PatchTST.py

import torch
import torch.nn as nn
import math
from torch import Tensor
import torch.nn.functional as F
import numpy as np
from typing import Callable, Optional

class RevIN(nn.Module):
    def __init__(self, num_features: int, eps=1e-5, affine=True, subtract_last=False):
        """
        :param num_features: the number of features or channels
        :param eps: a value added for numerical stability
        :param affine: if True, RevIN has learnable affine parameters
        """
        super(RevIN, self).__init__()
        self.num_features = num_features
        self.eps = eps
        self.affine = affine
        self.subtract_last = subtract_last
        if self.affine:
            self._init_params()

    def forward(self, x, mode:str):
        if mode == 'norm':
            self._get_statistics(x)
            x = self._normalize(x)
        elif mode == 'denorm':
            x = self._denormalize(x)
        else: raise NotImplementedError
        return x

    def _init_params(self):
        # initialize RevIN params: (C,)
        self.affine_weight = nn.Parameter(torch.ones(self.num_features))
        self.affine_bias = nn.Parameter(torch.zeros(self.num_features))

    def _get_statistics(self, x):
        dim2reduce = tuple(range(1, x.ndim-1))
        if self.subtract_last:
            self.last = x[:,-1,:].unsqueeze(1)
        else:
            self.mean = torch.mean(x, dim=dim2reduce, keepdim=True).detach()
        self.stdev = torch.sqrt(torch.var(x, dim=dim2reduce, keepdim=True, unbiased=False) + self.eps).detach()

    def _normalize(self, x):
        if self.subtract_last:
            x = x - self.last
        else:
            x = x - self.mean
        x = x / self.stdev
        if self.affine:
            x = x * self.affine_weight
            x = x + self.affine_bias
        return x

    def _denormalize(self, x):
        if self.affine:
            x = x - self.affine_bias
            x = x / (self.affine_weight + self.eps*self.eps)
        x = x * self.stdev
        if self.subtract_last:
            x = x + self.last
        else:
            x = x + self.mean
        return x
    
class Transpose(nn.Module):
    def __init__(self, *dims, contiguous=False): 
        super().__init__()
        self.dims, self.contiguous = dims, contiguous
    def forward(self, x):
        if self.contiguous: return x.transpose(*self.dims).contiguous()
        else: return x.transpose(*self.dims)

    
def get_activation_fn(activation):
    if callable(activation): return activation()
    elif activation.lower() == "relu": return nn.ReLU()
    elif activation.lower() == "gelu": return nn.GELU()
    raise ValueError(f'{activation} is not available. You can use "relu", "gelu", or a callable') 
    
    
# decomposition

class moving_avg(nn.Module):
    """
    Moving average block to highlight the trend of time series
    """
    def __init__(self, kernel_size, stride):
        super(moving_avg, self).__init__()
        self.kernel_size = kernel_size
        self.avg = nn.AvgPool1d(kernel_size=kernel_size, stride=stride, padding=0)

    def forward(self, x):
        # padding on the both ends of time series
        front = x[:, 0:1, :].repeat(1, (self.kernel_size - 1) // 2, 1)
        end = x[:, -1:, :].repeat(1, (self.kernel_size - 1) // 2, 1)
        x = torch.cat([front, x, end], dim=1)
        x = self.avg(x.permute(0, 2, 1))
        x = x.permute(0, 2, 1)
        return x


class series_decomp(nn.Module):
    """
    Series decomposition block
    """
    def __init__(self, kernel_size):
        super(series_decomp, self).__init__()
        self.moving_avg = moving_avg(kernel_size, stride=1)

    def forward(self, x):
        moving_mean = self.moving_avg(x)
        res = x - moving_mean
        return res, moving_mean
    
# pos_encoding

def PositionalEncoding(q_len, d_model, normalize=True):
    pe = torch.zeros(q_len, d_model)
    position = torch.arange(0, q_len).unsqueeze(1)
    div_term = torch.exp(torch.arange(0, d_model, 2) * -(math.log(10000.0) / d_model))
    pe[:, 0::2] = torch.sin(position * div_term)
    pe[:, 1::2] = torch.cos(position * div_term)
    if normalize:
        pe = pe - pe.mean()
        pe = pe / (pe.std() * 10)
    return pe

