使用DeepSpeed进行大模型推理加速的指南

pip install deepspeed
pip install torch==1.11.0 torchvision==0.12.0 --extra-index-url https://download.pytorch.org/whl/cu113 --upgrade -q
pip install deepspeed==0.7.0 --upgrade -q
pip install transformers[sentencepiece]==4.21.1 --upgrade -q
pip install datasets evaluate[evaluator]==0.2.2 seqeval --upgrade -q

pythonimport torch
from transformers import BertTokenizer, BertForSequenceClassification
import deepspeed

pythonmodel_name = 'bert-base-uncased'
tokenizer = BertTokenizer.from_pretrained(model_name)
model = BertForSequenceClassification.from_pretrained(model_name)

# 配置DeepSpeed参数
ds_config = {
    "train_batch_size": 1,
    "fp16": {
        "enabled": True
    },
    "inference": {
        "enabled": True,
        "replace_with_kernel_inject": True,
        "mp_size": 1,
        "dtype": "float16"
    }
}

# 初始化DeepSpeed
model_engine, _, _, _ = deepspeed.initialize(model=model, config_params=ds_config)

pythontext = "Hello, how are you?"
inputs = tokenizer(text, return_tensors="pt")

pythonwith torch.no_grad():
    outputs = model_engine(**inputs)
    logits = outputs.logits

import deepspeed
import torch
from transformers import BertTokenizer, BertForSequenceClassification

# 加载模型和tokenizer
model_name = 'bert-base-uncased'
tokenizer = BertTokenizer.from_pretrained(model_name)
model = BertForSequenceClassification.from_pretrained(model_name)

# 创建数据加载器
train_data = [...]  # 自定义训练数据
data_loader = torch.utils.data.DataLoader(train_data, batch_size=32)

# 初始化DeepSpeed引擎
model_engine, optimizer, _, _ = deepspeed.initialize(model=model, model_parameters=model.parameters(), training_data=data_loader)

pythonfor step, batch in enumerate(data_loader):
    loss = model_engine(batch)  # 前向传播
    model_engine.backward(loss)  # 反向传播
    model_engine.step()          # 更新权重

{
    "train_batch_size": 32,
    "gradient_accumulation_steps": 1,
    "fp16": {
        "enabled": true
    },
    "zero_optimization": {
        "stage": 2
    }
}

deepspeed --num_gpus=2 train.py --deepspeed ds_config.json

if step % save_interval == 0:
    model_engine.save_checkpoint(save_dir, client_sd=client_sd)