[Feature]: LoRA support for InternVLChatModel

[vidixha]

Your current environment

vllm version = 0.6.1

Model Input Dumps

No response

🐛 Describe the bug

The output of `command:`

vllm version = 0.6.1. InternVLChat is in list of supported models.

CUDA_VISIBLE_DEVICES=0 python3 -m vllm.entrypoints.openai.api_server --model OpenGVLab/InternVL2-8B --vllm_enable_lora=True --vllm_max_lora_rank=32 --lora-modules line_items=checkpoint-786/ --api-key=abcd  --host=0.0.0.0 --port=8817 --gpu_memory_utilization 0.95 --max_model_len=8192 --trust_remote_code --limit-mm-per-prompt 'image=16' 

rank0]:   File "/root/anaconda3/envs/msswift_latest/lib/python3.10/site-packages/vllm/engine/async_llm_engine.py", line 636, in __init__
[rank0]:     self.engine = self._init_engine(*args, **kwargs)
[rank0]:   File "/root/anaconda3/envs/msswift_latest/lib/python3.10/site-packages/vllm/engine/async_llm_engine.py", line 840, in _init_engine
[rank0]:     return engine_class(*args, **kwargs)
[rank0]:   File "/root/anaconda3/envs/msswift_latest/lib/python3.10/site-packages/vllm/engine/async_llm_engine.py", line 272, in __init__
[rank0]:     super().__init__(*args, **kwargs)
[rank0]:   File "/root/anaconda3/envs/msswift_latest/lib/python3.10/site-packages/vllm/engine/llm_engine.py", line 270, in __init__
[rank0]:     self.model_executor = executor_class(
[rank0]:   File "/root/anaconda3/envs/msswift_latest/lib/python3.10/site-packages/vllm/executor/executor_base.py", line 46, in __init__
[rank0]:     self._init_executor()
[rank0]:   File "/root/anaconda3/envs/msswift_latest/lib/python3.10/site-packages/vllm/executor/gpu_executor.py", line 39, in _init_executor
[rank0]:     self.driver_worker.load_model()
[rank0]:   File "/root/anaconda3/envs/msswift_latest/lib/python3.10/site-packages/vllm/worker/worker.py", line 182, in load_model
[rank0]:     self.model_runner.load_model()
[rank0]:   File "/root/anaconda3/envs/msswift_latest/lib/python3.10/site-packages/vllm/worker/model_runner.py", line 881, in load_model
[rank0]:     self.model = get_model(model_config=self.model_config,
[rank0]:   File "/root/anaconda3/envs/msswift_latest/lib/python3.10/site-packages/vllm/model_executor/model_loader/__init__.py", line 19, in get_model
[rank0]:     return loader.load_model(model_config=model_config,
[rank0]:   File "/root/anaconda3/envs/msswift_latest/lib/python3.10/site-packages/vllm/model_executor/model_loader/loader.py", line 341, in load_model
[rank0]:     model = _initialize_model(model_config, self.load_config,
[rank0]:   File "/root/anaconda3/envs/msswift_latest/lib/python3.10/site-packages/vllm/model_executor/model_loader/loader.py", line 170, in _initialize_model
[rank0]:     return build_model(
[rank0]:   File "/root/anaconda3/envs/msswift_latest/lib/python3.10/site-packages/vllm/model_executor/model_loader/loader.py", line 151, in build_model
[rank0]:     extra_kwargs = _get_model_initialization_kwargs(model_class, lora_config,
[rank0]:   File "/root/anaconda3/envs/msswift_latest/lib/python3.10/site-packages/vllm/model_executor/model_loader/loader.py", line 128, in _get_model_initialization_kwargs
[rank0]:     raise ValueError(
[rank0]: ValueError: Model InternVLChatModel does not support LoRA, but LoRA is enabled. Support for this model may be added in the future. If this is important to you, please open an issue on github

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[jeejeelee]

Could you provide your LoRA configuration? I might be able to implement this quickly

[vidixha]

@jeejeelee pls help..its quite urgent. thanks!

"lora_rank": 32, "lora_alpha": 64, "lora_dropout": 0.05, "lora_bias_trainable": "none", "lora_dtype": null, "lora_lr_ratio": null, "use_rslora": false, "use_dora": false, "init_lora_weights": true,

[jeejeelee]

I will. can you provibe target_modules info?

