faster_tokenizer_op.cc

/* Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
    http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */

#include "paddle/fluid/operators/string/faster_tokenizer_op.h"

#include <utf8proc.h>

#include <algorithm>
#include <chrono>
#include <codecvt>
#include <fstream>
#include <iostream>
#include <numeric>
#include <string>
#include <unordered_map>
#include <unordered_set>
#include <vector>

#include "paddle/fluid/framework/string_array.h"

namespace paddle {
namespace operators {

using std::ifstream;
using std::int64_t;
using std::size_t;
using std::string;
using std::unordered_map;
using std::unordered_set;
using std::vector;
using std::wstring;

const wstring kStripChars = L" \t\n\r\v\f";

inline bool IsControl(const wchar_t& ch) {
  if (ch == L'\t' || ch == L'\n' || ch == L'\r') return false;
  auto cat = utf8proc_category(ch);
  if (cat == UTF8PROC_CATEGORY_CC || cat == UTF8PROC_CATEGORY_CF) return true;
  return false;
}

inline bool IsChineseChar(const wchar_t& ch) {
  if ((ch >= 0x4E00 && ch <= 0x9FFF) || (ch >= 0x3400 && ch <= 0x4DBF) ||
      (ch >= 0x20000 && ch <= 0x2A6DF) || (ch >= 0x2A700 && ch <= 0x2B73F) ||
      (ch >= 0x2B740 && ch <= 0x2B81F) || (ch >= 0x2B820 && ch <= 0x2CEAF) ||
      (ch >= 0xF900 && ch <= 0xFAFF) || (ch >= 0x2F800 && ch <= 0x2FA1F))
    return true;
  return false;
}

inline bool IsWhiteSpace(const wchar_t& ch) {
  if (ch == L' ' || ch == L'\t' || ch == L'\n' || ch == L'\r') return true;
  auto cat = utf8proc_category(ch);
  if (cat == UTF8PROC_CATEGORY_ZS) return true;
  return false;
}

inline bool IsPunctuation(const wchar_t& ch) {
  if ((ch >= 33 && ch <= 47) || (ch >= 58 && ch <= 64) ||
      (ch >= 91 && ch <= 96) || (ch >= 123 && ch <= 126))
    return true;
  auto cat = utf8proc_category(ch);
  if (cat == UTF8PROC_CATEGORY_PD || cat == UTF8PROC_CATEGORY_PS ||
      cat == UTF8PROC_CATEGORY_PE || cat == UTF8PROC_CATEGORY_PC ||
      cat == UTF8PROC_CATEGORY_PO  // sometimes ¶ belong SO
      || cat == UTF8PROC_CATEGORY_PI || cat == UTF8PROC_CATEGORY_PF)
    return true;
  return false;
}

BasicTokenizer::BasicTokenizer(bool do_lower_case /* = true */)
    : do_lower_case_(do_lower_case) {}

wchar_t BasicTokenizer::do_lower_case(wchar_t ch) const {
  wchar_t new_ch = utf8proc_tolower(ch);
  return new_ch;
}

void BasicTokenizer::Tokenize(const string& text, vector<wstring>* res) const {
  std::wstring unicode_text;
  bool status = framework::ConvertStrToWstr(text, &unicode_text);
  if (!status) {
    // String is converted into wstring failedly.
    return;
  }
  std::wstring cache_text = L"";
  auto PushCacheText = [&]() {
    if (!cache_text.empty()) {
      res->emplace_back(cache_text);
      cache_text = L"";
    }
  };
  for (auto& ch : unicode_text) {
    if (ch == 0 || ch == 0xfffd || IsControl(ch)) {
      continue;
    }
    if (do_lower_case_) {
      ch = do_lower_case(ch);
    }
    if (IsChineseChar(ch) || IsPunctuation(ch)) {
      PushCacheText();
      res->emplace_back(std::wstring{ch});
    } else if (IsWhiteSpace(ch)) {
      PushCacheText();
    } else {
      cache_text += ch;
    }
  }
  PushCacheText();
}

