import os
import sys
import torch
import hashlib
from types import MethodType
from typing import List, Literal, Optional, Tuple
import transformers
from transformers import (
AutoConfig,
AutoModel,
AutoTokenizer,
HfArgumentParser,
Seq2SeqTrainingArguments,
BitsAndBytesConfig
)
from transformers.utils import check_min_version
from transformers.utils.versions import require_version
from transformers.modeling_utils import PreTrainedModel
from transformers.tokenization_utils import PreTrainedTokenizer
import datasets
from datasets import Dataset, concatenate_datasets, load_dataset
from peft import (
PeftModel,
TaskType,
LoraConfig,
get_peft_model
)
from peft.utils import CONFIG_NAME, WEIGHTS_NAME
from trl import AutoModelForCausalLMWithValueHead
from .config import (
ModelArguments,
DataTrainingArguments,
FinetuningArguments,
GeneratingArguments
)
from .other import (
get_logger,
load_trainable_params,
load_valuehead_params,
print_trainable_params,
prepare_model_for_training,
IGNORE_INDEX
)
check_min_version("4.27.4")
require_version("datasets>=2.10.0", "To fix: pip install datasets>=2.10.0")
require_version("accelerate>=0.19.0", "To fix: pip install accelerate>=0.19.0")
require_version("peft>=0.3.0", "To fix: pip install peft>=0.3.0")
require_version("trl>=0.4.4", "To fix: pip install trl>=0.4.4")
logger = get_logger(__name__)
def init_adapter(
model: PreTrainedModel,
model_args: ModelArguments,
finetuning_args: FinetuningArguments,
is_trainable: bool
) -> PreTrainedModel:
r"""
Initializes the adapters.
Support full-parameter, freeze, P-Tuning v2 and LoRA training.
Note that the trainable parameters must be cast to float32.
"""
if finetuning_args.finetuning_type == "none" and is_trainable:
raise ValueError("You cannot use finetuning_type=none while training.")
if finetuning_args.finetuning_type == "full":
logger.info("Fine-tuning method: Full")
model = model.float()
if finetuning_args.finetuning_type == "freeze":
logger.info("Fine-tuning method: Freeze")
for name, param in model.named_parameters():
if not any(trainable_layer in name for trainable_layer in finetuning_args.trainable_layers):
param.requires_grad_(False)
else:
param.data = param.data.to(torch.float32)
if model_args.checkpoint_dir is not None:
assert load_trainable_params(model, model_args.checkpoint_dir[0]), "Model checkpoint is not correctly loaded."
if finetuning_args.finetuning_type == "p_tuning":
logger.info("Fine-tuning method: P-Tuning v2")
if model_args.checkpoint_dir is not None:
assert load_trainable_params(model, model_args.checkpoint_dir[0]), "Model checkpoint is not correctly loaded."
if finetuning_args.finetuning_type == "lora":
logger.info("Fine-tuning method: LoRA")
lastest_checkpoint = None
if model_args.checkpoint_dir is not None:
assert os.path.exists(os.path.join(model_args.checkpoint_dir[0], WEIGHTS_NAME)), \
"Provided path ({}) does not contain a LoRA weight.".format(model_args.checkpoint_dir[0])
assert os.path.exists(os.path.join(model_args.checkpoint_dir[0], CONFIG_NAME)), \
"The given checkpoint may be not a LoRA checkpoint, please specify `--finetuning_type full/p_tuning/freeze` instead."
