mirror of
https://github.com/zebrajr/pytorch.git
synced 2025-12-06 12:20:52 +01:00
f81db8afb8
Summary:
I have some test code in there as well, along with a script "test_libtorch" to run it. You'll need to modify `test_libtorch` to point to where you have `pytorch` built. I currently require that `pybind11` is included as a subdirectory of the test, but added it to the `.gitignore` to make this reviewable.
Currently, something like this works:
```cpp
struct Foo {
int x, y;
Foo(): x(2), y(5){}
Foo(int x_, int y_) : x(x_), y(y_) {}
void display() {
cout<<"x: "<<x<<' '<<"y: "<<y<<endl;
}
int64_t add(int64_t z) {
return (x+y)*z;
}
};
static auto test = torch::jit::class_<Foo>("Foo")
.def(torch::jit::init<int64_t, int64_t>())
.def("display", &Foo::display)
.def("add", &Foo::add)
.def("combine", &Foo::combine);
```
with
```py
torch.jit.script
def f(x):
val = torch._C.Foo(5, 3)
val.display()
print(val.add(3))
```
results in
```
x: 5 y: 3
24
```
Current issues:
- [x] The python class created by torchscript doesn't interactly properly with the surrounding code.
```
torch.jit.script
def f(x):
val = torch._C.Foo(5, 3)
return val
```
- [x] Doesn't properly take in non-pointer classes. Can't define this function signature in cpp (We don't want to support this I believe).
```cpp
void combine(Foo x) {
```
- [x] Has some issues with memory for blobs when constructing multiple objects (fix constant propagation pass to not treat capsules as the same object).
```py
torch.jit.script
def f(x):
val = torch._C.Foo(5, 3)
val2 = torch._C.Foo(100, 0)
val.display()
print(val.add(3))
```
- [ ] Can't define multiple constructors (need to define overload string. Currently not possible since we don't support overloaded methods).
- [x] `init` is a little bit different syntax than `pybind`. `.init<...>()` instead of `.def(py::init<>())`
- [x] I couldn't figure out how to add some files into the build so they'd be copied to the `include/` directories, so I symlinked them manually.
- [ ] Currently, the conversion from Python into Torchscript doesn't work.
- [ ] Torchbind also currently requires Python/Pybind dependency. Fixing this would probably involve some kind of macro to bind into Python when possible.
- [ ] We pass back into Python by value, currently. There's no way of passing by reference.
- [x] Currently can only register one method with the same type signature. This is because we create a `static auto opRegistry`, and the function is templated on the type signature.
Somewhat blocked on https://github.com/pytorch/pytorch/pull/21177. We currently use some structures that will be refactored by his PR (namely `return_type_to_ivalue` and `ivalue_to_arg_type`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/21098
Differential Revision: D16634872
Pulled By: Chillee
fbshipit-source-id: 1408bb89ea649c27d560df59e2cf9920467fe1de
81 lines
2.3 KiB
Python
81 lines
2.3 KiB
Python
import unittest
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import torch
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from torch import ops
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import torch.jit as jit
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import glob
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import os
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def get_custom_class_library_path():
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library_filename = glob.glob("build/*custom_class*")
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assert (len(library_filename) == 1)
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library_filename = library_filename[0]
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path = os.path.abspath(library_filename)
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assert os.path.exists(path), path
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return path
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def test_equality(f, cmp_key):
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obj1 = f()
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obj2 = jit.script(f)()
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return (cmp_key(obj1), cmp_key(obj2))
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class TestCustomOperators(unittest.TestCase):
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def setUp(self):
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ops.load_library(get_custom_class_library_path())
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def test_no_return_class(self):
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def f():
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val = torch.classes.Foo(5, 3)
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return val.info()
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self.assertEqual(*test_equality(f, lambda x: x))
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def test_constructor_with_args(self):
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def f():
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val = torch.classes.Foo(5, 3)
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return val
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self.assertEqual(*test_equality(f, lambda x: x.info()))
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def test_function_call_with_args(self):
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def f():
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val = torch.classes.Foo(5, 3)
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val.increment(1)
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return val
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self.assertEqual(*test_equality(f, lambda x: x.info()))
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def test_function_method_wrong_type(self):
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def f():
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val = torch.classes.Foo(5, 3)
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val.increment("asdf")
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return val
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with self.assertRaisesRegex(RuntimeError, "Expected"):
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jit.script(f)()
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@unittest.skip("We currently don't support passing custom classes to custom methods.")
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def test_input_class_type(self):
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def f():
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val = torch.classes.Foo(1, 2)
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val2 = torch.classes.Foo(2, 3)
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val.combine(val2)
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return val
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self.assertEqual(*test_equality(f, lambda x: x.info()))
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def test_stack_string(self):
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def f():
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val = torch.classes.StackString(["asdf", "bruh"])
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return val.pop()
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self.assertEqual(*test_equality(f, lambda x: x))
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def test_stack_push_pop(self):
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def f():
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val = torch.classes.StackString(["asdf", "bruh"])
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val2 = torch.classes.StackString(["111", "222"])
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val.push(val2.pop())
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return val.pop() + val2.pop()
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self.assertEqual(*test_equality(f, lambda x: x))
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if __name__ == "__main__":
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unittest.main()
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