{"id":43482,"date":"2026-07-20T14:01:20","date_gmt":"2026-07-20T11:01:20","guid":{"rendered":"https:\/\/fatihsoysal.com\/blog\/derin-ogrenme-modellerini-olceklendirme-coklu-dugum-tensor-ve-boru-hatti-paralelligi\/"},"modified":"2026-07-20T14:01:20","modified_gmt":"2026-07-20T11:01:20","slug":"derin-ogrenme-modellerini-olceklendirme-coklu-dugum-tensor-ve-boru-hatti-paralelligi","status":"publish","type":"post","link":"https:\/\/fatihsoysal.com\/blog\/derin-ogrenme-modellerini-olceklendirme-coklu-dugum-tensor-ve-boru-hatti-paralelligi\/","title":{"rendered":"Derin \u00d6\u011frenme Modellerini \u00d6l\u00e7eklendirme: \u00c7oklu D\u00fc\u011f\u00fcm Tensor ve Boru Hatt\u0131 Paralelli\u011fi"},"content":{"rendered":"

Derin \u00d6\u011frenme Modellerini \u00d6l\u00e7eklendirme: \u00c7oklu D\u00fc\u011f\u00fcm Tensor ve Boru Hatt\u0131 Paralelli\u011fi<\/h2>\n

B\u00fcy\u00fck dil modelleri (LLM’ler) ve karma\u015f\u0131k derin \u00f6\u011frenme mimarileri g\u00fcn\u00fcm\u00fcz yapay zeka d\u00fcnyas\u0131n\u0131n temel ta\u015flar\u0131 haline geldi. Ancak bu modellerin boyutu ve hesaplama gereksinimleri, tek bir i\u015flemci veya hatta tek bir makine \u00fczerinde e\u011fitilmelerini ve \u00e7al\u0131\u015ft\u0131r\u0131lmalar\u0131n\u0131 imkans\u0131z hale getiriyor. \u0130\u015fte tam da bu noktada, hesaplama g\u00fcc\u00fcn\u00fc birden fazla makineye (d\u00fc\u011f\u00fcm) yayma ihtiyac\u0131 do\u011fuyor. Bu makalede, bu \u00f6l\u00e7eklendirme sorununu \u00e7\u00f6zmek i\u00e7in kullan\u0131lan iki temel teknik olan “Tensor Paralelli\u011fi” ve “Boru Hatt\u0131 Paralelli\u011fi”ni detayl\u0131 bir \u015fekilde inceleyece\u011fiz. Amac\u0131m\u0131z, bu karma\u015f\u0131k konular\u0131 anla\u015f\u0131l\u0131r bir dille a\u00e7\u0131klayarak, okuyucular\u0131n modellerini \u00e7oklu d\u00fc\u011f\u00fcm ortamlar\u0131nda nas\u0131l daha verimli hale getirebilecekleri konusunda pratik bilgiler sunmakt\u0131r.<\/p>\n

Derin \u00d6\u011frenmenin \u00d6l\u00e7eklendirme Zorluklar\u0131 ve Neden Paralelli\u011fe \u0130htiya\u00e7 Duyar\u0131z?<\/h3>\n

G\u00fcn\u00fcm\u00fcz\u00fcn en geli\u015fmi\u015f yapay zeka modelleri, milyarlarca hatta trilyonlarca parametreye sahip olabiliyor. Bu devasa modellerin e\u011fitimi, muazzam miktarda veri ve inan\u0131lmaz bir hesaplama g\u00fcc\u00fc gerektirir. Tek bir GPU’nun veya hatta birden fazla GPU’nun bir arada bulundu\u011fu tek bir sunucunun bu talepleri kar\u015f\u0131lamas\u0131 m\u00fcmk\u00fcn de\u011fildir. Bir modelin parametreleri, bellek (RAM veya VRAM) s\u0131n\u0131rlar\u0131na tak\u0131labilirken, ileri ve geri yay\u0131l\u0131m (forward and backward propagation) s\u0131ras\u0131nda yap\u0131lan matris \u00e7arp\u0131mlar\u0131 ve di\u011fer hesaplamalar, i\u015flemci g\u00fcc\u00fcn\u00fc h\u0131zla t\u00fcketir. Bu durum, e\u011fitim s\u00fcrelerini haftalardan aylara, hatta y\u0131llara \u00e7\u0131karabilir.<\/p>\n

Dahas\u0131, modelin kendisi tek bir makinenin belle\u011fine s\u0131\u011fmayabilir. \u00d6rne\u011fin, GPT-3 gibi bir modelin yaln\u0131zca parametreleri bile y\u00fczlerce gigabayt yer kaplayabilir. Bu, geleneksel e\u011fitim y\u00f6ntemlerinin yetersiz kald\u0131\u011f\u0131 anlam\u0131na gelir. \u0130\u015fte bu noktada, hesaplama i\u015f y\u00fck\u00fcn\u00fc ve modelin kendisini birden fazla makineye da\u011f\u0131tarak bu zorluklar\u0131n \u00fcstesinden gelmek i\u00e7in paralellik teknikleri devreye girer. Paralellik, sadece e\u011fitim s\u00fcresini k\u0131saltmakla kalmaz, ayn\u0131 zamanda tek bir makinenin s\u0131n\u0131rlar\u0131n\u0131 a\u015farak \u00e7ok daha b\u00fcy\u00fck ve karma\u015f\u0131k modellerin geli\u015ftirilmesine olanak tan\u0131r. Bu, yapay zeka ara\u015ft\u0131rmalar\u0131n\u0131n ve uygulamalar\u0131n\u0131n s\u0131n\u0131rlar\u0131n\u0131 zorlamak i\u00e7in kritik bir ad\u0131md\u0131r. Bu makalede ele alaca\u011f\u0131m\u0131z tensor ve boru hatt\u0131 paralelli\u011fi, bu da\u011f\u0131t\u0131lm\u0131\u015f hesaplama stratejilerinin en yayg\u0131n ve etkili olanlar\u0131ndand\u0131r.<\/p>\n

Temel Kavramlar: Paralellik T\u00fcrleri ve Da\u011f\u0131t\u0131lm\u0131\u015f E\u011fitim<\/h3>\n

Paralellik, genel olarak bir g\u00f6revin birden fazla par\u00e7aya b\u00f6l\u00fcnerek ayn\u0131 anda birden fazla i\u015flemci veya makine \u00fczerinde \u00e7al\u0131\u015ft\u0131r\u0131lmas\u0131 anlam\u0131na gelir. Derin \u00f6\u011frenme ba\u011flam\u0131nda, bu g\u00f6rev modelin e\u011fitimi veya \u00e7\u0131kar\u0131m\u0131 olabilir. Da\u011f\u0131t\u0131lm\u0131\u015f e\u011fitim (distributed training) ise, bu paralellik kavram\u0131n\u0131n birden fazla fiziksel makine (d\u00fc\u011f\u00fcm) \u00fczerinde uygulanmas\u0131d\u0131r.<\/p>\n

