wehub-resource-sync
135 lines
4.2 KiB
C++
135 lines
4.2 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 <type_traits>
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#include <vector>
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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_kernel.h"
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namespace phi {
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template <typename Context, typename T, int Rank>
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void Tile(const Context& dev_ctx,
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const DenseTensor& x,
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std::vector<int64_t> repeat_times,
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DenseTensor* out) {
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auto x_dims = x.dims();
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for (size_t i = 0; i < repeat_times.size(); ++i) {
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if (repeat_times[i] == 0) {
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dev_ctx.template Alloc<T>(out);
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return;
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}
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PADDLE_ENFORCE_GT(
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repeat_times[i],
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0,
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errors::InvalidArgument(
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"All elements of the input 'repeat_times' for tile op must "
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"be positive integers, but the value received is %d.",
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repeat_times[i]));
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}
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auto vec_x_dims = vectorize<int64_t>(x_dims);
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if (repeat_times.size() < vec_x_dims.size()) {
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int diff = vec_x_dims.size() - repeat_times.size();
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repeat_times.insert(repeat_times.begin(), diff, 1);
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} else {
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int diff = repeat_times.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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PADDLE_ENFORCE_EQ(
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repeat_times.size(),
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vec_x_dims.size(),
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errors::InvalidArgument(
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"The rank (%d) of the input 'x' and the rank (%d) of the input "
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"'repeat_times' for tile op must match after promotion.",
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vec_x_dims.size(),
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repeat_times.size()));
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if (Rank == 0) {
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Copy(dev_ctx, x, dev_ctx.GetPlace(), false, out);
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return;
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}
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Eigen::DSizes<int64_t, Rank> bcast_dims;
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for (size_t i = 0; i < repeat_times.size(); ++i) {
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bcast_dims[i] = repeat_times[i];
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}
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DDim new_x_dims = make_ddim(vec_x_dims);
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DDim out_dims(new_x_dims);
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for (size_t i = 0; i < repeat_times.size(); ++i) {
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out_dims[i] *= repeat_times[i];
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}
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out->Resize(out_dims);
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auto eigen_x = EigenTensor<T, Rank>::From(x, new_x_dims);
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dev_ctx.template Alloc<T>(out);
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auto eigen_out = EigenTensor<T, Rank>::From(*out, out_dims);
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auto& place = *dev_ctx.eigen_device();
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funcs::EigenBroadcast<std::decay_t<decltype(place)>, T, Rank>::Eval(
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place, eigen_out, eigen_x, bcast_dims);
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}
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template <typename T, typename Context>
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void TileKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const IntArray& repeat_times,
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DenseTensor* out) {
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if (x.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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auto rank = x.dims().size();
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auto& repeat_times_data = repeat_times.GetData();
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int repeat_times_size = repeat_times_data.size();
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rank = std::max(rank, repeat_times_size);
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switch (rank) {
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case 0:
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Tile<Context, T, 0>(dev_ctx, x, repeat_times_data, out);
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break;
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case 1:
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Tile<Context, T, 1>(dev_ctx, x, repeat_times_data, out);
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break;
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case 2:
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Tile<Context, T, 2>(dev_ctx, x, repeat_times_data, out);
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break;
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case 3:
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Tile<Context, T, 3>(dev_ctx, x, repeat_times_data, out);
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break;
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case 4:
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Tile<Context, T, 4>(dev_ctx, x, repeat_times_data, out);
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break;
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case 5:
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Tile<Context, T, 5>(dev_ctx, x, repeat_times_data, out);
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break;
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case 6:
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Tile<Context, T, 6>(dev_ctx, x, repeat_times_data, out);
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break;
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case 7:
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Tile<Context, T, 7>(dev_ctx, x, repeat_times_data, out);
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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 0 and 7. But "
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"received tensor's rank = %d.",
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rank));
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}
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}
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} // namespace phi
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