1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
|
import numpy as np
import torch
from worldalign.common import retrieval_metrics, sliced_wasserstein
from worldalign.energy import (
log_sinkhorn,
relation_field_energy,
standardized_relation,
)
from worldalign.gw import gw_pseudo_targets
from worldalign.models import Bridge, PrefixAdapter
from worldalign.vg_diagnose import bundle_signature
def test_shapes_and_retrieval():
bridge = Bridge(8, 12, hidden_dim=16)
prefix = PrefixAdapter(12, 20, prefix_length=4, hidden_dim=16)
x = torch.randn(5, 8)
y = bridge(x)
assert y.shape == (5, 12)
assert prefix(y).shape == (5, 4, 20)
metrics = retrieval_metrics(y, y)
assert metrics["i2t_r@1"] == 1.0
def test_sliced_wasserstein_identity():
x = torch.randn(32, 16)
assert sliced_wasserstein(x, x).item() < 1e-10
def test_gw_recovers_structure_without_pairs():
rng = np.random.default_rng(7)
z = rng.normal(size=(600, 6)).astype(np.float32)
q, _ = np.linalg.qr(rng.normal(size=(6, 6)))
x = torch.from_numpy(z)
y = torch.from_numpy((z @ q).astype(np.float32))[torch.randperm(len(z))]
result = gw_pseudo_targets(x, y, clusters=12, seed=3, max_iter=50)
assert result["coupling"].shape == (12, 12)
assert np.isfinite(result["gw_distance"])
def test_bundle_signature_is_view_permutation_and_rotation_invariant():
torch.manual_seed(5)
x = torch.randn(7, 8, 12)
q, _ = torch.linalg.qr(torch.randn(12, 12))
permuted = (x @ q)[:, torch.randperm(8)]
first = bundle_signature(x)
second = bundle_signature(permuted)
assert torch.allclose(first, second, atol=2e-5)
def test_log_sinkhorn_has_unit_marginals():
logits = torch.randn(9, 9)
coupling = log_sinkhorn(logits, temperature=0.7, iterations=30)
assert torch.allclose(coupling.sum(0), torch.ones(9), atol=1e-4)
assert torch.allclose(coupling.sum(1), torch.ones(9), atol=1e-4)
def test_transposition_delta_matches_brute_force():
from worldalign.manifold_gate import (
all_transposition_delta_mse,
build_channels,
permuted,
relation_mse,
)
generator = torch.Generator().manual_seed(11)
visual = torch.randn(24, 8, generator=generator)[:, None, :]
text = torch.randn(24, 10, generator=generator)[:, None, :]
text_channels, _ = build_channels(text, bundle=False)
visual_channels, _ = build_channels(visual, bundle=False)
delta = all_transposition_delta_mse(text_channels[0], visual_channels[0])
base = relation_mse(text_channels[0], visual_channels[0])
for p, q in [(0, 1), (3, 17), (5, 23), (10, 11)]:
permutation = torch.arange(24)
permutation[[p, q]] = permutation[[q, p]]
brute = (
relation_mse(permuted(text_channels[0], permutation), visual_channels[0])
- base
)
assert torch.allclose(delta[p, q], brute, atol=1e-10)
assert torch.allclose(delta[q, p], brute, atol=1e-10)
def test_gate_energy_matches_energy_module():
from worldalign.manifold_gate import assignment_energy, build_channels
generator = torch.Generator().manual_seed(3)
visual = torch.randn(16, 6, generator=generator)
text = torch.randn(16, 9, generator=generator)
text_channels, text_relation = build_channels(text[:, None, :], bundle=False)
visual_channels, visual_relation = build_channels(visual[:, None, :], bundle=False)
permutation = torch.randperm(16, generator=generator)
gate = assignment_energy(
text_channels, visual_channels, text_relation, visual_relation, permutation
)
reference_relation, reference_standardized = standardized_relation(visual)
mse, kl = relation_field_energy(
reference_relation, reference_standardized, text[permutation]
)
assert abs(gate["mse"] - float(mse)) < 1e-5
assert abs(gate["conditional_kl"] - float(kl)) < 1e-4
def test_k_derangement_moves_exactly_k():
from worldalign.manifold_gate import k_derangement
generator = torch.Generator().manual_seed(9)
for k in (2, 4, 16):
permutation = k_derangement(64, k, generator)
moved = (permutation != torch.arange(64)).sum()
assert int(moved) == k
def test_bundle_mean_channel_matches_view_mean_relation():
from worldalign.manifold_gate import view_bundle_channels
torch.manual_seed(4)
views = torch.nn.functional.normalize(torch.randn(6, 5, 8), dim=-1)
channels = view_bundle_channels(views)
