| | """Bailing MoE V2 model configuration""" |
| |
|
| | from transformers.configuration_utils import PretrainedConfig |
| |
|
| |
|
| | class BailingMoeV2Config(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, |
| | 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_types=None, |
| | sliding_window=256, |
| | hc_expand=1, |
| | quantize=False, |
| | **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.quantize = quantize |
| |
|
| | |
| | self.layer_types = layer_types |
| | if self.layer_types is None: |
| | self.layer_types = ["full_attention" for i in range(self.num_hidden_layers)] |
| | self.sliding_window = sliding_window |
| |
|
| | |
| | if hc_expand > 1: |
| | self.hc_expand = hc_expand |
| | self.hc = True |
| | else: |
| | self.hc = False |
| |
|
| | |
| | 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.first_k_dense_replace = first_k_dense_replace |
| | self.output_router_logits = output_router_logits |
| |
|
| | super().__init__( |
| | pad_token_id=pad_token_id, eos_token_id=eos_token_id, tie_word_embeddings=tie_word_embeddings, **kwargs |
| | ) |
| |
|