if is_trainable and model_args.resume_lora_training: # continually train on the lora weights
checkpoints_to_merge, lastest_checkpoint = model_args.checkpoint_dir[:-1], model_args.checkpoint_dir[-1]
else:
checkpoints_to_merge = model_args.checkpoint_dir
for checkpoint in checkpoints_to_merge:
model = PeftModel.from_pretrained(model, checkpoint)
model = model.merge_and_unload()
if len(checkpoints_to_merge) > 0:
logger.info("Merged {} model checkpoint(s).".format(len(checkpoints_to_merge)))
if lastest_checkpoint is not None: # resume lora training
model = PeftModel.from_pretrained(model, lastest_checkpoint, is_trainable=True)
if is_trainable and lastest_checkpoint is None: # create new lora weights while training
lora_config = LoraConfig(
task_type=TaskType.CAUSAL_LM, # we should regard ChatGLM as a causal LM
inference_mode=False,
r=finetuning_args.lora_rank,
lora_alpha=finetuning_args.lora_alpha,
lora_dropout=finetuning_args.lora_dropout,
target_modules=finetuning_args.lora_target
)
model = get_peft_model(model, lora_config)
if model_args.checkpoint_dir is not None:
logger.info("Loaded fine-tuned model from checkpoint(s): {}".format(",".join(model_args.checkpoint_dir)))
return model
def load_pretrained(
model_args: ModelArguments,
finetuning_args: FinetuningArguments,
is_trainable: Optional[bool] = False,
stage: Optional[Literal["sft", "rm", "ppo"]] = "sft"
) -> Tuple[PreTrainedModel, PreTrainedTokenizer]:
r"""
Loads pretrained model and tokenizer.
Support both training and inference.
"""
if (not is_trainable) and model_args.checkpoint_dir is None:
logger.warning("Checkpoint is not found at evaluation, load the original model.")
finetuning_args = FinetuningArguments(finetuning_type="none")
assert stage == "sft" or finetuning_args.finetuning_type == "lora", \
"RM and PPO training can only be performed with LoRA method."
quantization = None
if model_args.quantization_bit is not None:
if is_trainable:
if finetuning_args.finetuning_type == "full":
raise ValueError("Full-parameter fine-tuning does not support quantization.")
elif finetuning_args.finetuning_type == "p_tuning":
quantization = "cpm" # use cpm's quantization
else:
quantization = "bnb" # use bnb's quantization
else:
quantization = "cpm"
config_kwargs = {
"trust_remote_code": True,
"cache_dir": model_args.cache_dir,
"revision": model_args.model_revision,
"use_auth_token": True if model_args.use_auth_token else None,
}
tokenizer = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
use_fast=model_args.use_fast_tokenizer,
padding_side="left",
**config_kwargs
)
config = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path,
**config_kwargs
)
# P-Tuning v2 configurations. Use the built-in p-tuning method of ChatGLM.
if finetuning_args.finetuning_type == "p_tuning":
config.pre_seq_len = finetuning_args.pre_seq_len # enable this will fix other parameters automatically
config.prefix_projection = finetuning_args.prefix_projection
# Quantization configurations for Full, Freeze and LoRA in training (using bitsandbytes library).
if quantization == "bnb":
if model_args.quantization_bit == 8:
require_version("bitsandbytes>=0.37.0", "To fix: pip install bitsandbytes>=0.37.0")
config_kwargs["load_in_8bit"] = True
config_kwargs["quantization_config"] = BitsAndBytesConfig(
load_in_8bit=True,
llm_int8_threshold=6.0
)
elif model_args.quantization_bit == 4:
require_version("bitsandbytes>=0.39.0", "To fix: pip install bitsandbytes>=0.39.0")
require_version("transformers>=4.30.1", "To fix: pip install transformers>=4.30.1")
require_version("accelerate>=0.20.3", "To fix: pip install accelerate>=0.20.3")
require_version("peft>=0.4.0.dev0", "To fix: pip install git+https://github.com/huggingface/peft.git")
config_kwargs["load_in_4bit"] = True
config_kwargs["quantization_config"] = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_compute_dtype=model_args.compute_dtype,
bnb_4bit_use_double_quant=model_args.double_quantization,
bnb_4bit_quant_type=model_args.quantization_type
)
config_kwargs["device_map"] = {"": int(os.environ.get("LOCAL_RANK") or 0)}
if model_args.checkpoint_dir is not None and finetuning_args.finetuning_type == "full":
model_to_load = model_args.checkpoint_dir[0]
else:
