Fix LightningCLI not using ckpt_path hyperparameters to instantiate classes (#20310, #20801).

Author: mauvilsa

src/lightning/pytorch/CHANGELOG.md

@@ -42,6 +42,10 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 - Fixed `RichProgressBar` crashing when sanity checking using val dataloader with 0 len ([#21108](https://github.com/Lightning-AI/pytorch-lightning/pull/21108))
 
+
+- Fixed `LightningCLI` not using `ckpt_path` hyperparameters to instantiate classes ([#21116](https://github.com/Lightning-AI/pytorch-lightning/pull/21116))
+
+
 ---
 
 ## [2.5.3] - 2025-08-13

src/lightning/pytorch/cli.py

@@ -16,6 +16,7 @@
 import sys
 from collections.abc import Iterable
 from functools import partial, update_wrapper
+from pathlib import Path
 from types import MethodType
 from typing import Any, Callable, Optional, TypeVar, Union
 
@@ -397,6 +398,7 @@ def __init__(
         main_kwargs, subparser_kwargs = self._setup_parser_kwargs(self.parser_kwargs)
         self.setup_parser(run, main_kwargs, subparser_kwargs)
         self.parse_arguments(self.parser, args)
+        self._parse_ckpt_path()
 
         self.subcommand = self.config["subcommand"] if run else None
 
@@ -551,6 +553,19 @@ def parse_arguments(self, parser: LightningArgumentParser, args: ArgsType) -> No
         else:
             self.config = parser.parse_args(args)
 
+    def _parse_ckpt_path(self) -> None:
+        """If a checkpoint path is given, parse the hyperparameters from the checkpoint and update the config."""
+        if not self.config.get("subcommand"):
+            return
+        ckpt_path = self.config[self.config.subcommand].get("ckpt_path")
+        if ckpt_path and Path(ckpt_path).is_file():
+            ckpt = torch.load(ckpt_path, weights_only=True, map_location="cpu")
+            hparams = ckpt.get("hyper_parameters", {})
+            hparams.pop("_instantiator", None)
+            if hparams:
+                hparams = {self.config.subcommand: {"model": hparams}}
+                self.config = self.parser.parse_object(hparams, self.config)
+
     def _dump_config(self) -> None:
         if hasattr(self, "config_dump"):
             return

tests/tests_pytorch/test_cli.py

@@ -487,6 +487,40 @@ def test_lightning_cli_print_config():
     assert outval["ckpt_path"] is None
 
 
+class BoringCkptPathModel(BoringModel):
+    def __init__(self, out_dim: int = 2, hidden_dim: int = 2) -> None:
+        super().__init__()
+        self.save_hyperparameters()
+        self.layer = torch.nn.Linear(32, out_dim)
+
+
+def test_lightning_cli_ckpt_path_argument_hparams(cleandir):
+    class CkptPathCLI(LightningCLI):
+        def add_arguments_to_parser(self, parser):
+            parser.link_arguments("model.out_dim", "model.hidden_dim", compute_fn=lambda x: x * 2)
+
+    cli_args = ["fit", "--model.out_dim=3", "--trainer.max_epochs=1"]
+    with mock.patch("sys.argv", ["any.py"] + cli_args):
+        cli = CkptPathCLI(BoringCkptPathModel)
+
+    assert cli.config.fit.model.out_dim == 3
+    assert cli.config.fit.model.hidden_dim == 6
+    hparams_path = Path(cli.trainer.log_dir) / "hparams.yaml"
+    assert hparams_path.is_file()
+    hparams = yaml.safe_load(hparams_path.read_text())
+    assert hparams["out_dim"] == 3
+    assert hparams["hidden_dim"] == 6
+
+    checkpoint_path = next(Path(cli.trainer.log_dir, "checkpoints").glob("*.ckpt"))
+    cli_args = ["predict", f"--ckpt_path={checkpoint_path}"]
+    with mock.patch("sys.argv", ["any.py"] + cli_args):
+        cli = CkptPathCLI(BoringCkptPathModel)
+
+    assert cli.config.predict.model.out_dim == 3
+    assert cli.config.predict.model.hidden_dim == 6
+    assert cli.config_init.predict.model.layer.out_features == 3
+
+
 def test_lightning_cli_submodules(cleandir):
     class MainModule(BoringModel):
         def __init__(self, submodule1: LightningModule, submodule2: LightningModule, main_param: int = 1):