✨ Add Glow-like multi-scale flow

tests/test_flows.py

@@ -121,3 +121,51 @@ def test_autoregressive_transforms():
         assert (torch.triu(J, diagonal=1) == 0).all(), t
         assert (torch.tril(J[:4, :4], diagonal=-1) == 0).all(), t
         assert (torch.tril(J[4:, 4:], diagonal=-1) == 0).all(), t
+
+
+def test_Glow(tmp_path):
+    flow = Glow((3, 32, 32), context=[5, 0, 5])
+
+    # Evaluation of log_prob
+    x, y = randn(8, 3, 32, 32), [randn(5, 16, 16), None, randn(8, 5, 4, 4)]
+    log_p = flow(y).log_prob(x)
+
+    assert log_p.shape == (8,)
+    assert log_p.requires_grad
+
+    flow.zero_grad(set_to_none=True)
+    loss = -log_p.mean()
+    loss.backward()
+
+    for p in flow.parameters():
+        assert p.grad is not None
+
+    # Sampling
+    x = flow(y).sample()
+
+    assert x.shape == (8, 3, 32, 32)
+
+    # Reparameterization trick
+    x = flow(y).rsample()
+
+    flow.zero_grad(set_to_none=True)
+    loss = x.square().sum().sqrt()
+    loss.backward()
+
+    for p in flow.parameters():
+        assert p.grad is not None
+
+    # Saving
+    torch.save(flow, tmp_path / 'flow.pth')
+
+    # Loading
+    flow_bis = torch.load(tmp_path / 'flow.pth')
+
+    x, y = randn(3, 32, 32), [randn(5, 16, 16), None, randn(5, 4, 4)]
+
+    seed = torch.seed()
+    log_p = flow(y).log_prob(x)
+    torch.manual_seed(seed)
+    log_p_bis = flow_bis(y).log_prob(x)
+
+    assert torch.allclose(log_p, log_p_bis)

tests/test_nn.py

@@ -70,3 +70,20 @@ def test_MonotonicMLP():
     J = torch.autograd.functional.jacobian(net, x)
 
     assert (J >= 0).all()
+
+
+def test_FCN():
+    net = FCN(3, 5)
+
+    # Non-batched
+    x = randn(3, 64, 64)
+    y = net(x)
+
+    assert y.shape == (5, 64, 64)
+    assert y.requires_grad
+
+    # Batched
+    x = randn(8, 3, 32, 32)
+    y = net(x)
+
+    assert y.shape == (8, 5, 32, 32)

tests/test_transforms.py

@@ -57,6 +57,49 @@ def test_univariate_transforms():
         assert torch.allclose(t.inv.log_abs_det_jacobian(y, z), ladj, atol=1e-4), t
 
 
+def test_multivariate_transforms():
+    ts = [
+        LULinearTransform(randn(3, 3), dim=-2),
+        PermutationTransform(torch.randperm(3), dim=-2),
+        PixelShuffleTransform(dim=-2),
+    ]
+
+    for t in ts:
+        # Shapes
+        x = randn(256, 3, 8)
+        y = t(x)
+
+        assert t.forward_shape(x.shape) == y.shape, t
+        assert t.inverse_shape(y.shape) == x.shape, t
+
+        # Inverse
+        z = t.inv(y)
+
+        assert x.shape == z.shape, t
+        assert torch.allclose(x, z, atol=1e-4), t
+
+        # Jacobian
+        x = randn(3, 8)
+        y = t(x)
+
+        jacobian = torch.autograd.functional.jacobian(t, x)
+        jacobian = jacobian.reshape(3 * 8, 3 * 8)
+
+        _, ladj = torch.slogdet(jacobian)
+
+        assert torch.allclose(t.log_abs_det_jacobian(x, y), ladj, atol=1e-4), t
+
+        # Inverse Jacobian
+        z = t.inv(y)
+
+        jacobian = torch.autograd.functional.jacobian(t.inv, y)
+        jacobian = jacobian.reshape(3 * 8, 3 * 8)
+
+        _, ladj = torch.slogdet(jacobian)
+
+        assert torch.allclose(t.inv.log_abs_det_jacobian(y, z), ladj, atol=1e-4), t
+
+
 def test_FFJTransform():
     a = torch.randn(3)
     f = lambda x, t: a * x
@@ -79,20 +122,24 @@ def test_FFJTransform():
     assert ladj.shape == x.shape[:-1]
 
