PaddleX/paddlex/inference/models/common/tokenizer/llama_tokenizer.py

# Copyright (c) 2025 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.

import os
from shutil import copyfile
from typing import List, Optional, Tuple

from paddlex.inference.models.common.tokenizer.tokenizer_utils import (
    PretrainedTokenizer,
)


class LlamaTokenizer(PretrainedTokenizer):
    model_input_names = ["input_ids", "attention_mask", "position_ids"]
    resource_files_names = {
        "vocab_file": "sentencepiece.bpe.model",
    }
    pretrained_resource_files_map = {
        "vocab_file": {
            "__internal_testing__/micro-random-llama": "https://bj.bcebos.com/paddlenlp/models/transformers/llama/sentencepiece.bpe.model",
            "__internal_testing__/tiny-random-llama": "https://bj.bcebos.com/paddlenlp/models/transformers/llama/sentencepiece.bpe.model",
            "facebook/llama-7b": "https://bj.bcebos.com/paddlenlp/models/transformers/llama/sentencepiece.bpe.model",
            "facebook/llama-13b": "https://bj.bcebos.com/paddlenlp/models/transformers/llama/sentencepiece.bpe.model",
            "facebook/llama-30b": "https://bj.bcebos.com/paddlenlp/models/transformers/llama/sentencepiece.bpe.model",
            "facebook/llama-65b": "https://bj.bcebos.com/paddlenlp/models/transformers/llama/sentencepiece.bpe.model",
        },
    }

    pretrained_init_configuration = {
        "__internal_testing__/micro-random-llama": {},
        "__internal_testing__/tiny-random-llama": {},
        "facebook/llama-7b": {},
        "facebook/llama-13b": {},
        "facebook/llama-30b": {},
        "facebook/llama-65b": {},
    }
    padding_side = "left"

    def __init__(
        self,
        vocab_file,
        unk_token="<unk>",
        bos_token="<s>",
        eos_token="</s>",
        add_bos_token=True,
        add_eos_token=False,
        sp_model_kwargs=None,
        decode_with_prefix_space=False,
        **kwargs,
    ):
        self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
        super().__init__(
            bos_token=bos_token, eos_token=eos_token, unk_token=unk_token, **kwargs
        )

        self.vocab_file = vocab_file
        self.add_bos_token = add_bos_token
        self.add_eos_token = add_eos_token
        self.decode_with_prefix_space = decode_with_prefix_space
        self.sp_model = self.get_spm_processor(kwargs.pop("from_slow", True))

    @property
    def vocab_size(self):
        """Returns vocab size"""
        return self.sp_model.get_piece_size()

    def __len__(self):
        """
        Returns the vocabulary size. added_tokens_encoder has to be added in the sp_model
        """
        added_size = 0

        for id in self.added_tokens_decoder:
            if id >= self.sp_model.get_piece_size():
                added_size += 1

        return self.vocab_size + added_size

    @property
    def bos_token_id(self) -> Optional[int]:
        return self.sp_model.bos_id()

    @property
    def eos_token_id(self) -> Optional[int]:
        return self.sp_model.eos_id()

    def get_spm_processor(self, from_slow=True):
        import sentencepiece as spm
        from sentencepiece import sentencepiece_model_pb2 as model_pb2

        tokenizer = spm.SentencePieceProcessor(**self.sp_model_kwargs)
        if from_slow:  # no dependency on protobuf
            tokenizer.Load(self.vocab_file)
            return tokenizer

        with open(self.vocab_file, "rb") as f:
            sp_model = f.read()
            model = model_pb2.ModelProto.FromString(sp_model)
            normalizer_spec = model_pb2.NormalizerSpec()
            normalizer_spec.add_dummy_prefix = False
            model.normalizer_spec.MergeFrom(normalizer_spec)
            sp_model = model.SerializeToString()
            tokenizer.LoadFromSerializedProto(sp_model)
        return tokenizer

    def get_vocab(self):
        """Returns vocab as a dict"""
        vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
        vocab.update(self.added_tokens_encoder)
        return vocab

    def _tokenize(self, text):
        """Returns a tokenized string."""
        return self.sp_model.encode(text, out_type=str)

