* wow argmax is so good
* 1 less line
* clean up and better variable names
* is this torch thing right...?
* add more tests
* slap a TODO on it
* clean ups
* prettier looking code and fix ceil mode test
* add return types and some docs
* ok that was a bad example since indices == value, just no example
* extra/gemm/max_matmul: start of custom kernels for GEMM
* add an unoptimized FP16/FP16 MMA example
* add slow 3-stage fp16 acc example
* add correct 3-stage pipeline with unswizzled/flat smem input (slow)
* add acc fp16 example with 3 stages and swizzle (no bank conflicts)
* add max version of NV fp16_fp16_fp16
* fix up comments and removed unused code in max variations
* add start of no_xor example
* fix to account for UOps to Ops
* train_shakespeare_char.py works
* move aten.where.self_out to tiny_backend_out
* fix memory leak
* corealize in the backward_hook
* Update backend.py
---------
Co-authored-by: George Hotz <72895+geohot@users.noreply.github.com>
* jit the forward
* might timeout, idk just send it
* this is dumb
* naive bitonic lol
* idk if this is correct, but that squeeze before is definitly not
* vectorized bitonic sort, but still slow
* yay 1 layer is correct
* alright its pretty good
* good enough
* rerun CI
* nit improve comment
* add f16/f32 mfma support for MI300
- add 16x16 mfma shape support for f16 with f32 acc
- add ops_python mfma emulation
- add arch to AMDRenderer
* minor cleanup
* minor cleanup
* add mfma emulation task to ci
* add back todo
* hotfix: comment
* add tc=3 job to ci
* failed test case for threefry
not sure if it's always like this, but increment before _threefry_random_bits is incorrect. the counts should start with random numbers generated so far.
use jax to generate 20 + 20 + 10 random numbers, the first 20 + 20 matches and the last 10 are different. just moving increment after _threefry_random_bits matches the number but jit test failes
* workaround
* why is this different?
* revert those
* and that
* poc
* repeated values fail, sigh
* is this being timed out?
* fix up down names
* bitonic v2, does this run?
* bitonic v3, faster
* bitonic v3.1, faster
* bitonic v3.1.1, same speed unlucky
* support dim and indices
* bitonic v3.2, simpler code, TODO repeated indices
* bruv gimme green for once cmon
* cat (stack) implementation, slow but maybe one day when cat is fast meow
* revert to v3.2
* bitonic v4, who let the cats out edition
* clean up variable names
* figured out repeated indices :D
* ruff check --fix
* use sort for topk
* add Tensor.sort everywhere
* fix docs and add some types
* slightly better variable names
* am I doing torch inplace correctly?
* delegate sort to values_stable
* add a contig, faster first sort
* maybe don't test_inplace
---------
Co-authored-by: chenyu <chenyu@fastmail.com>
* np generates randoms
* hotfix: use generator for int dtype
* float32 as default dtype for float generator
* use np.float32 instead of stirng
* add dtype= to integers generator
* change import _to_np_dtype source