 
-def test_PermutationTransform():
-    t = PermutationTransform(torch.randperm(8))
+def test_DropTransform():
+    dist = Normal(randn(3), abs(randn(3)) + 1)
+    t = DropTransform(dist)
 
-    x = torch.randn(256, 8)
+    # Call
+    x = randn(256, 5)
     y = t(x)
 
-    assert x.shape == y.shape
-
-    match = x[:, :, None] == y[:, None, :]
-
-    assert (match.sum(dim=-1) == 1).all()
-    assert (match.sum(dim=-2) == 1).all()
+    assert t.forward_shape(x.shape) == y.shape
+    assert t.inverse_shape(y.shape) == x.shape
 
+    # Inverse
     z = t.inv(y)
 
     assert x.shape == z.shape
-    assert (x == z).all()
+    assert not torch.allclose(x, z)
+
+    # Jacobian
+    ladj = t.log_abs_det_jacobian(x, y)
+
+    assert ladj.shape == (256,)

zuko/distributions.py

@@ -372,7 +372,7 @@ def __init__(
     ):
         super().__init__(batch_shape=base.batch_shape)
 
-        assert len(base.event_shape) < 1, "'base' has to be univariate"
+        assert not base.event_shape, "'base' has to be univariate"
 
         self.base = base
         self.uniform = Uniform(base.cdf(lower), base.cdf(upper))
@@ -430,7 +430,7 @@ def __init__(
     ):
         super().__init__(batch_shape=base.batch_shape)
 
-        assert len(base.event_shape) < 1, "'base' has to be univariate"
+        assert not base.event_shape, "'base' has to be univariate"
 
         self.base = base
         self.n = n

zuko/flows.py

@@ -13,6 +13,8 @@
     'NAF',
     'FreeFormJacobianTransform',
     'CNF',
+    'ConvCouplingTransform',
+    'Glow',
 ]
 
 import abc
@@ -750,3 +752,190 @@ def __init__(
         )
 
