Node Classification
This tutorial shows how to train a multi-layer GraphSAGE for node classification on ogbn-arxiv provided by Open Graph Benchmark (OGB). The dataset contains around 170 thousand nodes and 1 million edges.
By the end of this tutorial, you will be able to
Train a GNN model for node classification on a single GPU with DGL’s neighbor sampling components.
Install DGL package
[1]:
# Install required packages.
import os
import torch
import numpy as np
os.environ['TORCH'] = torch.__version__
os.environ['DGLBACKEND'] = "pytorch"
# Install the CPU version in default. If you want to install CUDA version,
# please refer to https://www.dgl.ai/pages/start.html and change runtime type
# accordingly.
device = torch.device("cpu")
!pip install --pre dgl -f https://data.dgl.ai/wheels-test/repo.html
try:
import dgl
import dgl.graphbolt as gb
installed = True
except ImportError as error:
installed = False
print(error)
print("DGL installed!" if installed else "DGL not found!")
Looking in links: https://data.dgl.ai/wheels-test/repo.html
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DGL installed!
Loading Dataset
ogbn-arxiv is already prepared as BuiltinDataset in GraphBolt.
[2]:
dataset = gb.BuiltinDataset("ogbn-arxiv-seeds").load()
The dataset is already preprocessed.
/home/ubuntu/regression_test/dgl/python/dgl/graphbolt/impl/ondisk_dataset.py:855: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
return torch.load(graph_topology.path)
Dataset consists of graph, feature and tasks. You can get the training-validation-test set from the tasks. Seed nodes and corresponding labels are already stored in each training-validation-test set. Other metadata such as number of classes are also stored in the tasks. In this dataset, there is only one task: node classification.
[3]:
graph = dataset.graph.to(device)
feature = dataset.feature.to(device)
train_set = dataset.tasks[0].train_set
valid_set = dataset.tasks[0].validation_set
test_set = dataset.tasks[0].test_set
task_name = dataset.tasks[0].metadata["name"]
num_classes = dataset.tasks[0].metadata["num_classes"]
print(f"Task: {task_name}. Number of classes: {num_classes}")
Task: node_classification. Number of classes: 40
How DGL Handles Computation Dependency¶
The computation dependency for message passing of a single node can be described as a series of message flow graphs (MFG).
Defining Neighbor Sampler and Data Loader in DGL
DGL provides tools to iterate over the dataset in minibatches while generating the computation dependencies to compute their outputs with the MFGs above. For node classification, you can use dgl.graphbolt.DataLoader for iterating over the dataset. It accepts a data pipe that generates minibatches of nodes and their labels, sample neighbors for each node, and generate the computation dependencies in the form of MFGs. Feature fetching, block creation and copying to target device are also
supported. All these operations are split into separate stages in the data pipe, so that you can customize the data pipeline by inserting your own operations.
Let’s say that each node will gather messages from 4 neighbors on each layer. The code defining the data loader and neighbor sampler will look like the following.
[4]:
def create_dataloader(itemset, shuffle):
datapipe = gb.ItemSampler(itemset, batch_size=1024, shuffle=shuffle)
datapipe = datapipe.copy_to(device)
datapipe = datapipe.sample_neighbor(graph, [4, 4])
datapipe = datapipe.fetch_feature(feature, node_feature_keys=["feat"])
return gb.DataLoader(datapipe)
You can iterate over the data loader and a MiniBatch object is yielded.
