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[GraphBolt] gb.DataLoader can simply be a datapipe. (#7732)
This commit is contained in:
Muhammed Fatih BALIN
committed by
GitHub
GitHub
parent
b3eacd22d7
commit
37d1064c22
@@ -4,7 +4,6 @@ import torch
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import torch.utils.data as torch_data
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from .base import CopyTo
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from .datapipes import (
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datapipe_graph_to_adjlist,
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find_dps,
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@@ -15,6 +14,7 @@ from .feature_fetcher import FeatureFetcher, FeatureFetcherStartMarker
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from .impl.neighbor_sampler import SamplePerLayer
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from .internal_utils import gb_warning
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from .item_sampler import ItemSampler
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from .minibatch_transformer import MiniBatchTransformer
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__all__ = [
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@@ -75,7 +75,7 @@ class MultiprocessingWrapper(torch_data.IterDataPipe):
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yield from self.dataloader
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class DataLoader(torch_data.DataLoader):
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class DataLoader(MiniBatchTransformer):
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"""Multiprocessing DataLoader.
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Iterates over the data pipeline with everything before feature fetching
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@@ -122,32 +122,33 @@ class DataLoader(torch_data.DataLoader):
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datapipe = datapipe.mark_end()
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datapipe_graph = traverse_dps(datapipe)
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# (1) Insert minibatch distribution.
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# TODO(BarclayII): Currently I'm using sharding_filter() as a
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# concept demonstration. Later on minibatch distribution should be
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# merged into ItemSampler to maximize efficiency.
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item_samplers = find_dps(
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datapipe_graph,
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ItemSampler,
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)
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for item_sampler in item_samplers:
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datapipe_graph = replace_dp(
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if num_workers > 0:
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# (1) Insert minibatch distribution.
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# TODO(BarclayII): Currently I'm using sharding_filter() as a
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# concept demonstration. Later on minibatch distribution should be
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# merged into ItemSampler to maximize efficiency.
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item_samplers = find_dps(
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datapipe_graph,
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item_sampler,
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item_sampler.sharding_filter(),
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ItemSampler,
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)
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for item_sampler in item_samplers:
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datapipe_graph = replace_dp(
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datapipe_graph,
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item_sampler,
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item_sampler.sharding_filter(),
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)
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# (2) Cut datapipe at FeatureFetcher and wrap.
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datapipe_graph = _find_and_wrap_parent(
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datapipe_graph,
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FeatureFetcherStartMarker,
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MultiprocessingWrapper,
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num_workers=num_workers,
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persistent_workers=persistent_workers,
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)
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# (2) Cut datapipe at FeatureFetcher and wrap.
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datapipe_graph = _find_and_wrap_parent(
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datapipe_graph,
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FeatureFetcherStartMarker,
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MultiprocessingWrapper,
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num_workers=num_workers,
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persistent_workers=persistent_workers,
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)
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# (3) Limit the number of UVA threads used if the feature_fetcher has
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# overlapping optimization enabled.
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# (3) Limit the number of UVA threads used if the feature_fetcher
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# or any of the samplers have overlapping optimization enabled.
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if num_workers == 0 and torch.cuda.is_available():
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feature_fetchers = find_dps(
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datapipe_graph,
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@@ -187,6 +188,4 @@ class DataLoader(torch_data.DataLoader):
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),
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)
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# The stages after feature fetching is still done in the main process.
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# So we set num_workers to 0 here.
