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# -*- coding: utf-8 -*-
from typing import Dict, List, Optional, Union
from transformers.configuration_utils import PretrainedConfig
class RWKV7Config(PretrainedConfig):
model_type = 'rwkv7'
keys_to_ignore_at_inference = ['past_key_values']
def __init__(
self,
attn_mode: str = "chunk",
hidden_size: int = 2048,
hidden_ratio: Optional[int] = 4,
intermediate_size: Optional[int] = None,
num_hidden_layers: int = 24,
head_dim: Optional[int] = 64,
num_heads: Optional[int] = None,
decay_low_rank_dim: int = 64,
gate_low_rank_dim: int = 128,
a_low_rank_dim: int = 64,
v_low_rank_dim: int = 16,
hidden_act: str = "sqrelu",
max_position_embeddings: int = 2048,
norm_first: bool = True,
norm_bias: bool = True,
norm_eps: float = 1e-5,
attn: Optional[Dict] = None,
use_cache: bool = True,
pad_token_id: Optional[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_cross_entropy: bool = True,
use_l2warp: bool = True,
vocab_size: int = 32000,
value_dim: Optional[Union[int, List[int]]] = None,
**kwargs
):
self.attn_mode = attn_mode
self.hidden_size = hidden_size
self.hidden_ratio = hidden_ratio
self.intermediate_size = intermediate_size
self.norm_first = norm_first
self.num_hidden_layers = num_hidden_layers
if head_dim is None and num_heads is not None:
head_dim = int(hidden_size // num_heads)
elif head_dim is not None and num_heads is None:
num_heads = int(hidden_size // head_dim)
if value_dim is None:
value_dim = [hidden_size] * num_hidden_layers
elif isinstance(value_dim, int):
assert value_dim >= hidden_size, "value_dim must be greater than hidden_size"
assert value_dim % hidden_size == 0, "value_dim must be divisible by hidden_size"
value_dim = [value_dim] * num_hidden_layers
else:
assert len(value_dim) == num_hidden_layers, "value_dim must have the same length as num_hidden_layers"
for v in value_dim:
assert v >= hidden_size, "value_dim must be greater than hidden_size"
assert v % hidden_size == 0, "value_dim must be divisible by hidden_size"
self.head_dim = head_dim
self.num_heads = num_heads
self.value_dim = value_dim
self.decay_low_rank_dim = decay_low_rank_dim
self.gate_low_rank_dim = gate_low_rank_dim
self.a_low_rank_dim = a_low_rank_dim
self.v_low_rank_dim = v_low_rank_dim
self.hidden_act = hidden_act
self.max_position_embeddings = max_position_embeddings
self.norm_bias = norm_bias
self.norm_eps = norm_eps
self.attn = attn
self.use_cache = use_cache
self.initializer_range = initializer_range
self.fuse_norm = fuse_norm
self.fuse_cross_entropy = fuse_cross_entropy
self.use_l2warp = use_l2warp
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,
)
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