Source code for pytorch_lightning.callbacks.pruning
# 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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# See the License for the specific language governing permissions and
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r"""
ModelPruning
^^^^^^^^^^^^
"""
import inspect
import logging
from copy import deepcopy
from functools import partial
from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, Union
import torch
import torch.nn.utils.prune as pytorch_prune
from torch import nn
from typing_extensions import TypedDict
import pytorch_lightning as pl
from pytorch_lightning.callbacks.base import Callback
from pytorch_lightning.core.lightning import LightningModule
from pytorch_lightning.utilities.apply_func import apply_to_collection
from pytorch_lightning.utilities.distributed import rank_zero_debug, rank_zero_only
from pytorch_lightning.utilities.exceptions import MisconfigurationException
log = logging.getLogger(__name__)
_PYTORCH_PRUNING_FUNCTIONS = {
"ln_structured": pytorch_prune.ln_structured,
"l1_unstructured": pytorch_prune.l1_unstructured,
"random_structured": pytorch_prune.random_structured,
"random_unstructured": pytorch_prune.random_unstructured,
}
_PYTORCH_PRUNING_METHOD = {
"ln_structured": pytorch_prune.LnStructured,
"l1_unstructured": pytorch_prune.L1Unstructured,
"random_structured": pytorch_prune.RandomStructured,
"random_unstructured": pytorch_prune.RandomUnstructured,
}
_PARAM_TUPLE = Tuple[nn.Module, str]
_PARAM_LIST = Sequence[_PARAM_TUPLE]
_MODULE_CONTAINERS = (LightningModule, nn.Sequential, nn.ModuleList, nn.ModuleDict)
class _LayerRef(TypedDict):
data: nn.Module
names: List[Tuple[int, str]]
[docs]class ModelPruning(Callback):
PARAMETER_NAMES = ("weight", "bias")
def __init__(
self,
pruning_fn: Union[Callable, str],
parameters_to_prune: _PARAM_LIST = (),
parameter_names: Optional[List[str]] = None,
use_global_unstructured: bool = True,
amount: Union[int, float, Callable[[int], Union[int, float]]] = 0.5,
apply_pruning: Union[bool, Callable[[int], bool]] = True,
make_pruning_permanent: bool = True,
use_lottery_ticket_hypothesis: Union[bool, Callable[[int], bool]] = True,
resample_parameters: bool = False,
pruning_dim: Optional[int] = None,
pruning_norm: Optional[int] = None,
verbose: int = 0,
prune_on_train_epoch_end: bool = True,
) -> None:
"""Model pruning Callback, using PyTorch's prune utilities. This callback is responsible of pruning
networks parameters during training.
To learn more about pruning with PyTorch, please take a look at
`this tutorial <https://pytorch.org/tutorials/intermediate/pruning_tutorial.html>`_.
.. warning:: ``ModelPruning`` is in beta and subject to change.
.. code-block:: python
parameters_to_prune = [(model.mlp_1, "weight"), (model.mlp_2, "weight")]
trainer = Trainer(
callbacks=[
ModelPruning(
pruning_fn="l1_unstructured",
parameters_to_prune=parameters_to_prune,
amount=0.01,
use_global_unstructured=True,
)
]
)
When ``parameters_to_prune`` is ``None``, ``parameters_to_prune`` will contain all parameters from the model.
The user can override ``filter_parameters_to_prune`` to filter any ``nn.Module`` to be pruned.
Args:
pruning_fn: Function from torch.nn.utils.prune module or your own PyTorch ``BasePruningMethod`` subclass.
Can also be string e.g. `"l1_unstructured"`. See pytorch docs for more details.
parameters_to_prune: List of tuples ``(nn.Module, "parameter_name_string")``.
parameter_names: List of parameter names to be pruned from the nn.Module.
Can either be ``"weight"`` or ``"bias"``.
use_global_unstructured: Whether to apply pruning globally on the model.
If ``parameters_to_prune`` is provided, global unstructured will be restricted on them.
amount: Quantity of parameters to prune:
- ``float``. Between 0.0 and 1.0. Represents the fraction of parameters to prune.
