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gameloader
334
layers/ETSformer_EncDec.py
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334
layers/ETSformer_EncDec.py
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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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import torch.fft as fft
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from einops import rearrange, reduce, repeat
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import math, random
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from scipy.fftpack import next_fast_len
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class Transform:
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def __init__(self, sigma):
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self.sigma = sigma
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@torch.no_grad()
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def transform(self, x):
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return self.jitter(self.shift(self.scale(x)))
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def jitter(self, x):
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return x + (torch.randn(x.shape).to(x.device) * self.sigma)
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def scale(self, x):
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return x * (torch.randn(x.size(-1)).to(x.device) * self.sigma + 1)
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def shift(self, x):
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return x + (torch.randn(x.size(-1)).to(x.device) * self.sigma)
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def conv1d_fft(f, g, dim=-1):
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N = f.size(dim)
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M = g.size(dim)
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fast_len = next_fast_len(N + M - 1)
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F_f = fft.rfft(f, fast_len, dim=dim)
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F_g = fft.rfft(g, fast_len, dim=dim)
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F_fg = F_f * F_g.conj()
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out = fft.irfft(F_fg, fast_len, dim=dim)
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out = out.roll((-1,), dims=(dim,))
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idx = torch.as_tensor(range(fast_len - N, fast_len)).to(out.device)
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out = out.index_select(dim, idx)
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return out
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class ExponentialSmoothing(nn.Module):
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def __init__(self, dim, nhead, dropout=0.1, aux=False):
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super().__init__()
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self._smoothing_weight = nn.Parameter(torch.randn(nhead, 1))
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self.v0 = nn.Parameter(torch.randn(1, 1, nhead, dim))
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self.dropout = nn.Dropout(dropout)
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if aux:
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self.aux_dropout = nn.Dropout(dropout)
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def forward(self, values, aux_values=None):
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b, t, h, d = values.shape
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init_weight, weight = self.get_exponential_weight(t)
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output = conv1d_fft(self.dropout(values), weight, dim=1)
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output = init_weight * self.v0 + output
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if aux_values is not None:
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aux_weight = weight / (1 - self.weight) * self.weight
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aux_output = conv1d_fft(self.aux_dropout(aux_values), aux_weight)
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output = output + aux_output
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return output
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def get_exponential_weight(self, T):
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# Generate array [0, 1, ..., T-1]
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powers = torch.arange(T, dtype=torch.float, device=self.weight.device)
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# (1 - \alpha) * \alpha^t, for all t = T-1, T-2, ..., 0]
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weight = (1 - self.weight) * (self.weight ** torch.flip(powers, dims=(0,)))
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# \alpha^t for all t = 1, 2, ..., T
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init_weight = self.weight ** (powers + 1)
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return rearrange(init_weight, 'h t -> 1 t h 1'), \
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rearrange(weight, 'h t -> 1 t h 1')
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@property
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def weight(self):
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return torch.sigmoid(self._smoothing_weight)
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class Feedforward(nn.Module):
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def __init__(self, d_model, dim_feedforward, dropout=0.1, activation='sigmoid'):
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# Implementation of Feedforward model
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super().__init__()
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self.linear1 = nn.Linear(d_model, dim_feedforward, bias=False)
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self.dropout1 = nn.Dropout(dropout)
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self.linear2 = nn.Linear(dim_feedforward, d_model, bias=False)
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self.dropout2 = nn.Dropout(dropout)
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self.activation = getattr(F, activation)
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def forward(self, x):
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x = self.linear2(self.dropout1(self.activation(self.linear1(x))))
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return self.dropout2(x)
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class GrowthLayer(nn.Module):
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def __init__(self, d_model, nhead, d_head=None, dropout=0.1):
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super().__init__()
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self.d_head = d_head or (d_model // nhead)
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self.d_model = d_model
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self.nhead = nhead
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self.z0 = nn.Parameter(torch.randn(self.nhead, self.d_head))
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self.in_proj = nn.Linear(self.d_model, self.d_head * self.nhead)
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self.es = ExponentialSmoothing(self.d_head, self.nhead, dropout=dropout)
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self.out_proj = nn.Linear(self.d_head * self.nhead, self.d_model)