SinCosPosEncoding = PositionalEncoding

def Coord2dPosEncoding(q_len, d_model, exponential=False, normalize=True, eps=1e-3, verbose=False):
    x = .5 if exponential else 1
    i = 0
    for i in range(100):
        cpe = 2 * (torch.linspace(0, 1, q_len).reshape(-1, 1) ** x) * (torch.linspace(0, 1, d_model).reshape(1, -1) ** x) - 1
        # pv(f'{i:4.0f}  {x:5.3f}  {cpe.mean():+6.3f}', verbose) # WTF is this pv???
        print(f'{i:4.0f}  {x:5.3f}  {cpe.mean():+6.3f}', verbose) # maybe this?
        if abs(cpe.mean()) <= eps: break
        elif cpe.mean() > eps: x += .001
        else: x -= .001
        i += 1
    if normalize:
        cpe = cpe - cpe.mean()
        cpe = cpe / (cpe.std() * 10)
    return cpe

def Coord1dPosEncoding(q_len, exponential=False, normalize=True):
    cpe = (2 * (torch.linspace(0, 1, q_len).reshape(-1, 1)**(.5 if exponential else 1)) - 1)
    if normalize:
        cpe = cpe - cpe.mean()
        cpe = cpe / (cpe.std() * 10)
    return cpe

def positional_encoding(pe, learn_pe, q_len, d_model):
    # Positional encoding
    if pe == None:
        W_pos = torch.empty((q_len, d_model)) # pe = None and learn_pe = False can be used to measure impact of pe
        nn.init.uniform_(W_pos, -0.02, 0.02)
        learn_pe = False
    elif pe == 'zero':
        W_pos = torch.empty((q_len, 1))
        nn.init.uniform_(W_pos, -0.02, 0.02)
    elif pe == 'zeros':
        W_pos = torch.empty((q_len, d_model))
        nn.init.uniform_(W_pos, -0.02, 0.02)
    elif pe == 'normal' or pe == 'gauss':
        W_pos = torch.zeros((q_len, 1))
        torch.nn.init.normal_(W_pos, mean=0.0, std=0.1)
    elif pe == 'uniform':
        W_pos = torch.zeros((q_len, 1))
        nn.init.uniform_(W_pos, a=0.0, b=0.1)
    elif pe == 'lin1d': W_pos = Coord1dPosEncoding(q_len, exponential=False, normalize=True)
    elif pe == 'exp1d': W_pos = Coord1dPosEncoding(q_len, exponential=True, normalize=True)
    elif pe == 'lin2d': W_pos = Coord2dPosEncoding(q_len, d_model, exponential=False, normalize=True)
    elif pe == 'exp2d': W_pos = Coord2dPosEncoding(q_len, d_model, exponential=True, normalize=True)
    elif pe == 'sincos': W_pos = PositionalEncoding(q_len, d_model, normalize=True)
    else: raise ValueError(f"{pe} is not a valid pe (positional encoder. Available types: 'gauss'=='normal', \
        'zeros', 'zero', uniform', 'lin1d', 'exp1d', 'lin2d', 'exp2d', 'sincos', None.)")
    return nn.Parameter(W_pos, requires_grad=learn_pe)

# Cell
class PatchTST_backbone(nn.Module):
    def __init__(self, c_in:int, context_window:int, target_window:int, patch_len:int, stride:int, max_seq_len:Optional[int]=1024, 
                 n_layers:int=3, d_model=128, n_heads=16, d_k:Optional[int]=None, d_v:Optional[int]=None,
                 d_ff:int=256, norm:str='BatchNorm', attn_dropout:float=0., dropout:float=0., act:str="gelu", key_padding_mask:bool='auto',
                 padding_var:Optional[int]=None, attn_mask:Optional[Tensor]=None, res_attention:bool=True, pre_norm:bool=False, store_attn:bool=False,
                 pe:str='zeros', learn_pe:bool=True, fc_dropout:float=0., head_dropout = 0, padding_patch = None,
                 pretrain_head:bool=False, head_type = 'flatten', individual = False, revin = True, affine = True, subtract_last = False,
                 verbose:bool=False, **kwargs):
        
        super().__init__()
        
        # RevIn
        self.revin = revin
        if self.revin: self.revin_layer = RevIN(c_in, affine=affine, subtract_last=subtract_last)
        
        # Patching
        self.patch_len = patch_len
        self.stride = stride
        self.padding_patch = padding_patch
        patch_num = int((context_window - patch_len)/stride + 1)
        if padding_patch == 'end': # can be modified to general case
            self.padding_patch_layer = nn.ReplicationPad1d((0, stride)) 
            patch_num += 1
        
