File size: 10,796 Bytes
d809459 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 |
"""Dbrx configuration."""
from typing import Any, Optional
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging
logger = logging.get_logger(__name__)
DBRX_PRETRAINED_CONFIG_ARCHIVE_MAP = {}
class DbrxAttentionConfig(PretrainedConfig):
"""Configuration class for Dbrx Attention.
[`DbrxAttention`] class. It is used to instantiate attention layers
according to the specified arguments, defining the layers architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
attn_pdrop (`float`, *optional*, defaults to 0.0):
The dropout probability for the attention layers.
clip_qkv (`float`, *optional*, defualts to None):
If not `None`, clip the queries, keys, and values in the attention layer to this value.
kv_n_heads (Optional[int]): For grouped_query_attention only, allow user to specify number of kv heads.
rope_theta (float): The base frequency for rope.
"""
def __init__(
self,
attn_pdrop: float = 0,
clip_qkv: Optional[float] = None,
kv_n_heads: int = 1,
rope_theta: float = 10000.0,
**kwargs: Any,
):
super().__init__(**kwargs)
self.attn_pdrop = attn_pdrop
self.clip_qkv = clip_qkv
self.kv_n_heads = kv_n_heads
self.rope_theta = rope_theta
for k in ['model_type']:
if k in kwargs:
kwargs.pop(k)
if len(kwargs) != 0:
raise ValueError(f'Found unknown {kwargs=}')
@classmethod
def from_pretrained(cls, pretrained_model_name_or_path: str,
**kwargs: Any) -> 'PretrainedConfig':
cls._set_token_in_kwargs(kwargs)
config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path,
**kwargs)
if config_dict.get('model_type') == 'dbrx':
config_dict = config_dict['attn_config']
if 'model_type' in config_dict and hasattr(
cls,
'model_type') and config_dict['model_type'] != cls.model_type:
logger.warning(
f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
+
f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.'
)
return cls.from_dict(config_dict, **kwargs)
class DbrxFFNConfig(PretrainedConfig):
"""Configuration class for Dbrx FFN.
[`DbrxFFN`] class. It is used to instantiate feedforward layers according to
the specified arguments, defining the layers architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
ffn_act_fn (dict, optional): A dict specifying activation function for the FFN.
The dict should have a key 'name' with the value being the name of
the activation function along with any additional keyword arguments.
ffn_hidden_size (int, optional): The hidden size of the feedforward network.
moe_num_experts (int, optional): The number of experts in the mixture of experts layer.
moe_top_k (int, optional): The number of experts to use in the mixture of experts layer.
moe_jitter_eps (float, optional): The jitter epsilon for the mixture of experts layer.
moe_loss_weight (float, optional): The loss weight for the mixture of experts layer.
moe_normalize_expert_weights (float, optional): The normalization factor for the expert weights.
uniform_expert_assignment (bool, optional): Whether to use uniform expert assignment.
This should only be used for benchmarking purposes.
"""
def __init__(
self,
ffn_act_fn: Optional[dict] = None,
ffn_hidden_size: int = 3584,
moe_num_experts: int = 4,
moe_top_k: int = 1,
moe_jitter_eps: Optional[float] = None,
moe_loss_weight: float = 0.01,
moe_normalize_expert_weights: Optional[float] = 1,
uniform_expert_assignment: bool = False,
**kwargs: Any,
):
super().__init__()
if ffn_act_fn is None:
ffn_act_fn = {'name': 'silu'}
self.ffn_act_fn = ffn_act_fn
self.ffn_hidden_size = ffn_hidden_size
self.moe_num_experts = moe_num_experts
self.moe_top_k = moe_top_k
self.moe_jitter_eps = moe_jitter_eps
self.moe_loss_weight = moe_loss_weight
self.moe_normalize_expert_weights = moe_normalize_expert_weights
self.uniform_expert_assignment = uniform_expert_assignment
for k in ['model_type']:
if k in kwargs:
kwargs.pop(k)
if len(kwargs) != 0:
raise ValueError(f'Found unknown {kwargs=}')
@classmethod
def from_pretrained(cls, pretrained_model_name_or_path: str,
**kwargs: Any) -> 'PretrainedConfig':
cls._set_token_in_kwargs(kwargs)
config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path,
**kwargs)
if config_dict.get('model_type') == 'dbrx':
config_dict = config_dict['ffn_config']
if 'model_type' in config_dict and hasattr(
cls,
'model_type') and config_dict['model_type'] != cls.model_type:
logger.warning(
f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
+
f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.'
