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llama.cpp/README.md
compilade c2101a2e90
llama : support Mamba Selective State Space Models (#5328) 
* mamba : begin working on support for Mamba SSM

* mamba : begin figuring out how to (ab)use the kv cache for Mamba

* mamba : recurrent inference almost works, but incoherent

* mamba : recurrent inference WORKS!!!

* convert : optionally use d_conv and d_state from config.json for Mamba

* mamba : refactor recurrent conv, resulting in 20% perf increase

It's still slower than I'd like, but I did not really optimize `ggml_exp` yet.

I also refactored `ggml_exp` to work with tensors with more than 2 dimensions.

* ggml : parallelize ggml_exp

This results in 8% faster token generation for Mamba-130M.

* mamba : simplify the conv step with a self-overlapping view

Turns out the conv_state can be made smaller by one column.
Note that this breaks existing GGUFs of Mamba,
because the key_value_length field is tied to the conv_state size.

Convolution with a self-overlapping view is cool!
And it's much simpler than what I initially thought would be necessary
to make the convolution step work with more than 1 token at a time.

Next step is to make the SSM step work on batches of tokens too,
and thus I need to figure out a way to make a parallel selective scan
which will keep the ssm_state small and won't make it bigger
by a factor of (n_layer * batch_size).

* llama : fix Mamba KV self size wrongly displaying as f16 instead of f32

Relatedly, I also tried to see if other types than f32 worked for the states,
but they don't, because of the operators used.
It's probably better anyway to keep lots of precision there,
since the states are small anyway.

* mamba : fix self-overlapping view depth stride

* mamba : handle batches of more than 1 token

This means running Mamba no longer crashes when using the default settings!
And probably also slightly faster prompt processing.
Both batched and non-batched processing yield the same output.

Previously, the state was not cleared when starting a sequence.
Next step is to make the KV cache API work as expected for Mamba models.

* ggml: add ggml_ssm_scan to help with parallel selective scan

If the selective scan was implemented without a custom operator,
there would be waaay too many nodes in the graph. For example,
for Mamba-130M, with a batch size of 512 (the default),
a naive selective scan could add at least 24*512=12288 nodes,
which is more than LLAMA_MAX_NODES (8192),
and that's only for the smallest Mamba model.
So it's much cleaner with a custom operator.
Not sure about the name, though.

* ggml : in ggml_ssm_scan, merge multiple rows in the same vec operation

This will help with performance on CPU if ggml_vec_mul_f32
and ggml_vec_add_f32 are ever optimized with SIMD.

* mamba : very basic quantization support

Mostly works, but there is currently no difference
between the variants of a k-quant (e.g. Q4_K_S and Q4_K_M are the same).
Most of the SSM-specific weights can be kept in f32 without affecting
the size that much, since they are relatively small.
(the linear projection weights are responsible for most of Mamba's size)

Too much quantization seems to make the state degrade quite fast, and
the model begins to output gibberish.
It seems to affect bigger models to a lesser extent than small models,
but I'm not sure by how much.

Experimentation will be needed to figure out which weights are more important
for the _M (and _L?) variants of k-quants for Mamba.

* convert : fix wrong name for layer norm weight of offical Mamba models

I was using Q-bert/Mamba-* models before, which have a slighlty different
naming scheme for the weights.
(they start with "model.layers" instead of "backbone.layers")

* mamba : fuse more steps of the SSM scan in the ggml_ssm_scan operator

This increases performance on CPU by around 30% for prompt processing,
and by around 20% for text generation.

However, it also makes the ggml_exp and ggml_soft_plus operators unused.
Whether or not they should be kept will be decided later.

* convert : for Mamba, also consider the "MambaLMHeadModel" arch name

It's the name of the class of the official implementation,
though they don't use it (yet) in the "architectures" field of config.json

* mamba : fix vocab size problems with official models

The perplexity was waaaay to high for models with a non-round vocab size.
Not sure why, but it needed to be fixed in the metadata.

Note that this breaks existing GGUF-converted Mamba models,
but **only if** the vocab size was not already rounded.

* ggml : remove ggml_exp and ggml_soft_plus

They did not exist anyway outside of this branch,
and since ggml_ssm_scan fused operations together, they are unused.
It's always possible to bring them back if needed.

* mamba : remove some useless comments

No code change.

* convert : fix flake8 linter errors

* mamba : apply suggestions from code review

* mamba : remove unecessary branch for row-wise ssm_state and C multiplication

It was previously done to avoid permuting when only one token is processed
at a time (like when generating text), but permuting is cheap,
and dynamically changing the compute graph is not future-proof.

* ggml : in ggml_ssm_scan, use more appropriate asserts

* ggml : rename the destination pointer in ggml_compute_forward_ssm_scan_f32

* mamba : multiple sequences, but one at a time

This is a step towards making this Mamba implementation usable
with the server example (the way the system prompt is kept when clearing
the client slots will need to be changed before this can work, though).

The KV cache size for this kind of model is tied to the maximum number
of sequences kept at any single time.
For now, this number is obtained from n_parallel (plus one,
to have an extra sequence to dedicate to the system prompt),
but there might be a better way to do this which won't also
make the main example use 2 cells even if only 1 is really used.
(for this specific case, --parallel 0 helps)

Simultaneous sequence processing will probably require changes to
ggml_ssm_scan, and possibly a new operator for the conv step.

* mamba : support llama_kv_cache_seq_cp

This (mis)uses the logic around K shifts, because tokens in a state
can't be shifted anyway, and because inp_K_shift has the right shape and type.
Using ggml_get_rows is a nice way to do copies, but copy chains can't work.
Fortunately, copy chains don't really seem to be used in the examples.

Each KV cell is dedicated to the sequence ID corresponding to its own index.

* mamba : use a state mask

It's cleaner than the previous heuristic of
checking for the pos of the first token in the batch.

inp_KQ_mask could not be re-used for this, because it has the wrong shape
and because it seems more suited to the next step of
simultaneous sequence processing (helping with the problem of
remembering which token belongs to which sequence(s)/state(s)).

* llama : replace the usage of n_ctx with kv_self.size in many places

* mamba : use n_tokens directly instead of n_tok

* mamba : in comments, properly refer to KV cells instead of slots

* mamba : reduce memory usage of ggml_ssm_scan

From 290.37 MiB to 140.68 MiB of CPU compute buffer size
with Mamba 3B with a batch size of 512.

The result tensor of ggml_ssm_scan was previously a big part
of the CPU compute buffer size. To make it smaller,
it does not contain the intermediate ssm states anymore.
Both y and the last ssm state are combined in the result tensor,
because it seems only a single tensor can be returned by an operator
with the way the graph is built.

