[Model] Update cugraph-ops models for 23.04 release (#5540)

Co-authored-by: schmidt-ju <79865721+schmidt-ju@users.noreply.github.com>
Co-authored-by: Quan (Andy) Gan <coin2028@hotmail.com>
Co-authored-by: Mufei Li <mufeili1996@gmail.com>
Co-authored-by: Ubuntu <ubuntu@ip-172-31-36-188.ap-northeast-1.compute.internal>
Co-authored-by: peizhou001 <110809584+peizhou001@users.noreply.github.com>
Co-authored-by: Ubuntu <ubuntu@ip-172-31-16-19.ap-northeast-1.compute.internal>

python/dgl/nn/pytorch/conv/cugraph_base.py

@@ -0,0 +1,57 @@
+"""An abstract base class for cugraph-ops nn module."""
+import torch
+from torch import nn
+
+
+class CuGraphBaseConv(nn.Module):
+    r"""An abstract base class for cugraph-ops nn module."""
+
+    def __init__(self):
+        super().__init__()
+        self._cached_offsets_fg = None
+
+    def reset_parameters(self):
+        r"""Resets all learnable parameters of the module."""
+        raise NotImplementedError
+
+    def forward(self, *args):
+        r"""Runs the forward pass of the module."""
+        raise NotImplementedError
+
+    def pad_offsets(self, offsets: torch.Tensor, size: int) -> torch.Tensor:
+        r"""Pad zero-in-degree nodes to the end of offsets to reach size.
+
+        cugraph-ops often provides two variants of aggregation functions for a
+        specific model: one intended for sampled-graph use cases, one for
+        full-graph ones. The former is in general more performant, however, it
+        only works when the sample size (the max of in-degrees) is small (<200),
+        due to the limit of GPU shared memory. For graphs with a larger max
+        in-degree, we need to fall back to the full-graph option, which requires
+        to convert a DGL block to a full graph. With the csc-representation,
+        this is equivalent to pad zero-in-degree nodes to the end of the offsets
+        array (also called indptr or colptr).
+
+        Parameters
+        ----------
+        offsets :
+            The (monotonically increasing) index pointer array in a CSC-format
+            graph.
+        size : int
+            The length of offsets after padding.
+
+        Returns
+        -------
+        torch.Tensor
+            The augmented offsets array.
+        """
+        if self._cached_offsets_fg is None:
+            self._cached_offsets_fg = torch.empty(
+                size, dtype=offsets.dtype, device=offsets.device
+            )
+        elif self._cached_offsets_fg.numel() < size:
+            self._cached_offsets_fg.resize_(size)
+
+        self._cached_offsets_fg[: offsets.numel()] = offsets
+        self._cached_offsets_fg[offsets.numel() : size] = offsets[-1]
+
+        return self._cached_offsets_fg[:size]

python/dgl/nn/pytorch/conv/cugraph_gatconv.py

@@ -5,24 +5,27 @@
 import torch
 from torch import nn
 
+from .cugraph_base import CuGraphBaseConv
+
 try:
-    from pylibcugraphops import make_fg_csr, make_mfg_csr
-    from pylibcugraphops.torch.autograd import mha_gat_n2n as GATConvAgg
+    from pylibcugraphops.pytorch import SampledCSC, StaticCSC
+    from pylibcugraphops.pytorch.operators import mha_gat_n2n as GATConvAgg
+
+    HAS_PYLIBCUGRAPHOPS = True
 except ImportError:
-    has_pylibcugraphops = False
-else:
-    has_pylibcugraphops = True
+    HAS_PYLIBCUGRAPHOPS = False
 
 
-class CuGraphGATConv(nn.Module):
+class CuGraphGATConv(CuGraphBaseConv):
     r"""Graph attention layer from `Graph Attention Networks
     <https://arxiv.org/pdf/1710.10903.pdf>`__, with the sparse aggregation
     accelerated by cugraph-ops.
 
     See :class:`dgl.nn.pytorch.conv.GATConv` for mathematical model.
 
