diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/angle/angle_atom_energy_impl.cu b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/angle/angle_atom_energy_impl.cu deleted file mode 100644 index 4b5084008ba..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/angle/angle_atom_energy_impl.cu +++ /dev/null @@ -1,66 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "plugin/device/gpu/kernel/cuda_impl/sponge/angle/angle_atom_energy_impl.cuh" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/util.cuh" -#include "plugin/device/gpu/kernel/cuda_impl/sponge/common_sponge.cuh" - -__global__ void AngleAtomEnergyKernel(int angle_numbers, const UNSIGNED_INT_VECTOR *uint_crd, const VECTOR *scaler, - const int *atom_a, const int *atom_b, const int *atom_c, const float *angle_k, - const float *angle_theta0, float *atom_energy) { - int angle_i = blockDim.x * blockIdx.x + threadIdx.x; - if (angle_i < angle_numbers) { - int atom_i = atom_a[angle_i]; - int atom_j = atom_b[angle_i]; - int atom_k = atom_c[angle_i]; - - float theta0 = angle_theta0[angle_i]; - float k = angle_k[angle_i]; - - VECTOR drij = Get_Periodic_Displacement(uint_crd[atom_i], uint_crd[atom_j], scaler[0]); - VECTOR drkj = Get_Periodic_Displacement(uint_crd[atom_k], uint_crd[atom_j], scaler[0]); - - float rij_2 = 1. / (drij * drij); - float rkj_2 = 1. / (drkj * drkj); - float rij_1_rkj_1 = sqrtf(rij_2 * rkj_2); - - float costheta = drij * drkj * rij_1_rkj_1; - costheta = fmaxf(-0.999999, fminf(costheta, 0.999999)); - float theta = acosf(costheta); - - float dtheta = theta - theta0; - - atomicAdd(&atom_energy[atom_i], k * dtheta * dtheta); - } -} - -void AngleAtomEnergy(int angle_numbers, int atom_numbers, const int *uint_crd_f, const float *scaler_f, - const int *atom_a, const int *atom_b, const int *atom_c, const float *angle_k, - const float *angle_theta0, float *ene, cudaStream_t stream) { - Reset_List<<(atom_numbers) / 128), 128, 0, stream>>>(atom_numbers, ene, 0.); - size_t thread_per_block = 128; - size_t block_per_grid = ceilf(static_cast(angle_numbers) / 128); - UNSIGNED_INT_VECTOR *uint_crd = - const_cast(reinterpret_cast(uint_crd_f)); - VECTOR *scaler = const_cast(reinterpret_cast(scaler_f)); - - AngleAtomEnergyKernel<<>>(angle_numbers, uint_crd, scaler, atom_a, - atom_b, atom_c, angle_k, angle_theta0, ene); - return; -} -void AngleAtomEnergy(int angle_numbers, int atom_numbers, const int *uint_crd_f, const float *scaler_f, - const int *atom_a, const int *atom_b, const int *atom_c, const float *angle_k, - const float *angle_theta0, float *ene, cudaStream_t stream); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/angle/angle_atom_energy_impl.cuh b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/angle/angle_atom_energy_impl.cuh deleted file mode 100644 index 538652a1bc2..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/angle/angle_atom_energy_impl.cuh +++ /dev/null @@ -1,27 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_ANGLE_ANGLE_ATOM_ENERGY_IMPL_H_ -#define MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_ANGLE_ANGLE_ATOM_ENERGY_IMPL_H_ - -#include -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_device_info.h" - -CUDA_LIB_EXPORT void AngleAtomEnergy(int angle_numbers, int atom_numbers, const int *uint_crd_f, const float *scaler_f, - const int *atom_a, const int *atom_b, const int *atom_c, const float *angle_k, - const float *angle_theta0, float *ene, cudaStream_t stream); -#endif diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/angle/angle_energy_impl.cu b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/angle/angle_energy_impl.cu deleted file mode 100644 index c57befff8eb..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/angle/angle_energy_impl.cu +++ /dev/null @@ -1,63 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "plugin/device/gpu/kernel/cuda_impl/sponge/angle/angle_energy_impl.cuh" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/util.cuh" -#include "plugin/device/gpu/kernel/cuda_impl/sponge/common_sponge.cuh" - -__global__ void AngleEnergyKernel(int angle_numbers, const UNSIGNED_INT_VECTOR *uint_crd, const VECTOR *scaler, - const int *atom_a, const int *atom_b, const int *atom_c, const float *angle_k, - const float *angle_theta0, float *angle_energy) { - int angle_i = blockDim.x * blockIdx.x + threadIdx.x; - if (angle_i < angle_numbers) { - int atom_i = atom_a[angle_i]; - int atom_j = atom_b[angle_i]; - int atom_k = atom_c[angle_i]; - - float theta0 = angle_theta0[angle_i]; - float k = angle_k[angle_i]; - - VECTOR drij = Get_Periodic_Displacement(uint_crd[atom_i], uint_crd[atom_j], scaler[0]); - VECTOR drkj = Get_Periodic_Displacement(uint_crd[atom_k], uint_crd[atom_j], scaler[0]); - - float rij_2 = 1. / (drij * drij); - float rkj_2 = 1. / (drkj * drkj); - float rij_1_rkj_1 = sqrtf(rij_2 * rkj_2); - - float costheta = drij * drkj * rij_1_rkj_1; - costheta = fmaxf(-0.999999, fminf(costheta, 0.999999)); - float theta = acosf(costheta); - - float dtheta = theta - theta0; - - angle_energy[angle_i] = k * dtheta * dtheta; - } -} - -void AngleEnergy(int angle_numbers, const int *uint_crd_f, const float *scaler_f, const int *atom_a, const int *atom_b, - const int *atom_c, const float *angle_k, const float *angle_theta0, float *ene, cudaStream_t stream) { - size_t thread_per_block = 128; - size_t block_per_grid = ceilf(static_cast(angle_numbers) / 128); - UNSIGNED_INT_VECTOR *uint_crd = - const_cast(reinterpret_cast(uint_crd_f)); - VECTOR *scaler = const_cast(reinterpret_cast(scaler_f)); - - AngleEnergyKernel<<>>(angle_numbers, uint_crd, scaler, atom_a, atom_b, - atom_c, angle_k, angle_theta0, ene); - return; -} -void AngleEnergy(int angle_numbers, const int *uint_crd_f, const float *scaler_f, const int *atom_a, const int *atom_b, - const int *atom_c, const float *angle_k, const float *angle_theta0, float *ene, cudaStream_t stream); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/angle/angle_energy_impl.cuh b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/angle/angle_energy_impl.cuh deleted file mode 100644 index b1eeb53a72b..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/angle/angle_energy_impl.cuh +++ /dev/null @@ -1,27 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_ANGLE_ANGLE_ENERGY_IMPL_H_ -#define MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_ANGLE_ANGLE_ENERGY_IMPL_H_ - -#include -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_device_info.h" - -CUDA_LIB_EXPORT void AngleEnergy(int angle_numbers, const int *uint_crd_f, const float *scaler_f, const int *atom_a, - const int *atom_b, const int *atom_c, const float *angle_k, const float *angle_theta0, - float *ene, cudaStream_t stream); -#endif diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/angle/angle_force_impl.cu b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/angle/angle_force_impl.cu deleted file mode 100644 index 6457de26a8f..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/angle/angle_force_impl.cu +++ /dev/null @@ -1,86 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "plugin/device/gpu/kernel/cuda_impl/sponge/angle/angle_force_impl.cuh" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/util.cuh" -#include "plugin/device/gpu/kernel/cuda_impl/sponge/common_sponge.cuh" - -__global__ void AngleForceKernel(int angle_numbers, const UNSIGNED_INT_VECTOR *uint_crd, const VECTOR *scaler, - const int *atom_a, const int *atom_b, const int *atom_c, const float *angle_k, - const float *angle_theta0, VECTOR *frc) { - int angle_i = blockDim.x * blockIdx.x + threadIdx.x; - if (angle_i < angle_numbers) { - int atom_i = atom_a[angle_i]; - int atom_j = atom_b[angle_i]; - int atom_k = atom_c[angle_i]; - - float theta0 = angle_theta0[angle_i]; - float k = angle_k[angle_i]; - - VECTOR drij = Get_Periodic_Displacement(uint_crd[atom_i], uint_crd[atom_j], scaler[0]); - VECTOR drkj = Get_Periodic_Displacement(uint_crd[atom_k], uint_crd[atom_j], scaler[0]); - - float rij_2 = 1. / (drij * drij); - float rkj_2 = 1. / (drkj * drkj); - float rij_1_rkj_1 = sqrtf(rij_2 * rkj_2); - - float costheta = drij * drkj * rij_1_rkj_1; - costheta = fmaxf(-0.999999, fminf(costheta, 0.999999)); - float theta = acosf(costheta); - - float dtheta = theta - theta0; - k = -2 * k * dtheta / sinf(theta); - - float common_factor_cross = k * rij_1_rkj_1; - float common_factor_self = k * costheta; - - VECTOR fi = common_factor_self * rij_2 * drij - common_factor_cross * drkj; - VECTOR fk = common_factor_self * rkj_2 * drkj - common_factor_cross * drij; - - atomicAdd(&frc[atom_i].x, fi.x); - atomicAdd(&frc[atom_i].y, fi.y); - atomicAdd(&frc[atom_i].z, fi.z); - - atomicAdd(&frc[atom_k].x, fk.x); - atomicAdd(&frc[atom_k].y, fk.y); - atomicAdd(&frc[atom_k].z, fk.z); - - fi = -fi - fk; - - atomicAdd(&frc[atom_j].x, fi.x); - atomicAdd(&frc[atom_j].y, fi.y); - atomicAdd(&frc[atom_j].z, fi.z); - } -} - -void AngleForce(int angle_numbers, int atom_numbers, const int *uint_crd_f, const float *scaler_f, const int *atom_a, - const int *atom_b, const int *atom_c, const float *angle_k, const float *angle_theta0, float *frc_f, - cudaStream_t stream) { - Reset_List<<(3. * atom_numbers) / 128), 128, 0, stream>>>(3 * atom_numbers, frc_f, 0.); - size_t thread_per_block = 128; - size_t block_per_grid = ceilf(static_cast(angle_numbers) / 128); - UNSIGNED_INT_VECTOR *uint_crd = - const_cast(reinterpret_cast(uint_crd_f)); - VECTOR *frc = const_cast(reinterpret_cast(frc_f)); - VECTOR *scaler = const_cast(reinterpret_cast(scaler_f)); - - AngleForceKernel<<>>(angle_numbers, uint_crd, scaler, atom_a, atom_b, - atom_c, angle_k, angle_theta0, frc); - return; -} -void AngleForce(int angle_numbers, int atom_numbers, const int *uint_crd_f, const float *scaler_f, const int *atom_a, - const int *atom_b, const int *atom_c, const float *angle_k, const float *angle_theta0, float *frc_f, - cudaStream_t stream); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/angle/angle_force_impl.cuh b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/angle/angle_force_impl.cuh deleted file mode 100644 index 15314403924..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/angle/angle_force_impl.cuh +++ /dev/null @@ -1,27 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_ANGLE_ANGLE_FORCE_IMPL_H_ -#define MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_ANGLE_ANGLE_FORCE_IMPL_H_ - -#include -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_device_info.h" - -CUDA_LIB_EXPORT void AngleForce(int angle_numbers, int atom_numbers, const int *uint_crd_f, const float *scaler_f, - const int *atom_a, const int *atom_b, const int *atom_c, const float *angle_k, - const float *angle_theta0, float *frc_f, cudaStream_t stream); -#endif diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/angle/angle_force_with_atom_energy_impl.cu b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/angle/angle_force_with_atom_energy_impl.cu deleted file mode 100644 index b186683585a..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/angle/angle_force_with_atom_energy_impl.cu +++ /dev/null @@ -1,90 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "plugin/device/gpu/kernel/cuda_impl/sponge/angle/angle_force_with_atom_energy_impl.cuh" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/util.cuh" -#include "plugin/device/gpu/kernel/cuda_impl/sponge/common_sponge.cuh" - -__global__ void AngleForceWithAtomEnergyKernel(int angle_numbers, const UNSIGNED_INT_VECTOR *uint_crd, - const VECTOR *scaler, const int *atom_a, const int *atom_b, - const int *atom_c, const float *angle_k, const float *angle_theta0, - VECTOR *frc, float *atom_energy) { - int angle_i = blockDim.x * blockIdx.x + threadIdx.x; - if (angle_i < angle_numbers) { - int atom_i = atom_a[angle_i]; - int atom_j = atom_b[angle_i]; - int atom_k = atom_c[angle_i]; - - float theta0 = angle_theta0[angle_i]; - float k = angle_k[angle_i]; - float k2 = k; - - VECTOR drij = Get_Periodic_Displacement(uint_crd[atom_i], uint_crd[atom_j], scaler[0]); - VECTOR drkj = Get_Periodic_Displacement(uint_crd[atom_k], uint_crd[atom_j], scaler[0]); - - float rij_2 = 1. / (drij * drij); - float rkj_2 = 1. / (drkj * drkj); - float rij_1_rkj_1 = sqrtf(rij_2 * rkj_2); - - float costheta = drij * drkj * rij_1_rkj_1; - costheta = fmaxf(-0.999999, fminf(costheta, 0.999999)); - float theta = acosf(costheta); - - float dtheta = theta - theta0; - k = -2 * k * dtheta / sinf(theta); - - float common_factor_cross = k * rij_1_rkj_1; - float common_factor_self = k * costheta; - - VECTOR fi = common_factor_self * rij_2 * drij - common_factor_cross * drkj; - VECTOR fk = common_factor_self * rkj_2 * drkj - common_factor_cross * drij; - - atomicAdd(&frc[atom_i].x, fi.x); - atomicAdd(&frc[atom_i].y, fi.y); - atomicAdd(&frc[atom_i].z, fi.z); - - atomicAdd(&frc[atom_k].x, fk.x); - atomicAdd(&frc[atom_k].y, fk.y); - atomicAdd(&frc[atom_k].z, fk.z); - - fi = -fi - fk; - - atomicAdd(&frc[atom_j].x, fi.x); - atomicAdd(&frc[atom_j].y, fi.y); - atomicAdd(&frc[atom_j].z, fi.z); - - atomicAdd(&atom_energy[atom_i], k2 * dtheta * dtheta); - } -} - -void AngleForceWithAtomEnergy(int angle_numbers, int atom_numbers, const int *uint_crd_f, const float *scaler_f, - const int *atom_a, const int *atom_b, const int *atom_c, const float *angle_k, - const float *angle_theta0, float *frc_f, float *ene, cudaStream_t stream) { - Reset_List<<(3. * atom_numbers) / 128), 128, 0, stream>>>(3 * atom_numbers, frc_f, 0.); - size_t thread_per_block = 128; - size_t block_per_grid = ceilf(static_cast(angle_numbers) / 128); - UNSIGNED_INT_VECTOR *uint_crd = - const_cast(reinterpret_cast(uint_crd_f)); - VECTOR *frc = const_cast(reinterpret_cast(frc_f)); - VECTOR *scaler = const_cast(reinterpret_cast(scaler_f)); - - AngleForceWithAtomEnergyKernel<<>>( - angle_numbers, uint_crd, scaler, atom_a, atom_b, atom_c, angle_k, angle_theta0, frc, ene); - return; -} -void AngleForceWithAtomEnergy(int angle_numbers, int atom_numbers, const int *uint_crd_f, const float *scaler_f, - const int *atom_a, const int *atom_b, const int *atom_c, const float *angle_k, - const float *angle_theta0, float *frc_f, float *ene, cudaStream_t stream); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/angle/angle_force_with_atom_energy_impl.cuh b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/angle/angle_force_with_atom_energy_impl.cuh deleted file mode 100644 index ac2445b52dc..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/angle/angle_force_with_atom_energy_impl.cuh +++ /dev/null @@ -1,28 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_ANGLE_ANGLE_FORCE_WITH_ATOM_ENERGY_IMPL_H_ -#define MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_ANGLE_ANGLE_FORCE_WITH_ATOM_ENERGY_IMPL_H_ - -#include -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_device_info.h" - -CUDA_LIB_EXPORT void AngleForceWithAtomEnergy(int angle_numbers, int atom_numbers, const int *uint_crd_f, - const float *scaler_f, const int *atom_a, const int *atom_b, - const int *atom_c, const float *angle_k, const float *angle_theta0, - float *frc_f, float *ene, cudaStream_t stream); -#endif diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/bond/bond_atom_energy_cuda_gpu_impl.cu b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/bond/bond_atom_energy_cuda_gpu_impl.cu deleted file mode 100644 index bab1f905c68..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/bond/bond_atom_energy_cuda_gpu_impl.cu +++ /dev/null @@ -1,57 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "plugin/device/gpu/kernel/cuda_impl/sponge/bond/bond_atom_energy_cuda_gpu_impl.cuh" -#include "plugin/device/gpu/kernel/cuda_impl/sponge/common_sponge.cuh" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/util.cuh" - -__global__ void BondAtomEnergyCudaKernel(const int bond_numbers, const UNSIGNED_INT_VECTOR *uint_crd, - const VECTOR *scaler, const int *atom_a, const int *atom_b, - const float *bond_k, const float *bond_r0, float *atom_ene) { - int bond_i = blockDim.x * blockIdx.x + threadIdx.x; - if (bond_i < bond_numbers) { - int atom_i = atom_a[bond_i]; - int atom_j = atom_b[bond_i]; - - float k = bond_k[bond_i]; - float r0 = bond_r0[bond_i]; - - VECTOR dr = Get_Periodic_Displacement(uint_crd[atom_i], uint_crd[atom_j], scaler[0]); - - float r1 = norm3df(dr.x, dr.y, dr.z); - float tempf = r1 - r0; - - atomicAdd(&atom_ene[atom_i], k * tempf * tempf); - } -} - -void BondAtomEnergy(int bond_numbers, int atom_numbers, const int *uint_crd_f, const float *scaler_f, const int *atom_a, - const int *atom_b, const float *bond_k, const float *bond_r0, float *atom_ene, - cudaStream_t stream) { - Reset_List<<(atom_numbers) / 128), 128, 0, stream>>>(atom_numbers, atom_ene, 0.); - size_t thread_per_block = 128; - size_t block_per_grid = ceilf(static_cast(bond_numbers) / 128); - UNSIGNED_INT_VECTOR *uint_crd = - const_cast(reinterpret_cast(uint_crd_f)); - VECTOR *scaler = const_cast(reinterpret_cast(scaler_f)); - - BondAtomEnergyCudaKernel<<>>(bond_numbers, uint_crd, scaler, atom_a, - atom_b, bond_k, bond_r0, atom_ene); - return; -} - -void BondAtomEnergy(int bond_numbers, int atom_numbers, const int *uint_crd_f, const float *scaler_f, const int *atom_a, - const int *atom_b, const float *bond_k, const float *bond_r0, float *atom_ene, cudaStream_t stream); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/bond/bond_atom_energy_cuda_gpu_impl.cuh b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/bond/bond_atom_energy_cuda_gpu_impl.cuh deleted file mode 100644 index 96f97609e87..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/bond/bond_atom_energy_cuda_gpu_impl.cuh +++ /dev/null @@ -1,28 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_BOND_BOND_ATOM_ENERGY_CUDA_GPU_IMPL_H_ -#define MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_BOND_BOND_ATOM_ENERGY_GPU_IMPL_H_ - -#include -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_device_info.h" - -CUDA_LIB_EXPORT void BondAtomEnergy(int bond_numbers, int atom_numbers, const int *uint_crd_f, const float *scaler_f, - const int *atom_a, const int *atom_b, const float *bond_k, const float *bond_r0, - float *atom_ene, cudaStream_t stream); - -#endif // MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_BOND_ATOM_ENERGY_GPU_IMPL_H_ diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/bond/bond_energy_cuda_gpu_impl.cu b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/bond/bond_energy_cuda_gpu_impl.cu deleted file mode 100644 index bb1007036c6..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/bond/bond_energy_cuda_gpu_impl.cu +++ /dev/null @@ -1,61 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "plugin/device/gpu/kernel/cuda_impl/sponge/bond/bond_energy_cuda_gpu_impl.cuh" -#include "plugin/device/gpu/kernel/cuda_impl/sponge/common_sponge.cuh" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/util.cuh" - -__global__ void BondEnergyCudaKernel(const int bond_numbers, const UNSIGNED_INT_VECTOR *uint_crd, const VECTOR *scaler, - const int *atom_a, const int *atom_b, const float *bond_k, const float *bond_r0, - float *bond_ene) { - int bond_i = blockDim.x * blockIdx.x + threadIdx.x; - if (bond_i < bond_numbers) { - int atom_i = atom_a[bond_i]; - int atom_j = atom_b[bond_i]; - - float k = bond_k[bond_i]; - float r0 = bond_r0[bond_i]; - - VECTOR dr = Get_Periodic_Displacement(uint_crd[atom_i], uint_crd[atom_j], scaler[0]); - - float r1 = norm3df(dr.x, dr.y, dr.z); - float tempf = r1 - r0; - float temp = k * tempf * tempf; - bond_ene[bond_i] = temp; - } -} - -void BondEnergy(int bond_numbers, int atom_numbers, const unsigned int *uint_crd_f, const float *scaler_f, - const int *atom_a, const int *atom_b, const float *bond_k, const float *bond_r0, float *bond_ene, - cudaStream_t stream) { - size_t thread_per_block = 128; - size_t block_per_grid = ceilf(static_cast(bond_numbers) / 128); - UNSIGNED_INT_VECTOR *uint_crd = - const_cast(reinterpret_cast(uint_crd_f)); - VECTOR *scaler = const_cast(reinterpret_cast(scaler_f)); - - Reset_List<<<(unsigned int)ceilf(static_cast(bond_numbers) / 128), 128, 0, stream>>>(bond_numbers, - bond_ene, 0.); - - BondEnergyCudaKernel<<>>(bond_numbers, uint_crd, scaler, - atom_a, atom_b, bond_k, bond_r0, - bond_ene); - return; -} - -void BondEnergy(int bond_numbers, int atom_numbers, const unsigned int *uint_crd_f, const float *scaler_f, - const int *atom_a, const int *atom_b, const float *bond_k, const float *bond_r0, - float *bond_ene, cudaStream_t stream); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/bond/bond_energy_cuda_gpu_impl.cuh b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/bond/bond_energy_cuda_gpu_impl.cuh deleted file mode 100644 index cd728d86d4c..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/bond/bond_energy_cuda_gpu_impl.cuh +++ /dev/null @@ -1,28 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_BOND_BOND_ENERGY_CUDA_GPU_IMPL_H_ -#define MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_BOND_BOND_ENERGY_CUDA_GPU_IMPL_H_ - -#include -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_device_info.h" - -CUDA_LIB_EXPORT void BondEnergy(int bond_numbers, int atom_numbers, const unsigned int *uint_crd_f, - const float *scaler_f, const int *atom_a, const int *atom_b, const float *bond_k, - const float *bond_r0, float *bond_ene, cudaStream_t stream); - -#endif // MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_BOND_ENERGY_CUDA_GPU_IMPL_H_ diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/bond/bond_force_cuda_gpu_impl.cu b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/bond/bond_force_cuda_gpu_impl.cu deleted file mode 100644 index d3ae9f8f47b..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/bond/bond_force_cuda_gpu_impl.cu +++ /dev/null @@ -1,63 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "plugin/device/gpu/kernel/cuda_impl/sponge/bond/bond_force_cuda_gpu_impl.cuh" -#include "plugin/device/gpu/kernel/cuda_impl/sponge/common_sponge.cuh" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/util.cuh" - -__global__ void BondForceCudaKernel(int bond_numbers, const UNSIGNED_INT_VECTOR *uint_crd, const VECTOR *scaler, - const int *atom_a, const int *atom_b, const float *bond_k, const float *bond_r0, - VECTOR *frc) { - int bond_i = blockDim.x * blockIdx.x + threadIdx.x; - if (bond_i < bond_numbers) { - int atom_i = atom_a[bond_i]; - int atom_j = atom_b[bond_i]; - - float k = bond_k[bond_i]; - float r0 = bond_r0[bond_i]; - VECTOR dr = Get_Periodic_Displacement(uint_crd[atom_i], uint_crd[atom_j], scaler[0]); - float r_1 = rnorm3df(dr.x, dr.y, dr.z); - float tempf = 1.0 - r0 * r_1; - - VECTOR f = 2 * tempf * k * dr; - atomicAdd(&frc[atom_i].x, -f.x); - atomicAdd(&frc[atom_i].y, -f.y); - atomicAdd(&frc[atom_i].z, -f.z); - - atomicAdd(&frc[atom_j].x, f.x); - atomicAdd(&frc[atom_j].y, f.y); - atomicAdd(&frc[atom_j].z, f.z); - } -} - -void BondForce(int bond_numbers, int atom_numbers, const unsigned int *uint_crd_f, const float *scaler_f, - const int *atom_a, const int *atom_b, const float *bond_k, const float *bond_r0, float *frc_f, - cudaStream_t stream) { - Reset_List<<(3. * atom_numbers) / 128), 128, 0, stream>>>(3 * atom_numbers, frc_f, 0.); - size_t thread_per_block = 128; - size_t block_per_grid = ceilf(static_cast(bond_numbers) / 128); - UNSIGNED_INT_VECTOR *uint_crd = - const_cast(reinterpret_cast(uint_crd_f)); - VECTOR *scaler = const_cast(reinterpret_cast(scaler_f)); - VECTOR *frc = const_cast(reinterpret_cast(frc_f)); - BondForceCudaKernel<<>>(bond_numbers, uint_crd, scaler, atom_a, atom_b, - bond_k, bond_r0, frc); - return; -} - -void BondForce(int bond_numbers, int atom_numbers, const unsigned int *uint_crd_f, const float *scaler_f, - const int *atom_a, const int *atom_b, const float *bond_k, const float *bond_r0, - float *frc_f, cudaStream_t stream); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/bond/bond_force_cuda_gpu_impl.cuh b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/bond/bond_force_cuda_gpu_impl.cuh deleted file mode 100644 index 5e9899a8cb9..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/bond/bond_force_cuda_gpu_impl.cuh +++ /dev/null @@ -1,27 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_BOND_BOND_FORCE_CUDA_GPU_IMPL_H_ -#define MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_BOND_BOND_FORCE_CUDA_GPU_IMPL_H_ - -#include -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_device_info.h" - -CUDA_LIB_EXPORT void BondForce(int bond_numbers, int atom_numbers, const unsigned int *uint_crd_f, - const float *scaler_f, const int *atom_a, const int *atom_b, const float *bond_k, - const float *bond_r0, float *frc_f, cudaStream_t stream); -#endif // MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_BOND_FORCE_CUDA_GPU_IMPL_H_ diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/bond/bond_force_with_atom_energy_and_virial_impl.cu b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/bond/bond_force_with_atom_energy_and_virial_impl.cu deleted file mode 100644 index fab62a62d90..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/bond/bond_force_with_atom_energy_and_virial_impl.cu +++ /dev/null @@ -1,75 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "plugin/device/gpu/kernel/cuda_impl/sponge/bond/bond_force_with_atom_energy_and_virial_impl.cuh" -#include "plugin/device/gpu/kernel/cuda_impl/sponge/common_sponge.cuh" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/util.cuh" - -__global__ void BondForceWithAtomEnergyAndVirialKernel(const int bond_numbers, const UNSIGNED_INT_VECTOR *uint_crd, - const VECTOR *scaler, const int *atom_a, const int *atom_b, - const float *bond_k, const float *bond_r0, VECTOR *frc, - float *atom_energy, float *atom_virial) { - int bond_i = blockDim.x * blockIdx.x + threadIdx.x; - if (bond_i < bond_numbers) { - int atom_i = atom_a[bond_i]; - int atom_j = atom_b[bond_i]; - - float k = bond_k[bond_i]; - float r0 = bond_r0[bond_i]; - - VECTOR dr = Get_Periodic_Displacement(uint_crd[atom_i], uint_crd[atom_j], scaler[0]); - float abs_r = norm3df(dr.x, dr.y, dr.z); - float r_1 = 1. / abs_r; - - float tempf2 = abs_r - r0; - float tempf = 2 * tempf2 * k; - VECTOR f = tempf * r_1 * dr; - - atomicAdd(&frc[atom_i].x, -f.x); - atomicAdd(&frc[atom_i].y, -f.y); - atomicAdd(&frc[atom_i].z, -f.z); - - atomicAdd(&frc[atom_j].x, f.x); - atomicAdd(&frc[atom_j].y, f.y); - atomicAdd(&frc[atom_j].z, f.z); - - atomicAdd(&atom_virial[atom_i], -tempf * abs_r); - atomicAdd(&atom_energy[atom_i], k * tempf2 * tempf2); - } -} - -void BondForceWithAtomEnergyAndVirial(int bond_numbers, int atom_numbers, const unsigned int *uint_crd_f, - const float *scaler_f, const int *atom_a, const int *atom_b, const float *bond_k, - const float *bond_r0, float *frc_f, float *atom_energy, float *atom_v, - cudaStream_t stream) { - Reset_List<<(3. * atom_numbers) / 128), 128, 0, stream>>>(3 * atom_numbers, frc_f, 0.); - Reset_List<<(atom_numbers) / 128), 128, 0, stream>>>(atom_numbers, atom_v, 0.); - Reset_List<<(atom_numbers) / 128), 128, 0, stream>>>(atom_numbers, atom_energy, 0.); - size_t thread_per_block = 128; - size_t block_per_grid = ceilf(static_cast(bond_numbers) / 128); - UNSIGNED_INT_VECTOR *uint_crd = - const_cast(reinterpret_cast(uint_crd_f)); - VECTOR *frc = const_cast(reinterpret_cast(frc_f)); - VECTOR *scaler = const_cast(reinterpret_cast(scaler_f)); - BondForceWithAtomEnergyAndVirialKernel<<>>( - bond_numbers, uint_crd, scaler, atom_a, atom_b, bond_k, bond_r0, frc, atom_energy, atom_v); - return; -} - -void BondForceWithAtomEnergyAndVirial(int bond_numbers, int atom_numbers, const unsigned int *uint_crd_f, - const float *scaler_f, const int *atom_a, const int *atom_b, const float *bond_k, - const float *bond_r0, float *frc_f, float *atom_energy, float *atom_v, - cudaStream_t stream); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/bond/bond_force_with_atom_energy_and_virial_impl.cuh b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/bond/bond_force_with_atom_energy_and_virial_impl.cuh deleted file mode 100644 index fa5f0408729..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/bond/bond_force_with_atom_energy_and_virial_impl.cuh +++ /dev/null @@ -1,29 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_BOND_BOND_FORCE_WITH_ATOM_VIRIAL_IMPL_H_ -#define MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_BOND_BOND_FORCE_WITH_ATOM_VIRIAL_IMPL_H_ - -#include -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_device_info.h" - -CUDA_LIB_EXPORT void BondForceWithAtomEnergyAndVirial(int bond_numbers, int atom_numbers, - const unsigned int *uint_crd_f, const float *scaler_f, - const int *atom_a, const int *atom_b, const float *bond_k, - const float *bond_r0, float *frc_f, float *atom_energy, - float *atom_v, cudaStream_t stream); -#endif diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/bond/bond_force_with_atom_energy_impl.cu b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/bond/bond_force_with_atom_energy_impl.cu deleted file mode 100644 index 11064927158..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/bond/bond_force_with_atom_energy_impl.cu +++ /dev/null @@ -1,69 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "plugin/device/gpu/kernel/cuda_impl/sponge/bond/bond_force_with_atom_energy_impl.cuh" -#include "plugin/device/gpu/kernel/cuda_impl/sponge/common_sponge.cuh" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/util.cuh" - -__global__ void BondForceWithAtomEnergyKernel(int bond_numbers, const UNSIGNED_INT_VECTOR *uint_crd, - const VECTOR *scaler, const int *atom_a, const int *atom_b, - const float *bond_k, const float *bond_r0, VECTOR *frc, - float *atom_energy) { - int bond_i = blockDim.x * blockIdx.x + threadIdx.x; - if (bond_i < bond_numbers) { - int atom_i = atom_a[bond_i]; - int atom_j = atom_b[bond_i]; - - float k = bond_k[bond_i]; - float r0 = bond_r0[bond_i]; - - VECTOR dr = Get_Periodic_Displacement(uint_crd[atom_i], uint_crd[atom_j], scaler[0]); - - float abs_r = norm3df(dr.x, dr.y, dr.z); - float r_1 = 1. / abs_r; - float tempf = abs_r - r0; - VECTOR f = 2 * tempf * r_1 * k * dr; - - atomicAdd(&frc[atom_i].x, -f.x); - atomicAdd(&frc[atom_i].y, -f.y); - atomicAdd(&frc[atom_i].z, -f.z); - - atomicAdd(&frc[atom_j].x, f.x); - atomicAdd(&frc[atom_j].y, f.y); - atomicAdd(&frc[atom_j].z, f.z); - - atomicAdd(&atom_energy[atom_i], k * tempf * tempf); - } -} - -void BondForceWithAtomEnergy(int bond_numbers, int atom_numbers, const int *uint_crd_f, const float *scaler_f, - const int *atom_a, const int *atom_b, const float *bond_k, const float *bond_r0, - float *frc_f, float *atom_e, cudaStream_t stream) { - Reset_List<<(3. * atom_numbers) / 128), 128, 0, stream>>>(3 * atom_numbers, frc_f, 0.); - Reset_List<<(atom_numbers) / 128), 128, 0, stream>>>(atom_numbers, atom_e, 0.); - size_t thread_per_block = 128; - size_t block_per_grid = ceilf(static_cast(bond_numbers) / 128); - UNSIGNED_INT_VECTOR *uint_crd = - const_cast(reinterpret_cast(uint_crd_f)); - VECTOR *frc = const_cast(reinterpret_cast(frc_f)); - VECTOR *scaler = const_cast(reinterpret_cast(scaler_f)); - BondForceWithAtomEnergyKernel<<>>(bond_numbers, uint_crd, scaler, atom_a, - atom_b, bond_k, bond_r0, frc, atom_e); - return; -} -void BondForceWithAtomEnergy(int bond_numbers, int atom_numbers, const int *uint_crd_f, const float *scaler_f, - const int *atom_a, const int *atom_b, const float *bond_k, const float *bond_r0, - float *frc_f, float *atom_e, cudaStream_t stream); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/bond/bond_force_with_atom_energy_impl.cuh b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/bond/bond_force_with_atom_energy_impl.cuh deleted file mode 100644 index 4841de06336..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/bond/bond_force_with_atom_energy_impl.cuh +++ /dev/null @@ -1,28 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_BOND_BOND_FORCE_WITH_ATOM_ENERGY_IMPL_H_ -#define MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_BOND_BOND_FORCE_WITH_ATOM_ENERGY_IMPL_H_ - -#include -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_device_info.h" - -CUDA_LIB_EXPORT void BondForceWithAtomEnergy(int bond_numbers, int atom_numbers, const int *uint_crd_f, - const float *scaler_f, const int *atom_a, const int *atom_b, - const float *bond_k, const float *bond_r0, float *frc_f, float *atom_e, - cudaStream_t stream); -#endif diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/bond/bond_force_with_atom_virial_impl.cu b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/bond/bond_force_with_atom_virial_impl.cu deleted file mode 100644 index 78dd7f56754..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/bond/bond_force_with_atom_virial_impl.cu +++ /dev/null @@ -1,69 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "plugin/device/gpu/kernel/cuda_impl/sponge/bond/bond_force_with_atom_virial_impl.cuh" -#include "plugin/device/gpu/kernel/cuda_impl/sponge/common_sponge.cuh" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/util.cuh" - -__global__ void BondForceWithAtomVirialKernel(int bond_numbers, const UNSIGNED_INT_VECTOR *uint_crd, - const VECTOR *scaler, const int *atom_a, const int *atom_b, - const float *bond_k, const float *bond_r0, VECTOR *frc, - float *atom_virial) { - int bond_i = blockDim.x * blockIdx.x + threadIdx.x; - if (bond_i < bond_numbers) { - int atom_i = atom_a[bond_i]; - int atom_j = atom_b[bond_i]; - - float k = bond_k[bond_i]; - float r0 = bond_r0[bond_i]; - - VECTOR dr = Get_Periodic_Displacement(uint_crd[atom_i], uint_crd[atom_j], scaler[0]); - - float abs_r = norm3df(dr.x, dr.y, dr.z); - float r_1 = 1. / abs_r; - float tempf = (abs_r - r0) * k; - VECTOR f = 2 * tempf * r_1 * dr; - - atomicAdd(&frc[atom_i].x, -f.x); - atomicAdd(&frc[atom_i].y, -f.y); - atomicAdd(&frc[atom_i].z, -f.z); - - atomicAdd(&frc[atom_j].x, f.x); - atomicAdd(&frc[atom_j].y, f.y); - atomicAdd(&frc[atom_j].z, f.z); - - atomicAdd(&atom_virial[atom_i], abs_r * tempf); - } -} - -void BondForceWithAtomVirial(int bond_numbers, int atom_numbers, const int *uint_crd_f, const float *scaler_f, - const int *atom_a, const int *atom_b, const float *bond_k, const float *bond_r0, - float *frc_f, float *atom_v, cudaStream_t stream) { - Reset_List<<(3. * atom_numbers) / 128), 128, 0, stream>>>(3 * atom_numbers, frc_f, 0.); - Reset_List<<(atom_numbers) / 128), 128, 0, stream>>>(atom_numbers, atom_v, 0.); - size_t thread_per_block = 128; - size_t block_per_grid = ceilf(static_cast(bond_numbers) / 128); - UNSIGNED_INT_VECTOR *uint_crd = - const_cast(reinterpret_cast(uint_crd_f)); - VECTOR *frc = const_cast(reinterpret_cast(frc_f)); - VECTOR *scaler = const_cast(reinterpret_cast(scaler_f)); - BondForceWithAtomVirialKernel<<>>(bond_numbers, uint_crd, scaler, atom_a, - atom_b, bond_k, bond_r0, frc, atom_v); - return; -} -void BondForceWithAtomVirial(int bond_numbers, int atom_numbers, const int *uint_crd_f, const float *scaler_f, - const int *atom_a, const int *atom_b, const float *bond_k, const float *bond_r0, - float *frc_f, float *atom_v, cudaStream_t stream); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/bond/bond_force_with_atom_virial_impl.cuh b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/bond/bond_force_with_atom_virial_impl.cuh deleted file mode 100644 index 0ebe0dbb4a6..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/bond/bond_force_with_atom_virial_impl.cuh +++ /dev/null @@ -1,28 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_BOND_BOND_FORCE_WITH_ATOM_VIRIAL_IMPL_H_ -#define MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_BOND_BOND_FORCE_WITH_ATOM_VIRIAL_IMPL_H_ - -#include -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_device_info.h" - -CUDA_LIB_EXPORT void BondForceWithAtomVirial(int bond_numbers, int atom_numbers, const int *uint_crd_f, - const float *scaler_f, const int *atom_a, const int *atom_b, - const float *bond_k, const float *bond_r0, float *frc_f, float *atom_v, - cudaStream_t stream); -#endif diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/common/atomcrdtocv_impl.cu b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/common/atomcrdtocv_impl.cu deleted file mode 100644 index 8c9abb85541..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/common/atomcrdtocv_impl.cu +++ /dev/null @@ -1,123 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "plugin/device/gpu/kernel/cuda_impl/sponge/common/atomcrdtocv_impl.cuh" -#include "plugin/device/gpu/kernel/cuda_impl/sponge/common_sponge.cuh" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/util.cuh" - -__device__ __host__ float fc(float Rij) { - const float PI = 3.141592654; - const float Rc = 1000.0; - return 0.5 * cosf(PI / Rc * Rij) + 0.5; -} - -__global__ void Record_Box_Map_Times(int atom_numbers, const float *crd, const float *old_crd, float *box, - int *box_map_times) { - float half_box[3] = {0.5F * box[0], 0.5F * box[1], 0.5F * box[2]}; - int i = blockDim.x * blockIdx.x + threadIdx.x; - if (i < atom_numbers) { - if (crd[3 * i + 0] - old_crd[3 * i + 0] > half_box[0]) { - box_map_times[3 * i + 0] = box_map_times[3 * i + 0] - 1; - } else if (crd[3 * i + 0] - old_crd[3 * i + 0] < -half_box[0]) { - box_map_times[3 * i + 0] = box_map_times[3 * i + 0] + 1; - } - if (crd[3 * i + 1] - old_crd[3 * i + 1] > half_box[1]) { - box_map_times[3 * i + 1] = box_map_times[3 * i + 1] - 1; - } else if (crd[3 * i + 1] - old_crd[3 * i + 1] < -half_box[1]) { - box_map_times[3 * i + 1] = box_map_times[3 * i + 1] + 1; - } - if (crd[3 * i + 2] - old_crd[3 * i + 2] > half_box[2]) { - box_map_times[3 * i + 2] = box_map_times[3 * i + 2] - 1; - } else if (crd[3 * i + 2] - old_crd[3 * i + 2] < -half_box[2]) { - box_map_times[3 * i + 2] = box_map_times[3 * i + 2] + 1; - } - } -} - -__global__ void gen_nowarp_crd(int atom_numbers, const float *crd, float *box, int *box_map_times, float *nowarp_crd) { - int i = blockDim.x * blockIdx.x + threadIdx.x; - if (i < atom_numbers) { - nowarp_crd[3 * i + 0] = static_cast(box_map_times[3 * i + 0]) * box[0] + crd[3 * i + 0]; - nowarp_crd[3 * i + 1] = static_cast(box_map_times[3 * i + 1]) * box[1] + crd[3 * i + 1]; - nowarp_crd[3 * i + 2] = static_cast(box_map_times[3 * i + 2]) * box[2] + crd[3 * i + 2]; - } -} - -__global__ void G_Radial(const int start_serial, const int end_serial, const float *crd, float *g_radial) { - const float Rs = 0.5, Eta = 0.5; - int i = blockDim.x * blockIdx.x + threadIdx.x; - if (i >= start_serial && i < end_serial) { - float rij; - float g_radial_lin = 0.; - for (int j = start_serial; j < end_serial; j = j + 1) { - if (j != i) { - // rij = sqrtf((crd[3*i+0] - crd[j]) * (crd[i] - crd[j])); - rij = sqrtf(normfloat(crd, crd, i, j)); - g_radial_lin = g_radial_lin + expf(-Eta * (rij - Rs) * (rij - Rs)) * fc(rij); - } else { - continue; - } - } - g_radial[i] = g_radial_lin; - } -} - -__global__ void G_Angular(const int start_serial, const int end_serial, const float *crd, float *g_angular) { - const float Rs = 0.5, Thetas = 3.14, Eta = 0.5, Zeta = 2.0; - int i = blockDim.x * blockIdx.x + threadIdx.x; - if (i >= start_serial && i < end_serial) { - float rij, rik, rjk, theta_jik; - float g_angular_lin = 0.; - for (int j = start_serial; j < end_serial; j = j + 1) { - if (j != i) { - rij = sqrtf(normfloat(crd, crd, i, j)); - for (int k = j + 1; k < end_serial; k = k + 1) { - if (k != i) { - rik = sqrtf(normfloat(crd, crd, i, k)); - rjk = sqrtf(normfloat(crd, crd, j, k)); - theta_jik = - acosf(fmaxf(fminf((rij * rij + rik * rik - rjk * rjk) / (2. * rij * rik), 0.999999), -0.999999)); - g_angular_lin = g_angular_lin + powf(1. + cosf(theta_jik - Thetas), Zeta) * - expf(-Eta * powf(0.5 * (rij + rik) - Rs, 2.)) * fc(rij) * fc(rik); - } else { - continue; - } - } - } else { - continue; - } - } - g_angular[i] = powf(2., 1. - Zeta) * g_angular_lin; - } -} - -void AtomCrdToCV(int atom_numbers, int start_serial, int end_serial, int number, const float *crd_f, - const float *old_crd, float *nowarp_crd, int *box_map_times, float *box, float *g_radial, - float *g_angular, cudaStream_t stream) { - Reset_List<<(3. * atom_numbers) / 128), 128, 0, stream>>>(3 * atom_numbers, box_map_times, - 0); - Record_Box_Map_Times<<(3. * atom_numbers) / 128), 128, 0, stream>>>( - atom_numbers, crd_f, old_crd, box, box_map_times); - gen_nowarp_crd<<(3. * atom_numbers) / 128), 128, 0, stream>>>(atom_numbers, crd_f, box, - box_map_times, nowarp_crd); - G_Radial<<<1, number, 0, stream>>>(start_serial, end_serial, nowarp_crd, g_radial); - G_Angular<<<1, number, 0, stream>>>(start_serial, end_serial, nowarp_crd, g_angular); - return; -} - -void AtomCrdToCV(int atom_numbers, int start_serial, int end_serial, int number, const float *crd_f, - const float *old_crd, float *nowarp_crd, int *box_map_times, float *box, float *g_radial, - float *g_angular, cudaStream_t stream); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/common/atomcrdtocv_impl.cuh b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/common/atomcrdtocv_impl.cuh deleted file mode 100644 index 85338ade301..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/common/atomcrdtocv_impl.cuh +++ /dev/null @@ -1,27 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_COMMON_ATOMCRDTOCV_IMPL_H_ -#define MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_COMMON_ATOMCRDTOCV_IMPL_H_ - -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_device_info.h" - -CUDA_LIB_EXPORT void AtomCrdToCV(int atom_numbers, int start_serial, int end_serial, int number, const float *crd_f, - const float *old_crd, float *nowarp_crd, int *box_map_times, float *box, - float *g_radial, float *g_angular, cudaStream_t stream); - -#endif // MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_COMMON_ATOMCRDTOCV_IMPL_H_ diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/common/crd_to_uint_crd_impl.cu b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/common/crd_to_uint_crd_impl.cu deleted file mode 100644 index 83dd52f5420..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/common/crd_to_uint_crd_impl.cu +++ /dev/null @@ -1,51 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "plugin/device/gpu/kernel/cuda_impl/sponge/common_sponge.cuh" -#include "plugin/device/gpu/kernel/cuda_impl/sponge/common/crd_to_uint_crd_impl.cuh" - -__global__ void Crd_To_Uint_Crd(const int atom_numbers, const VECTOR *scale_factor, const VECTOR *crd, - UNSIGNED_INT_VECTOR *uint_crd) { - int atom_i = blockDim.x * blockIdx.x + threadIdx.x; - if (atom_i < atom_numbers) { - uint_crd[atom_i].uint_x = crd[atom_i].x * scale_factor[0].x; - uint_crd[atom_i].uint_y = crd[atom_i].y * scale_factor[0].y; - uint_crd[atom_i].uint_z = crd[atom_i].z * scale_factor[0].z; - /*uint_crd[atom_i].uint_x = 2 * uint_crd[atom_i].uint_x; - uint_crd[atom_i].uint_y = 2 * uint_crd[atom_i].uint_y; - uint_crd[atom_i].uint_z = 2 * uint_crd[atom_i].uint_z;*/ - uint_crd[atom_i].uint_x = uint_crd[atom_i].uint_x << 1; - uint_crd[atom_i].uint_y = uint_crd[atom_i].uint_y << 1; - uint_crd[atom_i].uint_z = uint_crd[atom_i].uint_z << 1; - } -} - -void CrdToUintCrd(const int atom_numbers, const float *crd_to_uint_crd_cof_f, const float *crd_f, - unsigned int *uint_crd_f, cudaStream_t stream) { - VECTOR *crd = const_cast(reinterpret_cast(crd_f)); - VECTOR *crd_to_uint_crd_cof = const_cast(reinterpret_cast(crd_to_uint_crd_cof_f)); - - UNSIGNED_INT_VECTOR *uint_crd = - const_cast(reinterpret_cast(uint_crd_f)); - - Crd_To_Uint_Crd<<(atom_numbers) / 128.0), 128, 0, stream>>>( - atom_numbers, crd_to_uint_crd_cof, crd, uint_crd); - - return; -} - -void CrdToUintCrd(const int atom_numbers, const float *crd_to_uint_crd_cof_f, const float *crd_f, - unsigned int *uint_crd_f, cudaStream_t stream); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/common/crd_to_uint_crd_impl.cuh b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/common/crd_to_uint_crd_impl.cuh deleted file mode 100644 index 99f13cdeac7..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/common/crd_to_uint_crd_impl.cuh +++ /dev/null @@ -1,27 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_CRD_TO_UINT_CRD_IMPL_H_ -#define MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_CRD_TO_UINT_CRD_IMPL_H_ - -#include -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_device_info.h" - -CUDA_LIB_EXPORT void CrdToUintCrd(const int atom_numbers, const float *crd_to_uint_crd_cof_f, const float *crd_f, - unsigned int *uint_crd_f, cudaStream_t stream); - -#endif // MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_CRD_TO_UINT_CRD_IMPL_H_ diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/common/crd_to_uint_crd_quarter_impl.cu b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/common/crd_to_uint_crd_quarter_impl.cu deleted file mode 100644 index 9f7aa284894..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/common/crd_to_uint_crd_quarter_impl.cu +++ /dev/null @@ -1,54 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -#include "plugin/device/gpu/kernel/cuda_impl/sponge/common/crd_to_uint_crd_quarter_impl.cuh" -#include "plugin/device/gpu/kernel/cuda_impl/sponge/common_sponge.cuh" - -__global__ void Crd_To_Uint_Crd_Quarter(const int atom_numbers, const VECTOR *scale_factor, const VECTOR *crd, - UNSIGNED_INT_VECTOR *uint_crd) { - int atom_i = blockDim.x * blockIdx.x + threadIdx.x; - if (atom_i < atom_numbers) { - INT_VECTOR tempi; - VECTOR temp = crd[atom_i]; - temp.x *= scale_factor[0].x; - temp.y *= scale_factor[0].y; - temp.z *= scale_factor[0].z; - - tempi.int_x = temp.x; - tempi.int_y = temp.y; - tempi.int_z = temp.z; - - uint_crd[atom_i].uint_x = (tempi.int_x << 2); - uint_crd[atom_i].uint_y = (tempi.int_y << 2); - uint_crd[atom_i].uint_z = (tempi.int_z << 2); - } -} - -void CrdToUintCrdQuarter(const int atom_numbers, const float *crd_to_uint_crd_cof_f, const float *crd_f, - unsigned int *uint_crd_f, cudaStream_t stream) { - VECTOR *crd = const_cast(reinterpret_cast(crd_f)); - VECTOR *crd_to_uint_crd_cof = const_cast(reinterpret_cast(crd_to_uint_crd_cof_f)); - - UNSIGNED_INT_VECTOR *uint_crd = - const_cast(reinterpret_cast(uint_crd_f)); - - Crd_To_Uint_Crd_Quarter<<(atom_numbers) / 128.0), 128, 0, stream>>>( - atom_numbers, crd_to_uint_crd_cof, crd, uint_crd); - - return; -} - -void CrdToUintCrdQuarter(const int atom_numbers, const float *crd_to_uint_crd_cof_f, const float *crd_f, - unsigned int *uint_crd_f, cudaStream_t stream); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/common/crd_to_uint_crd_quarter_impl.cuh b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/common/crd_to_uint_crd_quarter_impl.cuh deleted file mode 100644 index e2360b42fc8..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/common/crd_to_uint_crd_quarter_impl.cuh +++ /dev/null @@ -1,27 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_CRD_TO_UINT_CRD_QUARTER_IMPL_H_ -#define MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_CRD_TO_UINT_CRD_QUARTER_IMPL_H_ - -#include -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_device_info.h" - -CUDA_LIB_EXPORT void CrdToUintCrdQuarter(const int atom_numbers, const float *crd_to_uint_crd_cof_f, const float *crd_f, - unsigned int *uint_crd_f, cudaStream_t stream); - -#endif // MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_CRD_TO_UINT_CRD_QUARTER_IMPL_H_ diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/common/get_center_of_mass_impl.cu b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/common/get_center_of_mass_impl.cu deleted file mode 100644 index b581bac9d1f..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/common/get_center_of_mass_impl.cu +++ /dev/null @@ -1,44 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "plugin/device/gpu/kernel/cuda_impl/sponge/common_sponge.cuh" -#include "plugin/device/gpu/kernel/cuda_impl/sponge/common/get_center_of_mass_impl.cuh" - -__global__ void Get_Center_Of_Mass(int residue_numbers, int *start, int *end, VECTOR *crd, float *atom_mass, - float *residue_mass_inverse, VECTOR *center_of_mass) { - for (int residue_i = blockDim.x * blockIdx.x + threadIdx.x; residue_i < residue_numbers; - residue_i += gridDim.x * blockDim.x) { - VECTOR com_lin = {0.0f, 0.0f, 0.0f}; - for (int atom_i = start[residue_i]; atom_i < end[residue_i]; atom_i += 1) { - com_lin = com_lin + atom_mass[atom_i] * crd[atom_i]; - } - center_of_mass[residue_i] = residue_mass_inverse[residue_i] * com_lin; - } -} - -void GetCenterOfMass(int residue_numbers, int *start, int *end, float *crd_f, float *atom_mass, - float *residue_mass_inverse, float *center_of_mass_f, cudaStream_t stream) { - Reset_List<<(3. * residue_numbers) / 128), 128, 0, stream>>>(3 * residue_numbers, - center_of_mass_f, 0.); - VECTOR *crd = reinterpret_cast(crd_f); - VECTOR *center_of_mass = reinterpret_cast(center_of_mass_f); - Get_Center_Of_Mass<<<20, 32, 0, stream>>>(residue_numbers, start, end, crd, atom_mass, residue_mass_inverse, - center_of_mass); - return; -} - -void GetCenterOfMass(int residue_numbers, int *start, int *end, float *crd_f, float *atom_mass, - float *residue_mass_inverse, float *center_of_mass_f, cudaStream_t stream); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/common/get_center_of_mass_impl.cuh b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/common/get_center_of_mass_impl.cuh deleted file mode 100644 index 75bd130e5ac..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/common/get_center_of_mass_impl.cuh +++ /dev/null @@ -1,27 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_COMMON_GETCENTEROFMASS_IMPL_H_ -#define MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_COMMON_GETCENTEROFMASS_IMPL_H_ - -#include -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_device_info.h" - -CUDA_LIB_EXPORT void GetCenterOfMass(int residue_numbers, int *start, int *end, float *crd_f, float *atom_mass, - float *residue_mass_inverse, float *center_of_mass_f, cudaStream_t stream); - -#endif // MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_COMMON_GETCENTER_IMPL_H_ diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/common/getcenter_impl.cu b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/common/getcenter_impl.cu deleted file mode 100644 index 9e93f160a03..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/common/getcenter_impl.cu +++ /dev/null @@ -1,43 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "plugin/device/gpu/kernel/cuda_impl/sponge/common_sponge.cuh" -#include "plugin/device/gpu/kernel/cuda_impl/sponge/common/getcenter_impl.cuh" - -__global__ void GetCenterOfGeometryKernel(const int center_numbers, float center_numbers_inverse, - const int *center_atoms, const VECTOR *crd, VECTOR *center_of_geometry) { - int i = blockDim.x * blockIdx.x + threadIdx.x; - if (i < center_numbers) { - int atom_i = center_atoms[i]; - VECTOR temp = center_numbers_inverse * crd[atom_i]; - atomicAdd(¢er_of_geometry[0].x, temp.x); - atomicAdd(¢er_of_geometry[0].y, temp.y); - atomicAdd(¢er_of_geometry[0].z, temp.z); - } -} - -void GetCenterOfGeometry(const int center_numbers, float center_numbers_inverse, const int *center_atoms, - const float *crd_f, float *center_of_geometry_f, cudaStream_t stream) { - VECTOR *crd = const_cast(reinterpret_cast(crd_f)); - VECTOR *center_of_geometry = const_cast(reinterpret_cast(center_of_geometry_f)); - GetCenterOfGeometryKernel<<(center_numbers) / 32), 32, 0, stream>>>( - center_numbers, center_numbers_inverse, center_atoms, crd, center_of_geometry); - - return; -} - -void GetCenterOfGeometry(const int center_numbers, float center_numbers_inverse, const int *center_atoms, float *crd_f, - float *center_of_geometry_f, cudaStream_t stream); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/common/getcenter_impl.cuh b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/common/getcenter_impl.cuh deleted file mode 100644 index df366b2fede..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/common/getcenter_impl.cuh +++ /dev/null @@ -1,28 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_COMMON_GETCENTER_IMPL_H_ -#define MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_COMMON_GETCENTER_IMPL_H_ - -#include -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_device_info.h" - -CUDA_LIB_EXPORT void GetCenterOfGeometry(const int center_numbers, float center_numbers_inverse, - const int *center_atoms, const float *crd_f, float *center_of_geometry_f, - cudaStream_t stream); - -#endif // MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_COMMON_GETCENTER_IMPL_H_ diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/common/map_center_of_mass_impl.cu b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/common/map_center_of_mass_impl.cu deleted file mode 100644 index 01807f2e34d..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/common/map_center_of_mass_impl.cu +++ /dev/null @@ -1,51 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "plugin/device/gpu/kernel/cuda_impl/sponge/common_sponge.cuh" -#include "plugin/device/gpu/kernel/cuda_impl/sponge/common/map_center_of_mass_impl.cuh" - -__global__ void Map_Center_Of_Mass(int residue_numbers, int *start, int *end, - float *scaler, VECTOR *center_of_mass, VECTOR *box_length, VECTOR *no_wrap_crd, VECTOR *crd) { - VECTOR trans_vec; - VECTOR com; - for (int residue_i = blockDim.x*blockIdx.x + threadIdx.x; residue_i < residue_numbers; - residue_i += gridDim.x * blockDim.x) { - com = center_of_mass[residue_i]; - - trans_vec.x = com.x - floorf(com.x / box_length[0].x) * box_length[0].x; - trans_vec.y = com.y - floorf(com.y / box_length[0].y) * box_length[0].y; - trans_vec.z = com.z - floorf(com.z / box_length[0].z) * box_length[0].z; - trans_vec = scaler[0] * trans_vec - com; - - for (int atom_i = start[residue_i] + threadIdx.y; atom_i < end[residue_i]; atom_i += blockDim.y) { - crd[atom_i] = no_wrap_crd[atom_i] + trans_vec; - } - } -} - -void MapCenterOfMass(int residue_numbers, int *start, int *end, float *center_of_mass_f, - float *box_length_f, float *no_wrap_crd_f, float *crd_f, float* scaler, cudaStream_t stream) { - VECTOR *crd = reinterpret_cast(crd_f); - VECTOR *no_wrap_crd = reinterpret_cast(no_wrap_crd_f); - VECTOR *box_length = reinterpret_cast(box_length_f); - VECTOR *center_of_mass = reinterpret_cast(center_of_mass_f); - Map_Center_Of_Mass<<<20, { 32, 4 } , 0, stream>>>(residue_numbers, start, end, scaler, center_of_mass, box_length, - no_wrap_crd, crd); - return; -} - -void MapCenterOfMass(int residue_numbers, int *start, int *end, float *center_of_mass_f, - float *box_length_f, float *no_wrap_crd_f, float *crd_f, float* scaler, cudaStream_t stream); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/common/map_center_of_mass_impl.cuh b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/common/map_center_of_mass_impl.cuh deleted file mode 100644 index 151ba37eb15..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/common/map_center_of_mass_impl.cuh +++ /dev/null @@ -1,28 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_COMMON_MAPCENTEROFMASS_IMPL_H_ -#define MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_COMMON_MAPCENTEROFMASS_IMPL_H_ - -#include -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_device_info.h" - -CUDA_LIB_EXPORT void MapCenterOfMass(int residue_numbers, int *start, int *end, float *center_of_mass_f, - float *box_length_f, float *no_wrap_crd_f, float *crd_f, float *scaler, - cudaStream_t stream); - -#endif // MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_COMMON_MAPCENTEROFMASS_IMPL_H_ diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/common/mdtemperature_impl.cu b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/common/mdtemperature_impl.cu deleted file mode 100644 index 39ee4f6761f..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/common/mdtemperature_impl.cu +++ /dev/null @@ -1,49 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -#include "plugin/device/gpu/kernel/cuda_impl/sponge/common_sponge.cuh" -#include "plugin/device/gpu/kernel/cuda_impl/sponge/common/mdtemperature_impl.cuh" - -__global__ void MDTemperatureKernel(const int residue_numbers, const int *start, const int *end, const VECTOR *atom_vel, - const float *atom_mass, float *ek) { - int residue_i = blockDim.x * blockIdx.x + threadIdx.x; - if (residue_i < residue_numbers) { - VECTOR momentum = {0., 0., 0.}; - float res_mass = 0.; - int s = start[residue_i]; - int e = end[residue_i]; - float mass_lin; - for (int atom_i = s; atom_i < e; atom_i = atom_i + 1) { - mass_lin = atom_mass[atom_i]; - - momentum.x = momentum.x + mass_lin * atom_vel[atom_i].x; - momentum.y = momentum.y + mass_lin * atom_vel[atom_i].y; - momentum.z = momentum.z + mass_lin * atom_vel[atom_i].z; - res_mass = res_mass + mass_lin; - } - ek[residue_i] = 0.5 * (momentum.x * momentum.x + momentum.y * momentum.y + momentum.z * momentum.z) / res_mass * - 2. / 3. / CONSTANT_kB / residue_numbers; - } -} - -void MDTemperature(const int residue_numbers, const int *start, const int *end, const float *atom_vel_f, - const float *atom_mass, float *ek, cudaStream_t stream) { - VECTOR *atom_vel = const_cast(reinterpret_cast(atom_vel_f)); - MDTemperatureKernel<<(residue_numbers) / 32), 32, 0, stream>>>(residue_numbers, start, end, - atom_vel, atom_mass, ek); - return; -} -void MDTemperature(const int residue_numbers, const int *start, const int *end, const float *atom_vel_f, - const float *atom_mass, float *ek, cudaStream_t stream); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/common/mdtemperature_impl.cuh b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/common/mdtemperature_impl.cuh deleted file mode 100644 index b2b08648f3f..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/common/mdtemperature_impl.cuh +++ /dev/null @@ -1,26 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_COMMON_MDTEMPERATURE_IMPL_H_ -#define MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_COMMON_MDTEMPERATURE_IMPL_H_ - -#include -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_device_info.h" - -CUDA_LIB_EXPORT void MDTemperature(const int residue_numbers, const int *start, const int *end, const float *atom_vel_f, - const float *atom_mass, float *ek, cudaStream_t stream); -#endif // MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_COMMON_MDTEMPERATURE_IMPL_H_ diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/common/total_c6_get_impl.cu b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/common/total_c6_get_impl.cu deleted file mode 100644 index efe5c96689d..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/common/total_c6_get_impl.cu +++ /dev/null @@ -1,48 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "plugin/device/gpu/kernel/cuda_impl/sponge/common/total_c6_get_impl.cuh" -#include "plugin/device/gpu/kernel/cuda_impl/sponge/common_sponge.cuh" - -__global__ void Total_C6_Get(int atom_numbers, int *atom_lj_type, float *d_lj_b, float *d_factor) { - int i, j; - float temp_sum = 0; - d_factor[0] = 0; - int x, y; - int itype, jtype, atom_pair_LJ_type; - for (i = blockIdx.x * blockDim.x + threadIdx.x; i < atom_numbers; i += gridDim.x * blockDim.x) { - itype = atom_lj_type[i]; - for (j = blockIdx.y * blockDim.y + threadIdx.y; j < atom_numbers; j += gridDim.y * blockDim.y) { - jtype = atom_lj_type[j]; - y = (jtype - itype); - x = y >> 31; - y = (y ^ x) - x; - x = jtype + itype; - jtype = (x + y) >> 1; - x = (x - y) >> 1; - atom_pair_LJ_type = (jtype * (jtype + 1) >> 1) + x; - temp_sum += d_lj_b[atom_pair_LJ_type]; - } - } - atomicAdd(d_factor, temp_sum); -} - -void total_c6_get(int atom_numbers, int *atom_lj_type, float *d_lj_b, float *d_factor, cudaStream_t stream) { - Total_C6_Get<<<{4, 4}, {32, 32}, 0, stream>>>(atom_numbers, atom_lj_type, d_lj_b, d_factor); - return; -} - -void total_c6_get(int atom_numbers, int *atom_lj_type, float *d_lj_b, float *d_factor, cudaStream_t stream); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/common/total_c6_get_impl.cuh b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/common/total_c6_get_impl.cuh deleted file mode 100644 index 76d582b7ae5..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/common/total_c6_get_impl.cuh +++ /dev/null @@ -1,26 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_COMMON_TOTAL_C6_GET_IMPL_H_ -#define MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_COMMON_TOTAL_C6_GET_IMPL_H_ - -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_device_info.h" - -CUDA_LIB_EXPORT void total_c6_get(int atom_numbers, int *atom_lj_type, float *d_lj_b, float *d_factor, - cudaStream_t stream); - -#endif // MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_COMMON_TOTAL_C6_GET_IMPL_H_ diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/common_sponge.cuh b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/common_sponge.cuh deleted file mode 100644 index 349de212bec..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/common_sponge.cuh +++ /dev/null @@ -1,366 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_COMMON_SPONGE_H_ -#define MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_COMMON_SPONGE_H_ - -#include -#include -#include -#include - -#include - -#include -#include -#include -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" - -#define TWO_DIVIDED_BY_SQRT_PI 1.1283791670218446 -#define CONSTANT_kB 0.00198716 -#define CONSTANT_Pi 3.1415926535897932f -static dim3 thread_LJ(8, 32); - -__constant__ float XRD3D_Ma[4] = {1.0 / 6.0, -0.5, 0.5, -1.0 / 6.0}; -__constant__ float XRD3D_Mb[4] = {0, 0.5, -1, 0.5}; -__constant__ float XRD3D_Mc[4] = {0, 0.5, 0, -0.5}; -__constant__ float XRD3D_Md[4] = {0, 1.0 / 6.0, 4.0 / 6.0, 1.0 / 6.0}; -__constant__ float XRD3D_dMa[4] = {0.5, -1.5, 1.5, -0.5}; -__constant__ float XRD3D_dMb[4] = {0, 1, -2, 1}; -__constant__ float XRD3D_dMc[4] = {0, 0.5, 0, -0.5}; - -struct VECTOR { - float x; - float y; - float z; -}; - -struct INT_VECTOR { - int int_x; - int int_y; - int int_z; -}; - -struct UNSIGNED_INT_VECTOR { - unsigned int uint_x; - unsigned int uint_y; - unsigned int uint_z; -}; - -struct NEIGHBOR_LIST { - int atom_numbers; - int *atom_serial; -}; -struct UINT_VECTOR_LJ_TYPE { - unsigned int uint_x; - unsigned int uint_y; - unsigned int uint_z; - int LJ_type; - float charge; -}; - -struct ATOM_NEAR { - int *atom_serial; -}; - -struct GRID_BUCKET { - int *atom_serial; -}; - -struct GRID_POINTER { - int *grid_serial; -}; - -struct VIRTUAL_TYPE_0 { - float virtual_atom; - float from_1; - float h_double; -}; - -struct VIRTUAL_TYPE_1 { - float virtual_atom; - float from_1; - float from_2; - float a; -}; - -struct VIRTUAL_TYPE_2 { - float virtual_atom; - float from_1; - float from_2; - float from_3; - float a; - float b; -}; - -struct VIRTUAL_TYPE_3 { - float virtual_atom; - float from_1; - float from_2; - float from_3; - float d; - float k; -}; - -struct CONSTRAIN_PAIR { - int atom_i_serial; - int atom_j_serial; - float constant_r; - float constrain_k; -}; - -__device__ __host__ static inline VECTOR operator-(const VECTOR &veca, const VECTOR &vecb) { - VECTOR vec; - vec.x = veca.x - vecb.x; - vec.y = veca.y - vecb.y; - vec.z = veca.z - vecb.z; - return vec; -} - -__device__ __host__ static inline VECTOR Get_Periodic_Displacement(const UNSIGNED_INT_VECTOR uvec_a, - const UNSIGNED_INT_VECTOR uvec_b, - const VECTOR scaler) { - VECTOR dr; - dr.x = (static_cast(uvec_a.uint_x - uvec_b.uint_x)) * scaler.x; - dr.y = (static_cast(uvec_a.uint_y - uvec_b.uint_y)) * scaler.y; - dr.z = (static_cast(uvec_a.uint_z - uvec_b.uint_z)) * scaler.z; - return dr; -} - -__device__ __host__ static inline VECTOR Get_Periodic_Displacement(const UINT_VECTOR_LJ_TYPE uvec_a, - const UINT_VECTOR_LJ_TYPE uvec_b, - const VECTOR scaler) { - VECTOR dr; - dr.x = (static_cast(uvec_a.uint_x - uvec_b.uint_x)) * scaler.x; - dr.y = (static_cast(uvec_a.uint_y - uvec_b.uint_y)) * scaler.y; - dr.z = (static_cast(uvec_a.uint_z - uvec_b.uint_z)) * scaler.z; - return dr; -} - -__device__ __host__ static inline VECTOR Get_Periodic_Displacement(const VECTOR vec_a, const VECTOR vec_b, - const VECTOR box_length) { - VECTOR dr; - dr = vec_a - vec_b; - dr.x = dr.x - floorf(dr.x / box_length.x + 0.5) * box_length.x; - dr.y = dr.y - floorf(dr.y / box_length.y + 0.5) * box_length.y; - dr.z = dr.z - floorf(dr.z / box_length.z + 0.5) * box_length.z; - return dr; -} - -__device__ __host__ static inline VECTOR Get_Periodic_Displacement(const VECTOR vec_a, const VECTOR vec_b, - const VECTOR box_length, - const VECTOR box_length_inverse) { - VECTOR dr; - dr = vec_a - vec_b; - dr.x = dr.x - floorf(dr.x * box_length_inverse.x + 0.5) * box_length.x; - dr.y = dr.y - floorf(dr.y * box_length_inverse.y + 0.5) * box_length.y; - dr.z = dr.z - floorf(dr.z * box_length_inverse.z + 0.5) * box_length.z; - return dr; -} - -__device__ __host__ static inline VECTOR operator+(const VECTOR &veca, const VECTOR &vecb) { - VECTOR vec; - vec.x = veca.x + vecb.x; - vec.y = veca.y + vecb.y; - vec.z = veca.z + vecb.z; - return vec; -} - -__device__ __host__ static inline float operator*(const VECTOR &veca, const VECTOR &vecb) { - return veca.x * vecb.x + veca.y * vecb.y + veca.z * vecb.z; -} -__device__ __host__ static inline VECTOR operator*(const float &a, const VECTOR &vecb) { - VECTOR vec; - vec.x = a * vecb.x; - vec.y = a * vecb.y; - vec.z = a * vecb.z; - return vec; -} - -__device__ __host__ static inline VECTOR operator-(const VECTOR &vecb) { - VECTOR vec; - vec.x = -vecb.x; - vec.y = -vecb.y; - vec.z = -vecb.z; - return vec; -} - -__device__ __host__ static inline VECTOR operator^(const VECTOR &veca, const VECTOR &vecb) { - VECTOR vec; - vec.x = veca.y * vecb.z - veca.z * vecb.y; - vec.y = veca.z * vecb.x - veca.x * vecb.z; - vec.z = veca.x * vecb.y - veca.y * vecb.x; - return vec; -} - -__device__ __host__ static inline float normfloat(const float *x, const float *y, int i, int j) { - float s = 0; - s += (x[3 * i + 0] - y[3 * j + 0]) * (x[3 * i + 0] - y[3 * j + 0]); - s += (x[3 * i + 1] - y[3 * j + 1]) * (x[3 * i + 1] - y[3 * j + 1]); - s += (x[3 * i + 2] - y[3 * j + 2]) * (x[3 * i + 2] - y[3 * j + 2]); - return s; -} - -__global__ static void construct_neighbor_list_kernel(int atom_numbers, int max_neighbor_numbers, int *nl_atom_numbers, - int *nl_atom_serial, NEIGHBOR_LIST *nl) { - for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < atom_numbers; i += gridDim.x * blockDim.x) { - nl[i].atom_numbers = nl_atom_numbers[i]; - nl[i].atom_serial = nl_atom_serial + i * max_neighbor_numbers; - } -} - -__global__ static void construct_atom_near(int atom_numbers, int near_numbers, int *atom_serial, ATOM_NEAR *an) { - for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < atom_numbers; i += gridDim.x * blockDim.x) { - an[i].atom_serial = atom_serial + i * near_numbers; - } -} - -static inline bool Malloc_Safely(void **address, size_t size) { - address[0] = NULL; - address[0] = reinterpret_cast(malloc(size)); - if (address[0] != NULL) { - return true; - } else { - printf("malloc failed!\n"); - getchar(); - return false; - } -} - -static inline bool Cuda_Malloc_Safely(void **address, size_t size) { - cudaError_t cuda_error = cudaMalloc(&address[0], size); - if (cuda_error == 0) { - return true; - } else { - printf("cudaMalloc failed! error %d\n", cuda_error); - getchar(); - return false; - } -} - -__global__ static void construct_constrain_pair(int constrain_pair_numbers, const int *atom_i_serials, - const int *atom_j_serials, const float *constant_rs, - const float *constrain_ks, CONSTRAIN_PAIR *constrain_pair) { - int atom_i = blockDim.x * blockIdx.x + threadIdx.x; - if (atom_i < constrain_pair_numbers) { - constrain_pair[atom_i].atom_i_serial = atom_i_serials[atom_i]; - constrain_pair[atom_i].atom_j_serial = atom_j_serials[atom_i]; - constrain_pair[atom_i].constant_r = constant_rs[atom_i]; - constrain_pair[atom_i].constrain_k = constrain_ks[atom_i]; - } -} - -__global__ static void Copy_Crd_To_New_Crd_Start(const int atom_numbers, const UNSIGNED_INT_VECTOR *crd, - UINT_VECTOR_LJ_TYPE *new_crd, const int *LJ_type, - const float *charge) { - int atom_i = blockDim.x * blockIdx.x + threadIdx.x; - if (atom_i < atom_numbers) { - new_crd[atom_i].uint_x = crd[atom_i].uint_x; - new_crd[atom_i].uint_y = crd[atom_i].uint_y; - new_crd[atom_i].uint_z = crd[atom_i].uint_z; - new_crd[atom_i].LJ_type = LJ_type[atom_i]; - new_crd[atom_i].charge = charge[atom_i]; - } -} - -// void Constrain_Force_Cycle_With_Virial(int atom_numbers, int constrain_pair_numbers, const unsigned int *uint_crd_f, -// const float *scaler_f, float *constrain_pair_f, const float *pair_dr_f, -// const int *atom_i_serials, const int *atom_j_serials, const float -// *constant_rs, const float *constrain_ks, float *test_frc_f, float -// *d_atom_virial, cudaStream_t stream); - -__global__ static void Rand_Normal(const int float4_numbers, curandStatePhilox4_32_10_t *rand_state, - float4 *rand_float4) { - int i = blockDim.x * blockIdx.x + threadIdx.x; - if (i < float4_numbers) { - rand_float4[i] = curand_normal4(&rand_state[i]); - } -} - -__global__ static void Setup_Rand_Normal_Kernel(const int float4_numbers, curandStatePhilox4_32_10_t *rand_state, - const int seed) { - int id = threadIdx.x + blockIdx.x * blockDim.x; - /* Each thread gets same seed, a different sequence - number, no offset */ - if (id < float4_numbers) { - curand_init(seed, id, 0, &rand_state[id]); - } -} - -__global__ static void Reset_List(const int element_numbers, int *list, const int replace_element) { - int i = blockDim.x * blockIdx.x + threadIdx.x; - if (i < element_numbers) { - list[i] = replace_element; - } -} - -__global__ static void Reset_List(const int element_numbers, float *list, const float replace_element) { - int i = blockDim.x * blockIdx.x + threadIdx.x; - if (i < element_numbers) { - list[i] = replace_element; - } -} - -__global__ static void Sum_Of_List(const int element_numbers, const float *list, float *sum) { - if (threadIdx.x == 0) { - sum[0] = 0.; - } - __syncthreads(); - float lin = 0.; - for (int i = threadIdx.x; i < element_numbers; i = i + blockDim.x) { - lin = lin + list[i]; - } - atomicAdd(sum, lin); -} - -__global__ static void Scale_List(const int element_numbers, float *list, float scaler) { - int i = blockDim.x * blockIdx.x + threadIdx.x; - if (i < element_numbers) { - list[i] = list[i] * scaler; - } -} - -__global__ static void Copy_List(const int element_numbers, const int *origin_list, int *list) { - int i = blockDim.x * blockIdx.x + threadIdx.x; - if (i < element_numbers) { - list[i] = origin_list[i]; - } -} -__global__ static void Copy_List(const int element_numbers, const float *origin_list, float *list) { - int i = blockDim.x * blockIdx.x + threadIdx.x; - if (i < element_numbers) { - list[i] = origin_list[i]; - } -} - -__global__ static void Print(const size_t size, const float *input_x) { - for (size_t i = 0; i < size; i++) { - printf("%f\n", input_x[i]); - } - return; -} -__global__ static void Print(const size_t size, const int *input_x) { - for (size_t i = 0; i < size; i++) { - printf("%d\n", input_x[i]); - } - return; -} - -__device__ static VECTOR Make_Vector_Not_Exceed_Value(VECTOR vector, const float value) { - return fminf(1.0, value * rnorm3df(vector.x, vector.y, vector.z)) * vector; -} - -#endif // MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_COMMON_SPONGE_H_ diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/crdmcmap/cal_no_wrap_crd_impl.cu b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/crdmcmap/cal_no_wrap_crd_impl.cu deleted file mode 100644 index eef09d37a45..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/crdmcmap/cal_no_wrap_crd_impl.cu +++ /dev/null @@ -1,46 +0,0 @@ - -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "plugin/device/gpu/kernel/cuda_impl/sponge/crdmcmap/cal_no_wrap_crd_impl.cuh" -#include "plugin/device/gpu/kernel/cuda_impl/sponge/common_sponge.cuh" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/util.cuh" - -__global__ void Calculate_No_Wrap_Crd(int atom_numbers, INT_VECTOR *box_map_times, VECTOR *box, VECTOR *crd, - VECTOR *nowrap_crd) { - for (int i = threadIdx.x; i < atom_numbers; i = i + blockDim.x) { - nowrap_crd[i].x = static_cast(box_map_times[i].int_x) * box[0].x + crd[i].x; - nowrap_crd[i].y = static_cast(box_map_times[i].int_y) * box[0].y + crd[i].y; - nowrap_crd[i].z = static_cast(box_map_times[i].int_z) * box[0].z + crd[i].z; - } -} - -void calculatenowrapcrd(int atom_numbers, int *box_map_times_f, float *box_f, float *crd_f, float *nowrap_crd_f, - cudaStream_t stream) { - Reset_List<<(3. * atom_numbers) / 128), 128, 0, stream>>>(3 * atom_numbers, nowrap_crd_f, - 0.); - INT_VECTOR *box_map_times = reinterpret_cast(box_map_times_f); - VECTOR *box = reinterpret_cast(box_f); - VECTOR *crd = reinterpret_cast(crd_f); - VECTOR *nowrap_crd = reinterpret_cast(nowrap_crd_f); - - Calculate_No_Wrap_Crd<<<20, 256, 0, stream>>>(atom_numbers, box_map_times, box, crd, - nowrap_crd); - return; -} - -void calculatenowrapcrd(int atom_numbers, int *box_map_times_f, float *box_f, float *crd_f, float *nowrap_crd_f, - cudaStream_t stream); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/crdmcmap/cal_no_wrap_crd_impl.cuh b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/crdmcmap/cal_no_wrap_crd_impl.cuh deleted file mode 100644 index 3e925124f70..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/crdmcmap/cal_no_wrap_crd_impl.cuh +++ /dev/null @@ -1,28 +0,0 @@ - -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_CRDMCMAP_CAL_NO_WRAP_CRD_IMPL_H_ -#define MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_CRDMCMAP_CAL_NO_WRAP_CRD_IMPL_H_ - -#include -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_device_info.h" - -CUDA_LIB_EXPORT void calculatenowrapcrd(int atom_numbers, int *box_map_times_f, float *box_f, float *crd_f, - float *nowrap_crd_f, cudaStream_t stream); - -#endif // MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_CRDMCMAP_CAL_NO_WRAP_CRD_IMPL_H_ diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/crdmcmap/refresh_boxmaptimes_impl.cu b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/crdmcmap/refresh_boxmaptimes_impl.cu deleted file mode 100644 index c4adee2a760..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/crdmcmap/refresh_boxmaptimes_impl.cu +++ /dev/null @@ -1,47 +0,0 @@ - -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "plugin/device/gpu/kernel/cuda_impl/sponge/crdmcmap/refresh_boxmaptimes_impl.cuh" -#include "plugin/device/gpu/kernel/cuda_impl/sponge/common_sponge.cuh" - -__global__ void Refresh_BoxMapTimes_CUDA(int atom_numbers, VECTOR *box_length_inverse, VECTOR *crd, - INT_VECTOR *box_map_times, VECTOR *old_crd) { - VECTOR crd_i, old_crd_i; - for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < atom_numbers; i += gridDim.x * blockDim.x) { - crd_i = crd[i]; - old_crd_i = old_crd[i]; - box_map_times[i].int_x += floor((old_crd_i.x - crd_i.x) * box_length_inverse[0].x + 0.5); - box_map_times[i].int_y += floor((old_crd_i.y - crd_i.y) * box_length_inverse[0].y + 0.5); - box_map_times[i].int_z += floor((old_crd_i.z - crd_i.z) * box_length_inverse[0].z + 0.5); - old_crd[i] = crd_i; - } -} - -void refresh_boxmaptimes(int atom_numbers, float *box_length_inverse_f, float *crd_f, float *old_crd_f, - int *box_map_times_f, cudaStream_t stream) { - INT_VECTOR *box_map_times = reinterpret_cast(box_map_times_f); - VECTOR *box_length_inverse = reinterpret_cast(box_length_inverse_f); - VECTOR *crd = reinterpret_cast(crd_f); - VECTOR *old_crd = reinterpret_cast(old_crd_f); - - Refresh_BoxMapTimes_CUDA<<<1, 256, 0, stream>>>(atom_numbers, box_length_inverse, crd, - box_map_times, old_crd); - return; -} - -void refresh_boxmaptimes(int atom_numbers, float *box_length_inverse, float *crd_f, float *old_crd_f, - int *box_map_times_f, cudaStream_t stream); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/crdmcmap/refresh_boxmaptimes_impl.cuh b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/crdmcmap/refresh_boxmaptimes_impl.cuh deleted file mode 100644 index cbd8402bf35..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/crdmcmap/refresh_boxmaptimes_impl.cuh +++ /dev/null @@ -1,28 +0,0 @@ - -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_CRDMCMAP_REFRESH_BOXMAPTIMES_IMPL_H_ -#define MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_CRDMCMAP_REFRESH_BOXMAPTIMES_IMPL_H_ - -#include -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_device_info.h" - -CUDA_LIB_EXPORT void refresh_boxmaptimes(int atom_numbers, float *box_length_inverse, float *crd_f, float *old_crd_f, - int *box_map_times_f, cudaStream_t stream); - -#endif // MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_CRDMCMAP_REFRESH_BOXMAPTIMES_IMPL_H_ diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/dihedral/dihedral_atom_energy_impl.cu b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/dihedral/dihedral_atom_energy_impl.cu deleted file mode 100644 index 4803c11f25f..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/dihedral/dihedral_atom_energy_impl.cu +++ /dev/null @@ -1,84 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "plugin/device/gpu/kernel/cuda_impl/sponge/dihedral/dihedral_atom_energy_impl.cuh" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/util.cuh" -#include "plugin/device/gpu/kernel/cuda_impl/sponge/common_sponge.cuh" - -__global__ void DihedralAtomEnergyKernel(int dihedral_numbers, const UNSIGNED_INT_VECTOR *uint_crd, - const VECTOR *scaler, const int *atom_a, const int *atom_b, const int *atom_c, - const int *atom_d, const int *ipn, const float *pk, const float *gamc, - const float *gams, const float *pn, float *ene) { - int dihedral_i = blockDim.x * blockIdx.x + threadIdx.x; - if (dihedral_i < dihedral_numbers) { - int atom_i = atom_a[dihedral_i]; - int atom_j = atom_b[dihedral_i]; - int atom_k = atom_c[dihedral_i]; - int atom_l = atom_d[dihedral_i]; - - float temp_pk = pk[dihedral_i]; - float temp_pn = pn[dihedral_i]; - float temp_gamc = gamc[dihedral_i]; - float temp_gams = gams[dihedral_i]; - - VECTOR drij = Get_Periodic_Displacement(uint_crd[atom_i], uint_crd[atom_j], scaler[0]); - VECTOR drkj = Get_Periodic_Displacement(uint_crd[atom_k], uint_crd[atom_j], scaler[0]); - VECTOR drkl = Get_Periodic_Displacement(uint_crd[atom_k], uint_crd[atom_l], scaler[0]); - - VECTOR r1 = drij ^ drkj; - VECTOR r2 = drkl ^ drkj; - - float r1_1 = rnorm3df(r1.x, r1.y, r1.z); - float r2_1 = rnorm3df(r2.x, r2.y, r2.z); - float r1_1_r2_1 = r1_1 * r2_1; - - float phi = r1 * r2 * r1_1_r2_1; - phi = fmaxf(-0.999999, fminf(phi, 0.999999)); - phi = acosf(phi); - - float sign = (r2 ^ r1) * drkj; - copysignf(phi, sign); - - phi = CONSTANT_Pi - phi; - - float nphi = temp_pn * phi; - - float cos_nphi = cosf(nphi); - float sin_nphi = sinf(nphi); - - atomicAdd(&ene[atom_i], (temp_pk + cos_nphi * temp_gamc + sin_nphi * temp_gams)); - } -} - -void DihedralAtomEnergy(int dihedral_numbers, int atom_numbers, const int *uint_crd_f, const float *scaler_f, - const int *atom_a, const int *atom_b, const int *atom_c, const int *atom_d, const int *ipn, - const float *pk, const float *gamc, const float *gams, const float *pn, float *ene, - cudaStream_t stream) { - Reset_List<<(atom_numbers) / 128), 128, 0, stream>>>(atom_numbers, ene, 0.); - size_t thread_per_block = 128; - size_t block_per_grid = ceilf(static_cast(dihedral_numbers) / 128); - UNSIGNED_INT_VECTOR *uint_crd = - const_cast(reinterpret_cast(uint_crd_f)); - VECTOR *scaler = const_cast(reinterpret_cast(scaler_f)); - - DihedralAtomEnergyKernel<<>>( - dihedral_numbers, uint_crd, scaler, atom_a, atom_b, atom_c, atom_d, ipn, pk, gamc, gams, pn, ene); - return; -} -void DihedralAtomEnergy(int dihedral_numbers, int atom_numbers, const int *uint_crd_f, const float *scaler_f, - const int *atom_a, const int *atom_b, const int *atom_c, const int *atom_d, const int *ipn, - const float *pk, const float *gamc, const float *gams, const float *pn, float *ene, - cudaStream_t stream); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/dihedral/dihedral_atom_energy_impl.cuh b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/dihedral/dihedral_atom_energy_impl.cuh deleted file mode 100644 index adc6b16ad9e..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/dihedral/dihedral_atom_energy_impl.cuh +++ /dev/null @@ -1,28 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_DIHEDRAL_DIHEDRAL_ATOM_ENERGY_IMPL_H_ -#define MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_DIHEDRAL_DIHEDRAL_ATOM_ENERGY_IMPL_H_ - -#include -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_device_info.h" - -CUDA_LIB_EXPORT void DihedralAtomEnergy(int dihedral_numbers, int atom_numbers, const int *uint_crd_f, - const float *scaler_f, const int *atom_a, const int *atom_b, const int *atom_c, - const int *atom_d, const int *ipn, const float *pk, const float *gamc, - const float *gams, const float *pn, float *ene, cudaStream_t stream); -#endif diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/dihedral/dihedral_energy_impl.cu b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/dihedral/dihedral_energy_impl.cu deleted file mode 100644 index ea58fa92c31..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/dihedral/dihedral_energy_impl.cu +++ /dev/null @@ -1,81 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "plugin/device/gpu/kernel/cuda_impl/sponge/dihedral/dihedral_energy_impl.cuh" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/util.cuh" -#include "plugin/device/gpu/kernel/cuda_impl/sponge/common_sponge.cuh" - -__global__ void DihedralEnergyKernel(int dihedral_numbers, const UNSIGNED_INT_VECTOR *uint_crd, const VECTOR *scaler, - const int *atom_a, const int *atom_b, const int *atom_c, const int *atom_d, - const int *ipn, const float *pk, const float *gamc, const float *gams, - const float *pn, float *ene) { - int dihedral_i = blockDim.x * blockIdx.x + threadIdx.x; - if (dihedral_i < dihedral_numbers) { - int atom_i = atom_a[dihedral_i]; - int atom_j = atom_b[dihedral_i]; - int atom_k = atom_c[dihedral_i]; - int atom_l = atom_d[dihedral_i]; - - float temp_pk = pk[dihedral_i]; - float temp_pn = pn[dihedral_i]; - float temp_gamc = gamc[dihedral_i]; - float temp_gams = gams[dihedral_i]; - - VECTOR drij = Get_Periodic_Displacement(uint_crd[atom_i], uint_crd[atom_j], scaler[0]); - VECTOR drkj = Get_Periodic_Displacement(uint_crd[atom_k], uint_crd[atom_j], scaler[0]); - VECTOR drkl = Get_Periodic_Displacement(uint_crd[atom_k], uint_crd[atom_l], scaler[0]); - - VECTOR r1 = drij ^ drkj; - VECTOR r2 = drkl ^ drkj; - - float r1_1 = rnorm3df(r1.x, r1.y, r1.z); - float r2_1 = rnorm3df(r2.x, r2.y, r2.z); - float r1_1_r2_1 = r1_1 * r2_1; - - float phi = r1 * r2 * r1_1_r2_1; - phi = fmaxf(-0.999999, fminf(phi, 0.999999)); - phi = acosf(phi); - - float sign = (r2 ^ r1) * drkj; - copysignf(phi, sign); - - phi = CONSTANT_Pi - phi; - - float nphi = temp_pn * phi; - - float cos_nphi = cosf(nphi); - float sin_nphi = sinf(nphi); - - ene[dihedral_i] = (temp_pk + cos_nphi * temp_gamc + sin_nphi * temp_gams); - } -} - -void DihedralEnergy(int dihedral_numbers, const int *uint_crd_f, const float *scaler_f, const int *atom_a, - const int *atom_b, const int *atom_c, const int *atom_d, const int *ipn, const float *pk, - const float *gamc, const float *gams, const float *pn, float *ene, cudaStream_t stream) { - size_t thread_per_block = 128; - size_t block_per_grid = ceilf(static_cast(dihedral_numbers) / 128); - UNSIGNED_INT_VECTOR *uint_crd = - const_cast(reinterpret_cast(uint_crd_f)); - VECTOR *scaler = const_cast(reinterpret_cast(scaler_f)); - - DihedralEnergyKernel<<>>( - dihedral_numbers, uint_crd, scaler, atom_a, atom_b, atom_c, atom_d, ipn, pk, gamc, gams, pn, ene); - return; -} -void DihedralEnergy(int dihedral_numbers, const int *uint_crd_f, const float *scaler_f, const int *atom_a, - const int *atom_b, const int *atom_c, const int *atom_d, const int *ipn, const float *pk, - const float *gamc, const float *gams, const float *pn, float *ene, cudaStream_t stream); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/dihedral/dihedral_energy_impl.cuh b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/dihedral/dihedral_energy_impl.cuh deleted file mode 100644 index f8fcb5dfe72..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/dihedral/dihedral_energy_impl.cuh +++ /dev/null @@ -1,28 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_DIHEDRAL_DIHEDRAL_ENERGY_IMPL_H_ -#define MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_DIHEDRAL_DIHEDRAL_ENERGY_IMPL_H_ - -#include -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_device_info.h" - -CUDA_LIB_EXPORT void DihedralEnergy(int dihedral_numbers, const int *uint_crd_f, const float *scaler_f, - const int *atom_a, const int *atom_b, const int *atom_c, const int *atom_d, - const int *ipn, const float *pk, const float *gamc, const float *gams, - const float *pn, float *ene, cudaStream_t stream); -#endif diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/dihedral/dihedral_force_impl.cu b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/dihedral/dihedral_force_impl.cu deleted file mode 100644 index e90780cf1ec..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/dihedral/dihedral_force_impl.cu +++ /dev/null @@ -1,121 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "plugin/device/gpu/kernel/cuda_impl/sponge/dihedral/dihedral_force_impl.cuh" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/util.cuh" -#include "plugin/device/gpu/kernel/cuda_impl/sponge/common_sponge.cuh" - -__global__ void DihedralForceKernel(int dihedral_numbers, const UNSIGNED_INT_VECTOR *uint_crd, const VECTOR *scaler, - const int *atom_a, const int *atom_b, const int *atom_c, const int *atom_d, - const int *ipn, const float *pk, const float *gamc, const float *gams, - const float *pn, VECTOR *frc) { - int dihedral_i = blockDim.x * blockIdx.x + threadIdx.x; - if (dihedral_i < dihedral_numbers) { - int atom_i = atom_a[dihedral_i]; - int atom_j = atom_b[dihedral_i]; - int atom_k = atom_c[dihedral_i]; - int atom_l = atom_d[dihedral_i]; - - int temp_ipn = ipn[dihedral_i]; - - float temp_pn = pn[dihedral_i]; - float temp_gamc = gamc[dihedral_i]; - float temp_gams = gams[dihedral_i]; - - VECTOR drij = Get_Periodic_Displacement(uint_crd[atom_i], uint_crd[atom_j], scaler[0]); - VECTOR drkj = Get_Periodic_Displacement(uint_crd[atom_k], uint_crd[atom_j], scaler[0]); - VECTOR drkl = Get_Periodic_Displacement(uint_crd[atom_k], uint_crd[atom_l], scaler[0]); - - VECTOR r1 = drij ^ drkj; - VECTOR r2 = drkl ^ drkj; - - float r1_1 = rnorm3df(r1.x, r1.y, r1.z); - float r2_1 = rnorm3df(r2.x, r2.y, r2.z); - float r1_2 = r1_1 * r1_1; - float r2_2 = r2_1 * r2_1; - float r1_1_r2_1 = r1_1 * r2_1; - - float phi = r1 * r2 * r1_1_r2_1; - phi = fmaxf(-0.999999, fminf(phi, 0.999999)); - phi = acosf(phi); - - float sign = (r2 ^ r1) * drkj; - copysignf(phi, sign); - - phi = CONSTANT_Pi - phi; - - float nphi = temp_pn * phi; - - float cos_phi = cosf(phi); - float sin_phi = sinf(phi); - float cos_nphi = cosf(nphi); - float sin_nphi = sinf(nphi); - - float dE_dphi; - if (fabsf(sin_phi) < 1e-6) { - temp_ipn *= temp_ipn % 2; // (((temp_ipn - 1) & 1) ^ 1) - dE_dphi = temp_gamc * (temp_pn - temp_ipn + temp_ipn * cos_phi); - } else { - dE_dphi = temp_pn * (temp_gamc * sin_nphi - temp_gams * cos_nphi) / sin_phi; - } - - VECTOR dphi_dr1 = r1_1_r2_1 * r2 + cos_phi * r1_2 * r1; - VECTOR dphi_dr2 = r1_1_r2_1 * r1 + cos_phi * r2_2 * r2; - - VECTOR dE_dri = dE_dphi * drkj ^ dphi_dr1; - VECTOR dE_drl = dE_dphi * dphi_dr2 ^ drkj; - VECTOR dE_drj_part = dE_dphi * ((drij ^ dphi_dr1) + (drkl ^ dphi_dr2)); - - VECTOR fi = dE_dri; - VECTOR fj = dE_drj_part - dE_dri; - VECTOR fk = -dE_drl - dE_drj_part; - VECTOR fl = dE_drl; - - atomicAdd(&frc[atom_i].x, fi.x); - atomicAdd(&frc[atom_i].y, fi.y); - atomicAdd(&frc[atom_i].z, fi.z); - atomicAdd(&frc[atom_j].x, fj.x); - atomicAdd(&frc[atom_j].y, fj.y); - atomicAdd(&frc[atom_j].z, fj.z); - atomicAdd(&frc[atom_k].x, fk.x); - atomicAdd(&frc[atom_k].y, fk.y); - atomicAdd(&frc[atom_k].z, fk.z); - atomicAdd(&frc[atom_l].x, fl.x); - atomicAdd(&frc[atom_l].y, fl.y); - atomicAdd(&frc[atom_l].z, fl.z); - } -} - -void DihedralForce(int dihedral_numbers, int atom_numbers, const int *uint_crd_f, const float *scaler_f, - const int *atom_a, const int *atom_b, const int *atom_c, const int *atom_d, const int *ipn, - const float *pk, const float *gamc, const float *gams, const float *pn, float *frc_f, - cudaStream_t stream) { - Reset_List<<(3. * atom_numbers) / 128), 128, 0, stream>>>(3 * atom_numbers, frc_f, 0.); - size_t thread_per_block = 128; - size_t block_per_grid = ceilf(static_cast(dihedral_numbers) / 128); - UNSIGNED_INT_VECTOR *uint_crd = - const_cast(reinterpret_cast(uint_crd_f)); - VECTOR *frc = const_cast(reinterpret_cast(frc_f)); - VECTOR *scaler = const_cast(reinterpret_cast(scaler_f)); - - DihedralForceKernel<<>>( - dihedral_numbers, uint_crd, scaler, atom_a, atom_b, atom_c, atom_d, ipn, pk, gamc, gams, pn, frc); - return; -} -void DihedralForce(int dihedral_numbers, int atom_numbers, const int *uint_crd_f, const float *scaler_f, - const int *atom_a, const int *atom_b, const int *atom_c, const int *atom_d, const int *ipn, - const float *pk, const float *gamc, const float *gams, const float *pn, float *frc_f, - cudaStream_t stream); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/dihedral/dihedral_force_impl.cuh b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/dihedral/dihedral_force_impl.cuh deleted file mode 100644 index 33038fac47a..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/dihedral/dihedral_force_impl.cuh +++ /dev/null @@ -1,28 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_DIHEDRAL_DIHEDRAL_FORCE_IMPL_H_ -#define MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_DIHEDRAL_DIHEDRAL_FORCE_IMPL_H_ - -#include -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_device_info.h" - -CUDA_LIB_EXPORT void DihedralForce(int dihedral_numbers, int atom_numbers, const int *uint_crd_f, const float *scaler_f, - const int *atom_a, const int *atom_b, const int *atom_c, const int *atom_d, - const int *ipn, const float *pk, const float *gamc, const float *gams, - const float *pn, float *frc_f, cudaStream_t stream); -#endif diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/dihedral/dihedral_force_with_atom_energy_impl.cu b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/dihedral/dihedral_force_with_atom_energy_impl.cu deleted file mode 100644 index 46874a0c78a..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/dihedral/dihedral_force_with_atom_energy_impl.cu +++ /dev/null @@ -1,125 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "plugin/device/gpu/kernel/cuda_impl/sponge/dihedral/dihedral_force_with_atom_energy_impl.cuh" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/util.cuh" -#include "plugin/device/gpu/kernel/cuda_impl/sponge/common_sponge.cuh" - -__global__ void DihedralForceWithAtomEnergyKernel(int dihedral_numbers, const UNSIGNED_INT_VECTOR *uint_crd, - const VECTOR *scaler, const int *atom_a, const int *atom_b, - const int *atom_c, const int *atom_d, const int *ipn, const float *pk, - const float *gamc, const float *gams, const float *pn, VECTOR *frc, - float *ene) { - int dihedral_i = blockDim.x * blockIdx.x + threadIdx.x; - if (dihedral_i < dihedral_numbers) { - int atom_i = atom_a[dihedral_i]; - int atom_j = atom_b[dihedral_i]; - int atom_k = atom_c[dihedral_i]; - int atom_l = atom_d[dihedral_i]; - - int temp_ipn = ipn[dihedral_i]; - - float temp_pk = pk[dihedral_i]; - float temp_pn = pn[dihedral_i]; - float temp_gamc = gamc[dihedral_i]; - float temp_gams = gams[dihedral_i]; - - VECTOR drij = Get_Periodic_Displacement(uint_crd[atom_i], uint_crd[atom_j], scaler[0]); - VECTOR drkj = Get_Periodic_Displacement(uint_crd[atom_k], uint_crd[atom_j], scaler[0]); - VECTOR drkl = Get_Periodic_Displacement(uint_crd[atom_k], uint_crd[atom_l], scaler[0]); - - VECTOR r1 = drij ^ drkj; - VECTOR r2 = drkl ^ drkj; - - float r1_1 = rnorm3df(r1.x, r1.y, r1.z); - float r2_1 = rnorm3df(r2.x, r2.y, r2.z); - float r1_2 = r1_1 * r1_1; - float r2_2 = r2_1 * r2_1; - float r1_1_r2_1 = r1_1 * r2_1; - - float phi = r1 * r2 * r1_1_r2_1; - phi = fmaxf(-0.999999, fminf(phi, 0.999999)); - phi = acosf(phi); - - float sign = (r2 ^ r1) * drkj; - copysignf(phi, sign); - - phi = CONSTANT_Pi - phi; - - float nphi = temp_pn * phi; - - float cos_phi = cosf(phi); - float sin_phi = sinf(phi); - float cos_nphi = cosf(nphi); - float sin_nphi = sinf(nphi); - - float dE_dphi; - if (fabsf(sin_phi) < 1e-6) { - temp_ipn *= (((temp_ipn - 1) & 1) ^ 1); - dE_dphi = temp_gamc * (temp_pn - temp_ipn + temp_ipn * cos_phi); - } else { - dE_dphi = temp_pn * (temp_gamc * sin_nphi - temp_gams * cos_nphi) / sin_phi; - } - - VECTOR dphi_dr1 = r1_1_r2_1 * r2 + cos_phi * r1_2 * r1; - VECTOR dphi_dr2 = r1_1_r2_1 * r1 + cos_phi * r2_2 * r2; - - VECTOR dE_dri = dE_dphi * drkj ^ dphi_dr1; - VECTOR dE_drl = dE_dphi * dphi_dr2 ^ drkj; - VECTOR dE_drj_part = dE_dphi * ((drij ^ dphi_dr1) + (drkl ^ dphi_dr2)); - - VECTOR fi = dE_dri; - VECTOR fj = dE_drj_part - dE_dri; - VECTOR fk = -dE_drl - dE_drj_part; - VECTOR fl = dE_drl; - - atomicAdd(&frc[atom_i].x, fi.x); - atomicAdd(&frc[atom_i].y, fi.y); - atomicAdd(&frc[atom_i].z, fi.z); - atomicAdd(&frc[atom_j].x, fj.x); - atomicAdd(&frc[atom_j].y, fj.y); - atomicAdd(&frc[atom_j].z, fj.z); - atomicAdd(&frc[atom_k].x, fk.x); - atomicAdd(&frc[atom_k].y, fk.y); - atomicAdd(&frc[atom_k].z, fk.z); - atomicAdd(&frc[atom_l].x, fl.x); - atomicAdd(&frc[atom_l].y, fl.y); - atomicAdd(&frc[atom_l].z, fl.z); - - atomicAdd(&ene[atom_i], (temp_pk + cos_nphi * temp_gamc + sin_nphi * temp_gams)); - } -} - -void DihedralForceWithAtomEnergy(int dihedral_numbers, int atom_numbers, const int *uint_crd_f, const float *scaler_f, - const int *atom_a, const int *atom_b, const int *atom_c, const int *atom_d, - const int *ipn, const float *pk, const float *gamc, const float *gams, const float *pn, - float *frc_f, float *ene, cudaStream_t stream) { - Reset_List<<(3. * atom_numbers) / 128), 128, 0, stream>>>(3 * atom_numbers, frc_f, 0.); - size_t thread_per_block = 128; - size_t block_per_grid = ceilf(static_cast(dihedral_numbers) / 128); - UNSIGNED_INT_VECTOR *uint_crd = - const_cast(reinterpret_cast(uint_crd_f)); - VECTOR *frc = const_cast(reinterpret_cast(frc_f)); - VECTOR *scaler = const_cast(reinterpret_cast(scaler_f)); - - DihedralForceWithAtomEnergyKernel<<>>( - dihedral_numbers, uint_crd, scaler, atom_a, atom_b, atom_c, atom_d, ipn, pk, gamc, gams, pn, frc, ene); - return; -} -void DihedralForceWithAtomEnergy(int dihedral_numbers, int atom_numbers, const int *uint_crd_f, const float *scaler_f, - const int *atom_a, const int *atom_b, const int *atom_c, const int *atom_d, - const int *ipn, const float *pk, const float *gamc, const float *gams, const float *pn, - float *frc_f, float *ene, cudaStream_t stream); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/dihedral/dihedral_force_with_atom_energy_impl.cuh b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/dihedral/dihedral_force_with_atom_energy_impl.cuh deleted file mode 100644 index 82dc3e26bb6..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/dihedral/dihedral_force_with_atom_energy_impl.cuh +++ /dev/null @@ -1,29 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_DIHEDRAL_DIHEDRAL_FORCE_WITH_ATOM_ENERGY_IMPL_H_ -#define MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_DIHEDRAL_DIHEDRAL_FORCE_WITH_ATOM_ENERGY_IMPL_H_ - -#include -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_device_info.h" - -CUDA_LIB_EXPORT void DihedralForceWithAtomEnergy(int dihedral_numbers, int atom_numbers, const int *uint_crd_f, - const float *scaler_f, const int *atom_a, const int *atom_b, - const int *atom_c, const int *atom_d, const int *ipn, const float *pk, - const float *gamc, const float *gams, const float *pn, float *frc_f, - float *ene, cudaStream_t stream); -#endif diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/lj/lj_direct_cf_force_with_lj_virial_direct_cf_energy_impl.cu b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/lj/lj_direct_cf_force_with_lj_virial_direct_cf_energy_impl.cu deleted file mode 100644 index 4a77a61bb11..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/lj/lj_direct_cf_force_with_lj_virial_direct_cf_energy_impl.cu +++ /dev/null @@ -1,144 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - /** - * Note: - * LJForce. This is an experimental interface that is subject to change and/or deletion. - */ - -#include "plugin/device/gpu/kernel/cuda_impl/sponge/lj/lj_direct_cf_force_with_lj_virial_direct_cf_energy_impl.cuh" -#include "plugin/device/gpu/kernel/cuda_impl/sponge/common_sponge.cuh" - -__global__ void LJ_Direct_CF_Force_With_LJ_Virial_Direct_CF_Energy_CUDA( - const int atom_numbers, const NEIGHBOR_LIST *nl, const UINT_VECTOR_LJ_TYPE *uint_crd, const VECTOR *boxlength, - const float *LJ_type_A, const float *LJ_type_B, const float cutoff, VECTOR *frc, const float pme_beta, - const float sqrt_pi, float *atom_lj_virial, float *atom_direct_cf_energy) { - int atom_i = blockDim.x * blockIdx.x + threadIdx.x; - if (atom_i < atom_numbers) { - NEIGHBOR_LIST nl_i = nl[atom_i]; - int N = nl_i.atom_numbers; - int atom_j; - int int_x; - int int_y; - int int_z; - UINT_VECTOR_LJ_TYPE r1 = uint_crd[atom_i], r2; - VECTOR dr; - float dr_2; - float dr_4; - float dr_8; - float dr_6; - float frc_abs = 0.; - VECTOR frc_lin; - VECTOR frc_record = {0., 0., 0.}; - float charge_i = r1.charge; - float charge_j; - float dr_abs; - float dr_1; - float beta_dr; - float frc_cf_abs; - float virial_lin = 0.; - float energy_lin = 0.; - - int x, y; - int atom_pair_LJ_type; - for (int j = threadIdx.y; j < N; j = j + blockDim.y) { - atom_j = nl_i.atom_serial[j]; - r2 = uint_crd[atom_j]; - charge_j = r2.charge; - - int_x = r2.uint_x - r1.uint_x; - int_y = r2.uint_y - r1.uint_y; - int_z = r2.uint_z - r1.uint_z; - dr.x = boxlength[0].x * int_x; - dr.y = boxlength[0].y * int_y; - dr.z = boxlength[0].z * int_z; - dr_abs = norm3df(dr.x, dr.y, dr.z); - if (dr_abs < cutoff) { - dr_1 = 1. / dr_abs; - dr_2 = dr_1 * dr_1; - dr_4 = dr_2 * dr_2; - dr_8 = dr_4 * dr_4; - dr_6 = dr_4 * dr_2; - - y = (r2.LJ_type - r1.LJ_type); - x = y >> 31; - y = (y ^ x) - x; - x = r2.LJ_type + r1.LJ_type; - r2.LJ_type = (x + y) >> 1; - x = (x - y) >> 1; - atom_pair_LJ_type = (r2.LJ_type * (r2.LJ_type + 1) >> 1) + x; - - frc_abs = (-LJ_type_A[atom_pair_LJ_type] * dr_6 + LJ_type_B[atom_pair_LJ_type]) * dr_8; - beta_dr = pme_beta * dr_abs; - frc_cf_abs = beta_dr * sqrt_pi * expf(-beta_dr * beta_dr) + erfcf(beta_dr); - frc_cf_abs = frc_cf_abs * dr_2 * dr_1; - frc_cf_abs = charge_i * charge_j * frc_cf_abs; - energy_lin = energy_lin + charge_i * charge_j * erfcf(beta_dr) * dr_1; - virial_lin = virial_lin - frc_abs * dr_abs * dr_abs; - frc_abs = frc_abs - frc_cf_abs; - frc_lin.x = frc_abs * dr.x; - frc_lin.y = frc_abs * dr.y; - frc_lin.z = frc_abs * dr.z; - frc_record.x = frc_record.x + frc_lin.x; - frc_record.y = frc_record.y + frc_lin.y; - frc_record.z = frc_record.z + frc_lin.z; - atomicAdd(&frc[atom_j].x, -frc_lin.x); - atomicAdd(&frc[atom_j].y, -frc_lin.y); - atomicAdd(&frc[atom_j].z, -frc_lin.z); - } - } - atomicAdd(&frc[atom_i].x, frc_record.x); - atomicAdd(&frc[atom_i].y, frc_record.y); - atomicAdd(&frc[atom_i].z, frc_record.z); - - atomicAdd(&atom_direct_cf_energy[atom_i], energy_lin); - atomicAdd(&atom_lj_virial[atom_i], virial_lin); - } -} - -void LJ_Direct_CF_Force_With_LJ_Virial_Direct_CF_Energy( - const int atom_numbers, const float cutoff, const float pme_beta, const unsigned int *uint_crd_f, const int *LJtype, - const float *charge, const float *scaler_f, float *uint_crd_with_LJ, int *nl_atom_numbers, int *nl_atom_serial, - int *nl, const float *d_LJ_A, const float *d_LJ_B, float *frc_f, float *atom_lj_virial, float *atom_energy, - int max_neighbor_numbers, cudaStream_t stream) { - Reset_List<<(3. * atom_numbers) / 128), 128, 0, stream>>>(3 * atom_numbers, frc_f, 0.); - Reset_List<<(atom_numbers) / 128), 128, 0, stream>>>(atom_numbers, atom_energy, 0.); - Reset_List<<(atom_numbers) / 128), 128, 0, stream>>>(atom_numbers, atom_lj_virial, 0.); - VECTOR *frc = reinterpret_cast(frc_f); - VECTOR *scaler = const_cast(reinterpret_cast(scaler_f)); - NEIGHBOR_LIST *nl_a = reinterpret_cast(nl); - construct_neighbor_list_kernel<<(atom_numbers) / 128), 128, 0, stream>>>( - atom_numbers, max_neighbor_numbers, nl_atom_numbers, nl_atom_serial, nl_a); - - UINT_VECTOR_LJ_TYPE *uint_crd_with_LJ_a = reinterpret_cast(uint_crd_with_LJ); - - UNSIGNED_INT_VECTOR *uint_crd = - const_cast(reinterpret_cast(uint_crd_f)); - - Copy_Crd_To_New_Crd_Start<<(atom_numbers) / 32), 32, 0, stream>>>( - atom_numbers, uint_crd, uint_crd_with_LJ_a, LJtype, charge); - - LJ_Direct_CF_Force_With_LJ_Virial_Direct_CF_Energy_CUDA<<(atom_numbers) / 8), thread_LJ, 0, - stream>>>( - atom_numbers, nl_a, uint_crd_with_LJ_a, scaler, d_LJ_A, d_LJ_B, cutoff, frc, pme_beta, TWO_DIVIDED_BY_SQRT_PI, - atom_lj_virial, atom_energy); - return; -} - -void LJ_Direct_CF_Force_With_LJ_Virial_Direct_CF_Energy( - const int atom_numbers, const float cutoff, const float pme_beta, const unsigned int *uint_crd_f, const int *LJtype, - const float *charge, const float *scaler_f, float *uint_crd_with_LJ, int *nl_atom_numbers, int *nl_atom_serial, - int *nl, const float *d_LJ_A, const float *d_LJ_B, float *frc_f, float *atom_lj_virial, float *atom_energy, - int max_neighbor_numbers, cudaStream_t stream); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/lj/lj_direct_cf_force_with_lj_virial_direct_cf_energy_impl.cuh b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/lj/lj_direct_cf_force_with_lj_virial_direct_cf_energy_impl.cuh deleted file mode 100644 index 5ad9f2f4609..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/lj/lj_direct_cf_force_with_lj_virial_direct_cf_energy_impl.cuh +++ /dev/null @@ -1,34 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -/** - * Note: - * LJForce. This is an experimental interface that is subject to change and/or deletion. - */ - -#ifndef MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_LJ_DIRECT_CF_FORCE_WITH_LJ_VIRIAL_DIRECT_CF_ENERGY_IMPL_H_ -#define MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_LJ_DIRECT_CF_FORCE_WITH_LJ_VIRIAL_DIRECT_CF_ENERGY_IMPL_H_ - -#include -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_device_info.h" - -CUDA_LIB_EXPORT void LJ_Direct_CF_Force_With_LJ_Virial_Direct_CF_Energy( - const int atom_numbers, const float cutoff, const float pme_beta, const unsigned int *uint_crd_f, const int *LJtype, - const float *charge, const float *scaler_f, float *uint_crd_with_LJ, int *nl_atom_numbers, int *nl_atom_serial, - int *nl, const float *d_LJ_A, const float *d_LJ_B, float *frc_f, float *atom_lj_virial, float *atom_energy, - int max_neighbor_numbers, cudaStream_t stream); - -#endif diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/lj/lj_energy_impl.cu b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/lj/lj_energy_impl.cu deleted file mode 100644 index 5b87d36ea75..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/lj/lj_energy_impl.cu +++ /dev/null @@ -1,102 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "plugin/device/gpu/kernel/cuda_impl/sponge/lj/lj_energy_impl.cuh" -#include "plugin/device/gpu/kernel/cuda_impl/sponge/common_sponge.cuh" - -__global__ void LJ_Energy_CUDA(const int atom_numbers, const NEIGHBOR_LIST *nl, const UINT_VECTOR_LJ_TYPE *uint_crd, - const VECTOR *boxlength, const float *LJ_type_A, const float *LJ_type_B, - const float cutoff_square, float *lj_ene) { - int atom_i = blockDim.x * blockIdx.x + threadIdx.x; - if (atom_i < atom_numbers) { - NEIGHBOR_LIST nl_i = nl[atom_i]; - int N = nl_i.atom_numbers; - int atom_j; - int int_x; - int int_y; - int int_z; - UINT_VECTOR_LJ_TYPE r1 = uint_crd[atom_i], r2; - VECTOR dr; - float dr2; - float dr_2; - float dr_4; - float dr_6; - float ene_lin = 0.; - - int x, y; - int atom_pair_LJ_type; - for (int j = threadIdx.y; j < N; j = j + blockDim.y) { - atom_j = nl_i.atom_serial[j]; - r2 = uint_crd[atom_j]; - - int_x = r2.uint_x - r1.uint_x; - int_y = r2.uint_y - r1.uint_y; - int_z = r2.uint_z - r1.uint_z; - dr.x = boxlength[0].x * int_x; - dr.y = boxlength[0].y * int_y; - dr.z = boxlength[0].z * int_z; - - dr2 = dr.x * dr.x + dr.y * dr.y + dr.z * dr.z; - if (dr2 < cutoff_square) { - dr_2 = 1. / dr2; - dr_4 = dr_2 * dr_2; - dr_6 = dr_4 * dr_2; - - y = (r2.LJ_type - r1.LJ_type); - x = y >> 31; - y = (y ^ x) - x; - x = r2.LJ_type + r1.LJ_type; - r2.LJ_type = (x + y) >> 1; - x = (x - y) >> 1; - atom_pair_LJ_type = (r2.LJ_type * (r2.LJ_type + 1) >> 1) + x; - - dr_2 = (0.083333333 * LJ_type_A[atom_pair_LJ_type] * dr_6 - 0.166666666 * LJ_type_B[atom_pair_LJ_type]) * dr_6; - ene_lin = ene_lin + dr_2; - } - } - atomicAdd(&lj_ene[atom_i], ene_lin); - } -} - -void LJEnergy(const int atom_numbers, const float cutoff_square, const int *uint_crd_f, const int *LJtype, - const float *charge, const float *scaler_f, float *uint_crd_with_LJ, int *nl_atom_numbers, - int *nl_atom_serial, int *nl, const float *d_LJ_A, const float *d_LJ_B, float *d_LJ_energy_atom, - cudaStream_t stream) { - VECTOR *scaler = const_cast(reinterpret_cast(scaler_f)); - int max_neighbor_numbers = 800; - NEIGHBOR_LIST *nl_a = reinterpret_cast(nl); - construct_neighbor_list_kernel<<(atom_numbers) / 128), 128, 0, stream>>>( - atom_numbers, max_neighbor_numbers, nl_atom_numbers, nl_atom_serial, nl_a); - - UINT_VECTOR_LJ_TYPE *uint_crd_with_LJ_a = reinterpret_cast(uint_crd_with_LJ); - - UNSIGNED_INT_VECTOR *uint_crd = - const_cast(reinterpret_cast(uint_crd_f)); - - Copy_Crd_To_New_Crd_Start<<(atom_numbers) / 32), 32, 0, stream>>>( - atom_numbers, uint_crd, uint_crd_with_LJ_a, LJtype, charge); - - Reset_List<<(atom_numbers) / 32), 32, 0, stream>>>(atom_numbers, d_LJ_energy_atom, 0.); - - LJ_Energy_CUDA<<(atom_numbers) / 8), thread_LJ, 0, stream>>>( - atom_numbers, nl_a, uint_crd_with_LJ_a, scaler, d_LJ_A, d_LJ_B, cutoff_square, d_LJ_energy_atom); - - return; -} -void LJEnergy(const int atom_numbers, const float cutoff_square, const int *uint_crd_f, const int *LJtype, - const float *charge, const float *scaler_f, float *uint_crd_with_LJ, int *nl_atom_numbers, - int *nl_atom_serial, int *nl, const float *d_LJ_A, const float *d_LJ_B, float *d_LJ_energy_atom, - cudaStream_t stream); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/lj/lj_energy_impl.cuh b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/lj/lj_energy_impl.cuh deleted file mode 100644 index a97a462aad8..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/lj/lj_energy_impl.cuh +++ /dev/null @@ -1,28 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_LJ_LJ_ENERGY_IMPL_H_ -#define MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_LJ_LJ_ENERGY_IMPL_H_ - -#include -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_device_info.h" - -CUDA_LIB_EXPORT void LJEnergy(const int atom_numbers, const float cutoff_square, const int *uint_crd_f, - const int *LJtype, const float *charge, const float *scaler_f, float *uint_crd_with_LJ, - int *nl_atom_numbers, int *nl_atom_serial, int *nl, const float *d_LJ_A, - const float *d_LJ_B, float *d_LJ_energy_atom, cudaStream_t stream); -#endif diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/lj/lj_force_impl.cu b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/lj/lj_force_impl.cu deleted file mode 100644 index 38cc77f2b3e..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/lj/lj_force_impl.cu +++ /dev/null @@ -1,117 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "plugin/device/gpu/kernel/cuda_impl/sponge/lj/lj_force_impl.cuh" -#include "plugin/device/gpu/kernel/cuda_impl/sponge/common_sponge.cuh" - -__global__ void LJ_Force_CUDA(const int atom_numbers, const NEIGHBOR_LIST *nl, const UINT_VECTOR_LJ_TYPE *uint_crd, - const VECTOR *boxlength, const float *LJ_type_A, const float *LJ_type_B, - const float cutoff_square, VECTOR *frc) { - int atom_i = blockDim.x * blockIdx.x + threadIdx.x; - if (atom_i < atom_numbers) { - NEIGHBOR_LIST nl_i = nl[atom_i]; - int N = nl_i.atom_numbers; - int B = ceilf(static_cast(N) / blockDim.y); - int atom_j; - int int_x; - int int_y; - int int_z; - UINT_VECTOR_LJ_TYPE r1 = uint_crd[atom_i], r2; - VECTOR dr; - float dr2; - float dr_2; - float dr_4; - float dr_8; - float dr_14; - float frc_abs = 0.; - VECTOR frc_lin; - VECTOR frc_record = {0., 0., 0.}; - - int x, y; - int atom_pair_LJ_type; - for (int j = threadIdx.y * B; j < (threadIdx.y + 1) * B; j = j + 1) { - if (j < N) { - atom_j = nl_i.atom_serial[j]; - r2 = uint_crd[atom_j]; - int_x = r2.uint_x - r1.uint_x; - int_y = r2.uint_y - r1.uint_y; - int_z = r2.uint_z - r1.uint_z; - dr.x = boxlength[0].x * int_x; - dr.y = boxlength[0].y * int_y; - dr.z = boxlength[0].z * int_z; - dr2 = dr.x * dr.x + dr.y * dr.y + dr.z * dr.z; - if (dr2 < cutoff_square) { - dr_2 = 1. / dr2; - dr_4 = dr_2 * dr_2; - dr_8 = dr_4 * dr_4; - dr_14 = dr_8 * dr_4 * dr_2; - - y = (r2.LJ_type - r1.LJ_type); - x = y >> 31; - y = (y ^ x) - x; - x = r2.LJ_type + r1.LJ_type; - r2.LJ_type = (x + y) >> 1; - x = (x - y) >> 1; - atom_pair_LJ_type = (r2.LJ_type * (r2.LJ_type + 1) >> 1) + x; - - frc_abs = -LJ_type_A[atom_pair_LJ_type] * dr_14 + LJ_type_B[atom_pair_LJ_type] * dr_8; - frc_lin.x = frc_abs * dr.x; - frc_lin.y = frc_abs * dr.y; - frc_lin.z = frc_abs * dr.z; - - frc_record.x = frc_record.x + frc_lin.x; - frc_record.y = frc_record.y + frc_lin.y; - frc_record.z = frc_record.z + frc_lin.z; - - atomicAdd(&frc[atom_j].x, -frc_lin.x); - atomicAdd(&frc[atom_j].y, -frc_lin.y); - atomicAdd(&frc[atom_j].z, -frc_lin.z); - } - } - } - atomicAdd(&frc[atom_i].x, frc_record.x); - atomicAdd(&frc[atom_i].y, frc_record.y); - atomicAdd(&frc[atom_i].z, frc_record.z); - } -} - -void LJForce(const int atom_numbers, const float cutoff_square, const int *uint_crd_f, const int *LJtype, - const float *charge, const float *scaler_f, float *uint_crd_with_LJ, int *nl_atom_numbers, - int *nl_atom_serial, int *nl, const float *d_LJ_A, const float *d_LJ_B, float *frc_f, - cudaStream_t stream) { - Reset_List<<(3. * atom_numbers) / 128), 128, 0, stream>>>(3 * atom_numbers, frc_f, 0.); - VECTOR *frc = reinterpret_cast(frc_f); - VECTOR *scaler = const_cast(reinterpret_cast(scaler_f)); - int max_neighbor_numbers = 800; - NEIGHBOR_LIST *nl_a = reinterpret_cast(nl); - construct_neighbor_list_kernel<<(atom_numbers) / 128), 128, 0, stream>>>( - atom_numbers, max_neighbor_numbers, nl_atom_numbers, nl_atom_serial, nl_a); - - UINT_VECTOR_LJ_TYPE *uint_crd_with_LJ_a = reinterpret_cast(uint_crd_with_LJ); - - UNSIGNED_INT_VECTOR *uint_crd = - const_cast(reinterpret_cast(uint_crd_f)); - - Copy_Crd_To_New_Crd_Start<<(atom_numbers) / 32), 32, 0, stream>>>( - atom_numbers, uint_crd, uint_crd_with_LJ_a, LJtype, charge); - - LJ_Force_CUDA<<(atom_numbers) / 8), thread_LJ, 0, stream>>>( - atom_numbers, nl_a, uint_crd_with_LJ_a, scaler, d_LJ_A, d_LJ_B, cutoff_square, frc); - return; -} -void LJForce(const int atom_numbers, const float cutoff_square, const int *uint_crd_f, const int *LJtype, - const float *charge, const float *scaler_f, float *uint_crd_with_LJ, int *nl_atom_numbers, - int *nl_atom_serial, int *nl, const float *d_LJ_A, const float *d_LJ_B, float *frc_f, cudaStream_t stream); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/lj/lj_force_impl.cuh b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/lj/lj_force_impl.cuh deleted file mode 100644 index 5c8462144b6..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/lj/lj_force_impl.cuh +++ /dev/null @@ -1,29 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_LJ_LJ_FORCE_IMPL_H_ -#define MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_LJ_LJ_FORCE_IMPL_H_ - -#include -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_device_info.h" - -CUDA_LIB_EXPORT void LJForce(const int atom_numbers, const float cutoff_square, const int *uint_crd_f, - const int *LJtype, const float *charge, const float *scaler_f, float *uint_crd_with_LJ, - int *nl_atom_numbers, int *nl_atom_serial, int *nl, const float *d_LJ_A, - const float *d_LJ_B, float *frc_f, cudaStream_t stream); - -#endif diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/lj/lj_force_with_pme_direct_force_impl.cu b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/lj/lj_force_with_pme_direct_force_impl.cu deleted file mode 100644 index 237ddea3e95..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/lj/lj_force_with_pme_direct_force_impl.cu +++ /dev/null @@ -1,133 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "plugin/device/gpu/kernel/cuda_impl/sponge/lj/lj_force_with_pme_direct_force_impl.cuh" -#include "plugin/device/gpu/kernel/cuda_impl/sponge/common_sponge.cuh" - -__global__ void LJ_Force_With_Direct_CF_CUDA(const int atom_numbers, const NEIGHBOR_LIST *nl, - const UINT_VECTOR_LJ_TYPE *uint_crd, const VECTOR *boxlength, - const float *LJ_type_A, const float *LJ_type_B, const float cutoff, - VECTOR *frc, const float pme_beta, const float sqrt_pi) { - int atom_i = blockDim.x * blockIdx.x + threadIdx.x; - if (atom_i < atom_numbers) { - NEIGHBOR_LIST nl_i = nl[atom_i]; - int N = nl_i.atom_numbers; - int atom_j; - int int_x; - int int_y; - int int_z; - UINT_VECTOR_LJ_TYPE r1 = uint_crd[atom_i], r2; - VECTOR dr; - float dr_2; - float dr_4; - float dr_8; - float dr_6; - float frc_abs = 0.; - VECTOR frc_lin; - VECTOR frc_record = {0., 0., 0.}; - - float charge_i = r1.charge; - float charge_j; - float dr_abs; - float dr_1; - float beta_dr; - float frc_cf_abs; - - int x, y; - int atom_pair_LJ_type; - for (int j = threadIdx.y; j < N; j = j + blockDim.y) { - atom_j = nl_i.atom_serial[j]; - r2 = uint_crd[atom_j]; - charge_j = r2.charge; - - int_x = r2.uint_x - r1.uint_x; - int_y = r2.uint_y - r1.uint_y; - int_z = r2.uint_z - r1.uint_z; - dr.x = boxlength[0].x * int_x; - dr.y = boxlength[0].y * int_y; - dr.z = boxlength[0].z * int_z; - dr_abs = norm3df(dr.x, dr.y, dr.z); - if (dr_abs < cutoff) { - dr_1 = 1. / dr_abs; - dr_2 = dr_1 * dr_1; - dr_4 = dr_2 * dr_2; - dr_8 = dr_4 * dr_4; - dr_6 = dr_4 * dr_2; - - y = (r2.LJ_type - r1.LJ_type); - x = y >> 31; - y = (y ^ x) - x; - x = r2.LJ_type + r1.LJ_type; - r2.LJ_type = (x + y) >> 1; - x = (x - y) >> 1; - atom_pair_LJ_type = (r2.LJ_type * (r2.LJ_type + 1) >> 1) + x; - - frc_abs = (-LJ_type_A[atom_pair_LJ_type] * dr_6 + LJ_type_B[atom_pair_LJ_type]) * dr_8; - beta_dr = pme_beta * dr_abs; - frc_cf_abs = beta_dr * sqrt_pi * expf(-beta_dr * beta_dr) + erfcf(beta_dr); - frc_cf_abs = frc_cf_abs * dr_2 * dr_1; - frc_cf_abs = charge_i * charge_j * frc_cf_abs; - - frc_abs = frc_abs - frc_cf_abs; - - frc_lin.x = frc_abs * dr.x; - frc_lin.y = frc_abs * dr.y; - frc_lin.z = frc_abs * dr.z; - - frc_record.x = frc_record.x + frc_lin.x; - frc_record.y = frc_record.y + frc_lin.y; - frc_record.z = frc_record.z + frc_lin.z; - - atomicAdd(&frc[atom_j].x, -frc_lin.x); - atomicAdd(&frc[atom_j].y, -frc_lin.y); - atomicAdd(&frc[atom_j].z, -frc_lin.z); - } - } - atomicAdd(&frc[atom_i].x, frc_record.x); - atomicAdd(&frc[atom_i].y, frc_record.y); - atomicAdd(&frc[atom_i].z, frc_record.z); - } -} - -void LJForceWithPMEDirectForce(const int atom_numbers, const float cutoff, const float pme_beta, const int *uint_crd_f, - const int *LJtype, const float *charge, const float *scaler_f, float *uint_crd_with_LJ, - int *nl_atom_numbers, int *nl_atom_serial, int *nl, const float *d_LJ_A, - const float *d_LJ_B, float *frc_f, cudaStream_t stream) { - Reset_List<<(3. * atom_numbers) / 128), 128, 0, stream>>>(3 * atom_numbers, frc_f, 0.); - VECTOR *frc = reinterpret_cast(frc_f); - VECTOR *scaler = const_cast(reinterpret_cast(scaler_f)); - int max_neighbor_numbers = 800; - NEIGHBOR_LIST *nl_a = reinterpret_cast(nl); - construct_neighbor_list_kernel<<(atom_numbers) / 128), 128, 0, stream>>>( - atom_numbers, max_neighbor_numbers, nl_atom_numbers, nl_atom_serial, nl_a); - - UINT_VECTOR_LJ_TYPE *uint_crd_with_LJ_a = reinterpret_cast(uint_crd_with_LJ); - - UNSIGNED_INT_VECTOR *uint_crd = - const_cast(reinterpret_cast(uint_crd_f)); - - Copy_Crd_To_New_Crd_Start<<(atom_numbers) / 32), 32, 0, stream>>>( - atom_numbers, uint_crd, uint_crd_with_LJ_a, LJtype, charge); - - LJ_Force_With_Direct_CF_CUDA<<(atom_numbers) / 8), thread_LJ, 0, stream>>>( - atom_numbers, nl_a, uint_crd_with_LJ_a, scaler, d_LJ_A, d_LJ_B, cutoff, frc, pme_beta, TWO_DIVIDED_BY_SQRT_PI); - return; -} - -void LJForceWithPMEDirectForce(const int atom_numbers, const float cutoff, const float pme_beta, const int *uint_crd_f, - const int *LJtype, const float *charge, const float *scaler_f, float *uint_crd_with_LJ, - int *nl_atom_numbers, int *nl_atom_serial, int *nl, const float *d_LJ_A, - const float *d_LJ_B, float *frc_f, cudaStream_t stream); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/lj/lj_force_with_pme_direct_force_impl.cuh b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/lj/lj_force_with_pme_direct_force_impl.cuh deleted file mode 100644 index 22c603c9642..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/lj/lj_force_with_pme_direct_force_impl.cuh +++ /dev/null @@ -1,30 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_LJ_LJ_FORCE_WITH_PME_DIRECT_FORCE_IMPL_H_ -#define MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_LJ_LJ_FORCE_WITH_PME_DIRECT_FORCE_IMPL_H_ - -#include -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_device_info.h" - -CUDA_LIB_EXPORT void LJForceWithPMEDirectForce(const int atom_numbers, const float cutoff, const float pme_beta, - const int *uint_crd_f, const int *LJtype, const float *charge, - const float *scaler_f, float *uint_crd_with_LJ, int *nl_atom_numbers, - int *nl_atom_serial, int *nl, const float *d_LJ_A, const float *d_LJ_B, - float *frc_f, cudaStream_t stream); - -#endif diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/lj/lj_pme_direct_force_with_atom_energy_impl.cu b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/lj/lj_pme_direct_force_with_atom_energy_impl.cu deleted file mode 100644 index c94d8888e88..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/lj/lj_pme_direct_force_with_atom_energy_impl.cu +++ /dev/null @@ -1,147 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "plugin/device/gpu/kernel/cuda_impl/sponge/lj/lj_pme_direct_force_with_atom_energy_impl.cuh" -#include "plugin/device/gpu/kernel/cuda_impl/sponge/common_sponge.cuh" - -__global__ void LJ_Direct_CF_Force_With_Atom_Energy_CUDA(const int atom_numbers, const NEIGHBOR_LIST *nl, - const UINT_VECTOR_LJ_TYPE *uint_crd, const VECTOR *boxlength, - const float *LJ_type_A, const float *LJ_type_B, - const float cutoff, VECTOR *frc, const float pme_beta, - const float sqrt_pi, float *atom_energy) { - int atom_i = blockDim.x * blockIdx.x + threadIdx.x; - if (atom_i < atom_numbers) { - NEIGHBOR_LIST nl_i = nl[atom_i]; - int N = nl_i.atom_numbers; - int atom_j; - int int_x; - int int_y; - int int_z; - UINT_VECTOR_LJ_TYPE r1 = uint_crd[atom_i], r2; - VECTOR dr; - float dr_2; - float dr_4; - float dr_8; - float dr_6; - float frc_abs = 0.; - VECTOR frc_lin; - VECTOR frc_record = {0., 0., 0.}; - - float charge_i = r1.charge; - float charge_j; - float dr_abs; - float dr_1; - float beta_dr; - float frc_cf_abs; - - float ene_lin = 0.; - float ene_lin2 = 0.; - - int x, y; - int atom_pair_LJ_type; - for (int j = threadIdx.y; j < N; j = j + blockDim.y) { - atom_j = nl_i.atom_serial[j]; - r2 = uint_crd[atom_j]; - charge_j = r2.charge; - - int_x = r2.uint_x - r1.uint_x; - int_y = r2.uint_y - r1.uint_y; - int_z = r2.uint_z - r1.uint_z; - dr.x = boxlength[0].x * int_x; - dr.y = boxlength[0].y * int_y; - dr.z = boxlength[0].z * int_z; - dr_abs = norm3df(dr.x, dr.y, dr.z); - if (dr_abs < cutoff) { - dr_1 = 1. / dr_abs; - dr_2 = dr_1 * dr_1; - dr_4 = dr_2 * dr_2; - dr_8 = dr_4 * dr_4; - dr_6 = dr_4 * dr_2; - - y = (r2.LJ_type - r1.LJ_type); - x = y >> 31; - y = (y ^ x) - x; - x = r2.LJ_type + r1.LJ_type; - r2.LJ_type = (x + y) >> 1; - x = (x - y) >> 1; - atom_pair_LJ_type = (r2.LJ_type * (r2.LJ_type + 1) >> 1) + x; - - frc_abs = (-LJ_type_A[atom_pair_LJ_type] * dr_6 + LJ_type_B[atom_pair_LJ_type]) * dr_8; - beta_dr = pme_beta * dr_abs; - frc_cf_abs = beta_dr * sqrt_pi * expf(-beta_dr * beta_dr) + erfcf(beta_dr); - frc_cf_abs = frc_cf_abs * dr_2 * dr_1; - frc_cf_abs = charge_i * charge_j * frc_cf_abs; - - ene_lin2 = ene_lin2 + charge_i * charge_j * erfcf(beta_dr) * dr_1; - ene_lin = - ene_lin + - (0.083333333 * LJ_type_A[atom_pair_LJ_type] * dr_6 - 0.166666666 * LJ_type_B[atom_pair_LJ_type]) * dr_6; - - frc_abs = frc_abs - frc_cf_abs; - - frc_lin.x = frc_abs * dr.x; - frc_lin.y = frc_abs * dr.y; - frc_lin.z = frc_abs * dr.z; - - frc_record.x = frc_record.x + frc_lin.x; - frc_record.y = frc_record.y + frc_lin.y; - frc_record.z = frc_record.z + frc_lin.z; - - atomicAdd(&frc[atom_j].x, -frc_lin.x); - atomicAdd(&frc[atom_j].y, -frc_lin.y); - atomicAdd(&frc[atom_j].z, -frc_lin.z); - } - } - atomicAdd(&frc[atom_i].x, frc_record.x); - atomicAdd(&frc[atom_i].y, frc_record.y); - atomicAdd(&frc[atom_i].z, frc_record.z); - - atomicAdd(&atom_energy[atom_i], ene_lin + ene_lin2); - } -} - -void LJDirectCFForceWithAtomEnergy(const int atom_numbers, const float cutoff, const float pme_beta, - const int *uint_crd_f, const int *LJtype, const float *charge, const float *scaler_f, - float *uint_crd_with_LJ, int *nl_atom_numbers, int *nl_atom_serial, int *nl, - const float *d_LJ_A, const float *d_LJ_B, float *frc_f, float *atom_energy, - cudaStream_t stream) { - Reset_List<<(3. * atom_numbers) / 128), 128, 0, stream>>>(3 * atom_numbers, frc_f, 0.); - VECTOR *frc = reinterpret_cast(frc_f); - VECTOR *scaler = const_cast(reinterpret_cast(scaler_f)); - int max_neighbor_numbers = 800; - NEIGHBOR_LIST *nl_a = reinterpret_cast(nl); - construct_neighbor_list_kernel<<(atom_numbers) / 128), 128, 0, stream>>>( - atom_numbers, max_neighbor_numbers, nl_atom_numbers, nl_atom_serial, nl_a); - - UINT_VECTOR_LJ_TYPE *uint_crd_with_LJ_a = reinterpret_cast(uint_crd_with_LJ); - - UNSIGNED_INT_VECTOR *uint_crd = - const_cast(reinterpret_cast(uint_crd_f)); - - Copy_Crd_To_New_Crd_Start<<(atom_numbers) / 32), 32, 0, stream>>>( - atom_numbers, uint_crd, uint_crd_with_LJ_a, LJtype, charge); - - LJ_Direct_CF_Force_With_Atom_Energy_CUDA<<(atom_numbers) / 8), thread_LJ, 0, stream>>>( - atom_numbers, nl_a, uint_crd_with_LJ_a, scaler, d_LJ_A, d_LJ_B, cutoff, frc, pme_beta, TWO_DIVIDED_BY_SQRT_PI, - atom_energy); - return; -} - -void LJDirectCFForceWithAtomEnergy(const int atom_numbers, const float cutoff, const float pme_beta, - const int *uint_crd_f, const int *LJtype, const float *charge, const float *scaler_f, - float *uint_crd_with_LJ, int *nl_atom_numbers, int *nl_atom_serial, int *nl, - const float *d_LJ_A, const float *d_LJ_B, float *frc_f, float *atom_energy, - cudaStream_t stream); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/lj/lj_pme_direct_force_with_atom_energy_impl.cuh b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/lj/lj_pme_direct_force_with_atom_energy_impl.cuh deleted file mode 100644 index e23c5de34b6..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/lj/lj_pme_direct_force_with_atom_energy_impl.cuh +++ /dev/null @@ -1,31 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_LJ_LJ_PME_DIRECT_FORCE_WITH_ATOM_ENERGY_IMPL_H_ -#define MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_LJ_LJ_PME_DIRECT_FORCE_WITH_ATOM_ENERGY_IMPL_H_ - -#include -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_device_info.h" - -CUDA_LIB_EXPORT void LJDirectCFForceWithAtomEnergy(const int atom_numbers, const float cutoff, const float pme_beta, - const int *uint_crd_f, const int *LJtype, const float *charge, - const float *scaler_f, float *uint_crd_with_LJ, int *nl_atom_numbers, - int *nl_atom_serial, int *nl, const float *d_LJ_A, - const float *d_LJ_B, float *frc_f, float *atom_energy, - cudaStream_t stream); - -#endif diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/nb14/dihedral_14_cf_atom_energy_impl.cu b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/nb14/dihedral_14_cf_atom_energy_impl.cu deleted file mode 100644 index 723967fda0f..