# Copyright 2020-2026 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 asyncio import atexit import copy import inspect import math import textwrap import time from collections import defaultdict, deque from collections.abc import Callable from contextlib import nullcontext from pathlib import Path from typing import Any import numpy as np import pandas as pd import torch import torch.utils.data import transformers from accelerate.logging import get_logger from accelerate.utils import gather, gather_object, is_peft_model, set_seed from datasets import Dataset, IterableDataset from packaging.version import Version from torch import nn from torch.distributed.fsdp import FullyShardedDataParallel as FSDP from torch.utils.data import Sampler from transformers import ( AutoModelForSequenceClassification, AutoProcessor, AutoTokenizer, GenerationConfig, PreTrainedModel, PreTrainedTokenizerBase, ProcessorMixin, TrainerCallback, is_trackio_available, is_wandb_available, ) from transformers.utils import is_peft_available, is_rich_available from ..data_utils import apply_chat_template, is_conversational, prepare_multimodal_messages from ..extras.profiling import profiling_context, profiling_decorator from ..generation.vllm_generation import VLLMGeneration from ..models import prepare_deepspeed, prepare_fsdp, unwrap_model_for_generation from ..models.utils import disable_gradient_checkpointing from .base_trainer import _BaseTrainer from .callbacks import SyncRefModelCallback from .rloo_config import RLOOConfig from .utils import ( RepeatSampler, create_model_from_path, disable_dropout_in_model, entropy_from_logits, get_config_model_id, identity, nanmax, nanmin, nanstd, pad, print_prompt_completions_sample, selective_log_softmax, shuffle_sequence_dict, shutdown_event_loop_in_daemon, split_pixel_values_by_grid, split_tensor_dict, start_event_loop_in_daemon, unsplit_pixel_values_by_grid, use_adapter, ) if is_peft_available(): from peft import PeftConfig, PeftModel, get_peft_model if is_wandb_available(): import wandb if is_trackio_available(): import trackio logger = get_logger(__name__) # A reward function can be a string, interpreted as a model ID and loaded as a pretrained model, a pretrained model, or # a callable that returns a list of floats (the rewards). The callable receives prompts, completions, and additional # arguments from the trainer (refer to the trainer's source for details). To ensure forward compatibility, it should # accept **kwargs. RewardFunc = str | PreTrainedModel | Callable[..., list[float | None]] class RLOOTrainer(_BaseTrainer): """ Trainer for the Reinforce Leave One Out (RLOO) method. This algorithm was initially proposed in the paper [Back to Basics: Revisiting REINFORCE Style Optimization for Learning from Human Feedback in LLMs](https://huggingface.co/papers/2402.14740). Example: ```python from trl import RLOOTrainer from trl.rewards import accuracy_reward from datasets import load_dataset dataset = load_dataset("trl-lib/DeepMath-103K", split="train") trainer = RLOOTrainer( model="Qwen/Qwen2.5-0.5B-Instruct", reward_funcs=accuracy_reward, train_dataset=dataset, ) trainer.train() ``` Args: model (`str` or [`~transformers.PreTrainedModel`] or [`~peft.PeftModel`]): 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 `.from_pretrained` (where `` is derived from the model config) with the keyword arguments in `args.model_init_kwargs`. - A [`~transformers.PreTrainedModel`] object. Only causal language models are supported. - A [`~peft.PeftModel`] object. Only causal language models are supported. reward_funcs (`RewardFunc | list[RewardFunc]`): 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, such as: - A string: 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.AutoModelForSequenceClassification.from_pretrained`] with `num_labels=1` and the keyword arguments in `args.model_init_kwargs`. - A [`~transformers.PreTrainedModel`] object: Only sequence classification models are supported. - A custom reward function: The function is provided with the prompts and the generated completions, plus any additional columns in the dataset. It should return a list of rewards. Custom reward functions can be either synchronous or asynchronous and can also return `None` when the reward is not applicable to those samples. This is useful for multi-task training where different reward functions apply to different types of samples. When a reward function returns `None` for a sample, that reward function is excluded from the reward calculation for that sample. For more details, see [Using a custom reward function](#using-a-custom-reward-function). The trainer's state is also passed to the reward function. The trainer's state is an instance of [`~transformers.TrainerState`] and can be accessed by accessing the `trainer_state` argument to the reward function's signature. - A list of reward functions, where each item can independently be any of the above types. Mixing different types within the list (e.g., a string model ID and a custom reward function) is allowed. args ([`RLOOConfig`], *optional*): Configuration for this trainer. If `None`, a default configuration is used. train_dataset ([`~datasets.Dataset`] or [`~datasets.IterableDataset`]): Dataset to use for training. It must include a column `"prompt"`. Any additional columns in the dataset is ignored. The format of the samples can be either: - [Standard](dataset_formats#standard): Each sample contains plain text. - [Conversational](dataset_formats#conversational): Each sample contains structured messages (e.g., role and content). eval_dataset ([`~datasets.Dataset`], [`~datasets.IterableDataset`] or `dict[str, Dataset | IterableDataset]`): Dataset to use for evaluation. It must meet the same requirements as `train_dataset`. processing_class ([`~transformers.PreTrainedTokenizerBase`], [`~transformers.ProcessorMixin`], *optional*): Processing class used to process the data. The padding side must be set to "left". If `None`, the processing class is loaded from the model's name with [`~transformers.AutoProcessor.from_pretrained`]. A padding token, `tokenizer.pad_token`, must be set. If the processing class has not set a padding token, `tokenizer.eos_token` will be used as the default. reward_processing_classes ([`~transformers.PreTrainedTokenizerBase`] or `list[PreTrainedTokenizerBase]`, *optional*): 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`, or if an element of the list corresponding to a [`~transformers.PreTrainedModel`] is `None`, the tokenizer for the model is automatically loaded using [`~transformers.AutoTokenizer.from_pretrained`]. For elements in `reward_funcs` that are custom reward functions (not [`~transformers.PreTrainedModel`]), the corresponding entries in `reward_processing_classes` are ignored. callbacks (list of [`~transformers.TrainerCallback`], *optional*): List of callbacks to customize the training loop. Will add those to the list of default callbacks detailed in [here](https://huggingface.co/docs/transformers/main_classes/callback). If you want to remove one of the default callbacks used, use the [`~transformers.Trainer.remove_callback`] method. optimizers (`tuple[torch.optim.Optimizer | None, torch.optim.lr_scheduler.LambdaLR | None]`, *optional*, defaults to `(None, None)`): A tuple containing the optimizer and the scheduler to use. Will default to an instance of `AdamW` on your model and a scheduler given by [`~transformers.get_linear_schedule_with_warmup`] controlled by `args`. peft_config ([`~peft.PeftConfig`], *optional*): PEFT configuration used to wrap the model. If `None`, the model is not wrapped. """ _tag_names = ["trl", "rloo"] _name = "RLOO" _paper = { "title": "Back to Basics: Revisiting REINFORCE-Style Optimization for Learning from Human Feedback in LLMs", "id": "2402.14740", # docstyle-ignore "citation": textwrap.dedent("""\ @inproceedings{ahmadian2024back, title = {{Back to Basics: Revisiting REINFORCE-Style Optimization for Learning from Human Feedback in LLMs}}, author = {Arash Ahmadian and Chris Cremer and Matthias Gall{\'{e}} and Marzieh Fadaee and Julia Kreutzer and Olivier Pietquin and Ahmet {\"{U}}st{\"{u}}n and Sara