configuration_phi3_v.py
10.5 KB · 218 lines · python Raw
1 # coding=utf-8
2 # Copyright 2024 Microsoft and the HuggingFace Inc. team. All rights reserved.
3 #
4 # Licensed under the Apache License, Version 2.0 (the "License");
5 # you may not use this file except in compliance with the License.
6 # You may obtain a copy of the License at
7 #
8 # http://www.apache.org/licenses/LICENSE-2.0
9 #
10 # Unless required by applicable law or agreed to in writing, software
11 # distributed under the License is distributed on an "AS IS" BASIS,
12 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
13 # See the License for the specific language governing permissions and
14 # limitations under the License.
15
16 """ Phi-3-V model configuration"""
17
18
19 from transformers.configuration_utils import PretrainedConfig
20 from transformers.utils import logging
21
22
23 logger = logging.get_logger(__name__)
24
25 PHI3V_PRETRAINED_CONFIG_ARCHIVE_MAP = {
26 "microsoft/Phi-3-vision-128k-instruct": "https://huggingface.co/microsoft/Phi-3-vision-128k-instruct/resolve/main/config.json",
27 "microsoft/Phi-3.5-vision-instruct": "https://huggingface.co/microsoft/Phi-3.5-vision-instruct/resolve/main/config.json",
28 }
29
30
31 class Phi3VConfig(PretrainedConfig):
32 r"""
33 This is the configuration class to store the configuration of a [`Phi3VModel`]. It is used to instantiate a Phi-3
34 model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
35 defaults will yield a similar configuration to that of the
36 [microsoft/Phi-3-vision-128k-instruct](https://huggingface.co/microsoft/Phi-3-vision-128k-instruct).
37
38 Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
39 documentation from [`PretrainedConfig`] for more information.
40
41 Args:
42 vocab_size (`int`, *optional*, defaults to 32064):
43 Vocabulary size of the Phi-3-V model. Defines the number of different tokens that can be represented by the
44 `inputs_ids` passed when calling [`Phi3VModel`].
45 hidden_size (`int`, *optional*, defaults to 3072):
46 Dimension of the hidden representations.
47 intermediate_size (`int`, *optional*, defaults to 8192):
48 Dimension of the MLP representations.
49 num_hidden_layers (`int`, *optional*, defaults to 32):
50 Number of hidden layers in the Transformer decoder.
51 num_attention_heads (`int`, *optional*, defaults to 32):
52 Number of attention heads for each attention layer in the Transformer decoder.
53 num_key_value_heads (`int`, *optional*):
54 This is the number of key_value heads that should be used to implement Grouped Query Attention. If
55 `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
56 `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
57 converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
58 by meanpooling all the original heads within that group. For more details checkout [this
59 paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
60 `num_attention_heads`.
61 resid_pdrop (`float`, *optional*, defaults to 0.0):
62 Dropout probability for mlp outputs.
63 embd_pdrop (`int`, *optional*, defaults to 0.0):
64 The dropout ratio for the embeddings.
65 attention_dropout (`float`, *optional*, defaults to 0.0):
66 The dropout ratio after computing the attention scores.
67 hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
68 The non-linear activation function (function or string) in the decoder.
69 max_position_embeddings (`int`, *optional*, defaults to 4096):
70 The maximum sequence length that this model might ever be used with.
71 original_max_position_embeddings (`int`, *optional*, defaults to 4096):
72 The maximum sequence length that this model was trained with. This is used to determine the size of the
73 original RoPE embeddings when using long scaling.
74 initializer_range (`float`, *optional*, defaults to 0.02):
75 The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
76 rms_norm_eps (`float`, *optional*, defaults to 1e-05):
77 The epsilon value used for the RMSNorm.
78 use_cache (`bool`, *optional*, defaults to `True`):
79 Whether or not the model should return the last key/values attentions (not used by all models). Only
80 relevant if `config.is_decoder=True`. Whether to tie weight embeddings or not.
81 tie_word_embeddings (`bool`, *optional*, defaults to `False`):
82 Whether to tie weight embeddings
83 rope_theta (`float`, *optional*, defaults to 10000.0):
84 The base period of the RoPE embeddings.
85 rope_scaling (`dict`, *optional*):
86 The scaling strategy for the RoPE embeddings. If `None`, no scaling is applied. If a dictionary, it must
87 contain the following keys: `type`, `short_factor` and `long_factor`. The `type` must be either `su` or `yarn` and
88 the `short_factor` and `long_factor` must be lists of numbers with the same length as the hidden size
89 divided by the number of attention heads divided by 2.
