本文主要是介绍Vanilla Transformer,希望对大家解决编程问题提供一定的参考价值,需要的开发者们随着小编来一起学习吧!
Vanilla Transformer
flyfish
Vanilla 香草味就是老美的原味,所以vanilla这个单词就是“普通的、原始的、最基础版本”的意思。
冰淇淋有原味的,香草味,的巧克力味的
老美说,不,我们没有原味的的,我们的香草味就是原味的。Vanilla就是 having no special or extra features。就是ordinary
import math
import numpy as np
from typing import Optionalimport torch
import torch.nn as nnfrom neuralforecast.common._modules import (TransEncoderLayer, TransEncoder,TransDecoderLayer, TransDecoder,DataEmbedding, AttentionLayer,
)
from neuralforecast.common._base_windows import BaseWindowsfrom neuralforecast.losses.pytorch import MAE
import numpy as np
import pandas as pd
import pytorch_lightning as pl
import matplotlib.pyplot as pltfrom neuralforecast import NeuralForecast
from neuralforecast.models import MLP
from neuralforecast.losses.pytorch import MQLoss, DistributionLoss
from neuralforecast.tsdataset import TimeSeriesDataset
from neuralforecast.utils import AirPassengers, AirPassengersPanel, AirPassengersStatic, augment_calendar_dfclass TriangularCausalMask():def __init__(self, B, L, device="cpu"):mask_shape = [B, 1, L, L]with torch.no_grad():self._mask = torch.triu(torch.ones(mask_shape, dtype=torch.bool), diagonal=1).to(device)@propertydef mask(self):return self._maskclass FullAttention(nn.Module):def __init__(self, mask_flag=True, scale=None, attention_dropout=0.1, output_attention=False):super(FullAttention, self).__init__()self.scale = scaleself.mask_flag = mask_flagself.output_attention = output_attentionself.dropout = nn.Dropout(attention_dropout)def forward(self, queries, keys, values, attn_mask):B, L, H, E = queries.shape_, S, _, D = values.shapescale = self.scale or 1. / math.sqrt(E)scores = torch.einsum("blhe,bshe->bhls", queries, keys)if self.mask_flag:if attn_mask is None:attn_mask = TriangularCausalMask(B, L, device=queries.device)scores.masked_fill_(attn_mask.mask, -np.inf)A = self.dropout(torch.softmax(scale * scores, dim=-1))V = torch.einsum("bhls,bshd->blhd", A, values)if self.output_attention:return (V.contiguous(), A)else:return (V.contiguous(), None)class VanillaTransformer(BaseWindows):""" VanillaTransformerVanilla Transformer, following implementation of the Informer paper, used as baseline.The architecture has three distinctive features:- Full-attention mechanism with O(L^2) time and memory complexity.- An MLP multi-step decoder that predicts long time-series sequences in a single forward operation rather than step-by-step.The Vanilla Transformer model utilizes a three-component approach to define its embedding:- It employs encoded autoregressive features obtained from a convolution network.- It uses window-relative positional embeddings derived from harmonic functions.- Absolute positional embeddings obtained from calendar features are utilized.*Parameters:*<br>`h`: int, forecast horizon.<br>`input_size`: int, maximum sequence length for truncated train backpropagation. Default -1 uses all history.<br>`futr_exog_list`: str list, future exogenous columns.<br>`hist_exog_list`: str list, historic exogenous columns.<br>`stat_exog_list`: str list, static exogenous columns.<br>`decoder_input_size_multiplier`: float = 0.5, .<br>`hidden_size`: int=128, units of embeddings and encoders.<br>`n_head`: int=4, controls number of multi-head's attention.<br>`dropout`: float (0, 1), dropout throughout Informer architecture.<br>`conv_hidden_size`: int=32, channels of the convolutional encoder.<br>`activation`: str=`GELU`, activation from ['ReLU', 'Softplus', 'Tanh', 'SELU', 'LeakyReLU', 'PReLU', 'Sigmoid', 'GELU'].<br>`encoder_layers`: int=2, number of layers for the TCN encoder.<br>`decoder_layers`: int=1, number of layers for the MLP decoder.