Hooker}, year = 2024, booktitle = {Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), {ACL} 2024, Bangkok, Thailand, August 11-16, 2024}, pages = {12248--12267}, publisher = {Association for Computational Linguistics}, editor = {Lun{-}Wei Ku and Andre Martins and Vivek Srikumar}, }"""), } def __init__( self, model: "str | PreTrainedModel | PeftModel", reward_funcs: RewardFunc | list[RewardFunc], args: RLOOConfig | None = None, train_dataset: Dataset | IterableDataset | None = None, eval_dataset: Dataset | IterableDataset | dict[str, Dataset | IterableDataset] | None = None, processing_class: PreTrainedTokenizerBase | ProcessorMixin | None = None, reward_processing_classes: PreTrainedTokenizerBase | list[PreTrainedTokenizerBase] | None = None, callbacks: list[TrainerCallback] | None = None, optimizers: tuple[torch.optim.Optimizer | None, torch.optim.lr_scheduler.LambdaLR | None] = (None, None), peft_config: "PeftConfig | None" = None, ): # Args if args is None: model_name = model if isinstance(model, str) else get_config_model_id(model.config) model_name = model_name.split("/")[-1] args = RLOOConfig(f"{model_name}-RLOO") # Model if isinstance(model, str): model_init_kwargs = args.model_init_kwargs or {} # Distributed training requires device_map=None ("auto" fails) if args.distributed_state.distributed_type in ["MULTI_GPU", "DEEPSPEED"]: model_init_kwargs["device_map"] = None model = create_model_from_path(model, **model_init_kwargs) else: if args.model_init_kwargs is not None: logger.warning( "You passed `model_init_kwargs` to the `RLOOConfig`, but your model is already instantiated. " "The `model_init_kwargs` will be ignored." ) # Some models (SmolVLM/Idefics3) don't support `logits_to_keep` argument and error out if we pass it # Inspect the forward method before we wrap the model with PEFT self.model_kwarg_keys = ( inspect.signature(model.forward).parameters.keys() if not hasattr(model, "get_base_model") else inspect.signature(model.get_base_model().forward).parameters.keys() ) # Processing class if processing_class is None: processing_class = AutoProcessor.from_pretrained( get_config_model_id(model.config), truncation_side="left", padding_side="left" ) # 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_id = tokenizer.pad_token_id self.eos_token_id = tokenizer.eos_token_id if is_peft_available() and is_peft_model(model) and peft_config is not None: raise ValueError( "You passed a `PeftModel` instance together with a `peft_config` to the trainer. Please first merge " "and unload the existing adapter, save the resulting base model, and then pass that base model along " "with the new `peft_config` to the trainer." ) if is_peft_available() and is_peft_model(model): # If the model is a PEFT model with a pretrained adapter, we need to create a "ref" adapter that is a copy # of the "default" adapter, so that we can use it as the reference model during the training. model.add_adapter("ref", model.peft_config["default"]) for name, param in model.named_parameters(): if ".default." in name: ref_name = name.replace(".default.", ".ref.") ref_param = model.get_parameter(ref_name) ref_param.data.copy_(param.data) # Create PEFT model if peft_config is not None: model = get_peft_model(model, peft_config) # When using gradient checkpointing with PEFT, we need to enable input gradients. transformers.Trainer normally # handles this, but a bug currently prevents it; see https://github.com/huggingface/transformers/issues/42489 if is_peft_available() and is_peft_model(model) and args.gradient_checkpointing: model.enable_input_require_grads() # When using QLoRA, the PEFT adapter weights are converted to bf16 to follow the recommendations from the # original paper (see https://huggingface.co/papers/2305.14314, paragraph 3). Normally, this can be done by # passing `autocast_adapter_dtype=False` to `get_peft_model`, but this option is not yet supported for # quantized models. See: https://github.com/huggingface/peft/issues/2889 # Non-quantized models do not have the `is_loaded_in_{8,4}bit` attributes, whereas quantized models do if getattr(model, "is_loaded_in_4bit", False) or getattr(model, "is_loaded_in_8bit", False): for param in model.parameters(): if param.requires_grad: param.data = param.data.to(torch.bfloat16) # Reward functions if not isinstance(reward_funcs, list): reward_funcs = [reward_funcs] self.reward_func_names = [] for i, reward_func in enumerate(reward_funcs): if isinstance(reward_func, str): model_init_kwargs = args.model_init_kwargs or {} # Distributed training requires device_map=None ("auto" fails) if args.distributed_state.distributed_type in ["MULTI_GPU", "DEEPSPEED"]: model_init_kwargs["device_map"] = None reward_funcs[i] = AutoModelForSequenceClassification.from_pretrained( reward_func, num_labels=1, **model_init_kwargs ) if isinstance(reward_funcs[i], nn.Module): # Use Module over PretrainedModel for compat w/ compiled models self.reward_func_names.append(get_config_model_id(reward_funcs[i].config).split("/")[-1]) else: self.reward_func_names.append(reward_funcs[i].__name__) self.reward_funcs = reward_funcs self._has_async_reward_funcs = any(inspect.iscoroutinefunction(func) for func in self.reward_funcs) if self._has_async_reward_funcs: self.async_reward_loop_thread, self.async_reward_loop, self.async_reward_loop_ready_event = ( start_event_loop_in_daemon(name="RLOOTrainer-AsyncRewardLoop") ) # wait until the event loop is running in the daemon thread self.async_reward_loop_ready_event.wait() atexit.register(shutdown_event_loop_in_daemon, self.async_reward_loop_thread, self.async_reward_loop) # Reward weights if args.reward_weights is not None: if len(args.reward_weights) != len(reward_funcs): raise ValueError( f"Number of reward weights ({len(args.reward_weights)}) must match number of reward " f"functions ({len(reward_funcs)})" ) self.reward_weights = torch.tensor(args.reward_weights, dtype=torch.float32) else: self.reward_weights = torch.ones(len(reward_funcs), dtype=torch.float32) # Reward processing class 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] if len(reward_processing_classes) != len(reward_funcs): raise ValueError( f"The number of reward processing classes ({len(reward_processing_classes)}) must match the number of " f"reward functions ({len(reward_funcs)})." ) for i, (reward_processing_class, reward_func) in enumerate( zip(reward_processing_classes, reward_funcs, strict=True) ): if isinstance(reward_func, PreTrainedModel): if reward_processing_class is None: reward_processing_class = AutoTokenizer.from_pretrained(get_config_model_id(reward_func.config)) if reward_processing_class.pad_token_id is None: reward_processing_class.pad_token = reward_processing_class.eos_token # The reward model computes the reward for the latest non-padded token in the input sequence. # So it's important to set the pad token ID to the padding token ID of the processing class. reward_func.config.pad_token_id = reward_processing_class.pad_token_id reward_processing_classes[i] = reward_processing_class self.reward_processing_classes = reward_processing_classes # Training arguments self.max_completion_length = args.max_completion_length self.num_generations = args.num_generations self.num_generations_eval = args.num_generations_eval or self.num_generations self.chat_template_kwargs = args.chat_template_kwargs or {} 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.pad_to_multiple_of = args.pad_to_multiple_of self.use_vllm = args.use_vllm self.vllm_mode = args.vllm_mode self.vllm_gpu_memory_utilization = args.vllm_gpu_memory_utilization # only applies to colocation mode self.vllm_tensor_parallel_size = args.vllm_tensor_parallel_size # only applies to colocation mode self.normalize_advantages = args.normalize_advantages self.mask_truncated_completions = args.mask_truncated_completions self.reward_clip_range = args.reward_clip_range # Datasets self.shuffle_dataset = args.shuffle_dataset if train_dataset is None: raise ValueError("`train_dataset` is required") elif ( isinstance(train_dataset, IterableDataset) or isinstance(eval_dataset, IterableDataset) or ( isinstance(eval_dataset, dict) and any(isinstance(ds, IterableDataset) for ds in eval_dataset.values()) ) ): # See https://github.com/huggingface/trl/issues/3213 raise NotImplementedError( "Iterable datasets are not yet supported in RLOOTrainer. Please use a standard dataset instead." ) # Multi-step self.num_iterations = args.num_iterations self.epsilon_low = args.epsilon self.epsilon_high = args.epsilon_high if args.epsilon_high is not None else args.epsilon # Tracks the number of iterations (forward + backward passes), including those within a grad accum cycle self._step = 0 # Buffer the batch to reuse generated outputs across multiple updates. For more details, see # `_get_train_sampler` and `_prepare_inputs`. self._buffered_inputs = None # Transformers explicitly set use_reentrant=True in the past to silence a PyTorch warning, but the default was # never updated once PyTorch switched to recommending use_reentrant=False. Until that change lands upstream # (see https://github.com/huggingface/transformers/pull/43203) and is released (most likely in 5.0.0), we # default to the recommended non-reentrant behavior here, while preserving any user-provided value. if args.gradient_checkpointing and Version(transformers.