OSSForks/pytorch
1
0
Fork 0
mirror of https://github.com/zebrajr/pytorch.git synced 2025-12-07 00:21:07 +01:00
Code Issues Actions 38 Packages Projects Releases Wiki Activity
a0207c8471
pytorch/test/test_public_bindings.py
Aaron Orenstein 8c356ce3da Fix lint errors in fbcode (#135614) 
Summary: Fixed a bunch of fbcode imports that happened to work but confused autodeps.  After this autodeps still suggests "improvements" to TARGETS (which breaks our builds) but at least it can find all the imports.

Test Plan:
```
fbpython fbcode/tools/build/buck/linters/lint_autoformat.py --linter=autodeps --default-exec-timeout=1800 -- fbcode/caffe2/TARGETS fbcode/caffe2/test/TARGETS
```
Before:
```
ERROR while processing caffe2/test/TARGETS: Cannot find an owner for "test_export" (from caffe2/test/export/testing.py:229) when processing rule "test_export". Please make sure it's listed in the srcs parameter of another rule. See https://fbur$
ERROR while processing caffe2/test/TARGETS: Cannot find an owner for "testing" (from caffe2/test/export/test_export.py:87) when processing rule "test_export". Please make sure it's listed in the srcs parameter of another rule. See https://fburl$
ERROR while processing caffe2/test/TARGETS: Cannot find an owner for "test_export" (from caffe2/test/export/test_serdes.py:9) when processing rule "test_export". Please make sure it's listed in the srcs parameter of another rule. See https://fb$
ERROR while processing caffe2/test/TARGETS: Cannot find an owner for "testing" (from caffe2/test/export/test_serdes.py:10) when processing rule "test_export". Please make sure it's listed in the srcs parameter of another rule. See https://fburl$
ERROR while processing caffe2/test/TARGETS: Cannot find an owner for "testing" (from caffe2/test/export/test_retraceability.py:7) when processing rule "test_export". Please make sure it's listed in the srcs parameter of another rule. See https:$
ERROR while processing caffe2/test/TARGETS: Cannot find an owner for "test_export" (from caffe2/test/export/test_retraceability.py:6) when processing rule "test_export". Please make sure it's listed in the srcs parameter of another rule. See ht$
ERROR while processing caffe2/test/TARGETS: Cannot find an owner for "testing" (from caffe2/test/export/test_export_nonstrict.py:7) when processing rule "test_export". Please make sure it's listed in the srcs parameter of another rule. See http$
ERROR while processing caffe2/test/TARGETS: Cannot find an owner for "test_export" (from caffe2/test/export/test_export_nonstrict.py:6) when processing rule "test_export". Please make sure it's listed in the srcs parameter of another rule. See $
ERROR while processing caffe2/test/TARGETS: Cannot find an owner for "test_export" (from caffe2/test/export/test_export_training_ir_to_run_decomp.py:8) when processing rule "test_export". Please make sure it's listed in the srcs parameter of an$
ERROR while processing caffe2/test/TARGETS: Cannot find an owner for "testing" (from caffe2/test/export/test_export_training_ir_to_run_decomp.py:10) when processing rule "test_export". Please make sure it's listed in the srcs parameter of anoth$
ERROR while processing caffe2/test/TARGETS: Found "//python/typeshed_internal:typeshed_internal_library" owner for "cv2" but it is protected by visibility rules: [] (from caffe2/test/test_bundled_images.py:7) when processing rule "test_bundled_$
ERROR while processing caffe2/test/TARGETS: Cannot find an owner for "caffe2.test.profiler_test_cpp_thread_lib" (from caffe2/test/profiler/test_cpp_thread.py:29) when processing rule "profiler_test_cpp_thread". Please make sure it's listed in t$
ERROR while processing caffe2/test/TARGETS: Cannot find an owner for "torch._utils_internal.get_file_path_2" (from caffe2/test/test_custom_ops.py:23) when processing rule "custom_ops". Please make sure it's listed in the srcs parameter of anoth$