[vidixha]

@jeejeelee "target_modules": [ "fc2", "w2", "output", "mlp1.3", "fc1", "mlp1.1", "w3", "proj", "w1", "wqkv", "wo", "qkv" ]

[jeejeelee]

Currently, vllm does not support LoRA inference for the visual encoder and projector components. It only supports LoRA inference for language models. Even if I were to implement support lorafor internvl now, the infer results might not be correct.

[vidixha]

@jeejeelee what is the easiest workaround for this? deploying using merged lora is affecting performance..i would want to deploy the original weights..is there some alternative I can explore (fast inference) for prod deployment.

[jeejeelee]

deploying using merged lora is affecting performance

How many LoRAs do you have? If you only have one, merging the weights would lead to higher inference efficiency.

[vidixha]

i have 2 loras main and adalora. sharing the whole config here for reference:

{
  "model_type": "internvl2-8b",
  "model_id_or_path": "OpenGVLab/InternVL2-8B",
  "model_revision": "main",
  "full_determinism": false,
  "sft_type": "lora",
  "freeze_parameters": [],
  "freeze_vit": false,
  "freeze_parameters_ratio": 0.0,
  "additional_trainable_parameters": [],
  "tuner_backend": "peft",
  "template_type": "internvl2",
  "output_dir": "LLaMA-Factory/output/internvl2-8b/v0-20240912-105045",
  "add_output_dir_suffix": false,
  "ddp_backend": null,
  "ddp_find_unused_parameters": null,
  "ddp_broadcast_buffers": null,
  "ddp_timeout": 1800,
  "seed": 42,
  "resume_from_checkpoint": null,
  "resume_only_model": false,
  "ignore_data_skip": false,
  "dtype": "bf16",
  "packing": false,
  "train_backend": "transformers",
  "tp": 1,
  "pp": 1,
  "min_lr": null,
  "sequence_parallel": false,
  "model_kwargs": null,
  "loss_name": null,
  "dataset": [
    "train.jsonl"
  ],
  "val_dataset": [],
  "dataset_seed": 42,
  "dataset_test_ratio": 0.01,
  "use_loss_scale": false,
  "loss_scale_config_path": "LLaMA-Factory/swift/swift/llm/agent/default_loss_scale_config.json",
  "system": "你是由上海人工智能实验室联合商汤科技开发的书生多模态大模型，英文名叫InternVL, 是一个有用无害的人工智能助手。",
  "tools_prompt": "react_en",
  "max_length": 3072,
  "truncation_strategy": "delete",
  "check_dataset_strategy": "none",
  "streaming": false,
  "streaming_val_size": 0,
  "streaming_buffer_size": 16384,
  "model_name": [
    null,
    null
  ],
  "model_author": [
    null,
    null
  ],
  "quant_method": null,
  "quantization_bit": 0,
  "hqq_axis": 0,
  "hqq_dynamic_config_path": null,
  "bnb_4bit_comp_dtype": "bf16",
  "bnb_4bit_quant_type": "nf4",
  "bnb_4bit_use_double_quant": true,
  "bnb_4bit_quant_storage": null,
  "rescale_image": -1,
  "target_modules": [
    "fc2",
    "w2",
    "output",
    "mlp1.3",
    "fc1",
    "mlp1.1",
    "w3",
    "proj",
    "w1",
    "wqkv",
    "wo",
    "qkv"
  ],
  "target_regex": null,
  "modules_to_save": [],
  "lora_rank": 32,
  "lora_alpha": 64,
  "lora_dropout": 0.05,
  "lora_bias_trainable": "none",
  "lora_dtype": null,
  "lora_lr_ratio": null,
  "use_rslora": false,
  "use_dora": false,
  "init_lora_weights": true,
  "fourier_n_frequency": 2000,
  "fourier_scaling": 300.0,
  "rope_scaling": null,
  "boft_block_size": 4,
  "boft_block_num": 0,
  "boft_n_butterfly_factor": 1,
  "boft_dropout": 0.0,
  "vera_rank": 256,
  "vera_projection_prng_key": 0,
  "vera_dropout": 0.0,
  "vera_d_initial": 0.1,
  "adapter_act": "gelu",
  "adapter_length": 128,
  "use_galore": false,
  "galore_target_modules": null,
  "galore_rank": 128,
  "galore_update_proj_gap": 50,
  "galore_scale": 1.0,
  "galore_proj_type": "std",
  "galore_optim_per_parameter": false,
  "galore_with_embedding": false,
  "galore_quantization": false,
  "galore_proj_quant": false,
  "galore_proj_bits": 4,
  "galore_proj_group_size": 256,
  "galore_cos_threshold": 0.4,
  "galore_gamma_proj": 2,
  "galore_queue_size": 5,
  "adalora_target_r": 8,
  "adalora_init_r": 12,
