Mirrors/llama.cpp
1
0
Fork 0
mirror of https://github.com/ggerganov/llama.cpp.git synced 2024-11-01 07:30:17 +01:00
Code Issues Packages Projects Releases Wiki Activity
f702a90e24
llama.cpp/examples/llava/MobileVLM-README.md
Olivier Chafik 1c641e6aac
build: rename main → llama-cli, server → llama-server, llava-cli → llama-llava-cli, etc... (#7809) 
* `main`/`server`: rename to `llama` / `llama-server` for consistency w/ homebrew

* server: update refs -> llama-server

gitignore llama-server

* server: simplify nix package

* main: update refs -> llama

fix examples/main ref

* main/server: fix targets

* update more names

* Update build.yml

* rm accidentally checked in bins

* update straggling refs

* Update .gitignore

* Update server-llm.sh

* main: target name -> llama-cli

* Prefix all example bins w/ llama-

* fix main refs

* rename {main->llama}-cmake-pkg binary

* prefix more cmake targets w/ llama-

* add/fix gbnf-validator subfolder to cmake

* sort cmake example subdirs

* rm bin files

* fix llama-lookup-* Makefile rules

* gitignore /llama-*

* rename Dockerfiles

* rename llama|main -> llama-cli; consistent RPM bin prefixes

* fix some missing -cli suffixes

* rename dockerfile w/ llama-cli

* rename(make): llama-baby-llama

* update dockerfile refs

* more llama-cli(.exe)

* fix test-eval-callback

* rename: llama-cli-cmake-pkg(.exe)

* address gbnf-validator unused fread warning (switched to C++ / ifstream)

* add two missing llama- prefixes

* Updating docs for eval-callback binary to use new `llama-` prefix.

* Updating a few lingering doc references for rename of main to llama-cli

* Updating `run-with-preset.py` to use new binary names.
Updating docs around `perplexity` binary rename.

* Updating documentation references for lookup-merge and export-lora

* Updating two small `main` references missed earlier in the finetune docs.

* Update apps.nix

* update grammar/README.md w/ new llama-* names

* update llama-rpc-server bin name + doc

* Revert "update llama-rpc-server bin name + doc"

This reverts commit e474ef1df4.

