/usr/local/lib64/python3.6/site-packages/torch/ao/sparsity/experimental/pruner
Edit: /usr/local/lib64/python3.6/site-packages/torch/ao/sparsity/experimental/pruner/base_pruner.py (10170B)
import copy
import warnings
import abc
import torch
from torch import nn
from torch.nn.utils import parametrize
from torch.nn.modules.container import ModuleDict, ModuleList
from .parametrization import PruningParametrization, ZeroesParametrization, ActivationReconstruction, BiasHook
from torch.ao.sparsity import BaseSparsifier, module_to_fqn, fqn_to_module
SUPPORTED_MODULES = { # added to config if None given
nn.Linear,
nn.Conv2d,
nn.BatchNorm2d, # will need manual update to match conv2d
}
NEEDS_ZEROS = { # these layers should have pruned indices zero-ed, not removed
nn.BatchNorm2d
}
class BasePruner(BaseSparsifier):
r"""Base class for all pruners.
Abstract methods that need to be implemented:
- update_mask: Function to compute a new mask for all keys in the
`module_groups`.
Args:
- defaults [dict]: default configurations will be attached to the
configuration. Only the keys that don't exist in the `config` will
be updated.
- also_prune_bias [bool]: whether to prune bias in addition to weights (to prune full output channel)
or not; default=True.
"""
def __init__(self, defaults, also_prune_bias=True):
super().__init__(defaults)
self.prune_bias = also_prune_bias
def _prepare(self, use_path=False, *args, **kwargs):
r"""Adds mask parametrization to the layer weight
"""
self.activation_handles = [] # store removable hook handles
self.bias_handles = []
for config in self.module_groups:
modules = []
if use_path:
if type(config['module']) is tuple: # (Conv2d, BN)
for fqn in config['fqn']:
module = fqn_to_module(self.model, fqn)
modules.append(module)
else:
module = fqn_to_module(self.model, config['fqn'])
modules.append(module)
else:
if type(config['module']) is tuple:
for module in config['module']:
modules.append(module)
else:
module = config['module']
modules.append(module)
for module in modules:
if not isinstance(module, tuple(NEEDS_ZEROS)):
# add pruning parametrization and forward hooks
if getattr(module, 'mask', None) is None:
module.register_buffer('mask', torch.tensor(module.weight.shape[0]))
param = config.get('parametrization', PruningParametrization)
parametrize.register_parametrization(module, 'weight', param(module.mask), unsafe=True)
assert isinstance(module.parametrizations, ModuleDict) # make mypy happy
assert isinstance(module.parametrizations.weight, ModuleList)
if isinstance(module, tuple(SUPPORTED_MODULES)):
self.activation_handles.append(module.register_forward_hook(
ActivationReconstruction(module.parametrizations.weight[0])
))
else:
raise NotImplementedError("This module type is not supported yet.")
else: # needs zeros
if getattr(module, 'mask', None) is None:
module.register_buffer('mask', torch.tensor(module.weight.shape[0]))
param = config.get('parametrization', ZeroesParametrization)
parametrize.register_parametrization(module, 'weight', param(module.mask), unsafe=True)
if module.bias is not None:
module.register_parameter('_bias', nn.Parameter(module.bias.detach()))
module.bias = None
self.bias_handles.append(module.register_forward_hook(BiasHook(module.parametrizations.weight[0], self.prune_bias)))
if len(modules) == 2: # (Conv2d, BN)
# should have the same set of pruned outputs
modules[1].parametrizations.weight[0].pruned_outputs = modules[0].parametrizations.weight[0].pruned_outputs
def prepare(self, model, config):
r"""Prepares a model, by adding the parametrizations and forward post-hooks.
Note::
The model is modified inplace. If you need to preserve the original
model, use copy.deepcopy.
Args:
- model [nn.Module]: model to configure. The model itself is not saved
but used for the state_dict saving / loading.
- config [list]: configuration elements could either be instances of
nn.Module or dict maps. The dicts must have a key 'module' with the
value being an instance of a nn.Module.
"""
self.model = model # TODO: Need to figure out how to load without this.
self.config = config
# If no config -- try getting all the supported layers
if self.config is None:
# Add all models to the config
self.config = []
stack = [model]
while stack:
module = stack.pop()
for name, child in module.named_children():
if type(child) in SUPPORTED_MODULES:
self.config.append(child)
else:
if type(child) in NEEDS_ZEROS and self.prune_bias:
warnings.warn(f"Models with {type(child)} layers have config provided by user.")
stack.append(child)
for module_config in self.config:
if type(module_config) is tuple:
first_layer, next_layer = module_config
assert isinstance(first_layer, nn.Conv2d) and isinstance(next_layer, nn.BatchNorm2d)
module_config = {'module': module_config}
local_args = copy.deepcopy(self.defaults)
local_args.update(module_config)
fqn_list = []
for module in local_args['module']:
module_fqn = module_to_fqn(model, module)
if module_fqn and module_fqn[0] == '.':
module_fqn = module_fqn[1:]
fqn_list.append(module_fqn)
local_args['fqn'] = fqn_list
else:
if isinstance(module_config, nn.Module):
module_config = {'module': module_config}
local_args = copy.deepcopy(self.defaults)
local_args.update(module_config)
module = local_args['module']
module_fqn = module_to_fqn(model, module)
if module_fqn and module_fqn[0] == '.':
module_fqn = module_fqn[1:]
local_args['fqn'] = module_fqn
self.module_groups.append(local_args)
self._prepare()
def squash_mask(self, use_path=False, *args, **kwargs):
for config in self.module_groups:
modules = []
if use_path:
if type(config['module']) is tuple: # (Conv2d, BN)
for fqn in config['fqn']:
module = fqn_to_module(self.model, fqn)
modules.append(module)
else:
module = fqn_to_module(self.model, config['fqn'])
modules.append(module)
else:
if type(config['module']) is tuple:
for module in config['module']:
modules.append(module)
else:
module = config['module']
modules.append(module)
for module in modules:
parametrize.remove_parametrizations(module, 'weight',
leave_parametrized=True)
if getattr(module._parameters, 'mask', None):
del module._parameters['mask']
elif getattr(module._buffers, 'mask', None):
del module._buffers['mask']
delattr(module, 'mask')
def get_module_pruned_outputs(self, module):
r"""Returns the set of pruned indices of module"""
assert parametrize.is_parametrized(module) # can only get pruned indices of pruned module
modules = {config['module'] for config in self.module_groups}
module_list = set()
for m in modules:
if type(m) is tuple:
module_list.update(m)
else:
module_list.add(m)
assert module in module_list # check that module is in pruner.module_groups
return module.parametrizations.weight[0].pruned_outputs # assume only one parametrization attached
def step(self, use_path=False):
if not self.enable_mask_update:
return
with torch.no_grad():
for config in self.module_groups:
modules = []
if use_path:
if type(config['module']) is tuple: # (Conv2d, BN)
for fqn in config['fqn']:
module = fqn_to_module(self.model, fqn)
modules.append(module)
else:
module = fqn_to_module(self.model, config['fqn'])
modules.append(module)
else:
if type(config['module']) is tuple:
for module in config['module']:
modules.append(module)
else:
module = config['module']
modules.append(module)
# only need to update the first module in modules if len(modules) > 1
# since they should share the same set of pruned outputs
module = modules[0]
self.update_mask(module, **config)
@abc.abstractmethod
def update_mask(self, layer, **kwargs):
pass