Add test for identity bijections with maximum regularization

test/test_identity_bijections.py

@@ -0,0 +1,67 @@
+# Check that when all bijection parameters are set to 0, the bijections reduce to an identity map
+
+from normalizing_flows.bijections.finite.autoregressive.layers import (
+    AffineCoupling,
+    DSCoupling,
+    RQSCoupling,
+    InverseAffineCoupling,
+    LRSCoupling,
+    ShiftCoupling,
+    AffineForwardMaskedAutoregressive,
+    AffineInverseMaskedAutoregressive,
+    ElementwiseAffine,
+    ElementwiseRQSpline,
+    ElementwiseScale,
+    ElementwiseShift,
+    LinearAffineCoupling,
+    LinearLRSCoupling,
+    LinearRQSCoupling,
+    LinearShiftCoupling,
+    LRSForwardMaskedAutoregressive,
+    RQSForwardMaskedAutoregressive,
+    RQSInverseMaskedAutoregressive,
+    UMNNMaskedAutoregressive,
+
+)
+import torch
+import pytest
+
+
+@pytest.mark.parametrize(
+    'layer_class',
+    [
+        AffineCoupling,
+        DSCoupling,
+        RQSCoupling,
+        InverseAffineCoupling,
+        LRSCoupling,
+        ShiftCoupling,
+        AffineForwardMaskedAutoregressive,
+        AffineInverseMaskedAutoregressive,
+        ElementwiseAffine,
+        ElementwiseRQSpline,
+        ElementwiseScale,
+        ElementwiseShift,
+        LinearAffineCoupling,
+        LinearLRSCoupling,
+        LinearRQSCoupling,
+        LinearShiftCoupling,
+        LRSForwardMaskedAutoregressive,
+        RQSForwardMaskedAutoregressive,
+        RQSInverseMaskedAutoregressive,
+        # UMNNMaskedAutoregressive,  # Inexact due to numerics
+    ]
+)
+def test_basic(layer_class):
+    n_batch, n_dim = 2, 3
+
+    torch.manual_seed(0)
+    x = torch.randn(size=(n_batch, n_dim))
+    layer = layer_class(event_shape=torch.Size((n_dim,)))
+
+    # Set all conditioner parameters to 0
+    with torch.no_grad():
+        for p in layer.parameters():
+            p.data *= 0
+
+    assert torch.allclose(layer(x)[0], x, atol=1e-2)