__version__) < Version("5.0.0"): args.gradient_checkpointing_kwargs = args.gradient_checkpointing_kwargs or {} args.gradient_checkpointing_kwargs.setdefault("use_reentrant", False) super().__init__( model=model, args=args, data_collator=identity, # No data collation is needed in RLOO train_dataset=train_dataset, eval_dataset=eval_dataset, processing_class=processing_class, callbacks=callbacks, optimizers=optimizers, ) # Reference model self.beta = args.beta if self.beta == 0.0: # If beta is 0.0, the reference model is not needed self.ref_model = None elif is_peft_model(model): # If PEFT is used, the reference model is not needed since the adapter can be disabled # to revert to the initial model. self.ref_model = None else: # For deepspeed, fsdp or non-distributed models, create a reference model from scratch model_init_kwargs = args.model_init_kwargs or {} # Distributed training requires device_map=None ("auto" fails) if self.args.distributed_state.distributed_type in ["MULTI_GPU", "DEEPSPEED"]: model_init_kwargs["device_map"] = None self.ref_model = create_model_from_path(get_config_model_id(self.model.config), **model_init_kwargs) # Disable dropout in the models if args.disable_dropout: disable_dropout_in_model(model) if self.ref_model is not None: disable_dropout_in_model(self.ref_model) # Initialize the metrics self._metrics = {"train": defaultdict(list), "eval": defaultdict(list)} self._total_train_tokens = 0 self._current_train_step_time = 0.0 self.log_completions = args.log_completions self.log_unique_prompts = args.log_unique_prompts self.num_completions_to_print = args.num_completions_to_print # Keep logs sized to the generation batch to record only outputs from the latest model update. self._logs = { "images": deque(maxlen=args.generation_batch_size), "prompt": deque(maxlen=args.generation_batch_size), "completion": deque(maxlen=args.generation_batch_size), "rewards": defaultdict(lambda: deque(maxlen=args.generation_batch_size)), "advantages": deque(maxlen=args.generation_batch_size), "extra": defaultdict(lambda: deque(maxlen=args.generation_batch_size)), } # Buffers for user-logged data from reward functions, flushed after gathering self._pending_extra_logs = defaultdict(list) self._pending_metrics = defaultdict(list) # Ensure each process receives a unique seed to prevent duplicate completions when generating with # transformers if num_generations exceeds per_device_train_batch_size. We could skip it if we use vLLM, but # it's safer to set it in all cases. set_seed(args.seed, device_specific=True) if self.use_vllm: # Initialize vLLM generation backend self.vllm_generation = VLLMGeneration( model=self.model, accelerator=self.accelerator, is_fsdp_enabled=self.is_fsdp_enabled, processing_class=self.processing_class, # vLLM configuration mode=args.vllm_mode, structured_outputs_regex=args.vllm_structured_outputs_regex, # Server mode configuration server_base_url=args.vllm_server_base_url, server_host=args.vllm_server_host, server_port=args.vllm_server_port, group_port=args.vllm_group_port, server_timeout=args.vllm_server_timeout, # Colocate mode configuration tensor_parallel_size=args.vllm_tensor_parallel_size, gpu_memory_utilization=args.vllm_gpu_memory_utilization, max_model_length=args.vllm_max_model_length, max_num_seqs=args.per_device_train_batch_size * args.vllm_tensor_parallel_size * args.steps_per_generation, enable_sleep_mode=args.vllm_enable_sleep_mode, model_impl=args.vllm_model_impl, # Generation configuration repetition_penalty=self.repetition_penalty, temperature=self.temperature, top_p=self.top_p, top_k=self.top_k, min_p=self.min_p, max_completion_length=self.max_completion_length, logprobs=None, # we don't need logprobs from vLLM in RLOO generation_kwargs=args.generation_kwargs, ) self._last_loaded_step = -1 # tag to avoid useless loading during grad accumulation else: generation_kwargs = { "max_new_tokens": self.max_completion_length, "do_sample": True, "pad_token_id": tokenizer.pad_token_id, "bos_token_id": tokenizer.bos_token_id, "eos_token_id": tokenizer.eos_token_id, "temperature": self.temperature, "top_p": self.top_p, "top_k": self.top_k, "min_p": self.min_p, "repetition_penalty": self.repetition_penalty, "cache_implementation": args.cache_implementation, } if args.generation_kwargs is not None: generation_kwargs.update(args.generation_kwargs) self.generation_config = GenerationConfig(**generation_kwargs, disable_compile=True) # Keep training-specific generation kwargs to overwrite model's original generation config self.generation_kwargs = generation_kwargs # Gradient accumulation requires scaled loss. Normally, loss scaling in the parent class depends on whether the # model accepts loss-related kwargs. Since we compute our own loss, this check is irrelevant. We set # self.model_accepts_loss_kwargs to False to enable scaling. self.model_accepts_loss_kwargs = False # Add tags to the model self.model.add_model_tags(self._tag_names) if self.ref_model is not None: if self.is_deepspeed_enabled: self.ref_model = prepare_deepspeed(self.ref_model, self.accelerator) elif self.is_fsdp_enabled: self.ref_model = prepare_fsdp(self.ref_model, self.accelerator) else: self.ref_model = self.accelerator.prepare_model(self.ref_model, evaluation_mode=True) if args.sync_ref_model: if self.beta == 0.0: raise ValueError( "You passed `sync_ref_model=True` while `beta=0.0`, which means the reference model is not used " "during training. Consequently, RLOOTrainer does not create a `ref_model` instance, and there is " "nothing to synchronize. Please set `sync_ref_model=False`, or set `beta` to a non-zero value." ) if is_peft_model(model): raise NotImplementedError( "You passed `sync_ref_model=True` while using a PEFT model, which is currently not supported. " "With PEFT, RLOOTrainer does not keep a separate reference model in memory; instead, it recovers " "reference behavior by temporarily disabling the adapter. As a result, there is no standalone " "`ref_model` instance to synchronize. Use `sync_ref_model=False`, or opt for full fine-tuning if " "you need a synced reference model. If you need `sync_ref_model` to work with PEFT, please open a " "feature request at https://github.com/huggingface/trl/issues." ) self.add_callback(SyncRefModelCallback(ref_model=self.ref_model, accelerator=self.accelerator)) for i, reward_func in enumerate(self.reward_funcs): if isinstance(reward_func, PreTrainedModel): if self.is_deepspeed_enabled: self.reward_funcs[i] = prepare_deepspeed(reward_func, self.accelerator) else: # 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 ) def _set_signature_columns_if_needed(self): # If `self.args.remove_unused_columns` is True, non-signature columns are removed. # By default, this method sets `self._signature_columns` to the model's expected inputs (usually, "input_ids" # and "attention_mask"). In RLOOTrainer, we preprocess data, so using the model's signature columns doesn't # work. Instead, we set them to the columns expected by the `training_step` method, hence