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# Owner(s): ["oncall: jit"]
import os
import sys
import inspect
import unittest
from typing import Dict, List
import torch
from torch.testing import FileCheck
# 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, RUN_CUDA
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 TestBuiltins(JitTestCase):
"""
Tests for TorchScript support of Python builtin functions.
"""
def test_has_attr(self):
class HasA(torch.nn.Module):
def __init__(self):
super(HasA, self).__init__()
self.a = 0
class HasB(torch.nn.Module):
def __init__(self):
super(HasB, self).__init__()
self.b = 1
class Mod(torch.nn.Module):
def __init__(self):
super(Mod, self).__init__()
self.mods = torch.nn.ModuleList([HasA(), HasB()])
def forward(self):
# use a list to encode hasattr results
l = torch.jit.annotate(List[int], [])
for mod in self.mods:
l.append(int(hasattr(mod, "a")))
l.append(int(hasattr(mod, "b")))
# actually retrieve the attr to test static refinement
if hasattr(mod, "a"):
l.append(mod.a)
if hasattr(mod, "b"):
l.append(mod.b)
return l
self.checkModule(Mod(), ())
def test_has_attr_invalid_args(self):
class Mod(torch.nn.Module):
def __init__(self):
super(Mod, self).__init__()
self.mod = torch.nn.Linear(1, 1)
def forward(self, name):
# not allowed, `name` must be static.
return hasattr(self.mod, name)
with self.assertRaisesRegexWithHighlight(RuntimeError, "hasattr", "name"):
torch.jit.script(Mod())
class Mod(torch.nn.Module):
def __init__(self):
super(Mod, self).__init__()
def forward(self, name):
# not allowed, `torch.rand` is not a class type
return hasattr(torch.rand(2, 3), name)
with self.assertRaisesRegexWithHighlight(RuntimeError, "hasattr", "name"):
torch.jit.script(Mod())
def test_del(self):
def fn(x: List[int]) -> List[int]:
a = x * 2
del a
return x
self.checkScript(fn, ([1, 2, 3],))
with self.assertRaisesRegexWithHighlight(RuntimeError, "undefined value", "a"):
@torch.jit.script
def fn(x):
a = x ** 2
del a
return a
with self.assertRaisesRegexWithHighlight(RuntimeError, "undefined value", "a"):
@torch.jit.script
def fn(x):
a = x ** 2
if a:
del a
return a
with self.assertRaisesRegexWithHighlight(RuntimeError, "undefined value", "b"):
@torch.jit.script
def fn(x):
a = x ** 2
del b
return a
def test_del_multiple_operands(self):
def fn(x: List[int]) -> List[int]:
a, b, c = x[0], x[1], x[2]
del a, b, c
return x
self.checkScript(fn, ([1, 2, 3],))
def del_list_multiple_operands(x: List[int]) -> List[int]:
del x[0], x[1]
return x
py_out = del_list_multiple_operands([0, 1, 2])
jit_out = torch.jit.script(del_list_multiple_operands)([0, 1, 2])
self.assertEqual(py_out, jit_out)
def del_dict_multiple_operands(x: Dict[str, int]) -> Dict[str, int]:
del x['hi'], x['there']
return x
py_out = del_dict_multiple_operands({"hi": 5, "there": 6})
jit_out = torch.jit.script(del_dict_multiple_operands)({"hi": 5, "there": 6})
self.assertEqual(py_out, jit_out)
class TestTensorBuiltins(JitTestCase):
def test_tensor_properties(self):
def should_keep(tensor, name):
if inspect.isroutine(getattr(tensor, name)):
return False
if name.startswith('_'):
return False
return True
tensor = torch.arange(4, dtype=torch.float).view(2, 2)
keys = dir(tensor)
