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23.3 ms / token, so just ~1% slower than q4_0.
Achieves 290 GB/s memory throughput.
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
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Co-authored-by: Adam Treat <adam@nomic.ai>
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* metal : 8% faster q4_0
Avoid copying into local uchar4 anf float4.
* metal : 17% faster Q4_0
Use 64 threads in a thread group.
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Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
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* metal : add Q2_K implementation
27.1 ms / token on M2 Max 30-core GPU, so about the
same speed as Q4_0. Memory throughput is ~156 GB/s.
The access pattern used in the Q2_K
CUDA implementation resulted in significantly lower
performance (~31 ms/token).
* Fixing merge conflicts
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Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
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* Metal implementation for Q4_K
Very slow for now:
42 ms / token, Q4_0 runs in 28 ms/token on my
30-core M2 Max GPU.
* Optimizing Q4_K on metal
The first token always takes longer, I guess because
the metal kernel is being jit-compiled.
So, using n = 128 to measure time.
At this point Q4_K takes 29.5 ms / token
compared to 27.2 ms / token for Q4_0.
Quite a bit better than the initial attempt,
but still not good enough.
* Optimizing q4_K metal dot some more
For n = 256 it is now 28.1 ms/token compared to
27 ms/token for q4_0.
* Fix after merge with master
* Metal implementation for Q6_K
Similar to the CUDA implementation.
No idea if this is the optimum for Metal, but the few
alternative variants I tried all had a lower performance.
We get 36.5 ms / token on M2 Max with 30 GPU cores.
This corresponds to ~200 GB/second throughput.
* clang-tidy : add config back
* Much better Q6_K implementation for metal
28.3 ms / token for 7B. Subtracting ~9 ms that is spent in
other compute graph operations, we are left with ~19 ms
for the matrix multiplications. The model is ~5.5 GB,
so we are getting 1000 / 19 * 5.5 = 290 GB/s!
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Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
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* Metal implementation for Q4_K
Very slow for now:
42 ms / token, Q4_0 runs in 28 ms/token on my
30-core M2 Max GPU.
* Optimizing Q4_K on metal
The first token always takes longer, I guess because
the metal kernel is being jit-compiled.
So, using n = 128 to measure time.
At this point Q4_K takes 29.5 ms / token
compared to 27.2 ms / token for Q4_0.
Quite a bit better than the initial attempt,
but still not good enough.
* Optimizing q4_K metal dot some more
For n = 256 it is now 28.1 ms/token compared to
27 ms/token for q4_0.
* Fix after merge with master
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Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
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Co-authored-by: Spencer Sutton <Spencer.Sutton@precisely.com>
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* Use MTLDevice.newBufferWithBytesNoCopy to share buffers between CPU and GPU
* Page-align buffers used by Metal
* Remove trailing whitespace
* Only import unistd.h for Metal builds
* metal : remove unnecessary copies
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Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
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* mtl : export the LLaMA computation graph
* ci : disable temporary
* mtl : adapt the MNIST example as starter
* mtl : no need for mtl-export tool, add cli arg for main instead
* mtl : export just a small part of the graph for now to make it easier
* mtl : move MSL code into separate file for easy editing
* mtl : initial get_rows_q4_0 kernel
* mtl : confirmed get_rows_q4_0 is working correctly
* mtl : add rms_norm kernel + confirm working
* mtl : add mul kernel + confirm working
* mtl : initial mul_mat Q4 kernel (wrong results)
* mtl : mul_mat fixes (still wrong)
* mtl : another mul_mat Q4 (still does not work)
* mtl : working mul_mat q4
* ggml : fix handling of "view" ops in ggml_graph_import()
* mtl : add rope kernel
* mtl : add reshape and transpose handling
* ggml : store offset as opt arg for ggml_view_xd() operators
* mtl : add cpy kernel + handle view ops
* mtl : confirm f16 x f32 attention mul mat
* mtl : add scale kernel
* mtl : add diag_mask_inf kernel
* mtl : fix soft_max kernel
* ggml : update ggml_nbytes() to handle non-contiguous tensors
* mtl : verify V tensor contents
* mtl : add f32 -> f32 cpy kernel
* mtl : add silu kernel
* mtl : add non-broadcast mul kernel
* mtl : full GPU inference of the computation graph
* mtl : optimize rms_norm and soft_max kernels
* mtl : add f16 mat x f32 vec multiplication kernel
* mtl : fix bug in f16 x f32 mul mat + speed-up computation
* mtl : faster mul_mat_q4_0_f32 kernel
* mtl : fix kernel signature + roll inner loop
* mtl : more threads for rms_norm + better timing
* mtl : remove printfs from inner loop
* mtl : simplify implementation
* mtl : add save/load vocab to ggml file
* mtl : plug Metal inference into llama.cpp (very quick-n-dirty)
* mtl : make it work with main example
Lots of hacks but at least now it generates text
* mtl : preparing for merge
* mtl : clean-up ggml mtl interface + suport scratch / inplace
* mtl : remove temp / debug code
* metal : final refactoring and simplification
* Revert "ci : disable temporary"
This reverts commit 98c267fc77fe811082f672538fc91bcfc9072d63.
* metal : add comments
* metal : clean-up stuff, fix typos
* readme : add Metal instructions
* readme : add example for main
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