Pytorch lobpcg 不适用于复杂的稀疏数组？

问：

我目前正在尝试使用 Pytorch“lobpcg”使用我的 GPU 对角化大型稀疏数组。当对角化由实数值组成的数组时，该函数似乎工作得很好，但是每当数组包含复杂值时，它都会返回一些令人困惑的错误消息。作为参考，我在这里留下了我的代码片段和返回的错误消息

from scipy import sparse
import torch
from torch import lobpcg
import numpy as np
from scipy import sparse 
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
#%%
def Torchfy(arr): # storese sparse arrays in gpu
   arr = arr.tocoo() # Converting to "coordinate format" (type of way to store matrix in the GPU)
   # Here we create sparse pythorch tensor feeding in the indices: device was defined in first cell
   arr = torch.sparse_coo_tensor(indices=torch.tensor([arr.row, arr.col]), values=torch.tensor(arr.data), size=arr.shape).to(device)
   return arr
def H_L(xcoor, ycoor, zcoor, b = 1, g1 = 1, g2 = 1, g3 = 1, e = 1, m = 1, hbar = 1):
########################################### Pre Calculations ###############################################
   h2 = hbar ** 2 # hbar sqrd
   c1 = - h2/(2*m) * (g1 + 2.5 * g2) # setting constant for first term
   c2 = g2 * h2 / m # setting constant for second term
   c3 = g3 * h2/(2*m) # setting constant for third term
   e2 = e ** 2 # e-charge sqrd
   eh = e / hbar
   e2h2 = e2 / h2
   y = 0.5 * b * sparse.spdiags(ycoor.reshape(N3), np.array([0]), N3, N3) # repeated, put in diagonal form (N^3,N^3) 
   x = 0.5 * b * sparse.spdiags(xcoor.reshape(N3), np.array([0]), N3, N3) # repeated, put in diagonal form (N^3,N^3) 
   y2 = y ** 2
   x2 = x ** 2
############################################ First Term ####################################################
   lapl = ddxx + ddyy + ddzz # Laplacian
   a_nabl = -1j * eh * (x * ddy - y * ddx)  # A(r) * Nabla, where A generates B = b * \hat{z}
   a2 = e2h2 * (y2 + x2)
   tmp1 = c1 * (- lapl + a_nabl + a2) # summing the terms relevant to tmp1
   tmp1 = sparse.kron(tmp1, np.eye(4)) # No J_i coupling so we consider kron with identity 
############################################ Second Term ###################################################
   kx2 = - ddxx + 1j * eh * y * ddx + e2h2 * y2
   ky2 = - ddyy - 1j * eh * x * ddy + e2h2 * x2
   tmp2 = sparse.kron(kx2, J_x2) + sparse.kron(ky2, J_y2) + sparse.kron(-ddzz, J_z2)
############################################ Third Term #####################################################
   kxky = 0.5 * (- 2 * ddxy - 2 * e2h2 * x * y - 1j * eh *( x * ddx - y * ddy))
   kxkz = 0.5 * (- 2 * ddxz + 1j * eh * y * ddz)
   kykz = 0.5 * (- 2 * ddyz - 1j * eh * x * ddz)
   tmp3 = sparse.kron(kxky, jxjy) + sparse.kron(kxkz, jxjz) + sparse.kron(kykz, jyjz)
   return tmp1 + tmp2 + tmp3
h = H_L(X, Y, Z)
h = Torchfy(h)
# Using the "Locally optimal block preconditioned conjugate gradient method" to obtain the first 10 eigenvalues
# we focus on the lowest ones (i.e. largest = False)
eigenvalues, eigenvectors = lobpcg(h, k=10, largest=False)

