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fix import bug

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Former-commit-id: 35b91ea34caade45dd51813b94da5177b852aa4c
This commit is contained in:
hiyouga 2023-11-16 02:27:03 +08:00
parent f441932bd1
commit eb5a852dd5
6 changed files with 91 additions and 84 deletions
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4
src/llmtuner/model/__init__.py
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@ -1,3 +1,5 @@
# Level: loader > adapter > parser, utils
from llmtuner.model.loader import load_model_and_tokenizer
from llmtuner.model.parser import get_train_args, get_infer_args, get_eval_args
from llmtuner.model.utils import create_ref_model, create_reward_model, dispatch_model, generate_model_card
from llmtuner.model.utils import dispatch_model, generate_model_card, load_valuehead_params

7
src/llmtuner/model/parser.py
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@ -82,15 +82,13 @@ def get_train_args(args: Optional[Dict[str, Any]] = None) -> _TRAIN_CLS:
raise ValueError("Please enable `predict_with_generate` to save model predictions.")
if finetuning_args.stage in ["rm", "ppo"]:
if finetuning_args.finetuning_type != "lora":
raise ValueError("RM and PPO stages can only be performed with the LoRA method.")
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 == "ppo" and not training_args.do_train:
raise ValueError("PPO training does not support evaluation.")
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:
@ -131,6 +129,9 @@ def get_train_args(args: Optional[Dict[str, Any]] = None) -> _TRAIN_CLS:
if (not training_args.do_train) and model_args.quantization_bit is not None:
logger.warning("Evaluating model in 4/8-bit mode may cause lower scores.")
if (not training_args.do_train) and finetuning_args.stage == "dpo" and finetuning_args.ref_model is None:
logger.warning("Specify `ref_model` for computing rewards at evaluation.")
# postprocess training_args
if (
training_args.local_rank != -1

70
src/llmtuner/model/utils.py
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@ -1,5 +1,5 @@
import torch
from typing import TYPE_CHECKING, Any, Dict, List, Literal, Optional, Set, Tuple, Union
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Set, Tuple
from transformers.utils import cached_file
from transformers.trainer import WEIGHTS_NAME, SAFE_WEIGHTS_NAME
@ -7,83 +7,15 @@ from transformers.trainer import WEIGHTS_NAME, SAFE_WEIGHTS_NAME
from llmtuner.extras.constants import LAYERNORM_NAMES
from llmtuner.extras.logging import get_logger
from llmtuner.hparams import ModelArguments, FinetuningArguments
from llmtuner.model import load_model_and_tokenizer
if TYPE_CHECKING:
from transformers.modeling_utils import PreTrainedModel
from trl import AutoModelForCausalLMWithValueHead
from llmtuner.hparams import DataArguments
logger = get_logger(__name__)
def create_ref_model(
model_args: "ModelArguments",
finetuning_args: "FinetuningArguments",
stage: Literal["ppo", "dpo"]
) -> Union["PreTrainedModel", "AutoModelForCausalLMWithValueHead"]:
r"""
Creates reference model for PPO/DPO training. Evaluation mode is not supported.
The valuehead parameter is randomly initialized since it is useless for PPO training.
"""
if finetuning_args.ref_model is not None:
ref_model_args_dict = model_args.to_dict()
ref_model_args_dict.update(dict(
model_name_or_path=finetuning_args.ref_model,
checkpoint_dir=finetuning_args.ref_model_checkpoint,
quantization_bit=finetuning_args.ref_model_quantization_bit
))
ref_model_args = ModelArguments(**ref_model_args_dict)
ref_finetuning_args = FinetuningArguments(finetuning_type="lora")
ref_model, _ = load_model_and_tokenizer(ref_model_args, ref_finetuning_args, is_trainable=False, stage=stage)
logger.info("Created reference model from {}".format(finetuning_args.ref_model))
else:
if finetuning_args.finetuning_type == "lora":
ref_model = None
else:
ref_model, _ = load_model_and_tokenizer(model_args, finetuning_args, is_trainable=False, stage=stage)
logger.info("Created reference model from the model itself.")
return ref_model
def create_reward_model(
model: "AutoModelForCausalLMWithValueHead",
model_args: "ModelArguments",
finetuning_args: "FinetuningArguments"
) -> "AutoModelForCausalLMWithValueHead":
r"""
Creates reward model for PPO training.
"""
if finetuning_args.reward_model_type == "lora":
model.pretrained_model.load_adapter(finetuning_args.reward_model, "reward")
for name, param in model.named_parameters(): # https://github.com/huggingface/peft/issues/1090
if "default" in name:
param.data = param.data.to(torch.float32) # trainable params should in fp32
vhead_params = load_valuehead_params(model_args.checkpoint_dir[-1], model_args)
assert vhead_params is not None, "Reward model is not correctly loaded."
model.register_buffer("reward_head_weight", vhead_params["v_head.summary.weight"], persistent=False)
model.register_buffer("reward_head_bias", vhead_params["v_head.summary.bias"], persistent=False)
model.register_buffer("default_head_weight", torch.zeros_like(vhead_params["v_head.summary.weight"]), persistent=False)
model.register_buffer("default_head_bias", torch.zeros_like(vhead_params["v_head.summary.bias"]), persistent=False)
logger.info("Loaded adapter weights of reward model from {}".format(finetuning_args.reward_model))
return None
else:
reward_model_args_dict = model_args.to_dict()
reward_model_args_dict.update(dict(
model_name_or_path=finetuning_args.reward_model,
checkpoint_dir=finetuning_args.reward_model_checkpoint,
quantization_bit=finetuning_args.reward_model_quantization_bit
))
reward_model_args = ModelArguments(**reward_model_args_dict)
reward_finetuning_args = FinetuningArguments(finetuning_type="lora")
reward_model, _ = load_model_and_tokenizer(reward_model_args, reward_finetuning_args, is_trainable=False, stage="ppo")
logger.info("Load full weights of reward model from {}".format(finetuning_args.reward_model))
return reward_model
def dispatch_model(model: "PreTrainedModel") -> "PreTrainedModel":
r"""
Dispatches a pre-trained model to GPUs with balanced memory.

