support arbitrary number of feature dimensions in latent to discrete

fix number of feature dimensions

[docs] consistent notation

release

ignore files in aim

testing different number of latent dimensions

extend tests to more feature dimensions

Update tests/test_dvae_winci2020.py

Co-authored-by: Anahita Mansouri Bigvand <[email protected]>

Update tests/test_dvae_winci2020.py

Co-authored-by: Anahita Mansouri Bigvand <[email protected]>

Update tests/test_dvae_winci2020.py

Co-authored-by: Anahita Mansouri Bigvand <[email protected]>

Author: Vladimir Vargas Calderón

.gitignore

@@ -64,3 +64,6 @@ venv.bak/
 .mypy_cache/
 .dmypy.json
 dmypy.json
+
+# aim
+*.aim*

dwave/plugins/torch/models/discrete_variational_autoencoder.py

@@ -80,28 +80,26 @@ def __init__(
         self._decoder = decoder
         if latent_to_discrete is None:
 
-            def latent_to_discrete(
-                logits: torch.Tensor, n_samples: int
-            ) -> torch.Tensor:
-                # Logits is of shape (batch_size, n_discrete), we assume these logits
+            def latent_to_discrete(logits: torch.Tensor, n_samples: int) -> torch.Tensor:
+                # Logits is of shape (batch_size, l1, l2, ...), we assume these logits
                 # refer to the probability of each discrete variable being 1. To use the
                 # gumbel softmax function we need to reshape the logits to (batch_size,
-                # n_discrete, 1), and then stack the logits to a zeros tensor of the
+                # l1, l2, ..., 1), and then stack the logits to a zeros tensor of the
                 # same shape. This is done to ensure that the gumbel softmax function
                 # works correctly.
-
+                n_feature_dims = logits.dim() - 1
                 logits = logits.unsqueeze(-1)
                 logits = torch.cat((logits, torch.zeros_like(logits)), dim=-1)
                 # We now create a new leading dimension and repeat the logits n_samples
                 # times:
-                logits = logits.unsqueeze(1).repeat(1, n_samples, 1, 1)
-                one_hots = torch.nn.functional.gumbel_softmax(
-                    logits, tau=1 / 7, hard=True
+                logits = logits.unsqueeze(1).repeat(
+                    *((1, n_samples) + (1,) * n_feature_dims + (1,))
                 )
+                one_hots = torch.nn.functional.gumbel_softmax(logits, tau=1 / 7, hard=True)
                 # The constant 1/7 is used because it was used in
                 # https://iopscience.iop.org/article/10.1088/2632-2153/aba220
 
-                # one_hots is of shape (batch_size, n_samples, n_discrete, 2), we need
+                # one_hots is of shape (batch_size, n_samples, f_1, f_2, ..., 2), we need
                 # to take the first element of the last dimension and convert it to spin
                 # variables to make the latent space compatible with QPU models.
                 return one_hots[..., 0] * 2 - 1

releasenotes/notes/arbitrary-feature-dimension-in-latent-to-discrete-57917ab12f34bdd8.yaml

@@ -0,0 +1,9 @@
+---
+fixes:
+  - |
+    The default ``latent_to_discrete`` transformation in
+    ``dwave.plugins.torch.models.discrete_variational_autoencoder.DiscreteVariationalAutoencoder``
+    has been fixed to accommodate arbitrary encoders. Before, the default
+    transformation only allowed encoders whose output shape was (B, l). Now,
+    encoders can have an arbitrary number of feature dimensions, i.e., the
+    shape can be (B, l1, l2, ...).

tests/test_dvae_winci2020.py

@@ -18,7 +18,9 @@
 from parameterized import parameterized
 
 from dwave.plugins.torch.models.boltzmann_machine import GraphRestrictedBoltzmannMachine
-from dwave.plugins.torch.models.discrete_variational_autoencoder import DiscreteVariationalAutoencoder as DVAE
+from dwave.plugins.torch.models.discrete_variational_autoencoder import (
+    DiscreteVariationalAutoencoder as DVAE,
+)
 from dwave.plugins.torch.models.losses.kl_divergence import pseudo_kl_divergence_loss
 from dwave.samplers import SimulatedAnnealingSampler
 