* mamba : simultaneous sequence processing

A batch can now contain tokens from multiple sequences.

This is necessary for at least the parallel example, the server example,
and the HellaSwag test in the perplexity example.

However, for this to be useful, uses of llama_kv_cache_seq_rm/cp
will need to be changed to work on whole sequences.

* ggml : add ggml_ssm_conv as a new operator for the conv step of Mamba

This operator makes it possible to use and update the correct states
for each token of the batch in the same way as ggml_ssm_scan.
Other solutions which use existing operators would need loops which would
add too many nodes to the graph (at least the ones I thought of).

Using this operator further reduces the size of the CPU compute buffer
from 140.68 MiB to 103.20 MiB with Mamba 3B with a batch size of 512.
And (at least on CPU), it's a bit faster than before.

Note that "ggml_ssm_conv" is probably not the most appropriate name,
and it could be changed if a better one is found.

* llama : add inp_s_seq as a new input tensor

The most convenient implementation to select the correct state (for Mamba)
for each token is to directly get the correct index from a tensor.
This is why inp_s_seq is storing int32_t and not floats.

The other, less convenient way to select the correct state would be
to have inp_KQ_mask contain 1.0f for each state used by a token
and 0.0f otherwise. This complicates quickly fetching the first used
state of a token, and is also less efficient because a whole row
of the mask would always need to be read for each token.

Using indexes makes it easy to stop searching when there are
no more sequences for a token, and the first sequence assigned
is always very quickly available (it's the first element of each row).

* mamba : support llama_kv_cache_seq_cp copy chains

* mamba : support shifting and dividing the kv cache pos

* mamba : make the server and parallel examples work with whole sequences

A seq_id is dedicated to the system prompt in both cases.

* llama : make llama_kv_cache_seq_rm return whether it succeeded or not

* mamba : dedicate an input tensor for state copy indices

This is cleaner and makes it easier to adapt when/if token positions
(and by extension, inp_K_shift) are no longer integers.

* mamba : adapt perplexity, batched, and batched-bench examples

* perplexity : limit the max number of sequences

This adapts to what the loaded model can provide.

* llama : add llama_n_max_seq to get the upper limit for seq_ids

Used by the perplexity example.

* batched : pass n_parallel to the model's context params

This should have been there already, but it wasn't.

* batched-bench : reserve sequences to support Mamba

* batched-bench : fix tokens being put in wrong sequences

Generation quality isn't what's measured in there anyway,
but at least using the correct sequences avoids using non-consecutive
token positions.

* mamba : stop abusing attention metadata

This breaks existing converted-to-GGUF Mamba models,
but will allow supporting mixed architectures like MambaFormer
without needing to break Mamba models.

This will also allow changing the size of Mamba's states
without having to reconvert models in the future.
(e.g. using something else than d_conv - 1 columns for the conv_states
 will not require breaking existing converted Mamba models again)

* gguf-py : add new KV metadata key-value pairs for Mamba

* llama : add new metadata key-value pairs for Mamba

* llama : guard against divisions by zero when n_head is 0

* mamba : rename "unlimited" KV cache property to "recurrent"

* mamba : more correctly update the "used" field of the KV cache

* ggml : in ggml_ssm_scan, use a threshold for soft_plus

This is how the official Mamba implementation does it,
and it's also what torch.nn.Softplus does.

* convert : for Mamba, fallback to internal NeoX tokenizer

The resulting models are exactly the same
as if the tokenizer.json and tokenizer_config.json of GPT-NeoX were there.

* mamba : support state saving and restoring

* ggml : implicitly pass src tensors through dst for Mamba-related ops

* mamba : clarify some comments

* server : fix cache_tokens not getting correctly resized

Otherwise, when the "we have to evaluate at least 1 token" special case
was triggered, an extra token was kept in cache_tokens even if it was
removed from the KV cache.

For Mamba, this caused useless prompt reprocessing when the previous
request triggered the above case.

* convert-hf : support new metadata keys for Mamba

For the models available at
https://huggingface.co/collections/state-spaces/transformers-compatible-mamba-65e7b40ab87e5297e45ae406

* mamba : rename metadata to be more similar to transformers library

This breaks existing converted-to-GGUF models,
but the metadata names are more "standard".

* mamba : support mamba-*-hf models

These models share their token_embd.weight with their output.weight

* mamba : add missing spaces

This is purely a formatting change.

* convert-hf : omit output.weight when identical with token_embd.weight

Only for Mamba for now, but it might be relevant for other models eventually.
Most Mamba models actually share these two tensors, albeit implicitly.

* readme : add Mamba to supported models, and add recent API changes

* mamba : move state_seq and state_mask views outside layer loop

A few tensors were also missing `struct` in front of `ggml_tensor`.
2024-03-08 17:31:00 -05:00

56 KiB
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llama.cpp

llama

License: MIT

Roadmap / Project status / Manifesto / ggml

Inference of Meta's LLaMA model (and others) in pure C/C++

Recent API changes

Hot topics

Table of Contents
  1. Description
  2. Usage
  3. Contributing
  4. Coding guidelines
  5. Docs

Description

The main goal of llama.cpp is to enable LLM inference with minimal setup and state-of-the-art performance on a wide variety of hardware - locally and in the cloud.

  • Plain C/C++ implementation without any dependencies
  • Apple silicon is a first-class citizen - optimized via ARM NEON, Accelerate and Metal frameworks
  • AVX, AVX2 and AVX512 support for x86 architectures
  • 1.5-bit, 2-bit, 3-bit, 4-bit, 5-bit, 6-bit, and 8-bit integer quantization for faster inference and reduced memory use
  • Custom CUDA kernels for running LLMs on NVIDIA GPUs (support for AMD GPUs via HIP)
  • Vulkan, SYCL, and (partial) OpenCL backend support
  • CPU+GPU hybrid inference to partially accelerate models larger than the total VRAM capacity

Since its inception, the project has improved significantly thanks to many contributions. It is the main playground for developing new features for the ggml library.

Supported platforms:

  • Mac OS
  • Linux
  • Windows (via CMake)
  • Docker
  • FreeBSD

Supported models:

Typically finetunes of the base models below are supported as well.

Multimodal models:

HTTP server

llama.cpp web server is a lightweight OpenAI API compatible HTTP server that can be used to serve local models and easily connect them to existing clients.

