| |
|
|
| from typing import Dict, Optional |
|
|
| from transformers.configuration_utils import PretrainedConfig |
|
|
|
|
| class ABCConfig(PretrainedConfig): |
|
|
| model_type = 'abc' |
| keys_to_ignore_at_inference = ['past_key_values'] |
|
|
| def __init__( |
| self, |
| hidden_size: int = 2048, |
| gate_low_rank_dim: int = 16, |
| clamp_min: float = -32, |
| clamp_max: float = 32, |
| hidden_ratio: Optional[int] = 4, |
| intermediate_size: Optional[int] = None, |
| num_hidden_layers: int = 24, |
| num_heads: int = 4, |
| num_slots: Optional[int] = 64, |
| use_short_conv: bool = False, |
| conv_size: int = 4, |
| exapnd_k: float = 0.5, |
| exapnd_v: float = 1, |
| hidden_act: str = "swish", |
| max_position_embeddings: int = 2048, |
| elementwise_affine: Optional[bool] = True, |
| norm_eps: float = 1e-6, |
| use_rope: bool = True, |
| attn: Optional[Dict] = None, |
| 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_norm: bool = True, |
| fuse_swiglu: bool = True, |
| fuse_cross_entropy: bool = True, |
| vocab_size: int = 32000, |
| **kwargs |
| ): |
| self.hidden_size = hidden_size |
| self.gate_low_rank_dim = gate_low_rank_dim |
| self.clamp_min = clamp_min |
| self.clamp_max = clamp_max |
| self.hidden_ratio = hidden_ratio |
| self.intermediate_size = intermediate_size |
| self.num_hidden_layers = num_hidden_layers |
| self.num_heads = num_heads |
| self.num_slots = num_slots |
| self.use_short_conv = use_short_conv |
| self.conv_size = conv_size |
| self.expand_k = exapnd_k |
| self.expand_v = exapnd_v |
| self.hidden_act = hidden_act |
| self.max_position_embeddings = max_position_embeddings |
| self.elementwise_affine = elementwise_affine |
| self.norm_eps = norm_eps |
| self.use_rope = use_rope |
| self.attn = attn |
| self.use_cache = use_cache |
| self.initializer_range = initializer_range |
|
|
| self.fuse_norm = fuse_norm |
| self.fuse_swiglu = fuse_swiglu |
| self.fuse_cross_entropy = fuse_cross_entropy |
| self.vocab_size = vocab_size |
|
|
| if attn is not None: |
| if not isinstance(attn, Dict): |
| raise ValueError("attn must be a dictionary") |
| if 'layers' not in attn: |
| raise ValueError("Layer indices must be provided to initialize hybrid attention layers") |
| if 'num_heads' not in attn: |
| raise ValueError("Number of heads must be provided to initialize hybrid attention layers") |
| attn['num_kv_heads'] = attn.get('num_kv_heads', attn['num_heads']) |
| attn['qkv_bias'] = attn.get('qkv_bias', False) |
| attn['window_size'] = attn.get('window_size', None) |
| attn['rope_theta'] = attn.get('rope_theta', 10000.) |
|
|
| 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, |
| ) |
|
|
