llama.cpp/examples/llava/MobileVLM-README.md

MobileVLM

Currently this implementation supports MobileVLM-1.7B / MobileVLM_V2-1.7B variants.

for more information, please go to 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 as an example, the different conversion step will be shown.

Usage

Build with cmake or run make llava-cli to build it.

After building, run: ./llava-cli to see the usage. For example:

./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

• Clone mobileVLM-1.7B and clip-vit-large-patch14-336 locally:

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:

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 the arg is --projector-type ldpv2) to convert the LLaVA image encoder to GGUF:

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

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 convert.py to convert the LLaMA part of LLaVA to GGUF:

python ./convert.py path/to/MobileVLM-1.7B

5. Use quantize to convert LLaMA part's DataType from fp16 to q4_k

./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

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

/data/local/tmp/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

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

/data/local/tmp/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

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

Orin compile and run

compile

make LLAMA_CUDA=1 CUDA_DOCKER_ARCH=sm_87 LLAMA_CUDA_F16=1 -j 32

run on Orin

case 1

input

./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

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

./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

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

Minor shortcomings

The n_patch of output in ldp is 1/4 of the input. In order to implement quickly, we uniformly modified clip_n_patches function to a quarter. when counting the time consumption, the calculated time will be 4 times bigger than the real cost.

TODO

• 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.
  

• run MobileVLM on Jetson Orin

• Support more model variants, such as MobileVLM-3B.

contributor

zhangjidong05, yangyang260, huyiming03, chenxiaotao03