wehub-resource-sync
48 lines
1.9 KiB
ReStructuredText
48 lines
1.9 KiB
ReStructuredText
.. _guide-graph-gpu:
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1.6 Using DGLGraph on a GPU
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---------------------------
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:ref:`(中文版)<guide_cn-graph-gpu>`
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One can create a :class:`~dgl.DGLGraph` on a GPU by passing two GPU tensors during construction.
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Another approach is to use the :func:`~dgl.DGLGraph.to` API to copy a :class:`~dgl.DGLGraph` to a GPU, which
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copies the graph structure as well as the feature data to the given device.
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.. code::
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>>> import dgl
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>>> import torch as th
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>>> u, v = th.tensor([0, 1, 2]), th.tensor([2, 3, 4])
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>>> g = dgl.graph((u, v))
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>>> g.ndata['x'] = th.randn(5, 3) # original feature is on CPU
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>>> g.device
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device(type='cpu')
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>>> cuda_g = g.to('cuda:0') # accepts any device objects from backend framework
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>>> cuda_g.device
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device(type='cuda', index=0)
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>>> cuda_g.ndata['x'].device # feature data is copied to GPU too
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device(type='cuda', index=0)
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>>> # A graph constructed from GPU tensors is also on GPU
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>>> u, v = u.to('cuda:0'), v.to('cuda:0')
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>>> g = dgl.graph((u, v))
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>>> g.device
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device(type='cuda', index=0)
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Any operations involving a GPU graph are performed on a GPU. Thus, they require all
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tensor arguments to be placed on GPU already and the results (graph or tensor) will be on
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GPU too. Furthermore, a GPU graph only accepts feature data on a GPU.
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.. code::
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>>> cuda_g.in_degrees()
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tensor([0, 0, 1, 1, 1], device='cuda:0')
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>>> cuda_g.in_edges([2, 3, 4]) # ok for non-tensor type arguments
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(tensor([0, 1, 2], device='cuda:0'), tensor([2, 3, 4], device='cuda:0'))
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>>> cuda_g.in_edges(th.tensor([2, 3, 4]).to('cuda:0')) # tensor type must be on GPU
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(tensor([0, 1, 2], device='cuda:0'), tensor([2, 3, 4], device='cuda:0'))
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>>> cuda_g.ndata['h'] = th.randn(5, 4) # ERROR! feature must be on GPU too!
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DGLError: Cannot assign node feature "h" on device cpu to a graph on device
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cuda:0. Call DGLGraph.to() to copy the graph to the same device.
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