modify layer_stack transparency map

haiku/_src/layer_stack.py

@@ -89,22 +89,16 @@ def _split_params(
     name_map: LayerStackTransparencyMapping,
 ) -> base.Params:
   """Splits the stacked parameters."""
-
-  def _split(x):
-    return [jnp.squeeze(s, axis=0) for s in jnp.split(x, x.shape[0], axis=0)]
-
   params = {}
   for mod_name, mod_params in stacked_params.items():
-    split_mod_params = {k: _split(v) for k, v in mod_params.items()}
     for i in range(num_layers):
       new_mod_name = name_map.stacked_to_flat(mod_name, i)
       if new_mod_name in params:
         raise ValueError(
             f"Found conflicting unstacked module name for {mod_name} at"
             f" {new_mod_name}."
         )
-      params[new_mod_name] = {k: v[i] for k, v in split_mod_params.items()}
-
+      params[new_mod_name] = jax.tree_map(lambda x: x[i], mod_params)  # pylint:disable=cell-var-from-loop
   return params
 
 
@@ -114,32 +108,27 @@ def _stack_params(
     name_map: LayerStackTransparencyMapping,
 ) -> base.Params:
   """Stacks the split parameters."""
-  params = {}
-  make_empty_param_stack = lambda: ([None] * num_layers)
-
+  # Construct a separate tree for each loop iteration, which we will then
+  # multimap over in a call to jnp.stack. This formulation preserves custom
+  # pytree node types.
+  param_trees = [{} for _ in range(num_layers)]
   for mod_name, mod_params in split_params.items():
     stacked_name_idx = name_map.flat_to_stacked(mod_name)
+    # If the transparency map returns None, this param is not part of the stack.
     if stacked_name_idx is None:
       continue
     stacked_mod_name, idx = stacked_name_idx
-    if stacked_mod_name not in params:
-      params[stacked_mod_name] = collections.defaultdict(make_empty_param_stack)
-
+    if stacked_mod_name not in param_trees[idx]:
+      param_trees[idx][stacked_mod_name] = {}
     for k, v in mod_params.items():
-      if params[stacked_mod_name][k][idx] is not None:
+      if k in param_trees[idx][stacked_mod_name]:
         raise ValueError(
             f"Found conflicting values for param {stacked_mod_name}/{k} at"
             f" index {idx}."
         )
-      params[stacked_mod_name][k][idx] = v
-
-  for mod_name, mod_params in params.items():
-    for k, v in mod_params.items():
-      if None in v:
-        raise ValueError(f"Couldn't find all params for {mod_name}/{k}: {v}")
-      mod_params[k] = jnp.stack(v, axis=0)
+      param_trees[idx][stacked_mod_name][k] = v
 
-  return params
+  return jax.tree_map(lambda *args: jnp.stack(args, axis=0), *param_trees)
 
 
 class _LayerStack:

haiku/_src/layer_stack_test.py

@@ -17,7 +17,6 @@
 import functools
 import re
 from typing import Optional
-
 from absl.testing import absltest
 from absl.testing import parameterized
 from haiku._src import base
@@ -598,6 +597,83 @@ def stacked(x: jax.Array) -> jax.Array:
         rtol=1e-6,
     )
 
+  def test_layer_stack_transparent_with_custom_pytrees(self):
+    class TransparencyMap(layer_stack.LayerStackTransparencyMapping):
+
+      def stacked_to_flat(self, stacked_module_name: str, scan_idx: int) -> str:
+        return stacked_module_name.replace("0", str(scan_idx))
+
+      def flat_to_stacked(
+          self, unstacked_module_name: str
+      ) -> Optional[tuple[str, int]]:
+        idx = int(re.findall(r"\d+", unstacked_module_name)[0])
+        return unstacked_module_name.replace(str(idx), "0"), idx
+
+    @jax.tree_util.register_pytree_node_class
+    class CustomParam:
+
+      def __init__(self, param, name):
+        self.param = param
+        self.multiplier = name
+
+      def tree_flatten(self):
+        return ((self.param, self.multiplier), None)
+
+      @classmethod
+      def tree_unflatten(cls, aux, values):
+        del aux
+        return cls(*values)
+
+      @property
+      def shape(self) -> list[int]:
+        return self.param.shape
+
+    class CustomLinear:
+
+      def __init__(self, *args, **kwargs):
+        self.linear = basic.Linear(*args, **kwargs)
+
+      def __call__(self, x: CustomParam) -> CustomParam:
+        # Unwrap from CustomParam before invoking linear
+        return CustomParam(
+            self.linear(x.param * x.multiplier),
+            x.multiplier,
+        )
+
+    def block(x: CustomParam, i: int) -> CustomParam:
+      return CustomLinear(output_size=x.shape[-1], name=f"linear_{i}")(x)
+
+    def looped(x: CustomParam, num_layers: int = 1) -> CustomParam:
+      for i in range(num_layers):
+        x = block(x, i)
+      return x
+
+    def stacked(x: CustomParam) -> CustomParam:
+      return layer_stack.layer_stack(
+          num_layers=1, transparent=True, transparency_map=TransparencyMap()
+      )(lambda y: block(y, 0))(x)
+
+    looped = transform.transform(looped)
+    stacked = transform.transform(stacked)
+
+    x = CustomParam(jnp.ones((2, 2)), 0.3)
+    rng = jax.random.PRNGKey(0)
+    looped_params = looped.init(rng, x)
+    stacked_params = stacked.init(rng, x)
+
+    self.assertEqual(
+        jax.tree_util.tree_structure(looped_params),
+        jax.tree_util.tree_structure(stacked_params),
+    )
+
+    # Use same set of params for both calls since stacked_params have different
+    # value than looped params because differences in RNG splitting.
+    np.testing.assert_allclose(
+        looped.apply(looped_params, rng, x).param,
+        stacked.apply(looped_params, rng, x).param,
+        rtol=1e-6,
+    )
+
 
 if __name__ == "__main__":
   jax.config.update("jax_check_tracer_leaks", True)