File size: 15,913 Bytes
7a1cab8
01717dd
7a1cab8
 
01717dd
 
7a1cab8
 
01717dd
 
7a1cab8
 
 
 
 
01717dd
 
 
 
7a1cab8
01717dd
 
7a1cab8
01717dd
 
 
7a1cab8
01717dd
 
 
 
 
 
 
 
7a1cab8
 
01717dd
7a1cab8
 
 
 
 
 
 
01717dd
7a1cab8
 
 
 
 
 
 
 
 
 
01717dd
7a1cab8
 
 
 
 
 
 
 
 
 
 
01717dd
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
7a1cab8
 
 
 
01717dd
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
7a1cab8
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
01717dd
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
7a1cab8
 
01717dd
 
 
 
 
 
 
 
 
7a1cab8
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
01717dd
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
import os
from typing import Optional, Tuple, List, Union
from shutil import copyfile
import torch

from transformers import PreTrainedTokenizer, RobertaTokenizer, GPT2Tokenizer, BertTokenizer
from transformers.utils import logging
from transformers.tokenization_utils_base import BatchEncoding
from transformers.models.auto.tokenization_auto import get_tokenizer_config
from transformers.utils.generic import _is_torch_device
import sentencepiece as spm

logger = logging.get_logger(__name__)


class GLMBatchEncoding(BatchEncoding):
    def to(self, device: Union[str, "torch.device"]) -> "BatchEncoding":
        """
        Send all values to device by calling `v.to(device)` (PyTorch only).

        Args:
            device (`str` or `torch.device`): The device to put the tensors on.

        Returns:
            [`BatchEncoding`]: The same instance after modification.
        """

        # This check catches things like APEX blindly calling "to" on all inputs to a module
        # Otherwise it passes the casts down and casts the LongTensor containing the token idxs
        # into a HalfTensor
        if isinstance(device, str) or _is_torch_device(device) or isinstance(device, int):
            self.data = {k: v.to(device=device) if torch.is_tensor(v) else v for k, v in self.data.items()}
        else:
            logger.warning(f"Attempting to cast a BatchEncoding to type {str(device)}. This is not supported.")
        return self


class GLMTokenizerMixin:
    @property
    def sop_token(self) -> Optional[str]:
        return "<|startofpiece|>"

    @property
    def sop_token_id(self) -> Optional[int]:
        """
        `Optional[int]`: Id of the start token in the vocabulary, used when training a model with autoregressive blank filling.
        """
        return self.convert_tokens_to_ids(self.sop_token)

    @property
    def eop_token(self) -> Optional[str]:
        return "<|endofpiece|>"

    @property
    def eop_token_id(self) -> Optional[int]:
        """
        `Optional[int]`: Id of the end token in the vocabulary, used when training a model with autoregressive blank filling.
        """
        return self.convert_tokens_to_ids(self.eop_token)

    @property
    def gmask_token_id(self) -> int:
        return self.convert_tokens_to_ids("[gMASK]")

    @property
    def smask_token_id(self) -> int:
        return self.convert_tokens_to_ids("[sMASK]")

    @property
    def mask_token_ids(self):
        return [self.mask_token_id, self.smask_token_id, self.gmask_token_id]

    def _build_input_for_multiple_choice(self, context, choices):
        context_id = context["input_ids"]
        if torch.is_tensor(context_id):
            context_id = context_id.tolist()

        division = len(context_id)
        mask_position = context_id.index(self.mask_token_id)

        token = torch.tensor(context_id, dtype=torch.long)
        attention_mask = [context["attention_mask"].expand(division, -1)]
        position_id = torch.arange(division, dtype=torch.long)
        block_position_id = torch.zeros(division, dtype=torch.long)

        choice_ids, choice_indices = [], []

        for choice_str in choices:
            choice = torch.tensor(self(choice_str, add_special_tokens=False, padding=False)['input_ids'],
                                  dtype=torch.long)
            choice_ids.append(choice)
            choice_indices.append(torch.arange(len(token), len(token) + len(choice), dtype=torch.long))
            attention_mask.append(torch.tril(torch.ones((len(choice), len(choice)), dtype=torch.long)))

            token = torch.cat((token, torch.tensor([self.sop_token_id], dtype=torch.long), choice[:-1]))
            position_id = torch.cat((position_id, torch.tensor([mask_position] * len(choice), dtype=torch.long)))
            block_position_id = torch.cat((block_position_id, torch.arange(1, 1 + len(choice), dtype=torch.long)))

        attention_mask = torch.block_diag(*attention_mask)
        attention_mask[division:, :division] = context["attention_mask"].unsqueeze(0)

