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LLaMA-Factory-310P3
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fix #1567

This commit is contained in:
hiyouga 2023-11-20 18:46:36 +08:00
parent 00baaa990e
commit 99a3f06377
5 changed files with 34 additions and 36 deletions
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2
src/llmtuner/model/loader.py
View File

@ -1,4 +1,3 @@
import os
import math
import torch
from types import MethodType
@ -202,6 +201,7 @@ def load_model_and_tokenizer(
# Prepare model with valuehead for RLHF
if stage in ["rm", "ppo"]:
model: "AutoModelForCausalLMWithValueHead" = AutoModelForCausalLMWithValueHead.from_pretrained(model)
setattr(model, "_keys_to_ignore_on_save", [name for name, _ in model.named_parameters() if "pretrained_model" in name])
vhead_path = (
model_args.checkpoint_dir[-1] if model_args.checkpoint_dir is not None else model_args.model_name_or_path
)

59
src/llmtuner/model/parser.py
View File

@ -40,6 +40,18 @@ _EVAL_CLS = Tuple[
]
def _verify_model_args(model_args: "ModelArguments", finetuning_args: "FinetuningArguments") -> None:
if model_args.quantization_bit is not None and finetuning_args.finetuning_type != "lora":
raise ValueError("Quantization is only compatible with the LoRA method.")
if (
model_args.checkpoint_dir is not None
and len(model_args.checkpoint_dir) != 1
and finetuning_args.finetuning_type != "lora"
):
raise ValueError("Multiple checkpoints are only available for LoRA tuning.")
def parse_train_args(args: Optional[Dict[str, Any]] = None) -> _TRAIN_CLS:
parser = HfArgumentParser(_TRAIN_ARGS)
return parse_args(parser, args)
@ -81,19 +93,14 @@ def get_train_args(args: Optional[Dict[str, Any]] = None) -> _TRAIN_CLS:
if finetuning_args.stage == "sft" and training_args.do_predict and not training_args.predict_with_generate:
raise ValueError("Please enable `predict_with_generate` to save model predictions.")
if finetuning_args.stage in ["rm", "ppo"]:
if training_args.resume_from_checkpoint is not None:
raise ValueError("RM and PPO stages do not support `resume_from_checkpoint`.")
if training_args.load_best_model_at_end:
raise ValueError("RM and PPO stages do not support `load_best_model_at_end`.")
if finetuning_args.stage in ["rm", "ppo"] and training_args.load_best_model_at_end:
raise ValueError("RM and PPO stages do not support `load_best_model_at_end`.")
if finetuning_args.stage == "ppo" and not training_args.do_train:
raise ValueError("PPO training does not support evaluation, use the SFT stage to evaluate models.")
if finetuning_args.stage in ["rm", "dpo"]:
for dataset_attr in data_args.dataset_list:
if not dataset_attr.ranking:
raise ValueError("Please use ranked datasets for reward modeling or DPO training.")
if finetuning_args.stage in ["rm", "dpo"] and (not all([data_attr.ranking for data_attr in data_args.dataset_list])):
raise ValueError("Please use ranked datasets for reward modeling or DPO training.")
if finetuning_args.stage == "ppo" and model_args.shift_attn:
raise ValueError("PPO training is incompatible with S^2-Attn.")
@ -107,15 +114,7 @@ def get_train_args(args: Optional[Dict[str, Any]] = None) -> _TRAIN_CLS:
if training_args.do_train and finetuning_args.finetuning_type == "lora" and finetuning_args.lora_target is None:
raise ValueError("Please specify `lora_target` in LoRA training.")
if model_args.quantization_bit is not None and finetuning_args.finetuning_type != "lora":
raise ValueError("Quantization is only compatible with the LoRA method.")
if (
model_args.checkpoint_dir is not None
and len(model_args.checkpoint_dir) != 1
and finetuning_args.finetuning_type != "lora"
):
raise ValueError("Only LoRA tuning accepts multiple checkpoints.")
_verify_model_args(model_args, finetuning_args)
if training_args.do_train and model_args.quantization_bit is not None and (not finetuning_args.upcast_layernorm):
logger.warning("We recommend enable `upcast_layernorm` in quantized training.")
@ -154,9 +153,14 @@ def get_train_args(args: Optional[Dict[str, Any]] = None) -> _TRAIN_CLS:
training_args_dict = training_args.to_dict()
training_args_dict.update(dict(resume_from_checkpoint=last_checkpoint))
training_args = Seq2SeqTrainingArguments(**training_args_dict)
logger.info(
"Resuming from checkpoint. Change `output_dir` or use `overwrite_output_dir` to avoid."
)
logger.info("Resuming training from {}. Change `output_dir` or use `overwrite_output_dir` to avoid.".format(
training_args.resume_from_checkpoint
))
if finetuning_args.stage in ["rm", "ppo"] and training_args.resume_from_checkpoint is not None:
logger.warning("Add {} to `checkpoint_dir` to resume training from checkpoint.".format(
training_args.resume_from_checkpoint
))
# postprocess model_args
model_args.compute_dtype = (
@ -183,15 +187,7 @@ def get_infer_args(args: Optional[Dict[str, Any]] = None) -> _INFER_CLS:
if data_args.template is None:
raise ValueError("Please specify which `template` to use.")
if model_args.quantization_bit is not None and finetuning_args.finetuning_type != "lora":
raise ValueError("Quantization is only compatible with the LoRA method.")
if (
model_args.checkpoint_dir is not None
and len(model_args.checkpoint_dir) != 1
and finetuning_args.finetuning_type != "lora"
):
raise ValueError("Only LoRA tuning accepts multiple checkpoints.")
_verify_model_args(model_args, finetuning_args)
return model_args, data_args, finetuning_args, generating_args
@ -202,8 +198,7 @@ def get_eval_args(args: Optional[Dict[str, Any]] = None) -> _EVAL_CLS:
if data_args.template is None:
raise ValueError("Please specify which `template` to use.")
if model_args.quantization_bit is not None and finetuning_args.finetuning_type != "lora":
raise ValueError("Quantization is only compatible with the LoRA method.")
_verify_model_args(model_args, finetuning_args)
transformers.set_seed(eval_args.seed)

