[GraphBolt] Implement labor dependent minibatching - python side. #7208
Conversation
|
To trigger regression tests:
|
| ): | ||
| if sampler.__name__ == "sample_layer_neighbors": |
There was a problem hiding this comment.
Instead of checking the sampler name here, why not move the self._init_seed(batch_dependency) to init of LayerNeighborSampler?
| ): | ||
| datapipe = datapipe.transform( | ||
| partial(self._prepare, graph.node_type_to_id) | ||
| ) | ||
| is_labor = sampler.__name__ == "sample_layer_neighbors" | ||
| if is_labor: | ||
| datapipe = datapipe.transform(self._set_seed) |
There was a problem hiding this comment.
Same here, is it possible to move this sample_layer_neighbors specific code to its own class, instead of hack in the parent class.
There was a problem hiding this comment.
How do you propose we do that without replicating the whole implementation?
There was a problem hiding this comment.
it makes sense to replicating some code if needed, it is usually error prone to have parent class's behavior depend on child class's type, which is anti-"oop" pattern.
There was a problem hiding this comment.
I understand that. But I feel anti-oop pattern is better than replicating the code. I plan to base Temporal samplers into the NeighborSamplerImpl as well, so that the GPU sampling optimizations can be enabled for the temporal case as well.
Description
Depends on #7207.
Checklist
Please feel free to remove inapplicable items for your PR.
Changes