Delete models/ChatGLM

models/ChatGLM/config.json

@@ -1,28 +0,0 @@
-{
-  "_name_or_path": "THUDM/chatglm-6b",
-  "architectures": [
-    "ChatGLMModel"
-  ],
-  "auto_map": {
-    "AutoConfig": "configuration_chatglm.ChatGLMConfig",
-    "AutoModel": "modeling_chatglm.ChatGLMForConditionalGeneration",
-    "AutoModelForSeq2SeqLM": "modeling_chatglm.ChatGLMForConditionalGeneration"
-  },
-  "bos_token_id": 130004,
-  "eos_token_id": 130005,
-  "mask_token_id": 130000,
-  "gmask_token_id": 130001,
-  "pad_token_id": 3,
-  "hidden_size": 4096,
-  "inner_hidden_size": 16384,
-  "layernorm_epsilon": 1e-05,
-  "max_sequence_length": 2048,
-  "model_type": "chatglm",
-  "num_attention_heads": 32,
-  "num_layers": 28,
-  "position_encoding_2d": true,
-  "torch_dtype": "float16",
-  "transformers_version": "4.23.1",
-  "use_cache": true,
-  "vocab_size": 130528
-}

models/ChatGLM/configuration_chatglm.py

@@ -1,103 +0,0 @@
-""" ChatGLM model configuration """
-
-from transformers.configuration_utils import PretrainedConfig
-from transformers.utils import logging
-
-logger = logging.get_logger(__name__)
-
-
-class ChatGLMConfig(PretrainedConfig):
-    r"""
-    This is the configuration class to store the configuration of a [`~ChatGLMModel`].
-    It is used to instantiate an ChatGLM model according to the specified arguments, defining the model
-    architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of
-    the ChatGLM-6B [THUDM/ChatGLM-6B](https://huggingface.co/THUDM/chatglm-6b) architecture.
-
-    Configuration objects inherit from  [`PretrainedConfig`] and can be used
-    to control the model outputs. Read the documentation from  [`PretrainedConfig`]
-    for more information.
-
-
-    Args:
-        vocab_size (`int`, *optional*, defaults to 150528):
-            Vocabulary size of the ChatGLM-6B model. Defines the number of different tokens that can be represented by the
-            `inputs_ids` passed when calling [`~ChatGLMModel`] or
-            [`~TFChatGLMModel`].
-        hidden_size (`int`, *optional*, defaults to 4096):
-            Dimension of the encoder layers and the pooler layer.
-        num_hidden_layers (`int`, *optional*, defaults to 28):
-            Number of hidden layers in the Transformer encoder.
-        num_attention_heads (`int`, *optional*, defaults to 32):
-            Number of attention heads for each attention layer in the Transformer encoder.
-        inner_hidden_size (`int`, *optional*, defaults to 16384):
-            Dimension of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
-        max_sequence_length (`int`, *optional*, defaults to 512):
-            The maximum sequence length that this model might ever be used with.
-            Typically set this to something large just in case (e.g., 512 or 1024 or 2048).
-        layernorm_epsilon (`float`, *optional*, defaults to 1e-5):
-            The epsilon used by the layer normalization layers.
-        use_cache (`bool`, *optional*, defaults to `True`):
-            Whether the model should return the last key/values attentions (not used by all models).
-        Example:
-
-    ```python
-    >>> from configuration_chatglm import ChatGLMConfig
-    >>> from modeling_chatglm import ChatGLMModel
-
-    >>> # Initializing a ChatGLM-6B THUDM/ChatGLM-6B style configuration
-    >>> configuration = ChatGLMConfig()
-
-    >>> # Initializing a model from the THUDM/ChatGLM-6B style configuration
-    >>> model = ChatGLMModel(configuration)
-
-    >>> # Accessing the model configuration
-    >>> configuration = model.config
-    ```
-"""
-    model_type = "chatglm"
-
-    def __init__(
-            self,
-            vocab_size=150528,
-            hidden_size=4096,
-            num_layers=28,
-            num_attention_heads=32,
-            layernorm_epsilon=1e-5,
-            use_cache=False,
-            bos_token_id=150004,
-            eos_token_id=150005,
-            mask_token_id=150000,
-            gmask_token_id=150001,
-            pad_token_id=0,
-            max_sequence_length=2048,
-            inner_hidden_size=16384,
-            position_encoding_2d=True,
-            quantization_bit=0,
-            pre_seq_len=None,
-            prefix_projection=False,
-            **kwargs
-    ):
-        self.num_layers = num_layers
-        self.vocab_size = vocab_size
-        self.hidden_size = hidden_size
-        self.num_attention_heads = num_attention_heads
-        self.max_sequence_length = max_sequence_length
-        self.layernorm_epsilon = layernorm_epsilon
-        self.inner_hidden_size = inner_hidden_size
-        self.use_cache = use_cache
-        self.bos_token_id = bos_token_id
-        self.eos_token_id = eos_token_id
-        self.pad_token_id = pad_token_id
-        self.mask_token_id = mask_token_id
-        self.gmask_token_id = gmask_token_id
-        self.position_encoding_2d = position_encoding_2d
-        self.quantization_bit = quantization_bit
-        self.pre_seq_len = pre_seq_len
-        self.prefix_projection = prefix_projection
-
-        super().__init__(
-            pad_token_id=pad_token_id,
-            bos_token_id=bos_token_id,
-            eos_token_id=eos_token_id,
-            **kwargs
-        )

models/ChatGLM/tokenization_chatglm.py

@@ -1,443 +0,0 @@
-"""Tokenization classes for ChatGLM."""
