File size: 2,921 Bytes
70038b6 | 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 | from transformers.configuration_utils import PretrainedConfig
class DynamicMindMoEConfig(PretrainedConfig):
"""DynamicMind-MoE: sparse mixture-of-experts variant of DynamicMind-Mini.
The dense MLP (intermediate 768) is replaced by one always-on shared expert
plus `num_routed_experts` fine-grained experts (intermediate 256), of which
`num_experts_per_token` are selected. Shared + top-2 reproduces the dense
layer's exact active parameter count, so inference cost per token is
unchanged while total capacity grows ~3.4x.
"""
model_type = "dynamicmind_moe"
def __init__(
self,
vocab_size=8192,
hidden_size=256,
intermediate_size=768, # kept for dense layers / upcycling source
moe_intermediate_size=256, # per-expert width (768 / 3)
num_hidden_layers=9,
num_attention_heads=8,
num_key_value_heads=2,
num_routed_experts=14,
num_shared_experts=1,
num_experts_per_token=2,
first_k_dense_layers=0, # keep the first K blocks dense if desired
norm_topk_prob=True,
router_aux_loss_coef=0.01,
router_z_loss_coef=0.001,
router_bias_update_rate=0.001, # aux-loss-free balancing (DeepSeek-V3)
use_aux_loss_free_balancing=True,
max_position_embeddings=1024,
rms_norm_eps=1e-5,
rope_theta=10000.0,
attention_dropout=0.0,
tie_word_embeddings=True,
bos_token_id=0,
eos_token_id=0,
pad_token_id=1,
**kwargs,
):
super().__init__(
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
pad_token_id=pad_token_id,
tie_word_embeddings=tie_word_embeddings,
**kwargs,
)
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.moe_intermediate_size = moe_intermediate_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.num_key_value_heads = num_key_value_heads
self.num_routed_experts = num_routed_experts
self.num_shared_experts = num_shared_experts
self.num_experts_per_token = num_experts_per_token
self.first_k_dense_layers = first_k_dense_layers
self.norm_topk_prob = norm_topk_prob
self.router_aux_loss_coef = router_aux_loss_coef
self.router_z_loss_coef = router_z_loss_coef
self.router_bias_update_rate = router_bias_update_rate
self.use_aux_loss_free_balancing = use_aux_loss_free_balancing
self.max_position_embeddings = max_position_embeddings
self.rms_norm_eps = rms_norm_eps
self.rope_theta = rope_theta
self.attention_dropout = attention_dropout
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