mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-12-27 22:59:24 +01:00
15606309a0
* New Feature:
1. Sum_Rows:
fix cuda kernel overflow
fix block shape error when nrows too big
2. Im2Col:
Support Batch in cuda
Support f32 to f32 both in cpu && cuda
3. DepthWiseConv:
Support by Im2Col && MulMat
4. Pool_2d:
Supoort avg pooling in cuda
5. HardSigmoid:
Imp in cuda
6. HardSwish:
Imp in cuda
* fix tabs instead of spaces
* code clean
* CUDA POOL2D
* ADD POOL2D test case in test-backend-ops.cpp
* code clean
* fix pool2d_kernel
nits
* fix bug in pool2d kernel
* fix avg pooling, count_include_pad
nits
* test-backend-ops : add more pool_2d tests
* cuda : fix warnings and formatting
* ggml : check types in release builds too in pool_2d
* test-backend-ops : remove f16 pool_2d tests
* cuda : more style fixes
* Add assert in ggml_cuda_op_pool2d
* pool2d float padding fallback
* test-backend-ops : add dst_type to im2col
---------
Co-authored-by: slaren <slarengh@gmail.com>
186 lines
7.5 KiB
Markdown
186 lines
7.5 KiB
Markdown
# MobileVLM
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Currently this implementation supports [MobileVLM-v1.7](https://huggingface.co/mtgv/MobileVLM-1.7B) variants.
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for more information, please go to [Meituan-AutoML/MobileVLM](https://github.com/Meituan-AutoML/MobileVLM)
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The implementation is based on llava, and is compatible with llava and mobileVLM. The usage is basically same as llava.
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## Usage
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Build with cmake or run `make llava-cli` to build it.
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After building, run: `./llava-cli` to see the usage. For example:
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```sh
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./llava-cli -m MobileVLM-1.7B/ggml-model-q4_k.gguf \
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--mmproj MobileVLM-1.7B/mmproj-model-f16.gguf \
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--image path/to/an/image.jpg \
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-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:"
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```
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## Model conversion
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- Clone `mobileVLM-1.7B` and `clip-vit-large-patch14-336` locally:
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```sh
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git clone https://huggingface.co/mtgv/MobileVLM-1.7B
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git clone https://huggingface.co/openai/clip-vit-large-patch14-336
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```
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2. Use `llava-surgery.py` to split the LLaVA model to LLaMA and multimodel projector constituents:
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```sh
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python ./examples/llava/llava-surgery.py -m path/to/MobileVLM-1.7B
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```
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3. Use `convert-image-encoder-to-gguf.py` with `--projector-type ldp` to convert the LLaVA image encoder to GGUF:
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```sh
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python ./examples/llava/convert-image-encoder-to-gguf \
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-m path/to/clip-vit-large-patch14-336 \
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--llava-projector path/to/MobileVLM-1.7B/llava.projector \
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--output-dir path/to/MobileVLM-1.7B \
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--projector-type ldp
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```
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4. Use `convert.py` to convert the LLaMA part of LLaVA to GGUF:
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```sh
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python ./convert.py path/to/MobileVLM-1.7B
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```
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5. Use `quantize` to convert LLaMA part's DataType from `fp16` to `q4_k`
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```sh
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./quantize path/to/MobileVLM-1.7B/ggml-model-f16.gguf path/to/MobileVLM-1.7B/ggml-model-q4_k.gguf q4_k_s
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```
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Now both the LLaMA part and the image encoder is in the `MobileVLM-1.7B` directory.