        # Backbone 
        self.backbone = TSTiEncoder(c_in, patch_num=patch_num, patch_len=patch_len, max_seq_len=max_seq_len,
                                n_layers=n_layers, d_model=d_model, n_heads=n_heads, d_k=d_k, d_v=d_v, d_ff=d_ff,
                                attn_dropout=attn_dropout, dropout=dropout, act=act, key_padding_mask=key_padding_mask, padding_var=padding_var,
                                attn_mask=attn_mask, res_attention=res_attention, pre_norm=pre_norm, store_attn=store_attn,
                                pe=pe, learn_pe=learn_pe, verbose=verbose, **kwargs)

        # Head
        self.head_nf = d_model * patch_num
        self.n_vars = c_in
        self.pretrain_head = pretrain_head
        self.head_type = head_type
        self.individual = individual

        if self.pretrain_head: 
            self.head = self.create_pretrain_head(self.head_nf, c_in, fc_dropout) # custom head passed as a partial func with all its kwargs
        elif head_type == 'flatten': 
            self.head = Flatten_Head(self.individual, self.n_vars, self.head_nf, target_window, head_dropout=head_dropout)
        
    
    def forward(self, z):                                                                   # z: [bs x nvars x seq_len]
        # norm
        if self.revin: 
            z = z.permute(0,2,1)
            z = self.revin_layer(z, 'norm')
            z = z.permute(0,2,1)
            
        # do patching
        if self.padding_patch == 'end':
            z = self.padding_patch_layer(z)
        z = z.unfold(dimension=-1, size=self.patch_len, step=self.stride)                   # z: [bs x nvars x patch_num x patch_len]
        z = z.permute(0,1,3,2)                                                              # z: [bs x nvars x patch_len x patch_num]
        
        # model
        z = self.backbone(z)                                                                # z: [bs x nvars x d_model x patch_num]
        z = self.head(z)                                                                    # z: [bs x nvars x target_window] 
        
        # denorm
        if self.revin: 
            z = z.permute(0,2,1)
            z = self.revin_layer(z, 'denorm')
            z = z.permute(0,2,1)
        return z
    
    def create_pretrain_head(self, head_nf, vars, dropout):
        return nn.Sequential(nn.Dropout(dropout),
                    nn.Conv1d(head_nf, vars, 1)
                    )


class Flatten_Head(nn.Module):
    def __init__(self, individual, n_vars, nf, target_window, head_dropout=0):
        super().__init__()
        
        self.individual = individual
        self.n_vars = n_vars
        
        if self.individual:
            self.linears = nn.ModuleList()
            self.dropouts = nn.ModuleList()
            self.flattens = nn.ModuleList()
            for i in range(self.n_vars):
                self.flattens.append(nn.Flatten(start_dim=-2))
                self.linears.append(nn.Linear(nf, target_window))
                self.dropouts.append(nn.Dropout(head_dropout))
        else:
            self.flatten = nn.Flatten(start_dim=-2)
            self.linear = nn.Linear(nf, target_window)
            self.dropout = nn.Dropout(head_dropout)
            
    def forward(self, x):                                 # x: [bs x nvars x d_model x patch_num]
        if self.individual:
            x_out = []
            for i in range(self.n_vars):
                z = self.flattens[i](x[:,i,:,:])          # z: [bs x d_model * patch_num]
                z = self.linears[i](z)                    # z: [bs x target_window]
                z = self.dropouts[i](z)
                x_out.append(z)
            x = torch.stack(x_out, dim=1)                 # x: [bs x nvars x target_window]
        else:
            x = self.flatten(x)
            x = self.linear(x)
            x = self.dropout(x)
        return x
        
        
    
    
class TSTiEncoder(nn.Module):  #i means channel-independent
    def __init__(self, c_in, patch_num, patch_len, max_seq_len=1024,
                 n_layers=3, d_model=128, n_heads=16, d_k=None, d_v=None,
                 d_ff=256, norm='BatchNorm', attn_dropout=0., dropout=0., act="gelu", store_attn=False,
                 key_padding_mask='auto', padding_var=None, attn_mask=None, res_attention=True, pre_norm=False,
                 pe='zeros', learn_pe=True, verbose=False, **kwargs):
        
        
        super().__init__()
        
        self.patch_num = patch_num
        self.patch_len = patch_len
        
        # Input encoding
        q_len = patch_num
        self.W_P = nn.Linear(patch_len, d_model)        # Eq 1: projection of feature vectors onto a d-dim vector space
        self.seq_len = q_len