[5]:
data = next(iter(create_dataloader(train_set, shuffle=True)))
print(data)
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
Cell In[5], line 1
----> 1 data = next(iter(create_dataloader(train_set, shuffle=True)))
2 print(data)
File /opt/conda/envs/dgl-dev-cpu/lib/python3.10/site-packages/torch/utils/data/datapipes/_hook_iterator.py:180, in hook_iterator.<locals>.wrap_generator(*args, **kwargs)
178 response = gen.send(None)
179 else:
--> 180 response = gen.send(None)
182 while True:
183 datapipe._number_of_samples_yielded += 1
File /opt/conda/envs/dgl-dev-cpu/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/callable.py:125, in MapperIterDataPipe.__iter__(self)
124 def __iter__(self) -> Iterator[T_co]:
--> 125 for data in self.datapipe:
126 yield self._apply_fn(data)
File /opt/conda/envs/dgl-dev-cpu/lib/python3.10/site-packages/torch/utils/data/datapipes/_hook_iterator.py:180, in hook_iterator.<locals>.wrap_generator(*args, **kwargs)
178 response = gen.send(None)
179 else:
--> 180 response = gen.send(None)
182 while True:
183 datapipe._number_of_samples_yielded += 1
File ~/regression_test/dgl/python/dgl/graphbolt/base.py:385, in EndMarker.__iter__(self)
384 def __iter__(self):
--> 385 yield from self.datapipe
File /opt/conda/envs/dgl-dev-cpu/lib/python3.10/site-packages/torch/utils/data/datapipes/_hook_iterator.py:180, in hook_iterator.<locals>.wrap_generator(*args, **kwargs)
178 response = gen.send(None)
179 else:
--> 180 response = gen.send(None)
182 while True:
183 datapipe._number_of_samples_yielded += 1
File /opt/conda/envs/dgl-dev-cpu/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/callable.py:125, in MapperIterDataPipe.__iter__(self)
124 def __iter__(self) -> Iterator[T_co]:
--> 125 for data in self.datapipe:
126 yield self._apply_fn(data)
File /opt/conda/envs/dgl-dev-cpu/lib/python3.10/site-packages/torch/utils/data/datapipes/_hook_iterator.py:180, in hook_iterator.<locals>.wrap_generator(*args, **kwargs)
178 response = gen.send(None)
179 else:
--> 180 response = gen.send(None)
182 while True:
183 datapipe._number_of_samples_yielded += 1
File ~/regression_test/dgl/python/dgl/graphbolt/base.py:411, in Bufferer.__iter__(self)
410 def __iter__(self):
--> 411 for data in self.datapipe:
412 if len(self.buffer) < self.buffer.maxlen:
413 self.buffer.append(data)
File /opt/conda/envs/dgl-dev-cpu/lib/python3.10/site-packages/torch/utils/data/datapipes/_hook_iterator.py:180, in hook_iterator.<locals>.wrap_generator(*args, **kwargs)
178 response = gen.send(None)
179 else:
--> 180 response = gen.send(None)
182 while True:
183 datapipe._number_of_samples_yielded += 1
File /opt/conda/envs/dgl-dev-cpu/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/callable.py:125, in MapperIterDataPipe.__iter__(self)
124 def __iter__(self) -> Iterator[T_co]:
--> 125 for data in self.datapipe:
126 yield self._apply_fn(data)
File /opt/conda/envs/dgl-dev-cpu/lib/python3.10/site-packages/torch/utils/data/datapipes/_hook_iterator.py:180, in hook_iterator.<locals>.wrap_generator(*args, **kwargs)
178 response = gen.send(None)
179 else:
--> 180 response = gen.send(None)
182 while True:
183 datapipe._number_of_samples_yielded += 1
File ~/regression_test/dgl/python/dgl/graphbolt/base.py:411, in Bufferer.__iter__(self)
410 def __iter__(self):
--> 411 for data in self.datapipe:
412 if len(self.buffer) < self.buffer.maxlen:
413 self.buffer.append(data)
File /opt/conda/envs/dgl-dev-cpu/lib/python3.10/site-packages/torch/utils/data/datapipes/_hook_iterator.py:180, in hook_iterator.<locals>.wrap_generator(*args, **kwargs)
