Shortcuts

Source code for pytorch_lightning.callbacks.pruning

# Copyright The PyTorch Lightning 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.
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)

© Copyright Copyright (c) 2018-2021, William Falcon et al... Revision 46f718d2.

Built with Sphinx using a theme provided by Read the Docs.
Read the Docs v: latest
Versions
latest
stable
1.5.4
1.5.3
1.5.2
1.5.1
1.5.0
1.4.9
1.4.8
1.4.7
1.4.6
1.4.5
1.4.4
1.4.3
1.4.2
1.4.1
1.4.0
1.3.8
1.3.7
1.3.6
1.3.5
1.3.4
1.3.3
1.3.2
1.3.1
1.3.0
1.2.10
1.2.8
1.2.7
1.2.6
1.2.5
1.2.4
1.2.3
1.2.2
1.2.1
1.2.0
1.1.8
1.1.7
1.1.6
1.1.5
1.1.4
1.1.3
1.1.2
1.1.1
1.1.0
1.0.8
1.0.7
1.0.6
1.0.5
1.0.4
1.0.3
1.0.2
1.0.1
1.0.0
0.10.0
0.9.0
0.8.5
0.8.4
0.8.3
0.8.2
0.8.1
0.8.0
0.7.6
0.7.5
0.7.4
0.7.3
0.7.2
0.7.1
0.7.0
0.6.0
0.5.3
0.4.9
ipynb-update
docs-search
Downloads
html
On Read the Docs
Project Home
Builds

Free document hosting provided by Read the Docs.