Replace lap.lapjv() with scipy.optimize.linear_sum_assignment()

docs/requirements.txt

@@ -7,4 +7,4 @@ opt_einsum>=2.3.2
 pyro-api>=0.1.1
 tqdm>=4.36
 funsor[torch]
-setuptools<60
+setuptools

pyro/distributions/one_one_matching.py

@@ -160,19 +160,15 @@ def sample(self, sample_shape=torch.Size()):
     def mode(self):
         """
         Computes a maximum probability matching.
-
-        .. note:: This requires the `lap <https://pypi.org/project/lap/>`_
-            package and runs on CPU.
         """
         return maximum_weight_matching(self.logits)
 
 
 @torch.no_grad()
 def maximum_weight_matching(logits):
-    with warnings.catch_warnings():
-        warnings.filterwarnings("ignore", category=ImportWarning)
-        import lap
+    from scipy.optimize import linear_sum_assignment
+
     cost = -logits.cpu()
-    value = lap.lapjv(cost.numpy())[1]
+    value = linear_sum_assignment(cost.numpy())[1]
     value = torch.tensor(value, dtype=torch.long, device=logits.device)
     return value

pyro/distributions/one_two_matching.py

@@ -169,9 +169,6 @@ def sample(self, sample_shape=torch.Size()):
     def mode(self):
         """
         Computes a maximum probability matching.
-
-        .. note:: This requires the `lap <https://pypi.org/project/lap/>`_
-            package and runs on CPU.
         """
         return maximum_weight_matching(self.logits)
 
@@ -204,12 +201,11 @@ def enumerate_one_two_matchings(num_destins):
 
 @torch.no_grad()
 def maximum_weight_matching(logits):
-    with warnings.catch_warnings():
-        warnings.filterwarnings("ignore", category=ImportWarning)
-        import lap
+    from scipy.optimize import linear_sum_assignment
+
     cost = -logits.cpu()
     cost = torch.cat([cost, cost], dim=-1)  # Duplicate destinations.
-    value = lap.lapjv(cost.numpy())[1]
+    value = linear_sum_assignment(cost.numpy())[1]
     value = torch.tensor(value, dtype=torch.long, device=logits.device)
     value %= logits.size(1)
     return value

setup.py

@@ -75,7 +75,7 @@
     "scikit-learn",
     "seaborn>=0.11.0",
     "wget",
-    "lap",  # Requires setuptools<60
+    "scipy>=1.1",
     # 'biopython>=1.54',
     # 'scanpy>=1.4',  # Requires HDF5
     # 'scvi>=0.6',  # Requires loopy and other fragile packages
@@ -115,7 +115,6 @@
             "pytest-xdist",
             "pytest>=5.0",
             "ruff",
-            "scipy>=1.1",
         ],
         "profile": ["prettytable", "pytest-benchmark", "snakeviz"],
         "dev": EXTRAS_REQUIRE
@@ -131,7 +130,6 @@
             "pytest-xdist",
             "pytest>=5.0",
             "ruff",
-            "scipy>=1.1",
             "sphinx",
             "sphinx_rtd_theme",
             "yapf",

tests/common.py

@@ -164,12 +164,14 @@ def assert_tensors_equal(a, b, prec=0.0, msg=""):
     assert a.size() == b.size(), msg
     if isinstance(prec, numbers.Number) and prec == 0:
         assert (a == b).all(), msg
+        return
     if a.numel() == 0 and b.numel() == 0:
         return
     b = b.type_as(a)
     b = b.cuda(device=a.get_device()) if a.is_cuda else b.cpu()
     if not a.dtype.is_floating_point:
-        return (a == b).all()
+        assert (a == b).all(), msg
+        return
     # check that NaNs are in the same locations
     nan_mask = a != a
     assert torch.equal(nan_mask, b != b), msg

tests/distributions/test_one_one_matching.py

@@ -112,20 +112,19 @@ def test_grad_hard(num_nodes):
 @pytest.mark.parametrize("dtype", [torch.float, torch.double], ids=str)
 @pytest.mark.parametrize("num_nodes", [1, 2, 3, 4, 5, 6, 7, 8])
 def test_mode(num_nodes, dtype):
-    pytest.importorskip("lap")
     logits = torch.randn(num_nodes, num_nodes, dtype=dtype) * 10
     d = dist.OneOneMatching(logits)
     values = d.enumerate_support()
     i = d.log_prob(values).max(0).indices.item()
     expected = values[i]
     actual = d.mode()
     assert_equal(actual, expected)
+    assert (actual == expected).all()
 