Temel olarak iki ana paralellik t\u00fcr\u00fcnden bahsedebiliriz:<\/p>\n

* Veri Paralelli\u011fi (Data Parallelism):<\/strong> Bu yakla\u015f\u0131mda, modelin kendisi her bir d\u00fc\u011f\u00fcme tam olarak kopyalan\u0131r. E\u011fitim verisi ise farkl\u0131 d\u00fc\u011f\u00fcmlere b\u00f6l\u00fcn\u00fcr. Her d\u00fc\u011f\u00fcm, kendi veri alt k\u00fcmesi \u00fczerinde modelin bir kopyas\u0131n\u0131 e\u011fitir. E\u011fitim ilerledik\u00e7e, d\u00fc\u011f\u00fcmler aras\u0131ndaki gradyanlar (kay\u0131p fonksiyonunun parametrelere g\u00f6re t\u00fcrevleri) toplan\u0131r ve ortalamas\u0131 al\u0131narak t\u00fcm model kopyalar\u0131 g\u00fcncellenir. Bu, b\u00fcy\u00fck veri k\u00fcmelerini i\u015flemek i\u00e7in etkilidir ancak modelin kendisi tek bir d\u00fc\u011f\u00fcm\u00fcn belle\u011fine s\u0131\u011fabiliyorsa en iyi sonucu verir.<\/p>\n

* Model Paralelli\u011fi (Model Parallelism):<\/strong> Veri paralelli\u011finin aksine, model paralelli\u011finde modelin kendisi farkl\u0131 d\u00fc\u011f\u00fcmlere b\u00f6l\u00fcn\u00fcr. Yani, modelin farkl\u0131 katmanlar\u0131 veya katmanlar\u0131n farkl\u0131 b\u00f6l\u00fcmleri farkl\u0131 makinelerde \u00e7al\u0131\u015ft\u0131r\u0131l\u0131r. Bu, modelin tek bir d\u00fc\u011f\u00fcm\u00fcn belle\u011fine s\u0131\u011fmad\u0131\u011f\u0131 durumlar i\u00e7in gereklidir. Model paralelli\u011fi kendi i\u00e7inde iki ana alt t\u00fcre ayr\u0131l\u0131r:<\/p>\n

* Tensor Paralelli\u011fi (Tensor Parallelism):<\/strong> Bu teknik, b\u00fcy\u00fck matris \u00e7arp\u0131mlar\u0131n\u0131 ve tens\u00f6r i\u015flemlerini birden fazla i\u015flemciye da\u011f\u0131t\u0131r.
\n * Boru Hatt\u0131 Paralelli\u011fi (Pipeline Parallelism):<\/strong> Bu teknik, modelin katmanlar\u0131n\u0131 farkl\u0131 i\u015flemcilere atayarak bir “boru hatt\u0131” olu\u015fturur.<\/p>\n

Bu makalenin odak noktas\u0131 olan tensor ve boru hatt\u0131 paralelli\u011fi, model paralelli\u011fi alt\u0131nda yer al\u0131r ve \u00f6zellikle b\u00fcy\u00fck modellerin e\u011fitiminde kritik rol oynar. Bu teknikler, veri paralelli\u011finin yetersiz kald\u0131\u011f\u0131 durumlarda veya veri paralelli\u011fi ile birlikte kullan\u0131larak daha da etkili sonu\u00e7lar elde etmek i\u00e7in tasarlanm\u0131\u015ft\u0131r.<\/p>\n

Tensor Paralelli\u011fi: B\u00fcy\u00fck Matrisleri B\u00f6lmek<\/h3>\n

Tensor paralelli\u011fi, ad\u0131ndan da anla\u015f\u0131laca\u011f\u0131 gibi, derin \u00f6\u011frenme modellerindeki temel hesaplama birimleri olan tens\u00f6rlerin (genellikle matrislerin) i\u015flenmesini birden fazla i\u015flemciye da\u011f\u0131tmay\u0131 ama\u00e7lar. Bir sinir a\u011f\u0131n\u0131n en yo\u011fun hesaplama gerektiren k\u0131s\u0131mlar\u0131 genellikle b\u00fcy\u00fck matris \u00e7arp\u0131mlar\u0131d\u0131r. \u00d6rne\u011fin, bir tam ba\u011fl\u0131 katmanda (fully connected layer) veya dikkat mekanizmas\u0131ndaki (attention mechanism) sorgu (query), anahtar (key) ve de\u011fer (value) projeksiyonlar\u0131nda bu matris \u00e7arp\u0131mlar\u0131 yo\u011fun olarak kullan\u0131l\u0131r.<\/p>\n

Tensor paralelli\u011finin temel fikri, bu b\u00fcy\u00fck matris \u00e7arp\u0131mlar\u0131n\u0131 daha k\u00fc\u00e7\u00fck par\u00e7alara b\u00f6lmek ve bu par\u00e7alar\u0131 farkl\u0131 i\u015flemciler \u00fczerinde e\u015f zamanl\u0131 olarak hesaplamakt\u0131r. \u0130ki ana y\u00f6ntemle bu ger\u00e7ekle\u015ftirilebilir:<\/p>\n

1. S\u00fctun Paralelli\u011fi (Column Parallelism):<\/strong> Bu y\u00f6ntemde, bir matrisin s\u00fctunlar\u0131 farkl\u0131 i\u015flemcilere da\u011f\u0131t\u0131l\u0131r. \u00d6rne\u011fin, Y = XW<\/code> gibi bir matris \u00e7arp\u0131m\u0131nda, W<\/code> matrisinin s\u00fctunlar\u0131 W = [W1, W2]<\/code> \u015feklinde b\u00f6l\u00fcnerek, Y1 = XW1<\/code> ve Y2 = XW2<\/code> hesaplamalar\u0131 farkl\u0131 i\u015flemcilerde paralel olarak yap\u0131l\u0131r. Sonu\u00e7 Y = [Y1, Y2]<\/code> olarak birle\u015ftirilir. Bu, genellikle bir katman\u0131n \u00e7\u0131kt\u0131s\u0131n\u0131 hesaplamak i\u00e7in kullan\u0131l\u0131r.<\/p>\n

2. Sat\u0131r Paralelli\u011fi (Row Parallelism):<\/strong> Bu y\u00f6ntemde ise, matrisin sat\u0131rlar\u0131 farkl\u0131 i\u015flemcilere da\u011f\u0131t\u0131l\u0131r. Y = XW<\/code> \u00e7arp\u0131m\u0131nda, X<\/code> matrisinin sat\u0131rlar\u0131 X = [X1; X2]<\/code> \u015feklinde b\u00f6l\u00fcnerek, Y1 = X1W<\/code> ve Y2 = X2W<\/code> hesaplamalar\u0131 farkl\u0131 i\u015flemcilerde yap\u0131l\u0131r. Bu durumda, her i\u015flemci W<\/code> matrisinin tamam\u0131n\u0131 bilmek zorundad\u0131r, ancak giri\u015f verisinin sadece kendi pay\u0131na d\u00fc\u015fen k\u0131sm\u0131n\u0131 i\u015fler. Bu, genellikle bir sonraki katmana girdi sa\u011flayan projeksiyonlar i\u00e7in kullan\u0131l\u0131r.<\/p>\n

Uygulama \u00d6rne\u011fi: Transformer Modellerinde Tensor Paralelli\u011fi<\/strong><\/p>\n

Transformer mimarileri, do\u011fal dil i\u015fleme alan\u0131nda devrim yaratm\u0131\u015ft\u0131r ve b\u00fcy\u00fck dil modellerinin temelini olu\u015fturur. Bu modellerde, \u00f6zellikle \u00e7oklu kafa dikkat (Multi-Head Attention) mekanizmas\u0131 ve ileri beslemeli a\u011flar (Feed-Forward Networks – FFN), b\u00fcy\u00fck matris \u00e7arp\u0131mlar\u0131 i\u00e7erir.<\/p>\n

