【Error when merging LoRA weights】

[Luo-Z13]

Hello, I load pre-trained llava-llama3 SFT weights and fine-tune using LoRA, but get an error when merging weights:

scripts:
Training:

deepspeed --master_port=$((RANDOM + 10000)) --include localhost:0,1,2,3 llava/train/train_mem.py \
    --lora_enable True --lora_r 128 --lora_alpha 256 --mm_projector_lr 2e-5 \
    --deepspeed ./scripts/zero2.json \
    --model_name_or_path ./HuggingFace-Download-Accelerator/models--MBZUAI--LLaVA-Meta-Llama-3-8B-Instruct-FT \
    --version llama3 \
    --data_path train_data.json \
    --image_folder .data/image \
    --vision_tower clip-vit-large-patch14-336 \
    --pretrain_mm_mlp_adapter ./HuggingFace-Download-Accelerator/models--MBZUAI--LLaVA-Meta-Llama-3-8B-Instruct-pretrain/mm_projector.bin \
    ...

merge lora:

python scripts/merge_lora_weights.py \
    --model-path ./checkpoints/llava-v1.5-8b-finetune-lora_loadfrom_FT \
    --model-base ./HuggingFace-Download-Accelerator/models--MBZUAI--LLaVA-Meta-Llama-3-8B-Instruct-FT \
    --save-model-path ./checkpoints/merge_llava-llama3-finetune-lora_loadfrom_FT

Error:

Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
Loading LLaVA from base model...

Loading checkpoint shards:   0%|          | 0/4 [00:00<?, ?it/s]
Loading checkpoint shards:   0%|          | 0/4 [00:10<?, ?it/s]
Traceback (most recent call last):
  File "/LLaVA-pp/LLaVA/scripts/merge_lora_weights.py", line 24, in <module>
    merge_lora(args)
  File "/LLaVA-pp/LLaVA/scripts/merge_lora_weights.py", line 8, in merge_lora
    tokenizer, model, image_processor, context_len = load_pretrained_model(args.model_path, args.model_base, model_name, device_map='cpu')
  File "/LLaVA-pp/LLaVA/llava/model/builder.py", line 64, in load_pretrained_model
    model = LlavaLlamaForCausalLM.from_pretrained(model_base, low_cpu_mem_usage=True, config=lora_cfg_pretrained, **kwargs)
  File "/miniconda-3/envs/llava-llama/lib/python3.10/site-packages/transformers/modeling_utils.py", line 3682, in from_pretrained
    ) = cls._load_pretrained_model(
  File "/miniconda-3/envs/llava-llama/lib/python3.10/site-packages/transformers/modeling_utils.py", line 4109, in _load_pretrained_model
    new_error_msgs, offload_index, state_dict_index = _load_state_dict_into_meta_model(
  File "/miniconda-3/envs/llava-llama/lib/python3.10/site-packages/transformers/modeling_utils.py", line 887, in _load_state_dict_into_meta_model
    set_module_tensor_to_device(model, param_name, param_device, **set_module_kwargs)
  File "/miniconda-3/envs/llava-llama/lib/python3.10/site-packages/accelerate/utils/modeling.py", line 358, in set_module_tensor_to_device
    raise ValueError(
ValueError: Trying to set a tensor of shape torch.Size([128257, 4096]) in "weight" (which has shape torch.Size([128256, 4096])), this look incorrect.
/LLaVA-pp/LLaVA/llava/model/builder.py:54: UserWarning: There is `lora` in model name but no `model_base` is provided. If you are loading a LoRA model, please provide the `model_base` argument. Detailed instruction: https://github.com/haotian-liu/LLaVA#launch-a-model-worker-lora-weights-unmerged.
  warnings.warn('There is `lora` in model name but no `model_base` is provided. If you are loading a LoRA model, please provide the `model_base` argument. Detailed instruction: https://github.com/haotian-liu/LLaVA#launch-a-model-worker-lora-weights-unmerged.')
Traceback (most recent call last):
  File "/miniconda-3/envs/llava-llama/lib/python3.10/site-packages/transformers/utils/hub.py", line 398, in cached_file
    resolved_file = hf_hub_download(
  File "/miniconda-3/envs/llava-llama/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py", line 111, in _inner_fn
    validate_repo_id(arg_value)
  File "/miniconda-3/envs/llava-llama/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py", line 159, in validate_repo_id
    raise HFValidationError(
huggingface_hub.utils._validators.HFValidationError: Repo id must be in the form 'repo_name' or 'namespace/repo_name': '/LLaVA-pp/LLaVA/checkpoints/merge_llava-llama3-finetune-lora_loadfrom_FT'. Use `repo_type` argument if needed.

