typo

setup.py

@@ -35,9 +35,15 @@ def get_extensions():
     for main in main_files:
         name = main.split(os.sep)[-1][:-4]
 
-        sources = [main, osp.join(extensions_dir, 'cpu', name + '_cpu.cpp')]
-        if WITH_CUDA:
-            sources += [osp.join(extensions_dir, 'cuda', name + '_cuda.cu')]
+        sources = [main]
+
+        path = osp.join(extensions_dir, 'cpu', name + '_cpu.cpp')
+        if osp.exists(path):
+            sources += [path]
+
+        path = osp.join(extensions_dir, 'cuda', name + '_cuda.cpp')
+        if WITH_CUDA and osp.exists(path):
+            sources += [path]
 
         extension = Extension(
             'torch_scatter._' + name,

torch_scatter/__init__.py

+import os.path as osp
+
+import torch
+
 from .scatter import (scatter_sum, scatter_add, scatter_mean, scatter_min,
                       scatter_max, scatter)
 from .segment_csr import (segment_sum_csr, segment_add_csr, segment_mean_csr,
@@ -9,6 +13,10 @@ from .segment_coo import (segment_sum_coo, segment_add_coo, segment_mean_coo,
 from .composite import (scatter_std, scatter_logsumexp, scatter_softmax,
                         scatter_log_softmax)
 
+torch.ops.load_library(
+    osp.join(osp.dirname(osp.abspath(__file__)), '_version.so'))
+_version = torch.ops.torch_scatter.cuda_version()
+
 __version__ = '2.0.3'
 
 __all__ = [

torch_scatter/scatter.py

-import warnings
 import os.path as osp
 from typing import Optional, Tuple
 
@@ -6,21 +5,8 @@ import torch
 
 from .utils import broadcast
 
-try:
-    torch.ops.load_library(
-        osp.join(osp.dirname(osp.abspath(__file__)), '_scatter.so'))
-except OSError:
-    warnings.warn('Failed to load `scatter` binaries.')
-
-    def scatter_with_arg_placeholder(src: torch.Tensor, index: torch.Tensor,
-                                     dim: int, out: Optional[torch.Tensor],
-                                     dim_size: Optional[int]
-                                     ) -> Tuple[torch.Tensor, torch.Tensor]:
-        raise ImportError
-        return src, index
-
-    torch.ops.torch_scatter.scatter_min = scatter_with_arg_placeholder
-    torch.ops.torch_scatter.scatter_max = scatter_with_arg_placeholder
+torch.ops.load_library(
+    osp.join(osp.dirname(osp.abspath(__file__)), '_scatter.so'))
 
 
 @torch.jit.script

torch_scatter/segment_coo.py

-import warnings
 import os.path as osp
 from typing import Optional, Tuple
 
 import torch
 
-try:
-    torch.ops.load_library(
-        osp.join(osp.dirname(osp.abspath(__file__)), '_segment_coo.so'))
-except OSError:
-    warnings.warn('Failed to load `segment_coo` binaries.')
-
-    def segment_coo_placeholder(src: torch.Tensor, index: torch.Tensor,
-                                out: Optional[torch.Tensor],
-                                dim_size: Optional[int]) -> torch.Tensor:
-        raise ImportError
-        return src
-
-    def segment_coo_with_arg_placeholder(
-            src: torch.Tensor, index: torch.Tensor,
-            out: Optional[torch.Tensor],
-            dim_size: Optional[int]) -> Tuple[torch.Tensor, torch.Tensor]:
-        raise ImportError
-        return src, index
-
-    def gather_coo_placeholder(src: torch.Tensor, index: torch.Tensor,
-                               out: Optional[torch.Tensor]) -> torch.Tensor:
-        raise ImportError
-        return src
-
-    torch.ops.torch_scatter.segment_sum_coo = segment_coo_placeholder
-    torch.ops.torch_scatter.segment_mean_coo = segment_coo_placeholder
-    torch.ops.torch_scatter.segment_min_coo = segment_coo_with_arg_placeholder
-    torch.ops.torch_scatter.segment_max_coo = segment_coo_with_arg_placeholder
-    torch.ops.torch_scatter.gather_coo = gather_coo_placeholder
+torch.ops.load_library(
+    osp.join(osp.dirname(osp.abspath(__file__)), '_segment_coo.so'))
 
 
 @torch.jit.script

torch_scatter/segment_csr.py

-import warnings
 import os.path as osp
 from typing import Optional, Tuple
 
 import torch
 
-try:
-    torch.ops.load_library(
-        osp.join(osp.dirname(osp.abspath(__file__)), '_segment_csr.so'))
-except OSError:
-    warnings.warn('Failed to load `segment_csr` binaries.')
-
-    def segment_csr_placeholder(src: torch.Tensor, indptr: torch.Tensor,
-                                out: Optional[torch.Tensor]) -> torch.Tensor:
-        raise ImportError
-        return src
-
-    def segment_csr_with_arg_placeholder(
-            src: torch.Tensor, indptr: torch.Tensor,
-            out: Optional[torch.Tensor]) -> Tuple[torch.Tensor, torch.Tensor]:
-        raise ImportError
-        return src, indptr
-
-    def gather_csr_placeholder(src: torch.Tensor, indptr: torch.Tensor,
-                               out: Optional[torch.Tensor]) -> torch.Tensor:
-        raise ImportError
-        return src
-
-    torch.ops.torch_scatter.segment_sum_csr = segment_csr_placeholder
-    torch.ops.torch_scatter.segment_mean_csr = segment_csr_placeholder
-    torch.ops.torch_scatter.segment_min_csr = segment_csr_with_arg_placeholder
-    torch.ops.torch_scatter.segment_max_csr = segment_csr_with_arg_placeholder
-    torch.ops.torch_scatter.gather_csr = gather_csr_placeholder
+torch.ops.load_library(
+    osp.join(osp.dirname(osp.abspath(__file__)), '_segment_csr.so'))
 
 
 @torch.jit.script