mirror of
https://github.com/pytorch/pytorch.git
synced 2025-10-20 21:14:14 +08:00
354edb232a
In particular, when creating the PyTorch wheel, we use setuptools find_packages 551b3c6dca/setup.py (L1055) which explicitly skips packages without `__init__.py` files (namespace packages) https://setuptools.pypa.io/en/latest/userguide/package_discovery.html#finding-simple-packages.
So this PR is reverting the change to stop skipping these namespace packages as, even though they are in the codebase, they are not in the published binaries and so we're ok relaxing the public API and importability rules for them.
A manual diff of the two traversal methods:
```
torch._inductor.kernel.bmm
torch._inductor.kernel.conv
torch._inductor.kernel.flex_attention
torch._inductor.kernel.mm
torch._inductor.kernel.mm_common
torch._inductor.kernel.mm_plus_mm
torch._inductor.kernel.unpack_mixed_mm
torch._strobelight.examples.cli_function_profiler_example
torch._strobelight.examples.compile_time_profile_example
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.ao.quantization.experimental.APoT_tensor
torch.ao.quantization.experimental.adaround_fake_quantize
torch.ao.quantization.experimental.adaround_loss
torch.ao.quantization.experimental.adaround_optimization
torch.ao.quantization.experimental.apot_utils
torch.ao.quantization.experimental.fake_quantize
torch.ao.quantization.experimental.fake_quantize_function
torch.ao.quantization.experimental.linear
torch.ao.quantization.experimental.observer
torch.ao.quantization.experimental.qconfig
torch.ao.quantization.experimental.quantizer
torch.csrc.jit.tensorexpr.codegen_external
torch.csrc.jit.tensorexpr.scripts.bisect
torch.csrc.lazy.test_mnist
torch.distributed._tensor.examples.checkpoint_example
torch.distributed._tensor.examples.comm_mode_features_example
torch.distributed._tensor.examples.comm_mode_features_example_argparser
torch.distributed._tensor.examples.convnext_example
torch.distributed._tensor.examples.torchrec_sharding_example
torch.distributed._tensor.examples.visualize_sharding_example
torch.distributed.benchmarks.benchmark_ddp_rpc
torch.distributed.checkpoint.examples.async_checkpointing_example
torch.distributed.checkpoint.examples.fsdp_checkpoint_example
torch.distributed.checkpoint.examples.stateful_example
torch.distributed.examples.memory_tracker_example
torch.fx.experimental.shape_inference.infer_shape
torch.fx.experimental.shape_inference.infer_symbol_values
torch.include.fp16.avx
torch.include.fp16.avx2
torch.onnx._internal.fx.analysis.unsupported_nodes
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.utils._strobelight.examples.cli_function_profiler_example
torch.utils.benchmark.examples.sparse.compare
torch.utils.benchmark.examples.sparse.fuzzer
torch.utils.benchmark.examples.sparse.op_benchmark
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
torch.utils.tensorboard.summary
torch.utils.tensorboard.writer
```
These are all either namespace packages (which we want to remove) or package that are not importable (and tagged as such in the test).
Pull Request resolved: https://github.com/pytorch/pytorch/pull/130497
Approved by: https://github.com/aorenste
620 lines
26 KiB
Python
620 lines
26 KiB
Python
# Owner(s): ["module: autograd"]
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import importlib
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import inspect
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import json
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import os
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import pkgutil
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import unittest
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from typing import Callable
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import torch
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from torch._utils_internal import get_file_path_2
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from torch.testing._internal.common_utils import (
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IS_JETSON,
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IS_MACOS,
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IS_WINDOWS,
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run_tests,
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skipIfTorchDynamo,
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TestCase,
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)
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class TestPublicBindings(TestCase):
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def test_no_new_reexport_callables(self):
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"""
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This test aims to stop the introduction of new re-exported callables into
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torch whose names do not start with _. Such callables are made available as
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torch.XXX, which may not be desirable.
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"""
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reexported_callables = sorted(
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k
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for k, v in vars(torch).items()
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if callable(v) and not v.__module__.startswith("torch")
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)
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self.assertTrue(
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all(k.startswith("_") for k in reexported_callables), reexported_callables
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)
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def test_no_new_bindings(self):
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"""
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This test aims to stop the introduction of new JIT bindings into torch._C
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whose names do not start with _. Such bindings are made available as
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torch.XXX, which may not be desirable.
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If your change causes this test to fail, add your new binding to a relevant
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submodule of torch._C, such as torch._C._jit (or other relevant submodule of
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torch._C). If your binding really needs to be available as torch.XXX, add it
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to torch._C and add it to the allowlist below.
