网站开发案例详解 源代码,德宏企业网站建设公司,网片网格,人才招聘网站开发大模型LORA微调总结 大模型微调总结模型加载使用deepspeed不使用deepspeed使用lora加载分词器 数据加载构建source和target构建input_ids和labels标签补齐构建训练器LORA模型推理模型加载多batch推理构建lora微调推理合并模型权重 大模型微调总结
模型加载
使用deepspeed mod… 大模型LORA微调总结 大模型微调总结模型加载使用deepspeed不使用deepspeed使用lora加载分词器 数据加载构建source和target构建input_ids和labels标签补齐构建训练器LORA模型推理模型加载多batch推理构建lora微调推理合并模型权重 大模型微调总结
模型加载
使用deepspeed model transformers.AutoModelForCausalLM.from_pretrained(model_args.model_name_or_path,cache_dirtraining_args.cache_dir,torch_dtypeauto,# if model_args.model_name_or_path.find(falcon) ! -1 else Falsetrust_remote_codeTrue)不使用deepspeed
model transformers.AutoModelForCausalLM.from_pretrained(model_args.model_name_or_path,cache_dirtraining_args.cache_dir,device_mapauto,torch_dtypeauto,# if model_args.model_name_or_path.find(falcon) ! -1 else Falsetrust_remote_codeTrue)
使用lora
from peft import LoraConfig, get_peft_model
LORA_R 32
# LORA_ALPHA 16
LORA_DROPOUT 0.05
TARGET_MODULES [
o_proj,gate_proj, down_proj, up_proj
]config LoraConfig(
rLORA_R,
# lora_alphaLORA_ALPHA,
target_modulesTARGET_MODULES,
lora_dropoutLORA_DROPOUT,
biasnone,
task_typeCAUSAL_LM,
#加载配置
model get_peft_model(model, config)
#打印训练参数比例
model.print_trainable_parameters()加载分词器
tokenizer transformers.AutoTokenizer.from_pretrained(model_args.model_name_or_path, trust_remote_codeTrue)数据加载 通过Hugging Face的dateset库进行加载数据 使用dateset可以轻松加载数据样例如下所示
from datasets import load_dataset
dataset load_dataset(csv, data_filesmy_file.csv)
dataset load_dataset(csv, data_files[my_file_1.csv, my_file_2.csv, my_file_3.csv])
dataset load_dataset(csv, data_files{train:[my_train_file_1.csv,my_train_file_2.csv],test: my_test_file.csv})我们可以按下面方式加载数据
def load_dataset_from_own(data_path: Optional[str] None,cache_dir: Optional[str] cache_data) - Dataset:all_file_list [a.json,b.json,c.json]data_files {train: all_file_list}extension all_file_list[0].split(.)[-1]datasets load_dataset(extension,data_filesdata_files,cache_dircache_dir,)[train]return datasets构建source和target
构建prompt
PROMPT_DICT {prompt_input: (Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n\n### Instruction:\n{instruction}\n\n### Input:\n{input}\n\n### Response:),prompt_no_input: (Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{instruction}\n\n### Response:),
}根据prompt构建source
sources [prompt_input.format_map({instruction: ins_data[i], input: input_data[i]}) if input_data[i] ! else prompt_no_input.format_map({instruction: ins_data[i]})for i in range(len_)]
#限制长度
sources [i[:data_args.source_length] for i in sources]根据prompt构建targets
targets [f{example[:data_args.target_length-1]}{tokenizer.eos_token} for example in output]构建input_ids和labels 输入需要构建的text,输出构建好的ids def _tokenize_fn(strings: Sequence[str], tokenizer: transformers.PreTrainedTokenizer) - Dict:Tokenize a list of strings.tokenized_list [tokenizer(text,return_tensorspt,paddinglongest,max_lengthtokenizer.model_max_length,truncationTrue,)for text in strings]#获得idsinput_ids labels [tokenized.input_ids[0]for tokenized in tokenized_list]#终止符设置ne_pad_token_id IGNORE_INDEX if tokenizer.pad_token_id is None else tokenizer.pad_token_id#统计长度input_ids_lens labels_lens [tokenized.input_ids.ne(ne_pad_token_id).sum().item() for tokenized in tokenized_list]return dict(input_idsinput_ids,labelslabels,input_ids_lensinput_ids_lens,labels_lenslabels_lens,)构建input_ids 和label examples [s t for s, t in zip(sources, targets)]
#问题答案、问题
examples_tokenized, sources_tokenized [_tokenize_fn(strings, tokenizer) for strings in (examples, sources)]
input_ids examples_tokenized[input_ids]
labels copy.deepcopy(input_ids)
#构建labels
for label, source_len in zip(labels, sources_tokenized[input_ids_lens]):label[:source_len] IGNORE_INDEX标签补齐
在动态batching中我们需要一个data collator完成padding。这里不适用DataCollatorWithPadding来进行补齐操作因为这个函数仅对输入的键包括input_ids, attention_mask, token_type_ids进行补齐不会对labels进行补齐操作。还有在对labels进行补齐操作时使用的是-100而不是分词器的pad_token这么做到的目的是在计算损失函数的时候忽略掉这些padding token。
data_collator DataCollatorForSeq2Seq(tokenizertokenizer, modelmodel,
label_pad_token_idIGNORE_INDEX)构建训练器
from transformers import DataCollatorForSeq2Seq, Trainer
trainer Trainer(modelmodel,tokenizertokenizer,argstraining_args,train_datasettrain_dataset,eval_datasetNone,data_collatordata_collator)
trainer.train()
trainer.save_state()
trainer.save_model(output_dirtraining_args.output_dir)LORA模型推理
模型加载
base_model_name_or_path internlm-7b
lora_model_name_or_path checkpoint-9695model AutoModelForCausalLM.from_pretrained(base_model_name_or_path,torch_dtypeauto,# device_mapauto,# if model_args.model_name_or_path.find(falcon) ! -1 else Falsetrust_remote_codeTrue,
).cuda(0)model PeftModel.from_pretrained(model, model_idlora_model_name_or_path)
model.eval()
print(ok)tokenizer AutoTokenizer.from_pretrained(base_model_name_or_path, trust_remote_codeTrue, padding_sideleft
)多batch推理构建
def batch_generate_data(text_input: List[str], use_train_model: bool True, temp: float 0.7
):text_input_format [generate_input(i) for i in text_input]batch_inputs tokenizer.batch_encode_plus(text_input_format, paddinglongest, return_tensorspt)batch_inputs[input_ids] batch_inputs[input_ids].cuda()batch_inputs[attention_mask] batch_inputs[attention_mask].cuda()if use_train_model:# with model.disable_adapter():outputs model.generate(**batch_inputs,max_new_tokens256,do_sampleTrue,temperaturetemp,top_p0.8,)else:with model.disable_adapter():outputs model.generate(**batch_inputs,max_new_tokens256,do_sampleTrue,temperaturetemp,top_p0.8,)outputs tokenizer.batch_decode(outputs.cpu()[:, batch_inputs[input_ids].shape[-1] :],skip_special_tokensTrue,)return outputslora微调推理
text_input [工作压力太大怎么办\n] * 32
# lora 训练结果
batch_generate_data(text_input, use_train_modelTrue, temp0.8)
# 原来的模型
batch_generate_data(text_input, use_train_modelFalse, temp0.8)合并模型权重
model model.merge_and_unload()
model.save_pretrained(internlm-7b-lml)
tokenizer.save_pretrained(internlm-7b-lml)
