File size: 4,725 Bytes
7da73ee | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 | """Bailing MoE V2 model configuration"""
from transformers.configuration_utils import PretrainedConfig
class BailingMoeV3Config(PretrainedConfig):
def __init__(
self,
vocab_size=157184,
hidden_size=2048,
intermediate_size=5120,
num_hidden_layers=20,
num_attention_heads=16,
num_key_value_heads=4,
hidden_act="silu",
use_qkv_bias=False, # bailing only
use_bias=False, # bailing only
rms_norm_eps=1e-06,
tie_word_embeddings=False, # PretrainedConfig key, here change default value.
embedding_dropout=0.0,
attention_dropout=0.0,
output_dropout=0.0,
initializer_range=0.02,
max_position_embeddings=32768,
rope_theta=600000.0,
use_cache=True,
max_window_layers=20,
rope_scaling=None,
pad_token_id=156892,
eos_token_id=156892,
num_experts=256,
num_shared_experts=1,
num_experts_per_tok=8,
n_group=8,
topk_group=4,
moe_intermediate_size=512,
moe_shared_expert_intermediate_size=512,
first_k_dense_replace=1,
head_dim=128,
output_router_logits=False,
use_qk_norm=True,
num_nextn_predict_layers=0,
mtp_loss_scaling_factor=0,
moe_router_enable_expert_bias=True,
routed_scaling_factor=1.0,
layer_group_size=5,
kv_lora_rank=512,
q_lora_rank=None,
qk_rope_head_dim=64,
v_head_dim=128,
qk_nope_head_dim=128,
rope_interleave=True,
score_function="sigmoid",
scoring_func="sigmoid",
seq_aux=True,
topk_method="noaux_tc",
router_dtype="fp32",
gated_attention_proj_granularity_type=None,
no_kda_lora=False,
kda_safe_gate=False,
kda_lower_bound=None,
short_conv_kernel_size=4,
**kwargs,
):
self.num_hidden_layers = num_hidden_layers
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.num_attention_heads = num_attention_heads
self.num_key_value_heads = num_key_value_heads
self.hidden_act = hidden_act
self.use_qkv_bias = use_qkv_bias
self.use_bias = use_bias
self.rms_norm_eps = rms_norm_eps
self.embedding_dropout = embedding_dropout
self.attention_dropout = attention_dropout
self.output_dropout = output_dropout
self.num_nextn_predict_layers = num_nextn_predict_layers
self.mtp_loss_scaling_factor = mtp_loss_scaling_factor
self.initializer_range = initializer_range
self.max_position_embeddings = max_position_embeddings
self.rope_theta = rope_theta
self.use_cache = use_cache
self.max_window_layers = max_window_layers
self.head_dim = head_dim or self.hidden_size // self.num_attention_heads
self.rope_scaling = rope_scaling
self.use_qk_norm = use_qk_norm
self.moe_router_enable_expert_bias = moe_router_enable_expert_bias
self.routed_scaling_factor = routed_scaling_factor
# MoE configs
self.num_experts = num_experts
self.num_shared_experts = num_shared_experts
self.num_experts_per_tok = num_experts_per_tok
self.n_group = n_group
self.topk_group = topk_group
self.moe_intermediate_size = moe_intermediate_size
self.moe_shared_expert_intermediate_size = moe_shared_expert_intermediate_size
self.first_k_dense_replace = first_k_dense_replace
self.output_router_logits = output_router_logits
# Linear configs
self.layer_group_size = layer_group_size
# mla
self.kv_lora_rank = kv_lora_rank
self.q_lora_rank = q_lora_rank
self.qk_rope_head_dim = qk_rope_head_dim
self.score_function = score_function
self.scoring_func = scoring_func
self.seq_aux = seq_aux
self.topk_method = topk_method
self.v_head_dim = v_head_dim
self.qk_nope_head_dim = qk_nope_head_dim
self.qk_head_dim = qk_nope_head_dim + qk_rope_head_dim
self.rope_interleave = rope_interleave
self.router_dtype = router_dtype
self.gated_attention_proj_granularity_type = gated_attention_proj_granularity_type
self.no_kda_lora = no_kda_lora
self.kda_safe_gate = kda_safe_gate
self.kda_lower_bound = kda_lower_bound
self.short_conv_kernel_size = short_conv_kernel_size
super().__init__(
pad_token_id=pad_token_id, eos_token_id=eos_token_id, tie_word_embeddings=tie_word_embeddings, **kwargs
)
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