Pull request for adding UMNN to this repository. Implemented coupling and autoregressive version. Simple unit tests passed and adapted notebook examples work too.

AWehenkel · AWehenkel · commit 616c3ee2bf05 · 2021-01-06T15:38:00.000+01:00
diff --git a/environment.yml b/environment.yml
@@ -21,6 +21,7 @@ dependencies:
   - pip:
     - torchtestcase
     - -e .  # install package in development mode
+    - umnn
   - pytest
   - python
   - pytorch
diff --git a/nflows/transforms/UMNN/MonotonicNormalizer.py b/nflows/transforms/UMNN/MonotonicNormalizer.py
@@ -0,0 +1,83 @@
+import torch
+from UMNN import NeuralIntegral, ParallelNeuralIntegral
+import torch.nn as nn
+
+
+def _flatten(sequence):
+    flat = [p.contiguous().view(-1) for p in sequence]
+    return torch.cat(flat) if len(flat) > 0 else torch.tensor([])
+
+
+class ELUPlus(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.elu = nn.ELU()
+
+    def forward(self, x):
+        return self.elu(x) + 1.
+
+
+class IntegrandNet(nn.Module):
+    def __init__(self, hidden, cond_in):
+        super(IntegrandNet, self).__init__()
+        l1 = [1 + cond_in] + hidden
+        l2 = hidden + [1]
+        layers = []
+        for h1, h2 in zip(l1, l2):
+            layers += [nn.Linear(h1, h2), nn.ReLU()]
+        layers.pop()
+        layers.append(ELUPlus())
+        self.net = nn.Sequential(*layers)
+
+    def forward(self, x, h):
+        nb_batch, in_d = x.shape
+        x = torch.cat((x, h), 1)
+        x_he = x.view(nb_batch, -1, in_d).transpose(1, 2).contiguous().view(nb_batch * in_d, -1)
+        y = self.net(x_he).view(nb_batch, -1)
+        return y
+
+
+class MonotonicNormalizer(nn.Module):
+    def __init__(self, integrand_net, cond_size, nb_steps=20, solver="CC"):
+        super(MonotonicNormalizer, self).__init__()
+        if type(integrand_net) is list:
+            self.integrand_net = IntegrandNet(integrand_net, cond_size)
+        else:
+            self.integrand_net = integrand_net
+        self.solver = solver
+        self.nb_steps = nb_steps
+
+    def forward(self, x, h, context=None):
+        x0 = torch.zeros(x.shape).to(x.device)
+        xT = x
+        z0 = h[:, :, 0]
+        h = h.permute(0, 2, 1).contiguous().view(x.shape[0], -1)
+        if self.solver == "CC":
+            z = NeuralIntegral.apply(x0, xT, self.integrand_net, _flatten(self.integrand_net.parameters()),
+                                     h, self.nb_steps) + z0
+        elif self.solver == "CCParallel":
+            z = ParallelNeuralIntegral.apply(x0, xT, self.integrand_net,
+                                             _flatten(self.integrand_net.parameters()),
+                                             h, self.nb_steps) + z0
+        else:
+            return None
+        return z, self.integrand_net(x, h)
+
+    def inverse_transform(self, z, h, context=None):
+        # Old inversion by binary search
+        x_max = torch.ones_like(z) * 20
+        x_min = -torch.ones_like(z) * 20
+        z_max, _ = self.forward(x_max, h, context)
+        z_min, _ = self.forward(x_min, h, context)
+        for i in range(25):
+            x_middle = (x_max + x_min) / 2
+            z_middle, _ = self.forward(x_middle, h, context)
+            left = (z_middle > z).float()
+            right = 1 - left
+            x_max = left * x_middle + right * x_max
+            x_min = right * x_middle + left * x_min
+            z_max = left * z_middle + right * z_max
+            z_min = right * z_middle + left * z_min
+        return (x_max + x_min) / 2
+
+
diff --git a/nflows/transforms/UMNN/__init__.py b/nflows/transforms/UMNN/__init__.py
@@ -0,0 +1 @@
+from nflows.transforms.UMNN.MonotonicNormalizer import MonotonicNormalizer, IntegrandNet
diff --git a/nflows/transforms/__init__.py b/nflows/transforms/__init__.py
@@ -4,6 +4,7 @@
     MaskedPiecewiseLinearAutoregressiveTransform,
     MaskedPiecewiseQuadraticAutoregressiveTransform,
     MaskedPiecewiseRationalQuadraticAutoregressiveTransform,
+    MaskedUMNNAutoregressiveTransform,
 )
 from nflows.transforms.base import (
     CompositeTransform,
@@ -21,6 +22,7 @@
     PiecewiseLinearCouplingTransform,
     PiecewiseQuadraticCouplingTransform,
     PiecewiseRationalQuadraticCouplingTransform,
+    UMNNCouplingTransform,
 )
 from nflows.transforms.linear import NaiveLinear
 from nflows.transforms.lu import LULinear
diff --git a/nflows/transforms/autoregressive.py b/nflows/transforms/autoregressive.py
@@ -18,6 +18,7 @@
     unconstrained_rational_quadratic_spline,
 )
 from nflows.utils import torchutils
+from nflows.transforms.UMNN import *
 
