File size: 4,660 Bytes
6197b2f
 
 
 
 
 
 
 
 
 
 
 
 
 
a96f4dc
6197b2f
 
 
 
 
 
 
 
a11892f
 
6197b2f
6f1f2d9
 
 
 
6197b2f
 
 
a11892f
 
 
 
 
6197b2f
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
a11892f
972bc8d
6197b2f
 
a11892f
 
a96f4dc
a11892f
 
6197b2f
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
6f1f2d9
 
 
6197b2f
972bc8d
6f1f2d9
972bc8d
6f1f2d9
 
 
eb24dbc
 
6f1f2d9
a96f4dc
 
6197b2f
6f1f2d9
 
a11892f
 
6197b2f
972bc8d
6197b2f
a265819
eb24dbc
 
6197b2f
 
 
 
6f1f2d9
 
 
6197b2f
 
 
 
 
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
# coding=utf-8
# Copyright 2021 The Fairseq Authors and The HuggingFace Inc. team. 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.
""" DalleBart model configuration """
import warnings

from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging

logger = logging.get_logger(__name__)


class DalleBartConfig(PretrainedConfig):
    model_type = "dallebart"
    keys_to_ignore_at_inference = ["past_key_values"]
    attribute_map = {
        "num_attention_heads": "encoder_attention_heads",
        "hidden_size": "d_model",
    }

    def __init__(
        self,
        normalize_text=False,
        encoder_vocab_size=50264,
        image_vocab_size=16384,  # encoded image token space
        image_length=256,  # number of encoded tokens
        max_text_length=64,  # max number of text tokens
        encoder_layers=12,
        encoder_ffn_dim=4096,
        encoder_attention_heads=16,
        decoder_layers=12,
        decoder_ffn_dim=4096,
        decoder_attention_heads=16,
        encoder_layerdrop=0.0,
        decoder_layerdrop=0.0,
        activation_function="gelu",
        d_model=1024,
        dropout=0.1,
        attention_dropout=0.0,
        activation_dropout=0.0,
        init_std=0.02,
        classifier_dropout=0.0,
        scale_embedding=False,
        gradient_checkpointing=False,
        use_cache=True,
        is_encoder_decoder=True,
        forced_eos_token_id=None,
        tie_word_embeddings=False,  # different modalities and sizes
        **kwargs,
    ):
        self.normalize_text = normalize_text
        self.encoder_vocab_size = encoder_vocab_size
        self.image_vocab_size = image_vocab_size
        self.image_length = image_length
        self.max_text_length = max_text_length
        self.d_model = d_model
        self.encoder_ffn_dim = encoder_ffn_dim
        self.encoder_layers = encoder_layers
        self.encoder_attention_heads = encoder_attention_heads
        self.decoder_ffn_dim = decoder_ffn_dim
        self.decoder_layers = decoder_layers
        self.decoder_attention_heads = decoder_attention_heads
        self.dropout = dropout
        self.attention_dropout = attention_dropout
        self.activation_dropout = activation_dropout
        self.activation_function = activation_function
        self.init_std = init_std
        self.encoder_layerdrop = encoder_layerdrop
        self.decoder_layerdrop = decoder_layerdrop
        self.classifier_dropout = classifier_dropout
        self.use_cache = use_cache
        self.gradient_checkpointing = gradient_checkpointing
        self.scale_embedding = (
            scale_embedding  # scale factor will be sqrt(d_model) if True
        )

        # remove inferred keys to prevent errors when loading config (passed as kwargs)
        for k in [
            "pad_token_id",
            "bos_token_id",
            "eos_token_id",
            "decoder_start_token_id",
            "min_length",
            "max_length",
        ]:
            kwargs.pop(k, None)

        super().__init__(
            pad_token_id=image_vocab_size
            + 1,  # needed to avoid errors during generation (converted to jnp.array)
            bos_token_id=image_vocab_size + 1,  # set to unreachable values
            eos_token_id=image_vocab_size + 1,
            is_encoder_decoder=is_encoder_decoder,
            decoder_start_token_id=image_vocab_size,  # BOS appended to vocab
            forced_eos_token_id=forced_eos_token_id,
            tie_word_embeddings=tie_word_embeddings,
            min_length=image_length + 1,
            max_length=image_length + 1,
            **kwargs,
        )

        # ensure backward compatibility for BART CNN models
        if self.forced_bos_token_id is None and kwargs.get(
            "force_bos_token_to_be_generated", False
        ):
            self.forced_bos_token_id = self.bos_token_id
            warnings.warn(
                f"Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions."
                "The config can simply be saved and uploaded again to be fixed."
            )