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config.json ADDED
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+ { "architectures": [
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+ "LlamaForCausalLM"
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+ ],
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+ "bos_token_id": 1,
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+ "eos_token_id": 2,
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+ "hidden_size": 1024,
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+ "max_position_embeddings": 1024,
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+ "intermediate_size": 2752,
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+ "num_attention_heads": 8,
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+ "num_hidden_layers": 12,
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+ "hidden_act": "silu",
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+ "vocab_size": 64793,
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+ "rms_norm_eps": 1e-06,
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+ "tie_emb_prj_weight": true,
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+ "use_cache": true,
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+ "do_sample": true,
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+ "max_length": 256
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+ }
generation_config.json ADDED
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+ {
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+ "_from_model_config": true,
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+ "bos_token_id": 1,
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+ "eos_token_id": 2,
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+ "pad_token_id": 0
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+ }
pytorch_model.bin ADDED
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:379aeb666b0574ae48cc834a1c66b1b9454a038916a42855f5c8b67d0de81de2
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+ size 872665218
tokenization_chatglm.py ADDED
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+ import os
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+ import torch
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+ from typing import List, Optional, Union, Dict
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+ from sentencepiece import SentencePieceProcessor
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+ from transformers import PreTrainedTokenizer
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+ from transformers.utils import logging, PaddingStrategy
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+ from transformers.tokenization_utils_base import EncodedInput, BatchEncoding
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+
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+
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+ class SPTokenizer:
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+ def __init__(self, model_path: str):
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+ # reload tokenizer
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+ assert os.path.isfile(model_path), model_path
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+ self.sp_model = SentencePieceProcessor(model_file=model_path)
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+
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+ # BOS / EOS token IDs
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+ self.n_words: int = self.sp_model.vocab_size()
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+ self.bos_id: int = self.sp_model.bos_id()
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+ self.eos_id: int = self.sp_model.eos_id()
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+ self.pad_id: int = self.sp_model.unk_id()
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+ assert self.sp_model.vocab_size() == self.sp_model.get_piece_size()
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+
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+ special_tokens = ["[MASK]", "[gMASK]", "[sMASK]", "sop", "eop"]
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+ self.special_tokens = {}
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+ self.index_special_tokens = {}
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+ for token in special_tokens:
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+ self.special_tokens[token] = self.n_words
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+ self.index_special_tokens[self.n_words] = token
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+ self.n_words += 1
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+
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+ def tokenize(self, s: str):
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+ return self.sp_model.EncodeAsPieces(s)
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+
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+ def encode(self, s: str, bos: bool = False, eos: bool = False) -> List[int]:
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+ assert type(s) is str
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+ t = self.sp_model.encode(s)
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+ if bos:
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+ t = [self.bos_id] + t
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+ if eos:
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+ t = t + [self.eos_id]
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+ return t
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+
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+ def decode(self, t: List[int]) -> str:
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+ return self.sp_model.decode(t)
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+
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+ def decode_tokens(self, tokens: List[str]) -> str:
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+ text = self.sp_model.DecodePieces(tokens)
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+ return text
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+
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+ def convert_token_to_id(self, token):
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+ """ Converts a token (str) in an id using the vocab. """
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+ if token in self.special_tokens:
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+ return self.special_tokens[token]
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+ return self.sp_model.PieceToId(token)
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+
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+ def convert_id_to_token(self, index):
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+ """Converts an index (integer) in a token (str) using the vocab."""
