Merge pull request #189 from danielward27/reduce_coupling

Reduce coupling

Author: danielward27

flowjax/bijections/coupling.py

@@ -10,6 +10,7 @@
 import jax.numpy as jnp
 from jaxtyping import PRNGKeyArray
 
+from flowjax import wrappers
 from flowjax.bijections.bijection import AbstractBijection
 from flowjax.bijections.jax_transforms import Vmap
 from flowjax.utils import Array, get_ravelled_pytree_constructor
@@ -55,7 +56,11 @@ def __init__(
                 "Only unconditional transformers with shape () are supported.",
             )
 
-        constructor, num_params = get_ravelled_pytree_constructor(transformer)
+        constructor, num_params = get_ravelled_pytree_constructor(
+            transformer,
+            filter_spec=eqx.is_inexact_array,
+            is_leaf=lambda leaf: isinstance(leaf, wrappers.NonTrainable),
+        )
 
         self.transformer_constructor = constructor
         self.untransformed_dim = untransformed_dim

flowjax/bijections/masked_autoregressive.py

@@ -9,11 +9,11 @@
 import jax.numpy as jnp
 from jaxtyping import Array, Int, PRNGKeyArray
 
+from flowjax import wrappers
 from flowjax.bijections.bijection import AbstractBijection
 from flowjax.bijections.jax_transforms import Vmap
 from flowjax.masks import rank_based_mask
 from flowjax.utils import get_ravelled_pytree_constructor
-from flowjax.wrappers import Parameterize
 
 
 class MaskedAutoregressive(AbstractBijection):
@@ -58,7 +58,11 @@ def __init__(
                 "Only unconditional transformers with shape () are supported.",
             )
 
-        constructor, num_params = get_ravelled_pytree_constructor(transformer)
+        constructor, num_params = get_ravelled_pytree_constructor(
+            transformer,
+            filter_spec=eqx.is_inexact_array,
+            is_leaf=lambda leaf: isinstance(leaf, wrappers.NonTrainable),
+        )
 
         if cond_dim is None:
             self.cond_shape = None
@@ -162,7 +166,7 @@ def masked_autoregressive_mlp(
         masked_linear = eqx.tree_at(
             lambda linear: linear.weight,
             linear,
-            Parameterize(jnp.where, mask, linear.weight, 0),
+            wrappers.Parameterize(jnp.where, mask, linear.weight, 0),
         )
         masked_layers.append(masked_linear)
 

flowjax/utils.py

@@ -7,8 +7,6 @@
 from jax.flatten_util import ravel_pytree
 from jaxtyping import Array, ArrayLike
 
-import flowjax
-
 
 def inv_softplus(x: ArrayLike) -> Array:
     """The inverse of the softplus function, checking for positive inputs."""
@@ -70,28 +68,28 @@ def _shapes_to_str(shapes):
     return f"{in_shapes_str}->{out_shapes_str}"
 
 
-def get_ravelled_pytree_constructor(tree, filter_spec=eqx.is_inexact_array) -> tuple:
+def get_ravelled_pytree_constructor(
+    tree,
+    *args,
+    **kwargs,
+) -> tuple:
     """Get a pytree constructor taking ravelled parameters, and the number of params.
 
     The constructor takes a single argument as input, which is all the bijection
     parameters flattened into a single contiguous vector. This is useful when we wish to
     parameterize a pytree with a neural neural network. Calling the constructor
-    at the zero vector will return the initial pytree. Parameters warpped in
-    ``NonTrainable`` are treated as leaves during partitioning.
+    at the zero vector will return the initial pytree. When using, you may wish to
+    specify ``NonTrainable`` nodes as leaves, using the ``is_leaf`` argument.
 
     Args:
         tree: Pytree to form constructor for.
-        filter_spec: Filter function to specify parameters. Defaults to
-            eqx.is_inexact_array.
+        *args: Arguments passed to ``eqx.partition``.
+        **kwargs: Key word arguments passed to ``eqx.partition``.
 
     Returns:
         tuple: Tuple containing the constructor, and the number of parameters.
     """
-    params, static = eqx.partition(
-        tree,
-        filter_spec,
-        is_leaf=lambda leaf: isinstance(leaf, flowjax.wrappers.NonTrainable),
-    )
+    params, static = eqx.partition(tree, *args, **kwargs)
     init, unravel = ravel_pytree(params)
 
     def constructor(ravelled_params: Array):

pyproject.toml

@@ -16,7 +16,7 @@ license = { file = "LICENSE" }
 name = "flowjax"
 readme = "README.md"
 requires-python = ">=3.10"
-version = "15.1.0"
+version = "16.0.0"
 
 [project.urls]
 repository = "https://github.com/danielward27/flowjax"