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# Owner(s): ["module: dynamo"]
from unittest.mock import patch
import torch
import torch._dynamo
import torch._dynamo.test_case
from torch._dynamo.testing import CompileCounter
_variable = 0
_variable_2 = 0
def user_function():
return torch.compiler.is_compiling()
def user_generator():
for _ in range(1):
yield torch.compiler.is_compiling()
return
class MyModule(torch.nn.Module):
def __init__(self, mode: int):
super().__init__()
self.mode = mode
self.register_forward_pre_hook(self.pre_forward, with_kwargs=True)
def pre_forward(self, module, args, kwargs):
if self.mode == 5:
if user_function():
global _variable
_variable += 1
return args, kwargs
def forward(self, x):
global _variable, _variable_2
if self.mode == 1:
if torch.compiler.is_compiling():
_variable += 1
else:
_variable_2 += 1
elif self.mode == 2:
if user_function():
_variable += 1
elif self.mode == 3:
lambda_f = lambda: torch.compiler.is_compiling() # noqa: E731
if lambda_f():
_variable += 1
elif self.mode == 4:
for cond in user_generator():
if cond:
_variable += 1
elif self.mode == 5:
x += 1
elif self.mode == 6:
if user_function():
torch._dynamo.graph_break()
_variable += 1
return x
class SkipNonTensorTests(torch._dynamo.test_case.TestCase):
def test_add_tensor1(self):
def fn(a, b):
return a + b
counter = CompileCounter()
x = torch.randn(4)
y = 5
opt_fn = torch._dynamo.optimize_assert(counter)(fn)
opt_fn(x, y)
assert counter.op_count == 1
def test_add_tensor2(self):
def fn(a, b):
return torch.add(a, b)
counter = CompileCounter()
x = torch.randn(4)
y = 5
opt_fn = torch._dynamo.optimize_assert(counter)(fn)
opt_fn(x, y)
assert counter.op_count == 1
def test_add_tensor_list(self):
def fn(lst):
return lst[0] + lst[1]
counter = CompileCounter()
x = torch.randn(4)
y = 5
opt_fn = torch._dynamo.optimize_assert(counter)(fn)
opt_fn([x, y])
assert counter.op_count == 1
def test_add_tensor_dict(self):
def fn(dt):
return dt["a"] + dt["b"]
counter = CompileCounter()
x = torch.randn(4)
y = 5
opt_fn = torch._dynamo.optimize_assert(counter)(fn)
opt_fn({"a": x, "b": y})
assert counter.op_count == 1
def test_add_skip(self):
def fn(a, b):
return a + b
counter = CompileCounter()
opt_fn = torch._dynamo.optimize_assert(counter)(fn)
x = 4
y = 5
opt_fn(x, y)
assert counter.op_count == 0
@patch.object(torch._dynamo.config, "raise_on_ctx_manager_usage", False)
def test_recursive_list(self):
def fn(x):
return x
counter = CompileCounter()
x = []
x.append(x)
with torch._dynamo.optimize_assert(counter):
fn(x)
assert counter.op_count == 0
@patch.object(torch._dynamo.config, "raise_on_ctx_manager_usage", False)
def test_custom_list(self):
def fn(x):
return x[0] + x[1]
counter = CompileCounter()
class Foo(list):
def __iter__(self):
raise Exception # noqa: TRY002
def __len__(self):
raise Exception # noqa: TRY002
x = Foo()
x.append(torch.randn(4))
x.append(torch.randn(4))
with torch._dynamo.optimize_assert(counter):
fn(x)
assert counter.op_count == 0
def test_do_not_skip_side_effects(self):
# https://github.com/pytorch/pytorch/issues/110765
# By invoking torch.compiler.is_compiling(),
# there may be side-effects inconsistent with eager when
# compiling. Thus we force dynamo to commit the graph,
# even if it does not perform any tensor operation
global _variable, _variable_2
for mode in range(1, 7):
torch._dynamo.reset()
_variable = 0
_variable_2 = 0
mod = MyModule(mode=mode)
model = torch.compile(mod, backend="eager", fullgraph=mode != 6)
assert _variable == 0
assert _variable_2 == 0
model(torch.tensor([1]))
assert _variable == 1
assert _variable_2 == 0
model(torch.tensor([1]))
assert _variable == 2
assert _variable_2 == 0
if __name__ == "__main__":
from torch._dynamo.test_case import run_tests
run_tests()
|
