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import warnings
from transformers.configuration_utils import PretrainedConfig
class CombaConfig(PretrainedConfig):
model_type = 'comba'
keys_to_ignore_at_inference = ['past_key_values']
def __init__(
self,
attn_mode: str = "chunk",
hidden_size: int = 2048,
conv_size: int = 4,
head_dim: int = 256,
num_heads: int = 6,
num_v_heads: int | None = None,
expand_v: float = 2.0,
use_output_gate: bool = True,
use_short_conv: bool = True,
use_output_correction: bool = True,
use_inner_decay: bool = True,
correction_factor: float = 1.,
max_position_embeddings: int = 2048,
hidden_ratio: int | None = 4,
intermediate_size: int | None = None,
hidden_act: str = "swish",
num_hidden_layers: int = 21,
norm_eps: float = 1e-6,
attn: dict | None = None,
use_cache: bool = True,
pad_token_id: int | None = 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,
fuse_linear_cross_entropy: bool = False,
use_l2warp: bool = False,
vocab_size: int = 32000,
**kwargs,
):
self.attn_mode = attn_mode
self.hidden_size = hidden_size
self.conv_size = conv_size
self.head_dim = head_dim
self.num_heads = num_heads
self.num_v_heads = num_v_heads
self.expand_v = expand_v
self.use_output_gate = use_output_gate
self.use_short_conv = use_short_conv
self.use_output_correction = use_output_correction
self.correction_factor = correction_factor
self.use_inner_decay = use_inner_decay
self.max_position_embeddings = max_position_embeddings
self.hidden_ratio = hidden_ratio
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.num_hidden_layers = num_hidden_layers
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_swiglu = fuse_swiglu
self.fuse_cross_entropy = fuse_cross_entropy
self.fuse_linear_cross_entropy = fuse_linear_cross_entropy
self.use_l2warp = use_l2warp
self.vocab_size = vocab_size
if fuse_cross_entropy and fuse_linear_cross_entropy:
raise ValueError(
"`fuse_cross_entropy` and `fuse_linear_cross_entropy` cannot be True at the same time.",
)
if fuse_linear_cross_entropy:
warnings.warn(
"`fuse_linear_cross_entropy` is enabled, which can improves memory efficiency "
"at the potential cost of reduced precision. "
"If you observe issues like loss divergence, consider disabling this setting.",
)
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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