Bindings:

UI:

Unless otherwise noted these projects are open-source with permissive licensing:

Here is a typical run using LLaMA v2 13B on M2 Ultra:

$ make -j && ./main -m models/llama-13b-v2/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:\nStep 1:" -n 400 -e
I llama.cpp build info:
I UNAME_S:  Darwin
I UNAME_P:  arm
I UNAME_M:  arm64
I CFLAGS:   -I.            -O3 -std=c11   -fPIC -DNDEBUG -Wall -Wextra -Wpedantic -Wcast-qual -Wdouble-promotion -Wshadow -Wstrict-prototypes -Wpointer-arith -Wmissing-prototypes -pthread -DGGML_USE_K_QUANTS -DGGML_USE_ACCELERATE
I CXXFLAGS: -I. -I./common -O3 -std=c++11 -fPIC -DNDEBUG -Wall -Wextra -Wpedantic -Wcast-qual -Wno-unused-function -Wno-multichar -pthread -DGGML_USE_K_QUANTS
I LDFLAGS:   -framework Accelerate
I CC:       Apple clang version 14.0.3 (clang-1403.0.22.14.1)
I CXX:      Apple clang version 14.0.3 (clang-1403.0.22.14.1)

make: Nothing to be done for `default'.
main: build = 1041 (cf658ad)
main: seed  = 1692823051
llama_model_loader: loaded meta data with 16 key-value pairs and 363 tensors from models/llama-13b-v2/ggml-model-q4_0.gguf (version GGUF V1 (latest))
llama_model_loader: - type  f32:   81 tensors
llama_model_loader: - type q4_0:  281 tensors
llama_model_loader: - type q6_K:    1 tensors
llm_load_print_meta: format         = GGUF V1 (latest)
llm_load_print_meta: arch           = llama
llm_load_print_meta: vocab type     = SPM
llm_load_print_meta: n_vocab        = 32000
llm_load_print_meta: n_merges       = 0
llm_load_print_meta: n_ctx_train    = 4096
llm_load_print_meta: n_ctx          = 512
llm_load_print_meta: n_embd         = 5120
llm_load_print_meta: n_head         = 40
llm_load_print_meta: n_head_kv      = 40
llm_load_print_meta: n_layer        = 40
llm_load_print_meta: n_rot          = 128
llm_load_print_meta: n_gqa          = 1
llm_load_print_meta: f_norm_eps     = 1.0e-05
llm_load_print_meta: f_norm_rms_eps = 1.0e-05
llm_load_print_meta: n_ff           = 13824
llm_load_print_meta: freq_base      = 10000.0
llm_load_print_meta: freq_scale     = 1
llm_load_print_meta: model type     = 13B
llm_load_print_meta: model ftype    = mostly Q4_0
llm_load_print_meta: model size     = 13.02 B
llm_load_print_meta: general.name   = LLaMA v2
llm_load_print_meta: BOS token = 1 '<s>'
llm_load_print_meta: EOS token = 2 '</s>'
llm_load_print_meta: UNK token = 0 '<unk>'
llm_load_print_meta: LF token  = 13 '<0x0A>'
llm_load_tensors: ggml ctx size =    0.11 MB
llm_load_tensors: mem required  = 7024.01 MB (+  400.00 MB per state)
...................................................................................................
llama_new_context_with_model: kv self size  =  400.00 MB
llama_new_context_with_model: compute buffer total size =   75.41 MB

system_info: n_threads = 16 / 24 | AVX = 0 | AVX2 = 0 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | FMA = 0 | NEON = 1 | ARM_FMA = 1 | F16C = 0 | FP16_VA = 1 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 0 | VSX = 0 |
sampling: repeat_last_n = 64, repeat_penalty = 1.100000, presence_penalty = 0.000000, frequency_penalty = 0.000000, top_k = 40, tfs_z = 1.000000, top_p = 0.950000, typical_p = 1.000000, temp = 0.800000, mirostat = 0, mirostat_lr = 0.100000, mirostat_ent = 5.000000
generate: n_ctx = 512, n_batch = 512, n_predict = 400, n_keep = 0


 Building a website can be done in 10 simple steps:
Step 1: Find the right website platform.
Step 2: Choose your domain name and hosting plan.
Step 3: Design your website layout.
Step 4: Write your website content and add images.
Step 5: Install security features to protect your site from hackers or spammers
Step 6: Test your website on multiple browsers, mobile devices, operating systems etc…
Step 7: Test it again with people who are not related to you personally – friends or family members will work just fine!
Step 8: Start marketing and promoting the website via social media channels or paid ads
Step 9: Analyze how many visitors have come to your site so far, what type of people visit more often than others (e.g., men vs women) etc…
Step 10: Continue to improve upon all aspects mentioned above by following trends in web design and staying up-to-date on new technologies that can enhance user experience even further!
How does a Website Work?
A website works by having pages, which are made of HTML code. This code tells your computer how to display the content on each page you visit – whether it’s an image or text file (like PDFs). In order for someone else’s browser not only be able but also want those same results when accessing any given URL; some additional steps need taken by way of programming scripts that will add functionality such as making links clickable!
The most common type is called static HTML pages because they remain unchanged over time unless modified manually (either through editing files directly or using an interface such as WordPress). They are usually served up via HTTP protocols – this means anyone can access them without having any special privileges like being part of a group who is allowed into restricted areas online; however, there may still exist some limitations depending upon where one lives geographically speaking.
How to
llama_print_timings:        load time =   576.45 ms
llama_print_timings:      sample time =   283.10 ms /   400 runs   (    0.71 ms per token,  1412.91 tokens per second)
llama_print_timings: prompt eval time =   599.83 ms /    19 tokens (   31.57 ms per token,    31.68 tokens per second)
llama_print_timings:        eval time = 24513.59 ms /   399 runs   (   61.44 ms per token,    16.28 tokens per second)
llama_print_timings:       total time = 25431.49 ms

And here is another demo of running both LLaMA-7B and whisper.cpp on a single M1 Pro MacBook:

https://user-images.githubusercontent.com/1991296/224442907-7693d4be-acaa-4e01-8b4f-add84093ffff.mp4

Usage

Here are the end-to-end binary build and model conversion steps for most supported models.

Get the Code

git clone https://github.com/ggerganov/llama.cpp
cd llama.cpp

Build

In order to build llama.cpp you have three different options.

  • Using make:

    • On Linux or MacOS:

      make

    • On Windows:

      1. Download the latest fortran version of w64devkit.
      2. Extract w64devkit on your pc.
      3. Run w64devkit.exe.
      4. Use the cd command to reach the llama.cpp folder.
      5. From here you can run: 
        make

  • Using CMake:

    mkdir build
cd build
cmake ..
cmake --build . --config Release

  • Using Zig (version 0.11 or later):

    Building for optimization levels and CPU features can be accomplished using standard build arguments, for example AVX2, FMA, F16C, it's also possible to cross compile for other operating systems and architectures:

    zig build -Doptimize=ReleaseFast -Dtarget=x86_64-windows-gnu -Dcpu=x86_64+avx2+fma+f16c

    The zig targets command will give you valid options to use.