WordPieceTokenizer::WordPieceTokenizer(
    const framework::Vocab* vocab,
    const wstring& unk_token /* = L"[UNK]"*/,
    const size_t max_input_chars_per_word /* = 100 */)
    : vocab_(vocab),
      unk_token_(unk_token),
      max_input_chars_per_word_(max_input_chars_per_word) {
  unk_token_id_ = vocab_->at(unk_token_);
}

void WordPieceTokenizer::Tokenize(const wstring& text,
                                  vector<int64_t>* token_ids) const {
  size_t len = text.size();
  if (len > max_input_chars_per_word_) {
    token_ids->emplace_back(std::move(unk_token_id_));
    return;
  }

  auto it = vocab_->find(text);
  if (it != vocab_->end()) {
    token_ids->emplace_back(std::move(it->second));
    return;
  }

  size_t start = 0;
  vector<int64_t> wordpiece_ids;
  while (start < len) {
    size_t end = len;
    std::wstring cur_substr;
    int64_t cur_substr_id;
    while (start < end) {
      std::wstring sub = text.substr(start, end - start);
      if (start > 0) {
        sub.insert(0, L"##");
      }
      auto it = vocab_->find(sub);
      if (it != vocab_->end()) {
        cur_substr = sub;
        cur_substr_id = it->second;
        break;
      }
      end -= 1;
    }

    if (cur_substr.empty()) {
      token_ids->emplace_back(std::move(unk_token_id_));
      return;
    } else {
      start = end;
      wordpiece_ids.emplace_back(std::move(cur_substr_id));
    }
  }
  for (auto& token_id : wordpiece_ids) {
    token_ids->emplace_back(std::move(token_id));
  }
}

BertTokenizer::BertTokenizer(const framework::Vocab* vocab,
                             bool do_lower_case /* = false */,
                             const wstring& unk_token /* = L"[UNK]" */,
                             const wstring& pad_token /* = L"[PAD]" */,
                             const wstring& cls_token /* = L"[CLS]" */,
                             const wstring& mask_token /* = L"[MASK]" */,
                             const wstring& sep_token /* = L"[SEP]" */,
                             const string& padding_site /* = "right" */)
    : do_lower_case_(do_lower_case),
      unk_token_(unk_token),
      pad_token_(pad_token),
      cls_token_(cls_token),
      mask_token_(mask_token),
      sep_token_(sep_token),
      padding_site_(padding_site),
      vocab_(vocab),
      basic_tokenizer_(do_lower_case_),
      word_piece_tokenizer_(vocab_, unk_token) {
  unk_token_id_ = vocab_->at(unk_token_);
  pad_token_id_ = vocab_->at(pad_token_);
  cls_token_id_ = vocab_->at(cls_token_);
  mask_token_id_ = vocab_->at(mask_token_);
  sep_token_id_ = vocab_->at(sep_token_);

  all_special_tokens_ = vector<wstring>(
      {unk_token_, pad_token_, cls_token_, mask_token_, sep_token_});
  all_special_token_ids_ = unordered_set<int64_t>({unk_token_id_,
                                                   pad_token_id_,
                                                   cls_token_id_,
                                                   mask_token_id_,
                                                   sep_token_id_});
}

void BertTokenizer::Tokenize(const string& text,
                             vector<int64_t>* split_token_ids) const {
  std::vector<std::wstring> tmp_tokens;
  basic_tokenizer_.Tokenize(text, &tmp_tokens);
  if (tmp_tokens.empty()) return;
  split_token_ids->reserve(tmp_tokens.size());
  for (auto& w_token : tmp_tokens) {
    const auto& vec_size = w_token.size();
    if (vec_size == 1) {
      if (IsChineseChar(w_token[0])) {
        auto vocab_it = vocab_->find(w_token);
        if (vocab_it != vocab_->end()) {
          split_token_ids->emplace_back(std::move(vocab_it->second));
        } else {
          split_token_ids->emplace_back(std::move(unk_token_id_));
        }
      } else {
        word_piece_tokenizer_.Tokenize(w_token, split_token_ids);
      }
    } else if (vec_size > 1) {
      word_piece_tokenizer_.Tokenize(w_token, split_token_ids);
    } else {
      continue;
    }
  }
}