* add hot topic notice to README.md

* Update README.md

* Update README.md

* rename gguf-split & quantize bins refs in **/tests.sh

---------

Co-authored-by: HanClinto <hanclinto@gmail.com>
2024-06-13 00:41:52 +01:00

378 lines
18 KiB
Markdown
Raw Blame History

# MobileVLM
Currently this implementation supports [MobileVLM-1.7B](https://huggingface.co/mtgv/MobileVLM-1.7B) / [MobileVLM_V2-1.7B](https://huggingface.co/mtgv/MobileVLM_V2-1.7B) variants.
for more information, please go to [Meituan-AutoML/MobileVLM](https://github.com/Meituan-AutoML/MobileVLM)
The implementation is based on llava, and is compatible with llava and mobileVLM. The usage is basically same as llava.
Notice: The overall process of model inference for both **MobileVLM** and **MobileVLM_V2** models is the same, but the process of model conversion is a little different. Therefore, using **MobileVLM-1.7B** as an example, the different conversion step will be shown.
## Usage
Build with cmake or run `make llama-llava-cli` to build it.
After building, run: `./llama-llava-cli` to see the usage. For example:
```sh
./llama-llava-cli -m MobileVLM-1.7B/ggml-model-q4_k.gguf \
--mmproj MobileVLM-1.7B/mmproj-model-f16.gguf \
--image path/to/an/image.jpg \
-p "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: <image>\nWho is the author of this book? Answer the question using a single word or phrase. ASSISTANT:"
```
## Model conversion
1. Clone `mobileVLM-1.7B` and `clip-vit-large-patch14-336` locally:
```sh
git clone https://huggingface.co/mtgv/MobileVLM-1.7B
git clone https://huggingface.co/openai/clip-vit-large-patch14-336
```
2. Use `llava-surgery.py` to split the LLaVA model to LLaMA and multimodel projector constituents:
```sh
python ./examples/llava/llava-surgery.py -m path/to/MobileVLM-1.7B
```
3. Use `convert-image-encoder-to-gguf.py` with `--projector-type ldp` (for **V2** please use `--projector-type ldpv2`) to convert the LLaVA image encoder to GGUF:
```sh
python ./examples/llava/convert-image-encoder-to-gguf \
-m path/to/clip-vit-large-patch14-336 \
--llava-projector path/to/MobileVLM-1.7B/llava.projector \
--output-dir path/to/MobileVLM-1.7B \
--projector-type ldp
```
```sh
python ./examples/llava/convert-image-encoder-to-gguf \
-m path/to/clip-vit-large-patch14-336 \
--llava-projector path/to/MobileVLM-1.7B_V2/llava.projector \
--output-dir path/to/MobileVLM-1.7B_V2 \
--projector-type ldpv2
```
4. Use `examples/convert-legacy-llama.py` to convert the LLaMA part of LLaVA to GGUF:
```sh
python ./examples/convert-legacy-llama.py path/to/MobileVLM-1.7B
```
5. Use `quantize` to convert LLaMA part's DataType from `fp16` to `q4_k`
```sh
./llama-quantize path/to/MobileVLM-1.7B/ggml-model-f16.gguf path/to/MobileVLM-1.7B/ggml-model-q4_k.gguf q4_k_s
```
Now both the LLaMA part and the image encoder is in the `MobileVLM-1.7B` directory.
## Android compile and run
### compile
refer to `examples/llava/android/build_64.sh`
```sh
mkdir examples/llava/android/build_64
cd examples/llava/android/build_64
../build_64.sh
```
### run on Android
refer to `android/adb_run.sh`, modify resources' `name` and `path`
## Some result on Android with `Snapdragon 888` chip
### case 1
**input**
```sh
/data/local/tmp/llama-llava-cli \
-m /data/local/tmp/ggml-model-q4_k.gguf \
--mmproj /data/local/tmp/mmproj-model-f16.gguf \
-t 4 \
--image /data/local/tmp/demo.jpg \
-p "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: <image>\nWho is the author of this book? \nAnswer the question using a single word or phrase. ASSISTANT:"
```
**output**
```sh
encode_image_with_clip: image encoded in 21148.71 ms by CLIP ( 146.87 ms per image patch)
Susan Wise Bauer
llama_print_timings: load time = 23574.72 ms
llama_print_timings: sample time = 1.24 ms / 6 runs ( 0.21 ms per token, 4850.44 tokens per second)
llama_print_timings: prompt eval time = 12460.15 ms / 246 tokens ( 50.65 ms per token, 19.74 tokens per second)
llama_print_timings: eval time = 424.86 ms / 6 runs ( 70.81 ms per token, 14.12 tokens per second)
llama_print_timings: total time = 34731.93 ms
```
### case 2
**input**
```sh
/data/local/tmp/llama-llava-cli \
