| from transformers import PretrainedConfig | |
| class AsterConfig(PretrainedConfig): | |
| model_type = "aster" | |
| def __init__( | |
| self, vocab_size=32000, hidden_size=1152, num_hidden_layers=22, | |
| num_attention_heads=18, num_key_value_heads=2, intermediate_size=5504, | |
| max_position_embeddings=4096, rms_norm_eps=1e-6, rope_theta=10000.0, | |
| gqa_implementation="native", bos_token_id=1, eos_token_id=2, | |
| pad_token_id=3, tie_word_embeddings=True, **kwargs, | |
| ): | |
| super().__init__( | |
| bos_token_id=bos_token_id, eos_token_id=eos_token_id, | |
| pad_token_id=pad_token_id, tie_word_embeddings=tie_word_embeddings, | |
| **kwargs, | |
| ) | |
| self.vocab_size = vocab_size | |
| self.hidden_size = hidden_size | |
| self.num_hidden_layers = num_hidden_layers | |
| self.num_attention_heads = num_attention_heads | |
| self.num_key_value_heads = num_key_value_heads | |
| self.intermediate_size = intermediate_size | |
| self.max_position_embeddings = max_position_embeddings | |
| self.rms_norm_eps = rms_norm_eps | |
| self.rope_theta = rope_theta | |
| self.gqa_implementation = gqa_implementation | |
| if hidden_size % num_attention_heads or num_attention_heads % num_key_value_heads: | |
| raise ValueError("Invalid attention head configuration") | |
| if hidden_size // num_attention_heads != 64: | |
| raise ValueError("Expected 64-dimensional attention heads for Stage 1") | |
| if gqa_implementation not in ("native", "expanded"): | |
| raise ValueError("gqa_implementation must be native or expanded") | |