关于gpu内存

[Katieyyy]

System Info / 系統信息

gpu内存：18.8（gpu专用内存11）

Who can help? / 谁可以帮助到您？

No response

Information / 问题信息

• The official example scripts / 官方的示例脚本
• My own modified scripts / 我自己修改的脚本和任务

Reproduction / 复现过程

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

device = "cuda"

tokenizer = AutoTokenizer.from_pretrained("THUDM/glm-4-9b-chat",trust_remote_code=True)

query = "你好"

inputs = tokenizer.apply_chat_template([{"role": "user", "content": query}],
add_generation_prompt=True,
tokenize=True,
return_tensors="pt",
return_dict=True
)

inputs = inputs.to(device)
model = AutoModelForCausalLM.from_pretrained(
"THUDM/glm-4-9b-chat",
torch_dtype=torch.bfloat16,
low_cpu_mem_usage=True,
trust_remote_code=True
).to(device).eval()

gen_kwargs = {"max_length": 2500, "do_sample": True, "top_k": 1}
with torch.no_grad():
outputs = model.generate(**inputs, **gen_kwargs)
outputs = outputs[:, inputs['input_ids'].shape[1]:]
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 1.16 GiB. GPU 0 has a total capacity of 11.00 GiB of which 0 bytes is free. Of the allocated memory 16.39 GiB is allocated by PyTorch, and 1.01 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)

Expected behavior / 期待表现

怎么解决这个OOM

[zhipuch]

18.8G吗？有点少

[Katieyyy]

好的好的！感谢