N EURAL P RUNING VIA G ROWING R EGULARIZATION
A BSTRACT
Regularization has long been utilized to learn sparsity in deep neural network pruning.
However, its role is mainly explored in the small penalty strength regime.
In this work, we extend its application to a new scenario where the regularization grows large gradually to tackle two central problems of pruning: pruning schedule and weight importance scoring.
(1) The former topic is newly brought up in this work, which we ﬁnd critical to the pruning performance while receives little research attention.
Speciﬁcally, we propose an L 2 regularization variant with rising penalty factors and show it can bring signiﬁcant accuracy gains compared with its one-shot counterpart, even when the same weights are removed.
The growing penalty scheme also brings us an approach to exploit the Hessian information for more accurate pruning without knowing their speciﬁc values , thus not bothered by the common Hessian approximation problems.
Empirically, the proposed algorithms are easy to implement and scalable to large datasets and networks in both structured and unstructured pruning .
Their effectiveness is demonstrated with modern deep neural networks on the CIFAR and ImageNet datasets, achieving competitive results compared to many state-of-the-art algorithms.
Our code and trained models are publicly available at https://github.com/mingsuntse/regularization-pruning.
I NTRODUCTION
As deep neural networks advance in recent years LeCun et al.
(2015); Schmidhuber (2015)
, their remarkable effectiveness comes at a cost of rising storage, memory footprint, computing resources and energy consumption Cheng et al.
; Deng et al.
Neural network pruning Han et al.
; Li et al.
Wen et al.
; He et al.
Gale et al.
(2019) is deemed as a promising force to alleviate this problem.
Since its early debut Mozer & Smolensky (1989); Reed (1993), the central problem of neural network pruning has been (arguably) how to choose weights to discard, i.e., the weight importance scoring problem LeCun et al.
(1990); Hassibi & Stork (1993);
Molchanov et al.
(2017b; 2019); Wang et al. (2019a)
; He et al.
The approaches to the scoring problem generally fall into two groups: importance-based and regularization-based Reed (1993).
The former focuses on directly proposing certain theoretically sound importance criterion so that we can prune the unimportant weights once for all.
Thus, the pruning process is typically one-shot.
In contrast, regularization-based approaches typically select unimportant weights through training with a penalty term Han et al.
Wen et al.
; Liu et al.
However, the penalty strength is usually maintained in a small regime to avoid damaging the model expressivity.
Whereas, a large penalty strength can be helpful, speciﬁcally in two aspects.
(1) A large penalty can push unimportant weights rather close to zero, then the pruning later barely hurts the performance even if the simple weight magnitude is adopted as criterion.
It is well-known that different weights of a neural network lie on the regions with different local quadratic structures, i.e., Hessian information.
Many methods try to tap into this to build a more accurate scoring LeCun et al.
(1990); Hassibi & Stork (1993)
; Wang et al.
(2019a)
; Singh & Alistarh (2020).
However, for deep networks, it is especially hard to estimate Hessian.
Sometimes, even the computing itself can be intractable without resorting to proper approximation Wang et al.
(2019a).
On this problem, we ask: Is it possible to exploit the Hessian information without knowing their speciﬁc values ?
This is the second scenario where a growing regularization can help.
We will show under a growing regularization, the weight magnitude will naturally separate because of their different underlying local quadratic structure, therein we can pick the unimportant weights more faithfully even using the simple magnitude-based criterion.
Corresponding to these two aspects, we will present two algorithms based on a growing L 2 regularization paradigm, in which the ﬁrst highlights a better pruning schedule 1 and the second explores a better pruning criterion.
Our contributions .
(1) We propose a simple yet effective growing regularization scheme, which can help transfer the model expressivity to the remaining part during pruning.
The encouraging performance inspires us that the pruning schedule may be as critical as the weight importance criterion and deserve more research attention.
(2) We further adopt growing regularization to exploit
Hessian implicitly, without knowing their speciﬁc values.
The method can help choose the unimportant weights more faithfully with a theoretically sound basis.
In this regard, our paper is the ﬁrst to show the connection between magnitude-based pruning and Hessian-based pruning, pointing out that the latter can be turned into the ﬁrst one through our proposed growing regularization scheme.
The proposed two algorithms are easy to implement and scalable to large-scale datasets and networks.
We show their effectiveness compared with many state-of-the-arts.
Especially, the methods can work seamlessly for both ﬁlter pruning and unstructured pruning.
R ELATED W ORK
Regularization-based pruning .
The ﬁrst group of relevant works is those applying regularization to learn sparsity.
