# Owner(s): ["module: functionalization"] import torch from torch.testing._internal.common_utils import TestCase, run_tests from torch.fx.passes.reinplace import reinplace from torch.fx.experimental.proxy_tensor import make_fx from torch.fx.experimental.symbolic_shapes import ShapeEnv from torch._dynamo.source import ConstantSource from torch.fx.experimental.sym_node import SymNode try: from functorch.experimental import functionalize HAS_FUNCTIONALIZATION = True except Exception as e: HAS_FUNCTIONALIZATION = False class TestReinplacePass(TestCase): def test_reinplace_basic(self): # Basic test: the out-of-place add() call should be converted # into add_() def f(x): a = x.clone() b = a.add(1) return b inpt = torch.ones(2) f2 = reinplace(make_fx(f)(inpt), inpt) expected_out = f(inpt) actual_out = f2(inpt) self.assertEqual(actual_out, expected_out) self.assertExpectedInline(f2.code, """\ def forward(self, x_1): clone = torch.ops.aten.clone.default(x_1); x_1 = None add = torch.ops.aten.add_.Tensor(clone, 1); add = None return clone """) def test_reinplace_with_view(self): def f(x): a = x.clone() a_view = a.view(-1) # We shouldn't re-inplace the first add(), because an alias of a is re-used later in the program b = a.add(1) # Second add() is fine to re-inplace c = a_view.add(1) return c inpt = torch.ones(2) f2 = reinplace(make_fx(f)(inpt), inpt) expected_out = f(inpt) actual_out = f2(inpt) self.assertEqual(actual_out, expected_out) self.assertExpectedInline(f2.code, """\ def forward(self, x_1): clone = torch.ops.aten.clone.default(x_1); x_1 = None view = torch.ops.aten.view.default(clone, [-1]) add = torch.ops.aten.add.Tensor(clone, 1); clone = add = None add_1 = torch.ops.aten.add_.Tensor(view, 1); add_1 = None return view """) def test_reinplace_different_metadata(self): def f(a_): a = a_.clone() b = a + 1 # Naively, we shouldn't try to inplace the .ge() call, # because that would require resizing "b" (from a float to a bool tensor). c = torch.ge(b, a) return c inpt = torch.ones(4) f2 = reinplace(make_fx(f)(inpt), inpt) expected_out = f(inpt) actual_out = f2(inpt) self.assertEqual(actual_out, expected_out) # The .ge() should not be reinplaced. self.assertExpectedInline(f2.code, """\ def forward(self, a__1): clone = torch.ops.aten.clone.default(a__1); a__1 = None add = torch.ops.aten.add.Tensor(clone, 1) ge = torch.ops.aten.ge.Tensor(add, clone); add = clone = None return ge """) def test_reinplace_overlapping_memory(self): def f(a_): a = a_.clone() b = a.expand(4, 4) # Can't reinplace because b has overlapping memory. c = b.add(1) return c inpt = torch.ones(1) f2 = reinplace(make_fx(f)(inpt), inpt) expected_out = f(inpt) actual_out = f2(inpt) self.assertEqual(actual_out, expected_out) self.assertExpectedInline(f2.code, """\ def forward(self, a__1): clone = torch.ops.aten.clone.default(a__1); a__1 = None expand = torch.ops.aten.expand.default(clone, [4, 4]); clone = None add = torch.ops.aten.add.Tensor(expand, 1); expand = None return add """) # This test won't actually run in CI, because it requires functionalize() from functorch. # I'm planning on testing more comprehensively with torchbench models, # but we can make this testing better once functorch moves into pytorch/pytorch. def test_reinplace_scatter_op(self): def f(a_): # for now, don't test mutations to inputs a = a_.clone() e = a.view(-1) b = a.view(-1) c = b[0] d = c.view(-1) d.add_(1) return a + e if not HAS_FUNCTIONALIZATION: return inpt = torch.ones(4) f2 = reinplace(make_fx(functionalize(f))(inpt), inpt) expected_out = f(inpt) actual_out = f2(inpt) self.assertEqual(actual_out, expected_out) # NOTE: one slight pessimization here is the fact that # there are a bunch of redundant views in the graph. # Technically, half of these views are duplicates that we could de-dup. # This shouldn't really hurt performance though, since creating an extra view # is effectively just moving some metadata around (and allocating a new TensorImpl). # We can/should update the pass in the future to clean this up. self.assertExpectedInline(f2.code, """\ def forward(self, a__1): clone = torch.ops.aten.clone.default(a__1); a__1 = None view = torch.ops.aten.view.default(clone, [-1]); view = None view_1 = torch.ops.aten.view.default(clone, [-1]) select = torch.ops.aten.select.int(view_1, 0, 0); view_1 = None view_2 = torch.ops.aten.view.default(select, [-1]); select = None add = torch.ops.aten.add_.Tensor(view_2, 1); add = None view_3 = torch.ops.aten.view.default(clone, [-1]); clone = None select_1 = torch.ops.aten.select.int(view_3, 0, 0); select_1 = None view_4 = torch.ops.aten.view.default(view_2, []); view_2 = view_4 = None view_5 = torch.ops.aten.view.default(view_3, [4]); view_3 = None view_6 = torch.ops.aten.view.default(view_5, [-1]) select_2 = torch.ops.aten.select.int(view_6, 0, 0); view_6 = None view_7 = torch.ops.aten.view.default(select_2, [-1]); select_2 = view_7 = None view_8 = torch.ops.aten.view.default(view_5, [-1]) add_1 = torch.ops.aten.add_.Tensor(view_5, view_8); view_8 = add_1 = None return view_5 """) def test_reinplace_scatter_twice(self): def f(a_): # for now, don't test mutations to inputs a = a_.clone() b = a[:, 1] c = b[1] c.add_(1) return a if not HAS_FUNCTIONALIZATION: return inpt = torch.ones(4, 4) f2 = reinplace(make_fx(functionalize(f))(inpt), inpt) expected_out = f(inpt) actual_out = f2(inpt) self.assertEqual(actual_out, expected_out) self.assertExpectedInline(f2.code, """\ def forward(self, a__1): clone = torch.ops.aten.clone.default(a__1); a__1 = None slice_1 = torch.ops.aten.slice.Tensor(clone, 0, 0, 9223372036854775807) select = torch.ops.aten.select.int(slice_1, 1, 1); slice_1 = None select_1 = torch.ops.aten.select.int(select, 0, 1); select = None add = torch.ops.aten.add_.Tensor(select_1, 1); select_1 = add = None slice_2 = torch.ops.aten.slice.Tensor(clone, 0, 0, 9223372036854775807) select_2 = torch.ops.aten.select.int(slice_2, 1, 1); slice_2 = select_2 = None slice_3 = torch.ops.aten.slice.Tensor(clone, 0, 0, 9223372036854775807) select_3 = torch.ops.aten.select.int(slice_3, 1, 1); slice_3 = None select_4 = torch.ops.aten.select.int(select_3, 0, 1); select_3 = select_4 = None return clone """) def test_reinplace_scatter_twice_with_different_view_op_valid(self): def f(a_): a = a_.clone() b = a[:, 1] c = b[1] c_updated = c.add(1) good_mirror_of_b = a.as_strided((4,), (4,), 1) # good_mirror_of_b points to the same region of memory as b. # and this scatter op below tries to scatter c_updated into the same region # that c currently takes up. # reinplacing logic checks this by confirming that: # c_updated # good_mirror_of_b.select(0, 1) # have the same size/stride/storage_offset. b_updated = torch.select_scatter(good_mirror_of_b, c_updated, 0, 1) return b_updated inpt = torch.ones(4, 4) f2 = reinplace(make_fx(f)(inpt), inpt) expected_out = f(inpt) actual_out = f2(inpt) self.assertEqual(actual_out, expected_out) self.assertExpectedInline(f2.code, """\ def forward(self, a__1): clone = torch.ops.aten.clone.default(a__1); a__1 = None slice_1 = torch.ops.aten.slice.Tensor(clone, 0, 0, 9223372036854775807) select = torch.ops.aten.select.int(slice_1, 1, 1); slice_1 = None select_1 = torch.ops.aten.select.int(select, 0, 1); select = None add = torch.ops.aten.add_.Tensor(select_1, 1); select_1 = add = None as_strided = torch.ops.aten.as_strided.default(clone, [4], [4], 1); clone = None return as_strided """) # Test example where we have a scatter op, where the base tensor # has the same size/stride/storage offset (even though it is a different view), # making it valid to re-inplace def test_reinplace_scatter_twice_with_different_view_op_invalid(self): def f(a_): a = a_.clone() b = a[:, 1] c = b[1] c_updated = c.add(1) good_mirror_of_b = a.as_strided((4,), (4,), 1) # The first arg to select_scatter is an equivalent view to b. # However, the select_scatter call below tries to put c_updated # into a different slice of "b" than what "c" currently occupies. # b_updated = torch.select_scatter(good_mirror_of_b, c_updated, 0, 0) return b_updated inpt = torch.ones(4, 4) f2 = reinplace(make_fx(f)(inpt), inpt) expected_out = f(inpt) actual_out = f2(inpt) self.assertEqual(actual_out, expected_out) self.assertExpectedInline(f2.code, """\ def forward(self, a__1): clone = torch.ops.aten.clone.default(a__1); a__1 = None slice_1 = torch.ops.aten.slice.Tensor(clone, 0, 0, 9223372036854775807) select = torch.ops.aten.select.int(slice_1, 1, 1); slice_1 = None select_1 = torch.ops.aten.select.int(select, 0, 1); select = None add = torch.ops.aten.add.Tensor(select_1, 1); select_1 = None as_strided = torch.ops.aten.as_strided.default(clone, [4], [4], 1); clone = None select_int = torch.ops.aten.select.int(as_strided, 0, 0) copy__default = torch.ops.aten.copy_.default(select_int, add); select_int = add = copy__default = None return as_strided """) # noqa: B950 def test_reinplace_scatter_twice_with_different_view_op_invalid2(self): def f(a_): a = a_.clone() b = a[:, 1] c = b[1] c_updated = c.add(1) bad_mirror_of_b = a.as_strided((4,), (4,), 0) # The first arg to select_scatter points to a different than c's base. # This makes it invalid to re-inplace. b_updated = torch.select_scatter(bad_mirror_of_b, c_updated, 0, 1) return b_updated inpt = torch.ones(4, 4) f2 = reinplace(make_fx(f)(inpt), inpt) expected_out = f(inpt) actual_out = f2(inpt) # self.assertEqual(actual_out, expected_out) self.assertExpectedInline(f2.code, """\ def forward(self, a__1): clone = torch.ops.aten.clone.default(a__1); a__1 = None slice_1 = torch.ops.aten.slice.Tensor(clone, 0, 0, 9223372036854775807) select = torch.ops.aten.select.int(slice_1, 1, 1); slice_1 = None select_1 = torch.ops.aten.select.int(select, 0, 1); select = None add = torch.ops.aten.add.Tensor(select_1, 1); select_1 = None as_strided = torch.ops.aten.as_strided.default(clone, [4], [4], 0); clone = None select_int = torch.ops.aten.select.int(as_strided, 0, 1) copy__default = torch.ops.aten.copy_.default(select_int, add); select_int = add = copy__default = None return as_strided """) # noqa: B950 def test_out_node_updated(self): def f(): x = torch.zeros(2, 2) y = x.diagonal() y_updated = y.add(1) z = torch.diagonal_scatter(x, y_updated) # reinplace needs to know to replace output [z] with [x] return [z] if not HAS_FUNCTIONALIZATION: return f2 = reinplace(make_fx(functionalize(f))()) expected_out = f() actual_out = f2() self.assertEqual(actual_out, expected_out) self.assertExpectedInline(f2.code, """\ def forward(self): zeros = torch.ops.aten.zeros.default([2, 2], device = device(type='cpu'), pin_memory = False) diagonal = torch.ops.aten.diagonal.default(zeros) add = torch.ops.aten.add_.Tensor(diagonal, 1); diagonal = add = None return [zeros] """) def test_reinplace_index_mutation(self): def f(): a = torch.zeros(4, 4, 4) a[:, 2:] = torch.ones(4, 2, 4) return a if not HAS_FUNCTIONALIZATION: return f2 = reinplace(make_fx(functionalize(f))()) expected_out = f() actual_out = f2() self.assertEqual(actual_out, expected_out) self.assertExpectedInline(f2.code, """\ def forward(self): zeros = torch.ops.aten.zeros.default([4, 4, 4], device = device(type='cpu'), pin_memory = False) ones = torch.ops.aten.ones.default([4, 2, 4], device = device(type='cpu'), pin_memory = False) slice_1 = torch.ops.aten.slice.Tensor(zeros, 0, 0, 9223372036854775807) slice_2 = torch.ops.aten.slice.Tensor(slice_1, 1, 2, 9223372036854775807); slice_1 = None copy = torch.ops.aten.copy_.default(slice_2, ones); slice_2 = ones = copy = None slice_3 = torch.ops.aten.slice.Tensor(zeros, 0, 0, 9223372036854775807); slice_3 = None slice_4 = torch.ops.aten.slice.Tensor(zeros, 0, 0, 9223372036854775807) slice_5 = torch.ops.aten.slice.Tensor(slice_4, 1, 2, 9223372036854775807); slice_4 = slice_5 = None return zeros """) def test_reinplace_sym_input(self): # Symbolic input test: the out-of-place add() call should be converted # into add_(), and symbolic input won't cause any error. def f(x, index): a = torch.select(x, 0, index) clone = a.clone() b = clone.add(1) return b x = torch.randn((4, 8, 16, 16), requires_grad=False) index = 2 shape_env = ShapeEnv() symbol = shape_env.create_symbol(index, source=ConstantSource( f"__testing_only{len(shape_env.var_to_val)}")) sym_index = torch.SymInt(SymNode(symbol, shape_env, int, hint=index)) inpt = [x, sym_index] f2 = reinplace(make_fx(f)(*inpt), *inpt) real_inpt = [x, index] expected_out = f(*real_inpt) actual_out = f2(*real_inpt) self.assertEqual(actual_out, expected_out) print(f2.code) self.assertExpectedInline(f2.code, """\ def forward(self, x_1, index_1): select = torch.ops.aten.select.int(x_1, 0, index_1); x_1 = index_1 = None clone = torch.ops.aten.clone.default(select); select = None add = torch.ops.aten.add_.Tensor(clone, 1); add = None return clone """) if __name__ == '__main__': run_tests()