近似近傍探索エンジン Sannyを支える技術/sanny_inside

Fukuoka.go#11
https://fukuokago.connpass.com/event/87684/

Transcript

1. ࡾ୐༔հ / Pepabo R&D Institute, GMO Pepabo, Inc.
   2018.06.12 Fukuoka.go#11
   ۙࣅۙ๣୳ࡧΤϯδϯ
   SannyΛࢧ͑Δٕज़
   

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2. ϓϦϯγύϧΤϯδχΞ
   ࡾ୐༔հ!NPOPDISPNFHBOF
   (.0ϖύϘגࣜձࣾϖύϘݚڀॴ
   IUUQCMPHNPOPDISPNFHBOFDPN
   

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3. 1. Motivation
   2. Sanny
   3. Sanny inside
   3
   ໨࣍
   

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4. 1.
   Motivation
   

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5. • େن໛ͳECαΠτͷ৘ใաଟ໰୊ͷղܾͷͨΊɼ঎඼ͷࣗಈਪન͸ඞਢػೳ
   • ঎඼ಉ࢜ͷྨࣅੑΛݩʹਪન͕ߦΘΕΔͨΊɼػցతʹൺֱՄೳͳई౓ͱͯ͠
   ଟ࣍ݩͷ࣮਺ϕΫτϧར༻͢Δɽಛ௃ྔɽ
   • ਂ૚ֶशͷൃలʹΑΓɼ͜Ε·Ͱద੾ͳಛ௃ྔΛಋ͘͜ͱ͕೉͔ͬͨ͠ը૾΍
   ςΩετΛߴਫ਼౓ͳಛ௃ྔ΁ม׵͢Δ͜ͱ͕Ͱ͖ΔΑ͏ʹͳ͍ͬͯΔɽ
   5
   ਪનػೳͱಛ௃ྔ
   

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6. • m࣍ݩͷϕΫτϧxΛn࣍ݩͷϕΫτϧy΁ࣸ૾͢Δؔ਺fΛਪఆ͢Δֶशख๏
   6
   χϡʔϥϧωοτϫʔΫ: Ϟσϧ
   x1
   x2
   xm
   b
   W
   y1
   y2
   yn
   1
   2
   o1
   = σ(
   m
   ∑
   i=0
   w1i
   xi
   + b1
   )
   h
   3
   o = σ (Wx + b)
   w11
   w12
   . . w1m
   w21
   w22
   . . w2m
   . .
   wh1
   wh2
   . . whm
   x1
   x2
   . .
   xm
   +
   b1
   b2
   . .
   bh
   x
   f
   y
   h
   wh1
   wh2
   x1
   x2
   ೖྗ͝ͱʹॏΈXΛ৐ࢉόΠΞε
   ͨ͠΋ͷΛ׆ੑԽؔ਺ʹ௨͢
   શϊʔυʹର͢Δॲཧ͸ߦྻͱͯ͠ૢ࡞Ͱ͖Δ
   

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7. Similar images search system using ANN
   7
   Feature Similar items
   ANN
   [2048]float64
   query response
   Deep CNN
   index
   Features
   [n][2048]float64
   Deep CNN
   register
   find similar features
   

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8. • େن໛ͳECαΠτͰѻ͏ಛ௃ྔ͸ɼେن໛͔ͭߴ࣍ݩϕΫτϧͷू߹ͱͳΔ
   • ਪનػೳͰ͸ɼ͜ͷू߹͔Βݕࡧ࣭໰σʔλʹྨࣅ͢Δ͍͔ͭ͘ͷϕΫτϧΛ
   ೚ҙͷڑ཭ؔ਺Λ࢖ͬͯ୳ࡧ͢Δɽۙ๣୳ࡧɽ
   • ۙ๣୳ࡧʹ͓͍ͯɼਖ਼֬Ͱ͸͋Δ͕ɼσʔλ਺ͱ࣍ݩ਺ʹൺྫͯ͠ܭࢉྔ͕૿
   Ճ͢Δઢܗ୳ࡧ͸ݱ࣮తͰ͸ͳ͍
   • ۭؒ෼ׂ΍ہॴੑӶහܕϋογϡͳͲͷํࣜʹΑͬͯܭࢉྔΛ࡟ݮ
   • ಛʹେن໛͔ͭߴ࣍ݩϕΫτϧू߹ʹରͯ͠͸ɼਫ਼౓Λ٘ਜ਼ʹͯ͠ܭࢉྔΛ཈
   ͑Δۙࣅղ͕༻͍ΒΕΔɽۙࣅۙ๣୳ࡧɽ
   8
   ਪનػೳͱۙࣅۙ๣୳ࡧ
   

