| |
| |
|
|
| from transformers.configuration_utils import PretrainedConfig |
| from transformers.utils import logging |
|
|
| logger = logging.get_logger(__name__) |
|
|
|
|
| class Glm4MoeLiteSCMConfig(PretrainedConfig): |
| model_type = "glm4_moe_lite" |
| keys_to_ignore_at_inference = ["past_key_values"] |
|
|
| def __init__( |
| self, |
| vocab_size=154880, |
| hidden_size=2048, |
| intermediate_size=10240, |
| moe_intermediate_size=1536, |
| num_hidden_layers=47, |
| num_attention_heads=20, |
| num_key_value_heads=20, |
| n_shared_experts=1, |
| n_routed_experts=64, |
| routed_scaling_factor=1.8, |
| kv_lora_rank=512, |
| q_lora_rank=768, |
| qk_rope_head_dim=64, |
| v_head_dim=256, |
| qk_nope_head_dim=192, |
| n_group=1, |
| topk_group=1, |
| num_experts_per_tok=4, |
| norm_topk_prob=True, |
| topk_method="noaux_tc", |
| first_k_dense_replace=1, |
| num_nextn_predict_layers=1, |
| hidden_act="silu", |
| max_position_embeddings=202752, |
| initializer_range=0.02, |
| rms_norm_eps=1e-5, |
| use_cache=True, |
| pad_token_id=None, |
| bos_token_id=0, |
| eos_token_id=1, |
| tie_word_embeddings=False, |
| rope_theta=1000000, |
| rope_scaling=None, |
| rope_interleave=True, |
| attention_bias=False, |
| attention_dropout=0.0, |
| scoring_func="sigmoid", |
| mlp_layer_types=None, |
| **kwargs, |
| ): |
| self.vocab_size = vocab_size |
| self.max_position_embeddings = max_position_embeddings |
| 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.n_shared_experts = n_shared_experts |
| self.n_routed_experts = n_routed_experts |
| self.routed_scaling_factor = routed_scaling_factor |
| self.kv_lora_rank = kv_lora_rank |
| self.q_lora_rank = q_lora_rank |
| self.qk_rope_head_dim = qk_rope_head_dim |
| 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.head_dim = qk_rope_head_dim |
| self.n_group = n_group |
| self.topk_group = topk_group |
| self.num_experts_per_tok = num_experts_per_tok |
| self.norm_topk_prob = norm_topk_prob |
| self.topk_method = topk_method |
| self.first_k_dense_replace = first_k_dense_replace |
| self.num_nextn_predict_layers = num_nextn_predict_layers |
| 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.rope_interleave = rope_interleave |
| self.attention_bias = attention_bias |
| self.attention_dropout = attention_dropout |
| self.scoring_func = scoring_func |
|
|
| |
| if mlp_layer_types is not None: |
| self.mlp_layer_types = mlp_layer_types |
| else: |
| self.mlp_layer_types = ( |
| ["dense"] * first_k_dense_replace |
| + ["sparse"] * (num_hidden_layers - first_k_dense_replace) |
| ) |
|
|
| 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, |
| ) |
|
|
|
|
| __all__ = ["Glm4MoeLiteSCMConfig"] |
|
|
