A NT M AN : S PARSE L OW -R ANK C OMPRESSION TO A CCELERATE RNN INFERENCE
Anonymous authors
Paper under double-blind review
A BSTRACT
Wide adoption of complex RNN based models is hindered by their inference performance, cost and memory requirements.
To address this issue, we develop
AntMan, combining structured sparsity with low-rank decomposition synergistically, to reduce model computation, size and execution time of RNNs while attaining desired accuracy.
AntMan extends knowledge distillation based training to learn the compressed models efﬁciently.
Our evaluation shows that AntMan offers up to 100x computation reduction with less than 1pt accuracy drop for language and machine reading comprehension models.
Our evaluation also shows that for a given accuracy target, AntMan produces 5x smaller models than the state-ofart.
Lastly, we show that AntMan offers super-linear speed gains compared to theoretical speedup, demonstrating its practical value on commodity hardware.
I NTRODUCTION
Remarkable advances in deep learning (DL) have produced great models across a wide variety of tasks such as computer vision, machine reading, speech generation and image recognition (Goodfellow et al., 2016).
However, wide adoption of these models is still limited by their inference performance, cost and memory requirements.
On the client side, all pervasive devices like smart-phones, tablets and laptops have limited memory and computational resources to handle large complex DL models.
On the server side, intensive computation can render the models too slow to meet responsiveness requirements and too expensive to scale, preventing their deployment in production.
Model Compression is a ﬂourishing area that aims to reduce the computational and memory complexity of DL models to address the aforementioned problems without signiﬁcantly affecting accuracy.
Compressing Convolution Neural Networks (CNNs) have already been widely explored in the past few years (Cheng et al., 2017), while our work focuses on Recurrent Neural Networks (RNNs), which are broadly used among various natural language processing tasks (Mikolov et al., 2010; Seo et al., 2016; Zaremba et al., 2014).
It is well known that large RNN models are computation and memory intensive (Zhang et al.).
In particular, their computation increases linearly with sequence length, and their recurrent unit has to be computed sequentially, one step at a time with limited parallelism, both of which makes long execution time a crucial issue for RNN inference computation.
Compressing RNNs, however, is challenging, because a recurrent unit is shared across all the time steps in sequence, compressing the unit will aggressively affect all the steps.
Inducing sparsity is one of the prominent approaches used for RNN compression.
Narang et al.
(2017a) proposed a pruning approach that deletes up to 90% connections in RNNs.
The obtained sparse matrices, however, have an irregular/non-structured pattern of non-zero weights, which is unfriendly for efﬁcient computation in modern hardware systems (Lebedev & Lempitsky, 2016;
Wen et al., 2016).
To address this issue, Narang et al.
(2017b)
proposed inducing block-sparsity in RNNs via pruning or group lasso regularization.
Similarly, Wen et al.
(2017) introduces ISS, intrinsic structured sparsity for LSTMs (Hochreiter & Schmidhuber, 1997), a type of RNN , such that a sparse LSTM can be transformed into a dense one but with smaller size.
ISS conveniently turns sparsity into efﬁcient execution, but as its sparse structure is quite coarse-grained, it is hard to push out high sparsity without degrading accuracy, especially in RNNs where the hidden dimension is smaller than input dimension (elaborated in Section 5.1).
Our work explores a new line of structured sparsity on RNNs, using predeﬁned compact structures as opposed to pruning and regularization based approaches.
We take inspiration from predeﬁned compact CNN structures such as group convolutions (Zhang et al., 2017; Krizhevsky et al., 2012) and depth-wise separable convolutions (Chollet, 2017).
Speciﬁcally, we replace matrix-vector multiplications (MVs), the dominant part of RNN computations, with localized group projections (LGP).
LGP divides the input and output vectors into groups where the elements of the output group is computed as a linear combination of those from the corresponding input group.
In addition, to empower the information ﬂow across multiple groups along the steps of RNN computation, we use a permutation matrix or a dense-square matrix to combine outputs across groups, helping the compact structure to retain accuracy.
Furthermore, we combine LGP with low-rank matrix decomposition in order to further reduce the computations.