  • Using gmake (FreeBSD):

    1. Install and activate DRM in FreeBSD

    2. Add your user to video group

    3. Install compilation dependencies.

      sudo pkg install gmake automake autoconf pkgconf llvm15 clinfo clover \
    opencl clblast openblas

gmake CC=/usr/local/bin/clang15 CXX=/usr/local/bin/clang++15 -j4

    Notes: With this packages you can build llama.cpp with OPENBLAS and CLBLAST support for use OpenCL GPU acceleration in FreeBSD. Please read the instructions for use and activate this options in this document below.

Metal Build

On MacOS, Metal is enabled by default. Using Metal makes the computation run on the GPU. To disable the Metal build at compile time use the LLAMA_NO_METAL=1 flag or the LLAMA_METAL=OFF cmake option.

When built with Metal support, you can explicitly disable GPU inference with the --n-gpu-layers|-ngl 0 command-line argument.

MPI Build

MPI lets you distribute the computation over a cluster of machines. Because of the serial nature of LLM prediction, this won't yield any end-to-end speed-ups, but it will let you run larger models than would otherwise fit into RAM on a single machine.

First you will need MPI libraries installed on your system. The two most popular (only?) options are MPICH and OpenMPI. Either can be installed with a package manager (apt, Homebrew, MacPorts, etc).

Next you will need to build the project with LLAMA_MPI set to true on all machines; if you're building with make, you will also need to specify an MPI-capable compiler (when building with CMake, this is configured automatically):

  • Using make:

    make CC=mpicc CXX=mpicxx LLAMA_MPI=1

  • Using CMake:

    cmake -S . -B build -DLLAMA_MPI=ON

Once the programs are built, download/convert the weights on all of the machines in your cluster. The paths to the weights and programs should be identical on all machines.

Next, ensure password-less SSH access to each machine from the primary host, and create a hostfile with a list of the hostnames and their relative "weights" (slots). If you want to use localhost for computation, use its local subnet IP address rather than the loopback address or "localhost".

Here is an example hostfile:

192.168.0.1:2
malvolio.local:1

The above will distribute the computation across 2 processes on the first host and 1 process on the second host. Each process will use roughly an equal amount of RAM. Try to keep these numbers small, as inter-process (intra-host) communication is expensive.

Finally, you're ready to run a computation using mpirun:

mpirun -hostfile hostfile -n 3 ./main -m ./models/7B/ggml-model-q4_0.gguf -n 128

BLAS Build

Building the program with BLAS support may lead to some performance improvements in prompt processing using batch sizes higher than 32 (the default is 512). Support with CPU-only BLAS implementations doesn't affect the normal generation performance. We may see generation performance improvements with GPU-involved BLAS implementations, e.g. cuBLAS, hipBLAS and CLBlast. There are currently several different BLAS implementations available for build and use:

  • Accelerate Framework:

    This is only available on Mac PCs and it's enabled by default. You can just build using the normal instructions.

  • OpenBLAS:

    This provides BLAS acceleration using only the CPU. Make sure to have OpenBLAS installed on your machine.

    • Using make:

      • On Linux:

        make LLAMA_OPENBLAS=1

      • On Windows:

        1. Download the latest fortran version of w64devkit.

        2. Download the latest version of OpenBLAS for Windows.

        3. Extract w64devkit on your pc.

        4. From the OpenBLAS zip that you just downloaded copy libopenblas.a, located inside the lib folder, inside w64devkit\x86_64-w64-mingw32\lib.

        5. From the same OpenBLAS zip copy the content of the include folder inside w64devkit\x86_64-w64-mingw32\include.

        6. Run w64devkit.exe.

        7. Use the cd command to reach the llama.cpp folder.

        8. From here you can run:

          make LLAMA_OPENBLAS=1

    • Using CMake on Linux:

      mkdir build
cd build
cmake .. -DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS
cmake --build . --config Release

  • BLIS

    Check BLIS.md for more information.

  • SYCL

    SYCL is a higher-level programming model to improve programming productivity on various hardware accelerators.

    llama.cpp based on SYCL is used to support Intel GPU (Data Center Max series, Flex series, Arc series, Built-in GPU and iGPU).

    For detailed info, please refer to llama.cpp for SYCL.

  • Intel oneMKL

    Building through oneAPI compilers will make avx_vnni instruction set available for intel processors that do not support avx512 and avx512_vnni. Please note that this build config does not support Intel GPU. For Intel GPU support, please refer to llama.cpp for SYCL.

    • Using manual oneAPI installation: By default, LLAMA_BLAS_VENDOR is set to Generic, so if you already sourced intel environment script and assign -DLLAMA_BLAS=ON in cmake, the mkl version of Blas will automatically been selected. Otherwise please install oneAPI and follow the below steps:

      mkdir build
cd build
source /opt/intel/oneapi/setvars.sh # You can skip this step if  in oneapi-basekit docker image, only required for manual installation
cmake .. -DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=Intel10_64lp -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_NATIVE=ON
cmake --build . --config Release

    • Using oneAPI docker image: If you do not want to source the environment vars and install oneAPI manually, you can also build the code using intel docker container: oneAPI-basekit. Then, you can use the commands given above.

    Check Optimizing and Running LLaMA2 on Intel® CPU for more information.

  • cuBLAS

    This provides BLAS acceleration using the CUDA cores of your Nvidia GPU. Make sure to have the CUDA toolkit installed. You can download it from your Linux distro's package manager (e.g. apt install nvidia-cuda-toolkit) or from here: CUDA Toolkit.

    For Jetson user, if you have Jetson Orin, you can try this: Offical Support. If you are using an old model(nano/TX2), need some additional operations before compiling.

    • Using make:

      make LLAMA_CUBLAS=1

    • Using CMake:

      mkdir build
cd build
cmake .. -DLLAMA_CUBLAS=ON
cmake --build . --config Release

    The environment variable CUDA_VISIBLE_DEVICES can be used to specify which GPU(s) will be used. The following compilation options are also available to tweak performance:

Option Legal values Default Description
LLAMA_CUDA_FORCE_DMMV Boolean false Force the use of dequantization + matrix vector multiplication kernels instead of using kernels that do matrix vector multiplication on quantized data. By default the decision is made based on compute capability (MMVQ for 6.1/Pascal/GTX 1000 or higher). Does not affect k-quants.
LLAMA_CUDA_DMMV_X Positive integer >= 32 32 Number of values in x direction processed by the CUDA dequantization + matrix vector multiplication kernel per iteration. Increasing this value can improve performance on fast GPUs. Power of 2 heavily recommended. Does not affect k-quants.
LLAMA_CUDA_MMV_Y Positive integer 1 Block size in y direction for the CUDA mul mat vec kernels. Increasing this value can improve performance on fast GPUs. Power of 2 recommended.
LLAMA_CUDA_F16 Boolean false If enabled, use half-precision floating point arithmetic for the CUDA dequantization + mul mat vec kernels and for the q4_1 and q5_1 matrix matrix multiplication kernels. Can improve performance on relatively recent GPUs.
LLAMA_CUDA_KQUANTS_ITER 1 or 2 2 Number of values processed per iteration and per CUDA thread for Q2_K and Q6_K quantization formats. Setting this value to 1 can improve performance for slow GPUs.
LLAMA_CUDA_PEER_MAX_BATCH_SIZE Positive integer 128 Maximum batch size for which to enable peer access between multiple GPUs. Peer access requires either Linux or NVLink. When using NVLink enabling peer access for larger batch sizes is potentially beneficial.

  • hipBLAS

    This provides BLAS acceleration on HIP-supported AMD GPUs. Make sure to have ROCm installed. You can download it from your Linux distro's package manager or from here: ROCm Quick Start (Linux).

    • Using make:

      make LLAMA_HIPBLAS=1

    • Using CMake for Linux (assuming a gfx1030-compatible AMD GPU):

      CC=/opt/rocm/llvm/bin/clang CXX=/opt/rocm/llvm/bin/clang++ \
    cmake -H. -Bbuild -DLLAMA_HIPBLAS=ON -DAMDGPU_TARGETS=gfx1030 -DCMAKE_BUILD_TYPE=Release \
    && cmake --build build -- -j 16

      On Linux it is also possible to use unified memory architecture (UMA) to share main memory between the CPU and integrated GPU by setting -DLLAMA_HIP_UMA=ON". However, this hurts performance for non-integrated GPUs (but enables working with integrated GPUs).

    • Using make (example for target gfx1030, build with 16 CPU threads):

      make -j16 LLAMA_HIPBLAS=1 LLAMA_HIP_UMA=1 AMDGPU_TARGETS=gxf1030

    • Using CMake for Windows (using x64 Native Tools Command Prompt for VS, and assuming a gfx1100-compatible AMD GPU):

      set PATH=%HIP_PATH%\bin;%PATH%
mkdir build
cd build
cmake -G Ninja -DAMDGPU_TARGETS=gfx1100 -DLLAMA_HIPBLAS=ON -DCMAKE_C_COMPILER=clang -DCMAKE_CXX_COMPILER=clang++ ..
cmake --build .

      Make sure that AMDGPU_TARGETS is set to the GPU arch you want to compile for. The above example uses gfx1100 that corresponds to Radeon RX 7900XTX/XT/GRE. You can find a list of targets here Find your gpu version string by matching the most significant version information from rocminfo | grep gfx | head -1 | awk '{print $2}' with the list of processors, e.g. gfx1035 maps to gfx1030.

    The environment variable HIP_VISIBLE_DEVICES can be used to specify which GPU(s) will be used. If your GPU is not officially supported you can use the environment variable [HSA_OVERRIDE_GFX_VERSION] set to a similar GPU, for example 10.3.0 on RDNA2 (e.g. gfx1030, gfx1031, or gfx1035) or 11.0.0 on RDNA3. The following compilation options are also available to tweak performance (yes, they refer to CUDA, not HIP, because it uses the same code as the cuBLAS version above):

    Option Legal values Default Description
    LLAMA_CUDA_DMMV_X Positive integer >= 32 32 Number of values in x direction processed by the HIP dequantization + matrix vector multiplication kernel per iteration. Increasing this value can improve performance on fast GPUs. Power of 2 heavily recommended. Does not affect k-quants.
    LLAMA_CUDA_MMV_Y Positive integer 1 Block size in y direction for the HIP mul mat vec kernels. Increasing this value can improve performance on fast GPUs. Power of 2 recommended. Does not affect k-quants.
    LLAMA_CUDA_KQUANTS_ITER 1 or 2 2 Number of values processed per iteration and per HIP thread for Q2_K and Q6_K quantization formats. Setting this value to 1 can improve performance for slow GPUs.

  • CLBlast

    OpenCL acceleration is provided by the matrix multiplication kernels from the CLBlast project and custom kernels for ggml that can generate tokens on the GPU.

    You will need the OpenCL SDK.

    • For Ubuntu or Debian, the packages opencl-headers, ocl-icd may be needed.

    • For Windows, a pre-built SDK is available on the OpenCL Releases page.

    • Installing the OpenCL SDK from source 
      git clone --recurse-submodules https://github.com/KhronosGroup/OpenCL-SDK.git
mkdir OpenCL-SDK/build
cd OpenCL-SDK/build
cmake .. -DBUILD_DOCS=OFF \
  -DBUILD_EXAMPLES=OFF \
  -DBUILD_TESTING=OFF \
  -DOPENCL_SDK_BUILD_SAMPLES=OFF \
  -DOPENCL_SDK_TEST_SAMPLES=OFF
cmake --build . --config Release
cmake --install . --prefix /some/path
    Installing CLBlast

    Pre-built CLBlast binaries may be found on the CLBlast Releases page. For Unix variants, it may also be found in your operating system's packages.

    Alternatively, they may be built from source.

    • Windows: 
      set OPENCL_SDK_ROOT="C:/OpenCL-SDK-v2023.04.17-Win-x64"
git clone https://github.com/CNugteren/CLBlast.git
mkdir CLBlast\build
cd CLBlast\build
cmake .. -DBUILD_SHARED_LIBS=OFF -DOVERRIDE_MSVC_FLAGS_TO_MT=OFF -DTUNERS=OFF -DOPENCL_ROOT=%OPENCL_SDK_ROOT% -G "Visual Studio 17 2022" -A x64
cmake --build . --config Release
cmake --install . --prefix C:/CLBlast
    • Unix: 
      git clone https://github.com/CNugteren/CLBlast.git
mkdir CLBlast/build
cd CLBlast/build
cmake .. -DBUILD_SHARED_LIBS=OFF -DTUNERS=OFF
cmake --build . --config Release
cmake --install . --prefix /some/path

      Where /some/path is where the built library will be installed (default is /usr/local).

