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from typing import Dict, List, Optional, Union |
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from transformers.configuration_utils import PretrainedConfig |
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class RWKV7Config(PretrainedConfig): |
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model_type = 'rwkv7' |
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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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attn_mode: str = "chunk", |
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hidden_size: int = 2048, |
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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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head_dim: Optional[int] = 64, |
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num_heads: Optional[int] = None, |
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decay_low_rank_dim: int = 64, |
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gate_low_rank_dim: int = 128, |
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a_low_rank_dim: int = 64, |
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v_low_rank_dim: int = 16, |
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hidden_act: str = "sqrelu", |
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max_position_embeddings: int = 2048, |
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norm_first: bool = True, |
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norm_bias: bool = True, |
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norm_eps: float = 1e-5, |
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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 = 0.006, |
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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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value_dim: Optional[Union[int, List[int]]] = None, |
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**kwargs |
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): |
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self.attn_mode = attn_mode |
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self.hidden_size = hidden_size |
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self.hidden_ratio = hidden_ratio |
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self.intermediate_size = intermediate_size |
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self.norm_first = norm_first |
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self.num_hidden_layers = num_hidden_layers |
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if head_dim is None and num_heads is not None: |
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head_dim = int(hidden_size // num_heads) |
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elif head_dim is not None and num_heads is None: |
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num_heads = int(hidden_size // head_dim) |
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if value_dim is None: |
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value_dim = [hidden_size] * num_hidden_layers |
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elif isinstance(value_dim, int): |
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assert value_dim >= hidden_size, "value_dim must be greater than hidden_size" |
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assert value_dim % hidden_size == 0, "value_dim must be divisible by hidden_size" |
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value_dim = [value_dim] * num_hidden_layers |
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else: |
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assert len(value_dim) == num_hidden_layers, "value_dim must have the same length as num_hidden_layers" |
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for v in value_dim: |
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assert v >= hidden_size, "value_dim must be greater than hidden_size" |
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assert v % hidden_size == 0, "value_dim must be divisible by hidden_size" |
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self.head_dim = head_dim |
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self.num_heads = num_heads |
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self.value_dim = value_dim |
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self.decay_low_rank_dim = decay_low_rank_dim |
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self.gate_low_rank_dim = gate_low_rank_dim |
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self.a_low_rank_dim = a_low_rank_dim |
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self.v_low_rank_dim = v_low_rank_dim |
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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.norm_bias = norm_bias |
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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['qkv_bias'] = attn.get('qkv_bias', False) |
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attn['window_size'] = attn.get('window_size', None) |
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attn['rope_theta'] = attn.get('rope_theta', 10000.) |
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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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