-from typing import List, Optional, Union
-import os
-
-from transformers.tokenization_utils import PreTrainedTokenizer
-from transformers.utils import logging, PaddingStrategy
-from transformers.tokenization_utils_base import EncodedInput, BatchEncoding
-from typing import Dict
-import sentencepiece as spm
-import numpy as np
-
-logger = logging.get_logger(__name__)
-
-PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
-    "THUDM/chatglm-6b": 2048,
-}
-
-
-class TextTokenizer:
-    def __init__(self, model_path):
-        self.sp = spm.SentencePieceProcessor()
-        self.sp.Load(model_path)
-        self.num_tokens = self.sp.vocab_size()
-
-    def encode(self, text):
-        return self.sp.EncodeAsIds(text)
-
-    def decode(self, ids: List[int]):
-        return self.sp.DecodeIds(ids)
-
-    def tokenize(self, text):
-        return self.sp.EncodeAsPieces(text)
-
-    def convert_tokens_to_string(self, tokens):
-        return self.sp.DecodePieces(tokens)
-
-    def convert_tokens_to_ids(self, tokens):
-        return [self.sp.PieceToId(token) for token in tokens]
-
-    def convert_token_to_id(self, token):
-        return self.sp.PieceToId(token)
-
-    def convert_id_to_token(self, idx):
-        return self.sp.IdToPiece(idx)
-
-    def __len__(self):
-        return self.num_tokens
-
-
-class SPTokenizer:
-    def __init__(
-            self,
-            vocab_file,
-            num_image_tokens=20000,
-            max_blank_length=80,
-            byte_fallback=True,
-    ):
-        assert vocab_file is not None
-        self.vocab_file = vocab_file
-        self.num_image_tokens = num_image_tokens
-        self.special_tokens = ["[MASK]", "[gMASK]", "[sMASK]", "<unused_0>", "<sop>", "<eop>", "<ENC>", "<dBLOCK>"]
-        self.max_blank_length = max_blank_length
-        self.byte_fallback = byte_fallback
-        self.text_tokenizer = TextTokenizer(vocab_file)
-
-    def _get_text_tokenizer(self):
-        return self.text_tokenizer
-
-    @staticmethod
-    def get_blank_token(length: int):
-        assert length >= 2
-        return f"<|blank_{length}|>"
-
-    @staticmethod
-    def get_tab_token():
-        return f"<|tab|>"
-
-    @property
-    def num_text_tokens(self):
-        return self.text_tokenizer.num_tokens
-
-    @property
-    def num_tokens(self):
-        return self.num_image_tokens + self.num_text_tokens
-
-    @staticmethod
-    def _encode_whitespaces(text: str, max_len: int = 80):
-        text = text.replace("\t", SPTokenizer.get_tab_token())
-        for i in range(max_len, 1, -1):
-            text = text.replace(" " * i, SPTokenizer.get_blank_token(i))
-        return text
-
-    def _preprocess(self, text: str, linebreak=True, whitespaces=True):
-        if linebreak:
-            text = text.replace("\n", "<n>")
-        if whitespaces:
-            text = self._encode_whitespaces(text, max_len=self.max_blank_length)
-        return text
-
-    def encode(
-            self, text: str, linebreak=True, whitespaces=True, add_dummy_prefix=True
-    ) -> List[int]:
-        """
-        @param text: Text to encode.
-        @param linebreak: Whether to encode newline (\n) in text.
-        @param whitespaces: Whether to encode multiple whitespaces or tab in text, useful for source code encoding.
-        @param special_tokens: Whether to encode special token ([MASK], [gMASK], etc.) in text.
