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from typing import Optional, Tuple
from transformers import PretrainedConfig
# 1) --------------------------------------------------------------------------
# CONFIG
# -----------------------------------------------------------------------------
class SuperLinearConfig(PretrainedConfig):
"""
Configuration for the SuperLinear MoE time–series foundation model.
Only *model_type* must be unique inside transformers; the rest mirrors
the __init__ arguments of your original Config object.
"""
model_type = "super_linear"
def __init__(
self,
# Model architecture parameters
train_seq_len=512,
train_pred_len=96,
# MoE parameters
top_k_experts=12,
noisy_gating_std=0.1,
moe_temp=1.0,
moe_norm=False,
layer_type='RLinear',
comp_moe=12,
freeze_experts=True,
# FFT-based gating parameters
use_fft=True,
fft_len=5000,
# Expert configuration
freq_experts='mean_naive_1/4_1/6_1/7_1/8_1/12_1/14_1/16_1/21_1/24_1/28_1/30_1/32_1/36_1/42_1/48_1/52_1/56_1/60_1/72_1/84_1/90_1/96_1/120_1/144_1/168_1/180_1/224_1/252_1/288_1/336_1/365_1/504_1/672_1/1008_1/1440_1/2016_1/3600',
# Training parameters
resample_long_lookback=False,
# Auto-regressive and horizon parameters
long_horizon_scaling=1,
# Resampling and lookback-based parameters
lookback_resampling=1,
scale_list=[2,4,6],
threshold=0.2,
freq_bound=0.25,
penalty_scale=2.0,
**kwargs,
):
# Model architecture parameters
self.train_seq_len = train_seq_len
self.train_pred_len = train_pred_len
# MoE parameters
self.top_k_experts = top_k_experts
self.noisy_gating_std = noisy_gating_std
self.moe_temp = moe_temp
self.moe_norm = moe_norm
self.layer_type = layer_type
self.comp_moe = comp_moe
self.freeze_experts = freeze_experts
# FFT-based gating parameters
self.use_fft = use_fft
self.fft_len = fft_len
# Expert configuration
self.freq_experts = freq_experts
# Training parameters
self.resample_long_lookback = resample_long_lookback
# Auto-regressive and horizon parameters
self.long_horizon_scaling = long_horizon_scaling
# Resampling and lookback-based parameters
self.lookback_resampling = lookback_resampling
self.scale_list = scale_list
self.threshold = threshold
self.freq_bound = freq_bound
self.penalty_scale = penalty_scale
super().__init__(**kwargs) |