create type hint stub files for module torch by t-vi · Pull Request #12500 · pytorch/pytorch

t-vi · 2018-10-09T20:52:48Z

We have:

This is an initial stab at creating a type stub torch/init.pyi .
This is only tested on Python 3, since that's the only Python version mypy
works on.
So far, we only aim at doing this for torch functions and torch.Tensor.
Quite a few methods and functions have to be typed manually. These are
done in torch/init.pyi.in

For me, PyCharm (the non-paid one) didn't seem to indicate errors in the .pyi when opening and seemed to be able to get the type hint for the few functions I tried, but I don't use PyCharm for my usual PyTorch activities, so I didn't extensively try this out.

An example of a generated PYI is at this gist. https://gist.github.com/ezyang/bf9b6a5fa8827c52152858169bcb61b1

Original summary:

This is more a basis for discussion that a ready solution, as it does lots of funny things, and for many of them a better solution will be found, but to facilitate discussion, I'm putting this out.

After many improvements, this could contribute to fixing #7318.

We have

This is an initial stab at creating a type stub torch/__init__.pyi .
We only do this for Python 3 because we want to use type hint introspection.
So far, we only aim at doing this for torch functions and torch.Tensor.
We need to import the newly build torch. Thus we do this at the end of the build.
We use os.fork to only import the module in a child process.
We use an annotate decorator to be able to put type hints on the native python functions in a way that
a) they're available in the usual place for Python 3
b) we stay Python 2 compatible
Some annotations in torch/functional.py are provided as examples, but the remaining ones still need to be done if you are OK with the decorator approach. At first glance, it seems that the annotations are also available to Python3 introspection, e.g. on Jupyter.

For me, PyCharm (the non-paid one) didn't seem to indicate errors in the .pyi when opening and seemed to be able to get the type hint for the few functions I tried, but I don't use PyCharm for my usual PyTorch activities, so I didn't extensively try this out.

An example of a generated PYI is at this gist.

This is more a basis for discussion that a ready solution, as it does lots of funny things, and for many of them a better solution will be found. - Initial stab at creating a type torch/__init__.pyi . - We only do this for Python 3 because we want to use type hint introspection. - So far, we only aim at doing this for torch functions and torch.Tensor. - We need to import the newly build torch. Thus we do this at the end of the build. We use os.fork to only import the module in a child process. - We use an annotate decorator to be able to put type hints on the native python functions in a way that a) they're available in the usual place for Python 3 b) we stay Python 2 compatible - Some annotatons in torch/functional.py are provided as examples, but the remaining ones still need to be done. This could end up fixing pytorch#7318

ssnl

Nice!

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+                'double': 'float',
+                'Generator *': 'Generator',
+                'Generator*': 'Generator',
+                'std::vector<Tensor>': 'Tuple[Tensor, ...]',


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+                'Layout': 'layout',
+                'void*': 'int',  # dataptr
+                'std::string': 'str',
+                'real': 'float',


- Add manual tensor() annotation, thank you, @elliotwite. - Create class stubs for device etc. - Improve type mapping. Thank you, Simon, for the review comments.

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+                'std::string': 'str',
+                'real': 'Union[float, int]',
+                'accreal': 'Union[float, int]',
+                'IntegerTensor': 'Tensor',


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+
+def arg_to_type_hint(arg):
+    name = arg['name']
+    if name == 'from':  # keyword...


ssnl · 2018-10-11T04:27:15Z

Question: what do you think is a good way to test this? Maybe pick some functions, and construct inputs basing on arg type, and then assert that it doesn't throw error from arg parser (it may still error in later stages due to things like shape mismatch)?

ezyang · 2018-10-11T06:06:26Z

We'll definitely need some sort of testing strategy for this, otherwise it will bitrot faster than you can say leroy jenkins.

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+
+# annotation decorator to get annotations in a way that is compatible
+# with both Python 2 and 3
+def annotate(ret, **kwargs):


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+needed_modules = set()
+
+
+def type_to_python(typename):


t-vi · 2018-10-11T06:10:47Z

Re testing: So the type hints should ideally have two properties:

Code satisfying the type hints will be "correct" in some way. One could try to check this using an approach similar to the one you mention, parsing the error messages. If we are off here, programs/people will have wrong ideas about what can be passed, but it isn't worse than now.
Any correct code will satisfy the type hint restrictions.
I think this may be more important property - people will get upset if their correct code is marked as wrong by checkers. One testing approach I could think of is to put "known good code" (our tests? the examples/tutorials?) through a checker (mypy?) and see if it passes. The question is whether the tests are a tad too dynamic to be statically checked and where to get good test code from if they are.

