mirror of
https://github.com/pytorch/pytorch.git
synced 2025-10-20 21:14:14 +08:00
9edee09ed6
Pull Request resolved: https://github.com/pytorch/pytorch/pull/76319 Approved by: https://github.com/ezyang
561 lines
22 KiB
Python
561 lines
22 KiB
Python
# Owner(s): ["module: codegen"]
|
|
|
|
import torch
|
|
from torch.testing._internal.common_utils import TestCase, run_tests
|
|
from torch.testing._internal.logging_tensor import LoggingTensor, LoggingTensorReentrant, capture_logs, log_input
|
|
from torch.utils._pytree import tree_map
|
|
|
|
import logging
|
|
|
|
def are_aliased(x, y):
|
|
if x._base is None and y._base is None:
|
|
return False
|
|
if x._base is not None and y._base is None:
|
|
return x._base is y
|
|
if x._base is None and y._base is not None:
|
|
return y._base is x
|
|
return x._base is y._base
|
|
|
|
# Just for testing: a logging tensor that also transforms out-of-place ops into inplace ops.
|
|
# That way even if the outer wrapper is functionalized, the inner wrapper will also need functionalization.
|
|
class InplaceLoggingTensor(LoggingTensorReentrant):
|
|
@staticmethod
|
|
def __new__(cls, e):
|
|
r = torch.Tensor._make_wrapper_subclass(cls, e.shape, dtype=e.dtype, requires_grad=False)
|
|
r.elem = e
|
|
return r
|
|
|
|
__torch_function__ = torch._C._disabled_torch_function_impl
|
|
|
|
def __str__(self):
|
|
return f'InplaceLoggingTensor({self.elem})'
|
|
|
|
@classmethod
|
|
def __torch_dispatch__(cls, func, types, args=(), kwargs=None):
|
|
def unwrap(e):
|
|
if isinstance(e, InplaceLoggingTensor):
|
|
return e.elem
|
|
else:
|
|
return e
|
|
|
|
def wrap(e):
|
|
if isinstance(e, torch.Tensor):
|
|
return InplaceLoggingTensor(e)
|
|
else:
|
|
return e
|
|
f = func
|
|
# this subclass converts all `add()` ops into `add_()` ops
|
|
if f is torch.ops.aten.add.Tensor:
|
|
f = torch.ops.aten.add_.Tensor
|
|
|
|
with cls.context():
|
|
rs = tree_map(wrap, f(*tree_map(unwrap, args), **tree_map(unwrap, kwargs)))
|
|
# after running the (potentially transformed) op,
|
|
# log the original op that we saw.
|
|
logging.getLogger("LoggingTensor").info(f"{func.__module__}.{func.__name__}", args, kwargs, rs)
|
|
return rs
|
|
|
|
|
|
|
|
class TestFunctionalization(TestCase):
|
|
|
|
def get_logs(self, func, inpt, *, reapply_views=False):
|
|
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(reapply_views=reapply_views)
|
|
try:
|
|
func(input_functional_logging)
|
|
finally:
|
|
torch._disable_functionalization()
|
|
return logs
|
|
|
|
def assert_functionalization(self, func, inpt, *, reapply_views=False):
|
|
input_clone = inpt.clone()
|
|
input_clone2 = inpt.clone()
|
|
input_functional = torch._to_functional_tensor(input_clone2)
|
|
|
|
# Compare outputs (and mutated inputs), with and without functionalization.
|
|
out_ref = func(inpt)
|
|
|
|
torch._enable_functionalization(reapply_views=reapply_views)
|
|
try:
|
|
out_functional = func(input_functional)
|
|
finally:
|
|
torch._disable_functionalization()
|
|
|
|
# We need to sync the input tensors first, in case there are any queued mutations left.
|
|
torch._sync(input_functional)
|
|
torch._sync(out_functional)
|
|
self.assertEqual(out_ref, torch._from_functional_tensor(out_functional))
|
|
self.assertEqual(inpt, torch._from_functional_tensor(input_functional)) # input mutations should still occur
|
|
|
|
def test_simple(self):
|
|
def f(x):
|
|
# simple test: 1 view op, 1 inplace op
|
|
tmp = torch.ones(4, 2)
|
|
y = x.view(4, 2)
|
|
y.add_(tmp)
|
|
z = x * x
|
|
return y
|
|
self.assert_functionalization(f, torch.ones(4, 2))
|
|
logs = self.get_logs(f, torch.ones(4, 2))
|
|
self.assertExpectedInline('\n'.join(logs), """\
|
|
$0 = input('input')
|
|
$1 = torch._ops.aten.view_copy.default($0, [4, 2])
|
|
$2 = torch._ops.aten.add.Tensor($1, tensor([[1., 1.],
|
|
[1., 1.],
|
|
[1., 1.],
|
|
[1., 1.]]))