the override. if self._signature_columns is None: self._signature_columns = ["prompt", "image", "images"] # This method overrides `Trainer.get_train_dataloader` to support our custom batching strategy. # Instead of returning a standard per-step batch (i.e., `per_device_batch_size), our dataloader loads an # *generation* batch (i.e., `per_device_batch_size × steps_per_generation`). This allows us to generate completions # once every steps_per_generation step—rather than once per accumulation step—which is significantly more # efficient. The only change from the original implementation is multiplying the batch size by # `steps_per_generation`. Thus, `_prepare_inputs` is called with this *generation* batch, and it handles the # splitting internally. # Maintenance note: This method is a copy-paste of the original `Trainer.get_train_dataloader` with only one line # modification. def get_train_dataloader(self): return self._get_dataloader( dataset=self.train_dataset, description="Training", batch_size=self._train_batch_size * self.args.steps_per_generation, # < this is the change sampler_fn=self._get_train_sampler, is_training=True, ) def _get_train_sampler(self, dataset: Dataset | None = None) -> Sampler: # Returns a sampler that # 1. ensures each prompt is repeated across multiple processes. This guarantees that identical prompts are # distributed to different GPUs, allowing rewards to be computed and normalized correctly within each prompt # group. Using the same seed across processes ensures consistent prompt assignment, preventing discrepancies # in group formation. # 2. repeats the batch multiple times to allow reusing generations across multiple updates. Refer to # _prepare_inputs to see how the generations are stored and reused. # In the following figure, the values are the prompt indices. The first row shows the first sampled batch, the # second row shows the second sampled batch, and so on. # # | GPU 0 | GPU 1 | # # global_step step <-───> num_generations=2 # <-───────> per_device_train_batch_size=3 # grad_accum ▲ ▲ 0 0 0 0 1 1 2 2 <- Generate for the first `steps_per_generation` (prompts 0 to 11); store the completions; use the first slice to compute the loss # =2 ▼ | 0 1 3 3 4 4 5 5 <- Take the stored generations and use the second slice to compute the loss # | # | 1 2 6 6 7 7 8 8 <- Take the stored generations and use the third slice to compute the loss # steps_per_gen=4 ▼ 1 3 9 9 10 10 11 11 <- Take the stored generations and use the fourth slice to compute the loss # # 2 4 12 12 13 13 14 14 <- Generate for the second `steps_per_generation` (prompts 12 to 23); store the completions; use the first slice to compute the loss # 2 5 15 15 16 16 17 17 <- Take the stored generations and use the second slice to compute the loss # ... if dataset is None: dataset = self.train_dataset return RepeatSampler( data_source=dataset, mini_repeat_count=self.num_generations, batch_size=self.args.generation_batch_size // self.num_generations, repeat_count=self.num_iterations * self.args.steps_per_generation, shuffle=self.shuffle_dataset, seed=self.args.seed, ) def _get_eval_sampler(self, eval_dataset) -> Sampler: # See _get_train_sampler for an explanation of the sampler. return RepeatSampler( data_source=eval_dataset, mini_repeat_count=self.num_generations_eval, seed=self.args.seed, ) @profiling_decorator def _get_per_token_logps_and_entropies( self, model, input_ids, attention_mask, logits_to_keep, batch_size=None, compute_entropy=False, pixel_values=None, image_grid_thw=None, num_images=None, pixel_attention_mask=None, image_sizes=None, token_type_ids=None, mm_token_type_ids=None, image_position_ids=None, ) -> tuple[torch.Tensor, torch.Tensor | None]: """Compute log-probs and (optionally) entropies for each token.""" batch_size = batch_size or input_ids.size(0) # Chunk inputs into smaller batches to reduce memory peak all_logps = [] all_entropies = [] for start in range(0, input_ids.size(0), batch_size): input_ids_batch = input_ids[start : start + batch_size] attention_mask_batch = attention_mask[start : start + batch_size] # Build model inputs model_inputs = {"input_ids": input_ids_batch, "attention_mask": attention_mask_batch} if image_grid_thw is not None and pixel_values is not None: rows_per_image = image_grid_thw.prod(dim=-1) rows_per_sample = torch.split(rows_per_image, num_images) rows_per_sample = torch.stack([s.sum() for s in rows_per_sample]) cum_rows = torch.cat([torch.tensor([0], device=rows_per_sample.device), rows_per_sample.cumsum(0)]) row_start, row_end = cum_rows[start].item(), cum_rows[start + batch_size].item() model_inputs["pixel_values"] = pixel_values[row_start:row_end] cum_imgs = torch.tensor([0] + num_images).cumsum(0) img_start, img_end = cum_imgs[start], cum_imgs[start + batch_size] model_inputs["image_grid_thw"] = image_grid_thw[img_start:img_end] elif image_position_ids is not None and pixel_values is not None: cum_imgs = torch.tensor([0] + num_images).cumsum(0) img_start, img_end = cum_imgs[start], cum_imgs[start + batch_size] model_inputs["pixel_values"] = pixel_values[img_start:img_end] model_inputs["image_position_ids"] = image_position_ids[img_start:img_end] elif pixel_values is not None: model_inputs["pixel_values"] = pixel_values[start : start + batch_size] if pixel_attention_mask is not None: model_inputs["pixel_attention_mask"] = pixel_attention_mask[start : start + batch_size] if image_sizes is not None: model_inputs["image_sizes"] = image_sizes[start : start + batch_size] if token_type_ids is not None: model_inputs["token_type_ids"] = token_type_ids[start : start + batch_size] if mm_token_type_ids is not None: model_inputs["mm_token_type_ids"] = mm_token_type_ids[start : start + batch_size] # Only add logits_to_keep if the model supports it if "logits_to_keep" in self.model_kwarg_keys: # We add 1 to `logits_to_keep` because the last logits of the sequence is later excluded model_inputs["logits_to_keep"] = logits_to_keep + 1 model_inputs["use_cache"] = False # only used in generation; set False to suppress warnings logits = model(**model_inputs).logits # Exclude the last value: it corresponds to the next token pred logits = logits[:, :-1, :] # (B, L-1, H) # Only keep the last logits_to_keep. For model that support logits_to_keep, this is a no-op. logits = logits[:, -logits_to_keep:, :] # (B, logits_to_keep, H) # Divide logits by sampling temperature. # See https://huggingface.co/blog/the_n_implementation_details_of_rlhf_with_ppo#policy-training-implementation-details logits.div_(self.temperature) completion_ids = input_ids_batch[:, -logits_to_keep:] logps = selective_log_softmax(logits, completion_ids) # compute logprobs all_logps.append(logps) if compute_entropy: with torch.no_grad(): entropies = entropy_from_logits(logits) all_entropies.append(entropies) logps = torch.cat(all_logps, dim=0) entropies = torch.cat(all_entropies, dim=0) if compute_entropy else None return logps, entropies def training_step(self, model, inputs, num_items_in_batch): time_before = time.perf_counter() output = super().training_step(model, inputs, num_items_in_batch) self._step += 1 time_after = time.perf_counter() self._current_train_step_time += time_after - time_before if self._step % self.current_gradient_accumulation_steps == 0: self._metrics["train"]["step_time"].append(self._current_train_step_time) self._current_train_step_time = 0.0 return output @profiling_decorator def _prepare_inputs(self, generation_batch: dict[str, torch.Tensor | Any]) -> dict[str, torch.Tensor | Any]: # Prepares inputs for model training/evaluation by managing completion generation and batch handling. # During training: # - Receives the local generation batch (Per-GPU batch size × steps per generation) # from the modified training dataloader instead of the standard local batch # - Generates completions once for the entire generation batch and splits it into batches of size # `per_device_train_batch_size` # - Buffers these completions and returns the appropriate slice for the current accumulation step # - Optimizes by regenerating completions only periodically (every steps_per_generation * num_iterations) # During evaluation: # - The input is