PyTorch 实现复数的操作基于 apply_complex 这个方法。 defapply_complex(fr,fi,input,dtype=torch.complex64):return(fr(input.real)-fi(input.imag)).type(dtype)\+1j*(fr(input.imag)+fi(input.real)).type(dtype) 这个函数需要传入2个操作 (nn.Conv2d, nn.Linear 等等)和torch.complex64 类型的 input。
这里我们直接看文件 BinaryOpsKernel.cpp.DEFAULT.cpp 中add_kernel方法, voidadd_kernel(TensorIterator&iter,Scalaralpha_scalar){...// 处理 Bool 和 Complex 复数类型的分支AT_DISPATCH_ALL_TYPES_AND(kBFloat16,iter.dtype(),"add_cpu/sub_cpu",[&](){autoalpha=alpha_scalar.to<scalar_t>();autoalpha...
Install PyTorch export CMAKE_PREFIX_PATH="${CONDA_PREFIX:-'$(dirname $(which conda))/../'}:${CMAKE_PREFIX_PATH}" python setup.py develop On macOS python3 setup.py develop On Windows If you want to build legacy python code, please refer to Building on legacy code and CUDA CPU-only...
PyTorch的Autograd模块实现了深度学习的算法中的向传播求导数,在张量(Tensor类)上的所有操作,Autograd都能为他们自动提供微分,简化了手动计算导数的复杂过程。 在0.4以前的版本中,Pytorch使用Variable类来自动计算所有的梯度Variable类主要包含三个属性: data:保存Variable所包含的Tensor;grad:保存data对应的梯度,grad也是个...
Simple is better than complex. Complex is better than complicated. Flat is better than nested. Sparse is better than dense. Readability counts. Special cases aren't special enough to break the rules. Although practicality beats purity. Errors should never pass silently. ...
首先看看 AccumulateGrad 的定义,这里省略了 AccumulateGrad 部分成员函数。从构建函数可看出来,一个AccumulateGrad实例必须用一个Variable构建,内部成员变量就是Variable variable。apply调用接收一个Variable list 实例,这和Variable grad_accumulator_相关。 structTORCH_APIAccumulateGrad :publicNode {explicitAccumulateGrad(...
PyTorch Map: Unveiling the Power of PyTorch Mapping OperationsIntroductionIn the world of artificial intelligence and machine learning, PyTorch has emerged as a leading开源framework, enabling researchers and developers to create and optimize complex neural networks. Among its many useful features, PyTorch...
RotatEfrom Sunet al.:RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space(ICLR 2019) [Example] GNN operators and utilities:PyG comes with a rich set of neural network operators that are commonly used in many GNN models. They follow an extensible design: It is easy to app...
# 其次：将xq和xk转换为复数，因为旋转矩阵只适用于复数 xq_=torch.view_as_complex(xq.float().reshape(*xq.shape[:-1], -1, 2)).to(device) #xq_:[bsz, seq_len, n_heads, head_dim/2] xk_=torch.view_as_complex(xk.float().reshape(*xk.shape[:-1], -1, 2)).to(device) ...
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一篇,介绍自动微分(梯度计算)所涉及的部分 PyTorch 基础类。因为字数太多(1万两千字),所以拆分成上下两篇。 系列前几篇连接如下: 深度学习利器之自动微分(1)