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/nb14/dihedral_14_cf_atom_energy_impl.cu +++ /dev/null @@ -1,78 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "plugin/device/gpu/kernel/cuda_impl/sponge/nb14/dihedral_14_cf_atom_energy_impl.cuh" -#include "plugin/device/gpu/kernel/cuda_impl/sponge/common_sponge.cuh" - -__global__ void Dihedral14CFAtomEnergyKernel(const int dihedral_14_numbers, const UINT_VECTOR_LJ_TYPE *uint_crd, - const VECTOR *boxlength, const int *a_14, const int *b_14, - const float *cf_scale_factor, float *ene) { - int dihedral_14_i = blockDim.x * blockIdx.x + threadIdx.x; - if (dihedral_14_i < dihedral_14_numbers) { - int atom_i = a_14[dihedral_14_i]; - int atom_j = b_14[dihedral_14_i]; - - UINT_VECTOR_LJ_TYPE r1 = uint_crd[atom_i]; - UINT_VECTOR_LJ_TYPE r2 = uint_crd[atom_j]; - - int int_x; - int int_y; - int int_z; - VECTOR dr; - float r_1; - float ene_lin = 0.; - - int_x = r2.uint_x - r1.uint_x; - int_y = r2.uint_y - r1.uint_y; - int_z = r2.uint_z - r1.uint_z; - dr.x = boxlength[0].x * int_x; - dr.y = boxlength[0].y * int_y; - dr.z = boxlength[0].z * int_z; - r_1 = rnorm3df(dr.x, dr.y, dr.z); - - ene_lin = r1.charge * r2.charge * r_1; - - ene_lin *= cf_scale_factor[dihedral_14_i]; - - atomicAdd(&ene[atom_i], ene_lin); - } -} - -void Dihedral14CFAtomEnergy(const int dihedral_14_numbers, const int atom_numbers, const int *uint_crd_f, - const int *LJtype, const float *charge, const float *boxlength_f, const int *a_14, - const int *b_14, const float *cf_scale_factor, float *ene, cudaStream_t stream) { - size_t thread_per_block = 128; - size_t block_per_grid = ceilf(static_cast(atom_numbers) / 128); - UINT_VECTOR_LJ_TYPE *uint_crd_with_LJ = NULL; - Cuda_Malloc_Safely(reinterpret_cast(&uint_crd_with_LJ), sizeof(UINT_VECTOR_LJ_TYPE) * atom_numbers); - - UNSIGNED_INT_VECTOR *uint_crd = - const_cast(reinterpret_cast(uint_crd_f)); - - Copy_Crd_To_New_Crd_Start<<(atom_numbers) / 32), 32, 0, stream>>>( - atom_numbers, uint_crd, uint_crd_with_LJ, LJtype, charge); - - VECTOR *boxlength = const_cast(reinterpret_cast(boxlength_f)); - Reset_List<<(3. * atom_numbers) / 128), 128, 0, stream>>>(atom_numbers, ene, 0.); - Dihedral14CFAtomEnergyKernel<<>>( - dihedral_14_numbers, uint_crd_with_LJ, boxlength, a_14, b_14, cf_scale_factor, ene); - - return; -} - -void Dihedral14CFAtomEnergy(const int dihedral_14_numbers, const int atom_numbers, const int *uint_crd_f, - const int *LJtype, const float *charge, const float *boxlength_f, const int *a_14, - const int *b_14, const float *cf_scale_factor, float *ene, cudaStream_t stream); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/nb14/dihedral_14_cf_atom_energy_impl.cuh b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/nb14/dihedral_14_cf_atom_energy_impl.cuh deleted file mode 100644 index eb0f80a46ec..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/nb14/dihedral_14_cf_atom_energy_impl.cuh +++ /dev/null @@ -1,27 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -#ifndef MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_NB14_DIHEDRAL_14_CF_ATOM_ENERGY_IMPL_H -#define MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_NB14_DIHEDRAL_14_CF_ATOM_ENERGY_IMPL_H - -#include -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_device_info.h" - -CUDA_LIB_EXPORT void Dihedral14CFAtomEnergy(const int dihedral_14_numbers, const int atom_numbers, - const int *uint_crd_f, const int *LJtype, const float *charge, - const float *boxlength_f, const int *a_14, const int *b_14, - const float *cf_scale_factor, float *ene, cudaStream_t stream); -#endif // MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_NB14_DIHEDRAL_14_CF_ENERGY_IMPL_H diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/nb14/dihedral_14_cf_energy_impl.cu b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/nb14/dihedral_14_cf_energy_impl.cu deleted file mode 100644 index 5192821e406..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/nb14/dihedral_14_cf_energy_impl.cu +++ /dev/null @@ -1,78 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "plugin/device/gpu/kernel/cuda_impl/sponge/nb14/dihedral_14_cf_energy_impl.cuh" -#include "plugin/device/gpu/kernel/cuda_impl/sponge/common_sponge.cuh" - -__global__ void Dihedral14CFEnergyKernel(const int dihedral_14_numbers, const UINT_VECTOR_LJ_TYPE *uint_crd, - const VECTOR *boxlength, const int *a_14, const int *b_14, - const float *cf_scale_factor, float *ene) { - int dihedral_14_i = blockDim.x * blockIdx.x + threadIdx.x; - if (dihedral_14_i < dihedral_14_numbers) { - int atom_i = a_14[dihedral_14_i]; - int atom_j = b_14[dihedral_14_i]; - - UINT_VECTOR_LJ_TYPE r1 = uint_crd[atom_i]; - UINT_VECTOR_LJ_TYPE r2 = uint_crd[atom_j]; - - int int_x; - int int_y; - int int_z; - VECTOR dr; - float r_1; - float ene_lin = 0.; - - int_x = r2.uint_x - r1.uint_x; - int_y = r2.uint_y - r1.uint_y; - int_z = r2.uint_z - r1.uint_z; - dr.x = boxlength[0].x * int_x; - dr.y = boxlength[0].y * int_y; - dr.z = boxlength[0].z * int_z; - r_1 = rnorm3df(dr.x, dr.y, dr.z); - - ene_lin = r1.charge * r2.charge * r_1; - - ene_lin *= cf_scale_factor[dihedral_14_i]; - - ene[dihedral_14_i] = ene_lin; - } -} - -void Dihedral14CFEnergy(const int dihedral_14_numbers, const int atom_numbers, const int *uint_crd_f, const int *LJtype, - const float *charge, float *uint_crd_with_LJ_f, const float *boxlength_f, const int *a_14, - const int *b_14, const float *cf_scale_factor, float *ene, cudaStream_t stream) { - size_t thread_per_block = 32; - size_t block_per_grid = ceilf(static_cast(dihedral_14_numbers) / 32); - - UNSIGNED_INT_VECTOR *uint_crd = - const_cast(reinterpret_cast(uint_crd_f)); - - UINT_VECTOR_LJ_TYPE *uint_crd_with_LJ = reinterpret_cast(uint_crd_with_LJ_f); - - Copy_Crd_To_New_Crd_Start<<(atom_numbers) / 32), 32, 0, stream>>>( - atom_numbers, uint_crd, uint_crd_with_LJ, LJtype, charge); - - VECTOR *boxlength = const_cast(reinterpret_cast(boxlength_f)); - - Dihedral14CFEnergyKernel<<>>( - dihedral_14_numbers, uint_crd_with_LJ, boxlength, a_14, b_14, cf_scale_factor, ene); - - return; -} - -void Dihedral14CFEnergy(const int dihedral_14_numbers, const int atom_numbers, const int *uint_crd_f, const int *LJtype, - const float *charge, float *uint_crd_with_LJ_f, const float *boxlength_f, const int *a_14, - const int *b_14, const float *cf_scale_factor, float *ene, cudaStream_t stream); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/nb14/dihedral_14_cf_energy_impl.cuh b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/nb14/dihedral_14_cf_energy_impl.cuh deleted file mode 100644 index bc5aea7af1d..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/nb14/dihedral_14_cf_energy_impl.cuh +++ /dev/null @@ -1,27 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -#ifndef MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_NB14_DIHEDRAL_14_CF_ENERGY_IMPL_H -#define MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_NB14_DIHEDRAL_14_CF_ENERGY_IMPL_H - -#include -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_device_info.h" - -CUDA_LIB_EXPORT void Dihedral14CFEnergy(const int dihedral_14_numbers, const int atom_numbers, const int *uint_crd_f, - const int *LJtype, const float *charge, float *uint_crd_with_LJ_f, - const float *boxlength_f, const int *a_14, const int *b_14, - const float *cf_scale_factor, float *ene, cudaStream_t stream); -#endif // MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_NB14_DIHEDRAL_14_CF_ENERGY_IMPL_H diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/nb14/dihedral_14_lj_atom_energy_impl.cu b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/nb14/dihedral_14_lj_atom_energy_impl.cu deleted file mode 100644 index 5dc0093c75f..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/nb14/dihedral_14_lj_atom_energy_impl.cu +++ /dev/null @@ -1,102 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "plugin/device/gpu/kernel/cuda_impl/sponge/nb14/dihedral_14_lj_atom_energy_impl.cuh" -#include "plugin/device/gpu/kernel/cuda_impl/sponge/common_sponge.cuh" - -__global__ void Dihedral14LJAtomEnergyKernel(const int dihedral_14_numbers, const UINT_VECTOR_LJ_TYPE *uint_crd, - const VECTOR *boxlength, const int *a_14, const int *b_14, - const float *lj_scale_factor, const float *LJ_type_A, - const float *LJ_type_B, float *ene) { - int dihedral_14_i = blockDim.x * blockIdx.x + threadIdx.x; - if (dihedral_14_i < dihedral_14_numbers) { - int atom_i = a_14[dihedral_14_i]; - int atom_j = b_14[dihedral_14_i]; - - UINT_VECTOR_LJ_TYPE r1 = uint_crd[atom_i]; - UINT_VECTOR_LJ_TYPE r2 = uint_crd[atom_j]; - - int int_x; - int int_y; - int int_z; - VECTOR dr; - float dr2; - float dr_2; - float dr_4; - float dr_6; - float dr_12; - float ene_lin = 0.; - int x, y; - int atom_pair_LJ_type; - - int_x = r2.uint_x - r1.uint_x; - int_y = r2.uint_y - r1.uint_y; - int_z = r2.uint_z - r1.uint_z; - dr.x = boxlength[0].x * int_x; - dr.y = boxlength[0].y * int_y; - dr.z = boxlength[0].z * int_z; - dr2 = dr.x * dr.x + dr.y * dr.y + dr.z * dr.z; - - dr_2 = 1. / dr2; - dr_4 = dr_2 * dr_2; - dr_6 = dr_4 * dr_2; - dr_12 = dr_6 * dr_6; - - y = (r2.LJ_type - r1.LJ_type); - x = y >> 31; - y = (y ^ x) - x; - x = r2.LJ_type + r1.LJ_type; - r2.LJ_type = (x + y) >> 1; - x = (x - y) >> 1; - atom_pair_LJ_type = (r2.LJ_type * (r2.LJ_type + 1) >> 1) + x; - - ene_lin = 0.08333333 * LJ_type_A[atom_pair_LJ_type] * dr_12 - - 0.1666666 * LJ_type_B[atom_pair_LJ_type] * dr_6; // LJ的A,B系数已经乘以12和6因此要反乘 - ene_lin *= lj_scale_factor[dihedral_14_i]; - - atomicAdd(&ene[atom_i], ene_lin); - } -} - -void Dihedral14LJAtomEnergy(const int dihedral_14_numbers, const int atom_numbers, const int *uint_crd_f, - const int *LJtype, const float *charge, const float *boxlength_f, const int *a_14, - const int *b_14, const float *lj_scale_factor, const float *LJ_type_A, - const float *LJ_type_B, float *ene, cudaStream_t stream) { - size_t thread_per_block = 128; - size_t block_per_grid = ceilf(static_cast(atom_numbers) / 128); - UINT_VECTOR_LJ_TYPE *uint_crd_with_LJ = NULL; - Cuda_Malloc_Safely(reinterpret_cast(&uint_crd_with_LJ), sizeof(UINT_VECTOR_LJ_TYPE) * atom_numbers); - - UNSIGNED_INT_VECTOR *uint_crd = - const_cast(reinterpret_cast(uint_crd_f)); - - Copy_Crd_To_New_Crd_Start<<(atom_numbers) / 32), 32, 0, stream>>>( - atom_numbers, uint_crd, uint_crd_with_LJ, LJtype, charge); - - VECTOR *boxlength = const_cast(reinterpret_cast(boxlength_f)); - Reset_List<<(3. * atom_numbers) / 128), 128, 0, stream>>>(atom_numbers, ene, 0.); - Dihedral14LJAtomEnergyKernel<<>>( - dihedral_14_numbers, uint_crd_with_LJ, boxlength, a_14, b_14, lj_scale_factor, LJ_type_A, LJ_type_B, ene); - - cudaStreamSynchronize(stream); - - return; -} - -void Dihedral14LJAtomEnergy(const int dihedral_14_numbers, const int atom_numbers, const int *uint_crd_f, - const int *LJtype, const float *charge, const float *boxlength_f, const int *a_14, - const int *b_14, const float *lj_scale_factor, const float *LJ_type_A, - const float *LJ_type_B, float *ene, cudaStream_t stream); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/nb14/dihedral_14_lj_atom_energy_impl.cuh b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/nb14/dihedral_14_lj_atom_energy_impl.cuh deleted file mode 100644 index a1943aefe2f..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/nb14/dihedral_14_lj_atom_energy_impl.cuh +++ /dev/null @@ -1,28 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -#ifndef MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_NB14_DIHEDRAL_14_LJ_ATOM_ENERGY_IMPL_H -#define MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_NB14_DIHEDRAL_14_LJ_ATOM_ENERGY_IMPL_H - -#include -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_device_info.h" - -CUDA_LIB_EXPORT void Dihedral14LJAtomEnergy(const int dihedral_14_numbers, const int atom_numbers, - const int *uint_crd_f, const int *LJtype, const float *charge, - const float *boxlength_f, const int *a_14, const int *b_14, - const float *lj_scale_factor, const float *LJ_type_A, - const float *LJ_type_B, float *ene, cudaStream_t stream); -#endif // MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_NB14_DIHEDRAL_14_LJ_ATOM_ENERGY_IMPL_H diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/nb14/dihedral_14_lj_cf_force_with_atom_energy_and_virial_impl.cu b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/nb14/dihedral_14_lj_cf_force_with_atom_energy_and_virial_impl.cu deleted file mode 100644 index 8d3ba0585eb..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/nb14/dihedral_14_lj_cf_force_with_atom_energy_and_virial_impl.cu +++ /dev/null @@ -1,139 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "plugin/device/gpu/kernel/cuda_impl/sponge/nb14/dihedral_14_lj_cf_force_with_atom_energy_and_virial_impl.cuh" -#include "plugin/device/gpu/kernel/cuda_impl/sponge/common_sponge.cuh" - -__global__ void Dihedral14LJCFForceWithAtomEnergyAndVirialKernel( - const int dihedral_14_numbers, const UINT_VECTOR_LJ_TYPE *uint_crd, const VECTOR *scaler, const int *a_14, - const int *b_14, const float *lj_scale_factor, const float *cf_scale_factor, const float *LJ_type_A, - const float *LJ_type_B, VECTOR *frc, float *atom_energy, float *atom_virial) { - int dihedral_14_i = blockDim.x * blockIdx.x + threadIdx.x; - if (dihedral_14_i < dihedral_14_numbers) { - UINT_VECTOR_LJ_TYPE r1, r2; - VECTOR dr; - float dr_abs; - float dr2; - float dr_1; - float dr_2; - float dr_4; - float dr_8; - float dr_14; - float frc_abs = 0.; - VECTOR temp_frc; - - float ene_lin; - float ene_lin2; - - int x, y; - int atom_pair_LJ_type; - - int atom_i = a_14[dihedral_14_i]; - int atom_j = b_14[dihedral_14_i]; - - r1 = uint_crd[atom_i]; - r2 = uint_crd[atom_j]; - - dr = Get_Periodic_Displacement(r2, r1, scaler[0]); - - dr2 = dr.x * dr.x + dr.y * dr.y + dr.z * dr.z; - - dr_2 = 1.0 / dr2; - dr_4 = dr_2 * dr_2; - dr_8 = dr_4 * dr_4; - dr_14 = dr_8 * dr_4 * dr_2; - dr_abs = norm3df(dr.x, dr.y, dr.z); - dr_1 = 1. / dr_abs; - - // CF - float charge_i = r1.charge; - float charge_j = r2.charge; - float frc_cf_abs; - frc_cf_abs = cf_scale_factor[dihedral_14_i] * dr_2 * dr_1; - frc_cf_abs = -charge_i * charge_j * frc_cf_abs; - // LJ - y = (r2.LJ_type - r1.LJ_type); - x = y >> 31; - y = (y ^ x) - x; - x = r2.LJ_type + r1.LJ_type; - r2.LJ_type = (x + y) >> 1; - x = (x - y) >> 1; - atom_pair_LJ_type = (r2.LJ_type * (r2.LJ_type + 1) >> 1) + x; - - frc_abs = -LJ_type_A[atom_pair_LJ_type] * dr_14 + LJ_type_B[atom_pair_LJ_type] * dr_8; - frc_abs *= lj_scale_factor[dihedral_14_i]; - - frc_abs += frc_cf_abs; - temp_frc.x = frc_abs * dr.x; - temp_frc.y = frc_abs * dr.y; - temp_frc.z = frc_abs * dr.z; - - atomicAdd(&frc[atom_j].x, -temp_frc.x); - atomicAdd(&frc[atom_j].y, -temp_frc.y); - atomicAdd(&frc[atom_j].z, -temp_frc.z); - atomicAdd(&frc[atom_i].x, temp_frc.x); - atomicAdd(&frc[atom_i].y, temp_frc.y); - atomicAdd(&frc[atom_i].z, temp_frc.z); - - ene_lin = r1.charge * r2.charge * dr_1; - ene_lin *= cf_scale_factor[dihedral_14_i]; - ene_lin2 = 0.08333333 * LJ_type_A[atom_pair_LJ_type] * dr_4 * dr_8 - - 0.1666666 * LJ_type_B[atom_pair_LJ_type] * dr_4 * dr_2; // LJ的A,B系数已经乘以12和6因此要反乘 - ene_lin2 *= lj_scale_factor[dihedral_14_i]; - - atomicAdd(&atom_energy[atom_i], ene_lin + ene_lin2); - - atomicAdd(&atom_virial[atom_i], -temp_frc * dr); - } -} - -void Dihedral14LJCFForceWithAtomEnergyAndVirial(const int dihedral_14_numbers, const int atom_numbers, - const int *uint_crd_f, const int *LJtype, const float *charge, - float *uint_crd_with_LJ_f, const float *boxlength_f, const int *a_14, - const int *b_14, const float *lj_scale_factor, - const float *cf_scale_factor, const float *LJ_type_A, - const float *LJ_type_B, float *frc_f, float *atom_energy, - float *atom_virial, cudaStream_t stream) { - size_t thread_per_block = 128; - size_t block_per_grid = ceilf(static_cast(atom_numbers) / 128); - UNSIGNED_INT_VECTOR *uint_crd = - const_cast(reinterpret_cast(uint_crd_f)); - - UINT_VECTOR_LJ_TYPE *uint_crd_with_LJ = reinterpret_cast(uint_crd_with_LJ_f); - - Copy_Crd_To_New_Crd_Start<<(atom_numbers) / 32), 32, 0, stream>>>( - atom_numbers, uint_crd, uint_crd_with_LJ, LJtype, charge); - - Reset_List<<(3. * atom_numbers) / 128), 128, 0, stream>>>(3 * atom_numbers, frc_f, 0.); - Reset_List<<(3. * atom_numbers) / 128), 128, 0, stream>>>(atom_numbers, atom_energy, 0.); - Reset_List<<(3. * atom_numbers) / 128), 128, 0, stream>>>(atom_numbers, atom_virial, 0.); - VECTOR *boxlength = const_cast(reinterpret_cast(boxlength_f)); - VECTOR *frc = const_cast(reinterpret_cast(frc_f)); - - Dihedral14LJCFForceWithAtomEnergyAndVirialKernel<<>>( - dihedral_14_numbers, uint_crd_with_LJ, boxlength, a_14, b_14, lj_scale_factor, cf_scale_factor, LJ_type_A, - LJ_type_B, frc, atom_energy, atom_virial); - - return; -} - -void Dihedral14LJCFForceWithAtomEnergyAndVirial(const int dihedral_14_numbers, const int atom_numbers, - const int *uint_crd_f, const int *LJtype, const float *charge, - float *uint_crd_with_LJ_f, const float *boxlength_f, const int *a_14, - const int *b_14, const float *lj_scale_factor, - const float *cf_scale_factor, const float *LJ_type_A, - const float *LJ_type_B, float *frc_f, float *atom_energy, - float *atom_virial, cudaStream_t stream); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/nb14/dihedral_14_lj_cf_force_with_atom_energy_and_virial_impl.cuh b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/nb14/dihedral_14_lj_cf_force_with_atom_energy_and_virial_impl.cuh deleted file mode 100644 index 91018f1fac4..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/nb14/dihedral_14_lj_cf_force_with_atom_energy_and_virial_impl.cuh +++ /dev/null @@ -1,28 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -#ifndef MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_NB14_DIHEDRAL_14_LJ_CF_FORCE_WITH_ATOM_ENERGY_IMPL_H -#define MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_NB14_DIHEDRAL_14_LJ_CF_FORCE_WITH_ATOM_ENERGY_IMPL_H - -#include -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_device_info.h" - -CUDA_LIB_EXPORT void Dihedral14LJCFForceWithAtomEnergyAndVirial( - const int dihedral_14_numbers, const int atom_numbers, const int *uint_crd_f, const int *LJtype, const float *charge, - float *uint_crd_with_LJ_f, const float *boxlength_f, const int *a_14, const int *b_14, const float *lj_scale_factor, - const float *cf_scale_factor, const float *LJ_type_A, const float *LJ_type_B, float *frc_f, float *atom_energy, - float *atom_virial, cudaStream_t stream); -#endif // MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_NB14_DIHEDRAL_14_LJ_CF_FORCE_WITH_ATOM_ENERGY_IMPL_H diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/nb14/dihedral_14_lj_cf_force_with_atom_energy_impl.cu b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/nb14/dihedral_14_lj_cf_force_with_atom_energy_impl.cu deleted file mode 100644 index 6b848960e4b..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/nb14/dihedral_14_lj_cf_force_with_atom_energy_impl.cu +++ /dev/null @@ -1,140 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "plugin/device/gpu/kernel/cuda_impl/sponge/nb14/dihedral_14_lj_cf_force_with_atom_energy_impl.cuh" -#include "plugin/device/gpu/kernel/cuda_impl/sponge/common_sponge.cuh" - -__global__ void Dihedral14LJCFForceWithAtomEnergyKernel(const int dihedral_14_numbers, - const UINT_VECTOR_LJ_TYPE *uint_crd, const VECTOR *boxlength, - const int *a_14, const int *b_14, const float *lj_scale_factor, - const float *cf_scale_factor, const float *LJ_type_A, - const float *LJ_type_B, VECTOR *frc, float *atom_energy) { - int dihedral_14_i = blockDim.x * blockIdx.x + threadIdx.x; - if (dihedral_14_i < dihedral_14_numbers) { - int int_x; - int int_y; - int int_z; - UINT_VECTOR_LJ_TYPE r1, r2; - VECTOR dr; - float dr_abs; - float dr2; - float dr_1; - float dr_2; - float dr_4; - float dr_8; - float dr_14; - float frc_abs = 0.; - VECTOR temp_frc; - - float ene_lin; - float ene_lin2; - - int x, y; - int atom_pair_LJ_type; - - int atom_i = a_14[dihedral_14_i]; - int atom_j = b_14[dihedral_14_i]; - - r1 = uint_crd[atom_i]; - r2 = uint_crd[atom_j]; - int_x = r2.uint_x - r1.uint_x; - int_y = r2.uint_y - r1.uint_y; - int_z = r2.uint_z - r1.uint_z; - dr.x = boxlength[0].x * int_x; - dr.y = boxlength[0].y * int_y; - dr.z = boxlength[0].z * int_z; - dr2 = dr.x * dr.x + dr.y * dr.y + dr.z * dr.z; - - dr_2 = 1.0 / dr2; - dr_4 = dr_2 * dr_2; - dr_8 = dr_4 * dr_4; - dr_14 = dr_8 * dr_4 * dr_2; - dr_abs = norm3df(dr.x, dr.y, dr.z); - dr_1 = 1. / dr_abs; - - float charge_i = r1.charge; - float charge_j = r2.charge; - float frc_cf_abs; - frc_cf_abs = cf_scale_factor[dihedral_14_i] * dr_2 * dr_1; - frc_cf_abs = -charge_i * charge_j * frc_cf_abs; - - y = (r2.LJ_type - r1.LJ_type); - x = y >> 31; - y = (y ^ x) - x; - x = r2.LJ_type + r1.LJ_type; - r2.LJ_type = (x + y) >> 1; - x = (x - y) >> 1; - atom_pair_LJ_type = (r2.LJ_type * (r2.LJ_type + 1) >> 1) + x; - - frc_abs = -LJ_type_A[atom_pair_LJ_type] * dr_14 + LJ_type_B[atom_pair_LJ_type] * dr_8; - frc_abs *= lj_scale_factor[dihedral_14_i]; - - frc_abs += frc_cf_abs; - temp_frc.x = frc_abs * dr.x; - temp_frc.y = frc_abs * dr.y; - temp_frc.z = frc_abs * dr.z; - - atomicAdd(&frc[atom_j].x, -temp_frc.x); - atomicAdd(&frc[atom_j].y, -temp_frc.y); - atomicAdd(&frc[atom_j].z, -temp_frc.z); - atomicAdd(&frc[atom_i].x, temp_frc.x); - atomicAdd(&frc[atom_i].y, temp_frc.y); - atomicAdd(&frc[atom_i].z, temp_frc.z); - - ene_lin = r1.charge * r2.charge * dr_1; - ene_lin *= cf_scale_factor[dihedral_14_i]; - ene_lin2 = 0.08333333 * LJ_type_A[atom_pair_LJ_type] * dr_4 * dr_8 - - 0.1666666 * LJ_type_B[atom_pair_LJ_type] * dr_4 * dr_2; // LJ的A,B系数已经乘以12和6因此要反乘 - ene_lin2 *= lj_scale_factor[dihedral_14_i]; - - atomicAdd(&atom_energy[atom_i], ene_lin + ene_lin2); - } -} - -void Dihedral14LJCFForceWithAtomEnergy(const int dihedral_14_numbers, const int atom_numbers, const int *uint_crd_f, - const int *LJtype, const float *charge, float *uint_crd_with_LJ_f, - const float *boxlength_f, const int *a_14, const int *b_14, - const float *lj_scale_factor, const float *cf_scale_factor, - const float *LJ_type_A, const float *LJ_type_B, float *frc_f, float *atom_energy, - cudaStream_t stream) { - size_t thread_per_block = 128; - size_t block_per_grid = ceilf(static_cast(dihedral_14_numbers) / 128); - UNSIGNED_INT_VECTOR *uint_crd = - const_cast(reinterpret_cast(uint_crd_f)); - - UINT_VECTOR_LJ_TYPE *uint_crd_with_LJ = reinterpret_cast(uint_crd_with_LJ_f); - - Copy_Crd_To_New_Crd_Start<<(atom_numbers) / 32), 32, 0, stream>>>( - atom_numbers, uint_crd, uint_crd_with_LJ, LJtype, charge); - - Reset_List<<(3. * atom_numbers) / 128), 128, 0, stream>>>(3 * atom_numbers, frc_f, 0.); - Reset_List<<(atom_numbers) / 128), 128, 0, stream>>>(atom_numbers, atom_energy, 0.); - VECTOR *boxlength = const_cast(reinterpret_cast(boxlength_f)); - VECTOR *frc = const_cast(reinterpret_cast(frc_f)); - - Dihedral14LJCFForceWithAtomEnergyKernel<<>>( - dihedral_14_numbers, uint_crd_with_LJ, boxlength, a_14, b_14, lj_scale_factor, cf_scale_factor, LJ_type_A, - LJ_type_B, frc, atom_energy); - - return; -} - -void Dihedral14LJCFForceWithAtomEnergy(const int dihedral_14_numbers, const int atom_numbers, const int *uint_crd_f, - const int *LJtype, const float *charge, float *uint_crd_with_LJ_f, - const float *boxlength_f, const int *a_14, const int *b_14, - const float *lj_scale_factor, const float *cf_scale_factor, - const float *LJ_type_A, const float *LJ_type_B, float *frc_f, float *atom_energy, - cudaStream_t stream); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/nb14/dihedral_14_lj_cf_force_with_atom_energy_impl.cuh b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/nb14/dihedral_14_lj_cf_force_with_atom_energy_impl.cuh deleted file mode 100644 index 7d5560c78e0..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/nb14/dihedral_14_lj_cf_force_with_atom_energy_impl.cuh +++ /dev/null @@ -1,30 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -#ifndef MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_NB14_DIHEDRAL_14_LJ_CF_FORCE_WITH_ATOM_ENERGY_IMPL_H -#define MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_NB14_DIHEDRAL_14_LJ_CF_FORCE_WITH_ATOM_ENERGY_IMPL_H - -#include -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_device_info.h" - -CUDA_LIB_EXPORT void Dihedral14LJCFForceWithAtomEnergy(const int dihedral_14_numbers, const int atom_numbers, - const int *uint_crd_f, const int *LJtype, const float *charge, - float *uint_crd_with_LJ_f, const float *boxlength_f, - const int *a_14, const int *b_14, const float *lj_scale_factor, - const float *cf_scale_factor, const float *LJ_type_A, - const float *LJ_type_B, float *frc_f, float *atom_energy, - cudaStream_t stream); -#endif // MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_NB14_DIHEDRAL_14_LJ_CF_FORCE_WITH_ATOM_ENERGY_IMPL_H diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/nb14/dihedral_14_lj_energy_impl.cu b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/nb14/dihedral_14_lj_energy_impl.cu deleted file mode 100644 index 5e90f43e592..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/nb14/dihedral_14_lj_energy_impl.cu +++ /dev/null @@ -1,98 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "plugin/device/gpu/kernel/cuda_impl/sponge/nb14/dihedral_14_lj_energy_impl.cuh" -#include "plugin/device/gpu/kernel/cuda_impl/sponge/common_sponge.cuh" - -__global__ void Dihedral14LJEnergyKernel(const int dihedral_14_numbers, const UINT_VECTOR_LJ_TYPE *uint_crd, - const VECTOR *boxlength, const int *a_14, const int *b_14, - const float *lj_scale_factor, const float *LJ_type_A, const float *LJ_type_B, - float *ene) { - int dihedral_14_i = blockDim.x * blockIdx.x + threadIdx.x; - if (dihedral_14_i < dihedral_14_numbers) { - int atom_i = a_14[dihedral_14_i]; - int atom_j = b_14[dihedral_14_i]; - - UINT_VECTOR_LJ_TYPE r1 = uint_crd[atom_i]; - UINT_VECTOR_LJ_TYPE r2 = uint_crd[atom_j]; - - int int_x; - int int_y; - int int_z; - VECTOR dr; - float dr2; - float dr_2; - float dr_4; - float dr_6; - float dr_12; - float ene_lin = 0.; - int x, y; - int atom_pair_LJ_type; - - int_x = r2.uint_x - r1.uint_x; - int_y = r2.uint_y - r1.uint_y; - int_z = r2.uint_z - r1.uint_z; - dr.x = boxlength[0].x * int_x; - dr.y = boxlength[0].y * int_y; - dr.z = boxlength[0].z * int_z; - dr2 = dr.x * dr.x + dr.y * dr.y + dr.z * dr.z; - - dr_2 = 1. / dr2; - dr_4 = dr_2 * dr_2; - dr_6 = dr_4 * dr_2; - dr_12 = dr_6 * dr_6; - - y = (r2.LJ_type - r1.LJ_type); - x = y >> 31; - y = (y ^ x) - x; - x = r2.LJ_type + r1.LJ_type; - r2.LJ_type = (x + y) >> 1; - x = (x - y) >> 1; - atom_pair_LJ_type = (r2.LJ_type * (r2.LJ_type + 1) >> 1) + x; - - ene_lin = 0.08333333 * LJ_type_A[atom_pair_LJ_type] * dr_12 - - 0.1666666 * LJ_type_B[atom_pair_LJ_type] * dr_6; // LJ的A,B系数已经乘以12和6因此要反乘 - ene_lin *= lj_scale_factor[dihedral_14_i]; - - ene[dihedral_14_i] = ene_lin; - } -} - -void Dihedral14LJEnergy(const int dihedral_14_numbers, const int atom_numbers, const int *uint_crd_f, const int *LJtype, - const float *charge, float *uint_crd_with_LJ_f, const float *boxlength_f, const int *a_14, - const int *b_14, const float *lj_scale_factor, const float *LJ_type_A, const float *LJ_type_B, - float *ene, cudaStream_t stream) { - size_t thread_per_block = 32; - size_t block_per_grid = ceilf(static_cast(dihedral_14_numbers) / 32); - UNSIGNED_INT_VECTOR *uint_crd = - const_cast(reinterpret_cast(uint_crd_f)); - - UINT_VECTOR_LJ_TYPE *uint_crd_with_LJ = reinterpret_cast(uint_crd_with_LJ_f); - - Copy_Crd_To_New_Crd_Start<<(atom_numbers) / 32), 32, 0, stream>>>( - atom_numbers, uint_crd, uint_crd_with_LJ, LJtype, charge); - VECTOR *boxlength = const_cast(reinterpret_cast(boxlength_f)); - - Dihedral14LJEnergyKernel<<>>( - dihedral_14_numbers, uint_crd_with_LJ, boxlength, a_14, b_14, lj_scale_factor, LJ_type_A, LJ_type_B, ene); - - return; -} - -void Dihedral14LJEnergy(const int dihedral_14_numbers, const int atom_numbers, const int *uint_crd_f, const int *LJtype, - const float *charge, float *uint_crd_with_LJ_f, const float *boxlength_f, const int *a_14, - const int *b_14, const float *lj_scale_factor, const float *LJ_type_A, const float *LJ_type_B, - float *ene, cudaStream_t stream); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/nb14/dihedral_14_lj_energy_impl.cuh b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/nb14/dihedral_14_lj_energy_impl.cuh deleted file mode 100644 index 7202a596df3..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/nb14/dihedral_14_lj_energy_impl.cuh +++ /dev/null @@ -1,29 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -#ifndef MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_NB14_DIHEDRAL_14_LJ_ENERGY_IMPL_H -#define MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_NB14_DIHEDRAL_14_LJ_ENERGY_IMPL_H - -#include -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_device_info.h" - -CUDA_LIB_EXPORT void Dihedral14LJEnergy(const int dihedral_14_numbers, const int atom_numbers, const int *uint_crd_f, - const int *LJtype, const float *charge, float *uint_crd_with_LJ_f, - const float *boxlength_f, const int *a_14, const int *b_14, - const float *lj_scale_factor, const float *LJ_type_A, const float *LJ_type_B, - float *ene, cudaStream_t stream); - -#endif // MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_NB14_DIHEDRAL_14_LJ_ENERGY_IMPL_H diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/nb14/dihedral_14_lj_force_impl.cu b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/nb14/dihedral_14_lj_force_impl.cu deleted file mode 100644 index 4e731c62b26..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/nb14/dihedral_14_lj_force_impl.cu +++ /dev/null @@ -1,111 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "plugin/device/gpu/kernel/cuda_impl/sponge/nb14/dihedral_14_lj_force_impl.cuh" -#include "plugin/device/gpu/kernel/cuda_impl/sponge/common_sponge.cuh" - -__global__ void Dihedral14LJForceKernel(const int dihedral_14_numbers, const UINT_VECTOR_LJ_TYPE *uint_crd, - const VECTOR *boxlength, const int *a_14, const int *b_14, - const float *lj_scale_factor, const float *LJ_type_A, const float *LJ_type_B, - VECTOR *frc) { - int dihedral_14_i = blockDim.x * blockIdx.x + threadIdx.x; - if (dihedral_14_i < dihedral_14_numbers) { - int int_x; - int int_y; - int int_z; - UINT_VECTOR_LJ_TYPE r1, r2; - VECTOR dr; - float dr2; - float dr_2; - float dr_4; - float dr_8; - float dr_14; - float frc_abs = 0.; - VECTOR temp_frc; - int x, y; - int atom_pair_LJ_type; - - int atom_i = a_14[dihedral_14_i]; - int atom_j = b_14[dihedral_14_i]; - - r1 = uint_crd[atom_i]; - r2 = uint_crd[atom_j]; - - int_x = r2.uint_x - r1.uint_x; - int_y = r2.uint_y - r1.uint_y; - int_z = r2.uint_z - r1.uint_z; - dr.x = boxlength[0].x * int_x; - dr.y = boxlength[0].y * int_y; - dr.z = boxlength[0].z * int_z; - dr2 = dr.x * dr.x + dr.y * dr.y + dr.z * dr.z; - - dr_2 = 1.0 / dr2; - dr_4 = dr_2 * dr_2; - dr_8 = dr_4 * dr_4; - dr_14 = dr_8 * dr_4 * dr_2; - - y = (r2.LJ_type - r1.LJ_type); - x = y >> 31; - y = (y ^ x) - x; - x = r2.LJ_type + r1.LJ_type; - r2.LJ_type = (x + y) >> 1; - x = (x - y) >> 1; - atom_pair_LJ_type = (r2.LJ_type * (r2.LJ_type + 1) >> 1) + x; - - frc_abs = -LJ_type_A[atom_pair_LJ_type] * dr_14 + LJ_type_B[atom_pair_LJ_type] * dr_8; - frc_abs *= lj_scale_factor[dihedral_14_i]; - temp_frc.x = frc_abs * dr.x; - temp_frc.y = frc_abs * dr.y; - temp_frc.z = frc_abs * dr.z; - - atomicAdd(&frc[atom_j].x, -temp_frc.x); - atomicAdd(&frc[atom_j].y, -temp_frc.y); - atomicAdd(&frc[atom_j].z, -temp_frc.z); - atomicAdd(&frc[atom_i].x, temp_frc.x); - atomicAdd(&frc[atom_i].y, temp_frc.y); - atomicAdd(&frc[atom_i].z, temp_frc.z); - } -} - -void Dihedral14LJForce(const int dihedral_14_numbers, const int atom_numbers, const int *uint_crd_f, const int *LJtype, - const float *charge, const float *boxlength_f, const int *a_14, const int *b_14, - const float *lj_scale_factor, const float *LJ_type_A, const float *LJ_type_B, float *frc_f, - cudaStream_t stream) { - size_t thread_per_block = 128; - size_t block_per_grid = ceilf(static_cast(atom_numbers) / 128); - UINT_VECTOR_LJ_TYPE *uint_crd_with_LJ = NULL; - Cuda_Malloc_Safely(reinterpret_cast(&uint_crd_with_LJ), sizeof(UINT_VECTOR_LJ_TYPE) * atom_numbers); - - UNSIGNED_INT_VECTOR *uint_crd = - const_cast(reinterpret_cast(uint_crd_f)); - - Copy_Crd_To_New_Crd_Start<<(atom_numbers) / 32), 32, 0, stream>>>( - atom_numbers, uint_crd, uint_crd_with_LJ, LJtype, charge); - cudaStreamSynchronize(stream); - Reset_List<<(3. * atom_numbers) / 128), 128, 0, stream>>>(3 * atom_numbers, frc_f, 0.); - VECTOR *boxlength = const_cast(reinterpret_cast(boxlength_f)); - VECTOR *frc = const_cast(reinterpret_cast(frc_f)); - - Dihedral14LJForceKernel<<>>( - dihedral_14_numbers, uint_crd_with_LJ, boxlength, a_14, b_14, lj_scale_factor, LJ_type_A, LJ_type_B, frc); - cudaStreamSynchronize(stream); - return; -} - -void Dihedral14LJForce(const int dihedral_14_numbers, const int atom_numbers, const int *uint_crd_f, const int *LJtype, - const float *charge, const float *boxlength_f, const int *a_14, const int *b_14, - const float *lj_scale_factor, const float *LJ_type_A, const float *LJ_type_B, float *frc_f, - cudaStream_t stream); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/nb14/dihedral_14_lj_force_impl.cuh b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/nb14/dihedral_14_lj_force_impl.cuh deleted file mode 100644 index 61849b1abc8..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/nb14/dihedral_14_lj_force_impl.cuh +++ /dev/null @@ -1,28 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -#ifndef MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_NB14_DIHEDRAL_14_LJ_FORCE_IMPL_H -#define MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_NB14_DIHEDRAL_14_LJ_FORCE_IMPL_H - -#include -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_device_info.h" - -CUDA_LIB_EXPORT void Dihedral14LJForce(const int dihedral_14_numbers, const int atom_numbers, const int *uint_crd_f, - const int *LJtype, const float *charge, const float *boxlength_f, - const int *a_14, const int *b_14, const float *lj_scale_factor, - const float *LJ_type_A, const float *LJ_type_B, float *frc_f, - cudaStream_t stream); -#endif // MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_NB14_DIHEDRAL_14_LJ_FORCE_IMPL_H diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/nb14/dihedral_14_lj_force_with_direct_cf_impl.cu b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/nb14/dihedral_14_lj_force_with_direct_cf_impl.cu deleted file mode 100644 index eef6d6209a8..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/nb14/dihedral_14_lj_force_with_direct_cf_impl.cu +++ /dev/null @@ -1,124 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "plugin/device/gpu/kernel/cuda_impl/sponge/nb14/dihedral_14_lj_force_with_direct_cf_impl.cuh" -#include "plugin/device/gpu/kernel/cuda_impl/sponge/common_sponge.cuh" - -__global__ void Dihedral14LJForceWithDirectCFKernel(const int dihedral_14_numbers, const UINT_VECTOR_LJ_TYPE *uint_crd, - const VECTOR *boxlength, const int *a_14, const int *b_14, - const float *lj_scale_factor, const float *cf_scale_factor, - const float *LJ_type_A, const float *LJ_type_B, VECTOR *frc) { - int dihedral_14_i = blockDim.x * blockIdx.x + threadIdx.x; - if (dihedral_14_i < dihedral_14_numbers) { - int int_x; - int int_y; - int int_z; - UINT_VECTOR_LJ_TYPE r1, r2; - VECTOR dr; - float dr_abs; - float dr2; - float dr_1; - float dr_2; - float dr_4; - float dr_8; - float dr_14; - float frc_abs = 0.; - VECTOR temp_frc; - - int x, y; - int atom_pair_LJ_type; - - int atom_i = a_14[dihedral_14_i]; - int atom_j = b_14[dihedral_14_i]; - - r1 = uint_crd[atom_i]; - r2 = uint_crd[atom_j]; - int_x = r2.uint_x - r1.uint_x; - int_y = r2.uint_y - r1.uint_y; - int_z = r2.uint_z - r1.uint_z; - dr.x = boxlength[0].x * int_x; - dr.y = boxlength[0].y * int_y; - dr.z = boxlength[0].z * int_z; - dr2 = dr.x * dr.x + dr.y * dr.y + dr.z * dr.z; - - dr_2 = 1.0 / dr2; - dr_4 = dr_2 * dr_2; - dr_8 = dr_4 * dr_4; - dr_14 = dr_8 * dr_4 * dr_2; - dr_abs = norm3df(dr.x, dr.y, dr.z); - dr_1 = 1. / dr_abs; - - float charge_i = r1.charge; - float charge_j = r2.charge; - float frc_cf_abs; - frc_cf_abs = cf_scale_factor[dihedral_14_i] * dr_2 * dr_1; - frc_cf_abs = -charge_i * charge_j * frc_cf_abs; - // LJ - y = (r2.LJ_type - r1.LJ_type); - x = y >> 31; - y = (y ^ x) - x; - x = r2.LJ_type + r1.LJ_type; - r2.LJ_type = (x + y) >> 1; - x = (x - y) >> 1; - atom_pair_LJ_type = (r2.LJ_type * (r2.LJ_type + 1) >> 1) + x; - - frc_abs = -LJ_type_A[atom_pair_LJ_type] * dr_14 + LJ_type_B[atom_pair_LJ_type] * dr_8; - frc_abs *= lj_scale_factor[dihedral_14_i]; - - frc_abs += frc_cf_abs; - temp_frc.x = frc_abs * dr.x; - temp_frc.y = frc_abs * dr.y; - temp_frc.z = frc_abs * dr.z; - - atomicAdd(&frc[atom_j].x, -temp_frc.x); - atomicAdd(&frc[atom_j].y, -temp_frc.y); - atomicAdd(&frc[atom_j].z, -temp_frc.z); - atomicAdd(&frc[atom_i].x, temp_frc.x); - atomicAdd(&frc[atom_i].y, temp_frc.y); - atomicAdd(&frc[atom_i].z, temp_frc.z); - } -} - -void Dihedral14LJForceWithDirectCF(const int dihedral_14_numbers, const int atom_numbers, const int *uint_crd_f, - const int *LJtype, const float *charge, const float *boxlength_f, const int *a_14, - const int *b_14, const float *lj_scale_factor, const float *cf_scale_factor, - const float *LJ_type_A, const float *LJ_type_B, float *frc_f, cudaStream_t stream) { - size_t thread_per_block = 128; - size_t block_per_grid = ceilf(static_cast(atom_numbers) / 128); - UINT_VECTOR_LJ_TYPE *uint_crd_with_LJ = NULL; - Cuda_Malloc_Safely(reinterpret_cast(&uint_crd_with_LJ), sizeof(UINT_VECTOR_LJ_TYPE) * atom_numbers); - - UNSIGNED_INT_VECTOR *uint_crd = - const_cast(reinterpret_cast(uint_crd_f)); - - Copy_Crd_To_New_Crd_Start<<(atom_numbers) / 32), 32, 0, stream>>>( - atom_numbers, uint_crd, uint_crd_with_LJ, LJtype, charge); - cudaStreamSynchronize(stream); - VECTOR *boxlength = const_cast(reinterpret_cast(boxlength_f)); - Reset_List<<(3. * atom_numbers) / 128), 128, 0, stream>>>(3 * atom_numbers, frc_f, 0.); - VECTOR *frc = const_cast(reinterpret_cast(frc_f)); - - Dihedral14LJForceWithDirectCFKernel<<>>( - dihedral_14_numbers, uint_crd_with_LJ, boxlength, a_14, b_14, lj_scale_factor, cf_scale_factor, LJ_type_A, - LJ_type_B, frc); - - return; -} - -void Dihedral14LJForceWithDirectCF(const int dihedral_14_numbers, const int atom_numbers, const int *uint_crd_f, - const int *LJtype, const float *charge, const float *boxlength_f, const int *a_14, - const int *b_14, const float *lj_scale_factor, const float *cf_scale_factor, - const float *LJ_type_A, const float *LJ_type_B, float *frc_f, cudaStream_t stream); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/nb14/dihedral_14_lj_force_with_direct_cf_impl.cuh b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/nb14/dihedral_14_lj_force_with_direct_cf_impl.cuh deleted file mode 100644 index 901aa4f56cc..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/nb14/dihedral_14_lj_force_with_direct_cf_impl.cuh +++ /dev/null @@ -1,29 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -#ifndef MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_NB14_DIHEDRAL_14_LJ_FORCE_WITH_DIRECT_CF_IMPL_H -#define MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_NB14_DIHEDRAL_14_LJ_FORCE_WITH_DIRECT_CF_IMPL_H - -#include -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_device_info.h" - -CUDA_LIB_EXPORT void Dihedral14LJForceWithDirectCF(const int dihedral_14_numbers, const int atom_numbers, - const int *uint_crd_f, const int *LJtype, const float *charge, - const float *boxlength_f, const int *a_14, const int *b_14, - const float *lj_scale_factor, const float *cf_scale_factor, - const float *LJ_type_A, const float *LJ_type_B, float *frc_f, - cudaStream_t stream); -#endif // MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_NB14_DIHEDRAL_14_LJ_FORCE_WITH_DIRECT_CF_IMPL_H diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/neighbor_list/neighbor_list_impl.cu b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/neighbor_list/neighbor_list_impl.cu deleted file mode 100644 index c32a73a0264..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/neighbor_list/neighbor_list_impl.cu +++ /dev/null @@ -1,644 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/** - * Note: - * NeighborListUpdate. This is an experimental interface that is subject to change and/or deletion. - */ - -#include "plugin/device/gpu/kernel/cuda_impl/sponge/neighbor_list/neighbor_list_impl.cuh" -#include - -// common functions - -static __global__ void Copy_List(const int element_numbers, const float *origin_list, float *list) { - int i = blockDim.x * blockIdx.x + threadIdx.x; - if (i < element_numbers) { - list[i] = origin_list[i]; - } -} - -static __global__ void Crd_To_Uint_Crd(const int atom_numbers, float *scale_factor, const VECTOR *crd, - UNSIGNED_INT_VECTOR *uint_crd) { - int atom_i = blockDim.x * blockIdx.x + threadIdx.x; - if (atom_i < atom_numbers) { - INT_VECTOR tempi; - VECTOR temp = crd[atom_i]; - - temp.x *= scale_factor[0]; - temp.y *= scale_factor[1]; - temp.z *= scale_factor[2]; - - tempi.int_x = temp.x; - tempi.int_y = temp.y; - tempi.int_z = temp.z; - - uint_crd[atom_i].uint_x = (tempi.int_x << 2); - uint_crd[atom_i].uint_y = (tempi.int_y << 2); - uint_crd[atom_i].uint_z = (tempi.int_z << 2); - } -} - -static __global__ void Crd_Periodic_Map(const int atom_numbers, VECTOR *crd, const float *box_length) { - int atom_i = blockDim.x * blockIdx.x + threadIdx.x; - if (atom_i < atom_numbers) { - if (crd[atom_i].x >= 0) { - if (crd[atom_i].x < box_length[0]) { - } else { - crd[atom_i].x = crd[atom_i].x - box_length[0]; - } - } else { - crd[atom_i].x = crd[atom_i].x + box_length[0]; - } - - if (crd[atom_i].y >= 0) { - if (crd[atom_i].y < box_length[1]) { - } else { - crd[atom_i].y = crd[atom_i].y - box_length[1]; - } - } else { - crd[atom_i].y = crd[atom_i].y + box_length[1]; - } - if (crd[atom_i].z >= 0) { - if (crd[atom_i].z < box_length[2]) { - } else { - crd[atom_i].z = crd[atom_i].z - box_length[2]; - } - } else { - crd[atom_i].z = crd[atom_i].z + box_length[2]; - } - } -} - -static __global__ void Clear_Grid_Bucket(const int grid_numbers, int *atom_numbers_in_grid_bucket, - GRID_BUCKET *bucket) { - int grid_serial = blockDim.x * blockIdx.x + threadIdx.x; - if (grid_serial < grid_numbers) { - GRID_BUCKET bucket_i = bucket[grid_serial]; - for (int i = 0; i < atom_numbers_in_grid_bucket[grid_serial]; i = i + 1) { - bucket_i.atom_serial[i] = -1; - } - atom_numbers_in_grid_bucket[grid_serial] = 0; - } -} - -static __global__ void Find_Atom_In_Grid_Serial(const int atom_numbers, const float *grid_length_inverse, - const VECTOR *crd, const int *grid_N, const int gridxy, - int *atom_in_grid_serial) { - int atom_i = blockDim.x * blockIdx.x + threadIdx.x; - if (atom_i < atom_numbers) { - int Nx = static_cast(crd[atom_i].x) * grid_length_inverse[0]; - int Ny = static_cast(crd[atom_i].y) * grid_length_inverse[1]; - int Nz = static_cast(crd[atom_i].z) * grid_length_inverse[2]; - Nx = Nx & ((Nx - grid_N[0]) >> 31); - Ny = Ny & ((Ny - grid_N[1]) >> 31); - Nz = Nz & ((Nz - grid_N[2]) >> 31); - atom_in_grid_serial[atom_i] = Nz * gridxy + Ny * grid_N[0] + Nx; - } -} - -static __global__ void Put_Atom_In_Grid_Bucket(const int atom_numbers, const int *atom_in_grid_serial, - GRID_BUCKET *bucket, int *atom_numbers_in_grid_bucket) { - int atom_i = blockDim.x * blockIdx.x + threadIdx.x; - if (atom_i < atom_numbers) { - int grid_serial = atom_in_grid_serial[atom_i]; - GRID_BUCKET bucket_i = bucket[grid_serial]; - int a = atom_numbers_in_grid_bucket[grid_serial]; - atomicCAS(&bucket_i.atom_serial[a], -1, atom_i); - if (bucket_i.atom_serial[a] != atom_i) { - while (true) { - a = a + 1; - atomicCAS(&bucket_i.atom_serial[a], -1, atom_i); - if (bucket_i.atom_serial[a] == atom_i) { - atomicAdd(&atom_numbers_in_grid_bucket[grid_serial], 1); - break; - } - } - } else { - atomicAdd(&atom_numbers_in_grid_bucket[grid_serial], 1); - } - } -} - -static __global__ void Find_atom_neighbors(const int atom_numbers, const UNSIGNED_INT_VECTOR *uint_crd, - const float *uint_dr_to_dr_cof, const int *atom_in_grid_serial, - const GRID_POINTER *gpointer, const GRID_BUCKET *bucket, - const int *atom_numbers_in_grid_bucket, NEIGHBOR_LIST *nl, - const float cutoff_skin_square) { - int atom_i = blockDim.x * blockIdx.x + threadIdx.x; - if (atom_i < atom_numbers) { - int grid_serial = atom_in_grid_serial[atom_i]; - int grid_serial2; - int atom_numbers_in_nl_lin = 0; - int atom_j; - int int_x; - int int_y; - int int_z; - UNSIGNED_INT_VECTOR uint_crd_i = uint_crd[atom_i]; - NEIGHBOR_LIST nl_i = nl[atom_i]; - GRID_POINTER gpointer_i = gpointer[grid_serial]; - VECTOR dr; - float dr2; - for (int grid_cycle = 0; grid_cycle < 125; grid_cycle = grid_cycle + 1) { - grid_serial2 = gpointer_i.grid_serial[grid_cycle]; - GRID_BUCKET bucket_i = bucket[grid_serial2]; - for (int i = 0; i < atom_numbers_in_grid_bucket[grid_serial2]; i = i + 1) { - atom_j = bucket_i.atom_serial[i]; - if (atom_j > atom_i) { - int_x = uint_crd[atom_j].uint_x - uint_crd_i.uint_x; - int_y = uint_crd[atom_j].uint_y - uint_crd_i.uint_y; - int_z = uint_crd[atom_j].uint_z - uint_crd_i.uint_z; - dr.x = uint_dr_to_dr_cof[0] * int_x; - dr.y = uint_dr_to_dr_cof[1] * int_y; - dr.z = uint_dr_to_dr_cof[2] * int_z; - dr2 = dr.x * dr.x + dr.y * dr.y + dr.z * dr.z; - if (dr2 < cutoff_skin_square) { - nl_i.atom_serial[atom_numbers_in_nl_lin] = atom_j; - atom_numbers_in_nl_lin = atom_numbers_in_nl_lin + 1; - } - } - } - } - nl[atom_i].atom_numbers = atom_numbers_in_nl_lin; - } -} - -static __global__ void Delete_Excluded_Atoms_Serial_In_Neighbor_List(const int atom_numbers, NEIGHBOR_LIST *nl, - const int *excluded_list_start, - const int *excluded_list, - const int *excluded_atom_numbers) { - int atom_i = blockDim.x * blockIdx.x + threadIdx.x; - if (atom_i < atom_numbers) { - int excluded_number = excluded_atom_numbers[atom_i]; - if (excluded_number > 0) { - int list_start = excluded_list_start[atom_i]; - int atom_min = excluded_list[list_start]; - int list_end = list_start + excluded_number; - int atom_max = excluded_list[list_end - 1]; - NEIGHBOR_LIST nl_i = nl[atom_i]; - int atomnumbers_in_nl_lin = nl_i.atom_numbers; - int atom_j; - int excluded_atom_numbers_lin = list_end - list_start; - int excluded_atom_numbers_count = 0; - for (int i = 0; i < atomnumbers_in_nl_lin; i = i + 1) { - atom_j = nl_i.atom_serial[i]; - if (atom_j < atom_min || atom_j > atom_max) { - continue; - } else { - for (int j = list_start; j < list_end; j = j + 1) { - if (atom_j == excluded_list[j]) { - atomnumbers_in_nl_lin = atomnumbers_in_nl_lin - 1; - nl_i.atom_serial[i] = nl_i.atom_serial[atomnumbers_in_nl_lin]; - excluded_atom_numbers_count = excluded_atom_numbers_count + 1; - i = i - 1; - } - } - if (excluded_atom_numbers_count < excluded_atom_numbers_lin) { - } else { - break; - } - } - } - nl[atom_i].atom_numbers = atomnumbers_in_nl_lin; - } - } -} - -static __global__ void construct_neighbor_list_kernel(int atom_numbers, int max_neighbor_numbers, int *nl_atom_numbers, - int *nl_atom_serial, NEIGHBOR_LIST *nl) { - for (size_t i = blockIdx.x * blockDim.x + threadIdx.x; i < atom_numbers; i += gridDim.x * blockDim.x) { - nl[i].atom_numbers = nl_atom_numbers[i]; - nl[i].atom_serial = nl_atom_serial + i * max_neighbor_numbers; - } -} - -static __global__ void copy_neighbor_list_atom_number(int atom_numbers, int max_neighbor_numbers, NEIGHBOR_LIST *nl, - int *nl_atom_numbers, int *nl_atom_serial) { - int i, j; - for (i = blockIdx.x * blockDim.x + threadIdx.x; i < atom_numbers; i += gridDim.x * blockDim.x) { - nl_atom_numbers[i] = nl[i].atom_numbers; - for (j = blockIdx.y * blockDim.y + threadIdx.y; j < max_neighbor_numbers; j += gridDim.y * blockDim.y) { - if (j < nl_atom_numbers[i]) { - nl_atom_serial[i * max_neighbor_numbers + j] = nl[i].atom_serial[j]; - } else { - nl_atom_serial[i * max_neighbor_numbers + j] = 0; - } - } - } -} - -static __global__ void Mul_half(float *src, float *dst) { - int index = threadIdx.x; - if (index < 3) { - dst[index] = src[index] * 0.5; - } -} - -static __global__ void Mul_quarter(float *src, float *dst) { - int index = threadIdx.x; - if (index < 3) { - dst[index] = src[index] * 0.25; - } -} - -// old neighbor list update functions -__global__ void Crd_To_Uint_Crd_Half(const int atom_numbers, float *scale_factor, const VECTOR *crd, - UNSIGNED_INT_VECTOR *uint_crd) { - int atom_i = blockDim.x * blockIdx.x + threadIdx.x; - if (atom_i < atom_numbers) { - uint_crd[atom_i].uint_x = crd[atom_i].x * scale_factor[0]; - uint_crd[atom_i].uint_y = crd[atom_i].y * scale_factor[1]; - uint_crd[atom_i].uint_z = crd[atom_i].z * scale_factor[2]; - uint_crd[atom_i].uint_x = uint_crd[atom_i].uint_x << 1; - uint_crd[atom_i].uint_y = uint_crd[atom_i].uint_y << 1; - uint_crd[atom_i].uint_z = uint_crd[atom_i].uint_z << 1; - } -} - -__global__ void Vector_Translation(const int vector_numbers, VECTOR *vec_list, const VECTOR translation_vec) { - int i = blockDim.x * blockIdx.x + threadIdx.x; - if (i < vector_numbers) { - vec_list[i].x = vec_list[i].x + translation_vec.x; - vec_list[i].y = vec_list[i].y + translation_vec.y; - vec_list[i].z = vec_list[i].z + translation_vec.z; - } -} - -__global__ void Vector_Translation(const int vector_numbers, VECTOR *vec_list, const VECTOR *translation_vec) { - int i = blockDim.x * blockIdx.x + threadIdx.x; - if (i < vector_numbers) { - vec_list[i].x = vec_list[i].x + translation_vec[0].x; - vec_list[i].y = vec_list[i].y + translation_vec[0].y; - vec_list[i].z = vec_list[i].z + translation_vec[0].z; - } -} - -__global__ void Is_need_refresh_neighbor_list_cuda(const int atom_numbers, const VECTOR *crd, const VECTOR *old_crd, - const float half_skin_square, int *need_refresh_flag) { - int i = blockDim.x * blockIdx.x + threadIdx.x; - if (i < atom_numbers) { - VECTOR r1 = crd[i]; - VECTOR r2 = old_crd[i]; - r1.x = r1.x - r2.x; - r1.y = r1.y - r2.y; - r1.z = r1.z - r2.z; - float r1_2 = r1.x * r1.x + r1.y * r1.y + r1.z * r1.z; - if (r1_2 > half_skin_square) { - atomicExch(&need_refresh_flag[0], 1); - } - } -} - -void Refresh_Neighbor_List_Half(int *refresh_sign, const int thread, const int atom_numbers, VECTOR *crd, - VECTOR *old_crd, UNSIGNED_INT_VECTOR *uint_crd, float *crd_to_uint_crd_cof, - float *uint_dr_to_dr_cof, int *atom_in_grid_serial, const float skin, float *box_length, - const GRID_POINTER *gpointer, GRID_BUCKET *bucket, int *atom_numbers_in_grid_bucket, - NEIGHBOR_LIST *d_nl, int *excluded_list_start, int *excluded_list, - int *excluded_numbers, float cutoff_skin_square, int grid_numbers, - float *grid_length_inverse, int *grid_N, int nxy, cudaStream_t stream) { - std::vector h_refresh_sign(1); - cudaMemcpyAsync(h_refresh_sign.data(), refresh_sign, sizeof(int), cudaMemcpyDeviceToHost, stream); - if (h_refresh_sign[0] == 1) { - VECTOR trans_vec = {-skin, -skin, -skin}; - Clear_Grid_Bucket<<(grid_numbers) / thread), thread, 0, stream>>>( - grid_numbers, atom_numbers_in_grid_bucket, bucket); - - Vector_Translation<<(atom_numbers) / thread), thread, 0, stream>>>(atom_numbers, crd, - trans_vec); - - Crd_Periodic_Map<<(atom_numbers) / thread), thread, 0, stream>>>(atom_numbers, crd, - box_length); - - Find_Atom_In_Grid_Serial<<(atom_numbers) / thread), thread, 0, stream>>>( - atom_numbers, grid_length_inverse, crd, grid_N, nxy, atom_in_grid_serial); - - trans_vec.x = -trans_vec.x; - trans_vec.y = -trans_vec.y; - trans_vec.z = -trans_vec.z; - - Vector_Translation<<(atom_numbers) / thread), thread, 0, stream>>>(atom_numbers, crd, - trans_vec); - - Copy_List<<(3. * atom_numbers) / thread), thread, 0, stream>>>( - 3 * atom_numbers, reinterpret_cast(crd), reinterpret_cast(old_crd)); - - Put_Atom_In_Grid_Bucket<<(atom_numbers) / thread), thread, 0, stream>>>( - atom_numbers, atom_in_grid_serial, bucket, atom_numbers_in_grid_bucket); - - Crd_To_Uint_Crd_Half<<(atom_numbers) / thread), thread, 0, stream>>>( - atom_numbers, crd_to_uint_crd_cof, crd, uint_crd); - - Find_atom_neighbors<<(atom_numbers) / thread), thread, 0, stream>>>( - atom_numbers, uint_crd, uint_dr_to_dr_cof, atom_in_grid_serial, gpointer, bucket, atom_numbers_in_grid_bucket, - d_nl, cutoff_skin_square); - - Delete_Excluded_Atoms_Serial_In_Neighbor_List<<(atom_numbers) / thread), thread, 0, - stream>>>(atom_numbers, d_nl, excluded_list_start, excluded_list, - excluded_numbers); - h_refresh_sign[0] = 0; - } -} - -void Refresh_Neighbor_List_First_Time(int *refresh_sign, const int thread, const int atom_numbers, VECTOR *crd, - VECTOR *old_crd, UNSIGNED_INT_VECTOR *uint_crd, float *crd_to_uint_crd_cof, - float *uint_dr_to_dr_cof, int *atom_in_grid_serial, const float skin, - float *box_length, const GRID_POINTER *gpointer, GRID_BUCKET *bucket, - int *atom_numbers_in_grid_bucket, NEIGHBOR_LIST *d_nl, int *excluded_list_start, - int *excluded_list, int *excluded_numbers, float cutoff_skin_square, - int grid_numbers, float *grid_length_inverse, int *grid_N, int nxy, - cudaStream_t stream) { - VECTOR trans_vec = {skin, skin, skin}; - Clear_Grid_Bucket<<(grid_numbers) / 32), 32, 0, stream>>>( - grid_numbers, atom_numbers_in_grid_bucket, bucket); - Crd_Periodic_Map<<(atom_numbers) / 32), 32, 0, stream>>>(atom_numbers, crd, box_length); - Find_Atom_In_Grid_Serial<<(atom_numbers) / 32), 32, 0, stream>>>( - atom_numbers, grid_length_inverse, crd, grid_N, nxy, atom_in_grid_serial); - Vector_Translation<<(atom_numbers) / 32), 32, 0, stream>>>(atom_numbers, crd, trans_vec); - Copy_List<<(3. * atom_numbers) / 32), 32, 0, stream>>>( - 3 * atom_numbers, reinterpret_cast(crd), reinterpret_cast(old_crd)); - Put_Atom_In_Grid_Bucket<<(atom_numbers) / 32), 32, 0, stream>>>( - atom_numbers, atom_in_grid_serial, bucket, atom_numbers_in_grid_bucket); - Crd_To_Uint_Crd_Half<<(atom_numbers) / 32), 32, 0, stream>>>( - atom_numbers, crd_to_uint_crd_cof, crd, uint_crd); - - Find_atom_neighbors<<(atom_numbers) / thread), thread, 0, stream>>>( - atom_numbers, uint_crd, uint_dr_to_dr_cof, atom_in_grid_serial, gpointer, bucket, atom_numbers_in_grid_bucket, d_nl, - cutoff_skin_square); - Delete_Excluded_Atoms_Serial_In_Neighbor_List<<(atom_numbers) / thread), thread, 0, - stream>>>(atom_numbers, d_nl, excluded_list_start, excluded_list, - excluded_numbers); -} - -__global__ void copy_neighbor_list_atom_number(int atom_numbers, NEIGHBOR_LIST *nl, int *nl_atom_numbers) { - for (size_t i = blockIdx.x * blockDim.x + threadIdx.x; i < atom_numbers; i += gridDim.x * blockDim.x) { - nl_atom_numbers[i] = nl[i].atom_numbers; - } -} - -void ConstructNeighborListHalf(int atom_numbers, int max_neighbor_numbers, int *nl_atom_numbers, int *nl_atom_serial, - NEIGHBOR_LIST *nl, cudaStream_t stream) { - construct_neighbor_list_kernel<<(atom_numbers) / 128), 128, 0, stream>>>( - atom_numbers, max_neighbor_numbers, nl_atom_numbers, nl_atom_serial, nl); -} - -void CopyNeighborListHalf(int atom_numbers, NEIGHBOR_LIST *nl, int *nl_atom_numbers, cudaStream_t stream) { - copy_neighbor_list_atom_number<<(atom_numbers) / 128), 128, 0, stream>>>(atom_numbers, nl, - nl_atom_numbers); -} - -void Refresh_Neighbor_List_No_Check_Half(int grid_numbers, int atom_numbers, float skin, int nxy, - float cutoff_skin_square, int *grid_N, float *box_length, - int *atom_numbers_in_grid_bucket, float *grid_length_inverse, - int *atom_in_grid_serial, GRID_BUCKET *bucket, VECTOR *crd, VECTOR *old_crd, - float *crd_to_uint_crd_cof, UNSIGNED_INT_VECTOR *uint_crd, - float *uint_dr_to_dr_cof, GRID_POINTER *gpointer, NEIGHBOR_LIST *d_nl, - int *excluded_list_start, int *excluded_list, int *excluded_numbers, - cudaStream_t stream) { - VECTOR trans_vec = {-skin, -skin, -skin}; - - Clear_Grid_Bucket<<(grid_numbers) / 32), 32, 0, stream>>>( - grid_numbers, atom_numbers_in_grid_bucket, bucket); - - Vector_Translation<<(atom_numbers) / 32), 32, 0, stream>>>(atom_numbers, crd, trans_vec); - - Crd_Periodic_Map<<(atom_numbers) / 32), 32, 0, stream>>>(atom_numbers, crd, box_length); - - Find_Atom_In_Grid_Serial<<(atom_numbers) / 32), 32, 0, stream>>>( - atom_numbers, grid_length_inverse, crd, grid_N, nxy, atom_in_grid_serial); - trans_vec.x = -trans_vec.x; - trans_vec.y = -trans_vec.y; - trans_vec.z = -trans_vec.z; - Vector_Translation<<(atom_numbers) / 32), 32, 0, stream>>>(atom_numbers, crd, trans_vec); - - cudaMemcpyAsync(old_crd, crd, sizeof(VECTOR) * atom_numbers, cudaMemcpyDeviceToDevice, stream); - - Put_Atom_In_Grid_Bucket<<(atom_numbers) / 32), 32, 0, stream>>>( - atom_numbers, atom_in_grid_serial, bucket, atom_numbers_in_grid_bucket); - - Crd_To_Uint_Crd_Half<<(atom_numbers) / 32), 32, 0, stream>>>( - atom_numbers, crd_to_uint_crd_cof, crd, uint_crd); - - Find_atom_neighbors<<(atom_numbers) / 32), 32, 0, stream>>>( - atom_numbers, uint_crd, uint_dr_to_dr_cof, atom_in_grid_serial, gpointer, bucket, atom_numbers_in_grid_bucket, d_nl, - cutoff_skin_square); - - Delete_Excluded_Atoms_Serial_In_Neighbor_List<<(atom_numbers) / 32), 32, 0, stream>>>( - atom_numbers, d_nl, excluded_list_start, excluded_list, excluded_numbers); -} - -void NeighborListUpdate(int grid_numbers, int atom_numbers, int *d_refresh_count, int refresh_interval, - int not_first_time, float skin, int nxy, float cutoff_square, float cutoff_with_skin_square, - int *grid_N, float *box_length, int *atom_numbers_in_grid_bucket, float *grid_length_inverse, - int *atom_in_grid_serial, GRID_BUCKET *bucket, float *crd, float *old_crd, - float *crd_to_uint_crd_cof, float *half_crd_to_uint_crd_cof, unsigned int *uint_crd, - float *uint_dr_to_dr_cof, GRID_POINTER *gpointer, NEIGHBOR_LIST *d_nl, int *excluded_list_start, - int *excluded_list, int *excluded_numbers, float half_skin_square, - int *is_need_refresh_neighbor_list, cudaStream_t stream) { - if (not_first_time) { - if (refresh_interval > 0) { - std::vector refresh_count_list(1); - cudaMemcpyAsync(refresh_count_list.data(), d_refresh_count, sizeof(int), cudaMemcpyDeviceToHost, stream); - cudaStreamSynchronize(stream); - int refresh_count = refresh_count_list[0]; - - if (refresh_count % refresh_interval == 0) { - Mul_half<<<1, 3, 0, stream>>>(crd_to_uint_crd_cof, half_crd_to_uint_crd_cof); - Refresh_Neighbor_List_No_Check_Half( - grid_numbers, atom_numbers, skin, nxy, cutoff_square, grid_N, box_length, atom_numbers_in_grid_bucket, - grid_length_inverse, atom_in_grid_serial, bucket, reinterpret_cast(crd), - reinterpret_cast(old_crd), half_crd_to_uint_crd_cof, - reinterpret_cast(uint_crd), uint_dr_to_dr_cof, gpointer, d_nl, excluded_list_start, - excluded_list, excluded_numbers, stream); - } - refresh_count += 1; - cudaMemcpyAsync(d_refresh_count, &refresh_count, sizeof(int), cudaMemcpyHostToDevice, stream); - } else { - Is_need_refresh_neighbor_list_cuda<<(atom_numbers) / 128), 128, 0, stream>>>( - atom_numbers, reinterpret_cast(crd), reinterpret_cast(old_crd), half_skin_square, - is_need_refresh_neighbor_list); - Mul_half<<<1, 3, 0, stream>>>(crd_to_uint_crd_cof, half_crd_to_uint_crd_cof); - Refresh_Neighbor_List_Half(is_need_refresh_neighbor_list, 32, atom_numbers, reinterpret_cast(crd), - reinterpret_cast(old_crd), reinterpret_cast(uint_crd), - half_crd_to_uint_crd_cof, uint_dr_to_dr_cof, atom_in_grid_serial, skin, box_length, - gpointer, bucket, atom_numbers_in_grid_bucket, d_nl, excluded_list_start, - excluded_list, excluded_numbers, cutoff_with_skin_square, grid_numbers, - grid_length_inverse, grid_N, nxy, stream); - } - } else { - Mul_half<<<1, 3, 0, stream>>>(crd_to_uint_crd_cof, half_crd_to_uint_crd_cof); - Refresh_Neighbor_List_First_Time( - is_need_refresh_neighbor_list, 32, atom_numbers, reinterpret_cast(crd), - reinterpret_cast(old_crd), reinterpret_cast(uint_crd), half_crd_to_uint_crd_cof, - uint_dr_to_dr_cof, atom_in_grid_serial, skin, box_length, gpointer, bucket, atom_numbers_in_grid_bucket, d_nl, - excluded_list_start, excluded_list, excluded_numbers, cutoff_with_skin_square, grid_numbers, grid_length_inverse, - grid_N, nxy, stream); - } -} - -// new neighbor list update functions - -__device__ __host__ VECTOR Get_Periodic_Displacement_Update(const VECTOR vec_a, const VECTOR vec_b, - const VECTOR box_length) { - VECTOR dr; - dr.x = vec_a.x - vec_b.x; - dr.y = vec_a.y - vec_b.y; - dr.x = vec_a.z - vec_b.z; - - dr.x = dr.x - floorf(dr.x / box_length.x + 0.5) * box_length.x; - dr.y = dr.y - floorf(dr.y / box_length.y + 0.5) * box_length.y; - dr.z = dr.z - floorf(dr.z / box_length.z + 0.5) * box_length.z; - return dr; -} - -__global__ void Is_need_refresh_neighbor_list_cuda(const int atom_numbers, const VECTOR *crd, const VECTOR *old_crd, - const VECTOR *box_length, const float half_skin_square, - int *need_refresh_flag) { - int i = blockDim.x * blockIdx.x + threadIdx.x; - if (i < atom_numbers) { - VECTOR r1 = crd[i]; - VECTOR r2 = old_crd[i]; - r1 = Get_Periodic_Displacement_Update(r1, r2, box_length[0]); - float r1_2 = r1.x * r1.x + r1.y * r1.y + r1.z * r1.z; - if (r1_2 > half_skin_square) { - atomicExch(&need_refresh_flag[0], 1); - } - } -} - -void Refresh_Neighbor_List(int *refresh_sign, const int thread, const int atom_numbers, VECTOR *crd, VECTOR *old_crd, - UNSIGNED_INT_VECTOR *uint_crd, float *crd_to_uint_crd_cof, float *uint_dr_to_dr_cof, - int *atom_in_grid_serial, const float skin, float *box_length, const GRID_POINTER *gpointer, - GRID_BUCKET *bucket, int *atom_numbers_in_grid_bucket, NEIGHBOR_LIST *d_nl, - int *excluded_list_start, int *excluded_list, int *excluded_numbers, - float cutoff_skin_square, int grid_numbers, float *grid_length_inverse, int *grid_N, int nxy, - cudaStream_t stream) { - std::vector h_refresh_sign(1); - cudaMemcpyAsync(h_refresh_sign.data(), refresh_sign, sizeof(int), cudaMemcpyDeviceToHost, stream); - if (h_refresh_sign[0] == 1) { - Clear_Grid_Bucket<<(grid_numbers) / thread), thread, 0, stream>>>( - grid_numbers, atom_numbers_in_grid_bucket, bucket); - - Crd_Periodic_Map<<(atom_numbers) / thread), thread, 0, stream>>>(atom_numbers, crd, - box_length); - - Find_Atom_In_Grid_Serial<<(atom_numbers) / thread), thread, 0, stream>>>( - atom_numbers, grid_length_inverse, crd, grid_N, nxy, atom_in_grid_serial); - - Copy_List<<(3. * atom_numbers) / thread), thread, 0, stream>>>( - 3 * atom_numbers, reinterpret_cast(crd), reinterpret_cast(old_crd)); - - Put_Atom_In_Grid_Bucket<<(atom_numbers) / thread), thread, 0, stream>>>( - atom_numbers, atom_in_grid_serial, bucket, atom_numbers_in_grid_bucket); - - Crd_To_Uint_Crd<<(atom_numbers) / thread), thread, 0, stream>>>( - atom_numbers, crd_to_uint_crd_cof, crd, uint_crd); - - Find_atom_neighbors<<(atom_numbers) / thread), thread, 0, stream>>>( - atom_numbers, uint_crd, uint_dr_to_dr_cof, atom_in_grid_serial, gpointer, bucket, atom_numbers_in_grid_bucket, - d_nl, cutoff_skin_square); - - Delete_Excluded_Atoms_Serial_In_Neighbor_List<<(atom_numbers) / thread), thread, 0, - stream>>>(atom_numbers, d_nl, excluded_list_start, excluded_list, - excluded_numbers); - h_refresh_sign[0] = 0; - } -} - -void Refresh_Neighbor_List_No_Check(int grid_numbers, int atom_numbers, float skin, int nxy, float cutoff_skin_square, - int *grid_N, float *box_length, int *atom_numbers_in_grid_bucket, - float *grid_length_inverse, int *atom_in_grid_serial, GRID_BUCKET *bucket, - VECTOR *crd, VECTOR *old_crd, float *crd_to_uint_crd_cof, - UNSIGNED_INT_VECTOR *uint_crd, float *uint_dr_to_dr_cof, GRID_POINTER *gpointer, - NEIGHBOR_LIST *d_nl, int *excluded_list_start, int *excluded_list, - int *excluded_numbers, cudaStream_t stream) { - Clear_Grid_Bucket<<(grid_numbers) / 32), 32, 0, stream>>>( - grid_numbers, atom_numbers_in_grid_bucket, bucket); - - Crd_Periodic_Map<<(atom_numbers) / 32), 32, 0, stream>>>(atom_numbers, crd, box_length); - - Find_Atom_In_Grid_Serial<<(atom_numbers) / 32), 32, 0, stream>>>( - atom_numbers, grid_length_inverse, crd, grid_N, nxy, atom_in_grid_serial); - - cudaMemcpyAsync(old_crd, crd, sizeof(VECTOR) * atom_numbers, cudaMemcpyDeviceToDevice, stream); - - Put_Atom_In_Grid_Bucket<<(atom_numbers) / 32), 32, 0, stream>>>( - atom_numbers, atom_in_grid_serial, bucket, atom_numbers_in_grid_bucket); - - Crd_To_Uint_Crd<<(atom_numbers) / 32), 32, 0, stream>>>(atom_numbers, crd_to_uint_crd_cof, - crd, uint_crd); - - Find_atom_neighbors<<(atom_numbers) / 32), 32, 0, stream>>>( - atom_numbers, uint_crd, uint_dr_to_dr_cof, atom_in_grid_serial, gpointer, bucket, atom_numbers_in_grid_bucket, d_nl, - cutoff_skin_square); - - Delete_Excluded_Atoms_Serial_In_Neighbor_List<<(atom_numbers) / 32), 32, 0, stream>>>( - atom_numbers, d_nl, excluded_list_start, excluded_list, excluded_numbers); -} - -void CopyNeighborList(int atom_numbers, int max_neighbor_numbers, NEIGHBOR_LIST *nl, int *nl_atom_numbers, - int *nl_atom_serial, cudaStream_t stream) { - copy_neighbor_list_atom_number<<(atom_numbers) / 128), 128, 0, stream>>>( - atom_numbers, max_neighbor_numbers, nl, nl_atom_numbers, nl_atom_serial); -} - -void ConstructNeighborList(int atom_numbers, int max_neighbor_numbers, int *nl_atom_numbers, int *nl_atom_serial, - NEIGHBOR_LIST *nl, cudaStream_t stream) { - construct_neighbor_list_kernel<<(atom_numbers) / 128), 128, 0, stream>>>( - atom_numbers, max_neighbor_numbers, nl_atom_numbers, nl_atom_serial, nl); -} - -int refresh_count = 0; - -void NeighborListRefresh(int grid_numbers, int atom_numbers, int *d_refresh_count, int refresh_interval, - int not_first_time, float skin, int nxy, float cutoff_square, float cutoff_with_skin_square, - int *grid_N, float *box_length, int *atom_numbers_in_grid_bucket, float *grid_length_inverse, - int *atom_in_grid_serial, GRID_BUCKET *bucket, float *crd, float *old_crd, - float *crd_to_uint_crd_cof, float *half_crd_to_uint_crd_cof, unsigned int *uint_crd, - float *uint_dr_to_dr_cof, GRID_POINTER *gpointer, NEIGHBOR_LIST *d_nl, - int *excluded_list_start, int *excluded_list, int *excluded_numbers, float half_skin_square, - int *is_need_refresh_neighbor_list, int forced_update, int forced_check, cudaStream_t stream) { - if (forced_update) { - Mul_quarter<<<1, 3, 0, stream>>>(crd_to_uint_crd_cof, half_crd_to_uint_crd_cof); - Refresh_Neighbor_List_No_Check( - grid_numbers, atom_numbers, skin, nxy, cutoff_square, grid_N, box_length, atom_numbers_in_grid_bucket, - grid_length_inverse, atom_in_grid_serial, bucket, reinterpret_cast(crd), - reinterpret_cast(old_crd), half_crd_to_uint_crd_cof, reinterpret_cast(uint_crd), - uint_dr_to_dr_cof, gpointer, d_nl, excluded_list_start, excluded_list, excluded_numbers, stream); - - } else if (refresh_interval > 0 && !forced_check) { - if (refresh_count % refresh_interval == 0) { - Mul_quarter<<<1, 3, 0, stream>>>(crd_to_uint_crd_cof, half_crd_to_uint_crd_cof); - Refresh_Neighbor_List_No_Check(grid_numbers, atom_numbers, skin, nxy, cutoff_square, grid_N, box_length, - atom_numbers_in_grid_bucket, grid_length_inverse, atom_in_grid_serial, bucket, - reinterpret_cast(crd), reinterpret_cast(old_crd), - half_crd_to_uint_crd_cof, reinterpret_cast(uint_crd), - uint_dr_to_dr_cof, gpointer, d_nl, excluded_list_start, excluded_list, - excluded_numbers, stream); - } - refresh_count += 1; - } else { - Is_need_refresh_neighbor_list_cuda<<(atom_numbers) / 128), 128, 0, stream>>>( - atom_numbers, reinterpret_cast(crd), reinterpret_cast(old_crd), - reinterpret_cast(box_length), half_skin_square, is_need_refresh_neighbor_list); - Mul_quarter<<<1, 3, 0, stream>>>(crd_to_uint_crd_cof, half_crd_to_uint_crd_cof); - Refresh_Neighbor_List(is_need_refresh_neighbor_list, 32, atom_numbers, reinterpret_cast(crd), - reinterpret_cast(old_crd), reinterpret_cast(uint_crd), - half_crd_to_uint_crd_cof, uint_dr_to_dr_cof, atom_in_grid_serial, skin, box_length, gpointer, - bucket, atom_numbers_in_grid_bucket, d_nl, excluded_list_start, excluded_list, - excluded_numbers, cutoff_with_skin_square, grid_numbers, grid_length_inverse, grid_N, nxy, - stream); - } -} diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/neighbor_list/neighbor_list_impl.cuh b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/neighbor_list/neighbor_list_impl.cuh deleted file mode 100644 index f5204e8de84..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/neighbor_list/neighbor_list_impl.cuh +++ /dev/null @@ -1,82 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -/** - * Note: - * NeighborListUpdate. This is an experimental interface that is subject to change and/or deletion. - */ - -#ifndef MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_NEIGHBOR_LIST_IMPL_H_ -#define MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_NEIGHBOR_LIST_IMPL_H_ -#include -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_device_info.h" - -struct VECTOR { - float x; - float y; - float z; -}; -struct INT_VECTOR { - int int_x; - int int_y; - int int_z; -}; -struct UNSIGNED_INT_VECTOR { - unsigned int uint_x; - unsigned int uint_y; - unsigned int uint_z; -}; -struct NEIGHBOR_LIST { - int atom_numbers; - int *atom_serial; -}; -struct GRID_BUCKET { - int *atom_serial; -}; -struct GRID_POINTER { - int *grid_serial; -}; - -CUDA_LIB_EXPORT void ConstructNeighborList(int grid_numbers, int max_neighbor_numbers, int *nl_atom_numbers, - int *nl_atom_serial, NEIGHBOR_LIST *nl, cudaStream_t stream); - -CUDA_LIB_EXPORT void CopyNeighborList(int atom_numbers, int max_neighbor_numbers, NEIGHBOR_LIST *nl, - int *nl_atom_numbers, int *nl_atom_serial, cudaStream_t stream); - -CUDA_LIB_EXPORT void NeighborListRefresh( - int grid_numbers, int atom_numbers, int *d_refresh_count, int refresh_interval, int not_first_time, float skin, - int nxy, float cutoff_square, float cutoff_with_skin_square, int *grid_N, float *box_length, - int *atom_numbers_in_grid_bucket, float *grid_length_inverse, int *atom_in_grid_serial, GRID_BUCKET *bucket, - float *crd, float *old_crd, float *crd_to_uint_crd_cof, float *half_crd_to_uint_crd_cof, unsigned int *uint_crd, - float *uint_dr_to_dr_cof, GRID_POINTER *gpointer, NEIGHBOR_LIST *d_nl, int *excluded_list_start, int *excluded_list, - int *excluded_numbers, float half_skin_square, int *is_need_refresh_neighbor_list, int forced_update, - int forced_check, cudaStream_t stream); - -CUDA_LIB_EXPORT void ConstructNeighborListHalf(int grid_numbers, int max_neighbor_numbers, int *nl_atom_numbers, - int *nl_atom_serial, NEIGHBOR_LIST *nl, cudaStream_t stream); - -CUDA_LIB_EXPORT void CopyNeighborListHalf(int atom_numbers, NEIGHBOR_LIST *nl, int *nl_atom_numbers, - cudaStream_t stream); - -CUDA_LIB_EXPORT void NeighborListUpdate( - int grid_numbers, int atom_numbers, int *d_refresh_count, int refresh_interval, int not_first_time, float skin, - int nxy, float cutoff_square, float cutoff_with_skin_square, int *grid_N, float *box_length, - int *atom_numbers_in_grid_bucket, float *grid_length_inverse, int *atom_in_grid_serial, GRID_BUCKET *bucket, - float *crd, float *old_crd, float *crd_to_uint_crd_cof, float *half_crd_to_uint_crd_cof, unsigned int *uint_crd, - float *uint_dr_to_dr_cof, GRID_POINTER *gpointer, NEIGHBOR_LIST *d_nl, int *excluded_list_start, int *excluded_list, - int *excluded_numbers, float half_skin_square, int *is_need_refresh_neighbor_list, cudaStream_t stream); - -#endif diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/nvtit/md_iteration_gradient_descent_impl.cu b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/nvtit/md_iteration_gradient_descent_impl.cu deleted file mode 100644 index fc2102e52c0..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/nvtit/md_iteration_gradient_descent_impl.cu +++ /dev/null @@ -1,41 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "plugin/device/gpu/kernel/cuda_impl/sponge/nvtit/md_iteration_gradient_descent_impl.cuh" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/util.cuh" -#include "plugin/device/gpu/kernel/cuda_impl/sponge/common_sponge.cuh" - -__global__ void MD_Iteration_Gradient_Descent(const int atom_numbers, VECTOR *crd, VECTOR *frc, - const float learning_rate) { - int i = blockDim.x * blockIdx.x + threadIdx.x; - if (i < atom_numbers) { - crd[i].x = crd[i].x + learning_rate * frc[i].x; - crd[i].y = crd[i].y + learning_rate * frc[i].y; - crd[i].z = crd[i].z + learning_rate * frc[i].z; - - frc[i].x = 0.; - frc[i].y = 0.; - frc[i].z = 0.; - } -} - -void MDIterationGradientDescent(const int atom_numbers, float *crd, float *frc, const float learning_rate, - cudaStream_t stream) { - VECTOR *d_crd = reinterpret_cast(crd); - VECTOR *d_frc = reinterpret_cast(frc); - MD_Iteration_Gradient_Descent<<(atom_numbers) / 128), 128, 0, stream>>>( - atom_numbers, d_crd, d_frc, learning_rate); -} diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/nvtit/md_iteration_gradient_descent_impl.cuh b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/nvtit/md_iteration_gradient_descent_impl.cuh deleted file mode 100644 index 8299fc491ca..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/nvtit/md_iteration_gradient_descent_impl.cuh +++ /dev/null @@ -1,26 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_MD_ITERATION_GRADIENT_DESCENT_IMPL_H_ -#define MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_MD_ITERATION_GRADIENT_DESCENT_IMPL_H_ - -#include -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_device_info.h" -CUDA_LIB_EXPORT void MDIterationGradientDescent(const int atom_numbers, float *crd, float *frc, - const float learning_rate, cudaStream_t stream); - -#endif diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/nvtit/md_iteration_leap_frog_impl.cu b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/nvtit/md_iteration_leap_frog_impl.cu deleted file mode 100644 index 8d00fc222e1..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/nvtit/md_iteration_leap_frog_impl.cu +++ /dev/null @@ -1,54 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -/** - * Note: - * MDIterationLeapFrog. This is an experimental interface that is subject to change and/or deletion. - */ - -#include "plugin/device/gpu/kernel/cuda_impl/sponge/nvtit/md_iteration_leap_frog_impl.cuh" -#include "plugin/device/gpu/kernel/cuda_impl/sponge/common_sponge.cuh" - -__global__ void MD_Iteration_Leap_Frog(const int atom_numbers, VECTOR *vel, VECTOR *crd, VECTOR *frc, VECTOR *acc, - const float *inverse_mass, const float dt) { - int i = blockDim.x * blockIdx.x + threadIdx.x; - if (i < atom_numbers) { - acc[i].x = inverse_mass[i] * frc[i].x; - acc[i].y = inverse_mass[i] * frc[i].y; - acc[i].z = inverse_mass[i] * frc[i].z; - - vel[i].x = vel[i].x + dt * acc[i].x; - vel[i].y = vel[i].y + dt * acc[i].y; - vel[i].z = vel[i].z + dt * acc[i].z; - - crd[i].x = crd[i].x + dt * vel[i].x; - crd[i].y = crd[i].y + dt * vel[i].y; - crd[i].z = crd[i].z + dt * vel[i].z; - - frc[i].x = 0.; - frc[i].y = 0.; - frc[i].z = 0.; - } -} - -void MDIterationLeapFrog(const int atom_numbers, float *vel, float *crd, float *frc, float *acc, - const float *inverse_mass, const float dt, cudaStream_t stream) { - VECTOR *d_vel = reinterpret_cast(vel); - VECTOR *d_crd = reinterpret_cast(crd); - VECTOR *d_frc = reinterpret_cast(frc); - VECTOR *d_acc = reinterpret_cast(acc); - MD_Iteration_Leap_Frog<<(atom_numbers) / 128), 128, 0, stream>>>( - atom_numbers, d_vel, d_crd, d_frc, d_acc, inverse_mass, dt); -} diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/nvtit/md_iteration_leap_frog_impl.cuh b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/nvtit/md_iteration_leap_frog_impl.cuh deleted file mode 100644 index 61d149060b7..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/nvtit/md_iteration_leap_frog_impl.cuh +++ /dev/null @@ -1,31 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -/** - * Note: - * MDIterationLeapFrog. This is an experimental interface that is subject to change and/or deletion. - */ - -#ifndef MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_NVTIT_MD_ITERATION_LEAP_FROG_IMPL_H -#define MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_NVTIT_MD_ITERATION_LEAP_FROG_IMPL_H - -#include -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_device_info.h" - -CUDA_LIB_EXPORT void MDIterationLeapFrog(const int atom_numbers, float *vel, float *crd, float *frc, float *acc, - const float *inverse_mass, const float dt, cudaStream_t stream); - -#endif // MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_MD_ITERATION_LEAP_FROG_LIUJIAN_GPU_IMPL_H_ diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/nvtit/md_iteration_leap_frog_liujian_gpu_impl.cu b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/nvtit/md_iteration_leap_frog_liujian_gpu_impl.cu deleted file mode 100644 index a2a636726b8..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/nvtit/md_iteration_leap_frog_liujian_gpu_impl.cu +++ /dev/null @@ -1,67 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "plugin/device/gpu/kernel/cuda_impl/sponge/nvtit/md_iteration_leap_frog_liujian_gpu_impl.cuh" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/util.cuh" -#include "plugin/device/gpu/kernel/cuda_impl/sponge/common_sponge.cuh" - -__global__ void MD_Iteration_Leap_Frog_With_LiuJian_kernel(const int atom_numbers, const float half_dt, const float dt, - const float exp_gamma, float *inverse_mass, - float *sqrt_mass_inverse, VECTOR *vel, VECTOR *crd, - VECTOR *frc, VECTOR *acc, VECTOR *random_frc, - VECTOR *output) { - int i = blockDim.x * blockIdx.x + threadIdx.x; - - if (i < atom_numbers) { - acc[i].x = inverse_mass[i] * frc[i].x; - acc[i].y = inverse_mass[i] * frc[i].y; - acc[i].z = inverse_mass[i] * frc[i].z; - - vel[i].x = vel[i].x + dt * acc[i].x; - vel[i].y = vel[i].y + dt * acc[i].y; - vel[i].z = vel[i].z + dt * acc[i].z; - - output[i].x = crd[i].x + half_dt * vel[i].x; - output[i].y = crd[i].y + half_dt * vel[i].y; - output[i].z = crd[i].z + half_dt * vel[i].z; - - vel[i].x = exp_gamma * vel[i].x + sqrt_mass_inverse[i] * random_frc[i].x; - vel[i].y = exp_gamma * vel[i].y + sqrt_mass_inverse[i] * random_frc[i].y; - vel[i].z = exp_gamma * vel[i].z + sqrt_mass_inverse[i] * random_frc[i].z; - - output[i].x = output[i].x + half_dt * vel[i].x; - output[i].y = output[i].y + half_dt * vel[i].y; - output[i].z = output[i].z + half_dt * vel[i].z; - } -} - -void MD_Iteration_Leap_Frog_With_LiuJian(const int atom_numbers, const float half_dt, const float dt, - const float exp_gamma, int float4_numbers, float *inverse_mass, - float *sqrt_mass_inverse, float *vel, float *crd, float *frc, float *acc, - curandStatePhilox4_32_10_t *rand_state, float *rand_frc, float *output, - cudaStream_t stream) { - Rand_Normal<<(float4_numbers) / 32.), 32, 0, stream>>>(float4_numbers, rand_state, - reinterpret_cast(rand_frc)); - VECTOR *d_vel = reinterpret_cast(vel); - VECTOR *d_crd = reinterpret_cast(crd); - VECTOR *d_frc = reinterpret_cast(frc); - VECTOR *d_acc = reinterpret_cast(acc); - VECTOR *d_rand_frc = reinterpret_cast(rand_frc); - VECTOR *d_out = reinterpret_cast(output); - MD_Iteration_Leap_Frog_With_LiuJian_kernel<<(atom_numbers) / 32), 32, 0, stream>>>( - atom_numbers, half_dt, dt, exp_gamma, inverse_mass, sqrt_mass_inverse, d_vel, d_crd, d_frc, d_acc, d_rand_frc, - d_out); -} diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/nvtit/md_iteration_leap_frog_liujian_gpu_impl.cuh b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/nvtit/md_iteration_leap_frog_liujian_gpu_impl.cuh deleted file mode 100644 index 77e42a3f2d6..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/nvtit/md_iteration_leap_frog_liujian_gpu_impl.cuh +++ /dev/null @@ -1,29 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_MD_ITERATION_LEAP_FROG_LIUJIAN_GPU_IMPL_H_ -#define MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_MD_ITERATION_LEAP_FROG_LIUJIAN_GPU_IMPL_H_ - -#include -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_device_info.h" -CUDA_LIB_EXPORT void MD_Iteration_Leap_Frog_With_LiuJian(const int atom_numbers, const float half_dt, const float dt, - const float exp_gamma, int float4_numbers, float *inverse_mass, - float *sqrt_mass_inverse, float *vel, float *crd, float *frc, - float *acc, curandStatePhilox4_32_10_t *rand_state, - float *rand_frc, float *output, cudaStream_t stream); - -#endif // MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_MD_ITERATION_LEAP_FROG_LIUJIAN_GPU_IMPL_H_ diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/nvtit/md_iteration_leap_frog_liujian_with_max_vel_impl.cu b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/nvtit/md_iteration_leap_frog_liujian_with_max_vel_impl.cu deleted file mode 100644 index b8c53e91acf..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/nvtit/md_iteration_leap_frog_liujian_with_max_vel_impl.cu +++ /dev/null @@ -1,80 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "plugin/device/gpu/kernel/cuda_impl/sponge/nvtit/md_iteration_leap_frog_liujian_with_max_vel_impl.cuh" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/util.cuh" -#include "plugin/device/gpu/kernel/cuda_impl/sponge/common_sponge.cuh" - -__global__ void MD_Iteration_Leap_Frog_With_LiuJian_With_Max_Velocity( - const int atom_numbers, const float half_dt, const float dt, const float exp_gamma, const float *inverse_mass, - const float *sqrt_mass_inverse, VECTOR *vel, VECTOR *crd, VECTOR *frc, VECTOR *acc, VECTOR *random_frc, - VECTOR *output, const float max_vel) { - int i = blockDim.x * blockIdx.x + threadIdx.x; - float abs_vel; - if (i < atom_numbers) { - acc[i].x = inverse_mass[i] * frc[i].x; - acc[i].y = inverse_mass[i] * frc[i].y; - acc[i].z = inverse_mass[i] * frc[i].z; - - vel[i].x = vel[i].x + dt * acc[i].x; - vel[i].y = vel[i].y + dt * acc[i].y; - vel[i].z = vel[i].z + dt * acc[i].z; - - abs_vel = norm3df(vel[i].x, vel[i].y, vel[i].z); - if (abs_vel < max_vel) { - } else { - abs_vel = max_vel / abs_vel; - vel[i].x = abs_vel * vel[i].x; - vel[i].y = abs_vel * vel[i].y; - vel[i].z = abs_vel * vel[i].z; - } - - output[i].x = crd[i].x + half_dt * vel[i].x; - output[i].y = crd[i].y + half_dt * vel[i].y; - output[i].z = crd[i].z + half_dt * vel[i].z; - - vel[i].x = exp_gamma * vel[i].x + sqrt_mass_inverse[i] * random_frc[i].x; - vel[i].y = exp_gamma * vel[i].y + sqrt_mass_inverse[i] * random_frc[i].y; - vel[i].z = exp_gamma * vel[i].z + sqrt_mass_inverse[i] * random_frc[i].z; - - output[i].x = output[i].x + half_dt * vel[i].x; - output[i].y = output[i].y + half_dt * vel[i].y; - output[i].z = output[i].z + half_dt * vel[i].z; - - frc[i].x = 0.; - frc[i].y = 0.; - frc[i].z = 0.; - } -} -void MD_Iteration_Leap_Frog_With_LiuJian_With_Max_Vel(const int atom_numbers, const float half_dt, const float dt, - const float exp_gamma, int float4_numbers, float *inverse_mass, - float *sqrt_mass_inverse, float *vel, float *crd, float *frc, - float *acc, curandStatePhilox4_32_10_t *rand_state, - float *rand_frc, float *output, const float max_vel, - cudaStream_t stream) { - Rand_Normal<<(float4_numbers) / 32.), 32, 0, stream>>>(float4_numbers, rand_state, - reinterpret_cast(rand_frc)); - VECTOR *d_vel = reinterpret_cast(vel); - VECTOR *d_crd = reinterpret_cast(crd); - VECTOR *d_frc = reinterpret_cast(frc); - VECTOR *d_acc = reinterpret_cast(acc); - VECTOR *d_rand_frc = reinterpret_cast(rand_frc); - VECTOR *d_out = reinterpret_cast(output); - MD_Iteration_Leap_Frog_With_LiuJian_With_Max_Velocity<<(atom_numbers) / 32), 32, 0, - stream>>>(atom_numbers, half_dt, dt, exp_gamma, inverse_mass, - sqrt_mass_inverse, d_vel, d_crd, d_frc, d_acc, - d_rand_frc, d_out, max_vel); -} diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/nvtit/md_iteration_leap_frog_liujian_with_max_vel_impl.cuh b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/nvtit/md_iteration_leap_frog_liujian_with_max_vel_impl.cuh deleted file mode 100644 index 7872f70cfd8..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/nvtit/md_iteration_leap_frog_liujian_with_max_vel_impl.cuh +++ /dev/null @@ -1,28 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_MD_ITERATION_LEAP_FROG_LIUJIAN_WITH_MAX_VEL_IMPL_H_ -#define MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_MD_ITERATION_LEAP_FROG_LIUJIAN_WITH_MAX_VEL_IMPL_H_ - -#include -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_device_info.h" -CUDA_LIB_EXPORT void MD_Iteration_Leap_Frog_With_LiuJian_With_Max_Vel( - const int atom_numbers, const float half_dt, const float dt, const float exp_gamma, int float4_numbers, - float *inverse_mass, float *sqrt_mass_inverse, float *vel, float *crd, float *frc, float *acc, - curandStatePhilox4_32_10_t *rand_state, float *rand_frc, float *output, const float max_vel, cudaStream_t stream); - -#endif // MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_MD_ITERATION_LEAP_FROG_LIUJIAN_WITH_MAX_VEL_IMPL_H_ diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/nvtit/md_iteration_leap_frog_with_max_vel_impl.cu b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/nvtit/md_iteration_leap_frog_with_max_vel_impl.cu deleted file mode 100644 index 60d96b1c008..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/nvtit/md_iteration_leap_frog_with_max_vel_impl.cu +++ /dev/null @@ -1,44 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "plugin/device/gpu/kernel/cuda_impl/sponge/nvtit/md_iteration_leap_frog_with_max_vel_impl.cuh" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/util.cuh" -#include "plugin/device/gpu/kernel/cuda_impl/sponge/common_sponge.cuh" - -__global__ void MD_Iteration_Leap_Frog_With_Max_Velocity(const int atom_numbers, VECTOR *vel, VECTOR *crd, VECTOR *frc, - VECTOR *acc, const float *inverse_mass, const float dt, - const float max_velocity) { - int i = blockDim.x * blockIdx.x + threadIdx.x; - if (i < atom_numbers) { - VECTOR acc_i = inverse_mass[i] * frc[i]; - VECTOR vel_i = vel[i] + dt * acc_i; - vel_i = Make_Vector_Not_Exceed_Value(vel_i, max_velocity); - vel[i] = vel_i; - crd[i] = crd[i] + dt * vel_i; - frc[i] = {0.0f, 0.0f, 0.0f}; - } -} - -void MDIterationLeapFrogWithMaxVelocity(const int atom_numbers, float *vel, float *crd, float *frc, float *acc, - const float *inverse_mass, const float dt, const float max_velocity, - cudaStream_t stream) { - VECTOR *d_vel = reinterpret_cast(vel); - VECTOR *d_crd = reinterpret_cast(crd); - VECTOR *d_frc = reinterpret_cast(frc); - VECTOR *d_acc = reinterpret_cast(acc); - MD_Iteration_Leap_Frog_With_Max_Velocity<<(atom_numbers) / 128), 128, 0, stream>>>( - atom_numbers, d_vel, d_crd, d_frc, d_acc, inverse_mass, dt, max_velocity); -} diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/nvtit/md_iteration_leap_frog_with_max_vel_impl.cuh b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/nvtit/md_iteration_leap_frog_with_max_vel_impl.cuh deleted file mode 100644 index 3bdb3dd60dc..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/nvtit/md_iteration_leap_frog_with_max_vel_impl.cuh +++ /dev/null @@ -1,27 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_MD_ITERATION_LEAP_FROG_WITH_MAX_VEL_IMPL_H_ -#define MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_MD_ITERATION_LEAP_FROG_WITH_MAX_VEL_IMPL_H_ - -#include -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_device_info.h" -CUDA_LIB_EXPORT void MDIterationLeapFrogWithMaxVelocity(const int atom_numbers, float *vel, float *crd, float *frc, - float *acc, const float *inverse_mass, const float dt, - const float max_velocity, cudaStream_t stream); - -#endif // MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_MD_ITERATION_LEAP_FROG_WITH_MAX_VEL_IMPL_H_ diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/nvtit/md_iteration_setup_random_state_gpu_impl.cu b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/nvtit/md_iteration_setup_random_state_gpu_impl.cu deleted file mode 100644 index 54138754bd4..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/nvtit/md_iteration_setup_random_state_gpu_impl.cu +++ /dev/null @@ -1,28 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "plugin/device/gpu/kernel/cuda_impl/sponge/nvtit/md_iteration_setup_random_state_gpu_impl.cuh" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/util.cuh" -#include "plugin/device/gpu/kernel/cuda_impl/sponge/common_sponge.cuh" - -void MD_Iteration_Setup_Random_State(int float4_numbers, curandStatePhilox4_32_10_t *rand_state, int seed, - cudaStream_t stream) { - Setup_Rand_Normal_Kernel<<(float4_numbers) / 32.), 32, 0, stream>>>(float4_numbers, - rand_state, seed); -} - -void MD_Iteration_Setup_Random_State(int float4_numbers, curandStatePhilox4_32_10_t *rand_state, int seed, - cudaStream_t stream); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/nvtit/md_iteration_setup_random_state_gpu_impl.cuh b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/nvtit/md_iteration_setup_random_state_gpu_impl.cuh deleted file mode 100644 index 8fda94fc3a7..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/nvtit/md_iteration_setup_random_state_gpu_impl.cuh +++ /dev/null @@ -1,25 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -#ifndef MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_MD_ITERATION_SETUP_RANDOM_STATE_GPU_IMPL_H_ -#define MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_MD_ITERATION_SETUP_RANDOM_STATE_GPU_IMPL_H_ - -#include -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_device_info.h" - -CUDA_LIB_EXPORT void MD_Iteration_Setup_Random_State(int float4_numbers, curandStatePhilox4_32_10_t *rand_state, - int seed, cudaStream_t stream); -#endif diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/pme/fft_3d_impl.cu b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/pme/fft_3d_impl.cu deleted file mode 100644 index d3910756359..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/pme/fft_3d_impl.cu +++ /dev/null @@ -1,27 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -#include "plugin/device/gpu/kernel/cuda_impl/sponge/pme/fft_3d_impl.cuh" -#include "plugin/device/gpu/kernel/cuda_impl/sponge/pme/pme_common.cuh" - -template -void FFT3D(int Nfft, T *input_tensor, Complex *output_tensor, const cufftHandle &FFT_plan_r2c, cudaStream_t stream) { - cufftExecR2C(FFT_plan_r2c, input_tensor, reinterpret_cast(output_tensor)); - return; -} - -template CUDA_LIB_EXPORT -void FFT3D(int Nfft, float *input_tensor, Complex *output_tensor, - const cufftHandle &FFT_plan_r2c, cudaStream_t stream); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/pme/fft_3d_impl.cuh b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/pme/fft_3d_impl.cuh deleted file mode 100644 index 6df893b71ea..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/pme/fft_3d_impl.cuh +++ /dev/null @@ -1,28 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -#ifndef MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_PME_FFT_3D_IMPL_H_ -#define MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_PME_FFT_3D_IMPL_H_ - -#include -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/complex.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_device_info.h" - -template -CUDA_LIB_EXPORT void FFT3D(int Nfft, T *input_tensor, Complex *output_tensor, const cufftHandle &FFT_plan_r2c, - cudaStream_t stream); - -#endif diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/pme/ifft_3d_impl.cu b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/pme/ifft_3d_impl.cu deleted file mode 100644 index 6c6880f1e6a..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/pme/ifft_3d_impl.cu +++ /dev/null @@ -1,28 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -#include "plugin/device/gpu/kernel/cuda_impl/sponge/pme/ifft_3d_impl.cuh" -#include "plugin/device/gpu/kernel/cuda_impl/sponge/pme/pme_common.cuh" - -template -void IFFT3D(int Nfft, Complex *input_tensor, T *output_tensor, const cufftHandle &FFT_plan_c2r, - cudaStream_t stream) { - cufftExecC2R(FFT_plan_c2r, reinterpret_cast(input_tensor), output_tensor); - return; -} - -template CUDA_LIB_EXPORT -void IFFT3D(int Nfft, Complex *input_tensor, float *output_tensor, - const cufftHandle &FFT_plan_c2r, cudaStream_t stream); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/pme/ifft_3d_impl.cuh b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/pme/ifft_3d_impl.cuh deleted file mode 100644 index 0d740e97a6b..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/pme/ifft_3d_impl.cuh +++ /dev/null @@ -1,28 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -#ifndef MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_PME_IFFT_3D_IMPL_H_ -#define MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_PME_IFFT_3D_IMPL_H_ - -#include -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/complex.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_device_info.h" - -template -CUDA_LIB_EXPORT void IFFT3D(int Nfft, Complex *input_tensor, T *output_tensor, const cufftHandle &FFT_plan_c2r, - cudaStream_t stream); - -#endif diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/pme/pme_batched_fft_2d_impl.cu b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/pme/pme_batched_fft_2d_impl.cu deleted file mode 100644 index 5f7dc4281ae..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/pme/pme_batched_fft_2d_impl.cu +++ /dev/null @@ -1,29 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -#include "plugin/device/gpu/kernel/cuda_impl/sponge/pme/pme_batched_fft_2d_impl.cuh" -#include "plugin/device/gpu/kernel/cuda_impl/sponge/pme/pme_common.cuh" - -template -void PMEBatchedFFT2D(Complex *input_tensor, Complex *output_tensor, - const cufftHandle &FFT_plan_c2c, int direction, cudaStream_t stream) { - cufftExecC2C(FFT_plan_c2c, reinterpret_cast(input_tensor), - reinterpret_cast(output_tensor), direction); - return; -} - -template CUDA_LIB_EXPORT -void PMEBatchedFFT2D(Complex *input_tensor, Complex *output_tensor, - const cufftHandle &FFT_plan_c2c, int direction, cudaStream_t stream); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/pme/pme_batched_fft_2d_impl.cuh b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/pme/pme_batched_fft_2d_impl.cuh deleted file mode 100644 index 71f1aa919d5..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/pme/pme_batched_fft_2d_impl.cuh +++ /dev/null @@ -1,28 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -#ifndef MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_PME_PME_BATCHED_FFT_2D_IMPL_H_ -#define MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_PME_PME_BATCHED_FFT_2D_IMPL_H_ - -#include -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/complex.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_device_info.h" - -template -CUDA_LIB_EXPORT void PMEBatchedFFT2D(Complex *input_tensor, Complex *output_tensor, - const cufftHandle &FFT_plan_c2c, int direction, cudaStream_t stream); - -#endif diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/pme/pme_common.cuh b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/pme/pme_common.cuh deleted file mode 100644 index 58e17a73bdd..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/pme/pme_common.cuh +++ /dev/null @@ -1,357 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -/** - * Note: - * PME_Common. This is an experimental interface that is subject to change and/or deletion. - */ - -#ifndef MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_PME_PME_COMMON_H_ -#define MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_PME_PME_COMMON_H_ -#include "plugin/device/gpu/kernel/cuda_impl/sponge/common_sponge.cuh" -__constant__ float PME_Ma[4] = {1.0 / 6.0, -0.5, 0.5, -1.0 / 6.0}; -__constant__ float PME_Mb[4] = {0, 0.5, -1, 0.5}; -__constant__ float PME_Mc[4] = {0, 0.5, 0, -0.5}; -__constant__ float PME_Md[4] = {0, 1.0 / 6.0, 4.0 / 6.0, 1.0 / 6.0}; -__constant__ float PME_dMa[4] = {0.5, -1.5, 1.5, -0.5}; -__constant__ float PME_dMb[4] = {0, 1, -2, 1}; -__constant__ float PME_dMc[4] = {0, 0.5, 0, -0.5}; -#define PI 3.1415926 -const float periodic_factor_inverse = 2.3283064365387e-10; -static dim3 thread_PME; - -const float cutoff = 10.0; -const float tolerance = 0.00001; - -static float M_(float u, int n) { - if (n == 2) { - if (u > 2 || u < 0) return 0; - return 1 - abs(u - 1); - } else { - return u / (n - 1) * M_(u, n - 1) + (n - u) / (n - 1) * M_(u - 1, n - 1); - } -} - -static float Get_Beta(float cutoff, float tolerance) { - float beta, low, high, tempf; - int ilow, ihigh; - - high = 1.0; - ihigh = 1; - - while (1) { - tempf = erfc(high * cutoff) / cutoff; - if (tempf <= tolerance) break; - high *= 2; - ihigh++; - } - - ihigh += 50; - low = 0.0; - for (ilow = 1; ilow < ihigh; ilow++) { - beta = (low + high) / 2; - tempf = erfc(beta * cutoff) / cutoff; - if (tempf >= tolerance) - low = beta; - else - high = beta; - } - return beta; -} - -static cufftComplex expc(cufftComplex z) { - cufftComplex res; - float t = expf(z.x); - sincosf(z.y, &res.y, &res.x); - res.x *= t; - res.y *= t; - return res; -} - -static float getb(int k, int NFFT, int B_order) { - cufftComplex tempc, tempc2, res; - float tempf; - tempc2.x = 0; - tempc2.y = 0; - - tempc.x = 0; - tempc.y = 2 * (B_order - 1) * PI * k / NFFT; - res = expc(tempc); - - for (int kk = 0; kk < (B_order - 1); kk++) { - tempc.x = 0; - tempc.y = 2 * PI * k / NFFT * kk; - tempc = expc(tempc); - tempf = M_(kk + 1, B_order); - tempc2.x += tempf * tempc.x; - tempc2.y += tempf * tempc.y; - } - res = cuCdivf(res, tempc2); - return res.x * res.x + res.y * res.y; -} - -__global__ static void device_add(float *ene, float *factor, float *charge_sum) { - ene[0] += factor[0] * charge_sum[0] * charge_sum[0]; -} - -__global__ static void PME_Atom_Near(const UNSIGNED_INT_VECTOR *uint_crd, int *PME_atom_near, const int PME_Nin, - const float periodic_factor_inverse_x, const float periodic_factor_inverse_y, - const float periodic_factor_inverse_z, const int atom_numbers, const int fftx, - const int ffty, const int fftz, const UNSIGNED_INT_VECTOR *PME_kxyz, - UNSIGNED_INT_VECTOR *PME_uxyz, VECTOR *PME_frxyz) { - int atom = blockDim.x * blockIdx.x + threadIdx.x; - if (atom < atom_numbers) { - UNSIGNED_INT_VECTOR *temp_uxyz = &PME_uxyz[atom]; - int k, tempux, tempuy, tempuz; - float tempf; - tempf = static_cast(uint_crd[atom].uint_x) * periodic_factor_inverse_x; - tempux = static_cast(tempf); - PME_frxyz[atom].x = tempf - tempux; - - tempf = static_cast(uint_crd[atom].uint_y) * periodic_factor_inverse_y; - tempuy = static_cast(tempf); - PME_frxyz[atom].y = tempf - tempuy; - - tempf = static_cast(uint_crd[atom].uint_z) * periodic_factor_inverse_z; - tempuz = static_cast(tempf); - PME_frxyz[atom].z = tempf - tempuz; - - if (tempux != (*temp_uxyz).uint_x || tempuy != (*temp_uxyz).uint_y || tempuz != (*temp_uxyz).uint_z) { - (*temp_uxyz).uint_x = tempux; - (*temp_uxyz).uint_y = tempuy; - (*temp_uxyz).uint_z = tempuz; - int *temp_near = PME_atom_near + atom * 64; - int kx, ky, kz; - for (k = 0; k < 64; k++) { - UNSIGNED_INT_VECTOR temp_kxyz = PME_kxyz[k]; - kx = tempux - temp_kxyz.uint_x; - if (kx < 0) kx += fftx; - if (kx > fftx) kx -= fftx; - ky = tempuy - temp_kxyz.uint_y; - if (ky < 0) ky += ffty; - if (ky > ffty) ky -= ffty; - kz = tempuz - temp_kxyz.uint_z; - if (kz < 0) kz += fftz; - if (kz > fftz) kz -= fftz; - temp_near[k] = kx * PME_Nin + ky * fftz + kz; - } - } - } -} - -__global__ static void PME_Q_Spread(int *PME_atom_near, const float *charge, const VECTOR *PME_frxyz, float *PME_Q, - const UNSIGNED_INT_VECTOR *PME_kxyz, const int atom_numbers) { - int atom = blockDim.x * blockIdx.x + threadIdx.x; - - if (atom < atom_numbers) { - int k; - float tempf, tempQ, tempf2; - - int *temp_near = PME_atom_near + atom * 64; - VECTOR temp_frxyz = PME_frxyz[atom]; - float tempcharge = charge[atom]; - - UNSIGNED_INT_VECTOR temp_kxyz; - unsigned int kx; - - for (k = threadIdx.y; k < 64; k = k + blockDim.y) { - temp_kxyz = PME_kxyz[k]; - kx = temp_kxyz.uint_x; - tempf = (temp_frxyz.x); - tempf2 = tempf * tempf; - tempf = PME_Ma[kx] * tempf * tempf2 + PME_Mb[kx] * tempf2 + PME_Mc[kx] * tempf + PME_Md[kx]; - - tempQ = tempcharge * tempf; - - kx = temp_kxyz.uint_y; - tempf = (temp_frxyz.y); - tempf2 = tempf * tempf; - tempf = PME_Ma[kx] * tempf * tempf2 + PME_Mb[kx] * tempf2 + PME_Mc[kx] * tempf + PME_Md[kx]; - - tempQ = tempQ * tempf; - - kx = temp_kxyz.uint_z; - tempf = (temp_frxyz.z); - tempf2 = tempf * tempf; - tempf = PME_Ma[kx] * tempf * tempf2 + PME_Mb[kx] * tempf2 + PME_Mc[kx] * tempf + PME_Md[kx]; - tempQ = tempQ * tempf; - - atomicAdd(&PME_Q[temp_near[k]], tempQ); - } - } -} - -__global__ static void PME_Direct_Energy(const int atom_numbers, const NEIGHBOR_LIST *nl, - const UNSIGNED_INT_VECTOR *uint_crd, const VECTOR *boxlength, - const float *charge, const float beta, const float cutoff_square, - float *direct_ene) { - int atom_i = blockDim.x * blockIdx.x + threadIdx.x; - if (atom_i < atom_numbers) { - NEIGHBOR_LIST nl_i = nl[atom_i]; - int N = nl_i.atom_numbers; - int atom_j; - int int_x; - int int_y; - int int_z; - UNSIGNED_INT_VECTOR r1 = uint_crd[atom_i], r2; - VECTOR dr; - float dr2; - float dr_abs; - // float dr_inverse; - float ene_temp; - float charge_i = charge[atom_i]; - float ene_lin = 0.; - - // int x, y; - // int atom_pair_LJ_type; - for (int j = threadIdx.y; j < N; j = j + blockDim.y) { - atom_j = nl_i.atom_serial[j]; - r2 = uint_crd[atom_j]; - - int_x = r2.uint_x - r1.uint_x; - int_y = r2.uint_y - r1.uint_y; - int_z = r2.uint_z - r1.uint_z; - dr.x = boxlength[0].x * int_x; - dr.y = boxlength[0].y * int_y; - dr.z = boxlength[0].z * int_z; - - dr2 = dr.x * dr.x + dr.y * dr.y + dr.z * dr.z; - if (dr2 < cutoff_square) { - dr_abs = norm3df(dr.x, dr.y, dr.z); - ene_temp = charge_i * charge[atom_j] * erfcf(beta * dr_abs) / dr_abs; - ene_lin = ene_lin + ene_temp; - } - } - atomicAdd(direct_ene, ene_lin); - } -} - -__global__ static void PME_Direct_Atom_Energy(const int atom_numbers, const NEIGHBOR_LIST *nl, - const UNSIGNED_INT_VECTOR *uint_crd, const VECTOR *boxlength, - const float *charge, const float beta, const float cutoff_square, - float *direct_ene) { - int atom_i = blockDim.x * blockIdx.x + threadIdx.x; - if (atom_i < atom_numbers) { - NEIGHBOR_LIST nl_i = nl[atom_i]; - int N = nl_i.atom_numbers; - int atom_j; - int int_x; - int int_y; - int int_z; - UNSIGNED_INT_VECTOR r1 = uint_crd[atom_i], r2; - VECTOR dr; - float dr2; - float dr_abs; - // float dr_inverse; - float ene_temp; - float charge_i = charge[atom_i]; - float ene_lin = 0.; - - for (int j = threadIdx.y; j < N; j = j + blockDim.y) { - atom_j = nl_i.atom_serial[j]; - r2 = uint_crd[atom_j]; - - int_x = r2.uint_x - r1.uint_x; - int_y = r2.uint_y - r1.uint_y; - int_z = r2.uint_z - r1.uint_z; - dr.x = boxlength[0].x * int_x; - dr.y = boxlength[0].y * int_y; - dr.z = boxlength[0].z * int_z; - - dr2 = dr.x * dr.x + dr.y * dr.y + dr.z * dr.z; - if (dr2 < cutoff_square) { - dr_abs = norm3df(dr.x, dr.y, dr.z); - ene_temp = charge_i * charge[atom_j] * erfcf(beta * dr_abs) / dr_abs; - ene_lin = ene_lin + ene_temp; - } - } - atomicAdd(&direct_ene[atom_i], ene_lin); - } -} - -__global__ static void PME_Energy_Product(const int element_number, const float *list1, const float *list2, - float *sum) { - if (threadIdx.x == 0) { - sum[0] = 0.; - } - __syncthreads(); - float lin = 0.0; - for (int i = threadIdx.x; i < element_number; i = i + blockDim.x) { - lin = lin + list1[i] * list2[i]; - } - atomicAdd(sum, lin); -} - -__global__ static void PME_BCFQ(cufftComplex *PME_FQ, float *PME_BC, int PME_Nfft) { - int index = blockDim.x * blockIdx.x + threadIdx.x; - if (index < PME_Nfft) { - float tempf = PME_BC[index]; - cufftComplex tempc = PME_FQ[index]; - PME_FQ[index].x = tempc.x * tempf; - PME_FQ[index].y = tempc.y * tempf; - } -} - -__global__ static void PME_Excluded_Energy_Correction(const int atom_numbers, const UNSIGNED_INT_VECTOR *uint_crd, - const VECTOR *sacler, const float *charge, const float pme_beta, - const float sqrt_pi, const int *excluded_list_start, - const int *excluded_list, const int *excluded_atom_numbers, - float *ene) { - int atom_i = blockDim.x * blockIdx.x + threadIdx.x; - if (atom_i < atom_numbers) { - int excluded_number = excluded_atom_numbers[atom_i]; - if (excluded_number > 0) { - int list_start = excluded_list_start[atom_i]; - // int atom_min = excluded_list[list_start]; - int list_end = list_start + excluded_number; - int atom_j; - int int_x; - int int_y; - int int_z; - - float charge_i = charge[atom_i]; - float charge_j; - float dr_abs; - float beta_dr; - - UNSIGNED_INT_VECTOR r1 = uint_crd[atom_i], r2; - VECTOR dr; - float dr2; - - float ene_lin = 0.; - - for (int i = list_start; i < list_end; i = i + 1) { - atom_j = excluded_list[i]; - r2 = uint_crd[atom_j]; - charge_j = charge[atom_j]; - - int_x = r2.uint_x - r1.uint_x; - int_y = r2.uint_y - r1.uint_y; - int_z = r2.uint_z - r1.uint_z; - dr.x = sacler[0].x * int_x; - dr.y = sacler[0].y * int_y; - dr.z = sacler[0].z * int_z; - dr2 = dr.x * dr.x + dr.y * dr.y + dr.z * dr.z; - - dr_abs = sqrtf(dr2); - beta_dr = pme_beta * dr_abs; - - ene_lin -= charge_i * charge_j * erff(beta_dr) / dr_abs; - } - atomicAdd(ene, ene_lin); - } - } -} -#endif diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/pme/pme_energy_impl.cu b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/pme/pme_energy_impl.cu deleted file mode 100644 index 73ab1b89d03..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/pme/pme_energy_impl.cu +++ /dev/null @@ -1,85 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -/** - * Note: - * PMEEnergy. This is an experimental interface that is subject to change and/or deletion. - */ -#include "plugin/device/gpu/kernel/cuda_impl/sponge/pme/pme_energy_impl.cuh" -#include "plugin/device/gpu/kernel/cuda_impl/sponge/pme/pme_common.cuh" -#include "plugin/device/gpu/kernel/cuda_impl/sponge/common_sponge.cuh" - -__global__ void PME_Energy_Reciprocal(const int element_number, const cufftComplex *FQ, const float *BC, float *sum) { - if (threadIdx.x == 0) { - sum[0] = 0.; - } - __syncthreads(); - float lin = 0.0; - cufftComplex FQ_i; - for (int i = threadIdx.x; i < element_number; i = i + blockDim.x) { - FQ_i = FQ[i]; - lin = lin + (FQ_i.x * FQ_i.x + FQ_i.y * FQ_i.y) * BC[i]; - } - atomicAdd(sum, lin); -} - -void PMEEnergy(int fftx, int ffty, int fftz, int atom_numbers, float beta, float *PME_BC, int *pme_uxyz, - float *pme_frxyz, float *PME_Q, float *pme_fq, int *PME_atom_near, int *pme_kxyz, const int *uint_crd_f, - const float *charge, int *nl_atom_numbers, int *nl_atom_serial, int *nl, const float *scaler_f, - const int *excluded_list_start, const int *excluded_list, const int *excluded_atom_numbers, - float *d_reciprocal_ene, float *d_self_ene, float *d_direct_ene, float *d_correction_ene, - dim3 thread_PME, int PME_Nin, int PME_Nfft, int PME_Nall, const cufftHandle &PME_plan_r2c, - const cufftHandle &PME_plan_c2r, cudaStream_t stream) { - UNSIGNED_INT_VECTOR *uint_crd = - const_cast(reinterpret_cast(uint_crd_f)); - VECTOR *scaler = const_cast(reinterpret_cast(scaler_f)); - int max_neighbor_numbers = 800; - NEIGHBOR_LIST *nl_a = reinterpret_cast(nl); - construct_neighbor_list_kernel<<(atom_numbers) / 128), 128, 0, stream>>>( - atom_numbers, max_neighbor_numbers, nl_atom_numbers, nl_atom_serial, nl_a); - - UNSIGNED_INT_VECTOR *PME_uxyz = reinterpret_cast(pme_uxyz); - UNSIGNED_INT_VECTOR *PME_kxyz = reinterpret_cast(pme_kxyz); - VECTOR *PME_frxyz = reinterpret_cast(pme_frxyz); - cufftComplex *PME_FQ = reinterpret_cast(pme_fq); - - Reset_List<<<3 * atom_numbers / 32 + 1, 32, 0, stream>>>(3 * atom_numbers, reinterpret_cast(PME_uxyz), - 1 << 30); - PME_Atom_Near<<>>( - uint_crd, PME_atom_near, PME_Nin, periodic_factor_inverse * fftx, periodic_factor_inverse * ffty, - periodic_factor_inverse * fftz, atom_numbers, fftx, ffty, fftz, PME_kxyz, PME_uxyz, PME_frxyz); - - Reset_List<<>>(PME_Nall, PME_Q, 0); - - PME_Q_Spread<<>>(PME_atom_near, charge, PME_frxyz, PME_Q, - PME_kxyz, atom_numbers); - - cufftExecR2C(PME_plan_r2c, reinterpret_cast(PME_Q), reinterpret_cast(PME_FQ)); - - PME_Energy_Reciprocal<<<1, 1024, 0, stream>>>(PME_Nfft, PME_FQ, PME_BC, d_reciprocal_ene); - - PME_Energy_Product<<<1, 1024, 0, stream>>>(atom_numbers, charge, charge, d_self_ene); - Scale_List<<<1, 1, 0, stream>>>(1, d_self_ene, -beta / sqrtf(PI)); - - Reset_List<<<1, 1, 0, stream>>>(1, d_direct_ene, 0.0); - PME_Direct_Energy<<>>( - atom_numbers, nl_a, uint_crd, scaler, charge, beta, cutoff * cutoff, d_direct_ene); - - Reset_List<<<1, 1, 0, stream>>>(1, d_correction_ene, 0.0); - PME_Excluded_Energy_Correction<<>>( - atom_numbers, uint_crd, scaler, charge, beta, sqrtf(PI), excluded_list_start, excluded_list, excluded_atom_numbers, - d_correction_ene); - return; -} diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/pme/pme_energy_impl.cuh b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/pme/pme_energy_impl.cuh deleted file mode 100644 index 24b0db6d046..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/pme/pme_energy_impl.cuh +++ /dev/null @@ -1,32 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -#ifndef MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_PME_PME_ENERGY_IMPL_H_ -#define MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_PME_PME_ENERGY_IMPL_H_ - -#include -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_device_info.h" - -CUDA_LIB_EXPORT void PMEEnergy(int fftx, int ffty, int fftz, int atom_numbers, float beta, float *PME_BC, int *pme_uxyz, - float *pme_frxyz, float *PME_Q, float *pme_fq, int *PME_atom_near, int *pme_kxyz, - const int *uint_crd_f, const float *charge, int *nl_atom_numbers, int *nl_atom_serial, - int *nl, const float *scaler_f, const int *excluded_list_start, const int *excluded_list, - const int *excluded_atom_numbers, float *d_reciprocal_ene, float *d_self_ene, - float *d_direct_ene, float *d_correction_ene, dim3 thread_PME, int PME_Nin, int PME_Nfft, - int PME_Nall, const cufftHandle &PME_plan_r2c, const cufftHandle &PME_plan_c2r, - cudaStream_t stream); - -#endif diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/pme/pme_energy_update_impl.cu b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/pme/pme_energy_update_impl.cu deleted file mode 100644 index 4e9b3fa91d5..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/pme/pme_energy_update_impl.cu +++ /dev/null @@ -1,90 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -/** - * Note: - * PMEEnergyUpdate. This is an experimental interface that is subject to change and/or deletion. - */ -#include "plugin/device/gpu/kernel/cuda_impl/sponge/pme/pme_energy_update_impl.cuh" -#include "plugin/device/gpu/kernel/cuda_impl/sponge/pme/pme_common.cuh" -#include "plugin/device/gpu/kernel/cuda_impl/sponge/common_sponge.cuh" - -__global__ void PME_Energy_Reciprocal_update(const int element_number, const cufftComplex *FQ, const float *BC, - float *sum) { - if (threadIdx.x == 0) { - sum[0] = 0.; - } - __syncthreads(); - float lin = 0.0; - cufftComplex FQ_i; - for (int i = threadIdx.x; i < element_number; i = i + blockDim.x) { - FQ_i = FQ[i]; - lin = lin + (FQ_i.x * FQ_i.x + FQ_i.y * FQ_i.y) * BC[i]; - } - atomicAdd(sum, lin); -} - -void PMEEnergyUpdate(int fftx, int ffty, int fftz, int atom_numbers, float beta, float *PME_BC, int *pme_uxyz, - float *pme_frxyz, float *PME_Q, float *pme_fq, int *PME_atom_near, int *pme_kxyz, - const int *uint_crd_f, const float *charge, int *nl_atom_numbers, int *nl_atom_serial, int *nl, - const float *scaler_f, const int *excluded_list_start, const int *excluded_list, - const int *excluded_atom_numbers, float *d_reciprocal_ene, float *d_self_ene, float *d_direct_ene, - float *d_correction_ene, dim3 thread_PME, int PME_Nin, int PME_Nfft, int PME_Nall, - const cufftHandle &PME_plan_r2c, const cufftHandle &PME_plan_c2r, float *neutralizing_factor, - float *charge_sum, int max_neighbor_numbers, cudaStream_t stream) { - UNSIGNED_INT_VECTOR *uint_crd = - const_cast(reinterpret_cast(uint_crd_f)); - VECTOR *scaler = const_cast(reinterpret_cast(scaler_f)); - // int max_neighbor_numbers = 800; - NEIGHBOR_LIST *nl_a = reinterpret_cast(nl); - construct_neighbor_list_kernel<<(atom_numbers) / 128), 128, 0, stream>>>( - atom_numbers, max_neighbor_numbers, nl_atom_numbers, nl_atom_serial, nl_a); - - UNSIGNED_INT_VECTOR *PME_uxyz = reinterpret_cast(pme_uxyz); - UNSIGNED_INT_VECTOR *PME_kxyz = reinterpret_cast(pme_kxyz); - VECTOR *PME_frxyz = reinterpret_cast(pme_frxyz); - cufftComplex *PME_FQ = reinterpret_cast(pme_fq); - - Reset_List<<<3 * atom_numbers / 32 + 1, 32, 0, stream>>>(3 * atom_numbers, reinterpret_cast(PME_uxyz), - 1 << 30); - PME_Atom_Near<<>>( - uint_crd, PME_atom_near, PME_Nin, periodic_factor_inverse * fftx, periodic_factor_inverse * ffty, - periodic_factor_inverse * fftz, atom_numbers, fftx, ffty, fftz, PME_kxyz, PME_uxyz, PME_frxyz); - - Reset_List<<>>(PME_Nall, PME_Q, 0); - - PME_Q_Spread<<>>(PME_atom_near, charge, PME_frxyz, PME_Q, - PME_kxyz, atom_numbers); - - cufftExecR2C(PME_plan_r2c, reinterpret_cast(PME_Q), reinterpret_cast(PME_FQ)); - - PME_Energy_Reciprocal_update<<<1, 1024, 0, stream>>>(PME_Nfft, PME_FQ, PME_BC, d_reciprocal_ene); - - PME_Energy_Product<<<1, 1024, 0, stream>>>(atom_numbers, charge, charge, d_self_ene); - Scale_List<<<1, 1, 0, stream>>>(1, d_self_ene, -beta / sqrtf(PI)); - - Sum_Of_List<<<1, 1024>>>(atom_numbers, charge, charge_sum); - device_add<<<1, 1>>>(d_self_ene, neutralizing_factor, charge_sum); - - Reset_List<<<1, 1, 0, stream>>>(1, d_direct_ene, 0.0); - PME_Direct_Energy<<>>( - atom_numbers, nl_a, uint_crd, scaler, charge, beta, cutoff * cutoff, d_direct_ene); - - Reset_List<<<1, 1, 0, stream>>>(1, d_correction_ene, 0.0); - PME_Excluded_Energy_Correction<<>>( - atom_numbers, uint_crd, scaler, charge, beta, sqrtf(PI), excluded_list_start, excluded_list, excluded_atom_numbers, - d_correction_ene); - return; -} diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/pme/pme_energy_update_impl.cuh b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/pme/pme_energy_update_impl.cuh deleted file mode 100644 index 3a26a7eccc4..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/pme/pme_energy_update_impl.cuh +++ /dev/null @@ -1,34 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -#ifndef MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_PME_PME_ENERGY_UPDATE_IMPL_H_ -#define MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_PME_PME_ENERGY_UPDATE_IMPL_H_ - -#include -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_device_info.h" - -CUDA_LIB_EXPORT void PMEEnergyUpdate(int fftx, int ffty, int fftz, int atom_numbers, float beta, float *PME_BC, - int *pme_uxyz, float *pme_frxyz, float *PME_Q, float *pme_fq, int *PME_atom_near, - int *pme_kxyz, const int *uint_crd_f, const float *charge, int *nl_atom_numbers, - int *nl_atom_serial, int *nl, const float *scaler_f, - const int *excluded_list_start, const int *excluded_list, - const int *excluded_atom_numbers, float *d_reciprocal_ene, float *d_self_ene, - float *d_direct_ene, float *d_correction_ene, dim3 thread_PME, int PME_Nin, - int PME_Nfft, int PME_Nall, const cufftHandle &PME_plan_r2c, - const cufftHandle &PME_plan_c2r, float *neutralizing_factor, float *charge_sum, - int max_neighbor_numbers, cudaStream_t stream); - -#endif diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/pme/pme_excluded_force_impl.cu b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/pme/pme_excluded_force_impl.cu deleted file mode 100644 index 02885a6777c..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/pme/pme_excluded_force_impl.cu +++ /dev/null @@ -1,103 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -#include "plugin/device/gpu/kernel/cuda_impl/sponge/pme/pme_excluded_force_impl.cuh" -#include "plugin/device/gpu/kernel/cuda_impl/sponge/pme/pme_common.cuh" - -__global__ void PME_Excluded_Force_Correction(const int atom_numbers, const UNSIGNED_INT_VECTOR *uint_crd, - const VECTOR *sacler, const float *charge, const float pme_beta, - const float sqrt_pi, const int *excluded_list_start, - const int *excluded_list, const int *excluded_atom_numbers, VECTOR *frc) { - int atom_i = blockDim.x * blockIdx.x + threadIdx.x; - if (atom_i < atom_numbers) { - int excluded_numbers = excluded_atom_numbers[atom_i]; - if (excluded_numbers > 0) { - int list_start = excluded_list_start[atom_i]; - // int atom_min = excluded_list[list_start]; - int list_end = list_start + excluded_numbers; - int atom_j; - int int_x; - int int_y; - int int_z; - - float charge_i = charge[atom_i]; - float charge_j; - float dr_abs; - float beta_dr; - - UNSIGNED_INT_VECTOR r1 = uint_crd[atom_i], r2; - VECTOR dr; - float dr2; - - float frc_abs = 0.; - VECTOR frc_lin; - VECTOR frc_record = {0., 0., 0.}; - - for (int i = list_start; i < list_end; i = i + 1) { - atom_j = excluded_list[i]; - r2 = uint_crd[atom_j]; - charge_j = charge[atom_j]; - - int_x = r2.uint_x - r1.uint_x; - int_y = r2.uint_y - r1.uint_y; - int_z = r2.uint_z - r1.uint_z; - dr.x = sacler[0].x * int_x; - dr.y = sacler[0].y * int_y; - dr.z = sacler[0].z * int_z; - dr2 = dr.x * dr.x + dr.y * dr.y + dr.z * dr.z; - - dr_abs = sqrtf(dr2); - beta_dr = pme_beta * dr_abs; - // sqrt_pi= 2/sqrt(3.141592654); - frc_abs = beta_dr * sqrt_pi * expf(-beta_dr * beta_dr) + erfcf(beta_dr); - frc_abs = (frc_abs - 1.) / dr2 / dr_abs; - frc_abs = -charge_i * charge_j * frc_abs; - frc_lin.x = frc_abs * dr.x; - frc_lin.y = frc_abs * dr.y; - frc_lin.z = frc_abs * dr.z; - - frc_record.x = frc_record.x + frc_lin.x; - frc_record.y = frc_record.y + frc_lin.y; - frc_record.z = frc_record.z + frc_lin.z; - - atomicAdd(&frc[atom_j].x, -frc_lin.x); - atomicAdd(&frc[atom_j].y, -frc_lin.y); - atomicAdd(&frc[atom_j].z, -frc_lin.z); - } // atom_j cycle - atomicAdd(&frc[atom_i].x, frc_record.x); - atomicAdd(&frc[atom_i].y, frc_record.y); - atomicAdd(&frc[atom_i].z, frc_record.z); - } // if need excluded - } -} - -void PMEExcludedForce(const int atom_numbers, const float pme_beta, const int *uint_crd_f, const float *sacler_f, - const float *charge, const int *excluded_list_start, const int *excluded_list, - const int *excluded_atom_numbers, float *frc_f, cudaStream_t stream) { - Reset_List<<(3. * atom_numbers) / 128), 128, 0, stream>>>(3 * atom_numbers, frc_f, 0.); - UNSIGNED_INT_VECTOR *uint_crd = - const_cast(reinterpret_cast(uint_crd_f)); - VECTOR *frc = reinterpret_cast(frc_f); - VECTOR *sacler = const_cast(reinterpret_cast(sacler_f)); - - PME_Excluded_Force_Correction<<(atom_numbers) / 128), 128, 0, stream>>>( - atom_numbers, uint_crd, sacler, charge, pme_beta, TWO_DIVIDED_BY_SQRT_PI, excluded_list_start, excluded_list, - excluded_atom_numbers, frc); - return; -} - -void PMEExcludedForce(const int atom_numbers, const float pme_beta, const int *uint_crd_f, const float *sacler_f, - const float *charge, const int *excluded_list_start, const int *excluded_list, - const int *excluded_atom_numbers, float *frc_f, cudaStream_t stream); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/pme/pme_excluded_force_impl.cuh b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/pme/pme_excluded_force_impl.cuh deleted file mode 100644 index bd08b11c1d8..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/pme/pme_excluded_force_impl.cuh +++ /dev/null @@ -1,28 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -#ifndef MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_PME_PME_EXCLUDED_FORCE_IMPL_H_ -#define MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_PME_PME_EXCLUDED_FORCE_IMPL_H_ - -#include -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_device_info.h" - -CUDA_LIB_EXPORT void PMEExcludedForce(const int atom_numbers, const float pme_beta, const int *uint_crd_f, - const float *sacler_f, const float *charge, const int *excluded_list_start, - const int *excluded_list, const int *excluded_atom_numbers, float *frc_f, - cudaStream_t stream); - -#endif diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/pme/pme_fft_1d_impl.cu b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/pme/pme_fft_1d_impl.cu deleted file mode 100644 index 289b1d08ddd..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/pme/pme_fft_1d_impl.cu +++ /dev/null @@ -1,29 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -#include "plugin/device/gpu/kernel/cuda_impl/sponge/pme/pme_fft_1d_impl.cuh" -#include "plugin/device/gpu/kernel/cuda_impl/sponge/pme/pme_common.cuh" - -template -void PMEFFT1D(int Nfft, Complex *input_tensor, Complex *output_tensor, const cufftHandle &FFT_plan_c2c, - cudaStream_t stream) { - cufftExecC2C(FFT_plan_c2c, reinterpret_cast(input_tensor), - reinterpret_cast(output_tensor), CUFFT_FORWARD); - return; -} - -template CUDA_LIB_EXPORT -void PMEFFT1D(int Nfft, Complex *input_tensor, Complex *output_tensor, - const cufftHandle &FFT_plan_c2c, cudaStream_t stream); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/pme/pme_fft_1d_impl.cuh b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/pme/pme_fft_1d_impl.cuh deleted file mode 100644 index 0ecde226b81..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/pme/pme_fft_1d_impl.cuh +++ /dev/null @@ -1,27 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -#ifndef MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_PME_PME_FFT_1D_IMPL_H_ -#define MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_PME_PME_FFT_1D_IMPL_H_ - -#include -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/complex.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" - -template -CUDA_LIB_EXPORT void PMEFFT1D(int Nfft, Complex *input_tensor, Complex *output_tensor, - const cufftHandle &FFT_plan_c2c, cudaStream_t stream); - -#endif diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/pme/pme_fft_2d_impl.cu b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/pme/pme_fft_2d_impl.cu deleted file mode 100644 index f06d37a2429..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/pme/pme_fft_2d_impl.cu +++ /dev/null @@ -1,29 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -#include "plugin/device/gpu/kernel/cuda_impl/sponge/pme/pme_fft_2d_impl.cuh" -#include "plugin/device/gpu/kernel/cuda_impl/sponge/pme/pme_common.cuh" - -template -void PMEFFT2D(int Nfft, Complex *input_tensor, Complex *output_tensor, - const cufftHandle &FFT_plan_c2c, cudaStream_t stream) { - cufftExecC2C(FFT_plan_c2c, reinterpret_cast(input_tensor), - reinterpret_cast(output_tensor), CUFFT_FORWARD); - return; -} - -template CUDA_LIB_EXPORT -void PMEFFT2D(int Nfft, Complex *input_tensor, Complex *output_tensor, - const cufftHandle &FFT_plan_c2c, cudaStream_t stream); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/pme/pme_fft_2d_impl.cuh b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/pme/pme_fft_2d_impl.cuh deleted file mode 100644 index 3218a1ac46e..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/pme/pme_fft_2d_impl.cuh +++ /dev/null @@ -1,27 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -#ifndef MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_PME_PME_FFT_2D_IMPL_H_ -#define MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_PME_PME_FFT_2D_IMPL_H_ - -#include -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/complex.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" - -template -CUDA_LIB_EXPORT void PMEFFT2D(int Nfft, Complex *input_tensor, Complex *output_tensor, - const cufftHandle &FFT_plan_c2c, cudaStream_t stream); - -#endif diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/pme/pme_ifft_1d_impl.cu b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/pme/pme_ifft_1d_impl.cu deleted file mode 100644 index a73ca562b3f..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/pme/pme_ifft_1d_impl.cu +++ /dev/null @@ -1,29 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -#include "plugin/device/gpu/kernel/cuda_impl/sponge/pme/pme_ifft_1d_impl.cuh" -#include "plugin/device/gpu/kernel/cuda_impl/sponge/pme/pme_common.cuh" - -template -void PMEIFFT1D(int Nfft, Complex *input_tensor, Complex *output_tensor, const cufftHandle &FFT_plan_c2c, - cudaStream_t stream) { - cufftExecC2C(FFT_plan_c2c, reinterpret_cast(input_tensor), - reinterpret_cast(output_tensor), CUFFT_INVERSE); - return; -} - -template CUDA_LIB_EXPORT -void PMEIFFT1D(int Nfft, Complex *input_tensor, Complex *output_tensor, - const cufftHandle &FFT_plan_c2c, cudaStream_t stream); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/pme/pme_ifft_1d_impl.cuh b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/pme/pme_ifft_1d_impl.cuh deleted file mode 100644 index 524195b0d7c..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/pme/pme_ifft_1d_impl.cuh +++ /dev/null @@ -1,27 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -#ifndef MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_PME_PME_IFFT_1D_IMPL_H_ -#define MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_PME_PME_IFFT_1D_IMPL_H_ - -#include -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/complex.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" - -template -CUDA_LIB_EXPORT void PMEIFFT1D(int Nfft, Complex *input_tensor, Complex *output_tensor, - const cufftHandle &FFT_plan_c2c, cudaStream_t stream); - -#endif diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/pme/pme_ifft_2d_impl.cu b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/pme/pme_ifft_2d_impl.cu deleted file mode 100644 index 0ecdb5bf886..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/pme/pme_ifft_2d_impl.cu +++ /dev/null @@ -1,29 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -#include "plugin/device/gpu/kernel/cuda_impl/sponge/pme/pme_ifft_2d_impl.cuh" -#include "plugin/device/gpu/kernel/cuda_impl/sponge/pme/pme_common.cuh" - -template -void PMEIFFT2D(int Nfft, Complex *input_tensor, Complex *output_tensor, const cufftHandle &FFT_plan_c2c, - cudaStream_t stream) { - cufftExecC2C(FFT_plan_c2c, reinterpret_cast(input_tensor), - reinterpret_cast(output_tensor), CUFFT_INVERSE); - return; -} - -template CUDA_LIB_EXPORT -void PMEIFFT2D(int Nfft, Complex *input_tensor, Complex *output_tensor, - const cufftHandle &FFT_plan_c2c, cudaStream_t stream); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/pme/pme_ifft_2d_impl.cuh b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/pme/pme_ifft_2d_impl.cuh deleted file mode 100644 index 4d3e7f44a7e..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/pme/pme_ifft_2d_impl.cuh +++ /dev/null @@ -1,27 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -#ifndef MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_PME_PME_IFFT_2D_IMPL_H_ -#define MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_PME_PME_IFFT_2D_IMPL_H_ - -#include -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/complex.