The file pathing is significant when running models inside of Termux on Android devices. llama.cpp performance is improved with loading a .bin from the $HOME directory.
The original file name, `ggml-alpaca-7b-q4.bin`, implied the first-generation GGML. After the breaking changes (mentioned in https://github.com/ggerganov/llama.cpp/issues/382), `llama.cpp` requires GGML V3 now. Those model files are named `*ggmlv3*.bin`. We should change the example to an actually working model file, so that this thing is more likely to run out-of-the-box for more people, and less people would waste time downloading the old Alpaca model.
* embed index and add --path for choosing static dir
* allow server to multithread
because web browsers send a lot of garbage requests we want the server
to multithread when serving 404s for favicon's etc. To avoid blowing up
llama we just take a mutex when it's invoked.
* let's try this with the xxd tool instead and see if msvc is happier with that
* enable server in Makefiles
* add /completion.js file to make it easy to use the server from js
* slightly nicer css
* rework state management into session, expose historyTemplate to settings
server: add option to output probabilities for completion (#1962)
* server: add option to output probabilities for completion
* server: fix issue when handling probability output for incomplete tokens for multibyte character generation
* server: fix llama_sample_top_k order
* examples/common.h: put all bool variables in gpt_params together
Daniel Drake [Sat, 1 Jul 2023 18:31:44 +0000 (20:31 +0200)]
cmake : don't force -mcpu=native on aarch64 (#2063)
It's currently not possible to cross-compile llama.cpp for aarch64
because CMakeLists.txt forces -mcpu=native for that target.
-mcpu=native doesn't make sense if your build host is not the
target architecture, and clang rejects it for that reason, aborting the
build. This can be easily reproduced using the current Android NDK to build
for aarch64 on an x86_64 host.
If there is not a specific CPU-tuning target for aarch64 then -mcpu
should be omitted completely. I think that makes sense, there is not
enough variance in the aarch64 instruction set to warrant a fixed -mcpu
optimization at this point. And if someone is building natively and wishes
to enable any possible optimizations for the host device, then there is
already the LLAMA_NATIVE option available.
Kawrakow [Mon, 26 Jun 2023 16:43:07 +0000 (19:43 +0300)]
k-quants : support for super-block size of 64 (#2001)
* k_quants: WIP super-blocks with 64 weights
* k_quants: WIP super-blocks with 64 weights
Q6_K scalar and AVX2 works
* k_quants: WIP super-blocks with 64 weights
Q4_K scalar and AVX2 works
* k_quants: WIP super-blocks with 64 weights
Q2_K scalar and AVX2 works. Q2_K is way too slow (it is actually slower
than the scalar implementation)
* k_quants: WIP super-blocks with 64 weights
Q3_K scalar and AVX2 works.
* k_quants: WIP super-blocks with 64 weights
Q5_K scalar and AVX2 works, and with that all
k_quants are done on AVX2 and scalar
* k_quants: WIP super-blocks with 64 weights
Q6_K working on CUDA. Cannot make it run quite as gast as
with super-blocks with 256 weigths: 8% slower on 4080,
20% slower on the 1660 (but there we fit 1 less layer on the
GPU because pf the larger model size), so some fraction of
these 20% is due to that,
* k_quants: WIP super-blocks with 64 weights
Q4_K working on CUDA. ~10% slower on GTX-1660,
16% slower on 4080.
* k_quants: WIP super-blocks with 64 weights
Q2_K working on CUDA. ~3% slower on GTX-1660,
10% slower on 4080.
* k_quants: WIP super-blocks with 64 weights
Q3_K working on CUDA.
* k_quants: WIP super-blocks with 64 weights
Q5_K working on CUDA, and with this CUDA is done.
* k_quants: WIP super-blocks with 64 weights
Q6_K working on ARM_NEON
* k_quants: WIP super-blocks with 64 weights
Q4_K working on ARM_NEON, but quite a bit slower than 256 weights
* k_quants: WIP super-blocks with 64 weights
Q2_K working on ARM_NEON, but quite a bit slower than 256 weights
* k_quants: WIP super-blocks with 64 weights
Q3_K working on ARM_NEON, but quite a bit slower than 256 weights.
* k_quants: WIP super-blocks with 64 weights
Q5_K working on ARM_NEON, but quite a bit slower than 256 weights.
With that, we have full support for ARM_NEON, although
performance is not quite there.
