"""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,
)