The most famous probably is to use L 0 or L 1 regularization Louizos et al.
Liu et al.
; Ye et al.
(2018) due to their sparsity-inducing nature.
In addition, the common
L 2 regularization is also explored for approximated sparsity Han et al.
The early papers focus more on unstructured pruning, which is beneﬁcial to model compression yet not to acceleration.
For structured pruning in favor of acceleration, Group-wise Brain Damage Lebedev &
Lempitsky (2016) and SSL Wen et al.
(2016) propose to use Group LASSO Yuan & Lin (2006) to learn regular sparsity, where the penalty strength is still kept in small scale because the penalty is uniformly applied to all the weights.
To resolve this, Ding et al.
(2018) and Wang et al. (2019c) propose to employ different penalty factors for different weights, enabling large regularization.
Importance-based pruning .
Importance-based pruning tries to establish certain advanced importance criteria that can reﬂect the true relative importance among weights as faithfully as possible.
The pruned weights are usually decided immediately by some proposed formula instead of by training (although the whole pruning process can involve training, e.g., iterative pruning).
The most widely used criterion is the magnitude-based: weight absolute value for unstructured pruningHan et al.
(2015; 2016) or L 1 /L 2 -norm for structured pruning Li et al.
This heuristic criterion was proposed a long time ago Reed (1993) and has been argued to be inaccurate.
In this respect, improvement mainly comes from using Hessian information to obtain a more accurate approximation of the increased loss when a weight is removed LeCun et al.
; Hassibi & Stork (1993).
Hessian is intractable to compute for large networks, so some methods (e.g., EigenDamage Wang et al. (2019a), WoodFisher Singh & Alistarh (2020)) employ cheap approximation (such as K-FAC
Fisher Martens & Grosse (2015)) to make the 2nd-order criteria tractable on deep networks.
Note that, there is no a hard boundary between the importance-based and regularization-based.
Many papers present their schemes in the combination of the two Ding et al.
Wang et al.
(2019c).
The difference mainly lies in their emphasis: Regularization-based method focuses more on an advanced penalty scheme so that the subsequent pruning criterion can be simple; while the importance-based one focus more on an advanced importance criterion itself.
Meanwhile, regularization paradigm always involves iterative training, while the importance-based can be one-shot LeCun et al.
(1990); Hassibi & Stork (1993)
; Wang et al.
(2019a)
(no training for picking weights to prune) or involve iterative training Molchanov et al.
(2017b; 2019);
Ding et al.
(2019a;b).
Other model compression methods .
Apart from pruning, there are also many other model compression approaches, e.g., quantization Courbariaux & Bengio (2016); Courbariaux et al.
Rastegari et al.
knowledge distillation Buciluˇa et al.
; Hinton et al.
(2014), low- rank decomposition Denton et al.
; Jaderberg et al.
(2014); Lebedev et al.
; Zhang et al.
(2015), and efﬁcient architecture design or search Howard et al.
; Sandler et al.
Howard et al.
; Zhang et al.
; Tan & Le (2019); Zoph & Le (2017); Elsken et al.
They are orthogonal to network pruning and can work with the proposed methods to compress more.
P ROPOSED M ETHOD 3. P ROBLEM F ORMULATION
Pruning can be formulated as a transformation T ( ∗ ) that takes a pretrained big model w as input and output a small model w 1 , typically followed by a ﬁne-tuning process F ( ∗ ) , which gives us the ﬁnal output w 2 = F ( w 1 ) .
We do not focus on F ( ∗ ) since it is simply a standard neural network training process, but focus on the process of w 1 = T ( w ) .
The effect of pruning can be further speciﬁed into two sub-transformations: (1) M = T 1 ( w ) , which obtains a binary mask vector M that decides which weights will be removed; (2) T 2 ( w ) , which adjusts the values of remaining weights.
That is,
[Equation]
For one-shot pruning, there is no iterative training at T 1 .
It depends on a speciﬁc algorithm to decide whether to adjust the remaining weights.
For example, OBD LeCun et al.
and L 1norm pruning Li et al.
(2017) do not adjust the kept weights (i.e., T 2 is the identity function) while
OBS Hassibi & Stork (1993) does.
For learning-based pruning, both T 1 and T 2 involve iterative training and the kept weights will always be adjusted.
In the following, we will present our algorithms in the ﬁlter pruning scenario since we mainly focus on model acceleration instead of compression in this work.
Nevertheless, the methodology can seamlessly translate to the unstructured pruning case.
