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from transformers.configuration_utils import PretrainedConfig |
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class GPTPanguConfig(PretrainedConfig): |
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model_type = "gpt_pangu" |
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keys_to_ignore_at_inference = ["past_key_values"] |
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def __init__( |
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self, |
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vocab_size=40000, |
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max_position_embeddings=1024, |
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hidden_size=1024, |
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intermediate_size=None, |
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num_layers=24, |
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num_heads=16, |
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activation_function="gelu", |
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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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scale_attn_weights=True, |
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initializer_range=0.02, |
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summary_type="cls_index", |
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summary_use_proj=True, |
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summary_activation=None, |
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summary_proj_to_labels=True, |
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summary_first_dropout=0.1, |
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use_cache=True, |
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**kwargs, |
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): |
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self.vocab_size = vocab_size |
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self.max_position_embeddings = max_position_embeddings |
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self.hidden_size = hidden_size |
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self.intermediate_size = intermediate_size |
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self.num_layers = num_layers |
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self.num_heads = num_heads |
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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.scale_attn_weights = scale_attn_weights |
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self.initializer_range = initializer_range |
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self.summary_type = summary_type |
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self.summary_use_proj = summary_use_proj |
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self.summary_activation = summary_activation |
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self.summary_first_dropout = summary_first_dropout |
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self.summary_proj_to_labels = summary_proj_to_labels |
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self.use_cache = use_cache |
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super().__init__(**kwargs) |
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