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from transformers.configuration_utils import PretrainedConfig |
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CODESAGE_PRETRAINED_CONFIG_ARCHIVE_MAP = { |
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"codesage/codesage-small-v2": "https://huggingface.co/codesage/codesage-small-v2/resolve/main/config.json", |
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"codesage/codesage-base-v2": "https://huggingface.co/codesage/codesage-base-v2/resolve/main/config.json", |
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"codesage/codesage-large-v2": "https://huggingface.co/codesage/codesage-large-v2/resolve/main/config.json", |
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} |
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class CodeSageConfig(PretrainedConfig): |
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model_type = "codesage" |
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def __init__( |
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self, |
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vocab_size=50257, |
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max_position_embeddings=1024, |
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hidden_size=1024, |
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num_hidden_layers=24, |
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num_attention_heads=8, |
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intermediate_size=4096, |
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activation_function="gelu_new", |
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residual_dropout_prob=0.1, |
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embedding_dropout_prob=0.1, |
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attention_dropout_prob=0.1, |
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layer_norm_epsilon=1e-5, |
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initializer_range=0.02, |
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position_embedding_type='absolute', |
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bos_token_id=0, |
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eos_token_id=0, |
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pad_token_id=49153, |
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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.num_hidden_layers = num_hidden_layers |
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self.num_attention_heads = num_attention_heads |
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self.intermediate_size = intermediate_size |
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assert 'gelu' in activation_function |
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self.activation_function = activation_function |
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self.residual_dropout_prob = residual_dropout_prob |
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self.embedding_dropout_prob = embedding_dropout_prob |
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self.attention_dropout_prob = attention_dropout_prob |
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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.position_embedding_type = position_embedding_type |
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super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs) |
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