# Copyright 2020-2025 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os import re import textwrap import warnings from contextlib import nullcontext from functools import wraps from pathlib import Path from typing import Any, Callable, Optional, Union import jinja2 import torch import torch.nn as nn import torch.nn.functional as F import torch.utils.data from accelerate import logging from accelerate.utils import broadcast_object_list, gather_object, is_peft_model from datasets import Dataset from packaging import version from torch.distributed.fsdp import FullyShardedDataParallel as FSDP from torch.utils.data import DataLoader, IterableDataset from transformers import ( AutoModelForCausalLM, AutoModelForSequenceClassification, AutoTokenizer, DataCollator, GenerationConfig, PreTrainedModel, PreTrainedTokenizerBase, ProcessorMixin, Trainer, TrainerCallback, is_apex_available, is_wandb_available, ) from transformers.models.auto.modeling_auto import MODEL_FOR_IMAGE_TEXT_TO_TEXT_MAPPING_NAMES from transformers.trainer_utils import EvalPrediction, seed_worker from transformers.training_args import OptimizerNames from transformers.utils import ( is_flash_attn_2_available, is_peft_available, is_sagemaker_mp_enabled, ) from ..data_utils import apply_chat_template, is_conversational, maybe_apply_chat_template from ..extras.profiling import profiling_context from ..extras.vllm_client import VLLMClient from ..import_utils import is_vllm_available from ..models import create_reference_model, prepare_peft_model from ..models.utils import unwrap_model_for_generation from .judges import BasePairwiseJudge from .online_dpo_config import OnlineDPOConfig from .utils import ( SIMPLE_CHAT_TEMPLATE, DPODataCollatorWithPadding, disable_dropout_in_model, empty_cache, generate_model_card, get_comet_experiment_url, pad, prepare_deepspeed, truncate_right, ) if is_peft_available(): from peft import PeftConfig, PeftModel if is_apex_available(): from apex import amp if is_sagemaker_mp_enabled(): from smdistributed.modelparallel import __version__ as SMP_VERSION IS_SAGEMAKER_MP_POST_1_10 = version.parse(SMP_VERSION) >= version.parse("1.10") else: IS_SAGEMAKER_MP_POST_1_10 = False if is_vllm_available(): from vllm import LLM, SamplingParams from vllm.sampling_params import GuidedDecodingParams if is_wandb_available(): import wandb logger = logging.get_logger(__name__) # What we call a reward function is a callable that takes a list of prompts and completions and returns a list of # rewards. When it's a string, it's a model ID, so it's loaded as a pretrained model. RewardFunc = Union[str, PreTrainedModel, Callable[[list, list], list[float]]] class OnlineDPOTrainer(Trainer): r""" Initialize OnlineDPOTrainer. Args: model (`Union[str, nn.Module, PreTrainedModel]`): Model to be trained. Can be either: - A string, being the *model id* of a pretrained model hosted inside a model repo on huggingface.co, or a path to a *directory* containing model weights saved using [`~transformers.PreTrainedModel.save_pretrained`], e.g., `'./my_model_directory/'`. The model is loaded using [`~transformers.AutoModelForCausalLM.from_pretrained`] with the keyword arguments in `args.model_init_kwargs`. - A [`~transformers.PreTrainedModel`] object. Only causal language models are supported. ref_model (`transformers.PreTrainedModel` or `torch.nn.Module` or `None`): The reference model to use for training. If None is specified, the reference model will be created from the model. judge (`BasePairwiseJudge`): The judge to use for pairwise comparison of model completions. reward_funcs (`Union[RewardFunc, list[RewardFunc]]`, *optional*, defaults to `None`): Reward functions to be used for computing the rewards. To compute the rewards, we call all the reward functions with the prompts and completions and sum the rewards. Can be either: - A single reward function: Can be a string (path to model), a [`~transformers.PreTrainedModel`], or a custom callable function. - A list of reward functions: Must all be of compatible types. Note: Only one of `judge`, or `reward_funcs` should be provided. args (`OnlineDPOConfig`): The online DPO config arguments to use for training. data_collator (`transformers.DataCollator`): The data collator to use for training. If None is specified, the default data collator (`DPODataCollatorWithPadding`) will be used which will pad the sequences to the maximum length of the sequences in the batch, given a dataset of paired sequences. train_dataset ([`~datasets.Dataset`] or [`~datasets.IterableDataset`]): The dataset to use for training. eval_dataset ([`~datasets.Dataset`], [`~datasets.IterableDataset`] or `dict[str, Union[Dataset, IterableDataset]]`): The dataset to use for evaluation. processing_class ([`~transformers.PreTrainedTokenizerBase`] or [`~transformers.ProcessorMixin`], *optional*, defaults to `None`): Processing class used to process the data. If provided, will be used to automatically process the inputs for the model, and it will be saved along the model to make it easier to rerun an interrupted training or reuse the fine-tuned model. reward_processing_classes (`Union[PreTrainedTokenizerBase, list[PreTrainedTokenizerBase]]`, *optional*, defaults to `None`): Processing classes corresponding to the reward functions specified in `reward_funcs`. Can be either: - A single processing class: Used when `reward_funcs` contains only one reward function. - A list of processing classes: Must match the order and length of the reward functions in `reward_funcs`. If set to `None`, the tokenizer for each model-based reward function is automatically loaded using [`~transformers.AutoTokenizer.from_pretrained`]. peft_config ([`~peft.PeftConfig`], *optional*, defaults to `None`): PEFT configuration used to wrap the model. If `None`, the model is not wrapped. compute_metrics (`Callable[[EvalPrediction], dict]`, *optional*): The function to use to compute the metrics. Must take a `EvalPrediction` and return a dictionary string to metric values. callbacks (`list[transformers.TrainerCallback]`): The callbacks to use for training. optimizers (`tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR]`): The optimizer and scheduler to use for training. preprocess_logits_for_metrics (`Callable[[torch.Tensor, torch.Tensor], torch.Tensor]`): The function to use to preprocess the logits before computing the metrics. .. deprecated:: 0.22.0 The following parameters are deprecated and will be removed in a future version: * `reward_model`: Use `reward_funcs` instead. For example, change `reward_model=model` to `reward_funcs=model`. * `reward_processing_class`: Use `reward_processing_classes` instead. For example, change `reward_processing_class=tokenizer` to `reward_processing_classes=tokenizer`. """ _tag_names = ["trl", "online-dpo"] def __init__( self, model: Union[PreTrainedModel, nn.Module, str], ref_model: Union[PreTrainedModel, nn.Module, None] = None, reward_funcs: Optional[Union[RewardFunc, list[RewardFunc]]] = None, judge: Optional[BasePairwiseJudge] = None, args: Optional[OnlineDPOConfig] = None, data_collator: Optional[DataCollator] = None, train_dataset: Optional[Union[Dataset, IterableDataset]] = None, eval_dataset: Optional[Union[Dataset, IterableDataset, dict[str, Union[Dataset, IterableDataset]]]] = None, processing_class: Optional[Union[PreTrainedTokenizerBase, ProcessorMixin]] = None, reward_processing_classes: Optional[Union[PreTrainedTokenizerBase, list[PreTrainedTokenizerBase]]] = None, peft_config: Optional["PeftConfig"] = 