model_to_load = model_args.model_name_or_path
# Load and prepare pretrained models (without valuehead).
model = AutoModel.from_pretrained(model_to_load, config=config, **config_kwargs)
# Register auto class to save the custom code files.
if hasattr(config, "auto_map") and "AutoConfig" in config.auto_map:
config.__class__.register_for_auto_class()
if hasattr(config, "auto_map") and "AutoTokenizer" in config.auto_map:
tokenizer.__class__.register_for_auto_class()
if hasattr(config, "auto_map") and "AutoModel" in config.auto_map:
model.__class__.register_for_auto_class()
if model_args.use_v2:
assert tokenizer.eos_token_id is not None, "Please update the *.json and *.py files of ChatGLM2-6B from HuggingFace."
model.lm_head = model.transformer.output_layer
output_embedding_base_layer = model.transformer
output_embedding_layer_name = "output_layer"
else:
assert tokenizer.eos_token_id == 130005, "Please specify `use_v2` argument while using ChatGLM2-6B."
output_embedding_base_layer = model
output_embedding_layer_name = "lm_head"
# Initialize adapters
model = prepare_model_for_training(
model,
finetuning_args.finetuning_type,
output_embedding_base_layer,
output_embedding_layer_name
) if is_trainable else model
model = init_adapter(model, model_args, finetuning_args, is_trainable)
if not is_trainable:
model.requires_grad_(False) # fix all model params
model = model.half() # cast all params to float16 for inference
# Quantization with the built-in method for P-Tuning v2 training or evaluation.
# Model parameters should be cast to float16 in quantized P-Tuning setting.
if quantization == "cpm":
if is_trainable: # convert all params into half precision except prefix_encoder in training
for name, param in model.named_parameters():
if "prefix_encoder" not in name:
param.data = param.data.to(torch.float16)
model.quantize(model_args.quantization_bit) # built-in method in ChatGLM-6B, also an in-place operation
if quantization is not None:
logger.info("Quantized model to {} bit.".format(model_args.quantization_bit))
if stage == "rm" or stage == "ppo": # add value head
model = AutoModelForCausalLMWithValueHead.from_pretrained(model)
if stage == "rm" and model_args.checkpoint_dir is not None: # load valuehead weights to evaluate reward model
logger.warning("Only the last checkpoint containing valuehead will be loaded as the valuehead.")
if load_valuehead_params(model, model_args.checkpoint_dir[-1]):
model.v_head.load_state_dict({
"summary.weight": getattr(model, "reward_head_weight"),
"summary.bias": getattr(model, "reward_head_bias")
})
if stage == "ppo": # load reward model
assert is_trainable, "PPO stage cannot be performed at evaluation."
assert model_args.reward_model is not None, "Reward model is necessary for PPO training."
logger.info("Load reward model from {}".format(model_args.reward_model))
model.pretrained_model.load_adapter(model_args.reward_model, "reward", is_trainable=False)
assert load_valuehead_params(model, model_args.reward_model), "Reward model is not correctly loaded."
print_trainable_params(model)
return model, tokenizer
def prepare_args(
stage: Literal["sft", "rm", "ppo"]
) -> Tuple[ModelArguments, DataTrainingArguments, Seq2SeqTrainingArguments, FinetuningArguments]:
parser = HfArgumentParser((ModelArguments, DataTrainingArguments, Seq2SeqTrainingArguments, FinetuningArguments))
if len(sys.argv) == 2 and sys.argv[1].endswith(".json"): # Provide arguments with a json file.