ERROR while processing caffe2/test/TARGETS: Cannot find an owner for "torch._utils_internal.get_file_path_2" (from caffe2/test/test_public_bindings.py:13) when processing rule "public_bindings". Please make sure it's listed in the srcs paramete$
ERROR while processing caffe2/test/TARGETS: Cannot find an owner for "torch._C._profiler.symbolize_tracebacks" (from caffe2/test/test_cuda.py:3348) when processing rule "test_cuda". Please make sure it's listed in the srcs parameter of another $
ERROR while processing caffe2/test/TARGETS: Cannot find an owner for "torch._C._profiler.gather_traceback" (from caffe2/test/test_cuda.py:3348) when processing rule "test_cuda". Please make sure it's listed in the srcs parameter of another rule$
ERROR while processing caffe2/test/TARGETS: Cannot find an owner for include <torch/csrc/autograd/profiler_kineto.h> (from caffe2/test/profiler/test_cpp_thread.cpp:2) when processing profiler_test_cpp_thread_lib.  Some things to try:
```

Differential Revision: D62049222

Pull Request resolved: https://github.com/pytorch/pytorch/pull/135614
Approved by: https://github.com/oulgen, https://github.com/laithsakka
2024-09-13 02:04:34 +00:00

645 lines
27 KiB
Python
Raw Blame History

# Owner(s): ["module: autograd"]
import importlib
import inspect
import json
import logging
import os
import pkgutil
import unittest
from typing import Callable
import torch
from torch._utils_internal import get_file_path_2 # @manual
from torch.testing._internal.common_utils import (
IS_JETSON,
IS_MACOS,
IS_WINDOWS,
run_tests,
skipIfTorchDynamo,
TestCase,
)
log = logging.getLogger(__name__)
class TestPublicBindings(TestCase):
def test_no_new_reexport_callables(self):
"""
This test aims to stop the introduction of new re-exported callables into
torch whose names do not start with _. Such callables are made available as
torch.XXX, which may not be desirable.
"""
reexported_callables = sorted(
k
for k, v in vars(torch).items()
if callable(v) and not v.__module__.startswith("torch")
)
self.assertTrue(
all(k.startswith("_") for k in reexported_callables), reexported_callables
)
def test_no_new_bindings(self):
"""
This test aims to stop the introduction of new JIT bindings into torch._C
whose names do not start with _. Such bindings are made available as
torch.XXX, which may not be desirable.
If your change causes this test to fail, add your new binding to a relevant
submodule of torch._C, such as torch._C._jit (or other relevant submodule of
torch._C). If your binding really needs to be available as torch.XXX, add it
to torch._C and add it to the allowlist below.
If you have removed a binding, remove it from the allowlist as well.
"""
# This allowlist contains every binding in torch._C that is copied into torch at
# the time of writing. It was generated with
#
# {elem for elem in dir(torch._C) if not elem.startswith("_")}
torch_C_allowlist_superset = {
"AggregationType",
"AliasDb",
"AnyType",
"Argument",
"ArgumentSpec",
"AwaitType",
"autocast_decrement_nesting",
"autocast_increment_nesting",
"AVG",
"BenchmarkConfig",
"BenchmarkExecutionStats",
"Block",
"BoolType",
"BufferDict",
"StorageBase",
"CallStack",
"Capsule",
"ClassType",
"clear_autocast_cache",
"Code",
"CompilationUnit",
"CompleteArgumentSpec",
"ComplexType",
"ConcreteModuleType",
"ConcreteModuleTypeBuilder",
"cpp",
"CudaBFloat16TensorBase",
"CudaBoolTensorBase",
"CudaByteTensorBase",
"CudaCharTensorBase",
"CudaComplexDoubleTensorBase",
"CudaComplexFloatTensorBase",
"CudaDoubleTensorBase",
"CudaFloatTensorBase",
"CudaHalfTensorBase",
"CudaIntTensorBase",
"CudaLongTensorBase",
"CudaShortTensorBase",
"DeepCopyMemoTable",
"default_generator",
"DeserializationStorageContext",
"device",
"DeviceObjType",
"DictType",
"DisableTorchFunction",
"DisableTorchFunctionSubclass",
"DispatchKey",
"DispatchKeySet",
"dtype",
"EnumType",
"ErrorReport",
"ExcludeDispatchKeyGuard",