treated as a standard local batch (no accumulation, no multiple iterations) # - Completions are generated for each batch without buffering or reuse # Returns a single local batch in both cases. mode = "train" if self.model.training else "eval" if mode == "train": generate_every = self.args.steps_per_generation * self.num_iterations if self._step % generate_every == 0 or self._buffered_inputs is None: # self._buffered_inputs=None can occur when resuming from a checkpoint generation_batch = self._generate_and_score_completions(generation_batch) generation_batch = split_pixel_values_by_grid(generation_batch) generation_batch = shuffle_sequence_dict(generation_batch) generation_batches = split_tensor_dict(generation_batch, self.args.steps_per_generation) self._buffered_inputs = [unsplit_pixel_values_by_grid(batch) for batch in generation_batches] inputs = self._buffered_inputs[self._step % self.args.steps_per_generation] else: # In evaluation, there is neither batch grouping for generation, nor multiple iterations, hence # local generation batch == local eval batch inputs = self._generate_and_score_completions(generation_batch) return inputs def _log_completion_extra(self, column: str, values: list): """ Log extra columns to the completions table. Called from reward functions via the `log_extra` kwarg. Args: column (`str`): Name of the column to add. values (`list`): Values for the column, one per sample in the batch. """ self._pending_extra_logs[column].extend(values) def _log_metric(self, name: str, value: float): """ Log a scalar metric from a reward function. Called via the `log_metric` kwarg. Values are averaged over each logging step and reported alongside built-in metrics like `kl` and `entropy`. Args: name (`str`): Name of the metric. value (`float`): Scalar value for this batch. """ self._pending_metrics[name].append(value) @profiling_decorator def _calculate_rewards(self, inputs, prompts, completions, completion_ids_list): device = self.accelerator.device rewards_per_func = torch.zeros(len(prompts), len(self.reward_funcs), device=device) # Repeat all input columns (but "prompt", "completion", and "completion_ids") to match the num of generations keys = [key for key in inputs[0] if key not in ["prompt", "completion", "completion_ids"]] reward_kwargs = {key: [example[key] for example in inputs] for key in keys} # This allows for dynamic reward shaping based on training progress. reward_kwargs["trainer_state"] = self.state # Allow reward functions to log extra columns to the completions table. reward_kwargs["log_extra"] = self._log_completion_extra # Allow reward functions to log additional scalar metrics. reward_kwargs["log_metric"] = self._log_metric async_funcs_info = [] # async custom functions for asyncio.gather for i, (reward_func, reward_processing_class, reward_func_name) in enumerate( zip(self.reward_funcs, self.reward_processing_classes, self.reward_func_names, strict=True) ): if isinstance(reward_func, nn.Module): # Module (no PretrainedModel) for compat with compiled models with profiling_context(self, reward_func_name): if is_conversational(inputs[0]): messages = [{"messages": p + c} for p, c in zip(prompts, completions, strict=True)] texts = [ apply_chat_template(x, reward_processing_class, **self.chat_template_kwargs)["text"] for x in messages ] else: texts = [p + c for p, c in zip(prompts, completions, strict=True)] reward_inputs = reward_processing_class( text=texts, return_tensors="pt", padding=True, padding_side="right", add_special_tokens=False ) reward_inputs = super()._prepare_inputs(reward_inputs) with torch.inference_mode(): rewards_per_func[:, i] = reward_func(**reward_inputs).logits[:, 0] # Shape (B*G,) elif inspect.iscoroutinefunction(reward_func): # Separate async reward funcs to run them in parallel later async_funcs_info.append((i, reward_func, reward_func_name)) else: # Run synchronous reward function with profiling_context(self, reward_func_name): 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) # Execute async custom functions in parallel using asyncio.gather if async_funcs_info: async def _invoke_async_reward(index, func, func_name): with profiling_context(self, func_name): output = await func( prompts=prompts, completions=completions, completion_ids=completion_ids_list, **reward_kwargs ) output = [r if r is not None else torch.nan for r in output] return index, output async def _run_async_funcs(): coros = [_invoke_async_reward(i, func, func_name) for (i, func, func_name) in async_funcs_info] return await asyncio.gather(*coros) async_results = asyncio.run_coroutine_threadsafe(_run_async_funcs(), self.async_reward_loop).result() for idx, output_reward_func in async_results: rewards_per_func[:, idx] = torch.tensor(output_reward_func, dtype=torch.float32, device=device) # If all reward functions return None for a given row, issue a detailed warning if torch.isnan(rewards_per_func).all(dim=1).any(): nan_row_idx = torch.isnan(rewards_per_func).all(dim=1).nonzero(as_tuple=True)[0][0] row_reward_kwargs = { key: value[nan_row_idx] for key, value in reward_kwargs.items() if key not in ("trainer_state", "log_extra", "log_metric") } row_reward_kwargs["prompt"] = prompts[nan_row_idx] row_reward_kwargs["completion"] = completions[nan_row_idx] logger.warning( f"All reward functions returned None for the following kwargs:\n{row_reward_kwargs}\n" "Please ensure that at least one reward function returns a valid reward." ) # Gather the reward per function: this part is crucial, because the rewards are normalized per group and the # completions may be distributed across processes rewards_per_func = gather(rewards_per_func) return rewards_per_func def _tokenize_prompts(self, prompts: list): """Tokenize prompts and extract images/multimodal fields for generation.""" if is_conversational({"prompt": prompts[0]}): # Extract images from messages for VLM support images = [] has_images = False for prompt in prompts: prompt_images = [] for message in prompt: if isinstance(message["content"], list): for part in message["content"]: if part["type"] == "image": prompt_images.append(part["image"]) has_images = True images.append(prompt_images if prompt_images else None) images = images if has_images else None # Workaround for a bug in transformers 5.3.0 where some processors (e.g. Qwen2.5-VL) crash on # batched unpadded input (transformers#44514). # Fixed in transformers 5.4.0 (transformers#44563). needs_padding_workaround = Version("5.3.0") <= Version(transformers.__version__) < Version("5.4.0") tokenized = self.processing_class.apply_chat_template( conversation=prompts, add_generation_prompt=True, tokenize=True, return_dict=True, **({"padding": True} if needs_padding_workaround else {}), **self.chat_template_kwargs, ) if needs_padding_workaround: # Unpad input_ids: remove padding tokens using attention_mask to get per-sequence lists prompt_ids = [ [tok for tok, m in zip(ids, mask, strict=True) if m] for ids, mask in zip(tokenized["input_ids"], tokenized["attention_mask"], strict=True) ] else: prompt_ids = tokenized["input_ids"] # For VLMs, the processor returns extra multimodal fields (pixel_values, image_grid_thw, etc.) multimodal_fields = {k: v for k, v in tokenized.items() if k not in ("input_ids", "attention_mask")} else: prompt_ids = self.processing_class(text=prompts)["input_ids"] images = None multimodal_fields = {} return prompt_ids, images, multimodal_fields def _generate_single_turn(self, prompt_ids, images, multimodal_fields): device = self.accelerator.device mode = "train" if self.model.training else "eval" # Generate completions using either vLLM or regular generation if self.use_vllm: # Sync weights if training step changed if self.state.global_step != self._last_loaded_step: with profiling_context(self, "sync_weights"): self.vllm_generation.sync_weights() self._last_loaded_step = self.state.global_step # Generate using vLLM (note: RLOO doesn't use logprobs from generation, so we ignore them) num_generations = self.num_generations if mode == "train" else self.num_generations_eval _, completion_ids, _, _ = self.vllm_generation.generate( prompts=prompt_ids, images=images, num_generations=num_generations, profiler=profiling_context(self, "vLLM.generate"), ) elif