-m /data/local/tmp/ggml-model-q4_k.gguf \
--mmproj /data/local/tmp/mmproj-model-f16.gguf \
-t 4 \
--image /data/local/tmp/cat.jpeg \
-p "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: <image>\nWhat is in the image? ASSISTANT:"
```
**output**
```sh
encode_image_with_clip: image encoded in 21149.51 ms by CLIP ( 146.87 ms per image patch)
The image depicts a cat sitting in the grass near some tall green plants.
llama_print_timings: load time = 23257.32 ms
llama_print_timings: sample time = 5.25 ms / 18 runs ( 0.29 ms per token, 3430.53 tokens per second)
llama_print_timings: prompt eval time = 11900.73 ms / 232 tokens ( 51.30 ms per token, 19.49 tokens per second)
llama_print_timings: eval time = 1279.03 ms / 18 runs ( 71.06 ms per token, 14.07 tokens per second)
llama_print_timings: total time = 34570.79 ms
```
## Some result on Android with `Snapdragon 778G` chip
### MobileVLM-1.7B case
#### llava-cli release-b2005
**input**
```sh
/data/local/tmp/llama-llava-cli \
-m /data/local/tmp/ggml-model-q4_k.gguf \
--mmproj /data/local/tmp/mmproj-model-f16.gguf \
-t 4 \
--image /data/local/tmp/many_llamas.jpeg \
-p "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: <image>\nWhat's that? ASSISTANT:"
```
**output**
```sh
encode_image_with_clip: image encoded in 18728.52 ms by CLIP ( 130.06 ms per image patch)
system_prompt: A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER:
user_prompt: \nWhat's that? ASSISTANT:
A group of llamas are standing in a green pasture.
llama_print_timings: load time = 20357.33 ms
llama_print_timings: sample time = 2.96 ms / 14 runs ( 0.21 ms per token, 4734.53 tokens per second)
llama_print_timings: prompt eval time = 8119.49 ms / 191 tokens ( 42.51 ms per token, 23.52 tokens per second)
llama_print_timings: eval time = 1005.75 ms / 14 runs ( 71.84 ms per token, 13.92 tokens per second)
llama_print_timings: total time = 28038.34 ms / 205 tokens
```
#### llava-cli latest-version
**input**
Just the same as above.
**output**(seems to be much slower)
```sh
encode_image_with_clip: image embedding created: 144 tokens
encode_image_with_clip: image encoded in 288268.88 ms by CLIP ( 2001.87 ms per image patch)
system_prompt: A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER:
user_prompt: \nWhat's that? ASSISTANT:
It is a group of sheep standing together in a grass field.
llama_print_timings: load time = 818120.91 ms
llama_print_timings: sample time = 3.44 ms / 14 runs ( 0.25 ms per token, 4067.40 tokens per second)
llama_print_timings: prompt eval time = 529274.69 ms / 191 tokens ( 2771.07 ms per token, 0.36 tokens per second)
llama_print_timings: eval time = 43894.02 ms / 13 runs ( 3376.46 ms per token, 0.30 tokens per second)
llama_print_timings: total time = 865441.76 ms / 204 tokens
```
### MobileVLM_V2-1.7B case
#### llava-cli release-2005b
**input**
Just the same as above.
**output**
```sh
encode_image_with_clip: image encoded in 20609.61 ms by CLIP ( 143.12 ms per image patch)
system_prompt: A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER:
user_prompt: \nWhat's that? ASSISTANT:
This image captures a lively scene of 20 llamas in motion on an expansive, grassy field. The llama is scattered across the landscape with some standing and others sitting down as if taking rest or observing their surroundings from different vantage points within this verdant setting.
The background offers glimpses into a picturesque town nestled amidst hills under an overcast sky, adding depth to the scene while also emphasizing that distance between these llama and human-made structures like houses or roads in which they roam freely without any barriers around them. The image is framed by text at both right angles on white backgrounds against a contrasting blue backdrop with green foliage, further drawing attention to the llamas amidst their natural habitat while also inviting viewers into this picturesque landscape within town limits of Alta Llama
llama_print_timings: load time = 22406.77 ms
llama_print_timings: sample time = 49.26 ms / 186 runs ( 0.26 ms per token, 3776.27 tokens per second)
llama_print_timings: prompt eval time = 9044.54 ms / 191 tokens ( 47.35 ms per token, 21.12 tokens per second)
