wehub-resource-sync
460 lines
15 KiB
C++
460 lines
15 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 <set>
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#include "paddle/phi/core/dense_tensor.h"
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#include "paddle/phi/core/utils/data_type.h"
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#include "paddle/phi/kernels/funcs/concat_and_split_functor.h"
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#include "paddle/phi/kernels/funcs/math_function.h"
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namespace phi {
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namespace funcs {
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template <typename T>
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static bool NaNSafeEqual(const T& a, const T& b) {
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if constexpr (std::is_floating_point_v<T>) {
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if (std::isnan(a) && std::isnan(b)) {
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return &a == &b;
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}
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if (std::isnan(a) || std::isnan(b)) {
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return false;
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}
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}
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return a == b;
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}
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template <typename T>
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static bool NaNSafeLess(const T& a, const T& b) {
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if constexpr (std::is_floating_point_v<T>) {
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if (std::isnan(a) && !std::isnan(b)) {
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return false;
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}
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if (!std::isnan(a) && std::isnan(b)) {
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return true;
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}
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if (std::isnan(a) && std::isnan(b)) {
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return &a < &b;
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}
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}
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return a < b;
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}
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template <typename Context, typename InT>
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struct UniqueOpFunctor {
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const Context& dev_ctx_;
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DenseTensor* out_;
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DenseTensor* index_;
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const DenseTensor* in_;
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DenseTensor* count_;
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UniqueOpFunctor(const Context& dev_ctx,
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DenseTensor* out,
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DenseTensor* index,
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const DenseTensor* in,
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DenseTensor* count = nullptr)
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: dev_ctx_(dev_ctx), out_(out), index_(index), in_(in), count_(count) {}
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template <typename IndexT>
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void apply() const {
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auto* in_data = in_->data<InT>();
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auto* index_data = dev_ctx_.template Alloc<IndexT>(index_);
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int64_t j = 0;
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// TODO(fangzeyang): Should optimize performance here.
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std::unordered_map<InT, int64_t> dict;
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std::vector<InT> uniq;
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PADDLE_ENFORCE_LT(
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in_->numel(),
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pow(2, 31),
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common::errors::InvalidArgument(
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"The num of Input(X) elements should be less then INT_MAX, "
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"but received num is %d.",
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in_->numel()));
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for (auto i = 0; i < in_->numel(); i++) {
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auto it = dict.find(in_data[i]);
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if (it == dict.end()) {
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dict.emplace(std::make_pair(in_data[i], j));
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uniq.emplace_back(in_data[i]);
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index_data[i] = static_cast<IndexT>(j);
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j++;
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} else {
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index_data[i] = static_cast<IndexT>(it->second);
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}
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}
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if (count_ != nullptr) {
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// Resize the count tensor dims to allocate the memory
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count_->Resize({static_cast<int64_t>(uniq.size())});
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IndexT* count_data = dev_ctx_.template Alloc<IndexT>(count_);
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// init count_data to 0
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memset(count_data, 0, uniq.size() * sizeof(IndexT));
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const auto& index_type = index_->dtype();
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bool index_type_match =
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index_type == DataType::INT32 || index_type == DataType::INT64;
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PADDLE_ENFORCE_EQ(index_type_match,
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true,
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common::errors::InvalidArgument(
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"Index holds the wrong type, it holds %s, "
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"but desires to be %s or %s",
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DataTypeToString(index_type),
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DataTypeToString(DataType::INT32),
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DataTypeToString(DataType::INT64)));
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if (index_type == DataType::INT32) {
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for (auto i = 0; i < in_->numel(); ++i) {
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const IndexT& index = index_data[i];
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count_data[static_cast<int32_t>(index)] += static_cast<IndexT>(1);
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}
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} else {
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for (auto i = 0; i < in_->numel(); ++i) {
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const IndexT& index = index_data[i];
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count_data[static_cast<int64_t>(index)] += static_cast<IndexT>(1);
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}
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}
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}
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out_->Resize({static_cast<int64_t>(uniq.size())});
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auto* out_data = dev_ctx_.template Alloc<InT>(out_);
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std::memcpy(out_data, uniq.data(), uniq.size() * sizeof(InT));
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}
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};
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static std::vector<DenseTensor> Unbind(const DenseTensor& in) {
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int64_t size = in.dims()[0];
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std::vector<DenseTensor> tensors(size);
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for (int64_t i = 0; i < size; ++i) {
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tensors[i] = in.Slice(i, i + 1);
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}
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return tensors;
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}
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template <typename T>
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static bool Equal(const DenseTensor& a, const DenseTensor& b) {
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if (a.numel() != b.numel()) {
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return false;
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}
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for (int64_t i = 0; i < a.numel(); ++i) {
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if (!NaNSafeEqual(a.data<T>()[i], b.data<T>()[i])) {
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return false;
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}
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}
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return true;
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}
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template <typename Context, typename InT, typename IndexT>
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static void UniqueFlattenedTensor(const Context& dev_ctx,
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const DenseTensor& in,
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DenseTensor* out,
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DenseTensor* indices,
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DenseTensor* index,
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DenseTensor* count,
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bool return_index,
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bool return_inverse,
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bool return_counts) {
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const InT* in_data = in.data<InT>();
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auto nan_safe_comp = [](const InT& a, const InT& b) {
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return NaNSafeLess(a, b);
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};
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std::set<InT, decltype(nan_safe_comp)> unique(nan_safe_comp);
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for (int64_t i = 0; i < in.numel(); ++i) {
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unique.insert(in_data[i]);
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}
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out->Resize({static_cast<int64_t>(unique.size())});
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auto* out_data = dev_ctx.template Alloc<InT>(out);
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std::copy(unique.begin(), unique.end(), out_data);
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if (return_index) {
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indices->Resize({out->numel()});
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auto indices_data = dev_ctx.template Alloc<IndexT>(indices);
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std::unordered_map<InT, IndexT> indices_map;
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indices_map.reserve(out->numel());
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for (int64_t i = 0; i < in.numel(); ++i) {
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if (indices_map.find(in_data[i]) != indices_map.end()) continue;
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indices_map[in_data[i]] = i;
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}
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for (int64_t i = 0; i < out->numel(); ++i) {
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indices_data[i] = indices_map[out_data[i]];
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}
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}
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if (return_inverse) {
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index->Resize({in.numel()});
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auto inverse_data = dev_ctx.template Alloc<IndexT>(index);
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for (int64_t i = 0; i < in.numel(); ++i) {
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for (int64_t j = 0; j < out->numel(); ++j) {
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if (NaNSafeEqual(in_data[i], out_data[j])) {
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inverse_data[i] = j;
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break;
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}
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}
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}
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}
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if (return_counts) {
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count->Resize({out->numel()});
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auto count_data = dev_ctx.template Alloc<IndexT>(count);
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for (int64_t i = 0; i < out->numel(); ++i) {
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IndexT cnt = 0;
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for (int64_t j = 0; j < in.numel(); ++j) {
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if (NaNSafeEqual(out_data[i], in_data[j])) {
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cnt++;
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}
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}
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count_data[i] = cnt;
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}
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}
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}
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template <typename Context, typename ForwardIt, typename InT, typename IndexT>
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static ForwardIt UniqueDimImpl(const Context& dev_ctx UNUSED,
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ForwardIt first,
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ForwardIt last,
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const std::vector<IndexT>& sorted_indices_vec,
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std::vector<IndexT>* inverse_vec,
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std::vector<IndexT>* counts_vec,
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std::vector<IndexT>* indices_vec) {
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if (first == last) {
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return last;
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}
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(*inverse_vec)[sorted_indices_vec[0]] = 0;
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(*counts_vec)[0] = 1;
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(*indices_vec)[0] = sorted_indices_vec[0];
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ForwardIt begin = first;
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ForwardIt result = first;
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while (++first != last) {
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int64_t idx_first = std::distance(begin, first);
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int64_t idx_result = std::distance(begin, result);
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if (!Equal<InT>(*result, *first)) {
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if (++result != first) {
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*result = std::move(*first);
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}
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idx_result += 1;
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(*indices_vec)[idx_result] = sorted_indices_vec[idx_first];
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}
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(*inverse_vec)[sorted_indices_vec[idx_first]] = idx_result;
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(*counts_vec)[idx_result] += 1;
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}
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return ++result;
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}
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template <typename Context, typename InT, typename IndexT>
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static void UniqueDim(const Context& dev_ctx,
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const DenseTensor& in,
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DenseTensor* out,
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DenseTensor* indices,
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DenseTensor* index,
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DenseTensor* count,
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bool return_index,
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bool return_inverse,
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bool return_counts,
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int axis) {
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// transpose tensor: eg. axis=1, [dim0, dim1, dim2] -> [dim1, dim0, dim2]
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std::vector<int> permute(in.dims().size());
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std::iota(permute.begin(), permute.end(), 0);
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permute[axis] = 0;
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permute[0] = axis;
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std::vector<int64_t> in_trans_dims_vec(vectorize(in.dims()));
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in_trans_dims_vec[axis] = in.dims()[0];
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in_trans_dims_vec[0] = in.dims()[axis];
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DenseTensor in_trans;
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DDim in_trans_dims = make_ddim(in_trans_dims_vec);
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in_trans.Resize(in_trans_dims);
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dev_ctx.template Alloc<InT>(&in_trans);
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TransCompute<Context, InT>(in.dims().size(), dev_ctx, in, &in_trans, permute);
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// reshape tensor: eg. [dim1, dim0, dim2] -> [dim1, dim0*dim2]
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DDim in_trans_flat_dims = common::flatten_to_2d(in_trans_dims, 1);
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in_trans.Resize(in_trans_flat_dims);
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// sort indices
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std::vector<IndexT> sorted_indices_vec(in_trans.dims()[0]);
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std::iota(sorted_indices_vec.begin(), sorted_indices_vec.end(), 0);
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int64_t col = in_trans.dims()[1];
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const InT* in_trans_data = in_trans.data<InT>();
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std::sort(sorted_indices_vec.begin(),
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sorted_indices_vec.end(),
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[&](int64_t a, int64_t b) -> bool {
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for (int64_t i = 0; i < col; ++i) {
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InT lhs = in_trans_data[i + a * col];
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InT rhs = in_trans_data[i + b * col];
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if (lhs < rhs) {
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return true;
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} else if (lhs > rhs) {
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return false;
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}
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}
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return false;
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});
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// sort tensor according to indices
