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Source code for lightning.pytorch.callbacks.pruning

# 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.
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#     http://www.apache.org/licenses/LICENSE-2.0
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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.nn.utils.prune as pytorch_prune
from lightning_utilities.core.apply_func import apply_to_collection
from torch import Tensor, nn
from typing_extensions import TypedDict, override

import lightning.pytorch as pl
from lightning.pytorch.callbacks.callback import Callback
from lightning.pytorch.core.module import LightningModule
from lightning.pytorch.utilities.exceptions import MisconfigurationException
from lightning.pytorch.utilities.rank_zero import rank_zero_debug, rank_zero_only

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:: This is an :ref:`experimental <versioning:Experimental API>` feature. .. 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/v1.7.1/torch/nn/utils/prune.py#L1118-L1122 """ 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, Tensor) or not isinstance(src, 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, _ = 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] @override def setup(self, trainer: "pl.Trainer", pl_module: LightningModule, stage: str) -> 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] @override 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] @override def on_validation_epoch_end(self, trainer: "pl.Trainer", pl_module: 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] @override 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 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: Tensor) -> Tensor: # each tensor and move them on cpu return tensor.cpu() return apply_to_collection(state_dict, Tensor, move_to_cpu)
[docs] @override def on_save_checkpoint(self, trainer: "pl.Trainer", pl_module: LightningModule, checkpoint: Dict[str, Any]) -> None: 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)
[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_prune` 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)