MIT assistant professor Song Han introduced AutoML for Model Compression (AMC). We prune each of these matrices separately, calculating a threshold for each. !OZs$.nOxb3R>OB-j'IIHwM,)&v5250 Hn`6OV\CoBlE/nmODM\"yw ;Q^5m gcXF-)KN Pvs! b3"?kO|(d(]+'kJZ*}(j"=w:C0M{OH8";-iEw> c HBqfII7;ulI@HRdt)Y$2#,\tn@d`S[Cvj=&5i`(@kzl?23Vy 4,+f4EdsP:*dYzbx9fY7P)E7&7'P=SiI^qDK-Gr{;6$NW)Tsl!~H#tk(GZ\I$-Jb{DFU}>"N`p3ghbJ)AG\vpfnUh\#{lldL}.SpBC*`K(5q[xYgR@cHHo3znBX zZB "s`6Ul. focused while the background and other elements are discarded. over the eight problems. This, in turn, changes the sort-order of the absolute values of those weights, which changes the order that we prune them in. Train-ing BERT-Base from scratch costs $7k and emits (Strubell et al., 2019).Model compression (Bucila et al., 2006), whichattempts to shrink a model without losing accuracy,is a viable approach to decreasing GPU usage. Google), where storage space is at a premium (e.g. Methods in Natural Language Processing and 9th We believe this is because the larger datasets require larger models to fit, so complexity restriction becomes an issue earlier. Finally, we observe that fine-tuning BERT on a specific task does not improve its prunability. The main difference between L0 or L1 regularization and weight pruning is that the former induce sparsity via a penalty on the loss function, which is learned during gradient descent via stochastic relaxation. % The pre-training paradigm, while effective, still has some problems. This has led to various attempts of compressing such models, but existing methods have not considered the differences in the predictive power of various model components or in the generalizability of the compressed . This deletes the pre-training information associated with the weight but does not prevent the model from fitting downstream datasets by keeping the weight at zero during downstream training. Poll Campaigns Get Interesting with Deepfakes, Chatbots & AI Candidates, Decentralised, Distributed, Transparent: Blockchain to Disrupt Ad Industry, A Case for IT Professionals Switching Jobs Frequently, Interesting AI, ML, NLP Applications in Finance and Insurance, What Happened in Reinforcement Learning in 2021, Council Post: Moving From A Contributor To An AI Leader, A Guide to Automated String Cleaning and Encoding in Python, Hands-On Guide to Building Knowledge Graph for Named Entity Recognition, Version 3 Of StyleGAN Released: Major Updates & Features, Why Did Alphabet Launch A Separate Company For Drug Discovery. Pruning a neural network decreases the number of parameters required to specify the model, which decreases the disk space required to store it. Tests on real and simulated data sets using classification and regression trees and subset selection in linear regression show that bagging can give substantial gains in accuracy. A pIn sectio 3, a proposed KD-based approach is illustrated, and a case study based on an SLS system i prese ted in s ction 4. For everything else, email us at [emailprotected]. Ensemble methods are often utilised to improve the gen-eralisation of a model, by exploiting the diversity of multiple underlying learners trained on data of the same problem. "Model compression." If the synthetic data is drawn from a distribution that has little overlap to this manifold, the labeled synthetic points will fail to capture the target function in the region of interest, On the other hand, if the distribution from which the synthetic data is simpled is too broad, only a fraction of the points will be drawn from the true manifold and many more samples will be necessary to adequately sample the region of interest, The RANDOM method for generating pseudo data uses a nonparametric bootstrap approach, For each attribute, a value is selected uniformly at random from the multiset (bag) of all values for that attribute present in the train set, When attribute values are generated independently, all conditional structure is lost and the pseudo examples are generated from a distribution that is usually much broader than the true distribution of the data, As a consequence many of the generated pseudo examples will cover uninteresting parts of the space, and this may prevent the mimic model from focusing on the important regions, Estimate the joint distribution of attributes using the training set, then sample pseudo examples from this joint distribution, Assuming that the true joint distribution can be estimated well, the conditional structure of the domain would be preserved and the new artificial examples would cover well the interesting regions of the space, One way to estimate the joint distribution of a set of variables is to use mixture model algorithms. Meaning, the output