         super().__init__(transforms, base)
+
+
+class ConvCouplingTransform(TransformModule):
+    r"""Creates a convolution coupling transformation.
+
+    Arguments:
+        channels: The number of channels.
+        context: The number of context channels.
+        spatial: The number of spatial dimensions.
+        univariate: The univariate transformation constructor.
+        shapes: The shapes of the univariate transformation parameters.
+        kwargs: Keyword arguments passed to :class:`zuko.nn.FCN`.
+    """
+
+    def __init__(
+        self,
+        channels: int,
+        context: int = 0,
+        spatial: int = 2,
+        univariate: Callable[..., Transform] = MonotonicAffineTransform,
+        shapes: List[Size] = [(), ()],
+        **kwargs,
+    ):
+        super().__init__()
+
+        self.d = channels // 2
+        self.dim = -(spatial + 1)
+
+        # Univariate transformation
+        self.univariate = univariate
+        self.shapes = list(map(Size, shapes))
+        self.sizes = [s.numel() for s in self.shapes]
+
+        # Hyper network
+        kwargs.setdefault('activation', nn.ELU)
+        kwargs.setdefault('normalize', True)
+
+        self.hyper = FCN(
+            in_channels=self.d + context,
+            out_channels=(channels - self.d) * sum(self.sizes),
+            spatial=spatial,
+            **kwargs,
+        )
+
+    def extra_repr(self) -> str:
+        base = self.univariate(*map(torch.randn, self.shapes))
+
+        return f'(base): {base}'
+
+    def meta(self, y: Tensor, x: Tensor) -> Transform:
+        if y is not None:
+            x = torch.cat(broadcast(x, y, ignore=abs(self.dim)), dim=self.dim)
+
+        phi = self.hyper(x)
+        phi = phi.unflatten(self.dim, (-1, sum(self.sizes)))
+        phi = phi.movedim(self.dim, -1)
+        phi = phi.split(self.sizes, -1)
+        phi = (p.unflatten(-1, s + (1,)) for p, s in zip(phi, self.shapes))
+        phi = (p.squeeze(-1) for p in phi)
+
+        return self.univariate(*phi)
+
+    def forward(self, y: Tensor = None) -> Transform:
+        return CouplingTransform(partial(self.meta, y), self.d, self.dim)
+
+
+class Glow(FlowModule):
+    r"""Creates a Glow-like multi-scale flow.
+
+    References:
+        | Glow: Generative Flow with Invertible 1x1 Convolutions (Kingma et al., 2018)
+        | https://arxiv.org/abs/1807.03039
+
+    Arguments:
+        shape: The shape of a sample.
+        context: The number of context channels at each scale.
+        transforms: The number of coupling transformations at each scale.
+        kwargs: Keyword arguments passed to :class:`ConvCouplingTransform`.
+    """
+
+    def __init__(
+        self,
+        shape: Size,
+        context: Union[int, List[int]] = 0,
+        transforms: List[int] = [8, 8, 8],
+        **kwargs,
+    ):
+        nn.Module.__init__(self)
+
+        channels, *space = shape
+        spatial = len(space)
+        dim = -len(shape)
+        scales = len(transforms)
+
+        assert all(s % 2**scales == 0 for s in space), (
+            f"'shape' cannot be downscaled {scales} times"
+        )
+
+        if isinstance(context, int):
+            context = [context] * len(transforms)
+
+        self.flows = nn.ModuleList()
+        self.bases = nn.ModuleList()
+
+        for i, K in enumerate(transforms):
+            flow = []
+            flow.append(Unconditional(PixelShuffleTransform, dim=dim))
+
+            channels = channels * 2**spatial
+            space = [s // 2 for s in space]
+
+            for _ in range(K):
+                flow.extend([
+                    Unconditional(
+                        PermutationTransform,
+                        torch.randperm(channels),
+                        dim=dim,
+                        buffer=True,
+                    ),
+                    Unconditional(
+                        LULinearTransform,
+                        torch.eye(channels),
+                        dim=dim,
+                    ),
+                    ConvCouplingTransform(
+                        channels=channels,
+                        context=context[i],
+                        spatial=spatial,
+                        **kwargs,
+                    ),
+                ])
+
+            self.flows.append(nn.ModuleList(flow))
+            self.bases.append(
+                Unconditional(
+                    DiagNormal,
+                    torch.zeros(channels // 2, *space),
+                    torch.ones(channels // 2, *space),
+                    ndims=spatial + 1,
+                    buffer=True,
+                )
+            )
+
+            channels = channels // 2
+
+        self.bases.append(
+            Unconditional(
+                DiagNormal,
+                torch.zeros(channels, *space),
+                torch.ones(channels, *space),
+                ndims=spatial + 1,
+                buffer=True,
+            )
+        )
+
+    def forward(self, y: List[Tensor] = None) -> NormalizingFlow:
+        r"""
+        Arguments:
+            y: A list of contexts :math:`y`. There should be one element :math:`y_i`
+                per scale, but elements can be :py:`None`.
+
+        Returns:
+            A multi-scale flow :math:`p(X | y)`.
+        """
+
+        if y is None:
+            y = [None] * len(self.flows)
+
+        # Transforms
+        transforms = []
+
+        for flow, base, y_i in zip(self.flows, self.bases, y):
+            for t in flow:
+                transforms.append(t(y_i))
+
+            transforms.append(DropTransform(base(y_i)))
+
+        # Base
+        base = self.bases[-1](None)
+        dim = -len(base.event_shape)
+
+        batch_shapes = [y_i.shape[:dim] for y_i in y if y_i is not None]
+        batch_shape = torch.broadcast_shapes(*batch_shapes)
+
+        base = base.expand(batch_shape)
+
+        return NormalizingFlow(transforms, base)