def to_tree_arrays(list_of_trees):
"""Convert a list of pytrees into a pytree of stacked jnp.arrays.
Args:
list_of_trees: A list of pytrees containing numbers as leaves.
Returns:
A pytree of jnp.arrays having the same structure as the elements of
`list_of_trees`
Example:
>>> to_tree_arrays([
(1, {"a": jnp.array([1,2])}),
(2, {"a": jnp.array([3,4])})
])
(DeviceArray([1, 2], dtype=int32),
{'a': DeviceArray([[1, 2],
[3, 4]], dtype=int32)})
"""
if not list_of_trees:
return list_of_trees
trees_list = jax.tree_transpose(
jax.tree_structure([0] * len(list_of_trees)),
jax.tree_structure(list_of_trees[0]), list_of_trees)
trees_array = jax.tree_multimap(lambda _, ls: jnp.stack(ls),
list_of_trees[0], trees_list)
return trees_array
def test_tree_transpose(self):
outerdef = jax.tree_structure(FlatMap({"a": 1, "b": 2}))
innerdef = jax.tree_structure([1, 2])
self.assertEqual(
[FlatMap({"a": 3, "b": 5}), FlatMap({"a": 4, "b": 6})],
jax.tree_transpose(
outerdef, innerdef, FlatMap({"a": [3, 4], "b": [5, 6]})))
def point_project_tree(tree_point_cloud, ts, n_basis, basis):
def point_project_list(*args):
return list(point_project_array(args, ts, n_basis, basis))
out = jax.tree_multimap(point_project_list, *tree_point_cloud)
original_struct = jax.tree_structure(tree_point_cloud[0])
mapped_struct = jax.tree_structure(list(range(n_basis)))
return jax.tree_transpose(original_struct, mapped_struct, out)
def function_project_tree(source_params, source_basis, target_basis, n_basis):
def function_project_list(*args):
return list(
function_project_array(args, source_basis, target_basis, n_basis))
out = jax.tree_multimap(function_project_list, *source_params)
original_struct = jax.tree_structure(source_params[0])
mapped_struct = jax.tree_structure(list(range(n_basis)))
return jax.tree_transpose(original_struct, mapped_struct, out)
def tree_vmap(f, lst):
stacked = jax.tree_map(lambda args: jnp.stack(args), lst)
out_stacked = jax.vmap(f)(stacked)
_, outer_treedef = jax.tree_flatten([None] * len(lst))
_, inner_treedef = jax.tree_flatten(out_stacked)
out_unstacked_transposed = jax.tree_map(list, out_stacked)
out_unstacked = jax.tree_transpose(
outer_treedef, inner_treedef, out_unstacked_transposed
)
return out_unstacked
def write_gradient_histogram(writer, step, *, grads=None, updates=None):
"""Log computed gradients and/or updates histograms."""
histograms = {"grad": grads, "update": updates}
histograms = {k: v for k, v in histograms.items() if v is not None}
if not histograms: return
# Transpose a histograms dict from
# {"grad": {"param1": Tensor}, "update": {"param1": Tensor}} to
# {"param1": {"grad": Tensor, "update": Tensor}} such that the gradient and
# the transformed updates appear next to each other in tensorboard.
histograms = jax.tree_transpose(
jax.tree_structure({k: 0
for k in histograms.keys()}),
jax.tree_structure(next(iter(histograms.values()))), histograms)
histograms = utils.flatten_dict(histograms, sep=".")
writer.write_histograms(step, histograms)