This is possible as low rank and sparsity are complimentary to each other.
Low-rank decomposition such as SVD approximates a low-rank multiplication A x as PQ x , where P and Q are dense.
By imposing LGP-based sparsity on P and Q , we reduce the computation further.
For a given rank reduction factor of r , we reduce the computation cost and model size by O ( r 2 ) , compared to O ( r ) by using low-rank decomposition methods like SVD (Golub & Reinsch, 1970) alone.
We call our compression approach AntMan — ‘shrink in scale’ by synergistically combining structured sparsity and low-rank decomposition, but ‘increase in strength’ by enabling the ﬂow across structured groups along RNN sequence to retain accuracy.
To train RNN models with AntMan, we use teacher-student training paradigm (Bucilu et al., 2006) by combining the label loss with teacher-MSE-loss and teacher-KL-divergence-loss.
To improve the training efﬁciency, we develop a new technique to decide proper coefﬁcients to obtain high accuracy efﬁciently with minimal trials.
We evaluate AntMan on multiple RNN based models for machine reading comprehension and language modeling.
For a well-known MRC model (Seo et al., 2016), we reduce the computational complexity and model size of LSTMs (a particular type of RNN) by up to 25x with less than 1pt drop in F1 score.
For PTB (Zaremba et al., 2014) language model, we achieve a computational reduction of 50x with no drop in perplexity, and 100x with just a single point drop in perplexity.
We also construct language models for PTB with perplexities ranging from 64 to 70, but with 3x to 5x fewer overall model weights (5x to 25x reduction in RNN weights) than the state-of-art.
Last but not least, we develop efﬁcient implementations of inference kernels on CPUs to serve models compressed by AntMan.
We show that unlike computation with unstructured sparsity, AntMan offers signiﬁcant performance improvement for large RNN models even with modest levels of sparsity.
Our evaluations show that a 2x to 10x theoretical reduction in computation can result in up to 2x to 30x actual speedup, respectively, for moderate to large RNNs, demonstrating attractive practical value of AntMan on commodity hardware.
R ELATED W ORK
Compressing RNNs via Sparsity: Described in Section 1 and empirically compared in Section 5.1.
Compressing RNNs via Low-Rank Approximations: Prabhavalkar et al.
; Lu et al.
(2016) use SVD to compress LSTM models by 3-4x for acoustic modeling and speech recognition tasks with negligible loss in accuracy.
AntMan achieves signiﬁcantly higher compression rate than SVD based methods for the same rank reduction.
Ye et al.
(2017) uses Block Tensor Decomposition to compress LSTMs for vision tasks.
Their work is speciﬁcally designed to exploit redundancies present in the image vector (input to the LSTMs) obtained from upstream CNN layers, while
AntMan is designed to compress general RNNs, where the inputs do not exhibit such redundancies in many cases.
Teacher-Student training paradigm: Knowledge Distillation (KD) technique developed by Hinton et al.
(2015) is a popular approach to compress deep and wide networks into sparser ones, where the compressed model mimics the function learned by the complex model.
KD usually optimizes a weighted average of two different objective functions.
The ﬁrst objective function can be one of the following three: cross entropy, or mean square error, or Kullerback Leiber divergence, all computed with respect to the soft targets, and the second objective function is the cross entropy with the correct labels.
Several similar approaches (Romero et al., 2014; Luo et al., 2016; Chen et al., 2015; Balan et al., 2015; Zagoruyko & Komodakis, 2016) extend the idea of KD.
In contrast, AntMan optimally combines three objective functions, MSE loss, KL divergence loss and the cross entropy of the true labels, powered by an efﬁcient method of deciding their coefﬁcients.
A NT M AN D ESIGN AND I MPLEMENTATION
AntMan compresses RNN computation by combining beneﬁts of structured sparsity and low rank decomposition.
It consists of three components: i) localized group projections that sparsify matrix multiplications using block diagonal matrices, ii) group mixing that exchanges information across different local groups along the sequence of RNN computation, and iii) low rank approximation that uses SVD like decomposition to reduce the rank of the projection.
