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2.5.0

pyg-team/pytorch_geometric

版本发布时间: 2024-02-16 15:16:56

pyg-team/pytorch_geometric最新发布版本:2.5.3(2024-04-19 19:37:44)

We are excited to announce the release of PyG 2.5 🎉🎉🎉

PyG 2.5 is the culmination of work from 38 contributors who have worked on features and bug-fixes for a total of over 360 commits since torch-geometric==2.4.0.

Highlights

torch_geometric.distributed

We are thrilled to announce the first in-house distributed training solution for PyG via the torch_geometric.distributed sub-package. Developers and researchers can now take full advantage of distributed training on large-scale datasets which cannot be fully loaded in memory of one machine at the same time. This implementation doesn't require any additional packages to be installed on top of the default PyG stack.

Key Advantages

See here for the accompanying tutorial. In addition, we provide two distributed examples in examples/distributed/pyg to get started:

EdgeIndex Tensor Representation

torch-geometric==2.5.0 introduces the EdgeIndex class.

EdgeIndex is a torch.Tensor, that holds an edge_index representation of shape [2, num_edges]. Edges are given as pairwise source and destination node indices in sparse COO format. While EdgeIndex sub-classes a general torch.Tensor, it can hold additional (meta)data, i.e.:

Additionally, EdgeIndex caches data for fast CSR or CSC conversion in case its representation is sorted (i.e. its rowptr or colptr). Caches are filled based on demand (e.g., when calling EdgeIndex.sort_by()), or when explicitly requested via EdgeIndex.fill_cache_(), and are maintained and adjusted over its lifespan (e.g., when calling EdgeIndex.flip()).

from torch_geometric import EdgeIndex

edge_index = EdgeIndex(
    [[0, 1, 1, 2],
     [1, 0, 2, 1]]
    sparse_size=(3, 3),
    sort_order='row',
    is_undirected=True,
    device='cpu',
)
>>> EdgeIndex([[0, 1, 1, 2],
...            [1, 0, 2, 1]])
assert edge_index.is_sorted_by_row
assert edge_index.is_undirected

# Flipping order:
edge_index = edge_index.flip(0)
>>> EdgeIndex([[1, 0, 2, 1],
...            [0, 1, 1, 2]])
assert edge_index.is_sorted_by_col
assert edge_index.is_undirected

# Filtering:
mask = torch.tensor([True, True, True, False])
edge_index = edge_index[:, mask]
>>> EdgeIndex([[1, 0, 2],
...            [0, 1, 1]])
assert edge_index.is_sorted_by_col
assert not edge_index.is_undirected

# Sparse-Dense Matrix Multiplication:
out = edge_index.flip(0) @ torch.randn(3, 16)
assert out.size() == (3, 16)

EdgeIndex is implemented through extending torch.Tensor via the __torch_function__ interface (see here for the highly recommended tutorial).

EdgeIndex ensures for optimal computation in GNN message passing schemes, while preserving the ease-of-use of regular COO-based PyG workflows. EdgeIndex will fully deprecate the usage of SparseTensor from torch-sparse in later releases, leaving us with just a single source of truth for representing graph structure information in PyG.

RecSys Support

Previously, all/most of our link prediction models were trained and evaluated using binary classification metrics. However, this usually requires that we have a set of candidates in advance, from which we can then infer the existence of links. This is not necessarily practical, since in most cases, we want to find the top-k most likely links from the full set of O(N^2) pairs.

torch-geometric==2.5.0 brings full support for using GNNs as a recommender system (#8452), including support for

mips = MIPSKNNIndex(dst_emb)

for src_batch in src_loader:
    src_emb = model(src_batch.x_dict, src_batch.edge_index_dict)
    _, pred_index_mat = mips.search(src_emb, k)
    
    for metric in retrieval_metrics:
         metric.update(pred_index_mat, edge_label_index)
         
for metric in retrieval_metrics:
     metric.compute()

See here for the accompanying example.

PyTorch 2.2 Support

PyG 2.5 is fully compatible with PyTorch 2.2 (#8857), and supports the following combinations:

PyTorch 2.2 cpu cu118 cu121
Linux ✅ ✅ ✅
macOS ✅
Windows ✅ ✅ ✅

You can still install PyG 2.5 with an older PyTorch release up to PyTorch 1.12 in case you are not eager to update your PyTorch version.

Native torch.compile(...) and TorchScript Support

torch-geometric==2.5.0 introduces a full re-implementation of the MessagePassing interface, which makes it natively applicable to both torch.compile and TorchScript. As such, torch_geometric.compile is now fully deprecated in favor of torch.compile

- model = torch_geometric.compile(model)
+ model = torch.compile(model)

and MessagePassing.jittable() is now a no-op:

- conv = torch.jit.script(conv.jittable())
+ model = torch.jit.script(conv)

In addition, torch.compile usage has been fixed to not require disabling of extension packages such as torch-scatter or torch-sparse.

New Tutorials, Examples, Models and Improvements

Breaking Changes

Deprecations

Features

Package-wide Improvements

Temporal Graph Support

torch_geometric.datasets

torch_geometric.nn

torch_geometric.metrics

torch_geometric.explain

torch_geometric.transforms

Other Improvements

Bugfixes

Changes

New Contributors

Full Changelog: https://github.com/pyg-team/pytorch_geometric/compare/2.4.0...2.5.0

相关地址：原始地址 下载(tar) 下载(zip)

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