wehub-resource-sync
65 lines
2.0 KiB
Python
65 lines
2.0 KiB
Python
# pylint: disable=too-many-arguments, too-many-locals
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from collections import OrderedDict
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from itertools import product
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import dgl
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import pytest
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import torch
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from dgl.nn import CuGraphGATConv, GATConv
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options = OrderedDict(
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{
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"idtype_int": [False, True],
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"max_in_degree": [None, 8],
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"num_heads": [1, 3],
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"to_block": [False, True],
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}
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)
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def generate_graph():
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u = torch.tensor([0, 1, 0, 2, 3, 0, 4, 0, 5, 0, 6, 7, 0, 8, 9])
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v = torch.tensor([1, 9, 2, 9, 9, 4, 9, 5, 9, 6, 9, 9, 8, 9, 0])
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g = dgl.graph((u, v))
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return g
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@pytest.mark.parametrize(",".join(options.keys()), product(*options.values()))
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def test_gatconv_equality(idtype_int, max_in_degree, num_heads, to_block):
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device = "cuda:0"
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in_feat, out_feat = 10, 2
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args = (in_feat, out_feat, num_heads)
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kwargs = {"bias": False}
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g = generate_graph().to(device)
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if idtype_int:
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g = g.int()
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if to_block:
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g = dgl.to_block(g)
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feat = torch.rand(g.num_src_nodes(), in_feat).to(device)
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torch.manual_seed(0)
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conv1 = GATConv(*args, **kwargs, allow_zero_in_degree=True).to(device)
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out1 = conv1(g, feat)
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torch.manual_seed(0)
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conv2 = CuGraphGATConv(*args, **kwargs).to(device)
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dim = num_heads * out_feat
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with torch.no_grad():
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conv2.attn_weights.data[:dim] = conv1.attn_l.data.flatten()
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conv2.attn_weights.data[dim:] = conv1.attn_r.data.flatten()
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conv2.fc.weight.data[:] = conv1.fc.weight.data
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out2 = conv2(g, feat, max_in_degree=max_in_degree)
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assert torch.allclose(out1, out2, atol=1e-6)
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grad_out1 = torch.rand_like(out1)
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grad_out2 = grad_out1.clone().detach()
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out1.backward(grad_out1)
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out2.backward(grad_out2)
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assert torch.allclose(conv1.fc.weight.grad, conv2.fc.weight.grad, atol=1e-6)
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assert torch.allclose(
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torch.cat((conv1.attn_l.grad, conv1.attn_r.grad), dim=0),
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conv2.attn_weights.grad.view(2, num_heads, out_feat),
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atol=1e-6,
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)
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