# Copyright 2021 The NetKet Authors - All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Optional, Callable, Union
from functools import partial
import numpy as np
import jax
import jax.numpy as jnp
from netket.utils.types import DType, PyTree, Array
import netket.jax as nkjax
from netket.hilbert import AbstractHilbert
from netket.operator import ContinuousOperator
from netket.utils import HashableArray
def jacrev(f):
def jacfun(x):
y, vjp_fun = nkjax.vjp(f, x)
if y.size == 1:
eye = jnp.eye(y.size, dtype=x.dtype)[0]
J = jax.vmap(vjp_fun, in_axes=0)(eye)
else:
eye = jnp.eye(y.size, dtype=x.dtype)
J = jax.vmap(vjp_fun, in_axes=0)(eye)
return J
return jacfun
def jacfwd(f):
def jacfun(x):
jvp_fun = lambda s: jax.jvp(f, (x,), (s,))[1]
eye = jnp.eye(len(x), dtype=x.dtype)
J = jax.vmap(jvp_fun, in_axes=0)(eye)
return J
return jacfun
[docs]
class KineticEnergy(ContinuousOperator):
r"""This is the kinetic energy operator (hbar = 1). The local value is given by:
:math:`E_{kin} = -1/2 ( \sum_i \frac{1}{m_i} (\log(\psi))'^2 + (\log(\psi))'' )`
"""
[docs]
def __init__(
self,
hilbert: AbstractHilbert,
mass: Union[float, list[float]],
dtype: Optional[DType] = None,
):
r"""Args:
hilbert: The underlying Hilbert space on which the operator is defined
mass: float if all masses are the same, list indicating the mass of each particle otherwise
dtype: Data type of the mass
"""
self._mass = jnp.asarray(mass, dtype=dtype)
self._is_hermitian = np.allclose(self._mass.imag, 0.0)
self.__attrs = None
super().__init__(hilbert, self._mass.dtype)
@property
def mass(self):
return self._mass
@property
def is_hermitian(self):
return self._is_hermitian
def _expect_kernel_single(
self, logpsi: Callable, params: PyTree, x: Array, inverse_mass: Optional[PyTree]
):
def logpsi_x(x):
return logpsi(params, x)
dlogpsi_x = jacrev(logpsi_x)
dp_dx2 = jnp.diag(jacfwd(dlogpsi_x)(x)[0].reshape(x.shape[0], x.shape[0]))
dp_dx = dlogpsi_x(x)[0][0] ** 2
return -0.5 * jnp.sum(inverse_mass * (dp_dx2 + dp_dx), axis=-1)
@partial(jax.vmap, in_axes=(None, None, None, 0, None))
def _expect_kernel(
self, logpsi: Callable, params: PyTree, x: Array, coefficient: Optional[PyTree]
):
return self._expect_kernel_single(logpsi, params, x, coefficient)
def _pack_arguments(self) -> PyTree:
return 1.0 / self._mass
@property
def _attrs(self):
if self.__attrs is None:
self.__attrs = (self.hilbert, self.dtype, HashableArray(self.mass))
return self.__attrs
def __repr__(self):
return f"KineticEnergy(m={self._mass})"
