What Matters In The Structured Pruning of Generative Language Models?
Authors: Michael Santacroce
Zixin Wen
yelong shen
Weizhu Chen
Yuanzhi Li
Venue: Submitted to ICLR 2023 (2023)
Keywords: Neural Network Pruning
Natural Language Generation

Abstract: Auto-regressive large language models such as GPT-3 require enormous computational resources to use, leading to huge financial cost and environmental impact. Structured pruning methods traditionally reduce resource usage, however, their application to and efficacy for generative language models is heavily under-explored. We analyze the effects of magnitude, random, and movement (Lagunas et al., 2021) pruning on MLP layers in GPT-like models. We find that movement can under-perform for these models while random pruning nearly matches the best methods. By examining neuron-level redundancy measures, we discover that movement does not select neurons based on how unique they are compared to other neurons, leaving behind excess redundancy. In view of this, we introduce Globally Unique Movement (GUM) to select neurons based on both uniqueness and sensitivity. We then discuss the roles of our techniques on different redundancy metrics through careful comparisons and ablations.

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WHAT MATTERS IN THE STRUCTURED PRUNING OF GENERATIVE LANGUAGE MODELS?
Anonymous authors Paper under double-blind review
ABSTRACT
Auto-regressive large language models such as GPT-3 require enormous computa- tional resources to use, leading to huge financial cost and environmental impact. Structured pruning methods traditionally reduce resource usage, however, their ap- plication to and efficacy for generative language models is heavily under-explored. We analyze the effects of magnitude, random, and movement (Lagunas et al. 2021) pruning on MLP layers in GPT-like models. We find that movement can under- perform for these models while random pruning nearly matches the best methods. By examining neuron-leve redundancy measures, we discover that movement does not select neurons based how unique they are compared to other neurons, leaving behind excess redundancy. In view of this, we introduce Globally Unique Movement (GUM) select neurons based on both uniqueness and sensitivity. We then discuss the roles of our techniques on different redundancy metrics through careful comparisons and ablations.
INTRODUCTION
Large language models (LLMs), such as the state-of-the-art GPT-3 model (Brown et al. 2020) with up to 175 billion parameters, have achieved remarkable performance in natural language processing (NLP) tasks. However, training and deploying such massive models also poses significant challenges in terms of computational cost, energy consumption, and environmental impact. Therefore, it is crucial to develop effective methods reduce the size of LLMs without compromising their quality.
Neural network pruning is a long- standinanding model compression method (Janowsky 1989 Mozer & Smolensky 1988 Frankle & Carbin 2018 Karnin 1990 Blalock al.) 2020). can be broadly classified into two types: unstructured and structured. U Unstructured pruning removes individual weights from the network based on some criteria, such as magnitude or movement, resulting in sparse weight matrices that can be stored and processed more efficiently. Structured pruning, on the other hand, eliminates whole components, such as neurons, channels, or blocks, leading to smaller architectures reduce end-to-end inference latency. While unstructured pruning has been extensively studied and applied to LLMs (Wang et al., 2020b Xu et al. 2021] Zafrir et al. 2021 Li et al.) 2022), ) structured pruning is more challenging and less explored. However, structured pruning is also more desirable in many practical scenarios, such as deploying these models on resource-constrainee devices or providing fast and reliable services based LLMs.
Existing work on structured pruning for LLMs focuses on BERT-like networks (Devlin et al. 2018 that consist of encoder-decoder or an encoder-only architecture (Li et al.) 2020 Xia al.
2022; Zhang et al. 2022) Yao et al., (021). )) These models are mainly used for natural language understanding (NLU) tasks, such as question answering, sentiment analysis, or natural language
inference. Among the various methods, Block Movement Pruning (Lagunas et al. 2021) is a recent and popular technique that removes weight blocks based on movement. However, there of systematic research on structured pruning for decoder-only architectures such as GPT-2 Radford ct al. (2019), GPT-3 Brown et al. (2020), or GPT-Neo Black et al. (202I), which are mainly used for natural language generation (NLG) tasks, such as text summarization. machine translation, or text completion. While there are some works that apply unstructured pruning (Li et al. 2022 or many kinds of orthogonal compression techniques to decoder-only LLMs (Wang et al.) 2020a Li
lack
ctt al. 202T] Edalati et al., 2022) Tao et al.) 2022, Xu & Huj 2022) Chen et al.] (021). there is no comprehensive evaluation of traditional structured pruning for these models NL. tasks.

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In this work, compress decoder-only auto-regressive language models. Due the lack of prior literature towards the same goal, we evaluate the performance of several general-domain pruning methods NLG tasks, including magnitude and movement pruning. However, find these methods can struggle or under-perform compared to naive baselines, leading to the following question:
What determines the performance of structured pruning on generative language models?
We aim fill this gap by conducting systematic study of structured fine- fine-pruning (pruning while finetuning) methods for decoder-only LLMs on NLG tasks] and further proposing novel method that combines the strengths of different existing methods. Our main contributions are:
To our knowledge, we perform the first systematic evaluation several structured pruning methods to decoder-only LLMs NLG tasks. We find that they only achieve marginal improvements over randomly selecting neurons finetuning. We explain their limitations under our proposed analysis framework, and characterize their advantages and disadvantages via the metries we evaluated.
We propose an empirical analysis framework for structured pruning that relies on two fundamental measures of redundancy: sensitivity and uniqueness. Sensitivity reflects how much the removal of network component affects the output of the model, while uniqueness reflects how much the information provided by network component differs from others. Our framework allows us to understand and compare the behavior and performance of different pruning methods.
To show the impact made possible by our analysis, we propose proof-of-concept method, Globally Unique Movement (GUM), that aims to maximize both sensitivity and unique- ness by pruning network components based on their global movement and local uniqueness scores. GUM outperforms the existing methods on several NLG tasks and achieves compet- itive compression rates, proving that future methods should preserve both sensitivity and uniqueness. We also conduct ablation studies to validate the effectiveness of our method components and design choices.
2 BACKGROUND & METHODOLOGY
There are many general-domain pruning methods. We focus on fine-pruming, relevant technique for language models which performs automated gradual pruning (Zhu & Gupta 2017) while fine-tuning. We focus on pruning the MLPs of generative models. At inferencing time, generative models can cache attention vector states. Therefore, especially large models, MLPs account for more time than attention for new tokens. MLPs also seem to store factual knowledge (Petroni et al.. 2019 Meng ct al. 2022), making their reduction possibly challenging.