178 response = gen.send(None)
179 else:
--> 180 response = gen.send(None)
182 while True:
183 datapipe._number_of_samples_yielded += 1
File /opt/conda/envs/dgl-dev-cpu/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/callable.py:125, in MapperIterDataPipe.__iter__(self)
124 def __iter__(self) -> Iterator[T_co]:
--> 125 for data in self.datapipe:
126 yield self._apply_fn(data)
File /opt/conda/envs/dgl-dev-cpu/lib/python3.10/site-packages/torch/utils/data/datapipes/_hook_iterator.py:180, in hook_iterator.<locals>.wrap_generator(*args, **kwargs)
178 response = gen.send(None)
179 else:
--> 180 response = gen.send(None)
182 while True:
183 datapipe._number_of_samples_yielded += 1
File ~/regression_test/dgl/python/dgl/graphbolt/base.py:411, in Bufferer.__iter__(self)
410 def __iter__(self):
--> 411 for data in self.datapipe:
412 if len(self.buffer) < self.buffer.maxlen:
413 self.buffer.append(data)
File /opt/conda/envs/dgl-dev-cpu/lib/python3.10/site-packages/torch/utils/data/datapipes/_hook_iterator.py:180, in hook_iterator.<locals>.wrap_generator(*args, **kwargs)
178 response = gen.send(None)
179 else:
--> 180 response = gen.send(None)
182 while True:
183 datapipe._number_of_samples_yielded += 1
File /opt/conda/envs/dgl-dev-cpu/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/callable.py:125, in MapperIterDataPipe.__iter__(self)
124 def __iter__(self) -> Iterator[T_co]:
--> 125 for data in self.datapipe:
126 yield self._apply_fn(data)
File /opt/conda/envs/dgl-dev-cpu/lib/python3.10/site-packages/torch/utils/data/datapipes/_hook_iterator.py:180, in hook_iterator.<locals>.wrap_generator(*args, **kwargs)
178 response = gen.send(None)
179 else:
--> 180 response = gen.send(None)
182 while True:
183 datapipe._number_of_samples_yielded += 1
File /opt/conda/envs/dgl-dev-cpu/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/callable.py:125, in MapperIterDataPipe.__iter__(self)
124 def __iter__(self) -> Iterator[T_co]:
--> 125 for data in self.datapipe:
126 yield self._apply_fn(data)
[... skipping similar frames: hook_iterator.<locals>.wrap_generator at line 180 (7 times), MapperIterDataPipe.__iter__ at line 125 (6 times)]
File /opt/conda/envs/dgl-dev-cpu/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/callable.py:125, in MapperIterDataPipe.__iter__(self)
124 def __iter__(self) -> Iterator[T_co]:
--> 125 for data in self.datapipe:
126 yield self._apply_fn(data)
File /opt/conda/envs/dgl-dev-cpu/lib/python3.10/site-packages/torch/utils/data/datapipes/_hook_iterator.py:180, in hook_iterator.<locals>.wrap_generator(*args, **kwargs)
178 response = gen.send(None)
179 else:
--> 180 response = gen.send(None)
182 while True:
183 datapipe._number_of_samples_yielded += 1
File /opt/conda/envs/dgl-dev-cpu/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/callable.py:126, in MapperIterDataPipe.__iter__(self)
124 def __iter__(self) -> Iterator[T_co]:
125 for data in self.datapipe:
--> 126 yield self._apply_fn(data)
File /opt/conda/envs/dgl-dev-cpu/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/callable.py:91, in MapperIterDataPipe._apply_fn(self, data)
89 def _apply_fn(self, data):
90 if self.input_col is None and self.output_col is None:
---> 91 return self.fn(data)
93 if self.input_col is None:
94 res = self.fn(data)
File ~/regression_test/dgl/python/dgl/graphbolt/minibatch_transformer.py:38, in MiniBatchTransformer._transformer(self, minibatch)
37 def _transformer(self, minibatch):
---> 38 minibatch = self.transformer(minibatch)
39 assert isinstance(