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super().__init__(datapipe, batch_size=None, num_workers=0)
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super().__init__(datapipe)
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@@ -138,8 +138,7 @@ def test_gpu_sampling_DataLoader(
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bufferer_cnt += 2 * num_layers
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if asynchronous:
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bufferer_cnt += 2 * num_layers
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datapipe = dataloader.dataset
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datapipe_graph = traverse_dps(datapipe)
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datapipe_graph = traverse_dps(dataloader)
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bufferers = find_dps(
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datapipe_graph,
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dgl.graphbolt.Bufferer,
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@@ -64,55 +64,54 @@ def test_integration_link_prediction():
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[3, 2],
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[3, 2],
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[3, 3],
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[5, 0],
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[5, 0],
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[5, 2],
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[5, 1],
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[3, 4],
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[3, 3],
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[3, 0],
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[3, 5],
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[3, 3],
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[3, 3],
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[3, 2],
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[3, 0],
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[3, 4]]),
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sampled_subgraphs=[SampledSubgraphImpl(sampled_csc=CSCFormatBase(indptr=tensor([0, 1, 1, 2, 2, 2, 3], dtype=torch.int32),
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indices=tensor([0, 5, 4], dtype=torch.int32),
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sampled_subgraphs=[SampledSubgraphImpl(sampled_csc=CSCFormatBase(indptr=tensor([0, 1, 1, 1, 1, 2, 2], dtype=torch.int32),
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indices=tensor([4, 5], dtype=torch.int32),
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),
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original_row_node_ids=tensor([5, 1, 3, 2, 0, 4]),
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original_edge_ids=tensor([8, 5, 7]),
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original_column_node_ids=tensor([5, 1, 3, 2, 0, 4]),
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),
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SampledSubgraphImpl(sampled_csc=CSCFormatBase(indptr=tensor([0, 1, 1, 1, 1, 1, 2], dtype=torch.int32),
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indices=tensor([5, 4], dtype=torch.int32),
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),
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original_row_node_ids=tensor([5, 1, 3, 2, 0, 4]),
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original_row_node_ids=tensor([5, 1, 3, 2, 4, 0]),
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original_edge_ids=tensor([9, 7]),
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original_column_node_ids=tensor([5, 1, 3, 2, 0, 4]),
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original_column_node_ids=tensor([5, 1, 3, 2, 4, 0]),
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),
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SampledSubgraphImpl(sampled_csc=CSCFormatBase(indptr=tensor([0, 1, 1, 1, 1, 2, 2], dtype=torch.int32),
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indices=tensor([0, 5], dtype=torch.int32),
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),
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original_row_node_ids=tensor([5, 1, 3, 2, 4, 0]),
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original_edge_ids=tensor([8, 7]),
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original_column_node_ids=tensor([5, 1, 3, 2, 4, 0]),
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)],
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node_features={'feat': tensor([[0.5160, 0.2486],
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[0.6172, 0.7865],
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[0.8672, 0.2276],
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[0.2109, 0.1089],
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[0.9634, 0.2294],
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[0.5503, 0.8223]])},
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[0.5503, 0.8223],
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[0.9634, 0.2294]])},
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labels=tensor([1., 1., 1., 1., 0., 0., 0., 0., 0., 0., 0., 0.]),
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input_nodes=tensor([5, 1, 3, 2, 0, 4]),
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input_nodes=tensor([5, 1, 3, 2, 4, 0]),
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indexes=tensor([0, 1, 2, 3, 0, 0, 1, 1, 2, 2, 3, 3]),
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edge_features=[{'feat': tensor([[0.8972, 0.7511, 0.3617],
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[0.7885, 0.3414, 0.5485],
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edge_features=[{'feat': tensor([[0.5773, 0.2199, 0.3366],
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[0.0056, 0.9469, 0.4432]])},
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{'feat': tensor([[0.5773, 0.2199, 0.3366],
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{'feat': tensor([[0.8972, 0.7511, 0.3617],
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[0.0056, 0.9469, 0.4432]])}],
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compacted_seeds=tensor([[0, 1],
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[2, 3],
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[2, 3],
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[2, 2],
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[0, 4],
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[0, 4],
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[2, 2],
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[0, 3],
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[0, 1],