|
|
$3 = torch._ops.aten.view_copy.default($2, [4, 2])
|
|
$4 = torch._ops.aten.mul.Tensor($3, $3)""")
|
|
|
|
def test_simple_out(self):
|
|
def f(x):
|
|
tmp = torch.ones(4, 2)
|
|
y = x.view(4, 2)
|
|
# the out= tensor will get resized, since it has size=0 to start.
|
|
z = torch.empty(())
|
|
torch.add(y, tmp, out=z)
|
|
w = z * z
|
|
return w
|
|
self.assert_functionalization(f, torch.ones(4, 2))
|
|
logs = self.get_logs(f, torch.ones(4, 2))
|
|
self.assertExpectedInline('\n'.join(logs), """\
|
|
$0 = input('input')
|
|
$1 = torch._ops.aten.view_copy.default($0, [4, 2])
|
|
$2 = torch._ops.aten.add.Tensor($1, tensor([[1., 1.],
|
|
[1., 1.],
|
|
[1., 1.],
|
|
[1., 1.]]))
|
|
$3 = torch._ops.aten.mul.Tensor($2, $2)""")
|
|
|
|
def test_multi_out(self):
|
|
def f(x):
|
|
# aminmax.out returns a tuple of tensors.
|
|
# functionalization should properly handle the tuple.
|
|
out_min = torch.empty(4)
|
|
out_max = torch.empty(4)
|
|
torch.aminmax(x, dim=0, out=(out_max, out_min))
|
|
return out_max
|
|
self.assert_functionalization(f, torch.arange(8, dtype=torch.float32))
|
|
logs = self.get_logs(f, torch.arange(8, dtype=torch.float32))
|
|
self.assertExpectedInline('\n'.join(logs), """\
|
|
$0 = input('input')
|
|
$1, $2 = torch._ops.aten.aminmax.default($0, dim=0)""")
|
|
|
|
def test_tensor_ctr(self):
|
|
def f(x):
|
|
y = torch.tensor((1, 2, 3))
|
|
z = y.view(-1)
|
|
z.add_(1)
|
|
return y
|
|
self.assert_functionalization(f, torch.arange(3, dtype=torch.float32))
|
|
logs = self.get_logs(f, torch.arange(3, dtype=torch.float32))
|
|
self.assertExpectedInline('\n'.join(logs), """$0 = input('input')""")
|
|
|
|
def test_inplace_on_non_view(self):
|
|
def f(x):
|
|
# test for the case where we functionalize an inplace op on the other tensor - not a view.
|
|
# This is worth checking because the tensor will have an empty ViewMeta stack, which needs to be special cased.
|
|
tmp = torch.ones(4, 2)
|
|
y = x.view(4, 2)
|
|
x.add_(tmp)
|
|
return y
|
|
self.assert_functionalization(f, torch.ones(4, 2))
|
|
logs = self.get_logs(f, torch.ones(4, 2))
|
|
self.assertExpectedInline('\n'.join(logs), """\
|
|
$0 = input('input')
|
|
$1 = torch._ops.aten.view_copy.default($0, [4, 2])
|
|
$2 = torch._ops.aten.add.Tensor($0, tensor([[1., 1.],
|
|
[1., 1.],
|
|
[1., 1.],
|
|
[1., 1.]]))""")