40 minibatch, (MiniBatch,)
41 ), "The transformer output should be an instance of MiniBatch"
42 return minibatch
File ~/regression_test/dgl/python/dgl/graphbolt/impl/neighbor_sampler.py:474, in CompactPerLayer._compact_per_layer(self, minibatch)
469 seeds = minibatch._seed_nodes
470 if self.deduplicate:
471 (
472 original_row_node_ids,
473 compacted_csc_format,
--> 474 ) = unique_and_compact_csc_formats(subgraph.sampled_csc, seeds)
475 subgraph = SampledSubgraphImpl(
476 sampled_csc=compacted_csc_format,
477 original_column_node_ids=seeds,
478 original_row_node_ids=original_row_node_ids,
479 original_edge_ids=subgraph.original_edge_ids,
480 )
481 else:
File ~/regression_test/dgl/python/dgl/graphbolt/internal/sample_utils.py:221, in unique_and_compact_csc_formats(csc_formats, unique_dst_nodes, async_op)
213 dst_list = [torch.tensor([], dtype=dtype, device=device)] * len(
214 unique_dst_list
215 )
216 unique_fn = (
217 torch.ops.graphbolt.unique_and_compact_batched_async
218 if async_op
219 else torch.ops.graphbolt.unique_and_compact_batched
220 )
--> 221 results = unique_fn(indice_list, dst_list, unique_dst_list)
223 class _Waiter:
224 def __init__(self, future, csc_formats):
File /opt/conda/envs/dgl-dev-cpu/lib/python3.10/site-packages/torch/_ops.py:1061, in OpOverloadPacket.__call__(self_, *args, **kwargs)
1059 if self_._has_torchbind_op_overload and _must_dispatch_in_python(args, kwargs):
1060 return _call_overload_packet_from_python(self_, args, kwargs)
-> 1061 return self_._op(*args, **(kwargs or {}))
RuntimeError: graphbolt::unique_and_compact_batched() is missing value for argument '_3'. Declaration: graphbolt::unique_and_compact_batched(Tensor[] _0, Tensor[] _1, Tensor[] _2, int _3, int _4) -> ((Tensor, Tensor, Tensor, Tensor)[] _0)
This exception is thrown by __iter__ of CompactPerLayer(datapipe=SamplePerLayer, deduplicate=True)
You can get the input node IDs from MFGs.
[6]:
mfgs = data.blocks
input_nodes = mfgs[0].srcdata[dgl.NID]
print(f"Input nodes: {input_nodes}.")
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
Cell In[6], line 1
----> 1 mfgs = data.blocks
2 input_nodes = mfgs[0].srcdata[dgl.NID]
3 print(f"Input nodes: {input_nodes}.")
NameError: name 'data' is not defined
Defining Model
Let’s consider training a 2-layer GraphSAGE with neighbor sampling. The model can be written as follows:
[7]:
import torch.nn as nn
import torch.nn.functional as F
from dgl.nn import SAGEConv
class Model(nn.Module):
def __init__(self, in_feats, h_feats, num_classes):
super(Model, self).__init__()
self.conv1 = SAGEConv(in_feats, h_feats, aggregator_type="mean")
self.conv2 = SAGEConv(h_feats, num_classes, aggregator_type="mean")
self.h_feats = h_feats
def forward(self, mfgs, x):
h = self.conv1(mfgs[0], x)
h = F.relu(h)
h = self.conv2(mfgs[1], h)
return h
in_size = feature.size("node", None, "feat")[0]
model = Model(in_size, 64, num_classes).to(device)
Defining Training Loop
The following initializes the model and defines the optimizer.
[8]:
opt = torch.optim.Adam(model.parameters())
When computing the validation score for model selection, usually you can also do neighbor sampling. We can just reuse our create_dataloader function to create two separate dataloaders for training and validation.
[9]:
train_dataloader = create_dataloader(train_set, shuffle=True)
valid_dataloader = create_dataloader(valid_set, shuffle=False)
import sklearn.metrics
The following is a training loop that performs validation every epoch. It also saves the model with the best validation accuracy into a file.
