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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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**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 = freeze_experts |
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self.layer_type = layer_type |
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self.linear_checkpoints_path = linear_checkpoints_path |
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self.linear_checkpoints_dir = linear_checkpoints_dir |
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self.load_linear = load_linear |
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self.manual_moe = manual_moe |
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self.misc_moe = misc_moe |
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self.noisy_gating_std = noisy_gating_std |
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self.noisy_gating_std_decay = noisy_gating_std_decay |
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self.ker_len = ker_len |
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self.con = con |
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self.d_model = d_model |
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self.mlp_gating = mlp_gating |
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self.moe_temp = moe_temp |
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self.use_fft = use_fft |
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self.fft_len = fft_len |
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self.dropout = dropout |
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super().__init__(**kwargs) |
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