contains a smaller range of values compared to the input without losing much information in the process. As per global tech market advisory firm ABI Research, about 230 billion devices will be shipped with TinyML chipset by 2030. Model compression (Bucila et al., 2006), which attempts to shrink a model without losing accuracy, is a viable approach to decreasing GPU usage. This technique requires the addition of a selective attention network upstream of the existing AI system. One might remove weights, neurons, layers, channels, attention heads, etc. Section 6 concludes th paper. For every bit of information we delete from BERT, it appears only a fraction is useful for CoLA, and an even smaller fraction useful for QQP.161616We cant quantify this now, but perhaps compression will help quantify the universality of the LM task. Recent improvements in the predictive quality of natural language processing systems are often dependent on a substantial increase in the number of model parameters. It leverages reinforcement learning to offer a model compression policy at a higher compression ratio, accuracy, and lower human effort. This thesis presents a general framework for knowledge distillation, whereby a convenient model of the authors' choosing learns how to mimic a complex model, by observing the latter's behaviour and being penalized whenever it fails to reproduce it. However, several alternative compression approaches have been proposed to discard non-task-specific information. We trained most models for 13 epochs rather than 3. For magnitude weight pruning, weve shown that 30-40% of the weights do not encode any useful inductive bias and can be discarded without affecting BERTs universality. Knowledge Distillation. 20-23, 2006, W. Chan, N. Kitaev, K. Guu, M. Stern, and J. Uszkoreit (2019), KERMIT: generative insertion-based modeling for sequences, Sparse networks from scratch: faster training without losing performance, J. Devlin, M. Chang, K. Lee, and K. Toutanova (2018), BERT: pre-training of deep bidirectional transformers for language understanding, U. Evci, F. Pedregosa, A. N. Gomez, and E. Elsen (2019), The difficulty of training sparse neural networks, The lottery ticket hypothesis: finding sparse, trainable neural networks, International Conference on Learning Representations, The state of sparsity in deep neural networks, S. Han, X. Liu, H. Mao, J. Pu, A. Pedram, M. A. Horowitz, and W. J. Dally (2016), EIE: efficient inference engine on compressed deep neural network, Proceedings of the 43rd International Symposium on Computer Architecture, S. Han, J. In some applications, we might really want the most interpretable model of all: simple Why doesnt fine-tuning change which weights are pruned much? Figure 1 shows that the first 30-40% of weights pruned by magnitude weight pruning do not impact pre-training loss or inference on any downstream task. Magnitude weight pruning itself is a simple procedure: 1. But if that is not available, they suggest sampling from a non-parametric estimate of the unlabeled data density. Sparse Architecture Search Hinton et al. researchers introduced the Lottery Ticket Hypothesis by improving on the traditional pruning technique. (2014b) took the concept forward for modern deep learning by training the student to mimic the teacher's output probabilities. Regularization Even so, we might consider simply training on the downstream tasks for much longer, which would increase the difference in weights pruned. Model compression (Bucila et al., 2006), which attempts to shrink a model without losing accuracy, is a viable approach to decreasing GPU usage. Models with 70-90% information deletion required 15 epochs to fit the training data. on Knowledge Discovery and Data Mining, Philadelphia, PA, USA, August Journal of Computer Science and Technology. This work introduces a novel method for lossless compression of tree-based ensemble methods, focusing on random forests, based on probabilistic modeling of the ensembles trees, followed by model clustering via Bregman divergence. In most cases covered by the experiment, a complex NN/ensemble can be compressed to a single layer of hidden units, however, can this also apply to problems where we would use CNN/RNN? (2015) proposed Knowledge Distillation (KD), which uses softened softmax labels from teacher networks when Low levels of pruning (30-40%) do not affect pre-training loss or transfer to downstream tasks at all. Further, companies such as Arm have taken a shine to TinyML, an embedded software technology used to build low power consuming devices to run ML models. Model compression is the technique of deploying state-of-the-art deep networks in devices with low power and resources