The difference lies in how we deﬁne the weight group : For ﬁlter pruning, a 4-d tensor convolutional ﬁlter (or 2-d tensor for fully-connected layers) is regarded as a weight group, while for unstructured pruning, a single weight makes a group.
P RUNING S CHEDULE : GR EG -
Our ﬁrst method (GReg-1) is a variant of L 1 -norm pruning Li et al.
It obtains the mask M by L 1 -norm sorting but adjusts the kept weights via regularization .
Speciﬁcally, given a pre-trained model w and layer pruning ratio r l , we sort the ﬁlters by L 1 -norm and set the mask to zero for those with the least norms.
Then, unlike Li et al.
(2017) which removes the unimportant weights immediately (i.e., one-shot fashion), we impose a growing L 2 penalty to drive them to zero ﬁrst:
[Equation]
where λ j is the penalty factor for j -th weight; δλ is the granularity in which we add up the penalty.
Clearly, a smaller δλ means this regularization process smoother .
Besides, λ j is only updated every K u iterations, which is a buffer time to let the network adapt to the new regularization.
This algorithm is to explore whether the way we remove them (i.e., pruning schedule) leads to a difference given the same weights to prune.
Simple as it is, the scheme can bring signiﬁcant accuracy gains especially under a large pruning ratio (Tab. 1).
Note that, we intentionally set δλ the same for all the unimportant weights to keep the core idea simple.
Natural extensions of using different penalty factors for different weights (such as those in Ding et al.
; Wang et al. (2019c)) may be worth exploring but out of the scope of this work.
When λ j reaches a pre-set ceiling τ , we terminate the training and prune those with the least L 1norms, then ﬁne-tune.
Notably, the pruning will barely hurt the accuracy since the unimportant weights have been compressed to typically less than 1 1000 the magnitude of remaining weights.
3. I MPORTANCE C RITERION : GR EG -
Our second algorithm is to further take advantage of the growing regularization scheme, not for pruning schedule but scoring.
The training of neural networks is prone to overﬁtting, so regularization is normally employed.
L 2 regularization (or referred to as weight decay) is a standard technique for deep network training.
Given a dataset D , model parameters w , the total loss will typically be
[Equation]
where L is the task loss function.
When the training converges, there should be
[Equation]
where w ∗ i indicates the i -th weight at its local minimum .
Eq.
(4) shows that, for each speciﬁc weight element, its equilibrium position is determined by two forces: loss gradient (i.e., guidance from the task) and regularization gradient (i.e., guidance from our prior).
Our idea is to slightly increase the λ to break the equilibrium and see how it results in a new one.
A general impression is: If λ goes a little higher, the penalty force will drive the weights further towards origin and it will not stop unless proper loss gradient comes to halt it and then a new equilibrium is reached at ˆ w ∗ i .
Considering different weights have different scales, we deﬁne a ratio r i
=
ˆ w ∗ i /w ∗
i to describe how much the weight magnitude changes after increasing the penalty factor.
Our interest lies in how the r i differs from one another and how it relates to the underlying Hessian information.
Deep neural networks are well-known over-parameterized and highly non-convex.
To obtain a feasible analysis, we adopt a local quadratic approximation of the loss function based on Taylor series expansion Strang (1991) following common practices LeCun et al.
(1990); Hassibi & Stork (1993)
; Wang et al. (2019a).
Then when the model is converged, the error E can be described by the converged weights w ∗ and the underlying Hessian matrix H (note H is p.s.d.
since the model is converged).
After increasing the penalty λ by δλ , the new converged weights can be proved to be
[Equation]
where I stands for the identity matrix.
Here we meet with the common problem of estimating
Hessian and its inverse, which are well-known to be intractable for deep neural networks.
We explore two simpliﬁed cases to help us move forward.
H is diagonal, which is a common simpliﬁcation for Hessian LeCun et al.
(1990), implying that the weights are independent of each other.
For w ∗ i with second derivative h ii .
With L 2 penalty increased by δλ ( δλ > 0 ), the new converged weights can be proved to be
[Equation]
where r i ∈ [0 , 1) since h ii ≥ 0 and δλ > 0 .
As seen, larger h ii results in larger r i (closer to 1), meaning that the weight is relatively less moved towards the origin .
Our second algorithm primarily builds upon this ﬁnding, which implies when we add a penalty perturbation to the converged network, the way that different weights respond can reﬂect their underlying Hessian information.
(2) In practice, we know H is rarely diagonal.
How the dependency among weights affects the ﬁnding above is of interest.
To have a closed form of inverse Hessian in Eq.
