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Merged
qgallouedec merged 6 commits into from
Dec 16, 2025
Merged

Align GRPO and RLOO initialization #4685

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4d7345a
align rloo and grpo
qgallouedec Dec 12, 2025
21549b2
style
qgallouedec Dec 12, 2025
c336c9a
Merge branch 'main' into align-rloo
qgallouedec Dec 15, 2025
8be5fc0
Move `prepare_model_for_kbit_training`, `enable_gradient_checkpointin…
qgallouedec Dec 16, 2025
c0f649a
Merge branch 'main' into align-rloo
qgallouedec Dec 16, 2025
5d42fcd
Merge branch 'main' into align-rloo
qgallouedec Dec 16, 2025
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82 changes: 48 additions & 34 deletions trl/trainer/rloo_trainer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -27,15 +27,13 @@
import pandas as pd
import torch
import torch.utils.data
import transformers
from accelerate.logging import get_logger
from accelerate.utils import broadcast_object_list, gather, gather_object, is_peft_model, set_seed
from datasets import Dataset, IterableDataset
from torch import nn
from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
from torch.utils.data import DataLoader, Sampler
from transformers import (
AutoConfig,
AutoModelForSequenceClassification,
AutoProcessor,
AutoTokenizer,
Expand All @@ -60,13 +58,14 @@
from ..extras.profiling import profiling_context, profiling_decorator
from ..extras.vllm_client import VLLMClient
from ..import_utils import is_vllm_available
from ..models import prepare_deepspeed, prepare_fsdp, prepare_peft_model, unwrap_model_for_generation
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,
ensure_master_addr_port,
entropy_from_logits,
Expand All @@ -86,7 +85,7 @@


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

if is_vllm_available():
from vllm import LLM, SamplingParams
Expand Down Expand Up @@ -225,8 +224,8 @@ class RLOOTrainer(BaseTrainer):

def __init__(
self,
model: str | PreTrainedModel = None,
reward_funcs: RewardFunc | list[RewardFunc] = None,
model: str | PreTrainedModel,
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,
Expand All @@ -242,28 +241,14 @@ def __init__(
model_name = model_name.split("/")[-1]
args = RLOOConfig(f"{model_name}-RLOO")

# Models

@qgallouedec qgallouedec Dec 12, 2025

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all other changes come from #4577

# Trained model
model_init_kwargs = args.model_init_kwargs or {}
# Model
if isinstance(model, str):
model_id = model
dtype = model_init_kwargs.get("dtype", "auto")
if isinstance(dtype, torch.dtype) or dtype == "auto" or dtype is None:
pass # dtype is already a torch.dtype or "auto" or None
elif isinstance(dtype, str): # it's a str, but not "auto"
dtype = getattr(torch, dtype)
model_init_kwargs["dtype"] = dtype
else:
raise ValueError(
"Invalid `dtype` passed to `RLOOConfig`. Expected either 'auto' or a string representing "
f"a `torch.dtype` (e.g., 'float32'), but got {dtype}."
)
model_init_kwargs["device_map"] = model_init_kwargs.get("device_map", "auto")
config = AutoConfig.from_pretrained(model_id)
architecture = getattr(transformers, config.architectures[0])
model = architecture.from_pretrained(model_id, **model_init_kwargs)
model_init_kwargs = args.model_init_kwargs or {}
# Special case for DeepSpeed: requires device_map=None ("auto" fails)
if args.distributed_state.distributed_type == "DEEPSPEED":
model_init_kwargs["device_map"] = None
model = create_model_from_path(model, **model_init_kwargs)
else:
model_id = get_config_model_id(model.config)
if args.model_init_kwargs is not None:
logger.warning(
"You passed `model_init_kwargs` to the `RLOOConfig`, but your model is already instantiated. "
Expand All @@ -278,12 +263,11 @@ def __init__(
else inspect.signature(model.get_base_model().forward).parameters.keys()
)

if peft_config is not None or (is_peft_available() and isinstance(model, PeftModel)):
model = prepare_peft_model(model, peft_config, args)

# Processing class
if processing_class is None:
processing_class = AutoProcessor.from_pretrained(get_config_model_id(model.config), truncation_side="left")
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):
Expand All @@ -300,12 +284,40 @@ def __init__(
self.pad_token_id = tokenizer.pad_token_id
self.eos_token_id = tokenizer.eos_token_id

if is_peft_available() and isinstance(model, PeftModel) and peft_config is not None:
# If the model is already a PeftModel, we need to merge and unload it.
# Further information: https://huggingface.co/docs/trl/dpo_trainer#reference-model-considerations-with-peft
model = model.merge_and_unload()

# 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 isinstance(model, PeftModel) 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 {}
# Special case for DeepSpeed: requires device_map=None ("auto" fails)
if args.distributed_state.distributed_type == "DEEPSPEED":
model_init_kwargs["device_map"] = None
reward_funcs[i] = AutoModelForSequenceClassification.from_pretrained(
reward_func, num_labels=1, **model_init_kwargs
)
Expand Down Expand Up @@ -356,7 +368,7 @@ def __init__(
self.max_prompt_length = args.max_prompt_length
self.max_completion_length = args.max_completion_length
self.num_generations = args.num_generations
self.num_generations_eval = args.num_generations_eval or 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
Expand Down Expand Up @@ -427,9 +439,11 @@ def __init__(
self.ref_model = None
else:
# For deepspeed, fsdp or non-distributed models, create a reference model from scratch
config = AutoConfig.from_pretrained(model_id)
architecture = getattr(transformers, config.architectures[0])
self.ref_model = architecture.from_pretrained(model_id, **model_init_kwargs)
model_init_kwargs = args.model_init_kwargs or {}
# Special case for DeepSpeed: requires device_map=None ("auto" fails)
if self.args.distributed_state.distributed_type == "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:
Expand Down
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