Notation and Background We shall define some notations for the MLP layers. Let a(+) R. RT be an element-wisse activation function (e.g. GeLU), and let W1 E I W2 E Iddx be two weight matrices and E Rm be the bias vector. For an input token x E Rd the MI.P layer output of x is expressed as (x: x + Wzh(x) with intermediate output h(x)) a (W,,N) where LN represents layer normalization. We use @ to denote element-wise multiplications. Lastly, we use C to denote the loss of the task. We study methods of reducing m, the intermediate dimension, which is usually set at m = 4d.
Movement Pruning Movement Pruning (Sanh et al. 2020) a popular fine-pruning method. In
this paper, we focus on the block version of movement pruning (Lagunas et al., (021), and we first introduce the original unstructured method. Let L(W) ) be the task loss with weight parameters W. For each weight parameter Wi,,. we compute accumulated score S. at iteration T,, by the following expression?
Σ S OC(W(9)
S
1s
OW,,
1 'll code publicly available at (removed for peer-review).
Gradients caleulated ttraight-through to the mask scores, otherwise it is undefined (Bengio et al.
2013
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Magnitude
Gradual Random
Hard Mymt.
Soft Mvmt.
GUM
Score S Selection Structure Regularization
L2 norms
Random (frozen)
Eq. 1
Eq.
Eq.
(S)
Top,
Top, (5)
Threshold
Top,
Local
Local
Local
Global
Global
None
None
R(S)
R(S)
R(S) + Ruim (S) (Eq
Criteria
Magnitude
Random
Sensitivity
Sensitivity
Sensitivit&&Unniqunees
Table 1:
Comparison of pruning methods used. R(S) is defined in Section 2 and (S)((S) is descrihed in Eq
Afterwards, the scores are used compute a mask M with entries Ma E {0, 1}. And we apply the mask by W = M o W, D' = M @ b, and U = U @ M to remove the masked weights.
There are two ways to compute the mask M: M (S), for hard movement and M ())) for soft movement. T and u are both hyperparameters, and (S) is defined as:
1, TSp; = {
S, in top v% otherwise.
Additionally, mask scores are regularized via regularization term with multiplier Amvp of the form RRS) = Amvp Ei, sigmoid(S..)) Hard movement prunes all layers by the same amount. Soft movement, however, allows for adaptive sparsity for different layers, which is known be crucial for high-sparsity regimes (He et al. 2018b Mallya et al. (018), and is seen to be the superior method for NLU tasks in [Sanh et al. (2020). Block pruning expands on this method to operate on groups of weights by combining mask scores per block, allowing for structured pruning (Lguuaa al. 102
Magnitude Pruning We use mask version of block magnitude pruning (a block extension of group-lasso, like Shen et al. (202I) as the baseline. For each set G of parameters, we assign the mask score and gradually prune groups with the smallest scores. So (Z(i))ea W"yl"""'
Gradual Random pruning Random pruning approaches have been explored previously (Yu et al., 2017, Blalock et al. (2020). and in particular gradual, random pruning (Gale et al. (209)) has been found to perform relatively well. We further explore random pruning in conjunction with distillation. Our gradual random method freezes S random initialization for the duration of finetuning and prunes using TSp,(S)).
Knowledge Distillation In practice, pruning is often paired with knowledge distillation (Hinton et al. 2015) to boost performance. Distillation loss adds KL divergence between teacher model and smaller student model. When used, we distill from a finetuned version of the model being pruned.
FINE-PRUNING FOR GENERATIVE LANGUAGE MODELS
We present our framework for understanding the redundancy pruning methods. this work, we focus improving the seminal work of movement pruning proposed in Sanh et al. (2020). However, naively applying movement often results in incremental or worse performance compared to random. We dissect our results using systematic framework and analyze their behaviors and properties.
3.1 OBSERVATIONS OF PREVIOUS PRUNING METHODS
Soft Movement (BERT's Best Method) Struggles for GPT-like Models it shown in Lagunas jet al. (02T) that soft movement enjoys better performance over hard movement when block pruning encoder-decoder models. However, we find the method severely struggles when using the original implementatior on GPT-like models due to highly sensitive hyperparameters. For instance, the mask regularization parameter Anvp can either be too large and prune too aggressively, or too little,
'Soft movement is the best to our knowledge at time of writing. Code is available at attttps: / com/ hugg ing aageace/aaa wwwaaccnnnnnunnniiaeee pruning In this code, mask scores are added directly to the optimizer and are affected by optimizer algorithm other hyperparameters. We use this code for fair comparison between architectures, but manually updating the mask according to the definition might help (Zhang et al. 2022.
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Model
Method
50% / Distil
25% / Distil
10% Distil
Soft
63.81/65.25
63.23 65.23/65.4 65.04
62.996* 664.95
Læ-Magnitude
62.71 65.32
61.35 64.58
61.10/ 63.90
GPT-Neo- OPP-Nee-1125m
Gradual Random
64.29 65.74
63.06 65.10
62.27 / 64.63
Finetuned: 65.92
Hard/Top,
65.33 65.95 .44
64.88/ 65.79
64.23 65. 17
GUM
63.88 / 66.23
64.36 66.18
63.81 65.65
Table 2: GPT-Neo-125mn Performance in A on the validation set for decreasing amount leftover on WikiSQL. GUM outperforms compared to other methods, soft movement struggles to match other methods, and gradual random nearly performs as well as Top,- iidicatess having 1-3% excess leftover neurons unpruned.
Model
Method
50% + Distil
25% + Distil
10% / Distil
Soft
68.27 / 69.319 67.74 / 69.314
67.25* / 66.111*
Lg-Magnitude
68.33 / 69.62
66.92 / 68.96
66.02 / 68.43
GPT-2-sm Finetuned: 70.32
Gradual Random
69.07 / 69.61
67.78 69.35
66.77 / 69.00
Hard/Top, Top,
69.62 / 69.93
69.10 69.33
68.30 69.26
GUM
68.62 / 70.38
68.82 69.63
68.07 69.46
Table GPTT--smm: Performance in Accı on the validation set for decreasing amount leftover on WikiSQL. indicates having 1-3% excess leftover neurons unpruned.
resulting in under-pruning as shown below. Even after grid searching Amvp we still find subpar performance, and given the extremely high runtimes for this method as listed in Appendix A we find this method impractical to use.
Random Pruning Works Surprisingly Well One might expect movement to easily beat random pruning, however, we find their performances to only slightly differ or sometimes match, especially under distillation. Other works have noted random pruning's effectiveness (Gale al., 2019), but find the difference in generative tasks to be particularly slim. As shown in Tables 2 and 4 random pruning performs very close both hard and soft movement pruning over WikiSQL and Wikitext datasets. Moreover, when combined with distillation, the gaps are largely closed between random pruning and other methods, which is also another intriguing observation itself, as we discuss below.