- ``int``. Represents the absolute number of parameters to prune.
- ``Callable``. For dynamic values. Will be called every epoch. Should return a value.
apply_pruning: Whether to apply pruning.
- ``bool``. Always apply it or not.
- ``Callable[[epoch], bool]``. For dynamic values. Will be called every epoch.
make_pruning_permanent: Whether to remove all reparametrization pre-hooks and apply masks
when training ends or the model is saved.
use_lottery_ticket_hypothesis: See `The lottery ticket hypothesis <https://arxiv.org/abs/1803.03635>`_:
- ``bool``. Whether to apply it or not.
- ``Callable[[epoch], bool]``. For dynamic values. Will be called every epoch.
resample_parameters: Used with ``use_lottery_ticket_hypothesis``. If True, the model parameters will
be resampled, otherwise, the exact original parameters will be used.
pruning_dim: If you are using a structured pruning method you need to specify the dimension.
pruning_norm: If you are using ``ln_structured`` you need to specify the norm.
verbose: Verbosity level. 0 to disable, 1 to log overall sparsity, 2 to log per-layer sparsity
prune_on_train_epoch_end: whether to apply pruning at the end of the training epoch.
If this is ``False``, then the check runs at the end of the validation epoch.
Raises:
MisconfigurationException:
If ``parameter_names`` is neither ``"weight"`` nor ``"bias"``,
if the provided ``pruning_fn`` is not supported,
if ``pruning_dim`` is not provided when ``"unstructured"``,
if ``pruning_norm`` is not provided when ``"ln_structured"``,
if ``pruning_fn`` is neither ``str`` nor :class:`torch.nn.utils.prune.BasePruningMethod`, or
if ``amount`` is none of ``int``, ``float`` and ``Callable``.
"""
self._use_global_unstructured = use_global_unstructured
self._parameters_to_prune = parameters_to_prune
self._use_lottery_ticket_hypothesis = use_lottery_ticket_hypothesis
self._resample_parameters = resample_parameters
self._prune_on_train_epoch_end = prune_on_train_epoch_end
self._parameter_names = parameter_names or self.PARAMETER_NAMES
self._global_kwargs: Dict[str, Any] = {}
self._original_layers: Optional[Dict[int, _LayerRef]] = None
self._pruning_method_name: Optional[str] = None
for name in self._parameter_names:
if name not in self.PARAMETER_NAMES:
raise MisconfigurationException(
f"The provided `parameter_names` name: {name} isn't in {self.PARAMETER_NAMES}"
)
if isinstance(pruning_fn, str):
pruning_kwargs = {}
pruning_fn = pruning_fn.lower()
if pruning_fn not in _PYTORCH_PRUNING_FUNCTIONS:
raise MisconfigurationException(
f"The provided `pruning_fn` {pruning_fn} isn't available in PyTorch's"
f" built-in functions: {list(_PYTORCH_PRUNING_FUNCTIONS.keys())} "
)
if pruning_fn.endswith("_structured"):
if pruning_dim is None:
raise MisconfigurationException(
"When requesting `structured` pruning, the `pruning_dim` should be provided."
)
if pruning_fn == "ln_structured":
if pruning_norm is None:
raise MisconfigurationException(
"When requesting `ln_structured` pruning, the `pruning_norm` should be provided."
)
pruning_kwargs["n"] = pruning_norm
pruning_kwargs["dim"] = pruning_dim
pruning_fn = self._create_pruning_fn(pruning_fn, **pruning_kwargs)
elif self._is_pruning_method(pruning_fn):
if not use_global_unstructured:
raise MisconfigurationException(
"PyTorch `BasePruningMethod` is currently only supported with `use_global_unstructured=True`."
)
else:
raise MisconfigurationException(
f"`pruning_fn` is expected to be a str in {list(_PYTORCH_PRUNING_FUNCTIONS.keys())}"
f" or a PyTorch `BasePruningMethod`. Found: {pruning_fn}."
