mirror of
https://github.com/hiyouga/LLaMA-Factory.git
synced 2025-12-16 11:50:35 +08:00
@@ -1 +1,4 @@
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from llmtuner.train.ppo.workflow import run_ppo
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from .workflow import run_ppo
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__all__ = ["run_ppo"]
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@@ -13,15 +13,15 @@ from transformers.trainer_pt_utils import remove_dummy_checkpoint
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from trl import PPOTrainer
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from trl.core import PPODecorators, logprobs_from_logits
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from llmtuner.extras.callbacks import LogCallback, FixValueHeadModelCallback
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from llmtuner.extras.logging import get_logger
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from llmtuner.extras.misc import AverageMeter, count_parameters, get_logits_processor
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from llmtuner.train.ppo.utils import dump_layernorm, get_rewards_from_server, restore_layernorm, replace_model
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from ...extras.callbacks import LogCallback, FixValueHeadModelCallback
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from ...extras.logging import get_logger
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from ...extras.misc import AverageMeter, count_parameters, get_logits_processor
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from .utils import dump_layernorm, get_rewards_from_server, restore_layernorm, replace_model
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if TYPE_CHECKING:
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from transformers import Seq2SeqTrainingArguments, TrainerCallback
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from trl import AutoModelForCausalLMWithValueHead
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from llmtuner.hparams import ModelArguments, FinetuningArguments, GeneratingArguments
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from ...hparams import ModelArguments, FinetuningArguments, GeneratingArguments
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logger = get_logger(__name__)
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@@ -2,7 +2,7 @@ import json
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import torch
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from typing import TYPE_CHECKING, Dict, List, Literal, Optional
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from llmtuner.extras.packages import is_requests_available
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from ...extras.packages import is_requests_available
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if TYPE_CHECKING:
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from transformers import PreTrainedModel
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@@ -7,17 +7,17 @@ from typing import TYPE_CHECKING, Optional, List
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from transformers import DataCollatorWithPadding
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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 FixValueHeadModelCallback
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from llmtuner.extras.misc import fix_valuehead_checkpoint
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from llmtuner.extras.ploting import plot_loss
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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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from ...data import get_dataset
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from ...extras.callbacks import FixValueHeadModelCallback
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from ...extras.misc import fix_valuehead_checkpoint
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from ...extras.ploting import plot_loss
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from ...model import load_model_and_tokenizer
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from ...train.utils import create_ref_model, create_reward_model
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from ...train.ppo.trainer import CustomPPOTrainer
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if TYPE_CHECKING:
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from transformers import Seq2SeqTrainingArguments, TrainerCallback
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from llmtuner.hparams import ModelArguments, DataArguments, FinetuningArguments, GeneratingArguments
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from ...hparams import ModelArguments, DataArguments, FinetuningArguments, GeneratingArguments
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def run_ppo(
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@@ -28,9 +28,8 @@ def run_ppo(
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generating_args: "GeneratingArguments",
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callbacks: Optional[List["TrainerCallback"]] = None
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):
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dataset = get_dataset(model_args, data_args)
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model, tokenizer = load_model_and_tokenizer(model_args, finetuning_args, training_args.do_train, add_valuehead=True)
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dataset = preprocess_dataset(dataset, tokenizer, data_args, training_args, stage="ppo")
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dataset = get_dataset(model_args, data_args, tokenizer, training_args, stage="ppo")
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tokenizer.padding_side = "left" # use left-padding in generation while using right-padding in training
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data_collator = DataCollatorWithPadding(tokenizer=tokenizer)
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