# Copyright (c) 2023, Thundersoft. All rights reserved. import os from shutil import copyfile from typing import Any, Dict, List, Optional, Tuple import sentencepiece as spm from transformers.tokenization_utils import AddedToken, PreTrainedTokenizer from transformers.utils import logging from transformers.convert_slow_tokenizer import import_protobuf logger = logging.get_logger(__name__) VOCAB_FILES_NAMES = {"vocab_file": "tokenizer.model"} PRETRAINED_VOCAB_FILES_MAP = { "vocab_file": {}, "tokenizer_file": {}, } PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {} SPIECE_UNDERLINE = "▁" B_INST, E_INST = "[INST]", "[/INST]" B_SYS, E_SYS = "<>\n", "\n<>\n\n" # fmt: off DEFAULT_SYSTEM_PROMPT = """You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your \ answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure\ that your responses are socially unbiased and positive in nature. If a question does not make any sense, or is not factually coherent, explain why instead of answering something not \ correct. If you don't know the answer to a question, please don't share false information.""" # fmt: on class SDSATTokenizer(PreTrainedTokenizer): """ Construct a SDSAT tokenizer. Based on byte-level Byte-Pair-Encoding. The default padding token is unset as there is no padding token in the original model. Args: vocab_file (`str`): Path to the vocabulary file. legacy (`bool`, *optional*): Whether or not the `legacy` behavior of the tokenizer should be used. Legacy is before the merge of #24622 and #25224 which includes fixes to properly handle tokens that appear after special tokens. A simple example: - `legacy=True`: ```python >>> from transformers import T5Tokenizer >>> tokenizer = T5Tokenizer.from_pretrained("t5-base", legacy=True) >>> tokenizer.encode("Hello .") [8774, 32099, 3, 5, 1] ``` - `legacy=False`: ```python >>> from transformers import T5Tokenizer >>> tokenizer = T5Tokenizer.from_pretrained("t5-base", legacy=False) >>> tokenizer.encode("Hello .") # the extra space `[3]` is no longer here [8774, 32099, 5, 1] ``` Checkout the [pull request](https://github.com/huggingface/transformers/pull/24565) for more details. """ vocab_files_names = VOCAB_FILES_NAMES pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES model_input_names = ["input_ids", "attention_mask"] def __init__( self, vocab_file, unk_token="", bos_token="", eos_token="", pad_token=None, sp_model_kwargs: Optional[Dict[str, Any]] = None, add_bos_token=True, add_eos_token=False, clean_up_tokenization_spaces=False, use_default_system_prompt=True, spaces_between_special_tokens=False, legacy=None, **kwargs, ): self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs bos_token = AddedToken(bos_token, lstrip=False, rstrip=False) if isinstance(bos_token, str) else bos_token eos_token = AddedToken(eos_token, lstrip=False, rstrip=False) if isinstance(eos_token, str) else eos_token unk_token = AddedToken(unk_token, lstrip=False, rstrip=False) if isinstance(unk_token, str) else unk_token pad_token = AddedToken(pad_token, lstrip=False, rstrip=False) if isinstance(pad_token, str) else pad_token if legacy is None: logger.warning_once( f"You are using the default legacy behaviour of the {self.__class__}. If you see this, DO NOT PANIC! This is" " expected, and simply means that the `legacy` (previous) behavior will be used so nothing changes for you." " If you want to use the new behaviour, set `legacy=False`. This should only be set if you understand what it" " means, and thouroughly read the reason why this was added as explained in" " https://github.com/huggingface/transformers/pull/24565" ) legacy = True self.legacy = legacy self.vocab_file = vocab_file self.add_bos_token = add_bos_token self.add_eos_token = add_eos_token self.use_default_system_prompt = use_default_system_prompt self.sp_model = self.get_spm_processor() super().__init__( bos_token=bos_token, eos_token=eos_token, unk_token=unk_token, pad_token=pad_token, add_bos_token=add_bos_token, add_eos_token=add_eos_token, sp_model_kwargs=self.sp_model_kwargs, clean_up_tokenization_spaces=clean_up_tokenization_spaces, use_default_system_prompt=use_default_system_prompt, spaces_between_special_tokens=spaces_between_special_tokens, legacy=legacy, **kwargs, ) @property def unk_token_length(self): return len(self.sp_model.encode(str(self.unk_token))) # Copied from transformers.models.t5.tokenization_t5.T5Tokenizer.get_spm_processor def get_spm_processor(self): tokenizer = spm.SentencePieceProcessor(**self.sp_model_kwargs) if self.legacy: # no dependency on protobuf tokenizer.Load(self.vocab_file) return tokenizer with open(self.vocab_file, "rb") as f: sp_model = f.read() model_pb2 = import_protobuf(f"The new behaviour of {self.__class__.__name__} (with `self.legacy = False`)") 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 __getstate__(self): state = self.__dict__.copy() state["sp_model"] = None state["sp_model_proto"] = self.sp_model.serialized_model_proto() return state def __setstate__(self, d): self.