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If you have removed a binding, remove it from the allowlist as well.
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"""
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# This allowlist contains every binding in torch._C that is copied into torch at
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# the time of writing. It was generated with
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#
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# {elem for elem in dir(torch._C) if not elem.startswith("_")}
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torch_C_allowlist_superset = {
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"AggregationType",
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"AliasDb",
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"AnyType",
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"Argument",
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"ArgumentSpec",
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"AwaitType",
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"autocast_decrement_nesting",
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"autocast_increment_nesting",
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"AVG",
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"BenchmarkConfig",
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"BenchmarkExecutionStats",
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"Block",
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"BoolType",
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"BufferDict",
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"StorageBase",
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"CallStack",
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"Capsule",
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"ClassType",
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"clear_autocast_cache",
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"Code",
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"CompilationUnit",
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"CompleteArgumentSpec",
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"ComplexType",
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"ConcreteModuleType",
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"ConcreteModuleTypeBuilder",
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"cpp",
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"CudaBFloat16TensorBase",
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"CudaBoolTensorBase",
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"CudaByteTensorBase",
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"CudaCharTensorBase",
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"CudaComplexDoubleTensorBase",
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"CudaComplexFloatTensorBase",
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"CudaDoubleTensorBase",
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"CudaFloatTensorBase",
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"CudaHalfTensorBase",
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"CudaIntTensorBase",
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"CudaLongTensorBase",
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"CudaShortTensorBase",
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"DeepCopyMemoTable",
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"default_generator",
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"DeserializationStorageContext",
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"device",
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"DeviceObjType",
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"DictType",
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"DisableTorchFunction",
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"DisableTorchFunctionSubclass",
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"DispatchKey",
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"DispatchKeySet",
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"dtype",
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"EnumType",
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"ErrorReport",
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"ExcludeDispatchKeyGuard",
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"ExecutionPlan",
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"FatalError",
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"FileCheck",
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"finfo",
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"FloatType",
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"fork",
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"FunctionSchema",
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"Future",
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"FutureType",
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"Generator",
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"GeneratorType",
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"get_autocast_cpu_dtype",
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"get_autocast_dtype",
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"get_autocast_ipu_dtype",
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"get_default_dtype",
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"get_num_interop_threads",
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"get_num_threads",
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"Gradient",
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"Graph",
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"GraphExecutorState",
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"has_cuda",
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"has_cudnn",
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"has_lapack",
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"has_mkl",
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"has_mkldnn",
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"has_mps",
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"has_openmp",
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"has_spectral",
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"iinfo",
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"import_ir_module_from_buffer",
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"import_ir_module",
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"InferredType",
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"init_num_threads",
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"InterfaceType",
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"IntType",
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"SymFloatType",
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"SymBoolType",
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"SymIntType",
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"IODescriptor",
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"is_anomaly_enabled",
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"is_anomaly_check_nan_enabled",
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"is_autocast_cache_enabled",
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"is_autocast_cpu_enabled",
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"is_autocast_ipu_enabled",
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"is_autocast_enabled",
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"is_grad_enabled",
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"is_inference_mode_enabled",
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"JITException",
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"layout",
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"ListType",
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"LiteScriptModule",
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"LockingLogger",
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"LoggerBase",
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"memory_format",
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"merge_type_from_type_comment",
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"ModuleDict",
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"Node",
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"NoneType",
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"NoopLogger",
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"NumberType",
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"OperatorInfo",
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"OptionalType",
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"OutOfMemoryError",
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"ParameterDict",
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"parse_ir",
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"parse_schema",
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"parse_type_comment",
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"PyObjectType",
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"PyTorchFileReader",
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"PyTorchFileWriter",
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"qscheme",
