Add Distrax classes and methods to surjectors (#34)

Author: dirmeier

.github/workflows/examples.yaml

@@ -0,0 +1,41 @@
+name: examples
+
+on:
+  push:
+    branches: [ main ]
+  pull_request:
+    branches: [ main ]
+
+jobs:
+  precommit:
+    name: Pre-commit checks
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v3
+
+  examples:
+    runs-on: ubuntu-latest
+    needs:
+      - precommit
+    strategy:
+      matrix:
+        python-version: [3.11]
+    steps:
+      - uses: actions/checkout@v3
+      - name: Set up Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v3
+        with:
+          python-version: ${{ matrix.python-version }}
+      - name: Install dependencies
+        run: |
+          pip install hatch matplotlib
+      - name: Build package
+        run: |
+          pip install jaxlib jax
+          pip install .
+      - name: Run tests
+        run: |
+            python examples/autoregressive_inference_surjection.py --n-iter 10
+            python examples/conditional_density_estimation.py --n-iter 10 --model coupling
+            python examples/conditional_density_estimation.py --n-iter 10 --model autoregressive
+            python examples/coupling_inference_surjection.py --n-iter 10

examples/autoregressive_inference_surjection.py

@@ -1,3 +1,4 @@
+import argparse
 from collections import namedtuple
 
 import distrax
@@ -99,14 +100,14 @@ def loss_fn(params):
     return params, losses
 
 
-def run():
+def run(n_iter):
     n, p = 1000, 20
     rng_seq = hk.PRNGSequence(2)
     y = jr.normal(next(rng_seq), shape=(n, p))
     data = namedtuple("named_dataset", "y")(y)
 
     model = make_model(p)
-    params, losses = train(rng_seq, data, model)
+    params, losses = train(rng_seq, data, model, n_iter)
     plt.plot(losses)
     plt.show()
 
@@ -115,4 +116,7 @@ def run():
 
 
 if __name__ == "__main__":
-    run()
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--n-iter", type=int, default=1_000)
+    args = parser.parse_args()
+    run(args.n_iter)

examples/conditional_density_estimation.py

@@ -1,3 +1,5 @@
+import argparse
+
 import distrax
 import haiku as hk
 import jax
@@ -49,9 +51,7 @@ def _flow(method, **kwargs):
                 layer = MaskedAutoregressive(
                     bijector_fn=_bijector_fn,
                     conditioner=MADE(
-                        2,
-                        [32, 32, 2 * 2],
-                        2,
+                        2, [32, 32], 2,
                         w_init=hk.initializers.TruncatedNormal(0.01),
                         b_init=jnp.zeros,
                     ),
@@ -104,7 +104,7 @@ def loss_fn(params):
     return params, losses
 
 
-def run():
+def run(n_iter, model):
     n = 10000
     thetas = distrax.Normal(jnp.zeros(2), jnp.full(2, 10)).sample(
         seed=random.PRNGKey(0), sample_shape=(n,)
@@ -114,8 +114,8 @@ def run():
     )
     data = named_dataset(y, thetas)
 
-    model = make_model(2)
-    params, losses = train(hk.PRNGSequence(2), data, model)
+    model = make_model(2, model)
+    params, losses = train(hk.PRNGSequence(2), data, model, n_iter)
     samples = model.apply(
         params,
         random.PRNGKey(2),
@@ -129,4 +129,10 @@ def run():
 
 
 if __name__ == "__main__":
-    run()
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--n-iter", type=int, default=1_000)
+    parser.add_argument("--model", type=str, default="coupling")
+    args = parser.parse_args()
+    run(args.n_iter, args.model)
+
+

examples/coupling_inference_surjection.py

@@ -1,3 +1,4 @@
+import argparse
 from collections import namedtuple
 
 import distrax
@@ -99,14 +100,14 @@ def loss_fn(params):
     return params, losses
 