self.use_transformers_paged: with ( profiling_context(self, "transformers.generate_batch"), unwrap_model_for_generation( self.model_wrapped, 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(): # Continuous batching API expects 'inputs' arg only all_outputs = unwrapped_model.generate_batch( prompt_ids, generation_config=self.generation_config, progress_bar=False ) unwrapped_model.train() # restore training mode, as generate_batch forces eval mode completion_ids = [output.generated_tokens for output in all_outputs.values()] else: # Regular generation path: left-pad token IDs into tensors prompt_tensors = [torch.tensor(ids) for ids in prompt_ids] padded_ids = pad(prompt_tensors, padding_value=self.pad_token_id, padding_side="left") attention_mask = pad([torch.ones_like(t) for t in prompt_tensors], padding_value=0, padding_side="left") generate_inputs = {"input_ids": padded_ids, "attention_mask": attention_mask} # For VLMs, include multimodal fields as tensors (pixel_values, image_grid_thw, etc.) for k, v in multimodal_fields.items(): if isinstance(v, torch.Tensor): generate_inputs[k] = v elif isinstance(v, list) and v and isinstance(v[0], list): # Per-token field (e.g., token_type_ids): left-pad like input_ids generate_inputs[k] = pad([torch.tensor(x) for x in v], padding_value=0, padding_side="left") else: generate_inputs[k] = torch.tensor(np.array(v)) generate_inputs = super()._prepare_inputs(generate_inputs) with ( profiling_context(self, "transformers.generate"), unwrap_model_for_generation( self.model_wrapped, self.accelerator, gather_deepspeed3_params=self.args.ds3_gather_for_generation, generation_kwargs=self.generation_kwargs, # Override model.generation_config with generation_kwargs to fix transformers#42762 ) as unwrapped_model, torch.no_grad(), FSDP.summon_full_params(self.model_wrapped, recurse=False) if self.is_fsdp_enabled else nullcontext(), ): prompt_completion_ids = unwrapped_model.generate( **generate_inputs, generation_config=self.generation_config ) # Compute prompt length and extract completion ids prompt_length = generate_inputs["input_ids"].size(1) completion_ids = prompt_completion_ids[:, prompt_length:] # Mask everything after the first EOS token is_eos = completion_ids == self.eos_token_id eos_idx = torch.full((is_eos.size(0),), is_eos.size(1), dtype=torch.long, device=device) eos_idx[is_eos.any(dim=1)] = is_eos.int().argmax(dim=1)[is_eos.any(dim=1)] sequence_indices = torch.arange(is_eos.size(1), device=device).expand(is_eos.size(0), -1) completion_mask = (sequence_indices <= eos_idx.unsqueeze(1)).int() completion_ids = [ c[m].tolist() for c, m in zip(completion_ids.cpu(), completion_mask.bool().cpu(), strict=True) ] return completion_ids def _generate(self, prompts: list): device = self.accelerator.device mode = "train" if self.model.training else "eval" # Copy the prompts to avoid modifying the original list prompts = copy.deepcopy(prompts) prompt_ids, images, multimodal_fields = self._tokenize_prompts(prompts) completion_ids = self._generate_single_turn(prompt_ids, images, multimodal_fields) # Decode completions. It's important to use `parse_response` when possible, because it handles tool calls. if is_conversational({"prompt": prompts[0]}): contents = self.processing_class.batch_decode(completion_ids, skip_special_tokens=True) completions = [[{"role": "assistant", "content": content}] for content in contents] else: completions = self.processing_class.batch_decode(completion_ids, skip_special_tokens=True) # Get completion length per sequence, used for logging prompt_lengths = torch.tensor([len(ids) for ids in prompt_ids], device=device) completion_lengths = torch.tensor([len(ids) for ids in completion_ids], device=device) agg_prompt_lengths = self.accelerator.gather(prompt_lengths) agg_completion_lengths = self.accelerator.gather(completion_lengths) total_prompt_tokens = agg_prompt_lengths.sum() total_completion_tokens = agg_completion_lengths.sum() # = num_items_in_batch, required for the DAPO loss # Log the metrics if mode == "train": self.state.num_input_tokens_seen += (total_prompt_tokens + total_completion_tokens).item() self._metrics[mode]["num_tokens"] = [self.state.num_input_tokens_seen] # Log completion lengths, mean, min, max self._metrics[mode]["completions/mean_length"].append(agg_completion_lengths.float().mean().item()) self._metrics[mode]["completions/min_length"].append(agg_completion_lengths.float().min().item()) self._metrics[mode]["completions/max_length"].append(agg_completion_lengths.float().max().item()) # Identify sequences that terminated with EOS and log their lengths eos_and_pad = [self.eos_token_id, self.pad_token_id] is_truncated = torch.tensor([ids[-1] not in eos_and_pad for ids in completion_ids], device=device) agg_is_truncated = self.accelerator.gather(is_truncated) self._metrics[mode]["completions/clipped_ratio"].append(agg_is_truncated.float().mean().item()) term_completion_lengths = agg_completion_lengths[~agg_is_truncated] if len(term_completion_lengths) == 0: # edge case where no terminated sequences are found term_completion_lengths = torch.zeros(1, device=device) self._metrics[mode]["completions/mean_terminated_length"].append(term_completion_lengths.float().mean().item()) self._metrics[mode]["completions/min_terminated_length"].append(term_completion_lengths.float().min().item()) self._metrics[mode]["completions/max_terminated_length"].append(term_completion_lengths.float().max().item()) return prompt_ids, completion_ids, completions def _generate_and_score_completions( self, inputs: list[dict[str, torch.Tensor | Any]] ) -> dict[str, torch.Tensor | Any]: device = self.accelerator.device mode = "train" if self.model.training else "eval" prompts = [x["prompt"] for x in inputs] if "images" in inputs[0]: images = [example.get("images") for example in inputs] elif "image" in inputs[0]: images = [[example.get("image")] if example.get("image") is not None else None for example in inputs] else: images = None # Transformers requires at least one image in the batch, otherwise it throws an error if images is not None and all(img_list == [] for img_list in images): images = None # If the prompts are conversational and the inputs contain images, we need to convert the prompts from # [{"role": "user", "content": "What color is the sky?"}] to # [{"role": "user", "content": [{"type": "image", "image": }, {"type": "text", "text": "What color is the sky?"}]}] if images is not None: if not is_conversational(inputs[0]): raise ValueError( "Multimodal training requires conversational prompts. It looks like the dataset contains " "non-conversational inputs, likely because a chat template was applied before passing the dataset " "to the trainer. Please provide the raw conversational prompts and let the trainer apply the chat " "template internally." ) prompts = [ prepare_multimodal_messages(prompt, images=image_list) for prompt, image_list in zip(prompts, images, strict=True) ] prompt_ids_list, completion_ids_list, completions = self._generate(prompts) # Convert lists of token IDs to padded tensors prompt_ids = [torch.tensor(ids) for ids in prompt_ids_list] prompt_mask = [torch.ones_like(ids, dtype=torch.long) for ids in prompt_ids] prompt_ids = pad( prompt_ids, padding_value=self.pad_token_id, padding_side="left", pad_to_multiple_of=self.pad_to_multiple_of, ).to(device=device) prompt_mask = pad( prompt_mask, padding_value=0, padding_side="left", pad_to_multiple_of=self.pad_to_multiple_of ).to(device=device) completion_ids = [torch.tensor(ids) for ids in completion_ids_list] completion_mask = [torch.ones_like(ids, dtype=torch.long) for ids in completion_ids] completion_ids = pad( completion_ids, padding_value=self.pad_token_id, padding_side="right", pad_to_multiple_of=self.pad_to_multiple_of, ).to(device=device) completion_mask = pad( completion_mask, padding_value=0, padding_side="right", pad_to_multiple_of=self.pad_to_multiple_of ).to(device=device) # If mask_truncated_completions is enabled, zero out truncated completions in completion_mask if self.mask_truncated_completions: eos_and_pad = [self.eos_token_id, self.pad_token_id] # Mask