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9. 2.
   Sanny
   

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10. • ਪનػೳʹ͸ɼఏҊ಺༰ͷత֬͞ͱॆ෼ͳԠ౴଎౓͕ٻΊΒΕΔɽ
    • ਫ਼౓ͱ଎౓Λཱ྆͢Δۙࣅۙ๣୳ࡧͷ࢓૊Έ͕ඞཁɽ
    • ଟछଟ༷ͳۙࣅۙ๣୳ࡧͷΞϧΰϦζϜ΍ͦΕΒΛ࣮૷ͨ͠ϥΠϒϥϦ͕ެ։ɼ
    ར༻͞Ε͍ͯΔɽ
    • ΞϧΰϦζϜ͝ͱʹ௕ॴ୹ॴ͕͋ΓɼݸʑͷΞϧΰϦζϜվળ͸ޮՌ͕ݶఆత
    10
    ۙࣅۙ๣୳ࡧͷվળͱ՝୊
    • ҰํͰɼଟ͘ͷΞϧΰϦζϜ͸ɼॲཧର৅σʔλ਺ͱ࣍ݩ਺ʹੑೳ͕ґଘ͢Δ
    • ୳ࡧ͋ͨΓͷॲཧର৅ͷॖখ͕ɼશͯͷΞϧΰϦζϜʹޮՌత
    

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11. • ঎඼ಛੑΛΑ͘දݱ͓ͯ͠Γߴਫ਼౓ʹྨࣅ౓͕ൺֱՄೳͳߴ࣍ݩ͔ͭີͳϕΫ
    τϧͷू߹Λର৅ͱͨۙ͠ࣅۙ๣୳ࡧ
    • ֶशࡁΈCNNΛಛ௃நग़ثͱͯ͠ར༻ͯ͠ը૾͔ΒಘΒΕΔಛ௃ྔू߹
    • ςΩετΛ෼ࢄදݱ΁ม׵͢ΔWord2vec͔ΒಘΒΕΔಛ௃ྔू߹
    • ͜ΕΒ͕ɼݕࡧ࣭໰σʔλ(ΫΤϦ)ʹର͢Δߴ࣍ݩϕΫτϧू߹ͷۙ๣୳ࡧ݁
    Ռͷ্Ґू߹͕ɼΫΤϦͱߴ࣍ݩϕΫτϧू߹Λ೚ҙͷ࣍ݩ਺Ͱ౳෼ͨ͠෦෼
    ϕΫτϧ୯ҐͰۙ๣୳ࡧͨ݁͠Ռͷ্Ґू߹ͱྨࣅ͠΍͍͢͜ͱʹண໨
    11
    ఏҊख๏
    ෦෼͕ྨࣅ͢Ε͹શମ͕ྨࣅ͢ΔՄೳੑ͕ߴ͍σʔλಛੑ
    

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12. • ΫΤϦͱߴ࣍ݩϕΫτϧू߹Λ೚ҙͷ࣍ݩ਺Ͱ౳෼ͨ͠෦෼ϕΫτϧ୯ҐͰฒ
    ߦʹۙ๣୳ࡧͨ݁͠Ռͷ࿨ू߹Ͱ͋Δۙ๣ީิ͔Βɼ࠶౓ۙ๣୳ࡧΛߦ͏ɽ
    12
    ఏҊख๏
    R@ R@ R@
    9@ 9@ 9@
    /SFDPSET
    // R@ Y@O
    Y㱨9@ // R@ Y@O
    Y㱨9@ // R@ Y@O
    Y㱨9@
    \ ^ \ ^ \ ^
    BSHNJOE R Y
    Y㱨\ ^
    㱮 㱮
    ෼ղલ
    ෼ղޙ
    ᶃ௿࣍ݩۭؒͷฒߦͨۙ͠๣୳ࡧ
    ᶄݻఆ਺ͷۙ๣ީิͷू໿
    ᶅۙ๣ީิͷઢܗ୳ࡧ
    ᶃ ᶄ ᶅ ଎౓վળ
    ᶃ
    ᶄ
    ᶅ
    2VFSZ
    

