Add back support for logging in the gradient clipping hooks (#14298)

* Add back support for logging in the gradient clipping hooks

* Docs and CHANGELOG

* Fix tests

Author: carmocca

docs/source-pytorch/visualize/logging_advanced.rst

@@ -355,7 +355,7 @@ In LightningModule
    * - Method
      - on_step
      - on_epoch
-   * - on_after_backward, on_before_backward, on_before_optimizer_step, on_before_zero_grad, training_step, training_step_end
+   * - on_after_backward, on_before_backward, on_before_optimizer_step, optimizer_step, configure_gradient_clipping, on_before_zero_grad, training_step, training_step_end
      - True
      - False
    * - training_epoch_end, test_epoch_end, test_step, test_step_end, validation_epoch_end, validation_step, validation_step_end

src/pytorch_lightning/CHANGELOG.md

@@ -4,13 +4,14 @@ 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/).
 
-## [1.7.3] - 2022-08-24
+## [1.7.3] - 2022-08-23
 
 ### Fixed
 
 - Fixed an assertion error when using a `ReduceOnPlateau` scheduler with the Horovod strategy ([#14215](https://github.com/Lightning-AI/lightning/pull/14215))
 - Fixed an `AttributeError` when accessing `LightningModule.logger` and the Trainer has multiple loggers ([#14234](https://github.com/Lightning-AI/lightning/pull/14234))
 - Fixed wrong num padding for `RichProgressBar` ([#14296](https://github.com/Lightning-AI/lightning/pull/14296))
+- Added back support for `log`ging in the `configure_gradient_clipping` hook after unintended removal in v1.7.2 ([#14298](https://github.com/Lightning-AI/lightning/issues/14298))
 
 
 ## [1.7.2] - 2022-08-17

src/pytorch_lightning/trainer/connectors/logger_connector/fx_validator.py

@@ -44,8 +44,13 @@ class _LogOptions(TypedDict):
             allowed_on_step=(False, True), allowed_on_epoch=(False, True), default_on_step=True, default_on_epoch=False
         ),
         "lr_scheduler_step": None,
-        "configure_gradient_clipping": None,
-        "clip_gradients": None,
+        # should match `optimizer_step`
+        "configure_gradient_clipping": _LogOptions(
+            allowed_on_step=(False, True), allowed_on_epoch=(False, True), default_on_step=True, default_on_epoch=False
+        ),
+        "clip_gradients": _LogOptions(
+            allowed_on_step=(False, True), allowed_on_epoch=(False, True), default_on_step=True, default_on_epoch=False
+        ),
         "on_before_zero_grad": _LogOptions(
             allowed_on_step=(False, True), allowed_on_epoch=(False, True), default_on_step=True, default_on_epoch=False
         ),

tests/tests_pytorch/trainer/logging_/test_logger_connector.py

@@ -183,12 +183,7 @@ class HookedModel(BoringModel):
     def __init__(self, not_supported):
         super().__init__()
         pl_module_hooks = get_members(LightningModule)
-        pl_module_hooks.difference_update(
-            {
-                "log",
-                "log_dict",
-            }
-        )
+        pl_module_hooks.difference_update({"log", "log_dict"})
         # remove `nn.Module` hooks
         module_hooks = get_members(torch.nn.Module)
         pl_module_hooks.difference_update(module_hooks)
@@ -236,8 +231,6 @@ def test_fx_validator_integration(tmpdir):
         "on_validation_model_eval": "You can't",
         "on_validation_model_train": "You can't",
         "lr_scheduler_step": "You can't",
-        "configure_gradient_clipping": "You can't",
-        "clip_gradients": "You can't",
         "on_save_checkpoint": "You can't",
         "on_load_checkpoint": "You can't",
         "on_exception": "You can't",

tests/tests_pytorch/trainer/logging_/test_loop_logging.py

@@ -54,6 +54,8 @@ def _make_assertion(model, hooks, result_mock, on_step, on_epoch, extra_kwargs):
             "on_after_backward",
             "on_before_optimizer_step",
             "optimizer_step",
+            "configure_gradient_clipping",
+            "clip_gradients",
             "on_before_zero_grad",
             "optimizer_zero_grad",
             "training_step",