mirror of
https://github.com/KimMeen/Time-LLM.git
synced 2024-11-21 03:13:47 +08:00
136 lines
4.8 KiB
Python
136 lines
4.8 KiB
Python
import torch
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import torch.nn as nn
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import torch.nn.functional as F
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class ConvLayer(nn.Module):
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def __init__(self, c_in):
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super(ConvLayer, self).__init__()
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self.downConv = nn.Conv1d(in_channels=c_in,
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out_channels=c_in,
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kernel_size=3,
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padding=2,
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padding_mode='circular')
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self.norm = nn.BatchNorm1d(c_in)
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self.activation = nn.ELU()
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self.maxPool = nn.MaxPool1d(kernel_size=3, stride=2, padding=1)
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def forward(self, x):
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x = self.downConv(x.permute(0, 2, 1))
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x = self.norm(x)
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x = self.activation(x)
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x = self.maxPool(x)
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x = x.transpose(1, 2)
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return x
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class EncoderLayer(nn.Module):
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def __init__(self, attention, d_model, d_ff=None, dropout=0.1, activation="relu"):
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super(EncoderLayer, self).__init__()
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d_ff = d_ff or 4 * d_model
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self.attention = attention
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self.conv1 = nn.Conv1d(in_channels=d_model, out_channels=d_ff, kernel_size=1)
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self.conv2 = nn.Conv1d(in_channels=d_ff, out_channels=d_model, kernel_size=1)
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self.norm1 = nn.LayerNorm(d_model)
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self.norm2 = nn.LayerNorm(d_model)
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self.dropout = nn.Dropout(dropout)
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self.activation = F.relu if activation == "relu" else F.gelu
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def forward(self, x, attn_mask=None, tau=None, delta=None):
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new_x, attn = self.attention(
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x, x, x,
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attn_mask=attn_mask,
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tau=tau, delta=delta
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)
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x = x + self.dropout(new_x)
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y = x = self.norm1(x)
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y = self.dropout(self.activation(self.conv1(y.transpose(-1, 1))))
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y = self.dropout(self.conv2(y).transpose(-1, 1))
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return self.norm2(x + y), attn
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class Encoder(nn.Module):
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def __init__(self, attn_layers, conv_layers=None, norm_layer=None):
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super(Encoder, self).__init__()
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self.attn_layers = nn.ModuleList(attn_layers)
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self.conv_layers = nn.ModuleList(conv_layers) if conv_layers is not None else None
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self.norm = norm_layer
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def forward(self, x, attn_mask=None, tau=None, delta=None):
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# x [B, L, D]
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attns = []
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if self.conv_layers is not None:
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for i, (attn_layer, conv_layer) in enumerate(zip(self.attn_layers, self.conv_layers)):
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delta = delta if i == 0 else None
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x, attn = attn_layer(x, attn_mask=attn_mask, tau=tau, delta=delta)
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x = conv_layer(x)
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attns.append(attn)
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x, attn = self.attn_layers[-1](x, tau=tau, delta=None)
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attns.append(attn)
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else:
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for attn_layer in self.attn_layers:
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x, attn = attn_layer(x, attn_mask=attn_mask, tau=tau, delta=delta)
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attns.append(attn)
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if self.norm is not None:
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x = self.norm(x)
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return x, attns
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class DecoderLayer(nn.Module):
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def __init__(self, self_attention, cross_attention, d_model, d_ff=None,
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dropout=0.1, activation="relu"):
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super(DecoderLayer, self).__init__()
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d_ff = d_ff or 4 * d_model
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self.self_attention = self_attention
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self.cross_attention = cross_attention
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self.conv1 = nn.Conv1d(in_channels=d_model, out_channels=d_ff, kernel_size=1)
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self.conv2 = nn.Conv1d(in_channels=d_ff, out_channels=d_model, kernel_size=1)
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self.norm1 = nn.LayerNorm(d_model)
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self.norm2 = nn.LayerNorm(d_model)
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self.norm3 = nn.LayerNorm(d_model)
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self.dropout = nn.Dropout(dropout)
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self.activation = F.relu if activation == "relu" else F.gelu
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def forward(self, x, cross, x_mask=None, cross_mask=None, tau=None, delta=None):
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x = x + self.dropout(self.self_attention(
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x, x, x,
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attn_mask=x_mask,
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tau=tau, delta=None
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)[0])
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x = self.norm1(x)
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x = x + self.dropout(self.cross_attention(
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x, cross, cross,
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attn_mask=cross_mask,
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tau=tau, delta=delta
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)[0])
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y = x = self.norm2(x)
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y = self.dropout(self.activation(self.conv1(y.transpose(-1, 1))))
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y = self.dropout(self.conv2(y).transpose(-1, 1))
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return self.norm3(x + y)
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class Decoder(nn.Module):
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def __init__(self, layers, norm_layer=None, projection=None):
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super(Decoder, self).__init__()
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self.layers = nn.ModuleList(layers)
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self.norm = norm_layer
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self.projection = projection
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def forward(self, x, cross, x_mask=None, cross_mask=None, tau=None, delta=None):
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for layer in self.layers:
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x = layer(x, cross, x_mask=x_mask, cross_mask=cross_mask, tau=tau, delta=delta)
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if self.norm is not None:
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x = self.norm(x)
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if self.projection is not None:
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x = self.projection(x)
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return x
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