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# ====================================================================
# configuration_unified.py
# ====================================================================
"""
Configuration class for Unified Language Model
HuggingFace Transformers compatible configuration with AutoClass support
"""
from transformers import PretrainedConfig
from typing import Optional
class UnifiedModelConfig(PretrainedConfig):
"""
Configuration class for UnifiedModel.
Inherits from PretrainedConfig for full HuggingFace compatibility.
"""
model_type = "unified_model"
def __init__(
self,
vocab_size: int = None,
hidden_size: int = 256,
intermediate_size: int = 1024,
num_hidden_layers: int = 6,
num_attention_heads: int = 8,
num_key_value_heads: int = 4,
max_position_embeddings: int = 2048,
rms_norm_eps: float = 1e-6,
rope_theta: float = 10000.0,
attention_dropout: float = 0.1,
mlp_dropout: float = 0.1,
embedding_dropout: float = 0.1,
xielu_alpha_p_init: float = 0.8,
xielu_alpha_n_init: float = 0.8,
xielu_beta: float = 0.5,
tie_word_embeddings: bool = True, # HuggingFace standard parameter name
# LaX configuration (Linear only)
lax_enabled: bool = True,
lax_gate_type: str = "linear", # Only "linear" supported now
# Canon Layers configuration (A+C only)
canon_enabled: bool = True,
canon_kernel_size: int = 4,
canon_a_enabled: bool = True, # Before attention
canon_c_enabled: bool = True, # Before MLP
# Canon B and D are permanently disabled
# FANFormer configuration
fanformer_p: float = 0.15,
# HuggingFace standard parameters
pad_token_id: int = None,
bos_token_id: int = None,
eos_token_id: int = None,
**kwargs
):
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
)
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.intermediate_size = 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.max_position_embeddings = max_position_embeddings
self.rms_norm_eps = rms_norm_eps
self.rope_theta = rope_theta
self.attention_dropout = attention_dropout
self.mlp_dropout = mlp_dropout
self.embedding_dropout = embedding_dropout
self.xielu_alpha_p_init = xielu_alpha_p_init
self.xielu_alpha_n_init = xielu_alpha_n_init
self.xielu_beta = xielu_beta
self.tie_word_embeddings = tie_word_embeddings
# LaX configuration
self.lax_enabled = lax_enabled
self.lax_gate_type = lax_gate_type
# Canon Layers configuration
self.canon_enabled = canon_enabled
self.canon_kernel_size = canon_kernel_size
self.canon_a_enabled = canon_a_enabled
self.canon_c_enabled = canon_c_enabled
# FANFormer
self.fanformer_p = fanformer_p
# ✅ FIXED: Force complete auto_map in config.json
self.auto_map = {
"AutoConfig": "configuration_unified.UnifiedModelConfig",
"AutoModel": "modeling_unified.UnifiedModel",
"AutoModelForCausalLM": "modeling_unified.UnifiedModel"
}
def to_diff_dict(self):
"""
✅ FIXED: Fuerza la serialización de tie_word_embeddings en config.json
Sobreescribe to_diff_dict() para asegurar que tie_word_embeddings
siempre aparezca en el config.json, evitando problemas de carga
donde HuggingFace no reconoce el weight tying.
Returns:
Dict: Configuración optimizada con tie_word_embeddings forzado
"""
# Obtiene la serialización normal (solo diferencias)
output = super().to_diff_dict()
# ✅ FUERZA la inclusión de tie_word_embeddings
# Esto asegura que aparezca en config.json sin importar si HF
# considera que es "default" o no
output["tie_word_embeddings"] = self.tie_word_embeddings
return output |