By composing them, we construct a few variations of AntMan compression modules that exhibit varying degree of compression rate and accuracy impact.
We also analyze the cost complexity of AntMan modules and discuss efﬁcient implementation on commodity hardware such as CPUs using off-the-shelf BLAS libraries.
3. L OCALIZED G ROUP P ROJECTIONS
AntMan reduces the computation and size of RNNs by replacing dense matrix-vector product (MV) with sparse but structured MV.
It divides the input and output vectors into g local groups such that the elements of an output group is a weighted linear sum of the elements in the corresponding input group.
Since output elements of one group only depend on the input elements of the corresponding group, we call it localized group projections (LGP).
Mathematically, we replace the matrix vector product
A x with D g x , where D g is a block-diagonal matrix with g blocks.
In an RNN cell computation, the hidden-state vector at time-step t − 1 is an input to the MV used to compute the hidden-state vector at time-step t .
Therefore, using LGP to replace MV in RNN restricts the information ﬂow within a single local group across multiple time steps of the RNN.
This restriction reduces the expressibility of the RNN, potentially degrading accuracy.
AntMan uses ‘group mixing’ to address this issue.
3. G ROUP M IXING
To facilitate the information ﬂow across multiple localized groups along RNN sequence computation, AntMan multiplies the output (or input) vector of LGP with a square matrix, which we call mixing matrix.
We develop two types of mixing with varying memory and computational complexity — shufﬂe mix and dense mix — inspired by the shufﬂing layer (Zhang et al., 2017) used with group convolutions, or 1x1 convolutions used with depth-separable convolutions (Chollet, 2017).
Shufﬂe mix: The shufﬂe-mix matrix is a permutation matrix, which evenly distributes the elements of the same group across the entire output vector across different groups.
Figure 1a shows the effect of shufﬂe mix following LGP.
Mathematically, shufﬂe mix is equivalent to a transpose operation.
If the output vector v resulting from the block diagonal MV has m elements, we can represent the vector as a matrix O of size [ g, m/g ] , where each row represents an output group computed from the corresponding input group.
Shufﬂe mix simply transforms v to O T of shape [ m/g, g ] .
Dense mix: This technique uses a dense square matrix for group mixing when the matrix in the
MV is non-square.
Mathematically, given A x , where size of A is m x n , we can decompose it into
MD g x , when m < n , or D g M x , when n < m , and M is a dense-mixing matrix of size m x m , or n x n , respectively.
Figure 1b shows an example of dense mix preceding LGP.
Dense mix has added cost of the dense matrix vector multiply compared to shufﬂe mix (quadratic vs linear).
However, unlike shufﬂe mix that simply permutes the elements of the output vector, dense mix takes a weighted linear combination, making it more general.
It helps retain accuracy at the expense of additional computation.
When combined with low-rank decomposition discussed next, dense mix provides high compression while maintaining accuracy, which we elaborate further in evaluation (Table 4).
3. L OW - RANK D ECOMPOSITION
Low-rank decomposition such as SVD approximates a low-rank matrix-vector product
A x as PQ x , where A , P and Q are dense with shapes m x n , m x nr and nr x n , respectively, and nr is the reduced rank.
We combine it with LGP by adding LGP-based sparsity on P and Q , further reducing computation.
This combination is likely to obtain more compression than using either of the techniques alone because structured sparsity and low-rank decomposition operate in a complimentary fashion.
In particular, low rank reduces the computation by factorizing A into smaller matrices P and Q , while LGP reduces computation by sparsifying these matrices without changing their dimensions.
3. C OMPOSING C OMPONENTS INTO A NT M AN C OMPRESSION M ODULES
Composed from the three components, LGP , group mixing and low-rank decomposition , we construct variations of AntMan compression modules to address varying efﬁciency and accuracy demand across DL models.
Figure 1 shows three of such compression modules: (a) LGP-shufﬂe —
LGP with shufﬂe mix; (b) LGP-dense — LGP with dense mix; (c) LowRank-LGP — low rank with
LGP-dense.
We elaborate the compression modules by taking Figure 1(c), LowRank-LGP, as an example.