assert channels.shape == (4, 6, 6)
means = views.double().mean(1)
assert torch.allclose(channels[0], means @ means.T, atol=1e-10)
def test_batched_transposition_delta_matches_single():
from worldalign.manifold_gate import all_transposition_delta_mse
from worldalign.view_gate import batched_transposition_delta, standardized_field
torch.manual_seed(2)
views_a = torch.nn.functional.normalize(torch.randn(3, 6, 5), dim=-1)
views_b = torch.nn.functional.normalize(torch.randn(3, 6, 7), dim=-1)
fields_a = standardized_field(views_a)
fields_b = standardized_field(views_b)
batched = batched_transposition_delta(fields_a, fields_b)
for item in range(3):
single = all_transposition_delta_mse(fields_a[item], fields_b[item])
assert torch.allclose(batched[item], single, atol=1e-10)
def test_batched_descent_reaches_local_minimum():
from worldalign.view_gate import (
batched_descent,
batched_transposition_delta,
field_mse,
standardized_field,
)
torch.manual_seed(6)
visual = torch.nn.functional.normalize(torch.randn(4, 8, 5), dim=-1)
noise = torch.nn.functional.normalize(
visual + 0.1 * torch.randn(4, 8, 5), dim=-1
)
visual_fields = standardized_field(visual)
text_fields = standardized_field(noise)
starts = torch.stack([torch.randperm(8) for _ in range(4)])
final_cost, permutations = batched_descent(
text_fields.clone(), visual_fields, starts.clone(), sweeps=100
)
index = torch.arange(4)
permuted = text_fields[
index[:, None, None], permutations[:, :, None], permutations[:, None, :]
]
assert torch.allclose(field_mse(permuted, visual_fields), final_cost, atol=1e-10)
delta = batched_transposition_delta(permuted, visual_fields)
assert (delta >= -1e-9).all()
def test_ricci_flow_returns_finite_symmetric_geometry():
import numpy as np
from worldalign.ricci_control import ollivier_ricci_apsp
rng = np.random.default_rng(4)
points = rng.normal(size=(24, 3))
distance = np.linalg.norm(points[:, None] - points[None, :], axis=-1)
class FlowArgs:
neighbors = 5
lazy_alpha = 0.5
flow_step = 0.4
flowed = ollivier_ricci_apsp(distance, FlowArgs(), iterations=3)
assert np.isfinite(flowed).all()
assert np.allclose(flowed, flowed.T, atol=1e-9)
assert (np.diag(flowed) == 0).all()
def test_paired_relation_field_has_less_energy_than_permutation():
generator = torch.Generator().manual_seed(7)
visual = torch.randn(32, 12, generator=generator)
paired = visual @ torch.randn(12, 20, generator=generator)
visual_relation, visual_standardized = standardized_relation(visual)
paired_energy = relation_field_energy(
visual_relation, visual_standardized, paired
)[0]
permutation = torch.randperm(32, generator=generator)
shuffled_energy = relation_field_energy(
visual_relation, visual_standardized, paired[permutation]
)[0]
assert paired_energy < shuffled_energy
def test_tier0_factor_vector_is_one_hot_per_factor():
from worldalign.tier0_pipeline import factor_vector
vector = factor_vector(3, 2, 0, classes=10)
assert vector.shape == (17,)
assert vector[:10].sum() == 1.0 and vector[3] == 1.0
assert vector[10:14].sum() == 1.0 and vector[11] == 1.0
assert vector[14:].sum() == 1.0 and vector[14] == 1.0
def test_watershed_splits_touching_objects():
from worldalign.tier0_pipeline import extract_objects, group_objects
image = torch.full((3, 64, 64), 0.1)
# Two same-coloured discs whose supports touch at a single column.
ys, xs = torch.meshgrid(torch.arange(64), torch.arange(64), indexing="ij")
for centre in (22, 38):
disc = ((ys - 32) ** 2 + (xs - centre) ** 2) <= 8**2
image[:, disc] = torch.tensor([0.9, 0.2, 0.2])[:, None]
objects = extract_objects(image, peak_distance=4)
assert len(objects) == 2
groups = group_objects(objects, threshold=0.35)
assert len(groups) == 1 and groups[0]["members"] == 2
def test_caption_encoding_matches_declared_factors():
from worldalign.tier0_pipeline import encode_caption
ranks = {"red": 0, "blue": 1}
states = encode_caption("there are three small red circles, a blue square.", ranks, 2)
assert states.shape == (2, 9)
assert states[0][0] > 0 and states[0][2 + 2] > 0 and states[0][2 + 4 + 0] > 0
assert states[1][1] > 0 and states[1][2 + 0] > 0 and states[1][2 + 4 + 1] > 0
|
