Summary and Contributions: The paper proposes movement pruning, which is a first order weight pruning method that allows pruning to be more easily adaptive during fine tuning. This is compared to magnitude pruning, which has been effective for supervised learning settings. However, an advantage of movement pruning is that when the weights are shifting during fine tuning, movement pruning is more adaptive to this scenario.
Strengths: - nice explanation of the technique, particularly in section 4 - very nice figures displaying the technique and comparing it to magnitude pruning - strong performance on GLUE benchmark tasks and squad
Weaknesses: - there are very few experimental details about distillation - is this distillation only on the training set, or is there data augmentation? - it is difficult to understand e.g. figure 5, there are a lot of lines on top of each other - the main metrics reported are performance compared to remaining weights, but the authors could report flops or model size, to make this much more concrete
Correctness: overall looks good
Clarity: paper was clear and well written
Relation to Prior Work: yes, related works section looks good
Additional Feedback: - can the authors comment how movement pruning might work for generative tasks, for example if T5 or BART were finetuned? thanks authors for the review response.
Summary and Contributions: After author response: Thanks for clarifying that the reported sparsity is relative to BERT base. This makes the reported comparisons more fair than I originally realized. I've updated my scores accordingly. === This paper proposes an approach for pruning pretrained language models during the fine-tuning process. Compared to past work that prunes weights based on their magnitude, the authors instead propose to prune based on the degree to which weights move toward 0 during fine-tuning. They evaluate their approach on several NLP tasks and compare to several pruning baselines and other parameter reduction techniques (LayerDrop, MiniBERT). The proposed approach shows particularly strong performance relative to baselines in the high sparsity regime (>95% sparsity), although there is still a gap with the original unpruned model. The authors show that this gap can be further reduced with knowledge distillation.
Strengths: - The proposed approach is well motivated, straightforward to implement and performs well in high sparsity settings. - The evaluation appears solid and the proposed approach is compared to strong baselines. - There is decent theoretical and empirical analysis confirming that the proposed approach does what it claims to (prune weights moving toward 0).
Weaknesses: - While the baselines are strong, the way they are reported may be a bit misleading. In particular, models are compared based on the sparsity percentage, which puts models with fewer parameters (e.g., MiniBERT) at a disadvantage. - As with most work on pruning, it is not yet possible to realize efficiency gains on GPU.
Correctness: The claims are mostly well supported, although the comparison to non-sparse models with fewer parameters (e.g., MiniBERT, possibly LayerDrop) are not quite fair since they are based on relative sparsity percentage instead of total non-zero parameter count.
Clarity: The paper is very clearly written.
Relation to Prior Work: The comparison to related work seems quite comprehensive and I did not find any missing related work.
Additional Feedback: - For the results in Figure 2, what does the x-axis represent for models with different numbers of parameters? For example, if a MiniBERT model has half as many parameters as BERT-base, then comparing "10% remaining weights" seems a bit unfair. What would the figure look like if the x-axis were instead the number of non-zero parameters? - You evaluate on one span extraction and two paired sentence classification tasks, but no single sentence classification tasks. Why not replace one of the sentence pair tasks with SST-2, for example? I expect the results would be similar, but it would make the experiments a bit more compelling. Presentation suggestions: - In Figure 3, it's a bit hard to compare the results to those in Figure 2. Perhaps consider plotting the delta F1 or accuracy instead of absolute values. - The presentation in Section 4 is a bit jarring, because you switch from discussing movement pruning to L_0 back to movement pruning. It may be clearer to swap the L_0 and Method Interpretation paragraphs. - Could you please add the raw values from Figures 2/3 to the appendix? - In Figure 5, instead of having "Global Topv; False; True" in the legend, perhaps change to "Topv; Local; Global" - typo (line 115): "yield a similar update $L_0$" -> "yield a similar update as $L_0$" - typo (line 265): stray sentence fragment: "Privacy-preserving Machine Learning ."
Summary and Contributions: In this paper the authors propose a method for pruning neural network weights based on first-order statistics rather than 0th-order statistics (e.g. the common magnitude-based pruning), which they claim should work better in the transfer learning setting, i.e.fine-tuning a pre-trained language model. They show that their approach leads to better accuracy/F1 at a given sparsity over various baselines when used during fine-tuning on SQuAD, MNLI and QQP. Following discussion: The author response didn't change my opinion enough for me to increase my score (and they did not address my concerns with one of the related works I mentioned, maybe it's less relevant than I thought, but they had room to explain that), and I agree with R4 that I would like to see more analysis of what is happening at low sparsity levels.
