diff --git a/src/llmtuner/hparams/model_args.py b/src/llmtuner/hparams/model_args.py index b60492a0..a6e4b710 100644 --- a/src/llmtuner/hparams/model_args.py +++ b/src/llmtuner/hparams/model_args.py @@ -169,6 +169,10 @@ class ModelArguments: default=False, metadata={"help": "For debugging purposes, print the status of the parameters in the model."}, ) + use_mllm: bool = field( + default=False, + metadata={"help": "Whether use Multimodal LLM."}, + ) def __post_init__(self): self.compute_dtype = None diff --git a/src/llmtuner/model/__init__.py b/src/llmtuner/model/__init__.py index 2bd73365..f6be60d8 100644 --- a/src/llmtuner/model/__init__.py +++ b/src/llmtuner/model/__init__.py @@ -1,10 +1,9 @@ -from .loader import load_config, load_model, load_tokenizer, load_mm_model +from .loader import load_config, load_model, load_tokenizer, load_processor from .utils.misc import find_all_linear_modules, load_valuehead_params __all__ = [ "load_config", "load_model", - "load_mm_model", "load_tokenizer", "load_processor", "load_valuehead_params", diff --git a/src/llmtuner/model/adapter.py b/src/llmtuner/model/adapter.py index 8079c028..bcefee92 100644 --- a/src/llmtuner/model/adapter.py +++ b/src/llmtuner/model/adapter.py @@ -1,4 +1,4 @@ -from typing import TYPE_CHECKING +from typing import TYPE_CHECKING, Union import torch from peft import LoraConfig, LoraModel, PeftModel, TaskType, get_peft_model @@ -21,11 +21,11 @@ logger = get_logger(__name__) def init_adapter( config: "PretrainedConfig", - model: "PreTrainedModel", + model: Union["PreTrainedModel","AutoModelForVision2Seq"], model_args: "ModelArguments", finetuning_args: "FinetuningArguments", is_trainable: bool, -) -> "PreTrainedModel": +) -> Union["PreTrainedModel","AutoModelForVision2Seq"]: r""" Initializes the adapters. @@ -195,103 +195,4 @@ def init_adapter( if model_args.adapter_name_or_path is not None: logger.info("Loaded adapter(s): {}".format(",".join(model_args.adapter_name_or_path))) - return model - - -def init_mm_adapter( - model: "AutoModelForVision2Seq", model_args: "ModelArguments", - finetuning_args: "FinetuningArguments", - is_trainable: bool, - use_clm=True, -) -> "AutoModelForVision2Seq": - if finetuning_args.finetuning_type == "lora": - logger.info("Fine-tuning method: {}".format("DoRA" if finetuning_args.use_dora else "LoRA")) - adapter_to_resume = None - - if model_args.adapter_name_or_path is not None: - is_mergeable = True - if getattr(model, "quantization_method", None): # merge lora in quantized model is unstable - assert len(model_args.adapter_name_or_path) == 1, "Quantized model only accepts a single adapter." - is_mergeable = False - - if is_deepspeed_zero3_enabled(): - assert len(model_args.adapter_name_or_path) == 1, "Cannot use multiple adapters in DeepSpeed ZeRO-3." - is_mergeable = False - - if (is_trainable and not finetuning_args.create_new_adapter) or (not is_mergeable): - adapter_to_merge = model_args.adapter_name_or_path[:-1] - adapter_to_resume = model_args.adapter_name_or_path[-1] - else: - adapter_to_merge = model_args.adapter_name_or_path - - for adapter in adapter_to_merge: - model: "LoraModel" = PeftModel.from_pretrained( - model, adapter, offload_folder=model_args.offload_folder - ) - model = model.merge_and_unload() - - if len(adapter_to_merge) > 0: - logger.info("Merged {} adapter(s).".format(len(adapter_to_merge))) - - if adapter_to_resume is not None: # resume lora training - model = PeftModel.from_pretrained( - model, adapter_to_resume, is_trainable=is_trainable, offload_folder=model_args.offload_folder - ) - - if is_trainable and adapter_to_resume is None: # create new lora weights while