infer_auto_device_map import torch # 初始化模型和设备映射 cuda_list = '0,1,2,3'.split(',') # 指定可用的GPU memory = '10GiB' # 每个GPU的最大显存使用量 max_memory = {int(cuda): memory for cuda in cuda_list} # 假设你有一个预训练的模型路径 model_path = 'path/to/pretrained/mod...
还有需要注意的地方是,可以通过设置参数max_memory来限制每个GPU的显存使用数额(在函数infer_auto_device_map()函数设置)。当设置了参数max_memory,需要构建一个包含 GPU 索引的字典(例如0,10,1等),并且cpu这个 key 是指 CPU 离线加载的最大内存(可以设置占用内存,不然加载后电脑会卡)。字典的 value 值,可以是...
问vkMapMemory调用/(爆发中的device.map_memory)上的Vulkan中的分段错误EN内存管理的必要性 很早之前计算...
Scenario 3: After modifying line 116 of the code in accordance with Solution 2, an error occurs when kwargs["device_map"] = "auto" because the kwargs["max_memory"] values are the same. The full error message is: "The current device_map had weights offloaded to the disk. Please prov...
# # if we are in a distributed setting, we need to set the device map and max memory per device # if os.environ.get('LOCAL_RANK') is not None: # local_rank = int(os.environ.get('LOCAL_RANK', '0')) # device_map = {'': local_rank} training_args.ddp_find_unused_parameters...
pci_map_addr=pci_find_max_end_va(); bar_addr=pci_map_addr; pci_map_addr= RTE_PTR_ADD(bar_addr, (size_t) reg->size); maps[i].addr=bar_addr; maps[i].offset= reg->offset; maps[i].size= reg->size; maps[i].path= NULL;/*vfio doesn't have per-resource paths*/ret=pci_vfi...
I implement the "Altera User Flash Memory for I2C Interface Protocol" ip core in MAX V device, and I want to map the content of UFM's memory content to some I/O. Since I want to map the data of write operation to some I/0. But I don't...
I implement the "Altera User Flash Memory for I2C Interface Protocol" ip core in MAX V device, and I want to map the content of UFM's memory content to some I/O. Since I want to map the data of write operation to some I/0. But I don't know how t...
Could be 8 bit related, also happens in 4bit. Will have to try unquantized. I have unequal memory on this particular setup, so `device_map=auto` is suboptimal: First gpu is an A6000 (non Ada), 48GB. Second gpu is an RTX 3090, 24GB. With `auto`, it only uses 24GB on both ...
(tensor_input, max_length=32) generated = tokenizer.batch_decode(gen_tokens)[0] print(generated) print('---') # with device_map=auto model = AutoModelForCausalLM.from_pretrained(model_name, device_map='auto') with torch.no_grad(): tokenize_input = tokenizer.tokenize(sentence) tensor_...