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from typing import Optional, Tuple |
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import torch, torch.nn as nn, torch.nn.functional as F |
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from transformers import ( |
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PretrainedConfig, |
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PreTrainedModel, |
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GenerationMixin, |
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AutoConfig, |
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AutoModelForCausalLM, |
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) |
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from transformers.modeling_outputs import CausalLMOutputWithCrossAttentions |
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class SuperLinearConfig(PretrainedConfig): |
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""" |
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Configuration for the SuperLinear MoE time–series foundation model. |
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Only *model_type* must be unique inside transformers; the rest mirrors |
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the __init__ arguments of your original Config object. |
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""" |
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model_type = "super_linear" |
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def __init__( |
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self, |
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seq_len=512, |
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pred_len=96, |
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inf_pred_len=96, |
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max_horizon=96, |
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auto_regressive=1, |
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moe_n_experts=8, |
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top_k_experts=3, |
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moe =1, |
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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', |
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**kwargs, |
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): |
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self.seq_len = seq_len |
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self.moe = moe |
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self.pred_len = pred_len |
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self.inf_pred_len = inf_pred_len |
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self.max_horizon = max_horizon |
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self.auto_regressive = auto_regressive |
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self.moe_n_experts = moe_n_experts |
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self.top_k_experts = top_k_experts |
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self.freq_experts = freq_experts |
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self.freeze_experts = 1 |
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self.layer_type = "RLinear" |
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self.linear_checkpoints_path = '/cs/azencot_fsas/MoE/' |
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self.linear_checkpoints_dir = "checkpoints5" |
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self.load_linear = 0 |
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self.manual_moe = 0 |
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self.misc_moe = 1 |
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self.noisy_gating_std = 0.1 |
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self.noisy_gating_std_decay = 1 |
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self.ker_len = 50 |
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self.con = 0 |
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self.d_model = 512 |
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self.mlp_gating = 1 |
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self.moe_temp = 1 |
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self.use_fft = 1 |
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self.fft_len = 10000 |
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self.dropout = 0.0 |
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super().__init__(**kwargs) |
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