add rank warning (#1428)

* add rank warning

* changelog

* use rank_zero_warn

* user trainer_init

* replace warnings

* fix test

* flake8

* docs

* changelog

* bug lol

Author: Borda

CHANGELOG.md

@@ -4,6 +4,17 @@ 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/).
 
+## [0.7.3] - 2020-04-09
+
+### Added
+
+- Added `rank_zero_warn` for warning only in rank 0 ([#1428](https://github.com/PyTorchLightning/pytorch-lightning/pull/1428))
+
+### Fixed
+
+- Fixed default `DistributedSampler` for DDP training ([#1425](https://github.com/PyTorchLightning/pytorch-lightning/pull/1425))
+- Fixed workers warning not on windows ([#1430](https://github.com/PyTorchLightning/pytorch-lightning/pull/1430))
+
 ## [0.7.2] - 2020-04-07
 
 ### Added

pytorch_lightning/__init__.py

@@ -1,6 +1,6 @@
 """Root package info."""
 
-__version__ = '0.7.2'
+__version__ = '0.7.3rc1'
 __author__ = 'William Falcon et al.'
 __author_email__ = '[email protected]'
 __license__ = 'Apache-2.0'

pytorch_lightning/callbacks/early_stopping.py

@@ -6,12 +6,11 @@
 
 """
 
-import warnings
-
 import numpy as np
 
 from pytorch_lightning import _logger as log
 from pytorch_lightning.callbacks.base import Callback
+from pytorch_lightning.utilities import rank_zero_warn
 
 
 class EarlyStopping(Callback):
@@ -80,7 +79,7 @@ def check_metrics(self, logs):
             if self.strict:
                 raise RuntimeError(error_msg)
             if self.verbose > 0:
-                warnings.warn(error_msg, RuntimeWarning)
+                rank_zero_warn(error_msg, RuntimeWarning)
 
             return False
 
@@ -113,6 +112,6 @@ def on_epoch_end(self, trainer, pl_module):
 
     def on_train_end(self, trainer, pl_module):
         if self.stopped_epoch > 0 and self.verbose > 0:
-            warnings.warn('Displayed epoch numbers by `EarlyStopping` start from "1" until v0.6.x,'
-                          ' but will start from "0" in v0.8.0.', DeprecationWarning)
+            rank_zero_warn('Displayed epoch numbers by `EarlyStopping` start from "1" until v0.6.x,'
+                           ' but will start from "0" in v0.8.0.', DeprecationWarning)
             log.info(f'Epoch {self.stopped_epoch + 1:05d}: early stopping')

pytorch_lightning/callbacks/gradient_accumulation_scheduler.py

@@ -6,9 +6,8 @@
 
 """
 
-import warnings
-
 from pytorch_lightning.callbacks.base import Callback
+from pytorch_lightning.utilities import rank_zero_warn
 
 
 class GradientAccumulationScheduler(Callback):
@@ -46,8 +45,8 @@ def __init__(self, scheduling: dict):
                 raise TypeError("All epoches and accumulation factor must be integers")
 
         minimal_epoch = min(scheduling.keys())
-        warnings.warn('Epochs indexing of `scheduling` starts from "1" until v0.6.x,'
-                      ' but will start from "0" in v0.8.0.', DeprecationWarning)
+        rank_zero_warn('Epochs indexing of `scheduling` starts from "1" until v0.6.x,'
+                       ' but will start from "0" in v0.8.0.', DeprecationWarning)
         if minimal_epoch < 1:
             msg = f"Epochs indexing from 1, epoch {minimal_epoch} cannot be interpreted correct"
             raise IndexError(msg)

pytorch_lightning/callbacks/model_checkpoint.py

@@ -7,14 +7,13 @@
 """
 
 import os
-import shutil
-import warnings
 import re
 
 import numpy as np
 
-from pytorch_lightning.callbacks.base import Callback
 from pytorch_lightning import _logger as log
+from pytorch_lightning.callbacks.base import Callback
+from pytorch_lightning.utilities import rank_zero_warn
 