        # Positional encoding
        self.W_pos = positional_encoding(pe, learn_pe, q_len, d_model)

        # Residual dropout
        self.dropout = nn.Dropout(dropout)

        # Encoder
        self.encoder = TSTEncoder(q_len, d_model, n_heads, d_k=d_k, d_v=d_v, d_ff=d_ff, norm=norm, attn_dropout=attn_dropout, dropout=dropout,
                                   pre_norm=pre_norm, activation=act, res_attention=res_attention, n_layers=n_layers, store_attn=store_attn)

        
    def forward(self, x) -> Tensor:                                              # x: [bs x nvars x patch_len x patch_num]
        
        n_vars = x.shape[1]
        # Input encoding
        x = x.permute(0,1,3,2)                                                   # x: [bs x nvars x patch_num x patch_len]
        x = self.W_P(x)                                                          # x: [bs x nvars x patch_num x d_model]

        u = torch.reshape(x, (x.shape[0]*x.shape[1],x.shape[2],x.shape[3]))      # u: [bs * nvars x patch_num x d_model]
        u = self.dropout(u + self.W_pos)                                         # u: [bs * nvars x patch_num x d_model]

        # Encoder
        z = self.encoder(u)                                                      # z: [bs * nvars x patch_num x d_model]
        z = torch.reshape(z, (-1,n_vars,z.shape[-2],z.shape[-1]))                # z: [bs x nvars x patch_num x d_model]
        z = z.permute(0,1,3,2)                                                   # z: [bs x nvars x d_model x patch_num]
        
        return z    
            
            
    
# Cell
class TSTEncoder(nn.Module):
    def __init__(self, q_len, d_model, n_heads, d_k=None, d_v=None, d_ff=None, 
                        norm='BatchNorm', attn_dropout=0., dropout=0., activation='gelu',
                        res_attention=False, n_layers=1, pre_norm=False, store_attn=False):
        super().__init__()

        self.layers = nn.ModuleList([TSTEncoderLayer(q_len, d_model, n_heads=n_heads, d_k=d_k, d_v=d_v, d_ff=d_ff, norm=norm,
                                                      attn_dropout=attn_dropout, dropout=dropout,
                                                      activation=activation, res_attention=res_attention,
                                                      pre_norm=pre_norm, store_attn=store_attn) for i in range(n_layers)])
        self.res_attention = res_attention

    def forward(self, src:Tensor, key_padding_mask:Optional[Tensor]=None, attn_mask:Optional[Tensor]=None):
        output = src
        scores = None
        if self.res_attention:
            for mod in self.layers: output, scores = mod(output, prev=scores, key_padding_mask=key_padding_mask, attn_mask=attn_mask)
            return output
        else:
            for mod in self.layers: output = mod(output, key_padding_mask=key_padding_mask, attn_mask=attn_mask)
            return output



class TSTEncoderLayer(nn.Module):
    def __init__(self, q_len, d_model, n_heads, d_k=None, d_v=None, d_ff=256, store_attn=False,
                 norm='BatchNorm', attn_dropout=0, dropout=0., bias=True, activation="gelu", res_attention=False, pre_norm=False):
        super().__init__()
        assert not d_model%n_heads, f"d_model ({d_model}) must be divisible by n_heads ({n_heads})"
        d_k = d_model // n_heads if d_k is None else d_k
        d_v = d_model // n_heads if d_v is None else d_v

        # Multi-Head attention
        self.res_attention = res_attention
        self.self_attn = _MultiheadAttention(d_model, n_heads, d_k, d_v, attn_dropout=attn_dropout, proj_dropout=dropout, res_attention=res_attention)

        # Add & Norm
        self.dropout_attn = nn.Dropout(dropout)
        if "batch" in norm.lower():
            self.norm_attn = nn.Sequential(Transpose(1,2), nn.BatchNorm1d(d_model), Transpose(1,2))
        else:
            self.norm_attn = nn.LayerNorm(d_model)

        # Position-wise Feed-Forward
        self.ff = nn.Sequential(nn.Linear(d_model, d_ff, bias=bias),
                                get_activation_fn(activation),
                                nn.Dropout(dropout),
                                nn.Linear(d_ff, d_model, bias=bias))

