H OW L OW C AN W E G O : T RADING M EMORY FOR E RROR IN L OW -P RECISION T RAINING
Anonymous authors
Paper under double-blind review
A BSTRACT
Low-precision arithmetic trains deep learning models using less energy, less memory and less time.
However, we pay a price for the savings: lower precision may yield larger round-off error and hence larger prediction error.
As applications proliferate, users must choose which precision to use to train a new model, and chip manufacturers must decide which precisions to manufacture.
We view these precision choices as a hyperparameter tuning problem, and borrow ideas from meta-learning to learn the tradeoff between memory and error.
In this paper, we introduce Pareto Estimation to Pick the Perfect Precision (PEPPP).
We use matrix factorization to ﬁnd non-dominated conﬁgurations (the Pareto frontier) with a limited number of network evaluations.
For any given memory budget, the precision that minimizes error is a point on this frontier.
Practitioners can use the frontier to trade memory for error and choose the best precision for their goals.
I NTRODUCTION
Training modern-day neural networks is becoming increasingly expensive as task and model sizes increase.
The energy consumption of the corresponding computation has increased alarmingly, and raises doubts about the sustainability of modern training practices (Schwartz et al., 2019).
Low-precision training can reduce consumption of both computation (De Sa et al., 2018) and memory (Sohoni et al., 2019a), thus minimizing the cost and energy to train larger models and making deep learning more accessible to resource-limited users.
[Figure]
Test error vs memory for ResNet-18 acrosslow-precision ﬂoating point conﬁgurations.
Figure (a) shows the tradeoff on CIFAR-10.
(Non-dominated points are blue circles.)
Figure (b) shows that the best precision to use varies depending on the memory budget, onimage datasets.
See Section 4 for experimental details.
Low-precision training replaces 32-bit or 64-bit ﬂoating point numbers with ﬁxed or ﬂoating point numbers that allocate fewer bits for the activations, optimizers, and weights.
A rich variety of methods appear in recent literature, including bit-centering (De Sa et al., 2018), loss scaling (Micikevicius et al., 2017) and mixed-precision training (Zhou et al., 2016).
There is however a fundamental tradeoff: lowering the number of bits of precision increases the quantization error , which may disrupt convergence and increase downstream error (Zhang et al., 2016;
Courbariaux et al., 2014; Gupta et al., 2015).
The low-precision conﬁguration is thus a hyperparameter to be chosen according to the resource constraints of the practitioner.
How should we choose this hyperparameter?
Many believe the highest allowable precision (given a memory budget) generally produces the lowest error model.
However, there are typically many ways to use the same amount of memory.
As shown in Figure 1(a), some of these conﬁgurations produce much lower error than others!
Figure 1(b) shows that no one conﬁguration dominates all others.
We might consult previous literature to choose a precision; but this approach fails for new applications.
Our goal is to efﬁciently pick the best low-precision conﬁguration under a memory budget.
Efﬁciency is especially important for resource-constrained practitioners, such as individual users or early-stage startups.
To promote efﬁciency, we use a meta-learning (Lemke et al., 2015; Vanschoren, 2018; Hospedales et al., 2020) approach: we train a small number of cheap very-low-precision models on the dataset to choose the perfect precision.
The gains from choosing the right low-precision format can offset the cost of this extra training — but each precision must be chosen carefully to realize the beneﬁts of low-precision training.
We use ideas from multi-objective optimization to characterize the tradeoff between memory and error and identify the Pareto frontier : the set of non-dominated solutions.
Users will want to understand the tradeoff between error and memory so they can determine the resources needed to adequately train a model for a given machine learning task.
This tradeoff may also inﬂuence the design of application-speciﬁc low-precision hardware, with profound implications for the future (Hooker, 2020).
For example, among all 99 low-precision conﬁgurations we tried, we identiﬁed some conﬁgurations that are Pareto optimal across many different tasks (listed in Appendix D).
These results could help hardware manufacturers decide which precision settings are worth manufacturing.
Computing the Pareto frontier by training models for all low-precision conﬁgurations is expensive and unnecessary.
Cloud computing platforms like Google Cloud and Amazon EC2 charge more for machines with the same CPU and double memory: 25% more on Google Cloud 1 and 100% more on
Amazon EC2 2 as of mid-September 2021.
We use techniques from meta-learning to leverage the information from other low-precision training runs on related datasets.
This approach allows us to estimate the Pareto frontier without evaluating all of the low-precision conﬁgurations.
Our system, Pareto Estimation to Pick the Perfect Precision (PEPPP), has two goals.
The ﬁrst goal is to ﬁnd the Pareto frontiers of a collection of related tasks, and is called meta-training in the meta-learning literature.
Meta-training requires a set of measurements , each collected by training and testing a neural network with a given precision on a dataset.
This information can be gathered ofﬂine with a relatively large resource budget, or by crowdsourcing amongst the academic or open-source community.
Still, it is absurdly expensive to exhaustively evaluate all measurements: that is, every possible low-precision conﬁguration on every task.
Instead, we study how to choose a subset of the possible measurements to achieve the best estimate.
The second goal we call meta-test : using the information learned on previous tasks, how can we transfer that information to a new task to efﬁciently estimate its Pareto frontier?
This goal corresponds to a resource-constrained individual or startup who wants to determine the best low-precision conﬁguration for a new dataset.
Both meta-training and meta-test rely on matrix completion and active learning techniques to avoid exhaustive search: we make a subset of all possible measurements and predict the rest.
We then estimate the Pareto frontier to help the user make an informed choice of the best conﬁguration.
We consider two sampling schemes: uniform and non-uniform sampling.
