mirror of
https://github.com/hiyouga/LLaMA-Factory.git
synced 2025-08-04 04:32:50 +08:00
fix import bug
Former-commit-id: 35b91ea34caade45dd51813b94da5177b852aa4c
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@ -1,3 +1,5 @@
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# Level: loader > adapter > parser, utils
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from llmtuner.model.loader import load_model_and_tokenizer
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from llmtuner.model.parser import get_train_args, get_infer_args, get_eval_args
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from llmtuner.model.utils import create_ref_model, create_reward_model, dispatch_model, generate_model_card
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from llmtuner.model.utils import dispatch_model, generate_model_card, load_valuehead_params
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@ -82,15 +82,13 @@ def get_train_args(args: Optional[Dict[str, Any]] = None) -> _TRAIN_CLS:
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raise ValueError("Please enable `predict_with_generate` to save model predictions.")
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if finetuning_args.stage in ["rm", "ppo"]:
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if finetuning_args.finetuning_type != "lora":
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raise ValueError("RM and PPO stages can only be performed with the LoRA method.")
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if training_args.resume_from_checkpoint is not None:
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raise ValueError("RM and PPO stages do not support `resume_from_checkpoint`.")
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if training_args.load_best_model_at_end:
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raise ValueError("RM and PPO stages do not support `load_best_model_at_end`.")
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if finetuning_args.stage == "ppo" and not training_args.do_train:
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raise ValueError("PPO training does not support evaluation.")
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raise ValueError("PPO training does not support evaluation, use the SFT stage to evaluate models.")
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if finetuning_args.stage in ["rm", "dpo"]:
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for dataset_attr in data_args.dataset_list:
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@ -131,6 +129,9 @@ def get_train_args(args: Optional[Dict[str, Any]] = None) -> _TRAIN_CLS:
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if (not training_args.do_train) and model_args.quantization_bit is not None:
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logger.warning("Evaluating model in 4/8-bit mode may cause lower scores.")
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if (not training_args.do_train) and finetuning_args.stage == "dpo" and finetuning_args.ref_model is None:
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logger.warning("Specify `ref_model` for computing rewards at evaluation.")
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# postprocess training_args
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if (
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training_args.local_rank != -1
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@ -1,5 +1,5 @@
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import torch
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from typing import TYPE_CHECKING, Any, Dict, List, Literal, Optional, Set, Tuple, Union
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from typing import TYPE_CHECKING, Any, Dict, List, Optional, Set, Tuple
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from transformers.utils import cached_file
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from transformers.trainer import WEIGHTS_NAME, SAFE_WEIGHTS_NAME
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@ -7,83 +7,15 @@ from transformers.trainer import WEIGHTS_NAME, SAFE_WEIGHTS_NAME
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from llmtuner.extras.constants import LAYERNORM_NAMES
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from llmtuner.extras.logging import get_logger
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from llmtuner.hparams import ModelArguments, FinetuningArguments
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from llmtuner.model import load_model_and_tokenizer
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if TYPE_CHECKING:
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from transformers.modeling_utils import PreTrainedModel
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from trl import AutoModelForCausalLMWithValueHead
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from llmtuner.hparams import DataArguments
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logger = get_logger(__name__)
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def create_ref_model(
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model_args: "ModelArguments",
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finetuning_args: "FinetuningArguments",
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stage: Literal["ppo", "dpo"]
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) -> Union["PreTrainedModel", "AutoModelForCausalLMWithValueHead"]:
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r"""
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Creates reference model for PPO/DPO training. Evaluation mode is not supported.
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The valuehead parameter is randomly initialized since it is useless for PPO training.
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"""
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if finetuning_args.ref_model is not None:
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ref_model_args_dict = model_args.to_dict()
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ref_model_args_dict.update(dict(
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model_name_or_path=finetuning_args.ref_model,
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checkpoint_dir=finetuning_args.ref_model_checkpoint,
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quantization_bit=finetuning_args.ref_model_quantization_bit
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))
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ref_model_args = ModelArguments(**ref_model_args_dict)
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ref_finetuning_args = FinetuningArguments(finetuning_type="lora")
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ref_model, _ = load_model_and_tokenizer(ref_model_args, ref_finetuning_args, is_trainable=False, stage=stage)
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logger.info("Created reference model from {}".format(finetuning_args.ref_model))
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else:
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if finetuning_args.finetuning_type == "lora":
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ref_model = None
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else:
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ref_model, _ = load_model_and_tokenizer(model_args, finetuning_args, is_trainable=False, stage=stage)
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logger.info("Created reference model from the model itself.")