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" - -template -CUDA_LIB_EXPORT void PMEIFFT2D(int Nfft, Complex *input_tensor, Complex *output_tensor, - const cufftHandle &FFT_plan_c2c, cudaStream_t stream); - -#endif diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/pme/pme_irfft_2d_impl.cu b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/pme/pme_irfft_2d_impl.cu deleted file mode 100644 index 38121dc53c2..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/pme/pme_irfft_2d_impl.cu +++ /dev/null @@ -1,28 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -#include "plugin/device/gpu/kernel/cuda_impl/sponge/pme/pme_irfft_2d_impl.cuh" -#include "plugin/device/gpu/kernel/cuda_impl/sponge/pme/pme_common.cuh" - -template -void PMEIRFFT2D(int Nfft, Complex *input_tensor, T *output_tensor, const cufftHandle &FFT_plan_c2r, - cudaStream_t stream) { - cufftExecC2R(FFT_plan_c2r, reinterpret_cast(input_tensor), output_tensor); - return; -} - -template CUDA_LIB_EXPORT -void PMEIRFFT2D(int Nfft, Complex *input_tensor, float *output_tensor, - const cufftHandle &FFT_plan_c2r, cudaStream_t stream); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/pme/pme_irfft_2d_impl.cuh b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/pme/pme_irfft_2d_impl.cuh deleted file mode 100644 index 3610537d68d..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/pme/pme_irfft_2d_impl.cuh +++ /dev/null @@ -1,27 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -#ifndef MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_PME_PME_IRFFT_2D_IMPL_H_ -#define MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_PME_PME_IRFFT_2D_IMPL_H_ - -#include -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/complex.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" - -template -CUDA_LIB_EXPORT void PMEIRFFT2D(int Nfft, Complex *input_tensor, T *output_tensor, const cufftHandle &FFT_plan_c2r, - cudaStream_t stream); - -#endif diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/pme/pme_reciprocal_force_impl.cu b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/pme/pme_reciprocal_force_impl.cu deleted file mode 100644 index c8971f2fff8..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/pme/pme_reciprocal_force_impl.cu +++ /dev/null @@ -1,107 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -/** - * Note: - * PMEReciprocalForce. This is an experimental interface that is subject to change and/or deletion. - */ -#include "plugin/device/gpu/kernel/cuda_impl/sponge/pme/pme_reciprocal_force_impl.cuh" -#include "plugin/device/gpu/kernel/cuda_impl/sponge/pme/pme_common.cuh" - -__global__ void PME_Final(int *PME_atom_near, const float *charge, const float *PME_Q, VECTOR *force, - const VECTOR *PME_frxyz, const UNSIGNED_INT_VECTOR *PME_kxyz, - const _VECTOR PME_inverse_box_vector, const int atom_numbers) { - int atom = blockDim.x * blockIdx.x + threadIdx.x; - if (atom < atom_numbers) { - int k, kx; - float tempdQx, tempdQy, tempdQz, tempdx, tempdy, tempdz, tempx, tempy, tempz, tempdQf; - float tempf, tempf2; - float temp_charge = charge[atom]; - int *temp_near = PME_atom_near + atom * 64; - UNSIGNED_INT_VECTOR temp_kxyz; - VECTOR temp_frxyz = PME_frxyz[atom]; - for (k = threadIdx.y; k < 64; k = k + blockDim.y) { - temp_kxyz = PME_kxyz[k]; - tempdQf = -PME_Q[temp_near[k]] * temp_charge; - - kx = temp_kxyz.uint_x; - tempf = (temp_frxyz.x); - tempf2 = tempf * tempf; - tempx = PME_Ma[kx] * tempf * tempf2 + PME_Mb[kx] * tempf2 + PME_Mc[kx] * tempf + PME_Md[kx]; - tempdx = PME_dMa[kx] * tempf2 + PME_dMb[kx] * tempf + PME_dMc[kx]; - - kx = temp_kxyz.uint_y; - tempf = (temp_frxyz.y); - tempf2 = tempf * tempf; - tempy = PME_Ma[kx] * tempf * tempf2 + PME_Mb[kx] * tempf2 + PME_Mc[kx] * tempf + PME_Md[kx]; - tempdy = PME_dMa[kx] * tempf2 + PME_dMb[kx] * tempf + PME_dMc[kx]; - - kx = temp_kxyz.uint_z; - tempf = (temp_frxyz.z); - tempf2 = tempf * tempf; - tempz = PME_Ma[kx] * tempf * tempf2 + PME_Mb[kx] * tempf2 + PME_Mc[kx] * tempf + PME_Md[kx]; - tempdz = PME_dMa[kx] * tempf2 + PME_dMb[kx] * tempf + PME_dMc[kx]; - - tempdQx = tempdx * tempy * tempz * PME_inverse_box_vector.x; - tempdQy = tempdy * tempx * tempz * PME_inverse_box_vector.y; - tempdQz = tempdz * tempx * tempy * PME_inverse_box_vector.z; - - atomicAdd(&force[atom].x, tempdQf * tempdQx); - atomicAdd(&force[atom].y, tempdQf * tempdQy); - atomicAdd(&force[atom].z, tempdQf * tempdQz); - } - } -} - -void PMEReciprocalForce(int fftx, int ffty, int fftz, int atom_numbers, float beta, float *PME_BC, int *pme_uxyz, - float *pme_frxyz, float *PME_Q, float *pme_fq, int *PME_atom_near, int *pme_kxyz, - const int *uint_crd_f, const float *charge, float *force, int PME_Nin, int PME_Nall, - int PME_Nfft, const cufftHandle &PME_plan_r2c, const cufftHandle &PME_plan_c2r, - const _VECTOR &PME_inverse_box_vector, cudaStream_t stream) { - Reset_List<<(3. * atom_numbers) / 128), 128, 0, stream>>>(3 * atom_numbers, force, 0.); - UNSIGNED_INT_VECTOR *uint_crd = - const_cast(reinterpret_cast(uint_crd_f)); - UNSIGNED_INT_VECTOR *PME_uxyz = reinterpret_cast(pme_uxyz); - UNSIGNED_INT_VECTOR *PME_kxyz = reinterpret_cast(pme_kxyz); - Reset_List<<<3 * atom_numbers / 32 + 1, 32, 0, stream>>>(3 * atom_numbers, reinterpret_cast(PME_uxyz), - 1 << 30); - - VECTOR *PME_frxyz = reinterpret_cast(pme_frxyz); - VECTOR *frc = reinterpret_cast(force); - - cufftComplex *PME_FQ = reinterpret_cast(pme_fq); - - // initial end - Reset_List<<(3. * atom_numbers) / 128), 128, 0, stream>>>( - 3 * atom_numbers, reinterpret_cast(frc), 0.); - - PME_Atom_Near<<>>( - uint_crd, PME_atom_near, PME_Nin, periodic_factor_inverse * fftx, periodic_factor_inverse * ffty, - periodic_factor_inverse * fftz, atom_numbers, fftx, ffty, fftz, PME_kxyz, PME_uxyz, PME_frxyz); - - Reset_List<<>>(PME_Nall, PME_Q, 0); - - PME_Q_Spread<<>>(PME_atom_near, charge, PME_frxyz, PME_Q, - PME_kxyz, atom_numbers); - - cufftExecR2C(PME_plan_r2c, reinterpret_cast(PME_Q), reinterpret_cast(PME_FQ)); - PME_BCFQ<<>>(PME_FQ, PME_BC, PME_Nfft); - - cufftExecC2R(PME_plan_c2r, reinterpret_cast(PME_FQ), reinterpret_cast(PME_Q)); - - PME_Final<<>>(PME_atom_near, charge, PME_Q, frc, PME_frxyz, - PME_kxyz, PME_inverse_box_vector, atom_numbers); - return; -} diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/pme/pme_reciprocal_force_impl.cuh b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/pme/pme_reciprocal_force_impl.cuh deleted file mode 100644 index 5faa797bdef..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/pme/pme_reciprocal_force_impl.cuh +++ /dev/null @@ -1,34 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -#ifndef MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_PME_PME_RECIPROCAL_FORCE_IMPL_H_ -#define MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_PME_PME_RECIPROCAL_FORCE_IMPL_H_ - -#include -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" - -struct _VECTOR { - float x; - float y; - float z; -}; -CUDA_LIB_EXPORT void PMEReciprocalForce(int fftx, int ffty, int fftz, int atom_numbers, float beta, float *PME_BC, - int *pme_uxyz, float *pme_frxyz, float *PME_Q, float *pme_fq, - int *PME_atom_near, int *pme_kxyz, const int *uint_crd_f, const float *charge, - float *force, int PME_Nin, int PME_Nall, int PME_Nfft, - const cufftHandle &PME_plan_r2c, const cufftHandle &PME_plan_c2r, - const _VECTOR &PME_inverse_box_vector, cudaStream_t stream); - -#endif diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/pme/pme_rfft_2d_impl.cu b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/pme/pme_rfft_2d_impl.cu deleted file mode 100644 index 58eee14dfc5..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/pme/pme_rfft_2d_impl.cu +++ /dev/null @@ -1,28 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -#include "plugin/device/gpu/kernel/cuda_impl/sponge/pme/pme_rfft_2d_impl.cuh" -#include "plugin/device/gpu/kernel/cuda_impl/sponge/pme/pme_common.cuh" - -template -void PMERFFT2D(int Nfft, T *input_tensor, Complex *output_tensor, - const cufftHandle &FFT_plan_r2c, cudaStream_t stream) { - cufftExecR2C(FFT_plan_r2c, input_tensor, reinterpret_cast(output_tensor)); - return; -} - -template CUDA_LIB_EXPORT -void PMERFFT2D(int Nfft, float *input_tensor, Complex *output_tensor, - const cufftHandle &FFT_plan_r2c, cudaStream_t stream); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/pme/pme_rfft_2d_impl.cuh b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/pme/pme_rfft_2d_impl.cuh deleted file mode 100644 index f6648bb33e2..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/pme/pme_rfft_2d_impl.cuh +++ /dev/null @@ -1,28 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -#ifndef MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_PME_PME_RFFT_2D_IMPL_H_ -#define MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_PME_PME_RFFT_2D_IMPL_H_ - -#include -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/complex.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_device_info.h" - -template -CUDA_LIB_EXPORT void PMERFFT2D(int Nfft, T *input_tensor, Complex *output_tensor, const cufftHandle &FFT_plan_r2c, - cudaStream_t stream); - -#endif diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/restrain/restrain_energy_impl.cu b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/restrain/restrain_energy_impl.cu deleted file mode 100644 index 546f1a96f72..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/restrain/restrain_energy_impl.cu +++ /dev/null @@ -1,45 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "plugin/device/gpu/kernel/cuda_impl/sponge/restrain/restrain_energy_impl.cuh" -#include "plugin/device/gpu/kernel/cuda_impl/sponge/common_sponge.cuh" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/util.cuh" - -__global__ void restrain_energy_kernel(const int restrain_numbers, const int *restrain_list, const VECTOR *crd, - const VECTOR *crd_ref, const float weight, const VECTOR boxlength, float *ene) { - int i = blockDim.x * blockIdx.x + threadIdx.x; - if (i < restrain_numbers) { - int atom_i = restrain_list[i]; - VECTOR dr = Get_Periodic_Displacement(crd_ref[atom_i], crd[atom_i], boxlength); - ene[i] = weight * dr * dr; - } -} - -void restrainenergy(int restrain_numbers, int atom_numbers, float weight, const int *restrain_list, const float *crd_f, - const float *crd_ref_f, const float *boxlength_f, float *ene, cudaStream_t stream) { - Reset_List<<(restrain_numbers) / 128), 128, 0, stream>>>(restrain_numbers, ene, 0.); - size_t thread_per_block = 128; - size_t block_per_grid = ceilf(static_cast(restrain_numbers) / 128); - const VECTOR *crd = reinterpret_cast(crd_f); - const VECTOR *crd_ref = reinterpret_cast(crd_ref_f); - const VECTOR *boxlength = reinterpret_cast(boxlength_f); - restrain_energy_kernel<<>>(restrain_numbers, restrain_list, crd, crd_ref, - weight, boxlength[0], ene); - return; -} - -void restrainenergy(int restrain_numbers, int atom_numbers, float weight, const int *restrain_list, const float *crd_f, - const float *crd_ref, const float *boxlength_f, float *ene, cudaStream_t stream); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/restrain/restrain_energy_impl.cuh b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/restrain/restrain_energy_impl.cuh deleted file mode 100644 index 6402e158c1d..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/restrain/restrain_energy_impl.cuh +++ /dev/null @@ -1,27 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_RESTRAIN_RESTRAIN_ENERGY_IMPL_H_ -#define MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_RESTRAIN_RESTRAIN_ENERGY_IMPL_H_ - -#include -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_device_info.h" - -CUDA_LIB_EXPORT void restrainenergy(int restrain_numbers, int atom_numbers, float weight, const int *restrain_list, - const float *crd_f, const float *crd_ref, const float *boxlength_f, float *ene, - cudaStream_t stream); -#endif // MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_RESTRAIN_RESTRAIN_ENERGY_IMPL_H_ diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/restrain/restrain_force_atom_energy_virial_impl.cu b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/restrain/restrain_force_atom_energy_virial_impl.cu deleted file mode 100644 index 2e7766babcc..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/restrain/restrain_force_atom_energy_virial_impl.cu +++ /dev/null @@ -1,59 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "plugin/device/gpu/kernel/cuda_impl/sponge/restrain/restrain_force_atom_energy_virial_impl.cuh" -#include "plugin/device/gpu/kernel/cuda_impl/sponge/common_sponge.cuh" - -__global__ void restrain_force_with_atom_energy_and_virial(int restrain_numbers, const int *restrain_list, - const VECTOR *crd, const VECTOR *crd_ref, const float weight, - const VECTOR *boxlength, float *atom_energy, - float *atom_virial, VECTOR *frc) { - int i = blockDim.x * blockIdx.x + threadIdx.x; - if (i < restrain_numbers) { - int atom_i = restrain_list[i]; - VECTOR dr = Get_Periodic_Displacement(crd_ref[atom_i], crd[atom_i], boxlength[0]); - - VECTOR temp_force = 2 * weight * dr; - float virial = temp_force * dr; - - atom_energy[atom_i] += 0.5 * virial; - atom_virial[atom_i] -= virial; - frc[atom_i] = frc[atom_i] + temp_force; - } -} - -void restrainforcewithatomenergyandvirial(int restrain_numbers, int atom_numbers, const int *restrain_list, - const float *crd_f, const float *crd_ref_f, const float weight, - const float *boxlength_f, float *atom_ene, float *atom_virial, float *frc_f, - cudaStream_t stream) { - Reset_List<<(3. * atom_numbers) / 128), 128, 0, stream>>>(3 * atom_numbers, frc_f, 0.); - Reset_List<<(atom_numbers) / 128), 128, 0, stream>>>(atom_numbers, atom_ene, 0.); - Reset_List<<(atom_numbers) / 128), 128, 0, stream>>>(atom_numbers, atom_virial, 0.); - size_t thread_per_block = 128; - size_t block_per_grid = ceilf(static_cast(restrain_numbers) / 128); - const VECTOR *crd = reinterpret_cast(crd_f); - const VECTOR *crd_ref = reinterpret_cast(crd_ref_f); - const VECTOR *boxlength = reinterpret_cast(boxlength_f); - VECTOR *frc = const_cast(reinterpret_cast(frc_f)); - restrain_force_with_atom_energy_and_virial<<>>( - restrain_numbers, restrain_list, crd, crd_ref, weight, boxlength, atom_ene, atom_virial, frc); - return; -} - -void restrainforcewithatomenergyandvirial(int restrain_numbers, int atom_numbers, const int *restrain_list, - const float *crd_f, const float *crd_ref_f, const float weight, - const float *boxlength_f, float *atom_ene, float *atom_virial, float *frc_f, - cudaStream_t stream); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/restrain/restrain_force_atom_energy_virial_impl.cuh b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/restrain/restrain_force_atom_energy_virial_impl.cuh deleted file mode 100644 index 099bd7435a3..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/restrain/restrain_force_atom_energy_virial_impl.cuh +++ /dev/null @@ -1,29 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_RESTRAIN_RESTRAIN_FORCE_ATOM_ENERGY_VIRIAL_IMPL_H_ -#define MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_RESTRAIN_RESTRAIN_FORCE_ATOM_ENERGY_VIRIAL_IMPL_H_ - -#include -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_device_info.h" - -CUDA_LIB_EXPORT void restrainforcewithatomenergyandvirial(int restrain_numbers, int atom_numbers, - const int *restrain_list, const float *crd_f, - const float *crd_ref_f, const float weight, - const float *boxlength_f, float *atom_ene, float *atom_virial, - float *frc_f, cudaStream_t stream); -#endif // MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_RESTRAIN_RESTRAIN_FORCE_ATOM_ENERGY_VIRIAL_IMPL_H_ diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/restrain/restrain_force_impl.cu b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/restrain/restrain_force_impl.cu deleted file mode 100644 index cff6a302b80..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/restrain/restrain_force_impl.cu +++ /dev/null @@ -1,56 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "plugin/device/gpu/kernel/cuda_impl/sponge/restrain/restrain_force_impl.cuh" -#include "plugin/device/gpu/kernel/cuda_impl/sponge/common_sponge.cuh" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/util.cuh" - -__global__ void restrainforcekernel(int restrain_numbers, const int *restrain_list, const UNSIGNED_INT_VECTOR *uint_crd, - const UNSIGNED_INT_VECTOR *uint_crd_ref, const float factor, const VECTOR *scaler, - VECTOR *frc) { - int i = blockDim.x * blockIdx.x + threadIdx.x; - if (i < restrain_numbers) { - int atom_i = restrain_list[i]; - VECTOR dr = Get_Periodic_Displacement(uint_crd_ref[atom_i], uint_crd[atom_i], scaler[0]); - - atomicAdd(&frc[atom_i].x, factor * dr.x); - atomicAdd(&frc[atom_i].y, factor * dr.y); - atomicAdd(&frc[atom_i].z, factor * dr.z); - } -} - -void restrainforce(int restrain_numbers, int atom_numbers, const int *restrain_list, const int *uint_crd_f, - const int *uint_crd_ref_f, const float factor, const float *scaler_f, float *frc_f, - cudaStream_t stream) { - Reset_List<<(3. * atom_numbers) / 128), 128, 0, stream>>>(3 * atom_numbers, frc_f, 0.); - size_t thread_per_block = 128; - size_t block_per_grid = ceilf(static_cast(restrain_numbers) / 128); - UNSIGNED_INT_VECTOR *uint_crd = - const_cast(reinterpret_cast(uint_crd_f)); - - UNSIGNED_INT_VECTOR *uint_crd_ref = - const_cast(reinterpret_cast(uint_crd_ref_f)); - - VECTOR *scaler = const_cast(reinterpret_cast(scaler_f)); - VECTOR *frc = const_cast(reinterpret_cast(frc_f)); - restrainforcekernel<<>>(restrain_numbers, restrain_list, uint_crd, - uint_crd_ref, factor, scaler, frc); - return; -} - -void restrainforce(int restrain_numbers, int atom_numbers, const int *restrain_list, const int *uint_crd_f, - const int *uint_crd_ref, const float factor, const float *scaler_f, float *frc_f, - cudaStream_t stream); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/restrain/restrain_force_impl.cuh b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/restrain/restrain_force_impl.cuh deleted file mode 100644 index f1cd09b0298..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/restrain/restrain_force_impl.cuh +++ /dev/null @@ -1,27 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_RESTRAIN_RESTAIN_FORCE_IMPL_H_ -#define MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_RESTRAIN_RESTAIN_FORCE_IMPL_H_ - -#include -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_device_info.h" - -CUDA_LIB_EXPORT void restrainforce(int restrain_numbers, int atom_numbers, const int *restrain_list, - const int *uint_crd_f, const int *uint_crd_ref, const float factor, - const float *scaler_f, float *frc_f, cudaStream_t stream); -#endif // MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_RESTRAIN_RESTAIN_FORCE_IMPL_H_ diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/simple_constrain/constrain_force_cycle_impl.cu b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/simple_constrain/constrain_force_cycle_impl.cu deleted file mode 100644 index 49445cfe81a..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/simple_constrain/constrain_force_cycle_impl.cu +++ /dev/null @@ -1,82 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -/** - * Note: - * Constrain_Force_Cycle. This is an experimental interface that is subject to change and/or deletion. - */ - -#include "plugin/device/gpu/kernel/cuda_impl/sponge/common_sponge.cuh" -#include "plugin/device/gpu/kernel/cuda_impl/sponge/simple_constrain/constrain_force_cycle_impl.cuh" - -__global__ void Constrain_Force_Cycle_Kernel(const int constrain_pair_numbers, const UNSIGNED_INT_VECTOR *uint_crd, - const VECTOR *scaler, const CONSTRAIN_PAIR *constrain_pair, - const VECTOR *pair_dr, VECTOR *test_frc) { - int pair_i = blockDim.x * blockIdx.x + threadIdx.x; - if (pair_i < constrain_pair_numbers) { - CONSTRAIN_PAIR cp = constrain_pair[pair_i]; - float r_1; - VECTOR dr; - float frc_abs; - VECTOR frc_lin; - - dr.x = (static_cast(uint_crd[cp.atom_i_serial].uint_x - uint_crd[cp.atom_j_serial].uint_x)) * scaler[0].x; - dr.y = (static_cast(uint_crd[cp.atom_i_serial].uint_y - uint_crd[cp.atom_j_serial].uint_y)) * scaler[0].y; - dr.z = (static_cast(uint_crd[cp.atom_i_serial].uint_z - uint_crd[cp.atom_j_serial].uint_z)) * scaler[0].z; - r_1 = rnorm3df(dr.x, dr.y, dr.z); - frc_abs = (1. - cp.constant_r * r_1) * cp.constrain_k; - - frc_lin.x = frc_abs * pair_dr[pair_i].x; - frc_lin.y = frc_abs * pair_dr[pair_i].y; - frc_lin.z = frc_abs * pair_dr[pair_i].z; - - atomicAdd(&test_frc[cp.atom_j_serial].x, frc_lin.x); - atomicAdd(&test_frc[cp.atom_j_serial].y, frc_lin.y); - atomicAdd(&test_frc[cp.atom_j_serial].z, frc_lin.z); - - atomicAdd(&test_frc[cp.atom_i_serial].x, -frc_lin.x); - atomicAdd(&test_frc[cp.atom_i_serial].y, -frc_lin.y); - atomicAdd(&test_frc[cp.atom_i_serial].z, -frc_lin.z); - } -} - -void Constrain_Force_Cycle(int atom_numbers, int constrain_pair_numbers, const unsigned int *uint_crd_f, - const float *scaler_f, float *constrain_pair_f, const float *pair_dr_f, - const int *atom_i_serials, const int *atom_j_serials, const float *constant_rs, - const float *constrain_ks, float *test_frc_f, cudaStream_t stream) { - Reset_List<<(3 * atom_numbers) / 128), 128, 0, stream>>>(3 * atom_numbers, test_frc_f, 0.); - size_t thread_per_block = 128; - size_t block_per_grid = ceilf(static_cast(atom_numbers) / 128); - const UNSIGNED_INT_VECTOR *uint_crd = reinterpret_cast(uint_crd_f); - const VECTOR *scaler = reinterpret_cast(scaler_f); - const VECTOR *pair_dr = reinterpret_cast(pair_dr_f); - - VECTOR *test_frc = reinterpret_cast(test_frc_f); - - CONSTRAIN_PAIR *constrain_pair = reinterpret_cast(constrain_pair_f); - - construct_constrain_pair<<(constrain_pair_numbers) / 128), 128, 0, stream>>>( - constrain_pair_numbers, atom_i_serials, atom_j_serials, constant_rs, constrain_ks, constrain_pair); - - Constrain_Force_Cycle_Kernel<<>>( - constrain_pair_numbers, uint_crd, scaler, constrain_pair, pair_dr, test_frc); - - return; -} - -void Constrain_Force_Cycle(int atom_numbers, int constrain_pair_numbers, const unsigned int *uint_crd_f, - const float *scaler_f, float *constrain_pair_f, const float *pair_dr_f, - const int *atom_i_serials, const int *atom_j_serials, const float *constant_rs, - const float *constrain_ks, float *test_frc_f, cudaStream_t stream); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/simple_constrain/constrain_force_cycle_impl.cuh b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/simple_constrain/constrain_force_cycle_impl.cuh deleted file mode 100644 index 502e6e65823..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/simple_constrain/constrain_force_cycle_impl.cuh +++ /dev/null @@ -1,34 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -/** - * Note: - * Constrain_Force_Cycle. This is an experimental interface that is subject to change and/or deletion. - */ - -#ifndef MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_SIMPLE_CONSTRAIN_CONSTRAIN_FORCE_CYCLE_IMPL_H_ -#define MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_SIMPLE_CONSTRAIN_CONSTRAIN_FORCE_CYCLE_IMPL_H_ - -#include -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_device_info.h" - -CUDA_LIB_EXPORT void Constrain_Force_Cycle(int atom_numbers, int constrain_pair_numbers, const unsigned int *uint_crd_f, - const float *scaler_f, float *constrain_pair_f, const float *pair_dr_f, - const int *atom_i_serials, const int *atom_j_serials, - const float *constant_rs, const float *constrain_ks, float *test_frc_f, - cudaStream_t stream); - -#endif diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/simple_constrain/constrain_force_cycle_with_virial_impl.cu b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/simple_constrain/constrain_force_cycle_with_virial_impl.cu deleted file mode 100644 index 4530efed91c..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/simple_constrain/constrain_force_cycle_with_virial_impl.cu +++ /dev/null @@ -1,78 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -/** - * Note: - * ConstrainForceCycleVirial. This is an experimental interface that is subject to change and/or deletion. - */ - -#include "plugin/device/gpu/kernel/cuda_impl/sponge/common_sponge.cuh" -#include "plugin/device/gpu/kernel/cuda_impl/sponge/simple_constrain/constrain_force_cycle_with_virial_impl.cuh" - -__global__ void Constrain_Force_Cycle_With_Virial(int constrain_pair_numbers, const UNSIGNED_INT_VECTOR *uint_crd, - const VECTOR *scaler, CONSTRAIN_PAIR *constrain_pair, - const VECTOR *pair_dr, VECTOR *test_frc, float *d_atom_virial) { - int pair_i = blockDim.x * blockIdx.x + threadIdx.x; - if (pair_i < constrain_pair_numbers) { - CONSTRAIN_PAIR cp = constrain_pair[pair_i]; - VECTOR dr0 = pair_dr[pair_i]; - VECTOR dr = Get_Periodic_Displacement(uint_crd[cp.atom_i_serial], uint_crd[cp.atom_j_serial], scaler[0]); - float r_1 = rnorm3df(dr.x, dr.y, dr.z); - float frc_abs = (1. - cp.constant_r * r_1) * cp.constrain_k; - VECTOR frc_lin = frc_abs * dr0; - d_atom_virial[pair_i] -= frc_lin * dr0; - - atomicAdd(&test_frc[cp.atom_j_serial].x, frc_lin.x); - atomicAdd(&test_frc[cp.atom_j_serial].y, frc_lin.y); - atomicAdd(&test_frc[cp.atom_j_serial].z, frc_lin.z); - - atomicAdd(&test_frc[cp.atom_i_serial].x, -frc_lin.x); - atomicAdd(&test_frc[cp.atom_i_serial].y, -frc_lin.y); - atomicAdd(&test_frc[cp.atom_i_serial].z, -frc_lin.z); - } -} - -void Constrain_Force_Cycle_With_Virial(int atom_numbers, int constrain_pair_numbers, const unsigned int *uint_crd_f, - const float *scaler_f, float *constrain_pair_f, const float *pair_dr_f, - const int *atom_i_serials, const int *atom_j_serials, const float *constant_rs, - const float *constrain_ks, float *test_frc_f, float *d_atom_virial, - cudaStream_t stream) { - Reset_List<<(3 * atom_numbers) / 128), 128, 0, stream>>>(3 * atom_numbers, test_frc_f, 0.); - Reset_List<<(constrain_pair_numbers) / 128), 128, 0, stream>>>(constrain_pair_numbers, - d_atom_virial, 0.); - size_t thread_per_block = 128; - size_t block_per_grid = ceilf(static_cast(atom_numbers) / 128); - const UNSIGNED_INT_VECTOR *uint_crd = reinterpret_cast(uint_crd_f); - const VECTOR *scaler = reinterpret_cast(scaler_f); - const VECTOR *pair_dr = reinterpret_cast(pair_dr_f); - - VECTOR *test_frc = reinterpret_cast(test_frc_f); - - CONSTRAIN_PAIR *constrain_pair = reinterpret_cast(constrain_pair_f); - - construct_constrain_pair<<(constrain_pair_numbers) / 128), 128, 0, stream>>>( - constrain_pair_numbers, atom_i_serials, atom_j_serials, constant_rs, constrain_ks, constrain_pair); - - Constrain_Force_Cycle_With_Virial<<>>( - constrain_pair_numbers, uint_crd, scaler, constrain_pair, pair_dr, test_frc, d_atom_virial); - - return; -} - -void Constrain_Force_Cycle_With_Virial(int atom_numbers, int constrain_pair_numbers, const unsigned int *uint_crd_f, - const float *scaler_f, float *constrain_pair_f, const float *pair_dr_f, - const int *atom_i_serials, const int *atom_j_serials, const float *constant_rs, - const float *constrain_ks, float *test_frc_f, float *d_atom_virial, - cudaStream_t stream); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/simple_constrain/constrain_force_cycle_with_virial_impl.cuh b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/simple_constrain/constrain_force_cycle_with_virial_impl.cuh deleted file mode 100644 index bbc9064d1e9..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/simple_constrain/constrain_force_cycle_with_virial_impl.cuh +++ /dev/null @@ -1,34 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -/** - * Note: - * ConstrainForceCycleVirial. This is an experimental interface that is subject to change and/or deletion. - */ - -#ifndef MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_SIMPLE_CONSTRAIN_CONSTRAIN_FORCE_CYCLE_WITH_VIRIAL_IMPL_H_ -#define MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_SIMPLE_CONSTRAIN_CONSTRAIN_FORCE_CYCLE_WITH_VIRIAL_IMPL_H_ - -#include -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_device_info.h" - -CUDA_LIB_EXPORT void Constrain_Force_Cycle_With_Virial(int atom_numbers, int constrain_pair_numbers, - const unsigned int *uint_crd_f, const float *scaler_f, - float *constrain_pair_f, const float *pair_dr_f, - const int *atom_i_serials, const int *atom_j_serials, - const float *constant_rs, const float *constrain_ks, - float *test_frc_f, float *d_atom_virial, cudaStream_t stream); -#endif diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/simple_constrain/constrain_force_virial_impl.cu b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/simple_constrain/constrain_force_virial_impl.cu deleted file mode 100644 index 904270580c2..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/simple_constrain/constrain_force_virial_impl.cu +++ /dev/null @@ -1,180 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -/** - * Note: - * ConstrainForceCycleVirial. This is an experimental interface that is subject to change and/or deletion. - */ - -#include "plugin/device/gpu/kernel/cuda_impl/sponge/common_sponge.cuh" -#include "plugin/device/gpu/kernel/cuda_impl/sponge/simple_constrain/constrain_force_virial_impl.cuh" - -__global__ void constrain_force_cycle_with_virial_update_kernel(int constrain_pair_numbers, - const UNSIGNED_INT_VECTOR *uint_crd, - const VECTOR *scaler, CONSTRAIN_PAIR *constrain_pair, - const VECTOR *pair_dr, VECTOR *test_frc, - float *d_atom_virial) { - int pair_i = blockDim.x * blockIdx.x + threadIdx.x; - if (pair_i < constrain_pair_numbers) { - CONSTRAIN_PAIR cp = constrain_pair[pair_i]; - VECTOR dr0 = pair_dr[pair_i]; - VECTOR dr = Get_Periodic_Displacement(uint_crd[cp.atom_i_serial], uint_crd[cp.atom_j_serial], scaler[0]); - float r_1 = rnorm3df(dr.x, dr.y, dr.z); - float frc_abs = (1. - cp.constant_r * r_1) * cp.constrain_k; - VECTOR frc_lin = frc_abs * dr0; - d_atom_virial[pair_i] -= frc_lin * dr0; - - atomicAdd(&test_frc[cp.atom_j_serial].x, frc_lin.x); - atomicAdd(&test_frc[cp.atom_j_serial].y, frc_lin.y); - atomicAdd(&test_frc[cp.atom_j_serial].z, frc_lin.z); - - atomicAdd(&test_frc[cp.atom_i_serial].x, -frc_lin.x); - atomicAdd(&test_frc[cp.atom_i_serial].y, -frc_lin.y); - atomicAdd(&test_frc[cp.atom_i_serial].z, -frc_lin.z); - } -} - -__global__ void constrain_force_cycle_update_kernel(const int constrain_pair_numbers, - const UNSIGNED_INT_VECTOR *uint_crd, const VECTOR *scaler, - const CONSTRAIN_PAIR *constrain_pair, const VECTOR *pair_dr, - VECTOR *test_frc) { - int pair_i = blockDim.x * blockIdx.x + threadIdx.x; - if (pair_i < constrain_pair_numbers) { - CONSTRAIN_PAIR cp = constrain_pair[pair_i]; - float r_1; - VECTOR dr; - float frc_abs; - VECTOR frc_lin; - - dr.x = (static_cast(uint_crd[cp.atom_i_serial].uint_x - uint_crd[cp.atom_j_serial].uint_x)) * scaler[0].x; - dr.y = (static_cast(uint_crd[cp.atom_i_serial].uint_y - uint_crd[cp.atom_j_serial].uint_y)) * scaler[0].y; - dr.z = (static_cast(uint_crd[cp.atom_i_serial].uint_z - uint_crd[cp.atom_j_serial].uint_z)) * scaler[0].z; - r_1 = rnorm3df(dr.x, dr.y, dr.z); - frc_abs = (1. - cp.constant_r * r_1) * cp.constrain_k; - - frc_lin.x = frc_abs * pair_dr[pair_i].x; - frc_lin.y = frc_abs * pair_dr[pair_i].y; - frc_lin.z = frc_abs * pair_dr[pair_i].z; - - atomicAdd(&test_frc[cp.atom_j_serial].x, frc_lin.x); - atomicAdd(&test_frc[cp.atom_j_serial].y, frc_lin.y); - atomicAdd(&test_frc[cp.atom_j_serial].z, frc_lin.z); - - atomicAdd(&test_frc[cp.atom_i_serial].x, -frc_lin.x); - atomicAdd(&test_frc[cp.atom_i_serial].y, -frc_lin.y); - atomicAdd(&test_frc[cp.atom_i_serial].z, -frc_lin.z); - } -} - -__global__ void refresh_uint_crd_update_kernel(int atom_numbers, const VECTOR *crd, - const VECTOR *quarter_crd_to_uint_crd_cof, UNSIGNED_INT_VECTOR *uint_crd, - VECTOR *test_frc, const float *mass_inverse, - const float half_exp_gamma_plus_half) { - int atom_i = blockDim.x * blockIdx.x + threadIdx.x; - if (atom_i < atom_numbers) { - INT_VECTOR tempi; - VECTOR crd_lin = crd[atom_i]; - VECTOR frc_lin = test_frc[atom_i]; - float mass_lin = mass_inverse[atom_i]; - - crd_lin.x = crd_lin.x + half_exp_gamma_plus_half * frc_lin.x * mass_lin; - crd_lin.y = crd_lin.y + half_exp_gamma_plus_half * frc_lin.y * mass_lin; - crd_lin.z = crd_lin.z + half_exp_gamma_plus_half * frc_lin.z * mass_lin; - - tempi.int_x = crd_lin.x * quarter_crd_to_uint_crd_cof[0].x; - tempi.int_y = crd_lin.y * quarter_crd_to_uint_crd_cof[0].y; - tempi.int_z = crd_lin.z * quarter_crd_to_uint_crd_cof[0].z; - - uint_crd[atom_i].uint_x = tempi.int_x << 2; - uint_crd[atom_i].uint_y = tempi.int_y << 2; - uint_crd[atom_i].uint_z = tempi.int_z << 2; - } -} - -void constrain_force_cycle_update(int atom_numbers, int constrain_pair_numbers, const unsigned int *uint_crd_f, - const float *scaler_f, float *constrain_pair_f, const float *pair_dr_f, - const int *atom_i_serials, const int *atom_j_serials, const float *constant_rs, - const float *constrain_ks, float *test_frc_f, cudaStream_t stream) { - size_t thread_per_block = 128; - size_t block_per_grid = ceilf(static_cast(atom_numbers) / 128); - const UNSIGNED_INT_VECTOR *uint_crd = reinterpret_cast(uint_crd_f); - const VECTOR *scaler = reinterpret_cast(scaler_f); - const VECTOR *pair_dr = reinterpret_cast(pair_dr_f); - - VECTOR *test_frc = reinterpret_cast(test_frc_f); - - CONSTRAIN_PAIR *constrain_pair = reinterpret_cast(constrain_pair_f); - - construct_constrain_pair<<(constrain_pair_numbers) / 128), 128, 0, stream>>>( - constrain_pair_numbers, atom_i_serials, atom_j_serials, constant_rs, constrain_ks, constrain_pair); - - constrain_force_cycle_update_kernel<<>>( - constrain_pair_numbers, uint_crd, scaler, constrain_pair, pair_dr, test_frc); - - return; -} - -void constrain_force_cycle_with_virial_update(int atom_numbers, int constrain_pair_numbers, - const unsigned int *uint_crd_f, const float *scaler_f, - float *constrain_pair_f, const float *pair_dr_f, - const int *atom_i_serials, const int *atom_j_serials, - const float *constant_rs, const float *constrain_ks, float *test_frc_f, - float *d_atom_virial, cudaStream_t stream) { - size_t thread_per_block = 128; - size_t block_per_grid = ceilf(static_cast(atom_numbers) / 128); - const UNSIGNED_INT_VECTOR *uint_crd = reinterpret_cast(uint_crd_f); - const VECTOR *scaler = reinterpret_cast(scaler_f); - const VECTOR *pair_dr = reinterpret_cast(pair_dr_f); - - VECTOR *test_frc = reinterpret_cast(test_frc_f); - - CONSTRAIN_PAIR *constrain_pair = reinterpret_cast(constrain_pair_f); - - construct_constrain_pair<<(constrain_pair_numbers) / 128), 128, 0, stream>>>( - constrain_pair_numbers, atom_i_serials, atom_j_serials, constant_rs, constrain_ks, constrain_pair); - - constrain_force_cycle_with_virial_update_kernel<<>>( - constrain_pair_numbers, uint_crd, scaler, constrain_pair, pair_dr, test_frc, d_atom_virial); - - return; -} - -void refresh_uint_crd_update(int atom_numbers, float half_exp_gamma_plus_half, const float *crd_f, - const float *quarter_crd_to_uint_crd_cof_f, float *test_frc_f, const float *mass_inverse, - unsigned int *uint_crd_f, cudaStream_t stream) { - size_t thread_per_block = 128; - size_t block_per_grid = ceilf(static_cast(atom_numbers) / 128); - const VECTOR *crd = reinterpret_cast(crd_f); - const VECTOR *quarter_crd_to_uint_crd_cof = reinterpret_cast(quarter_crd_to_uint_crd_cof_f); - VECTOR *test_frc = reinterpret_cast(test_frc_f); - UNSIGNED_INT_VECTOR *uint_crd = reinterpret_cast(uint_crd_f); - - refresh_uint_crd_update_kernel<<>>( - atom_numbers, crd, quarter_crd_to_uint_crd_cof, uint_crd, test_frc, mass_inverse, half_exp_gamma_plus_half); - return; -} - -void set_zero_force_with_virial(int atom_numbers, int constrain_pair_numbers, float *test_frc_f, float *d_atom_virial, - cudaStream_t stream) { - Reset_List<<(3 * atom_numbers) / 128), 128, 0, stream>>>(3 * atom_numbers, test_frc_f, 0.); - Reset_List<<(constrain_pair_numbers) / 128), 128, 0, stream>>>(constrain_pair_numbers, - d_atom_virial, 0.); - return; -} - -void set_zero(int numbers, float *x, cudaStream_t stream) { - Reset_List<<(numbers) / 128), 128, 0, stream>>>(numbers, x, 0.); - return; -} diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/simple_constrain/constrain_force_virial_impl.cuh b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/simple_constrain/constrain_force_virial_impl.cuh deleted file mode 100644 index 289455b9327..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/simple_constrain/constrain_force_virial_impl.cuh +++ /dev/null @@ -1,49 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -/** - * Note: - * ConstrainForce. This is an experimental interface that is subject to change and/or deletion. - */ - -#ifndef MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_SIMPLE_CONSTRAIN_CONSTRAIN_FORCE_VIRIAL_IMPL_H_ -#define MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_SIMPLE_CONSTRAIN_CONSTRAIN_FORCE_VIRIAL_IMPL_H_ - -#include -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_device_info.h" - -CUDA_LIB_EXPORT void constrain_force_cycle_update(int atom_numbers, int constrain_pair_numbers, - const unsigned int *uint_crd_f, const float *scaler_f, - float *constrain_pair_f, const float *pair_dr_f, - const int *atom_i_serials, const int *atom_j_serials, - const float *constant_rs, const float *constrain_ks, - float *test_frc_f, cudaStream_t stream); - -CUDA_LIB_EXPORT void constrain_force_cycle_with_virial_update( - int atom_numbers, int constrain_pair_numbers, const unsigned int *uint_crd_f, const float *scaler_f, - float *constrain_pair_f, const float *pair_dr_f, const int *atom_i_serials, const int *atom_j_serials, - const float *constant_rs, const float *constrain_ks, float *test_frc_f, float *d_atom_virial, cudaStream_t stream); - -CUDA_LIB_EXPORT void refresh_uint_crd_update(int atom_numbers, float half_exp_gamma_plus_half, const float *crd_f, - const float *quarter_crd_to_uint_crd_cof_f, float *test_frc_f, - const float *mass_inverse, unsigned int *uint_crd_f, cudaStream_t stream); - -CUDA_LIB_EXPORT void set_zero_force_with_virial(int atom_numbers, int constrain_pair_numbers, float *test_frc_f, - float *d_atom_virial, cudaStream_t stream); - -CUDA_LIB_EXPORT void set_zero(int numbers, float *x, cudaStream_t stream); - -#endif diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/simple_constrain/last_crd_to_dr_impl.cu b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/simple_constrain/last_crd_to_dr_impl.cu deleted file mode 100644 index 56081c1b87b..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/simple_constrain/last_crd_to_dr_impl.cu +++ /dev/null @@ -1,84 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "plugin/device/gpu/kernel/cuda_impl/sponge/simple_constrain/last_crd_to_dr_impl.cuh" -#include "plugin/device/gpu/kernel/cuda_impl/sponge/common_sponge.cuh" - -__global__ void Last_Crd_To_dr(const int constarin_pair_numbers, const VECTOR *atom_crd, - const VECTOR *quarter_crd_to_uint_crd_cof, const VECTOR *uint_dr_to_dr, - CONSTRAIN_PAIR *constrain_pair, VECTOR *pair_dr) { - int pair_i = blockDim.x * blockIdx.x + threadIdx.x; - if (pair_i < constarin_pair_numbers) { - INT_VECTOR tempi; - INT_VECTOR tempj; - UNSIGNED_INT_VECTOR uint_crd_i; - UNSIGNED_INT_VECTOR uint_crd_j; - CONSTRAIN_PAIR cp = constrain_pair[pair_i]; - VECTOR dr; - - tempi.int_x = atom_crd[cp.atom_i_serial].x * quarter_crd_to_uint_crd_cof[0].x; - tempi.int_y = atom_crd[cp.atom_i_serial].y * quarter_crd_to_uint_crd_cof[0].y; - tempi.int_z = atom_crd[cp.atom_i_serial].z * quarter_crd_to_uint_crd_cof[0].z; - - tempj.int_x = atom_crd[cp.atom_j_serial].x * quarter_crd_to_uint_crd_cof[0].x; - tempj.int_y = atom_crd[cp.atom_j_serial].y * quarter_crd_to_uint_crd_cof[0].y; - tempj.int_z = atom_crd[cp.atom_j_serial].z * quarter_crd_to_uint_crd_cof[0].z; - - uint_crd_i.uint_x = tempi.int_x << 2; - uint_crd_i.uint_y = tempi.int_y << 2; - uint_crd_i.uint_z = tempi.int_z << 2; - - uint_crd_j.uint_x = tempj.int_x << 2; - uint_crd_j.uint_y = tempj.int_y << 2; - uint_crd_j.uint_z = tempj.int_z << 2; - - dr.x = (static_cast(uint_crd_i.uint_x - uint_crd_j.uint_x)) * uint_dr_to_dr[0].x; - dr.y = (static_cast(uint_crd_i.uint_y - uint_crd_j.uint_y)) * uint_dr_to_dr[0].y; - dr.z = (static_cast(uint_crd_i.uint_z - uint_crd_j.uint_z)) * uint_dr_to_dr[0].z; - - pair_dr[pair_i] = dr; - } -} - -void lastcrdtodr(int constrain_pair_numbers, const float *atom_crd_f, const float *quarter_crd_to_uint_crd_cof_f, - const float *uint_dr_to_dr_f, float *constrain_pair_f, const int *atom_i_serials, - const int *atom_j_serials, const float *constant_rs, const float *constrain_ks, float *pair_dr_f, - cudaStream_t stream) { - Reset_List<<(3 * constrain_pair_numbers) / 128), 128, 0, stream>>>( - 3 * constrain_pair_numbers, pair_dr_f, 0.); - size_t thread_per_block = 128; - size_t block_per_grid = ceilf(static_cast(constrain_pair_numbers) / 128); - const VECTOR *atom_crd = reinterpret_cast(atom_crd_f); - const VECTOR *quarter_crd_to_uint_crd_cof = reinterpret_cast(quarter_crd_to_uint_crd_cof_f); - const VECTOR *uint_dr_to_dr = reinterpret_cast(uint_dr_to_dr_f); - - CONSTRAIN_PAIR *constrain_pair = reinterpret_cast(constrain_pair_f); - - VECTOR *pair_dr = reinterpret_cast(pair_dr_f); - - construct_constrain_pair<<(constrain_pair_numbers) / 128), 128, 0, stream>>>( - constrain_pair_numbers, atom_i_serials, atom_j_serials, constant_rs, constrain_ks, constrain_pair); - - Last_Crd_To_dr<<>>(constrain_pair_numbers, atom_crd, - quarter_crd_to_uint_crd_cof, uint_dr_to_dr, - constrain_pair, pair_dr); - return; -} - -void lastcrdtodr(int constrain_pair_numbers, const float *atom_crd_f, const float *quarter_crd_to_uint_crd_cof_f, - const float *uint_dr_to_dr_f, float *constrain_pair_f, const int *atom_i_serials, - const int *atom_j_serials, const float *constant_rs, const float *constrain_ks, float *pair_dr_f, - cudaStream_t stream); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/simple_constrain/last_crd_to_dr_impl.cuh b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/simple_constrain/last_crd_to_dr_impl.cuh deleted file mode 100644 index becb141bfe0..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/simple_constrain/last_crd_to_dr_impl.cuh +++ /dev/null @@ -1,29 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_SIMPLE_CONSTRAIN_LAST_CRD_TO_DR_IMPL_H_ -#define MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_SIMPLE_CONSTRAIN_LAST_CRD_TO_DR_IMPL_H_ - -#include -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_device_info.h" - -CUDA_LIB_EXPORT void lastcrdtodr(int constrain_pair_numbers, const float *atom_crd_f, - const float *quarter_crd_to_uint_crd_cof_f, const float *uint_dr_to_dr_f, - float *constrain_pair_f, const int *atom_i_serials, const int *atom_j_serials, - const float *constant_rs, const float *constrain_ks, float *pair_dr_f, - cudaStream_t stream); -#endif // MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_SIMPLE_CONSTRAIN_LAST_CRD_TO_DR_IMPL_H_ diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/simple_constrain/refresh_crd_vel_impl.cu b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/simple_constrain/refresh_crd_vel_impl.cu deleted file mode 100644 index 996381d83b2..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/simple_constrain/refresh_crd_vel_impl.cu +++ /dev/null @@ -1,61 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "plugin/device/gpu/kernel/cuda_impl/sponge/simple_constrain/refresh_crd_vel_impl.cuh" -#include "plugin/device/gpu/kernel/cuda_impl/sponge/common_sponge.cuh" - -__global__ void Refresh_Crd_Vel(int atom_numbers, float dt_inverse, float dt, float exp_gamma, - float half_exp_gamma_plus_half, VECTOR *test_frc, float *mass_inverse, VECTOR *crd, - VECTOR *vel) { - int atom_i = blockDim.x * blockIdx.x + threadIdx.x; - if (atom_i < atom_numbers) { - VECTOR crd_lin = crd[atom_i]; - VECTOR frc_lin = test_frc[atom_i]; - VECTOR vel_lin = vel[atom_i]; - float mass_lin = mass_inverse[atom_i]; - - frc_lin.x = frc_lin.x * mass_lin; - frc_lin.y = frc_lin.y * mass_lin; - frc_lin.z = frc_lin.z * mass_lin; // mass实际为mass的倒数，frc_lin已经乘以dt^2 - - crd_lin.x = crd_lin.x + half_exp_gamma_plus_half * frc_lin.x; - crd_lin.y = crd_lin.y + half_exp_gamma_plus_half * frc_lin.y; - crd_lin.z = crd_lin.z + half_exp_gamma_plus_half * frc_lin.z; - - vel_lin.x = (vel_lin.x + exp_gamma * frc_lin.x * dt_inverse); - vel_lin.y = (vel_lin.y + exp_gamma * frc_lin.y * dt_inverse); - vel_lin.z = (vel_lin.z + exp_gamma * frc_lin.z * dt_inverse); - - crd[atom_i] = crd_lin; - vel[atom_i] = vel_lin; - } -} - -void refreshcrdvel(int atom_numbers, float dt_inverse, float dt, float exp_gamma, float half_exp_gamma_plus_half, - float *test_frc_f, float *mass_inverse, float *crd_f, float *vel_f, cudaStream_t stream) { - size_t thread_per_block = 128; - size_t block_per_grid = ceilf(static_cast(atom_numbers) / 128); - VECTOR *crd = reinterpret_cast(crd_f); - VECTOR *vel = reinterpret_cast(vel_f); - VECTOR *test_frc = reinterpret_cast(test_frc_f); - - Refresh_Crd_Vel<<>>( - atom_numbers, dt_inverse, dt, exp_gamma, half_exp_gamma_plus_half, test_frc, mass_inverse, crd, vel); - return; -} - -void refreshcrdvel(int atom_numbers, float dt_inverse, float dt, float exp_gamma, float half_exp_gamma_plus_half, - float *test_frc_f, float *mass_inverse, float *crd_f, float *vel_f, cudaStream_t stream); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/simple_constrain/refresh_crd_vel_impl.cuh b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/simple_constrain/refresh_crd_vel_impl.cuh deleted file mode 100644 index f3c27147830..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/simple_constrain/refresh_crd_vel_impl.cuh +++ /dev/null @@ -1,27 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_SIMPLE_CONSTRAIN_REFRESH_CRD_VEL_IMPL_H_ -#define MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_SIMPLE_CONSTRAIN_REFRESH_CRD_VEL_IMPL_H_ - -#include -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_device_info.h" - -CUDA_LIB_EXPORT void refreshcrdvel(int atom_numbers, float dt_inverse, float dt, float exp_gamma, - float half_exp_gamma_plus_half, float *test_frc_f, float *mass_inverse, float *crd_f, - float *vel_f, cudaStream_t stream); -#endif // MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_SIMPLE_CONSTRAIN_REFRESH_CRD_VEL_IMPL_H_ diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/simple_constrain/refresh_uint_crd_impl.cu b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/simple_constrain/refresh_uint_crd_impl.cu deleted file mode 100644 index 43aaf3142a8..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/simple_constrain/refresh_uint_crd_impl.cu +++ /dev/null @@ -1,63 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "plugin/device/gpu/kernel/cuda_impl/sponge/simple_constrain/refresh_uint_crd_impl.cuh" -#include "plugin/device/gpu/kernel/cuda_impl/sponge/common_sponge.cuh" - -__global__ void Refresh_Uint_Crd(int atom_numbers, const VECTOR *crd, const VECTOR *quarter_crd_to_uint_crd_cof, - UNSIGNED_INT_VECTOR *uint_crd, const VECTOR *test_frc, const float *mass_inverse, - const float half_exp_gamma_plus_half) { - int atom_i = blockDim.x * blockIdx.x + threadIdx.x; - if (atom_i < atom_numbers) { - INT_VECTOR tempi; - VECTOR crd_lin = crd[atom_i]; - VECTOR frc_lin = test_frc[atom_i]; - float mass_lin = mass_inverse[atom_i]; - - crd_lin.x = crd_lin.x + half_exp_gamma_plus_half * frc_lin.x * mass_lin; - crd_lin.y = crd_lin.y + half_exp_gamma_plus_half * frc_lin.y * mass_lin; - crd_lin.z = crd_lin.z + half_exp_gamma_plus_half * frc_lin.z * mass_lin; - - tempi.int_x = crd_lin.x * quarter_crd_to_uint_crd_cof[0].x; - tempi.int_y = crd_lin.y * quarter_crd_to_uint_crd_cof[0].y; - tempi.int_z = crd_lin.z * quarter_crd_to_uint_crd_cof[0].z; - - uint_crd[atom_i].uint_x = tempi.int_x << 2; - uint_crd[atom_i].uint_y = tempi.int_y << 2; - uint_crd[atom_i].uint_z = tempi.int_z << 2; - } -} - -void refreshuintcrd(int atom_numbers, float half_exp_gamma_plus_half, const float *crd_f, - const float *quarter_crd_to_uint_crd_cof_f, const float *test_frc_f, const float *mass_inverse, - unsigned int *uint_crd_f, cudaStream_t stream) { - size_t thread_per_block = 128; - size_t block_per_grid = ceilf(static_cast(atom_numbers) / 128); - const VECTOR *crd = reinterpret_cast(crd_f); - const VECTOR *quarter_crd_to_uint_crd_cof = reinterpret_cast(quarter_crd_to_uint_crd_cof_f); - const VECTOR *test_frc = reinterpret_cast(test_frc_f); - UNSIGNED_INT_VECTOR *uint_crd = reinterpret_cast(uint_crd_f); - - Refresh_Uint_Crd<<>>(atom_numbers, crd, - quarter_crd_to_uint_crd_cof, - uint_crd, test_frc, mass_inverse, - half_exp_gamma_plus_half); - return; -} - -void refreshuintcrd(int atom_numbers, float half_exp_gamma_plus_half, const float *crd_f, - const float *quarter_crd_to_uint_crd_cof_f, const float *test_frc_f, const float *mass_inverse, - unsigned int *uint_crd_f, cudaStream_t stream); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/simple_constrain/refresh_uint_crd_impl.cuh b/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/simple_constrain/refresh_uint_crd_impl.cuh deleted file mode 100644 index 1a6e3df7d9c..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/cuda_impl/sponge/simple_constrain/refresh_uint_crd_impl.cuh +++ /dev/null @@ -1,27 +0,0 @@ -/** - * Copyright 2021 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_SIMPLE_CONSTRAIN_REFRESH_UINT_CRD_IMPL_H_ -#define MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_SIMPLE_CONSTRAIN_REFRESH_UINT_CRD_IMPL_H_ - -#include -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_device_info.h" - -CUDA_LIB_EXPORT void refreshuintcrd(int atom_numbers, float half_exp_gamma_plus_half, const float *crd_f, - const float *quarter_crd_to_uint_crd_cof_f, const float *test_frc_f, - const float *mass_inverse, unsigned int *uint_crd_f, cudaStream_t stream); -#endif // MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_SPONGE_SIMPLE_CONSTRAIN_REFRESH_UINT_CRD_IMPL_H_ diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/OWNERS b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/OWNERS deleted file mode 100644 index 2d07c75f316..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/OWNERS +++ /dev/null @@ -1,2 +0,0 @@ -approvers: -- wang_zi_dong diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/angle/angle_atom_energy_kernel.cc b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/angle/angle_atom_energy_kernel.cc deleted file mode 100644 index f3345ea0ffa..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/angle/angle_atom_energy_kernel.cc +++ /dev/null @@ -1,88 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "plugin/device/gpu/kernel/sponge/angle/angle_atom_energy_kernel.h" -#include "ops/angle_atom_energy.h" - -namespace mindspore { -namespace kernel { -using KernelRunFunc = AngleAtomEnergyGpuKernelMod::KernelRunFunc; -const std::vector> &AngleAtomEnergyGpuKernelMod::GetFuncList() const { - static const std::vector> func_list = { - {KernelAttr() - .AddInputAttr(kNumberTypeUInt32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddOutputAttr(kNumberTypeFloat32), - &AngleAtomEnergyGpuKernelMod::LaunchKernel}}; - return func_list; -} - -bool AngleAtomEnergyGpuKernelMod::Init(const BaseOperatorPtr &base_operator, const std::vector &inputs, - const std::vector &outputs) { - if (!MatchKernelFunc(base_operator, inputs, outputs)) { - return false; - } - auto kernel_ptr = std::dynamic_pointer_cast(base_operator); - MS_EXCEPTION_IF_NULL(kernel_ptr); - kernel_name_ = kernel_ptr->name(); - angle_numbers_ = static_cast(kernel_ptr->get_angle_numbers()); - return true; -} - -int AngleAtomEnergyGpuKernelMod::Resize(const BaseOperatorPtr &base_operator, - const std::vector &inputs, - const std::vector &outputs, - const std::map &inputsOnHost) { - if (int ret = KernelMod::Resize(base_operator, inputs, outputs, inputsOnHost); ret != KRET_OK) { - return ret; - } - auto shape_uint_crd = inputs[0]->GetShapeVector(); - ele_uint_crd_ = SizeOf(shape_uint_crd); - return KRET_OK; -} - -template -bool AngleAtomEnergyGpuKernelMod::LaunchKernel(const std::vector &inputs, const std::vector &, - const std::vector &outputs) { - auto uint_crd_f = GetDeviceAddress(inputs, 0); - auto scaler_f = GetDeviceAddress(inputs, 1); - auto atom_a = GetDeviceAddress(inputs, 2); - auto atom_b = GetDeviceAddress(inputs, 3); - auto atom_c = GetDeviceAddress(inputs, 4); - auto angle_k = GetDeviceAddress(inputs, 5); - auto angle_theta0 = GetDeviceAddress(inputs, 6); - - auto ene = GetDeviceAddress(outputs, 0); - AngleAtomEnergy(angle_numbers_, ele_uint_crd_, uint_crd_f, scaler_f, atom_a, atom_b, atom_c, angle_k, angle_theta0, - ene, reinterpret_cast(stream_ptr_)); - return true; -} - -bool AngleAtomEnergyGpuKernelMod::Launch(const std::vector &inputs, - const std::vector &workspace, - const std::vector &outputs, void *stream_ptr) { - stream_ptr_ = stream_ptr; - return kernel_func_(this, inputs, workspace, outputs); -} - -MS_KERNEL_FACTORY_REG(NativeGpuKernelMod, AngleAtomEnergy, AngleAtomEnergyGpuKernelMod); -} // namespace kernel -} // namespace mindspore diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/angle/angle_atom_energy_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/angle/angle_atom_energy_kernel.h deleted file mode 100644 index e22ea7183f2..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/angle/angle_atom_energy_kernel.h +++ /dev/null @@ -1,62 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef MINDSPORE_CCSRC_PLUGIN_DEVICE_GPU_KERNEL_SPONGE_ANGLE_ANGLE_ATOM_ENERGY_KERNEL_H_ -#define MINDSPORE_CCSRC_PLUGIN_DEVICE_GPU_KERNEL_SPONGE_ANGLE_ANGLE_ATOM_ENERGY_KERNEL_H_ -#include -#include -#include -#include -#include -#include "plugin/device/gpu/kernel/gpu_kernel.h" -#include "plugin/device/gpu/kernel/gpu_kernel_factory.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" -#include "plugin/device/gpu/kernel/cuda_impl/sponge/angle/angle_atom_energy_impl.cuh" - -namespace mindspore { -namespace kernel { -class AngleAtomEnergyGpuKernelMod : public NativeGpuKernelMod, public MatchKernelHelper { - public: - AngleAtomEnergyGpuKernelMod() = default; - ~AngleAtomEnergyGpuKernelMod() override = default; - - const std::vector> &GetFuncList() const override; - - std::vector GetOpSupport() override { return OpSupport(); }; - - bool Init(const BaseOperatorPtr &base_operator, const std::vector &inputs, - const std::vector &outputs) override; - - int Resize(const BaseOperatorPtr &base_operator, const std::vector &inputs, - const std::vector &outputs, - const std::map &inputsOnHost) override; - - bool Launch(const std::vector &inputs, const std::vector &workspace, - const std::vector &outputs, void *stream_ptr) override; - - private: - template - bool LaunchKernel(const std::vector &inputs, const std::vector &workspace, - const std::vector &outputs); - - int angle_numbers_{0}; - size_t ele_uint_crd_{1}; - void *stream_ptr_{nullptr}; -}; -} // namespace kernel -} // namespace mindspore - -#endif // MINDSPORE_CCSRC_PLUGIN_DEVICE_GPU_KERNEL_SPONGE_ANGLE_ANGLE_ATOM_ENERGY_KERNEL_H_ diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/angle/angle_energy_kernel.cc b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/angle/angle_energy_kernel.cc deleted file mode 100644 index 99a30601a17..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/angle/angle_energy_kernel.cc +++ /dev/null @@ -1,33 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "plugin/device/gpu/kernel/sponge/angle/angle_energy_kernel.h" - -namespace mindspore { -namespace kernel { -MS_REG_GPU_KERNEL_TWO(AngleEnergy, - KernelAttr() - .AddInputAttr(kNumberTypeUInt32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddOutputAttr(kNumberTypeFloat32), - AngleEnergyGpuKernelMod, float, int) -} // namespace kernel -} // namespace mindspore diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/angle/angle_energy_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/angle/angle_energy_kernel.h deleted file mode 100644 index fe0fcc43bdd..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/angle/angle_energy_kernel.h +++ /dev/null @@ -1,99 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONGE_ANGLE_ANGLE_ENERGY_KERNEL_H_ -#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONGE_ANGLE_ANGLE_ENERGY_KERNEL_H_ -#include -#include -#include -#include -#include "plugin/device/gpu/kernel/gpu_kernel.h" -#include "plugin/device/gpu/kernel/gpu_kernel_factory.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" -#include "plugin/device/gpu/kernel/cuda_impl/sponge/angle/angle_energy_impl.cuh" -namespace mindspore { -namespace kernel { -template -class AngleEnergyGpuKernelMod : public DeprecatedNativeGpuKernelMod { - public: - AngleEnergyGpuKernelMod() : ele_uint_crd(1) {} - ~AngleEnergyGpuKernelMod() override = default; - - bool Init(const CNodePtr &kernel_node) override { - kernel_node_ = kernel_node; - angle_numbers = static_cast(GetAttr(kernel_node, "angle_numbers")); - auto shape_uint_crd = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto shape_scaler = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - auto shape_atom_a = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); - auto shape_atom_b = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); - auto shape_atom_c = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 4); - auto shape_angle_k = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 5); - auto shape_angle_theta0 = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 6); - - ele_uint_crd *= SizeOf(shape_uint_crd); - ele_scaler *= SizeOf(shape_scaler); - ele_atom_a *= SizeOf(shape_atom_a); - ele_atom_b *= SizeOf(shape_atom_b); - ele_atom_c *= SizeOf(shape_atom_c); - ele_angle_k *= SizeOf(shape_angle_k); - ele_angle_theta0 *= SizeOf(shape_angle_theta0); - - InitSizeLists(); - return true; - } - - bool Launch(const std::vector &inputs, const std::vector &, - const std::vector &outputs, void *stream_ptr) override { - auto uint_crd_f = GetDeviceAddress(inputs, 0); - auto scaler_f = GetDeviceAddress(inputs, 1); - auto atom_a = GetDeviceAddress(inputs, 2); - auto atom_b = GetDeviceAddress(inputs, 3); - auto atom_c = GetDeviceAddress(inputs, 4); - auto angle_k = GetDeviceAddress(inputs, 5); - auto angle_theta0 = GetDeviceAddress(inputs, 6); - - auto ene = GetDeviceAddress(outputs, 0); - AngleEnergy(angle_numbers, uint_crd_f, scaler_f, atom_a, atom_b, atom_c, angle_k, angle_theta0, ene, - reinterpret_cast(stream_ptr)); - return true; - } - - protected: - void InitSizeLists() override { - input_size_list_.push_back(ele_uint_crd * sizeof(T1)); - input_size_list_.push_back(ele_scaler * sizeof(T)); - input_size_list_.push_back(ele_atom_a * sizeof(T1)); - input_size_list_.push_back(ele_atom_b * sizeof(T1)); - input_size_list_.push_back(ele_atom_c * sizeof(T1)); - input_size_list_.push_back(ele_angle_k * sizeof(T)); - input_size_list_.push_back(ele_angle_theta0 * sizeof(T)); - - output_size_list_.push_back(angle_numbers * sizeof(T)); - } - - private: - size_t ele_uint_crd = 1; - size_t ele_scaler = 1; - size_t ele_atom_a = 1; - size_t ele_atom_b = 1; - size_t ele_atom_c = 1; - size_t ele_angle_k = 1; - size_t ele_angle_theta0 = 1; - int angle_numbers; -}; -} // namespace kernel -} // namespace mindspore -#endif diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/angle/angle_force_kernel.cc b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/angle/angle_force_kernel.cc deleted file mode 100644 index 0732fe5a5e9..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/angle/angle_force_kernel.cc +++ /dev/null @@ -1,33 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "plugin/device/gpu/kernel/sponge/angle/angle_force_kernel.h" - -namespace mindspore { -namespace kernel { -MS_REG_GPU_KERNEL_TWO(AngleForce, - KernelAttr() - .AddInputAttr(kNumberTypeUInt32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddOutputAttr(kNumberTypeFloat32), - AngleForceGpuKernelMod, float, int) -} // namespace kernel -} // namespace mindspore diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/angle/angle_force_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/angle/angle_force_kernel.h deleted file mode 100644 index 553f9bee9f8..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/angle/angle_force_kernel.h +++ /dev/null @@ -1,100 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONGE_ANGLE_ANGLE_FORCE_KERNEL_H_ -#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONGE_ANGLE_ANGLE_FORCE_KERNEL_H_ -#include -#include -#include -#include -#include "plugin/device/gpu/kernel/gpu_kernel.h" -#include "plugin/device/gpu/kernel/gpu_kernel_factory.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" -#include "plugin/device/gpu/kernel/cuda_impl/sponge/angle/angle_force_impl.cuh" -namespace mindspore { -namespace kernel { -template -class AngleForceGpuKernelMod : public DeprecatedNativeGpuKernelMod { - public: - AngleForceGpuKernelMod() : ele_uint_crd(1) {} - ~AngleForceGpuKernelMod() override = default; - - bool Init(const CNodePtr &kernel_node) override { - kernel_node_ = kernel_node; - angle_numbers = static_cast(GetAttr(kernel_node, "angle_numbers")); - auto shape_uint_crd = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto shape_scaler = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - auto shape_atom_a = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); - auto shape_atom_b = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); - auto shape_atom_c = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 4); - auto shape_angle_k = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 5); - auto shape_angle_theta0 = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 6); - - ele_uint_crd *= SizeOf(shape_uint_crd); - ele_scaler *= SizeOf(shape_scaler); - ele_atom_a *= SizeOf(shape_atom_a); - ele_atom_b *= SizeOf(shape_atom_b); - ele_atom_c *= SizeOf(shape_atom_c); - ele_angle_k *= SizeOf(shape_angle_k); - ele_angle_theta0 *= SizeOf(shape_angle_theta0); - - InitSizeLists(); - return true; - } - - bool Launch(const std::vector &inputs, const std::vector &, - const std::vector &outputs, void *stream_ptr) override { - auto uint_crd_f = GetDeviceAddress(inputs, 0); - auto scaler_f = GetDeviceAddress(inputs, 1); - auto atom_a = GetDeviceAddress(inputs, 2); - auto atom_b = GetDeviceAddress(inputs, 3); - auto atom_c = GetDeviceAddress(inputs, 4); - auto angle_k = GetDeviceAddress(inputs, 5); - auto angle_theta0 = GetDeviceAddress(inputs, 6); - - auto frc_f = GetDeviceAddress(outputs, 0); - AngleForce(angle_numbers, ele_uint_crd, uint_crd_f, scaler_f, atom_a, atom_b, atom_c, angle_k, angle_theta0, frc_f, - reinterpret_cast(stream_ptr)); - return true; - } - - protected: - void InitSizeLists() override { - input_size_list_.push_back(ele_uint_crd * sizeof(T1)); - input_size_list_.push_back(ele_scaler * sizeof(T)); - input_size_list_.push_back(ele_atom_a * sizeof(T1)); - input_size_list_.push_back(ele_atom_b * sizeof(T1)); - input_size_list_.push_back(ele_atom_c * sizeof(T1)); - input_size_list_.push_back(ele_angle_k * sizeof(T)); - input_size_list_.push_back(ele_angle_theta0 * sizeof(T)); - - output_size_list_.push_back(ele_uint_crd * 3 * sizeof(T)); - } - - private: - size_t ele_uint_crd = 1; - size_t ele_scaler = 1; - size_t ele_atom_a = 1; - size_t ele_atom_b = 1; - size_t ele_atom_c = 1; - size_t ele_angle_k = 1; - size_t ele_angle_theta0 = 1; - - int angle_numbers; -}; -} // namespace kernel -} // namespace mindspore -#endif diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/angle/angle_force_with_atom_energy_kernel.cc b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/angle/angle_force_with_atom_energy_kernel.cc deleted file mode 100644 index dc81e9505b9..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/angle/angle_force_with_atom_energy_kernel.cc +++ /dev/null @@ -1,34 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "plugin/device/gpu/kernel/sponge/angle/angle_force_with_atom_energy_kernel.h" - -namespace mindspore { -namespace kernel { -MS_REG_GPU_KERNEL_TWO(AngleForceWithAtomEnergy, - KernelAttr() - .AddInputAttr(kNumberTypeUInt32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddOutputAttr(kNumberTypeFloat32) - .AddOutputAttr(kNumberTypeFloat32), - AngleForceWithAtomEnergyGpuKernelMod, float, int) -} // namespace kernel -} // namespace mindspore diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/angle/angle_force_with_atom_energy_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/angle/angle_force_with_atom_energy_kernel.h deleted file mode 100644 index b7bdc046b25..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/angle/angle_force_with_atom_energy_kernel.h +++ /dev/null @@ -1,101 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONGE_ANGLE_ANGLE_FORCE_WITH_ATOM_ENERGY_KERNEL_H_ -#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONGE_ANGLE_ANGLE_FORCE_WITH_ATOM_ENERGY_KERNEL_H_ -#include -#include -#include -#include -#include "plugin/device/gpu/kernel/gpu_kernel.h" -#include "plugin/device/gpu/kernel/gpu_kernel_factory.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" -#include "plugin/device/gpu/kernel/cuda_impl/sponge/angle/angle_force_with_atom_energy_impl.cuh" -namespace mindspore { -namespace kernel { -template -class AngleForceWithAtomEnergyGpuKernelMod : public DeprecatedNativeGpuKernelMod { - public: - AngleForceWithAtomEnergyGpuKernelMod() : ele_uint_crd(1) {} - ~AngleForceWithAtomEnergyGpuKernelMod() override = default; - - bool Init(const CNodePtr &kernel_node) override { - kernel_node_ = kernel_node; - angle_numbers = static_cast(GetAttr(kernel_node, "angle_numbers")); - auto shape_uint_crd = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto shape_scaler = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - auto shape_atom_a = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); - auto shape_atom_b = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); - auto shape_atom_c = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 4); - auto shape_angle_k = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 5); - auto shape_angle_theta0 = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 6); - - ele_uint_crd *= SizeOf(shape_uint_crd); - ele_scaler *= SizeOf(shape_scaler); - ele_atom_a *= SizeOf(shape_atom_a); - ele_atom_b *= SizeOf(shape_atom_b); - ele_atom_c *= SizeOf(shape_atom_c); - ele_angle_k *= SizeOf(shape_angle_k); - ele_angle_theta0 *= SizeOf(shape_angle_theta0); - InitSizeLists(); - return true; - } - - bool Launch(const std::vector &inputs, const std::vector &, - const std::vector &outputs, void *stream_ptr) override { - auto uint_crd_f = GetDeviceAddress(inputs, 0); - auto scaler_f = GetDeviceAddress(inputs, 1); - auto atom_a = GetDeviceAddress(inputs, 2); - auto atom_b = GetDeviceAddress(inputs, 3); - auto atom_c = GetDeviceAddress(inputs, 4); - auto angle_k = GetDeviceAddress(inputs, 5); - auto angle_theta0 = GetDeviceAddress(inputs, 6); - - auto frc_f = GetDeviceAddress(outputs, 0); - auto ene = GetDeviceAddress(outputs, 1); - AngleForceWithAtomEnergy(angle_numbers, ele_uint_crd, uint_crd_f, scaler_f, atom_a, atom_b, atom_c, angle_k, - angle_theta0, frc_f, ene, reinterpret_cast(stream_ptr)); - return true; - } - - protected: - void InitSizeLists() override { - input_size_list_.push_back(ele_uint_crd * sizeof(T1)); - input_size_list_.push_back(ele_scaler * sizeof(T)); - input_size_list_.push_back(ele_atom_a * sizeof(T1)); - input_size_list_.push_back(ele_atom_b * sizeof(T1)); - input_size_list_.push_back(ele_atom_c * sizeof(T1)); - input_size_list_.push_back(ele_angle_k * sizeof(T)); - input_size_list_.push_back(ele_angle_theta0 * sizeof(T)); - - output_size_list_.push_back(ele_uint_crd * 3 * sizeof(T)); - output_size_list_.push_back(ele_uint_crd * sizeof(T)); - } - - private: - size_t ele_uint_crd = 1; - size_t ele_scaler = 1; - size_t ele_atom_a = 1; - size_t ele_atom_b = 1; - size_t ele_atom_c = 1; - size_t ele_angle_k = 1; - size_t ele_angle_theta0 = 1; - - int angle_numbers; -}; -} // namespace kernel -} // namespace mindspore -#endif diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/bond/bond_atom_energy_cuda_gpu_kernel.cc b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/bond/bond_atom_energy_cuda_gpu_kernel.cc deleted file mode 100644 index 331c8f06775..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/bond/bond_atom_energy_cuda_gpu_kernel.cc +++ /dev/null @@ -1,32 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "plugin/device/gpu/kernel/sponge/bond/bond_atom_energy_cuda_gpu_kernel.h" - -namespace mindspore { -namespace kernel { -MS_REG_GPU_KERNEL_TWO(BondAtomEnergy, - KernelAttr() - .AddInputAttr(kNumberTypeUInt32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddOutputAttr(kNumberTypeFloat32), - BondAtomEnergyCudaGpuKernelMod, float, int) -} // namespace kernel -} // namespace mindspore diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/bond/bond_atom_energy_cuda_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/bond/bond_atom_energy_cuda_gpu_kernel.h deleted file mode 100644 index 9b51a2beec4..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/bond/bond_atom_energy_cuda_gpu_kernel.h +++ /dev/null @@ -1,102 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONG_BOND_BOND_ATOM_ENERGY_CUDA_GPU_KERNEL_H_ -#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONG_BOND_BOND_ATOM_ENERGY_CUDA_GPU_KERNEL_H_ - -#include "plugin/device/gpu/kernel/cuda_impl/sponge/bond/bond_atom_energy_cuda_gpu_impl.cuh" - -#include -#include -#include -#include - -#include "plugin/device/gpu/kernel/gpu_kernel.h" -#include "plugin/device/gpu/kernel/gpu_kernel_factory.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" - -namespace mindspore { -namespace kernel { -template -class BondAtomEnergyCudaGpuKernelMod : public DeprecatedNativeGpuKernelMod { - public: - BondAtomEnergyCudaGpuKernelMod() : ele_uint_crd(1) {} - ~BondAtomEnergyCudaGpuKernelMod() override = default; - - bool Init(const CNodePtr &kernel_node) override { - kernel_node_ = kernel_node; - bond_numbers = static_cast(GetAttr(kernel_node, "bond_numbers")); - atom_numbers = static_cast(GetAttr(kernel_node, "atom_numbers")); - auto shape_uint_crd = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto shape_scaler = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - auto shape_atom_a = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); - auto shape_atom_b = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); - auto shape_bond_k = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 4); - auto shape_bond_r0 = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 5); - - ele_uint_crd *= SizeOf(shape_uint_crd); - ele_scaler *= SizeOf(shape_scaler); - ele_atom_a *= SizeOf(shape_atom_a); - ele_atom_b *= SizeOf(shape_atom_b); - ele_bond_k *= SizeOf(shape_bond_k); - ele_bond_r0 *= SizeOf(shape_bond_r0); - - InitSizeLists(); - return true; - } - - bool Launch(const std::vector &inputs, const std::vector &, - const std::vector &outputs, void *stream_ptr) override { - auto uint_crd_f = GetDeviceAddress(inputs, 0); - auto scaler_f = GetDeviceAddress(inputs, 1); - auto atom_a = GetDeviceAddress(inputs, 2); - auto atom_b = GetDeviceAddress(inputs, 3); - auto bond_k = GetDeviceAddress(inputs, 4); - auto bond_r0 = GetDeviceAddress(inputs, 5); - - auto atom_ene = GetDeviceAddress(outputs, 0); - - BondAtomEnergy(bond_numbers, atom_numbers, uint_crd_f, scaler_f, atom_a, atom_b, bond_k, bond_r0, atom_ene, - reinterpret_cast(stream_ptr)); - return true; - } - - protected: - void InitSizeLists() override { - input_size_list_.push_back(ele_uint_crd * sizeof(T1)); - input_size_list_.push_back(ele_scaler * sizeof(T)); - input_size_list_.push_back(ele_atom_a * sizeof(T1)); - input_size_list_.push_back(ele_atom_b * sizeof(T1)); - input_size_list_.push_back(ele_bond_k * sizeof(T)); - input_size_list_.push_back(ele_bond_r0 * sizeof(T)); - - output_size_list_.push_back(atom_numbers * sizeof(T)); - } - - private: - size_t ele_uint_crd = 1; - size_t ele_scaler = 1; - size_t ele_atom_a = 1; - size_t ele_atom_b = 1; - size_t ele_bond_k = 1; - size_t ele_bond_r0 = 1; - - int bond_numbers; - int atom_numbers; -}; -} // namespace kernel -} // namespace mindspore -#endif // MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONG_BOND_BOND_ATOM_ENERGY_CUDA_GPU_KERNEL_H_ diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/bond/bond_energy_cuda_gpu_kernel.cc b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/bond/bond_energy_cuda_gpu_kernel.cc deleted file mode 100644 index bfb93d78576..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/bond/bond_energy_cuda_gpu_kernel.cc +++ /dev/null @@ -1,32 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "plugin/device/gpu/kernel/sponge/bond/bond_energy_cuda_gpu_kernel.h" - -namespace mindspore { -namespace kernel { -MS_REG_GPU_KERNEL_THREE(BondEnergy, - KernelAttr() - .AddInputAttr(kNumberTypeUInt32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddOutputAttr(kNumberTypeFloat32), - BondEnergyCudaGpuKernelMod, float, int, unsigned int) -} // namespace kernel -} // namespace mindspore diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/bond/bond_energy_cuda_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/bond/bond_energy_cuda_gpu_kernel.h deleted file mode 100644 index 035befcb539..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/bond/bond_energy_cuda_gpu_kernel.h +++ /dev/null @@ -1,103 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONG_BOND_BOND_ENERGY_CUDA_GPU_KERNEL_H_ -#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONG_BOND_BOND_ENERGY_CUDA_GPU_KERNEL_H_ - -#include "plugin/device/gpu/kernel/cuda_impl/sponge/bond/bond_energy_cuda_gpu_impl.cuh" - -#include -#include -#include -#include - -#include "plugin/device/gpu/kernel/gpu_kernel.h" -#include "plugin/device/gpu/kernel/gpu_kernel_factory.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" - -namespace mindspore { -namespace kernel { -template -class BondEnergyCudaGpuKernelMod : public DeprecatedNativeGpuKernelMod { - public: - BondEnergyCudaGpuKernelMod() : ele_uint_crd(1) {} - ~BondEnergyCudaGpuKernelMod() override = default; - - bool Init(const CNodePtr &kernel_node) override { - // get bond_numbers - kernel_node_ = kernel_node; - bond_numbers = static_cast(GetAttr(kernel_node, "bond_numbers")); - atom_numbers = static_cast(GetAttr(kernel_node, "atom_numbers")); - auto shape_uint_crd = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto shape_scaler = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - auto shape_atom_a = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); - auto shape_atom_b = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); - auto shape_bond_k = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 4); - auto shape_bond_r0 = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 5); - - ele_uint_crd *= SizeOf(shape_uint_crd); - ele_scaler *= SizeOf(shape_scaler); - ele_atom_a *= SizeOf(shape_atom_a); - ele_atom_b *= SizeOf(shape_atom_b); - ele_bond_k *= SizeOf(shape_bond_k); - ele_bond_r0 *= SizeOf(shape_bond_r0); - - InitSizeLists(); - return true; - } - - bool Launch(const std::vector &inputs, const std::vector &, - const std::vector &outputs, void *stream_ptr) override { - auto uint_crd_f = GetDeviceAddress(inputs, 0); - auto scaler_f = GetDeviceAddress(inputs, 1); - auto atom_a = GetDeviceAddress(inputs, 2); - auto atom_b = GetDeviceAddress(inputs, 3); - auto bond_k = GetDeviceAddress(inputs, 4); - auto bond_r0 = GetDeviceAddress(inputs, 5); - - auto bond_ene = GetDeviceAddress(outputs, 0); - - BondEnergy(bond_numbers, atom_numbers, uint_crd_f, scaler_f, atom_a, atom_b, bond_k, bond_r0, bond_ene, - reinterpret_cast(stream_ptr)); - return true; - } - - protected: - void InitSizeLists() override { - input_size_list_.push_back(ele_uint_crd * sizeof(T2)); - input_size_list_.push_back(ele_scaler * sizeof(T)); - input_size_list_.push_back(ele_atom_a * sizeof(T1)); - input_size_list_.push_back(ele_atom_b * sizeof(T1)); - input_size_list_.push_back(ele_bond_k * sizeof(T)); - input_size_list_.push_back(ele_bond_r0 * sizeof(T)); - - output_size_list_.push_back(bond_numbers * sizeof(T)); - } - - private: - size_t ele_uint_crd = 1; - size_t ele_scaler = 1; - size_t ele_atom_a = 1; - size_t ele_atom_b = 1; - size_t ele_bond_k = 1; - size_t ele_bond_r0 = 1; - - int bond_numbers; - int atom_numbers; -}; -} // namespace kernel -} // namespace mindspore -#endif // MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONG_BOND_BOND_ENERGY_CUDA_GPU_KERNEL_H_ diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/bond/bond_force_cuda_gpu_kernel.cc b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/bond/bond_force_cuda_gpu_kernel.cc deleted file mode 100644 index a8e9fde0479..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/bond/bond_force_cuda_gpu_kernel.cc +++ /dev/null @@ -1,32 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "plugin/device/gpu/kernel/sponge/bond/bond_force_cuda_gpu_kernel.h" - -namespace mindspore { -namespace kernel { -MS_REG_GPU_KERNEL_THREE(BondForce, - KernelAttr() - .AddInputAttr(kNumberTypeUInt32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddOutputAttr(kNumberTypeFloat32), - BondForceCudaGpuKernelMod, float, int, unsigned int) -} // namespace kernel -} // namespace mindspore diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/bond/bond_force_cuda_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/bond/bond_force_cuda_gpu_kernel.h deleted file mode 100644 index e6466e5ae55..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/bond/bond_force_cuda_gpu_kernel.h +++ /dev/null @@ -1,103 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONG_BOND_BOND_FORCE_CUDA_GPU_KERNEL_H_ -#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONG_BOND_BOND_FORCE_CUDA_GPU_KERNEL_H_ - -#include "plugin/device/gpu/kernel/cuda_impl/sponge/bond/bond_force_cuda_gpu_impl.cuh" - -#include -#include -#include -#include - -#include "plugin/device/gpu/kernel/gpu_kernel.h" -#include "plugin/device/gpu/kernel/gpu_kernel_factory.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" - -namespace mindspore { -namespace kernel { -template -class BondForceCudaGpuKernelMod : public DeprecatedNativeGpuKernelMod { - public: - BondForceCudaGpuKernelMod() : ele_uint_crd(1) {} - ~BondForceCudaGpuKernelMod() override = default; - - bool Init(const CNodePtr &kernel_node) override { - // get bond_numbers - kernel_node_ = kernel_node; - bond_numbers = static_cast(GetAttr(kernel_node, "bond_numbers")); - atom_numbers = static_cast(GetAttr(kernel_node, "atom_numbers")); - auto shape_uint_crd = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto shape_scaler = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - auto shape_atom_a = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); - auto shape_atom_b = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); - auto shape_bond_k = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 4); - auto shape_bond_r0 = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 5); - - ele_uint_crd *= SizeOf(shape_uint_crd); - ele_scaler *= SizeOf(shape_scaler); - ele_atom_a *= SizeOf(shape_atom_a); - ele_atom_b *= SizeOf(shape_atom_b); - ele_bond_k *= SizeOf(shape_bond_k); - ele_bond_r0 *= SizeOf(shape_bond_r0); - - InitSizeLists(); - return true; - } - - bool Launch(const std::vector &inputs, const std::vector &, - const std::vector &outputs, void *stream_ptr) override { - auto uint_crd_f = GetDeviceAddress(inputs, 0); - auto scaler_f = GetDeviceAddress(inputs, 1); - auto atom_a = GetDeviceAddress(inputs, 2); - auto atom_b = GetDeviceAddress(inputs, 3); - auto bond_k = GetDeviceAddress(inputs, 4); - auto bond_r0 = GetDeviceAddress(inputs, 5); - - auto frc_f = GetDeviceAddress(outputs, 0); - - BondForce(bond_numbers, atom_numbers, uint_crd_f, scaler_f, atom_a, atom_b, bond_k, bond_r0, frc_f, - reinterpret_cast(stream_ptr)); - return true; - } - - protected: - void InitSizeLists() override { - input_size_list_.push_back(ele_uint_crd * sizeof(T2)); - input_size_list_.push_back(ele_scaler * sizeof(T)); - input_size_list_.push_back(ele_atom_a * sizeof(T1)); - input_size_list_.push_back(ele_atom_b * sizeof(T1)); - input_size_list_.push_back(ele_bond_k * sizeof(T)); - input_size_list_.push_back(ele_bond_r0 * sizeof(T)); - - output_size_list_.push_back(atom_numbers * 3 * sizeof(T)); - } - - private: - size_t ele_uint_crd = 1; - size_t ele_scaler = 1; - size_t ele_atom_a = 1; - size_t ele_atom_b = 1; - size_t ele_bond_k = 1; - size_t ele_bond_r0 = 1; - - int bond_numbers; - int atom_numbers; -}; -} // namespace kernel -} // namespace mindspore -#endif // MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONG_BOND_BOND_FORCE_CUDA_GPU_KERNEL_H_ diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/bond/bond_force_with_atom_energy_and_virial_kernel.cc b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/bond/bond_force_with_atom_energy_and_virial_kernel.cc deleted file mode 100644 index cb1dea83757..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/bond/bond_force_with_atom_energy_and_virial_kernel.cc +++ /dev/null @@ -1,34 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "plugin/device/gpu/kernel/sponge/bond/bond_force_with_atom_energy_and_virial_kernel.h" - -namespace mindspore { -namespace kernel { -MS_REG_GPU_KERNEL_THREE(BondForceWithAtomEnergyAndVirial, - KernelAttr() - .AddInputAttr(kNumberTypeUInt32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddOutputAttr(kNumberTypeFloat32) - .AddOutputAttr(kNumberTypeFloat32) - .AddOutputAttr(kNumberTypeFloat32), - BondForceWithAtomEnergyAndVirialGpuKernelMod, float, int, unsigned int) -} // namespace kernel -} // namespace mindspore diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/bond/bond_force_with_atom_energy_and_virial_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/bond/bond_force_with_atom_energy_and_virial_kernel.h deleted file mode 100644 index fb077750270..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/bond/bond_force_with_atom_energy_and_virial_kernel.h +++ /dev/null @@ -1,105 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONG_BOND_BOND_FORCE_WITH_ATOM_ENERGY_AND_VIRIAL_KERNEL_H_ -#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONG_BOND_BOND_FORCE_WITH_ATOM_ENERGY_AND_VIRIAL_KERNEL_H_ - -#include "plugin/device/gpu/kernel/cuda_impl/sponge/bond/bond_force_with_atom_energy_and_virial_impl.cuh" - -#include -#include -#include -#include - -#include "plugin/device/gpu/kernel/gpu_kernel.h" -#include "plugin/device/gpu/kernel/gpu_kernel_factory.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" - -namespace mindspore { -namespace kernel { -template -class BondForceWithAtomEnergyAndVirialGpuKernelMod : public DeprecatedNativeGpuKernelMod { - public: - BondForceWithAtomEnergyAndVirialGpuKernelMod() : ele_uint_crd(1) {} - ~BondForceWithAtomEnergyAndVirialGpuKernelMod() override = default; - - bool Init(const CNodePtr &kernel_node) override { - // get bond_numbers - kernel_node_ = kernel_node; - bond_numbers = static_cast(GetAttr(kernel_node, "bond_numbers")); - atom_numbers = static_cast(GetAttr(kernel_node, "atom_numbers")); - auto shape_uint_crd = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto shape_scaler = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - auto shape_atom_a = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); - auto shape_atom_b = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); - auto shape_bond_k = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 4); - auto shape_bond_r0 = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 5); - - ele_uint_crd *= SizeOf(shape_uint_crd); - ele_scaler *= SizeOf(shape_scaler); - ele_atom_a *= SizeOf(shape_atom_a); - ele_atom_b *= SizeOf(shape_atom_b); - ele_bond_k *= SizeOf(shape_bond_k); - ele_bond_r0 *= SizeOf(shape_bond_r0); - InitSizeLists(); - return true; - } - - bool Launch(const std::vector &inputs, const std::vector &, - const std::vector &outputs, void *stream_ptr) override { - auto uint_crd_f = GetDeviceAddress(inputs, 0); - auto scaler_f = GetDeviceAddress(inputs, 1); - auto atom_a = GetDeviceAddress(inputs, 2); - auto atom_b = GetDeviceAddress(inputs, 3); - auto bond_k = GetDeviceAddress(inputs, 4); - auto bond_r0 = GetDeviceAddress(inputs, 5); - - auto frc_f = GetDeviceAddress(outputs, 0); - auto atom_energy = GetDeviceAddress(outputs, 1); - auto atom_v = GetDeviceAddress(outputs, 2); - BondForceWithAtomEnergyAndVirial(bond_numbers, atom_numbers, uint_crd_f, scaler_f, atom_a, atom_b, bond_k, bond_r0, - frc_f, atom_energy, atom_v, reinterpret_cast(stream_ptr)); - return true; - } - - protected: - void InitSizeLists() override { - input_size_list_.push_back(ele_uint_crd * sizeof(T2)); - input_size_list_.push_back(ele_scaler * sizeof(T)); - input_size_list_.push_back(ele_atom_a * sizeof(T1)); - input_size_list_.push_back(ele_atom_b * sizeof(T1)); - input_size_list_.push_back(ele_bond_k * sizeof(T)); - input_size_list_.push_back(ele_bond_r0 * sizeof(T)); - - output_size_list_.push_back(atom_numbers * 3 * sizeof(T)); - output_size_list_.push_back(atom_numbers * sizeof(T)); - output_size_list_.push_back(atom_numbers * sizeof(T)); - } - - private: - size_t ele_uint_crd = 1; - size_t ele_scaler = 1; - size_t ele_atom_a = 1; - size_t ele_atom_b = 1; - size_t ele_bond_k = 1; - size_t ele_bond_r0 = 1; - - int bond_numbers; - int atom_numbers; -}; -} // namespace kernel -} // namespace mindspore -#endif diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/bond/bond_force_with_atom_energy_kernel.cc b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/bond/bond_force_with_atom_energy_kernel.cc deleted file mode 100644 index ce4dca2e80b..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/bond/bond_force_with_atom_energy_kernel.cc +++ /dev/null @@ -1,33 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "plugin/device/gpu/kernel/sponge/bond/bond_force_with_atom_energy_kernel.h" - -namespace mindspore { -namespace kernel { -MS_REG_GPU_KERNEL_TWO(BondForceWithAtomEnergy, - KernelAttr() - .AddInputAttr(kNumberTypeUInt32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddOutputAttr(kNumberTypeFloat32) - .AddOutputAttr(kNumberTypeFloat32), - BondForceWithAtomEnergyGpuKernelMod, float, int) -} // namespace kernel -} // namespace mindspore diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/bond/bond_force_with_atom_energy_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/bond/bond_force_with_atom_energy_kernel.h deleted file mode 100644 index 2ffa77abcbe..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/bond/bond_force_with_atom_energy_kernel.h +++ /dev/null @@ -1,103 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONG_BOND_BOND_FORCE_WITH_ATOM_ENERGY_KERNEL_H_ -#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONG_BOND_BOND_FORCE_WITH_ATOM_ENERGY_KERNEL_H_ - -#include "plugin/device/gpu/kernel/cuda_impl/sponge/bond/bond_force_with_atom_energy_impl.cuh" - -#include -#include -#include -#include - -#include "plugin/device/gpu/kernel/gpu_kernel.h" -#include "plugin/device/gpu/kernel/gpu_kernel_factory.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" - -namespace mindspore { -namespace kernel { -template -class BondForceWithAtomEnergyGpuKernelMod : public DeprecatedNativeGpuKernelMod { - public: - BondForceWithAtomEnergyGpuKernelMod() : ele_uint_crd(1) {} - ~BondForceWithAtomEnergyGpuKernelMod() override = default; - - bool Init(const CNodePtr &kernel_node) override { - // get bond_numbers - kernel_node_ = kernel_node; - bond_numbers = static_cast(GetAttr(kernel_node, "bond_numbers")); - atom_numbers = static_cast(GetAttr(kernel_node, "atom_numbers")); - auto shape_uint_crd = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto shape_scaler = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - auto shape_atom_a = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); - auto shape_atom_b = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); - auto shape_bond_k = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 4); - auto shape_bond_r0 = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 5); - - ele_uint_crd *= SizeOf(shape_uint_crd); - ele_scaler *= SizeOf(shape_scaler); - ele_atom_a *= SizeOf(shape_atom_a); - ele_atom_b *= SizeOf(shape_atom_b); - ele_bond_k *= SizeOf(shape_bond_k); - ele_bond_r0 *= SizeOf(shape_bond_r0); - InitSizeLists(); - return true; - } - - bool Launch(const std::vector &inputs, const std::vector &, - const std::vector &outputs, void *stream_ptr) override { - auto uint_crd_f = GetDeviceAddress(inputs, 0); - auto scaler_f = GetDeviceAddress(inputs, 1); - auto atom_a = GetDeviceAddress(inputs, 2); - auto atom_b = GetDeviceAddress(inputs, 3); - auto bond_k = GetDeviceAddress(inputs, 4); - auto bond_r0 = GetDeviceAddress(inputs, 5); - - auto frc_f = GetDeviceAddress(outputs, 0); - auto atom_e = GetDeviceAddress(outputs, 1); - BondForceWithAtomEnergy(bond_numbers, atom_numbers, uint_crd_f, scaler_f, atom_a, atom_b, bond_k, bond_r0, frc_f, - atom_e, reinterpret_cast(stream_ptr)); - return true; - } - - protected: - void InitSizeLists() override { - input_size_list_.push_back(ele_uint_crd * sizeof(T1)); - input_size_list_.push_back(ele_scaler * sizeof(T)); - input_size_list_.push_back(ele_atom_a * sizeof(T1)); - input_size_list_.push_back(ele_atom_b * sizeof(T1)); - input_size_list_.push_back(ele_bond_k * sizeof(T)); - input_size_list_.push_back(ele_bond_r0 * sizeof(T)); - - output_size_list_.push_back(atom_numbers * 3 * sizeof(T)); - output_size_list_.push_back(atom_numbers * sizeof(T)); - } - - private: - size_t ele_uint_crd = 1; - size_t ele_scaler = 1; - size_t ele_atom_a = 1; - size_t ele_atom_b = 1; - size_t ele_bond_k = 1; - size_t ele_bond_r0 = 1; - - int bond_numbers; - int atom_numbers; -}; -} // namespace kernel -} // namespace mindspore -#endif diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/bond/bond_force_with_atom_virial_kernel.cc b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/bond/bond_force_with_atom_virial_kernel.cc deleted file mode 100644 index e9ae2d9341c..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/bond/bond_force_with_atom_virial_kernel.cc +++ /dev/null @@ -1,33 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "plugin/device/gpu/kernel/sponge/bond/bond_force_with_atom_virial_kernel.h" - -namespace mindspore { -namespace kernel { -MS_REG_GPU_KERNEL_TWO(BondForceWithAtomVirial, - KernelAttr() - .AddInputAttr(kNumberTypeUInt32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddOutputAttr(kNumberTypeFloat32) - .AddOutputAttr(kNumberTypeFloat32), - BondForceWithAtomVirialGpuKernelMod, float, int) -} // namespace kernel -} // namespace mindspore diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/bond/bond_force_with_atom_virial_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/bond/bond_force_with_atom_virial_kernel.h deleted file mode 100644 index 876a975b9a3..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/bond/bond_force_with_atom_virial_kernel.h +++ /dev/null @@ -1,103 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONG_BOND_BOND_FORCE_WITH_ATOM_VIRIAL_KERNEL_H_ -#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONG_BOND_BOND_FORCE_WITH_ATOM_VIRIAL_KERNEL_H_ - -#include "plugin/device/gpu/kernel/cuda_impl/sponge/bond/bond_force_with_atom_virial_impl.cuh" - -#include -#include -#include -#include - -#include "plugin/device/gpu/kernel/gpu_kernel.h" -#include "plugin/device/gpu/kernel/gpu_kernel_factory.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" - -namespace mindspore { -namespace kernel { -template -class BondForceWithAtomVirialGpuKernelMod : public DeprecatedNativeGpuKernelMod { - public: - BondForceWithAtomVirialGpuKernelMod() : ele_uint_crd(1) {} - ~BondForceWithAtomVirialGpuKernelMod() override = default; - - bool Init(const CNodePtr &kernel_node) override { - // get bond_numbers - kernel_node_ = kernel_node; - bond_numbers = static_cast(GetAttr(kernel_node, "bond_numbers")); - atom_numbers = static_cast(GetAttr(kernel_node, "atom_numbers")); - auto shape_uint_crd = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto shape_scaler = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - auto shape_atom_a = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); - auto shape_atom_b = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); - auto shape_bond_k = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 4); - auto shape_bond_r0 = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 5); - - ele_uint_crd *= SizeOf(shape_uint_crd); - ele_scaler *= SizeOf(shape_scaler); - ele_atom_a *= SizeOf(shape_atom_a); - ele_atom_b *= SizeOf(shape_atom_b); - ele_bond_k *= SizeOf(shape_bond_k); - ele_bond_r0 *= SizeOf(shape_bond_r0); - InitSizeLists(); - return true; - } - - bool Launch(const std::vector &inputs, const std::vector &, - const std::vector &outputs, void *stream_ptr) override { - auto uint_crd_f = GetDeviceAddress(inputs, 0); - auto scaler_f = GetDeviceAddress(inputs, 1); - auto atom_a = GetDeviceAddress(inputs, 2); - auto atom_b = GetDeviceAddress(inputs, 3); - auto bond_k = GetDeviceAddress(inputs, 4); - auto bond_r0 = GetDeviceAddress(inputs, 5); - - auto frc_f = GetDeviceAddress(outputs, 0); - auto atom_v = GetDeviceAddress(outputs, 1); - BondForceWithAtomVirial(bond_numbers, atom_numbers, uint_crd_f, scaler_f, atom_a, atom_b, bond_k, bond_r0, frc_f, - atom_v, reinterpret_cast(stream_ptr)); - return true; - } - - protected: - void InitSizeLists() override { - input_size_list_.push_back(ele_uint_crd * sizeof(T1)); - input_size_list_.push_back(ele_scaler * sizeof(T)); - input_size_list_.push_back(ele_atom_a * sizeof(T1)); - input_size_list_.push_back(ele_atom_b * sizeof(T1)); - input_size_list_.push_back(ele_bond_k * sizeof(T)); - input_size_list_.push_back(ele_bond_r0 * sizeof(T)); - - output_size_list_.push_back(atom_numbers * 3 * sizeof(T)); - output_size_list_.push_back(atom_numbers * sizeof(T)); - } - - private: - size_t ele_uint_crd = 1; - size_t ele_scaler = 1; - size_t ele_atom_a = 1; - size_t ele_atom_b = 1; - size_t ele_bond_k = 1; - size_t ele_bond_r0 = 1; - - int bond_numbers; - int atom_numbers; -}; -} // namespace kernel -} // namespace mindspore -#endif diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/common/atomcrdtocv_kernel.cc b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/common/atomcrdtocv_kernel.cc deleted file mode 100644 index 6c26d16f7bd..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/common/atomcrdtocv_kernel.cc +++ /dev/null @@ -1,32 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "plugin/device/gpu/kernel/sponge/common/atomcrdtocv_kernel.h" - -namespace mindspore { -namespace kernel { -MS_REG_GPU_KERNEL_TWO(TransferCrd, - KernelAttr() - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddOutputAttr(kNumberTypeFloat32) - .AddOutputAttr(kNumberTypeFloat32) - .AddOutputAttr(kNumberTypeFloat32) - .AddOutputAttr(kNumberTypeInt32), - AtomCrdToCVGpuKernelMod, float, int) -} // namespace kernel -} // namespace mindspore diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/common/atomcrdtocv_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/common/atomcrdtocv_kernel.h deleted file mode 100644 index 6dd8a53587e..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/common/atomcrdtocv_kernel.h +++ /dev/null @@ -1,99 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONG_COMMON_ATOMCRDTOCV_KERNEL_H_ -#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONG_COMMON_ATOMCRDTOCV_KERNEL_H_ - -#include "plugin/device/gpu/kernel/cuda_impl/sponge/common/atomcrdtocv_impl.cuh" - -#include -#include -#include -#include - -#include "plugin/device/gpu/kernel/gpu_kernel.h" -#include "plugin/device/gpu/kernel/gpu_kernel_factory.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" - -namespace mindspore { -namespace kernel { -template -class AtomCrdToCVGpuKernelMod : public DeprecatedNativeGpuKernelMod { - public: - AtomCrdToCVGpuKernelMod() : ele_crd(1) {} - ~AtomCrdToCVGpuKernelMod() override = default; - - bool Init(const CNodePtr &kernel_node) override { - kernel_node_ = kernel_node; - start_serial = static_cast(GetAttr(kernel_node, "start_serial")); - end_serial = static_cast(GetAttr(kernel_node, "end_serial")); - number = static_cast(GetAttr(kernel_node, "number")); - atom_numbers = static_cast(GetAttr(kernel_node, "atom_numbers")); - auto shape_crd = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto shape_old_crd = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - auto shape_box = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - - ele_crd *= SizeOf(shape_crd); - ele_old_crd *= SizeOf(shape_old_crd); - ele_box *= SizeOf(shape_box); - - InitSizeLists(); - return true; - } - - bool Launch(const std::vector &inputs, const std::vector &workspace, - const std::vector &outputs, void *stream_ptr) override { - auto crd = GetDeviceAddress(inputs, 0); - auto old_crd = GetDeviceAddress(inputs, 1); - auto box = GetDeviceAddress(inputs, 2); - - auto g_radial = GetDeviceAddress(outputs, 0); - auto g_angular = GetDeviceAddress(outputs, 1); - - auto nowarp_crd = GetDeviceAddress(outputs, 2); - auto box_map_times = GetDeviceAddress(outputs, 3); - - AtomCrdToCV(atom_numbers, start_serial, end_serial, number, crd, old_crd, nowarp_crd, box_map_times, box, g_radial, - g_angular, reinterpret_cast(stream_ptr)); - return true; - } - - protected: - void InitSizeLists() override { - input_size_list_.push_back(ele_crd * sizeof(T)); - input_size_list_.push_back(ele_old_crd * sizeof(T)); - input_size_list_.push_back(ele_box * sizeof(T)); - - output_size_list_.push_back(number * sizeof(T)); - output_size_list_.push_back(number * sizeof(T)); - - output_size_list_.push_back(3 * atom_numbers * sizeof(T)); - output_size_list_.push_back(3 * atom_numbers * sizeof(T1)); - } - - private: - size_t ele_crd = 1; - size_t ele_old_crd = 1; - size_t ele_box = 1; - - int end_serial; - int start_serial; - int number; - int atom_numbers; -}; -} // namespace kernel -} // namespace mindspore -#endif // MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONG_COMMON_ATOMCRDTOCV_KERNEL_H_ diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/common/crd_to_uint_crd_kernel.cc b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/common/crd_to_uint_crd_kernel.cc deleted file mode 100644 index fb80d292791..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/common/crd_to_uint_crd_kernel.cc +++ /dev/null @@ -1,26 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "plugin/device/gpu/kernel/sponge/common/crd_to_uint_crd_kernel.h" - -namespace mindspore { -namespace kernel { -MS_REG_GPU_KERNEL_TWO( - CrdToUintCrd, - KernelAttr().AddInputAttr(kNumberTypeFloat32).AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeUInt32), - CrdToUintCrdGpuKernelMod, float, unsigned int) -} // namespace kernel -} // namespace mindspore diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/common/crd_to_uint_crd_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/common/crd_to_uint_crd_kernel.h deleted file mode 100644 index 6f6a33655e3..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/common/crd_to_uint_crd_kernel.h +++ /dev/null @@ -1,79 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONG_COMMON_CRD_TO_UINT_CRD_KERNEL_H_ -#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONG_COMMON_CRD_TO_UINT_CRD_KERNEL_H_ - -#include -#include -#include -#include -#include "plugin/device/gpu/kernel/gpu_kernel.h" -#include "plugin/device/gpu/kernel/gpu_kernel_factory.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" -#include "plugin/device/gpu/kernel/cuda_impl/sponge/common/crd_to_uint_crd_impl.cuh" - -namespace mindspore { -namespace kernel { -template -class CrdToUintCrdGpuKernelMod : public DeprecatedNativeGpuKernelMod { - public: - CrdToUintCrdGpuKernelMod() : ele_crd(1) {} - ~CrdToUintCrdGpuKernelMod() override = default; - - bool Init(const CNodePtr &kernel_node) override { - kernel_node_ = kernel_node; - atom_numbers = static_cast(GetAttr(kernel_node, "atom_numbers")); - - auto shape_crd_to_uint_crd_cof = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto shape_crd = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - - ele_crd_to_uint_crd_cof *= SizeOf(shape_crd_to_uint_crd_cof); - ele_crd *= SizeOf(shape_crd); - - InitSizeLists(); - return true; - } - - bool Launch(const std::vector &inputs, const std::vector &, - const std::vector &outputs, void *stream_ptr) override { - auto crd_to_uint_crd_cof = GetDeviceAddress(inputs, 0); - auto crd = GetDeviceAddress(inputs, 1); - - auto uint_crd = GetDeviceAddress(outputs, 0); - - CrdToUintCrd(atom_numbers, crd_to_uint_crd_cof, crd, uint_crd, reinterpret_cast(stream_ptr)); - - return true; - } - - protected: - void InitSizeLists() override { - input_size_list_.push_back(ele_crd_to_uint_crd_cof * sizeof(T)); - input_size_list_.push_back(ele_crd * sizeof(T)); - - output_size_list_.push_back(3 * atom_numbers * sizeof(T)); - } - - private: - size_t ele_crd_to_uint_crd_cof = 1; - size_t ele_crd = 1; - - int atom_numbers; -}; -} // namespace kernel -} // namespace mindspore -#endif // MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONG_COMMON_CRD_TO_UINT_CRD_KERNEL_H_ diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/common/crd_to_uint_crd_quarter_kernel.cc b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/common/crd_to_uint_crd_quarter_kernel.cc deleted file mode 100644 index 63905c0a42b..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/common/crd_to_uint_crd_quarter_kernel.cc +++ /dev/null @@ -1,26 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "plugin/device/gpu/kernel/sponge/common/crd_to_uint_crd_quarter_kernel.h" - -namespace mindspore { -namespace kernel { -MS_REG_GPU_KERNEL_TWO( - CrdToUintCrdQuarter, - KernelAttr().AddInputAttr(kNumberTypeFloat32).AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeUInt32), - CrdToUintCrdQuarterGpuKernelMod, float, unsigned int) -} // namespace kernel -} // namespace mindspore diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/common/crd_to_uint_crd_quarter_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/common/crd_to_uint_crd_quarter_kernel.h deleted file mode 100644 index bc5c3058f33..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/common/crd_to_uint_crd_quarter_kernel.h +++ /dev/null @@ -1,79 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONG_COMMON_CRD_TO_UINT_CRD_QUARTER_KERNEL_H_ -#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONG_COMMON_CRD_TO_UINT_CRD_QUARTER_KERNEL_H_ - -#include -#include -#include -#include -#include "plugin/device/gpu/kernel/gpu_kernel.h" -#include "plugin/device/gpu/kernel/gpu_kernel_factory.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" -#include "plugin/device/gpu/kernel/cuda_impl/sponge/common/crd_to_uint_crd_quarter_impl.cuh" - -namespace mindspore { -namespace kernel { -template -class CrdToUintCrdQuarterGpuKernelMod : public DeprecatedNativeGpuKernelMod { - public: - CrdToUintCrdQuarterGpuKernelMod() : ele_crd(1) {} - ~CrdToUintCrdQuarterGpuKernelMod() override = default; - - bool Init(const CNodePtr &kernel_node) override { - kernel_node_ = kernel_node; - atom_numbers = static_cast(GetAttr(kernel_node, "atom_numbers")); - - auto shape_crd_to_uint_crd_cof = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto shape_crd = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - - ele_crd_to_uint_crd_cof *= SizeOf(shape_crd_to_uint_crd_cof); - ele_crd *= SizeOf(shape_crd); - - InitSizeLists(); - return true; - } - - bool Launch(const std::vector &inputs, const std::vector &, - const std::vector &outputs, void *stream_ptr) override { - auto crd_to_uint_crd_cof = GetDeviceAddress(inputs, 0); - auto crd = GetDeviceAddress(inputs, 1); - - auto uint_crd = GetDeviceAddress(outputs, 0); - - CrdToUintCrdQuarter(atom_numbers, crd_to_uint_crd_cof, crd, uint_crd, reinterpret_cast(stream_ptr)); - - return true; - } - - protected: - void InitSizeLists() override { - input_size_list_.push_back(ele_crd_to_uint_crd_cof * sizeof(T)); - input_size_list_.push_back(ele_crd * sizeof(T)); - - output_size_list_.push_back(3 * atom_numbers * sizeof(T)); - } - - private: - size_t ele_crd_to_uint_crd_cof = 1; - size_t ele_crd = 1; - - int atom_numbers; -}; -} // namespace kernel -} // namespace mindspore -#endif // MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONG_COMMON_CRD_TO_UINT_CRD_KERNEL_H_ diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/common/get_center_of_mass_kernel.cc b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/common/get_center_of_mass_kernel.cc deleted file mode 100644 index 6b37707852b..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/common/get_center_of_mass_kernel.cc +++ /dev/null @@ -1,31 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "plugin/device/gpu/kernel/sponge/common/get_center_of_mass_kernel.h" - -namespace mindspore { -namespace kernel { -MS_REG_GPU_KERNEL_TWO(GetCenterOfMass, - KernelAttr() - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddOutputAttr(kNumberTypeFloat32), - GetCenterOfMassGpuKernelMod, float, int) -} // namespace kernel -} // namespace mindspore diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/common/get_center_of_mass_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/common/get_center_of_mass_kernel.h deleted file mode 100644 index a80a7e4d765..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/common/get_center_of_mass_kernel.h +++ /dev/null @@ -1,94 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONG_COMMON_GET_CENTER_OF_MASS_KERNEL_H_ -#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONG_COMMON_GET_CENTER_OF_MASS_KERNEL_H_ - -#include -#include -#include -#include -#include "plugin/device/gpu/kernel/gpu_kernel.h" -#include "plugin/device/gpu/kernel/gpu_kernel_factory.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" -#include "plugin/device/gpu/kernel/cuda_impl/sponge/common/get_center_of_mass_impl.cuh" - -namespace mindspore { -namespace kernel { -template -class GetCenterOfMassGpuKernelMod : public DeprecatedNativeGpuKernelMod { - public: - GetCenterOfMassGpuKernelMod() : ele_start(1) {} - ~GetCenterOfMassGpuKernelMod() override = default; - - bool Init(const CNodePtr &kernel_node) override { - kernel_node_ = kernel_node; - residue_numbers = static_cast(GetAttr(kernel_node, "residue_numbers")); - - auto shape_start = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto shape_end = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - auto shape_crd = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); - auto shape_atom_mass = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); - auto shape_residue_mass_inverse = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 4); - - ele_start *= SizeOf(shape_start); - ele_end *= SizeOf(shape_end); - ele_crd *= SizeOf(shape_crd); - ele_atom_mass *= SizeOf(shape_atom_mass); - ele_residue_mass_inverse *= SizeOf(shape_residue_mass_inverse); - - InitSizeLists(); - return true; - } - - bool Launch(const std::vector &inputs, const std::vector &, - const std::vector &outputs, void *stream_ptr) override { - auto start = GetDeviceAddress(inputs, 0); - auto end = GetDeviceAddress(inputs, 1); - auto crd = GetDeviceAddress(inputs, 2); - auto atom_mass = GetDeviceAddress(inputs, 3); - auto residue_mass_inverse = GetDeviceAddress(inputs, 4); - - auto center_of_mass = GetDeviceAddress(outputs, 0); - - GetCenterOfMass(residue_numbers, start, end, crd, atom_mass, residue_mass_inverse, center_of_mass, - reinterpret_cast(stream_ptr)); - - return true; - } - - protected: - void InitSizeLists() override { - input_size_list_.push_back(ele_start * sizeof(T1)); - input_size_list_.push_back(ele_end * sizeof(T1)); - input_size_list_.push_back(ele_crd * sizeof(T)); - input_size_list_.push_back(ele_atom_mass * sizeof(T)); - input_size_list_.push_back(ele_residue_mass_inverse * sizeof(T)); - output_size_list_.push_back(3 * sizeof(T) * residue_numbers); - } - - private: - size_t ele_start = 1; - size_t ele_end = 1; - size_t ele_crd = 1; - size_t ele_atom_mass = 1; - size_t ele_residue_mass_inverse = 1; - - int residue_numbers; -}; -} // namespace kernel -} // namespace mindspore -#endif // MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONG_COMMON_GET_CENTER_OF_MASS_KERNEL_H_ diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/common/getcenter_kernel.cc b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/common/getcenter_kernel.cc deleted file mode 100644 index 78eb13f04e7..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/common/getcenter_kernel.cc +++ /dev/null @@ -1,26 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "plugin/device/gpu/kernel/sponge/common/getcenter_kernel.h" - -namespace mindspore { -namespace kernel { -MS_REG_GPU_KERNEL_TWO( - GetCenterOfGeometry, - KernelAttr().AddInputAttr(kNumberTypeInt32).AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32), - GetCenterOfGeometryGpuKernelMod, float, int) -} // namespace kernel -} // namespace mindspore diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/common/getcenter_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/common/getcenter_kernel.h deleted file mode 100644 index c4116facfe7..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/common/getcenter_kernel.h +++ /dev/null @@ -1,82 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONG_COMMON_GETCENTER_KERNEL_H_ -#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONG_COMMON_GETCENTER_KERNEL_H_ - -#include -#include -#include -#include -#include "plugin/device/gpu/kernel/gpu_kernel.h" -#include "plugin/device/gpu/kernel/gpu_kernel_factory.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" -#include "plugin/device/gpu/kernel/cuda_impl/sponge/common/getcenter_impl.cuh" - -namespace mindspore { -namespace kernel { -template -class GetCenterOfGeometryGpuKernelMod : public DeprecatedNativeGpuKernelMod { - public: - GetCenterOfGeometryGpuKernelMod() : ele_center_atoms(1) {} - ~GetCenterOfGeometryGpuKernelMod() override = default; - - bool Init(const CNodePtr &kernel_node) override { - kernel_node_ = kernel_node; - center_numbers = static_cast(GetAttr(kernel_node, "center_numbers")); - center_numbers_inverse = static_cast(GetAttr(kernel_node, "center_numbers_inverse")); - - auto shape_center_atoms = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto shape_crd = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - - ele_center_atoms *= SizeOf(shape_center_atoms); - ele_crd *= SizeOf(shape_crd); - - InitSizeLists(); - return true; - } - - bool Launch(const std::vector &inputs, const std::vector &, - const std::vector &outputs, void *stream_ptr) override { - auto center_atoms = GetDeviceAddress(inputs, 0); - auto crd = GetDeviceAddress(inputs, 1); - - auto center_of_geometry = GetDeviceAddress(outputs, 0); - - GetCenterOfGeometry(center_numbers, center_numbers_inverse, center_atoms, crd, center_of_geometry, - reinterpret_cast(stream_ptr)); - - return true; - } - - protected: - void InitSizeLists() override { - input_size_list_.push_back(ele_center_atoms * sizeof(T1)); - input_size_list_.push_back(ele_crd * sizeof(T)); - - output_size_list_.push_back(3 * sizeof(T)); - } - - private: - size_t ele_center_atoms = 1; - size_t ele_crd = 1; - - int center_numbers; - float center_numbers_inverse; -}; -} // namespace kernel -} // namespace mindspore -#endif // MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONG_COMMON_GETCENTER_KERNEL_H_ diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/common/map_center_of_mass_kernel.cc b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/common/map_center_of_mass_kernel.cc deleted file mode 100644 index 08c27d7710c..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/common/map_center_of_mass_kernel.cc +++ /dev/null @@ -1,33 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "plugin/device/gpu/kernel/sponge/common/map_center_of_mass_kernel.h" - -namespace mindspore { -namespace kernel { -MS_REG_GPU_KERNEL_TWO(MapCenterOfMass, - KernelAttr() - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddOutputAttr(kNumberTypeFloat32), - MapCenterOfMassGpuKernelMod, float, int) -} // namespace kernel -} // namespace mindspore diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/common/map_center_of_mass_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/common/map_center_of_mass_kernel.h deleted file mode 100644 index a711e610331..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/common/map_center_of_mass_kernel.h +++ /dev/null @@ -1,99 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONG_COMMON_MAP_CENTER_OF_MASS_KERNEL_H_ -#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONG_COMMON_MAP_CENTER_OF_MASS_KERNEL_H_ - -#include -#include -#include -#include -#include "plugin/device/gpu/kernel/gpu_kernel.h" -#include "plugin/device/gpu/kernel/gpu_kernel_factory.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" -#include "plugin/device/gpu/kernel/cuda_impl/sponge/common/map_center_of_mass_impl.cuh" - -namespace mindspore { -namespace kernel { -template -class MapCenterOfMassGpuKernelMod : public DeprecatedNativeGpuKernelMod { - public: - MapCenterOfMassGpuKernelMod() : ele_start(1) {} - ~MapCenterOfMassGpuKernelMod() override = default; - - bool Init(const CNodePtr &kernel_node) override { - kernel_node_ = kernel_node; - residue_numbers = static_cast(GetAttr(kernel_node, "residue_numbers")); - - auto shape_start = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto shape_end = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - auto shape_center_of_mass = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); - auto shape_box_length = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); - auto shape_no_wrap_crd = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 4); - auto shape_crd = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 5); - - ele_start *= SizeOf(shape_start); - ele_end *= SizeOf(shape_end); - ele_center_of_mass *= SizeOf(shape_center_of_mass); - ele_box_length *= SizeOf(shape_box_length); - ele_no_wrap_crd *= SizeOf(shape_no_wrap_crd); - ele_crd *= SizeOf(shape_crd); - - InitSizeLists(); - return true; - } - - bool Launch(const std::vector &inputs, const std::vector &, - const std::vector &outputs, void *stream_ptr) override { - auto start = GetDeviceAddress(inputs, 0); - auto end = GetDeviceAddress(inputs, 1); - auto center_of_mass = GetDeviceAddress(inputs, 2); - auto box_length = GetDeviceAddress(inputs, 3); - auto no_wrap_crd = GetDeviceAddress(inputs, 4); - auto crd = GetDeviceAddress(inputs, 5); - auto scaler = GetDeviceAddress(inputs, 6); - - MapCenterOfMass(residue_numbers, start, end, center_of_mass, box_length, no_wrap_crd, crd, scaler, - reinterpret_cast(stream_ptr)); - - return true; - } - - protected: - void InitSizeLists() override { - input_size_list_.push_back(ele_start * sizeof(T1)); - input_size_list_.push_back(ele_end * sizeof(T1)); - input_size_list_.push_back(ele_center_of_mass * sizeof(T)); - input_size_list_.push_back(ele_box_length * sizeof(T)); - input_size_list_.push_back(ele_no_wrap_crd * sizeof(T)); - input_size_list_.push_back(ele_crd * sizeof(T)); - input_size_list_.push_back(sizeof(T)); - output_size_list_.push_back(sizeof(T)); - } - - private: - size_t ele_start = 1; - size_t ele_end = 1; - size_t ele_center_of_mass = 1; - size_t ele_box_length = 1; - size_t ele_no_wrap_crd = 1; - size_t ele_crd = 1; - - int residue_numbers; -}; -} // namespace kernel -} // namespace mindspore -#endif // MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONG_COMMON_MAP_CENTER_OF_MASS_KERNEL_H_ diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/common/mdtemperature_kernel.cc b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/common/mdtemperature_kernel.cc deleted file mode 100644 index e4e0a5808c9..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/common/mdtemperature_kernel.cc +++ /dev/null @@ -1,30 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "plugin/device/gpu/kernel/sponge/common/mdtemperature_kernel.h" - -namespace mindspore { -namespace kernel { -MS_REG_GPU_KERNEL_TWO(MDTemperature, - KernelAttr() - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddOutputAttr(kNumberTypeFloat32), - MDTemperatureGpuKernelMod, float, int) -} // namespace kernel -} // namespace mindspore diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/common/mdtemperature_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/common/mdtemperature_kernel.h deleted file mode 100644 index 7e4558144f0..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/common/mdtemperature_kernel.h +++ /dev/null @@ -1,89 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONG_COMMON_MDTEMPERATURE_KERNEL_H_ -#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONG_COMMON_MDTEMPERATURE_KERNEL_H_ - -#include -#include -#include -#include -#include "plugin/device/gpu/kernel/gpu_kernel.h" -#include "plugin/device/gpu/kernel/gpu_kernel_factory.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" -#include "plugin/device/gpu/kernel/cuda_impl/sponge/common/mdtemperature_impl.cuh" - -namespace mindspore { -namespace kernel { -template -class MDTemperatureGpuKernelMod : public DeprecatedNativeGpuKernelMod { - public: - MDTemperatureGpuKernelMod() : ele_start(1) {} - ~MDTemperatureGpuKernelMod() override = default; - - bool Init(const CNodePtr &kernel_node) override { - kernel_node_ = kernel_node; - residue_numbers = static_cast(GetAttr(kernel_node, "residue_numbers")); - - auto shape_start = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto shape_end = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - auto shape_atom_vel = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); - auto shape_atom_mass = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); - - ele_start *= SizeOf(shape_start); - ele_end *= SizeOf(shape_end); - ele_atom_vel *= SizeOf(shape_atom_vel); - ele_atom_mass *= SizeOf(shape_atom_mass); - - InitSizeLists(); - return true; - } - - bool Launch(const std::vector &inputs, const std::vector &, - const std::vector &outputs, void *stream_ptr) override { - auto start = GetDeviceAddress(inputs, 0); - auto end = GetDeviceAddress(inputs, 1); - auto atom_vel_f = GetDeviceAddress(inputs, 2); - auto atom_mass = GetDeviceAddress(inputs, 3); - - auto ek = GetDeviceAddress(outputs, 0); - - MDTemperature(residue_numbers, start, end, atom_vel_f, atom_mass, ek, reinterpret_cast(stream_ptr)); - - return true; - } - - protected: - void InitSizeLists() override { - input_size_list_.push_back(ele_start * sizeof(T1)); - input_size_list_.push_back(ele_end * sizeof(T1)); - input_size_list_.push_back(ele_atom_vel * sizeof(T)); - input_size_list_.push_back(ele_atom_mass * sizeof(T)); - - output_size_list_.push_back(residue_numbers * sizeof(T)); - } - - private: - size_t ele_start = 1; - size_t ele_end = 1; - size_t ele_atom_vel = 1; - size_t ele_atom_mass = 1; - - int residue_numbers; -}; -} // namespace kernel -} // namespace mindspore -#endif // MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONG_COMMON_MDTEMPERATURE_KERNEL_H_ diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/common/total_c6_get_kernel.cc b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/common/total_c6_get_kernel.cc deleted file mode 100644 index d1411045db9..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/common/total_c6_get_kernel.cc +++ /dev/null @@ -1,30 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -/** - * Note: - * Totalc6get. This is an experimental interface that is subject to change and/or deletion. - */ - -#include "plugin/device/gpu/kernel/sponge/common/total_c6_get_kernel.h" - -namespace mindspore { -namespace kernel { -MS_REG_GPU_KERNEL_TWO( - Totalc6get, - KernelAttr().AddInputAttr(kNumberTypeInt32).AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32), - TotalC6GetGpuKernelMod, float, int) -} // namespace kernel -} // namespace mindspore diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/common/total_c6_get_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/common/total_c6_get_kernel.h deleted file mode 100644 index 3dc019a64b2..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/common/total_c6_get_kernel.h +++ /dev/null @@ -1,79 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONG_COMMON_TOTAL_C6_GET_KERNEL_H_ -#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONG_COMMON_TOTAL_C6_GET_KERNEL_H_ - -#include -#include -#include -#include -#include "plugin/device/gpu/kernel/gpu_kernel.h" -#include "plugin/device/gpu/kernel/gpu_kernel_factory.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" -#include "plugin/device/gpu/kernel/cuda_impl/sponge/common/total_c6_get_impl.cuh" - -namespace mindspore { -namespace kernel { -template -class TotalC6GetGpuKernelMod : public DeprecatedNativeGpuKernelMod { - public: - TotalC6GetGpuKernelMod() : ele_atom_lj_type(1) {} - ~TotalC6GetGpuKernelMod() override = default; - - bool Init(const CNodePtr &kernel_node) override { - kernel_node_ = kernel_node; - atom_numbers = static_cast(GetAttr(kernel_node, "atom_numbers")); - - auto shape_atom_lj_type = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto shape_lj_b = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - - ele_atom_lj_type *= SizeOf(shape_atom_lj_type); - ele_lj_b *= SizeOf(shape_lj_b); - - InitSizeLists(); - return true; - } - - bool Launch(const std::vector &inputs, const std::vector &, - const std::vector &outputs, void *stream_ptr) override { - auto atom_lj_type = GetDeviceAddress(inputs, 0); - auto lj_b = GetDeviceAddress(inputs, 1); - - auto factor = GetDeviceAddress(outputs, 0); - - total_c6_get(atom_numbers, atom_lj_type, lj_b, factor, reinterpret_cast(stream_ptr)); - - return true; - } - - protected: - void InitSizeLists() override { - input_size_list_.push_back(ele_atom_lj_type * sizeof(T1)); - input_size_list_.push_back(ele_lj_b * sizeof(T)); - - output_size_list_.push_back(sizeof(T)); - } - - private: - size_t ele_atom_lj_type = 1; - size_t ele_lj_b = 1; - - int atom_numbers; -}; -} // namespace kernel -} // namespace mindspore -#endif // MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONG_COMMON_GETCENTER_KERNEL_H_ diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/crdmcmap/cal_no_wrap_crd_kernel.cc b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/crdmcmap/cal_no_wrap_crd_kernel.cc deleted file mode 100644 index f39f46e7b94..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/crdmcmap/cal_no_wrap_crd_kernel.cc +++ /dev/null @@ -1,33 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -/** - * Note: - * CalculateNowrapCrd. This is an experimental interface that is subject to change and/or deletion. - */ - -#include "plugin/device/gpu/kernel/sponge/crdmcmap/cal_no_wrap_crd_kernel.h" - -namespace mindspore { -namespace kernel { -MS_REG_GPU_KERNEL_TWO(CalculateNowrapCrd, - KernelAttr() - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeInt32) - .AddOutputAttr(kNumberTypeFloat32), - CalculateNoWrapCrdGpuKernelMod, float, int) -} // namespace kernel -} // namespace mindspore diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/crdmcmap/cal_no_wrap_crd_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/crdmcmap/cal_no_wrap_crd_kernel.h deleted file mode 100644 index 959c3d0674d..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/crdmcmap/cal_no_wrap_crd_kernel.h +++ /dev/null @@ -1,85 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONG_CRDMCMAP_CAL_NO_WRAP_CRD_KERNEL_H_ -#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONG_CRDMCMAP_CAL_NO_WRAP_CRD_KERNEL_H_ - -#include "plugin/device/gpu/kernel/cuda_impl/sponge/crdmcmap/cal_no_wrap_crd_impl.cuh" - -#include -#include -#include -#include - -#include "plugin/device/gpu/kernel/gpu_kernel.h" -#include "plugin/device/gpu/kernel/gpu_kernel_factory.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" - -namespace mindspore { -namespace kernel { -template -class CalculateNoWrapCrdGpuKernelMod : public DeprecatedNativeGpuKernelMod { - public: - CalculateNoWrapCrdGpuKernelMod() : ele_crd(1) {} - ~CalculateNoWrapCrdGpuKernelMod() override = default; - - bool Init(const CNodePtr &kernel_node) override { - // get bond_numbers - kernel_node_ = kernel_node; - atom_numbers = static_cast(GetAttr(kernel_node, "atom_numbers")); - auto shape_crd = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto shape_box = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - auto shape_box_map_times = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); - - ele_crd *= SizeOf(shape_crd); - ele_box *= SizeOf(shape_box); - ele_box_map_times *= SizeOf(shape_box_map_times); - - InitSizeLists(); - return true; - } - - bool Launch(const std::vector &inputs, const std::vector &, - const std::vector &outputs, void *stream_ptr) override { - auto crd = GetDeviceAddress(inputs, 0); - auto box = GetDeviceAddress(inputs, 1); - auto box_map_times = GetDeviceAddress(inputs, 2); - - auto nowrap_crd = GetDeviceAddress(outputs, 0); - - calculatenowrapcrd(atom_numbers, box_map_times, box, crd, nowrap_crd, reinterpret_cast(stream_ptr)); - return true; - } - - protected: - void InitSizeLists() override { - input_size_list_.push_back(ele_crd * sizeof(T)); - input_size_list_.push_back(ele_box * sizeof(T)); - input_size_list_.push_back(ele_box_map_times * sizeof(T1)); - - output_size_list_.push_back(3 * atom_numbers * sizeof(T)); - } - - private: - size_t ele_crd = 1; - size_t ele_box = 1; - size_t ele_box_map_times = 1; - - int atom_numbers; -}; -} // namespace kernel -} // namespace mindspore -#endif // MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONG_CRDMCMAP_CAL_NO_WRAP_CRD_KERNEL_H_ diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/crdmcmap/refresh_boxmaptimes_kernel.cc b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/crdmcmap/refresh_boxmaptimes_kernel.cc deleted file mode 100644 index 4e0c49179a5..