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dalle-mini / tools

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feat: update mini config
d9a16f2

boris commited on

feat: implement transformer variants (#144)
542378c
unverified

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feat(train): log norm and histograms (#143)
b7b619a
unverified

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feat(data): super conditioning (#141)
7939874
unverified

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feat: support pod (#139)
803ccbf
unverified

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fix: no gradient checkpointing for new model
2e02683

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feat: no gradient checkpointing for params init
b798ed3

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feat: update configs
79557f9

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fix(train): consider schedule offset
bc4734f

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feat(train): local jax cache
9f5e879

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feat: add bucket reference to artifact
d368fb6

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style: lint
d5d442a

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feat: handle gradient checkpointing
5173ec7

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feat: load from bucket
1c4e839

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feat(train): save to bucket
50498e6

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feat: reduce artifact space + offset step
34cf91c

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feat(demo): update reference
e558000

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feat: restore weights on CPU
5f954fc

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feat(train): simplify tokenizer loading
4cb21dd

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feat: cleanup notebook
5a390e8

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feat(demo): uncomment pip install
094e178

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feat: improve inference demo
35fe578

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feat(train): use compilation cache
da9367c

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fix: style
d483294

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Merge branch 'main' of https://github.com/borisdayma/dalle-mini into main
0a691de

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feat: log num_parameters early
7cfe576

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fix: distributed shampoo class
696422e

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feat: update distributed_shampoo
5996680

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feat(train) - handle multiple nodes (#130)
0952927
unverified

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feat: handle model parallel
1bb3269

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feat(train): more custom x-axis
5f28cd2

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fix(train): opt_state_shape for distributed_shampoo
225b6ff

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feat(train): split artifact into model/state
fa5b058

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feat(train): another 25% faster
14abe8c

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feat(train): overhead from 70% to 1% 🥳
2b7f5f1

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feat(pjit): follow t5x style
7b5868f

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fix(train): grads spec
00710bc

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feat(train): improve pjit speed
f254058

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fix(train): consider correct batch size
b7c7458

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feat(train): custom start_preconditioning_step
8149924

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feat(train): handle distributed_shampoo in pjit
032f623

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feat: update distributed_shampoo + fix None spec
8a9e367

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feat(train): distributed_shampoo with pjit
cc34d07

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fix style
f044cb8

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feat(train): restore opt_state efficiently
1bfc1b5

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feat(model): clean way to load on cpu
12f323d

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feat(train): load model on CPU
3d43591

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feat(train): different rng per node
2d212d8

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feat(train): no batch dimension with pjit
df1fe19

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feat(train): progress on pjit
49597a2

boris commited on