(5), we explore the 2-d case, namely, w ∗ =
(cid:0) w ∗ 1 w ∗ 2 (cid:1) , H = (cid:0) h 11 h
[Equation]
12 h 22 (cid:1) , ˆH = (cid:0) h
+ δλ
[Equation] [Equation]
h
+ δλ (cid:1) .
The new converged weights can be analytically solved below, where the approximation equality is because that δλ is rather small,
[Equation]
As seen, h 11 > h 22 also leads to r 1 > r 2 , in line with the ﬁnding above.
The existence of weight dependency (i.e., the h 12 ) actually does not affect the conclusion since it is included in both ratios.
These theoretical analyses show us that when the penalty is increased at the same pace, because of different local curvature structures, the weights actually respond differently – weights with larger curvature will be less moved.
As such, the magnitude discrepancy among weights will be magniﬁed as λ grows.
Ultimately, the weights will naturally separate (see Fig.
1 for an empirical validation).
When the discrepancy is large enough, even the simple L 1 -norm can make an accurate criterion.
Notably, the whole process happens itself with the uniformly rising L 2 penalty, no need to know the
Hessian values , thus not bothered by any issue arising from Hessian approximation in relevant prior arts LeCun et al.
(1990); Hassibi & Stork (1993)
; Wang et al.
(2019a)
; Singh & Alistarh (2020).
In terms of the speciﬁc algorithm, all the penalty factor is increased at the same pace, λ j = λ j + δλ, for all j.
When λ j reaches some ceiling τ (cid:48) , the magnitude gap turns large enough to let L 1 -norm do scoring faithfully.
After this, the procedures are similar to those in GReg-1: λ for the unimportant weights are further increased.
One extra step is to bring back the kept weights to the normal magnitude.
Although they are the “survivors” during the previous competition under a large penalty, their expressivity are also hurt.
To be exact, we adopt negative penalty factor for the kept weights to encourage them to recover.
When the λ for unimportant weights reaches the threshold τ (akin to that of GReg-1), the training is terminated.
L 1 -pruning is conducted and then ﬁne-tune to regain accuracy.
To this end, the proposed two algorithms can be summarized in Algorithm 1.
Pruning ratios .
We employ pre-speciﬁed pruning ratios in this work to keep the core method neat (see Appendix for more discussion).
Exploring layer-wise sensitivity is out of the scope of this work, but clearly any method that ﬁnds more proper pruning ratios can readily work with our approaches.
Discussion: differences from IncReg .
Although our work shares a general spirit of growing regularization with IncReg Wang et al.
(2019c;b), our work is actually starkly different from theirs in many axes:
• Motivation.
The motivations for using the growing regularization are different.
Wang et al.
(2019c;b) adopt growing regularization to select the unimportant weights by training.
Namely, they focus on the importance criterion problem.
In contrast, we use growing regularization to investigate the pruning schedule problem (for GReg-1) or exploit the underlying Hessian information (for GReg-2).
The importance criterion is simply L 1 -norm.
• Algorithm design.
Wang et al.
(2019c;b) assign different regularization factors to different weight groups based on their relative importance, while we assign them with the same factors.
For GReg-1, this may not be a substantial difference, while for GReg-2, the difference is fundamental because the theoretical analysis of GReg-2 (Sec. 3.3) relies on the fact that regularization factors are kept the same for different weights.
• Theoretical analysis.
The algorithm in Wang et al.
(2019c;b) is generally heuristic-based, while our work provides rigorous theoretical analyses (Sec. 3.3) to support the proposed algorithm GReg-2.
• Empirical performance.
Both our methods are signiﬁcantly better than Wang et al.
(2019c;b) on the large-scale ImageNet dataset, which will be shown in the experiment section (Tab. 3).
Discussion: other regularization forms .
The proposed methods in this work adopts L 2 regularization.
Here we discuss the possibility to generalize the method to other regularization forms ( L 1 and
L 0 ).
(1) For GReg-1, it can be easily generalized to other regularization forms like L 1 .
For GReg-2, since the theoretical basis in Sec.
3.3 relies on the local quadratic approximation, L 2 regularization meets this requirement while L 1 does not.
Therefore, GReg-2 cannot be (easily) generalized to the
[Table]
L 1 regularization as far as we can see currently.
(2) For L 0 regularization, it is well-known NP-hard.
In practice, it is typically converted to the L 1 regularization case, which we just discussed.
E XPERIMENTAL R ESULTS
Datasets and networks .
We ﬁrst conduct analyses on the CIFAR10/100 datasets Krizhevsky (2009) with ResNet56 He et al.
