1. 背景

上一篇介绍了基于训练加速框架Unsloth，微调训练Llama3的显卡资源占用及训练时间对比。

近期Unsloth新增了Qwen1.5的模型适配，因此本qiang~马不停蹄地又进行了一次实验对比。

对Unsloth的简介，感兴趣的客观可以查阅上一篇《LLM微调加速神器：Unsloth + LLama3》。

2. 实战

本着眼过千遍不如手过一遍的宗旨，本qiang~针对Unsloth做了一个对比实现。

对比的实验环境为： A800，模型为Qwen1.5-32B-Chat。

可以使用如下命令进行更新unsloth​。

pip install "unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git"

2.1 比对维度

维度	说明
显卡	是否支持bf16
最大文本长度	max_seq_length
批次大小	per_device_train_batch_size
梯度累加步长	gradient_accumulation_steps
秩	LoRA的rank
dropout	lora_droput

2.2 源码

源码请见附件，由于Qwen1.5和Llama3的prompt模板不一致，因此源码层面上也稍加了改动。

3 实验结果

3.1 A800

3.2 结论

针对于Qwen1.5-32B-Chat进行unsloth训练，与基于transformers框架训练进行比对，结论如下：

集成unsloth后，显卡占用确实更少，平均减少20%-25%，训练效率确实更快，不管是哪种维度，平均训练时间减少了27%-41%。

使用40G显存的单卡如A40，即可进行微调训练。

4. 总结

一句话足矣~

本文主要是使用unsloth框架针对Qwen1.5的高效微调实验，提供了详细的对比代码以及不同维度的对比分析结果。

之后会研读一遍unsloth的底层源码，具体是如何使用triton语言实现加速的，以及如何手写的前馈网络和反向传播的实现~

5. 参考

1. unsloth: https://github.com/unslothai/unsloth

6. 附件

from unsloth import FastLanguageModel
import torch
from datasets import load_dataset
from trl import SFTTrainer
from transformers import TrainingArguments, TextStreamer, AutoModelForCausalLM, set_seed, AutoTokenizer, BitsAndBytesConfig
from peft import get_peft_model, LoraConfig, prepare_model_for_kbit_training
import gc
 
set_seed(42)
 
 
def train_unsloth(dtype,
                  max_seq_length,
                  per_device_train_batch_size, 
                  gradient_accumulation_steps, 
                  rank,  
                  lora_alpha=16, 
                  lora_dropout=0, 
                  max_steps=50, 
                  save_steps=50,
                  seed=42,
                  warmup_steps=5,
                  learning_rate=2e-4,
                  logging_steps=5):
    print(f'dtype:{dtype}, max_seq_length:{max_seq_length}, per_device_train_batch_size:{per_device_train_batch_size}, gradient_accumulation_steps:{gradient_accumulation_steps}, rank:{rank}, lora_dropout:{lora_dropout}')
    load_in_4bit = True
 
    model, tokenizer = FastLanguageModel.from_pretrained(
        model_name='pretrain_models/Qwen/Qwen1.5-32B-Chat/',
        max_seq_length=max_seq_length,
        dtype=dtype,
        load_in_4bit=load_in_4bit
    )
 
    model = FastLanguageModel.get_peft_model(
        model,
        r = rank,
        target_modules=['q_proj', 'k_proj', 'v_proj', 'o_proj', 'gate_proj', 'up_proj', 'down_proj'],
        lora_alpha=lora_alpha,
        lora_dropout=lora_dropout,
        bias='none',
        use_gradient_checkpointing=True,
        random_state=seed,
        use_rslora=False,
        max_seq_length=max_seq_length
    )
 
