| |
|
|
| from typing import Optional |
|
|
| from transformers.configuration_utils import PretrainedConfig |
|
|
|
|
| class LinearAttentionConfig(PretrainedConfig): |
|
|
| model_type = 'linear_attn' |
| keys_to_ignore_at_inference = ['past_key_values'] |
|
|
| def __init__( |
| self, |
| vocab_size: int = 32000, |
| hidden_size: int = 2048, |
| expand_k: int = 1, |
| expand_v: int = 1, |
| hidden_ratio: Optional[int] = 4, |
| intermediate_size: Optional[int] = None, |
| num_hidden_layers: int = 24, |
| num_heads: int = 4, |
| attn_mode: str = "fused_chunk", |
| feature_map: str = "elementwise_product", |
| tie_feature_map_qk: bool = False, |
| norm_q: bool = False, |
| norm_k: bool = False, |
| norm_feature_map: bool = False, |
| hidden_act: str = "swish", |
| max_position_embeddings: int = 2048, |
| elementwise_affine: Optional[bool] = True, |
| norm_eps: float = 1e-6, |
| use_cache: bool = True, |
| pad_token_id: int = None, |
| bos_token_id: int = 1, |
| eos_token_id: int = 2, |
| tie_word_embeddings: bool = False, |
| initializer_range: float = 0.02, |
| fuse_cross_entropy: bool = True, |
| **kwargs |
| ): |
| self.vocab_size = vocab_size |
| self.max_position_embeddings = max_position_embeddings |
| self.hidden_size = hidden_size |
| self.expand_k = expand_k |
| self.expand_v = expand_v |
| self.hidden_ratio = hidden_ratio |
| self.intermediate_size = intermediate_size |
| self.num_hidden_layers = num_hidden_layers |
| self.num_heads = num_heads |
| self.attn_mode = attn_mode |
| self.feature_map = feature_map |
| self.tie_feature_map_qk = tie_feature_map_qk |
| self.norm_q = norm_q |
| self.norm_k = norm_k |
| self.norm_feature_map = norm_feature_map |
| self.hidden_act = hidden_act |
| self.elementwise_affine = elementwise_affine |
| self.norm_eps = norm_eps |
| self.use_cache = use_cache |
| self.initializer_range = initializer_range |
| self.fuse_cross_entropy = fuse_cross_entropy |
|
|
| super().__init__( |
| pad_token_id=pad_token_id, |
| bos_token_id=bos_token_id, |
| eos_token_id=eos_token_id, |
| tie_word_embeddings=tie_word_embeddings, |
| **kwargs, |
| ) |
|
|
