suport v2.1.0

readthedocs.yml

+version: 2
+
+build:
+   image: latest
+
+python:
+   version: 3.8
+   system_packages: true
+   install:
+      - requirements: docs/requirements.txt
+      - method: setuptools
+        path: .
+
+formats: []

setup.cfg

 [metadata]
-description-file = README.md
+long_description=file: README.md
+long_description_content_type=text/markdown
+
+classifiers =
+    Development Status :: 5 - Production/Stable
+    License :: OSI Approved :: MIT License
+    Programming Language :: Python
+    Programming Language :: Python :: 3.7
+    Programming Language :: Python :: 3.8
+    Programming Language :: Python :: 3.9
+    Programming Language :: Python :: 3.10
+    Programming Language :: Python :: 3 :: Only
 
 [aliases]
 test = pytest
 
 [tool:pytest]
-addopts = --capture=no --cov
-
-[egg_info]
-tag_build = 
-tag_date = 0
-
+addopts = --capture=no

test/composite/test_logsumexp.py

+import torch
+from torch_scatter import scatter_logsumexp
+
+
+def test_logsumexp():
+    inputs = torch.tensor([
+        0.5, 0.5, 0.0, -2.1, 3.2, 7.0, -1.0, -100.0,
+        float('-inf'),
+        float('-inf'), 0.0
+    ])
+    inputs.requires_grad_()
+    index = torch.tensor([0, 0, 1, 1, 1, 2, 4, 4, 5, 6, 6])
+    splits = [2, 3, 1, 0, 2, 1, 2]
+
+    outputs = scatter_logsumexp(inputs, index)
+
+    for src, out in zip(inputs.split(splits), outputs.unbind()):
+        assert out.tolist() == torch.logsumexp(src, dim=0).tolist()
+
+    outputs.backward(torch.randn_like(outputs))
+
+    jit = torch.jit.script(scatter_logsumexp)
+    assert jit(inputs, index).tolist() == outputs.tolist()

test/composite/test_softmax.py

+import torch
+from torch_scatter import scatter_log_softmax, scatter_softmax
+
+
+def test_softmax():
+    src = torch.tensor([0.2, 0, 0.2, -2.1, 3.2, 7, -1, float('-inf')])
+    src.requires_grad_()
+    index = torch.tensor([0, 1, 0, 1, 1, 2, 4, 4])
+
+    out = scatter_softmax(src, index)
+
+    out0 = torch.softmax(torch.tensor([0.2, 0.2]), dim=-1)
+    out1 = torch.softmax(torch.tensor([0, -2.1, 3.2]), dim=-1)
+    out2 = torch.softmax(torch.tensor([7], dtype=torch.float), dim=-1)
+    out4 = torch.softmax(torch.tensor([-1, float('-inf')]), dim=-1)
+
+    expected = torch.stack([
+        out0[0], out1[0], out0[1], out1[1], out1[2], out2[0], out4[0], out4[1]
+    ], dim=0)
+
+    assert torch.allclose(out, expected)
+
+    out.backward(torch.randn_like(out))
+
+    jit = torch.jit.script(scatter_softmax)
+    assert jit(src, index).tolist() == out.tolist()
+
+
+def test_log_softmax():
+    src = torch.tensor([0.2, 0, 0.2, -2.1, 3.2, 7, -1, float('-inf')])
+    src.requires_grad_()
+    index = torch.tensor([0, 1, 0, 1, 1, 2, 4, 4])
+
+    out = scatter_log_softmax(src, index)
+
+    out0 = torch.log_softmax(torch.tensor([0.2, 0.2]), dim=-1)
+    out1 = torch.log_softmax(torch.tensor([0, -2.1, 3.2]), dim=-1)
+    out2 = torch.log_softmax(torch.tensor([7], dtype=torch.float), dim=-1)
+    out4 = torch.log_softmax(torch.tensor([-1, float('-inf')]), dim=-1)
+
+    expected = torch.stack([
+        out0[0], out1[0], out0[1], out1[1], out1[2], out2[0], out4[0], out4[1]
+    ], dim=0)
+
+    assert torch.allclose(out, expected)
+
+    out.backward(torch.randn_like(out))
+
+    jit = torch.jit.script(scatter_log_softmax)
+    assert jit(src, index).tolist() == out.tolist()

torch_scatter.egg-info/requires.txt

 
 [test]
 pytest
-pytest-runner
 pytest-cov