chore: import upstream snapshot with attribution
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wehub-resource-sync
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// Copyright (c) 2024 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 <numeric> // std::iota
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#include "paddle/phi/common/memory_utils.h"
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#include "paddle/phi/core/kernel_registry.h"
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#include "paddle/phi/core/mixed_vector.h"
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#include "paddle/phi/kernels/funcs/eigen/common.h"
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#include "paddle/phi/kernels/funcs/math_function.h"
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namespace phi {
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template <typename Context, typename T>
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struct SequenceExpandFunctor {
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void operator()(const Context& dev_ctx,
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const DenseTensor& x,
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const Vector<size_t>& x_lod, /*expand source lod*/
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const Vector<size_t>& ref_lod, /*expand referenced lod*/
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DenseTensor* out);
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};
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template <typename Context, typename T>
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struct SequenceExpandGradFunctor {
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void operator()(const Context& dev_ctx,
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const DenseTensor& dout,
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const Vector<size_t>& x_lod, /*expand source lod*/
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const Vector<size_t>& ref_lod, /*expand referenced lod*/
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DenseTensor* dx);
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};
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template <typename T, typename Context>
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void SequenceExpandKernel(const Context& dev_ctx,
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const DenseTensor& x_in,
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const DenseTensor& y_in,
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int ref_level,
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DenseTensor* out) {
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// From InferShape
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const auto& x_dims = x_in.dims();
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auto out_dims = x_dims;
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auto& x_lod = x_in.lod();
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auto& y_lod = y_in.lod();
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PADDLE_ENFORCE_LE(x_lod.size(),
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1UL,
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common::errors::InvalidArgument(
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"Level of Input(X)'s lod should not be "
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"greater than 1. But received: lod level %u.",
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x_lod.size()));
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PADDLE_ENFORCE_GT(y_lod.size(),
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0UL,
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common::errors::InvalidArgument(
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"Level of Input(Y)'s lod should be greater than 0. But "
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"received: lod level %u.",
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y_lod.size()));
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PADDLE_ENFORCE_EQ(
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ref_level == -1 ||
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(ref_level >= 0 && ref_level < static_cast<int>(y_lod.size())),
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true,
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common::errors::InvalidArgument(
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"Invalid `ref_level`, which should be either equal to -1 "
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"or in [0, %d), but received `ref_level` = %u.",
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y_lod.size(),
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ref_level));
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if (ref_level == -1) ref_level = static_cast<int>(y_lod.size() - 1);
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if (!x_lod.empty()) {
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PADDLE_ENFORCE_EQ(
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x_lod[0].size(),
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y_lod[ref_level].size(),
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common::errors::InvalidArgument(
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"Level number of Input(X)'s lod could be 0. Otherwise "
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"size of Input(X)'s first level lod should be equal to "
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"size of Input(Y)'s referred level lod. But received: "
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"Input(X).lod[0].size() = %u, Input(Y).lod[%d].size() = "
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"%u",
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x_lod[0].size(),
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ref_level,
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y_lod[ref_level].size()));
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} else {
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PADDLE_ENFORCE_EQ(x_dims[0],
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static_cast<int64_t>(y_lod[ref_level].size()) - 1,
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common::errors::InvalidArgument(
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"When Input(X)'s lod is null, the dims[0] of "
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"Input(X) should match the "
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"size of Input(Y)'s referred level lod. But received "
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"Input(X): input rank %u, input shape [%s]; received "
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"Input(Y).lod[%d].size() - 1 = %d.",
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x_dims.size(),
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x_dims,
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ref_level,
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static_cast<int64_t>(y_lod[ref_level].size()) - 1));
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}
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int64_t out_first_dim = 0;
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if (y_lod[ref_level].size() <= 1) {
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out_first_dim = x_dims[0];
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} else {
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for (size_t i = 1; i < y_lod[ref_level].size(); ++i) {
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int x_seq_len = 1;
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if (x_lod.size() == 1) {
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x_seq_len = static_cast<int>(x_lod[0][i] - x_lod[0][i - 1]);
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}
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out_first_dim += static_cast<int64_t>(
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(y_lod[ref_level][i] - y_lod[ref_level][i - 1]) * x_seq_len);
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}
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}
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out_dims[0] = out_first_dim;
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out->Resize(out_dims);
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auto* x = &x_in;
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PADDLE_ENFORCE_EQ(
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y_lod.empty(),
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false,
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common::errors::InvalidArgument(
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"Input(Y) DenseTensor of SequenceExpandOp does not contain "
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"LoD information."));
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if (ref_level == -1) ref_level = y_lod.size() - 1;
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dev_ctx.template Alloc<T>(out);
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if (y_lod[ref_level].size() <= 1) {
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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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// x lod level is at most 1.
