Large-Scale Matrix Factorization with Distributed Stochastic Gradient Descent

Transcription

1 Large-Scale Matrix Factorization with Distributed Stochastic Gradient Descent KDD 2011 Rainer Gemulla, Peter J. Haas, Erik Nijkamp and Yannis Sismanis Presenter: Jiawen Yao Dept. CSE, UT Arlington 1 1

2 Outline Matrix Factorization Stochastic Gradient Descent Distributed SGD Experiments Summary Dept. CSE, UT Arlington 2

3 Matrix completion Dept. CSE, UT Arlington 3

4 Matrix Factorization As Web 2.0 and enterprise-cloud applications proliferate, data mining becomes more important Real application Set of users Set of items (movies, books, products, ) Feedback (ratings, purchase, tags, ) Predict additional items a user may like Assumption: Similar feedback Similar taste Matrix Factorization Dept. CSE, UT Arlington 4

5 Matrix Factorization Example - Netflix competition 500k users, 20k movies, 100M movie ratings, 3M question marks Avatar The Matrix Up Alice? 4 2 Bob 3 2? Charlie 5? 3 The goal is to predict missing entries (denoted by?) Dept. CSE, UT Arlington 5

6 Matrix Factorization A general machine learning problem Recommender systems, text indexing, face recognition, Training data V: m n input matrix (e.g., rating matrix) Z: training set of indexes in V (e.g., subset of known ratings) Output find a approximation V WH which have the smallest loss argmin W,H L(V, W, H) Dept. CSE, UT Arlington 6

7 The loss function Loss function Nonzero squared loss L NZSL = L NZSL i,j:v ij 0 V ij WH ij 2 A sum of local losses over the entries in V ij L = (i,j) Z l V ij, W i, H j Focus on the class of nonzero decompositions Z = i, j : V ij 0 Dept. CSE, UT Arlington 7

8 The loss function Find best model argmin W,H (i,j) Z L ij (W i, H j ) W i row i of matrix W H j column j of matrix H To avoid trivialities, we assume there is at least one training point in every row and in every column. Dept. CSE, UT Arlington 8

9 Prior Work Specialized algorithms Designed for a small class of loss functions GKL loss Generic algorithms Handle all differentiable loss functions that decompose into summation form Distributed gradient descent (DGD), Partitioned SGD (PSGD) The proposed Dept. CSE, UT Arlington 9

10 Successful applications Movie recommendation >12M users, >20k movies, 2.4B ratings 36GB data, 9.2GB model Website recommendation 51M users, 15M URLs, 1.2B clicks 17.8GB data, 161GB metadata, 49GB model News personalization Dept. CSE, UT Arlington 10

11 Stochastic Gradient Descent Find minimum θ of function L Dept. CSE, UT Arlington 11

12 Stochastic Gradient Descent Find minimum θ of function L Pick a starting point θ 0 Dept. CSE, UT Arlington 12

13 Stochastic Gradient Descent Find minimum θ of function L Pick a starting point θ 0 Dept. CSE, UT Arlington 13

14 Stochastic Gradient Descent Find minimum θ of function L Pick a starting point θ 0 Dept. CSE, UT Arlington 14

15 Stochastic Gradient Descent Find minimum θ of function L Pick a starting point θ 0 Approximate gradient L (θ 0 ) Dept. CSE, UT Arlington 15

16 Stochastic Gradient Descent Find minimum θ of function L Pick a starting point θ 0 Approximate gradient L (θ 0 ) Stochastic difference equation θ n+1 = θ n ε n L (θ n ) Under certain conditions, asymptotically approximates (continuous) gradient descent Dept. CSE, UT Arlington 16

17 Stochastic Gradient Descent for Matrix Factorization Set θ = (W, H) L(θ) = and use (i,j) Z L ij W i, H j L (θ) = (i,j) Z L ij W i, H j L (θ, z) = NL z (W iz, H jz ) Where N = Z and training point z is chosen randomly from the training set. Z = i, j : V ij 0 Dept. CSE, UT Arlington 17