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, # Deprecated parameters reward_model: Optional[Union[PreTrainedModel, nn.Module]] = None, reward_processing_class: Optional[PreTrainedTokenizerBase] = None, ) -> None: if ref_model is model: raise ValueError( "`model` and `ref_model` cannot be the same object. If you want `ref_model` to be the " "same as `model`, either omit the `ref_model` argument or pass `None`." ) self.ref_model = ref_model # Handle deprecated parameters for backward compatibility if reward_model is not None: warnings.warn( "The `reward_model` parameter is deprecated and will be removed in version 0.25.0. " "Please use `reward_funcs` instead. For example, change `reward_model=model` to `reward_funcs=model`.", ) # Convert old reward_model to new reward_funcs format if reward_funcs is None: reward_funcs = reward_model else: warnings.warn( "Both `reward_model` and `reward_funcs` are provided. Using `reward_funcs` and ignoring " "`reward_model`.", ) if reward_processing_class is not None: warnings.warn( "The `reward_processing_class` parameter is deprecated and will be removed in version 0.25.0. " "Please use `reward_processing_classes` instead. For example, change " "`reward_processing_class=tokenizer` to `reward_processing_classes=tokenizer`.", ) # Convert old reward_processing_class to new reward_processing_classes format if reward_processing_classes is None: reward_processing_classes = reward_processing_class else: warnings.warn( "Both `reward_processing_class` and `reward_processing_classes` are provided. Using " "`reward_processing_classes` and ignoring `reward_processing_class`.", ) # Validate reward configuration - must have exactly one of: judge, or reward_funcs reward_configs = sum(x is not None for x in [judge, reward_funcs]) if reward_configs == 0: raise ValueError("One of `judge` or `reward_funcs` must be provided.") elif reward_configs > 1: if judge is not None: logger.warning( "Both `judge` and `reward_funcs` are provided. Using `judge` and ignoring `reward_funcs`.", UserWarning, ) reward_funcs = None self.judge = judge # Handle reward_funcs if reward_funcs is not None: if not isinstance(reward_funcs, list): reward_funcs = [reward_funcs] self.reward_func_names = [] # Process reward functions (convert strings to models, collect names) model_init_kwargs = args.model_init_kwargs or {} for i, reward_func in enumerate(reward_funcs): if isinstance(reward_func, str): # Load model from string path reward_funcs[i] = AutoModelForSequenceClassification.from_pretrained( reward_func, num_labels=1, **model_init_kwargs ) if isinstance(reward_funcs[i], nn.Module): self.reward_func_names.append(reward_funcs[i].config._name_or_path.split("/")[-1]) else: self.reward_func_names.append(reward_funcs[i].__name__) self.reward_funcs = reward_funcs # Handle reward processing classes for reward_funcs if reward_processing_classes is None: reward_processing_classes = [None] * len(reward_funcs) elif not isinstance(reward_processing_classes, list): reward_processing_classes = [reward_processing_classes] else: if len(reward_processing_classes) != len(reward_funcs): raise ValueError( "The number of reward processing classes must match the number of reward functions." ) self.reward_processing_classes = [] for reward_processing_class_i, reward_func in zip(reward_processing_classes, reward_funcs): if isinstance(reward_func, PreTrainedModel): if reward_processing_class_i is None: reward_processing_class_i = AutoTokenizer.from_pretrained(reward_func.config._name_or_path) if reward_processing_class_i.pad_token_id is None: reward_processing_class_i.pad_token = reward_processing_class_i.eos_token # Set pad token ID on reward model config reward_func.config.pad_token_id = reward_processing_class_i.pad_token_id self.reward_processing_classes.append(reward_processing_class_i) else: self.reward_funcs = None self.reward_func_names = [] self.reward_processing_classes = [] # Handle reward_weights if reward_funcs is not None: if args.reward_weights is not None: if len(args.reward_weights) != len(self.reward_funcs): raise ValueError( f"Number of reward weights ({len(args.reward_weights)}) must match number of reward " f"functions ({len(self.reward_funcs)})" ) self.reward_weights = torch.tensor(args.reward_weights, dtype=torch.float32) else: self.reward_weights = torch.ones(len(self.reward_funcs), dtype=torch.float32) else: self.reward_weights = None if args.missing_eos_penalty is not None and reward_funcs is None and judge is None: # Check if this is the old reward_model case if reward_model is not None: logger.warning( "The `missing_eos_penalty` parameter is deprecated when used with the deprecated `reward_model` parameter. " "Please use `reward_funcs` instead of `reward_model` to continue using this feature.", DeprecationWarning, stacklevel=2, ) else: raise ValueError("`missing_eos_penalty` is only supported when `reward_funcs` is provided.") if args is None: raise ValueError("`args` must be provided.") # Check that the processing_class is provided if processing_class is None: raise ValueError("`processing_class` must be provided.") model_init_kwargs = args.model_init_kwargs or {} if isinstance(model, str): model_id = model # Handle dtype in model_init_kwargs dtype = model_init_kwargs.get("dtype") if isinstance(dtype, torch.dtype) or dtype == "auto" or dtype is None: pass elif isinstance(dtype, str): dtype = getattr(torch, dtype) model_init_kwargs["dtype"] = dtype else: raise ValueError( "Invalid `dtype` passed to `OnlineDPOConfig`. Expected either 'auto' or a string " f"representing a `torch.dtype` (e.g., 'float32'), but got {dtype}." ) model = AutoModelForCausalLM.from_pretrained(model_id, **model_init_kwargs) else: if args.model_init_kwargs is not None: raise ValueError( "You passed `model_init_kwargs` to the `OnlineDPOConfig`, but your model is already instantiated. " "This argument can only be used when the `model` argument is a string." ) self.is_encoder_decoder = model.config.is_encoder_decoder self.is_vision_model = model.config.model_type in MODEL_FOR_IMAGE_TEXT_TO_TEXT_MAPPING_NAMES.keys() if peft_config is not None or (is_peft_available() and isinstance(model, PeftModel)): model = prepare_peft_model(model, peft_config, args) # Enable gradient checkpointing if requested if args.gradient_checkpointing: model = self._enable_gradient_checkpointing(model, args) # Disable dropout in the model and reference model if args.disable_dropout: disable_dropout_in_model(model) if self.ref_model is not None: disable_dropout_in_model(self.ref_model) # Handle the ref_model # Usually, the user wants the ref model to be the initial version of the model. When using PEFT, it's easy to # get the ref model, as it's just the model with a disabled adapter. When not using PEFT, we need to create # the ref model from the model by copying it and disable the gradients and set it in evaluation mode. if ref_model is None: # No ref model provided, the most common case if peft_config is None: self.ref_model = create_reference_model(model) # copy, disable gradients, set eval mode else: self.ref_model = None # we don't need a ref model here, we can just disable the adapter. else: # rare case, the user provided a ref model self.ref_model = ref_model self.ref_model.eval() # Disable the gradient and set the reward model in eval mode if reward_funcs is not None: for reward_func in reward_funcs: if isinstance(reward_func, PreTrainedModel): reward_func.eval() self.max_length = args.max_length self.stats = { "objective/kl": [], "objective/entropy": [], "objective/non_score_reward": [], "rewards/chosen": [], "rewards/rejected": [], "rewards/accuracies": [], "rewards/margins": [], "logps/chosen": [], "logps/rejected": [], "val/contain_eos_token": [], "beta": [], } if self.reward_funcs is not None: self.stats["objective/rlhf_reward"] = [] self.stats["objective/scores_margin"] = [] self.stats["objective/scores"] = [] # Store generation parameters for later use self.use_vllm = args.use_vllm self.num_generations = 2 # Generate 2 completions per prompt for Online DPO self.temperature = args.temperature self.top_p = args.top_p self.top_k = args.top_k self.min_p = args.min_p self.repetition_penalty = args.repetition_penalty self.use_transformers_paged = args.use_transformers_paged self.vllm_mode = args.vllm_mode if args.use_vllm else None self.vllm_gpu_memory_utilization = args.vllm_gpu_memory_utilization self.vllm_tensor_parallel_size = args.vllm_tensor_parallel_size self.vllm_model_impl = args.vllm_model_impl # Handle pad token for processors or tokenizers if isinstance(processing_class, ProcessorMixin): tokenizer = processing_class.tokenizer elif isinstance(processing_class, PreTrainedTokenizerBase): tokenizer = processing_class else: raise TypeError("The `processing_class` must be either a `PreTrainedTokenizerBase` or a `ProcessorMixin`") if tokenizer.pad_token is None: tokenizer.pad_token = tokenizer.eos_token self.pad_token = tokenizer.pad_token self.pad_token_id = tokenizer.pad_token_id self.eos_token_id = tokenizer.eos_token_id # Vision tokens for VLM support self.image_token_id = getattr(processing_class, "image_token_id", None) self.vision_start_token_id = getattr(processing_class, "vision_start_token_id", None) self.vision_end_token_id = getattr(processing_class, "vision_end_token_id", None) # Get the image token string for token collapsing self.image_token = None if self.image_token_id is not None: self.image_token = tokenizer.decode([self.image_token_id]) # Define the collator if not provided if data_collator is None: data_collator = DPODataCollatorWithPadding(pad_token_id=self.pad_token_id) # The trainer estimates the number of FLOPs (floating-point operations) using the number of elements in the # input tensor associated with the key "input_ids". However, in Online DPO, the sampled data does not include # the "input_ids" key. As a result, the trainer issues the warning: "Could not estimate the number of tokens # of the input, floating-point operations will not be computed." To suppress this warning, we set the # "estimate_tokens" key in the model's "warnings_issued" dictionary to True. This acts as a flag to indicate # that the warning has already been issued. model.warnings_issued["estimate_tokens"] = True super().__init__( model=model, args=args, data_collator=data_collator, train_dataset=train_dataset, eval_dataset=eval_dataset, processing_class=processing_class, 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) self._beta = args.beta # Set up generation configuration and vLLM after super().__init__ if self.use_vllm: if not is_vllm_available(): raise ImportError( "vLLM is not available and `use_vllm` is set to True. Please install vLLM with " "`pip install vllm` to use it." ) if self.vllm_mode == "server": if self.accelerator.is_main_process: if args.vllm_server_base_url is not None: base_url = args.vllm_server_base_url else: base_url = f"http://{args.vllm_server_host}:{args.vllm_server_port}" self.vllm_client = VLLMClient(base_url=base_url, connection_timeout=args.vllm_server_timeout) self.vllm_client.init_communicator(device=torch.cuda.current_device()) else: self.vllm_client = None elif self.vllm_mode == "colocate": # vLLM dynamically adjusts the size of the key-value cache based on available GPU memory at instantiation. # A larger cache size improves speed, so we would expect gpu_memory_utilization=1. # However, at this stage, the optimizer's weights are not yet loaded onto the GPU; they will be loaded # after the first optimizer step and remain in GPU memory throughout training. So we must reserve enough # space for them. # Configure vLLM parameters vllm_kwargs = { "model": model.name_or_path, "tensor_parallel_size": self.vllm_tensor_parallel_size, "gpu_memory_utilization": self.vllm_gpu_memory_utilization, "model_impl": self.vllm_model_impl, "max_num_seqs": self.args.per_device_train_batch_size * self.vllm_tensor_parallel_size, "max_model_len": args.max_length + args.max_new_tokens, # max_length includes prompt + completion "distributed_executor_backend": "external_launcher", # Feed identical seed for tp groups to ensure sampling results are the same across workers "seed": self.accelerator.process_index // self.vllm_tensor_parallel_size, # Latest vLLM v1 memory profiler is misled by the high default value (i.e., 32768) "max_num_batched_tokens": 4096, } # vLLM requires the environment variables to be set for distributed training. os.environ["RANK"] = str(self.accelerator.process_index) os.environ["LOCAL_RANK"] = str(self.accelerator.local_process_index) os.environ["WORLD_SIZE"] = str(self.accelerator.num_processes) os.environ["MASTER_ADDR"] = os.environ.get("MASTER_ADDR", "localhost") os.environ["MASTER_PORT"] = os.environ.get("MASTER_PORT", "12345") self.llm = LLM(**vllm_kwargs) else: raise ValueError(f"vllm_mode must be either 'server' or 'colocate', got '{self.vllm_mode}'.") # vLLM specific sampling arguments self.guided_decoding_regex = args.vllm_guided_decoding_regex self._last_loaded_step = -1 # tag to avoid useless loading during grad accumulation # Set up vLLM generation config generation_params = { "n": 2, # 2 generations per prompt for Online DPO "repetition_penalty": self.repetition_penalty, "temperature": self.temperature, "top_p": self.top_p, "top_k": -1 if self.top_k is None else self.top_k, "min_p": 0.0 if self.min_p is None else self.min_p, "max_tokens": args.max_new_tokens, "detokenize": False, # to avoid vllm to decode (we don't need it) } if args.generation_kwargs is not None: generation_params.update(args.generation_kwargs) if self.guided_decoding_regex: generation_params["guided_decoding"] = GuidedDecodingParams(regex=self.guided_decoding_regex) self.generation_config = SamplingParams(**generation_params) # When using vLLM, the main process is responsible for loading the model weights. This can cause process # desynchronization and seems to lead to DeepSpeed hanging during initialization. To prevent this, we # synchronize all processes after vLLM has been fully initialized. self.accelerator.wait_for_everyone() else: # Set up transformers generation config generation_kwargs = { "max_new_tokens": args.max_new_tokens, "do_sample": True, "pad_token_id": self.pad_token_id, "bos_token_id": tokenizer.bos_token_id, "eos_token_id": self.eos_token_id, "temperature": self.temperature, "top_k": self.top_k, "top_p": self.top_p, "repetition_penalty": self.repetition_penalty, "use_cache": True if not self.args.gradient_checkpointing else False, } # Add min_p if supported if self.min_p is not None: generation_kwargs["min_p"] = self.min_p if args.generation_kwargs is not None: generation_kwargs.update(args.generation_kwargs) if self.use_transformers_paged: generation_kwargs["max_batch_tokens"] = 512 generation_kwargs["num_blocks"] = 1024 generation_kwargs["block_size"] = 128 # Remove None values generation_kwargs = {k: v for k, v in generation_kwargs.items() if v is not None} self.generation_config = GenerationConfig(**generation_kwargs) if self.is_deepspeed_enabled: if self.ref_model