        # Add & Norm
        self.dropout_ffn = nn.Dropout(dropout)
        if "batch" in norm.lower():
            self.norm_ffn = nn.Sequential(Transpose(1,2), nn.BatchNorm1d(d_model), Transpose(1,2))
        else:
            self.norm_ffn = nn.LayerNorm(d_model)

        self.pre_norm = pre_norm
        self.store_attn = store_attn


    def forward(self, src:Tensor, prev:Optional[Tensor]=None, key_padding_mask:Optional[Tensor]=None, attn_mask:Optional[Tensor]=None) -> Tensor:

        # Multi-Head attention sublayer
        if self.pre_norm:
            src = self.norm_attn(src)
        ## Multi-Head attention
        if self.res_attention:
            src2, attn, scores = self.self_attn(src, src, src, prev, key_padding_mask=key_padding_mask, attn_mask=attn_mask)
        else:
            src2, attn = self.self_attn(src, src, src, key_padding_mask=key_padding_mask, attn_mask=attn_mask)
        if self.store_attn:
            self.attn = attn
        ## Add & Norm
        src = src + self.dropout_attn(src2) # Add: residual connection with residual dropout
        if not self.pre_norm:
            src = self.norm_attn(src)

        # Feed-forward sublayer
        if self.pre_norm:
            src = self.norm_ffn(src)
        ## Position-wise Feed-Forward
        src2 = self.ff(src)
        ## Add & Norm
        src = src + self.dropout_ffn(src2) # Add: residual connection with residual dropout
        if not self.pre_norm:
            src = self.norm_ffn(src)

        if self.res_attention:
            return src, scores
        else:
            return src




class _MultiheadAttention(nn.Module):
    def __init__(self, d_model, n_heads, d_k=None, d_v=None, res_attention=False, attn_dropout=0., proj_dropout=0., qkv_bias=True, lsa=False):
        """Multi Head Attention Layer
        Input shape:
            Q:       [batch_size (bs) x max_q_len x d_model]
            K, V:    [batch_size (bs) x q_len x d_model]
            mask:    [q_len x q_len]
        """
        super().__init__()
        d_k = d_model // n_heads if d_k is None else d_k
        d_v = d_model // n_heads if d_v is None else d_v

        self.n_heads, self.d_k, self.d_v = n_heads, d_k, d_v

        self.W_Q = nn.Linear(d_model, d_k * n_heads, bias=qkv_bias)
        self.W_K = nn.Linear(d_model, d_k * n_heads, bias=qkv_bias)
        self.W_V = nn.Linear(d_model, d_v * n_heads, bias=qkv_bias)

        # Scaled Dot-Product Attention (multiple heads)
        self.res_attention = res_attention
        self.sdp_attn = _ScaledDotProductAttention(d_model, n_heads, attn_dropout=attn_dropout, res_attention=self.res_attention, lsa=lsa)

        # Poject output
        self.to_out = nn.Sequential(nn.Linear(n_heads * d_v, d_model), nn.Dropout(proj_dropout))


    def forward(self, Q:Tensor, K:Optional[Tensor]=None, V:Optional[Tensor]=None, prev:Optional[Tensor]=None,
                key_padding_mask:Optional[Tensor]=None, attn_mask:Optional[Tensor]=None):

        bs = Q.size(0)
        if K is None: K = Q
        if V is None: V = Q

        # Linear (+ split in multiple heads)
        q_s = self.W_Q(Q).view(bs, -1, self.n_heads, self.d_k).transpose(1,2)       # q_s    : [bs x n_heads x max_q_len x d_k]
        k_s = self.W_K(K).view(bs, -1, self.n_heads, self.d_k).permute(0,2,3,1)     # k_s    : [bs x n_heads x d_k x q_len] - transpose(1,2) + transpose(2,3)
        v_s = self.W_V(V).view(bs, -1, self.n_heads, self.d_v).transpose(1,2)       # v_s    : [bs x n_heads x q_len x d_v]

        # Apply Scaled Dot-Product Attention (multiple heads)
        if self.res_attention:
            output, attn_weights, attn_scores = self.sdp_attn(q_s, k_s, v_s, prev=prev, key_padding_mask=key_padding_mask, attn_mask=attn_mask)
        else:
            output, attn_weights = self.sdp_attn(q_s, k_s, v_s, key_padding_mask=key_padding_mask, attn_mask=attn_mask)
        # output: [bs x n_heads x q_len x d_v], attn: [bs x n_heads x q_len x q_len], scores: [bs x n_heads x max_q_len x q_len]

