14791803374

Committed 02 May 2025 08:37AM UTC coverage: 83.686%. First build

Build # 14791803374

Build Type

Pull #190

github

Committed by

web-flow

Commit Message

Merge f2e5d9375 into 20fe7ebf9

Pull Request Pull Request #190: Fix memory

Run Details

940 of 1316 branches covered (71.43%)

Branch coverage included in aggregate %.

141 of 231 new or added lines in 21 files covered. (61.04%)

4477 of 5157 relevant lines covered (86.81%)

0.87 hits per line

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58.33

/qmctorch/wavefunction/orbitals/backflow/kernels/backflow_kernel_autodiff_inverse.py

import torch
from torch import nn
from .backflow_kernel_base import BackFlowKernelBase
from .....scf import Molecule

class BackFlowKernelAutoInverse(BackFlowKernelBase):
    def __init__(self, mol: Molecule, cuda: bool, weight: float = 0.0) -> None:
        """Compute the back flow kernel, i.e. the function
        f(rij) where rij is the distance between electron i and j
        This kernel is used in the backflow transformation
        .. math:
            q_i = r_i + \\sum_{j\\neq i} f(r_{ij}) (r_i-r_j)
        """
        super().__init__(mol, cuda)
        self.weight = nn.Parameter(torch.as_tensor([weight]))

    def _backflow_kernel(self, ree:torch.Tensor) -> torch.Tensor:
        """Computes the kernel via autodiff

        Args:
            ree ([type]): [description]

        Returns:
            [type]: [description]
        """
        eye = torch.eye(self.nelec, self.nelec).to(self.device)
        mask = torch.ones_like(ree) - eye
        return self.weight * mask * (1.0 / (ree + eye) - eye)

1	import torch	1✔
2	from torch import nn	1✔
3	from .backflow_kernel_base import BackFlowKernelBase	1✔
4	from .....scf import Molecule	1✔
5
6	class BackFlowKernelAutoInverse(BackFlowKernelBase):	1✔
7	def __init__(self, mol: Molecule, cuda: bool, weight: float = 0.0) -> None:	1✔
8	"""Compute the back flow kernel, i.e. the function
9	f(rij) where rij is the distance between electron i and j
10	This kernel is used in the backflow transformation
11	.. math:
12	q_i = r_i + \\sum_{j\\neq i} f(r_{ij}) (r_i-r_j)
13	"""
14	super().__init__(mol, cuda)	×
NEW 15	self.weight = nn.Parameter(torch.as_tensor([weight]))	×
16
17	def _backflow_kernel(self, ree:torch.Tensor) -> torch.Tensor:	1✔
18	"""Computes the kernel via autodiff
19
20	Args:
21	ree ([type]): [description]
22
23	Returns:
24	[type]: [description]
25	"""
26	eye = torch.eye(self.nelec, self.nelec).to(self.device)	×
27	mask = torch.ones_like(ree) - eye	×
28	return self.weight * mask * (1.0 / (ree + eye) - eye)	×

NLESC-JCER / QMCTorch / 14791803374

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