{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#| default_exp models.vanillatransformer" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Vanilla Transformer" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Vanilla Transformer, following implementation of the Informer paper, used as baseline.\n", "\n", "The architecture has three distinctive features:\n", "- Full-attention mechanism with O(L^2) time and memory complexity.\n", "- Classic encoder-decoder proposed by Vaswani et al. (2017) with a multi-head attention mechanism.\n", "- An MLP multi-step decoder that predicts long time-series sequences in a single forward operation rather than step-by-step.\n", "\n", "The Vanilla Transformer model utilizes a three-component approach to define its embedding:\n", "- It employs encoded autoregressive features obtained from a convolution network.\n", "- It uses window-relative positional embeddings derived from harmonic functions.\n", "- Absolute positional embeddings obtained from calendar features are utilized." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**References**
\n", "- [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)
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![Figure 1. Transformer Architecture.](imgs_models/vanilla_transformer.png)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#| export\n", "import math\n", "import numpy as np\n", "from typing import Optional\n", "\n", "import torch\n", "import torch.nn as nn\n", "\n", "from neuralforecast.common._modules import (\n", " TransEncoderLayer, TransEncoder,\n", " TransDecoderLayer, TransDecoder,\n", " DataEmbedding, AttentionLayer,\n", ")\n", "from neuralforecast.common._base_windows import BaseWindows\n", "\n", "from neuralforecast.losses.pytorch import MAE" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#| hide\n", "from fastcore.test import test_eq\n", "from nbdev.showdoc import show_doc" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. Auxiliary Functions" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#| export\n", "class TriangularCausalMask():\n", " def __init__(self, B, L, device=\"cpu\"):\n", " mask_shape = [B, 1, L, L]\n", " with torch.no_grad():\n", " self._mask = torch.triu(torch.ones(mask_shape, dtype=torch.bool), diagonal=1).to(device)\n", "\n", " @property\n", " def mask(self):\n", " return self._mask\n", "\n", "class FullAttention(nn.Module):\n", " def __init__(self, mask_flag=True, scale=None, attention_dropout=0.1, output_attention=False):\n", " super(FullAttention, self).__init__()\n", " self.scale = scale\n", " self.mask_flag = mask_flag\n", " self.output_attention = output_attention\n", " self.dropout = nn.Dropout(attention_dropout)\n", "\n", " def forward(self, queries, keys, values, attn_mask):\n", " B, L, H, E = queries.shape\n", " _, S, _, D = values.shape\n", " scale = self.scale or 1. / math.sqrt(E)\n", "\n", " scores = torch.einsum(\"blhe,bshe->bhls\", queries, keys)\n", " \n", " if self.mask_flag:\n", " if attn_mask is None:\n", " attn_mask = TriangularCausalMask(B, L, device=queries.device)\n", "\n", " scores.masked_fill_(attn_mask.mask, -np.inf)\n", "\n", " A = self.dropout(torch.softmax(scale * scores, dim=-1))\n", " V = torch.einsum(\"bhls,bshd->blhd\", A, values)\n", "\n", " if self.output_attention:\n", " return (V.contiguous(), A)\n", " else:\n", " return (V.contiguous(), None)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2. VanillaTransformer" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#| export\n", "class VanillaTransformer(BaseWindows):\n", " \"\"\" VanillaTransformer\n", "\n", " Vanilla Transformer, following implementation of the Informer paper, used as baseline.\n", "\n", " The architecture has three distinctive features:\n", " - Full-attention mechanism with O(L^2) time and memory complexity.\n", " - An MLP multi-step decoder that predicts long time-series sequences in a single forward operation rather than step-by-step.\n", "\n", " The Vanilla Transformer model utilizes a three-component approach to define its embedding:\n", " - It employs encoded autoregressive features obtained from a convolution network.\n", " - It uses window-relative positional embeddings derived from harmonic functions.\n", " - Absolute positional embeddings obtained from calendar features are utilized.\n", "\n", " *Parameters:*
\n", " `h`: int, forecast horizon.
\n", " `input_size`: int, maximum sequence length for truncated train backpropagation. Default -1 uses all history.
\n", " `futr_exog_list`: str list, future exogenous columns.
\n", " `hist_exog_list`: str list, historic exogenous columns.
\n", " `stat_exog_list`: str list, static exogenous columns.
