`Error while merging hparams` when using LightningCLI and YAML

[cgebbe]

Bug description

The minimal example below throws the error RuntimeError: Error while merging hparams: the keys ['_class_path'] are present in both the LightningModule's and LightningDataModule's hparams but have different values.

I though this was supposed to work. Would really appreciate workaround tips (that also work with checkpointing) or a fix.

What version are you seeing the problem on?

v2.4

How to reproduce the bug

Run the code below using e.g. python main.py --config config.yaml

# config.yaml
data:
  class_path: MNISTDataModule

model:
  class_path: LitAutoEncoder

# main.py
import os
from torch import optim, nn, utils
from torchvision.datasets import MNIST
from torchvision.transforms import ToTensor
import lightning as L
from lightning.pytorch import cli
from torch.utils.data import random_split, DataLoader
import torch
from torchvision import transforms


class LitAutoEncoder(L.LightningModule):
    """From https://lightning.ai/docs/pytorch/stable/starter/introduction.html"""

    def __init__(self, dim: int = 64):
        super().__init__()
        self.save_hyperparameters()
        self.encoder = nn.Sequential(
            nn.Linear(28 * 28, dim), nn.ReLU(), nn.Linear(dim, 3)
        )
        self.decoder = nn.Sequential(
            nn.Linear(3, dim), nn.ReLU(), nn.Linear(dim, 28 * 28)
        )

    def training_step(self, batch, batch_idx):
        x, _ = batch
        x = x.view(x.size(0), -1)
        z = self.encoder(x)
        x_hat = self.decoder(z)
        loss = nn.functional.mse_loss(x_hat, x)
        self.log("train_loss", loss)
        return loss

    def configure_optimizers(self):
        return optim.Adam(self.parameters(), lr=1e-3)


class MNISTDataModule(L.LightningDataModule):
    """From https://lightning.ai/docs/pytorch/stable/data/datamodule.html"""

    def __init__(self, data_dir: str = "./"):
        super().__init__()
        self.save_hyperparameters()
        self.data_dir = data_dir
        self.transform = transforms.Compose(
            [transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))]
        )

    def prepare_data(self):
        # download
        MNIST(self.data_dir, train=True, download=True)
        MNIST(self.data_dir, train=False, download=True)

    def setup(self, stage: str):
        # Assign train/val datasets for use in dataloaders
        if stage == "fit":
            mnist_full = MNIST(self.data_dir, train=True, transform=self.transform)
            self.mnist_train, self.mnist_val = random_split(
                mnist_full, [55000, 5000], generator=torch.Generator().manual_seed(42)
            )

        # Assign test dataset for use in dataloader(s)
        if stage == "test":
            self.mnist_test = MNIST(
                self.data_dir, train=False, transform=self.transform
            )

        if stage == "predict":
            self.mnist_predict = MNIST(
                self.data_dir, train=False, transform=self.transform
            )

    def train_dataloader(self):
        return DataLoader(self.mnist_train, batch_size=32)

    def val_dataloader(self):
        return DataLoader(self.mnist_val, batch_size=32)

    def test_dataloader(self):
        return DataLoader(self.mnist_test, batch_size=32)

    def predict_dataloader(self):
        return DataLoader(self.mnist_predict, batch_size=32)


cli.LightningCLI()

Error messages and logs

RuntimeError: Error while merging hparams: the keys ['_class_path'] are present in both the LightningModule's and LightningDataModule's hparams but have different values.