 
 class AutoregressiveTransform(Transform):
@@ -127,6 +128,71 @@ def _unconstrained_scale_and_shift(self, autoregressive_params):
         return unconstrained_scale, shift
 
 
+class MaskedUMNNAutoregressiveTransform(AutoregressiveTransform):
+    """An unconstrained monotonic neural networks autoregressive layer that transforms the variables.
+
+        Reference:
+        > A. Wehenkel and G. Louppe, Unconstrained Monotonic Neural Networks, NeurIPS2019.
+
+        ---- Specific arguments ----
+        integrand_net_layers: the layers dimension to put in the integrand network.
+        cond_size: The embedding size for the conditioning factors.
+        nb_steps: The number of integration steps.
+        solver: The quadrature algorithm - CC or CCParallel. Both implements Clenshaw-Curtis quadrature with
+        Leibniz rule for backward computation. CCParallel pass all the evaluation points (nb_steps) at once, it is faster
+        but requires more memory.
+        """
+    def __init__(
+        self,
+        features,
+        hidden_features,
+        context_features=None,
+        num_blocks=2,
+        use_residual_blocks=True,
+        random_mask=False,
+        activation=F.relu,
+        dropout_probability=0.0,
+        use_batch_norm=False,
+        integrand_net_layers=[50, 50, 50],
+        cond_size=20,
+        nb_steps=20,
+        solver="CCParallel",
+    ):
+        self.features = features
+        self.cond_size = cond_size
+        made = made_module.MADE(
+            features=features,
+            hidden_features=hidden_features,
+            context_features=context_features,
+            num_blocks=num_blocks,
+            output_multiplier=self._output_dim_multiplier(),
+            use_residual_blocks=use_residual_blocks,
+            random_mask=random_mask,
+            activation=activation,
+            dropout_probability=dropout_probability,
+            use_batch_norm=use_batch_norm,
+        )
+        self._epsilon = 1e-3
+        super().__init__(made)
+        self.transformer = MonotonicNormalizer(integrand_net_layers, cond_size, nb_steps, solver)
+
+
+    def _output_dim_multiplier(self):
+        return self.cond_size
+
+    def _elementwise_forward(self, inputs, autoregressive_params):
+        z, jac = self.transformer(inputs, autoregressive_params.reshape(inputs.shape[0], inputs.shape[1], -1))
+        log_det_jac = jac.log().sum(1)
+        return z, log_det_jac
+
+    def _elementwise_inverse(self, inputs, autoregressive_params):
+        x = self.transformer.inverse_transform(inputs, autoregressive_params.reshape(inputs.shape[0], inputs.shape[1], -1))
+        z, jac = self.transformer(x, autoregressive_params.reshape(inputs.shape[0], inputs.shape[1], -1))
+        log_det_jac = -jac.log().sum(1)
+        return x, log_det_jac
+
+
+
 class MaskedPiecewiseLinearAutoregressiveTransform(AutoregressiveTransform):
     def __init__(
         self,
diff --git a/nflows/transforms/coupling.py b/nflows/transforms/coupling.py
@@ -13,6 +13,7 @@
     PiecewiseRationalQuadraticCDF,
 )
 from nflows.utils import torchutils
+from nflows.transforms.UMNN import *
 
 
 class CouplingTransform(Transform):
@@ -140,6 +141,73 @@ def _coupling_transform_inverse(self, inputs, transform_params):
         raise NotImplementedError()
 
