def test_kwarg_only(self) -> None:
with capture_logs() as logs:
x = LoggingTensor(torch.ones(1))
y = LoggingTensor(torch.ones(1, 1))
z = LoggingTensor(torch.ones(1))
log_input("x", x)
log_input("y", y)
log_input("z", z)
torch.addmv(x, y, z)
torch.addmv(x, y, z, beta=1)
torch.addmv(x, y, z, beta=2)
torch.addmv(x, y, z, alpha=2)
torch.addmv(x, y, z, beta=2, alpha=2)
# The expectation is that beta/alpha don't show up when they're
# defaulted. This is even if the user explicitly specified it.
self.assertExpectedInline('\n'.join(logs), '''\
$0 = input('x')
$1 = input('y')
$2 = input('z')
$3 = torch._ops.aten.addmv($0, $1, $2)
$4 = torch._ops.aten.addmv($0, $1, $2)
$5 = torch._ops.aten.addmv($0, $1, $2, beta=2)
$6 = torch._ops.aten.addmv($0, $1, $2, alpha=2)
$7 = torch._ops.aten.addmv($0, $1, $2, beta=2, alpha=2)''')
def test_basic(self) -> None:
with capture_logs() as logs:
x = LoggingTensor(torch.tensor([3.0], requires_grad=True))
log_input("x", x)
y = x * x
saved_x = y.grad_fn._saved_self
grad_y = LoggingTensor(torch.tensor([1.0]))
log_input("grad_y", grad_y)
g, = torch.autograd.grad((y,), (x,), (grad_y,))
self.assertEqual(g.elem, torch.tensor([6.0]))
with torch.no_grad():
self.assertEqual(saved_x, x)
self.assertEqual(saved_x._version, x._version)
x.add_(2)
self.assertEqual(saved_x, x)
# TODO: figure out why broken
# self.assertEqual(saved_x._version, x._version)
self.assertExpectedInline('\n'.join(logs), '''\
$0 = input('x')
$1 = torch._ops.aten.mul($0, $0)
$2 = input('grad_y')
$3 = torch._ops.aten.mul($2, $0)
$4 = torch._ops.aten.mul($2, $0)
$5 = torch._ops.aten.add($4, $3)''')
def test_version(self) -> None:
x = LoggingTensor(torch.ones(1))
prev_vc = x._version
x.detach().add_(2)
cur_vc = x._version
self.assertNotEqual(prev_vc, cur_vc)
x.data.add_(2)
self.assertEqual(cur_vc, x._version)
def test_detach_appears_twice_when_called_once(self) -> None:
with capture_logs() as logs:
x = LoggingTensor(torch.tensor([3.0]), requires_grad=True)
log_input("x", x)
x.detach()
# FIXME: We actually want this to emit a single detach. However,
# it currently emits two, for reasons unclear to us. Leaving
# this test here to make sure we don't regress even further (it
# would be bad if calling .detach() once emits 3+ detaches).
self.assertExpectedInline('\n'.join(logs), '''\
$0 = input('x')
$1 = torch._ops.aten.detach.default($0)
$2 = torch._ops.aten.detach.default($1)''')
def test_out(self) -> None:
with capture_logs() as logs:
x = LoggingTensor(torch.ones(1))
y = LoggingTensor(torch.zeros(1))
log_input("x", x)
log_input("y", y)
torch.abs(x, out=y)
self.assertEqual(y.elem, torch.ones(1))
# TODO: arguably this shouldn't pass and we should complain
# that out isn't a kwarg
self.assertExpectedInline('\n'.join(logs), '''\
$0 = input('x')
$1 = input('y')
$2 = torch._ops.aten.abs($0, out=$1)''')
def test_format(self) -> None:
x = LoggingTensor(torch.ones(1))
s1 = str(x)
s2 = repr(x)
s3 = f"{x}"
self.assertExpectedInline(s1, """LoggingTensor(tensor([1.]))""")
