[bugfix] TPU test hangs to barrier on 1 process

CHANGELOG.md

@@ -83,6 +83,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Prevent `WandbLogger` from dropping values ([#5931](https://github.com/PyTorchLightning/pytorch-lightning/pull/5931))
 
 
+- Fixed `trainer.test` from `best_path` hangs after calling `trainer.fit` ([#6272](https://github.com/PyTorchLightning/pytorch-lightning/pull/6272))
+
+
 - Fixed duplicate logs appearing in console when using the python logging module ([#5509](https://github.com/PyTorchLightning/pytorch-lightning/pull/5509), [#6275](https://github.com/PyTorchLightning/pytorch-lightning/pull/6275))
 
 

pytorch_lightning/accelerators/tpu.py

@@ -31,10 +31,8 @@ def setup(self, trainer: 'Trainer', model: 'LightningModule') -> None:
  raise MisconfigurationException("TPUs only support a single tpu core or tpu spawn training.")
  return super().setup(trainer, model)
 
- def run_optimizer_step(
- self, optimizer: Optimizer, optimizer_idx: int, lambda_closure: Callable, **kwargs: Any
- ) -> None:
- xm.optimizer_step(optimizer, optimizer_args={'closure': lambda_closure, **kwargs})
+ def run_optimizer_step(self, optimizer: Optimizer, optimizer_idx: int, lambda_closure: Callable, **kwargs):
+ xm.optimizer_step(optimizer, barrier=False, optimizer_args={'closure': lambda_closure, **kwargs})
 
  def all_gather(self, tensor: torch.Tensor, group: Optional[Any] = None, sync_grads: bool = False) -> torch.Tensor:
  """

pytorch_lightning/plugins/training_type/horovod.py

@@ -15,6 +15,7 @@
 from typing import Any, List, Optional, Union
 
 import torch
+import torch.distributed as torch_distrib
 from torch.optim.lr_scheduler import _LRScheduler, Optimizer
 
 from pytorch_lightning.core.optimizer import LightningOptimizer
@@ -116,7 +117,8 @@ def start_predicting(self, trainer):
  hvd.join()
 
  def barrier(self, *args, **kwargs):
- hvd.join()
+ if torch_distrib.is_initialized():
+ hvd.join()
 
  def broadcast(self, obj: object, src: int = 0) -> object:
  obj = hvd.broadcast_object(obj, src)

pytorch_lightning/plugins/training_type/tpu_spawn.py

@@ -17,6 +17,7 @@
 from typing import Any, Dict, Iterable, List, Optional, Union
 
 import torch
+import torch.distributed as torch_distrib
 import torch.multiprocessing as mp
 
 from pytorch_lightning.core.lightning import LightningModule
@@ -125,7 +126,8 @@ def model_to_device(self) -> None:
  self._model.to(xm.xla_device())
 
  def barrier(self, name: Optional[str] = None) -> None:
- rendezvous(f"pl.Trainer.{name}")
+ if torch_distrib.is_initialized():
+ rendezvous(f"pl.Trainer.{name}")
 
  def transfer_distrib_spawn_state_on_fit_end(self, results):
  # TODO: is there a better way than accessing callback through model -> trainer -> callback?
@@ -139,14 +141,28 @@ def transfer_distrib_spawn_state_on_fit_end(self, results):
  # TODO: is there a better way than accessing trainer through model -> trainer?
  if not self.lightning_module.trainer.testing and best_model_path is not None and len(best_model_path) > 0:
  last_path = re.sub(".ckpt", ".tmp_end.ckpt", best_model_path)
- xm.save(self.lightning_module.state_dict(), last_path)
+ self.save(self.lightning_module.state_dict(), last_path)
 
  if self.global_rank == 0:
  # todo, pass complete checkpoint as state dictionary
  self.mp_queue.put(best_model_path)
  self.mp_queue.put(last_path)
  self.mp_queue.put(results)
 
+ def save(self, state_dict: Dict, path: str) -> None:
+ """
+ Saving with ``xm.save`` can be unstable and miss the rendez-vous after ``torch.save``.
+ The rendez-vous doesn't affect directly saving.
+ We can ignore the ``RuntimeError`` to reduce friction with TPUs.
+ """
+ try:
+ xm.save(state_dict, path)
+ except RuntimeError as e:
+ if "Failed to meet rendezvous" in str(e):
+ pass
+ else:
+ raise e
+
  def broadcast(self, obj: object, src: int = 0) -> object:
  buffer = io.BytesIO()
  torch.save(obj, buffer)
@@ -294,4 +310,4 @@ def save_checkpoint(self, filepath, weights_only: bool = False):
  # dump states as a checkpoint dictionary object
  _checkpoint = self.lightning_module.trainer.checkpoint_connector.dump_checkpoint(weights_only)
  # Todo: TypeError: 'mappingproxy' object does not support item assignment
- xm.save({k: v for k, v in _checkpoint.items() if k != "callbacks"}, filepath)
+ self.save({k: v for k, v in _checkpoint.items() if k != "callbacks"}, filepath)

pytorch_lightning/trainer/connectors/accelerator_connector.py

@@ -496,7 +496,7 @@ def set_distributed_mode(self, distributed_backend: Optional[str] = None):
  # define the max CPU available
  self.num_processes = os.cpu_count()
  # special case with TPUs
- elif self.distributed_backend == 'tpu':
+ elif self.distributed_backend == 'tpu' or self.tpu_cores is not None:
  self._device_type = DeviceType.TPU
  elif self.distributed_backend and self._distrib_type is None:
  self._distrib_type = DistributedType(self.distributed_backend)

pytorch_lightning/trainer/trainer.py

@@ -56,7 +56,7 @@
 from pytorch_lightning.trainer.training_loop import TrainLoop
 from pytorch_lightning.trainer.training_tricks import TrainerTrainingTricksMixin
 from pytorch_lightning.tuner.tuning import Tuner
-from pytorch_lightning.utilities import DeviceType, rank_zero_warn
+from pytorch_lightning.utilities import rank_zero_warn
 from pytorch_lightning.utilities.cloud_io import load as pl_load
 from pytorch_lightning.utilities.debugging import InternalDebugger
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
@@ -912,8 +912,8 @@ def __test_using_best_weights(self, ckpt_path, test_dataloaders):
  f'specify a path for a checkpoint .test(ckpt_path=PATH)'
  )
  return {}
- if not self._device_type == DeviceType.TPU:
-  self.accelerator.barrier()
+
+ self.training_type_plugin.barrier()
 
  ckpt = pl_load(ckpt_path, map_location=lambda storage, loc: storage)
  model.load_state_dict(ckpt['state_dict'])

tests/models/test_tpu.py

@@ -177,8 +177,6 @@ def test_model_16bit_tpu_cores_8(tmpdir):
 def test_model_tpu_early_stop(tmpdir):
  """Test if single TPU core training works"""
 
- # todo: Test on 8 cores - hanging.
-
  class CustomBoringModel(BoringModel):
 
  def validation_step(self, *args, **kwargs):
@@ -195,9 +193,10 @@ def validation_step(self, *args, **kwargs):
  max_epochs=2,
  limit_train_batches=2,
  limit_val_batches=2,
- tpu_cores=[1],
+ tpu_cores=8,
  )
  trainer.fit(model)
+ trainer.test(test_dataloaders=DataLoader(RandomDataset(32, 2000), batch_size=32))
 
 
 @pytest.mark.skipif(not _TPU_AVAILABLE, reason="test requires TPU machine")