utils/my_trainer.py

import os
import dataclasses
import inspect
import warnings
from functools import wraps
from typing import Callable, Dict, List, Optional, Tuple, Union, final
from torch.utils.data import DataLoader
import torch
import torch.nn as nn
from accelerate.state import PartialState
import datasets
from model.qformer import Blip2QformerPatch
from datasets import Dataset
from datasets.arrow_writer import SchemaInferenceError
from datasets.builder import DatasetGenerationError
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
    DataCollator,
    DataCollatorForLanguageModeling,
    PreTrainedModel,
    PreTrainedTokenizerBase,
    Trainer,
    TrainingArguments,
)
from transformers.modeling_utils import unwrap_model
from transformers.trainer_callback import TrainerCallback
from transformers.trainer_utils import EvalPrediction
# from transformers.trainer import _is_peft_model
from trl.extras.dataset_formatting import get_formatting_func_from_dataset
from trl.import_utils import is_peft_available
from trl.trainer.utils import (
    ConstantLengthDataset,
    DataCollatorForCompletionOnlyLM,
    RichProgressCallback,
    neftune_post_forward_hook,
    peft_module_casting_to_bf16,
    trl_sanitze_kwargs_for_tagging,
)
from transformers.utils import is_datasets_available
from transformers.trainer_utils import seed_worker
from transformers.models.auto.modeling_auto import (
    MODEL_FOR_CAUSAL_LM_MAPPING_NAMES,
    MODEL_MAPPING_NAMES,
)

if is_peft_available():
    from peft import PeftConfig, PeftModel, get_peft_model, prepare_model_for_kbit_training


def maybe_zero_3(param, ignore_status=False, name=None):
    from deepspeed import zero
    from deepspeed.runtime.zero.partition_parameters import ZeroParamStatus
    if hasattr(param, "ds_id"):
        if param.ds_status == ZeroParamStatus.NOT_AVAILABLE:
            if not ignore_status:
                print(name, 'no ignore status')
        with zero.GatheredParameters([param]):
            param = param.data.detach().cpu().clone()
    else:
        param = param.detach().cpu().clone()
    return param


def get_mm_adapter_state_maybe_zero_3(named_params, keys_to_match):
    # to_return = {k: t for k, t in named_params if any(key_match in k for key_match in keys_to_match)}
    to_return = {k: t for k, t in named_params if not any(key_match in k for key_match in keys_to_match)}
    to_return = {k: maybe_zero_3(v, ignore_status=True, name=k).cpu() for k, v in to_return.items()}
    return to_return

class CustomTrainer(Trainer):
    def __init__(
        self,
        model: Optional[Union[PreTrainedModel, nn.Module, str]] = None,
        args: Optional[TrainingArguments] = None,
        data_collator: Optional[DataCollator] = None,  # type: ignore
        train_dataset: Optional[Dataset] = None,
        eval_dataset: Optional[Union[Dataset, Dict[str, Dataset]]] = None,
        tokenizer: Optional[PreTrainedTokenizerBase] = None,
        model_init: Optional[Callable[[], PreTrainedModel]] = None,
        compute_metrics: Optional[Callable[[EvalPrediction], Dict]] = None,
        callbacks: Optional[List[TrainerCallback]] = None,
        optimizers: Tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR] = (None, None),
        preprocess_logits_for_metrics: Optional[Callable[[torch.Tensor, torch.Tensor], torch.Tensor]] = None,
        peft_config: Optional["PeftConfig"] = None,
        dataset_text_field: Optional[str] = None,
        packing: Optional[bool] = False,
        formatting_func: Optional[Callable] = None,
        max_seq_length: Optional[int] = None,
        infinite: Optional[bool] = None,
        num_of_sequences: Optional[int] = 1024,
        chars_per_token: Optional[float] = 3.6,
        dataset_num_proc: Optional[int] = None,
        dataset_batch_size: int = 1000,
        neftune_noise_alpha: Optional[float] = None,
        model_init_kwargs: Optional[Dict] = None,
        dataset_kwargs: Optional[Dict] = None,
        eval_packing: Optional[bool] = None,
    ):
        if model_init_kwargs is None:
            model_init_kwargs = {}
        elif not isinstance(model, str):
            raise ValueError("You passed model_kwargs to the SFTTrainer. But your model is already instantiated.")