-        @param add_dummy_prefix: Whether to add dummy blank space in the beginning.
-        """
-        text = self._preprocess(text, linebreak, whitespaces)
-        if not add_dummy_prefix:
-            text = "<n>" + text
-        tmp = self._get_text_tokenizer().encode(text)
-        tokens = [x + self.num_image_tokens for x in tmp]
-        return tokens if add_dummy_prefix else tokens[2:]
-
-    def postprocess(self, text):
-        text = text.replace("<n>", "\n")
-        text = text.replace(SPTokenizer.get_tab_token(), "\t")
-        for i in range(2, self.max_blank_length + 1):
-            text = text.replace(self.get_blank_token(i), " " * i)
-        return text
-
-    def decode(self, text_ids: List[int]) -> str:
-        ids = [int(_id) - self.num_image_tokens for _id in text_ids]
-        ids = [_id for _id in ids if _id >= 0]
-        text = self._get_text_tokenizer().decode(ids)
-        text = self.postprocess(text)
-        return text
-
-    def decode_tokens(self, tokens: List[str]) -> str:
-        text = self._get_text_tokenizer().convert_tokens_to_string(tokens)
-        text = self.postprocess(text)
-        return text
-
-    def tokenize(
-            self, text: str, linebreak=True, whitespaces=True, add_dummy_prefix=True
-    ) -> List[str]:
-        """
-        @param text: Text to encode.
-        @param linebreak: Whether to encode newline (\n) in text.
-        @param whitespaces: Whether to encode multiple whitespaces or tab in text, useful for source code encoding.
-        @param special_tokens: Whether to encode special token ([MASK], [gMASK], etc.) in text.
-        @param add_dummy_prefix: Whether to add dummy blank space in the beginning.
-        """
-        text = self._preprocess(text, linebreak, whitespaces)
-        if not add_dummy_prefix:
-            text = "<n>" + text
-        tokens = self._get_text_tokenizer().tokenize(text)
-        return tokens if add_dummy_prefix else tokens[2:]
-
-    def __getitem__(self, x: Union[int, str]):
-        if isinstance(x, int):
-            if x < self.num_image_tokens:
-                return "<image_{}>".format(x)
-            else:
-                return self.text_tokenizer.convert_id_to_token(x - self.num_image_tokens)
-        elif isinstance(x, str):
-            if x.startswith("<image_") and x.endswith(">") and x[7:-1].isdigit():
-                return int(x[7:-1])
-            else:
-                return self.text_tokenizer.convert_token_to_id(x) + self.num_image_tokens
-        else:
-            raise ValueError("The key should be str or int.")
-
-
-class ChatGLMTokenizer(PreTrainedTokenizer):
-    """
-    Construct a ChatGLM tokenizer. Based on byte-level Byte-Pair-Encoding.
-
-    Args:
-        vocab_file (`str`):
-            Path to the vocabulary file.
-    """
-
-    vocab_files_names = {"vocab_file": "ice_text.model"}
-    max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
-    model_input_names = ["input_ids", "attention_mask", "position_ids"]
-
-    def __init__(
-            self,
-            vocab_file,
-            do_lower_case=False,
-            remove_space=False,
-            bos_token='<sop>',
-            eos_token='<eop>',
-            end_token='</s>',
-            mask_token='[MASK]',
-            gmask_token='[gMASK]',
-            padding_side="left",
-            pad_token="<pad>",
-            unk_token="<unk>",
-            num_image_tokens=20000,
-            **kwargs
-    ) -> None:
-        super().__init__(
-            do_lower_case=do_lower_case,
-            remove_space=remove_space,
-            padding_side=padding_side,
-            bos_token=bos_token,
-            eos_token=eos_token,
-            end_token=end_token,
-            mask_token=mask_token,
-            gmask_token=gmask_token,
-            pad_token=pad_token,
-            unk_token=unk_token,
-            num_image_tokens=num_image_tokens,
-            **kwargs
-        )
-
-        self.do_lower_case = do_lower_case
-        self.remove_space = remove_space
-        self.vocab_file = vocab_file
-
-        self.bos_token = bos_token
-        self.eos_token = eos_token
-        self.end_token = end_token
-        self.mask_token = mask_token
-        self.gmask_token = gmask_token
-
-        self.sp_tokenizer = SPTokenizer(vocab_file, num_image_tokens=num_image_tokens)
-
-        """ Initialisation """
-
-    @property
-    def gmask_token_id(self) -> Optional[int]:
-        if self.gmask_token is None:
-            return None
-        return self.convert_tokens_to_ids(self.gmask_token)
-
-    @property
-    def end_token_id(self) -> Optional[int]:
-        """
-        `Optional[int]`: Id of the end of context token in the vocabulary. Returns `None` if the token has not been
-        set.