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assert self.d_head * self.nhead == self.d_model, "d_model must be divisible by nhead"
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def forward(self, inputs):
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"""
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:param inputs: shape: (batch, seq_len, dim)
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:return: shape: (batch, seq_len, dim)
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"""
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b, t, d = inputs.shape
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values = self.in_proj(inputs).view(b, t, self.nhead, -1)
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values = torch.cat([repeat(self.z0, 'h d -> b 1 h d', b=b), values], dim=1)
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values = values[:, 1:] - values[:, :-1]
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out = self.es(values)
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out = torch.cat([repeat(self.es.v0, '1 1 h d -> b 1 h d', b=b), out], dim=1)
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out = rearrange(out, 'b t h d -> b t (h d)')
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return self.out_proj(out)
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class FourierLayer(nn.Module):
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def __init__(self, d_model, pred_len, k=None, low_freq=1):
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super().__init__()
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self.d_model = d_model
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self.pred_len = pred_len
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self.k = k
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self.low_freq = low_freq
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def forward(self, x):
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"""x: (b, t, d)"""
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b, t, d = x.shape
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x_freq = fft.rfft(x, dim=1)
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if t % 2 == 0:
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x_freq = x_freq[:, self.low_freq:-1]
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f = fft.rfftfreq(t)[self.low_freq:-1]
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else:
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x_freq = x_freq[:, self.low_freq:]
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f = fft.rfftfreq(t)[self.low_freq:]
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x_freq, index_tuple = self.topk_freq(x_freq)
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f = repeat(f, 'f -> b f d', b=x_freq.size(0), d=x_freq.size(2))
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f = rearrange(f[index_tuple], 'b f d -> b f () d').to(x_freq.device)
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return self.extrapolate(x_freq, f, t)
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def extrapolate(self, x_freq, f, t):
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x_freq = torch.cat([x_freq, x_freq.conj()], dim=1)
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f = torch.cat([f, -f], dim=1)
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t_val = rearrange(torch.arange(t + self.pred_len, dtype=torch.float),
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't -> () () t ()').to(x_freq.device)
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amp = rearrange(x_freq.abs() / t, 'b f d -> b f () d')
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phase = rearrange(x_freq.angle(), 'b f d -> b f () d')
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x_time = amp * torch.cos(2 * math.pi * f * t_val + phase)
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return reduce(x_time, 'b f t d -> b t d', 'sum')
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def topk_freq(self, x_freq):
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values, indices = torch.topk(x_freq.abs(), self.k, dim=1, largest=True, sorted=True)
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mesh_a, mesh_b = torch.meshgrid(torch.arange(x_freq.size(0)), torch.arange(x_freq.size(2)))
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index_tuple = (mesh_a.unsqueeze(1).to(indices.device), indices, mesh_b.unsqueeze(1).to(indices.device))
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x_freq = x_freq[index_tuple]
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return x_freq, index_tuple
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class LevelLayer(nn.Module):
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def __init__(self, d_model, c_out, dropout=0.1):
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super().__init__()
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self.d_model = d_model
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self.c_out = c_out
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self.es = ExponentialSmoothing(1, self.c_out, dropout=dropout, aux=True)
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self.growth_pred = nn.Linear(self.d_model, self.c_out)
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self.season_pred = nn.Linear(self.d_model, self.c_out)
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def forward(self, level, growth, season):
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b, t, _ = level.shape
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growth = self.growth_pred(growth).view(b, t, self.c_out, 1)
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season = self.season_pred(season).view(b, t, self.c_out, 1)
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growth = growth.view(b, t, self.c_out, 1)
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season = season.view(b, t, self.c_out, 1)
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level = level.view(b, t, self.c_out, 1)
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out = self.es(level - season, aux_values=growth)
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out = rearrange(out, 'b t h d -> b t (h d)')
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return out
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class EncoderLayer(nn.Module):
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def __init__(self, d_model, nhead, c_out, seq_len, pred_len, k, dim_feedforward=None, dropout=0.1,
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activation='sigmoid', layer_norm_eps=1e-5):
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super().__init__()
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self.d_model = d_model
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self.nhead = nhead
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self.c_out = c_out
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self.seq_len = seq_len
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self.pred_len = pred_len
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dim_feedforward = dim_feedforward or 4 * d_model
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self.dim_feedforward = dim_feedforward
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self.growth_layer = GrowthLayer(d_model, nhead, dropout=dropout)
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self.seasonal_layer = FourierLayer(d_model, pred_len, k=k)
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self.level_layer = LevelLayer(d_model, c_out, dropout=dropout)
