Enable inference mode for testing and predicting

CHANGELOG.md

@@ -107,9 +107,13 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 - Added `on_exception` callback hook ([#9183](https://github.com/PyTorchLightning/pytorch-lightning/pull/9183))
 
+
 - Add a warning to deepspeed when inferring batch size ([#9221](https://github.com/PyTorchLightning/pytorch-lightning/pull/9221))
 
 
+- Added `inference_mode` for evaluation and prediction ([8813](https://github.com/PyTorchLightning/pytorch-lightning/pull/8813))
+
+
 ### Changed
 
 - Parsing of the `gpus` Trainer argument has changed: `gpus="n"` (str) no longer selects the GPU index n and instead selects the first n devices. ([#8770](https://github.com/PyTorchLightning/pytorch-lightning/pull/8770))
@@ -286,7 +290,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Fixed `EarlyStopping` running on train epoch end when `check_val_every_n_epoch>1` is set ([#9156](https://github.com/PyTorchLightning/pytorch-lightning/pull/9156))
 
 
-- Fixed an issue with logger outputs not being finalized correctly after prediction runs ([#8333](https://github.com/PyTorchLightning/pytorch-lightning/issues/8333))
+- Fixed an issue with logger outputs not being finalized correctly after prediction runs ([#8685](https://github.com/PyTorchLightning/pytorch-lightning/pull/8685))
 
 
 - Fixed the Apex and DeepSpeed plugin closure running after the `on_before_optimizer_step` hook ([#9288](https://github.com/PyTorchLightning/pytorch-lightning/issues/9288))

pytorch_lightning/trainer/trainer.py

@@ -16,9 +16,10 @@
 import os
 import traceback
 import warnings
+from contextlib import contextmanager
 from datetime import timedelta
 from pathlib import Path
-from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple, Union
+from typing import Any, Callable, Dict, Generator, Iterable, List, Optional, Tuple, Union
 from weakref import proxy
 
 import torch
@@ -76,7 +77,7 @@
 from pytorch_lightning.utilities.debugging import InternalDebugger
 from pytorch_lightning.utilities.distributed import distributed_available
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
-from pytorch_lightning.utilities.imports import _fault_tolerant_training
+from pytorch_lightning.utilities.imports import _fault_tolerant_training, _TORCH_GREATER_EQUAL_1_9
 from pytorch_lightning.utilities.model_helpers import is_overridden
 from pytorch_lightning.utilities.model_summary import ModelSummary, summarize
 from pytorch_lightning.utilities.seed import reset_seed
@@ -1146,7 +1147,7 @@ def _run_evaluate(self) -> _EVALUATE_OUTPUT:
         # reset trainer on this loop and all child loops in case user connected a custom loop
         self._evaluation_loop.trainer = self
 
-        with self.profiler.profile(f"run_{self.state.stage}_evaluation"), torch.no_grad():
+        with self.profiler.profile(f"run_{self.state.stage}_evaluation"), self._evaluation_context():
             eval_loop_results = self._evaluation_loop.run()
 
         # remove the tensors from the eval results
@@ -1162,7 +1163,7 @@ def _run_predict(self) -> Optional[_PREDICT_OUTPUT]:
         self.reset_predict_dataloader(self.lightning_module)
         # reset trainer on this loop and all child loops in case user connected a custom loop
         self.predict_loop.trainer = self
-        with torch.no_grad():
+        with self._evaluation_context():
             return self.predict_loop.run()
 
     def _run_sanity_check(self, ref_model):
@@ -1391,3 +1392,8 @@ def _on_exception(self):
         # save a checkpoint for fault tolerant training. we don't use `log_dir` to minimize the chances of failure.
         file_path = os.path.join(self.default_root_dir, ".pl_auto_save.ckpt")
         self.save_checkpoint(file_path)
+
+    @contextmanager
+    def _evaluation_context(self) -> Generator:
+        with torch.inference_mode() if _TORCH_GREATER_EQUAL_1_9 else torch.no_grad():
+            yield