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# Owner(s): ["module: pytree"]
import torch
from torch.testing._internal.common_utils import TestCase, run_tests
from torch.utils._pytree import tree_flatten, tree_map, tree_unflatten, TreeSpec, LeafSpec
from torch.utils._pytree import _broadcast_to_and_flatten, tree_map_only, tree_all
from torch.utils._pytree import tree_any, tree_all_only, tree_any_only
from collections import namedtuple, OrderedDict
from torch.testing._internal.common_utils import parametrize, subtest, instantiate_parametrized_tests
class TestPytree(TestCase):
def test_treespec_equality(self):
self.assertTrue(LeafSpec() == LeafSpec())
self.assertTrue(TreeSpec(list, None, []) == TreeSpec(list, None, []))
self.assertTrue(TreeSpec(list, None, [LeafSpec()]) == TreeSpec(list, None, [LeafSpec()]))
self.assertFalse(TreeSpec(tuple, None, []) == TreeSpec(list, None, []))
self.assertTrue(TreeSpec(tuple, None, []) != TreeSpec(list, None, []))
def test_flatten_unflatten_leaf(self):
def run_test_with_leaf(leaf):
values, treespec = tree_flatten(leaf)
self.assertEqual(values, [leaf])
self.assertEqual(treespec, LeafSpec())
unflattened = tree_unflatten(values, treespec)
self.assertEqual(unflattened, leaf)
run_test_with_leaf(1)
run_test_with_leaf(1.)
run_test_with_leaf(None)
run_test_with_leaf(bool)
run_test_with_leaf(torch.randn(3, 3))
def test_flatten_unflatten_list(self):
def run_test(lst):
expected_spec = TreeSpec(list, None, [LeafSpec() for _ in lst])
values, treespec = tree_flatten(lst)
self.assertTrue(isinstance(values, list))
self.assertEqual(values, lst)
self.assertEqual(treespec, expected_spec)
unflattened = tree_unflatten(values, treespec)
self.assertEqual(unflattened, lst)
self.assertTrue(isinstance(unflattened, list))
run_test([])
run_test([1., 2])
run_test([torch.tensor([1., 2]), 2, 10, 9, 11])
def test_flatten_unflatten_tuple(self):
def run_test(tup):
expected_spec = TreeSpec(tuple, None, [LeafSpec() for _ in tup])
values, treespec = tree_flatten(tup)
self.assertTrue(isinstance(values, list))
self.assertEqual(values, list(tup))
self.assertEqual(treespec, expected_spec)
unflattened = tree_unflatten(values, treespec)
self.assertEqual(unflattened, tup)
self.assertTrue(isinstance(unflattened, tuple))
run_test(())
run_test((1.,))
run_test((1., 2))
run_test((torch.tensor([1., 2]), 2, 10, 9, 11))
def test_flatten_unflatten_odict(self):
def run_test(odict):
expected_spec = TreeSpec(
OrderedDict,
list(odict.keys()),
[LeafSpec() for _ in odict.values()])
values, treespec = tree_flatten(odict)
self.assertTrue(isinstance(values, list))
self.assertEqual(values, list(odict.values()))
self.assertEqual(treespec, expected_spec)
unflattened = tree_unflatten(values, treespec)
self.assertEqual(unflattened, odict)
self.assertTrue(isinstance(unflattened, OrderedDict))
od = OrderedDict()
run_test(od)
od['b'] = 1
od['a'] = torch.tensor(3.14)
run_test(od)
def test_flatten_unflatten_namedtuple(self):
Point = namedtuple('Point', ['x', 'y'])
def run_test(tup):
expected_spec = TreeSpec(namedtuple, Point, [LeafSpec() for _ in tup])
values, treespec = tree_flatten(tup)
self.assertTrue(isinstance(values, list))
self.assertEqual(values, list(tup))
self.assertEqual(treespec, expected_spec)
unflattened = tree_unflatten(values, treespec)
self.assertEqual(unflattened, tup)
self.assertTrue(isinstance(unflattened, Point))
run_test(Point(1., 2))
run_test(Point(torch.tensor(1.), 2))
@parametrize("op", [
subtest(torch.max, name='max'),
subtest(torch.min, name='min'),
])
def test_flatten_unflatten_return_type(self, op):
x = torch.randn(3, 3)
expected = op(x, dim=0)
values, spec = tree_flatten(expected)
# Check that values is actually List[Tensor] and not (ReturnType(...),)
for value in values:
self.assertTrue(isinstance(value, torch.Tensor))
result = tree_unflatten(values, spec)
self.assertEqual(type(result), type(expected))
self.assertEqual(result, expected)
def test_flatten_unflatten_dict(self):
def run_test(tup):
expected_spec = TreeSpec(dict, list(tup.keys()),
[LeafSpec() for _ in tup.values()])
values, treespec = tree_flatten(tup)
self.assertTrue(isinstance(values, list))
self.assertEqual(values, list(tup.values()))
self.assertEqual(treespec, expected_spec)
unflattened = tree_unflatten(values, treespec)
self.assertEqual(unflattened, tup)
self.assertTrue(isinstance(unflattened, dict))
run_test({})
run_test({'a': 1})
run_test({'abcdefg': torch.randn(2, 3)})
run_test({1: torch.randn(2, 3)})
run_test({'a': 1, 'b': 2, 'c': torch.randn(2, 3)})
def test_flatten_unflatten_nested(self):
def run_test(pytree):
values, treespec = tree_flatten(pytree)
self.assertTrue(isinstance(values, list))
self.assertEqual(len(values), treespec.num_leaves)
