* bump tg
* bump tg
* assign
* bump
* cpu llvm
* frame buffer updated in place, no need to return
* don't bake in stale pointers
* fix update image output indices
* lint
* bump
* modeld: quiet do_chunk output during scons build
SCons default-prints Python function actions with all their args.
The do_chunk function has 1259 tinygrad source files as deps, causing
a wall of text during builds. Wrap in SAction with a short strfunction.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* split compile and chunk into separate Commands
cleaner fix: do_chunk only depends on the pkl, not tinygrad files
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
* Chunk tinygrad pkl below GitHub max size
* pull that out
* rm glob
* make work
* Single name def
* unused comment
* more cleanups
* revert that
* 10MB overhead
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Co-authored-by: Adeeb Shihadeh <adeebshihadeh@gmail.com>
* Revert "Revert tgwarp again (#37161)"
This reverts commit 45099e7fcd.
* Weird uv sizes
* Fix interleaving
* Fix on CPU
* make CPU safe
* Prevent corruption without clone
* Claude knows speeed
* fix interleaving
* less kernels
* blob caching
* This is still slightly faster
* Comment for blob cache
Hi! The point of this pr is to make the model run easier to read. On the latest tinygrad numpy().flatten() empirically does the same thing as the internal contiguous().realize().uop.base.buffer.numpy(). numpy() is also documented (docstrings), which can assist new contributors in learning what each potential execution does. Torq_boi or yassine, I know you want proof in the code base, so here it is. As of tinygrad commit 2f55005:
in tinygrad_repo/tinygrad/tensor.py
Lines 316-318 (def _buffer): ensure the tenso is contiguous() and realized() before accessing the raw buffer.
Line 378 (def numpy): Wraps the buffer access and adds a reshape to match the tensor shape.
self._buffer() is what executes contiguous().realize() and returns the buffer object.
Calling numpy() on that buffer object returns a 1D array (defined in tinygrad/device.py:193 via np.frombuffer).
The reshape(self.shape) at the end of Tensor.numpy() then adds dimensions to that 1D array. The added .flatten() removes those dimensions, flattening it back to a 1D array. Effectively the same as what is currently done, but less complex.
* Revert "revert tg calib and opencl cleanup (#37113)"
This reverts commit 51312afd3d.
* power draw is a lil higher
* just don't miss a cycle
* fix warp targets
* fix tinygrad dep
* Revert "Remove all the OpenCL (#37105)"
This reverts commit d5cbb89d84.
* Revert "rm common/mat.h"
This reverts commit 4ce701150a.
* Revert "Calibrate in tg (#36621)"
This reverts commit 593c3a0c8e.
* fix non-determinism in selfservice model build
also trim down model compile dependencies to the minimum required
* Apply suggestions from code review
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Co-authored-by: Shane Smiskol <shane@smiskol.com>
* Remove cython for transformations
* Add new test
* Switch back to program to fix mac builds
* Convert to Python instead
* Fix failing builds
* lint
* Implement conversion in pure python/numpy
* Add more tests
* Fix bugs in tests
Bruce Wayne