|
|
|
|
def test_tensor_list_composite(self):
|
|
def f(x):
|
|
# Test an op with TensorList input
|
|
y = torch.block_diag(x, x)
|
|
return y
|
|
self.assert_functionalization(f, torch.ones(2, 2))
|
|
logs = self.get_logs(f, torch.ones(2, 2))
|
|
self.assertExpectedInline('\n'.join(logs), """\
|
|
$0 = input('input')
|
|
$1 = torch._ops.aten.expand_copy.default($0, [2, 2])
|
|
$2 = torch._ops.aten.slice_scatter.default(tensor([[0., 0., 0., 0.],
|
|
[0., 0., 0., 0.]]), $1, 1, 0, 2)
|
|
$3 = torch._ops.aten.slice_scatter.default(tensor([[0., 0., 0., 0.],
|
|
[0., 0., 0., 0.],
|
|
[0., 0., 0., 0.],
|
|
[0., 0., 0., 0.]]), $2, 0, 0, 2)
|
|
$4 = torch._ops.aten.slice_copy.Tensor($3, 0, 2, 4)
|
|
$5 = torch._ops.aten.slice_copy.Tensor($4, 1, 2, 4)
|
|
$6 = torch._ops.aten.expand_copy.default($0, [2, 2])""")
|
|
|
|
def test_cat(self):
|
|
def f(x):
|
|
out = torch.empty(0)
|
|
torch.cat((x,), out=out)
|
|
return out
|
|
self.assert_functionalization(f, torch.ones(2, 2))
|
|
logs = self.get_logs(f, torch.ones(2, 2))
|
|
self.assertExpectedInline('\n'.join(logs), """\
|
|
$0 = input('input')
|
|
$1 = torch._ops.aten.cat.default([LoggingTensor(tensor([[1., 1.],
|
|
[1., 1.]]))])""")
|
|
|
|
def test_diagonal(self):
|
|
def f(x):
|
|
# test: view ops that take a subset of the original tensor (select/diagonal)
|
|
tmp = torch.ones(2)
|
|
y = x.diagonal()
|
|
y.add_(tmp)
|
|
z = x * x
|
|
return z
|
|
self.assert_functionalization(f, torch.ones(2, 2))
|
|
logs = self.get_logs(f, torch.ones(2, 2))
|
|
self.assertExpectedInline('\n'.join(logs), """\
|
|
$0 = input('input')
|
|
$1 = torch._ops.aten.diagonal_copy.default($0)
|
|
$2 = torch._ops.aten.add.Tensor($1, tensor([1., 1.]))
|
|
$3 = torch._ops.aten.diagonal_scatter.default($0, $2)
|
|
$4 = torch._ops.aten.mul.Tensor($3, $3)""")
|
|
|
|
def test_diagonal_mutated_input(self):
|
|
def f(x):
|
|
# simple test: there are pending updates afterwards, which the test syncs manually
|
|
tmp = torch.ones(2)
|
|
y = x.diagonal()
|
|
y.add_(tmp)
|
|
return x
|
|
x = torch.ones(2, 2)
|
|
self.assert_functionalization(f, x)
|
|
|
|
def test_split(self):
|
|
def f(x):
|
|
# test: view ops that return multiple tensors (split)
|
|
tmp = torch.ones(2)
|
|
y1, y2 = x.split(2)
|
|
y3 = y2.diagonal()
|
|
y3.add_(tmp)
|
|
z = x * x
|
|
return y3
|
|
self.assert_functionalization(f, torch.ones(4, 2))
|
|
logs = self.get_logs(f, torch.ones(4, 2))
|
|
self.assertExpectedInline('\n'.join(logs), """\
|
|
$0 = input('input')
|
|
$1, $2 = torch._ops.aten.split_copy.Tensor($0, 2)
|
|
$3 = torch._ops.aten.diagonal_copy.default($2)
|
|
$4 = torch._ops.aten.add.Tensor($3, tensor([1., 1.]))
|
|
$5, $6 = torch._ops.aten.split_copy.Tensor($0, 2)
|
|
$7 = torch._ops.aten.diagonal_scatter.default($6, $4)
|
|
$8 = torch._ops.aten.slice_scatter.default($0, $7, 0, 2, 4)
|
|
$9 = torch._ops.aten.mul.Tensor($8, $8)""")
|
|
|
|
def test_view_inplace(self):
|
|
def f(x):
|
|
# test: view + inplace op (transpose_)
|
|
tmp = torch.ones(4)
|
|
x.transpose_(1, 0)
|
|
y = x[0]
|
|
y.add_(tmp)
|
|
return y
|
|
self.assert_functionalization(f, torch.ones(4, 2))
|
|
logs = self.get_logs(f, torch.ones(4, 2))
|
|
self.assertExpectedInline('\n'.join(logs), """\
|
|
$0 = input('input')
|
|
$1 = torch._ops.aten.transpose_copy.int($0, 1, 0)
|
|
$2 = torch._ops.aten.select_copy.int($1, 0, 0)
|
|
$3 = torch._ops.aten.add.Tensor($2, tensor([1., 1., 1., 1.]))""")
