Source code for pytorch_lightning.utilities.model_summary
# Copyright The PyTorch Lightning team.
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# you may not use this file except in compliance with the License.
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# http://www.apache.org/licenses/LICENSE-2.0
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"""Utilities related to model weights summary."""
import contextlib
import logging
from collections import OrderedDict
from typing import Any, Dict, List, Optional, Tuple, Union
import numpy as np
import torch
import torch.nn as nn
from torch import Tensor
from torch.utils.hooks import RemovableHandle
import pytorch_lightning as pl
from pytorch_lightning.utilities.warnings import WarningCache
log = logging.getLogger(__name__)
warning_cache = WarningCache()
PARAMETER_NUM_UNITS = [" ", "K", "M", "B", "T"]
UNKNOWN_SIZE = "?"
[docs]class LayerSummary:
"""Summary class for a single layer in a :class:`~pytorch_lightning.core.lightning.LightningModule`. It
collects the following information:
- Type of the layer (e.g. Linear, BatchNorm1d, ...)
- Input shape
- Output shape
- Number of parameters
The input and output shapes are only known after the example input array was
passed through the model.
Example::
>>> model = torch.nn.Conv2d(3, 8, 3)
>>> summary = LayerSummary(model)
>>> summary.num_parameters
224
>>> summary.layer_type
'Conv2d'
>>> output = model(torch.rand(1, 3, 5, 5))
>>> summary.in_size
[1, 3, 5, 5]
>>> summary.out_size
[1, 8, 3, 3]
Args:
module: A module to summarize
"""
def __init__(self, module: nn.Module) -> None:
super().__init__()
self._module = module
self._hook_handle = self._register_hook()
self._in_size: Optional[Union[str, List]] = None
self._out_size: Optional[Union[str, List]] = None
def __del__(self) -> None:
self.detach_hook()
def _register_hook(self) -> Optional[RemovableHandle]:
"""Registers a hook on the module that computes the input- and output size(s) on the first forward pass. If
the hook is called, it will remove itself from the from the module, meaning that recursive models will only
record their input- and output shapes once. Registering hooks on :class:`~torch.jit.ScriptModule` is not
supported.
Return:
A handle for the installed hook, or ``None`` if registering the hook is not possible.
"""
def hook(_: nn.Module, inp: Any, out: Any) -> None:
if len(inp) == 1:
inp = inp[0]
self._in_size = parse_batch_shape(inp)
self._out_size = parse_batch_shape(out)
assert self._hook_handle is not None
self._hook_handle.remove()
handle = None
if not isinstance(self._module, torch.jit.ScriptModule):
handle = self._module.register_forward_hook(hook)
return handle
[docs] def detach_hook(self) -> None:
"""Removes the forward hook if it was not already removed in the forward pass.
Will be called after the summary is created.
"""
if self._hook_handle is not None:
self._hook_handle.remove()
@property
def in_size(self) -> Union[str, List]:
return self._in_size or UNKNOWN_SIZE
@property
def out_size(self) -> Union[str, List]:
return self._out_size or UNKNOWN_SIZE
@property
def layer_type(self) -> str:
"""Returns the class name of the module."""
return str(self._module.__class__.__name__)
@property
def num_parameters(self) -> int:
"""Returns the number of parameters in this module."""
return sum(np.prod(p.shape) if not _is_lazy_weight_tensor(p) else 0 for p in self._module.parameters())
[docs]class ModelSummary:
"""Generates a summary of all layers in a :class:`~pytorch_lightning.core.lightning.LightningModule`.
Args:
model: The model to summarize (also referred to as the root module).
max_depth: Maximum depth of modules to show. Use -1 to show all modules or 0 to show no
summary. Defaults to 1.
The string representation of this summary prints a table with columns containing
the name, type and number of parameters for each layer.
The root module may also have an attribute ``example_input_array`` as shown in the example below.
If present, the root module will be called with it as input to determine the
intermediate input- and output shapes of all layers. Supported are tensors and
nested lists and tuples of tensors. All other types of inputs will be skipped and show as `?`
in the summary table. The summary will also display `?` for layers not used in the forward pass.
Example::
>>> import pytorch_lightning as pl
>>> class LitModel(pl.LightningModule):
...
... def __init__(self):
... super().__init__()
... self.net = nn.Sequential(nn.Linear(256, 512), nn.BatchNorm1d(512))
... self.example_input_array = torch.zeros(10, 256) # optional
...
