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# Owner(s): ["oncall: jit"]
import os
import sys
import torch
from torch.testing import FileCheck
from typing import List
# Make the helper files in test/ importable
pytorch_test_dir = os.path.dirname(os.path.dirname(os.path.realpath(__file__)))
sys.path.append(pytorch_test_dir)
from torch.testing._internal.jit_utils import JitTestCase, freeze_rng_state
if __name__ == '__main__':
raise RuntimeError("This test file is not meant to be run directly, use:\n\n"
"\tpython test/test_jit.py TESTNAME\n\n"
"instead.")
class TestRemoveMutation(JitTestCase):
def test_aten_inplace(self):
def test_not_new_alias(x):
y = x[0]
y.add_(2)
return y
fn = torch.jit.script(test_not_new_alias)
graph = fn.graph
self.run_pass('remove_mutation', graph)
FileCheck().check("aten::add_").run(graph)
self.assertEqual(fn(torch.ones([2, 2])), test_not_new_alias(torch.ones([2, 2])))
def test_no_lowering():
x = torch.tensor([2, 2])
x[0] = 3
return x
# there is no functional equivalent of x[0] = ...
fn = torch.jit.script(test_no_lowering)
graph = fn.graph
self.run_pass('remove_mutation', graph)
FileCheck().check("aten::copy_").run(graph)
self.assertEqual(fn(), test_no_lowering())
def test_move_before_not_valid():
y = torch.tensor([2, 2])
z = y + 2
y.add_(2)
return y, z
fn = torch.jit.script(test_move_before_not_valid)
graph = fn.graph
self.run_pass('remove_mutation', graph)
FileCheck().check("aten::add_").run(graph)
self.assertEqual(fn(), test_move_before_not_valid())
def test_successful():
x = torch.tensor([2, 2])
x.add_(1)
x.add_(3)
y = x + 4
return x, y
fn = torch.jit.script(test_successful)
graph = fn.graph
self.run_pass('remove_mutation', graph)
FileCheck().check_not("aten::add_").run(graph)
self.assertEqual(test_successful(), fn())
def test_intermediary_use():
x = torch.tensor([2, 2])
x.add_(1)
y = x + 4
x.add_(3)
return x, y
fn = torch.jit.script(test_intermediary_use)
graph = fn.graph
FileCheck().check_count("aten::add_", 2).run(graph)
self.run_pass('remove_mutation', graph)
# Unable to remove the second add_ because of the y = x + 4 use
# In the future we could duplicating the value of x as a temporary and replacing
# its intermediary use (so long as aliasing is safe)
FileCheck().check_count("aten::add_", 1).run(graph)
self.assertEqual(test_intermediary_use(), fn())
def test_if_output(self):
def foo(x, cond: bool):
if cond:
y = x + 5
else:
y = x + 2
y.add_(4)
return y
out_eager = foo(torch.tensor(5), True)
foo_script = torch.jit.script(foo)
FileCheck().check("aten::add_").run(foo_script.graph)
self.run_pass('remove_mutation', foo_script.graph)
FileCheck().check_not("aten::add_").run(foo_script.graph)
self.assertEqual(out_eager, foo_script(torch.tensor(5), True))
def test_if_output_fail(self):
@torch.jit.script
def foo(cond: bool):
li = []
if cond:
x = torch.tensor(1)
li.append(x)
else:
x = torch.tensor(2)
y = x.add_(2)
return y, li
self.run_pass('inline', foo.graph)
self.run_pass('remove_mutation', foo.graph)
FileCheck().check("aten::add_").run(foo.graph)
@torch.jit.script
def foo(cond: bool, y):
if cond:
x = y
else:
x = torch.tensor(2)
z = x.add_(2)
return z
self.run_pass('inline', foo.graph)
self.run_pass('remove_mutation', foo.graph)
FileCheck().check("aten::add_").run(foo.graph)
def test_special_mapped_op(self):
def test_successful():
x = torch.tensor([2, 2])
y = torch.tensor([2, 4])
x.zero_()
y.fill_(3)
return x, y
fn = torch.jit.script(test_successful)
graph = fn.graph
self.run_pass('remove_mutation', graph)
FileCheck().check_not("aten::zero_").check_not("aten::fill_").run(graph)
self.assertEqual(test_successful(), fn())
# full_like is not implemented for a tensor fill value
def test_successful():
x = torch.tensor([2, 2])
y = torch.tensor([2, 4])
x.fill_(y)
return x + x
fn = torch.jit.script(test_successful)
graph = fn.graph
