dmlc--dgl
Tingyu Wang
bcf9923b32
* add CuGraphSAGEConv model * fix lint issues * update model to reflect changes in make_mfg_csr(), move max_in_degree to forward() * lintrunner * allow reset_parameters() * remove norm option, simplify test * allow full graph fallback option, add example * address comments * address reviews --------- Co-authored-by: Mufei Li <mufeili1996@gmail.com>
70 行
2.0 KiB
Python
70 行
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 CuGraphSAGEConv, SAGEConv
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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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"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.skip()
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@pytest.mark.parametrize(",".join(options.keys()), product(*options.values()))
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def test_SAGEConv_equality(idtype_int, max_in_degree, to_block):
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device = "cuda:0"
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in_feat, out_feat = 5, 2
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kwargs = {"aggregator_type": "mean"}
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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 = SAGEConv(in_feat, out_feat, **kwargs).to(device)
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torch.manual_seed(0)
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conv2 = CuGraphSAGEConv(in_feat, out_feat, **kwargs).to(device)
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with torch.no_grad():
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conv2.linear.weight.data[:, :in_feat] = conv1.fc_neigh.weight.data
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conv2.linear.weight.data[:, in_feat:] = conv1.fc_self.weight.data
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conv2.linear.bias.data[:] = conv1.fc_self.bias.data
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out1 = conv1(g, feat)
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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-06)
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grad_out = torch.rand_like(out1)
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out1.backward(grad_out)
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out2.backward(grad_out)
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assert torch.allclose(
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conv1.fc_neigh.weight.grad,
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conv2.linear.weight.grad[:, :in_feat],
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atol=1e-6,
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)
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assert torch.allclose(
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conv1.fc_self.weight.grad,
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conv2.linear.weight.grad[:, in_feat:],
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atol=1e-6,
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)
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assert torch.allclose(
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conv1.fc_self.bias.grad, conv2.linear.bias.grad, atol=1e-6
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)
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