    Building Llama with CLBlast
    • Build with make: 
      make LLAMA_CLBLAST=1
    • CMake (Unix): 
      mkdir build
cd build
cmake .. -DLLAMA_CLBLAST=ON -DCLBlast_DIR=/some/path
cmake --build . --config Release
    • CMake (Windows): 
      set CL_BLAST_CMAKE_PKG="C:/CLBlast/lib/cmake/CLBlast"
git clone https://github.com/ggerganov/llama.cpp
cd llama.cpp
mkdir build
cd build
cmake .. -DBUILD_SHARED_LIBS=OFF -DLLAMA_CLBLAST=ON -DCMAKE_PREFIX_PATH=%CL_BLAST_CMAKE_PKG% -G "Visual Studio 17 2022" -A x64
cmake --build . --config Release
cmake --install . --prefix C:/LlamaCPP
    Running Llama with CLBlast

    The CLBlast build supports --gpu-layers|-ngl like the CUDA version does.

    To select the correct platform (driver) and device (GPU), you can use the environment variables GGML_OPENCL_PLATFORM and GGML_OPENCL_DEVICE. The selection can be a number (starting from 0) or a text string to search:

    GGML_OPENCL_PLATFORM=1 ./main ...
GGML_OPENCL_DEVICE=2 ./main ...
GGML_OPENCL_PLATFORM=Intel ./main ...
GGML_OPENCL_PLATFORM=AMD GGML_OPENCL_DEVICE=1 ./main ...

    The default behavior is to find the first GPU device, but when it is an integrated GPU on a laptop, for instance, the selectors are useful. Using the variables it is possible to select a CPU-based driver as well, if so desired.

    You can get a list of platforms and devices from the clinfo -l command, etc.

  • Vulkan

    With docker:

    You don't need to install Vulkan SDK. It will be installed inside the container.

    # Build the image
docker build -t llama-cpp-vulkan -f .devops/main-vulkan.Dockerfile .

# Then, use it:
docker run -it --rm -v "$(pwd):/app:Z" --device /dev/dri/renderD128:/dev/dri/renderD128 --device /dev/dri/card1:/dev/dri/card1 llama-cpp-vulkan -m "/app/models/YOUR_MODEL_FILE" -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 33

    Without docker:

    Firstly, you need to make sure you have installed Vulkan SDK

    For example, on Ubuntu 22.04 (jammy), use the command below:

    wget -qO - https://packages.lunarg.com/lunarg-signing-key-pub.asc | apt-key add -
wget -qO /etc/apt/sources.list.d/lunarg-vulkan-jammy.list https://packages.lunarg.com/vulkan/lunarg-vulkan-jammy.list
apt update -y
apt-get install -y vulkan-sdk
# To verify the installation, use the command below:
vulkaninfo

    Alternatively your package manager might be able to provide the appropiate libraries. For example for Ubuntu 22.04 you can install libvulkan-dev instead.

    Then, build llama.cpp using the cmake command below:

    mkdir -p build
cd build
cmake .. -DLLAMA_VULKAN=1
cmake --build . --config Release
# Test the output binary (with "-ngl 33" to offload all layers to GPU)
./bin/main -m "PATH_TO_MODEL" -p "Hi you how are you" -n 50 -e -ngl 33 -t 4

# You should see in the output, ggml_vulkan detected your GPU. For example:
# ggml_vulkan: Using Intel(R) Graphics (ADL GT2) | uma: 1 | fp16: 1 | warp size: 32

Prepare and Quantize

To obtain the official LLaMA 2 weights please see the Obtaining and using the Facebook LLaMA 2 model section. There is also a large selection of pre-quantized gguf models available on Hugging Face.

# obtain the official LLaMA model weights and place them in ./models
ls ./models
llama-2-7b tokenizer_checklist.chk tokenizer.model
# [Optional] for models using BPE tokenizers
ls ./models
<folder containing weights and tokenizer json> vocab.json
# [Optional] for PyTorch .bin models like Mistral-7B
ls ./models
<folder containing weights and tokenizer json>

# install Python dependencies
python3 -m pip install -r requirements.txt

# convert the model to ggml FP16 format
python3 convert.py models/mymodel/

# [Optional] for models using BPE tokenizers
python convert.py models/mymodel/ --vocab-type bpe

# quantize the model to 4-bits (using Q4_K_M method)
./quantize ./models/mymodel/ggml-model-f16.gguf ./models/mymodel/ggml-model-Q4_K_M.gguf Q4_K_M

# update the gguf filetype to current version if older version is now unsupported
./quantize ./models/mymodel/ggml-model-Q4_K_M.gguf ./models/mymodel/ggml-model-Q4_K_M-v2.gguf COPY

Run the quantized model

# start inference on a gguf model
./main -m ./models/mymodel/ggml-model-Q4_K_M.gguf -n 128

When running the larger models, make sure you have enough disk space to store all the intermediate files.

Running on Windows with prebuilt binaries

You will find prebuilt Windows binaries on the release page.

Simply download and extract the latest zip package of choice: (e.g. llama-b1380-bin-win-avx2-x64.zip)

From the unzipped folder, open a terminal/cmd window here and place a pre-converted .gguf model file. Test out the main example like so:

.\main -m llama-2-7b.Q4_0.gguf -n 128

Memory/Disk Requirements

As the models are currently fully loaded into memory, you will need adequate disk space to save them and sufficient RAM to load them. At the moment, memory and disk requirements are the same.

Model Original size Quantized size (Q4_0)
7B 13 GB 3.9 GB
13B 24 GB 7.8 GB
30B 60 GB 19.5 GB
65B 120 GB 38.5 GB

Quantization

Several quantization methods are supported. They differ in the resulting model disk size and inference speed.

(outdated)

Model Measure F16 Q4_0 Q4_1 Q5_0 Q5_1 Q8_0
7B perplexity 5.9066 6.1565 6.0912 5.9862 5.9481 5.9070
7B file size 13.0G 3.5G 3.9G 4.3G 4.7G 6.7G
7B ms/tok @ 4th 127 55 54 76 83 72
7B ms/tok @ 8th 122 43 45 52 56 67
7B bits/weight 16.0 4.5 5.0 5.5 6.0 8.5
13B perplexity 5.2543 5.3860 5.3608 5.2856 5.2706 5.2548
13B file size 25.0G 6.8G 7.6G 8.3G 9.1G 13G
13B ms/tok @ 4th - 103 105 148 160 131
13B ms/tok @ 8th - 73 82 98 105 128
13B bits/weight 16.0 4.5 5.0 5.5 6.0 8.5

Perplexity (measuring model quality)

You can use the perplexity example to measure perplexity over a given prompt (lower perplexity is better). For more information, see https://huggingface.co/docs/transformers/perplexity.

The perplexity measurements in table above are done against the wikitext2 test dataset (https://paperswithcode.com/dataset/wikitext-2), with context length of 512. The time per token is measured on a MacBook M1 Pro 32GB RAM using 4 and 8 threads.

