| # 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) |
| self.assertEqual(inpt, torch._from_functional_tensor(input_functional)) # input mutations should still occur |
| |
| # Handle tests with multi-tensor outputs |
| if isinstance(out_ref, tuple) and isinstance(out_functional, tuple): |
| out_refs, out_functionals = list(out_ref), list(out_functional) |
| else: |
| out_refs, out_functionals = [out_ref], [out_functional] |
| |
| for out_ref_, out_functional_ in zip(out_refs, out_functionals): |
| self.assertEqual(out_ref_.size(), out_functional_.size()) |
| torch._sync(out_functional_) |
| out_functional_unwrapped = torch._from_functional_tensor(out_functional_) |
| self.assertEqual(out_ref_, out_functional_unwrapped) |
| |
| def test_multiple_views_of_same_base(self): |
| def f(x): |
| y = x.view(-1) |
| z = x.view(-1) |
| x.add_(1) |
| # y should have been updated. |
| y2 = y + 1 |
| # z should have been updated too. |
| z2 = z + 1 |
| return z2 |
| self.assert_functionalization(f, torch.ones(4)) |
| |
| 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)) |
| |
| 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.]]))""") |
| |
| # Some ops that are mutable are neither inplace nor out= ops. |
| # They also need special handling. |
| def test_mutable_op_not_inplace_or_other(self): |
| def f(x): |
| return torch._fused_moving_avg_obs_fq_helper(x, x, x, x, x, x, x, 1.0, 0, 1, 0) |
| |
| logs = self.get_logs(f, torch.ones(1)) |
| self.assertExpectedInline('\n'.join(logs), """\ |
| $0 = input('input') |
| $1, $2, $3, $4, $5, $6 = torch._ops.aten._fused_moving_avg_obs_fq_helper.functional($0, $0, $0, $0, $0, $0, $0, 1.0, 0, 1, 0)""") |
| |
| def test_as_strided(self): |
| def f(x): |
| y = x.as_strided((2,), (2,), 1) |
| y.add_(1) |
| return x |
| self.assert_functionalization(f, torch.ones(9)) |
| logs = self.get_logs(f, torch.ones(9)) |
| self.assertExpectedInline('\n'.join(logs), """\ |
| $0 = input('input') |
| $1 = torch._ops.aten.as_strided_copy.default($0, [2], [2], 1) |
| $2 = torch._ops.aten.add.Tensor($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.block_diag.default([LoggingTensor(tensor([[1., 1.], |
| [1., 1.]])), LoggingTensor(tensor([[1., 1.], |
| [1., 1.]]))])""") |
| |
| 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 x |
| 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_metadata_change(self): |
| def f(x): |
| # ops like ge_() are allowed to change the dtype of the input. |
| # functionalization should pick up on that. |
| return x.ge_(0) |
| 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.ge.Scalar($0, 0) |
| $2 = torch._ops.aten._to_copy.default($1, dtype=torch.float32, layout=torch.strided)""") |
| |
| 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.unsqueeze_copy.default($4, 0) |
| $6 = torch._ops.aten.squeeze_copy.default($5) |
| $7, $8 = torch._ops.aten.split_copy.Tensor($6, 2) |
| $9 = torch._ops.aten.add.Tensor($7, tensor([[1., 1.], |
| [1., 1.]])) |
| $10 = torch._ops.aten.select_copy.int($3, 0, 0) |
| $11 = torch._ops.aten.clone.default($9, memory_format=torch.contiguous_format) |
| $12 = torch._ops.aten._unsafe_view.default($11, [4]) |
| $13 = torch._ops.aten.view_copy.default($1, [8]) |
| $14 = torch._ops.aten._reshape_alias_copy.default($13, [2, 4], [4, 1]) |
| $15 = torch._ops.aten.transpose_copy.int($14, 1, 0) |
| $16 = torch._ops.aten.unsqueeze_copy.default($15, 0) |
| $17 = torch._ops.aten.squeeze_copy.default($16) |
| $18 = torch._ops.aten.slice_scatter.default($17, $9, 0, 0, 2) |
| $19 = torch._ops.aten.unsqueeze_copy.default($18, 0) |
| $20 = torch._ops.aten.squeeze_copy.dim($19, 0) |
| $21 = torch._ops.aten.transpose_copy.int($20, 1, 0) |
| $22 = torch._ops.aten._reshape_alias_copy.default($21, [8], [1]) |
| $23 = torch._ops.aten.view_copy.default($22, [4, 2]) |