<br>`loss`: PyTorch module, instantiated train loss class from [losses collection](https://nixtla.github.io/neuralforecast/losses.pytorch.html).<br>`max_steps`: int=1000, maximum number of training steps.<br>`learning_rate`: float=1e-3, Learning rate between (0, 1).<br>`num_lr_decays`: int=-1, Number of learning rate decays, evenly distributed across max_steps.<br>`early_stop_patience_steps`: int=-1, Number of validation iterations before early stopping.<br>`val_check_steps`: int=100, Number of training steps between every validation loss check.<br>`batch_size`: int=32, number of different series in each batch.<br>`valid_batch_size`: int=None, number of different series in each validation and test batch, if None uses batch_size.<br>`windows_batch_size`: int=1024, number of windows to sample in each training batch, default uses all.<br>`inference_windows_batch_size`: int=1024, number of windows to sample in each inference batch.<br>`start_padding_enabled`: bool=False, if True, the model will pad the time series with zeros at the beginning, by input size.<br>`scaler_type`: str='robust', type of scaler for temporal inputs normalization see [temporal scalers](https://nixtla.github.io/neuralforecast/common.scalers.html).<br>`random_seed`: int=1, random_seed for pytorch initializer and numpy generators.<br>`num_workers_loader`: int=os.cpu_count(), workers to be used by `TimeSeriesDataLoader`.<br>`drop_last_loader`: bool=False, if True `TimeSeriesDataLoader` drops last non-full batch.<br>`alias`: str, optional, Custom name of the model.<br>`optimizer`: Subclass of 'torch.optim.Optimizer', optional, user specified optimizer instead of the default choice (Adam).<br>`optimizer_kwargs`: dict, optional, list of parameters used by the user specified `optimizer`.<br>`**trainer_kwargs`: int, keyword trainer arguments inherited from [PyTorch Lighning's trainer](https://pytorch-lightning.readthedocs.io/en/stable/api/pytorch_lightning.trainer.trainer.Trainer.html?highlight=trainer).<br>*References*<br>- [Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, Wancai Zhang. "Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting"](https://arxiv.org/abs/2012.07436)<br>"""# Class attributesSAMPLING_TYPE = 'windows'def __init__(self,h: int,input_size: int,stat_exog_list=None,hist_exog_list=None,futr_exog_list=None,decoder_input_size_multiplier: float = 0.5,hidden_size: int = 128,dropout: float = 0.05,n_head: int = 4,conv_hidden_size: int = 32,activation: str = 'gelu',encoder_layers: int = 2,decoder_layers: int = 1,loss=MAE(),valid_loss=None,max_steps: int = 5000,learning_rate: float = 1e-4,num_lr_decays: int = -1,early_stop_patience_steps: int = -1,val_check_steps: int = 100,batch_size: int = 32,valid_batch_size: Optional[int] = None,windows_batch_size=1024,inference_windows_batch_size: int = 1024,start_padding_enabled=False,step_size: int = 1,scaler_type: str = 'identity',random_seed: int = 1,num_workers_loader: int = 0,drop_last_loader: bool = False,optimizer=None,optimizer_kwargs=None,**trainer_kwargs):super(VanillaTransformer, self).__init__(h=h,input_size=input_size,hist_exog_list=hist_exog_list,stat_exog_list=stat_exog_list,futr_exog_list=futr_exog_list,loss=loss,valid_loss=valid_loss,max_steps=max_steps,learning_rate=learning_rate,num_lr_decays=num_lr_decays,early_stop_patience_steps=early_stop_patience_steps,val_check_steps=val_check_steps,batch_size=batch_size,valid_batch_size=valid_batch_size,windows_batch_size=windows_batch_size,inference_windows_batch_size=inference_windows_batch_size,start_padding_enabled=start_padding_enabled,step_size=step_size,scaler_type=scaler_type,num_workers_loader=num_workers_loader,drop_last_loader=drop_last_loader,random_seed=random_seed,optimizer=optimizer,optimizer_kwargs=optimizer_kwargs,**trainer_kwargs)# Architectureself.futr_input_size = len(self.futr_exog_list)self.hist_input_size = len(self.hist_exog_list)self.stat_input_size = len(self.stat_exog_list)if self.stat_input_size > 0:raise Exception('VanillaTransformer does not support static variables yet')if self.hist_input_size > 0:raise Exception('VanillaTransformer does not support historical variables yet')self.label_len = int(np.ceil(input_size * decoder_input_size_multiplier))if (self.label_len >= input_size) or (self.label_len <= 0):raise Exception(f'Check decoder_input_size_multiplier={decoder_input_size_multiplier}, range (0,1)')if activation not in ['relu', 'gelu']:raise Exception(f'Check activation={activation}')self.c_out = self.loss.outputsize_multiplierself.output_attention = Falseself.enc_in = 1self.dec_in = 1# Embeddingself.enc_embedding = DataEmbedding(c_in=self.enc_in,exog_input_size=self.hist_input_size,hidden_size=hidden_size,pos_embedding=True,dropout=dropout)self.dec_embedding = DataEmbedding(self.dec_in,exog_input_size=self.hist_input_size,hidden_size=hidden_size,pos_embedding=True,dropout=dropout)# Encoderself.encoder = TransEncoder([TransEncoderLayer(AttentionLayer(FullAttention(mask_flag=False,attention_dropout=dropout,output_attention=self.output_attention),hidden_size, n_head),hidden_size,conv_hidden_size,dropout=dropout,activation=activation) for l in range(encoder_layers)],norm_layer=torch.nn.LayerNorm(hidden_size))# Decoderself.decoder = TransDecoder([TransDecoderLayer(AttentionLayer(FullAttention(mask_flag=True, attention_dropout=dropout, output_attention=False),hidden_size, n_head),AttentionLayer(FullAttention(mask_flag=False, attention_dropout=dropout, output_attention=False),hidden_size, n_head),hidden_size,conv_hidden_size,dropout=dropout,activation=activation,)for l in range(decoder_layers)],norm_layer=torch.nn.LayerNorm(hidden_size),projection=nn.Linear(hidden_size, self.c_out, bias=True))def forward(self, windows_batch):# Parse windows_batchinsample_y = windows_batch['insample_y']# insample_mask = windows_batch['insample_mask']# hist_exog = windows_batch['hist_exog']# stat_exog = windows_batch['stat_exog']futr_exog = windows_batch['futr_exog']insample_y = insample_y.unsqueeze(-1) # [Ws,L,1]if self.futr_input_size > 0:x_mark_enc = futr_exog[:, :self.input_size, :]x_mark_dec = futr_exog[:, -(self.label_len + self.h):, :]else:x_mark_enc = Nonex_mark_dec = Nonex_dec = torch.zeros(size=(len(insample_y), self.h, 1)).to(insample_y.device)x_dec = torch.cat([insample_y[:, -self.label_len:, :], x_dec], dim=1)enc_out = self.enc_embedding(insample_y, x_mark_enc)enc_out, _ = self.encoder(enc_out, attn_mask=None) # attns visualizationdec_out = self.dec_embedding(x_dec, x_mark_dec)dec_out = self.decoder(dec_out, enc_out, x_mask=None,cross_mask=None)forecast = self.loss.domain_map(dec_out[:, -self.h:])return forecastAirPassengersPanel, calendar_cols = augment_calendar_df(df=AirPassengersPanel, freq='M')Y_train_df = AirPassengersPanel[AirPassengersPanel.ds<AirPassengersPanel['ds'].values[-12]] # 132 train
Y_test_df = AirPassengersPanel[AirPassengersPanel.ds>=AirPassengersPanel['ds'].values[-12]].reset_index(drop=True) # 12 testmodel = VanillaTransformer(h=12,input_size=24,hidden_size=16,conv_hidden_size=32,n_head=2,loss=MAE(),futr_exog_list=calendar_cols,scaler_type='robust',learning_rate=1e-3,max_steps=500,val_check_steps=50,early_stop_patience_steps=2)print(model.encoder)
print(model.decoder)print(model.enc_in)
print(model.dec_in)print(model.enc_embedding)
print(model.dec_embedding)
print(model.output_attention)nf = NeuralForecast(models=[model],freq='M'
)