completion_mask for attention masking is_truncated = torch.tensor([ids[-1] not in eos_and_pad for ids in completion_ids_list], device=device) completion_mask = completion_mask * (~is_truncated).unsqueeze(1).int() # Concatenate prompt_mask with completion_mask for logit computation prompt_completion_ids = torch.cat([prompt_ids, completion_ids], dim=1) # (B, P+C) attention_mask = torch.cat([prompt_mask, completion_mask], dim=1) # (B, P+C) logits_to_keep = completion_ids.size(1) # we only need to compute the logits for the completion tokens batch_size = self.args.per_device_train_batch_size if mode == "train" else self.args.per_device_eval_batch_size num_images = [len(img_list) if img_list else 0 for img_list in images] if images is not None else None # Get forward_kwargs for models with multimodal inputs if images is not None: prompts_text = [ apply_chat_template({"prompt": prompt}, self.processing_class, **self.chat_template_kwargs)["prompt"] for prompt in prompts ] prompt_inputs = self.processing_class(images=images, text=prompts_text, padding=True, return_tensors="pt") prompt_inputs = super()._prepare_inputs(prompt_inputs) forward_kwargs = {k: v for k, v in prompt_inputs.items() if k not in ["input_ids", "attention_mask"]} else: forward_kwargs = {} # If token_type_ids are used, extend them with zeros for the completion part if "token_type_ids" in forward_kwargs: token_type_ids = forward_kwargs["token_type_ids"] if self.pad_to_multiple_of is not None: # Needed only with pad_to_multiple_of: otherwise prompt_ids and token_type_ids must have equal len padding_size = prompt_ids.size(1) - token_type_ids.size(1) if padding_size > 0: token_type_ids = torch.cat( [token_type_ids.new_zeros((token_type_ids.size(0), padding_size)), token_type_ids], dim=1 ) forward_kwargs["token_type_ids"] = torch.cat( [token_type_ids, token_type_ids.new_zeros(completion_ids.shape)], dim=1 ) # If mm_token_type_ids are used, extend them with zeros for the completion part if "mm_token_type_ids" in forward_kwargs: mm_token_type_ids = forward_kwargs["mm_token_type_ids"] if self.pad_to_multiple_of is not None: # Needed only with pad_to_multiple_of: otherwise prompt_ids and mm_token_type_ids must have equal len padding_size = prompt_ids.size(1) - mm_token_type_ids.size(1) if padding_size > 0: mm_token_type_ids = torch.cat( [mm_token_type_ids.new_zeros((mm_token_type_ids.size(0), padding_size)), mm_token_type_ids], dim=1, ) forward_kwargs["mm_token_type_ids"] = torch.cat( [mm_token_type_ids, mm_token_type_ids.new_zeros(completion_ids.shape)], dim=1 ) # When gradient checkpointing is enabled with use_reentrant=True (non default), calling the model inside a # torch.no_grad() block triggers a harmless PyTorch warning ("None of the inputs have requires_grad=True"). # Temporarily disable checkpointing to avoid this warning during inference. with torch.no_grad(), disable_gradient_checkpointing(self.model, self.args.gradient_checkpointing_kwargs): # Compute the per-token log probabilities for the current model old_per_token_logps, _ = self._get_per_token_logps_and_entropies( self.model, prompt_completion_ids, attention_mask, logits_to_keep, batch_size, num_images=num_images, **forward_kwargs, # may contain pixel_values, image_grid_thw, pixel_attention_mask, image_sizes, image_position_ids ) old_logps = (old_per_token_logps * completion_mask).sum(1) # mask out padding and tokens after EOS # Compute the per-token log probabilities for the reference model if self.beta != 0.0: if self.ref_model is not None: ref_per_token_logps, _ = self._get_per_token_logps_and_entropies( self.ref_model, prompt_completion_ids, attention_mask, logits_to_keep, batch_size=batch_size, num_images=num_images, **forward_kwargs, # may contain pixel_values, image_grid_thw, pixel_attention_mask, image_sizes, image_position_ids ) else: # When training a PEFT adapter, how we obtain the reference depends on the setup: # - New adapter: disabling adapters yields the base model. # - Re-training an existing adapter: an initial copy is loaded under the name "ref". model = self.accelerator.unwrap_model(self.model) with use_adapter(model, adapter_name="ref" if "ref" in model.peft_config else None): ref_per_token_logps, _ = self._get_per_token_logps_and_entropies( self.model, prompt_completion_ids, attention_mask, logits_to_keep, batch_size=batch_size, num_images=num_images, **forward_kwargs, # may contain pixel_values, image_grid_thw, pixel_attention_mask, image_sizes, image_position_ids ) else: ref_per_token_logps = None # Decode prompts_text = self.processing_class.batch_decode(prompt_ids, skip_special_tokens=True) completions_text = self.processing_class.batch_decode(completion_ids, skip_special_tokens=True) # Calculate rewards for each reward function. rewards_per_func aggregates rewards across all processes. This is # important because rewards will be normalized per group, and completions are distributed. We will later slice # rewards_per_func to extract each process's subset. rewards_per_func = self._calculate_rewards(inputs, prompts, completions, completion_ids_list) num_generations = self.num_generations if mode == "train" else self.num_generations_eval # Apply weights to each reward function's output and sum rewards = (rewards_per_func * self.reward_weights.to(device).unsqueeze(0)).nansum(dim=1) # Apply reward clipping if specified if self.reward_clip_range: rewards = rewards.clamp(min=self.reward_clip_range[0], max=self.reward_clip_range[1]) # Include the KL penalty in the reward if self.beta != 0.0: per_token_kl = old_per_token_logps - ref_per_token_logps # Apply sequence-level KL penalty to rewards (sum KL across tokens first, then apply to each sequence) kl = (per_token_kl * completion_mask).sum(-1) kl = gather(kl) # rewards are gathered, so kl must be too rewards = rewards - self.beta * kl grouped_rewards = rewards.view(-1, num_generations) mean_grouped_rewards = grouped_rewards.mean(dim=1) if num_generations > 1: std_rewards = grouped_rewards.std(dim=1) else: # doesn't occur during training, but could occur in eval when num_generations_eval=1 std_rewards = torch.zeros_like(mean_grouped_rewards) # RLOO advantages computation grouped_sum = grouped_rewards.sum(dim=1, keepdim=True) # (num_prompts, 1) if num_generations > 1: baselines = (grouped_sum - grouped_rewards) / (num_generations - 1) # (num_prompts, num_generations) baselines = baselines.view(-1) # Flatten back to match rewards shape advantages = rewards - baselines else: # this case doesn't occur during training, but could in eval when num_generations_eval=1 advantages = torch.zeros_like(rewards) # Normalize advantages if self.normalize_advantages: advantages = (advantages - advantages.mean()) / (advantages.std() + 1e-4) is_std_zero = torch.isclose(std_rewards, torch.zeros_like(std_rewards)) # for logging # Slice to keep only the local part of the data process_slice = slice( self.accelerator.process_index * len(prompts), (self.accelerator.process_index + 1) * len(prompts), ) all_process_advantages = advantages.clone() # keep the aggregated advantages for logging advantages = advantages[process_slice] # Calculate and log the mean KL divergence between current and reference model if self.beta != 0.0: mean_kl = (per_token_kl * completion_mask).sum() / completion_mask.sum().clamp(min=1.0) self._metrics[mode]["kl"].append(self.accelerator.gather(mean_kl).nanmean().item()) # Calculate mean reward per function, but only for samples where the function was applied (non-NaN values) for i, reward_func_name in enumerate(self.reward_func_names): mean_rewards = torch.nanmean(rewards_per_func[:, i]).item() self._metrics[mode][f"rewards/{reward_func_name}/mean"].append(mean_rewards) std_func_rewards = nanstd(rewards_per_func[:, i]).item() self._metrics[mode][f"rewards/{reward_func_name}/std"].append(std_func_rewards) rewards = (rewards_per_func * self.reward_weights.to(rewards_per_func.device).unsqueeze(0)).nansum(dim=1) self._metrics[mode]["reward"].append(rewards.mean().item()) self._metrics[mode]["reward_std"].append(rewards.std().item()) self._metrics[mode]["frac_reward_zero_std"].append(is_std_zero.float().mean().item()) # Log prompt and completion texts