Rewrite the tests structure (#92)

* update tests

* style

tests/extensions.py

@@ -2,24 +2,28 @@
 import torch
 from torch_struct import LogSemiring
 import itertools
+from hypothesis.strategies import integers, composite, floats
+from hypothesis.extra.numpy import arrays
+import numpy as np
 
 
 class LinearChainTest:
-    def __init__(self, semiring=LogSemiring):
-        self.semiring = semiring
-
     @staticmethod
-    def _rand(min_n=2):
-        b = torch.randint(2, 4, (1,))
-        N = torch.randint(min_n, 4, (1,))
-        C = torch.randint(2, 4, (1,))
-        return torch.rand(b, N, C, C), (b.item(), (N + 1).item())
+    @composite
+    def logpotentials(draw, min_n=2):
+        b = draw(integers(min_value=2, max_value=3))
+        N = draw(integers(min_value=min_n, max_value=3))
+        C = draw(integers(min_value=2, max_value=3))
+        logp = draw(
+            arrays(np.float, (b, N, C, C), floats(min_value=-100.0, max_value=100.0))
+        )
+        return torch.tensor(logp), (b, (N + 1))
 
     ### Tests
-
-    def enumerate(self, edge, lengths=None):
-        model = torch_struct.LinearChain(self.semiring)
-        semiring = self.semiring
+    @staticmethod
+    def enumerate(semiring, edge, lengths=None):
+        model = torch_struct.LinearChain(semiring)
+        semiring = semiring
         ssize = semiring.size()
         edge, batch, N, C, lengths = model._check_potentials(edge, lengths)
         chains = [[([c], semiring.one_(torch.zeros(ssize, batch))) for c in range(C)]]
@@ -66,17 +70,18 @@ def enumerate(self, edge, lengths=None):
 
 
 class DepTreeTest:
-    def __init__(self, semiring=LogSemiring):
-        self.semiring = semiring
-
     @staticmethod
-    def _rand():
-        b = torch.randint(2, 4, (1,))
-        N = torch.randint(2, 4, (1,))
-        return torch.rand(b, N, N), (b.item(), N.item())
+    @composite
+    def logpotentials(draw):
+        b = draw(integers(min_value=2, max_value=3))
+        N = draw(integers(min_value=2, max_value=3))
+        logp = draw(
+            arrays(np.float, (b, N, N), floats(min_value=-10.0, max_value=10.0))
+        )
+        return torch.tensor(logp), (b, N)
 
-    def enumerate(self, arc_scores, non_proj=False, multi_root=True):
-        semiring = self.semiring
+    @staticmethod
+    def enumerate(semiring, arc_scores, non_proj=False, multi_root=True):
         parses = []
         q = []
         arc_scores = torch_struct.convert(arc_scores)
@@ -101,21 +106,23 @@ def enumerate(self, arc_scores, non_proj=False, multi_root=True):
 
 
 class SemiMarkovTest:
-    def __init__(self, semiring=LogSemiring):
-        self.semiring = semiring
 
     # Tests
 
     @staticmethod
-    def _rand():
-        b = torch.randint(2, 4, (1,))
-        N = torch.randint(2, 4, (1,))
-        K = torch.randint(2, 4, (1,))
-        C = torch.randint(2, 4, (1,))
-        return torch.rand(b, N, K, C, C), (b.item(), (N + 1).item())
+    @composite
+    def logpotentials(draw):
+        b = draw(integers(min_value=2, max_value=3))
+        N = draw(integers(min_value=2, max_value=3))
+        K = draw(integers(min_value=2, max_value=3))
+        C = draw(integers(min_value=2, max_value=3))
+        logp = draw(
+            arrays(np.float, (b, N, K, C, C), floats(min_value=-100.0, max_value=100.0))
+        )
+        return torch.tensor(logp), (b, (N + 1))
 
-    def enumerate(self, edge):
-        semiring = self.semiring
+    @staticmethod
+    def enumerate(semiring, edge):
         ssize = semiring.size()
         batch, N, K, C, _ = edge.shape
         edge = semiring.convert(edge)
@@ -213,12 +220,22 @@ def _is_projective(parse):
 
 
 class CKY_CRFTest:
-    def __init__(self, semiring=LogSemiring):
-        self.semiring = semiring
+    @staticmethod
+    @composite
+    def logpotentials(draw):
+        batch = draw(integers(min_value=2, max_value=4))
+        N = draw(integers(min_value=2, max_value=4))
+        NT = draw(integers(min_value=2, max_value=4))
+        logp = draw(
+            arrays(
+                np.float, (batch, N, N, NT), floats(min_value=-100.0, max_value=100.0)
+            )
+        )
+        return torch.tensor(logp), (batch, N)
 
-    # For testing
-    def enumerate(self, scores):
-        semiring = self.semiring
+    @staticmethod
+    def enumerate(semiring, scores):
+        semiring = semiring
         batch, N, _, NT = scores.shape
 
         def enumerate(x, start, end):
@@ -243,22 +260,36 @@ def enumerate(x, start, end):
 
         return semiring.sum(torch.stack(ls, dim=-1)), None
 
-    @staticmethod
-    def _rand():
-        batch = torch.randint(2, 5, (1,))
-        N = torch.randint(2, 5, (1,))
-        NT = torch.randint(2, 5, (1,))
-        scores = torch.rand(batch, N, N, NT)
-        return scores, (batch.item(), N.item())
-
 
 class CKYTest:
-    def __init__(self, semiring=LogSemiring):
-        self.semiring = semiring
+    @staticmethod
+    @composite
+    def logpotentials(draw):
+        batch = draw(integers(min_value=2, max_value=3))
+        N = draw(integers(min_value=2, max_value=4))
+        NT = draw(integers(min_value=2, max_value=3))
+        T = draw(integers(min_value=2, max_value=3))
+        terms = draw(
+            arrays(np.float, (batch, N, T), floats(min_value=-100.0, max_value=100.0))
+        )
+        rules = draw(
+            arrays(
+                np.float,
+                (batch, NT, NT + T, NT + T),
+                floats(min_value=-100.0, max_value=100.0),
+            )
+        )
+        roots = draw(
+            arrays(np.float, (batch, NT), floats(min_value=-100.0, max_value=100.0))
+        )
+        return (torch.tensor(terms), torch.tensor(rules), torch.tensor(roots)), (
+            batch,
+            N,
+        )
 
-    def enumerate(self, scores):
+    @staticmethod
+    def enumerate(semiring, scores):
         terms, rules, roots = scores
-        semiring = self.semiring
         batch, N, T = terms.shape
         _, NT, _, _ = rules.shape
 
@@ -283,17 +314,6 @@ def enumerate(x, start, end):
             ls += [semiring.times(s, roots[:, nt]) for s, _ in enumerate(nt, 0, N)]
         return semiring.sum(torch.stack(ls, dim=-1)), None
 
-    @staticmethod
-    def _rand():
-        batch = torch.randint(2, 5, (1,))
-        N = torch.randint(2, 5, (1,))
-        NT = torch.randint(2, 5, (1,))
-        T = torch.randint(2, 5, (1,))
-        terms = torch.rand(batch, N, T)
-        rules = torch.rand(batch, NT, (NT + T), (NT + T))
-        roots = torch.rand(batch, NT)
-        return (terms, rules, roots), (batch.item(), N.item())
-
 
 class AlignmentTest:
     def __init__(self, semiring=LogSemiring):