| 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 |
|
|
| |
| |
| |
|
|
|
|
| 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, |
| auto_regressive=1, |
| moe_n_experts=8, |
| top_k_experts=3, |
| moe =1, |
| freq_experts='mean_naive_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_1/96_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', |
| **kwargs, |
| ): |
| 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 = 1 |
| self.layer_type = "RLinear" |
| self.linear_checkpoints_path = '/cs/azencot_fsas/MoE/' |
| self.linear_checkpoints_dir = "checkpoints5" |
| self.load_linear = 0 |
| self.manual_moe = 0 |
| self.misc_moe = 1 |
| self.noisy_gating_std = 0.1 |
| self.noisy_gating_std_decay = 1 |
| self.ker_len = 50 |
| self.con = 0 |
| self.d_model = 512 |
| self.mlp_gating = 1 |
| self.moe_temp = 1 |
| self.use_fft = 1 |
| self.fft_len = 10000 |
| self.dropout = 0.0 |
| super().__init__(**kwargs) |
|
|