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/crdmcmap/refresh_boxmaptimes_kernel.cc +++ /dev/null @@ -1,34 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -/** - * Note: - * RefreshBoxmapTimes. This is an experimental interface that is subject to change and/or deletion. - */ - -#include "plugin/device/gpu/kernel/sponge/crdmcmap/refresh_boxmaptimes_kernel.h" - -namespace mindspore { -namespace kernel { -MS_REG_GPU_KERNEL_TWO(RefreshBoxmapTimes, - KernelAttr() - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeInt32) - .AddOutputAttr(kNumberTypeFloat32), - RefreshBoxmaptimesGpuKernelMod, float, int) -} // namespace kernel -} // namespace mindspore diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/crdmcmap/refresh_boxmaptimes_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/crdmcmap/refresh_boxmaptimes_kernel.h deleted file mode 100644 index 8e8e9b156ca..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/crdmcmap/refresh_boxmaptimes_kernel.h +++ /dev/null @@ -1,89 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONG_CRDMCMAP_REFRESH_BOXMAPTIMES_KERNEL_H_ -#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONG_CRDMCMAP_REFRESH_BOXMAPTIMES_KERNEL_H_ - -#include "plugin/device/gpu/kernel/cuda_impl/sponge/crdmcmap/refresh_boxmaptimes_impl.cuh" - -#include -#include -#include -#include - -#include "plugin/device/gpu/kernel/gpu_kernel.h" -#include "plugin/device/gpu/kernel/gpu_kernel_factory.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" - -namespace mindspore { -namespace kernel { -template -class RefreshBoxmaptimesGpuKernelMod : public DeprecatedNativeGpuKernelMod { - public: - RefreshBoxmaptimesGpuKernelMod() : ele_crd(1) {} - ~RefreshBoxmaptimesGpuKernelMod() override = default; - - bool Init(const CNodePtr &kernel_node) override { - // get bond_numbers - kernel_node_ = kernel_node; - atom_numbers = static_cast(GetAttr(kernel_node, "atom_numbers")); - auto shape_crd = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto shape_old_crd = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - auto shape_box_length_inverse = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); - auto shape_box_map_times = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); - - ele_crd *= SizeOf(shape_crd); - ele_old_crd *= SizeOf(shape_old_crd); - ele_box_length_inverse *= SizeOf(shape_box_length_inverse); - ele_box_map_times *= SizeOf(shape_box_map_times); - - InitSizeLists(); - return true; - } - - bool Launch(const std::vector &inputs, const std::vector &, - const std::vector &outputs, void *stream_ptr) override { - auto crd = GetDeviceAddress(inputs, 0); - auto old_crd = GetDeviceAddress(inputs, 1); - auto box_length_inverse = GetDeviceAddress(inputs, 2); - auto box_map_times = GetDeviceAddress(inputs, 3); - - refresh_boxmaptimes(atom_numbers, box_length_inverse, crd, old_crd, box_map_times, - reinterpret_cast(stream_ptr)); - return true; - } - - protected: - void InitSizeLists() override { - input_size_list_.push_back(ele_crd * sizeof(T)); - input_size_list_.push_back(ele_old_crd * sizeof(T)); - input_size_list_.push_back(ele_box_length_inverse * sizeof(T)); - input_size_list_.push_back(ele_box_map_times * sizeof(T1)); - - output_size_list_.push_back(sizeof(T)); - } - - private: - size_t ele_crd = 1; - size_t ele_box_length_inverse = 1; - size_t ele_old_crd = 1; - size_t ele_box_map_times = 1; - - int atom_numbers; -}; -} // namespace kernel -} // namespace mindspore -#endif // MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONG_CRDMCMAP_CAL_NO_WRAP_CRD_KERNEL_H_ diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/dihedral/dihedral_atom_energy_kernel.cc b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/dihedral/dihedral_atom_energy_kernel.cc deleted file mode 100644 index 0af1b5608db..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/dihedral/dihedral_atom_energy_kernel.cc +++ /dev/null @@ -1,37 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "plugin/device/gpu/kernel/sponge/dihedral/dihedral_atom_energy_kernel.h" - -namespace mindspore { -namespace kernel { -MS_REG_GPU_KERNEL_TWO(DihedralAtomEnergy, - KernelAttr() - .AddInputAttr(kNumberTypeUInt32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddOutputAttr(kNumberTypeFloat32), - DihedralAtomEnergyGpuKernelMod, float, int) -} // namespace kernel -} // namespace mindspore diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/dihedral/dihedral_atom_energy_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/dihedral/dihedral_atom_energy_kernel.h deleted file mode 100644 index a51c6f230cc..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/dihedral/dihedral_atom_energy_kernel.h +++ /dev/null @@ -1,119 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONGE_DIHEDRAL_DIHEDRAL_ATOM_ENERGY_KERNEL_H_ -#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONGE_DIHEDRAL_DIHEDRAL_ATOM_ENERGY_KERNEL_H_ -#include -#include -#include -#include -#include "plugin/device/gpu/kernel/gpu_kernel.h" -#include "plugin/device/gpu/kernel/gpu_kernel_factory.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" -#include "plugin/device/gpu/kernel/cuda_impl/sponge/dihedral/dihedral_atom_energy_impl.cuh" -namespace mindspore { -namespace kernel { -template -class DihedralAtomEnergyGpuKernelMod : public DeprecatedNativeGpuKernelMod { - public: - DihedralAtomEnergyGpuKernelMod() : ele_uint_crd(1) {} - ~DihedralAtomEnergyGpuKernelMod() override = default; - - bool Init(const CNodePtr &kernel_node) override { - kernel_node_ = kernel_node; - dihedral_numbers = static_cast(GetAttr(kernel_node, "dihedral_numbers")); - auto shape_uint_crd = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto shape_scaler = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - auto shape_atom_a = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); - auto shape_atom_b = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); - auto shape_atom_c = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 4); - auto shape_atom_d = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 5); - auto shape_ipn = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 6); - auto shape_pk = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 7); - auto shape_gamc = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 8); - auto shape_gams = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 9); - auto shape_pn = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 10); - - ele_uint_crd *= SizeOf(shape_uint_crd); - ele_scaler *= SizeOf(shape_scaler); - ele_atom_a *= SizeOf(shape_atom_a); - ele_atom_b *= SizeOf(shape_atom_b); - ele_atom_c *= SizeOf(shape_atom_c); - ele_atom_d *= SizeOf(shape_atom_d); - ele_ipn *= SizeOf(shape_ipn); - ele_pk *= SizeOf(shape_pk); - ele_gamc *= SizeOf(shape_gamc); - ele_gams *= SizeOf(shape_gams); - ele_pn *= SizeOf(shape_pn); - InitSizeLists(); - return true; - } - - bool Launch(const std::vector &inputs, const std::vector &, - const std::vector &outputs, void *stream_ptr) override { - auto uint_crd_f = GetDeviceAddress(inputs, 0); - auto scaler_f = GetDeviceAddress(inputs, 1); - auto atom_a = GetDeviceAddress(inputs, 2); - auto atom_b = GetDeviceAddress(inputs, 3); - auto atom_c = GetDeviceAddress(inputs, 4); - auto atom_d = GetDeviceAddress(inputs, 5); - auto ipn = GetDeviceAddress(inputs, 6); - auto pk = GetDeviceAddress(inputs, 7); - auto gamc = GetDeviceAddress(inputs, 8); - auto gams = GetDeviceAddress(inputs, 9); - auto pn = GetDeviceAddress(inputs, 10); - - auto ene = GetDeviceAddress(outputs, 0); - DihedralAtomEnergy(dihedral_numbers, ele_uint_crd, uint_crd_f, scaler_f, atom_a, atom_b, atom_c, atom_d, ipn, pk, - gamc, gams, pn, ene, reinterpret_cast(stream_ptr)); - return true; - } - - protected: - void InitSizeLists() override { - input_size_list_.push_back(ele_uint_crd * sizeof(T1)); - input_size_list_.push_back(ele_scaler * sizeof(T)); - input_size_list_.push_back(ele_atom_a * sizeof(T1)); - input_size_list_.push_back(ele_atom_b * sizeof(T1)); - input_size_list_.push_back(ele_atom_c * sizeof(T1)); - input_size_list_.push_back(ele_atom_d * sizeof(T1)); - input_size_list_.push_back(ele_ipn * sizeof(T1)); - input_size_list_.push_back(ele_pk * sizeof(T)); - input_size_list_.push_back(ele_gamc * sizeof(T)); - input_size_list_.push_back(ele_gams * sizeof(T)); - input_size_list_.push_back(ele_pn * sizeof(T)); - - output_size_list_.push_back(ele_uint_crd * sizeof(T)); - } - - private: - size_t ele_uint_crd = 1; - size_t ele_scaler = 1; - size_t ele_atom_a = 1; - size_t ele_atom_b = 1; - size_t ele_atom_c = 1; - size_t ele_atom_d = 1; - size_t ele_ipn = 1; - size_t ele_pk = 1; - size_t ele_gamc = 1; - size_t ele_gams = 1; - size_t ele_pn = 1; - - int dihedral_numbers; -}; -} // namespace kernel -} // namespace mindspore -#endif diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/dihedral/dihedral_energy_kernel.cc b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/dihedral/dihedral_energy_kernel.cc deleted file mode 100644 index af84b47b29b..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/dihedral/dihedral_energy_kernel.cc +++ /dev/null @@ -1,37 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "plugin/device/gpu/kernel/sponge/dihedral/dihedral_energy_kernel.h" - -namespace mindspore { -namespace kernel { -MS_REG_GPU_KERNEL_TWO(DihedralEnergy, - KernelAttr() - .AddInputAttr(kNumberTypeUInt32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddOutputAttr(kNumberTypeFloat32), - DihedralEnergyGpuKernelMod, float, int) -} // namespace kernel -} // namespace mindspore diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/dihedral/dihedral_energy_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/dihedral/dihedral_energy_kernel.h deleted file mode 100644 index e5a42d440f3..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/dihedral/dihedral_energy_kernel.h +++ /dev/null @@ -1,119 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONGE_DIHEDRAL_DIHEDRAL_ENERGY_KERNEL_H_ -#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONGE_DIHEDRAL_DIHEDRAL_ENERGY_KERNEL_H_ -#include -#include -#include -#include -#include "plugin/device/gpu/kernel/gpu_kernel.h" -#include "plugin/device/gpu/kernel/gpu_kernel_factory.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" -#include "plugin/device/gpu/kernel/cuda_impl/sponge/dihedral/dihedral_energy_impl.cuh" -namespace mindspore { -namespace kernel { -template -class DihedralEnergyGpuKernelMod : public DeprecatedNativeGpuKernelMod { - public: - DihedralEnergyGpuKernelMod() : ele_uint_crd(1) {} - ~DihedralEnergyGpuKernelMod() override = default; - - bool Init(const CNodePtr &kernel_node) override { - kernel_node_ = kernel_node; - dihedral_numbers = static_cast(GetAttr(kernel_node, "dihedral_numbers")); - auto shape_uint_crd = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto shape_scaler = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - auto shape_atom_a = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); - auto shape_atom_b = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); - auto shape_atom_c = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 4); - auto shape_atom_d = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 5); - auto shape_ipn = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 6); - auto shape_pk = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 7); - auto shape_gamc = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 8); - auto shape_gams = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 9); - auto shape_pn = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 10); - - ele_uint_crd *= SizeOf(shape_uint_crd); - ele_scaler *= SizeOf(shape_scaler); - ele_atom_a *= SizeOf(shape_atom_a); - ele_atom_b *= SizeOf(shape_atom_b); - ele_atom_c *= SizeOf(shape_atom_c); - ele_atom_d *= SizeOf(shape_atom_d); - ele_ipn *= SizeOf(shape_ipn); - ele_pk *= SizeOf(shape_pk); - ele_gamc *= SizeOf(shape_gamc); - ele_gams *= SizeOf(shape_gams); - ele_pn *= SizeOf(shape_pn); - InitSizeLists(); - return true; - } - - bool Launch(const std::vector &inputs, const std::vector &, - const std::vector &outputs, void *stream_ptr) override { - auto uint_crd_f = GetDeviceAddress(inputs, 0); - auto scaler_f = GetDeviceAddress(inputs, 1); - auto atom_a = GetDeviceAddress(inputs, 2); - auto atom_b = GetDeviceAddress(inputs, 3); - auto atom_c = GetDeviceAddress(inputs, 4); - auto atom_d = GetDeviceAddress(inputs, 5); - auto ipn = GetDeviceAddress(inputs, 6); - auto pk = GetDeviceAddress(inputs, 7); - auto gamc = GetDeviceAddress(inputs, 8); - auto gams = GetDeviceAddress(inputs, 9); - auto pn = GetDeviceAddress(inputs, 10); - - auto ene = GetDeviceAddress(outputs, 0); - DihedralEnergy(dihedral_numbers, uint_crd_f, scaler_f, atom_a, atom_b, atom_c, atom_d, ipn, pk, gamc, gams, pn, ene, - reinterpret_cast(stream_ptr)); - return true; - } - - protected: - void InitSizeLists() override { - input_size_list_.push_back(ele_uint_crd * sizeof(T1)); - input_size_list_.push_back(ele_scaler * sizeof(T)); - input_size_list_.push_back(ele_atom_a * sizeof(T1)); - input_size_list_.push_back(ele_atom_b * sizeof(T1)); - input_size_list_.push_back(ele_atom_c * sizeof(T1)); - input_size_list_.push_back(ele_atom_d * sizeof(T1)); - input_size_list_.push_back(ele_ipn * sizeof(T1)); - input_size_list_.push_back(ele_pk * sizeof(T)); - input_size_list_.push_back(ele_gamc * sizeof(T)); - input_size_list_.push_back(ele_gams * sizeof(T)); - input_size_list_.push_back(ele_pn * sizeof(T)); - - output_size_list_.push_back(dihedral_numbers * sizeof(T)); - } - - private: - size_t ele_uint_crd = 1; - size_t ele_scaler = 1; - size_t ele_atom_a = 1; - size_t ele_atom_b = 1; - size_t ele_atom_c = 1; - size_t ele_atom_d = 1; - size_t ele_ipn = 1; - size_t ele_pk = 1; - size_t ele_gamc = 1; - size_t ele_gams = 1; - size_t ele_pn = 1; - - int dihedral_numbers; -}; -} // namespace kernel -} // namespace mindspore -#endif diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/dihedral/dihedral_force_kernel.cc b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/dihedral/dihedral_force_kernel.cc deleted file mode 100644 index 6802a89c475..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/dihedral/dihedral_force_kernel.cc +++ /dev/null @@ -1,37 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "plugin/device/gpu/kernel/sponge/dihedral/dihedral_force_kernel.h" - -namespace mindspore { -namespace kernel { -MS_REG_GPU_KERNEL_TWO(DihedralForce, - KernelAttr() - .AddInputAttr(kNumberTypeUInt32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddOutputAttr(kNumberTypeFloat32), - DihedralForceGpuKernelMod, float, int) -} // namespace kernel -} // namespace mindspore diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/dihedral/dihedral_force_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/dihedral/dihedral_force_kernel.h deleted file mode 100644 index 945c8a5588a..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/dihedral/dihedral_force_kernel.h +++ /dev/null @@ -1,119 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONGE_DIHEDRAL_DIHEDRAL_FORCE_KERNEL_H_ -#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONGE_DIHEDRAL_DIHEDRAL_FORCE_KERNEL_H_ -#include -#include -#include -#include -#include "plugin/device/gpu/kernel/gpu_kernel.h" -#include "plugin/device/gpu/kernel/gpu_kernel_factory.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" -#include "plugin/device/gpu/kernel/cuda_impl/sponge/dihedral/dihedral_force_impl.cuh" -namespace mindspore { -namespace kernel { -template -class DihedralForceGpuKernelMod : public DeprecatedNativeGpuKernelMod { - public: - DihedralForceGpuKernelMod() : ele_uint_crd(1) {} - ~DihedralForceGpuKernelMod() override = default; - - bool Init(const CNodePtr &kernel_node) override { - kernel_node_ = kernel_node; - dihedral_numbers = static_cast(GetAttr(kernel_node, "dihedral_numbers")); - auto shape_uint_crd = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto shape_scaler = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - auto shape_atom_a = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); - auto shape_atom_b = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); - auto shape_atom_c = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 4); - auto shape_atom_d = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 5); - auto shape_ipn = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 6); - auto shape_pk = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 7); - auto shape_gamc = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 8); - auto shape_gams = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 9); - auto shape_pn = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 10); - - ele_uint_crd *= SizeOf(shape_uint_crd); - ele_scaler *= SizeOf(shape_scaler); - ele_atom_a *= SizeOf(shape_atom_a); - ele_atom_b *= SizeOf(shape_atom_b); - ele_atom_c *= SizeOf(shape_atom_c); - ele_atom_d *= SizeOf(shape_atom_d); - ele_ipn *= SizeOf(shape_ipn); - ele_pk *= SizeOf(shape_pk); - ele_gamc *= SizeOf(shape_gamc); - ele_gams *= SizeOf(shape_gams); - ele_pn *= SizeOf(shape_pn); - InitSizeLists(); - return true; - } - - bool Launch(const std::vector &inputs, const std::vector &, - const std::vector &outputs, void *stream_ptr) override { - auto uint_crd_f = GetDeviceAddress(inputs, 0); - auto scaler_f = GetDeviceAddress(inputs, 1); - auto atom_a = GetDeviceAddress(inputs, 2); - auto atom_b = GetDeviceAddress(inputs, 3); - auto atom_c = GetDeviceAddress(inputs, 4); - auto atom_d = GetDeviceAddress(inputs, 5); - auto ipn = GetDeviceAddress(inputs, 6); - auto pk = GetDeviceAddress(inputs, 7); - auto gamc = GetDeviceAddress(inputs, 8); - auto gams = GetDeviceAddress(inputs, 9); - auto pn = GetDeviceAddress(inputs, 10); - - auto frc_f = GetDeviceAddress(outputs, 0); - DihedralForce(dihedral_numbers, ele_uint_crd, uint_crd_f, scaler_f, atom_a, atom_b, atom_c, atom_d, ipn, pk, gamc, - gams, pn, frc_f, reinterpret_cast(stream_ptr)); - return true; - } - - protected: - void InitSizeLists() override { - input_size_list_.push_back(ele_uint_crd * sizeof(T1)); - input_size_list_.push_back(ele_scaler * sizeof(T)); - input_size_list_.push_back(ele_atom_a * sizeof(T1)); - input_size_list_.push_back(ele_atom_b * sizeof(T1)); - input_size_list_.push_back(ele_atom_c * sizeof(T1)); - input_size_list_.push_back(ele_atom_d * sizeof(T1)); - input_size_list_.push_back(ele_ipn * sizeof(T1)); - input_size_list_.push_back(ele_pk * sizeof(T)); - input_size_list_.push_back(ele_gamc * sizeof(T)); - input_size_list_.push_back(ele_gams * sizeof(T)); - input_size_list_.push_back(ele_pn * sizeof(T)); - - output_size_list_.push_back(ele_uint_crd * 3 * sizeof(T)); - } - - private: - size_t ele_uint_crd = 1; - size_t ele_scaler = 1; - size_t ele_atom_a = 1; - size_t ele_atom_b = 1; - size_t ele_atom_c = 1; - size_t ele_atom_d = 1; - size_t ele_ipn = 1; - size_t ele_pk = 1; - size_t ele_gamc = 1; - size_t ele_gams = 1; - size_t ele_pn = 1; - - int dihedral_numbers; -}; -} // namespace kernel -} // namespace mindspore -#endif diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/dihedral/dihedral_force_with_atom_energy_kernel.cc b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/dihedral/dihedral_force_with_atom_energy_kernel.cc deleted file mode 100644 index 04436b949a9..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/dihedral/dihedral_force_with_atom_energy_kernel.cc +++ /dev/null @@ -1,38 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "plugin/device/gpu/kernel/sponge/dihedral/dihedral_force_with_atom_energy_kernel.h" - -namespace mindspore { -namespace kernel { -MS_REG_GPU_KERNEL_TWO(DihedralForceWithAtomEnergy, - KernelAttr() - .AddInputAttr(kNumberTypeUInt32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddOutputAttr(kNumberTypeFloat32) - .AddOutputAttr(kNumberTypeFloat32), - DihedralForceWithAtomEnergyGpuKernelMod, float, int) -} // namespace kernel -} // namespace mindspore diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/dihedral/dihedral_force_with_atom_energy_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/dihedral/dihedral_force_with_atom_energy_kernel.h deleted file mode 100644 index df2291272f0..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/dihedral/dihedral_force_with_atom_energy_kernel.h +++ /dev/null @@ -1,121 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONGE_DIHEDRAL_DIHEDRAL_FORCE_WITH_ATOM_ENERGY_KERNEL_H_ -#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONGE_DIHEDRAL_DIHEDRAL_FORCE_WITH_ATOM_ENERGY_KERNEL_H_ -#include -#include -#include -#include -#include "plugin/device/gpu/kernel/gpu_kernel.h" -#include "plugin/device/gpu/kernel/gpu_kernel_factory.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" -#include "plugin/device/gpu/kernel/cuda_impl/sponge/dihedral/dihedral_force_with_atom_energy_impl.cuh" -namespace mindspore { -namespace kernel { -template -class DihedralForceWithAtomEnergyGpuKernelMod : public DeprecatedNativeGpuKernelMod { - public: - DihedralForceWithAtomEnergyGpuKernelMod() : ele_uint_crd(1) {} - ~DihedralForceWithAtomEnergyGpuKernelMod() override = default; - - bool Init(const CNodePtr &kernel_node) override { - kernel_node_ = kernel_node; - dihedral_numbers = static_cast(GetAttr(kernel_node, "dihedral_numbers")); - auto shape_uint_crd = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto shape_scaler = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - auto shape_atom_a = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); - auto shape_atom_b = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); - auto shape_atom_c = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 4); - auto shape_atom_d = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 5); - auto shape_ipn = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 6); - auto shape_pk = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 7); - auto shape_gamc = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 8); - auto shape_gams = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 9); - auto shape_pn = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 10); - - ele_uint_crd *= SizeOf(shape_uint_crd); - ele_scaler *= SizeOf(shape_scaler); - ele_atom_a *= SizeOf(shape_atom_a); - ele_atom_b *= SizeOf(shape_atom_b); - ele_atom_c *= SizeOf(shape_atom_c); - ele_atom_d *= SizeOf(shape_atom_d); - ele_ipn *= SizeOf(shape_ipn); - ele_pk *= SizeOf(shape_pk); - ele_gamc *= SizeOf(shape_gamc); - ele_gams *= SizeOf(shape_gams); - ele_pn *= SizeOf(shape_pn); - InitSizeLists(); - return true; - } - - bool Launch(const std::vector &inputs, const std::vector &, - const std::vector &outputs, void *stream_ptr) override { - auto uint_crd_f = GetDeviceAddress(inputs, 0); - auto scaler_f = GetDeviceAddress(inputs, 1); - auto atom_a = GetDeviceAddress(inputs, 2); - auto atom_b = GetDeviceAddress(inputs, 3); - auto atom_c = GetDeviceAddress(inputs, 4); - auto atom_d = GetDeviceAddress(inputs, 5); - auto ipn = GetDeviceAddress(inputs, 6); - auto pk = GetDeviceAddress(inputs, 7); - auto gamc = GetDeviceAddress(inputs, 8); - auto gams = GetDeviceAddress(inputs, 9); - auto pn = GetDeviceAddress(inputs, 10); - - auto frc_f = GetDeviceAddress(outputs, 0); - auto ene = GetDeviceAddress(outputs, 1); - DihedralForceWithAtomEnergy(dihedral_numbers, ele_uint_crd, uint_crd_f, scaler_f, atom_a, atom_b, atom_c, atom_d, - ipn, pk, gamc, gams, pn, frc_f, ene, reinterpret_cast(stream_ptr)); - return true; - } - - protected: - void InitSizeLists() override { - input_size_list_.push_back(ele_uint_crd * sizeof(T1)); - input_size_list_.push_back(ele_scaler * sizeof(T)); - input_size_list_.push_back(ele_atom_a * sizeof(T1)); - input_size_list_.push_back(ele_atom_b * sizeof(T1)); - input_size_list_.push_back(ele_atom_c * sizeof(T1)); - input_size_list_.push_back(ele_atom_d * sizeof(T1)); - input_size_list_.push_back(ele_ipn * sizeof(T1)); - input_size_list_.push_back(ele_pk * sizeof(T)); - input_size_list_.push_back(ele_gamc * sizeof(T)); - input_size_list_.push_back(ele_gams * sizeof(T)); - input_size_list_.push_back(ele_pn * sizeof(T)); - - output_size_list_.push_back(ele_uint_crd * 3 * sizeof(T)); - output_size_list_.push_back(ele_uint_crd * sizeof(T)); - } - - private: - size_t ele_uint_crd = 1; - size_t ele_scaler = 1; - size_t ele_atom_a = 1; - size_t ele_atom_b = 1; - size_t ele_atom_c = 1; - size_t ele_atom_d = 1; - size_t ele_ipn = 1; - size_t ele_pk = 1; - size_t ele_gamc = 1; - size_t ele_gams = 1; - size_t ele_pn = 1; - - int dihedral_numbers; -}; -} // namespace kernel -} // namespace mindspore -#endif diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/lj/lj_direct_cf_force_with_lj_virial_direct_cf_energy_kernel.cc b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/lj/lj_direct_cf_force_with_lj_virial_direct_cf_energy_kernel.cc deleted file mode 100644 index 4faf780a09f..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/lj/lj_direct_cf_force_with_lj_virial_direct_cf_energy_kernel.cc +++ /dev/null @@ -1,39 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -/** - * Note: - * LJForceWithVirialEnergy. This is an experimental interface that is subject to change and/or deletion. - */ -#include "plugin/device/gpu/kernel/sponge/lj/lj_direct_cf_force_with_lj_virial_direct_cf_energy_kernel.h" - -namespace mindspore { -namespace kernel { -MS_REG_GPU_KERNEL_THREE(LJForceWithVirialEnergy, - KernelAttr() - .AddInputAttr(kNumberTypeUInt32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddOutputAttr(kNumberTypeFloat32) - .AddOutputAttr(kNumberTypeFloat32) - .AddOutputAttr(kNumberTypeFloat32), - LJForceWithVirialEnergyGpuKernelMod, float, int, unsigned int) -} // namespace kernel -} // namespace mindspore diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/lj/lj_direct_cf_force_with_lj_virial_direct_cf_energy_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/lj/lj_direct_cf_force_with_lj_virial_direct_cf_energy_kernel.h deleted file mode 100644 index 8c562943f2e..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/lj/lj_direct_cf_force_with_lj_virial_direct_cf_energy_kernel.h +++ /dev/null @@ -1,135 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -/** - * Note: - * LJForceWithVirialEnergy. This is an experimental interface that is subject to change and/or deletion. - */ -#ifndef MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_LJ_DIRECT_CF_FORCE_WITH_LJ_VIRIAL_DIRECT_CF_ENERGY_KERNEL_H_ -#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_LJ_DIRECT_CF_FORCE_WITH_LJ_VIRIAL_DIRECT_CF_ENERGY_KERNEL_H_ -#include -#include -#include -#include -#include "plugin/device/gpu/kernel/gpu_kernel.h" -#include "plugin/device/gpu/kernel/gpu_kernel_factory.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" -#include "plugin/device/gpu/kernel/cuda_impl/sponge/lj/lj_direct_cf_force_with_lj_virial_direct_cf_energy_impl.cuh" -namespace mindspore { -namespace kernel { -template -class LJForceWithVirialEnergyGpuKernelMod : public DeprecatedNativeGpuKernelMod { - public: - LJForceWithVirialEnergyGpuKernelMod() : ele_uint_crd(1) {} - ~LJForceWithVirialEnergyGpuKernelMod() override = default; - - bool Init(const CNodePtr &kernel_node) override { - kernel_node_ = kernel_node; - atom_numbers = static_cast(GetAttr(kernel_node, "atom_numbers")); - max_neighbor_numbers = static_cast(GetAttr(kernel_node, "max_neighbor_numbers")); - cutoff = static_cast(GetAttr(kernel_node, "cutoff")); - pme_beta = static_cast(GetAttr(kernel_node, "pme_beta")); - - auto shape_uint_crd = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto shape_LJtype = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - auto shape_charge = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); - auto shape_scaler = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); - auto shape_nl_numbers = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 4); - auto shape_nl_serial = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 5); - auto shape_d_LJ_a = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 6); - auto shape_d_LJ_b = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 7); - - ele_uint_crd *= SizeOf(shape_uint_crd); - ele_LJtype *= SizeOf(shape_LJtype); - ele_charge *= SizeOf(shape_charge); - ele_scaler *= SizeOf(shape_scaler); - ele_d_LJ_a *= SizeOf(shape_d_LJ_a); - ele_d_LJ_b *= SizeOf(shape_d_LJ_b); - - InitSizeLists(); - return true; - } - - bool Launch(const std::vector &inputs, const std::vector &workspace, - const std::vector &outputs, void *stream_ptr) override { - auto uint_crd = GetDeviceAddress(inputs, 0); - auto LJtype = GetDeviceAddress(inputs, 1); - auto charge = GetDeviceAddress(inputs, 2); - auto scaler = GetDeviceAddress(inputs, 3); - auto nl_numbers = GetDeviceAddress(inputs, 4); - auto nl_serial = GetDeviceAddress(inputs, 5); - auto d_LJ_a = GetDeviceAddress(inputs, 6); - auto d_LJ_b = GetDeviceAddress(inputs, 7); - - auto uint_crd_with_LJ = GetDeviceAddress(workspace, 0); - auto nl = GetDeviceAddress(workspace, 1); - - auto frc = GetDeviceAddress(outputs, 0); - auto atom_lj_virial = GetDeviceAddress(outputs, 1); - auto atom_energy = GetDeviceAddress(outputs, 2); - LJ_Direct_CF_Force_With_LJ_Virial_Direct_CF_Energy(atom_numbers, cutoff, pme_beta, uint_crd, LJtype, charge, scaler, - uint_crd_with_LJ, nl_numbers, nl_serial, nl, d_LJ_a, d_LJ_b, frc, - atom_lj_virial, atom_energy, max_neighbor_numbers, - reinterpret_cast(stream_ptr)); - return true; - } - - protected: - void InitSizeLists() override { - input_size_list_.push_back(ele_uint_crd * sizeof(T2)); - input_size_list_.push_back(ele_LJtype * sizeof(T1)); - input_size_list_.push_back(ele_charge * sizeof(T)); - input_size_list_.push_back(ele_scaler * sizeof(T)); - input_size_list_.push_back(atom_numbers * sizeof(T1)); - input_size_list_.push_back(max_neighbor_numbers * sizeof(T1)); - input_size_list_.push_back(ele_d_LJ_a * sizeof(T)); - input_size_list_.push_back(ele_d_LJ_b * sizeof(T)); - - workspace_size_list_.push_back(atom_numbers * max_neighbor_numbers * sizeof(T1)); - workspace_size_list_.push_back(atom_numbers * sizeof(UINT_VECTOR_LJ_TYPE)); - - output_size_list_.push_back(atom_numbers * 3 * sizeof(T)); - output_size_list_.push_back(atom_numbers * sizeof(T)); - output_size_list_.push_back(atom_numbers * sizeof(T)); - } - - private: - size_t ele_uint_crd = 1; - size_t ele_LJtype = 1; - size_t ele_charge = 1; - size_t ele_scaler = 1; - size_t ele_nl = 1; - size_t ele_d_LJ_a = 1; - size_t ele_d_LJ_b = 1; - - int atom_numbers; - int max_neighbor_numbers; - float pme_beta; - float cutoff; - struct UINT_VECTOR_LJ_TYPE { - unsigned int uint_x; - unsigned int uint_y; - unsigned int uint_z; - int LJ_type; - float charge; - }; - struct NEIGHBOR_LIST { - int atom_numbers; - int *atom_serial; - }; -}; -} // namespace kernel -} // namespace mindspore -#endif diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/lj/lj_energy_kernel.cc b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/lj/lj_energy_kernel.cc deleted file mode 100644 index 361e853cf58..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/lj/lj_energy_kernel.cc +++ /dev/null @@ -1,33 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -#include "plugin/device/gpu/kernel/sponge/lj/lj_energy_kernel.h" - -namespace mindspore { -namespace kernel { -MS_REG_GPU_KERNEL_TWO(LJEnergy, - KernelAttr() - .AddInputAttr(kNumberTypeUInt32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddOutputAttr(kNumberTypeFloat32), - LJEnergyGpuKernelMod, float, int) -} // namespace kernel -} // namespace mindspore diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/lj/lj_energy_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/lj/lj_energy_kernel.h deleted file mode 100644 index 6a5c8c217fd..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/lj/lj_energy_kernel.h +++ /dev/null @@ -1,122 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -#ifndef MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONGE_LJ_LJ_ENERGY_KERNEL_H_ -#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONGE_LJ_LJ_ENERGY_KERNEL_H_ -#include -#include -#include -#include -#include "plugin/device/gpu/kernel/gpu_kernel.h" -#include "plugin/device/gpu/kernel/gpu_kernel_factory.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" -#include "plugin/device/gpu/kernel/cuda_impl/sponge/lj/lj_energy_impl.cuh" -namespace mindspore { -namespace kernel { -template -class LJEnergyGpuKernelMod : public DeprecatedNativeGpuKernelMod { - public: - LJEnergyGpuKernelMod() : ele_uint_crd(1) {} - ~LJEnergyGpuKernelMod() override = default; - - bool Init(const CNodePtr &kernel_node) override { - kernel_node_ = kernel_node; - atom_numbers = static_cast(GetAttr(kernel_node, "atom_numbers")); - cutoff_square = static_cast(GetAttr(kernel_node, "cutoff_square")); - - auto shape_uint_crd = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto shape_LJtype = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - auto shape_charge = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); - auto shape_scaler = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); - auto shape_nl_numbers = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 4); - auto shape_nl_serial = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 5); - auto shape_d_LJ_a = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 6); - auto shape_d_LJ_b = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 7); - - ele_uint_crd *= SizeOf(shape_uint_crd); - ele_LJtype *= SizeOf(shape_LJtype); - ele_charge *= SizeOf(shape_charge); - ele_scaler *= SizeOf(shape_scaler); - ele_d_LJ_a *= SizeOf(shape_d_LJ_a); - ele_d_LJ_b *= SizeOf(shape_d_LJ_b); - - InitSizeLists(); - return true; - } - - bool Launch(const std::vector &inputs, const std::vector &workspace, - const std::vector &outputs, void *stream_ptr) override { - auto uint_crd = GetDeviceAddress(inputs, 0); - auto LJtype = GetDeviceAddress(inputs, 1); - auto charge = GetDeviceAddress(inputs, 2); - auto scaler = GetDeviceAddress(inputs, 3); - auto nl_numbers = GetDeviceAddress(inputs, 4); - auto nl_serial = GetDeviceAddress(inputs, 5); - auto d_LJ_a = GetDeviceAddress(inputs, 6); - auto d_LJ_b = GetDeviceAddress(inputs, 7); - - auto uint_crd_with_LJ = GetDeviceAddress(workspace, 0); - auto nl = GetDeviceAddress(workspace, 1); - - auto d_LJ_energy_atom = GetDeviceAddress(outputs, 0); - LJEnergy(atom_numbers, cutoff_square, uint_crd, LJtype, charge, scaler, uint_crd_with_LJ, nl_numbers, nl_serial, nl, - d_LJ_a, d_LJ_b, d_LJ_energy_atom, reinterpret_cast(stream_ptr)); - return true; - } - - protected: - void InitSizeLists() override { - input_size_list_.push_back(ele_uint_crd * sizeof(T1)); - input_size_list_.push_back(ele_LJtype * sizeof(T1)); - input_size_list_.push_back(ele_charge * sizeof(T)); - input_size_list_.push_back(ele_scaler * sizeof(T)); - input_size_list_.push_back(atom_numbers * sizeof(T1)); - input_size_list_.push_back(max_nl_numbers * sizeof(T1)); - input_size_list_.push_back(ele_d_LJ_a * sizeof(T)); - input_size_list_.push_back(ele_d_LJ_b * sizeof(T)); - - workspace_size_list_.push_back(atom_numbers * max_nl_numbers * sizeof(T1)); - workspace_size_list_.push_back(atom_numbers * sizeof(UINT_VECTOR_LJ_TYPE)); - - output_size_list_.push_back(atom_numbers * sizeof(T)); - } - - private: - size_t ele_uint_crd = 1; - size_t ele_LJtype = 1; - size_t ele_charge = 1; - size_t ele_scaler = 1; - size_t ele_nl = 1; - size_t ele_d_LJ_a = 1; - size_t ele_d_LJ_b = 1; - - int atom_numbers; - float cutoff_square; - int max_nl_numbers = 800; - struct UINT_VECTOR_LJ_TYPE { - unsigned int uint_x; - unsigned int uint_y; - unsigned int uint_z; - int LJ_type; - float charge; - }; - struct NEIGHBOR_LIST { - int atom_numbers; - int *atom_serial; - }; -}; -} // namespace kernel -} // namespace mindspore -#endif diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/lj/lj_force_kernel.cc b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/lj/lj_force_kernel.cc deleted file mode 100644 index 0887aade6ad..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/lj/lj_force_kernel.cc +++ /dev/null @@ -1,33 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -#include "plugin/device/gpu/kernel/sponge/lj/lj_force_kernel.h" - -namespace mindspore { -namespace kernel { -MS_REG_GPU_KERNEL_TWO(LJForce, - KernelAttr() - .AddInputAttr(kNumberTypeUInt32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddOutputAttr(kNumberTypeFloat32), - LJForceGpuKernelMod, float, int) -} // namespace kernel -} // namespace mindspore diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/lj/lj_force_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/lj/lj_force_kernel.h deleted file mode 100644 index 9c1f12a82dd..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/lj/lj_force_kernel.h +++ /dev/null @@ -1,121 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -#ifndef MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONGE_LJ_LJ_FORCE_KERNEL_H_ -#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONGE_LJ_LJ_FORCE_KERNEL_H_ -#include -#include -#include -#include -#include "plugin/device/gpu/kernel/gpu_kernel.h" -#include "plugin/device/gpu/kernel/gpu_kernel_factory.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" -#include "plugin/device/gpu/kernel/cuda_impl/sponge/lj/lj_force_impl.cuh" -namespace mindspore { -namespace kernel { -template -class LJForceGpuKernelMod : public DeprecatedNativeGpuKernelMod { - public: - LJForceGpuKernelMod() : ele_uint_crd(1) {} - ~LJForceGpuKernelMod() override = default; - - bool Init(const CNodePtr &kernel_node) override { - kernel_node_ = kernel_node; - atom_numbers = static_cast(GetAttr(kernel_node, "atom_numbers")); - cutoff_square = static_cast(GetAttr(kernel_node, "cutoff_square")); - - auto shape_uint_crd = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto shape_LJtype = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - auto shape_charge = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); - auto shape_scaler = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); - auto shape_nl_numbers = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 4); - auto shape_nl_serial = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 5); - auto shape_d_LJ_a = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 6); - auto shape_d_LJ_b = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 7); - - ele_uint_crd *= SizeOf(shape_uint_crd); - ele_LJtype *= SizeOf(shape_LJtype); - ele_charge *= SizeOf(shape_charge); - ele_scaler *= SizeOf(shape_scaler); - ele_d_LJ_a *= SizeOf(shape_d_LJ_a); - ele_d_LJ_b *= SizeOf(shape_d_LJ_b); - - InitSizeLists(); - return true; - } - - bool Launch(const std::vector &inputs, const std::vector &workspace, - const std::vector &outputs, void *stream_ptr) override { - auto uint_crd = GetDeviceAddress(inputs, 0); - auto LJtype = GetDeviceAddress(inputs, 1); - auto charge = GetDeviceAddress(inputs, 2); - auto scaler = GetDeviceAddress(inputs, 3); - auto nl_numbers = GetDeviceAddress(inputs, 4); - auto nl_serial = GetDeviceAddress(inputs, 5); - auto d_LJ_a = GetDeviceAddress(inputs, 6); - auto d_LJ_b = GetDeviceAddress(inputs, 7); - - auto uint_crd_with_LJ = GetDeviceAddress(workspace, 0); - auto nl = GetDeviceAddress(workspace, 1); - - auto frc = GetDeviceAddress(outputs, 0); - LJForce(atom_numbers, cutoff_square, uint_crd, LJtype, charge, scaler, uint_crd_with_LJ, nl_numbers, nl_serial, nl, - d_LJ_a, d_LJ_b, frc, reinterpret_cast(stream_ptr)); - return true; - } - - protected: - void InitSizeLists() override { - input_size_list_.push_back(ele_uint_crd * sizeof(T1)); - input_size_list_.push_back(ele_LJtype * sizeof(T1)); - input_size_list_.push_back(ele_charge * sizeof(T)); - input_size_list_.push_back(ele_scaler * sizeof(T)); - input_size_list_.push_back(atom_numbers * sizeof(T1)); - input_size_list_.push_back(max_nl_numbers * sizeof(T1)); - input_size_list_.push_back(ele_d_LJ_a * sizeof(T)); - input_size_list_.push_back(ele_d_LJ_b * sizeof(T)); - - workspace_size_list_.push_back(atom_numbers * max_nl_numbers * sizeof(T1)); - workspace_size_list_.push_back(atom_numbers * sizeof(UINT_VECTOR_LJ_TYPE)); - - output_size_list_.push_back(atom_numbers * 3 * sizeof(T)); - } - - private: - size_t ele_uint_crd = 1; - size_t ele_LJtype = 1; - size_t ele_charge = 1; - size_t ele_scaler = 1; - size_t ele_d_LJ_a = 1; - size_t ele_d_LJ_b = 1; - - int atom_numbers; - float cutoff_square; - int max_nl_numbers = 800; - struct UINT_VECTOR_LJ_TYPE { - unsigned int uint_x; - unsigned int uint_y; - unsigned int uint_z; - int LJ_type; - float charge; - }; - struct NEIGHBOR_LIST { - int atom_numbers; - int *atom_serial; - }; -}; -} // namespace kernel -} // namespace mindspore -#endif diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/lj/lj_force_with_pme_direct_force_kernel.cc b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/lj/lj_force_with_pme_direct_force_kernel.cc deleted file mode 100644 index 20eb3981f51..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/lj/lj_force_with_pme_direct_force_kernel.cc +++ /dev/null @@ -1,33 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -#include "plugin/device/gpu/kernel/sponge/lj/lj_force_with_pme_direct_force_kernel.h" - -namespace mindspore { -namespace kernel { -MS_REG_GPU_KERNEL_TWO(LJForceWithPMEDirectForce, - KernelAttr() - .AddInputAttr(kNumberTypeUInt32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddOutputAttr(kNumberTypeFloat32), - LJForceWithPMEDirectForceGpuKernelMod, float, int) -} // namespace kernel -} // namespace mindspore diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/lj/lj_force_with_pme_direct_force_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/lj/lj_force_with_pme_direct_force_kernel.h deleted file mode 100644 index b47a1de9c6e..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/lj/lj_force_with_pme_direct_force_kernel.h +++ /dev/null @@ -1,125 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -#ifndef MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONGE_LJ_LJ_FORCE_WITH_PME_DIRECT_FORCE_KERNEL_H_ -#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONGE_LJ_LJ_FORCE_WITH_PME_DIRECT_FORCE_KERNEL_H_ -#include -#include -#include -#include -#include "plugin/device/gpu/kernel/gpu_kernel.h" -#include "plugin/device/gpu/kernel/gpu_kernel_factory.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" -#include "plugin/device/gpu/kernel/cuda_impl/sponge/lj/lj_force_with_pme_direct_force_impl.cuh" -namespace mindspore { -namespace kernel { -template -class LJForceWithPMEDirectForceGpuKernelMod : public DeprecatedNativeGpuKernelMod { - public: - LJForceWithPMEDirectForceGpuKernelMod() : ele_uint_crd(1) {} - ~LJForceWithPMEDirectForceGpuKernelMod() override = default; - - bool Init(const CNodePtr &kernel_node) override { - kernel_node_ = kernel_node; - atom_numbers = static_cast(GetAttr(kernel_node, "atom_numbers")); - cutoff = static_cast(GetAttr(kernel_node, "cutoff")); - pme_beta = static_cast(GetAttr(kernel_node, "pme_beta")); - - auto shape_uint_crd = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto shape_LJtype = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - auto shape_charge = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); - auto shape_scaler = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); - auto shape_nl_numbers = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 4); - auto shape_nl_serial = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 5); - auto shape_d_LJ_a = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 6); - auto shape_d_LJ_b = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 7); - - ele_uint_crd *= SizeOf(shape_uint_crd); - ele_LJtype *= SizeOf(shape_LJtype); - ele_charge *= SizeOf(shape_charge); - ele_scaler *= SizeOf(shape_scaler); - ele_d_LJ_a *= SizeOf(shape_d_LJ_a); - ele_d_LJ_b *= SizeOf(shape_d_LJ_b); - - InitSizeLists(); - return true; - } - - bool Launch(const std::vector &inputs, const std::vector &workspace, - const std::vector &outputs, void *stream_ptr) override { - auto uint_crd = GetDeviceAddress(inputs, 0); - auto LJtype = GetDeviceAddress(inputs, 1); - auto charge = GetDeviceAddress(inputs, 2); - auto scaler = GetDeviceAddress(inputs, 3); - auto nl_numbers = GetDeviceAddress(inputs, 4); - auto nl_serial = GetDeviceAddress(inputs, 5); - auto d_LJ_a = GetDeviceAddress(inputs, 6); - auto d_LJ_b = GetDeviceAddress(inputs, 7); - - auto uint_crd_with_LJ = GetDeviceAddress(workspace, 0); - auto nl = GetDeviceAddress(workspace, 1); - - auto frc = GetDeviceAddress(outputs, 0); - LJForceWithPMEDirectForce(atom_numbers, cutoff, pme_beta, uint_crd, LJtype, charge, scaler, uint_crd_with_LJ, - nl_numbers, nl_serial, nl, d_LJ_a, d_LJ_b, frc, - reinterpret_cast(stream_ptr)); - return true; - } - - protected: - void InitSizeLists() override { - input_size_list_.push_back(ele_uint_crd * sizeof(T1)); - input_size_list_.push_back(ele_LJtype * sizeof(T1)); - input_size_list_.push_back(ele_charge * sizeof(T)); - input_size_list_.push_back(ele_scaler * sizeof(T)); - input_size_list_.push_back(atom_numbers * sizeof(T1)); - input_size_list_.push_back(max_nl_numbers * sizeof(T1)); - input_size_list_.push_back(ele_d_LJ_a * sizeof(T)); - input_size_list_.push_back(ele_d_LJ_b * sizeof(T)); - - workspace_size_list_.push_back(atom_numbers * max_nl_numbers * sizeof(T1)); - workspace_size_list_.push_back(atom_numbers * sizeof(UINT_VECTOR_LJ_TYPE)); - - output_size_list_.push_back(atom_numbers * 3 * sizeof(T)); - } - - private: - size_t ele_uint_crd = 1; - size_t ele_LJtype = 1; - size_t ele_charge = 1; - size_t ele_scaler = 1; - size_t ele_nl = 1; - size_t ele_d_LJ_a = 1; - size_t ele_d_LJ_b = 1; - - int atom_numbers; - float pme_beta; - float cutoff; - int max_nl_numbers = 800; - struct UINT_VECTOR_LJ_TYPE { - unsigned int uint_x; - unsigned int uint_y; - unsigned int uint_z; - int LJ_type; - float charge; - }; - struct NEIGHBOR_LIST { - int atom_numbers; - int *atom_serial; - }; -}; -} // namespace kernel -} // namespace mindspore -#endif diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/lj/lj_force_with_pme_direct_force_update_kernel.cc b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/lj/lj_force_with_pme_direct_force_update_kernel.cc deleted file mode 100644 index a7c9664e870..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/lj/lj_force_with_pme_direct_force_update_kernel.cc +++ /dev/null @@ -1,38 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -/** - * Note: - * LJForceWithPMEDirectForceUpdate. This is an experimental interface that is subject to change and/or deletion. - */ -#include "plugin/device/gpu/kernel/sponge/lj/lj_force_with_pme_direct_force_update_kernel.h" - -namespace mindspore { -namespace kernel { -MS_REG_GPU_KERNEL_TWO(LJForceWithPMEDirectForceUpdate, - KernelAttr() - .AddInputAttr(kNumberTypeUInt32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddOutputAttr(kNumberTypeFloat32), - LJForceWithPMEDirectForceUpdateGpuKernelMod, float, int) -} // namespace kernel -} // namespace mindspore diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/lj/lj_force_with_pme_direct_force_update_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/lj/lj_force_with_pme_direct_force_update_kernel.h deleted file mode 100644 index 39827ddbeb5..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/lj/lj_force_with_pme_direct_force_update_kernel.h +++ /dev/null @@ -1,144 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -/** - * Note: - * LJForceWithPMEDirectForceUpdate. This is an experimental interface that is subject to change and/or deletion. - */ -#ifndef MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONGE_LJ_LJ_FORCE_WITH_PME_DIRECT_FORCE_KERNEL_H_ -#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONGE_LJ_LJ_FORCE_WITH_PME_DIRECT_FORCE_KERNEL_H_ -#include -#include -#include -#include -#include "plugin/device/gpu/kernel/gpu_kernel.h" -#include "plugin/device/gpu/kernel/gpu_kernel_factory.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" -#include "plugin/device/gpu/kernel/cuda_impl/sponge/lj/lj_force_with_pme_direct_force_impl.cuh" -namespace mindspore { -namespace kernel { -template -class LJForceWithPMEDirectForceUpdateGpuKernelMod : public DeprecatedNativeGpuKernelMod { - public: - LJForceWithPMEDirectForceUpdateGpuKernelMod() : ele_uint_crd(1) {} - ~LJForceWithPMEDirectForceUpdateGpuKernelMod() override = default; - - bool Init(const CNodePtr &kernel_node) override { - kernel_node_ = kernel_node; - atom_numbers = static_cast(GetAttr(kernel_node, "atom_numbers")); - cutoff = static_cast(GetAttr(kernel_node, "cutoff")); - pme_beta = static_cast(GetAttr(kernel_node, "pme_beta")); - need_update = static_cast(GetAttr(kernel_node, "need_update")); - - auto shape_uint_crd = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto shape_LJtype = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - auto shape_charge = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); - auto shape_scaler = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); - auto shape_nl_numbers = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 4); - auto shape_nl_serial = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 5); - auto shape_d_LJ_a = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 6); - auto shape_d_LJ_b = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 7); - - ele_uint_crd *= SizeOf(shape_uint_crd); - ele_LJtype *= SizeOf(shape_LJtype); - ele_charge *= SizeOf(shape_charge); - ele_scaler *= SizeOf(shape_scaler); - ele_d_LJ_a *= SizeOf(shape_d_LJ_a); - ele_d_LJ_b *= SizeOf(shape_d_LJ_b); - - InitSizeLists(); - return true; - } - - bool Launch(const std::vector &inputs, const std::vector &workspace, - const std::vector &outputs, void *stream_ptr) override { - auto uint_crd = GetDeviceAddress(inputs, 0); - auto LJtype = GetDeviceAddress(inputs, 1); - auto charge = GetDeviceAddress(inputs, 2); - auto scaler = GetDeviceAddress(inputs, 3); - auto nl_numbers = GetDeviceAddress(inputs, 4); - auto nl_serial = GetDeviceAddress(inputs, 5); - auto d_LJ_a = GetDeviceAddress(inputs, 6); - auto d_LJ_b = GetDeviceAddress(inputs, 7); - auto d_beta = GetDeviceAddress(inputs, 8); - - if (need_update) { - cudaMemcpyAsync(&pme_beta, d_beta, sizeof(float), cudaMemcpyDeviceToHost, - reinterpret_cast(stream_ptr)); - cudaStreamSynchronize(reinterpret_cast(stream_ptr)); - } - - auto uint_crd_with_LJ = GetDeviceAddress(workspace, 0); - auto nl = GetDeviceAddress(workspace, 1); - - auto frc = GetDeviceAddress(outputs, 0); - LJForceWithPMEDirectForce(atom_numbers, cutoff, pme_beta, uint_crd, LJtype, charge, scaler, uint_crd_with_LJ, - nl_numbers, nl_serial, nl, d_LJ_a, d_LJ_b, frc, - reinterpret_cast(stream_ptr)); - return true; - } - - protected: - void InitSizeLists() override { - input_size_list_.push_back(ele_uint_crd * sizeof(T1)); - input_size_list_.push_back(ele_LJtype * sizeof(T1)); - input_size_list_.push_back(ele_charge * sizeof(T)); - input_size_list_.push_back(ele_scaler * sizeof(T)); - input_size_list_.push_back(atom_numbers * sizeof(T1)); - input_size_list_.push_back(max_nl_numbers * sizeof(T1)); - input_size_list_.push_back(ele_d_LJ_a * sizeof(T)); - input_size_list_.push_back(ele_d_LJ_b * sizeof(T)); - input_size_list_.push_back(sizeof(T)); - - workspace_size_list_.push_back(atom_numbers * max_nl_numbers * sizeof(T1)); - workspace_size_list_.push_back(atom_numbers * sizeof(UINT_VECTOR_LJ_TYPE)); - - output_size_list_.push_back(atom_numbers * 3 * sizeof(T)); - } - - private: - size_t ele_uint_crd = 1; - size_t ele_LJtype = 1; - size_t ele_charge = 1; - size_t ele_scaler = 1; - size_t ele_nl = 1; - size_t ele_d_LJ_a = 1; - size_t ele_d_LJ_b = 1; - - int atom_numbers; - float pme_beta; - float cutoff; - int need_update; - int max_nl_numbers = 800; - struct UINT_VECTOR_LJ_TYPE { - unsigned int uint_x; - unsigned int uint_y; - unsigned int uint_z; - int LJ_type; - float charge; - }; - struct NEIGHBOR_LIST { - int atom_numbers; - int *atom_serial; - }; - struct VECTOR { - float x; - float y; - float z; - }; -}; -} // namespace kernel -} // namespace mindspore -#endif diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/lj/lj_force_with_virial_energy_update_kernel.cc b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/lj/lj_force_with_virial_energy_update_kernel.cc deleted file mode 100644 index 46667e01eee..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/lj/lj_force_with_virial_energy_update_kernel.cc +++ /dev/null @@ -1,40 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -/** - * Note: - * LJForceWithVirialEnergyUpdate. This is an experimental interface that is subject to change and/or deletion. - */ -#include "plugin/device/gpu/kernel/sponge/lj/lj_force_with_virial_energy_update_kernel.h" - -namespace mindspore { -namespace kernel { -MS_REG_GPU_KERNEL_THREE(LJForceWithVirialEnergyUpdate, - KernelAttr() - .AddInputAttr(kNumberTypeUInt32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddOutputAttr(kNumberTypeFloat32) - .AddOutputAttr(kNumberTypeFloat32) - .AddOutputAttr(kNumberTypeFloat32), - LJForceWithVirialEnergyUpdateGpuKernelMod, float, int, unsigned int) -} // namespace kernel -} // namespace mindspore diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/lj/lj_force_with_virial_energy_update_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/lj/lj_force_with_virial_energy_update_kernel.h deleted file mode 100644 index 5cdb6bc3320..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/lj/lj_force_with_virial_energy_update_kernel.h +++ /dev/null @@ -1,145 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -/** - * Note: - * LJForceWithVirialEnergyUpdate. This is an experimental interface that is subject to change and/or deletion. - */ -#ifndef MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_LJ_FORCE_WITH_VIRIAL_ENERGY_UPDATE_KERNEL_H_ -#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_LJ_FORCE_WITH_VIRIAL_ENERGY_UPDATE_KERNEL_H_ -#include -#include -#include -#include -#include "plugin/device/gpu/kernel/gpu_kernel.h" -#include "plugin/device/gpu/kernel/gpu_kernel_factory.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" -#include "plugin/device/gpu/kernel/cuda_impl/sponge/lj/lj_direct_cf_force_with_lj_virial_direct_cf_energy_impl.cuh" -namespace mindspore { -namespace kernel { -template -class LJForceWithVirialEnergyUpdateGpuKernelMod : public DeprecatedNativeGpuKernelMod { - public: - LJForceWithVirialEnergyUpdateGpuKernelMod() : ele_uint_crd(1) {} - ~LJForceWithVirialEnergyUpdateGpuKernelMod() override = default; - - bool Init(const CNodePtr &kernel_node) override { - kernel_node_ = kernel_node; - atom_numbers = static_cast(GetAttr(kernel_node, "atom_numbers")); - max_neighbor_numbers = static_cast(GetAttr(kernel_node, "max_neighbor_numbers")); - cutoff = static_cast(GetAttr(kernel_node, "cutoff")); - pme_beta = static_cast(GetAttr(kernel_node, "pme_beta")); - need_update = static_cast(GetAttr(kernel_node, "need_update")); - - auto shape_uint_crd = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto shape_LJtype = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - auto shape_charge = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); - auto shape_scaler = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); - auto shape_nl_numbers = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 4); - auto shape_nl_serial = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 5); - auto shape_d_LJ_a = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 6); - auto shape_d_LJ_b = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 7); - - ele_uint_crd *= SizeOf(shape_uint_crd); - ele_LJtype *= SizeOf(shape_LJtype); - ele_charge *= SizeOf(shape_charge); - ele_scaler *= SizeOf(shape_scaler); - ele_d_LJ_a *= SizeOf(shape_d_LJ_a); - ele_d_LJ_b *= SizeOf(shape_d_LJ_b); - - InitSizeLists(); - return true; - } - - bool Launch(const std::vector &inputs, const std::vector &workspace, - const std::vector &outputs, void *stream_ptr) override { - auto uint_crd = GetDeviceAddress(inputs, 0); - auto LJtype = GetDeviceAddress(inputs, 1); - auto charge = GetDeviceAddress(inputs, 2); - auto scaler = GetDeviceAddress(inputs, 3); - auto nl_numbers = GetDeviceAddress(inputs, 4); - auto nl_serial = GetDeviceAddress(inputs, 5); - auto d_LJ_a = GetDeviceAddress(inputs, 6); - auto d_LJ_b = GetDeviceAddress(inputs, 7); - auto d_beta = GetDeviceAddress(inputs, 8); - - auto uint_crd_with_LJ = GetDeviceAddress(workspace, 0); - auto nl = GetDeviceAddress(workspace, 1); - - auto frc = GetDeviceAddress(outputs, 0); - auto atom_lj_virial = GetDeviceAddress(outputs, 1); - auto atom_energy = GetDeviceAddress(outputs, 2); - - if (need_update) { - cudaMemcpyAsync(&pme_beta, d_beta, sizeof(float), cudaMemcpyDeviceToHost, - reinterpret_cast(stream_ptr)); - cudaStreamSynchronize(reinterpret_cast(stream_ptr)); - } - - LJ_Direct_CF_Force_With_LJ_Virial_Direct_CF_Energy(atom_numbers, cutoff, pme_beta, uint_crd, LJtype, charge, scaler, - uint_crd_with_LJ, nl_numbers, nl_serial, nl, d_LJ_a, d_LJ_b, frc, - atom_lj_virial, atom_energy, max_neighbor_numbers, - reinterpret_cast(stream_ptr)); - return true; - } - - protected: - void InitSizeLists() override { - input_size_list_.push_back(ele_uint_crd * sizeof(T2)); - input_size_list_.push_back(ele_LJtype * sizeof(T1)); - input_size_list_.push_back(ele_charge * sizeof(T)); - input_size_list_.push_back(ele_scaler * sizeof(T)); - input_size_list_.push_back(atom_numbers * sizeof(T1)); - input_size_list_.push_back(max_neighbor_numbers * sizeof(T1)); - input_size_list_.push_back(ele_d_LJ_a * sizeof(T)); - input_size_list_.push_back(ele_d_LJ_b * sizeof(T)); - - workspace_size_list_.push_back(atom_numbers * max_neighbor_numbers * sizeof(T1)); - workspace_size_list_.push_back(atom_numbers * sizeof(UINT_VECTOR_LJ_TYPE)); - - output_size_list_.push_back(atom_numbers * 3 * sizeof(T)); - output_size_list_.push_back(atom_numbers * sizeof(T)); - output_size_list_.push_back(atom_numbers * sizeof(T)); - } - - private: - size_t ele_uint_crd = 1; - size_t ele_LJtype = 1; - size_t ele_charge = 1; - size_t ele_scaler = 1; - size_t ele_nl = 1; - size_t ele_d_LJ_a = 1; - size_t ele_d_LJ_b = 1; - - int atom_numbers; - int max_neighbor_numbers; - float pme_beta; - float cutoff; - int need_update; - struct UINT_VECTOR_LJ_TYPE { - unsigned int uint_x; - unsigned int uint_y; - unsigned int uint_z; - int LJ_type; - float charge; - }; - struct NEIGHBOR_LIST { - int atom_numbers; - int *atom_serial; - }; -}; -} // namespace kernel -} // namespace mindspore -#endif diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/nb14/dihedral_14_cf_atom_energy_kernel.cc b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/nb14/dihedral_14_cf_atom_energy_kernel.cc deleted file mode 100644 index cae02b472de..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/nb14/dihedral_14_cf_atom_energy_kernel.cc +++ /dev/null @@ -1,33 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "plugin/device/gpu/kernel/sponge/nb14/dihedral_14_cf_atom_energy_kernel.h" - -namespace mindspore { -namespace kernel { -MS_REG_GPU_KERNEL_TWO(Dihedral14CFAtomEnergy, - KernelAttr() - .AddInputAttr(kNumberTypeUInt32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeFloat32) - .AddOutputAttr(kNumberTypeFloat32), - Dihedral14CFAtomEnergyGpuKernelMod, float, int) -} // namespace kernel -} // namespace mindspore diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/nb14/dihedral_14_cf_atom_energy_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/nb14/dihedral_14_cf_atom_energy_kernel.h deleted file mode 100644 index 64dd2f34bfb..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/nb14/dihedral_14_cf_atom_energy_kernel.h +++ /dev/null @@ -1,106 +0,0 @@ -/** - * Copyright 2019-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONG_NB14_DIHEDRAL_14_CF_ATOM_ENERGY_KERNEL_H_ -#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONG_NB14_DIHEDRAL_14_CF_ATOM_ENERGY_KERNEL_H_ - -#include -#include -#include -#include -#include "plugin/device/gpu/kernel/gpu_kernel.h" -#include "plugin/device/gpu/kernel/gpu_kernel_factory.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" -#include "plugin/device/gpu/kernel/cuda_impl/sponge/nb14/dihedral_14_cf_atom_energy_impl.cuh" - -namespace mindspore { -namespace kernel { -template -class Dihedral14CFAtomEnergyGpuKernelMod : public DeprecatedNativeGpuKernelMod { - public: - Dihedral14CFAtomEnergyGpuKernelMod() : ele_uint_crd(1) {} - ~Dihedral14CFAtomEnergyGpuKernelMod() override = default; - - bool Init(const CNodePtr &kernel_node) override { - kernel_node_ = kernel_node; - dihedral_14_numbers = static_cast(GetAttr(kernel_node, "dihedral_14_numbers")); - atom_numbers = static_cast(GetAttr(kernel_node, "atom_numbers")); - - auto shape_uint_crd = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto shape_LJtype = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - auto shape_charge = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); - auto shape_boxlength_f = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); - auto shape_a_14 = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 4); - auto shape_b_14 = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 5); - auto shape_cf_scale_factor = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 6); - - ele_uint_crd *= SizeOf(shape_uint_crd); - ele_LJtype *= SizeOf(shape_LJtype); - ele_charge *= SizeOf(shape_charge); - ele_boxlength_f *= SizeOf(shape_boxlength_f); - ele_a_14 *= SizeOf(shape_a_14); - ele_b_14 *= SizeOf(shape_b_14); - ele_cf_scale_factor *= SizeOf(shape_cf_scale_factor); - - InitSizeLists(); - return true; - } - - bool Launch(const std::vector &inputs, const std::vector &, - const std::vector &outputs, void *stream_ptr) override { - auto uint_crd_f = GetDeviceAddress(inputs, 0); - auto LJtype = GetDeviceAddress(inputs, 1); - auto charge = GetDeviceAddress(inputs, 2); - auto boxlength_f = GetDeviceAddress(inputs, 3); - auto a_14 = GetDeviceAddress(inputs, 4); - auto b_14 = GetDeviceAddress(inputs, 5); - auto cf_scale_factor = GetDeviceAddress(inputs, 6); - auto ene = GetDeviceAddress(outputs, 0); - - Dihedral14CFAtomEnergy(dihedral_14_numbers, atom_numbers, uint_crd_f, LJtype, charge, boxlength_f, a_14, b_14, - cf_scale_factor, ene, reinterpret_cast(stream_ptr)); - - return true; - } - - protected: - void InitSizeLists() override { - input_size_list_.push_back(ele_uint_crd * sizeof(T1)); - input_size_list_.push_back(ele_LJtype * sizeof(T1)); - input_size_list_.push_back(ele_charge * sizeof(T)); - input_size_list_.push_back(ele_boxlength_f * sizeof(T)); - input_size_list_.push_back(ele_a_14 * sizeof(T1)); - input_size_list_.push_back(ele_b_14 * sizeof(T1)); - input_size_list_.push_back(ele_cf_scale_factor * sizeof(T)); - - output_size_list_.push_back(atom_numbers * sizeof(T)); - } - - private: - size_t ele_uint_crd = 1; - size_t ele_LJtype = 1; - size_t ele_charge = 1; - size_t ele_boxlength_f = 1; - size_t ele_a_14 = 1; - size_t ele_b_14 = 1; - size_t ele_cf_scale_factor = 1; - - int dihedral_14_numbers; - int atom_numbers; -}; -} // namespace kernel -} // namespace mindspore -#endif // MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONG_NB14_DIHEDRAL_14_CF_ATOM_ENERGY_KERNEL_H_ diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/nb14/dihedral_14_cf_energy_kernel.cc b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/nb14/dihedral_14_cf_energy_kernel.cc deleted file mode 100644 index b4142216476..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/nb14/dihedral_14_cf_energy_kernel.cc +++ /dev/null @@ -1,33 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "plugin/device/gpu/kernel/sponge/nb14/dihedral_14_cf_energy_kernel.h" - -namespace mindspore { -namespace kernel { -MS_REG_GPU_KERNEL_TWO(Dihedral14CFEnergy, - KernelAttr() - .AddInputAttr(kNumberTypeUInt32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeFloat32) - .AddOutputAttr(kNumberTypeFloat32), - Dihedral14CFEnergyGpuKernelMod, float, int) -} // namespace kernel -} // namespace mindspore diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/nb14/dihedral_14_cf_energy_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/nb14/dihedral_14_cf_energy_kernel.h deleted file mode 100644 index 16f3123a95c..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/nb14/dihedral_14_cf_energy_kernel.h +++ /dev/null @@ -1,114 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONG_NB14_DIHEDRAL_14_CF_ENERGY_KERNEL_H_ -#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONG_NB14_DIHEDRAL_14_CF_ENERGY_KERNEL_H_ - -#include -#include -#include -#include -#include "plugin/device/gpu/kernel/gpu_kernel.h" -#include "plugin/device/gpu/kernel/gpu_kernel_factory.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" -#include "plugin/device/gpu/kernel/cuda_impl/sponge/nb14/dihedral_14_cf_energy_impl.cuh" - -namespace mindspore { -namespace kernel { -template -class Dihedral14CFEnergyGpuKernelMod : public DeprecatedNativeGpuKernelMod { - public: - Dihedral14CFEnergyGpuKernelMod() : ele_uint_crd(1) {} - ~Dihedral14CFEnergyGpuKernelMod() override = default; - - bool Init(const CNodePtr &kernel_node) override { - kernel_node_ = kernel_node; - dihedral_14_numbers = static_cast(GetAttr(kernel_node, "dihedral_14_numbers")); - atom_numbers = static_cast(GetAttr(kernel_node, "atom_numbers")); - - auto shape_uint_crd = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto shape_LJtype = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - auto shape_charge = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); - auto shape_boxlength_f = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); - auto shape_a_14 = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 4); - auto shape_b_14 = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 5); - auto shape_cf_scale_factor = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 6); - - ele_uint_crd *= SizeOf(shape_uint_crd); - ele_LJtype *= SizeOf(shape_LJtype); - ele_charge *= SizeOf(shape_charge); - ele_boxlength_f *= SizeOf(shape_boxlength_f); - ele_a_14 *= SizeOf(shape_a_14); - ele_b_14 *= SizeOf(shape_b_14); - ele_cf_scale_factor *= SizeOf(shape_cf_scale_factor); - - InitSizeLists(); - return true; - } - - bool Launch(const std::vector &inputs, const std::vector &workspace, - const std::vector &outputs, void *stream_ptr) override { - auto uint_crd_f = GetDeviceAddress(inputs, 0); - auto LJtype = GetDeviceAddress(inputs, 1); - auto charge = GetDeviceAddress(inputs, 2); - auto boxlength_f = GetDeviceAddress(inputs, 3); - auto a_14 = GetDeviceAddress(inputs, 4); - auto b_14 = GetDeviceAddress(inputs, 5); - auto cf_scale_factor = GetDeviceAddress(inputs, 6); - auto ene = GetDeviceAddress(outputs, 0); - auto uint_crd_with_LJ = GetDeviceAddress(workspace, 0); - - Dihedral14CFEnergy(dihedral_14_numbers, atom_numbers, uint_crd_f, LJtype, charge, uint_crd_with_LJ, boxlength_f, - a_14, b_14, cf_scale_factor, ene, reinterpret_cast(stream_ptr)); - - return true; - } - - protected: - void InitSizeLists() override { - input_size_list_.push_back(ele_uint_crd * sizeof(T1)); - input_size_list_.push_back(ele_LJtype * sizeof(T1)); - input_size_list_.push_back(ele_charge * sizeof(T)); - input_size_list_.push_back(ele_boxlength_f * sizeof(T)); - input_size_list_.push_back(ele_a_14 * sizeof(T1)); - input_size_list_.push_back(ele_b_14 * sizeof(T1)); - input_size_list_.push_back(ele_cf_scale_factor * sizeof(T)); - workspace_size_list_.push_back(atom_numbers * sizeof(UINT_VECTOR_LJ_TYPE)); - output_size_list_.push_back(dihedral_14_numbers * sizeof(T)); - } - - private: - size_t ele_uint_crd = 1; - size_t ele_LJtype = 1; - size_t ele_charge = 1; - size_t ele_boxlength_f = 1; - size_t ele_a_14 = 1; - size_t ele_b_14 = 1; - size_t ele_cf_scale_factor = 1; - - int dihedral_14_numbers; - int atom_numbers; - struct UINT_VECTOR_LJ_TYPE { - unsigned int uint_x; - unsigned int uint_y; - unsigned int uint_z; - int LJ_type; - float charge; - }; -}; -} // namespace kernel -} // namespace mindspore -#endif // MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONG_NB14_DIHEDRAL_14_LJ_FORCE_WITH_DIRECT_CF_KERNEL_H_ diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/nb14/dihedral_14_lj_atom_energy_kernel.cc b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/nb14/dihedral_14_lj_atom_energy_kernel.cc deleted file mode 100644 index 0954a2df89f..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/nb14/dihedral_14_lj_atom_energy_kernel.cc +++ /dev/null @@ -1,35 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "plugin/device/gpu/kernel/sponge/nb14/dihedral_14_lj_atom_energy_kernel.h" - -namespace mindspore { -namespace kernel { -MS_REG_GPU_KERNEL_TWO(Dihedral14LJAtomEnergy, - KernelAttr() - .AddInputAttr(kNumberTypeUInt32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddOutputAttr(kNumberTypeFloat32), - Dihedral14LJAtomEnergyGpuKernelMod, float, int) -} // namespace kernel -} // namespace mindspore diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/nb14/dihedral_14_lj_atom_energy_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/nb14/dihedral_14_lj_atom_energy_kernel.h deleted file mode 100644 index 8bbb560f739..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/nb14/dihedral_14_lj_atom_energy_kernel.h +++ /dev/null @@ -1,115 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONG_NB14_DIHEDRAL_14_LJ_ATOM_ENERGY_KERNEL_H_ -#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONG_NB14_DIHEDRAL_14_LJ_ATOM_ENERGY_KERNEL_H_ - -#include -#include -#include -#include -#include "plugin/device/gpu/kernel/gpu_kernel.h" -#include "plugin/device/gpu/kernel/gpu_kernel_factory.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" -#include "plugin/device/gpu/kernel/cuda_impl/sponge/nb14/dihedral_14_lj_atom_energy_impl.cuh" - -namespace mindspore { -namespace kernel { -template -class Dihedral14LJAtomEnergyGpuKernelMod : public DeprecatedNativeGpuKernelMod { - public: - Dihedral14LJAtomEnergyGpuKernelMod() : ele_uint_crd(1) {} - ~Dihedral14LJAtomEnergyGpuKernelMod() override = default; - - bool Init(const CNodePtr &kernel_node) override { - kernel_node_ = kernel_node; - dihedral_14_numbers = static_cast(GetAttr(kernel_node, "dihedral_14_numbers")); - atom_numbers = static_cast(GetAttr(kernel_node, "atom_numbers")); - - auto shape_uint_crd = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto shape_LJtype = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - auto shape_charge = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); - auto shape_boxlength_f = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); - auto shape_a_14 = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 4); - auto shape_b_14 = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 5); - auto shape_lj_scale_factor = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 6); - auto shape_LJ_type_A = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 7); - auto shape_LJ_type_B = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 8); - - ele_uint_crd *= SizeOf(shape_uint_crd); - ele_LJtype *= SizeOf(shape_LJtype); - ele_charge *= SizeOf(shape_charge); - ele_boxlength_f *= SizeOf(shape_boxlength_f); - ele_a_14 *= SizeOf(shape_a_14); - ele_b_14 *= SizeOf(shape_b_14); - ele_lj_scale_factor *= SizeOf(shape_lj_scale_factor); - ele_LJ_type_A *= SizeOf(shape_LJ_type_A); - ele_LJ_type_B *= SizeOf(shape_LJ_type_B); - - InitSizeLists(); - return true; - } - - bool Launch(const std::vector &inputs, const std::vector &, - const std::vector &outputs, void *stream_ptr) override { - auto uint_crd_f = GetDeviceAddress(inputs, 0); - auto LJtype = GetDeviceAddress(inputs, 1); - auto charge = GetDeviceAddress(inputs, 2); - auto boxlength_f = GetDeviceAddress(inputs, 3); - auto a_14 = GetDeviceAddress(inputs, 4); - auto b_14 = GetDeviceAddress(inputs, 5); - auto lj_scale_factor = GetDeviceAddress(inputs, 6); - auto LJ_type_A = GetDeviceAddress(inputs, 7); - auto LJ_type_B = GetDeviceAddress(inputs, 8); - auto ene = GetDeviceAddress(outputs, 0); - - Dihedral14LJAtomEnergy(dihedral_14_numbers, atom_numbers, uint_crd_f, LJtype, charge, boxlength_f, a_14, b_14, - lj_scale_factor, LJ_type_A, LJ_type_B, ene, reinterpret_cast(stream_ptr)); - return true; - } - - protected: - void InitSizeLists() override { - input_size_list_.push_back(ele_uint_crd * sizeof(T1)); - input_size_list_.push_back(ele_LJtype * sizeof(T1)); - input_size_list_.push_back(ele_charge * sizeof(T)); - input_size_list_.push_back(ele_boxlength_f * sizeof(T)); - input_size_list_.push_back(ele_a_14 * sizeof(T1)); - input_size_list_.push_back(ele_b_14 * sizeof(T1)); - input_size_list_.push_back(ele_lj_scale_factor * sizeof(T)); - input_size_list_.push_back(ele_LJ_type_A * sizeof(T)); - input_size_list_.push_back(ele_LJ_type_B * sizeof(T)); - - output_size_list_.push_back(atom_numbers * sizeof(T)); - } - - private: - size_t ele_uint_crd = 1; - size_t ele_LJtype = 1; - size_t ele_charge = 1; - size_t ele_boxlength_f = 1; - size_t ele_a_14 = 1; - size_t ele_b_14 = 1; - size_t ele_lj_scale_factor = 1; - size_t ele_LJ_type_A = 1; - size_t ele_LJ_type_B = 1; - - int dihedral_14_numbers; - int atom_numbers; -}; -} // namespace kernel -} // namespace mindspore -#endif // MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONG_NB14_DIHEDRAL_14_LJ_ATOM_ENERGY_KERNEL_H_ diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/nb14/dihedral_14_lj_cf_force_with_atom_energy_and_virial_kernel.cc b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/nb14/dihedral_14_lj_cf_force_with_atom_energy_and_virial_kernel.cc deleted file mode 100644 index 024d06ea47d..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/nb14/dihedral_14_lj_cf_force_with_atom_energy_and_virial_kernel.cc +++ /dev/null @@ -1,38 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "plugin/device/gpu/kernel/sponge/nb14/dihedral_14_lj_cf_force_with_atom_energy_and_virial_kernel.h" - -namespace mindspore { -namespace kernel { -MS_REG_GPU_KERNEL_TWO(Dihedral14ForceWithAtomEnergyVirial, - KernelAttr() - .AddInputAttr(kNumberTypeUInt32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddOutputAttr(kNumberTypeFloat32) - .AddOutputAttr(kNumberTypeFloat32) - .AddOutputAttr(kNumberTypeFloat32), - Dihedral14LJCFForceWithAtomEnergyAndVirialGpuKernelMod, float, int) -} // namespace kernel -} // namespace mindspore diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/nb14/dihedral_14_lj_cf_force_with_atom_energy_and_virial_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/nb14/dihedral_14_lj_cf_force_with_atom_energy_and_virial_kernel.h deleted file mode 100644 index 7f534620ed3..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/nb14/dihedral_14_lj_cf_force_with_atom_energy_and_virial_kernel.h +++ /dev/null @@ -1,138 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONG_NB14_DIHEDRAL_14_LJ_CF_FORCE_WITH_ATOM_ENE_VIRIAL_KERNEL_H_ -#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONG_NB14_DIHEDRAL_14_LJ_CF_FORCE_WITH_ATOM_ENE_VIRIAL_KERNEL_H_ - -#include -#include -#include -#include -#include "plugin/device/gpu/kernel/gpu_kernel.h" -#include "plugin/device/gpu/kernel/gpu_kernel_factory.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" -#include "plugin/device/gpu/kernel/cuda_impl/sponge/nb14/dihedral_14_lj_cf_force_with_atom_energy_and_virial_impl.cuh" - -namespace mindspore { -namespace kernel { - -template -class Dihedral14LJCFForceWithAtomEnergyAndVirialGpuKernelMod : public DeprecatedNativeGpuKernelMod { - public: - Dihedral14LJCFForceWithAtomEnergyAndVirialGpuKernelMod() : ele_uint_crd(1) {} - ~Dihedral14LJCFForceWithAtomEnergyAndVirialGpuKernelMod() override = default; - - bool Init(const CNodePtr &kernel_node) override { - kernel_node_ = kernel_node; - dihedral_14_numbers = static_cast(GetAttr(kernel_node, "dihedral_14_numbers")); - atom_numbers = static_cast(GetAttr(kernel_node, "atom_numbers")); - - auto shape_uint_crd = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto shape_LJtype = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - auto shape_charge = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); - auto shape_boxlength_f = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); - auto shape_a_14 = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 4); - auto shape_b_14 = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 5); - auto shape_lj_scale_factor = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 6); - auto shape_cf_scale_factor = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 7); - auto shape_LJ_type_A = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 8); - auto shape_LJ_type_B = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 9); - - ele_uint_crd *= SizeOf(shape_uint_crd); - ele_LJtype *= SizeOf(shape_LJtype); - ele_charge *= SizeOf(shape_charge); - ele_boxlength_f *= SizeOf(shape_boxlength_f); - ele_a_14 *= SizeOf(shape_a_14); - ele_b_14 *= SizeOf(shape_b_14); - ele_lj_scale_factor *= SizeOf(shape_lj_scale_factor); - ele_cf_scale_factor *= SizeOf(shape_cf_scale_factor); - ele_LJ_type_A *= SizeOf(shape_LJ_type_A); - ele_LJ_type_B *= SizeOf(shape_LJ_type_B); - - InitSizeLists(); - return true; - } - - bool Launch(const std::vector &inputs, const std::vector &workspace, - const std::vector &outputs, void *stream_ptr) override { - auto uint_crd_f = GetDeviceAddress(inputs, 0); - auto LJtype = GetDeviceAddress(inputs, 1); - auto charge = GetDeviceAddress(inputs, 2); - auto boxlength_f = GetDeviceAddress(inputs, 3); - auto a_14 = GetDeviceAddress(inputs, 4); - auto b_14 = GetDeviceAddress(inputs, 5); - auto lj_scale_factor = GetDeviceAddress(inputs, 6); - auto cf_scale_factor = GetDeviceAddress(inputs, 7); - auto LJ_type_A = GetDeviceAddress(inputs, 8); - auto LJ_type_B = GetDeviceAddress(inputs, 9); - auto frc_f = GetDeviceAddress(outputs, 0); - auto atom_energy = GetDeviceAddress(outputs, 1); - auto atom_virial = GetDeviceAddress(outputs, 2); - - auto uint_crd_with_LJ = GetDeviceAddress(workspace, 0); - - Dihedral14LJCFForceWithAtomEnergyAndVirial(dihedral_14_numbers, atom_numbers, uint_crd_f, LJtype, charge, - uint_crd_with_LJ, boxlength_f, a_14, b_14, lj_scale_factor, - cf_scale_factor, LJ_type_A, LJ_type_B, frc_f, atom_energy, atom_virial, - reinterpret_cast(stream_ptr)); - - return true; - } - - protected: - void InitSizeLists() override { - input_size_list_.push_back(ele_uint_crd * sizeof(T1)); - input_size_list_.push_back(ele_LJtype * sizeof(T1)); - input_size_list_.push_back(ele_charge * sizeof(T)); - input_size_list_.push_back(ele_boxlength_f * sizeof(T)); - input_size_list_.push_back(ele_a_14 * sizeof(T1)); - input_size_list_.push_back(ele_b_14 * sizeof(T1)); - input_size_list_.push_back(ele_lj_scale_factor * sizeof(T)); - input_size_list_.push_back(ele_cf_scale_factor * sizeof(T)); - input_size_list_.push_back(ele_LJ_type_A * sizeof(T)); - input_size_list_.push_back(ele_LJ_type_B * sizeof(T)); - workspace_size_list_.push_back(atom_numbers * sizeof(UINT_VECTOR_LJ_TYPE)); - - output_size_list_.push_back(3 * atom_numbers * sizeof(T)); - output_size_list_.push_back(atom_numbers * sizeof(T)); - output_size_list_.push_back(atom_numbers * sizeof(T)); - } - - private: - size_t ele_uint_crd = 1; - size_t ele_LJtype = 1; - size_t ele_charge = 1; - size_t ele_boxlength_f = 1; - size_t ele_a_14 = 1; - size_t ele_b_14 = 1; - size_t ele_lj_scale_factor = 1; - size_t ele_cf_scale_factor = 1; - size_t ele_LJ_type_A = 1; - size_t ele_LJ_type_B = 1; - - int dihedral_14_numbers; - int atom_numbers; - struct UINT_VECTOR_LJ_TYPE { - unsigned int uint_x; - unsigned int uint_y; - unsigned int uint_z; - int LJ_type; - float charge; - }; -}; -} // namespace kernel -} // namespace mindspore -#endif diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/nb14/dihedral_14_lj_cf_force_with_atom_energy_kernel.cc b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/nb14/dihedral_14_lj_cf_force_with_atom_energy_kernel.cc deleted file mode 100644 index fa2b36fb501..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/nb14/dihedral_14_lj_cf_force_with_atom_energy_kernel.cc +++ /dev/null @@ -1,37 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "plugin/device/gpu/kernel/sponge/nb14/dihedral_14_lj_cf_force_with_atom_energy_kernel.h" - -namespace mindspore { -namespace kernel { -MS_REG_GPU_KERNEL_TWO(Dihedral14LJCFForceWithAtomEnergy, - KernelAttr() - .AddInputAttr(kNumberTypeUInt32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddOutputAttr(kNumberTypeFloat32) - .AddOutputAttr(kNumberTypeFloat32), - Dihedral14LJCFForceWithAtomEnergyGpuKernelMod, float, int) -} // namespace kernel -} // namespace mindspore diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/nb14/dihedral_14_lj_cf_force_with_atom_energy_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/nb14/dihedral_14_lj_cf_force_with_atom_energy_kernel.h deleted file mode 100644 index 289d4f4f4c4..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/nb14/dihedral_14_lj_cf_force_with_atom_energy_kernel.h +++ /dev/null @@ -1,134 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONG_NB14_DIHEDRAL_14_LJ_CF_FORCE_WITH_ATOM_ENERGY_KERNEL_H_ -#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONG_NB14_DIHEDRAL_14_LJ_CF_FORCE_WITH_ATOM_ENERGY_KERNEL_H_ - -#include -#include -#include -#include -#include "plugin/device/gpu/kernel/gpu_kernel.h" -#include "plugin/device/gpu/kernel/gpu_kernel_factory.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" -#include "plugin/device/gpu/kernel/cuda_impl/sponge/nb14/dihedral_14_lj_cf_force_with_atom_energy_impl.cuh" - -namespace mindspore { -namespace kernel { -template -class Dihedral14LJCFForceWithAtomEnergyGpuKernelMod : public DeprecatedNativeGpuKernelMod { - public: - Dihedral14LJCFForceWithAtomEnergyGpuKernelMod() : ele_uint_crd(1) {} - ~Dihedral14LJCFForceWithAtomEnergyGpuKernelMod() override = default; - - bool Init(const CNodePtr &kernel_node) override { - kernel_node_ = kernel_node; - dihedral_14_numbers = static_cast(GetAttr(kernel_node, "dihedral_14_numbers")); - atom_numbers = static_cast(GetAttr(kernel_node, "atom_numbers")); - - auto shape_uint_crd = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto shape_LJtype = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - auto shape_charge = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); - auto shape_boxlength_f = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); - auto shape_a_14 = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 4); - auto shape_b_14 = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 5); - auto shape_lj_scale_factor = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 6); - auto shape_cf_scale_factor = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 7); - auto shape_LJ_type_A = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 8); - auto shape_LJ_type_B = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 9); - - ele_uint_crd *= SizeOf(shape_uint_crd); - ele_LJtype *= SizeOf(shape_LJtype); - ele_charge *= SizeOf(shape_charge); - ele_boxlength_f *= SizeOf(shape_boxlength_f); - ele_a_14 *= SizeOf(shape_a_14); - ele_b_14 *= SizeOf(shape_b_14); - ele_lj_scale_factor *= SizeOf(shape_lj_scale_factor); - ele_cf_scale_factor *= SizeOf(shape_cf_scale_factor); - ele_LJ_type_A *= SizeOf(shape_LJ_type_A); - ele_LJ_type_B *= SizeOf(shape_LJ_type_B); - - InitSizeLists(); - return true; - } - - bool Launch(const std::vector &inputs, const std::vector &workspace, - const std::vector &outputs, void *stream_ptr) override { - auto uint_crd_f = GetDeviceAddress(inputs, 0); - auto LJtype = GetDeviceAddress(inputs, 1); - auto charge = GetDeviceAddress(inputs, 2); - auto boxlength_f = GetDeviceAddress(inputs, 3); - auto a_14 = GetDeviceAddress(inputs, 4); - auto b_14 = GetDeviceAddress(inputs, 5); - auto lj_scale_factor = GetDeviceAddress(inputs, 6); - auto cf_scale_factor = GetDeviceAddress(inputs, 7); - auto LJ_type_A = GetDeviceAddress(inputs, 8); - auto LJ_type_B = GetDeviceAddress(inputs, 9); - auto frc_f = GetDeviceAddress(outputs, 0); - auto atom_energy = GetDeviceAddress(outputs, 1); - - auto uint_crd_with_LJ = GetDeviceAddress(workspace, 0); - - Dihedral14LJCFForceWithAtomEnergy(dihedral_14_numbers, atom_numbers, uint_crd_f, LJtype, charge, uint_crd_with_LJ, - boxlength_f, a_14, b_14, lj_scale_factor, cf_scale_factor, LJ_type_A, LJ_type_B, - frc_f, atom_energy, reinterpret_cast(stream_ptr)); - - return true; - } - - protected: - void InitSizeLists() override { - input_size_list_.push_back(ele_uint_crd * sizeof(T1)); - input_size_list_.push_back(ele_LJtype * sizeof(T1)); - input_size_list_.push_back(ele_charge * sizeof(T)); - input_size_list_.push_back(ele_boxlength_f * sizeof(T)); - input_size_list_.push_back(ele_a_14 * sizeof(T1)); - input_size_list_.push_back(ele_b_14 * sizeof(T1)); - input_size_list_.push_back(ele_lj_scale_factor * sizeof(T)); - input_size_list_.push_back(ele_cf_scale_factor * sizeof(T)); - input_size_list_.push_back(ele_LJ_type_A * sizeof(T)); - input_size_list_.push_back(ele_LJ_type_B * sizeof(T)); - workspace_size_list_.push_back(atom_numbers * sizeof(UINT_VECTOR_LJ_TYPE)); - - output_size_list_.push_back(3 * atom_numbers * sizeof(T)); - output_size_list_.push_back(atom_numbers * sizeof(T)); - } - - private: - size_t ele_uint_crd = 1; - size_t ele_LJtype = 1; - size_t ele_charge = 1; - size_t ele_boxlength_f = 1; - size_t ele_a_14 = 1; - size_t ele_b_14 = 1; - size_t ele_lj_scale_factor = 1; - size_t ele_cf_scale_factor = 1; - size_t ele_LJ_type_A = 1; - size_t ele_LJ_type_B = 1; - - int dihedral_14_numbers; - int atom_numbers; - struct UINT_VECTOR_LJ_TYPE { - unsigned int uint_x; - unsigned int uint_y; - unsigned int uint_z; - int LJ_type; - float charge; - }; -}; -} // namespace kernel -} // namespace mindspore -#endif // MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONG_NB14_DIHEDRAL_14_LJ_CF_FORCE_WITH_ATOM_ENERGY_KERNEL_H_ diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/nb14/dihedral_14_lj_energy_kernel.cc b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/nb14/dihedral_14_lj_energy_kernel.cc deleted file mode 100644 index eb47127df5b..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/nb14/dihedral_14_lj_energy_kernel.cc +++ /dev/null @@ -1,35 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "plugin/device/gpu/kernel/sponge/nb14/dihedral_14_lj_energy_kernel.h" - -namespace mindspore { -namespace kernel { -MS_REG_GPU_KERNEL_TWO(Dihedral14LJEnergy, - KernelAttr() - .AddInputAttr(kNumberTypeUInt32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddOutputAttr(kNumberTypeFloat32), - Dihedral14LJEnergyGpuKernelMod, float, int) -} // namespace kernel -} // namespace mindspore diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/nb14/dihedral_14_lj_energy_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/nb14/dihedral_14_lj_energy_kernel.h deleted file mode 100644 index ab65bfabf39..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/nb14/dihedral_14_lj_energy_kernel.h +++ /dev/null @@ -1,126 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONG_NB14_DIHEDRAL_14_LJ_ENERGY_KERNEL_H_ -#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONG_NB14_DIHEDRAL_14_LJ_ENERGY_KERNEL_H_ - -#include -#include -#include -#include -#include "plugin/device/gpu/kernel/gpu_kernel.h" -#include "plugin/device/gpu/kernel/gpu_kernel_factory.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" -#include "plugin/device/gpu/kernel/cuda_impl/sponge/nb14/dihedral_14_lj_energy_impl.cuh" - -namespace mindspore { -namespace kernel { -template -class Dihedral14LJEnergyGpuKernelMod : public DeprecatedNativeGpuKernelMod { - public: - Dihedral14LJEnergyGpuKernelMod() : ele_uint_crd(1) {} - ~Dihedral14LJEnergyGpuKernelMod() override = default; - - bool Init(const CNodePtr &kernel_node) override { - kernel_node_ = kernel_node; - dihedral_14_numbers = static_cast(GetAttr(kernel_node, "dihedral_14_numbers")); - atom_numbers = static_cast(GetAttr(kernel_node, "atom_numbers")); - - auto shape_uint_crd = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto shape_LJtype = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - auto shape_charge = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); - auto shape_boxlength_f = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); - auto shape_a_14 = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 4); - auto shape_b_14 = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 5); - auto shape_lj_scale_factor = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 6); - auto shape_LJ_type_A = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 7); - auto shape_LJ_type_B = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 8); - - ele_uint_crd *= SizeOf(shape_uint_crd); - ele_LJtype *= SizeOf(shape_LJtype); - ele_charge *= SizeOf(shape_charge); - ele_boxlength_f *= SizeOf(shape_boxlength_f); - ele_a_14 *= SizeOf(shape_a_14); - ele_b_14 *= SizeOf(shape_b_14); - ele_lj_scale_factor *= SizeOf(shape_lj_scale_factor); - ele_LJ_type_A *= SizeOf(shape_LJ_type_A); - ele_LJ_type_B *= SizeOf(shape_LJ_type_B); - - InitSizeLists(); - return true; - } - - bool Launch(const std::vector &inputs, const std::vector &workspace, - const std::vector &outputs, void *stream_ptr) override { - auto uint_crd_f = GetDeviceAddress(inputs, 0); - auto LJtype = GetDeviceAddress(inputs, 1); - auto charge = GetDeviceAddress(inputs, 2); - auto boxlength_f = GetDeviceAddress(inputs, 3); - auto a_14 = GetDeviceAddress(inputs, 4); - auto b_14 = GetDeviceAddress(inputs, 5); - auto lj_scale_factor = GetDeviceAddress(inputs, 6); - auto LJ_type_A = GetDeviceAddress(inputs, 7); - auto LJ_type_B = GetDeviceAddress(inputs, 8); - auto ene = GetDeviceAddress(outputs, 0); - auto uint_crd_with_LJ = GetDeviceAddress(workspace, 0); - - Dihedral14LJEnergy(dihedral_14_numbers, atom_numbers, uint_crd_f, LJtype, charge, uint_crd_with_LJ, boxlength_f, - a_14, b_14, lj_scale_factor, LJ_type_A, LJ_type_B, ene, - reinterpret_cast(stream_ptr)); - - return true; - } - - protected: - void InitSizeLists() override { - input_size_list_.push_back(ele_uint_crd * sizeof(T1)); - input_size_list_.push_back(ele_LJtype * sizeof(T1)); - input_size_list_.push_back(ele_charge * sizeof(T)); - input_size_list_.push_back(ele_boxlength_f * sizeof(T)); - input_size_list_.push_back(ele_a_14 * sizeof(T1)); - input_size_list_.push_back(ele_b_14 * sizeof(T1)); - input_size_list_.push_back(ele_lj_scale_factor * sizeof(T)); - input_size_list_.push_back(ele_LJ_type_A * sizeof(T)); - input_size_list_.push_back(ele_LJ_type_B * sizeof(T)); - workspace_size_list_.push_back(atom_numbers * sizeof(UINT_VECTOR_LJ_TYPE)); - - output_size_list_.push_back(dihedral_14_numbers * sizeof(T)); - } - - private: - size_t ele_uint_crd = 1; - size_t ele_LJtype = 1; - size_t ele_charge = 1; - size_t ele_boxlength_f = 1; - size_t ele_a_14 = 1; - size_t ele_b_14 = 1; - size_t ele_lj_scale_factor = 1; - size_t ele_LJ_type_A = 1; - size_t ele_LJ_type_B = 1; - - int dihedral_14_numbers; - int atom_numbers; - struct UINT_VECTOR_LJ_TYPE { - unsigned int uint_x; - unsigned int uint_y; - unsigned int uint_z; - int LJ_type; - float charge; - }; -}; -} // namespace kernel -} // namespace mindspore -#endif // MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONG_NB14_DIHEDRAL_14_LJ_ENERGY_KERNEL_H_ diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/nb14/dihedral_14_lj_force_gpu_kernel.cc b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/nb14/dihedral_14_lj_force_gpu_kernel.cc deleted file mode 100644 index b6cb2639697..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/nb14/dihedral_14_lj_force_gpu_kernel.cc +++ /dev/null @@ -1,35 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "plugin/device/gpu/kernel/sponge/nb14/dihedral_14_lj_force_gpu_kernel.h" - -namespace mindspore { -namespace kernel { -MS_REG_GPU_KERNEL_TWO(Dihedral14LJForce, - KernelAttr() - .AddInputAttr(kNumberTypeUInt32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddOutputAttr(kNumberTypeFloat32), - Dihedral14LJForceGpuKernelMod, float, int) -} // namespace kernel -} // namespace mindspore diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/nb14/dihedral_14_lj_force_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/nb14/dihedral_14_lj_force_gpu_kernel.h deleted file mode 100644 index 082987b7d06..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/nb14/dihedral_14_lj_force_gpu_kernel.h +++ /dev/null @@ -1,114 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONG_NB14_DIHEDRAL_14_LJ_FORCE_KERNEL_H_ -#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONG_NB14_DIHEDRAL_14_LJ_FORCE_KERNEL_H_ - -#include -#include -#include -#include -#include "plugin/device/gpu/kernel/gpu_kernel.h" -#include "plugin/device/gpu/kernel/gpu_kernel_factory.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" -#include "plugin/device/gpu/kernel/cuda_impl/sponge/nb14/dihedral_14_lj_force_impl.cuh" - -namespace mindspore { -namespace kernel { -template -class Dihedral14LJForceGpuKernelMod : public DeprecatedNativeGpuKernelMod { - public: - Dihedral14LJForceGpuKernelMod() : ele_uint_crd(1) {} - ~Dihedral14LJForceGpuKernelMod() override = default; - - bool Init(const CNodePtr &kernel_node) override { - kernel_node_ = kernel_node; - dihedral_14_numbers = static_cast(GetAttr(kernel_node, "dihedral_14_numbers")); - atom_numbers = static_cast(GetAttr(kernel_node, "atom_numbers")); - - auto shape_uint_crd = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto shape_LJtype = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - auto shape_charge = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); - auto shape_boxlength_f = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); - auto shape_a_14 = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 4); - auto shape_b_14 = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 5); - auto shape_lj_scale_factor = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 6); - auto shape_LJ_type_A = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 7); - auto shape_LJ_type_B = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 8); - - ele_uint_crd *= SizeOf(shape_uint_crd); - ele_LJtype *= SizeOf(shape_LJtype); - ele_charge *= SizeOf(shape_charge); - ele_boxlength_f *= SizeOf(shape_boxlength_f); - ele_a_14 *= SizeOf(shape_a_14); - ele_b_14 *= SizeOf(shape_b_14); - ele_lj_scale_factor *= SizeOf(shape_lj_scale_factor); - ele_LJ_type_A *= SizeOf(shape_LJ_type_A); - ele_LJ_type_B *= SizeOf(shape_LJ_type_B); - - InitSizeLists(); - return true; - } - - bool Launch(const std::vector &inputs, const std::vector &, - const std::vector &outputs, void *stream_ptr) override { - auto uint_crd_f = GetDeviceAddress(inputs, 0); - auto LJtype = GetDeviceAddress(inputs, 1); - auto charge = GetDeviceAddress(inputs, 2); - auto boxlength_f = GetDeviceAddress(inputs, 3); - auto a_14 = GetDeviceAddress(inputs, 4); - auto b_14 = GetDeviceAddress(inputs, 5); - auto lj_scale_factor = GetDeviceAddress(inputs, 6); - auto LJ_type_A = GetDeviceAddress(inputs, 7); - auto LJ_type_B = GetDeviceAddress(inputs, 8); - auto frc_f = GetDeviceAddress(outputs, 0); - Dihedral14LJForce(dihedral_14_numbers, atom_numbers, uint_crd_f, LJtype, charge, boxlength_f, a_14, b_14, - lj_scale_factor, LJ_type_A, LJ_type_B, frc_f, reinterpret_cast(stream_ptr)); - return true; - } - - protected: - void InitSizeLists() override { - input_size_list_.push_back(ele_uint_crd * sizeof(T1)); - input_size_list_.push_back(ele_LJtype * sizeof(T1)); - input_size_list_.push_back(ele_charge * sizeof(T)); - input_size_list_.push_back(ele_boxlength_f * sizeof(T)); - input_size_list_.push_back(ele_a_14 * sizeof(T1)); - input_size_list_.push_back(ele_b_14 * sizeof(T1)); - input_size_list_.push_back(ele_lj_scale_factor * sizeof(T)); - input_size_list_.push_back(ele_LJ_type_A * sizeof(T)); - input_size_list_.push_back(ele_LJ_type_B * sizeof(T)); - - output_size_list_.push_back(atom_numbers * 3 * sizeof(T)); - } - - private: - size_t ele_uint_crd = 1; - size_t ele_LJtype = 1; - size_t ele_charge = 1; - size_t ele_boxlength_f = 1; - size_t ele_a_14 = 1; - size_t ele_b_14 = 1; - size_t ele_lj_scale_factor = 1; - size_t ele_LJ_type_A = 1; - size_t ele_LJ_type_B = 1; - - int dihedral_14_numbers; - int atom_numbers; -}; -} // namespace kernel -} // namespace mindspore -#endif // MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONG_NB14_DIHEDRAL_14_LJ_FORCE_KERNEL_H_ diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/nb14/dihedral_14_lj_force_with_direct_cf_kernel.cc b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/nb14/dihedral_14_lj_force_with_direct_cf_kernel.cc deleted file mode 100644 index d3635b716c7..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/nb14/dihedral_14_lj_force_with_direct_cf_kernel.cc +++ /dev/null @@ -1,36 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "plugin/device/gpu/kernel/sponge/nb14/dihedral_14_lj_force_with_direct_cf_kernel.h" - -namespace mindspore { -namespace kernel { -MS_REG_GPU_KERNEL_TWO(Dihedral14LJForceWithDirectCF, - KernelAttr() - .AddInputAttr(kNumberTypeUInt32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddOutputAttr(kNumberTypeFloat32), - Dihedral14LJForceWithDirectCFGpuKernelMod, float, int) -} // namespace kernel -} // namespace mindspore diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/nb14/dihedral_14_lj_force_with_direct_cf_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/nb14/dihedral_14_lj_force_with_direct_cf_kernel.h deleted file mode 100644 index 27b080c00df..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/nb14/dihedral_14_lj_force_with_direct_cf_kernel.h +++ /dev/null @@ -1,122 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONG_NB14_DIHEDRAL_14_LJ_FORCE_WITH_DIRECT_CF_KERNEL_H_ -#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONG_NB14_DIHEDRAL_14_LJ_FORCE_WITH_DIRECT_CF_KERNEL_H_ - -#include -#include -#include -#include -#include "plugin/device/gpu/kernel/gpu_kernel.h" -#include "plugin/device/gpu/kernel/gpu_kernel_factory.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" -#include "plugin/device/gpu/kernel/cuda_impl/sponge/nb14/dihedral_14_lj_force_with_direct_cf_impl.cuh" - -namespace mindspore { -namespace kernel { -template -class Dihedral14LJForceWithDirectCFGpuKernelMod : public DeprecatedNativeGpuKernelMod { - public: - Dihedral14LJForceWithDirectCFGpuKernelMod() : ele_uint_crd(1) {} - ~Dihedral14LJForceWithDirectCFGpuKernelMod() override = default; - - bool Init(const CNodePtr &kernel_node) override { - kernel_node_ = kernel_node; - dihedral_14_numbers = static_cast(GetAttr(kernel_node, "dihedral_14_numbers")); - atom_numbers = static_cast(GetAttr(kernel_node, "atom_numbers")); - - auto shape_uint_crd = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto shape_LJtype = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - auto shape_charge = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); - auto shape_boxlength_f = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); - auto shape_a_14 = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 4); - auto shape_b_14 = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 5); - auto shape_lj_scale_factor = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 6); - auto shape_cf_scale_factor = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 7); - auto shape_LJ_type_A = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 8); - auto shape_LJ_type_B = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 9); - - ele_uint_crd *= SizeOf(shape_uint_crd); - ele_LJtype *= SizeOf(shape_LJtype); - ele_charge *= SizeOf(shape_charge); - ele_boxlength_f *= SizeOf(shape_boxlength_f); - ele_a_14 *= SizeOf(shape_a_14); - ele_b_14 *= SizeOf(shape_b_14); - ele_lj_scale_factor *= SizeOf(shape_lj_scale_factor); - ele_cf_scale_factor *= SizeOf(shape_cf_scale_factor); - ele_LJ_type_A *= SizeOf(shape_LJ_type_A); - ele_LJ_type_B *= SizeOf(shape_LJ_type_B); - - InitSizeLists(); - return true; - } - - bool Launch(const std::vector &inputs, const std::vector &, - const std::vector &outputs, void *stream_ptr) override { - auto uint_crd_f = GetDeviceAddress(inputs, 0); - auto LJtype = GetDeviceAddress(inputs, 1); - auto charge = GetDeviceAddress(inputs, 2); - auto boxlength_f = GetDeviceAddress(inputs, 3); - auto a_14 = GetDeviceAddress(inputs, 4); - auto b_14 = GetDeviceAddress(inputs, 5); - auto lj_scale_factor = GetDeviceAddress(inputs, 6); - auto cf_scale_factor = GetDeviceAddress(inputs, 7); - auto LJ_type_A = GetDeviceAddress(inputs, 8); - auto LJ_type_B = GetDeviceAddress(inputs, 9); - auto frc_f = GetDeviceAddress(outputs, 0); - - Dihedral14LJForceWithDirectCF(dihedral_14_numbers, atom_numbers, uint_crd_f, LJtype, charge, boxlength_f, a_14, - b_14, lj_scale_factor, cf_scale_factor, LJ_type_A, LJ_type_B, frc_f, - reinterpret_cast(stream_ptr)); - - return true; - } - - protected: - void InitSizeLists() override { - input_size_list_.push_back(ele_uint_crd * sizeof(T1)); - input_size_list_.push_back(ele_LJtype * sizeof(T1)); - input_size_list_.push_back(ele_charge * sizeof(T)); - input_size_list_.push_back(ele_boxlength_f * sizeof(T)); - input_size_list_.push_back(ele_a_14 * sizeof(T1)); - input_size_list_.push_back(ele_b_14 * sizeof(T1)); - input_size_list_.push_back(ele_lj_scale_factor * sizeof(T)); - input_size_list_.push_back(ele_cf_scale_factor * sizeof(T)); - input_size_list_.push_back(ele_LJ_type_A * sizeof(T)); - input_size_list_.push_back(ele_LJ_type_B * sizeof(T)); - - output_size_list_.push_back(atom_numbers * 3 * sizeof(T)); - } - - private: - size_t ele_uint_crd = 1; - size_t ele_LJtype = 1; - size_t ele_charge = 1; - size_t ele_boxlength_f = 1; - size_t ele_a_14 = 1; - size_t ele_b_14 = 1; - size_t ele_lj_scale_factor = 1; - size_t ele_cf_scale_factor = 1; - size_t ele_LJ_type_A = 1; - size_t ele_LJ_type_B = 1; - - int dihedral_14_numbers; - int atom_numbers; -}; -} // namespace kernel -} // namespace mindspore -#endif // MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONG_NB14_DIHEDRAL_14_LJ_FORCE_WITH_DIRECT_CF_KERNEL_H_ diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/neighbor_list/neighbor_list_update_kernel.cc b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/neighbor_list/neighbor_list_update_kernel.cc deleted file mode 100644 index e3d0d4230cd..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/neighbor_list/neighbor_list_update_kernel.cc +++ /dev/null @@ -1,49 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -/** - * Note: - * NeighborListUpdate. This is an experimental interface that is subject to change and/or deletion. - */ - -#include "plugin/device/gpu/kernel/sponge/neighbor_list/neighbor_list_update_kernel.h" - -namespace mindspore { -namespace kernel { -MS_REG_GPU_KERNEL_TWO(NeighborListUpdate, - KernelAttr() - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeUInt32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeInt32) - .AddOutputAttr(kNumberTypeFloat32), - NeighborListUpdateGpuKernelMod, int, float) -} // namespace kernel -} // namespace mindspore diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/neighbor_list/neighbor_list_update_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/neighbor_list/neighbor_list_update_kernel.h deleted file mode 100644 index 5140b57a609..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/neighbor_list/neighbor_list_update_kernel.h +++ /dev/null @@ -1,198 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -/** - * Note: - * NeighborListUpdate. This is an experimental interface that is subject to change and/or deletion. - */ - -#ifndef MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONGE_NEIGHBOR_LIST_UPDATE_KERNEL_H_ -#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONGE_NEIGHBOR_LIST_UPDATE_KERNEL_H_ - -#include -#include -#include -#include -#include "plugin/device/gpu/kernel/gpu_kernel.h" -#include "plugin/device/gpu/kernel/gpu_kernel_factory.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" -#include "plugin/device/gpu/kernel/cuda_impl/sponge/neighbor_list/neighbor_list_impl.cuh" - -namespace mindspore { -namespace kernel { -constexpr float kSkinDef = 2.0; -constexpr float kCutoffDef = 10.0; -constexpr int kMaxAtomInGridNumDef = 64; -constexpr int kMaxNbNumDef = 800; - -constexpr size_t kIdx3 = 3; -constexpr size_t kIdx5 = 5; -constexpr size_t kIdx6 = 6; -constexpr size_t kIdx8 = 8; -constexpr size_t kIdx2 = 2; -constexpr size_t kIdx4 = 4; -constexpr size_t kIdx9 = 9; -constexpr size_t kIdx16 = 16; -constexpr size_t kIdx10 = 10; -constexpr size_t kIdx7 = 7; -constexpr size_t kIdx14 = 14; -constexpr size_t kIdx12 = 12; -constexpr size_t kIdx11 = 11; -constexpr size_t kIdx13 = 13; -constexpr size_t kIdx18 = 18; -constexpr size_t kIdx15 = 15; -constexpr size_t kIdx17 = 17; - -template -class NeighborListUpdateGpuKernelMod : public DeprecatedNativeGpuKernelMod { - public: - NeighborListUpdateGpuKernelMod() - : skin(kSkinDef), - cutoff(kCutoffDef), - max_atom_in_grid_numbers(kMaxAtomInGridNumDef), - max_neighbor_numbers(kMaxNbNumDef) {} - ~NeighborListUpdateGpuKernelMod() override = default; - bool Init(const CNodePtr &kernel_node) override { - kernel_node_ = kernel_node; - grid_numbers = static_cast(GetAttr(kernel_node, "grid_numbers")); - atom_numbers = static_cast(GetAttr(kernel_node, "atom_numbers")); - refresh_interval = static_cast(GetAttr(kernel_node, "refresh_interval")); - not_first_time = static_cast(GetAttr(kernel_node, "not_first_time")); - nxy = static_cast(GetAttr(kernel_node, "nxy")); - excluded_atom_numbers = static_cast(GetAttr(kernel_node, "excluded_atom_numbers")); - - cutoff_square = static_cast(GetAttr(kernel_node, "cutoff_square")); - half_skin_square = static_cast(GetAttr(kernel_node, "half_skin_square")); - cutoff_with_skin = static_cast(GetAttr(kernel_node, "cutoff_with_skin")); - half_cutoff_with_skin = static_cast(GetAttr(kernel_node, "half_cutoff_with_skin")); - cutoff_with_skin_square = static_cast(GetAttr(kernel_node, "cutoff_with_skin_square")); - h_bucket.resize(grid_numbers); - h_gpointer.resize(grid_numbers); - InitSizeLists(); - return true; - } - - bool Launch(const std::vector &inputs, const std::vector &workspaces, - const std::vector &outputs, void *stream_ptr) override { - auto atom_numbers_in_grid_bucket = GetDeviceAddress(inputs, 0); - auto bucket = GetDeviceAddress(inputs, 1); - auto crd = GetDeviceAddress(inputs, kIdx2); - auto box_length = GetDeviceAddress(inputs, kIdx3); - auto grid_n = GetDeviceAddress(inputs, kIdx4); - auto grid_length_inverse = GetDeviceAddress(inputs, kIdx5); - auto atom_in_grid_serial = GetDeviceAddress(inputs, kIdx6); - auto old_crd = GetDeviceAddress(inputs, kIdx7); - auto crd_to_uint_crd_cof = GetDeviceAddress(inputs, kIdx8); - auto uint_crd = GetDeviceAddress(inputs, kIdx9); - auto gpointer = GetDeviceAddress(inputs, kIdx10); - auto nl_atom_numbers = GetDeviceAddress(inputs, kIdx11); - auto nl_atom_serial = GetDeviceAddress(inputs, kIdx12); - auto uint_dr_to_dr_cof = GetDeviceAddress(inputs, kIdx13); - auto excluded_list_start = GetDeviceAddress(inputs, kIdx14); - auto excluded_list = GetDeviceAddress(inputs, kIdx15); - auto excluded_numbers = GetDeviceAddress(inputs, kIdx16); - auto need_refresh_flag = GetDeviceAddress(inputs, kIdx17); - auto d_refresh_count = GetDeviceAddress(inputs, kIdx18); - - GRID_BUCKET *d_bucket = reinterpret_cast(GetDeviceAddress(workspaces, 0)); - GRID_POINTER *d_gpointer = reinterpret_cast(GetDeviceAddress(workspaces, 1)); - NEIGHBOR_LIST *nl = GetDeviceAddress(workspaces, kIdx2); - float *half_crd_to_uint_crd_cof = GetDeviceAddress(workspaces, kIdx3); - - for (size_t i = 0; i < h_bucket.size(); i += 1) { - h_bucket[i].atom_serial = bucket + i * max_atom_in_grid_numbers; - } - const size_t kGridSize = 125; - for (size_t i = 0; i < h_gpointer.size(); i += 1) { - h_gpointer[i].grid_serial = gpointer + i * kGridSize; - } - - cudaMemcpyAsync(d_bucket, h_bucket.data(), sizeof(GRID_BUCKET) * grid_numbers, cudaMemcpyHostToDevice, - reinterpret_cast(stream_ptr)); - cudaMemcpyAsync(d_gpointer, h_gpointer.data(), sizeof(GRID_POINTER) * grid_numbers, cudaMemcpyHostToDevice, - reinterpret_cast(stream_ptr)); - ConstructNeighborListHalf(atom_numbers, max_neighbor_numbers, nl_atom_numbers, nl_atom_serial, nl, - reinterpret_cast(stream_ptr)); - NeighborListUpdate(grid_numbers, atom_numbers, d_refresh_count, refresh_interval, not_first_time, skin, nxy, - cutoff_square, cutoff_with_skin_square, grid_n, box_length, atom_numbers_in_grid_bucket, - grid_length_inverse, atom_in_grid_serial, d_bucket, crd, old_crd, crd_to_uint_crd_cof, - half_crd_to_uint_crd_cof, uint_crd, uint_dr_to_dr_cof, d_gpointer, nl, excluded_list_start, - excluded_list, excluded_numbers, half_skin_square, need_refresh_flag, - reinterpret_cast(stream_ptr)); - CopyNeighborListHalf(atom_numbers, nl, nl_atom_numbers, reinterpret_cast(stream_ptr)); - return true; - } - - protected: - void InitSizeLists() override { - input_size_list_.push_back(sizeof(int) * grid_numbers); - input_size_list_.push_back(sizeof(int) * max_atom_in_grid_numbers * grid_numbers); - input_size_list_.push_back(sizeof(VECTOR) * atom_numbers); - input_size_list_.push_back(sizeof(VECTOR)); - - input_size_list_.push_back(sizeof(INT_VECTOR)); - input_size_list_.push_back(sizeof(VECTOR)); - input_size_list_.push_back(sizeof(int) * atom_numbers); - - input_size_list_.push_back(sizeof(VECTOR) * atom_numbers); - input_size_list_.push_back(sizeof(VECTOR)); - input_size_list_.push_back(sizeof(UNSIGNED_INT_VECTOR) * atom_numbers); - - const size_t kGridSize = 125; - input_size_list_.push_back(sizeof(int) * grid_numbers * kGridSize); - input_size_list_.push_back(sizeof(int) * atom_numbers); - input_size_list_.push_back(sizeof(int) * atom_numbers * max_neighbor_numbers); - input_size_list_.push_back(sizeof(VECTOR)); - - input_size_list_.push_back(sizeof(int) * atom_numbers); - input_size_list_.push_back(sizeof(int) * excluded_atom_numbers); - input_size_list_.push_back(sizeof(int) * atom_numbers); - - input_size_list_.push_back(sizeof(int)); - input_size_list_.push_back(sizeof(int)); - - workspace_size_list_.push_back(sizeof(GRID_BUCKET) * grid_numbers); - workspace_size_list_.push_back(sizeof(GRID_POINTER) * grid_numbers); - workspace_size_list_.push_back(sizeof(NEIGHBOR_LIST) * atom_numbers); - const size_t kCrdSize = 3; - workspace_size_list_.push_back(sizeof(float) * kCrdSize); - - output_size_list_.push_back(sizeof(float)); - } - - private: - float skin; - float cutoff; - int not_first_time; - int grid_numbers; - int refresh_interval; - int atom_numbers; - int nxy; - int max_atom_in_grid_numbers; - int max_neighbor_numbers; - int excluded_atom_numbers; - float half_skin_square; - float cutoff_square; - float cutoff_with_skin; - float half_cutoff_with_skin; - float cutoff_with_skin_square; - - std::vector h_bucket; - std::vector h_gpointer; -}; -} // namespace kernel -} // namespace mindspore - -#endif diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/neighbor_list/neighbor_list_update_new_kernel.cc b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/neighbor_list/neighbor_list_update_new_kernel.cc deleted file mode 100644 index a224be13136..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/neighbor_list/neighbor_list_update_new_kernel.cc +++ /dev/null @@ -1,49 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -/** - * Note: - * NeighborListRefresh. This is an experimental interface that is subject to change and/or deletion. - */ - -#include "plugin/device/gpu/kernel/sponge/neighbor_list/neighbor_list_update_new_kernel.h" - -namespace mindspore { -namespace kernel { -MS_REG_GPU_KERNEL_TWO(NeighborListRefresh, - KernelAttr() - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeUInt32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeInt32) - .AddOutputAttr(kNumberTypeFloat32), - NeighborListUpdateNewGpuKernelMod, int, float) -} // namespace kernel -} // namespace mindspore diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/neighbor_list/neighbor_list_update_new_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/neighbor_list/neighbor_list_update_new_kernel.h deleted file mode 100644 index bbfdbb671e5..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/neighbor_list/neighbor_list_update_new_kernel.h +++ /dev/null @@ -1,203 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -/** - * Note: - * NeighborListRefresh. This is an experimental interface that is subject to change and/or deletion. - */ - -#ifndef MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONGE_NEIGHBOR_LIST_UPDATE_NEW_KERNEL_H_ -#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONGE_NEIGHBOR_LIST_UPDATE_NEW_KERNEL_H_ - -#include -#include -#include -#include "plugin/device/gpu/kernel/gpu_kernel.h" -#include "plugin/device/gpu/kernel/gpu_kernel_factory.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" -#include "plugin/device/gpu/kernel/cuda_impl/sponge/neighbor_list/neighbor_list_impl.cuh" - -namespace mindspore { -namespace kernel { -constexpr float kSkinDef = 2.0; -constexpr float kCutoffDef = 9.0; -constexpr int kMaxAtomInGridNumDef = 64; -constexpr int kMaxNbNumDef = 800; - -constexpr size_t kIdx2 = 2; -constexpr size_t kIdx3 = 3; -constexpr size_t kIdx4 = 4; -constexpr size_t kIdx5 = 5; -constexpr size_t kIdx6 = 6; -constexpr size_t kIdx7 = 7; -constexpr size_t kIdx8 = 8; -constexpr size_t kIdx9 = 9; -constexpr size_t kIdx10 = 10; -constexpr size_t kIdx11 = 11; -constexpr size_t kIdx12 = 12; -constexpr size_t kIdx13 = 13; -constexpr size_t kIdx14 = 14; -constexpr size_t kIdx15 = 15; -constexpr size_t kIdx16 = 16; -constexpr size_t kIdx17 = 17; -constexpr size_t kIdx18 = 18; - -template -class NeighborListUpdateNewGpuKernelMod : public DeprecatedNativeGpuKernelMod { - public: - NeighborListUpdateNewGpuKernelMod() - : skin(kSkinDef), - cutoff(kCutoffDef), - max_atom_in_grid_numbers(kMaxAtomInGridNumDef), - max_neighbor_numbers(kMaxNbNumDef) {} - ~NeighborListUpdateNewGpuKernelMod() override = default; - bool Init(const CNodePtr &kernel_node) override { - kernel_node_ = kernel_node; - grid_numbers = static_cast(GetAttr(kernel_node, "grid_numbers")); - atom_numbers = static_cast(GetAttr(kernel_node, "atom_numbers")); - refresh_interval = static_cast(GetAttr(kernel_node, "refresh_interval")); - not_first_time = static_cast(GetAttr(kernel_node, "not_first_time")); - nxy = static_cast(GetAttr(kernel_node, "nxy")); - excluded_atom_numbers = static_cast(GetAttr(kernel_node, "excluded_atom_numbers")); - - cutoff_square = static_cast(GetAttr(kernel_node, "cutoff_square")); - half_skin_square = static_cast(GetAttr(kernel_node, "half_skin_square")); - cutoff_with_skin = static_cast(GetAttr(kernel_node, "cutoff_with_skin")); - half_cutoff_with_skin = static_cast(GetAttr(kernel_node, "half_cutoff_with_skin")); - cutoff_with_skin_square = static_cast(GetAttr(kernel_node, "cutoff_with_skin_square")); - forced_update = static_cast(GetAttr(kernel_node, "forced_update")); - forced_check = static_cast(GetAttr(kernel_node, "forced_check")); - h_bucket.resize(grid_numbers); - h_gpointer.resize(grid_numbers); - InitSizeLists(); - return true; - } - - bool Launch(const std::vector &inputs, const std::vector &workspaces, - const std::vector &outputs, void *stream_ptr) override { - auto atom_numbers_in_grid_bucket = GetDeviceAddress(inputs, 0); - auto bucket = GetDeviceAddress(inputs, 1); - auto crd = GetDeviceAddress(inputs, kIdx2); - auto box_length = GetDeviceAddress(inputs, kIdx3); - auto grid_n = GetDeviceAddress(inputs, kIdx4); - auto grid_length_inverse = GetDeviceAddress(inputs, kIdx5); - auto atom_in_grid_serial = GetDeviceAddress(inputs, kIdx6); - auto old_crd = GetDeviceAddress(inputs, kIdx7); - auto crd_to_uint_crd_cof = GetDeviceAddress(inputs, kIdx8); - auto uint_crd = GetDeviceAddress(inputs, kIdx9); - auto gpointer = GetDeviceAddress(inputs, kIdx10); - auto nl_atom_numbers = GetDeviceAddress(inputs, kIdx11); - auto nl_atom_serial = GetDeviceAddress(inputs, kIdx12); - auto uint_dr_to_dr_cof = GetDeviceAddress(inputs, kIdx13); - auto excluded_list_start = GetDeviceAddress(inputs, kIdx14); - auto excluded_list = GetDeviceAddress(inputs, kIdx15); - auto excluded_numbers = GetDeviceAddress(inputs, kIdx16); - auto need_refresh_flag = GetDeviceAddress(inputs, kIdx17); - auto d_refresh_count = GetDeviceAddress(inputs, kIdx18); - - GRID_BUCKET *d_bucket = reinterpret_cast(GetDeviceAddress(workspaces, 0)); - GRID_POINTER *d_gpointer = reinterpret_cast(GetDeviceAddress(workspaces, 1)); - NEIGHBOR_LIST *nl = GetDeviceAddress(workspaces, kIdx2); - float *half_crd_to_uint_crd_cof = GetDeviceAddress(workspaces, kIdx3); - - for (size_t i = 0; i < h_bucket.size(); i += 1) { - h_bucket[i].atom_serial = bucket + i * max_atom_in_grid_numbers; - } - const int kGridSize = 125; - for (size_t i = 0; i < h_gpointer.size(); i += 1) { - h_gpointer[i].grid_serial = gpointer + i * kGridSize; - } - - cudaMemcpyAsync(d_bucket, h_bucket.data(), sizeof(GRID_BUCKET) * grid_numbers, cudaMemcpyHostToDevice, - reinterpret_cast(stream_ptr)); - cudaMemcpyAsync(d_gpointer, h_gpointer.data(), sizeof(GRID_POINTER) * grid_numbers, cudaMemcpyHostToDevice, - reinterpret_cast(stream_ptr)); - ConstructNeighborList(atom_numbers, max_neighbor_numbers, nl_atom_numbers, nl_atom_serial, nl, - reinterpret_cast(stream_ptr)); - - NeighborListRefresh(grid_numbers, atom_numbers, d_refresh_count, refresh_interval, not_first_time, skin, nxy, - cutoff_square, cutoff_with_skin_square, grid_n, box_length, atom_numbers_in_grid_bucket, - grid_length_inverse, atom_in_grid_serial, d_bucket, crd, old_crd, crd_to_uint_crd_cof, - half_crd_to_uint_crd_cof, uint_crd, uint_dr_to_dr_cof, d_gpointer, nl, excluded_list_start, - excluded_list, excluded_numbers, half_skin_square, need_refresh_flag, forced_update, - forced_check, reinterpret_cast(stream_ptr)); - CopyNeighborList(atom_numbers, max_neighbor_numbers, nl, nl_atom_numbers, nl_atom_serial, - reinterpret_cast(stream_ptr)); - return true; - } - - protected: - void InitSizeLists() override { - input_size_list_.push_back(sizeof(int) * grid_numbers); - input_size_list_.push_back(sizeof(int) * max_atom_in_grid_numbers * grid_numbers); - input_size_list_.push_back(sizeof(VECTOR) * atom_numbers); - input_size_list_.push_back(sizeof(VECTOR)); - - input_size_list_.push_back(sizeof(INT_VECTOR)); - input_size_list_.push_back(sizeof(VECTOR)); - input_size_list_.push_back(sizeof(int) * atom_numbers); - - input_size_list_.push_back(sizeof(VECTOR) * atom_numbers); - input_size_list_.push_back(sizeof(VECTOR)); - input_size_list_.push_back(sizeof(UNSIGNED_INT_VECTOR) * atom_numbers); - - const size_t kGridSize = 125; - input_size_list_.push_back(sizeof(int) * grid_numbers * kGridSize); - input_size_list_.push_back(sizeof(int) * atom_numbers); - input_size_list_.push_back(sizeof(int) * atom_numbers * max_neighbor_numbers); - input_size_list_.push_back(sizeof(VECTOR)); - - input_size_list_.push_back(sizeof(int) * atom_numbers); - input_size_list_.push_back(sizeof(int) * excluded_atom_numbers); - input_size_list_.push_back(sizeof(int) * atom_numbers); - - input_size_list_.push_back(sizeof(int)); - input_size_list_.push_back(sizeof(int)); - - workspace_size_list_.push_back(sizeof(GRID_BUCKET) * grid_numbers); - workspace_size_list_.push_back(sizeof(GRID_POINTER) * grid_numbers); - workspace_size_list_.push_back(sizeof(NEIGHBOR_LIST) * atom_numbers); - const size_t kCrtSize = 3; - workspace_size_list_.push_back(sizeof(float) * kCrtSize); - - output_size_list_.push_back(sizeof(float)); - } - - private: - float skin; - float cutoff; - int not_first_time; - int atom_numbers; - int grid_numbers; - int refresh_interval; - int nxy; - int max_atom_in_grid_numbers; - int max_neighbor_numbers; - int excluded_atom_numbers; - float half_skin_square; - float cutoff_square; - float cutoff_with_skin; - float half_cutoff_with_skin; - float cutoff_with_skin_square; - int forced_update; - int forced_check; - - std::vector h_bucket; - std::vector h_gpointer; -}; -} // namespace kernel -} // namespace mindspore - -#endif diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/nvtit/md_iteration_gradient_descent_kernel.cc b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/nvtit/md_iteration_gradient_descent_kernel.cc deleted file mode 100644 index 4bcfb32bf3f..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/nvtit/md_iteration_gradient_descent_kernel.cc +++ /dev/null @@ -1,26 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "plugin/device/gpu/kernel/sponge/nvtit/md_iteration_gradient_descent_kernel.h" - -namespace mindspore { -namespace kernel { -MS_REG_GPU_KERNEL_ONE( - MDIterationGradientDescent, - KernelAttr().AddInputAttr(kNumberTypeFloat32).AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32), - MDIterationGradientDescentGpuKernelMod, float) -} // namespace kernel -} // namespace mindspore diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/nvtit/md_iteration_gradient_descent_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/nvtit/md_iteration_gradient_descent_kernel.h deleted file mode 100644 index 08cf221881f..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/nvtit/md_iteration_gradient_descent_kernel.h +++ /dev/null @@ -1,70 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_MD_ITERATION_GRADIENT_DESCENT_KERNEL_H_ -#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_MD_ITERATION_GRADIENT_DESCENT_KERNEL_H_ - -#include "plugin/device/gpu/kernel/cuda_impl/sponge/nvtit/md_iteration_gradient_descent_impl.cuh" -#include -#include -#include -#include - -#include "plugin/device/gpu/kernel/gpu_kernel.h" -#include "plugin/device/gpu/kernel/gpu_kernel_factory.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" - -namespace mindspore { -namespace kernel { -template -class MDIterationGradientDescentGpuKernelMod : public DeprecatedNativeGpuKernelMod { - public: - MDIterationGradientDescentGpuKernelMod() {} - ~MDIterationGradientDescentGpuKernelMod() override = default; - - bool Init(const CNodePtr &kernel_node) override { - // get bond_numbers - kernel_node_ = kernel_node; - atom_numbers = static_cast(GetAttr(kernel_node, "atom_numbers")); - learning_rate = static_cast(GetAttr(kernel_node, "learning_rate")); - InitSizeLists(); - return true; - } - - bool Launch(const std::vector &inputs, const std::vector &workspace, - const std::vector &outputs, void *stream_ptr) override { - auto crd = GetDeviceAddress(inputs, 0); - auto frc = GetDeviceAddress(inputs, 1); - - MDIterationGradientDescent(atom_numbers, crd, frc, learning_rate, reinterpret_cast(stream_ptr)); - return true; - } - - protected: - void InitSizeLists() override { - input_size_list_.push_back(atom_numbers * 3 * sizeof(T)); - input_size_list_.push_back(atom_numbers * 3 * sizeof(T)); - - output_size_list_.push_back(sizeof(T)); - } - - private: - int atom_numbers; - float learning_rate; -}; -} // namespace kernel -} // namespace mindspore -#endif diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/nvtit/md_iteration_leap_frog_kernel.cc b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/nvtit/md_iteration_leap_frog_kernel.cc deleted file mode 100644 index 422e18a285a..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/nvtit/md_iteration_leap_frog_kernel.cc +++ /dev/null @@ -1,31 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "plugin/device/gpu/kernel/sponge/nvtit/md_iteration_leap_frog_kernel.h" - -namespace mindspore { -namespace kernel { -MS_REG_GPU_KERNEL_ONE(MDIterationLeapFrog, - KernelAttr() - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddOutputAttr(kNumberTypeFloat32), - MDIterationLeapFrogGpuKernelMod, float) -} // namespace kernel -} // namespace mindspore diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/nvtit/md_iteration_leap_frog_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/nvtit/md_iteration_leap_frog_kernel.h deleted file mode 100644 index ff694288559..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/nvtit/md_iteration_leap_frog_kernel.h +++ /dev/null @@ -1,79 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -/** - * Note: - * MDIterationLeapFrog. This is an experimental interface that is subject to change and/or deletion. - */ - -#ifndef MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_MD_ITERATION_LEAP_FROG_KERNEL_H_ -#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_MD_ITERATION_LEAP_FROG_KERNEL_H_ - -#include -#include -#include -#include - -#include "plugin/device/gpu/kernel/gpu_kernel.h" -#include "plugin/device/gpu/kernel/gpu_kernel_factory.