blob: b205c6330edff59a47879994737bcae380140066 [file] [log] [blame]
# Owner(s): ["module: dynamo"]
import torch
import torch._dynamo.test_case
import torch._dynamo.testing
import torch._dynamo.utils
from torch._dynamo.utils import dynamo_timed
class DynamoProfilerTests(torch._dynamo.test_case.TestCase):
def test_dynamo_timed_profiling_isolated(self):
# @dynamo_timed functions should appear in profile traces.
@dynamo_timed
def inner_fn(x):
return x.sin()
def outer_fn(x, y):
return inner_fn(x) * y
x, y = [torch.rand((2, 2)) for _ in range(2)]
with torch.profiler.profile(with_stack=False) as prof:
outer_fn(x, y)
self.assertTrue(
any("inner_fn (dynamo_timed)" in evt.name for evt in prof.events())
)
def test_dynamo_timed_profiling_backend_compile(self):
# @dynamo_timed functions should appear in profile traces.
# this checks whether these actually appear in actual dynamo execution.
# "backend_compile" is just chosen as an example; if it gets renamed
# this test can be replaced or deleted
fn_name = "call_user_compiler"
def fn(x, y):
return x.sin() * y.cos()
x, y = [torch.rand((2, 2)) for _ in range(2)]
with torch.profiler.profile(with_stack=False) as prof:
torch._dynamo.optimize("aot_eager")(fn)(x, y)
self.assertTrue(
any(f"{fn_name} (dynamo_timed)" in evt.name for evt in prof.events())
)
if __name__ == "__main__":
from torch._dynamo.test_case import run_tests
run_tests()