How to run

  1. Download/extract: https://huggingface.co/datasets/ggml-org/ci/resolve/main/wikitext-2-raw-v1.zip
  2. Run ./perplexity -m models/7B/ggml-model-q4_0.gguf -f wiki.test.raw
  3. Output:
perplexity : calculating perplexity over 655 chunks
24.43 seconds per pass - ETA 4.45 hours
[1]4.5970,[2]5.1807,[3]6.0382,...

And after 4.45 hours, you will have the final perplexity.

Interactive mode

If you want a more ChatGPT-like experience, you can run in interactive mode by passing -i as a parameter. In this mode, you can always interrupt generation by pressing Ctrl+C and entering one or more lines of text, which will be converted into tokens and appended to the current context. You can also specify a reverse prompt with the parameter -r "reverse prompt string". This will result in user input being prompted whenever the exact tokens of the reverse prompt string are encountered in the generation. A typical use is to use a prompt that makes LLaMA emulate a chat between multiple users, say Alice and Bob, and pass -r "Alice:".

Here is an example of a few-shot interaction, invoked with the command

# default arguments using a 7B model
./examples/chat.sh

# advanced chat with a 13B model
./examples/chat-13B.sh

# custom arguments using a 13B model
./main -m ./models/13B/ggml-model-q4_0.gguf -n 256 --repeat_penalty 1.0 --color -i -r "User:" -f prompts/chat-with-bob.txt

Note the use of --color to distinguish between user input and generated text. Other parameters are explained in more detail in the README for the main example program.

image

Persistent Interaction

The prompt, user inputs, and model generations can be saved and resumed across calls to ./main by leveraging --prompt-cache and --prompt-cache-all. The ./examples/chat-persistent.sh script demonstrates this with support for long-running, resumable chat sessions. To use this example, you must provide a file to cache the initial chat prompt and a directory to save the chat session, and may optionally provide the same variables as chat-13B.sh. The same prompt cache can be reused for new chat sessions. Note that both prompt cache and chat directory are tied to the initial prompt (PROMPT_TEMPLATE) and the model file.

# Start a new chat
PROMPT_CACHE_FILE=chat.prompt.bin CHAT_SAVE_DIR=./chat/default ./examples/chat-persistent.sh

# Resume that chat
PROMPT_CACHE_FILE=chat.prompt.bin CHAT_SAVE_DIR=./chat/default ./examples/chat-persistent.sh

# Start a different chat with the same prompt/model
PROMPT_CACHE_FILE=chat.prompt.bin CHAT_SAVE_DIR=./chat/another ./examples/chat-persistent.sh

# Different prompt cache for different prompt/model
PROMPT_TEMPLATE=./prompts/chat-with-bob.txt PROMPT_CACHE_FILE=bob.prompt.bin \
    CHAT_SAVE_DIR=./chat/bob ./examples/chat-persistent.sh

Constrained output with grammars

llama.cpp supports grammars to constrain model output. For example, you can force the model to output JSON only:

./main -m ./models/13B/ggml-model-q4_0.gguf -n 256 --grammar-file grammars/json.gbnf -p 'Request: schedule a call at 8pm; Command:'

The grammars/ folder contains a handful of sample grammars. To write your own, check out the GBNF Guide.

For authoring more complex JSON grammars, you can also check out https://grammar.intrinsiclabs.ai/, a browser app that lets you write TypeScript interfaces which it compiles to GBNF grammars that you can save for local use. Note that the app is built and maintained by members of the community, please file any issues or FRs on its repo and not this one.

Instruct mode

  1. First, download and place the ggml model into the ./models folder
  2. Run the main tool like this:
./examples/alpaca.sh

Sample run:

== Running in interactive mode. ==
 - Press Ctrl+C to interject at any time.
 - Press Return to return control to LLaMA.
 - If you want to submit another line, end your input in '\'.

 Below is an instruction that describes a task. Write a response that appropriately completes the request.

> How many letters are there in the English alphabet?
There 26 letters in the English Alphabet
> What is the most common way of transportation in Amsterdam?
The majority (54%) are using public transit. This includes buses, trams and metros with over 100 lines throughout the city which make it very accessible for tourists to navigate around town as well as locals who commute by tram or metro on a daily basis
> List 5 words that start with "ca".
cadaver, cauliflower, cabbage (vegetable), catalpa (tree) and Cailleach.
>

Obtaining and using the Facebook LLaMA 2 model

Seminal papers and background on the models

If your issue is with model generation quality, then please at least scan the following links and papers to understand the limitations of LLaMA models. This is especially important when choosing an appropriate model size and appreciating both the significant and subtle differences between LLaMA models and ChatGPT:

Android

Building the Project using Android NDK

You can easily run llama.cpp on Android device with termux.

First, install the essential packages for termux:

pkg install clang wget git cmake

Second, obtain the Android NDK and then build with CMake:

$ mkdir build-android
$ cd build-android
$ export NDK=<your_ndk_directory>
$ cmake -DCMAKE_TOOLCHAIN_FILE=$NDK/build/cmake/android.toolchain.cmake -DANDROID_ABI=arm64-v8a -DANDROID_PLATFORM=android-23 -DCMAKE_C_FLAGS=-march=armv8.4a+dotprod ..
$ make

Install termux on your device and run termux-setup-storage to get access to your SD card. Finally, copy the llama binary and the model files to your device storage. Here is a demo of an interactive session running on Pixel 5 phone:

https://user-images.githubusercontent.com/271616/225014776-1d567049-ad71-4ef2-b050-55b0b3b9274c.mp4

Building the Project using Termux (F-Droid)

Termux from F-Droid offers an alternative route to execute the project on an Android device. This method empowers you to construct the project right from within the terminal, negating the requirement for a rooted device or SD Card.

Outlined below are the directives for installing the project using OpenBLAS and CLBlast. This combination is specifically designed to deliver peak performance on recent devices that feature a GPU.

