使用Accelerate库在多GPU上进行LLM推理

大型语言模型(llm)已经彻底改变了自然语言处理领域。随着这些模型在规模和复杂性上的增长，推理的计算需求也显着增加。为了应对这一挑战利用多个gpu变得至关重要。

因此，这篇文章将在多个GPU上同时进行推理，内容主要包括：介绍Accelerate库、简单的方法和工作代码示例，以及使用多个GPU进行性能基准测试

本文将使用多个3090将llama2-7b的推理扩展在多个GPU上

基本示例

我们首先介绍一个简单的示例来演示使用Accelerate进行多gpu“消息传递” 。

from accelerate import Accelerator from accelerate.utils import gather_object  accelerator = Accelerator()  # each GPU creates a string message=[ f"Hello this is GPU {accelerator.process_index}" ]   # collect the messages from all GPUs messages=gather_object(message)  # output the messages only on the main process with accelerator.print()  accelerator.print(messages)

输出如下：

['Hello this is GPU 0', 'Hello this is GPU 1', 'Hello this is GPU 2', 'Hello this is GPU 3', 'Hello this is GPU 4']

多GPU推理

下面是一个简单的、非批处理的推理方法。代码很简单，因为Accelerate库已经帮我们做了很多工作，我们直接使用就可以：

from accelerate import Accelerator from accelerate.utils import gather_object from transformers import AutoModelForCausalLM, AutoTokenizer from statistics import mean import torch, time, json  accelerator = Accelerator()  # 10*10 Prompts. Source: https://www.penguin.co.uk/articles/2022/04/best-first-lines-in-books prompts_all=["The King is dead. Long live the Queen.","Once there were four children whose names were Peter, Susan, Edmund, and Lucy.","The story so far: in the beginning, the universe was created.","It was a bright cold day in April, and the clocks were striking thirteen.","It is a truth universally acknowledged, that a single man in possession of a good fortune, must be in want of a wife.","The sweat wis lashing oafay Sick Boy; he wis trembling.","124 was spiteful. Full of Baby's venom.","As Gregor Samsa awoke one morning from uneasy dreams he found himself transformed in his bed into a gigantic insect.","I write this sitting in the kitchen sink.","We were somewhere around Barstow on the edge of the desert when the drugs began to take hold.", ] * 10  # load a base model and tokenizer model_path="models/llama2-7b" model = AutoModelForCausalLM.from_pretrained(model_path,device_map={"": accelerator.process_index},torch_dtype=torch.bfloat16, ) tokenizer = AutoTokenizer.from_pretrained(model_path)   # sync GPUs and start the timer accelerator.wait_for_everyone() start=time.time()  # divide the prompt list onto the available GPUs  with accelerator.split_between_processes(prompts_all) as prompts:# store output of generations in dictresults=dict(outputs=[], num_tokens=0) # have each GPU do inference, prompt by promptfor prompt in prompts:prompt_tokenized=tokenizer(prompt, return_tensors="pt").to("cuda")output_tokenized = model.generate(**prompt_tokenized, max_new_tokens=100)[0] # remove prompt from output output_tokenized=output_tokenized[len(prompt_tokenized["input_ids"][0]):] # store outputs and number of tokens in result{}results["outputs"].append( tokenizer.decode(output_tokenized) )results["num_tokens"] += len(output_tokenized) results=[ results ] # transform to list, otherwise gather_object() will not collect correctly  # collect results from all the GPUs results_gathered=gather_object(results)  if accelerator.is_main_process:timediff=time.time()-startnum_tokens=sum([r["num_tokens"] for r in results_gathered ]) print(f"tokens/sec: {num_tokens//timediff}, time {timediff}, total tokens {num_tokens}, total prompts {len(prompts_all)}")

使用多个gpu会导致一些通信开销:性能在4个gpu时呈线性增长，然后在这种特定设置中趋于稳定。当然这里的性能取决于许多参数，如模型大小和量化、提示长度、生成的令牌数量和采样策略，所以我们只讨论一般的情况

