Summary:
https://github.com/pytorch/pytorch/issues/20153
I believe you need 2 passes for this. Take this example
```python
torch.jit.script
def f():
x = torch.ones(10, 9, 8, 7, 6)
return x[..., None, None].shape
```
which results in `[10, 9, 8, 7, 6, 1, 1]`
vs
```
torch.jit.script
def f():
x = torch.ones(10, 9, 8, 7, 6)
return x[..., None, None, :].shape
```
which results in `[10, 9, 8, 7, 1, 1, 6]`
After only processing `x[..., None, None` we don't know whether we should be creating a new dimension at the end of the dimension list or somewhere in the middle. What we do depends on the elements to the right of it.
Thus, I do 2 passes - one to collect all the dimensions that the index operations operate on, and another that executes the index operations.
This still doesn't work for an ellipse index followed by a tensor index, but it wasn't working previously either.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/22905
Differential Revision: D16433558
Pulled By: Chillee
fbshipit-source-id: c1b303cb97b1af8b6e405bad33495ef3b4c27c4a
Summary:
This fixes up the test suite (mostly just adding `ignore` decorations
to tests that need to call Python function) so that it all passes with
recursive script enabled.
The main user-facing result of this change is that Python functions are
compiled without any decorators, so non-TorchScriptable code must be
decorated with `torch.jit.ignore` (or
`torch.jit.ignore(drop_on_export=True` to maintain the functionality of
the current `ignore`)
Details can be found in #20939
](https://our.intern.facebook.com/intern/diff/16277608/)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/22887
Pulled By: driazati
Differential Revision: D16277608
fbshipit-source-id: 0abd0dc4291cf40651a1719bff813abb2b559640
Summary:
Add support for breaks and continues in the jit. We do with a Graph transform pre-SSA.
A graph of the form
```
def test():
while i < 5:
if i == 3:
break
i += 1
print(i)
```
has the body of the loop transformed to
```
if i == 3:
did_break = True
else:
did_break = False
if did_break:
loop_exit = True
else:
i += 1
print(i)
loop_exit = i < 5
```
I am going to add more tests but I think it is ready for review now.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/21692
Differential Revision: D16215807
Pulled By: eellison
fbshipit-source-id: 365102f42de4861d9323caaeb39a96de7619a667
Summary:
The error for `test_error_stack_module`:
```
Traceback (most recent call last):
File "../test.py", line 35, in <module>
scripted = torch.jit.script(M())
File "/home/davidriazati/other/pytorch/torch/jit/__init__.py", line 1119, in script
return _convert_to_script_module(obj)
File "/home/davidriazati/other/pytorch/torch/jit/__init__.py", line 1825, in _convert_to_script_module
raise e
RuntimeError:
d(int x) -> int:
Expected a value of type 'int' for argument 'x' but instead found type 'str'.
:
at ../test.py:11:12
def c(x):
return d("hello") + d(x)
~ <--- HERE
'c' is being compiled since it was called from 'b'
at ../test.py:14:12
def b(x):
return c(x)
~~~ <--- HERE
'b' is being compiled since it was called from 'forward'
at ../test.py:22:16
def forward(self, x):
return b(x)
~~~ <--- HERE
'forward' is being compiled since it was called from 'forward'
at ../test.py:31:20
def forward(self, x):
return x + self.submodule(x)
~~~~~~~~~~~~~~~~ <--- HERE
```
This also unifies our error reporting in the front end with `ErrorReport`
TODO
* Include module names in message, #22207 should make this easy
](https://our.intern.facebook.com/intern/diff/16060781/)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/22280
Pulled By: driazati
Differential Revision: D16060781
fbshipit-source-id: c42968b53aaddb774ac69d5abbf7e60c23df8eed
Summary:
* Deletes all weak script decorators / associated data structures / methods
* In order to keep supporting the standard library in script, this enables recursive script on any function defined in `torch.nn`
* Most changes in `torch/nn` are the result of `ag -Q "weak" torch/nn/ -l | xargs sed -i '/weak/d'`, only `rnn.py` needed manual editing to use the `ignore` and `export` to continue supporting the overloaded `forward` methods
* `Sequential`/`ModuleList` no longer need to be added to constants since they are compiled on demand
This should also fix https://github.com/pytorch/pytorch/issues/22212
Pull Request resolved: https://github.com/pytorch/pytorch/pull/22212
Differential Revision: D15988346
Pulled By: driazati
fbshipit-source-id: af223e3ad0580be895377312949997a70e988e4f
Summary:
In talks with smessmer, we decided that it'd be better to put the logic in `list`, as optimal behavior requires knowing `.capacity()`
Results on my cpu (for the benchmark here: https://twitter.com/VahidK/status/1138674536679821312) now look like this:
```
Pytorch batch_gather took 0.018311 seconds.
Pytorch batch_gather jit took 0.013921 seconds.
Pytorch vectorized batch_gather took 0.001384 seconds.
```
Previously, `batch_gather jit` took 3x as long as `batch_gather`.
Some logic taken from https://github.com/pytorch/pytorch/pull/21690. Note that these two PR's are somewhat orthogonal. That PR handles this benchmark by looking at the alias analysis, while this PR specializes for `+=`.
Note that we can't jit the vectorized version as we think `torch.arange` returns a float tensor.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/21896
Differential Revision: D15998628
Pulled By: Chillee
fbshipit-source-id: b0085960da4613578b94deb98ac62c0a4532a8c3