LowRank-LGP combines structured sparsity with low rank decomposition.
First, it decomposes an MV into an SVD-like form, i.e., A x ← PQ x , where A is a matrix of size m x n , P and Q are decomposed matrices of size m x nr and nr x n , and nr represents the reduced rank.
Next, we replace P and Q using LGP-Dense, i.e., A x ← D g out M out M in D g in x , where D g in and D g out are block-diagonal matrices of size m x nr and nr x n , and g in and g out are the number of diagonal blocks.
M in and M out are both square matrices of size nr x nr .
It can be further simpliﬁed into
A x ← D g out M r D g in x , where M r =
M out M in .
This module, combining all three components of AntMan, exhibits the potential of achieving signiﬁcantly higher cost reduction than using SVD alone, which we quantify shortly.
3. C OMPUTATION AND M ODEL S IZE R EDUCTION
[Table]
[Equation]
Table 1 discusses the reduction in computation and model size over the original A x , where A is a matrix of size m x n .
The third column reports the total number of multiply-add operations, which is also the size of weight matrix in the case of MV.
The ﬁnal column represents the reduction in computation (that is equal to the reduction in model size) compared to the original MV.
We highlight two key messages: (1) LGP-Dense reduces the total cost by ≈ max ( m,n ) min ( m,n ) when g (cid:29) max ( m,n ) min ( m,n ) , i.e., the larger difference between m and n , the more reduction it gets.
(2) When g out and g in are large enough, LowRank-LGP can enable signiﬁcantly higher cost reduction over SVD, while maintaining the same reduced rank.
To see this, let’s assume m = n , and g out
= g in = g .
In this case, the cost reduction from SVD is r/ 2 , while the cost reduction from LowRank-LGP is r 2 g 2 r + g .
Now, if g ≥ r , then the cost reduction is at least r 2 / 3 , and it goes up to r 2 when g (cid:29) r .
Therefore, the reduction in computational cost scales as O ( r ) for SVD, while it scales as O ( r 2 ) for
LowRank-LGP assuming g ≥ r .
As a concrete example, consider a MV of size 1000 x 400 , where the number of parameters and the number of multiply-add (MADD) operations in 400K. Using LGP-Shufﬂe with g = 10 , we can reduce both the number of parameters and MADD operations to 40K. Using LGP-Dense with g = 10 , we can reduce them to 200K (40K from LGP + 160K from dense mix).
Using LowRankLGP with g = 10 and r = 4 , we can reduce the parameters and MADD operations to 1000 ∗ 400 4 ∗+ 400 ∗ 400 4 ∗+ 400 ∗ 400 4 ∗ 10 , which is 24K.
3. E FFICIENT I MPLEMENTATION
We develop efﬁcient implementation of AntMan modules (LGP-Shufﬂe, LGP-Dense, LowRankLGP) on CPUs to empower their usage in practice.
The implementation consists of three building blocks: i) Regular matrix-vector multiply for dense mix, ii) shufﬂe-mix multiply and iii) blockdiagonal MVs.
BLAS libraries such as Intel MKL already provides efﬁcient implementation of matrix-vector multiplication.
Shufﬂe mix is implemented efﬁciently as a matrix transpose operation as described in Section 3.1.
The block-diagonal MV is viewed as multiple smaller MVs, each corresponding to one of the blocks in the block-diagonal matrix.
With multicores, each of these blocks can be computed in parallel using OpenMP for parallelization and Intel MKL for MV computation.
In summary, AntMan modules can be implemented efﬁciently on commodity hardware, such as
CPUs, conveniently applicable to various devices on cloud and on edge.
T RAINING A NT M AN U SING K NOWLEDGE D ISTILLATION
We observe that while training AntMan models directly on target labels alone does not generalize well on test data, using knowledge distillation or teacher-student training helps greatly on retaining accuracy.
We use the original uncompressed model as the teacher, and train the compressed model (student) to imitate the output distribution of the teacher, in addition to training on the target labels.
We describe how we apply and extend teacher-student training.