void BertTokenizer::BuildInputsWithSpecialTokens(
    vector<int64_t>* inputs,
    const vector<int64_t>& token_ids_0,
    const vector<int64_t>& token_ids_1 /* = vector<int64_t>() */) const {
  if (token_ids_1.empty()) {
    inputs->clear();
    inputs->resize(token_ids_0.size() + 2);
    inputs->at(0) = std::move(cls_token_id_);
    size_t i = 1;
    for (auto& token_id : token_ids_0) {
      inputs->at(i) = std::move(token_id);
      ++i;
    }
    inputs->at(i) = std::move(sep_token_id_);
  } else {
    inputs->clear();
    inputs->resize(token_ids_0.size() + token_ids_1.size() + 3);
    inputs->at(0) = std::move(cls_token_id_);
    size_t i = 1;
    for (auto& token_id : token_ids_0) {
      inputs->at(i) = std::move(token_id);
      ++i;
    }
    inputs->at(i) = std::move(sep_token_id_);
    ++i;
    for (auto& token_id : token_ids_1) {
      inputs->at(i) = std::move(token_id);
      ++i;
    }
    inputs->at(i) = std::move(sep_token_id_);
  }
}

int64_t BertTokenizer::GetNumSpecialTokensToAdd(const bool pair) const {
  if (pair) {
    return 3;
  } else {
    return 2;
  }
}

void BertTokenizer::CreateTokenTypeIdsFromSequences(
    vector<int64_t>* token_type_ids,
    const vector<int64_t>& token_ids_0,
    const vector<int64_t>& token_ids_1 /* = vector<int64_t>() */) const {
  if (token_ids_1.empty()) {
    vector<int64_t> tmp(token_ids_0.size() + 2, 0);
    token_type_ids->swap(tmp);
  } else {
    vector<int64_t> tmp(token_ids_0.size() + token_ids_1.size() + 3, 0);
    for (size_t i = token_ids_0.size() + 2; i < tmp.size(); i++) {
      tmp[i] = 1;
    }
    token_type_ids->swap(tmp);
  }
}

void BertTokenizer::TruncateSequence(
    vector<int64_t>* ids,
    vector<int64_t>* pair_ids,
    const size_t num_tokens_to_remove /* = 0 */,
    const size_t stride /* = 0 */) const {
  for (size_t i = 0; i < num_tokens_to_remove; i++) {
    if ((pair_ids->empty()) || (ids->size() > pair_ids->size())) {
      ids->pop_back();
    } else {
      pair_ids->pop_back();
    }
  }
}

int64_t BertTokenizer::GetPadTokenID() const { return pad_token_id_; }

int BertTokenizer::Encode(
    unordered_map<string, vector<int64_t>>* encoded_inputs,
    const string& text,
    const string& text_pair /* = "" */,
    bool is_split_into_words /* = false */,
    const size_t max_seq_len /* = 0 */,
    bool pad_to_max_seq_len /* = false */) const {
  vector<int64_t> ids;
  vector<int64_t> pair_ids;
  if (!is_split_into_words) {
    Tokenize(text, &ids);
    if (ids.empty()) return 0;
    if (!text_pair.empty()) {
      Tokenize(text_pair, &pair_ids);
      if (pair_ids.empty()) return 0;
    }
  } else {
    std::wstring unicode_text;
    bool status_a = framework::ConvertStrToWstr(text, &unicode_text);
    if (!status_a) {
      return 0;
    }
    for (size_t i = 0; i < unicode_text.size(); i++) {
      wstring token = unicode_text.substr(i, 1);
      auto it = vocab_->find(token);
      if (it != vocab_->end()) {
        ids.emplace_back(std::move(it->second));
      } else {
        ids.emplace_back(std::move(unk_token_id_));
      }
    }
  }

  bool pair = false;
  if (!pair_ids.empty()) {
    pair = true;
  }

  size_t len_ids = ids.size();
  size_t len_pair_ids = pair_ids.size();