     This module depends on :code:`pylibcugraphops` package, which can be
-    installed via :code:`conda install -c nvidia pylibcugraphops>=23.02`.
+    installed via :code:`conda install -c nvidia pylibcugraphops=23.04`.
+    :code:`pylibcugraphops` 23.04 requires python 3.8.x or 3.10.x.
 
     .. note::
         This is an **experimental** feature.
@@ -78,7 +81,7 @@ class CuGraphGATConv(nn.Module):
             [ 1.6477, -1.9986],
             [ 1.1138, -1.9302]]], device='cuda:0', grad_fn=<ViewBackward0>)
     """
-    MAX_IN_DEGREE_MFG = 500
+    MAX_IN_DEGREE_MFG = 200
 
     def __init__(
         self,
@@ -91,10 +94,11 @@ def __init__(
         activation=None,
         bias=True,
     ):
-        if has_pylibcugraphops is False:
+        if HAS_PYLIBCUGRAPHOPS is False:
             raise ModuleNotFoundError(
-                f"{self.__class__.__name__} requires pylibcugraphops >= 23.02. "
-                f"Install via `conda install -c nvidia 'pylibcugraphops>=23.02'`."
+                f"{self.__class__.__name__} requires pylibcugraphops=23.04. "
+                f"Install via `conda install -c nvidia 'pylibcugraphops=23.04'`."
+                f"pylibcugraphops requires Python 3.8 or 3.10."
             )
         super().__init__()
         self.in_feats = in_feats
@@ -170,25 +174,17 @@ def forward(self, g, feat, max_in_degree=None):
                 max_in_degree = g.in_degrees().max().item()
 
             if max_in_degree < self.MAX_IN_DEGREE_MFG:
-                _graph = make_mfg_csr(
-                    g.dstnodes(),
+                _graph = SampledCSC(
                     offsets,
                     indices,
                     max_in_degree,
                     g.num_src_nodes(),
                 )
             else:
-                offsets_fg = torch.empty(
-                    g.num_src_nodes() + 1,
-                    dtype=offsets.dtype,
-                    device=offsets.device,
-                )
-                offsets_fg[: offsets.numel()] = offsets
-                offsets_fg[offsets.numel() :] = offsets[-1]
-
-                _graph = make_fg_csr(offsets_fg, indices)
+                offsets_fg = self.pad_offsets(offsets, g.num_src_nodes() + 1)
+                _graph = StaticCSC(offsets_fg, indices)
         else:
-            _graph = make_fg_csr(offsets, indices)
+            _graph = StaticCSC(offsets, indices)
 
         feat = self.feat_drop(feat)
         feat_transformed = self.fc(feat)
@@ -199,7 +195,6 @@ def forward(self, g, feat, max_in_degree=None):
             self.num_heads,
             "LeakyReLU",
             self.negative_slope,
-            add_own_node=False,
             concat_heads=True,
         )[: g.num_dst_nodes()].view(-1, self.num_heads, self.out_feats)
 

python/dgl/nn/pytorch/conv/cugraph_relgraphconv.py

@@ -6,18 +6,20 @@
 import torch
 from torch import nn
 
+from .cugraph_base import CuGraphBaseConv
+
 try:
-    from pylibcugraphops import make_fg_csr_hg, make_mfg_csr_hg
-    from pylibcugraphops.torch.autograd import (
+    from pylibcugraphops.pytorch import SampledHeteroCSC, StaticHeteroCSC
+    from pylibcugraphops.pytorch.operators import (
         agg_hg_basis_n2n_post as RelGraphConvAgg,
     )
+
+    HAS_PYLIBCUGRAPHOPS = True
 except ImportError:
-    has_pylibcugraphops = False
-else:
-    has_pylibcugraphops = True
+    HAS_PYLIBCUGRAPHOPS = False
 
 
-class CuGraphRelGraphConv(nn.Module):
+class CuGraphRelGraphConv(CuGraphBaseConv):
     r"""An accelerated relational graph convolution layer from `Modeling
     Relational Data with Graph Convolutional Networks
     <https://arxiv.org/abs/1703.06103>`__ that leverages the highly-optimized
@@ -26,7 +28,8 @@ class CuGraphRelGraphConv(nn.Module):
     See :class:`dgl.nn.pytorch.conv.RelGraphConv` for mathematical model.
 
