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...
t.Tensor()和t.tensor()的区别是 torch.Tensor是torch.tensor 和 torch.empty的集合. 为了避免歧义和困惑,才把 torch.Tensor 分拆成了torch.tensor 和`torch.empty. 所以t.tensor和t.Tensor()可以互换,没有谁更好。不过推荐使用t.tensor(),就类似list(),np.array()一样。 而t.tensor()和t.as_tensor()...
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...
The output offunctioncan contain non-Tensor values and gradient recording is only performed for the Tensor values. Note that if the output consists of nested structures (ex: custom objects, lists, dicts etc.) consisting of Tensors, these Tensors nested in custom structures will not be considere...
🐛 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...
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...
torch_npu.npu_fusion_attention(Tensor query, Tensor key, Tensor value, int head_num, str input_layout, Tensor? pse=None, Tensor? padding_mask=None, Tensor? atten_mask=None, float scale=1., float keep_prob=1., int pre_tockens=2147483647, int next_tockens=2147483647, int inner_precise=...
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...
device): Desired device of returned tensor. Returns: (torch.Tensor): A tensor of shape (num_grid, size[0]*size[1], 2) that contains coordinates for the regular grids. """ affine_trans = torch.tensor([[[1., 0., 0.], [0., 1., 0.]]], device=device) grid = F.affine_grid...
...通常我们这样指定使用CUDA:device = torch.device("cuda" if torch.cuda.is_available() else "cpu")inputs.to(device)...这样就把input这个tensor转换成了CUDA 类型。...正确的做法是:device = torch.device("cuda" if torch.cuda.is_available() else "cpu")inputs = inputs.to(device...