先说说fp16和fp32,当前的深度学习框架大都采用的都是fp32来进行权重参数的存储,比如Python float的类型为双精度浮点数fp64,PyTorch Tensor的默认类型为单精度浮点数fp32。随着模型越来越大,加速训练模型的需求就产生了。在深度学习模型中使用fp32主要存在几个问题,第一模型尺寸大,训练的时候对显卡的显存要求高;第二...
在学习torch应用进行采样时,创建了一个小测试用例,意外的发现运行异常 RuntimeError: Expected all tensors to be on the same device. Expected NPU tensor, please check whether the input tensor device is correct. [ERROR] 2024-10-14-19:05:41 (PID:18047, Device:0, RankID:-1) ERR01002 OPS inva...
RuntimeError: All input tensors must be on the same device. Received cuda:0 and cpu Instead: RuntimeError: iter.device(arg).is_cuda() INTERNAL ASSERT FAILED at "C:/cb/pytorch_1000000000000/work/aten/src\ATen/native/cuda/Loops.cuh":61, please report a bug to PyTorch. ...
in compile_check_fn tensor_guards = TensorGuards( TypeError: expected Tensor() You can suppress this exception and fall back to eager by setting: torch._dynamo.config.suppress_errors = True The above exception was the direct cause of the following exception: Traceback (most recent call last)...
torch.save(state_dict, os.path.join(save_dir, "pytorch_model.bin")) hf_model_config.save_pretrained(save_dir) dist.barrier() Note that the option FullStateDictConfig(rank0_only=True, offload_to_cpu=True) is to gather the model on the CPU of the 0th rank device to save memory when...
- 如果不启用输入的梯度,在重新计算时会出现错误,因为PyTorch无法通过不需要梯度的tensor进行反向传播 举例说明: # 没有gradient checkpointing时的正常流程 input_ids -> embedding -> hidden_states -> output (保存在内存中) # 使用gradient checkpointing时 ...
python -c "import mindspore;mindspore.set_context(device_target='Ascend');mindspore.run_check()" 刘思铭 创建了Question 7个月前 i-robot 成员 7个月前 复制链接地址 Please assign maintainer to check this issue. 请为此issue分配处理人。 @liu-siming-hw i-robot 成员 7个月前 复制链接地址...
-gpu (Optional; run in GPU mode on given device IDs separated by ','.Use '-gpu all' to run on all available GPUs. The effective training batch size is multiplied by the number of devices.) type: string default: "" -iterations (The number of iterations to run.) type: int32 default...
-gpu (Optional; run in GPU mode on given device IDs separated by ','.Use '-gpu all' to run on all available GPUs. The effective training batch size is multiplied by the number of devices.) type: string default: "" -iterations (The number of iterations to run.) type: int32 default...
I convert the Pytorch model(CIResNet22_RPN.pth) from SiamDW project to ONNX model and there is no error to get the artifact (siamdw.onnx in attached). After that, i ref the workaround (may be incorrect) to shape inference conflict and convert ONNX model(siamdw.onnx) to Ope...