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
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# Unless required by applicable law or agreed to in writing, software
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"""
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__)


[docs]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
[docs] @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 if trainer.is_global_zero: self.summarize(summary_data, total_parameters, trainable_parameters, model_size, **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, **summarize_kwargs: Any, ) -> None: summary_table = _format_summary_table( total_parameters, trainable_parameters, model_size, *summary_data, ) log.info("\n" + summary_table)