\n", "\t`decoder_input_size_multiplier`: float = 0.5, .
\n", " `hidden_size`: int=128, units of embeddings and encoders.
\n", " `n_head`: int=4, controls number of multi-head's attention.
\n", " `dropout`: float (0, 1), dropout throughout Informer architecture.
\n", "\t`conv_hidden_size`: int=32, channels of the convolutional encoder.
\n", "\t`activation`: str=`GELU`, activation from ['ReLU', 'Softplus', 'Tanh', 'SELU', 'LeakyReLU', 'PReLU', 'Sigmoid', 'GELU'].
\n", " `encoder_layers`: int=2, number of layers for the TCN encoder.
\n", " `decoder_layers`: int=1, number of layers for the MLP decoder.
\n", " `loss`: PyTorch module, instantiated train loss class from [losses collection](https://nixtla.github.io/neuralforecast/losses.pytorch.html).
\n", " `max_steps`: int=1000, maximum number of training steps.
\n", " `learning_rate`: float=1e-3, Learning rate between (0, 1).
\n", " `num_lr_decays`: int=-1, Number of learning rate decays, evenly distributed across max_steps.
\n", " `early_stop_patience_steps`: int=-1, Number of validation iterations before early stopping.
\n", " `val_check_steps`: int=100, Number of training steps between every validation loss check.
\n", " `batch_size`: int=32, number of different series in each batch.
\n", " `valid_batch_size`: int=None, number of different series in each validation and test batch, if None uses batch_size.
\n", " `windows_batch_size`: int=1024, number of windows to sample in each training batch, default uses all.
\n", " `inference_windows_batch_size`: int=1024, number of windows to sample in each inference batch.
\n", " `start_padding_enabled`: bool=False, if True, the model will pad the time series with zeros at the beginning, by input size.
\n", " `scaler_type`: str='robust', type of scaler for temporal inputs normalization see [temporal scalers](https://nixtla.github.io/neuralforecast/common.scalers.html).
\n", " `random_seed`: int=1, random_seed for pytorch initializer and numpy generators.
\n", " `num_workers_loader`: int=os.cpu_count(), workers to be used by `TimeSeriesDataLoader`.
\n", " `drop_last_loader`: bool=False, if True `TimeSeriesDataLoader` drops last non-full batch.
\n", " `alias`: str, optional, Custom name of the model.
\n", " `optimizer`: Subclass of 'torch.optim.Optimizer', optional, user specified optimizer instead of the default choice (Adam).
\n", " `optimizer_kwargs`: dict, optional, list of parameters used by the user specified `optimizer`.
\n", " `**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).
\n", "\n", "\t*References*
\n", "\t- [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)
\n", " \"\"\"\n", " # Class attributes\n", " SAMPLING_TYPE = 'windows'\n", "\n", " def __init__(self,\n", " h: int, \n", " input_size: int,\n", " stat_exog_list = None,\n", " hist_exog_list = None,\n", " futr_exog_list = None,\n", " decoder_input_size_multiplier: float = 0.5,\n", " hidden_size: int = 128, \n", " dropout: float = 0.05,\n", " n_head: int = 4,\n", " conv_hidden_size: int = 32,\n", " activation: str = 'gelu',\n", " encoder_layers: int = 2, \n", " decoder_layers: int = 1, \n", " loss = MAE(),\n", " valid_loss = None,\n", " max_steps: int = 5000,\n", " learning_rate: float = 1e-4,\n", " num_lr_decays: int = -1,\n", " early_stop_patience_steps: int =-1,\n", " val_check_steps: int = 100,\n", " batch_size: int = 32,\n", " valid_batch_size: Optional[int] = None,\n", " windows_batch_size = 1024,\n", " inference_windows_batch_size: int = 1024,\n", " start_padding_enabled = False,\n", " step_size: int = 1,\n", " scaler_type: str = 'identity',\n", " random_seed: int = 1,\n", " num_workers_loader: int = 0,\n", " drop_last_loader: bool = False,\n", " optimizer = None,\n", " optimizer_kwargs = None,\n", " **trainer_kwargs):\n", " super(VanillaTransformer, self).