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424 lines
16 KiB
424 lines
16 KiB
/* Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
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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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http://www.apache.org/licenses/LICENSE-2.0
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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 <algorithm>
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#include <cmath>
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#include <numeric>
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#include <set>
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#include <unordered_map>
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#include <utility>
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#include <vector>
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#include "paddle/fluid/framework/op_registry.h"
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#include "paddle/fluid/operators/math/concat_and_split.h"
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#include "paddle/fluid/operators/math/math_function.h"
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#include "paddle/fluid/operators/transpose_op.h"
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namespace paddle {
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namespace operators {
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template <typename InT>
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struct UniqueOpFunctor {
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framework::Tensor* out_;
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framework::Tensor* index_;
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const framework::Tensor* in_;
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framework::Tensor* count_;
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UniqueOpFunctor(framework::Tensor* out, framework::Tensor* index,
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const framework::Tensor* in,
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framework::Tensor* count = nullptr)
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: 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 = index_->mutable_data<IndexT>(platform::CPUPlace());
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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(), pow(2, 31),
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platform::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(framework::make_ddim({static_cast<int64_t>(uniq.size())}));
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IndexT* count_data = count_->mutable_data<IndexT>(platform::CPUPlace());
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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_->type();
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bool index_type_match = index_type == framework::proto::VarType::INT32 ||
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index_type == framework::proto::VarType::INT64;
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PADDLE_ENFORCE_EQ(index_type_match, true,
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platform::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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paddle::framework::DataTypeToString(index_type),
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paddle::framework::DataTypeToString(
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framework::proto::VarType::INT32),
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paddle::framework::DataTypeToString(
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framework::proto::VarType::INT64)));
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if (index_type == framework::proto::VarType::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(framework::make_ddim({static_cast<int64_t>(uniq.size())}));
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auto out_data = out_->mutable_data<InT>(platform::CPUPlace());
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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<framework::Tensor> Unbind(const framework::Tensor& in) {
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int64_t size = in.dims()[0];
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std::vector<framework::Tensor> 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 framework::Tensor& a, const framework::Tensor& 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 (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 InT, typename IndexT>
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static void UniqueFlattendTensor(const framework::ExecutionContext& context,
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const framework::Tensor& in,
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framework::Tensor* out, bool return_index,
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bool return_inverse, bool return_counts) {
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const InT* in_data = in.data<InT>();
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std::set<InT> unique(in_data, in_data + in.numel());
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out->Resize(framework::make_ddim({static_cast<int64_t>(unique.size())}));
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auto out_data = out->mutable_data<InT>(context.GetPlace());
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std::copy(unique.begin(), unique.end(), out_data);
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if (return_index) {
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auto* indices = context.Output<framework::Tensor>("Indices");
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indices->Resize(framework::make_ddim({out->numel()}));
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auto indices_data = indices->mutable_data<IndexT>(context.GetPlace());
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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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auto* inverse = context.Output<framework::Tensor>("Index");
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inverse->Resize(framework::make_ddim({in.numel()}));
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auto inverse_data = inverse->mutable_data<IndexT>(context.GetPlace());
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std::unordered_map<InT, IndexT> inverse_map;
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inverse_map.reserve(out->numel());
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for (int64_t i = 0; i < out->numel(); ++i) {
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inverse_map[out_data[i]] = i;
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}
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for (int64_t i = 0; i < in.numel(); ++i) {
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inverse_data[i] = inverse_map[in_data[i]];
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}
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}
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if (return_counts) {
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auto* count = context.Output<framework::Tensor>("Counts");
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count->Resize(framework::make_ddim({out->numel()}));
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auto count_data = count->mutable_data<IndexT>(context.GetPlace());
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std::unordered_map<InT, IndexT> counts_map;
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counts_map.reserve(out->numel());
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for (int64_t i = 0; i < out->numel(); ++i) {
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counts_map[out_data[i]] = 0;
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}
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for (int64_t i = 0; i < in.numel(); i++) {
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counts_map[in_data[i]] += 1;
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}
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for (int64_t i = 0; i < out->numel(); i++) {
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count_data[i] = counts_map[out_data[i]];
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}
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}
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}
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template <class ForwardIt, typename InT, typename IndexT>
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static ForwardIt UniqueDimImpl(const framework::ExecutionContext& context,
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ForwardIt first, 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 DeviceContext, typename InT, typename IndexT>
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static void UniqueDim(const framework::ExecutionContext& context,
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const framework::Tensor& in, framework::Tensor* out,
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bool return_index, bool return_inverse,
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bool return_counts, 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(framework::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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framework::Tensor in_trans;
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framework::DDim in_trans_dims = framework::make_ddim(in_trans_dims_vec);
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in_trans.Resize(in_trans_dims);
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in_trans.mutable_data<InT>(context.GetPlace());
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auto& dev_ctx = context.template device_context<DeviceContext>();