Strengths: - well-motivated and effective (seemingly good empirical results) technique for task-specific compression of fine-tuned pre-trained language models, an important area of research to make these models usable in "real", deployed scenarios, and more accessible generally - A nice analysis of results
Weaknesses: - At least one highly related work is missing (see below) - empirical results don't compare to any other published work, only self-implemented baselines. I'm not convinced that there exist no other relevant works on pruning pre-trained LMs that could be compared to. I could definitely be convinced that this is acceptable given existing work, but I'd like to even see an explanation of why existing work isn't comparable in the paper.
Correctness: The results presented seem correct, but I'm concerned about the lack of comparison to other approaches for compressing LMs during fine-tuning. For example, https://arxiv.org/abs/1909.12486 seems comparable, also https://arxiv.org/abs/2002.08307, https://arxiv.org/abs/1910.06360, and https://arxiv.org/abs/2002.11794
Clarity: The paper is very well written and easy to follow. There are not enough details on optimization included in the main paper (I did not read the appendix) to replicate this work.
Relation to Prior Work: A highly related work from NeurIPS 2019 is missing: Sparse Networks from Scratch: Faster Training without Losing Performance (https://arxiv.org/abs/1907.04840). It may even make sense to compare to this work as a baseline. See also papers under "Correctness" (I think some of these are included and some are not.).
Additional Feedback: - you may want to consider reformatting table 2, it is quite hard to parse - similarly, Figure 2 is quite busy and hard to read; you may want to remove some of the lines from this figure, and include the full plots as supplemental material
Summary and Contributions: This paper shows that the magnitude pruning methods are less effective in the case of pruning of pre-trained models for task-specific fine-tuning, and proposed a simple pruning approach, movement pruning (mvp), which is first-order and more adaptive. Rather than pruning weights with small absolute values like magnitude pruning, mvp prunes weights that are shrinking towards 0. The authors conducted experiments on BERT fine-tuning, which show that mvp outperforms magnitude pruning method and other 1st order methods at high sparsity level.
Strengths: This paper is well-written. The proposed approachs and conducted experiments are clearly described. The visualization and analysis about the differences between magnitude pruning and movement pruning (in particular, Figure 1 and Figure 4) in the paper is very intuitive and intriguing. For example, they found the distribution of remaining weights after pruning with movement pruning method smoother than that with magnitude pruning. Moreover, the weights that are close to 0 are also less important, which is consistent with magnitude pruning. The proposed approach is simple and effective, compared with the similar 1st-order method, L0 regularization. Besides, the experimental results demonstrate that the approach (and its soft version) works well at high sparsity level.
Weaknesses: The designed movement pruning approach is somehow lack of novelty, as various pruning heuristics (based on activations, redundancy, second derivatives, channels, etc.) have been proposed. Besides, the experimental results show that the movement pruning method performs worse than magnitude pruning method when at low sparsity level but the authors did not explain why. Does the poor performance at low sparsity level mean that the proposed importance criterion is not suitable for low sparsity pruning?
Correctness: The claims, method, and empirical methodology in the paper are correct.
Clarity: Overall, the paper is well-written. The method and experiments are clearly described. Also, the figures and tables in the paper well expressed the relationship and difference between their method and the related methods. It would be better to make Section 4 clearer, especially the part about L0 regularization.
Relation to Prior Work: Yes, the authors compare their proposed method with magnitude pruning and L0 regularization. The characteristics of these methods are highlighted in Table 1. The difference between movement pruning and magnitude pruning is depicted in Figure 1 and Figure 4.
Additional Feedback: More analysis about the reasons why movement pruning performs worse than magnitude pruning at low sparsity level is suggested. As shown in Figure 4, the movement pruning is more general and adaptive than magnitude pruning, but the results at low sparsity level show that simply removing weights with small absolute values yields better performance. That means, when removing a small proportion of connections, the distance from 0 is a better importance criterion than movement. But when removing a large proportion of connections, some important weights but with relatively small absolute values are wrongly removed by magnitude pruning. Can you give some intuitive explanations or deeper analysis about this phenomenon?