Use jax.tree_util.tree_map in place of deprecated tree_multimap.

The latter is a simple alias of the former, so this change is a no-op.

PiperOrigin-RevId: 461045645

kfac_ferminet_alpha/curvature_blocks.py

@@ -372,9 +372,9 @@ def multiply_matpower(
       raise ValueError("Neither `has_scale` nor `has_shift`.")
     factors = jax.tree_map(lambda x: x + diagonal_weight, factors)
     if exp == 1:
-      return jax.tree_multimap(jnp.multiply, vec, factors)
+      return jax.tree_map(jnp.multiply, vec, factors)
     elif exp == -1:
-      return jax.tree_multimap(jnp.divide, vec, factors)
+      return jax.tree_map(jnp.divide, vec, factors)
     else:
       raise NotImplementedError()
 

kfac_ferminet_alpha/estimator.py

@@ -180,7 +180,7 @@ def vec_block_apply(
     """Executes func for each approximation block on vectors."""
     per_block_vectors = self.vectors_to_blocks(parameter_structured_vector)
     assert len(per_block_vectors) == len(self.blocks)
-    results = jax.tree_multimap(func, tuple(self.blocks.values()),
+    results = jax.tree_map(func, tuple(self.blocks.values()),
                                 per_block_vectors)
     parameter_structured_result = self.blocks_to_vectors(results)
     utils.check_structure_shapes_and_dtype(parameter_structured_vector,

kfac_ferminet_alpha/optimizer.py

@@ -462,7 +462,7 @@ def _step(
     )
 
     # Update parameters: params = params + delta
-    params = jax.tree_multimap(jnp.add, params, delta)
+    params = jax.tree_map(jnp.add, params, delta)
 
     # Optionally compute the reduction ratio and update the damping
     self.estimator.damping = None
@@ -607,5 +607,5 @@ def velocities_and_delta(
     assert len(vectors) == len(coefficients)
     delta = utils.scalar_mul(vectors[0], coefficients[0])
     for vi, wi in zip(vectors[1:], coefficients[1:]):
-      delta = jax.tree_multimap(jnp.add, delta, utils.scalar_mul(vi, wi))
+      delta = jax.tree_map(jnp.add, delta, utils.scalar_mul(vi, wi))
     return delta, delta

kfac_ferminet_alpha/utils.py

@@ -120,7 +120,7 @@ def extract_func_outputs(
 def inner_product(obj1: T, obj2: T) -> jnp.ndarray:
   if jax.tree_structure(obj1) != jax.tree_structure(obj2):
     raise ValueError("The two structures are not identical.")
-  elements_product = jax.tree_multimap(lambda x, y: jnp.sum(x * y), obj1, obj2)
+  elements_product = jax.tree_map(lambda x, y: jnp.sum(x * y), obj1, obj2)
   return sum(jax.tree_flatten(elements_product)[0])