jais-30b-v3 / configuration_jais.py
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# Copyright 2023 The OpenAI Team Authors and HuggingFace Inc. team.
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# Copyright 2023 Cerebras Systems.
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""" JAIS configuration"""
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging
logger = logging.get_logger(__name__)
class JAISConfig(PretrainedConfig):
"""
This is the configuration class to store the configuration of a [`JAISModel`]. It is used to instantiate a JAIS
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:
vocab_size (`int`, *optional*, defaults to 50257):
Vocabulary size of the JAIS model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`JAISModel`].
n_positions (`int`, *optional*, defaults to 1024):
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).
n_embd (`int`, *optional*, defaults to 768):
Dimensionality of the embeddings and hidden states.
n_layer (`int`, *optional*, defaults to 12):
Number of hidden layers in the Transformer encoder.
n_head (`int`, *optional*, defaults to 12):
Number of attention heads for each attention layer in the Transformer encoder.
n_inner (`int`, *optional*, defaults to None):
Dimensionality of the inner feed-forward layers. `None` will set it to 4 times n_embd
activation_function (`str`, *optional*, defaults to `"gelu"`):
Activation function, to be selected in the list `["relu", "silu", "gelu", "tanh", "gelu_new", "swiglu"]`.
resid_pdrop (`float`, *optional*, defaults to 0.1):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
embd_pdrop (`float`, *optional*, defaults to 0.1):
The dropout ratio for the embeddings.
attn_pdrop (`float`, *optional*, defaults to 0.1):
The dropout ratio for the attention.
layer_norm_epsilon (`float`, *optional*, defaults to 1e-5):
The epsilon to use in the layer normalization layers.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
scale_attn_weights (`bool`, *optional*, defaults to `True`):
Scale attention weights by dividing by sqrt(hidden_size)..
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models).
scale_attn_by_inverse_layer_idx (`bool`, *optional*, defaults to `False`):
Whether to additionally scale attention weights by `1 / layer_idx + 1`.
reorder_and_upcast_attn (`bool`, *optional*, defaults to `False`):
Whether to scale keys (K) prior to computing attention (dot-product) and upcast attention
dot-product/softmax to float() when training with mixed precision.
position_embedding_type (`str`, *optional*, defaults to `"learned"`):
Positional embedding can be either `"alibi"` or `"learned"`.
mup_width_scale (`float`, *optional*, defaults to 1.0):
muP parameter to scale learning rate and initializers. Calculated as (`d_model,0 / d_model`), where
`d_model` is the model's width and `d_model,0` is the proxy model's width.
mup_embeddings_scale (`float`, *optional*, defaults to 1.0):
muP parameter to scale token and position embeddings.
mup_output_alpha (`float`, *optional*, defaults to 1.0):
muP parameter to scale output logits (`output_logits_scale = mup_output_alpha * mup_width_scale`).
mup_scale_qk_dot_by_d (`bool`, *optional*, defaults to `False`):
Scale attention weights by dividing by hidden_size instead of sqrt(hidden_size). Need to set
scale_attn_weights to `True` as well.
alibi_scaling (`Dict`, *optional*):
Dictionary containing the scaling configuration for ALiBi embeddings. Currently only supports linear
scaling strategy. Can specify either the scaling `factor` (must be a float greater than 1) for fixed scaling
or `train_seq_len` for dynamic scaling on input samples with sequence length > `train_seq_len`. The expected
formats are `{"type": strategy name, "factor": scaling factor}` or
`{"type": strategy name, "train_seq_len": training sequence length}`.
Example:
```python
>>> from transformers import JAISConfig, JAISModel
>>> # Initializing a JAIS configuration
>>> configuration = JAISConfig()
>>> # Initializing a model (with random weights) from the configuration
>>> model = JAISModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "jais"
keys_to_ignore_at_inference = ["past_key_values"]
attribute_map = {
"hidden_size": "n_embd",
"max_position_embeddings": "n_positions",
"num_attention_heads": "n_head",
"num_hidden_layers": "n_layer",
}
def __init__(
self,
vocab_size=50257,
n_positions=1024,
n_embd=768,
n_layer=12,
n_head=12,
n_inner=None,
activation_function="gelu_new",
resid_pdrop=0.1,
embd_pdrop=0.1,
attn_pdrop=0.1,
layer_norm_epsilon=1e-5,
initializer_range=0.02,
scale_attn_weights=True,
use_cache=True,
bos_token_id=50256,
eos_token_id=50256,
scale_attn_by_inverse_layer_idx=False,
reorder_and_upcast_attn=False,
position_embedding_type="learned",
mup_width_scale=1.0,
mup_embeddings_scale=1.0,
mup_output_alpha=1.0,
mup_scale_qk_dot_by_d=False,
alibi_scaling=None,
**kwargs,
):
self.vocab_size = vocab_size
self.n_positions = n_positions
self.n_embd = n_embd
self.n_layer = n_layer
self.n_head = n_head
self.n_inner = n_inner
self.activation_function = activation_function
self.resid_pdrop = resid_pdrop
self.embd_pdrop = embd_pdrop
self.attn_pdrop = attn_pdrop
self.layer_norm_epsilon = layer_norm_epsilon
self.initializer_range = initializer_range
self.scale_attn_weights = scale_attn_weights
self.use_cache = use_cache
self.scale_attn_by_inverse_layer_idx = scale_attn_by_inverse_layer_idx
self.reorder_and_upcast_attn = reorder_and_upcast_attn
self.bos_token_id = bos_token_id
self.eos_token_id = eos_token_id
self.position_embedding_type = position_embedding_type
self.mup_width_scale = mup_width_scale
self.mup_embeddings_scale = mup_embeddings_scale
self.mup_output_alpha = mup_output_alpha
self.mup_scale_qk_dot_by_d = mup_scale_qk_dot_by_d
self.alibi_scaling = alibi_scaling
self._alibi_scaling_validation()
super().__init__(bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
def _alibi_scaling_validation(self):
"""
Validate the `alibi_scaling` configuration.
"""
if self.alibi_scaling is None:
return
if not isinstance(self.alibi_scaling, dict) or len(self.alibi_scaling) != 2:
raise ValueError(
"`alibi_scaling` must be a dictionary with two fields, `type` and `factor` or `type` and `train_seq_len`, "
f"got {self.alibi_scaling}"
)
alibi_scaling_type = self.alibi_scaling.get("type", None)
alibi_scaling_factor = self.alibi_scaling.get("factor", None)
alibi_dynamic_scaling = self.alibi_scaling.get("train_seq_len", None)
if alibi_scaling_type is None or alibi_scaling_type != "linear":
raise ValueError(
f"`alibi_scaling`'s type field must be 'linear', got {alibi_scaling_type}"
)
if alibi_scaling_factor is not None:
if not isinstance(alibi_scaling_factor, float) or alibi_scaling_factor <= 1.0:
raise ValueError(f"`alibi_scaling`'s factor field must be a float > 1.0, got {alibi_scaling_factor}")
if alibi_dynamic_scaling is not None:
if not isinstance(alibi_dynamic_scaling, int) or alibi_dynamic_scaling <= 1:
raise ValueError(f"`alibi_scaling`'s `train_seq_len` field must be an integer > 1, got {alibi_dynamic_scaling}")