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import torch.nn as nn
import torch.nn.functional as F
from torch.nn.utils import weight_norm
import math
import numpy as np
class PositionalEmbedding(nn.Module):
def __init__(self, d_model, max_len=5000):
super(PositionalEmbedding, self).__init__()
# Compute the positional encodings once in log space.
pe = torch.zeros(max_len, d_model).float()
pe.require_grad = False
position = torch.arange(0, max_len).float().unsqueeze(1)
div_term = (
torch.arange(0, d_model, 2).float() * -(math.log(10000.0) / d_model)
).exp()
pe[:, 0::2] = torch.sin(position * div_term)
pe[:, 1::2] = torch.cos(position * div_term)
pe = pe.unsqueeze(0)
self.register_buffer("pe", pe)
def forward(self, x):
return self.pe[:, : x.size(1)]
class TokenEmbedding(nn.Module):
def __init__(self, c_in, d_model):
super(TokenEmbedding, self).__init__()
padding = 1 if torch.__version__ >= "1.5.0" else 2
self.tokenConv = nn.Conv1d(
in_channels=c_in,
out_channels=d_model,
kernel_size=3,
padding=padding,
padding_mode="circular",
bias=False,
)
for m in self.modules():
if isinstance(m, nn.Conv1d):
nn.init.kaiming_normal_(
m.weight, mode="fan_in", nonlinearity="leaky_relu"
)
def forward(self, x):
x = self.tokenConv(x.permute(0, 2, 1)).transpose(1, 2)
return x
class FixedEmbedding(nn.Module):
def __init__(self, c_in, d_model):
super(FixedEmbedding, self).__init__()
w = torch.zeros(c_in, d_model).float()
w.require_grad = False
position = torch.arange(0, c_in).float().unsqueeze(1)
div_term = (
torch.arange(0, d_model, 2).float() * -(math.log(10000.0) / d_model)
).exp()
w[:, 0::2] = torch.sin(position * div_term)
w[:, 1::2] = torch.cos(position * div_term)
self.emb = nn.Embedding(c_in, d_model)
self.emb.weight = nn.Parameter(w, requires_grad=False)
def forward(self, x):
return self.emb(x).detach()
class TemporalEmbedding(nn.Module):
def __init__(self, d_model, embed_type="fixed", freq="h"):
super(TemporalEmbedding, self).__init__()
minute_size = 4
hour_size = 24
weekday_size = 7
day_size = 32
month_size = 13
Embed = FixedEmbedding if embed_type == "fixed" else nn.Embedding
if freq == "t":
self.minute_embed = Embed(minute_size, d_model)
self.hour_embed = Embed(hour_size, d_model)
self.weekday_embed = Embed(weekday_size, d_model)
self.day_embed = Embed(day_size, d_model)
self.month_embed = Embed(month_size, d_model)
def forward(self, x):
x = x.long()
minute_x = (
self.minute_embed(x[:, :, 4]) if hasattr(self, "minute_embed") else 0.0
)
hour_x = self.hour_embed(x[:, :, 3])
weekday_x = self.weekday_embed(x[:, :, 2])
day_x = self.day_embed(x[:, :, 1])
month_x = self.month_embed(x[:, :, 0])
return hour_x + weekday_x + day_x + month_x + minute_x
class TimeFeatureEmbedding(nn.Module):
def __init__(self, d_model, embed_type="timeF", freq="h"):
super(TimeFeatureEmbedding, self).__init__()
freq_map = {"h": 4, "t": 5, "s": 6, "m": 1, "a": 1, "w": 2, "d": 3, "b": 3}
d_inp = freq_map[freq]
self.embed = nn.Linear(d_inp, d_model, bias=False)
def forward(self, x):
return self.embed(x)
class DataEmbedding(nn.Module):
"""
Data Embedding for LSTiT
- value_emb + temporal_emb
> use relative PE instead of absolute PE
"""
def __init__(
self, c_in, d_model, embed_type="fixed", freq="h", dropout=0.1, use_abs_pe=True
):
super(DataEmbedding, self).__init__()
self.value_embedding = TokenEmbedding(c_in=c_in, d_model=d_model)
self.temporal_embedding = (
TemporalEmbedding(d_model=d_model, embed_type=embed_type, freq=freq)
if embed_type != "timeF"
else TimeFeatureEmbedding(d_model=d_model, embed_type=embed_type, freq=freq)
)
self.dropout = nn.Dropout(p=dropout)
self.use_abs_pe = use_abs_pe
if self.use_abs_pe:
self.position_embedding = PositionalEmbedding(d_model=d_model)
self.pe_fc = nn.Linear(d_model, d_model)
def forward(self, x, x_mark):
x = self.value_embedding(x) + self.temporal_embedding(x_mark)
if self.use_abs_pe:
x = x + self.pe_fc(self.position_embedding(x))
return self.dropout(x)
class RelativeSinPE(nn.Module):
"""
Relative Sine-PE to enable Frequency info as inductive bias
"""
def __init__(self, d_model, max_len=5000, linear_freq=False):
"""
:param d_model: The dimension of PE
:param max_len: The maximum length allowed
:param linear_freq: Use Linear Freq (DFT) instead of Exponential Freq
"""
super().__init__()