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+ if index in self.index_special_tokens or index in [self.eos_id, self.bos_id, self.pad_id] or index < 0:
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+ return ""
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+ return self.sp_model.IdToPiece(index)
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+
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+
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+ class ChatGLMTokenizer(PreTrainedTokenizer):
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+ vocab_files_names = {"vocab_file": "tokenizer.model"}
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+
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+ model_input_names = ["input_ids", "attention_mask", "position_ids"]
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+
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+ def __init__(self, vocab_file, padding_side="left", clean_up_tokenization_spaces=False, **kwargs):
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+ self.name = "GLMTokenizer"
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+
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+ self.vocab_file = vocab_file
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+ self.tokenizer = SPTokenizer(vocab_file)
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+ self.special_tokens = {
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+ "<bos>": self.tokenizer.bos_id,
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+ "<eos>": self.tokenizer.eos_id,
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+ "<pad>": self.tokenizer.pad_id
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+ }
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+ super().__init__(padding_side=padding_side, clean_up_tokenization_spaces=clean_up_tokenization_spaces, **kwargs)
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+
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+ def get_command(self, token):
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+ if token in self.special_tokens:
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+ return self.special_tokens[token]
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+ assert token in self.tokenizer.special_tokens, f"{token} is not a special token for {self.name}"
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+ return self.tokenizer.special_tokens[token]
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+
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+ @property
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+ def unk_token(self) -> str:
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+ return "<unk>"
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+
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+ @property
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+ def pad_token(self) -> str:
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+ return "<unk>"
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+
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+ @property
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+ def pad_token_id(self):
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+ return self.get_command("<pad>")
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+
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+ @property
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+ def eos_token(self) -> str:
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+ return "</s>"
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+
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+ @property
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+ def eos_token_id(self):
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+ return self.get_command("<eos>")
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+
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+ @property
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+ def vocab_size(self):
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+ return self.tokenizer.n_words
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+
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+ def get_vocab(self):
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+ """ Returns vocab as a dict """
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+ vocab = {self._convert_id_to_token(i): i for i in range(self.vocab_size)}
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+ vocab.update(self.added_tokens_encoder)
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+ return vocab
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+
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+ def _tokenize(self, text, **kwargs):
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+ return self.tokenizer.tokenize(text)
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+
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+ def _convert_token_to_id(self, token):
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+ """ Converts a token (str) in an id using the vocab. """
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+ return self.tokenizer.convert_token_to_id(token)
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+
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+ def _convert_id_to_token(self, index):
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+ """Converts an index (integer) in a token (str) using the vocab."""
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+ return self.tokenizer.convert_id_to_token(index)
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+
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+ def convert_tokens_to_string(self, tokens: List[str]) -> str:
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+ return self.tokenizer.decode_tokens(tokens)
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+
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+ def save_vocabulary(self, save_directory, filename_prefix=None):
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+ """
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+ Save the vocabulary and special tokens file to a directory.
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+ Args:
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+ save_directory (`str`):
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+ The directory in which to save the vocabulary.
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+ filename_prefix (`str`, *optional*):
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+ An optional prefix to add to the named of the saved files.
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+ Returns:
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+ `Tuple(str)`: Paths to the files saved.
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+ """
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+ if os.path.isdir(save_directory):
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+ vocab_file = os.path.join(
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+ save_directory, self.vocab_files_names["vocab_file"]
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+ )
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+ else:
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+ vocab_file = save_directory
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+
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+ with open(self.vocab_file, 'rb') as fin:
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+ proto_str = fin.read()
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+
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+ with open(vocab_file, "wb") as writer:
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+ writer.write(proto_str)
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+
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+ return (vocab_file,)
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+
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+ def get_prefix_tokens(self):
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+ prefix_tokens = [self.get_command("[gMASK]"), self.get_command("sop")]
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+ return prefix_tokens
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+
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+ def build_prompt(self, query, history=None):
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+ if history is None:
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+ history = []
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+ prompt = ""
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+ for i, (old_query, response) in enumerate(history):
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+ prompt += "[Round {}]\n\n问:{}\n\n答:{}\n\n".format(i + 1, old_query, response)
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+ prompt += "[Round {}]\n\n问:{}\n\n答:".format(len(history) + 1, query)
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+ return prompt
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+
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+ def build_inputs_with_special_tokens(
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+ self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
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+ ) -> List[int]:
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+ """
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+ Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
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+ adding special tokens. A BERT sequence has the following format:
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+ - single sequence: `[CLS] X [SEP]`
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+ - pair of sequences: `[CLS] A [SEP] B [SEP]`
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+ Args:
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+ token_ids_0 (`List[int]`):
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+ List of IDs to which the special tokens will be added.
