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53bfb00ee1
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/68395 At the time that I wrote the pass, I thought that `c10::TensorList` and `c10::List<Tensor>` were the same thing. But it looks like a `TensorList` is actually an `ArrayRef<Tensor>`. This led to a nasty bug when I tried to add conditional functionalization to `block_diag`, where in the boxed kernel, I would: (1) unwrap the first `IValue` by calling `.toTensorList()` (this actually returns a `List<Tensor>`, not a `TensorList`). (2) call `TensorList to_functional_tensor(List<Tensor>)` to get out a `TensorList` with the functionalized tensors (3) wrap that back into an `IValue` and put in on the stack. Somewhere in that sequence of operations, something bad happens and we segfault. Fixing up the signature of `to_functional_tensor` to be `List<Tensor> to_functional_tensor(List<Tensor>)` fixes the bug. I have a feeling that there's a latent TensorList-related bug in the boxing/unboxing logic that made this worse, but I'm okay to stick with my narrow fix for now. Additionally tested by running `pytest test/test_ops.py test/test_vmap.py -v -k block_diag` on top of this PR: https://github.com/pytorch/functorch/pull/235 Test Plan: Imported from OSS Reviewed By: zou3519 Differential Revision: D32448258 Pulled By: bdhirsh fbshipit-source-id: 3b2b6c7cd5e4c29533d0502f24272d826bfe03c1
311 lines
12 KiB
Python
311 lines
12 KiB
Python
import torch
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from torch.testing._internal.common_utils import TestCase, run_tests
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from torch.testing._internal.logging_tensor import LoggingTensor, capture_logs, log_input
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def are_aliased(x, y):
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if x._base is None and y._base is None:
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return False
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if x._base is not None and y._base is None:
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return x._base is y
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if x._base is None and y._base is not None:
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return y._base is x
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return x._base is y._base
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class TestFunctionalization(TestCase):
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def get_logs(self, func, inpt):
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input_clone_logging = LoggingTensor(inpt.clone())
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input_functional_logging = torch._to_functional_tensor(input_clone_logging)
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with capture_logs() as logs:
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log_input("input", input_clone_logging)
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torch._enable_functionalization()
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try:
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func(input_functional_logging)
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finally:
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torch._disable_functionalization()
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return logs
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def assert_functionalization(self, func, inpt):
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input_clone = inpt.clone()
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input_clone2 = inpt.clone()
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input_functional = torch._to_functional_tensor(input_clone2)
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# Compare outputs (and mutated inputs), with and without functionalization.
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out_ref = func(inpt)
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torch._enable_functionalization()
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try:
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out_functional = func(input_functional)
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finally:
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torch._disable_functionalization()
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# We need to sync the input tensors first, in case there are any queued mutations left.
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torch._sync(input_functional)
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torch._sync(out_functional)
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self.assertEqual(out_ref, torch._from_functional_tensor(out_functional))
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self.assertEqual(inpt, torch._from_functional_tensor(input_functional)) # input mutations should still occur
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def test_simple(self):
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def f(x):
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# simple test: 1 view op, 1 inplace op
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tmp = torch.ones(4, 2)
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y = x.view(4, 2)
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y.add_(tmp)
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z = x * x
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return y
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self.assert_functionalization(f, torch.ones(4, 2))
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logs = self.get_logs(f, torch.ones(4, 2))
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self.assertExpectedInline('\n'.join(logs), """\
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$0 = input('input')
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$1 = torch._ops.aten.view($0, [4, 2])
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$2 = torch._ops.aten.add($1, tensor([[1., 1.],
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[1., 1.],
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[1., 1.],
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[1., 1.]]))
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$3 = torch._ops.aten.view($2, [4, 2])
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$4 = torch._ops.aten.mul($3, $3)""")
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def test_inplace_on_non_view(self):
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def f(x):
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# test for the case where we functionalize an inplace op on the other tensor - not a view.
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# This is worth checking because the tensor will have an empty ViewMeta stack, which needs to be special cased.
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tmp = torch.ones(4, 2)
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y = x.view(4, 2)
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x.add_(tmp)
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return y
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self.assert_functionalization(f, torch.ones(4, 2))
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logs = self.get_logs(f, torch.ones(4, 2))
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self.assertExpectedInline('\n'.join(logs), """\
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$0 = input('input')
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$1 = torch._ops.aten.view($0, [4, 2])
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$2 = torch._ops.aten.add($0, tensor([[1., 1.],
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[1., 1.],
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[1., 1.],
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[1., 1.]]))""")
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def test_tensor_list_composite(self):
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def f(x):
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# Test an op with TensorList input
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y = torch.block_diag(x, x)
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return y
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self.assert_functionalization(f, torch.ones(2, 2))
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logs = self.get_logs(f, torch.ones(2, 2))
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# Only seeing copy_() calls in the logs are actually expected:
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# - block_diag is a CompositeImplicitAutograd op, implemented in terms of copy_() and a few other ops.
