* different way to write torch backend
* both backends
* more work
* simpler code
* more work
* test both
* imply unwrap/wrap
* FORWARD_ONLY=1 TINY_BACKEND=1 python3 test/test_ops.py TestOps.test_add works
* ready to start making test_ops work in torch backend
* backward pass, TINY_BACKEND=1 python3 test/test_ops.py TestOps.test_add works
* FORWARD_ONLY=1 TINY_BACKEND=1 python3 test/test_ops.py TestOps.test_simple_conv2d works
* matmul backward is broken with as_strided
* 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>
* Make logcumsumexp numerically stable
* Refactor
* Refactor for special case ndim=0
* Refactor
* Use the correct device for mask
---------
Co-authored-by: chenyu <chenyu@fastmail.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>
* Switch to dawn, all tests passing locally
* Use dawn-python
* Skip failing test
* Skip midcast and fix timestamp on metal ci
* Autogen webgpu
* Try fetch dawn lib again
* /usr/lib
* Without lib prefix
* Test autogen diff
* Delete webgpu support, move everything to ops_webgpu
* mypy fix
* Simplify, refactor
* Line savings
* No ResultContainer
* Type annotation for result
* Some more simplifications
* Why was this explicit sync used at all?
* Refactor: delete functions that are only used once
* Create shader module inline
* Clear unit tests cache, maybe that solves it
* That wasn't it
* Try deleting cache to pass failing weight compare
* weights_only=False for pytorch 2.6
* Simplify ctype array creation
* Remove nanosecond precision timestamps
* Simplify error handling
* Refactor, add back type annotations
* Deleted custom submit function, refactor
* read_buffer simplify
* Fix use after free, refactor
* Simplify supported_features
* Runtime docs
---------
Co-authored-by: George Hotz <72895+geohot@users.noreply.github.com>
* more conditions for shift rewrite mul/idiv
* make ptx test uint so the new condition is true
* delete idiv test
* rewrite to 0 is wrong for idiv, as denominator is cast to 0 before division
* mul/div by 2**(large count) is unsupported anyway
* implemented in tensor
* apply onnx tests to asymmetrical pads
* better onnx op ordering
* correct ceil_mode asymmetrical
* fix onnx_ops comments
* a few more TODOs and fix some stupidity
* fix some typing
* fix test
* mypy still a little messed up
* refactor out pad struct transformation
* add simple docs for now
* add whatever tests possible
* add tests for _resolve_pool_pads
* better err msg
* whoops didn't mean to include this
* retry CI
* enable asymmetric pads onnx tests
* better docs
---------
Co-authored-by: chenyu <chenyu@fastmail.com>
* _padding2d -> _resolve_pool_pads
* rephrase err msg
* even better error msg
* check asymmetric first os people don't hit error twice
* test against torch
it's a python style mod. possibily can be cleaner with a floor div
relaxed the vmin for MOD slightly for cstyle negatives mod, it's more correct and might fix other bugs
* implemented
* this implementation is now correct
* this is fine I guess
* better variable names
* finally correct gathernd
* add a note
* eh just leave it at this for now
* teeny adjustment
* start work on new gradient
* more correct
* working tests
* more tests
* work
* add (faliing) gradient test
* add view and reduce gradient
* test_add works, many failing test_ops
* add max and reduce max
* add max and reduce max
* 129 failing
* 108 failed
* better view drawing
* 101 failed
* i got 99 failures
* 94 failures
* it's tons of terrible code, but only 50 tests fail
* only 19 failures
* same 19 but shorter
* minimal doesn't matter
* shorter
* lil simpler
* simpler
* simpler
* simpler
* 13 test failures
* nine tests fail
* all ops tests pass
* add contiguous gradient + fix sched tests
* faster by removing toposort calls
* missed one
* add jax to testing