Uniform sampling is straightforward.
Non-uniform sampling estimates the Pareto frontier more efﬁciently by making fewer or even no high-memory measurements.
To the best of our knowledge, PEPPP is the ﬁrst to study the error-memory tradeoff in low-precision training and inference without exhaustive search.
Some previous works show the beneﬁt of lowprecision arithmetic in signiﬁcant energy reduction at the cost of a small accuracy decrease (Hashemi et al., 2017), and propose hardware-software codesign frameworks to select the desired model (Langroudi et al., 2019).
Many other papers focus on low-precision inference only and use it for model compression (Wu et al., 2018;
Cai & Vasconcelos, 2020).
Another line of work seeks state-of-the-art (SOTA) performance with low-precision training, using carefully designed learning schedules (Sun et al., 2019; 2020).
Our work imagines low-precision as a way to reduce training or deployment costs of customized models trained on proprietary datasets (not SOTA models on ML benchmarks).
Even so, we show in Section 4 that our work picks promising models for every memory budget, which enables near-SOTA results on CIFAR-10.
Figure 2 shows a ﬂowchart of PEPPP.
The rest of this paper is organized as follows.
Sectionintroduces notation and terminology.
Section 3 describes the main ideas we use to actively sample conﬁgurations and approximate Pareto frontiers.
Section 4 shows experimental results.
[Figure]
N OTATION AND TERMINOLOGY
Low-precision formats.
We use ﬂoating point numbers for low-precision training in this paper.
[Figure] [Figure]
As an example, Figure 3 shows an 8-bit ﬂoating point number with 3 exponent bits and 4 mantissa bits.
A speciﬁc low-precision representation with a certain number of bits for each part is called a low-precision format .
We may use different low-precision formats for the weights, activations, and optimizer.
In the case of using two formats to train and represent a neural network (as discussed in Section 3.1), these two formats are called Format A and B .
A speciﬁc combination of these two formats is a hyperparameter setting of the neural network, and we call it a low-precision conﬁguration .
Math basics.
We deﬁne [ n ] = { 1 , . . . , n } for a positive integer n .
With a Boolean variable X , the indicator function 1 ( X ) equals 1 if X is true, and 0 otherwise.
With a scalar variable x , we use x + to denote max { x, 0 } .
⊆
and ⊂ denote subset and strict subset, respectively.
Linear algebra.
We denote vector and matrix variables respectively by lowercase letters ( a ) and capital letters ( A ).
All vectors are column vectors.
The Euclidean norm of a vector a ∈ R n is (cid:107) a (cid:107) :=
(cid:112)(cid:80) ni
=1 a 2 i .
To denote a part of a matrix A ∈ R n × d , we use a colon to denote the varying dimension: A i, : and A : ,j (or a j ) denote the i th row and j th column of A , respectively, and A ij denotes the ( i, j ) -th entry.
With an ordered index set S = { s 1 , . . .
, s k } where s 1 < . . .
< s k ∈ [ d ] , we denote A : S :=
[ A : ,s· · ·
A : ,s k ] ∈ R n × k .
Given two vectors x, y ∈ R n , x (cid:22) y means x i ≤ y
i for each i ∈ [ n ] .
Given a matrix A ∈ R n × d and a set of observed indices as Ω ⊆ [ n ] × [ d ] , the partially observed matrix P Ω ( A ) ∈ R n × d has entries ( P Ω ( A )) ij =
A ij if ( i, j ) ∈ Ω , and 0 otherwise.
Pareto frontier.
Multi-objective optimization simultaneously minimizes n costs { c i } i ∈ [ n ] .
A feasible point c (1) =
( c (1) 1 , . . .
, c (1) n ) is Pareto optimal if for any other feasible point c (2)
=
( c (2) 1 , . . .
, c (2) n ) , c (2) (cid:22) c (1) implies c (2) =
c (1) (Boyd & Vandenberghe, 2004).
The set of Pareto optimal points is the
Pareto frontier .
Tasks, datasets, measurements and evaluations.
A task carries out a process (classiﬁcation, regression, image segmentation, etc.) on a dataset .
Given a deep learning model and a dataset, the training and testing of the model on the dataset is called a measurement .
In our low-precision context, we evaluate a conﬁguration on a dataset to make a measurement.
Meta-learning.
Meta-learning transfers knowledge from past ( meta-training ) tasks to better understand the new ( meta-test ) task.
To evaluate the error of meta-training, we use meta-leave-one-out cross-validation (meta-LOOCV): on a collection of tasks, each time we treat one task as meta-test and the others as meta-training, and average the results over all splits.
[Figure]
Dataset 1: CIFAR-10, 2: CIFAR-100 (fruit and vegetables), 3: ImageNet-stick (Deng et al., 2009).
Conﬁguration Format A (exponent bits, mantissa bits),
Format B (exponent bits, mantissa bits).
a: (3, 1), (6, 7); b: (3, 4), (7, 7); c: (4, 3), (8, 7); d: (5, 3), (6, 7).
Error matrix and memory matrix.
Given a neural network, the errors of different low-precision conﬁgurations on meta-training datasets form an error matrix , whose ( i, j ) -th entry E ij is the test error of the j th conﬁguration on the i th dataset.
To compute the error matrix, we split the i -th dataset into training and test subsets (or use a given split), train the neural network at the j -th conﬁguration on the training subset, and evaluate the test error on the test subset.
The memory required for each measurement forms a memory matrix M , which has the same shape as the corresponding error matrix.
Example error and memory matrices are shown in Figure 4.
M ETHODOLOGY
PEPPP operates in two phases: meta-training and meta-test.