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13. 13
    Sanny: ఏҊख๏ͷ࣮૷
    4BOOZ 4BOOZ
    4BOOZ
    // //
    㱮
    //
    //
    ᶃ
    ᶄ
    ᶅ
    2VFSZ
    "MHPSJTN "MHPSJTN "MHPSJTN
    • ΫΤϦฒͼʹ୳ࡧର৅σʔλͷ೚ҙ࣍ݩ΁ͷ౳෼ͱ݁Ռͷू໿Λ୲౰͢Δ
    • ෦෼ϕΫτϧͷۙ๣୳ࡧΞϧΰϦζϜ͸໰Θͳ͍
    • ෦෼ϕΫτϧ͝ͱͷ୳ࡧॲཧ͸ಠཱͷͨΊ෼ࢄߏ੒͕Մೳ
    

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14. ύϑΥʔϚϯε
    14
    • ॎ࣠͸୳ࡧඵ਺ͷٯ਺දࣔɼ্ʹ
    ߦ͘΄Ͳߴ଎ɽԣ࣠͸ద߹཰ɽ
    • ݩͷΞϧΰϦζϜʹରͯ͠ఏҊख๏
    Λద༻ͨ͜͠ͱͰਫ਼౓ɼ଎౓ͷվ
    ળ͕ݟΒΕͨ
    ʰSanny: େن໛ECαΠτͷͨΊͷਫ਼౓ͱ଎౓Λཱ྆ͨ͠෼ࢄՄೳͳۙࣅۙ๣୳ࡧΤϯδϯʱ https://blog.monochromegane.com/blog/2018/05/16/wsa_2_sanny/
    

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15. 3.
    Sanny inside
    

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16. Euclidean distance
    

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17. • SannyͰ͸ۙ๣ީิͷू߹͔ΒϢʔΫϦουڑ཭ʹΑΔۙ๣୳ࡧΛߦ͏
    • ࣍ݩ਺ͱϨίʔυ਺ʹൺྫ͢Δઢܗ୳ࡧͳͷͰॲཧ࣌ؒΛ୹ॖ͍ͨ͠
    • ɹɹɹɹ͸֤࣍ݩ͝ͱʹಠཱ͍ͯ͠ΔͷͰฒߦॲཧ͕Մೳ
    17
    ϢʔΫϦουڑ཭ܭࢉ
    d(p, q) =
    v
    u
    u
    t
    n
    X
    i=1
    (pi qi)2
    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
    (pi qi)2
    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18. • CPU໋ྩͷ͏ͪɼҰͭͷ໋ྩͰෳ਺σʔλΛॲཧͰ͖Δ΋ͷ
    • x86ΞʔΩςΫνϟϓϩηοαͩͱSSE໋ྩ܈΍AVX໋ྩ܈ͳͲ
    • AVXͩͱ256bit௕ͷϨδελΛར༻Ͱ͖ΔͨΊɼ32bitͷσʔλΛ8ݸฒྻʹ
    ॲཧͰ͖Δ
    18
    SIMD (Single Instruction Multiple Data)
    

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19. 19
    Hello, SIMD
    CJU CJU
    CJU CJU
    
    4*.%6OJU
    
    CJU
    CJU CJU
    CJU CJU
    x = float32 * 16 y = float32 * 16
    z = float32 * 16
    const size_t n = 16;
    float *x, *y, *z;
    // Align with 256bit
    x = (float *)_mm_malloc(sizeof(float) * n, 32);
    y = (float *)_mm_malloc(sizeof(float) * n, 32);
    z = (float *)_mm_malloc(sizeof(float) * n, 32);
    __m256 *vz = (__m256 *)z;
    __m256 *vx = (__m256 *)x;
    __m256 *vy = (__m256 *)y;
    const size_t end = n / 8;
    for(size_t i=0; ivz[i] = _mm256_add_ps(vx[i], vy[i]);
    _mm_free(x);
    _mm_free(y);
    _mm_free(z);
    