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# - copy_() doesn't have an out-of-place variant, so the pass leaves it alone
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# - the other ops are all not called on the input tensor, which means that the LoggingTensor doesn't see them
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# We can update the output of this test if/when these tests eventually use LoggingTensor with PythonMode
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self.assertExpectedInline('\n'.join(logs), """\
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$0 = input('input')
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$1 = torch._ops.aten.copy_(tensor([[1., 1.],
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[1., 1.]]), $0)
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$2 = torch._ops.aten.copy_(tensor([[1., 1.],
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[1., 1.]]), $0)""")
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def test_diagonal(self):
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def f(x):
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# test: view ops that take a subset of the original tensor (select/diagonal)
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tmp = torch.ones(2)
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y = x.diagonal()
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y.add_(tmp)
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z = x * x
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return z
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self.assert_functionalization(f, torch.ones(2, 2))
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logs = self.get_logs(f, torch.ones(2, 2))
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self.assertExpectedInline('\n'.join(logs), """\
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$0 = input('input')
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$1 = torch._ops.aten.diagonal($0)
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$2 = torch._ops.aten.add($1, tensor([1., 1.]))
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$3 = torch._ops.aten.diagonal_scatter($0, $2)
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$4 = torch._ops.aten.mul($3, $3)""")
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def test_diagonal_mutated_input(self):
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def f(x):
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# simple test: there are pending updates afterwards, which the test syncs manually
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tmp = torch.ones(2)
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y = x.diagonal()
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y.add_(tmp)
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return x
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x = torch.ones(2, 2)
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self.assert_functionalization(f, x)
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def test_split(self):
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def f(x):
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# test: view ops that return multiple tensors (split)
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tmp = torch.ones(2)
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y1, y2 = x.split(2)
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y3 = y2.diagonal()
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y3.add_(tmp)
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z = x * x
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return y3
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self.assert_functionalization(f, torch.ones(4, 2))
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logs = self.get_logs(f, torch.ones(4, 2))
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self.assertExpectedInline('\n'.join(logs), """\
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$0 = input('input')
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$1, $2 = torch._ops.aten.split($0, 2)
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$3 = torch._ops.aten.diagonal($2)
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$4 = torch._ops.aten.add($3, tensor([1., 1.]))
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$5, $6 = torch._ops.aten.split($0, 2)
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$7 = torch._ops.aten.diagonal_scatter($6, $4)
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$8 = torch._ops.aten.slice_scatter($0, $7, 0, 2, 4)
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$9 = torch._ops.aten.mul($8, $8)""")
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def test_view_inplace(self):
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def f(x):
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# test: view + inplace op (transpose_)
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tmp = torch.ones(4)
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x.transpose_(1, 0)
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y = x[0]
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y.add_(tmp)
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return y
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self.assert_functionalization(f, torch.ones(4, 2))
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logs = self.get_logs(f, torch.ones(4, 2))
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self.assertExpectedInline('\n'.join(logs), """\
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$0 = input('input')
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$1 = torch._ops.aten.transpose($0, 1, 0)
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$2 = torch._ops.aten.select($1, 0, 0)
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$3 = torch._ops.aten.add($2, tensor([1., 1., 1., 1.]))""")
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def test_scalars(self):
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def f(x):
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# test: the pass can handle scalar inputs properly
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tmp = torch.ones(4, 2)
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y = x.view(4, 2)
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y.add_(1)
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z = 2 * y
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z.div_(1)
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return z
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self.assert_functionalization(f, torch.ones(4, 2))
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logs = self.get_logs(f, torch.ones(4, 2))
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self.assertExpectedInline('\n'.join(logs), """\
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$0 = input('input')
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$1 = torch._ops.aten.view($0, [4, 2])
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$2 = torch._ops.aten.add($1, tensor(1))
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$3 = torch._ops.aten.mul($2, tensor(2))
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$4 = torch._ops.aten.div($3, tensor(1))""")
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def test_everything(self):
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def f(x):
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# test: everything
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tmp = torch.ones(2, 2)
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y = x.view(8)
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z0 = y.reshape(2, 4)
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z1 = z0.transpose(1, 0)
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z1.unsqueeze_(0)
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z1.squeeze_()
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z2, z3 = z1.split(2)
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z2.add_(tmp)
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z4 = z0[0] + z2.reshape(4)
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return z2
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self.assert_functionalization(f, torch.ones(4, 2))
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logs = self.get_logs(f, torch.ones(4, 2))
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self.assertExpectedInline('\n'.join(logs), """\
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$0 = input('input')
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$1 = torch._ops.aten.view($0, [8])
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$2 = torch._ops.aten._reshape_alias($1, [2, 4], [4, 1])
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$3 = torch._ops.aten.transpose($2, 1, 0)
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$4 = torch._ops.aten.view($0, [8])
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$5 = torch._ops.aten._reshape_alias($4, [2, 4], [4, 1])
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$6 = torch._ops.aten.transpose($5, 1, 0)
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$7 = torch._ops.aten.unsqueeze($6, 0)
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$8 = torch._ops.aten.view($0, [8])
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$9 = torch._ops.aten._reshape_alias($8, [2, 4], [4, 1])
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$10 = torch._ops.aten.transpose($9, 1, 0)
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$11 = torch._ops.aten.unsqueeze($10, 0)
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$12 = torch._ops.aten.squeeze($11)
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$13, $14 = torch._ops.aten.split($12, 2)
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$15 = torch._ops.aten.add($13, tensor([[1., 1.],
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[1., 1.]]))