     This module depends on :code:`pylibcugraphops` package, which can be
-    installed via :code:`conda install -c nvidia pylibcugraphops>=23.02`.
+    installed via :code:`conda install -c nvidia pylibcugraphops=23.04`.
+    :code:`pylibcugraphops` 23.04 requires python 3.8.x or 3.10.x.
 
     .. note::
         This is an **experimental** feature.
@@ -92,10 +95,11 @@ def __init__(
         dropout=0.0,
         apply_norm=False,
     ):
-        if has_pylibcugraphops is False:
+        if HAS_PYLIBCUGRAPHOPS is False:
             raise ModuleNotFoundError(
-                f"{self.__class__.__name__} requires pylibcugraphops >= 23.02 "
-                f"to be installed."
+                f"{self.__class__.__name__} requires pylibcugraphops=23.04. "
+                f"Install via `conda install -c nvidia 'pylibcugraphops=23.04'`."
+                f"pylibcugraphops requires Python 3.8 or 3.10."
             )
         super().__init__()
         self.in_feat = in_feat
@@ -176,53 +180,36 @@ def forward(self, g, feat, etypes, max_in_degree=None):
         torch.Tensor
             New node features. Shape: :math:`(|V|, D_{out})`.
         """
-        # Create csc-representation and cast etypes to int32.
         offsets, indices, edge_ids = g.adj_tensors("csc")
         edge_types_perm = etypes[edge_ids.long()].int()
 
-        # Create cugraph-ops graph.
         if g.is_block:
             if max_in_degree is None:
                 max_in_degree = g.in_degrees().max().item()
 
             if max_in_degree < self.MAX_IN_DEGREE_MFG:
-                _graph = make_mfg_csr_hg(
-                    g.dstnodes(),
+                _graph = SampledHeteroCSC(
                     offsets,
                     indices,
+                    edge_types_perm,
                     max_in_degree,
                     g.num_src_nodes(),
-                    n_node_types=0,
-                    n_edge_types=self.num_rels,
-                    out_node_types=None,
-                    in_node_types=None,
-                    edge_types=edge_types_perm,
+                    self.num_rels,
                 )
             else:
-                offsets_fg = torch.empty(
-                    g.num_src_nodes() + 1,
-                    dtype=offsets.dtype,
-                    device=offsets.device,
-                )
-                offsets_fg[: offsets.numel()] = offsets
-                offsets_fg[offsets.numel() :] = offsets[-1]
-
-                _graph = make_fg_csr_hg(
+                offsets_fg = self.pad_offsets(offsets, g.num_src_nodes() + 1)
+                _graph = StaticHeteroCSC(
                     offsets_fg,
                     indices,
-                    n_node_types=0,
-                    n_edge_types=self.num_rels,
-                    node_types=None,
-                    edge_types=edge_types_perm,
+                    edge_types_perm,
+                    self.num_rels,
                 )
         else:
-            _graph = make_fg_csr_hg(
+            _graph = StaticHeteroCSC(
                 offsets,
                 indices,
-                n_node_types=0,
-                n_edge_types=self.num_rels,
-                node_types=None,
-                edge_types=edge_types_perm,
+                edge_types_perm,
+                self.num_rels,
             )
 
         h = RelGraphConvAgg(

python/dgl/nn/pytorch/conv/cugraph_sageconv.py

@@ -2,19 +2,20 @@
 cugraph-ops"""
 # pylint: disable=no-member, arguments-differ, invalid-name, too-many-arguments
 
-import torch
 from torch import nn
 
+from .cugraph_base import CuGraphBaseConv
+
 try:
-    from pylibcugraphops import make_fg_csr, make_mfg_csr
-    from pylibcugraphops.torch.autograd import agg_concat_n2n as SAGEConvAgg
+    from pylibcugraphops.pytorch import SampledCSC, StaticCSC
+    from pylibcugraphops.pytorch.operators import agg_concat_n2n as SAGEConvAgg
+
+    HAS_PYLIBCUGRAPHOPS = True
 except ImportError:
-    has_pylibcugraphops = False
-else:
-    has_pylibcugraphops = True
+    HAS_PYLIBCUGRAPHOPS = False
 