8
src/llmtuner/train/dpo/workflow.py
View File

@ -6,10 +6,10 @@ from transformers import Seq2SeqTrainingArguments
from llmtuner.data import get_dataset, preprocess_dataset, split_dataset
from llmtuner.extras.constants import IGNORE_INDEX
from llmtuner.extras.logging import get_logger
from llmtuner.extras.ploting import plot_loss
from llmtuner.hparams import ModelArguments
from llmtuner.model import create_ref_model, generate_model_card, load_model_and_tokenizer
from llmtuner.model import generate_model_card, load_model_and_tokenizer
from llmtuner.train.utils import create_ref_model
from llmtuner.train.dpo.collator import DPODataCollatorWithPadding
from llmtuner.train.dpo.trainer import CustomDPOTrainer
@ -18,9 +18,6 @@ if TYPE_CHECKING:
from llmtuner.hparams import DataArguments, FinetuningArguments
logger = get_logger(__name__)
def run_dpo(
model_args: "ModelArguments",
data_args: "DataArguments",
@ -74,7 +71,6 @@ def run_dpo(
if training_args.do_eval:
metrics = trainer.evaluate(metric_key_prefix="eval")
if id(model) == id(ref_model): # unable to compute rewards without a reference model
logger.warning("Specify `ref_model` for computing rewards at evaluation.")
remove_keys = [key for key in metrics.keys() if "rewards" in key]
for key in remove_keys:
metrics.pop(key)

7
src/llmtuner/train/ppo/workflow.py
View File

@ -9,9 +9,9 @@ from transformers.optimization import get_scheduler
from llmtuner.data import get_dataset, preprocess_dataset
from llmtuner.extras.callbacks import SavePeftModelCallback
from llmtuner.extras.logging import get_logger
from llmtuner.extras.ploting import plot_loss
from llmtuner.model import create_ref_model, create_reward_model, load_model_and_tokenizer
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
if TYPE_CHECKING:
@ -19,9 +19,6 @@ if TYPE_CHECKING:
from llmtuner.hparams import ModelArguments, DataArguments, FinetuningArguments, GeneratingArguments
logger = get_logger(__name__)
def run_ppo(
model_args: "ModelArguments",
data_args: "DataArguments",