self.assertEqual(s1, s2)
self.assertEqual(s1, s3)
def test_subclass_creation(self):
# Make sure these statements runs without error
# In particular checking that when internal detach returns
# subclasses, these are cleanly overwritten.
class Foo(torch.Tensor):
pass
err_msg = "subclass Foo but.*already associated to a python object of type LoggingTensor"
with self.assertRaisesRegex(RuntimeError, err_msg):
a = torch.Tensor._make_subclass(Foo, LoggingTensor(torch.rand(2)))
with self.assertRaisesRegex(RuntimeError, err_msg):
b = LoggingTensor(torch.rand(2)).as_subclass(Foo)
with self.assertRaisesRegex(RuntimeError, err_msg):
Foo(LoggingTensor(torch.rand(2)))
with self.assertRaisesRegex(TypeError, "Foo must define __torch_dispatch__"):
torch.Tensor._make_wrapper_subclass(Foo, (2, 2))
def test_enable_python_mode_error(self) -> None:
with self.assertRaisesRegex(ValueError, "__torch_dispatch__"):
with enable_python_mode(torch.Tensor):
pass
z = LoggingTensor(torch.empty([]))
with self.assertRaisesRegex(ValueError, "must be the type"):
with enable_python_mode(z):
pass
def test_mixed_wrappers_valid(self):
def f(x, y):
z = x + y
z.add_(1)
return z
x1_not_functional = LoggingTensor(torch.ones(4))
x2_functional = torch._to_functional_tensor(LoggingTensor(torch.ones(4)))
with capture_logs() as logs:
y = f(x1_not_functional, x2_functional)
# Make sure that functionalization ran the "+" kernel
# with a functional + non-functional tensor, and wrapped the output appropriately.
self.assertExpectedInline('\n'.join(logs), """\
$2 = torch._ops.aten.add.Tensor($0, $1)
$3 = torch._ops.aten.add.Tensor($2, 1)""")
def test_enable_torch_dispatch_mode_error(self) -> None:
z = LoggingTensor(torch.empty([]))
with self.assertRaisesRegex(
ValueError,
"expected to get TorchDispatchMode, Tensor-like class, or None"
):
with enable_torch_dispatch_mode(z):
pass
def test_multiple_levels_of_wrapping(self):
def f(x):
# call an inplace op and have it get logged twice (by the outer + inner wrapper)
x.add_(1)
# Test 1: both the inner and outer wrapper are "functionalized"
x_inner_and_outer_functional = torch._to_functional_tensor(
InplaceLoggingTensor(
torch._to_functional_tensor(LoggingTensor(torch.ones(4)))))
with capture_logs() as logs:
f(x_inner_and_outer_functional)
# Since both wrappers were unctionalized, they both log "add"
self.assertExpectedInline(
'\n'.join(logs), """\
$1 = torch._ops.aten.add.Tensor($0, 1)
$3 = torch._ops.aten.add.Tensor($2, 1)""")
# Test 2: only the inner wrapper is "functionalized"
x_only_inner_functional = InplaceLoggingTensor(
torch._to_functional_tensor(LoggingTensor(torch.ones(4))))
with capture_logs() as logs:
f(x_only_inner_functional)
# Since only the inner wrapper is functionalized, then the inner (first) log is functionalized
self.assertExpectedInline(
'\n'.join(logs), """\
$1 = torch._ops.aten.add.Tensor($0, 1)
$3 = torch._ops.aten.add_.Tensor($2, 1)""")
# Test 3: only the inner wrapper is "functionalized"
x_only_outer_functional = torch._to_functional_tensor(
InplaceLoggingTensor(LoggingTensor(torch.ones(4))))
with capture_logs() as logs:
f(x_only_outer_functional)
# Only the outer add_ is functionalized
# Since only the outer wrapper is functionalized, then the outer (second) log is functionalized
self.assertExpectedInline(