        # if infinite is not None:
        #     warnings.warn(
        #         "The `infinite` argument is deprecated and will be removed in a future version of TRL. Use `TrainingArguments.max_steps` or `TrainingArguments.num_train_epochs` instead to control training length."
        #     )

        # if isinstance(model, str):
        #     warnings.warn(
        #         "You passed a model_id to the SFTTrainer. This will automatically create an "
        #         "`AutoModelForCausalLM` or a `PeftModel` (if you passed a `peft_config`) for you."
        #     )
        #     model = AutoModelForCausalLM.from_pretrained(model, **model_init_kwargs)

        if packing and data_collator is not None and isinstance(data_collator, DataCollatorForCompletionOnlyLM):
            raise ValueError(
                "You passed a `DataCollatorForCompletionOnlyLM` to the SFTTrainer. This is not compatible with the `packing` argument."
            )

        if is_peft_available() and peft_config is not None:
            if not isinstance(peft_config, PeftConfig):
                raise ValueError(
                    "If you want to use the PeftModel, you need to pass a PeftConfig object to the SFTTrainer."
                    f" and you passed a {type(peft_config)}."
                )

            if not isinstance(model, PeftModel):
                _support_gc_kwargs = hasattr(
                    args, "gradient_checkpointing_kwargs"
                ) and "gradient_checkpointing_kwargs" in list(
                    inspect.signature(prepare_model_for_kbit_training).parameters
                )
                gradient_checkpointing_kwargs = getattr(args, "gradient_checkpointing_kwargs", None) or {}
                is_sharded_qlora = False
                # Below is to support QLoRA + FSDP / DS-Zero3 - one should never call
                # peft_module_casting_to_bf16 or prepare_model_for_kbit_training when doing
                # QLoRA + FSDP / DS-Zero3
                if getattr(model, "is_loaded_in_4bit", False):
                    for _, param in model.named_parameters():
                        if param.__class__.__name__ == "Params4bit":
                            is_sharded_qlora = param.data.device.type == "cpu"
                            break
                if getattr(model, "is_loaded_in_8bit", False) or (
                    getattr(model, "is_loaded_in_4bit", False) and not is_sharded_qlora
                ):
                    prepare_model_kwargs = {
                        "use_gradient_checkpointing": getattr(args, "gradient_checkpointing", False)
                    }

                    if _support_gc_kwargs:
                        prepare_model_kwargs["gradient_checkpointing_kwargs"] = gradient_checkpointing_kwargs

                    model = prepare_model_for_kbit_training(model, **prepare_model_kwargs)

                    if args is not None:
                        args = dataclasses.replace(args, gradient_checkpointing=False)
                elif getattr(args, "gradient_checkpointing", False) and (
                    "use_reentrant" not in gradient_checkpointing_kwargs
                    or gradient_checkpointing_kwargs["use_reentrant"]
                ):
                    # For backward compatibility with older versions of transformers
                    if hasattr(model, "enable_input_require_grads"):
                        model.enable_input_require_grads()
                    else:

                        def make_inputs_require_grad(module, input, output):
                            output.requires_grad_(True)

                        model.get_input_embeddings().register_forward_hook(make_inputs_require_grad)

                model = get_peft_model(model, peft_config)
                if (
                    args is not None
                    and args.bf16
                    and getattr(model, "is_loaded_in_4bit", False)
                    and not is_sharded_qlora
                ):
                    peft_module_casting_to_bf16(model)

        if tokenizer is None:
            raise Exception("pleae provide a tokenizer")
            tokenizer = AutoTokenizer.from_pretrained(model.config._name_or_path)
            if getattr(tokenizer, "pad_token", None) is None:
                tokenizer.pad_token = tokenizer.eos_token

        if max_seq_length is None:
            # to overcome some issues with broken tokenizers
            max_seq_length = min(tokenizer.model_max_length, 1024)

