| """ |
| LLaDA MoE configuration |
| """ |
|
|
| from transformers.configuration_utils import PretrainedConfig |
| from transformers.modeling_rope_utils import rope_config_validation |
|
|
|
|
| class LLaDAConfig(PretrainedConfig): |
| model_type = "llada" |
| keys_to_ignore_at_inference = ["past_key_values"] |
|
|
| def __init__( |
| self, |
| vocab_size=-1, |
| hidden_size=-1, |
| dense_intermediate_size=-1, |
| expert_intermediate_size=-1, |
| shared_expert_intermediate_size=-1, |
| num_hidden_layers=-1, |
| num_attention_heads=-1, |
| num_key_value_heads=None, |
| hidden_act="silu", |
| max_position_embeddings=4096, |
| initializer_range=0.02, |
| rms_norm_eps=1e-05, |
| use_cache=False, |
| pad_token_id=1, |
| bos_token_id=None, |
| eos_token_id=50279, |
| tie_word_embeddings=False, |
| rope_theta=-1, |
| partial_rotary_factor=-1, |
| rope_scaling=None, |
| attention_bias=False, |
| attention_dropout=0.0, |
| clip_qkv=None, |
| num_experts_per_tok=-1, |
| num_experts=-1, |
| output_router_logits=False, |
| router_aux_loss_coef=0.01, |
| norm_topk_prob=None, |
| qk_layernorm=None, |
| moe_layer_freq=[], |
| moe_router_enable_expert_bias=None, |
| moe_router_score_function=None, |
| routed_scaling_factor=1, |
| router_num_group=-2, |
| router_topk_group=-2, |
| **kwargs, |
| ): |
| self.vocab_size = vocab_size |
| self.max_position_embeddings = max_position_embeddings |
| self.hidden_size = hidden_size |
| self.expert_intermediate_size = expert_intermediate_size |
| self.dense_intermediate_size = dense_intermediate_size |
| self.shared_expert_intermediate_size = shared_expert_intermediate_size |
| self.num_hidden_layers = num_hidden_layers |
| self.num_attention_heads = num_attention_heads |
| if num_key_value_heads is None: |
| num_key_value_heads = num_attention_heads |
| self.num_key_value_heads = num_key_value_heads |
|
|
| 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.attention_bias = attention_bias |
| self.attention_dropout = attention_dropout |
| self.clip_qkv = clip_qkv |
| self.num_experts_per_tok = num_experts_per_tok |
| self.num_experts = num_experts |
| self.output_router_logits = output_router_logits |
| self.router_aux_loss_coef = router_aux_loss_coef |
| self.norm_topk_prob = norm_topk_prob |
| self.qk_layernorm = qk_layernorm |
| self.moe_layer_freq = moe_layer_freq |
| self.moe_router_enable_expert_bias = moe_router_enable_expert_bias |
| self.moe_router_score_function = moe_router_score_function |
| self.partial_rotary_factor = partial_rotary_factor |
| self.routed_scaling_factor = routed_scaling_factor |
| self.router_num_group = router_num_group |
| self.router_topk_group = router_topk_group |
|
|
| if self.rope_scaling is not None and "type" in self.rope_scaling: |
| self.rope_scaling["rope_type"] = self.rope_scaling["type"] |
| rope_config_validation(self) |
|
|
| 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, |
| ) |
|
|
