File size: 5,081 Bytes
9f0f627
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
3a9a7ca
9f0f627
 
 
 
3a9a7ca
9f0f627
 
3a9a7ca
9f0f627
 
 
 
 
 
3a9a7ca
 
 
 
9f0f627
3a9a7ca
9f0f627
 
 
3a9a7ca
 
 
9f0f627
 
3a9a7ca
9f0f627
 
2027efa
9f0f627
2027efa
3a9a7ca
9f0f627
 
 
 
 
 
 
 
 
 
 
3a9a7ca
9f0f627
3a9a7ca
 
9f0f627
 
3a9a7ca
9f0f627
 
 
 
 
 
 
3a9a7ca
9f0f627
 
 
 
 
 
2027efa
9f0f627
 
 
 
 
 
 
 
 
 
 
 
 
 
2027efa
9f0f627
 
 
 
 
 
 
 
 
 
 
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
# coding=utf-8
# Copyright 2023 The OpenAI Team Authors and HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION.  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.
""" RWKV configuration"""

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


logger = logging.get_logger(__name__)

RWKV5_PRETRAINED_CONFIG_ARCHIVE_MAP = {}


class Rwkv5Config(PretrainedConfig):
    """
    This is the configuration class to store the configuration of a [`Rwkv5Model`]. It is used to instantiate a RWKV5
    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 RWVK-4
    [RWKV/rwkv-5-world-1b5](https://huggingface.co/RWKV/rwkv-5-world-1b5) 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 65536):
            Vocabulary size of the RWKV5 model. Defines the number of different tokens that can be represented by the
            `inputs_ids` passed when calling [`Rwkv5Model`].
        hidden_size (`int`, *optional*, defaults to 768):
            Dimensionality of the embeddings and hidden states.
        num_hidden_layers (`int`, *optional*, defaults to 24):
            Number of hidden layers in the model.
        attention_hidden_size (`int`, *optional*):
            Dimensionality of the attention hidden states. Will default to `hidden_size` if unset.
        num_attention_heads (`int`, *optional*, defaults to 64):
            The attention heads to use in rwkv5 self_attention module.
        head_size (`int`, *optional*, defaults to 64): head_size of rwkv5 self_attention module.
        intermediate_size (`int`, *optional*):
            Dimensionality of the inner feed-forward layers. Will default to 4 times `hidden_size` if unset.
        layer_norm_epsilon (`float`, *optional*, defaults to 1e-05):
            The epsilon to use in the layer normalization layers.
        bos_token_id (`int`, *optional*, defaults to 0):
            The id of the beginning of sentence token in the vocabulary. Defaults to 0.
        eos_token_id (`int`, *optional*, defaults to 0):
            The id of the end of sentence token in the vocabulary. Defaults to 0.
        rescale_every (`int`, *optional*, defaults to 6):
            At inference, the hidden states (and weights of the correponding output layers) are divided by 2 every
            `rescale_every` layer. If set to 0 or a negative number, no rescale is done.
        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
            Whether or not to tie the word embeddings with the input token embeddings.
        use_cache (`bool`, *optional*, defaults to `True`):
            Whether or not the model should return the last state.


    Example:

    ```python
    >>> from transformers import Rwkv5Config, Rwkv5Model

    >>> # Initializing a Rwkv5 configuration
    >>> configuration = Rwkv5Config()

    >>> # Initializing a model (with random weights) from the configuration
    >>> model = Rwkv5Model(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```"""

    model_type = "rwkv5"

    def __init__(
        self,
        vocab_size=65536,
        hidden_size=768,
        num_hidden_layers=24,
        attention_hidden_size=None,
        head_size=64,
        head_size_divisor=8,
        intermediate_size=None,
        layer_norm_epsilon=1e-5,
        bos_token_id=0,
        eos_token_id=0,
        rescale_every=6,
        tie_word_embeddings=False,
        use_cache=True,
        **kwargs,
    ):
        self.vocab_size = vocab_size
        self.hidden_size = hidden_size
        self.num_hidden_layers = num_hidden_layers
        self.attention_hidden_size = attention_hidden_size if attention_hidden_size is not None else hidden_size
        self.head_size = head_size
        self.head_size_divisor = head_size_divisor
        self.intermediate_size = None
        self.layer_norm_epsilon = layer_norm_epsilon
        self.rescale_every = rescale_every
        self.use_cache = use_cache

        self.bos_token_id = bos_token_id
        self.eos_token_id = eos_token_id

        super().__init__(
            tie_word_embeddings=tie_word_embeddings, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs
        )