[mmaaz60]

Hi @Luo-Z13,

Thank you for your interest in our work. The error you are getting because you have not added the PAD token and resized the tokenizer embeddings. Please ensure that you are using the builder.py that is provided in our repository at https://github.com/mbzuai-oryx/LLaVA-pp/blob/main/LLaMA-3-V/builder.py.

Specifically, the pad token is added at

LLaVA-pp/LLaMA-3-V/builder.py

Line 65 in b93d9c8

print(f"Adding pad token as '<pad>'")

[Luo-Z13]

Hi @Luo-Z13,

Thank you for your interest in our work. The error you are getting because you have not added the PAD token and resized the tokenizer embeddings. Please ensure that you are using the builder.py that is provided in our repository at https://github.com/mbzuai-oryx/LLaVA-pp/blob/main/LLaMA-3-V/builder.py.

Specifically, the pad token is added at

LLaVA-pp/LLaMA-3-V/builder.py

Line 65 in b93d9c8

print(f"Adding pad token as '<pad>'")

Hello, I checked my file and I have copied it using cp LLaMA-3-V/builder.py LLaVA/llava/model/builder.py before training.
I notice that the error happens in line 64 before the pad token beening added:

LLaVA-pp/LLaMA-3-V/builder.py

Line 64 in b93d9c8

model = LlavaLlamaForCausalLM.from_pretrained(model_base, low_cpu_mem_usage=True, config=lora_cfg_pretrained, **kwargs)

so maybe this error is caused by something else?

[Luo-Z13]

Hello, I try to conduct inference using the provided weight directly:

python llava/eval/model_vqa.py \
    --model-path ./HuggingFace-Download-Accelerator/models--MBZUAI--LLaVA-Meta-Llama-3-8B-Instruct-FT \
    --question-file vqa_question.jsonl \
    --answers-file vqa_answer_llava-llama3.jsonl \
    --image-folder ./data/img

And the warning also appears:

Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
model_base:None

Loading checkpoint shards:   0%|          | 0/4 [00:00<?, ?it/s]
Loading checkpoint shards:  25%|██▌       | 1/4 [00:48<02:25, 48.52s/it]
Loading checkpoint shards:  50%|█████     | 2/4 [01:34<01:34, 47.19s/it]
Loading checkpoint shards:  75%|███████▌  | 3/4 [02:21<00:47, 47.03s/it]
Loading checkpoint shards: 100%|██████████| 4/4 [02:31<00:00, 32.23s/it]
Loading checkpoint shards: 100%|██████████| 4/4 [02:31<00:00, 37.80s/it]
Some weights of the model checkpoint at ./HuggingFace-Download-Accelerator/models--MBZUAI--LLaVA-Meta-Llama-3-8B-Instruct-FT were not used when initializing LlavaLlamaForCausalLM: ['model.vision_tower.vision_tower.vision_model.embeddings.class_embedding', 'model.vision_tower.vision_tower.vision_model.embeddings.patch_embedding.weight', 'model.vision_tower.vision_tower.vision_model.embeddings.position_embedding.weight', 'model.vision_tower.vision_tower.vision_model.encoder.layers.0.layer_norm1.bias', 'model.vision_tower.vision_tower.vision_model.encoder.layers.0.layer_norm1.weight', 'model.vision_tower.vision_tower.vision_model.encoder.layers.0.layer_norm2.bias', 'model.vision_tower.vision_tower.vision_model.encoder.layers.0.layer_norm2.weight', 'model.vision_tower.vision_tower.vision_model.encoder.layers.0.mlp.fc1.bias', 'model.vision_tower.vision_tower.vision_model.encoder.layers.0.mlp.fc1.weight', 'model.vision_tower.vision_tower.vision_model.encoder.layers.0.mlp.fc2.bias', 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- This IS expected if you are initializing LlavaLlamaForCausalLM 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 LlavaLlamaForCausalLM from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
miniconda-3/envs/llava-llama/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
  return self.fget.__get__(instance, owner)()

  0%|          | 0/5556 [00:00<?, ?it/s]The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.
Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.

  0%|          | 1/5556 [00:10<15:58:44, 10.36s/it]The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.
Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.

  0%|          | 2/5556 [00:11<7:38:32,  4.95s/it] The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.
Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.

  0%|          | 3/5556 [00:11<4:15:24,  2.76s/it]The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.
Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.

What may be the possible reasons?