"ExecutionPlan",
"FatalError",
"FileCheck",
"finfo",
"FloatType",
"fork",
"FunctionSchema",
"Future",
"FutureType",
"Generator",
"GeneratorType",
"get_autocast_cpu_dtype",
"get_autocast_dtype",
"get_autocast_ipu_dtype",
"get_default_dtype",
"get_num_interop_threads",
"get_num_threads",
"Gradient",
"Graph",
"GraphExecutorState",
"has_cuda",
"has_cudnn",
"has_lapack",
"has_mkl",
"has_mkldnn",
"has_mps",
"has_openmp",
"has_spectral",
"iinfo",
"import_ir_module_from_buffer",
"import_ir_module",
"InferredType",
"init_num_threads",
"InterfaceType",
"IntType",
"SymFloatType",
"SymBoolType",
"SymIntType",
"IODescriptor",
"is_anomaly_enabled",
"is_anomaly_check_nan_enabled",
"is_autocast_cache_enabled",
"is_autocast_cpu_enabled",
"is_autocast_ipu_enabled",
"is_autocast_enabled",
"is_grad_enabled",
"is_inference_mode_enabled",
"JITException",
"layout",
"ListType",
"LiteScriptModule",
"LockingLogger",
"LoggerBase",
"memory_format",
"merge_type_from_type_comment",
"ModuleDict",
"Node",
"NoneType",
"NoopLogger",
"NumberType",
"OperatorInfo",
"OptionalType",
"OutOfMemoryError",
"ParameterDict",
"parse_ir",
"parse_schema",
"parse_type_comment",
"PyObjectType",
"PyTorchFileReader",
"PyTorchFileWriter",
"qscheme",
"read_vitals",
"RRefType",
"ScriptClass",
"ScriptClassFunction",
"ScriptDict",
"ScriptDictIterator",
"ScriptDictKeyIterator",
"ScriptList",
"ScriptListIterator",
"ScriptFunction",
"ScriptMethod",
"ScriptModule",
"ScriptModuleSerializer",
"ScriptObject",
"ScriptObjectProperty",
"SerializationStorageContext",
"set_anomaly_enabled",
"set_autocast_cache_enabled",
"set_autocast_cpu_dtype",
"set_autocast_dtype",
"set_autocast_ipu_dtype",
"set_autocast_cpu_enabled",
"set_autocast_ipu_enabled",
"set_autocast_enabled",
"set_flush_denormal",
"set_num_interop_threads",
"set_num_threads",
"set_vital",
"Size",
"StaticModule",
"Stream",
"StreamObjType",
"Event",
"StringType",
"SUM",
"SymFloat",
"SymInt",
"TensorType",
"ThroughputBenchmark",
"TracingState",
"TupleType",
"Type",
"unify_type_list",
"UnionType",
"Use",
"Value",
"set_autocast_gpu_dtype",
"get_autocast_gpu_dtype",
"vitals_enabled",
"wait",
"Tag",
"set_autocast_xla_enabled",
"set_autocast_xla_dtype",
"get_autocast_xla_dtype",
"is_autocast_xla_enabled",
}
torch_C_bindings = {elem for elem in dir(torch._C) if not elem.startswith("_")}
# torch.TensorBase is explicitly removed in torch/__init__.py, so included here (#109940)
explicitly_removed_torch_C_bindings = {"TensorBase"}
torch_C_bindings = torch_C_bindings - explicitly_removed_torch_C_bindings
# Check that the torch._C bindings are all in the allowlist. Since
# bindings can change based on how PyTorch was compiled (e.g. with/without
# CUDA), the two may not be an exact match but the bindings should be
# a subset of the allowlist.
difference = torch_C_bindings.difference(torch_C_allowlist_superset)
msg = f"torch._C had bindings that are not present in the allowlist:\n{difference}"
self.assertTrue(torch_C_bindings.issubset(torch_C_allowlist_superset), msg)
@staticmethod
def _is_mod_public(modname):
split_strs = modname.split(".")
for elem in split_strs:
if elem.startswith("_"):
return False
return True
@unittest.skipIf(
IS_WINDOWS or IS_MACOS,
"Inductor/Distributed modules hard fail on windows and macos",
)
@skipIfTorchDynamo("Broken and not relevant for now")
def test_modules_can_be_imported(self):
failures = []
def onerror(modname):
failures.append(
(modname, ImportError("exception occurred importing package"))
)
for mod in pkgutil.walk_packages(torch.__path__, "torch.", onerror=onerror):
modname = mod.name
try:
# TODO: fix "torch/utils/model_dump/__main__.py"
# which calls sys.exit() when we try to import it
if "__main__" in modname:
continue
importlib.import_module(modname)
except Exception as e:
# Some current failures are not ImportError
log.exception("import_module failed")
failures.append((modname, e))