* k_quants: WIP super-blocks with 64 weights
Slightly more efficient Q3_K and Q5_K
* k_quants: WIP super-blocks with 64 weights
Another small improvement for Q3_K and Q5_K on ARM_NEON
* k_quants: WIP super-blocks with 64 weights
Yet another speedup for Q5_K on ARM_NEON.
We are now within 10% of the QK_K = 256 version.
* k_quants: WIP super-blocks with 64 weights
* We are able to pass preprocessor macros to the Metal
compiler
* Q6_K works and is actually slightly more efficient than
the QK_K = 256 version (25.2 ms vs 25.8 ms)
* k_quants: WIP super-blocks with 64 weights
Q4_K works on Metal and is actually slightly faster
than QK_K = 256 (21.95 ms vs 24.0 ms).
* k_quants: WIP super-blocks with 64 weights
Q2_K works on Metal and is very slightly faster
than QK_K = 256 (23.8 ms vs 24.2 ms).
* k_quants: WIP super-blocks with 64 weights
Q3_K works on Metal and is slightly faster
than QK_K = 256 (26.6 ms vs 28.3 ms).
* k_quants: WIP super-blocks with 64 weights
Q5_K works on Metal and is slightly faster
than QK_K = 256 (23.7 ms vs 26.3 ms).
* k_quants: call them _K, not _k, also on Metal
* k_quants: correctly define QK_K in llama.cpp
* Fixed bug in q4_K quantization added with the 64-block addition
* Simplify via lambda
* k_quants: swicth Q3_K to 4-bit scales when QK_K = 64
Otherwise there isn't much benefit from this
quantization type. There is some very slight loss
in accuracy, but we reduce size by ~7%.
E.g., for OpenLLaMA-3B, Q3_K_S perplexity is
8.6131 with 8-bit scales and 8.6352 with 4-bit,
while file size decreases from 1.53G to 1.44G.
* k_quants: switch Q4_K to 4-bit scales when QK_K = 64
Here the loss in accuracy is greater than for Q3_K,
but the Q4_K points still move further to the left on
the perplexity vs size curve.
* k_quants: forgot to add the Metal changes in last commit
* k_quants: change Q5_K to be type 0 when QK_K = 64
Still needs AVX2 implementation
* k_quants: AVX2 implementation for new 64-weight Q5_K
* k_quants: 10% faster ARM_NEON Q5_K dot product
* k_quants: fixed issue caused by merging with master
Alex Renda [Sat, 24 Jun 2023 10:15:01 +0000 (03:15 -0700)]
llama : fix top-p sampling to match the canonical definition (#1953)
* Fix top-p sampling to match the standard definition (smallest set that has probability mass at least p, not largest set with probability mass less than p)
Erik Scholz [Thu, 22 Jun 2023 12:20:47 +0000 (14:20 +0200)]
rework convert.py to read hyper-parameters from config.json (#1958)
* Read hyper-parameters from HuggingFace-transformer config.json, if they exist, and fall back to guessing, like before otherwise.
This allows converting open_llama 3B and other non-standard model designs.
Kawrakow [Mon, 19 Jun 2023 15:14:09 +0000 (18:14 +0300)]
cuda : faster k-quants on older GPUs (#1930)
* k_quants: hopefully much faster Q4_K on older GPUs
On the GTX-1660 that I have available to represent
"old GPUs", token prediction drops from 65.5 ms/tok
to 41.5 ms/tok!
* k_quants: hopefully much faster Q3_K on older GPUs
On the GTX-1660 that I have available to represent
"old GPUs", token prediction drops from 60.3 ms/tok
to 41.0 ms/tok!
* k_quants: faster Q2_K on older GPUs
It looks like I didn't need to change anything
compared to what we already had, so this is just
adding clarifying comments. But I now measure
36.3 ms/tok on the GTX-1660, instead fo the
47.2 ms/tok that I have written in the faster
k-quants PR.
* k_quants: faster Q5_K on older GPUs
68.5 ms/tok -> 62.0 ms/tok on GTX-1660.
For some reason the same access pattern that leads
to such resounding success for Q2_K to Q4_K did not
work at all for Q5_K.
It is also more difficult to measure because for Q5_K_S
we only have 32 layers on the GTX-1660, so output, tok embeddings
and kv cache are done on the CPU.
overview / pkg / ggml / sources / llama.cpp / log