# Owner(s): ["module: codegen"] import torch from torch.testing._internal.common_utils import TestCase, run_tests, skipIfTorchDynamo, TEST_WITH_TORCHDYNAMO from torch.testing._internal.logging_tensor import LoggingTensor, capture_logs from torch.utils._pytree import tree_map from torch.fx.experimental.proxy_tensor import make_fx from torch.fx.passes.reinplace import reinplace import unittest 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 # We can unify testing and use functionalize() here instead # if/when functorch moves into core. # This is basically a crappy version of `functionalize()` for single-tensor-arg inputs. def _functionalize(f, *, reapply_views: bool): def wrapped(a): input_functional = torch._to_functional_tensor(a) torch._enable_functionalization(reapply_views=reapply_views) try: out = f(input_functional) finally: torch._disable_functionalization() torch._sync(input_functional) inpt_new = torch._from_functional_tensor(input_functional) if inpt_new is not a: # Existing deficiency in functionalize(): # we don't correctly mutate input metadata (yet?) if inpt_new.shape == a.shape: a.copy_(inpt_new) tree_map(torch._sync, out) out_unwrapped = tree_map(torch._from_functional_tensor, out) return out_unwrapped return wrapped @unittest.skipIf(TEST_WITH_TORCHDYNAMO, "https://github.com/pytorch/pytorch/issues/81457") class TestFunctionalization(TestCase): def get_logs(self, func, inpt, *, reapply_views=False, run_reinplace=False): inpt_clone = inpt.clone() traced_f = make_fx(_functionalize(func, reapply_views=reapply_views))(inpt) if run_reinplace: traced_f = reinplace(traced_f, inpt_clone) return traced_f.code def assert_functionalization(self, func, inpt, *, reapply_views=False, mutated_input_metadata=False): input_clone = inpt.clone() input_clone2 = inpt.clone() input_clone3 = inpt.clone() # Compare outputs (and mutated inputs), with and without functionalization. out_ref = func(inpt) out_functional = _functionalize(func, reapply_views=reapply_views)(input_clone) # The reinplacing pass is only valid to run with reapply_views=True. functional_func = make_fx(_functionalize(func, reapply_views=True))(input_clone2) reinplace_func = reinplace(make_fx(_functionalize(func, reapply_views=True))(input_clone2), input_clone2) # NOTE: for now, need to pass in fresh inputs here, because make_fx # will directly mutate the inputs that you trace with. # Once this is fixed we can clean this up. out_reinplace = reinplace_func(input_clone3) # functionalize() deficiency: input metadata mutations aren't propagated properly, # so we just need to skip checks here for the tests that exercise that. if not mutated_input_metadata: self.assertEqual(inpt, input_clone) # input mutations should still occur self.assertEqual(inpt, input_clone3) # Handle tests with multi-tensor outputs if isinstance(out_ref, tuple): out_refs, out_functionals, out_reinplaces = list(out_ref), list(out_functional), list(out_reinplace) else: out_refs, out_functionals, out_reinplaces = [out_ref], [out_functional], [out_reinplace] for out_ref_, out_functional_, out_reinplace_ in zip(out_refs, out_functionals, out_reinplaces): self.assertEqual(out_ref_, out_functional_) self.assertEqual(out_ref_, out_reinplace_) def test_save_for_backwards_segfault(self): inp = torch._to_functional_tensor(LoggingTensor(torch.randn(2, 2))).requires_grad_(True) inp.exp() 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(logs, """\ def forward(self, a_1): ones = torch.ops.aten.ones.default([4, 2], device = device(type='cpu'), pin_memory = False) view_copy = torch.ops.aten.view_copy.default(a_1, [4, 2]) add = torch.ops.aten.add.Tensor(view_copy, ones); view_copy = ones = None view_copy_1 = torch.ops.aten.view_copy.default(add, [4, 2]) mul = torch.ops.aten.mul.Tensor(view_copy_1, view_copy_1) copy_ = torch.ops.aten.copy_.default(a_1, view_copy_1); a_1 = view_copy_1 = None return add """) reinplaced_logs = self.get_logs(f, torch.ones(4, 2), reapply_views=True, run_reinplace=True) self.assertExpectedInline(reinplaced_logs, """\ def forward(self, a_1): ones = torch.ops.aten.ones.default([4, 2], device = device(type='cpu'), pin_memory = False) view = torch.ops.aten.view.default(a_1, [4, 2]) add = torch.ops.aten.add.Tensor(view, ones); view = ones = None view_1 = torch.ops.aten.view.default(add, [4, 2]) mul = torch.ops.aten.mul.Tensor(view_1, view_1) copy_ = torch.ops.aten.copy_.default(a_1, view_1); a_1 = view_1 = None return add """) 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(logs, """\ def forward(self, a_1): ones = torch.ops.aten.ones.default([4, 2], device = device(type='cpu'), pin_memory = False) view_copy = torch.ops.aten.view_copy.default(a_1, [4, 2]); a_1 = None empty = torch.ops.aten.empty.memory_format([], device = device(type='cpu'), pin_memory = False) add = torch.ops.aten.add.Tensor(view_copy, ones); view_copy = ones = None mul = torch.ops.aten.mul.Tensor(add, add); add = None return mul """) reinplaced_logs = self.get_logs(f, torch.ones(4, 2), reapply_views=True, run_reinplace=True) self.assertExpectedInline(reinplaced_logs, """\ def forward(self, a_1): ones = torch.ops.aten.ones.default([4, 2], device = device(type='cpu'), pin_memory = False) view = torch.ops.aten.view.default(a_1, [4, 2]); a_1 = None empty = torch.ops.aten.empty.memory_format([], device = device(type='cpu'), pin_memory = False) add = torch.ops.aten.add.Tensor(view, ones); view = ones = None mul = torch.ops.aten.mul.Tensor(add, add); add = None return mul """) 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(logs, """\ def forward(self, a_1): empty = torch.ops.aten.empty.memory_format([4], device = device(type='cpu'), pin_memory = False) empty_1 = torch.ops.aten.empty.memory_format([4], device = device(type='cpu'), pin_memory = False) aminmax = torch.ops.aten.aminmax.default(a_1, dim = 0); a_1 = None getitem = aminmax[0] getitem_1 = aminmax[1]; aminmax = None return getitem """) reinplaced_logs = self.get_logs(f, torch.arange(8, dtype=torch.float32), reapply_views=True, run_reinplace=True) self.assertExpectedInline(reinplaced_logs, """\ def forward(self, a_1): empty = torch.ops.aten.empty.memory_format([4], device = device(type='cpu'), pin_memory = False) empty_1 = torch.ops.aten.empty.memory_format([4], device = device(type='cpu'), pin_memory = False) aminmax = torch.ops.aten.aminmax.default(a_1, dim = 0); a_1 = None getitem = aminmax[0] getitem_1 = aminmax[1]; aminmax = None return getitem """) def test_tensor_ctr(self): def f(x): y = torch.tensor((1, 2, 3)) z = y.view(-1) z.add_(1) return y inpt = torch.arange(3, dtype=torch.float32) self.assert_functionalization(f, inpt) logs = self.get_logs(f, inpt) self.assertExpectedInline(logs, """\ def forward(self, a_1): _tensor_constant0 = self._tensor_constant0 lift_fresh_copy = torch.ops.aten.lift_fresh_copy.default(_tensor_constant0); _tensor_constant0 = None view_copy = torch.ops.aten.view_copy.default(lift_fresh_copy, [-1]); lift_fresh_copy = None add = torch.ops.aten.add.Tensor(view_copy, 1); view_copy = None view_copy_1 = torch.ops.aten.view_copy.default(add, [3]); add = None return view_copy_1 """) reinplaced_logs = self.get_logs(f, inpt, reapply_views=True, run_reinplace=True) self.assertExpectedInline(reinplaced_logs, """\ def forward(self, a_1): _tensor_constant0 = self._tensor_constant0 lift_fresh_copy = torch.ops.aten.lift_fresh_copy.default(_tensor_constant0); _tensor_constant0 = None view = torch.ops.aten.view.default(lift_fresh_copy, [-1]); lift_fresh_copy = None add = torch.ops.aten.add_.Tensor(view, 1) view_1 = torch.ops.aten.view.default(view, [3]); view = None return view_1 """) def test_tensor_list_mixed_functional_nonfunctional(self): nonfunctional_tensor = torch.ones(2, dtype=torch.long) def f(x): # simple test: 1 view op, 1 inplace op functional_tensor = torch.ones(2, dtype=torch.long) out = x[functional_tensor, nonfunctional_tensor] return out out = f(torch.ones(2, 2)) out_functional = _functionalize(f, reapply_views=True)(torch.ones(2, 2)) self.assertEqual(out, out_functional) 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(logs, """\ def forward(self, a_1): ones = torch.ops.aten.ones.default([4, 2], device = device(type='cpu'), pin_memory = False) view_copy = torch.ops.aten.view_copy.default(a_1, [4, 2]) add = torch.ops.aten.add.Tensor(a_1, ones); ones = None copy_ = torch.ops.aten.copy_.default(a_1, add); a_1 = None view_copy_1 = torch.ops.aten.view_copy.default(add, [4, 2]); add = None return view_copy_1 """) reinplaced_logs = self.get_logs(f, torch.ones(4, 2), reapply_views=True, run_reinplace=True) self.assertExpectedInline(reinplaced_logs, """\ def forward(self, a_1): ones = torch.ops.aten.ones.default([4, 2], device = device(type='cpu'), pin_memory = False) view = torch.ops.aten.view.default(a_1, [4, 2]) add = torch.ops.aten.add.Tensor(a_1, ones); ones = None copy_ = torch.ops.aten.copy_.default(a_1, add); a_1 = None view_1 = torch.ops.aten.view.default(add, [4, 2]); add = None return view_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(logs, """\ def forward(self, a_1): _fused_moving_avg_obs_fq_helper_functional = torch.ops.aten._fused_moving_avg_obs_fq_helper_functional.default(a_1, a_1, a_1, a_1, a_1, a_1, a_1, 1.0, 0, 1, 0) getitem = _fused_moving_avg_obs_fq_helper_functional[0] getitem_1 = _fused_moving_avg_obs_fq_helper_functional[1] getitem_2 = _fused_moving_avg_obs_fq_helper_functional[2] getitem_3 = _fused_moving_avg_obs_fq_helper_functional[3] getitem_4 = _fused_moving_avg_obs_fq_helper_functional[4] getitem_5 = _fused_moving_avg_obs_fq_helper_functional[5]; _fused_moving_avg_obs_fq_helper_functional = None copy_ = torch.ops.aten.copy_.default(a_1, getitem_5); a_1 = getitem_5 = None return (getitem, getitem_1) """) # noqa: B950 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(logs, """\ def forward(self, a_1): as_strided_copy = torch.ops.aten.as_strided_copy.default(a_1, [2], [2], 1) add = torch.ops.aten.add.Tensor(as_strided_copy, 1); as_strided_copy = None as_strided_scatter = torch.ops.aten.as_strided_scatter.default(a_1, add, [2], [2], 1); add = None copy_ = torch.ops.aten.copy_.default(a_1, as_strided_scatter); a_1 = None return as_strided_scatter """) 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(logs, """\ def forward(self, a_1): block_diag = torch.ops.aten.block_diag.default([a_1, a_1]); a_1 = None return block_diag """) 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(logs, """\ def forward(self, a_1): empty = torch.ops.aten.empty.memory_format([0], device = device(type='cpu'), pin_memory = False) cat = torch.ops.aten.cat.default([a_1]); a_1 = None return cat """) reinplaced_logs = self.get_logs(f, torch.ones(2, 2), reapply_views=True, run_reinplace=True) self.assertExpectedInline(reinplaced_logs, """\ def forward(self, a_1): empty = torch.ops.aten.empty.memory_format([0], device = device(type='cpu'), pin_memory = False) cat = torch.ops.aten.cat.default([a_1]); a_1 = None return cat """) 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.clone().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(logs, """\ def forward(self, a_1): ones = torch.ops.aten.ones.default([2], device = device(type='cpu'), pin_memory = False) clone = torch.ops.aten.clone.default(a_1) diagonal_copy = torch.ops.aten.diagonal_copy.default(clone); clone = None add = torch.ops.aten.add.Tensor(diagonal_copy, ones); diagonal_copy = ones = None mul = torch.ops.aten.mul.Tensor(a_1, a_1); a_1 = None return mul """) reinplaced_logs = self.get_logs(f, torch.ones(2, 2), reapply_views=True, run_reinplace=True) self.assertExpectedInline(reinplaced_logs, """\ def forward(self, a_1): ones = torch.ops.aten.ones.default([2], device = device(type='cpu'), pin_memory = False) clone = torch.ops.aten.clone.default(a_1) diagonal = torch.ops.aten.diagonal.default(clone); clone = None add = torch.ops.aten.add_.Tensor(diagonal, ones); diagonal = ones = None mul = torch.ops.aten.mul.Tensor(a_1, a_1); a_1 = None return mul """) 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) logs = self.get_logs(f, torch.ones(2, 2)) self.assertExpectedInline(logs, """\ def forward(self, a_1): ones = torch.ops.aten.ones.default([2], device = device(type='cpu'), pin_memory = False) diagonal_copy = torch.ops.aten.diagonal_copy.default(a_1) add = torch.ops.aten.add.Tensor(diagonal_copy, ones); diagonal_copy = ones = None diagonal_scatter = torch.ops.aten.diagonal_scatter.default(a_1, add); add = None copy_ = torch.ops.aten.copy_.default(a_1, diagonal_scatter); a_1 = None return diagonal_scatter """) 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(logs, """\ def forward(self, a_1): ones = torch.ops.aten.ones.default([2], device = device(type='cpu'), pin_memory = False) split_copy = torch.ops.aten.split_copy.Tensor(a_1, 2) getitem = split_copy[0] getitem_1 = split_copy[1]; split_copy = None diagonal_copy = torch.ops.aten.diagonal_copy.default(getitem_1); getitem_1 = None add = torch.ops.aten.add.Tensor(diagonal_copy, ones); diagonal_copy = ones = None split_copy_1 = torch.ops.aten.split_copy.Tensor(a_1, 2) getitem_2 = split_copy_1[0] getitem_3 = split_copy_1[1]; split_copy_1 = None diagonal_scatter = torch.ops.aten.diagonal_scatter.default(getitem_3, add); getitem_3 = None slice_scatter = torch.ops.aten.slice_scatter.default(a_1, diagonal_scatter, 0, 2, 4); diagonal_scatter = None mul = torch.ops.aten.mul.Tensor(slice_scatter, slice_scatter) copy_ = torch.ops.aten.copy_.default(a_1, slice_scatter); a_1 = slice_scatter = None return add """) # noqa: B950 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), mutated_input_metadata=True) logs = self.get_logs(f, torch.ones(4, 2)) self.assertExpectedInline(logs, """\ def forward(self, a_1): ones = torch.ops.aten.ones.default([4], device = device(type='cpu'), pin_memory = False) transpose_copy = torch.ops.aten.transpose_copy.int(a_1, 1, 0) select_copy = torch.ops.aten.select_copy.int(transpose_copy, 0, 0); transpose_copy = None add = torch.ops.aten.add.Tensor(select_copy, ones); select_copy = ones = None transpose_copy_1 = torch.ops.aten.transpose_copy.int(a_1, 1, 0); a_1 = None select_scatter = torch.ops.aten.select_scatter.default(transpose_copy_1, add, 0, 0); transpose_copy_1 = add = None transpose_copy_2 = torch.ops.aten.transpose_copy.int(select_scatter, 1, 0); select_scatter = None transpose_copy_3 = torch.ops.aten.transpose_copy.int(transpose_copy_2, 1, 0); transpose_copy_2 = None return transpose_copy_3 """) # noqa: B950 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(logs, """\ def forward(self, a_1): view_copy = torch.ops.aten.view_copy.default(a_1, [8]) arange = torch.ops.aten.arange.default(4, device = device(type='cpu'), pin_memory = False) arange_1 = torch.ops.aten.arange.default(4, dtype = torch.float32, device = device(type='cpu'), pin_memory = False) index_put = torch.ops.aten.index_put.default(view_copy, [arange], arange_1); view_copy = arange = arange_1 = None view_copy_1 = torch.ops.aten.view_copy.default(index_put, [4, 2]) copy_ = torch.ops.aten.copy_.default(a_1, view_copy_1); a_1 = view_copy_1 = None return index_put """) # noqa: B950 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(logs, """\ def forward(self, a_1): ones = torch.ops.aten.ones.default([4, 2], device = device(type='cpu'), pin_memory = False) view_copy = torch.ops.aten.view_copy.default(a_1, [4, 2]) add = torch.ops.aten.add.Tensor(view_copy, 1); view_copy = None mul = torch.ops.aten.mul.Tensor(add, 2) div = torch.ops.aten.div.Tensor(mul, 1); mul = None view_copy_1 = torch.ops.aten.view_copy.default(add, [4, 2]); add = None copy_ = torch.ops.aten.copy_.default(a_1, view_copy_1); a_1 = view_copy_1 = None return div """) @skipIfTorchDynamo("Test does not work with TorchDynamo") 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. y = x.clone() out = y.ge_(0) return out self.assert_functionalization(f, torch.ones(4, 2)) logs = self.get_logs(f, torch.ones(4, 2)) self.assertExpectedInline(logs, """\ def forward(self, a_1): clone = torch.ops.aten.clone.default(a_1); a_1 = None ge = torch.ops.aten.ge.Scalar(clone, 0); clone = None _to_copy = torch.ops.aten._to_copy.default(ge, dtype = torch.float32, layout = torch.strided); ge = None return _to_copy """) reinplaced_logs = self.get_logs(f, torch.ones(2, 2), reapply_views=True, run_reinplace=True) self.assertExpectedInline(reinplaced_logs, """\ def forward(self, a_1): clone = torch.ops.aten.clone.default(a_1); a_1 = None ge = torch.ops.aten.ge.Scalar(clone, 0); clone = None _to_copy = torch.ops.aten._to_copy.default(ge, dtype = torch.float32, layout = torch.strided); ge = None return _to_copy """) # noqa: B950 @skipIfTorchDynamo("Test does not work with TorchDynamo") def test_metadata_change_out_op(self): def f(t, y): out_1 = torch.ones(1) return torch.add(t, y, out=out_1) inpt1, inpt2 = torch.tensor([1]), torch.tensor([1]) inpt1_func, inpt2_func = torch._to_functional_tensor(inpt1), torch._to_functional_tensor(inpt2) out_ref = f(inpt1, inpt2) torch._enable_functionalization(reapply_views=True) try: out_functional = f(inpt1_func, inpt2_func) finally: torch._disable_functionalization() self.assertEqual(out_ref, torch._from_functional_tensor(out_functional)) 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(logs, """\ def forward(self, a_1): view_copy = torch.ops.aten.view_copy.default(a_1, [4, 2]); a_1 = None return view_copy """) 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(logs, """\ def forward(self, a_1): ones = torch.ops.aten.ones.default([2, 2], device = device(type='cpu'), pin_memory = False) add = torch.ops.aten.add.Tensor(a_1, a_1); a_1 = None view_copy = torch.ops.aten.view_copy.default(add, [8]) _reshape_alias_copy = torch.ops.aten._reshape_alias_copy.default(view_copy, [2, 4], [4, 1]); view_copy = None transpose_copy = torch.ops.aten.transpose_copy.int(_reshape_alias_copy, 1, 0) unsqueeze_copy = torch.ops.aten.unsqueeze_copy.default(transpose_copy, 0); transpose_copy = None squeeze_copy = torch.ops.aten.squeeze_copy.default(unsqueeze_copy); unsqueeze_copy = None split_copy = torch.ops.aten.split_copy.Tensor(squeeze_copy, 2); squeeze_copy = None getitem = split_copy[0] getitem_1 = split_copy[1]; split_copy = None add_1 = torch.ops.aten.add.Tensor(getitem, ones); getitem = ones = None select_copy = torch.ops.aten.select_copy.int(_reshape_alias_copy, 0, 0); _reshape_alias_copy = None clone = torch.ops.aten.clone.default(add_1, memory_format = torch.contiguous_format) _unsafe_view = torch.ops.aten._unsafe_view.default(clone, [4]); clone = None view_copy_1 = torch.ops.aten.view_copy.default(add, [8]); add = None _reshape_alias_copy_1 = torch.ops.aten._reshape_alias_copy.default(view_copy_1, [2, 4], [4, 1]); view_copy_1 = None transpose_copy_1 = torch.ops.aten.transpose_copy.int(_reshape_alias_copy_1, 1, 0); _reshape_alias_copy_1 = None unsqueeze_copy_1 = torch.ops.aten.unsqueeze_copy.default(transpose_copy_1, 0); transpose_copy_1 = None squeeze_copy_1 = torch.ops.aten.squeeze_copy.default(unsqueeze_copy_1); unsqueeze_copy_1 = None slice_scatter = torch.ops.aten.slice_scatter.default(squeeze_copy_1, add_1, 0, 0, 2); squeeze_copy_1 = None unsqueeze_copy_2 = torch.ops.aten.unsqueeze_copy.default(slice_scatter, 0); slice_scatter = None squeeze_copy_2 = torch.ops.aten.squeeze_copy.dim(unsqueeze_copy_2, 0); unsqueeze_copy_2 = None transpose_copy_2 = torch.ops.aten.transpose_copy.int(squeeze_copy_2, 1, 0); squeeze_copy_2 = None _reshape_alias_copy_2 = torch.ops.aten._reshape_alias_copy.default(transpose_copy_2, [8], [1]); transpose_copy_2 = None view_copy_2 = torch.ops.aten.view_copy.default(_reshape_alias_copy_2, [4, 2]); _reshape_alias_copy_2 = None view_copy_3 = torch.ops.aten.view_copy.default(view_copy_2, [8]); view_copy_2 = None _reshape_alias_copy_3 = torch.ops.aten._reshape_alias_copy.default(view_copy_3, [2, 4], [4, 1]); view_copy_3 = None select_copy_1 = torch.ops.aten.select_copy.int(_reshape_alias_copy_3, 0, 0); _reshape_alias_copy_3 = None add_2 = torch.ops.aten.add.Tensor(select_copy_1, _unsafe_view); select_copy_1 = _unsafe_view = None return add_1 """) # noqa: B950 reinplaced_logs = self.get_logs(f, torch.ones(4, 2), reapply_views=True, run_reinplace=True) self.assertExpectedInline(reinplaced_logs, """\ def forward(self, a_1): ones = torch.ops.aten.ones.default([2, 2], device = device(type='cpu'), pin_memory = False) add = torch.ops.aten.add.Tensor(a_1, a_1); a_1 = None view = torch.ops.aten.view.default(add, [8]) _reshape_alias = torch.ops.aten._reshape_alias.default(view, [2, 4], [4, 1]); view = None transpose = torch.ops.aten.transpose.int(_reshape_alias, 1, 0) unsqueeze = torch.ops.aten.unsqueeze.default(transpose, 0); transpose = None squeeze = torch.ops.aten.squeeze.default(unsqueeze); unsqueeze = None split = torch.ops.aten.split.Tensor(squeeze, 2); squeeze = None getitem = split[0] getitem_1 = split[1]; split = None add_1 = torch.ops.aten.add_.Tensor(getitem, ones); ones = None select = torch.ops.aten.select.int(_reshape_alias, 0, 0); _reshape_alias = None clone = torch.ops.aten.clone.default(getitem, memory_format = torch.contiguous_format) _unsafe_view = torch.ops.aten._unsafe_view.default(clone, [4]); clone = None view_1 = torch.ops.aten.view.default(add, [8]); add = None _reshape_alias_1 = torch.ops.aten._reshape_alias.default(view_1, [2, 4], [4, 1]); view_1 = None transpose_1 = torch.ops.aten.transpose.int(_reshape_alias_1, 1, 0); _reshape_alias_1 = None unsqueeze_1 = torch.ops.aten.unsqueeze.default(transpose_1, 0); transpose_1 = None squeeze_1 = torch.ops.aten.squeeze.default(unsqueeze_1); unsqueeze_1 = None unsqueeze_2 = torch.ops.aten.unsqueeze.default(squeeze_1, 0); squeeze_1 = None squeeze_2 = torch.ops.aten.squeeze.dim(unsqueeze_2, 0); unsqueeze_2 = None transpose_2 = torch.ops.aten.transpose.int(squeeze_2, 1, 0); squeeze_2 = None _reshape_alias_2 = torch.ops.aten._reshape_alias.default(transpose_2, [8], [1]); transpose_2 = None view_2 = torch.ops.aten.view.default(_reshape_alias_2, [4, 2]); _reshape_alias_2 = None view_3 = torch.ops.aten.view.default(view_2, [8]); view_2 = None _reshape_alias_3 = torch.ops.aten._reshape_alias.default(view_3, [2, 4], [4, 1]); view_3 = None select_1 = torch.ops.aten.select.int(_reshape_alias_3, 0, 0); _reshape_alias_3 = None add_2 = torch.ops.aten.add.Tensor(select_1, _unsafe_view); select_1 = _unsafe_view = None return getitem """) 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(logs, """\ def forward(self, a_1): ones = torch.ops.aten.ones.default([4, 2], device = device(type='cpu'), pin_memory = False) view = torch.ops.aten.view.default(a_1, [4, 2]) add = torch.ops.aten.add.Tensor(view, ones); view = ones = None view_1 = torch.ops.aten.view.default(add, [4, 2]) mul = torch.ops.aten.mul.Tensor(view_1, view_1) copy_ = torch.ops.aten.copy_.default(a_1, view_1); a_1 = view_1 = None return add """) 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) 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(logs, """\ def forward(self, a_1): zeros = torch.ops.aten.zeros.default([2, 2], device = device(type='cpu'), pin_memory = False) diagonal_copy = torch.ops.aten.diagonal_copy.default(zeros); zeros = None add = torch.ops.aten.add.Tensor(a_1, a_1); a_1 = None return add """) reinplaced_logs = self.get_logs(f, torch.ones(2), reapply_views=True, run_reinplace=True) self.assertExpectedInline(reinplaced_logs, """\ def forward(self, a_1): zeros = torch.ops.aten.zeros.default([2, 2], device = device(type='cpu'), pin_memory = False) diagonal = torch.ops.aten.diagonal.default(zeros); zeros = None add = torch.ops.aten.add.Tensor(a_1, a_1); a_1 = None return add """) # 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(logs, """\ def forward(self, a_1): zeros = torch.ops.aten.zeros.default([2, 2], device = device(type='cpu'), pin_memory = False) diagonal_copy = torch.ops.aten.diagonal_copy.default(zeros); zeros = None expand_copy = torch.ops.aten.expand_copy.default(a_1, [2]) add = torch.ops.aten.add.Tensor(expand_copy, a_1); expand_copy = a_1 = None return add """) reinplaced_logs = self.get_logs(f, torch.ones(1), reapply_views=True, run_reinplace=True) self.assertExpectedInline(reinplaced_logs, """\ def forward(self, a_1): zeros = torch.ops.aten.zeros.default([2, 2], device = device(type='cpu'), pin_memory = False) diagonal = torch.ops.aten.diagonal.default(zeros); zeros = None expand_copy = torch.ops.aten.expand_copy.default(a_1, [2]) add = torch.ops.aten.add_.Tensor(expand_copy, a_1); a_1 = None return expand_copy """) # 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(logs, """\ def forward(self, a_1): zeros = torch.ops.aten.zeros.default([2, 2], device = device(type='cpu'), pin_memory = False) diagonal_copy = torch.ops.aten.diagonal_copy.default(zeros); zeros = None _to_copy = torch.ops.aten._to_copy.default(a_1, dtype = torch.float32, layout = torch.strided, device = device(type='cpu'), pin_memory = False) add = torch.ops.aten.add.Tensor(_to_copy, a_1); _to_copy = a_1 = None return add """) # noqa: B950 reinplaced_logs = self.get_logs(f, torch.ones(2, dtype=torch.long), reapply_views=True, run_reinplace=True) self.assertExpectedInline(reinplaced_logs, """\ def forward(self, a_1): zeros = torch.ops.aten.zeros.default([2, 2], device = device(type='cpu'), pin_memory = False) diagonal = torch.ops.aten.diagonal.default(zeros); zeros = None _to_copy = torch.ops.aten._to_copy.default(a_1, dtype = torch.float32, layout = torch.strided, device = device(type='cpu'), pin_memory = False) add = torch.ops.aten.add_.Tensor(_to_copy, a_1); a_1 = None return _to_copy """) # noqa: B950 # 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(logs, """\ def forward(self, a_1): zeros = torch.ops.aten.zeros.default([2, 2], device = device(type='cpu'), pin_memory = False) diagonal_copy = torch.ops.aten.diagonal_copy.default(zeros); zeros = None _to_copy = torch.ops.aten._to_copy.default(a_1, dtype = torch.float32, layout = torch.strided, device = device(type='cpu'), pin_memory = False) expand_copy = torch.ops.aten.expand_copy.default(_to_copy, [2]); _to_copy = None add = torch.ops.aten.add.Tensor(expand_copy, a_1); expand_copy = a_1 = None return add """) # noqa: B950 reinplaced_logs = self.get_logs(f, torch.ones(1, dtype=torch.long), reapply_views=True, run_reinplace=True) self.assertExpectedInline(reinplaced_logs, """\ def forward(self, a_1): zeros = torch.ops.aten.zeros.default([2, 2], device = device(type='cpu'), pin_memory = False) diagonal = torch.ops.aten.diagonal.default(zeros); zeros = None _to_copy = torch.ops.aten._to_copy.default(a_1, dtype = torch.float32, layout = torch.strided, device = device(type='cpu'), pin_memory = False) expand_copy = torch.ops.aten.expand_copy.default(_to_copy, [2]); _to_copy = None add = torch.ops.aten.add_.Tensor(expand_copy, a_1); a_1 = None return expand_copy """) # noqa: B950 def test_expand_symint(self): # Once some existing SymInt bugs are ironed out, we should update # this test to plumb FakeSymbolicTensors through it def f(x): return x.expand(x.size(0), x.size(1)) self.assert_functionalization(f, torch.ones(2, 2)) logs = self.get_logs(f, torch.ones(2, 2)) self.assertExpectedInline(logs, """\ def forward(self, a_1): expand_copy = torch.ops.aten.expand_copy.default(a_1, [2, 2]); a_1 = None return expand_copy """) 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(logs, """\ def forward(self, a_1): add = torch.ops.aten.add.Tensor(a_1, a_1); a_1 = None diagonal_copy = torch.ops.aten.diagonal_copy.default(add) fill = torch.ops.aten.fill.Scalar(diagonal_copy, 0); diagonal_copy = None diagonal_scatter = torch.ops.aten.diagonal_scatter.default(add, fill); add = fill = None return diagonal_scatter """) reinplaced_logs = self.get_logs(f, torch.ones(2, 2), reapply_views=True, run_reinplace=True) self.assertExpectedInline(reinplaced_logs, """\ def forward(self, a_1): add = torch.ops.aten.add.Tensor(a_1, a_1); a_1 = None diagonal = torch.ops.aten.diagonal.default(add) fill = torch.ops.aten.fill_.Scalar(diagonal, 0); diagonal = None return add """) 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(logs, """\ def forward(self, a_1): add = torch.ops.aten.add.Tensor(a_1, 1); a_1 = None view_copy = torch.ops.aten.view_copy.default(add, [4, 4]) resize = torch.ops.aten.resize.default(view_copy, [3, 3]) as_strided_copy = torch.ops.aten.as_strided_copy.default(view_copy, [3, 3], [3, 1]); view_copy = None view_copy_1 = torch.ops.aten.view_copy.default(as_strided_copy, [-1]); as_strided_copy = None add_1 = torch.ops.aten.add.Tensor(view_copy_1, 1); view_copy_1 = None view_copy_2 = torch.ops.aten.view_copy.default(add, [4, 4]); add = None as_strided_copy_1 = torch.ops.aten.as_strided_copy.default(view_copy_2, [3, 3], [3, 1]) view_copy_3 = torch.ops.aten.view_copy.default(add_1, [3, 3]); add_1 = None as_strided_scatter = torch.ops.aten.as_strided_scatter.default(view_copy_2, view_copy_3, [3, 3], [3, 1]); view_copy_2 = view_copy_3 = None view_copy_4 = torch.ops.aten.view_copy.default(as_strided_scatter, [8, 2]); as_strided_scatter = None view_copy_5 = torch.ops.aten.view_copy.default(view_copy_4, [4, 4]); view_copy_4 = None as_strided_copy_2 = torch.ops.aten.as_strided_copy.default(view_copy_5, [3, 3], [3, 1]); view_copy_5 = None add_2 = torch.ops.aten.add.Tensor(as_strided_copy_2, 1); as_strided_copy_2 = None return add_2 """) # noqa: B950 reinplaced_logs = self.get_logs(f, torch.ones(8, 2), reapply_views=True, run_reinplace=True) self.assertExpectedInline(reinplaced_logs, """\ def forward(self, a_1): add = torch.ops.aten.add.Tensor(a_1, 1); a_1 = None view = torch.ops.aten.view.default(add, [4, 4]) resize = torch.ops.aten.resize.default(view, [3, 3]) as_strided = torch.ops.aten.as_strided.default(view, [3, 3], [3, 1]); view = None view_1 = torch.ops.aten.view.default(as_strided, [-1]); as_strided = None add_1 = torch.ops.aten.add_.Tensor(view_1, 1) view_2 = torch.ops.aten.view.default(add, [4, 4]); add = None as_strided_1 = torch.ops.aten.as_strided.default(view_2, [3, 3], [3, 1]) view_3 = torch.ops.aten.view.default(view_1, [3, 3]); view_1 = None view_4 = torch.ops.aten.view.default(view_2, [8, 2]); view_2 = None view_5 = torch.ops.aten.view.default(view_4, [4, 4]); view_4 = None as_strided_2 = torch.ops.aten.as_strided.default(view_5, [3, 3], [3, 1]); view_5 = None add_2 = torch.ops.aten.add_.Tensor(as_strided_2, 1) return as_strided_2 """) 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(logs, """\ def forward(self, a_1): add = torch.ops.aten.add.Tensor(a_1, 1); a_1 = None resize = torch.ops.aten.resize.default(add, [5, 5]); add = None view_copy = torch.ops.aten.view_copy.default(resize, [25]); resize = None fill = torch.ops.aten.fill.Scalar(view_copy, 1); view_copy = None view_copy_1 = torch.ops.aten.view_copy.default(fill, [5, 5]); fill = None add_1 = torch.ops.aten.add.Tensor(view_copy_1, 1) return (view_copy_1, add_1) """) reinplaced_logs = self.get_logs(f, torch.ones(8, 2), reapply_views=True, run_reinplace=True) self.assertExpectedInline(reinplaced_logs, """\ def forward(self, a_1): add = torch.ops.aten.add.Tensor(a_1, 1); a_1 = None resize = torch.ops.aten.resize_.default(add, [5, 5]) view = torch.ops.aten.view.default(add, [25]); add = None fill = torch.ops.aten.fill_.Scalar(view, 1) view_1 = torch.ops.aten.view.default(view, [5, 5]); view = None add_1 = torch.ops.aten.add.Tensor(view_1, 1) return (view_1, add_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 + non functional 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) def test_index_mutation_on_non_input(self): def f(x): tmp = torch.zeros(10) tmp[5].fill_(1) return tmp self.assert_functionalization(f, torch.ones(2)) logs = self.get_logs(f, torch.ones(2)) self.assertExpectedInline(logs, """\ def forward(self, a_1): zeros = torch.ops.aten.zeros.default([10], device = device(type='cpu'), pin_memory = False) select_copy = torch.ops.aten.select_copy.int(zeros, 0, 5) fill = torch.ops.aten.fill.Scalar(select_copy, 1); select_copy = None select_scatter = torch.ops.aten.select_scatter.default(zeros, fill, 0, 5); zeros = fill = None return select_scatter """) # noqa: B950 reinplaced_logs = self.get_logs(f, torch.ones(2), reapply_views=True, run_reinplace=True) self.assertExpectedInline(reinplaced_logs, """\ def forward(self, a_1): zeros = torch.ops.aten.zeros.default([10], device = device(type='cpu'), pin_memory = False) select = torch.ops.aten.select.int(zeros, 0, 5) fill = torch.ops.aten.fill_.Scalar(select, 1); select = None return zeros """) if __name__ == '__main__': run_tests()