Support DDP with LRFinder

docs/source-pytorch/advanced/training_tricks.rst

@@ -270,7 +270,13 @@ initial learning rate.
 .. warning::
 
     For the moment, this feature only works with models having a single optimizer.
-    LR Finder support for DDP and any of its variations is not implemented yet. It is coming soon.
+
+
+.. note::
+
+    With DDP: Since all the processes run in isolation, only process with ``global_rank=0`` will make the decision to stop the
+    learning rate finder and broadcast its results to all other ranks. That means, at the end of LR finder, each process will be running with
+    the learning rate found on ``global_rank=0``.
 
 
 Using Lightning's built-in LR finder

src/pytorch_lightning/CHANGELOG.md

@@ -10,6 +10,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 ### Added
 
+- Added support for DDP with `LRFinder` ([#15304](https://github.com/Lightning-AI/lightning/pull/15304))
+
+
 - Added utilities to migrate checkpoints from one Lightning version to another ([#15237](https://github.com/Lightning-AI/lightning/pull/15237))
 
 - Added back the accidentally removed `pytorch_lightning.utilities.distributed.rank_zero_only` function ([#15536](https://github.com/Lightning-AI/lightning/pull/15536))

src/pytorch_lightning/tuner/lr_finder.py

@@ -217,6 +217,7 @@ def lr_find(
 
     # Save initial model, that is loaded after learning rate is found
     ckpt_path = os.path.join(trainer.default_root_dir, f".lr_find_{uuid.uuid4()}.ckpt")
+    ckpt_path = trainer.strategy.broadcast(ckpt_path)
     trainer.save_checkpoint(ckpt_path)
 
     # Arguments we adjust during the lr finder, save for restoring
@@ -252,6 +253,7 @@ def lr_find(
         trainer.progress_bar_callback.enable()
 
     # Update lr attr if required
+    lr_finder.results = trainer.strategy.broadcast(lr_finder.results)
     if update_attr:
         lr = lr_finder.suggestion()
 
@@ -311,6 +313,7 @@ def __lr_finder_restore_params(trainer: "pl.Trainer", params: Dict[str, Any]) ->
     loop = trainer.fit_loop
     loop.load_state_dict(deepcopy(params["loop_state_dict"]))
     loop.restarting = False
+    trainer.should_stop = False
 
 
 class _LRCallback(Callback):
@@ -380,10 +383,12 @@ def on_train_batch_end(
         # Check if we diverging
         if self.early_stop_threshold is not None:
             if current_step > 1 and smoothed_loss > self.early_stop_threshold * self.best_loss:
-                trainer.fit_loop.max_steps = current_step  # stop signal
+                trainer.should_stop = True  # stop signal
                 if self.progress_bar:
                     self.progress_bar.close()
 
+        trainer.should_stop = trainer.strategy.broadcast(trainer.should_stop)
+
         # Save best loss for diverging checking
         if smoothed_loss < self.best_loss or current_step == 1:
             self.best_loss = smoothed_loss

src/pytorch_lightning/tuner/tuning.py

@@ -59,7 +59,7 @@ def _tune(
 
         self.trainer.strategy.connect(model)
 
-        is_tuning = self.trainer.auto_scale_batch_size or self.trainer.auto_lr_find
+        is_tuning = self.trainer.auto_scale_batch_size
         if self.trainer._accelerator_connector.is_distributed and is_tuning:
             raise MisconfigurationException(
                 "`trainer.tune()` is currently not supported with"

tests/tests_pytorch/tuner/test_lr_finder.py

@@ -93,6 +93,7 @@ def test_trainer_reset_correctly(tmpdir):
         "max_steps",
         "fit_loop.max_steps",
         "strategy.setup_optimizers",
+        "should_stop",
     ]
     expected = {ca: getattr_recursive(trainer, ca) for ca in changed_attributes}
 
@@ -438,3 +439,27 @@ def test_if_lr_finder_callback_already_configured():
 
     with pytest.raises(MisconfigurationException, match="Trainer is already configured with a .* callback"):
         trainer.tune(model)
+
+
+@mock.patch.dict(os.environ, os.environ.copy(), clear=True)
+@RunIf(standalone=True)
+def test_lr_finder_with_ddp(tmpdir):
+    seed_everything(7)
+
+    init_lr = 1e-4
+    dm = ClassifDataModule()
+    model = ClassificationModel(lr=init_lr)
+
+    trainer = Trainer(
+        default_root_dir=tmpdir,
+        max_epochs=1,
+        strategy="ddp",
+        devices=2,
+        accelerator="cpu",
+    )
+
+    trainer.tuner.lr_find(model, datamodule=dm, update_attr=True, num_training=20)
+    lr = trainer.lightning_module.lr
+    lr = trainer.strategy.broadcast(lr)
+    assert trainer.lightning_module.lr == lr
+    assert lr != init_lr