(2016)/VGG19 Simonyan & Zisserman (2015).
Then we evaluate our methods on the large-scale ImageNet dataset Deng et al.
(2009) with ResNet34 and
50 He et al.
For CIFAR datasets, we train our baseline models with accuracies comparable to those in the original papers.
For ImageNet, we take the ofﬁcial PyTorch Paszke et al.
(2019) pre-trained models 2 as baseline to maintain comparability with other methods.
Training settings .
To control the irrelevant factors as we can, for comparison methods that release their pruning ratios, we will adopt their ratios; otherwise, we will use our speciﬁed ones.
We compare the speedup (measured by FLOPs reduction) since we mainly target model acceleration rather than compression.
Detailed training settings (e.g., hyper-parameters and layer pruning ratios) are summarized in the Appendix.
4. R ES N ET 56/VGG19 ON CIFAR-10/
Pruning schedule: GReg-1 .
First, we explore the effect of different pruning schedules on the performance of pruning.
Speciﬁcally, we conduct two sets of experiments for comparison: (1) prune by L 1 -norm sorting and ﬁne-tune Li et al.
(2017) (shorted as “ L 1 +one-shot”); (2) employ the proposed growing regularization scheme (“GReg-1”) and ﬁne-tune.
We use a uniform pruning ratio scheme here:
Pruning ratio r is the same for all l -th conv layer (the ﬁrst layer is not pruned following common practice Gale et al.
For ResNet56, since it has the residual addition restriction, we only prune the ﬁrst conv layer in a block as previous works do Li et al.
For comprehensive comparisons, the pruning ratios vary in a large spectrum, covering acceleration ratios from around 2 × to 44 × .
Note that we do not intend to obtain the best performance here but systematically explore the effect of different pruning schedules, so we employ relatively simple settings (e.g., the uniform pruning ratios).
For fair comparisons, the ﬁne-tuning scheme (e.g., number of epochs, learning rate schedule, etc.) is the same for different methods.
Therefore, the key comparison here is to see which method can deliver a better base model before ﬁne-tuning.
The results are shown in Tab. 1.
We have the following observations:
(1) On the whole, the proposed
GReg-1 consistently outperforms L 1 +one-shot.
It is important to reiterate that the two settings have exactly the same pruned weights, so the only difference is how they are removed.
The accuracy gaps show that apart from importance scoring, pruning schedule is also a critical factor.
In the
Appendix D, we present more results to demonstrate this ﬁnding actually is general , not merely limited to the case of L 1 -norm criterion.
The proposed regularization-based pruning schedule is consistently more favorable than the one-shot counterpart.
(2) The larger pruning ratio, the more
[Table]
[Figure]
Method
[Table] [Table]
[Figure]
[Table]
[Figure]
[Table]
[Figure]
[Table]
pronounced of the gain.
This is reasonable since when more weights are pruned, the network cannot recover by its inherent plasticity
Mittal et al.
(2018), then the regularization-based way is more helpful because it helps the model transfer its expressive power to the remaining part.
When the pruning ratio is relatively small (such as ResNet56, r = 50% ) , the plasticity of the model is enough to heal, so the beneﬁt from GReg-1 is less signiﬁcant compared with the one-shot counterpart.
Importance criterion: GReg-2 .
Here we empirically validate our ﬁnding in Sec.
3.3, that is, with uniformly rising L 2 penalty, the weights should naturally separate.
We claim, if h 11 > h 22 , there should be r 1 > r 2 , where r 1 =
ˆ w 1 w 1 , r 2 =
ˆ w 2 w 2 (the * mark indicating the local minimum is omitted here for readability).
r 1 > r 2 leads to ˆ w 1 w 1 > ˆ w 2 w 2 , namely, r 1 =
ˆ w 1 ˆ w 2 > w 1 w 2 .
This shows that, after the L 2 penalty grows a little, the new magnitude ratio of weight 1 over weight 2 will be magniﬁed if h 11 > h 22 ( w 1 , w 2 are positive in the analysis here, while the conclusion still holds if either of them is negative).
In Fig.
1 (Row 1), we plot the standard deviation (divided by the means for normalization since the magnitude varies over iterations) of ﬁlter L 1 -norms as the regularization grows.
As seen, the normalized L 1 -norm stddev grows larger and larger as λ grows.
This phenomenon consistently appears across different models and datasets.
To ﬁguratively understand how the increasing penalty affects the relative magnitude over time, in Fig.
1 (Row 2), we plot the relative L 1 -norms (divided by the max L 1 -norm for normalization) at different iterations.
