test: replacing tmpdir with tmp_path in tests_pytorch/utilities (…

…#19654)

tests/tests_pytorch/utilities/migration/test_migration.py

@@ -86,8 +86,8 @@ def test_migrate_loop_current_epoch_to_progress_tracking():
 
 
 @pytest.mark.parametrize("model_class", [BoringModel, ManualOptimBoringModel])
-def test_migrate_loop_batches_that_stepped(tmpdir, model_class):
-    trainer = Trainer(max_steps=1, limit_val_batches=0, default_root_dir=tmpdir)
+def test_migrate_loop_batches_that_stepped(tmp_path, model_class):
+    trainer = Trainer(max_steps=1, limit_val_batches=0, default_root_dir=tmp_path)
     model = model_class()
     trainer.fit(model)
     ckpt_path = trainer.checkpoint_callback.best_model_path
@@ -103,7 +103,7 @@ def on_train_start(self) -> None:
             assert self.trainer.global_step == 1
             assert self.trainer.fit_loop.epoch_loop._batches_that_stepped == 1
 
-    trainer = Trainer(max_steps=2, limit_val_batches=0, default_root_dir=tmpdir)
+    trainer = Trainer(max_steps=2, limit_val_batches=0, default_root_dir=tmp_path)
     model = TestModel()
     trainer.fit(model, ckpt_path=ckpt_path)
     new_loop = trainer.fit_loop.epoch_loop

tests/tests_pytorch/utilities/test_all_gather_grad.py

@@ -52,7 +52,7 @@ def test_all_gather_ddp_spawn():
 
 
 @RunIf(min_cuda_gpus=2, skip_windows=True, standalone=True)
-def test_all_gather_collection(tmpdir):
+def test_all_gather_collection(tmp_path):
     class TestModel(BoringModel):
         on_train_epoch_end_called = False
 
@@ -87,7 +87,7 @@ def on_train_epoch_end(self):
 
     model = TestModel()
     trainer = Trainer(
-        default_root_dir=tmpdir,
+        default_root_dir=tmp_path,
         limit_train_batches=8,
         limit_val_batches=0,
         max_epochs=1,
@@ -105,7 +105,7 @@ def on_train_epoch_end(self):
 
 
 @RunIf(min_cuda_gpus=2, skip_windows=True, standalone=True)
-def test_all_gather_sync_grads(tmpdir):
+def test_all_gather_sync_grads(tmp_path):
     class TestModel(BoringModel):
         training_step_called = False
 
@@ -118,7 +118,7 @@ def training_step(self, batch, batch_idx):
 
     model = TestModel()
     trainer = Trainer(
-        default_root_dir=tmpdir,
+        default_root_dir=tmp_path,
         limit_train_batches=1,
         limit_val_batches=0,
         max_epochs=1,

tests/tests_pytorch/utilities/test_auto_restart.py

@@ -71,7 +71,7 @@ def val_dataloader(self):
 
 
 def _fit_model(
-    tmpdir, should_signal, val_check_interval, failure_on_step, failure_on_training, on_last_batch, status=None
+    tmp_path, should_signal, val_check_interval, failure_on_step, failure_on_training, on_last_batch, status=None
 ):
     seed_everything(42)
     model = TestAutoRestartModelUnderSignal(should_signal, failure_on_step, failure_on_training, on_last_batch)
@@ -87,13 +87,13 @@ def on_exception(self, trainer, pl_module, exception):
 
     test_callback = MyTestCallback()
     trainer = Trainer(
-        default_root_dir=tmpdir,
+        default_root_dir=tmp_path,
         max_epochs=1,
         limit_train_batches=4,
         limit_val_batches=4,
         val_check_interval=val_check_interval,
         num_sanity_val_steps=0,
-        callbacks=[test_callback, OnExceptionCheckpoint(tmpdir)],
+        callbacks=[test_callback, OnExceptionCheckpoint(tmp_path)],
     )
     if should_signal:
         with pytest.raises(SIGTERMException):
@@ -111,7 +111,7 @@ def on_exception(self, trainer, pl_module, exception):
 @pytest.mark.parametrize("failure_on_training", [False, True])
 @pytest.mark.parametrize("failure_on_step", [False, True])
 @RunIf(skip_windows=True)
-def test_auto_restart_under_signal(on_last_batch, val_check_interval, failure_on_training, failure_on_step, tmpdir):
+def test_auto_restart_under_signal(on_last_batch, val_check_interval, failure_on_training, failure_on_step, tmp_path):
     if failure_on_step:
         if on_last_batch:
             if failure_on_training:
@@ -130,4 +130,4 @@ def test_auto_restart_under_signal(on_last_batch, val_check_interval, failure_on
             # `on_train_epoch_end` happens after `on_validation_epoch_end` since Lightning v1.4
             status = "_FitLoop:on_advance_end" if failure_on_training else "_TrainingEpochLoop:on_advance_end"
 
-    _fit_model(tmpdir, True, val_check_interval, failure_on_step, failure_on_training, on_last_batch, status=status)
+    _fit_model(tmp_path, True, val_check_interval, failure_on_step, failure_on_training, on_last_batch, status=status)

tests/tests_pytorch/utilities/test_deepspeed_collate_checkpoint.py

@@ -23,11 +23,11 @@
 
 
 @RunIf(min_cuda_gpus=2, standalone=True, deepspeed=True)
-def test_deepspeed_collate_checkpoint(tmpdir):
+def test_deepspeed_collate_checkpoint(tmp_path):
     """Test to ensure that with DeepSpeed Stage 3 we can collate the sharded checkpoints into a single file."""
     model = BoringModel()
     trainer = Trainer(
-        default_root_dir=tmpdir,
+        default_root_dir=tmp_path,
         strategy=DeepSpeedStrategy(stage=3),
         accelerator="gpu",
         devices=2,
@@ -37,12 +37,12 @@ def test_deepspeed_collate_checkpoint(tmpdir):
         enable_model_summary=False,
     )
     trainer.fit(model)
-    checkpoint_path = os.path.join(tmpdir, "model.pt")
+    checkpoint_path = os.path.join(tmp_path, "model.pt")
     checkpoint_path = trainer.strategy.broadcast(checkpoint_path)
     trainer.save_checkpoint(checkpoint_path)
     if trainer.is_global_zero:
         # ensure function call works
-        output_path = os.path.join(tmpdir, "single_model.pt")
+        output_path = os.path.join(tmp_path, "single_model.pt")
         convert_zero_checkpoint_to_fp32_state_dict(checkpoint_path, output_path)
         _assert_checkpoint_equal(model, output_path)
 

tests/tests_pytorch/utilities/test_dtype_device_mixin.py

@@ -45,10 +45,10 @@ def on_train_batch_start(self, trainer, model, batch, batch_idx):
 
 
 @RunIf(min_cuda_gpus=2)
-def test_submodules_multi_gpu_ddp_spawn(tmpdir):
+def test_submodules_multi_gpu_ddp_spawn(tmp_path):
     model = TopModule()
     trainer = Trainer(
-        default_root_dir=tmpdir,
+        default_root_dir=tmp_path,
         strategy="ddp_spawn",
         accelerator="gpu",
         devices=2,