)
return cls.from_dict(config_dict, **kwargs)
class DbrxConfig(PretrainedConfig):
"""Configuration class for Dbrx.
[`DbrxModel`]. It is used to instantiate a Dbrx model according to the
specified arguments, defining the model architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
d_model (`int`, *optional*, defaults to 6144):
Dimensionality of the embeddings and hidden states.
n_heads (`int`, *optional*, defaults to 48):
Number of attention heads for each attention layer in the Transformer encoder.
n_layers (`int`, *optional*, defaults to 40):
Number of hidden layers in the Transformer encoder.
max_seq_len (`int`, *optional*, defaults to 32768):
The maximum sequence length of the model.
vocab_size (`int`, *optional*, defaults to 100352):
Vocabulary size of the Dbrx model. Defines the maximum number of different tokens that can be represented by
the `inputs_ids` passed when calling [`DbrxModel`].
resid_pdrop (`float`, *optional*, defaults to 0.0):
The dropout probability applied to the attention output before combining with residual.
emb_pdrop (`float`, *optional*, defaults to 0.0):
The dropout probability for the embedding layer.
attn_config (`dict`, *optional*):
A dictionary used to configure the model's attention module.
ffn_config (`dict`, *optional*):
A dictionary used to configure the model's FFN module.
use_cache (`bool`, *optional*, defaults to `False`):
Whether or not the model should return the last key/values attentions (not used by all models).
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
output_router_logits (`bool`, *optional*, defaults to `False`):
Whether or not the router logits should be returned by the model. Enabling this will also
allow the model to output the auxiliary loss. See [here]() for more details
router_aux_loss_coef (`float`, *optional*, defaults to 0.001):
The aux loss factor for the total loss.
Example:
```python
>>> from transformers import DbrxConfig, DbrxModel
>>> # Initializing a Dbrx configuration
>>> configuration = DbrxConfig()
>>> # Initializing a model (with random weights) from the configuration
>>> model = DbrxModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```
"""
model_type = 'dbrx'
attribute_map = {
'num_attention_heads': 'n_heads',
'hidden_size': 'd_model',
'num_hidden_layers': 'n_layers',
'max_position_embeddings': 'max_seq_len'
}
def __init__(
self,
d_model: int = 2048,
n_heads: int = 16,
n_layers: int = 24,
max_seq_len: int = 2048,
vocab_size: int = 32000,
resid_pdrop: float = 0.0,
emb_pdrop: float = 0.0,
attn_config: Optional[DbrxAttentionConfig] = None,
ffn_config: Optional[DbrxFFNConfig] = None,
use_cache: bool = True,
initializer_range: float = 0.02,
output_router_logits: bool = False,
router_aux_loss_coef: float = 0.05,
**kwargs: Any,
):
if attn_config is None:
self.attn_config = DbrxAttentionConfig()
elif isinstance(attn_config, dict):
self.attn_config = DbrxAttentionConfig(**attn_config)
else:
self.attn_config = attn_config
if ffn_config is None:
self.ffn_config = DbrxFFNConfig()
elif isinstance(ffn_config, dict):
self.ffn_config = DbrxFFNConfig(**ffn_config)
else:
self.ffn_config = ffn_config
self.d_model = d_model
self.n_heads = n_heads
self.n_layers = n_layers
self.max_seq_len = max_seq_len
self.vocab_size = vocab_size
self.resid_pdrop = resid_pdrop
self.emb_pdrop = emb_pdrop
self.use_cache = use_cache
self.initializer_range = initializer_range
self.output_router_logits = output_router_logits
self.router_aux_loss_coef = router_aux_loss_coef
tie_word_embeddings = kwargs.pop('tie_word_embeddings', False)
if tie_word_embeddings:
raise ValueError(
'tie_word_embeddings is not supported for Dbrx models.')
super().__init__(
tie_word_embeddings=tie_word_embeddings,
**kwargs,
)
|