... def forward(self, x):
... return self.net(x)
...
>>> model = LitModel()
>>> ModelSummary(model, max_depth=1) # doctest: +NORMALIZE_WHITESPACE
| Name | Type | Params | In sizes | Out sizes
------------------------------------------------------------
0 | net | Sequential | 132 K | [10, 256] | [10, 512]
------------------------------------------------------------
132 K Trainable params
0 Non-trainable params
132 K Total params
0.530 Total estimated model params size (MB)
>>> ModelSummary(model, max_depth=-1) # doctest: +NORMALIZE_WHITESPACE
| Name | Type | Params | In sizes | Out sizes
--------------------------------------------------------------
0 | net | Sequential | 132 K | [10, 256] | [10, 512]
1 | net.0 | Linear | 131 K | [10, 256] | [10, 512]
2 | net.1 | BatchNorm1d | 1.0 K | [10, 512] | [10, 512]
--------------------------------------------------------------
132 K Trainable params
0 Non-trainable params
132 K Total params
0.530 Total estimated model params size (MB)
"""
def __init__(self, model: "pl.LightningModule", max_depth: int = 1) -> None:
self._model = model
if not isinstance(max_depth, int) or max_depth < -1:
raise ValueError(f"`max_depth` can be -1, 0 or > 0, got {max_depth}.")
self._max_depth = max_depth
self._layer_summary = self.summarize()
# 1 byte -> 8 bits
# TODO: how do we compute precision_megabytes in case of mixed precision?
precision = self._model.precision if isinstance(self._model.precision, int) else 32
self._precision_megabytes = (precision / 8.0) * 1e-6
@property
def named_modules(self) -> List[Tuple[str, nn.Module]]:
mods: List[Tuple[str, nn.Module]]
if self._max_depth == 0:
mods = []
elif self._max_depth == 1:
# the children are the top-level modules
mods = list(self._model.named_children())
else:
mods = self._model.named_modules()
mods = list(mods)[1:] # do not include root module (LightningModule)
return mods
@property
def layer_names(self) -> List[str]:
return list(self._layer_summary.keys())
@property
def layer_types(self) -> List[str]:
return [layer.layer_type for layer in self._layer_summary.values()]
@property
def in_sizes(self) -> List:
return [layer.in_size for layer in self._layer_summary.values()]
@property
def out_sizes(self) -> List:
return [layer.out_size for layer in self._layer_summary.values()]
@property
def param_nums(self) -> List[int]:
return [layer.num_parameters for layer in self._layer_summary.values()]
@property
def total_parameters(self) -> int:
return sum(p.numel() if not _is_lazy_weight_tensor(p) else 0 for p in self._model.parameters())
@property
def trainable_parameters(self) -> int:
return sum(
p.numel() if not _is_lazy_weight_tensor(p) else 0 for p in self._model.parameters() if p.requires_grad
)
@property
def model_size(self) -> float:
# todo: seems it does not work with quantized models - it returns 0.0
return self.total_parameters * self._precision_megabytes
def summarize(self) -> Dict[str, LayerSummary]:
summary = OrderedDict((name, LayerSummary(module)) for name, module in self.named_modules)
if self._model.example_input_array is not None:
self._forward_example_input()
for layer in summary.values():
layer.detach_hook()
if self._max_depth >= 1:
# remove summary entries with depth > max_depth
for k in [k for k in summary if k.count(".") >= self._max_depth]:
del summary[k]
return summary
def _forward_example_input(self) -> None:
"""Run the example input through each layer to get input- and output sizes."""
