mirror of
https://github.com/zebrajr/pytorch.git
synced 2025-12-06 12:20:52 +01:00
d362b8e9e6
fixes #81457 fixes #81216 fixes #81212 fixes #81207 fixes #81206 fixes #81218 fixes #81203 fixes #81202 fixes #81214 fixes #81220 fixes #81205 fixes #81200 fixes #81204 fixes #81221 fixes #81209 fixes #81210 fixes #81215 fixes #81217 fixes #81222 fixes #81211 fixes #81201 fixes #81208 As part of this PR I'm also re-enabling all of the functionalization tests that got marked as flaky in CI (they're not actually flaky - I think they got marked because a PR that should have changed their expect-test output made it to master without the changes. I'll let CI run on this PR to confirm though). reland of https://github.com/pytorch/pytorch/pull/80897 Pull Request resolved: https://github.com/pytorch/pytorch/pull/82407 Approved by: https://github.com/ezyang
252 lines
10 KiB
Python
252 lines
10 KiB
Python
# Owner(s): ["module: functionalization"]
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import torch
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from torch.testing._internal.common_utils import TestCase, run_tests
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from torch.fx.passes.reinplace import reinplace
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from torch.fx.experimental.proxy_tensor import make_fx
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try:
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from functorch.experimental import functionalize
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HAS_FUNCTIONALIZATION = True
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except Exception as e:
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HAS_FUNCTIONALIZATION = False
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class TestReinplacePass(TestCase):
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def test_reinplace_basic(self):
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# Basic test: the out-of-place add() call should be converted
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# into add_()
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def f(x):
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a = x.clone()
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b = a.add(1)
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return b
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inpt = torch.ones(2)
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f2 = reinplace(make_fx(f)(inpt), inpt)
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expected_out = f(inpt)
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actual_out = f2(inpt)
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self.assertEqual(actual_out, expected_out)
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self.assertExpectedInline(f2.code, """\
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def forward(self, x_1):
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clone_default = torch.ops.aten.clone.default(x_1); x_1 = None
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add_tensor = torch.ops.aten.add_.Tensor(clone_default, 1)
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return clone_default
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""")
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def test_reinplace_with_view(self):
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def f(x):
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a = x.clone()
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a_view = a.view(-1)
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# We shouldn't re-inplace the first add(), because an alias of a is re-used later in the program
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b = a.add(1)
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# Second add() is fine to re-inplace
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c = a_view.add(1)
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return c
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inpt = torch.ones(2)
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f2 = reinplace(make_fx(f)(inpt), inpt)
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expected_out = f(inpt)
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actual_out = f2(inpt)
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self.assertEqual(actual_out, expected_out)
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self.assertExpectedInline(f2.code, """\
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def forward(self, x_1):
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clone_default = torch.ops.aten.clone.default(x_1); x_1 = None
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view_default = torch.ops.aten.view.default(clone_default, [-1])
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add_tensor = torch.ops.aten.add.Tensor(clone_default, 1); clone_default = None
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add_tensor_1 = torch.ops.aten.add_.Tensor(view_default, 1)
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return view_default
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""")
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# This test won't actually run in CI, because it requires functionalize() from functorch.
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# I'm planning on testing more comprehensively with torchbench models,
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# but we can make this testing better once functorch moves into pytorch/pytorch.
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def test_reinplace_scatter_op(self):
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def f(a_):
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# for now, don't test mutations to inputs
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a = a_.clone()
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e = a.view(-1)
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b = a.view(-1)
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c = b[0]
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d = c.view(-1)
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d.add_(1)
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return a + e
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if not HAS_FUNCTIONALIZATION:
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return
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inpt = torch.ones(4)
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f2 = reinplace(make_fx(functionalize(f))(inpt), inpt)
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expected_out = f(inpt)
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actual_out = f2(inpt)
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self.assertEqual(actual_out, expected_out)
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# NOTE: one slight pessimization here is the fact that
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# there are a bunch of redundant views in the graph.
