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""" Bloom 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 RWConfig(PretrainedConfig): |
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model_type = "RefinedWeb" |
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keys_to_ignore_at_inference = ["past_key_values"] |
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attribute_map = { |
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"num_hidden_layers": "n_layer", |
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"num_attention_heads": "n_head", |
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} |
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def __init__( |
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self, |
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vocab_size=250880, |
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hidden_size=64, |
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n_layer=2, |
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n_head=8, |
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layer_norm_epsilon=1e-5, |
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initializer_range=0.02, |
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use_cache=True, |
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bos_token_id=1, |
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eos_token_id=2, |
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apply_residual_connection_post_layernorm=False, |
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hidden_dropout=0.0, |
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attention_dropout=0.0, |
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n_head_kv=None, |
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alibi=False, |
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**kwargs, |
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): |
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self.vocab_size = vocab_size |
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n_embed = kwargs.pop("n_embed", None) |
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self.hidden_size = hidden_size if n_embed is None else n_embed |
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self.n_layer = n_layer |
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self.n_head = n_head |
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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.use_cache = use_cache |
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self.apply_residual_connection_post_layernorm = apply_residual_connection_post_layernorm |
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self.hidden_dropout = hidden_dropout |
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self.attention_dropout = attention_dropout |
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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.n_head_kv = n_head if n_head_kv is None else n_head_kv |
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self.alibi = alibi |
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super().__init__(bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs) |
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@property |
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def head_dim(self): |
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return self.hidden_size // self.n_head |
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@property |
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def rotary(self): |
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return not self.alibi |
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