运行上述代码的完整版本后，我收到错误消息：RuntimeError：预期标量类型 ComplexDouble 但找到 Float。有什么解决办法吗？
提前致谢

编辑：
运行 h = torch.complex（h.real， h.imag）.to（device） 我收到错误消息：NotImplementedError：无法使用来自“SparseCPU”后端的参数运行“aten：：view_as_real”。这可能是因为此后端不存在运算符，或者在选择性/自定义生成过程中省略了运算符（如果使用自定义生成）。如果您是在移动设备上使用 PyTorch 的 Facebook 员工，请访问 https://fburl.com/ptmfixes 了解可能的解决方案。“aten：：view_as_real”仅适用于以下后端：[CPU、Meta、BackendSelect、Python、FuncTorchDynamicLayerBackMode、Functionalize、Named、Conjugate、Negative、ZeroTensor、ADInplaceOrView、AutogradOther、AutogradCPU、AutogradCUDA、AutogradHIP、AutogradXLA、AutogradMPS、AutogradIPU、AutogradXPU、AutogradHPU、AutogradVE、AutogradLazy、AutogradMeta、AutogradMTIA、AutogradPrivateUse1、AutogradPrivateUse2、AutogradPrivateUse3、AutogradNestedTensor、Tracer、AutocastCPU、AutocastCUDA、FuncTorchBatched、FuncTorchVmapMode、Batched、VmapMode、FuncTorchGradWrapper、PythonTLSSnapshot、FuncTorchDynamicLayerFrontMode、PythonDispatcher]。