without compromising on the models accuracy. Since pre-training information deletion plays a central role in performance degradation while over-pruning, we might expect that downstream fine-tuning would improve prunability by making important weights more salient (increasing their magnitude). [3] propose an algorithm to train a single neural network by mimicking the output of an ensemble of models. Tang et al. However, Dettmers and Zettlemoyer (2019) and Mostafa and Wang (2019) present methods to do this by allowing SGD to search over the space of possible subnetworks. Here, we collapse the trained additive resp. This information is not equally useful to each task; tasks degrade linearly with pre-train loss, but at different rates. Individual task results are in Table 1. pruned models, so complexity restriction is a secondary cause of performance degradation. One additional way to avoid overfitting next to Laplace estimates is model compression (Bucila et al. Often the best performing supervised learning models are ensembles of hundreds or thousands of base-level classifiers. Replace an ensemble by a simpler neural network (let a NN replicate the decisions a larger model would take), The technique is similar to having humans go through unlabeled data, labeling them, and then passing them along to a neural network to learn, MUNGE is some sort of nearest neighbor data generator (takes example e, find the closest example e' in the training data set and uses the features of these two examples to create a new example), We show how to train compact artificial neural nets to mimic the function learned by ensemble selection, We use the ensemble to label a large unlabeled data set and then train the neural net on this much larger, ensemble labeled, data set, The key difficulty when compressing complex ensembles into simpler models this way is the need for a large unlabeled data set, The main idea behind model compression is to use a fast and compact model to approximate the function learned by a slower, larger, but better performing model, An important question is how do we get the pseudo data (to train the model), It is important that the synthetic data match well the distribution of the real train and future test cases, Usually real data lay in a small submanifold of the complete attribute space. I am a technology journalist with AIM. The goal is to have the same distribution in the student model as available in the teacher model. Attention Head Pruning 4. Figure 2 shows that the pre-training loss linearly predicts the effects of information deletion on downstream accuracy. To train a neural net on ADULT, these attributes must first be converted to 14, 16, and 41 distinct binary attributes. 3. Want to hear about new tools we're making? Pruning can also increase inference speed if whole neurons or convolutional channels are pruned, which reduces GPU usage.111If weights are pruned, however, the weight matrices become sparse. Currently, the only way to know how much to prune is by trial and (dev-set) error. Is this information equally useful to all tasks? These sparse architectures, along with the appropriate initializations, are sometimes referred to as lottery tickets.333Sparse networks are difficult to train from scratch (Evci et al., 2019). View 3 excerpts, cites background and methods. Knowledge distillation is another approach to retain accuracy with model compression. Its unclear, however, whether this is because the pre-training task is less relevant to QQP or whether QQP simply has a bigger dataset with more information content.171717Hendrycks et al. We perform weight magnitude pruning on a pre-trained BERT-Base model.999https://github.com/google-research/bert We select sparsities from 0% to 90% in increments of 10% and gradually prune BERT to this sparsity over the first 10k steps of training. Pruning an extra 30% of BERTs weights is worth only one accuracy point on QQP but 10 points on CoLA. compression: 1quantization: quantization-aware-training (QAT), High-Bit (>2b) (DoReFa/Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference)Low-Bit (2b)/Ternary and Binary (TWN/BNN/XNOR-Net); post-training-quantization (PTQ . from publication: Model compression | Often the best performing supervised learning models are ensembles of hundreds or thousands of base-level classiers. ArXiv. We repeat this for learning rates in [2,3,4,5]105 and show the results with the best development accuracy in Figure 1 / Table 1. Does compressing BERT impede its ability to transfer to new tasks? Our experiments suggest that training on downstream data before pruning is too blunt an instrument to improve prunability. We might expect that BERT would be more compressible after downstream