added 3.5 dependency

setup.py

@@ -35,7 +35,7 @@ def get_extensions():
     if WITH_CUDA:
         Extension = CUDAExtension
         define_macros += [('WITH_CUDA', None)]
-        extra_compile_args['cxx'] += ['-O0']
+        # extra_compile_args['cxx'] += ['-O0']
         nvcc_flags = os.getenv('NVCC_FLAGS', '')
         nvcc_flags = [] if nvcc_flags == '' else nvcc_flags.split(' ')
         nvcc_flags += ['-arch=sm_35', '--expt-relaxed-constexpr']
@@ -52,12 +52,12 @@ def get_extensions():
     for main in main_files:
         name = main.split(os.sep)[-1][:-4]
 
-        sources = [main, osp.join(extensions_dir, 'cpu', f'{name}_cpu.cpp')]
+        sources = [main, osp.join(extensions_dir, 'cpu', name + '_cpu.cpp')]
         if WITH_CUDA:
-            sources += [osp.join(extensions_dir, 'cuda', f'{name}_cuda.cu')]
+            sources += [osp.join(extensions_dir, 'cuda', name + '_cuda.cu')]
 
         extension = Extension(
-            f'torch_scatter._{name}',
+            'torch_scatter._' + name,
             sources,
             include_dirs=[extensions_dir],
             define_macros=define_macros,
@@ -82,7 +82,7 @@ setup(
     description='PyTorch Extension Library of Optimized Scatter Operations',
     keywords=['pytorch', 'scatter', 'segment', 'gather'],
     license='MIT',
-    python_requires='>=3.6',
+    python_requires='>=3.5',
     install_requires=install_requires,
     setup_requires=setup_requires,
     tests_require=tests_require,

test/test_scatter.py

@@ -91,10 +91,10 @@ def test_forward(test, reduce, dtype, device):
     dim = test['dim']
     expected = tensor(test[reduce], dtype, device)
 
-    out = getattr(torch_scatter, f'scatter_{reduce}')(src, index, dim)
+    out = getattr(torch_scatter, 'scatter_' + reduce)(src, index, dim)
     if isinstance(out, tuple):
         out, arg_out = out
-        arg_expected = tensor(test[f'arg_{reduce}'], torch.long, device)
+        arg_expected = tensor(test['arg_' + reduce], torch.long, device)
         assert torch.all(arg_out == arg_expected)
     assert torch.all(out == expected)
 
@@ -121,7 +121,7 @@ def test_out(test, reduce, dtype, device):
 
     out = torch.full_like(expected, -2)
 
-    getattr(torch_scatter, f'scatter_{reduce}')(src, index, dim, out)
+    getattr(torch_scatter, 'scatter_' + reduce)(src, index, dim, out)
 
     if reduce == 'sum' or reduce == 'add':
         expected = expected - 2
@@ -150,9 +150,9 @@ def test_non_contiguous(test, reduce, dtype, device):
     if index.dim() > 1:
         index = index.transpose(0, 1).contiguous().transpose(0, 1)
 
-    out = getattr(torch_scatter, f'scatter_{reduce}')(src, index, dim)
+    out = getattr(torch_scatter, 'scatter_' + reduce)(src, index, dim)
     if isinstance(out, tuple):
         out, arg_out = out
-        arg_expected = tensor(test[f'arg_{reduce}'], torch.long, device)
+        arg_expected = tensor(test['arg_' + reduce], torch.long, device)
         assert torch.all(arg_out == arg_expected)
     assert torch.all(out == expected)

test/test_segment.py

@@ -91,17 +91,17 @@ def test_forward(test, reduce, dtype, device):
     indptr = tensor(test['indptr'], torch.long, device)
     expected = tensor(test[reduce], dtype, device)
 
-    out = getattr(torch_scatter, f'segment_{reduce}_csr')(src, indptr)
+    out = getattr(torch_scatter, 'segment_' + reduce + '_csr')(src, indptr)
     if isinstance(out, tuple):
         out, arg_out = out
-        arg_expected = tensor(test[f'arg_{reduce}'], torch.long, device)
+        arg_expected = tensor(test['arg_' + reduce], torch.long, device)
         assert torch.all(arg_out == arg_expected)
     assert torch.all(out == expected)
 
-    out = getattr(torch_scatter, f'segment_{reduce}_coo')(src, index)
+    out = getattr(torch_scatter, 'segment_' + reduce + '_coo')(src, index)
     if isinstance(out, tuple):
         out, arg_out = out
-        arg_expected = tensor(test[f'arg_{reduce}'], torch.long, device)
+        arg_expected = tensor(test['arg_' + reduce], torch.long, device)
         assert torch.all(arg_out == arg_expected)
     assert torch.all(out == expected)
 
@@ -129,12 +129,12 @@ def test_out(test, reduce, dtype, device):
 
     out = torch.full_like(expected, -2)
 
-    getattr(torch_scatter, f'segment_{reduce}_csr')(src, indptr, out)
+    getattr(torch_scatter, 'segment_' + reduce + '_csr')(src, indptr, out)
     assert torch.all(out == expected)
 
     out.fill_(-2)
 
-    getattr(torch_scatter, f'segment_{reduce}_coo')(src, index, out)
+    getattr(torch_scatter, 'segment_' + reduce + '_coo')(src, index, out)
 
     if reduce == 'sum' or reduce == 'add':
         expected = expected - 2
@@ -165,16 +165,16 @@ def test_non_contiguous(test, reduce, dtype, device):
     if indptr.dim() > 1:
         indptr = indptr.transpose(0, 1).contiguous().transpose(0, 1)
 
-    out = getattr(torch_scatter, f'segment_{reduce}_csr')(src, indptr)
+    out = getattr(torch_scatter, 'segment_' + reduce + '_csr')(src, indptr)
     if isinstance(out, tuple):
         out, arg_out = out
-        arg_expected = tensor(test[f'arg_{reduce}'], torch.long, device)
+        arg_expected = tensor(test['arg_' + reduce], torch.long, device)
         assert torch.all(arg_out == arg_expected)
     assert torch.all(out == expected)
 
-    out = getattr(torch_scatter, f'segment_{reduce}_coo')(src, index)
+    out = getattr(torch_scatter, 'segment_' + reduce + '_coo')(src, index)
     if isinstance(out, tuple):
         out, arg_out = out
-        arg_expected = tensor(test[f'arg_{reduce}'], torch.long, device)
+        arg_expected = tensor(test['arg_' + reduce], torch.long, device)
         assert torch.all(arg_out == arg_expected)
     assert torch.all(out == expected)