* Automatically applies ruff rule 401. Turns loops into equivalent list comprehensions which are faster and do not leak the scope of the loop variables.
* list comprehensions not only often have better typing, but are 50+% faster than for loops on overhead. They also preserve length information etc and are better for the interpreter to optimize.
* Manually went back and made mypy happy after the change.
* Also fixed style lints in files covered by flake8 but not by pyfmt
Pull Request resolved: https://github.com/pytorch/pytorch/pull/140980
Approved by: https://github.com/justinchuby, https://github.com/malfet
Summary: same function in observer and quantize, consolidated to a
single function. Note the definitions were slightly different, I've
changed the definition to be maximally inclusive so that the name of the
function is more accurate
Test Plan: python test/test_public_bindings.py
python test/test_quantization.py
Reviewers:
Subscribers:
Tasks:
Tags:
Differential Revision: [D40709276](https://our.internmc.facebook.com/intern/diff/D40709276)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/87520
Approved by: https://github.com/jcaip
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/76561
User model had syntax like `torch.cat(tensors=[x])`. This PR fixes two errors
to unbreak this in NS shadow model:
1. skip nodes which only have kwargs (instead of throwing an exception)
2. explicitly skip shadowing of `torch.cat` (since it's not supported anyways)
Test Plan:
```
python test/test_quantization.py -k test_op_with_only_kwargs_skips_shadowing
python test/test_quantization.py -k test_op_mul_add_cat_skips_shadowing
```
Reviewed By: hx89
Differential Revision: D36017356
Pulled By: vkuzo
fbshipit-source-id: 0da4840a62c2dac183f8294c2cec4fce262474b3
(cherry picked from commit 88409c1576e7f690708957b2baa285fc7961e9d6)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/76504
Shadowing for add and mul is not implemented, this PR fixes the skipping
logic to also skip the `operator.add` and `operator.mul` flavor of these
operators.
Test Plan:
```
python test/test_quantization.py -k test_mul_add_skips_shadowing
```
Reviewed By: dzdang
Differential Revision: D35985997
Pulled By: vkuzo
fbshipit-source-id: f832e54a5461d3b182df4bb905357d6c66742e98
(cherry picked from commit 93ae9592f68873865ebfdc438bffb1c9486dd1c1)
Summary:
The tests were disabled by https://github.com/pytorch/pytorch/pull/61687, but
this specific behavior broke some time after while these tests were disabled.
The issue was that:
1. `torch.add` is present in these models
2. In the common codepath of comparing fp32 to int8, torch.ops.quantized.add was already filtered out because it did not have a dtype specified
3. In the less common codepath of comparing fp32 to fp32, torch.add was eligible for shadowing, but the logic was broken
This PR fixes (3) by disabling shadowing on ops which do not support it, by op type.
The support may be built later, if needed.
Test plan:
```
python test/test_quantization.py TestFXNumericSuiteCoreAPIsModels.test_resnet18
python test/test_quantization.py TestFXNumericSuiteCoreAPIsModels.test_mobilenet_v2
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/75472
Approved by: https://github.com/jerryzh168
Summary:
In https://github.com/pytorch/pytorch/pull/61687, a couple of FX Numeric Suite
tests were disabled.
This PR reenables one of these tests. We update the dtype inference logic
of NS to always return a specific type instead of sometimes returning
"fp32 or int8". When the type cannot be deduced by the current logic,
we do not shadow the node.
As a better version of dtype inference becomes available in FX Graph Mode Quantization,
we could migrate this code to use it.
Future PRs in the stack will unbreak other things to enable NS for FX to
work on torchvision again.
Test plan:
```
python test/test_quantization.py -k NumericSuite
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/75471
Approved by: https://github.com/jerryzh168
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/66380
Description:
1. creates doc pages for Eager and FX numeric suites
2. adds a link from main quantization doc to (1)
3. formats docblocks in Eager NS to render well
4. adds example code and docblocks to FX numeric suite
Test Plan:
```
cd docs
make html
python -m http.server
// renders well
```
Reviewed By: jerryzh168
Differential Revision: D31543173
Pulled By: vkuzo
fbshipit-source-id: feb291bcbe92747495f45165f738631fa5cbffbd
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/66222
Description:
1. creates doc pages for Eager and FX numeric suites
2. adds a link from main quantization doc to (1)
3. formats docblocks in Eager NS to render well
4. adds example code and docblocks to FX numeric suite
Test Plan:
```
cd docs
make html
python -m http.server
// renders well
```
Reviewed By: jerryzh168
Differential Revision: D31447610
Pulled By: vkuzo
fbshipit-source-id: 441170c4a6c3ddea1e7c7c5cc2f1e1cd5aa65f2f
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/66058
After the initial migration from `torch.quantization` to `torch.ao.quantization`, some of the files did not change.
This happened because the migration was done in parallel, and some of the files were landed while the others were still in the original location.
This is the last fix in the AO migration phase 1, which completely enables the ao.quantization namespace.
Test Plan: `python test/test_quantization.py`
Reviewed By: vkuzo
Differential Revision: D31366066
Pulled By: z-a-f
fbshipit-source-id: bf4a74885be89d098df2d87e685795a2a64026c5
