Bug: Please enable floatfftw in the cmake to use float fft when precision is set to single using GPU

[OutisLi]

Describe the bug

I use this INPUT file to test my build from source code:

INPUT_PARAMETERS
#Parameters	(General)
calculation             scf
pseudo_dir		.
orbital_dir 		.
ntype 1
#Parameters (Accuracy)
symmetry                1
basis_type		pw	# PW; LCAO in pw; LCAO
ecutwfc			100	# energy cutoff for wave functions
scf_nmax		100
scf_thr 1e-6 #1e-5
kspacing 0.05
cal_force 1
cal_stress 1
smearing_method  gauss
smearing_sigma 0.015
mixing_type broyden
mixing_beta 0.3 

ks_solver bpcg

device gpu
precision single

But it cannot run and throws a error shown : Please enable floatfftw in the cmake to use float fft

Expected behavior

No response

To Reproduce

I build abacus from source code using toolchain:
file toolchain_gnu.sh:

#!/bin/bash
#SBATCH -J install
#SBATCH -N 1
#SBATCH -n 16
#SBATCH -o compile.log
#SBATCH -e compile.err

# JamesMisaka in 2023-09-16
# install abacus dependency by gnu-toolchain
# one can use mpich or openmpi.
# openmpi will be faster, but not compatible in some cases.
# libtorch and libnpy are for deepks support, which can be =no
# if you want to run EXX calculation, you should set --with-libri=install
# mpich (and intel toolchain) is recommended for EXX support
# gpu-lcao supporting modify: CUDA_PATH and --enable-cuda
# export CUDA_PATH=/usr/local/cuda

./install_abacus_toolchain.sh \
--with-gcc=system \
--with-intel=no \
--with-openblas=install \
--with-openmpi=install \
--with-cmake=install \
--with-scalapack=install \
--with-libxc=install \
--with-fftw=install \
--with-elpa=install \
--with-cereal=install \
--with-rapidjson=install \
--with-libtorch=install \
--with-libnpy=install \
--with-libri=install \
--with-libcomm=install \
--with-4th-openmpi=no \
--enable-cuda \
--gpu-ver=120 \
| tee compile.log
# to use openmpi-version4: set --with-4th-openmpi=yes
# to enable gpu-lcao, add the following lines:
# --enable-cuda \
# --gpu-ver=75 \ 
# one should check your gpu compute capability number 
# and use it in --gpu-ver

and file build_abacus_gnu.sh:

#!/bin/bash
#SBATCH -J build
#SBATCH -N 1
#SBATCH -n 16
#SBATCH -o install.log
#SBATCH -e install.err
# JamesMisaka in 2025.03.09

# Build ABACUS by gnu-toolchain

# module load openmpi

ABACUS_DIR=..
TOOL=$(pwd)
INSTALL_DIR=$TOOL/install
source $INSTALL_DIR/setup
cd $ABACUS_DIR
ABACUS_DIR=$(pwd)

BUILD_DIR=build_abacus_gnu
rm -rf $BUILD_DIR

PREFIX=$ABACUS_DIR
LAPACK=$INSTALL_DIR/openblas-0.3.28/lib
SCALAPACK=$INSTALL_DIR/scalapack-2.2.1/lib
ELPA=$INSTALL_DIR/elpa-2025.01.001/nvidia # for gpu-lcao
FFTW3=$INSTALL_DIR/fftw-3.3.10
CEREAL=$INSTALL_DIR/cereal-1.3.2/include/cereal
LIBXC=$INSTALL_DIR/libxc-7.0.0
RAPIDJSON=$INSTALL_DIR/rapidjson-1.1.0/
LIBRI=$INSTALL_DIR/LibRI-0.2.1.0
LIBCOMM=$INSTALL_DIR/LibComm-0.1.1
LIBTORCH=$INSTALL_DIR/libtorch-2.1.2/share/cmake/Torch
LIBNPY=$INSTALL_DIR/libnpy-1.0.1/include
# DEEPMD=$HOME/apps/anaconda3/envs/deepmd # v3.0 might have problem