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" -#include "plugin/device/gpu/kernel/cuda_impl/sponge/nvtit/md_iteration_leap_frog_impl.cuh" - -namespace mindspore { -namespace kernel { -template -class MDIterationLeapFrogGpuKernelMod : public DeprecatedNativeGpuKernelMod { - public: - MDIterationLeapFrogGpuKernelMod() {} - ~MDIterationLeapFrogGpuKernelMod() override = default; - - bool Init(const CNodePtr &kernel_node) override { - kernel_node_ = kernel_node; - atom_numbers = static_cast(GetAttr(kernel_node, "atom_numbers")); - dt = static_cast(GetAttr(kernel_node, "dt")); - InitSizeLists(); - return true; - } - - bool Launch(const std::vector &inputs, const std::vector &workspace, - const std::vector &outputs, void *stream_ptr) override { - auto vel = GetDeviceAddress(inputs, 0); - auto crd = GetDeviceAddress(inputs, 1); - auto frc = GetDeviceAddress(inputs, 2); - auto acc = GetDeviceAddress(inputs, 3); - auto inverse_mass = GetDeviceAddress(inputs, 4); - - MDIterationLeapFrog(atom_numbers, vel, crd, frc, acc, inverse_mass, dt, reinterpret_cast(stream_ptr)); - return true; - } - - protected: - void InitSizeLists() override { - input_size_list_.push_back(atom_numbers * 3 * sizeof(T)); - input_size_list_.push_back(atom_numbers * 3 * sizeof(T)); - input_size_list_.push_back(atom_numbers * 3 * sizeof(T)); - input_size_list_.push_back(atom_numbers * 3 * sizeof(T)); - input_size_list_.push_back(atom_numbers * sizeof(T)); - - output_size_list_.push_back(sizeof(T)); - } - - private: - int atom_numbers; - float dt; -}; -} // namespace kernel -} // namespace mindspore -#endif diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/nvtit/md_iteration_leap_frog_liujian_gpu_kernel.cc b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/nvtit/md_iteration_leap_frog_liujian_gpu_kernel.cc deleted file mode 100644 index 327bb97a5ab..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/nvtit/md_iteration_leap_frog_liujian_gpu_kernel.cc +++ /dev/null @@ -1,34 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "plugin/device/gpu/kernel/sponge/nvtit/md_iteration_leap_frog_liujian_gpu_kernel.h" - -namespace mindspore { -namespace kernel { -MS_REG_GPU_KERNEL_TWO(MDIterationLeapFrogLiujian, - KernelAttr() - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddOutputAttr(kNumberTypeFloat32), - MDIterationLeapFrogLiujianCudaGpuKernelMod, float, int) -} // namespace kernel -} // namespace mindspore diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/nvtit/md_iteration_leap_frog_liujian_gpu_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/nvtit/md_iteration_leap_frog_liujian_gpu_kernel.h deleted file mode 100644 index ed2bebf05c7..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/nvtit/md_iteration_leap_frog_liujian_gpu_kernel.h +++ /dev/null @@ -1,104 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_MD_ITERATION_LEAP_FROG_LIUJIAN_GPU_KERNEL_H_ -#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_MD_ITERATION_LEAP_FROG_LIUJIAN_GPU_KERNEL_H_ - -#include "plugin/device/gpu/kernel/cuda_impl/sponge/nvtit/md_iteration_leap_frog_liujian_gpu_impl.cuh" -#include -#include -#include -#include - -#include "plugin/device/gpu/kernel/gpu_kernel_factory.h" -#include "plugin/device/gpu/kernel/gpu_kernel.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" - -namespace mindspore { -namespace kernel { -constexpr size_t kIdx5 = 5; -constexpr size_t kIdx4 = 4; -constexpr size_t kIdx2 = 2; -constexpr size_t kIdx6 = 6; -constexpr size_t kIdx7 = 7; -constexpr size_t kIdx3 = 3; - -template -class MDIterationLeapFrogLiujianCudaGpuKernelMod : public DeprecatedNativeGpuKernelMod { - public: - MDIterationLeapFrogLiujianCudaGpuKernelMod() {} - ~MDIterationLeapFrogLiujianCudaGpuKernelMod() override = default; - - bool Init(const CNodePtr &kernel_node) override { - // get bond_numbers - kernel_node_ = kernel_node; - half_dt = static_cast(GetAttr(kernel_node, "half_dt")); - atom_numbers = static_cast(GetAttr(kernel_node, "atom_numbers")); - exp_gamma = static_cast(GetAttr(kernel_node, "exp_gamma")); - dt = static_cast(GetAttr(kernel_node, "dt")); - const double kCoef1 = 3.; - const double kCoef2 = 4.; - float4_numbers = ceil(kCoef1 * static_cast(atom_numbers) / kCoef2); - InitSizeLists(); - return true; - } - - bool Launch(const std::vector &inputs, const std::vector &workspace, - const std::vector &outputs, void *stream_ptr) override { - auto inverse_mass = GetDeviceAddress(inputs, 0); - auto sqrt_mass_inverse = GetDeviceAddress(inputs, 1); - auto vel = GetDeviceAddress(inputs, kIdx2); - auto frc = GetDeviceAddress(inputs, kIdx4); - auto crd = GetDeviceAddress(inputs, kIdx3); - auto acc = GetDeviceAddress(inputs, kIdx5); - auto rand_frc = GetDeviceAddress(inputs, kIdx7); - auto rand_state = GetDeviceAddress(inputs, kIdx6); - - auto output = GetDeviceAddress(outputs, 0); - - MD_Iteration_Leap_Frog_With_LiuJian(atom_numbers, half_dt, dt, exp_gamma, float4_numbers, inverse_mass, - sqrt_mass_inverse, vel, crd, frc, acc, - reinterpret_cast(rand_state), rand_frc, output, - reinterpret_cast(stream_ptr)); - return true; - } - - protected: - void InitSizeLists() override { - const int kAtomSize = 3; - - input_size_list_.push_back(atom_numbers * sizeof(float)); - input_size_list_.push_back(atom_numbers * sizeof(float)); - input_size_list_.push_back(atom_numbers * kAtomSize * sizeof(float)); - input_size_list_.push_back(atom_numbers * kAtomSize * sizeof(float)); - input_size_list_.push_back(atom_numbers * kAtomSize * sizeof(float)); - input_size_list_.push_back(atom_numbers * kAtomSize * sizeof(float)); - input_size_list_.push_back(float4_numbers * sizeof(curandStatePhilox4_32_10_t)); - input_size_list_.push_back(atom_numbers * kAtomSize * sizeof(float)); - - output_size_list_.push_back(atom_numbers * kAtomSize * sizeof(T)); - } - - private: - float half_dt; - float exp_gamma; - float dt; - int atom_numbers; - int float4_numbers; -}; -} // namespace kernel -} // namespace mindspore -#endif diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/nvtit/md_iteration_leap_frog_liujian_gpu_with_max_vel_kernel.cc b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/nvtit/md_iteration_leap_frog_liujian_gpu_with_max_vel_kernel.cc deleted file mode 100644 index 8931ab5793d..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/nvtit/md_iteration_leap_frog_liujian_gpu_with_max_vel_kernel.cc +++ /dev/null @@ -1,34 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "plugin/device/gpu/kernel/sponge/nvtit/md_iteration_leap_frog_liujian_gpu_with_max_vel_kernel.h" - -namespace mindspore { -namespace kernel { -MS_REG_GPU_KERNEL_TWO(MDIterationLeapFrogLiujianWithMaxVel, - KernelAttr() - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddOutputAttr(kNumberTypeFloat32), - MDIterationLeapFrogLiujianMaxVelCudaGpuKernelMod, float, int) -} // namespace kernel -} // namespace mindspore diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/nvtit/md_iteration_leap_frog_liujian_gpu_with_max_vel_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/nvtit/md_iteration_leap_frog_liujian_gpu_with_max_vel_kernel.h deleted file mode 100644 index ac511bb13c7..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/nvtit/md_iteration_leap_frog_liujian_gpu_with_max_vel_kernel.h +++ /dev/null @@ -1,105 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONG_MD_ITERATION_LEAP_FROG_LIUJIAN_GPU_WITH_MAX_VEL_KERNEL_H_ -#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONG_MD_ITERATION_LEAP_FROG_LIUJIAN_GPU_WITH_MAX_VEL_KERNEL_H_ - -#include "plugin/device/gpu/kernel/cuda_impl/sponge/nvtit/md_iteration_leap_frog_liujian_with_max_vel_impl.cuh" -#include -#include -#include -#include - -#include "plugin/device/gpu/kernel/gpu_kernel.h" -#include "plugin/device/gpu/kernel/gpu_kernel_factory.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" - -namespace mindspore { -namespace kernel { -constexpr size_t kIdx2 = 2; -constexpr size_t kIdx3 = 3; -constexpr size_t kIdx4 = 4; -constexpr size_t kIdx5 = 5; -constexpr size_t kIdx6 = 6; -constexpr size_t kIdx7 = 7; -template -class MDIterationLeapFrogLiujianMaxVelCudaGpuKernelMod : public DeprecatedNativeGpuKernelMod { - public: - MDIterationLeapFrogLiujianMaxVelCudaGpuKernelMod() {} - ~MDIterationLeapFrogLiujianMaxVelCudaGpuKernelMod() override = default; - - bool Init(const CNodePtr &kernel_node) override { - // get bond_numbers - kernel_node_ = kernel_node; - atom_numbers = static_cast(GetAttr(kernel_node, "atom_numbers")); - half_dt = static_cast(GetAttr(kernel_node, "half_dt")); - dt = static_cast(GetAttr(kernel_node, "dt")); - exp_gamma = static_cast(GetAttr(kernel_node, "exp_gamma")); - const double kCoef1 = 3.; - const double kCoef2 = 4.; - float4_numbers = ceil(kCoef1 * static_cast(atom_numbers) / kCoef2); - max_vel = static_cast(GetAttr(kernel_node, "max_vel")); - InitSizeLists(); - return true; - } - - bool Launch(const std::vector &inputs, const std::vector &workspace, - const std::vector &outputs, void *stream_ptr) override { - auto inverse_mass = GetDeviceAddress(inputs, 0); - auto sqrt_mass_inverse = GetDeviceAddress(inputs, 1); - auto vel = GetDeviceAddress(inputs, kIdx2); - auto crd = GetDeviceAddress(inputs, kIdx3); - auto frc = GetDeviceAddress(inputs, kIdx4); - auto acc = GetDeviceAddress(inputs, kIdx5); - auto rand_state = GetDeviceAddress(inputs, kIdx6); - auto rand_frc = GetDeviceAddress(inputs, kIdx7); - - auto output = GetDeviceAddress(outputs, 0); - - MD_Iteration_Leap_Frog_With_LiuJian_With_Max_Vel( - atom_numbers, half_dt, dt, exp_gamma, float4_numbers, inverse_mass, sqrt_mass_inverse, vel, crd, frc, acc, - reinterpret_cast(rand_state), rand_frc, output, max_vel, - reinterpret_cast(stream_ptr)); - return true; - } - - protected: - void InitSizeLists() override { - const int kAtomSize = 3; - - input_size_list_.push_back(atom_numbers * sizeof(float)); - input_size_list_.push_back(atom_numbers * sizeof(float)); - input_size_list_.push_back(atom_numbers * kAtomSize * sizeof(float)); - input_size_list_.push_back(atom_numbers * kAtomSize * sizeof(float)); - input_size_list_.push_back(atom_numbers * kAtomSize * sizeof(float)); - input_size_list_.push_back(atom_numbers * kAtomSize * sizeof(float)); - input_size_list_.push_back(float4_numbers * sizeof(curandStatePhilox4_32_10_t)); - input_size_list_.push_back(atom_numbers * kAtomSize * sizeof(float)); - - output_size_list_.push_back(atom_numbers * kAtomSize * sizeof(T)); - } - - private: - int atom_numbers; - float half_dt; - float dt; - float exp_gamma; - int float4_numbers; - float max_vel; -}; -} // namespace kernel -} // namespace mindspore -#endif diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/nvtit/md_iteration_leap_frog_with_max_vel_kernel.cc b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/nvtit/md_iteration_leap_frog_with_max_vel_kernel.cc deleted file mode 100644 index 4ab86af2906..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/nvtit/md_iteration_leap_frog_with_max_vel_kernel.cc +++ /dev/null @@ -1,31 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "plugin/device/gpu/kernel/sponge/nvtit/md_iteration_leap_frog_with_max_vel_kernel.h" - -namespace mindspore { -namespace kernel { -MS_REG_GPU_KERNEL_ONE(MDIterationLeapFrogWithMaxVel, - KernelAttr() - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddOutputAttr(kNumberTypeFloat32), - MDIterationLeapFrogWithMaxVelGpuKernelMod, float) -} // namespace kernel -} // namespace mindspore diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/nvtit/md_iteration_leap_frog_with_max_vel_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/nvtit/md_iteration_leap_frog_with_max_vel_kernel.h deleted file mode 100644 index cca0bd568da..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/nvtit/md_iteration_leap_frog_with_max_vel_kernel.h +++ /dev/null @@ -1,79 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_MD_ITERATION_LEAP_FROG_WITH_MAX_VEL_KERNEL_H_ -#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_MD_ITERATION_LEAP_FROG_WITH_MAX_VEL_KERNEL_H_ - -#include "plugin/device/gpu/kernel/cuda_impl/sponge/nvtit/md_iteration_leap_frog_with_max_vel_impl.cuh" -#include -#include -#include -#include - -#include "plugin/device/gpu/kernel/gpu_kernel.h" -#include "plugin/device/gpu/kernel/gpu_kernel_factory.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" - -namespace mindspore { -namespace kernel { -template -class MDIterationLeapFrogWithMaxVelGpuKernelMod : public DeprecatedNativeGpuKernelMod { - public: - MDIterationLeapFrogWithMaxVelGpuKernelMod() {} - ~MDIterationLeapFrogWithMaxVelGpuKernelMod() override = default; - - bool Init(const CNodePtr &kernel_node) override { - // get bond_numbers - kernel_node_ = kernel_node; - atom_numbers = static_cast(GetAttr(kernel_node, "atom_numbers")); - dt = static_cast(GetAttr(kernel_node, "dt")); - max_velocity = static_cast(GetAttr(kernel_node, "max_velocity")); - InitSizeLists(); - return true; - } - - bool Launch(const std::vector &inputs, const std::vector &workspace, - const std::vector &outputs, void *stream_ptr) override { - auto vel = GetDeviceAddress(inputs, 0); - auto crd = GetDeviceAddress(inputs, 1); - auto frc = GetDeviceAddress(inputs, 2); - auto acc = GetDeviceAddress(inputs, 3); - auto inverse_mass = GetDeviceAddress(inputs, 4); - - MDIterationLeapFrogWithMaxVelocity(atom_numbers, vel, crd, frc, acc, inverse_mass, dt, max_velocity, - reinterpret_cast(stream_ptr)); - return true; - } - - protected: - void InitSizeLists() override { - input_size_list_.push_back(atom_numbers * 3 * sizeof(T)); - input_size_list_.push_back(atom_numbers * 3 * sizeof(T)); - input_size_list_.push_back(atom_numbers * 3 * sizeof(T)); - input_size_list_.push_back(atom_numbers * 3 * sizeof(T)); - input_size_list_.push_back(atom_numbers * sizeof(T)); - - output_size_list_.push_back(sizeof(T)); - } - - private: - int atom_numbers; - float dt; - float max_velocity; -}; -} // namespace kernel -} // namespace mindspore -#endif diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/nvtit/md_iteration_setup_random_state.cc b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/nvtit/md_iteration_setup_random_state.cc deleted file mode 100644 index 0dec3545eb0..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/nvtit/md_iteration_setup_random_state.cc +++ /dev/null @@ -1,24 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "plugin/device/gpu/kernel/sponge/nvtit/md_iteration_setup_random_state.h" - -namespace mindspore { -namespace kernel { -MS_REG_GPU_KERNEL_TWO(MDIterationSetupRandState, KernelAttr().AddOutputAttr(kNumberTypeFloat32), - MDIterationSetupRandStateGpuKernelMod, float, int) -} // namespace kernel -} // namespace mindspore diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/nvtit/md_iteration_setup_random_state.h b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/nvtit/md_iteration_setup_random_state.h deleted file mode 100644 index 8dea9ef84a2..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/nvtit/md_iteration_setup_random_state.h +++ /dev/null @@ -1,67 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_MD_ITERATION_SETUP_RANDOM_STATE_GPU_KERNEL_H_ -#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_MD_ITERATION_SETUP_RANDOM_STATE_GPU_KERNEL_H_ - -#include "plugin/device/gpu/kernel/cuda_impl/sponge/nvtit/md_iteration_setup_random_state_gpu_impl.cuh" -#include -#include -#include -#include - -#include "plugin/device/gpu/kernel/gpu_kernel.h" -#include "plugin/device/gpu/kernel/gpu_kernel_factory.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" - -namespace mindspore { -namespace kernel { -template -class MDIterationSetupRandStateGpuKernelMod : public DeprecatedNativeGpuKernelMod { - public: - MDIterationSetupRandStateGpuKernelMod() {} - ~MDIterationSetupRandStateGpuKernelMod() override = default; - - bool Init(const CNodePtr &kernel_node) override { - // get bond_numbers - kernel_node_ = kernel_node; - atom_numbers = static_cast(GetAttr(kernel_node, "atom_numbers")); - seed = static_cast(GetAttr(kernel_node, "seed")); - float4_numbers = ceil(3. * static_cast(atom_numbers) / 4.); - InitSizeLists(); - return true; - } - - bool Launch(const std::vector &inputs, const std::vector &, - const std::vector &outputs, void *stream_ptr) override { - auto output = GetDeviceAddress(outputs, 0); - curandStatePhilox4_32_10_t *rand_state = reinterpret_cast(output); - MD_Iteration_Setup_Random_State(float4_numbers, rand_state, seed, reinterpret_cast(stream_ptr)); - - return true; - } - - protected: - void InitSizeLists() override { output_size_list_.push_back(sizeof(curandStatePhilox4_32_10_t) * float4_numbers); } - - private: - int atom_numbers; - int seed; - int float4_numbers; -}; -} // namespace kernel -} // namespace mindspore -#endif diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/pme/fft_3d_kernel.cc b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/pme/fft_3d_kernel.cc deleted file mode 100644 index d7d664a2c28..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/pme/fft_3d_kernel.cc +++ /dev/null @@ -1,23 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -#include "plugin/device/gpu/kernel/sponge/pme/fft_3d_kernel.h" - -namespace mindspore { -namespace kernel { -MS_REG_GPU_KERNEL_ONE(FFT3D, KernelAttr().AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeComplex64), - FFT3DGpuKernelMod, float) -} // namespace kernel -} // namespace mindspore diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/pme/fft_3d_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/pme/fft_3d_kernel.h deleted file mode 100644 index e861ee2f76b..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/pme/fft_3d_kernel.h +++ /dev/null @@ -1,77 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -#ifndef MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONGE_PME_FFT_3D_KERNEL_H_ -#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONGE_PME_FFT_3D_KERNEL_H_ -#include -#include -#include -#include -#include -#include "plugin/device/gpu/kernel/gpu_kernel.h" -#include "plugin/device/gpu/kernel/gpu_kernel_factory.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" -#include "plugin/device/gpu/kernel/cuda_impl/sponge/pme/fft_3d_impl.cuh" -namespace mindspore { -namespace kernel { -template -using Complex = mindspore::utils::Complex; -template -class FFT3DGpuKernelMod : public DeprecatedNativeGpuKernelMod { - public: - FFT3DGpuKernelMod() = default; - ~FFT3DGpuKernelMod() override = default; - - bool Init(const CNodePtr &kernel_node) override { - kernel_node_ = kernel_node; - fftx = static_cast(GetAttr(kernel_node, "fftx")); - ffty = static_cast(GetAttr(kernel_node, "ffty")); - fftz = static_cast(GetAttr(kernel_node, "fftz")); - Nall = fftx * ffty * fftz; - Nfft = fftx * ffty * (fftz / 2 + 1); - - cufftPlan3d(&FFT_plan_r2c, fftx, ffty, fftz, CUFFT_R2C); - - InitSizeLists(); - return true; - } - - bool Launch(const std::vector &inputs, const std::vector &workspace, - const std::vector &outputs, void *stream_ptr) override { - auto input_tensor = GetDeviceAddress(inputs, 0); - auto output_tensor = GetDeviceAddress>(outputs, 0); - - cufftSetStream(FFT_plan_r2c, reinterpret_cast(stream_ptr)); - FFT3D(Nfft, input_tensor, output_tensor, FFT_plan_r2c, reinterpret_cast(stream_ptr)); - return true; - } - - protected: - void InitSizeLists() override { - input_size_list_.push_back(Nall * sizeof(T)); - output_size_list_.push_back(Nfft * sizeof(Complex)); - } - - private: - int fftx; - int ffty; - int fftz; - int Nall; - int Nfft; - cufftHandle FFT_plan_r2c; -}; -} // namespace kernel -} // namespace mindspore -#endif diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/pme/ifft_3d_kernel.cc b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/pme/ifft_3d_kernel.cc deleted file mode 100644 index d7c8f064ddb..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/pme/ifft_3d_kernel.cc +++ /dev/null @@ -1,23 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -#include "plugin/device/gpu/kernel/sponge/pme/ifft_3d_kernel.h" - -namespace mindspore { -namespace kernel { -MS_REG_GPU_KERNEL_ONE(IFFT3D, KernelAttr().AddInputAttr(kNumberTypeComplex64).AddOutputAttr(kNumberTypeFloat32), - IFFT3DGpuKernelMod, float) -} // namespace kernel -} // namespace mindspore diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/pme/ifft_3d_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/pme/ifft_3d_kernel.h deleted file mode 100644 index d60570fc1a8..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/pme/ifft_3d_kernel.h +++ /dev/null @@ -1,77 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -#ifndef MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONGE_PME_IFFT_3D_KERNEL_H_ -#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONGE_PME_IFFT_3D_KERNEL_H_ -#include -#include -#include -#include -#include -#include "plugin/device/gpu/kernel/gpu_kernel.h" -#include "plugin/device/gpu/kernel/gpu_kernel_factory.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" -#include "plugin/device/gpu/kernel/cuda_impl/sponge/pme/ifft_3d_impl.cuh" -namespace mindspore { -namespace kernel { -template -using Complex = mindspore::utils::Complex; -template -class IFFT3DGpuKernelMod : public DeprecatedNativeGpuKernelMod { - public: - IFFT3DGpuKernelMod() = default; - ~IFFT3DGpuKernelMod() override = default; - - bool Init(const CNodePtr &kernel_node) override { - kernel_node_ = kernel_node; - fftx = static_cast(GetAttr(kernel_node, "fftx")); - ffty = static_cast(GetAttr(kernel_node, "ffty")); - fftz = static_cast(GetAttr(kernel_node, "fftz")); - Nfft = fftx * ffty * fftz; - Nall = fftx * ffty * (fftz - 1) * 2; - - cufftPlan3d(&FFT_plan_c2r, fftx, ffty, (fftz - 1) * 2, CUFFT_C2R); - - InitSizeLists(); - return true; - } - - bool Launch(const std::vector &inputs, const std::vector &workspace, - const std::vector &outputs, void *stream_ptr) override { - auto input_tensor = GetDeviceAddress>(inputs, 0); - auto output_tensor = GetDeviceAddress(outputs, 0); - - cufftSetStream(FFT_plan_c2r, reinterpret_cast(stream_ptr)); - IFFT3D(Nfft, input_tensor, output_tensor, FFT_plan_c2r, reinterpret_cast(stream_ptr)); - return true; - } - - protected: - void InitSizeLists() override { - input_size_list_.push_back(Nfft * sizeof(Complex)); - output_size_list_.push_back(Nall * sizeof(T)); - } - - private: - int fftx; - int ffty; - int fftz; - int Nall; - int Nfft; - cufftHandle FFT_plan_c2r; -}; -} // namespace kernel -} // namespace mindspore -#endif diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/pme/pme_batched_fft_2d_kernel.cc b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/pme/pme_batched_fft_2d_kernel.cc deleted file mode 100644 index e8991b98f4c..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/pme/pme_batched_fft_2d_kernel.cc +++ /dev/null @@ -1,24 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -#include "plugin/device/gpu/kernel/sponge/pme/pme_batched_fft_2d_kernel.h" - -namespace mindspore { -namespace kernel { -MS_REG_GPU_KERNEL_ONE(PMEBatchedFFT2D, - KernelAttr().AddInputAttr(kNumberTypeComplex64).AddOutputAttr(kNumberTypeComplex64), - PMEBatchedFFT2DGpuKernelMod, float) -} // namespace kernel -} // namespace mindspore diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/pme/pme_batched_fft_2d_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/pme/pme_batched_fft_2d_kernel.h deleted file mode 100644 index d4f68f9be7d..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/pme/pme_batched_fft_2d_kernel.h +++ /dev/null @@ -1,82 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -#ifndef MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONGE_PME_PME_BATCHED_FFT_2D_KERNEL_H_ -#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONGE_PME_PME_BATCHED_FFT_2D_KERNEL_H_ -#include -#include -#include -#include -#include -#include "plugin/device/gpu/kernel/gpu_kernel.h" -#include "plugin/device/gpu/kernel/gpu_kernel_factory.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" -#include "plugin/device/gpu/kernel/cuda_impl/sponge/pme/pme_batched_fft_2d_impl.cuh" -namespace mindspore { -namespace kernel { -template -using Complex = mindspore::utils::Complex; -template -class PMEBatchedFFT2DGpuKernelMod : public DeprecatedNativeGpuKernelMod { - public: - PMEBatchedFFT2DGpuKernelMod() = default; - ~PMEBatchedFFT2DGpuKernelMod() override = default; - - bool Init(const CNodePtr &kernel_node) override { - // direction: forward 1, inverse -1 - direction = static_cast(GetAttr(kernel_node, "direction")); - fftx = static_cast(GetAttr(kernel_node, "fftx")); - ffty = static_cast(GetAttr(kernel_node, "ffty")); - fftz = static_cast(GetAttr(kernel_node, "fftz")); - Nall = fftx * ffty * fftz; - - // config plan - int batch = fftx; - int dimSize[] = {ffty, fftz}; - int rank = sizeof(dimSize) / sizeof(dimSize[0]); - cufftPlanMany(&FFT_plan_c2c, rank, dimSize, NULL, 0, 0, NULL, 0, 0, CUFFT_C2C, batch); - - InitSizeLists(); - return true; - } - - bool Launch(const std::vector &inputs, const std::vector &workspace, - const std::vector &outputs, void *stream_ptr) override { - auto input_tensor = GetDeviceAddress>(inputs, 0); - auto output_tensor = GetDeviceAddress>(outputs, 0); - - cufftSetStream(FFT_plan_c2c, reinterpret_cast(stream_ptr)); - PMEBatchedFFT2D(input_tensor, output_tensor, FFT_plan_c2c, direction, - reinterpret_cast(stream_ptr)); - return true; - } - - protected: - void InitSizeLists() override { - input_size_list_.push_back(Nall * sizeof(Complex)); - output_size_list_.push_back(Nall * sizeof(Complex)); - } - - private: - int direction; - int fftx; - int ffty; - int fftz; - int Nall; - cufftHandle FFT_plan_c2c; -}; -} // namespace kernel -} // namespace mindspore -#endif diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/pme/pme_energy_kernel.cc b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/pme/pme_energy_kernel.cc deleted file mode 100644 index e26e5bcfef0..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/pme/pme_energy_kernel.cc +++ /dev/null @@ -1,36 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -#include "plugin/device/gpu/kernel/sponge/pme/pme_energy_kernel.h" - -namespace mindspore { -namespace kernel { -MS_REG_GPU_KERNEL_TWO(PMEEnergy, - KernelAttr() - .AddInputAttr(kNumberTypeUInt32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeInt32) - .AddOutputAttr(kNumberTypeFloat32) - .AddOutputAttr(kNumberTypeFloat32) - .AddOutputAttr(kNumberTypeFloat32) - .AddOutputAttr(kNumberTypeFloat32), - PMEEnergyGpuKernelMod, float, int) -} // namespace kernel -} // namespace mindspore diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/pme/pme_energy_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/pme/pme_energy_kernel.h deleted file mode 100644 index 73633c65848..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/pme/pme_energy_kernel.h +++ /dev/null @@ -1,272 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -#ifndef MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONGE_PME_PME_ENERGY_KERNEL_H_ -#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONGE_PME_PME_ENERGY_KERNEL_H_ -#include -#include -#include -#include "plugin/device/gpu/kernel/gpu_kernel.h" -#include "plugin/device/gpu/kernel/gpu_kernel_factory.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" -#include "plugin/device/gpu/kernel/cuda_impl/sponge/pme/pme_energy_impl.cuh" -namespace mindspore { -namespace kernel { -template -class PMEEnergyGpuKernelMod : public DeprecatedNativeGpuKernelMod { - public: - PMEEnergyGpuKernelMod() : ele_uint_crd(1) {} - ~PMEEnergyGpuKernelMod() override = default; - - bool Init(const CNodePtr &kernel_node) override { - kernel_node_ = kernel_node; - atom_numbers = static_cast(GetAttr(kernel_node, "atom_numbers")); - excluded_numbers = static_cast(GetAttr(kernel_node, "excluded_numbers")); - beta = static_cast(GetAttr(kernel_node, "beta")); - fftx = static_cast(GetAttr(kernel_node, "fftx")); - ffty = static_cast(GetAttr(kernel_node, "ffty")); - fftz = static_cast(GetAttr(kernel_node, "fftz")); - - float box_length_0 = static_cast(GetAttr(kernel_node, "box_length_0")); - float box_length_1 = static_cast(GetAttr(kernel_node, "box_length_1")); - float box_length_2 = static_cast(GetAttr(kernel_node, "box_length_2")); - std::vector h_box_length{box_length_0, box_length_1, box_length_2}; - VECTOR *box_length = reinterpret_cast(h_box_length.data()); - cufftPlan3d(&PME_plan_r2c, fftx, ffty, fftz, CUFFT_R2C); - cufftPlan3d(&PME_plan_c2r, fftx, ffty, fftz, CUFFT_C2R); - _thread_PME.x = 8; - _thread_PME.y = 8; - PME_Nin = ffty * fftz; - PME_Nfft = fftx * ffty * (fftz / 2 + 1); - PME_Nall = fftx * ffty * fftz; - PME_kxyz_cpu.resize(64); - volume = box_length[0].x * box_length[0].y * box_length[0].z; - int kx, ky, kz, kxrp, kyrp, kzrp, index; - for (kx = 0; kx < 4; kx++) { - for (ky = 0; ky < 4; ky++) { - for (kz = 0; kz < 4; kz++) { - index = kx * 16 + ky * 4 + kz; - PME_kxyz_cpu[index].uint_x = kx; - PME_kxyz_cpu[index].uint_y = ky; - PME_kxyz_cpu[index].uint_z = kz; - } - } - } - - B1.resize(fftx); - B2.resize(ffty); - B3.resize(fftz); - PME_BC0.resize(PME_Nfft); - for (kx = 0; kx < fftx; kx++) { - B1[kx] = getb(kx, fftx, 4); - } - - for (ky = 0; ky < ffty; ky++) { - B2[ky] = getb(ky, ffty, 4); - } - - for (kz = 0; kz < fftz; kz++) { - B3[kz] = getb(kz, fftz, 4); - } - float mprefactor = PI * PI / -beta / beta; - - float msq; - for (kx = 0; kx < fftx; kx++) { - kxrp = kx; - if (kx > fftx / 2) kxrp = fftx - kx; - for (ky = 0; ky < ffty; ky++) { - kyrp = ky; - if (ky > ffty / 2) kyrp = ffty - ky; - for (kz = 0; kz <= fftz / 2; kz++) { - kzrp = kz; - - msq = kxrp * kxrp / box_length[0].x / box_length[0].x + kyrp * kyrp / box_length[0].y / box_length[0].y + - kzrp * kzrp / box_length[0].z / box_length[0].z; - index = kx * ffty * (fftz / 2 + 1) + ky * (fftz / 2 + 1) + kz; - if ((kx + ky + kz) == 0) { - PME_BC0[index] = 0; - } else { - PME_BC0[index] = 1.0 / PI / msq * exp(mprefactor * msq) / volume; - } - - PME_BC0[index] *= B1[kx] * B2[ky] * B3[kz]; - } - } - } - - InitSizeLists(); - return true; - } - - bool Launch(const std::vector &inputs, const std::vector &workspace, - const std::vector &outputs, void *stream_ptr) override { - auto uint_crd = GetDeviceAddress(inputs, 0); - auto charge = GetDeviceAddress(inputs, 1); - auto nl_numbers = GetDeviceAddress(inputs, 2); - auto nl_serial = GetDeviceAddress(inputs, 3); - auto scaler = GetDeviceAddress(inputs, 4); - auto excluded_list_start = GetDeviceAddress(inputs, 5); - auto excluded_list = GetDeviceAddress(inputs, 6); - auto excluded_atom_numbers = GetDeviceAddress(inputs, 7); - - auto pme_uxyz = GetDeviceAddress(workspace, 0); // workspace - auto pme_frxyz = GetDeviceAddress(workspace, 1); // workspace - auto pme_q = GetDeviceAddress(workspace, 2); // workspace - auto pme_fq = GetDeviceAddress(workspace, 3); // workspace - auto pme_atom_near = GetDeviceAddress(workspace, 4); // workspace - auto pme_bc = GetDeviceAddress(workspace, 5); // workspace - auto pme_kxyz = GetDeviceAddress(workspace, 6); // workspace - auto nl = GetDeviceAddress(workspace, 7); - - auto reciprocal_ene = GetDeviceAddress(outputs, 0); - auto self_ene = GetDeviceAddress(outputs, 1); - auto direct_ene = GetDeviceAddress(outputs, 2); - auto correction_ene = GetDeviceAddress(outputs, 3); - - cufftSetStream(PME_plan_r2c, reinterpret_cast(stream_ptr)); - cufftSetStream(PME_plan_c2r, reinterpret_cast(stream_ptr)); - cudaMemcpyAsync(pme_kxyz, PME_kxyz_cpu.data(), sizeof(UNSIGNED_INT_VECTOR) * 64, cudaMemcpyHostToDevice, - reinterpret_cast(stream_ptr)); - cudaMemcpyAsync(pme_bc, PME_BC0.data(), sizeof(float) * PME_Nfft, cudaMemcpyHostToDevice, - reinterpret_cast(stream_ptr)); - - PMEEnergy(fftx, ffty, fftz, atom_numbers, beta, pme_bc, pme_uxyz, pme_frxyz, pme_q, pme_fq, pme_atom_near, pme_kxyz, - uint_crd, charge, nl_numbers, nl_serial, nl, scaler, excluded_list_start, excluded_list, - excluded_atom_numbers, reciprocal_ene, self_ene, direct_ene, correction_ene, _thread_PME, PME_Nin, - PME_Nfft, PME_Nall, PME_plan_r2c, PME_plan_c2r, reinterpret_cast(stream_ptr)); - return true; - } - - protected: - void InitSizeLists() override { - input_size_list_.push_back(atom_numbers * sizeof(UNSIGNED_INT_VECTOR)); - input_size_list_.push_back(atom_numbers * sizeof(VECTOR)); - input_size_list_.push_back(atom_numbers * sizeof(T1)); - input_size_list_.push_back(max_nl_numbers * sizeof(T1)); - input_size_list_.push_back(sizeof(VECTOR)); - - input_size_list_.push_back(atom_numbers * sizeof(T1)); - input_size_list_.push_back(excluded_numbers * sizeof(T1)); - input_size_list_.push_back(atom_numbers * sizeof(T1)); - - workspace_size_list_.push_back(atom_numbers * sizeof(UNSIGNED_INT_VECTOR)); - workspace_size_list_.push_back(atom_numbers * sizeof(VECTOR)); - workspace_size_list_.push_back(PME_Nall * sizeof(T)); - workspace_size_list_.push_back(PME_Nfft * sizeof(cufftComplex)); - workspace_size_list_.push_back(atom_numbers * 64 * sizeof(int)); - workspace_size_list_.push_back(PME_Nfft * sizeof(float)); - workspace_size_list_.push_back(64 * sizeof(UNSIGNED_INT_VECTOR)); - workspace_size_list_.push_back(atom_numbers * max_nl_numbers * sizeof(T1)); - - output_size_list_.push_back(sizeof(T)); - output_size_list_.push_back(sizeof(T)); - output_size_list_.push_back(sizeof(T)); - output_size_list_.push_back(sizeof(T)); - } - - cufftComplex expc(cufftComplex z) { - cufftComplex res; - float t = expf(z.x); - mindspore::kernel::math::SinCosf(z.y, &res.y, &res.x); - res.x *= t; - res.y *= t; - return res; - } - - float M_(float u, int n) { - if (n == 2) { - if (u > 2 || u < 0) return 0; - return 1 - abs(u - 1); - } else { - return u / (n - 1) * M_(u, n - 1) + (n - u) / (n - 1) * M_(u - 1, n - 1); - } - } - - float getb(int k, int NFFT, int B_order) { - cufftComplex tempc, tempc2, res; - float tempf; - tempc2.x = 0; - tempc2.y = 0; - - tempc.x = 0; - if (NFFT == 0) { - MS_LOG(ERROR) << "Divide by zero."; - } else { - tempc.y = 2 * (B_order - 1) * PI * k / NFFT; - } - res = expc(tempc); - - for (int kk = 0; kk < (B_order - 1); kk++) { - tempc.x = 0; - if (NFFT == 0) { - MS_LOG(ERROR) << "Divide by zero."; - break; - } else { - tempc.y = 2 * PI * k / NFFT * kk; - } - tempc = expc(tempc); - tempf = M_(kk + 1, B_order); - tempc2.x += tempf * tempc.x; - tempc2.y += tempf * tempc.y; - } - res = cuCdivf(res, tempc2); - return res.x * res.x + res.y * res.y; - } - - private: - size_t ele_uint_crd = 1; - - std::vector B1; - std::vector B2; - std::vector B3; - std::vector PME_BC0; - - int atom_numbers; - int excluded_numbers; - int max_nl_numbers = 800; - int fftx; - int ffty; - int fftz; - float beta; - int PME_Nin; - int PME_Nall; - int PME_Nfft; - float volume; - float PI = 3.1415926; - cufftHandle PME_plan_r2c; - cufftHandle PME_plan_c2r; - - dim3 _thread_PME; - - struct VECTOR { - float x; - float y; - float z; - }; - - struct UNSIGNED_INT_VECTOR { - unsigned int uint_x; - unsigned int uint_y; - unsigned int uint_z; - }; - std::vector PME_kxyz_cpu; - struct NEIGHBOR_LIST { - int atom_numbers; - int *atom_serial; - }; -}; -} // namespace kernel -} // namespace mindspore -#endif diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/pme/pme_energy_update_kernel.cc b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/pme/pme_energy_update_kernel.cc deleted file mode 100644 index ed53552993a..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/pme/pme_energy_update_kernel.cc +++ /dev/null @@ -1,42 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -/** - * Note: - * PMEEnergyUpdate. This is an experimental interface that is subject to change and/or deletion. - */ -#include "plugin/device/gpu/kernel/sponge/pme/pme_energy_update_kernel.h" - -namespace mindspore { -namespace kernel { -MS_REG_GPU_KERNEL_TWO(PMEEnergyUpdate, - KernelAttr() - .AddInputAttr(kNumberTypeUInt32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddOutputAttr(kNumberTypeFloat32) - .AddOutputAttr(kNumberTypeFloat32) - .AddOutputAttr(kNumberTypeFloat32) - .AddOutputAttr(kNumberTypeFloat32), - PMEEnergyUpdateGpuKernelMod, float, int) -} // namespace kernel -} // namespace mindspore diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/pme/pme_energy_update_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/pme/pme_energy_update_kernel.h deleted file mode 100644 index 226d45c5c22..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/pme/pme_energy_update_kernel.h +++ /dev/null @@ -1,305 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -/** - * Note: - * PMEEnergyUpdate. This is an experimental interface that is subject to change and/or deletion. - */ -#ifndef MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONGE_PME_PME_ENERGY_UPDATE_KERNEL_H_ -#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONGE_PME_PME_ENERGY_UPDATE_KERNEL_H_ -#include -#include -#include -#include "plugin/device/gpu/kernel/gpu_kernel.h" -#include "plugin/device/gpu/kernel/gpu_kernel_factory.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" -#include "plugin/device/gpu/kernel/cuda_impl/sponge/pme/pme_energy_update_impl.cuh" - -namespace mindspore { -namespace kernel { -template -class PMEEnergyUpdateGpuKernelMod : public DeprecatedNativeGpuKernelMod { - public: - PMEEnergyUpdateGpuKernelMod() : ele_uint_crd(1) {} - ~PMEEnergyUpdateGpuKernelMod() override = default; - - bool Init(const CNodePtr &kernel_node) override { - kernel_node_ = kernel_node; - atom_numbers = static_cast(GetAttr(kernel_node, "atom_numbers")); - excluded_numbers = static_cast(GetAttr(kernel_node, "excluded_numbers")); - beta = static_cast(GetAttr(kernel_node, "beta")); - fftx = static_cast(GetAttr(kernel_node, "fftx")); - ffty = static_cast(GetAttr(kernel_node, "ffty")); - fftz = static_cast(GetAttr(kernel_node, "fftz")); - - float box_length_0 = static_cast(GetAttr(kernel_node, "box_length_0")); - float box_length_1 = static_cast(GetAttr(kernel_node, "box_length_1")); - float box_length_2 = static_cast(GetAttr(kernel_node, "box_length_2")); - max_neighbor_numbers = static_cast(GetAttr(kernel_node, "max_neighbor_numbers")); - need_update = static_cast(GetAttr(kernel_node, "need_update")); - - cufftPlan3d(&PME_plan_r2c, fftx, ffty, fftz, CUFFT_R2C); - cufftPlan3d(&PME_plan_c2r, fftx, ffty, fftz, CUFFT_C2R); - _thread_PME.x = 8; - _thread_PME.y = 8; - PME_Nin = ffty * fftz; - PME_Nfft = fftx * ffty * (fftz / 2 + 1); - PME_Nall = fftx * ffty * fftz; - PME_kxyz_cpu.resize(64); - - std::vector h_box_length{box_length_0, box_length_1, box_length_2}; - VECTOR *box_length = reinterpret_cast(h_box_length.data()); - - volume = box_length[0].x * box_length[0].y * box_length[0].z; - int kx, ky, kz, kxrp, kyrp, kzrp, index; - for (kx = 0; kx < 4; kx++) { - for (ky = 0; ky < 4; ky++) { - for (kz = 0; kz < 4; kz++) { - index = kx * 16 + ky * 4 + kz; - PME_kxyz_cpu[index].uint_x = kx; - PME_kxyz_cpu[index].uint_y = ky; - PME_kxyz_cpu[index].uint_z = kz; - } - } - } - - B1.resize(fftx); - B2.resize(ffty); - B3.resize(fftz); - PME_BC0.resize(PME_Nfft); - for (kx = 0; kx < fftx; kx++) { - B1[kx] = getb(kx, fftx, 4); - } - - for (ky = 0; ky < ffty; ky++) { - B2[ky] = getb(ky, ffty, 4); - } - - for (kz = 0; kz < fftz; kz++) { - B3[kz] = getb(kz, fftz, 4); - } - float mprefactor = PI * PI / -beta / beta; - - float msq; - for (kx = 0; kx < fftx; kx++) { - kxrp = kx; - if (kx > fftx / 2) kxrp = fftx - kx; - for (ky = 0; ky < ffty; ky++) { - kyrp = ky; - if (ky > ffty / 2) kyrp = ffty - ky; - for (kz = 0; kz <= fftz / 2; kz++) { - kzrp = kz; - - msq = kxrp * kxrp / box_length[0].x / box_length[0].x + kyrp * kyrp / box_length[0].y / box_length[0].y + - kzrp * kzrp / box_length[0].z / box_length[0].z; - index = kx * ffty * (fftz / 2 + 1) + ky * (fftz / 2 + 1) + kz; - if ((kx + ky + kz) == 0) { - PME_BC0[index] = 0; - } else { - PME_BC0[index] = 1.0 / PI / msq * exp(mprefactor * msq) / volume; - } - - PME_BC0[index] *= B1[kx] * B2[ky] * B3[kz]; - } - } - } - - InitSizeLists(); - return true; - } - - bool Launch(const std::vector &inputs, const std::vector &workspace, - const std::vector &outputs, void *stream_ptr) override { - auto uint_crd = GetDeviceAddress(inputs, 0); - auto charge = GetDeviceAddress(inputs, 1); - auto nl_numbers = GetDeviceAddress(inputs, 2); - auto nl_serial = GetDeviceAddress(inputs, 3); - auto scaler = GetDeviceAddress(inputs, 4); - auto excluded_list_start = GetDeviceAddress(inputs, 5); - auto excluded_list = GetDeviceAddress(inputs, 6); - auto excluded_atom_numbers = GetDeviceAddress(inputs, 7); - auto neutralizing_factor = GetDeviceAddress(inputs, 8); - auto d_beta = GetDeviceAddress(inputs, 9); - - auto pme_uxyz = GetDeviceAddress(workspace, 0); // workspace - auto pme_frxyz = GetDeviceAddress(workspace, 1); // workspace - auto pme_q = GetDeviceAddress(workspace, 2); // workspace - auto pme_fq = GetDeviceAddress(workspace, 3); // workspace - auto pme_atom_near = GetDeviceAddress(workspace, 4); // workspace - auto pme_bc = GetDeviceAddress(workspace, 5); // workspace - auto pme_kxyz = GetDeviceAddress(workspace, 6); // workspace - auto nl = GetDeviceAddress(workspace, 7); - auto charge_sum = GetDeviceAddress(workspace, 8); - - auto reciprocal_ene = GetDeviceAddress(outputs, 0); - auto self_ene = GetDeviceAddress(outputs, 1); - auto direct_ene = GetDeviceAddress(outputs, 2); - auto correction_ene = GetDeviceAddress(outputs, 3); - - h_beta = beta; - if (need_update) { - cudaMemcpyAsync(&h_beta, d_beta, sizeof(float), cudaMemcpyDeviceToHost, - reinterpret_cast(stream_ptr)); - cudaStreamSynchronize(reinterpret_cast(stream_ptr)); - factor = h_beta / beta; - double factor_inverse = 1.0 / factor; - - for (int i = 0; i < PME_Nfft; i++) { - PME_BC0[i] *= factor_inverse; // update PME_BC0 - } - } - - cufftSetStream(PME_plan_r2c, reinterpret_cast(stream_ptr)); - cufftSetStream(PME_plan_c2r, reinterpret_cast(stream_ptr)); - cudaMemcpyAsync(pme_kxyz, PME_kxyz_cpu.data(), sizeof(UNSIGNED_INT_VECTOR) * 64, cudaMemcpyHostToDevice, - reinterpret_cast(stream_ptr)); - cudaMemcpyAsync(pme_bc, PME_BC0.data(), sizeof(float) * PME_Nfft, cudaMemcpyHostToDevice, - reinterpret_cast(stream_ptr)); - - PMEEnergyUpdate(fftx, ffty, fftz, atom_numbers, h_beta, pme_bc, pme_uxyz, pme_frxyz, pme_q, pme_fq, pme_atom_near, - pme_kxyz, uint_crd, charge, nl_numbers, nl_serial, nl, scaler, excluded_list_start, excluded_list, - excluded_atom_numbers, reciprocal_ene, self_ene, direct_ene, correction_ene, _thread_PME, PME_Nin, - PME_Nfft, PME_Nall, PME_plan_r2c, PME_plan_c2r, neutralizing_factor, charge_sum, - max_neighbor_numbers, reinterpret_cast(stream_ptr)); - return true; - } - - protected: - void InitSizeLists() override { - input_size_list_.push_back(atom_numbers * sizeof(UNSIGNED_INT_VECTOR)); - input_size_list_.push_back(atom_numbers * sizeof(VECTOR)); - input_size_list_.push_back(atom_numbers * sizeof(T1)); - input_size_list_.push_back(max_neighbor_numbers * sizeof(T1)); - input_size_list_.push_back(sizeof(VECTOR)); - - input_size_list_.push_back(atom_numbers * sizeof(T1)); - input_size_list_.push_back(excluded_numbers * sizeof(T1)); - input_size_list_.push_back(atom_numbers * sizeof(T1)); - input_size_list_.push_back(sizeof(T)); - input_size_list_.push_back(sizeof(T)); - - workspace_size_list_.push_back(atom_numbers * sizeof(UNSIGNED_INT_VECTOR)); - workspace_size_list_.push_back(atom_numbers * sizeof(VECTOR)); - workspace_size_list_.push_back(PME_Nall * sizeof(T)); - workspace_size_list_.push_back(PME_Nfft * sizeof(cufftComplex)); - workspace_size_list_.push_back(atom_numbers * 64 * sizeof(int)); - workspace_size_list_.push_back(PME_Nfft * sizeof(float)); - workspace_size_list_.push_back(64 * sizeof(UNSIGNED_INT_VECTOR)); - workspace_size_list_.push_back(atom_numbers * max_neighbor_numbers * sizeof(T1)); - workspace_size_list_.push_back(sizeof(T)); - - output_size_list_.push_back(sizeof(T)); - output_size_list_.push_back(sizeof(T)); - output_size_list_.push_back(sizeof(T)); - output_size_list_.push_back(sizeof(T)); - } - - cufftComplex expc(cufftComplex z) { - cufftComplex res; - float t = expf(z.x); - mindspore::kernel::math::SinCosf(z.y, &res.y, &res.x); - res.x *= t; - res.y *= t; - return res; - } - - float M_(float u, int n) { - if (n == 2) { - if (u > 2 || u < 0) return 0; - return 1 - abs(u - 1); - } else { - return u / (n - 1) * M_(u, n - 1) + (n - u) / (n - 1) * M_(u - 1, n - 1); - } - } - - float getb(int k, int NFFT, int B_order) { - cufftComplex tempc, tempc2, res; - float tempf; - tempc2.x = 0; - tempc2.y = 0; - - tempc.x = 0; - if (NFFT == 0) { - MS_LOG(ERROR) << "Divide by zero."; - } else { - tempc.y = 2 * (B_order - 1) * PI * k / NFFT; - } - res = expc(tempc); - - for (int kk = 0; kk < (B_order - 1); kk++) { - tempc.x = 0; - if (NFFT == 0) { - MS_LOG(ERROR) << "Divide by zero."; - break; - } else { - tempc.y = 2 * PI * k / NFFT * kk; - } - tempc = expc(tempc); - tempf = M_(kk + 1, B_order); - tempc2.x += tempf * tempc.x; - tempc2.y += tempf * tempc.y; - } - res = cuCdivf(res, tempc2); - return res.x * res.x + res.y * res.y; - } - - private: - size_t ele_uint_crd = 1; - - std::vector B1; - std::vector B2; - std::vector B3; - std::vector PME_BC0; - - int atom_numbers; - int excluded_numbers; - int max_neighbor_numbers; - int fftx; - int ffty; - int fftz; - float beta; - float factor; - float h_beta; - int PME_Nin; - int PME_Nall; - int PME_Nfft; - float volume; - float PI = 3.1415926; - int need_update; - cufftHandle PME_plan_r2c; - cufftHandle PME_plan_c2r; - - dim3 _thread_PME; - - struct VECTOR { - float x; - float y; - float z; - }; - - struct UNSIGNED_INT_VECTOR { - unsigned int uint_x; - unsigned int uint_y; - unsigned int uint_z; - }; - std::vector PME_kxyz_cpu; - struct NEIGHBOR_LIST { - int atom_numbers; - int *atom_serial; - }; -}; -} // namespace kernel -} // namespace mindspore -#endif diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/pme/pme_excluded_force_kernel.cc b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/pme/pme_excluded_force_kernel.cc deleted file mode 100644 index 21864bbe06d..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/pme/pme_excluded_force_kernel.cc +++ /dev/null @@ -1,31 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -#include "plugin/device/gpu/kernel/sponge/pme/pme_excluded_force_kernel.h" - -namespace mindspore { -namespace kernel { -MS_REG_GPU_KERNEL_TWO(PMEExcludedForce, - KernelAttr() - .AddInputAttr(kNumberTypeUInt32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeInt32) - .AddOutputAttr(kNumberTypeFloat32), - PMEExcludedForceGpuKernelMod, float, int) -} // namespace kernel -} // namespace mindspore diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/pme/pme_excluded_force_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/pme/pme_excluded_force_kernel.h deleted file mode 100644 index a98d67d3478..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/pme/pme_excluded_force_kernel.h +++ /dev/null @@ -1,93 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -/** - * Note: - * PMEExcludedForce. This is an experimental interface that is subject to change and/or deletion. - */ -#ifndef MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONGE_PME_PME_EXCLUDED_FORCE_KERNEL_H_ -#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONGE_PME_PME_EXCLUDED_FORCE_KERNEL_H_ -#include -#include -#include -#include "plugin/device/gpu/kernel/gpu_kernel.h" -#include "plugin/device/gpu/kernel/gpu_kernel_factory.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" -#include "plugin/device/gpu/kernel/cuda_impl/sponge/pme/pme_excluded_force_impl.cuh" - -namespace mindspore { -namespace kernel { -template -class PMEExcludedForceGpuKernelMod : public DeprecatedNativeGpuKernelMod { - public: - PMEExcludedForceGpuKernelMod() : ele_uint_crd(1) {} - ~PMEExcludedForceGpuKernelMod() override = default; - - bool Init(const CNodePtr &kernel_node) override { - kernel_node_ = kernel_node; - atom_numbers = static_cast(GetAttr(kernel_node, "atom_numbers")); - excluded_numbers = static_cast(GetAttr(kernel_node, "excluded_numbers")); - beta = static_cast(GetAttr(kernel_node, "beta")); - InitSizeLists(); - return true; - } - - bool Launch(const std::vector &inputs, const std::vector &workspace, - const std::vector &outputs, void *stream_ptr) override { - auto uint_crd = GetDeviceAddress(inputs, 0); - auto sacler = GetDeviceAddress(inputs, 1); - auto charge = GetDeviceAddress(inputs, 2); - auto excluded_list_start = GetDeviceAddress(inputs, 3); - auto excluded_list = GetDeviceAddress(inputs, 4); - auto excluded_atom_numbers = GetDeviceAddress(inputs, 5); - - auto force = GetDeviceAddress(outputs, 0); - PMEExcludedForce(atom_numbers, beta, uint_crd, sacler, charge, excluded_list_start, excluded_list, - excluded_atom_numbers, force, reinterpret_cast(stream_ptr)); - return true; - } - - protected: - void InitSizeLists() override { - input_size_list_.push_back(atom_numbers * sizeof(UNSIGNED_INT_VECTOR)); - input_size_list_.push_back(sizeof(VECTOR)); - input_size_list_.push_back(atom_numbers * sizeof(T)); - input_size_list_.push_back(atom_numbers * sizeof(T1)); - input_size_list_.push_back(excluded_numbers * sizeof(T1)); - input_size_list_.push_back(atom_numbers * sizeof(T1)); - - output_size_list_.push_back(atom_numbers * 3 * sizeof(T)); - } - - private: - size_t ele_uint_crd = 1; - int atom_numbers; - int excluded_numbers; - float beta; - struct VECTOR { - float x; - float y; - float z; - }; - - struct UNSIGNED_INT_VECTOR { - unsigned int uint_x; - unsigned int uint_y; - unsigned int uint_z; - }; -}; -} // namespace kernel -} // namespace mindspore -#endif diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/pme/pme_excluded_force_update_kernel.cc b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/pme/pme_excluded_force_update_kernel.cc deleted file mode 100644 index de0ec83a10c..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/pme/pme_excluded_force_update_kernel.cc +++ /dev/null @@ -1,36 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -/** - * Note: - * PMEExcludedForceUpdate. This is an experimental interface that is subject to change and/or deletion. - */ -#include "plugin/device/gpu/kernel/sponge/pme/pme_excluded_force_update_kernel.h" - -namespace mindspore { -namespace kernel { -MS_REG_GPU_KERNEL_TWO(PMEExcludedForceUpdate, - KernelAttr() - .AddInputAttr(kNumberTypeUInt32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeFloat32) - .AddOutputAttr(kNumberTypeFloat32), - PMEExcludedForceUpdateGpuKernelMod, float, int) -} // namespace kernel -} // namespace mindspore diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/pme/pme_excluded_force_update_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/pme/pme_excluded_force_update_kernel.h deleted file mode 100644 index 2792be490e9..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/pme/pme_excluded_force_update_kernel.h +++ /dev/null @@ -1,106 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -/** - * Note: - * PMEExcludedForceUpdate. This is an experimental interface that is subject to change and/or deletion. - */ -#ifndef MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONGE_PME_PME_EXCLUDED_FORCE_KERNEL_H_ -#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONGE_PME_PME_EXCLUDED_FORCE_KERNEL_H_ - -#include -#include -#include -#include -#include -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" -#include "plugin/device/gpu/kernel/gpu_kernel.h" -#include "plugin/device/gpu/kernel/gpu_kernel_factory.h" -#include "plugin/device/gpu/kernel/cuda_impl/sponge/pme/pme_excluded_force_impl.cuh" - -namespace mindspore { -namespace kernel { -template -class PMEExcludedForceUpdateGpuKernelMod : public DeprecatedNativeGpuKernelMod { - public: - PMEExcludedForceUpdateGpuKernelMod() : ele_uint_crd(1) {} - ~PMEExcludedForceUpdateGpuKernelMod() override = default; - - bool Init(const CNodePtr &kernel_node) override { - kernel_node_ = kernel_node; - atom_numbers = static_cast(GetAttr(kernel_node, "atom_numbers")); - excluded_numbers = static_cast(GetAttr(kernel_node, "excluded_numbers")); - beta = static_cast(GetAttr(kernel_node, "beta")); - need_update = static_cast(GetAttr(kernel_node, "need_update")); - InitSizeLists(); - return true; - } - - bool Launch(const std::vector &inputs, const std::vector &workspace, - const std::vector &outputs, void *stream_ptr) override { - auto uint_crd = GetDeviceAddress(inputs, 0); - auto sacler = GetDeviceAddress(inputs, 1); - auto charge = GetDeviceAddress(inputs, 2); - auto excluded_list_start = GetDeviceAddress(inputs, 3); - auto excluded_list = GetDeviceAddress(inputs, 4); - auto excluded_atom_numbers = GetDeviceAddress(inputs, 5); - auto d_beta = GetDeviceAddress(inputs, 6); - if (need_update) { - cudaMemcpyAsync(&beta, d_beta, sizeof(float), cudaMemcpyDeviceToHost, reinterpret_cast(stream_ptr)); - cudaStreamSynchronize(reinterpret_cast(stream_ptr)); - } - - auto force = GetDeviceAddress(outputs, 0); - PMEExcludedForce(atom_numbers, beta, uint_crd, sacler, charge, excluded_list_start, excluded_list, - excluded_atom_numbers, force, reinterpret_cast(stream_ptr)); - return true; - } - - protected: - void InitSizeLists() override { - input_size_list_.push_back(atom_numbers * sizeof(UNSIGNED_INT_VECTOR)); - input_size_list_.push_back(sizeof(VECTOR)); - input_size_list_.push_back(atom_numbers * sizeof(T)); - input_size_list_.push_back(atom_numbers * sizeof(T1)); - input_size_list_.push_back(excluded_numbers * sizeof(T1)); - input_size_list_.push_back(atom_numbers * sizeof(T1)); - input_size_list_.push_back(sizeof(T)); - - output_size_list_.push_back(atom_numbers * 3 * sizeof(T)); - } - - private: - size_t ele_uint_crd = 1; - - int atom_numbers; - int excluded_numbers; - float beta; - float factor; - int need_update; - struct VECTOR { - float x; - float y; - float z; - }; - - struct UNSIGNED_INT_VECTOR { - unsigned int uint_x; - unsigned int uint_y; - unsigned int uint_z; - }; -}; -} // namespace kernel -} // namespace mindspore -#endif diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/pme/pme_fft_1d_kernel.cc b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/pme/pme_fft_1d_kernel.cc deleted file mode 100644 index d3d81f1f706..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/pme/pme_fft_1d_kernel.cc +++ /dev/null @@ -1,23 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -#include "plugin/device/gpu/kernel/sponge/pme/pme_fft_1d_kernel.h" - -namespace mindspore { -namespace kernel { -MS_REG_GPU_KERNEL_ONE(PMEFFT1D, KernelAttr().AddInputAttr(kNumberTypeComplex64).AddOutputAttr(kNumberTypeComplex64), - PMEFFT1DGpuKernelMod, float) -} // namespace kernel -} // namespace mindspore diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/pme/pme_fft_1d_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/pme/pme_fft_1d_kernel.h deleted file mode 100644 index 55a692252ec..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/pme/pme_fft_1d_kernel.h +++ /dev/null @@ -1,73 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -#ifndef MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONGE_PME_PME_FFT_1D_KERNEL_H_ -#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONGE_PME_PME_FFT_1D_KERNEL_H_ -#include -#include -#include -#include -#include -#include "plugin/device/gpu/kernel/gpu_kernel.h" -#include "plugin/device/gpu/kernel/gpu_kernel_factory.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" -#include "plugin/device/gpu/kernel/cuda_impl/sponge/pme/pme_fft_1d_impl.cuh" -namespace mindspore { -namespace kernel { -template -using Complex = mindspore::utils::Complex; -template -class PMEFFT1DGpuKernelMod : public DeprecatedNativeGpuKernelMod { - public: - PMEFFT1DGpuKernelMod() = default; - ~PMEFFT1DGpuKernelMod() override = default; - - bool Init(const CNodePtr &kernel_node) override { - fftx = static_cast(GetAttr(kernel_node, "fftx")); - Nall = fftx; - Nfft = fftx; - - cufftPlan1d(&FFT_plan_c2c, fftx, CUFFT_C2C, batch); - - InitSizeLists(); - return true; - } - - bool Launch(const std::vector &inputs, const std::vector &workspace, - const std::vector &outputs, void *stream_ptr) override { - auto input_tensor = GetDeviceAddress>(inputs, 0); - auto output_tensor = GetDeviceAddress>(outputs, 0); - - cufftSetStream(FFT_plan_c2c, reinterpret_cast(stream_ptr)); - PMEFFT1D(Nfft, input_tensor, output_tensor, FFT_plan_c2c, reinterpret_cast(stream_ptr)); - return true; - } - - protected: - void InitSizeLists() override { - input_size_list_.push_back(Nall * sizeof(Complex)); - output_size_list_.push_back(Nfft * sizeof(Complex)); - } - - private: - int batch = 1; - int fftx; - int Nall; - int Nfft; - cufftHandle FFT_plan_c2c; -}; -} // namespace kernel -} // namespace mindspore -#endif diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/pme/pme_fft_2d_kernel.cc b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/pme/pme_fft_2d_kernel.cc deleted file mode 100644 index 588bc98d5ae..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/pme/pme_fft_2d_kernel.cc +++ /dev/null @@ -1,23 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -#include "plugin/device/gpu/kernel/sponge/pme/pme_fft_2d_kernel.h" - -namespace mindspore { -namespace kernel { -MS_REG_GPU_KERNEL_ONE(PMEFFT2D, KernelAttr().AddInputAttr(kNumberTypeComplex64).AddOutputAttr(kNumberTypeComplex64), - PMEFFT2DGpuKernelMod, float) -} // namespace kernel -} // namespace mindspore diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/pme/pme_fft_2d_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/pme/pme_fft_2d_kernel.h deleted file mode 100644 index 8e486a0c53f..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/pme/pme_fft_2d_kernel.h +++ /dev/null @@ -1,74 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -#ifndef MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONGE_PME_PME_FFT_2D_KERNEL_H_ -#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONGE_PME_PME_FFT_2D_KERNEL_H_ -#include -#include -#include -#include -#include -#include "plugin/device/gpu/kernel/gpu_kernel.h" -#include "plugin/device/gpu/kernel/gpu_kernel_factory.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" -#include "plugin/device/gpu/kernel/cuda_impl/sponge/pme/pme_fft_2d_impl.cuh" -namespace mindspore { -namespace kernel { -template -using Complex = mindspore::utils::Complex; -template -class PMEFFT2DGpuKernelMod : public DeprecatedNativeGpuKernelMod { - public: - PMEFFT2DGpuKernelMod() = default; - ~PMEFFT2DGpuKernelMod() override = default; - - bool Init(const CNodePtr &kernel_node) override { - fftx = static_cast(GetAttr(kernel_node, "fftx")); - ffty = static_cast(GetAttr(kernel_node, "ffty")); - Nall = fftx * ffty; - Nfft = fftx * ffty; - - cufftPlan2d(&FFT_plan_c2c, fftx, ffty, CUFFT_C2C); - - InitSizeLists(); - return true; - } - - bool Launch(const std::vector &inputs, const std::vector &workspace, - const std::vector &outputs, void *stream_ptr) override { - auto input_tensor = GetDeviceAddress>(inputs, 0); - auto output_tensor = GetDeviceAddress>(outputs, 0); - - cufftSetStream(FFT_plan_c2c, reinterpret_cast(stream_ptr)); - PMEFFT2D(Nfft, input_tensor, output_tensor, FFT_plan_c2c, reinterpret_cast(stream_ptr)); - return true; - } - - protected: - void InitSizeLists() override { - input_size_list_.push_back(Nall * sizeof(Complex)); - output_size_list_.push_back(Nfft * sizeof(Complex)); - } - - private: - int fftx; - int ffty; - int Nall; - int Nfft; - cufftHandle FFT_plan_c2c; -}; -} // namespace kernel -} // namespace mindspore -#endif diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/pme/pme_ifft_1d_kernel.cc b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/pme/pme_ifft_1d_kernel.cc deleted file mode 100644 index 137ce5bcf6c..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/pme/pme_ifft_1d_kernel.cc +++ /dev/null @@ -1,23 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -#include "plugin/device/gpu/kernel/sponge/pme/pme_ifft_1d_kernel.h" - -namespace mindspore { -namespace kernel { -MS_REG_GPU_KERNEL_ONE(PMEIFFT1D, KernelAttr().AddInputAttr(kNumberTypeComplex64).AddOutputAttr(kNumberTypeComplex64), - PMEIFFT1DGpuKernelMod, float) -} // namespace kernel -} // namespace mindspore diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/pme/pme_ifft_1d_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/pme/pme_ifft_1d_kernel.h deleted file mode 100644 index 5aedb6cecee..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/pme/pme_ifft_1d_kernel.h +++ /dev/null @@ -1,73 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -#ifndef MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONGE_PME_PME_IFFT_1D_KERNEL_H_ -#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONGE_PME_PME_IFFT_1D_KERNEL_H_ -#include -#include -#include -#include -#include -#include "plugin/device/gpu/kernel/gpu_kernel.h" -#include "plugin/device/gpu/kernel/gpu_kernel_factory.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" -#include "plugin/device/gpu/kernel/cuda_impl/sponge/pme/pme_ifft_1d_impl.cuh" -namespace mindspore { -namespace kernel { -template -using Complex = mindspore::utils::Complex; -template -class PMEIFFT1DGpuKernelMod : public DeprecatedNativeGpuKernelMod { - public: - PMEIFFT1DGpuKernelMod() = default; - ~PMEIFFT1DGpuKernelMod() override = default; - - bool Init(const CNodePtr &kernel_node) override { - fftx = static_cast(GetAttr(kernel_node, "fftx")); - Nfft = fftx; - Nall = fftx; - - cufftPlan1d(&FFT_plan_c2c, fftx, CUFFT_C2C, batch); - - InitSizeLists(); - return true; - } - - bool Launch(const std::vector &inputs, const std::vector &workspace, - const std::vector &outputs, void *stream_ptr) override { - auto input_tensor = GetDeviceAddress>(inputs, 0); - auto output_tensor = GetDeviceAddress>(outputs, 0); - - cufftSetStream(FFT_plan_c2c, reinterpret_cast(stream_ptr)); - PMEIFFT1D(Nfft, input_tensor, output_tensor, FFT_plan_c2c, reinterpret_cast(stream_ptr)); - return true; - } - - protected: - void InitSizeLists() override { - input_size_list_.push_back(Nfft * sizeof(Complex)); - output_size_list_.push_back(Nall * sizeof(Complex)); - } - - private: - int batch = 1; - int fftx; - int Nall; - int Nfft; - cufftHandle FFT_plan_c2c; -}; -} // namespace kernel -} // namespace mindspore -#endif diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/pme/pme_ifft_2d_kernel.cc b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/pme/pme_ifft_2d_kernel.cc deleted file mode 100644 index a7554a6fbb4..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/pme/pme_ifft_2d_kernel.cc +++ /dev/null @@ -1,23 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -#include "plugin/device/gpu/kernel/sponge/pme/pme_ifft_2d_kernel.h" - -namespace mindspore { -namespace kernel { -MS_REG_GPU_KERNEL_ONE(PMEIFFT2D, KernelAttr().AddInputAttr(kNumberTypeComplex64).AddOutputAttr(kNumberTypeComplex64), - PMEIFFT2DGpuKernelMod, float) -} // namespace kernel -} // namespace mindspore diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/pme/pme_ifft_2d_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/pme/pme_ifft_2d_kernel.h deleted file mode 100644 index 30dd017f041..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/pme/pme_ifft_2d_kernel.h +++ /dev/null @@ -1,74 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -#ifndef MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONGE_PME_PME_IFFT_2D_KERNEL_H_ -#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONGE_PME_PME_IFFT_2D_KERNEL_H_ -#include -#include -#include -#include -#include -#include "plugin/device/gpu/kernel/gpu_kernel.h" -#include "plugin/device/gpu/kernel/gpu_kernel_factory.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" -#include "plugin/device/gpu/kernel/cuda_impl/sponge/pme/pme_ifft_2d_impl.cuh" -namespace mindspore { -namespace kernel { -template -using Complex = mindspore::utils::Complex; -template -class PMEIFFT2DGpuKernelMod : public DeprecatedNativeGpuKernelMod { - public: - PMEIFFT2DGpuKernelMod() = default; - ~PMEIFFT2DGpuKernelMod() override = default; - - bool Init(const CNodePtr &kernel_node) override { - fftx = static_cast(GetAttr(kernel_node, "fftx")); - ffty = static_cast(GetAttr(kernel_node, "ffty")); - Nfft = fftx * ffty; - Nall = fftx * ffty; - - cufftPlan2d(&FFT_plan_c2c, fftx, ffty, CUFFT_C2C); - - InitSizeLists(); - return true; - } - - bool Launch(const std::vector &inputs, const std::vector &workspace, - const std::vector &outputs, void *stream_ptr) override { - auto input_tensor = GetDeviceAddress>(inputs, 0); - auto output_tensor = GetDeviceAddress>(outputs, 0); - - cufftSetStream(FFT_plan_c2c, reinterpret_cast(stream_ptr)); - PMEIFFT2D(Nfft, input_tensor, output_tensor, FFT_plan_c2c, reinterpret_cast(stream_ptr)); - return true; - } - - protected: - void InitSizeLists() override { - input_size_list_.push_back(Nfft * sizeof(Complex)); - output_size_list_.push_back(Nall * sizeof(Complex)); - } - - private: - int fftx; - int ffty; - int Nall; - int Nfft; - cufftHandle FFT_plan_c2c; -}; -} // namespace kernel -} // namespace mindspore -#endif diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/pme/pme_irfft_2d_kernel.cc b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/pme/pme_irfft_2d_kernel.cc deleted file mode 100644 index a827e44058f..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/pme/pme_irfft_2d_kernel.cc +++ /dev/null @@ -1,23 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -#include "plugin/device/gpu/kernel/sponge/pme/pme_irfft_2d_kernel.h" - -namespace mindspore { -namespace kernel { -MS_REG_GPU_KERNEL_ONE(PMEIRFFT2D, KernelAttr().AddInputAttr(kNumberTypeComplex64).AddOutputAttr(kNumberTypeFloat32), - PMEIRFFT2DGpuKernelMod, float) -} // namespace kernel -} // namespace mindspore diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/pme/pme_irfft_2d_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/pme/pme_irfft_2d_kernel.h deleted file mode 100644 index 9c0bad02f4a..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/pme/pme_irfft_2d_kernel.h +++ /dev/null @@ -1,75 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -#ifndef MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONGE_PME_PME_IRFFT_2D_KERNEL_H_ -#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONGE_PME_PME_IRFFT_2D_KERNEL_H_ -#include -#include -#include -#include -#include -#include "plugin/device/gpu/kernel/gpu_kernel.h" -#include "plugin/device/gpu/kernel/gpu_kernel_factory.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" -#include "plugin/device/gpu/kernel/cuda_impl/sponge/pme/pme_irfft_2d_impl.cuh" -namespace mindspore { -namespace kernel { -template -using Complex = mindspore::utils::Complex; -template -class PMEIRFFT2DGpuKernelMod : public DeprecatedNativeGpuKernelMod { - public: - PMEIRFFT2DGpuKernelMod() = default; - ~PMEIRFFT2DGpuKernelMod() override = default; - - bool Init(const CNodePtr &kernel_node) override { - constexpr int NUM_2 = 2; - fftx = static_cast(GetAttr(kernel_node, "fftx")); - ffty = static_cast(GetAttr(kernel_node, "ffty")); - Nfft = fftx * ffty; - Nall = fftx * (ffty - 1) * NUM_2; - - cufftPlan2d(&FFT_plan_c2r, fftx, (ffty - 1) * NUM_2, CUFFT_C2R); - - InitSizeLists(); - return true; - } - - bool Launch(const std::vector &inputs, const std::vector &workspace, - const std::vector &outputs, void *stream_ptr) override { - auto input_tensor = GetDeviceAddress>(inputs, 0); - auto output_tensor = GetDeviceAddress(outputs, 0); - - cufftSetStream(FFT_plan_c2r, reinterpret_cast(stream_ptr)); - PMEIRFFT2D(Nfft, input_tensor, output_tensor, FFT_plan_c2r, reinterpret_cast(stream_ptr)); - return true; - } - - protected: - void InitSizeLists() override { - input_size_list_.push_back(Nfft * sizeof(Complex)); - output_size_list_.push_back(Nall * sizeof(T)); - } - - private: - int fftx; - int ffty; - int Nall; - int Nfft; - cufftHandle FFT_plan_c2r; -}; -} // namespace kernel -} // namespace mindspore -#endif diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/pme/pme_reciprocal_force_kernel.cc b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/pme/pme_reciprocal_force_kernel.cc deleted file mode 100644 index d8ed969b054..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/pme/pme_reciprocal_force_kernel.cc +++ /dev/null @@ -1,25 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -#include "plugin/device/gpu/kernel/sponge/pme/pme_reciprocal_force_kernel.h" - -namespace mindspore { -namespace kernel { -MS_REG_GPU_KERNEL_TWO( - PMEReciprocalForce, - KernelAttr().AddInputAttr(kNumberTypeUInt32).AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32), - PMEReciprocalForceGpuKernelMod, float, int) -} // namespace kernel -} // namespace mindspore diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/pme/pme_reciprocal_force_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/pme/pme_reciprocal_force_kernel.h deleted file mode 100644 index a2fa1a76dea..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/pme/pme_reciprocal_force_kernel.h +++ /dev/null @@ -1,243 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -/** - * Note: - * PMEReciprocalForce. This is an experimental interface that is subject to change and/or deletion. - */ -#ifndef MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONGE_PME_PME_RECIPROCAL_FORCE_KERNEL_H_ -#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONGE_PME_PME_RECIPROCAL_FORCE_KERNEL_H_ -#include -#include -#include -#include -#include -#include "plugin/device/gpu/kernel/gpu_kernel.h" -#include "plugin/device/gpu/kernel/gpu_kernel_factory.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" -#include "plugin/device/gpu/kernel/cuda_impl/sponge/pme/pme_reciprocal_force_impl.cuh" -namespace mindspore { -namespace kernel { -template -class PMEReciprocalForceGpuKernelMod : public DeprecatedNativeGpuKernelMod { - public: - PMEReciprocalForceGpuKernelMod() : ele_uint_crd(1) {} - ~PMEReciprocalForceGpuKernelMod() override = default; - - bool Init(const CNodePtr &kernel_node) override { - kernel_node_ = kernel_node; - atom_numbers = static_cast(GetAttr(kernel_node, "atom_numbers")); - beta = static_cast(GetAttr(kernel_node, "beta")); - fftx = static_cast(GetAttr(kernel_node, "fftx")); - ffty = static_cast(GetAttr(kernel_node, "ffty")); - fftz = static_cast(GetAttr(kernel_node, "fftz")); - PME_Nall = fftx * ffty * fftz; - PME_Nfft = fftx * ffty * (fftz / 2 + 1); - PME_Nin = ffty * fftz; - - float box_length_0 = static_cast(GetAttr(kernel_node, "box_length_0")); - float box_length_1 = static_cast(GetAttr(kernel_node, "box_length_1")); - float box_length_2 = static_cast(GetAttr(kernel_node, "box_length_2")); - std::vector h_box_length{box_length_0, box_length_1, box_length_2}; - VECTOR *box_length = reinterpret_cast(h_box_length.data()); - PME_inverse_box_vector.x = static_cast(fftx) / box_length[0].x; - PME_inverse_box_vector.y = static_cast(ffty) / box_length[0].y; - PME_inverse_box_vector.z = static_cast(fftz) / box_length[0].z; - cufftPlan3d(&PME_plan_r2c, fftx, ffty, fftz, CUFFT_R2C); - cufftPlan3d(&PME_plan_c2r, fftx, ffty, fftz, CUFFT_C2R); - - float volume = box_length[0].x * box_length[0].y * box_length[0].z; - PME_kxyz_cpu.resize(64); - int kx, ky, kz, kxrp, kyrp, kzrp, index; - for (kx = 0; kx < 4; kx++) { - for (ky = 0; ky < 4; ky++) { - for (kz = 0; kz < 4; kz++) { - index = kx * 16 + ky * 4 + kz; - PME_kxyz_cpu[index].uint_x = kx; - PME_kxyz_cpu[index].uint_y = ky; - PME_kxyz_cpu[index].uint_z = kz; - } - } - } - B1.resize(fftx); - B2.resize(ffty); - B3.resize(fftz); - PME_BC0.resize(PME_Nfft); - - for (kx = 0; kx < fftx; kx++) { - B1[kx] = getb(kx, fftx, 4); - } - - for (ky = 0; ky < ffty; ky++) { - B2[ky] = getb(ky, ffty, 4); - } - - for (kz = 0; kz < fftz; kz++) { - B3[kz] = getb(kz, fftz, 4); - } - float mprefactor = PI * PI / -beta / beta; - float msq; - for (kx = 0; kx < fftx; kx++) { - kxrp = kx; - if (kx > fftx / 2) kxrp = fftx - kx; - for (ky = 0; ky < ffty; ky++) { - kyrp = ky; - if (ky > ffty / 2) kyrp = ffty - ky; - for (kz = 0; kz <= fftz / 2; kz++) { - kzrp = kz; - - msq = kxrp * kxrp / box_length[0].x / box_length[0].x + kyrp * kyrp / box_length[0].y / box_length[0].y + - kzrp * kzrp / box_length[0].z / box_length[0].z; - index = kx * ffty * (fftz / 2 + 1) + ky * (fftz / 2 + 1) + kz; - if ((kx + ky + kz) == 0) { - PME_BC0[index] = 0; - } else { - PME_BC0[index] = 1.0 / PI / msq * exp(mprefactor * msq) / volume; - } - - PME_BC0[index] *= B1[kx] * B2[ky] * B3[kz]; - } - } - } - - InitSizeLists(); - return true; - } - - bool Launch(const std::vector &inputs, const std::vector &workspace, - const std::vector &outputs, void *stream_ptr) override { - auto uint_crd = GetDeviceAddress(inputs, 0); - auto charge = GetDeviceAddress(inputs, 1); - - auto pme_uxyz = GetDeviceAddress(workspace, 0); // workspace - auto pme_frxyz = GetDeviceAddress(workspace, 1); // workspace - auto pme_q = GetDeviceAddress(workspace, 2); // workspace - auto pme_fq = GetDeviceAddress(workspace, 3); // workspace - auto pme_atom_near = GetDeviceAddress(workspace, 4); // workspace - auto pme_bc = GetDeviceAddress(workspace, 5); // workspace - auto pme_kxyz = GetDeviceAddress(workspace, 6); // workspace - - auto force = GetDeviceAddress(outputs, 0); - cufftSetStream(PME_plan_r2c, reinterpret_cast(stream_ptr)); - cufftSetStream(PME_plan_c2r, reinterpret_cast(stream_ptr)); - cudaMemcpyAsync(pme_kxyz, PME_kxyz_cpu.data(), sizeof(UNSIGNED_INT_VECTOR) * 64, cudaMemcpyHostToDevice, - reinterpret_cast(stream_ptr)); - cudaMemcpyAsync(pme_bc, PME_BC0.data(), sizeof(float) * PME_Nfft, cudaMemcpyHostToDevice, - reinterpret_cast(stream_ptr)); - PMEReciprocalForce(fftx, ffty, fftz, atom_numbers, beta, pme_bc, pme_uxyz, pme_frxyz, pme_q, pme_fq, pme_atom_near, - pme_kxyz, uint_crd, charge, force, PME_Nin, PME_Nall, PME_Nfft, PME_plan_r2c, PME_plan_c2r, - PME_inverse_box_vector, reinterpret_cast(stream_ptr)); - return true; - } - - protected: - void InitSizeLists() override { - input_size_list_.push_back(atom_numbers * sizeof(UNSIGNED_INT_VECTOR)); - input_size_list_.push_back(atom_numbers * sizeof(float)); - - workspace_size_list_.push_back(atom_numbers * sizeof(UNSIGNED_INT_VECTOR)); - workspace_size_list_.push_back(atom_numbers * sizeof(VECTOR)); - workspace_size_list_.push_back(PME_Nall * sizeof(T)); - workspace_size_list_.push_back(PME_Nfft * sizeof(cufftComplex)); - workspace_size_list_.push_back(atom_numbers * 64 * sizeof(int)); - workspace_size_list_.push_back(PME_Nfft * sizeof(float)); - workspace_size_list_.push_back(64 * sizeof(UNSIGNED_INT_VECTOR)); - - output_size_list_.push_back(atom_numbers * sizeof(VECTOR)); - } - - cufftComplex expc(cufftComplex z) { - cufftComplex res; - float t = expf(z.x); - mindspore::kernel::math::SinCosf(z.y, &res.y, &res.x); - res.x *= t; - res.y *= t; - return res; - } - - float M_(float u, int n) { - if (n == 2) { - if (u > 2 || u < 0) return 0; - return 1 - abs(u - 1); - } else { - return u / (n - 1) * M_(u, n - 1) + (n - u) / (n - 1) * M_(u - 1, n - 1); - } - } - - float getb(int k, int NFFT, int B_order) { - cufftComplex tempc, tempc2, res; - float tempf; - tempc2.x = 0; - tempc2.y = 0; - - tempc.x = 0; - if (NFFT == 0) { - MS_LOG(ERROR) << "Divide by zero."; - } else { - tempc.y = 2 * (B_order - 1) * PI * k / NFFT; - } - res = expc(tempc); - - for (int kk = 0; kk < (B_order - 1); kk++) { - tempc.x = 0; - if (NFFT == 0) { - MS_LOG(ERROR) << "Divide by zero."; - break; - } else { - tempc.y = 2 * PI * k / NFFT * kk; - } - tempc = expc(tempc); - tempf = M_(kk + 1, B_order); - tempc2.x += tempf * tempc.x; - tempc2.y += tempf * tempc.y; - } - res = cuCdivf(res, tempc2); - return res.x * res.x + res.y * res.y; - } - - private: - size_t ele_uint_crd = 1; - int atom_numbers; - int fftx; - int ffty; - int fftz; - float beta; - int PME_Nall; - int PME_Nfft; - int PME_Nin; - float PI = 3.1415926; - std::vector B1; - std::vector B2; - std::vector B3; - std::vector PME_BC0; - - cufftHandle PME_plan_r2c; - cufftHandle PME_plan_c2r; - struct VECTOR { - float x; - float y; - float z; - }; - _VECTOR PME_inverse_box_vector; - struct UNSIGNED_INT_VECTOR { - unsigned int uint_x; - unsigned int uint_y; - unsigned int uint_z; - }; - std::vector PME_kxyz_cpu; -}; -} // namespace kernel -} // namespace mindspore -#endif diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/pme/pme_reciprocal_force_update_kernel.cc b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/pme/pme_reciprocal_force_update_kernel.cc deleted file mode 100644 index d25884edb97..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/pme/pme_reciprocal_force_update_kernel.cc +++ /dev/null @@ -1,32 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -/** - * Note: - * PMEReciprocalForceUpdate. This is an experimental interface that is subject to change and/or deletion. - */ -#include "plugin/device/gpu/kernel/sponge/pme/pme_reciprocal_force_update_kernel.h" - -namespace mindspore { -namespace kernel { -MS_REG_GPU_KERNEL_TWO(PMEReciprocalForceUpdate, - KernelAttr() - .AddInputAttr(kNumberTypeUInt32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddOutputAttr(kNumberTypeFloat32), - PMEReciprocalForceUpdateGpuKernelMod, float, int) -} // namespace kernel -} // namespace mindspore diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/pme/pme_reciprocal_force_update_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/pme/pme_reciprocal_force_update_kernel.h deleted file mode 100644 index 80e73c41cb5..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/pme/pme_reciprocal_force_update_kernel.h +++ /dev/null @@ -1,273 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -/** - * Note: - * PMEReciprocalForceUpdate. This is an experimental interface that is subject to change and/or deletion. - */ -#ifndef MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONGE_PME_PME_RECIPROCAL_FORCE_UPDATE_KERNEL_H_ -#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONGE_PME_PME_RECIPROCAL_FORCE_UPDATE_KERNEL_H_ -#include -#include -#include -#include "plugin/device/gpu/kernel/gpu_kernel.h" -#include "plugin/device/gpu/kernel/gpu_kernel_factory.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" -#include "plugin/device/gpu/kernel/cuda_impl/sponge/pme/pme_reciprocal_force_impl.cuh" - -namespace mindspore { -namespace kernel { -template -class PMEReciprocalForceUpdateGpuKernelMod : public DeprecatedNativeGpuKernelMod { - public: - PMEReciprocalForceUpdateGpuKernelMod() : ele_uint_crd(1) {} - ~PMEReciprocalForceUpdateGpuKernelMod() override = default; - - bool Init(const CNodePtr &kernel_node) override { - kernel_node_ = kernel_node; - atom_numbers = static_cast(GetAttr(kernel_node, "atom_numbers")); - beta = static_cast(GetAttr(kernel_node, "beta")); - fftx = static_cast(GetAttr(kernel_node, "fftx")); - ffty = static_cast(GetAttr(kernel_node, "ffty")); - fftz = static_cast(GetAttr(kernel_node, "fftz")); - - float box_length_0 = static_cast(GetAttr(kernel_node, "box_length_0")); - float box_length_1 = static_cast(GetAttr(kernel_node, "box_length_1")); - float box_length_2 = static_cast(GetAttr(kernel_node, "box_length_2")); - - need_update = static_cast(GetAttr(kernel_node, "need_update")); - PME_Nall = fftx * ffty * fftz; - PME_Nfft = fftx * ffty * (fftz / 2 + 1); - PME_Nin = ffty * fftz; - cufftPlan3d(&PME_plan_r2c, fftx, ffty, fftz, CUFFT_R2C); - cufftPlan3d(&PME_plan_c2r, fftx, ffty, fftz, CUFFT_C2R); - - std::vector h_box_length{box_length_0, box_length_1, box_length_2}; - VECTOR *box_length = reinterpret_cast(h_box_length.data()); - - PME_inverse_box_vector0.x = static_cast(fftx) / box_length[0].x; - PME_inverse_box_vector0.y = static_cast(ffty) / box_length[0].y; - PME_inverse_box_vector0.z = static_cast(fftz) / box_length[0].z; - PME_inverse_box_vector.x = PME_inverse_box_vector0.x; - PME_inverse_box_vector.y = PME_inverse_box_vector0.y; - PME_inverse_box_vector.z = PME_inverse_box_vector0.z; - float volume = box_length[0].x * box_length[0].y * box_length[0].z; - PME_kxyz_cpu.resize(64); - int kx, ky, kz, kxrp, kyrp, kzrp, index; - for (kx = 0; kx < 4; kx++) { - for (ky = 0; ky < 4; ky++) { - for (kz = 0; kz < 4; kz++) { - index = kx * 16 + ky * 4 + kz; - PME_kxyz_cpu[index].uint_x = kx; - PME_kxyz_cpu[index].uint_y = ky; - PME_kxyz_cpu[index].uint_z = kz; - } - } - } - B1.resize(fftx); - B2.resize(ffty); - B3.resize(fftz); - PME_BC0.resize(PME_Nfft); - - for (kx = 0; kx < fftx; kx++) { - B1[kx] = getb(kx, fftx, 4); - } - - for (ky = 0; ky < ffty; ky++) { - B2[ky] = getb(ky, ffty, 4); - } - - for (kz = 0; kz < fftz; kz++) { - B3[kz] = getb(kz, fftz, 4); - } - float mprefactor = PI * PI / -beta / beta; - float msq; - for (kx = 0; kx < fftx; kx++) { - kxrp = kx; - if (kx > fftx / 2) kxrp = fftx - kx; - for (ky = 0; ky < ffty; ky++) { - kyrp = ky; - if (ky > ffty / 2) kyrp = ffty - ky; - for (kz = 0; kz <= fftz / 2; kz++) { - kzrp = kz; - - msq = kxrp * kxrp / box_length[0].x / box_length[0].x + kyrp * kyrp / box_length[0].y / box_length[0].y + - kzrp * kzrp / box_length[0].z / box_length[0].z; - index = kx * ffty * (fftz / 2 + 1) + ky * (fftz / 2 + 1) + kz; - if ((kx + ky + kz) == 0) { - PME_BC0[index] = 0; - } else { - PME_BC0[index] = 1.0 / PI / msq * exp(mprefactor * msq) / volume; - } - - PME_BC0[index] *= B1[kx] * B2[ky] * B3[kz]; - } - } - } - PME_BC = PME_BC0; - InitSizeLists(); - return true; - } - - bool Launch(const std::vector &inputs, const std::vector &workspace, - const std::vector &outputs, void *stream_ptr) override { - auto uint_crd = GetDeviceAddress(inputs, 0); - auto charge = GetDeviceAddress(inputs, 1); - auto d_beta = GetDeviceAddress(inputs, 2); - - auto pme_uxyz = GetDeviceAddress(workspace, 0); // workspace - auto pme_frxyz = GetDeviceAddress(workspace, 1); // workspace - auto pme_q = GetDeviceAddress(workspace, 2); // workspace - auto pme_fq = GetDeviceAddress(workspace, 3); // workspace - auto pme_atom_near = GetDeviceAddress(workspace, 4); // workspace - auto pme_bc = GetDeviceAddress(workspace, 5); // workspace - auto pme_kxyz = GetDeviceAddress(workspace, 6); // workspace - - auto force = GetDeviceAddress(outputs, 0); - h_beta = beta; - if (need_update) { - cudaMemcpyAsync(&h_beta, d_beta, sizeof(float), cudaMemcpyDeviceToHost, - reinterpret_cast(stream_ptr)); - cudaStreamSynchronize(reinterpret_cast(stream_ptr)); - factor = h_beta / beta; - double factor_inverse = 1.0 / factor; - - PME_inverse_box_vector.x = PME_inverse_box_vector0.x * factor_inverse; - PME_inverse_box_vector.y = PME_inverse_box_vector0.y * factor_inverse; - PME_inverse_box_vector.z = PME_inverse_box_vector0.z * factor_inverse; - - for (int i = 0; i < PME_Nfft; i++) { - PME_BC[i] = PME_BC0[i] * factor_inverse; - } - } - - cufftSetStream(PME_plan_r2c, reinterpret_cast(stream_ptr)); - cufftSetStream(PME_plan_c2r, reinterpret_cast(stream_ptr)); - cudaMemcpyAsync(pme_kxyz, PME_kxyz_cpu.data(), sizeof(UNSIGNED_INT_VECTOR) * 64, cudaMemcpyHostToDevice, - reinterpret_cast(stream_ptr)); - cudaMemcpyAsync(pme_bc, PME_BC.data(), sizeof(float) * PME_Nfft, cudaMemcpyHostToDevice, - reinterpret_cast(stream_ptr)); - PMEReciprocalForce(fftx, ffty, fftz, atom_numbers, beta, pme_bc, pme_uxyz, pme_frxyz, pme_q, pme_fq, pme_atom_near, - pme_kxyz, uint_crd, charge, force, PME_Nin, PME_Nall, PME_Nfft, PME_plan_r2c, PME_plan_c2r, - PME_inverse_box_vector, reinterpret_cast(stream_ptr)); - return true; - } - - protected: - void InitSizeLists() override { - input_size_list_.push_back(atom_numbers * sizeof(UNSIGNED_INT_VECTOR)); - input_size_list_.push_back(atom_numbers * sizeof(float)); - input_size_list_.push_back(sizeof(float)); - - workspace_size_list_.push_back(atom_numbers * sizeof(UNSIGNED_INT_VECTOR)); - workspace_size_list_.push_back(atom_numbers * sizeof(VECTOR)); - workspace_size_list_.push_back(PME_Nall * sizeof(T)); - workspace_size_list_.push_back(PME_Nfft * sizeof(cufftComplex)); - workspace_size_list_.push_back(atom_numbers * 64 * sizeof(int)); - workspace_size_list_.push_back(PME_Nfft * sizeof(float)); - workspace_size_list_.push_back(64 * sizeof(UNSIGNED_INT_VECTOR)); - - output_size_list_.push_back(atom_numbers * sizeof(VECTOR)); - } - - cufftComplex expc(cufftComplex z) { - cufftComplex res; - float t = expf(z.x); - mindspore::kernel::math::SinCosf(z.y, &res.y, &res.x); - res.x *= t; - res.y *= t; - return res; - } - - float M_(float u, int n) { - if (n == 2) { - if (u > 2 || u < 0) return 0; - return 1 - abs(u - 1); - } else { - return u / (n - 1) * M_(u, n - 1) + (n - u) / (n - 1) * M_(u - 1, n - 1); - } - } - - float getb(int k, int NFFT, int B_order) { - cufftComplex tempc, tempc2, res; - float tempf; - tempc2.x = 0; - tempc2.y = 0; - - tempc.x = 0; - if (NFFT == 0) { - MS_LOG(ERROR) << "Divide by zero."; - } else { - tempc.y = 2 * (B_order - 1) * PI * k / NFFT; - } - res = expc(tempc); - - for (int kk = 0; kk < (B_order - 1); kk++) { - tempc.x = 0; - if (NFFT == 0) { - MS_LOG(ERROR) << "Divide by zero."; - break; - } else { - tempc.y = 2 * PI * k / NFFT * kk; - } - tempc = expc(tempc); - tempf = M_(kk + 1, B_order); - tempc2.x += tempf * tempc.x; - tempc2.y += tempf * tempc.y; - } - res = cuCdivf(res, tempc2); - return res.x * res.x + res.y * res.y; - } - - private: - size_t ele_uint_crd = 1; - - int atom_numbers; - int fftx; - int ffty; - int fftz; - float beta; - float h_beta; - float factor; - int PME_Nall; - int PME_Nfft; - int PME_Nin; - float PI = 3.1415926; - int need_update; - std::vector B1; - std::vector B2; - std::vector B3; - std::vector PME_BC0; - std::vector PME_BC; - - cufftHandle PME_plan_r2c; - cufftHandle PME_plan_c2r; - struct VECTOR { - float x; - float y; - float z; - }; - _VECTOR PME_inverse_box_vector0; - _VECTOR PME_inverse_box_vector; - struct UNSIGNED_INT_VECTOR { - unsigned int uint_x; - unsigned int uint_y; - unsigned int uint_z; - }; - std::vector PME_kxyz_cpu; -}; -} // namespace kernel -} // namespace mindspore -#endif diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/pme/pme_rfft_2d_kernel.cc b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/pme/pme_rfft_2d_kernel.cc deleted file mode 100644 index 47d86f609ce..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/pme/pme_rfft_2d_kernel.cc +++ /dev/null @@ -1,23 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -#include "plugin/device/gpu/kernel/sponge/pme/pme_rfft_2d_kernel.h" - -namespace mindspore { -namespace kernel { -MS_REG_GPU_KERNEL_ONE(PMERFFT2D, KernelAttr().AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeComplex64), - PMERFFT2DGpuKernelMod, float) -} // namespace kernel -} // namespace mindspore diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/pme/pme_rfft_2d_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/pme/pme_rfft_2d_kernel.h deleted file mode 100644 index e13f2e9109a..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/pme/pme_rfft_2d_kernel.h +++ /dev/null @@ -1,75 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -#ifndef MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONGE_PME_PME_RFFT_2D_KERNEL_H_ -#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONGE_PME_PME_RFFT_2D_KERNEL_H_ -#include -#include -#include -#include -#include -#include "plugin/device/gpu/kernel/gpu_kernel.h" -#include "plugin/device/gpu/kernel/gpu_kernel_factory.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" -#include "plugin/device/gpu/kernel/cuda_impl/sponge/pme/pme_rfft_2d_impl.cuh" -namespace mindspore { -namespace kernel { -template -using Complex = mindspore::utils::Complex; -template -class PMERFFT2DGpuKernelMod : public DeprecatedNativeGpuKernelMod { - public: - PMERFFT2DGpuKernelMod() = default; - ~PMERFFT2DGpuKernelMod() override = default; - - bool Init(const CNodePtr &kernel_node) override { - constexpr int NUM_2 = 2; - fftx = static_cast(GetAttr(kernel_node, "fftx")); - ffty = static_cast(GetAttr(kernel_node, "ffty")); - Nall = fftx * ffty; - Nfft = fftx * (ffty / NUM_2 + 1); - - cufftPlan2d(&FFT_plan_r2c, fftx, ffty, CUFFT_R2C); - - InitSizeLists(); - return true; - } - - bool Launch(const std::vector &inputs, const std::vector &workspace, - const std::vector &outputs, void *stream_ptr) override { - auto input_tensor = GetDeviceAddress(inputs, 0); - auto output_tensor = GetDeviceAddress>(outputs, 0); - - cufftSetStream(FFT_plan_r2c, reinterpret_cast(stream_ptr)); - PMERFFT2D(Nfft, input_tensor, output_tensor, FFT_plan_r2c, reinterpret_cast(stream_ptr)); - return true; - } - - protected: - void InitSizeLists() override { - input_size_list_.push_back(Nall * sizeof(T)); - output_size_list_.push_back(Nfft * sizeof(Complex)); - } - - private: - int fftx; - int ffty; - int Nall; - int Nfft; - cufftHandle FFT_plan_r2c; -}; -} // namespace kernel -} // namespace mindspore -#endif diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/restrain/restrain_energy_kernel.cc b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/restrain/restrain_energy_kernel.cc deleted file mode 100644 index c75fd71b850..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/restrain/restrain_energy_kernel.cc +++ /dev/null @@ -1,34 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -/** - * Note: - * RestrainEnergy. This is an experimental interface that is subject to change and/or deletion. - */ - -#include "plugin/device/gpu/kernel/sponge/restrain/restrain_energy_kernel.h" - -namespace mindspore { -namespace kernel { -MS_REG_GPU_KERNEL_TWO(RestrainEnergy, - KernelAttr() - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddOutputAttr(kNumberTypeFloat32), - RestrainEnergyGpuKernelMod, float, int) -} // namespace kernel -} // namespace mindspore diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/restrain/restrain_energy_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/restrain/restrain_energy_kernel.h deleted file mode 100644 index e81b99be9be..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/restrain/restrain_energy_kernel.h +++ /dev/null @@ -1,95 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONG_RESTRAIN_RESTRAIN_FORCE_KERNEL_H_ -#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONG_RESTRAIN_RESTRAIN_FORCE_KERNEL_H_ - -#include "plugin/device/gpu/kernel/cuda_impl/sponge/restrain/restrain_energy_impl.cuh" - -#include -#include -#include -#include - -#include "plugin/device/gpu/kernel/gpu_kernel.h" -#include "plugin/device/gpu/kernel/gpu_kernel_factory.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" - -namespace mindspore { -namespace kernel { -template -class RestrainEnergyGpuKernelMod : public DeprecatedNativeGpuKernelMod { - public: - RestrainEnergyGpuKernelMod() : ele_crd(1) {} - ~RestrainEnergyGpuKernelMod() override = default; - - bool Init(const CNodePtr &kernel_node) override { - // get bond_numbers - kernel_node_ = kernel_node; - restrain_numbers = static_cast(GetAttr(kernel_node, "restrain_numbers")); - atom_numbers = static_cast(GetAttr(kernel_node, "atom_numbers")); - weight = static_cast(GetAttr(kernel_node, "weight")); - auto shape_restrain_list = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto shape_crd = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - auto shape_crd_ref = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); - auto shape_scaler = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); - - ele_crd *= SizeOf(shape_crd); - ele_scaler *= SizeOf(shape_scaler); - ele_restrain_list *= SizeOf(shape_restrain_list); - ele_crd_ref *= SizeOf(shape_crd_ref); - - InitSizeLists(); - return true; - } - - bool Launch(const std::vector &inputs, const std::vector &, - const std::vector &outputs, void *stream_ptr) override { - auto restrain_list = GetDeviceAddress(inputs, 0); - auto crd_f = GetDeviceAddress(inputs, 1); - auto crd_ref = GetDeviceAddress(inputs, 2); - auto scaler_f = GetDeviceAddress(inputs, 3); - - auto ene = GetDeviceAddress(outputs, 0); - - restrainenergy(restrain_numbers, atom_numbers, weight, restrain_list, crd_f, crd_ref, scaler_f, ene, - reinterpret_cast(stream_ptr)); - return true; - } - - protected: - void InitSizeLists() override { - input_size_list_.push_back(ele_restrain_list * sizeof(T1)); - input_size_list_.push_back(ele_crd * sizeof(T)); - input_size_list_.push_back(ele_crd_ref * sizeof(T)); - input_size_list_.push_back(ele_scaler * sizeof(T)); - - output_size_list_.push_back(atom_numbers * sizeof(T)); - } - - private: - size_t ele_crd = 1; - size_t ele_scaler = 1; - size_t ele_restrain_list = 1; - size_t ele_crd_ref = 1; - - int restrain_numbers; - int atom_numbers; - float weight; -}; -} // namespace kernel -} // namespace mindspore -#endif // MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONG_RESTRAIN_RESTRAIN_FORCE_KERNEL_H_ diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/restrain/restrain_force_atom_energy_virial_impl_kernel.cc b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/restrain/restrain_force_atom_energy_virial_impl_kernel.cc deleted file mode 100644 index 00e17f37856..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/restrain/restrain_force_atom_energy_virial_impl_kernel.cc +++ /dev/null @@ -1,36 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -/** - * Note: - * RestrainForceWithAtomEnergyVirial. This is an experimental interface that is subject to change and/or deletion. - */ - -#include "plugin/device/gpu/kernel/sponge/restrain/restrain_force_atom_energy_virial_kernel.h" - -namespace mindspore { -namespace kernel { -MS_REG_GPU_KERNEL_TWO(RestrainForceWithAtomEnergyVirial, - KernelAttr() - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddOutputAttr(kNumberTypeFloat32) - .AddOutputAttr(kNumberTypeFloat32) - .AddOutputAttr(kNumberTypeFloat32), - RestrainForceWithAtomenergyAndVirialGpuKernelMod, float, int) -} // namespace kernel -} // namespace mindspore diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/restrain/restrain_force_atom_energy_virial_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/restrain/restrain_force_atom_energy_virial_kernel.h deleted file mode 100644 index 39b679f3e57..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/restrain/restrain_force_atom_energy_virial_kernel.h +++ /dev/null @@ -1,100 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONG_RESTRAIN_RESTRAIN_FORCE_ATOM_ENERGY_VIRIAL_KERNEL_H_ -#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONG_RESTRAIN_RESTRAIN_FORCE_ATOM_ENERGY_VIRIAL_KERNEL_H_ - -#include "plugin/device/gpu/kernel/cuda_impl/sponge/restrain/restrain_force_atom_energy_virial_impl.cuh" - -#include -#include -#include -#include - -#include "plugin/device/gpu/kernel/gpu_kernel.h" -#include "plugin/device/gpu/kernel/gpu_kernel_factory.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" - -namespace mindspore { -namespace kernel { -template -class RestrainForceWithAtomenergyAndVirialGpuKernelMod : public DeprecatedNativeGpuKernelMod { - public: - RestrainForceWithAtomenergyAndVirialGpuKernelMod() : ele_crd(1) {} - ~RestrainForceWithAtomenergyAndVirialGpuKernelMod() override = default; - - bool Init(const CNodePtr &kernel_node) override { - // get bond_numbers - kernel_node_ = kernel_node; - restrain_numbers = static_cast(GetAttr(kernel_node, "restrain_numbers")); - atom_numbers = static_cast(GetAttr(kernel_node, "atom_numbers")); - weight = static_cast(GetAttr(kernel_node, "weight")); - auto shape_restrain_list = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto shape_crd = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - auto shape_crd_ref = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); - auto shape_scaler = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); - - ele_crd *= SizeOf(shape_crd); - ele_scaler *= SizeOf(shape_scaler); - ele_restrain_list *= SizeOf(shape_restrain_list); - ele_crd_ref *= SizeOf(shape_crd_ref); - - InitSizeLists(); - return true; - } - - bool Launch(const std::vector &inputs, const std::vector &, - const std::vector &outputs, void *stream_ptr) override { - auto restrain_list = GetDeviceAddress(inputs, 0); - auto crd_f = GetDeviceAddress(inputs, 1); - auto crd_ref = GetDeviceAddress(inputs, 2); - auto scaler_f = GetDeviceAddress(inputs, 3); - - auto atom_ene = GetDeviceAddress(outputs, 0); - auto atom_virial = GetDeviceAddress(outputs, 1); - auto frc = GetDeviceAddress(outputs, 2); - - restrainforcewithatomenergyandvirial(restrain_numbers, atom_numbers, restrain_list, crd_f, crd_ref, weight, - scaler_f, atom_ene, atom_virial, frc, - reinterpret_cast(stream_ptr)); - return true; - } - - protected: - void InitSizeLists() override { - input_size_list_.push_back(ele_restrain_list * sizeof(T1)); - input_size_list_.push_back(ele_crd * sizeof(T)); - input_size_list_.push_back(ele_crd_ref * sizeof(T)); - input_size_list_.push_back(ele_scaler * sizeof(T)); - - output_size_list_.push_back(atom_numbers * sizeof(T)); - output_size_list_.push_back(atom_numbers * sizeof(T)); - output_size_list_.push_back(3 * atom_numbers * sizeof(T)); - } - - private: - size_t ele_crd = 1; - size_t ele_scaler = 1; - size_t ele_restrain_list = 1; - size_t ele_crd_ref = 1; - - int restrain_numbers; - int atom_numbers; - float weight; -}; -} // namespace kernel -} // namespace mindspore -#endif // MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONG_RESTRAIN_RESTRAIN_FORCE_KERNEL_H_ diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/restrain/restrain_force_kernel.cc b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/restrain/restrain_force_kernel.cc deleted file mode 100644 index 843616974dd..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/restrain/restrain_force_kernel.cc +++ /dev/null @@ -1,30 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "plugin/device/gpu/kernel/sponge/restrain/restrain_force_kernel.h" - -namespace mindspore { -namespace kernel { -MS_REG_GPU_KERNEL_TWO(RestrainForce, - KernelAttr() - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeUInt32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeFloat32) - .AddOutputAttr(kNumberTypeFloat32), - RestrainForceGpuKernelMod, float, int) -} // namespace kernel -} // namespace mindspore diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/restrain/restrain_force_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/restrain/restrain_force_kernel.h deleted file mode 100644 index 471acf5b720..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/restrain/restrain_force_kernel.h +++ /dev/null @@ -1,95 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONG_RESTRAIN_RESTRAIN_FORCE_KERNEL_H_ -#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONG_RESTRAIN_RESTRAIN_FORCE_KERNEL_H_ - -#include "plugin/device/gpu/kernel/cuda_impl/sponge/restrain/restrain_force_impl.cuh" - -#include -#include -#include -#include - -#include "plugin/device/gpu/kernel/gpu_kernel.h" -#include "plugin/device/gpu/kernel/gpu_kernel_factory.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" - -namespace mindspore { -namespace kernel { -template -class RestrainForceGpuKernelMod : public DeprecatedNativeGpuKernelMod { - public: - RestrainForceGpuKernelMod() : ele_uint_crd(1) {} - ~RestrainForceGpuKernelMod() override = default; - - bool Init(const CNodePtr &kernel_node) override { - // get bond_numbers - kernel_node_ = kernel_node; - restrain_numbers = static_cast(GetAttr(kernel_node, "restrain_numbers")); - atom_numbers = static_cast(GetAttr(kernel_node, "atom_numbers")); - factor = static_cast(GetAttr(kernel_node, "factor")); - auto shape_restrain_list = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto shape_uint_crd = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - auto shape_uint_crd_ref = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); - auto shape_scaler = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); - - ele_uint_crd *= SizeOf(shape_uint_crd); - ele_scaler *= SizeOf(shape_scaler); - ele_restrain_list *= SizeOf(shape_restrain_list); - ele_uint_crd_ref *= SizeOf(shape_uint_crd_ref); - - InitSizeLists(); - return true; - } - - bool Launch(const std::vector &inputs, const std::vector &, - const std::vector &outputs, void *stream_ptr) override { - auto restrain_list = GetDeviceAddress(inputs, 0); - auto uint_crd_f = GetDeviceAddress(inputs, 1); - auto uint_crd_ref = GetDeviceAddress(inputs, 2); - auto scaler_f = GetDeviceAddress(inputs, 3); - - auto frc_f = GetDeviceAddress(outputs, 0); - - restrainforce(restrain_numbers, atom_numbers, restrain_list, uint_crd_f, uint_crd_ref, factor, scaler_f, frc_f, - reinterpret_cast(stream_ptr)); - return true; - } - - protected: - void InitSizeLists() override { - input_size_list_.push_back(ele_restrain_list * sizeof(T1)); - input_size_list_.push_back(ele_uint_crd * sizeof(T1)); - input_size_list_.push_back(ele_uint_crd_ref * sizeof(T1)); - input_size_list_.push_back(ele_scaler * sizeof(T)); - - output_size_list_.push_back(atom_numbers * 3 * sizeof(T)); - } - - private: - size_t ele_uint_crd = 1; - size_t ele_scaler = 1; - size_t ele_restrain_list = 1; - size_t ele_uint_crd_ref = 1; - - int restrain_numbers; - int atom_numbers; - float factor; -}; -} // namespace kernel -} // namespace mindspore -#endif // MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONG_RESTRAIN_RESTRAIN_FORCE_KERNEL_H_ diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/simple_constrain/constrain_force_cycle_kernel.cc b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/simple_constrain/constrain_force_cycle_kernel.cc deleted file mode 100644 index 745353432e3..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/simple_constrain/constrain_force_cycle_kernel.cc +++ /dev/null @@ -1,33 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "plugin/device/gpu/kernel/sponge/simple_constrain/constrain_force_cycle_kernel.h" - -namespace mindspore { -namespace kernel { -MS_REG_GPU_KERNEL_THREE(ConstrainForceCycle, - KernelAttr() - .AddInputAttr(kNumberTypeUInt32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddOutputAttr(kNumberTypeFloat32), - ConstrainForceCycleGpuKernelMod, float, int, unsigned int) -} // namespace kernel -} // namespace mindspore diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/simple_constrain/constrain_force_cycle_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/simple_constrain/constrain_force_cycle_kernel.h deleted file mode 100644 index d2d80bb59df..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/simple_constrain/constrain_force_cycle_kernel.h +++ /dev/null @@ -1,119 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONG_SIMPLE_CONSTRAIN_CONSTRAIN_FORCE_CYCLE_KERNEL_H_ -#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONG_SIMPLE_CONSTRAIN_CONSTRAIN_FORCE_CYCLE_KERNEL_H_ - -#include "plugin/device/gpu/kernel/cuda_impl/sponge/simple_constrain/constrain_force_cycle_impl.cuh" - -#include -#include -#include -#include - -#include "plugin/device/gpu/kernel/gpu_kernel.h" -#include "plugin/device/gpu/kernel/gpu_kernel_factory.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" - -namespace mindspore { -namespace kernel { -template -class ConstrainForceCycleGpuKernelMod : public DeprecatedNativeGpuKernelMod { - public: - ConstrainForceCycleGpuKernelMod() : ele_uint_crd(1) {} - ~ConstrainForceCycleGpuKernelMod() override = default; - - bool Init(const CNodePtr &kernel_node) override { - // get bond_numbers - kernel_node_ = kernel_node; - atom_numbers = static_cast(GetAttr(kernel_node, "atom_numbers")); - constrain_pair_numbers = static_cast(GetAttr(kernel_node, "constrain_pair_numbers")); - - auto shape_uint_crd = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto shape_scaler = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - auto shape_pair_dr = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); - auto shape_atom_i_serials = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); - auto shape_atom_j_serials = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 4); - auto shape_constant_rs = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 5); - auto shape_constrain_ks = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 6); - - ele_uint_crd *= SizeOf(shape_uint_crd); - ele_scaler *= SizeOf(shape_scaler); - ele_pair_dr *= SizeOf(shape_pair_dr); - ele_atom_i_serials *= SizeOf(shape_atom_i_serials); - ele_atom_j_serials *= SizeOf(shape_atom_j_serials); - ele_constant_rs *= SizeOf(shape_constant_rs); - ele_constrain_ks *= SizeOf(shape_constrain_ks); - - InitSizeLists(); - return true; - } - - bool Launch(const std::vector &inputs, const std::vector &workspace, - const std::vector &outputs, void *stream_ptr) override { - auto uint_crd = GetDeviceAddress(inputs, 0); - auto scaler = GetDeviceAddress(inputs, 1); - auto pair_dr = GetDeviceAddress(inputs, 2); - auto atom_i_serials = GetDeviceAddress(inputs, 3); - auto atom_j_serials = GetDeviceAddress(inputs, 4); - auto constant_rs = GetDeviceAddress(inputs, 5); - auto constrain_ks = GetDeviceAddress(inputs, 6); - - auto constrain_pair = GetDeviceAddress(workspace, 0); - - auto test_frc_f = GetDeviceAddress(outputs, 0); - - Constrain_Force_Cycle(atom_numbers, constrain_pair_numbers, uint_crd, scaler, constrain_pair, pair_dr, - atom_i_serials, atom_j_serials, constant_rs, constrain_ks, test_frc_f, - reinterpret_cast(stream_ptr)); - return true; - } - - protected: - void InitSizeLists() override { - input_size_list_.push_back(ele_uint_crd * sizeof(T2)); - input_size_list_.push_back(ele_scaler * sizeof(T)); - input_size_list_.push_back(ele_pair_dr * sizeof(T)); - input_size_list_.push_back(ele_atom_i_serials * sizeof(T1)); - input_size_list_.push_back(ele_atom_j_serials * sizeof(T1)); - input_size_list_.push_back(ele_constant_rs * sizeof(T)); - input_size_list_.push_back(ele_constrain_ks * sizeof(T)); - - workspace_size_list_.push_back(constrain_pair_numbers * sizeof(CONSTRAIN_PAIR)); - output_size_list_.push_back(3 * atom_numbers * sizeof(T)); - } - - private: - size_t ele_uint_crd = 1; - size_t ele_scaler = 1; - size_t ele_pair_dr = 1; - size_t ele_atom_i_serials = 1; - size_t ele_atom_j_serials = 1; - size_t ele_constant_rs = 1; - size_t ele_constrain_ks = 1; - - int atom_numbers; - int constrain_pair_numbers; - struct CONSTRAIN_PAIR { - int atom_i_serial; - int atom_j_serial; - float constant_r; - float constrain_k; - }; -}; -} // namespace kernel -} // namespace mindspore -#endif diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/simple_constrain/constrain_force_cycle_with_virial_kernel.cc b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/simple_constrain/constrain_force_cycle_with_virial_kernel.cc deleted file mode 100644 index 02f5e921ce7..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/simple_constrain/constrain_force_cycle_with_virial_kernel.cc +++ /dev/null @@ -1,38 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -/** - * Note: - * ConstrainForceCycleWithVirial. This is an experimental interface that is subject to change and/or deletion. - */ - -#include "plugin/device/gpu/kernel/sponge/simple_constrain/constrain_force_cycle_with_virial_kernel.h" - -namespace mindspore { -namespace kernel { -MS_REG_GPU_KERNEL_THREE(ConstrainForceCycleWithVirial, - KernelAttr() - .AddInputAttr(kNumberTypeUInt32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddOutputAttr(kNumberTypeFloat32) - .AddOutputAttr(kNumberTypeFloat32), - ConstrainForceCycleWithVirialGpuKernelMod, float, int, unsigned int) -} // namespace kernel -} // namespace mindspore diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/simple_constrain/constrain_force_cycle_with_virial_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/simple_constrain/constrain_force_cycle_with_virial_kernel.h deleted file mode 100644 index 34d6567cbc0..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/simple_constrain/constrain_force_cycle_with_virial_kernel.h +++ /dev/null @@ -1,120 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONG_SIMPLE_CONSTRAIN_CONSTRAIN_FORCE_CYCLE_WITH_VIRIAL_KERNEL_H_ -#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONG_SIMPLE_CONSTRAIN_CONSTRAIN_FORCE_CYCLE_WITH_VIRIAL_KERNEL_H_ - -#include "plugin/device/gpu/kernel/cuda_impl/sponge/simple_constrain/constrain_force_cycle_with_virial_impl.cuh" - -#include -#include -#include -#include - -#include "plugin/device/gpu/kernel/gpu_kernel.h" -#include "plugin/device/gpu/kernel/gpu_kernel_factory.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" - -namespace mindspore { -namespace kernel { -template -class ConstrainForceCycleWithVirialGpuKernelMod : public DeprecatedNativeGpuKernelMod { - public: - ConstrainForceCycleWithVirialGpuKernelMod() : ele_uint_crd(1) {} - ~ConstrainForceCycleWithVirialGpuKernelMod() override = default; - - bool Init(const CNodePtr &kernel_node) override { - // get bond_numbers - kernel_node_ = kernel_node; - atom_numbers = static_cast(GetAttr(kernel_node, "atom_numbers")); - constrain_pair_numbers = static_cast(GetAttr(kernel_node, "constrain_pair_numbers")); - - auto shape_uint_crd = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto shape_scaler = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - auto shape_pair_dr = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); - auto shape_atom_i_serials = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); - auto shape_atom_j_serials = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 4); - auto shape_constant_rs = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 5); - auto shape_constrain_ks = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 6); - - ele_uint_crd *= SizeOf(shape_uint_crd); - ele_scaler *= SizeOf(shape_scaler); - ele_pair_dr *= SizeOf(shape_pair_dr); - ele_atom_i_serials *= SizeOf(shape_atom_i_serials); - ele_atom_j_serials *= SizeOf(shape_atom_j_serials); - ele_constant_rs *= SizeOf(shape_constant_rs); - ele_constrain_ks *= SizeOf(shape_constrain_ks); - - InitSizeLists(); - return true; - } - - bool Launch(const std::vector &inputs, const std::vector &workspace, - const std::vector &outputs, void *stream_ptr) override { - auto uint_crd = GetDeviceAddress(inputs, 0); - auto scaler = GetDeviceAddress(inputs, 1); - auto pair_dr = GetDeviceAddress(inputs, 2); - auto atom_i_serials = GetDeviceAddress(inputs, 3); - auto atom_j_serials = GetDeviceAddress(inputs, 4); - auto constant_rs = GetDeviceAddress(inputs, 5); - auto constrain_ks = GetDeviceAddress(inputs, 6); - auto constrain_pair = GetDeviceAddress(workspace, 0); - - auto test_frc_f = GetDeviceAddress(outputs, 0); - auto d_atom_virial = GetDeviceAddress(outputs, 1); - - Constrain_Force_Cycle_With_Virial(atom_numbers, constrain_pair_numbers, uint_crd, scaler, constrain_pair, pair_dr, - atom_i_serials, atom_j_serials, constant_rs, constrain_ks, test_frc_f, - d_atom_virial, reinterpret_cast(stream_ptr)); - return true; - } - - protected: - void InitSizeLists() override { - input_size_list_.push_back(ele_uint_crd * sizeof(T2)); - input_size_list_.push_back(ele_scaler * sizeof(T)); - input_size_list_.push_back(ele_pair_dr * sizeof(T)); - input_size_list_.push_back(ele_atom_i_serials * sizeof(T1)); - input_size_list_.push_back(ele_atom_j_serials * sizeof(T1)); - input_size_list_.push_back(ele_constant_rs * sizeof(T)); - input_size_list_.push_back(ele_constrain_ks * sizeof(T)); - - workspace_size_list_.push_back(constrain_pair_numbers * sizeof(CONSTRAIN_PAIR)); - output_size_list_.push_back(3 * atom_numbers * sizeof(T)); - output_size_list_.push_back(atom_numbers * sizeof(T)); - } - - private: - size_t ele_uint_crd = 1; - size_t ele_scaler = 1; - size_t ele_pair_dr = 1; - size_t ele_atom_i_serials = 1; - size_t ele_atom_j_serials = 1; - size_t ele_constant_rs = 1; - size_t ele_constrain_ks = 1; - - int atom_numbers; - int constrain_pair_numbers; - struct CONSTRAIN_PAIR { - int atom_i_serial; - int atom_j_serial; - float constant_r; - float constrain_k; - }; -}; -} // namespace kernel -} // namespace mindspore -#endif diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/simple_constrain/constrain_force_kernel.cc b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/simple_constrain/constrain_force_kernel.cc deleted file mode 100644 index 0c4a0b13d17..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/simple_constrain/constrain_force_kernel.cc +++ /dev/null @@ -1,40 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -/** - * Note: - * ConstrainForce. This is an experimental interface that is subject to change and/or deletion. - */ -#include "plugin/device/gpu/kernel/sponge/simple_constrain/constrain_force_kernel.h" - -namespace mindspore { -namespace kernel { -MS_REG_GPU_KERNEL_THREE(ConstrainForce, - KernelAttr() - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddOutputAttr(kNumberTypeUInt32) - .AddOutputAttr(kNumberTypeFloat32) - .AddOutputAttr(kNumberTypeFloat32), - ConstrainForceGpuKernelMod, float, int, unsigned int) -} // namespace kernel -} // namespace mindspore diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/simple_constrain/constrain_force_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/simple_constrain/constrain_force_kernel.h deleted file mode 100644 index f8c0c2a5b6b..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/simple_constrain/constrain_force_kernel.h +++ /dev/null @@ -1,154 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -/** - * Note: - * ConstrainForce. This is an experimental interface that is subject to change and/or deletion. - */ - -#ifndef MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONG_SIMPLE_CONSTRAIN_CONSTRAIN_FORCE_KERNEL_H_ -#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONG_SIMPLE_CONSTRAIN_CONSTRAIN_FORCE_KERNEL_H_ - -#include "plugin/device/gpu/kernel/cuda_impl/sponge/simple_constrain/constrain_force_virial_impl.cuh" - -#include -#include -#include -#include - -#include "plugin/device/gpu/kernel/gpu_kernel.h" -#include "plugin/device/gpu/kernel/gpu_kernel_factory.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" - -namespace mindspore { -namespace kernel { -template -class ConstrainForceGpuKernelMod : public DeprecatedNativeGpuKernelMod { - public: - ConstrainForceGpuKernelMod() : ele_crd(1) {} - ~ConstrainForceGpuKernelMod() override = default; - - bool Init(const CNodePtr &kernel_node) override { - kernel_node_ = kernel_node; - atom_numbers = static_cast(GetAttr(kernel_node, "atom_numbers")); - constrain_pair_numbers = static_cast(GetAttr(kernel_node, "constrain_pair_numbers")); - iteration_numbers = static_cast(GetAttr(kernel_node, "iteration_numbers")); - half_exp_gamma_plus_half = static_cast(GetAttr(kernel_node, "half_exp_gamma_plus_half")); - - auto shape_crd = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto shape_quarter_cof = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - auto shape_mass_inverse = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); - - auto shape_scaler = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); - auto shape_pair_dr = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 4); - auto shape_atom_i_serials = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 5); - auto shape_atom_j_serials = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 6); - auto shape_constant_rs = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 7); - auto shape_constrain_ks = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 8); - - ele_scaler *= SizeOf(shape_scaler); - ele_pair_dr *= SizeOf(shape_pair_dr); - ele_atom_i_serials *= SizeOf(shape_atom_i_serials); - ele_atom_j_serials *= SizeOf(shape_atom_j_serials); - ele_constant_rs *= SizeOf(shape_constant_rs); - ele_constrain_ks *= SizeOf(shape_constrain_ks); - ele_crd *= SizeOf(shape_crd); - ele_quarter_cof *= SizeOf(shape_quarter_cof); - ele_mass_inverse *= SizeOf(shape_mass_inverse); - - InitSizeLists(); - return true; - } - - bool Launch(const std::vector &inputs, const std::vector &workspace, - const std::vector &outputs, void *stream_ptr) override { - auto crd = GetDeviceAddress(inputs, 0); - auto quarter_cof = GetDeviceAddress(inputs, 1); - auto mass_inverse = GetDeviceAddress(inputs, 2); - - auto scaler = GetDeviceAddress(inputs, 3); - auto pair_dr = GetDeviceAddress(inputs, 4); - auto atom_i_serials = GetDeviceAddress(inputs, 5); - auto atom_j_serials = GetDeviceAddress(inputs, 6); - auto constant_rs = GetDeviceAddress(inputs, 7); - auto constrain_ks = GetDeviceAddress(inputs, 8); - - auto constrain_pair = GetDeviceAddress(workspace, 0); - - auto uint_crd = GetDeviceAddress(outputs, 0); - - auto test_frc_f = GetDeviceAddress(outputs, 1); - auto d_atom_virial = GetDeviceAddress(outputs, 2); - - set_zero_force_with_virial(atom_numbers, constrain_pair_numbers, test_frc_f, d_atom_virial, - reinterpret_cast(stream_ptr)); - - for (int i = 0; i < iteration_numbers; i++) { - refresh_uint_crd_update(atom_numbers, half_exp_gamma_plus_half, crd, quarter_cof, test_frc_f, mass_inverse, - uint_crd, reinterpret_cast(stream_ptr)); - - constrain_force_cycle_update(atom_numbers, constrain_pair_numbers, uint_crd, scaler, constrain_pair, pair_dr, - atom_i_serials, atom_j_serials, constant_rs, constrain_ks, test_frc_f, - reinterpret_cast(stream_ptr)); - } - return true; - } - - protected: - void InitSizeLists() override { - input_size_list_.push_back(ele_crd * sizeof(T)); - input_size_list_.push_back(ele_quarter_cof * sizeof(T)); - input_size_list_.push_back(ele_mass_inverse * sizeof(T)); - - input_size_list_.push_back(ele_scaler * sizeof(T)); - input_size_list_.push_back(ele_pair_dr * sizeof(T)); - input_size_list_.push_back(ele_atom_i_serials * sizeof(T1)); - input_size_list_.push_back(ele_atom_j_serials * sizeof(T1)); - input_size_list_.push_back(ele_constant_rs * sizeof(T)); - input_size_list_.push_back(ele_constrain_ks * sizeof(T)); - - workspace_size_list_.push_back(constrain_pair_numbers * sizeof(CONSTRAIN_PAIR)); - - output_size_list_.push_back(3 * atom_numbers * sizeof(T2)); - output_size_list_.push_back(3 * atom_numbers * sizeof(T)); - output_size_list_.push_back(constrain_pair_numbers * sizeof(T)); - } - - private: - size_t ele_scaler = 1; - size_t ele_pair_dr = 1; - size_t ele_atom_i_serials = 1; - size_t ele_atom_j_serials = 1; - size_t ele_constant_rs = 1; - size_t ele_constrain_ks = 1; - size_t ele_crd = 1; - size_t ele_quarter_cof = 1; - size_t ele_mass_inverse = 1; - - int atom_numbers; - int constrain_pair_numbers; - int iteration_numbers; - int need_pressure; - float half_exp_gamma_plus_half; - struct CONSTRAIN_PAIR { - int atom_i_serial; - int atom_j_serial; - float constant_r; - float constrain_k; - }; -}; -} // namespace kernel -} // namespace mindspore -#endif diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/simple_constrain/constrain_force_virial_kernel.cc b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/simple_constrain/constrain_force_virial_kernel.cc deleted file mode 100644 index 9b01f9000d7..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/simple_constrain/constrain_force_virial_kernel.cc +++ /dev/null @@ -1,41 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -/** - * Note: - * ConstrainForceVirial. This is an experimental interface that is subject to change and/or deletion. - */ - -#include "plugin/device/gpu/kernel/sponge/simple_constrain/constrain_force_virial_kernel.h" - -namespace mindspore { -namespace kernel { -MS_REG_GPU_KERNEL_THREE(ConstrainForceVirial, - KernelAttr() - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddOutputAttr(kNumberTypeUInt32) - .AddOutputAttr(kNumberTypeFloat32) - .AddOutputAttr(kNumberTypeFloat32), - ConstrainForceVirialGpuKernelMod, float, int, unsigned int) -} // namespace kernel -} // namespace mindspore diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/simple_constrain/constrain_force_virial_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/simple_constrain/constrain_force_virial_kernel.h deleted file mode 100644 index 08621b810d4..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/simple_constrain/constrain_force_virial_kernel.h +++ /dev/null @@ -1,151 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -/** - * Note: - * ConstrainForceVirial. This is an experimental interface that is subject to change and/or deletion. - */ -#ifndef MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONG_SIMPLE_CONSTRAIN_CONSTRAIN_FORCE_VIRIAL_KERNEL_H_ -#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONG_SIMPLE_CONSTRAIN_CONSTRAIN_FORCE_VIRIAL_KERNEL_H_ - -#include "plugin/device/gpu/kernel/cuda_impl/sponge/simple_constrain/constrain_force_virial_impl.cuh" - -#include -#include -#include -#include - -#include "plugin/device/gpu/kernel/gpu_kernel.h" -#include "plugin/device/gpu/kernel/gpu_kernel_factory.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" - -namespace mindspore { -namespace kernel { -template -class ConstrainForceVirialGpuKernelMod : public DeprecatedNativeGpuKernelMod { - public: - ConstrainForceVirialGpuKernelMod() : ele_crd(1) {} - ~ConstrainForceVirialGpuKernelMod() override = default; - - bool Init(const CNodePtr &kernel_node) override { - kernel_node_ = kernel_node; - atom_numbers = static_cast(GetAttr(kernel_node, "atom_numbers")); - constrain_pair_numbers = static_cast(GetAttr(kernel_node, "constrain_pair_numbers")); - iteration_numbers = static_cast(GetAttr(kernel_node, "iteration_numbers")); - half_exp_gamma_plus_half = static_cast(GetAttr(kernel_node, "half_exp_gamma_plus_half")); - - auto shape_crd = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto shape_quarter_cof = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - auto shape_mass_inverse = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); - - auto shape_scaler = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); - auto shape_pair_dr = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 4); - auto shape_atom_i_serials = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 5); - auto shape_atom_j_serials = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 6); - auto shape_constant_rs = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 7); - auto shape_constrain_ks = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 8); - - ele_scaler *= SizeOf(shape_scaler); - ele_pair_dr *= SizeOf(shape_pair_dr); - ele_atom_i_serials *= SizeOf(shape_atom_i_serials); - ele_atom_j_serials *= SizeOf(shape_atom_j_serials); - ele_constant_rs *= SizeOf(shape_constant_rs); - ele_constrain_ks *= SizeOf(shape_constrain_ks); - ele_crd *= SizeOf(shape_crd); - ele_quarter_cof *= SizeOf(shape_quarter_cof); - ele_mass_inverse *= SizeOf(shape_mass_inverse); - - InitSizeLists(); - return true; - } - - bool Launch(const std::vector &inputs, const std::vector &workspace, - const std::vector &outputs, void *stream_ptr) override { - auto crd = GetDeviceAddress(inputs, 0); - auto quarter_cof = GetDeviceAddress(inputs, 1); - auto mass_inverse = GetDeviceAddress(inputs, 2); - auto scaler = GetDeviceAddress(inputs, 3); - auto pair_dr = GetDeviceAddress(inputs, 4); - auto atom_i_serials = GetDeviceAddress(inputs, 5); - auto atom_j_serials = GetDeviceAddress(inputs, 6); - auto constant_rs = GetDeviceAddress(inputs, 7); - auto constrain_ks = GetDeviceAddress(inputs, 8); - auto constrain_pair = GetDeviceAddress(workspace, 0); - auto uint_crd = GetDeviceAddress(outputs, 0); - auto test_frc_f = GetDeviceAddress(outputs, 1); - auto d_atom_virial = GetDeviceAddress(outputs, 2); - - set_zero_force_with_virial(atom_numbers, constrain_pair_numbers, test_frc_f, d_atom_virial, - reinterpret_cast(stream_ptr)); - - for (int i = 0; i < iteration_numbers; i++) { - refresh_uint_crd_update(atom_numbers, half_exp_gamma_plus_half, crd, quarter_cof, test_frc_f, mass_inverse, - uint_crd, reinterpret_cast(stream_ptr)); - - constrain_force_cycle_with_virial_update(atom_numbers, constrain_pair_numbers, uint_crd, scaler, constrain_pair, - pair_dr, atom_i_serials, atom_j_serials, constant_rs, constrain_ks, - test_frc_f, d_atom_virial, reinterpret_cast(stream_ptr)); - } - - return true; - } - - protected: - void InitSizeLists() override { - input_size_list_.push_back(ele_crd * sizeof(T)); - input_size_list_.push_back(ele_quarter_cof * sizeof(T)); - input_size_list_.push_back(ele_mass_inverse * sizeof(T)); - - input_size_list_.push_back(ele_scaler * sizeof(T)); - input_size_list_.push_back(ele_pair_dr * sizeof(T)); - input_size_list_.push_back(ele_atom_i_serials * sizeof(T1)); - input_size_list_.push_back(ele_atom_j_serials * sizeof(T1)); - input_size_list_.push_back(ele_constant_rs * sizeof(T)); - input_size_list_.push_back(ele_constrain_ks * sizeof(T)); - - workspace_size_list_.push_back(constrain_pair_numbers * sizeof(CONSTRAIN_PAIR)); - - output_size_list_.push_back(3 * atom_numbers * sizeof(T2)); - - output_size_list_.push_back(3 * atom_numbers * sizeof(T)); - output_size_list_.push_back(constrain_pair_numbers * sizeof(T)); - } - - private: - size_t ele_scaler = 1; - size_t ele_pair_dr = 1; - size_t ele_atom_i_serials = 1; - size_t ele_atom_j_serials = 1; - size_t ele_constant_rs = 1; - size_t ele_constrain_ks = 1; - size_t ele_crd = 1; - size_t ele_quarter_cof = 1; - size_t ele_mass_inverse = 1; - - int atom_numbers; - int constrain_pair_numbers; - int iteration_numbers; - int need_pressure; - float half_exp_gamma_plus_half; - struct CONSTRAIN_PAIR { - int atom_i_serial; - int atom_j_serial; - float constant_r; - float constrain_k; - }; -}; -} // namespace kernel -} // namespace mindspore -#endif diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/simple_constrain/constrain_kernel.cc b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/simple_constrain/constrain_kernel.cc deleted file mode 100644 index e2f0bcd862c..