model_args, data_args, training_args, finetuning_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
else:
model_args, data_args, training_args, finetuning_args = parser.parse_args_into_dataclasses()
# Setup logging
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
log_level = training_args.get_process_log_level()
datasets.utils.logging.set_verbosity(log_level)
transformers.utils.logging.set_verbosity(log_level)
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Check arguments (do not check finetuning_args since it may be loaded from checkpoints)
assert stage == "sft" or (not training_args.predict_with_generate), \
"`predict_with_generate` cannot be set as True at PT, RM and PPO stages."
assert not (training_args.do_train and training_args.predict_with_generate), \
"`predict_with_generate` cannot be set as True while training."
assert (not training_args.do_predict) or training_args.predict_with_generate, \
"Please enable `predict_with_generate` to save model predictions."
if model_args.quantization_bit is not None:
assert finetuning_args.finetuning_type != "full" and finetuning_args.finetuning_type != "freeze", \
"Quantization is incompatible with the full-parameter and freeze tuning."
assert not (finetuning_args.finetuning_type == "p_tuning" and training_args.fp16), \
"FP16 training conflicts with quantized P-Tuning."
if not training_args.do_train:
logger.warning("Evaluating model in 4/8-bit mode may cause lower scores.")
assert model_args.checkpoint_dir is None or finetuning_args.finetuning_type == "lora" \
or len(model_args.checkpoint_dir) == 1, "Only LoRA tuning accepts multiple checkpoints."
if training_args.do_train and (not training_args.fp16):
logger.warning("We recommend enable fp16 mixed precision training for ChatGLM-6B.")
if training_args.local_rank != -1 and training_args.ddp_find_unused_parameters is None:
logger.warning("`ddp_find_unused_parameters` needs to be set as False in DDP training.")
training_args.ddp_find_unused_parameters = False
training_args.optim = "adamw_torch" if training_args.optim == "adamw_hf" else training_args.optim # suppress warning
if model_args.quantization_bit is not None:
if training_args.fp16:
model_args.compute_dtype = torch.float16
elif training_args.bf16:
model_args.compute_dtype = torch.bfloat16
else:
model_args.compute_dtype = torch.float32
# Log on each process the small summary:
logger.info(
f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}\n"
+ f" distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}"
)
logger.info(f"Training/evaluation parameters {training_args}")
# Set seed before initializing model.
transformers.set_seed(training_args.seed)
return model_args, data_args, training_args, finetuning_args
def prepare_infer_args() -> Tuple[ModelArguments, FinetuningArguments, GeneratingArguments]:
parser = HfArgumentParser((ModelArguments, FinetuningArguments, GeneratingArguments))
if len(sys.argv) == 2 and sys.argv[1].endswith(".json"): # Provide arguments with a json file.
model_args, finetuning_args, generating_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
else:
model_args, finetuning_args, generating_args = parser.parse_args_into_dataclasses()
assert model_args.checkpoint_dir is None or finetuning_args.finetuning_type == "lora" \
or len(model_args.checkpoint_dir) == 1, "Only LoRA tuning accepts multiple checkpoints."
return model_args, finetuning_args, generating_args
def prepare_data(
model_args: ModelArguments,
data_args: DataTrainingArguments
) -> Dataset:
def checksum(file_path, hash):
with open(file_path, "rb") as datafile:
binary_data = datafile.read()
sha1 = hashlib.sha1(binary_data).hexdigest()
if sha1 != hash:
logger.warning("Checksum failed for {}. It may vary depending on the platform.".format(file_path))
ext2type = {
"csv": "csv",
"json": "json",
"jsonl": "json"
}
max_samples = data_args.max_samples
all_datasets: List[Dataset] = [] # support multiple datasets
for dataset_attr in data_args.dataset_list:
logger.info("Loading dataset {}...".format(dataset_attr))
if dataset_attr.load_from == "hf_hub":
data_path = dataset_attr.dataset_name
data_files = None
elif dataset_attr.load_from == "script":
data_path = os.path.join(data_args.dataset_dir, dataset_attr.dataset_name)
data_files = None
elif dataset_attr.load_from == "file":
data_path = None
data_files: List[str] = []
if os.path.isdir(os.path.join(data_args.dataset_dir, dataset_attr.dataset_name)):
for file_name in os.listdir(os.path.join(data_args.dataset_dir, dataset_attr.dataset_name)):
data_files.append(os.path.join(data_args.dataset_dir, dataset_attr.dataset_name, file_name))
if data_path is None:
data_path = ext2type.get(data_files[0].split(".")[-1], None)
else:
assert ext2type.get(data_files[-1].split(".")[-1], None) == data_path, "file type does not match."