model = self._model
trainer = self._model.trainer
input_ = model.example_input_array
input_ = model._apply_batch_transfer_handler(input_)
mode = model.training
model.eval()
forward_context = contextlib.nullcontext() if trainer is None else trainer.precision_plugin.forward_context()
with torch.no_grad(), forward_context:
# let the model hooks collect the input- and output shapes
if isinstance(input_, (list, tuple)):
model(*input_)
elif isinstance(input_, dict):
model(**input_)
else:
model(input_)
model.train(mode) # restore mode of module
def _get_summary_data(self) -> List[Tuple[str, List[str]]]:
"""Makes a summary listing with:
Layer Name, Layer Type, Number of Parameters, Input Sizes, Output Sizes, Model Size
"""
arrays = [
(" ", list(map(str, range(len(self._layer_summary))))),
("Name", self.layer_names),
("Type", self.layer_types),
("Params", list(map(get_human_readable_count, self.param_nums))),
]
if self._model.example_input_array is not None:
arrays.append(("In sizes", [str(x) for x in self.in_sizes]))
arrays.append(("Out sizes", [str(x) for x in self.out_sizes]))
return arrays
def __str__(self) -> str:
arrays = self._get_summary_data()
total_parameters = self.total_parameters
trainable_parameters = self.trainable_parameters
model_size = self.model_size
return _format_summary_table(total_parameters, trainable_parameters, model_size, *arrays)
def __repr__(self) -> str:
return str(self)
def parse_batch_shape(batch: Any) -> Union[str, List]:
if hasattr(batch, "shape"):
return list(batch.shape)
if isinstance(batch, (list, tuple)):
shape = [parse_batch_shape(el) for el in batch]
return shape
return UNKNOWN_SIZE
def _format_summary_table(
total_parameters: int,
trainable_parameters: int,
model_size: float,
*cols: Tuple[str, List[str]],
) -> str:
"""Takes in a number of arrays, each specifying a column in the summary table, and combines them all into one
big string defining the summary table that are nicely formatted."""
n_rows = len(cols[0][1])
n_cols = 1 + len(cols)
# Get formatting width of each column
col_widths = []
for c in cols:
col_width = max(len(str(a)) for a in c[1]) if n_rows else 0
col_width = max(col_width, len(c[0])) # minimum length is header length
col_widths.append(col_width)
# Formatting
s = "{:<{}}"
total_width = sum(col_widths) + 3 * n_cols
header = [s.format(c[0], l) for c, l in zip(cols, col_widths)]
# Summary = header + divider + Rest of table
summary = " | ".join(header) + "\n" + "-" * total_width
for i in range(n_rows):
line = []
for c, l in zip(cols, col_widths):
line.append(s.format(str(c[1][i]), l))
summary += "\n" + " | ".join(line)
summary += "\n" + "-" * total_width
summary += "\n" + s.format(get_human_readable_count(trainable_parameters), 10)
summary += "Trainable params"
summary += "\n" + s.format(get_human_readable_count(total_parameters - trainable_parameters), 10)
summary += "Non-trainable params"
summary += "\n" + s.format(get_human_readable_count(total_parameters), 10)
summary += "Total params"
summary += "\n" + s.format(get_formatted_model_size(model_size), 10)
summary += "Total estimated model params size (MB)"
return summary
def get_formatted_model_size(total_model_size: float) -> str:
return f"{total_model_size:,.3f}"
[docs]def get_human_readable_count(number: int) -> str:
"""Abbreviates an integer number with K, M, B, T for thousands, millions, billions and trillions, respectively.
Examples:
>>> get_human_readable_count(123)
'123 '
>>> get_human_readable_count(1234) # (one thousand)
'1.2 K'
>>> get_human_readable_count(2e6) # (two million)
'2.0 M'
>>> get_human_readable_count(3e9) # (three billion)
'3.0 B'
>>> get_human_readable_count(4e14) # (four hundred trillion)
'400 T'
>>> get_human_readable_count(5e15) # (more than trillion)
'5,000 T'
Args:
number: a positive integer number
Return:
A string formatted according to the pattern described above.
"""
assert number >= 0
labels = PARAMETER_NUM_UNITS
num_digits = int(np.floor(np.log10(number)) + 1 if number > 0 else 1)
num_groups = int(np.ceil(num_digits / 3))
num_groups = min(num_groups, len(labels)) # don't abbreviate beyond trillions
shift = -3 * (num_groups - 1)
number = number * (10 ** shift)
index = num_groups - 1
if index < 1 or number >= 100:
return f"{int(number):,d} {labels[index]}"
return f"{number:,.1f} {labels[index]}"
def _is_lazy_weight_tensor(p: Tensor) -> bool:
from torch.nn.parameter import UninitializedParameter
if isinstance(p, UninitializedParameter):
warning_cache.warn(
"A layer with UninitializedParameter was found. "
"Thus, the total number of parameters detected may be inaccurate."
)
return True
return False
[docs]def summarize(lightning_module: "pl.LightningModule", max_depth: int = 1) -> ModelSummary:
"""Summarize the LightningModule specified by `lightning_module`.
Args:
lightning_module: `LightningModule` to summarize.
max_depth: The maximum depth of layer nesting that the summary will include. A value of 0 turns the
layer summary off. Default: 1.
Return:
The model summary object
"""
return ModelSummary(lightning_module, max_depth=max_depth)