            warnings.warn(
                f"You didn't pass a `max_seq_length` argument to the SFTTrainer, this will default to {max_seq_length}"
            )

        self.dataset_num_proc = dataset_num_proc
        self.dataset_batch_size = dataset_batch_size

        self._trainer_supports_neftune = hasattr(args, "neftune_noise_alpha")

        if neftune_noise_alpha is not None and self._trainer_supports_neftune:
            args.neftune_noise_alpha = neftune_noise_alpha
            warnings.warn(
                "You passed a `neftune_noise_alpha` argument to the SFTTrainer, the value you passed will override the one in the `TrainingArguments`."
            )
            # self.neftune_noise_alpha is done at Trainer level
        elif not self._trainer_supports_neftune:
            self.neftune_noise_alpha = neftune_noise_alpha

        if formatting_func is None and dataset_text_field is None:
            # check if dataset has ChatML format or instruction format and is supported
            # if not stays #None
            formatting_func = get_formatting_func_from_dataset(train_dataset, tokenizer)

        if not packing:
            if dataset_text_field is None and formatting_func is None:
                raise ValueError(
                    "You passed `packing=False` to the SFTTrainer, but you didn't pass a `dataset_text_field` or `formatting_func` argument."
                )

            if data_collator is None:
                data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False)

        # Pre-process the datasets only once per node. The remaining processes will use the cache.
        with PartialState().local_main_process_first():
            if dataset_kwargs is None:
                dataset_kwargs = {}
            if train_dataset is not None:
                train_dataset = self._prepare_dataset(
                    train_dataset,
                    tokenizer,
                    packing,
                    dataset_text_field,
                    max_seq_length,
                    formatting_func,
                    num_of_sequences,
                    chars_per_token,
                    remove_unused_columns=args.remove_unused_columns if args is not None else True,
                    **dataset_kwargs,
                )
            if eval_dataset is not None:
                _multiple = isinstance(eval_dataset, dict)
                _eval_datasets = eval_dataset if _multiple else {"singleton": eval_dataset}

                eval_packing = packing if eval_packing is None else eval_packing

                for _eval_dataset_name, _eval_dataset in _eval_datasets.items():
                    _eval_datasets[_eval_dataset_name] = self._prepare_dataset(
                        _eval_dataset,
                        tokenizer,
                        eval_packing,
                        dataset_text_field,
                        max_seq_length,
                        formatting_func,
                        num_of_sequences,
                        chars_per_token,
                        remove_unused_columns=args.remove_unused_columns if args is not None else True,
                        **dataset_kwargs,
                    )
                if not _multiple:
                    eval_dataset = _eval_datasets["singleton"]

        if tokenizer.padding_side is not None and tokenizer.padding_side != "right":
            warnings.warn(
                "You passed a tokenizer with `padding_side` not equal to `right` to the SFTTrainer. This might lead to some unexpected behaviour due to "
                "overflow issues when training a model in half-precision. You might consider adding `tokenizer.padding_side = 'right'` to your code."
            )

        super().__init__(
            model=model,
            args=args,
            data_collator=data_collator,
            train_dataset=train_dataset,
            eval_dataset=eval_dataset,
            tokenizer=tokenizer,
            model_init=model_init,
            compute_metrics=compute_metrics,
            callbacks=callbacks,
            optimizers=optimizers,
            preprocess_logits_for_metrics=preprocess_logits_for_metrics,
        )

        # Add tags for models that have been loaded with the correct transformers version
        if hasattr(self.model, "add_model_tags"):
            self.model.add_model_tags(self._tag_names)

        if self.args.max_steps > 0 and packing:
            warnings.warn(
                "You passed `packing=True` to the SFTTrainer, and you are training your model with `max_steps` strategy. The dataset will be iterated until the `max_steps` are reached."
            )
            self.train_dataset.infinite = True
        elif self.args.max_steps == -1 and packing:
            self.train_dataset.infinite = False

        if any(isinstance(callback, RichProgressCallback) for callback in self.callback_handler.callbacks):
            for callback in self.callback_handler.callbacks:
                # Remove the PrinterCallback to avoid duplicated prints in case we passed a `RichProgressCallback`
                if callback.__class__.__name__ == "PrinterCallback":
                    self.callback_handler.pop_callback(callback)

    def _prepare_dataset(
        self,
        dataset,
        tokenizer,
        packing,
        dataset_text_field,
        max_seq_length,
        formatting_func,
        num_of_sequences,
        chars_per_token,
        remove_unused_columns=True,
        append_concat_token=True,
        add_special_tokens=True,
    ):
        if dataset is None:
            raise ValueError("The dataset should not be None")

