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# Owner(s): ["oncall: jit"]
import os
import sys
import torch
import unittest
# 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.")
# NOTE: FIXING FAILING TESTS
# If you are seeing a test failure from this file, congrats, you improved
# parity between JIT and Python API. Before you fix the test, you must also update
# the corresponding section in documentation that states the unsupported behavior.
# see: `jit_unsupported.rst`
class TestUnsupportedOps(JitTestCase):
def test_factory_ops_requires_grad_fail(self):
# Keyword argument {name} unknown is a JIT-only error message,
# so these functions are succeeding in eager and failing in JIT
# Complete issue and set of ops is https://github.com/pytorch/pytorch/issues/30761
# only testing some because they should be fixed all at once
def ones():
return torch.ones([2], requires_grad=True)
with self.assertRaisesRegexWithHighlight(Exception,
"Keyword argument requires_grad unknown",
"torch.ones"):
torch.jit.script(ones)
def randn():
return torch.randn([2], requires_grad=True)
with self.assertRaisesRegexWithHighlight(Exception,
"Keyword argument requires_grad unknown",
"torch.randn"):
torch.jit.script(randn)
def zeros():
return torch.zeros([2], requires_grad=True)
with self.assertRaisesRegexWithHighlight(Exception,
"Keyword argument requires_grad unknown",
"torch.zeros"):
torch.jit.script(zeros)
@unittest.skipIf(not torch._C.has_lapack, "PyTorch compiled without Lapack")
def test_init_ops(self):
def calculate_gain():
return torch.nn.init.calculate_gain('leaky_relu', 0.2)
def eye_():
return torch.nn.init.eye_(torch.zeros([2, 2]))
def dirac_():
return torch.nn.init.dirac_(torch.empty(3, 16, 5, 5))
def kaiming_uniform_():
return torch.nn.init.kaiming_normal_(torch.empty(3, 5))
def orthogonal_():
return torch.nn.init.orthogonal_(torch.empty(3, 5))
def sparse():
return torch.nn.init.sparse_(torch.empty(3, 5), sparsity=.1)
for func in [calculate_gain, eye_, dirac_, kaiming_uniform_, orthogonal_, sparse]:
# doesn't error in eager
func()
with self.assertRaisesRegex(Exception, ""):
torch.jit.script(func)
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