mirror of
https://github.com/zebrajr/pytorch.git
synced 2025-12-06 12:20:52 +01:00
6d9432c44c
A context manager that disables the decomposition of certain ops during dynamo tracing. The approach is to temporarily hijack the operator callable with PT2 custom operator. The custom operator will not be decomposed and will show up as a single node to be exported to ONNX. For the time being the decomposition of these ops is otherwise unavoidable. https://github.com/pytorch/pytorch/issues/116684 https://github.com/pytorch/pytorch/issues/115883 This solution will no longer be required once the issue is resolved. Pull Request resolved: https://github.com/pytorch/pytorch/pull/117314 Approved by: https://github.com/justinchuby, https://github.com/malfet
552 lines
24 KiB
Python
552 lines
24 KiB
Python
# Owner(s): ["module: autograd"]
|
|
|
|
from torch.testing._internal.common_utils import TestCase, run_tests, IS_JETSON, IS_WINDOWS
|
|
from torch._utils_internal import get_file_path_2
|
|
|
|
import pkgutil
|
|
import torch
|
|
import importlib
|
|
from typing import Callable
|
|
import inspect
|
|
import json
|
|
import os
|
|
import unittest
|
|
from importlib import import_module
|
|
from itertools import chain
|
|
from pathlib import Path
|
|
|
|
def _find_all_importables(pkg):
|
|
"""Find all importables in the project.
|
|
|
|
Return them in order.
|
|
"""
|
|
return sorted(
|
|
set(
|
|
chain.from_iterable(
|
|
_discover_path_importables(Path(p), pkg.__name__)
|
|
for p in pkg.__path__
|
|
),
|
|
),
|
|
)
|
|
|
|
|
|
def _discover_path_importables(pkg_pth, pkg_name):
|
|
"""Yield all importables under a given path and package.
|
|
|
|
This is like pkgutil.walk_packages, but does *not* skip over namespace
|
|
packages. Taken from https://stackoverflow.com/questions/41203765/init-py-required-for-pkgutil-walk-packages-in-python3
|
|
"""
|
|
for dir_path, _d, file_names in os.walk(pkg_pth):
|
|
pkg_dir_path = Path(dir_path)
|
|
|
|
if pkg_dir_path.parts[-1] == '__pycache__':
|
|
continue
|
|
|
|
if all(Path(_).suffix != '.py' for _ in file_names):
|
|
continue
|
|
|
|
rel_pt = pkg_dir_path.relative_to(pkg_pth)
|
|
pkg_pref = '.'.join((pkg_name, ) + rel_pt.parts)
|
|
yield from (
|
|
pkg_path
|
|
for _, pkg_path, _ in pkgutil.walk_packages(
|
|
(str(pkg_dir_path), ), prefix=f'{pkg_pref}.',
|
|
)
|
|
)
|
|
|
|
|
|
class TestPublicBindings(TestCase):
|
|
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_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",
|
|
"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_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",
|
|
"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
|
|
|
|
|
|
def test_modules_can_be_imported(self):
|
|
failures = []
|
|
for _, modname, _ in _discover_path_importables(str(torch.__path__), "torch"):
|
|
try:
|
|
# TODO: fix "torch/utils/model_dump/__main__.py"
|
|
# which calls sys.exit() when we try to import it
|
|
if "__main__" in modname:
|
|
continue
|
|
import_module(modname)
|
|
except Exception as e:
|
|
# Some current failures are not ImportError
|
|
failures.append((modname, type(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",
|
|
"torch.onnx._internal.fx._pass",
|
|
"torch.onnx._internal.fx.analysis",
|
|
"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.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.pipe_with_ddp_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.utils.tensorboard._caffe2_graph",
|
|
"torch._inductor.codegen.cuda.cuda_template",
|
|
"torch._inductor.codegen.cuda.gemm_template",
|
|
"torch._inductor.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",
|
|
}
|
|
|
|
# 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.pipeline.sync",
|
|
"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",
|
|
}
|
|
|
|
errors = []
|
|
for mod, excep_type 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 {excep_type}")
|
|
|
|
self.assertEqual("", "\n".join(errors))
|
|
|
|
# AttributeError: module 'torch.distributed' has no attribute '_shard'
|
|
@unittest.skipIf(IS_WINDOWS or IS_JETSON, "Distributed Attribute Error")
|
|
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)
|
|
if not (isinstance(obj, Callable) 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 _, modname, _ in _discover_path_importables(str(torch.__path__), "torch"):
|
|
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()
|