If you opt to utilize OpenBLAS, you'll need to install the corresponding package.

apt install libopenblas

Subsequently, if you decide to incorporate CLBlast, you'll first need to install the requisite OpenCL packages:

apt install ocl-icd opencl-headers opencl-clhpp clinfo

In order to compile CLBlast, you'll need to first clone the respective Git repository, which can be found at this URL: https://github.com/CNugteren/CLBlast. Alongside this, clone this repository into your home directory. Once this is done, navigate to the CLBlast folder and execute the commands detailed below:

cmake .
make
cp libclblast.so* $PREFIX/lib
cp ./include/clblast.h ../llama.cpp

Following the previous steps, navigate to the LlamaCpp directory. To compile it with OpenBLAS and CLBlast, execute the command provided below:

cp /data/data/com.termux/files/usr/include/openblas/cblas.h .
cp /data/data/com.termux/files/usr/include/openblas/openblas_config.h .
make LLAMA_CLBLAST=1 //(sometimes you need to run this command twice)

Upon completion of the aforementioned steps, you will have successfully compiled the project. To run it using CLBlast, a slight adjustment is required: a command must be issued to direct the operations towards your device's physical GPU, rather than the virtual one. The necessary command is detailed below:

GGML_OPENCL_PLATFORM=0
GGML_OPENCL_DEVICE=0
export LD_LIBRARY_PATH=/vendor/lib64:$LD_LIBRARY_PATH

(Note: some Android devices, like the Zenfone 8, need the following command instead - "export LD_LIBRARY_PATH=/system/vendor/lib64:$LD_LIBRARY_PATH". Source: https://www.reddit.com/r/termux/comments/kc3ynp/opencl_working_in_termux_more_in_comments/ )

For easy and swift re-execution, consider documenting this final part in a .sh script file. This will enable you to rerun the process with minimal hassle.

Place your desired model into the ~/llama.cpp/models/ directory and execute the ./main (...) script.

Docker

Prerequisites

  • Docker must be installed and running on your system.
  • Create a folder to store big models & intermediate files (ex. /llama/models)

Images

We have three Docker images available for this project:

  1. ghcr.io/ggerganov/llama.cpp:full: This image includes both the main executable file and the tools to convert LLaMA models into ggml and convert into 4-bit quantization. (platforms: linux/amd64, linux/arm64)
  2. ghcr.io/ggerganov/llama.cpp:light: This image only includes the main executable file. (platforms: linux/amd64, linux/arm64)
  3. ghcr.io/ggerganov/llama.cpp:server: This image only includes the server executable file. (platforms: linux/amd64, linux/arm64)

Additionally, there the following images, similar to the above:

  • ghcr.io/ggerganov/llama.cpp:full-cuda: Same as full but compiled with CUDA support. (platforms: linux/amd64)
  • ghcr.io/ggerganov/llama.cpp:light-cuda: Same as light but compiled with CUDA support. (platforms: linux/amd64)
  • ghcr.io/ggerganov/llama.cpp:server-cuda: Same as server but compiled with CUDA support. (platforms: linux/amd64)
  • ghcr.io/ggerganov/llama.cpp:full-rocm: Same as full but compiled with ROCm support. (platforms: linux/amd64, linux/arm64)
  • ghcr.io/ggerganov/llama.cpp:light-rocm: Same as light but compiled with ROCm support. (platforms: linux/amd64, linux/arm64)
  • ghcr.io/ggerganov/llama.cpp:server-rocm: Same as server but compiled with ROCm support. (platforms: linux/amd64, linux/arm64)

The GPU enabled images are not currently tested by CI beyond being built. They are not built with any variation from the ones in the Dockerfiles defined in .devops/ and the GitHub Action defined in .github/workflows/docker.yml. If you need different settings (for example, a different CUDA or ROCm library, you'll need to build the images locally for now).

Usage

The easiest way to download the models, convert them to ggml and optimize them is with the --all-in-one command which includes the full docker image.

Replace /path/to/models below with the actual path where you downloaded the models.

docker run -v /path/to/models:/models ghcr.io/ggerganov/llama.cpp:full --all-in-one "/models/" 7B

On completion, you are ready to play!

docker run -v /path/to/models:/models ghcr.io/ggerganov/llama.cpp:full --run -m /models/7B/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 512

or with a light image:

docker run -v /path/to/models:/models ghcr.io/ggerganov/llama.cpp:light -m /models/7B/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 512

or with a server image:

docker run -v /path/to/models:/models -p 8000:8000 ghcr.io/ggerganov/llama.cpp:server -m /models/7B/ggml-model-q4_0.gguf --port 8000 --host 0.0.0.0 -n 512

Docker With CUDA

Assuming one has the nvidia-container-toolkit properly installed on Linux, or is using a GPU enabled cloud, cuBLAS should be accessible inside the container.

Building Locally

docker build -t local/llama.cpp:full-cuda -f .devops/full-cuda.Dockerfile .
docker build -t local/llama.cpp:light-cuda -f .devops/main-cuda.Dockerfile .
docker build -t local/llama.cpp:server-cuda -f .devops/server-cuda.Dockerfile .

You may want to pass in some different ARGS, depending on the CUDA environment supported by your container host, as well as the GPU architecture.

The defaults are:

  • CUDA_VERSION set to 11.7.1
  • CUDA_DOCKER_ARCH set to all

The resulting images, are essentially the same as the non-CUDA images:

  1. local/llama.cpp:full-cuda: This image includes both the main executable file and the tools to convert LLaMA models into ggml and convert into 4-bit quantization.
  2. local/llama.cpp:light-cuda: This image only includes the main executable file.
  3. local/llama.cpp:server-cuda: This image only includes the server executable file.

Usage

After building locally, Usage is similar to the non-CUDA examples, but you'll need to add the --gpus flag. You will also want to use the --n-gpu-layers flag.

docker run --gpus all -v /path/to/models:/models local/llama.cpp:full-cuda --run -m /models/7B/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 512 --n-gpu-layers 1
docker run --gpus all -v /path/to/models:/models local/llama.cpp:light-cuda -m /models/7B/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 512 --n-gpu-layers 1
docker run --gpus all -v /path/to/models:/models local/llama.cpp:server-cuda -m /models/7B/ggml-model-q4_0.gguf --port 8000 --host 0.0.0.0 -n 512 --n-gpu-layers 1

Contributing

  • Contributors can open PRs
  • Collaborators can push to branches in the llama.cpp repo and merge PRs into the master branch
  • Collaborators will be invited based on contributions
  • Any help with managing issues and PRs is very appreciated!
  • Make sure to read this: Inference at the edge
  • A bit of backstory for those who are interested: Changelog podcast

Coding guidelines

  • Avoid adding third-party dependencies, extra files, extra headers, etc.
  • Always consider cross-compatibility with other operating systems and architectures
  • Avoid fancy looking modern STL constructs, use basic for loops, avoid templates, keep it simple
  • There are no strict rules for the code style, but try to follow the patterns in the code (indentation, spaces, etc.). Vertical alignment makes things more readable and easier to batch edit
  • Clean-up any trailing whitespaces, use 4 spaces for indentation, brackets on the same line, void * ptr, int & a
  • See good first issues for tasks suitable for first contributions
  • Tensors store data in row-major order. We refer to dimension 0 as columns, 1 as rows, 2 as matrices
  • Matrix multiplication is unconventional: z = ggml_mul_mat(ctx, x, y) means zT = x @ yT

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