使用Accelerate库在多GPU上进行LLM推理

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)

['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']

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 dict
    results=dict(outputs=[], num_tokens=0)
 
    # have each GPU do inference, prompt by prompt
    for 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()-start
    num_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)}")

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 json
    with 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 batches
    prompt_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. tokens
        outputs_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 accelerate
        results["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()-start
    num_tokens=sum([r["num_tokens"] for r in results_gathered ])
 
    print(f"tokens/sec: {num_tokens//timediff}, time elapsed: {timediff}, num_tokens {num_tokens}")