🐛 Describe the bug Got errorRuntimeError: Unexpected floating ScalarType in at::autocast::prioritizewhen running following code on CUDA. It works well for CPU or CUDA with dtype=torch.float16. import torch de
Conv2d(in_channels=3, out_channels=6, kernel_size=5) def forward(self, x): with autocast(device_type="cuda", enabled=True): return self.conv1(x) device = torch.device('cuda') # Create an instance of the network net = SimpleCNN() net.to(device) # Create a sample input tensor ...
参数处理:要使 AMP Cast 缓存有效,参数需要requires_grad=True。遗憾的是,在 NeMo 转录 API 中,此标志设置为 False(requires_grad=False),从而阻止缓存工作,并导致不必要的 Cast 开销。 频繁的缓存清除:每次退出torch.amp.autocast上下文管理器时,都会清除转换缓存。用户通常会在上下文管理器中包装单个推理...
In order to enable FP8 operations, TE modules need to be wrapped inside the fp8_autocast context manager. [2]: import transformer_engine.pytorch as te import torch torch.manual_seed(12345) my_linear = te.Linear(768, 768, bias=True) inp = torch.rand((1024, 768)).cuda() with te....
label = data[1].squeeze(-1).to(device=device, non_blocking=True) # use mixed precision to take advantage of bfloat16 support with torch.autocast(device_type='cuda', dtype=torch.bfloat16): outputs = model(inputs) loss = criterion(outputs, label) ...
metric=evaluate.load("wer")eval_dataloader=DataLoader(common_voice["test"],batch_size=8,collate_fn=data_collator)model.eval()forstep,batchinenumerate(tqdm(eval_dataloader)):withtorch.cuda.amp.autocast():withtorch.no_grad():generated_tokens=(model.generate(input_features=batch["input_features...
YOLOv5 v7.0-114-g3c0a6e66 Python-3.9.0 torch-1.13.1+cu116 CUDA:0 (NVIDIA GeForce GTX 1050, 4096MiB) hyperparameters: lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0,...
with torch.autocast(“cuda”) and (torch.inference_mode() if not train else torch.enable_grad()): images = images.to(self.device) labels = labels.to(self.device) t = self.sample_timesteps(images.shape[0]).to(self.device) x_t, noise = self.noise_images(images, t) ...
outold_state_dict=model.state_dict model.state_dict=(lambdaself,*_,**__:get_peft_model_state_dict(self,old_state_dict())).__get__(model,type(model))...# if load_in_8bit=True, need to cast data type during trainingwithtorch.autocast('cuda'):trainer.train(resume_from_checkpoint=...
fp16, instead of the default floating point 32 implementation with CUDA then I set the PyTorch data type to each torch.float16 and set the use_auth_token equal to the input into the function parameter of use_token I then cast the pipeline to be processed on the GPU with the to ...