GPU plugin for PySCF

Installation

Note

The compiled binary packages support compute capability 6.0 and later (Pascal and later, such as Tesla P100, RTX 10 series and later).

Run nvcc --version in your terminal to check the installed CUDA toolkit version. Then, choose the proper package based on your CUDA toolkit version.

Platform	Command	cutensor (highly recommended)
CUDA 11.x	pip3 install gpu4pyscf-cuda11x	pip3 install cutensor-cu11
CUDA 12.x	pip3 install gpu4pyscf-cuda12x	pip3 install cutensor-cu12

Compilation

One can compile the package with

git clone https://github.com/pyscf/gpu4pyscf.git
cd gpu4pyscf
cmake -S gpu4pyscf/lib -B build/temp.gpu4pyscf
cmake --build build/temp.gpu4pyscf -j 4
CURRENT_PATH=`pwd`
export PYTHONPATH="${PYTHONPATH}:${CURRENT_PATH}"

Then install cutensor and cupy for acceleration (please switch the versions according to your nvcc version!)

pip3 install cutensor-cu12 cupy-cuda12x

There shouldn't be cupy or cutensor compilation during pip install process. If you see the following warning at the beginning of a gpu4pyscf job, it implies problems with cupy and cutensor installation (likely a version mismatch, or multiple versions of same package installed).

<repo_path>/gpu4pyscf/lib/cutensor.py:<line_number>: UserWarning: using cupy as the tensor contraction engine.

The package also provides multiple dockerfiles in dockerfiles. One can use them as references to create the compilation envrionment.

Features

• Density fitting scheme and direct SCF scheme;
• SCF, analytical Gradient, and analytical Hessian calculations for Hartree-Fock and DFT;
• LDA, GGA, mGGA, hybrid, and range-separated functionals via libXC;
• Geometry optimization and transition state search via geomeTRIC;
• Dispersion corrections via DFTD3 and DFTD4;
• Nonlocal functional correction (vv10) for SCF and gradient;
• ECP is supported and calculated on CPU;
• PCM models, SMD model, their analytical gradients, and semi-analytical Hessian matrix;
• Unrestricted Hartree-Fock and Unrestricted DFT, gradient, and Hessian;
• MP2/DF-MP2 and CCSD (experimental);
• Polarizability, IR, and NMR shielding
• QM/MM with PBC
• CHELPG, ESP, and RESP atomic charge

Limitations

• Rys roots up to 9 for density fitting scheme and direct scf scheme;
• Atomic basis up to g orbitals;
• Auxiliary basis up to i orbitals;
• Density fitting scheme up to ~168 atoms with def2-tzvpd basis, bounded by CPU memory;
• meta-GGA without density laplacian;
• Double hybrid functionals are not supported;

Examples

import pyscf
from gpu4pyscf.dft import rks

atom ='''
O       0.0000000000    -0.0000000000     0.1174000000
H      -0.7570000000    -0.0000000000    -0.4696000000
H       0.7570000000     0.0000000000    -0.4696000000
'''

mol = pyscf.M(atom=atom, basis='def2-tzvpp')
mf = rks.RKS(mol, xc='LDA').density_fit()

e_dft = mf.kernel()  # compute total energy
print(f"total energy = {e_dft}")

g = mf.nuc_grad_method()
g_dft = g.kernel()   # compute analytical gradient

h = mf.Hessian()
h_dft = h.kernel()   # compute analytical Hessian

to_gpu is supported since PySCF 2.5.0

import pyscf
from pyscf.dft import rks

atom ='''
O       0.0000000000    -0.0000000000     0.1174000000
H      -0.7570000000    -0.0000000000    -0.4696000000
H       0.7570000000     0.0000000000    -0.4696000000
'''

mol = pyscf.M(atom=atom, basis='def2-tzvpp')
mf = rks.RKS(mol, xc='LDA').density_fit().to_gpu()  # move PySCF object to GPU4PySCF object
e_dft = mf.kernel()  # compute total energy

Find more examples in gpu4pyscf/examples

Benchmarks

Speedup with GPU4PySCF v0.6.0 on A100-80G over Q-Chem 6.1 on 32-cores CPU (Desity fitting, SCF, def2-tzvpp, def2-universal-jkfit, B3LYP, (99,590))

mol	natm	LDA	PBE	B3LYP	M06	wB97m-v
020_Vitamin_C	20	2.86	6.09	13.11	11.58	17.46
031_Inosine	31	13.14	15.87	16.57	25.89	26.14
033_Bisphenol_A	33	12.31	16.88	16.54	28.45	28.82
037_Mg_Porphin	37	13.85	19.03	20.53	28.31	30.27
042_Penicillin_V	42	10.34	13.35	15.34	22.01	24.2
045_Ochratoxin_A	45	13.34	15.3	19.66	27.08	25.41
052_Cetirizine	52	17.79	17.44	19	24.41	25.87
057_Tamoxifen	57	14.7	16.57	18.4	24.86	25.47
066_Raffinose	66	13.77	14.2	20.47	22.94	25.35
084_Sphingomyelin	84	14.24	12.82	15.96	22.11	24.46
095_Azadirachtin	95	5.58	7.72	24.18	26.84	25.21
113_Taxol	113	5.44	6.81	24.58	29.14	nan

Find more benchmarks in gpu4pyscf/benchmarks

References

@misc{li2024introducting,
      title={Introducing GPU-acceleration into the Python-based Simulations of Chemistry Framework}, 
      author={Rui Li and Qiming Sun and Xing Zhang and Garnet Kin-Lic Chan},
      year={2024},
      eprint={2407.09700},
      archivePrefix={arXiv},
      primaryClass={physics.comp-ph},
      url={https://arxiv.org/abs/2407.09700}, 
}

@misc{wu2024enhancing,
      title={Enhancing GPU-acceleration in the Python-based Simulations of Chemistry Framework}, 
      author={Xiaojie Wu and Qiming Sun and Zhichen Pu and Tianze Zheng and Wenzhi Ma and Wen Yan and Xia Yu and Zhengxiao Wu and Mian Huo and Xiang Li and Weiluo Ren and Sheng Gong and Yumin Zhang and Weihao Gao},
      year={2024},
      eprint={2404.09452},
      archivePrefix={arXiv},
      primaryClass={physics.comp-ph},
      url={https://arxiv.org/abs/2404.09452}, 
}