# It is ok to add new entries here but please be careful that these modules
# do not get imported by public code.
private_allowlist = {
"torch._inductor.codegen.cuda.cuda_kernel",
# TODO(#133647): Remove the onnx._internal entries after
# onnx and onnxscript are installed in CI.
"torch.onnx._internal.exporter",
"torch.onnx._internal.exporter._analysis",
"torch.onnx._internal.exporter._building",
"torch.onnx._internal.exporter._capture_strategies",
"torch.onnx._internal.exporter._compat",
"torch.onnx._internal.exporter._core",
"torch.onnx._internal.exporter._decomp",
"torch.onnx._internal.exporter._dispatching",
"torch.onnx._internal.exporter._fx_passes",
"torch.onnx._internal.exporter._ir_passes",
"torch.onnx._internal.exporter._isolated",
"torch.onnx._internal.exporter._onnx_program",
"torch.onnx._internal.exporter._registration",
"torch.onnx._internal.exporter._reporting",
"torch.onnx._internal.exporter._schemas",
"torch.onnx._internal.exporter._tensors",
"torch.onnx._internal.exporter._verification",
"torch.onnx._internal.fx._pass",
"torch.onnx._internal.fx.analysis",
"torch.onnx._internal.fx.analysis.unsupported_nodes",
"torch.onnx._internal.fx.decomposition_skip",
"torch.onnx._internal.fx.diagnostics",
"torch.onnx._internal.fx.fx_onnx_interpreter",
"torch.onnx._internal.fx.fx_symbolic_graph_extractor",
"torch.onnx._internal.fx.onnxfunction_dispatcher",
"torch.onnx._internal.fx.op_validation",
"torch.onnx._internal.fx.passes",
"torch.onnx._internal.fx.passes._utils",
"torch.onnx._internal.fx.passes.decomp",
"torch.onnx._internal.fx.passes.functionalization",
"torch.onnx._internal.fx.passes.modularization",
"torch.onnx._internal.fx.passes.readability",
"torch.onnx._internal.fx.passes.type_promotion",
"torch.onnx._internal.fx.passes.virtualization",
"torch.onnx._internal.fx.type_utils",
"torch.testing._internal.common_distributed",
"torch.testing._internal.common_fsdp",
"torch.testing._internal.dist_utils",
"torch.testing._internal.distributed.common_state_dict",
"torch.testing._internal.distributed._shard.sharded_tensor",
"torch.testing._internal.distributed._shard.test_common",
"torch.testing._internal.distributed._tensor.common_dtensor",
"torch.testing._internal.distributed.ddp_under_dist_autograd_test",
"torch.testing._internal.distributed.distributed_test",
"torch.testing._internal.distributed.distributed_utils",
"torch.testing._internal.distributed.fake_pg",
"torch.testing._internal.distributed.multi_threaded_pg",
"torch.testing._internal.distributed.nn.api.remote_module_test",
"torch.testing._internal.distributed.rpc.dist_autograd_test",
"torch.testing._internal.distributed.rpc.dist_optimizer_test",
"torch.testing._internal.distributed.rpc.examples.parameter_server_test",
"torch.testing._internal.distributed.rpc.examples.reinforcement_learning_rpc_test",
"torch.testing._internal.distributed.rpc.faulty_agent_rpc_test",
"torch.testing._internal.distributed.rpc.faulty_rpc_agent_test_fixture",
"torch.testing._internal.distributed.rpc.jit.dist_autograd_test",
"torch.testing._internal.distributed.rpc.jit.rpc_test",
"torch.testing._internal.distributed.rpc.jit.rpc_test_faulty",
"torch.testing._internal.distributed.rpc.rpc_agent_test_fixture",
"torch.testing._internal.distributed.rpc.rpc_test",
"torch.testing._internal.distributed.rpc.tensorpipe_rpc_agent_test_fixture",
"torch.testing._internal.distributed.rpc_utils",
"torch._inductor.codegen.cuda.cuda_template",
"torch._inductor.codegen.cuda.gemm_template",
"torch._inductor.codegen.cpp_template",
"torch._inductor.codegen.cpp_gemm_template",
"torch._inductor.codegen.cpp_micro_gemm",