# NB: python basic data structures (dict list tuple) all have
# contents equality defined on them, so the following works for them.
unflattened = tree_unflatten(values, treespec)
self.assertEqual(unflattened, pytree)
cases = [
[()],
([],),
{'a': ()},
{'a': 0, 'b': [{'c': 1}]},
{'a': 0, 'b': [1, {'c': 2}, torch.randn(3)], 'c': (torch.randn(2, 3), 1)},
]
def test_treemap(self):
def run_test(pytree):
def f(x):
return x * 3
sm1 = sum(map(tree_flatten(pytree)[0], f))
sm2 = tree_flatten(tree_map(f, pytree))[0]
self.assertEqual(sm1, sm2)
def invf(x):
return x // 3
self.assertEqual(tree_flatten(tree_flatten(pytree, f), invf), pytree)
cases = [
[()],
([],),
{'a': ()},
{'a': 1, 'b': [{'c': 2}]},
{'a': 0, 'b': [2, {'c': 3}, 4], 'c': (5, 6)},
]
for case in cases:
run_test(case)
def test_tree_only(self):
self.assertEqual(tree_map_only(int, lambda x: x + 2, [0, "a"]), [2, "a"])
def test_tree_all_any(self):
self.assertTrue(tree_all(lambda x: x % 2, [1, 3]))
self.assertFalse(tree_all(lambda x: x % 2, [0, 1]))
self.assertTrue(tree_any(lambda x: x % 2, [0, 1]))
self.assertFalse(tree_any(lambda x: x % 2, [0, 2]))
self.assertTrue(tree_all_only(int, lambda x: x % 2, [1, 3, "a"]))
self.assertFalse(tree_all_only(int, lambda x: x % 2, [0, 1, "a"]))
self.assertTrue(tree_any_only(int, lambda x: x % 2, [0, 1, "a"]))
self.assertFalse(tree_any_only(int, lambda x: x % 2, [0, 2, "a"]))
def test_treespec_repr(self):
# Check that it looks sane
pytree = (0, [0, 0, 0])
_, spec = tree_flatten(pytree)
self.assertEqual(
repr(spec), 'TreeSpec(tuple, None, [*, TreeSpec(list, None, [*, *, *])])')
def test_broadcast_to_and_flatten(self):
cases = [
(1, (), []),
# Same (flat) structures
((1,), (0,), [1]),
([1], [0], [1]),
((1, 2, 3), (0, 0, 0), [1, 2, 3]),
({'a': 1, 'b': 2}, {'a': 0, 'b': 0}, [1, 2]),
# Mismatched (flat) structures
([1], (0,), None),
([1], (0,), None),
((1,), [0], None),
((1, 2, 3), (0, 0), None),
({'a': 1, 'b': 2}, {'a': 0}, None),
({'a': 1, 'b': 2}, {'a': 0, 'c': 0}, None),
({'a': 1, 'b': 2}, {'a': 0, 'b': 0, 'c': 0}, None),
# Same (nested) structures
((1, [2, 3]), (0, [0, 0]), [1, 2, 3]),
((1, [(2, 3), 4]), (0, [(0, 0), 0]), [1, 2, 3, 4]),
# Mismatched (nested) structures
((1, [2, 3]), (0, (0, 0)), None),
((1, [2, 3]), (0, [0, 0, 0]), None),
# Broadcasting single value
(1, (0, 0, 0), [1, 1, 1]),
(1, [0, 0, 0], [1, 1, 1]),
(1, {'a': 0, 'b': 0}, [1, 1]),
(1, (0, [0, [0]], 0), [1, 1, 1, 1]),
(1, (0, [0, [0, [], [[[0]]]]], 0), [1, 1, 1, 1, 1]),
# Broadcast multiple things
((1, 2), ([0, 0, 0], [0, 0]), [1, 1, 1, 2, 2]),
((1, 2), ([0, [0, 0], 0], [0, 0]), [1, 1, 1, 1, 2, 2]),
(([1, 2, 3], 4), ([0, [0, 0], 0], [0, 0]), [1, 2, 2, 3, 4, 4]),
]
for pytree, to_pytree, expected in cases:
_, to_spec = tree_flatten(to_pytree)
result = _broadcast_to_and_flatten(pytree, to_spec)
self.assertEqual(result, expected, msg=str([pytree, to_spec, expected]))
instantiate_parametrized_tests(TestPytree)
if __name__ == '__main__':
run_tests()
|
