# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 # This file was automatically generated from src/transformers/models/laguna/modular_laguna.py. # Do NOT edit this file manually as any edits will be overwritten by the generation of # the file from the modular. If any change should be done, please apply the change to the # modular_laguna.py file directly. One of our CI enforces this. # 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 # Copyright 2026 Poolside and the HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import Any, Literal from huggingface_hub.dataclasses import strict from ...configuration_utils import PreTrainedConfig from ...modeling_rope_utils import RopeParameters from ...utils import auto_docstring @auto_docstring(checkpoint="poolside/laguna-XS.2") @strict class LagunaConfig(PreTrainedConfig): r""" num_attention_heads_per_layer (`list[int]`, *optional*): Per-layer override for ``num_attention_heads``. Length must equal ``num_hidden_layers``. mlp_layer_types (`list[str]`, *optional*): Per-layer MLP type — ``"dense"`` or ``"sparse"``. Length must equal ``num_hidden_layers``. Defaults to first layer dense, rest sparse. moe_routed_scaling_factor (`float`, *optional*, defaults to 1.0): Scalar applied to routed-expert output before combining with the shared-expert output. moe_apply_router_weight_on_input (`bool`, *optional*, defaults to `False`): Whether to apply router weights to the MoE input rather than the output. Not supported in transformers yet; ``True`` will raise a ``NotImplementedError`` for now. moe_router_logit_softcapping (`float`, *optional*, defaults to 0.0): Scaling factor when applying tanh softcapping on the logits of the MoE router logits. Example: ```python >>> from transformers import LagunaModel, LagunaConfig >>> configuration = LagunaConfig() >>> model = LagunaModel(configuration) >>> configuration = model.config ``` """ model_type = "laguna" keys_to_ignore_at_inference = ["past_key_values"] base_model_tp_plan = { "layers.*.self_attn.q_proj": "colwise", "layers.*.self_attn.k_proj": "colwise", "layers.*.self_attn.v_proj": "colwise", "layers.*.self_attn.g_proj": "colwise", "layers.*.self_attn.o_proj": "rowwise", "layers.*.self_attn.q_norm": "replicated_with_grad_allreduce", "layers.*.self_attn.k_norm": "replicated_with_grad_allreduce", "layers.*.mlp.gate_proj": "colwise", "layers.*.mlp.up_proj": "colwise", "layers.*.mlp.down_proj": "rowwise", "layers.*.mlp.experts.gate_up_proj": "packed_colwise", "layers.*.mlp.experts.down_proj": "rowwise", "layers.*.mlp.experts": "moe_tp_experts", "layers.*.mlp.shared_experts.gate_proj": "colwise", "layers.*.mlp.shared_experts.up_proj": "colwise", "layers.*.mlp.shared_experts.down_proj": "rowwise", } base_model_pp_plan = { "embed_tokens": (["input_ids"], ["inputs_embeds"]), "layers": (["hidden_states", "attention_mask"], ["hidden_states"]), "norm": (["hidden_states"], ["hidden_states"]), } vocab_size: int = 100352 hidden_size: int = 2048 intermediate_size: int = 8192 num_hidden_layers: int = 40 num_attention_heads: int = 48 num_key_value_heads: int = 8 hidden_act: str = "silu" max_position_embeddings: int = 131072 initializer_range: float = 0.02 rms_norm_eps: float = 1e-6 use_cache: bool = True tie_word_embeddings: bool = False rope_parameters: RopeParameters | dict | None = None sliding_window: int = 512 attention_dropout: float | int = 0.0 moe_intermediate_size: int = 512 shared_expert_intermediate_size: int = 512 num_experts_per_tok: int = 8 num_experts: int = 256 output_router_logits: bool = False router_aux_loss_coef: float = 0.001 layer_types: list[str] | None = None pad_token_id: int | None = None bos_token_id: int | None = None eos_token_id: int | list[int] | None = None # Laguna-specific attention head_dim: int = 128 attention_bias: bool = False num_attention_heads_per_layer: list[int] | None = None # Laguna-specific MoE mlp_layer_types: list[str] | None = None moe_routed_scaling_factor: float = 1.0 moe_apply_router_weight_on_input: bool = False moe_router_logit_softcapping: float = 0.0 def __post_init__(self, **kwargs): if self.layer_types is None: self.layer_types = ["full_attention"] * self.num_hidden_layers if self.mlp_layer_types is None: self.mlp_layer_types = ["dense"] + ["sparse"] * (self.num_hidden_layers - 1) if self.num_attention_heads_per_layer is None: self.num_attention_heads_per_layer = [self.num_attention_heads] * self.num_hidden_layers default_rope_params: dict[Literal["full_attention", "sliding_attention"], dict[str, Any]] = { "full_attention": {"rope_type": "default", "rope_theta": 500000.0, "partial_rotary_factor": 0.5}, "sliding_attention": {"rope_type": "default", "rope_theta": 10000.0, "partial_rotary_factor": 1.0}, } if self.rope_parameters is None: self.rope_parameters = default_rope_params # rope_parameters is keyed by layer type; tell the validator those keys are intentional. super().__post_init__(**kwargs, ignore_keys_at_rope_validation={"sliding_attention", "full_attention"}) def convert_rope_params_to_dict(self, **kwargs): # No need to handle BC for new models, because they have no old-format `rope_scaling` return kwargs def validate_architecture(self): """Part of ``@strict``-powered validation.""" if self.moe_apply_router_weight_on_input: raise NotImplementedError( "moe_apply_router_weight_on_input=True is not yet supported in the " "transformers implementation of Laguna." ) if ( self.num_attention_heads_per_layer is not None and len(self.num_attention_heads_per_layer) != self.num_hidden_layers ): raise ValueError( f"num_attention_heads_per_layer length ({len(self.num_attention_heads_per_layer)}) " f"must equal num_hidden_layers ({self.num_hidden_layers})." ) if len(self.layer_types) != self.num_hidden_layers: raise ValueError( f"layer_types length ({len(self.layer_types)}) " f"must equal num_hidden_layers ({self.num_hidden_layers})." ) if len(self.mlp_layer_types) != self.num_hidden_layers: raise ValueError( f"mlp_layer_types length ({len(self.mlp_layer_types)}) " f"must equal num_hidden_layers ({self.num_hidden_layers})." ) __all__ = ["LagunaConfig"]