        # check if torch dataset / dataloader and do nothing
        if isinstance(dataset, (torch.utils.data.IterableDataset, torch.utils.data.Dataset, ConstantLengthDataset)):
            return dataset

        return self._prepare_non_packed_dataloader(
            tokenizer,
            dataset,
            dataset_text_field,
            max_seq_length,
            formatting_func,
            add_special_tokens,
            remove_unused_columns,
        )

    def _prepare_non_packed_dataloader(
        self,
        tokenizer,
        dataset,
        dataset_text_field,
        max_seq_length,
        formatting_func=None,
        add_special_tokens=True,
        remove_unused_columns=True,
    ):
        use_formatting_func = formatting_func is not None and dataset_text_field is None
        self._dataset_sanity_checked = False

        # Inspired from: https://huggingface.co/learn/nlp-course/chapter7/6?fw=pt
        def tokenize(element):
            outputs = tokenizer(
                # element[dataset_text_field] if not use_formatting_func else formatting_func(element),
                element if not use_formatting_func else formatting_func(element),
                add_special_tokens=add_special_tokens,
                truncation=True,
                padding=False,
                max_length=max_seq_length,
                return_overflowing_tokens=False,
                return_length=False,
            )

            if use_formatting_func and not self._dataset_sanity_checked:
                if not isinstance(formatting_func(element), list):
                    raise ValueError(
                        "The `formatting_func` should return a list of processed strings since it can lead to silent bugs."
                    )
                else:
                    self._dataset_sanity_checked = True

            return {"input_ids": outputs["input_ids"], "attention_mask": outputs["attention_mask"]}

        def tokenize_instruct(element):
            outputs = tokenizer(
                # element[dataset_text_field] if not use_formatting_func else formatting_func(element),
                element if not use_formatting_func else formatting_func(element),
                add_special_tokens=add_special_tokens,
                truncation=True,
                padding=False,
                max_length=max_seq_length,
                return_overflowing_tokens=False,
                return_length=False,
            )

            if use_formatting_func and not self._dataset_sanity_checked:
                if not isinstance(formatting_func(element), list):
                    raise ValueError(
                        "The `formatting_func` should return a list of processed strings since it can lead to silent bugs."
                    )
                else:
                    self._dataset_sanity_checked = True

            return {"input_ids_instruct": outputs["input_ids"], "attention_mask_instruct": outputs["attention_mask"]}

        signature_columns = ["input_ids", "labels", "attention_mask"]

        extra_columns = list(set(dataset.column_names) - set(signature_columns))

        if not remove_unused_columns and len(extra_columns) > 0:
            warnings.warn(
                "You passed `remove_unused_columns=False` on a non-packed dataset. This might create some issues with the default collator and yield to errors. If you want to "
                f"inspect dataset other columns (in this case {extra_columns}), you can subclass `DataCollatorForLanguageModeling` in case you used the default collator and create your own data collator in order to inspect the unused dataset columns."
            )

        tokenized_dataset = dataset.map(
            tokenize,
            batched=False,
            remove_columns=['text'],
            num_proc=self.dataset_num_proc,
            batch_size=self.dataset_batch_size,
            input_columns=['text'],
       )
       ########## image text interaction ##########
        if 'text_input' in dataset.column_names:
            print('tokenize instruction text!')
            tokenized_dataset = tokenized_dataset.map(
                tokenize_instruct,
                batched=False,
                remove_columns=['text_input'],
                num_proc=self.dataset_num_proc,
                batch_size=self.dataset_batch_size,
                input_columns=['text_input'],
            )
        return tokenized_dataset

    def get_train_dataloader(self) -> DataLoader:
        """
        Returns the training [`~torch.utils.data.DataLoader`].

