wehub-resource-sync
188 lines
6.9 KiB
C++
188 lines
6.9 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/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/impl/expand_kernel_impl.h"
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namespace phi {
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template <typename Context, typename T, int Dims>
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void ExpandBackward(const Context& dev_ctx,
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const DenseTensor& out_grad,
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const std::vector<int>& reshape_dims_vec,
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const std::vector<int>& reduce_dims_vec,
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DenseTensor* in_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>(in_grad);
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in_grad->data<T>();
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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 in_grad_fp32;
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in_grad_fp32.Resize(in_grad->dims());
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dev_ctx.template Alloc<float>(&in_grad_fp32);
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auto x_grad = EigenVector<float>::Flatten(in_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 out_grad0 = 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, x_grad, out_grad0, 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, in_grad_fp32, DataType::FLOAT16, in_grad);
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} else {
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CastKernel<float, Context>(
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dev_ctx, in_grad_fp32, DataType::BFLOAT16, in_grad);
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}
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} else {
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auto x_grad = EigenVector<T>::Flatten(*in_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 out_grad0 = 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, x_grad, out_grad0, 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 ExpandGradKernel(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& shape,
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DenseTensor* in_grad) {
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auto x_dims = x.dims();
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auto out_grad_dims = out_grad.dims();
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std::vector<int64_t> expand_shape = vectorize<int64_t>(out_grad_dims);
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if (x.numel() == 0 || out_grad.numel() == 0 ||
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(in_grad && in_grad->numel() == 0)) {
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dev_ctx.template Alloc<T>(in_grad);
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if (in_grad->numel() != 0) {
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Full<T, Context>(dev_ctx, in_grad->dims(), 0, in_grad);
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}
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return;
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}
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if (in_grad->dims() == out_grad_dims) {
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Copy(dev_ctx, out_grad, dev_ctx.GetPlace(), false, in_grad);
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return;
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}
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auto vec_in_dims = vectorize<int64_t>(x_dims);
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auto diff = expand_shape.size() - vec_in_dims.size();
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vec_in_dims.insert(vec_in_dims.begin(), diff, 1);
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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<int> repeat_times(vec_in_dims.size());
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for (size_t i = 0; i < vec_in_dims.size(); ++i) {
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repeat_times[i] = expand_shape[i] / vec_in_dims[i];
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}
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std::vector<int> reshape_dims_vec;
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std::vector<int> reduce_dims_vec;
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for (size_t i = 0; i < repeat_times.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[i]);
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reshape_dims_vec.push_back(vec_in_dims[i]);
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}
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int dims = reduce_dims_vec.size();
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PADDLE_ENFORCE_GE(dims,
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0,
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common::errors::InvalidArgument(
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"The rank of the input 'Out@GRAD' for "
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"expand_v2_grad op must be greater than or "
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"equal to 0, but 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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common::errors::InvalidArgument(
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"The rank of the input 'Out@GRAD' for "
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"expand_v2_grad op 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 0:
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ExpandBackward<Context, T, 1>(
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dev_ctx, out_grad, reshape_dims_vec, reduce_dims_vec, in_grad);
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break;
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case 1:
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ExpandBackward<Context, T, 1>(
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dev_ctx, out_grad, reshape_dims_vec, reduce_dims_vec, in_grad);
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break;
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case 2:
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ExpandBackward<Context, T, 2>(
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dev_ctx, out_grad, reshape_dims_vec, reduce_dims_vec, in_grad);
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break;
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case 3:
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ExpandBackward<Context, T, 3>(
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dev_ctx, out_grad, reshape_dims_vec, reduce_dims_vec, in_grad);
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break;
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case 4:
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ExpandBackward<Context, T, 4>(
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dev_ctx, out_grad, reshape_dims_vec, reduce_dims_vec, in_grad);
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break;
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case 5:
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ExpandBackward<Context, T, 5>(
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dev_ctx, out_grad, reshape_dims_vec, reduce_dims_vec, in_grad);
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break;
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case 6:
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ExpandBackward<Context, T, 6>(
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dev_ctx, out_grad, reshape_dims_vec, reduce_dims_vec, in_grad);
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break;
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case 7:
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ExpandBackward<Context, T, 7>(
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dev_ctx, out_grad, reshape_dims_vec, reduce_dims_vec, in_grad);
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break;
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case 8:
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ExpandBackward<Context, T, 8>(
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dev_ctx, out_grad, reshape_dims_vec, reduce_dims_vec, in_grad);
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break;
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default:
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PADDLE_THROW(common::errors::InvalidArgument(
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"Only support tensor with rank being between 1 and %d. But "
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"received tensor's rank = %d.",
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MAX_RANK_SUPPORTED,
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dims));
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}
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}
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} // namespace phi
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