Environment

Current environment

• CUDA:
  - GPU:
  - NVIDIA A10G
  - available: True
  - version: 12.1
• Lightning:
  - lightning: 2.4.0
  - lightning-utilities: 0.11.4
  - pytorch-lightning: 2.3.3
  - torch: 2.3.1
  - torchdata: 0.7.1
  - torchmetrics: 1.0.3
  - torchsummary: 1.5.1
  - torchvision: 0.18.1
• Packages:
  - absl-py: 2.1.0
  - affine: 2.4.0
  - aiobotocore: 2.13.0
  - aiohttp: 3.9.5
  - aioitertools: 0.11.0
  - aiosignal: 1.3.1
  - albucore: 0.0.12
  - albumentations: 1.4.11
  - altair: 5.3.0
  - annotated-types: 0.7.0
  - ansi2html: 1.9.1
  - ansicolors: 1.1.8
  - antlr4-python3-runtime: 4.9.3
  - anyio: 4.4.0
  - appdirs: 1.4.4
  - argon2-cffi: 23.1.0
  - argon2-cffi-bindings: 21.2.0
  - arrow: 1.3.0
  - asciitree: 0.3.3
  - assertpy: 1.1
  - asttokens: 2.4.1
  - astunparse: 1.6.3
  - async-lru: 2.0.4
  - attrs: 23.2.0
  - automation-api-gateway-client: 12.0.165
  - az-annotation-io: 0.18.8
  - az-cp-aws-utils: 1.2.5
  - az-cp-datadictionary-definitions: 0.2024.119.post1
  - az-cp-drclib: 0.1.0
  - az-cp-holoviews-compressed-rgb: 0.0.1rc3
  - az-cp-imagekit-ventana-bif: 0.2.2
  - az-cp-logging: 0.15.0
  - az-cp-ooportal: 1.4.7
  - az-cp-pathviz: 0.0.1rc7
  - az-cp-pita: 1.10.1
  - az-cp-predictino-container: 3.10.3
  - az-drc2polygons: 2.2.2
  - az-git-utils: 0.14.0
  - babel: 2.15.0
  - beautifulsoup4: 4.12.3
  - bleach: 6.1.0
  - blessed: 1.20.0
  - blinker: 1.8.2
  - bokeh: 3.4.1
  - boto3: 1.34.106
  - botocore: 1.34.106
  - bpython: 0.24
  - braceexpand: 0.1.7
  - cachetools: 5.3.3
  - cerberus: 1.3.5
  - certifi: 2024.2.2
  - cffi: 1.16.0
  - cfgv: 3.4.0
  - charset-normalizer: 3.3.2
  - click: 8.1.7
  - click-plugins: 1.1.1
  - cligj: 0.7.2
  - cloudpickle: 3.0.0
  - cmake: 3.25.0
  - colorama: 0.4.6
  - colorcet: 3.1.0
  - comm: 0.2.2
  - contourpy: 1.2.1
  - coreg-tools: 0.3.0
  - cubinlinker-cu11: 0.3.0.post2
  - cuda-python: 11.8.3
  - cudf-cu11: 24.6.1
  - cuml-cu11: 24.6.1
  - cupy-cuda11x: 13.2.0
  - curtsies: 0.4.2
  - cwcwidth: 0.1.9
  - cycler: 0.12.1
  - dash: 2.17.0
  - dash-core-components: 2.0.0
  - dash-html-components: 2.0.0
  - dash-table: 5.0.0
  - dask: 2024.5.1
  - dask-cuda: 24.6.0
  - dask-cudf-cu11: 24.6.1
  - dask-expr: 1.1.1
  - datashader: 0.16.2
  - debugpy: 1.8.1
  - decorator: 5.1.1
  - definiens-autocli: 4.5.3
  - definiens-ia-algorithms: 3.39.1
  - definiens-imagekit: 5.6.6
  - definiens-parallel: 1.3.0
  - defusedxml: 0.7.1
  - deprecated: 1.2.14
  - distlib: 0.3.8
  - distributed: 2024.5.1
  - distributed-ucxx-cu11: 0.38.0
  - dm-tree: 0.1.8
  - dnspython: 2.6.1
  - docker-pycreds: 0.4.0
  - docstring-parser: 0.16
  - ec2-metadata: 2.13.0
  - editor: 1.6.6
  - entrypoints: 0.4
  - eval-type-backport: 0.2.0
  - executing: 2.0.1
  - fancycompleter: 0.9.1
  - fasteners: 0.19
  - fastjsonschema: 2.19.1
  - fastrlock: 0.8.2
  - filelock: 3.14.0
  - fiona: 1.9.6
  - flask: 3.0.3
  - flatbuffers: 24.3.25
  - fonttools: 4.52.4
  - fqdn: 1.5.1
  - frozendict: 2.4.4
  - frozenlist: 1.4.1
  - fsspec: 2024.5.0
  - fvcore: 0.1.5.post20221221
  - gast: 0.6.0
  - geopandas: 1.0.1
  - geopolars: 0.1.0a4
  - gitdb: 4.0.11
  - gitpython: 3.1.43
  - google-auth: 2.32.0
  - google-auth-oauthlib: 1.0.0
  - google-pasta: 0.2.0
  - greenlet: 3.0.3
  - grpcio: 1.64.1
  - h11: 0.14.0
  - h5py: 3.11.0
  - holoviews: 1.19.0
  - httpcore: 1.0.5
  - httpx: 0.27.0
  - huggingface-hub: 0.24.0
  - hvplot: 0.10.0
  - identify: 2.5.36
  - idna: 3.7
  - imagecodecs: 2024.1.1
  - imageio: 2.34.1