cmake -B $BUILD_DIR -DCMAKE_INSTALL_PREFIX=$PREFIX \
        -DCMAKE_CXX_COMPILER=g++ \
        -DMPI_CXX_COMPILER=mpicxx \
        -DLAPACK_DIR=$LAPACK \
        -DSCALAPACK_DIR=$SCALAPACK \
        -DELPA_DIR=$ELPA \
        -DFFTW3_DIR=$FFTW3 \
        -DCEREAL_INCLUDE_DIR=$CEREAL \
        -DLibxc_DIR=$LIBXC \
        -DENABLE_LCAO=ON \
        -DENABLE_LIBXC=ON \
        -DUSE_OPENMP=ON \
        -DUSE_ELPA=ON \
        -DENABLE_RAPIDJSON=ON \
        -DRapidJSON_DIR=$RAPIDJSON \
        -DUSE_CUDA=ON \
        -DENABLE_DEEPKS=1 \
        -DTorch_DIR=$LIBTORCH \
        -Dlibnpy_INCLUDE_DIR=$LIBNPY \
        -DENABLE_LIBRI=ON \
        -DLIBRI_DIR=$LIBRI \
        -DLIBCOMM_DIR=$LIBCOMM \
# 	      -DDeePMD_DIR=$DEEPMD \
        #-DENABLE_CUSOLVERMP=ON \
        #-D CAL_CUSOLVERMP_PATH=/opt/nvidia/hpc_sdk/Linux_x86_64/2x.xx/math_libs/1x.x/targets/x86_64-linux/lib

# # add mkl env for libtorch to link
# if one want to install libtorch, mkl should be load in build process
# for -lmkl when load libtorch
# module load mkl

# if one want's to include deepmd, your system gcc version should be >= 11.3.0 for glibc requirements

cmake --build $BUILD_DIR -j `nproc` 
cmake --install $BUILD_DIR 2>/dev/null

# generate abacus_env.sh
cat << EOF > "${TOOL}/abacus_env.sh"
#!/bin/bash
source $INSTALL_DIR/setup
export PATH="${PREFIX}/bin":\${PATH}
EOF

# generate information
cat << EOF
========================== usage =========================
Done!
To use the installed ABACUS version
You need to source ${TOOL}/abacus_env.sh first !
"""
EOF

Environment

OS: Ubuntu 24.04.2 LTS on Windows 10 x86_64
CPU: AMD Ryzen 9 7950X3D (32) @ 4.192GHz
GPU: 2950:00:00.0 Microsoft Corporation Basic Render Driver (5090D on windows)

Additional Context

if the precision is set to double(by default) this program runs normally.
Additionally, when I install abacus from conda-forge, single precision can run, but in the 10th or 11th loop, the RAM usage rises suddenly(normally 4G, but rises above RAM limit then in a few seconds), and killed by system

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[OutisLi]

It still raise the same error after I pulled the latest code and rebuild abacus.

OMP_NUM_THREADS=1 mpirun --use-hwthread-cpus -np 24 abacus
Info: Local MPI proc number: 24,OpenMP thread number: 1,Total thread number: 24,Local thread limit: 32
                                                                                     
                              ABACUS v3.9.0.6

               Atomic-orbital Based Ab-initio Computation at UStc                    

                     Website: http://abacus.ustc.edu.cn/                             
               Documentation: https://abacus.deepmodeling.com/                       
                  Repository: https://github.com/abacusmodeling/abacus-develop       
                              https://github.com/deepmodeling/abacus-develop         
                      Commit: cac721c5f (Tue May 27 18:44:28 2025 +0800)