is not None: self.ref_model = prepare_deepspeed( self.ref_model, args.per_device_train_batch_size, args.fp16, args.bf16 ) # Prepare reward function models for DeepSpeed if self.reward_funcs is not None: for i, reward_func in enumerate(self.reward_funcs): if isinstance(reward_func, PreTrainedModel): self.reward_funcs[i] = prepare_deepspeed(reward_func, self.accelerator) else: if self.ref_model is not None: self.ref_model = self.ref_model.to(self.accelerator.device) # Prepare reward function models for FSDP/regular training if self.reward_funcs is not None: for i, reward_func in enumerate(self.reward_funcs): if isinstance(reward_func, PreTrainedModel): # Set device placement to True to make `prepare_model` move `reward_func` to device when using fsdp self.reward_funcs[i] = self.accelerator.prepare_model( reward_func, evaluation_mode=True, device_placement=True ) @property def beta(self): if isinstance(self._beta, list): epoch = self.state.epoch return self._beta[epoch] if epoch < len(self._beta) else self._beta[-1] else: return self._beta @staticmethod def tokenize_row(feature, is_encoder_decoder: bool, tokenizer: PreTrainedTokenizerBase) -> dict[str, Any]: """Tokenize a single row from a DPO specific dataset.""" if not is_encoder_decoder: batch = tokenizer(feature["prompt"], add_special_tokens=False) # Add BOS token to head of prompt. Avoid adding if it's already there if tokenizer.bos_token_id is not None: prompt_len_input_ids = len(batch["input_ids"]) if prompt_len_input_ids == 0 or tokenizer.bos_token_id != batch["input_ids"][0]: batch["input_ids"] = [tokenizer.bos_token_id] + batch["input_ids"] batch["attention_mask"] = [1] + batch["attention_mask"] else: batch = tokenizer(feature["prompt"], add_special_tokens=True) batch = {f"prompt_{key}": value for key, value in batch.items()} return batch # Same as Trainer.get_train_dataloader but skip the "remove_unused_columns". @wraps(Trainer.get_train_dataloader) def get_train_dataloader(self) -> DataLoader: if self.train_dataset is None: raise ValueError("Trainer: training requires a train_dataset.") train_dataset = self.train_dataset data_collator = self.data_collator 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)) # Same as Trainer.get_eval_dataloader but skip the "remove_unused_columns". @wraps(Trainer.get_eval_dataloader) def get_eval_dataloader(self, eval_dataset: Optional[Union[str, Dataset]] = None) -> DataLoader: 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 dataloader_key = eval_dataset if isinstance(eval_dataset, str) else "eval" if ( hasattr(self, "_eval_dataloaders") and dataloader_key in self._eval_dataloaders and self.args.dataloader_persistent_workers ): return self.accelerator.prepare(self._eval_dataloaders[dataloader_key]) eval_dataset = ( self.eval_dataset[eval_dataset] if isinstance(eval_dataset, str) else eval_dataset if eval_dataset is not None else self.eval_dataset ) data_collator = self.data_collator 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: if hasattr(self, "_eval_dataloaders"): self._eval_dataloaders[dataloader_key] = eval_dataloader else: self._eval_dataloaders = {dataloader_key: eval_dataloader} return self.accelerator.prepare(eval_dataloader) def _enable_gradient_checkpointing(self, model: PreTrainedModel, args: OnlineDPOConfig) -> PreTrainedModel: """Enables gradient checkpointing for the model.""" # Ensure use_cache is disabled model.config.use_cache = False # Enable gradient checkpointing on the base model for PEFT if is_peft_model(model): model.base_model.gradient_checkpointing_enable() # Enable gradient checkpointing for non-PEFT models else: model.gradient_checkpointing_enable() gradient_checkpointing_kwargs = args.gradient_checkpointing_kwargs or {} use_reentrant = ( "use_reentrant" not in gradient_checkpointing_kwargs or gradient_checkpointing_kwargs["use_reentrant"] ) if use_reentrant: model.enable_input_require_grads() return model def _generate_vllm(self, prompts, images=None): eos_token_id = self.eos_token_id pad_token_id = self.pad_token_id # Generate completion_ids and prompt_ids based on mode if self.vllm_mode == "server": completion_ids, prompt_ids = self._generate_vllm_server(prompts, images) elif self.vllm_mode == "colocate": completion_ids, prompt_ids = self._generate_vllm_colocate(prompts, images) # Shared padding, masking, and tensor conversion logic max_prompt_length = max(len(ids) for ids in prompt_ids) prompt_mask = [[0] * (max_prompt_length - len(ids)) + [1] * len(ids) for ids in prompt_ids] prompt_ids = [[pad_token_id] * (max_prompt_length - len(ids)) + ids for ids in prompt_ids] max_tokens = self.generation_config.max_tokens completion_mask = [[1] * len(ids) + [0] * (max_tokens - len(ids)) for ids in completion_ids] completion_ids = [ ids + [eos_token_id] if ids[-1] != eos_token_id and len(ids) < max_tokens else ids for ids in completion_ids ] completion_ids = [ids + [pad_token_id] * (max_tokens - len(ids)) for ids in completion_ids] # Convert to tensors prompt_ids = torch.tensor(prompt_ids, device=self.accelerator.device) prompt_mask = torch.tensor(prompt_mask, device=self.accelerator.device) completion_ids = torch.tensor(completion_ids, device=self.accelerator.device) completion_mask = torch.tensor(completion_mask, device=self.accelerator.device) return prompt_ids, prompt_mask, completion_ids, completion_mask def _generate_vllm_server(self, prompts, images=None): """Generate completions using vLLM server mode""" has_images = images is not None # Update vLLM server weights if needed if hasattr(self, "_last_loaded_step") and self.state.global_step != self._last_loaded_step: self._move_model_to_vllm() self._last_loaded_step = self.state.global_step elif not hasattr(self, "_last_loaded_step"): self._move_model_to_vllm() self._last_loaded_step = self.state.global_step # Apply chat template if conversational if is_conversational({"prompt": prompts[0]}): prompts_text = [apply_chat_template({"prompt": p}, self.processing_class)["prompt"] for p in prompts] else: prompts_text = prompts # Gather all prompts to main process all_prompts = gather_object(prompts_text) if has_images: all_images = gather_object(images) if self.accelerator.is_main_process: # Since 'prompts' contains 'num_generations' duplicates, we first take unique prompts, and generate # num_generations outputs for each one. This is faster than generating outputs for each duplicate # prompt individually. ordered_set_of_prompts = all_prompts[:: self.num_generations] if has_images: ordered_set_of_images = all_images[:: self.num_generations] else: ordered_set_of_images = None completion_ids = self.vllm_client.generate( prompts=ordered_set_of_prompts, images=ordered_set_of_images, n=self.num_generations, repetition_penalty=self.repetition_penalty, temperature=self.temperature, top_p=self.top_p, top_k=-1 if self.top_k is None else self.top_k, min_p=0.0 if self.min_p is None else self.min_p, max_tokens=self.generation_config.max_tokens, guided_decoding_regex=self.guided_decoding_regex if hasattr(self, "guided_decoding_regex") else None, generation_kwargs=self.args.generation_kwargs, ) # Flatten: each prompt generates 2 completions completion_ids = [[comp_id] for prompt_completions in completion_ids for comp_id in prompt_completions] else: completion_ids = [None] * (len(all_prompts) * 2) # Broadcast completions to all processes completion_ids = broadcast_object_list(completion_ids, from_process=0) # Each process takes its slice process_slice = slice( self.accelerator.process_index * len(prompts) * 