Source code for torch.nn.utils.prune

r"""
Pruning methods
"""
import numbers
from abc import ABC, abstractmethod
from collections.abc import Iterable
from typing import Tuple

import torch

[docs]class BasePruningMethod(ABC): r"""Abstract base class for creation of new pruning techniques. Provides a skeleton for customization requiring the overriding of methods such as :meth:compute_mask and :meth:apply. """ _tensor_name: str def __init__(self): pass def __call__(self, module, inputs): r"""Multiplies the mask (stored in module[name + '_mask']) into the original tensor (stored in module[name + '_orig']) and stores the result into module[name] by using :meth:apply_mask. Args: module (nn.Module): module containing the tensor to prune inputs: not used. """ setattr(module, self._tensor_name, self.apply_mask(module))
[docs] @abstractmethod def compute_mask(self, t, default_mask): r"""Computes and returns a mask for the input tensor t. Starting from a base default_mask (which should be a mask of ones if the tensor has not been pruned yet), generate a random mask to apply on top of the default_mask according to the specific pruning method recipe. Args: t (torch.Tensor): tensor representing the importance scores of the parameter to prune. default_mask (torch.Tensor): Base mask from previous pruning iterations, that need to be respected after the new mask is applied. Same dims as t. Returns: mask (torch.Tensor): mask to apply to t, of same dims as t """ pass
[docs] def apply_mask(self, module): r"""Simply handles the multiplication between the parameter being pruned and the generated mask. Fetches the mask and the original tensor from the module and returns the pruned version of the tensor. Args: module (nn.Module): module containing the tensor to prune Returns: pruned_tensor (torch.Tensor): pruned version of the input tensor """ # to carry out the multiplication, the mask needs to have been computed, # so the pruning method must know what tensor it's operating on assert self._tensor_name is not None, "Module {} has to be pruned".format( module ) # this gets set in apply() mask = getattr(module, self._tensor_name + "_mask") orig = getattr(module, self._tensor_name + "_orig") pruned_tensor = mask.to(dtype=orig.dtype) * orig return pruned_tensor
[docs] @classmethod def apply(cls, module, name, *args, importance_scores=None, **kwargs): r"""Adds the forward pre-hook that enables pruning on the fly and the reparametrization of a tensor in terms of the original tensor and the pruning mask. Args: module (nn.Module): module containing the tensor to prune name (str): parameter name within module on which pruning will act. args: arguments passed on to a subclass of :class:BasePruningMethod importance_scores (torch.Tensor): tensor of importance scores (of same shape as module parameter) used to compute mask for pruning. The values in this tensor indicate the importance of the corresponding elements in the parameter being pruned. If unspecified or None, the parameter will be used in its place. kwargs: keyword arguments passed on to a subclass of a :class:BasePruningMethod """ def _get_composite_method(cls, module, name, *args, **kwargs): # Check if a pruning method has already been applied to # module[name]. If so, store that in old_method. old_method = None found = 0 # there should technically be only 1 hook with hook.name == name # assert this using found hooks_to_remove = [] for k, hook in module._forward_pre_hooks.items(): # if it exists, take existing thing, remove hook, then # go through normal thing if isinstance(hook, BasePruningMethod) and hook._tensor_name == name: old_method = hook hooks_to_remove.append(k) found += 1 assert ( found <= 1 ), "Avoid adding multiple pruning hooks to the\ same tensor {} of module {}. Use a PruningContainer.".format( name, module ) for k in hooks_to_remove: del module._forward_pre_hooks[k] # Apply the new pruning method, either from scratch or on top of # the previous one. method = cls(*args, **kwargs) # new pruning # Have the pruning method remember what tensor it's been applied to method._tensor_name = name # combine methods with old_method, if old_method exists if old_method is not None: # meaning that there was a hook # if the hook is already a pruning container, just add the # new pruning method to the container if isinstance(old_method, PruningContainer): old_method.add_pruning_method(method) method = old_method # rename old_method --> method # if the hook is simply a single pruning method, create a # container, add the old pruning method and the new one elif isinstance(old_method, BasePruningMethod): container = PruningContainer(old_method) # Have the pruning method remember the name of its tensor # setattr(container, '_tensor_name', name) container.add_pruning_method(method) method = container # rename container --> method return method method = _get_composite_method(cls, module, name, *args, **kwargs) # at this point we have no forward_pre_hooks but we could have an # active reparametrization of the tensor if another pruning method # had been applied (in which case method would be a PruningContainer # and not a simple pruning method). # Pruning is to be applied to the module's tensor named name, # starting from the state it is found in prior to this iteration of # pruning. The pruning mask is calculated based on importances scores. orig = getattr(module, name) if importance_scores is not None: assert ( importance_scores.shape == orig.shape ), "importance_scores should have the same shape as parameter \ {} of {}".format( name, module ) else: importance_scores = orig # If this is the first time pruning is applied, take care of moving # the original tensor to a new