[GraphBolt] torch.compile() support for gb.expand_indptr.

graphbolt/src/expand_indptr.cc

@@ -5,6 +5,7 @@
  * @brief ExpandIndptr operators.
  */
 #include <graphbolt/cuda_ops.h>
+#include <torch/autograd.h>
 
 #include "./macro.h"
 #include "./utils.h"
@@ -29,5 +30,19 @@ torch::Tensor ExpandIndptr(
       indptr.diff(), 0, output_size);
 }
 
+TORCH_LIBRARY_IMPL(graphbolt, CPU, m) {
+  m.impl("expand_indptr", &ExpandIndptr);
+}
+
+#ifdef GRAPHBOLT_USE_CUDA
+TORCH_LIBRARY_IMPL(graphbolt, CUDA, m) {
+  m.impl("expand_indptr", &ExpandIndptrImpl);
+}
+#endif
+
+TORCH_LIBRARY_IMPL(graphbolt, Autograd, m) {
+  m.impl("expand_indptr", torch::autograd::autogradNotImplementedFallback());
+}
+
 }  // namespace ops
 }  // namespace graphbolt

graphbolt/src/python_binding.cc

@@ -88,11 +88,21 @@ TORCH_LIBRARY(graphbolt, m) {
   m.def("isin", &IsIn);
   m.def("index_select", &ops::IndexSelect);
   m.def("index_select_csc", &ops::IndexSelectCSC);
-  m.def("expand_indptr", &ops::ExpandIndptr);
   m.def("set_seed", &RandomEngine::SetManualSeed);
 #ifdef GRAPHBOLT_USE_CUDA
   m.def("set_max_uva_threads", &cuda::set_max_uva_threads);
 #endif
+#ifdef HAS_IMPL_ABSTRACT_PYSTUB
+  m.impl_abstract_pystub("dgl.graphbolt.base", "//dgl.graphbolt.base");
+#endif
+  m.def(
+      "expand_indptr(Tensor indptr, ScalarType dtype, Tensor? node_ids, "
+      "SymInt? output_size) -> Tensor"
+#ifdef HAS_PT2_COMPLIANT_TAG
+      ,
+      {at::Tag::pt2_compliant_tag}
+#endif
+  );
 }
 
 }  // namespace sampling

python/dgl/graphbolt/__init__.py

@@ -5,25 +5,6 @@
 import torch
 
 from .._ffi import libinfo
-from .base import *
-from .minibatch import *
-from .dataloader import *
-from .dataset import *
-from .feature_fetcher import *
-from .feature_store import *
-from .impl import *
-from .itemset import *
-from .item_sampler import *
-from .minibatch_transformer import *
-from .negative_sampler import *
-from .sampled_subgraph import *
-from .subgraph_sampler import *
-from .internal import (
-    compact_csc_format,
-    unique_and_compact,
-    unique_and_compact_csc_formats,
-)
-from .utils import add_reverse_edges, add_reverse_edges_2, exclude_seed_edges
 
 
 def load_graphbolt():
@@ -53,3 +34,24 @@ def load_graphbolt():
 
 
 load_graphbolt()
+
+# pylint: disable=wrong-import-position
+from .base import *
+from .minibatch import *
+from .dataloader import *
+from .dataset import *
+from .feature_fetcher import *
+from .feature_store import *
+from .impl import *
+from .itemset import *
+from .item_sampler import *
+from .minibatch_transformer import *
+from .negative_sampler import *
+from .sampled_subgraph import *
+from .subgraph_sampler import *
+from .internal import (
+    compact_csc_format,
+    unique_and_compact,
+    unique_and_compact_csc_formats,
+)
+from .utils import add_reverse_edges, add_reverse_edges_2, exclude_seed_edges

python/dgl/graphbolt/base.py

@@ -4,6 +4,7 @@
 from dataclasses import dataclass
 
 import torch
+from torch.torch_version import TorchVersion
 from torch.utils.data import functional_datapipe
 from torchdata.datapipes.iter import IterDataPipe
 
@@ -63,6 +64,18 @@ def isin(elements, test_elements):
     return torch.ops.graphbolt.isin(elements, test_elements)
 
 
+if TorchVersion(torch.__version__) >= TorchVersion("2.2.0a0"):
+
+    @torch.library.impl_abstract("graphbolt::expand_indptr")
+    def expand_indptr_abstract(indptr, dtype, node_ids, output_size):
+        """Abstract implementation of expand_indptr for torch.compile() support."""
+        if output_size is None:
+            output_size = torch.library.get_ctx().new_dynamic_size()
+        if dtype is None:
+            dtype = node_ids.dtype
+        return indptr.new_empty(output_size, dtype=dtype)
+
+
 def expand_indptr(indptr, dtype=None, node_ids=None, output_size=None):
     """Converts a given indptr offset tensor to a COO format tensor. If
     node_ids is not given, it is assumed to be equal to

tests/python/pytorch/graphbolt/test_base.py

@@ -7,6 +7,7 @@
 import dgl.graphbolt as gb
 import pytest
 import torch
+from torch.torch_version import TorchVersion
 
 from . import gb_test_utils
 
@@ -296,6 +297,32 @@ def test_expand_indptr(nodes, dtype):
     gb_result = gb.expand_indptr(indptr, dtype, nodes, indptr[-1].item())
     assert torch.equal(torch_result, gb_result)
 
+    if TorchVersion(torch.__version__) >= TorchVersion("2.2.0a0"):
+        import torch._dynamo as dynamo
+        from torch.testing._internal.optests import opcheck
+
+        # Tests torch.compile compatibility
+        for output_size in [None, indptr[-1].item()]:
+            kwargs = {"node_ids": nodes, "output_size": output_size}
+            opcheck(
+                torch.ops.graphbolt.expand_indptr,
+                (indptr, dtype),
+                kwargs,
+                test_utils=[
+                    "test_schema",
+                    "test_autograd_registration",
+                    "test_faketensor",
+                    "test_aot_dispatch_dynamic",
+                ],
+                raise_exception=True,
+            )
+
+            explanation = dynamo.explain(gb.expand_indptr)(
+                indptr, dtype, nodes, output_size
+            )
+            expected_breaks = -1 if output_size is None else 0
+            assert explanation.graph_break_count == expected_breaks
+
 
 def test_csc_format_base_representation():
     csc_format_base = gb.CSCFormatBase(