        Will use no sampler if `train_dataset` does not implement `__len__`, a random sampler (adapted to distributed
        training if necessary) otherwise.

        Subclass and override this method if you want to inject some custom behavior.
        """
        if self.train_dataset is None:
            raise ValueError("Trainer: training requires a train_dataset.")

        train_dataset = self.train_dataset
        data_collator = self.data_collator
        # if is_datasets_available() and isinstance(train_dataset, datasets.Dataset):
        #     train_dataset = self._remove_unused_columns(train_dataset, description="training")
        # else:
        #     data_collator = self._get_collator_with_removed_columns(data_collator, description="training")

        dataloader_params = {
            "batch_size": self._train_batch_size,
            "collate_fn": data_collator,
            "num_workers": self.args.dataloader_num_workers,
            "pin_memory": self.args.dataloader_pin_memory,
            "persistent_workers": self.args.dataloader_persistent_workers,
        }

        if not isinstance(train_dataset, torch.utils.data.IterableDataset):
            dataloader_params["sampler"] = self._get_train_sampler()
            dataloader_params["drop_last"] = self.args.dataloader_drop_last
            dataloader_params["worker_init_fn"] = seed_worker
            dataloader_params["prefetch_factor"] = self.args.dataloader_prefetch_factor

        return self.accelerator.prepare(DataLoader(train_dataset, **dataloader_params))
    
    def get_eval_dataloader(self, eval_dataset: Optional[Dataset] = None) -> DataLoader:
        """
        Returns the evaluation [`~torch.utils.data.DataLoader`].

        Subclass and override this method if you want to inject some custom behavior.

        Args:
            eval_dataset (`torch.utils.data.Dataset`, *optional*):
                If provided, will override `self.eval_dataset`. If it is a [`~datasets.Dataset`], columns not accepted
                by the `model.forward()` method are automatically removed. It must implement `__len__`.
        """
        if eval_dataset is None and self.eval_dataset is None:
            raise ValueError("Trainer: evaluation requires an eval_dataset.")

        # If we have persistent workers, don't do a fork bomb especially as eval datasets
        # don't change during training
        if hasattr(self, "_eval_dataloader") and self.args.dataloader_persistent_workers:
            return self.accelerator.prepare(self._eval_dataloader)
        eval_dataset = eval_dataset if eval_dataset is not None else self.eval_dataset
        data_collator = self.data_collator

        # if is_datasets_available() and isinstance(eval_dataset, datasets.Dataset):
        #     eval_dataset = self._remove_unused_columns(eval_dataset, description="evaluation")
        # else:
        #     data_collator = self._get_collator_with_removed_columns(data_collator, description="evaluation")

        dataloader_params = {
            "batch_size": self.args.eval_batch_size,
            "collate_fn": data_collator,
            "num_workers": self.args.dataloader_num_workers,
            "pin_memory": self.args.dataloader_pin_memory,
            "persistent_workers": self.args.dataloader_persistent_workers,
        }

        if not isinstance(eval_dataset, torch.utils.data.IterableDataset):
            dataloader_params["sampler"] = self._get_eval_sampler(eval_dataset)
            dataloader_params["drop_last"] = self.args.dataloader_drop_last
            dataloader_params["prefetch_factor"] = self.args.dataloader_prefetch_factor

        # accelerator.free_memory() will destroy the references, so
        # we need to store the non-prepared version
        eval_dataloader = DataLoader(eval_dataset, **dataloader_params)
        if self.args.dataloader_persistent_workers:
            self._eval_dataloader = eval_dataloader

        return self.accelerator.prepare(eval_dataloader)

    def compute_loss(self, model, inputs, return_outputs=False):
        if self.label_smoother is not None and "labels" in inputs:
            labels = inputs.pop("labels")
        else:
            labels = None
        outputs = model(**inputs)
        # Save past state if it exists
        # TODO: this needs to be fixed and made cleaner later.
        if self.args.past_index >= 0:
            self._past = outputs[self.args.past_index]

