has_torch_function()) { return handle_torch_function( r, args, kwargs, THPVariableFunctionsModule, "torch"); } if (r.idx == 0) { if (r.isNone(3)) { auto high = r.toInt64(0); auto size = r.intlist(1); auto generator = r.generator(2); // NOTE: r.scalartype(X) ...
t_inputs = cast(Tuple[torch.Tensor, ...], (inputs,) if is_tensor_like(inputs) else tuple(inputs)) overridable_args = t_outputs + t_inputs if has_torch_function(overridable_args): return handle_torch_function( grad, overridable_args, outputs, inputs, grad_outputs=grad_outputs, retai...
ifhas_torch_function_variadic(input, target, weight): returnhandle_torch_function( cross_entropy, (input, target, weight), input, target, weight=weight, size_average=size_average, ignore_index=ignore_index, reduce=reduce, reduction=reduction, label_smoothing=label_smoothing, ) ifsize_averageisnot...
torch.nn.functional.softmax函数源码如下,可以看到ret = input.softmax(dim)实际上调用了torch._C._VariableFunctions中的softmax函数 defsoftmax(input: Tensor, dim:Optional[int] =None, _stacklevel:int=3, dtype:Optional[DType] =None) -> Tensor:r"""Applies a softmax function. Softmax is defined...
既然PyTorch本身在编译期间并不知道torch-xla的存在,那么当用户使用一个xla device上的Tensor作为一个torch function的输入的时候,又经历了怎样一个过程调用到pytorch-xla中的东西呢? 从XLATensor开始的溯源 尽管我们现在并不知道怎么调用到torch-xla中的,但我们知道PyTorch Tensor一定要转换成XLATensor(参考tensor.h),...
这个函数返回一个 句柄(handle)。它有一个方法handle.remove(),可以用这个方法将hook从module移除。 例子: 代码语言:javascript 代码运行次数:0 运行 AI代码解释 v=Variable(torch.Tensor([0,0,0]),requires_grad=True)h=v.register_hook(lambda grad:grad*2)# double the gradient ...
当然也有一些第三方的库来简化Pytorch的训练过程比如PyTorch Lightning、TorchHandle等但是终究不是官方的库。最后总结 最适合你的深度学习框架将取决于你的具体需求和要求 TensorFlow 和 PyTorch 都提供了广泛的功能和高级特性，并且这两个框架都已被研发社区广泛采用。 作为高级用户，我的个人建议是深入学习一个库，另外...
在 PyTorch 中，这可以很容易实现：class MyFunction ( torch . autograd . Function ):@staticmethoddef forward ( ctx , input ):ctx . save_for_backward ( input )output = torch . sign ( input )return outputdef backward ( ctx , grad_output ...
Ithinkthis emerges becausefull_backward_hookis technically atorch.autograd.Functionand so when you call anytorch.funcmethod it requires asetup_contextin order to handle any outputs within pytorch2.0. In previous versions of pytorch,full_backward_hookmethods were skipped entirely if I recall correctly...
By default, the activation function is GELU. If you would like an alternative activation function, you can pass in the class to the keyword ff_activation. import torch from reformer_pytorch import ReformerLM from torch import nn model = ReformerLM( num_tokens= 20000, dim = 512, depth = 6...