First in meta-training, we learn conﬁguration embeddings by training a neural network of a speciﬁc architecture at different low-precision conﬁgurations on different datasets.
As listed in Appendix A, we study image classiﬁcation tasks and a range of low-precision formats that vary in the number of bits for the exponent and mantissa for the activations, optimizer, and weights.
Our hope is to avoid enumerating every possible conﬁguration since exhaustive search is prohibitive in practice.
To this end, we make a few measurements and then predict the other test errors by active learning techniques.
We also compute the memory matrix to understand the memory consumption of each measurement.
Then in meta-test, we assume that we have completed meta-training, and hence know the conﬁguration embeddings.
The meta-test goal is to predict the tradeoff between memory usage and test error on a new (meta-test) dataset.
This step corresponds to inference in traditional machine learning; it must be quick and cheap to satisfy the needs of resource-constrained practitioners.
To select the conﬁguration that has smallest test error and takes less than the memory limit, we measure a few selected (informative and cheap) conﬁgurations on the meta-test dataset, and use the information of their test errors to predict the rest.
The problem of choosing measurements is known as the “cold-start” problem in the literature on recommender systems.
Then we use the model built on the meta-training datasets to estimate values for the other measurements on the meta-test dataset.
3. M ETA - TRAINING
On the n meta-training datasets, we ﬁrst theoretically compute the memory needed to evaluate each of the d low-precision conﬁgurations to obtain the full memory matrix M ∈ R n × d .
The total memory consists of memory needed for model weights, activations, and the optimizer (gradients, gradient accumulators, etc.)
(Sohoni et al., 2019b).
Among these three types, activations typically dominate, as shown in Figure 5.
Thus using lower precision for activations drastically reduces memory usage.
Additionally, empirical studies (Zhou et al., 2016) have shown that adopting higher precision formats for the optimizer can substantially improve the accuracy of the trained model.
Since the optimizer usually requires a relatively small memory, it is often the best to use a higher precision format for this component.
Thus for each low-precision conﬁguration, it is typical to use a lower precision for network weights and activations, and a higher precision for the optimizer, resulting in combinatorially many choices in total.
An example of the memory usage in this low-precision scenario is also shown in Figure 5.
[Figure]
In practice, ML tasks are often correlated: for example, meta-training datasets might be subsets of a large dataset like CIFAR-100 or ImageNet.
The ranking of different conﬁgurations tends to be similar on similar tasks.
For example, we computed the test errors of 99 low-precision conﬁgurations on 87 datasets (both listed in Appendix A) to form a performance vector in R 99 for each dataset.
We use the Kendall tau correlation to characterize the alignment between the ranking of errors incurred by different conﬁgurations on two datasets: the Kendall tau correlation is 1 if the order is the same andif the order is reversed.
As shown in Figure 6(a), similar datasets have larger correlations: for example, datasets with indices 38–87 correspond to ImageNet subproblems such as distinguishing types of fruit or boat.
Notice also that some dataset pairs have conﬁguration performance rankings that are negatively correlated.
The corresponding error matrix
E concatenates the performance vector for each dataset.
It is not low rank, but its singular values decay rapidly, as shown in Figure 6(b).
Hence we expect low rank approximation of this matrix from a few measurements to work well: it is not necessary to measure every conﬁguration.
[Figure]
In the uniform sampling scheme, we sample measurements uniformly at random to obtain a partially observed error matrix P Ω ( E ) .
We then estimate the full error matrix E using a low rank matrix completion method to form estimate (cid:98) E .
In this paper, we use S OFT I MPUTE
(Mazumder et al., 2010; Hastie et al., 2015)
(Algorithm 1, Appendix B).
Using the estimated error matrix (cid:98) E and computed memory matrix M , we compute the Pareto frontier for each dataset to understand the (estimated) error-memory tradeoff.
Section 4 Figure 10 shows an example.
In the non-uniform sampling scheme, we sample measurements with non-uniform probabilities, and use a weighted variant of S OFT I MPUTE , weighting by the inverse sampling probability, to complete the error matrix (Ma & Chen, 2019).
Then we estimate the Pareto frontier in the same way as above.
Non-uniform sampling is useful to reduce the memory needed for measurements.
To this end, we construct a probability matrix P by entry-wise transformation P ij = σ (1 /M ij ) , in which the monotonically increasing σ : R → [0 , 1] maps inverse memory usage to a sampling probability.
In this way, we make more low-memory measurements, and thus reduce the total memory usage.
3. M ETA - TEST
Having estimated the embedding of each conﬁguration, our goal is to quickly compute the errormemory tradeoff on the meta-test dataset, avoiding the exhaustive search of all possible measurements.
We ﬁrst compute the memory usage of each low-precision conﬁguration on the meta-test dataset.
With this information and guided by meta-training, we evaluate only a few (cheap but informative) conﬁgurations and predict the rest.
Users then ﬁnally select the non-dominated conﬁguration with highest allowable memory.
An example of the process is shown in Figure 7.
PEPPP uses Experiment Design with Matrix Factorization (ED-MF) described below to choose informative conﬁgurations.
With more time, ED-MF evaluates more conﬁgurations, improving our estimates.
We may also set a hard cutoff on memory: we do not evaluate low-precision conﬁgurations exceeding the memory limit.
This setting has been studied in different contexts, and is called active learning or sequential decision making .
ED-MF picks measurements to minimize the variance of the resulting estimate.