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20. Euclidean distance using SIMD and cgo
    20
    /*
    #cgo CFLAGS: -mavx -std=c99
    #include
    #include
    #include
    #include
    float avx_euclidean_distance(const size_t n, float *x, float *y)
    {
    __m256 *vx = (__m256 *)x;
    __m256 *vy = (__m256 *)y;
    __m256 vsub = {0};
    __m256 vsum = {0};
    const size_t end = n / 8;
    for(size_t i=0; ivsub = _mm256_sub_ps(vx[i], vy[i]);
    vsum = _mm256_add_ps(vsum, _mm256_mul_ps(vsub, vsub));
    }
    __attribute__((aligned(32))) float t[8] = {0};
    _mm256_store_ps(t, vsum);
    return sqrt(t[0] + t[1] + t[2] + t[3] + t[4] + t[5] + t[6] + t[7]);
    }
    */
    

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21. Euclidean distance using SIMD and cgo
    21
    func MmMalloc(size int) []float32 {
    size_ := size
    size = align(size)
    ptr := C._mm_malloc((C.size_t)(C.sizeof_float*size), 32)
    hdr := reflect.SliceHeader{
    Data: uintptr(unsafe.Pointer(ptr)),
    Len: size,
    Cap: size,
    }
    goSlice := *(*[]float32)(unsafe.Pointer(&hdr))
    if size_ != size {
    for i := size_; i < size; i++ {
    goSlice[i] = 0.0
    }
    }
    return goSlice
    }
    func MmFree(v []float32) {
    C._mm_free(unsafe.Pointer(&v[0]))
    }
    func align(size int) int {
    return int(math.Ceil(float64(size)/8.0) * 8.0)
    }
    

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22. Euclidean distance using SIMD and cgo
    22
    func EuclideanDistance(size int, x, y []float32) float32 {
    size = align(size)
    dot := C.avx_euclidean_distance((C.size_t)(size), (*C.float)(&x[0]), (*C.float)(&y[0]))
    return float32(dot)
    }
    BenchmarkEuclideanDistance-8 30000 59465 ns/op 0 B/op 0 allocs/op
    BenchmarkEuclideanDistanceGoroutine-8 2000 1034087 ns/op 479 B/op 5 allocs/op
    BenchmarkHypot-8 50000 34059 ns/op 0 B/op 0 allocs/op
    BenchmarkEuclideanDistanceAVX-8 5000000 359 ns/op 0 B/op 0 allocs/op
    Benchmark (2048dim)
    

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23. TCP Connection
    

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24. • SannyͰ͸֤෦෼ϕΫτϧू߹͔Βͷۙࣅۙ๣୳ࡧ݁ՌΛۙ๣ީิͱͯ͠ू໿
    ͢Δɽ
    • ෼ࢄߏ੒ͷ৔߹͸ωοτϫʔΫӽ͠ʹΫΤϦͱ୳ࡧ݁Ռͷ΍ΓͱΓ͕ൃੜ͢Δɽ
    ·ͨɼΫΤϦ͸සൟʹൃߦ͞Εɼ୳ࡧʹ͸زڐ͔ͷॲཧ࣌ؒΛཁ͢Δɽ
    • TCPίωΫγϣϯଟॏԽͷ՝୊ղܾҊͱͯ͠୯Ұ઀ଓʹ͓͚ΔԾ૝తͳૹड৴
    ͷଟॏԽ -> HTTP/2όΠφϦϑϨʔϛϯάϨΠϠ
    • ϓϩτίϧΦʔόʔϔουͷআڈͱϓϩτίϧґଘ͔Βͷ୤٫Λ໨ࢦͨ͠ɼΑ
    ΓݶఆతͰߴ଎ͳιέοτ௨৴ͷݕ౼
    24
    αʔόؒ૬ޓ௨৴ʹ͓͚ΔϘτϧωοΫͱͦͷղܾࡦ
    

