Comparison Between TreeValue and Jax LibTree

In this section, we will take a look at the feature and performance of the jax-libtree library, which is developed by Google.

[1]:
_TREE_DATA_1 = {'a': 1, 'b': 2, 'x': {'c': 3, 'd': 4}}

Mapping Operation

TreeValue’s Mapping

[2]:
from treevalue import mapping, FastTreeValue

t = FastTreeValue(_TREE_DATA_1)
mapping(t, lambda x: x ** 2)
/tmp/tmp4q2ykbbk/bd171e47eddd5b95747dbb64747e18068f6b3274/treevalue/tree/integration/torch.py:21: FutureWarning: `torch.utils._pytree._register_pytree_node` is deprecated. Please use `torch.utils._pytree.register_pytree_node` instead.
  register_for_torch(TreeValue)
/tmp/tmp4q2ykbbk/bd171e47eddd5b95747dbb64747e18068f6b3274/treevalue/tree/integration/torch.py:22: FutureWarning: `torch.utils._pytree._register_pytree_node` is deprecated. Please use `torch.utils._pytree.register_pytree_node` instead.
  register_for_torch(FastTreeValue)
[2]:
../_images/comparison_jax_libtree.result_5_1.svg
[3]:
%timeit mapping(t, lambda x: x ** 2)
2.61 µs ± 14.3 ns per loop (mean ± std. dev. of 7 runs, 100,000 loops each)
[4]:
mapping(t, lambda x, p: (x ** 2, p))
[4]:
../_images/comparison_jax_libtree.result_7_0.svg
[5]:
%timeit mapping(t, lambda x, p: (x ** 2, p))
2.69 µs ± 9.34 ns per loop (mean ± std. dev. of 7 runs, 100,000 loops each)

pytree’s tree_map

[6]:
from jax.tree_util import tree_map

tree_map(lambda x: x ** 2, _TREE_DATA_1)
[6]:
{'a': 1, 'b': 4, 'x': {'c': 9, 'd': 16}}
[7]:
%timeit tree_map(lambda x: x ** 2, _TREE_DATA_1)
4.6 µs ± 17 ns per loop (mean ± std. dev. of 7 runs, 100,000 loops each)

Flatten and Unflatten Operation

TreeValue’s Performance

[8]:
from treevalue import flatten, flatten_keys, flatten_values

t_flatted = flatten(t)
t_flatted
[8]:
[(('a',), 1), (('b',), 2), (('x', 'c'), 3), (('x', 'd'), 4)]
[9]:
%timeit flatten(t)
504 ns ± 4.2 ns per loop (mean ± std. dev. of 7 runs, 1,000,000 loops each)
[10]:
from treevalue import flatten_keys

flatten_keys(t)
[10]:
[('a',), ('b',), ('x', 'c'), ('x', 'd')]
[11]:
%timeit flatten_keys(t)
467 ns ± 3.71 ns per loop (mean ± std. dev. of 7 runs, 1,000,000 loops each)
[12]:
from treevalue import flatten_values

flatten_values(t)
[12]:
[1, 2, 3, 4]
[13]:
%timeit flatten_values(t)
330 ns ± 3.05 ns per loop (mean ± std. dev. of 7 runs, 1,000,000 loops each)
[14]:
from treevalue import unflatten

unflatten(t_flatted)
[14]:
../_images/comparison_jax_libtree.result_20_0.svg
[15]:
%timeit unflatten(t_flatted)
581 ns ± 3.6 ns per loop (mean ± std. dev. of 7 runs, 1,000,000 loops each)

pytree’s Performance

[16]:
from jax.tree_util import tree_flatten

leaves, treedef = tree_flatten(_TREE_DATA_1)
print('Leaves:', leaves)
print('Treedef:', treedef)
Leaves: [1, 2, 3, 4]
Treedef: PyTreeDef({'a': *, 'b': *, 'x': {'c': *, 'd': *}})
[17]:
%timeit tree_flatten(_TREE_DATA_1)
1.37 µs ± 7.22 ns per loop (mean ± std. dev. of 7 runs, 1,000,000 loops each)
[18]:
from jax.tree_util import tree_unflatten

tree_unflatten(treedef, leaves)
[18]:
{'a': 1, 'b': 2, 'x': {'c': 3, 'd': 4}}
[19]:
%timeit tree_unflatten(treedef, leaves)
682 ns ± 3.09 ns per loop (mean ± std. dev. of 7 runs, 1,000,000 loops each)

All Operation

TreeValue’s Performance

[20]:
all(flatten_values(t))
[20]:
True
[21]:
%timeit all(flatten_values(t))
424 ns ± 3.85 ns per loop (mean ± std. dev. of 7 runs, 1,000,000 loops each)

pytree.tree_all’s performance

[22]:
from jax.tree_util import tree_all
[23]:
tree_all(_TREE_DATA_1)
[23]:
True
[24]:
%timeit tree_all(_TREE_DATA_1)
1.57 µs ± 7.52 ns per loop (mean ± std. dev. of 7 runs, 1,000,000 loops each)

Reduce Operation

TreeValue’s Reduce

[25]:
from functools import reduce

def _flatten_reduce(tree):
    values = flatten_values(tree)
    return reduce(lambda x, y: x + y, values)

_flatten_reduce(t)
[25]:
10
[26]:
%timeit _flatten_reduce(t)
834 ns ± 6.28 ns per loop (mean ± std. dev. of 7 runs, 1,000,000 loops each)
[27]:
def _flatten_reduce_with_init(tree):
    values = flatten_values(tree)
    return reduce(lambda x, y: x + y, values, 0)

