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""" JAIS configuration""" |
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from transformers.configuration_utils import PretrainedConfig |
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from transformers.utils import logging |
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logger = logging.get_logger(__name__) |
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class JAISConfig(PretrainedConfig): |
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""" |
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This is the configuration class to store the configuration of a [`JAISModel`]. It is used to |
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instantiate a JAIS model according to the specified arguments, defining the model architecture. |
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Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the |
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documentation from [`PretrainedConfig`] for more information. |
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Args: |
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vocab_size (`int`, *optional*, defaults to 50257): |
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Vocabulary size of the JAIS model. Defines the number of different tokens that can be represented by the |
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`inputs_ids` passed when calling [`JAISModel`]. |
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n_positions (`int`, *optional*, defaults to 1024): |
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The maximum sequence length that this model might ever be used with. Typically set this to something large |
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just in case (e.g., 512 or 1024 or 2048). |
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n_embd (`int`, *optional*, defaults to 768): |
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Dimensionality of the embeddings and hidden states. |
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n_layer (`int`, *optional*, defaults to 12): |
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Number of hidden layers in the Transformer encoder. |
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n_head (`int`, *optional*, defaults to 12): |
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Number of attention heads for each attention layer in the Transformer encoder. |
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n_inner (`int`, *optional*, defaults to None): |
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Dimensionality of the inner feed-forward layers. `None` will set it to 4 times n_embd |
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activation_function (`str`, *optional*, defaults to `"gelu"`): |
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Activation function, to be selected in the list `["relu", "silu", "gelu", "tanh", "gelu_new", "swiglu"]`. |
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resid_pdrop (`float`, *optional*, defaults to 0.1): |
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The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. |
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embd_pdrop (`float`, *optional*, defaults to 0.1): |
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The dropout ratio for the embeddings. |
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attn_pdrop (`float`, *optional*, defaults to 0.1): |
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The dropout ratio for the attention. |
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layer_norm_epsilon (`float`, *optional*, defaults to 1e-5): |
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The epsilon to use in the layer normalization layers. |
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initializer_range (`float`, *optional*, defaults to 0.02): |
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The standard deviation of the truncated_normal_initializer for initializing all weight matrices. |
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scale_attn_weights (`bool`, *optional*, defaults to `True`): |
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Scale attention weights by dividing by sqrt(hidden_size).. |
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use_cache (`bool`, *optional*, defaults to `True`): |
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Whether or not the model should return the last key/values attentions (not used by all models). |
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scale_attn_by_inverse_layer_idx (`bool`, *optional*, defaults to `False`): |
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Whether to additionally scale attention weights by `1 / layer_idx + 1`. |
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reorder_and_upcast_attn (`bool`, *optional*, defaults to `False`): |
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Whether to scale keys (K) prior to computing attention (dot-product) and upcast attention |
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dot-product/softmax to float() when training with mixed precision. |
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position_embedding_type (`str`, *optional*, defaults to `"learned"`): |
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Positional embedding can be either `"alibi"` or `"learned"`. |
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width_scale (`float`, *optional*, defaults to 1.0): |
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muP parameter to scale output logits and initializers. Calculated as (`d_model,0 / d_model`), |
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where `d_model` is the model's width and `d_model,0` is the proxy model's width. |
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embeddings_scale (`float`, *optional*, defaults to 1.0): |
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muP parameter to scale token and position embeddings. |
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scale_qk_dot_by_d (`bool`, *optional*, defaults to `False`): |
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Scale attention weights by dividing by hidden_size instead of sqrt(hidden_size). |
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Need to set scale_attn_weights to `True` as well. |
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""" |
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model_type = "jais" |
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keys_to_ignore_at_inference = ["past_key_values"] |
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attribute_map = { |
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"hidden_size": "n_embd", |
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"max_position_embeddings": "n_positions", |
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"num_attention_heads": "n_head", |
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"num_hidden_layers": "n_layer", |
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} |
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def __init__( |
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self, |
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vocab_size=50257, |
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n_positions=1024, |
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n_embd=768, |
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n_layer=12, |
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n_head=12, |
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n_inner=None, |
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activation_function="gelu_new", |
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resid_pdrop=0.1, |
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embd_pdrop=0.1, |
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attn_pdrop=0.1, |
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layer_norm_epsilon=1e-5, |
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initializer_range=0.02, |
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scale_attn_weights=True, |
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use_cache=True, |
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bos_token_id=50256, |
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eos_token_id=50256, |
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scale_attn_by_inverse_layer_idx=False, |
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reorder_and_upcast_attn=False, |
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position_embedding_type="learned", |
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width_scale=1.0, |
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embeddings_scale=1.0, |
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scale_qk_dot_by_d=False, |
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**kwargs, |
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): |
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self.vocab_size = vocab_size |
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self.n_positions = n_positions |
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self.n_embd = n_embd |
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self.n_layer = n_layer |
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self.n_head = n_head |
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self.n_inner = n_inner |
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self.activation_function = activation_function |
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self.resid_pdrop = resid_pdrop |
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self.embd_pdrop = embd_pdrop |
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self.attn_pdrop = attn_pdrop |
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self.layer_norm_epsilon = layer_norm_epsilon |
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self.initializer_range = initializer_range |
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self.scale_attn_weights = scale_attn_weights |
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self.use_cache = use_cache |
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self.scale_attn_by_inverse_layer_idx = scale_attn_by_inverse_layer_idx |
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self.reorder_and_upcast_attn = reorder_and_upcast_attn |
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self.bos_token_id = bos_token_id |
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self.eos_token_id = eos_token_id |
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self.position_embedding_type = position_embedding_type |
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self.width_scale = width_scale |
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self.embeddings_scale = embeddings_scale |
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self.scale_qk_dot_by_d = scale_qk_dot_by_d |
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super().__init__(bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs) |
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