Brunobkr/llama.cpp_AlgMor24_github

1# MobileVLM2 3Currently 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.4 5for more information, please go to [Meituan-AutoML/MobileVLM](https://github.com/Meituan-AutoML/MobileVLM)6 7The implementation is based on llava, and is compatible with llava and mobileVLM. The usage is basically same as llava.8 9Notice: 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.10 11## Usage12 13Build the `llama-mtmd-cli` binary.14 15After building, run: `./llama-mtmd-cli` to see the usage. For example:16 17```sh18./llama-mtmd-cli -m MobileVLM-1.7B/ggml-model-q4_k.gguf \19    --mmproj MobileVLM-1.7B/mmproj-model-f16.gguf \20    --chat-template deepseek21```22 23## Model conversion24 251. Clone `mobileVLM-1.7B` and `clip-vit-large-patch14-336` locally:26 27```sh28git clone https://huggingface.co/mtgv/MobileVLM-1.7B29 30git clone https://huggingface.co/openai/clip-vit-large-patch14-33631```32 332. Use `llava_surgery.py` to split the LLaVA model to LLaMA and multimodel projector constituents:34 35```sh36python ./tools/mtmd/llava_surgery.py -m path/to/MobileVLM-1.7B37```38 393. 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:40 41```sh42python ./tools/mtmd/convert_image_encoder_to_gguf.py \43    -m path/to/clip-vit-large-patch14-336 \44    --llava-projector path/to/MobileVLM-1.7B/llava.projector \45    --output-dir path/to/MobileVLM-1.7B \46    --projector-type ldp47```48 49```sh50python ./tools/mtmd/convert_image_encoder_to_gguf.py \51    -m path/to/clip-vit-large-patch14-336 \52    --llava-projector path/to/MobileVLM-1.7B_V2/llava.projector \53    --output-dir path/to/MobileVLM-1.7B_V2 \54    --projector-type ldpv255```56 574. Use `examples/convert_legacy_llama.py` to convert the LLaMA part of LLaVA to GGUF:58 59```sh60python ./examples/convert_legacy_llama.py path/to/MobileVLM-1.7B --skip-unknown61```62 635. Use `quantize` to convert LLaMA part's DataType from `fp32` to `q4_k`64```sh65./llama-quantize path/to/MobileVLM-1.7B/ggml-model-F32.gguf path/to/MobileVLM-1.7B/ggml-model-q4_k.gguf q4_k_s66```67 68Now both the LLaMA part and the image encoder is in the `MobileVLM-1.7B` directory.69 70## Android compile and run71### compile72refer to `tools/mtmd/android/build_64.sh`73```sh74mkdir tools/mtmd/android/build_6475cd tools/mtmd/android/build_6476../build_64.sh77```78### run on Android79refer to `android/adb_run.sh`, modify resources' `name` and `path`80 81## Some result on Android with `Snapdragon 888` chip82### case 183**input**84```sh85/data/local/tmp/llama-mtmd-cli \86    -m /data/local/tmp/ggml-model-q4_k.gguf \87    --mmproj /data/local/tmp/mmproj-model-f16.gguf \88    -t 4 \89    --image /data/local/tmp/demo.jpg \90    -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:"91```92**output**93```sh94encode_image_with_clip: image encoded in 21148.71 ms by CLIP (  146.87 ms per image patch)95 Susan Wise Bauer96llama_print_timings:        load time =   23574.72 ms97llama_print_timings:      sample time =       1.24 ms /     6 runs   (    0.21 ms per token,  4850.44 tokens per second)98llama_print_timings: prompt eval time =   12460.15 ms /   246 tokens (   50.65 ms per token,    19.74 tokens per second)99llama_print_timings:        eval time =     424.86 ms /     6 runs   (   70.81 ms per token,    14.12 tokens per second)100llama_print_timings:       total time =   34731.93 ms101```102### case 2103**input**104```sh105/data/local/tmp/llama-mtmd-cli \106    -m /data/local/tmp/ggml-model-q4_k.gguf \107    --mmproj /data/local/tmp/mmproj-model-f16.gguf \108    -t 4 \109    --image /data/local/tmp/cat.jpeg \110    -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:"111```112**output**113```sh114encode_image_with_clip: image encoded in 21149.51 ms by CLIP (  146.87 ms per image patch)115 The image depicts a cat sitting in the grass near some tall green plants.116llama_print_timings:        load time =   23257.32 ms117llama_print_timings:      sample time =       5.25 ms /    18 runs   (    0.29 ms per token,  3430.53 tokens per second)118llama_print_timings: prompt eval time =   11900.73 ms /   232 tokens (   51.30 ms per token,    19.49 tokens per second)119llama_print_timings:        eval time =    1279.03 ms /    18 runs   (   71.06 ms per token,    14.07 tokens per second)120llama_print_timings:       total time =   34570.79 ms121```122 123 124## Some result on Android with `Snapdragon 778G` chip125### MobileVLM-1.7B case126#### mtmd-cli release-b2005127**input**128```sh129/data/local/tmp/llama-mtmd-cli \130    -m /data/local/tmp/ggml-model-q4_k.gguf \131    --mmproj /data/local/tmp/mmproj-model-f16.gguf \132    -t 4 \133    --image /data/local/tmp/many_llamas.jpeg \134    -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:"135```136**output**137```sh138encode_image_with_clip: image encoded in 18728.52 ms by CLIP (  130.06 ms per image patch)139system_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:140user_prompt: \nWhat's that? ASSISTANT:141 142 A group of llamas are standing in a green pasture.143 144llama_print_timings:        load time =   20357.33 ms145llama_print_timings:      sample time =       2.96 ms /    14 runs   (    0.21 ms per token,  4734.53 tokens per second)146llama_print_timings: prompt eval time =    8119.49 ms /   191 tokens (   42.51 ms per token,    23.52 tokens per second)147llama_print_timings:        eval time =    1005.75 ms /    14 runs   (   71.84 ms per token,    13.92 tokens per second)148llama_print_timings:       total time =   28038.34 ms /   205 tokens149```150#### mtmd-cli latest-version151**input**152 153Just the same as above.154 155**output**(seems to be much slower)156```sh157encode_image_with_clip: image embedding created: 144 tokens158 159encode_image_with_clip: image encoded in 288268.88 ms by CLIP ( 2001.87 ms per image patch)160system_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:161user_prompt: \nWhat's that? ASSISTANT:162 163 It is a group of sheep standing together in a grass field.164 165llama_print_timings:        load time =  818120.91 ms166llama_print_timings:      sample time =       3.44 ms /    14 runs   (    0.25 ms per token,  4067.40 tokens per second)167llama_print_timings: prompt eval time =  529274.69 ms /   191 tokens ( 2771.07 ms per token,     0.36 tokens per second)168llama_print_timings:        eval time =   43894.02 ms /    13 runs   ( 3376.46 ms per token,     0.30 tokens per second)169llama_print_timings:       total time =  865441.76 ms /   204 tokens170```171### MobileVLM_V2-1.7B case172#### mtmd-cli release-2005b173**input**174 175Just the same as above.176 177**output**178```sh179encode_image_with_clip: image encoded in 20609.61 ms by CLIP (  143.12 ms per image patch)180system_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:181user_prompt: \nWhat's that? ASSISTANT:182 183 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.184 185The 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 Llama186 187llama_print_timings:        load time =   22406.77 ms188llama_print_timings:      sample time =      49.26 ms /   186 runs   (    0.26 ms per token,  3776.27 tokens per second)189llama_print_timings: prompt eval time =    9044.54 ms /   191 tokens (   47.35 ms per token,    21.12 tokens per second)190llama_print_timings:        eval time =   14497.49 ms /   186 runs   (   77.94 ms per token,    12.83 tokens per second)191llama_print_timings:       total time =   44411.01 ms /   377 tokens192```193 194## Orin compile and run195### compile196```sh197make GGML_CUDA=1 CUDA_DOCKER_ARCH=sm_87 -j 32198```199### run on Orin200### case 1201**input**202```sh203./llama-mtmd-cli \204    -m /data/local/tmp/ggml-model-q4_k.gguf \205    --mmproj /data/local/tmp/mmproj-model-f16.gguf \206    --image /data/local/tmp/demo.jpeg \207    -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:" \208    --n-gpu-layers 999209```210**output**211```sh212 213encode_image_with_clip: image encoded in   296.62 ms by CLIP (    2.06 ms per image patch)214 215 Susan Wise Bauer216 217llama_print_timings:        load time =    1067.64 ms218llama_print_timings:      sample time =       1.53 ms /     6 runs   (    0.25 ms per token,  3934.43 tokens per second)219llama_print_timings: prompt eval time =     306.84 ms /   246 tokens (    1.25 ms per token,   801.72 tokens per second)220llama_print_timings:        eval time =      91.50 ms /     6 runs   (   15.25 ms per token,    65.58 tokens per second)221llama_print_timings:       total time =    1352.63 ms /   252 tokens222```223 224### case 2225**input**226```sh227./llama-mtmd-cli \228    -m /data/local/tmp/ggml-model-q4_k.gguf \229    --mmproj /data/local/tmp/mmproj-model-f16.gguf \230    -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:" \231    --n-gpu-layers 999232 233```234**output**235```sh236encode_image_with_clip: image encoded in   302.15 ms by CLIP (    2.10 ms per image patch)237 238 The image features a cat lying in the grass.239 240llama_print_timings:        load time =    1057.07 ms241llama_print_timings:      sample time =       3.27 ms /    11 runs   (    0.30 ms per token,  3360.83 tokens per second)242llama_print_timings: prompt eval time =     213.60 ms /   232 tokens (    0.92 ms per token,  1086.14 tokens per second)243llama_print_timings:        eval time =     166.65 ms /    11 runs   (   15.15 ms per token,    66.01 tokens per second)244llama_print_timings:       total time =    1365.47 ms /   243 tokens245```246 247## Running on Intel(R) Core(TM) i7-10750H248### Operating system249Ubuntu22.04250### compile251```sh252make -j32253```254### MobileVLM-1.7B case255**input**256```sh257-m /path/to/ggml-model-q4_k.gguf \258    --mmproj /path/to/mmproj-model-f16.gguf \259    --image /path/to/many_llamas.jpeg260    -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:" \261```262**output**263```sh264encode_image_with_clip: image embedding created: 144 tokens265 266encode_image_with_clip: image encoded in  2730.94 ms by CLIP (   18.96 ms per image patch)267system_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:268user_prompt: \nWhat's that?ASSISTANT:269 270 A group of llamas are walking together in a field.271 272llama_print_timings:        load time =    5506.60 ms273llama_print_timings:      sample time =       0.44 ms /    13 runs   (    0.03 ms per token, 29545.45 tokens per second)274llama_print_timings: prompt eval time =    2031.58 ms /   190 tokens (   10.69 ms per token,    93.52 tokens per second)275llama_print_timings:        eval time =     438.92 ms /    12 runs   (   36.58 ms per token,    27.34 tokens per second)276llama_print_timings:       total time =    5990.25 ms /   202 tokens277```278 279### MobileVLM_V2-1.7B case280**input**281 282Just the same as above.283 284**output**285```sh286encode_image_with_clip: image embedding created: 144 tokens287 288encode_image_with_clip: image encoded in  3223.89 ms by CLIP (   22.39 ms per image patch)289system_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:290user_prompt: \nWhat's that?ASSISTANT:291 292 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.293 294The 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.295 296The 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.297 298llama_print_timings:        load time =    6642.61 ms299llama_print_timings:      sample time =       8.15 ms /   223 runs   (    0.04 ms per token, 27358.61 tokens per second)300llama_print_timings: prompt eval time =    2475.07 ms /   190 tokens (   13.03 ms per token,    76.77 tokens per second)301llama_print_timings:        eval time =    8760.60 ms /   222 runs   (   39.46 ms per token,    25.34 tokens per second)302llama_print_timings:       total time =   15513.95 ms /   412 tokens303```304 305## Run on Intel(R) Core(TM) Ultra7 115H306### operation system307Windows11308### compile309```sh310make -j32311```312### MobileVLM-1.7B case313**input**314```sh315-m /path/to/ggml-model-q4_k.gguf \316    --mmproj /path/to/tmp/mmproj-model-f16.gguf \317    -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:" \318```319**output**320```sh321encode_image_with_clip: image encoded in  4902.81 ms by CLIP (   34.05 ms per image patch)322system_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:323user_prompt: \nWhat's that? ASSISTANT:324 325 The image features a group of brown and white llamas standing in a grassy field.326 327llama_print_timings:        load time =    7441.06 ms328llama_print_timings:      sample time =       0.72 ms /    19 runs   (    0.04 ms per token, 26279.39 tokens per second)329llama_print_timings: prompt eval time =    2090.71 ms /   191 tokens (   10.95 ms per token,    91.36 tokens per second)330llama_print_timings:        eval time =     512.35 ms /    18 runs   (   28.46 ms per token,    35.13 tokens per second)331llama_print_timings:       total time =    7987.23 ms /   209 tokens332```333 334### MobileVLM_V2-1.7B case335**input**336 337Just the same as above.338 339**output**340```sh341encode_image_with_clip: image encoded in  4682.44 ms by CLIP (   32.52 ms per image patch)342system_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:343user_prompt: \nWhat's that? ASSISTANT:344 345 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. One346 of them, possibly the first in the line, has its back turned, perhaps observing something in the distance.347 348The 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.349 350The 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 in351 front is not visible, indicating that it might not be the main focus of the photo.352 353The 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.354 355 356llama_print_timings:        load time =    7015.35 ms357llama_print_timings:      sample time =      10.61 ms /   256 runs   (    0.04 ms per token, 24119.09 tokens per second)358llama_print_timings: prompt eval time =    2052.45 ms /   191 tokens (   10.75 ms per token,    93.06 tokens per second)359llama_print_timings:        eval time =    7259.43 ms /   255 runs   (   28.47 ms per token,    35.13 tokens per second)360llama_print_timings:       total time =   14371.19 ms /   446 tokens361```362 363## TODO364 365- [x] Support non-CPU backend for the new operators, such as `depthwise`, `hardswish`, `hardsigmoid`366- [ ] Optimize LDP projector performance367 368      - Optimize the structure definition to avoid unnecessary memory rearrangements, to reduce the use of `ggml_permute_cpy`;369      - Optimize operator implementation (ARM CPU/NVIDIA GPU): such as depthwise conv, hardswish, hardsigmoid, etc.370- [x] run MobileVLM on `Jetson Orin`371- [ ] Support more model variants, such as `MobileVLM-3B`.372 373 374## contributor375```sh376zhangjidong05, yangyang260, huyiming03, chenxiaotao03, ZiangWu-77377```378