ICODE-ADC/lammps
4
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

Properly decorate energy/force compute

Browse Source
This commit is contained in:
rohskopf
2023-05-24 11:39:05 -06:00
parent 28c9c274be
commit d66504be81
2 changed files with 25 additions and 6 deletions
Download Patch File Download Diff File Expand all files Collapse all files

2
examples/mliap/jax/in.run
View File

@ -32,4 +32,4 @@ fix 1 all nve
#dump 4 all custom 1 forces.xyz fx fy fz #dump 4 all custom 1 forces.xyz fx fy fz
thermo 50 thermo 50
run 250 run 100

29
examples/mliap/jax/mliap_unified_jax.py
View File

@ -1,5 +1,9 @@
from lammps.mliap.mliap_unified_abc import MLIAPUnified from lammps.mliap.mliap_unified_abc import MLIAPUnified
import numpy as np import numpy as np
import jax
import jax.numpy as jnp
from jax import jit
from functools import partial
class MLIAPUnifiedJAX(MLIAPUnified): class MLIAPUnifiedJAX(MLIAPUnified):
@ -13,6 +17,7 @@ class MLIAPUnifiedJAX(MLIAPUnified):
# pair_coeff * * 1 1 # pair_coeff * * 1 1
self.epsilon = epsilon self.epsilon = epsilon
self.sigma = sigma self.sigma = sigma
self.npair_max = 250000
def compute_gradients(self, data): def compute_gradients(self, data):
"""Test compute_gradients.""" """Test compute_gradients."""
@ -22,12 +27,26 @@ class MLIAPUnifiedJAX(MLIAPUnified):
def compute_forces(self, data): def compute_forces(self, data):
"""Test compute_forces.""" """Test compute_forces."""
eij, fij = self.compute_pair_ef(data) rij = data.rij
data.update_pair_energy(eij)
data.update_pair_forces(fij)
def compute_pair_ef(self, data): # TODO: Take max npairs from the LAMMPS Cython side.
rij = data.rij if (data.npairs > self.npair_max):
self.npair_max = data.npairs
npad = self.npair_max - data.npairs
# TODO: Take pre-padded rij from the LAMMPS Cython side.
# This might account for ~2-3x slowdown compared to original LJ.
rij = np.pad(rij, ((0,npad), (0,0)), 'constant')
eij, fij = self.compute_pair_ef(rij)
data.update_pair_energy(np.array(np.double(eij)))
data.update_pair_forces(np.array(np.double(fij)))
#@jax.jit # <-- This will error! See https://github.com/google/jax/issues/1251
# @partial takes a function (e.g. jax.jit) as an arg.
@partial(jax.jit, static_argnums=(0,))
def compute_pair_ef(self, rij):
r2inv = 1.0 / np.sum(rij ** 2, axis=1) r2inv = 1.0 / np.sum(rij ** 2, axis=1)
r6inv = r2inv * r2inv * r2inv r6inv = r2inv * r2inv * r2inv