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|
# 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:
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()
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