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Can use Lora+base model. but for merging Lora+base is error #18

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hellangleZ opened this issue May 5, 2024 · 4 comments
Open

Can use Lora+base model. but for merging Lora+base is error #18

hellangleZ opened this issue May 5, 2024 · 4 comments

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@hellangleZ
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Lora+base is working good
image
image

When merge

(/data2/llava-phi) root@A100:/data2/LLaVA-pp# python -m llava.serve.model_worker --host 0.0.0.0 --controller http://localhost:10000 --port 40000 --worker http://localhost:40000 --model-path /data2/phi3-vlm3
2024-05-05 10:36:00 | INFO | model_worker | args: Namespace(host='0.0.0.0', port=40000, worker_address='http://localhost:40000', controller_address='http://localhost:10000', model_path='/data2/phi3-vlm3', model_base=None, model_name=None, device='cuda', multi_modal=False, limit_model_concurrency=5, stream_interval=1, no_register=False, load_8bit=False, load_4bit=False, use_flash_attn=False)
2024-05-05 10:36:00 | INFO | model_worker | Loading the model phi3-vlm3 on worker c734db ...
Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
Loading checkpoint shards: 0%| | 0/2 [00:00<?, ?it/s]
Loading checkpoint shards: 50%|ΖΖΖΖΖΖΖΖΖΖΖΖΖΖΖΖΖΖΖΖΖΖΖΖΖΖ? | 1/2 [00:00<00:00, 1.38it/s]
Loading checkpoint shards: 100%|ΖΖΖΖΖΖΖΖΖΖΖΖΖΖΖΖΖΖΖΖΖΖΖΖΖΖΖΖΖΖΖΖΖΖΖΖΖΖΖΖΖΖΖΖΖΖΖΖΖΖΖΖΖ| 2/2 [00:01<00:00, 1.97it/s]
Loading checkpoint shards: 100%|ΖΖΖΖΖΖΖΖΖΖΖΖΖΖΖΖΖΖΖΖΖΖΖΖΖΖΖΖΖΖΖΖΖΖΖΖΖΖΖΖΖΖΖΖΖΖΖΖΖΖΖΖΖ| 2/2 [00:01<00:00, 1.85it/s]
2024-05-05 10:36:07 | ERROR | stderr |
Some weights of the model checkpoint at /data2/phi3-vlm3 were not used when initializing LlavaPhiForCausalLM: ['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', 'model.vision_tower.vision_tower.vision_model.encoder.layers.0.mlp.fc2.weight', 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'model.vision_tower.vision_tower.vision_model.post_layernorm.bias', 'model.vision_tower.vision_tower.vision_model.post_layernorm.weight', 'model.vision_tower.vision_tower.vision_model.pre_layrnorm.bias', 'model.vision_tower.vision_tower.vision_model.pre_layrnorm.weight']

  • This IS expected if you are initializing LlavaPhiForCausalLM 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 LlavaPhiForCausalLM from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
    2024-05-05 10:36:07 | INFO | model_worker | Register to controller
    2024-05-05 10:36:07 | ERROR | stderr | INFO: Started server process [7571]
    2024-05-05 10:36:07 | ERROR | stderr | INFO: Waiting for application startup.
    2024-05-05 10:36:07 | ERROR | stderr | INFO: Application startup complete.
    2024-05-05 10:36:07 | ERROR | stderr | INFO: Uvicorn running on http://0.0.0.0:40000 (Press CTRL+C to quit)
    2024-05-05 10:36:19 | INFO | stdout | INFO: 127.0.0.1:51532 - "POST /worker_get_status HTTP/1.1" 200 OK
    2024-05-05 10:36:22 | INFO | model_worker | Send heart beat. Models: ['phi3-vlm3']. Semaphore: None. global_counter: 0
    2024-05-05 10:36:25 | INFO | model_worker | Send heart beat. Models: ['phi3-vlm3']. Semaphore: Semaphore(value=4, locked=False). global_counter: 1
    2024-05-05 10:36:25 | INFO | stdout | INFO: 127.0.0.1:36292 - "POST /worker_generate_stream HTTP/1.1" 200 OK
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@hellangleZ
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Hi @mmaaz60 Could you please help to check , Thanks

I use the merge.py in /scripts folder

python merge.py --model-path /data2/LLaVA-pp/LLaVA/checkpoints/llava-v1.5-phi3-mini-lora_code_change --model-base microsoft/Phi-3-mini-4k-instruct --save-model-path /data2/phi3-vlm

@mmaaz60
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mmaaz60 commented May 5, 2024

Hi @hellangleZ,

Thank you, I am not sure what is going wrong. Ideally, after merging, the model should work as well. Can you please verify if your model is working with the provided Google Colab. This way we may be able to narrow down the root cause. Thank You.