2, (self.accelerator.process_index + 1) * len(prompts) * 2, ) completion_ids = completion_ids[process_slice] # Create prompt_ids by tokenizing locally prompt_inputs = self.processing_class( text=prompts_text, return_tensors="pt", padding=True, padding_side="left", add_special_tokens=False, ) prompt_ids = [] for prompt_tokens in prompt_inputs["input_ids"]: prompt_ids.extend([prompt_tokens.tolist(), prompt_tokens.tolist()]) # 2 copies for 2 completions return completion_ids, prompt_ids def _generate_vllm_colocate(self, prompts, images=None): """Generate completions using vLLM colocate mode""" # Update model weights if needed self._move_model_to_vllm() # Apply chat template if conversational if is_conversational({"prompt": prompts[0]}): prompts_text = [apply_chat_template({"prompt": p}, self.processing_class)["prompt"] for p in prompts] else: prompts_text = prompts # Prepare vLLM inputs with images if available if images is not None: vllm_inputs = [] for prompt, image in zip(prompts_text, images): if image is not None: vllm_inputs.append({"prompt": prompt, "multi_modal_data": {"image": image}}) else: vllm_inputs.append(prompt) else: vllm_inputs = prompts_text outputs = self.llm.generate(vllm_inputs, self.generation_config, use_tqdm=False) completion_ids = [list(output.outputs[i].token_ids) for i in range(2) for output in outputs] prompt_ids = [list(output.prompt_token_ids) for _ in range(2) for output in outputs] return completion_ids, prompt_ids def _move_model_to_vllm(self): """Synchronize model weights to vLLM server with support for PEFT, DeepSpeed, and FSDP""" # For DeepSpeed ZeRO-3 and FSDP, we need to gather all parameters before operations deepspeed_plugin = self.accelerator.state.deepspeed_plugin zero_stage_3 = deepspeed_plugin is not None and deepspeed_plugin.zero_stage == 3 if zero_stage_3: import deepspeed gather_if_zero3 = deepspeed.zero.GatheredParameters else: gather_if_zero3 = nullcontext if is_peft_model(self.model): # With PEFT and FSDP/DeepSpeed ZeRO Stage 3, we must gather the full model at once before merging, as # merging adapters in a sharded manner is not supported. # TODO: does this work with FSDP? with gather_if_zero3(list(self.model.parameters())): self.model.merge_adapter() # Update vLLM weights while parameters are gathered if self.is_fsdp_enabled: # note if using FSDP, gather_if_zero3 is nullcontext # Update vLLM weights while parameters are gathered # For PEFT with FSDP we need to use the memory efficient post-order traversal fsdp_plugin = getattr(self.accelerator.state, "fsdp_plugin", None) fsdp_version = getattr(fsdp_plugin, "fsdp_version", 1) if fsdp_plugin else 1 if fsdp_version == 1: # use memory-efficient post-order traversal for FSDP self._sync_fsdp1_params_to_vllm(self.model) elif fsdp_version == 2: self._sync_fsdp2_params_to_vllm(self.model) else: # DeepSpeed ZeRO-3 with PEFT for name, param in self.model.named_parameters(): # When using PEFT, we need to recover the original parameter name and discard some parameters name = name.removeprefix("base_model.model.").replace(".base_layer", "") if self.model.prefix in name: continue # When module to save, remove its prefix and discard the original module if "original_module" in name: continue name = self._fix_param_name_to_vllm(name, extra_prefixes=["modules_to_save.default."]) if self.vllm_mode == "server" and self.accelerator.is_main_process: self.vllm_client.update_named_param(name, param.data) elif self.vllm_mode == "colocate": llm_model = self.llm.llm_engine.model_executor.driver_worker.model_runner.model llm_model.load_weights([(name, param.data)]) # Unmerge adapters while parameters are still gathered self.model.unmerge_adapter() # Parameters will automatically be repartitioned when exiting the context else: # For non-PEFT models, simply gather (if needed) and update each parameter individually. if self.is_fsdp_enabled: fsdp_plugin = getattr(self.accelerator.state, "fsdp_plugin", None) fsdp_version = getattr(fsdp_plugin, "fsdp_version", 1) if fsdp_plugin else 1 if fsdp_version == 1: self._sync_fsdp1_params_to_vllm(self.model) # use memory-efficient post-order traversal for FSDP elif fsdp_version == 2: self._sync_fsdp2_params_to_vllm(self.model) else: for name, param in self.model.named_parameters(): name = self._fix_param_name_to_vllm(name) with gather_if_zero3([param]): if self.vllm_mode == "server" and self.accelerator.is_main_process: self.vllm_client.update_named_param(name, param.data) elif self.vllm_mode == "colocate": llm_model = self.llm.llm_engine.model_executor.driver_worker.model_runner.model llm_model.load_weights([(name, param.data)]) # Reset cache on vLLM if self.vllm_mode == "server" and self.accelerator.is_main_process: self.vllm_client.reset_prefix_cache() elif self.vllm_mode == "colocate": self.llm.reset_prefix_cache() def _sync_fsdp1_params_to_vllm(self, module: nn.Module, prefix: str = "", visited=None): """Memory-efficient post-order traversal of FSDP modules to extract full parameters and sync with vLLM.""" # For FSDP1, we need to recurse into children and also use summon_full_params if visited is None: visited = set() for child_name, child_module in module.named_children(): child_prefix = f"{prefix}.{child_name}" if prefix else child_name self._sync_fsdp1_params_to_vllm( child_module, prefix=child_prefix, visited=visited ) # recurse into the child if isinstance(module, FSDP): with FSDP.summon_full_params(module, recurse=False, writeback=False): for param_name, param in module.named_parameters(): full_name = f"{prefix}.{param_name}" if prefix else param_name full_name = self._fix_param_name_to_vllm(full_name, extra_prefixes=["_fsdp_wrapped_module."]) if full_name in visited: continue # skip FSDP subtrees already traversed visited.add(full_name) if self.vllm_mode == "server" and self.accelerator.is_main_process: self.vllm_client.update_named_param(full_name, param.data) elif self.vllm_mode == "colocate": llm_model = self.llm.llm_engine.model_executor.driver_worker.model_runner.model llm_model.load_weights([(full_name, param.data)]) def _sync_fsdp2_params_to_vllm(self, module: nn.Module): # For FSDP2, module.state_dict() already covers all parameters, so no need for recursion for name, param in module.state_dict().items(): if param.is_cpu: param = param.to(torch.device("cuda")) param = param.full_tensor() if self.vllm_mode == "server" and self.accelerator.is_main_process: self.vllm_client.update_named_param(name, param) elif self.vllm_mode == "colocate": llm_model = self.llm.llm_engine.model_executor.driver_worker.model_runner.model llm_model.load_weights([(name, param)]) def _fix_param_name_to_vllm(self, name, extra_prefixes: Optional[list[str]] = None): """Clean parameter names for vLLM compatibility""" extra_prefixes = extra_prefixes or [] prefixes = ["_checkpoint_wrapped_module."] + extra_prefixes for prefix in prefixes: name = name.replace(prefix, "") return name def process_vision_row( self, features: dict[str, Union[list, torch.Tensor]], processing_class=None ) -> dict[str, list[int]]: """ Process a vision row for VLM models (adapted from DPO trainer) """ processor = processing_class or self.processing_class processed_features = processor(images=[features["image"]], text=features["prompt"], add_special_tokens=False) prompt_input_ids = processed_features["input_ids"][0] # Create the output dict with required fields output = { "prompt_input_ids": prompt_input_ids, "prompt_attention_mask": processed_features["attention_mask"][0], } # Add vision-specific fields if "pixel_values" in