        # back to the original inputs dimensions
        output = output.transpose(1, 2).contiguous().view(bs, -1, self.n_heads * self.d_v) # output: [bs x q_len x n_heads * d_v]
        output = self.to_out(output)

        if self.res_attention: return output, attn_weights, attn_scores
        else: return output, attn_weights


class _ScaledDotProductAttention(nn.Module):
    r"""Scaled Dot-Product Attention module (Attention is all you need by Vaswani et al., 2017) with optional residual attention from previous layer
    (Realformer: Transformer likes residual attention by He et al, 2020) and locality self sttention (Vision Transformer for Small-Size Datasets
    by Lee et al, 2021)"""

    def __init__(self, d_model, n_heads, attn_dropout=0., res_attention=False, lsa=False):
        super().__init__()
        self.attn_dropout = nn.Dropout(attn_dropout)
        self.res_attention = res_attention
        head_dim = d_model // n_heads
        self.scale = nn.Parameter(torch.tensor(head_dim ** -0.5), requires_grad=lsa)
        self.lsa = lsa

    def forward(self, q:Tensor, k:Tensor, v:Tensor, prev:Optional[Tensor]=None, key_padding_mask:Optional[Tensor]=None, attn_mask:Optional[Tensor]=None):
        '''
        Input shape:
            q               : [bs x n_heads x max_q_len x d_k]
            k               : [bs x n_heads x d_k x seq_len]
            v               : [bs x n_heads x seq_len x d_v]
            prev            : [bs x n_heads x q_len x seq_len]
            key_padding_mask: [bs x seq_len]
            attn_mask       : [1 x seq_len x seq_len]
        Output shape:
            output:  [bs x n_heads x q_len x d_v]
            attn   : [bs x n_heads x q_len x seq_len]
            scores : [bs x n_heads x q_len x seq_len]
        '''

        # Scaled MatMul (q, k) - similarity scores for all pairs of positions in an input sequence
        attn_scores = torch.matmul(q, k) * self.scale      # attn_scores : [bs x n_heads x max_q_len x q_len]

        # Add pre-softmax attention scores from the previous layer (optional)
        if prev is not None: attn_scores = attn_scores + prev

        # Attention mask (optional)
        if attn_mask is not None:                                     # attn_mask with shape [q_len x seq_len] - only used when q_len == seq_len
            if attn_mask.dtype == torch.bool:
                attn_scores.masked_fill_(attn_mask, -np.inf)
            else:
                attn_scores += attn_mask

        # Key padding mask (optional)
        if key_padding_mask is not None:                              # mask with shape [bs x q_len] (only when max_w_len == q_len)
            attn_scores.masked_fill_(key_padding_mask.unsqueeze(1).unsqueeze(2), -np.inf)

        # normalize the attention weights
        attn_weights = F.softmax(attn_scores, dim=-1)                 # attn_weights   : [bs x n_heads x max_q_len x q_len]
        attn_weights = self.attn_dropout(attn_weights)

        # compute the new values given the attention weights
        output = torch.matmul(attn_weights, v)                        # output: [bs x n_heads x max_q_len x d_v]

        if self.res_attention: return output, attn_weights, attn_scores
        else: return output, attn_weights

class PatchTST(nn.Module):
    def __init__(self, 
                 enc_in=321, 
                 seq_len=336, 
                 pred_len=96, 
                 e_layers=3, 
                 n_heads=16, 
                 d_model=128, 
                 d_ff=256,
                 dropout=0.2, 
                 fc_dropout=0.2, 
                 head_dropout=0.2, 
                 patch_len=16, 
                 stride=8, 
                 individual=0,
                 padding_patch="end", 
                 revin=1, 
                 affine=0, 
                 subtract_last=0, 
                 decomposition=0, 
                 kernel_size=25,
                 max_seq_len:Optional[int]=1024, 
                 d_k:Optional[int]=None, 
                 d_v:Optional[int]=None,
                 norm:str='BatchNorm', 
                 attn_dropout:float=0., 
                 act:str="gelu",
                 key_padding_mask:bool='auto',
                 padding_var:Optional[int]=None,
                 attn_mask:Optional[Tensor]=None, 
                 res_attention:bool=True, 
                 pre_norm:bool=False, 
                 store_attn:bool=False, 
                 pe:str='zeros',
                 learn_pe:bool=True, 
                 pretrain_head:bool=False, 
                 head_type='flatten',
                 verbose:bool=False, 
                 **kwargs):
        
        super().__init__()