_flatten_reduce_with_init(t)
[27]:
10
[28]:
%timeit _flatten_reduce_with_init(t)
894 ns ± 8.63 ns per loop (mean ± std. dev. of 7 runs, 1,000,000 loops each)

pytree.tree_reduce

[29]:
from jax.tree_util import tree_reduce

tree_reduce(lambda x, y: x + y, _TREE_DATA_1)
[29]:
10
[30]:
%timeit tree_reduce(lambda x, y: x + y, _TREE_DATA_1)
2.08 µs ± 8.55 ns per loop (mean ± std. dev. of 7 runs, 100,000 loops each)
[31]:
tree_reduce(lambda x, y: x + y, _TREE_DATA_1, 0)
[31]:
10
[32]:
%timeit tree_reduce(lambda x, y: x + y, _TREE_DATA_1, 0)
2.14 µs ± 6.99 ns per loop (mean ± std. dev. of 7 runs, 100,000 loops each)

Structure Transpose

Subside and Rise in TreeValue

[33]:
from treevalue import subside

value = {
    'a': FastTreeValue({'a': 1, 'b': {'x': 2, 'y': 3}}),
    'b': FastTreeValue({'a': 10, 'b': {'x': 20, 'y': 30}}),
    'c': {
        'x': FastTreeValue({'a': 100, 'b': {'x': 200, 'y': 300}}),
        'y': FastTreeValue({'a': 400, 'b': {'x': 500, 'y': 600}}),
    },
}
subside(value)
[33]:
../_images/comparison_jax_libtree.result_48_0.svg
[34]:
%timeit subside(value)
10.3 µs ± 45.3 ns per loop (mean ± std. dev. of 7 runs, 100,000 loops each)
[35]:
from treevalue import raw, rise

value = FastTreeValue({
    'a': raw({'a': 1, 'b': {'x': 2, 'y': 3}}),
    'b': raw({'a': 10, 'b': {'x': 20, 'y': 30}}),
    'c': {
        'x': raw({'a': 100, 'b': {'x': 200, 'y': 300}}),
        'y': raw({'a': 400, 'b': {'x': 500, 'y': 600}}),
    },
})
rise(value)
[35]:
{'b': {'y': <FastTreeValue 0x7f854ce49730>
  ├── 'a' --> 3
  ├── 'b' --> 30
  └── 'c' --> <FastTreeValue 0x7f854ce497c0>
      ├── 'x' --> 300
      └── 'y' --> 600,
  'x': <FastTreeValue 0x7f854ce49f10>
  ├── 'a' --> 2
  ├── 'b' --> 20
  └── 'c' --> <FastTreeValue 0x7f854ce49550>
      ├── 'x' --> 200
      └── 'y' --> 500},
 'a': <FastTreeValue 0x7f854ce49940>
 ├── 'a' --> 1
 ├── 'b' --> 10
 └── 'c' --> <FastTreeValue 0x7f854ce49250>
     ├── 'x' --> 100
     └── 'y' --> 400}
[36]:
%timeit rise(value)
11.3 µs ± 74.1 ns per loop (mean ± std. dev. of 7 runs, 100,000 loops each)
[37]:
vt = {'a': None, 'b': {'x': None, 'y': None}}
rise(value, template=vt)
[37]:
{'a': <FastTreeValue 0x7f854ceb94f0>
 ├── 'a' --> 1
 ├── 'b' --> 10
 └── 'c' --> <FastTreeValue 0x7f854ceb9a00>
     ├── 'x' --> 100
     └── 'y' --> 400,
 'b': {'x': <FastTreeValue 0x7f854ceb9550>
  ├── 'a' --> 2
  ├── 'b' --> 20
  └── 'c' --> <FastTreeValue 0x7f854ceb9a90>
      ├── 'x' --> 200
      └── 'y' --> 500,
  'y': <FastTreeValue 0x7f8610e6f130>
  ├── 'a' --> 3
  ├── 'b' --> 30
  └── 'c' --> <FastTreeValue 0x7f8610e6f2b0>
      ├── 'x' --> 300
      └── 'y' --> 600}}
[38]:
%timeit rise(value, template=vt)
9.09 µs ± 43 ns per loop (mean ± std. dev. of 7 runs, 100,000 loops each)

pytree.tree_transpose

[39]:
from jax.tree_util import tree_structure, tree_transpose

sto = tree_structure({'a': 1, 'b': 2, 'c': {'x': 3, 'y': 4}})
sti = tree_structure({'a': 1, 'b': {'x': 2, 'y': 3}})

value = (
    {'a': 1, 'b': {'x': 2, 'y': 3}},
    {
        'a': {'a': 10, 'b': {'x': 20, 'y': 30}},
        'b': [
            {'a': 100, 'b': {'x': 200, 'y': 300}},
            {'a': 400, 'b': {'x': 500, 'y': 600}},
        ],
    }
)
tree_transpose(sto, sti, value)
[39]:
{'a': {'a': 1, 'b': 10, 'c': {'x': 100, 'y': 400}},
 'b': {'x': {'a': 2, 'b': 20, 'c': {'x': 200, 'y': 500}},
  'y': {'a': 3, 'b': 30, 'c': {'x': 300, 'y': 600}}}}
[40]:
%timeit tree_transpose(sto, sti, value)
10.1 µs ± 83.7 ns per loop (mean ± std. dev. of 7 runs, 100,000 loops each)
[ ]: