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78 lines
2.2 KiB
Python
78 lines
2.2 KiB
Python
#
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# Copyright (c) 2022 by Contributors
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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import unittest
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import backend as F
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import dgl
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from utils import parametrize_idtype
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D = 5
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def generate_graph(idtype, grad=False, add_data=True):
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g = dgl.graph([]).to(F.ctx(), dtype=idtype)
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g.add_nodes(10)
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u, v = [], []
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# create a graph where 0 is the source and 9 is the sink
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for i in range(1, 9):
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u.append(0)
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v.append(i)
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u.append(i)
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v.append(9)
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# add a back flow from 9 to 0
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u.append(9)
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v.append(0)
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g.add_edges(u, v)
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if add_data:
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ncol = F.randn((10, D))
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ecol = F.randn((17, D))
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if grad:
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ncol = F.attach_grad(ncol)
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ecol = F.attach_grad(ecol)
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g.ndata["h"] = ncol
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g.edata["l"] = ecol
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return g
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@unittest.skipIf(not F.gpu_ctx(), reason="only necessary with GPU")
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@parametrize_idtype
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def test_gpu_cache(idtype):
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g = generate_graph(idtype)
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cache = dgl.cuda.GPUCache(5, D, idtype)
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h = g.ndata["h"]
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t = 5
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keys = F.arange(0, t, dtype=idtype)
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values, m_idx, m_keys = cache.query(keys)
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m_values = h[F.tensor(m_keys, F.int64)]
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values[F.tensor(m_idx, F.int64)] = m_values
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cache.replace(m_keys, m_values)
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keys = F.arange(3, 8, dtype=idtype)
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values, m_idx, m_keys = cache.query(keys)
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assert m_keys.shape[0] == 3 and m_idx.shape[0] == 3
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m_values = h[F.tensor(m_keys, F.int64)]
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values[F.tensor(m_idx, F.int64)] = m_values
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assert (values != h[F.tensor(keys, F.int64)]).sum().item() == 0
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cache.replace(m_keys, m_values)
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if __name__ == "__main__":
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test_gpu_cache(F.int64)
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test_gpu_cache(F.int32)
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