* move reduce over 0 len axis logic to lazy.py
this fixed uneven shard reduce case if the uneven one has length 0
* fix interpreted backends
* fix backwards for 0 shape tensors too
* 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>
* initial multitensor jit support and tests
* Added graphs to multitensor jit and updated tests
* update unbind api
* fix set device, add TinyJit to resnet
* update_stats includes device
---------
Co-authored-by: ramenguy99 <ramenguy99@gmail.com>
* 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
* cached size
* simplify simplify
* 0 doesn't have base
* fix test
* cleaner cache
* hmm, metal is flaky on this...might be real(ish) but useless as test
* short circuit reshape/expand properly
* better reshape bypass
* make Embedding device aware for multigpu
* split line instead of igore because that's cheating
* add test incomplete
* add test complete
* remove comment
* fix white space
* remove nn.Embedding
* add a failing test for LR scheduler when using multigpu
* fix calculation order and unnecessary tensor created for float
* min_lr is no longer tensor
* simple multitensor API
* test multitensor
* mt work
* new api
* copies
* all but data parallel
* allreduce there
* works, but axis sharded
* fix all mt tests
* features/multi
* work
* backprop
* fix tests
* tests passing
* mt progress
* cleanups
* less lines
* tensor cleanup
* save more lines
* mypy passes
* fix tests
* skip for cuda too
* bump download cache
