Upload multi_headed_attention.py

nets/multi_headed_attention.py

@@ -0,0 +1,103 @@
+import torch
+import torch.nn.functional as F
+import numpy as np
+from torch import nn
+import math
+
+
+class MultiHeadAttention(nn.Module):
+    def __init__(
+            self,
+            n_heads,
+            input_dim,
+            embed_dim=None,
+            val_dim=None,
+            key_dim=None
+    ):
+        super(MultiHeadAttention, self).__init__()
+
+        if val_dim is None:
+            assert embed_dim is not None, "Provide either embed_dim or val_dim"
+            val_dim = embed_dim // n_heads
+        if key_dim is None:
+            key_dim = val_dim
+
+        self.n_heads = n_heads
+        self.input_dim = input_dim
+        self.embed_dim = embed_dim
+        self.val_dim = val_dim
+        self.key_dim = key_dim
+
+        self.norm_factor = 1 / math.sqrt(key_dim)  # See Attention is all you need
+
+        self.W_query = nn.Parameter(torch.Tensor(n_heads, input_dim, key_dim), requires_grad=True)
+        self.W_key = nn.Parameter(torch.Tensor(n_heads, input_dim, key_dim), requires_grad=True)
+        self.W_val = nn.Parameter(torch.Tensor(n_heads, input_dim, val_dim), requires_grad=True)
+
+        if embed_dim is not None:
+            self.W_out = nn.Parameter(torch.Tensor(n_heads, key_dim, embed_dim), requires_grad=True)
+
+        self.init_parameters()
+
+    def init_parameters(self):
+
+        for param in self.parameters():
+            stdv = 1. / math.sqrt(param.size(-1))
+            param.data.uniform_(-stdv, stdv)
+
+
+    def forward(self, q, h=None, mask=None):
+        """
+        :param q: queries (batch_size, n_query, input_dim)
+        :param h: data (batch_size, graph_size, input_dim)
+        :param mask: mask (batch_size, n_query, graph_size) or viewable as that (i.e. can be 2 dim if n_query == 1)
+        Mask should contain 1 if attention is not possible (i.e. mask is negative adjacency)
+        :return:
+        """
+        if h is None:
+            h = q  # compute self-attention
+
+        # h should be (batch_size, graph_size, input_dim)
+        batch_size, graph_size, input_dim = h.size()
+        n_query = q.size(1)
+        assert q.size(0) == batch_size
+        assert q.size(2) == input_dim
+        assert input_dim == self.input_dim, "Wrong embedding dimension of input"
+
+        hflat = h.contiguous().view(-1, input_dim)
+        qflat = q.contiguous().view(-1, input_dim)
+
+        # last dimension can be different for keys and values
+        shp = (self.n_heads, batch_size, graph_size, -1)
+        shp_q = (self.n_heads, batch_size, n_query, -1)
+
+        # Calculate queries, (n_heads, n_query, graph_size, key/val_size)
+        Q = torch.matmul(qflat, self.W_query).view(shp_q)
+        # Calculate keys and values (n_heads, batch_size, graph_size, key/val_size)
+        K = torch.matmul(hflat, self.W_key).view(shp)
+        V = torch.matmul(hflat, self.W_val).view(shp)
+
+        # Calculate compatibility (n_heads, batch_size, n_query, graph_size)
+        compatibility = self.norm_factor * torch.matmul(Q, K.transpose(2, 3))
+
+        # Optionally apply mask to prevent attention
+        if mask is not None:
+            mask = mask.view(1, batch_size, n_query, graph_size).expand_as(compatibility)
+            compatibility[mask] = -np.inf
+
+        attn = F.softmax(compatibility, dim=-1)
+
+        # If there are nodes with no neighbours then softmax returns nan so we fix them to 0
+        if mask is not None:
+            attnc = attn.clone()
+            attnc[mask] = 0
+            attn = attnc
+
+        heads = torch.matmul(attn, V)
+
+        out = torch.mm(
+            heads.permute(1, 2, 0, 3).contiguous().view(-1, self.n_heads * self.val_dim),
+            self.W_out.view(-1, self.embed_dim)
+        ).view(batch_size, n_query, self.embed_dim)
+
+        return out