wehub-resource-sync
149 lines
4.8 KiB
Python
149 lines
4.8 KiB
Python
"""Torch Module for GraphSAGE layer using the aggregation primitives in
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cugraph-ops"""
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# pylint: disable=no-member, arguments-differ, invalid-name, too-many-arguments
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from torch import nn
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from .cugraph_base import CuGraphBaseConv
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try:
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from pylibcugraphops.pytorch import SampledCSC, StaticCSC
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from pylibcugraphops.pytorch.operators import agg_concat_n2n as SAGEConvAgg
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HAS_PYLIBCUGRAPHOPS = True
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except ImportError:
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HAS_PYLIBCUGRAPHOPS = False
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class CuGraphSAGEConv(CuGraphBaseConv):
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r"""An accelerated GraphSAGE layer from `Inductive Representation Learning
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on Large Graphs <https://arxiv.org/pdf/1706.02216.pdf>`__ that leverages the
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highly-optimized aggregation primitives in cugraph-ops:
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.. math::
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h_{\mathcal{N}(i)}^{(l+1)} &= \mathrm{aggregate}
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\left(\{h_{j}^{l}, \forall j \in \mathcal{N}(i) \}\right)
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h_{i}^{(l+1)} &= W \cdot \mathrm{concat}
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(h_{i}^{l}, h_{\mathcal{N}(i)}^{(l+1)})
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This module depends on :code:`pylibcugraphops` package, which can be
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installed via :code:`conda install -c nvidia pylibcugraphops=23.04`.
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:code:`pylibcugraphops` 23.04 requires python 3.8.x or 3.10.x.
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.. note::
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This is an **experimental** feature.
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Parameters
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----------
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in_feats : int
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Input feature size.
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out_feats : int
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Output feature size.
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aggregator_type : str
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Aggregator type to use (``mean``, ``sum``, ``min``, ``max``).
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feat_drop : float
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Dropout rate on features, default: ``0``.
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bias : bool
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If True, adds a learnable bias to the output. Default: ``True``.
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Examples
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--------
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>>> import dgl
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>>> import torch
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>>> from dgl.nn import CuGraphSAGEConv
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>>> device = 'cuda'
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>>> g = dgl.graph(([0,1,2,3,2,5], [1,2,3,4,0,3])).to(device)
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>>> g = dgl.add_self_loop(g)
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>>> feat = torch.ones(6, 10).to(device)
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>>> conv = CuGraphSAGEConv(10, 2, 'mean').to(device)
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>>> res = conv(g, feat)
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>>> res
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tensor([[-1.1690, 0.1952],
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[-1.1690, 0.1952],
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[-1.1690, 0.1952],
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[-1.1690, 0.1952],
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[-1.1690, 0.1952],
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[-1.1690, 0.1952]], device='cuda:0', grad_fn=<AddmmBackward0>)
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"""
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MAX_IN_DEGREE_MFG = 500
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def __init__(
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self,
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in_feats,
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out_feats,
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aggregator_type="mean",
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feat_drop=0.0,
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bias=True,
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):
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if HAS_PYLIBCUGRAPHOPS is False:
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raise ModuleNotFoundError(
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f"{self.__class__.__name__} requires pylibcugraphops=23.04. "
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f"Install via `conda install -c nvidia 'pylibcugraphops=23.04'`."
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f"pylibcugraphops requires Python 3.8 or 3.10."
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)
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valid_aggr_types = {"max", "min", "mean", "sum"}
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if aggregator_type not in valid_aggr_types:
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raise ValueError(
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f"Invalid aggregator_type. Must be one of {valid_aggr_types}. "
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f"But got '{aggregator_type}' instead."
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)
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super().__init__()
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self.in_feats = in_feats
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self.out_feats = out_feats
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self.aggr = aggregator_type
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self.feat_drop = nn.Dropout(feat_drop)
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self.linear = nn.Linear(2 * in_feats, out_feats, bias=bias)
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def reset_parameters(self):
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r"""Reinitialize learnable parameters."""
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self.linear.reset_parameters()
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def forward(self, g, feat, max_in_degree=None):
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r"""Forward computation.
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Parameters
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----------
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g : DGLGraph
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The graph.
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feat : torch.Tensor
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Node features. Shape: :math:`(N, D_{in})`.
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max_in_degree : int
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Maximum in-degree of destination nodes. It is only effective when
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:attr:`g` is a :class:`DGLBlock`, i.e., bipartite graph. When
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:attr:`g` is generated from a neighbor sampler, the value should be
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set to the corresponding :attr:`fanout`. If not given,
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:attr:`max_in_degree` will be calculated on-the-fly.
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Returns
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-------
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torch.Tensor
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Output node features. Shape: :math:`(N, D_{out})`.
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"""
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offsets, indices, _ = g.adj_tensors("csc")
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if g.is_block:
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if max_in_degree is None:
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max_in_degree = g.in_degrees().max().item()
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if max_in_degree < self.MAX_IN_DEGREE_MFG:
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_graph = SampledCSC(
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offsets,
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indices,
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max_in_degree,
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g.num_src_nodes(),
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)
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else:
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offsets_fg = self.pad_offsets(offsets, g.num_src_nodes() + 1)
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_graph = StaticCSC(offsets_fg, indices)
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else:
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_graph = StaticCSC(offsets, indices)
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feat = self.feat_drop(feat)
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h = SAGEConvAgg(feat, _graph, self.aggr)[: g.num_dst_nodes()]
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h = self.linear(h)
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return h
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