transformers 不同精度float16、bfloat16、float32加载模型对比

news2024/12/27 13:09:54

参考：

https://github.com/chunhuizhang/pytorch_distribute_tutorials/blob/main/tutorials/amp_autocast_mixed_precision_training.ipynb 在这里插入图片描述

from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto

model = AutoModelForCausalLM.from_pretrained(
    "Qwen1.5-7B-Chat",
    torch_dtype="auto",
    device_map="auto"
)
# tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2-7B-Instruct")
print(model.get_memory_footprint()/(1024**2))

for name, para in model.named_parameters():
    print(para.dtype, name, para.device)

默认bfloat16
在这里插入图片描述

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
device = "cuda" # the device to load the model onto

model = AutoModelForCausalLM.from_pretrained(
    "Qwen1.5-7B-Chat",
    torch_dtype=torch.float16,,
    device_map="auto"
)
# tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2-7B-Instruct")
print(model.get_memory_footprint()/(1024**2))

for name, para in model.named_parameters():
    print(para.dtype, name, para.device)

float16与bfloat16加载空间需要差不多，差不多GPU需要15G多
在这里插入图片描述

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
device = "cuda" # the device to load the model onto

model = AutoModelForCausalLM.from_pretrained(
    "Qwen1.5-7B-Chat",
    torch_dtype=torch.float32,,
    device_map="auto"
)
# tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2-7B-Instruct")
print(model.get_memory_footprint()/(1024**2))

for name, para in model.named_parameters():
    print(para.dtype, name, para.device)