Loss function: We deﬁne the loss function of the compressed model as a weighted combination of three losses — the raw loss from the target labels, and the MSE and the KL divergence losses of the student’s output distribution with respect to the teacher’s corresponding output distribution:
[Equation]
where C target , C mse , C kl are the coefﬁcient values corresponding to the target loss, MSE loss and
KL divergence loss, respectively.
S
o , T o are the output distributions of student and teacher model, respectively, whereas T target is the target distribution.
Deciding loss coefﬁcients: The ﬁnal performance of the compressed model signiﬁcantly depends on the values of the loss coefﬁcients, C target , C mse and C kl .
Searching for appropriate values for these coefﬁcients via grid or random search is time and resource consuming.
We develop an efﬁcient method to decide them with the following intuition.
The direction of the gradient updates to the model is dictated by the relative magnitudes of the individual losses.
If one is signiﬁcantly smaller than the other, then it would have minimal impact on the direction of the updates during training.
Therefore, we want to scale each of the three losses such that the overall magnitude of each of the three terms in Eqn.
1 is roughly the same.
To this end, we initially train the compressed model separately using each of the three losses and record the loss values once the model converges.
Then we use these values as reference to identify loss coefﬁcients such that each of the three terms is roughly the same.
We use these coefﬁcients to train the compressed model to optimize accuracy.
Effectiveness: We demonstrate the effectiveness of our approach by training a 10x compressed language model constructed by replacing the LSTMs in Zaremba et al.
(2014) with LGP-Shufﬂe with g = 10 .
For this compressed model, the validation loss values at convergence when training separately with the three individual losses were: target = 4 .
110 , MSE = 0 .
133 and KL = 0 .
Table 3 shows the test perplexity values (lower the better) obtained by training the compressed model with varying C mse and C KL , while ﬁxing C target = 1 .
Note that the lowest perplexity is achieved by setting coefﬁcient values of C target = 1 , C mse = 30 and C KL = 1000 .
At these values, each term in Eqn.
1 is roughly equal to 4 , demonstrating the effectiveness of our method.
Table 3 also shows the beneﬁts of combining three losses.
Note that when C mse = 0 , the best achievable perplexity is 121 . 97 .
Similarly, when C KL = 0 , the best achievable perplexity is 75 . 61 .
However, combining all three gives the lowest perplexity of 74 . 69 .
[Table] [Table]
E XPERIMENTS
We evaluate AntMan on three aspects.
(1) We use AntMan to obtain order(s) of magnitude computation reduction for language modeling and machine reading comprehension tasks while getting similar accuracy.
(2) We use AntMan to construct models with several times fewer parameters than the state-of-art models with the same accuracy targets.
(3) Not limited by theoretical speedup, we measure real speedup of AntMan on CPUs and observe super-linear computational efﬁciency on large RNNs, demonstrating attractive practical value of AntMan on commodity hardware.
5. C OMPUTATION R EDUCTION
We evaluate the effectiveness of AntMan on reducing model computation: (1) on Zaremba et al.
(2014) model for word level completion task, we obtain 50 x reduction without sacriﬁcing any accuracy; (2) on Seo et al.
(2016) model for machine reading compression task, we obtain up to 25 x reduction with less than 1pt drop on F1 score.
5.1. W ORD LEVEL COMPLETION
Word level completion task predicts the next word given a partial input sequence.
Dataset and Model: We use Penn Tree Bank(PTB) dataset (Marcus et al., 1993) that consists of 929k training words, 73k validation words and 82k test words.
As the teacher model, we chose the model in Zaremba et al.
(2014) consisting of 2 layered LSTMs each with hidden dimension of 1500.
For the student model, we replace all the MVs in the LSTMs with LGP-Shufﬂe, and use g = 10 to g = 100 groups.
We do not use any low-rank decomposition for this model.
Results: Table 2 shows the perplexity values of the compressed models for different levels of computation reductions.
Matching the perplexity of the original model, AntMan ( g = 50 ) achieves 50x computation reduction.
With g = 10 , AntMan achieves 10 x computation reduction while 3pt better perplexity.
With g = 100 , AntMan achieves 100 x computation reduction with only 1pt loss in perplexity.
