* global -> group
* allow None for local_size in custom function
* lil local
* comment on shape
* fix cuda
* smart local cast
* better local heuristic
* fix ptx, and work_dim cleanup
* fix metal
* fix ops test
* fix openpilot jit
* no more optlocal
* might fix metal tests
* try metal now
* see generated metal code
* test free removal. REVERT THIS
* mergable
* Make GPU the default device
* Compile EfficientNet with CPU
* don't print device
* use METAL and CUDA if possible
* Revert some changes to workflow
* Fix import error when checking device availability
* device lookup is now optional
* hopefully fix linter and tests
* fix workflow
* Skip device if not available
* don't change default if CPU=1
* simplify device selection
* Default to CPU if no GPU
* don't print device name...
* No need to change default in llama
* Make GPU the default device
* Compile EfficientNet with CPU
* don't print device
* use METAL and CUDA if possible
* Revert some changes to workflow
* Fix import error when checking device availability
* device lookup is now optional
* hopefully fix linter and tests
* fix workflow
* Skip device if not available
* don't change default if CPU=1
* simplify device selection
* Default to CPU if no GPU
* don't print device name...
* No need to change default in llama
* run github workflow
* Fix logic to select default
* pass if an error occurs
* use separate function for try except
* Less, LessOrEqual, Greater, GreaterOrEqual, Equal
* lint fix
* using built in functions
* overriding __eq__ breaks things
* backwards pass for less - foward only tests
* one other spot
* removing backwards for comparison ops to match pytorch
* raise runtime error
* more tests for comparison ops
* fixed the lineup
* added number upcast tests
* conv2d is an hlop
* shorter conv
* KOPT=-1
* alt imp
* MULACC
* smarter mulacc
* pop conv
* 7x7 -> 5x5
* didn't fix, that's not going to work
* this is faster and matches old behavior
* oh, non lazy just won't work with mulacc
* mulacc in torch
* bool types were creeping in
* optimizer is actually better with hlop conv
* fix pushing permutes issue
* refactor einsum_mulacc
* fix up readme
* update readme
* _image_conv2d
* fix bias addition location
* pushing permutes gets back to 200 kernels
* conv cleanup
* disable hlop conv
* don't hide that in helpers
This commit resolves issue https://github.com/geohot/tinygrad/issues/453
In the example code in the README.md, when it is run, it prints for Tiny
Grad the tensors as:
<Tensor <LB (3, 3) op:MovementOps.RESHAPE> with grad None>
<Tensor <LB (1, 3) op:MovementOps.RESHAPE> with grad None>
But to be equivalent to the output of the Torch example, we need
to use numpy() to get it to show:
[[ 2. 2. 2.]
[ 0. 0. 0.]
[-2. -2. -2.]]
[[1. 1. 1.]]
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