-
Notifications
You must be signed in to change notification settings - Fork 25.3k
/
configuration_realm.py
186 lines (165 loc) 路 8.54 KB
/
configuration_realm.py
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
# coding=utf-8
# Copyright 2022 The REALM authors and The HuggingFace Inc. team.
#
# 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.
""" REALM model configuration."""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
logger = logging.get_logger(__name__)
REALM_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"google/realm-cc-news-pretrained-embedder": (
"https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/config.json"
),
"google/realm-cc-news-pretrained-encoder": (
"https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/config.json"
),
"google/realm-cc-news-pretrained-scorer": (
"https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/config.json"
),
"google/realm-cc-news-pretrained-openqa": (
"https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/config.json"
),
"google/realm-orqa-nq-openqa": "https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/config.json",
"google/realm-orqa-nq-reader": "https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/config.json",
"google/realm-orqa-wq-openqa": "https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/config.json",
"google/realm-orqa-wq-reader": "https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/config.json",
# See all REALM models at https://huggingface.co/models?filter=realm
}
class RealmConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of
1. [`RealmEmbedder`]
2. [`RealmScorer`]
3. [`RealmKnowledgeAugEncoder`]
4. [`RealmRetriever`]
5. [`RealmReader`]
6. [`RealmForOpenQA`]
It is used to instantiate an REALM 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 REALM
[google/realm-cc-news-pretrained-embedder](https://huggingface.co/google/realm-cc-news-pretrained-embedder)
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 30522):
Vocabulary size of the REALM model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`RealmEmbedder`], [`RealmScorer`], [`RealmKnowledgeAugEncoder`], or
[`RealmReader`].
hidden_size (`int`, *optional*, defaults to 768):
Dimension of the encoder layers and the pooler layer.
retriever_proj_size (`int`, *optional*, defaults to 128):
Dimension of the retriever(embedder) projection.
num_hidden_layers (`int`, *optional*, defaults to 12):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 12):
Number of attention heads for each attention layer in the Transformer encoder.
num_candidates (`int`, *optional*, defaults to 8):
Number of candidates inputted to the RealmScorer or RealmKnowledgeAugEncoder.
intermediate_size (`int`, *optional*, defaults to 3072):
Dimension of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
hidden_act (`str` or `function`, *optional*, defaults to `"gelu_new"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"selu"` and `"gelu_new"` are supported.
hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler.
attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
The dropout ratio for the attention probabilities.
max_position_embeddings (`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).
type_vocab_size (`int`, *optional*, defaults to 2):
The vocabulary size of the `token_type_ids` passed when calling [`RealmEmbedder`], [`RealmScorer`],
[`RealmKnowledgeAugEncoder`], or [`RealmReader`].
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layer_norm_eps (`float`, *optional*, defaults to 1e-12):
The epsilon used by the layer normalization layers.
span_hidden_size (`int`, *optional*, defaults to 256):
Dimension of the reader's spans.
max_span_width (`int`, *optional*, defaults to 10):
Max span width of the reader.
reader_layer_norm_eps (`float`, *optional*, defaults to 1e-3):
The epsilon used by the reader's layer normalization layers.
reader_beam_size (`int`, *optional*, defaults to 5):
Beam size of the reader.
reader_seq_len (`int`, *optional*, defaults to 288+32):
Maximum sequence length of the reader.
num_block_records (`int`, *optional*, defaults to 13353718):
Number of block records.
searcher_beam_size (`int`, *optional*, defaults to 5000):
Beam size of the searcher. Note that when eval mode is enabled, *searcher_beam_size* will be the same as
*reader_beam_size*.
Example:
```python
>>> from transformers import RealmConfig, RealmEmbedder
>>> # Initializing a REALM realm-cc-news-pretrained-* style configuration
>>> configuration = RealmConfig()
>>> # Initializing a model (with random weights) from the google/realm-cc-news-pretrained-embedder style configuration
>>> model = RealmEmbedder(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "realm"
def __init__(
self,
vocab_size=30522,
hidden_size=768,
retriever_proj_size=128,
num_hidden_layers=12,
num_attention_heads=12,
num_candidates=8,
intermediate_size=3072,
hidden_act="gelu_new",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=512,
type_vocab_size=2,
initializer_range=0.02,
layer_norm_eps=1e-12,
span_hidden_size=256,
max_span_width=10,
reader_layer_norm_eps=1e-3,
reader_beam_size=5,
reader_seq_len=320, # 288 + 32
num_block_records=13353718,
searcher_beam_size=5000,
pad_token_id=1,
bos_token_id=0,
eos_token_id=2,
**kwargs,
):
super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
# Common config
self.vocab_size = vocab_size
self.max_position_embeddings = max_position_embeddings
self.hidden_size = hidden_size
self.retriever_proj_size = retriever_proj_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.num_candidates = num_candidates
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.initializer_range = initializer_range
self.type_vocab_size = type_vocab_size
self.layer_norm_eps = layer_norm_eps
# Reader config
self.span_hidden_size = span_hidden_size
self.max_span_width = max_span_width
self.reader_layer_norm_eps = reader_layer_norm_eps
self.reader_beam_size = reader_beam_size
self.reader_seq_len = reader_seq_len
# Retrieval config
self.num_block_records = num_block_records
self.searcher_beam_size = searcher_beam_size