parameter called name + '_orig' and # and deleting the original parameter if not isinstance(method, PruningContainer): # copy module[name] to module[name + '_orig'] module.register_parameter(name + "_orig", orig) # temporarily delete module[name] del module._parameters[name] default_mask = torch.ones_like(orig) # temp # If this is not the first time pruning is applied, all of the above # has been done before in a previous pruning iteration, so we're good # to go else: default_mask = ( getattr(module, name + "_mask") .detach() .clone(memory_format=torch.contiguous_format) ) # Use try/except because if anything goes wrong with the mask # computation etc., you'd want to roll back. try: # get the final mask, computed according to the specific method mask = method.compute_mask(importance_scores, default_mask=default_mask) # reparametrize by saving mask to module[name + '_mask']... module.register_buffer(name + "_mask", mask) # ... and the new pruned tensor to module[name] setattr(module, name, method.apply_mask(module)) # associate the pruning method to the module via a hook to # compute the function before every forward() (compile by run) module.register_forward_pre_hook(method) except Exception as e: if not isinstance(method, PruningContainer): orig = getattr(module, name + "_orig") module.register_parameter(name, orig) del module._parameters[name + "_orig"] raise e return method
[docs] def prune(self, t, default_mask=None, importance_scores=None): r"""Computes and returns a pruned version of input tensor t according to the pruning rule specified in :meth:compute_mask. Args: t (torch.Tensor): tensor to prune (of same dimensions as default_mask). importance_scores (torch.Tensor): tensor of importance scores (of same shape as t) used to compute mask for pruning t. The values in this tensor indicate the importance of the corresponding elements in the t that is being pruned. If unspecified or None, the tensor t will be used in its place. default_mask (torch.Tensor, optional): mask from previous pruning iteration, if any. To be considered when determining what portion of the tensor that pruning should act on. If None, default to a mask of ones. Returns: pruned version of tensor t. """ if importance_scores is not None: assert ( importance_scores.shape == t.shape ), "importance_scores should have the same shape as tensor t" else: importance_scores = t default_mask = default_mask if default_mask is not None else torch.ones_like(t) return t * self.compute_mask(importance_scores, default_mask=default_mask)
[docs] def remove(self, module): r"""Removes the pruning reparameterization from a module. The pruned parameter named name remains permanently pruned, and the parameter named name+'_orig' is removed from the parameter list. Similarly, the buffer named name+'_mask' is removed from the buffers. Note: Pruning itself is NOT undone or reversed! """ # before removing pruning from a tensor, it has to have been applied assert ( self._tensor_name is not None ), "Module {} has to be pruned\ before pruning can be removed".format( module ) # this gets set in apply() # to update module[name] to latest trained weights weight = self.apply_mask(module) # masked weights # delete and reset if hasattr(module, self._tensor_name): delattr(module, self._tensor_name) orig = module._parameters[self._tensor_name + "_orig"] orig.data = weight.data del module._parameters[self._tensor_name + "_orig"] del module._buffers[self._tensor_name + "_mask"] setattr(module, self._tensor_name, orig)
[docs]class PruningContainer(BasePruningMethod): """Container holding a sequence of pruning methods for iterative pruning. Keeps track of the order in which pruning methods are applied and handles combining successive pruning calls. Accepts as argument an instance of a BasePruningMethod or an iterable of them. """ def __init__(self, *args): self._pruning_methods: Tuple["BasePruningMethod", ...] = tuple() if not isinstance(args, Iterable): # only 1 item self._tensor_name = args._tensor_name self.add_pruning_method(args) elif len(args) == 1: # only 1 item in a tuple self._tensor_name = args[0]._tensor_name self.add_pruning_method(args[0]) else: # manual construction from list or other iterable (or no args) for method in args: self.add_pruning_method(method)
[docs] def add_pruning_method(self, method): r"""Adds a child pruning method to the container. Args: method (subclass of BasePruningMethod): child pruning method to be added to the container. """ # check that we're adding a pruning method to the container if not isinstance(method, BasePruningMethod) and method is not None: raise TypeError( "{} is not a BasePruningMethod subclass".format(type(method)) ) elif method is not None and self._tensor_name != method._tensor_name: raise ValueError( "Can only add pruning methods acting on " "the parameter named '{}' to PruningContainer {}.".format( self._tensor_name, self ) + " Found '{}'".format(method._tensor_name) ) # if all checks passed, add to _pruning_methods tuple self._pruning_methods += (method,)
def __len__(self): return len(self._pruning_methods) def __iter__(self): return iter(self._pruning_methods) def __getitem__(self, idx): return self._pruning_methods[idx]