 
 class ModelCheckpoint(Callback):
@@ -83,7 +82,7 @@ def __init__(self, filepath: str, monitor: str = 'val_loss', verbose: bool = Fal
                  mode: str = 'auto', period: int = 1, prefix: str = ''):
         super().__init__()
         if save_top_k > 0 and os.path.isdir(filepath) and len(os.listdir(filepath)) > 0:
-            warnings.warn(
+            rank_zero_warn(
                 f"Checkpoint directory {filepath} exists and is not empty with save_top_k != 0."
                 "All files in this directory will be deleted when a checkpoint is saved!"
             )
@@ -115,9 +114,7 @@ def __init__(self, filepath: str, monitor: str = 'val_loss', verbose: bool = Fal
         }
 
         if mode not in mode_dict:
-            warnings.warn(
-                f'ModelCheckpoint mode {mode} is unknown, '
-                'fallback to auto mode.', RuntimeWarning)
+            rank_zero_warn(f'ModelCheckpoint mode {mode} is unknown, fallback to auto mode.', RuntimeWarning)
             mode = 'auto'
 
         self.monitor_op, self.kth_value, self.mode = mode_dict[mode]
@@ -206,7 +203,7 @@ def on_validation_end(self, trainer, pl_module):
             current = metrics.get(self.monitor)
 
             if current is None:
-                warnings.warn(f'Can save best model only with {self.monitor} available, skipping.', RuntimeWarning)
+                rank_zero_warn(f'Can save best model only with {self.monitor} available, skipping.', RuntimeWarning)
             elif self.check_monitor_top_k(current):
                 self._do_check_save(filepath, current, epoch)
             elif self.verbose > 0:

pytorch_lightning/core/decorators.py

@@ -1,4 +1,4 @@
-import warnings
+from pytorch_lightning.utilities import rank_zero_warn
 
 
 def data_loader(fn):
@@ -7,7 +7,7 @@ def data_loader(fn):
     Warnings:
         This decorator deprecated in v0.7.0 and it will be removed v0.9.0.
     """
-    warnings.warn('`data_loader` decorator deprecated in v0.7.0. Will be removed v0.9.0', DeprecationWarning)
+    rank_zero_warn('`data_loader` decorator deprecated in v0.7.0. Will be removed v0.9.0', DeprecationWarning)
 
     def inner_fx(self):
         return fn(self)

pytorch_lightning/core/lightning.py

@@ -1,7 +1,6 @@
 import collections
 import inspect
 import os
-import warnings
 from abc import ABC, abstractmethod
 from argparse import Namespace
 from typing import Any, Callable, Dict, List, Optional, Tuple, Union, Sequence
@@ -20,6 +19,7 @@
 from pytorch_lightning.core.saving import ModelIO, load_hparams_from_tags_csv
 from pytorch_lightning.overrides.data_parallel import LightningDistributedDataParallel
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
+from pytorch_lightning.utilities import rank_zero_warn
 
 try:
     import torch_xla.core.xla_model as xm
@@ -225,7 +225,7 @@ def training_step(self, batch, batch_idx, hiddens):
             The loss value shown in the progress bar is smoothed (averaged) over the last values,
             so it differs from the actual loss returned in train/validation step.
         """
-        warnings.warn('`training_step` must be implemented to be used with the Lightning Trainer')
+        rank_zero_warn('`training_step` must be implemented to be used with the Lightning Trainer')
 
     def training_end(self, *args, **kwargs):
         """
@@ -1088,7 +1088,7 @@ def configure_optimizers(self):
                   }
 
         """
-        warnings.warn('`configure_optimizers` must be implemented to be used with the Lightning Trainer')
+        rank_zero_warn('`configure_optimizers` must be implemented to be used with the Lightning Trainer')
 
     def optimizer_step(
             self,
@@ -1291,16 +1291,16 @@ def train_dataloader(self):
                     return loader
 