As shown, it is hard to tell which ﬁlters are really important by the initial ﬁlter magnitude (Iter 0), but under a large penalty later, their discrepancy turns more and more obvious and ﬁnally it is very easy to identify which ﬁlters are more important.
Since the magnitude gap is so large, the simple
L 1 -norm can make a sufﬁciently faithful criterion.
CIFAR benchmarks .
Finally, we compare the proposed algorithms with existing methods on the
CIFAR datasets (Tab. 2).
Here we adopt non-uniform pruning ratios (see the Appendix for speciﬁc numbers) for the best accuracy-FLOPs trade-off.
On CIFAR10, compared with AMC He et al.
[Table] [Table]
(2018b), though it adopts better layer-wise pruning ratios via reinforcement-learning, our algorithms can still deliver more favorable performance using sub-optimal human-speciﬁed ratios.
AFP Ding et al.
(2018) is another work exploring large regularization, while they do not adopt the growing scheme as we do.
Its performance is also less favorable on CIFAR10 as shown in the table.
Although our methods perform a little worse than C-SGD Ding et al. (2019a) on CIFAR10, on the large-scale
ImageNet dataset, we will show our methods are signiﬁcantly better than C-SGD.
Notably, on CIFAR100, Kron-OBD/OBS (an extension by Wang et al. (2019a) of the original
OBD/OBS from unstructured pruning to structured pruning) are believed to be more accurate than
L 1 -norm in terms of capturing relative weight importance LeCun et al.
(1990); Hassibi & Stork (1993)
; Wang et al. (2019a).
Yet, they are signiﬁcantly outperformed by our GReg-1 based on the simple L 1 -norm scoring.
This may inspire us that an average pruning schedule (like the one-shot fashion) can offset the gain from a more advanced importance scoring scheme.
4. R ES N ET 34/50 ON I MAGE N ET
Then we evaluate our methods on the standard large-scale ImageNet benchmarks with ResNets
He et al.
We refer to the ofﬁcial PyTorch ImageNet training example 3 to make sure the imple- mentation (such as data augmentation, weight decay, momentum, etc.) is standard.
Please refer to the summarized training setting in the Appendix for details.
The results are shown in Tab. 3.
Methods with similar speedup are grouped together for easy comparison.
In general, our method achieves comparable or better performance across various speedups on ResNet34 and 50.
Concretely, (1) On both ResNet34 and 50, when the speedup is small (less than 2 × ), only our methods (and AOFP Ding et al. (2019b) for ResNet50) can even improve the top-1 accuracy.
This phenomenon is broadly found by previous works Wen et al.
; Wang et al.
; He et al.
(2017) but mainly on small datasets like CIFAR, while we make it on the much challenging ImageNet benchmark.
(2) Similar to the results on CIFAR (Tab. 1), when the speedup is larger, the advantage of our method is more obvious.
For example, ours GReg-2 only outperforms
Taylor-FO Molchanov et al.
(2019) by 0.86%
top-1 accuracy at the ∼ 2 × setting, while at ∼ 3 × ,
GReg-2 is better by 2.21% top-1 accuracy.
(3) Many methods work on the weight importance criterion problem, including some very recent ones (ProvableFP Liebenwein et al.
LFPC He et al.
Yet as shown, our simple variant of L 1 -norm pruning can still be a strong competitor in terms of accuracy-FLOPs trade-off.
This reiterates one of our key ideas in this work that the pruning schedule may be as important as weight importance scoring and worth more research attention.
Unstructured pruning .
Although we mainly target ﬁlter pruning in this work, the proposed methods actually can be applied to unstructured pruning as effectively.
In Tab. 4, we present the results of unstructured pruning on ResNet50.
WoodFisher Singh & Alistarh (2020) is the state-of-the-art
Hessian-based unstructured pruning approach.
Notably, without any Hessian approximation, our
GReg-2 can achieve comparable performance with it (better absolute accuracy, yet slightly worse accuracy drop).
Besides, the simple magnitude pruning variant GReg-1 delivers more favorable result, implying that a better pruning schedule also matters in the unstructured pruning case.
C ONCLUSION
Regularization is long deemed as a sparsity-learning tool in neural network pruning, which usually works in the small strength regime.
In this work, we present two algorithms that exploit regularization in a new fashion that the penalty factor is uniformly raised to a large level.
Two central problems regarding deep neural pruning are tackled by the proposed methods, pruning schedule and weight importance criterion.
