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.]]
* bringing back reshape and permute
* done with E701
* 4x4 works in generic way
* max and sum not vectorizing...
* special case single float
* support comparing to MPS
* improve matmul speed, consider generic principles
* GlobalCounter
* fix op tracking
* faster
* comment that out for now
* err, it needs that
* fix minor issues
* fix global_mem
* chonker will make llvm fast
* work
* better speed tests, we will make them fast
* with the cache add is the same speed
* relu and neg are fast
* fix sum speed
* maximum maxnum?
* hack for gemm opt
* gemm very slow
* zeros like
* test_permute
* shapetracker returns self
* fix shapetracker factorization
* err, int strides
* permutes are faster now in tinygrad than pytorch
* support -1 in expand
* gemm unrolled
* improve final test case
* WIP GEMM
* why isn't GEMM fast?
* revert cache dim
* ffp contract works on clang, not llvm?
* ignore llvm ir
* this makes fma work at least, but no faster
* USE_4x4
* 63 GFLOPS
* 87 GFLOPS
* that wasn't matmul, 44 GFLOPS now
* 82 GFLOPS permuted
* this permute too
* a little speed for the convs
* 45 GFLOPS
* speed tests pass again
* clean up prints
* fix FMA WHAT A WASTE OF TIME
* colors
* moar fair
* GPU
* useless on chonker
* cleanups
* improve factorized shapetracker
* better threshold
* label conv
* work
* ops test pass again
* hot load the index
* run the last view, no need to create
* ZeroView needs a repr for the key to work
* fix segfault on out of bounds
* one more test
* start amx, and llvm.initialize_native_asmparser
* amx works
* nice AMX class
* nicer AMX class
* refactor get_idxs
* amx working
* is slower...
* useless flip
* cache
* SZ_X
* AMX_SZ_X/Y work alone
* Contiguous mlop
* test gemm packed
* PREPARE in packed
* use_amx factor
* prefetch isn't faster
* loop
* same 3ms
* 2.24 ms
* allow double on store in TG
* amx reduce is the same speed as non amx reduce
* include memory bandwidth
* clean up shapetracker
* flip returns stride
* prepare for upstream
* Update ops_llvm.py (#426)
* permutes are yellow and green now
* faster conv
* llvm cleanups
* Show optimised IR under debug 4 (#428)
* ASTKernel class
* Make tinygrad work with older python version (#427)
* Make tinygrad work with older python version
* Use partialmethod instead of partial
* smiple chonker is chonking
* remove junk from test speed vs torch
* fix linker and types
* AMX is only here now
* add LLVM tests, it's a valid backend now
* oops, run llvm test
* contiguous_op
* fix loadops compare
* dedup reduceops
Co-authored-by: calledit <1573053+calledit@users.noreply.github.com>
batch_invstd can be falsely assumed to be set, even though it is None
since hasattr will not return false in this case
BatchNorm2D a reshape will be attempted then, which causes an exception
* gemm
* off by factor of 5
* 50 GFLOPS
* works
* 91 gflops
* working at 50G
* works
* iy
* 150 GFLOPS
* 150 GFLOPS
* N=2048 is still fast
* threading soon
* multithread
* pinning
* throttling is sad
* Align matrices to cacheline width (#361)
Co-authored-by: cloud <Cloud11665@gmail.com>