llama_print_timings: eval time = 14497.49 ms / 186 runs ( 77.94 ms per token, 12.83 tokens per second)
llama_print_timings: total time = 44411.01 ms / 377 tokens
```
## Orin compile and run
### compile
```sh
make LLAMA_CUDA=1 CUDA_DOCKER_ARCH=sm_87 LLAMA_CUDA_F16=1 -j 32
```
### run on Orin
### case 1
**input**
```sh
./llama-llava-cli \
-m /data/local/tmp/ggml-model-q4_k.gguf \
--mmproj /data/local/tmp/mmproj-model-f16.gguf \
--image /data/local/tmp/demo.jpeg \
-p "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: <image>\nWho is the author of this book? \nAnswer the question using a single word or phrase. ASSISTANT:" \
--n-gpu-layers 999
```
**output**
```sh
encode_image_with_clip: image encoded in 296.62 ms by CLIP ( 2.06 ms per image patch)
Susan Wise Bauer
llama_print_timings: load time = 1067.64 ms
llama_print_timings: sample time = 1.53 ms / 6 runs ( 0.25 ms per token, 3934.43 tokens per second)
llama_print_timings: prompt eval time = 306.84 ms / 246 tokens ( 1.25 ms per token, 801.72 tokens per second)
llama_print_timings: eval time = 91.50 ms / 6 runs ( 15.25 ms per token, 65.58 tokens per second)
llama_print_timings: total time = 1352.63 ms / 252 tokens
```
### case 2
**input**
```sh
./llama-llava-cli \
-m /data/local/tmp/ggml-model-q4_k.gguf \
--mmproj /data/local/tmp/mmproj-model-f16.gguf \
-p "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: <image>\nWhat is in the image? ASSISTANT:" \
--n-gpu-layers 999
```
**output**
```sh
encode_image_with_clip: image encoded in 302.15 ms by CLIP ( 2.10 ms per image patch)
The image features a cat lying in the grass.
llama_print_timings: load time = 1057.07 ms
llama_print_timings: sample time = 3.27 ms / 11 runs ( 0.30 ms per token, 3360.83 tokens per second)
llama_print_timings: prompt eval time = 213.60 ms / 232 tokens ( 0.92 ms per token, 1086.14 tokens per second)
llama_print_timings: eval time = 166.65 ms / 11 runs ( 15.15 ms per token, 66.01 tokens per second)
llama_print_timings: total time = 1365.47 ms / 243 tokens
```
## Running on Intel(R) Core(TM) i7-10750H
### Operating system
Ubuntu22.04
### compile
```sh
make -j32
```
### MobileVLM-1.7B case
**input**
```sh
-m /path/to/ggml-model-q4_k.gguf \
--mmproj /path/to/mmproj-model-f16.gguf \
--image /path/to/many_llamas.jpeg
-p "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: <image>\nWhat's that? ASSISTANT:" \
```
**output**
```sh
encode_image_with_clip: image embedding created: 144 tokens
encode_image_with_clip: image encoded in 2730.94 ms by CLIP ( 18.96 ms per image patch)
system_prompt: A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER:
user_prompt: \nWhat's that?ASSISTANT:
A group of llamas are walking together in a field.
llama_print_timings: load time = 5506.60 ms
llama_print_timings: sample time = 0.44 ms / 13 runs ( 0.03 ms per token, 29545.45 tokens per second)
llama_print_timings: prompt eval time = 2031.58 ms / 190 tokens ( 10.69 ms per token, 93.52 tokens per second)
llama_print_timings: eval time = 438.92 ms / 12 runs ( 36.58 ms per token, 27.34 tokens per second)
llama_print_timings: total time = 5990.25 ms / 202 tokens
```
### MobileVLM_V2-1.7B case
**input**
Just the same as above.
**ouput**
```sh
encode_image_with_clip: image embedding created: 144 tokens
encode_image_with_clip: image encoded in 3223.89 ms by CLIP ( 22.39 ms per image patch)
system_prompt: A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER:
user_prompt: \nWhat's that?ASSISTANT:
The image captures a tranquil scene in a park, where a group of approximately 20 llamas are gathered. The llamas, a mix of white and black, are standing in a line, their black and white patterns contrasting with the lush green grass of the park. The lamas are arranged in a line, suggesting a social order.
The park itself is lush and green, with trees dotting the landscape in the background. A sign reading "Llamas Tico Ana" is also visible in the image, possibly indicating the location or the breed of the llamas. The image seems to be taken from a distance, providing a wide view of the scene and the surrounding environment.
