* init
* add failed case
* fix: temp comment out MULACC cast
* is this right?
* add test case
* oops, forgot to get rid of temp test
* WOOOOOO TOOK OUT 2 TRANSPOSES IN GATHER YAY
* cleaner
* comment cleanup
* update docs
* resolve conflict
* oops
* SUPA FAST
* comment out a test
* del some print statements
* use new broadcast stuff
* more clean up
* move try except
* skip fancy indexing for python backend test_ops
* search: add a BEAM_COMPARE env to optionally not compare to hc/tc
setting BEAM_COMPARE=0 will prevent additional memory allocation
needed to do the timing tests assuming the BEAM result is in
the diskcache.
* change to optionally use Buffer.allocate
* initial version
* heh gimme grrrreen
* version 2
* clean ups
* some test confusion
* fix onnx
* rename to _broadcast_tensors
* improved errors and test
* fixed?
* some test fixup
* version 3 lol
* comments
* cleaner
* add failure test for expand to 0 test
* 1 more assertRaises test
* make err msg better
* also rewrite the expand onnx op? :s
the annoying thing to remove all FlopCounter is that for device that does not support local, matmul index alu is huge.
we can remove the dtype first.
sneak in updating `ruff` command to `ruff check`
* tensor cores
* Merge from master
* faster program start in llvm (#3897)
* Fix the result permutation in einsum (#3895)
* Fix permutation of result indices in einsum.
* Delete stray line used for breaking tests
* Fix linter error by renaming twice-used variable
---------
Co-authored-by: chenyu <chenyu@fastmail.com>
* touchup einsum (#3900)
don't need rhs_letters
* hotfix check ckpts before writing achieved model (#3901)
this killed tinybox green run
* replace dtype.name str with render_dtype (#3903)
fixed some bf16 cast issue since it does not have `.name`.
also more robust if there are lang specific type override
* add --minimal flag to nvrtc (#3899)
* wmma: fix the AMD TC threads to split the first 16 threads (#3904)
previously it was incorrectly aliasing 16 into the size 8 upcast
on the store alias. now it splits it properly into 8 and the
remaining 2 into the correct local stride
* training cifar with BF16 on CUDA (#3905)
* training cifar with BF16 on CUDA
memory usage is between float and half due to numpy calls on dataset preprocessing, which converts into float.
* simpler bf16 functions
* bf16 cifar works for HSA too just very slow
* simpler bf16 functions, we love cuda
* include negative float in test_dtype (#3884)
* include negative float in test_dtype
* that is ub
* too annoying
* pack can overflow
* add to benchmark
* change var name to satisfy mypy
* spacing
* Update to new TensorCore format
* Spacing
---------
Co-authored-by: nimlgen <138685161+nimlgen@users.noreply.github.com>
Co-authored-by: Alejandro F Queiruga <33233447+afqueiruga@users.noreply.github.com>
Co-authored-by: chenyu <chenyu@fastmail.com>
Co-authored-by: sekstini <127142660+sekstini@users.noreply.github.com>
Co-authored-by: Francis Lam <flam@alum.mit.edu>
Co-authored-by: George Hotz <72895+geohot@users.noreply.github.com>
* mulacc
* Move more stuff to pattern matcher
* disable callable from the == check
* disable function passing in pattern matcher
* Add set of dtypes pattern matching + refactor mulacc pattern
* HWCopyQueue in KFD
* hw compute queue
* test
* move test
* more tests
* fix wait
* fix multimap
* mes crash
* tests pass but slow
* stuff is working
* one more test
