Source code for lightning.pytorch.callbacks.model_summary

# 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.
"""
Model Summary
=============

Generates a summary of all layers in a :class:`~lightning.pytorch.core.LightningModule`.

The string representation of this summary prints a table with columns containing
the name, type and number of parameters for each layer.

"""

import logging
from typing import Any, Dict, List, Tuple, Union

from typing_extensions import override

import lightning.pytorch as pl
from lightning.pytorch.callbacks.callback import Callback
from lightning.pytorch.utilities.model_summary import DeepSpeedSummary, summarize
from lightning.pytorch.utilities.model_summary import ModelSummary as Summary
from lightning.pytorch.utilities.model_summary.model_summary import _format_summary_table

log = logging.getLogger(__name__)


class ModelSummary(Callback):
    r"""Generates a summary of all layers in a :class:`~lightning.pytorch.core.LightningModule`.

    Args:
        max_depth: The maximum depth of layer nesting that the summary will include. A value of 0 turns the
            layer summary off.
        **summarize_kwargs: Additional arguments to pass to the `summarize` method.

    Example::

        >>> from lightning.pytorch import Trainer
        >>> from lightning.pytorch.callbacks import ModelSummary
        >>> trainer = Trainer(callbacks=[ModelSummary(max_depth=1)])

    """

    def __init__(self, max_depth: int = 1, **summarize_kwargs: Any) -> None:
        self._max_depth: int = max_depth
        self._summarize_kwargs: Dict[str, Any] = summarize_kwargs

    @override
    def on_fit_start(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
        if not self._max_depth:
            return

        model_summary = self._summary(trainer, pl_module)
        summary_data = model_summary._get_summary_data()
        total_parameters = model_summary.total_parameters
        trainable_parameters = model_summary.trainable_parameters
        model_size = model_summary.model_size
        total_training_modes = model_summary.total_training_modes

        if trainer.is_global_zero:
            self.summarize(
                summary_data,
                total_parameters,
                trainable_parameters,
                model_size,
                total_training_modes,
                **self._summarize_kwargs,
            )

    def _summary(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> Union[DeepSpeedSummary, Summary]:
        from lightning.pytorch.strategies.deepspeed import DeepSpeedStrategy

        if isinstance(trainer.strategy, DeepSpeedStrategy) and trainer.strategy.zero_stage_3:
            return DeepSpeedSummary(pl_module, max_depth=self._max_depth)
        return summarize(pl_module, max_depth=self._max_depth)

    @staticmethod
    def summarize(
        summary_data: List[Tuple[str, List[str]]],
        total_parameters: int,
        trainable_parameters: int,
        model_size: float,
        total_training_modes: Dict[str, int],
        **summarize_kwargs: Any,
    ) -> None:
        summary_table = _format_summary_table(
            total_parameters,
            trainable_parameters,
            model_size,
            total_training_modes,
            *summary_data,
        )
        log.info("\n" + summary_table)