[Bugfix] Accessing data from the indexes stored in same device #4242
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To trigger regression tests:
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| @@ -133,6 +133,7 @@ def run(proc_id, n_gpus, args, devices, data): | |||
| # blocks. | |||
| tic_step = time.time() | |||
| for step, (input_nodes, pos_graph, neg_graph, blocks) in enumerate(dataloader): | |||
| input_nodes = input_nodes.to(nfeat.device) | |||
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When features are on the CPU, input_nodes will first be copied to GPU in the dataloader and then copied back to the CPU for indexing. It's not caused by this PR and can be eliminated by unifying --graph_device and --data_device just like other examples. Perhaps we should take a note and fix it when we refactor this example in the future.
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I generally agree. It does sound odd that copying input_nodes back and forth and it should be fixed when we refactor it. However, here, it seems input_nodes always belong to the same device as dataloader (--gpu), which is configured/controlled differently than nfeat (--data-device) and g (--graph-device)...
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With the new sampling pipeline, we can specify prefetch_node_features to the sampler and don't need batch_inputs = nfeat[input_nodes].to(device) anymore.
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I see... Thanks Xin for noting this. I will keep it in mind :)
| pos_graph = pos_graph.to(device) | ||
| neg_graph = neg_graph.to(device) | ||
| blocks = [block.int().to(device) for block in blocks] | ||
| blocks = [block.int() for block in blocks] |
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Since pos_graph, neg_graph, blocks all reside in the same device as dataloader (L105), moving to device is redundant.
…4242) * First update to fix two examples * Update to fix RGCN/graphsage example and dataloader * Update
Description
To address #4234, this PR fixes the crashing example cases, rgcn and graphsage, due to recent pytorch update
Checklist
Please feel free to remove inapplicable items for your PR.
or have been fixed to be compatible with this change
Changes
Move index to the same device as data.