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/simple_constrain/constrain_kernel.cc +++ /dev/null @@ -1,41 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -/** - * Note: - * Constrain. This is an experimental interface that is subject to change and/or deletion. - */ -#include "plugin/device/gpu/kernel/sponge/simple_constrain/constrain_kernel.h" - -namespace mindspore { -namespace kernel { -MS_REG_GPU_KERNEL_THREE(Constrain, - KernelAttr() - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeInt32) - .AddOutputAttr(kNumberTypeUInt32) - .AddOutputAttr(kNumberTypeFloat32) - .AddOutputAttr(kNumberTypeFloat32), - ConstrainGpuKernelMod, float, int, unsigned int) -} // namespace kernel -} // namespace mindspore diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/simple_constrain/constrain_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/simple_constrain/constrain_kernel.h deleted file mode 100644 index 0adc081764a..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/simple_constrain/constrain_kernel.h +++ /dev/null @@ -1,168 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -/** - * Note: - * Constrain. This is an experimental interface that is subject to change and/or deletion. - */ -#ifndef MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONG_SIMPLE_CONSTRAIN_CONSTRAIN_KERNEL_H_ -#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONG_SIMPLE_CONSTRAIN_CONSTRAIN_KERNEL_H_ - -#include "plugin/device/gpu/kernel/cuda_impl/sponge/simple_constrain/constrain_force_virial_impl.cuh" - -#include -#include -#include -#include - -#include "plugin/device/gpu/kernel/gpu_kernel.h" -#include "plugin/device/gpu/kernel/gpu_kernel_factory.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" - -namespace mindspore { -namespace kernel { -template -class ConstrainGpuKernelMod : public DeprecatedNativeGpuKernelMod { - public: - ConstrainGpuKernelMod() : ele_crd(1), step(1) {} - ~ConstrainGpuKernelMod() override = default; - - bool Init(const CNodePtr &kernel_node) override { - // get bond_numbers - kernel_node_ = kernel_node; - atom_numbers = static_cast(GetAttr(kernel_node, "atom_numbers")); - constrain_pair_numbers = static_cast(GetAttr(kernel_node, "constrain_pair_numbers")); - iteration_numbers = static_cast(GetAttr(kernel_node, "iteration_numbers")); - half_exp_gamma_plus_half = static_cast(GetAttr(kernel_node, "half_exp_gamma_plus_half")); - - auto shape_crd = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto shape_quarter_cof = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - auto shape_mass_inverse = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); - - auto shape_scaler = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); - auto shape_pair_dr = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 4); - auto shape_atom_i_serials = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 5); - auto shape_atom_j_serials = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 6); - auto shape_constant_rs = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 7); - auto shape_constrain_ks = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 8); - - ele_scaler *= SizeOf(shape_scaler); - ele_pair_dr *= SizeOf(shape_pair_dr); - ele_atom_i_serials *= SizeOf(shape_atom_i_serials); - ele_atom_j_serials *= SizeOf(shape_atom_j_serials); - ele_constant_rs *= SizeOf(shape_constant_rs); - ele_constrain_ks *= SizeOf(shape_constrain_ks); - ele_crd *= SizeOf(shape_crd); - ele_quarter_cof *= SizeOf(shape_quarter_cof); - ele_mass_inverse *= SizeOf(shape_mass_inverse); - - InitSizeLists(); - return true; - } - - bool Launch(const std::vector &inputs, const std::vector &workspace, - const std::vector &outputs, void *stream_ptr) override { - auto crd = GetDeviceAddress(inputs, 0); - auto quarter_cof = GetDeviceAddress(inputs, 1); - auto mass_inverse = GetDeviceAddress(inputs, 2); - auto scaler = GetDeviceAddress(inputs, 3); - auto pair_dr = GetDeviceAddress(inputs, 4); - auto atom_i_serials = GetDeviceAddress(inputs, 5); - auto atom_j_serials = GetDeviceAddress(inputs, 6); - auto constant_rs = GetDeviceAddress(inputs, 7); - auto constrain_ks = GetDeviceAddress(inputs, 8); - auto d_need_pressure = GetDeviceAddress(inputs, 9); - - auto constrain_pair = GetDeviceAddress(workspace, 0); - - auto uint_crd = GetDeviceAddress(outputs, 0); - - auto test_frc_f = GetDeviceAddress(outputs, 1); - auto d_atom_virial = GetDeviceAddress(outputs, 2); - - cudaMemcpyAsync(&need_pressure, d_need_pressure, sizeof(int), cudaMemcpyDeviceToHost, - reinterpret_cast(stream_ptr)); - cudaStreamSynchronize(reinterpret_cast(stream_ptr)); - - set_zero_force_with_virial(atom_numbers, constrain_pair_numbers, test_frc_f, d_atom_virial, - reinterpret_cast(stream_ptr)); - - for (int i = 0; i < iteration_numbers; i++) { - refresh_uint_crd_update(atom_numbers, half_exp_gamma_plus_half, crd, quarter_cof, test_frc_f, mass_inverse, - uint_crd, reinterpret_cast(stream_ptr)); - - if (need_pressure) { - constrain_force_cycle_with_virial_update(atom_numbers, constrain_pair_numbers, uint_crd, scaler, constrain_pair, - pair_dr, atom_i_serials, atom_j_serials, constant_rs, constrain_ks, - test_frc_f, d_atom_virial, reinterpret_cast(stream_ptr)); - } else { - constrain_force_cycle_update(atom_numbers, constrain_pair_numbers, uint_crd, scaler, constrain_pair, pair_dr, - atom_i_serials, atom_j_serials, constant_rs, constrain_ks, test_frc_f, - reinterpret_cast(stream_ptr)); - } - } - step++; - - return true; - } - - protected: - void InitSizeLists() override { - input_size_list_.push_back(ele_crd * sizeof(T)); - input_size_list_.push_back(ele_quarter_cof * sizeof(T)); - input_size_list_.push_back(ele_mass_inverse * sizeof(T)); - - input_size_list_.push_back(ele_scaler * sizeof(T)); - input_size_list_.push_back(ele_pair_dr * sizeof(T)); - input_size_list_.push_back(ele_atom_i_serials * sizeof(T1)); - input_size_list_.push_back(ele_atom_j_serials * sizeof(T1)); - input_size_list_.push_back(ele_constant_rs * sizeof(T)); - input_size_list_.push_back(ele_constrain_ks * sizeof(T)); - - workspace_size_list_.push_back(constrain_pair_numbers * sizeof(CONSTRAIN_PAIR)); - - output_size_list_.push_back(3 * atom_numbers * sizeof(T2)); - - output_size_list_.push_back(3 * atom_numbers * sizeof(T)); - output_size_list_.push_back(constrain_pair_numbers * sizeof(T)); - } - - private: - size_t ele_scaler = 1; - size_t ele_pair_dr = 1; - size_t ele_atom_i_serials = 1; - size_t ele_atom_j_serials = 1; - size_t ele_constant_rs = 1; - size_t ele_constrain_ks = 1; - size_t ele_crd = 1; - size_t ele_quarter_cof = 1; - size_t ele_mass_inverse = 1; - - int atom_numbers; - int constrain_pair_numbers; - int iteration_numbers; - int need_pressure; - float half_exp_gamma_plus_half; - int step; - struct CONSTRAIN_PAIR { - int atom_i_serial; - int atom_j_serial; - float constant_r; - float constrain_k; - }; -}; -} // namespace kernel -} // namespace mindspore -#endif diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/simple_constrain/last_crd_to_dr_kernel.cc b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/simple_constrain/last_crd_to_dr_kernel.cc deleted file mode 100644 index 5f9325b4c03..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/simple_constrain/last_crd_to_dr_kernel.cc +++ /dev/null @@ -1,36 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -/** - * Note: - * LastCrdToDr. This is an experimental interface that is subject to change and/or deletion. - */ -#include "plugin/device/gpu/kernel/sponge/simple_constrain/last_crd_to_dr_kernel.h" - -namespace mindspore { -namespace kernel { -MS_REG_GPU_KERNEL_TWO(LastCrdToDr, - KernelAttr() - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeInt32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddOutputAttr(kNumberTypeFloat32), - LastCrdToDrGpuKernelMod, float, int) -} // namespace kernel -} // namespace mindspore diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/simple_constrain/last_crd_to_dr_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/simple_constrain/last_crd_to_dr_kernel.h deleted file mode 100644 index 9d2d787017d..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/simple_constrain/last_crd_to_dr_kernel.h +++ /dev/null @@ -1,117 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONG_SIMPLE_CONSTRAIN_LAST_CRD_TO_DR_KERNEL_H_ -#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONG_SIMPLE_CONSTRAIN_LAST_CRD_TO_DR_KERNEL_H_ - -#include "plugin/device/gpu/kernel/cuda_impl/sponge/simple_constrain/last_crd_to_dr_impl.cuh" - -#include -#include -#include -#include - -#include "plugin/device/gpu/kernel/gpu_kernel.h" -#include "plugin/device/gpu/kernel/gpu_kernel_factory.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" - -namespace mindspore { -namespace kernel { -template -class LastCrdToDrGpuKernelMod : public DeprecatedNativeGpuKernelMod { - public: - LastCrdToDrGpuKernelMod() : ele_atom_crd(1) {} - ~LastCrdToDrGpuKernelMod() override = default; - - bool Init(const CNodePtr &kernel_node) override { - // get bond_numbers - kernel_node_ = kernel_node; - constrain_pair_numbers = static_cast(GetAttr(kernel_node, "constrain_pair_numbers")); - - auto shape_atom_crd = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto shape_quater_cof = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - auto shape_uint_dr_to_dr = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); - auto shape_atom_i_serials = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); - auto shape_atom_j_serials = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 4); - auto shape_constant_rs = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 5); - auto shape_constrain_ks = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 6); - - ele_atom_crd *= SizeOf(shape_atom_crd); - ele_quater_cof *= SizeOf(shape_quater_cof); - ele_uint_dr_to_dr *= SizeOf(shape_uint_dr_to_dr); - ele_atom_i_serials *= SizeOf(shape_atom_i_serials); - ele_atom_j_serials *= SizeOf(shape_atom_j_serials); - ele_constant_rs *= SizeOf(shape_constant_rs); - ele_constrain_ks *= SizeOf(shape_constrain_ks); - - InitSizeLists(); - return true; - } - - bool Launch(const std::vector &inputs, const std::vector &workspace, - const std::vector &outputs, void *stream_ptr) override { - auto atom_crd = GetDeviceAddress(inputs, 0); - auto quater_cof = GetDeviceAddress(inputs, 1); - auto uint_dr_to_dr = GetDeviceAddress(inputs, 2); - auto atom_i_serials = GetDeviceAddress(inputs, 3); - auto atom_j_serials = GetDeviceAddress(inputs, 4); - auto constant_rs = GetDeviceAddress(inputs, 5); - auto constrain_ks = GetDeviceAddress(inputs, 6); - - auto constrain_pair = GetDeviceAddress(workspace, 0); - - auto pair_dr = GetDeviceAddress(outputs, 0); - - lastcrdtodr(constrain_pair_numbers, atom_crd, quater_cof, uint_dr_to_dr, constrain_pair, atom_i_serials, - atom_j_serials, constant_rs, constrain_ks, pair_dr, reinterpret_cast(stream_ptr)); - return true; - } - - protected: - void InitSizeLists() override { - input_size_list_.push_back(ele_atom_crd * sizeof(T)); - input_size_list_.push_back(ele_quater_cof * sizeof(T)); - input_size_list_.push_back(ele_uint_dr_to_dr * sizeof(T)); - input_size_list_.push_back(ele_atom_i_serials * sizeof(T1)); - input_size_list_.push_back(ele_atom_j_serials * sizeof(T1)); - input_size_list_.push_back(ele_constant_rs * sizeof(T)); - input_size_list_.push_back(ele_constrain_ks * sizeof(T)); - - workspace_size_list_.push_back(constrain_pair_numbers * sizeof(CONSTRAIN_PAIR)); - - output_size_list_.push_back(3 * constrain_pair_numbers * sizeof(T)); - } - - private: - size_t ele_atom_crd = 1; - size_t ele_quater_cof = 1; - size_t ele_uint_dr_to_dr = 1; - size_t ele_atom_i_serials = 1; - size_t ele_atom_j_serials = 1; - size_t ele_constant_rs = 1; - size_t ele_constrain_ks = 1; - - int constrain_pair_numbers; - struct CONSTRAIN_PAIR { - int atom_i_serial; - int atom_j_serial; - float constant_r; - float constrain_k; - }; -}; -} // namespace kernel -} // namespace mindspore -#endif diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/simple_constrain/refresh_crd_vel_kernel.cc b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/simple_constrain/refresh_crd_vel_kernel.cc deleted file mode 100644 index 52859060f14..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/simple_constrain/refresh_crd_vel_kernel.cc +++ /dev/null @@ -1,34 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -/** - * Note: - * RefreshCrdVel. This is an experimental interface that is subject to change and/or deletion. - */ - -#include "plugin/device/gpu/kernel/sponge/simple_constrain/refresh_crd_vel_kernel.h" - -namespace mindspore { -namespace kernel { -MS_REG_GPU_KERNEL_TWO(RefreshCrdVel, - KernelAttr() - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddOutputAttr(kNumberTypeFloat32), - RefreshCrdVelGpuKernelMod, float, unsigned int) -} // namespace kernel -} // namespace mindspore diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/simple_constrain/refresh_crd_vel_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/simple_constrain/refresh_crd_vel_kernel.h deleted file mode 100644 index 170cd7e9543..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/simple_constrain/refresh_crd_vel_kernel.h +++ /dev/null @@ -1,97 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONG_SIMPLE_CONSTRAIN_REFRESH_CRD_VEL_KERNEL_H_ -#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONG_SIMPLE_CONSTRAIN_REFRESH_CRD_VEL_KERNEL_H_ - -#include "plugin/device/gpu/kernel/cuda_impl/sponge/simple_constrain/refresh_crd_vel_impl.cuh" - -#include -#include -#include -#include - -#include "plugin/device/gpu/kernel/gpu_kernel.h" -#include "plugin/device/gpu/kernel/gpu_kernel_factory.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" - -namespace mindspore { -namespace kernel { -template -class RefreshCrdVelGpuKernelMod : public DeprecatedNativeGpuKernelMod { - public: - RefreshCrdVelGpuKernelMod() : ele_crd(1) {} - ~RefreshCrdVelGpuKernelMod() override = default; - - bool Init(const CNodePtr &kernel_node) override { - // get bond_numbers - kernel_node_ = kernel_node; - atom_numbers = static_cast(GetAttr(kernel_node, "atom_numbers")); - dt_inverse = static_cast(GetAttr(kernel_node, "dt_inverse")); - dt = static_cast(GetAttr(kernel_node, "dt")); - exp_gamma = static_cast(GetAttr(kernel_node, "exp_gamma")); - half_exp_gamma_plus_half = static_cast(GetAttr(kernel_node, "half_exp_gamma_plus_half")); - auto shape_crd = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto shape_vel = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - auto shape_test_frc = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); - auto shape_mass_inverse = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); - - ele_crd *= SizeOf(shape_crd); - ele_vel *= SizeOf(shape_vel); - ele_test_frc *= SizeOf(shape_test_frc); - ele_mass_inverse *= SizeOf(shape_mass_inverse); - - InitSizeLists(); - return true; - } - - bool Launch(const std::vector &inputs, const std::vector &, - const std::vector &outputs, void *stream_ptr) override { - auto crd = GetDeviceAddress(inputs, 0); - auto vel = GetDeviceAddress(inputs, 1); - auto test_frc = GetDeviceAddress(inputs, 2); - auto mass_inverse = GetDeviceAddress(inputs, 3); - - refreshcrdvel(atom_numbers, dt_inverse, dt, exp_gamma, half_exp_gamma_plus_half, test_frc, mass_inverse, crd, vel, - reinterpret_cast(stream_ptr)); - return true; - } - - protected: - void InitSizeLists() override { - input_size_list_.push_back(ele_crd * sizeof(T)); - input_size_list_.push_back(ele_vel * sizeof(T)); - input_size_list_.push_back(ele_test_frc * sizeof(T)); - input_size_list_.push_back(ele_mass_inverse * sizeof(T)); - - output_size_list_.push_back(sizeof(T)); - } - - private: - size_t ele_crd = 1; - size_t ele_vel = 1; - size_t ele_test_frc = 1; - size_t ele_mass_inverse = 1; - - int atom_numbers; - float dt_inverse; - float dt; - float exp_gamma; - float half_exp_gamma_plus_half; -}; -} // namespace kernel -} // namespace mindspore -#endif // MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONG_SIMPLE_CONSTRAIN_REFRESH_CRD_VEL_KERNEL_H_ diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/simple_constrain/refresh_uint_crd_kernel.cc b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/simple_constrain/refresh_uint_crd_kernel.cc deleted file mode 100644 index a4ca9a0e907..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/simple_constrain/refresh_uint_crd_kernel.cc +++ /dev/null @@ -1,33 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -/** - * Note: - * RefreshUintCrd. This is an experimental interface that is subject to change and/or deletion. - */ -#include "plugin/device/gpu/kernel/sponge/simple_constrain/refresh_uint_crd_kernel.h" - -namespace mindspore { -namespace kernel { -MS_REG_GPU_KERNEL_TWO(RefreshUintCrd, - KernelAttr() - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddInputAttr(kNumberTypeFloat32) - .AddOutputAttr(kNumberTypeUInt32), - RefreshUintCrdGpuKernelMod, float, unsigned int) -} // namespace kernel -} // namespace mindspore diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/simple_constrain/refresh_uint_crd_kernel.h b/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/simple_constrain/refresh_uint_crd_kernel.h deleted file mode 100644 index 2b8bba9cb53..00000000000 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/sponge/simple_constrain/refresh_uint_crd_kernel.h +++ /dev/null @@ -1,93 +0,0 @@ -/** - * Copyright 2021-2022 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONG_SIMPLE_CONSTRAIN_REFRESH_UINT_CRD_KERNEL_H_ -#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONG_SIMPLE_CONSTRAIN_REFRESH_UINT_CRD_KERNEL_H_ - -#include "plugin/device/gpu/kernel/cuda_impl/sponge/simple_constrain/refresh_uint_crd_impl.cuh" - -#include -#include -#include -#include - -#include "plugin/device/gpu/kernel/gpu_kernel.h" -#include "plugin/device/gpu/kernel/gpu_kernel_factory.h" -#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h" - -namespace mindspore { -namespace kernel { -template -class RefreshUintCrdGpuKernelMod : public DeprecatedNativeGpuKernelMod { - public: - RefreshUintCrdGpuKernelMod() : ele_crd(1) {} - ~RefreshUintCrdGpuKernelMod() override = default; - - bool Init(const CNodePtr &kernel_node) override { - // get bond_numbers - kernel_node_ = kernel_node; - atom_numbers = static_cast(GetAttr(kernel_node, "atom_numbers")); - half_exp_gamma_plus_half = static_cast(GetAttr(kernel_node, "half_exp_gamma_plus_half")); - auto shape_crd = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0); - auto shape_quarter_cof = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1); - auto shape_test_frc = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 2); - auto shape_mass_inverse = common::AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 3); - - ele_crd *= SizeOf(shape_crd); - ele_quarter_cof *= SizeOf(shape_quarter_cof); - ele_test_frc *= SizeOf(shape_test_frc); - ele_mass_inverse *= SizeOf(shape_mass_inverse); - - InitSizeLists(); - return true; - } - - bool Launch(const std::vector &inputs, const std::vector &, - const std::vector &outputs, void *stream_ptr) override { - auto crd = GetDeviceAddress(inputs, 0); - auto quarter_cof = GetDeviceAddress(inputs, 1); - auto test_frc = GetDeviceAddress(inputs, 2); - auto mass_inverse = GetDeviceAddress(inputs, 3); - - auto uint_crd = GetDeviceAddress(outputs, 0); - - refreshuintcrd(atom_numbers, half_exp_gamma_plus_half, crd, quarter_cof, test_frc, mass_inverse, uint_crd, - reinterpret_cast(stream_ptr)); - return true; - } - - protected: - void InitSizeLists() override { - input_size_list_.push_back(ele_crd * sizeof(T)); - input_size_list_.push_back(ele_quarter_cof * sizeof(T)); - input_size_list_.push_back(ele_test_frc * sizeof(T)); - input_size_list_.push_back(ele_mass_inverse * sizeof(T)); - - output_size_list_.push_back(3 * atom_numbers * sizeof(T1)); - } - - private: - size_t ele_crd = 1; - size_t ele_quarter_cof = 1; - size_t ele_test_frc = 1; - size_t ele_mass_inverse = 1; - - int atom_numbers; - float half_exp_gamma_plus_half; -}; -} // namespace kernel -} // namespace mindspore -#endif // MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_SPONG_SIMPLE_CONSTRAIN_REFRESH_UINT_CRD_KERNEL_H_ diff --git a/mindspore/python/mindspore/ops/operations/__init__.py b/mindspore/python/mindspore/ops/operations/__init__.py index da46c2b8261..f77efe6abcc 100644 --- a/mindspore/python/mindspore/ops/operations/__init__.py +++ b/mindspore/python/mindspore/ops/operations/__init__.py @@ -104,23 +104,6 @@ from .random_ops import (RandomChoiceWithMask, StandardNormal, Gamma, RandomGamm LogUniformCandidateSampler, TruncatedNormal) from .rl_ops import (BufferAppend, BufferGetItem, BufferSample) from .sparse_ops import (SparseToDense, SparseTensorDenseMatmul, SparseTensorDenseAdd) -from .sponge_ops import (BondForce, BondEnergy, BondAtomEnergy, BondForceWithAtomEnergy, BondForceWithAtomVirial, - DihedralForce, DihedralEnergy, DihedralAtomEnergy, DihedralForceWithAtomEnergy, AngleForce, - AngleEnergy, AngleAtomEnergy, AngleForceWithAtomEnergy, PMEReciprocalForce, - LJForce, LJEnergy, LJForceWithPMEDirectForce, PMEExcludedForce, PMEEnergy, Dihedral14LJForce, - Dihedral14LJForceWithDirectCF, Dihedral14LJEnergy, Dihedral14LJCFForceWithAtomEnergy, - Dihedral14LJAtomEnergy, Dihedral14CFEnergy, Dihedral14CFAtomEnergy, - MDTemperature, MDIterationLeapFrogLiujian, - CrdToUintCrd, MDIterationSetupRandState, TransferCrd, FFT3D, IFFT3D, NeighborListUpdate) -from .sponge_update_ops import (ConstrainForceCycleWithVirial, RefreshUintCrd, LastCrdToDr, RefreshCrdVel, - CalculateNowrapCrd, RefreshBoxmapTimes, Totalc6get, CrdToUintCrdQuarter, - MDIterationLeapFrogLiujianWithMaxVel, GetCenterOfMass, MapCenterOfMass, - NeighborListRefresh, MDIterationLeapFrog, MDIterationLeapFrogWithMaxVel, - MDIterationGradientDescent, BondForceWithAtomEnergyAndVirial, ConstrainForceCycle, - LJForceWithVirialEnergy, LJForceWithPMEDirectForceUpdate, PMEReciprocalForceUpdate, - PMEExcludedForceUpdate, LJForceWithVirialEnergyUpdate, - Dihedral14ForceWithAtomEnergyVirial, PMEEnergyUpdate, - ConstrainForceVirial, ConstrainForce, Constrain) __all__ = [ 'HSVToRGB', @@ -499,8 +482,6 @@ __all__ = [ "TensorScatterMul", "TensorScatterDiv", "SoftShrink", - "FFT3D", - "IFFT3D", "HShrink", "PyFunc", "BufferAppend", @@ -522,77 +503,11 @@ __all__ = [ "Cummax", ] -__sponge__ = [ - "Cross", - "BondForce", - "BondEnergy", - "BondAtomEnergy", - "BondForceWithAtomEnergy", - "BondForceWithAtomVirial", - "DihedralForce", - "DihedralEnergy", - "DihedralAtomEnergy", - "DihedralForceWithAtomEnergy", - "AngleForce", - "AngleEnergy", - "AngleAtomEnergy", - "AngleForceWithAtomEnergy", - 'PMEReciprocalForce', - 'LJForce', - 'LJForceWithPMEDirectForce', - 'LJEnergy', - 'PMEExcludedForce', - 'PMEEnergy', - "Dihedral14LJForce", - "Dihedral14LJEnergy", - "Dihedral14LJForceWithDirectCF", - "Dihedral14LJCFForceWithAtomEnergy", - "Dihedral14LJAtomEnergy", - "Dihedral14CFEnergy", - "MDIterationLeapFrog", - "Dihedral14CFAtomEnergy", - "MDTemperature", - "NeighborListUpdate", - "MDIterationLeapFrogLiujian", - "CrdToUintCrd", - "MDIterationSetupRandState", - "TransferCrd", - # Update - "ConstrainForceCycleWithVirial", - "RefreshUintCrd", - "LastCrdToDr", - "RefreshCrdVel", - "CalculateNowrapCrd", - "RefreshBoxmapTimes", - "Totalc6get", - "CrdToUintCrdQuarter", - "MDIterationLeapFrogLiujianWithMaxVel", - "GetCenterOfMass", - "MapCenterOfMass", - "NeighborListRefresh", - "MDIterationLeapFrog", - "MDIterationLeapFrogWithMaxVel", - "MDIterationGradientDescent", - "BondForceWithAtomEnergyAndVirial", - "ConstrainForceCycle", - "LJForceWithVirialEnergy", - "LJForceWithPMEDirectForceUpdate", - "PMEReciprocalForceUpdate", - "PMEExcludedForceUpdate", - "LJForceWithVirialEnergyUpdate", - "Dihedral14ForceWithAtomEnergyVirial", - "PMEEnergyUpdate", - "ConstrainForceVirial", - "ConstrainForce", - "Constrain", -] - __custom__ = [ "ms_kernel", "kernel", ] -__all__.extend(__sponge__) __all__.extend(__custom__) __all__.sort() diff --git a/mindspore/python/mindspore/ops/operations/sponge_ops.py b/mindspore/python/mindspore/ops/operations/sponge_ops.py deleted file mode 100644 index f2f53799694..00000000000 --- a/mindspore/python/mindspore/ops/operations/sponge_ops.py +++ /dev/null @@ -1,3498 +0,0 @@ -# Copyright 2021 Huawei Technologies Co., Ltd -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================ - -""" -Note: - SPONGE operators. This is an experimental interface that is subject to change and/or deletion. -""" - -import math - -from ..primitive import Primitive, PrimitiveWithInfer, prim_attr_register -from ..._checkparam import Rel -from ..._checkparam import Validator as validator -from ...common import dtype as mstype - - -class BondForce(PrimitiveWithInfer): - """ - Calculate the force exerted by the simple harmonic bond on the corresponding atoms. - Assume the number of harmonic bonds is m and the number of atoms is n. - - Because there is a large amount of inputs and each of them are related, - there is no way to construct `Examples` using random methods. For details, refer the webpage `SPONGE in MindSpore - `_. - - .. math:: - - dr = (x_1-x_2, y_1-y_2, z_1-z_2) - - .. math:: - - F = (F_x, F_y, F_z) = 2*k*(1 - r_0/|dr|)*dr - - Args: - atom_numbers(int32): the number of atoms n. - bond_numbers(int32): the number of harmonic bonds m. - - Inputs: - - **uint_crd_f** (Tensor) - The unsigned int coordinate value of each atom. - The data type is uint32 and the shape is :math:`(n, 3)`. - - **scaler_f** (Tensor) - The 3-D scale factor (x, y, z), - between the real space float coordinates and the unsigned int coordinates. - The data type is float32 and the shape is :math:`(3,)`. - - **atom_a** (Tensor) - The first atom index of each bond. - The data type is int32 and the shape is :math:`(m,)`. - - **atom_b** (Tensor) - The second atom index of each bond. - The data type is int32 and the shape is :math:`(m,)`. - - **bond_k** (Tensor) - The force constant of each bond. - The data type is float32 and the shape is :math:`(m,)`. - - **bond_r0** (Tensor) - The equlibrium length of each bond. - The data type is float32 and the shape is :math:`(m,)`. - - Outputs: - - **frc_f** (Tensor) - The force felt by each atom. - The data type is float32 and the shape is :math:`(n, 3)`. - - Supported Platforms: - ``GPU`` - """ - - @prim_attr_register - def __init__(self, bond_numbers, atom_numbers): - """Initialize BondForce.""" - validator.check_value_type('bond_numbers', bond_numbers, int, self.name) - validator.check_value_type('atom_numbers', atom_numbers, int, self.name) - self.bond_numbers = bond_numbers - self.atom_numbers = atom_numbers - self.add_prim_attr('bond_numbers', self.bond_numbers) - self.add_prim_attr('atom_numbers', self.atom_numbers) - self.init_prim_io_names(inputs=['uint_crd_f', 'scaler_f', 'atom_a', 'atom_b', 'bond_k', 'bond_r0'], - outputs=['frc_f']) - - def infer_shape(self, uint_crd_f_shape, scaler_f_shape, atom_a_shape, atom_b_shape, bond_k_shape, bond_r0_shape): - cls_name = self.name - n = self.atom_numbers - m = self.bond_numbers - validator.check_int(len(uint_crd_f_shape), 2, Rel.EQ, "uint_crd_f_dim", cls_name) - validator.check_int(len(scaler_f_shape), 1, Rel.EQ, "scaler_f_dim", cls_name) - validator.check_int(len(atom_a_shape), 1, Rel.EQ, "atom_a_dim", cls_name) - validator.check_int(len(atom_b_shape), 1, Rel.EQ, "atom_b_dim", cls_name) - validator.check_int(len(bond_k_shape), 1, Rel.EQ, "bond_k_dim", cls_name) - validator.check_int(len(bond_r0_shape), 1, Rel.EQ, "bond_r0_dim", cls_name) - validator.check_int(uint_crd_f_shape[0], n, Rel.EQ, "uint_crd_f_shape[0]", cls_name) - validator.check_int(uint_crd_f_shape[1], 3, Rel.EQ, "uint_crd_f_shape[1]", cls_name) - validator.check_int(scaler_f_shape[0], 3, Rel.EQ, "scaler_f_shape", cls_name) - validator.check_int(atom_a_shape[0], m, Rel.EQ, "uint_crd_f_shape", cls_name) - validator.check_int(atom_b_shape[0], m, Rel.EQ, "atom_b_shape", cls_name) - validator.check_int(bond_k_shape[0], m, Rel.EQ, "bond_k_shape", cls_name) - validator.check_int(bond_r0_shape[0], m, Rel.EQ, "bond_r0_shape", cls_name) - return uint_crd_f_shape - - def infer_dtype(self, uint_crd_f_dtype, scaler_f_type, atom_a_type, atom_b_type, bond_k_type, bond_r0_type): - validator.check_tensor_dtype_valid('uint_crd_f', uint_crd_f_dtype, [mstype.uint32], self.name) - validator.check_tensor_dtype_valid('scaler_f', scaler_f_type, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('atom_a', atom_a_type, [mstype.int32], self.name) - validator.check_tensor_dtype_valid('atom_b', atom_b_type, [mstype.int32], self.name) - validator.check_tensor_dtype_valid('bond_k', bond_k_type, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('bond_r0', bond_r0_type, [mstype.float32], self.name) - return bond_r0_type - - -class BondEnergy(PrimitiveWithInfer): - """ - Calculate the harmonic potential energy between each bonded atom pair. - Assume our system has n atoms and m harmonic bonds. - - Because there is a large amount of inputs and each of them are related, - there is no way to construct `Examples` using random methods. For details, refer the webpage `SPONGE in MindSpore - `_. - - .. math:: - - dr = (x_1-x_2, y_1-y_2, z_1-z_2) - - .. math:: - - E = k*(|dr| - r_0)^2 - - Args: - atom_numbers(int32): the number of atoms n. - bond_numbers(int32): the number of harmonic bonds m. - - Inputs: - - **uint_crd_f** (Tensor) - The unsigned int coordinate value of each atom. - The data type is uint32 and the shape is :math:`(n, 3)`. - - **scaler_f** (Tensor) - The 3-D scale factor (x, y, z), - between the real space float coordinates and the unsigned int coordinates. - The data type is float32 and the shape is :math:`(3,)`. - - **atom_a** (Tensor) - The first atom index of each bond. - The data type is int32 and the shape is :math:`(m,)`. - - **atom_b** (Tensor) - The second atom index of each bond. - The data type is int32 and the shape is :math:`(m,)`. - - **bond_k** (Tensor) - The force constant of each bond. - The data type is float32 and the shape is :math:`(m,)`. - - **bond_r0** (Tensor) - The equlibrium length of each bond. - The data type is float32 and the shape is :math:`(m,)`. - - Outputs: - - **bond_ene** (Tensor) - The harmonic potential energy for each bond. - The data type is float32 and the shape is :math:`(m,)`. - - Supported Platforms: - ``GPU`` - """ - - @prim_attr_register - def __init__(self, bond_numbers, atom_numbers): - """Initialize BondEnergy.""" - validator.check_value_type('bond_numbers', bond_numbers, int, self.name) - validator.check_value_type('atom_numbers', atom_numbers, int, self.name) - self.bond_numbers = bond_numbers - self.atom_numbers = atom_numbers - self.add_prim_attr('bond_numbers', self.bond_numbers) - self.add_prim_attr('atom_numbers', self.atom_numbers) - self.init_prim_io_names(inputs=['uint_crd_f', 'scaler_f', 'atom_a', 'atom_b', 'bond_k', 'bond_r0'], - outputs=['bond_ene']) - - def infer_shape(self, uint_crd_f_shape, scaler_f_shape, atom_a_shape, atom_b_shape, bond_k_shape, bond_r0_shape): - cls_name = self.name - n = self.atom_numbers - m = self.bond_numbers - validator.check_int(len(uint_crd_f_shape), 2, Rel.EQ, "uint_crd_f_dim", cls_name) - validator.check_int(len(scaler_f_shape), 1, Rel.EQ, "scaler_f_dim", cls_name) - validator.check_int(len(atom_a_shape), 1, Rel.EQ, "atom_a_dim", cls_name) - validator.check_int(len(atom_b_shape), 1, Rel.EQ, "atom_b_dim", cls_name) - validator.check_int(len(bond_k_shape), 1, Rel.EQ, "bond_k_dim", cls_name) - validator.check_int(len(bond_r0_shape), 1, Rel.EQ, "bond_r0_dim", cls_name) - validator.check_int(uint_crd_f_shape[0], n, Rel.EQ, "uint_crd_f_shape[0]", cls_name) - validator.check_int(uint_crd_f_shape[1], 3, Rel.EQ, "uint_crd_f_shape[1]", cls_name) - validator.check_int(scaler_f_shape[0], 3, Rel.EQ, "scaler_f_shape", cls_name) - validator.check_int(atom_a_shape[0], m, Rel.EQ, "uint_crd_f_shape", cls_name) - validator.check_int(atom_b_shape[0], m, Rel.EQ, "atom_b_shape", cls_name) - validator.check_int(bond_k_shape[0], m, Rel.EQ, "bond_k_shape", cls_name) - validator.check_int(bond_r0_shape[0], m, Rel.EQ, "bond_r0_shape", cls_name) - - return bond_k_shape - - def infer_dtype(self, uint_crd_f_dtype, scaler_f_type, atom_a_type, atom_b_type, bond_k_type, bond_r0_type): - validator.check_tensor_dtype_valid('uint_crd_f', uint_crd_f_dtype, [mstype.uint32], self.name) - validator.check_tensor_dtype_valid('scaler_f', scaler_f_type, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('atom_a', atom_a_type, [mstype.int32], self.name) - validator.check_tensor_dtype_valid('atom_b', atom_b_type, [mstype.int32], self.name) - validator.check_tensor_dtype_valid('bond_k', bond_k_type, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('bond_r0', bond_r0_type, [mstype.float32], self.name) - return bond_r0_type - - -class BondAtomEnergy(PrimitiveWithInfer): - """ - Add the potential energy caused by simple harmonic bonds to the total - potential energy of each atom. - - The calculation formula is the same as operator BondEnergy(). - - Because there is a large amount of inputs and each of them are related, - there is no way to construct `Examples` using random methods. For details, refer the webpage `SPONGE in MindSpore - `_. - - Args: - atom_numbers(int32): the number of atoms n. - bond_numbers(int32): the number of harmonic bonds m. - - Inputs: - - **uint_crd_f** (Tensor) - The unsigned int coordinate value of each atom. - The data type is uint32 and the shape is :math:`(n, 3)`. - - **scaler_f** (Tensor) - The 3-D scale factor (x, y, z), - between the real space float coordinates and the unsigned int coordinates. - The data type is float32 and the shape is :math:`(3,)`. - - **atom_a** (Tensor) - The first atom index of each bond. - The data type is int32 and the shape is :math:`(m,)`. - - **atom_b** (Tensor) - The second atom index of each bond. - The data type is int32 and the shape is :math:`(m,)`. - - **bond_k** (Tensor) - The force constant of each bond. - The data type is float32 and the shape is :math:`(m,)`. - - **bond_r0** (Tensor) - The equlibrium length of each bond. - The data type is float32 and the shape is :math:`(m,)`. - - Outputs: - - **atom_ene** (Tensor) - The accumulated potential energy for each atom. - The data type is float32 and the shape is :math:`(n,)`. - - Supported Platforms: - ``GPU`` - """ - - @prim_attr_register - def __init__(self, bond_numbers, atom_numbers): - """Initialize BondAtomEnergy.""" - validator.check_value_type('bond_numbers', bond_numbers, int, self.name) - validator.check_value_type('atom_numbers', atom_numbers, int, self.name) - self.bond_numbers = bond_numbers - self.atom_numbers = atom_numbers - self.add_prim_attr('bond_numbers', self.bond_numbers) - self.add_prim_attr('atom_numbers', self.atom_numbers) - self.init_prim_io_names(inputs=['uint_crd_f', 'scaler_f', 'atom_a', 'atom_b', 'bond_k', 'bond_r0'], - outputs=['atom_ene']) - - def infer_shape(self, uint_crd_f_shape, scaler_f_shape, atom_a_shape, atom_b_shape, bond_k_shape, bond_r0_shape): - cls_name = self.name - n = self.atom_numbers - m = self.bond_numbers - validator.check_int(len(uint_crd_f_shape), 2, Rel.EQ, "uint_crd_f_dim", cls_name) - validator.check_int(len(scaler_f_shape), 1, Rel.EQ, "scaler_f_dim", cls_name) - validator.check_int(len(atom_a_shape), 1, Rel.EQ, "atom_a_dim", cls_name) - validator.check_int(len(atom_b_shape), 1, Rel.EQ, "atom_b_dim", cls_name) - validator.check_int(len(bond_k_shape), 1, Rel.EQ, "bond_k_dim", cls_name) - validator.check_int(len(bond_r0_shape), 1, Rel.EQ, "bond_r0_dim", cls_name) - validator.check_int(uint_crd_f_shape[0], n, Rel.EQ, "uint_crd_f_shape[0]", cls_name) - validator.check_int(uint_crd_f_shape[1], 3, Rel.EQ, "uint_crd_f_shape[1]", cls_name) - validator.check_int(scaler_f_shape[0], 3, Rel.EQ, "scaler_f_shape", cls_name) - validator.check_int(atom_a_shape[0], m, Rel.EQ, "uint_crd_f_shape", cls_name) - validator.check_int(atom_b_shape[0], m, Rel.EQ, "atom_b_shape", cls_name) - validator.check_int(bond_k_shape[0], m, Rel.EQ, "bond_k_shape", cls_name) - validator.check_int(bond_r0_shape[0], m, Rel.EQ, "bond_r0_shape", cls_name) - return [n,] - - def infer_dtype(self, uint_crd_f_dtype, scaler_f_type, atom_a_type, atom_b_type, bond_k_type, bond_r0_type): - validator.check_tensor_dtype_valid('uint_crd_f', uint_crd_f_dtype, [mstype.uint32], self.name) - validator.check_tensor_dtype_valid('scaler_f', scaler_f_type, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('atom_a', atom_a_type, [mstype.int32], self.name) - validator.check_tensor_dtype_valid('atom_b', atom_b_type, [mstype.int32], self.name) - validator.check_tensor_dtype_valid('bond_k', bond_k_type, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('bond_r0', bond_r0_type, [mstype.float32], self.name) - return bond_r0_type - - -class BondForceWithAtomEnergy(PrimitiveWithInfer): - """ - Calculate bond force and harmonic potential energy together. - - The calculation formula is the same as operator BondForce() and BondEnergy(). - - Because there is a large amount of inputs and each of them are related, - there is no way to construct `Examples` using random methods. For details, refer the webpage `SPONGE in MindSpore - `_. - - Args: - atom_numbers(int32): the number of atoms n. - bond_numbers(int32): the number of harmonic bonds m. - - Inputs: - - **uint_crd_f** (Tensor) - The unsigned int coordinate value of each atom. - The data type is uint32 and the shape is :math:`(n, 3)`. - - **scaler_f** (Tensor) - The 3-D scale factor (x, y, z), - between the real space float coordinates and the unsigned int coordinates. - The data type is float32 and the shape is :math:`(3,)`. - - **atom_a** (Tensor) - The first atom index of each bond. - The data type is int32 and the shape is :math:`(m,)`. - - **atom_b** (Tensor) - The second atom index of each bond. - The data type is int32 and the shape is :math:`(m,)`. - - **bond_k** (Tensor) - The force constant of each bond. - The data type is float32 and the shape is :math:`(m,)`. - - **bond_r0** (Tensor) - The equlibrium length of each bond. - The data type is float32 and the shape is :math:`(m,)`. - - Outputs: - - **frc_f** (Tensor) - The force felt by each atom. - The data type is float32 and the shape is :math:`(n, 3)`. - - **atom_e** (Tensor) - The accumulated potential energy for each atom. - The data type is float32 and the shape is :math:`(n,)`. - - Supported Platforms: - ``GPU`` - """ - - @prim_attr_register - def __init__(self, bond_numbers, atom_numbers): - """Initialize BondForceWithAtomEnergy.""" - validator.check_value_type('bond_numbers', bond_numbers, int, self.name) - validator.check_value_type('atom_numbers', atom_numbers, int, self.name) - self.bond_numbers = bond_numbers - self.atom_numbers = atom_numbers - self.add_prim_attr('bond_numbers', self.bond_numbers) - self.add_prim_attr('atom_numbers', self.atom_numbers) - self.init_prim_io_names(inputs=['uint_crd_f', 'scaler_f', 'atom_a', 'atom_b', 'bond_k', 'bond_r0'], - outputs=['frc_f', 'atom_e']) - - def infer_shape(self, uint_crd_f_shape, scaler_f_shape, atom_a_shape, atom_b_shape, bond_k_shape, bond_r0_shape): - cls_name = self.name - n = self.atom_numbers - m = self.bond_numbers - validator.check_int(len(uint_crd_f_shape), 2, Rel.EQ, "uint_crd_f_dim", cls_name) - validator.check_int(len(scaler_f_shape), 1, Rel.EQ, "scaler_f_dim", cls_name) - validator.check_int(len(atom_a_shape), 1, Rel.EQ, "atom_a_dim", cls_name) - validator.check_int(len(atom_b_shape), 1, Rel.EQ, "atom_b_dim", cls_name) - validator.check_int(len(bond_k_shape), 1, Rel.EQ, "bond_k_dim", cls_name) - validator.check_int(len(bond_r0_shape), 1, Rel.EQ, "bond_r0_dim", cls_name) - validator.check_int(uint_crd_f_shape[0], n, Rel.EQ, "uint_crd_f_shape[0]", cls_name) - validator.check_int(uint_crd_f_shape[1], 3, Rel.EQ, "uint_crd_f_shape[1]", cls_name) - validator.check_int(scaler_f_shape[0], 3, Rel.EQ, "scaler_f_shape", cls_name) - validator.check_int(atom_a_shape[0], m, Rel.EQ, "uint_crd_f_shape", cls_name) - validator.check_int(atom_b_shape[0], m, Rel.EQ, "atom_b_shape", cls_name) - validator.check_int(bond_k_shape[0], m, Rel.EQ, "bond_k_shape", cls_name) - validator.check_int(bond_r0_shape[0], m, Rel.EQ, "bond_r0_shape", cls_name) - - return uint_crd_f_shape, [n,] - - def infer_dtype(self, uint_crd_f_dtype, scaler_f_type, atom_a_type, atom_b_type, bond_k_type, bond_r0_type): - validator.check_tensor_dtype_valid('uint_crd_f', uint_crd_f_dtype, [mstype.uint32], self.name) - validator.check_tensor_dtype_valid('scaler_f', scaler_f_type, [mstype.float32], self.name) - - validator.check_tensor_dtype_valid('atom_a', atom_a_type, [mstype.int32], self.name) - validator.check_tensor_dtype_valid('atom_b', atom_b_type, [mstype.int32], self.name) - - validator.check_tensor_dtype_valid('bond_k', bond_k_type, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('bond_r0', bond_r0_type, [mstype.float32], self.name) - return bond_r0_type, bond_r0_type - - -class BondForceWithAtomVirial(PrimitiveWithInfer): - """ - Calculate bond force and the virial coefficient caused by simple harmonic - bond for each atom together. - - The calculation formula of the force part is the same as operator BondForce(). - - Because there is a large amount of inputs and each of them are related, - there is no way to construct `Examples` using random methods. For details, refer the webpage `SPONGE in MindSpore - `_. - - The Virial part is as follows: - - .. math:: - - dr = (x_1-x_2, y_1-y_2, z_1-z_2) - - .. math:: - - virial = |dr|*(|dr| - r_0)*k - - Args: - atom_numbers(int32): the number of atoms n. - bond_numbers(int32): the number of harmonic bonds m. - - Inputs: - - **uint_crd_f** (Tensor) - The unsigned int coordinate value of each atom. - The data type is uint32 and the shape is :math:`(n, 3)`. - - **scaler_f** (Tensor) - The 3-D scale factor (x, y, z), - between the real space float coordinates and the unsigned int coordinates. - The data type is float32 and the shape is :math:`(3,)`. - - **atom_a** (Tensor) - The first atom index of each bond. - The data type is int32 and the shape is :math:`(m,)`. - - **atom_b** (Tensor) - The second atom index of each bond. - The data type is int32 and the shape is :math:`(m,)`. - - **bond_k** (Tensor) - The force constant of each bond. - The data type is float32 and the shape is :math:`(m,)`. - - **bond_r0** (Tensor) - The equlibrium length of each bond. - The data type is float32 and the shape is :math:`(m,)`. - - Outputs: - - **frc_f** (Tensor) - Same as operator BondForce(). - The data type is float32 and the shape is :math:`(n, 3)`. - - **atom_v** (Tensor) - The accumulated virial coefficient for each atom. - The data type is float32 and the shape is :math:`(n,)`. - - Supported Platforms: - ``GPU`` - """ - - @prim_attr_register - def __init__(self, bond_numbers, atom_numbers): - """Initialize BondForceWithAtomVirial.""" - validator.check_value_type('bond_numbers', bond_numbers, int, self.name) - validator.check_value_type('atom_numbers', atom_numbers, int, self.name) - self.bond_numbers = bond_numbers - self.atom_numbers = atom_numbers - self.add_prim_attr('bond_numbers', self.bond_numbers) - self.add_prim_attr('atom_numbers', self.atom_numbers) - self.init_prim_io_names(inputs=['uint_crd_f', 'scaler_f', 'atom_a', 'atom_b', 'bond_k', 'bond_r0'], - outputs=['frc_f', 'atom_v']) - - def infer_shape(self, uint_crd_f_shape, scaler_f_shape, atom_a_shape, atom_b_shape, bond_k_shape, bond_r0_shape): - cls_name = self.name - n = self.atom_numbers - m = self.bond_numbers - validator.check_int(len(uint_crd_f_shape), 2, Rel.EQ, "uint_crd_f_dim", cls_name) - validator.check_int(len(scaler_f_shape), 1, Rel.EQ, "scaler_f_dim", cls_name) - validator.check_int(len(atom_a_shape), 1, Rel.EQ, "atom_a_dim", cls_name) - validator.check_int(len(atom_b_shape), 1, Rel.EQ, "atom_b_dim", cls_name) - validator.check_int(len(bond_k_shape), 1, Rel.EQ, "bond_k_dim", cls_name) - validator.check_int(len(bond_r0_shape), 1, Rel.EQ, "bond_r0_dim", cls_name) - validator.check_int(uint_crd_f_shape[0], n, Rel.EQ, "uint_crd_f_shape[0]", cls_name) - validator.check_int(uint_crd_f_shape[1], 3, Rel.EQ, "uint_crd_f_shape[1]", cls_name) - validator.check_int(scaler_f_shape[0], 3, Rel.EQ, "scaler_f_shape", cls_name) - validator.check_int(atom_a_shape[0], m, Rel.EQ, "uint_crd_f_shape", cls_name) - validator.check_int(atom_b_shape[0], m, Rel.EQ, "atom_b_shape", cls_name) - validator.check_int(bond_k_shape[0], m, Rel.EQ, "bond_k_shape", cls_name) - validator.check_int(bond_r0_shape[0], m, Rel.EQ, "bond_r0_shape", cls_name) - - return uint_crd_f_shape, [n,] - - def infer_dtype(self, uint_crd_f_dtype, scaler_f_type, atom_a_type, atom_b_type, bond_k_type, bond_r0_type): - validator.check_tensor_dtype_valid('uint_crd_f', uint_crd_f_dtype, [mstype.uint32], self.name) - validator.check_tensor_dtype_valid('scaler_f', scaler_f_type, [mstype.float32], self.name) - - validator.check_tensor_dtype_valid('atom_a', atom_a_type, [mstype.int32], self.name) - validator.check_tensor_dtype_valid('atom_b', atom_b_type, [mstype.int32], self.name) - - validator.check_tensor_dtype_valid('bond_k', bond_k_type, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('bond_r0', bond_r0_type, [mstype.float32], self.name) - return bond_r0_type, bond_r0_type - - -class DihedralForce(PrimitiveWithInfer): - """ - Calculate the force exerted by the dihedral term which made of 4-atoms - on the corresponding atoms. Assume the number of dihedral terms is m and - the number of atoms is n. - - Because there is a large amount of inputs and each of them are related, - there is no way to construct `Examples` using random methods. For details, refer the webpage `SPONGE in MindSpore - `_. - - Args: - dihedral_numbers(int32): the number of dihedral terms m. - - Inputs: - - **uint_crd_f** (Tensor) - The unsigned int coordinates - value of each atom. The data type is uint32 and the shape is :math:`(n, 3)`. - - **scaler_f** (Tensor) - The 3-D scale factor between - the real space float coordinates and the unsigned int coordinates. - The data type is float32 and the shape is :math:`(3,)`. - - **atom_a** (Tensor) - The 1st atom index of each dihedral. - The data type is int32 and the shape is :math:`(m,)`. - - **atom_b** (Tensor) - The 2nd atom index of each dihedral. - The data type is int32 and the shape is :math:`(m,)`. - - **atom_c** (Tensor) - The 3rd atom index of each dihedral. - The data type is int32 and the shape is :math:`(m,)`. - - **atom_d** (Tensor) - The 4th atom index of each dihedral. - 4 atoms are connected in the form a-b-c-d. - The data type is int32 and the shape is :math:`(m,)`. - - **ipn** (Tensor) - The period of dihedral angle of each dihedral. - The data type is int32 and the shape is :math:`(m,)`. - - **pk** (Tensor) - The force constant of each dihedral. - The data type is float32 and the shape is :math:`(m,)`. - - **gamc** (Tensor) - k*cos(phi_0) of each dihedral. - The data type is float32 and the shape is :math:`(m,)`. - - **gams** (Tensor) - k*sin(phi_0) of each dihedral. - The data type is float32 and the shape is :math:`(m,)`. - - **pn** (Tensor) - The floating point form of ipn. - The data type is float32 and the shape is :math:`(m,)`. - - Outputs: - - **frc_f** (Tensor) - The force felt by each atom. - The data type is float32 and the shape is :math:`(n, 3)`. - - Supported Platforms: - ``GPU`` - """ - - @prim_attr_register - def __init__(self, dihedral_numbers): - """Initialize DihedralForce.""" - validator.check_value_type('dihedral_numbers', dihedral_numbers, int, self.name) - self.dihedral_numbers = dihedral_numbers - self.init_prim_io_names(inputs=['uint_crd_f', 'scaler_f', 'atom_a', 'atom_b', 'atom_c', 'atom_d', 'ipn', 'pk', - 'gamc', 'gams', 'pn'], - outputs=['frc_f']) - self.add_prim_attr('dihedral_numbers', self.dihedral_numbers) - - def infer_shape(self, uint_crd_f_shape, scaler_f_shape, atom_a_shape, atom_b_shape, atom_c_shape, atom_d_shape, - ipn_shape, pk_shape, gamc_shape, gams_shape, pn_shape): - cls_name = self.name - m = self.dihedral_numbers - validator.check_int(len(uint_crd_f_shape), 2, Rel.EQ, "uint_crd_f_dim", cls_name) - validator.check_int(len(scaler_f_shape), 1, Rel.EQ, "scaler_f_dim", cls_name) - validator.check_int(len(atom_a_shape), 1, Rel.EQ, "atom_a_dim", cls_name) - validator.check_int(len(atom_b_shape), 1, Rel.EQ, "atom_b_dim", cls_name) - validator.check_int(len(atom_c_shape), 1, Rel.EQ, "atom_c_dim", cls_name) - validator.check_int(len(atom_d_shape), 1, Rel.EQ, "atom_d_dim", cls_name) - validator.check_int(len(ipn_shape), 1, Rel.EQ, "ipn_dim", cls_name) - validator.check_int(len(pk_shape), 1, Rel.EQ, "pk_dim", cls_name) - validator.check_int(len(gamc_shape), 1, Rel.EQ, "gamc_dim", cls_name) - validator.check_int(len(gams_shape), 1, Rel.EQ, "gams_dim", cls_name) - validator.check_int(len(pn_shape), 1, Rel.EQ, "pn_dim", cls_name) - - validator.check_int(uint_crd_f_shape[1], 3, Rel.EQ, "uint_crd_f_shape[1]", cls_name) - validator.check_int(scaler_f_shape[0], 3, Rel.EQ, "scaler_f_shape", cls_name) - validator.check_int(atom_a_shape[0], m, Rel.EQ, "atom_a_shape", cls_name) - validator.check_int(atom_b_shape[0], m, Rel.EQ, "atom_b_shape", cls_name) - validator.check_int(atom_c_shape[0], m, Rel.EQ, "atom_c_shape", cls_name) - validator.check_int(atom_d_shape[0], m, Rel.EQ, "atom_d_shape", cls_name) - validator.check_int(ipn_shape[0], m, Rel.EQ, "ipn_shape", cls_name) - validator.check_int(pk_shape[0], m, Rel.EQ, "pk_shape", cls_name) - validator.check_int(gamc_shape[0], m, Rel.EQ, "gamc_shape", cls_name) - validator.check_int(gams_shape[0], m, Rel.EQ, "gams_shape", cls_name) - validator.check_int(pn_shape[0], m, Rel.EQ, "pn_shape", cls_name) - return uint_crd_f_shape - - def infer_dtype(self, uint_crd_f_dtype, scaler_f_type, atom_a_type, atom_b_type, atom_c_type, atom_d_type, - ipn_type, pk_type, gamc_type, gams_type, pn_type): - validator.check_tensor_dtype_valid('uint_crd_f', uint_crd_f_dtype, [mstype.uint32], self.name) - validator.check_tensor_dtype_valid('scaler_f', scaler_f_type, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('atom_a', atom_a_type, [mstype.int32], self.name) - validator.check_tensor_dtype_valid('atom_b', atom_b_type, [mstype.int32], self.name) - validator.check_tensor_dtype_valid('atom_c', atom_c_type, [mstype.int32], self.name) - validator.check_tensor_dtype_valid('atom_d', atom_d_type, [mstype.int32], self.name) - validator.check_tensor_dtype_valid('ipn', ipn_type, [mstype.int32], self.name) - validator.check_tensor_dtype_valid('pk', pk_type, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('gamc', gamc_type, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('gams', gams_type, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('pn', pn_type, [mstype.float32], self.name) - - return pn_type - - -class DihedralEnergy(PrimitiveWithInfer): - """ - Calculate the potential energy caused by dihedral terms for each 4-atom pair. - Assume our system has n atoms and m dihedral terms. - - Because there is a large amount of inputs and each of them are related, - there is no way to construct `Examples` using random methods. For details, refer the webpage `SPONGE in MindSpore - `_. - - Args: - dihedral_numbers(int32): the number of dihedral terms m. - - Inputs: - - **uint_crd_f** (Tensor) - The unsigned int coordinates - value of each atom. - The data type is uint32 and the shape is :math:`(n, 3)`. - - **scaler_f** (Tensor) - The 3-D scale factor between - the real space float coordinates and the unsigned int coordinates. - The data type is float32 and the shape is :math:`(3,)`. - - **atom_a** (Tensor) - The 1st atom index of each dihedral. - The data type is int32 and the shape is :math:`(m,)`. - - **atom_b** (Tensor) - The 2nd atom index of each dihedral. - The data type is int32 and the shape is :math:`(m,)`. - - **atom_c** (Tensor) - The 3rd atom index of each dihedral. - The data type is int32 and the shape is :math:`(m,)`. - - **atom_d** (Tensor) - The 4th atom index of each dihedral. - 4 atoms are connected in the form a-b-c-d. - The data type is int32 and the shape is :math:`(m,)`. - - **ipn** (Tensor) - The period of dihedral angle of each dihedral. - The data type is int32 and the shape is :math:`(m,)`. - - **pk** (Tensor) - The force constant of each dihedral. - The data type is int32 and the shape is :math:`(m,)`. - - **gamc** (Tensor) - k*cos(phi_0) of each dihedral. - The data type is float32 and the shape is :math:`(m,)`. - - **gams** (Tensor) - k*sin(phi_0) of each dihedral. - The data type is float32 and the shape is :math:`(m,)`. - - **pn** (Tensor) - The floating point form of ipn. - The data type is float32 and the shape is :math:`(m,)`. - - Outputs: - - **ene** (Tensor) - The potential energy for each - dihedral term. The data type is float32 and the shape is :math:`(m,)`. - - Supported Platforms: - ``GPU`` - """ - - @prim_attr_register - def __init__(self, dihedral_numbers): - """Initialize DihedralEnergy.""" - validator.check_value_type('dihedral_numbers', dihedral_numbers, int, self.name) - self.dihedral_numbers = dihedral_numbers - self.init_prim_io_names(inputs=['uint_crd_f', 'scaler_f', 'atom_a', 'atom_b', 'atom_c', 'atom_d', 'ipn', 'pk', - 'gamc', 'gams', 'pn'], - outputs=['ene']) - self.add_prim_attr('dihedral_numbers', self.dihedral_numbers) - - def infer_shape(self, uint_crd_f_shape, scaler_f_shape, atom_a_shape, atom_b_shape, atom_c_shape, atom_d_shape, - ipn_shape, pk_shape, gamc_shape, gams_shape, pn_shape): - cls_name = self.name - m = self.dihedral_numbers - validator.check_int(len(uint_crd_f_shape), 2, Rel.EQ, "uint_crd_f_dim", cls_name) - validator.check_int(len(scaler_f_shape), 1, Rel.EQ, "scaler_f_dim", cls_name) - validator.check_int(len(atom_a_shape), 1, Rel.EQ, "atom_a_dim", cls_name) - validator.check_int(len(atom_b_shape), 1, Rel.EQ, "atom_b_dim", cls_name) - validator.check_int(len(atom_c_shape), 1, Rel.EQ, "atom_c_dim", cls_name) - validator.check_int(len(atom_d_shape), 1, Rel.EQ, "atom_d_dim", cls_name) - validator.check_int(len(ipn_shape), 1, Rel.EQ, "ipn_dim", cls_name) - validator.check_int(len(pk_shape), 1, Rel.EQ, "pk_dim", cls_name) - validator.check_int(len(gamc_shape), 1, Rel.EQ, "gamc_dim", cls_name) - validator.check_int(len(gams_shape), 1, Rel.EQ, "gams_dim", cls_name) - validator.check_int(len(pn_shape), 1, Rel.EQ, "pn_dim", cls_name) - - validator.check_int(uint_crd_f_shape[1], 3, Rel.EQ, "uint_crd_f_shape[1]", cls_name) - validator.check_int(scaler_f_shape[0], 3, Rel.EQ, "scaler_f_shape", cls_name) - validator.check_int(atom_a_shape[0], m, Rel.EQ, "atom_a_shape", cls_name) - validator.check_int(atom_b_shape[0], m, Rel.EQ, "atom_b_shape", cls_name) - validator.check_int(atom_c_shape[0], m, Rel.EQ, "atom_c_shape", cls_name) - validator.check_int(atom_d_shape[0], m, Rel.EQ, "atom_d_shape", cls_name) - validator.check_int(ipn_shape[0], m, Rel.EQ, "ipn_shape", cls_name) - validator.check_int(pk_shape[0], m, Rel.EQ, "pk_shape", cls_name) - validator.check_int(gamc_shape[0], m, Rel.EQ, "gamc_shape", cls_name) - validator.check_int(gams_shape[0], m, Rel.EQ, "gams_shape", cls_name) - validator.check_int(pn_shape[0], m, Rel.EQ, "pn_shape", cls_name) - return [m,] - - def infer_dtype(self, uint_crd_f_dtype, scaler_f_type, atom_a_type, atom_b_type, atom_c_type, atom_d_type, - ipn_type, pk_type, gamc_type, gams_type, pn_type): - validator.check_tensor_dtype_valid('uint_crd_f', uint_crd_f_dtype, [mstype.uint32], self.name) - validator.check_tensor_dtype_valid('scaler_f', scaler_f_type, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('atom_a', atom_a_type, [mstype.int32], self.name) - validator.check_tensor_dtype_valid('atom_b', atom_b_type, [mstype.int32], self.name) - validator.check_tensor_dtype_valid('atom_c', atom_c_type, [mstype.int32], self.name) - validator.check_tensor_dtype_valid('atom_d', atom_d_type, [mstype.int32], self.name) - validator.check_tensor_dtype_valid('ipn', ipn_type, [mstype.int32], self.name) - validator.check_tensor_dtype_valid('pk', pk_type, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('gamc', gamc_type, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('gams', gams_type, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('pn', pn_type, [mstype.float32], self.name) - - return pn_type - - -class DihedralAtomEnergy(PrimitiveWithInfer): - """ - Add the potential energy caused by dihedral terms to the total potential - energy of each atom. - - Because there is a large amount of inputs and each of them are related, - there is no way to construct `Examples` using random methods. For details, refer the webpage `SPONGE in MindSpore - `_. - - The calculation formula is the same as operator DihedralEnergy(). - - Args: - dihedral_numbers(int32): the number of dihedral terms m. - - Inputs: - - **uint_crd_f** (Tensor) - The unsigned int coordinates - value of each atom. The data type is uint32 and the shape is :math:`(n, 3)`. - - **scaler_f** (Tensor) - The 3-D scale factor between - the real space float coordinates and the unsigned int coordinates. - The data type is float32 and the shape is :math:`(3,)`. - - **atom_a** (Tensor) - The 1st atom index of each dihedral. - The data type is int32 and the shape is :math:`(m,)`. - - **atom_b** (Tensor) - The 2nd atom index of each dihedral. - The data type is int32 and the shape is :math:`(m,)`. - - **atom_c** (Tensor) - The 3rd atom index of each dihedral. - The data type is int32 and the shape is :math:`(m,)`. - - **atom_d** (Tensor) - The 4th atom index of each dihedral. - 4 atoms are connected in the form a-b-c-d. The data type is int32 and the shape is :math:`(m,)`. - - **ipn** (Tensor) - The period of dihedral angle of each dihedral. - The data type is int32 and the shape is :math:`(m,)`. - - **pk** (Tensor) - The force constant of each dihedral. - The data type is float32 and the shape is :math:`(m,)`. - - **gamc** (Tensor) - k*cos(phi_0) of each dihedral. - The data type is float32 and the shape is :math:`(m,)`. - - **gams** (Tensor) - k*sin(phi_0) of each dihedral. - The data type is float32 and the shape is :math:`(m,)`. - - **pn** (Tensor) - The floating point form of ipn. - The data type is float32 and the shape is :math:`(m,)`. - - Outputs: - - **ene** (Tensor) - The accumulated potential - energy for each atom. The data type is float32 and the shape is :math:`(n,)`. - - Supported Platforms: - ``GPU`` - """ - - @prim_attr_register - def __init__(self, dihedral_numbers): - """Initialize DihedralAtomEnergy.""" - validator.check_value_type('dihedral_numbers', dihedral_numbers, int, self.name) - self.dihedral_numbers = dihedral_numbers - self.init_prim_io_names(inputs=['uint_crd_f', 'scaler_f', 'atom_a', 'atom_b', 'atom_c', 'atom_d', 'ipn', 'pk', - 'gamc', 'gams', 'pn'], - outputs=['ene']) - self.add_prim_attr('dihedral_numbers', self.dihedral_numbers) - - def infer_shape(self, uint_crd_f_shape, scaler_f_shape, atom_a_shape, atom_b_shape, atom_c_shape, atom_d_shape, - ipn_shape, pk_shape, gamc_shape, gams_shape, pn_shape): - cls_name = self.name - n = uint_crd_f_shape[0] - m = self.dihedral_numbers - validator.check_int(len(uint_crd_f_shape), 2, Rel.EQ, "uint_crd_f_dim", cls_name) - validator.check_int(len(scaler_f_shape), 1, Rel.EQ, "scaler_f_dim", cls_name) - validator.check_int(len(atom_a_shape), 1, Rel.EQ, "atom_a_dim", cls_name) - validator.check_int(len(atom_b_shape), 1, Rel.EQ, "atom_b_dim", cls_name) - validator.check_int(len(atom_c_shape), 1, Rel.EQ, "atom_c_dim", cls_name) - validator.check_int(len(atom_d_shape), 1, Rel.EQ, "atom_d_dim", cls_name) - validator.check_int(len(ipn_shape), 1, Rel.EQ, "ipn_dim", cls_name) - validator.check_int(len(pk_shape), 1, Rel.EQ, "pk_dim", cls_name) - validator.check_int(len(gamc_shape), 1, Rel.EQ, "gamc_dim", cls_name) - validator.check_int(len(gams_shape), 1, Rel.EQ, "gams_dim", cls_name) - validator.check_int(len(pn_shape), 1, Rel.EQ, "pn_dim", cls_name) - - validator.check_int(uint_crd_f_shape[1], 3, Rel.EQ, "uint_crd_f_shape[1]", cls_name) - validator.check_int(scaler_f_shape[0], 3, Rel.EQ, "scaler_f_shape", cls_name) - validator.check_int(atom_a_shape[0], m, Rel.EQ, "atom_a_shape", cls_name) - validator.check_int(atom_b_shape[0], m, Rel.EQ, "atom_b_shape", cls_name) - validator.check_int(atom_c_shape[0], m, Rel.EQ, "atom_c_shape", cls_name) - validator.check_int(atom_d_shape[0], m, Rel.EQ, "atom_d_shape", cls_name) - validator.check_int(ipn_shape[0], m, Rel.EQ, "ipn_shape", cls_name) - validator.check_int(pk_shape[0], m, Rel.EQ, "pk_shape", cls_name) - validator.check_int(gamc_shape[0], m, Rel.EQ, "gamc_shape", cls_name) - validator.check_int(gams_shape[0], m, Rel.EQ, "gams_shape", cls_name) - validator.check_int(pn_shape[0], m, Rel.EQ, "pn_shape", cls_name) - return [n,] - - def infer_dtype(self, uint_crd_f_dtype, scaler_f_type, atom_a_type, atom_b_type, atom_c_type, atom_d_type, - ipn_type, pk_type, gamc_type, gams_type, pn_type): - validator.check_tensor_dtype_valid('uint_crd_f', uint_crd_f_dtype, [mstype.uint32], self.name) - validator.check_tensor_dtype_valid('scaler_f', scaler_f_type, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('atom_a', atom_a_type, [mstype.int32], self.name) - validator.check_tensor_dtype_valid('atom_b', atom_b_type, [mstype.int32], self.name) - validator.check_tensor_dtype_valid('atom_c', atom_c_type, [mstype.int32], self.name) - validator.check_tensor_dtype_valid('atom_d', atom_d_type, [mstype.int32], self.name) - validator.check_tensor_dtype_valid('ipn', ipn_type, [mstype.int32], self.name) - validator.check_tensor_dtype_valid('pk', pk_type, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('gamc', gamc_type, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('gams', gams_type, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('pn', pn_type, [mstype.float32], self.name) - - return pn_type - - -class DihedralForceWithAtomEnergy(PrimitiveWithInfer): - """ - Calculate dihedral force and potential energy together. - - The calculation formula is the same as operator DihedralForce() and DihedralEnergy(). - - Because there is a large amount of inputs and each of them are related, - there is no way to construct `Examples` using random methods. For details, refer the webpage `SPONGE in MindSpore - `_. - - Args: - dihedral_numbers(int32): the number of dihedral terms m. - - Inputs: - - **uint_crd_f** (Tensor) - The unsigned int coordinates - value of each atom. The data type is uint32 and the shape is :math:`(n, 3)`. - - **scaler_f** (Tensor) - The 3-D scale factor between - the real space float coordinates and the unsigned int coordinates. - The data type is float32 and the shape is :math:`(3,)`. - - **atom_a** (Tensor) - The 1st atom index of each dihedral. - The data type is int32 and the shape is :math:`(m,)`. - - **atom_b** (Tensor) - The 2nd atom index of each dihedral. - The data type is int32 and the shape is :math:`(m,)`. - - **atom_c** (Tensor) - The 3rd atom index of each dihedral. - The data type is int32 and the shape is :math:`(m,)`. - - **atom_d** (Tensor) - The 4th atom index of each dihedral. - 4 atoms are connected in the form a-b-c-d. The data type is int32 and the shape is :math:`(m,)`. - - **ipn** (Tensor) - The period of dihedral angle of each dihedral. - The data type is int32 and the shape is :math:`(m,)`. - - **pk** (Tensor) - The force constant of each dihedral. - The data type is float32 and the shape is :math:`(m,)`. - - **gamc** (Tensor) - k*cos(phi_0) of each dihedral. - The data type is float32 and the shape is :math:`(m,)`. - - **gams** (Tensor) - k*sin(phi_0) of each dihedral. - The data type is float32 and the shape is :math:`(m,)`. - - **pn** (Tensor) - The floating point form of ipn. - The data type is float32 and the shape is :math:`(m,)`. - - Outputs: - - **frc_f** (Tensor) - Same as operator DihedralForce(). - The data type is float32 and the shape is :math:`(n, 3)`. - - **ene** (Tensor) - Same as operator DihedralAtomEnergy(). - The data type is float32 and the shape is :math:`(n,)`. - - Supported Platforms: - ``GPU`` - """ - - @prim_attr_register - def __init__(self, dihedral_numbers): - """Initialize DihedralForceWithAtomEnergy.""" - validator.check_value_type('dihedral_numbers', dihedral_numbers, int, self.name) - self.dihedral_numbers = dihedral_numbers - self.init_prim_io_names(inputs=['uint_crd_f', 'scaler_f', 'atom_a', 'atom_b', 'atom_c', 'atom_d', 'ipn', 'pk', - 'gamc', 'gams', 'pn'], - outputs=['frc_f', 'ene']) - self.add_prim_attr('dihedral_numbers', self.dihedral_numbers) - - def infer_shape(self, uint_crd_f_shape, scaler_f_shape, atom_a_shape, atom_b_shape, atom_c_shape, atom_d_shape, - ipn_shape, pk_shape, gamc_shape, gams_shape, pn_shape): - cls_name = self.name - n = uint_crd_f_shape[0] - m = self.dihedral_numbers - validator.check_int(len(uint_crd_f_shape), 2, Rel.EQ, "uint_crd_f_dim", cls_name) - validator.check_int(len(scaler_f_shape), 1, Rel.EQ, "scaler_f_dim", cls_name) - validator.check_int(len(atom_a_shape), 1, Rel.EQ, "atom_a_dim", cls_name) - validator.check_int(len(atom_b_shape), 1, Rel.EQ, "atom_b_dim", cls_name) - validator.check_int(len(atom_c_shape), 1, Rel.EQ, "atom_c_dim", cls_name) - validator.check_int(len(atom_d_shape), 1, Rel.EQ, "atom_d_dim", cls_name) - validator.check_int(len(ipn_shape), 1, Rel.EQ, "ipn_dim", cls_name) - validator.check_int(len(pk_shape), 1, Rel.EQ, "pk_dim", cls_name) - validator.check_int(len(gamc_shape), 1, Rel.EQ, "gamc_dim", cls_name) - validator.check_int(len(gams_shape), 1, Rel.EQ, "gams_dim", cls_name) - validator.check_int(len(pn_shape), 1, Rel.EQ, "pn_dim", cls_name) - - validator.check_int(uint_crd_f_shape[1], 3, Rel.EQ, "uint_crd_f_shape[1]", cls_name) - validator.check_int(scaler_f_shape[0], 3, Rel.EQ, "scaler_f_shape", cls_name) - validator.check_int(atom_a_shape[0], m, Rel.EQ, "atom_a_shape", cls_name) - validator.check_int(atom_b_shape[0], m, Rel.EQ, "atom_b_shape", cls_name) - validator.check_int(atom_c_shape[0], m, Rel.EQ, "atom_c_shape", cls_name) - validator.check_int(atom_d_shape[0], m, Rel.EQ, "atom_d_shape", cls_name) - validator.check_int(ipn_shape[0], m, Rel.EQ, "ipn_shape", cls_name) - validator.check_int(pk_shape[0], m, Rel.EQ, "pk_shape", cls_name) - validator.check_int(gamc_shape[0], m, Rel.EQ, "gamc_shape", cls_name) - validator.check_int(gams_shape[0], m, Rel.EQ, "gams_shape", cls_name) - validator.check_int(pn_shape[0], m, Rel.EQ, "pn_shape", cls_name) - return uint_crd_f_shape, [n,] - - def infer_dtype(self, uint_crd_f_dtype, scaler_f_type, atom_a_type, atom_b_type, atom_c_type, atom_d_type, - ipn_type, pk_type, gamc_type, gams_type, pn_type): - validator.check_tensor_dtype_valid('uint_crd_f', uint_crd_f_dtype, [mstype.uint32], self.name) - validator.check_tensor_dtype_valid('scaler_f', scaler_f_type, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('atom_a', atom_a_type, [mstype.int32], self.name) - validator.check_tensor_dtype_valid('atom_b', atom_b_type, [mstype.int32], self.name) - validator.check_tensor_dtype_valid('atom_c', atom_c_type, [mstype.int32], self.name) - validator.check_tensor_dtype_valid('atom_d', atom_d_type, [mstype.int32], self.name) - validator.check_tensor_dtype_valid('ipn', ipn_type, [mstype.int32], self.name) - validator.check_tensor_dtype_valid('pk', pk_type, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('gamc', gamc_type, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('gams', gams_type, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('pn', pn_type, [mstype.float32], self.name) - - return pn_type, pn_type - - -class AngleForce(PrimitiveWithInfer): - """ - Calculate the force exerted by angles made of 3 atoms on the - corresponding atoms. Assume the number of angles is m and the - number of atoms is n. - - Because there is a large amount of inputs and each of them are related, - there is no way to construct `Examples` using random methods. For details, refer the webpage `SPONGE in MindSpore - `_. - - .. math:: - dr_{ab} = (x_b-x_a, y_b-y_a, z_b-z_a) - .. math:: - dr_{cb} = (x_b-x_c, y_b-y_c, z_b-z_c) - .. math:: - theta = arccos(inner_product(dr_{ab}, dr_{cb})/|dr_{ab}|/|dr_{cb}|) - .. math:: - F_a = -2*k*(theta-theta_0)/sin(theta)*[cos(theta)/|dr_{ab}|^2*dr_{ab} - - 1/|dr_{ab}|/|dr_{cb}|*dr_{cb}] - .. math:: - F_c = -2*k*(theta-theta_0)/sin(theta)*[cos(theta)/|dr_{cb}|^2*dr_{cb} - - 1/|dr_{cb}|/|dr_{ab}|*dr_{ab}] - .. math:: - F_b = -F_a - F_c - - Args: - angle_numbers(int32): the number of angles m. - - Inputs: - - **uint_crd_f** (Tensor) - The unsigned int coordinate value of each atom. - The data type is uint32 and the shape is :math:`(n, 3)`. - - **scaler_f** (Tensor) - The 3-D scale factor between - the real space float coordinates and the unsigned int coordinates. - The data type is float32 and the shape is :math:`(3,)`. - - **atom_a** (Tensor) - The 1st atom index of each angle. - The data type is int32 and the shape is :math:`(m,)`. - - **atom_b** (Tensor) - The 2nd and the central atom index of each angle. - The data type is int32 and the shape is :math:`(m,)`. - - **atom_c** (Tensor) - The 3rd atom index of each angle. - The data type is int32 and the shape is :math:`(m,)`. - - **angle_k** (Tensor) - The force constant for each angle. - The data type is float32 and the shape is :math:`(m,)`. - - **angle_theta0** (Tensor) - The equilibrium position value for each angle. - The data type is float32 and the shape is :math:`(m,)`. - - Outputs: - - **frc_f** (Tensor) - The force felt by each atom. - The data type is float32 and the shape is :math:`(n, 3)`. - - Supported Platforms: - ``GPU`` - """ - - @prim_attr_register - def __init__(self, angle_numbers): - """Initialize AngleForce.""" - validator.check_value_type('angle_numbers', angle_numbers, int, self.name) - self.angle_numbers = angle_numbers - self.init_prim_io_names(inputs=['uint_crd_f', 'scaler_f', 'atom_a', 'atom_b', 'atom_c', 'angle_k', - 'angle_theta0'], - outputs=['frc_f']) - self.add_prim_attr('angle_numbers', self.angle_numbers) - - def infer_shape(self, uint_crd_f_shape, scaler_f_shape, atom_a_shape, atom_b_shape, atom_c_shape, angle_k_shape, - angle_theta0_shape): - cls_name = self.name - m = self.angle_numbers - validator.check_int(len(uint_crd_f_shape), 2, Rel.EQ, "uint_crd_f_dim", cls_name) - validator.check_int(len(scaler_f_shape), 1, Rel.EQ, "scaler_f_dim", cls_name) - validator.check_int(len(atom_a_shape), 1, Rel.EQ, "atom_a_dim", cls_name) - validator.check_int(len(atom_b_shape), 1, Rel.EQ, "atom_b_dim", cls_name) - validator.check_int(len(atom_c_shape), 1, Rel.EQ, "atom_c_dim", cls_name) - validator.check_int(len(angle_k_shape), 1, Rel.EQ, "angle_k_dim", cls_name) - validator.check_int(len(angle_theta0_shape), 1, Rel.EQ, "angle_theta0_dim", cls_name) - - validator.check_int(uint_crd_f_shape[1], 3, Rel.EQ, "uint_crd_f_shape[1]", cls_name) - validator.check_int(scaler_f_shape[0], 3, Rel.EQ, "scaler_f_shape", cls_name) - validator.check_int(atom_a_shape[0], m, Rel.EQ, "atom_a_shape", cls_name) - validator.check_int(atom_b_shape[0], m, Rel.EQ, "atom_b_shape", cls_name) - validator.check_int(atom_c_shape[0], m, Rel.EQ, "atom_c_shape", cls_name) - validator.check_int(angle_k_shape[0], m, Rel.EQ, "angle_k_shape", cls_name) - validator.check_int(angle_theta0_shape[0], m, Rel.EQ, "angle_theta0_shape", cls_name) - return uint_crd_f_shape - - def infer_dtype(self, uint_crd_f_dtype, scaler_f_type, atom_a_type, atom_b_type, atom_c_type, angle_k_type, - angle_theta0_type): - validator.check_tensor_dtype_valid('uint_crd_f', uint_crd_f_dtype, [mstype.uint32], self.name) - validator.check_tensor_dtype_valid('scaler_f', scaler_f_type, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('atom_a', atom_a_type, [mstype.int32], self.name) - validator.check_tensor_dtype_valid('atom_b', atom_b_type, [mstype.int32], self.name) - validator.check_tensor_dtype_valid('atom_c', atom_c_type, [mstype.int32], self.name) - validator.check_tensor_dtype_valid('angle_k', angle_k_type, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('angle_theta0', angle_theta0_type, [mstype.float32], self.name) - return angle_k_type - - -class AngleEnergy(PrimitiveWithInfer): - """ - Calculate the energy caused by 3-atoms angle term. Assume the number of angles is m and the - number of atoms is n. - - Because there is a large amount of inputs and each of them are related, - there is no way to construct `Examples` using random methods. For details, refer the webpage `SPONGE in MindSpore - `_. - - .. math:: - dr_{ab} = (x_b-x_a, y_b-y_a, z_b-z_a) - .. math:: - dr_{cb} = (x_b-x_c, y_b-y_c, z_b-z_c) - .. math:: - theta = arccos(inner_product(dr_{ab}, dr_{cb})/|dr_{ab}|/|dr_{cb}|) - .. math:: - E = k*(theta - theta_0)^2 - - Args: - angle_numbers(int32): the number of angles m. - - Inputs: - - **uint_crd_f** (Tensor) - The unsigned int coordinate value of each atom. - The data type is uint32 and the shape is :math:`(n, 3)`. - - **scaler_f** (Tensor) - The 3-D scale factor between - the real space float coordinates and the unsigned int coordinates. - The data type is float32 and the shape is :math:`(3,)`. - - **atom_a** (Tensor) - The 1st atom index of each angle. - The data type is int32 and the shape is :math:`(m,)`. - - **atom_b** (Tensor) - The 2nd and the central atom index of each angle. - The data type is int32 and the shape is :math:`(m,)`. - - **atom_c** (Tensor) - The 3rd atom index of each angle. - The data type is int32 and the shape is :math:`(m,)`. - - **angle_k** (Tensor) - The force constant for each angle. - The data type is float32 and the shape is :math:`(m,)`. - - **angle_theta0** (Tensor) - The equilibrium position value for each angle. - The data type is float32 and the shape is :math:`(m,)`. - - Outputs: - - **ene** (Tensor) - The potential energy for each angle term. - The data type is float32 and the shape is :math:`(m,)`. - - Supported Platforms: - ``GPU`` - """ - - @prim_attr_register - def __init__(self, angle_numbers): - """Initialize AngleEnergy.""" - validator.check_value_type('angle_numbers', angle_numbers, int, self.name) - self.angle_numbers = angle_numbers - self.init_prim_io_names(inputs=['uint_crd_f', 'scaler_f', 'atom_a', 'atom_b', 'atom_c', 'angle_k', - 'angle_theta0'], - outputs=['ene']) - self.add_prim_attr('angle_numbers', self.angle_numbers) - - def infer_shape(self, uint_crd_f_shape, scaler_f_shape, atom_a_shape, atom_b_shape, atom_c_shape, angle_k_shape, - angle_theta0_shape): - cls_name = self.name - m = self.angle_numbers - validator.check_int(len(uint_crd_f_shape), 2, Rel.EQ, "uint_crd_f_dim", cls_name) - validator.check_int(len(scaler_f_shape), 1, Rel.EQ, "scaler_f_dim", cls_name) - validator.check_int(len(atom_a_shape), 1, Rel.EQ, "atom_a_dim", cls_name) - validator.check_int(len(atom_b_shape), 1, Rel.EQ, "atom_b_dim", cls_name) - validator.check_int(len(atom_c_shape), 1, Rel.EQ, "atom_c_dim", cls_name) - validator.check_int(len(angle_k_shape), 1, Rel.EQ, "angle_k_dim", cls_name) - validator.check_int(len(angle_theta0_shape), 1, Rel.EQ, "angle_theta0_dim", cls_name) - - validator.check_int(uint_crd_f_shape[1], 3, Rel.EQ, "uint_crd_f_shape[1]", cls_name) - validator.check_int(scaler_f_shape[0], 3, Rel.EQ, "scaler_f_shape", cls_name) - validator.check_int(atom_a_shape[0], m, Rel.EQ, "atom_a_shape", cls_name) - validator.check_int(atom_b_shape[0], m, Rel.EQ, "atom_b_shape", cls_name) - validator.check_int(atom_c_shape[0], m, Rel.EQ, "atom_c_shape", cls_name) - validator.check_int(angle_k_shape[0], m, Rel.EQ, "angle_k_shape", cls_name) - validator.check_int(angle_theta0_shape[0], m, Rel.EQ, "angle_theta0_shape", cls_name) - return [m,] - - def infer_dtype(self, uint_crd_f_dtype, scaler_f_type, atom_a_type, atom_b_type, atom_c_type, angle_k_type, - angle_theta0_type): - validator.check_tensor_dtype_valid('uint_crd_f', uint_crd_f_dtype, [mstype.uint32], self.name) - validator.check_tensor_dtype_valid('scaler_f', scaler_f_type, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('atom_a', atom_a_type, [mstype.int32], self.name) - validator.check_tensor_dtype_valid('atom_b', atom_b_type, [mstype.int32], self.name) - validator.check_tensor_dtype_valid('atom_c', atom_c_type, [mstype.int32], self.name) - validator.check_tensor_dtype_valid('angle_k', angle_k_type, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('angle_theta0', angle_theta0_type, [mstype.float32], self.name) - return angle_k_type - - -class AngleAtomEnergy(Primitive): - """ - Add the potential energy caused by angle terms to the total potential - energy of each atom. Assume the number of angles is m and the - number of atoms is n. - - The calculation formula is the same as operator AngleEnergy(). - - Because there is a large amount of inputs and each of them are related, - there is no way to construct `Examples` using random methods. For details, refer the webpage `SPONGE in MindSpore - `_. - - Args: - angle_numbers(int32): the number of angles m. - - Inputs: - - **uint_crd_f** (Tensor) - The unsigned int coordinate value of each atom. - The data type is uint32 and the shape is :math:`(n, 3)`. - - **scaler_f** (Tensor) - The 3-D scale factor between - the real space float coordinates and the unsigned int coordinates. - The data type is float32 and the shape is :math:`(3,)`. - - **atom_a** (Tensor) - The 1st atom index of each angle. - The data type is int32 and the shape is :math:`(m,)`. - - **atom_b** (Tensor) - The 2nd and the central atom index of each angle. - The data type is int32 and the shape is :math:`(m,)`. - - **atom_c** (Tensor) - The 3rd atom index of each angle. - The data type is int32 and the shape is :math:`(m,)`. - - **angle_k** (Tensor) - The force constant for each angle. - The data type is float32 and the shape is :math:`(m,)`. - - **angle_theta0** (Tensor) - The equilibrium position value for each angle. - The data type is float32 and the shape is :math:`(m,)`. - - Outputs: - - **ene** (Tensor) - The accumulated potential energy for each atom. - The data type is float32 and the shape is :math:`(n,)`. - - Supported Platforms: - ``GPU`` - """ - - @prim_attr_register - def __init__(self, angle_numbers): - """Initialize AngleAtomEnergy.""" - validator.check_value_type('angle_numbers', angle_numbers, int, self.name) - self.angle_numbers = angle_numbers - self.init_prim_io_names(inputs=['uint_crd_f', 'scaler_f', 'atom_a', 'atom_b', 'atom_c', 'angle_k', - 'angle_theta0'], - outputs=['ene']) - self.add_prim_attr('angle_numbers', self.angle_numbers) - - -class AngleForceWithAtomEnergy(PrimitiveWithInfer): - """ - Calculate angle force and potential energy together. Assume the number of angles is m and the - number of atoms is n. - - The calculation formula is the same as operator AngleForce() and AngleEnergy(). - - Because there is a large amount of inputs and each of them are related, - there is no way to construct `Examples` using random methods. For details, refer the webpage `SPONGE in MindSpore - `_. - - Args: - angle_numbers(int32): the number of angles m. - - Inputs: - - **uint_crd_f** (Tensor) - The unsigned int coordinate value of each atom. - The data type is uint32 and the shape is :math:`(n, 3)`. - - **scaler_f** (Tensor) - The 3-D scale factor between - the real space float coordinates and the unsigned int coordinates. - The data type is float and the shape is :math:`(3,)`. - - **atom_a** (Tensor) - The 1st atom index of each angle. - The data type is int32 and the shape is :math:`(m,)`. - - **atom_b** (Tensor) - The 2nd and the central atom index of each angle. - The data type is int32 and the shape is :math:`(m,)`. - - **atom_c** (Tensor) - The 3rd atom index of each angle. - The data type is int32 and the shape is :math:`(m,)`. - - **angle_k** (Tensor) - The force constant for each angle. - The data type is float32 and the shape is :math:`(m,)`. - - **angle_theta0** (Tensor) - The equilibrium position value for each angle. - The data type is float32 and the shape is :math:`(m,)`. - - Outputs: - - **frc_f** (Tensor) - same as operator AngleForce(). - The data type is float32 and the shape is :math:`(n, 3)`. - - **ene** (Tensor) - same as operator AngleAtomEnergy(). - The data type is float and the shape is :math:`(n,)`. - - Supported Platforms: - ``GPU`` - """ - - @prim_attr_register - def __init__(self, angle_numbers): - """Initialize AngleForceWithAtomEnergy.""" - validator.check_value_type('angle_numbers', angle_numbers, int, self.name) - self.angle_numbers = angle_numbers - self.init_prim_io_names(inputs=['uint_crd_f', 'scaler_f', 'atom_a', 'atom_b', 'atom_c', 'angle_k', - 'angle_theta0'], - outputs=['frc_f', 'ene']) - self.add_prim_attr('angle_numbers', self.angle_numbers) - - def infer_shape(self, uint_crd_f_shape, scaler_f_shape, atom_a_shape, atom_b_shape, atom_c_shape, angle_k_shape, - angle_theta0_shape): - cls_name = self.name - n = uint_crd_f_shape[0] - m = self.angle_numbers - validator.check_int(len(uint_crd_f_shape), 2, Rel.EQ, "uint_crd_f_dim", cls_name) - validator.check_int(len(scaler_f_shape), 1, Rel.EQ, "scaler_f_dim", cls_name) - validator.check_int(len(atom_a_shape), 1, Rel.EQ, "atom_a_dim", cls_name) - validator.check_int(len(atom_b_shape), 1, Rel.EQ, "atom_b_dim", cls_name) - validator.check_int(len(atom_c_shape), 1, Rel.EQ, "atom_c_dim", cls_name) - validator.check_int(len(angle_k_shape), 1, Rel.EQ, "angle_k_dim", cls_name) - validator.check_int(len(angle_theta0_shape), 1, Rel.EQ, "angle_theta0_dim", cls_name) - - validator.check_int(uint_crd_f_shape[1], 3, Rel.EQ, "uint_crd_f_shape[1]", cls_name) - validator.check_int(scaler_f_shape[0], 3, Rel.EQ, "scaler_f_shape", cls_name) - validator.check_int(atom_a_shape[0], m, Rel.EQ, "atom_a_shape", cls_name) - validator.check_int(atom_b_shape[0], m, Rel.EQ, "atom_b_shape", cls_name) - validator.check_int(atom_c_shape[0], m, Rel.EQ, "atom_c_shape", cls_name) - validator.check_int(angle_k_shape[0], m, Rel.EQ, "angle_k_shape", cls_name) - validator.check_int(angle_theta0_shape[0], m, Rel.EQ, "angle_theta0_shape", cls_name) - return uint_crd_f_shape, [n,] - - def infer_dtype(self, uint_crd_f_dtype, scaler_f_type, atom_a_type, atom_b_type, atom_c_type, angle_k_type, - angle_theta0_type): - validator.check_tensor_dtype_valid('uint_crd_f', uint_crd_f_dtype, [mstype.uint32], self.name) - validator.check_tensor_dtype_valid('scaler_f', scaler_f_type, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('atom_a', atom_a_type, [mstype.int32], self.name) - validator.check_tensor_dtype_valid('atom_b', atom_b_type, [mstype.int32], self.name) - validator.check_tensor_dtype_valid('atom_c', atom_c_type, [mstype.int32], self.name) - validator.check_tensor_dtype_valid('angle_k', angle_k_type, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('angle_theta0', angle_theta0_type, [mstype.float32], self.name) - return angle_k_type, angle_k_type - - -class Dihedral14LJForce(PrimitiveWithInfer): - """ - Calculate the Lennard-Jones part of 1,4 dihedral force correction - for each necessary dihedral terms on the corresponding atoms. - - Assume the number of necessary dihedral 1,4 terms is m, the number of atoms is n, - and the number of Lennard-Jones types for all atoms is P, which means - there will be q = P*(P+1)/2 types of possible Lennard-Jones interactions - for all kinds of atom pairs. - - Because there is a large amount of inputs and each of them are related, - there is no way to construct `Examples` using random methods. For details, refer the webpage `SPONGE in MindSpore - `_. - - .. math:: - dr = (x_a-x_b, y_a-y_b, z_a-z_b) - .. math:: - F = k*(-12*A/|dr|^{14} + 6*B/|dr|^{8})*dr - - Args: - nb14_numbers (int32): the number of necessary dihedral 1,4 terms m. - atom_numbers (int32): the number of atoms n. - - Inputs: - - **uint_crd_f** (Tensor) - The unsigned int coordinate value of each atom. - The data type is uint32 and the shape is :math:`(n, 3)`. - - **LJ_type** (Tensor) - The Lennard-Jones type of each atom. - The data type is int32 and the shape is :math:`(n,)`. - - **charge** (Tensor) - The charge of each atom. - The data type is float32 and the shape is :math:`(n,)`. - - **boxlength_f** (Tensor) - The length of molecular simulation box in 3 dimensions. - The data type is float32 and the shape is :math:`(3,)`. - - **a_14** (Tensor) - The first atom index of each dihedral 1,4 term. - The data type is int32 and the shape is :math:`(m,)`. - - **b_14** (Tensor) - The second atom index of each dihedral 1,4 term. - The data type is int32 and the shape is :math:`(m,)`. - - **lj_scale_factor** (Tensor) - The scale factor for the - Lennard-Jones part of force correction of each dihedral 1,4 term. - The data type is float32 and the shape is :math:`(m,)`. - - **LJ_type_A** (Tensor) - The A parameter in Lennard-Jones scheme of each atom pair type. - q is the number of atom pair. The data type is float32 and the shape is :math:`(q,)`. - - **LJ_type_B** (Tensor) - The B parameter in Lennard-Jones scheme of each atom pair type. - q is the number of atom pair. The data type is float32 and the shape is :math:`(q,)`. - - Outputs: - - **frc_f** (Tensor) - The force felt by each atom. - The data type is float32 and the shape is :math:`(n, 3)`. - - Supported Platforms: - ``GPU`` - """ - - @prim_attr_register - def __init__(self, nb14_numbers, atom_numbers): - """Initialize Dihedral14LJForce.""" - validator.check_value_type('nb14_numbers', nb14_numbers, int, self.name) - validator.check_value_type('atom_numbers', atom_numbers, int, self.name) - self.dihedral_14_numbers = nb14_numbers - self.atom_numbers = atom_numbers - self.init_prim_io_names( - inputs=['uint_crd_f', 'LJtype', 'charge', 'boxlength_f', 'a_14', 'b_14', 'lj_scale_factor', - 'LJ_type_A', 'LJ_type_B'], - outputs=['frc_f']) - self.add_prim_attr('dihedral_14_numbers', self.dihedral_14_numbers) - self.add_prim_attr('atom_numbers', self.atom_numbers) - - def infer_shape(self, uint_crd_f_shape, ljtype_shape, charge_shape, boxlength_f_shape, a_14_shape, b_14_shape, - lj_scale_factor_shape, lj_type_a_shape, lj_type_b_shape): - cls_name = self.name - n = self.atom_numbers - m = self.dihedral_14_numbers - q = lj_type_a_shape[0] - validator.check_int(len(uint_crd_f_shape), 2, Rel.EQ, "uint_crd_f_dim", cls_name) - validator.check_int(len(ljtype_shape), 1, Rel.EQ, "LJtype_dim", cls_name) - validator.check_int(len(charge_shape), 1, Rel.EQ, "charge_dim", cls_name) - validator.check_int(len(boxlength_f_shape), 1, Rel.EQ, "boxlength_f_dim", cls_name) - validator.check_int(len(a_14_shape), 1, Rel.EQ, "a_14_dim", cls_name) - validator.check_int(len(b_14_shape), 1, Rel.EQ, "b_14_dim", cls_name) - validator.check_int(len(lj_scale_factor_shape), 1, Rel.EQ, "lj_scale_factor_dim", cls_name) - validator.check_int(len(lj_type_b_shape), 1, Rel.EQ, "LJ_type_B_dim", cls_name) - - validator.check_int(uint_crd_f_shape[0], n, Rel.EQ, "uint_crd_f[0]", cls_name) - validator.check_int(uint_crd_f_shape[1], 3, Rel.EQ, "uint_crd_f[1]", cls_name) - validator.check_int(ljtype_shape[0], n, Rel.EQ, "LJtype", cls_name) - validator.check_int(charge_shape[0], n, Rel.EQ, "charge", cls_name) - validator.check_int(boxlength_f_shape[0], 3, Rel.EQ, "boxlength_f", cls_name) - validator.check_int(lj_type_b_shape[0], q, Rel.EQ, "LJ_type_B", cls_name) - validator.check_int(a_14_shape[0], m, Rel.EQ, "a_14_shape", cls_name) - validator.check_int(b_14_shape[0], m, Rel.EQ, "b_14_shape", cls_name) - validator.check_int(lj_scale_factor_shape[0], m, Rel.EQ, "lj_scale_factor_shape", cls_name) - return uint_crd_f_shape - - def infer_dtype(self, uint_crd_f_dtype, ljtype_dtype, charge_dtype, boxlength_f_type, a_14_type, b_14_type, - lj_scale_factor_type, lj_type_a_type, lj_type_b_type): - validator.check_tensor_dtype_valid('uint_crd_f', uint_crd_f_dtype, [mstype.uint32], self.name) - validator.check_tensor_dtype_valid('LJtype', ljtype_dtype, [mstype.int32], self.name) - validator.check_tensor_dtype_valid('charge', charge_dtype, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('boxlength_f', boxlength_f_type, [mstype.float32], self.name) - - validator.check_tensor_dtype_valid('a_14', a_14_type, [mstype.int32], self.name) - validator.check_tensor_dtype_valid('b_14', b_14_type, [mstype.int32], self.name) - - validator.check_tensor_dtype_valid('lj_scale_factor', lj_scale_factor_type, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('LJ_type_A', lj_type_a_type, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('LJ_type_B', lj_type_b_type, [mstype.float32], self.name) - return lj_type_b_type - - -class Dihedral14LJEnergy(PrimitiveWithInfer): - """ - Calculate the Lennard-Jones part of 1,4 dihedral energy correction for - each necessary dihedral terms on the corresponding atoms. - - Because there is a large amount of inputs and each of them are related, - there is no way to construct `Examples` using random methods. For details, refer the webpage `SPONGE in MindSpore - `_. - - .. math:: - dr = (x_a-x_b, y_a-y_b, z_a-z-b) - .. math:: - E = k*(A/|dr|^{12} - B/|dr|^{6}) - - Args: - nb14_numbers (int32): the number of necessary dihedral 1,4 terms m. - atom_numbers (int32): the number of atoms n. - - Inputs: - - **uint_crd_f** (Tensor) - The unsigned int coordinate value of each atom. - The data type is uint32 and the shape is :math:`(n, 3)`. - - **LJ_type** (Tensor) - The Lennard-Jones type of each atom. - The data type is int32 and the shape is :math:`(n,)`. - - **charge** (Tensor) - The charge of each atom. - The data type is float32 and the shape is :math:`(n,)`. - - **boxlength_f** (Tensor) - The length of molecular simulation box in 3 dimensions. - The data type is float32 and the shape is :math:`(3,)`. - - **a_14** (Tensor) - The first atom index of each dihedral 1,4 term. - The data type is int32 and the shape is :math:`(m,)`. - - **b_14** (Tensor) - The second atom index of each dihedral 1,4 term. - The data type is int32 and the shape is :math:`(m,)`. - - **lj_scale_factor** (Tensor) - The scale factor for the - Lennard-Jones part of force correction of each dihedral 1,4 term. - The data type is float32 and the shape is :math:`(m,)`. - - **LJ_type_A** (Tensor) - The A parameter in Lennard-Jones scheme of each atom pair type. - q is the number of atom pair. The data type is float32 and the shape is :math:`(q,)`. - - **LJ_type_B** (Tensor) - The B parameter in Lennard-Jones scheme of each atom pair type. - q is the number of atom pair. The data type is float32 and the shape is :math:`(q,)`. - - Outputs: - - **ene** (Tensor) - The Lennard-Jones potential energy correction. - The data type is float32 and the shape is :math:`(m,)`. - - Supported Platforms: - ``GPU`` - """ - - @prim_attr_register - def __init__(self, nb14_numbers, atom_numbers): - """Initialize Dihedral14LJEnergy""" - validator.check_value_type('nb14_numbers', nb14_numbers, int, self.name) - validator.check_value_type('atom_numbers', atom_numbers, int, self.name) - self.dihedral_14_numbers = nb14_numbers - self.atom_numbers = atom_numbers - - self.init_prim_io_names( - inputs=['uint_crd_f', 'LJtype', 'charge', 'boxlength_f', 'a_14', 'b_14', 'lj_scale_factor', - 'LJ_type_A', 'LJ_type_B'], - outputs=['ene']) - self.add_prim_attr('dihedral_14_numbers', self.dihedral_14_numbers) - self.add_prim_attr('atom_numbers', self.atom_numbers) - - def infer_shape(self, uint_crd_f_shape, ljtype_shape, charge_shape, boxlength_f_shape, a_14_shape, b_14_shape, - lj_scale_factor_shape, lj_type_a_shape, lj_type_b_shape): - cls_name = self.name - n = self.atom_numbers - m = self.dihedral_14_numbers - q = lj_type_a_shape[0] - validator.check_int(len(uint_crd_f_shape), 2, Rel.EQ, "uint_crd_f_dim", cls_name) - validator.check_int(len(ljtype_shape), 1, Rel.EQ, "LJtype_dim", cls_name) - validator.check_int(len(charge_shape), 1, Rel.EQ, "charge_dim", cls_name) - validator.check_int(len(boxlength_f_shape), 1, Rel.EQ, "boxlength_f_dim", cls_name) - validator.check_int(len(a_14_shape), 1, Rel.EQ, "a_14_dim", cls_name) - validator.check_int(len(b_14_shape), 1, Rel.EQ, "b_14_dim", cls_name) - validator.check_int(len(lj_scale_factor_shape), 1, Rel.EQ, "lj_scale_factor_dim", cls_name) - validator.check_int(len(lj_type_b_shape), 1, Rel.EQ, "LJ_type_B_dim", cls_name) - - validator.check_int(uint_crd_f_shape[0], n, Rel.EQ, "uint_crd_f[0]", cls_name) - validator.check_int(uint_crd_f_shape[1], 3, Rel.EQ, "uint_crd_f[1]", cls_name) - validator.check_int(ljtype_shape[0], n, Rel.EQ, "LJtype", cls_name) - validator.check_int(charge_shape[0], n, Rel.EQ, "charge", cls_name) - validator.check_int(boxlength_f_shape[0], 3, Rel.EQ, "boxlength_f", cls_name) - validator.check_int(lj_type_b_shape[0], q, Rel.EQ, "LJ_type_B", cls_name) - validator.check_int(a_14_shape[0], m, Rel.EQ, "a_14_shape", cls_name) - validator.check_int(b_14_shape[0], m, Rel.EQ, "b_14_shape", cls_name) - validator.check_int(lj_scale_factor_shape[0], m, Rel.EQ, "lj_scale_factor_shape", cls_name) - return [self.dihedral_14_numbers,] - - def infer_dtype(self, uint_crd_f_dtype, ljtype_dtype, charge_dtype, boxlength_f_type, a_14_type, b_14_type, - lj_scale_factor_type, lj_type_a_type, lj_type_b_type): - validator.check_tensor_dtype_valid('uint_crd_f', uint_crd_f_dtype, [mstype.uint32], self.name) - validator.check_tensor_dtype_valid('LJtype', ljtype_dtype, [mstype.int32], self.name) - validator.check_tensor_dtype_valid('charge', charge_dtype, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('boxlength_f', boxlength_f_type, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('a_14', a_14_type, [mstype.int32], self.name) - validator.check_tensor_dtype_valid('b_14', b_14_type, [mstype.int32], self.name) - validator.check_tensor_dtype_valid('lj_scale_factor', lj_scale_factor_type, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('LJ_type_A', lj_type_a_type, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('LJ_type_B', lj_type_b_type, [mstype.float32], self.name) - - return lj_type_a_type - - -class Dihedral14LJForceWithDirectCF(PrimitiveWithInfer): - """ - Calculate the Lennard-Jones part and the Coulomb part of force correction - for each necessary dihedral 1,4 terms. - - Because there is a large amount of inputs and each of them are related, - there is no way to construct `Examples` using random methods. For details, refer the webpage `SPONGE in MindSpore - `_. - - The calculation formula of the Lennard-Jones part is the same as operator - Dihedral14LJForce(), and the Coulomb part is as follows: - - .. math:: - dr = (x_a-x_b, y_a-y_b, z_a-z_b) - .. math:: - F = -k*q_a*q_b/|r|^3*dr - - Args: - nb14_numbers (int32): the number of necessary dihedral 1,4 terms m. - atom_numbers (int32): the number of atoms n. - - Inputs: - - **uint_crd_f** (Tensor) - The unsigned int coordinate value of each atom. - The data type is uint32 and the shape is :math:`(n, 3)`. - - **LJ_type** (Tensor) - The Lennard-Jones type of each atom. - The data type is int32 and the shape is :math:`(n,)`. - - **charge** (Tensor) - The charge of each atom. - The data type is float32 and the shape is :math:`(n,)`. - - **boxlength_f** (Tensor) - The length of molecular simulation box in 3 dimensions. - The data type is float32 and the shape is :math:`(3,)`. - - **a_14** (Tensor) - The first atom index of each dihedral 1,4 term. - The data type is int32 and the shape is :math:`(m,)`. - - **b_14** (Tensor) - The second atom index of each dihedral 1,4 term. - The data type is int32 and the shape is :math:`(m,)`. - - **lj_scale_factor** (Tensor) - The scale factor for the - Lennard-Jones part of force correction of each dihedral 1,4 term. - The data type is float32 and the shape is :math:`(m,)`. - - **cf_scale_factor** (Tensor) - The scale factor for the - Coulomb part of force correction for each dihedral 1,4 terms. - The data type is float32 and the shape is :math:`(m,)`. - - **LJ_type_A** (Tensor) - The A parameter in Lennard-Jones scheme of each atom pair type. - q is the number of atom pair. The data type is float32 and the shape is :math:`(q,)`. - - **LJ_type_B** (Tensor) - The B parameter in Lennard-Jones shceme of each atom pair type. - q is the number of atom pair. The data type is float32 and the shape is :math:`(q,)`. - - Outputs: - - **frc_f** (Tensor) - The force felt by each atom. - The data type is float32 and the shape is :math:`(n, 3)` - - Supported Platforms: - ``GPU`` - """ - - @prim_attr_register - def __init__(self, nb14_numbers, atom_numbers): - """Initialize Dihedral14LJForceWithDirectCF.""" - validator.check_value_type('nb14_numbers', nb14_numbers, int, self.name) - validator.check_value_type('atom_numbers', atom_numbers, int, self.name) - self.dihedral_14_numbers = nb14_numbers - self.atom_numbers = atom_numbers - - self.init_prim_io_names( - inputs=['uint_crd_f', 'LJtype', 'charge', 'boxlength_f', 'a_14', 'b_14', 'lj_scale_factor', - 'cf_scale_factor', - 'LJ_type_A', 'LJ_type_B'], - outputs=['frc_f']) - self.add_prim_attr('dihedral_14_numbers', self.dihedral_14_numbers) - self.add_prim_attr('atom_numbers', self.atom_numbers) - - def infer_shape(self, uint_crd_f_shape, ljtype_shape, charge_shape, boxlength_f_shape, a_14_shape, b_14_shape, - lj_scale_factor_shape, cf_scale_factor_shape, lj_type_a_shape, lj_type_b_shape): - cls_name = self.name - n = self.atom_numbers - m = self.dihedral_14_numbers - q = lj_type_a_shape[0] - validator.check_int(len(uint_crd_f_shape), 2, Rel.EQ, "uint_crd_f_dim", cls_name) - validator.check_int(len(ljtype_shape), 1, Rel.EQ, "LJtype_dim", cls_name) - validator.check_int(len(charge_shape), 1, Rel.EQ, "charge_dim", cls_name) - validator.check_int(len(boxlength_f_shape), 1, Rel.EQ, "boxlength_f_dim", cls_name) - validator.check_int(len(a_14_shape), 1, Rel.EQ, "a_14_dim", cls_name) - validator.check_int(len(b_14_shape), 1, Rel.EQ, "b_14_dim", cls_name) - validator.check_int(len(lj_scale_factor_shape), 1, Rel.EQ, "lj_scale_factor_dim", cls_name) - validator.check_int(len(cf_scale_factor_shape), 1, Rel.EQ, "cf_scale_factor_dim", cls_name) - validator.check_int(len(lj_type_b_shape), 1, Rel.EQ, "LJ_type_B_dim", cls_name) - - validator.check_int(uint_crd_f_shape[0], n, Rel.EQ, "uint_crd_f_shape[0]", cls_name) - validator.check_int(uint_crd_f_shape[1], 3, Rel.EQ, "uint_crd_f_shape[1]", cls_name) - validator.check_int(ljtype_shape[0], n, Rel.EQ, "LJtype_shape", cls_name) - validator.check_int(charge_shape[0], n, Rel.EQ, "charge_shape", cls_name) - validator.check_int(boxlength_f_shape[0], 3, Rel.EQ, "boxlength_f_shape", cls_name) - validator.check_int(lj_type_b_shape[0], q, Rel.EQ, "LJ_type_B_shape", cls_name) - validator.check_int(a_14_shape[0], m, Rel.EQ, "a_14_shape", cls_name) - validator.check_int(b_14_shape[0], m, Rel.EQ, "b_14_shape", cls_name) - validator.check_int(lj_scale_factor_shape[0], m, Rel.EQ, "lj_scale_factor_shape", cls_name) - validator.check_int(cf_scale_factor_shape[0], m, Rel.EQ, "cf_scale_factor_shape", cls_name) - return [self.atom_numbers, 3] - - def infer_dtype(self, uint_crd_f_dtype, ljtype_dtype, charge_dtype, boxlength_f_type, a_14_type, b_14_type, - lj_scale_factor_type, cf_scale_factor_type, lj_type_a_type, lj_type_b_type): - validator.check_tensor_dtype_valid('uint_crd_f', uint_crd_f_dtype, [mstype.uint32], self.name) - validator.check_tensor_dtype_valid('LJtype', ljtype_dtype, [mstype.int32], self.name) - validator.check_tensor_dtype_valid('charge', charge_dtype, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('boxlength_f', boxlength_f_type, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('a_14', a_14_type, [mstype.int32], self.name) - validator.check_tensor_dtype_valid('b_14', b_14_type, [mstype.int32], self.name) - validator.check_tensor_dtype_valid('lj_scale_factor', lj_scale_factor_type, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('cf_scale_factor', cf_scale_factor_type, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('LJ_type_A', lj_type_a_type, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('LJ_type_B', lj_type_b_type, [mstype.float32], self.name) - - return lj_type_a_type - - -class Dihedral14LJCFForceWithAtomEnergy(PrimitiveWithInfer): - """ - Calculate the Lennard-Jones and Coulumb energy correction and force correction - for each necessary dihedral 1,4 terms together and add them to the total force - and potential energy for each atom. - - The calculation formula of force correction is the same as operator - :class:`Dihedral14LJForceWithDirectCF`, and the energy correction part is the same - as operator :class:`Dihedral14LJEnergy` and :class:`Dihedral14CFEnergy`. - - Because there is a large amount of inputs and each of them are related, - there is no way to construct `Examples` using random methods. For details, refer the webpage `SPONGE in MindSpore - `_. - - Args: - nb14_numbers (int32): the number of necessary dihedral 1,4 terms m. - atom_numbers (int32): the number of atoms n. - - Inputs: - - **uint_crd_f** (Tensor) - The unsigned int coordinate value of each atom. - The data type is uint32 and the shape is :math:`(n, 3)`. - - **LJ_type** (Tensor) - The Lennard-Jones type of each atom. - The data type is int32 and the shape is :math:`(n,)`. - - **charge** (Tensor) - The charge of each atom. - The data type is float32 and the shape is :math:`(n,)`. - - **boxlength_f** (Tensor) - The length of molecular simulation box in 3 dimensions. - The data type is float32 and the shape is :math:`(3,)`. - - **a_14** (Tensor) - The first atom index of each dihedral 1,4 term. - The data type is int32 and the shape is :math:`(m,)`. - - **b_14** (Tensor) - The second atom index of each dihedral 1,4 term. - The data type is int32 and the shape is :math:`(m,)`. - - **lj_scale_factor** (Tensor) - The scale factor for the - Lennard-Jones part of force correction of each dihedral 1,4 term. - The data type is float32 and the shape is :math:`(m,)`. - - **cf_scale_factor** (Tensor) - The scale factor for the - Coulomb part of force correction for each dihedral 1,4 terms. - The data type is float32 and the shape is :math:`(m,)`. - - **LJ_type_A** (Tensor) - The A parameter in Lennard-Jones scheme of each atom pair type. - q is the number of atom pair. The data type is float32 and the shape is :math:`(q,)`. - - **LJ_type_B** (Tensor) - The B parameter in Lennard-Jones scheme of each atom pair type. - q is the number of atom pair. The data type is float32 and the shape is :math:`(q,)`. - - Outputs: - - **frc_f** (Tensor) - The force felt by each atom. - The data type is float32 and the shape is :math:`(n, 3)`. - - **atom_energy** (Tensor) - The accumulated potential energy for each atom. - The data type is float32 and the shape is :math:`(n,)`. - - Supported Platforms: - ``GPU`` - """ - - @prim_attr_register - def __init__(self, nb14_numbers, atom_numbers): - """Initialize Dihedral14LJCFForceWithAtomEnergy.""" - validator.check_value_type('nb14_numbers', nb14_numbers, int, self.name) - validator.check_value_type('atom_numbers', atom_numbers, int, self.name) - self.dihedral_14_numbers = nb14_numbers - self.atom_numbers = atom_numbers - - self.init_prim_io_names( - inputs=['uint_crd_f', 'LJtype', 'charge', 'boxlength_f', 'a_14', 'b_14', 'lj_scale_factor', - 'cf_scale_factor', - 'LJ_type_A', 'LJ_type_B'], - outputs=['frc_f', 'atom_energy']) - self.add_prim_attr('dihedral_14_numbers', self.dihedral_14_numbers) - self.add_prim_attr('atom_numbers', self.atom_numbers) - - def infer_shape(self, uint_crd_f_shape, ljtype_shape, charge_shape, boxlength_f_shape, a_14_shape, b_14_shape, - lj_scale_factor_shape, cf_scale_factor_shape, lj_type_a_shape, lj_type_b_shape): - cls_name = self.name - n = self.atom_numbers - m = self.dihedral_14_numbers - q = lj_type_a_shape[0] - validator.check_int(len(uint_crd_f_shape), 2, Rel.EQ, "uint_crd_f_dim", cls_name) - validator.check_int(len(ljtype_shape), 1, Rel.EQ, "LJtype_dim", cls_name) - validator.check_int(len(charge_shape), 1, Rel.EQ, "charge_dim", cls_name) - validator.check_int(len(boxlength_f_shape), 1, Rel.EQ, "boxlength_f_dim", cls_name) - validator.check_int(len(a_14_shape), 1, Rel.EQ, "a_14_dim", cls_name) - validator.check_int(len(b_14_shape), 1, Rel.EQ, "b_14_dim", cls_name) - validator.check_int(len(lj_scale_factor_shape), 1, Rel.EQ, "lj_scale_factor_dim", cls_name) - validator.check_int(len(cf_scale_factor_shape), 1, Rel.EQ, "cf_scale_factor_dim", cls_name) - validator.check_int(len(lj_type_b_shape), 1, Rel.EQ, "LJ_type_B_dim", cls_name) - - validator.check_int(uint_crd_f_shape[0], n, Rel.EQ, "uint_crd_f_shape[0]", cls_name) - validator.check_int(uint_crd_f_shape[1], 3, Rel.EQ, "uint_crd_f_shape[1]", cls_name) - validator.check_int(ljtype_shape[0], n, Rel.EQ, "LJtype_shape", cls_name) - validator.check_int(charge_shape[0], n, Rel.EQ, "charge_shape", cls_name) - validator.check_int(boxlength_f_shape[0], 3, Rel.EQ, "boxlength_f_shape", cls_name) - validator.check_int(lj_type_b_shape[0], q, Rel.EQ, "LJ_type_B_shape", cls_name) - validator.check_int(a_14_shape[0], m, Rel.EQ, "a_14_shape", cls_name) - validator.check_int(b_14_shape[0], m, Rel.EQ, "b_14_shape", cls_name) - validator.check_int(lj_scale_factor_shape[0], m, Rel.EQ, "lj_scale_factor_shape", cls_name) - validator.check_int(cf_scale_factor_shape[0], m, Rel.EQ, "cf_scale_factor_shape", cls_name) - return uint_crd_f_shape, charge_shape - - def infer_dtype(self, uint_crd_f_dtype, ljtype_dtype, charge_dtype, boxlength_f_type, a_14_type, b_14_type, - lj_scale_factor_type, cf_scale_factor_type, lj_type_a_type, lj_type_b_type): - validator.check_tensor_dtype_valid('uint_crd_f', uint_crd_f_dtype, [mstype.uint32], self.name) - validator.check_tensor_dtype_valid('LJtype', ljtype_dtype, [mstype.int32], self.name) - validator.check_tensor_dtype_valid('charge', charge_dtype, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('boxlength_f', boxlength_f_type, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('a_14', a_14_type, [mstype.int32], self.name) - validator.check_tensor_dtype_valid('b_14', b_14_type, [mstype.int32], self.name) - validator.check_tensor_dtype_valid('lj_scale_factor', lj_scale_factor_type, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('cf_scale_factor', cf_scale_factor_type, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('LJ_type_A', lj_type_a_type, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('LJ_type_B', lj_type_b_type, [mstype.float32], self.name) - - return charge_dtype, charge_dtype - - -class Dihedral14LJAtomEnergy(PrimitiveWithInfer): - """ - Add the potential energy caused by Lennard-Jones energy correction for each - necessary dihedral 1,4 terms to the total potential energy of each atom. - - The calculation formula is the same as operator Dihedral14LJEnergy(). - - Because there is a large amount of inputs and each of them are related, - there is no way to construct `Examples` using random methods. For details, refer the webpage `SPONGE in MindSpore - `_. - - Args: - nb14_numbers (int32): the number of necessary dihedral 1,4 terms m. - atom_numbers (int32): the number of atoms n. - - Inputs: - - **uint_crd_f** (Tensor) - The unsigned int coordinate value of each atom. - The data type is uint32 and the shape is :math:`(n, 3)`. - - **LJ_type** (Tensor) - The Lennard-Jones type of each atom. - The data type is int32 and the shape is :math:`(n,)`. - - **charge** (Tensor) - The charge of each atom. - The data type is float32 and the shape is :math:`(n,)`. - - **boxlength_f** (Tensor) - The length of molecular simulation box in 3 dimensions. - The data type is float32 and the shape is :math:`(3,)`. - - **a_14** (Tensor) - The first atom index of each dihedral 1,4 term. - The data type is int32 and the shape is :math:`(m,)`. - - **b_14** (Tensor) - The second atom index of each dihedral 1,4 term. - The data type is int32 and the shape is :math:`(m,)`. - - **lj_scale_factor** (Tensor) - The scale factor for the - Lennard-Jones part of force correction of each dihedral 1,4 term. - The data type is float32 and the shape is :math:`(m,)`. - - **cf_scale_factor** (Tensor) - The scale factor for the - Coulomb part of force correction for each dihedral 1,4 terms. - The data type is float32 and the shape is :math:`(m,)`. - - **LJ_type_A** (Tensor) - The A parameter in Lennard-Jones scheme of each atom pair type. - q is the number of atom pair. The data type is float32 and the shape is :math:`(q,)`. - - **LJ_type_B** (Tensor) - The B parameter in Lennard-Jones scheme of each atom pair type. - q is the number of atom pair. The data type is float32 and the shape is :math:`(q,)`. - - Outputs: - - **ene** (Tensor) - The accumulated potential energy of each atom. - The data type is float32 and the shape is :math:`(n,)`. - - Supported Platforms: - ``GPU`` - """ - - @prim_attr_register - def __init__(self, nb14_numbers, atom_numbers): - """Initialize Dihedral14LJAtomEnergy.""" - validator.check_value_type('nb14_numbers', nb14_numbers, int, self.name) - validator.check_value_type('atom_numbers', atom_numbers, int, self.name) - self.dihedral_14_numbers = nb14_numbers - self.atom_numbers = atom_numbers - - self.init_prim_io_names( - inputs=['uint_crd_f', 'LJtype', 'charge', 'boxlength_f', 'a_14', 'b_14', 'lj_scale_factor', - 'LJ_type_A', 'LJ_type_B'], - outputs=['ene']) - self.add_prim_attr('dihedral_14_numbers', self.dihedral_14_numbers) - self.add_prim_attr('atom_numbers', self.atom_numbers) - - def infer_shape(self, uint_crd_f_shape, ljtype_shape, charge_shape, boxlength_f_shape, a_14_shape, b_14_shape, - lj_scale_factor_shape, lj_type_a_shape, lj_type_b_shape): - cls_name = self.name - n = self.atom_numbers - q = lj_type_a_shape[0] - validator.check_int(len(uint_crd_f_shape), 2, Rel.EQ, "uint_crd_f_dim", cls_name) - validator.check_int(len(ljtype_shape), 1, Rel.EQ, "LJtype_dim", cls_name) - validator.check_int(len(charge_shape), 1, Rel.EQ, "charge_dim", cls_name) - validator.check_int(len(boxlength_f_shape), 1, Rel.EQ, "boxlength_f_dim", cls_name) - validator.check_int(len(a_14_shape), 1, Rel.EQ, "a_14_dim", cls_name) - validator.check_int(len(b_14_shape), 1, Rel.EQ, "b_14_dim", cls_name) - validator.check_int(len(lj_scale_factor_shape), 1, Rel.EQ, "lj_scale_factor_dim", cls_name) - validator.check_int(len(lj_type_b_shape), 1, Rel.EQ, "LJ_type_B_dim", cls_name) - - validator.check_int(uint_crd_f_shape[0], n, Rel.EQ, "uint_crd_f_shape[0]", cls_name) - validator.check_int(uint_crd_f_shape[1], 3, Rel.EQ, "uint_crd_f_shape[1]", cls_name) - validator.check_int(ljtype_shape[0], n, Rel.EQ, "LJtype_shape", cls_name) - validator.check_int(charge_shape[0], n, Rel.EQ, "charge_shape", cls_name) - validator.check_int(boxlength_f_shape[0], 3, Rel.EQ, "boxlength_f_shape", cls_name) - validator.check_int(lj_type_b_shape[0], q, Rel.EQ, "LJ_type_B_shape", cls_name) - m = self.dihedral_14_numbers - validator.check_int(a_14_shape[0], m, Rel.EQ, "a_14_shape", cls_name) - validator.check_int(b_14_shape[0], m, Rel.EQ, "b_14_shape", cls_name) - validator.check_int(lj_scale_factor_shape[0], m, Rel.EQ, "lj_scale_factor_shape", cls_name) - return ljtype_shape - - def infer_dtype(self, uint_crd_f_dtype, ljtype_dtype, charge_dtype, boxlength_f_type, a_14_type, b_14_type, - lj_scale_factor_type, lj_type_a_type, lj_type_b_type): - validator.check_tensor_dtype_valid('uint_crd_f', uint_crd_f_dtype, [mstype.uint32], self.name) - validator.check_tensor_dtype_valid('LJtype', ljtype_dtype, [mstype.int32], self.name) - validator.check_tensor_dtype_valid('charge', charge_dtype, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('boxlength_f', boxlength_f_type, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('a_14', a_14_type, [mstype.int32], self.name) - validator.check_tensor_dtype_valid('b_14', b_14_type, [mstype.int32], self.name) - validator.check_tensor_dtype_valid('lj_scale_factor', lj_scale_factor_type, [mstype.float32], - self.name) - validator.check_tensor_dtype_valid('LJ_type_A', lj_type_a_type, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('LJ_type_B', lj_type_b_type, [mstype.float32], self.name) - - return lj_type_a_type - - -class Dihedral14CFEnergy(PrimitiveWithInfer): - """ - Calculate the Coulumb part of 1,4 dihedral energy correction for - each necessary dihedral terms on the corresponding atoms. - - Because there is a large amount of inputs and each of them are related, - there is no way to construct `Examples` using random methods. For details, refer the webpage `SPONGE in MindSpore - `_. - - .. math:: - - dr = (x_a-x_b, y_a-y_b, z_a-z_b) - - .. math:: - E = k*q_a*q_b/|dr| - - Args: - nb14_numbers (int32): the number of necessary dihedral 1,4 terms m. - atom_numbers (int32): the number of atoms n. - - Inputs: - - **uint_crd_f** (Tensor) - The unsigned int coordinate value of each atom. - The data type is uint32 and the shape is :math:`(n, 3)`. - - **LJ_type** (Tensor) - The Lennard-Jones type of each atom. - The data type is int32 and the shape is :math:`(n,)`. - - **charge** (Tensor) - The charge of each atom. - The data type is float32 and the shape is :math:`(n,)`. - - **boxlength_f** (Tensor) - The length of molecular simulation box in 3 dimensions. - The data type is float32 and the shape is :math:`(3,)`. - - **a_14** (Tensor) - The first atom index of each dihedral 1,4 term. - The data type is int32 and the shape is :math:`(m,)`. - - **b_14** (Tensor) - The second atom index of each dihedral 1,4 term. - The data type is int32 and the shape is :math:`(m,)`. - - **cf_scale_factor** (Tensor) - The scale factor for the - Coulomb part of force correction for each dihedral 1,4 terms. - The data type is float32 and the shape is :math:`(m,)`. - - Outputs: - - **ene** (Tensor) - The accumulated potential energy of each atom. - The data type is float32 and the shape is :math:`(m,)`. - - Supported Platforms: - ``GPU`` - """ - - @prim_attr_register - def __init__(self, nb14_numbers, atom_numbers): - """Initialize Dihedral14CFEnergy.""" - validator.check_value_type('nb14_numbers', nb14_numbers, int, self.name) - validator.check_value_type('atom_numbers', atom_numbers, int, self.name) - self.dihedral_14_numbers = nb14_numbers - self.atom_numbers = atom_numbers - - self.init_prim_io_names( - inputs=['uint_crd_f', 'LJtype', 'charge', 'boxlength_f', 'a_14', 'b_14', 'cj_scale_factor'], - outputs=['ene']) - self.add_prim_attr('dihedral_14_numbers', self.dihedral_14_numbers) - self.add_prim_attr('atom_numbers', self.atom_numbers) - - def infer_shape(self, uint_crd_f_shape, ljtype_shape, charge_shape, boxlength_f_shape, a_14_shape, b_14_shape, - cf_scale_factor_shape): - cls_name = self.name - n = self.atom_numbers - validator.check_int(len(uint_crd_f_shape), 2, Rel.EQ, "uint_crd_f_dim", cls_name) - validator.check_int(len(ljtype_shape), 1, Rel.EQ, "LJtype_dim", cls_name) - validator.check_int(len(charge_shape), 1, Rel.EQ, "charge_dim", cls_name) - validator.check_int(len(boxlength_f_shape), 1, Rel.EQ, "boxlength_f_dim", cls_name) - validator.check_int(len(a_14_shape), 1, Rel.EQ, "a_14_dim", cls_name) - validator.check_int(len(b_14_shape), 1, Rel.EQ, "b_14_dim", cls_name) - validator.check_int(len(cf_scale_factor_shape), 1, Rel.EQ, "cf_scale_factor_dim", cls_name) - - validator.check_int(uint_crd_f_shape[0], n, Rel.EQ, "uint_crd_f_shape[0]", cls_name) - validator.check_int(uint_crd_f_shape[1], 3, Rel.EQ, "uint_crd_f_shape[1]", cls_name) - validator.check_int(ljtype_shape[0], n, Rel.EQ, "LJtype_shape", cls_name) - validator.check_int(charge_shape[0], n, Rel.EQ, "charge_shape", cls_name) - validator.check_int(boxlength_f_shape[0], 3, Rel.EQ, "boxlength_f_shape", cls_name) - m = self.dihedral_14_numbers - validator.check_int(a_14_shape[0], m, Rel.EQ, "a_14_shape", cls_name) - validator.check_int(b_14_shape[0], m, Rel.EQ, "b_14_shape", cls_name) - validator.check_int(cf_scale_factor_shape[0], m, Rel.EQ, "cf_scale_factor_shape", cls_name) - return [self.dihedral_14_numbers,] - - def infer_dtype(self, uint_crd_f_dtype, ljtype_dtype, charge_dtype, boxlength_f_type, a_14_type, b_14_type, - cf_scale_factor_type): - validator.check_tensor_dtype_valid('uint_crd_f', uint_crd_f_dtype, [mstype.uint32], self.name) - validator.check_tensor_dtype_valid('LJtype', ljtype_dtype, [mstype.int32], self.name) - validator.check_tensor_dtype_valid('charge', charge_dtype, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('boxlength_f', boxlength_f_type, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('a_14', a_14_type, [mstype.int32], self.name) - validator.check_tensor_dtype_valid('b_14', b_14_type, [mstype.int32], self.name) - validator.check_tensor_dtype_valid('lj_scale_factor', cf_scale_factor_type, [mstype.float32], - self.name) - - return charge_dtype - - -class Dihedral14CFAtomEnergy(PrimitiveWithInfer): - """ - Add the potential energy caused by Coulumb energy correction for each - necessary dihedral 1,4 terms to the total potential energy of each atom. - - The calculation formula is the same as operator :class:`Dihedral14CFEnergy`. - - Because there is a large amount of inputs and each of them are related, - there is no way to construct `Examples` using random methods. For details, refer the webpage `SPONGE in MindSpore - `_. - - Args: - nb14_numbers (int32): the number of necessary dihedral 1,4 terms m. - atom_numbers (int32): the number of atoms n. - - Inputs: - - **uint_crd_f** (Tensor) - The unsigned int coordinate value of each atom. - The data type is uint32 and the shape is :math:`(n, 3)`. - - **LJ_type** (Tensor) - The Lennard-Jones type of each atom. - The data type is int32 and the shape is :math:`(n,)`. - - **charge** (Tensor) - The charge of each atom. - The data type is float32 and the shape is :math:`(n,)`. - - **boxlength_f** (Tensor) - The length of molecular simulation box in 3 dimensions. - The data type is float32 and the shape is :math:`(3,)`. - - **a_14** (Tensor) - The first atom index of each dihedral 1,4 term. - The data type is int32 and the shape is :math:`(m,)`. - - **b_14** (Tensor) - The second atom index of each dihedral 1,4 term. - The data type is int32 and the shape is :math:`(m,)`. - - **cf_scale_factor** (Tensor) - The scale factor for the - Coulomb part of force correction for each dihedral 1,4 terms. - The data type is float32 and the shape is :math:`(m,)`. - - Outputs: - - **ene** (Tensor) - The accumulated potential energy of each atom. - The data type is float32 and the shape is :math:`(n,)` - - - Supported Platforms: - ``GPU`` - """ - - @prim_attr_register - def __init__(self, nb14_numbers, atom_numbers): - """Initialize Dihedral14CFAtomEnergy.""" - validator.check_value_type('nb14_numbers', nb14_numbers, int, self.name) - validator.check_value_type('atom_numbers', atom_numbers, int, self.name) - self.dihedral_14_numbers = nb14_numbers - self.atom_numbers = atom_numbers - - self.init_prim_io_names( - inputs=['uint_crd_f', 'LJtype', 'charge', 'boxlength_f', 'a_14', 'b_14', 'cf_scale_factor'], - outputs=['ene']) - self.add_prim_attr('dihedral_14_numbers', self.dihedral_14_numbers) - self.add_prim_attr('atom_numbers', self.atom_numbers) - - def infer_shape(self, uint_crd_f_shape, ljtype_shape, charge_shape, boxlength_f_shape, a_14_shape, b_14_shape, - cf_scale_factor_shape): - cls_name = self.name - n = self.atom_numbers - validator.check_int(len(uint_crd_f_shape), 2, Rel.EQ, "uint_crd_f_dim", cls_name) - validator.check_int(len(ljtype_shape), 1, Rel.EQ, "LJtype_dim", cls_name) - validator.check_int(len(charge_shape), 1, Rel.EQ, "charge_dim", cls_name) - validator.check_int(len(boxlength_f_shape), 1, Rel.EQ, "boxlength_f_dim", cls_name) - validator.check_int(len(a_14_shape), 1, Rel.EQ, "a_14_dim", cls_name) - validator.check_int(len(b_14_shape), 1, Rel.EQ, "b_14_dim", cls_name) - validator.check_int(len(cf_scale_factor_shape), 1, Rel.EQ, "cf_scale_factor_dim", cls_name) - - validator.check_int(uint_crd_f_shape[0], n, Rel.EQ, "uint_crd_f_shape[0]", cls_name) - validator.check_int(uint_crd_f_shape[1], 3, Rel.EQ, "uint_crd_f_shape[1]", cls_name) - validator.check_int(ljtype_shape[0], n, Rel.EQ, "LJtype_shape", cls_name) - validator.check_int(charge_shape[0], n, Rel.EQ, "charge_shape", cls_name) - validator.check_int(boxlength_f_shape[0], 3, Rel.EQ, "boxlength_f_shape", cls_name) - m = self.dihedral_14_numbers - validator.check_int(a_14_shape[0], m, Rel.EQ, "a_14_shape", cls_name) - validator.check_int(b_14_shape[0], m, Rel.EQ, "b_14_shape", cls_name) - validator.check_int(cf_scale_factor_shape[0], m, Rel.EQ, "cf_scale_factor_shape", cls_name) - return ljtype_shape - - def infer_dtype(self, uint_crd_f_dtype, ljtype_dtype, charge_dtype, boxlength_f_type, a_14_type, b_14_type, - cf_scale_factor_type): - validator.check_tensor_dtype_valid('uint_crd_f', uint_crd_f_dtype, [mstype.uint32], self.name) - validator.check_tensor_dtype_valid('LJtype', ljtype_dtype, [mstype.int32], self.name) - validator.check_tensor_dtype_valid('charge', charge_dtype, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('boxlength_f', boxlength_f_type, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('a_14', a_14_type, [mstype.int32], self.name) - validator.check_tensor_dtype_valid('b_14', b_14_type, [mstype.int32], self.name) - validator.check_tensor_dtype_valid('cf_scale_factor', cf_scale_factor_type, [mstype.float32], - self.name) - - return charge_dtype - - -class PMEReciprocalForce(PrimitiveWithInfer): - """ - Calculate the reciprocal part of long-range Coulumb force using - PME(Particle Meshed Ewald) method. Assume the number of atoms is n. - - The detailed calculation formula of PME(Particle Meshed Ewald) method - can be found in this paper: A Smooth Particle Mesh Ewald Method. DOI: - 10.1063/1.470117. - - Because there is a large amount of inputs and each of them are related, - there is no way to construct `Examples` using random methods. For details, refer the webpage `SPONGE in MindSpore - `_. - - Args: - atom_numbers(int32): the number of atoms, n. - beta(float32): the PME beta parameter, determined by the - non-bond cutoff value and simulation precision tolerance. - fftx(int32): the number of points for Fourier transform in dimension X. - ffty(int32): the number of points for Fourier transform in dimension Y. - fftz(int32): the number of points for Fourier transform in dimension Z. - box_length_0(float32): the value of boxlength idx 0 - box_length_1(float32): the value of boxlength idx 1 - box_length_2(float32): the value of boxlength idx 2 - - Inputs: - - **uint_crd** (Tensor) - The unsigned int coordinates value of each atom. - The data type is uint32 and the shape is :math:`(n, 3)` - - **charge** (Tensor) - The charge carried by each atom. - The data type is float32 and the shape is :math:`(n,)` - - Outputs: - - **force** (Tensor) - The force felt by each atom. - The data type is float32 and the shape is :math:`(n, 3)` - - Supported Platforms: - ``GPU`` - """ - - @prim_attr_register - def __init__(self, atom_numbers, beta, fftx, ffty, fftz, box_length_0, box_length_1, box_length_2): - """Initialize PMEReciprocalForce.""" - validator.check_value_type('atom_numbers', atom_numbers, int, self.name) - validator.check_value_type('beta', beta, float, self.name) - validator.check_value_type('fftx', fftx, int, self.name) - validator.check_value_type('ffty', ffty, int, self.name) - validator.check_value_type('fftz', fftz, int, self.name) - validator.check_value_type('box_length_0', box_length_0, float, self.name) - validator.check_value_type('box_length_1', box_length_1, float, self.name) - validator.check_value_type('box_length_2', box_length_2, float, self.name) - self.atom_numbers = atom_numbers - self.beta = beta - self.fftx = fftx - self.ffty = ffty - self.fftz = fftz - self.box_length_0 = box_length_0 - self.box_length_1 = box_length_1 - self.box_length_2 = box_length_2 - - self.init_prim_io_names(inputs=['boxlength', 'uint_crd', 'charge'], - outputs=['force']) - self.add_prim_attr('atom_numbers', self.atom_numbers) - self.add_prim_attr('beta', self.beta) - self.add_prim_attr('fftx', self.fftx) - self.add_prim_attr('ffty', self.ffty) - self.add_prim_attr('fftz', self.fftz) - self.add_prim_attr('box_length_0', self.box_length_0) - self.add_prim_attr('box_length_1', self.box_length_1) - self.add_prim_attr('box_length_2', self.box_length_2) - - def infer_shape(self, uint_crd_shape, charge_shape): - cls_name = self.name - n = self.atom_numbers - validator.check_int(len(uint_crd_shape), 2, Rel.EQ, "uint_crd_dim", cls_name) - validator.check_int(len(charge_shape), 1, Rel.EQ, "charge_dim", cls_name) - - validator.check_int(uint_crd_shape[0], n, Rel.EQ, "uint_crd_shape[0]", cls_name) - validator.check_int(uint_crd_shape[1], 3, Rel.EQ, "uint_crd_shape[1]", cls_name) - validator.check_int(charge_shape[0], n, Rel.EQ, "charge_shape", cls_name) - return uint_crd_shape - - def infer_dtype(self, uint_crd_type, charge_type): - validator.check_tensor_dtype_valid('uint_crd', uint_crd_type, [mstype.uint32], self.name) - validator.check_tensor_dtype_valid('charge', charge_type, [mstype.float32], self.name) - return