* 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>
* 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>
* refactoring thneed
* continue
* minor update
* looks like it's working
* big refactor
* confirm thneed got the right output
* code is there but it's broken
* works now
* always OPTWG, input -> dat
* fix type issue
* ngrl stuff
* fngrl
* fix typo in compile script
* workflow dispatch
* new models in tests
* dont need to up this threshold
Co-authored-by: HaraldSchafer <harald.the.engineer@gmail.com>
* quick math: 0 + x = x.
* gradient w.r.t. x using cherry for conv
* gradient w.r.t. w for conv on cherry but doing vector dot products
* small optimization
* [cherry] optimize conv backpass for large channel count
* get rid of numpy einsum
* added resnets
* fix minor
* fix minor
* resnet in models
* added resnet test
* added resnet train test
* added linear, conv2d nn tests
* fix minor in extra/training
* resnet in models
* fix minor
* fix tolerance for linear in nn test
* fix eval, this causes cpu and gpu UT failing
* revert transformer test
* fix minor for CPU test
* improved model get_params for sequential layer
* fix minor for params counting
* commented broken ops tests
* improved train for resnet