The proposed approaches rely on few impractical assumptions, have a sound theoretical basis, and are scalable to large datasets and networks.
Apart from the methodology itself, the encouraging results on CIFAR and ImageNet also justify our general ideas in this paper: (1)
In addition to weight importance scoring, pruning schedule is another pivotal factor in deep neural pruning which may deserve more research attention.
(2) Without any Hessian approximation, we can still tap into its power for pruning with the help of growing L 2 regularization.
A CKNOWLEDGEMENTS
The work is supported by the National Science Foundation Award ECCS-1916839 and the
U.S. Army Research Ofﬁce Award W911NF-17-1-0367.
A A PPENDIX A. E XPERIMENTAL S ETTING D ETAILS
Training setting summary .
About the networks evaluated, we intentionally avoid AlexNet and
VGG on the ImageNet benchmark because the single-branch architecture is no longer representative of the modern deep network architectures with residuals (but still keep VGG19 on the CIFAR analysis to make sure the ﬁndings are not limited to one speciﬁc architecture).
Apart from some key settings stated in the paper, a more detailed training setting summary is shown as Tab. 5.
[Table]
Pruning ratios .
Although several recent methods Ding et al. (2019b); Singh & Alistarh (2020) can automatically decide pruning ratios, in this paper we opt to consider pruning independent with the pruning ratio choosing.
The main consideration is that pruning ratio is broadly believed to reﬂect the redundancy of different layers LeCun et al.
Wen et al.
; He et al.
(2017), which is an inherent characteristic of the model , thus should not be coupled with the subsequent pruning algorithms.
Before we list the speciﬁc pruning ratios, we explain how we set them.
(1) For a ResNet, if it has N stages, we will use a list of N ﬂoats to represent its pruning ratios for the N stages.
For example,
ResNet56 has 4 stages in conv layers, then “[0, 0.5, 0.5, 0.5]” means “for the ﬁrst stage (which is also the ﬁrst conv layer), the pruning ratio is 0; the other three stages have pruning ratio of 0.5”.
Besides, since we do not prune the last conv in a residual block, which means for a two-layer residual block (for ResNet56), we only prune the ﬁrst layer; for a three-layer bottleneck block (for ResNet34 and 50), we only prune the ﬁrst and second layers.
(2) For VGG19, we use the following pruning ratio setting.
For example, “[0:0, 1-9:0.3, 10-15:0.5]” means “for the ﬁrst layer (index starting from 0), the pruning ratio is 0; for layer 1 to 9, the pruning ratio is 0.3; for layer 10 to 15, the pruning ratio is 0.5”.
With these, the speciﬁc pruning ratio for each of our experiments in the paper are listed in Tab. 6.
We do not have strong rules to set them, except one, which is setting the pruning ratios of higher stages smaller , because the FLOPs of higher layers are relatively smaller (due to the fact that the spatial feature map sizes are smaller) and we are targeting more acceleration than compression.
Of course, this scheme only is quite crude, yet as our results (Tab. 3 and 4) show, even with these crude settings, the performances are still competitive.
B P ROOF OF E Q .
When a quadratic function E converges at w ∗ with Hessian matrix H , it can be formulated as
[Equation]
(10) where C is a constant.
Now a new function is made by increasing the L 2 penalty by small amount δλ , namely,
[Equation]
Let the new converged values be ˆw ∗ , then similar to Eq.
10, ˆ E can be formulated as
[Equation]
[Table]
Meanwhile, combine Eq.
10 and Eq.
11, we can obtain
[Equation]
[Equation]
Compare Eq.
13 with Eq.
12, we have
[Equation]
[Equation]
C P ROOF OF E Q .
[Equation]
Therefore, ˆw ∗ = ˆH − 1 Hw ∗ ⇒
[Equation]
D GR EG -1 + OBD
In Sec. 4.1, we show when pruning the same weights, GReg-1 is signiﬁcantly better than the oneshot counterpart, where the pruned weights are selected by the L 1 -norm criterion.
Here we conduct the same comparison just with a different pruning criterion introduced in OBD LeCun et al.
OBD is also an one-shot pruning method, using a Hessian-based criterion which is believed to be more advanced than L 1 -norm.
Results are shown in Tab.
As seen, using this more advanced importance criterion, our pruning scheme based on growing regularization is still consistently better than the one-shot counterpart.
Besides, it is also veriﬁed here that a better pruning schedule can bring more accuracy gain when the speedup is larger .
E F ILTER L1- NORM CHANGE OF VGG
In Fig.
1 (Row 2), we plot the ﬁlter L 1 -norm change over time for ResNet50 on ImageNet.
