VLM训练——Trainer源码解读

本文将以LLaVa源码为例，解析如何使用Trainer训练/微调一个VLM。

1. 参数解析
- - ModelArguments
  - DataArguments
  - TrainingArguments
2. 加载模型
3. 加载数据
4. 创建Trainer开始训练

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1. 参数解析

VLM 和 LLM 相关训练框架都会引入 ModelArguments、DataArguments、TrainingArguments、GeneratingArguments 并通过 Transformer.HfArgumentParser 进行整合，然后再用parse_args_into_dataclasses()方法解析成 hf 的标准形式model_args, data_args, training_args，实现了两行代码处理训练全程的参数问题。这些命令行参数会从.sh的Shell 代码文件中导入。

from typing import Optional
from dataclasses import dataclass, field
import transformers
 
 
...
 
    添加上述的 Argument Class
 
...
 
 
if __name__ == '__main__':
    parser = transformers.HfArgumentParser((ModelArguments, DataArguments, TrainingArguments, GeneratingArguments))
    model_args, data_args, training_args, generate_args = parser.parse_args_into_dataclasses()
 
    print(model_args)
    print(data_args)
    print(training_args)
    print(generate_args)

ModelArguments

ModelArguments 通常包含模型路径，以及一些架构上的参数。
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DataArguments

DataArguments 通常包含数据路径，以及一些预处理参数。
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TrainingArguments

TrainingArguments 通常包含模型训练的一些必要参数，如优化器、学习率等参数。
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2. 加载模型

对于我们不仅要加载 LLM 还需要加载 Image Encoder 和 Projector，因此我们可以直接写一个VLM Model（继承transformer库中的LLM）

		model = LlavaLlamaForCausalLM.from_pretrained(
                model_args.model_name_or_path,
                cache_dir=training_args.cache_dir,
                attn_implementation=attn_implementation,
                torch_dtype=(torch.bfloat16 if training_args.bf16 else None),
                **bnb_model_from_pretrained_args
            )

LlavaLlamaForCausalLM 继承了LLM（transformer.LlamaForCausalLM）和 VLM抽象类（LlavaMetaForCausalLM），LlavaLlamaForCausalLM中的Visual Modules是 LlavaLlamaModel 用于加载 Image Encoder 和 Projector。其多模态forward的流程就是，先对 image 和 text 计算 embedding，然后将其多模态的 tokens 拼接在一起送入LLM。

class LlavaLlamaForCausalLM(LlamaForCausalLM, LlavaMetaForCausalLM):
    config_class = LlavaConfig

    def __init__(self, config):
        super(LlamaForCausalLM, self).__init__(config)
        self.model = LlavaLlamaModel(config)
        self.pretraining_tp = config.pretraining_tp
        self.vocab_size = config.vocab_size
        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)

        # Initialize weights and apply final processing
        self.post_init()

    def get_model(self):
        return self.model

    def forward(
        self,
        input_ids: torch.LongTensor = None,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_values: Optional[List[torch.FloatTensor]] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        labels: Optional[torch.LongTensor] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        images: Optional[torch.FloatTensor] = None,
        image_sizes: Optional[List[List[int]]] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple, CausalLMOutputWithPast]:

        if inputs_embeds is None:
            (
                input_ids,
                position_ids,
                attention_mask,
                past_key_values,
                inputs_embeds,
                labels
            ) = self.prepare_inputs_labels_for_multimodal(
                input_ids,
                position_ids,
                attention_mask,
                past_key_values,
                labels,
                images,
                image_sizes
            )

        return super().forward(
            input_ids=input_ids,
            attention_mask=attention_mask,
            position_ids=position_ids,
            past_key_values=past_key_values,
            inputs_embeds=inputs_embeds,
            labels=labels,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict
        )

    @torch.no_grad()
    def generate(
        self,
        inputs: Optional[torch.Tensor] = None,
        images: Optional[torch.Tensor] = None,
        image_sizes: Optional[torch.Tensor] = None,
        **kwargs,
    ) -> Union[GenerateOutput, torch.LongTensor]:
        position_ids = kwargs.pop("position_ids", None)
        attention_mask = kwargs.pop("attention_mask", None)
        if "inputs_embeds" in kwargs:
            raise NotImplementedError("`inputs_embeds` is not supported")

        if images is not None:
            (
                inputs,
                position_ids,
                attention_mask,
                _,
                inputs_embeds,
                _
            ) = self.prepare_inputs_labels_for_multimodal(
                inputs,
                position_ids,
                attention_mask,
                None,
                None,
                images,
                image_sizes=image_sizes
            )
        else:
            inputs_embeds = self.get_model().embed_tokens(inputs)

        return super().generate(
            position_ids=position_ids,
            attention_mask=attention_mask,
            inputs_embeds=inputs_embeds,
            **kwargs
        )

    def prepare_inputs_for_generation(self, input_ids, past_key_values=None,
                                      inputs_embeds=None, **kwargs):
        images = kwargs.pop("images", None)
        image_sizes = kwargs.pop("image_sizes", None)
        inputs = super().prepare_inputs_for_generation(
            input_ids, past_key_values=past_key_values, inputs_embeds=inputs_embeds, **kwargs
        )
        if images is not None:
            inputs['images'] = images
        if image_sizes is not None:
            inputs['image_sizes'] = image_sizes
        return inputs

另外，我们还需要加载Tokenizer，并设置其词表：

		tokenizer = transformers.AutoTokenizer.from_pretrained(
            model_args.model_name_or_path,
            cache_dir=training_args.cache_dir,
            model_max_length=training_args.model_max_length,
            padding_side="right",
            use_fast=False,
        )