 
+class UMNNCouplingTransform(CouplingTransform):
+    """An unconstrained monotonic neural networks coupling layer that transforms the variables.
+
+    Reference:
+    > A. Wehenkel and G. Louppe, Unconstrained Monotonic Neural Networks, NeurIPS2019.
+
+    ---- Specific arguments ----
+        integrand_net_layers: the layers dimension to put in the integrand network.
+        cond_size: The embedding size for the conditioning factors.
+        nb_steps: The number of integration steps.
+        solver: The quadrature algorithm - CC or CCParallel. Both implements Clenshaw-Curtis quadrature with
+        Leibniz rule for backward computation. CCParallel pass all the evaluation points (nb_steps) at once, it is faster
+        but requires more memory.
+
+    """
+    def __init__(
+        self,
+        mask,
+        transform_net_create_fn,
+        integrand_net_layers=[50, 50, 50],
+        cond_size=20,
+        nb_steps=20,
+        solver="CCParallel",
+        apply_unconditional_transform=False
+    ):
+
+        if apply_unconditional_transform:
+            unconditional_transform = lambda features: MonotonicNormalizer(integrand_net_layers, 0, nb_steps, solver)
+        else:
+            unconditional_transform = None
+        self.cond_size = cond_size
+        super().__init__(
+            mask,
+            transform_net_create_fn,
+            unconditional_transform=unconditional_transform,
+        )
+
+        self.transformer = MonotonicNormalizer(integrand_net_layers, cond_size, nb_steps, solver)
+
+    def _transform_dim_multiplier(self):
+        return self.cond_size
+
+    def _coupling_transform_forward(self, inputs, transform_params):
+        if len(inputs.shape) == 2:
+            z, jac = self.transformer(inputs, transform_params.reshape(inputs.shape[0], inputs.shape[1], -1))
+            log_det_jac = jac.log().sum(1)
+            return z, log_det_jac
+        else:
+            B, C, H, W = inputs.shape
+            z, jac = self.transformer(inputs.permute(0, 2, 3, 1).reshape(-1, inputs.shape[1]), transform_params.permute(0, 2, 3, 1).reshape(-1, 1, transform_params.shape[1]))
+            log_det_jac = jac.log().reshape(B, -1).sum(1)
+            return z.reshape(B, H, W, C).permute(0, 3, 1, 2), log_det_jac
+
+    def _coupling_transform_inverse(self, inputs, transform_params):
+        if len(inputs.shape) == 2:
+            x = self.transformer.inverse_transform(inputs, transform_params.reshape(inputs.shape[0], inputs.shape[1], -1))
+            z, jac = self.transformer(x, transform_params.reshape(inputs.shape[0], inputs.shape[1], -1))
+            log_det_jac = -jac.log().sum(1)
+            return x, log_det_jac
+        else:
+            B, C, H, W = inputs.shape
+            x = self.transformer.inverse_transform(inputs.permute(0, 2, 3, 1).reshape(-1, inputs.shape[1]), transform_params.permute(0, 2, 3, 1).reshape(-1, 1, transform_params.shape[1]))
+            z, jac = self.transformer(x, transform_params.permute(0, 2, 3, 1).reshape(-1, 1, transform_params.shape[1]))
+            log_det_jac = -jac.log().reshape(B, -1).sum(1)
+            return x.reshape(B, H, W, C).permute(0, 3, 1, 2), log_det_jac
+
+
 class AffineCouplingTransform(CouplingTransform):
     """An affine coupling layer that scales and shifts part of the variables.
 
