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from transformers import PreTrainedModel, PretrainedConfig |
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class Seq2SeqConfig(PretrainedConfig): |
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def __init__( |
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self, |
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vocab_size=30522, |
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hidden_size=768, |
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num_encoder_layers=6, |
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num_decoder_layers=12, |
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num_attention_heads=12, |
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num_key_value_heads=4, |
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intermediate_size=3072, |
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hidden_act="silu", |
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hidden_dropout_prob=0.0, |
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attention_probs_dropout_prob=0.0, |
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max_position_embeddings=512, |
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initializer_range=0.02, |
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layer_norm_eps=1e-12, |
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pad_token_id=0, |
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bos_token_id=1, |
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eos_token_id=2, |
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use_cache=True, |
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rotary_emb_dim=0, |
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rotary_emb_base=10000.0, |
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rotary_emb_scale_base=None, |
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rotary_emb_interleaved=False, |
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**kwargs |
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): |
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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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self.vocab_size = vocab_size |
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self.hidden_size = hidden_size |
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self.num_encoder_layers = num_encoder_layers |
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self.num_decoder_layers = num_decoder_layers |
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self.num_attention_heads = num_attention_heads |
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self.num_key_value_heads = num_key_value_heads |
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self.hidden_act = hidden_act |
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self.intermediate_size = intermediate_size |
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self.hidden_dropout_prob = hidden_dropout_prob |
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self.attention_probs_dropout_prob = attention_probs_dropout_prob |
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self.max_position_embeddings = max_position_embeddings |
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self.initializer_range = initializer_range |
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self.layer_norm_eps = layer_norm_eps |
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self.use_cache = use_cache |
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self.rotary_emb_base = rotary_emb_base |
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self.rotary_emb_scale_base = rotary_emb_scale_base |
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self.rotary_emb_interleaved = rotary_emb_interleaved |
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self.head_dim = self.hidden_size // self.num_attention_heads |
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self.rotary_emb_dim = kwargs.get('rotary_emb_dim', self.head_dim // 2) |
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if self.rotary_emb_dim > self.head_dim: |
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print(f"Warning: rotary_emb_dim ({self.rotary_emb_dim}) is larger than head_dim ({self.head_dim}). Setting rotary_emb_dim to head_dim.") |
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self.rotary_emb_dim = self.head_dim |