* Add MobileVLM_V2 backup * Update MobileVLM-README.md * Update examples/llava/MobileVLM-README.md Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * Update examples/llava/convert-image-encoder-to-gguf.py Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * clip : fix whitespace * fix deifinition mistake in clip.cpp --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
8.1 KiB
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 MobilVLM and MobilVLM_V2 models is the same, but the process of model conversion is a little different. Therefore, using MobiVLM 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
andclip-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
- 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
- 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
- Use
convert.py
to convert the LLaMA part of LLaVA to GGUF:
python ./convert.py path/to/MobileVLM-1.7B
- Use
quantize
to convert LLaMA part's DataType fromfp16
toq4_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_CUBLAS=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