[docs] def compute_mask(self, t, default_mask): r"""Applies the latest method by computing the new partial masks and returning its combination with the default_mask. The new partial mask should be computed on the entries or channels that were not zeroed out by the default_mask. Which portions of the tensor t the new mask will be calculated from depends on the PRUNING_TYPE (handled by the type handler): * for 'unstructured', the mask will be computed from the raveled list of nonmasked entries; * for 'structured', the mask will be computed from the nonmasked channels in the tensor; * for 'global', the mask will be computed across all entries. Args: t (torch.Tensor): tensor representing the parameter to prune (of same dimensions as default_mask). default_mask (torch.Tensor): mask from previous pruning iteration. Returns: mask (torch.Tensor): new mask that combines the effects of the default_mask and the new mask from the current pruning method (of same dimensions as default_mask and t). """ def _combine_masks(method, t, mask): r""" Args: method (a BasePruningMethod subclass): pruning method currently being applied. t (torch.Tensor): tensor representing the parameter to prune (of same dimensions as mask). mask (torch.Tensor): mask from previous pruning iteration Returns: new_mask (torch.Tensor): new mask that combines the effects of the old mask and the new mask from the current pruning method (of same dimensions as mask and t). """ new_mask = mask # start off from existing mask new_mask = new_mask.to(dtype=t.dtype) # compute a slice of t onto which the new pruning method will operate if method.PRUNING_TYPE == "unstructured": # prune entries of t where the mask is 1 slc = mask == 1 # for struct pruning, exclude channels that have already been # entirely pruned elif method.PRUNING_TYPE == "structured": if not hasattr(method, "dim"): raise AttributeError( "Pruning methods of PRUNING_TYPE " '"structured" need to have the attribute dim defined.' ) # find the channels to keep by removing the ones that have been # zeroed out already (i.e. where sum(entries) == 0) n_dims = t.dim() # "is this a 2D tensor? 3D? ..." dim = method.dim # convert negative indexing if dim < 0: dim = n_dims + dim # if dim is still negative after subtracting it from n_dims if dim < 0: raise IndexError( "Index is out of bounds for tensor with dimensions {}".format( n_dims ) ) # find channels along dim = dim that aren't already tots 0ed out keep_channel = mask.sum(dim=[d for d in range(n_dims) if d != dim]) != 0 # create slice to identify what to prune slc = [slice(None)] * n_dims slc[dim] = keep_channel elif method.PRUNING_TYPE == "global": n_dims = len(t.shape) # "is this a 2D tensor? 3D? ..." slc = [slice(None)] * n_dims else: raise ValueError( "Unrecognized PRUNING_TYPE {}".format(method.PRUNING_TYPE) ) # compute the new mask on the unpruned slice of the tensor t partial_mask = method.compute_mask(t[slc], default_mask=mask[slc]) new_mask[slc] = partial_mask.to(dtype=new_mask.dtype) return new_mask method = self._pruning_methods[-1] mask = _combine_masks(method, t, default_mask) return mask
[docs]class Identity(BasePruningMethod): r"""Utility pruning method that does not prune any units but generates the pruning parametrization with a mask of ones. """ PRUNING_TYPE = "unstructured" def compute_mask(self, t, default_mask): mask = default_mask return mask
[docs] @classmethod def apply(cls, module, name): r"""Adds the forward pre-hook that enables pruning on the fly and the reparametrization of a tensor in terms of the original tensor and the pruning mask. Args: module (nn.Module): module containing the tensor to prune name (str): parameter name within module on which pruning will act. """ return super(Identity, cls).apply(module, name)
[docs]class RandomUnstructured(BasePruningMethod): r"""Prune (currently unpruned) units in a tensor at random. Args: name (str): parameter name within module on which pruning will act. amount (int or float): quantity of parameters to prune. If float, should be between 0.0 and 1.0 and represent the fraction of parameters to prune. If int, it represents the absolute number of parameters to prune. """ PRUNING_TYPE = "unstructured" def __init__(self, amount): # Check range of validity of pruning amount _validate_pruning_amount_init(amount) self.amount = amount def compute_mask(self, t, default_mask): # Check that the amount of units to prune is not > than the number of # parameters in t tensor_size = t.nelement() # Compute number of units to prune: amount if int, # else amount * tensor_size nparams_toprune = _compute_nparams_toprune(self.amount, tensor_size) # This should raise an error if the number of units to prune is larger # than the number of units in the tensor _validate_pruning_amount(nparams_toprune, tensor_size) mask = default_mask.clone(memory_format=torch.contiguous_format) if nparams_toprune != 0: # k=0 not supported by torch.kthvalue prob = torch.rand_like(t) topk = torch.topk(prob.view(-1), k=nparams_toprune) mask.view(-1)[topk.indices] = 0 return mask
[docs] @classmethod def apply(cls, module, name, amount): r"""Adds the forward pre-hook that enables pruning on the fly and the reparametrization of a tensor in terms of the original tensor and the pruning mask. Args: module (nn.Module): module containing the tensor to prune name (str): parameter name within module on which pruning will act. amount (int or float): quantity of parameters to prune. If float, should be between 0.0 and 1.0 and represent the fraction of parameters to prune. If int, it represents the absolute number of parameters to prune. """ return super(RandomUnstructured, cls).apply(module, name, amount=amount)