[10]:
from tqdm.auto import tqdm
for epoch in range(10):
model.train()
with tqdm(train_dataloader) as tq:
for step, data in enumerate(tq):
x = data.node_features["feat"]
labels = data.labels
predictions = model(data.blocks, x)
loss = F.cross_entropy(predictions, labels)
opt.zero_grad()
loss.backward()
opt.step()
accuracy = sklearn.metrics.accuracy_score(
labels.cpu().numpy(),
predictions.argmax(1).detach().cpu().numpy(),
)
tq.set_postfix(
{"loss": "%.03f" % loss.item(), "acc": "%.03f" % accuracy},
refresh=False,
)
model.eval()
predictions = []
labels = []
with tqdm(valid_dataloader) as tq, torch.no_grad():
for data in tq:
x = data.node_features["feat"]
labels.append(data.labels.cpu().numpy())
predictions.append(model(data.blocks, x).argmax(1).cpu().numpy())
predictions = np.concatenate(predictions)
labels = np.concatenate(labels)
accuracy = sklearn.metrics.accuracy_score(labels, predictions)
print("Epoch {} Validation Accuracy {}".format(epoch, accuracy))
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
Cell In[10], line 7
4 model.train()
6 with tqdm(train_dataloader) as tq:
----> 7 for step, data in enumerate(tq):
8 x = data.node_features["feat"]
9 labels = data.labels
File /opt/conda/envs/dgl-dev-cpu/lib/python3.10/site-packages/tqdm/notebook.py:250, in tqdm_notebook.__iter__(self)
248 try:
249 it = super().__iter__()
--> 250 for obj in it:
251 # return super(tqdm...) will not catch exception
252 yield obj
253 # NB: except ... [ as ...] breaks IPython async KeyboardInterrupt
File /opt/conda/envs/dgl-dev-cpu/lib/python3.10/site-packages/tqdm/std.py:1181, in tqdm.__iter__(self)
1178 time = self._time
1180 try:
-> 1181 for obj in iterable:
1182 yield obj
1183 # Update and possibly print the progressbar.
1184 # Note: does not call self.update(1) for speed optimisation.
File /opt/conda/envs/dgl-dev-cpu/lib/python3.10/site-packages/torch/utils/data/datapipes/_hook_iterator.py:180, in hook_iterator.<locals>.wrap_generator(*args, **kwargs)
178 response = gen.send(None)
179 else:
--> 180 response = gen.send(None)
182 while True:
183 datapipe._number_of_samples_yielded += 1
File /opt/conda/envs/dgl-dev-cpu/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/callable.py:125, in MapperIterDataPipe.__iter__(self)
124 def __iter__(self) -> Iterator[T_co]:
--> 125 for data in self.datapipe:
126 yield self._apply_fn(data)
File /opt/conda/envs/dgl-dev-cpu/lib/python3.10/site-packages/torch/utils/data/datapipes/_hook_iterator.py:180, in hook_iterator.<locals>.wrap_generator(*args, **kwargs)
178 response = gen.send(None)
179 else:
--> 180 response = gen.send(None)
182 while True:
183 datapipe._number_of_samples_yielded += 1
File ~/regression_test/dgl/python/dgl/graphbolt/base.py:385, in EndMarker.__iter__(self)
384 def __iter__(self):
--> 385 yield from self.datapipe
File /opt/conda/envs/dgl-dev-cpu/lib/python3.10/site-packages/torch/utils/data/datapipes/_hook_iterator.py:180, in hook_iterator.<locals>.wrap_generator(*args, **kwargs)
178 response = gen.send(None)
179 else:
--> 180 response = gen.send(None)
182 while True:
183 datapipe._number_of_samples_yielded += 1
File /opt/conda/envs/dgl-dev-cpu/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/callable.py:125, in MapperIterDataPipe.__iter__(self)
124 def __iter__(self) -> Iterator[T_co]:
--> 125 for data in self.datapipe:
126 yield self._apply_fn(data)
File /opt/conda/envs/dgl-dev-cpu/lib/python3.10/site-packages/torch/utils/data/datapipes/_hook_iterator.py:180, in hook_iterator.<locals>.wrap_generator(*args, **kwargs)
178 response = gen.send(None)