  - importlib-metadata: 7.1.0
  - iniconfig: 2.0.0
  - inquirer: 3.2.4
  - ioda: 0.19.1
  - ioda-readout-service-client: 0.0.5
  - ioda-result-service-client: 0.6.1
  - iopath: 0.1.10
  - ipp: 2021.4.0
  - ipykernel: 6.29.4
  - ipython: 8.24.0
  - ipytree: 0.2.2
  - ipywidgets: 8.1.3
  - isoduration: 20.11.0
  - itables: 2.1.0
  - itsdangerous: 2.2.0
  - jax: 0.4.30
  - jaxlib: 0.4.30
  - jedi: 0.19.1
  - jinja2: 3.1.4
  - jmespath: 1.0.1
  - joblib: 1.4.2
  - json5: 0.9.25
  - jsonargparse: 4.29.0
  - jsonpointer: 2.4
  - jsonschema: 4.22.0
  - jsonschema-specifications: 2023.12.1
  - jupyter: 1.0.0
  - jupyter-bokeh: 4.0.5
  - jupyter-client: 8.6.2
  - jupyter-console: 6.6.3
  - jupyter-core: 5.7.2
  - jupyter-dash: 0.4.2
  - jupyter-events: 0.10.0
  - jupyter-lsp: 2.2.5
  - jupyter-server: 2.14.1
  - jupyter-server-terminals: 0.5.3
  - jupyterlab: 4.2.1
  - jupyterlab-pygments: 0.3.0
  - jupyterlab-server: 2.27.2
  - jupyterlab-vim: 4.1.3
  - jupyterlab-widgets: 3.0.11
  - kaleido: 0.2.1
  - keras: 2.12.0
  - kiwisolver: 1.4.5
  - lazy-loader: 0.4
  - leb128: 1.0.7
  - libclang: 18.1.1
  - libucx-cu11: 1.15.0.post1
  - lightning: 2.4.0
  - lightning-utilities: 0.11.4
  - linkify-it-py: 2.0.3
  - lit: 15.0.7
  - litdata: 0.2.16
  - llvmlite: 0.42.0
  - locket: 1.0.0
  - loguru: 0.7.2
  - lxml: 4.9.4
  - markdown: 3.6
  - markdown-it-py: 3.0.0
  - markupsafe: 2.1.5
  - matplotlib: 3.9.0
  - matplotlib-inline: 0.1.7
  - mdit-py-plugins: 0.4.1
  - mdurl: 0.1.2
  - mistune: 3.0.2
  - ml-dtypes: 0.4.0
  - mpmath: 1.3.0
  - msgpack: 1.0.8
  - multidict: 6.0.5
  - multipledispatch: 1.0.0
  - namex: 0.0.8
  - nbclient: 0.10.0
  - nbconvert: 7.16.4
  - nbformat: 5.10.4
  - nest-asyncio: 1.6.0
  - networkx: 3.3
  - newrelic: 8.11.0
  - nodeenv: 1.9.0
  - notebook: 7.2.0
  - notebook-cgebbe: 0.1.0
  - notebook-shim: 0.2.4
  - nrai-wrapper: 2.19.339839
  - numba: 0.59.1
  - numcodecs: 0.12.1
  - numpy: 1.26.4
  - nvidia-cublas-cu12: 12.1.3.1
  - nvidia-cuda-cupti-cu12: 12.1.105
  - nvidia-cuda-nvrtc-cu12: 12.1.105
  - nvidia-cuda-runtime-cu12: 12.1.105
  - nvidia-cudnn-cu12: 8.9.2.26
  - nvidia-cufft-cu12: 11.0.2.54
  - nvidia-curand-cu12: 10.3.2.106
  - nvidia-cusolver-cu12: 11.4.5.107
  - nvidia-cusparse-cu12: 12.1.0.106
  - nvidia-dali-cuda120: 1.39.0
  - nvidia-nccl-cu12: 2.20.5
  - nvidia-nvimgcodec-cu12: 0.2.0.7
  - nvidia-nvjitlink-cu12: 12.5.82
  - nvidia-nvtx-cu12: 12.1.105
  - nvtx: 0.2.10
  - oauthlib: 3.2.2
  - ome-types: 0.5.1.post1
  - omegaconf: 2.3.0
  - opencv-python-headless: 4.9.0.80
  - openslide-python: 1.3.1
  - opt-einsum: 3.3.0
  - optree: 0.12.1
  - outcome: 1.3.0.post0
  - overrides: 7.7.0
  - packaging: 24.0
  - pandas: 2.1.4
  - pandocfilters: 1.5.1
  - panel: 1.4.4
  - papermill: 2.6.0
  - param: 2.1.0
  - parso: 0.8.4
  - partd: 1.4.2
  - pdbpp: 0.10.3
  - pexpect: 4.9.0
  - pillow: 10.3.0
  - pip: 24.1.2
  - pivottablejs: 0.9.0
  - platformdirs: 4.2.2
  - plotly: 5.22.0
  - pluggy: 1.5.0
  - polars: 0.20.30
  - portal-test-utils: 8.100.0
  - portalocker: 2.10.1
  - pre-commit: 3.7.1
  - prometheus-client: 0.20.0
  - prompt-toolkit: 3.0.45
  - protobuf: 3.20.0
  - psutil: 5.9.8
  - psycopg2-binary: 2.9.9
  - ptpython: 3.0.27
  - ptxcompiler-cu11: 0.8.1.post1
  - ptyprocess: 0.7.0
  - pudb: 2024.1
  - pure-eval: 0.2.2
  - pyarrow: 16.1.0
  - pyasn1: 0.6.0
  - pyasn1-modules: 0.4.0
  - pycparser: 2.22
  - pyct: 0.5.0
  - pydantic: 2.7.3
  - pydantic-compat: 0.1.2
  - pydantic-core: 2.18.4