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$16 = torch._ops.aten.select($2, 0, 0)
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$17 = torch._ops.aten.clone($15, memory_format=0)
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$18 = torch._ops.aten._unsafe_view($17, [4])
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$19 = torch._ops.aten.view($0, [8])
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$20 = torch._ops.aten._reshape_alias($19, [2, 4], [4, 1])
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$21 = torch._ops.aten.transpose($20, 1, 0)
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$22 = torch._ops.aten.unsqueeze($21, 0)
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$23 = torch._ops.aten.squeeze($22)
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$24 = torch._ops.aten.slice_scatter($23, $15, 0, 0, 2)
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$25 = torch._ops.aten.unsqueeze($24, 0)
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$26 = torch._ops.aten.squeeze($25, 0)
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$27 = torch._ops.aten.transpose($26, 1, 0)
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$28 = torch._ops.aten._reshape_alias($27, [8], [1])
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$29 = torch._ops.aten.view($28, [4, 2])
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$30 = torch._ops.aten.view($29, [8])
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$31 = torch._ops.aten._reshape_alias($30, [2, 4], [4, 1])
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$32 = torch._ops.aten.select($31, 0, 0)
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$33 = torch._ops.aten.add($32, $18)""")
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def test_aliases_maintained_after_pass(self):
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def f(x):
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tmp = torch.ones(4, 2)
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y = x.view(4, 2)
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z = x.view(4, 2)
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y.add_(tmp)
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return y, z
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input_functional = torch._to_functional_tensor(torch.ones(4, 2))
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torch._enable_functionalization()
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try:
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y, z = f(input_functional)
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torch._sync(y)
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torch._sync(z)
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finally:
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torch._disable_functionalization()
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# y and z are aliases inside of the function, and that aliasing relationship should be maintained.
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_y = torch._from_functional_tensor(y)
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_z = torch._from_functional_tensor(z)
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self.assertTrue(are_aliased(_y, _z))
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# copy_() gets its own test, because it is special cased in functionalization.
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# self.copy_(src) decomposes into src.to(self).expand_as(self).
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def test_copy_(self):
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def f(x):
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tmp = torch.zeros(2, 2)
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# NOTE: LoggingTensor isn't a mode, which means that the diagonal call
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# will not be logged. This is fine for testing.
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tmp_slice = tmp.diagonal()
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y = tmp_slice.copy_(x)
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z = y.add_(x)
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return z
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# Test 1: copy_() with same dtype and shape
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# to() is a composite op that noops when the dtype/shape match, so nothing gets logged.
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self.assert_functionalization(f, torch.ones(2))
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logs = self.get_logs(f, torch.ones(2))
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self.assertExpectedInline('\n'.join(logs), """\
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$0 = input('input')
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$1 = torch._ops.aten.expand($0, [2])
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$2 = torch._ops.aten.add($1, $0)""")
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# Test 2: copy_() with same dtype, different shape
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self.assert_functionalization(f, torch.ones(1))
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logs = self.get_logs(f, torch.ones(1))
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self.assertExpectedInline('\n'.join(logs), """\
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$0 = input('input')
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$1 = torch._ops.aten.expand($0, [2])
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$2 = torch._ops.aten.add($1, $0)""")
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# Test 3: copy_() with different dtype, same shape
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self.assert_functionalization(f, torch.ones(2, dtype=torch.long))
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logs = self.get_logs(f, torch.ones(2, dtype=torch.long))
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self.assertExpectedInline('\n'.join(logs), """\
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$0 = input('input')
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$1 = torch._ops.aten._to_copy($0, dtype=6, layout=0, device=device(type='cpu'), pin_memory=False)
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$2 = torch._ops.aten.expand($1, [2])
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$3 = torch._ops.aten.add($2, $0)""")
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# Test 4: copy_() with different dtype, different shape
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self.assert_functionalization(f, torch.ones(1, dtype=torch.long))
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logs = self.get_logs(f, torch.ones(1, dtype=torch.long))
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self.assertExpectedInline('\n'.join(logs), """\
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$0 = input('input')
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$1 = torch._ops.aten._to_copy($0, dtype=6, layout=0, device=device(type='cpu'), pin_memory=False)
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$2 = torch._ops.aten.expand($1, [2])
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$3 = torch._ops.aten.add($2, $0)""")
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if __name__ == '__main__':
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run_tests()
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