Speciﬁcally, we factorize the true (or estimated) error matrix E ∈ R n × d (or (cid:98) E ) into its best rank- k approximation, and get dataset embeddings X ∈ R n × k and conﬁguration embeddings
Y ∈ R d × k as E ≈ X (cid:62) Y (Fusi et al., 2018;
Yang et al., 2019).
On a meta-test dataset, we denote the error and memory vectors of the conﬁgurations as e new ∈ R d and m new ∈ R d , respectively.
We model the error on the new dataset as e new =
Y (cid:62) x new + (cid:15) ∈ R d , where x new ∈ R k is the embedding of the new dataset and (cid:15) ∈ R d accounts for the errors from both measurement and low rank decomposition.
We estimate the embedding x new from a few measurements (entries) of e new by least squares.
Note that this requires at least k measurements on the meta-test dataset to make meaningful estimations, in which k is the rank for matrix factorization.
[Figure]
If (cid:15) ∼ N (0 , σ 2 I ) , the variance of the estimator is (cid:16) (cid:80) j ∈ S
y j y (cid:62) j (cid:17) − 1 , in which y j is the j th column of Y .
D-optimal experiment design selects measurements on the new dataset by minimizing (a scalarization of) the variance, minimize log det (cid:16)
[Equation] subject to | S | ≤ l S ⊆
[ d ] , (1) to ﬁnd S , the set of indices of conﬁgurations to evaluate.
The positive integer l bounds the number of measurements.
Given a memory cap m max , we replace the second constraint above by S ⊆ T , in which T = { j ∈ [ d ] | m new j ≤ m max } is the set of feasible conﬁgurations.
Since
Problem 1 is combinatorially hard, we may either relax it to a convex optimization problem by allowing decision variables to have non-integer values between 0 and 1, or use a greedy method to incrementally choose measurements (initialized by measurements chosen by the column-pivoted QR decomposition (Gu & Eisenstat, 1996; Golub & Van Loan, 2012)).
We compare these two approaches in Appendix C.
In Section 4.2, we compare ED-MF with competing techniques and show it works the best to estimate the Pareto frontier and select the ﬁnal conﬁguration.
E XPERIMENTS AND DISCUSSIONS
The code for PEPPP and experiments is in the anonymous GitHub repository at https://github. com/barterer/lp .
We use QPyTorch (Zhang et al., 2019) to simulate low-precision formats on standard hardware.
The 87 datasets and 99 low-precision conﬁgurations in experiments are listed in
Appendix A.
The datasets consist of natural and medical images from various domains.
Apart from
CIFAR-10, the datasets include 20 CIFAR-100 partitions from mutually exclusive subsets, based on the superclass labels.
They also include 50 subsets of ImageNet (Deng et al., 2009), which contains over 20,000 classes grouped into multiple major hierarchical categories like fungus and amphibian; each of the 50 datasets come from different hierarchies.
Finally, we use 16 datasets from the visual domain benchmark (Wallace & Hariharan, 2020) to increase the diversity of domains.
The 99 low-precision conﬁgurations use mixed-precision as described in Section 3.1.
Format A (for the activations and weights) uses 5 to 9 bits; Format B (for the optimizer) ranges from 14 to 20 bits.
For each, these bits may be split arbitrarily between the exponent and mantissa.
Across all conﬁgurations, we use ResNet-18, ResNet-34 and VGG (Simonyan & Zisserman, 2014) variants (11, 13, 16, 19) with learning rate 0.001, momentum 0.9, weight decay 0.0005 and batch size 32.
Each training uses only 10 epochs as an early stopping strategy (Yao et al., 2007) to prevent overﬁtting.
All the measurements take 35 GPU days on NVIDIA ® GeForce ® RTX 3090.
Developing a method to select the above hyperparameters at the same time as the low-precision format is an important topic for future research, but not our focus here.
In Section 4.3 we demonstrate how
PEPPP can naturally extend to efﬁciently select both optimization and low-precision hyperparameters.
A number of metrics may be used to evaluate the quality of the obtained Pareto frontier in a multiobjective optimization problem (Wu & Azarm, 2001; Li et al., 2014; Audet et al., 2020): the distance between approximated and true frontiers (convergence), the uniformity of the distances between neighboring Pareto optimal points (uniformity), how well the frontier is covered by the approximated points (spread), etc.
In our setting, the memory is accurate and the test error is estimated, so we do not have full control of the uniformity and spread of the 2-dimensional frontiers between test error and memory.
As illustrated in Figure 8, we evaluate the quality of our estimated Pareto frontiers by the following two metrics:
[Figure]
Convergence (Deb et al., 2002) between the sets of and points
P , (cid:98) P is 1 | (cid:98) P| (cid:80) v ∈ (cid:98) P dist ( v, P ) , where the distance between point v and set P is dist ( v, P )
=
min {(cid:107) v − w (cid:107) : w ∈ P} .
This is a surrogate for the distance between Pareto frontiers.
Hypervolume difference (HyperDiff) (Zitzler & Thiele, 1998; Coello & Sierra, 1999; Wu &
Azarm, 2001) is the absolute difference between the volumes of solution spaces dominated by the true and estimated Pareto frontiers.
This metric improves with better convergence, uniformity, and spread.
Its computation requires an upper boundary for each resource.
When computing these metrics, we normalize the memories by proportionally scaling them to between 0 and 1.
To evaluate the matrix completion performance, we use the relative error deﬁned as (cid:107) (cid:98) v − v (cid:107) / (cid:107) v (cid:107) , in which (cid:98) v is the predicted vector and v is the true vector.
4. M ETA - TRAINING
We study the effectiveness of uniform and non-uniform sampling schemes.
For simplicity, we regard all 87 available datasets as meta-training in this section.
