* shard llama
* sharding works
* simpler
* simpler
* consume option
* disable that test
* save a line
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Co-authored-by: George Hotz <george@tinygrad.org>
* add llama attention test for multigpu
* test fails
* kv cache trying to shrink on sharded axis
* mask None works for scale dot product
* kv cache seems to be working but scale dot product breaks
* scaled dot product works, but the last linear layer failed
* running into the reshape case where it could be wrong for multigpu
* making sure it was the reshape
* adding contiguous doesn't solve
* need to shard more properly
* remove reshape test
* minor adjustment to scale dot product attention test
* weights are sharded wrong
* continue fix new weight sharding
* clean up
* fix attention when start_pos is 0
* remove print
* add TODOs for the best mutigpu interface
* cleanup llama apply_rotary_emb and other helpers
used ellipsis and other higher level tensor function.
disabled the half @ half -> half tensor core as it fails uop dtype checks
* keep hip 8x8->8 wmma
* lazy rewrite, try 2
* min fix tests
* pass contig test
* put broken pads back
* move that to realize
* no contig child fixes array packing
* so wrong
* now that's correct
* base children
* fix bind issues
* disable to_image_idx
* fix tests
* that failure shouldn't break other tests
* more fixes
* fix torch
* skip failing tests in CI
* 1e-7
* half is broken
* 1e-6 margin of error
* add name support
* use fetch in gpt2
* remove requests from main lib, networkx also optional
* umm, keep that assert
* updates to fetch
* i love the walrus so much
* stop bundling mnist with tinygrad
* err, https
* download cache names
* add DOWNLOAD_CACHE_VERSION
* need env.
* ugh, wrong path
* replace get_child