Distillation Closes the Gaps Between Different Methods As shown in Table 2 and Table 3 methods with very different performances would perform rather similar if distilled from non-pruned, finetuned model. Indeed, both WikiSQL. and Wikitext experiments in Table|Aand Table (able(5]showed showed that when the network has fewer left-over neurons (c.g., 10% or 25%), the difference of accuracies or perplexities often fall below half of the difference without distillation. This observation remains consistent across models of different sizes, architectures. and tasks. Results for GPT-neo with 1.3- billion parameters in Table 6 shows that pruning larger model can still benefit from distillation. Knowledge distillation often boosts the performance of weaker methods even more, which might suggest the differences between methods are largely due to the inability to learn more diverse features set in fine-pruning, as suggested by the work of Allen-Zhu Li 2020).
3.2 TWO TYPES OF REDUNDANCY MEASURES: SENSITIVITY AND UNIQUENESS
In order understand why these pruning methods display such behaviors, devise framework to characterize the leftover neurons pruned network based on two criteria: sensitivity and uniqueness"i Sensitivity captures how much neuron contributes the task objective L. while uniqueness captures how much information it provides that is not already captured by other neurons. We formalize these notions of redundancy as follows:
*These are known concepts in literature, but have been both combined into one pruning method.
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Definition 3.1 (Redundancy Criteria) Given set of neurons (ig())) and input X, call one neuron h2 redundant if it meets at least one of the following two conditions:
1. Sensitivity/aalinny:: the neuron is not salient if its outpus either negligible has small gradient when optimizing for the downstream task, mathematically described as
ac E [X)( ah,(X)
= 0
2. Uniqueness: the neuron is not unique if its oulputs could be reconstructed entirely with linear combination of the outputs from other neurons, mathematically described as
(x(X) spann (gan(()((2) (hy(())
over all impuls X
Intuitively, a sensitive neuron has outputs that greatly contribute to the final output, while a unique neuron has outputs which are different from that of others. These metrics are independent from one another, neuron could be highly salient but replaceable by other neurons, unique but ultimately contribute little to the network. Consider toy example where two neurons h; and by have the same non-zero weights and large gradient. Neuron h; could easily be removed by doubling the outputs of h1. so they are not unique, but both are highly salient.
could be highly
we find the following important trends. Figure and Figure show that without distillation, different methods often have preference of focus on one of the redundancy measures. We now comment on trends across all experimental result tables (Table 2 balance between both measures, establishing strong correlative link between these metrics and final pruning performance. Under distillation, however, sensitivity seemingly concentrates across methods. Regardless of method, as more pruning occurs, sensitivity decreases and uniqueness increases in general. For individual methods, can further dive deeper below:
The General Trade-off Between Saliency and Uniqueness Equipped with Definition 3.1
8 The best performing methods strike
Magnitude pruming universally scores worst on both metrics, explaining its poorer perfor- mance in all experiments. However, with distillation, gaps of sensitivity between methods noticcably decreases, which partially describes why distillation improves it significantly.
Random pruning obtains similar distillation sensitivity and uniqueness, though slightly lower, to hard movement, lending credence to its overall high performance. However, sensitivity is markedly lower without distillation is reflected in all figures. This is proof that hard movement does not target uniqueness, given random pruning does not target uniqueness.
WikiSQL
Wikitext-103
0.80x
0.80x
Uniqueness Sensitivity
Sensitivlly
0.60x
0.60x
0.40x
0.20x
0.00x
0.00x
1.40c
Uniqueness
1.00x
1.00k
0.80x
50%
25%
75%
75%
Leftover (%)
Leftover (%)
Magnitude
GUM
Random
Soft
Top.
wro Distil
WiDistl
Sensitivity and Uniqueness measured on the training set for GPT-Neo- 125m. The vertical axis is defined as the ratio of the corresponding metric between the pruned model and a baseline model (which is non-pruned and fully fine-tuned) with a maximum 1x. We are able use these graphs to analyze and compare the performance of different pruning methods. Details of measurements are given Appendix F
Figure 1:
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WikisQL
Wikitext-103
Sensifivity
Sensitivity
0.20x
1.01x
Uniqueness
Uniqueness
2.00x
0.90x
1.00x
0.90x
60%
25%
10%
50%
25%
10%
75%
50%
25% 76%
50%
25%
Leftover (%)
Leftover (%)
Magnilude
GUM
Random
Soft
Top.
w/o Distil
WiDistl
Figure 2: Sensitivity and Uniqueness measured on the training set for GPT-2-sm. The vertical axis
defined as the ratio of the corresponding metric between the pruned model and baseline model (which is non- non-pruned and fully fine-tuned) with maximum of 1x. Details of measurements are given Appendix E
Soft movement pruning also usually scores poorly on both metrics, and sometimes abysmally as its sensitivity in Figure TT. helping describe its overall poor performance.
Hard movement pruming consistently obtains the highest sensitivity with not-far- far-behind uniqueness across different datasets and architectures. This correlates with the high perfor- mance when distillation is not used. However, when combined with distillation, the gaps of sensitivity between methods converge, and the advantage of hard movement fades.
GUM, our proof-of-coneep method, nearly always obtains best uniqueness while maintain- ing decent sensitivity, further improved using distillation. explaining its superiority across various tasks. However, GUM has a larger performance increase for GPT-Neo- 125m than for GPT-2-sm on WikiSQL; this is explained in Figure 2 pruned GPT-2 already has high baseline uniqueness for WikiSQL (~2xx) so further increase incurs diminishing returns.
Given the traininggvalidation split and general noise in the datasets, there are some outlier points, for instance, GUM's surprisingly poor distilled uniqueness for Wikitext in Figure [ We observe higher absolute uniqueness on Wikitext in general (around 95% of neurons are unique per cosine similarity), meaning uniqueness varies over datasets and improving uniqueness is difficult.
GLOBALLY UNIQUE MOVEMENT
After observing the lack of uniqueness amongst leftover neurons, we set out improve the perfor- mance of hard movement. We introduce two techniques which together comprise Globally Unique Movement (GUM). In essence, encourage score- weighte uniqueness term by multiplying the score regularizer and the cosine similarity together, to obtain a balance of uniqueness and sensitivity.
