66 lines
2.1 KiB
Python
66 lines
2.1 KiB
Python
from lammps.mliap.mliap_unified_abc import MLIAPUnified
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import numpy as np
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import jax
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import jax.numpy as jnp
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from jax import jit
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from functools import partial
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# /- ensure epsilon and sigma are treated as compile-time constants
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@partial(jit, static_argnums=(0, 1))
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def lj_potential(epsilon: float, sigma: float, rij):
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# a pure function we can differentiate:
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def _tot_e(rij):
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r2inv = 1.0 / jnp.sum(rij ** 2, axis=1)
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r6inv = r2inv * r2inv * r2inv
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lj1 = 4.0 * epsilon * sigma**12
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lj2 = 4.0 * epsilon * sigma**6
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eij = r6inv * (lj1 * r6inv - lj2)
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return jnp.sum(eij)
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# /- construct a function computing _tot_e and its derivative
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tot_e, fij = jax.value_and_grad(_tot_e)(rij)
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return tot_e, fij
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class MLIAPUnifiedJAX(MLIAPUnified):
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"""Test implementation for MLIAPUnified."""
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epsilon: float
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sigma: float
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def __init__(self, element_types, epsilon=1.0, sigma=1.0, rcutfac=1.25):
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# ARGS: interface, element_types, ndescriptors, nparams, rcutfac
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super().__init__(None, element_types, 1, 3, rcutfac)
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# Mimicking the LJ pair-style:
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# pair_style lj/cut 2.5
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# pair_coeff * * 1 1
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self.epsilon = epsilon
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self.sigma = sigma
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# TODO: Take this from the LAMMPS Cython side.
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self.npair_max = 250000
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def compute_gradients(self, data):
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"""Test compute_gradients."""
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def compute_descriptors(self, data):
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"""Test compute_descriptors."""
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def compute_forces(self, data):
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"""Test compute_forces."""
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rij = data.rij
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# TODO: Take max npairs from the LAMMPS Cython side.
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if (data.npairs > self.npair_max):
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self.npair_max = data.npairs
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npad = self.npair_max - data.npairs
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# TODO: Take pre-padded rij from the LAMMPS Cython side.
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# This might account for ~2-3x slowdown compared to original LJ.
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rij = np.pad(rij, ((0,npad), (0,0)), 'constant')
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e_tot, fij = lj_potential(rij)
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data.energy = e_tot.item()
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data.update_pair_forces(np.array(fij, dtype=np.float64))
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