 Wed May 28 12:21:56 2025
 MAKE THE DIR         : OUT.ABACUS/
 RUNNING WITH DEVICE  : GPU / NVIDIA GeForce RTX 4080

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 Warning: the number of valence electrons in pseudopotential > 6 for Mo: [Kr] 4d5 5s1
 Pseudopotentials with additional electrons can yield (more) accurate outcomes, but may be less efficient.
 If you're confident that your chosen pseudopotential is appropriate, you can safely ignore this warning.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

 
 ---------------------------------------------------------
                         !NOTICE!                         
 ---------------------------------------------------------
 
 Please enable float fftw in the cmake to use float fft
 CHECK IN FILE : OUT.ABACUS/warning.log
 
 For detailed manual of ABACUS, please see the website
 https://abacus.deepmodeling.com
 For any questions, propose issues on the website
 https://github.com/deepmodeling/abacus-develop/issues
 ---------------------------------------------------------
                         !NOTICE!                         
 ---------------------------------------------------------

TIME STATISTICS
-----------------------------------------------------
 CLASS_NAME    NAME    TIME/s  CALLS   AVG/s  PER/%  
-----------------------------------------------------
            total      0.65   1        0.65   100.00 
 Driver     reading    0.43   1        0.43   66.74  
-----------------------------------------------------

 See output information in : OUT.ABACUS/
--------------------------------------------------------------------------
prterun detected that one or more processes exited with non-zero status,
thus causing the job to be terminated. The first process to do so was:

   Process name: [prterun-Li-PC-PKU-1240488@1,19]
   Exit code:    1
--------------------------------------------------------------------------

@A-006

[OutisLi]

toolchain_gnu_gpu.sh:

#!/bin/bash
#SBATCH -J install
#SBATCH -N 1
#SBATCH -n 16
#SBATCH -o compile.log
#SBATCH -e compile.err

# JamesMisaka in 2023-09-16
# install abacus dependency by gnu-toolchain
# one can use mpich or openmpi.
# openmpi will be faster, but not compatible in some cases.
# libtorch and libnpy are for deepks support, which can be =no
# if you want to run EXX calculation, you should set --with-libri=install
# mpich (and intel toolchain) is recommended for EXX support
# gpu-lcao supporting modify: CUDA_PATH and --enable-cuda
# export CUDA_PATH=/usr/local/cuda

./install_abacus_toolchain.sh \
--with-gcc=system \
--with-intel=no \
--with-openblas=install \
--with-openmpi=install \
--with-cmake=install \
--with-scalapack=install \
--with-libxc=install \
--with-fftw=install \
--with-elpa=install \
--with-cereal=install \
--with-rapidjson=install \
--with-libtorch=install \
--with-libnpy=install \
--with-libri=install \
--with-libcomm=install \
--with-4th-openmpi=no \
--enable-cuda \
--gpu-ver=89 \
| tee compile.log
# to use openmpi-version4: set --with-4th-openmpi=yes
# to enable gpu-lcao, add the following lines:
# --enable-cuda \
# --gpu-ver=75 \
# one should check your gpu compute capability number
# and use it in --gpu-ver

[OutisLi]

build_abacus_gnu_gpu.sh:

#!/bin/bash
#SBATCH -J build
#SBATCH -N 1
#SBATCH -n 16
#SBATCH -o install.log
#SBATCH -e install.err
# JamesMisaka in 2025.03.09

# Build ABACUS by gnu-toolchain

# module load gcc cuda/12.6.0

ABACUS_DIR=..
TOOL=$(pwd)
INSTALL_DIR=$TOOL/install
source $INSTALL_DIR/setup
cd $ABACUS_DIR
ABACUS_DIR=$(pwd)