Tackling Non-Unique Redundancy Regularizing or pruning via similarity is well-explored topic (Ayinde et al. 2019 Zhu et al. [2018) Srinivas & Babu 2015) SSntacroce et al.) 2020) and existing techniques would increase uniqueness. However, integrate
cleanly with movement
to insulate weights from regularization, with a small increase in training time as listed in Appendix [A]
Our approach regularizes mask scores based on cosine similarity Cosine similarity between the outputs of any two neurons given input X (for example, a batch of tokens) is defined simply as
(e(X)TTy(>)
sim h4 (X),,, = ||; (X)l22
*Solving for linear combinations of neurons during training is prohibitively expensive, consider cosine similarity "first-order" proxy.

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Algorithm 1 Running Cosine Similarity Update
Require: a set of neurons hi(() for E [m], inputs from a set Z (usually intermediate outputs of attention layers), an update multiplier Asimi
1: Initialize running similarity sim (0) (hs h2) = 0 for i,j E [m]. running inner products C
and running output vector norms ( = 0 for E [m]:
2: while still training do
3: Sample a input X E Z, compute the output vector neuron Ity (X) for j E [m]
4: Update ((++1) < (1 Ami)) CC + Asim (x(X)) hx))), Vi,j E [m]
5: Update Q. (1 Asim )Q² Asim [X);(X)] Vi E [m]
6: Update similarity by:
sim (hs, t ((t+1)) / Q(+1))
vi,j e [m]
7: end while
However, calculating similarity with only intra-batch estimates noisy and unreliable, so we introduce running version of its estimates in Algorithm to obtain cosine similarity sim (h;,h)) between neurons h-)-) and (.). Now, we build on the regularization term Rsim (S) of movement pruning to define a new regularization: Let Nien be the number leftover neurons, for each group j E [m]] and its corresponding score Sj. define term U, Nico Eie(m) sim(hy, h4), and then multiply U, to the original terms R(S) to obtain
Rsim (S) = Amvp Σ, Uj sigmoid(S))
Global Top,, for Soft-Like Movement Hard movement removes the same amount of weights per layer independently. Global Top, instead uses Top, function on the set of all mask scores in the network jointly. Global Top, was originally explored for movement (Sanh al.) (0200) and was found to perform similarly. We find when used in conjunction with uniqueness regularization, Global outperforms Local. Global Top, intuitively allows for more flexibility when pruning. When pruning locally. it is necessary to choose the least common pruning percent if one layer requires 50% neurons before severe degradation, all layers must keep 50%. Global comparison removes this loophole in similar manner to soft movement i
5 RESULTS
In this section, we present results on three different kinds of generative language modeling tasks: language modeling with Wikitext-103 Merity language understanding with SAMsum Gliwa et al. (2019), and exact match measurable text-to-code generation with WikiSQL W When distilling, the teacher model used is the finetuned version of the model. To ensure trends hold when scaling up, we present one experiment with GPT-Neo-L.3b in section For all pruning amounts, we will present in terms of final percentage leftover i.e., 75% of neurons remain after pruning. For soft movement, final prune percentage is shown parentheses when it differs from desired by large amount.
(2016), text-to-text generation and natural
al. (0117). Details and hyperparameters are listed in Appendix F
In general, GUM is found to outperform Top, by a margin similar to the difference between Top, and gradual random pruning, with some exceptions. While small, we argue this gap shows the effectiveness of preserving neuron uniqueness alongside saliency.
Wikitext-103 Results on the Wikitext- 103 dataset Merity al. (2016), of the most popular datasets for causal language modeling, are shown in Tables and Because performance on Wikitext-1003 is in perplexity (PPL), it a highly consistent and discriminatory dataset prune on. We are unable to ever fully recover original model performance after pruning, suggesting that any compression increases uncertainty. Distillation generally hurts performance across all methods.
AAppendix D shows example pruning distribution for pruned network.
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Model
Method
75% + Distil
50% + Distil
25% / Distil
Soft
17.814/ 77.814/17.651
19,470 19.053
2..169/20.460
Lz-Magnitude
18.524 18.048
20.834 20.041
23.692 22.604
GPT-Neo- 125m
Gradual Random
17.307 17.144
18.900 18.410
21,458 / 20.546
Finetuned: 16. 138
Hard/Top.
16.974 17.142
18.253 18.369
20.495 / 20.194
GUM
16.822 17.158
17.881 18.314
20.059 19.833
Table 4: GPT-Neo- 125m: Performance in perplexity (PPL) on the validation set decreasing amount leftover Wikitext-103. indicates having 1-3% excess leftover neurons unpruned.
Model
Method
75% / Distil
50% + Distil
25% / + Distil
Soft
16.754 / (6.954 16.950
18.261 18.051
19.948 / 19,473'
Lz-Magnitude
17.399 17.414
19.595 19.178
22.593 / 21.667
GPT-2-sm
Gradual Random
16.574 16.823
17.974 17.862
20.444 / 19.7 19.798
Finetuned: 15,571
Hard/Top,
16.363 16.730
17.611 17.742
20.016 19.663
GUM
16.242 16.680
17.444 17.692
19.877 19.681
Table 5: GPT-2-sm: Performance in perplexity (PPL) on the validation set for decreasing amount leftover Wikitext-103, * indicates having 1-3% excess leftover neurons unpruned.
WikiSQL As opposed to the other datasets, WikiSQL Zhong et al. (2077) contains hard ground- truth labels for comparison via Exact Match (EM). Due to this, our best performance is achieved in WikiSQL, where GUM is able to remove up to 75% of neurons while maintaining performance
GPT-Neo. Results are shown in Tables[and and We also present results for GPT-Neo- 1.3b only on Results for this experiment follow similar trend to smaller models.
WikiSQL in Table 6
Model
Method
50% / + Distil
25% / Distil
10% + Distil
GPT-Neo- 1.3B
Gradual Random
70.38 / 73.83
72.77 72.77
Finetuned: 74.88
Hard/Top.
73.33 / 74.75
72.18 / 74.54
71.14 73.77
GUM
72.88 / 74.70
71.80 / 74.62
71.35 74.157
Table 6: GPT-Neo-1.3B: Performance in Accıf for decreasing amount leftover on WikiSQL.
SAMsum Results on SAMsum Gliwa et al. (2019) are presented in Tables 7 and Popular for encoder-decoder models, this dataset entails summarizing short instant message conversations. Larger generative models have been explored for this task (Feng et al. [2021] Zhang et al.) 2019). achieving competitive results. We use this dataset test the natural language understanding and summarization skills of small models under pruning. We note poor relative baseline results relative to encoder-decoder models as expected, however, pruning trends follow that of other datasets and GUM generally outperforms Top..