|
|
|
|
def test_optional_tensor_list(self):
|
|
def f(x):
|
|
# test: an operator that takes in a List[Optional[Tensor]] argument
|
|
# (index_put)
|
|
y = x.view(8)
|
|
indices = torch.arange(4)
|
|
values = torch.arange(4, dtype=y.dtype)
|
|
y.index_put_((indices,), values, accumulate=False)
|
|
return y
|
|
self.assert_functionalization(f, torch.ones(4, 2))
|
|
logs = self.get_logs(f, torch.ones(4, 2))
|
|
self.assertExpectedInline('\n'.join(logs), """\
|
|
$0 = input('input')
|
|
$1 = torch._ops.aten.view_copy.default($0, [8])
|
|
$2 = torch._ops.aten.index_put.default($1, [tensor([0, 1, 2, 3])], tensor([0., 1., 2., 3.]))""")
|
|
|
|
def test_scalars(self):
|
|
def f(x):
|
|
# test: the pass can handle scalar inputs properly
|
|
tmp = torch.ones(4, 2)
|
|
y = x.view(4, 2)
|
|
y.add_(1)
|
|
z = 2 * y
|
|
z.div_(1)
|
|
return z
|
|
self.assert_functionalization(f, torch.ones(4, 2))
|
|
logs = self.get_logs(f, torch.ones(4, 2))
|
|
self.assertExpectedInline('\n'.join(logs), """\
|
|
$0 = input('input')
|
|
$1 = torch._ops.aten.view_copy.default($0, [4, 2])
|
|
$2 = torch._ops.aten.add.Tensor($1, 1)
|
|
$3 = torch._ops.aten.mul.Tensor($2, 2)
|
|
$4 = torch._ops.aten.div.Tensor($3, 1)""")
|
|
|
|
def test_only_one_view(self):
|
|
def f(x):
|
|
# This tests that we don't have any unnecessary views in the trace.
|
|
# If the input wasn't mutated, we don't need to regenerate it,
|
|
# so there should be a total of 1 op in the output trace.
|
|
return x.view(4, 2)
|
|
logs = self.get_logs(f, torch.ones(4, 2))
|
|
self.assertExpectedInline('\n'.join(logs), """\
|
|
$0 = input('input')
|
|
$1 = torch._ops.aten.view_copy.default($0, [4, 2])""")
|
|
|
|
def test_everything(self):
|
|
def f(x):
|
|
# test: everything
|
|
tmp = torch.ones(2, 2)
|
|
x2 = x + x
|
|
y = x2.view(8)
|
|
z0 = y.reshape(2, 4)
|
|
z1 = z0.transpose(1, 0)
|
|
z1.unsqueeze_(0)
|
|
z1.squeeze_()
|
|
z2, z3 = z1.split(2)
|
|
z2.add_(tmp)
|
|
z4 = z0[0] + z2.reshape(4)
|
|
return z2
|
|
self.assert_functionalization(f, torch.ones(4, 2))
|
|
logs = self.get_logs(f, torch.ones(4, 2))
|
|
self.assertExpectedInline('\n'.join(logs), """\
|
|
$0 = input('input')
|
|
$1 = torch._ops.aten.add.Tensor($0, $0)
|
|
$2 = torch._ops.aten.view_copy.default($1, [8])
|
|
$3 = torch._ops.aten._reshape_alias_copy.default($2, [2, 4], [4, 1])
|
|
$4 = torch._ops.aten.transpose_copy.int($3, 1, 0)
|
|
$5 = torch._ops.aten.view_copy.default($1, [8])
|
|
$6 = torch._ops.aten._reshape_alias_copy.default($5, [2, 4], [4, 1])
|
|
$7 = torch._ops.aten.transpose_copy.int($6, 1, 0)
|
|
$8 = torch._ops.aten.unsqueeze_copy.default($7, 0)
|
|
$9 = torch._ops.aten.view_copy.default($1, [8])
|
|
$10 = torch._ops.aten._reshape_alias_copy.default($9, [2, 4], [4, 1])
|
|
$11 = torch._ops.aten.transpose_copy.int($10, 1, 0)
|
|
$12 = torch._ops.aten.unsqueeze_copy.default($11, 0)
|
|
$13 = torch._ops.aten.squeeze_copy.default($12)
|
|
$14, $15 = torch._ops.aten.split_copy.Tensor($13, 2)
|
|
$16 = torch._ops.aten.add.Tensor($14, tensor([[1., 1.],
|
|
[1., 1.]]))
