qmctorch.utils.torch_utils module¶

qmctorch.utils.torch_utils.set_torch_double_precision()[source]¶: Set the default precision to double for all torch tensors.

qmctorch.utils.torch_utils.set_torch_single_precision()[source]¶: Set the default precision to single for all torch tensors.

qmctorch.utils.torch_utils.fast_power(x, k, mask0=None, mask2=None)[source]¶

Computes x**k when k have elements 0, 1, 2

Parameters:	x (torch.tensor) – input k (torch.tensor) – exponents mask0 (torch.tensor) – precomputed mask of the elements of that are 0 (Defaults to None and computed here) mask2 (torch.tensor) – precomputed mask of the elements of that are 2 (Defaults to None and computed here)
Returns:	values of x**k
Return type:	torch.tensor

qmctorch.utils.torch_utils.gradients(out, inp)[source]¶

Return the gradients of out wrt inp

Parameters:	out ([type]) – [description] inp ([type]) – [description]

qmctorch.utils.torch_utils.diagonal_hessian(out, inp, return_grads=False)[source]¶

return the diagonal hessian of out wrt to inp

Parameters:	out ([type]) – [description] inp ([type]) – [description]
Returns:	[description]
Return type:	[type]

class qmctorch.utils.torch_utils.DataSet(data)[source]¶

Bases: sphinx.ext.autodoc.importer._MockObject

Creates a torch data set

Parameters:	{torch.tensor} -- data (data) –

class qmctorch.utils.torch_utils.Loss(wf, method='energy', clip=False)[source]¶

Bases: sphinx.ext.autodoc.importer._MockObject

Defines the loss to use during the optimization

Keyword Arguments:
Parameters:	{WaveFunction} -- wave function object used (wf) –
	{str} -- method to use (default (method) – {‘energy’}) (energy, variance, weighted-energy, weighted-variance) {bool} -- clip the values that are +/- % sigma away from (clip) – the mean (default: {False})

forward(pos, no_grad=False, deactivate_weight=False)[source]¶

Computes the loss

Keyword Arguments:
Parameters:	{torch.tensor} -- positions of the walkers in that batch (pos) –
	{bool} -- computes the gradient of the loss (no_grad) – (default: {False})
Returns:	torch.tensor, torch.tensor – value of the loss, local energies

static get_grad_mode(no_grad)[source]¶

Returns enable_grad or no_grad

Parameters:	{bool} -- [description] (no_grad) –

get_clipping_mask(local_energies)[source]¶

computes the clipping mask

Parameters:	{torch.tensor} -- values of the local energies (local_energies) –

get_sampling_weights(pos, deactivate_weight)[source]¶: Get the weight needed when resampling is not done at every step

class qmctorch.utils.torch_utils.OrthoReg(alpha=0.1)[source]¶

Bases: sphinx.ext.autodoc.importer._MockObject

Add a penalty loss to keep the MO orthogonalized

Keyword Arguments:
	{float} -- strength of the penaly (default (alpha) – {0.1})