    def formatting_prompts_func(examples):
        instructions = examples["instruction"]
        inputs       = examples["input"]
        outputs      = examples["output"]
        texts = []
        for instruction, input, output in zip(instructions, inputs, outputs):
            text = tokenizer.apply_chat_template(
                [
                    {"role": "system", "content": "You are a helpful assistant."},
                    {"role": "user", "content": f'{instruction}. {input}'},
                    {'role': 'assistant', 'content': f'{output}'}
                ],
                tokenize=False,
                add_generation_prompt=False
            )
            texts.append(text)
        return { "text" : texts}
    pass
 
 
    dataset = load_dataset("yahma/alpaca-cleaned", split = "train")
    dataset = dataset.map(formatting_prompts_func, batched = True)
 
    trainer = SFTTrainer(
        model=model,
        tokenizer=tokenizer,
        train_dataset=dataset,
        dataset_text_field='text',
        max_seq_length=max_seq_length,
        packing=False,
        args = TrainingArguments(
            per_device_train_batch_size=per_device_train_batch_size,
            gradient_accumulation_steps=gradient_accumulation_steps,
            warmup_steps=warmup_steps,
            learning_rate=learning_rate,
            fp16 = not torch.cuda.is_bf16_supported(),
            bf16 = torch.cuda.is_bf16_supported(),
            logging_steps=logging_steps,
            optim='adamw_8bit',
            weight_decay=0.01,
            lr_scheduler_type='linear',
            seed=seed,
            output_dir='output/llame3-8b-instruct-unsloth',
            save_steps=save_steps,
            max_steps=max_steps
        )
    )
 
    gpu_stats = torch.cuda.get_device_properties(0)
    start_gpu_memory = round(torch.cuda.max_memory_reserved()/1024/1024/1024, 3)
    max_memory = round(gpu_stats.total_memory/1024/1024/1024, 3)
    print(f"GPU = {gpu_stats.name}. Max memory = {max_memory} GB.")
    print(f"{start_gpu_memory} GB of memory reserved.")
 
    trainer_stats = trainer.train()
 
    used_memory = round(torch.cuda.max_memory_reserved()/1024/1024/1024, 3)
    used_memory_for_lora = round(used_memory - start_gpu_memory)
    used_percentage = round(used_memory/max_memory*100, 3)
    lora_percentage = round(used_memory_for_lora/max_memory*100, 3)
    print(f"{trainer_stats.metrics['train_runtime']} seconds used for training.")
    print(f"{round(trainer_stats.metrics['train_runtime']/60, 2)} minutes used for training.")
    print(f"Peak reserved memory = {used_memory} GB.")
    print(f"Peak reserved memory for training = {used_memory_for_lora} GB.")
    print(f"Peak reserved memory % of max memory = {used_percentage} %.")
    print(f"Peak reserved memory for training % of max memory = {lora_percentage} %.")
 
    model.save_pretrained("output/llame3-8b-instruct-unsloth-lora") # Local saving
    tokenizer.save_pretrained("output/llame3-8b-instruct-unsloth-lora")
 
    # model.save_pretrained_merged("model", tokenizer, save_method = "merged_16bit",)  # Merge to 16bit
    # model.save_pretrained_merged("model", tokenizer, save_method = "merged_4bit",) # Merge to 4bit
    # model.save_pretrained_merged("model", tokenizer, save_method = "lora",) # Just LoRA adapters
    # model.save_pretrained_gguf("model", tokenizer,)   # Save to 8bit Q8_0
    # model.save_pretrained_gguf("model", tokenizer, quantization_method = "f16")   # Save to 16bit GGUF
    # model.save_pretrained_gguf("model", tokenizer, quantization_method = "q4_k_m")    # Save to q4_k_m GGUF
    del model
    del tokenizer
 
    torch.cuda.empty_cache()
    for _ in range(3):
        gc.collect()
 