Here we plot the case of VGG19 on CIFAR100 to show the weight separation phenomenon under growing regularization is a general one across different datasets and networks.
F H YPER - PARAMETERS AND SENSITIVITY ANALYSIS
There are ﬁve introduced values in our methods: regularization ceiling τ , ceiling for picking τ (cid:48) , interval K u , K s , granularity δλ .
Their settings are summarized in Tab. 8.
Among them, the ceilings
[Table]
[Figure]
li z
[Figure]
d
L
[Figure]
are set through validation: τ
= 1 is set to make sure the unimportant weights are pushed down enough (as stated in the main paper, normally after the regularization training, their magnitudes are too small to cause signiﬁcant accuracy degradation if they are completely removed).
τ (cid:48)
= 0 .
01 is set generally for the same goal as τ , but since it is applied to all the weight (not just the unimportant ones), we only expect it to be moderately large (thus smaller than τ ) so that the important and unimportant can be differentiated with a clear boundary.
For the δλ , we use a very small regularization granularity δλ , which our theoretical analysis is based on.
We set its value to 1e-4 for GReg-1 and 1e-5 for GReg-2 with reference to the original weight decay value 5 ×
10 − 4 (for CIFAR models) and 10 − 4 (for ImageNet models).
Note that, these values come from our methods per se, not directly related to datasets and networks, thus are invariant to them.
This is why we can employ the same setting of these three hyper-parameters in all our experiments, freeing practitioners from heavy tuning when dealing with different networks or datasets.
[Table]
A little bit of change is for K u , K s .
Both are generally to let the network have enough time to converge to the new equilibrium.
Generally, we prefer large update intervals, yet we also need to consider the time complexity: Too large of them will bring too many iterations, which may be unnecessary.
Among them, K s is less important since it is to stabilize the large regularization ( τ = 1 ).
We introduce it simply to make sure the training is fully converged.
Therefore, the possibly more sensitive hyper-parameter is the K u (set to 5 for ImageNet and 10 for CIFAR).
Here we will show the performance is insensitive to the varying K u .
As shown in Tab. 9, the peak performance appears at around K u = 15 for ResNet56 and K u = 10 for VGG19.
We simply adopt 10 for a uniform setting in our paper.
We did not heavily tune these hyper-parameters, yet as seen, they work pretty well across different networks and datasets.
Notably, even for the worst cases in Tab.
9 (in blue color), they are still signiﬁcantly better than those of the “ L 1 +one-shot” scheme, demonstrating the robustness of the proposed algorithm.
[Table]
G M ORE RESULTS OF PRUNING SCHEDULE COMPARISON
In Tab. 1, we show using L 1 -norm sorting, our proposed GReg-1 can consistently surpass the oneshot schedule even pruning the same weights.
Here we ask a more general question: Can the beneﬁts from a regularization-based schedule consistently appear, agnostic to the weight importance scoring criterion ?
This question is important because it will show if the gain from a better pruning schedule is only a bonus concurrent with the L 1 criterion or a really universal phenomenon.
Since there are literally so many weight importance criteria, we cannot ablate them one by one.
Nevertheless, given a pre-trained model and a pruning ratio r , no matter what criterion, its role is to select a ﬁlter subset .
For example, if there are 100 ﬁlters in a layer and r = 0 . 5 , then they are at most (cid:0) 10050 (cid:1) importance criteria in theory for this layer.
We can simply randomly pick a subset of ﬁlters (which corresponds to certain criterion, albeit unknown) and compare the one-shot way with regularization-based way on the subset.
Based on this idea, we conduct ﬁve random runs on the ResNet56 and VGG19 to explore this.
The pruning ratio is chosen as 90% for ResNet56 and 70% for VGG19 because under this ratio the compression (or acceleration) ratio is about 10 times, neither too large nor too small (where the network can heal itself regardless of pruning methods).
The results are shown in Tab. 10.
Here is a sanity check: Compared with Tab. 1, the mean accuracy of pruning randomly picked ﬁlters should be less than pruning those picked by L 1 -norm, conﬁrmed by 86.85% vs. 87.34% for ResNet56 and 65.04% vs. 66.05% for VGG19.
As seen, in each run, the regularization-based way also signiﬁcantly surpasses its one-shot counterpart.
Although ﬁve random runs are still too few given the exploding potential combinations, yet as shown by the accuracy standard deviations, the results are stable and thus qualiﬁed to support our ﬁnding that the regularization-based pruning schedule is better to the one-shot counterpart.
[Table]