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# Implementation of Feedforward model
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self.ff = Feedforward(d_model, dim_feedforward, dropout=dropout, activation=activation)
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self.norm1 = nn.LayerNorm(d_model, eps=layer_norm_eps)
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self.norm2 = nn.LayerNorm(d_model, eps=layer_norm_eps)
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self.dropout1 = nn.Dropout(dropout)
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self.dropout2 = nn.Dropout(dropout)
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def forward(self, res, level, attn_mask=None):
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season = self._season_block(res)
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res = res - season[:, :-self.pred_len]
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growth = self._growth_block(res)
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res = self.norm1(res - growth[:, 1:])
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res = self.norm2(res + self.ff(res))
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level = self.level_layer(level, growth[:, :-1], season[:, :-self.pred_len])
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return res, level, growth, season
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def _growth_block(self, x):
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x = self.growth_layer(x)
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return self.dropout1(x)
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def _season_block(self, x):
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x = self.seasonal_layer(x)
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return self.dropout2(x)
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class Encoder(nn.Module):
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def __init__(self, layers):
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super().__init__()
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self.layers = nn.ModuleList(layers)
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def forward(self, res, level, attn_mask=None):
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growths = []
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seasons = []
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for layer in self.layers:
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res, level, growth, season = layer(res, level, attn_mask=None)
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growths.append(growth)
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seasons.append(season)
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return level, growths, seasons
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class DampingLayer(nn.Module):
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def __init__(self, pred_len, nhead, dropout=0.1):
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super().__init__()
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self.pred_len = pred_len
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self.nhead = nhead
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self._damping_factor = nn.Parameter(torch.randn(1, nhead))
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self.dropout = nn.Dropout(dropout)
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def forward(self, x):
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x = repeat(x, 'b 1 d -> b t d', t=self.pred_len)
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b, t, d = x.shape
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powers = torch.arange(self.pred_len).to(self._damping_factor.device) + 1
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powers = powers.view(self.pred_len, 1)
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damping_factors = self.damping_factor ** powers
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damping_factors = damping_factors.cumsum(dim=0)
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x = x.view(b, t, self.nhead, -1)
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x = self.dropout(x) * damping_factors.unsqueeze(-1)
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return x.view(b, t, d)
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@property
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def damping_factor(self):
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return torch.sigmoid(self._damping_factor)
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class DecoderLayer(nn.Module):
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def __init__(self, d_model, nhead, c_out, pred_len, dropout=0.1):
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super().__init__()
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self.d_model = d_model
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self.nhead = nhead
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self.c_out = c_out
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self.pred_len = pred_len
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self.growth_damping = DampingLayer(pred_len, nhead, dropout=dropout)
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self.dropout1 = nn.Dropout(dropout)
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def forward(self, growth, season):
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growth_horizon = self.growth_damping(growth[:, -1:])
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growth_horizon = self.dropout1(growth_horizon)
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seasonal_horizon = season[:, -self.pred_len:]
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return growth_horizon, seasonal_horizon
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class Decoder(nn.Module):
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def __init__(self, layers):
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super().__init__()
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self.d_model = layers[0].d_model
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self.c_out = layers[0].c_out
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self.pred_len = layers[0].pred_len
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self.nhead = layers[0].nhead
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self.layers = nn.ModuleList(layers)
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self.pred = nn.Linear(self.d_model, self.c_out)
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def forward(self, growths, seasons):
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growth_repr = []
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season_repr = []
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for idx, layer in enumerate(self.layers):
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growth_horizon, season_horizon = layer(growths[idx], seasons[idx])
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growth_repr.append(growth_horizon)
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season_repr.append(season_horizon)
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growth_repr = sum(growth_repr)
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season_repr = sum(season_repr)
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return self.pred(growth_repr), self.pred(season_repr)
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