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import torch
import torch.nn.functional as F
from torch.testing._internal.common_nn import wrap_functional
"""
`sample_functional` is used by `test_cpp_api_parity.py` to test that Python / C++ API
parity test harness works for `torch.nn.functional` functions.
When `has_parity=true` is passed to `sample_functional`, behavior of `sample_functional`
is the same as the C++ equivalent.
When `has_parity=false` is passed to `sample_functional`, behavior of `sample_functional`
is different from the C++ equivalent.
"""
def sample_functional(x, has_parity):
if has_parity:
return x * 2
else:
return x * 4
torch.nn.functional.sample_functional = sample_functional
SAMPLE_FUNCTIONAL_CPP_SOURCE = """\n
namespace torch {
namespace nn {
namespace functional {
struct C10_EXPORT SampleFunctionalFuncOptions {
SampleFunctionalFuncOptions(bool has_parity) : has_parity_(has_parity) {}
TORCH_ARG(bool, has_parity);
};
Tensor sample_functional(Tensor x, SampleFunctionalFuncOptions options) {
return x * 2;
}
} // namespace functional
} // namespace nn
} // namespace torch
"""
functional_tests = [
dict(
constructor=wrap_functional(F.sample_functional, has_parity=True),
cpp_options_args="F::SampleFunctionalFuncOptions(true)",
input_size=(1, 2, 3),
fullname="sample_functional_has_parity",
has_parity=True,
),
dict(
constructor=wrap_functional(F.sample_functional, has_parity=False),
cpp_options_args="F::SampleFunctionalFuncOptions(false)",
input_size=(1, 2, 3),
fullname="sample_functional_no_parity",
has_parity=False,
),
# This is to test that setting the `test_cpp_api_parity=False` flag skips
# the C++ API parity test accordingly (otherwise this test would run and
# throw a parity error).
dict(
constructor=wrap_functional(F.sample_functional, has_parity=False),
cpp_options_args="F::SampleFunctionalFuncOptions(false)",
input_size=(1, 2, 3),
fullname="sample_functional_THIS_TEST_SHOULD_BE_SKIPPED",
test_cpp_api_parity=False,
),
]
|