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"read_vitals",
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"RRefType",
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"ScriptClass",
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"ScriptClassFunction",
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"ScriptDict",
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"ScriptDictIterator",
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"ScriptDictKeyIterator",
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"ScriptList",
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"ScriptListIterator",
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"ScriptFunction",
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"ScriptMethod",
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"ScriptModule",
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"ScriptModuleSerializer",
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"ScriptObject",
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"ScriptObjectProperty",
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"SerializationStorageContext",
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"set_anomaly_enabled",
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"set_autocast_cache_enabled",
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"set_autocast_cpu_dtype",
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"set_autocast_dtype",
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"set_autocast_ipu_dtype",
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"set_autocast_cpu_enabled",
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"set_autocast_ipu_enabled",
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"set_autocast_enabled",
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"set_flush_denormal",
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"set_num_interop_threads",
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"set_num_threads",
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"set_vital",
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"Size",
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"StaticModule",
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"Stream",
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"StreamObjType",
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"Event",
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"StringType",
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"SUM",
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"SymFloat",
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"SymInt",
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"TensorType",
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"ThroughputBenchmark",
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"TracingState",
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"TupleType",
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"Type",
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"unify_type_list",
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"UnionType",
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"Use",
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"Value",
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"set_autocast_gpu_dtype",
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"get_autocast_gpu_dtype",
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"vitals_enabled",
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"wait",
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"Tag",
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"set_autocast_xla_enabled",
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"set_autocast_xla_dtype",
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"get_autocast_xla_dtype",
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"is_autocast_xla_enabled",
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}
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torch_C_bindings = {elem for elem in dir(torch._C) if not elem.startswith("_")}
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# torch.TensorBase is explicitly removed in torch/__init__.py, so included here (#109940)
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explicitly_removed_torch_C_bindings = {
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"TensorBase",
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}
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torch_C_bindings = torch_C_bindings - explicitly_removed_torch_C_bindings
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# Check that the torch._C bindings are all in the allowlist. Since
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# bindings can change based on how PyTorch was compiled (e.g. with/without
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# CUDA), the two may not be an exact match but the bindings should be
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# a subset of the allowlist.
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difference = torch_C_bindings.difference(torch_C_allowlist_superset)
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msg = f"torch._C had bindings that are not present in the allowlist:\n{difference}"
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self.assertTrue(torch_C_bindings.issubset(torch_C_allowlist_superset), msg)
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@staticmethod
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def _is_mod_public(modname):
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split_strs = modname.split(".")
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for elem in split_strs:
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if elem.startswith("_"):
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return False
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return True
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@unittest.skipIf(
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IS_WINDOWS or IS_MACOS,
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"Inductor/Distributed modules hard fail on windows and macos",
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)
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@skipIfTorchDynamo("Broken and not relevant for now")
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def test_modules_can_be_imported(self):
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failures = []
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def onerror(modname):
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failures.append((modname, ImportError))
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for mod in pkgutil.walk_packages(torch.__path__, "torch.", onerror=onerror):
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modname = mod.name
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try:
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# TODO: fix "torch/utils/model_dump/__main__.py"
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# which calls sys.exit() when we try to import it
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if "__main__" in modname:
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continue
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importlib.import_module(modname)
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except Exception as e:
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# Some current failures are not ImportError
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failures.append((modname, type(e)))
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# It is ok to add new entries here but please be careful that these modules
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# do not get imported by public code.
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private_allowlist = {
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"torch._inductor.codegen.cuda.cuda_kernel",
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"torch.onnx._internal.fx._pass",
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"torch.onnx._internal.fx.analysis",
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"torch.onnx._internal.fx.analysis.unsupported_nodes",
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"torch.onnx._internal.fx.decomposition_skip",
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"torch.onnx._internal.fx.diagnostics",
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"torch.onnx._internal.fx.fx_onnx_interpreter",
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"torch.onnx._internal.fx.fx_symbolic_graph_extractor",
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"torch.onnx._internal.fx.onnxfunction_dispatcher",
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"torch.onnx._internal.fx.op_validation",
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"torch.onnx._internal.fx.passes",
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"torch.onnx._internal.fx.passes._utils",
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"torch.onnx._internal.fx.passes.decomp",
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"torch.onnx._internal.fx.passes.functionalization",
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"torch.onnx._internal.fx.passes.modularization",