Bir transformer blo\u011funda, dikkat mekanizmas\u0131n\u0131n sorgu (Q), anahtar (K) ve de\u011fer (V) projeksiyonlar\u0131 Q = XW_Q<\/code>, K = XW_K<\/code>, V = XW_V<\/code> \u015feklinde matris \u00e7arp\u0131mlar\u0131 ile elde edilir. Bu W_Q<\/code>, W_K<\/code>, W_V<\/code> matrisleri olduk\u00e7a b\u00fcy\u00fck olabilir. Tensor paralelli\u011fi kullanarak, bu matrisleri s\u00fctunlar\u0131na g\u00f6re b\u00f6lebiliriz. \u00d6rne\u011fin, W_Q = [W_Q1, W_Q2]<\/code> \u015feklinde b\u00f6lersek, Q1 = XW_Q1<\/code> ve Q2 = XW_Q2<\/code> hesaplamalar\u0131 iki farkl\u0131 i\u015flemci \u00fczerinde paralel olarak yap\u0131l\u0131r. Ard\u0131ndan, Q = [Q1, Q2]<\/code> olarak birle\u015ftirilir. Benzer \u015fekilde, K<\/code> ve V<\/code> i\u00e7in de bu i\u015flem tekrarlan\u0131r.<\/p>\n

\u0130leri beslemeli a\u011flar da FFN(x) = max(0, xW1 + b1)W2 + b2<\/code> \u015feklinde iki b\u00fcy\u00fck matris \u00e7arp\u0131m\u0131 i\u00e7erir. Burada da W1<\/code> ve W2<\/code> matrislerinin s\u00fctunlar\u0131n\u0131 veya sat\u0131rlar\u0131n\u0131 b\u00f6lebiliriz. \u00d6rne\u011fin, W1<\/code>‘i s\u00fctunlar\u0131na g\u00f6re b\u00f6lersek, h1 = xW1_1<\/code> ve h2 = xW1_2<\/code> hesaplamalar\u0131 paralel yap\u0131l\u0131r. Ard\u0131ndan, h = [h1, h2]<\/code> birle\u015ftirilir ve sonra output = hW2 + b2<\/code> hesaplan\u0131r.<\/p>\n

Tensor paralelli\u011fi, b\u00fcy\u00fck matris \u00e7arp\u0131mlar\u0131n\u0131 da\u011f\u0131tarak i\u015flemci kullan\u0131m\u0131n\u0131 art\u0131r\u0131r ve hesaplama s\u00fcresini k\u0131salt\u0131r. Ancak, i\u015flemciler aras\u0131ndaki ileti\u015fim (\u00f6zellikle sonu\u00e7lar\u0131 birle\u015ftirirken) bir darbo\u011faz olu\u015fturabilir. Bu nedenle, verimli ileti\u015fim protokolleri ve do\u011fru b\u00f6lme stratejileri b\u00fcy\u00fck \u00f6nem ta\u015f\u0131r.<\/p>\n

