TorchBasedFeature

class dgl.graphbolt.TorchBasedFeature(torch_feature: Tensor, metadata: Dict | None = None)

Bases: Feature

A wrapper of pytorch based feature.

Initialize a torch based feature store by a torch feature. Note that the feature can be either in memory or on disk.

Parameters:

torch_feature (torch.Tensor) – The torch feature. Note that the dimension of the tensor should be greater than 1.

Examples

>>> import torch
>>> from dgl import graphbolt as gb

1. The feature is in memory.

>>> torch_feat = torch.arange(10).reshape(2, -1)
>>> feature = gb.TorchBasedFeature(torch_feat)
>>> feature.read()
tensor([[0, 1, 2, 3, 4],
        [5, 6, 7, 8, 9]])
>>> feature.read(torch.tensor([0]))
tensor([[0, 1, 2, 3, 4]])
>>> feature.update(torch.tensor([[1 for _ in range(5)]]),
...                      torch.tensor([1]))
>>> feature.read(torch.tensor([0, 1]))
tensor([[0, 1, 2, 3, 4],
        [1, 1, 1, 1, 1]])
>>> feature.size()
torch.Size([5])

2. The feature is on disk.

>>> import numpy as np
>>> arr = np.array([[1, 2], [3, 4]])
>>> np.save("/tmp/arr.npy", arr)
>>> torch_feat = torch.from_numpy(np.load("/tmp/arr.npy", mmap_mode="r+"))
>>> feature = gb.TorchBasedFeature(torch_feat)
>>> feature.read()
tensor([[1, 2],
        [3, 4]])
>>> feature.read(torch.tensor([0]))
tensor([[1, 2]])

3. Pinned CPU feature.

>>> torch_feat = torch.arange(10).reshape(2, -1).pin_memory()
>>> feature = gb.TorchBasedFeature(torch_feat)
>>> feature.read().device
device(type='cuda', index=0)
>>> feature.read(torch.tensor([0]).cuda()).device
device(type='cuda', index=0)

is_pinned()

Returns True if the stored feature is pinned.

metadata()

Get the metadata of the feature.

Returns:

The metadata of the feature.

Return type:

Dict

pin_memory_()

In-place operation to copy the feature to pinned memory. Returns the same object modified in-place.

read(ids: Tensor | None = None)

Read the feature by index.

If the feature is on pinned CPU memory and ids is on GPU or pinned CPU memory, it will be read by GPU and the returned tensor will be on GPU. Otherwise, the returned tensor will be on CPU.

Parameters:

ids (torch.Tensor, optional) – The index of the feature. If specified, only the specified indices of the feature are read. If None, the entire feature is returned.

Returns:

The read feature.

Return type:

torch.Tensor

size()

Get the size of the feature.

Returns:

The size of the feature.

Return type:

torch.Size

to(device)

Copy TorchBasedFeature to the specified device.

update(value: Tensor, ids: Tensor | None = None)

Update the feature store.

Parameters:

• value (torch.Tensor) – The updated value of the feature.

• ids (torch.Tensor, optional) – The indices of the feature to update. If specified, only the specified indices of the feature will be updated. For the feature, the ids[i] row is updated to value[i]. So the indices and value must have the same length. If None, the entire feature will be updated.