18 Stochastic Gradient Descent for Matrix Factorization SGD for Matrix Factorization Input: A training set Z, initial values W0 and H0 1. Pick a random entry z Z 2. Compute approximate gradient L (θ, z) 3. Update parameters θ n+1 = θ n ε n L (θ n, z) 4. Repeat N times In practice, an additional projection is used θ n+1 = Π H [θ n ε n L θ n, z ] to keep the iterative in a given constraint set H = {θ: θ 0}. Dept. CSE, UT Arlington 18

19 Stochastic Gradient Descent for Matrix Factorization Why stochastic is good? Easy obtain May help in escaping local minima Exploit repetition with the data Dept. CSE, UT Arlington 19

20 Distributed SGD (DSGD) SGD steps depend on each other How to distribute? θ n+1 = θ n ε n L (θ n, z) Parameter mixing (ISGD) Map: Run independent instances of SGD on subsets of the data Reduce: Average results once at the end Does not converge to correct solution Iterative Parameter mixing (PSGD) Map: Run independent instances of SGD on subsets of the data (for some time) Reduce: Average results after each pass over the data Converges slowly Dept. CSE, UT Arlington 20

21 Distributed SGD (DSGD) Dept. CSE, UT Arlington 21

22 Stratified SGD Proposed Stratified SGD to obtain an efficient DSGD for matrix factorization L θ = ω 1 L 1 θ + ω 2 L 2 θ + + ω q L q θ The sum of loss function L s, and s is a stratum. A stratum is a part or partition of dataset SSGD runs standard SGD on a single stratum at a time, but switches strata in a way that guarantees correctness Dept. CSE, UT Arlington 22

23 Stratified SGD algorithm Suppose a stratum sequence {γ n }, each γ n takes values in {1,, q} θ n+1 = Π H [θ n ε n L γn θ n ] Appropriate sufficient conditions for the convergence of SSGD can be from stochastic approximation Dept. CSE, UT Arlington 23

24 Distribute SSGD SGD steps depend on each other θ n+1 = Π H [θ n ε n L γn θ n ] An SGD step on example z Z 1. Reads W iz, H jz 2. Performs gradient computation L ij 3. Updates and W iz H jz W iz, H jz Dept. CSE, UT Arlington 24

25 Problem Structure SGD steps depend on each other θ n+1 = Π H [θ n ε n L γn θ n ] An SGD step on example z Z 1. Reads W iz, H jz 2. Performs gradient computation L ij 3. Updates and W iz H jz W iz, H jz Dept. CSE, UT Arlington 25

26 Problem Structure SGD steps depend on each other θ n+1 = Π H [θ n ε n L γn θ n ] An SGD step on example z Z 1. Reads W iz, H jz 2. Performs gradient computation L ij 3. Updates and W iz H jz W iz, H jz Dept. CSE, UT Arlington 26

27 Problem Structure SGD steps depend on each other θ n+1 = Π H [θ n ε n L γn θ n ] An SGD step on example z Z 1. Reads W iz, H jz 2. Performs gradient computation L ij 3. Updates and W iz H jz W iz, H jz Not all steps are dependent Dept. CSE, UT Arlington 27

28 Interchangeability Definition 1. Two elements neither row nor column z 1, z 2 Z are interchangeable if they share Whenz n, z n+1 are interchangeable, the SGD steps θ n+1 = θ n ε L θ n, z n θ n+2 = θ n ε L θ n, z n ε L (θ n+1, z n+1 ) = θ n ε L θ n, z n ε L (θ n, z n+1 ) Dept. CSE, UT Arlington 28

29 A simple case Denote by the set of training points in block. Suppose we run T steps of SSGD on Z, starting from some initial point θ 0 = (W 0, H 0 ) and using a fixed step size ε. Describe an instance of the SGD process by a training sequence ω = z 0, z 1,, z T 1 of T training points. Z b θ n+1 ω = θ n ω + ε T 1 n=0 Y n (ω) Z b Dept. CSE, UT Arlington 29

