deepspeed+transformers模型微调

一、目录

代码讲解

二、实现。

1、代码讲解，trainer 实现。

transformers通过trainer 集成deepspeed功能 ，所以中需要进行文件配置，即可实现deepspeed的训练。

微调代码：参数定义--->数据处理---->模型创建/评估方式---->trainer 框架训练

注意： V100 显卡，不包括float16 精度训练。

bash 复制代码

import deepspeed
deepspeed.ops.op_builder.CPUAdamBuilder().load()
import nltk
import torch
import evaluate
import datasets
import numpy as np
from nltk.tokenize import sent_tokenize
from torch.nn.utils.rnn import pad_sequence
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
from transformers import Seq2SeqTrainer, Seq2SeqTrainingArguments
nltk.download("punkt")
import gc
import torch

######################################定义参数####################################
dataset_name = "samsum" # 数据集名称
#model_name="google/flan-t5-xxl" # 模型名称
model_name="google/flan-t5-xl" # 模型名称
max_input_length = 256
max_gen_length = 128
output_dir = "checkpoints"
num_train_epochs = 5
learning_rate = 5e-5
deepspeed_config = "ds_config.json" #          deepspeed配置文件
per_device_train_batch_size=5 # batch size设置为1，因为太大导致OOM
per_device_eval_batch_size=5
gradient_accumulation_steps=10 # 由于单卡的batch size为1，为了扩展batch size，使用梯度累加

#################################加载数据集，与数据预处理#########################################
tokenizer = AutoTokenizer.from_pretrained(model_name)
dataset = datasets.load_dataset(dataset_name)
print(dataset["train"][0])

def preprocess(examples):
    dialogues = ["summarize:" + dia for dia in examples["dialogue"]]
    # summaries = [summ for summ in examples["summary"]]
    model_inputs = tokenizer(dialogues, max_length=max_input_length, truncation=True)
    labels = tokenizer(text_target=examples["summary"], max_length=max_gen_length, truncation=True)
    model_inputs["labels"] = labels["input_ids"]
    return model_inputs

tokenized_dataset = dataset.map(preprocess, batched=True, remove_columns=["dialogue", "summary", "id"])
# print(tokenized_dataset["train"]["input_ids"][0]) # 打印结果    对map后的数据进行查看。


def collate_fn(features):
    batch_input_ids = [torch.LongTensor(feature["input_ids"]) for feature in features]
    batch_attention_mask = [torch.LongTensor(feature["attention_mask"]) for feature in features]
    batch_labels = [torch.LongTensor(feature["labels"]) for feature in features]
    batch_input_ids = pad_sequence(batch_input_ids, batch_first=True, padding_value=tokenizer.pad_token_id)
    batch_attention_mask = pad_sequence(batch_attention_mask, batch_first=True, padding_value=0)
    batch_labels = pad_sequence(batch_labels, batch_first=True, padding_value=-100)
    return {
        "input_ids": batch_input_ids,
        "attention_mask": batch_attention_mask,
        "labels": batch_labels
    }

##############################加载模型,采用seq2seqLM模型，并进行测试##############################
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

#用于测试的代码
#dataloader = DataLoader(tokenized_dataset["test"], shuffle=False, batch_size=4, collate_fn=collate_fn)
# batch = next(iter(dataloader))
# #print(batch)
# # 用于测试的代码
# dataloader = DataLoader(tokenized_dataset["test"], shuffle=False, batch_size=4, collate_fn=collate_fn)
# batch = next(iter(dataloader))
# output = model(**batch)
#print(output)
#############################################模型训练，并采用trainer 架构####################################
print("==========train....================")
metric = evaluate.load("rouge")
def compute_metrics(eval_preds):
    preds, labels = eval_preds
    if isinstance(preds, tuple):
        preds = preds[0]
    decoded_preds = tokenizer.batch_decode(preds, skip_special_tokens=True)
    labels = np.where(labels != -100, labels, tokenizer.pad_token_id)
    decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)
    decoded_preds = ["\n".join(sent_tokenize(pred.strip())) for pred in decoded_preds]
    decoded_labels = ["\n".join(sent_tokenize(label.strip())) for label in decoded_labels]
    result = metric.compute(predictions=decoded_preds, references=decoded_labels, use_stemmer=True)
    result = {k: round(v * 100, 4) for k, v in result.items()}
    prediction_lens = [np.count_nonzero(pred != tokenizer.pad_token_id) for pred in preds]
    result["gen_len"] = np.mean(prediction_lens)
    return result