BUILD_DIR=build_abacus_gnu_gpu
rm -rf $BUILD_DIR

PREFIX=$ABACUS_DIR
LAPACK=$INSTALL_DIR/openblas-0.3.29/lib
SCALAPACK=$INSTALL_DIR/scalapack-2.2.2/lib
ELPA=$INSTALL_DIR/elpa-2025.01.001/nvidia # for gpu-lcao
FFTW3=$INSTALL_DIR/fftw-3.3.10
CEREAL=$INSTALL_DIR/cereal-master/include/cereal
LIBXC=$INSTALL_DIR/libxc-7.0.0
RAPIDJSON=$INSTALL_DIR/rapidjson-master/
LIBRI=$INSTALL_DIR/LibRI-0.2.1.0
LIBCOMM=$INSTALL_DIR/LibComm-master
LIBTORCH=$INSTALL_DIR/libtorch-2.1.2/share/cmake/Torch
LIBNPY=$INSTALL_DIR/libnpy-1.0.1/include
# DEEPMD=$HOME/apps/anaconda3/envs/deepmd # v3.0 might have problem

cmake -B $BUILD_DIR -DCMAKE_INSTALL_PREFIX=$PREFIX \
    -DCMAKE_CXX_COMPILER=g++ \
    -DMPI_CXX_COMPILER=mpicxx \
    -DLAPACK_DIR=$LAPACK \
    -DSCALAPACK_DIR=$SCALAPACK \
    -DELPA_DIR=$ELPA \
    -DFFTW3_DIR=$FFTW3 \
    -DCEREAL_INCLUDE_DIR=$CEREAL \
    -DLibxc_DIR=$LIBXC \
    -DENABLE_LCAO=ON \
    -DENABLE_LIBXC=ON \
    -DUSE_OPENMP=ON \
    -DUSE_ELPA=ON \
    -DENABLE_RAPIDJSON=ON \
    -DRapidJSON_DIR=$RAPIDJSON \
    -DUSE_CUDA=ON \
    -DENABLE_DEEPKS=1 \
    -DTorch_DIR=$LIBTORCH \
    -Dlibnpy_INCLUDE_DIR=$LIBNPY \
    -DENABLE_LIBRI=ON \
    -DLIBRI_DIR=$LIBRI \
    -DLIBCOMM_DIR=$LIBCOMM
# 	      -DDeePMD_DIR=$DEEPMD \
#-DENABLE_CUSOLVERMP=ON \
#-D CAL_CUSOLVERMP_PATH=/opt/nvidia/hpc_sdk/Linux_x86_64/2x.xx/math_libs/1x.x/targets/x86_64-linux/lib

# # add mkl env for libtorch to link
# if one want to install libtorch, mkl should be load in build process
# for -lmkl when load libtorch
# module load mkl

# if one want's to include deepmd, your system gcc version should be >= 11.3.0 for glibc requirements

# cmake --build $BUILD_DIR -j `nproc`
cmake --build $BUILD_DIR -j 16
cmake --install $BUILD_DIR 2>/dev/null

# generate abacus_env.sh
cat <<EOF >"${TOOL}/abacus_env.sh"
#!/bin/bash
source $INSTALL_DIR/setup
export PATH="${PREFIX}/bin":\${PATH}
EOF

# generate information
cat <<EOF
========================== usage =========================
Done!
To use the installed ABACUS version
You need to source ${TOOL}/abacus_env.sh first !
"""
EOF

[OutisLi]

INPUT:

INPUT_PARAMETERS
#Parameters	(General)
calculation             scf
pseudo_dir		.
orbital_dir 		.
ntype 1
#Parameters (Accuracy)
symmetry                1
basis_type		lcao	# PW; LCAO in pw; LCAO
ecutwfc			100	# energy cutoff for wave functions
scf_nmax		100
scf_thr 1e-6 #1e-5
kspacing 0.05
cal_force 1
cal_stress 1
smearing_method  gauss
smearing_sigma 0.015
mixing_type broyden
mixing_beta 0.3 

device gpu
precision single

[A-006]

Thank you for your request. The LCAO method does not support single precision, so if you set device=gpu, precision=single, basis_type=lcao, it will still run in double precision. We will implement this feature once the LCAO basis supports single precision.