    def _convert_token_to_id(self, token):
        """Converts a token (str) in an id using the vocab."""
        return self.sp_model.piece_to_id(token)

    def _convert_id_to_token(self, index):
        """Converts an index (integer) in a token (str) using the vocab."""
        token = self.sp_model.id_to_piece(index)
        return token

    def convert_tokens_to_string(self, tokens):
        """Converts a sequence of tokens (string) in a single string."""
        current_sub_tokens = []
        out_string = ""
        prev_is_special = False
        for i, token in enumerate(tokens):
            # make sure that special tokens are not decoded using sentencepiece model
            if token in self.all_special_tokens:
                if not prev_is_special and i != 0:
                    out_string += " "
                out_string += self.sp_model.decode(current_sub_tokens) + token
                prev_is_special = True
                current_sub_tokens = []
            else:
                current_sub_tokens.append(token)
                prev_is_special = False
        out_string += self.sp_model.decode(current_sub_tokens)
        return out_string

    def save_vocabulary(
        self, save_directory, filename_prefix: Optional[str] = None
    ) -> Tuple[str]:
        """
        Save the vocabulary and special tokens file to a directory.
        Args:
            save_directory (`str`):
                The directory in which to save the vocabulary.
        Returns:
            `Tuple(str)`: Paths to the files saved.
        """
        if not os.path.isdir(save_directory):
            raise ValueError(
                f"Vocabulary path ({save_directory}) should be a directory"
            )

        out_vocab_file = os.path.join(
            save_directory,
            (filename_prefix + "-" if filename_prefix else "")
            + self.resource_files_names["vocab_file"],
        )

        if os.path.abspath(self.vocab_file) != os.path.abspath(
            out_vocab_file
        ) and os.path.isfile(self.vocab_file):
            copyfile(self.vocab_file, out_vocab_file)
        elif not os.path.isfile(self.vocab_file):
            with open(out_vocab_file, "wb") as fi:
                content_spiece_model = self.sp_model.serialized_model_proto()
                fi.write(content_spiece_model)

        return (out_vocab_file,)

    def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
        if self.add_bos_token:
            bos_token_ids = [self.bos_token_id]
        else:
            bos_token_ids = []

        output = bos_token_ids + token_ids_0

        if token_ids_1 is not None:
            output = output + token_ids_1

        if self.add_eos_token:
            output = output + [self.eos_token_id]

        return output

    def get_special_tokens_mask(
        self,
        token_ids_0: List[int],
        token_ids_1: Optional[List[int]] = None,
        already_has_special_tokens: bool = False,
    ) -> List[int]:
        """
        Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
        special tokens using the tokenizer `prepare_for_model` method.
        Args:
            token_ids_0 (`List[int]`):
                List of IDs.
            token_ids_1 (`List[int]`, *optional*):
                Optional second list of IDs for sequence pairs.
            already_has_special_tokens (`bool`, *optional*, defaults to `False`):
                Whether or not the token list is already formatted with special tokens for the model.
        Returns:
            `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
        """
        if already_has_special_tokens:
            return super().get_special_tokens_mask(
                token_ids_0=token_ids_0,
                token_ids_1=token_ids_1,
                already_has_special_tokens=True,
            )

        if token_ids_1 is None:
            return [1] + ([0] * len(token_ids_0)) + [1]
        return [1] + ([0] * len(token_ids_0)) + [1, 1] + ([0] * len(token_ids_1)) + [1]

    def create_token_type_ids_from_sequences(
        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
    ) -> List[int]:
        """
        Create a mask from the two sequences passed to be used in a sequence-pair classification task. T5 does not make
        use of token type ids, therefore a list of zeros is returned.

        Args:
            token_ids_0 (`List[int]`):
                List of IDs.
            token_ids_1 (`List[int]`, *optional*):
                Optional second list of IDs for sequence pairs.
        Returns:
            `List[int]`: List of zeros.
        """
        eos = [self.eos_token_id]

        if token_ids_1 is None:
            return len(token_ids_0 + eos) * [0]
        return len(token_ids_0 + eos + token_ids_1 + eos) * [0]