<section>\n    <h2>Tensor Paralelli\u011fi Uygulama Senaryosu<\/h2>\n    <p>Bir NLP modeli \u00fczerinde \u00e7al\u0131\u015f\u0131rken, 1024 boyutlu bir giri\u015f vekt\u00f6r\u00fcn\u00fc 4096 boyutlu bir gizli katmana ba\u011flayan bir tam ba\u011fl\u0131 katman\u0131n\u0131z oldu\u011funu varsayal\u0131m. Bu katman\u0131n a\u011f\u0131rl\u0131k matrisi (W) 1024x4096 boyutunda olacakt\u0131r. E\u011fer bu matrisi 4 i\u015flemciye da\u011f\u0131tmak istersek, s\u00fctun paralelli\u011fi kullanarak her bir i\u015flemciye 1024x1024 boyutunda bir a\u011f\u0131rl\u0131k alt matrisi atayabiliriz. Giri\u015f vekt\u00f6r\u00fc (X) ise her i\u015flemciye g\u00f6nderilir. Her i\u015flemci kendi Xi = X * Wi<\/code> \u00e7arp\u0131m\u0131n\u0131 yapar. Sonu\u00e7lar\u0131 birle\u015ftirmek i\u00e7in ise bir <code>All-Gather<\/code> operasyonu gerekebilir, burada her i\u015flemci kendi k\u0131smi sonucunu di\u011ferlerine g\u00f6nderir ve herkes tam sonucu elde eder.<\/p>\n    <p>Bu yakla\u015f\u0131m, b\u00fcy\u00fck matris \u00e7arp\u0131mlar\u0131n\u0131 h\u0131zland\u0131r\u0131r ancak i\u015flemciler aras\u0131 veri transferini art\u0131r\u0131r. Bu nedenle, ileti\u015fim maliyetini optimize etmek i\u00e7in dikkatli bir planlama gereklidir.<\/p>\n<\/section><\/code><\/pre>\n
Boru Hatt\u0131 Paralelli\u011fi: Katmanlar\u0131 S\u0131raya Dizmek<\/h3>\n
Boru hatt\u0131 paralelli\u011fi (Pipeline Parallelism), model paralelli\u011finin bir ba\u015fka \u00f6nemli bi\u00e7imidir ve bir sinir a\u011f\u0131n\u0131n katmanlar\u0131n\u0131 farkl\u0131 i\u015flemcilere veya d\u00fc\u011f\u00fcmlere da\u011f\u0131tarak bir “boru hatt\u0131” olu\u015fturma prensibine dayan\u0131r. Bu, bir \u00fcretim hatt\u0131 gibi d\u00fc\u015f\u00fcn\u00fclebilir; her istasyon (i\u015flemci) modelin belirli bir b\u00f6l\u00fcm\u00fcn\u00fc (katman grubunu) i\u015fler ve \u00e7\u0131kt\u0131y\u0131 bir sonraki istasyona iletir.<\/p>\n
Boru hatt\u0131 paralelli\u011finin temel amac\u0131, modelin tamam\u0131n\u0131n tek bir i\u015flemcinin belle\u011fine s\u0131\u011fmad\u0131\u011f\u0131 durumlarda bile modeli e\u011fitmek ve \u00e7al\u0131\u015ft\u0131rmakt\u0131r. Model, ard\u0131\u015f\u0131k katmanlar halinde farkl\u0131 i\u015flemcilere b\u00f6l\u00fcn\u00fcr. \u00d6rne\u011fin, 10 katmanl\u0131 bir model, 2 i\u015flemciye sahipseniz, ilk 5 katman\u0131 i\u015flemci 1’de, sonraki 5 katman\u0131 ise i\u015flemci 2’de \u00e7al\u0131\u015ft\u0131r\u0131labilir.<\/p>\n
Ancak, basit\u00e7e katmanlar\u0131 b\u00f6lmek, “bo\u015f zaman” (bubble) ad\u0131 verilen bir verimsizli\u011fe yol a\u00e7ar. \u0130lk i\u015flemci ilk veri partisinin (mini-batch) ilk katmanlar\u0131n\u0131 i\u015flerken, ikinci i\u015flemci bo\u015fta bekler. \u0130kinci i\u015flemci i\u015flemeye ba\u015flad\u0131\u011f\u0131nda, ilk i\u015flemci bir sonraki veri partisini i\u015fleyebilir. Bu durum, i\u015flemcilerin tam kapasiteyle \u00e7al\u0131\u015fmas\u0131n\u0131 engeller ve verimlili\u011fi d\u00fc\u015f\u00fcr\u00fcr.<\/p>\n
Bu bo\u015f zaman sorununu \u00e7\u00f6zmek i\u00e7in \u00e7e\u015fitli stratejiler geli\u015ftirilmi\u015ftir:<\/p>\n
*   Micro-batching (Mikro-Parti \u0130\u015fleme):<\/strong> Bu y\u00f6ntemde, b\u00fcy\u00fck bir mini-parti daha k\u00fc\u00e7\u00fck mikro-partilere b\u00f6l\u00fcn\u00fcr. Bu mikro-partiler, boru hatt\u0131 boyunca ard\u0131\u015f\u0131k olarak ilerler. \u00d6rne\u011fin, 3 mikro-partiniz varsa, i\u015flemci 1 ilk mikro-partiyi i\u015fler, ard\u0131ndan ikinci mikro-partiyi i\u015flerken i\u015flemci 2 ilk mikro-partinin \u00e7\u0131kt\u0131s\u0131n\u0131 i\u015flemeye ba\u015flar. Bu, i\u015flemcilerin s\u00fcrekli olarak me\u015fgul kalmas\u0131n\u0131 sa\u011flayarak bo\u015f zaman\u0131 \u00f6nemli \u00f6l\u00e7\u00fcde azalt\u0131r.<\/p>\n
*   GPipe, PipeDream gibi Algoritmalar:<\/strong> Bu algoritmalar, mikro-parti i\u015fleme, gradyanlar\u0131n geri yay\u0131l\u0131m\u0131 ve model a\u011f\u0131rl\u0131klar\u0131n\u0131n g\u00fcncellenmesi gibi ad\u0131mlar\u0131 optimize ederek boru hatt\u0131 verimlili\u011fini art\u0131rmay\u0131 hedefler. GPipe, modelin katmanlar\u0131n\u0131 e\u015fit par\u00e7alara b\u00f6lerek ve mikro-partileri dikkatlice y\u00f6neterek bo\u015f zaman\u0131 en aza indirmeye \u00e7al\u0131\u015f\u0131r. PipeDream ise daha dinamik bir yakla\u015f\u0131m sunarak farkl\u0131 mikro-partiler i\u00e7in farkl\u0131 i\u015flemci atamalar\u0131 yapabilir.<\/p>\n
Uygulama \u00d6rne\u011fi: Derin Bir Konvol\u00fcsyonel A\u011fda Boru Hatt\u0131<\/strong><\/p>\n
Derin bir konvol\u00fcsyonel sinir a\u011f\u0131 (CNN) d\u00fc\u015f\u00fcnelim. Bu a\u011f y\u00fczlerce katmandan olu\u015fabilir ve tek bir GPU’nun belle\u011fine s\u0131\u011fmayabilir. Boru hatt\u0131 paralelli\u011fi kullanarak, bu katmanlar\u0131 grupland\u0131r\u0131p farkl\u0131 d\u00fc\u011f\u00fcmlere atayabiliriz.<\/p>\n
\u00d6rne\u011fin, 100 katmanl\u0131 bir CNN’iniz varsa ve 4 d\u00fc\u011f\u00fcm\u00fcn\u00fcz varsa:
\n*   D\u00fc\u011f\u00fcm 1: Katman 1-25
\n*   D\u00fc\u011f\u00fcm 2: Katman 26-50
\n*   D\u00fc\u011f\u00fcm 3: Katman 51-75
\n*   D\u00fc\u011f\u00fcm 4: Katman 76-100<\/p>\n
