111 lines
3.7 KiB
Python
111 lines
3.7 KiB
Python
import torch.nn as nn
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import torch
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from torchinfo import summary
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class AttentionLayer(nn.Module):
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"""Perform attention across the -2 dim (the -1 dim is `model_dim`).
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Make sure the tensor is permuted to correct shape before attention.
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E.g.
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- Input shape (batch_size, in_steps, num_nodes, model_dim).
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- Then the attention will be performed across the nodes.
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Also, it supports different src and tgt length.
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But must `src length == K length == V length`.
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"""
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def __init__(self, model_dim, num_heads=8, mask=False):
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super().__init__()
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self.model_dim = model_dim # 152
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self.num_heads = num_heads
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self.mask = mask
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self.head_dim = model_dim // num_heads
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self.FC_Q = nn.Linear(model_dim, model_dim) # [152,152]
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self.FC_K = nn.Linear(model_dim, model_dim)
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self.FC_V = nn.Linear(model_dim, model_dim)
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self.out_proj = nn.Linear(model_dim, model_dim)
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def forward(self, query, key, value):
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# Q (batch_size, ..., tgt_length, model_dim)
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# K, V (batch_size, ..., src_length, model_dim)
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batch_size = query.shape[0] # 16 #64
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tgt_length = query.shape[-2] # 12 #170
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src_length = key.shape[-2] # 12 #170
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query = self.FC_Q(query) # [64,6,170,152]
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key = self.FC_K(key)
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value = self.FC_V(value)
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# Qhead, Khead, Vhead (num_heads * batch_size, ..., length, head_dim)
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query = torch.cat(
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torch.split(query, self.head_dim, dim=-1), dim=0
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) # [512,6,170,24]
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key = torch.cat(torch.split(key, self.head_dim, dim=-1), dim=0)
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value = torch.cat(torch.split(value, self.head_dim, dim=-1), dim=0)
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key = key.transpose(
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-1, -2
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) # (num_heads * batch_size, ..., head_dim, src_length)
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attn_score = ( # [64,170,12,12]
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query @ key
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) / self.head_dim**0.5 # (num_heads * batch_size, ..., tgt_length, src_length)
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if self.mask:
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mask = torch.ones(
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tgt_length, src_length, dtype=torch.bool, device=query.device
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).tril() # lower triangular part of the matrix
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attn_score.masked_fill_(~mask, -torch.inf) # fill in-place
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attn_score = torch.softmax(attn_score, dim=-1) # [64,170,12,12]
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out = attn_score @ value
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out = torch.cat(
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torch.split(out, batch_size, dim=0), dim=-1
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) # (batch_size, ..., tgt_length, head_dim * num_heads = model_dim)[16,170,12,152]
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out = self.out_proj(out) # [64,6,170,152]
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return out
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class SelfAttentionLayer(nn.Module):
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def __init__(
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self, model_dim, feed_forward_dim=2048, num_heads=8, dropout=0, mask=False
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):
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super().__init__()
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self.attn = AttentionLayer(model_dim, num_heads, mask)
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self.feed_forward = nn.Sequential(
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nn.Linear(model_dim, feed_forward_dim), # [152,256]
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nn.ReLU(inplace=True),
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nn.Linear(feed_forward_dim, model_dim), # [256.152]
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)
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self.ln1 = nn.LayerNorm(model_dim)
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self.ln2 = nn.LayerNorm(model_dim)
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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, x, dim=-2):
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x = x.transpose(dim, -2)
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# x: (batch_size, ..., length, model_dim)
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residual = x
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out = self.attn(x, x, x) # (batch_size, ..., length, model_dim)[16,170,12,152]
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out = self.dropout1(out)
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out = self.ln1(residual + out)
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residual = out
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out = self.feed_forward(out) # (batch_size, ..., length, model_dim)
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out = self.dropout2(out)
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out = self.ln2(residual + out)
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out = out.transpose(dim, -2) # [64,6,170,152]
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return out
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