dims_vec;
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for (size_t i = 0; i < repeat_times_data.size(); ++i) {
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reduce_dims_vec.push_back(reshape_dims_vec.size());
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reshape_dims_vec.push_back(repeat_times_data[i]);
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reshape_dims_vec.push_back(vec_x_dims[i]);
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}
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int dims = reduce_dims_vec.size();
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bool just_copy = true;
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for (size_t i = 0; i < repeat_times_data.size(); i++) {
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if (repeat_times_data[i] != 1) {
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just_copy = false;
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break;
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}
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}
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// no need reduce, just copy
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if (just_copy) {
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dev_ctx.template Alloc<T>(x_grad);
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Copy(dev_ctx, out_grad, dev_ctx.GetPlace(), false, x_grad);
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// TensorCopy may change the dims of dx
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x_grad->Resize(x_dims);
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} else {
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PADDLE_ENFORCE_GE(dims,
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1,
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errors::InvalidArgument(
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"The rank of the input 'Out@GRAD' for tile_grad op "
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"must be greater than or equal to 1, but "
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"the value received is %d.",
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dims));
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PADDLE_ENFORCE_LE(dims,
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MAX_RANK_SUPPORTED,
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errors::InvalidArgument(
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"The rank of the input 'Out@GRAD' for tile_grad op "
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"must be less than or equal "
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"to %d, but the value received is %d.",
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MAX_RANK_SUPPORTED,
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dims));
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switch (dims) {
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case 1:
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TileBackward<Context, T, 1>(
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dev_ctx, out_grad, reshape_dims_vec, reduce_dims_vec, x_grad);
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break;
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case 2:
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TileBackward<Context, T, 2>(
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dev_ctx, out_grad, reshape_dims_vec, reduce_dims_vec, x_grad);
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break;
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case 3:
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TileBackward<Context, T, 3>(
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dev_ctx, out_grad, reshape_dims_vec, reduce_dims_vec, x_grad);
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break;
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case 4:
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TileBackward<Context, T, 4>(
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dev_ctx, out_grad, reshape_dims_vec, reduce_dims_vec, x_grad);
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break;
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case 5:
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TileBackward<Context, T, 5>(
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dev_ctx, out_grad, reshape_dims_vec, reduce_dims_vec, x_grad);
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break;
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case 6:
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TileBackward<Context, T, 6>(
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dev_ctx, out_grad, reshape_dims_vec, reduce_dims_vec, x_grad);
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break;
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case 7:
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TileBackward<Context, T, 7>(
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dev_ctx, out_grad, reshape_dims_vec, reduce_dims_vec, x_grad);
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break;
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default:
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PADDLE_THROW(errors::InvalidArgument(
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"Only support tensor with rank being between 1 and 7. But "
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"received tensor's rank = %d.",
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dims));
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}
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}
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}
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} // namespace phi
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