[docs]class L1Unstructured(BasePruningMethod): r"""Prune (currently unpruned) units in a tensor by zeroing out the ones with the lowest L1-norm. Args: amount (int or float): quantity of parameters to prune. If float, should be between 0.0 and 1.0 and represent the fraction of parameters to prune. If int, it represents the absolute number of parameters to prune. """ PRUNING_TYPE = "unstructured" def __init__(self, amount): # Check range of validity of pruning amount _validate_pruning_amount_init(amount) self.amount = amount def compute_mask(self, t, default_mask): # Check that the amount of units to prune is not > than the number of # parameters in t tensor_size = t.nelement() # Compute number of units to prune: amount if int, # else amount * tensor_size nparams_toprune = _compute_nparams_toprune(self.amount, tensor_size) # This should raise an error if the number of units to prune is larger # than the number of units in the tensor _validate_pruning_amount(nparams_toprune, tensor_size) mask = default_mask.clone(memory_format=torch.contiguous_format) if nparams_toprune != 0: # k=0 not supported by torch.kthvalue # largest=True --> top k; largest=False --> bottom k # Prune the smallest k topk = torch.topk(torch.abs(t).view(-1), k=nparams_toprune, largest=False) # topk will have .indices and .values mask.view(-1)[topk.indices] = 0 return mask
[docs] @classmethod def apply(cls, module, name, amount, importance_scores=None): r"""Adds the forward pre-hook that enables pruning on the fly and the reparametrization of a tensor in terms of the original tensor and the pruning mask. Args: module (nn.Module): module containing the tensor to prune name (str): parameter name within module on which pruning will act. amount (int or float): quantity of parameters to prune. If float, should be between 0.0 and 1.0 and represent the fraction of parameters to prune. If int, it represents the absolute number of parameters to prune. importance_scores (torch.Tensor): tensor of importance scores (of same shape as module parameter) used to compute mask for pruning. The values in this tensor indicate the importance of the corresponding elements in the parameter being pruned. If unspecified or None, the module parameter will be used in its place. """ return super(L1Unstructured, cls).apply( module, name, amount=amount, importance_scores=importance_scores )
[docs]class RandomStructured(BasePruningMethod): r"""Prune entire (currently unpruned) channels in a tensor at random. Args: amount (int or float): quantity of parameters to prune. If float, should be between 0.0 and 1.0 and represent the fraction of parameters to prune. If int, it represents the absolute number of parameters to prune. dim (int, optional): index of the dim along which we define channels to prune. Default: -1. """ PRUNING_TYPE = "structured" def __init__(self, amount, dim=-1): # Check range of validity of amount _validate_pruning_amount_init(amount) self.amount = amount self.dim = dim
[docs] def compute_mask(self, t, default_mask): r"""Computes and returns a mask for the input tensor t. Starting from a base default_mask (which should be a mask of ones if the tensor has not been pruned yet), generate a random mask to apply on top of the default_mask by randomly zeroing out channels along the specified dim of the tensor. Args: t (torch.Tensor): tensor representing the parameter to prune default_mask (torch.Tensor): Base mask from previous pruning iterations, that need to be respected after the new mask is applied. Same dims as t. Returns: mask (torch.Tensor): mask to apply to t, of same dims as t Raises: IndexError: if self.dim >= len(t.shape) """ # Check that tensor has structure (i.e. more than 1 dimension) such # that the concept of "channels" makes sense _validate_structured_pruning(t) # Check that self.dim is a valid dim to index t, else raise IndexError _validate_pruning_dim(t, self.dim) # Check that the amount of channels to prune is not > than the number of # channels in t along the dim to prune tensor_size = t.shape[self.dim] # Compute number of units to prune: amount if int, # else amount * tensor_size nparams_toprune = _compute_nparams_toprune(self.amount, tensor_size) # This should raise an error if the number of units to prune is larger # than the number of units in the tensor _validate_pruning_amount(nparams_toprune, tensor_size) # Compute binary mask by initializing it to all 0s and then filling in # 1s wherever topk.indices indicates, along self.dim. # mask has the same shape as tensor t def make_mask(t, dim, nchannels, nchannels_toprune): # generate a random number in [0, 1] to associate to each channel prob = torch.rand(nchannels) # generate mask for each channel by 0ing out the channels that # got assigned the k = nchannels_toprune lowest values in prob threshold = torch.kthvalue(prob, k=nchannels_toprune).values channel_mask = prob > threshold mask = torch.zeros_like(t) slc = [slice(None)] * len(t.shape) slc[dim] = channel_mask mask[slc] = 1 return mask if nparams_toprune == 0: # k=0 not supported by torch.kthvalue mask = default_mask else: # apply the new structured mask on top of prior (potentially # unstructured) mask mask = make_mask(t, self.dim, tensor_size, nparams_toprune) mask *= default_mask.to(dtype=mask.dtype) return mask
[docs] @classmethod def apply(cls, module, name, amount, dim=-1): r"""Adds the forward pre-hook that enables pruning on the fly and the reparametrization of a tensor in terms of the original tensor and the pruning mask. Args: module (nn.Module): module containing the tensor to prune name (str): parameter name within module on which pruning will act. amount (int or float): quantity of parameters to prune. If float, should be between 0.0 and 1.0 and represent the fraction of parameters to prune. If int, it represents the absolute number of parameters to prune. dim (int, optional): index of the dim along which we define channels to prune. Default: -1. """ return super(RandomStructured, cls).apply(module, name, amount=amount, dim=dim)