'\n'.join(logs), """\
$1 = torch._ops.aten.add_.Tensor($0, 1)
$3 = torch._ops.aten.add.Tensor($2, 1)""")
def test_wrapper_subclass_serializes(self) -> None:
with tempfile.TemporaryFile() as f:
x = LoggingTensor(torch.randn(3))
torch.save(x, f)
f.seek(0)
x_loaded = torch.load(f)
self.assertTrue(type(x_loaded) is type(x))
self.assertEqual(x.elem, x_loaded.elem)
self.assertFalse(x is x_loaded)
def test_torch_ops(self):
r = make_tensor((2,), device='cpu', dtype=torch.float)
self.assertEqual(torch.ops.prims.sin(r), torch.sin(r))
r = LoggingTensor(r)
with capture_logs() as logs:
log_input("input", r)
prims.sin(r)
self.assertExpectedInline('\n'.join(logs), """\
$0 = input('input')
$1 = torch._ops.prims.sin.default($0)""")
def test_kwarg_only_and_positional_default(self) -> None:
with capture_logs() as logs:
x = LoggingTensor(torch.ones(1))
y = LoggingTensor(torch.ones(1))
log_input("x", x)
log_input("y", y)
torch.ops.aten.kl_div(x, y)
torch.ops.aten.kl_div(x, y, 2)
torch.ops.aten.kl_div(x, y, log_target=True)
torch.ops.aten.kl_div(x, y, 2, log_target=True)
# What we are testing here is that we omit reduction
# if it is defaulted, even if a kwarg is set
self.assertExpectedInline('\n'.join(logs), '''\
$0 = input('x')
$1 = input('y')
$2 = torch._ops.aten.kl_div($0, $1)
$3 = torch._ops.aten.kl_div($0, $1, 2)
$4 = torch._ops.aten.kl_div($0, $1, log_target=True)
$5 = torch._ops.aten.kl_div($0, $1, 2, log_target=True)''')
def get_logs(self, func, inpt):
input_clone_logging = LoggingTensor(inpt.clone())
input_functional_logging = torch._to_functional_tensor(input_clone_logging)
with capture_logs() as logs:
log_input("input", input_clone_logging)
torch._enable_functionalization()
try:
func(input_functional_logging)
finally:
torch._disable_functionalization()
return logs
def test_mixed_wrappers_valid(self):
def f(x, y):
z = x + y
z.add_(1)
return z
x1_not_functional = LoggingTensor(torch.ones(4))
x2_functional = torch._to_functional_tensor(
LoggingTensor(torch.ones(4)))
with capture_logs() as logs:
y = f(x1_not_functional, x2_functional)
# I think the alias trace is coming from the fact that x2 is technically *not*
# a LoggingTensor (instead it *contains* a LoggingTensor), but x1 *is* a LoggingTensor.
# The important thing here though is that functionalization ran the "+" kernel
# with a functional + non-functional tensor, and wrapped the output appropriately.
self.assertExpectedInline(
'\n'.join(logs), """\
$2 = torch._ops.aten.add.Tensor($0, $1)
$3 = torch._ops.aten.alias.default($2)
$4 = torch._ops.aten.add.Tensor($3, tensor(1))""")
def test_tolist_numpy_with_python_mode(self) -> None:
x = LoggingTensor(torch.tensor([2.0, 3.0]))
with self.assertRaisesRegex(RuntimeError, "is not supported for tensor subclasses."):
x.tolist()
with self.assertRaisesRegex(RuntimeError, "is not supported for tensor subclasses."):
x.numpy()
with self.assertRaises(AssertionError):
self.assertEqual(x, None)
def test_nesting_with_same_enable_torch_dispatch_mode(self) -> None:
# "nested" enable_torch_dispatch_modes are allowed if they're the same mode. It's the equivalent of
# a noop, so it will only write once to the log
with capture_logs() as logs:
x = LoggingTensor(torch.tensor([3.]))