  "adalora_tinit": 0,
  "adalora_tfinal": 0,
  "adalora_deltaT": 1,
  "adalora_beta1": 0.85,
  "adalora_beta2": 0.85,
  "adalora_orth_reg_weight": 0.5,
  "ia3_feedforward_modules": [],
  "llamapro_num_new_blocks": 4,
  "llamapro_num_groups": null,
  "neftune_noise_alpha": null,
  "neftune_backend": "transformers",
  "lisa_activated_layers": 0,
  "lisa_step_interval": 20,
  "reft_layer_key": null,
  "reft_layers": null,
  "reft_rank": 4,
  "reft_intervention_type": "LoreftIntervention",
  "reft_args": null,
  "use_liger": false,
  "gradient_checkpointing": true,
  "deepspeed": null,
  "batch_size": 1,
  "eval_batch_size": 1,
  "auto_find_batch_size": false,
  "num_train_epochs": 3,
  "max_steps": -1,
  "optim": "adamw_torch",
  "adam_beta1": 0.9,
  "adam_beta2": 0.95,
  "adam_epsilon": 1e-08,
  "learning_rate": 5e-05,
  "weight_decay": 0.1,
  "gradient_accumulation_steps": 1,
  "max_grad_norm": 1,
  "predict_with_generate": false,
  "lr_scheduler_type": "cosine",
  "lr_scheduler_kwargs": {},
  "warmup_ratio": 0.05,
  "warmup_steps": 0,
  "eval_steps": 100,
  "save_steps": 100,
  "save_only_model": false,
  "save_total_limit": 2,
  "logging_steps": 5,
  "acc_steps": 1,
  "dataloader_num_workers": 1,
  "dataloader_pin_memory": true,
  "dataloader_drop_last": false,
  "push_to_hub": false,
  "hub_model_id": null,
  "hub_token": null,
  "hub_private_repo": false,
  "hub_strategy": "every_save",
  "test_oom_error": false,
  "disable_tqdm": false,
  "lazy_tokenize": true,
  "preprocess_num_proc": 1,
  "use_flash_attn": true,
  "ignore_args_error": true,
  "check_model_is_latest": true,
  "logging_dir": "LLaMA-Factory/output/internvl2-8b/v0-20240912-105045/runs",
  "report_to": [
    "tensorboard"
  ],
  "acc_strategy": "token",
  "save_on_each_node": false,
  "evaluation_strategy": "steps",
  "save_strategy": "steps",
  "save_safetensors": true,
  "gpu_memory_fraction": null,
  "include_num_input_tokens_seen": false,
  "local_repo_path": null,
  "custom_register_path": null,
  "custom_dataset_info": null,
  "device_map_config": null,
  "device_max_memory": [],
  "max_new_tokens": 2048,
  "do_sample": null,
  "temperature": null,
  "top_k": null,
  "top_p": null,
  "repetition_penalty": null,
  "num_beams": 1,
  "fsdp": "",
  "fsdp_config": null,
  "sequence_parallel_size": 1,
  "model_layer_cls_name": null,
  "metric_warmup_step": 0,
  "fsdp_num": 1,
  "per_device_train_batch_size": null,
  "per_device_eval_batch_size": null,
  "eval_strategy": null,
  "self_cognition_sample": 0,
  "train_dataset_mix_ratio": 0.0,
  "train_dataset_mix_ds": [
    "ms-bench"
  ],
  "train_dataset_sample": -1,
  "val_dataset_sample": null,
  "safe_serialization": null,
  "only_save_model": null,
  "neftune_alpha": null,
  "deepspeed_config_path": null,
  "model_cache_dir": null,
  "lora_dropout_p": null,
  "lora_target_modules": [
    "fc2",
    "w2",
    "output",
    "mlp1.3",
    "fc1",
    "mlp1.1",
    "w3",
    "proj",
    "w1",
    "wqkv",
    "wo",
    "qkv"
  ],
  "lora_target_regex": null,
  "lora_modules_to_save": [],
  "boft_target_modules": [],
  "boft_modules_to_save": [],
  "vera_target_modules": [],
  "vera_modules_to_save": [],
  "ia3_target_modules": [],
  "ia3_modules_to_save": [],
  "custom_train_dataset_path": [],
  "custom_val_dataset_path": [],
  "device_map_config_path": null,
  "push_hub_strategy": null,
  "use_self_cognition": false,
  "is_multimodal": true,
  "is_vision": true,
  "lora_use_embedding": false,
  "lora_use_all": true,
  "lora_m2s_use_embedding": false,
  "lora_m2s_use_ln": false,
  "torch_dtype": "torch.bfloat16",
  "fp16": false,
  "bf16": true,
  "rank": -1,
  "local_rank": -1,
  "world_size": 1,
  "local_world_size": 1,
  "bnb_4bit_compute_dtype": "torch.bfloat16",
  "load_in_4bit": false,
  "load_in_8bit": false,
  "train_sampler_random": true,