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+ token_ids_1 (`List[int]`, *optional*):
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+ Optional second list of IDs for sequence pairs.
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+ Returns:
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+ `List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
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+ """
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+ prefix_tokens = self.get_prefix_tokens()
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+ token_ids_0 = prefix_tokens + token_ids_0
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+ if token_ids_1 is not None:
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+ token_ids_0 = token_ids_0 + token_ids_1 + [self.get_command("<eos>")]
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+ return token_ids_0
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+
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+ def _pad(
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+ self,
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+ encoded_inputs: Union[Dict[str, EncodedInput], BatchEncoding],
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+ max_length: Optional[int] = None,
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+ padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
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+ pad_to_multiple_of: Optional[int] = None,
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+ return_attention_mask: Optional[bool] = None,
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+ ) -> dict:
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+ """
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+ Pad encoded inputs (on left/right and up to predefined length or max length in the batch)
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+ Args:
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+ encoded_inputs:
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+ Dictionary of tokenized inputs (`List[int]`) or batch of tokenized inputs (`List[List[int]]`).
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+ max_length: maximum length of the returned list and optionally padding length (see below).
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+ Will truncate by taking into account the special tokens.
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+ padding_strategy: PaddingStrategy to use for padding.
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+ - PaddingStrategy.LONGEST Pad to the longest sequence in the batch
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+ - PaddingStrategy.MAX_LENGTH: Pad to the max length (default)
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+ - PaddingStrategy.DO_NOT_PAD: Do not pad
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+ The tokenizer padding sides are defined in self.padding_side:
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+ - 'left': pads on the left of the sequences
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+ - 'right': pads on the right of the sequences
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+ pad_to_multiple_of: (optional) Integer if set will pad the sequence to a multiple of the provided value.
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+ This is especially useful to enable the use of Tensor Core on NVIDIA hardware with compute capability
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+ `>= 7.5` (Volta).
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+ return_attention_mask:
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+ (optional) Set to False to avoid returning attention mask (default: set to model specifics)
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+ """
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+ # Load from model defaults
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+ assert self.padding_side == "left"
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+
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+ required_input = encoded_inputs[self.model_input_names[0]]
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+ seq_length = len(required_input)
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+
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+ if padding_strategy == PaddingStrategy.LONGEST:
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+ max_length = len(required_input)
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+
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+ if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
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+ max_length = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of
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+
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+ needs_to_be_padded = padding_strategy != PaddingStrategy.DO_NOT_PAD and len(required_input) != max_length
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+
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+ # Initialize attention mask if not present.
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+ if "attention_mask" not in encoded_inputs:
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+ encoded_inputs["attention_mask"] = [1] * seq_length
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+
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+ if "position_ids" not in encoded_inputs:
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+ encoded_inputs["position_ids"] = list(range(seq_length))
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+
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+ if needs_to_be_padded:
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+ difference = max_length - len(required_input)
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+
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+ if "attention_mask" in encoded_inputs:
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+ encoded_inputs["attention_mask"] = [0] * difference + encoded_inputs["attention_mask"]
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+ if "position_ids" in encoded_inputs:
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+ encoded_inputs["position_ids"] = [0] * difference + encoded_inputs["position_ids"]
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+ encoded_inputs[self.model_input_names[0]] = [self.pad_token_id] * difference + required_input
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+
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+ return encoded_inputs
tokenizer.model ADDED
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:e7dc4c393423b76e4373e5157ddc34803a0189ba96b21ddbb40269d31468a6f2
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+ size 1018370
tokenizer_config.json ADDED
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+ {
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+ "name_or_path": "THUDM/chatglm2-6b",
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+ "remove_space": false,
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+ "do_lower_case": false,
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+ "tokenizer_class": "ChatGLMTokenizer",
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+ "auto_map": {
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+ "AutoTokenizer": [
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+ "tokenization_chatglm.ChatGLMTokenizer",
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+ null
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+ ]
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+ }
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+ }