transformers之SFT和VLLM部署Llama3-8b模型_vllm 微调命令

1. 环境安装

pip install -q -U bitsandbytes
pip install -q -U git+https://github.com/huggingface/transformers.git
pip install -q -U git+https://github.com/huggingface/peft.git
pip install -q -U git+https://github.com/huggingface/accelerate.git
pip install trl
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2. accelerator准备

import os
import torch
from datasets import load_dataset
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
    BitsAndBytesConfig,
    HfArgumentParser,
    TrainingArguments,
    pipeline,
    logging,
)
from peft import LoraConfig, PeftModel
from trl import SFTTrainer
from accelerate import FullyShardedDataParallelPlugin, Accelerator
from torch.distributed.fsdp.fully_sharded_data_parallel import FullOptimStateDictConfig, FullStateDictConfig


fsdp_plugin = FullyShardedDataParallelPlugin(
    state_dict_config=FullStateDictConfig(offload_to_cpu=True, rank0_only=False),
    optim_state_dict_config=FullOptimStateDictConfig(offload_to_cpu=True, rank0_only=False),
)

accelerator = Accelerator(fsdp_plugin=fsdp_plugin)
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3. 加载llama3和数据

因为使用的是base模型，所以没有一个严格的提示模板需要遵循。使用的数据集遵循LLama3的模板格式，因此对于使用Llama3聊天格式的下游任务来说应该没问题。如果你使用自己的数据，你可以自定义格式，在下游任务中也使用相同的格式即可。

base_model_id = "meta-llama/Meta-Llama-3-8B"
dataset_name = "scooterman/guanaco-llama3-1k"
new_model = "llama3-8b-SFT"


from datasets import load_dataset
dataset = load_dataset(dataset_name, split="train")

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig

model = AutoModelForCausalLM.from_pretrained(base_model_id, device_map="auto")
tokenizer = AutoTokenizer.from_pretrained(
    base_model_id,
    add_eos_token=True,
    add_bos_token=True, 
)
tokenizer.pad_token = tokenizer.eos_token

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4. 训练参数配置

许多教程只是简单地粘贴一个参数列表，让读者自己去弄清楚每个参数的作用。下面我添加了注释来解释每个参数的作用!

# Output directory where the results and checkpoint are stored
output_dir = "./results"

# Number of training epochs - how many times does the model see the whole dataset
num_train_epochs = 1 #Increase this for a larger finetune

# Enable fp16/bf16 training. This is the type of each weight. Since we are on an A100
# we can set bf16 to true because it can handle that type of computation
bf16 = True

# Batch size is the number of training examples used to train a single forward and backward pass. 
per_device_train_batch_size = 4

# Gradients are accumulated over multiple mini-batches before updating the model weights. 
# This allows for effectively training with a larger batch size on hardware with limited memory
gradient_accumulation_steps = 2

# memory optimization technique that reduces RAM usage during training by intermittently storing 
# intermediate activations instead of retaining them throughout the entire forward pass, trading 
# computational time for lower memory consumption.
gradient_checkpointing = True

# Maximum gradient normal (gradient clipping)
max_grad_norm = 0.3

# Initial learning rate (AdamW optimizer)
learning_rate = 2e-4

# Weight decay to apply to all layers except bias/LayerNorm weights
weight_decay = 0.001

# Optimizer to use
optim = "paged_adamw_32bit"

# Number of training steps (overrides num_train_epochs)
max_steps = 5

# Ratio of steps for a linear warmup (from 0 to learning rate)
warmup_ratio = 0.03

# Group sequences into batches with same length
# Saves memory and speeds up training considerably
group_by_length = True

# Save checkpoint every X updates steps
save_steps = 100

# Log every X updates steps
logging_steps = 5
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5. 微调

建立一个wandb帐户来监控这次微调任务。

pip install wandb
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import wandb
training_arguments = TrainingArguments(
    output_dir=output_dir,
    num_train_epochs=num_train_epochs,
    per_device_train_batch_size=per_device_train_batch_size,
    gradient_accumulation_steps=gradient_accumulation_steps,
    optim=optim,
    save_steps=save_steps,
    logging_steps=logging_steps,
    learning_rate=learning_rate,
    weight_decay=weight_decay,
    bf16=bf16,
    max_grad_norm=max_grad_norm,
    max_steps=max_steps,
    warmup_ratio=warmup_ratio,
    group_by_length=group_by_length,
    report_to="wandb"
)

trainer = SFTTrainer(
    model=model,
    train_dataset=dataset,
    dataset_text_field="text",
    tokenizer=tokenizer,
    args=training_arguments,
)


trainer.train()

# Save trained model
trainer.model.save_pretrained(new_model)
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6. vllm部署

为了部署这个模型以进行极快的推理，使用VLLM并托管一个OpenAI兼容端点。可能需要重新启动内核，然后运行下面的单元。

pip install vllm
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python -O -u -m vllm.entrypoints.openai.api_server \
    --host=127.0.0.1 \
    --port=8000 \
    --model=brev-llama3-8b-SFT \
    --tokenizer=meta-llama/Meta-Llama-3-8B \
    --tensor-parallel-size=2
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7. Llama-3-8b-instruct的使用

Instruct 版本对话prompt结构:

<|begin_of_text|><|start_header_id|>system<|end_header_id|>

{{ system_prompt }}<|eot_id|><|start_header_id|>user<|end_header_id|>

{{ user_msg_1 }}<|eot_id|><|start_header_id|>assistant<|end_header_id|>

{{ model_answer_1 }}<|eot_id|>

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16 GB 的 RAM，包括 3090 或 4090 等消费级 GPU

import transformers
import torch

model_id = "meta-llama/Meta-Llama-3-8B-Instruct"

pipeline = transformers.pipeline(
    "text-generation",
    model=model_id,
    model_kwargs={"torch_dtype": torch.bfloat16},
    device="cuda",
)

messages = [
    {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
    {"role": "user", "content": "Who are you?"},
]

prompt = pipeline.tokenizer.apply_chat_template(
        messages, 
        tokenize=False, 
        add_generation_prompt=True
)

terminators = [
    pipeline.tokenizer.eos_token_id,
    pipeline.tokenizer.convert_tokens_to_ids("<|eot_id|>")
]

outputs = pipeline(
    prompt,
    max_new_tokens=256,
    eos_token_id=terminators,
    do_sample=True,
    temperature=0.6,
    top_p=0.9,
)
print(outputs[0]["generated_text"][len(prompt):])

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量化版，4 bits加载需要大约 7 GB 的内存运行

pipeline = transformers.pipeline(
    "text-generation",
    model=model_id,
    model_kwargs={
        "torch_dtype": torch.float16,
        "quantization_config": {"load_in_4bit": True},
        "low_cpu_mem_usage": True,
    },
)


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参考

1. https://huggingface.co/blog/llama3#how-to-prompt-llama-3
2. https://ai.meta.com/blog/meta-llama-3/
3. https://pytorch.org/torchtune/stable/tutorials/llama3.html