training_args = Seq2SeqTrainingArguments(
    output_dir=output_dir,
    per_device_train_batch_size=per_device_train_batch_size,
    per_device_eval_batch_size=per_device_eval_batch_size,
    gradient_accumulation_steps=gradient_accumulation_steps,
    eval_accumulation_steps=1, # 防止评估时导致OOM
    predict_with_generate=True,
    learning_rate=learning_rate,
    num_train_epochs=num_train_epochs,
    # logging & evaluation strategies
    logging_dir="logs",
    logging_strategy="steps",
    logging_steps=50, # 每50个step打印一次log
    evaluation_strategy="steps",
    eval_steps=500, # 每500个step进行一次评估
    save_steps=500,
    save_total_limit=2,
    load_best_model_at_end=True,
    deepspeed=deepspeed_config,                        # deepspeed配置文件的位置
    report_to="all"
)

trainer = Seq2SeqTrainer(
    model=model,
    args=training_args,
    train_dataset=tokenized_dataset["train"],
    eval_dataset=tokenized_dataset["validation"],
    data_collator=collate_fn,
    compute_metrics=compute_metrics,
)
trainer.train()
gc.collect()
torch.cuda.empty_cache()
# 打印验证集上的结果
# print(trainer.evaluate(tokenized_dataset["validation"]))
# # 打印测试集上的结果
# print(trainer.evaluate(tokenized_dataset["test"]))
# 保存最优模型
trainer.save_model("best.pt")
#export NCCL_IB_DISABLE=1; export NCCL_P2P_DISABLE=1; NCCL_DEBUG=INFO deepspeed --include=localhost:0,1 test1.py

配置文件：ds_config.json

bash 复制代码

{
   "fp16": {
        "enabled": "auto"
    },
  "optimizer": {
    "type": "AdamW",
    "params": {
      "lr": "auto",
      "betas": "auto",
      "eps": "auto",
      "weight_decay": "auto"
    }
  },
  "scheduler": {
    "type": "WarmupLR",
    "params": {
      "warmup_min_lr": "auto",
      "warmup_max_lr": "auto",
      "warmup_num_steps": "auto"
    }
  },
  "zero_optimization": {
    "stage": 3,
    "offload_optimizer": {
      "device": "cpu",
      "pin_memory": true
    },
    "offload_param": {
      "device": "cpu",
      "pin_memory": true
    },
    "overlap_comm": true,
    "contiguous_gradients": true,
    "sub_group_size": 1e9,
    "reduce_bucket_size": "auto",
    "stage3_prefetch_bucket_size": "auto",
    "stage3_param_persistence_threshold": "auto",
    "stage3_max_live_parameters": 1e9,
    "stage3_max_reuse_distance": 1e9,
    "stage3_gather_16bit_weights_on_model_save": false
  },
  "gradient_accumulation_steps": "auto",
  "gradient_clipping": "auto",
  "steps_per_print": 2000,
  "train_batch_size": "auto",
  "train_micro_batch_size_per_gpu": "auto",
  "wall_clock_breakdown": false
}

启动：单机多卡

bash 复制代码

export NCCL_IB_DISABLE=1; export NCCL_P2P_DISABLE=1; NCCL_DEBUG=INFO deepspeed --include=localhost:0,1 test1.py>output.log 2>&1 &

二、代码讲解，peft微调+trainer 实现。

bash 复制代码

import os
import torch
import random
import datasets
import numpy as np
from typing import Dict
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
    DataCollatorForSeq2Seq,
    TrainingArguments,
    Trainer
)
from peft import (
    LoraConfig,
    TaskType,
    get_peft_model,
    get_peft_model_state_dict,
)

def set_random_seed(seed):
    if seed is not None and seed > 0:
        random.seed(seed)
        np.random.seed(seed)
        torch.manual_seed(seed)
        torch.random.manual_seed(seed)
        torch.cuda.manual_seed(seed)
        torch.cuda.manual_seed_all(seed)
        torch.backends.cudnn.deterministic = True

set_random_seed(1234)

# 1. 设置参数
# LoRA参数
LORA_R = 8
LORA_ALPHA = 32
LORA_DROPOUT = 0.1
# 训练参数
EPOCHS=3
LEARNING_RATE=5e-5
OUTPUT_DIR="./checkpoints"
BATCH_SIZE=4 # 2
GRADIENT_ACCUMULATION_STEPS=3
# 其他参数
MODEL_PATH = "bigscience/bloomz-7b1-mt"
DATA_PATH = "./data/belle_open_source_1M.train.json"
MAX_LENGTH = 512
PATTERN = "{}\n{}"
DS_CONFIG = "ds_zero2_config.json"
tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH) # 加载tokenizer
# 加载数据
dataset = datasets.load_dataset("json", data_files=DATA_PATH)
# print(dataset["train"][0])


