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from typing import Dict, Optional |
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from transformers.configuration_utils import PretrainedConfig |
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class ABCConfig(PretrainedConfig): |
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model_type = 'abc' |
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keys_to_ignore_at_inference = ['past_key_values'] |
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def __init__( |
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self, |
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hidden_size: int = 2048, |
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gate_low_rank_dim: int = 16, |
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clamp_min: float = -32, |
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clamp_max: float = 32, |
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hidden_ratio: Optional[int] = 4, |
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intermediate_size: Optional[int] = None, |
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num_hidden_layers: int = 24, |
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num_heads: int = 4, |
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num_slots: Optional[int] = 64, |
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use_short_conv: bool = False, |
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conv_size: int = 4, |
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exapnd_k: float = 0.5, |
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exapnd_v: float = 1, |
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hidden_act: str = "swish", |
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max_position_embeddings: int = 2048, |
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elementwise_affine: Optional[bool] = True, |
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norm_eps: float = 1e-6, |
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attn: Optional[Dict] = None, |
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use_cache: bool = True, |
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pad_token_id: int = None, |
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bos_token_id: int = 1, |
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eos_token_id: int = 2, |
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tie_word_embeddings: bool = False, |
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initializer_range: float = 0.02, |
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fuse_norm: bool = True, |
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fuse_cross_entropy: bool = True, |
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vocab_size: int = 32000, |
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**kwargs |
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): |
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self.hidden_size = hidden_size |
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self.gate_low_rank_dim = gate_low_rank_dim |
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self.clamp_min = clamp_min |
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self.clamp_max = clamp_max |
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self.hidden_ratio = hidden_ratio |
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self.intermediate_size = intermediate_size |
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self.num_hidden_layers = num_hidden_layers |
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self.num_heads = num_heads |
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self.num_slots = num_slots |
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self.use_short_conv = use_short_conv |
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self.conv_size = conv_size |
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self.expand_k = exapnd_k |
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self.expand_v = exapnd_v |
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self.hidden_act = hidden_act |
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self.max_position_embeddings = max_position_embeddings |
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self.elementwise_affine = elementwise_affine |
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self.norm_eps = norm_eps |
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self.attn = attn |
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self.use_cache = use_cache |
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self.initializer_range = initializer_range |
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self.fuse_norm = fuse_norm |
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self.fuse_cross_entropy = fuse_cross_entropy |
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self.vocab_size = vocab_size |
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if attn is not None: |
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if not isinstance(attn, Dict): |
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raise ValueError("attn must be a dictionary") |
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if 'layers' not in attn: |
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raise ValueError("Layer indices must be provided to initialize hybrid attention layers") |
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if 'num_heads' not in attn: |
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raise ValueError("Number of heads must be provided to initialize hybrid attention layers") |
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attn['num_kv_heads'] = attn.get('num_kv_heads', attn['num_heads']) |
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attn['window_size'] = attn.get('window_size', None) |
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super().__init__( |
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pad_token_id=pad_token_id, |
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bos_token_id=bos_token_id, |
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eos_token_id=eos_token_id, |
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tie_word_embeddings=tie_word_embeddings, |
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**kwargs, |
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) |
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