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# Owner(s): ["module: dynamo"]
import unittest.mock
import torch
import torch._dynamo.test_case
import torch._dynamo.testing
from torch._dynamo.testing import same
try:
from diffusers.models import unet_2d
except ImportError:
unet_2d = None
def maybe_skip(fn):
if unet_2d is None:
return unittest.skip("requires diffusers")(fn)
return fn
class TestBaseOutput(torch._dynamo.test_case.TestCase):
@maybe_skip
def test_create(self):
def fn(a):
tmp = unet_2d.UNet2DOutput(a + 1)
return tmp
torch._dynamo.testing.standard_test(self, fn=fn, nargs=1, expected_ops=1)
@maybe_skip
def test_assign(self):
def fn(a):
tmp = unet_2d.UNet2DOutput(a + 1)
tmp.sample = a + 2
return tmp
args = [torch.randn(10)]
obj1 = fn(*args)
cnts = torch._dynamo.testing.CompileCounter()
opt_fn = torch._dynamo.optimize_assert(cnts)(fn)
obj2 = opt_fn(*args)
self.assertTrue(same(obj1.sample, obj2.sample))
self.assertEqual(cnts.frame_count, 1)
self.assertEqual(cnts.op_count, 2)
def _common(self, fn, op_count):
args = [
unet_2d.UNet2DOutput(
sample=torch.randn(10),
)
]
obj1 = fn(*args)
cnts = torch._dynamo.testing.CompileCounter()
opt_fn = torch._dynamo.optimize_assert(cnts)(fn)
obj2 = opt_fn(*args)
self.assertTrue(same(obj1, obj2))
self.assertEqual(cnts.frame_count, 1)
self.assertEqual(cnts.op_count, op_count)
@maybe_skip
def test_getattr(self):
def fn(obj: unet_2d.UNet2DOutput):
x = obj.sample * 10
return x
self._common(fn, 1)
@maybe_skip
def test_getitem(self):
def fn(obj: unet_2d.UNet2DOutput):
x = obj["sample"] * 10
return x
self._common(fn, 1)
@maybe_skip
def test_tuple(self):
def fn(obj: unet_2d.UNet2DOutput):
a = obj.to_tuple()
return a[0] * 10
self._common(fn, 1)
@maybe_skip
def test_index(self):
def fn(obj: unet_2d.UNet2DOutput):
return obj[0] * 10
self._common(fn, 1)
if __name__ == "__main__":
from torch._dynamo.test_case import run_tests
run_tests()
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