# real and imag are only implemented for complex tensors.
self.assertRaises(RuntimeError, lambda: should_keep(tensor, 'imag'))
keys.remove('imag')
properties = [p for p in keys if should_keep(tensor, p)]
code_template = """
def fn(x):
return x.{}
"""
EQUALITY_MISMATCH = set([
# TorchScript doesn't have real enums so they return an int instead
# of the actual value
'dtype',
'layout',
])
MISSING_PROPERTIES = set([
'grad_fn',
# This is an undocumented property so it's not included
"output_nr",
# This has a longer implementation, maybe not worth copying to
# TorchScript if named tensors don't work there anyways
'names',
])
for p in properties:
if p in MISSING_PROPERTIES:
continue
code = code_template.format(p)
cu = torch.jit.CompilationUnit()
cu.define(code)
if p in EQUALITY_MISMATCH:
continue
self.assertEqual(getattr(tensor, p), cu.fn(tensor))
def test_tensor_subscript_assign(self):
def fn1(x):
a = torch.zeros_like(x, dtype=torch.uint8)
a[torch.tensor(0)] = torch.tensor(2, dtype=torch.uint8)
return a
def fn2(x):
a = torch.zeros_like(x, dtype=torch.uint8)
a[0] = 2
return a
def fn3(x):
a = torch.zeros_like(x, dtype=torch.uint8)
a[torch.tensor(0)] = 2
return a
def fn4(x):
a = torch.zeros_like(x, dtype=torch.uint8)
a[0] = torch.tensor(2, dtype=torch.uint8)
return a
def fn5(x):
a = torch.zeros_like(x, dtype=torch.float32)
a[torch.tensor(0)] = 2
return a
for fn in (fn1, fn2, fn3, fn4, fn5):
self.checkScript(fn, (torch.zeros(2, dtype=torch.uint8),))
@unittest.skipIf(not RUN_CUDA, "requires CUDA")
def test_tensor_subscript_assign_device(self):
def fn6(x):
a = torch.zeros_like(x, dtype=torch.float32, device="cuda")
a[torch.tensor(0)] = 2
return a
self.checkScript(fn6, (torch.zeros(2, dtype=torch.float32, device="cuda"),))
def test_tensor_item(self):
def test_scalar_cast(x):
scalar = x.item()
return int(scalar), float(scalar)
graph = torch.jit.script(test_scalar_cast).graph
FileCheck().check("(int, float) = prim::TupleConstruct").run(graph)
self.checkScript(test_scalar_cast, (torch.tensor(1.0),))
self.checkScript(test_scalar_cast, (torch.tensor(1),))
def test_method_on_number(self):
def func():
c = 1
return c.add(1)
with self.assertRaisesRegex(RuntimeError, 'object has no attribute or method'):
torch.jit.script(func)
# testing implicit conversion of tensors to scalars to match function arguments
def test_scalar_to_num_conversions(self):
@torch.jit.script
def multiple_defs(x):
c = 1
x = x + c
return x
self.assertTrue("ImplicitTensorToNum" not in str(multiple_defs.graph))
@torch.jit.script
def tensor_to_int_script(x, tensor):
return x.unsqueeze(tensor)
# location present in error message
with self.assertRaisesRegex(RuntimeError, "x.unsqueeze"):
tensor_to_int_script(torch.tensor([2]), torch.tensor([2, 2]))
def tensor_to_int(x, tensor):
return x.unsqueeze(tensor)
@torch.jit.script
def tensor_to_float_script(x, tensor):
return x.addcmul(tensor, tensor, value=tensor)
def tensor_to_float(x, tensor):
return x.addcmul(tensor, tensor, value=tensor)
x = torch.zeros(10)
# float tensor, float tensor with grad, int tensor (can't set grad on int tensor)
tensors = [torch.tensor(1.1),
torch.tensor(1.1, requires_grad=True),
torch.tensor(0),
torch.tensor([2])]
script_funs = [tensor_to_int_script, tensor_to_float_script]
funs = [tensor_to_int, tensor_to_float]
# return the result, or whether exception was thrown
def test_func(func, x, tensor):
try:
result = func(x, tensor)
except RuntimeError as e:
result = True
except TypeError as e:
result = True
return result
# assert result or exception equal for each (function, inputs)
for tensor in tensors:
for i in range(len(script_funs)):
self.assertEqual(test_func(script_funs[i], x, tensor), test_func(funs[i], x, tensor))
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