"torch._inductor.codegen.cpp_template_kernel",
"torch._inductor.runtime.triton_helpers",
"torch.ao.pruning._experimental.data_sparsifier.lightning.callbacks.data_sparsity",
"torch.backends._coreml.preprocess",
"torch.contrib._tensorboard_vis",
"torch.distributed._composable",
"torch.distributed._functional_collectives",
"torch.distributed._functional_collectives_impl",
"torch.distributed._shard",
"torch.distributed._sharded_tensor",
"torch.distributed._sharding_spec",
"torch.distributed._spmd.api",
"torch.distributed._spmd.batch_dim_utils",
"torch.distributed._spmd.comm_tensor",
"torch.distributed._spmd.data_parallel",
"torch.distributed._spmd.distribute",
"torch.distributed._spmd.experimental_ops",
"torch.distributed._spmd.parallel_mode",
"torch.distributed._tensor",
"torch.distributed.algorithms._checkpoint.checkpoint_wrapper",
"torch.distributed.algorithms._optimizer_overlap",
"torch.distributed.rpc._testing.faulty_agent_backend_registry",
"torch.distributed.rpc._utils",
"torch.ao.pruning._experimental.data_sparsifier.benchmarks.dlrm_utils",
"torch.ao.pruning._experimental.data_sparsifier.benchmarks.evaluate_disk_savings",
"torch.ao.pruning._experimental.data_sparsifier.benchmarks.evaluate_forward_time",
"torch.ao.pruning._experimental.data_sparsifier.benchmarks.evaluate_model_metrics",
"torch.ao.pruning._experimental.data_sparsifier.lightning.tests.test_callbacks",
"torch.csrc.jit.tensorexpr.scripts.bisect",
"torch.csrc.lazy.test_mnist",
"torch.distributed._shard.checkpoint._fsspec_filesystem",
"torch.distributed._tensor.examples.visualize_sharding_example",
"torch.distributed.checkpoint._fsspec_filesystem",
"torch.distributed.examples.memory_tracker_example",
"torch.testing._internal.distributed.rpc.fb.thrift_rpc_agent_test_fixture",
"torch.utils._cxx_pytree",
"torch.utils.tensorboard._convert_np",
"torch.utils.tensorboard._embedding",
"torch.utils.tensorboard._onnx_graph",
"torch.utils.tensorboard._proto_graph",
"torch.utils.tensorboard._pytorch_graph",
"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.elastic.rendezvous.etcd_rendezvous",
"torch.distributed.elastic.rendezvous.etcd_rendezvous_backend",
"torch.distributed.elastic.rendezvous.etcd_store",
"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 = []
for mod, exc in failures:
if mod in public_allowlist:
# TODO: Ensure this is the right error type
continue
if mod in private_allowlist:
continue
errors.append(
f"{mod} failed to import with error {type(exc).__qualname__}: {str(exc)}"
)
self.assertEqual("", "\n".join(errors))
# AttributeError: module 'torch.distributed' has no attribute '_shard'
@unittest.skipIf(IS_WINDOWS or IS_JETSON or IS_MACOS, "Distributed Attribute Error")
@skipIfTorchDynamo("Broken and not relevant for now")
def test_correct_module_names(self):
"""
An API is considered public, if its `__module__` starts with `torch.`
and there is no name in `__module__` or the object itself that starts with "_".
Each public package should either:
- (preferred) Define `__all__` and all callables and classes in there must have their
`__module__` start with the current submodule's path. Things not in `__all__` should
NOT have their `__module__` start with the current submodule.
- (for simple python-only modules) Not define `__all__` and all the elements in `dir(submod)` must have their
`__module__` that start with the current submodule.
"""
failure_list = []
with open(
get_file_path_2(os.path.dirname(__file__), "allowlist_for_publicAPI.json")
) as json_file:
# no new entries should be added to this allow_dict.
# New APIs must follow the public API guidelines.
allow_dict = json.load(json_file)