" HINT: if passing a `BasePruningMethod`, pass the the class, not an instance"
)
# need to ignore typing here since pytorch base class does not define the PRUNING_TYPE attribute
if use_global_unstructured and pruning_fn.PRUNING_TYPE != "unstructured": # type: ignore
raise MisconfigurationException(
'Only the "unstructured" PRUNING_TYPE is supported with `use_global_unstructured=True`.' # type: ignore
f" Found method {pruning_fn} of type {pruning_fn.PRUNING_TYPE}. "
)
self.pruning_fn = pruning_fn
self._apply_pruning = apply_pruning
self._make_pruning_permanent = make_pruning_permanent
if not (isinstance(amount, (int, float)) or callable(amount)):
raise MisconfigurationException(
"`amount` should be provided and be either an int, a float or Callable function."
)
self.amount = amount
if verbose not in (0, 1, 2):
raise MisconfigurationException("`verbose` must be any of (0, 1, 2)")
self._verbose = verbose
[docs] def filter_parameters_to_prune(self, parameters_to_prune: _PARAM_LIST = ()) -> _PARAM_LIST:
"""This function can be overridden to control which module to prune."""
return parameters_to_prune
def _create_pruning_fn(self, pruning_fn: str, **kwargs: Any) -> Union[Callable, pytorch_prune.BasePruningMethod]:
"""This function takes `pruning_fn`, a function name.
IF use_global_unstructured, pruning_fn will be resolved into its associated ``PyTorch BasePruningMethod`` ELSE,
pruning_fn will be resolved into its function counterpart from `torch.nn.utils.prune`.
"""
pruning_meth = (
_PYTORCH_PRUNING_METHOD[pruning_fn]
if self._use_global_unstructured
else _PYTORCH_PRUNING_FUNCTIONS[pruning_fn]
)
assert callable(pruning_meth), "Selected pruning method is not callable"
if self._use_global_unstructured:
self._global_kwargs = kwargs
# save the function __name__ now because partial does not include it
# and there are issues setting the attribute manually in ddp.
self._pruning_method_name = pruning_meth.__name__
if self._use_global_unstructured:
return pruning_meth
return ModelPruning._wrap_pruning_fn(pruning_meth, **kwargs)
@staticmethod
def _wrap_pruning_fn(pruning_fn: Callable, **kwargs: Any) -> Callable:
return partial(pruning_fn, **kwargs)
[docs] def make_pruning_permanent(self, module: nn.Module) -> None:
"""Removes pruning buffers from any pruned modules.
Adapted from https://github.com/pytorch/pytorch/blob/1.7.1/torch/nn/utils/prune.py#L1176-L1180
"""
for _, module in module.named_modules():
for k in list(module._forward_pre_hooks):
hook = module._forward_pre_hooks[k]
if isinstance(hook, pytorch_prune.BasePruningMethod):
hook.remove(module)
del module._forward_pre_hooks[k]
@staticmethod
def _copy_param(new: nn.Module, old: nn.Module, name: str) -> None:
dst = getattr(new, name)
src = getattr(old, name)
if dst is None or src is None or not isinstance(dst, torch.Tensor) or not isinstance(src, torch.Tensor):
return
dst.data = src.data.to(dst.device)
[docs] def apply_lottery_ticket_hypothesis(self) -> None:
r"""
Lottery ticket hypothesis algorithm (see page 2 of the paper):
1. Randomly initialize a neural network :math:`f(x; \theta_0)` (where :math:`\theta_0 \sim \mathcal{D}_\theta`).
2. Train the network for :math:`j` iterations, arriving at parameters :math:`\theta_j`.
3. Prune :math:`p\%` of the parameters in :math:`\theta_j`, creating a mask :math:`m`.
4. Reset the remaining parameters to their values in :math:`\theta_0`, creating the winning ticket :math:`f(x; m \odot \theta_0)`.
This function implements the step 4.