def train_trans(dtype, 
                max_seq_length, 
                per_device_train_batch_size, 
                gradient_accumulation_steps, 
                rank, 
                lora_alpha=16, 
                lora_dropout=0, 
                max_steps=50, 
                save_steps=50,
                seed=42,
                warmup_steps=5,
                learning_rate=2e-4,
                logging_steps=5):
    print(f'dtype:{dtype}, max_seq_length:{max_seq_length}, per_device_train_batch_size:{per_device_train_batch_size}, gradient_accumulation_steps:{gradient_accumulation_steps}, rank:{rank}, lora_dropout:{lora_dropout}')
 
    model_path = 'pretrain_models/Qwen/Qwen1.5-32B-Chat/'
    
    tokenizer = AutoTokenizer.from_pretrained(model_path, padding_side='right', model_max_length=8192)
 
    quantization_config = BitsAndBytesConfig(
        load_in_4bit=True,
        bnb_4bit_compute_dtype=dtype,
        bnb_4bit_use_double_quant=True,
        bnb_4bit_quant_type="nf4",
        llm_int8_threshold=6.0,
        llm_int8_has_fp16_weight=False,
    )
 
    model = AutoModelForCausalLM.from_pretrained(
        model_path,
        torch_dtype=dtype,
        quantization_config=quantization_config,
    )
 
    model = prepare_model_for_kbit_training(model, use_gradient_checkpointing=True)
    model.enable_input_require_grads()
 
    config = LoraConfig(
        r=rank,
        lora_alpha=lora_alpha,
        target_modules=['q_proj', 'k_proj', 'v_proj', 'o_proj', 'gate_proj', 'up_proj', 'down_proj'],
        lora_dropout=lora_dropout,
        bias="none",
        task_type="CAUSAL_LM",
        use_rslora=False
    )
 
    model = get_peft_model(model, peft_config=config)
    model.gradient_checkpointing_enable()
 
    def formatting_prompts_func(examples):
        instructions = examples["instruction"]
        inputs       = examples["input"]
        outputs      = examples["output"]
        texts = []
        for instruction, input, output in zip(instructions, inputs, outputs):
            text = tokenizer.apply_chat_template(
                [
                    {"role": "system", "content": "You are a helpful assistant."},
                    {"role": "user", "content": f'{instruction}. {input}'},
                    {'role': 'assistant', 'content': f'{output}'}
                ],
                tokenize=False,
                add_generation_prompt=False
            )
            texts.append(text)
        return { "text" : texts}
 
    dataset = load_dataset("yahma/alpaca-cleaned", split = "train")
    dataset = dataset.map(formatting_prompts_func, batched = True,)
 
    trainer = SFTTrainer(
        model=model,
        tokenizer=tokenizer,
        train_dataset=dataset,
        dataset_text_field='text',
        max_seq_length=max_seq_length,
        packing=False,
        args = TrainingArguments(
            per_device_train_batch_size=per_device_train_batch_size,
            gradient_accumulation_steps=gradient_accumulation_steps,
            warmup_steps=warmup_steps,
            learning_rate=learning_rate,
            fp16 = not torch.cuda.is_bf16_supported(),
            bf16 = torch.cuda.is_bf16_supported(),
            logging_steps=logging_steps,
            optim='adamw_8bit',
            weight_decay=0.01,
            lr_scheduler_type='linear',
            seed=seed,
            output_dir='output/llame3-8b-instruct-unsloth',
            save_steps=save_steps,
            max_steps=max_steps
        )
    )
 
    gpu_stats = torch.cuda.get_device_properties(0)
    start_gpu_memory = round(torch.cuda.max_memory_reserved()/1024/1024/1024, 3)
    max_memory = round(gpu_stats.total_memory/1024/1024/1024, 3)
    print(f"GPU = {gpu_stats.name}. Max memory = {max_memory} GB.")
    print(f"{start_gpu_memory} GB of memory reserved.")
 
