| """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, |
| use_bias=False, |
| rms_norm_eps=1e-06, |
| tie_word_embeddings=False, |
| 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 |
|
|
| |
| 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 |
|
|
| |
| self.layer_group_size = layer_group_size |
| |
| 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 |
| ) |
|
|