# Because we want minimal modifications to the `allowlist_for_publicAPI.json`,
# we are adding the entries for the migrated modules here from the original
# locations.
for modname in allow_dict["being_migrated"]:
if modname in allow_dict:
allow_dict[allow_dict["being_migrated"][modname]] = allow_dict[
modname
]
def test_module(modname):
try:
if "__main__" in modname:
return
mod = importlib.import_module(modname)
except Exception:
# It is ok to ignore here as we have a test above that ensures
# this should never happen
return
if not self._is_mod_public(modname):
return
# verifies that each public API has the correct module name and naming semantics
def check_one_element(elem, modname, mod, *, is_public, is_all):
obj = getattr(mod, elem)
# torch.dtype is not a class nor callable, so we need to check for it separately
if not (
isinstance(obj, (Callable, torch.dtype)) or inspect.isclass(obj)
):
return
elem_module = getattr(obj, "__module__", None)
# Only used for nice error message below
why_not_looks_public = ""
if elem_module is None:
why_not_looks_public = (
"because it does not have a `__module__` attribute"
)
# If a module is being migrated from foo.a to bar.a (that is entry {"foo": "bar"}),
# the module's starting package would be referred to as the new location even
# if there is a "from foo import a" inside the "bar.py".
modname = allow_dict["being_migrated"].get(modname, modname)
elem_modname_starts_with_mod = (
elem_module is not None
and elem_module.startswith(modname)
and "._" not in elem_module
)
if not why_not_looks_public and not elem_modname_starts_with_mod:
why_not_looks_public = (
f"because its `__module__` attribute (`{elem_module}`) is not within the "
f"torch library or does not start with the submodule where it is defined (`{modname}`)"
)
# elem's name must NOT begin with an `_` and it's module name
# SHOULD start with it's current module since it's a public API
looks_public = not elem.startswith("_") and elem_modname_starts_with_mod
if not why_not_looks_public and not looks_public:
why_not_looks_public = f"because it starts with `_` (`{elem}`)"
if is_public != looks_public:
if modname in allow_dict and elem in allow_dict[modname]:
return
if is_public:
why_is_public = (
f"it is inside the module's (`{modname}`) `__all__`"
if is_all
else "it is an attribute that does not start with `_` on a module that "
"does not have `__all__` defined"
)
fix_is_public = (
f"remove it from the modules's (`{modname}`) `__all__`"
if is_all
else f"either define a `__all__` for `{modname}` or add a `_` at the beginning of the name"
)
else:
assert is_all
why_is_public = (
f"it is not inside the module's (`{modname}`) `__all__`"
)
fix_is_public = (
f"add it from the modules's (`{modname}`) `__all__`"
)
if looks_public:
why_looks_public = (
"it does look public because it follows the rules from the doc above "
"(does not start with `_` and has a proper `__module__`)."
)
fix_looks_public = "make its name start with `_`"
else:
why_looks_public = why_not_looks_public
if not elem_modname_starts_with_mod:
fix_looks_public = (
"make sure the `__module__` is properly set and points to a submodule "
f"of `{modname}`"
)
else:
fix_looks_public = (
"remove the `_` at the beginning of the name"
)
failure_list.append(f"# {modname}.{elem}:")
is_public_str = "" if is_public else " NOT"
failure_list.append(
f" - Is{is_public_str} public: {why_is_public}"
)
looks_public_str = "" if looks_public else " NOT"
failure_list.append(
f" - Does{looks_public_str} look public: {why_looks_public}"
)
# Swap the str below to avoid having to create the NOT again
failure_list.append(
" - You can do either of these two things to fix this problem:"
)
failure_list.append(
f" - To make it{looks_public_str} public: {fix_is_public}"
)
failure_list.append(
f" - To make it{is_public_str} look public: {fix_looks_public}"
)
if hasattr(mod, "__all__"):
public_api = mod.__all__
all_api = dir(mod)
for elem in all_api:
check_one_element(
elem, modname, mod, is_public=elem in public_api, is_all=True
)
else:
all_api = dir(mod)
for elem in all_api:
if not elem.startswith("_"):
check_one_element(
elem, modname, mod, is_public=True, is_all=False
)
for mod in pkgutil.walk_packages(torch.__path__, "torch."):
modname = mod.name
test_module(modname)
test_module("torch")
msg = (
"All the APIs below do not meet our guidelines for public API from "
"https://github.com/pytorch/pytorch/wiki/Public-API-definition-and-documentation.\n"
)
msg += (
"Make sure that everything that is public is expected (in particular that the module "
"has a properly populated `__all__` attribute) and that everything that is supposed to be public "
"does look public (it does not start with `_` and has a `__module__` that is properly populated)."
)
msg += "\n\nFull list:\n"
msg += "\n".join(map(str, failure_list))
# empty lists are considered false in python
self.assertTrue(not failure_list, msg)
if __name__ == "__main__":
run_tests()
Reference in New Issue View Git Blame Copy Permalink