-        """
-        if self.end_token is None:
-            return None
-        return self.convert_tokens_to_ids(self.end_token)
-
-    @property
-    def vocab_size(self):
-        """ Returns vocab size """
-        return self.sp_tokenizer.num_tokens
-
-    def get_vocab(self):
-        """ Returns vocab as a dict """
-        vocab = {self._convert_id_to_token(i): i for i in range(self.vocab_size)}
-        vocab.update(self.added_tokens_encoder)
-        return vocab
-
-    def preprocess_text(self, inputs):
-        if self.remove_space:
-            outputs = " ".join(inputs.strip().split())
-        else:
-            outputs = inputs
-
-        if self.do_lower_case:
-            outputs = outputs.lower()
-
-        return outputs
-
-    def _tokenize(self, text, **kwargs):
-        """ Returns a tokenized string. """
-        text = self.preprocess_text(text)
-
-        seq = self.sp_tokenizer.tokenize(text)
-
-        return seq
-
-    def convert_tokens_to_string(self, tokens: List[str]) -> str:
-        return self.sp_tokenizer.decode_tokens(tokens)
-
-    def _decode(
-            self,
-            token_ids: Union[int, List[int]],
-            **kwargs
-    ) -> str:
-        if isinstance(token_ids, int):
-            token_ids = [token_ids]
-        if len(token_ids) == 0:
-            return ""
-        if self.pad_token_id in token_ids:  # remove pad
-            token_ids = list(filter((self.pad_token_id).__ne__, token_ids))
-        return super()._decode(token_ids, **kwargs)
-
-    def _convert_token_to_id(self, token):
-        """ Converts a token (str) in an id using the vocab. """
-        return self.sp_tokenizer[token]
-
-    def _convert_id_to_token(self, index):
-        """Converts an index (integer) in a token (str) using the vocab."""
-        return self.sp_tokenizer[index]
-
-    def save_vocabulary(self, save_directory, filename_prefix=None):
-        """
-        Save the vocabulary and special tokens file to a directory.
-
-        Args:
-            save_directory (`str`):
-                The directory in which to save the vocabulary.
-            filename_prefix (`str`, *optional*):
-                An optional prefix to add to the named of the saved files.
-
-        Returns:
-            `Tuple(str)`: Paths to the files saved.
-        """
-        if os.path.isdir(save_directory):
-            vocab_file = os.path.join(
-                save_directory, self.vocab_files_names["vocab_file"]
-            )
-        else:
-            vocab_file = save_directory
-
-        with open(self.vocab_file, 'rb') as fin:
-            proto_str = fin.read()
-
-        with open(vocab_file, "wb") as writer:
-            writer.write(proto_str)
-
-        return (vocab_file,)
-
-    def build_inputs_with_special_tokens(
-            self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
-    ) -> List[int]:
-        """
-        Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
-        adding special tokens. A BERT sequence has the following format:
-
-        - single sequence: `[CLS] X [SEP]`
-        - pair of sequences: `[CLS] A [SEP] B [SEP]`
-
-        Args:
-            token_ids_0 (`List[int]`):
-                List of IDs to which the special tokens will be added.
-            token_ids_1 (`List[int]`, *optional*):
-                Optional second list of IDs for sequence pairs.
-
-        Returns:
-            `List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
-        """
-        gmask_id = self.sp_tokenizer[self.gmask_token]
-        eos_id = self.sp_tokenizer[self.eos_token]
-        token_ids_0 = token_ids_0 + [gmask_id, self.sp_tokenizer[self.bos_token]]
-        if token_ids_1 is not None:
-            token_ids_0 = token_ids_0 + token_ids_1 + [eos_id]
-        return token_ids_0
-
-    def _pad(
-            self,
-            encoded_inputs: Union[Dict[str, EncodedInput], BatchEncoding],
-            max_length: Optional[int] = None,
-            padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
-            pad_to_multiple_of: Optional[int] = None,
-            return_attention_mask: Optional[bool] = None,
-    ) -> dict:
-        """
-        Pad encoded inputs (on left/right and up to predefined length or max length in the batch)
-
-        Args:
-            encoded_inputs:
-                Dictionary of tokenized inputs (`List[int]`) or batch of tokenized inputs (`List[List[int]]`).
-            max_length: maximum length of the returned list and optionally padding length (see below).
-                Will truncate by taking into account the special tokens.