fine-tuning. To do so, we evaluate it on the training set and learn a single Poisson model per count variable. (e.g., through weight pruning and quantization), while comparatively little effort has been spent on devising techniques for encoding and compressing. Discover special offers, top stories, upcoming events, and more. (2019) showed that after fine-tuning on MNLI, up to 40% of attention heads can be pruned from BERT without affecting test accuracy. We also fine-tune on downstream tasks until training loss becomes comparable to models with no pruning. A common paradigm is to pre-train a feature extractor on large amounts of data then fine-tune it as part of a deep learning model on some downstream task (i.e. Our findings suggest that these methods might be used to train sparse BERT from scratch. We present a method for "compressing" large, complex ensembles into, 2016 IEEE 16th International Conference on Data Mining (ICDM). This similarity may imply that global pruning would perform similarly to matrix-local pruning. But the best performing information-deleted models have the lowest training error of all, so overfitting seems unlikely.151515We are reminded of the double-descent risk curve proposed by Belkin et al. Figure 3 shows fine-tuning changes which weights are pruned by less than 6%. arXiv as responsive web pages so you The three attributes with the highest arity have 14, 16, 41 unique values. Fitting an ensemble with a decision tree might give interpretable rules that could be scrutinized for legal/ethical compliance. ThoughtWorks Bats Thoughtfully, calls for Leveraging Tech Responsibly. We explore weight pruning for BERT and ask: how does compression during pre-training affect transfer learning? Pruning involves two steps: it deletes the information stored in a weight by setting it to 0 and then regularizes the model by preventing that weight from changing during further training. However, these models do not recover all evaluation accuracy, despite matching un-pruned models training loss. Why does pruning at these levels hurt downstream performance? @Tom_Rochette PDAs), and where computational power is limited (e.g. Predictors of performance degradation while pruning might help us decide which level of sparsity is appropriate for a given trained network without trying many at once. These models follow a pre-training paradigm: they are trained on a large amount of unlabeled text via a task that resembles language modeling (Yang et al., 2019; Chan et al., 2019) and are then fine-tuned on a smaller amount of downstream data, which is labeled for a specific task. While there is a separate key, query, and value projection matrix for each attention head, implementations typically stack matrices from each attention head, resulting in only 3 parameter matrices: one for key projections, one for value projections, and one for query projections. However, the complexity and size of the model may not necessarily . depending on which heuristic is used. Similarly, the performance of information-deletion models is a proxy for how much of that information was useful for each task. :L Selective attention: Only the objects or elements of interest are. This may be caused by dropout, or it may be a general property of our training regime (SGD). These approaches demonstrated substantial success in improving generalization capabilities of AIs as well as in reducing computational overheads ( Iandola et al . However, pre-training is effective precisely because the pre-training dataset is much larger than the labeled downstream dataset, which allows learning of more robust representations. In their KDD 2006 paper ("Model Compression"), Bucila, Caruana, and Niculescu-Mizil recommend simply using a large pool of unlabeled data, which is readily acquired in some applications, at least more easily than labeled data. Code. That is sometimes possible, e.g., Model Compression, Bucila et al., KDD 2006. Pre-trained feature extractors, such as BERT (Devlin et al., 2018) for natural language processing and VGG (Simonyan and Zisserman, 2014) for computer vision, have become effective methods for improving the performance of deep learning models. We conclude that BERT can be pruned once during pre-training rather than separately for each task without affecting performance. Stay up to date with our latest news, receive exclusive deals, and more. We might consider the pre-training loss as a proxy for how much pre-training information weve deleted in total. However, Figure 1 shows that models pruned after downstream fine-tuning do not surpass the development accuracies of models pruned during pre-training, despite achieving similar training losses. This paper applies state-of-the-art