elif os.path.isfile(os.path.join(data_args.dataset_dir, dataset_attr.dataset_name)):
data_files.append(os.path.join(data_args.dataset_dir, dataset_attr.dataset_name))
data_path = ext2type.get(data_files[0].split(".")[-1], None)
else:
raise ValueError("File not found.")
assert data_path, "File extension must be csv, json or jsonl."
if len(data_files) == 1 and dataset_attr.dataset_sha1 is not None:
checksum(data_files[0], dataset_attr.dataset_sha1)
else:
logger.warning("Checksum failed: missing SHA-1 hash value in dataset_info.json or too many files.")
else:
raise NotImplementedError
raw_datasets = load_dataset(
data_path,
data_files=data_files,
cache_dir=model_args.cache_dir,
use_auth_token=True if model_args.use_auth_token else None
)
dataset = raw_datasets[data_args.split]
if max_samples is not None:
max_samples_temp = min(len(dataset), max_samples)
dataset = dataset.select(range(max_samples_temp))
dummy_data = [None] * len(dataset)
for column_name, target_name in [
("prompt_column", "prompt"),
("query_column", "query"),
("response_column", "response"),
("history_column", "history")
]: # every dataset will have 4 columns same as each other
if getattr(dataset_attr, column_name) != target_name:
if getattr(dataset_attr, column_name):
dataset = dataset.rename_column(getattr(dataset_attr, column_name), target_name)
else: # None or empty string
dataset = dataset.add_column(target_name, dummy_data)
all_datasets.append(dataset)
if len(data_args.dataset_list) == 1:
all_datasets = all_datasets[0]
else:
all_datasets = concatenate_datasets(all_datasets)
return all_datasets
def preprocess_data(
dataset: Dataset,
tokenizer: PreTrainedTokenizer,
data_args: DataTrainingArguments,
training_args: Seq2SeqTrainingArguments,
stage: Literal["sft", "rm", "ppo"]
) -> Dataset:
column_names = list(dataset.column_names)
prefix = data_args.source_prefix if data_args.source_prefix is not None else ""
def format_example(examples): # support question with a single answer or multiple answers
for i in range(len(examples["prompt"])):
if examples["prompt"][i] and examples["response"][i]:
query, answer = examples["prompt"][i], examples["response"][i]
query = query + examples["query"][i] if examples["query"][i] else query
history = examples["history"][i] if examples["history"][i] else []
prompt = ""
for j, (old_query, response) in enumerate(history):
prompt += "[Round {}]\n\n问：{}\n\n答：{}\n\n".format(j+1, old_query, response)
prompt += "[Round {}]\n\n问：{}\n\n答：".format(len(history)+1, query)
prompt = prefix + prompt
yield prompt, answer
def preprocess_supervised_dataset(examples):
# v1: build inputs with format `X [gMASK] <sop> Y <eop>` and labels with format `[IGNORE] ... [IGNORE] Y <eop>`
# v2: build inputs with format `[gMASK] sop X Y </s>` and labels with format `[IGNORE] ... [IGNORE] Y </s>`
model_inputs = {"input_ids": [], "labels": []}
for prompt, answer in format_example(examples):