  // Truncation: Handle max sequence length
  // If max_seq_len == 0, then do nothing and keep the real length.
  // If max_seq_len > 0 and
  // all the input sequence len is over the max_seq_len,
  // then we truncate it.
  size_t total_len = len_ids + len_pair_ids + GetNumSpecialTokensToAdd(pair);
  if (max_seq_len > 0 && total_len > max_seq_len) {
    TruncateSequence(&ids, &pair_ids, total_len - max_seq_len);
  }

  // Add special tokens
  vector<int64_t> sequence;
  BuildInputsWithSpecialTokens(&sequence, ids, pair_ids);
  size_t seq_len = sequence.size();
  vector<int64_t> token_type_ids;
  CreateTokenTypeIdsFromSequences(&token_type_ids, ids, pair_ids);

  // Build output dictionnary
  encoded_inputs->emplace("input_ids", sequence);
  encoded_inputs->emplace("token_type_ids", token_type_ids);
  // Check lengths
  if (max_seq_len > 0 && seq_len > max_seq_len) {
    VLOG(3) << "There is something wrong with the input sequence length."
               " Please check it.";
    // Failed.
    return 0;
  }

  // Padding
  bool needs_to_be_padded = false;
  if (pad_to_max_seq_len && max_seq_len > 0 && (seq_len < max_seq_len)) {
    needs_to_be_padded = true;
  }

  if (needs_to_be_padded) {
    int64_t difference = max_seq_len - seq_len;
    size_t pad_start = max_seq_len - 1 - difference;
    encoded_inputs->at("token_type_ids").resize(max_seq_len);
    for (size_t i = max_seq_len - 1; i > pad_start; i--) {
      encoded_inputs->at("token_type_ids")[i] = pad_token_id_;
    }

    encoded_inputs->at("input_ids").resize(max_seq_len);
    for (size_t i = max_seq_len - 1; i > pad_start; i--) {
      encoded_inputs->at("input_ids")[i] = pad_token_id_;
    }
  }
  return 1;
}

void BertTokenizer::BatchEncode(
    vector<unordered_map<string, vector<int64_t>>>* batch_encode_inputs,
    const framework::Strings& batch_text,
    const framework::Strings& batch_text_pair /* = vector<string>() */,
    bool is_split_into_words /* = false */,
    const size_t max_seq_len /* = 0 */,
    bool pad_to_max_seq_len /* = false */) const {
  bool has_text_pair = false;
  if (batch_text_pair.size() != 0) {
    has_text_pair = true;
  }

  size_t batch_size = batch_text.size();
#ifdef PADDLE_WITH_MKLML
#pragma omp parallel for
#endif
  for (size_t i = 0; i < batch_size; i++) {
    unordered_map<string, vector<int64_t>> res;
    if (has_text_pair) {
      auto status = Encode(&res,
                           batch_text[i],
                           batch_text_pair[i],
                           is_split_into_words,
                           max_seq_len,
                           pad_to_max_seq_len);
      if (!status) {
        res["input_ids"] =
            std::vector<int64_t>{cls_token_id_, sep_token_id_, cls_token_id_};
        res["token_type_ids"] = std::vector<int64_t>{0, 0, 1};
      }
    } else {
      auto status = Encode(&res,
                           batch_text[i],
                           {},
                           is_split_into_words,
                           max_seq_len,
                           pad_to_max_seq_len);

      if (!status) {
        res["input_ids"] = std::vector<int64_t>{cls_token_id_, sep_token_id_};
        res["token_type_ids"] = std::vector<int64_t>{0, 0};
      }
    }
    batch_encode_inputs->at(i) = std::move(res);
  }
}

