inference/relax_adsorbate_on_slab.py

import time, torch, numpy as np
from ase.build import fcc100, add_adsorbate, molecule
from ase.optimize import LBFGS

# 自动定位 UMA 旋转基文件 Jd.pt
import os
_REPO_ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
_JD_PATH = os.path.join(_REPO_ROOT, "weight", "Jd.pt")
if os.path.isfile(_JD_PATH):
    os.environ.setdefault("ONESCIENCE_UMA_JD_PATH", _JD_PATH)

from onescience.utils.uma.units.mlip_unit import load_predict_unit
from onescience.utils.uma.calculate.ase_calculator import FAIRChemCalculator

# === 工具函数 ===
def _sync():
    if torch.cuda.is_available():
        torch.cuda.synchronize()

def clear_cache(atoms):
    # 避免命中 ASE/Calculator 的上次结果缓存，确保测到真实推理时间
    if hasattr(atoms, "calc") and hasattr(atoms.calc, "results"):
        atoms.calc.results.clear()

# === 加载模型 ===
predictor = load_predict_unit(
    "../weight/uma-s-1p1_converted.pt",
    device="cuda"
)
calc = FAIRChemCalculator(predictor, task_name="oc20")

# === 构建体系 ===
slab = fcc100("Cu", (3, 3, 3), vacuum=8, periodic=True)
adsorbate = molecule("CO")
add_adsorbate(slab, adsorbate, 2.0, "bridge")
slab.calc = calc

# === 预热（把模型/张量搬到显存，避免首轮开销影响统计）===
_ = slab.get_potential_energy(); _sync()
_ = slab.get_forces();            _sync()

# === 计时：LBFGS 优化（总耗时 & 平均每步）===
t0 = time.perf_counter()
opt = LBFGS(slab, logfile=None)  # 如需日志，改成 "lbfgs.log"
opt.run(fmax=0.05, steps=100)
_sync()
lbfgs_total = time.perf_counter() - t0
steps_done = getattr(opt, "nsteps", None) or 100

# === 计时：单次能量 & 力 推理延迟 ===
clear_cache(slab); t0 = time.perf_counter()
energy = slab.get_potential_energy(); _sync()
t_energy = time.perf_counter() - t0

clear_cache(slab); t0 = time.perf_counter()
forces = slab.get_forces(); _sync()
t_forces = time.perf_counter() - t0

# === 打印结果 ===
print(f"Predicted energy: {energy}")
print(f"Predicted forces shape: {forces.shape}")
# 如需打印具体力矩阵，取消下一行注释（可能很长）
# print(forces)

print(f"[E] latency: {t_energy*1e3:.2f} ms/call")
print(f"[F] latency: {t_forces*1e3:.2f} ms/call")
print(f"[LBFGS] total: {lbfgs_total:.3f} s, per-step: {lbfgs_total/steps_done*1e3:.2f} ms/step")