@hellangleZ
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Hi @hellangleZ,

Thank you, I am not sure what is going wrong. Ideally, after merging, the model should work as well. Can you please verify if your model is working with the provided Google Colab. This way we may be able to narrow down the root cause. Thank You.

Hi @mmaaz60

image

image

Pop this error at this step

image

please help to check

@hellangleZ
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@mmaaz60 Even I not use Lora use full_FT, the model ialso could not be used, it should be a merge Clip and phi3 issue,

In [1]: import requests
...: from PIL import Image
...:
...: import torch
...: from transformers import AutoProcessor, LlavaForConditionalGeneration

In [2]: model_id = "/aml/LLaVA-pp/LLaVA/checkpoints/llava-v1.5-8b-finetune-FT"

In [3]:
...: prompt = ("<|start_header_id|>user<|end_header_id|>\n\n\nWhat are these?<|eot_id|>"
...: "<|start_header_id|>assistant<|end_header_id|>\n\n")

In [4]: image_file = "http://images.cocodataset.org/val2017/000000039769.jpg"

In [5]: model = LlavaForConditionalGeneration.from_pretrained(
...: model_id,
...: torch_dtype=torch.float16,
...: low_cpu_mem_usage=True,
...: ).to(0)
You are using a model of type llava_llama to instantiate a model of type llava. This is not supported for all configurations of models and can yield errors.
/aml/llava/lib/python3.10/site-packages/transformers/models/llava/configuration_llava.py:103: FutureWarning: The vocab_size argument is deprecated and will be removed in v4.42, since it can be inferred from the text_config. Passing this argument has no effect
warnings.warn(
Loading checkpoint shards: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 4/4 [00:00<00:00, 12.25it/s]
Some weights of LlavaForConditionalGeneration were not initialized from the model checkpoint at /aml/LLaVA-pp/LLaVA/checkpoints/llava-v1.5-8b-finetune-FT and are newly initialized: ['model.language_model.lm_head.weight', 'model.language_model.model.embed_tokens.weight', 'model.language_model.model.layers.0.input_layernorm.weight', 'model.language_model.model.layers.0.mlp.down_proj.weight', 'model.language_model.model.layers.0.mlp.gate_proj.weight', 'model.language_model.model.layers.0.mlp.up_proj.weight', 'model.language_model.model.layers.0.post_attention_layernorm.weight', 'model.language_model.model.layers.0.self_attn.k_proj.weight', 'model.language_model.model.layers.0.self_attn.o_proj.weight', 'model.language_model.model.layers.0.self_attn.q_proj.weight', 'model.language_model.model.layers.0.self_attn.v_proj.weight', 'model.language_model.model.layers.1.input_layernorm.weight', 'model.language_model.model.layers.1.mlp.down_proj.weight', 'model.language_model.model.layers.1.mlp.gate_proj.weight', 'model.language_model.model.layers.1.mlp.up_proj.weight', 'model.language_model.model.layers.1.post_attention_layernorm.weight', 'model.language_model.model.layers.1.self_attn.k_proj.weight', 'model.language_model.model.layers.1.self_attn.o_proj.weight', 'model.language_model.model.layers.1.self_attn.q_proj.weight', 'model.language_model.model.layers.1.self_attn.v_proj.weight', 'model.language_model.model.layers.10.input_layernorm.weight', 'model.language_model.model.layers.10.mlp.down_proj.weight', 'model.language_model.model.layers.10.mlp.gate_proj.weight', 'model.language_model.model.layers.10.mlp.up_proj.weight', 'model.language_model.model.layers.10.post_attention_layernorm.weight', 'model.language_model.model.layers.10.self_attn.k_proj.weight', 'model.language_model.model.layers.10.self_attn.o_proj.weight', 'model.language_model.model.layers.10.self_attn.q_proj.weight', 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'model.vision_tower.vision_model.encoder.layers.8.mlp.fc2.bias', 'model.vision_tower.vision_model.encoder.layers.8.mlp.fc2.weight', 'model.vision_tower.vision_model.encoder.layers.8.self_attn.k_proj.bias', 'model.vision_tower.vision_model.encoder.layers.8.self_attn.k_proj.weight', 'model.vision_tower.vision_model.encoder.layers.8.self_attn.out_proj.bias', 'model.vision_tower.vision_model.encoder.layers.8.self_attn.out_proj.weight', 'model.vision_tower.vision_model.encoder.layers.8.self_attn.q_proj.bias', 'model.vision_tower.vision_model.encoder.layers.8.self_attn.q_proj.weight', 'model.vision_tower.vision_model.encoder.layers.8.self_attn.v_proj.bias', 'model.vision_tower.vision_model.encoder.layers.8.self_attn.v_proj.weight', 'model.vision_tower.vision_model.encoder.layers.9.layer_norm1.bias', 'model.vision_tower.vision_model.encoder.layers.9.layer_norm1.weight', 'model.vision_tower.vision_model.encoder.layers.9.layer_norm2.bias', 'model.vision_tower.vision_model.encoder.layers.9.layer_norm2.weight', 'model.vision_tower.vision_model.encoder.layers.9.mlp.fc1.bias', 'model.vision_tower.vision_model.encoder.layers.9.mlp.fc1.weight', 'model.vision_tower.vision_model.encoder.layers.9.mlp.fc2.bias', 'model.vision_tower.vision_model.encoder.layers.9.mlp.fc2.weight', 'model.vision_tower.vision_model.encoder.layers.9.self_attn.k_proj.bias', 'model.vision_tower.vision_model.encoder.layers.9.self_attn.k_proj.weight', 'model.vision_tower.vision_model.encoder.layers.9.self_attn.out_proj.bias', 'model.vision_tower.vision_model.encoder.layers.9.self_attn.out_proj.weight', 'model.vision_tower.vision_model.encoder.layers.9.self_attn.q_proj.bias', 'model.vision_tower.vision_model.encoder.layers.9.self_attn.q_proj.weight', 'model.vision_tower.vision_model.encoder.layers.9.self_attn.v_proj.bias', 'model.vision_tower.vision_model.encoder.layers.9.self_attn.v_proj.weight', 'model.vision_tower.vision_model.post_layernorm.bias', 'model.vision_tower.vision_model.post_layernorm.weight', 'model.vision_tower.vision_model.pre_layrnorm.bias', 'model.vision_tower.vision_model.pre_layrnorm.weight']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.