-            padding_strategy: PaddingStrategy to use for padding.
-
-                - PaddingStrategy.LONGEST Pad to the longest sequence in the batch
-                - PaddingStrategy.MAX_LENGTH: Pad to the max length (default)
-                - PaddingStrategy.DO_NOT_PAD: Do not pad
-                The tokenizer padding sides are defined in self.padding_side:
-
-                    - 'left': pads on the left of the sequences
-                    - 'right': pads on the right of the sequences
-            pad_to_multiple_of: (optional) Integer if set will pad the sequence to a multiple of the provided value.
-                This is especially useful to enable the use of Tensor Core on NVIDIA hardware with compute capability
-                `>= 7.5` (Volta).
-            return_attention_mask:
-                (optional) Set to False to avoid returning attention mask (default: set to model specifics)
-        """
-        # Load from model defaults
-        bos_token_id = self.sp_tokenizer[self.bos_token]
-        mask_token_id = self.sp_tokenizer[self.mask_token]
-        gmask_token_id = self.sp_tokenizer[self.gmask_token]
-        assert self.padding_side == "left"
-
-        required_input = encoded_inputs[self.model_input_names[0]]
-        seq_length = len(required_input)
-
-        if padding_strategy == PaddingStrategy.LONGEST:
-            max_length = len(required_input)
-
-        if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
-            max_length = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of
-
-        needs_to_be_padded = padding_strategy != PaddingStrategy.DO_NOT_PAD and len(required_input) != max_length
-
-        # Initialize attention mask if not present.
-        if max_length is not None:
-            if "attention_mask" not in encoded_inputs:
-                if bos_token_id in required_input:
-                    context_length = required_input.index(bos_token_id)
-                else:
-                    context_length = seq_length
-                attention_mask = np.ones((1, seq_length, seq_length))
-                attention_mask = np.tril(attention_mask)
-                attention_mask[:, :, :context_length] = 1
-                attention_mask = np.bool_(attention_mask < 0.5)
-                encoded_inputs["attention_mask"] = attention_mask
-
-            if "position_ids" not in encoded_inputs:
-                if bos_token_id in required_input:
-                    context_length = required_input.index(bos_token_id)
-                else:
-                    context_length = seq_length
-                position_ids = np.arange(seq_length, dtype=np.int64)
-                mask_token = mask_token_id if mask_token_id in required_input else gmask_token_id
-                if mask_token in required_input:
-                    mask_position = required_input.index(mask_token)
-                    position_ids[context_length:] = mask_position
-                block_position_ids = np.concatenate(
-                    [np.zeros(context_length, dtype=np.int64),
-                     np.arange(1, seq_length - context_length + 1, dtype=np.int64)])
-                encoded_inputs["position_ids"] = np.stack([position_ids, block_position_ids], axis=0)
-
-        if needs_to_be_padded:
-            difference = max_length - len(required_input)
-
-            if "attention_mask" in encoded_inputs:
-                encoded_inputs["attention_mask"] = np.pad(encoded_inputs["attention_mask"],
-                                                          pad_width=[(0, 0), (difference, 0), (difference, 0)],
-                                                          mode='constant', constant_values=True)
-            if "token_type_ids" in encoded_inputs:
-                encoded_inputs["token_type_ids"] = [self.pad_token_type_id] * difference + encoded_inputs[
-                    "token_type_ids"
-                ]
-            if "special_tokens_mask" in encoded_inputs:
-                encoded_inputs["special_tokens_mask"] = [1] * difference + encoded_inputs["special_tokens_mask"]
-            if "position_ids" in encoded_inputs:
-                encoded_inputs["position_ids"] = np.pad(encoded_inputs["position_ids"],
-                                                        pad_width=[(0, 0), (difference, 0)])
-            encoded_inputs[self.model_input_names[0]] = [self.pad_token_id] * difference + required_input
-
-        return encoded_inputs

models/ChatGLM/tokenizer_config.json

@@ -1,20 +0,0 @@
-{
-  "name_or_path": "THUDM/chatglm-6b",
-  "bos_token": "<sop>",
-  "eos_token": "<eop>",
-  "end_token": "</s>",
-  "gmask_token": "[gMASK]",
-  "mask_token": "[MASK]",
-  "pad_token": "<pad>",
-  "unk_token": "<unk>",
-  "remove_space": false,
-  "do_lower_case": false,
-  "tokenizer_class": "ChatGLMTokenizer",
-  "num_image_tokens": 0,
-  "auto_map": {
-    "AutoTokenizer": [
-      "tokenization_chatglm.ChatGLMTokenizer",
-      null
-      ]
-  }
-}