|
|
$17 = torch._ops.aten.select_copy.int($3, 0, 0)
|
|
$18 = torch._ops.aten.clone.default($16, memory_format=0)
|
|
$19 = torch._ops.aten._unsafe_view.default($18, [4])
|
|
$20 = torch._ops.aten.view_copy.default($1, [8])
|
|
$21 = torch._ops.aten._reshape_alias_copy.default($20, [2, 4], [4, 1])
|
|
$22 = torch._ops.aten.transpose_copy.int($21, 1, 0)
|
|
$23 = torch._ops.aten.unsqueeze_copy.default($22, 0)
|
|
$24 = torch._ops.aten.squeeze_copy.default($23)
|
|
$25 = torch._ops.aten.slice_scatter.default($24, $16, 0, 0, 2)
|
|
$26 = torch._ops.aten.unsqueeze_copy.default($25, 0)
|
|
$27 = torch._ops.aten.squeeze_copy.dim($26, 0)
|
|
$28 = torch._ops.aten.transpose_copy.int($27, 1, 0)
|
|
$29 = torch._ops.aten._reshape_alias_copy.default($28, [8], [1])
|
|
$30 = torch._ops.aten.view_copy.default($29, [4, 2])
|
|
$31 = torch._ops.aten.view_copy.default($30, [8])
|
|
$32 = torch._ops.aten._reshape_alias_copy.default($31, [2, 4], [4, 1])
|
|
$33 = torch._ops.aten.select_copy.int($32, 0, 0)
|
|
$34 = torch._ops.aten.add.Tensor($33, $19)""")
|
|
|
|
def test_reapply_views_simple(self):
|
|
def f(x):
|
|
tmp = torch.ones(4, 2)
|
|
y = x.view(4, 2)
|
|
y.add_(tmp)
|
|
z = x * x
|
|
return y
|
|
self.assert_functionalization(f, torch.ones(4, 2), reapply_views=True)
|
|
logs = self.get_logs(f, torch.ones(4, 2), reapply_views=True)
|
|
self.assertExpectedInline('\n'.join(logs), """\
|
|
$0 = input('input')
|
|
$1 = torch._ops.aten.view.default($0, [4, 2])
|
|
$2 = torch._ops.aten.add.Tensor($1, tensor([[1., 1.],
|
|
[1., 1.],
|
|
[1., 1.],
|
|
[1., 1.]]))
|
|
$3 = torch._ops.aten.view.default($2, [4, 2])
|
|
$4 = torch._ops.aten.mul.Tensor($3, $3)""")
|
|
|
|
def test_aliases_maintained_after_pass_when_reapplying_views(self):
|
|
def f(x):
|
|
tmp = torch.ones(4, 2)
|
|
y = x.view(4, 2)
|
|
z = x.view(4, 2)
|
|
y.add_(tmp)
|
|
return y, z
|
|
|
|
input_functional = torch._to_functional_tensor(torch.ones(4, 2))
|
|
torch._enable_functionalization(reapply_views=True)
|
|
try:
|
|
y, z = f(input_functional)
|
|
torch._sync(y)
|
|
torch._sync(z)
|
|
finally:
|
|
torch._disable_functionalization()
|
|
|
|
# y and z are aliases inside of the function, and that aliasing relationship should be maintained.
|
|
_y = torch._from_functional_tensor(y)
|
|
_z = torch._from_functional_tensor(z)
|
|
self.assertTrue(are_aliased(_y, _z))
|
|
|
|
# copy_() gets its own test, because it is special cased in functionalization.
|
|
# self.copy_(src) decomposes into src.to(self).expand_as(self).
|
|
def test_copy_(self):
|
|
def f(x):
|
|
tmp = torch.zeros(2, 2)
