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# Owner(s): ["oncall: jit"]
import os
import sys
import torch
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
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.")
# Tests that Python slice class is supported in TorchScript
class TestSlice(JitTestCase):
def test_slice_kwarg(self):
def slice_kwarg(x: List[int]):
return x[slice(1, stop=2)]
with self.assertRaisesRegex(RuntimeError, "Slice does not accept any keyword arguments"):
torch.jit.script(slice_kwarg)
def test_slice_three_nones(self):
def three_nones(x: List[int]):
return x[slice(None, None, None)]
self.checkScript(three_nones, (range(10),))
def test_slice_two_nones(self):
def two_nones(x: List[int]):
return x[slice(None, None)]
self.checkScript(two_nones, (range(10),))
def test_slice_one_none(self):
def one_none(x: List[int]):
return x[slice(None)]
self.checkScript(one_none, (range(10),))
def test_slice_stop_only(self):
def fn(x: List[int]):
return x[slice(5)]
self.checkScript(fn, (range(10),))
def test_slice_stop_only_with_nones(self):
def fn(x: List[int]):
return x[slice(None, 5, None)]
self.checkScript(fn, (range(10),))
def test_slice_start_stop(self):
def fn(x: List[int]):
return x[slice(1, 5)]
self.checkScript(fn, (range(10),))
def test_slice_start_stop_with_none(self):
def fn(x: List[int]):
return x[slice(1, 5, None)]
self.checkScript(fn, (range(10),))
def test_slice_start_stop_step(self):
def fn(x: List[int]):
return x[slice(0, 6, 2)]
self.checkScript(fn, (range(10),))
def test_slice_string(self):
def fn(x: str):
return x[slice(None, 3, 1)]
self.checkScript(fn, ("foo_bar",))
def test_slice_tensor(self):
def fn(x: torch.Tensor):
return x[slice(None, 3, 1)]
self.checkScript(fn, (torch.ones(10),))
def test_slice_tensor_multidim(self):
def fn(x: torch.Tensor):
return x[slice(None, 3, 1), 0]
self.checkScript(fn, (torch.ones((10, 10)),))
def test_slice_tensor_multidim_with_dots(self):
def fn(x: torch.Tensor):
return x[slice(None, 3, 1), ...]
self.checkScript(fn, (torch.ones((10, 10)),))
def test_slice_as_variable(self):
def fn(x: List[int]):
a = slice(1)
return x[a]
self.checkScript(fn, (range(10),))
def test_slice_stop_clipped(self):
def fn(x: List[int]):
return x[slice(1000)]
self.checkScript(fn, (range(10),))
def test_slice_dynamic_index(self):
def t(x):
slice1 = x[0:1]
zero = 0
one = zero + 1
slice2 = x[zero:one]
return slice1 + slice2
self.checkScript(t, (torch.zeros(3, 2, 3),))
def test_tuple_slicing(self):
def tuple_slice(a):
if bool(a):
b = (1, 2, 3, 4)
else:
b = (4, 3, 2, 1)
c = b[-4:4]
e = c[1:-1]
return e
self.checkScript(tuple_slice, (torch.tensor([1]),), optimize=True)
scripted_fn = torch.jit.script(tuple_slice)
self.assertEqual(scripted_fn(torch.tensor(1)), (2, 3))
tuple_graph = scripted_fn.graph
slices = tuple_graph.findAllNodes("prim::TupleConstruct")
num_outputs = set(len(x.output().type().elements()) for x in slices)
# there should be only one tupleSlice with length of 2
self.assertTrue(num_outputs == {2})
self.run_pass('lower_all_tuples', tuple_graph)
self.assertTrue('Tuple' not in str(tuple_graph))
def test_module_list_slicing(self):
class Bar(torch.nn.Module):
def __init__(self, identifier: str):
super().__init__()
self.identifier = identifier
def forward(self):
return 0
class Foo(torch.nn.Module):
def __init__(self):
super().__init__()
module_list = [Bar("A"), Bar("B"), Bar("C"), Bar("D"), Bar("E")]
self.test = torch.nn.ModuleList(module_list)
def forward(self):
return self.test[::-2], self.test[1:4:]
scripted_foo = torch.jit.script(Foo())
result1, result2 = scripted_foo()
self.assertEqual(len(result1), 3)
self.assertEqual(result1[0].identifier, "E")
self.assertEqual(result1[1].identifier, "C")
self.assertEqual(result1[2].identifier, "A")
self.assertEqual(len(result2), 3)
self.assertEqual(result2[0].identifier, "B")
self.assertEqual(result2[1].identifier, "C")
self.assertEqual(result2[2].identifier, "D")
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