        # load parameters
        c_in = enc_in
        context_window = seq_len
        target_window = pred_len
        n_layers = e_layers
        
        # model
        self.decomposition = decomposition
        if self.decomposition:
            self.decomp_module = series_decomp(kernel_size)
            self.model_trend = PatchTST_backbone(c_in=c_in, context_window = context_window, target_window=target_window, patch_len=patch_len, stride=stride, 
                                  max_seq_len=max_seq_len, n_layers=n_layers, d_model=d_model,
                                  n_heads=n_heads, d_k=d_k, d_v=d_v, d_ff=d_ff, norm=norm, attn_dropout=attn_dropout,
                                  dropout=dropout, act=act, key_padding_mask=key_padding_mask, padding_var=padding_var, 
                                  attn_mask=attn_mask, res_attention=res_attention, pre_norm=pre_norm, store_attn=store_attn,
                                  pe=pe, learn_pe=learn_pe, fc_dropout=fc_dropout, head_dropout=head_dropout, padding_patch = padding_patch,
                                  pretrain_head=pretrain_head, head_type=head_type, individual=individual, revin=revin, affine=affine,
                                  subtract_last=subtract_last, verbose=verbose, **kwargs)
            self.model_res = PatchTST_backbone(c_in=c_in, context_window = context_window, target_window=target_window, patch_len=patch_len, stride=stride, 
                                  max_seq_len=max_seq_len, n_layers=n_layers, d_model=d_model,
                                  n_heads=n_heads, d_k=d_k, d_v=d_v, d_ff=d_ff, norm=norm, attn_dropout=attn_dropout,
                                  dropout=dropout, act=act, key_padding_mask=key_padding_mask, padding_var=padding_var, 
                                  attn_mask=attn_mask, res_attention=res_attention, pre_norm=pre_norm, store_attn=store_attn,
                                  pe=pe, learn_pe=learn_pe, fc_dropout=fc_dropout, head_dropout=head_dropout, padding_patch = padding_patch,
                                  pretrain_head=pretrain_head, head_type=head_type, individual=individual, revin=revin, affine=affine,
                                  subtract_last=subtract_last, verbose=verbose, **kwargs)
        else:
            self.model = PatchTST_backbone(c_in=c_in, context_window = context_window, target_window=target_window, patch_len=patch_len, stride=stride, 
                                  max_seq_len=max_seq_len, n_layers=n_layers, d_model=d_model,
                                  n_heads=n_heads, d_k=d_k, d_v=d_v, d_ff=d_ff, norm=norm, attn_dropout=attn_dropout,
                                  dropout=dropout, act=act, key_padding_mask=key_padding_mask, padding_var=padding_var, 
                                  attn_mask=attn_mask, res_attention=res_attention, pre_norm=pre_norm, store_attn=store_attn,
                                  pe=pe, learn_pe=learn_pe, fc_dropout=fc_dropout, head_dropout=head_dropout, padding_patch = padding_patch,
                                  pretrain_head=pretrain_head, head_type=head_type, individual=individual, revin=revin, affine=affine,
                                  subtract_last=subtract_last, verbose=verbose, **kwargs)
            
    def forward(self, x):           
        x = x[..., 0] # x: [Batch, Input length, Channel]
        if self.decomposition:
            res_init, trend_init = self.decomp_module(x)
            res_init, trend_init = res_init.permute(0,2,1), trend_init.permute(0,2,1)  # x: [Batch, Channel, Input length]
            res = self.model_res(res_init)
            trend = self.model_trend(trend_init)
            x = res + trend
            x = x.permute(0,2,1)    # x: [Batch, Input length, Channel]
        else:
            x = x.permute(0,2,1)    # x: [Batch, Channel, Input length]
            x = self.model(x)
            x = x.permute(0,2,1)    # x: [Batch, Input length, Channel]
        return x.unsqueeze(-1)
    
if __name__ == "__main__":
    from torchinfo import summary
    model = PatchTST()
    summary(model, [64, 336, 321, 1])