* support same uidx in multiple shape positions
* rename var
* update comment
* add contiguous index check to global_store too
* update comment
* small change
* is this better?
* smh
* smaller change?
* get rid of more changes
* get rid of more changes
* is this even making anything better
* comment
* fix test
---------
Co-authored-by: George Hotz <72895+geohot@users.noreply.github.com>
* fix LtNode simplification when lhs and rhs contain same variables
`(Variable("a", 1, 5) < Variable("a", 1, 5))` should eval to `NumNode(0)`
* fix with less perf impact
* Fix numpy uint/int overflow
* lol
* Works
* Update
* Move overflow test to float64/float32
* One line
* Update
* One more
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Co-authored-by: Patrick Tsai <patosai@users.noreply.github.com>
it's recommended to use __getnewargs__ to update the args of classes that use __new__ when unpickling.
It's preferred because it does not change the __new__ behavior.
* do not truncate float64 precision
* use l suffix to try avoid overload confusion
* long line, ruff bloats the function otherwise
* fmt
* remove long double suffix (l), it's sufficient to have the float32 (f) suffix to avoid function overload ambigouity; add test showcasing rtol=1e-12 precision increase, the test fails without the renderer changes
* use more reasonable test values, same as test_int_to_float_unary_func
* disable test for CUDACPU, does not support half and segfaults on some operations per dtypes_alu test
* disable test for HIP, renderer does not support f64 precision
* do not use noqa E501, break up condition
* remove cpu and torch backends
* don't copy to cpu
* use clang instead of cpu
* multitensor gathers on the first device
* clang is cpu + use default
* fixup
* bugfix
* remove float cast
* cast scalars to the correct value in creation time
* cast scalar in the correct place
* wrong, use y_dtype
* make consts have a unique cache key
* add cast_scalar back
* test_load_cache_const_bufs
* add bool dtype
* test_const_dtype
* fix linters
* generic rendering of half and bf16
hotfix
* fix uops + regression test
* fix the test for metal's half4
* uop.uop fixup
* mypy with --strict-equality, fix ops_gpu
* set metal fast math default to 0 (disabled)
It's a correctness fix because we use inf and nan. Let's see how slow it is
* skip failed onnx tests
* tmp DISABLE_COMPILER_CACHE=1 in metal benchmark
* Revert "tmp DISABLE_COMPILER_CACHE=1 in metal benchmark"
This reverts commit 22267df380.
* env var METAL_FAST_MATH to disable fastmath for metal
use this to test impact of fast math. might need to disable compiler cache with DISABLE_COMPILER_CACHE
* failed onnx test with fast math
METAL_FAST_MATH=0 DISABLE_COMPILER_CACHE=1 NOOPT=1 python -m pytest -n=auto test/external/external_test_onnx_backend.py -k test_MaxPool3d_stride_padding_cpu
Fully UNROLLing the first_reduce should not change the number of
local_dims.
Fully UNROLLing a GROUP dim should reduce the number of
group_for_reduces by one.
Also changed group_for_reduces to be a count as the axis number
isn't used anywhere (they are always the first reduce dims).
* ops_python: add HIP tensor core mock and refactor METAL
* Add tests to CI
* add DEBUG=2 to full tests
---------
Co-authored-by: George Hotz <72895+geohot@users.noreply.github.com>
* start uop emu
* tiny_add passes
* more ops
* emulate the whole warp
* test_gemm passes
* metal gemm test pass
* works on big gemm
* works on big gemm
* more tests pass
* touch ups
* fix mypy
* cleanups
* exp2 mypy
* arch is where it belongs
* actually emulate tensor cores
* fix test
* new style
run on TORCH since it's the fastest one on CI.
caught a bug in multinomial, and update the behavior of fancy index and gather to move the indices Tensor to same device as self.
fix when correction is too big. it seems to only work when input size is 0 though.
torch can output -inf in var when correction is too big, which does not make sense.
* fix Tensor.mean to compute the mean correctly with 0-length axes are selected
* add a regression test
* rename sum variable to sum_t to avoid conflict with built it function
* refactor Tensor.mean to has less lines
* skip matacc opt if the all src buffers of mul op are const buffers
* add noqa directive for long test
* unskip MALACC opt
* ensure that a_axes at least includes summation axes in order to perform np.einsum correctly
* add regression test for mulacc op
* compute a_slices using a_axes
* refactor helper of function to retrieve axes and slices for nonzero strides as well as summation axes
* include a regression test that uses and to test the behaviour indirectly