        return {
            "input_ids": token,
            "position_ids": torch.stack((position_id, block_position_id)),
            "attention_mask": attention_mask,
            "choice_ids": choice_ids,
            "choice_indices": choice_indices
        }

    def _pad_batch(self, tokens, position_ids, attention_mask, max_seq_length):
        pad_length = max_seq_length - len(tokens)
        attention_mask = torch.nn.functional.pad(
            attention_mask,
            (0, pad_length, 0, pad_length),
            mode="constant",
            value=0,
        )
        tokens = torch.cat((tokens, torch.zeros(pad_length, dtype=torch.long)))
        position_ids = torch.cat((position_ids, position_ids[..., -1:].expand(-1, pad_length)), dim=-1)
        return tokens, position_ids, attention_mask

    def _collate(self, samples):
        TILE = 1
        length_to_pad = (max(map(lambda spl: len(spl["input_ids"]), samples)) + TILE - 1) // TILE * TILE

        token_batch, position_id_batch, attention_mask_batch = [], [], []
        choices_batch, choice_target_ids_batch = [], []

        for sample in samples:
            token, position_id, attention_mask = self._pad_batch(
                sample["input_ids"], sample["position_ids"], sample["attention_mask"], length_to_pad
            )
            token_batch.append(token)
            position_id_batch.append(position_id)
            attention_mask_batch.append(attention_mask)
            choices_batch.append(sample["choice_ids"])
            choice_target_ids_batch.append(sample["choice_indices"])
        return {
            "input_ids": torch.stack(token_batch),
            "position_ids": torch.stack(position_id_batch),
            "attention_mask": torch.stack(attention_mask_batch).unsqueeze(1),
            "choice_ids": choices_batch,
            "choice_indices": choice_target_ids_batch,
        }

    def build_inputs_for_multiple_choice(self, model_input: BatchEncoding, choices, max_length=None):
        samples = [{key: value[i] for key, value in model_input.items()} for i in range(len(model_input["input_ids"]))]
        samples = [self._build_input_for_multiple_choice(sample, choice) for sample, choice in
                   zip(samples, choices)]
        inputs = self._collate(samples)
        return GLMBatchEncoding(inputs)

    def build_inputs_for_generation(self, model_input: BatchEncoding, max_gen_length=512, targets=None, padding=False):
        mask_ids = self.mask_token_ids
        input_ids = model_input.input_ids
        batch_size, seq_length = input_ids.shape[:2]
        position_id, block_position_id = list(range(seq_length)), [0 for _ in range(seq_length)]
        position_ids, block_position_ids = [], []
        labels = None
        if targets is not None:
            is_batched = isinstance(targets, (list, tuple))
            targets = self(targets, add_special_tokens=False, padding=False).input_ids
            if not is_batched:
                targets = [targets]
            assert len(targets) == len(input_ids)
            targets = [(target + [self.eop_token_id])[:max_gen_length] for target in targets]
            if not padding:
                max_gen_length = max(map(len, targets))
            targets = [[self.sop_token_id] + target for target in targets]
            labels = [target[1:] for target in targets]
            targets = [target + [self.pad_token_id] * (max_gen_length + 1 - len(target)) for target in targets]
            labels = [label + [-100] * (max_gen_length - len(label)) for label in labels]
            targets = torch.tensor(targets, dtype=input_ids.dtype, device=input_ids.device)
            labels = torch.tensor(labels, dtype=input_ids.dtype, device=input_ids.device)
            labels = torch.cat((input_ids.new_full((batch_size, seq_length), -100), labels), dim=1)
        for i in range(batch_size):
            mask_positions = []
            for mask_id in mask_ids:
                mask_positions += (input_ids[i] == mask_id).nonzero(as_tuple=True)[0].tolist()
            if not mask_positions:
                raise ValueError("Cannot find mask token in the input")
            mask_positions.sort()
            mask_pos = mask_positions[0]
            position_ids.append(position_id + [mask_pos] * max_gen_length)
            block_position_ids.append(block_position_id + list(range(1, max_gen_length + 1)))
        position_ids = torch.tensor(position_ids, dtype=input_ids.dtype, device=input_ids.device)
        block_position_ids = torch.tensor(block_position_ids, dtype=input_ids.dtype, device=input_ids.device)
        position_ids = torch.stack((position_ids, block_position_ids), dim=1)
        attention_mask = model_input.attention_mask
        attention_mask = attention_mask.unsqueeze(1).expand(-1, seq_length + max_gen_length, -1)
        generation_attention_mask = torch.cat([attention_mask.new_zeros((seq_length, max_gen_length)),
                                               torch.tril(attention_mask.new_ones((max_gen_length, max_gen_length)))],
                                              dim=0).unsqueeze(0).expand(batch_size, -1, -1)
        attention_mask = torch.cat((attention_mask, generation_attention_mask), dim=2)
        attention_mask = attention_mask.unsqueeze(1)
        if targets is None:
            input_ids = torch.cat((input_ids, input_ids.new_full((batch_size, 1), self.sop_token_id)), dim=-1)
        else:
            input_ids = torch.cat((input_ids, targets[:, :-1]), dim=1)
        batch = {"input_ids": input_ids, "position_ids": position_ids}
        if labels is None:
            batch["generation_attention_mask"] = attention_mask
        else:
            batch["attention_mask"] = attention_mask
            batch["labels"] = labels
        return BatchEncoding(batch)