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DenseTensor input_sorted;
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input_sorted.Resize(in_trans_dims);
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dev_ctx.template Alloc<InT>(&input_sorted);
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InT* input_sorted_data = input_sorted.data<InT>();
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for (size_t i = 0; i < sorted_indices_vec.size(); ++i) {
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memcpy(input_sorted_data + i * col,
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in_trans_data + static_cast<int64_t>(sorted_indices_vec[i]) * col,
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col * sizeof(InT));
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}
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std::vector<DenseTensor> input_unbind = Unbind(input_sorted);
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std::vector<IndexT> inverse_vec(sorted_indices_vec.size(), 0);
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std::vector<IndexT> counts_vec(sorted_indices_vec.size(), 0);
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std::vector<IndexT> indices_vec(sorted_indices_vec.size(), 0);
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auto last = UniqueDimImpl<Context, std::vector<DenseTensor>::iterator, InT>(
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dev_ctx,
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input_unbind.begin(),
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input_unbind.end(),
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sorted_indices_vec,
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&inverse_vec,
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&counts_vec,
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&indices_vec);
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input_unbind.erase(last, input_unbind.end());
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counts_vec.erase(counts_vec.begin() + input_unbind.size(), counts_vec.end());
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indices_vec.erase(indices_vec.begin() + input_unbind.size(),
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indices_vec.end());
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funcs::ConcatFunctor<Context, InT> concat_functor;
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DenseTensor out_trans;
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std::vector<int64_t> out_trans_dims_vec = in_trans_dims_vec;
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out_trans_dims_vec[0] = input_unbind.size();
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out_trans.Resize(out_trans_dims_vec);
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dev_ctx.template Alloc<InT>(&out_trans);
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std::swap(out_trans_dims_vec[0], out_trans_dims_vec[axis]);
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out->Resize(out_trans_dims_vec);
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dev_ctx.template Alloc<InT>(out);
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concat_functor(dev_ctx, input_unbind, 0, &out_trans);
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TransCompute<Context, InT>(
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out_trans.dims().size(), dev_ctx, out_trans, out, permute);
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if (return_inverse) {
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TensorFromVector(inverse_vec, dev_ctx, index);
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}
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if (return_counts) {
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TensorFromVector(counts_vec, dev_ctx, count);
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}
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if (return_index) {
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TensorFromVector(indices_vec, dev_ctx, indices);
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}
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}
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template <typename Context, typename InT>
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struct UniqueFlattenedTensorFunctor {
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const Context& dev_ctx_; /* */
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const DenseTensor& in_;
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DenseTensor* out_;
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DenseTensor* indices_;
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DenseTensor* index_;
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DenseTensor* count_;
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const bool return_index_;
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const bool return_inverse_;
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const bool return_counts_;
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UniqueFlattenedTensorFunctor(const Context& dev_ctx,
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const DenseTensor& in,
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DenseTensor* out,
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DenseTensor* indices,
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DenseTensor* index,
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DenseTensor* count,
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bool return_index,
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bool return_inverse,
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bool return_counts)
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: dev_ctx_(dev_ctx),
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in_(in),
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out_(out),
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indices_(indices),
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index_(index),
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count_(count),
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return_index_(return_index),
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return_inverse_(return_inverse),
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return_counts_(return_counts) {}
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template <typename IndexT>
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void apply() const {
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UniqueFlattenedTensor<Context, InT, IndexT>(dev_ctx_,
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in_,
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out_,
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indices_,
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index_,
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count_,
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return_index_,
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return_inverse_,
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return_counts_);
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}
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};
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template <typename Context, typename InT>
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struct UniqueDimFunctor {
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const Context& dev_ctx_;
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const DenseTensor& in_;
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DenseTensor* out_;
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DenseTensor* indices_;
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DenseTensor* index_;
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DenseTensor* count_;
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const int axis_;
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const bool return_index_;
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const bool return_inverse_;
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const bool return_counts_;
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UniqueDimFunctor(const Context& dev_ctx,
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const DenseTensor& in,
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DenseTensor* out,
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DenseTensor* indices,
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DenseTensor* index,
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DenseTensor* count,
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const int axis,
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bool return_index,
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bool return_inverse,
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bool return_counts)
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: dev_ctx_(dev_ctx),
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in_(in),
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out_(out),
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indices_(indices),
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index_(index),
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count_(count),
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axis_(axis),
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return_index_(return_index),
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return_inverse_(return_inverse),
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return_counts_(return_counts) {}
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template <typename IndexT>
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void apply() const {
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UniqueDim<Context, InT, IndexT>(dev_ctx_,
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in_,
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out_,
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indices_,
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index_,
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count_,
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return_index_,
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return_inverse_,
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return_counts_,
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axis_);
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}
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};
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} // namespace funcs
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} // namespace phi
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