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