mirror of
https://github.com/wassname/seq2seq-time.git
synced 2026-06-27 17:31:58 +08:00
wassname
80 lines
2.9 KiB
Python
80 lines
2.9 KiB
Python
import torch
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from torch import nn
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from torch.nn import functional as F
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from ..util import mask_upper_triangular
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class CrossAttention(nn.Module):
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"""
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A single transformer, using cross attention, like in the determistic encoder in attentive neural processes.
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"""
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def __init__(self, x_dim, y_dim, attention_dropout=0, nhead=8, nlayers=8, hidden_size=32, min_std=0.01):
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super().__init__()
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self._min_std = min_std
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enc_x_dim = x_dim + y_dim
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self.v_encoder = nn.Linear(enc_x_dim, hidden_size)
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self.k_encoder = nn.Linear(x_dim, hidden_size)
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self.q_encoder = nn.Linear(x_dim, hidden_size)
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self.self_attn_k = torch.nn.MultiheadAttention(
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hidden_size, nhead, bias=False, dropout=attention_dropout
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)
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self.self_attn_q = torch.nn.MultiheadAttention(
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hidden_size, nhead, bias=False, dropout=attention_dropout
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)
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self.self_attn_v = torch.nn.MultiheadAttention(
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hidden_size, nhead, bias=False, dropout=attention_dropout
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)
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self.cross_attn = torch.nn.MultiheadAttention(
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hidden_size, nhead, bias=False, dropout=attention_dropout
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)
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encoder_norm = nn.LayerNorm(hidden_size)
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layer_enc = nn.TransformerEncoderLayer(
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d_model=hidden_size,
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dim_feedforward=hidden_size*8,
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dropout=attention_dropout,
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nhead=nhead,
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# activation
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)
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self.transformer = nn.TransformerEncoder(
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layer_enc, num_layers=nlayers, norm=encoder_norm
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)
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self.mean = nn.Linear(hidden_size, y_dim)
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self.std = nn.Linear(hidden_size, y_dim)
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def forward(self, past_x, past_y, future_x, future_y=None):
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device = next(self.parameters()).device
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context = torch.cat([past_x, past_y], -1).detach()
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# Masks
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B, C, _ = past_x.shape
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past_causal_mask = mask_upper_triangular(C, device)
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B, T, _ = future_x.shape
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future_causal_mask = mask_upper_triangular(T, device)
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# Change feature size
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k = self.k_encoder(past_x).permute(1, 0, 2)
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q = self.q_encoder(future_x).permute(1, 0, 2)
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v = self.v_encoder(context).permute(1, 0, 2)
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# # Self attention with causal mask
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# v = self.self_attn_v(v, v, v, attn_mask=past_causal_mask)[0]
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# q = self.self_attn_q(q, q, q, attn_mask=future_causal_mask)[0]
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# k = self.self_attn_k(k, k, k, attn_mask=past_causal_mask)[0]
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# Cross attention
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h = self.cross_attn(query=q, key=k, value=v)[0]
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# Transformer
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outputs = self.transformer(h, mask=future_causal_mask)
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outputs = outputs.permute(1, 0, 2)
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# Head
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mean = self.mean(outputs)
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log_sigma = self.std(outputs)
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sigma = self._min_std + (1 - self._min_std) * F.softplus(log_sigma)
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return torch.distributions.Normal(mean, sigma), {}
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