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+
+
+def do_gen_py(build_lib_path):
+    while '' in sys.path:


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+        decls = [d for d in decls if d['name'] != fname + '_out']
+    for decl in decls:
+        skip = 'Type' in [a['dynamic_type'] for a in decl['arguments']]
+        if not skip:


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+    return name + ': ' + typename + default
+
+
+def generate_type_hints(fname, decls, is_tensor=False):


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+                python_returns_s = python_returns[0]
+            type_hint = "def {}({}) -> {}: ...".format(fname, python_args_s, python_returns_s)
+            numargs = len(decl['arguments'])
+            have_vararg_version = (numargs > 0 and decl['arguments'][0]['dynamic_type'] in {'IntList', 'TensorList'} and


ezyang · 2018-10-11T06:38:26Z

OK, so I briefly skimmed the generation code, and I think the best thing we can do to make this presentable is to do some Declarations.yaml cleanup, which should simplify a bit of the hacking around you had to do here. #12562 records some overall directions we want to take here, but probably the most important prereqs for this patch are:

Centralizing TensorOptions to kwargs transformation
Fix some types (e.g., Generator * and std::vector<Tensor>) to no longer be directly C++ types
Rename dynamic_type to type (after having eliminated what we previously called type)
Eliminate all mentions of Type from Declarations.yaml
Centralized logic for determining if a method is inplace or not
Change method_of to something simpler like function and/or method. Then don't have to use the "does it have a self argument" heuristic.

t-vi · 2018-10-11T09:44:50Z

So what does the #12562 washlist mean for progressing here?
I'll certainly fix the immediate comments, thanks for these!
Is it fair to say that you don't object to the annotation approach using the decorator? Then I would see to sprinkling that a bit more on functional.py.
Regarding code for testing: On the bug report, @elliotwaite mentioned the documentation examples as a source for code. That seems a great idea and we do have all the docstrings, so maybe we canmake a .py from these and run mypy.

ezyang · 2018-10-11T18:44:58Z

So what does the #12562 washlist mean for progressing here?

Let me look again at the code and work out exactly what would be affected by the changes.

Is it fair to say that you don't object to the annotation approach using the decorator? Then I would see to sprinkling that a bit more on functional.py.

Yeah, I don't personally object to it. It seems like a quite nice way to add the annotation.

Regarding code for testing: On the bug report, @elliotwaite mentioned the documentation examples as a source for code. That seems a great idea and we do have all the docstrings, so maybe we canmake a .py from these and run mypy.

This sounds like a great idea.

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+    return type_hints
+
+
+def parameters_from_signature(sig):


ezyang · 2018-10-11T18:57:13Z

These spots look like they would make Declarations.yaml refactoring harder

pytorch/tools/pyi/gen_pyi.py

Line 56 in a327e18

has_out = fname + '_out' in dnames

pytorch/tools/pyi/gen_pyi.py

Line 188 in a327e18

if not name.startswith('_'):

Don't want to hard-code _out handling and inplace handling; should be centralized.

pytorch/tools/pyi/gen_pyi.py

Line 60 in a327e18

skip = 'Type' in [a['dynamic_type'] for a in decl['arguments']]

Type is going away. This one's pretty harmless though, easy enough to fix once Type is killed.

pytorch/tools/pyi/gen_pyi.py

Line 65 in a327e18

if 'self: Tensor' in python_args:

Hard-coded handling of self. Maybe this is OK and we'll keep this in the end. CC @zdevito

pytorch/tools/pyi/gen_pyi.py

Line 75 in a327e18

python_args += ["dtype: dtype=None",

Hard-coded TensorOptions expansion

pytorch/tools/pyi/gen_pyi.py

Line 87 in a327e18

    
           have_vararg_version = (numargs > 0 and decl['arguments'][0]['dynamic_type'] in {'IntList', 'TensorList'} and

I actually have no idea what is going on with varargs.

ezyang · 2018-10-11T19:00:32Z

As a whole, after reviewing this patch, I feel a bit better about landing it before the refactor happens, after cleanups happen. I don't think it will be that much more burden when we make adjustments. (Maybe this means I'm signing up to do the adjustments :) cc @zdevito @gchanan

t-vi · 2018-10-11T19:15:08Z

The _ filtering for "internal" functions does not work as is BTW, as we need annotations for __mul__ .

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+class create_pyi(distutils.command.build.build):
+    def run(self):
+        print("-- Building .pyi --")
+        if sys.version_info[0] == 3:


ezyang · 2019-01-24T19:36:37Z

After a lot of whacking, the type annotation script no longer requires import torch to generate annotations.

t-vi · 2019-01-24T19:41:26Z

Awesome stuff, @ezyang . ❤️ Probably would have been easier to not base it on my feeble attempts. 😕

ezyang · 2019-01-24T19:49:01Z

Let's be clear now, your diff helped a lot :) Would have been 10x more work without it.