         """
-        warnings.warn('`train_dataloader` must be implemented to be used with the Lightning Trainer')
+        rank_zero_warn('`train_dataloader` must be implemented to be used with the Lightning Trainer')
 
     def tng_dataloader(self):  # todo: remove in v1.0.0
         """
         Warnings:
             Deprecated in v0.5.0. Use :meth:`train_dataloader` instead. Will be removed in 1.0.0.
         """
         output = self.train_dataloader()
-        warnings.warn("`tng_dataloader` has been renamed to `train_dataloader` since v0.5.0."
-                      " and this method will be removed in v1.0.0", DeprecationWarning)
+        rank_zero_warn("`tng_dataloader` has been renamed to `train_dataloader` since v0.5.0."
+                       " and this method will be removed in v1.0.0", DeprecationWarning)
         return output
 
     def test_dataloader(self) -> Union[DataLoader, List[DataLoader]]:
@@ -1407,7 +1407,7 @@ def load_from_metrics(cls, weights_path, tags_csv, map_location=None):
             Deprecated in version 0.7.0. You should use :meth:`load_from_checkpoint` instead.
             Will be removed in v0.9.0.
         """
-        warnings.warn(
+        rank_zero_warn(
             "`load_from_metrics` method has been unified with `load_from_checkpoint` in v0.7.0."
             " The deprecated method will be removed in v0.9.0.", DeprecationWarning
         )
@@ -1519,7 +1519,7 @@ def _load_model_state(cls, checkpoint: Dict[str, Any], *args, **kwargs) -> 'Ligh
                 is_namespace = checkpoint.get('hparams_type', 'namespace') == 'namespace'
                 hparams = Namespace(**ckpt_hparams) if is_namespace else ckpt_hparams
             else:
-                warnings.warn(
+                rank_zero_warn(
                     f"Checkpoint does not contain hyperparameters but {cls.__name__}'s __init__ "
                     f"contains argument 'hparams'. Will pass in an empty Namespace instead."
                     " Did you forget to store your model hyperparameters in self.hparams?"

pytorch_lightning/core/model_saving.py

@@ -3,9 +3,9 @@
   The deprecated module name will be removed in v0.8.0.
 """
 
-import warnings
+from pytorch_lightning.utilities import rank_zero_warn
 
-warnings.warn("`model_saving` module has been renamed to `saving` since v0.6.0."
-              " The deprecated module name will be removed in v0.8.0.", DeprecationWarning)
+rank_zero_warn("`model_saving` module has been renamed to `saving` since v0.6.0."
+               " The deprecated module name will be removed in v0.8.0.", DeprecationWarning)
 
 from pytorch_lightning.core.saving import *  # noqa: F403

pytorch_lightning/core/root_module.py

@@ -3,9 +3,9 @@
  The deprecated module name will be removed in v0.8.0.
 """
 
-import warnings
+from pytorch_lightning.utilities import rank_zero_warn
 
-from pytorch_lightning.core.lightning import *  # noqa: F403
+rank_zero_warn("`root_module` module has been renamed to `lightning` since v0.6.0."
+               " The deprecated module name will be removed in v0.8.0.", DeprecationWarning)
 
-warnings.warn("`root_module` module has been renamed to `lightning` since v0.6.0."
-              " The deprecated module name will be removed in v0.8.0.", DeprecationWarning)
+from pytorch_lightning.core.lightning import *  # noqa: F403

pytorch_lightning/logging/__init__.py

@@ -3,10 +3,10 @@
  The deprecated package name will be removed in v0.9.0.
 """
 
-import warnings
+from pytorch_lightning.utilities import rank_zero_warn
 
-warnings.warn("`logging` package has been renamed to `loggers` since v0.7.0"
-              " The deprecated package name will be removed in v0.9.0.", DeprecationWarning)
+rank_zero_warn("`logging` package has been renamed to `loggers` since v0.7.0"
+               " The deprecated package name will be removed in v0.9.0.", DeprecationWarning)
 
 from pytorch_lightning.loggers import *  # noqa: F403
 from pytorch_lightning.loggers import base, tensorboard  # noqa: F403