[docs]class LnStructured(BasePruningMethod): r"""Prune entire (currently unpruned) channels in a tensor based on their Ln-norm. Args: amount (int or float): quantity of channels to prune. If float, should be between 0.0 and 1.0 and represent the fraction of parameters to prune. If int, it represents the absolute number of parameters to prune. n (int, float, inf, -inf, 'fro', 'nuc'): See documentation of valid entries for argument p in :func:torch.norm. dim (int, optional): index of the dim along which we define channels to prune. Default: -1. """ PRUNING_TYPE = "structured" def __init__(self, amount, n, dim=-1): # Check range of validity of amount _validate_pruning_amount_init(amount) self.amount = amount self.n = n self.dim = dim
[docs] def compute_mask(self, t, default_mask): r"""Computes and returns a mask for the input tensor t. Starting from a base default_mask (which should be a mask of ones if the tensor has not been pruned yet), generate a mask to apply on top of the default_mask by zeroing out the channels along the specified dim with the lowest Ln-norm. Args: t (torch.Tensor): tensor representing the parameter to prune default_mask (torch.Tensor): Base mask from previous pruning iterations, that need to be respected after the new mask is applied. Same dims as t. Returns: mask (torch.Tensor): mask to apply to t, of same dims as t Raises: IndexError: if self.dim >= len(t.shape) """ # Check that tensor has structure (i.e. more than 1 dimension) such # that the concept of "channels" makes sense _validate_structured_pruning(t) # Check that self.dim is a valid dim to index t, else raise IndexError _validate_pruning_dim(t, self.dim) # Check that the amount of channels to prune is not > than the number of # channels in t along the dim to prune tensor_size = t.shape[self.dim] # Compute number of units to prune: amount if int, # else amount * tensor_size nparams_toprune = _compute_nparams_toprune(self.amount, tensor_size) nparams_tokeep = tensor_size - nparams_toprune # This should raise an error if the number of units to prune is larger # than the number of units in the tensor _validate_pruning_amount(nparams_toprune, tensor_size) # Structured pruning prunes entire channels so we need to know the # L_n norm along each channel to then find the topk based on this # metric norm = _compute_norm(t, self.n, self.dim) # largest=True --> top k; largest=False --> bottom k # Keep the largest k channels along dim=self.dim topk = torch.topk(norm, k=nparams_tokeep, largest=True) # topk will have .indices and .values # Compute binary mask by initializing it to all 0s and then filling in # 1s wherever topk.indices indicates, along self.dim. # mask has the same shape as tensor t def make_mask(t, dim, indices): # init mask to 0 mask = torch.zeros_like(t) # e.g.: slc = [None, None, None], if len(t.shape) = 3 slc = [slice(None)] * len(t.shape) # replace a None at position=dim with indices # e.g.: slc = [None, None, [0, 2, 3]] if dim=2 & indices=[0,2,3] slc[dim] = indices # use slc to slice mask and replace all its entries with 1s # e.g.: mask[:, :, [0, 2, 3]] = 1 mask[slc] = 1 return mask if nparams_toprune == 0: # k=0 not supported by torch.kthvalue mask = default_mask else: mask = make_mask(t, self.dim, topk.indices) mask *= default_mask.to(dtype=mask.dtype) return mask
[docs] @classmethod def apply(cls, module, name, amount, n, dim, importance_scores=None): r"""Adds the forward pre-hook that enables pruning on the fly and the reparametrization of a tensor in terms of the original tensor and the pruning mask. Args: module (nn.Module): module containing the tensor to prune name (str): parameter name within module on which pruning will act. amount (int or float): quantity of parameters to prune. If float, should be between 0.0 and 1.0 and represent the fraction of parameters to prune. If int, it represents the absolute number of parameters to prune. n (int, float, inf, -inf, 'fro', 'nuc'): See documentation of valid entries for argument p in :func:torch.norm. dim (int): index of the dim along which we define channels to prune. importance_scores (torch.Tensor): tensor of importance scores (of same shape as module parameter) used to compute mask for pruning. The values in this tensor indicate the importance of the corresponding elements in the parameter being pruned. If unspecified or None, the module parameter will be used in its place. """ return super(LnStructured, cls).apply( module, name, amount=amount, n=n, dim=dim, importance_scores=importance_scores, )
[docs] @classmethod def apply(cls, module, name, mask): r"""Adds the forward pre-hook that enables pruning on the fly and the reparametrization of a tensor in terms of the original tensor and the pruning mask. Args: module (nn.Module): module containing the tensor to prune name (str): parameter name within module on which pruning will act. """ return super(CustomFromMask, cls).apply(module, name, mask=mask)
[docs]def identity(module, name): r"""Applies pruning reparametrization to the tensor corresponding to the parameter called name in module without actually pruning any units. Modifies module in place (and also return the modified module) by: 1) adding a named buffer called name+'_mask' corresponding to the binary mask applied to the parameter name by the pruning method. 2) replacing the parameter name by its pruned version, while the original (unpruned) parameter is stored in a new parameter named name+'_orig'. Note: The mask is a tensor of ones. Args: module (nn.Module): module containing the tensor to prune. name (str): parameter name within module on which pruning will act. Returns: module (nn.Module): modified (i.e. pruned) version of the input module Examples: >>> m = prune.identity(nn.Linear(2, 3), 'bias') >>> print(m.bias_mask) tensor([1., 1., 1.]) """ Identity.apply(module, name) return module