 
-def run():
+def run(n_iter):
     n, p = 1000, 20
     rng_seq = hk.PRNGSequence(2)
     y = jr.normal(next(rng_seq), shape=(n, p))
     data = namedtuple("named_dataset", "y")(y)
 
     model = make_model(p)
-    params, losses = train(rng_seq, data, model)
+    params, losses = train(rng_seq, data, model, n_iter)
     plt.plot(losses)
     plt.show()
 
@@ -115,4 +116,9 @@ def run():
 
 
 if __name__ == "__main__":
-    run()
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--n-iter", type=int, default=1_000)
+    args = parser.parse_args()
+    run(args.n_iter)
+
+

surjectors/__init__.py

@@ -1,6 +1,8 @@
 """surjectors: Surjection layers for density estimation with normalizing flows."""
 
-__version__ = "0.3.2"
+__version__ = "0.3.3"
+
+from distrax import ScalarAffine
 
 from surjectors._src.bijectors.affine_masked_autoregressive import (
     AffineMaskedAutoregressive,
@@ -60,4 +62,5 @@
     "MLPInferenceFunnel",
     "Slice",
     # "Augment",
+    "ScalarAffine",
 ]

surjectors/_src/bijectors/permutation.py

@@ -1,9 +1,10 @@
-import distrax
 from jax import numpy as jnp
 
+from surjectors._src.bijectors.bijector import Bijector
+
 
 # pylint: disable=arguments-renamed
-class Permutation(distrax.Bijector):
+class Permutation(Bijector):
     """Permute the dimensions of a vector.
 
     Args:
@@ -20,13 +21,13 @@ class Permutation(distrax.Bijector):
     """
 
     def __init__(self, permutation, event_ndims_in: int):
-        super().__init__(event_ndims_in)
         self.permutation = permutation
+        self.event_ndims_in = event_ndims_in
 
-    def _forward_and_likelihood_contribution(self, z):
+    def _forward_and_likelihood_contribution(self, z, **kwargs):
         return z[..., self.permutation], jnp.full(jnp.shape(z)[:-1], 0.0)
 
-    def _inverse_and_likelihood_contribution(self, y):
+    def _inverse_and_likelihood_contribution(self, y, **kwargs):
         size = self.permutation.size
         permutation_inv = (
             jnp.zeros(size, dtype=jnp.result_type(int))

surjectors/_src/distributions/transformed_distribution.py

@@ -1,8 +1,11 @@
+from typing import Union
+
 import chex
 import distrax
 import haiku as hk
 from distrax import Distribution
 from jax import Array
+from tensorflow_probability.substrates.jax import distributions as tfd
 
 from surjectors._src.surjectors.surjector import Surjector
 
@@ -31,7 +34,11 @@ class TransformedDistribution:
         >>> )
     """
 
-    def __init__(self, base_distribution: Distribution, transform: Surjector):
+    def __init__(
+        self,
+        base_distribution: Union[Distribution, tfd.Distribution],
+        transform: Surjector,
+    ):
         self.base_distribution = base_distribution
         self.transform = transform
 
@@ -118,10 +125,17 @@ def sample_and_log_prob(self, sample_shape=(), x: Array = None):
         if x is not None:
             chex.assert_equal(sample_shape[0], x.shape[0])
 
-        z, lp_z = self.base_distribution.sample_and_log_prob(
-            seed=hk.next_rng_key(),
-            sample_shape=sample_shape,
-        )
+        try:
+            z, lp_z = self.base_distribution.sample_and_log_prob(
+                seed=hk.next_rng_key(),
+                sample_shape=sample_shape,
+            )
+        except AttributeError:
+            z, lp_z = self.base_distribution.experimental_sample_and_log_prob(
+                seed=hk.next_rng_key(),
+                sample_shape=sample_shape,
+            )
+
         y, fldj = self.transform.forward_and_likelihood_contribution(z, x=x)
         lp = lp_z - fldj
         return y, lp