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[2, 4],
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[2, 2],
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[2, 0],
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[2, 2],
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[2, 2],
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[2, 5]]),
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blocks=[Block(num_src_nodes=6, num_dst_nodes=6, num_edges=3),
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[2, 3],
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[2, 5],
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[2, 4]]),
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blocks=[Block(num_src_nodes=6, num_dst_nodes=6, num_edges=2),
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Block(num_src_nodes=6, num_dst_nodes=6, num_edges=2)],
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)"""
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),
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@@ -121,103 +120,97 @@ def test_integration_link_prediction():
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[4, 3],
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[4, 4],
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[0, 4],
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[3, 1],
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[3, 4],
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[3, 5],
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[4, 2],
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[4, 5],
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[4, 1],
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[4, 4],
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[4, 3],
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[4, 4],
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[4, 5],
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[0, 1],
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[0, 5]]),
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sampled_subgraphs=[SampledSubgraphImpl(sampled_csc=CSCFormatBase(indptr=tensor([0, 0, 0, 0, 1, 1, 2], dtype=torch.int32),
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indices=tensor([4, 0], dtype=torch.int32),
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[0, 3]]),
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sampled_subgraphs=[SampledSubgraphImpl(sampled_csc=CSCFormatBase(indptr=tensor([0, 0, 0, 0, 0, 1], dtype=torch.int32),
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indices=tensor([3], dtype=torch.int32),
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),
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original_row_node_ids=tensor([3, 4, 0, 1, 5, 2]),
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original_edge_ids=tensor([0, 1]),
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original_column_node_ids=tensor([3, 4, 0, 1, 5, 2]),
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original_row_node_ids=tensor([3, 4, 0, 5, 1]),
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original_edge_ids=tensor([0]),
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original_column_node_ids=tensor([3, 4, 0, 5, 1]),
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),
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SampledSubgraphImpl(sampled_csc=CSCFormatBase(indptr=tensor([0, 0, 0, 0, 1, 2, 3], dtype=torch.int32),
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indices=tensor([4, 4, 0], dtype=torch.int32),
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SampledSubgraphImpl(sampled_csc=CSCFormatBase(indptr=tensor([0, 0, 0, 0, 1, 2], dtype=torch.int32),
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indices=tensor([3, 3], dtype=torch.int32),
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),
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original_row_node_ids=tensor([3, 4, 0, 1, 5, 2]),
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original_edge_ids=tensor([0, 8, 1]),
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original_column_node_ids=tensor([3, 4, 0, 1, 5, 2]),
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original_row_node_ids=tensor([3, 4, 0, 5, 1]),
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original_edge_ids=tensor([8, 0]),
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original_column_node_ids=tensor([3, 4, 0, 5, 1]),
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)],
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node_features={'feat': tensor([[0.8672, 0.2276],
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[0.5503, 0.8223],
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[0.9634, 0.2294],
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[0.6172, 0.7865],
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[0.5160, 0.2486],
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[0.2109, 0.1089]])},
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[0.6172, 0.7865]])},
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labels=tensor([1., 1., 1., 1., 0., 0., 0., 0., 0., 0., 0., 0.]),
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input_nodes=tensor([3, 4, 0, 1, 5, 2]),
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input_nodes=tensor([3, 4, 0, 5, 1]),
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indexes=tensor([0, 1, 2, 3, 0, 0, 1, 1, 2, 2, 3, 3]),
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edge_features=[{'feat': tensor([[0.5123, 0.1709, 0.6150],
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[0.1476, 0.1902, 0.1314]])},
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{'feat': tensor([[0.5123, 0.1709, 0.6150],
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[0.8972, 0.7511, 0.3617],
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[0.1476, 0.1902, 0.1314]])}],
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edge_features=[{'feat': tensor([[0.5123, 0.1709, 0.6150]])},
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{'feat': tensor([[0.8972, 0.7511, 0.3617],
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[0.5123, 0.1709, 0.6150]])}],
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compacted_seeds=tensor([[0, 0],
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[1, 0],
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[1, 1],
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[2, 1],
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[0, 1],
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[0, 3],
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[0, 4],
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[1, 5],
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[1, 4],
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[1, 1],