source_ids = tokenizer.encode(text=prompt, add_special_tokens=False)
target_ids = tokenizer.encode(text=answer, add_special_tokens=False)
if len(source_ids) > data_args.max_source_length - 2: # gmask and sop tokens
source_ids = source_ids[:data_args.max_source_length - 2]
if len(target_ids) > data_args.max_target_length - 1: # eos token
target_ids = target_ids[:data_args.max_target_length - 1]
context_length = len(source_ids) + 2 # gmask and sop tokens
input_ids = tokenizer.build_inputs_with_special_tokens(source_ids, target_ids)
labels = [IGNORE_INDEX] * context_length + input_ids[context_length:]
model_inputs["input_ids"].append(input_ids)
model_inputs["labels"].append(labels)
return model_inputs
def preprocess_evaluation_dataset(examples):
# v1: build inputs with format `X [gMASK] <sop>` and labels with format `Y [gMASK] <sop>`
# v2: build inputs with format `[gMASK] sop X` and labels with format `[gMASK] sop Y`
model_inputs = {"input_ids": [], "labels": []}
for prompt, answer in format_example(examples):
source_ids = tokenizer.encode(text=prompt, add_special_tokens=False)
target_ids = tokenizer.encode(text=answer, add_special_tokens=False)
if len(source_ids) > data_args.max_source_length - 2: # gmask and sop tokens
source_ids = source_ids[:data_args.max_source_length - 2]
if len(target_ids) > data_args.max_target_length - 2: # gmask and sop tokens
target_ids = target_ids[:data_args.max_target_length - 2]
input_ids = tokenizer.build_inputs_with_special_tokens(source_ids)
labels = tokenizer.build_inputs_with_special_tokens(target_ids)
model_inputs["input_ids"].append(input_ids)
model_inputs["labels"].append(labels)
return model_inputs
def preprocess_pairwise_dataset(examples):
# v1: build input pairs with format `X [gMASK] <sop> Y1 <eop>` and `X [gMASK] <sop> Y2 <eop>`
# v2: build input pairs with format `[gMASK] sop X Y1 </s>` and `[gMASK] sop X Y2 </s>`
model_inputs = {"accept_ids": [], "reject_ids": []}
for prompt, answer in format_example(examples):
source_ids = tokenizer.encode(text=prompt, add_special_tokens=False)
accept_ids = tokenizer.encode(text=answer[0], add_special_tokens=False)
reject_ids = tokenizer.encode(text=answer[1], add_special_tokens=False)
if len(source_ids) > data_args.max_source_length - 2: # gmask and sop tokens
source_ids = source_ids[:data_args.max_source_length - 2]
if len(accept_ids) > data_args.max_target_length - 1: # eos token
accept_ids = accept_ids[:data_args.max_target_length - 1]
if len(reject_ids) > data_args.max_target_length - 1: # eos token
reject_ids = reject_ids[:data_args.max_target_length - 1]
accept_ids = tokenizer.build_inputs_with_special_tokens(source_ids[:], accept_ids) # avoid copying error
reject_ids = tokenizer.build_inputs_with_special_tokens(source_ids[:], reject_ids)
model_inputs["accept_ids"].append(accept_ids)
model_inputs["reject_ids"].append(reject_ids)
return model_inputs
def print_sft_dataset_example(example):