In addition, comparing with the state-of-art compressed model in Wen et al.
(2017) using
ISS, AntMan reduces computations further by 5-10x under comparable test perplexities.
5.1. M ACHINE R EADING C OMPREHENSION (MRC)
MRC tasks have gained signiﬁcant popularity in last few years within NLP and computer vision communities.
The models answer a query about a given context paragraph, evaluated based on exact match (EM) and F1 score (higher the better).
Dataset: We use Stanford Question Answering Dataset (SQuAD) (Rajpurkar et al., 2016), which consists of a large set of Wikipedia articles and more than 100,000 questions.
The answer to every question is always a small excerpt of the article.
Teacher Model: We chose our teacher model as the
BiDirectional Attention Flow Model (BiDAF) (Seo et al., 2016), which is a hierarchical multi-stage model with 6 layers.
We focus on compressing the layers having RNNs, which are also the most computationally expensive ones.
Speciﬁcally, the modeling layer uses 2 layers of bi-directional
LSTMs, denoted by ModFwd1, ModBwd1, ModFwd2, ModBwd2, while the output layer has a single bi-directional LSTM, denoted by OutFwd, OutBwd.
Compressed Models: We created three compressed models using AntMan with different levels of compression to replace the LSTMs in the BiDAF model: i) LGP-Shufﬂe ( g im = 10 , g hm = 4) ,
[Table]
The computation reduction for each is shown in
Results: Table 4 shows that both LGP-Shufﬂe and LowRank-LGP achieve signiﬁcant computation reduction over the original; their reduction is much higher than the existing work ISS (Wen et al., 2017) with better EM and F1 scores.
ISS compresses an RNN by reducing hidden dimension.
The amount of computation per LSTM step for ISS is proportional to ( i + h/r ) ∗ ( h/r ) , where i is the input dimension, h is the hidden dimension, and 1 /r is fraction of hidden dimension removed by
ISS.
When i (cid:29) h , the compression is proportional to r .
In BiDAF, i (cid:29) h in the ﬁrst modeling layers (800 vs 100).
Therefore, compression in these layers is proportional to the reduction in the hidden dimension.
However, h = 100 is already very small.
By reducing it further, ISS experiences near 2pt drop in F1 score with less than 2.5x compression on the ﬁrst modeling layers.
LGP-Shufﬂe uses structured sparsity to compress both the input and hidden MVs without reducing hidden dimension.
For a comparable EM and F1 scores to ISS, LGP-shufﬂe achieves signiﬁcantly higher reduction on the ﬁrst modeling layers, while doing modestly better on all other layers.
LowRank-LGP improves further upon LGP-Shufﬂe, increasing accuracy by leveraging dense mix to enrich the connection among multiple localized groups, and reducing computation by combining low-rank decomposition.
It achieves signiﬁcantly higher computation reduction across all layers than both ISS and LGP-Shufﬂe, while achieving nearly 1pt higher F1 scores.
5. O PTIMIZED MODEL SIZE FOR DIFFERENT ACCURACY TARGETS
Different applications have various accuracy requirements while the devices they are running on also impose different constraints on the model size.
For given accuracy targets, smaller models are desired; and for given model sizes, higher accuracy is desired.
We show that AntMan improves the Pareto curve of model size against accuracy, providing more compressed models with the same accuracy targets of several recent models at word level completion task.
Teacher Model: We use the state-of-art language model as of Sept, 2018, AWD-LSTM (Merity et al., 2017), consisting of 3-layer LSTMs with 1150 hidden units and an embedding size of 400.
Compressed Models: Our compressed models replace all the MVs in the LSTMs of AWD-LSTM with AntMan (LGP-Shufﬂe with g = 5 to g = 50 groups).
Results: Figure 2 compares AntMan with other models.
LGP-Shufﬂe ( g = 5 ) achieves perplexity of 63 with 5x fewer LSTM parameters and 3x fewer total parameters than NAS-Cell (Zoph & Le, 2016), the state-of-art model obtaining this range of accuracy.