[docs]def random_unstructured(module, name, amount): r"""Prunes tensor corresponding to parameter called name in module by removing the specified amount of (currently unpruned) units selected at random. Modifies module in place (and also return the modified module) by: 1) adding a named buffer called name+'_mask' corresponding to the binary mask applied to the parameter name by the pruning method. 2) replacing the parameter name by its pruned version, while the original (unpruned) parameter is stored in a new parameter named name+'_orig'. Args: module (nn.Module): module containing the tensor to prune name (str): parameter name within module on which pruning will act. amount (int or float): quantity of parameters to prune. If float, should be between 0.0 and 1.0 and represent the fraction of parameters to prune. If int, it represents the absolute number of parameters to prune. Returns: module (nn.Module): modified (i.e. pruned) version of the input module Examples: >>> m = prune.random_unstructured(nn.Linear(2, 3), 'weight', amount=1) >>> torch.sum(m.weight_mask == 0) tensor(1) """ RandomUnstructured.apply(module, name, amount) return module
[docs]def l1_unstructured(module, name, amount, importance_scores=None): r"""Prunes tensor corresponding to parameter called name in module by removing the specified amount of (currently unpruned) units with the lowest L1-norm. Modifies module in place (and also return the modified module) by: 1) adding a named buffer called name+'_mask' corresponding to the binary mask applied to the parameter name by the pruning method. 2) replacing the parameter name by its pruned version, while the original (unpruned) parameter is stored in a new parameter named name+'_orig'. Args: module (nn.Module): module containing the tensor to prune name (str): parameter name within module on which pruning will act. amount (int or float): quantity of parameters to prune. If float, should be between 0.0 and 1.0 and represent the fraction of parameters to prune. If int, it represents the absolute number of parameters to prune. importance_scores (torch.Tensor): tensor of importance scores (of same shape as module parameter) used to compute mask for pruning. The values in this tensor indicate the importance of the corresponding elements in the parameter being pruned. If unspecified or None, the module parameter will be used in its place. Returns: module (nn.Module): modified (i.e. pruned) version of the input module Examples: >>> m = prune.l1_unstructured(nn.Linear(2, 3), 'weight', amount=0.2) >>> m.state_dict().keys() odict_keys(['bias', 'weight_orig', 'weight_mask']) """ L1Unstructured.apply( module, name, amount=amount, importance_scores=importance_scores ) return module
[docs]def random_structured(module, name, amount, dim): r"""Prunes tensor corresponding to parameter called name in module by removing the specified amount of (currently unpruned) channels along the specified dim selected at random. Modifies module in place (and also return the modified module) by: 1) adding a named buffer called name+'_mask' corresponding to the binary mask applied to the parameter name by the pruning method. 2) replacing the parameter name by its pruned version, while the original (unpruned) parameter is stored in a new parameter named name+'_orig'. Args: module (nn.Module): module containing the tensor to prune name (str): parameter name within module on which pruning will act. amount (int or float): quantity of parameters to prune. If float, should be between 0.0 and 1.0 and represent the fraction of parameters to prune. If int, it represents the absolute number of parameters to prune. dim (int): index of the dim along which we define channels to prune. Returns: module (nn.Module): modified (i.e. pruned) version of the input module Examples: >>> m = prune.random_structured( nn.Linear(5, 3), 'weight', amount=3, dim=1 ) >>> columns_pruned = int(sum(torch.sum(m.weight, dim=0) == 0)) >>> print(columns_pruned) 3 """ RandomStructured.apply(module, name, amount, dim) return module
[docs]def ln_structured(module, name, amount, n, dim, importance_scores=None): r"""Prunes tensor corresponding to parameter called name in module by removing the specified amount of (currently unpruned) channels along the specified dim with the lowest Ln-norm. Modifies module in place (and also return the modified module) by: 1) adding a named buffer called name+'_mask' corresponding to the binary mask applied to the parameter name by the pruning method. 2) replacing the parameter name by its pruned version, while the original (unpruned) parameter is stored in a new parameter named name+'_orig'. Args: module (nn.Module): module containing the tensor to prune name (str): parameter name within module on which pruning will act. amount (int or float): quantity of parameters to prune. If float, should be between 0.0 and 1.0 and represent the fraction of parameters to prune. If int, it represents the absolute number of parameters to prune. n (int, float, inf, -inf, 'fro', 'nuc'): See documentation of valid entries for argument p in :func:torch.norm. dim (int): index of the dim along which we define channels to prune. importance_scores (torch.Tensor): tensor of importance scores (of same shape as module parameter) used to compute mask for pruning. The values in this tensor indicate the importance of the corresponding elements in the parameter being pruned. If unspecified or None, the module parameter will be used in its place. Returns: module (nn.Module): modified (i.e. pruned) version of the input module Examples: >>> m = prune.ln_structured( nn.Conv2d(5, 3, 2), 'weight', amount=0.3, dim=1, n=float('-inf') ) """ LnStructured.apply( module, name, amount, n, dim, importance_scores=importance_scores ) return module