In S OFT I MPUTE , we use λ = 0 .
1 (chosen by cross-validation from a logarithmic scale); and we choose a rank 5 approximation, which accounts for 78% of the variance in the error matrix (as shown in Figure 6(b)).
In the uniform sampling scheme, we investigate how many samples we need for accurate estimates: we sample the error matrix at a number of different ratios, ranging from 5% to 50%, and complete the error matrix from each set of sampled measurements.
In uniform sampling, the sampling ratio is also the percentage of memory we need to make the measurements in parallel, compared to exhaustive search.
Hence we show the relationship between Pareto frontier estimation performance and sampling ratio in Figure 9.
We can see the estimates are more accurate at larger sampling ratios, but sampling 20% of the entries already sufﬁces for good performance.
As a more intuitive example, Figure 10 shows the error-memory tradeoff on CIFAR-10.
Compared to the estimated Pareto optimal points at sampling ratio 5%, the ones at 20% are closer to the true
Pareto frontier (better convergence), lie more evenly (better uniformity) and cover the true Pareto frontier better (better spread), as shown by the respective convergence and HyperDiff values.
The non-uniform sampling scheme has a similar trend and is shown in Appendix E.2.
4. M ETA - LEAVE - ONE - OUT CROSS - VALIDATION ( META -LOOCV)
Now suppose that we have already collected measurements on the meta-training datasets to form a meta-training error matrix E (or its low rank approximation (cid:98) E ).
On the meta-test dataset, PEPPP estimates the Pareto frontier by the active learning technique ED-MF.
We compare ED-MF with a few other active learning techniques: Random selection with matrix factorization (R ANDOM -MF),
[Figure]
violins individual the range
The red error bars show the standard deviation of the error on CIFAR-10 across 100 random of the
Figure a) the matrix completion error
Figure (
) and
) show the performance around 0.1)
good
[Figure]
(convergence 0.09 and HyperDiff 0.16).
QR decomposition with column pivoting and matrix factorization (QR-MF) and two Bayesian optimization techniques (BO-MF and BO- FULL ), to understand whether the strong assumptions in
ED-MF (low rank and Gaussian errors) are a hindrance or a help.
An introduction to these techniques can be found in Appendix E.1.
[Table]
We use rank 3 for matrix factorization: Y : ,j ∈ R 3 for each j ∈ [ d ] .
In BO, we tune hyperparameters on a logarithmic scale and choose the RBF kernel with length scale 20 , white noise with variance 1 , and ξ = 0 . 01 .
Table 1 shows the meta-LOOCV settings for each acquisition technique.
We compare the techniques at a range of number of conﬁgurations to measure in each meta-LOOCV split, resembling what practitioners do in hyperparameter tuning: evaluate an informative subset and infer the rest.
Setting I is the most basic, Setting IV and VI are the most practical.
We only show results of
Setting I and IV in the main paper, and defer the rest to Appendix E.
In Setting I, we do meta-LOOCV with the full meta-training error matrix in each split and do not cap the memory for meta-test.
This means we evaluate every conﬁguration on the meta-training datasets.
We can see from Figure 11(a) and 11(b) that:
• ED-MF stably outperforms under both metrics, especially with fewer measurements.
• QR-MF overall matches the performance of ED-MF.
• BO-MF, BO- FULL and RANDOM -MF underperform ED-MF and QR-MF at lower memory usage, but often match their performance with higher memory.
Practitioners may have only collected part of the meta-training performance, and desire or are limited by a memory cap when they do meta-test on the new dataset.
In Setting IV, we cap the single-conﬁguration memory usage for meta-test at 816MB, the median memory of all possible measurements across conﬁgurations and datasets.
Additionally, we uniformly sample 20% conﬁgurations from the meta-training error matrix in each split.
In Figure 11(c) and 11(d), we can see similar trends as Setting I, except that QR-MF is slightly worse.
Ultimately, users would want to select a conﬁguration that both achieves a small error and takes lower memory than the limit.
As shown in Figure 2, PEPPP offers users the predicted Pareto frontier and chooses the non-dominated conﬁguration with the highest allowable memory and hence the lowest error.
We compare the acquisition techniques with the “random high-memory” baseline that randomly chooses a conﬁguration that takes the highest allowable memory: an approach that follows the “higher memory, lower error” intuition.
Figure 12 shows an example.
[Figure] [Figure]
when making 3 measurements (memory usage ∼ 10%) on 10 ImageNet partitions.
ED-MF outperforms in most cases.
Overall, among all approaches and across all memory usage, ED-MF outperforms, especially at a smaller number of measurements.
Compared to BO techniques, ED-MF also enjoys the beneﬁt of having less hyperparameters to tune.
Although techniques like QR-MF and RANDOMMF are easier to implement, the additional cost of ED-MF is much smaller than the cost of making measurements: neural network training and testing.
4. T UNING OPTIMIZATION HYPERPARAMETERS
The low-precision networks still underﬁt after 10 epochs of training.
This situation is typical: underﬁtting due to budget constraints is unfortunately common in deep learning.
Luckily, meta-learning the best precision will succeed so long as the validation errors are correctly ordered , even if they all overestimate the error of the corresponding fully trained model.
Indeed, our validation errors correlate well with the errors achieved after further training: on CIFAR10, the Kendall tau correlation between ResNet-18 errors at 10 epochs and 100 epochs is 0.73, shown in Figure 19.
The lowest error at 100 epochs is 9.7%, only approximately 2% higher than
SOTA (Bungert et al., 2021).
Previously, we have shown that PEPPP can estimate the error-memory tradeoff and select a promising conﬁguration with other hyperparameters ﬁxed.