30 A simple case θ n+1 ω = θ n ω + ε T 1 n=0 Y n (ω) Consider the subsequence σ b (ω) means training points from block Z b ; the length T b ω = σ b ω. The following theorem suggests we can run SGD on each block independently and then sum up the results. Theorem 3 Using the definitions above θ T ω = θ 0 + ε d b=1 T b ω 1 k=0 Y k (σ b (ω)) Dept. CSE, UT Arlington 30

31 A simple case If we divide into d independent map tasks Γ 1, Γ d. Task Γ b is responsible for subsequence σ b (ω) : It takes Z b, W b and H b as input, performs the block-local updates σ b (ω) Dept. CSE, UT Arlington 31

32 The General Case Theorem 3 can also be applied into a general case. d-monomial Dept. CSE, UT Arlington 32

33 The General Case Dept. CSE, UT Arlington 33

34 Exploitation Block and distribute the input matrix V High-level approach (Map only) 1. Pick a diagonal 2. Run SGD on the diagonal (in parallel) 3. Merge the results 4. Move on to next diagonal Steps 1-3 form a cycle Dept. CSE, UT Arlington 34

35 Exploitation Block and distribute the input matrix V High-level approach (Map only) 1. Pick a diagonal 2. Run SGD on the diagonal (in parallel) 3. Merge the results 4. Move on to next diagonal Steps 1-3 form a cycle Dept. CSE, UT Arlington 35

36 Exploitation Block and distribute the input matrix V High-level approach (Map only) 1. Pick a diagonal 2. Run SGD on the diagonal (in parallel) 3. Merge the results 4. Move on to next diagonal Steps 1-3 form a cycle Dept. CSE, UT Arlington 36

37 Exploitation Block and distribute the input matrix V High-level approach (Map only) 1. Pick a diagonal 2. Run SGD on the diagonal (in parallel) 3. Merge the results 4. Move on to next diagonal Steps 1-3 form a cycle Step 2: Simulate sequential SGD 1. Interchangeable blocks 2. Throw dice of how many iterations per block 3. Throw dice of which step sizes per block Dept. CSE, UT Arlington 37

38 Exploitation Block and distribute the input matrix V High-level approach (Map only) 1. Pick a diagonal 2. Run SGD on the diagonal (in parallel) 3. Merge the results 4. Move on to next diagonal Steps 1-3 form a cycle Step 2: Simulate sequential SGD 1. Interchangeable blocks 2. Throw dice of how many iterations per block 3. Throw dice of which step sizes per block Dept. CSE, UT Arlington 38

39 Exploitation Block and distribute the input matrix V High-level approach (Map only) 1. Pick a diagonal 2. Run SGD on the diagonal (in parallel) 3. Merge the results 4. Move on to next diagonal Steps 1-3 form a cycle Step 2: Simulate sequential SGD 1. Interchangeable blocks 2. Throw dice of how many iterations per block 3. Throw dice of which step sizes per block Dept. CSE, UT Arlington 39

40 Experiments Compare with PSGD, DGD, ALS methods Data Netflix Competition 100M ratings from 480k customers on 18k movies Synthetic dataset 10M rows, 1M columns, 1B nonzero entries Dept. CSE, UT Arlington 40

41 Experiments Test on two well-known loss functions L NZSL = i,j:v ij 0 V ij WH ij 2 L L2 = L NZSL + λ W F 2 + H F 2 It works well on a variety kind of loss functions, results for other loss functions can be found in their technique report. Dept. CSE, UT Arlington 41

42 Experiments The proposed DSGD converges faster and achieves better results than others Dept. CSE, UT Arlington 42

43 Experiments 1. The processing time remains constant as the size increase 2. To very large datasets on larger clusters, the overall running time increases by a modest 30% Dept. CSE, UT Arlington 43

44 Summary Matrix factorization Widely applicable via customized loss functions Large instances (millions * millions with billions of entries) Distributed Stochastic Gradient Descent Simple and versatile Achieves Fully distributed data/model Fully distributed processing Same or better loss Faster Good scalability Dept. CSE, UT Arlington 44

45 Thank you! Dept. CSE, UT Arlington 45