6 ADDITIONAL RELATED WORKS
General Domain Pruning Neural net pruning has been proposed years before the explosion deep
learning research (Janowsky) 1989
Mozer & Smolensky 1988 Karnin 1990), and are summarized
an outdated survey Reed 1993). Previous works have explored many approaches of pruning neural
nets (Wen et al. 2016 Han et al. 2015b a Li et al. 2016). Recently, the lottery ficket hypothesis Frankle & Carbin (0118) proposed a new direction to prune at initialization instead. However, there also massive divergence of methods claims that it is not worthwhile Liu al. (2018); Blalock] ct al. (2020). Regardless, many strong techniques exist in modern incarnations across all kinds of
architectures (Yang al. 2016 Luo al. 2017 He et al. 20
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Method
Leftover %
Rouge_1
Rouge_2 2
Rouge_L.
Rouge_LSUM
No Prune
38.68
14.74
31.73
31.76
50% / + Distil
35.54 36.82
12.71
13,40
29.11 30.28
29.04 30.27
Gradual Random
25% / + Distil
33.11 35.71
11.01
13.13
27.39 29.50
27.37 29.48
10% / + Distil
31.83 /34.60
10.02 11.72
26.49 / 28.40
26.46 28.34
50% / + Distil
37.68 36.94
14.17
13.72
31.12 30.64
31,09/ 30.62
Hard/Top,
25% / + Distil
36.38 37.34
13.00 14.24
29.96 / 31.17
29.95 31.15
10% / Distil
33.07 /6.122
10.95 12.62
27.70 / 29.70
27.68 29.67
50% / + Distil
37.22 38.45
13.79/ 14.27
30.72 / 31.35
30.72 31.36
GUM
25% / + Distil
36.18 37.57
13.18 13.71
29.99 30.91
30.00/ 30.93
10% / + Distil
34.72/36.55 36.52
11.88 13.40
28.82 / 29.97
28.79 / 29.96
Table 7: OPP-Nee-125mm: Validation results on SAMsum. Higher is better for all metries. In general, more pruning hurts performance, and GUM outperforms Top.-
Method
Leftover
Rouge_1
Rouge
Rouge
Rouge LSUM
Prune
40.83
16.85
33.72
33.70
50% + Distil
39.29 39.69
15.25 / 15.74
32.16 /32.72
32.15
Gradual Random
25% / + Distil
38,09 / 39.73
14.43 / 15.35
31.16 32.72
31.15 / 32.66
10% + Distil
36.91 / 38.88 88
13.02 / 14.71
30.13 31.85
30.13 31.83
50% / + Distil
39.93 / 40.23
16,43 / 16.07
33,40/ 33.39
33.38 / 33.39
Hard/Top,
25% Distil
38.84/ / 40.49
14.88 / 16.12
31.83/ / 33,46
31.82 33,45
10% + Distil
38.28 39.49
14.58 15.32
31.36 32.57
31.37 32.57
50% Distil
38.80/ 40.74
15.52 16.22
32.27 33.99
32.23 33,95
GUM
25% + Distil
37.56/ / 39.74
14.24 / 15.72
30.94 32.93
30,91 / 32,93
10% / + Distil
39.12/ 39.80
15.01 15.79
32.20 32.96
32.21 32,95
Table 8: GPT-2-sm: Validation results SAMsum. Higher is better for all metrics. In general, more pruning hurts performance, and GUM outperforms Topp..
Compressing Language Models Compressing LLMs in particular has spawned a unique kind of pruning. Given that LLMs first undergo pre- pre-training on massive amounts of data, works such as Xu et al. 2021); Zafrir et al. (202T): Li et al. (022) find ways to prune and finetune these models on downstream data. Building on automated gradual pruning (Zhu & Gupta (017) and learned threshold pruning (Azarian et al. 2020). movement pruning (Sanh et al.) 2020) and further block movement (Lagunas et al., 2021) have become highly popular methods for fine-pruning, combining finetuning with pruning for overall best performance. Since then, many works have attempted to improve
movement (Yoo et al.] [2222] Zhang et al.) [2022) Xia et al.) 2022] Kwon et al.) (022). As previously mentioned, however, are unable to find any comparable works systematically exploring structured pruning for decoder-only models.
7 CONCLUSION & FUTURE WORK
In this paper, we have performed an evaluation of structured pruming on generative language models. finding existing methods to improve over random pruning less than expected. In addition, we have proposed framework for analyzing pruning methods in terms of uniqueness and saliency, two impor- tant criteria for preserving model quality and diversity. We have presented novel method based on these metrics, GUM, for structured pruning generative models, based on uniqueness regularization and global Top,, pruning. Our method can be applied to the MLP layers of various generative models, but there are still many open questions and challenges for pruming other components, such as attention heads MoE modules. We also acknowledge the limitations of our method, which reduce saliency and performance, suggesting possible directions for improving uniqueness pruning. Our work is an initial step towards understanding and improving structured pruning of generative models.

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Under review as a conference paper at ICLR 2023
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A COMPUTATIONAL. RUNTIME COMPARISON
The training runtime of all pruning methods are compared in Tables I0 and II For all experiments, Soft movement has greatly increased runtime compared to other pruning methods. This is due to the over-pruning problem previously described: if soft movement prunes too many neurons, must default to hard movement backup. To reach specific pruning percentage, this must occur, resulting in significantly more computation.
GUM is certainly slower than hard movement, however, we find the difference to be minimal. Compared to no pruning, all pruning methods add a significant amount time to training, especially when also combined with distillation. Therefore, the additional runtime incurred by GUM is small in comparison.
Model
No Prune
L2
Random
Hard
GUM
Soft
GPT-Neo-122m
4.75
10.5
9.93
10.26
11.46
25.33
GPT-Neo-125mn + Distil
13.83
13.83
13.93
15.4
31.5
GPT-2-sm
6.83
11.62
10.06
10.07
12.83
27.5
GPT-2-sm + Distil
9.28
14.22
14.55
17.08
32.67
GPT-Neo-1.3b
3.92
11.83
12.25
12.83
GPT-Neo-1.3b + Distil
14.5
14.8
15.56
Table 9: WikiSQL Training Runtime in Hours. GPT-Neo- 125mm and GPT-2-sm were run on 8xV100 GPUs, while GPT-Neo- 1.3b was run 8xA100 GPUs. All results are averaged over all pruning runs, which are comparable given neuron removal occurs after training.