__init__(h=h,\n", " input_size=input_size,\n", " hist_exog_list=hist_exog_list,\n", " stat_exog_list=stat_exog_list,\n", " futr_exog_list = futr_exog_list,\n", " loss=loss,\n", " valid_loss=valid_loss,\n", " max_steps=max_steps,\n", " learning_rate=learning_rate,\n", " num_lr_decays=num_lr_decays,\n", " early_stop_patience_steps=early_stop_patience_steps,\n", " val_check_steps=val_check_steps,\n", " batch_size=batch_size,\n", " valid_batch_size=valid_batch_size,\n", " windows_batch_size=windows_batch_size,\n", " inference_windows_batch_size=inference_windows_batch_size,\n", " start_padding_enabled=start_padding_enabled,\n", " step_size=step_size,\n", " scaler_type=scaler_type,\n", " num_workers_loader=num_workers_loader,\n", " drop_last_loader=drop_last_loader,\n", " random_seed=random_seed,\n", " optimizer=optimizer,\n", " optimizer_kwargs=optimizer_kwargs,\n", " **trainer_kwargs)\n", "\n", " # Architecture\n", " self.futr_input_size = len(self.futr_exog_list)\n", " self.hist_input_size = len(self.hist_exog_list)\n", " self.stat_input_size = len(self.stat_exog_list)\n", "\n", " if self.stat_input_size > 0:\n", " raise Exception('VanillaTransformer does not support static variables yet')\n", " \n", " if self.hist_input_size > 0:\n", " raise Exception('VanillaTransformer does not support historical variables yet')\n", "\n", " self.label_len = int(np.ceil(input_size * decoder_input_size_multiplier))\n", " if (self.label_len >= input_size) or (self.label_len <= 0):\n", " raise Exception(f'Check decoder_input_size_multiplier={decoder_input_size_multiplier}, range (0,1)')\n", "\n", " if activation not in ['relu', 'gelu']:\n", " raise Exception(f'Check activation={activation}')\n", " \n", " self.c_out = self.loss.outputsize_multiplier\n", " self.output_attention = False\n", " self.enc_in = 1 \n", " self.dec_in = 1\n", "\n", " # Embedding\n", " self.enc_embedding = DataEmbedding(c_in=self.enc_in,\n", " exog_input_size=self.hist_input_size,\n", " hidden_size=hidden_size, \n", " pos_embedding=True,\n", " dropout=dropout)\n", " self.dec_embedding = DataEmbedding(self.dec_in,\n", " exog_input_size=self.hist_input_size,\n", " hidden_size=hidden_size, \n", " pos_embedding=True,\n", " dropout=dropout)\n", "\n", " # Encoder\n", " self.encoder = TransEncoder(\n", " [\n", " TransEncoderLayer(\n", " AttentionLayer(\n", " FullAttention(mask_flag=False,\n", " attention_dropout=dropout,\n", " output_attention=self.output_attention),\n", " hidden_size, n_head),\n", " hidden_size,\n", " conv_hidden_size,\n", " dropout=dropout,\n", " activation=activation\n", " ) for l in range(encoder_layers)\n", " ],\n", " norm_layer=torch.nn.LayerNorm(hidden_size)\n", " )\n", " # Decoder\n", " self.decoder = TransDecoder(\n", " [\n", " TransDecoderLayer(\n", " AttentionLayer(\n", " FullAttention(mask_flag=True, attention_dropout=dropout, output_attention=False),\n", " hidden_size, n_head),\n", " AttentionLayer(\n", " FullAttention(mask_flag=False, attention_dropout=dropout, output_attention=False),\n", " hidden_size, n_head),\n", " hidden_size,\n", " conv_hidden_size,\n", " dropout=dropout,\n", " activation=activation,\n", " )\n", " for l in range(decoder_layers)\n", " ],\n", " norm_layer=torch.nn.LayerNorm(hidden_size),\n", " projection=nn.Linear(hidden_size, self.c_out, bias=True)\n", " )\n", "\n", " def forward(self, windows_batch):\n", " # Parse windows_batch\n", " insample_y = windows_batch['insample_y']\n", " #insample_mask = windows_batch['insample_mask']\n", " #hist_exog = windows_batch['hist_exog']\n", " #stat_exog = windows_batch['stat_exog']\n", "\n", " futr_exog = windows_batch['futr_exog']\n", "\n", " insample_y = insample_y.unsqueeze(-1) # [Ws,L,1]\n", "\n", " if self.futr_input_size > 0:\n", " x_mark_enc = futr_exog[:,:self.input_size,:]\n", " x_mark_dec = futr_exog[:,-(self.label_len+self.h):,:]\n", " else:\n", " x_mark_enc = None\n", " x_mark_dec = None\n", "\n", " x_dec = torch.zeros(size=(len(insample_y),self.h,1), device=insample_y.device)\n", " x_dec = torch.cat([insample_y[:,-self.label_len:,:], x_dec], dim=1)\n", "\n", " enc_out = self.enc_embedding(insample_y, x_mark_enc)\n", " enc_out, _ = self.encoder(enc_out, attn_mask=None) # attns visualization\n", "\n", " dec_out = self.dec_embedding(x_dec, x_mark_dec)\n", " dec_out = self.decoder(dec_out, enc_out, x_mask=None, \n", " cross_mask=None)\n", "\n", " forecast = self.loss.domain_map(dec_out[:, -self.h:])\n", " return forecast" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "show_doc(VanillaTransformer)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "show_doc(VanillaTransformer.fit, name='VanillaTransformer.fit')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "show_doc(VanillaTransformer.predict, name='VanillaTransformer.predict')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Usage Example" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#| eval: false\n", "import numpy as np\n", "import pandas as pd\n", "import pytorch_lightning as pl\n", "import matplotlib.pyplot as plt\n", "\n", "from neuralforecast import NeuralForecast\n", "from neuralforecast.models import MLP\n", "from neuralforecast.losses.pytorch import MQLoss, DistributionLoss\n", "from neuralforecast.tsdataset import TimeSeriesDataset\n", "from neuralforecast.utils import AirPassengers, AirPassengersPanel, AirPassengersStatic, augment_calendar_df\n", "\n", "AirPassengersPanel, calendar_cols = augment_calendar_df(df=AirPassengersPanel, freq='M')\n", "\n", "Y_train_df = AirPassengersPanel[AirPassengersPanel.ds=AirPassengersPanel['ds'].values[-12]].reset_index(drop=True) # 12 test\n", "\n", "model = VanillaTransformer(h=12,\n", " input_size=24,\n", " hidden_size=16,\n", " conv_hidden_size=32,\n", " n_head=2,\n", " loss=MAE(),\n", " futr_exog_list=calendar_cols,\n", " scaler_type='robust',\n", " learning_rate=1e-3,\n", " max_steps=500,\n", " val_check_steps=50,\n", " early_stop_patience_steps=2)\n", "\n", "nf = NeuralForecast(\n", " models=[model],\n", " freq='M'\n", ")\n", "nf.fit(df=Y_train_df, static_df=AirPassengersStatic, val_size=12)\n", "forecasts = nf.predict(futr_df=Y_test_df)\n", "\n", "Y_hat_df = forecasts.reset_index(drop=False).drop(columns=['unique_id','ds'])\n", "plot_df = pd.concat([Y_test_df, Y_hat_df], axis=1)\n", "plot_df = pd.concat([Y_train_df, plot_df])\n", "\n", "if model.loss.is_distribution_output:\n", " plot_df = plot_df[plot_df.unique_id=='Airline1'].drop('unique_id', axis=1)\n", " plt.plot(plot_df['ds'], plot_df['y'], c='black', label='True')\n", " plt.plot(plot_df['ds'], plot_df['VanillaTransformer-median'], c='blue', label='median')\n", " plt.fill_between(x=plot_df['ds'][-12:], \n", " y1=plot_df['VanillaTransformer-lo-90'][-12:].values, \n", " y2=plot_df['VanillaTransformer-hi-90'][-12:].values,\n", " alpha=0.4, label='level 90')\n", " plt.grid()\n", " plt.legend()\n", " plt.plot()\n", "else:\n", " plot_df = plot_df[plot_df.unique_id=='Airline1'].drop('unique_id', axis=1)\n", " plt.plot(plot_df['ds'], plot_df['y'], c='black', label='True')\n", " plt.plot(plot_df['ds'], plot_df['VanillaTransformer'], c='blue', label='Forecast')\n", " plt.legend()\n", " plt.grid()" ] } ], "metadata": { "kernelspec": { "display_name": "python3", "language": "python", "name": "python3" } }, "nbformat": 4, "nbformat_minor": 4 }