34 lines
1.3 KiB
Python
34 lines
1.3 KiB
Python
import torch
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from torch import nn
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from layers.Transformer_EncDec import Encoder, EncoderLayer
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from layers.SelfAttention_Family import FullAttention, AttentionLayer
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from layers.Embed import PatchEmbedding
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from transformers import AutoModelForCausalLM
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class Model(nn.Module):
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def __init__(self, configs):
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"""
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patch_len: int, patch len for patch_embedding
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stride: int, stride for patch_embedding
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"""
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super().__init__()
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self.model = AutoModelForCausalLM.from_pretrained('thuml/sundial-base-128m', trust_remote_code=True)
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self.task_name = configs.task_name
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self.seq_len = configs.seq_len
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self.pred_len = configs.pred_len
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def forecast(self, x_enc, x_mark_enc, x_dec, x_mark_dec):
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outputs = []
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for i in range(x_enc.shape[-1]):
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output = self.model.generate(x_enc[...,i], max_new_tokens=self.pred_len, num_samples=20)
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output = output.mean(dim=1)
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outputs.append(output)
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dec_out = torch.stack(outputs, dim=-1)
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return dec_out
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def forward(self, x_enc, x_mark_enc, x_dec, x_mark_dec, mask=None):
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if self.task_name == 'zero_shot_forecast':
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dec_out = self.forecast(x_enc, x_mark_enc, x_dec, x_mark_dec)
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return dec_out
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return None
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