class GLMRobertaTokenizer(RobertaTokenizer, GLMTokenizerMixin):
    model_input_names = ["input_ids", "position_ids", "attention_mask"]
    truncation_side: str = "left"

    @property
    def gmask_token_id(self) -> int:
        raise NotImplementedError("The model doesn't support gMASK")

    @property
    def smask_token_id(self) -> int:
        raise NotImplementedError("The model doesn't support sMASK")

    @property
    def mask_token_ids(self):
        return [self.mask_token_id]


class GLMChineseTokenizer(PreTrainedTokenizer, GLMTokenizerMixin):
    vocab_files_names = {"vocab_file": "cog-pretrain.model"}
    truncation_side: str = "left"

    def __init__(self, vocab_file, **kwargs):
        super().__init__(**kwargs)
        self.vocab_file = vocab_file
        self.sp_model = spm.SentencePieceProcessor()
        self.sp_model.Load(vocab_file)

    @property
    def vocab_size(self):
        return len(self.sp_model)

    def get_vocab(self):
        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, **kwargs):
        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.PieceToId(token)

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

    def convert_tokens_to_string(self, tokens):
        return self.sp_model.decode(tokens)

    def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
        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 "") + self.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: 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 (:obj:`List[int]`):
                List of IDs to which the special tokens will be added.
            token_ids_1 (:obj:`List[int]`, `optional`):
                Optional second list of IDs for sequence pairs.

        Returns:
            :obj:`List[int]`: List of `input IDs <../glossary.html#input-ids>`__ with the appropriate special tokens.
        """
        assert token_ids_1 is None
        cls = [self.cls_token_id]
        eos = [self.eos_token_id]
        return cls + token_ids_0 + eos


class GLMGPT2Tokenizer(GPT2Tokenizer, GLMTokenizerMixin):
    model_input_names = ["input_ids", "position_ids", "attention_mask"]
    truncation_side: str = "left"

    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 (:obj:`List[int]`):
                List of IDs to which the special tokens will be added.
            token_ids_1 (:obj:`List[int]`, `optional`):
                Optional second list of IDs for sequence pairs.

        Returns:
            :obj:`List[int]`: List of `input IDs <../glossary.html#input-ids>`__ with the appropriate special tokens.
        """
        assert token_ids_1 is None
        cls = [self.cls_token_id]
        eos = [self.eos_token_id]
        return cls + token_ids_0 + eos


class GLMBertTokenizer(BertTokenizer, GLMTokenizerMixin):
    model_input_names = ["input_ids", "position_ids", "attention_mask"]
    truncation_side: str = "left"

    @property
    def gmask_token_id(self) -> int:
        raise NotImplementedError("The model doesn't support gMASK")

    @property
    def smask_token_id(self) -> int:
        raise NotImplementedError("The model doesn't support sMASK")

    @property
    def mask_token_ids(self):
        return [self.mask_token_id]


class GLMTokenizer:
    @classmethod
    def from_pretrained(cls, pretrained_model_name_or_path, *inputs, **kwargs):
        tokenizer_config = get_tokenizer_config(pretrained_model_name_or_path, **kwargs)
        config_tokenizer_class = tokenizer_config.get("tokenizer_class")
        if config_tokenizer_class == "GLMRobertaTokenizer":
            tokenizer_class = GLMRobertaTokenizer
        elif config_tokenizer_class == "GLMChineseTokenizer":
            tokenizer_class = GLMChineseTokenizer
        elif config_tokenizer_class == "GLMGPT2Tokenizer":
            tokenizer_class = GLMGPT2Tokenizer
        elif config_tokenizer_class == "GLMBertTokenizer":
            tokenizer_class = GLMBertTokenizer
        else:
            raise NotImplementedError("Not implemented tokenizer type:", config_tokenizer_class)
        return tokenizer_class.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs)