File size: 7,882 Bytes
d66d4ca
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
9a24831
 
 
d66d4ca
9a24831
 
d66d4ca
 
 
 
 
9a24831
 
d66d4ca
9a24831
d66d4ca
 
 
9a24831
 
 
 
 
d66d4ca
 
 
 
9a24831
d66d4ca
9a24831
d66d4ca
9a24831
d66d4ca
9a24831
d66d4ca
9a24831
 
 
 
 
d66d4ca
9a24831
d66d4ca
 
 
 
 
9a24831
 
 
d66d4ca
9a24831
 
 
 
d66d4ca
9a24831
d66d4ca
 
 
9a24831
d66d4ca
9a24831
 
 
 
 
 
 
d66d4ca
 
 
 
 
 
 
 
 
9a24831
 
d66d4ca
9a24831
d66d4ca
 
9a24831
d66d4ca
 
 
 
9a24831
 
 
 
 
 
 
 
 
 
 
 
 
 
d66d4ca
9a24831
d66d4ca
9a24831
 
 
 
 
d66d4ca
9a24831
 
 
 
d66d4ca
 
 
 
9a24831
 
 
d66d4ca
9a24831
 
 
 
d66d4ca
 
 
 
 
 
 
 
 
 
 
 
 
 
9a24831
 
 
 
 
 
d66d4ca
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
# coding=utf-8
# Copyright 2024 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.
""" Gemmoe model configuration"""

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


logger = logging.get_logger(__name__)

GEMMOE_PRETRAINED_CONFIG_ARCHIVE_MAP = {
    "Crystalcareai/GemMoE-Beta-1": "https://huggingface.co/Crystalcareai/GemMoE-Beta-1/resolve/main/config.json",
}


class GemmoeConfig(PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`GemmoeModel`]. It is used to instantiate a Gemmoe
    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 Gemmoe-7B.

    e.g. [mhenrichsen/gemmoe-7b](https://huggingface.co/mhenrichsen/gemmoe-7b)

    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 256000):
            Vocabulary size of the Gemmoe model. Defines the number of different tokens that can be represented by the
            `inputs_ids` passed when calling [`GemmoeModel`]
        hidden_size (`int`, *optional*, defaults to 3072):
            Dimension of the hidden representations.
        intermediate_size (`int`, *optional*, defaults to 24576):
            Dimension of the MLP representations.
        num_hidden_layers (`int`, *optional*, defaults to 28):
            Number of hidden layers in the Transformer decoder.
        num_attention_heads (`int`, *optional*, defaults to 16):
            Number of attention heads for each attention layer in the Transformer decoder.
        num_key_value_heads (`int`, *optional*, defaults to 16):
            This is the number of key_value heads that should be used to implement Grouped Query Attention. If
            `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
            `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
            converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
            by meanpooling all the original heads within that group. For more details checkout [this
            paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to 
            `num_attention_heads`.
        head_dim (`int`, *optional*, defaults to 256):
            The attention head dimension.
        hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
            The non-linear activation function (function or string) in the decoder.  
        max_position_embeddings (`int`, *optional*, defaults to 8192):
            The maximum sequence length that this model might ever be used with.
        initializer_range (`float`, *optional*, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        rms_norm_eps (`float`, *optional*, defaults to 1e-6):  
            The epsilon used by the rms normalization layers.
        use_cache (`bool`, *optional*, defaults to `True`):
            Whether or not the model should return the last key/values attentions (not used by all models). Only
            relevant if `config.is_decoder=True`.
        pad_token_id (`int`, *optional*, defaults to 0):
            Padding token id.
        eos_token_id (`int`, *optional*, defaults to 1):
            End of stream token id.  
        bos_token_id (`int`, *optional*, defaults to 2):
            Beginning of stream token id.
        tie_word_embeddings (`bool`, *optional*, defaults to `True`): 
            Whether to tie weight embeddings
        rope_theta (`float`, *optional*, defaults to 10000.0):
            The base period of the RoPE embeddings.
        attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):
            Whether to use a bias in the query, key, value and output projection layers during self-attention.
        attention_dropout (`float`, *optional*, defaults to 0.0):
            The dropout ratio for the attention probabilities.
        num_experts_per_tok (`int`, *optional*, defaults to 2):
            The number of experts used in the sparse mixture of experts layer.
        num_local_experts (`int`, *optional*, defaults to 8):  
            The number of local experts used in the sparse mixture of experts layer.
        router_aux_loss_coef (`float`, *optional*, defaults to 0.01):
            The coefficient for the auxiliary loss of the router.
        output_router_logits (`bool`, *optional*, defaults to `False`):
            Whether or not to output the logits of the routers. They are useful for computing the router loss, and
            should not be returned during inference.

    ```python
    >>> from transformers import GemmoeModel, GemmoeConfig

    >>> # Initializing a Gemmoe gemmoe-7b style configuration
    >>> configuration = GemmoeConfig()

    >>> # Initializing a model from the gemmoe-7b style configuration
    >>> model = GemmoeModel(configuration)

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

    model_type = "gemmoe"
    keys_to_ignore_at_inference = ["past_key_values"]

    def __init__(
        self,
        vocab_size=256000,
        hidden_size=3072,
        intermediate_size=24576,
        num_hidden_layers=28,
        num_attention_heads=16,
        num_key_value_heads=16,
        head_dim=256,
        hidden_act="gelu",
        max_position_embeddings=8192,
        initializer_range=0.02,
        rms_norm_eps=1e-6,
        use_cache=True,
        pad_token_id=0,
        eos_token_id=1,
        bos_token_id=2,
        tie_word_embeddings=True,
        rope_theta=10000.0,
        attention_bias=False,
        attention_dropout=0.0,
        num_experts_per_tok=2,
        num_local_experts=8,
        router_aux_loss_coef=0.02,
        output_router_logits=False,
        **kwargs,
    ):
        self.vocab_size = vocab_size
        self.max_position_embeddings = max_position_embeddings        
        self.hidden_size = hidden_size
        self.intermediate_size = intermediate_size
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads
        self.head_dim = head_dim
        self.num_key_value_heads = num_key_value_heads
        self.hidden_act = hidden_act
        self.initializer_range = initializer_range
        self.rms_norm_eps = rms_norm_eps
        self.use_cache = use_cache
        self.rope_theta = rope_theta
        self.attention_bias = attention_bias 
        self.attention_dropout = attention_dropout
        self.num_experts_per_tok = num_experts_per_tok
        self.num_local_experts = num_local_experts
        self.router_aux_loss_coef = router_aux_loss_coef
        self.output_router_logits = output_router_logits
         
        super().__init__(
            pad_token_id=pad_token_id,
            bos_token_id=bos_token_id,
            eos_token_id=eos_token_id,
            tie_word_embeddings=tie_word_embeddings,
            **kwargs,
        )