* Start from andredaprato:webgpu-clean
* Fix infs
* inf wgsl function is not needed
* Emulated ulong for threefry, more tests passing
* Randomness tests passing
* Update model export to support new changes in webgpu, efficientnet export works again
* Simplify shift emulation in wgsl
* Delete test file
* Fix bigger than u32 u32 literal
* Why was skip copies added here?
* Python3.12 for webgpu tests
* Fix model export syntax error
* Get test ops passing with some skips
* Fix lint
* Much simpler shift
* Run more tests
* Timestamp queries are not supported in CI, so skip search tests
* All fancy indexing passing
* r is ctx
* Run more dtype tests by using is_dtype_supported
* Cleanup ulong shift rendering
* UPat -> Pat, UOps -> Ops
* Pat -> UPat
* Refactor render_ushift if-else
* Pattern to avoid ulong mul
* Remove vals_dtype
* is_nan trick + rewrite, test_isnan passing
* Rewrite a * select(1, nan, gate) -> select(a, nan, gate)
* No arg, just op
* Support char, uchar, short, ushort
* Run test_index_mnis now that we have uint8
* Fix pyling
* Save 3 lines by using base Compiler
* No more long emulation
* Remove fixup_binops
* No more external_local_bufx wgsl specific cstyle modif, use base extra_pm
* Simpler, faster copyin/out
* Skip some new tests that use long
* Fix typo
* copyout touchup
* Save lines by using render_cast
* WebGL is not supported in core, delete it from is_dtype_supported
* More narrow test skips for some unary tests
* TernaryOps, UnaryOps -> Ops
* TinyGrad supports WebGPU
* StableDiffusion demo: f16tof32 gpu is a lib, update UI
* Packed load/store, no more scale_size, no core tinygrad changes
* Rename copyin, copyout
* Device -> dev
* Fix lint
* Pattern matcher rule for packed load/store
* Refactor
* Shorter packed load/store
* this should fix lint
* Fix mypy
* SD compile script working
* New SD webgpu UI
* New default prompt
* New SD weights
* Fix title when webgpu not available
* Run symbolic tests, simplify is_nan, use round_up
* Show step time on UI
* Bump minimum wgpu version to v0.19
* Fix latent
---------
Co-authored-by: George Hotz <72895+geohot@users.noreply.github.com>
use "nvidia/Llama-3.1-Nemotron-70B-Instruct-HF".
```
PYTHONPATH=. JITBEAM=2 python3 examples/llama3.py --download_model --size 70B --quantize int8 --benchmark
```
on M4 Max, 40 sec to load the model and
```
enqueue in 165.15 ms
total 328.54 ms, 3.04 tok/s, 247.46 GB/s, param 221.20 GB/s
enqueue in 5.31 ms
total 168.48 ms, 5.94 tok/s, 482.54 GB/s, param 431.34 GB/s
enqueue in 5.32 ms
total 168.77 ms, 5.93 tok/s, 481.71 GB/s, param 430.60 GB/s
enqueue in 5.69 ms
total 169.51 ms, 5.90 tok/s, 479.61 GB/s, param 428.72 GB/s
enqueue in 5.41 ms
total 168.60 ms, 5.93 tok/s, 482.20 GB/s, param 431.04 GB/s
enqueue in 5.18 ms
total 168.98 ms, 5.92 tok/s, 481.12 GB/s, param 430.08 GB/s
enqueue in 5.43 ms
total 168.82 ms, 5.92 tok/s, 481.59 GB/s, param 430.49 GB/s
enqueue in 5.27 ms
total 168.94 ms, 5.92 tok/s, 481.23 GB/s, param 430.17 GB/s
```