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return ref_model
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def create_reward_model(
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model: "AutoModelForCausalLMWithValueHead",
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model_args: "ModelArguments",
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finetuning_args: "FinetuningArguments"
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) -> "AutoModelForCausalLMWithValueHead":
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r"""
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Creates reward model for PPO training.
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"""
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if finetuning_args.reward_model_type == "lora":
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model.pretrained_model.load_adapter(finetuning_args.reward_model, "reward")
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for name, param in model.named_parameters(): # https://github.com/huggingface/peft/issues/1090
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if "default" in name:
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param.data = param.data.to(torch.float32) # trainable params should in fp32
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vhead_params = load_valuehead_params(model_args.checkpoint_dir[-1], model_args)
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assert vhead_params is not None, "Reward model is not correctly loaded."
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model.register_buffer("reward_head_weight", vhead_params["v_head.summary.weight"], persistent=False)
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model.register_buffer("reward_head_bias", vhead_params["v_head.summary.bias"], persistent=False)
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model.register_buffer("default_head_weight", torch.zeros_like(vhead_params["v_head.summary.weight"]), persistent=False)
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model.register_buffer("default_head_bias", torch.zeros_like(vhead_params["v_head.summary.bias"]), persistent=False)
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logger.info("Loaded adapter weights of reward model from {}".format(finetuning_args.reward_model))
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return None
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else:
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reward_model_args_dict = model_args.to_dict()
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reward_model_args_dict.update(dict(
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model_name_or_path=finetuning_args.reward_model,
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checkpoint_dir=finetuning_args.reward_model_checkpoint,
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quantization_bit=finetuning_args.reward_model_quantization_bit
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))
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reward_model_args = ModelArguments(**reward_model_args_dict)
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reward_finetuning_args = FinetuningArguments(finetuning_type="lora")
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reward_model, _ = load_model_and_tokenizer(reward_model_args, reward_finetuning_args, is_trainable=False, stage="ppo")
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logger.info("Load full weights of reward model from {}".format(finetuning_args.reward_model))
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return reward_model
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def dispatch_model(model: "PreTrainedModel") -> "PreTrainedModel":
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r"""
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Dispatches a pre-trained model to GPUs with balanced memory.
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@ -6,10 +6,10 @@ from transformers import Seq2SeqTrainingArguments
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from llmtuner.data import get_dataset, preprocess_dataset, split_dataset
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from llmtuner.extras.constants import IGNORE_INDEX
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from llmtuner.extras.logging import get_logger
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from llmtuner.extras.ploting import plot_loss
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from llmtuner.hparams import ModelArguments
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from llmtuner.model import create_ref_model, generate_model_card, load_model_and_tokenizer
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from llmtuner.model import generate_model_card, load_model_and_tokenizer
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from llmtuner.train.utils import create_ref_model
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from llmtuner.train.dpo.collator import DPODataCollatorWithPadding
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from llmtuner.train.dpo.trainer import CustomDPOTrainer
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@ -18,9 +18,6 @@ if TYPE_CHECKING:
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from llmtuner.hparams import DataArguments, FinetuningArguments
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logger = get_logger(__name__)
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def run_dpo(
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model_args: "ModelArguments",
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data_args: "DataArguments",
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@ -74,7 +71,6 @@ def run_dpo(
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if training_args.do_eval:
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metrics = trainer.evaluate(metric_key_prefix="eval")
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if id(model) == id(ref_model): # unable to compute rewards without a reference model
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logger.warning("Specify `ref_model` for computing rewards at evaluation.")
