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b5cd3a80bb
Summary:
# What's this
Just a small bug fix related to typing stubs.
I haven't open an issue. I will do so if I must open it, but this PR is very small (only 6 lines diff).
## What I encountered
pytorch 1.5.0 with mypy 0.770 behaves odd. The code is following:
```python
import torch
def f() -> int: # Mypy says: `error: Missing return statement`
with torch.no_grad():
return 1
```
No mypy error is expected, but actually mypy 0.770 warns about `Missing return statement`.
## This is because
`mypy >= 0.730` with `--warn-unreachable` says it's unreachable because `torch.no_grad()` may "swallows" the error in the return statement.
http://mypy-lang.blogspot.com/2019/09/mypy-730-released.html
Here is a small "swallowing" example:
```python
from typing import Generator
from contextlib import contextmanager
contextmanager
def swallow_zerodiv() -> Generator[None, None, None]:
try:
yield None
except ZeroDivisionError:
pass
finally:
pass
def div(a: int, b: int) -> float: # This function seems `(int, int) -> float` but actually `(int, int) -> Optional[float]` because ` return a / b` may be swallowed
with swallow_zerodiv():
return a / b
if __name__ == '__main__':
result = div(1, 0)
print(result, type(result)) # None <class 'NoneType'>
```
To supress this behavior, one can tell mypy not to swallow any exceptions, with returning `Literal[False]` or `None` in `__exit__` method of the context manager.
# What I did
Return `None` instead of `bool` to tell mypy that "I never swallow your exception".
I chose `None` because I cannot interpret `Literal[False]` without typing_extensions in `python <=3.7`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/39324
Differential Revision: D21833651
Pulled By: albanD
fbshipit-source-id: d5cad2e5e19068bd68dc773e997bf13f7e60f4de
107 lines
4.2 KiB
Python
107 lines
4.2 KiB
Python
import torch
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import warnings
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from typing import Any
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class detect_anomaly(object):
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r"""Context-manager that enable anomaly detection for the autograd engine.
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This does two things:
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- Running the forward pass with detection enabled will allow the backward
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pass to print the traceback of the forward operation that created the failing
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backward function.
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- Any backward computation that generate "nan" value will raise an error.
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.. warning::
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This mode should be enabled only for debugging as the different tests
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will slow down your program execution.
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Example:
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>>> import torch
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>>> from torch import autograd
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>>> class MyFunc(autograd.Function):
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... @staticmethod
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... def forward(ctx, inp):
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... return inp.clone()
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... @staticmethod
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... def backward(ctx, gO):
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... # Error during the backward pass
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... raise RuntimeError("Some error in backward")
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... return gO.clone()
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>>> def run_fn(a):
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... out = MyFunc.apply(a)
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... return out.sum()
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>>> inp = torch.rand(10, 10, requires_grad=True)
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>>> out = run_fn(inp)
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>>> out.backward()
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Traceback (most recent call last):
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File "<stdin>", line 1, in <module>
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File "/your/pytorch/install/torch/tensor.py", line 93, in backward
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torch.autograd.backward(self, gradient, retain_graph, create_graph)
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File "/your/pytorch/install/torch/autograd/__init__.py", line 90, in backward
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allow_unreachable=True) # allow_unreachable flag
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File "/your/pytorch/install/torch/autograd/function.py", line 76, in apply
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return self._forward_cls.backward(self, *args)
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File "<stdin>", line 8, in backward
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RuntimeError: Some error in backward
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>>> with autograd.detect_anomaly():
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... inp = torch.rand(10, 10, requires_grad=True)
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... out = run_fn(inp)
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... out.backward()
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Traceback of forward call that caused the error:
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File "tmp.py", line 53, in <module>
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out = run_fn(inp)
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File "tmp.py", line 44, in run_fn
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out = MyFunc.apply(a)
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Traceback (most recent call last):
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File "<stdin>", line 4, in <module>
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File "/your/pytorch/install/torch/tensor.py", line 93, in backward
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torch.autograd.backward(self, gradient, retain_graph, create_graph)
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File "/your/pytorch/install/torch/autograd/__init__.py", line 90, in backward
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allow_unreachable=True) # allow_unreachable flag
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File "/your/pytorch/install/torch/autograd/function.py", line 76, in apply
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return self._forward_cls.backward(self, *args)
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File "<stdin>", line 8, in backward
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RuntimeError: Some error in backward
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"""
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def __init__(self) -> None:
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self.prev = torch.is_anomaly_enabled()
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warnings.warn('Anomaly Detection has been enabled. '
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'This mode will increase the runtime '
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'and should only be enabled for debugging.', stacklevel=2)
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def __enter__(self) -> None:
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torch.set_anomaly_enabled(True)
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def __exit__(self, *args: Any) -> None:
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torch.set_anomaly_enabled(self.prev)
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class set_detect_anomaly(object):
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r"""Context-manager that sets the anomaly detection for the autograd engine on or off.
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``set_detect_anomaly`` will enable or disable the autograd anomaly detection
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based on its argument :attr:`mode`.
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It can be used as a context-manager or as a function.
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See ``detect_anomaly`` above for details of the anomaly detection behaviour.
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Arguments:
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mode (bool): Flag whether to enable anomaly detection (``True``),
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or disable (``False``).
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"""
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def __init__(self, mode: bool) -> None:
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self.prev = torch.is_anomaly_enabled()
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torch.set_anomaly_enabled(mode)
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def __enter__(self) -> None:
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pass
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def __exit__(self, *args: Any) -> None:
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torch.set_anomaly_enabled(self.prev)
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