self.run_pass('remove_mutation', graph)
FileCheck().check_not('aten::fill_').run(graph)
def normal():
return torch.rand(2, 1, 3, 4).normal_()
fn = torch.jit.script(normal)
graph = fn.graph
self.run_pass('remove_mutation', graph)
FileCheck().check_not("normal_").run(graph)
with freeze_rng_state():
out_eager = normal()
with freeze_rng_state():
out_script = fn()
self.assertEqual(out_eager, out_script)
def test_lists_append(self):
def successful_remove():
return [i for i in range(5)] # noqa: C416
fn = torch.jit.script(successful_remove)
graph = fn.graph
self.run_pass('loop_unrolling', graph)
self.run_pass('remove_mutation', graph)
self.run_pass('constant_propagation', graph)
FileCheck().check("graph").check_next("Constant").check_next("return").run(graph)
self.assertEqual(successful_remove(), successful_remove())
def intermediary_use():
a = [1, 2]
b = len(a)
a.append(3)
return a
fn = torch.jit.script(intermediary_use)
graph = fn.graph
FileCheck().check("append").run(graph)
self.run_pass('remove_mutation', graph)
# it is possible to remove the append here but don't currently have the logic for it
FileCheck().check_not("append").run(graph)
self.assertEqual(intermediary_use(), fn())
def test_lists_insert(self):
def successful_remove():
a : List[int] = []
a.insert(0, 1)
a.insert(0, 2)
a.insert(-10, 3)
a.insert(-9, 4)
a.insert(10, 5)
return a
fn = torch.jit.script(successful_remove)
graph = fn.graph
torch._C._jit_pass_remove_mutation(graph)
torch._C._jit_pass_constant_propagation(graph)
FileCheck().check("graph").check_next("Constant").check_next("return").run(graph)
self.assertEqual(successful_remove(), fn())
def test_list_indexing_removal(self):
@torch.jit.script
def out_of_bounds():
x = [1, 2]
x[4] = 3
return x
torch._C._jit_pass_remove_mutation(out_of_bounds.graph)
FileCheck().check("set_item").run(out_of_bounds.graph)
@torch.jit.script
def unknown(y: int):
x = [1, 2]
x[y] = 3
return x
torch._C._jit_pass_remove_mutation(out_of_bounds.graph)
FileCheck().check("set_item").run(out_of_bounds.graph)
def successful():
x = [1, 2, 3]
x[0] = 4
x[-1] = 0
return x
scripted_fn = torch.jit.script(successful)
torch._C._jit_pass_remove_mutation(scripted_fn.graph)
FileCheck().check_not("set_item").run(scripted_fn.graph)
self.checkScript(successful, ())
def successful():
x = [1, 2, 3]
x[0] = 4
x[-1] = 0
return x
scripted_fn = torch.jit.script(successful)
torch._C._jit_pass_remove_mutation(scripted_fn.graph)
FileCheck().check_not("set_item").run(scripted_fn.graph)
self.checkScript(successful, ())
def successful():
x = [1]
x[-1] = 3
return x
scripted_fn = torch.jit.script(successful)
torch._C._jit_pass_remove_mutation(scripted_fn.graph)
FileCheck().check_not("set_item").run(scripted_fn.graph)
self.checkScript(successful, ())
def test_common_pytorch_list_ops(self):
for op in ["cat", "stack", "vstack", "hstack", "dstack"]:
class OpMod(torch.nn.Module):
def __init__(self, op):
super(OpMod, self).__init__()
self.op = torch_op
def forward(self):
x = torch.tensor([1, 2, 3, 4])
x.add_(3)
y = [x, x]
return self.op(y) + 3
torch_op = getattr(torch, op)
mod = OpMod(torch_op)
mod_script = torch.jit.script(mod)
self.run_pass('remove_mutation', mod_script.forward.graph)
FileCheck().check_not("aten::add_").run(mod_script.forward.graph)
self.assertEqual(mod(), mod_script())
# test that the output doesnt alias the input
for inputs in [torch.rand(2, 2)], [torch.rand(2, 2) for _ in range(2)]:
result = torch_op(inputs)
sums = [ten.sum() for ten in result]
for inp in inputs:
inp.fill_(10)
self.assertEqual(sums, [ten.sum() for ten in result])
@torch.jit.script
def test_multiple_uses():
x = torch.tensor([1, 2, 3, 4])
x.add_(3)
y = [x, x]
return torch.cat(y), y
self.run_pass('remove_mutation', mod_script.forward.graph)
FileCheck().check("aten::add_").run(test_multiple_uses.graph)
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