In practice, users may also want to tune hyperparameters like learning rate to achieve the lowest error.
In Appendix E.4, we tune learning rate in addition to precision, and show that the methodology can be used in broader settings of hyperparameter tuning.
4. M ETA - LEARNING ACROSS ARCHITECTURES
The meta-learning in previous sections were conducted on ResNet-18.
In Appendix E.5, we show that on 10 ImageNet partitions, PEPPP is also capable of estimating the error-memory tradeoff of ResNet-34, VGG-11, VGG-13, VGG-16 and VGG-19 competitively.
Moreover, the meta-learning across architectures works better than considering each architecture separately.
C ONCLUSION
This paper proposes PEPPP, a meta-learning system to select low-precision conﬁgurations that leverages training information from related tasks to efﬁciently pick the perfect precision given a memory budget.
Built on low rank matrix completion with active learning, PEPPP estimates the Pareto frontier between memory usage and model performance to ﬁnd the best low-precision conﬁguration at each memory level.
By reducing the cost of hyperparameter tuning in low-precision training, PEPPP allows practitioners to efﬁciently train accurate models.
A D ATASETS AND L OW -P RECISION F ORMATS
The 87 datasets chosen in our experiment are listed in Table 2.
Note that the datasets contain images of two different resolutions: 32 denotes resolution 3 × 32 × 32 , and 64 denotes 3 × 64 × 64 .
We list our choices of Format A and B in Table 3 and 4, respectively.
In each evaluation, the low-precision conﬁguration is composed of one Format A (for the activations and weights) and one Format B (for the optimizer), as detailed in Section 3.1.
Therefore, the total number of low-precision conﬁgurations used in our experiment is 99.
B T HE S OFT I MPUTE A LGORITHM
There are several versions of S OFT I MPUTE ; (Hastie et al., 2015) gives a nice overview.
We use the version in (Mazumder et al., 2010).
At a high level, S OFT I MPUTE iteratively applies a soft-thresholding operator S λ on the partially observed error matrix with a series of decreasing λ values.
Each S λ replaces the singular values { σ i } with { ( σ i − λ ) + } .
The regularization parameter λ can be set in advance or ad hoc, by convergence dynamics.
The pseudocode for the general S OFT
I MPUTE algorithm is shown as Algorithm 1, in which P Ω ( E ) is a matrix with the same shape as E , and has the ( i, j ) -th entry being E ij if ( i, j ) ∈ Ω , andotherwise.
In our implementation, λ i =
λt
i for each step i , and t i is the step size from TFOCS backtracking (Becker et al., 2011).
Input: a partially observed matrix P Ω ( E ) ∈ R n × d , number of iterations I , a series of decreasing λ values { λ i } Ii = Output: an estimate (cid:98) E for i = 1 to I do (cid:101) E ← P Ω ( E ) + P Ω
[Equation]
[Equation]
C A LGORITHMS FOR E XPERIMENT D ESIGN
As mentioned in Section 3.2, there are mainly two algorithms to solve Problem 1, the D-optimal experiment design problem: convexiﬁcation and greedy.
The convexiﬁcation approach relaxes the combinatorial optimization problem to the convex optimization problem minimize
[Equation]
subject to
[Equation]
2) that can be solved by a convex solver (like SLSQP).
Then we sort the entries in the optimal solution v ∗ ∈ R d and set the largest l entries to 1 and the rest to 0.
The greedy approach (Madan et al., 2019; Yang et al., 2020) maximizes the submodular objective function by ﬁrst choosing an initial set of conﬁgurations by column-pivoted QR decomposition, and then greedily adding new conﬁguration to the solution set S in each step until | S | = l .
The greedy stepwise selection algorithm is shown as Algorithm 2, in which the new conﬁguration is chosen by the Matrix Determinant Lemma 3 (Harville, 1998), and X − 1 t is updated by the close form from the
Sherman-Morrison Formula 4 (Sherman & Morrison, 1950).
The column-pivoted QR decomposition selects top k pivot columns of Y ∈ R k ×
d to get the index set S 0 , ensuring that X 0 = (cid:80) j ∈ S
y j y (cid:62) j is non-singular.
Input: design vectors { y j } dj =1 , in which y j ∈ R k ; maximum number of selected conﬁgurations l ; initial set of conﬁgurations S 0 ⊆
[ d ] , s.t. X
= (cid:80) j ∈ S
y j y (cid:62) j is non-singular
Output: the selected set of designs S ⊆
[ d ]
S ← S 0while | S | ≤ l do i ← argmax j ∈ [ d
[Equation]
y (cid:62)
[Equation]
y j
[Equation]
The convexiﬁcation approach empirically works because most entries of the optimal solution v ∗ are close to either 0 or 1.
The histogram in Figure 13 shows an example when we use rank k = 5 to factorize the entire error matrix and set l = 20 .
[Figure]
In terms of solution quality, the relative error plot in Figure 13 shows that the greedy approach consistently outperforms in terms of the relative matrix completion error for each dataset in ED-MF solutions.
Since the greedy approach is also more than 10 × faster than convexiﬁcation (implemented by scipy ), we use the greedy approach throughout all experiments for the rest of this paper.
D I NFORMATION TO HARDWARE DESIGN
Among all 87 datasets (each has its own error-memory tradeoff), the promising conﬁgurations that we identify among all 99 conﬁgurations are as below (number of sign bits - number of exponent bits -number of mantissa bits).
