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from typing import Optional, Tuple
import torch, torch.nn as nn, torch.nn.functional as F
from transformers import (
PretrainedConfig,
PreTrainedModel,
GenerationMixin,
AutoConfig,
AutoModelForCausalLM,
)
from transformers.modeling_outputs import CausalLMOutputWithCrossAttentions
# 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,
seq_len=512,
pred_len=96,
inf_pred_len=96,
max_horizon=96,
moe_n_experts=12,
top_k_experts=5,
moe =1,
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',
auto_regressive= 1,
d_model= 128,
dropout= 0.0,
fft_len= 5000,
freeze_experts= 1,
layer_type= "RLinear",
linear_checkpoints_dir= "checkpoints5",
linear_checkpoints_path= "/cs/azencot_fsas/MoE/",
load_linear = 0,
load_weights =0,
misc_moe = 10,
mlp_gating = 0,
moe_norm = 1,
model_type= "super_linear",
moe_temp = 1,
noisy_gating_std = 0.1,
noisy_gating_std_decay = 1,
torch_dtype = "float32",
transformers_version = "4.40.1",
use_fft = 1,
**kwargs, # any extra CLI args
):
self.seq_len = seq_len
self.moe = moe
self.pred_len = pred_len
self.inf_pred_len = inf_pred_len
self.max_horizon = max_horizon
self.auto_regressive = auto_regressive
self.moe_n_experts = moe_n_experts
self.top_k_experts = top_k_experts
self.freq_experts = freq_experts
self.freeze_experts = freeze_experts
self.layer_type = layer_type
self.linear_checkpoints_path = linear_checkpoints_path
self.linear_checkpoints_dir = linear_checkpoints_dir
self.load_linear = load_linear
self.load_weights = load_weights
self.misc_moe = misc_moe
self.noisy_gating_std = noisy_gating_std
self.noisy_gating_std_decay = noisy_gating_std_decay
self.d_model = d_model
self.mlp_gating = mlp_gating
self.moe_norm = moe_norm
self.moe_temp = moe_temp
self.use_fft = use_fft
self.fft_len = fft_len
self.dropout = dropout
super().__init__(**kwargs)
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