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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 CodeGenConfig(PretrainedConfig): |
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model_type = "codegen" |
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def __init__( |
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self, |
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vocab_size=50400, |
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n_positions=2048, |
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n_ctx=2048, |
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n_embd=4096, |
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n_layer=28, |
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n_head=16, |
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rotary_dim=64, |
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n_inner=None, |
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activation_function="gelu_new", |
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resid_pdrop=0.0, |
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embd_pdrop=0.0, |
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attn_pdrop=0.0, |
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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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gradient_checkpointing=False, |
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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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**kwargs |
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): |
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super().__init__(bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs) |
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self.vocab_size = vocab_size |
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self.n_ctx = n_ctx |
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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.rotary_dim = rotary_dim |
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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.gradient_checkpointing = gradient_checkpointing |
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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.bos_token_id = bos_token_id |
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self.eos_token_id = eos_token_id |
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@property |
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def max_position_embeddings(self): |
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return self.n_positions |
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@property |
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def hidden_size(self): |
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return self.n_embd |
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@property |
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def num_attention_heads(self): |
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return self.n_head |
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@property |
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def num_hidden_layers(self): |
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return self.n_layer |
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