3. 加载数据

在开始构造Trainer勋训练之前，我们还需要创建dataset和data collator：

def make_supervised_data_module(tokenizer: transformers.PreTrainedTokenizer,
                                data_args) -> Dict:
    """Make dataset and collator for supervised fine-tuning."""
    train_dataset = LazySupervisedDataset(tokenizer=tokenizer,
                                data_path=data_args.data_path,
                                data_args=data_args)
    data_collator = DataCollatorForSupervisedDataset(tokenizer=tokenizer)
    return dict(train_dataset=train_dataset,
                eval_dataset=None,
                data_collator=data_collator)

4. 创建Trainer开始训练

    trainer = LLaVATrainer(model=model,
                    tokenizer=tokenizer,
                    args=training_args,
                    **data_module)
	trainer.train()
    trainer.save_state()

构造VLM的Trainer，继承Trainer，重写_get_train_sampler、create_optimizer、_save_checkpoint、_save即可。

class LLaVATrainer(Trainer):

    def _get_train_sampler(self) -> Optional[torch.utils.data.Sampler]:
        if self.train_dataset is None or not has_length(self.train_dataset):
            return None

        if self.args.group_by_modality_length:
            lengths = self.train_dataset.modality_lengths
            return LengthGroupedSampler(
                self.args.train_batch_size,
                world_size=self.args.world_size * self.args.gradient_accumulation_steps,
                lengths=lengths,
                group_by_modality=True,
            )
        else:
            return super()._get_train_sampler()

    def create_optimizer(self):
        """
        Setup the optimizer.

        We provide a reasonable default that works well. If you want to use something else, you can pass a tuple in the
        Trainer's init through `optimizers`, or subclass and override this method in a subclass.
        """
        if is_sagemaker_mp_enabled():
            return super().create_optimizer()

        opt_model = self.model

        if self.optimizer is None:
            decay_parameters = get_parameter_names(opt_model, ALL_LAYERNORM_LAYERS)
            decay_parameters = [name for name in decay_parameters if "bias" not in name]
            if self.args.mm_projector_lr is not None:
                projector_parameters = [name for name, _ in opt_model.named_parameters() if "mm_projector" in name]
                optimizer_grouped_parameters = [
                    {
                        "params": [
                            p for n, p in opt_model.named_parameters() if (n in decay_parameters and n not in projector_parameters and p.requires_grad)
                        ],
                        "weight_decay": self.args.weight_decay,
                    },
                    {
                        "params": [
                            p for n, p in opt_model.named_parameters() if (n not in decay_parameters and n not in projector_parameters and p.requires_grad)
                        ],
                        "weight_decay": 0.0,
                    },
                    {
                        "params": [
                            p for n, p in opt_model.named_parameters() if (n in decay_parameters and n in projector_parameters and p.requires_grad)
                        ],
                        "weight_decay": self.args.weight_decay,
                        "lr": self.args.mm_projector_lr,
                    },
                    {
                        "params": [
                            p for n, p in opt_model.named_parameters() if (n not in decay_parameters and n in projector_parameters and p.requires_grad)
                        ],
                        "weight_decay": 0.0,
                        "lr": self.args.mm_projector_lr,
                    },
                ]
            else:
                optimizer_grouped_parameters = [
                    {
                        "params": [
                            p for n, p in opt_model.named_parameters() if (n in decay_parameters and p.requires_grad)
                        ],
                        "weight_decay": self.args.weight_decay,
                    },
                    {
                        "params": [
                            p for n, p in opt_model.named_parameters() if (n not in decay_parameters and p.requires_grad)
                        ],
                        "weight_decay": 0.0,
                    },
                ]

            optimizer_cls, optimizer_kwargs = Trainer.get_optimizer_cls_and_kwargs(self.args)

            self.optimizer = optimizer_cls(optimizer_grouped_parameters, **optimizer_kwargs)
            if optimizer_cls.__name__ == "Adam8bit":
                import bitsandbytes

                manager = bitsandbytes.optim.GlobalOptimManager.get_instance()

                skipped = 0
                for module in opt_model.modules():
                    if isinstance(module, nn.Embedding):
                        skipped += sum({p.data_ptr(): p.numel() for p in module.parameters()}.values())
                        logger.info(f"skipped {module}: {skipped/2**20}M params")
                        manager.register_module_override(module, "weight", {"optim_bits": 32})
                        logger.debug(f"bitsandbytes: will optimize {module} in fp32")
                logger.info(f"skipped: {skipped/2**20}M params")

        return self.optimizer

    def _save_checkpoint(self, model, trial, metrics=None):
        if getattr(self.args, 'tune_mm_mlp_adapter', False):
            from transformers.trainer_utils import PREFIX_CHECKPOINT_DIR
            checkpoint_folder = f"{PREFIX_CHECKPOINT_DIR}-{self.state.global_step}"

            run_dir = self._get_output_dir(trial=trial)
            output_dir = os.path.join(run_dir, checkpoint_folder)

            # Only save Adapter
            keys_to_match = ['mm_projector', 'vision_resampler']
            if getattr(self.args, "use_im_start_end", False):
                keys_to_match.extend(['embed_tokens', 'embed_in'])

            weight_to_save = get_mm_adapter_state_maybe_zero_3(self.model.named_parameters(), keys_to_match)

            if self.args.local_rank == 0 or self.args.local_rank == -1:
                self.model.config.save_pretrained(output_dir)
                torch.save(weight_to_save, os.path.join(output_dir, f'mm_projector.bin'))
        else:
            super(LLaVATrainer, self)._save_checkpoint(model, trial, metrics)

    def _save(self, output_dir: Optional[str] = None, state_dict=None):
        if getattr(self.args, 'tune_mm_mlp_adapter', False):
            pass
        else:
            super(LLaVATrainer, self)._save(output_dir, state_dict)