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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"`. |
| width_scale (`float`, *optional*, defaults to 1.0): |
| muP parameter to scale output logits 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. |
| embeddings_scale (`float`, *optional*, defaults to 1.0): |
| muP parameter to scale token and position embeddings. |
| 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. |
| |
| """ |
|
|
| 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", |
| width_scale=1.0, |
| embeddings_scale=1.0, |
| scale_qk_dot_by_d=False, |
| **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.width_scale = width_scale |
| self.embeddings_scale = embeddings_scale |
| self.scale_qk_dot_by_d = scale_qk_dot_by_d |
|
|
| super().__init__(bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs) |
|
|