 
 @pytest.mark.parametrize("dtype", [torch.float, torch.double], ids=str)
 @pytest.mark.parametrize("num_nodes", [3, 5, 8, 13, 100, 1000])
 def test_mode_smoke(num_nodes, dtype):
-    pytest.importorskip("lap")
     logits = torch.randn(num_nodes, num_nodes, dtype=dtype) * 10
     d = dist.OneOneMatching(logits)
     value = d.mode()
@@ -136,7 +135,6 @@ def test_mode_smoke(num_nodes, dtype):
 @pytest.mark.parametrize("num_nodes", [2, 3, 4, 5, 6])
 @pytest.mark.parametrize("bp_iters", [None, BP_ITERS], ids=["exact", "bp"])
 def test_sample(num_nodes, dtype, bp_iters):
-    pytest.importorskip("lap")
     logits = torch.randn(num_nodes, num_nodes, dtype=dtype) * 10
     d = dist.OneOneMatching(logits, bp_iters=bp_iters)
 

tests/distributions/test_one_two_matching.py

@@ -175,7 +175,6 @@ def test_grad_phylo(num_leaves):
 @pytest.mark.parametrize("dtype", [torch.float, torch.double], ids=str)
 @pytest.mark.parametrize("num_destins", [1, 2, 3, 4, 5])
 def test_mode_full(num_destins, dtype):
-    pytest.importorskip("lap")
     num_sources = 2 * num_destins
     logits = torch.randn(num_sources, num_destins, dtype=dtype) * 10
     d = dist.OneTwoMatching(logits)
@@ -189,7 +188,6 @@ def test_mode_full(num_destins, dtype):
 @pytest.mark.parametrize("dtype", [torch.float, torch.double], ids=str)
 @pytest.mark.parametrize("num_leaves", [2, 3, 4, 5, 6])
 def test_mode_phylo(num_leaves, dtype):
-    pytest.importorskip("lap")
     logits, times = random_phylo_logits(num_leaves, dtype)
     d = dist.OneTwoMatching(logits)
     values = d.enumerate_support()
@@ -202,7 +200,6 @@ def test_mode_phylo(num_leaves, dtype):
 @pytest.mark.parametrize("dtype", [torch.float, torch.double], ids=str)
 @pytest.mark.parametrize("num_destins", [3, 5, 8, 13, 100, 1000])
 def test_mode_full_smoke(num_destins, dtype):
-    pytest.importorskip("lap")
     num_sources = 2 * num_destins
     logits = torch.randn(num_sources, num_destins, dtype=dtype) * 10
     d = dist.OneTwoMatching(logits)
@@ -213,7 +210,6 @@ def test_mode_full_smoke(num_destins, dtype):
 @pytest.mark.parametrize("dtype", [torch.float, torch.double], ids=str)
 @pytest.mark.parametrize("num_leaves", [3, 5, 8, 13, 100, 1000])
 def test_mode_phylo_smoke(num_leaves, dtype):
-    pytest.importorskip("lap")
     logits, times = random_phylo_logits(num_leaves, dtype)
     d = dist.OneTwoMatching(logits, bp_iters=10)
     value = d.mode()
@@ -224,7 +220,6 @@ def test_mode_phylo_smoke(num_leaves, dtype):
 @pytest.mark.parametrize("num_destins", [2, 3, 4])
 @pytest.mark.parametrize("bp_iters", [None, BP_ITERS], ids=["exact", "bp"])
 def test_sample_full(num_destins, dtype, bp_iters):
-    pytest.importorskip("lap")
     num_sources = 2 * num_destins
     logits = torch.randn(num_sources, num_destins, dtype=dtype) * 10
     d = dist.OneTwoMatching(logits, bp_iters=bp_iters)
@@ -251,7 +246,6 @@ def test_sample_full(num_destins, dtype, bp_iters):
 @pytest.mark.parametrize("num_leaves", [3, 4, 5])
 @pytest.mark.parametrize("bp_iters", [None, BP_ITERS], ids=["exact", "bp"])
 def test_sample_phylo(num_leaves, dtype, bp_iters):
-    pytest.importorskip("lap")
     logits, times = random_phylo_logits(num_leaves, dtype)
     num_sources, num_destins = logits.shape
     d = dist.OneTwoMatching(logits, bp_iters=bp_iters)