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# Technically, half of these views are duplicates that we could de-dup.
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# This shouldn't really hurt performance though, since creating an extra view
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# is effectively just moving some metadata around (and allocating a new TensorImpl).
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# We can/should update the pass in the future to clean this up.
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self.assertExpectedInline(f2.code, """\
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def forward(self, a__1):
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clone_default = torch.ops.aten.clone.default(a__1); a__1 = None
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view_default = torch.ops.aten.view.default(clone_default, [-1])
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view_default_1 = torch.ops.aten.view.default(clone_default, [-1])
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select_int = torch.ops.aten.select.int(view_default_1, 0, 0); view_default_1 = None
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view_default_2 = torch.ops.aten.view.default(select_int, [-1]); select_int = None
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add_tensor = torch.ops.aten.add_.Tensor(view_default_2, 1)
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view_default_3 = torch.ops.aten.view.default(clone_default, [-1]); clone_default = None
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select_int_1 = torch.ops.aten.select.int(view_default_3, 0, 0)
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view_default_4 = torch.ops.aten.view.default(view_default_2, []); view_default_2 = None
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view_default_5 = torch.ops.aten.view.default(view_default_3, [4]); view_default_3 = None
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view_default_6 = torch.ops.aten.view.default(view_default_5, [-1])
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add_tensor_1 = torch.ops.aten.add_.Tensor(view_default_5, view_default_6); view_default_6 = None
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return view_default_5
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""")
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def test_reinplace_scatter_twice(self):
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def f(a_):
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# for now, don't test mutations to inputs
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a = a_.clone()
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b = a[:, 1]
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c = b[1]
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c.add_(1)
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return a
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if not HAS_FUNCTIONALIZATION:
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return
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inpt = torch.ones(4, 4)
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f2 = reinplace(make_fx(functionalize(f))(inpt), inpt)
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expected_out = f(inpt)
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actual_out = f2(inpt)
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self.assertEqual(actual_out, expected_out)
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self.assertExpectedInline(f2.code, """\
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def forward(self, a__1):
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clone_default = torch.ops.aten.clone.default(a__1); a__1 = None
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slice_tensor = torch.ops.aten.slice.Tensor(clone_default, 0, 0, 9223372036854775807)
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select_int = torch.ops.aten.select.int(slice_tensor, 1, 1); slice_tensor = None
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select_int_1 = torch.ops.aten.select.int(select_int, 0, 1); select_int = None
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add_tensor = torch.ops.aten.add_.Tensor(select_int_1, 1); select_int_1 = None
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slice_tensor_1 = torch.ops.aten.slice.Tensor(clone_default, 0, 0, 9223372036854775807)
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select_int_2 = torch.ops.aten.select.int(slice_tensor_1, 1, 1); slice_tensor_1 = None
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return clone_default
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""")
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def test_reinplace_scatter_twice_with_different_view_op_valid(self):
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def f(a_):
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a = a_.clone()
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b = a[:, 1]
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c = b[1]
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c_updated = c.add(1)
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good_mirror_of_b = a.as_strided((4,), (4,), 1)
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# good_mirror_of_b points to the same region of memory as b.
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# and this scatter op below tries to scatter c_updated into the same region
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# that c currently takes up.
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# reinplacing logic checks this by confirming that:
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# c_updated
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# good_mirror_of_b.select(0, 1)
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# have the same size/stride/storage_offset.
