1.4.9 release commit

Author: awaelchli

CHANGELOG.md

@@ -5,13 +5,14 @@ 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/).
 
 
-## [unreleased] - 2021-??-??
+## [1.4.9] - 2021-09-30
 
 - Moved the gradient unscaling in `NativeMixedPrecisionPlugin` from `pre_optimizer_step` to `post_backward` ([#9606](https://github.com/PyTorchLightning/pytorch-lightning/pull/9606))
 - Fixed gradient unscaling being called too late, causing gradient clipping and gradient norm tracking to be applied incorrectly ([#9606](https://github.com/PyTorchLightning/pytorch-lightning/pull/9606))
 - Fixed `lr_find` to generate same results on multiple calls ([#9704](https://github.com/PyTorchLightning/pytorch-lightning/pull/9704))
 - Fixed `reset` metrics on validation epoch end ([#9717](https://github.com/PyTorchLightning/pytorch-lightning/pull/9717))
-
+- Fixed input validation for `gradient_clip_val`, `gradient_clip_algorithm`, `track_grad_norm` and `terminate_on_nan` Trainer arguments ([#9595](https://github.com/PyTorchLightning/pytorch-lightning/pull/9595))
+- Reset metrics before each task starts ([#9410](https://github.com/PyTorchLightning/pytorch-lightning/pull/9410))
 
 
 ## [1.4.8] - 2021-09-22

pytorch_lightning/__about__.py

@@ -1,7 +1,7 @@
 import time
 
 _this_year = time.strftime("%Y")
-__version__ = "1.4.8"
+__version__ = "1.4.9"
 __author__ = "William Falcon et al."
 __author_email__ = "[email protected]"
 __license__ = "Apache-2.0"

pytorch_lightning/trainer/trainer.py

@@ -1112,7 +1112,7 @@ def _run_sanity_check(self, ref_model):
             self.logger_connector.reset_results()
             self.logger_connector.reset_metrics()
 
-            self.call_hook("on_sanity_check_start")
+            self.on_sanity_check_start()
 
             # reload dataloaders
             self._evaluation_loop.reload_evaluation_dataloaders()

tests/checkpointing/test_trainer_checkpoint.py

@@ -75,9 +75,6 @@ def validation_step(self, batch, batch_idx):
         results.append(deepcopy(trainer.callback_metrics))
         best_model_paths.append(trainer.checkpoint_callback.best_model_path)
 
-    for idx in range(len(results) - 1):
-        assert results[idx]["val_loss"] > results[idx + 1]["val_loss"]
-
     for idx, best_model_path in enumerate(best_model_paths):
         if idx == 0:
             assert best_model_path.endswith(f"epoch=0{idx}.ckpt")