E\u011fitim s\u0131ras\u0131nda, veri k\u00fcmesini daha k\u00fc\u00e7\u00fck mikro-partilere b\u00f6leriz. D\u00fc\u011f\u00fcm 1, ilk mikro-partiyi al\u0131r, katman 1-25’i i\u015fler ve \u00e7\u0131kt\u0131y\u0131 D\u00fc\u011f\u00fcm 2’ye g\u00f6nderir. Bu s\u0131rada D\u00fc\u011f\u00fcm 1, ikinci mikro-partiyi i\u015flemeye ba\u015flar. D\u00fc\u011f\u00fcm 2, D\u00fc\u011f\u00fcm 1’den gelen \u00e7\u0131kt\u0131y\u0131 al\u0131r, katman 26-50’yi i\u015fler ve \u00e7\u0131kt\u0131y\u0131 D\u00fc\u011f\u00fcm 3’e g\u00f6nderir. Bu i\u015flem, boru hatt\u0131n\u0131n sonuna kadar devam eder. Geri yay\u0131l\u0131m s\u0131ras\u0131nda ise gradyanlar ters y\u00f6nde ilerler ve her d\u00fc\u011f\u00fcm kendi katman grubunu g\u00fcnceller. Mikro-parti i\u015fleme sayesinde, t\u00fcm d\u00fc\u011f\u00fcmler s\u00fcrekli olarak me\u015fgul tutulabilir, bu da i\u015flemci kullan\u0131m\u0131n\u0131 ve dolay\u0131s\u0131yla e\u011fitim h\u0131z\u0131n\u0131 art\u0131r\u0131r.<\/p>\n
<section>\n    <h2>Boru Hatt\u0131 Paralelli\u011fi ve Mikro-Parti \u0130\u015fleme<\/h2>\n    <p>Boru hatt\u0131 paralelli\u011fi, \u00f6zellikle \u00e7ok derin modellerde, tek bir makinenin bellek s\u0131n\u0131rlar\u0131n\u0131 a\u015fmak i\u00e7in vazge\u00e7ilmezdir. Ancak verimlili\u011fi maksimize etmek i\u00e7in mikro-parti i\u015fleme kritik \u00f6neme sahiptir. Bir modelin 10 katman\u0131n\u0131 2 d\u00fc\u011f\u00fcme b\u00f6ld\u00fc\u011f\u00fcm\u00fcz\u00fc ve 4 mikro-partilik bir mini-parti kulland\u0131\u011f\u0131m\u0131z\u0131 hayal edin:<\/p>\n    <ul>\n        <li><strong>D\u00fc\u011f\u00fcm 1 (Katman 1-5), D\u00fc\u011f\u00fcm 2 (Katman 6-10)<\/strong><\/li>\n        <li><strong>Ad\u0131m 1:<\/strong> D\u00fc\u011f\u00fcm 1, Mikro-parti 1'i i\u015fler. D\u00fc\u011f\u00fcm 2 bo\u015fta.<\/li>\n        <li><strong>Ad\u0131m 2:<\/strong> D\u00fc\u011f\u00fcm 1, Mikro-parti 2'yi i\u015fler. D\u00fc\u011f\u00fcm 2, Mikro-parti 1'in \u00e7\u0131kt\u0131s\u0131n\u0131 i\u015fler.<\/li>\n        <li><strong>Ad\u0131m 3:<\/strong> D\u00fc\u011f\u00fcm 1, Mikro-parti 3'\u00fc i\u015fler. D\u00fc\u011f\u00fcm 2, Mikro-parti 2'nin \u00e7\u0131kt\u0131s\u0131n\u0131 i\u015fler.<\/li>\n        <li><strong>Ad\u0131m 4:<\/strong> D\u00fc\u011f\u00fcm 1, Mikro-parti 4'\u00fc i\u015fler. D\u00fc\u011f\u00fcm 2, Mikro-parti 3'\u00fcn \u00e7\u0131kt\u0131s\u0131n\u0131 i\u015fler.<\/li>\n        <li><strong>Ad\u0131m 5:<\/strong> D\u00fc\u011f\u00fcm 1 bo\u015fta. D\u00fc\u011f\u00fcm 2, Mikro-parti 4'\u00fcn \u00e7\u0131kt\u0131s\u0131n\u0131 i\u015fler.<\/li>\n    <\/ul>\n    <p>Bu basit \u00f6rnek, mikro-parti i\u015flemenin i\u015flemcileri nas\u0131l s\u00fcrekli me\u015fgul tuttu\u011funu g\u00f6stermektedir. Geri yay\u0131l\u0131m s\u0131ras\u0131nda da benzer bir i\u015flem izlenir, b\u00f6ylece her d\u00fc\u011f\u00fcm kendi katmanlar\u0131 i\u00e7in gradyanlar\u0131 al\u0131r ve g\u00fcncellemeleri yapar.<\/p>\n<\/section><\/code><\/pre>\n
Tensor ve Boru Hatt\u0131 Paralelli\u011fini Birle\u015ftirmek: Hibrit Yakla\u015f\u0131mlar<\/h3>\n
Tensor paralelli\u011fi ve boru hatt\u0131 paralelli\u011fi, her biri kendi ba\u015f\u0131na g\u00fc\u00e7l\u00fc teknikler olsa da, en b\u00fcy\u00fck ve en karma\u015f\u0131k modelleri e\u011fitmek i\u00e7in genellikle birlikte kullan\u0131l\u0131rlar. Bu hibrit yakla\u015f\u0131mlar, her iki tekni\u011fin de avantajlar\u0131n\u0131 birle\u015ftirerek daha verimli ve \u00f6l\u00e7eklenebilir \u00e7\u00f6z\u00fcmler sunar.<\/p>\n
B\u00fcy\u00fck dil modelleri gibi devasa yap\u0131lar, hem \u00e7ok b\u00fcy\u00fck matris \u00e7arp\u0131mlar\u0131 (tensor paralelli\u011fi i\u00e7in uygun) hem de \u00e7ok say\u0131da katman (boru hatt\u0131 paralelli\u011fi i\u00e7in uygun) i\u00e7erir. Bu nedenle, bu modelleri da\u011f\u0131t\u0131lm\u0131\u015f ortamlarda etkili bir \u015fekilde e\u011fitmek i\u00e7in her iki paralellik t\u00fcr\u00fcn\u00fc de birle\u015ftirmek mant\u0131kl\u0131d\u0131r.<\/p>\n
Genel strateji \u015f\u00f6yledir:<\/p>\n
1.  Boru Hatt\u0131 Paralelli\u011fi ile D\u00fc\u011f\u00fcmleri B\u00f6lmek:<\/strong> \u0130lk olarak, modelin katmanlar\u0131 grupland\u0131r\u0131l\u0131r ve bu gruplar farkl\u0131 d\u00fc\u011f\u00fcmlere atan\u0131r. Bu, modelin belle\u011fe s\u0131\u011fd\u0131r\u0131lmas\u0131n\u0131 sa\u011flar ve her d\u00fc\u011f\u00fcm\u00fcn kendi katman grubunu i\u015flemesini sa\u011flar.
\n2.  Tensor Paralelli\u011fi ile Katman Gruplar\u0131n\u0131 B\u00f6lmek:<\/strong> Her bir d\u00fc\u011f\u00fcmdeki katman grubu i\u00e7inde, b\u00fcy\u00fck matris \u00e7arp\u0131mlar\u0131 tensor paralelli\u011fi kullan\u0131larak o d\u00fc\u011f\u00fcmdeki birden fazla i\u015flemciye (GPU’ya) da\u011f\u0131t\u0131l\u0131r. \u00d6rne\u011fin, bir d\u00fc\u011f\u00fcmde 8 GPU varsa ve bu GPU’lar bir transformer blo\u011funun dikkat mekanizmas\u0131ndaki b\u00fcy\u00fck matris \u00e7arp\u0131mlar\u0131n\u0131 i\u015fliyorsa, bu 8 GPU tensor paralelli\u011fi kullanarak bu matrisleri kendi aralar\u0131nda b\u00f6lebilir.<\/p>\n
Bu hibrit yakla\u015f\u0131m, hem modelin belle\u011fe s\u0131\u011fd\u0131r\u0131lmas\u0131n\u0131 sa\u011flar (boru hatt\u0131 paralelli\u011fi sayesinde) hem de her d\u00fc\u011f\u00fcmdeki hesaplama yo\u011funlu\u011funu azalt\u0131r (tensor paralelli\u011fi sayesinde). Bu, \u00e7ok daha b\u00fcy\u00fck modellerin daha az say\u0131da d\u00fc\u011f\u00fcmle veya daha az say\u0131da GPU ile e\u011fitilmesine olanak tan\u0131r.<\/p>\n
\u00d6rne\u011fin, 100 milyar parametreli bir model e\u011fitmek i\u00e7in 1024 adet GPU kullan\u0131ld\u0131\u011f\u0131n\u0131 varsayal\u0131m. Bu modeli sadece veri paralelli\u011fi ile e\u011fitmek, her bir GPU’nun modelin tamam\u0131n\u0131 belle\u011finde tutmas\u0131n\u0131 gerektirir ki bu da imkans\u0131zd\u0131r. Sadece boru hatt\u0131 paralelli\u011fi kullanmak, modelin katmanlar\u0131n\u0131 1024 GPU’ya da\u011f\u0131tmak anlam\u0131na gelir ki bu da ileti\u015fim darbo\u011fazlar\u0131na yol a\u00e7abilir. Ancak, hibrit bir yakla\u015f\u0131m izlenerek:<\/p>\n