processed_features: output["pixel_values"] = processed_features["pixel_values"][0] if "pixel_attention_mask" in processed_features: output["pixel_attention_mask"] = processed_features["pixel_attention_mask"][0] if "image_sizes" in processed_features: output["image_sizes"] = processed_features["image_sizes"][0] return output def _generate(self, model, prompts, images=None): """Generate completions using the model""" device = next(model.parameters()).device eos_token_id = self.eos_token_id pad_token_id = self.pad_token_id # Apply chat template and tokenize the input inputs = [{"prompt": prompt} for prompt in prompts] # Add images if provided (VLM support) if images is not None: for i, image in enumerate(images): inputs[i]["image"] = image # Apply chat template to get text prompts prompts_text = [maybe_apply_chat_template(x, self.processing_class)["prompt"] for x in inputs] # Handle image token collapsing/removal # The chat template sometimes inserts a single image token into the prompt text. However, when this text is # later tokenized, the single image token string is expanded into multiple image token IDs, depending on the # image size. We need to handle this properly. if self.image_token is not None and images is not None: escaped_img_token = re.escape(self.image_token) # Search for the image token in the chat template if hasattr(self.processing_class, "chat_template") and self.processing_class.chat_template: if re.search(escaped_img_token, self.processing_class.chat_template): # Collapse repeated image tokens back into a single token prompts_text = [ re.sub(rf"({escaped_img_token})+", self.image_token, text) for text in prompts_text ] else: # If the chat template doesn't use the image token, remove all instances if self.vision_end_token_id is not None: escaped_eoi_token = re.escape( self.processing_class.tokenizer.decode([self.vision_end_token_id]) ) prompts_text = [ re.sub(rf"({escaped_img_token})+{escaped_eoi_token}", "", text) for text in prompts_text ] else: # If vision_end_token_id is None, just remove the image tokens prompts_text = [re.sub(rf"({escaped_img_token})+", "", text) for text in prompts_text] # Prepare kwargs for processing class kwargs = {} if images is not None: kwargs = {"images": [[img] for img in images]} # Process inputs using the processing class (handles both VLM and LLM) prompt_inputs = self.processing_class( text=prompts_text, return_tensors="pt", padding=True, padding_side="left", add_special_tokens=False, **kwargs, ) prompt_inputs = {k: v.to(device) for k, v in prompt_inputs.items()} # Convert vision inputs to model's dtype for proper computation if "pixel_values" in prompt_inputs: # Handle DataParallel wrapped models model_dtype = getattr(model, "dtype", None) if model_dtype is None and hasattr(model, "module"): model_dtype = model.module.dtype if model_dtype is not None: prompt_inputs["pixel_values"] = prompt_inputs["pixel_values"].to(model_dtype) # Sample 2 completions per prompt of size `max_new_tokens` from the model prompt_ids = prompt_inputs["input_ids"].repeat(2, 1) prompt_mask = prompt_inputs["attention_mask"].repeat(2, 1) # Prepare vision inputs if available vision_generation_kwargs = {} if self.is_vision_model and images is not None: if "pixel_values" in prompt_inputs: vision_generation_kwargs["pixel_values"] = prompt_inputs["pixel_values"].repeat(2, 1, 1, 1) if "pixel_attention_mask" in prompt_inputs: vision_generation_kwargs["pixel_attention_mask"] = prompt_inputs["pixel_attention_mask"].repeat(2, 1) if "image_sizes" in prompt_inputs: vision_generation_kwargs["image_sizes"] = prompt_inputs["image_sizes"].repeat(2, 1) if "image_grid_thw" in prompt_inputs: vision_generation_kwargs["image_grid_thw"] = prompt_inputs["image_grid_thw"].repeat(2, 1) if self.use_transformers_paged: previous_attn = self.model_wrapped.config._attn_implementation if is_flash_attn_2_available(): self.model_wrapped.config._attn_implementation = "paged_attention" else: self.model_wrapped.config._attn_implementation = "sdpa_paged" with ( profiling_context(self, "transformers.generate_batch"), unwrap_model_for_generation( model, self.accelerator, gather_deepspeed3_params=self.args.ds3_gather_for_generation ) as unwrapped_model, torch.no_grad(), FSDP.summon_full_params(self.model_wrapped, recurse=False) if self.is_fsdp_enabled else nullcontext(), ): # Cast to the appropriate dtype based on training configuration if self.args.bf16: unwrapped_model.to(torch.bfloat16) elif self.args.fp16: unwrapped_model.to(torch.float16) with torch.inference_mode(): all_outputs = unwrapped_model.generate_batch( prompt_ids.tolist(), generation_config=self.generation_config, progress_bar=False, ) completion_ids = [output.generated_tokens for output in all_outputs.values()] completion_ids = [torch.tensor(ids, device=device) for ids in completion_ids] completion_ids = pad(completion_ids, padding_value=self.pad_token_id, padding_side="right") prompt_completion_ids = torch.cat([prompt_ids, completion_ids], dim=1) # Restore the original attention implementation, training mode self.model_wrapped.config._attn_implementation = previous_attn # Extract completion_ids and create completion_mask prompt_length = prompt_ids.size(1) completion_ids = prompt_completion_ids[:, prompt_length:] completion_ids, completion_mask = truncate_right(completion_ids, eos_token_id, pad_token_id) return prompt_ids, prompt_mask, completion_ids, completion_mask else: # Regular generation path with ( profiling_context(self, "transformers.generate"), unwrap_model_for_generation( model, self.accelerator, gather_deepspeed3_params=self.args.ds3_gather_for_generation ) as unwrapped_model, torch.no_grad(), FSDP.summon_full_params(self.model_wrapped, recurse=False) if self.is_fsdp_enabled else nullcontext(), ): # Setup cache implementation if specified if self.args.cache_implementation is not None: unwrapped_model.generation_config.cache_implementation = self.args.cache_implementation # Standard generation output = unwrapped_model.generate( input_ids=prompt_ids, attention_mask=prompt_mask, generation_config=self.generation_config, **vision_generation_kwargs, ) completion_ids = output[:, prompt_ids.size(1) :] completion_ids, completion_mask = truncate_right(completion_ids, eos_token_id, pad_token_id) return prompt_ids, prompt_mask, completion_ids, completion_mask def _calculate_rewards_from_functions(self, prompts, completions, completion_ids_list, **reward_kwargs): """ Calculate rewards using reward functions """ device = self.accelerator.device rewards_per_func = torch.zeros(len(prompts), len(self.reward_funcs), device=device) # Add trainer state to reward kwargs for dynamic reward shaping reward_kwargs["trainer_state"] = self.state for i, (reward_func, reward_processing_class) in enumerate( zip(self.reward_funcs, self.reward_processing_classes) ): if isinstance(reward_func, nn.Module): # Model-based reward function # Handle conversational vs text input if is_conversational({"prompt": prompts[0]}): messages = [{"messages": p + c} for p, c in zip(prompts, completions)] texts = [apply_chat_template(x, reward_processing_class)["text"] for x in messages] else: texts = [p + c for p, c in zip(prompts, completions)] # Tokenize and get reward scores reward_inputs = reward_processing_class( text=texts, return_tensors="pt", padding=True, padding_side="right", add_special_tokens=False ) reward_inputs = {k: v.to(device) for k, v in reward_inputs.items()} with torch.inference_mode(): rewards_per_func[:, i] = reward_func(**reward_inputs).logits[:, 