179 else:
--> 180 response = gen.send(None)
182 while True:
183 datapipe._number_of_samples_yielded += 1
File ~/regression_test/dgl/python/dgl/graphbolt/base.py:411, in Bufferer.__iter__(self)
410 def __iter__(self):
--> 411 for data in self.datapipe:
412 if len(self.buffer) < self.buffer.maxlen:
413 self.buffer.append(data)
File /opt/conda/envs/dgl-dev-cpu/lib/python3.10/site-packages/torch/utils/data/datapipes/_hook_iterator.py:180, in hook_iterator.<locals>.wrap_generator(*args, **kwargs)
178 response = gen.send(None)
179 else:
--> 180 response = gen.send(None)
182 while True:
183 datapipe._number_of_samples_yielded += 1
File /opt/conda/envs/dgl-dev-cpu/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/callable.py:125, in MapperIterDataPipe.__iter__(self)
124 def __iter__(self) -> Iterator[T_co]:
--> 125 for data in self.datapipe:
126 yield self._apply_fn(data)
File /opt/conda/envs/dgl-dev-cpu/lib/python3.10/site-packages/torch/utils/data/datapipes/_hook_iterator.py:180, in hook_iterator.<locals>.wrap_generator(*args, **kwargs)
178 response = gen.send(None)
179 else:
--> 180 response = gen.send(None)
182 while True:
183 datapipe._number_of_samples_yielded += 1
File ~/regression_test/dgl/python/dgl/graphbolt/base.py:411, in Bufferer.__iter__(self)
410 def __iter__(self):
--> 411 for data in self.datapipe:
412 if len(self.buffer) < self.buffer.maxlen:
413 self.buffer.append(data)
File /opt/conda/envs/dgl-dev-cpu/lib/python3.10/site-packages/torch/utils/data/datapipes/_hook_iterator.py:180, in hook_iterator.<locals>.wrap_generator(*args, **kwargs)
178 response = gen.send(None)
179 else:
--> 180 response = gen.send(None)
182 while True:
183 datapipe._number_of_samples_yielded += 1
File /opt/conda/envs/dgl-dev-cpu/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/callable.py:125, in MapperIterDataPipe.__iter__(self)
124 def __iter__(self) -> Iterator[T_co]:
--> 125 for data in self.datapipe:
126 yield self._apply_fn(data)
File /opt/conda/envs/dgl-dev-cpu/lib/python3.10/site-packages/torch/utils/data/datapipes/_hook_iterator.py:180, in hook_iterator.<locals>.wrap_generator(*args, **kwargs)
178 response = gen.send(None)
179 else:
--> 180 response = gen.send(None)
182 while True:
183 datapipe._number_of_samples_yielded += 1
File ~/regression_test/dgl/python/dgl/graphbolt/base.py:411, in Bufferer.__iter__(self)
410 def __iter__(self):
--> 411 for data in self.datapipe:
412 if len(self.buffer) < self.buffer.maxlen:
413 self.buffer.append(data)
File /opt/conda/envs/dgl-dev-cpu/lib/python3.10/site-packages/torch/utils/data/datapipes/_hook_iterator.py:180, in hook_iterator.<locals>.wrap_generator(*args, **kwargs)
178 response = gen.send(None)
179 else:
--> 180 response = gen.send(None)
182 while True:
183 datapipe._number_of_samples_yielded += 1
File /opt/conda/envs/dgl-dev-cpu/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/callable.py:125, in MapperIterDataPipe.__iter__(self)
124 def __iter__(self) -> Iterator[T_co]:
--> 125 for data in self.datapipe:
126 yield self._apply_fn(data)
File /opt/conda/envs/dgl-dev-cpu/lib/python3.10/site-packages/torch/utils/data/datapipes/_hook_iterator.py:180, in hook_iterator.<locals>.wrap_generator(*args, **kwargs)
178 response = gen.send(None)
179 else:
--> 180 response = gen.send(None)
182 while True:
183 datapipe._number_of_samples_yielded += 1
File /opt/conda/envs/dgl-dev-cpu/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/callable.py:125, in MapperIterDataPipe.__iter__(self)
124 def __iter__(self) -> Iterator[T_co]:
--> 125 for data in self.datapipe:
126 yield self._apply_fn(data)
[... skipping similar frames: hook_iterator.<locals>.wrap_generator at line 180 (7 times), MapperIterDataPipe.__iter__ at line 125 (6 times)]
File /opt/conda/envs/dgl-dev-cpu/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/callable.py:125, in MapperIterDataPipe.__iter__(self)
124 def __iter__(self) -> Iterator[T_co]:
--> 125 for data in self.datapipe:
126 yield self._apply_fn(data)
File /opt/conda/envs/dgl-dev-cpu/lib/python3.10/site-packages/torch/utils/data/datapipes/_hook_iterator.py:180, in hook_iterator.<locals>.wrap_generator(*args, **kwargs)
178 response = gen.send(None)
179 else:
--> 180 response = gen.send(None)
182 while True:
183 datapipe._number_of_samples_yielded += 1
File /opt/conda/envs/dgl-dev-cpu/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/callable.py:126, in MapperIterDataPipe.__iter__(self)
124 def __iter__(self) -> Iterator[T_co]:
125 for data in self.datapipe:
--> 126 yield self._apply_fn(data)
File /opt/conda/envs/dgl-dev-cpu/lib/python3.10/site-packages/torch/utils/data/datapipes/iter/callable.py:91, in MapperIterDataPipe._apply_fn(self, data)
89 def _apply_fn(self, data):
90 if self.input_col is None and self.output_col is None:
---> 91 return self.fn(data)
93 if self.input_col is None:
94 res = self.fn(data)
File ~/regression_test/dgl/python/dgl/graphbolt/minibatch_transformer.py:38, in MiniBatchTransformer._transformer(self, minibatch)
37 def _transformer(self, minibatch):
---> 38 minibatch = self.transformer(minibatch)
39 assert isinstance(
40 minibatch, (MiniBatch,)
41 ), "The transformer output should be an instance of MiniBatch"
42 return minibatch
File ~/regression_test/dgl/python/dgl/graphbolt/impl/neighbor_sampler.py:474, in CompactPerLayer._compact_per_layer(self, minibatch)
469 seeds = minibatch._seed_nodes
470 if self.deduplicate:
471 (
472 original_row_node_ids,
473 compacted_csc_format,
--> 474 ) = unique_and_compact_csc_formats(subgraph.sampled_csc, seeds)
475 subgraph = SampledSubgraphImpl(
476 sampled_csc=compacted_csc_format,
477 original_column_node_ids=seeds,
478 original_row_node_ids=original_row_node_ids,
479 original_edge_ids=subgraph.original_edge_ids,
480 )
481 else:
File ~/regression_test/dgl/python/dgl/graphbolt/internal/sample_utils.py:221, in unique_and_compact_csc_formats(csc_formats, unique_dst_nodes, async_op)
213 dst_list = [torch.tensor([], dtype=dtype, device=device)] * len(
214 unique_dst_list
215 )
216 unique_fn = (
217 torch.ops.graphbolt.unique_and_compact_batched_async
218 if async_op
219 else torch.ops.graphbolt.unique_and_compact_batched
220 )
--> 221 results = unique_fn(indice_list, dst_list, unique_dst_list)
223 class _Waiter:
224 def __init__(self, future, csc_formats):
File /opt/conda/envs/dgl-dev-cpu/lib/python3.10/site-packages/torch/_ops.py:1061, in OpOverloadPacket.__call__(self_, *args, **kwargs)
1059 if self_._has_torchbind_op_overload and _must_dispatch_in_python(args, kwargs):
1060 return _call_overload_packet_from_python(self_, args, kwargs)
-> 1061 return self_._op(*args, **(kwargs or {}))
RuntimeError: graphbolt::unique_and_compact_batched() is missing value for argument '_3'. Declaration: graphbolt::unique_and_compact_batched(Tensor[] _0, Tensor[] _1, Tensor[] _2, int _3, int _4) -> ((Tensor, Tensor, Tensor, Tensor)[] _0)
This exception is thrown by __iter__ of CompactPerLayer(datapipe=SamplePerLayer, deduplicate=True)
Conclusion
In this tutorial, you have learned how to train a multi-layer GraphSAGE with neighbor sampling.