infer_auto_device_map()(或在load_checkpoint_and_dispatch()中设置device_map="auto")是按照 GPU、CPU 和硬盘的顺序分配模型模块(防止循环操作),因此如果你的第一个层需要的 GPU 显存空间大于 GPU 显存时,有可能在 CPU/硬盘上出先奇怪的东西(第一个层不要太大,不然会发生奇怪的事情)。 l
在这个上下文中,self.fast_dtype是torch.set_autocast_dtype函数的参数,指定了应该设置的数据类型。 解释# type: ignore[arg-type]注解的用途: # type: ignore[arg-type]是一个类型检查注解,用于告诉类型检查器(如mypy)忽略某个特定的类型错误。在这个例子中,它可能是因为在调用torch.set_autocast_dtype时,...
(As #13076 deals with a number of issues, I opened #13195 to focus on torch_dtype with AutoModel issue.) 👍 1 hwijeen added 3 commits August 20, 2021 13:03 check torch_dtype in config as well 4df0a8c support dtypes other than auto adfd847 apply black and isort 3d0820b ...
import torch from transformers import AutoTokenizer, AutoModelForCausalLM path = "/home/noah/.cache/huggingface/transformers/1386e39caf0b158682709eb063f0231e03f868a0f87846c1eb777a79f161f87d.ce4d05ebacaac5ad33896c20e5373d786588147616bced327805834cb4beaf8f" model = torch.load(path) f...
在SD根目录下modules\devices.py的最后一行,将return torch.autocast("cuda")改为torch.autocast("cuda", dtype=torch.float32, enabled=True),这样启动显存占用减半。下面是N卡工作模式的相关资料,感谢星光2213大佬的帖子。Nvidia显卡有两种工作模式:TCC:Tesla 计算集群(Tesla Compute Cluster,简称 TCC)模式...
lower() 343 - if dtype == "auto": 344 - if config_dtype == torch.float32: 345 - # Following the common practice, we use float16 for float32 models.346 - torch_dtype = torch.float16 342 + if isinstance(dtype, str): 343 + dtype = dtype.lower() ...
("CompVis/stable-diffusion-v1-4", revision="fp16", torch_dtype=torch.float16, use_auth_token=True) prompt = "Pineapple on a white table" pipe.to("mps") # Found docs suggested this to get around a bug with autocast('mps'): _ = pipe(prompt, num_inference_steps=1) with auto...
torch_dtype in ["auto", None] else getattr(torch, model_args.torch_dtype) ) # int8 is not compatible with DeepSpeed (require not to pass device_map) if training_args.use_int8_training: print_rank_0("int8 is not compatible with DeepSpeed. ", log_file, global_rank) device_map = (...
auto input_ptr = input.const_data_ptr<scalar_t>(); int64_t remaining = outer_size; int64_t chunk_size = (1L << 30L) / dim_size; while(remaining > 0) { dispatch_softmax_forward<scalar_t, scalar_t, accscalar_t, is_log_softmax, false>( output_ptr, input_ptr, dim_siz...
auto dtype = r.isNone(3) ? at::ScalarType::Long : r.scalartype(3); This comment was marked as off-topic. Sign in to view tools/autograd/templates/python_torch_functions.cpp Outdated auto high = r.toInt64(1); auto size = r.intlist(2); // NOTE: r.scalartype(X) give...