# Compute the positional encodings once in log space.
BASE = 10000.0
pe = torch.zeros(max_len, d_model).float()
pe.require_grad = False
position = torch.arange(0, max_len).float().unsqueeze(1)
if linear_freq:
div_term = (torch.arange(0, d_model, 2).float()) / d_model * BASE
else:
div_term = (
torch.arange(0, d_model, 2).float() * -(math.log(10000.0) / d_model)
).exp()
pe[:, 0::2] = torch.sin(position * div_term)
pe[:, 1::2] = torch.cos(position * div_term)
# (L, d_model)
pe = F.pad(pe, (0, 0, 1, 0), mode="constant", value=0.0)
# pe = torch.cat([torch.zeros_like(pe[:1]), pe], dim=0)
pe = pe.unsqueeze(0)
self.register_buffer("pe", pe) # (1, max_len, d)
@torch.no_grad()
def forward(self, x_enc, x_dec=None, overlap_len=0):
enc_idx = torch.arange(x_enc.size(1))
enc_enc = enc_idx.unsqueeze(1) - enc_idx.unsqueeze(0) # (L_in, L_in)
if x_dec is None:
return self.pe, enc_enc
dec_idx = torch.arange(x_dec.size(1)) + x_enc.size(1) - max(overlap_len, 0)
dec_dec = dec_idx.unsqueeze(1) - dec_idx.unsqueeze(0) # (L_out, L_out)
dec_enc = dec_idx.unsqueeze(1) - enc_idx.unsqueeze(0) # (L_out, L_in)
return self.pe, enc_enc, dec_enc, dec_dec
class RelativeFreqPE(nn.Module):
"""
Relative Frequency Based PE
"""
def __init__(self, d_pe=128, max_len=2000):
super().__init__()
# Compute the positional encodings once in log space.
BASE = 10000.0
self.max_len = max_len
pe = torch.zeros(max_len + 1, max_len).float()
pe.require_grad = False
position = torch.arange(max_len)
for freq in range(1, max_len):
mask = position % freq == 0
pe[freq][mask] = 1.0
pe[0][0] = 1.0 # identity identification
pe[-1] = 0.0 # out of range indicator
pe = pe[:, :d_pe] # take top d_pe as the PE
self.register_buffer("pe", pe) # (d+1, d)
@torch.no_grad()
def forward(self, x_enc, x_dec=None, overlap_len=0):
enc_idx = torch.arange(x_enc.size(1))
enc_enc = enc_idx.unsqueeze(1) - enc_idx.unsqueeze(0) # (L_in, L_in)
if x_dec is None:
return self.pe, enc_enc
dec_idx = torch.arange(x_dec.size(1)) + x_enc.size(1) - max(overlap_len, 0)
dec_dec = dec_idx.unsqueeze(1) - dec_idx.unsqueeze(0) # (L_out, L_out)
dec_enc = dec_idx.unsqueeze(1) - enc_idx.unsqueeze(0) # (L_out, L_in)
enc_enc = torch.abs(enc_enc)
dec_dec = torch.abs(dec_dec)
dec_enc = torch.abs(dec_enc)
enc_enc = torch.masked_fill(enc_enc, enc_enc > self.max_len - 1, self.max_len)
dec_dec = torch.masked_fill(dec_dec, dec_dec > self.max_len - 1, self.max_len)
dec_enc = torch.masked_fill(dec_enc, dec_enc > self.max_len - 1, self.max_len)
return self.pe, enc_enc, dec_enc, dec_dec
class SinDegEncoder(nn.Module):
def __init__(self, hidden_dim=64, constant=10000):
super().__init__()
self.eps = (
100 # to make the wave smaller to aovid better sensitivity on smaller value
)
self.hidden_dim = hidden_dim
self.fc = nn.Linear(hidden_dim, hidden_dim)
div = torch.exp(
torch.arange(0, self.hidden_dim, 2) * (-np.log(constant) / self.hidden_dim)
)
self.register_buffer("div", div)
def forward(self, batch):
deg = batch.deg
deg = deg.flatten(0) * self.eps # [B]
degenc = (
deg.unsqueeze(-1) * self.div
) # auto broadcast: [B, 1] x [D/2] --> [B, D/2]
degenc = torch.cat(
[torch.sin(degenc), torch.cos(degenc)], dim=2
) # [B, D/2] --> [B, D]
batch.x = batch.x + self.fc(degenc) if "x" in batch else self.fc(degenc)
return batch
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