training - if len(finetuning_args.lora_target) == 1 and finetuning_args.lora_target[0] == "all": - target_modules = find_all_linear_modules(model) - else: - target_modules = finetuning_args.lora_target - - if finetuning_args.use_llama_pro: - target_modules = find_expanded_modules(model, target_modules, finetuning_args.num_layer_trainable) - - if ( - finetuning_args.use_dora - and getattr(model, "quantization_method", None) is not None - and getattr(model, "quantization_method", None) != QuantizationMethod.BITS_AND_BYTES - ): - raise ValueError("DoRA is not compatible with PTQ-quantized models.") - - peft_kwargs = { - "r": finetuning_args.lora_rank, - "target_modules": target_modules, - "lora_alpha": finetuning_args.lora_alpha, - "lora_dropout": finetuning_args.lora_dropout, - "use_rslora": finetuning_args.use_rslora, - "modules_to_save": finetuning_args.additional_target, - } - - if model_args.use_unsloth: - from unsloth import FastLanguageModel # type: ignore - - unsloth_peft_kwargs = { - "model": model, - "max_seq_length": model_args.model_max_length, - "use_gradient_checkpointing": "unsloth", - } - model = FastLanguageModel.get_peft_model(**peft_kwargs, **unsloth_peft_kwargs) - else: - if use_clm: - lora_config = LoraConfig( - task_type=TaskType.CAUSAL_LM, - inference_mode=False, - use_dora=finetuning_args.use_dora, - **peft_kwargs, - ) - else: - lora_config = LoraConfig( - inference_mode=False, - use_dora=finetuning_args.use_dora, - **peft_kwargs, - ) - model = get_peft_model(model, lora_config) - - if (not finetuning_args.pure_bf16) and (not finetuning_args.use_badam): - for param in filter(lambda p: p.requires_grad, model.parameters()): - param.data = param.data.to(torch.float32) - - if model_args.adapter_name_or_path is not None: - logger.info("Loaded adapter(s): {}".format(",".join(model_args.adapter_name_or_path))) - return model + return model \ No newline at end of file diff --git a/src/llmtuner/model/loader.py b/src/llmtuner/model/loader.py index a6c37922..3712a592 100644 --- a/src/llmtuner/model/loader.py +++ b/src/llmtuner/model/loader.py @@ -1,11 +1,11 @@ -from typing import TYPE_CHECKING, Any, Dict +from typing import TYPE_CHECKING, Any, Dict, Union from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, AutoProcessor, AutoModelForVision2Seq from trl import AutoModelForCausalLMWithValueHead from ..extras.logging import get_logger from ..extras.misc import count_parameters, try_download_model_from_ms -from .adapter import init_adapter, init_mm_adapter +from .adapter import init_adapter from .patcher import patch_config, patch_model, patch_tokenizer, patch_valuehead_model from .utils.misc import load_valuehead_params, register_autoclass from .utils.mod import convert_pretrained_model_to_mod, load_mod_pretrained_model @@ -106,12 +106,12 @@ def load_config(model_args: "ModelArguments") -> "PretrainedConfig": def load_model( - tokenizer: "PreTrainedTokenizer", - model_args: "ModelArguments", - finetuning_args: "FinetuningArguments", - is_trainable: bool = False, - add_valuehead: bool = False, -) -> "PreTrainedModel": + tokenizer: "PreTrainedTokenizer", + model_args: "ModelArguments", + finetuning_args: "FinetuningArguments", + is_trainable: bool = False, + add_valuehead: bool = False, +) -> Union["PreTrainedModel", "AutoModelForVision2Seq"]: r""" Loads pretrained model. """ @@ -134,7 +134,10 @@ def load_model( if model_args.mixture_of_depths == "load": model = load_mod_pretrained_model(**init_kwargs) else: - model = AutoModelForCausalLM.from_pretrained(**init_kwargs) + if model_args.use_mllm: + model = AutoModelForVision2Seq.from_pretrained(**init_kwargs) + else: + model = AutoModelForCausalLM.from_pretrained(**init_kwargs) if model_args.mixture_of_depths == "convert": model = convert_pretrained_model_to_mod(model, config, model_args) @@ -182,56 +185,4 @@ def load_model( ) ) - return model - - -def load_mm_model( - processor: "AutoProcessor", - model_args: "ModelArguments", - finetuning_args: "FinetuningArguments", - is_trainable: bool = False, - add_valuehead: bool = False, - use_clm=True, -) -> "AutoModelForVision2Seq": - r""" - Loads pretrained model. Must after load_tokenizer. - """ - tokenizer = processor.tokenizer - init_kwargs = _get_init_kwargs(model_args) - config = AutoConfig.from_pretrained(model_args.model_name_or_path, **init_kwargs) - patch_config(config, tokenizer, model_args, init_kwargs, is_trainable) - - model = None - if model is None: - init_kwargs["config"] = config - init_kwargs["pretrained_model_name_or_path"] = model_args.model_name_or_path - model: "AutoModelForVision2Seq" = AutoModelForVision2Seq.from_pretrained(**init_kwargs) - patch_model(model, tokenizer, model_args, is_trainable) - register_autoclass(config, model, tokenizer) - - model = init_mm_adapter(model, model_args, finetuning_args, is_trainable, use_clm) - - if not is_trainable: - model.requires_grad_(False) - model.eval() - else: - model.train() - - trainable_params, all_param = count_parameters(model) - if is_trainable: - param_stats = "trainable params: {:d} || all params: {:d} || trainable%: {:.4f}".format( - trainable_params, all_param, 100 * trainable_params / all_param - ) - else: - param_stats = "all params: {:d}".format(all_param) - logger.info(param_stats) - - if model_args.print_param_status: - for name, param in model.named_parameters(): - print( - "name: {}, dtype: {}, device: {}, trainable: {}".format( - name, param.dtype, param.device, param.requires_grad - ) - ) - - return model + return model \ No newline at end of file diff --git a/src/llmtuner/train/sftmm/workflow.py b/src/llmtuner/train/sftmm/workflow.py index 21f4aebf..7afd8f6f 100644 --- a/src/llmtuner/train/sftmm/workflow.py +++ b/src/llmtuner/train/sftmm/workflow.py @@ -4,7 +4,7 @@ from typing import TYPE_CHECKING, List, Optional from ...data import split_dataset, get_mm_dataset from ...extras.misc import get_logits_processor from ...extras.ploting import plot_loss -from ...model import load_tokenizer, load_processor, load_mm_model +from ...model import load_tokenizer, load_processor, load_model from ..utils import create_modelcard_and_push from .metric import ComputeMetrics from .trainer import CustomSeq2SeqTrainer @@ -29,10 +29,7 @@ def run_sft_mm( CHAT_TEMPLATE = """{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. {% for message in messages %}{% if message['role'] == 'user' %}USER: {% else %}ASSISTANT: {% endif %}{% for item in message['content'] %}{% if item['type'] == 'text' %}{{ item['text'] }}{% elif item['type'] == 'image' %}{% endif %}{% endfor %}{% if message['role'] == 'user' %} {% else %}{{eos_token}}{% endif %}{% endfor %}{% if add_generation_prompt %}ASSISTANT: {% endif %}""" tokenizer.chat_template = CHAT_TEMPLATE processor.tokenizer = tokenizer - use_clm = True - if "blip" in model_args.model_name_or_path: - use_clm = False - model = load_mm_model(processor, model_args, finetuning_args, training_args.do_train, use_clm=use_clm) + model = load_model(processor.tokenizer, model_args, finetuning_args, training_args.do_train) dataset = get_mm_dataset(processor, model_args, data_args, training_args, stage="sft") if getattr(model, "is_quantized", False) and not training_args.do_train: setattr(model, "_hf_peft_config_loaded", True) # hack here: make model compatible with prediction