Fix ModelPruning(make_pruning_permanent=True) buffers getting removed when saved during training (#6073)

Co-authored-by: chaton <[email protected]>

Author: carmocca

CHANGELOG.md

@@ -5,6 +5,20 @@ All notable changes to this project will be documented in this file.
 The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 
+## [1.2.3] - 2021-03-09
+
+### Added
+
+
+### Changed
+
+
+### Fixed
+
+- Fixed `ModelPruning(make_pruning_permanent=True)` pruning buffers getting removed when saved during training ([#6073](https://github.com/PyTorchLightning/pytorch-lightning/pull/6073))
+
+
+
 ## [1.2.2] - 2021-03-02
 
 ### Added

pytorch_lightning/callbacks/pruning.py

@@ -18,7 +18,7 @@
 import inspect
 from copy import deepcopy
 from functools import partial
-from typing import Any, Callable, List, Optional, Tuple, Union
+from typing import Any, Callable, Dict, List, Optional, Tuple, Union
 
 import torch
 import torch.nn.utils.prune as pytorch_prune
@@ -27,7 +27,7 @@
 from pytorch_lightning import _logger as log
 from pytorch_lightning.callbacks.base import Callback
 from pytorch_lightning.core.lightning import LightningModule
-from pytorch_lightning.utilities import rank_zero_only
+from pytorch_lightning.utilities.distributed import rank_zero_debug, rank_zero_only
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
 
 _PYTORCH_PRUNING_FUNCTIONS = {
@@ -246,14 +246,18 @@ def _create_pruning_fn(self, pruning_fn: str, **kwargs) -> Union[Callable, pytor
     def _wrap_pruning_fn(pruning_fn, **kwargs):
         return partial(pruning_fn, **kwargs)
 
-    def make_pruning_permanent(self):
-        """ Makes ``parameters_to_prune`` current pruning permanent. """
-        for module, param_name in self._parameters_to_prune:
-            try:
-                pytorch_prune.remove(module, param_name)
-            except ValueError:
-                # pruning already made permanent
-                pass
+    def make_pruning_permanent(self, pl_module: LightningModule):
+        """
+        Removes pruning buffers from any pruned modules
+
+        Adapted from https://github.com/pytorch/pytorch/blob/1.7.1/torch/nn/utils/prune.py#L1176-L1180
+        """
+        for _, module in pl_module.named_modules():
+            for k in list(module._forward_pre_hooks):
+                hook = module._forward_pre_hooks[k]
+                if isinstance(hook, pytorch_prune.BasePruningMethod):
+                    hook.remove(module)
+                    del module._forward_pre_hooks[k]
 
     def _restore_original_weights(self, module: nn.Module, orig_module: nn.Module, tensor_name: str):
         trained = getattr(module, tensor_name)
@@ -351,7 +355,7 @@ def _log_sparsity_stats(
                     f" {curr_mask_zeros} ({curr_mask_zeros / curr_mask_size:.2%})"
                 )
 
-    def on_before_accelerator_backend_setup(self, trainer, pl_module):
+    def on_before_accelerator_backend_setup(self, trainer, pl_module: LightningModule):
         parameters_to_prune = self.sanitize_parameters_to_prune(
             pl_module, self._parameters_to_prune, parameter_names=self._parameter_names
         )
@@ -367,7 +371,7 @@ def on_before_accelerator_backend_setup(self, trainer, pl_module):
                 self._original_layers.setdefault(id_, {"data": deepcopy(module), "names": []})
                 self._original_layers[id_]["names"].append((i, name))
 
-    def on_train_epoch_end(self, trainer, pl_module, *args):
+    def on_train_epoch_end(self, trainer, pl_module: LightningModule, outputs):
         current_epoch = trainer.current_epoch
         prune = self._apply_pruning(current_epoch) if isinstance(self._apply_pruning, Callable) else self._apply_pruning
         amount = self.amount(current_epoch) if isinstance(self.amount, Callable) else self.amount
@@ -381,13 +385,20 @@ def on_train_epoch_end(self, trainer, pl_module, *args):
         ):
             self.apply_lottery_ticket_hypothesis()
 
-    def on_train_end(self, *args):
+    def on_train_end(self, trainer, pl_module: LightningModule):
         if self._make_pruning_permanent:
-            self.make_pruning_permanent()
+            rank_zero_debug("`ModelPruning.on_train_end`. Pruning is made permanent for this checkpoint.")
+            self.make_pruning_permanent(pl_module)
 