self._logs["prompt"].extend(gather_object(prompts_text)) self._logs["completion"].extend(gather_object(completions_text)) for i, name in enumerate(self.reward_func_names): self._logs["rewards"][name].extend(rewards_per_func[:, i].tolist()) self._logs["advantages"].extend(all_process_advantages.tolist()) # Flush user-logged extra columns (from log_extra), gathering across processes. # Keys must be sorted so that all ranks call gather_object in the same order, otherwise values # get mis-attributed across columns (dict insertion order may differ between processes). for column in sorted(self._pending_extra_logs): self._logs["extra"][column].extend(gather_object(self._pending_extra_logs[column])) self._pending_extra_logs.clear() # Flush user-logged metrics (from log_metric), averaging across processes. # Keys must be sorted so that all ranks call accelerator.gather in the same order, otherwise values # get mis-attributed across metrics (dict insertion order may differ between processes). for name in sorted(self._pending_metrics): values = self._pending_metrics[name] local_mean = sum(values) / len(values) global_mean = self.accelerator.gather(torch.tensor(local_mean, device=device)).mean().item() self._metrics[mode][name].append(global_mean) self._pending_metrics.clear() if images is not None: self._logs["images"].extend(gather_object(images)) output = { "prompt_ids": prompt_ids, "prompt_mask": prompt_mask, "completion_ids": completion_ids, "completion_mask": completion_mask, "old_logps": old_logps, "advantages": advantages, } if "pixel_values" in forward_kwargs: output["pixel_values"] = forward_kwargs["pixel_values"] if "image_grid_thw" in forward_kwargs: output["image_grid_thw"] = forward_kwargs["image_grid_thw"] if "pixel_attention_mask" in forward_kwargs: output["pixel_attention_mask"] = forward_kwargs["pixel_attention_mask"] if "image_sizes" in forward_kwargs: output["image_sizes"] = forward_kwargs["image_sizes"] if "token_type_ids" in forward_kwargs: output["token_type_ids"] = forward_kwargs["token_type_ids"] if "mm_token_type_ids" in forward_kwargs: output["mm_token_type_ids"] = forward_kwargs["mm_token_type_ids"] if "image_position_ids" in forward_kwargs: output["image_position_ids"] = forward_kwargs["image_position_ids"] if images is not None: output["num_images"] = num_images return output @profiling_decorator def compute_loss(self, model, inputs, return_outputs=False, num_items_in_batch=None): if return_outputs: raise ValueError("The RLOOTrainer does not support returning outputs") return self._compute_loss(model, inputs) def _compute_loss(self, model, inputs): # Compute the per-token log probabilities for the model prompt_ids, prompt_mask = inputs["prompt_ids"], inputs["prompt_mask"] completion_ids, completion_mask = inputs["completion_ids"], inputs["completion_mask"] input_ids = torch.cat([prompt_ids, completion_ids], dim=1) attention_mask = torch.cat([prompt_mask, completion_mask], dim=1) logits_to_keep = completion_ids.size(1) # we only need to compute the logits for the completion tokens # Compute the per_token_logps and the entropy at each position in the completion per_token_logps, entropies = self._get_per_token_logps_and_entropies( model, input_ids, attention_mask, logits_to_keep, compute_entropy=True, pixel_values=inputs.get("pixel_values"), image_grid_thw=inputs.get("image_grid_thw"), num_images=inputs.get("num_images"), pixel_attention_mask=inputs.get("pixel_attention_mask"), image_sizes=inputs.get("image_sizes"), token_type_ids=inputs.get("token_type_ids"), mm_token_type_ids=inputs.get("mm_token_type_ids"), image_position_ids=inputs.get("image_position_ids"), ) logps = (per_token_logps * completion_mask).sum(1) # mask out padding and tokens after EOS old_logps = inputs["old_logps"] log_ratio = logps - old_logps # Compute the loss advantages = inputs["advantages"] coef_1 = torch.exp(log_ratio) coef_2 = torch.clamp(coef_1, 1 - self.epsilon_low, 1 + self.epsilon_high) per_sequence_loss1 = coef_1 * advantages per_sequence_loss2 = coef_2 * advantages per_sequence_loss = -torch.min(per_sequence_loss1, per_sequence_loss2) loss = per_sequence_loss.mean() # Log the metrics mode = "train" if self.model.training else "eval" # Entropy mean_entropy = (entropies * completion_mask).sum() / completion_mask.sum().clamp(min=1.0) self._metrics[mode]["entropy"].append(self.accelerator.gather(mean_entropy).nanmean().item()) # Compute the clipped probability ratios is_low_clipped = (coef_1 < 1 - self.epsilon_low) & (advantages < 0) is_high_clipped = (coef_1 > 1 + self.epsilon_high) & (advantages > 0) is_region_clipped = is_low_clipped | is_high_clipped gathered_low_clip = self.accelerator.gather(is_low_clipped.float().mean()) self._metrics[mode]["clip_ratio/low_mean"].append(gathered_low_clip.nanmean().item()) self._metrics[mode]["clip_ratio/low_min"].append(nanmin(gathered_low_clip).item()) gathered_high_clip = self.accelerator.gather(is_high_clipped.float().mean()) self._metrics[mode]["clip_ratio/high_mean"].append(gathered_high_clip.nanmean().item()) self._metrics[mode]["clip_ratio/high_max"].append(nanmax(gathered_high_clip).item()) gathered_clip_ratio = self.accelerator.gather(is_region_clipped.float().mean()) self._metrics[mode]["clip_ratio/region_mean"].append(gathered_clip_ratio.nanmean().item()) return loss # During eval, Trainer calls prediction_step. If no labels are present in the inputs, it only runs forward and # returns logits. We override prediction_step to force compute_loss, because this trainer doesn't involve labels. def prediction_step(self, model, inputs, prediction_loss_only, ignore_keys: list[str] | None = None): inputs = self._prepare_inputs(inputs) with torch.no_grad(): with self.compute_loss_context_manager(): loss = self.compute_loss(model, inputs) loss = loss.mean().detach() return loss, None, None def log(self, logs: dict[str, float], start_time: float | None = None) -> None: mode = "train" if self.model.training else "eval" # Average the metrics metrics = {} for key, val in self._metrics[mode].items(): # Filter out NaN values before averaging. A reward function that returns None for all samples # in a batch produces NaN for that batch's metric. With logging_steps > 1, a naive sum()/len() # would let a single NaN contaminate valid data from other batches. Only return None when no # valid values remain (e.g. JSON loggers crash on float NaN). valid = [v for v in val if not math.isnan(v)] metrics[key] = sum(valid) / len(valid) if valid else None # This method can be called both in training and evaluation. When called in evaluation, the keys in `logs` # start with "eval_". We need to add the prefix "eval_" to the keys in `metrics` to match the format. if mode == "eval": metrics = {f"eval_{key}": val for key, val in metrics.items()} logs = {**logs, **metrics} super().log(logs, start_time) self._metrics[mode].clear() if self.accelerator.is_main_process and self.log_completions: if is_rich_available(): print_prompt_completions_sample( self._logs["prompt"], self._logs["completion"], self._logs["rewards"], self._logs["advantages"], self.state.global_step, self.num_completions_to_print, extra=dict(self._logs["extra"]), ) logging_backends = [] if self.args.report_to and "wandb" in self.args.report_to and wandb.run is not None: logging_backends.append(wandb) if self.args.report_to and "trackio" in self.args.report_to: logging_backends.append(trackio) table = { "step": [self.state.global_step] * len(self._logs["prompt"]), "prompt": self._logs["prompt"], "completion": self._logs["completion"], **self._logs["rewards"], **self._logs["extra"], "advantage": self._logs["advantages"], } df_base = pd.DataFrame(table) images_raw = self._logs["images"] or [] for logging_backend in logging_backends: if images_raw: images = [] for image_list in self._logs["images"]: images.append([logging_backend.Image(image) for image in image_list]) df = pd.concat( [df_base, pd.Series(images, name="image")], axis=1, copy=False, ) else: df = df_base if self.log_unique_prompts: df = df.drop_duplicates(subset=["prompt"]) logging_backend.log({"completions": logging_backend.Table(dataframe=df)}) # 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)