| $24 = torch._ops.aten.view_copy.default($23, [8]) |
| $25 = torch._ops.aten._reshape_alias_copy.default($24, [2, 4], [4, 1]) |
| $26 = torch._ops.aten.select_copy.int($25, 0, 0) |
| $27 = torch._ops.aten.add.Tensor($26, $12)""") |
| |
| 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.copy.default(tensor([0., 0.]), $0) |
| $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.copy.default(tensor([0., 0.]), $0) |
| $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.copy.default(tensor([0., 0.]), $0) |
| $2 = torch._ops.aten.add.Tensor($1, $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.copy.default(tensor([0., 0.]), $0) |
| $2 = torch._ops.aten.add.Tensor($1, $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_resize_smaller(self): |
| def f(w): |
| # Resizing to a smaller size doesn't affect storage |
| x = w + 1 |
| y = x.view(4, 4) |
| y.resize_(3, 3) |
| y2 = y.view(-1) |
| y2.add_(1) |
| z = y + 1 |
| return z |
| |
| self.assert_functionalization(f, torch.ones(8, 2)) |
| logs = self.get_logs(f, torch.ones(8, 2)) |
| self.assertExpectedInline('\n'.join(logs), """\ |
| $0 = input('input') |
| $1 = torch._ops.aten.add.Tensor($0, 1) |
| $2 = torch._ops.aten.view_copy.default($1, [4, 4]) |
| $3 = torch._ops.aten.resize.functional($2, [3, 3]) |
| $4 = torch._ops.aten.as_strided_copy.default($2, [3, 3], [3, 1]) |
| $5 = torch._ops.aten.view_copy.default($4, [-1]) |
| $6 = torch._ops.aten.add.Tensor($5, 1) |
| $7 = torch._ops.aten.view_copy.default($1, [4, 4]) |
| $8 = torch._ops.aten.as_strided_copy.default($7, [3, 3], [3, 1]) |
| $9 = torch._ops.aten.view_copy.default($6, [3, 3]) |
| $10 = torch._ops.aten.as_strided_scatter.default($7, $9, [3, 3], [3, 1]) |
| $11 = torch._ops.aten.view_copy.default($10, [8, 2]) |
| $12 = torch._ops.aten.view_copy.default($11, [4, 4]) |
| $13 = torch._ops.aten.as_strided_copy.default($12, [3, 3], [3, 1]) |
| $14 = torch._ops.aten.add.Tensor($13, 1)""") |
| |
| def test_resize_larger_valid(self): |
| def f(x): |
| y = x + 1 |
| # resizing a tensor to a larger size is only currently allowed |
| # if the tensor-to-resize is not a view / has no outstanding views. |
| # See Note [resize_() in functionalization pass] |
| y.resize_(5, 5) |
| y2 = y.view(25) |
| # Do a mutation to ensure that aliases of the output of resize_() |
| # propagate mutations correctly. |
| # I'm using fill_ specifically because I want to guarantee that |
| # none of the output has uninitialized memory at the end |
| # (since these tests compare the data output against a reference impl) |
| y2.fill_(1) |
| out = y + 1 |
| return y, out |
| |
| self.assert_functionalization(f, torch.ones(8, 2)) |
| logs = self.get_logs(f, torch.ones(8, 2)) |
| self.assertExpectedInline('\n'.join(logs), """\ |
| $0 = input('input') |
| $1 = torch._ops.aten.add.Tensor($0, 1) |
| $2 = torch._ops.aten.resize.functional($1, [5, 5]) |
| $3 = torch._ops.aten.view_copy.default($2, [25]) |
| $4 = torch._ops.aten.fill.Scalar($3, 1) |
| $5 = torch._ops.aten.view_copy.default($4, [5, 5]) |
| $6 = torch._ops.aten.add.Tensor($5, 1)""") |
| |
| def test_resize_larger_invalid(self): |
| def f(x): |
| y = x + 1 |
| z = y.view(4, 4) |
| # resizing a tensor to a larger size is only currently allowed |
| # if the tensor-to-resize is not a view / has no outstanding views. |
| # See Note [resize_() in functionalization pass] |
| # This should fail |
| z.resize_(5, 5) |
| z2 = z.view(25) |
| z2.fill_(1) |
| out = z + 1 |
| return y, out |
| |
| with self.assertRaisesRegex( |
| RuntimeError, |
| r'Attempted to resize a view tensor to a larger size. This is not allowed in the functionalization pass'): |
| self.assert_functionalization(f, torch.ones(8, 2)) |
| |
| 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() |