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[1, 0],
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[2, 3],
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[2, 4]]),
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blocks=[Block(num_src_nodes=6, num_dst_nodes=6, num_edges=2),
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Block(num_src_nodes=6, num_dst_nodes=6, num_edges=3)],
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[1, 1],
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[1, 3],
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[2, 4],
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[2, 0]]),
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blocks=[Block(num_src_nodes=5, num_dst_nodes=5, num_edges=1),
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Block(num_src_nodes=5, num_dst_nodes=5, num_edges=2)],
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)"""
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),
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str(
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"""MiniBatch(seeds=tensor([[5, 5],
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[4, 5],
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[5, 0],
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[5, 4],
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[5, 5],
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[5, 5],
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[4, 0],
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[4, 1]]),
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sampled_subgraphs=[SampledSubgraphImpl(sampled_csc=CSCFormatBase(indptr=tensor([0, 0, 1, 1, 2], dtype=torch.int32),
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indices=tensor([1, 0], dtype=torch.int32),
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[4, 0]]),
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sampled_subgraphs=[SampledSubgraphImpl(sampled_csc=CSCFormatBase(indptr=tensor([0, 0, 1, 1], dtype=torch.int32),
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indices=tensor([1], dtype=torch.int32),
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),
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original_row_node_ids=tensor([5, 4, 0, 1]),
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original_edge_ids=tensor([6, 0]),
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original_column_node_ids=tensor([5, 4, 0, 1]),
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original_row_node_ids=tensor([5, 4, 0]),
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original_edge_ids=tensor([6]),
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original_column_node_ids=tensor([5, 4, 0]),
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),
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SampledSubgraphImpl(sampled_csc=CSCFormatBase(indptr=tensor([0, 0, 1, 1, 2], dtype=torch.int32),
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indices=tensor([1, 0], dtype=torch.int32),
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SampledSubgraphImpl(sampled_csc=CSCFormatBase(indptr=tensor([0, 0, 1, 1], dtype=torch.int32),
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indices=tensor([2], dtype=torch.int32),
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),
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original_row_node_ids=tensor([5, 4, 0, 1]),
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original_edge_ids=tensor([6, 0]),
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original_column_node_ids=tensor([5, 4, 0, 1]),
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original_row_node_ids=tensor([5, 4, 0]),
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original_edge_ids=tensor([7]),
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original_column_node_ids=tensor([5, 4, 0]),
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)],
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node_features={'feat': tensor([[0.5160, 0.2486],
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[0.5503, 0.8223],
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[0.9634, 0.2294],
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[0.6172, 0.7865]])},
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[0.9634, 0.2294]])},
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labels=tensor([1., 1., 0., 0., 0., 0.]),
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input_nodes=tensor([5, 4, 0, 1]),
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input_nodes=tensor([5, 4, 0]),
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indexes=tensor([0, 1, 0, 0, 1, 1]),
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edge_features=[{'feat': tensor([[0.4088, 0.8200, 0.1851],
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[0.5123, 0.1709, 0.6150]])},
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{'feat': tensor([[0.4088, 0.8200, 0.1851],
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[0.5123, 0.1709, 0.6150]])}],
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edge_features=[{'feat': tensor([[0.4088, 0.8200, 0.1851]])},
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{'feat': tensor([[0.0056, 0.9469, 0.4432]])}],
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compacted_seeds=tensor([[0, 0],
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[1, 0],
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[0, 2],
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[0, 1],
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[0, 0],
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[0, 0],
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[1, 2],
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[1, 3]]),
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blocks=[Block(num_src_nodes=4, num_dst_nodes=4, num_edges=2),
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Block(num_src_nodes=4, num_dst_nodes=4, num_edges=2)],
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[1, 2]]),
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blocks=[Block(num_src_nodes=3, num_dst_nodes=3, num_edges=1),
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Block(num_src_nodes=3, num_dst_nodes=3, num_edges=1)],
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)"""
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),
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]
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for step, data in enumerate(dataloader):
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assert expected[step] == str(data), print(data)
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assert expected[step] == str(data), print(step, data)