The ``resample_parameters`` argument can be used to reset the parameters with a new :math:`\theta_z \sim \mathcal{D}_\theta`
""" # noqa: E501
assert self._original_layers is not None
for d in self._original_layers.values():
copy = d["data"]
names = d["names"]
if self._resample_parameters and hasattr(copy, "reset_parameters") and callable(copy.reset_parameters):
copy = deepcopy(copy) # keep the original parameters
copy.reset_parameters()
for i, name in names:
new, new_name = self._parameters_to_prune[i]
self._copy_param(new, copy, name)
def _apply_local_pruning(self, amount: float) -> None:
for module, name in self._parameters_to_prune:
self.pruning_fn(module, name=name, amount=amount)
def _resolve_global_kwargs(self, amount: float) -> Dict[str, Any]:
self._global_kwargs["amount"] = amount
params = set(inspect.signature(self.pruning_fn).parameters)
params.discard("self")
return {k: v for k, v in self._global_kwargs.items() if k in params}
def _apply_global_pruning(self, amount: float) -> None:
pytorch_prune.global_unstructured(
self._parameters_to_prune, pruning_method=self.pruning_fn, **self._resolve_global_kwargs(amount)
)
@staticmethod
def _get_pruned_stats(module: nn.Module, name: str) -> Tuple[int, int]:
attr = f"{name}_mask"
if not hasattr(module, attr):
return 0, 1
mask = getattr(module, attr)
return (mask == 0).sum().item(), mask.numel()
[docs] def apply_pruning(self, amount: Union[int, float]) -> None:
"""Applies pruning to ``parameters_to_prune``."""
if self._verbose:
prev_stats = [self._get_pruned_stats(m, n) for m, n in self._parameters_to_prune]
if self._use_global_unstructured:
self._apply_global_pruning(amount)
else:
self._apply_local_pruning(amount)
if self._verbose:
curr_stats = [self._get_pruned_stats(m, n) for m, n in self._parameters_to_prune]
self._log_sparsity_stats(prev_stats, curr_stats, amount=amount)
@rank_zero_only
def _log_sparsity_stats(
self, prev: List[Tuple[int, int]], curr: List[Tuple[int, int]], amount: Union[int, float] = 0
) -> None:
total_params = sum(p.numel() for layer, _ in self._parameters_to_prune for p in layer.parameters())
prev_total_zeros = sum(zeros for zeros, _ in prev)
curr_total_zeros = sum(zeros for zeros, _ in curr)
log.info(
f"Applied `{self._pruning_method_name}`. Pruned:"
f" {prev_total_zeros}/{total_params} ({prev_total_zeros / total_params:.2%}) ->"
f" {curr_total_zeros}/{total_params} ({curr_total_zeros / total_params:.2%})"
)
if self._verbose == 2:
for i, (module, name) in enumerate(self._parameters_to_prune):
prev_mask_zeros, prev_mask_size = prev[i]
curr_mask_zeros, curr_mask_size = curr[i]
log.info(
f"Applied `{self._pruning_method_name}` to `{module!r}.{name}` with amount={amount}. Pruned:"
f" {prev_mask_zeros} ({prev_mask_zeros / prev_mask_size:.2%}) ->"
f" {curr_mask_zeros} ({curr_mask_zeros / curr_mask_size:.2%})"
)
[docs] def on_before_accelerator_backend_setup(self, trainer: "pl.Trainer", pl_module: LightningModule) -> None:
parameters_to_prune = self.sanitize_parameters_to_prune(
pl_module, self._parameters_to_prune, parameter_names=self._parameter_names
)
self._parameters_to_prune = self.filter_parameters_to_prune(parameters_to_prune)
if self._use_lottery_ticket_hypothesis:
# group modules by id. Each entry has a copy of the initial data
# and a list of the associated parameter names to prune
self._original_layers = {}
for i, (module, name) in enumerate(self._parameters_to_prune):
id_ = id(module)
self._original_layers.setdefault(id_, _LayerRef(data=deepcopy(module), names=[]))
self._original_layers[id_]["names"].append((i, name))
def _run_pruning(self, current_epoch: int) -> None:
prune = self._apply_pruning(current_epoch) if callable(self._apply_pruning) else self._apply_pruning
amount = self.amount(current_epoch) if callable(self.amount) else self.amount
if not prune or not amount:
return
self.apply_pruning(amount)
if (
self._use_lottery_ticket_hypothesis(current_epoch)
if callable(self._use_lottery_ticket_hypothesis)
else self._use_lottery_ticket_hypothesis
):
self.apply_lottery_ticket_hypothesis()
[docs] def on_train_epoch_end(self, trainer: "pl.Trainer", pl_module: LightningModule) -> None:
if self._prune_on_train_epoch_end:
rank_zero_debug("`ModelPruning.on_train_epoch_end`. Applying pruning")
self._run_pruning(pl_module.current_epoch)
[docs] def on_validation_epoch_end(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
if not trainer.sanity_checking and not self._prune_on_train_epoch_end:
rank_zero_debug("`ModelPruning.on_validation_epoch_end`. Applying pruning")
self._run_pruning(pl_module.current_epoch)
[docs] def on_train_end(self, trainer: "pl.Trainer", pl_module: LightningModule) -> None:
if self._make_pruning_permanent:
rank_zero_debug("`ModelPruning.on_train_end`. Pruning is made permanent for this checkpoint")
self.make_pruning_permanent(pl_module)
def _make_pruning_permanent_on_state_dict(self, pl_module: LightningModule) -> Dict[str, Any]:
state_dict = pl_module.state_dict()