[docs]def global_unstructured(parameters, pruning_method, importance_scores=None, **kwargs): r""" Globally prunes tensors corresponding to all parameters in parameters by applying the specified pruning_method. Modifies modules in place by: 1) adding a named buffer called name+'_mask' corresponding to the binary mask applied to the parameter name by the pruning method. 2) replacing the parameter name by its pruned version, while the original (unpruned) parameter is stored in a new parameter named name+'_orig'. Args: parameters (Iterable of (module, name) tuples): parameters of the model to prune in a global fashion, i.e. by aggregating all weights prior to deciding which ones to prune. module must be of type :class:nn.Module, and name must be a string. pruning_method (function): a valid pruning function from this module, or a custom one implemented by the user that satisfies the implementation guidelines and has PRUNING_TYPE='unstructured'. importance_scores (dict): a dictionary mapping (module, name) tuples to the corresponding parameter's importance scores tensor. The tensor should be the same shape as the parameter, and is used for computing mask for pruning. If unspecified or None, the parameter will be used in place of its importance scores. kwargs: other keyword arguments such as: amount (int or float): quantity of parameters to prune across the specified parameters. If float, should be between 0.0 and 1.0 and represent the fraction of parameters to prune. If int, it represents the absolute number of parameters to prune. Raises: TypeError: if PRUNING_TYPE != 'unstructured' Note: Since global structured pruning doesn't make much sense unless the norm is normalized by the size of the parameter, we now limit the scope of global pruning to unstructured methods. Examples: >>> net = nn.Sequential(OrderedDict([ ('first', nn.Linear(10, 4)), ('second', nn.Linear(4, 1)), ])) >>> parameters_to_prune = ( (net.first, 'weight'), (net.second, 'weight'), ) >>> prune.global_unstructured( parameters_to_prune, pruning_method=prune.L1Unstructured, amount=10, ) >>> print(sum(torch.nn.utils.parameters_to_vector(net.buffers()) == 0)) tensor(10, dtype=torch.uint8) """ # ensure parameters is a list or generator of tuples if not isinstance(parameters, Iterable): raise TypeError("global_unstructured(): parameters is not an Iterable") importance_scores = importance_scores if importance_scores is not None else {} if not isinstance(importance_scores, dict): raise TypeError("global_unstructured(): importance_scores must be of type dict") # flatten importance scores to consider them all at once in global pruning relevant_importance_scores = torch.nn.utils.parameters_to_vector( [ importance_scores.get((module, name), getattr(module, name)) for (module, name) in parameters ] ) # similarly, flatten the masks (if they exist), or use a flattened vector # of 1s of the same dimensions as t default_mask = torch.nn.utils.parameters_to_vector( [ getattr(module, name + "_mask", torch.ones_like(getattr(module, name))) for (module, name) in parameters ] ) # use the canonical pruning methods to compute the new mask, even if the # parameter is now a flattened out version of parameters container = PruningContainer() container._tensor_name = "temp" # to make it match that of method method = pruning_method(**kwargs) method._tensor_name = "temp" # to make it match that of container if method.PRUNING_TYPE != "unstructured": raise TypeError( 'Only "unstructured" PRUNING_TYPE supported for ' "the pruning_method. Found method {} of type {}".format( pruning_method, method.PRUNING_TYPE ) ) container.add_pruning_method(method) # use the compute_mask method from PruningContainer to combine the # mask computed by the new method with the pre-existing mask final_mask = container.compute_mask(relevant_importance_scores, default_mask) # Pointer for slicing the mask to match the shape of each parameter pointer = 0 for module, name in parameters: param = getattr(module, name) # The length of the parameter num_param = param.numel() # Slice the mask, reshape it param_mask = final_mask[pointer : pointer + num_param].view_as(param) # Assign the correct pre-computed mask to each parameter and add it # to the forward_pre_hooks like any other pruning method custom_from_mask(module, name, mask=param_mask) # Increment the pointer to continue slicing the final_mask pointer += num_param
[docs]def custom_from_mask(module, name, mask): r"""Prunes tensor corresponding to parameter called name in module by applying the pre-computed mask in mask. Modifies module in place (and also return the modified module) by: 1) adding a named buffer called name+'_mask' corresponding to the binary mask applied to the parameter name by the pruning method. 2) replacing the parameter name by its pruned version, while the original (unpruned) parameter is stored in a new parameter named name+'_orig'. Args: module (nn.Module): module containing the tensor to prune name (str): parameter name within module on which pruning will act. mask (Tensor): binary mask to be applied to the parameter. Returns: module (nn.Module): modified (i.e. pruned) version of the input module Examples: >>> m = prune.custom_from_mask( nn.Linear(5, 3), name='bias', mask=torch.tensor([0, 1, 0]) ) >>> print(m.bias_mask) tensor([0., 1., 0.]) """ CustomFromMask.apply(module, name, mask) return module