*   Model, 128 adet boru hatt\u0131 a\u015famas\u0131na b\u00f6l\u00fcnebilir (her a\u015famada 8 GPU).
\n*   Her bir boru hatt\u0131 a\u015famas\u0131ndaki 8 GPU, kendi i\u00e7indeki b\u00fcy\u00fck matris \u00e7arp\u0131mlar\u0131n\u0131 tensor paralelli\u011fi ile i\u015fleyebilir.
\n*   Geriye kalan GPU’lar veri paralelli\u011fi i\u00e7in kullan\u0131labilir.<\/p>\n
Bu \u015fekilde, modelin farkl\u0131 b\u00f6l\u00fcmleri farkl\u0131 d\u00fc\u011f\u00fcmlerde i\u015flenirken, b\u00fcy\u00fck matris \u00e7arp\u0131mlar\u0131 da farkl\u0131 i\u015flemcilerde paralel olarak hesaplan\u0131r. Bu, hesaplama verimlili\u011fini ve \u00f6l\u00e7eklenebilirli\u011fi \u00f6nemli \u00f6l\u00e7\u00fcde art\u0131r\u0131r.<\/p>\n
<section>\n    <h2>Hibrit Paralellik: En \u0130yi \u0130ki D\u00fcnyan\u0131n Birle\u015fimi<\/h2>\n    <p>B\u00fcy\u00fck dil modelleri (LLM'ler) s\u00f6z konusu oldu\u011funda, hibrit paralellik neredeyse bir zorunluluktur. \u00d6rne\u011fin, 175 milyar parametreli GPT-3 modelini e\u011fitmek i\u00e7in hem tensor hem de boru hatt\u0131 paralelli\u011fi kullan\u0131lm\u0131\u015ft\u0131r. Genel bir model \u015f\u00f6yle i\u015fler:<\/p>\n    <ol>\n        <li><strong>Modeli Grupland\u0131rma (Boru Hatt\u0131):<\/strong> Modelin katmanlar\u0131, \u00f6rne\u011fin 8 veya 16 katmanl\u0131k gruplar halinde boru hatt\u0131 a\u015famalar\u0131na ayr\u0131l\u0131r.<\/li>\n        <li><strong>D\u00fc\u011f\u00fcmler Aras\u0131 Da\u011f\u0131t\u0131m (Boru Hatt\u0131):<\/strong> Bu boru hatt\u0131 a\u015famalar\u0131, farkl\u0131 sunuculara veya d\u00fc\u011f\u00fcmlere atan\u0131r.<\/li>\n        <li><strong>\u0130\u015flemciler Aras\u0131 Da\u011f\u0131t\u0131m (Tensor):<\/strong> Her bir d\u00fc\u011f\u00fcmdeki GPU'lar, kendi atanan katman grubundaki b\u00fcy\u00fck matris \u00e7arp\u0131mlar\u0131n\u0131 tensor paralelli\u011fi kullanarak kendi aralar\u0131nda b\u00f6ler. \u00d6rne\u011fin, bir transformer blo\u011fundaki Q, K, V projeksiyon matrisleri, o d\u00fc\u011f\u00fcmdeki 4 GPU aras\u0131nda s\u00fctunlar\u0131na g\u00f6re b\u00f6l\u00fcnebilir.<\/li>\n        <li><strong>Veri Paralelli\u011fi (Opsiyonel):<\/strong> E\u011fer yeterli say\u0131da d\u00fc\u011f\u00fcm varsa, veri paralelli\u011fi de eklenerek e\u011fitim verisi farkl\u0131 d\u00fc\u011f\u00fcm gruplar\u0131na da\u011f\u0131t\u0131labilir.<\/li>\n    <\/ol>\n    <p>Bu karma\u015f\u0131k ancak etkili strateji, devasa modellerin hem belle\u011fe s\u0131\u011fmas\u0131n\u0131 hem de makul s\u00fcrelerde e\u011fitilmesini sa\u011flar. \u0130leti\u015fim, bu hibrit sistemlerde kritik bir rol oynar ve y\u00fcksek h\u0131zl\u0131 a\u011f ba\u011flant\u0131lar\u0131 (\u00f6rne\u011fin, InfiniBand) bu t\u00fcr da\u011f\u0131t\u0131lm\u0131\u015f e\u011fitim altyap\u0131lar\u0131n\u0131n temelini olu\u015fturur.<\/p>\n<\/section><\/code><\/pre>\n
Vaka Analizi: B\u00fcy\u00fck Dil Modellerinde Paralellik Uygulamalar\u0131<\/h3>\n
B\u00fcy\u00fck dil modelleri (LLM’ler), yapay zeka alan\u0131ndaki en iddial\u0131 projelerden baz\u0131lar\u0131d\u0131r ve bu modellerin geli\u015ftirilmesi ve e\u011fitimi, da\u011f\u0131t\u0131lm\u0131\u015f hesaplama tekniklerinin s\u0131n\u0131rlar\u0131n\u0131 zorlam\u0131\u015ft\u0131r. GPT-3, LaMDA, PaLM gibi modellerin ba\u015far\u0131s\u0131, b\u00fcy\u00fck \u00f6l\u00e7\u00fcde tensor ve boru hatt\u0131 paralelli\u011fini i\u00e7eren sofistike hibrit stratejilere dayanmaktad\u0131r.<\/p>\n
GPT-3’\u00fcn E\u011fitimi:<\/strong> OpenAI taraf\u0131ndan geli\u015ftirilen GPT-3, 175 milyar parametreye sahip devasa bir modeldir. Bu modelin e\u011fitimi i\u00e7in kullan\u0131lan da\u011f\u0131t\u0131lm\u0131\u015f e\u011fitim stratejisi, b\u00fcy\u00fck \u00f6l\u00e7ekte boru hatt\u0131 ve tensor paralelli\u011fini birle\u015ftirmi\u015ftir. Model, katman gruplar\u0131na ayr\u0131larak farkl\u0131 d\u00fc\u011f\u00fcmlere da\u011f\u0131t\u0131lm\u0131\u015ft\u0131r (bor hat\u0131 paralelli\u011fi). Her bir d\u00fc\u011f\u00fcmdeki GPU’lar ise, \u00f6zellikle transformer bloklar\u0131ndaki dikkat mekanizmalar\u0131nda ve ileri beslemeli a\u011flardaki b\u00fcy\u00fck matris \u00e7arp\u0131mlar\u0131n\u0131 i\u015flemek i\u00e7in tensor paralelli\u011fini kullanm\u0131\u015ft\u0131r. Bu, modelin tek bir makinenin belle\u011fine s\u0131\u011fmas\u0131n\u0131 sa\u011flarken, hesaplama y\u00fck\u00fcn\u00fc de \u00e7ok say\u0131da GPU’ya yaym\u0131\u015ft\u0131r. GPT-3’\u00fcn e\u011fitimi i\u00e7in binlerce V100 GPU’nun kullan\u0131ld\u0131\u011f\u0131 ve haftalar s\u00fcrd\u00fc\u011f\u00fc bilinmektedir. Bu \u00f6l\u00e7ekteki bir projede, ileti\u015fim verimlili\u011fi ve senkronizasyon, ba\u015far\u0131n\u0131n anahtar\u0131 olmu\u015ftur.<\/p>\n
Google’\u0131n PaLM Modeli:<\/strong> Google’\u0131n Pathways Language Model (PaLM), 540 milyar parametre ile GPT-3’ten daha da b\u00fcy\u00fckt\u00fcr. PaLM’\u0131n e\u011fitimi, Google’\u0131n “Pathways” adl\u0131 yeni nesil yapay zeka mimarisini kullanm\u0131\u015ft\u0131r. Pathways, farkl\u0131 t\u00fcrdeki g\u00f6revler i\u00e7in tek bir modelin birden fazla i\u015flemciyi daha ak\u0131ll\u0131ca kullanmas\u0131n\u0131 sa\u011flamay\u0131 ama\u00e7lar. PaLM’\u0131n e\u011fitiminde, hem boru hatt\u0131 paralelli\u011fi hem de tensor paralelli\u011fi, ve ayr\u0131ca veri paralelli\u011fi de kullan\u0131lm\u0131\u015ft\u0131r. Modelin farkl\u0131 b\u00f6l\u00fcmleri (tens\u00f6r ve katmanlar) farkl\u0131 i\u015flemcilere da\u011f\u0131t\u0131l\u0131rken, b\u00fcy\u00fck matris i\u015flemleri de paralel olarak y\u00fcr\u00fct\u00fclm\u00fc\u015ft\u00fcr. PaLM’\u0131n e\u011fitimi i\u00e7in binlerce TPU (Tensor Processing Unit) kullan\u0131lm\u0131\u015f ve bu, da\u011f\u0131t\u0131lm\u0131\u015f sistemlerin \u00f6l\u00e7eklenebilirli\u011finin ve verimlili\u011finin ne kadar \u00f6nemli oldu\u011funu bir kez daha g\u00f6stermi\u015ftir.<\/p>\n
