结果: before changed: tensor([0, 1, 2, 3]) tensor([0., 1., 2., 3.]) tensor([0, 1, 2, 3]) tensor([0, 1, 2, 3]) changed: tensor([3, 2, 1, 0]) tensor([0., 1., 2., 3.]) tensor([0, 1, 2, 3]) tensor([3, 2, 1, 0]) 3. .detach()和.clone() .clo...
1 transpose、permute等维度变换操作后,tensor在内存中不再是连续存储的,而view操作要求tensor的内存连续存储,所以需要contiguous来返回一个contiguous copy; 2 维度变换后的变量是之前变量的浅拷贝,指向同一区域,即view操作会连带原来的变量一同变形,这是不合法的,所以也会报错;--- 这个解释有部分道理,也即contiguous返...
With random or constant values 具有随机值或常量值 shape是张量维度的元组。在下面的函数中,它确定输出张量的维数。 shape = (2,3,) rand_tensor = torch.rand(shape) ones_tensor = torch.ones(shape) zeros_tensor = torch.zeros(shape)print(f"Random Tensor: \n{rand_tensor}\n")print(f"Ones Tens...
call_functionapplies a free function to some values.nameis similarly the name of the value to assign to.targetis the function to be applied.argsandkwargsrepresent the arguments to the function, following the Python calling convention call_moduleapplies a module in the module hierarchy’sforward(...
device="cuda"e4m3 = torch.tensor(1., device=device, dtype=e4m3_type)e5m2 = torch.tensor(1., device=device, dtype=e5m2_type) 也可以强制转换为FP8。在下面的代码中,我们生成一个随机的浮点张量,并比较将它们转换为四种不同的浮点类型的结果: ...
...## 第一次裁剪,初始化default_mask,将原param tensor移动到一个新参数name + '_orig' 并删除原来 parameterifnotisinstance(method, PruningContainer):# copy `module[name]` to `module[name + '_orig']`module.register_parameter(name +"_orig", orig)# temporarily delete `module[name]`delmodule...
rank = 0 is initialized rank = 1 is initialized tensor([1, 2, 3, 4], device='cuda:1') tensor([1, 2, 3, 4], device='cuda:0') 2. launch 代码语言:javascript 代码运行次数:0 运行 AI代码解释 $ python3 -m torch.distributed.launch --配置train.py --args参数 # 通过外部命令运行 ...
Rewrote DTensor (Tensor Parallel) APIs to improve UX (#114732) In PyTorch 2.1 or before, users can use ParallelStyles like PairwiseParallel and specify input/output layout with functions like make_input_replicate_1d or make_output_replicate_1d. And we have default values for _prepare_input ...
self).__init__() self.layers = ModuleList([copy.deepcopy(decoder_layer) for _ in range(num_layers)]) def forward(self, tgt: Tensor, memory: Tensor, tgt_mask: Optional[Tensor] = None,memory_mask: Optional[Tensor] = None, tgt_key_padding_mask: Optional[Tensor] ...
它们的API分别是:index_select(dim, index)与masked_select(mask),dim表示Tensor的维度,index是选取的索引,且index必须是与被调Tensor类型一致的Tensor,mask表示Bool Tensor,下面请看具体示例: 除此以外,类似的方法还包括:index_fill_、index_copy_、index_add_、masked_fill_、masked_fill等。 <11>gather、...