* 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
* add Tensor.split (#2677)
* fix mypy errors
* add list support for Tensor.split
* fix ruff comments
* match tensor.split api
* simplify split and test_split
---------
Co-authored-by: George Hotz <72895+geohot@users.noreply.github.com>
the correct condition is that PADTO cannot be applied to reduce axis, not Reduce.MAX in ops.
even for Reduce.SUM it's possible that the reduce axis had a div before, and the padded 0 became inf then sum over it is incorrect.
* return bool
* add tests to the type spec
* fix multinomial
* fix tril
* fix round
* fix NegativeLogLikelihoodLoss
* rm debug
* webgpu
* more webgpu
* bitwise or for adding two bools
* onnx ops dont need to cast anymore
* Revert "bitwise or for adding two bools"
This reverts commit b413babffa.
* workaround for metal neg
* just the tests in the type spec
* test dtypes of return values of cumsum, argmax/min, multinomial
cumsum behaves like sum, and functions that return an index return in dtypes.default_int
* because webgpu is different
* ww/Fixed Tensor.randint() to accept shape tuples ()
* ww/Wrote a test to cover this typo
* ww/Updated Tensor random objects to optionally take (,) or *() to be more consistent
* ww/no lint no worries
* ww/Made peace with linter
* ww/Added new line can't reduce line size without reducing readablitity
* ww/reverted to using .mul
* space removal in formula and a single test to cover it
* space in torch einsum as well
* replacing spaces in a var formula to support truncating all the spaces
* better support for platform dependent flags
* osx test support
* removed unused import and made line length <150
* changed osx ci shm
* lstrip in case SharedMemory._name is passed
* 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
* remove the all_int(shape) check in Tensor._loadop
we can support jittable symbolic shape random with custom rand now, and we can formalize it in the test after threefry is ready
* MOCKHIP false positive