 
-class CuGraphSAGEConv(nn.Module):
+class CuGraphSAGEConv(CuGraphBaseConv):
     r"""An accelerated GraphSAGE layer from `Inductive Representation Learning
     on Large Graphs <https://arxiv.org/pdf/1706.02216.pdf>`__ that leverages the
     highly-optimized aggregation primitives in cugraph-ops:
@@ -27,7 +28,8 @@ class CuGraphSAGEConv(nn.Module):
         (h_{i}^{l}, h_{\mathcal{N}(i)}^{(l+1)})
 
     This module depends on :code:`pylibcugraphops` package, which can be
-    installed via :code:`conda install -c nvidia pylibcugraphops>=23.02`.
+    installed via :code:`conda install -c nvidia pylibcugraphops=23.04`.
+    :code:`pylibcugraphops` 23.04 requires python 3.8.x or 3.10.x.
 
     .. note::
         This is an **experimental** feature.
@@ -74,10 +76,11 @@ def __init__(
         feat_drop=0.0,
         bias=True,
     ):
-        if has_pylibcugraphops is False:
+        if HAS_PYLIBCUGRAPHOPS is False:
             raise ModuleNotFoundError(
-                f"{self.__class__.__name__} requires pylibcugraphops >= 23.02. "
-                f"Install via `conda install -c nvidia 'pylibcugraphops>=23.02'`."
+                f"{self.__class__.__name__} requires pylibcugraphops=23.04. "
+                f"Install via `conda install -c nvidia 'pylibcugraphops=23.04'`."
+                f"pylibcugraphops requires Python 3.8 or 3.10."
             )
 
         valid_aggr_types = {"max", "min", "mean", "sum"}
@@ -126,25 +129,17 @@ def forward(self, g, feat, max_in_degree=None):
                 max_in_degree = g.in_degrees().max().item()
 
             if max_in_degree < self.MAX_IN_DEGREE_MFG:
-                _graph = make_mfg_csr(
-                    g.dstnodes(),
+                _graph = SampledCSC(
                     offsets,
                     indices,
                     max_in_degree,
                     g.num_src_nodes(),
                 )
             else:
-                offsets_fg = torch.empty(
-                    g.num_src_nodes() + 1,
-                    dtype=offsets.dtype,
-                    device=offsets.device,
-                )
-                offsets_fg[: offsets.numel()] = offsets
-                offsets_fg[offsets.numel() :] = offsets[-1]
-
-                _graph = make_fg_csr(offsets_fg, indices)
+                offsets_fg = self.pad_offsets(offsets, g.num_src_nodes() + 1)
+                _graph = StaticCSC(offsets_fg, indices)
         else:
-            _graph = make_fg_csr(offsets, indices)
+            _graph = StaticCSC(offsets, indices)
 
         feat = self.feat_drop(feat)
         h = SAGEConvAgg(feat, _graph, self.aggr)[: g.num_dst_nodes()]

tests/cugraph/cugraph-ops/test_cugraph_gatconv.py

@@ -24,7 +24,6 @@ def generate_graph():
     return g
 
 
-@pytest.mark.skip()
 @pytest.mark.parametrize(",".join(options.keys()), product(*options.values()))
 def test_gatconv_equality(idtype_int, max_in_degree, num_heads, to_block):
     device = "cuda:0"

tests/cugraph/cugraph-ops/test_cugraph_relgraphconv.py

@@ -27,7 +27,6 @@ def generate_graph():
     return g
 
 
-@pytest.mark.skip()
 @pytest.mark.parametrize(",".join(options.keys()), product(*options.values()))
 def test_relgraphconv_equality(
     idtype_int, max_in_degree, num_bases, regularizer, self_loop, to_block

tests/cugraph/cugraph-ops/test_cugraph_sageconv.py

@@ -23,7 +23,6 @@ def generate_graph():
     return g
 
 
-@pytest.mark.skip()
 @pytest.mark.parametrize(",".join(options.keys()), product(*options.values()))
 def test_SAGEConv_equality(idtype_int, max_in_degree, to_block):
     device = "cuda:0"