  - pydeck: 0.9.1
  - pygments: 2.18.0
  - pylibraft-cu11: 24.6.0
  - pymongo: 4.8.0
  - pynvml: 11.4.1
  - pyogrio: 0.9.0
  - pyotp: 2.9.0
  - pyparsing: 3.1.2
  - pypeln: 0.4.9
  - pyportal: 2.33.2
  - pyproj: 3.6.1
  - pyrepl: 0.9.0
  - pysocks: 1.7.1
  - pytest: 8.2.2
  - python-dateutil: 2.9.0.post0
  - python-dotenv: 1.0.1
  - python-gitlab: 3.15.0
  - python-json-logger: 2.0.7
  - python-magic: 0.4.27
  - python-on-whales: 0.71.0
  - pytorch-lightning: 2.3.3
  - pytz: 2024.1
  - pyviz-comms: 3.0.2
  - pyxdg: 0.28
  - pyyaml: 6.0.1
  - pyzmq: 26.0.3
  - qtconsole: 5.5.2
  - qtpy: 2.4.1
  - raft-dask-cu11: 24.6.0
  - rai: 2.4.1
  - rapids-dask-dependency: 24.6.0
  - rasterio: 1.3.10
  - ray: 2.31.0
  - rdi: 2.19.339839
  - readchar: 4.1.0
  - referencing: 0.35.1
  - requests: 2.32.2
  - requests-oauthlib: 2.0.0
  - requests-toolbelt: 1.0.0
  - retrying: 1.3.4
  - rfc3339-validator: 0.1.4
  - rfc3986-validator: 0.1.1
  - rich: 13.7.1
  - rmm-cu11: 24.6.0
  - rpds-py: 0.18.1
  - rsa: 4.9
  - rtree: 1.2.0
  - ruamel.yaml: 0.18.6
  - ruamel.yaml.clib: 0.2.8
  - ruff: 0.5.1
  - runs: 1.2.2
  - s3cmd: 2.4.0
  - s3fs: 2024.5.0
  - s3transfer: 0.10.1
  - safetensors: 0.4.3
  - scikit-image: 0.23.2
  - scikit-learn: 1.5.1
  - scipy: 1.13.1
  - seaborn: 0.13.2
  - segment-anything: 1.0
  - selenium: 4.22.0
  - semantic-segmentation: 0.1.0
  - send2trash: 1.8.3
  - sentry-sdk: 2.6.0
  - setproctitle: 1.3.3
  - setuptools: 63.4.3
  - shapely: 2.0.4
  - shellingham: 1.5.4
  - six: 1.16.0
  - smmap: 5.0.1
  - sniffio: 1.3.1
  - snuggs: 1.4.7
  - sortedcontainers: 2.4.0
  - soupsieve: 2.5
  - spatialpandas: 0.4.10
  - sqlalchemy: 2.0.31
  - stack-data: 0.6.3
  - stopit: 1.1.2
  - streamlit: 1.36.0
  - submitit: 1.5.1
  - sympy: 1.12
  - tabulate: 0.9.0
  - tblib: 3.0.0
  - tenacity: 8.3.0
  - tensorboard: 2.12.3
  - tensorboard-data-server: 0.7.2
  - tensorflow: 2.12.1
  - tensorflow-estimator: 2.12.0
  - tensorflow-io: 0.37.1
  - tensorflow-io-gcs-filesystem: 0.37.1
  - termcolor: 2.4.0
  - terminado: 0.18.1
  - threadpoolctl: 3.5.0
  - tifffile: 2024.5.22
  - timm: 1.0.7
  - tinycss2: 1.3.0
  - toml: 0.10.2
  - tomli: 2.0.1
  - toolz: 0.12.1
  - torch: 2.3.1
  - torchdata: 0.7.1
  - torchmetrics: 1.0.3
  - torchsummary: 1.5.1
  - torchvision: 0.18.1
  - tornado: 6.4
  - tqdm: 4.66.4
  - traitlets: 5.14.3
  - treelite: 4.1.2
  - trio: 0.25.1
  - trio-websocket: 0.11.1
  - triton: 2.3.1
  - typer: 0.12.3
  - types-python-dateutil: 2.9.0.20240316
  - typing-extensions: 4.12.2
  - typing-utils: 0.1.0
  - tzdata: 2024.1
  - uc-micro-py: 1.0.3
  - ucx-py-cu11: 0.38.0
  - ucxx-cu11: 0.38.0
  - uri-template: 1.3.0
  - urllib3: 2.0.7
  - urwid: 2.6.12
  - urwid-readline: 0.14
  - ventanamripy: 0.3
  - virtualenv: 20.26.2
  - wandb: 0.17.2
  - watchdog: 4.0.1
  - wcwidth: 0.2.13
  - webcolors: 1.13
  - webdataset: 0.2.93
  - webencodings: 0.5.1
  - websocket-client: 1.8.0
  - werkzeug: 3.0.3
  - wheel: 0.43.0
  - widgetsnbextension: 4.0.11
  - wmctrl: 0.5
  - wrapt: 1.14.1
  - wsproto: 1.2.0
  - xarray: 2024.6.0
  - xformers: 0.0.27
  - xmod: 1.8.1
  - xsdata: 24.3.1
  - xyzservices: 2024.6.0
  - yacs: 0.1.8
  - yarl: 1.9.4
  - zarr: 2.18.2
  - zict: 3.0.0
  - zipp: 3.19.1
• System:
  - OS: Linux
  - architecture:
  - 64bit
  - ELF
  - processor: x86_64
  - python: 3.11.9
  - release: 5.15.0-1066-aws
  - version: Trainer.fit() crashes if no checkpoint callback is provided #72~20.04.1-Ubuntu SMP Thu Jul 18 10:41:27 UTC 2024

