llava : update documentations (#13055)
* llava : update documentations * fix typo
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docs/multimodal/granitevision.md
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docs/multimodal/granitevision.md
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# Granite Vision
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Download the model and point your `GRANITE_MODEL` environment variable to the path.
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```bash
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$ git clone https://huggingface.co/ibm-granite/granite-vision-3.2-2b
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$ export GRANITE_MODEL=./granite-vision-3.2-2b
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```
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### 1. Running llava surgery v2.
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First, we need to run the llava surgery script as shown below:
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`python llava_surgery_v2.py -C -m $GRANITE_MODEL`
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You should see two new files (`llava.clip` and `llava.projector`) written into your model's directory, as shown below.
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```bash
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$ ls $GRANITE_MODEL | grep -i llava
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llava.clip
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llava.projector
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```
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We should see that the projector and visual encoder get split out into the llava files. Quick check to make sure they aren't empty:
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```python
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import os
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import torch
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MODEL_PATH = os.getenv("GRANITE_MODEL")
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if not MODEL_PATH:
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raise ValueError("env var GRANITE_MODEL is unset!")
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encoder_tensors = torch.load(os.path.join(MODEL_PATH, "llava.clip"))
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projector_tensors = torch.load(os.path.join(MODEL_PATH, "llava.projector"))
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assert len(encoder_tensors) > 0
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assert len(projector_tensors) > 0
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```
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If you actually inspect the `.keys()` of the loaded tensors, you should see a lot of `vision_model` tensors in the `encoder_tensors`, and 5 tensors (`'multi_modal_projector.linear_1.bias'`, `'multi_modal_projector.linear_1.weight'`, `'multi_modal_projector.linear_2.bias'`, `'multi_modal_projector.linear_2.weight'`, `'image_newline'`) in the multimodal `projector_tensors`.
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### 2. Creating the Visual Component GGUF
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Next, create a new directory to hold the visual components, and copy the llava.clip/projector files, as shown below.
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```bash
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$ ENCODER_PATH=$PWD/visual_encoder
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$ mkdir $ENCODER_PATH
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$ cp $GRANITE_MODEL/llava.clip $ENCODER_PATH/pytorch_model.bin
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$ cp $GRANITE_MODEL/llava.projector $ENCODER_PATH/
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```
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Now, we need to write a config for the visual encoder. In order to convert the model, be sure to use the correct `image_grid_pinpoints`, as these may vary based on the model. You can find the `image_grid_pinpoints` in `$GRANITE_MODEL/config.json`.
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```json
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{
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"_name_or_path": "siglip-model",
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"architectures": [
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"SiglipVisionModel"
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],
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"image_grid_pinpoints": [
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[384,384],
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[384,768],
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[384,1152],
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[384,1536],
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[384,1920],
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[384,2304],
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[384,2688],
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[384,3072],
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[384,3456],
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[384,3840],
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[768,384],
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[768,768],
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[768,1152],
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[768,1536],
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[768,1920],
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[1152,384],
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[1152,768],
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[1152,1152],
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[1536,384],
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[1536,768],
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[1920,384],
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[1920,768],
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[2304,384],
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[2688,384],
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[3072,384],
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[3456,384],
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[3840,384]
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],
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"mm_patch_merge_type": "spatial_unpad",
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"hidden_size": 1152,
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"image_size": 384,
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"intermediate_size": 4304,
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"model_type": "siglip_vision_model",
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"num_attention_heads": 16,
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"num_hidden_layers": 27,
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"patch_size": 14,
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"layer_norm_eps": 1e-6,
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"hidden_act": "gelu_pytorch_tanh",
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"projection_dim": 0,
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"vision_feature_layer": [-24, -20, -12, -1]
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}
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```
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At this point you should have something like this:
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```bash
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$ ls $ENCODER_PATH
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config.json llava.projector pytorch_model.bin
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```
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Now convert the components to GGUF; Note that we also override the image mean/std dev to `[.5,.5,.5]` since we use the SigLIP visual encoder - in the transformers model, you can find these numbers in the `preprocessor_config.json`.
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```bash
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$ python convert_image_encoder_to_gguf.py \
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-m $ENCODER_PATH \
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--llava-projector $ENCODER_PATH/llava.projector \
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--output-dir $ENCODER_PATH \
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--clip-model-is-vision \
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--clip-model-is-siglip \
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--image-mean 0.5 0.5 0.5 \
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--image-std 0.5 0.5 0.5
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```
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This will create the first GGUF file at `$ENCODER_PATH/mmproj-model-f16.gguf`; we will refer to the absolute path of this file as the `$VISUAL_GGUF_PATH.`
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### 3. Creating the LLM GGUF.
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The granite vision model contains a granite LLM as its language model. For now, the easiest way to get the GGUF for LLM is by loading the composite model in `transformers` and exporting the LLM so that it can be directly converted with the normal conversion path.
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First, set the `LLM_EXPORT_PATH` to the path to export the `transformers` LLM to.
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```bash
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$ export LLM_EXPORT_PATH=$PWD/granite_vision_llm
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```
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```python
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import os
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import transformers
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MODEL_PATH = os.getenv("GRANITE_MODEL")
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if not MODEL_PATH:
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raise ValueError("env var GRANITE_MODEL is unset!")
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LLM_EXPORT_PATH = os.getenv("LLM_EXPORT_PATH")
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if not LLM_EXPORT_PATH:
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raise ValueError("env var LLM_EXPORT_PATH is unset!")
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tokenizer = transformers.AutoTokenizer.from_pretrained(MODEL_PATH)
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# NOTE: granite vision support was added to transformers very recently (4.49);
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# if you get size mismatches, your version is too old.
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# If you are running with an older version, set `ignore_mismatched_sizes=True`
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# as shown below; it won't be loaded correctly, but the LLM part of the model that
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# we are exporting will be loaded correctly.
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model = transformers.AutoModelForImageTextToText.from_pretrained(MODEL_PATH, ignore_mismatched_sizes=True)
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tokenizer.save_pretrained(LLM_EXPORT_PATH)
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model.language_model.save_pretrained(LLM_EXPORT_PATH)
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```
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Now you can convert the exported LLM to GGUF with the normal converter in the root of the llama cpp project.
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```bash
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$ LLM_GGUF_PATH=$LLM_EXPORT_PATH/granite_llm.gguf
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...
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$ python convert_hf_to_gguf.py --outfile $LLM_GGUF_PATH $LLM_EXPORT_PATH
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```
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### 4. Quantization
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If you want to quantize the LLM, you can do so with `llama-quantize` as you would any other LLM. For example:
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```bash
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$ ./build/bin/llama-quantize $LLM_EXPORT_PATH/granite_llm.gguf $LLM_EXPORT_PATH/granite_llm_q4_k_m.gguf Q4_K_M
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$ LLM_GGUF_PATH=$LLM_EXPORT_PATH/granite_llm_q4_k_m.gguf
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```
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Note that currently you cannot quantize the visual encoder because granite vision models use SigLIP as the visual encoder, which has tensor dimensions that are not divisible by 32.
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### 5. Running the Model in Llama cpp
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Build llama cpp normally; you should have a target binary named `llama-mtmd-cli`, which you can pass two binaries to. As an example, we pass the the llama.cpp banner.
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```bash
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$ ./build/bin/llama-mtmd-cli -m $LLM_GGUF_PATH \
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--mmproj $VISUAL_GGUF_PATH \
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-c 16384 \
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--temp 0
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```
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