class FasterTokenizerOp : public framework::OperatorWithKernel {
 public:
  using framework::OperatorWithKernel::OperatorWithKernel;

  void InferShape(framework::InferShapeContext* ctx) const override {
    OP_INOUT_CHECK(ctx->HasInput("Text"), "Input", "Text", "Tokenizer");
    OP_INOUT_CHECK(ctx->HasInput("Vocab"), "Input", "Vocab", "Tokenizer");
    OP_INOUT_CHECK(
        ctx->HasOutput("InputIds"), "Output", "InputIds", "Tokenizer");
    OP_INOUT_CHECK(
        ctx->HasOutput("SegmentIds"), "Output", "SegmentIds", "Tokenizer");

    ctx->SetOutputDim("InputIds", {-1, -1});
    ctx->SetOutputDim("SegmentIds", {-1, -1});
  }

 protected:
  phi::KernelKey GetExpectedKernelType(
      const framework::ExecutionContext& ctx) const override {
    return phi::KernelKey(framework::proto::VarType::INT64,
                          paddle::platform::CPUPlace());
  }

  phi::KernelKey GetKernelTypeForVar(
      const std::string& var_name,
      const phi::DenseTensor& tensor,
      const phi::KernelKey& expected_kernel_type) const override {
    return phi::KernelKey(phi::Backend::ALL_BACKEND,
                          tensor.layout(),
                          expected_kernel_type.dtype());
  }
};

class FasterTokenizerOpMaker : public framework::OpProtoAndCheckerMaker {
 public:
  void Make() override {
    AddInput("Vocab",
             "(std::map<std::wstring, std::int>), The vocab to map "
             "token string to token id.");
    AddInput("Text",
             "(std::vector<std::string>), The sequence to be processed. "
             "One sequence is a string, a list of strings, "
             "or a list of integers depending on whether it "
             "has been pretokenized and converted to ids. ");
    AddInput("TextPair",
             "(std::vector<std::string>), Same as `text` argument, "
             "while it represents for the latter sequence of the "
             "sequence pair.")
        .AsDispensable();
    AddOutput("InputIds", "(Tensor), The token ids of the input text.");
    AddOutput("SegmentIds", "(Tensor), The segments ids of the input text.");
    AddAttr<bool>(
        "do_lower_case",
        "(bool), Whether or not to lowercase the input when tokenizing.")
        .SetDefault(false);
    AddAttr<bool>(
        "is_split_into_words",
        "(bool), Whether or not the input is already pre-tokenized "
        "(e.g., split into words). If set to True, the tokenizer "
        "assumes the input is already split into words (for instance, "
        "by splitting it on whitespace) which it will tokenize. This "
        "is useful for NER or token classification.")
        .SetDefault(false);
    AddAttr<int>("max_seq_len",
                 "(int), If set to a positive number, will limit the "
                 "total sequence returned so that it has a maximum length."
                 " If there are overflowing tokens, those overflowing "
                 "tokens will be added to the returned dictionary  when "
                 "`return_overflowing_tokens` is `True`.")
        .SetDefault(0);
    AddAttr<bool>("pad_to_max_seq_len",
                  "(bool), If set to `True`, the returned sequences would be"
                  " padded up to `max_seq_len` specified length according to"
                  " padding side and padding token id.")
        .SetDefault(false);
    AddComment(R"DOC(Performs tokenization and uses the tokenized tokens to "
    "prepare model inputs. It supports sequence or sequence pair as input, "
    "and batch input is not allowed.)DOC");
  }
};

}  // namespace operators
}  // namespace paddle

namespace ops = paddle::operators;
REGISTER_OPERATOR(faster_tokenizer,
                  ops::FasterTokenizerOp,
                  ops::FasterTokenizerOpMaker);

PD_REGISTER_STRUCT_KERNEL(
    faster_tokenizer, CPU, ALL_LAYOUT, ops::FasterTokenizerKernel, int64_t) {}