refactor(loader): NodeLoader and LinkLoader as base implementation classes, part 4
#5404
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NodeLoader and LinkLoader as base implementation classes, part 5NodeLoader and LinkLoader as base implementation classes, part 4
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Padarn
reviewed
Sep 10, 2022
| # Initialize sampler with keyword arguments: | ||
| # NOTE sampler is an attribute of 'DataLoader': | ||
| self.node_sampler = node_sampler | ||
| if initialize_sampler: |
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Is there a use case for not doing this?
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Yeah, see LightningNodeData and LightningLinkData for examples of this (we initialize a sampler once, and use it for multiple dataloaders).
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Ahh I see. Lightning always gives caes I hadn't thought of. Thanks
yaoyaowd
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Sep 12, 2022
rusty1s
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Sep 13, 2022
mananshah99
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Sep 13, 2022
…5418) This PR builds off of #5404 by refactoring `HGTLoader` behind the sampler and loader interface; the simplicity of this refactor also shows the flexibility of the interface. In doing so, it defines `HGTSampler` that inherits from `BaseSampler`, and uses `HGTSampler` as part of the `HGTLoader(NodeLoader)` construction. With this setup, it should also be trivial to add an HGT link-level loader, but this task is left as a TODO.
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This PR continues the effort to consolidate PyG's sampling interface in preparation for moving
sample(...)behind theGraphStoreinterface. This effort is somewhat large in scope and will be broken into multiple PRs for ease of review. It builds off of #5402, and makes a significant move to abstract data loading behind adata: Union[Data, HeteroData, Tuple[FeatureStore, GraphStore]]and asampler: BaseSampler.It does so by introducing two base implementation classes:
NodeLoaderandLinkLoader.NodeLoaderperforms sampling from nodes (usingsample_from_nodes), andLinkLoaderdoes the same from edges (usingsample_from_edges). They both expose parameters in their initializers that are intended for loading (that is, the process of using a sampler to get subgraphs, using a feature fetcher to get features, and joining these together to construct aHeteroDataobject to pass downstream). Samplers are intended to expose parameters that are used for sampling (that are particular to the sampling method).The implementations of
NeighborLoaderandLinkNeighborLoaderare now very simple: they pass theNeighborSamplerand any necessary initialization parameters directly in__init__, with no other change.