print(nf)
exit(0)nf.fit(df=Y_train_df, static_df=AirPassengersStatic, val_size=12)
forecasts = nf.predict(futr_df=Y_test_df)Y_hat_df = forecasts.reset_index(drop=False).drop(columns=['unique_id','ds'])
plot_df = pd.concat([Y_test_df, Y_hat_df], axis=1)
plot_df = pd.concat([Y_train_df, plot_df])if model.loss.is_distribution_output:plot_df = plot_df[plot_df.unique_id=='Airline1'].drop('unique_id', axis=1)plt.plot(plot_df['ds'], plot_df['y'], c='black', label='True')plt.plot(plot_df['ds'], plot_df['VanillaTransformer-median'], c='blue', label='median')plt.fill_between(x=plot_df['ds'][-12:],y1=plot_df['VanillaTransformer-lo-90'][-12:].values,y2=plot_df['VanillaTransformer-hi-90'][-12:].values,alpha=0.4, label='level 90')plt.grid()plt.legend()plt.plot()
else:plot_df = plot_df[plot_df.unique_id=='Airline1'].drop('unique_id', axis=1)plt.plot(plot_df['ds'], plot_df['y'], c='black', label='True')plt.plot(plot_df['ds'], plot_df['VanillaTransformer'], c='blue', label='Forecast')plt.legend()plt.grid()
结构
TransEncoder((attn_layers): ModuleList((0-1): 2 x TransEncoderLayer((attention): AttentionLayer((inner_attention): FullAttention((dropout): Dropout(p=0.05, inplace=False))(query_projection): Linear(in_features=16, out_features=16, bias=True)(key_projection): Linear(in_features=16, out_features=16, bias=True)(value_projection): Linear(in_features=16, out_features=16, bias=True)(out_projection): Linear(in_features=16, out_features=16, bias=True))(conv1): Conv1d(16, 32, kernel_size=(1,), stride=(1,))(conv2): Conv1d(32, 16, kernel_size=(1,), stride=(1,))(norm1): LayerNorm((16,), eps=1e-05, elementwise_affine=True)(norm2): LayerNorm((16,), eps=1e-05, elementwise_affine=True)(dropout): Dropout(p=0.05, inplace=False)))(norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)
)
TransDecoder((layers): ModuleList((0): TransDecoderLayer((self_attention): AttentionLayer((inner_attention): FullAttention((dropout): Dropout(p=0.05, inplace=False))(query_projection): Linear(in_features=16, out_features=16, bias=True)(key_projection): Linear(in_features=16, out_features=16, bias=True)(value_projection): Linear(in_features=16, out_features=16, bias=True)(out_projection): Linear(in_features=16, out_features=16, bias=True))(cross_attention): AttentionLayer((inner_attention): FullAttention((dropout): Dropout(p=0.05, inplace=False))(query_projection): Linear(in_features=16, out_features=16, bias=True)(key_projection): Linear(in_features=16, out_features=16, bias=True)(value_projection): Linear(in_features=16, out_features=16, bias=True)(out_projection): Linear(in_features=16, out_features=16, bias=True))(conv1): Conv1d(16, 32, kernel_size=(1,), stride=(1,))(conv2): Conv1d(32, 16, kernel_size=(1,), stride=(1,))(norm1): LayerNorm((16,), eps=1e-05, elementwise_affine=True)(norm2): LayerNorm((16,), eps=1e-05, elementwise_affine=True)(norm3): LayerNorm((16,), eps=1e-05, elementwise_affine=True)(dropout): Dropout(p=0.05, inplace=False)))(norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)(projection): Linear(in_features=16, out_features=1, bias=True)
)
1
1
DataEmbedding((value_embedding): TokenEmbedding((tokenConv): Conv1d(1, 16, kernel_size=(3,), stride=(1,), padding=(1,), bias=False, padding_mode=circular))(position_embedding): PositionalEmbedding()(dropout): Dropout(p=0.05, inplace=False)
)
DataEmbedding((value_embedding): TokenEmbedding((tokenConv): Conv1d(1, 16, kernel_size=(3,), stride=(1,), padding=(1,), bias=False, padding_mode=circular))(position_embedding): PositionalEmbedding()(dropout): Dropout(p=0.05, inplace=False)
)
False
<neuralforecast.core.NeuralForecast object at 0x00000219A2B4B280>
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