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25. • ୯ҰίωΫγϣϯͷԾ૝ଟॏԽΛఏڙ
    • ΞϓϦέʔγϣϯ͸ಠࣗͷ؆ܿͳϓϩτίϧʹΑͬͯετϦʔϜܦ༝Ͱσʔλ
    Λૹड৴͢Δ
    25
    Smux (Socket Multiplexer)
    ʰGoݴޠͰTCP΍ιέοτ௨৴ΛଟॏԽɼߴ଎Խ͢Δsmux(ιέοτϚϧνϓϨΫα)Λͭͬͨ͘ʱ https://blog.monochromegane.com/blog/2018/05/03/smux/
    3FRVFTU
    Payload
    Header
    Frame
    3FRVFTU
    Stream 1
    Stream 2
    Payload
    Header
    3FTQPOTF
    Payload
    Header
    3FTQPOTF
    Payload
    Header
    Payload
    Header
    Stream 1
    Payload
    Header
    Stream 2
    Payload
    Header
    Stream 1
    Payload
    Header
    Stream 1
    Payload
    Header
    Stream 2
    TCP Connection
    Req
    Res
    

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26. Smux (Socket Multiplexer)
    26
    // smux server
    server := smux.Server{
    Network: "tcp", // or "unix"
    Address: "localhost:3000", // or "sockfile"
    Handler: smux.HandlerFunc(func(w io.Writer, r io.Reader) {
    io.Copy(ioutil.Discard, r)
    fmt.Fprint(w, "Hello, smux client!")
    }),
    }
    server.ListenAndServe()
    // smux client
    client := smux.Client{
    Network: "tcp", // or "unix"
    Address: "localhost:3000", // or "sockfile"
    }
    body, _ := client.Post([]byte("Hello, smux server!"))
    fmt.Printf("%s\n", body) // "Hello, smux client!"
    Server
    Client
    

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27. ύϑΥʔϚϯε
    27
    • smux, HTTP/1.1, HTTP/2ͷϕϯνϚʔΫ
    • ίωΫγϣϯ਺ or ετϦʔϜ਺Λ૿Ճ͞
    ͤͳ͕ΒҰఆ਺ͷϦΫΤετΛࡹ࣌ؒ͘
    Λܭଌ
    • αʔόαΠυॲཧ୅ସʹ਺ेmsͷsleep
    • ख๏ʹΑͬͯଟॏ౓૿Ճʹ൐͍ੑೳ͕಄
    ଧͪʹͳΔͳ͔smux͕ߴ͍ੑೳΛࣔͨ͠
    

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28. ઌߦ࣮૷ΛؚΊͨύϑΥʔϚϯε
    28
    • ઌߦ࣮૷ͱͯ͠hashicorp/yamax, xtaci/
    smux(ssmux)ΛՃ͑ͨධՁ
    • yamaxʹରͯ͠͸ಉ౳Ҏ্͕ͩssmux͕
    ༏लͰ͋ͬͨ
    • ະ࣮૷ͷϑϩʔ੍ޚͳͲΛ࣮૷ࢀߟʹύ
    ϑΥʔϚϯεݟ௚͠Λݕ౼
    

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29. 4.
    ·ͱΊ
    

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30. • ۙࣅۙ๣୳ࡧΛߴ଎Խ͢ΔͨΊɼσʔλಛੑʹண໨ͨۙ͠ࣅۙ๣୳ࡧΤϯδϯ
    SannyΛ࣮૷ͨ͠
    • ࣮૷؍఺Ͱͷߴ଎ԽͷͨΊɼϢʔΫϦουڑ཭ܭࢉͰSIMDԋࢉΛɼTCP઀ଓ
    ͷԾ૝ଟॏԽΛ࠾༻ͨ͠
    • ύϑΥʔϚϯεվળ͸༷ʑͳϨΠϠͰՄೳͳͷͰ෯޿͘ݕ౼Ͱ͖Δ
    • Goݴޠ͔Βѻ͑Δͷ͕࠷ߴͰ͢Ͷ
    30
    ·ͱΊ
    

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31. View Slide