@@ -32,10 +34,7 @@ def setUp(self):
         latent_features = 2
 
         # Data in corners of unit square:
-        self.data = torch.tensor([[1.0, 1.0],
-                                  [1.0, 0.0],
-                                  [0.0, 0.0],
-                                  [0.0, 1.0]])
+        self.data = torch.tensor([[1.0, 1.0], [1.0, 0.0], [0.0, 0.0], [0.0, 1.0]])
 
         # The encoder maps input data to logits. We make this encoder without parameters
         # for simplicity. The encoder will map 1s to 10s and 0s to -10s, so that the
@@ -48,16 +47,45 @@ def setUp(self):
         # [1, -1].
 
         class Encoder(torch.nn.Module):
+            def __init__(self, n_latent_dims: int):
+                super().__init__()
+                self.n_latent_dims = n_latent_dims
+
+            def forward(self, x: torch.Tensor) -> torch.Tensor:
+                # x is always two-dimensional of shape (batch_size, features_size)
+                dims_to_add = self.n_latent_dims - 1
+                output = x * 20 - 10
+                for _ in range(dims_to_add):
+                    output = output.unsqueeze(-2)
+                return output
+
+        class Decoder(torch.nn.Module):
+            def __init__(self, latent_features: int, input_features: int):
+                super().__init__()
+                self.linear = torch.nn.Linear(latent_features, input_features)
+
             def forward(self, x: torch.Tensor) -> torch.Tensor:
-                return x * 20 - 10
+                # x is of shape (batch_size, replica_size, l1, l2, ...)
+                n_latent_dims_to_remove = x.ndim - 3
+                for _ in range(n_latent_dims_to_remove):
+                    x = x.squeeze(1)
+                return self.linear(x)
 
-        self.encoder = Encoder()
-        self.decoder = torch.nn.Linear(latent_features, input_features)
+        # self.encoders is a dict whose keys are the number of latent dims and the values
+        # are the models themselves
+        self.encoders = {i: Encoder(i) for i in range(1, 3)}
+        # self.decoders is independent of number of latent dims, but we also create a dict to separate
+        # them
+        self.decoders = {i: Decoder(latent_features, input_features) for i in range(1, 3)}
 
-        self.dvae = DVAE(self.encoder, self.decoder)
+        # self.dvaes is a dict whose keys are the numbers of latent dims and the values are the models
+        # themselves
+
+        self.dvaes = {i: DVAE(self.encoders[i], self.decoders[i]) for i in range(1, 3)}
 
         self.boltzmann_machine = GraphRestrictedBoltzmannMachine(
-            nodes=(0, 1), edges=[(0, 1)],
+            nodes=(0, 1),
+            edges=[(0, 1)],
             linear={0: 0.1, 1: -0.2},
             quadratic={(0, 1): -1.2},
         )
@@ -66,8 +94,11 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
 
     def test_mappings(self):
         """Test the mapping between data and logits."""
-        # Let's make sure that indeed the maps are correct:
-        _, discretes, _ = self.dvae(self.data, n_samples=1)
+        # Let's make sure that indeed the maps are correct. For this, we use only the first
+        # autoencoder, which is the one whose encoder maps data to a single feature dimension. The
+        # second autoencoder maps data to two feature dimensions (the last one is a dummy dimension)
+        _, discretes, _ = self.dvaes[1](self.data, n_samples=1)
+        # squeeze the replica dimension:
         discretes = discretes.squeeze(1)
         # map [1, 1] to [1, 1]:
         torch.testing.assert_close(torch.tensor([1, 1]).float(), discretes[0])
@@ -78,19 +109,18 @@ def test_mappings(self):
         # map [0, 1] to [-1, 1]:
         torch.testing.assert_close(torch.tensor([-1, 1]).float(), discretes[3])
 
