* start
* still need the float16 workaround in
* tiny nit for correctness
* idk hacks, I need to understand this device stuff better
* no-op?
* remove that assert for true nooooooop
* add fallback_context
* don't run linearize().uop tests in get_action_space test
this part takes 2 minutes in CI and has nothing to do with action space. also not sure if the "for some reason" comment is still relevant
* -n=auto test/models
* Implement private _linalg_eigh function for tensor eigenvalue decomposition in torch backend
* Add unit test for linalg.eigh function in TestTorchBackend
This test verifies the eigenvalue decomposition of a 2x2 tensor using the linalg.eigh function, ensuring the computed eigenvalues and reconstructed tensor match the expected results.
* enumerate cases of Tensors in the JIT
* optional fused optimizers
* add fused optimizer test
* move that there
* ugh
* work on beautiful_cifar
* speed close to hlb_cifar
* schedule to corealize all
* one line sched step
* less lines
* Add mmapeak implementation for 7900 XTX
* Change identation
* Use a template instead of multiple assebly files
* Fix output formatting
* Reduce register file bank conflicts
* More accurate measurement for quick instructions
* Add support for gfx1201
* RDNA4 wmma requires less VGRPs
* RDNA4 does not have s_cmpk instructions
* Add v_wmma_i32_16x16x32_iu4 for gfx1201
* Add sparse wmma instructions
---------
Co-authored-by: George Hotz <72895+geohot@users.noreply.github.com>
- Implemented a new function `equal` in the torch backend to compare two tensors for equality.
- Added unit tests for the `equal` function to verify its correctness with different tensor inputs.
* work on minrf example
* more
* jit sample
* t is tensor not const
* fixes
* more convs
* fix dropout
* don't print
* 504
* big patch
* onehot
* touch
* use embeddings
* dumb uses final layer
* act
* non fl
* match
* tp
* 3
* of
* ppsz
* normal
* add adln
* no t
* weird transformer
* weird transformer
* contig
* actual speed fix
* dumb
* cb
* 0
* t is 0
* mort-t
* args
* dumb days are over
* readable
* contig
* no more t mask
* mask_t
* init to zero
* clean
* steps
* work
* tt
* t
* solid
* Enhance tensor random functions with dtype support
- Updated `aten.uniform_` and `aten.normal_` to include dtype parameter in backend.py
- Added unit tests for uniform and normal tensor generation with specific dtypes in test.py
* Refactor test name for clarity
- Renamed `test_normal_dtype` to `test_normal` in `extra/torch_backend/test.py`
- Aims to improve readability and better reflect the test's purpose