• Format A: 1-4-1, Format B: 1-6-7 (appears on 60 out of 87 Pareto frontiers) • Format A: 1-4-1, Format B: 1-7-7 (appears on 33 out of 87 Pareto frontiers) • Format A: 1-3-1, Format B: 1-7-7 (appears on 30 out of 87 Pareto frontiers) • Format A: 1-4-2, Format B: 1-6-7 (appears on 24 out of 87 Pareto frontiers) • Format A: 1-5-2, Format B: 1-6-7 (appears on 21 out of 87 Pareto frontiers)
E M ORE DETAILS ON EXPERIMENTS
We ﬁrst show the plot of explained variances of top principal components in Figure 14: how much variance in our data do the ﬁrst several principal components account for (Bishop, 2006).
This quantity is computed by the ratio of sum of squares of the ﬁrst k singular values to that of all singular values.
In Figure 14, we vary k from 1 to 30, corresponding to the decay of singular values shown in
Figure 6(b).
We can see the ﬁrst singular value already accounts for 99 . 0% of the total variance, and the ﬁrst two singular values account for more than 99 .
This means we can pick a small rank for PCA in meta-training and still keep the most information in our meta-training data. A − 1 a ) [Figure]
[Figure]
i
[Figure] [Figure] We use the ratio of incorrectly classiﬁed images as our error metric. Figure 15 shows histograms of error and memory values in our error and memory matrices from evaluating 99 conﬁgurations ondatasets.
The vertical dashed lines show the respective medians: 0 .
78 for test error and 816MB for memory.
We can see both the test error and memory values span a wide range.
The errors come from training a wide range of low-precision conﬁgurations with optimization hyperparameters not ﬁnetuned, and are thus larger than SOTA results.
In Section 4.3 of the main paper and Section E.4 here, we show that training for a larger number of epochs and with some other optimization hyperparameters yield the same error-memory tradeoff as Figure 1(a) in the main paper.
In meta-LOOCV settings with a meta-test memory cap at the median memory 816MB, Figureshows a histogram of number of feasible conﬁgurations on each of the 87 datasets.
There are “cheap” (resolution 32) datasets on which each of the conﬁgurations takes less than the cap, and “expensive” (resolution 64) datasets on which the feasible conﬁgurations are far less than 99.
E. I NTRODUCTION TO R ANDOM -MF, QR-MF AND BO
• Random selection with matrix factorization (R ANDOM -MF).
Same as ED-MF, R ANDOMMF predicts the unevaluated conﬁgurations by linear regression, except that it selects the conﬁgurations to evaluate by random sampling.
• QR decomposition with column pivoting and matrix factorization (QR-MF).
QR-MF ﬁrst selects the conﬁgurations to evaluate by QR decomposition with column pivoting: EP = QR , in which the permutation matrix P gives the most informative conﬁgurations.
Then it predicts unevaluated conﬁgurations in the same way as ED-MF and R ANDOM -MF.
• Bayesian optimization (BO) .
Bayesian optimization is a sequential decision making framework that learns and optimizes a black-box function by incrementally building surrogate models and choosing new measurements (Frazier, 2018).
It works best for black-box functions that are expensive to evaluate and lack special structures or derivatives.
We compare ED-MF with two BO techniques.
The ﬁrst technique, BO-MF, applies BO to the function f 1 : R k
→ R that maps low-dimensional conﬁguration embeddings { Y : ,j } dj =1 to the test errors of the conﬁgurations on the meta-test dataset { e new j } dj =
(Fusi et al., 2018).
The embeddings come from the same low-rank factorization of the error matrix E as in ED-MF.
The second, BO- FULL , applies BO to the function f 2 : R n → R that directly maps columns of the error matrix { E : ,j } dj = to { e new j } dj =1 .
To learn either of these black-box functions, we start by evaluating a subset of conﬁgurations S ⊆
[ d ] and then incrementally select new conﬁgurations that maximize the expected improvement ( [Table] [Table]
E. A DDITIONAL META - TRAINING RESULTS : NON - UNIFORM SAMPLING
[Figure]
In non-uniform sampling, we sample each entry of the error matrix E with probabilities
P ij = σ (1 /W ij ) , in which σ maps { 1 /W ij } into an interval [0 , p max ] ⊆
[0 , 1] according to the cumulative distribution function of { 1 /W ij } .
By varying p max , we change how we aggressively sample the conﬁgurations, how different the sampling probabilities are between large and small memory conﬁgurations, and also the percentage of memory needed.
In Figure 17, we vary p max from 0.1 to 1 and see that the quality of the estimated Pareto frontier improves with more memory.
E. A DDITIONAL M ETA -LOOCV R ESULTS
In the main paper, we have shown the performance of Pareto frontier estimates and conﬁguration selection for Setting I and IV.
For the rest of the settings in Table 1, we conduct meta-LOOCV in the same way and show the results of Pareto frontier estimates in Figure 18.
Setting II.
We have the 816MB memory cap for meta-test, and have the full meta-training error matrix in each split
[Figure]
an 816MB meta-test memory cap). Each error bar is the standard error across datasets.
ED-MF is among the best in every setting and under both metrics.
Setting III.
We have no memory cap for meta-test, and uniformly sample 20% conﬁgurations for meta-training in each split.
Setting V.
We have no memory cap for meta-test, and non-uniformly sample 20% conﬁgurations for meta-training in each split.
The sampling method is the same as in meta-training (Figure 17).
Setting VI.
We have the 816MB memory cap for meta-test, and non-uniformly sample 20% metatraining conﬁgurations in the same way as Setting V above. [Figure]
E. T UNING O PTIMIZATION H YPERPARAMETERS
Figure 19 shows the validation errors of 99 conﬁgurations on the ResNet-18 architecture that is trained for different numbers of epochs.