|
|
# NOTE: LoggingTensor isn't a mode, which means that the diagonal call
|
|
# will not be logged. This is fine for testing.
|
|
tmp_slice = tmp.diagonal()
|
|
y = tmp_slice.copy_(x)
|
|
z = y.add_(x)
|
|
return z
|
|
|
|
# Test 1: copy_() with same dtype and shape
|
|
# to() is a composite op that noops when the dtype/shape match, so nothing gets logged.
|
|
self.assert_functionalization(f, torch.ones(2))
|
|
logs = self.get_logs(f, torch.ones(2))
|
|
self.assertExpectedInline('\n'.join(logs), """\
|
|
$0 = input('input')
|
|
$1 = torch._ops.aten.expand_copy.default($0, [2])
|
|
$2 = torch._ops.aten.add.Tensor($1, $0)""")
|
|
|
|
# Test 2: copy_() with same dtype, different shape
|
|
self.assert_functionalization(f, torch.ones(1))
|
|
logs = self.get_logs(f, torch.ones(1))
|
|
self.assertExpectedInline('\n'.join(logs), """\
|
|
$0 = input('input')
|
|
$1 = torch._ops.aten.expand_copy.default($0, [2])
|
|
$2 = torch._ops.aten.add.Tensor($1, $0)""")
|
|
|
|
# Test 3: copy_() with different dtype, same shape
|
|
self.assert_functionalization(f, torch.ones(2, dtype=torch.long))
|
|
logs = self.get_logs(f, torch.ones(2, dtype=torch.long))
|
|
self.assertExpectedInline('\n'.join(logs), """\
|
|
$0 = input('input')
|
|
$1 = torch._ops.aten._to_copy.default($0, dtype=6, layout=0, device=device(type='cpu'), pin_memory=False)
|
|
$2 = torch._ops.aten.expand_copy.default($1, [2])
|
|
$3 = torch._ops.aten.add.Tensor($2, $0)""")
|
|
|
|
# Test 4: copy_() with different dtype, different shape
|
|
self.assert_functionalization(f, torch.ones(1, dtype=torch.long))
|
|
logs = self.get_logs(f, torch.ones(1, dtype=torch.long))
|
|
self.assertExpectedInline('\n'.join(logs), """\
|
|
$0 = input('input')
|
|
$1 = torch._ops.aten._to_copy.default($0, dtype=6, layout=0, device=device(type='cpu'), pin_memory=False)
|
|
$2 = torch._ops.aten.expand_copy.default($1, [2])
|
|
$3 = torch._ops.aten.add.Tensor($2, $0)""")
|
|
|
|
def test_fill_(self):
|
|
def f(x):
|
|
y = x + x
|
|
z = y.diagonal()
|
|
z.fill_(0)
|
|
return y
|
|
|
|
self.assert_functionalization(f, torch.ones(2, 2))
|
|
logs = self.get_logs(f, torch.ones(2, 2))
|
|
self.assertExpectedInline('\n'.join(logs), """\
|
|
$0 = input('input')
|
|
$1 = torch._ops.aten.add.Tensor($0, $0)
|
|
$2 = torch._ops.aten.diagonal_copy.default($1)
|
|
$3 = torch._ops.aten.fill.Scalar($2, 0)""")
|
|
|
|
def test_nested_functions_propagate_updates(self):
|
|
def g(x):
|
|
# Create a view of x
|
|
y = x[0]
|
|
y.add_(1)
|
|
# The view, y, gets deallocated at the end of this function
|
|
|
|
def f(x):
|
|
# Calling g(x) should mutate x
|
|
g(x)
|
|
# We expect x to be synced here, even though the alias created in g() has been deallocated!
|
|
y = x + x
|
|
return y
|
|
|
|
self.assert_functionalization(f, torch.ones(2, 2))
|
|
|
|
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_mixed_wrappers_invalid(self):
|
|
x1_not_functional = torch.ones(4)
|
|
x2_functional = torch._to_functional_tensor(torch.ones(4))
|
|
|
|
# When dealing with mixed functional + nonfunctional tensors,
|
|
# normal_tensor.add_(functional_tensor) is not valid
|
|
# because normal_tensor would need to be "promoted" to a functional tensor.
|
|
with self.assertRaises(RuntimeError):
|
|
x1_not_functional.add_(x2_functional)
|
|
|
|
# This tests the behavior of functionalization with multiple layers of wrapped tensor subclasses.
|
|
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)""")
|
|
|
|
if __name__ == '__main__':
|
|
run_tests()
|