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support function calling

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hiyouga
2024-01-18 09:54:23 +08:00
parent 28135d787d
commit d9f1cae351
69 changed files with 1329 additions and 1085 deletions
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19
src/llmtuner/train/ppo/workflow.py
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@@ -7,17 +7,17 @@ from typing import TYPE_CHECKING, Optional, List
from transformers import DataCollatorWithPadding
from transformers.optimization import get_scheduler
from llmtuner.data import get_dataset, preprocess_dataset
from llmtuner.extras.callbacks import FixValueHeadModelCallback
from llmtuner.extras.misc import fix_valuehead_checkpoint
from llmtuner.extras.ploting import plot_loss
from llmtuner.model import load_model_and_tokenizer
from llmtuner.train.utils import create_ref_model, create_reward_model
from llmtuner.train.ppo.trainer import CustomPPOTrainer
from ...data import get_dataset
from ...extras.callbacks import FixValueHeadModelCallback
from ...extras.misc import fix_valuehead_checkpoint
from ...extras.ploting import plot_loss
from ...model import load_model_and_tokenizer
from ...train.utils import create_ref_model, create_reward_model
from ...train.ppo.trainer import CustomPPOTrainer
if TYPE_CHECKING:
from transformers import Seq2SeqTrainingArguments, TrainerCallback
from llmtuner.hparams import ModelArguments, DataArguments, FinetuningArguments, GeneratingArguments
from ...hparams import ModelArguments, DataArguments, FinetuningArguments, GeneratingArguments
def run_ppo(
@@ -28,9 +28,8 @@ def run_ppo(
generating_args: "GeneratingArguments",
callbacks: Optional[List["TrainerCallback"]] = None
):
dataset = get_dataset(model_args, data_args)
model, tokenizer = load_model_and_tokenizer(model_args, finetuning_args, training_args.do_train, add_valuehead=True)
dataset = preprocess_dataset(dataset, tokenizer, data_args, training_args, stage="ppo")
dataset = get_dataset(model_args, data_args, tokenizer, training_args, stage="ppo")
tokenizer.padding_side = "left" # use left-padding in generation while using right-padding in training
data_collator = DataCollatorWithPadding(tokenizer=tokenizer)