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[Model] Update GAT model code (dmlc#622)
* fix gat code to use latest edge softmax module * avoid transpose * update README * use edge_softmax op * mxnet edge softmax op * mxnet gat * update README * fix unittest * fix ci * fix mxnet nn test; relax criteria for prod reducer
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,135 @@ | ||
| """ | ||
| Graph Attention Networks in DGL using SPMV optimization. | ||
| References | ||
| ---------- | ||
| Paper: https://arxiv.org/abs/1710.10903 | ||
| Author's code: https://github.com/PetarV-/GAT | ||
| Pytorch implementation: https://github.com/Diego999/pyGAT | ||
| """ | ||
|
|
||
| import mxnet as mx | ||
| from mxnet import gluon | ||
| import mxnet.ndarray as nd | ||
| import mxnet.gluon.nn as nn | ||
| import dgl.function as fn | ||
| from dgl.nn.mxnet import edge_softmax | ||
|
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||
|
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| class GraphAttention(gluon.Block): | ||
| def __init__(self, | ||
| g, | ||
| in_dim, | ||
| out_dim, | ||
| num_heads, | ||
| feat_drop, | ||
| attn_drop, | ||
| alpha, | ||
| residual=False): | ||
| super(GraphAttention, self).__init__() | ||
| self.g = g | ||
| self.num_heads = num_heads | ||
| self.fc = nn.Dense(num_heads * out_dim, use_bias=False, | ||
| weight_initializer=mx.init.Xavier()) | ||
| if feat_drop: | ||
| self.feat_drop = nn.Dropout(feat_drop) | ||
| else: | ||
| self.feat_drop = lambda x : x | ||
| if attn_drop: | ||
| self.attn_drop = nn.Dropout(attn_drop) | ||
| else: | ||
| self.attn_drop = lambda x : x | ||
| self.attn_l = self.params.get("left_att", grad_req="add", | ||
| shape=(1, num_heads, out_dim), | ||
| init=mx.init.Xavier()) | ||
| self.attn_r = self.params.get("right_att", grad_req="add", | ||
| shape=(1, num_heads, out_dim), | ||
| init=mx.init.Xavier()) | ||
| self.alpha = alpha | ||
| self.softmax = edge_softmax | ||
| self.residual = residual | ||
| if residual: | ||
| if in_dim != out_dim: | ||
| self.res_fc = nn.Dense(num_heads * out_dim, use_bias=False, | ||
| weight_initializer=mx.init.Xavier()) | ||
| else: | ||
| self.res_fc = None | ||
|
|
||
| def forward(self, inputs): | ||
| # prepare | ||
| h = self.feat_drop(inputs) # NxD | ||
| ft = self.fc(h).reshape((h.shape[0], self.num_heads, -1)) # NxHxD' | ||
| a1 = (ft * self.attn_l.data(ft.context)).sum(axis=-1).expand_dims(-1) # N x H x 1 | ||
| a2 = (ft * self.attn_r.data(ft.context)).sum(axis=-1).expand_dims(-1) # N x H x 1 | ||
| self.g.ndata.update({'ft' : ft, 'a1' : a1, 'a2' : a2}) | ||
| # 1. compute edge attention | ||
| self.g.apply_edges(self.edge_attention) | ||
| # 2. compute softmax | ||
| self.edge_softmax() | ||
| # 3. compute the aggregated node features | ||
| self.g.update_all(fn.src_mul_edge('ft', 'a_drop', 'ft'), | ||
| fn.sum('ft', 'ft')) | ||
| ret = self.g.ndata['ft'] | ||
| # 4. residual | ||
| if self.residual: | ||
| if self.res_fc is not None: | ||
| resval = self.res_fc(h).reshape( | ||
| (h.shape[0], self.num_heads, -1)) # NxHxD' | ||
| else: | ||
| resval = nd.expand_dims(h, axis=1) # Nx1xD' | ||
| ret = resval + ret | ||
| return ret | ||
|
|
||
| def edge_attention(self, edges): | ||
| # an edge UDF to compute unnormalized attention values from src and dst | ||
| a = nd.LeakyReLU(edges.src['a1'] + edges.dst['a2'], slope=self.alpha) | ||
| return {'a' : a} | ||
|
|
||
| def edge_softmax(self): | ||
| attention = self.softmax(self.g, self.g.edata.pop('a')) | ||
| # Dropout attention scores and save them | ||
| self.g.edata['a_drop'] = self.attn_drop(attention) | ||
|
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||
|
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||
| class GAT(nn.Block): | ||
| def __init__(self, | ||
| g, | ||
| num_layers, | ||
| in_dim, | ||
| num_hidden, | ||
| num_classes, | ||
| heads, | ||
| activation, | ||
| feat_drop, | ||
| attn_drop, | ||
| alpha, | ||
| residual): | ||
| super(GAT, self).__init__() | ||
| self.g = g | ||
| self.num_layers = num_layers | ||
| self.gat_layers = [] | ||
| self.activation = activation | ||
| # input projection (no residual) | ||
| self.gat_layers.append(GraphAttention( | ||
| g, in_dim, num_hidden, heads[0], | ||
| feat_drop, attn_drop, alpha, False)) | ||
| # hidden layers | ||
| for l in range(1, num_layers): | ||
| # due to multi-head, the in_dim = num_hidden * num_heads | ||
| self.gat_layers.append(GraphAttention( | ||
| g, num_hidden * heads[l-1], num_hidden, heads[l], | ||
| feat_drop, attn_drop, alpha, residual)) | ||
| # output projection | ||
| self.gat_layers.append(GraphAttention( | ||
| g, num_hidden * heads[-2], num_classes, heads[-1], | ||
| feat_drop, attn_drop, alpha, residual)) | ||
| for i, layer in enumerate(self.gat_layers): | ||
| self.register_child(layer, "gat_layer_{}".format(i)) | ||
|
|
||
| def forward(self, inputs): | ||
| h = inputs | ||
| for l in range(self.num_layers): | ||
| h = self.gat_layers[l](h).flatten() | ||
| h = self.activation(h) | ||
| # output projection | ||
| logits = self.gat_layers[-1](h).mean(1) | ||
| return logits |
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