  "training_args": "Seq2SeqTrainingArguments(output_dir='LLaMA-Factory/output/internvl2-8b/v0-20240912-105045', overwrite_output_dir=False, do_train=False, do_eval=True, do_predict=False, eval_strategy=<IntervalStrategy.STEPS: 'steps'>, prediction_loss_only=False, per_device_train_batch_size=1, per_device_eval_batch_size=1, per_gpu_train_batch_size=None, per_gpu_eval_batch_size=None, gradient_accumulation_steps=1, eval_accumulation_steps=None, eval_delay=0, torch_empty_cache_steps=None, learning_rate=5e-05, weight_decay=0.1, adam_beta1=0.9, adam_beta2=0.95, adam_epsilon=1e-08, max_grad_norm=1, num_train_epochs=3, max_steps=-1, lr_scheduler_type=<SchedulerType.COSINE: 'cosine'>, lr_scheduler_kwargs={}, warmup_ratio=0.05, warmup_steps=0, log_level='passive', log_level_replica='warning', log_on_each_node=True, logging_dir='LLaMA-Factory/output/internvl2-8b/v0-20240912-105045/runs', logging_strategy=<IntervalStrategy.STEPS: 'steps'>, logging_first_step=True, logging_steps=5, logging_nan_inf_filter=True, save_strategy=<IntervalStrategy.STEPS: 'steps'>, save_steps=100, save_total_limit=2, save_safetensors=True, save_on_each_node=False, save_only_model=False, restore_callback_states_from_checkpoint=False, no_cuda=False, use_cpu=False, use_mps_device=False, seed=42, data_seed=None, jit_mode_eval=False, use_ipex=False, bf16=True, fp16=False, fp16_opt_level='O1', half_precision_backend='auto', bf16_full_eval=False, fp16_full_eval=False, tf32=None, local_rank=0, ddp_backend=None, tpu_num_cores=None, tpu_metrics_debug=False, debug=[], dataloader_drop_last=False, eval_steps=100, dataloader_num_workers=1, dataloader_prefetch_factor=None, past_index=-1, run_name='LLaMA-Factory/output/internvl2-8b/v0-20240912-105045', disable_tqdm=False, remove_unused_columns=False, label_names=None, load_best_model_at_end=False, metric_for_best_model='loss', greater_is_better=False, ignore_data_skip=False, fsdp=[], fsdp_min_num_params=0, fsdp_config={'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}, fsdp_transformer_layer_cls_to_wrap=None, accelerator_config=AcceleratorConfig(split_batches=False, dispatch_batches=False, even_batches=True, use_seedable_sampler=True, non_blocking=False, gradient_accumulation_kwargs=None, use_configured_state=False), deepspeed=None, label_smoothing_factor=0.0, optim=<OptimizerNames.ADAMW_TORCH: 'adamw_torch'>, optim_args=None, adafactor=False, group_by_length=False, length_column_name='length', report_to=['tensorboard'], ddp_find_unused_parameters=None, ddp_bucket_cap_mb=None, ddp_broadcast_buffers=None, dataloader_pin_memory=True, dataloader_persistent_workers=False, skip_memory_metrics=True, use_legacy_prediction_loop=False, push_to_hub=False, resume_from_checkpoint=None, hub_model_id=None, hub_strategy=<HubStrategy.EVERY_SAVE: 'every_save'>, hub_token=None, hub_private_repo=False, hub_always_push=False, gradient_checkpointing=True, gradient_checkpointing_kwargs=None, include_inputs_for_metrics=False, eval_do_concat_batches=True, fp16_backend='auto', evaluation_strategy=None, push_to_hub_model_id=None, push_to_hub_organization=None, push_to_hub_token=None, mp_parameters='', auto_find_batch_size=False, full_determinism=False, torchdynamo=None, ray_scope='last', ddp_timeout=1800, torch_compile=False, torch_compile_backend=None, torch_compile_mode=None, dispatch_batches=None, split_batches=None, include_tokens_per_second=False, include_num_input_tokens_seen=False, neftune_noise_alpha=None, optim_target_modules=None, batch_eval_metrics=False, eval_on_start=False, eval_use_gather_object=False, sortish_sampler=True, predict_with_generate=False, generation_max_length=None, generation_num_beams=None, generation_config=GenerationConfig {\n  \"eos_token_id\": 92542,\n  \"max_new_tokens\": 2048,\n  \"pad_token_id\": 2\n}\n, train_sampler_random=True, acc_strategy='token', loss_name=None, additional_saved_files=[], metric_warmup_step=0, train_dataset_sample=-1)"
}