@@ -151,7 +219,7 @@ def _transform_dim_multiplier(self):
         return 2
 
     def _scale_and_shift(self, transform_params):
-        unconstrained_scale = transform_params[:, self.num_transform_features :, ...]
+        unconstrained_scale = transform_params[:, self.num_transform_features:, ...]
         shift = transform_params[:, : self.num_transform_features, ...]
         # scale = (F.softplus(unconstrained_scale) + 1e-3).clamp(0, 3)
         scale = torch.sigmoid(unconstrained_scale + 2) + 1e-3
diff --git a/tests/transforms/autoregressive_test.py b/tests/transforms/autoregressive_test.py
@@ -114,6 +114,24 @@ def test_forward_inverse_are_consistent(self):
         self.assert_forward_inverse_are_consistent(transform, inputs)
 
 
+class MaskedUMNNAutoregressiveTranformTest(TransformTest):
+    def test_forward_inverse_are_consistent(self):
+        batch_size = 10
+        features = 20
+        inputs = torch.rand(batch_size, features)
+        self.eps = 1e-4
+
+        transform = autoregressive.MaskedUMNNAutoregressiveTransform(
+            cond_size=10,
+            features=features,
+            hidden_features=30,
+            num_blocks=5,
+            use_residual_blocks=True,
+        )
+
+        self.assert_forward_inverse_are_consistent(transform, inputs)
+
+
 class MaskedPiecewiseCubicAutoregressiveTranformTest(TransformTest):
     def test_forward_inverse_are_consistent(self):
         batch_size = 10
diff --git a/tests/transforms/coupling_test.py b/tests/transforms/coupling_test.py
@@ -112,6 +112,53 @@ def test_forward_inverse_are_consistent(self):
                 self.assert_forward_inverse_are_consistent(transform, inputs)
 
 
+class UMNNTransformTest(TransformTest):
+    shapes = [[20], [2, 4, 4]]
+
+    def test_forward(self):
+        for shape in self.shapes:
+            inputs = torch.randn(batch_size, *shape)
+            transform, mask = create_coupling_transform(
+                coupling.UMNNCouplingTransform, shape, integrand_net_layers=[50, 50, 50],
+                cond_size=20,
+                nb_steps=20,
+                solver="CC"
+            )
+            outputs, logabsdet = transform(inputs)
+            with self.subTest(shape=shape):
+                self.assert_tensor_is_good(outputs, [batch_size] + shape)
+                self.assert_tensor_is_good(logabsdet, [batch_size])
+                self.assertEqual(outputs[:, mask <= 0, ...], inputs[:, mask <= 0, ...])
+
+    def test_inverse(self):
+        for shape in self.shapes:
+            inputs = torch.randn(batch_size, *shape)
+            transform, mask = create_coupling_transform(
+                coupling.UMNNCouplingTransform, shape, integrand_net_layers=[50, 50, 50],
+                cond_size=20,
+                nb_steps=20,
+                solver="CC"
+            )
+            outputs, logabsdet = transform(inputs)
+            with self.subTest(shape=shape):
+                self.assert_tensor_is_good(outputs, [batch_size] + shape)
+                self.assert_tensor_is_good(logabsdet, [batch_size])
+                self.assertEqual(outputs[:, mask <= 0, ...], inputs[:, mask <= 0, ...])
+
+    def test_forward_inverse_are_consistent(self):
+        self.eps = 1e-6
+        for shape in self.shapes:
+            inputs = torch.randn(batch_size, *shape)
+            transform, mask = create_coupling_transform(
+                coupling.UMNNCouplingTransform, shape, integrand_net_layers=[50, 50, 50],
+                cond_size=20,
+                nb_steps=20,
+                solver="CC"
+            )
+            with self.subTest(shape=shape):
+                self.assert_forward_inverse_are_consistent(transform, inputs)
+
+
 class PiecewiseCouplingTransformTest(TransformTest):
     classes = [
         coupling.PiecewiseLinearCouplingTransform,

Original file line number	Diff line number	Diff line change
`@@ -0,0 +1 @@`
	`1`	`+from nflows.transforms.UMNN.MonotonicNormalizer import MonotonicNormalizer, IntegrandNet`