print("input_ids:\n{}".format(example["input_ids"]))
print("inputs:\n{}".format(tokenizer.decode(example["input_ids"], skip_special_tokens=False)))
print("label_ids:\n{}".format(example["labels"]))
print("labels:\n{}".format(
tokenizer.decode([d if d != IGNORE_INDEX else tokenizer.pad_token_id for d in example["labels"]],
skip_special_tokens=False)
))
def print_pairwise_dataset_example(example):
print("accept_ids:\n{}".format(example["accept_ids"]))
print("accepts:\n{}".format(tokenizer.decode(example["accept_ids"], skip_special_tokens=False)))
print("reject_ids:\n{}".format(example["reject_ids"]))
print("rejects:\n{}".format(tokenizer.decode(example["reject_ids"], skip_special_tokens=False)))
def print_ppo_dataset_example(example):
print("input_ids:\n{}".format(example["input_ids"]))
print("inputs:\n{}".format(tokenizer.decode(example["input_ids"], skip_special_tokens=False)))
if stage == "sft":
preprocess_function = preprocess_evaluation_dataset \
if training_args.predict_with_generate else preprocess_supervised_dataset
elif stage == "rm":
preprocess_function = preprocess_pairwise_dataset
elif stage == "ppo":
preprocess_function = preprocess_evaluation_dataset
with training_args.main_process_first(desc="dataset map pre-processing"):
dataset = dataset.map(
preprocess_function,
batched=True,
num_proc=data_args.preprocessing_num_workers,
remove_columns=column_names,
load_from_cache_file=not data_args.overwrite_cache,
desc="Running tokenizer on dataset"
)
if stage == "sft":
print_sft_dataset_example(dataset[0])
elif stage == "rm":
print_pairwise_dataset_example(dataset[0])
elif stage == "ppo":
print_ppo_dataset_example(dataset[0])
return dataset这段代码主要用于初始化模型和适配器，并根据参数配置对模型进行微调。下面是对每行代码的详细解释：
from transformers.utils import check_min_version：从 transformers 库导入check_min_version函数，用于检查 transformers 库的版本是否满足最低要求。from transformers.utils.versions import require_version：从 transformers 库导入require_version函数，用于检查特定库的版本是否满足要求。from transformers.modeling_utils import PreTrainedModel：从 transformers 库导入PreTrainedModel类，该类是所有预训练模型的基类。from transformers.tokenization_utils import PreTrainedTokenizer：从 transformers 库导入PreTrainedTokenizer类，该类是所有预训练分词器的基类。import datasets：导入 datasets 库，该库包含大量的公开数据集和评估度量。from datasets import Dataset, concatenate_datasets, load_dataset：从 datasets 库导入Dataset类、concatenate_datasets函数和load_dataset函数。这些都用于处理数据集。- 从 peft 库导入
PeftModel、TaskType、LoraConfig和get_peft_model。Peft 库应该是用于特殊的模型微调和参数设置的库，但在 2021 年 9 月的知识截止日期之前并未找到详细信息。 from peft.utils import CONFIG_NAME, WEIGHTS_NAME：从 peft 库的 utils 模块导入CONFIG_NAME和WEIGHTS_NAME，这两个常量可能是用来指定模型配置和权重的文件名。from trl import AutoModelForCausalLMWithValueHead：从 trl 库导入AutoModelForCausalLMWithValueHead类，用于生成一个带有值头的因果语言模型，这可能是进行强化学习训练的模型。from .config import (ModelArguments, DataTrainingArguments, FinetuningArguments, GeneratingArguments)：从本地的 config 模块导入四个类，这些类定义了模型、训练、微调和生成等过程中的参数。