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remove_keys = [key for key in metrics.keys() if "rewards" in key]
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for key in remove_keys:
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metrics.pop(key)
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@ -9,9 +9,9 @@ from transformers.optimization import get_scheduler
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from llmtuner.data import get_dataset, preprocess_dataset
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from llmtuner.extras.callbacks import SavePeftModelCallback
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from llmtuner.extras.logging import get_logger
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from llmtuner.extras.ploting import plot_loss
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from llmtuner.model import create_ref_model, create_reward_model, load_model_and_tokenizer
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from llmtuner.model import load_model_and_tokenizer
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from llmtuner.train.utils import create_ref_model, create_reward_model
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from llmtuner.train.ppo.trainer import CustomPPOTrainer
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if TYPE_CHECKING:
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@ -19,9 +19,6 @@ if TYPE_CHECKING:
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from llmtuner.hparams import ModelArguments, DataArguments, FinetuningArguments, GeneratingArguments
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logger = get_logger(__name__)
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def run_ppo(
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model_args: "ModelArguments",
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data_args: "DataArguments",
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79
src/llmtuner/train/utils.py
Normal file
79
src/llmtuner/train/utils.py
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@ -0,0 +1,79 @@
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import torch
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from typing import TYPE_CHECKING, Literal, Union
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from llmtuner.extras.logging import get_logger
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from llmtuner.hparams import ModelArguments, FinetuningArguments
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from llmtuner.model import load_model_and_tokenizer, load_valuehead_params
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if TYPE_CHECKING:
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from transformers.modeling_utils import PreTrainedModel
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from trl import AutoModelForCausalLMWithValueHead
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logger = get_logger(__name__)
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def create_ref_model(
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model_args: "ModelArguments",
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finetuning_args: "FinetuningArguments",
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stage: Literal["ppo", "dpo"]
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) -> Union["PreTrainedModel", "AutoModelForCausalLMWithValueHead"]:
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r"""
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Creates reference model for PPO/DPO training. Evaluation mode is not supported.
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The valuehead parameter is randomly initialized since it is useless for PPO training.
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"""
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if finetuning_args.ref_model is not None:
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ref_model_args_dict = model_args.to_dict()
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ref_model_args_dict.update(dict(
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model_name_or_path=finetuning_args.ref_model,
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checkpoint_dir=finetuning_args.ref_model_checkpoint,
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quantization_bit=finetuning_args.ref_model_quantization_bit
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))
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ref_model_args = ModelArguments(**ref_model_args_dict)
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ref_finetuning_args = FinetuningArguments(finetuning_type="lora")
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ref_model, _ = load_model_and_tokenizer(ref_model_args, ref_finetuning_args, is_trainable=False, stage=stage)
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logger.info("Created reference model from {}".format(finetuning_args.ref_model))
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else:
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if finetuning_args.finetuning_type == "lora":
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ref_model = None
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else:
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ref_model, _ = load_model_and_tokenizer(model_args, finetuning_args, is_trainable=False, stage=stage)
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logger.info("Created reference model from the model itself.")
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return ref_model
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def create_reward_model(
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model: "AutoModelForCausalLMWithValueHead",
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model_args: "ModelArguments",
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finetuning_args: "FinetuningArguments"
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) -> "AutoModelForCausalLMWithValueHead":
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r"""
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Creates reward model for PPO training.
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"""
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if finetuning_args.reward_model_type == "lora":
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model.pretrained_model.load_adapter(finetuning_args.reward_model, "reward")
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for name, param in model.named_parameters(): # https://github.com/huggingface/peft/issues/1090
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if "default" in name:
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param.data = param.data.to(torch.float32) # trainable params should in fp32
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vhead_params = load_valuehead_params(model_args.checkpoint_dir[-1], model_args)
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assert vhead_params is not None, "Reward model is not correctly loaded."
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model.register_buffer("reward_head_weight", vhead_params["v_head.summary.weight"], persistent=False)
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model.register_buffer("reward_head_bias", vhead_params["v_head.summary.bias"], persistent=False)
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model.register_buffer("default_head_weight", torch.zeros_like(vhead_params["v_head.summary.weight"]), persistent=False)
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model.register_buffer("default_head_bias", torch.zeros_like(vhead_params["v_head.summary.bias"]), persistent=False)
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logger.info("Loaded adapter weights of reward model from {}".format(finetuning_args.reward_model))
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return None
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else:
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reward_model_args_dict = model_args.to_dict()
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reward_model_args_dict.update(dict(
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model_name_or_path=finetuning_args.reward_model,
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checkpoint_dir=finetuning_args.reward_model_checkpoint,
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quantization_bit=finetuning_args.reward_model_quantization_bit
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))
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reward_model_args = ModelArguments(**reward_model_args_dict)
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reward_finetuning_args = FinetuningArguments(finetuning_type="lora")
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reward_model, _ = load_model_and_tokenizer(reward_model_args, reward_finetuning_args, is_trainable=False, stage="ppo")
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logger.info("Load full weights of reward model from {}".format(finetuning_args.reward_model))
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return reward_model
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