0] # Shape (B*G,) else: # Custom reward function output_reward_func = reward_func( prompts=prompts, completions=completions, completion_ids=completion_ids_list, **reward_kwargs ) # Convert None values to NaN output_reward_func = [reward if reward is not None else torch.nan for reward in output_reward_func] rewards_per_func[:, i] = torch.tensor(output_reward_func, dtype=torch.float32, device=device) # Weight and sum across all reward functions if self.reward_weights is not None: total_rewards = (rewards_per_func * self.reward_weights.to(device).unsqueeze(0)).nansum(dim=1) else: total_rewards = rewards_per_func.nansum(dim=1) return total_rewards def _forward(self, model, prompt_ids, prompt_mask, completion_ids, completion_mask, vision_inputs=None): # Get the number of tokens to truncate from prompt num_tokens_to_truncate = max(prompt_ids.size(1) + completion_ids.size(1) - self.max_length, 0) # Truncate left to avoid oom prompt_ids = prompt_ids[:, num_tokens_to_truncate:] prompt_mask = prompt_mask[:, num_tokens_to_truncate:] # Concat the prompt and completion prompt_completion_ids = torch.cat((prompt_ids, completion_ids), dim=1) prompt_completion_mask = torch.cat((prompt_mask, completion_mask), dim=1) # Prepare model kwargs with vision inputs if available model_kwargs = {"attention_mask": prompt_completion_mask} if vision_inputs is not None: if "pixel_values" in vision_inputs: model_kwargs["pixel_values"] = vision_inputs["pixel_values"] if "pixel_attention_mask" in vision_inputs: model_kwargs["pixel_attention_mask"] = vision_inputs["pixel_attention_mask"] if "image_sizes" in vision_inputs: model_kwargs["image_sizes"] = vision_inputs["image_sizes"] if "image_grid_thw" in vision_inputs: model_kwargs["image_grid_thw"] = vision_inputs["image_grid_thw"] # Get the logprobs of the completions from the model output = model(prompt_completion_ids, **model_kwargs) # There is 1 offset, because the model predict the next token prompt_len = prompt_ids.size(1) start_idx = prompt_len - 1 if prompt_len > 0 else 0 logits = output.logits[:, start_idx:-1] # Take the completion tokens logprob logprobs = torch.take_along_dim(logits.log_softmax(dim=-1), completion_ids.unsqueeze(-1), dim=2).squeeze(-1) return logprobs def training_step( self, model: nn.Module, inputs: dict[str, Union[torch.Tensor, Any]], num_items_in_batch: Optional[int] = None ) -> torch.Tensor: model.train() prompts = inputs["prompt"] batch_size = len(prompts) # Handle images for VLM support has_images = "image" in inputs images = None if has_images: images = inputs["image"] # Convert conversational prompts to include image tokens for prompt in prompts: if isinstance(prompt, list): for message in prompt: if not isinstance(message, dict): continue content = message.get("content") role = message.get("role") if isinstance(content, str): if role == "user": message["content"] = [{"type": "image"}, {"type": "text", "text": content}] elif role == "system": message["content"] = [{"type": "text", "text": content}] if self.args.use_vllm: prompt_ids, prompt_mask, completion_ids, completion_mask = self._generate_vllm(prompts, images) else: prompt_ids, prompt_mask, completion_ids, completion_mask = self._generate(model, prompts, images) contain_eos_token = torch.any(completion_ids == self.eos_token_id, dim=-1) # Extract vision inputs if available for VLM support vision_inputs = None if has_images and self.is_vision_model and not self.args.use_vllm: # For vision models with transformers generation, we need to prepare vision inputs # Process the images to get vision inputs that can be passed through the forward pass vision_inputs = {} kwargs = {"images": [[img] for img in images]} processed = self.processing_class( text=[""] * len(images), # Dummy text for vision processing return_tensors="pt", **kwargs, ) # Handle DataParallel wrapped models model_device = getattr(model, "device", None) model_dtype = getattr(model, "dtype", None) if model_device is None and hasattr(model, "module"): model_device = model.module.device model_dtype = model.module.dtype # Move vision tensors to device and convert to model dtype # Need to duplicate for 2 completions per prompt if "pixel_values" in processed: vision_inputs["pixel_values"] = ( processed["pixel_values"].to(model_device, dtype=model_dtype).repeat(2, 1, 1, 1) ) if "pixel_attention_mask" in processed: vision_inputs["pixel_attention_mask"] = processed["pixel_attention_mask"].to(model_device).repeat(2, 1) if "image_sizes" in processed: vision_inputs["image_sizes"] = processed["image_sizes"].to(model_device).repeat(2, 1) if "image_grid_thw" in processed: vision_inputs["image_grid_thw"] = processed["image_grid_thw"].to(model_device).repeat(2, 1) logprobs = self._forward(model, prompt_ids, prompt_mask, completion_ids, completion_mask, vision_inputs) with torch.no_grad(): if self.ref_model is not None: ref_logprobs = self._forward( self.ref_model, prompt_ids, prompt_mask, completion_ids, completion_mask, vision_inputs ) else: # peft case: we just need to disable the adapter with self.model.disable_adapter(): ref_logprobs = self._forward( self.model, prompt_ids, prompt_mask, completion_ids, completion_mask, vision_inputs ) # Decode the completions, and format them if the input is conversational device = logprobs.device completions = self.processing_class.batch_decode(completion_ids, skip_special_tokens=True) if is_conversational({"prompt": prompts[0]}): completions = [[{"role": "assistant", "content": completion}] for completion in completions] # Get the reward from reward functions, judge, or deprecated reward_model if self.reward_funcs is not None: # First create completion_ids_list for custom reward functions completion_ids_list = [completion_ids[i].tolist() for i in range(completion_ids.shape[0])] # Extract additional fields from inputs for reward functions reward_kwargs = {} keys = [key for key in inputs if key not in ["prompt"]] for key in keys: if isinstance(inputs[key], (list, tuple)): # Repeat input fields to match number of completions (2 per prompt) reward_kwargs[key] = inputs[key] * 2 else: reward_kwargs[key] = inputs[key] # Calculate rewards using reward functions rewards = self._calculate_rewards_from_functions( prompts=2 * prompts, completions=completions, completion_ids_list=completion_ids_list, **reward_kwargs ) # Apply missing EOS penalty if configured if self.args.missing_eos_penalty is not None: rewards[~contain_eos_token] -= self.args.missing_eos_penalty # Split rewards into chosen/rejected pairs first_half, second_half = rewards.split(batch_size) mask = first_half >= second_half elif self.judge is not None: # Once formatted, conversational data may contain special tokens (such as <|im_start|>) that are not # directly understandable by the judge and could alter its judgment. To avoid this and make the judge # independent of the model's chat template, we use the raw conversation data, and apply our own chat # template to it. if is_conversational({"prompt": prompts[0]}): environment = jinja2.Environment() template = environment.from_string(SIMPLE_CHAT_TEMPLATE) prompts = [template.render(messages=prompt) for prompt in prompts] completions = [template.render(messages=completion) for completion in completions] ranks_of_first_completion = self.judge.judge( prompts, list(zip(completions[:batch_size], completions[batch_size:])) ) # convert ranks to a True/False mask: # when rank == 0, it means the first completion is the best # when rank == 1, it means the second completion is the best mask = torch.tensor([rank == 0 for rank in ranks_of_first_completion], device=device) batch_range = torch.arange(batch_size, device=device) chosen_indices = batch_range + (~mask * batch_size) rejected_indices = batch_range + (mask * batch_size) # Build tensor so that the first half is the chosen examples and the second half the rejected examples cr_indices = torch.cat((chosen_indices, rejected_indices), dim=0) # cr = chosen and rejected cr_logprobs = logprobs[cr_indices] cr_ref_logprobs = ref_logprobs[cr_indices] # mask out the padding tokens padding_mask = ~completion_mask.bool() cr_padding_mask = padding_mask[cr_indices] cr_logprobs_sum = (cr_logprobs * ~cr_padding_mask).sum(1) cr_ref_logprobs_sum = (cr_ref_logprobs * ~cr_padding_mask).sum(1) # Split the chosen and rejected examples chosen_logprobs_sum, rejected_logprobs_sum = torch.split(cr_logprobs_sum, batch_size) chosen_ref_logprobs_sum, rejected_ref_logprobs_sum = torch.split(cr_ref_logprobs_sum, batch_size) pi_logratios = chosen_logprobs_sum - rejected_logprobs_sum ref_logratios = chosen_ref_logprobs_sum - rejected_ref_logprobs_sum logits = pi_logratios - ref_logratios if self.args.loss_type == "sigmoid": losses = -F.logsigmoid(self.beta * logits) elif self.args.loss_type == "ipo": losses = (logits - 1 / (2 * self.beta)) ** 2 else: raise NotImplementedError(f"invalid loss type {self.loss_type}") loss = losses.mean() # Log everything if self.reward_funcs is not None: # When using reward_funcs, we have rewards instead of scores scores_margin = rewards[chosen_indices] - rewards[rejected_indices] self.stats["objective/scores_margin"].append( self.accelerator.gather_for_metrics(scores_margin.mean()).mean().item() ) self.stats["objective/scores"].append(self.accelerator.gather_for_metrics(rewards.mean()).mean().item()) self.stats["val/contain_eos_token"].append(contain_eos_token.float().mean().item()) self.stats["logps/chosen"].append(self.accelerator.gather_for_metrics(chosen_logprobs_sum).mean().item()) self.stats["logps/rejected"].append(self.accelerator.gather_for_metrics(rejected_logprobs_sum).mean().item()) kl = logprobs - ref_logprobs mean_kl = kl.sum(1).mean() self.stats["objective/kl"].append(self.accelerator.gather_for_metrics(mean_kl).mean().item()) non_score_reward = (-self.beta * kl).sum(1) mean_non_score_reward = non_score_reward.mean() self.stats["objective/non_score_reward"].append( self.accelerator.gather_for_metrics(mean_non_score_reward).mean().item() ) if self.reward_funcs is not None: # Calculate RLHF reward by combining rewards with non_score_reward rlhf_reward = rewards + non_score_reward self.stats["objective/rlhf_reward"].append(self.accelerator.gather_for_metrics(rlhf_reward).mean().item()) mean_entropy = -logprobs.sum(1).mean() self.stats["objective/entropy"].append(self.accelerator.gather_for_metrics(mean_entropy).mean().item()) chosen_rewards = self.beta * (chosen_logprobs_sum - chosen_ref_logprobs_sum) gathered_chosen_rewards = self.accelerator.gather_for_metrics(chosen_rewards) self.stats["rewards/chosen"].append(gathered_chosen_rewards.mean().item()) rejected_rewards = self.beta * (rejected_logprobs_sum - rejected_ref_logprobs_sum) gathered_rejected_rewards = self.accelerator.gather_for_metrics(rejected_rewards) self.stats["rewards/rejected"].append(gathered_rejected_rewards.mean().item()) margin = gathered_chosen_rewards - gathered_rejected_rewards self.stats["rewards/margins"].append(margin.mean().item()) accuracy = margin > 0 self.stats["rewards/accuracies"].append(accuracy.float().mean().item()) self.stats["beta"].append(self.beta) if ( self.args.torch_empty_cache_steps is not None and self.state.global_step % self.args.torch_empty_cache_steps == 0 ): empty_cache() kwargs = {} # For LOMO optimizers you need to explicitly use the learning rate if self.args.optim in [OptimizerNames.LOMO, OptimizerNames.ADALOMO]: kwargs["learning_rate"] = self._get_learning_rate() if self.args.n_gpu > 1: loss = loss.mean() # mean() to average on multi-gpu parallel training if self.use_apex: with amp.scale_loss(loss, self.optimizer) as scaled_loss: scaled_loss.backward() else: self.accelerator.backward(loss, **kwargs) return loss.detach() / self.args.gradient_accumulation_steps # Same as Trainer._maybe_log_save_evaluate but log our metrics def _maybe_log_save_evaluate( self, tr_loss, grad_norm, model, trial, epoch, ignore_keys_for_eval, start_time, learning_rate=None ): 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 if learning_rate is not None: logs["learning_rate"] = learning_rate else: logs["learning_rate"] = self._get_learning_rate() # Add our metrics for key, val in self.stats.items(): logs[key] = sum(val) / len(val) self.stats = {key: [] for key in self.stats} # reset stats self._total_loss_scalar += tr_loss_scalar self._globalstep_last_logged = self.state.global_step self.store_flos() self.log(logs, start_time) metrics = None if self.control.should_evaluate: metrics = self._evaluate(trial, ignore_keys_for_eval) is_new_best_metric = self._determine_best_metric(metrics=metrics, trial=trial) if self.args.save_strategy == "best": self.control.should_save = is_new_best_metric if self.control.should_save: self._save_checkpoint(model, trial) self.control = self.callback_handler.on_save(self.args, self.state, self.control) # Ensure the model card is saved along with the checkpoint def _save_checkpoint(self, model, trial): if self.args.hub_model_id is None: model_name = Path(self.args.output_dir).name else: model_name = self.args.hub_model_id.split("/")[-1] self.create_model_card(model_name=model_name) super()._save_checkpoint(model, trial) def create_model_card( self, model_name: Optional[str] = None, dataset_name: Optional[str] = None, tags: Union[str, list[str], None] = None, ): """ Creates a draft of a model card using the information available to the `Trainer`. Args: model_name (`str` or `None`, *optional*, defaults to `None`): Name of the model. dataset_name (`str` or `None`, *optional*, defaults to `None`): Name of the dataset used for training. tags (`str`, `list[str]` or `None`, *optional*, defaults to `None`): Tags to be associated with the model card. """ if not self.is_world_process_zero(): return if hasattr(self.model.config, "_name_or_path") and not os.path.isdir(self.model.config._name_or_path): base_model = self.model.config._name_or_path else: base_model = None # normalize `tags` to a mutable set if tags is None: tags = set() elif isinstance(tags, str): tags = {tags} else: tags = set(tags) if hasattr(self.model.config, "unsloth_version"): tags.add("unsloth") if "JOB_ID" in os.environ: tags.add("hf_jobs") tags.update(self._tag_names) # docstyle-ignore citation = textwrap.dedent("""\ @article{guo2024direct, title = {{Direct Language Model Alignment from Online AI Feedback}}, author = {Shangmin Guo and Biao Zhang and Tianlin Liu and Tianqi Liu and Misha Khalman and Felipe Llinares and Alexandre Ram{\'{e}} and Thomas Mesnard and Yao Zhao and Bilal Piot and Johan Ferret and Mathieu Blondel}, year = 2024, eprint = {arXiv:2402.04792} }""") model_card = generate_model_card( base_model=base_model, model_name=model_name, hub_model_id=self.hub_model_id, dataset_name=dataset_name, tags=tags, wandb_url=wandb.run.url if is_wandb_available() and wandb.run is not None else None, comet_url=get_comet_experiment_url(), trainer_name="Online DPO", trainer_citation=citation, paper_title="Direct Language Model Alignment from Online AI Feedback", paper_id="2402.04792", ) model_card.save(os.path.join(self.args.output_dir, "README.md"))