model compression techniques to create compact versions of several of models extensively trained with large computational budgets, evaluating them in terms of efficiency, model simplicity and environmental foot-print. Attention aspiring data scientists and analytics enthusiasts: Genpact is holding a career day in September! Universal feature extractors, such as BERT for natural language processing and VGG for computer vision, have become effective methods for improving deep learning models without requiring more labeled data. Some of the major breakthroughs in recent years in model compression include: Workshop, VirtualBuilding Data Solutions on AWS19th Nov, 2022, Conference, in-person (Bangalore)Machine Learning Developers Summit (MLDS) 202319-20th Jan, 2023, Conference, in-person (Bangalore)Rising 2023 | Women in Tech Conference16-17th Mar, 2023, Conference, in-person (Bangalore)Data Engineering Summit (DES) 202327-28th Apr, 2023, Conference, in-person (Bangalore)MachineCon 202323rd Jun, 2023, Stay Connected with a larger ecosystem of data science and ML Professionals. Unfortunately, the space required to store this many classifiers, and the time required to execute them at run-time, prohibits their use in applications where test sets are large (e.g. Protecting neural network model supply chain. In this work, we focus on weight magnitude pruning because it is one of the most fine-grained and effective pruning methods. Fitting an ensemble with a decision tree might give interpretable rules that could be scrutinized for legal/ethical compliance. (Gale et al., 2019). model. Recall that self-attention first projects layer inputs into key, query, and value embeddings via linear projections. Finally, we observe that fine-tuning BERT on a specific task does not improve its prunability or change the order of pruning by a meaningful amount. While some claim that language model pre-training is a universal language learning task (Radford et al., 2019), there is no theoretical justification for this, only empirical evidence. However, the complexity and size of the model may not necessarily translate to good performance. In 2019, MIT. These approaches demonstrated substantial success in improving generalization capabilities of AIs as well as in reducing computational overheads SqueezeNet:2016 , in cases of knowledge . In an extensive set of experiments, it is shown that compressing dynamic forecasting ensembles into an individual model leads to a comparable predictive performance and a drastic reduction in computational costs. micronet, a model compression and deploy lib. We might be concerned that poorly performing models are over-fitting, since they have lower training losses than unpruned models. When applied to Deep Learning, this requires the use of very large models that have to be trained multiple times, and the requirements of . Context Learned in this study Things to explore. Pre-training loss increases as we prune weights necessary for fitting the pre-training data (Table 1). (2019) used BERT as a knowledge distillation teacher to compress relevant information into smaller Bi-LSTMs, while Kuncoro et al. Section 5 showed that downstream fine-tuning does not increase prunability. It might also be used to trade accuracy for memory in some low-resource cases, such as deploying to smartphones for real-time prediction. We see that the main obstacle to compressing pre-trained models is maintaining the inductive bias of the model learned during pre-training. In 2019, researchers introduced a Multi-LayerPruning method (MLPrune) to decide compression ratios for all layers automatically. Compressing BERT for Specific Tasks Future work on compressing pre-trained models should focus on maintaining that inductive bias and quantifying its relevance to various tasks during accuracy/memory trade-offs. BERT-Large can only be used with access to a Google TPU, and BERT-Base requires some optimization tricks such as gradient checkpointing or gradient accumulation to be trained effectively on consumer hardware (Sohoni et al., 2019). (2019) suggest that pruning these weights might have a hidden cost: decreasing model robustness. Compression Alternatives to AI salvaging include model compression (Bucila et al., 2006), knowledge distillation (Hinton et al., 2015), and privileged information (Vapnik and Izmailov, 2017). Model compression (Bucila et al., 2006) rst introduced the idea of knowledge distillation by compressing an ensemble of models into a smaller network. That is sometimes possible, e.g., Model Compression, Bucila et al., KDD 2006. TRUE DIST RANDOM NBE MUNGE Figure 1: Synthetic data generated for a simple 2D problem.
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