LGP-Shufﬂe ( g = 10 ) achieves perplexity of 66 with 10x fewer LSTM parameters and 4x fewer total parameters than Var-RHN
(Zilly et al., 2016), and LGP-Shufﬂe ( g = 50 ) achieves perplexity of 74 with 50x fewer LSTM parameters and 5x fewer total parameters than Var-LSTM-avg
(Inan et al., 2016).
These results notably improve the Pareto curve of the task by reducing model sizes against different accuracy targets.
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5. T HEORETICAL VS A CTUAL S PEEDUP
By using efﬁcient implementation of AntMan described in Section 3.
we turn the theoretical speedup (computation reduction) to actual speedup (execution time reduction) in practice.
Furthermore we show that the actual speedup can be signiﬁcantly higher than the theoretical speedup for large problem sizes.
The result of our evaluation is shown in Table 5.
Problem Conﬁguration: We measure the execution time of LSTMs with and without AntMan varying input and hidden dimensions from 100 to 1600.
We use a batch size of 1, which is common in serving scenarios, and a sequence length of 100.
LSTM Implementation: We use an efﬁcient implementation as discussed in Elsen: Fuse 4 input
MVs across all time steps into a single large matrix multiplication, and fuse 4 hidden MVs within each time step.
Platform: The experiments are run on a single core of Intel CPU E5-2650 v4 @
2.20GHz.
We use just a single core for two reasons: i) to emulate the limited resource availability in many use cases such as laptops and smart phones, ii) performance of multi-core RNN is highly implementation dependent (Zhang et al.)
even for regular RNNs and therefore is difﬁcult to make apple-to-apple comparison.
We use Intel MKL library for GEMM implementation.
Discussion: Table 5 shows that, for very small problem size, AntMan offers no speedup regardless of the reduction in the computation.
This is expected as GEMM performance gets worse as the problem size decreases (Zhang et al.
; Rajbhandari et al., 2017).
However, as the problem size is already very small, memory reduction or performance improvement is less crucial for such problems.
For medium sized problems, AntMan offers good actual speedup compared to the theoretical speedup.
Notice that unlike unstructured sparsity, where signiﬁcant levels of sparsity is necessary to see actual performance improvement, with AntMan, even a modest 50% sparsity or 2x computation reduction results in signiﬁcant performance gain at problem size 400 and 800.
Furthermore, for large problem sizes the actual speedup is signiﬁcantly larger than the theoretical speedup.
At problem size ofand 1600, the weight matrices in the LSTM are too large to ﬁt in L3 cache (30 MB in this case), thus spilling into memory.
These LSTMs have much lower efﬁciency as the memory bandwidth of a
CPU is much lower than the L3 cache bandwidth.
By reducing the memory footprint, AntMan-based
LSTM ﬁts in L3 cache, leading to an actual speed up that is considerably higher than the theoretical speedup.
These results demonstrate attractive practical value of AntMan on commodity hardware.
C ONCLUSION
We develop AntMan, combining structured sparsity and low-rank decomposition, to reduce the computation, size and execution time of RNN models by order(s) of magnitude while achieving similar accuracy.
We hope its compression efﬁciency and effectiveness would help unblock and enable many great RNN-based models deployed in practice. A PPENDIX
In this section, we justify our choice of ISS as the strongest baseline for comparing against AntMan by demonstrating the drawbacks of various other compression techniques.
We also discuss how
AntMan compares to ISS in the absence of Knowledge Distillation, and present some additional experiemtal details.
A. C HOICE OF B ASELINE
We considered several compression techniques to identify the strongest baselines to compare with
AntMan.
Eventually, we chose ISS as the main baseline in the paper, because for RNNs ISS check two important criteria that we believe are most critical for model compression techniques, i)
Good theoretical reduction in compute and memory achievable without sacriﬁcing accuracy, ii) Good computation efﬁciency of the compressed model to fully exploit the theoretical reduction in computation.
Most compression techniques do not satisfy both of these criteria.
Below we discuss several compression techniques, and presents results when applicable comparing these techniques to AntMan, that we hope will justify to the readers, why they were not considered as the primary baselines.