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"torch.onnx._internal.fx.passes.readability",
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"torch.onnx._internal.fx.passes.type_promotion",
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"torch.onnx._internal.fx.passes.virtualization",
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"torch.onnx._internal.fx.type_utils",
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"torch.testing._internal.common_distributed",
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"torch.testing._internal.common_fsdp",
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"torch.testing._internal.dist_utils",
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"torch.testing._internal.distributed.common_state_dict",
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"torch.testing._internal.distributed._shard.sharded_tensor",
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"torch.testing._internal.distributed._shard.test_common",
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"torch.testing._internal.distributed._tensor.common_dtensor",
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"torch.testing._internal.distributed.ddp_under_dist_autograd_test",
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"torch.testing._internal.distributed.distributed_test",
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"torch.testing._internal.distributed.distributed_utils",
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"torch.testing._internal.distributed.fake_pg",
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"torch.testing._internal.distributed.multi_threaded_pg",
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"torch.testing._internal.distributed.nn.api.remote_module_test",
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"torch.testing._internal.distributed.rpc.dist_autograd_test",
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"torch.testing._internal.distributed.rpc.dist_optimizer_test",
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"torch.testing._internal.distributed.rpc.examples.parameter_server_test",
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"torch.testing._internal.distributed.rpc.examples.reinforcement_learning_rpc_test",
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"torch.testing._internal.distributed.rpc.faulty_agent_rpc_test",
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"torch.testing._internal.distributed.rpc.faulty_rpc_agent_test_fixture",
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"torch.testing._internal.distributed.rpc.jit.dist_autograd_test",
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"torch.testing._internal.distributed.rpc.jit.rpc_test",
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"torch.testing._internal.distributed.rpc.jit.rpc_test_faulty",
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"torch.testing._internal.distributed.rpc.rpc_agent_test_fixture",
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"torch.testing._internal.distributed.rpc.rpc_test",
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"torch.testing._internal.distributed.rpc.tensorpipe_rpc_agent_test_fixture",
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"torch.testing._internal.distributed.rpc_utils",
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"torch._inductor.codegen.cuda.cuda_template",
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"torch._inductor.codegen.cuda.gemm_template",
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"torch._inductor.codegen.cpp_template",
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"torch._inductor.codegen.cpp_gemm_template",
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"torch._inductor.codegen.cpp_micro_gemm",
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"torch._inductor.codegen.cpp_template_kernel",
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"torch._inductor.runtime.triton_helpers",
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"torch.ao.pruning._experimental.data_sparsifier.lightning.callbacks.data_sparsity",
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"torch.backends._coreml.preprocess",
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"torch.contrib._tensorboard_vis",
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"torch.distributed._composable",
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"torch.distributed._functional_collectives",
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"torch.distributed._functional_collectives_impl",
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"torch.distributed._shard",
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"torch.distributed._sharded_tensor",
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"torch.distributed._sharding_spec",
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"torch.distributed._spmd.api",
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"torch.distributed._spmd.batch_dim_utils",
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"torch.distributed._spmd.comm_tensor",
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"torch.distributed._spmd.data_parallel",
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"torch.distributed._spmd.distribute",
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"torch.distributed._spmd.experimental_ops",
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"torch.distributed._spmd.parallel_mode",
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"torch.distributed._tensor",
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"torch.distributed.algorithms._checkpoint.checkpoint_wrapper",
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"torch.distributed.algorithms._optimizer_overlap",
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"torch.distributed.rpc._testing.faulty_agent_backend_registry",
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"torch.distributed.rpc._utils",
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"torch.ao.pruning._experimental.data_sparsifier.benchmarks.dlrm_utils",
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"torch.ao.pruning._experimental.data_sparsifier.benchmarks.evaluate_disk_savings",
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"torch.ao.pruning._experimental.data_sparsifier.benchmarks.evaluate_forward_time",
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"torch.ao.pruning._experimental.data_sparsifier.benchmarks.evaluate_model_metrics",
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"torch.ao.pruning._experimental.data_sparsifier.lightning.tests.test_callbacks",
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"torch.csrc.jit.tensorexpr.scripts.bisect",
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"torch.csrc.lazy.test_mnist",
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"torch.distributed._shard.checkpoint._fsspec_filesystem",
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"torch.distributed._tensor.examples.visualize_sharding_example",
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"torch.distributed.checkpoint._fsspec_filesystem",
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"torch.distributed.examples.memory_tracker_example",
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"torch.testing._internal.distributed.rpc.fb.thrift_rpc_agent_test_fixture",
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"torch.utils._cxx_pytree",
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"torch.utils.tensorboard._convert_np",
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"torch.utils.tensorboard._embedding",
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"torch.utils.tensorboard._onnx_graph",
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"torch.utils.tensorboard._proto_graph",
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"torch.utils.tensorboard._pytorch_graph",
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"torch.utils.tensorboard._utils",
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}
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# No new entries should be added to this list.
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# All public modules should be importable on all platforms.
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public_allowlist = {
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"torch.distributed.algorithms.ddp_comm_hooks",
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"torch.distributed.algorithms.model_averaging.averagers",
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"torch.distributed.algorithms.model_averaging.hierarchical_model_averager",
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"torch.distributed.algorithms.model_averaging.utils",
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"torch.distributed.checkpoint",
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"torch.distributed.constants",
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"torch.distributed.distributed_c10d",
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"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, 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 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."):
|
|
mod = 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()
|