In [6]: processor = AutoProcessor.from_pretrained(model_id)

KeyError Traceback (most recent call last)
File /aml/llava/lib/python3.10/site-packages/transformers/models/auto/configuration_auto.py:948, in AutoConfig.from_pretrained(cls, pretrained_model_name_or_path, **kwargs)
947 try:
--> 948 config_class = CONFIG_MAPPING[config_dict["model_type"]]
949 except KeyError:

File /aml/llava/lib/python3.10/site-packages/transformers/models/auto/configuration_auto.py:650, in _LazyConfigMapping.getitem(self, key)
649 if key not in self._mapping:
--> 650 raise KeyError(key)
651 value = self._mapping[key]

KeyError: 'llava_llama'

During handling of the above exception, another exception occurred:

ValueError Traceback (most recent call last)
Cell In[6], line 1
----> 1 processor = AutoProcessor.from_pretrained(model_id)

File /aml/llava/lib/python3.10/site-packages/transformers/models/auto/processing_auto.py:285, in AutoProcessor.from_pretrained(cls, pretrained_model_name_or_path, **kwargs)
282 if processor_class is None:
283 # Otherwise, load config, if it can be loaded.
284 if not isinstance(config, PretrainedConfig):
--> 285 config = AutoConfig.from_pretrained(
286 pretrained_model_name_or_path, trust_remote_code=trust_remote_code, **kwargs
287 )
289 # And check if the config contains the processor class.
290 processor_class = getattr(config, "processor_class", None)

File /aml/llava/lib/python3.10/site-packages/transformers/models/auto/configuration_auto.py:950, in AutoConfig.from_pretrained(cls, pretrained_model_name_or_path, **kwargs)
948 config_class = CONFIG_MAPPING[config_dict["model_type"]]
949 except KeyError:
--> 950 raise ValueError(
951 f"The checkpoint you are trying to load has model type {config_dict['model_type']} "
952 "but Transformers does not recognize this architecture. This could be because of an "
953 "issue with the checkpoint, or because your version of Transformers is out of date."
954 )
955 return config_class.from_dict(config_dict, **unused_kwargs)
956 else:
957 # Fallback: use pattern matching on the string.
958 # We go from longer names to shorter names to catch roberta before bert (for instance)

ValueError: The checkpoint you are trying to load has model type llava_llama but Transformers does not recognize this architecture. This could be because of an issue with the checkpoint, or because your version o

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