Signed-off-by: Edward Z. Yang <ezyang@fb.com>

ezyang · 2019-01-25T16:23:55Z

Ugh, CI failure doesn't repro locally. Time to fire up the Docker image, I guess...

Signed-off-by: Edward Z. Yang <ezyang@fb.com>

ezyang · 2019-01-28T20:29:37Z

What I ran into trouble with was to make sure mypy picks up the right PyTorch. (Which is why I did this temporary directory dance.)

So, in the end, you were right! The problem is specific to when you install PyTorch, because mypy treats installed packages differently from local packages. It will refuse to attempt to typecheck using installed packages unless they are registered on typeshed, which explains why the imports cannot be found. Your symlink trick solved the problem, because it made the package look local again. There doesn't seem to be a way to otherwise force mypy to treat the package as typecheckable when it's not registered on typeshed.

I think I am just going to resurrect this code verbatim with a comment.

Signed-off-by: Edward Z. Yang <ezyang@fb.com>

* create type hint stub files for module torch This is more a basis for discussion that a ready solution, as it does lots of funny things, and for many of them a better solution will be found. - Initial stab at creating a type torch/__init__.pyi . - We only do this for Python 3 because we want to use type hint introspection. - So far, we only aim at doing this for torch functions and torch.Tensor. - We need to import the newly build torch. Thus we do this at the end of the build. We use os.fork to only import the module in a child process. - We use an annotate decorator to be able to put type hints on the native python functions in a way that a) they're available in the usual place for Python 3 b) we stay Python 2 compatible - Some annotatons in torch/functional.py are provided as examples, but the remaining ones still need to be done. This could end up fixing #7318 This was backported from #12500 but with tests removed and the stub file checked in directly, so we don't have to also make sure all build shenanigans work. Master will be properly tested. The stub file was generated by running 'python setup.py create_pyi' and then copying the generated pyi file in the build directory (find -name *.pyi) to torch/ * Ignore pyi in flake8 Signed-off-by: Edward Z. Yang <ezyang@fb.com> * Real fix for lint error Signed-off-by: Edward Z. Yang <ezyang@fb.com>

ezyang · 2019-01-29T04:28:26Z

Oh my god it passed all the tests LOL

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@ezyang is landing this pull request. If you are a Facebook employee, you can view this diff on Phabricator.

vsiles · 2019-02-11T13:23:34Z

I'm curious: why didn't you just annotate torch/tensor.py directly ?

ezyang · 2019-02-12T22:48:32Z

@vsiles For a pretty silly reason, actually: the original patch @t-vi wrote didn't do that, and when I tried it, PyCharm shows up the module as torch.tensor rather than torch (in reality, I tried a version where I just added a type stub for torch._C and then let things work out; in that case, I got torch._C.)

This isn't set in stone, esp. if people don't mind the originating module look ugly. But that's the reason.

Summary: - Fix broken sparse_coo_examples, update output - Tensor(...) to tensor(...) - Fix arguments to math.log to be floats While the last might be debateable, mypy currently complains when passing an int to math.log. As it is not essential for our examples, let's be clean w.r.t. other people's expectations. These popped up while checking examples in the context of pytorch#12500 . Pull Request resolved: pytorch#12707 Differential Revision: D10415256 Pulled By: SsnL fbshipit-source-id: c907b576b02cb0f89d8f261173dbf4b3175b4b8d

Summary: We have: - This is an initial stab at creating a type stub `torch/__init__.pyi` . - This is only tested on Python 3, since that's the only Python version mypy works on. - So far, we only aim at doing this for torch functions and torch.Tensor. - Quite a few methods and functions have to be typed manually. These are done in `torch/__init__.pyi.in` For me, PyCharm (the non-paid one) didn't seem to indicate errors in the .pyi when opening and seemed to be able to get the type hint for the few functions I tried, but I don't use PyCharm for my usual PyTorch activities, so I didn't extensively try this out. An example of a generated PYI is at [this gist](https://gist.github.com/ezyang/bf9b6a5fa8827c52152858169bcb61b1). Pull Request resolved: pytorch#12500 Differential Revision: D13695553 Pulled By: ezyang fbshipit-source-id: 4566c71913ede4e4c23ebc4a72c17151f94e8e21

t-vi added 2 commits October 9, 2018 22:06

the future...

2e3ee60

t-vi requested review from apaszke, colesbury, ezyang, gchanan, soumith and zdevito as code owners October 9, 2018 20:52

ssnl reviewed Oct 9, 2018

View reviewed changes

Improve type mapping, add manual annotations

a327e18

- Add manual tensor() annotation, thank you, @elliotwite. - Create class stubs for device etc. - Improve type mapping. Thank you, Simon, for the review comments.