# find the mask and the original weights.
map_pruned_params = {k.replace("_mask", "") for k in state_dict.keys() if k.endswith("_mask")}
for tensor_name in map_pruned_params:
orig = state_dict.pop(tensor_name + "_orig")
mask = state_dict.pop(tensor_name + "_mask")
# make weights permanent
state_dict[tensor_name] = mask.to(dtype=orig.dtype) * orig
def move_to_cpu(tensor: torch.Tensor) -> torch.Tensor:
# each tensor and move them on cpu
return tensor.cpu()
return apply_to_collection(state_dict, torch.Tensor, move_to_cpu)
[docs] def on_save_checkpoint(
self, trainer: "pl.Trainer", pl_module: LightningModule, checkpoint: Dict[str, Any]
) -> Dict[str, Any]:
if self._make_pruning_permanent:
rank_zero_debug("`ModelPruning.on_save_checkpoint`. Pruning is made permanent for this checkpoint")
# manually prune the weights so training can keep going with the same buffers
checkpoint["state_dict"] = self._make_pruning_permanent_on_state_dict(pl_module)
return checkpoint
[docs] @staticmethod
def sanitize_parameters_to_prune(
pl_module: LightningModule, parameters_to_prune: _PARAM_LIST = (), parameter_names: Sequence[str] = ()
) -> _PARAM_LIST:
"""This function is responsible of sanitizing ``parameters_to_prune`` and ``parameter_names``. If
``parameters_to_prune is None``, it will be generated with all parameters of the model.
Raises:
MisconfigurationException:
If ``parameters_to_prune`` doesn't exist in the model, or
if ``parameters_to_prune`` is neither a list nor a tuple.
"""
parameters = parameter_names or ModelPruning.PARAMETER_NAMES
current_modules = [m for m in pl_module.modules() if not isinstance(m, _MODULE_CONTAINERS)]
if not parameters_to_prune:
parameters_to_prune = [
(m, p) for p in parameters for m in current_modules if getattr(m, p, None) is not None
]
elif (
isinstance(parameters_to_prune, (list, tuple))
and len(parameters_to_prune) > 0
and all(len(p) == 2 for p in parameters_to_prune)
and all(isinstance(a, nn.Module) and isinstance(b, str) for a, b in parameters_to_prune)
):
missing_modules, missing_parameters = [], []
for module, name in parameters_to_prune:
if module not in current_modules:
missing_modules.append(module)
continue
if not hasattr(module, name):
missing_parameters.append(name)
if missing_modules or missing_parameters:
raise MisconfigurationException(
"Some provided `parameters_to_tune` don't exist in the model."
f" Found missing modules: {missing_modules} and missing parameters: {missing_parameters}"
)
else:
raise MisconfigurationException(
"The provided `parameters_to_prune` should either be list of tuple"
" with 2 elements: (nn.Module, parameter_name_to_prune) or None"
)
return parameters_to_prune
@staticmethod
def _is_pruning_method(method: Any) -> bool:
if not inspect.isclass(method):
return False
return issubclass(method, pytorch_prune.BasePruningMethod)