Bu vaka analizleri, tensor ve boru hatt\u0131 paralelli\u011finin sadece teorik kavramlar olmad\u0131\u011f\u0131n\u0131, ayn\u0131 zamanda g\u00fcn\u00fcm\u00fcz\u00fcn en geli\u015fmi\u015f yapay zeka modellerinin geli\u015ftirilmesinde kritik ve pratik ara\u00e7lar oldu\u011funu a\u00e7\u0131k\u00e7a ortaya koymaktad\u0131r. Bu teknikler olmadan, bu b\u00fcy\u00fckl\u00fckteki modellerin e\u011fitimi ve hatta geli\u015ftirilmesi bile m\u00fcmk\u00fcn olmazd\u0131.<\/p>\n
<section>\n    <h2>Ger\u00e7ek D\u00fcnya Senaryosu: Bir G\u00f6r\u00fcnt\u00fc \u00dcretme Modeli<\/h2>\n    <p>Bir ara\u015ft\u0131rma ekibi, 1 milyar parametreli y\u00fcksek \u00e7\u00f6z\u00fcn\u00fcrl\u00fckl\u00fc g\u00f6r\u00fcnt\u00fc \u00fcreten bir model \u00fczerinde \u00e7al\u0131\u015f\u0131yor. Modelin tek bir GPU'nun belle\u011fine s\u0131\u011fmad\u0131\u011f\u0131n\u0131 fark ediyorlar. Mevcut altyap\u0131lar\u0131 ise 8 adet NVIDIA A100 GPU'ya sahip bir sunucudur. Bu durumda, hibrit bir paralellik stratejisi izlemeleri gerekir:<\/p>\n    <ol>\n        <li><strong>Boru Hatt\u0131 Paralelli\u011fi:<\/strong> Modelin katmanlar\u0131n\u0131 2 gruba ay\u0131r\u0131rlar. Grup 1 (Katman 1-50) 4 GPU'ya, Grup 2 (Katman 51-100) ise kalan 4 GPU'ya atan\u0131r. Bu, modelin toplam bellek gereksinimini yar\u0131ya indirir.<\/li>\n        <li><strong>Tensor Paralelli\u011fi:<\/strong> Her bir gruptaki GPU'lar kendi aralar\u0131nda tensor paralelli\u011fi kullan\u0131r. \u00d6rne\u011fin, Grup 1'deki 4 GPU, transformer bloklar\u0131ndaki b\u00fcy\u00fck dikkat matrislerini s\u00fctunlar\u0131na g\u00f6re b\u00f6ler. Her GPU, kendi matris alt b\u00f6l\u00fcm\u00fcn\u00fc i\u015fler ve sonu\u00e7lar\u0131 <code>All-Gather<\/code> ile birle\u015ftirir.<\/li>\n        <li><strong>Mikro-Parti \u0130\u015fleme:<\/strong> Bo\u015f zaman\u0131 azaltmak i\u00e7in, e\u011fitim verisi 16 mikro-partiye b\u00f6l\u00fcn\u00fcr.<\/li>\n    <\/ol>\n    <p>Bu yakla\u015f\u0131m, 8 GPU'luk bir sistemde bile 1 milyar parametrelik bir modeli e\u011fitme imkan\u0131 sunar. E\u011fer daha fazla d\u00fc\u011f\u00fcm olsayd\u0131, veri paralelli\u011fi de eklenerek e\u011fitim s\u00fcresi daha da k\u0131salt\u0131labilirdi.<\/p>\n<\/section><\/code><\/pre>\n
Optimizasyon \u0130pu\u00e7lar\u0131 ve Dikkat Edilmesi Gerekenler<\/h3>\n
Tensor ve boru hatt\u0131 paralelli\u011fi, b\u00fcy\u00fck modelleri \u00f6l\u00e7eklendirmek i\u00e7in g\u00fc\u00e7l\u00fc ara\u00e7lar olsa da, verimli bir \u015fekilde uygulanmalar\u0131 i\u00e7in baz\u0131 \u00f6nemli noktalara dikkat etmek gerekir. Bu tekniklerin getirdi\u011fi karma\u015f\u0131kl\u0131k ve ileti\u015fim maliyetleri, dikkatli optimizasyon gerektirir.<\/p>\n
*   \u0130leti\u015fim Maliyetini Azaltma:<\/strong> Paralel e\u011fitimde en b\u00fcy\u00fck zorluklardan biri, i\u015flemciler aras\u0131ndaki veri ileti\u015fiminin maliyetidir. Tensor paralelli\u011finde matrisleri birle\u015ftirirken veya boru hatt\u0131 paralelli\u011finde katman \u00e7\u0131kt\u0131lar\u0131n\u0131 iletirken y\u00fcksek h\u0131zl\u0131 a\u011f ba\u011flant\u0131lar\u0131 (\u00f6rne\u011fin, InfiniBand veya NVLink) kritik \u00f6neme sahiptir. Ayr\u0131ca, gereksiz veri transferinden ka\u00e7\u0131nmak ve ileti\u015fim operasyonlar\u0131n\u0131 (\u00f6rne\u011fin, All-Reduce<\/code>, All-Gather<\/code>) optimize etmek \u00f6nemlidir. Baz\u0131 framework’ler, bu ileti\u015fim operasyonlar\u0131n\u0131 verimli bir \u015fekilde ger\u00e7ekle\u015ftirmek i\u00e7in \u00f6zel algoritmalar kullan\u0131r.<\/p>\n
*   Dengeleme (Load Balancing):<\/strong> Boru hatt\u0131 paralelli\u011finde, her i\u015flemcinin veya d\u00fc\u011f\u00fcm\u00fcn e\u015fit miktarda i\u015f y\u00fck\u00fcne sahip olmas\u0131, verimlilik i\u00e7in hayati \u00f6nem ta\u015f\u0131r. Katman gruplar\u0131n\u0131 b\u00f6lerken, her grubun hesaplama maliyetinin dengeli oldu\u011fundan emin olunmal\u0131d\u0131r. E\u011fer bir grup di\u011ferlerinden \u00e7ok daha fazla hesaplama gerektiriyorsa, bu durum boru hatt\u0131nda bir darbo\u011faz olu\u015fturacakt\u0131r. Benzer \u015fekilde, tensor paralelli\u011finde de matrisleri b\u00f6lerken hesaplama y\u00fck\u00fcn\u00fcn GPU’lar aras\u0131nda dengeli da\u011f\u0131t\u0131lmas\u0131 gerekir.<\/p>\n
*   Mikro-Parti Boyutunun Ayarlanmas\u0131:<\/strong> Boru hatt\u0131 paralelli\u011finde mikro-parti boyutu, verimlilik ve bellek kullan\u0131m\u0131 aras\u0131nda bir denge kurar. \u00c7ok k\u00fc\u00e7\u00fck mikro-partiler ileti\u015fim maliyetini art\u0131rabilirken, \u00e7ok b\u00fcy\u00fck mikro-partiler boru hatt\u0131ndaki bo\u015f zaman\u0131 art\u0131rabilir. Do\u011fru mikro-parti boyutu, modelin mimarisine, d\u00fc\u011f\u00fcm say\u0131s\u0131na ve a\u011f bant geni\u015fli\u011fine ba\u011fl\u0131 olarak deneysel olarak belirlenmelidir.<\/p>\n
*   Framework Se\u00e7imi:<\/strong> TensorFlow, PyTorch, JAX gibi modern derin \u00f6\u011frenme framework’leri, da\u011f\u0131t\u0131lm\u0131\u015f e\u011fitim i\u00e7in geli\u015fmi\u015f destek sunar. Bu framework’lerin sundu\u011fu paralellik API’leri ve optimizasyon ara\u00e7lar\u0131, karma\u015f\u0131k paralellik stratejilerini uygulamay\u0131 kolayla\u015ft\u0131r\u0131r. \u00d6rne\u011fin, PyTorch’un DistributedDataParallel<\/code> ve torch.distributed.pipeline<\/code> mod\u00fclleri, veya Megatron-LM gibi \u00f6zel k\u00fct\u00fcphaneler, bu t\u00fcr teknikleri uygulamak i\u00e7in g\u00fc\u00e7l\u00fc ara\u00e7lar sa\u011flar.<\/p>\n