Model
No Prune
L2
Random
Hard
GUM
Soft
OPT-Neo-125m
5.42
8.55
6.82
7.1
8.87
18.08
GPT-Neo-1255 + Distil GPT-2-sm
11.9
11.9
11.8
13.45
23.33
4.72
6.45
6.17
6.12
9.1
17.13
GPT-2-sm Distil
11.33
10.88
11.12
13.48
22.17
Table 10: Wikitext Training Runtime in Hours. GPT-Neo-122 and GPT-2-sm were run on Vx00 GPUs. All results are averaged over all pruning runs, which are comparable given neuron removal occurs after training.
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Model
No Prune
Random
Hard
GUM
GPT-Neo-1255
1.72
2.22
2.37
2.71
GPT-Neo- 125mm + Distil
4.45
5.13
5.33
GPT-2-sm
1.55
2.41
2.27
2.79
GPT-2-sm + Distil
3.96
3.93
4.17
Table 11: SAMsum Training Runtime in Hours. GPT-Neo- 125m and GPT-2-sm were run on 8xV100 GPUs. All results are averaged over all pruning runs, which are comparable given neuron removal occurs after training.
B E2E NLG CHALLENGE RESULTS
also tested pruning on the E2E NLG Challenge Dušek et al. (220 in Tables 12 and 13 highly popular dataset, performance in this domain is measured by variety of metrics that all measure the quality of the output indirectly as opposed to the direct measurement versus ground truth other experiments.
After many rounds of hyperparameter optimization, results for GPT-2-sm loosely follow previously seen trends, however, GPT-Neo- 125m results are highly inconsistent. For this model removing 90% of neurons can result best performance, and increasing pruning does not monotonically affect performance, two highly concerning phenomenon unseen in other datasets.
We speculate these inconsistencies could be due to many reasons; such as the open-endedness of the problem domain, little training data. Measuring the performance of generative models is non-trivial task, especially when model outputs are highly similar as is the case when pruning.
For these reasons, do not further experiment this dataset and leave out of main analysis. However, we include this experiment to show that in some cases, pruning results can be inconsistent for language models, Further exploration is required into this area.
Method & Leftover %
BLEU
NIST
METEOR
ROUGE_L
CIDEr
No Prune
68.22
8.6315
0.4479
0.7103
2.3141
Top, 75%
68.26
8.6660
0.4486
0.7108
2.3120
Top, 25%
68.80
8.6780
0.4487
0.7126
2.3238
Top,. 10%
68. 15
8.6377
0.4472
0.7144
2.2955
GUM 75%
68.07
8,5698
0.4458
0.7090
2.2835
GUM 25%
68.22
8.6597
0.4495
0.7093
2.3230
GUM 10%
68.26
8. 3.6684
0.4490
0.7109
2.3012
Table 12: GPT-Neo-125m: Testing results on the E2E NLG Challenge. Higher is better for all
metrics. Even at 10% leftover, performance is similar to the baseline, for both Top,, and GUM.
Method & Leftover %
BLEU
NIST
METEOR
ROUGE L
CIDEr
No Prune
68.05
8.6547
0.4623
0.7143
2.4500
Top 75%
66.79
8.5064
0.4590
0.7119
2,4308
Top,, 25%
66.85
8.5115
0.4587
0.7098
2.4322
Top,, 10%
66.63
8.4783
0.4588
0.7091
2.4368
GUM 75%
68.02
8.6552
0.4619
0.7134
2.4502
GUM 25%
66.58
8,4853
0.4588
0.7098
2,4286
GUM 10%
67.06
8.5237
0,4601
0.7122
2.4417
Table 13: GPT-2-sm: Testing results on the E2E NLG Challenge. Higher is better for all metrics. In general, more pruning results in worse performance, and GUM outperforms Top,--
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C UNIQUENESS REGULARIZATION PER LAYER
The goal of uniqueness regularization punish similarity between neurons, especially those with high similarity. Figure shows that GUM is generally successful in removing neurons with high similarity across all layers.
Layer1
Layer
Layar
100%
60%
40%
20%
Layer
Layer
100%
60%
20%
Layer
Layer 10
Layer 11
Layer 12
80%
60%
40%
20%
GUM
TopK
Figure 3: For each layer, this graph shows the percentage of neurons with at least one similarity per range. Similarity is defined as the absolute value of cosine similarity over the entire validation dataset, increasing from to 1. Top, and GUM are compared, training on WikiSQL with GPT-Nco- 125m. Total leftover neurons exactly 25% of all neurons.
D GLOBAL TOP, REMOVAL PER LAYER
natural question arises with Global Top,, pruning: what is the final prune percentage per-layer? Figure 4 shows the prune percentages layer for one sample training run. From this test, later layers are clearly prioritized, with a large emphasis on the last layer. While the exact layers pruned more or less will vary with noise, model, and dataset, we observe this trend to generally hold true.
E UNIQUENESS AND SENSITIVITY GRAPHING
To measure sensitivity for a model, measure the global sum of sensitivity for all neurons (ee()))i in feedforward layers {1 L} on the training dataset via (taking the absolute value to account for sign):
ac (h;(X)
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30
10
Figure 4: The percentage leftover for layers 1-12 after Global Top, pruning, using GUM training on WikiSQL with GPT-Neo-125m. Total leftover neurons is exactly 25% of all neurons.
To measure uniqueness for model, we measure the cosine similarity of each neuron with each other neuron in equation over the entire dataset, measuring each pair only once. This can be measured using the running cosine similarity without decay multiplier. Then, we measure the percentage of neurons with at least similarity above 0.8 to another neuron (i.e. these neurons nearly match each other) across the entire network.
Both sensitivity and uniqueness are not useful on their own, but are useful relative to other models. We therefore divide both metries by the value obtained for the fully finetuned version of the model.
Overall, uniqueness values for Wikitext-103 103 and for WikiSQL were quite different. Neurons on Wikitext-103 seem to already be highly unique, with some specific layers containing large amount of redundancy, while WikiSQL has redundancy throughout the entire network.
Figures and 6 show non-re-scaled results for the previous graphs. These graphs show uniqueness as a percent of all neurons (i.e. 50% neurons are unique) and sensitivity as a raw value.
WikiSQL
Wikitext-103
0.15
Sensitivity
Sensitivity
0.10
0.4
0.05
0.3
Uniqueness
Uniqueness
98%
40%
98%
97%
10% Leftover (%)
Leftover (%)
Magnitude
GUM
Random
Soft
Top,
w/o Distil
w Disti
Figure 5: Sensitivity and Uniqueness measured without re-scaling, for GPT-2-sm.
