Loading Vision_tower from model checkpoint is not successful

[daulettoibazar]

Hi,
I was trying to fine-tune the weight of vision encoder along with mm_projector by setting:

model_config_finetune = {
"use_cluster": True,
"freeze": True,
"mm_tune": True,
"vision_tune": True....

which I expect will freeze LLM but then unfreeze projector and vision tower. Later using this fine-tuned model for inference gives me following warning:

Some weights of the model checkpoint at ./chatunivi-v1.5-13b_fv4 were not used when initializing ChatUniViLlamaForCausalLM: ['model.vision_tower.vision_tower.vision_model.encoder.layers.12.self_attn.k_proj.bias', 'model.vision_tower.vision_tower.vision_model.encoder.layers.16.mlp.fc1.bias', 'model.vision_tower.vision_tower.vision_model.encoder.layers.4.self_attn.v_proj.bias', 'model.vision_tower.vision_tower.vision_model.encoder.layers.13.mlp.fc2.bias', 'model.vision_tower.vision_tower.vision_model.encoder.layers.0.layer_norm2.weight', 'model.vision_tower.vision_tower.vision_model.encoder.layers.5.self_attn.k_proj.weight', 'model.vision_tower.vision_tower.vision_model.encoder.layers.7.self_attn.v_proj.bias', 'model.vision_tower.vision_tower.vision_model.encoder.layers.19.mlp.fc2.weight', 'model.vision_tower.vision_tower.vision_model.encoder.layers.3.self_attn.v_proj.weight', 'model.vision_tower.vision_tower.vision_model.encoder.layers.19.mlp.fc1.bias', 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- This IS expected if you are initializing ChatUniViLlamaForCausalLM from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
- This IS NOT expected if you are initializing ChatUniViLlamaForCausalLM from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
Some weights of ChatUniViLlamaForCausalLM were not initialized from the model checkpoint at ./chatunivi-v1.5-13b_fv4 and are newly initialized: ['model.mm_projector.bias', 'model.mm_projector.weight']

So I went through the model architecture and found that loading of vision tower happens in this line:

        self.image_processor = CLIPImageProcessor.from_pretrained(self.vision_tower_name)

, which loads clip from hugging face checkpoint.

So my question: is it possible to load CLIP weights from the checkpoint file saved after fine-tuning process?

[jpthu17]

You can set delay_load=False to ensure that the CLIP weights are loaded directly from the checkpoint file.

Chat-UniVi/ChatUniVi/model/arch.py

Line 16 in ec0b3db

self.vision_tower = build_vision_tower(config, delay_load=True)