In between 100 epochs and 10 epochs, the errors have a
Kendall tau correlation of 0.73.
[Figure] Figure 20 shows that the error-memory tradeoff still exists with a ﬁne-tuned learning rate for each conﬁguration.
With the learning rate tuned across { 0 . 01 , 0 .
001 , 0 . 0001 } , the test errors are in general smaller, but high-memory conﬁgurations still achieve lower errors in general.
Thus the need to efﬁciently select a low-precision conﬁguration persists.
Our approach can naturally extend to efﬁciently selecting optimization and low-precision hyperparameters.
We perform the meta-training and meta-LOOCV experiments on a subset of CIFARpartitions with multiple learning rates { 0 . 01 , 0 . 001 , 0 . 0001 } .
The error and memory matrices we use here have 45 rows and 99 columns, respectively.
Learning rate and low-precision conﬁguration are collapsed into the same dimension: each row corresponds to a combination of one CIFAR-100 subset and one of the learning rates { 0 . 01 , 0 .
001 , 0 . 0001 } , as shown in Table 5.
We say that these error and memory matrices are LR-tuned .
Figure 21 shows the LR-tuned error matrix also has a fast singular value decay.
The other hyperparameters are the same as in LR-ﬁxed experiments, except that we use batch size 128 and train for 100 epochs. [Figure] [Figure] sampling ratio 0 .0 .0 .0 .
H y p
e r D i ﬀ [Figure] [Figure] sampling ratio 0 .0 .0 .0 .
H y p
e r D i ﬀ
[Figure]
The meta-training and meta-LOOCV results are consistent with those in Sections 4.1 and 4.2, respectively.
In meta-training, we ﬁrst uniformly sample the error matrix and study the performance of matrix completion and Pareto frontier estimation.
Figure 22 shows the matrix completion error and Pareto frontier estimation metrics.
Then we do non-uniformly sampling and get Figure 23, the LR-tuned version of Figure 17 in the main paper.
In meta-test, we evaluate settings in Table 1 in the main paper.
We get the performance of Pareto frontier estimates in Figure 24, the LR-tuned version of Figure 11 in the main paper.
ED-MF steadily outperforms.
E. L EARNING ACROSS ARCHITECTURES
We show that on 10 ImageNet partitions, PEPPP with ED-MF is able to estimate the error-memory tradeoff of the low-precision conﬁgurations on of ResNet-34, VGG-11, VGG-13, VGG-16 and VGG19.
The 10 ImageNet partitions have WordNet IDs {n02470899, n01482071, n02022684, n03546340, n07566340, n00019613, n01772222, n03915437, n02489589, n02127808} and are randomly selected from the 50 ImageNet subsets on which we collected the error matrix.
On these ImageNet partitions, we use the performance of ResNet-18 as meta-training data, and either the performance of ResNetor VGG variants as meta-test data.
In Figure 25, we can see that ED-MF is steadily among the best in Pareto frontier estimation, and there is no statistical difference between the estimation performance on ResNet-34 and VGG variants. [Figure] [Figure]
Next, we compare the performance of the following two cases:
i Meta-learning across datasets with performance from the same architecture:
For example, to learn the error-memory tradeoff of ResNet-18 on n02470899, we only use the tradeoffs of ResNet-18 on 9 other ImageNet partitions as the meta-training data.
ii Meta-learning across datasets with performance from both the same and other architectures: For example, the error-memory tradeoff of ResNet-18 on n02470899, we not only use the tradeoffs of ResNet-18 on 9 other ImageNet partitions, but also use those of ResNet-34, VGG-11, VGG-13, VGG-16 and VGG-19 on the 9 partitions as the meta-training data.
Figure 26 shows that Case ii outperforms Case i in better estimating the error-memory tradeoffs on different architectures and datasets. [Figure]
[Table]
stick) 6750 42 ImageNet(turtle) 6750 43 ImageNet(ﬁnch) 6750 44 ImageNet(watchdog) 6404 45 ImageNet(footwear) 6587 46 ImageNet(salamander) 6750 47 ImageNet(anthropoidape) 6750 48 ImageNet(elasmobranch) 6750 49 ImageNet(shorebird) 6750 50 ImageNet(housing) 6605 51 ImageNet(foodstuff) 6750 52 ImageNet(substance) 6643 53 ImageNet(spider) 8100 54 ImageNet(percussioninstrument) 8082 55 ImageNet(NewWorldmonkey) 8100 56 ImageNet(bigcat) 8100 57 ImageNet(box) 7829 58 ImageNet(fabric) 7862 59 ImageNet(kitchenappliance)
60 ImageNet(mollusk) 8100 61 ImageNet(handtool) 8054 62 ImageNet(butterﬂy) 8100 63 ImageNet(stringedinstrument) 8100 64 ImageNet(boat) 8006 65 ImageNet(rodent) 8006 66 ImageNet(toiletry)
67 ImageNet(computer) 7696 68 ImageNet(shop) 9400 69 ImageNet(mustelinemammal) 9450 70 ImageNet(OldWorldmonkey)
9450 71 ImageNet(bottle) 9205 72 ImageNet(fungus) 9450 73 ImageNet(truck)
74 ImageNet(spaniel) 9119 75 ImageNet(sportsequipment) 9450 76 ImageNet(gamebird) 9450 77 ImageNet(seat) 9126 78 ImageNet(fruit) 9450 79 ImageNet(weapon) 9450 80 ImageNet(beetle) 10800 81 ImageNet(toydog) 9832
ImageNet(decapodcrustacean
[Table] [Table]
[Table] [Table]