wehub-resource-sync
127 lines
4.1 KiB
Plaintext
127 lines
4.1 KiB
Plaintext
// 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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#include "paddle/phi/kernels/tile_kernel.h"
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#include "paddle/phi/backends/gpu/gpu_context.h"
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#include "paddle/phi/core/kernel_registry.h"
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#include "paddle/phi/kernels/funcs/broadcast_function.h"
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namespace phi {
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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 x_dims = x.dims();
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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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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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for (size_t i = 0; i < repeat_times_data.size(); ++i) {
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if (repeat_times_data[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_data[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_data[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_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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PADDLE_ENFORCE_EQ(
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repeat_times_data.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_data.size()));
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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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DenseTensor new_x = x;
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vec_x_dims.insert(vec_x_dims.begin(), 1, 1);
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for (size_t i = 0; i < repeat_times_data.size(); ++i) {
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out_dims[i] *= repeat_times_data[i];
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new_x.Resize(vec_x_dims);
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std::vector<const DenseTensor*> ins = {&new_x};
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vec_x_dims[i] *= repeat_times_data[i];
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if (i != repeat_times_data.size() - 1) {
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if (repeat_times_data[i] != 1) {
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DenseTensor tmp_out;
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tmp_out.Resize(vec_x_dims);
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dev_ctx.template Alloc<T>(&tmp_out);
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std::vector<DenseTensor*> outs = {&tmp_out};
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funcs::BroadcastKernel<T>(
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dev_ctx, ins, &outs, kps::IdentityFunctor<T>(), i);
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tmp_out.Resize(out_dims);
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new_x = tmp_out;
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}
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vec_x_dims[i] *= vec_x_dims[i + 1];
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vec_x_dims[i + 1] = 1;
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} else {
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out->Resize(vec_x_dims);
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dev_ctx.template Alloc<T>(out);
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std::vector<DenseTensor*> outs = {out};
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funcs::BroadcastKernel<T>(
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dev_ctx, ins, &outs, kps::IdentityFunctor<T>(), i);
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out->Resize(out_dims);
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}
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}
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}
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} // namespace phi
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PD_REGISTER_KERNEL(tile,
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GPU,
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ALL_LAYOUT,
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phi::TileKernel,
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bool,
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float,
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double,
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int,
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int64_t,
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int8_t,
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int16_t,
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uint8_t,
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phi::float16,
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phi::bfloat16,
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phi::float8_e4m3fn,
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phi::float8_e5m2,
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phi::complex64,
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phi::complex128) {}
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