view_as_complex(xq_) xk_ = torch.view_as_complex(xk_) # 应用旋转操作,然后将结果转回实数域 # xq_out.shape = [batch_size, seq_len, dim] xq_out = torch.view_as_real(xq_ * freqs_cis).flatten(2) xk_out = torch.view_as_real(xk_ * freqs_cis).flatten(2) return xq_out.type_...
Add view_as_real, view_as_complex for complex tensors (#39099)Summary: Pull Request resolved: #39099 Test Plan: Imported from OSS Differential Revision: D22057886 Pulled By: anjali411 fbshipit-source-id: bad5ba7097ba0dd13f2c549b2463094dee9afa14 main· whc_flight_4bc2caa7fdf006894eff7af...
Pytorch export onnx: RuntimeError Exporting the operator view_as_complex to ONNX opset version 9 is not supported. Please open a bug to request ONNX export support for the missing operator also :onnx/onnx#3173 cc@houseroad@spandantiwari@lara-hdr@BowenBao@neginraoof ...
_neg_view(self, *args, **kwargs) _nnz(self, *args, **kwargs) _values(self, *args, **kwargs) __add__(self, *args, **kwargs) __and__(self, *args, **kwargs) __bool__(self, *args, **kwargs) __complex__(self, *args, **kwargs) __delitem__(self, *args, **kwarg...
class DistributedDataParallel(Module): def __init__(self, module, device_ids=None, output_device=None, dim=0, broadcast_buffers=True, process_group=None, bucket_cap_mb=25, find_unused_parameters=False, check_reduction=False, gradient_as_bucket_view=False): super(DistributedDataParallel, self)...
x=x.view(batch_size,-1)#Flatten,变成想要的全连接网络需要的输入 x=self.fc(x)#用全连接层做变换 return x#因为要做交叉熵损失,最后一层不做激活 model=Net() criterion = torch.nn.CrossEntropyLoss() optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.5) ...
本系列将通过大概十篇左右文章来分析 PyTorch 的自动微分功能如何实现。本文是前向传播的第三篇,介绍具体实现机制。 在反向传播时候,当拿到了一个张量,引擎需要知道: 如何对此张量调用梯度计算,即从哪里找到计算梯度的函数 F。 拿到函数 F 之后,这个函数的输入就是此张量本身,但是函数 F 需要知道输入参数(本张量)...
本系列将通过大概十篇左右文章来分析 PyTorch 的自动微分功能如何实现。本文是前向传播的第三篇,介绍具体实现机制。 在反向传播时候,当拿到了一个张量,引擎需要知道: 如何对此张量调用梯度计算,即从哪里找到计算梯度的函数 F。 拿到函数 F 之后,这个函数的输入就是此张量本身,但是函数 F 需要知道输入参数(本张量)...
'sgn', 'asin', 'acos', 'sub', 'div', 'cat', 'view_as_complex', 'index_put', 'neg', 'complex', 'select', '_s_where', 'as_strided', 'slice', 'constant_pad_nd', 'unbind', 'split', 'split_with_sizes', 'unsafe_split', 'split_with_sizes_backward', ...
Most frameworks such as TensorFlow, Theano, Caffe, and CNTK have a static view of the world. One has to build a neural network and reuse the same structure again and again. Changing the way the network behaves means that one has to start from scratch. With PyTorch, we use a technique ...