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TransCompute<DeviceContext, InT>(in.dims().size(), dev_ctx, in, &in_trans,
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permute);
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// reshape tensor: eg. [dim1, dim0, dim2] -> [dim1, dim0*dim2]
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framework::DDim in_trans_flat_dims =
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framework::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(), 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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framework::Tensor input_sorted;
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input_sorted.Resize(in_trans_dims);
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input_sorted.mutable_data<InT>(context.GetPlace());
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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<framework::Tensor> 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<std::vector<framework::Tensor>::iterator, InT>(
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context, input_unbind.begin(), input_unbind.end(), sorted_indices_vec,
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&inverse_vec, &counts_vec, &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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math::ConcatFunctor<DeviceContext, InT> concat_functor;
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framework::Tensor 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(framework::make_ddim(out_trans_dims_vec));
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out_trans.mutable_data<InT>(context.GetPlace());
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std::swap(out_trans_dims_vec[0], out_trans_dims_vec[axis]);
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out->Resize(framework::make_ddim(out_trans_dims_vec));
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out->mutable_data<InT>(context.GetPlace());
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concat_functor(dev_ctx, input_unbind, 0, &out_trans);
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TransCompute<DeviceContext, InT>(out_trans.dims().size(), dev_ctx, out_trans,
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out, permute);
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if (return_inverse) {
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auto* inverse = context.Output<framework::Tensor>("Index");
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framework::TensorFromVector(inverse_vec, context.device_context(), inverse);
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}
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if (return_counts) {
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auto* count = context.Output<framework::Tensor>("Counts");
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framework::TensorFromVector(counts_vec, context.device_context(), count);
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}
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if (return_index) {
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auto* indices = context.Output<framework::Tensor>("Indices");
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framework::TensorFromVector(indices_vec, context.device_context(), indices);
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}
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}
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template <typename DeviceContext, typename InT>
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struct UniqueFlattendTensorFunctor {
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const framework::ExecutionContext& ctx_;
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const framework::Tensor& in_;
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framework::Tensor* out_;
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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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UniqueFlattendTensorFunctor(const framework::ExecutionContext& context,
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const framework::Tensor& in,
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framework::Tensor* out, bool return_index,
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bool return_inverse, bool return_counts)
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: ctx_(context),
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in_(in),
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out_(out),
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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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UniqueFlattendTensor<InT, IndexT>(ctx_, in_, out_, return_index_,
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return_inverse_, return_counts_);
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}
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};
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template <typename DeviceContext, typename InT>
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struct UniqueDimFunctor {
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const framework::ExecutionContext& ctx_;
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const framework::Tensor& in_;
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framework::Tensor* out_;
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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 framework::ExecutionContext& context,
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const framework::Tensor& in, framework::Tensor* out,
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const int axis, bool return_index, bool return_inverse,
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bool return_counts)
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: ctx_(context),
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in_(in),
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out_(out),
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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<DeviceContext, InT, IndexT>(
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ctx_, in_, out_, return_index_, return_inverse_, return_counts_, axis_);
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}
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};
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template <typename DeviceContext, typename T>
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class UniqueKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& context) const override {
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auto* x = context.Input<framework::Tensor>("X");
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auto* out = context.Output<framework::Tensor>("Out");
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auto data_type = static_cast<framework::proto::VarType::Type>(
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context.Attr<int>("dtype"));
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if (data_type == framework::proto::VarType::INT32) {
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PADDLE_ENFORCE_LE(
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x->numel(), INT_MAX,
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platform::errors::InvalidArgument(
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"The number of elements in Input(X) should be less than or "
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"equal to INT_MAX, but received num is %d. Please set `dtype` to "
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"int64.",
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x->numel()));
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}
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if (!context.Attr<bool>("is_sorted")) {
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auto* index = context.Output<framework::Tensor>("Index");
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framework::VisitDataType(data_type, UniqueOpFunctor<T>(out, index, x));
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return;
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}
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std::vector<int> axis_vec = context.Attr<std::vector<int>>("axis");
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bool return_index = context.Attr<bool>("return_index");
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bool return_inverse = context.Attr<bool>("return_inverse");
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bool return_counts = context.Attr<bool>("return_counts");
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if (axis_vec.empty()) {
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framework::VisitDataTypeSmall(
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data_type,
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UniqueFlattendTensorFunctor<DeviceContext, T>(
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context, *x, out, return_index, return_inverse, return_counts));
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} else {
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int axis = axis_vec[0];
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framework::VisitDataTypeSmall(
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data_type, UniqueDimFunctor<DeviceContext, T>(
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context, *x, out, axis, return_index, return_inverse,
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return_counts));
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}
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}
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};
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} // namespace operators
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} // namespace paddle
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