import torch device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") model = torch.load("model_path") # pytorch模型加载 model.eval() x = torch.randn((1, 3, 320, 320)) # 生成张量 x = (device) torch.onnx
Shard:先将Tensor切片,分布式放置在多个GPU上,我们需要指定分割的维度。example:Shard(1),分割维度为1 Replicate:将Tensor拷贝n份,分布式放置在n个GPU上。 _Partial: 使得Tensor,在device mesh设备网格的特定维度上进行reduce,也就是在数个GPU设备(并非全部被)上进行reduce操作。 torch2.3给我们提供了5个ParallelStyle...
consisting of Tensors, these Tensors nested in custom structures will not be considered as part of autograd.原文大意如下:检查点技术通过以计算量为代价来节省内存,即不保存整个计算图的所有中间激活值以计算反向传播,而是在反向传播时重新计算这些激活值。它可以应用于模型的任何部分。具体而言,在前向传递中,...
torch.rsqrt(a) 返回平方根的倒数torch.mean std prod sum var tanh max min(input) 返回均值 标准差 累乘 求和 方差 双曲正切 最大 最小值torch.equal(Tensor1,Tensor2)两个张量进行比较,如果相等返回true,否则返回falsetorch.bmm(a, b) 执行两个张量之间的批矩阵间乘积( batch matrix-matrix product),记...
File "/usr/local/python3.7.5/lib/python3.7/site-packages/torch_npu/utils/device_guard.py", line 38, in wrapper return func(*args, **kwargs) File "/usr/local/python3.7.5/lib/python3.7/site-packages/torch_npu/utils/tensor_methods.py", line 66, in _npu return torch_npu._C.npu...
注意到XLATensor只有下面这一个创建函数接受at::Tensor作为输入,因此就在这里面打印调用栈。 代码语言:javascript 代码运行次数:0 运行 AI代码解释 XLATensor XLATensor::Create(constat::Tensor&tensor,constDevice&device) 测试的用例很简单,我们让两个xla device上的Tensor相乘:...
🐛 Bug The function torch.pow doesn't seem to check if the input tensors are on the same device. To Reproduce Steps to reproduce the behavior: a = torch.tensor(2.0, device=torch.device('cuda:0')) b = torch.tensor(1.0) torch.pow(a,b) Expec...
... [ 15%] Building NVCC (Device) object lib/THC/CMakeFiles/THC.dir/THC_generated_THCTensorMathReduce.cu.o 2 errors detected in the compilation of "/tmp/tmpxft_00002141_00000000-4_THCTensorMath.cpp4.ii". CMake Error at THC_generated_THCTensorMath.cu.o.cmake:267 (message): Error ...
RuntimeError: torch_xla/csrc/tensor.cpp:486 : Check failed: data_ != nullptr *** Begin stack trace *** tensorflow::CurrentStackTrace() torch_xla::XLATensor::data() const torch_xla::XLATensor::GetIrValue() const torch_xla::XLATensor::native_batch_norm_backward(torch_xla::XLATensor cons...
[1] = input_tensor[0][1].sub_(0.456).div_(0.224); 97 input_tensor[0][2] = input_tensor[0][2].sub_(0.406).div_(0.225); 98 // to GPU 99 // input_tensor = input_tensor.to(at::kCUDA); 100 101 torch::Tensor out_tensor = module.forward({input_tensor}).toTensor(); 102 ...