1 GPU: 44个token /秒，时间:225.5 s

2个GPU：每秒处理88个token，总共用时112.9秒

3个GPU：每秒处理128个令牌，总共耗时77.6秒

4 gpu: 137个token /秒，时间:72.7s

5个gpu：每秒处理119个token，总共需要83.8秒的时间

在多GPU上进行批处理

现实世界中，我们可以使用批处理推理来加快速度。这会减少GPU之间的通讯，加快推理速度。我们只需要增加prepare_prompts函数将一批数据而不是单条数据输入到模型即可：

from accelerate import Accelerator from accelerate.utils import gather_object from transformers import AutoModelForCausalLM, AutoTokenizer from statistics import mean import torch, time, json  accelerator = Accelerator()  def write_pretty_json(file_path, data):import jsonwith open(file_path, "w") as write_file:json.dump(data, write_file, indent=4)  # 10*10 Prompts. Source: https://www.penguin.co.uk/articles/2022/04/best-first-lines-in-books prompts_all=["The King is dead. Long live the Queen.","Once there were four children whose names were Peter, Susan, Edmund, and Lucy.","The story so far: in the beginning, the universe was created.","It was a bright cold day in April, and the clocks were striking thirteen.","It is a truth universally acknowledged, that a single man in possession of a good fortune, must be in want of a wife.","The sweat wis lashing oafay Sick Boy; he wis trembling.","124 was spiteful. Full of Baby's venom.","As Gregor Samsa awoke one morning from uneasy dreams he found himself transformed in his bed into a gigantic insect.","I write this sitting in the kitchen sink.","We were somewhere around Barstow on the edge of the desert when the drugs began to take hold.", ] * 10  # load a base model and tokenizer model_path="models/llama2-7b" model = AutoModelForCausalLM.from_pretrained(model_path,device_map={"": accelerator.process_index},torch_dtype=torch.bfloat16, ) tokenizer = AutoTokenizer.from_pretrained(model_path)  tokenizer.pad_token = tokenizer.eos_token  # batch, left pad (for inference), and tokenize def prepare_prompts(prompts, tokenizer, batch_size=16):batches=[prompts[i:i + batch_size] for i in range(0, len(prompts), batch_size)]batches_tok=[]tokenizer.padding_side="left" for prompt_batch in batches:batches_tok.append(tokenizer(prompt_batch, return_tensors="pt", padding='longest', truncatinotallow=False, pad_to_multiple_of=8,add_special_tokens=False).to("cuda") )tokenizer.padding_side="right"return batches_tok  # sync GPUs and start the timer accelerator.wait_for_everyone() start=time.time()  # divide the prompt list onto the available GPUs  with accelerator.split_between_processes(prompts_all) as prompts:results=dict(outputs=[], num_tokens=0) # have each GPU do inference in batchesprompt_batches=prepare_prompts(prompts, tokenizer, batch_size=16) for prompts_tokenized in prompt_batches:outputs_tokenized=model.generate(**prompts_tokenized, max_new_tokens=100) # remove prompt from gen. tokensoutputs_tokenized=[ tok_out[len(tok_in):] for tok_in, tok_out in zip(prompts_tokenized["input_ids"], outputs_tokenized) ]  # count and decode gen. tokens num_tokens=sum([ len(t) for t in outputs_tokenized ])outputs=tokenizer.batch_decode(outputs_tokenized) # store in results{} to be gathered by accelerateresults["outputs"].extend(outputs)results["num_tokens"] += num_tokens results=[ results ] # transform to list, otherwise gather_object() will not collect correctly  # collect results from all the GPUs results_gathered=gather_object(results)  if accelerator.is_main_process:timediff=time.time()-startnum_tokens=sum([r["num_tokens"] for r in results_gathered ]) print(f"tokens/sec: {num_tokens//timediff}, time elapsed: {timediff}, num_tokens {num_tokens}")

可以看到批处理会大大加快速度。

需要重写的内容是：1个GPU：520个令牌/秒，时间：19.2秒

两张GPU的算力为每秒900个代币，计算时间为11.1秒

3 gpu: 1205个token /秒，时间:8.2s

四张GPU：1655个令牌/秒，所需时间为6.0秒

5个GPU: 每秒1658个令牌，时间：6.0秒

总结

截止到本文为止，llama.cpp，ctransformer还不支持多GPU推理，好像llama.cpp在6月有个多GPU的merge，但是我没看到官方更新，所以这里暂时确定不支持多GPU。如果有小伙伴确认可以支持多GPU请留言。

huggingface的Accelerate包则为我们使用多GPU提供了一个很方便的选择，使用多个GPU推理可以显着提高性能，但gpu之间通信的开销随着gpu数量的增加而显着增加。

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