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)。 load_checkpoint_and...
import torch from transformers import AutoTokenizer, AutoModelForCausalLM path = "/home/noah/.cache/huggingface/transformers/1386e39caf0b158682709eb063f0231e03f868a0f87846c1eb777a79f161f87d.ce4d05ebacaac5ad33896c20e5373d786588147616bced327805834cb4beaf8f" model = torch.load(path) f...
tokenizer = T5Tokenizer.from_pretrained("t5-small") model = T5EncoderModel.from_pretrained("t5-small", device_map="auto", torch_dtype=torch.half) input_text = "translate English to German: How old are you?" input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to("cuda")...
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 = (...
"tokenizer=AutoTokenizer.from_pretrained(model_name)device=torch.device("cuda"iftorch.cuda.is_available()else"cpu")model=AutoModelForCausalLM.from_pretrained(model_name,torch_dtype=torch.bfloat16).to(device)optimizer=torch.optim.Adam(model.parameters(),lr=5e-5)input_ids=tokenizer.encode(input,...
(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 ...
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 Sign in to view tools/autograd/templates/python_torch_functions.cpp Outdated auto high = r.toInt64(1); auto size = r.intlist(2); // NOTE: r.scalartype(X) give...
("CompVis/stable-diffusion-v1-4", revision="fp16", torch_dtype=torch.float16, use_auth_token=True) prompt = "Very good prompt" pipe.to("mps") # Found docs suggested this to get around a bug with autocast('mps'): _ = pipe(prompt, num_inference_steps=1) with autocast('mps')...
auto mask = make_dim_mask(dim, ndim); allocate_reduction_result(result, self, mask, keepdim, out_dtype); auto viewed_result = review_reduce_result(result, ndim, mask, keepdim); namedinference::propagate_names_for_reduction(result, self, dim, keepdim); if (self.scalar_type() =...
num_train_epochs // 10 } # Load the pretrained model and tokenizer tokenizer = AutoTokenizer.from_pretrained( model_args.model_name_or_path, cache_dir=training_args.cache_dir, model_max_length=training_args.model_max_length, truncation_side="left", ) model = AutoModelForCausalLM.from_...