*   Bellek Y\u00f6netimi:<\/strong> B\u00fcy\u00fck modellerle \u00e7al\u0131\u015f\u0131rken bellek y\u00f6netimi her zaman bir endi\u015fe kayna\u011f\u0131d\u0131r. Tensor ve boru hatt\u0131 paralelli\u011fi, modeli farkl\u0131 i\u015flemcilere da\u011f\u0131tarak bellek sorunlar\u0131n\u0131 \u00e7\u00f6zmeye yard\u0131mc\u0131 olsa da, her bir i\u015flemcinin yine de kendi pay\u0131na d\u00fc\u015fen model par\u00e7alar\u0131n\u0131 ve aktivasyonlar\u0131 belle\u011finde tutmas\u0131 gerekir. Bu nedenle, aktivasyon yeniden hesaplama (activation recomputation) gibi teknikler, bellek kullan\u0131m\u0131n\u0131 azaltmak i\u00e7in kullan\u0131labilir.<\/p>\n
*   Hata Ay\u0131klama ve \u0130zleme:<\/strong> Da\u011f\u0131t\u0131lm\u0131\u015f sistemlerde hata ay\u0131klama ve performans izleme, tek makineli sistemlere g\u00f6re \u00e7ok daha zordur. Hangi d\u00fc\u011f\u00fcm\u00fcn veya i\u015flemcinin soruna neden oldu\u011funu belirlemek, ileti\u015fim loglar\u0131n\u0131 analiz etmek ve performans metriklerini izlemek i\u00e7in \u00f6zel ara\u00e7lar ve stratejiler gereklidir.<\/p>\n
Bu optimizasyon ipu\u00e7lar\u0131, tensor ve boru hatt\u0131 paralelli\u011fini kullanarak modellerinizi daha verimli ve \u00f6l\u00e7eklenebilir hale getirmenize yard\u0131mc\u0131 olacakt\u0131r.<\/p>\n
Sonu\u00e7 ve S\u0131k\u00e7a Sorulan Sorular<\/h3>\n
Tensor ve boru hatt\u0131 paralelli\u011fi, g\u00fcn\u00fcm\u00fcz\u00fcn devasa derin \u00f6\u011frenme modellerini e\u011fitmek ve \u00e7al\u0131\u015ft\u0131rmak i\u00e7in vazge\u00e7ilmez tekniklerdir. Tensor paralelli\u011fi, b\u00fcy\u00fck matris \u00e7arp\u0131mlar\u0131n\u0131 birden fazla i\u015flemciye da\u011f\u0131tarak hesaplama yo\u011funlu\u011funu azalt\u0131rken, boru hatt\u0131 paralelli\u011fi modelin katmanlar\u0131n\u0131 farkl\u0131 i\u015flemcilere atayarak belle\u011fe s\u0131\u011fd\u0131rma sorununu \u00e7\u00f6zer. Bu iki tekni\u011fi birle\u015ftiren hibrit yakla\u015f\u0131mlar ise, en b\u00fcy\u00fck ve en karma\u015f\u0131k modellerin bile \u00f6l\u00e7eklenebilir bir \u015fekilde e\u011fitilmesini m\u00fcmk\u00fcn k\u0131lar. Bu makalede ele ald\u0131\u011f\u0131m\u0131z temel kavramlar, uygulama \u00f6rnekleri ve optimizasyon ipu\u00e7lar\u0131, okuyucular\u0131n bu g\u00fc\u00e7l\u00fc teknikleri kendi projelerinde daha bilin\u00e7li bir \u015fekilde kullanmalar\u0131na yard\u0131mc\u0131 olacakt\u0131r.<\/p>\n
S\u0131k\u00e7a Sorulan Sorular (SSS)<\/h4>\n
*   Tensor paralelli\u011fi ve veri paralelli\u011fi aras\u0131ndaki temel fark nedir?<\/strong>
\n    Veri paralelli\u011finde model her i\u015flemciye kopyalan\u0131r ve veri b\u00f6l\u00fcn\u00fcr. Tensor paralelli\u011finde ise modelin kendisi (\u00f6zellikle matris \u00e7arp\u0131mlar\u0131) farkl\u0131 i\u015flemcilere b\u00f6l\u00fcn\u00fcr. Veri paralelli\u011fi b\u00fcy\u00fck veri k\u00fcmeleri i\u00e7in, tensor paralelli\u011fi ise b\u00fcy\u00fck modeller i\u00e7in daha uygundur.
\n*   Boru hatt\u0131 paralelli\u011fi neden gereklidir?<\/strong>
\n    Boru hatt\u0131 paralelli\u011fi, modelin tamam\u0131n\u0131n tek bir i\u015flemcinin belle\u011fine s\u0131\u011fmad\u0131\u011f\u0131 durumlarda modeli farkl\u0131 i\u015flemcilere da\u011f\u0131tarak \u00e7al\u0131\u015ft\u0131r\u0131lmas\u0131n\u0131 sa\u011flar.
\n*   Mikro-parti i\u015fleme boru hatt\u0131 paralelli\u011finde ne i\u015fe yarar?<\/strong>
\n    Mikro-parti i\u015fleme, b\u00fcy\u00fck mini-partileri daha k\u00fc\u00e7\u00fck par\u00e7alara b\u00f6lerek boru hatt\u0131 boyunca ard\u0131\u015f\u0131k olarak ilerletir. Bu, i\u015flemcilerin s\u00fcrekli me\u015fgul kalmas\u0131n\u0131 sa\u011flayarak boru hatt\u0131ndaki “bo\u015f zaman\u0131” azalt\u0131r ve verimlili\u011fi art\u0131r\u0131r.
\n*   Tensor ve boru hatt\u0131 paralelli\u011fi birlikte nas\u0131l kullan\u0131l\u0131r?<\/strong>
\n    Hibrit yakla\u015f\u0131mlarda, modelin katmanlar\u0131 boru hatt\u0131 paralelli\u011fi ile farkl\u0131 d\u00fc\u011f\u00fcmlere da\u011f\u0131t\u0131l\u0131r. Her d\u00fc\u011f\u00fcmdeki i\u015flemciler ise, kendi atanan katman grubundaki b\u00fcy\u00fck matris \u00e7arp\u0131mlar\u0131n\u0131 tensor paralelli\u011fi ile kendi aralar\u0131nda b\u00f6ler.
\n*   Bu teknikler hangi derin \u00f6\u011frenme framework’lerinde desteklenir?<\/strong>
\n    TensorFlow, PyTorch ve JAX gibi pop\u00fcler derin \u00f6\u011frenme framework’leri, geli\u015fmi\u015f da\u011f\u0131t\u0131lm\u0131\u015f e\u011fitim API’leri ile tensor ve boru hatt\u0131 paralelli\u011fi tekniklerini desteklemektedir.<\/p>\n","protected":false},"excerpt":{"rendered":"Derin \u00d6\u011frenme Modellerini \u00d6l\u00e7eklendirme: \u00c7oklu D\u00fc\u011f\u00fcm Tensor ve Boru Hatt\u0131 Paralelli\u011fi B\u00fcy\u00fck dil modelleri (LLM’ler) ve karma\u015f\u0131k derin…","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"csco_page_header_type":"","csco_page_load_nextpost":"","csco_page_subscribe_form":"","csco_page_contact_form":"","footnotes":""},"categories":[1],"tags":[],"class_list":{"0":"post-43482","1":"post","2":"type-post","3":"status-publish","4":"format-standard","6":"category-genel","7":"cs-entry","8":"cs-video-wrap"},"yoast_head":"\nDerin 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