Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/23886
This is a series of PRs that will allow us to support adding [padding to conv](https://github.com/pytorch/pytorch/pull/22484) and also reduce the friction of adding method overloads that was brought up in https://github.com/pytorch/pytorch/pull/23266.
Support for overloaded functions following the specification in [PEP 484](https://www.python.org/dev/peps/pep-0484/#function-method-overloading).
The usage is:
```
torch.jit.overload
def add(x: int, y: int) -> int: ...
torch.jit.overload
def add(x: float, y: float) -> float: ...
def add:
return x + y
```
Follow up PRs:
- Add same API for methods
- A couple of cleanups for functions:
- don't require default params specified on the overload as well
- potentially error if invocation could be matched to multiple overloads. now it just chooses the first one, mypy does the same thing currently
Test Plan: Imported from OSS
Differential Revision: D16694863
Pulled By: eellison
fbshipit-source-id: f94f2933bc1c97fa58f31846acfe962b0630068c
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/22956
As Title says: remove the extra function arguments for better engineering.
Differential Revision: D16297724
fbshipit-source-id: a31be17708d13508c4ce9a3ce7eb5238e8d17984
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/23779
Mangling is two underscores, not one :(. We want this method to be
private so that inheritors who define a `__construct` do not interfere
with Module initialization
Test Plan: Imported from OSS
Differential Revision: D16645156
Pulled By: suo
fbshipit-source-id: b9060cb35bfaa0391ff200b63fb78b1ac15fee39
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/23680
Now when initializing a ScriptModule during the torch.jit.load()
process, there is already a cpp module backing the thing. That means
that setting training will overwrite whatever the initialized
ScriptModule had.
This PR splits apart the common "set up internal state" part of the
Module __init__ and calls that from ScriptModule.__init__ and
Module.__init__, leaving the "nn.Module-specific" part (setting
`self.training`) for the nn.Module __init__
Test Plan: Imported from OSS
Differential Revision: D16606959
Pulled By: suo
fbshipit-source-id: f7ea6b36551ff4e4472b7685f65731d5cfab87fd
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/23577
This diff is fixing a model size issue introduced in #23291. After that PR, the model size after in8 quantization is the same as that of the original unquantized model. The reason is that we save original weight for int8 quantization even when that's not needed anymore. This diff fixes that by only saving original weight for fp16 quantization path.
Reviewed By: llyfacebook
Differential Revision: D16557619
fbshipit-source-id: f924ae8d155a0d525b86a7440b3c7147d5bead0a
Summary:
Previously these were left out which would lead to confusing messages,
now it looks something like:
```
torch.jit.frontend.UnsupportedNodeError: import statements aren't
supported
:
at ../test.py:13:9
def bad_fn(self):
import pdb
~~~~~~ <--- HERE
'__torch__.X' is being compiled since it was called from 'fn'
at ../test.py:16:12
def fn(x):
return X(10)
~~~~ <--- HERE
```
Fixes#23453
Pull Request resolved: https://github.com/pytorch/pytorch/pull/23454
Pulled By: driazati
Differential Revision: D16567930
fbshipit-source-id: 251b6f91f37a2816e06bb4c803f9bc172fa1d91b
Summary:
Previously these were left out which would lead to confusing messages,
now it looks something like:
```
torch.jit.frontend.UnsupportedNodeError: import statements aren't
supported
:
at ../test.py:13:9
def bad_fn(self):
import pdb
~~~~~~ <--- HERE
'__torch__.X' is being compiled since it was called from 'fn'
at ../test.py:16:12
def fn(x):
return X(10)
~~~~ <--- HERE
```
Fixes#23453
](https://our.intern.facebook.com/intern/diff/16526027/)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/23454
Pulled By: driazati
Differential Revision: D16526027
fbshipit-source-id: 109f2968430dbf51ee91b1b3409badfd557d19a4
Summary:
Use the recursive script API in the existing docs
TODO:
* Migration guide for 1.1 -> 1.2
Pull Request resolved: https://github.com/pytorch/pytorch/pull/21612
Pulled By: driazati
Differential Revision: D16553734
fbshipit-source-id: fb6be81a950224390bd5d19b9b3de2d97b3dc515
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/23347
This diff replaces uint8 with int8 to match with the underlying kernel implementation. When we do int8 quantization, we are computing with uint8 (input activation) * int8 (weight) -> uint8 (output activation). The weight is quantized into int8.
Reviewed By: jianyuh
Differential Revision: D16469435
fbshipit-source-id: a697655b0e97833fc601e5980970aec4dba53c39
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/23291
This diff implements LSTM with FP16 weights based on FBGEMM.
At a high level, here are the steps:
1. Quantize and pack weight in every layer of LSTM
2. Pass weights from step 1 to the ATen `quantized_lstm` function which does matrix multiplication with FP16 weight. The following code shows the dtype of each variable used in MM:
Y = X * W + B
(fp32, fp32, fp16, fp32)
Reviewed By: jianyuh
Differential Revision: D16389595
fbshipit-source-id: c26ae4e153c667a941f4af64e9d07fc251403cee
Summary:
Previously we looked at the stack frame of the function that called
`script` to resolve variables. This doesn't work if someone calls script
with a function defined somewhere else that references captured
variables. We already have a mechanism to look at the closed over
variables for a function, so this changes the `rcb` to use that.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/22270
Pulled By: driazati
Differential Revision: D16391346
fbshipit-source-id: ad9b314ae86c249251b106079e76a5d7cf6c04c2
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:
Since recursive script creates a ScriptModule from an `nn.Module`,
there's no ties to the original module to pull a type name from, so we
have to explicitly pass it in.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/22873
Pulled By: driazati
Differential Revision: D16268547
fbshipit-source-id: 902a30e6e36427c6ba7033ded027a29d9dcbc1ee
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/22323
This diff adds an interface to use quantized Linear op in JIT.
Reviewed By: jamesr66a
Differential Revision: D16040724
fbshipit-source-id: 90e90aff9973c96ea076ed6a21ae02c349ee2bcf
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