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## Android compile and run
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### compile
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refer to `examples/llava/android/build_64.sh`
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```sh
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mkdir examples/llava/android/build_64
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cd examples/llava/android/build_64
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../build_64.sh
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```
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### run on Android
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refer to `android/adb_run.sh`, modify resources' `name` and `path`
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## some result on Android with `Snapdragon 888` chip
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### case 1
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**input**
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```sh
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/data/local/tmp/llava-cli \
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-m /data/local/tmp/ggml-model-q4_k.gguf \
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--mmproj /data/local/tmp/mmproj-model-f16.gguf \
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-t 4 \
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--image /data/local/tmp/demo.jpg \
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-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:"
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```
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**output**
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```sh
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encode_image_with_clip: image encoded in 21148.71 ms by CLIP ( 146.87 ms per image patch)
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Susan Wise Bauer
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llama_print_timings: load time = 23574.72 ms
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llama_print_timings: sample time = 1.24 ms / 6 runs ( 0.21 ms per token, 4850.44 tokens per second)
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llama_print_timings: prompt eval time = 12460.15 ms / 246 tokens ( 50.65 ms per token, 19.74 tokens per second)
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llama_print_timings: eval time = 424.86 ms / 6 runs ( 70.81 ms per token, 14.12 tokens per second)
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llama_print_timings: total time = 34731.93 ms
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```
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### case 2
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**input**
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```sh
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/data/local/tmp/llava-cli \
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-m /data/local/tmp/ggml-model-q4_k.gguf \
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--mmproj /data/local/tmp/mmproj-model-f16.gguf \
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-t 4 \
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--image /data/local/tmp/cat.jpeg \
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-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:"
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```
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**output**
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```sh
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encode_image_with_clip: image encoded in 21149.51 ms by CLIP ( 146.87 ms per image patch)
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The image depicts a cat sitting in the grass near some tall green plants.
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llama_print_timings: load time = 23257.32 ms
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llama_print_timings: sample time = 5.25 ms / 18 runs ( 0.29 ms per token, 3430.53 tokens per second)
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llama_print_timings: prompt eval time = 11900.73 ms / 232 tokens ( 51.30 ms per token, 19.49 tokens per second)
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llama_print_timings: eval time = 1279.03 ms / 18 runs ( 71.06 ms per token, 14.07 tokens per second)
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llama_print_timings: total time = 34570.79 ms
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```
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## Orin compile and run
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### compile
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```sh
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make LLAMA_CUBLAS=1 CUDA_DOCKER_ARCH=sm_87 LLAMA_CUDA_F16=1 -j 32
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```
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### run on Orin
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### case 1
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**input**
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```sh
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./llava-cli \
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-m /data/local/tmp/ggml-model-q4_k.gguf \
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--mmproj /data/local/tmp/mmproj-model-f16.gguf \
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--image /data/local/tmp/demo.jpeg \
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-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:" \
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--n-gpu-layers 999
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```
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**output**
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```sh
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encode_image_with_clip: image encoded in 296.62 ms by CLIP ( 2.06 ms per image patch)
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Susan Wise Bauer
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llama_print_timings: load time = 1067.64 ms
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llama_print_timings: sample time = 1.53 ms / 6 runs ( 0.25 ms per token, 3934.43 tokens per second)
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llama_print_timings: prompt eval time = 306.84 ms / 246 tokens ( 1.25 ms per token, 801.72 tokens per second)
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llama_print_timings: eval time = 91.50 ms / 6 runs ( 15.25 ms per token, 65.58 tokens per second)
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llama_print_timings: total time = 1352.63 ms / 252 tokens
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```
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### case 2
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**input**
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```sh
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./llava-cli \
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-m /data/local/tmp/ggml-model-q4_k.gguf \
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--mmproj /data/local/tmp/mmproj-model-f16.gguf \
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-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:" \
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--n-gpu-layers 999
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```
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**output**
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```sh
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encode_image_with_clip: image encoded in 302.15 ms by CLIP ( 2.10 ms per image patch)
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The image features a cat lying in the grass.
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llama_print_timings: load time = 1057.07 ms
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llama_print_timings: sample time = 3.27 ms / 11 runs ( 0.30 ms per token, 3360.83 tokens per second)
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llama_print_timings: prompt eval time = 213.60 ms / 232 tokens ( 0.92 ms per token, 1086.14 tokens per second)
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llama_print_timings: eval time = 166.65 ms / 11 runs ( 15.15 ms per token, 66.01 tokens per second)
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llama_print_timings: total time = 1365.47 ms / 243 tokens
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```
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## Minor shortcomings
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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.
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## TODO
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- [x] Support non-CPU backend for the new operators, such as `depthwise`, `hardswish`, `hardsigmoid`
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- [ ] Optimize LDP projector performance
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- Optimize the structure definition to avoid unnecessary memory rearrangements, to reduce the use of `ggml_permute_cpy`;
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- Optimize operator implementation (ARM CPU/NVIDIA GPU): such as depthwise conv, hardswish, hardsigmoid, etc.
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- [x] run MobileVLM on `Jetson Orin`
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- [ ] Support more model variants, such as `MobileVLM-3B`.
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## contributor
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```sh
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zhangjidong05, yangyang260, huyiming03, chenxiaotao03
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```
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