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Remove public_allowlist from TestPublicBindings.test_correct_module_names and ensure private_allowlist-ed things are actually private (#145620)

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This passes locally, also sanity checked importing these modules on [colab](https://colab.research.google.com/drive/1edynWX1mlQNZIBxtb3g81_ZeTpAqWi19?usp=sharing)

Pull Request resolved: https://github.com/pytorch/pytorch/pull/145620
Approved by: https://github.com/albanD
This commit is contained in:
Mikayla Gawarecki
2025-01-24 08:30:37 -08:00
committed by PyTorch MergeBot
parent 5d01a2874f
commit 7db20ffd68
1 changed files with 3 additions and 43 deletions
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46
test/test_public_bindings.py
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@ -288,6 +288,7 @@ class TestPublicBindings(TestCase):
# It is ok to add new entries here but please be careful that these modules # It is ok to add new entries here but please be careful that these modules
# do not get imported by public code. # do not get imported by public code.
# DO NOT add public modules here.
private_allowlist = { private_allowlist = {
"torch._inductor.codegen.cuda.cuda_kernel", "torch._inductor.codegen.cuda.cuda_kernel",
# TODO(#133647): Remove the onnx._internal entries after # TODO(#133647): Remove the onnx._internal entries after
@ -404,52 +405,11 @@ class TestPublicBindings(TestCase):
"torch.utils.tensorboard._utils", "torch.utils.tensorboard._utils",
} }
# No new entries should be added to this list.
# All public modules should be importable on all platforms.
public_allowlist = {
"torch.distributed.algorithms.ddp_comm_hooks",
"torch.distributed.algorithms.model_averaging.averagers",
"torch.distributed.algorithms.model_averaging.hierarchical_model_averager",
"torch.distributed.algorithms.model_averaging.utils",
"torch.distributed.checkpoint",
"torch.distributed.constants",
"torch.distributed.distributed_c10d",
"torch.distributed.elastic.agent.server",
"torch.distributed.elastic.rendezvous",
"torch.distributed.fsdp",
"torch.distributed.launch",
"torch.distributed.launcher",
"torch.distributed.nn",
"torch.distributed.nn.api.remote_module",
"torch.distributed.optim",
"torch.distributed.optim.optimizer",
"torch.distributed.rendezvous",
"torch.distributed.rpc.api",
"torch.distributed.rpc.backend_registry",
"torch.distributed.rpc.constants",
"torch.distributed.rpc.internal",
"torch.distributed.rpc.options",
"torch.distributed.rpc.rref_proxy",
"torch.distributed.rpc.server_process_global_profiler",
"torch.distributed.run",
"torch.distributed.tensor.parallel",
"torch.distributed.utils",
"torch.utils.tensorboard",
"torch.utils.tensorboard.summary",
"torch.utils.tensorboard.writer",
"torch.ao.quantization.experimental.fake_quantize",
"torch.ao.quantization.experimental.linear",
"torch.ao.quantization.experimental.observer",
"torch.ao.quantization.experimental.qconfig",
}
errors = [] errors = []
for mod, exc in failures: for mod, exc in failures:
if mod in public_allowlist:
# TODO: Ensure this is the right error type
continue
if mod in private_allowlist: if mod in private_allowlist:
# make sure mod is actually private
assert any(t.startswith("_") for t in mod.split("."))
continue continue
errors.append( errors.append(
f"{mod} failed to import with error {type(exc).__qualname__}: {str(exc)}" f"{mod} failed to import with error {type(exc).__qualname__}: {str(exc)}"