Skip to content

Sanitize None params during pruning #6836

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 3 commits into from
Apr 5, 2021
Merged

Sanitize None params during pruning #6836

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
3 commits
Select commit Hold shift + click to select a range
e15e49c
sanitize none params during pruning
karthikprasad Apr 5, 2021
8fcfe8a
amend
karthikprasad Apr 5, 2021
07b442d
Apply suggestions from code review
rohitgr7 Apr 5, 2021
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
3 changes: 3 additions & 0 deletions CHANGELOG.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -170,6 +170,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).

### Fixed

- Sanitize `None` params during pruning ([#6836](https://github.com/PyTorchLightning/pytorch-lightning/pull/6836))


- Made the `Plugin.reduce` method more consistent across all Plugins to reflect a mean-reduction by default ([#6011](https://github.com/PyTorchLightning/pytorch-lightning/pull/6011))


Expand Down
4 changes: 3 additions & 1 deletion pytorch_lightning/callbacks/pruning.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -422,7 +422,9 @@ def sanitize_parameters_to_prune(
current_modules = [m for m in pl_module.modules() if not isinstance(m, _MODULE_CONTAINERS)]

if parameters_to_prune is None:
parameters_to_prune = [(m, p) for p in parameters for m in current_modules if hasattr(m, p)]
parameters_to_prune = [
(m, p) for p in parameters for m in current_modules if getattr(m, p, None) is not None
]
elif (
isinstance(parameters_to_prune, (list, tuple)) and len(parameters_to_prune) > 0
and all(len(p) == 2 for p in parameters_to_prune)
Expand Down
19 changes: 11 additions & 8 deletions tests/callbacks/test_pruning.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -36,7 +36,7 @@ def __init__(self):
self.layer = Sequential(
OrderedDict([
("mlp_1", nn.Linear(32, 32)),
("mlp_2", nn.Linear(32, 32)),
("mlp_2", nn.Linear(32, 32, bias=False)),
("mlp_3", nn.Linear(32, 2)),
])
)
Expand Down Expand Up @@ -85,7 +85,10 @@ def train_with_pruning_callback(
if parameters_to_prune:
pruning_kwargs["parameters_to_prune"] = [(model.layer.mlp_1, "weight"), (model.layer.mlp_2, "weight")]
else:
pruning_kwargs["parameter_names"] = ["weight"]
if isinstance(pruning_fn, str) and pruning_fn.endswith("_structured"):
pruning_kwargs["parameter_names"] = ["weight"]
else:
pruning_kwargs["parameter_names"] = ["weight", "bias"]
if isinstance(pruning_fn, str) and pruning_fn.endswith("_structured"):
pruning_kwargs["pruning_dim"] = 0
if pruning_fn == "ln_structured":
Expand Down Expand Up @@ -249,14 +252,14 @@ def test_multiple_pruning_callbacks(tmpdir, caplog, make_pruning_permanent: bool
actual = [m for m in actual if m.startswith("Applied")]
assert actual == [
"Applied `L1Unstructured`. Pruned: 0/1122 (0.00%) -> 544/1122 (48.48%)",
"Applied `L1Unstructured` to `Linear(in_features=32, out_features=32, bias=True).weight` with amount=0.5. Pruned: 0 (0.00%) -> 506 (49.41%)", # noqa: E501
"Applied `L1Unstructured` to `Linear(in_features=32, out_features=2, bias=True).weight` with amount=0.5. Pruned: 0 (0.00%) -> 38 (59.38%)", # noqa: E501
"Applied `L1Unstructured` to `Linear(in_features=32, out_features=32, bias=True).weight` with amount=0.5. Pruned: 0 (0.00%) -> 500 (48.83%)", # noqa: E501
"Applied `L1Unstructured` to `Linear(in_features=32, out_features=2, bias=True).weight` with amount=0.5. Pruned: 0 (0.00%) -> 44 (68.75%)", # noqa: E501
"Applied `RandomUnstructured`. Pruned: 544/1122 (48.48%) -> 680/1122 (60.61%)",
"Applied `RandomUnstructured` to `Linear(in_features=32, out_features=32, bias=True).weight` with amount=0.25. Pruned: 506 (49.41%) -> 633 (61.82%)", # noqa: E501
"Applied `RandomUnstructured` to `Linear(in_features=32, out_features=2, bias=True).weight` with amount=0.25. Pruned: 38 (59.38%) -> 47 (73.44%)", # noqa: E501
"Applied `RandomUnstructured` to `Linear(in_features=32, out_features=32, bias=True).weight` with amount=0.25. Pruned: 500 (48.83%) -> 635 (62.01%)", # noqa: E501
"Applied `RandomUnstructured` to `Linear(in_features=32, out_features=2, bias=True).weight` with amount=0.25. Pruned: 44 (68.75%) -> 45 (70.31%)", # noqa: E501
"Applied `L1Unstructured`. Pruned: 680/1122 (60.61%) -> 884/1122 (78.79%)",
"Applied `L1Unstructured` to `Linear(in_features=32, out_features=32, bias=True).weight` with amount=0.5. Pruned: 633 (61.82%) -> 828 (80.86%)", # noqa: E501
"Applied `L1Unstructured` to `Linear(in_features=32, out_features=2, bias=True).weight` with amount=0.5. Pruned: 47 (73.44%) -> 56 (87.50%)", # noqa: E501
"Applied `L1Unstructured` to `Linear(in_features=32, out_features=32, bias=True).weight` with amount=0.5. Pruned: 635 (62.01%) -> 830 (81.05%)", # noqa: E501
"Applied `L1Unstructured` to `Linear(in_features=32, out_features=2, bias=True).weight` with amount=0.5. Pruned: 45 (70.31%) -> 54 (84.38%)", # noqa: E501
]

filepath = str(tmpdir / "foo.ckpt")
Expand Down