        if labels is not None:
            unwrapped_model = unwrap_model(model)
            # if _is_peft_model(unwrapped_model):
            #     model_name = unwrapped_model.base_model.model._get_name()
            # else:
            #     model_name = unwrapped_model._get_name()
            model_name = unwrapped_model._get_name()
            if model_name in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES.values():
                loss = self.label_smoother(outputs, labels, shift_labels=True)
            else:
                loss = self.label_smoother(outputs, labels)
        else:
            if isinstance(outputs, dict) and "loss" not in outputs:
                raise ValueError(
                    "The model did not return a loss from the inputs, only the following keys: "
                    f"{','.join(outputs.keys())}. For reference, the inputs it received are {','.join(inputs.keys())}."
                )
            # We don't use .loss here since the model may return tuples instead of ModelOutput.
            loss = outputs["loss"] if isinstance(outputs, dict) else outputs[0]

        return (loss, outputs) if return_outputs else loss
    
class QformerTrainer(CustomTrainer):
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.logging_loss_scalar = {
            "total_loss": 0.0,
            "loss_itc": 0.0,
            "loss_itm": 0.0,
            "loss_lm": 0.0,
        }

    def _prepare_non_packed_dataloader(
        self,
        tokenizer,
        dataset,
        dataset_text_field,
        max_seq_length,
        formatting_func=None,
        add_special_tokens=True,
        remove_unused_columns=True,
    ):
        return dataset

    def _maybe_log_save_evaluate(self, tr_loss, grad_norm, model, trial, epoch, ignore_keys_for_eval):
        if self.control.should_log and self.state.global_step > self._globalstep_last_logged:

            logs: Dict[str, float] = {}

            # all_gather + mean() to get average loss over all processes
            tr_loss_scalar = self._nested_gather(tr_loss).mean().item()

            # reset tr_loss to zero
            tr_loss -= tr_loss

            logs["loss"] = round(tr_loss_scalar / (self.state.global_step - self._globalstep_last_logged), 4)
            if grad_norm is not None:
                logs["grad_norm"] = grad_norm.detach().item() if isinstance(grad_norm, torch.Tensor) else grad_norm
            logs["learning_rate"] = self._get_learning_rate()

            # 平均并记录自定义的loss日志
            logs["total_loss"] = round(self.logging_loss_scalar["total_loss"] / (self.state.global_step - self._globalstep_last_logged), 4)
            logs["loss_itc"] = round(self.logging_loss_scalar["loss_itc"] / (self.state.global_step - self._globalstep_last_logged), 4)
            logs["loss_itm"] = round(self.logging_loss_scalar["loss_itm"] / (self.state.global_step - self._globalstep_last_logged), 4)
            logs["loss_lm"] = round(self.logging_loss_scalar["loss_lm"] / (self.state.global_step - self._globalstep_last_logged), 4)

            self._total_loss_scalar += tr_loss_scalar
            self._globalstep_last_logged = self.state.global_step
            self.store_flos()

            # 重置累积的loss
            self.logging_loss_scalar = {
                "total_loss": 0.0,
                "loss_itc": 0.0,
                "loss_itm": 0.0,
                "loss_lm": 0.0,
                }

            self.log(logs)

        metrics = None
        if self.control.should_evaluate:
            metrics = self._evaluate(trial, ignore_keys_for_eval)

        if self.control.should_save:
            self._save_checkpoint(model, trial, metrics=metrics)
            self.control = self.callback_handler.on_save(self.args, self.state, self.control)
    
    def compute_loss(self, model, inputs, return_outputs=False):
        outputs = model(**inputs)
        loss = outputs["loss"]

        self.logging_loss_scalar['total_loss'] += loss.item() / self.args.gradient_accumulation_steps
        self.logging_loss_scalar['loss_itc'] += outputs["loss_itc"].item() / self.args.gradient_accumulation_steps
        self.logging_loss_scalar['loss_itm'] += outputs["loss_itm"].item() / self.args.gradient_accumulation_steps
        self.logging_loss_scalar['loss_lm'] += outputs["loss_lm"].item() / self.args.gradient_accumulation_steps

        return (loss, outputs) if return_outputs else loss