-    def test_train(self):
+    @parameterized.expand([1, 2])
+    def test_train(self, n_latent_dims):
         """Test training simple dataset."""
+        dvae = self.dvaes[n_latent_dims]
         optimiser = torch.optim.SGD(
-            list(self.dvae.parameters())
-            + list(self.boltzmann_machine.parameters()),
+            list(dvae.parameters()) + list(self.boltzmann_machine.parameters()),
             lr=0.01,
             momentum=0.9,
         )
         N_SAMPLES = 1
         for _ in range(1000):
-            latents, discretes, reconstructed_data = self.dvae(
-                self.data, n_samples=N_SAMPLES
-            )
+            latents, discretes, reconstructed_data = dvae(self.data, n_samples=N_SAMPLES)
             true_data = self.data.unsqueeze(1).repeat(1, N_SAMPLES, 1)
 
             # Measure the reconstruction loss
@@ -114,38 +144,43 @@ def test_train(self):
         torch.testing.assert_close(true_data, reconstructed_data)
         # Furthermore, the GRBM should learn that all spin strings of length 2 are
         # equally likely, so the h and J parameters should be close to 0:
-        torch.testing.assert_close(self.boltzmann_machine.linear, torch.zeros(2),
-                                   rtol=1e-2, atol=1e-2)
-        torch.testing.assert_close(self.boltzmann_machine.quadratic, torch.tensor([0.0]).float(),
-                                   rtol=1e-2, atol=1e-2)
-
-    @parameterized.expand([
-        (
-            1,
-            torch.tensor([[[1.,  1.]], [[1., -1.]], [[-1., -1.]], [[-1.,  1.]]])
-        ),
-        (
-            5,
-            torch.tensor([[[1.,  1.]] * 5, [[1., -1.]] * 5, [[-1., -1.]] * 5, [[-1.,  1.]] * 5])
-        ),
-    ])
+        torch.testing.assert_close(
+            self.boltzmann_machine.linear, torch.zeros(2), rtol=1e-2, atol=1e-2
+        )
+        torch.testing.assert_close(
+            self.boltzmann_machine.quadratic, torch.tensor([0.0]).float(), rtol=1e-2, atol=1e-2
+        )
+
+    @parameterized.expand(
+        [
+            (1, torch.tensor([[[1.0, 1.0]], [[1.0, -1.0]], [[-1.0, -1.0]], [[-1.0, 1.0]]])),
+            (
+                5,
+                torch.tensor(
+                    [[[1.0, 1.0]] * 5, [[1.0, -1.0]] * 5, [[-1.0, -1.0]] * 5, [[-1.0, 1.0]] * 5]
+                ),
+            ),
+        ]
+    )
     def test_latent_to_discrete(self, n_samples, expected):
         """Test the latent_to_discrete default method."""
-        latents = self.encoder(self.data)
-        discretes = self.dvae.latent_to_discrete(latents, n_samples)
+        # All encoders and dvaes only differ in the number of dummy feature dimensions in the latent
+        # space. For this reason, this test can only be done with the case of one feature dimension,
+        # which corresponds to the first encoder and dvae.
+        latents = self.encoders[1](self.data)
+        discretes = self.dvaes[1].latent_to_discrete(latents, n_samples)
         assert torch.equal(discretes, expected)
 
-    @parameterized.expand([0, 1, 5, 1000])
-    def test_forward(self, n_samples):
+    @parameterized.expand([(i, j) for i in range(1, 3) for j in [0, 1, 5, 1000]])
+    def test_forward(self, n_latent_dims, n_samples):
         """Test the forward method."""
-        latents = self.encoder(self.data)
-        discretes = self.dvae.latent_to_discrete(latents, n_samples)
-        reconstructed_x = self.decoder(discretes)
+        latents = self.encoders[n_latent_dims](self.data)
+        discretes = self.dvaes[n_latent_dims].latent_to_discrete(latents, n_samples)
+        reconstructed_x = self.decoders[n_latent_dims](discretes)
 
-        expected_latents, expected_discretes, expected_reconstructed_x = self.dvae.forward(
-            x=self.data,
-            n_samples=n_samples
-        )
+        expected_latents, expected_discretes, expected_reconstructed_x = self.dvaes[
+            n_latent_dims
+        ].forward(x=self.data, n_samples=n_samples)
 
         assert torch.equal(reconstructed_x, expected_reconstructed_x)
         assert torch.equal(discretes, expected_discretes)