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def test_integration_node_classification():
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@@ -275,10 +268,10 @@ def test_integration_node_classification():
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str(
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"""MiniBatch(seeds=tensor([5, 1]),
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sampled_subgraphs=[SampledSubgraphImpl(sampled_csc=CSCFormatBase(indptr=tensor([0, 1, 2], dtype=torch.int32),
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indices=tensor([2, 0], dtype=torch.int32),
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indices=tensor([0, 0], dtype=torch.int32),
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),
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original_row_node_ids=tensor([5, 1, 4]),
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original_edge_ids=tensor([9, 0]),
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original_row_node_ids=tensor([5, 1]),
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original_edge_ids=tensor([8, 0]),
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original_column_node_ids=tensor([5, 1]),
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),
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SampledSubgraphImpl(sampled_csc=CSCFormatBase(indptr=tensor([0, 1, 2], dtype=torch.int32),
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@@ -289,51 +282,49 @@ def test_integration_node_classification():
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original_column_node_ids=tensor([5, 1]),
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)],
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node_features={'feat': tensor([[0.5160, 0.2486],
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[0.6172, 0.7865],
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[0.5503, 0.8223]])},
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[0.6172, 0.7865]])},
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labels=None,
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input_nodes=tensor([5, 1, 4]),
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input_nodes=tensor([5, 1]),
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indexes=None,
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edge_features=[{'feat': tensor([[0.5773, 0.2199, 0.3366],
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edge_features=[{'feat': tensor([[0.8972, 0.7511, 0.3617],
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[0.5123, 0.1709, 0.6150]])},
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{'feat': tensor([[0.8972, 0.7511, 0.3617],
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[0.5123, 0.1709, 0.6150]])}],
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compacted_seeds=None,
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blocks=[Block(num_src_nodes=3, num_dst_nodes=2, num_edges=2),
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blocks=[Block(num_src_nodes=2, num_dst_nodes=2, num_edges=2),
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Block(num_src_nodes=2, num_dst_nodes=2, num_edges=2)],
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)"""
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),
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str(
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"""MiniBatch(seeds=tensor([2, 4]),
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sampled_subgraphs=[SampledSubgraphImpl(sampled_csc=CSCFormatBase(indptr=tensor([0, 1, 2, 3, 3], dtype=torch.int32),
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||||
sampled_subgraphs=[SampledSubgraphImpl(sampled_csc=CSCFormatBase(indptr=tensor([0, 1, 2, 3], dtype=torch.int32),
|
||||
indices=tensor([2, 1, 2], dtype=torch.int32),
|
||||
),
|
||||
original_row_node_ids=tensor([2, 4, 3, 0]),
|
||||
original_edge_ids=tensor([2, 6, 3]),
|
||||
original_column_node_ids=tensor([2, 4, 3, 0]),
|
||||
original_row_node_ids=tensor([2, 4, 3]),
|
||||
original_edge_ids=tensor([1, 6, 3]),
|
||||
original_column_node_ids=tensor([2, 4, 3]),
|
||||
),
|
||||
SampledSubgraphImpl(sampled_csc=CSCFormatBase(indptr=tensor([0, 1, 2], dtype=torch.int32),
|
||||
indices=tensor([2, 3], dtype=torch.int32),
|
||||
indices=tensor([2, 1], dtype=torch.int32),
|
||||
),
|
||||
original_row_node_ids=tensor([2, 4, 3, 0]),
|
||||
original_edge_ids=tensor([2, 7]),
|
||||
original_row_node_ids=tensor([2, 4, 3]),
|
||||
original_edge_ids=tensor([2, 6]),
|
||||
original_column_node_ids=tensor([2, 4]),
|
||||
)],
|
||||
node_features={'feat': tensor([[0.2109, 0.1089],
|
||||
[0.5503, 0.8223],
|
||||
[0.8672, 0.2276],
|
||||
[0.9634, 0.2294]])},
|
||||
[0.8672, 0.2276]])},
|
||||
labels=None,
|
||||
input_nodes=tensor([2, 4, 3, 0]),
|
||||
input_nodes=tensor([2, 4, 3]),
|
||||
indexes=None,
|
||||
edge_features=[{'feat': tensor([[0.2582, 0.5203, 0.6228],
|
||||
edge_features=[{'feat': tensor([[0.1476, 0.1902, 0.1314],
|
||||
[0.4088, 0.8200, 0.1851],
|
||||
[0.3708, 0.7631, 0.2683]])},
|
||||
{'feat': tensor([[0.2582, 0.5203, 0.6228],
|
||||
[0.0056, 0.9469, 0.4432]])}],
|
||||
[0.4088, 0.8200, 0.1851]])}],
|
||||
compacted_seeds=None,
|
||||
blocks=[Block(num_src_nodes=4, num_dst_nodes=4, num_edges=3),
|
||||
Block(num_src_nodes=4, num_dst_nodes=2, num_edges=2)],
|
||||
blocks=[Block(num_src_nodes=3, num_dst_nodes=3, num_edges=3),
|
||||
Block(num_src_nodes=3, num_dst_nodes=2, num_edges=2)],
|
||||
)"""
|
||||
),
|
||||
str(
|
||||
@@ -342,14 +333,14 @@ def test_integration_node_classification():
|
||||
indices=tensor([0], dtype=torch.int32),
|
||||
),
|
||||
original_row_node_ids=tensor([3, 0]),
|
||||
original_edge_ids=tensor([4]),
|
||||
original_edge_ids=tensor([3]),
|
||||
original_column_node_ids=tensor([3, 0]),
|
||||
),
|
||||
SampledSubgraphImpl(sampled_csc=CSCFormatBase(indptr=tensor([0, 1, 1], dtype=torch.int32),
|
||||
indices=tensor([0], dtype=torch.int32),
|
||||
),
|
||||
original_row_node_ids=tensor([3, 0]),
|
||||
original_edge_ids=tensor([4]),
|
||||
original_edge_ids=tensor([3]),
|
||||
original_column_node_ids=tensor([3, 0]),
|
||||
)],
|
||||
node_features={'feat': tensor([[0.8672, 0.2276],
|
||||
@@ -357,8 +348,8 @@ def test_integration_node_classification():
|
||||
labels=None,
|
||||
input_nodes=tensor([3, 0]),
|
||||
indexes=None,
|
||||
edge_features=[{'feat': tensor([[0.2126, 0.7878, 0.7225]])},
|
||||
{'feat': tensor([[0.2126, 0.7878, 0.7225]])}],
|
||||
edge_features=[{'feat': tensor([[0.3708, 0.7631, 0.2683]])},
|
||||
{'feat': tensor([[0.3708, 0.7631, 0.2683]])}],
|
||||
compacted_seeds=None,
|
||||
blocks=[Block(num_src_nodes=2, num_dst_nodes=2, num_edges=1),
|
||||
Block(num_src_nodes=2, num_dst_nodes=2, num_edges=1)],
|
||||
|
||||
Reference in New Issue
Block a user