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See https://github.com/pytorch/pytorch/pull/129751#issue-2380881501. Most changes are auto-generated by linter. You can review these PRs via: ```bash git diff --ignore-all-space --ignore-blank-lines HEAD~1 ``` Pull Request resolved: https://github.com/pytorch/pytorch/pull/129758 Approved by: https://github.com/ezyang
73 lines
2.1 KiB
Python
73 lines
2.1 KiB
Python
import torch
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import torch.nn.functional as F
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from torch.testing._internal.common_nn import wrap_functional
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"""
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`sample_functional` is used by `test_cpp_api_parity.py` to test that Python / C++ API
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parity test harness works for `torch.nn.functional` functions.
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When `has_parity=true` is passed to `sample_functional`, behavior of `sample_functional`
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is the same as the C++ equivalent.
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When `has_parity=false` is passed to `sample_functional`, behavior of `sample_functional`
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is different from the C++ equivalent.
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"""
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def sample_functional(x, has_parity):
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if has_parity:
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return x * 2
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else:
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return x * 4
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torch.nn.functional.sample_functional = sample_functional
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SAMPLE_FUNCTIONAL_CPP_SOURCE = """\n
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namespace torch {
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namespace nn {
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namespace functional {
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struct C10_EXPORT SampleFunctionalFuncOptions {
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SampleFunctionalFuncOptions(bool has_parity) : has_parity_(has_parity) {}
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TORCH_ARG(bool, has_parity);
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};
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Tensor sample_functional(Tensor x, SampleFunctionalFuncOptions options) {
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return x * 2;
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}
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} // namespace functional
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} // namespace nn
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} // namespace torch
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"""
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functional_tests = [
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dict(
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constructor=wrap_functional(F.sample_functional, has_parity=True),
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cpp_options_args="F::SampleFunctionalFuncOptions(true)",
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input_size=(1, 2, 3),
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fullname="sample_functional_has_parity",
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has_parity=True,
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),
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dict(
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constructor=wrap_functional(F.sample_functional, has_parity=False),
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cpp_options_args="F::SampleFunctionalFuncOptions(false)",
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input_size=(1, 2, 3),
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fullname="sample_functional_no_parity",
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has_parity=False,
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),
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# This is to test that setting the `test_cpp_api_parity=False` flag skips
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# the C++ API parity test accordingly (otherwise this test would run and
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# throw a parity error).
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dict(
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constructor=wrap_functional(F.sample_functional, has_parity=False),
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cpp_options_args="F::SampleFunctionalFuncOptions(false)",
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input_size=(1, 2, 3),
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fullname="sample_functional_THIS_TEST_SHOULD_BE_SKIPPED",
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test_cpp_api_parity=False,
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),
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]
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