from .other import (get_logger, load_trainable_params, load_valuehead_params, print_trainable_params, prepare_model_for_training, IGNORE_INDEX)：从本地的 other 模块导入多个函数和常量，这些函数和常量用于日志记录、加载参数、打印参数、准备训练模型等。check_min_version("4.27.4")：检查 transformers 库的版本是否至少为 4.27.4。require_version("datasets>=2.10.0", "To fix: pip install datasets>=2.10.0")：检查 datasets 库的版本是否至少为 2.10.0，如果不满足，则提示如何修复。require_version("accelerate>=0.19.0", "To fix: pip install accelerate>=0.19.0")：检查 accelerate 库的版本是否至少为 0.19.0，如果不满足，则提示如何修复。require_version("peft>=0.3.0", "To fix: pip install peft>=0.3.0")：检查 peft 库的版本是否至少为 0.3.0，如果不满足，则提示如何修复。require_version("trl>=0.4.4", "To fix: pip install trl>=0.4.4")：检查 trl 库的版本是否至少为 0.4.4，如果不满足，则提示如何修复。logger = get_logger(__name__)：通过 get_logger 函数获取一个日志记录器，name 是当前模块的名称。
接下来是 init_adapter 函数的定义，它用于初始化适配器并根据参数对模型进行微调。输入参数包括预训练模型、模型参数、微调参数和一个指示模型是否可训练的布尔值，返回值是经过微调的模型。函数的主要步骤包括：
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def init_adapter(model: PreTrainedModel, model_args: ModelArguments, finetuning_args: FinetuningArguments, is_trainable: bool) -> PreTrainedModel：定义函数init_adapter，输入是模型、模型参数、微调参数和一个布尔值，输出是微调后的模型。 -
通过
finetuning_args.finetuning_type判断微调的类型，并根据微调的类型进行不同的处理。例如，如果微调类型为 "full"，则将模型的所有参数转换为 float 类型；如果微调类型为 "freeze"，则冻结一些层的参数；如果微调类型为 "p_tuning" 或 "lora"，则通过特定的方式加载参数等。 -
在微调过程中，根据
model_args.checkpoint_dir检查并加载模型检查点。 -
如果微调类型为 "lora"，则进一步创建或加载 LoRA 权重。
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最后，如果存在模型检查点，那么在日志中记录已加载的模型检查点信息，并返回微调后的模型。
注意：本段代码是用于模型微调的一部分，其中包含了许多特定的函数和类，如 PeftModel、get_peft_model、LoraConfig、load_trainable_params 等，对这些函数和类的具体功能和实现，需要结合相关的库和代码进行理解。
接下来的代码段细分讨论这个 init_adapter 函数：
18-24. if finetuning_args.finetuning_type == "none" and is_trainable: raise ValueError("You cannot use finetuning_type=none while training.")：如果微调类型为 "none" 并且模型是可训练的，那么抛出 ValueError 异常。这是因为在可训练的情况下，不应设置微调类型为 "none"。
25-29. if finetuning_args.finetuning_type == "full": logger.info("Fine-tuning method: Full") model = model.float()：如果微调类型为 "full"，则在日志中记录微调方法为 "Full"，并将模型的所有参数转换为浮点类型。
30-39. if finetuning_args.finetuning_type == "freeze": ...：如果微调类型为 "freeze"，则在日志中记录微调方法为 "Freeze"，然后遍历模型的所有参数，如果参数不在 finetuning_args.trainable_layers 列表中，那么将参数的 requires_grad 属性设置为 False，即冻结参数；否则，将参数的数据类型转换为浮点类型。然后，如果 model_args.checkpoint_dir 不为空，那么从检查点目录加载模型的可训练参数。
40-44. if finetuning_args.finetuning_type == "p_tuning": ...：如果微调类型为 "p_tuning"，则在日志中记录微调方法为 "P-Tuning v2"。然后，如果 model_args.checkpoint_dir 不为空，那么从检查点目录加载模型的可训练参数。
45-76. if finetuning_args.finetuning_type == "lora": ...：如果微调类型为 "lora"，则在日志中记录微调方法为 "LoRA"，然后执行一系列操作，包括检查检查点目录是否包含 LoRA 权重，从检查点目录加载 LoRA 权重，合并和卸载模型，从最新的检查点重新加载模型，创建新的 LoRA 权重等。
77-78. if model_args.checkpoint_dir is not None: logger.info("Loaded fine-tuned model from checkpoint(s): {}".format(",".join(model_args.checkpoint_dir)))：如果 model_args.checkpoint_dir 不为空，那么在日志中记录已经从检查点加载了微调后的模型。
return model：返回经过微调的模型。
总结：此函数主要用于初始化适配器并进行模型的微调。首先根据微调的类型（"full"、"freeze"、"p_tuning" 或 "lora"）对模型进行不同的处理，然后如果提供了模型检查点，那么从检查点中加载模型的可训练参数或者 LoRA 权重。最后，返回经过微调的模型。