CPU：在 /opt/conda/conda-bld/pytorch_1682343904035/work/build/aten/src/ATen/RegisterCPU.cpp：31034 [内核] Meta：在 /opt/conda/conda-bld/pytorch_1682343904035/work/build/aten/src/ATen/RegisterMeta.cpp：26824 [内核] BackendSelect：在 /opt/conda/conda-bld/pytorch_1682343904035/work/aten/src/ATen/core/BackendSelectFallbackKernel.cpp：3 [后端回退] Python：在/opt/conda/conda-bld/pytorch_1682343904035/work/aten/src/ATen/core/PythonFallbackKernel.cpp：144 [后端回退] FuncTorchDynamicLayerBackMode：在 /opt/conda/conda-bld/pytorch_1682343904035/work/aten/src/ATen/functorch/DynamicLayer.cpp：491 [后端回退] 功能化：在 /opt/conda/conda-bld/pytorch_1682343904035/work/build/aten/src/ATen/RegisterFunctionalization_2.cpp：21384 [内核] 命名：注册于/opt/conda/conda-bld/pytorch_1682343904035/work/aten/src/ATen/core/NamedRegistrations.cpp：7 [后端回退] 共轭：在 /opt/conda/conda-bld/pytorch_1682343904035/work/aten/src/ATen/ConjugateFallback.cpp：21 注册的 fallthrough：否定：在 /opt/conda/conda-bld/pytorch_1682343904035/work/aten/src/ATen/native/NegateFallback.cpp：23 注册的 fallthrough] ZeroTensor：在 /opt/conda/conda-bld/pytorch_1682343904035/work/ 注册的 fallthroughaten/src/ATen/ZeroTensorFallback.cpp：90 [内核] ADInplaceOrView：在 /opt/conda/conda-bld/pytorch_1682343904035/work/torch/csrc/autograd/generated/ADInplaceOrViewType_0.cpp：4733 [内核] AutogradOther：在 /opt/conda/conda-bld/pytorch_1682343904035/work/torch/csrc/autograd/generated/VariableType_4.cpp：15288 注册 [autograd 内核] AutogradCPU：在 /opt/conda/conda-bld/pytorch_1682343904035/work/torch/csrc/autograd/generated/VariableType_ 注册4.cpp：15288 [autograd 内核] AutogradCUDA：在 /opt/conda/conda-bld/pytorch_1682343904035/work/torch/csrc/autograd/generated/VariableType_4.cpp：15288 注册 [autograd 内核] AutogradHIP：在 /opt/conda/conda-bld/pytorch_1682343904035/work/torch/csrc/autograd/generated/VariableType_4.cpp：15288 注册 [autograd 内核] AutogradXLA：在 /opt/conda/conda-bld/pytorch_1682343904035/work/torch/csrc/autograd/generated/VariableType_4.cpp：15288 注册 [autograd内核] AutogradMPS：在 /opt/conda/conda-bld/pytorch_1682343904035/work/torch/csrc/autograd/generated/VariableType_4.cpp：15288 注册 [autograd 内核] AutogradIPU：在 /opt/conda/conda-bld/pytorch_1682343904035/work/torch/csrc/autograd/generated/VariableType_4.cpp：15288 注册 [autograd 内核] AutogradXPU：在 /opt/conda/conda-bld/pytorch_1682343904035/work/torch/csrc/autograd/generated/VariableType_4.cpp：15288 注册 [autograd 内核] AutogradHPU： 在 /opt/conda/conda-bld/pytorch_1682343904035/work/torch/csrc/autograd/generated/VariableType_4.cpp：15288 注册 [autograd 内核] AutogradVE：在 /opt/conda/conda-bld/pytorch_1682343904035/work/torch/csrc/autograd/generated/VariableType_4.cpp：15288 注册 [autograd 内核] AutogradLazy：在 /opt/conda/conda-bld/pytorch_1682343904035/work/torch/csrc/autograd/generated/VariableType_4.cpp：15288 注册 [autograd 内核] AutogradMeta： 在 /opt/conda/conda-bld/pytorch_1682343904035/work/torch/csrc/autograd/generated/VariableType_4.cpp：15288 注册 [autograd 内核] AutogradMTIA：在 /opt/conda/conda-bld/pytorch_1682343904035/work/torch/csrc/autograd/generated/VariableType_4.cpp：15288 注册 [autograd 内核] AutogradPrivateUse1：在 /opt/conda/con 注册da-bld/pytorch_1682343904035/work/torch/csrc/autograd/generated/VariableType_4.cpp：15288 [autograd 内核] AutogradPrivateUse2：在 /opt/conda/conda-bld/pytorch_1682343904035/work/torch/csrc/autograd/generated/VariableType_4.cpp：15288 注册 [autograd 内核] AutogradPrivateUse3：在 /opt/conda/conda-bld/pytorch_1682343904035/work/torch/csrc/autograd/generated/VariableType_4.cpp：15288 注册 AutogradNestedTensor：注册于/opt/conda/conda-bld/pytorch_1682343904035/work/torch/csrc/autograd/generated/VariableType_4.cpp：15288 [autograd 内核] Tracer：在 /opt/conda/conda-bld/pytorch_1682343904035/work/torch/csrc/autograd/generated/TraceType_4.cpp：12892 注册 [内核] AutocastCPU：fallthrough 注册于 /opt/conda/conda-bld/pytorch_1682343904035/work/aten/src/ATen/autocast_mode.cpp：487 [后端回退] AutocastCUDA：fallthrough 注册于/opt/conda/conda-bld/pytorch_1682343904035/work/aten/src/ATen/autocast_mode.cpp：354 [后端回退] FuncTorchBatched：在 /opt/conda/conda-bld/pytorch_1682343904035/work/aten/src/ATen/functorch/BatchRulesUnaryOps.cpp：82 注册 [内核] FuncTorchVmapMode：在 /opt/conda/conda-bld/pytorch_1682343904035/work/aten/src/ATen/functorch/VmapModeRegistrations.cpp：28 注册的 [后端回退] 已批处理：注册于/opt/conda/conda-bld/pytorch_1682343904035/work/aten/src/ATen/LegacyBatchingRegistrations.cpp：1077 [内核] VmapMode：在 /opt/conda/conda-bld/pytorch_1682343904035/work/aten/src/ATen/VmapModeRegistrations.cpp：33 [后端回退] FuncTorchGradWrapper：在 /opt/conda/conda-bld/pytorch_1682343904035/work/aten/src/ATen/functorch/TensorWrapper.cpp：210 注册 [后端回退] PythonTLSSnapshot：注册于/opt/conda/conda-bld/pytorch_1682343904035/work/aten/src/ATen/core/PythonFallbackKernel.cpp：152 [后端回退] FuncTorchDynamicLayerFrontMode：在 /opt/conda/conda-bld/pytorch_1682343904035/work/aten/src/ATen/functorch/DynamicLayer.cpp：487 [后端回退] PythonDispatcher：在 /opt/conda/conda-bld/pytorch_1682343904035/work/aten/src/ATen/core/PythonFallbackKernel.cpp：148 [后端回退]