Pyraformer_EncDec.py

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.modules.linear import Linear
from layers.SelfAttention_Family import AttentionLayer, FullAttention
from layers.Embed import DataEmbedding
import math


def get_mask(input_size, window_size, inner_size):
    """Get the attention mask of PAM-Naive"""
    # Get the size of all layers
    all_size = []
    all_size.append(input_size)
    for i in range(len(window_size)):
        layer_size = math.floor(all_size[i] / window_size[i])
        all_size.append(layer_size)

    seq_length = sum(all_size)
    mask = torch.zeros(seq_length, seq_length)

    # get intra-scale mask
    inner_window = inner_size // 2
    for layer_idx in range(len(all_size)):
        start = sum(all_size[:layer_idx])
        for i in range(start, start + all_size[layer_idx]):
            left_side = max(i - inner_window, start)
            right_side = min(i + inner_window + 1, start + all_size[layer_idx])
            mask[i, left_side:right_side] = 1

    # get inter-scale mask
    for layer_idx in range(1, len(all_size)):
        start = sum(all_size[:layer_idx])
        for i in range(start, start + all_size[layer_idx]):
            left_side = (start - all_size[layer_idx - 1]) + \
                (i - start) * window_size[layer_idx - 1]
            if i == (start + all_size[layer_idx] - 1):
                right_side = start
            else:
                right_side = (
                    start - all_size[layer_idx - 1]) + (i - start + 1) * window_size[layer_idx - 1]
            mask[i, left_side:right_side] = 1
            mask[left_side:right_side, i] = 1

    mask = (1 - mask).bool()

    return mask, all_size


def refer_points(all_sizes, window_size):
    """Gather features from PAM's pyramid sequences"""
    input_size = all_sizes[0]
    indexes = torch.zeros(input_size, len(all_sizes))

    for i in range(input_size):
        indexes[i][0] = i
        former_index = i
        for j in range(1, len(all_sizes)):
            start = sum(all_sizes[:j])
            inner_layer_idx = former_index - (start - all_sizes[j - 1])
            former_index = start + \
                min(inner_layer_idx // window_size[j - 1], all_sizes[j] - 1)
            indexes[i][j] = former_index

    indexes = indexes.unsqueeze(0).unsqueeze(3)

    return indexes.long()


class RegularMask():
    def __init__(self, mask):
        self._mask = mask.unsqueeze(1)

    @property
    def mask(self):
        return self._mask


class EncoderLayer(nn.Module):
    """ Compose with two layers """

    def __init__(self, d_model, d_inner, n_head, dropout=0.1, normalize_before=True):
        super(EncoderLayer, self).__init__()

        self.slf_attn = AttentionLayer(
            FullAttention(mask_flag=True, factor=0,
                          attention_dropout=dropout, output_attention=False),
            d_model, n_head)
        self.pos_ffn = PositionwiseFeedForward(
            d_model, d_inner, dropout=dropout, normalize_before=normalize_before)

    def forward(self, enc_input, slf_attn_mask=None):
        attn_mask = RegularMask(slf_attn_mask)
        enc_output, _ = self.slf_attn(
            enc_input, enc_input, enc_input, attn_mask=attn_mask)
        enc_output = self.pos_ffn(enc_output)
        return enc_output


class Encoder(nn.Module):
    """ A encoder model with self attention mechanism. """

    def __init__(self, configs, window_size, inner_size):
        super().__init__()

        d_bottleneck = configs.d_model//4

        self.mask, self.all_size = get_mask(
            configs.seq_len, window_size, inner_size)
        self.indexes = refer_points(self.all_size, window_size)
        self.layers = nn.ModuleList([
            EncoderLayer(configs.d_model, configs.d_ff, configs.n_heads, dropout=configs.dropout,
                         normalize_before=False) for _ in range(configs.e_layers)
        ])  # naive pyramid attention

        self.enc_embedding = DataEmbedding(
            configs.enc_in, configs.d_model, configs.dropout)
        self.conv_layers = Bottleneck_Construct(
            configs.d_model, window_size, d_bottleneck)

    def forward(self, x_enc, x_mark_enc):
        seq_enc = self.enc_embedding(x_enc, x_mark_enc)

        mask = self.mask.repeat(len(seq_enc), 1, 1).to(x_enc.device)
        seq_enc = self.conv_layers(seq_enc)

        for i in range(len(self.layers)):
            seq_enc = self.layers[i](seq_enc, mask)

        indexes = self.indexes.repeat(seq_enc.size(
            0), 1, 1, seq_enc.size(2)).to(seq_enc.device)
        indexes = indexes.view(seq_enc.size(0), -1, seq_enc.size(2))
        all_enc = torch.gather(seq_enc, 1, indexes)
        seq_enc = all_enc.view(seq_enc.size(0), self.all_size[0], -1)

        return seq_enc


class ConvLayer(nn.Module):
    def __init__(self, c_in, window_size):
        super(ConvLayer, self).__init__()
        self.downConv = nn.Conv1d(in_channels=c_in,
                                  out_channels=c_in,
                                  kernel_size=window_size,
                                  stride=window_size)
        self.norm = nn.BatchNorm1d(c_in)
        self.activation = nn.ELU()

    def forward(self, x):
        x = self.downConv(x)
        x = self.norm(x)
        x = self.activation(x)
        return x


class Bottleneck_Construct(nn.Module):
    """Bottleneck convolution CSCM"""

    def __init__(self, d_model, window_size, d_inner):
        super(Bottleneck_Construct, self).__init__()
        if not isinstance(window_size, list):
            self.conv_layers = nn.ModuleList([
                ConvLayer(d_inner, window_size),
                ConvLayer(d_inner, window_size),
                ConvLayer(d_inner, window_size)
            ])
        else:
            self.conv_layers = []
            for i in range(len(window_size)):
                self.conv_layers.append(ConvLayer(d_inner, window_size[i]))
            self.conv_layers = nn.ModuleList(self.conv_layers)
        self.up = Linear(d_inner, d_model)
        self.down = Linear(d_model, d_inner)
        self.norm = nn.LayerNorm(d_model)

    def forward(self, enc_input):
        temp_input = self.down(enc_input).permute(0, 2, 1)
        all_inputs = []
        for i in range(len(self.conv_layers)):
            temp_input = self.conv_layers[i](temp_input)
            all_inputs.append(temp_input)

        all_inputs = torch.cat(all_inputs, dim=2).transpose(1, 2)
        all_inputs = self.up(all_inputs)
        all_inputs = torch.cat([enc_input, all_inputs], dim=1)

        all_inputs = self.norm(all_inputs)
        return all_inputs


class PositionwiseFeedForward(nn.Module):
    """ Two-layer position-wise feed-forward neural network. """

    def __init__(self, d_in, d_hid, dropout=0.1, normalize_before=True):
        super().__init__()

        self.normalize_before = normalize_before

        self.w_1 = nn.Linear(d_in, d_hid)
        self.w_2 = nn.Linear(d_hid, d_in)

        self.layer_norm = nn.LayerNorm(d_in, eps=1e-6)
        self.dropout = nn.Dropout(dropout)

    def forward(self, x):
        residual = x
        if self.normalize_before:
            x = self.layer_norm(x)

        x = F.gelu(self.w_1(x))
        x = self.dropout(x)
        x = self.w_2(x)
        x = self.dropout(x)
        x = x + residual

        if not self.normalize_before:
            x = self.layer_norm(x)
        return x