    trainer_stats = trainer.train()
 
    used_memory = round(torch.cuda.max_memory_reserved()/1024/1024/1024, 3)
    used_memory_for_lora = round(used_memory - start_gpu_memory)
    used_percentage = round(used_memory/max_memory*100, 3)
    lora_percentage = round(used_memory_for_lora/max_memory*100, 3)
    print(f"{trainer_stats.metrics['train_runtime']} seconds used for training.")
    print(f"{round(trainer_stats.metrics['train_runtime']/60, 2)} minutes used for training.")
    print(f"Peak reserved memory = {used_memory} GB.")
    print(f"Peak reserved memory for training = {used_memory_for_lora} GB.")
    print(f"Peak reserved memory % of max memory = {used_percentage} %.")
    print(f"Peak reserved memory for training % of max memory = {lora_percentage} %.")
 
    model.save_pretrained("output/llame3-8b-instruct-unsloth-lora") # Local saving
    tokenizer.save_pretrained("output/llame3-8b-instruct-unsloth-lora")
    
    del model
    del tokenizer
 
    torch.cuda.empty_cache()
    for _ in range(3):
        gc.collect()
 
def infer():
 
    model, tokenizer = FastLanguageModel.from_pretrained(
        model_name='output/llame3-8b-instruct-unsloth-lora',
        max_seq_length=2048,
        dtype=torch.float16,
        load_in_4bit=True
    )
 
    # 2x的速率进行推理
    FastLanguageModel.for_inference(model)
 
    inputs = tokenizer([alpaca_prompt.format('Continue the fibonnaci sequence.', '1, 1, 2, 3, 5, 8', '')], return_tensors = "pt").to('cuda')
    outputs = model.generate(**inputs, max_new_tokens=1024, use_cache=True)
    print(tokenizer.batch_decode(outputs))
 
    text_streamer = TextStreamer(tokenizer)
    outputs = model.generate(**inputs, max_new_tokens=1024, streamer=text_streamer)
    print(tokenizer.batch_decode(outputs))
 
 
if __name__ == '__main__':
 
    train_unsloth(dtype=torch.bfloat16, max_seq_length=1024, per_device_train_batch_size=1, gradient_accumulation_steps=16, rank=8, lora_dropout=0)
    train_unsloth(dtype=torch.bfloat16, max_seq_length=1024, per_device_train_batch_size=1, gradient_accumulation_steps=16, rank=64, lora_dropout=0)
    train_unsloth(dtype=torch.bfloat16, max_seq_length=2048, per_device_train_batch_size=1, gradient_accumulation_steps=16, rank=64, lora_dropout=0)
    train_unsloth(dtype=torch.bfloat16, max_seq_length=2048, per_device_train_batch_size=4, gradient_accumulation_steps=4, rank=64, lora_dropout=0)
    train_unsloth(dtype=torch.bfloat16, max_seq_length=2048, per_device_train_batch_size=4, gradient_accumulation_steps=4, rank=64, lora_dropout=0.05)
    train_unsloth(dtype=torch.bfloat16, max_seq_length=2048, per_device_train_batch_size=16, gradient_accumulation_steps=4, rank=64, lora_dropout=0.05)
    
    train_trans(dtype=torch.bfloat16, max_seq_length=1024, per_device_train_batch_size=1, gradient_accumulation_steps=16, rank=8, lora_dropout=0)
    train_trans(dtype=torch.bfloat16, max_seq_length=1024, per_device_train_batch_size=1, gradient_accumulation_steps=16, rank=64, lora_dropout=0)
    train_trans(dtype=torch.bfloat16, max_seq_length=2048, per_device_train_batch_size=1, gradient_accumulation_steps=16, rank=64, lora_dropout=0)
    train_trans(dtype=torch.bfloat16, max_seq_length=2048, per_device_train_batch_size=4, gradient_accumulation_steps=4, rank=64, lora_dropout=0)
    train_trans(dtype=torch.bfloat16, max_seq_length=2048, per_device_train_batch_size=4, gradient_accumulation_steps=4, rank=64, lora_dropout=0.05)

转至:https://blog.csdn.net/MENGERN/article/details/138974731