5
src/llmtuner/train/ppo/trainer.py
View File

@ -74,10 +74,13 @@ class CustomPPOTrainer(PPOTrainer, Trainer):
else:
self.reward_model = self.accelerator.prepare_model(self.reward_model, evaluation_mode=True)
def ppo_train(self) -> None:
def ppo_train(self, resume_from_checkpoint: Optional[str] = None) -> None:
r"""
Implements training loop for the PPO stage, like _inner_training_loop() in Huggingface's Trainer.
"""
if resume_from_checkpoint is not None:
raise ValueError("`resume_from_checkpoint` will be supported in the future version.")
total_train_batch_size = (
self.args.per_device_train_batch_size * self.args.gradient_accumulation_steps * self.args.world_size
)

2
src/llmtuner/train/ppo/workflow.py
View File

@ -94,7 +94,7 @@ def run_ppo(
# Training
if training_args.do_train:
ppo_trainer.ppo_train()
ppo_trainer.ppo_train(resume_from_checkpoint=training_args.resume_from_checkpoint)
ppo_trainer.save_model()
ppo_trainer.save_state() # must be called after save_model to have a folder
if ppo_trainer.is_world_process_zero() and finetuning_args.plot_loss:

2
src/llmtuner/train/rm/workflow.py
View File

@ -47,7 +47,7 @@ def run_rm(
# Training
if training_args.do_train:
train_result = trainer.train()
train_result = trainer.train(resume_from_checkpoint=training_args.resume_from_checkpoint)
trainer.save_model()
trainer.log_metrics("train", train_result.metrics)
trainer.save_metrics("train", train_result.metrics)