Remove the unused `utilities.finite_checks` (#16682)

docs/source-pytorch/api_references.rst

@@ -254,7 +254,6 @@ utilities
     data
     deepspeed
     distributed
-    finite_checks
     memory
     model_summary
     parsing

src/lightning/pytorch/CHANGELOG.md

@@ -184,6 +184,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 - Removed `Trainer.model` setter ([#16462](https://github.com/Lightning-AI/lightning/pull/16462))
 
+- Removed the unused `lightning.pytorch.utilities.finite_checks.print_nan_gradients` function ([#16682](https://github.com/Lightning-AI/lightning/pull/16682))
+- Removed the unused `lightning.pytorch.utilities.finite_checks.detect_nan_parameters` function ([#16682](https://github.com/Lightning-AI/lightning/pull/16682))
+
 - Tuner removal
   * Removed the deprecated `trainer.tuning` property ([#16379](https://github.com/Lightning-AI/lightning/pull/16379))
   * Removed the deprecated `TrainerFn.TUNING` and `RunningStage.TUNING` enums ([#16379](https://github.com/Lightning-AI/lightning/pull/16379))

src/lightning/pytorch/utilities/finite_checks.py

@@ -1,44 +0,0 @@
-# Copyright The Lightning AI team.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""Helper functions to detect NaN/Inf values."""
-
-import logging
-
-import torch
-import torch.nn as nn
-
-log = logging.getLogger(__name__)
-
-
-def print_nan_gradients(model: nn.Module) -> None:
-    """Iterates over model parameters and prints out parameter + gradient information if NaN."""
-    for param in model.parameters():
-        if (param.grad is not None) and torch.isnan(param.grad.float()).any():
-            log.info(f"{param}, {param.grad}")
-
-
-def detect_nan_parameters(model: nn.Module) -> None:
-    """Iterates over model parameters and prints gradients if any parameter is not finite.
-
-    Raises:
-        ValueError:
-            If ``NaN`` or ``inf`` values are found
-    """
-    for name, param in model.named_parameters():
-        if not torch.isfinite(param).all():
-            print_nan_gradients(model)
-            raise ValueError(
-                f"Detected nan and/or inf values in `{name}`."
-                " Check your forward pass for numerically unstable operations."
-            )

tests/tests_pytorch/utilities/test_finite_checks.py

@@ -1,20 +0,0 @@
-import math
-
-import pytest
-import torch
-import torch.nn as nn
-
-from lightning.pytorch.utilities.finite_checks import detect_nan_parameters
-
-
-@pytest.mark.parametrize("value", (math.nan, math.inf, -math.inf))
-def test_detect_nan_parameters(value):
-    model = nn.Linear(2, 3)
-
-    detect_nan_parameters(model)
-
-    nn.init.constant_(model.bias, value)
-    assert not torch.isfinite(model.bias).all()
-
-    with pytest.raises(ValueError, match=r".*Detected nan and/or inf values in `bias`.*"):
-        detect_nan_parameters(model)