File "/home/chenkc/code/pytorch/repeat_function.py", line 9, in <module> cat_ab = torch.cat([a, b], dim = 0) RuntimeError: Sizes of tensors must match except in dimension 0. Got 1 and 3 in dimension 1 ''' References:1. 《TensorFlow深度学习》...
class MyAddFunction : public torch::autograd::Function<MyAddFunction> { public: static Tensor forward(AutogradContext *ctx, torch::Tensor self, torch::Tensor other) { at::AutoNonVariableTypeMode g; return myadd(self, other); } static tensor_list backward(AutogradContext *ctx, tensor_list grad...
batch_size=batch_size, num_workers=4) loss_function = torch.nn.CrossEntropyLoss() t0 = time.perf_counter() summ = 0 count = 0 for idx, (inputs, target) in enumerate(data_loader, start=1): inputs = inputs.to(...
下面是一个简单的分布式模型,其中包括对 torch.distributed.all_reduce 的调用。 模型在 eager 模式下按预期运行,但在graph编译期间失败并出现“attribute error”(torch.classes.c10d.ProcessGroup does not have a field with name ‘shape’)。 我们需要将日志级别提高到 INFO,然后发现发现错误在计算的“第 3 步...
loss = loss_function(outputs, targets) loss.backward() optimizer.step() batch_time = time.perf_counter() - t0 if idx > 10: # skip first few steps summ += batch_time count += 1 t0 = time.perf_counter() if idx > 500: break ...
builtins.bool = False,backend: Union[str, Callable] = "inductor",mode: Union[str, None] = None,options: Optional[Dict[str, Union[str, builtins.int, builtins.bool]]] = None,disable: builtins.bool = False) -> Callable:"""Optimizes given model/...
因此,在编写自动求导内核之前,让我们编写一个调度函数,该函数调用调度程序以找到适合您操作符的正确内核。这个函数构成了您操作符的公共 C++ API - 实际上,PyTorch 的 C++ API 中的所有张量函数都在底层以相同的方式调用调度程序。调度函数如下所示: Tensor myadd(const Tensor& self, const Tensor& other) { ...
inputs若为True,则渐变wrt离开时图形的一部分，但不显示inputs不会被计算和累积torch.autograd.Function...
(inputs) loss = loss_function(outputs, targets) loss.backward() optimizer.step() batch_time = time.perf_counter() - t0 if idx > 10: # skip first few steps summ += batch_time count += 1 t0 = time.perf_counter() if idx > 500: break print(f'average step time: {summ/count}'...
可以使用repeat()函数实现张量的维度复制 x = torch.tensor([[1,2,3], [4,5,6], [7,8,9]]) x = einops.repeat(x, 'c h w -> (2 c) h w') x = x.repeat(2,1,1) x # tensor([[[1, 2, 3], # [4, 5, 6], # [7, 8, 9]], ...