TPU Deep Dive
CourseReview SlidesExamsSources

Sources

  • SB4All the Transformer Math You Need to KnowCounting dots
  • SB4All the Transformer Math You Need to KnowGlobal FLOPs and params
  • SB4All the Transformer Math You Need to KnowAttention share
  • SB4All the Transformer Math You Need to KnowMoE
  • SB4All the Transformer Math You Need to KnowRemat
  • SB4All the Transformer Math You Need to KnowKV caching
  • SB4All the Transformer Math You Need to KnowProblems
  • SB4All the Transformer Math You Need to KnowFlash attention
  • SB5How to Parallelize a Transformer for TrainingLLaMA-2-13B problems
  • SB6Training LLaMA 3 on TPUsLLaMA-3-70B counts
  • SB7All About Transformer InferenceAttention intensity
  • SB7All About Transformer InferenceWorked problems
  • SB10Programming TPUs in JAXMoE with ragged_dot
  • R-tpuinftpu-inference (being renamed vLLM JAX)megablox, fused MoE kernels
  • D-7xTPU7x (Ironwood)
  • G-8ddGupta & Mugazambi, "TPU 8t and TPU 8i technical deep dive"
Chapter 4. Day 2, second block, 55 minutes

Transformer accounting on TPU

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Question 1 of 12
Derivation: enter a number
How many FLOPs does the contraction A[I,J,K,L]⋅B[I,J,M,N,O]→C[K,L,M,N,O]A[I,J,K,L] \cdot B[I,J,M,N,O] \to C[K,L,M,N,O]A[I,J,K,L]⋅B[I,J,M,N,O]→C[K,L,M,N,O] take, for I=J=K=L=M=N=O=4I = J = K = L = M = N = O = 4I=J=K=L=M=N=O=4?
FLOPs
TPU Deep Dive

A free course on TPU hardware and software for GPU engineers.

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