F TRAINING HYPERPARAMETERS
All training hyp hyperparameters provided. Only random seed attempted per training We note that it is possible different pruning amounts require different hyperparameters and therefore performance could be better, however, searching for each possible combination would be far too
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WikisQL
Wikitext-103
Sensitivity
Sensitivity
Uniqueness
80%
Uniqueness
00%
40%
10% Leftover (%)
50%
25% 75%
Leftover (%)
Magnitude
GUM
Random
Soft
Topy
wio Distil
Wisil
Figure 6: Sensitivity and Uniqueness measured without re-scaling, for GPT-Neo- 125m.
expensive. Therefore, the same hyperparameters used for different pruning percentages. Only the best combination for each model is listed, but each model had its hyperparameters tuned manually, We explored hyperparameters by starting with known vaules from literature, then performing grid searches on relevant pruning vaules (i.e. distillation temperature, or Agum).
LI regularization was used on the mask scores for all models. Learning weight decayed linearly for all models. LR means Learning Rate, WD means Weight Decay, and LS means Label Smoothing. When distillation was used, the teacher was trained using the same hyperparameters (sans pruning arguments).
Adam 31
Adam B2
Adam e
Batch
LR Warmup Percent
GUM A
0.9
0.999
le-4
10%
0.99
Table 14: Shared hyperparameters for all runs.
WikiSQL A max token length 512 was used. Strings were not converted lowercase. Special tokens were added by the tokenizer.
GPT-Neo-122 125m used a mask LR of le-2 for Hard Movement/GUM and for Soft Movement. GPT-2-sm used mask LR of le-2 for Hard Movement/GUM and lel for Soft Movement. Soft movement required large mask learning rate as would otherwise not converge other combinations of more regularization were attempted.
Model
LR
WD
Epochs
Amep
Agam
LS
Distil
Distil Temp.
GPT-Neo- 125m
5e-4
0.05
10
le2
0.05
0.5
GPT-2-sm
3e-4
0.1
11
2
le1
0.05
0.9
GPT-Neo-1133
2e-4
0.05
lel
0
0.5
Table 15: WikiSQL hyperparameters.
Wikitext-103 A max token length of 1024 was used. Strings were not converted to lowercase. Special tokens were added by the tokenizer.
GPT-Nco- 125m used a mask LR of le-2 for Hard Movemen//UMM and lel for Soft Movement. GPT-2-sm used mask LR of 1e-2 for Hard Movement/GUM and lel for Soft Movement. Soft
17

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Under review as a conference paper at ICLR 2023
movement required large mask learning rate as it would otherwise not converge other combinations of more regularization were attempted.
For Wikitext- 103 specifically, Soft Movement also required extremely large Amop- This causes a large increase in pruning at the beginning of training, which could explain the overall poor performance. Without this large value, however, Soft Movement would not converge to the desired pruning amount.
Model
LR
WD
Epochs
Amop
Agum
LS
Distil a
Distil Temp.
GPT-Neo- 125m
5e-4
0.05
2, 1e2 (Soft)
lel
0.05
0.9
GPT-2-sm
3e-4
0.1
2. le2 (Soft)
lel
0.05
0.9
Table 16: Wikitext-103 hyperparameters.
SAMsum A max token length of 1024 used. Strings were not converted lowercase, and whitespace was not stripped. More than or epochs starts to result in overtraining for both models, so both were limited to not overtrain.
For GPT-Nco- 125m, both methods used a mask LR of 1e-2. For GPT-2-sm, GUM required a mask LR of le-3, while TopK used a mask LR of le-2. However, for both models Xyam must be lel for no distillation, and le2 for distillation, as too high Agum results in poor non-distil performance. Soft movement was not attempted on this dataset.
Model
LR
WD
Epochs
Amup
Agum
LS
Distil a
Distil Temp.
GPT-Neo-125m
5c-4
0.01
4
2
le1/1e2
0.01
0.5
1
GPT--2-sm
3e-4
0.05
6
2
lel
0.01
0.9
1
Table 17: SAMsum hyperparameters.
E2E A max token length of 512 was used, When testing. BEAM search was used with 10 beams, length penalty of 99, and ngram repeat size of 4.
GPT-Nee- 125m used a mask LR of 1e-2 for Hard Movement/GUM and lel for Soft Movement. GPT-2-sm used mask LR of 1e-2 for Hard Movemen//UUM and lel for Soft Movement. Soft movement required a large mask learning rate as it would otherwise not converge other combinations of more regularization were attempted.
Model
LR
WD
Epochs
Amep
Agum
LS
Distil
Distil Temp.
GPT-Neo-125m
5e-4
0.01
2, lel (Soft)
lel
0.05
0.5
2
GPT-2-sm
3e-4
0.1
2, 1e2 (Soft)
lel
0.05
0.9
Table 18: E2E NLG Challenge hyperparameters.
