* WebGPU on Windows
* Fix dawn-python install
* New test
* pydeps
* Minor fix
* Only install dawn-python on windows webgpu
---------
Co-authored-by: George Hotz <72895+geohot@users.noreply.github.com>
* opt transforms the ast into an optimized ast
* fix get_kernel order and to_function_name
* function_name property
* update docs
* copy from kernel.py
* improve docs
* ci didn't trigger?
* add kernelize
* remove that
* kernelize returns self
* update abstractions2.py
* kernelize in test_schedule
* temp: assert BUFFER_VIEW's existence
* ASSIGN must have a buffer or subbuffer target
* assert and shrink
* fix
* padded setitem
* var
* toposort once
* extra
* base_buffer
* end with BUFFER_VIEW
* setitem for disk
* test_setitem_becomes_subbuffer
* mul slice test
* torch backend fix 1
* non-deterministic
* keep subbuffer
`AMDComputeQueue.__del__` frees `hw_page` which is safe because
`AMDAllocator._free` does `self.dev.synchronize()` which is supposed
to wait for execution of IB to finish, however that doesn't happen if
AMDComputeQueue is dropped right after submit before timeline signal is
incremented, which it is in most places leading to a race if .bind() is
also used (required for multi-xcc because bug in mec fw treats all
PACKET3_PRED_EXECs outside IBs as if they had EXEC_COUNT of zero).
* why does max_unpool2d feel slower than out.gradient ...
* slightly cleaner
* what happened to ruff
* need to think about this some more
* slightly faster now?
* clean up, 1 more failing edge case
* ok good
* working TINY_BACKEND
* nit doc wording
* retry CI
* 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>
* terrible but somewhat working impl
* linux behaves differently than macos?
* slightly better impl
* small clean up; haven't figured this out yet
* better
* torch has different behavior on linux and macos for duplicated values
* add sum docs
* fix test
* add torch return_type test
* add an exception test
* wrap_fxn instead, and move op lower in order
* better repeated values test
* rerun ci
* add `Tensor.isclose()`
* support `equal_nan`
so as to match PyTorch's behavior
* update unit tests
* remove some tests temporarily
* re-enable one test
* re-enable other test
* try to fix failing tests during CI
* save one line of code
---------
Co-authored-by: George Hotz <72895+geohot@users.noreply.github.com>
* pytorch scatter -> scatter_reduce
* WIP scatter_reduce implementation
* _pre_scatter return type hint
* split out src, mask to satisfy linter
* Add src cast back in
* dict of lambdas instead of ifs
* sum and prod reduction ops with include_self
* add reduce arg error message
* add amax and amin reduction ops
* Fix include_self for higher dims
* Simplify
* Simplify amax and amin too
* Pull include_self logic out into _inv_mask function
* reduce arg cannot be None for scatter_reduce
* Fix self-mask issue
* Add mean reduce op
* Add tests
* any() not needed here
* remove comment
* End support for Tensor src with reduce arg in tinygrad scatter
* Process index, dim inside actual functions
* Add scatter_reduce to onnx
* Add excluded onnx ScatterElements reduction tests back in
* Save 2 lines on the mask helpers
* Update docs
* Add include_self=False tests
* cleanup
* Remove unneeded helper function
---------
Co-authored-by: chenyu <chenyu@fastmail.com>
* add some docs about speed [pr]
* better torch gemm
* enable locals on llvm/clang
* disable locals for beam speed on LLVM/CLANG
* 0x20 alignment in llvm allows ymm use
