File size: 3,509 Bytes
2281a21 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 |
"""
LLaDA MoE configuration
"""
from transformers.configuration_utils import PretrainedConfig
from transformers.modeling_rope_utils import rope_config_validation
class LLaDAConfig(PretrainedConfig):
model_type = "llada"
keys_to_ignore_at_inference = ["past_key_values"]
def __init__(
self,
vocab_size=-1,
hidden_size=-1,
dense_intermediate_size=-1,
expert_intermediate_size=-1,
shared_expert_intermediate_size=-1,
num_hidden_layers=-1,
num_attention_heads=-1,
num_key_value_heads=None,
hidden_act="silu",
max_position_embeddings=4096,
initializer_range=0.02,
rms_norm_eps=1e-05,
use_cache=False,
pad_token_id=1,
bos_token_id=None,
eos_token_id=50279,
tie_word_embeddings=False,
rope_theta=-1,
partial_rotary_factor=-1,
rope_scaling=None,
attention_bias=False,
attention_dropout=0.0,
clip_qkv=None,
num_experts_per_tok=-1,
num_experts=-1,
output_router_logits=False,
router_aux_loss_coef=0.01,
norm_topk_prob=None,
qk_layernorm=None,
moe_layer_freq=[],
moe_router_enable_expert_bias=None,
moe_router_score_function=None,
routed_scaling_factor=1,
router_num_group=-2,
router_topk_group=-2,
**kwargs,
):
self.vocab_size = vocab_size
self.max_position_embeddings = max_position_embeddings
self.hidden_size = hidden_size
self.expert_intermediate_size = expert_intermediate_size
self.dense_intermediate_size = dense_intermediate_size
self.shared_expert_intermediate_size = shared_expert_intermediate_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
if num_key_value_heads is None:
num_key_value_heads = num_attention_heads
self.num_key_value_heads = num_key_value_heads
self.hidden_act = hidden_act
self.initializer_range = initializer_range
self.rms_norm_eps = rms_norm_eps
self.use_cache = use_cache
self.rope_theta = rope_theta
self.rope_scaling = rope_scaling
self.attention_bias = attention_bias
self.attention_dropout = attention_dropout
self.clip_qkv = clip_qkv
self.num_experts_per_tok = num_experts_per_tok
self.num_experts = num_experts
self.output_router_logits = output_router_logits
self.router_aux_loss_coef = router_aux_loss_coef
self.norm_topk_prob = norm_topk_prob
self.qk_layernorm = qk_layernorm
self.moe_layer_freq = moe_layer_freq
self.moe_router_enable_expert_bias = moe_router_enable_expert_bias
self.moe_router_score_function = moe_router_score_function
self.partial_rotary_factor = partial_rotary_factor
self.routed_scaling_factor = routed_scaling_factor
self.router_num_group = router_num_group
self.router_topk_group = router_topk_group
if self.rope_scaling is not None and "type" in self.rope_scaling:
self.rope_scaling["rope_type"] = self.rope_scaling["type"]
rope_config_validation(self)
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,
)
|