__dict__ = d self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs) self.sp_model.LoadFromSerializedProto(self.sp_model_proto) @property def vocab_size(self): """Returns vocab size""" return self.sp_model.get_piece_size() 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 # Copied from transformers.models.t5.tokenization_t5.T5Tokenizer.tokenize def tokenize(self, text: "TextInput", add_special_tokens=False, **kwargs) -> List[str]: """ Converts a string to a list of tokens. If `self.legacy` is set to `False`, a prefix token is added unless the first token is special. """ if self.legacy or len(text) == 0: return super().tokenize(text, **kwargs) # tokens = super().tokenize(SPIECE_UNDERLINE + text.replace(SPIECE_UNDERLINE, " "), **kwargs) tokens = super().tokenize(text.replace(SPIECE_UNDERLINE, " "), **kwargs) # liu if len(tokens) > 1 and tokens[0] == SPIECE_UNDERLINE and tokens[1] in self.all_special_tokens: tokens = tokens[1:] return tokens # Copied from transformers.models.t5.tokenization_t5.T5Tokenizer._tokenize def _tokenize(self, text, **kwargs): """ Returns a tokenized string. We de-activated the `add_dummy_prefix` option, thus the sentencepiece internals will always strip any SPIECE_UNDERLINE. For example: `self.sp_model.encode(f"{SPIECE_UNDERLINE}Hey", out_type = str)` will give `['H', 'e', 'y']` instead of `['▁He', 'y']`. Thus we always encode `f"{unk_token}text"` and strip the `unk_token`. Here is an example with `unk_token = ""` and `unk_token_length = 4`. `self.tokenizer.sp_model.encode(" Hey", out_type = str)[4:]`. """ tokens = self.sp_model.encode(text, out_type=str) if self.legacy or not text.startswith((SPIECE_UNDERLINE, " ")): return tokens # 1. Encode string + prefix ex: " Hey" tokens = self.sp_model.encode(self.unk_token + text, out_type=str) # 2. Remove self.unk_token from ['<','unk','>', '▁Hey'] return tokens[self.unk_token_length :] if len(tokens) >= self.unk_token_length else tokens 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.IdToPiece(index) return token def convert_tokens_to_string(self, tokens): """Converts a sequence of tokens (string) in a single string.""" # since we manually add the prefix space, we have to remove it when decoding if tokens[0].startswith(SPIECE_UNDERLINE): tokens[0] = tokens[0][1:] 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 and self.legacy: 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): logger.error(f"Vocabulary path ({save_directory}) should be a directory") return out_vocab_file = os.path.join( save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_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): bos_token_id = [self.bos_token_id] if self.add_bos_token else [] eos_token_id = [self.eos_token_id] if self.add_eos_token else [] output = bos_token_id + token_ids_0 + eos_token_id if token_ids_1 is not None: output = output + bos_token_id + token_ids_1 + 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 ) bos_token_id = [1] if self.add_bos_token else [] eos_token_id = [1] if self.add_eos_token else [] if token_ids_1 is None: return bos_token_id + ([0] * len(token_ids_0)) + eos_token_id return ( bos_token_id + ([0] * len(token_ids_0)) + eos_token_id + bos_token_id + ([0] * len(token_ids_1)) + eos_token_id ) def create_token_type_ids_from_sequences( self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None ) -> List[int]: """ Creates a mask from the two sequences passed to be used in a sequence-pair classification task. An ALBERT sequence pair mask has the following format: ``` 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 | first sequence | second sequence | ``` if token_ids_1 is None, only returns the first portion of the mask (0s). 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 [token type IDs](../glossary#token-type-ids) according to the given sequence(s). """ bos_token_id = [self.bos_token_id] if self.add_bos_token else [] eos_token_id = [self.eos_token_id] if self.add_eos_token else [] output = [0] * len(bos_token_id + token_ids_0 + eos_token_id) if token_ids_1 is not None: output += [1] * len(bos_token_id + token_ids_1 + eos_token_id) return output def _build_conversation_input_ids(self, conversation: "Conversation") -> List[int]: r"""Builds the input ids for a conversation. This is the format used in the provided examples. System prompts should be manually added at the beginning of the conversation. If no system prompt is given, the `DEFAULT_SYSTEM_PROMPT` will be used. ``` [INST] B_SYS SytemPrompt E_SYS Prompt [/INST] Answer [INST] Prompt [/INST] Answer [INST] Prompt [/INST] ``` If you want to use your own system prompt, make sure to use both `B_SYS` and `E_SYS` use the following: ```python >>> from transformers import Conversation >>> Conversation( ... "<>\n Only answer with emojis, and charades\n<>\n\nHow can I build a house in 10 septs?" ... ) # doctest: +IGNORE_RESULT ``` Args: conversation (`Conversation`): Conversation to build input ids for. Returns: `List[int]`: Input ids for the conversation. """ if self.use_default_system_prompt: if len(conversation.past_user_inputs) > 0: if ( not conversation.past_user_inputs[0].startswith(B_SYS) or E_SYS not in conversation.past_user_inputs[0] ): conversation.past_user_inputs[0] = ( B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + conversation.past_user_inputs[0] ) elif conversation.new_user_input: if not conversation.new_user_input.startswith(B_SYS) or E_SYS not in conversation.new_user_input: conversation.new_user_input = B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + conversation.new_user_input else: raise ValueError("Last message must be from user") dialogue = list(conversation.iter_texts()) if not all([is_user for is_user, msg in dialogue[::2]]) or not all( [not is_user for is_user, msg in dialogue[1::2]] ): raise ValueError( "The model only supports 'user' and 'assistant' roles, starting with user and alternating (u/a/u/a/u...)" ) dialog_tokens: List[int] = [] dialog_tokens += sum( [ [self.bos_token_id] + self.encode( f"{B_INST} {(prompt[1]).strip()} {E_INST} {(answer[1]).strip()} ", add_special_tokens=False ) + [self.eos_token_id] for prompt, answer in zip(dialogue[::2], dialogue[1::2]) ], [], ) dialog_tokens += [self.bos_token_id] + self.encode( f"{B_INST} {(dialogue[-1][1]).strip()} {E_INST}", add_special_tokens=False ) return dialog_tokens