# 2. tokenize
def tokenize(text: str, add_eos_token=True):
    result = tokenizer(
        text,
        truncation=True,
        max_length=MAX_LENGTH,
        padding=False,
        return_tensors=None)
    # 判断是否要添加eos_token
    if (result["input_ids"][-1] != tokenizer.eos_token_id
        and len(result["input_ids"]) < MAX_LENGTH
        and add_eos_token):
        result["input_ids"].append(tokenizer.eos_token_id)
        result["attention_mask"].append(1)
    result["labels"] = result["input_ids"].copy()
    return result


def preprocess(example: Dict, train_on_inputs: bool = False):
    prompt = example["input"]
    response = example["target"]
    text = PATTERN.format(prompt, response)
    tokenized_inp = tokenize(text)
    # 若train_on_inputs为False，则将label中与input相关的token替换为-100
    if not train_on_inputs:
        tokenized_prompt = tokenize(prompt,add_eos_token=False)
        prompt_tokens_len = len(tokenized_prompt["input_ids"])
        tokenized_inp["labels"] = [-100]*prompt_tokens_len + tokenized_inp["labels"][prompt_tokens_len:]
    return tokenized_inp


train_data = dataset["train"].shuffle().map(preprocess, remove_columns=["id", "input", "target"])
print(train_data[0])

# pad_to_multiple_of=8表示padding的长度是8的倍数
collate_fn = DataCollatorForSeq2Seq(tokenizer, pad_to_multiple_of=8, return_tensors="pt", padding=True)

# 2. 加载模型
evice_map = {"": int(os.environ.get("LOCAL_RANK") or 0)}
# device_map指定模型加载的GPU;troch_dtype=torch.float16表示半精度加载模型
model = AutoModelForCausalLM.from_pretrained(MODEL_PATH, torch_dtype=torch.float16, device_map=device_map)


# 3. LoRA相关
lora_config = LoraConfig(
    task_type=TaskType.CAUSAL_LM,
    inference_mode=False,
    r=LORA_R,                         # LoRA中低秩近似的秩
    lora_alpha=LORA_ALPHA,            # 见上文中的低秩矩阵缩放超参数
    lora_dropout=LORA_DROPOUT,       # LoRA层的dropout
)
# 转换模型
model = get_peft_model(model, lora_config)
model.config.use_cache = False
old_state_dict = model.state_dict
model.state_dict = (
    lambda self, *_, **__: get_peft_model_state_dict(self, old_state_dict())
).__get__(model, type(model))
# 打印模型中的可训练参数
model.print_trainable_parameters()


# 4. 训练参数
args = TrainingArguments(
    output_dir=OUTPUT_DIR, # checkpoint的存储目录
    per_device_train_batch_size=BATCH_SIZE, # 单设备上的batch size
    gradient_accumulation_steps=GRADIENT_ACCUMULATION_STEPS, # 梯度累加的step数
    warmup_steps=100,
    num_train_epochs=EPOCHS,
    learning_rate=LEARNING_RATE,
    fp16=True, # 使用混合精度训练
    logging_steps=50,
    evaluation_strategy="no", # 不进行评估
    save_strategy="steps",
    save_steps=2000, # 保存checkpoint的step数
    save_total_limit=5, # 最多保存5个checkpoint
    deepspeed=DS_CONFIG                               #deepspeed 配置
)


# 5. 模型训练
trainer = Trainer(
    model=model,
    train_dataset=train_data,
    eval_dataset=None,
    args=args,
    data_collator=collate_fn
)
trainer.train()
model.save_pretrained("best_model")

bash 复制代码

{
  "train_micro_batch_size_per_gpu": "auto",
  "gradient_accumulation_steps": "auto",
  "steps_per_print": 50,
  "gradient_clipping": 1.0,
  "zero_optimization": {
    "stage": 2,
    "offload_optimizer": {
            "device": "cpu"
    },
    "contiguous_gradients": true,
    "overlap_comm": true
  },
  "zero_allow_untested_optimizer": true,
  "fp16": {
    "enabled": true,
    "loss_scale": 0,
    "loss_scale_window": 1000,
    "hysteresis": 2,
    "min_loss_scale": 1
  },
  "optimizer": {
    "type": "Adam",
    "params": {
      "lr": "auto",
      "betas": "auto",
      "eps": "auto",
      "weight_decay": "auto"
    }
  },
  "activation_checkpointing": {
    "partition_activations": true,
    "contiguous_memory_optimization": true
  },
  "wall_clock_breakdown": false
}