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Vector<size_t> out_lod;
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if (x_lod.size() == 1) {
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out_lod.push_back(0);
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int out_offset = 0;
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for (size_t i = 1; i < y_lod[ref_level].size(); ++i) {
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int repeat_num = y_lod[ref_level][i] - y_lod[ref_level][i - 1];
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int x_start = x_lod[0][i - 1];
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int x_end = x_lod[0][i];
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int x_seq_len = x_end - x_start;
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for (int j = 0; j < repeat_num; ++j) {
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out_lod.push_back(out_lod.back() + x_seq_len);
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out_offset++;
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}
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}
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// write lod to out if x has lod
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auto& ref_lod = *out->mutable_lod();
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ref_lod[0] = out_lod;
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}
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Vector<size_t> ref_x_lod;
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if (x->lod().size() == 1) {
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ref_x_lod = x->lod()[0];
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} else {
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// x_lod doesn't has lod, use fake x lod, level = 0
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ref_x_lod.resize(x->dims()[0] + 1);
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std::iota(ref_x_lod.begin(), ref_x_lod.end(), 0);
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}
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SequenceExpandFunctor<Context, T> functor;
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functor(dev_ctx, *x, ref_x_lod, y_lod[ref_level], out);
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}
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template <typename T, typename Context>
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void SequenceExpandGradKernel(const Context& dev_ctx,
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const DenseTensor& x_in,
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const DenseTensor& y_in,
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const DenseTensor& out_grad,
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int ref_level,
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DenseTensor* x_grad) {
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auto* g_out = &out_grad;
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auto* x = &x_in;
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auto* y = &y_in;
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auto* g_x = x_grad;
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dev_ctx.template Alloc<T>(g_x);
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g_x->set_lod(x->lod());
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funcs::SetConstant<Context, T> set_zero;
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set_zero(dev_ctx, g_x, static_cast<T>(0));
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auto& y_lod = y->lod();
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if (ref_level == -1) ref_level = y_lod.size() - 1;
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// just copy the gradient
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if (y_lod[ref_level].size() <= 1) {
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Copy(dev_ctx, *g_out, dev_ctx.GetPlace(), false, g_x);
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return;
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}
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Vector<size_t> ref_x_lod;
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Vector<size_t> ref_lod = y_lod[ref_level];
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if (x->lod().size() == 1) {
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ref_x_lod = x->lod()[0];
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} else {
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// x_lod doesn't has lod, use fake x lod, level = 0
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ref_x_lod.resize(x->dims()[0] + 1);
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std::iota(ref_x_lod.begin(), ref_x_lod.end(), 0);
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}
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SequenceExpandGradFunctor<Context, T> functor;
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functor(dev_ctx, *g_out, ref_x_lod, ref_lod, g_x);
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}
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// for GPU kernel
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inline void GetOutputOffset(const Vector<size_t>& x_lod,
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const Vector<size_t>& ref_lod,
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Vector<size_t>* out_offset) {
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size_t offset = 0;
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int lod_size = static_cast<int>(x_lod.size());
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for (int i = 0; i < static_cast<int>(x_lod.size()); ++i) {
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(*out_offset)[i] = offset;
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if (i < lod_size - 1) {
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offset += (ref_lod[i + 1] - ref_lod[i]) * (x_lod[i + 1] - x_lod[i]);
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}
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}
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}
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} // namespace phi
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