More info

Relevant existing issues

• pytorch_lightning.utilities.exceptions.MisconfigurationException: Error while merging hparams: the keys <keys> are present in both the LightningModule's and LightningDataModule's hparams. #8716
• With yaml config file for LightningCLI, self.save_hyperparameters() behavior abnormal #19977

[cgebbe]

FYI: I worked around it now by adding the following to my LightningModule. A bit brittle, but seems to work for now.

    def setup(self, stage: str):
        self._save_config()

    def _save_config(self):
        # P: Currently, we do not log all hyperparameters:
        #    lightning.CLI saves the config.yaml only locally, not well to W&B.
        #    self.save_hyperparameters() does not work well with YAML, see
        #    https://github.com/Lightning-AI/pytorch-lightning/issues/20182
        # S: Read saved local config and save it as hparams.
        #    You should disable any other `self.save_hyperparameters()`.
        # NOTE: This logs the RESOLVED config using YAML and CLI arguments.
        import torch.distributed as dist

        if dist.is_initialized() and dist.get_rank() != 0:
            # only save config with rank0
            return

        if self.trainer.fast_dev_run:
            # in fast_dev_run mode, loggers are replaced by DummyLogger
            return

        config_yaml_path = Path(self.logger.save_dir) / "config.yaml"
        assert config_yaml_path.exists()
        with open(config_yaml_path) as f:
            dct = yaml.safe_load(f)
        self.save_hyperparameters(dct)
        print("=== Saved config.yaml as hyperparameter")

[tshu-w]

cc @mauvilsa @awaelchli

[mauvilsa]

I was not aware of this merging of parameters. I guess this happens always when both model and data do save_hyperparameters. This should be easy to fix by excluding the special keys _class_path and _instantiator. Though, maybe better to exclude all keys starting with _ since parameter names should not start with this, and thus "Force User Decisions To Best Practices".

[DucoG]

Ran into same issue.

Simple temporary workaround:
In either LightningModule or LightningDataModule call:

self.save_hyperparameters(ignore=['_class_path'])

similar issues: #9492

[mauvilsa]

I created pull request #20221 to fix this.