18


Reviews:
[{"review_id": "mZNDkh9ZtLt", "reviewer": "Reviewer_MVrZ", "summary": "", "questions": "", "limitations": "", "rating": 6, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "The authors are interested in structured pruning of generative language models. In particular, this work builds on top of [Movement Pruning](https://arxiv.org/abs/2005.07683) and [Block movement pruning](https://arxiv.org/abs/2109.04838) to prune entire structures (and not individual weights) in models similar to GPT2 and the fine-tuning stage.\n\nThe authors first notice that current structure pruning applied to decoder only language models on NLG tasks perform relatively similarly at pruning rates between 10% and 50% (percentage of remaining weights), and more surprisingly, similarly to random pruning.\n\nThe authors then introduce two fundamental measures of redundancy called “sensitivity” and “uniqueness” which respectively measure how much a group of parameters impact the training objective and how unique a group of parameters is compared to other groups of parameters. This motivates the introduction of Globally Unique Movement, a method that essentially encourages remaining neurons to be dissimilar (as measured by cosine similarity), by modifying the regularization in movement pruning.\n", "summary_of_the_review": "This paper extends movement pruning and block movement pruning by introducing a \"uniqueness\" metric in the training objective which encourages the model to prune similar neurons.\nWhile the method and insights are relatively novel, the numbers and comparisons of previous pruning methods are somewhat weak.\n", "strength_and_weaknesses": "Strengths:\n- The problem is relatively well-motivated and the paper makes a noticeable effort to be didactic.\n- The contributions are somewhat novel, and experiments are well conducted on reasonably large setups.\n- The result about gradual pruning performing similarly to previous state-of-the-art structured pruning is surprising and insightful.\n\nWeaknesses:\n- The numbers showing the superiority of the method are weak or show only weak improved performance or trends.\n- The connection between sensitivity/uniqueness and fine-pruning performance is not well articulated (see questions).\n- I have doubts about whether the choice of benchmarks is the most appropriate. For instance, the authors note that on E2E, pruned models perform better than the non-pruned baseline.\n", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work.", "empirical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work."}, "extra_text": {}}, {"review_id": "lrMbidAySCP", "reviewer": "Reviewer_jU8R", "summary": "", "questions": "", "limitations": "", "rating": 6, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "The authors conduct a study of structured pruning methods on natural language generation tasks. They demonstrate that the prior art do not improve significantly over the naive random pruning baseline. Then the authors attempt to understand the results through two measures that they introduce: sensitivity and uniqueness. The insights from these two measures allow the authors to construct a new pruning method, GUM, that explicitly enforces uniqueness through a cosine similarity proxy of the measure. The methods are implemented on large GPT-like models on a collection of downstream tasks.", "summary_of_the_review": "There are useful contributions in this work, which I think will benefit the community. The work is very well-presented too. The topic of pruning for natural language generation is an important one, in my opinion, so I recommend weak acceptance.", "strength_and_weaknesses": "Strengths:\n\n1. The topic of compressing LLM for generation tasks is an important one and I am happy to see that the authors focused on it.\n\n2. The introduced sensitivity and uniqueness measures will be useful to future work in the area. \n\n3. The experiments are thorough and demonstrate meaningful trends.\n\nWeaknesses:\n\n1. There are marginal improvements and not all of them are consistent. That is OK, but it probably signals that better methods than GUM can be constructed. I am curious to learn what the opinion of the authors is.\n\n2. What happens if we use these pruning methods on non-generative tasks? I think it would be good to see that experiment in the paper, because the GUM method, by construction, is agnostic to the type of downstream task.\n\nMinor:\n\n* The results for the E2E NLG Challenge are not linked in the paragraph in the main text. Please link to Table 7.", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "4: All of the claims and statements are well-supported and correct.", "technical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work.", "empirical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work."}, "extra_text": {}}, {"review_id": "WofGnhyMBGw", "reviewer": "Reviewer_AWKv", "summary": "", "questions": "", "limitations": "", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "The paper systematically studied the task of structured pruning of generative language models. It tests the performance of several different pruning methods on GPT-2 and GPT-Neo. The author also proposes two redundancy measures for each neuron in the MLP layer and shows that there is a strong correlation between these measures and the performance of pruning methods. Based on the observation, the author proposes a new pruning method, named GUM. The method combines a uniqueness regularization and a global top_v strategy to achieve strong performance on several pruning tasks.", "summary_of_the_review": "Overall the paper proposes two interesting redundancy measures for pruning and a promising method GUM. But the current experiment section can be further improved and more analysis on the movement methods can be done.", "strength_and_weaknesses": "Strength:\n1. The paper conducted a systematic evaluation of several structured pruning methods on the generative language model.\n2. The proposed redundancy measures are intuitive and are shown to have a strong correlation with the pruning results.\n3. The newly proposed GUM achieves strong performance when compared to the baseline methods.\n\nWeaknesses\n1. Most of the experiments focus on the WikiSQL and Wikitext tasks. More experiments and diverse tasks are needed to prove that the GUM can generalize to different tasks.\n2. The result of E2E NLG is confusing. Although the author provided some explanation, it's still unclear what point the experiment wants to make. If the E2E NLG is not a good task to test the method, another text-to-text task should be used.", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work.", "empirical_novelty_and_significance": "2: The contributions are only marginally significant or novel."}, "extra_text": {}}, {"review_id": "25uUtIwVLbW", "reviewer": "Reviewer_25Qh", "summary": "", "questions": "", "limitations": "", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "This paper investigated pruning for generative language models. They compared several existing pruning methods on decoder-only language models which had not been empirically evaluated before. The authors found that popular pruning methods such as movement pruning do not perform robustly on causal language models. In contrast, random pruning was shown to be a strong baseline. The authors propose two metrics to analyze the effectiveness of pruning. Based on these insights, they propose a new pruning method (GUM) which considers uniqueness of neurons. They evaluated this method on three benchmarks and showed some improvement over the prior methods. ", "summary_of_the_review": "This paper studies an important problem and provides some novel insights. The analysis and empirical evaluation part is mostly well executed except that the evaluation tasks/datasets could be expanded to improve representativeness and coverage. The effectiveness of the proposed method is not very clear with current results due to 1) improvements are small and may not be statistically significant. 2) the proposed method has high complexity which may slow down training and consume more memory. More clarity on both and some justification on whether the improvement outweigh the complexity would make it a strong paper. ", "strength_and_weaknesses": "Strengths:\n- This paper studies an important problem, i.e. pruning LLMs, for increasing the accessibility of LLMs as they keep growing larger and larger. This problem is also under-explored so far where both empirical results and theoretical understandings are lacking. \n- This authors carefully analyzed existing methods through neuron redundancy metrics which shed lights on their performance in pruning. \n- The proposed method is novel and interesting.   \n- The writing is clear and easy to follow. \n\nWeaknesses:\n- The evaluation tasks and datasets do not have full coverage of how generative LLM are often evaluated. The authors evaluated on three tasks: language modeling (wikitext-103), text-to-code generation (wikiSQL), text-to-text generation (E2E NLG challenge). There are a couple of problems. First, E2E NLG challenge is not very diagnostic as the authors found that \"E2E NLG challenge is not very diagnostic \" and \"speculate these discrepancies are due to the open-endedness of the problem domain and underfitting the data.\" I would expect the authors to evaluate on a more robust text-to-text task (e.g. summarization) to derive more informative evaluation results. Second, besides language modeling and text-to-code generation, LLMs are often used for language understanding and reasoning tasks, e.g. those evaluated in the GPT-3 paper. Experiments on pruning should be conducted on some of those tasks. \n- The proposed approach computes cosine similarity for any two neurons' outputs. Although it's a sound solution, it is computationally expensive. Also, I wonder whether the running cosine similarity has high variance and how it affects the effectiveness of pruning. \n- Improvements from GUM are minor especially on language modeling. \n- An ablation on components and design choices of the proposed method is missing, although it's mentioned in the abstract. For example, how does GUM compare to LUM (Locally Unique Movement)?", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work.", "empirical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work."}, "extra_text": {}}]

Author rebuttal:

Decision: Reject