90 bos_token_id (`int`, *optional*, defaults to 1):
91 The id of the "beginning-of-sequence" token.
92 eos_token_id (`int`, *optional*, defaults to 32000):
93 The id of the "end-of-sequence" token.
94 pad_token_id (`int`, *optional*, defaults to 32000):
95 The id of the padding token.
96 sliding_window (`int`, *optional*):
97 Sliding window attention window size. If `None`, no sliding window is applied.
98 embd_layer (`str`, *optional*, defaults to `"default"`):
99 The embedding layer to use. Can be either `"default"` or `"image"`. "default" uses the standard embedding for text.
100
101 Example:
102
103 ```python
104 >>> from transformers import Phi3VModel, Phi3VConfig
105
106 >>> # Initializing a Phi-3-V style configuration
107 >>> configuration = Phi3Config.from_pretrained("microsoft/Phi-3-vision-128k-instruct")
108
109 >>> # Initializing a model from the configuration
110 >>> model = Phi3VModel(configuration)
111
112 >>> # Accessing the model configuration
113 >>> configuration = model.config
114 ```"""
115
116 model_type = "phi3_v"
117 keys_to_ignore_at_inference = ["past_key_values"]
118
119 def __init__(
120 self,
121 vocab_size=32064,
122 hidden_size=3072,
123 intermediate_size=8192,
124 num_hidden_layers=32,
125 num_attention_heads=32,
126 num_key_value_heads=None,
127 resid_pdrop=0.0,
128 embd_pdrop=0.0,
129 attention_dropout=0.0,
130 hidden_act="silu",
131 max_position_embeddings=4096,
132 original_max_position_embeddings=4096,
133 initializer_range=0.02,
134 rms_norm_eps=1e-5,
135 use_cache=True,
136 tie_word_embeddings=False,
137 rope_theta=10000.0,
138 rope_scaling=None,
139 bos_token_id=1,
140 eos_token_id=32000,
141 pad_token_id=32000,
142 sliding_window=None,
143 embd_layer: str = "default",
144 **kwargs,
145 ):
146 self.vocab_size = vocab_size
147 self.hidden_size = hidden_size
148 self.intermediate_size = intermediate_size
149 self.num_hidden_layers = num_hidden_layers
150 self.num_attention_heads = num_attention_heads
151
152 if num_key_value_heads is None:
153 num_key_value_heads = num_attention_heads
154
155 self.num_key_value_heads = num_key_value_heads
156 self.resid_pdrop = resid_pdrop
157 self.embd_pdrop = embd_pdrop
158 self.attention_dropout = attention_dropout
159 self.hidden_act = hidden_act
160 self.max_position_embeddings = max_position_embeddings
161 self.original_max_position_embeddings = original_max_position_embeddings
162 self.initializer_range = initializer_range
163 self.rms_norm_eps = rms_norm_eps
164 self.use_cache = use_cache
165 self.rope_theta = rope_theta
166 self.rope_scaling = rope_scaling
167 self._rope_scaling_validation()
168 self.sliding_window = sliding_window
169 self.embd_layer = embd_layer
170
171
172 super().__init__(
173 bos_token_id=bos_token_id,
174 eos_token_id=eos_token_id,
175 pad_token_id=pad_token_id,
176 tie_word_embeddings=tie_word_embeddings,
177 **kwargs,
178 )
179
180 def _rope_scaling_validation(self):
181 """
182 Validate the `rope_scaling` configuration.
183 """
184 if self.rope_scaling is None:
185 return
186
187 if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 3:
188 raise ValueError(
189 "`rope_scaling` must be a dictionary with three fields, `type`, `short_factor` and `long_factor`, "
190 f"got {self.rope_scaling}"
191 )
192 rope_scaling_type = self.rope_scaling.get("type", None)
193 rope_scaling_short_factor = self.rope_scaling.get("short_factor", None)
194 rope_scaling_long_factor = self.rope_scaling.get("long_factor", None)
195 if rope_scaling_type is None or rope_scaling_type not in ["su", "yarn"]:
196 raise ValueError(f"`rope_scaling`'s type field must be one of ['su', 'yarn'], got {rope_scaling_type}")
197 if not (
198 isinstance(rope_scaling_short_factor, list)
199 and all(isinstance(x, (int, float)) for x in rope_scaling_short_factor)
200 ):
201 raise ValueError(
202 f"`rope_scaling`'s short_factor field must be a list of numbers, got {rope_scaling_short_factor}"
203 )
204 if not len(rope_scaling_short_factor) == self.hidden_size // self.num_attention_heads // 2:
205 raise ValueError(
206 f"`rope_scaling`'s short_factor field must have length {self.hidden_size // self.num_attention_heads // 2}, got {len(rope_scaling_short_factor)}"
207 )
208 if not (
209 isinstance(rope_scaling_long_factor, list)
210 and all(isinstance(x, (int, float)) for x in rope_scaling_long_factor)
211 ):
212 raise ValueError(
213 f"`rope_scaling`'s long_factor field must be a list of numbers, got {rope_scaling_long_factor}"
214 )
215 if not len(rope_scaling_long_factor) == self.hidden_size // self.num_attention_heads // 2:
216 raise ValueError(
217 f"`rope_scaling`'s long_factor field must have length {self.hidden_size // self.num_attention_heads // 2}, got {len(rope_scaling_long_factor)}"
218 )