Quantization : Up to 8-bit quantization (original 32-bit) can be supported fairly easily on commodity hardware, resulting in a maximum compression of 4x.
On the other hand, we have demonstrated that Ant-Man can achieve up to 100x reduction in computation without loss in accuracy.
Furthermore, quantization can be applied to AntMan to further reduce the computation, as quantization is not orthogonal but rather complementary to AntMan.
Pruning Pruning can be used to generate both unstructured and structured sparsity.
The former is not computationally efﬁcient while the latter requires specialized implementation for efﬁcient execution.
While we did not present pruning results in the paper, we did try out techniques on both PTB and
BiDAF models to generate random sparsity as well as blocked sparsity.
In both cases, we were able to get more that 10x reduction in computation even in the absence of Knowledge distillation.
Therefore pruning provides excellent computation reduction.
However, as discussed in the paper, those theoretical computational reductions cannot be efﬁciently converted into practical performance gains: Unstructured sparsity resulting from pruning suffers from poor computation efﬁciency; a 10x theoretical reduction leads to less than 4x improvement in performance while AntMan achieves 30x performance gain with 10x reduction for PTB like models.
(Table 7)
It is possible to achieve structured sparsity such as block sparsity through pruning.
However, structured sparsity requires implementing specialized kernels to take advantage of the computation reduction.
The compute efﬁciency greatly depends on the implementation, and in general is far from ideal.
On the contrary both ISS and AntMan achieves good computation reduction, and can be efﬁciently executed using readily available BLAS libraries such as Intel MKL resulting in super linear speedups as shown in the paper.
Direct Design : We compared AntMan with smaller RNN models (with smaller hidden dimension) trained using the larger teacher model.
Our results show that for the same level of compression
AntMan achieves much higher accuracy.
(Table 6)
SVD RNN :
We constructed compressed models by replacing matrix-multiplication with SVD of various rank, and trained the SVD based models using knowledge distillation.
Once again, we ﬁnd that for same level of compression, AntMan achieves much higher accuracy than SVD.
(Table 6)
Block Tensor Decomposition (BTD) :
BTD is designed to compress RNNs whose inputs are produced by convolution based models, and contain certain redundancies.
AntMan, on the other hand, is generic to all RNN based models.
Also, BTD is designed to compress only the input vector and not the hidden vectors.
This hinders the performance of BTD over a range of RNNs, where the hidden vectors are also large and hence we didnt consider BTD as a strong baseline to compare against.
6. ISS VS A NT M AN WITHOUT K NOWLEDGE D ISTILLATION
[Table]
Here, we compare the performance of AntMan with ISS, without using any knowledge distillation.
Please note that knowledge distillation is part of the training process for AntMan, but it is not for
ISS.
Nevertheless, it is interesting to see how AntMan performs in the absence of a teacher.
When trained without knowledge distillation, our experiemnts show that AntMan and ISS have complimentary strengths.
On the PTB dataset, with a 10x compute reduction, AntMan does not generalize well without a teacher, while ISS incurs less than 1pt loss in perplexity compared to the original model.
This is demonstrated by the ﬁrst row and column in Table 3, and the third row in Table 2. On the contrary, for the BiDAF, AntMan incurs less than 1pt reduction in F1 score for nearly 10x compute reduction 2 , while ISS incurs nearly 2pt reduction in F1 score with less than 5x compute reduction on average. This is shown in Table 8.
AntMan can sucessfully compress BiDAF, while ISS fails because ISS compresses an LSTM by effectively reducing its hidden dimension, while AntMan preserves the hidden dimension size.
The
LSTMs in the BiDAF model have large input dimensions making them computationally expensive, but they have very small hidden dimensions.
Therefore, reducing the already small hidden dimension results in signiﬁcant loss of accuracy.
On the contrary, the PTB model has large input as well as hidden dimensions, allowing ISS to work effectively.
6. A DDITIONAL E XPERIMENT DETAILS
Optimizers All the models in the evaluation section were trained using ADAM optimizer.
Hyperparameters For both PTB and SQUAD, all the hyper-parameters for training the respective models were set as default from their standard and recommended implementations on github (PTB, BiDAF 4 ).