[vidixha]

@jeejeelee efficiency is better but results are worse than unmerged one

[jeejeelee]

@AkshataABhat II have preliminarily completed the work of supporting LoRA in InternVL, but note that it can still only add LoRA to the language model. You can try it out. See my branch: https://github.com/jeejeelee/vllm/tree/internvl-lora .

Additionally, I suggest you could try adding LoRA only to the language model and retrain it

[vidixha]

@jeejeelee I discussed this with my team..and they want support for the lora for vision model as well..

[zzf2grx]

@AkshataABhat II have preliminarily completed the work of supporting LoRA in InternVL, but note that it can still only add LoRA to the language model. You can try it out. See my branch: https://github.com/jeejeelee/vllm/tree/internvl-lora .

Additionally, I suggest you could try adding LoRA only to the language model and retrain it

Hi~ I have some questions about this branch. Does it support InternVL2-8B (awq) with multi LoRAs? Thank you!

[jeejeelee]

@AkshataABhat II have preliminarily completed the work of supporting LoRA in InternVL, but note that it can still only add LoRA to the language model. You can try it out. See my branch: https://github.com/jeejeelee/vllm/tree/internvl-lora . Additionally, I suggest you could try adding LoRA only to the language model and retrain it

Hi~ I have some questions about this branch. Does it support InternVL2-8B (awq) with multi LoRAs? Thank you!

If internvl supports awq, I think it should be fine. You can try it out, and if you have any issues, I'll help you solve them.

[Zhiy-Zhang]

@AkshataABhat II have preliminarily completed the work of supporting LoRA in InternVL, but note that it can still only add LoRA to the language model. You can try it out. See my branch: https://github.com/jeejeelee/vllm/tree/internvl-lora . Additionally, I suggest you could try adding LoRA only to the language model and retrain it

Hi~ I have some questions about this branch. Does it support InternVL2-8B (awq) with multi LoRAs? Thank you!

If internvl supports awq, I think it should be fine. You can try it out, and if you have any issues, I'll help you solve them.

@jeejeelee hi~, I have some questions about awq with LoRAs? When language model(awq) with multi LoRAs, LoRA weights need to be quantized？

[jeejeelee]

@AkshataABhat II have preliminarily completed the work of supporting LoRA in InternVL, but note that it can still only add LoRA to the language model. You can try it out. See my branch: https://github.com/jeejeelee/vllm/tree/internvl-lora . Additionally, I suggest you could try adding LoRA only to the language model and retrain it

Hi~ I have some questions about this branch. Does it support InternVL2-8B (awq) with multi LoRAs? Thank you!

If internvl supports awq, I think it should be fine. You can try it out, and if you have any issues, I'll help you solve them.

@jeejeelee hi~, I have some questions about awq with LoRAs? When language model(awq) with multi LoRAs, LoRA weights need to be quantized？

You don't need to

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