The llamas' positions relative to each other, the sign, and the trees create a harmonious composition. The image does not contain any discernible text. The overall scene is one of peace and natural beauty, with the llamas in their natural habitat, surrounded by the vibrant colors and lush greenery of the park.
llama_print_timings: load time = 6642.61 ms
llama_print_timings: sample time = 8.15 ms / 223 runs ( 0.04 ms per token, 27358.61 tokens per second)
llama_print_timings: prompt eval time = 2475.07 ms / 190 tokens ( 13.03 ms per token, 76.77 tokens per second)
llama_print_timings: eval time = 8760.60 ms / 222 runs ( 39.46 ms per token, 25.34 tokens per second)
llama_print_timings: total time = 15513.95 ms / 412 tokens
```
## Run on Intel(R) Core(TM) Ultra7 115H
### operation system
Windows11
### comiple
```sh
make -j32
```
### MobileVLM-1.7B case
**input**
```sh
-m /path/to/ggml-model-q4_k.gguf \
--mmproj /path/to/tmp/mmproj-model-f16.gguf \
-p "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: <image>\nWhat's that? ASSISTANT:" \
```
**output**
```sh
encode_image_with_clip: image encoded in 4902.81 ms by CLIP ( 34.05 ms per image patch)
system_prompt: A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER:
user_prompt: \nWhat's that? ASSISTANT:
The image features a group of brown and white llamas standing in a grassy field.
llama_print_timings: load time = 7441.06 ms
llama_print_timings: sample time = 0.72 ms / 19 runs ( 0.04 ms per token, 26279.39 tokens per second)
llama_print_timings: prompt eval time = 2090.71 ms / 191 tokens ( 10.95 ms per token, 91.36 tokens per second)
llama_print_timings: eval time = 512.35 ms / 18 runs ( 28.46 ms per token, 35.13 tokens per second)
llama_print_timings: total time = 7987.23 ms / 209 tokens
```
### MobileVLM_V2-1.7B case
**input**
Just the same as above.
**output**
```sh
encode_image_with_clip: image encoded in 4682.44 ms by CLIP ( 32.52 ms per image patch)
system_prompt: A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER:
user_prompt: \nWhat's that? ASSISTANT:
This image captures a lively scene of a group of 14 llamas in a grassy field. The llamas, with their distinctive black and white coats, are standing and walking in a line, seemingly engaged in a social activity. One
of them, possibly the first in the line, has its back turned, perhaps observing something in the distance.
The llama in the front of the line stands out due to its black and white coloring, which is quite unusual for llama patterns. The llama in the front also seems to be more aware of its surroundings, as it faces the camera, giving a sense of engagement with the viewer.
The image is taken from the side of the llama, providing a clear view of the llama in the front and its companions. The lameness in the llama in
front is not visible, indicating that it might not be the main focus of the photo.
The background of the image features a grassy field, with a fence and a tree visible in the distance. The tree appears to be bare, suggesting that it might be during a time of year when most trees are dormant or have shed their leaves.
llama_print_timings: load time = 7015.35 ms
llama_print_timings: sample time = 10.61 ms / 256 runs ( 0.04 ms per token, 24119.09 tokens per second)
llama_print_timings: prompt eval time = 2052.45 ms / 191 tokens ( 10.75 ms per token, 93.06 tokens per second)
llama_print_timings: eval time = 7259.43 ms / 255 runs ( 28.47 ms per token, 35.13 tokens per second)
llama_print_timings: total time = 14371.19 ms / 446 tokens
```
## TODO
- [x] Support non-CPU backend for the new operators, such as `depthwise`, `hardswish`, `hardsigmoid`
- [ ] Optimize LDP projector performance
- Optimize the structure definition to avoid unnecessary memory rearrangements, to reduce the use of `ggml_permute_cpy`;
- Optimize operator implementation (ARM CPU/NVIDIA GPU): such as depthwise conv, hardswish, hardsigmoid, etc.
- [x] run MobileVLM on `Jetson Orin`
- [ ] Support more model variants, such as `MobileVLM-3B`.
## contributor
```sh
zhangjidong05, yangyang260, huyiming03, chenxiaotao03, ZiangWu-77
```
Reference in New Issue View Git Blame Copy Permalink