[docs]def remove(module, name): r"""Removes the pruning reparameterization from a module and the pruning method from the forward hook. The pruned parameter named name remains permanently pruned, and the parameter named name+'_orig' is removed from the parameter list. Similarly, the buffer named name+'_mask' is removed from the buffers. Note: Pruning itself is NOT undone or reversed! Args: module (nn.Module): module containing the tensor to prune name (str): parameter name within module on which pruning will act. Examples: >>> m = random_unstructured(nn.Linear(5, 7), name='weight', amount=0.2) >>> m = remove(m, name='weight') """ for k, hook in module._forward_pre_hooks.items(): if isinstance(hook, BasePruningMethod) and hook._tensor_name == name: hook.remove(module) del module._forward_pre_hooks[k] return module raise ValueError( "Parameter '{}' of module {} has to be pruned " "before pruning can be removed".format(name, module) )
[docs]def is_pruned(module): r"""Check whether module is pruned by looking for forward_pre_hooks in its modules that inherit from the :class:BasePruningMethod. Args: module (nn.Module): object that is either pruned or unpruned Returns: binary answer to whether module is pruned. Examples: >>> m = nn.Linear(5, 7) >>> print(prune.is_pruned(m)) False >>> prune.random_unstructured(m, name='weight', amount=0.2) >>> print(prune.is_pruned(m)) True """ for _, submodule in module.named_modules(): for _, hook in submodule._forward_pre_hooks.items(): if isinstance(hook, BasePruningMethod): return True return False
def _validate_pruning_amount_init(amount): r"""Validation helper to check the range of amount at init. Args: amount (int or float): quantity of parameters to prune. If float, should be between 0.0 and 1.0 and represent the fraction of parameters to prune. If int, it represents the absolute number of parameters to prune. Raises: ValueError: if amount is a float not in [0, 1], or if it's a negative integer. TypeError: if amount is neither a float nor an integer. Note: This does not take into account the number of parameters in the tensor to be pruned, which is known only at prune. """ if not isinstance(amount, numbers.Real): raise TypeError( "Invalid type for amount: {}. Must be int or float." "".format(amount) ) if (isinstance(amount, numbers.Integral) and amount < 0) or ( not isinstance(amount, numbers.Integral) # so it's a float and (float(amount) > 1.0 or float(amount) < 0.0) ): raise ValueError( "amount={} should either be a float in the " "range [0, 1] or a non-negative integer" "".format(amount) ) def _validate_pruning_amount(amount, tensor_size): r"""Validation helper to check that the amount of parameters to prune is meaningful wrt to the size of the data (tensor_size). Args: amount (int or float): quantity of parameters to prune. If float, should be between 0.0 and 1.0 and represent the fraction of parameters to prune. If int, it represents the absolute number of parameters to prune. tensor_size (int): absolute number of parameters in the tensor to prune. """ # TODO: consider removing this check and allowing users to specify # a number of units to prune that is greater than the number of units # left to prune. In this case, the tensor will just be fully pruned. if isinstance(amount, numbers.Integral) and amount > tensor_size: raise ValueError( "amount={} should be smaller than the number of " "parameters to prune={}".format(amount, tensor_size) ) def _validate_structured_pruning(t): r"""Validation helper to check that the tensor to be pruned is multi- dimensional, such that the concept of "channels" is well-defined. Args: t (torch.Tensor): tensor representing the parameter to prune Raises: ValueError: if the tensor t is not at least 2D. """ shape = t.shape if len(shape) <= 1: raise ValueError( "Structured pruning can only be applied to " "multidimensional tensors. Found tensor of shape " "{} with {} dims".format(shape, len(shape)) ) def _compute_nparams_toprune(amount, tensor_size): r"""Since amount can be expressed either in absolute value or as a percentage of the number of units/channels in a tensor, this utility function converts the percentage to absolute value to standardize the handling of pruning. Args: amount (int or float): quantity of parameters to prune. If float, should be between 0.0 and 1.0 and represent the fraction of parameters to prune. If int, it represents the absolute number of parameters to prune. tensor_size (int): absolute number of parameters in the tensor to prune. Returns: int: the number of units to prune in the tensor """ # incorrect type already checked in _validate_pruning_amount_init if isinstance(amount, numbers.Integral): return amount else: return int(round(amount * tensor_size)) # int needed for Python 2 def _validate_pruning_dim(t, dim): r""" Args: t (torch.Tensor): tensor representing the parameter to prune dim (int): index of the dim along which we define channels to prune """ if dim >= t.dim(): raise IndexError("Invalid index {} for tensor of size {}".format(dim, t.shape)) def _compute_norm(t, n, dim): r"""Compute the L_n-norm across all entries in tensor t along all dimension except for the one identified by dim. Example: if t is of shape, say, 3x2x4 and dim=2 (the last dim), then norm will have Size [4], and each entry will represent the L_n-norm computed using the 3x2=6 entries for each of the 4 channels. Args: t (torch.Tensor): tensor representing the parameter to prune n (int, float, inf, -inf, 'fro', 'nuc'): See documentation of valid entries for argument p in torch.norm dim (int): dim identifying the channels to prune Returns: norm (torch.Tensor): L_n norm computed across all dimensions except for dim. By construction, norm.shape = t.shape[-1]. """ # dims = all axes, except for the one identified by dim dims = list(range(t.dim())) # convert negative indexing if dim < 0: dim = dims[dim] dims.remove(dim) norm = torch.norm(t, p=n, dim=dims) return norm