log_input("x", x)
with enable_torch_dispatch_mode(LoggingTensor):
with enable_torch_dispatch_mode(LoggingTensor):
x + x
self.assertExpectedInline(
'\n'.join(logs), '''\
$0 = input('x')
$1 = torch._ops.aten.add.Tensor($0, $0)''')
def test_custom_autograd(self) -> None:
escape = [None]
class Square(torch.autograd.Function):
@staticmethod
def forward(ctx, x):
y = x**2
ctx.save_for_backward(x)
return y
@staticmethod
def backward(ctx, grad_output):
assert isinstance(grad_output, LoggingTensor)
x, = ctx.saved_tensors
assert isinstance(x, LoggingTensor)
escape[0] = x
return grad_output * 2 * x
with capture_logs() as logs:
x = LoggingTensor(torch.ones(1), requires_grad=True)
log_input("x", x)
x.grad = LoggingTensor(torch.zeros(1))
log_input("x.grad", x.grad)
y = Square.apply(x)
grad_output = LoggingTensor(torch.ones(1))
log_input("grad_output", grad_output)
y.backward(grad_output)
with torch.no_grad():
self.assertEqual(escape[0], x)
self.assertEqual(escape[0]._version, x._version)
# TODO: figure out why x.requires_grad = False doesn't
# trigger an error for LoggingTensor
x.add_(2)
self.assertEqual(escape[0], x)
# TODO: figure out why this is broken
# self.assertEqual(escape[0]._version, x._version)
self.assertExpectedInline(
'\n'.join(logs), '''\
$0 = input('x')
$1 = input('x.grad')
$2 = torch._ops.aten.pow.Tensor_Scalar($0, 2)
$3 = input('grad_output')
$4 = torch._ops.aten.mul.Tensor($3, 2)
$5 = torch._ops.aten.mul.Tensor($4, $0)
$6 = torch._ops.aten.add_.Tensor($1, $5)''')
def test_autograd_in_attr(self):
# We want the wrapped Tensor to require gradients!
true_t = torch.rand(2, requires_grad=True)
t = LoggingTensor(true_t)
out = t + 2
self.assertFalse(out.requires_grad)
self.assertIsNone(out.grad_fn)
self.assertTrue(out.elem.requires_grad)
self.assertIsNotNone(out.elem.grad_fn)
with self.assertRaisesRegex(RuntimeError, "does not require grad"):
out.sum().backward()
out.elem.sum().backward()
self.assertIsNone(t.grad)
self.assertIsNotNone(t.elem.grad)
def test_storage_can_be_converted_to_python_object(self):
with enable_python_mode(LoggingTensor):
s = torch.Storage()
z = LoggingTensor(torch.empty([]))
z.set_(s)
class SubclassInfo:
__slots__ = ['name', 'create_fn', 'closed_under_ops']
def __init__(self, name, create_fn, closed_under_ops=True):
self.name = name
self.create_fn = create_fn # create_fn(shape) -> tensor instance
self.closed_under_ops = closed_under_ops
subclass_db = {
torch.Tensor:
SubclassInfo('base_tensor', create_fn=lambda shape: torch.randn(shape)),
NonWrapperTensor:
SubclassInfo('non_wrapper_tensor',
create_fn=lambda shape: NonWrapperTensor(torch.randn(shape))),
LoggingTensor:
SubclassInfo('logging_tensor',
create_fn=lambda shape: LoggingTensor(torch.randn(shape))),
SparseTensor:
SubclassInfo('sparse_tensor',
create_fn=lambda shape: SparseTensor.from_dense(
torch.randn(shape).relu())),
DiagTensorBelow:
SubclassInfo(
'diag_tensor_below',
create_fn=lambda shape: DiagTensorBelow(torch.randn(shape)),
closed_under_ops=False # sparse semantics
),
}
def test_deepcopy_wrapper_subclass(self) -> None:
x = LoggingTensor(torch.randn(3))
x_copy = deepcopy(x)
self.assertTrue(type(x_copy) is type(x))
self.assertEqual(x.elem, x_copy.elem)
self.assertFalse(x is x_copy)
def test_storage(self) -> None:
# For now, just make sure it doesn't crash. Ideally, we should
# return some virtual storage that is safe to work with
x = LoggingTensor(torch.ones(1))
self.assertRaises(RuntimeError, lambda: x.storage())
def test_metadata_change_not_allowed(self) -> None:
x = LoggingTensor(torch.ones(1))
y = x.data
self.assertIsInstance(y, LoggingTensor)
self.assertRaises(RuntimeError, lambda: y.resize_(4))
def test_save_for_backwards_segfault(self):
inp = torch._to_functional_tensor(LoggingTensor(torch.randn(2, 2))).requires_grad_(True)
inp.exp()