79
src/llmtuner/train/utils.py Normal file
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@ -0,0 +1,79 @@
import torch
from typing import TYPE_CHECKING, Literal, Union
from llmtuner.extras.logging import get_logger
from llmtuner.hparams import ModelArguments, FinetuningArguments
from llmtuner.model import load_model_and_tokenizer, load_valuehead_params
if TYPE_CHECKING:
from transformers.modeling_utils import PreTrainedModel
from trl import AutoModelForCausalLMWithValueHead
logger = get_logger(__name__)
def create_ref_model(
model_args: "ModelArguments",
finetuning_args: "FinetuningArguments",
stage: Literal["ppo", "dpo"]
) -> Union["PreTrainedModel", "AutoModelForCausalLMWithValueHead"]:
r"""
Creates reference model for PPO/DPO training. Evaluation mode is not supported.
The valuehead parameter is randomly initialized since it is useless for PPO training.
"""
if finetuning_args.ref_model is not None:
ref_model_args_dict = model_args.to_dict()
ref_model_args_dict.update(dict(
model_name_or_path=finetuning_args.ref_model,
checkpoint_dir=finetuning_args.ref_model_checkpoint,
quantization_bit=finetuning_args.ref_model_quantization_bit
))
ref_model_args = ModelArguments(**ref_model_args_dict)
ref_finetuning_args = FinetuningArguments(finetuning_type="lora")
ref_model, _ = load_model_and_tokenizer(ref_model_args, ref_finetuning_args, is_trainable=False, stage=stage)
logger.info("Created reference model from {}".format(finetuning_args.ref_model))
else:
if finetuning_args.finetuning_type == "lora":
ref_model = None
else:
ref_model, _ = load_model_and_tokenizer(model_args, finetuning_args, is_trainable=False, stage=stage)
logger.info("Created reference model from the model itself.")
return ref_model
def create_reward_model(
model: "AutoModelForCausalLMWithValueHead",
model_args: "ModelArguments",
finetuning_args: "FinetuningArguments"
) -> "AutoModelForCausalLMWithValueHead":
r"""
Creates reward model for PPO training.
"""
if finetuning_args.reward_model_type == "lora":
model.pretrained_model.load_adapter(finetuning_args.reward_model, "reward")
for name, param in model.named_parameters(): # https://github.com/huggingface/peft/issues/1090
if "default" in name:
param.data = param.data.to(torch.float32) # trainable params should in fp32
vhead_params = load_valuehead_params(model_args.checkpoint_dir[-1], model_args)
assert vhead_params is not None, "Reward model is not correctly loaded."
model.register_buffer("reward_head_weight", vhead_params["v_head.summary.weight"], persistent=False)
model.register_buffer("reward_head_bias", vhead_params["v_head.summary.bias"], persistent=False)
model.register_buffer("default_head_weight", torch.zeros_like(vhead_params["v_head.summary.weight"]), persistent=False)
model.register_buffer("default_head_bias", torch.zeros_like(vhead_params["v_head.summary.bias"]), persistent=False)
logger.info("Loaded adapter weights of reward model from {}".format(finetuning_args.reward_model))
return None
else:
reward_model_args_dict = model_args.to_dict()
reward_model_args_dict.update(dict(
model_name_or_path=finetuning_args.reward_model,
checkpoint_dir=finetuning_args.reward_model_checkpoint,
quantization_bit=finetuning_args.reward_model_quantization_bit
))
reward_model_args = ModelArguments(**reward_model_args_dict)
reward_finetuning_args = FinetuningArguments(finetuning_type="lora")
reward_model, _ = load_model_and_tokenizer(reward_model_args, reward_finetuning_args, is_trainable=False, stage="ppo")
logger.info("Load full weights of reward model from {}".format(finetuning_args.reward_model))
return reward_model