-    def on_save_checkpoint(self, *args):
+    def on_save_checkpoint(self, trainer, pl_module: LightningModule, checkpoint: Dict[str, Any]):
         if self._make_pruning_permanent:
-            self.make_pruning_permanent()
+            rank_zero_debug("`ModelPruning.on_save_checkpoint`. Pruning is made permanent for this checkpoint.")
+            prev_device = pl_module.device
+            # prune a copy so training can continue with the same buffers
+            copy = deepcopy(pl_module.to("cpu"))
+            self.make_pruning_permanent(copy)
+            checkpoint["state_dict"] = copy.state_dict()
+            pl_module.to(prev_device)
 
     @staticmethod
     def sanitize_parameters_to_prune(

tests/callbacks/test_pruning.py

@@ -15,7 +15,6 @@
 import platform
 from collections import OrderedDict
 from logging import INFO
-from unittest import mock
 
 import pytest
 import torch
@@ -24,7 +23,7 @@
 from torch.nn import Sequential
 
 from pytorch_lightning import seed_everything, Trainer
-from pytorch_lightning.callbacks import ModelPruning
+from pytorch_lightning.callbacks import ModelCheckpoint, ModelPruning
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
 from tests.helpers import BoringModel
 
@@ -42,6 +41,10 @@ def __init__(self):
             ])
         )
 
+    def training_step(self, batch, batch_idx):
+        self.log("test", -batch_idx)
+        return super().training_step(batch, batch_idx)
+
 
 class TestPruningMethod(pytorch_prune.BasePruningMethod):
     PRUNING_TYPE = "unstructured"
@@ -219,7 +222,6 @@ def apply_lottery_ticket_hypothesis(self):
 
 
 @pytest.mark.parametrize("make_pruning_permanent", (False, True))
-@mock.patch.dict(os.environ, {}, clear=True)
 def test_multiple_pruning_callbacks(tmpdir, caplog, make_pruning_permanent):
     seed_everything(0)
     model = TestModel()
@@ -244,8 +246,9 @@ def test_multiple_pruning_callbacks(tmpdir, caplog, make_pruning_permanent):
     with caplog.at_level(INFO):
         trainer.fit(model)
 
-    actual = [m.strip() for m in caplog.messages[-9:]]
-    expected = [
+    actual = [m.strip() for m in caplog.messages]
+    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
@@ -256,11 +259,54 @@ def test_multiple_pruning_callbacks(tmpdir, caplog, make_pruning_permanent):
         "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
     ]
-    assert actual == expected
 
     filepath = str(tmpdir / "foo.ckpt")
     trainer.save_checkpoint(filepath)
 
     model.load_from_checkpoint(filepath, strict=False)
     has_pruning = hasattr(model.layer.mlp_1, "weight_orig")
     assert not has_pruning if make_pruning_permanent else has_pruning
+
+
+def test_permanent_when_model_is_saved_multiple_times(tmpdir, caplog):
+    """
+    When a model is saved multiple times and make_permanent=True, we need to
+    make sure a copy is pruned and not the trained model if we want to continue
+    with the same pruning buffers.
+    """
+    seed_everything(0)
+
+    class TestPruning(ModelPruning):
+
+        def on_save_checkpoint(self, trainer, pl_module, checkpoint):
+            super().on_save_checkpoint(trainer, pl_module, checkpoint)
+            assert "layer.mlp_3.weight_orig" not in checkpoint["state_dict"]
+            assert hasattr(pl_module.layer.mlp_3, "weight_orig")
+
+    model = TestModel()
+    pruning_callback = TestPruning(
+        "random_unstructured",
+        parameters_to_prune=[(model.layer.mlp_3, "weight")],
+        verbose=1,
+        make_pruning_permanent=True
+    )
+    ckpt_callback = ModelCheckpoint(monitor="test", save_top_k=2, save_last=True)
+    trainer = Trainer(callbacks=[pruning_callback, ckpt_callback], max_epochs=3, progress_bar_refresh_rate=0)
+    with caplog.at_level(INFO):
+        trainer.fit(model)
+
+    actual = [m.strip() for m in caplog.messages]
+    actual = [m for m in actual if m.startswith("Applied")]
+    assert actual == [
+        "Applied `RandomUnstructured`. Pruned: 0/66 (0.00%) -> 32/66 (48.48%)",
+        "Applied `RandomUnstructured`. Pruned: 32/66 (48.48%) -> 48/66 (72.73%)",
+        "Applied `RandomUnstructured`. Pruned: 48/66 (72.73%) -> 56/66 (84.85%)",
+    ]
+
+    # removed on_train_end
+    assert not hasattr(model.layer.mlp_3, "weight_orig")
+
+    model.load_from_checkpoint(trainer.checkpoint_callback.kth_best_model_path)
+    assert not hasattr(model.layer.mlp_3, "weight_orig")
+    model.load_from_checkpoint(trainer.checkpoint_callback.last_model_path)
+    assert not hasattr(model.layer.mlp_3, "weight_orig")