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b_updated = torch.select_scatter(good_mirror_of_b, c_updated, 0, 1)
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return b_updated
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inpt = torch.ones(4, 4)
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f2 = reinplace(make_fx(f)(inpt), inpt)
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expected_out = f(inpt)
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actual_out = f2(inpt)
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self.assertEqual(actual_out, expected_out)
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self.assertExpectedInline(f2.code, """\
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def forward(self, a__1):
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clone_default = torch.ops.aten.clone.default(a__1); a__1 = None
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slice_tensor = torch.ops.aten.slice.Tensor(clone_default, 0, 0, 9223372036854775807)
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select_int = torch.ops.aten.select.int(slice_tensor, 1, 1); slice_tensor = None
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select_int_1 = torch.ops.aten.select.int(select_int, 0, 1); select_int = None
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add_tensor = torch.ops.aten.add_.Tensor(select_int_1, 1); select_int_1 = None
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as_strided_default = torch.ops.aten.as_strided.default(clone_default, [4], [4], 1); clone_default = None
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return as_strided_default
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""")
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# Test example where we have a scatter op, where the base tensor
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# has the same size/stride/storage offset (even though it is a different view),
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# making it valid to re-inplace
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def test_reinplace_scatter_twice_with_different_view_op_invalid(self):
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def f(a_):
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a = a_.clone()
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b = a[:, 1]
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c = b[1]
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c_updated = c.add(1)
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good_mirror_of_b = a.as_strided((4,), (4,), 1)
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# The first arg to select_scatter is an equivalent view to b.
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# However, the select_scatter call below tries to put c_updated
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# into a different slice of "b" than what "c" currently occupies.
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#
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b_updated = torch.select_scatter(good_mirror_of_b, c_updated, 0, 0)
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return b_updated
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inpt = torch.ones(4, 4)
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f2 = reinplace(make_fx(f)(inpt), inpt)
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expected_out = f(inpt)
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actual_out = f2(inpt)
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self.assertEqual(actual_out, expected_out)
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self.assertExpectedInline(f2.code, """\
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def forward(self, a__1):
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clone_default = torch.ops.aten.clone.default(a__1); a__1 = None
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slice_tensor = torch.ops.aten.slice.Tensor(clone_default, 0, 0, 9223372036854775807)
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select_int = torch.ops.aten.select.int(slice_tensor, 1, 1); slice_tensor = None
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select_int_1 = torch.ops.aten.select.int(select_int, 0, 1); select_int = None
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add_tensor = torch.ops.aten.add.Tensor(select_int_1, 1); select_int_1 = None
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as_strided_default = torch.ops.aten.as_strided.default(clone_default, [4], [4], 1); clone_default = None
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select_scatter_default = torch.ops.aten.select_scatter.default(as_strided_default, add_tensor, 0, 0); as_strided_default = add_tensor = None
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return select_scatter_default
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""") # noqa: B950
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def test_reinplace_scatter_twice_with_different_view_op_invalid2(self):
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def f(a_):
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a = a_.clone()
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b = a[:, 1]
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c = b[1]
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c_updated = c.add(1)
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bad_mirror_of_b = a.as_strided((4,), (4,), 0)
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# The first arg to select_scatter points to a different than c's base.
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# This makes it invalid to re-inplace.
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b_updated = torch.select_scatter(bad_mirror_of_b, c_updated, 0, 1)
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return b_updated
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inpt = torch.ones(4, 4)
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f2 = reinplace(make_fx(f)(inpt), inpt)
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expected_out = f(inpt)
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actual_out = f2(inpt)
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# self.assertEqual(actual_out, expected_out)
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self.assertExpectedInline(f2.code, """\
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def forward(self, a__1):
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clone_default = torch.ops.aten.clone.default(a__1); a__1 = None
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slice_tensor = torch.ops.aten.slice.Tensor(clone_default, 0, 0, 9223372036854775807)
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select_int = torch.ops.aten.select.int(slice_tensor, 1, 1); slice_tensor = None
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select_int_1 = torch.ops.aten.select.int(select_int, 0, 1); select_int = None
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add_tensor = torch.ops.aten.add.Tensor(select_int_1, 1); select_int_1 = None
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as_strided_default = torch.ops.aten.as_strided.default(clone_default, [4], [4], 0); clone_default = None
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select_scatter_default = torch.ops.aten.select_scatter.default(as_strided_default, add_tensor, 0, 1); as_strided_default = add_tensor = None
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return select_scatter_default
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""") # noqa: B950
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if __name__ == '__main__':
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run_tests()
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