charge_type - - -class PMEExcludedForce(PrimitiveWithInfer): - """ - Calculate the excluded part of long-range Coulumb force using - PME(Particle Meshed Ewald) method. Assume the number of atoms is - n, and the length of excluded list is E. - - Because there is a large amount of inputs and each of them are related, - there is no way to construct `Examples` using random methods. For details, refer the webpage `SPONGE in MindSpore - `_. - - Args: - atom_numbers(int32): the number of atoms, n. - excluded_numbers(int32): the length of excluded list, E. - beta(float32): the PME beta parameter, determined by the - non-bond cutoff value and simulation precision tolerance. - - Inputs: - - **uint_crd** (Tensor) - The unsigned int coordinates value of each atom. - The data type is uint32 and the shape is :math:`(n, 3)` - - **scaler** (Tensor) - The scale factor between real space - coordinates and its unsigned int value. The data type is float32 and the shape is :math:`(3,)` - - **charge** (Tensor) - The charge carried by each atom. - The data type is float32 and the shape is :math:`(n,)` - - **excluded_list_start** (Tensor) - The start excluded index - in excluded list for each atom. The data type is int32 and the shape is :math:`(n,)` - - **excluded_list** (Tensor) - The contiguous join of excluded - list of each atom. E is the number of excluded atoms. The data type is int32 and the shape is :math:`(E,)` - - **excluded_atom_numbers** (Tensor) - The number of atom excluded - in excluded list for each atom. The data type is int32 and the shape is :math:`(n,)` - - Outputs: - - **force** (Tensor) - The force felt by each atom. - The data type is float32 and the shape is :math:`(n, 3)` - - Supported Platforms: - ``GPU`` - """ - - @prim_attr_register - def __init__(self, atom_numbers, excluded_numbers, beta): - """Initialize PMEExcludedForce.""" - validator.check_value_type('atom_numbers', atom_numbers, int, self.name) - validator.check_value_type('excluded_numbers', excluded_numbers, int, self.name) - validator.check_value_type('beta', beta, float, self.name) - self.atom_numbers = atom_numbers - self.excluded_numbers = excluded_numbers - self.beta = beta - self.init_prim_io_names( - inputs=['uint_crd', 'sacler', 'charge', 'excluded_list_start', 'excluded_list', 'excluded_atom_numbers'], - outputs=['force']) - self.add_prim_attr('atom_numbers', self.atom_numbers) - self.add_prim_attr('excluded_numbers', self.excluded_numbers) - self.add_prim_attr('beta', self.beta) - - def infer_shape(self, uint_crd_shape, sacler_shape, charge_shape, excluded_list_start_shape, excluded_list_shape, - excluded_atom_numbers_shape): - cls_name = self.name - n = self.atom_numbers - validator.check_int(len(uint_crd_shape), 2, Rel.EQ, "uint_crd_dim", cls_name) - validator.check_int(len(sacler_shape), 1, Rel.EQ, "sacler_dim", cls_name) - validator.check_int(len(charge_shape), 1, Rel.EQ, "charge_dim", cls_name) - validator.check_int(len(excluded_list_start_shape), 1, Rel.EQ, "excluded_list_start_dim", cls_name) - validator.check_int(len(excluded_atom_numbers_shape), 1, Rel.EQ, "excluded_atom_numbers_dim", cls_name) - - validator.check_int(uint_crd_shape[0], n, Rel.EQ, "uint_crd_shape[0]", cls_name) - validator.check_int(uint_crd_shape[1], 3, Rel.EQ, "uint_crd_shape[1]", cls_name) - validator.check_int(sacler_shape[0], 3, Rel.EQ, "sacler_shape", cls_name) - validator.check_int(charge_shape[0], n, Rel.EQ, "charge_shape", cls_name) - validator.check_int(excluded_list_start_shape[0], n, Rel.EQ, "excluded_list_start_shape", cls_name) - validator.check_int(excluded_atom_numbers_shape[0], n, Rel.EQ, "excluded_atom_numbers_shape", cls_name) - return uint_crd_shape - - def infer_dtype(self, uint_crd_type, sacler_type, charge_type, excluded_list_start_type, excluded_list_type, - excluded_atom_numbers_type): - validator.check_tensor_dtype_valid('sacler', sacler_type, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('uint_crd', uint_crd_type, [mstype.uint32], self.name) - validator.check_tensor_dtype_valid('charge', charge_type, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('excluded_list_start', excluded_list_start_type, [mstype.int32], - self.name) - validator.check_tensor_dtype_valid('excluded_list', excluded_list_type, [mstype.int32], - self.name) - validator.check_tensor_dtype_valid('excluded_atom_numbers', excluded_atom_numbers_type, [mstype.int32], - self.name) - return charge_type - - -class PMEEnergy(PrimitiveWithInfer): - """ - Calculate the Coulumb energy of the system using PME method. - - Because there is a large amount of inputs and each of them are related, - there is no way to construct `Examples` using random methods. For details, refer the webpage `SPONGE in MindSpore - `_. - - .. math:: - - E = sum_{ij} q_iq_j/r_{ij} - - Args: - atom_numbers(int32): the number of atoms, n. - excluded_numbers(int32): the length of excluded list, E. - beta(float32): the PME beta parameter, determined by the - non-bond cutoff value and simulation precision tolerance. - fftx(int32): the number of points for Fourier transform in dimension X. - ffty(int32): the number of points for Fourier transform in dimension Y. - fftz(int32): the number of points for Fourier transform in dimension Z. - box_length_0(float32): the value of boxlength idx 0 - box_length_1(float32): the value of boxlength idx 1 - box_length_2(float32): the value of boxlength idx 2 - - - Inputs: - - **uint_crd** (Tensor) - The unsigned int coordinates value of each atom. - The data type is uint32 and the shape is :math:`(n, 3)` - - **charge** (Tensor) - The charge carried by each atom. - The data type is float32 and the shape is :math:`(n,)` - - **nl_numbers** - (Tensor) - The each atom. - The data type is int32 and the shape is :math:`(n, 3)` - - **nl_serial** - (Tensor) - The neighbor list of each atom, the max number is 800. - The data type is int32 and the shape is :math:`(n, 800)` - - **scaler** (Tensor) - The scale factor between real space - coordinates and its unsigned int value. The data type is float32 and the shape is :math:`(3,)` - - **excluded_list_start** (Tensor) - The start excluded index - in excluded list for each atom. The data type is int32 and the shape is :math:`(n,)` - - **excluded_list** (Tensor) - The contiguous join of excluded - list of each atom. E is the number of excluded atoms. The data type is int32 and the shape is :math:`(E,)` - - **excluded_atom_numbers** (Tensor) - The number of atom excluded - in excluded list for each atom. The data type is int32 and the shape is :math:`(n,)` - - Outputs: - - **reciprocal_ene** (Tensor) - The reciprocal term of PME energy. - The data type is float32 and the the shape is :math:`(1,)`. - - **self_ene** (Tensor) - The self term of PME energy. - The data type is float32 and the the shape is :math:`(1,)`. - - **direct_ene** (Tensor) - The direct term of PME energy. - The data type is float32 and the the shape is :math:`(1,)`. - - **correction_ene** (Tensor) - The correction term of PME energy. - The data type is float32 and the the shape is :math:`(1,)`. - - Supported Platforms: - ``GPU`` - """ - - @prim_attr_register - def __init__(self, atom_numbers, excluded_numbers, beta, fftx, ffty, fftz, box_length_0, box_length_1, - box_length_2): - """Initialize PMEEnergy.""" - validator.check_value_type('atom_numbers', atom_numbers, int, self.name) - validator.check_value_type('excluded_numbers', excluded_numbers, int, self.name) - validator.check_value_type('beta', beta, float, self.name) - validator.check_value_type('fftx', fftx, int, self.name) - validator.check_value_type('ffty', ffty, int, self.name) - validator.check_value_type('fftz', fftz, int, self.name) - validator.check_value_type('box_length_0', box_length_0, float, self.name) - validator.check_value_type('box_length_1', box_length_1, float, self.name) - validator.check_value_type('box_length_2', box_length_2, float, self.name) - self.atom_numbers = atom_numbers - self.excluded_numbers = excluded_numbers - self.beta = beta - self.fftx = fftx - self.ffty = ffty - self.fftz = fftz - self.box_length_0 = box_length_0 - self.box_length_1 = box_length_1 - self.box_length_2 = box_length_2 - self.init_prim_io_names( - inputs=['box_length', 'uint_crd', 'charge', 'nl_numbers', 'nl_serial', 'scaler', 'excluded_list_start', - 'excluded_list', 'excluded_atom_numbers'], - outputs=['reciprocal_ene', 'self_ene', 'direct_ene', 'correction_ene']) - self.add_prim_attr('atom_numbers', self.atom_numbers) - self.add_prim_attr('excluded_numbers', self.excluded_numbers) - self.add_prim_attr('beta', self.beta) - self.add_prim_attr('fftx', self.fftx) - self.add_prim_attr('ffty', self.ffty) - self.add_prim_attr('fftz', self.fftz) - self.add_prim_attr('box_length_0', self.box_length_0) - self.add_prim_attr('box_length_1', self.box_length_1) - self.add_prim_attr('box_length_2', self.box_length_2) - - def infer_shape(self, uint_crd, charge, nl_numbers, nl_serial, scaler, excluded_list_start, - excluded_list, excluded_atom_numbers): - cls_name = self.name - n = self.atom_numbers - validator.check_int(len(uint_crd), 2, Rel.EQ, "uint_crd_dim", cls_name) - validator.check_int(len(charge), 1, Rel.EQ, "charge_dim", cls_name) - validator.check_int(len(nl_numbers), 1, Rel.EQ, "nl_numbers_dim", cls_name) - validator.check_int(len(nl_serial), 2, Rel.LE, "nl_serial_dim", cls_name) - validator.check_int(len(excluded_list_start), 1, Rel.EQ, "excluded_list_start_dim", cls_name) - validator.check_int(len(excluded_atom_numbers), 1, Rel.EQ, "excluded_atom_numbers_dim", cls_name) - validator.check_int(len(excluded_list), 1, Rel.GE, "excluded_list", cls_name) - - validator.check_int(uint_crd[0], n, Rel.EQ, "uint_crd_shape[0]", cls_name) - validator.check_int(uint_crd[1], 3, Rel.EQ, "uint_crd_shape[1]", cls_name) - validator.check_int(charge[0], n, Rel.EQ, "charge_shape", cls_name) - validator.check_int(nl_numbers[0], n, Rel.EQ, "nl_numbers_shape[0]", cls_name) - validator.check_int(nl_serial[0], n, Rel.LE, "nl_serial_shape[0]", cls_name) - validator.check_int(nl_serial[1], 800, Rel.LE, "nl_serial_shape[1]", cls_name) - validator.check_int(excluded_list_start[0], n, Rel.EQ, "excluded_list_start_shape", cls_name) - validator.check_int(excluded_atom_numbers[0], n, Rel.EQ, "excluded_atom_numbers_shape", cls_name) - validator.check_int(excluded_list[0], 0, Rel.GE, "excluded_list_shape", cls_name) - return (1,), (1,), (1,), (1,) - - def infer_dtype(self, uint_crd, charge, nl_numbers, nl_serial, scaler, excluded_list_start, - excluded_list, excluded_atom_numbers): - validator.check_tensor_dtype_valid('uint_crd', uint_crd, [mstype.uint32], self.name) - validator.check_tensor_dtype_valid('charge', charge, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('nl_numbers', nl_numbers, [mstype.int32], self.name) - validator.check_tensor_dtype_valid('nl_serial', nl_serial, [mstype.int32], self.name) - validator.check_tensor_dtype_valid('scaler', scaler, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('excluded_list_start', excluded_list_start, [mstype.int32], - self.name) - validator.check_tensor_dtype_valid('excluded_list', excluded_list, [mstype.int32], - self.name) - validator.check_tensor_dtype_valid('excluded_atom_numbers', excluded_atom_numbers, [mstype.int32], - self.name) - return charge, charge, charge, charge - - -class LJEnergy(PrimitiveWithInfer): - """ - Calculate the Van der Waals interaction energy described by Lennard-Jones - potential for each atom. Assume the number of atoms is n, and the number - of Lennard-Jones types for all atoms is P, which means there will be - q = P*(P+1)/2 types of possible Lennard-Jones interactions for all kinds - of atom pairs. - - Because there is a large amount of inputs and each of them are related, - there is no way to construct `Examples` using random methods. For details, refer the webpage `SPONGE in MindSpore - `_. - - .. math:: - - dr = (x_a-x_b, y_a-y_b, z_a-z_b) - - .. math:: - E = A/|dr|^{12} - B/|dr|^{6} - - Args: - atom_numbers(int32): the number of atoms, n. - cutoff_square(float32): the square value of cutoff. - - Inputs: - - **uint_crd** (Tensor) - The unsigned int coordinate value of each atom. - The data type is uint32 and the shape is :math:`(n, 3)` - - **LJtype** (Tensor) - The Lennard-Jones type of each atom. - The data type is int32 and the shape is :math:`(n,)` - - **charge** (Tensor) - The charge carried by each atom. - The data type is float32 and the shape is :math:`(n,)` - - **scaler** (Tensor) - The scale factor between real - space coordinate and its unsigned int value. The data type is float32 and the shape is :math:`(3,)` - - **nl_numbers** - (Tensor) - The each atom. - The data type is int32 and the shape is :math:`(n,)` - - **nl_serial** - (Tensor) - The neighbor list of each atom, the max number is 800. - The data type is int32 and the shape is :math:`(n, 800)`. - - **d_LJ_A** (Tensor) - The Lennard-Jones A coefficient of each kind of atom pair. - q is the number of atom pair. The data type is float32 and the shape is :math:`(q,)`. - - **d_LJ_B** (Tensor) - The Lennard-Jones B coefficient of each kind of atom pair. - q is the number of atom pair. The data type is float32 and the shape is :math:`(q,)`. - - Outputs: - - **d_LJ_energy_atom** (Tensor) - The Lennard-Jones potential energy of each atom. - The data type is float32 and the shape is :math:`(n,)`. - - **d_LJ_energy_sum** (Scalar), the sum of Lennard-Jones potential energy of each atom. - The data type is float32. - - Supported Platforms: - ``GPU`` - """ - - @prim_attr_register - def __init__(self, atom_numbers, cutoff_square): - """Initialize LJEnergy.""" - validator.check_value_type('atom_numbers', atom_numbers, int, self.name) - validator.check_value_type('cutoff_square', cutoff_square, float, self.name) - self.atom_numbers = atom_numbers - self.cutoff_square = cutoff_square - self.init_prim_io_names( - inputs=['uint_crd', 'LJtype', 'charge', 'scaler', 'nl_numbers', 'nl_serial', 'd_LJ_A', 'd_LJ_B'], - outputs=['d_LJ_energy_atom']) - self.add_prim_attr('atom_numbers', self.atom_numbers) - self.add_prim_attr('cutoff_square', self.cutoff_square) - - def infer_shape(self, uint_crd, ljtype, charge, scaler, nl_numbers, nl_serial, d_lj_a, d_lj_b): - cls_name = self.name - n = self.atom_numbers - q = d_lj_a[0] - validator.check_int(len(uint_crd), 2, Rel.EQ, "uint_crd_dim", cls_name) - validator.check_int(len(ljtype), 1, Rel.EQ, "LJtype_dim", cls_name) - validator.check_int(len(charge), 1, Rel.EQ, "charge_dim", cls_name) - validator.check_int(len(scaler), 1, Rel.EQ, "scaler_dim", cls_name) - validator.check_int(len(nl_numbers), 1, Rel.EQ, "nl_numbers_dim", cls_name) - validator.check_int(len(nl_serial), 2, Rel.EQ, "nl_serial_dim", cls_name) - validator.check_int(len(d_lj_b), 1, Rel.EQ, "d_LJ_B_dim", cls_name) - - validator.check_int(uint_crd[0], n, Rel.EQ, "uint_crd_shape[0]", cls_name) - validator.check_int(uint_crd[1], 3, Rel.EQ, "uint_crd_shape[1]", cls_name) - validator.check_int(ljtype[0], n, Rel.EQ, "LJtype_shape", cls_name) - validator.check_int(charge[0], n, Rel.EQ, "charge_shape", cls_name) - validator.check_int(scaler[0], 3, Rel.EQ, "scaler_shape", cls_name) - validator.check_int(nl_numbers[0], n, Rel.EQ, "nl_numbers_shape", cls_name) - validator.check_int(nl_serial[0], n, Rel.EQ, "nl_serial_shape[0]", cls_name) - validator.check_int(nl_serial[1], 800, Rel.LE, "nl_serial_shape[1]", cls_name) - validator.check_int(len(d_lj_a), 1, Rel.EQ, "d_LJ_A_dim", cls_name) - validator.check_int(d_lj_a[0], q, Rel.EQ, "d_LJ_A_shape[0]", cls_name) - validator.check_int(d_lj_b[0], q, Rel.EQ, "d_LJ_B_shape[0]", cls_name) - return charge - - def infer_dtype(self, uint_crd, ljtype, charge, scaler, nl_numbers, nl_serial, d_lj_a, d_lj_b): - validator.check_tensor_dtype_valid('uint_crd', uint_crd, [mstype.uint32], self.name) - validator.check_tensor_dtype_valid('LJtype', ljtype, [mstype.int32], self.name) - validator.check_tensor_dtype_valid('charge', charge, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('scaler', scaler, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('nl_numbers', nl_numbers, [mstype.int32], self.name) - validator.check_tensor_dtype_valid('nl_serial', nl_serial, [mstype.int32], self.name) - validator.check_tensor_dtype_valid('d_LJ_A', d_lj_a, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('d_LJ_B', d_lj_b, [mstype.float32], self.name) - return charge - - -class LJForce(PrimitiveWithInfer): - """ - Calculate the Van der Waals interaction force described by Lennard-Jones - potential energy for each atom. - - Because there is a large amount of inputs and each of them are related, - there is no way to construct `Examples` using random methods. For details, refer the webpage `SPONGE in MindSpore - `_. - - .. math:: - - dr = (x_a-x_b, y_a-y_b, z_a-z_b) - - .. math:: - - F = (-12*A/|dr|^{14} + 6*B/|dr|^{8}) * dr - - Args: - atom_numbers(int32): the number of atoms, n. - cutoff_square(float32): the square value of cutoff. - - Inputs: - - **uint_crd** (Tensor) - The unsigned int coordinates value of each atom. - The data type is uint32 and the shape is :math:`(n, 3)` - - **LJtype** (Tensor) - The Lennard-Jones type of each atom. - The data type is int32 and the shape is :math:`(n,)` - - **charge** (Tensor) - The charge carried by each atom. - The data type is float32 and the shape is :math:`(n,)` - - **scaler** (Tensor) - The scale factor between real space - coordinates and its unsigned int value. The data type is float32 and the shape is :math:`(3,)` - - **nl_numbers** - (Tensor) - The each atom. - The data type is int32 and the shape is :math:`(n,)` - - **nl_serial** - (Tensor) - The neighbor list of each atom, the max number is 800. - The data type is int32 and the shape is :math:`(n, 800)`. - - **d_LJ_A** (Tensor) - The Lennard-Jones A coefficient of each kind of atom pair. - q is the number of atom pair. The data type is float32 and the shape is :math:`(q,)`. - - **d_LJ_B** (Tensor) - The Lennard-Jones B coefficient of each kind of atom pair. - q is the number of atom pair. The data type is float32 and the shape is :math:`(q,)`. - - outputs: - - **frc** (Tensor) - The force felt by each atom. - The data type is float32 and the shape is :math:`(n, 3)`. - - Supported Platforms: - ``GPU`` - """ - - @prim_attr_register - def __init__(self, atom_numbers, cutoff_square): - """Initialize LJForce.""" - validator.check_value_type('atom_numbers', atom_numbers, int, self.name) - validator.check_value_type('cutoff_square', cutoff_square, float, self.name) - self.atom_numbers = atom_numbers - self.cutoff_square = cutoff_square - self.init_prim_io_names( - inputs=['uint_crd', 'LJtype', 'charge', 'scaler', 'nl_numbers', 'nl_serial', 'd_LJ_A', 'd_LJ_B'], - outputs=['frc']) - self.add_prim_attr('atom_numbers', self.atom_numbers) - self.add_prim_attr('cutoff_square', self.cutoff_square) - - def infer_shape(self, uint_crd, ljtype, charge, scaler, nl_numbers, nl_serial, d_lj_a, d_lj_b): - cls_name = self.name - n = self.atom_numbers - q = d_lj_a[0] - validator.check_int(len(uint_crd), 2, Rel.EQ, "uint_crd_dim", cls_name) - validator.check_int(len(ljtype), 1, Rel.EQ, "LJtype_dim", cls_name) - validator.check_int(len(charge), 1, Rel.EQ, "charge_dim", cls_name) - validator.check_int(len(scaler), 1, Rel.EQ, "scaler_dim", cls_name) - validator.check_int(len(nl_numbers), 1, Rel.EQ, "nl_numbers_dim", cls_name) - validator.check_int(len(nl_serial), 2, Rel.EQ, "nl_serial_dim", cls_name) - validator.check_int(len(d_lj_b), 1, Rel.EQ, "d_LJ_B_dim", cls_name) - - validator.check_int(uint_crd[0], n, Rel.EQ, "uint_crd_shape[0]", cls_name) - validator.check_int(uint_crd[1], 3, Rel.EQ, "uint_crd_shape[1]", cls_name) - validator.check_int(ljtype[0], n, Rel.EQ, "LJtype_shape", cls_name) - validator.check_int(charge[0], n, Rel.EQ, "charge_shape", cls_name) - validator.check_int(scaler[0], 3, Rel.EQ, "scaler_shape", cls_name) - validator.check_int(nl_numbers[0], n, Rel.EQ, "nl_numbers_shape", cls_name) - validator.check_int(nl_serial[0], n, Rel.EQ, "nl_serial_shape[0]", cls_name) - validator.check_int(nl_serial[1], 800, Rel.EQ, "nl_serial_shape[1]", cls_name) - validator.check_int(len(d_lj_a), 1, Rel.EQ, "d_LJ_A_dim", cls_name) - validator.check_int(d_lj_a[0], q, Rel.EQ, "d_LJ_A_shape[0]", cls_name) - validator.check_int(d_lj_b[0], q, Rel.EQ, "d_LJ_B_shape[0]", cls_name) - return uint_crd - - def infer_dtype(self, uint_crd, ljtype, charge, scaler, nl_numbers, nl_serial, d_lj_a, d_lj_b): - validator.check_tensor_dtype_valid('uint_crd', uint_crd, [mstype.uint32], self.name) - validator.check_tensor_dtype_valid('LJtype', ljtype, [mstype.int32], self.name) - validator.check_tensor_dtype_valid('charge', charge, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('scaler', scaler, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('nl_numbers', nl_numbers, [mstype.int32], self.name) - validator.check_tensor_dtype_valid('nl_serial', nl_serial, [mstype.int32], self.name) - validator.check_tensor_dtype_valid('d_LJ_A', d_lj_a, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('d_LJ_B', d_lj_b, [mstype.float32], self.name) - return charge - - -class LJForceWithPMEDirectForce(PrimitiveWithInfer): - """ - Calculate the Lennard-Jones force and PME direct force together. - - The calculation formula of Lennard-Jones part is the same as operator - LJForce(), and the PME direct part is within PME method. - - Because there is a large amount of inputs and each of them are related, - there is no way to construct `Examples` using random methods. For details, refer the webpage `SPONGE in MindSpore - `_. - - Args: - atom_numbers(int32): the number of atoms, n. - cutoff_square(float32): the square value of cutoff. - pme_beta(float32): PME beta parameter, same as operator PMEReciprocalForce(). - - Inputs: - - **uint_crd** (Tensor) - The unsigned int coordinate value of each atom. - The data type is uint32 and the shape is :math:`(n, 3)`. - - **LJtype** (Tensor) - The Lennard-Jones type of each atom. - The data type is int32 and the shape is :math:`(n,)`. - - **charge** (Tensor) - The charge carried by each atom. - The data type is float32 and the shape is :math:`(n,)`. - - **scaler** (Tensor) - The scale factor between real - space coordinate and its unsigned int value. - The data type is float32 and the shape is :math:`(3,)`. - - **nl_numbers** - (Tensor) - The each atom. - The data type is int32 and the shape is :math:`(n,)`. - - **nl_serial** - (Tensor) - The neighbor list of each atom, the max number is 800. - The data type is int32 and the shape is :math:`(n, 800)`. - - **d_LJ_A** (Tensor) - The Lennard-Jones A coefficient of each kind of atom pair. - q is the number of atom pair. The data type is float32 and the shape is :math:`(q,)`. - - **d_LJ_B** (Tensor) - The Lennard-Jones B coefficient of each kind of atom pair. - q is the number of atom pair. The data type is float32 and the shape is :math:`(q,)`. - - Outputs: - - **frc** (Tensor), The force felt by each atom. - The data type is float32 and the shape is :math:`(n, 3)`. - - Supported Platforms: - ``GPU`` - """ - - @prim_attr_register - def __init__(self, atom_numbers, cutoff, pme_beta): - """Initialize LJForceWithPMEDirectForce.""" - validator.check_value_type('atom_numbers', atom_numbers, int, self.name) - validator.check_value_type('cutoff', cutoff, float, self.name) - validator.check_value_type('pme_beta', pme_beta, float, self.name) - self.atom_numbers = atom_numbers - self.cutoff = cutoff - self.pme_beta = pme_beta - self.init_prim_io_names( - inputs=['uint_crd', 'LJtype', 'charge', 'scaler', 'nl_numbers', 'nl_serial', 'd_LJ_A', 'd_LJ_B'], - outputs=['frc']) - self.add_prim_attr('atom_numbers', self.atom_numbers) - self.add_prim_attr('cutoff', self.cutoff) - self.add_prim_attr('pme_beta', self.pme_beta) - - def infer_shape(self, uint_crd, ljtype, charge, scaler, nl_numbers, nl_serial, d_lj_a, d_lj_b): - cls_name = self.name - n = self.atom_numbers - q = d_lj_a[0] - validator.check_int(len(uint_crd), 2, Rel.EQ, "uint_crd_dim", cls_name) - validator.check_int(len(ljtype), 1, Rel.EQ, "LJtype_dim", cls_name) - validator.check_int(len(charge), 1, Rel.EQ, "charge_dim", cls_name) - validator.check_int(len(scaler), 1, Rel.EQ, "scaler_dim", cls_name) - validator.check_int(len(nl_numbers), 1, Rel.EQ, "nl_numbers_dim", cls_name) - validator.check_int(len(nl_serial), 2, Rel.EQ, "nl_serial_dim", cls_name) - validator.check_int(len(d_lj_b), 1, Rel.EQ, "d_LJ_B_dim", cls_name) - - validator.check_int(uint_crd[0], n, Rel.EQ, "uint_crd_shape[0]", cls_name) - validator.check_int(uint_crd[1], 3, Rel.EQ, "uint_crd_shape[1]", cls_name) - validator.check_int(ljtype[0], n, Rel.EQ, "LJtype_shape", cls_name) - validator.check_int(charge[0], n, Rel.EQ, "charge_shape", cls_name) - validator.check_int(scaler[0], 3, Rel.EQ, "scaler_shape", cls_name) - validator.check_int(nl_numbers[0], n, Rel.EQ, "nl_numbers_shape", cls_name) - validator.check_int(nl_serial[0], n, Rel.EQ, "nl_serial_shape[0]", cls_name) - validator.check_int(nl_serial[1], 800, Rel.EQ, "nl_serial_shape[1]", cls_name) - validator.check_int(len(d_lj_a), 1, Rel.EQ, "d_LJ_A_dim", cls_name) - validator.check_int(d_lj_a[0], q, Rel.EQ, "d_LJ_A_shape[0]", cls_name) - validator.check_int(d_lj_b[0], q, Rel.EQ, "d_LJ_B_shape[0]", cls_name) - return uint_crd - - def infer_dtype(self, uint_crd, ljtype, charge, scaler, nl_numbers, nl_serial, d_lj_a, d_lj_b): - validator.check_tensor_dtype_valid('uint_crd', uint_crd, [mstype.uint32], self.name) - validator.check_tensor_dtype_valid('LJtype', ljtype, [mstype.int32], self.name) - validator.check_tensor_dtype_valid('charge', charge, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('scaler', scaler, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('nl_numbers', nl_numbers, [mstype.int32], self.name) - validator.check_tensor_dtype_valid('nl_serial', nl_serial, [mstype.int32], self.name) - validator.check_tensor_dtype_valid('d_LJ_A', d_lj_a, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('d_LJ_B', d_lj_b, [mstype.float32], self.name) - return charge - - -class MDTemperature(PrimitiveWithInfer): - """ - Compute the MD temperature. - - Because there is a large amount of inputs and each of them are related, - there is no way to construct `Examples` using random methods. For details, refer the webpage `SPONGE in MindSpore - `_. - - Args: - residue_numbers (int32): the number of residues m. - atom_numbers (int32): the number of atoms n. - - Inputs: - - **start** (Tensor) - The start atom index of each residue. - The data type is int32 and the shape is :math:`(m,)`. - - **end** (Tensor) - The end atom index of each residue. - The data type is int32 and the shape is :math:`(m,)`. - - **atom_vel_f** (Tensor) - The velocity of each atom. - The data type is float32 and the shape is :math:`(n, 3)`. - - **atom_mass** (Tensor) - The mass of each atom. - The data type is float32 and the shape is :math:`(n,)`. - - Outputs: - - **ek** (Tensor) - The temperature of each atom. - The data type is float32 and the shape is :math:`(n,)`. - - Supported Platforms: - ``GPU`` - """ - - @prim_attr_register - def __init__(self, residue_numbers, atom_numbers): - """Initialize MDTemperature.""" - validator.check_value_type('residue_numbers', residue_numbers, int, self.name) - validator.check_value_type('atom_numbers', atom_numbers, int, self.name) - self.residue_numbers = residue_numbers - self.atom_numbers = atom_numbers - self.add_prim_attr('residue_numbers', self.residue_numbers) - self.add_prim_attr('atom_numbers', self.atom_numbers) - self.init_prim_io_names( - inputs=['start', 'end', 'atom_vel_f', 'atom_mass'], - outputs=['ek']) - - def infer_shape(self, start_shape, end_shape, atom_vel_f_shape, atom_mass_shape): - cls_name = self.name - n = self.residue_numbers - m = self.atom_numbers - validator.check_int(len(start_shape), 1, Rel.EQ, "start", cls_name) - validator.check_int(start_shape[0], n, Rel.EQ, "end", cls_name) - validator.check_int(len(end_shape), 1, Rel.EQ, "start", cls_name) - validator.check_int(end_shape[0], n, Rel.EQ, "end", cls_name) - validator.check_int(atom_vel_f_shape[0], m, Rel.EQ, "atom_vel_f", cls_name) - validator.check_int(atom_vel_f_shape[1], 3, Rel.EQ, "atom_vel_f", cls_name) - validator.check_int(len(atom_mass_shape), 1, Rel.EQ, "atom_mass", cls_name) - validator.check_int(atom_mass_shape[0], m, Rel.EQ, "atom_mass", cls_name) - return [n,] - - def infer_dtype(self, start_dtype, end_dtype, atom_vel_f_dtype, atom_mass_dtype): - validator.check_tensor_dtype_valid('start', start_dtype, [mstype.int32], self.name) - validator.check_tensor_dtype_valid('end', end_dtype, [mstype.int32], self.name) - validator.check_tensor_dtype_valid('atom_vel_f', atom_vel_f_dtype, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('atom_mass', atom_mass_dtype, [mstype.float32], self.name) - return atom_mass_dtype - - -class MDIterationLeapFrogWithRF(PrimitiveWithInfer): - """ - One step of classical leap frog algorithm to solve the finite difference - Hamiltonian equations of motion for certain system, using Langevin dynamics - with Liu's thermostat scheme. Assume the number of atoms is n and the target - control temperature is T. - - Detailed iteration formula can be found in this paper: A unified thermostat - scheme for efficient configurational sampling for classical/quantum canonical - ensembles via molecular dynamics. DOI: 10.1063/1.4991621. - - Because there is a large amount of inputs and each of them are related, - there is no way to construct `Examples` using random methods. For details, refer the webpage `SPONGE in MindSpore - `_. - - Inputs: - - **float4_numbers** (Scalar) - total length to store random numbers. - The data type is int32. - - **atom_numbers** (Scalar) - The number of atoms n. - The data type is int32. - - **dt** (Scalar) - time step for finite difference. The data type is float32. - - **half_dt** (Scalar) - half of time step for finite difference. - The data type is float32. - - **exp_gamma** (Scalar) - parameter in Liu's dynamic, equals - exp(-gamma_ln * dt), where gamma_ln is the firction factor in Langvin - dynamics. The data type is float32. - - **max_velocity** (Scalar) - The upper limit of velocity, when the - velocity overflows, scale it to the upper limit. The data type is float32. - - **is_max_velocity** (Scalar) - whether the max velocity control is - open or not. The data type is int32. - - **mass_inverse** (Tensor) - The inverse value of - mass of each atom. The data type is float32 and the shape is :math:`(n,)`. - - **sqrt_mass** (Tensor) - The inverse square root value - of effect mass in Liu's dynamics of each atom. - The data type is float32 and the shape is :math:`(n,)`. - - **vel** (Tensor) - The velocity of each atom. - The data type is float32 and the shape is :math:`(n, 3)`. - - **crd** (Tensor) - The coordinate of each atom. - The data type is float32 and the shape is :math:`(n, 3)`. - - **frc** (Tensor) - The force felt by each atom. - The data type is float32 and the shape is :math:`(n, 3)`. - - **acc** (Tensor) - The acceleration of each atom. - The data type is float32 and the shape is :math:`(n, 3)`. - - **random force** (Tensor) - The random forces. - The data type is float32 and the shape is :math:`(n, 3)`. - - Outputs: - - **res** (Scalar) - The data type is float32. - - Supported Platforms: - ``GPU`` - Examples: - """ - - @prim_attr_register - def __init__(self, float4_numbers, atom_numbers, half_dt, dt, exp_gamma, is_max_velocity, max_velocity): - """Initialize MDIterationLeapFrogWithRF.""" - validator.check_value_type('float4_numbers', float4_numbers, int, self.name) - validator.check_value_type('atom_numbers', atom_numbers, int, self.name) - validator.check_value_type('half_dt', half_dt, float, self.name) - validator.check_value_type('dt', dt, float, self.name) - validator.check_value_type('exp_gamma', exp_gamma, float, self.name) - validator.check_value_type('is_max_velocity', is_max_velocity, int, self.name) - validator.check_value_type('max_velocity', max_velocity, float, self.name) - self.float4_numbers = float4_numbers - self.atom_numbers = atom_numbers - self.half_dt = half_dt - self.dt = dt - self.exp_gamma = exp_gamma - self.is_max_velocity = is_max_velocity - self.max_velocity = max_velocity - - self.init_prim_io_names( - inputs=['mass_inverse', 'sqrt_mass', 'vel_in', 'crd_in', 'frc_in', 'acc_in', 'random_force'], - outputs=['res']) - self.add_prim_attr('float4_numbers', self.float4_numbers) - self.add_prim_attr('atom_numbers', self.atom_numbers) - self.add_prim_attr('half_dt', self.half_dt) - self.add_prim_attr('dt', self.dt) - self.add_prim_attr('exp_gamma', self.exp_gamma) - self.add_prim_attr('is_max_velocity', self.is_max_velocity) - self.add_prim_attr('max_velocity', self.max_velocity) - - def infer_shape(self, mass_inverse_shape, sqrt_mass_shape, vel_in_shape, crd_in_shape, frc_in_shape, acc_in_shape, - random_force_shape): - n = self.atom_numbers - validator.check_int(len(mass_inverse_shape), 1, Rel.EQ, "mass_inverse", self.name) - validator.check_int(len(sqrt_mass_shape), 1, Rel.EQ, "mass_inverse", self.name) - validator.check_int(mass_inverse_shape[0], n, Rel.EQ, "mass_inverse", self.name) - validator.check_int(sqrt_mass_shape[0], n, Rel.EQ, "mass_inverse", self.name) - validator.check_int(vel_in_shape[0], n, Rel.EQ, "vel_in", self.name) - validator.check_int(vel_in_shape[1], 3, Rel.EQ, "vel_in", self.name) - validator.check_int(crd_in_shape[0], n, Rel.EQ, "crd_in", self.name) - validator.check_int(crd_in_shape[1], 3, Rel.EQ, "crd_in", self.name) - validator.check_int(frc_in_shape[0], n, Rel.EQ, "frc_in", self.name) - validator.check_int(frc_in_shape[1], 3, Rel.EQ, "frc_in", self.name) - validator.check_int(acc_in_shape[0], n, Rel.EQ, "acc_in", self.name) - validator.check_int(acc_in_shape[1], 3, Rel.EQ, "acc_in", self.name) - validator.check_int(random_force_shape[0], n, Rel.EQ, "random_force", self.name) - validator.check_int(random_force_shape[1], 3, Rel.EQ, "random_force", self.name) - - return [1,] - - def infer_dtype(self, mass_inverse_dtype, sqrt_mass_dtype, vel_in_dtype, crd_in_dtype, frc_in_dtype, acc_in_dtype, - rf_dtype): - validator.check_tensor_dtype_valid('mass_inverse', mass_inverse_dtype, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('sqrt_mass', sqrt_mass_dtype, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('vel_in', vel_in_dtype, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('crd_in', crd_in_dtype, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('frc_in', frc_in_dtype, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('acc_in', acc_in_dtype, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('rf', rf_dtype, [mstype.float32], self.name) - return mstype.float32 - - -class MDIterationLeapFrogLiujian(PrimitiveWithInfer): - """ - One step of classical leap frog algorithm to solve the finite difference - Hamiltonian equations of motion for certain system, using Langevin dynamics - with Liu's thermostat scheme. Assume the number of atoms is n and the target - control temperature is T. - - Detailed iteration formula can be found in this paper: A unified thermostat - scheme for efficient configurational sampling for classical/quantum canonical - ensembles via molecular dynamics. DOI: 10.1063/1.4991621. - - Because there is a large amount of inputs and each of them are related, - there is no way to construct `Examples` using random methods. For details, refer the webpage `SPONGE in MindSpore - `_. - - Args: - atom_numbers(int32): the number of atoms n. - dt(float32): time step for finite difference. - half_dt(float32): half of time step for finite difference. - exp_gamma(float32): parameter in Liu's dynamic. - - Inputs: - - **inverse_mass** (Tensor) - The inverse value of - mass of each atom. The data type is float32 and the shape is :math:`(n)`. - - **sqrt_mass_inverse** (Tensor) - The inverse square root value - of effect mass in Liu's dynamics of each atom. - The data type is float32 and the shape is :math:`(n,)`. - - **vel** (Tensor) - The velocity of each atom. - The data type is float32 and the shape is :math:`(n, 3)`. - - **crd** (Tensor) - The coordinate of each atom. - The data type is float32 and the shape is :math:`(n, 3)`. - - **frc** (Tensor) - The force felt by each atom. - The data type is float32 and the shape is :math:`(n, 3)`. - - **acc** (Tensor) - The acceleration of each atom. - The data type is float32 and the shape is :math:`(n, 3)`. - - **rand_state** (Tensor) - Random state to generate - random force. The data type is float32 and the shape is :math:`(math.ceil(n * 3.0 / 4.0) * 16, )`. - - **rand_frc** (Tensor) - The random forces. - The data type is float32 and the shape is :math:`(n, 3)`. - - Outputs: - - **output** (Tensor) - The output coordinates. - The data type is float32, and the shape is :math:`(n, 3)`. - - Supported Platforms: - ``GPU`` - """ - - @prim_attr_register - def __init__(self, atom_numbers, half_dt, dt, exp_gamma): - """Initialize MDIterationLeapFrogLiujian.""" - self.atom_numbers = atom_numbers - self.half_dt = half_dt - self.dt = dt - self.exp_gamma = exp_gamma - - self.add_prim_attr('atom_numbers', self.atom_numbers) - self.add_prim_attr('half_dt', self.half_dt) - self.add_prim_attr('dt', self.dt) - self.add_prim_attr('exp_gamma', self.exp_gamma) - self.init_prim_io_names( - inputs=['inverse_mass', 'sqrt_mass_inverse', 'vel', 'crd', 'frc', 'acc', 'rand_state', 'rand_frc'], - outputs=['output']) - self.add_prim_attr('side_effect_mem', True) - - def infer_shape(self, inverse_mass, sqrt_mass_inverse, vel, crd, frc, acc, rand_state, rand_frc): - n = self.atom_numbers - validator.check_int(len(inverse_mass), 1, Rel.EQ, "inverse_mass", self.name) - validator.check_int(len(sqrt_mass_inverse), 1, Rel.EQ, "sqrt_mass_inverse", self.name) - validator.check_int(inverse_mass[0], n, Rel.EQ, "inverse_mass", self.name) - validator.check_int(sqrt_mass_inverse[0], n, Rel.EQ, "sqrt_mass_inverse", self.name) - validator.check_int(len(rand_state), 1, Rel.EQ, "rand_state_dim", self.name) - validator.check_int(len(rand_frc), 2, Rel.EQ, "rand_frc_dim", self.name) - validator.check_int(len(vel), 2, Rel.EQ, "vel_dim", self.name) - validator.check_int(len(crd), 2, Rel.EQ, "crd_dim", self.name) - validator.check_int(len(frc), 2, Rel.EQ, "frc_dim", self.name) - validator.check_int(len(acc), 2, Rel.EQ, "acc_dim", self.name) - validator.check_int(vel[0], n, Rel.EQ, "vel_shape[0]", self.name) - validator.check_int(vel[1], 3, Rel.EQ, "vel_shape[1]", self.name) - validator.check_int(crd[0], n, Rel.EQ, "crd_shape[0]", self.name) - validator.check_int(crd[1], 3, Rel.EQ, "crd_shape[1]", self.name) - validator.check_int(frc[0], n, Rel.EQ, "frc_shape[0]", self.name) - validator.check_int(frc[1], 3, Rel.EQ, "frc_shape[1]", self.name) - validator.check_int(acc[0], n, Rel.EQ, "acc_shape[0]", self.name) - validator.check_int(acc[1], 3, Rel.EQ, "acc_shape[1]", self.name) - validator.check_int(rand_frc[0], n, Rel.EQ, "rand_frc_shape[0]", self.name) - validator.check_int(rand_frc[1], 3, Rel.EQ, "rand_frc_shape[1]", self.name) - validator.check_int(rand_state[0], math.ceil(self.atom_numbers * 3 / 4.0) * 16, Rel.EQ, "rand_state", self.name) - return [self.atom_numbers, 3] - - def infer_dtype(self, inverse_mass, sqrt_mass_inverse, vel, crd, frc, acc, rand_state, rand_frc): - validator.check_tensor_dtype_valid('inverse_mass', inverse_mass, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('sqrt_mass_inverse', sqrt_mass_inverse, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('vel', vel, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('crd', crd, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('frc', frc, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('acc', acc, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('rand_frc', rand_frc, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('rand_state', rand_state, [mstype.float32], self.name) - return mstype.float32 - - -class CrdToUintCrd(PrimitiveWithInfer): - """ - Convert FP32 coordinate to Uint32 coordinate. - - Because there is a large amount of inputs and each of them are related, - there is no way to construct `Examples` using random methods. For details, refer the webpage `SPONGE in MindSpore - `_. - - Args: - atom_numbers(int32): the number of atoms n. - - Inputs: - - **crd_to_uint_crd_cof** (Tensor) - The scale factor - between the unsigned int value and the real space coordinates. - The data type is float32 and the shape is :math:`(3,)`. - - **crd** (Tensor) - The coordinate of each atom. - The data type is float32 and the shape is :math:`(n, 3)`. - - Outputs: - - **output** (Scalar) - The data type is uint32. - - Supported Platforms: - ``GPU`` - """ - - @prim_attr_register - def __init__(self, atom_numbers): - """Initialize CrdToUintCrd.""" - self.atom_numbers = atom_numbers - self.add_prim_attr('atom_numbers', self.atom_numbers) - self.init_prim_io_names( - inputs=['crd_to_uint_crd_cof', 'crd'], - outputs=['output']) - - def infer_shape(self, crd_to_uint_crd_cof, crd): - validator.check_int(crd_to_uint_crd_cof[0], 3, Rel.EQ, "crd_to_uint_crd_cof_shape", self.name) - validator.check_int(crd[0], self.atom_numbers, Rel.EQ, "crd[0]", self.name) - validator.check_int(crd[1], 3, Rel.EQ, "crd[1]", self.name) - return crd - - def infer_dtype(self, crd_to_uint_crd_cof, crd): - validator.check_tensor_dtype_valid('crd_to_uint_crd_cof', crd_to_uint_crd_cof, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('crd', crd, [mstype.float32], self.name) - return mstype.uint32 - - -class MDIterationSetupRandState(PrimitiveWithInfer): - """ - Compute the random state of the iteration. - - Because there is a large amount of inputs and each of them are related, - there is no way to construct `Examples` using random methods. For details, refer the webpage `SPONGE in MindSpore - `_. - - Args: - atom_numbers(int32): the number of atoms n. - seed(int32): random seed. - - Outputs: - - **output** (Tensor) random state. - The data type is float32 and the shape is :math:`(ceil(n * 3 / 4),)`. - - Supported Platforms: - ``GPU`` - """ - - @prim_attr_register - def __init__(self, atom_numbers, seed): - """Initialize MDIterationSetupRandState.""" - self.atom_numbers = atom_numbers - self.seed = seed - self.add_prim_attr('atom_numbers', self.atom_numbers) - self.add_prim_attr('seed', self.seed) - self.init_prim_io_names( - inputs=[], - outputs=['output']) - - def infer_shape(self): - float4_numbers = math.ceil(self.atom_numbers * 3 / 4.0) - curandsize = 64 / 4 - return [float4_numbers * int(curandsize),] - - def infer_dtype(self): - return mstype.float32 - - -class TransferCrd(PrimitiveWithInfer): - """ - Transfer the coordinates to angular and radial. - - Because there is a large amount of inputs and each of them are related, - there is no way to construct `Examples` using random methods. For details, refer the webpage `SPONGE in MindSpore - `_. - - Args: - start_serial(int32): the index start position. - end_serial(int32): the index end position. - number(int32): the length of angular and radial. - - Inputs: - - **crd** (Tensor) - The coordinate of each atom. - n is the number of atoms. The data type is float32 and the shape is :math:`(n, 3)`. - - **old_crd** (Tensor) - The last coordinate of each atom. - n is the number of atoms. The data type is float32 and the shape is :math:`(n, 3)`. - - **box** (Tensor) - The length of 3 dimensions of the simulation box. - The data type is float32 and the shape is :math:`(3,)`. - - Outputs: - - **radial** (Tensor) - The array of radial transferred from coordinates. - The data type is float32 and the shape is :math:`(number,)`. - - **angular** (Tensor) - The array of angular transferred from coordinates. - The data type is float32 and the shape is :math:`(number,)`. - - **nowarp_crd** (Tensor) - The modified coordinate of each atom for - computing radial and angular. The data type is float32 and the shape is :math:`(n, 3)`. - - **box_map_times** (Tensor) - The box map times for radial and angular. - The data type is int32 and the shape is :math:`(n, 3)`. - - Supported Platforms: - ``GPU`` - """ - - @prim_attr_register - def __init__(self, start_serial, end_serial, number, atom_numbers): - """Initialize TransferCrd.""" - validator.check_value_type('start_serial', start_serial, int, self.name) - validator.check_value_type('end_serial', end_serial, int, self.name) - validator.check_value_type('number', number, int, self.name) - validator.check_value_type('atom_numbers', atom_numbers, int, self.name) - self.start_serial = start_serial - self.end_serial = end_serial - self.number = number - self.atom_numbers = atom_numbers - self.add_prim_attr('start_serial', self.start_serial) - self.add_prim_attr('end_serial', self.end_serial) - self.add_prim_attr('number', self.number) - self.add_prim_attr('atom_numbers', self.atom_numbers) - self.init_prim_io_names( - inputs=['crd', 'old_crd', 'box'], - outputs=['radial', 'angular', 'nowarp_crd', 'box_map_times']) - - def infer_shape(self, crd_shape, old_crd_shape, box_shape): - n = self.atom_numbers - validator.check_int(len(crd_shape), 2, Rel.EQ, "crd_dim", self.name) - validator.check_int(crd_shape[0], n, Rel.EQ, "crd_shape[0]", self.name) - validator.check_int(crd_shape[1], 3, Rel.EQ, "crd_shape[1]", self.name) - validator.check_int(len(old_crd_shape), 2, Rel.EQ, "old_crd_dim", self.name) - validator.check_int(old_crd_shape[0], n, Rel.EQ, "old_crd_shape[0]", self.name) - validator.check_int(old_crd_shape[1], 3, Rel.EQ, "old_crd_shape[1]", self.name) - validator.check_int(len(box_shape), 1, Rel.EQ, "box_dim", self.name) - validator.check_int(box_shape[0], 3, Rel.EQ, "box_shape[0]", self.name) - return [self.number,], [self.number,], [n, 3], [n, 3] - - def infer_dtype(self, crd_dtype, old_crd_dtype, box_dtype): - validator.check_tensor_dtype_valid('crd', crd_dtype, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('old_crd', old_crd_dtype, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('box', box_dtype, [mstype.float32], self.name) - return mstype.float32, mstype.float32, mstype.float32, mstype.int32 - - -class PMEBatchedFFT2D(PrimitiveWithInfer): - """ - Forward FFT with N-Dimensional Input. currently this is only batched ifft2. - - .. warning:: - This is an experimental prototype that is subject to change and/or deletion. - - Inputs: - - **input_tensor** (Tensor) - Three dimentsional tensor, supported - data type is complex64. - - Outputs: - - **output_tensor** (Tensor) - The tensor after undergoing fast Fourier - transform, the data type is complex64. - - Supported Platforms: - ``GPU`` - """ - - @prim_attr_register - def __init__(self, direction): - self.init_prim_io_names( - inputs=['input_tensor'], - outputs=['output_tensor']) - - validator.check_value_type('direction', direction, int, self.name) - self.direction = direction - self.add_prim_attr('direction', self.direction) - - def infer_shape(self, input_shape): - self.add_prim_attr("fftx", input_shape[0]) - self.add_prim_attr("ffty", input_shape[1]) - self.add_prim_attr("fftz", input_shape[2]) - return [input_shape[0], input_shape[1], input_shape[2]] - - def infer_dtype(self, input_dtype): - validator.check_tensor_dtype_valid('input_tensor', input_dtype, [mstype.complex64], self.name) - return mstype.complex64 - - -class PMEFFT1D(PrimitiveWithInfer): - """ - Forward FFT with One-Dimensional Input. - - .. warning:: - This is an experimental prototype that is subject to change and/or deletion. - - Inputs: - - **input_tensor** (Tensor) - Three dimentsional tensor, supported - data type is complex64. - - Outputs: - - **output_tensor** (Tensor) - The tensor after undergoing fast Fourier - transform, the data type is complex64. - - Supported Platforms: - ``GPU`` - """ - - @prim_attr_register - def __init__(self): - self.init_prim_io_names( - inputs=['input_tensor'], - outputs=['output_tensor']) - - def infer_shape(self, input_shape): - self.add_prim_attr('fftx', input_shape[0]) - return [input_shape[0]] - - def infer_dtype(self, input_dtype): - validator.check_tensor_dtype_valid('input_tensor', input_dtype, [mstype.complex64], self.name) - return mstype.complex64 - - -class PMEIFFT1D(PrimitiveWithInfer): - """ - Inverse FFT with One-Dimensional Input. - - .. warning:: - This is an experimental prototype that is subject to change and/or deletion. - - Inputs: - - **input_tensor** (Tensor) - Three dimentsional input tensor, supported data - type is complex64. - - Outputs: - - **output_tensor** (Tensor) - Returns the tensor after undergoing - inverse Fourier transform, the data type is complex64. - - Supported Platforms: - ``GPU`` - """ - - @prim_attr_register - def __init__(self): - self.init_prim_io_names( - inputs=['input_tensor'], - outputs=['output_tensor']) - - def infer_shape(self, input_shape): - self.add_prim_attr('fftx', input_shape[0]) - return [input_shape[0]] - - def infer_dtype(self, input_dtype): - validator.check_tensor_dtype_valid('input_tensor', input_dtype, [mstype.complex64], self.name) - return mstype.complex64 - - -class PMEFFT2D(PrimitiveWithInfer): - """ - Forward FFT with Two-Dimensional Input. - - .. warning:: - This is an experimental prototype that is subject to change and/or deletion. - - Inputs: - - **input_tensor** (Tensor) - Three dimentsional tensor, supported - data type is complex64. - - Outputs: - - **output_tensor** (Tensor) - The tensor after undergoing fast Fourier - transform, the data type is complex64. - - Supported Platforms: - ``GPU`` - """ - - @prim_attr_register - def __init__(self): - self.init_prim_io_names( - inputs=['input_tensor'], - outputs=['output_tensor']) - - def infer_shape(self, input_shape): - self.add_prim_attr('fftx', input_shape[0]) - self.add_prim_attr('ffty', input_shape[1]) - return [input_shape[0], input_shape[1]] - - def infer_dtype(self, input_dtype): - validator.check_tensor_dtype_valid('input_tensor', input_dtype, [mstype.complex64], self.name) - return mstype.complex64 - - -class PMEIFFT2D(PrimitiveWithInfer): - """ - Inverse FFT with Two-Dimensional Input. - - .. warning:: - This is an experimental prototype that is subject to change and/or deletion. - - Inputs: - - **input_tensor** (Tensor) - Three dimentsional input tensor, supported data - type is complex64. - - Outputs: - - **output_tensor** (Tensor) - Return the tensor after undergoing - inverse Fourier transform, the data type is complex64. - - Supported Platforms: - ``GPU`` - """ - - @prim_attr_register - def __init__(self): - self.init_prim_io_names( - inputs=['input_tensor'], - outputs=['output_tensor']) - - def infer_shape(self, input_shape): - self.add_prim_attr('fftx', input_shape[0]) - self.add_prim_attr('ffty', input_shape[1]) - return [input_shape[0], input_shape[1]] - - def infer_dtype(self, input_dtype): - validator.check_tensor_dtype_valid('input_tensor', input_dtype, [mstype.complex64], self.name) - return mstype.complex64 - - -class PMERFFT2D(PrimitiveWithInfer): - """ - Forward FFT with Two-Dimensional Input for real -> complex. - - .. warning:: - This is an experimental prototype that is subject to change and/or deletion. - - Inputs: - - **input_tensor** (Tensor) - Three dimentsional tensor, supported - data type is float32. - - Outputs: - - **output_tensor** (Tensor) - The tensor after undergoing fast Fourier - transform, the data type is complex64. - - Supported Platforms: - ``GPU`` - """ - - @prim_attr_register - def __init__(self): - self.init_prim_io_names( - inputs=['input_tensor'], - outputs=['output_tensor']) - - def infer_shape(self, input_shape): - self.add_prim_attr('fftx', input_shape[0]) - self.add_prim_attr('ffty', input_shape[1]) - return [input_shape[0], int(input_shape[1]/2)+1] - - def infer_dtype(self, input_dtype): - validator.check_tensor_dtype_valid('input_tensor', input_dtype, [mstype.float32], self.name) - return mstype.complex64 - - -class PMEIRFFT2D(PrimitiveWithInfer): - """ - Inverse RFFT with Two-Dimensional Input. - - .. warning:: - This is an experimental prototype that is subject to change and/or deletion. - - Inputs: - - **input_tensor** (Tensor) - Three dimentsional input tensor, supported data - type is complex64. - - Outputs: - - **output_tensor** (Tensor) - Return the tensor after undergoing - inverse Fourier transform, the data type is float32. - - Supported Platforms: - ``GPU`` - """ - - @prim_attr_register - def __init__(self): - self.init_prim_io_names( - inputs=['input_tensor'], - outputs=['output_tensor']) - - def infer_shape(self, input_shape): - self.add_prim_attr('fftx', input_shape[0]) - self.add_prim_attr('ffty', input_shape[1]) - return [input_shape[0], 2*(input_shape[1]-1)] - - def infer_dtype(self, input_dtype): - validator.check_tensor_dtype_valid('input_tensor', input_dtype, [mstype.complex64], self.name) - return mstype.float32 - - -class FFT3D(PrimitiveWithInfer): - """ - Forward FFT with Three-Dimensional Input. - - .. warning:: - This is an experimental prototype that is subject to change and/or deletion. - - Inputs: - - **input_tensor** (Tensor) - Three dimensional tensor, supported - data type is float32. - - Outputs: - - **output_tensor** (Tensor) - The tensor after undergoing fast Fourier - transform, the data type is complex64. - - Supported Platforms: - ``GPU`` - """ - - @prim_attr_register - def __init__(self): - self.init_prim_io_names( - inputs=['input_tensor'], - outputs=['output_tensor']) - - def infer_shape(self, input_shape): - self.add_prim_attr('fftx', input_shape[0]) - self.add_prim_attr('ffty', input_shape[1]) - self.add_prim_attr('fftz', input_shape[2]) - return [input_shape[0], input_shape[1], int(input_shape[2]/2)+1] - - def infer_dtype(self, input_dtype): - validator.check_tensor_dtype_valid('input_tensor', input_dtype, [mstype.float32], self.name) - return mstype.complex64 - - -class IFFT3D(PrimitiveWithInfer): - """ - Inverse FFT with Three-Dimensional Input. - - .. warning:: - This is an experimental prototype that is subject to change and/or deletion. - - Inputs: - - **input_tensor** (Tensor) - Three dimensional input tensor, supported data - type is complex64. - - Outputs: - - **output_tensor** (Tensor) - Returns the tensor after undergoing - inverse Fourier transform, the data type is float32. - - Supported Platforms: - ``GPU`` - """ - - @prim_attr_register - def __init__(self): - self.init_prim_io_names( - inputs=['input_tensor'], - outputs=['output_tensor']) - - def infer_shape(self, input_shape): - self.add_prim_attr('fftx', input_shape[0]) - self.add_prim_attr('ffty', input_shape[1]) - self.add_prim_attr('fftz', input_shape[2]) - return [input_shape[0], input_shape[1], (input_shape[2]-1)*2] - - def infer_dtype(self, input_dtype): - validator.check_tensor_dtype_valid('input_tensor', input_dtype, [mstype.complex64], self.name) - return mstype.float32 - - -class NeighborListUpdate(PrimitiveWithInfer): - """ - Update (or construct if first time) the Verlet neighbor list for the - calculation of short-ranged force. Assume the number of atoms is N, - the number of grids divided is G, the maximum number of atoms in one - grid is m, the maximum number of atoms in single atom's neighbor list - is L, and the number of total atom in excluded list is E. - - Args: - grid_numbers (int32): the total number of grids divided G. - atom_numbers (int32): the number of atoms n. - not_first_time (int32): whether to construct the neighbor list first time or not. - nxy (int32): the total number of grids divided in xy plane. - excluded_atom_numbers (int32): the total atom numbers in the excluded list E. - cutoff_square (float32): the cutoff square distance for short-range force calculation. - half_skin_square (float32): the maximum square value of the distance atom allowed to move between two updates. - cutoff_with_skin (float32): cutoff + skin, indicates the radius of the neighbor list for each atom. - half_cutoff_with_skin (float32): cutoff_with_skin/2. - cutoff_with_skin_square (float32): the square value of cutoff_with_skin. - refresh_interval (int32): the number of iteration steps between two updates of neighbor list. Default: 20. - cutoff (float32): the cutoff distance for short-range force calculation. Default: 10.0. - skin (float32): the maximum value of the distance atom allowed to move. Default: 2.0. - max_atom_in_grid_numbers (int32): the maximum number of atoms in one grid k. Default: 64. - max_neighbor_numbers (int32): The maximum number of neighbors m. Default: 800. - - Inputs: - - **atom_numbers_in_grid_bucket** (Tensor) - The number of atoms in each grid bucket. - The data type is int32 and the shape is :math:`(G,)`. - - **bucket** (Tensor) - (Tensor) - The atom indices in each grid bucket. - The data type is int32 and the shape is :math:`(G, k)`. - - **crd** (Tensor) - The coordinates of each atom. - The data type is float32 and the shape is :math:`(n, 3)`. - - **box_length** (Tensor) - The box length of the simulation box. - The data type is float32 and the shape is :math:`(3,)`. - - **grid_N** (Tensor) - The number of grids divided of 3 dimensions of the simulation box. - The data type is int32 and the shape is :math:`(3,)`. - - **grid_length_inverse** (Tensor) - The inverse value of grid length. - The data type is float32 and the shape is :math:`(3,)`. - - **atom_in_grid_serial** (Tensor) - The grid index for each atom. - The data type is int32 and the shape is :math:`(n,)`. - - **old_crd** (Tensor) - The coordinates before update of each atom. - The data type is float32 and the shape is :math:`(n, 3)`. - - **crd_to_uint_crd_cof** (Tensor) - The scale factor between the unsigned int coordinate and the real one. - The data type is float32 and the shape is :math:`(3,)`. - - **uint_crd** (Tensor) - The unsigned int coordinates value fo each atom. - The data type is unsigned int32 and the shape is :math:`(n, 3)`. - - **gpointer** (Tensor) - The nearest neighbor grids (including self) of each grid. - The data type is int32 and the shape is :math:`(G, 125)`. - - **nl_atom_numbers** (Tensor) - The number of atoms in neighbor list of each atom. - The data type is int32 and the shape is :math:`(n,)`. - - **nl_atom_serial** (Tensor) - The indices of atoms in neighbor list of each atom. - The data type is int32 and the shape is :math:`(n, m)`. - - **uint_dr_to_dr_cof** (Tensor) - The scale factor. - The data type is float32 and the shape is :math:`(3,)`. - - **excluded_list_start** (Tensor) - The start excluded index in excluded list for each atom. - The data type is int32 and the shape is :math:`(n,)`. - - **excluded_list** (Tensor) - The contiguous join of excluded list of each atom. - The data type is int32 and the shape is :math:`(E,)`. - - **excluded_numbers** (Tensor) - The number of atom excluded in excluded list for each atom. - The data type is int32 and the shape is :math:`(n,)`. - - **need_refresh_flag** (Tensor) - Whether the neighbor list of each atom need update or not. - The data type is int32 and the shape is :math:`(1,)`. - - **refresh_count** (Tensor) - Count how many iteration steps have passed since last update. - The data type is int32 and the shape is :math:`(1,)` or :math:`()`. - - Outputs: - - **res** (Tensor) - The return value after updating successfully. - The data type is float32 and the shape is :math:`(1,)`. - - Supported Platforms: - ``GPU`` - """ - - @prim_attr_register - def __init__(self, grid_numbers, atom_numbers, not_first_time, nxy, excluded_atom_numbers, - cutoff_square, half_skin_square, cutoff_with_skin, half_cutoff_with_skin, cutoff_with_skin_square, - refresh_interval=20, cutoff=10.0, skin=2.0, max_atom_in_grid_numbers=64, max_neighbor_numbers=800): - self.grid_numbers = grid_numbers - self.atom_numbers = atom_numbers - self.refresh_interval = refresh_interval - self.not_first_time = not_first_time - self.cutoff = cutoff - self.skin = skin - self.max_atom_in_grid_numbers = max_atom_in_grid_numbers - self.nxy = nxy - self.excluded_atom_numbers = excluded_atom_numbers - self.cutoff_square = cutoff_square - self.half_skin_square = half_skin_square - self.cutoff_with_skin = cutoff_with_skin - self.half_cutoff_with_skin = half_cutoff_with_skin - self.cutoff_with_skin_square = cutoff_with_skin_square - self.max_neighbor_numbers = max_neighbor_numbers - validator.check_value_type('grid_numbers', grid_numbers, int, self.name) - validator.check_value_type('atom_numbers', atom_numbers, int, self.name) - validator.check_value_type('refresh_interval', refresh_interval, int, self.name) - validator.check_value_type('not_first_time', not_first_time, int, self.name) - validator.check_value_type('cutoff', cutoff, float, self.name) - validator.check_value_type('skin', skin, float, self.name) - validator.check_value_type('max_atom_in_grid_numbers', max_atom_in_grid_numbers, int, self.name) - validator.check_value_type('nxy', nxy, int, self.name) - validator.check_value_type('excluded_atom_numbers', excluded_atom_numbers, int, self.name) - validator.check_value_type('cutoff_square', cutoff_square, float, self.name) - validator.check_value_type('half_skin_square', half_skin_square, float, self.name) - validator.check_value_type('cutoff_with_skin', cutoff_with_skin, float, self.name) - validator.check_value_type('half_cutoff_with_skin', half_cutoff_with_skin, float, self.name) - validator.check_value_type('cutoff_with_skin_square', cutoff_with_skin_square, float, self.name) - validator.check_value_type('max_neighbor_numbers', max_neighbor_numbers, int, self.name) - self.init_prim_io_names( - inputs=['atom_numbers_in_grid_bucket', 'bucket', 'crd', 'box_length', 'grid_N', 'grid_length_inverse', - 'atom_in_grid_serial', 'old_crd', 'crd_to_uint_crd_cof', 'uint_crd', 'gpointer', 'nl_atom_numbers', - 'nl_atom_serial', 'uint_dr_to_dr_cof', 'excluded_list_start', 'excluded_list', 'excluded_numbers', - 'need_refresh_flag', 'refresh_count'], outputs=['res']) - - self.add_prim_attr('grid_numbers', self.grid_numbers) - self.add_prim_attr('atom_numbers', self.atom_numbers) - self.add_prim_attr('refresh_interval', self.refresh_interval) - self.add_prim_attr('not_first_time', self.not_first_time) - self.add_prim_attr('cutoff', self.cutoff) - self.add_prim_attr('skin', self.skin) - self.add_prim_attr('max_atom_in_grid_numbers', self.max_atom_in_grid_numbers) - self.add_prim_attr('nxy', self.nxy) - self.add_prim_attr('excluded_atom_numbers', self.excluded_atom_numbers) - self.add_prim_attr('cutoff_square', self.cutoff_square) - self.add_prim_attr('half_skin_square', self.half_skin_square) - self.add_prim_attr('cutoff_with_skin', self.cutoff_with_skin) - self.add_prim_attr('half_cutoff_with_skin', self.half_cutoff_with_skin) - self.add_prim_attr('cutoff_with_skin_square', self.cutoff_with_skin_square) - self.add_prim_attr('side_effect_mem', True) - - def infer_shape(self, atom_numbers_in_grid_bucket_shape, bucket_shape, crd_shape, box_length_shape, grid_n_shape, - grid_length_inverse_shape, atom_in_grid_serial_shape, old_crd_shape, crd_to_uint_crd_cof_shape, - uint_crd_shape, gpointer_shape, nl_atom_numbers_shape, nl_atom_serial_shape, - uint_dr_to_dr_cof_shape, excluded_list_start_shape, excluded_list_shape, excluded_numbers_shape, - need_refresh_flag_shape, refresh_count_shape): - validator.check_int(len(atom_numbers_in_grid_bucket_shape), 1, Rel.EQ, - "atom_numbers_in_grid_bucket_dim", self.name) - validator.check_int(len(bucket_shape), 2, Rel.EQ, "bucket_dim", self.name) - validator.check_int(len(crd_shape), 2, Rel.EQ, "crd_dim", self.name) - validator.check_int(len(box_length_shape), 1, Rel.EQ, "box_length_dim", self.name) - validator.check_int(len(grid_n_shape), 1, Rel.EQ, "grid_n_dim", self.name) - validator.check_int(len(grid_length_inverse_shape), 1, Rel.EQ, "grid_length_inverse_dim", self.name) - validator.check_int(len(atom_in_grid_serial_shape), 1, Rel.EQ, "atom_in_grid_serial_dim", self.name) - validator.check_int(len(old_crd_shape), 2, Rel.EQ, "old_crd_dim", self.name) - validator.check_int(len(crd_to_uint_crd_cof_shape), 1, Rel.EQ, "crd_to_uint_crd_cof_dim", self.name) - validator.check_int(len(uint_crd_shape), 2, Rel.EQ, "uint_crd_dim", self.name) - validator.check_int(len(gpointer_shape), 2, Rel.EQ, "gpointer_dim", self.name) - validator.check_int(len(nl_atom_numbers_shape), 1, Rel.EQ, "nl_atom_numbers_dim", self.name) - validator.check_int(len(nl_atom_serial_shape), 2, Rel.EQ, "nl_atom_serial_dim", self.name) - validator.check_int(len(uint_dr_to_dr_cof_shape), 1, Rel.EQ, "uint_dr_to_dr_cof_dim", self.name) - validator.check_int(len(excluded_list_start_shape), 1, Rel.EQ, "excluded_list_start_dim", self.name) - validator.check_int(len(excluded_list_shape), 1, Rel.EQ, "excluded_list_dim", self.name) - validator.check_int(len(excluded_numbers_shape), 1, Rel.EQ, "excluded_numbers_dim", self.name) - validator.check_int(len(need_refresh_flag_shape), 1, Rel.EQ, "need_refresh_flag_dim", self.name) - validator.check_int(len(refresh_count_shape), 1, Rel.LE, "refresh_count_dim", self.name) - validator.check_int(atom_numbers_in_grid_bucket_shape[0], self.grid_numbers, Rel.EQ, - "atom_numbers_in_grid_bucket", self.name) - validator.check_int(bucket_shape[0], self.grid_numbers, Rel.EQ, "bucket", self.name) - validator.check_int(bucket_shape[1], self.max_atom_in_grid_numbers, Rel.EQ, "bucket", self.name) - validator.check_int(crd_shape[0], self.atom_numbers, Rel.EQ, "crd", self.name) - validator.check_int(crd_shape[1], 3, Rel.EQ, "crd", self.name) - validator.check_int(box_length_shape[0], 3, Rel.EQ, "box_length", self.name) - validator.check_int(grid_n_shape[0], 3, Rel.EQ, "grid_N", self.name) - validator.check_int(grid_length_inverse_shape[0], 3, Rel.EQ, "grid_length_inverse", self.name) - validator.check_int(atom_in_grid_serial_shape[0], self.atom_numbers, Rel.EQ, "atom_in_grid_serial", - self.name) - validator.check_int(old_crd_shape[0], self.atom_numbers, Rel.EQ, "old_crd", self.name) - validator.check_int(old_crd_shape[1], 3, Rel.EQ, "old_crd", self.name) - validator.check_int(crd_to_uint_crd_cof_shape[0], 3, Rel.EQ, "crd_to_uint_crd_cof", self.name) - validator.check_int(uint_crd_shape[0], self.atom_numbers, Rel.EQ, "uint_crd", self.name) - validator.check_int(uint_crd_shape[1], 3, Rel.EQ, "uint_crd", self.name) - validator.check_int(gpointer_shape[0], self.grid_numbers, Rel.EQ, "gpointer", self.name) - validator.check_int(gpointer_shape[1], 125, Rel.EQ, "gpointer", self.name) - validator.check_int(nl_atom_numbers_shape[0], self.atom_numbers, Rel.EQ, "nl_atom_numbers", self.name) - validator.check_int(nl_atom_serial_shape[0], self.atom_numbers, Rel.EQ, "nl_atom_serial", self.name) - validator.check_int(nl_atom_serial_shape[1], self.max_neighbor_numbers, Rel.EQ, "nl_atom_serial", - self.name) - validator.check_int(uint_dr_to_dr_cof_shape[0], 3, Rel.EQ, "uint_dr_to_dr_cof", self.name) - validator.check_int(excluded_list_start_shape[0], self.atom_numbers, Rel.EQ, "excluded_list_start", - self.name) - validator.check_int(excluded_list_shape[0], self.excluded_atom_numbers, Rel.EQ, "excluded_list", - self.name) - validator.check_int(excluded_numbers_shape[0], self.atom_numbers, Rel.EQ, "excluded_numbers", self.name) - validator.check_int(need_refresh_flag_shape[0], 1, Rel.EQ, "need_refresh_flag", self.name) - if refresh_count_shape: - validator.check_int(refresh_count_shape[0], 1, Rel.EQ, "refresh_count_shape", self.name) - return [1,] - - def infer_dtype(self, atom_numbers_in_grid_bucket_dtype, bucket_dtype, crd_dtype, box_length_dtype, grid_n_dtype, - grid_length_inverse_dtype, atom_in_grid_serial_dtype, old_crd_dtype, crd_to_uint_crd_cof_dtype, - uint_crd_dtype, gpointer_dtype, nl_atom_numbers_dtype, nl_atom_serial_dtype, - uint_dr_to_dr_cof_dtype, excluded_list_start_dtype, excluded_list_dtype, excluded_numbers_dtype, - need_refresh_flag_dtype, refresh_count_dtype): - validator.check_tensor_dtype_valid('atom_numbers_in_grid_bucket', atom_numbers_in_grid_bucket_dtype, - [mstype.int32], self.name) - validator.check_tensor_dtype_valid('bucket', bucket_dtype, [mstype.int32], self.name) - validator.check_tensor_dtype_valid('crd', crd_dtype, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('box_length', box_length_dtype, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('grid_N', grid_n_dtype, [mstype.int32], self.name) - validator.check_tensor_dtype_valid('grid_length_inverse', grid_length_inverse_dtype, [mstype.float32], - self.name) - validator.check_tensor_dtype_valid('atom_in_grid_serial', atom_in_grid_serial_dtype, [mstype.int32], - self.name) - validator.check_tensor_dtype_valid('old_crd', old_crd_dtype, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('crd_to_uint_crd_cof', crd_to_uint_crd_cof_dtype, [mstype.float32], - self.name) - validator.check_tensor_dtype_valid('uint_crd', uint_crd_dtype, [mstype.uint32], self.name) - validator.check_tensor_dtype_valid('gpointer', gpointer_dtype, [mstype.int32], self.name) - validator.check_tensor_dtype_valid('nl_atom_numbers', nl_atom_numbers_dtype, [mstype.int32], self.name) - validator.check_tensor_dtype_valid('nl_atom_serial', nl_atom_serial_dtype, [mstype.int32], self.name) - validator.check_tensor_dtype_valid('uint_dr_to_dr_cof', uint_dr_to_dr_cof_dtype, [mstype.float32], - self.name) - validator.check_tensor_dtype_valid('excluded_list_start', excluded_list_start_dtype, [mstype.int32], - self.name) - validator.check_tensor_dtype_valid('excluded_list', excluded_list_dtype, [mstype.int32], self.name) - validator.check_tensor_dtype_valid('excluded_numbers', excluded_numbers_dtype, [mstype.int32], self.name) - validator.check_tensor_dtype_valid('need_refresh_flag', need_refresh_flag_dtype, [mstype.int32], - self.name) - validator.check_tensor_dtype_valid('refresh_count', refresh_count_dtype, [mstype.int32], - self.name) - return mstype.float32 diff --git a/mindspore/python/mindspore/ops/operations/sponge_update_ops.py b/mindspore/python/mindspore/ops/operations/sponge_update_ops.py deleted file mode 100644 index c82762b4991..00000000000 --- a/mindspore/python/mindspore/ops/operations/sponge_update_ops.py +++ /dev/null @@ -1,2546 +0,0 @@ -# Copyright 2021 Huawei Technologies Co., Ltd -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================ - -""" -Note: - SPONGE operators for new modules. This is an experimental interface that is subject to change and/or deletion. -""" -import math -from ..primitive import PrimitiveWithInfer, prim_attr_register -from ..._checkparam import Rel -from ..._checkparam import Validator as validator -from ...common import dtype as mstype - - -class RefreshUintCrd(PrimitiveWithInfer): - """ - Refresh the unsigned coordinate of each constrained atom in each constrain iteration. - - .. warning:: - This is an experimental prototype that is subject to change and/or deletion. - - Args: - atom_numbers (int32): the number of atoms n. - half_exp_gamma_plus_half (float32): constant value (1.0 + exp(gamma * dt)) if Langvin-Liu thermostat is used, - where gamma is friction coefficient and dt is the simulation time step, 1.0 otherwise. - - Inputs: - - **crd** (Tensor) - The coordinate of each atom. - The data type is float32 and the shape is :math:`(n, 3)`. - - **quarter_cof** (Tensor) - The 3-D scale factor. - The data type is float32 and the shape is :math:`(3,)`. - - **test_frc** (Tensor) - The constraint force. - The data type is float32 and the shape is :math:`(n, 3)`. - - **mass_inverse** (Tensor) - The inverse value of mass of each atom. - The data type is float32 and the shape is :math:`(n,)`. - - Outputs: - - **uint_crd** (Tensor) - The unsigned int coordinate value of each atom. - The data type is uint32 and the shape is :math:`(n, 3)`. - - Supported Platforms: - ``GPU`` - """ - - @prim_attr_register - def __init__(self, atom_numbers, half_exp_gamma_plus_half): - validator.check_value_type('atom_numbers', atom_numbers, int, self.name) - validator.check_value_type('half_exp_gamma_plus_half', half_exp_gamma_plus_half, float, self.name) - self.atom_numbers = atom_numbers - self.half_exp_gamma_plus_half = half_exp_gamma_plus_half - self.add_prim_attr('atom_numbers', self.atom_numbers) - self.add_prim_attr('half_exp_gamma_plus_half', self.half_exp_gamma_plus_half) - self.init_prim_io_names( - inputs=['crd', 'quarter_cof', 'test_frc', 'mass_inverse'], - outputs=['uint_crd']) - - def infer_shape(self, crd_shape, quarter_cof_shape, test_frc_shape, mass_inverse_shape): - cls_name = self.name - n = self.atom_numbers - validator.check_int(len(crd_shape), 2, Rel.EQ, "crd_dim", cls_name) - validator.check_int(len(quarter_cof_shape), 1, Rel.EQ, "quarter_cof_dim", cls_name) - validator.check_int(len(test_frc_shape), 2, Rel.EQ, "test_frc_dim", cls_name) - validator.check_int(len(mass_inverse_shape), 1, Rel.EQ, "mass_inverse_dim", cls_name) - - validator.check_int(crd_shape[0], n, Rel.EQ, "crd_shape[0]", cls_name) - validator.check_int(crd_shape[1], 3, Rel.EQ, "crd_shape[1]", cls_name) - validator.check_int(quarter_cof_shape[0], 3, Rel.EQ, "quarter_cof_shape", cls_name) - validator.check_int(test_frc_shape[0], n, Rel.EQ, "test_frc_shape[0]", cls_name) - validator.check_int(test_frc_shape[1], 3, Rel.EQ, "test_frc_shape[1]", cls_name) - validator.check_int(mass_inverse_shape[0], n, Rel.EQ, "mass_inverse_shape", cls_name) - - return [n, 3] - - def infer_dtype(self, crd_dtype, quarter_cof_dtype, test_frc_dtype, mass_inverse_dtype): - validator.check_tensor_dtype_valid('crd', crd_dtype, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('quarter_cof', quarter_cof_dtype, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('test_frc', test_frc_dtype, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('mass_inverse', mass_inverse_dtype, [mstype.float32], self.name) - return mstype.uint32 - - -class ConstrainForceCycleWithVirial(PrimitiveWithInfer): - """ - Calculate the constraint force and virial in each iteration. - - .. warning:: - This is an experimental prototype that is subject to change and/or deletion. - - Args: - atom_numbers (int32): the number of atoms n. - constrain_pair_numbers (int32): the number of constrain pairs m. - - Inputs: - - **uint_crd** (Tensor) - The unsigned int coordinate value of each atom. - The data type is uint32 and the shape is :math:`(n, 3)`. - - **scaler** (Tensor) - The 3-D scale factor (x, y, z), - The data type is float32 and the shape is :math:`(3,)`. - - **pair_dr** (Tensor) - The displacement vector of each constrained atom pair. - The data type is float32 and the shape is :math:`(m, 3)`. - - **atom_i_serials** (Tensor) - The first atom index of each constrained atom pair. - The data type is int32 and the shape is :math:`(m,)`. - - **atom_j_serials** (Tensor) - The second atom index of each constrained atom pair. - The data type is int32 and the shape is :math:`(m,)`. - - **constant_rs** (Tensor) - The constrained distance of each constrained atom pair. - The data type is float32 and the shape is :math:`(m,)`. - - **constrain_ks** (Tensor) - The coefficient of each constrained atom pair. - The data type is float32 and the shape is :math:`(m,)`. - - Outputs: - - **test_frc** (Tensor) - The constraint force. - The data type is float32 and the shape is :math:`(n, 3)`. - - **atom_virial** (Tensor) - The virial caused by constraint force of each atom. - The data type is float32 and the shape is :math:`(m,)`. - - Supported Platforms: - ``GPU`` - """ - - @prim_attr_register - def __init__(self, atom_numbers, constrain_pair_numbers): - validator.check_value_type('atom_numbers', atom_numbers, int, self.name) - validator.check_value_type('constrain_pair_numbers', constrain_pair_numbers, int, self.name) - self.atom_numbers = atom_numbers - self.constrain_pair_numbers = constrain_pair_numbers - self.add_prim_attr('atom_numbers', self.atom_numbers) - self.add_prim_attr('constrain_pair_numbers', self.constrain_pair_numbers) - self.init_prim_io_names( - inputs=['uint_crd', 'scaler', 'pair_dr', 'atom_i_serials', 'atom_j_serials', - 'constant_rs', 'constrain_ks'], - outputs=['test_frc', 'atom_virial']) - - def infer_shape(self, uint_crd_shape, scaler_shape, pair_dr_shape, atom_i_serials_shape, - atom_j_serials_shape, constant_rs_shape, constrain_ks_shape): - cls_name = self.name - n = self.atom_numbers - m = self.constrain_pair_numbers - validator.check_int(len(uint_crd_shape), 2, Rel.EQ, "uint_crd_dim", cls_name) - validator.check_int(len(scaler_shape), 1, Rel.EQ, "scaler_dim", cls_name) - validator.check_int(len(pair_dr_shape), 2, Rel.EQ, "pair_dr_dim", cls_name) - validator.check_int(len(atom_i_serials_shape), 1, Rel.EQ, "atom_i_serials_dim", cls_name) - validator.check_int(len(atom_j_serials_shape), 1, Rel.EQ, "atom_j_serials_dim", cls_name) - validator.check_int(len(constant_rs_shape), 1, Rel.EQ, "constant_rs_dim", cls_name) - validator.check_int(len(constrain_ks_shape), 1, Rel.EQ, "constrain_ks_dim", cls_name) - - validator.check_int(uint_crd_shape[0], n, Rel.EQ, "uint_crd_shape[0]", cls_name) - validator.check_int(uint_crd_shape[1], 3, Rel.EQ, "uint_crd_shape[1]", cls_name) - validator.check_int(scaler_shape[0], 3, Rel.EQ, "scaler_shape", cls_name) - validator.check_int(pair_dr_shape[0], m, Rel.EQ, "pair_dr_shape[0]", cls_name) - validator.check_int(pair_dr_shape[1], 3, Rel.EQ, "pair_dr_shape[1]", cls_name) - validator.check_int(atom_i_serials_shape[0], m, Rel.EQ, "atom_i_serials_shape", cls_name) - validator.check_int(atom_j_serials_shape[0], m, Rel.EQ, "atom_j_serials_shape", cls_name) - validator.check_int(constant_rs_shape[0], m, Rel.EQ, "constant_rs_shape", cls_name) - validator.check_int(constrain_ks_shape[0], m, Rel.EQ, "constrain_ks_shape", cls_name) - return [n, 3], [m,] - - def infer_dtype(self, uint_crd_dtype, scaler_dtype, pair_dr_dtype, atom_i_serials_dtype, - atom_j_serials_dtype, constant_rs_dtype, constrain_ks_dtype): - validator.check_tensor_dtype_valid('uint_crd', uint_crd_dtype, [mstype.uint32], self.name) - validator.check_tensor_dtype_valid('scaler', scaler_dtype, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('pair_dr', pair_dr_dtype, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('atom_i_serials', atom_i_serials_dtype, [mstype.int32], self.name) - validator.check_tensor_dtype_valid('atom_j_serials', atom_j_serials_dtype, [mstype.int32], self.name) - validator.check_tensor_dtype_valid('constant_rs', constant_rs_dtype, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('constrain_ks', constrain_ks_dtype, [mstype.float32], self.name) - return mstype.float32, mstype.float32 - - -class LastCrdToDr(PrimitiveWithInfer): - """ - Calculate the displacement vector of each constrained atom pair. - - .. warning:: - This is an experimental prototype that is subject to change and/or deletion. - - Args: - atom_numbers (int32): the number of atoms n. - constrain_pair_numbers (int32): the number of constrain pairs m. - - Inputs: - - **crd** (Tensor) - The coordinate of each atom. - The data type is float32 and the shape is :math:`(n, 3)`. - - **quarter_cof** (Tensor) - The 3-D scale factor. - The data type is float32 and the shape is :math:`(3,)`. - - **uint_dr_to_dr** (Tensor) - The 3-D scale factor (x, y, z) - The data type is int32 and the shape is :math:`(3,)`.. - - **atom_i_serials** (Tensor) - The first atom index of each constrained atom pair. - The data type is int32 and the shape is :math:`(m,)`. - - **atom_j_serials** (Tensor) - The second atom index of each constrained atom pair. - The data type is int32 and the shape is :math:`(m,)`. - - **constant_rs** (Tensor) - The constrained distance of each constrained atom pair. - The data type is float32 and the shape is :math:`(m,)`. - - **constrain_ks** (Tensor) - The coefficient of each constrained atom pair. - The data type is float32 and the shape is :math:`(m,)`. - - Outputs: - - **pair_dr** (Tensor) - The displacement vector of each constrained atom pair. - The data type is float32 and the shape is :math:`(m, 3)`. - - Supported Platforms: - ``GPU`` - """ - - @prim_attr_register - def __init__(self, atom_numbers, constrain_pair_numbers): - validator.check_value_type('atom_numbers', atom_numbers, int, self.name) - validator.check_value_type('constrain_pair_numbers', constrain_pair_numbers, int, self.name) - self.constrain_pair_numbers = constrain_pair_numbers - self.atom_numbers = atom_numbers - self.add_prim_attr('constrain_pair_numbers', self.constrain_pair_numbers) - self.init_prim_io_names( - inputs=['crd', 'quarter_cof', 'uint_dr_to_dr', 'atom_i_serials', 'atom_j_serials', - 'constant_rs', 'constrain_ks'], - outputs=['pair_dr']) - - def infer_shape(self, crd_shape, quarter_cof_shape, uint_dr_to_dr_shape, atom_i_serials_shape, - atom_j_serials_shape, constant_rs_shape, constrain_ks_shape): - cls_name = self.name - n = self.atom_numbers - m = self.constrain_pair_numbers - validator.check_int(len(crd_shape), 2, Rel.EQ, "crd_dim", cls_name) - validator.check_int(len(quarter_cof_shape), 1, Rel.EQ, "quarter_cof_dim", cls_name) - validator.check_int(len(uint_dr_to_dr_shape), 1, Rel.EQ, "quarter_cof_dim", cls_name) - validator.check_int(len(atom_i_serials_shape), 1, Rel.EQ, "atom_i_serials_dim", cls_name) - validator.check_int(len(atom_j_serials_shape), 1, Rel.EQ, "atom_j_serials_dim", cls_name) - validator.check_int(len(constant_rs_shape), 1, Rel.EQ, "constant_rs_dim", cls_name) - validator.check_int(len(constrain_ks_shape), 1, Rel.EQ, "constrain_ks_dim", cls_name) - - validator.check_int(crd_shape[0], n, Rel.EQ, "crd_shape[0]", cls_name) - validator.check_int(crd_shape[1], 3, Rel.EQ, "crd_shape[1]", cls_name) - validator.check_int(quarter_cof_shape[0], 3, Rel.EQ, "quarter_cof_shape", cls_name) - validator.check_int(uint_dr_to_dr_shape[0], 3, Rel.EQ, "uint_dr_to_dr_shape", cls_name) - validator.check_int(atom_i_serials_shape[0], m, Rel.EQ, "atom_i_serials_shape", cls_name) - validator.check_int(atom_j_serials_shape[0], m, Rel.EQ, "atom_j_serials_shape", cls_name) - validator.check_int(constant_rs_shape[0], m, Rel.EQ, "constant_rs_shape", cls_name) - validator.check_int(constrain_ks_shape[0], m, Rel.EQ, "constrain_ks_shape", cls_name) - - return [m, 3] - - def infer_dtype(self, crd_dtype, quarter_cof_dtype, uint_dr_to_dr_dtype, atom_i_serials_dtype, - atom_j_serials_dtype, constant_rs_dtype, constrain_ks_dtype): - validator.check_tensor_dtype_valid('crd', crd_dtype, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('quarter_cof', quarter_cof_dtype, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('uint_dr_to_dr', uint_dr_to_dr_dtype, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('atom_i_serials', atom_i_serials_dtype, [mstype.int32], self.name) - validator.check_tensor_dtype_valid('atom_j_serials', atom_j_serials_dtype, [mstype.int32], self.name) - validator.check_tensor_dtype_valid('constant_rs', constant_rs_dtype, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('constrain_ks', constrain_ks_dtype, [mstype.float32], self.name) - return mstype.float32 - - -class RefreshCrdVel(PrimitiveWithInfer): - """ - Refresh the coordinate and velocity of each constrained atom after all iterations have ended. - - .. warning:: - This is an experimental prototype that is subject to change and/or deletion. - - Args: - atom_numbers (int32): the number of atoms n. - dt_inverse (float32): the inverse value of simulation time step. - dt (float32): the simulation time step. - exp_gamma (float32): constant value exp(gamma * dt). - half_exp_gamma_plus_half (float32): constant value (1 + exp_gamma)/2. - - Inputs: - - **crd** (Tensor) - The coordinate of each atom. - The data type is float32 and the shape is :math:`(n, 3)`. - - **vel** (Tensor) - The velocity of each atom. - The data type is float32 and the shape is :math:`(n, 3)`. - - **test_frc** (Tensor) - The constraint force calculated in the last iteration. - The data type is float32 and the shape is :math:`(n, 3)`. - - **mass_inverse** (Tensor) - The inverse value of mass of each atom. - The data type is float32 and the shape is :math:`(n,)`. - - Outputs: - - **res** (Tensor) - The return value after updating successfully. - The data type is float32 and the shape is :math:`(1,)`. - - Supported Platforms: - ``GPU`` - """ - - @prim_attr_register - def __init__(self, atom_numbers, dt_inverse, dt, exp_gamma, half_exp_gamma_plus_half): - validator.check_value_type('atom_numbers', atom_numbers, int, self.name) - validator.check_value_type('dt', dt, float, self.name) - validator.check_value_type('dt_inverse', dt_inverse, float, self.name) - validator.check_value_type('exp_gamma', exp_gamma, float, self.name) - validator.check_value_type('half_exp_gamma_plus_half', half_exp_gamma_plus_half, float, self.name) - self.atom_numbers = atom_numbers - self.dt_inverse = dt_inverse - self.dt = dt - self.exp_gamma = exp_gamma - self.half_exp_gamma_plus_half = half_exp_gamma_plus_half - self.add_prim_attr('atom_numbers', self.atom_numbers) - self.add_prim_attr('dt_inverse', self.dt_inverse) - self.add_prim_attr('dt', self.dt) - self.add_prim_attr('exp_gamma', self.exp_gamma) - self.add_prim_attr('half_exp_gamma_plus_half', self.half_exp_gamma_plus_half) - self.init_prim_io_names( - inputs=['crd', 'vel', 'test_frc', 'mass_inverse'], - outputs=['res']) - self.add_prim_attr('side_effect_mem', True) - - def infer_shape(self, crd_shape, vel_shape, test_frc_shape, mass_inverse_shape): - cls_name = self.name - n = self.atom_numbers - validator.check_int(len(crd_shape), 2, Rel.EQ, "crd_dim", cls_name) - validator.check_int(len(vel_shape), 2, Rel.EQ, "vel_dim", cls_name) - validator.check_int(len(test_frc_shape), 2, Rel.EQ, "test_frc_dim", cls_name) - validator.check_int(len(mass_inverse_shape), 1, Rel.EQ, "mass_inverse_dim", cls_name) - - validator.check_int(crd_shape[0], n, Rel.EQ, "crd_shape[0]", cls_name) - validator.check_int(crd_shape[1], 3, Rel.EQ, "crd_shape[1]", cls_name) - validator.check_int(vel_shape[0], n, Rel.EQ, "vel_shape[0]", cls_name) - validator.check_int(vel_shape[1], 3, Rel.EQ, "vel_shape[1]", cls_name) - validator.check_int(test_frc_shape[0], n, Rel.EQ, "test_frc_shape[0]", cls_name) - validator.check_int(test_frc_shape[1], 3, Rel.EQ, "test_frc_shape[1]", cls_name) - validator.check_int(mass_inverse_shape[0], n, Rel.EQ, "mass_inverse_shape[0]", cls_name) - return [1,] - - def infer_dtype(self, crd_dtype, vel_dtype, test_frc_dtype, mass_inverse_dtype): - validator.check_tensor_dtype_valid('crd', crd_dtype, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('vel', vel_dtype, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('test_frc', test_frc_dtype, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('mass_inverse', mass_inverse_dtype, [mstype.float32], self.name) - return mstype.float32 - - -class CalculateNowrapCrd(PrimitiveWithInfer): - """ - Calculate the inside-box periodic image of each atom. - - .. warning:: - This is an experimental prototype that is subject to change and/or deletion. - - Args: - atom_numbers (int32): the number of atoms n. - - Inputs: - - **crd** (Tensor) - The coordinate of each atom. - The data type is float32 and the shape is :math:`(n, 3)`. - - **box** (Tensor) - The 3-D size of system. - The data type is float32 and the shape is :math:`(3, )`. - - **box_map_times** (Tensor) - The number of times each atom has crossed the box. - The data type is int32 and the shape is :math:`(n, 3)`. - - Outputs: - - **nowrap_crd** (Tensor) - The inside-box periodic image of each atom. - The data type is float32 and the shape is :math:`(n, 3)`. - - Supported Platforms: - ``GPU`` - """ - - @prim_attr_register - def __init__(self, atom_numbers): - validator.check_value_type('atom_numbers', atom_numbers, int, self.name) - self.atom_numbers = atom_numbers - self.add_prim_attr('atom_numbers', self.atom_numbers) - self.init_prim_io_names( - inputs=['crd', 'box', 'box_map_times'], - outputs=['nowrap_crd']) - - def infer_shape(self, crd_shape, box_shape, box_map_times_shape): - cls_name = self.name - n = self.atom_numbers - validator.check_int(len(crd_shape), 2, Rel.EQ, "crd_dim", cls_name) - validator.check_int(len(box_shape), 1, Rel.EQ, "box_dim", cls_name) - validator.check_int(len(box_map_times_shape), 2, Rel.EQ, "box_map_times_dim", cls_name) - - validator.check_int(crd_shape[0], n, Rel.EQ, "crd_shape[0]", cls_name) - validator.check_int(crd_shape[1], 3, Rel.EQ, "crd_shape[1]", cls_name) - validator.check_int(box_shape[0], 3, Rel.EQ, "box_shape[0]", cls_name) - validator.check_int(box_map_times_shape[0], n, Rel.EQ, "box_map_times_shape[0]", cls_name) - validator.check_int(box_map_times_shape[1], 3, Rel.EQ, "box_map_times_shape[1]", cls_name) - return [n, 3] - - def infer_dtype(self, crd_dtype, box_dtype, box_map_times_dtype): - validator.check_tensor_dtype_valid('crd', crd_dtype, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('box', box_dtype, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('box_map_times', box_map_times_dtype, - [mstype.float32], self.name) - return mstype.float32 - - -class RefreshBoxmapTimes(PrimitiveWithInfer): - """ - Refresh the box-crossing times of each atom. - - .. warning:: - This is an experimental prototype that is subject to change and/or deletion. - - Args: - atom_numbers (int32): the number of atoms n. - - Inputs: - - **crd** (Tensor) - The coordinate of each atom. - The data type is float32 and the shape is :math:`(n, 3)`. - - **old_crd** (Tensor) - The coordinate of each atom at last update. - The data type is float32 and the shape is :math:`(n, 3)`. - - **box_length_inverse** (Tensor) - The inverse value of box length in 3 dimensions. - The data type is float32 and the shape is :math:`(3,)`. - - **box_map_times** (Tensor) - The number of times each atom has crossed the box. - The data type is int32 and the shape is :math:`(n, 3)`. - - Outputs: - - **res** (Tensor) - The return value after updating successfully. - The data type is float32 and the shape is :math:`(1,)`. - - Supported Platforms: - ``GPU`` - """ - - @prim_attr_register - def __init__(self, atom_numbers): - validator.check_value_type('atom_numbers', atom_numbers, int, self.name) - self.atom_numbers = atom_numbers - self.add_prim_attr('atom_numbers', self.atom_numbers) - self.init_prim_io_names( - inputs=['crd', 'old_crd', 'box_length_inverse', 'box_map_times'], - outputs=['res']) - - def infer_shape(self, crd_shape, old_crd_shape, box_length_inverse_shape, box_map_times_shape): - cls_name = self.name - n = self.atom_numbers - validator.check_int(len(crd_shape), 2, Rel.EQ, "crd_dim", cls_name) - validator.check_int(len(old_crd_shape), 2, Rel.EQ, "old_crd_dim", cls_name) - validator.check_int(len(box_length_inverse_shape), 1, Rel.EQ, "box_length_inverse_dim", cls_name) - validator.check_int(len(box_map_times_shape), 2, Rel.EQ, "box_map_times_dim", cls_name) - - validator.check_int(crd_shape[0], n, Rel.EQ, "crd_shape[0]", cls_name) - validator.check_int(crd_shape[1], 3, Rel.EQ, "crd_shape[1]", cls_name) - validator.check_int(old_crd_shape[0], n, Rel.EQ, "old_crd_shape[0]", cls_name) - validator.check_int(old_crd_shape[1], 3, Rel.EQ, "old_crd_shape[1]", cls_name) - validator.check_int(box_length_inverse_shape[0], 3, Rel.EQ, "box_length_inverse_shape[0]", cls_name) - validator.check_int(box_map_times_shape[0], n, Rel.EQ, "box_map_times_shape[0]", cls_name) - validator.check_int(box_map_times_shape[1], 3, Rel.EQ, "box_map_times_shape[1]", cls_name) - return [1,] - - def infer_dtype(self, crd_dtype, old_crd_dtype, box_length_inverse_dtype, box_map_times_dtype): - validator.check_tensor_dtype_valid('crd', crd_dtype, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('old_crd', old_crd_dtype, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('box_length_inverse', box_length_inverse_dtype, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('box_map_times', box_map_times_dtype, - [mstype.int32], self.name) - return mstype.float32 - - -class Totalc6get(PrimitiveWithInfer): - """ - Get the average dispersion constant of short range Lennard-Jones interaction, - for the subsequent long range correction energy and virial. Assume system has m Lennard-Jones types of atoms. - - .. warning:: - This is an experimental prototype that is subject to change and/or deletion. - - Args: - atom_numbers (int32): the number of atoms n. - - Inputs: - - **atom_lj_type** (Tensor) - The Lennard-Jones type of each atom. - The data type is float32 and the shape is :math:`(n,)`. - - **lj_b** (Tensor) - The attraction coefficient of each type. the number of pair atoms is m. - The data type is float32 and the shape is :math:`(m,)`. - - Outputs: - - **factor** (Tensor) - The average dispersion constant of Lennard-Jones interaction. - The data type is float32 and the shape is :math:`(1,)`. - - Supported Platforms: - ``GPU`` - """ - - @prim_attr_register - def __init__(self, atom_numbers): - validator.check_value_type('atom_numbers', atom_numbers, int, self.name) - self.atom_numbers = atom_numbers - self.add_prim_attr('atom_numbers', self.atom_numbers) - self.init_prim_io_names( - inputs=['atom_lj_type', 'lj_b'], - outputs=['factor']) - - def infer_shape(self, atom_lj_type, lj_b): - cls_name = self.name - n = self.atom_numbers - validator.check_int(len(atom_lj_type), 1, Rel.EQ, "atom_lj_type_dim", cls_name) - validator.check_int(len(lj_b), 1, Rel.EQ, "LJ_b_dim", cls_name) - validator.check_int(atom_lj_type[0], n, Rel.EQ, "atom_lj_type_shape[0]", cls_name) - return [1,] - - def infer_dtype(self, atom_lj_type, lj_b): - validator.check_tensor_dtype_valid('atom_lj_type', atom_lj_type, mstype.int32, self.name) - validator.check_tensor_dtype_valid('lj_b', lj_b, mstype.float32, self.name) - return mstype.float32 - - -class CrdToUintCrdQuarter(PrimitiveWithInfer): - """ - Convert FP32 coordinate to Uint32 coordinate. - - .. warning:: - This is an experimental prototype that is subject to change and/or deletion. - - Args: - atom_numbers (int32): the number of atoms n. - - Inputs: - - **crd_to_uint_crd_cof** (Tensor) - The crd_to_uint_crd coefficient. - The data type is float32 and the shape is :math:`(3,)`. - - **crd** (Tensor) - The coordinate of each atom. - The data type is float32 and the shape is :math:`(n, 3)`. - - Outputs: - - **output** (Tensor) - The unsigned int coordinates. - The data type is unsigned int32 and the shape is :math:`(n, 3)`. - - Supported Platforms: - ``GPU`` - """ - - @prim_attr_register - def __init__(self, atom_numbers): - """Initialize CrdToUintCrdQuarter""" - validator.check_value_type('atom_numbers', atom_numbers, int, self.name) - self.atom_numbers = atom_numbers - self.add_prim_attr('atom_numbers', self.atom_numbers) - self.init_prim_io_names( - inputs=['crd_to_uint_crd_cof', 'crd'], - outputs=['output']) - - def infer_shape(self, crd_to_uint_crd_cof, crd): - cls_name = self.name - n = self.atom_numbers - validator.check_int(len(crd), 2, Rel.EQ, "crd_dim", cls_name) - validator.check_int(len(crd_to_uint_crd_cof), 1, Rel.EQ, "crd_to_uint_crd_cof_dim", cls_name) - validator.check_int(crd_to_uint_crd_cof[0], 3, Rel.EQ, "crd_to_uint_crd_cof_shape", self.name) - validator.check_int(crd[0], n, Rel.EQ, "crd[0]", self.name) - validator.check_int(crd[1], 3, Rel.EQ, "crd[1]", self.name) - return crd - - def infer_dtype(self, crd_to_uint_crd_cof, crd): - validator.check_tensor_dtype_valid('crd_to_uint_crd_cof', crd_to_uint_crd_cof, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('crd', crd, [mstype.float32], self.name) - return mstype.uint32 - - -class MDIterationLeapFrogLiujianWithMaxVel(PrimitiveWithInfer): - """ - One step of classical leap frog algorithm to solve the finite difference - Hamiltonian equations of motion for certain system, using Langevin dynamics - with Liu's thermostat scheme, but with an maximum velocity limit. Assume the - number of atoms is n and the target control temperature is T. - - Detailed iteration formula can be found in this paper: A unified thermostat - scheme for efficient configurational sampling for classical/quantum canonical - ensembles via molecular dynamics. DOI: 10.1063/1.4991621. - - .. warning:: - This is an experimental prototype that is subject to change and/or deletion. - - Args: - atom_numbers (int32): the number of atoms n. - dt (float32): time step for finite difference. - half_dt (float32): half of time step for finite difference. - exp_gamma (float32): parameter in Liu's dynamic, exp(-gamma_ln * dt). - max_vel (float32): the maximum velocity limit. - - Inputs: - - **inverse_mass** (Tensor) - The inverse value of mass of each atom. - The data type is float32 and the shape is :math:`(n,)`. - - **sqrt_mass_inverse** (Tensor) - The inverse sqrt of the mass in Liu's dynamics of each atom. - The data type is float32 and the shape is :math:`(n,)`. - - **vel** (Tensor) - The velocity of each atom. - The data type is float32 and the shape is :math:`(n, 3)`. - - **crd** (Tensor) - The coordinate of each atom. - The data type is float32 and the shape is :math:`(n, 3)`. - - **frc** (Tensor) - The force felt by each atom. - The data type is float32 and the shape is :math:`(n, 3)`. - - **acc** (Tensor) - The acceleration of each atom. - The data type is float32 and the shape is :math:`(n, 3)`. - - **rand_state** (Tensor) - Random state to generate random force. - The data type is float32 and the shape is :math:`(math.ceil(n * 3.0 / 4.0) * 16, )`. - - **rand_frc** (Tensor) - The random forces. - The data type is float32 and the shape is :math:`(n, 3)`. - - Outputs: - - **output** (float32) - The output coordinate of each atom. - The data type is float32 and the shape is :math:`(n, 3)`. - - Supported Platforms: - ``GPU`` - """ - - @prim_attr_register - def __init__(self, atom_numbers, half_dt, dt, exp_gamma, max_vel): - """Initialize MDIterationLeapFrogLiujian""" - validator.check_value_type('atom_numbers', atom_numbers, int, self.name) - validator.check_value_type('half_dt', half_dt, float, self.name) - validator.check_value_type('dt', dt, float, self.name) - validator.check_value_type('exp_gamma', exp_gamma, float, self.name) - validator.check_value_type('max_vel', max_vel, float, self.name) - self.atom_numbers = atom_numbers - self.half_dt = half_dt - self.dt = dt - self.exp_gamma = exp_gamma - self.max_vel = max_vel - - self.add_prim_attr('atom_numbers', self.atom_numbers) - self.add_prim_attr('half_dt', self.half_dt) - self.add_prim_attr('dt', self.dt) - self.add_prim_attr('exp_gamma', self.exp_gamma) - self.add_prim_attr('max_vel', self.max_vel) - self.init_prim_io_names( - inputs=['inverse_mass', 'sqrt_mass_inverse', 'vel', 'crd', 'frc', 'acc', 'rand_state', 'rand_frc'], - outputs=['output']) - self.add_prim_attr('side_effect_mem', True) - - def infer_shape(self, inverse_mass, sqrt_mass_inverse, vel, crd, frc, acc, rand_state, rand_frc): - n = self.atom_numbers - validator.check_int(len(inverse_mass), 1, Rel.EQ, "inverse_mass_dim", self.name) - validator.check_int(len(sqrt_mass_inverse), 1, Rel.EQ, "sqrt_mass_inverse_dim", self.name) - validator.check_int(len(rand_state), 1, Rel.EQ, "rand_state_dim", self.name) - validator.check_int(len(rand_frc), 2, Rel.EQ, "rand_frc_dim", self.name) - validator.check_int(len(vel), 2, Rel.EQ, "vel_dim", self.name) - validator.check_int(len(crd), 2, Rel.EQ, "crd_dim", self.name) - validator.check_int(len(frc), 2, Rel.EQ, "frc_dim", self.name) - validator.check_int(len(acc), 2, Rel.EQ, "acc_dim", self.name) - validator.check_int(inverse_mass[0], n, Rel.EQ, "inverse_mass", self.name) - validator.check_int(sqrt_mass_inverse[0], n, Rel.EQ, "sqrt_mass_inverse", self.name) - validator.check_int(vel[0], n, Rel.EQ, "vel_shape[0]", self.name) - validator.check_int(vel[1], 3, Rel.EQ, "vel_shape[1]", self.name) - validator.check_int(crd[0], n, Rel.EQ, "crd_shape[0]", self.name) - validator.check_int(crd[1], 3, Rel.EQ, "crd_shape[1]", self.name) - validator.check_int(frc[0], n, Rel.EQ, "frc_shape[0]", self.name) - validator.check_int(frc[1], 3, Rel.EQ, "frc_shape[1]", self.name) - validator.check_int(acc[0], n, Rel.EQ, "acc_shape[0]", self.name) - validator.check_int(acc[1], 3, Rel.EQ, "acc_shape[1]", self.name) - validator.check_int(rand_frc[0], n, Rel.EQ, "rand_frc_shape[0]", self.name) - validator.check_int(rand_frc[1], 3, Rel.EQ, "rand_frc_shape[1]", self.name) - validator.check_int(rand_state[0], math.ceil(self.atom_numbers * 3 / 4.0) * 16, Rel.EQ, "rand_state", self.name) - return [self.atom_numbers, 3] - - def infer_dtype(self, inverse_mass, sqrt_mass_inverse, vel, crd, frc, acc, rand_state, rand_frc): - validator.check_tensor_dtype_valid('inverse_mass', inverse_mass, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('sqrt_mass_inverse', sqrt_mass_inverse, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('vel', vel, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('crd', crd, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('frc', frc, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('acc', acc, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('rand_frc', rand_frc, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('rand_state', rand_state, [mstype.float32], self.name) - return mstype.float32 - - -class GetCenterOfMass(PrimitiveWithInfer): - """ - Get coordinate of centroid of each residue. Assume system has n atoms. - - .. warning:: - This is an experimental prototype that is subject to change and/or deletion. - - Args: - residue_numbers (int32): the number of residues m. - - Inputs: - - **start** (Tensor) - The start atom index of each residue. - The data type is int32 and the shape is :math:`(m,)`. - - **end** (Tensor) - The end atom index of each residue. - The data type is int32 and the shape is :math:`(m,)`. - - **crd** (Tensor) - The coordinate of each atom. - The data type is float32 and the shape is :math:`(n, 3)`. - - **atom_mass** (Tensor) - The mass of each atom and the atom number is n. - The data type is float32 and the shape is :math:`(n,)`. - - **residue_mass_inverse** (Tensor) - The inverse of mass of each residue. - The data type is float32 and the shape is :math:`(m,)`. - - Outputs: - - **center_of_mass** (Tensor) - The coordinate of centroid of each residue. - The data type is float32 and the shape is :math:`(m, 3)`. - - Supported Platforms: - ``GPU`` - """ - - @prim_attr_register - def __init__(self, residue_numbers): - """Initialize GetCenterOfMass""" - validator.check_value_type('residue_numbers', residue_numbers, int, self.name) - self.residue_numbers = residue_numbers - self.add_prim_attr('residue_numbers', self.residue_numbers) - self.init_prim_io_names( - inputs=['start', 'end', 'crd', 'atom_mass', 'residue_mass_inverse'], - outputs=['center_of_mass']) - - def infer_shape(self, start, end, crd, atom_mass, residue_mass_inverse): - n = crd[0] - m = self.residue_numbers - validator.check_int(len(start), 1, Rel.EQ, "start_dim", self.name) - validator.check_int(len(end), 1, Rel.EQ, "end_dim", self.name) - validator.check_int(len(crd), 2, Rel.EQ, "crd_dim", self.name) - validator.check_int(len(atom_mass), 1, Rel.EQ, "atom_mass_dim", self.name) - validator.check_int(len(residue_mass_inverse), 1, Rel.EQ, "residue_mass_inverse_dim", self.name) - validator.check_int(start[0], m, Rel.EQ, "start_shape", self.name) - validator.check_int(end[0], m, Rel.EQ, "end_shape", self.name) - validator.check_int(crd[0], n, Rel.EQ, "crd_shape[0]", self.name) - validator.check_int(crd[1], 3, Rel.EQ, "crd_shape[1]", self.name) - validator.check_int(atom_mass[0], n, Rel.EQ, "atom_mass_shape[0]", self.name) - validator.check_int(residue_mass_inverse[0], m, Rel.EQ, "residue_mass_inverse_shape", self.name) - return [m, 3] - - def infer_dtype(self, start, end, crd, atom_mass, residue_mass_inverse): - validator.check_tensor_dtype_valid('start', start, [mstype.int32], self.name) - validator.check_tensor_dtype_valid('end', end, [mstype.int32], self.name) - validator.check_tensor_dtype_valid('crd', crd, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('atom_mass', atom_mass, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('residue_mass_inverse', residue_mass_inverse, [mstype.float32], self.name) - return mstype.float32 - - -class MapCenterOfMass(PrimitiveWithInfer): - """ - Map all atoms in the same residue to the same periodic box, scale if necessary (usually in pressurestat). - Assume system has n atoms. - - .. warning:: - This is an experimental prototype that is subject to change and/or deletion. - - Args: - residue_numbers (int32): the number of residues m. - - Inputs: - - **start** (Tensor) - The start atom index of each residue. - The data type is int32 and the shape is :math:`(m,)`. - - **end** (Tensor) - The end atom index of each residue. - The data type is int32 and the shape is :math:`(m,)`. - - **center_of_mass** (Tensor) - The coordinate of centroid of each residue. - The data type is float32 and the shape is :math:`(m, 3)`. - - **box_length** (Tensor) - The box length of the simulation box. - The data type is float32 and the shape is :math:`(3,)`. - - **no_wrap_crd** (Tensor) - The coordinate of each atom before wrap. - The data type is float32 and the shape is :math:`(n, 3)`. - - **crd** (Tensor) - The coordinate of each atom after wrap. - The data type is float32 and the shape is :math:`(n, 3)`. - - **scaler** (Tensor) - The scaler of system. - The data type is float32 and the shape is :math:`(1,)`. - - Outputs: - - **res** (Tensor) - The return value after updating successfully. - The data type is float32 and the shape is :math:`(1,)`. - - Supported Platforms: - ``GPU`` - """ - - @prim_attr_register - def __init__(self, residue_numbers): - """Initialize MapCenterOfMass""" - validator.check_value_type('residue_numbers', residue_numbers, int, self.name) - self.residue_numbers = residue_numbers - self.add_prim_attr('residue_numbers', self.residue_numbers) - self.init_prim_io_names( - inputs=['start', 'end', 'center_of_mass', 'box_length', - 'no_wrap_crd', 'crd', 'scaler'], - outputs=['res']) - - def infer_shape(self, start, end, center_of_mass, box_length, no_wrap_crd, crd, scaler): - m = self.residue_numbers - n = crd[0] - validator.check_int(len(start), 1, Rel.EQ, "start_dim", self.name) - validator.check_int(len(end), 1, Rel.EQ, "end_dim", self.name) - validator.check_int(len(center_of_mass), 2, Rel.EQ, "center_of_mass_dim", self.name) - validator.check_int(len(box_length), 1, Rel.EQ, "box_length_dim", self.name) - validator.check_int(len(no_wrap_crd), 2, Rel.EQ, "no_wrap_crd_dim", self.name) - validator.check_int(len(crd), 2, Rel.EQ, "crd_dim", self.name) - validator.check_int(len(scaler), 1, Rel.EQ, "scaler_dim", self.name) - - validator.check_int(start[0], m, Rel.EQ, "start_shape", self.name) - validator.check_int(end[0], m, Rel.EQ, "end_shape", self.name) - validator.check_int(center_of_mass[0], m, Rel.EQ, "center_of_mass_shape[0]", self.name) - validator.check_int(center_of_mass[1], 3, Rel.EQ, "center_of_mass_shape[1]", self.name) - validator.check_int(box_length[0], 3, Rel.EQ, "box_length_shape", self.name) - validator.check_int(scaler[0], 1, Rel.EQ, "scaler_shape", self.name) - validator.check_int(no_wrap_crd[0], n, Rel.EQ, "no_wrap_crd_shape[0]", self.name) - validator.check_int(no_wrap_crd[1], 3, Rel.EQ, "no_wrap_crd_shape[1]", self.name) - validator.check_int(crd[0], n, Rel.EQ, "crd_shape[0]", self.name) - validator.check_int(crd[1], 3, Rel.EQ, "crd_shape[1]", self.name) - return [1,] - - def infer_dtype(self, start, end, center_of_mass, box_length, no_wrap_crd, crd, scaler): - validator.check_tensor_dtype_valid('start', start, [mstype.int32], self.name) - validator.check_tensor_dtype_valid('end', end, [mstype.int32], self.name) - validator.check_tensor_dtype_valid('crd', crd, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('center_of_mass', center_of_mass, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('box_length', box_length, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('no_wrap_crd', no_wrap_crd, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('scaler', scaler, [mstype.float32], self.name) - return mstype.float32 - - -class NeighborListRefresh(PrimitiveWithInfer): - """ - Update (or construct if first time) the Verlet neighbor list for the - calculation of short-ranged force. - - .. warning:: - This is an experimental prototype that is subject to change and/or deletion. - - Args: - grid_numbers (int32): the total number of grids divided G. - atom_numbers (int32): the number of atoms n. - not_first_time (int32): whether to construct the neighbor list first time or not. - nxy (int32): the total number of grids divided in xy plane. - excluded_atom_numbers (int32): the total atom numbers in the excluded list E. - cutoff_square (float32): the cutoff square distance for short-range force calculation. - half_skin_square (float32): the maximum square value of the distance atom allowed to move between two updates. - cutoff_with_skin (float32): cutoff + skin, indicates the radius of the neighbor list for each atom. - half_cutoff_with_skin (float32): cutoff_with_skin/2. - cutoff_with_skin_square (float32): the square value of cutoff_with_skin. - refresh_interval (int32): the number of iteration steps between two updates of neighbor list. Default: 20. - cutoff (float32): the cutoff distance for short-range force calculation. Default: 10.0. - skin (float32): the maximum value of the distance atom allowed to move. Default: 2.0. - max_atom_in_grid_numbers (int32): the maximum number of atoms in one grid k. Default: 64. - max_neighbor_numbers (int32): The maximum number of neighbors m. Default: 800. - forced_update (int32): the flag that decides whether to force an update. Default: 0. - forced_check (int32): the flag that decides whether to force an check. Default: 0. - - Inputs: - - **atom_numbers_in_grid_bucket** (Tensor) - The number of atoms in each grid bucket. - The data type is int32 and the shape is :math:`(G,)`. - - **bucket** (Tensor) - (Tensor) - The atom indices in each grid bucket. - The data type is int32 and the shape is :math:`(G, k)`. - - **crd** (Tensor) - The coordinates of each atom. - The data type is float32 and the shape is :math:`(n, 3)`. - - **box_length** (Tensor) - The box length of the simulation box. - The data type is float32 and the shape is :math:`(3,)`. - - **grid_n** (Tensor) - The number of grids divided of 3 dimensions of the simulation box. - The data type is int32 and the shape is :math:`(3,)`. - - **grid_length_inverse** (Tensor) - The inverse value of grid length. - The data type is float32 and the shape is :math:`(3,)`. - - **atom_in_grid_serial** (Tensor) - The grid index for each atom. - The data type is int32 and the shape is :math:`(n,)`. - - **old_crd** (Tensor) - The coordinates before update of each atom. - The data type is float32 and the shape is :math:`(n, 3)`. - - **crd_to_uint_crd_cof** (Tensor) - The scale factor between the unsigned int coordinate and the real one. - The data type is float32 and the shape is :math:`(3,)`. - - **uint_crd** (Tensor) - The unsigned int coordinates value fo each atom. - The data type is unsigned int32 and the shape is :math:`(n, 3)`. - - **gpointer** (Tensor) - The nearest neighbor grids (including self) of each grid. - The data type is int32 and the shape is :math:`(G, 125)`. - - **nl_atom_numbers** (Tensor) - The number of atoms in neighbor list of each atom. - The data type is int32 and the shape is :math:`(n,)`. - - **nl_atom_serial** (Tensor) - The indices of atoms in neighbor list of each atom. - The data type is int32 and the shape is :math:`(n, m)`. - - **uint_dr_to_dr_cof** (Tensor) - The scale factor. - The data type is float32 and the shape is :math:`(3,)`. - - **excluded_list_start** (Tensor) - The start excluded index in excluded list for each atom. - The data type is int32 and the shape is :math:`(n,)`. - - **excluded_list** (Tensor) - The contiguous join of excluded list of each atom. - The data type is int32 and the shape is :math:`(E,)`. - - **excluded_numbers** (Tensor) - The number of atom excluded in excluded list for each atom. - The data type is int32 and the shape is :math:`(n,)`. - - **need_refresh_flag** (Tensor) - Whether the neighbor list of each atom need update or not. - The data type is int32 and the shape is :math:`(1,)`. - - **refresh_count** (Union[Tensor, Scalar]) - Count how many iteration steps have passed since last update. - The data type is int32 and the shape is :math:`(1,)` or :math:`()`. - - Outputs: - - **res** (Tensor) - The return value after updating successfully. - The data type is float32 and the shape is :math:`(1,)`. - - Supported Platforms: - ``GPU`` - """ - - @prim_attr_register - def __init__(self, grid_numbers, atom_numbers, not_first_time, nxy, excluded_atom_numbers, - cutoff_square, half_skin_square, cutoff_with_skin, half_cutoff_with_skin, cutoff_with_skin_square, - refresh_interval=20, cutoff=10.0, skin=2.0, max_atom_in_grid_numbers=64, max_neighbor_numbers=800, - forced_update=0, forced_check=0): - """Initialize NeighborListRefresh""" - self.grid_numbers = grid_numbers - self.atom_numbers = atom_numbers - self.refresh_interval = refresh_interval - self.not_first_time = not_first_time - self.cutoff = cutoff - self.skin = skin - self.max_atom_in_grid_numbers = max_atom_in_grid_numbers - self.nxy = nxy - self.excluded_atom_numbers = excluded_atom_numbers - self.cutoff_square = cutoff_square - self.half_skin_square = half_skin_square - self.cutoff_with_skin = cutoff_with_skin - self.half_cutoff_with_skin = half_cutoff_with_skin - self.cutoff_with_skin_square = cutoff_with_skin_square - self.max_neighbor_numbers = max_neighbor_numbers - self.forced_update = forced_update - self.forced_check = forced_check - validator.check_value_type('grid_numbers', grid_numbers, int, self.name) - validator.check_value_type('atom_numbers', atom_numbers, int, self.name) - validator.check_value_type('refresh_interval', refresh_interval, int, self.name) - validator.check_value_type('not_first_time', not_first_time, int, self.name) - validator.check_value_type('cutoff', cutoff, float, self.name) - validator.check_value_type('skin', skin, float, self.name) - validator.check_value_type('max_atom_in_grid_numbers', max_atom_in_grid_numbers, int, self.name) - validator.check_value_type('nxy', nxy, int, self.name) - validator.check_value_type('excluded_atom_numbers', excluded_atom_numbers, int, self.name) - validator.check_value_type('cutoff_square', cutoff_square, float, self.name) - validator.check_value_type('half_skin_square', half_skin_square, float, self.name) - validator.check_value_type('cutoff_with_skin', cutoff_with_skin, float, self.name) - validator.check_value_type('half_cutoff_with_skin', half_cutoff_with_skin, float, self.name) - validator.check_value_type('cutoff_with_skin_square', cutoff_with_skin_square, float, self.name) - validator.check_value_type('max_neighbor_numbers', max_neighbor_numbers, int, self.name) - validator.check_value_type('forced_update', forced_update, int, self.name) - validator.check_value_type('forced_check', forced_check, int, self.name) - self.init_prim_io_names( - inputs=['atom_numbers_in_grid_bucket', 'bucket', 'crd', 'box_length', 'grid_n', 'grid_length_inverse', - 'atom_in_grid_serial', 'old_crd', 'crd_to_uint_crd_cof', 'uint_crd', 'gpointer', 'nl_atom_numbers', - 'nl_atom_serial', 'uint_dr_to_dr_cof', 'excluded_list_start', 'excluded_list', 'excluded_numbers', - 'need_refresh_flag', 'refresh_count'], outputs=['res']) - - self.add_prim_attr('grid_numbers', self.grid_numbers) - self.add_prim_attr('atom_numbers', self.atom_numbers) - self.add_prim_attr('refresh_interval', self.refresh_interval) - self.add_prim_attr('not_first_time', self.not_first_time) - self.add_prim_attr('cutoff', self.cutoff) - self.add_prim_attr('skin', self.skin) - self.add_prim_attr('max_atom_in_grid_numbers', self.max_atom_in_grid_numbers) - self.add_prim_attr('nxy', self.nxy) - self.add_prim_attr('excluded_atom_numbers', self.excluded_atom_numbers) - self.add_prim_attr('cutoff_square', self.cutoff_square) - self.add_prim_attr('half_skin_square', self.half_skin_square) - self.add_prim_attr('cutoff_with_skin', self.cutoff_with_skin) - self.add_prim_attr('half_cutoff_with_skin', self.half_cutoff_with_skin) - self.add_prim_attr('cutoff_with_skin_square', self.cutoff_with_skin_square) - self.add_prim_attr('forced_update', self.forced_update) - self.add_prim_attr('forced_check', self.forced_check) - self.add_prim_attr('side_effect_mem', True) - - def infer_shape(self, atom_numbers_in_grid_bucket_shape, bucket_shape, crd_shape, box_length_shape, grid_n_shape, - grid_length_inverse_shape, atom_in_grid_serial_shape, old_crd_shape, crd_to_uint_crd_cof_shape, - uint_crd_shape, gpointer_shape, nl_atom_numbers_shape, nl_atom_serial_shape, - uint_dr_to_dr_cof_shape, excluded_list_start_shape, excluded_list_shape, excluded_numbers_shape, - need_refresh_flag_shape, refresh_count_shape): - validator.check_int(len(atom_numbers_in_grid_bucket_shape), 1, Rel.EQ, - "atom_numbers_in_grid_bucket_dim", self.name) - validator.check_int(len(bucket_shape), 2, Rel.EQ, "bucket_dim", self.name) - validator.check_int(len(crd_shape), 2, Rel.EQ, "crd_dim", self.name) - validator.check_int(len(box_length_shape), 1, Rel.EQ, "box_length_dim", self.name) - validator.check_int(len(grid_n_shape), 1, Rel.EQ, "grid_n_dim", self.name) - validator.check_int(len(grid_length_inverse_shape), 1, Rel.EQ, "grid_length_inverse_dim", self.name) - validator.check_int(len(atom_in_grid_serial_shape), 1, Rel.EQ, "atom_in_grid_serial_dim", self.name) - validator.check_int(len(old_crd_shape), 2, Rel.EQ, "old_crd_dim", self.name) - validator.check_int(len(crd_to_uint_crd_cof_shape), 1, Rel.EQ, "crd_to_uint_crd_cof_dim", self.name) - validator.check_int(len(uint_crd_shape), 2, Rel.EQ, "uint_crd_dim", self.name) - validator.check_int(len(gpointer_shape), 2, Rel.EQ, "gpointer_dim", self.name) - validator.check_int(len(nl_atom_numbers_shape), 1, Rel.EQ, "nl_atom_numbers_dim", self.name) - validator.check_int(len(nl_atom_serial_shape), 2, Rel.EQ, "nl_atom_serial_dim", self.name) - validator.check_int(len(uint_dr_to_dr_cof_shape), 1, Rel.EQ, "uint_dr_to_dr_cof_dim", self.name) - validator.check_int(len(excluded_list_start_shape), 1, Rel.EQ, "excluded_list_start_dim", self.name) - validator.check_int(len(excluded_list_shape), 1, Rel.EQ, "excluded_list_dim", self.name) - validator.check_int(len(excluded_numbers_shape), 1, Rel.EQ, "excluded_numbers_dim", self.name) - validator.check_int(len(need_refresh_flag_shape), 1, Rel.EQ, "need_refresh_flag_dim", self.name) - validator.check_int(len(refresh_count_shape), 1, Rel.LE, "refresh_count_dim", self.name) - validator.check_int(atom_numbers_in_grid_bucket_shape[0], self.grid_numbers, Rel.EQ, - "atom_numbers_in_grid_bucket", self.name) - validator.check_int(bucket_shape[0], self.grid_numbers, Rel.EQ, "bucket", self.name) - validator.check_int(bucket_shape[1], self.max_atom_in_grid_numbers, Rel.EQ, "bucket", self.name) - validator.check_int(crd_shape[0], self.atom_numbers, Rel.EQ, "crd", self.name) - validator.check_int(crd_shape[1], 3, Rel.EQ, "crd", self.name) - validator.check_int(box_length_shape[0], 3, Rel.EQ, "box_length", self.name) - validator.check_int(grid_n_shape[0], 3, Rel.EQ, "grid_n", self.name) - validator.check_int(grid_length_inverse_shape[0], 3, Rel.EQ, "grid_length_inverse", self.name) - validator.check_int(atom_in_grid_serial_shape[0], self.atom_numbers, Rel.EQ, "atom_in_grid_serial", - self.name) - validator.check_int(old_crd_shape[0], self.atom_numbers, Rel.EQ, "old_crd", self.name) - validator.check_int(old_crd_shape[1], 3, Rel.EQ, "old_crd", self.name) - validator.check_int(crd_to_uint_crd_cof_shape[0], 3, Rel.EQ, "crd_to_uint_crd_cof", self.name) - validator.check_int(uint_crd_shape[0], self.atom_numbers, Rel.EQ, "uint_crd", self.name) - validator.check_int(uint_crd_shape[1], 3, Rel.EQ, "uint_crd", self.name) - validator.check_int(gpointer_shape[0], self.grid_numbers, Rel.EQ, "gpointer", self.name) - validator.check_int(gpointer_shape[1], 125, Rel.EQ, "gpointer", self.name) - validator.check_int(nl_atom_numbers_shape[0], self.atom_numbers, Rel.EQ, "nl_atom_numbers", self.name) - validator.check_int(nl_atom_serial_shape[0], self.atom_numbers, Rel.EQ, "nl_atom_serial", self.name) - validator.check_int(nl_atom_serial_shape[1], self.max_neighbor_numbers, Rel.EQ, "nl_atom_serial", - self.name) - validator.check_int(uint_dr_to_dr_cof_shape[0], 3, Rel.EQ, "uint_dr_to_dr_cof", self.name) - validator.check_int(excluded_list_start_shape[0], self.atom_numbers, Rel.EQ, "excluded_list_start", - self.name) - validator.check_int(excluded_list_shape[0], self.excluded_atom_numbers, Rel.EQ, "excluded_list", - self.name) - validator.check_int(excluded_numbers_shape[0], self.atom_numbers, Rel.EQ, "excluded_numbers", self.name) - validator.check_int(need_refresh_flag_shape[0], 1, Rel.EQ, "need_refresh_flag", self.name) - if refresh_count_shape: - validator.check_int(refresh_count_shape[0], 1, Rel.EQ, "refresh_count_shape", self.name) - return [1,] - - def infer_dtype(self, atom_numbers_in_grid_bucket_dtype, bucket_dtype, crd_dtype, box_length_dtype, grid_n_dtype, - grid_length_inverse_dtype, atom_in_grid_serial_dtype, old_crd_dtype, crd_to_uint_crd_cof_dtype, - uint_crd_dtype, gpointer_dtype, nl_atom_numbers_dtype, nl_atom_serial_dtype, - uint_dr_to_dr_cof_dtype, excluded_list_start_dtype, excluded_list_dtype, excluded_numbers_dtype, - need_refresh_flag_dtype, refresh_count_dtype): - validator.check_tensor_dtype_valid('atom_numbers_in_grid_bucket', atom_numbers_in_grid_bucket_dtype, - [mstype.int32], self.name) - validator.check_tensor_dtype_valid('bucket', bucket_dtype, [mstype.int32], self.name) - validator.check_tensor_dtype_valid('crd', crd_dtype, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('box_length', box_length_dtype, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('grid_n', grid_n_dtype, [mstype.int32], self.name) - validator.check_tensor_dtype_valid('grid_length_inverse', grid_length_inverse_dtype, [mstype.float32], - self.name) - validator.check_tensor_dtype_valid('atom_in_grid_serial', atom_in_grid_serial_dtype, [mstype.int32], - self.name) - validator.check_tensor_dtype_valid('old_crd', old_crd_dtype, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('crd_to_uint_crd_cof', crd_to_uint_crd_cof_dtype, [mstype.float32], - self.name) - validator.check_tensor_dtype_valid('uint_crd', uint_crd_dtype, [mstype.uint32], self.name) - validator.check_tensor_dtype_valid('gpointer', gpointer_dtype, [mstype.int32], self.name) - validator.check_tensor_dtype_valid('nl_atom_numbers', nl_atom_numbers_dtype, [mstype.int32], self.name) - validator.check_tensor_dtype_valid('nl_atom_serial', nl_atom_serial_dtype, [mstype.int32], self.name) - validator.check_tensor_dtype_valid('uint_dr_to_dr_cof', uint_dr_to_dr_cof_dtype, [mstype.float32], - self.name) - validator.check_tensor_dtype_valid('excluded_list_start', excluded_list_start_dtype, [mstype.int32], - self.name) - validator.check_tensor_dtype_valid('excluded_list', excluded_list_dtype, [mstype.int32], self.name) - validator.check_tensor_dtype_valid('excluded_numbers', excluded_numbers_dtype, [mstype.int32], self.name) - validator.check_tensor_dtype_valid('need_refresh_flag', need_refresh_flag_dtype, [mstype.int32], - self.name) - validator.check_tensor_dtype_valid('refresh_count', refresh_count_dtype, [mstype.int32], - self.name) - - return mstype.float32 - - -class MDIterationLeapFrog(PrimitiveWithInfer): - """ - One step of classical leap frog algorithm to solve the finite difference - Hamiltonian equations of motion for certain system. - - .. warning:: - This is an experimental prototype that is subject to change and/or deletion. - - Args: - atom_numbers (int32): the number of atoms n. - dt (float32): the simulation time step. - - Inputs: - - **sqrt_mass_inverse** (Tensor) - The square root of the inverse value of the mass of each atom. - The data type is float32 and the shape is :math:`(n,)`. - - **vel** (Tensor) - The velocity of each atom. - The data type is float32 and the shape is :math:`(n, 3)`. - - **crd** (Tensor) - The coordinate of each atom. - The data type is float32 and the shape is :math:`(n, 3)`. - - **frc** (Tensor) - The force felt by each atom. - The data type is float32 and the shape is :math:`(n, 3)`. - - **acc** (Tensor) - The acceleration of each atom. - The data type is float32 and the shape is :math:`(n, 3)`. - - **inverse_mass** (Tensor) - The inverse value of mass of each atom. - The data type is float32 and the shape is :math:`(n,)`. - - Outputs: - - **res** (Tensor) - The return value after updating successfully. - The data type is float32 and the shape is :math:`(1,)`. - - Supported Platforms: - ``GPU`` - """ - - @prim_attr_register - def __init__(self, atom_numbers, dt): - """Initialize MDIterationLeapFrog""" - validator.check_value_type('atom_numbers', atom_numbers, int, self.name) - validator.check_value_type('dt', dt, float, self.name) - self.atom_numbers = atom_numbers - self.dt = dt - self.add_prim_attr('atom_numbers', self.atom_numbers) - self.add_prim_attr('dt', self.dt) - self.init_prim_io_names( - inputs=['sqrt_mass_inverse', 'vel', 'crd', 'frc', 'acc', 'inverse_mass'], - outputs=['res']) - self.add_prim_attr('side_effect_mem', True) - - def infer_shape(self, vel, crd, frc, acc, inverse_mass): - n = self.atom_numbers - validator.check_int(len(vel), 2, Rel.EQ, "vel_dim", self.name) - validator.check_int(len(crd), 2, Rel.EQ, "crd_dim", self.name) - validator.check_int(len(frc), 2, Rel.EQ, "frc_dim", self.name) - validator.check_int(len(acc), 2, Rel.EQ, "acc_dim", self.name) - validator.check_int(len(inverse_mass), 1, Rel.EQ, "inverse_mass_dim", self.name) - validator.check_int(vel[0], n, Rel.EQ, "vel_shape[0]", self.name) - validator.check_int(vel[1], 3, Rel.EQ, "vel_shape[1]", self.name) - validator.check_int(crd[0], n, Rel.EQ, "crd_shape[0]", self.name) - validator.check_int(crd[1], 3, Rel.EQ, "crd_shape[1]", self.name) - validator.check_int(frc[0], n, Rel.EQ, "frc_shape[0]", self.name) - validator.check_int(frc[1], 3, Rel.EQ, "frc_shape[1]", self.name) - validator.check_int(acc[0], n, Rel.EQ, "acc_shape[0]", self.name) - validator.check_int(acc[1], 3, Rel.EQ, "acc_shape[1]", self.name) - validator.check_int(inverse_mass[0], n, Rel.EQ, "inverse_mass_shape", self.name) - return [1,] - - def infer_dtype(self, vel, crd, frc, acc, inverse_mass): - validator.check_tensor_dtype_valid('inverse_mass', inverse_mass, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('vel', vel, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('crd', crd, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('frc', frc, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('acc', acc, [mstype.float32], self.name) - return mstype.float32 - - -class MDIterationLeapFrogWithMaxVel(PrimitiveWithInfer): - """ - Leap frog algorithm to solve the Hamiltonian equations of motion with a maximum velocity limit. - - .. warning:: - This is an experimental prototype that is subject to change and/or deletion. - - Args: - atom_numbers (int32): the number of atoms n. - dt (float32): the simulation time step. - max_velocity (float32): the maximum velocity limit. - - Inputs: - - **vel** (Tensor) - The velocity of each atom. - The data type is float32 and the shape is :math:`(n, 3)`. - - **crd** (Tensor) - The coordinate of each atom. - The data type is float32 and the shape is :math:`(n, 3)`. - - **frc** (Tensor) - The force felt by each atom. - The data type is float32 and the shape is :math:`(n, 3)`. - - **acc** (Tensor) - The acceleration of each atom. - The data type is float32 and the shape is :math:`(n, 3)`. - - **inverse_mass** (Tensor) - The inverse value of mass of each atom. - The data type is float32 and the shape is :math:`(n,)`. - - Outputs: - - **res** (Tensor) - The return value after updating successfully. - The data type is float32 and the shape is :math:`(1,)`. - - - Supported Platforms: - ``GPU`` - """ - - @prim_attr_register - def __init__(self, atom_numbers, dt, max_velocity): - """Initialize MDIterationLeapFrogWithMaxVel""" - validator.check_value_type('atom_numbers', atom_numbers, int, self.name) - validator.check_value_type('dt', dt, float, self.name) - validator.check_value_type('max_velocity', max_velocity, float, self.name) - self.atom_numbers = atom_numbers - self.dt = dt - self.max_velocity = max_velocity - - self.add_prim_attr('atom_numbers', self.atom_numbers) - self.add_prim_attr('dt', self.dt) - self.add_prim_attr('max_velocity', self.max_velocity) - self.init_prim_io_names( - inputs=['vel', 'crd', 'frc', 'acc', 'inverse_mass'], - outputs=['res']) - self.add_prim_attr('side_effect_mem', True) - - def infer_shape(self, vel, crd, frc, acc, inverse_mass): - n = self.atom_numbers - validator.check_int(len(vel), 2, Rel.EQ, "vel_dim", self.name) - validator.check_int(len(crd), 2, Rel.EQ, "crd_dim", self.name) - validator.check_int(len(frc), 2, Rel.EQ, "frc_dim", self.name) - validator.check_int(len(acc), 2, Rel.EQ, "acc_dim", self.name) - validator.check_int(len(inverse_mass), 1, Rel.EQ, "inverse_mass_dim", self.name) - validator.check_int(inverse_mass[0], n, Rel.EQ, "inverse_mass_shape", self.name) - validator.check_int(vel[0], n, Rel.EQ, "vel_shape[0]", self.name) - validator.check_int(vel[1], 3, Rel.EQ, "vel_shape[1]", self.name) - validator.check_int(crd[0], n, Rel.EQ, "crd_shape[0]", self.name) - validator.check_int(crd[1], 3, Rel.EQ, "crd_shape[1]", self.name) - validator.check_int(frc[0], n, Rel.EQ, "frc_shape[0]", self.name) - validator.check_int(frc[1], 3, Rel.EQ, "frc_shape[1]", self.name) - validator.check_int(acc[0], n, Rel.EQ, "acc_shape[0]", self.name) - validator.check_int(acc[1], 3, Rel.EQ, "acc_shape[1]", self.name) - return [1,] - - def infer_dtype(self, vel, crd, frc, acc, inverse_mass): - validator.check_tensor_dtype_valid('inverse_mass', inverse_mass, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('vel', vel, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('crd', crd, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('frc', frc, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('acc', acc, [mstype.float32], self.name) - return mstype.float32 - - -class MDIterationGradientDescent(PrimitiveWithInfer): - """ - Update the coordinate of each atom in the direction of potential for energy minimization. - - .. warning:: - This is an experimental prototype that is subject to change and/or deletion. - - Args: - atom_numbers (int32): the number of atoms n. - learning_rate (float32): the update step length. - - Inputs: - - **crd** (Tensor) - The coordinate of each atom. - The data type is float32 and the shape is :math:`(n, 3)`. - - **frc** (Tensor), The force felt by each atom. - The data type is float32 and the shape is :math:`(n, 3)`. - - Output: - - **res** (Tensor) - The return value after updating successfully. - The data type is float32 and the shape is :math:`(1,)`. - - Supported Platforms: - ``GPU`` - """ - - @prim_attr_register - def __init__(self, atom_numbers, learning_rate): - """Initialize MDIterationGradientDescent""" - validator.check_value_type('atom_numbers', atom_numbers, int, self.name) - validator.check_value_type('learning_rate', learning_rate, float, self.name) - self.atom_numbers = atom_numbers - self.learning_rate = learning_rate - self.add_prim_attr('atom_numbers', self.atom_numbers) - self.add_prim_attr('learning_rate', self.learning_rate) - self.init_prim_io_names( - inputs=['crd', 'frc'], - outputs=['res']) - self.add_prim_attr('side_effect_mem', True) - - def infer_shape(self, crd, frc): - n = self.atom_numbers - validator.check_int(len(crd), 2, Rel.EQ, "crd_dim", self.name) - validator.check_int(len(frc), 2, Rel.EQ, "frc_dim", self.name) - validator.check_int(crd[0], n, Rel.EQ, "crd_shape[0]", self.name) - validator.check_int(crd[1], 3, Rel.EQ, "crd_shape[1]", self.name) - validator.check_int(frc[0], n, Rel.EQ, "frc_shape[0]", self.name) - validator.check_int(frc[1], 3, Rel.EQ, "frc_shape[1]", self.name) - return [1,] - - def infer_dtype(self, crd, frc): - validator.check_tensor_dtype_valid('crd', crd, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('frc', frc, [mstype.float32], self.name) - return mstype.float32 - - -class BondForceWithAtomEnergyAndVirial(PrimitiveWithInfer): - """ - Calculate bond force, harmonic potential energy and atom virial together. - - The calculation formula is the same as operator BondForce() and BondEnergy(). - - Because there is a large amount of inputs and each of them are related, - there is no way to construct `Examples` using random methods. For details, refer the webpage `SPONGE in MindSpore - `_. - - .. warning:: - This is an experimental prototype that is subject to change and/or deletion. - - Args: - atom_numbers (int32): the number of atoms n. - bond_numbers (int32): the number of harmonic bonds m. - - Inputs: - - **uint_crd_f** (Tensor) - The unsigned int coordinate value of each atom. - The data type is uint32 and the shape is :math:`(n, 3)`. - - **scaler_f** (Tensor) - The 3-D scale factor (x, y, z), - The data type is float32 and the shape is :math:`(3,)`. - - **atom_a** (Tensor) - The first atom index of each bond. - The data type is int32 and the shape is :math:`(m,)`. - - **atom_b** (Tensor) - The second atom index of each bond. - The data type is int32 and the shape is :math:`(m,)`. - - **bond_k** (Tensor) - The force constant of each bond. - The data type is float32 and the shape is :math:`(m,)`. - - **bond_r0** (Tensor) - The equlibrium length of each bond. - The data type is float32 and the shape is :math:`(m,)`. - - Outputs: - - **frc_f** (Tensor) - The force of each atom. - The data type is float32 and the shape is :math:`(n, 3)`. - - **atom_e** (Tensor) - The energy of each atom. - The data type is float32 and the shape is :math:`(n,)`. - - **atom_virial** (Tensor) - The virial of each atom. - The data type is float32 and the shape is :math:`(n,)`. - - Supported Platforms: - ``GPU`` - """ - - @prim_attr_register - def __init__(self, bond_numbers, atom_numbers): - """Initialize BondForceWithAtomEnergyAndVirial""" - validator.check_value_type('bond_numbers', bond_numbers, int, self.name) - validator.check_value_type('atom_numbers', atom_numbers, int, self.name) - self.bond_numbers = bond_numbers - self.atom_numbers = atom_numbers - self.add_prim_attr('bond_numbers', self.bond_numbers) - self.add_prim_attr('atom_numbers', self.atom_numbers) - self.init_prim_io_names(inputs=['uint_crd_f', 'scaler_f', 'atom_a', 'atom_b', 'bond_k', 'bond_r0'], - outputs=['frc_f', 'atom_e', 'atom_virial']) - - def infer_shape(self, uint_crd_f_shape, scaler_f_shape, atom_a_shape, atom_b_shape, bond_k_shape, bond_r0_shape): - cls_name = self.name - n = self.atom_numbers - m = self.bond_numbers - validator.check_int(len(uint_crd_f_shape), 2, Rel.EQ, "uint_crd_f_dim", cls_name) - validator.check_int(len(scaler_f_shape), 1, Rel.EQ, "scaler_f_dim", cls_name) - validator.check_int(len(atom_a_shape), 1, Rel.EQ, "atom_a_dim", cls_name) - validator.check_int(len(atom_b_shape), 1, Rel.EQ, "atom_b_dim", cls_name) - validator.check_int(len(bond_k_shape), 1, Rel.EQ, "bond_k_dim", cls_name) - validator.check_int(len(bond_r0_shape), 1, Rel.EQ, "bond_r0_dim", cls_name) - validator.check_int(uint_crd_f_shape[0], n, Rel.EQ, "uint_crd_f_shape[0]", cls_name) - validator.check_int(uint_crd_f_shape[1], 3, Rel.EQ, "uint_crd_f_shape[1]", cls_name) - validator.check_int(scaler_f_shape[0], 3, Rel.EQ, "scaler_f_shape", cls_name) - validator.check_int(atom_a_shape[0], m, Rel.EQ, "atom_a_shape", cls_name) - validator.check_int(atom_b_shape[0], m, Rel.EQ, "atom_b_shape", cls_name) - validator.check_int(bond_k_shape[0], m, Rel.EQ, "bond_k_shape", cls_name) - validator.check_int(bond_r0_shape[0], m, Rel.EQ, "bond_r0_shape", cls_name) - - return uint_crd_f_shape, [n,], [n,] - - def infer_dtype(self, uint_crd_f_dtype, scaler_f_type, atom_a_type, atom_b_type, bond_k_type, bond_r0_type): - validator.check_tensor_dtype_valid('uint_crd_f', uint_crd_f_dtype, [mstype.uint32], self.name) - validator.check_tensor_dtype_valid('scaler_f', scaler_f_type, [mstype.float32], self.name) - - validator.check_tensor_dtype_valid('atom_a', atom_a_type, [mstype.int32], self.name) - validator.check_tensor_dtype_valid('atom_b', atom_b_type, [mstype.int32], self.name) - - validator.check_tensor_dtype_valid('bond_k', bond_k_type, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('bond_r0', bond_r0_type, [mstype.float32], self.name) - return mstype.float32, mstype.float32, mstype.float32 - - -class LJForceWithVirialEnergy(PrimitiveWithInfer): - """ - Calculate the Lennard-Jones force, virial and atom energy together. - - The calculation formula of Lennard-Jones part is the same as operator - LJForce(), and the PME direct part is within PME method. - - Because there is a large amount of inputs and each of them are related, - there is no way to construct `Examples` using random methods. For details, refer the webpage `SPONGE in MindSpore - `_. - - .. warning:: - This is an experimental prototype that is subject to change and/or deletion. - - Args: - atom_numbers (int32): the number of atoms, n. - cutoff (float32): the square value of cutoff. - pme_beta (float32): PME beta parameter, same as operator PMEReciprocalForce(). - max_neighbor_numbers (int32): the max neighbor numbers, default 800. - - Inputs: - - **uint_crd** (Tensor) - The unsigned int coordinate value of each atom. - The data type is uint32 and the shape is :math:`(n, 3)`. - - **LJtype** (Tensor) - The Lennard-Jones type of each atom. - The data type is int32 and the shape is :math:`(n,)`. - - **charge** (Tensor) - The charge carried by each atom. - The data type is float32 and the shape is :math:`(n,)`. - - **scaler** (Tensor) - The scale factor between real - space coordinate and its unsigned int value. - The data type is float32 and the shape is :math:`(3,)`. - - **nl_numbers** (Tensor) - The each atom. - The data type is int32 and the shape is :math:`(n,)`. - - **nl_serial** (Tensor) - The neighbor list of each atom, the max number is 800. - The data type is int32 and the shape is :math:`(n, 800)`. - - **d_LJ_A** (Tensor) - The Lennard-Jones A coefficient of each kind of atom pair. - The number of atom pair is q. The data type is float32 and the shape is :math:`(q,)`. - - **d_LJ_B** (Tensor) - The Lennard-Jones B coefficient of each kind of atom pair. - The number of atom pair is q. The data type is float32 and the shape is :math:`(q,)`. - - Outputs: - - **frc** (Tensor), The force felt by each atom. - The data type is float32 and the shape is :math:`(n, 3)`. - - **virial** (Tensor), The virial felt by each atom. - The data type is float32 and the shape is :math:`(n,)`. - - **atom_energy** (Tensor), The atom energy felt by each atom. - The data type is float32 and the shape is :math:`(n, 3)`. - - Supported Platforms: - ``GPU`` - """ - - @prim_attr_register - def __init__(self, atom_numbers, cutoff, pme_beta, max_neighbor_numbers=800): - """Initialize LJForceWithVirialEnergy""" - validator.check_value_type('atom_numbers', atom_numbers, int, self.name) - validator.check_value_type('cutoff', cutoff, float, self.name) - validator.check_value_type('pme_beta', pme_beta, float, self.name) - validator.check_value_type('max_neighbor_numbers', max_neighbor_numbers, int, self.name) - self.atom_numbers = atom_numbers - self.cutoff = cutoff - self.pme_beta = pme_beta - self.max_neighbor_numbers = max_neighbor_numbers - self.init_prim_io_names( - inputs=['uint_crd', 'LJtype', 'charge', 'scaler', 'nl_numbers', 'nl_serial', 'd_LJ_A', 'd_LJ_B'], - outputs=['frc', 'virial', 'atom_energy']) - self.add_prim_attr('atom_numbers', self.atom_numbers) - self.add_prim_attr('cutoff', self.cutoff) - self.add_prim_attr('pme_beta', self.pme_beta) - self.add_prim_attr('max_neighbor_numbers', self.max_neighbor_numbers) - - def infer_shape(self, uint_crd, ljtype, charge, scaler, nl_numbers, nl_serial, d_lj_a, d_lj_b): - cls_name = self.name - n = self.atom_numbers - q = d_lj_a[0] - m = self.max_neighbor_numbers - validator.check_int(len(uint_crd), 2, Rel.EQ, "uint_crd_dim", cls_name) - validator.check_int(len(ljtype), 1, Rel.EQ, "LJtype_dim", cls_name) - validator.check_int(len(charge), 1, Rel.EQ, "charge_dim", cls_name) - validator.check_int(len(scaler), 1, Rel.EQ, "scaler_dim", cls_name) - validator.check_int(len(nl_numbers), 1, Rel.EQ, "nl_numbers_dim", cls_name) - validator.check_int(len(nl_serial), 2, Rel.EQ, "nl_serial_dim", cls_name) - validator.check_int(len(d_lj_a), 1, Rel.EQ, "d_LJ_A_dim", cls_name) - validator.check_int(len(d_lj_b), 1, Rel.EQ, "d_LJ_B_dim", cls_name) - - validator.check_int(uint_crd[0], n, Rel.EQ, "uint_crd_shape[0]", cls_name) - validator.check_int(uint_crd[1], 3, Rel.EQ, "uint_crd_shape[1]", cls_name) - validator.check_int(ljtype[0], n, Rel.EQ, "LJtype_shape", cls_name) - validator.check_int(charge[0], n, Rel.EQ, "charge_shape", cls_name) - validator.check_int(scaler[0], 3, Rel.EQ, "scaler_shape", cls_name) - validator.check_int(nl_numbers[0], n, Rel.EQ, "nl_numbers_shape", cls_name) - validator.check_int(nl_serial[0], n, Rel.EQ, "nl_serial_shape[0]", cls_name) - validator.check_int(nl_serial[1], m, Rel.EQ, "nl_serial_shape[1]", cls_name) - validator.check_int(d_lj_a[0], q, Rel.EQ, "d_LJ_A_shape[0]", cls_name) - validator.check_int(d_lj_b[0], q, Rel.EQ, "d_LJ_B_shape[0]", cls_name) - return [n, 3], [n,], [n,] - - def infer_dtype(self, uint_crd, ljtype, charge, scaler, nl_numbers, nl_serial, d_lj_a, d_lj_b): - validator.check_tensor_dtype_valid('uint_crd', uint_crd, [mstype.uint32], self.name) - validator.check_tensor_dtype_valid('LJtype', ljtype, [mstype.int32], self.name) - validator.check_tensor_dtype_valid('charge', charge, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('scaler', scaler, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('nl_numbers', nl_numbers, [mstype.int32], self.name) - validator.check_tensor_dtype_valid('nl_serial', nl_serial, [mstype.int32], self.name) - validator.check_tensor_dtype_valid('d_LJ_A', d_lj_a, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('d_LJ_B', d_lj_b, [mstype.float32], self.name) - return mstype.float32, mstype.float32, mstype.float32 - - -class LJForceWithPMEDirectForceUpdate(PrimitiveWithInfer): - """ - Calculate the Lennard-Jones force and PME direct force together for pressure. - - The calculation formula of Lennard-Jones part is the same as operator - LJForce(), and the PME direct part is within PME method. - - Because there is a large amount of inputs and each of them are related, - there is no way to construct `Examples` using random methods. For details, refer the webpage `SPONGE in MindSpore - `_. - - .. warning:: - This is an experimental prototype that is subject to change and/or deletion. - - Args: - atom_numbers (int32): the number of atoms, n. - cutoff (float32): the square value of cutoff. - pme_beta (float32): PME beta parameter, same as operator PMEReciprocalForce(). - need_update (int32): if need_update = 1, calculate the pressure, default 0. - - Inputs: - - **uint_crd** (Tensor) - The unsigned int coordinate value of each atom. - The data type is uint32 and the shape is :math:`(n, 3)`. - - **LJtype** (Tensor) - The Lennard-Jones type of each atom. - The data type is int32 and the shape is :math:`(n,)`. - - **charge** (Tensor) - The charge carried by each atom. - The data type is float32 and the shape is :math:`(n,)`. - - **scaler** (Tensor) - The scale factor between real - space coordinate and its unsigned int value. - The data type is float32 and the shape is :math:`(3,)`. - - **nl_numbers** (Tensor) - The each atom. - The data type is int32 and the shape is :math:`(n,)`. - - **nl_serial** (Tensor) - The neighbor list of each atom, the max number is 800. - The data type is int32 and the shape is :math:`(n, 800)`. - - **d_LJ_A** (Tensor) - The Lennard-Jones A coefficient of each kind of atom pair. - The number of atom pair is q. The data type is float32 and the shape is :math:`(q,)`. - - **d_LJ_B** (Tensor) - The Lennard-Jones B coefficient of each kind of atom pair. - The number of atom pair is q. The data type is float32 and the shape is :math:`(q,)`. - - **beta** (Tensor) - PME beta parameter. The data type is float32 and the shape is :math:`(1,)`. - - Outputs: - - **frc** (Tensor) - The force felt by each atom. - The data type is float32 and the shape is :math:`(n, 3)`. - - Supported Platforms: - ``GPU`` - """ - - @prim_attr_register - def __init__(self, atom_numbers, cutoff, pme_beta, need_update=0): - """Initialize LJForceWithPMEDirectForce""" - validator.check_value_type('atom_numbers', atom_numbers, int, self.name) - validator.check_value_type('cutoff', cutoff, float, self.name) - validator.check_value_type('pme_beta', pme_beta, float, self.name) - validator.check_value_type('need_update', need_update, int, self.name) - self.atom_numbers = atom_numbers - self.cutoff = cutoff - self.pme_beta = pme_beta - self.need_update = need_update - self.init_prim_io_names( - inputs=['uint_crd', 'LJtype', 'charge', 'scaler', 'nl_numbers', 'nl_serial', 'd_LJ_A', 'd_LJ_B', 'beta'], - outputs=['frc']) - self.add_prim_attr('atom_numbers', self.atom_numbers) - self.add_prim_attr('cutoff', self.cutoff) - self.add_prim_attr('pme_beta', self.pme_beta) - self.add_prim_attr('need_update', self.need_update) - - def infer_shape(self, uint_crd, ljtype, charge, scaler, nl_numbers, nl_serial, d_lj_a, d_lj_b, beta): - cls_name = self.name - n = self.atom_numbers - q = d_lj_a[0] - m = nl_serial[1] - validator.check_int(len(uint_crd), 2, Rel.EQ, "uint_crd_dim", cls_name) - validator.check_int(len(ljtype), 1, Rel.EQ, "LJtype_dim", cls_name) - validator.check_int(len(charge), 1, Rel.EQ, "charge_dim", cls_name) - validator.check_int(len(scaler), 1, Rel.EQ, "scaler_dim", cls_name) - validator.check_int(len(nl_numbers), 1, Rel.EQ, "nl_numbers_dim", cls_name) - validator.check_int(len(nl_serial), 2, Rel.EQ, "nl_serial_dim", cls_name) - validator.check_int(len(d_lj_a), 1, Rel.EQ, "d_LJ_A_dim", cls_name) - validator.check_int(len(d_lj_b), 1, Rel.EQ, "d_LJ_B_dim", cls_name) - validator.check_int(len(beta), 1, Rel.EQ, "beta_dim", cls_name) - - validator.check_int(uint_crd[0], n, Rel.EQ, "uint_crd_shape[0]", cls_name) - validator.check_int(uint_crd[1], 3, Rel.EQ, "uint_crd_shape[1]", cls_name) - validator.check_int(ljtype[0], n, Rel.EQ, "LJtype_shape", cls_name) - validator.check_int(charge[0], n, Rel.EQ, "charge_shape", cls_name) - validator.check_int(scaler[0], 3, Rel.EQ, "scaler_shape", cls_name) - validator.check_int(nl_numbers[0], n, Rel.EQ, "nl_numbers_shape", cls_name) - validator.check_int(nl_serial[0], n, Rel.EQ, "nl_serial_shape[0]", cls_name) - validator.check_int(nl_serial[1], m, Rel.EQ, "nl_serial_shape[1]", cls_name) - validator.check_int(d_lj_a[0], q, Rel.EQ, "d_LJ_A_shape[0]", cls_name) - validator.check_int(d_lj_b[0], q, Rel.EQ, "d_LJ_B_shape[0]", cls_name) - validator.check_int(beta[0], 1, Rel.EQ, "beta_shape", cls_name) - return [n, 3] - - def infer_dtype(self, uint_crd, ljtype, charge, scaler, nl_numbers, nl_serial, d_lj_a, d_lj_b, beta): - validator.check_tensor_dtype_valid('uint_crd', uint_crd, [mstype.uint32], self.name) - validator.check_tensor_dtype_valid('LJtype', ljtype, [mstype.int32], self.name) - validator.check_tensor_dtype_valid('charge', charge, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('scaler', scaler, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('nl_numbers', nl_numbers, [mstype.int32], self.name) - validator.check_tensor_dtype_valid('nl_serial', nl_serial, [mstype.int32], self.name) - validator.check_tensor_dtype_valid('d_LJ_A', d_lj_a, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('d_LJ_B', d_lj_b, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('beta', beta, [mstype.float32], self.name) - return mstype.float32 - - -class PMEReciprocalForceUpdate(PrimitiveWithInfer): - """ - Calculate the reciprocal part of long-range Coulumb force using - PME(Particle Meshed Ewald) method for pressure. Assume the number of atoms is n. - - The detailed calculation formula of PME(Particle Meshed Ewald) method - can be found in this paper: A Smooth Particle Mesh Ewald Method. DOI: - 10.1063/1.470117. - - Because there is a large amount of inputs and each of them are related, - there is no way to construct `Examples` using random methods. For details, refer the webpage `SPONGE in MindSpore - `_. - - .. warning:: - This is an experimental prototype that is subject to change and/or deletion. - - Args: - atom_numbers (int32): the number of atoms, n. - beta (float32): the PME beta parameter, determined by the - non-bond cutoff value and simulation precision tolerance. - fftx (int32): the number of points for Fourier transform in dimension X. - ffty (int32): the number of points for Fourier transform in dimension Y. - fftz (int32): the number of points for Fourier transform in dimension Z. - box_length_0 (float32): the value of boxlength idx 0 - box_length_1 (float32): the value of boxlength idx 1 - box_length_2 (float32): the value of boxlength idx 2 - need_update (int32): if need_update = 1, calculate the pressure, default 0. - - Inputs: - - **uint_crd** (Tensor) - The unsigned int coordinate value of each atom. - The data type is uint32 and the shape is :math:`(n, 3)`. - - **charge** (Tensor) - The charge carried by each atom. - The data type is float32 and the shape is :math:`(n,)` - - **beta** (Tensor) - The PME beta parameter to be updated in pressure calculation. - The data type is float32 and the shape is :math:`(1,)` - - Outputs: - - **force** (Tensor) - The force felt by each atom. - The data type is float32 and the shape is :math:`(n, 3)` - - Supported Platforms: - ``GPU`` - """ - - @prim_attr_register - def __init__(self, atom_numbers, beta, fftx, ffty, fftz, - box_length_0, box_length_1, box_length_2, need_update=0): - """Initialize PMEReciprocalForce""" - validator.check_value_type('atom_numbers', atom_numbers, int, self.name) - validator.check_value_type('beta', beta, float, self.name) - validator.check_value_type('fftx', fftx, int, self.name) - validator.check_value_type('ffty', ffty, int, self.name) - validator.check_value_type('fftz', fftz, int, self.name) - validator.check_value_type('box_length_0', box_length_0, float, self.name) - validator.check_value_type('box_length_1', box_length_1, float, self.name) - validator.check_value_type('box_length_2', box_length_2, float, self.name) - validator.check_value_type('need_update', need_update, int, self.name) - self.atom_numbers = atom_numbers - self.beta = beta - self.fftx = fftx - self.ffty = ffty - self.fftz = fftz - self.box_length_0 = box_length_0 - self.box_length_1 = box_length_1 - self.box_length_2 = box_length_2 - self.need_update = need_update - - self.init_prim_io_names(inputs=['uint_crd', 'charge', 'beta'], - outputs=['force']) - self.add_prim_attr('atom_numbers', self.atom_numbers) - self.add_prim_attr('beta', self.beta) - self.add_prim_attr('fftx', self.fftx) - self.add_prim_attr('ffty', self.ffty) - self.add_prim_attr('fftz', self.fftz) - self.add_prim_attr('box_length_0', self.box_length_0) - self.add_prim_attr('box_length_1', self.box_length_1) - self.add_prim_attr('box_length_2', self.box_length_2) - self.add_prim_attr('need_update', self.need_update) - - def infer_shape(self, uint_crd_shape, charge_shape, beta): - cls_name = self.name - n = self.atom_numbers - validator.check_int(len(uint_crd_shape), 2, Rel.EQ, "uint_crd_dim", cls_name) - validator.check_int(len(charge_shape), 1, Rel.EQ, "charge_dim", cls_name) - validator.check_int(len(beta), 1, Rel.EQ, "beta_dim", cls_name) - validator.check_int(uint_crd_shape[0], n, Rel.EQ, "uint_crd_shape[0]", cls_name) - validator.check_int(uint_crd_shape[1], 3, Rel.EQ, "uint_crd_shape[1]", cls_name) - validator.check_int(charge_shape[0], n, Rel.EQ, "charge_shape", cls_name) - validator.check_int(beta[0], 1, Rel.EQ, "beta_shape", cls_name) - return uint_crd_shape - - def infer_dtype(self, uint_crd_type, charge_type, beta): - validator.check_tensor_dtype_valid('uint_crd', uint_crd_type, [mstype.uint32], self.name) - validator.check_tensor_dtype_valid('charge', charge_type, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('beta', beta, [mstype.float32], self.name) - return charge_type - - -class PMEExcludedForceUpdate(PrimitiveWithInfer): - """ - Calculate the excluded part of long-range Coulumb force using - PME(Particle Meshed Ewald) method for pressure. Assume the number of atoms is - n, and the length of excluded list is E. - - Because there is a large amount of inputs and each of them are related, - there is no way to construct `Examples` using random methods. For details, refer the webpage `SPONGE in MindSpore - `_. - - .. warning:: - This is an experimental prototype that is subject to change and/or deletion. - - Args: - atom_numbers (int32): the number of atoms, n. - excluded_numbers (int32): the length of excluded list, E. - beta (float32): the PME beta parameter, determined by the - non-bond cutoff value and simulation precision tolerance. - need_update (int32): if need_update = 1, calculate the pressure, default 0. - - Inputs: - - **uint_crd** (Tensor) - The unsigned int coordinates value of each atom. - The data type is uint32 and the shape is :math:`(n, 3)` - - **scaler** (Tensor) - The scale factor between real space - coordinates and its unsigned int value. The data type is float32 and the shape is :math:`(3,)` - - **charge** (Tensor) - The charge carried by each atom. - The data type is float32 and the shape is :math:`(n,)` - - **excluded_list_start** (Tensor) - The start excluded index - in excluded list for each atom. The data type is int32 and the shape is :math:`(n,)` - - **excluded_list** (Tensor) - The contiguous join of excluded - list of each atom. E is the number of excluded atoms. The data type is int32 and the shape is :math:`(E,)` - - **excluded_atom_numbers** (Tensor) - The number of atom excluded - in excluded list for each atom. The data type is int32 and the shape is :math:`(n,)` - - **beta** (Tensor) - The PME beta parameter to be updated in pressure calculation. - The data type is float32 and the shape is :math:`(1,)` - - Outputs: - - **force** (Tensor) - The force felt by each atom. - The data type is float32 and the shape is :math:`(n, 3)` - - Supported Platforms: - ``GPU`` - """ - - @prim_attr_register - def __init__(self, atom_numbers, excluded_numbers, beta, need_update=0): - """Initialize PMEExcludedForce""" - validator.check_value_type('atom_numbers', atom_numbers, int, self.name) - validator.check_value_type('excluded_numbers', excluded_numbers, int, self.name) - validator.check_value_type('beta', beta, float, self.name) - validator.check_value_type('need_update', need_update, int, self.name) - self.atom_numbers = atom_numbers - self.excluded_numbers = excluded_numbers - self.beta = beta - self.need_update = need_update - self.init_prim_io_names( - inputs=['uint_crd', 'scaler', 'charge', 'excluded_list_start', 'excluded_list', - 'excluded_atom_numbers', 'beta'], - outputs=['force']) - self.add_prim_attr('atom_numbers', self.atom_numbers) - self.add_prim_attr('excluded_numbers', self.excluded_numbers) - self.add_prim_attr('beta', self.beta) - self.add_prim_attr('need_update', self.need_update) - - def infer_shape(self, uint_crd_shape, scaler_shape, charge_shape, excluded_list_start_shape, excluded_list_shape, - excluded_atom_numbers_shape, beta): - cls_name = self.name - n = self.atom_numbers - e = self.excluded_numbers - validator.check_int(len(uint_crd_shape), 2, Rel.EQ, "uint_crd_dim", cls_name) - validator.check_int(len(scaler_shape), 1, Rel.EQ, "scaler_dim", cls_name) - validator.check_int(len(charge_shape), 1, Rel.EQ, "charge_dim", cls_name) - validator.check_int(len(excluded_list_start_shape), 1, Rel.EQ, "excluded_list_start_dim", cls_name) - validator.check_int(len(excluded_atom_numbers_shape), 1, Rel.EQ, "excluded_atom_numbers_dim", cls_name) - validator.check_int(len(excluded_list_shape), 1, Rel.EQ, "excluded_list_dim", cls_name) - validator.check_int(len(beta), 1, Rel.EQ, "beta_dim", cls_name) - validator.check_int(uint_crd_shape[0], n, Rel.EQ, "uint_crd_shape[0]", cls_name) - validator.check_int(uint_crd_shape[1], 3, Rel.EQ, "uint_crd_shape[1]", cls_name) - validator.check_int(scaler_shape[0], 3, Rel.EQ, "scaler_shape", cls_name) - validator.check_int(charge_shape[0], n, Rel.EQ, "charge_shape", cls_name) - validator.check_int(excluded_list_start_shape[0], n, Rel.EQ, "excluded_list_start_shape", cls_name) - validator.check_int(excluded_atom_numbers_shape[0], n, Rel.EQ, "excluded_atom_numbers_shape", cls_name) - validator.check_int(excluded_list_shape[0], e, Rel.EQ, "excluded_list_shape", cls_name) - validator.check_int(beta[0], 1, Rel.EQ, "beta_shape", cls_name) - return [n, 3] - - def infer_dtype(self, uint_crd_type, scaler_type, charge_type, excluded_list_start_type, excluded_list_type, - excluded_atom_numbers_type, beta): - validator.check_tensor_dtype_valid('scaler', scaler_type, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('uint_crd', uint_crd_type, [mstype.uint32], self.name) - validator.check_tensor_dtype_valid('charge', charge_type, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('excluded_list_start', excluded_list_start_type, [mstype.int32], - self.name) - validator.check_tensor_dtype_valid('excluded_list', excluded_list_type, [mstype.int32], - self.name) - validator.check_tensor_dtype_valid('excluded_atom_numbers', excluded_atom_numbers_type, [mstype.int32], - self.name) - validator.check_tensor_dtype_valid('beta', beta, [mstype.float32], self.name) - return mstype.float32 - - -class LJForceWithVirialEnergyUpdate(PrimitiveWithInfer): - """ - Calculate the Lennard-Jones force and PME direct force together for pressure. - - The calculation formula of Lennard-Jones part is the same as operator - LJForce(), and the PME direct part is within PME method. - - Because there is a large amount of inputs and each of them are related, - there is no way to construct `Examples` using random methods. For details, refer the webpage `SPONGE in MindSpore - `_. - - .. warning:: - This is an experimental prototype that is subject to change and/or deletion. - - Args: - atom_numbers (int32): the number of atoms, n. - cutoff (float32): the square value of cutoff. - pme_beta (float32): PME beta parameter, same as operator PMEReciprocalForce(). - max_neighbor_numbers (int32): the max neighbor numbers, default 800. - need_update (int32): if need_update = 1, calculate the pressure, default 0. - - Inputs: - - **uint_crd** (Tensor) - The unsigned int coordinate value of each atom. - The data type is uint32 and the shape is :math:`(n, 3)`. - - **LJtype** (Tensor) - The Lennard-Jones type of each atom. - The data type is int32 and the shape is :math:`(n,)`. - - **charge** (Tensor) - The charge carried by each atom. - The data type is float32 and the shape is :math:`(n,)`. - - **scaler** (Tensor) - The scale factor. - The data type is float32 and the shape is :math:`(3,)`. - - **nl_numbers** (Tensor) - The each atom. - The data type is int32 and the shape is :math:`(n,)`. - - **nl_serial** (Tensor) - The neighbor list of each atom, the max number is 800. - The data type is int32 and the shape is :math:`(n, 800)`. - - **d_LJ_A** (Tensor) - The Lennard-Jones A coefficient of each kind of atom pair. - The number of atom pair is q. The data type is float32 and the shape is :math:`(q,)`. - - **d_LJ_B** (Tensor) - The Lennard-Jones B coefficient of each kind of atom pair. - The number of atom pair is q. The data type is float32 and the shape is :math:`(q,)`. - - **beta** (Tensor) - The PME beta parameter to be updated in pressure calculation. - The data type is float32 and the shape is :math:`(1,)` - - Outputs: - - **frc** (Tensor) - The force felt by each atom. - The data type is float32 and the shape is :math:`(n, 3)`. - - **virial** (Tensor) - The accumulated potential virial for each atom. - The data type is float32 and the shape is :math:`(n, )`. - - **atom_energy** (Tensor) - The accumulated potential energy for each atom. - The data type is float32 and the shape is :math:`(n, )`. - - Supported Platforms: - ``GPU`` - """ - - @prim_attr_register - def __init__(self, atom_numbers, cutoff, pme_beta, max_neighbor_numbers=800, need_update=0): - """Initialize LJForceWithPMEDirectForce""" - validator.check_value_type('atom_numbers', atom_numbers, int, self.name) - validator.check_value_type('cutoff', cutoff, float, self.name) - validator.check_value_type('pme_beta', pme_beta, float, self.name) - validator.check_value_type('max_neighbor_numbers', max_neighbor_numbers, int, self.name) - validator.check_value_type('need_update', need_update, int, self.name) - self.atom_numbers = atom_numbers - self.cutoff = cutoff - self.pme_beta = pme_beta - self.max_neighbor_numbers = max_neighbor_numbers - self.need_update = need_update - self.init_prim_io_names( - inputs=['uint_crd', 'LJtype', 'charge', 'scaler', 'nl_numbers', 'nl_serial', 'd_LJ_A', 'd_LJ_B', 'beta'], - outputs=['frc', 'virial', 'atom_energy']) - self.add_prim_attr('atom_numbers', self.atom_numbers) - self.add_prim_attr('cutoff', self.cutoff) - self.add_prim_attr('pme_beta', self.pme_beta) - self.add_prim_attr('max_neighbor_numbers', self.max_neighbor_numbers) - self.add_prim_attr('need_update', self.need_update) - - def infer_shape(self, uint_crd, ljtype, charge, scaler, nl_numbers, nl_serial, d_lj_a, d_lj_b, beta): - cls_name = self.name - n = self.atom_numbers - q = d_lj_a[0] - m = self.max_neighbor_numbers - validator.check_int(len(uint_crd), 2, Rel.EQ, "uint_crd_dim", cls_name) - validator.check_int(len(ljtype), 1, Rel.EQ, "LJtype_dim", cls_name) - validator.check_int(len(charge), 1, Rel.EQ, "charge_dim", cls_name) - validator.check_int(len(scaler), 1, Rel.EQ, "scaler_dim", cls_name) - validator.check_int(len(nl_numbers), 1, Rel.EQ, "nl_numbers_dim", cls_name) - validator.check_int(len(nl_serial), 2, Rel.EQ, "nl_serial_dim", cls_name) - validator.check_int(len(d_lj_a), 1, Rel.EQ, "d_LJ_A_dim", cls_name) - validator.check_int(len(d_lj_b), 1, Rel.EQ, "d_LJ_B_dim", cls_name) - validator.check_int(len(beta), 1, Rel.EQ, "beta_dim", cls_name) - validator.check_int(uint_crd[0], n, Rel.EQ, "uint_crd_shape[0]", cls_name) - validator.check_int(uint_crd[1], 3, Rel.EQ, "uint_crd_shape[1]", cls_name) - validator.check_int(ljtype[0], n, Rel.EQ, "LJtype_shape", cls_name) - validator.check_int(charge[0], n, Rel.EQ, "charge_shape", cls_name) - validator.check_int(scaler[0], 3, Rel.EQ, "scaler_shape", cls_name) - validator.check_int(nl_numbers[0], n, Rel.EQ, "nl_numbers_shape", cls_name) - validator.check_int(nl_serial[0], n, Rel.EQ, "nl_serial_shape[0]", cls_name) - validator.check_int(nl_serial[1], m, Rel.EQ, "nl_serial_shape[1]", cls_name) - validator.check_int(d_lj_a[0], q, Rel.EQ, "d_LJ_A_shape[0]", cls_name) - validator.check_int(d_lj_b[0], q, Rel.EQ, "d_LJ_B_shape[0]", cls_name) - validator.check_int(beta[0], 1, Rel.EQ, "beta_shape[0]", cls_name) - return [n, 3], [n,], [n,] - - def infer_dtype(self, uint_crd, ljtype, charge, scaler, nl_numbers, nl_serial, d_lj_a, d_lj_b, beta): - validator.check_tensor_dtype_valid('uint_crd', uint_crd, [mstype.uint32], self.name) - validator.check_tensor_dtype_valid('LJtype', ljtype, [mstype.int32], self.name) - validator.check_tensor_dtype_valid('charge', charge, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('scaler', scaler, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('nl_numbers', nl_numbers, [mstype.int32], self.name) - validator.check_tensor_dtype_valid('nl_serial', nl_serial, [mstype.int32], self.name) - validator.check_tensor_dtype_valid('d_LJ_A', d_lj_a, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('d_LJ_B', d_lj_b, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('beta', beta, [mstype.float32], self.name) - return mstype.float32, mstype.float32, mstype.float32 - - -class Dihedral14ForceWithAtomEnergyVirial(PrimitiveWithInfer): - """ - Calculate the Lennard-Jones and Coulumb energy correction and force correction - for each necessary dihedral 1,4 terms together and add them to the total force - and potential energy for each atom. - - The calculation formula of force correction is the same as operator - :class:`Dihedral14LJForceWithDirectCF`, and the energy correction part is the same - as operator :class:`Dihedral14LJEnergy` and :class:`Dihedral14CFEnergy`. - - .. warning:: - This is an experimental prototype that is subject to change and/or deletion. - - Args: - nb14_numbers (int32): the number of necessary dihedral 1,4 terms m. - atom_numbers (int32): the number of atoms n. - - Inputs: - - **uint_crd_f** (Tensor) - The unsigned int coordinate value of each atom. - The data type is uint32 and the shape is :math:`(n, 3)`. - - **LJtype** (Tensor) - The Lennard-Jones type of each atom. - The data type is int32 and the shape is :math:`(n,)`. - - **charge** (Tensor) - The charge carried by each atom. - The data type is float32 and the shape is :math:`(n,)`. - - **boxlength** (Tensor) - The length of molecular simulation box in 3 dimensions. - The data type is float32 and the shape is :math:`(3,)`. - - **a_14** (Tensor) - The first atom index of each dihedral 1,4 term. - The data type is int32 and the shape is :math:`(m,)`. - - **b_14** (Tensor) - The second atom index of each dihedral 1,4 term. - The data type is int32 and the shape is :math:`(m,)`. - - **lj_scale_factor** (Tensor) - The scale factor for the - Lennard-Jones part of force correction of each dihedral 1,4 term. - - **cf_scale_factor** (Tensor) - The scale factor for the Coulomb force. - The data type is float32 and the shape is :math:`(m,)`. - - **LJ_type_A** (Tensor) - The A parameter in Lennard-Jones scheme of each atom pair type. - The number of atom pair is q. The data type is float32 and the shape is :math:`(q,)`. - - **LJ_type_B** (Tensor) - The B parameter in Lennard-Jones scheme of each atom pair type. - The number of atom pair is q. The data type is float32 and the shape is :math:`(q,)`. - - Outputs: - - **frc** (Tensor) - The force felt by each atom. - The data type is float32 and the shape is :math:`(n, 3)`. - - **atom_energy** (Tensor) - The accumulated potential energy for each atom. - The data type is float32 and the shape is :math:`(n, )`. - - **atom_virial** (Tensor) - The accumulated potential virial for each atom. - The data type is float32 and the shape is :math:`(n, )`. - - Supported Platforms: - ``GPU`` - """ - - @prim_attr_register - def __init__(self, nb14_numbers, atom_numbers): - """Initialize Dihedral14LJCFForceWithAtomEnergy""" - validator.check_value_type('nb14_numbers', nb14_numbers, int, self.name) - validator.check_value_type('atom_numbers', atom_numbers, int, self.name) - self.dihedral_14_numbers = nb14_numbers - self.atom_numbers = atom_numbers - - self.init_prim_io_names( - inputs=['uint_crd_f', 'LJtype', 'charge', 'boxlength', 'a_14', 'b_14', 'lj_scale_factor', - 'cf_scale_factor', 'LJ_type_A', 'LJ_type_B'], - outputs=['frc', 'atom_energy', 'atom_virial']) - self.add_prim_attr('dihedral_14_numbers', self.dihedral_14_numbers) - self.add_prim_attr('atom_numbers', self.atom_numbers) - - def infer_shape(self, uint_crd_f_shape, ljtype_shape, charge_shape, boxlength_f_shape, a_14_shape, b_14_shape, - lj_scale_factor_shape, cf_scale_factor_shape, lj_type_a_shape, lj_type_b_shape): - cls_name = self.name - n = self.atom_numbers - m = self.dihedral_14_numbers - q = lj_type_a_shape[0] - validator.check_int(len(uint_crd_f_shape), 2, Rel.EQ, "uint_crd_f_dim", cls_name) - validator.check_int(len(ljtype_shape), 1, Rel.EQ, "LJtype_dim", cls_name) - validator.check_int(len(charge_shape), 1, Rel.EQ, "charge_dim", cls_name) - validator.check_int(len(boxlength_f_shape), 1, Rel.EQ, "boxlength_f_dim", cls_name) - validator.check_int(len(a_14_shape), 1, Rel.EQ, "a_14_dim", cls_name) - validator.check_int(len(b_14_shape), 1, Rel.EQ, "b_14_dim", cls_name) - validator.check_int(len(lj_scale_factor_shape), 1, Rel.EQ, "lj_scale_factor_dim", cls_name) - validator.check_int(len(cf_scale_factor_shape), 1, Rel.EQ, "cf_scale_factor_dim", cls_name) - validator.check_int(len(lj_type_a_shape), 1, Rel.EQ, "LJ_type_A_dim", cls_name) - validator.check_int(len(lj_type_b_shape), 1, Rel.EQ, "LJ_type_B_dim", cls_name) - - validator.check_int(uint_crd_f_shape[0], n, Rel.EQ, "uint_crd_f_shape[0]", cls_name) - validator.check_int(uint_crd_f_shape[1], 3, Rel.EQ, "uint_crd_f_shape[1]", cls_name) - validator.check_int(ljtype_shape[0], n, Rel.EQ, "LJtype_shape", cls_name) - validator.check_int(charge_shape[0], n, Rel.EQ, "charge_shape", cls_name) - validator.check_int(boxlength_f_shape[0], 3, Rel.EQ, "boxlength_f_shape", cls_name) - validator.check_int(lj_type_a_shape[0], q, Rel.EQ, "LJ_type_A_shape", cls_name) - validator.check_int(lj_type_b_shape[0], q, Rel.EQ, "LJ_type_B_shape", cls_name) - validator.check_int(a_14_shape[0], m, Rel.EQ, "a_14_shape", cls_name) - validator.check_int(b_14_shape[0], m, Rel.EQ, "b_14_shape", cls_name) - validator.check_int(lj_scale_factor_shape[0], m, Rel.EQ, "lj_scale_factor_shape", cls_name) - validator.check_int(cf_scale_factor_shape[0], m, Rel.EQ, "cf_scale_factor_shape", cls_name) - return [n, 3], [n,], [n,] - - def infer_dtype(self, uint_crd_f_dtype, ljtype_dtype, charge_dtype, boxlength_f_type, a_14_type, b_14_type, - lj_scale_factor_type, cf_scale_factor_type, lj_type_a_type, lj_type_b_type): - validator.check_tensor_dtype_valid('uint_crd_f', uint_crd_f_dtype, [mstype.uint32], self.name) - validator.check_tensor_dtype_valid('LJtype', ljtype_dtype, [mstype.int32], self.name) - validator.check_tensor_dtype_valid('charge', charge_dtype, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('boxlength_f', boxlength_f_type, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('a_14', a_14_type, [mstype.int32], self.name) - validator.check_tensor_dtype_valid('b_14', b_14_type, [mstype.int32], self.name) - validator.check_tensor_dtype_valid('lj_scale_factor', lj_scale_factor_type, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('cf_scale_factor', cf_scale_factor_type, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('LJ_type_A', lj_type_a_type, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('LJ_type_B', lj_type_b_type, [mstype.float32], self.name) - - return mstype.float32, mstype.float32, mstype.float32 - - -class PMEEnergyUpdate(PrimitiveWithInfer): - """ - Calculate the Coulumb energy of the system using PME method for pressure. - - Because there is a large amount of inputs and each of them are related, - there is no way to construct `Examples` using random methods. For details, refer the webpage `SPONGE in MindSpore - `_. - - .. warning:: - This is an experimental prototype that is subject to change and/or deletion. - - Args: - atom_numbers (int32): the number of atoms, n. - excluded_numbers (int32): the length of excluded list, E. - beta (float32): the PME beta parameter, determined by the - non-bond cutoff value and simulation precision tolerance. - fftx (int32): the number of points for Fourier transform in dimension X. - ffty (int32): the number of points for Fourier transform in dimension Y. - fftz (int32): the number of points for Fourier transform in dimension Z. - box_length_0 (float32): the value of boxlength idx 0. - box_length_1 (float32): the value of boxlength idx 1. - box_length_2 (float32): the value of boxlength idx 2. - max_neighbor_numbers (int32): the max neighbor numbers, m, default 800. - need_update (int32): if need_update = 1, calculate the pressure, default 0. - - Inputs: - - **uint_crd** (Tensor) - The unsigned int coordinates value of each atom. - The data type is uint32 and the shape is :math:`(n, 3)` - - **charge** (Tensor) - The charge carried by each atom. - The data type is float32 and the shape is :math:`(n,)` - - **nl_numbers** - (Tensor) - The each atom. - The data type is int32 and the shape is :math:`(n, 3)` - - **nl_serial** - (Tensor) - The neighbor list of each atom, the max number is 800. - The data type is int32 and the shape is :math:`(n, m)` - - **scaler** (Tensor) - The scale factor between real space - coordinates and its unsigned int value. The data type is float32 and the shape is :math:`(3,)` - - **excluded_list_start** (Tensor) - The start excluded index - in excluded list for each atom. The data type is int32 and the shape is :math:`(n,)` - - **excluded_list** (Tensor) - The contiguous join of excluded - list of each atom. E is the number of excluded atoms. The data type is int32 and the shape is :math:`(E,)` - - **excluded_atom_numbers** (Tensor) - The number of atom excluded - in excluded list for each atom. The data type is int32 and the shape is :math:`(n,)` - - **factor** (Tensor) - The factor parameter to be updated in pressure calculation. - The data type is float32 and the shape is :math:`(1,)` - - **beta** (Tensor) - The PME beta parameter to be updated in pressure calculation. - The data type is float32 and the shape is :math:`(1,)` - - Outputs: - - **reciprocal_ene** (Tensor) - The reciprocal term of PME energy. - The data type is float32 and the the shape is :math:`(1,)`. - - **self_ene** (Tensor) - The self term of PME energy. - The data type is float32 and the the shape is :math:`(1,)`. - - **direct_ene** (Tensor) - The direct term of PME energy. - The data type is float32 and the the shape is :math:`(1,)`. - - **correction_ene** (Tensor) - The correction term of PME energy. - The data type is float32 and the the shape is :math:`(1,)`. - - Supported Platforms: - ``GPU`` - """ - - @prim_attr_register - def __init__(self, atom_numbers, excluded_numbers, beta, fftx, ffty, fftz, box_length_0, box_length_1, - box_length_2, max_neighbor_numbers=800, need_update=0): - """Initialize PMEEnergyUpdate""" - validator.check_value_type('atom_numbers', atom_numbers, int, self.name) - validator.check_value_type('excluded_numbers', excluded_numbers, int, self.name) - validator.check_value_type('beta', beta, float, self.name) - validator.check_value_type('fftx', fftx, int, self.name) - validator.check_value_type('ffty', ffty, int, self.name) - validator.check_value_type('fftz', fftz, int, self.name) - validator.check_value_type('box_length_0', box_length_0, float, self.name) - validator.check_value_type('box_length_1', box_length_1, float, self.name) - validator.check_value_type('box_length_2', box_length_2, float, self.name) - validator.check_value_type('max_neighbor_numbers', max_neighbor_numbers, int, self.name) - validator.check_value_type('need_update', need_update, int, self.name) - self.atom_numbers = atom_numbers - self.excluded_numbers = excluded_numbers - self.beta = beta - self.fftx = fftx - self.ffty = ffty - self.fftz = fftz - self.box_length_0 = box_length_0 - self.box_length_1 = box_length_1 - self.box_length_2 = box_length_2 - self.max_neighbor_numbers = max_neighbor_numbers - self.need_update = need_update - self.init_prim_io_names( - inputs=['uint_crd', 'charge', 'nl_numbers', 'nl_serial', 'scaler', 'excluded_list_start', - 'excluded_list', 'excluded_atom_numbers', 'factor', 'beta'], - outputs=['reciprocal_ene', 'self_ene', 'direct_ene', 'correction_ene']) - self.add_prim_attr('atom_numbers', self.atom_numbers) - self.add_prim_attr('excluded_numbers', self.excluded_numbers) - self.add_prim_attr('beta', self.beta) - self.add_prim_attr('fftx', self.fftx) - self.add_prim_attr('ffty', self.ffty) - self.add_prim_attr('fftz', self.fftz) - self.add_prim_attr('box_length_0', self.box_length_0) - self.add_prim_attr('box_length_1', self.box_length_1) - self.add_prim_attr('box_length_2', self.box_length_2) - self.add_prim_attr('max_neighbor_numbers', self.max_neighbor_numbers) - self.add_prim_attr('need_update', self.need_update) - - def infer_shape(self, uint_crd, charge, nl_numbers, nl_serial, scaler, excluded_list_start, - excluded_list, excluded_atom_numbers, factor, beta): - cls_name = self.name - n = self.atom_numbers - m = self.max_neighbor_numbers - e = self.excluded_numbers - validator.check_int(len(uint_crd), 2, Rel.EQ, "uint_crd_dim", cls_name) - validator.check_int(len(charge), 1, Rel.EQ, "charge_dim", cls_name) - validator.check_int(len(nl_numbers), 1, Rel.EQ, "nl_numbers_dim", cls_name) - validator.check_int(len(nl_serial), 2, Rel.EQ, "nl_serial_dim", cls_name) - validator.check_int(len(excluded_list_start), 1, Rel.EQ, "excluded_list_start_dim", cls_name) - validator.check_int(len(excluded_atom_numbers), 1, Rel.EQ, "excluded_atom_numbers_dim", cls_name) - validator.check_int(len(excluded_list), 1, Rel.EQ, "excluded_list_dim", cls_name) - validator.check_int(len(scaler), 1, Rel.EQ, "scaler_dim", cls_name) - validator.check_int(len(factor), 1, Rel.EQ, "factor_dim", cls_name) - validator.check_int(len(beta), 1, Rel.EQ, "beta_dim", cls_name) - validator.check_int(uint_crd[0], n, Rel.EQ, "uint_crd_shape[0]", cls_name) - validator.check_int(uint_crd[1], 3, Rel.EQ, "uint_crd_shape[1]", cls_name) - validator.check_int(charge[0], n, Rel.EQ, "charge_shape", cls_name) - validator.check_int(nl_numbers[0], n, Rel.EQ, "nl_numbers_shape[0]", cls_name) - validator.check_int(nl_serial[0], n, Rel.EQ, "nl_serial_shape[0]", cls_name) - validator.check_int(nl_serial[1], m, Rel.EQ, "nl_serial_shape[1]", cls_name) - validator.check_int(excluded_list_start[0], n, Rel.EQ, "excluded_list_start_shape", cls_name) - validator.check_int(excluded_atom_numbers[0], n, Rel.EQ, "excluded_atom_numbers_shape", cls_name) - validator.check_int(excluded_list[0], e, Rel.EQ, "excluded_list_shape", cls_name) - validator.check_int(factor[0], 1, Rel.EQ, "factor_shape", cls_name) - validator.check_int(beta[0], 1, Rel.EQ, "beta_shape", cls_name) - validator.check_int(scaler[0], 3, Rel.EQ, "scaler_shape", cls_name) - return [1,], [1,], [1,], [1,] - - def infer_dtype(self, uint_crd, charge, nl_numbers, nl_serial, scaler, excluded_list_start, - excluded_list, excluded_atom_numbers, factor, beta): - validator.check_tensor_dtype_valid('uint_crd', uint_crd, [mstype.uint32], self.name) - validator.check_tensor_dtype_valid('charge', charge, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('nl_numbers', nl_numbers, [mstype.int32], self.name) - validator.check_tensor_dtype_valid('nl_serial', nl_serial, [mstype.int32], self.name) - validator.check_tensor_dtype_valid('scaler', scaler, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('excluded_list_start', excluded_list_start, [mstype.int32], - self.name) - validator.check_tensor_dtype_valid('excluded_list', excluded_list, [mstype.int32], - self.name) - validator.check_tensor_dtype_valid('excluded_atom_numbers', excluded_atom_numbers, [mstype.int32], - self.name) - validator.check_tensor_dtype_valid('factor', factor, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('beta', beta, [mstype.float32], self.name) - return charge, charge, charge, charge - - -class ConstrainForceCycle(PrimitiveWithInfer): - """ - Calculate the constraint force in each iteration. - - .. warning:: - This is an experimental prototype that is subject to change and/or deletion. - - Args: - atom_numbers (int32): the number of atoms n. - constrain_pair_numbers (int32): the number of constrain pairs m. - - Inputs: - - **uint_crd** (Tensor) - The unsigned int coordinate value of each atom. - The data type is uint32 and the shape is :math:`(n, 3)`. - - **scaler** (Tensor) - The 3-D scale factor (x, y, z), - The data type is float32 and the shape is :math:`(3,)`. - - **pair_dr** (Tensor) - The displacement vector of each constrained atom pair. - The data type is float32 and the shape is :math:`(m, 3)`. - - **atom_i_serials** (Tensor) - The first atom index of each constrained atom pair. - The data type is int32 and the shape is :math:`(m,)`. - - **atom_j_serials** (Tensor) - The second atom index of each constrained atom pair. - The data type is int32 and the shape is :math:`(m,)`. - - **constant_rs** (Tensor) - The constrained distance of each constrained atom pair. - The data type is float32 and the shape is :math:`(m,)`. - - **constrain_ks** (Tensor) - The coefficient of each constrained atom pair. - The data type is float32 and the shape is :math:`(m,)`. - - Outputs: - - **test_frc** (Tensor) - The constraint force. - The data type is float32 and the shape is :math:`(n, 3)`. - - Supported Platforms: - ``GPU`` - """ - - @prim_attr_register - def __init__(self, atom_numbers, constrain_pair_numbers): - validator.check_value_type('atom_numbers', atom_numbers, int, self.name) - validator.check_value_type('constrain_pair_numbers', constrain_pair_numbers, int, self.name) - self.atom_numbers = atom_numbers - self.constrain_pair_numbers = constrain_pair_numbers - self.add_prim_attr('atom_numbers', self.atom_numbers) - self.add_prim_attr('constrain_pair_numbers', self.constrain_pair_numbers) - self.init_prim_io_names( - inputs=['uint_crd', 'scaler', 'pair_dr', 'atom_i_serials', 'atom_j_serials', - 'constant_rs', 'constrain_ks'], - outputs=['test_frc']) - - def infer_shape(self, uint_crd_shape, scaler_shape, pair_dr_shape, atom_i_serials_shape, - atom_j_serials_shape, constant_rs_shape, constrain_ks_shape): - cls_name = self.name - n = self.atom_numbers - m = self.constrain_pair_numbers - validator.check_int(len(uint_crd_shape), 2, Rel.EQ, "uint_crd_dim", cls_name) - validator.check_int(len(scaler_shape), 1, Rel.EQ, "scaler_dim", cls_name) - validator.check_int(len(pair_dr_shape), 2, Rel.EQ, "pair_dr_dim", cls_name) - validator.check_int(len(atom_i_serials_shape), 1, Rel.EQ, "atom_i_serials_dim", cls_name) - validator.check_int(len(atom_j_serials_shape), 1, Rel.EQ, "atom_j_serials_dim", cls_name) - validator.check_int(len(constant_rs_shape), 1, Rel.EQ, "constant_rs_dim", cls_name) - validator.check_int(len(constrain_ks_shape), 1, Rel.EQ, "constrain_ks_dim", cls_name) - - validator.check_int(uint_crd_shape[0], n, Rel.EQ, "uint_crd_shape[0]", cls_name) - validator.check_int(uint_crd_shape[1], 3, Rel.EQ, "uint_crd_shape[1]", cls_name) - validator.check_int(scaler_shape[0], 3, Rel.EQ, "scaler_shape", cls_name) - validator.check_int(pair_dr_shape[0], m, Rel.EQ, "pair_dr_shape[0]", cls_name) - validator.check_int(pair_dr_shape[1], 3, Rel.EQ, "pair_dr_shape[1]", cls_name) - validator.check_int(atom_i_serials_shape[0], m, Rel.EQ, "atom_i_serials_shape[0]", cls_name) - validator.check_int(atom_j_serials_shape[0], m, Rel.EQ, "atom_j_serials_shape[0]", cls_name) - validator.check_int(constant_rs_shape[0], m, Rel.EQ, "constant_rs_shape[0]", cls_name) - validator.check_int(constrain_ks_shape[0], m, Rel.EQ, "constrain_ks_shape[0]", cls_name) - return [n, 3] - - def infer_dtype(self, uint_crd_dtype, scaler_dtype, pair_dr_dtype, atom_i_serials_dtype, - atom_j_serials_dtype, constant_rs_dtype, constrain_ks_dtype): - validator.check_tensor_dtype_valid('uint_crd', uint_crd_dtype, [mstype.uint32], self.name) - validator.check_tensor_dtype_valid('scaler', scaler_dtype, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('pair_dr', pair_dr_dtype, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('atom_i_serials', atom_i_serials_dtype, [mstype.int32], self.name) - validator.check_tensor_dtype_valid('atom_j_serials', atom_j_serials_dtype, [mstype.int32], self.name) - validator.check_tensor_dtype_valid('constant_rs', constant_rs_dtype, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('constrain_ks', constrain_ks_dtype, [mstype.float32], self.name) - return mstype.float32 - - -class ConstrainForceVirial(PrimitiveWithInfer): - """ - Calculate the constraint force and virial in a step with iteration numbers. - - .. warning:: - This is an experimental prototype that is subject to change and/or deletion. - - Args: - atom_numbers (int32): the number of atoms n. - constrain_pair_numbers (int32): the number of constrain pairs m. - iteration_numbers (int32): the number of iteration numbers p. - half_exp_gamma_plus_half (float32): half exp_gamma plus half q. - - Inputs: - - **crd** (Tensor) - The coordinate of each atom. - The data type is float32 and the shape is :math:`(n, 3)`. - - **quarter_cof** (Tensor) - The 3-D scale factor. - The data type is float32 and the shape is :math:`(3,)`. - - **mass_inverse** (Tensor) - The inverse value of mass of each atom. - The data type is float32 and the shape is :math:`(n,)`. - - **scaler** (Tensor) - The 3-D scale factor (x, y, z), - The data type is float32 and the shape is :math:`(3,)`. - - **pair_dr** (Tensor) - The displacement vector of each constrained atom pair. - The data type is float32 and the shape is :math:`(m, 3)`. - - **atom_i_serials** (Tensor) - The first atom index of each constrained atom pair. - The data type is int32 and the shape is :math:`(m,)`. - - **atom_j_serials** (Tensor) - The second atom index of each constrained atom pair. - The data type is int32 and the shape is :math:`(m,)`. - - **constant_rs** (Tensor) - The constrained distance of each constrained atom pair. - The data type is float32 and the shape is :math:`(m,)`. - - **constrain_ks** (Tensor) - The coefficient of each constrained atom pair. - The data type is float32 and the shape is :math:`(m,)`. - - Outputs: - - **uint_crd** (Tensor) - The unsigned int coordinate value of each atom. - The data type is uint32 and the shape is :math:`(n, 3)`. - - **frc** (Tensor) - The force felt by each atom. - The data type is float32 and the shape is :math:`(n, 3)`. - - **virial** (Tensor) - The constraint virial on each atom. - The data type is float32 and the shape is :math:`(m,)`. - - Supported Platforms: - ``GPU`` - """ - - @prim_attr_register - def __init__(self, atom_numbers, constrain_pair_numbers, iteration_numbers, half_exp_gamma_plus_half): - validator.check_value_type('atom_numbers', atom_numbers, int, self.name) - validator.check_value_type('constrain_pair_numbers', constrain_pair_numbers, int, self.name) - validator.check_value_type('iteration_numbers', iteration_numbers, int, self.name) - validator.check_value_type('half_exp_gamma_plus_half', half_exp_gamma_plus_half, float, self.name) - self.atom_numbers = atom_numbers - self.constrain_pair_numbers = constrain_pair_numbers - self.iteration_numbers = iteration_numbers - self.half_exp_gamma_plus_half = half_exp_gamma_plus_half - self.add_prim_attr('atom_numbers', self.atom_numbers) - self.add_prim_attr('constrain_pair_numbers', self.constrain_pair_numbers) - self.add_prim_attr('iteration_numbers', self.iteration_numbers) - self.add_prim_attr('half_exp_gamma_plus_half', self.half_exp_gamma_plus_half) - - self.init_prim_io_names( - inputs=['crd', 'quarter_cof', 'mass_inverse', - 'scaler', 'pair_dr', 'atom_i_serials', 'atom_j_serials', - 'constant_rs', 'constrain_ks'], - outputs=['uint_crd', 'frc', 'virial']) - - def infer_shape(self, crd, quarter_cof, mass_inverse, scaler_shape, pair_dr_shape, atom_i_serials_shape, - atom_j_serials_shape, constant_rs_shape, constrain_ks_shape): - cls_name = self.name - n = self.atom_numbers - m = self.constrain_pair_numbers - validator.check_int(len(crd), 2, Rel.EQ, "crd_dim", cls_name) - validator.check_int(len(quarter_cof), 1, Rel.EQ, "quarter_cof_dim", cls_name) - validator.check_int(len(mass_inverse), 1, Rel.EQ, "mass_inverse_dim", cls_name) - validator.check_int(len(scaler_shape), 1, Rel.EQ, "scaler_dim", cls_name) - validator.check_int(len(pair_dr_shape), 2, Rel.EQ, "pair_dr_dim", cls_name) - validator.check_int(len(atom_i_serials_shape), 1, Rel.EQ, "atom_i_serials_dim", cls_name) - validator.check_int(len(atom_j_serials_shape), 1, Rel.EQ, "atom_j_serials_dim", cls_name) - validator.check_int(len(constant_rs_shape), 1, Rel.EQ, "constant_rs_dim", cls_name) - validator.check_int(len(constrain_ks_shape), 1, Rel.EQ, "constrain_ks_dim", cls_name) - validator.check_int(crd[0], n, Rel.EQ, "crd_shape[0]", cls_name) - validator.check_int(crd[1], 3, Rel.EQ, "crd_shape[1]", cls_name) - validator.check_int(quarter_cof[0], 3, Rel.EQ, "quarter_cof_shape", cls_name) - validator.check_int(mass_inverse[0], n, Rel.EQ, "mass_inverse_shape", cls_name) - validator.check_int(scaler_shape[0], 3, Rel.EQ, "scaler_shape", cls_name) - validator.check_int(pair_dr_shape[0], m, Rel.EQ, "pair_dr_shape[0]", cls_name) - validator.check_int(pair_dr_shape[1], 3, Rel.EQ, "pair_dr_shape[1]", cls_name) - validator.check_int(atom_i_serials_shape[0], m, Rel.EQ, "atom_i_serials_shape[0]", cls_name) - validator.check_int(atom_j_serials_shape[0], m, Rel.EQ, "atom_j_serials_shape[0]", cls_name) - validator.check_int(constant_rs_shape[0], m, Rel.EQ, "constant_rs_shape[0]", cls_name) - validator.check_int(constrain_ks_shape[0], m, Rel.EQ, "constrain_ks_shape[0]", cls_name) - return [n, 3], [n, 3], [m,] - - def infer_dtype(self, crd, quarter_cof, mass_inverse, scaler_dtype, pair_dr_dtype, atom_i_serials_dtype, - atom_j_serials_dtype, constant_rs_dtype, constrain_ks_dtype): - validator.check_tensor_dtype_valid('crd', crd, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('quarter_cof', quarter_cof, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('mass_inverse', mass_inverse, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('scaler', scaler_dtype, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('pair_dr', pair_dr_dtype, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('atom_i_serials', atom_i_serials_dtype, [mstype.int32], self.name) - validator.check_tensor_dtype_valid('atom_j_serials', atom_j_serials_dtype, [mstype.int32], self.name) - validator.check_tensor_dtype_valid('constant_rs', constant_rs_dtype, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('constrain_ks', constrain_ks_dtype, [mstype.float32], self.name) - return mstype.uint32, mstype.float32, mstype.float32 - - -class ConstrainForce(PrimitiveWithInfer): - """ - Calculate the constraint force in a step with iteration numbers. - - .. warning:: - This is an experimental prototype that is subject to change and/or deletion. - - Args: - atom_numbers (int32): the number of atoms n. - constrain_pair_numbers (int32): the number of constrain pairs m. - iteration_numbers (int32): the number of iteration numbers p. - half_exp_gamma_plus_half (float32): half exp_gamma plus half q. - - Inputs: - - **crd** (Tensor) - The coordinate of each atom. - The data type is float32 and the shape is :math:`(n, 3)`. - - **quarter_cof** (Tensor) - The 3-D scale factor. - The data type is float32 and the shape is :math:`(3,)`. - - **mass_inverse** (Tensor) - The inverse value of mass of each atom. - The data type is float32 and the shape is :math:`(n,)`. - - **scaler** (Tensor) - The 3-D scale factor (x, y, z), - The data type is float32 and the shape is :math:`(3,)`. - - **pair_dr** (Tensor) - The displacement vector of each constrained atom pair. - The data type is float32 and the shape is :math:`(m, 3)`. - - **atom_i_serials** (Tensor) - The first atom index of each constrained atom pair. - The data type is int32 and the shape is :math:`(m,)`. - - **atom_j_serials** (Tensor) - The second atom index of each constrained atom pair. - The data type is int32 and the shape is :math:`(m,)`. - - **constant_rs** (Tensor) - The constrained distance of each constrained atom pair. - The data type is float32 and the shape is :math:`(m,)`. - - **constrain_ks** (Tensor) - The coefficient of each constrained atom pair. - The data type is float32 and the shape is :math:`(m,)`. - - Outputs: - - **uint_crd** (Tensor) - The unsigned int coordinate value of each atom. - The data type is uint32 and the shape is :math:`(n, 3)`. - - **frc** (Tensor) - The constraint force on each atom. - The data type is float32 and the shape is :math:`(n, 3)`. - - **virial** (Tensor) - The constraint virial on each atom and it is zero. - The data type is float32 and the shape is :math:`(m,)`. - - Supported Platforms: - ``GPU`` - """ - - @prim_attr_register - def __init__(self, atom_numbers, constrain_pair_numbers, iteration_numbers, half_exp_gamma_plus_half): - validator.check_value_type('atom_numbers', atom_numbers, int, self.name) - validator.check_value_type('constrain_pair_numbers', constrain_pair_numbers, int, self.name) - validator.check_value_type('iteration_numbers', iteration_numbers, int, self.name) - validator.check_value_type('half_exp_gamma_plus_half', half_exp_gamma_plus_half, float, self.name) - self.atom_numbers = atom_numbers - self.constrain_pair_numbers = constrain_pair_numbers - self.iteration_numbers = iteration_numbers - self.half_exp_gamma_plus_half = half_exp_gamma_plus_half - self.add_prim_attr('atom_numbers', self.atom_numbers) - self.add_prim_attr('constrain_pair_numbers', self.constrain_pair_numbers) - self.add_prim_attr('iteration_numbers', self.iteration_numbers) - self.add_prim_attr('half_exp_gamma_plus_half', self.half_exp_gamma_plus_half) - - self.init_prim_io_names( - inputs=['crd', 'quarter_cof', 'mass_inverse', - 'scaler', 'pair_dr', 'atom_i_serials', 'atom_j_serials', 'constant_rs', 'constrain_ks'], - outputs=['uint_crd', 'frc', 'virial']) - - def infer_shape(self, crd, quarter_cof, mass_inverse, scaler_shape, pair_dr_shape, atom_i_serials_shape, - atom_j_serials_shape, constant_rs_shape, constrain_ks_shape): - cls_name = self.name - n = self.atom_numbers - m = self.constrain_pair_numbers - validator.check_int(len(crd), 2, Rel.EQ, "crd_dim", cls_name) - validator.check_int(len(quarter_cof), 1, Rel.EQ, "quarter_cof_dim", cls_name) - validator.check_int(len(mass_inverse), 1, Rel.EQ, "mass_inverse_dim", cls_name) - validator.check_int(len(scaler_shape), 1, Rel.EQ, "scaler_dim", cls_name) - validator.check_int(len(pair_dr_shape), 2, Rel.EQ, "pair_dr_dim", cls_name) - validator.check_int(len(atom_i_serials_shape), 1, Rel.EQ, "atom_i_serials_dim", cls_name) - validator.check_int(len(atom_j_serials_shape), 1, Rel.EQ, "atom_j_serials_dim", cls_name) - validator.check_int(len(constant_rs_shape), 1, Rel.EQ, "constant_rs_dim", cls_name) - validator.check_int(len(constrain_ks_shape), 1, Rel.EQ, "constrain_ks_dim", cls_name) - validator.check_int(crd[0], n, Rel.EQ, "crd_shape[0]", cls_name) - validator.check_int(crd[1], 3, Rel.EQ, "crd_shape[1]", cls_name) - validator.check_int(quarter_cof[0], 3, Rel.EQ, "quarter_cof_shape", cls_name) - validator.check_int(mass_inverse[0], n, Rel.EQ, "mass_inverse_shape", cls_name) - validator.check_int(scaler_shape[0], 3, Rel.EQ, "scaler_shape", cls_name) - validator.check_int(pair_dr_shape[0], m, Rel.EQ, "pair_dr_shape[0]", cls_name) - validator.check_int(pair_dr_shape[1], 3, Rel.EQ, "pair_dr_shape[1]", cls_name) - validator.check_int(atom_i_serials_shape[0], m, Rel.EQ, "atom_i_serials_shape[0]", cls_name) - validator.check_int(atom_j_serials_shape[0], m, Rel.EQ, "atom_j_serials_shape[0]", cls_name) - validator.check_int(constant_rs_shape[0], m, Rel.EQ, "constant_rs_shape[0]", cls_name) - validator.check_int(constrain_ks_shape[0], m, Rel.EQ, "constrain_ks_shape[0]", cls_name) - return [n, 3], [n, 3], [m,] - - def infer_dtype(self, crd, quarter_cof, mass_inverse, scaler_dtype, pair_dr_dtype, atom_i_serials_dtype, - atom_j_serials_dtype, constant_rs_dtype, constrain_ks_dtype): - validator.check_tensor_dtype_valid('crd', crd, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('quarter_cof', quarter_cof, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('mass_inverse', mass_inverse, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('scaler', scaler_dtype, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('pair_dr', pair_dr_dtype, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('atom_i_serials', atom_i_serials_dtype, [mstype.int32], self.name) - validator.check_tensor_dtype_valid('atom_j_serials', atom_j_serials_dtype, [mstype.int32], self.name) - validator.check_tensor_dtype_valid('constant_rs', constant_rs_dtype, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('constrain_ks', constrain_ks_dtype, [mstype.float32], self.name) - return mstype.uint32, mstype.float32, mstype.float32 - - -class Constrain(PrimitiveWithInfer): - """ - Calculate the constraint force and virial depends on pressure calculation. - - .. warning:: - This is an experimental prototype that is subject to change and/or deletion. - - Args: - atom_numbers (int32): the number of atoms n. - constrain_pair_numbers (int32): the number of constrain pairs m. - iteration_numbers (int32): the number of iteration numbers p. - half_exp_gamma_plus_half (float32): half exp_gamma plus half q. - update_interval (int32): the number of update interval, default 10. - - Inputs: - - **crd** (Tensor) - The coordinate of each atom. - The data type is float32 and the shape is :math:`(n, 3)`. - - **quarter_cof** (Tensor) - The 3-D scale factor. - The data type is float32 and the shape is :math:`(3,)`. - - **mass_inverse** (Tensor) - The inverse value of mass of each atom. - The data type is float32 and the shape is :math:`(n,)`. - - **scaler** (Tensor) - The 3-D scale factor (x, y, z), - The data type is float32 and the shape is :math:`(3,)`. - - **pair_dr** (Tensor) - The displacement vector of each constrained atom pair. - The data type is float32 and the shape is :math:`(m, 3)`. - - **atom_i_serials** (Tensor) - The first atom index of each constrained atom pair. - The data type is int32 and the shape is :math:`(m,)`. - - **atom_j_serials** (Tensor) - The second atom index of each constrained atom pair. - The data type is int32 and the shape is :math:`(m,)`. - - **constant_rs** (Tensor) - The constrained distance of each constrained atom pair. - The data type is float32 and the shape is :math:`(m,)`. - - **constrain_ks** (Tensor) - The coefficient of each constrained atom pair. - The data type is float32 and the shape is :math:`(m,)`. - - **need_pressure** (Tensor) - If need pressure, 1 else 0. - The data type is int32 and the shape is :math:`(1,)` or :math:`()`. - - Outputs: - - **uint_crd** (Tensor) - The unsigned int coordinate value of each atom. - The data type is uint32 and the shape is :math:`(n, 3)`. - - **frc** (Tensor) - The constraint force on each atom. - The data type is float32 and the shape is :math:`(n, 3)`. - - **virial** (Tensor) - The constraint virial on each atom. - The data type is float32 and the shape is :math:`(m,)`. - - Supported Platforms: - ``GPU`` - """ - - @prim_attr_register - def __init__(self, atom_numbers, constrain_pair_numbers, iteration_numbers, half_exp_gamma_plus_half, - update_interval=10): - validator.check_value_type('atom_numbers', atom_numbers, int, self.name) - validator.check_value_type('constrain_pair_numbers', constrain_pair_numbers, int, self.name) - validator.check_value_type('iteration_numbers', iteration_numbers, int, self.name) - validator.check_value_type('half_exp_gamma_plus_half', half_exp_gamma_plus_half, float, self.name) - validator.check_value_type('update_interval', update_interval, int, self.name) - self.atom_numbers = atom_numbers - self.constrain_pair_numbers = constrain_pair_numbers - self.iteration_numbers = iteration_numbers - self.half_exp_gamma_plus_half = half_exp_gamma_plus_half - self.update_interval = update_interval - self.add_prim_attr('atom_numbers', self.atom_numbers) - self.add_prim_attr('constrain_pair_numbers', self.constrain_pair_numbers) - self.add_prim_attr('iteration_numbers', self.iteration_numbers) - self.add_prim_attr('half_exp_gamma_plus_half', self.half_exp_gamma_plus_half) - self.add_prim_attr('update_interval', self.update_interval) - - self.init_prim_io_names( - inputs=['crd', 'quarter_cof', 'mass_inverse', - 'scaler', 'pair_dr', 'atom_i_serials', 'atom_j_serials', - 'constant_rs', 'constrain_ks', 'need_pressure'], - outputs=['uint_crd', 'frc', 'virial']) - - def infer_shape(self, crd, quarter_cof, mass_inverse, scaler_shape, pair_dr_shape, atom_i_serials_shape, - atom_j_serials_shape, constant_rs_shape, constrain_ks_shape, need_pressure): - cls_name = self.name - n = self.atom_numbers - m = self.constrain_pair_numbers - validator.check_int(len(crd), 2, Rel.EQ, "crd_dim", cls_name) - validator.check_int(len(quarter_cof), 1, Rel.EQ, "quarter_cof_dim", cls_name) - validator.check_int(len(mass_inverse), 1, Rel.EQ, "mass_inverse_dim", cls_name) - validator.check_int(len(scaler_shape), 1, Rel.EQ, "scaler_dim", cls_name) - validator.check_int(len(pair_dr_shape), 2, Rel.EQ, "pair_dr_dim", cls_name) - validator.check_int(len(atom_i_serials_shape), 1, Rel.EQ, "atom_i_serials_dim", cls_name) - validator.check_int(len(atom_j_serials_shape), 1, Rel.EQ, "atom_j_serials_dim", cls_name) - validator.check_int(len(constant_rs_shape), 1, Rel.EQ, "constant_rs_dim", cls_name) - validator.check_int(len(constrain_ks_shape), 1, Rel.EQ, "constrain_ks_dim", cls_name) - validator.check_int(len(need_pressure), 1, Rel.LE, "need_pressure_dim", cls_name) - validator.check_int(crd[0], n, Rel.EQ, "crd_shape[0]", cls_name) - validator.check_int(crd[1], 3, Rel.EQ, "crd_shape[1]", cls_name) - validator.check_int(quarter_cof[0], 3, Rel.EQ, "quarter_cof_shape", cls_name) - validator.check_int(mass_inverse[0], n, Rel.EQ, "mass_inverse_shape", cls_name) - validator.check_int(scaler_shape[0], 3, Rel.EQ, "scaler_shape", cls_name) - validator.check_int(pair_dr_shape[0], m, Rel.EQ, "pair_dr_shape[0]", cls_name) - validator.check_int(pair_dr_shape[1], 3, Rel.EQ, "pair_dr_shape[1]", cls_name) - validator.check_int(atom_i_serials_shape[0], m, Rel.EQ, "atom_i_serials_shape[0]", cls_name) - validator.check_int(atom_j_serials_shape[0], m, Rel.EQ, "atom_j_serials_shape[0]", cls_name) - validator.check_int(constant_rs_shape[0], m, Rel.EQ, "constant_rs_shape[0]", cls_name) - validator.check_int(constrain_ks_shape[0], m, Rel.EQ, "constrain_ks_shape[0]", cls_name) - if need_pressure: - validator.check_int(need_pressure[0], 1, Rel.EQ, "need_pressure_shape", self.name) - return [n, 3], [n, 3], [m,] - - def infer_dtype(self, crd, quarter_cof, mass_inverse, scaler_dtype, pair_dr_dtype, atom_i_serials_dtype, - atom_j_serials_dtype, constant_rs_dtype, constrain_ks_dtype, need_pressure): - validator.check_tensor_dtype_valid('crd', crd, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('quarter_cof', quarter_cof, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('mass_inverse', mass_inverse, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('scaler', scaler_dtype, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('pair_dr', pair_dr_dtype, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('atom_i_serials', atom_i_serials_dtype, [mstype.int32], self.name) - validator.check_tensor_dtype_valid('atom_j_serials', atom_j_serials_dtype, [mstype.int32], self.name) - validator.check_tensor_dtype_valid('constant_rs', constant_rs_dtype, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('constrain_ks', constrain_ks_dtype, [mstype.float32], self.name) - validator.check_tensor_dtype_valid('need_pressure', need_pressure, [mstype.int32], self.name) - return mstype.uint32, mstype.float32, mstype.float32 diff --git a/tests/st/sponge/__init__.py b/tests/st/sponge/__init__.py deleted file mode 100644 index e69de29bb2d..00000000000 diff --git a/tests/st/sponge/data/NVT_290_10ns.in b/tests/st/sponge/data/NVT_290_10ns.in deleted file mode 100644 index 54f9ab22f84..00000000000 --- a/tests/st/sponge/data/NVT_290_10ns.in +++ /dev/null @@ -1,15 +0,0 @@ -290K 200ns - - mode = 1, - dt= 0.001, - step_limit = 1000, - langevin_gamma=1.0, - target_temperature=290, - thermostat=1, - write_information_interval=100, - amber_irest=0, - cut=10.0, - - - - diff --git a/tests/st/sponge/data/WATER_ALA.parm7 b/tests/st/sponge/data/WATER_ALA.parm7 deleted file mode 100644 index 46842e27559..00000000000 --- a/tests/st/sponge/data/WATER_ALA.parm7 +++ /dev/null @@ -1,4764 +0,0 @@ -%VERSION VERSION_STAMP = V0001.000 DATE = 04/15/21 15:06:14 -%FLAG TITLE -%FORMAT(20a4) -ALA -%FLAG POINTERS -%FORMAT(10I8) - 2298 9 2283 14 32 18 57 44 0 0 - 3169 759 14 18 44 10 14 20 10 1 - 0 0 0 0 0 0 0 1 10 0 - 0 -%FLAG ATOM_NAME -%FORMAT(20a4) -N H CA HA CB HB1 HB2 HB3 C O N H CA HA CB HB1 HB2 HB3 C O -N H CA HA CB HB1 HB2 HB3 C O O H1 H2 O H1 H2 O H1 H2 O -H1 H2 O H1 H2 O H1 H2 O H1 H2 O H1 H2 O H1 H2 O H1 H2 -O H1 H2 O H1 H2 O H1 H2 O H1 H2 O H1 H2 O H1 H2 O H1 -H2 O H1 H2 O H1 H2 O H1 H2 O H1 H2 O H1 H2 O H1 H2 O -H1 H2 O H1 H2 O H1 H2 O H1 H2 O H1 H2 O H1 H2 O H1 H2 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8 9 9 8 9 9 8 - 9 9 8 9 9 8 9 9 8 9 - 9 8 9 9 8 9 9 8 9 9 - 8 9 9 8 9 9 8 9 9 8 - 9 9 8 9 9 8 9 9 8 9 - 9 8 9 9 8 9 9 8 9 9 - 8 9 9 8 9 9 8 9 9 8 - 9 9 8 9 9 8 9 9 8 9 - 9 8 9 9 8 9 9 8 9 9 - 8 9 9 8 9 9 8 9 9 8 - 9 9 8 9 9 8 9 9 8 9 - 9 8 9 9 8 9 9 8 9 9 - 8 9 9 8 9 9 8 9 9 8 - 9 9 8 9 9 8 9 9 8 9 - 9 8 9 9 8 9 9 8 9 9 - 8 9 9 8 9 9 8 9 9 8 - 9 9 8 9 9 8 9 9 8 9 - 9 8 9 9 8 9 9 8 9 9 - 8 9 9 8 9 9 8 9 9 8 - 9 9 8 9 9 8 9 9 8 9 - 9 8 9 9 8 9 9 8 9 9 - 8 9 9 8 9 9 8 9 9 8 - 9 9 8 9 9 8 9 9 8 9 - 9 8 9 9 8 9 9 8 9 9 - 8 9 9 8 9 9 8 9 9 8 - 9 9 8 9 9 8 9 9 8 9 - 9 8 9 9 8 9 9 8 9 9 - 8 9 9 8 9 9 8 9 9 8 - 9 9 8 9 9 8 9 9 8 9 - 9 8 9 9 8 9 9 8 9 9 - 8 9 9 8 9 9 8 9 9 8 - 9 9 8 9 9 8 9 9 8 9 - 9 8 9 9 8 9 9 8 9 9 - 8 9 9 8 9 9 8 9 9 8 - 9 9 8 9 9 8 9 9 8 9 - 9 8 9 9 8 9 9 8 9 9 - 8 9 9 8 9 9 8 9 9 8 - 9 9 8 9 9 8 9 9 8 9 - 9 8 9 9 8 9 9 8 9 9 - 8 9 9 8 9 9 8 9 9 8 - 9 9 8 9 9 8 9 9 8 9 - 9 8 9 9 8 9 9 8 9 9 - 8 9 9 8 9 9 8 9 9 8 - 9 9 8 9 9 8 9 9 8 9 - 9 8 9 9 8 9 9 8 9 9 - 8 9 9 8 9 9 8 9 9 8 - 9 9 8 9 9 8 9 9 8 9 - 9 8 9 9 8 9 9 8 9 9 - 8 9 9 8 9 9 8 9 9 8 - 9 9 8 9 9 8 9 9 8 9 - 9 8 9 9 8 9 9 8 9 9 - 8 9 9 8 9 9 8 9 9 8 - 9 9 8 9 9 8 9 9 8 9 - 9 8 9 9 8 9 9 8 9 9 - 8 9 9 8 9 9 8 9 9 8 - 9 9 8 9 9 8 9 9 8 9 - 9 8 9 9 8 9 9 8 9 9 - 8 9 9 8 9 9 8 9 9 8 - 9 9 8 9 9 8 9 9 8 9 - 9 8 9 9 8 9 9 8 9 9 - 8 9 9 8 9 9 8 9 9 8 - 9 9 8 9 9 8 9 9 8 9 - 9 8 9 9 8 9 9 8 9 9 - 8 9 9 8 9 9 8 9 9 8 - 9 9 8 9 9 8 9 9 8 9 - 9 8 9 9 8 9 9 8 9 9 - 8 9 9 8 9 9 8 9 9 8 - 9 9 8 9 9 8 9 9 8 9 - 9 8 9 9 8 9 9 8 9 9 - 8 9 9 8 9 9 8 9 9 8 - 9 9 8 9 9 8 9 9 8 9 - 9 8 9 9 8 9 9 8 9 9 - 8 9 9 8 9 9 8 9 9 8 - 9 9 8 9 9 8 9 9 8 9 - 9 8 9 9 8 9 9 8 9 9 - 8 9 9 8 9 9 8 9 9 8 - 9 9 8 9 9 8 9 9 8 9 - 9 8 9 9 8 9 9 8 9 9 - 8 9 9 8 9 9 8 9 9 8 - 9 9 8 9 9 8 9 9 8 9 - 9 8 9 9 8 9 9 8 9 9 - 8 9 9 8 9 9 8 9 9 8 - 9 9 8 9 9 8 9 9 8 9 - 9 8 9 9 8 9 9 8 9 9 - 8 9 9 8 9 9 8 9 9 8 - 9 9 8 9 9 8 9 9 8 9 - 9 8 9 9 8 9 9 8 9 9 - 8 9 9 8 9 9 8 9 9 8 - 9 9 8 9 9 8 9 9 8 9 - 9 8 9 9 8 9 9 8 9 9 - 8 9 9 8 9 9 8 9 9 8 - 9 9 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9 -%FLAG NUMBER_EXCLUDED_ATOMS -%FORMAT(10I8) - 10 4 10 7 6 3 2 1 7 3 - 10 4 10 7 6 3 2 1 7 3 - 9 4 7 6 5 3 2 1 1 1 - 2 1 1 2 1 1 2 1 1 2 - 1 1 2 1 1 2 1 1 2 1 - 1 2 1 1 2 1 1 2 1 1 - 2 1 1 2 1 1 2 1 1 2 - 1 1 2 1 1 2 1 1 2 1 - 1 2 1 1 2 1 1 2 1 1 - 2 1 1 2 1 1 2 1 1 2 - 1 1 2 1 1 2 1 1 2 1 - 1 2 1 1 2 1 1 2 1 1 - 2 1 1 2 1 1 2 1 1 2 - 1 1 2 1 1 2 1 1 2 1 - 1 2 1 1 2 1 1 2 1 1 - 2 1 1 2 1 1 2 1 1 2 - 1 1 2 1 1 2 1 1 2 1 - 1 2 1 1 2 1 1 2 1 1 - 2 1 1 2 1 1 2 1 1 2 - 1 1 2 1 1 2 1 1 2 1 - 1 2 1 1 2 1 1 2 1 1 - 2 1 1 2 1 1 2 1 1 2 - 1 1 2 1 1 2 1 1 2 1 - 1 2 1 1 2 1 1 2 1 1 - 2 1 1 2 1 1 2 1 1 2 - 1 1 2 1 1 2 1 1 2 1 - 1 2 1 1 2 1 1 2 1 1 - 2 1 1 2 1 1 2 1 1 2 - 1 1 2 1 1 2 1 1 2 1 - 1 2 1 1 2 1 1 2 1 1 - 2 1 1 2 1 1 2 1 1 2 - 1 1 2 1 1 2 1 1 2 1 - 1 2 1 1 2 1 1 2 1 1 - 2 1 1 2 1 1 2 1 1 2 - 1 1 2 1 1 2 1 1 2 1 - 1 2 1 1 2 1 1 2 1 1 - 2 1 1 2 1 1 2 1 1 2 - 1 1 2 1 1 2 1 1 2 1 - 1 2 1 1 2 1 1 2 1 1 - 2 1 1 2 1 1 2 1 1 2 - 1 1 2 1 1 2 1 1 2 1 - 1 2 1 1 2 1 1 2 1 1 - 2 1 1 2 1 1 2 1 1 2 - 1 1 2 1 1 2 1 1 2 1 - 1 2 1 1 2 1 1 2 1 1 - 2 1 1 2 1 1 2 1 1 2 - 1 1 2 1 1 2 1 1 2 1 - 1 2 1 1 2 1 1 2 1 1 - 2 1 1 2 1 1 2 1 1 2 - 1 1 2 1 1 2 1 1 2 1 - 1 2 1 1 2 1 1 2 1 1 - 2 1 1 2 1 1 2 1 1 2 - 1 1 2 1 1 2 1 1 2 1 - 1 2 1 1 2 1 1 2 1 1 - 2 1 1 2 1 1 2 1 1 2 - 1 1 2 1 1 2 1 1 2 1 - 1 2 1 1 2 1 1 2 1 1 - 2 1 1 2 1 1 2 1 1 2 - 1 1 2 1 1 2 1 1 2 1 - 1 2 1 1 2 1 1 2 1 1 - 2 1 1 2 1 1 2 1 1 2 - 1 1 2 1 1 2 1 1 2 1 - 1 2 1 1 2 1 1 2 1 1 - 2 1 1 2 1 1 2 1 1 2 - 1 1 2 1 1 2 1 1 2 1 - 1 2 1 1 2 1 1 2 1 1 - 2 1 1 2 1 1 2 1 1 2 - 1 1 2 1 1 2 1 1 2 1 - 1 2 1 1 2 1 1 2 1 1 - 2 1 1 2 1 1 2 1 1 2 - 1 1 2 1 1 2 1 1 2 1 - 1 2 1 1 2 1 1 2 1 1 - 2 1 1 2 1 1 2 1 1 2 - 1 1 2 1 1 2 1 1 2 1 - 1 2 1 1 2 1 1 2 1 1 - 2 1 1 2 1 1 2 1 1 2 - 1 1 2 1 1 2 1 1 2 1 - 1 2 1 1 2 1 1 2 1 1 - 2 1 1 2 1 1 2 1 1 2 - 1 1 2 1 1 2 1 1 2 1 - 1 2 1 1 2 1 1 2 1 1 - 2 1 1 2 1 1 2 1 1 2 - 1 1 2 1 1 2 1 1 2 1 - 1 2 1 1 2 1 1 2 1 1 - 2 1 1 2 1 1 2 1 1 2 - 1 1 2 1 1 2 1 1 2 1 - 1 2 1 1 2 1 1 2 1 1 - 2 1 1 2 1 1 2 1 1 2 - 1 1 2 1 1 2 1 1 2 1 - 1 2 1 1 2 1 1 2 1 1 - 2 1 1 2 1 1 2 1 1 2 - 1 1 2 1 1 2 1 1 2 1 - 1 2 1 1 2 1 1 2 1 1 - 2 1 1 2 1 1 2 1 1 2 - 1 1 2 1 1 2 1 1 2 1 - 1 2 1 1 2 1 1 2 1 1 - 2 1 1 2 1 1 2 1 1 2 - 1 1 2 1 1 2 1 1 2 1 - 1 2 1 1 2 1 1 2 1 1 - 2 1 1 2 1 1 2 1 1 2 - 1 1 2 1 1 2 1 1 2 1 - 1 2 1 1 2 1 1 2 1 1 - 2 1 1 2 1 1 2 1 1 2 - 1 1 2 1 1 2 1 1 2 1 - 1 2 1 1 2 1 1 2 1 1 - 2 1 1 2 1 1 2 1 1 2 - 1 1 2 1 1 2 1 1 2 1 - 1 2 1 1 2 1 1 2 1 1 - 2 1 1 2 1 1 2 1 1 2 - 1 1 2 1 1 2 1 1 2 1 - 1 2 1 1 2 1 1 2 1 1 - 2 1 1 2 1 1 2 1 1 2 - 1 1 2 1 1 2 1 1 2 1 - 1 2 1 1 2 1 1 2 1 1 - 2 1 1 2 1 1 2 1 1 2 - 1 1 2 1 1 2 1 1 2 1 - 1 2 1 1 2 1 1 2 1 1 - 2 1 1 2 1 1 2 1 1 2 - 1 1 2 1 1 2 1 1 2 1 - 1 2 1 1 2 1 1 2 1 1 - 2 1 1 2 1 1 2 1 1 2 - 1 1 2 1 1 2 1 1 2 1 - 1 2 1 1 2 1 1 2 1 1 - 2 1 1 2 1 1 2 1 1 2 - 1 1 2 1 1 2 1 1 2 1 - 1 2 1 1 2 1 1 2 1 1 - 2 1 1 2 1 1 2 1 1 2 - 1 1 2 1 1 2 1 1 2 1 - 1 2 1 1 2 1 1 2 1 1 - 2 1 1 2 1 1 2 1 1 2 - 1 1 2 1 1 2 1 1 2 1 - 1 2 1 1 2 1 1 2 1 1 - 2 1 1 2 1 1 2 1 1 2 - 1 1 2 1 1 2 1 1 2 1 - 1 2 1 1 2 1 1 2 1 1 - 2 1 1 2 1 1 2 1 1 2 - 1 1 2 1 1 2 1 1 2 1 - 1 2 1 1 2 1 1 2 1 1 - 2 1 1 2 1 1 2 1 1 2 - 1 1 2 1 1 2 1 1 2 1 - 1 2 1 1 2 1 1 2 1 1 - 2 1 1 2 1 1 2 1 1 2 - 1 1 2 1 1 2 1 1 2 1 - 1 2 1 1 2 1 1 2 1 1 - 2 1 1 2 1 1 2 1 1 2 - 1 1 2 1 1 2 1 1 2 1 - 1 2 1 1 2 1 1 2 1 1 - 2 1 1 2 1 1 2 1 1 2 - 1 1 2 1 1 2 1 1 2 1 - 1 2 1 1 2 1 1 2 1 1 - 2 1 1 2 1 1 2 1 1 2 - 1 1 2 1 1 2 1 1 2 1 - 1 2 1 1 2 1 1 2 1 1 - 2 1 1 2 1 1 2 1 1 2 - 1 1 2 1 1 2 1 1 2 1 - 1 2 1 1 2 1 1 2 1 1 - 2 1 1 2 1 1 2 1 1 2 - 1 1 2 1 1 2 1 1 2 1 - 1 2 1 1 2 1 1 2 1 1 - 2 1 1 2 1 1 2 1 1 2 - 1 1 2 1 1 2 1 1 2 1 - 1 2 1 1 2 1 1 2 1 1 - 2 1 1 2 1 1 2 1 1 2 - 1 1 2 1 1 2 1 1 2 1 - 1 2 1 1 2 1 1 2 1 1 - 2 1 1 2 1 1 2 1 1 2 - 1 1 2 1 1 2 1 1 2 1 - 1 2 1 1 2 1 1 2 1 1 - 2 1 1 2 1 1 2 1 1 2 - 1 1 2 1 1 2 1 1 2 1 - 1 2 1 1 2 1 1 2 1 1 - 2 1 1 2 1 1 2 1 1 2 - 1 1 2 1 1 2 1 1 2 1 - 1 2 1 1 2 1 1 2 1 1 - 2 1 1 2 1 1 2 1 1 2 - 1 1 2 1 1 2 1 1 2 1 - 1 2 1 1 2 1 1 2 1 1 - 2 1 1 2 1 1 2 1 1 2 - 1 1 2 1 1 2 1 1 2 1 - 1 2 1 1 2 1 1 2 1 1 - 2 1 1 2 1 1 2 1 1 2 - 1 1 2 1 1 2 1 1 2 1 - 1 2 1 1 2 1 1 2 1 1 - 2 1 1 2 1 1 2 1 1 2 - 1 1 2 1 1 2 1 1 2 1 - 1 2 1 1 2 1 1 2 1 1 - 2 1 1 2 1 1 2 1 1 2 - 1 1 2 1 1 2 1 1 2 1 - 1 2 1 1 2 1 1 2 1 1 - 2 1 1 2 1 1 2 1 1 2 - 1 1 2 1 1 2 1 1 2 1 - 1 2 1 1 2 1 1 2 1 1 - 2 1 1 2 1 1 2 1 1 2 - 1 1 2 1 1 2 1 1 2 1 - 1 2 1 1 2 1 1 2 1 1 - 2 1 1 2 1 1 2 1 1 2 - 1 1 2 1 1 2 1 1 2 1 - 1 2 1 1 2 1 1 2 1 1 - 2 1 1 2 1 1 2 1 1 2 - 1 1 2 1 1 2 1 1 2 1 - 1 2 1 1 2 1 1 2 1 1 - 2 1 1 2 1 1 2 1 1 2 - 1 1 2 1 1 2 1 1 2 1 - 1 2 1 1 2 1 1 2 1 1 - 2 1 1 2 1 1 2 1 1 2 - 1 1 2 1 1 2 1 1 2 1 - 1 2 1 1 2 1 1 2 1 1 - 2 1 1 2 1 1 2 1 1 2 - 1 1 2 1 1 2 1 1 2 1 - 1 2 1 1 2 1 1 2 1 1 - 2 1 1 2 1 1 2 1 1 2 - 1 1 2 1 1 2 1 1 2 1 - 1 2 1 1 2 1 1 2 1 1 - 2 1 1 2 1 1 2 1 1 2 - 1 1 2 1 1 2 1 1 2 1 - 1 2 1 1 2 1 1 2 1 1 - 2 1 1 2 1 1 2 1 1 2 - 1 1 2 1 1 2 1 1 2 1 - 1 2 1 1 2 1 1 2 1 1 - 2 1 1 2 1 1 2 1 1 2 - 1 1 2 1 1 2 1 1 2 1 - 1 2 1 1 2 1 1 2 1 1 - 2 1 1 2 1 1 2 1 1 2 - 1 1 2 1 1 2 1 1 2 1 - 1 2 1 1 2 1 1 2 1 1 - 2 1 1 2 1 1 2 1 1 2 - 1 1 2 1 1 2 1 1 2 1 - 1 2 1 1 2 1 1 2 1 1 - 2 1 1 2 1 1 2 1 1 2 - 1 1 2 1 1 2 1 1 -%FLAG NONBONDED_PARM_INDEX -%FORMAT(10I8) - 1 2 4 7 11 16 22 29 37 2 - 3 5 8 12 17 23 30 38 4 5 - 6 9 13 18 24 31 39 7 8 9 - 10 14 19 25 32 40 11 12 13 14 - 15 20 26 33 41 16 17 18 19 20 - 21 27 34 42 22 23 24 25 26 27 - 28 35 43 29 30 31 32 33 34 35 - 36 -1 37 38 39 40 41 42 43 -1 - 45 -%FLAG RESIDUE_LABEL -%FORMAT(20a4) -ALA ALA ALA WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT -WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT -WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT -WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT -WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT -WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT -WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT -WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT -WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT -WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT -WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT -WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT -WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT -WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT -WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT -WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT -WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT -WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT -WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT -WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT -WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT -WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT -WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT -WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT -WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT -WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT -WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT -WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT -WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT -WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT -WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT -WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT -WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT -WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT -WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT -WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT -WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT -WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT WAT -%FLAG RESIDUE_POINTER -%FORMAT(10I8) - 1 11 21 31 34 37 40 43 46 49 - 52 55 58 61 64 67 70 73 76 79 - 82 85 88 91 94 97 100 103 106 109 - 112 115 118 121 124 127 130 133 136 139 - 142 145 148 151 154 157 160 163 166 169 - 172 175 178 181 184 187 190 193 196 199 - 202 205 208 211 214 217 220 223 226 229 - 232 235 238 241 244 247 250 253 256 259 - 262 265 268 271 274 277 280 283 286 289 - 292 295 298 301 304 307 310 313 316 319 - 322 325 328 331 334 337 340 343 346 349 - 352 355 358 361 364 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CX H1 CT HC HC HC C O -N H CX H1 CT HC HC HC C O OW HW HW OW HW HW OW HW HW OW -HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW -OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW -HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW -HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW -OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW -HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW -HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW -OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW -HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW -HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW -OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW -HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW -HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW -OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW -HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW -HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW -OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW -HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW -HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW -OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW -HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW -HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW -OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW -HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW -HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW -OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW -HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW -HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW -OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW -HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW -HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW -OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW -HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW -HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW -OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW -HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW -HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW -OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW -HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW -HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW -OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW -HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW -HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW -OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW -HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW -HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW -OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW -HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW -HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW -OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW -HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW -HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW -OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW -HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW -HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW -OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW -HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW -HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW -OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW -HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW -HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW -OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW -HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW -HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW -OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW -HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW -HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW -OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW -HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW -HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW -OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW -HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW -HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW -OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW -HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW -HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW -OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW -HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW -HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW -OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW -HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW -HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW -OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW -HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW -HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW -OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW -HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW -HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW -OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW -HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW -HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW -OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW -HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW -HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW -OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW -HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW -HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW -OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW -HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW -HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW -OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW -HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW -HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW -OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW -HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW -HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW -OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW -HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW -HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW -OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW -HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW -HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW -OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW OW HW HW -%FLAG TREE_CHAIN_CLASSIFICATION -%FORMAT(20a4) -M E M E 3 E E E M E M E M E 3 E E E M E -M E M E 3 E E E M E BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA -BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA -BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA -BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA -BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA -BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA -BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA -BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA -BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA -BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA -BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA -BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA -BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA -BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA -BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA -BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA -BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA -BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA -BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA -BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA -BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA -BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA -BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA -BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA -BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA -BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA -BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA -BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA -BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA -BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA -BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA -BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA -BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA -BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA -BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA -BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA -BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA -BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA -BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA -BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA -BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA -BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA -BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA -BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA -BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA -BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA -BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA -BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA -BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA -BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA -BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA -BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA -BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA -BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA -BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA -BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA -BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA -BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA -BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA -BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA -BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA -BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA -BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA -BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA -BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA -BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA -BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA -BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA -BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA -BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA -BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA -BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA -BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA -BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA -BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA -BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA -BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA -BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA -BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA -BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA -BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA -BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA -BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA -BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA -BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA -BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA -BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA -BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA -BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA -BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA -BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA -BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA -BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA -BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA -BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA -BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA -BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA -BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA -BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA -BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA -BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA -BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA -BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA -BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA -BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA -BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA -BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA -BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA -BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA -BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA -BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA BLA 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5.6481250 - 7.0265180 11.4291130 6.2162830 4.3212280 4.4789470 7.9433300 - 4.8726870 4.5884810 7.1686550 4.8355790 4.8527070 8.6588530 - 14.1650970 14.9645690 9.9896570 13.6881530 15.7717880 9.7969180 - 13.4812760 14.3124020 10.1422780 5.1683790 11.1295510 12.9781270 - 4.2712320 11.3535480 13.2254670 5.1936660 11.2530450 12.0292670 - 3.9955010 8.0008830 11.5538640 3.8562780 8.8731630 11.9226040 - 3.1136370 7.6581640 11.4086580 10.3184390 12.9184700 2.8603770 - 10.7580300 13.6609030 2.4459160 10.2571280 13.1611270 3.7842730 - 5.7935360 13.9430530 13.7623790 5.7650790 14.3473480 12.8952220 - 5.6300330 13.0140890 13.5995390 11.0100170 15.6198480 10.7882780 - 11.5244840 15.5306490 11.5905210 10.1669250 15.9658180 11.0810690 - 5.4642950 15.6852350 1.9938400 5.2885430 14.7687890 1.7806200 - 4.6098690 16.0439640 2.2336200 12.2530630 15.0404090 2.8513270 - 12.7087580 14.2593470 3.1651890 12.5923150 15.7510910 3.3954450 - 17.0007440 3.9702940 13.1837710 16.3851710 4.4782610 12.6553170 - 16.6803860 3.0701270 13.1263650 9.0621760 2.8094830 10.6499900 - 8.6001130 3.0177850 11.4619840 8.6599870 1.9954180 10.3470570 - 14.0626270 4.0303780 11.7796560 13.2119860 3.7309530 12.1005630 - 13.9028060 4.2745790 10.8680380 11.1575980 5.5305530 13.8795350 - 11.4829390 4.6969280 13.5397580 11.9456310 6.0534050 14.0273680 - 4.5666170 5.0659880 2.7745830 4.3407090 4.8497050 3.6792450 - 3.7858700 5.4988340 2.4291800 7.0065380 5.9645460 13.4406100 - 6.9108980 5.0240190 13.2906500 7.8497360 6.1860530 13.0454170 - 10.4028800 3.7086790 3.4039760 10.7958370 4.0258990 2.5908450 - 10.7645650 2.8298670 3.5184280 8.7120970 2.4594100 14.6636620 - 8.0219290 2.2666300 15.2982710 9.4553270 2.7428740 15.1960920 - 10.9883940 6.2992540 7.4990540 10.2565060 6.8079350 7.8480640 - 10.5908280 5.4822520 7.1979320 7.4261330 2.9746740 5.9043030 - 7.4297390 2.5336690 5.0547570 8.3212450 3.2964070 6.0114850 - 3.2763710 2.8573230 4.8637240 2.9363760 3.3348460 5.6204290 - 2.5141030 2.4038350 4.5038340 7.7269300 7.0348520 7.6508440 - 8.0939430 6.5607090 8.3969780 7.5374360 7.9090090 7.9916780 - 5.5168670 6.0757160 9.9692650 5.2125740 6.9549140 10.1943010 - 6.4715130 6.1356950 10.0050090 14.8038440 9.2783510 6.3585890 - 15.5237560 9.7738800 5.9682000 14.6020260 8.6001400 5.7139760 - 10.1857240 9.3492570 12.2454890 10.1194550 8.4288240 11.9912480 - 9.5562380 9.4468010 12.9599530 4.0887860 9.9498530 4.2038310 - 4.6066530 9.1466790 4.1495200 3.6371880 9.8877670 5.0455140 - 4.7745520 7.3744140 14.0003560 4.4745660 7.5201320 13.1031380 - 5.5456760 6.8149430 13.9076950 10.1173970 12.1713140 11.9958690 - 10.1318510 11.2394100 11.7777600 9.4099070 12.2572900 12.6348460 - 16.3285180 8.8120480 11.3427270 15.6033370 9.4049260 11.1456660 - 16.8560640 8.8044020 10.5440640 11.8584770 10.5380650 4.8411120 - 12.2108590 9.9408780 4.1812490 10.9747100 10.2124880 5.0119370 - 7.9448050 11.2978340 2.2481530 8.6319180 11.8447550 2.6289120 - 7.1580470 11.8419070 2.2831140 7.2475260 8.3024370 1.8606890 - 7.7038040 9.1129620 2.0867080 6.8963470 7.9847510 2.6925380 - 9.5507870 13.8130710 9.0899190 10.1950090 14.0190090 9.7672610 - 9.9137310 13.0519110 8.6370040 10.8239950 11.8163060 7.8076640 - 11.4662180 11.1156220 7.9208810 11.3489300 12.5961500 7.6273530 - 12.5977570 12.6157960 11.0112700 12.9333810 13.2024650 11.6890620 - 11.6777910 12.4850670 11.2410490 8.4266150 12.6649290 14.1800760 - 8.1063020 11.9458530 14.7246450 7.7524510 13.3404360 14.2536820 - 11.6419750 2.8975450 9.5437560 12.0100570 2.0770660 9.8717130 - 10.7817950 2.9577340 9.9593210 6.9765810 4.1318340 1.7683000 - 6.2681960 4.6004680 2.2096610 7.2610290 3.4740350 2.4028190 - 8.6021740 5.5515040 9.7675530 9.1006990 6.0260860 10.4327380 - 8.7444690 4.6278560 9.9745560 8.0958030 16.0924300 2.4806770 - 8.3499530 16.0545690 1.5586140 7.1470510 15.9657630 2.4735020 - 15.8088600 1.7683000 10.6103920 15.3062200 2.5088400 10.9497730 - 16.6245260 2.1567000 10.2940710 14.1010610 7.0116180 4.0565280 - 13.1600750 7.0266410 4.2312970 14.4154680 6.2252140 4.5025580 - 16.1036190 14.1825030 5.7319540 16.2283610 15.0056200 6.2043350 - 16.9701750 13.9715760 5.3843690 3.5753720 16.2756240 5.4036670 - 4.3116200 15.7271700 5.6745240 3.9594640 17.1412250 5.2642660 - 2.5812000 15.4602310 14.5922250 3.3824730 15.7814360 15.0057650 - 2.0663820 15.0997890 15.3142160 7.3547750 9.3546760 13.0693820 - 6.5893420 9.9231760 12.9848490 7.5363710 9.0622970 12.1762050 - 2.3332890 6.0905360 14.4308370 2.3738230 5.7919230 15.3393600 - 3.1878390 6.4912970 14.2715890 4.2738700 4.6697460 12.3982860 - 3.7435160 4.1788410 11.7706200 4.9426770 5.0981660 11.8640790 - 13.1716760 9.1366920 12.1624050 13.5857780 9.2912000 13.0114470 - 12.2367760 9.2622140 12.3249830 13.6438930 6.4763080 14.1663200 - 14.2259700 5.7215400 14.0783680 13.8200700 7.0070520 13.3894680 - 7.9331990 9.0887400 10.1319980 8.3520100 9.9320130 9.9596220 - 7.0140620 9.2259440 9.9026760 10.2695890 6.8203580 11.5157820 - 10.3892100 6.2244720 12.2552660 11.1289890 6.8573270 11.0959120 - 12.6083790 7.3740770 10.0920330 12.0819010 7.8983390 9.4885470 - 12.9515130 8.0068160 10.7230060 5.7839410 15.3940410 11.2477710 - 5.2460500 16.1186470 10.9286410 6.0373370 14.9170750 10.4575050 - 7.0364660 3.3624930 12.7733640 7.6264350 3.0540780 13.4611460 - 6.2184900 2.8904680 12.9293540 11.8558090 2.9790740 13.0066660 - 11.1673610 2.3172280 12.9416650 12.5764110 2.5329010 13.4515120 - 8.6401620 16.6291620 11.5064360 8.5660320 17.5326760 11.8136730 - 7.7380460 16.3103520 11.4786430 4.6390830 12.2875220 2.4699950 - 4.3386370 12.1640190 3.3703860 4.5328320 11.4283810 2.0615680 - 17.8699180 9.6669200 4.5202760 18.2408010 8.8836520 4.1138730 - 18.5101110 10.3558300 4.3420060 14.8532940 11.0505780 9.9333080 - 14.0392780 11.4802940 10.1958870 15.2607140 11.6624190 9.3202130 - 16.2023340 6.1893980 11.4602300 15.9709430 5.9606730 10.5600250 - 16.4627890 7.1093530 11.4147190 16.0874680 5.2679950 5.1270020 - 16.6112160 4.7742600 4.4960190 16.6734100 5.4030310 5.8717590 - 16.2664400 5.3519820 8.6489570 15.3167110 5.3183360 8.7634560 - 16.4477410 6.2619930 8.4139340 17.0061810 3.1614250 3.6000110 - 16.8961090 3.0070290 4.5382380 17.7669400 2.6335190 3.3575520 - 14.9108990 15.8024380 3.4601350 15.5068060 15.4808950 2.7835770 - 15.1687140 15.3296200 4.2514620 2.8171560 12.2342290 13.6091980 - 2.7741190 11.8635200 14.4906440 2.9482570 13.1719860 13.7493800 - 6.5177590 13.7901880 9.2101700 6.2768950 14.0576440 8.3232220 - 7.4580230 13.9607610 9.2652560 2.6559590 15.1804540 10.7289760 - 2.0619910 15.0489240 11.4679800 2.6483430 14.3442680 10.2631860 - 15.4621200 16.4519680 7.2899080 15.0024170 17.2035490 6.9157100 - 14.8333660 15.7326150 7.2313880 5.8916920 14.7607920 6.4647210 - 6.4081940 14.1495280 5.9395470 6.5414300 15.2636700 6.9558220 - 8.6312420 15.6516410 7.2916190 8.9190230 15.3970710 6.4149200 - 9.1435640 15.0993770 7.8821760 8.6984780 16.5732510 14.6584390 - 9.6252090 16.3376780 14.7020350 8.6835210 17.4004510 14.1770420 - 36.0047690 33.3989010 30.2224290 90.0000000 90.0000000 90.0000000 diff --git a/tests/st/sponge/functions/__init__.py b/tests/st/sponge/functions/__init__.py deleted file mode 100644 index e69de29bb2d..00000000000 diff --git a/tests/st/sponge/functions/angle_energy.py b/tests/st/sponge/functions/angle_energy.py deleted file mode 100644 index c5cc59036bf..00000000000 --- a/tests/st/sponge/functions/angle_energy.py +++ /dev/null @@ -1,64 +0,0 @@ -# Copyright 2021 Huawei Technologies Co., Ltd -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================ -'''angle energy''' -from mindspore import numpy as np - -from .common import get_periodic_displacement - - -def angle_energy(angle_numbers, uint_crd_f, scalar_f, atom_a, atom_b, atom_c, angle_k, angle_theta0): - """ - Calculate the energy caused by 3-atoms angle term. Assume the number of angles is M and the - number of atoms is N. - - .. math:: - dr_{ab} = (x_b-x_a, y_b-y_a, z_b-z_a) - .. math:: - dr_{cb} = (x_b-x_c, y_b-y_c, z_b-z_c) - .. math:: - theta = arccos(inner_product(dr_{ab}, dr_{cb})/|dr_{ab}|/|dr_{cb}|) - .. math:: - E = k*(theta - theta_0)^2 - - Args: - angle_numbers (int): the number of angles M. - uint_crd_f (Tensor, uint32): [N, 3], the unsigned int coordinate value of each atom. - scalar_f (Tensor, float32): [3,], the 3-D scale factor between - the real space float coordinates and the unsigned int coordinates. - atom_a (Tensor, int32): [M,], the 1st atom index of each angle. - atom_b (Tensor, int32): [M,], the 2nd and the central atom index of each angle. - atom_c (Tensor, int32): [M,], the 3rd atom index of each angle. - angle_k (Tensor, float32): [M,], the force constant for each angle. - angle_theta0 (Tensor, float32): [M,], the equilibrium position value for each angle. - - Outputs: - ene (Tensor, float32): [M,], the potential energy for each angle term. - - Supported Platforms: - ``GPU`` - """ - drij = get_periodic_displacement(uint_crd_f[atom_a], uint_crd_f[atom_b], scalar_f) - drkj = get_periodic_displacement(uint_crd_f[atom_c], uint_crd_f[atom_b], scalar_f) - - rij_2 = 1 / np.sum(drij ** 2, -1) - rkj_2 = 1 / np.sum(drkj ** 2, -1) - rij_1_rkj_1 = np.sqrt(rij_2 * rkj_2) - - costheta = np.sum(drij * drkj, -1) * rij_1_rkj_1 - costheta = np.clip(costheta, -0.999999, 0.999999) - theta = np.arccos(costheta) - - dtheta = theta - angle_theta0 - return angle_k * dtheta * dtheta diff --git a/tests/st/sponge/functions/angle_force_with_atom_energy.py b/tests/st/sponge/functions/angle_force_with_atom_energy.py deleted file mode 100644 index 90177cd4484..00000000000 --- a/tests/st/sponge/functions/angle_force_with_atom_energy.py +++ /dev/null @@ -1,79 +0,0 @@ -# Copyright 2021 Huawei Technologies Co., Ltd -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================ -'''angle force with atom energy''' -from mindspore import numpy as np -from mindspore import ops - -from .common import get_periodic_displacement - - -def angle_force_with_atom_energy(angle_numbers, uint_crd_f, scalar_f, atom_a, atom_b, atom_c, angle_k, angle_theta0): - """ - Calculate angle force and potential energy together. Assume the number of angles is M and the - number of atoms is N. - - The calculation formula is the same as operator AngleForce() and AngleEnergy(). - - Args: - angle_numbers (int): the number of angles M. - uint_crd_f (Tensor, uint32): [N, 3], the unsigned int coordinate value of each atom. - scalar_f (Tensor, float32): [3,], the 3-D scale factor between - the real space float coordinates and the unsigned int coordinates. - atom_a (Tensor, int32): [M,], the 1st atom index of each angle. - atom_b (Tensor, int32): [M,], the 2nd and the central atom index of each angle. - atom_c (Tensor, int32): [M,], the 3rd atom index of each angle. - angle_k (Tensor, float32): [M,], the force constant for each angle. - angle_theta0 (Tensor, float32): [M,], the equilibrium position value for each angle. - - Outputs: - frc_f (Tensor, float32): [N, 3], same as operator AngleForce(). - ene (Tensor, float) - [N,], same as operator AngleAtomEnergy(). - - Supported Platforms: - ``GPU`` - """ - atom_numbers = uint_crd_f.shape[0] - k2 = angle_k - frc = np.zeros((atom_numbers, 3)) - atom_energy = np.zeros(atom_numbers) - - drij = get_periodic_displacement(uint_crd_f[atom_a], uint_crd_f[atom_b], scalar_f) - drkj = get_periodic_displacement(uint_crd_f[atom_c], uint_crd_f[atom_b], scalar_f) - - rij_2 = 1. / np.sum(drij ** 2, -1) - rkj_2 = 1. / np.sum(drkj ** 2, -1) - rij_1_rkj_1 = np.sqrt(rij_2 * rkj_2) - - costheta = np.sum(drij * drkj, -1) * rij_1_rkj_1 - costheta = np.clip(costheta, -0.999999, 0.999999) - theta = np.arccos(costheta) - - dtheta = theta - angle_theta0 - angle_k = -2 * angle_k * dtheta / np.sin(theta) - - common_factor_cross = np.expand_dims(angle_k * rij_1_rkj_1, -1) - common_factor_self = angle_k * costheta - - fi = np.expand_dims(common_factor_self * rij_2, -1) * drij - common_factor_cross * drkj - fk = np.expand_dims(common_factor_self * rkj_2, -1) * drkj - common_factor_cross * drij - - frc = ops.tensor_scatter_add(frc, np.expand_dims(atom_a, -1), fi) - frc = ops.tensor_scatter_add(frc, np.expand_dims(atom_c, -1), fk) - frc = ops.tensor_scatter_add(frc, np.expand_dims(atom_b, -1), -fi - fk) - - atom_energy_val = k2 * dtheta * dtheta - atom_energy = ops.tensor_scatter_add(atom_energy, np.expand_dims(atom_a, -1), atom_energy_val) - - return frc, atom_energy diff --git a/tests/st/sponge/functions/bond_energy.py b/tests/st/sponge/functions/bond_energy.py deleted file mode 100644 index 206035406be..00000000000 --- a/tests/st/sponge/functions/bond_energy.py +++ /dev/null @@ -1,63 +0,0 @@ -# Copyright 2021 Huawei Technologies Co., Ltd -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================ -'''bond energy''' -import mindspore.numpy as mnp -from mindspore import ops - - -def bond_energy(atom_numbers, bond_numbers, uint_crd_f, scaler_f, atom_a, atom_b, bond_k, bond_r0): - """ - Calculate the harmonic potential energy between each bonded atom pair. - Assume our system has N atoms and M harmonic bonds. - - .. math:: - - dr = (x_1-x_2, y_1-y_2, z_1-z_2) - - .. math:: - - E = k*(|dr| - r_0)^2 - - Args: - atom_numbers (int): the number of atoms N. - bond_numbers (int): the number of harmonic bonds M. - uint_crd_f (Tensor, uint32 ) - [N, 3], the unsigned int coordinate value of each atom. - scaler_f (Tensor, float32): [3,], the 3-D scale factor (x, y, z), - between the real space float coordinates and the unsigned int coordinates. - atom_a (Tensor, int32): [M,], the first atom index of each bond. - atom_b (Tensor, int32): [M,], the second atom index of each bond. - bond_k (Tensor, float32): [M,], the force constant of each bond. - bond_r0 (Tensor, float32): [M,], the equlibrium length of each bond. - - Outputs: - bond_ene (Tensor, float32): [M,], the harmonic potential energy for each bond. - - Supported Platforms: - ``GPU`` - """ - uint_vec_a = uint_crd_f[atom_a] # (M, 3) - uint_vec_b = uint_crd_f[atom_b] # (M, 3) - - uint_vec_dr = uint_vec_a - uint_vec_b - uint_vec_dr = ops.depend(uint_vec_dr, uint_vec_dr) - - int_vec_dr = uint_vec_dr.astype('int32') - int_vec_dr = ops.depend(int_vec_dr, int_vec_dr) - - vec_dr = int_vec_dr * scaler_f - dr_dot = mnp.sum(vec_dr * vec_dr, 1) - tempf = mnp.sqrt(dr_dot) - bond_r0 - - return bond_k * tempf * tempf diff --git a/tests/st/sponge/functions/bond_force_with_atom_energy.py b/tests/st/sponge/functions/bond_force_with_atom_energy.py deleted file mode 100644 index 6aa16d4ede2..00000000000 --- a/tests/st/sponge/functions/bond_force_with_atom_energy.py +++ /dev/null @@ -1,61 +0,0 @@ -# Copyright 2021 Huawei Technologies Co., Ltd -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================ -'''bond force with atom energy''' -from mindspore import numpy as np -from mindspore import ops - -from .common import get_periodic_displacement - - -def bond_force_with_atom_energy(atom_numbers, bond_numbers, uint_crd_f, scalar_f, atom_a, atom_b, bond_k, bond_r0): - """ - Calculate bond force and harmonic potential energy together. - - The calculation formula is the same as operator BondForce() and BondEnergy(). - - Args: - atom_numbers (int): the number of atoms N. - bond_numbers (int): the number of harmonic bonds M. - uint_crd_f (Tensor, uint32 ) - [N, 3], the unsigned int coordinate value of each atom. - scalar_f (Tensor, float32): [3,], the 3-D scale factor (x, y, z), - between the real space float coordinates and the unsigned int coordinates. - atom_a (Tensor, int32): [M,], the first atom index of each bond. - atom_b (Tensor, int32): [M,], the second atom index of each bond. - bond_k (Tensor, float32): [M,], the force constant of each bond. - bond_r0 (Tensor, float32): [M,], the equlibrium length of each bond. - - Outputs: - frc_f (Tensor, float32): [N, 3], same as operator BondForce(). - atom_energy (Tensor, float32): [N,], same as atom_ene in operator BondAtomEnergy(). - - Supported Platforms: - ``GPU`` - """ - frc = np.zeros(uint_crd_f.shape, np.float32) - atom_energy = np.zeros(atom_numbers) - - dr = get_periodic_displacement(uint_crd_f[atom_a], uint_crd_f[atom_b], scalar_f) - abs_r = np.norm(dr, axis=-1) - r_1 = 1. / abs_r - tempf = abs_r - bond_r0 - f = 2 * np.expand_dims(tempf * r_1 * bond_k, -1) * dr - - frc = ops.tensor_scatter_add(frc, np.expand_dims(atom_a, -1), -f) - frc = ops.tensor_scatter_add(frc, np.expand_dims(atom_b, -1), f) - - atom_energy_val = bond_k * tempf * tempf - atom_energy = ops.tensor_scatter_add(atom_energy, np.expand_dims(atom_a, -1), atom_energy_val) - - return frc, atom_energy diff --git a/tests/st/sponge/functions/bond_force_with_atom_energy_with_virial.py b/tests/st/sponge/functions/bond_force_with_atom_energy_with_virial.py deleted file mode 100644 index e20f9fece6b..00000000000 --- a/tests/st/sponge/functions/bond_force_with_atom_energy_with_virial.py +++ /dev/null @@ -1,64 +0,0 @@ -# Copyright 2021 Huawei Technologies Co., Ltd -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================ -'''bond force with atom energy with virial''' - -import mindspore.numpy as np -import mindspore.ops as ops -from .common import get_periodic_displacement - - -def bond_force_with_atom_energy_with_virial(atom_numbers, uint_crd_f, scalar_f, atom_a, atom_b, bond_k, - bond_r0): - """ - Calculate bond force and harmonic potential energy together. - - The calculation formula is the same as operator BondForce() and BondEnergy(). - - Args: - atom_numbers (int): the number of atoms N. - uint_crd_f (Tensor, uint32 ) - [N, 3], the unsigned int coordinate value of each atom. - scalar_f (Tensor, float32): [3,], the 3-D scale factor (x, y, z), - between the real space float coordinates and the unsigned int coordinates. - atom_a (Tensor, int32): [M,], the first atom index of each bond. - atom_b (Tensor, int32): [M,], the second atom index of each bond. - bond_k (Tensor, float32): [M,], the force constant of each bond. - bond_r0 (Tensor, float32): [M,], the equlibrium length of each bond. - - Outputs: - frc_f (Tensor, float32): [N, 3], same as operator BondForce(). - atom_energy (Tensor, float32): [N,], same as atom_ene in operator BondAtomEnergy(). - atom_virial (Tensor, float32): [N,], the virial of each atom - - Supported Platforms: - ``GPU`` - """ - frc = np.zeros(uint_crd_f.shape, np.float32) - atom_energy = np.zeros(atom_numbers) - atom_virial = np.zeros(atom_numbers) - - dr = get_periodic_displacement(uint_crd_f[atom_a], uint_crd_f[atom_b], scalar_f) - abs_r = np.norm(dr, axis=-1) - r_1 = 1. / abs_r - tempf = abs_r - bond_r0 - f = 2 * np.expand_dims(tempf * r_1 * bond_k, -1) * dr - - frc = ops.tensor_scatter_add(frc, np.expand_dims(atom_a, -1), -f) - frc = ops.tensor_scatter_add(frc, np.expand_dims(atom_b, -1), f) - - atom_energy_val = bond_k * tempf * tempf - atom_virial_val = -2 * tempf * bond_k * abs_r - atom_energy = ops.tensor_scatter_add(atom_energy, np.expand_dims(atom_a, -1), atom_energy_val) - atom_virial = ops.tensor_scatter_add(atom_virial, np.expand_dims(atom_a, -1), atom_virial_val) - return frc, atom_energy, atom_virial diff --git a/tests/st/sponge/functions/common.py b/tests/st/sponge/functions/common.py deleted file mode 100644 index 2adb194637f..00000000000 --- a/tests/st/sponge/functions/common.py +++ /dev/null @@ -1,198 +0,0 @@ -# Copyright 2021 Huawei Technologies Co., Ltd -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================ -'''common''' -import math - -import numpy as onp - -from mindspore import ops -from mindspore.ops import constexpr -import mindspore.numpy as np - -PI = 3.1415926 - - -@constexpr -def get_excluded_index(atom_numbers, excluded_list_size): - return np.tile(np.arange(excluded_list_size), (atom_numbers, 1)) - - -@constexpr -def get_neighbour_index(atom_numbers, neighbour_list_size): - return np.tile(np.arange(neighbour_list_size), (atom_numbers, 1)) - - -@constexpr -def get_atom_list_tensor(atom_numbers): - return np.arange(atom_numbers).reshape(-1, 1) - - -@constexpr -def get_zero_tensor(shape, dtype=np.float32): - return np.zeros(shape, dtype) - - -@constexpr -def get_full_tensor(shape, fill_value, dtype=np.float32): - return np.full(shape, fill_value, dtype) - - -@constexpr -def get_range_tensor(stop): - return np.arange(stop) - - -def get_csr_excluded_index(ele_list, start, num): - '''get csr excluded index''' - row_idx = [0] - col_idx = [] - value = [] - for s, n in zip(start, num): - if n > 0: - col_idx += list(range(n)) - row_idx.append(len(col_idx)) - value += ele_list[s : s + n].tolist() - else: - row_idx.append(len(col_idx)) - return row_idx, col_idx, value - - -def broadcast_with_excluded_list(excluded_indptr): - atom_excluded_index = [] - for i, v in enumerate(excluded_indptr[1:]): - atom_excluded_index += [i] * (v - excluded_indptr[i]) - return np.array(atom_excluded_index) - - -def get_periodic_displacement(x, y, scaler): - int_dr = x - y - int_dr = int_dr.astype('int32') - int_dr = ops.depend(int_dr, int_dr) - return int_dr * scaler - - -def get_csr_residual_index(atom_numbers, start, end): - """Re-format residual list""" - indptr = onp.append(start, end[-1]).astype("int32") - indices = onp.arange(atom_numbers).astype("int32") - data = onp.ones(atom_numbers, dtype="float32") - return indptr, indices, data - - -def cucdiv(real1, imag1, real2, imag2): - """ - Compute the quotient of the given complex numbers. - Assuming that `x` is the complex number composed of - the real part `real1` and imaginary part `imag1`, - while `y` consists of `real2` and `imag2`, - the result is computed by the equation `x / y`. - """ - divisor = real2 * real2 + imag2 * imag2 - res_real = (real1 * real2 + imag1 * imag2) / divisor - res_imag = (imag1 * real2 - real1 * imag2) / divisor - return res_real, res_imag - - -def expc(real, imag): - t = math.exp(real) - res_real = math.cos(imag) * t - res_imag = math.sin(imag) * t - return res_real, res_imag - - -def m_(u, n): - if n == 2: - if u > 2 or u < 0: - return 0 - return 1 - abs(u - 1) - return u / (n - 1) * m_(u, n - 1) + (n - u) / (n - 1) * m_(u - 1, n - 1) - - -def getb(k, nfft, b_order): - '''getb''' - tempc2_real = 0. - tempc2_imag = 0. - tempc_real = 0. - tempc_imag = 2 * (b_order - 1) * PI * k / nfft - res_real, res_imag = expc(tempc_real, tempc_imag) - for kk in range(b_order - 1): - tempc_real = 0 - tempc_imag = 2 * PI * k / nfft * kk - tempc_real, tempc_imag = expc(tempc_real, tempc_imag) - tempf = m_(kk + 1, b_order) - tempc2_real += tempf * tempc_real - tempc2_imag += tempf * tempc_imag - res_real, res_imag = cucdiv(res_real, res_imag, tempc2_real, tempc2_imag) - return res_real * res_real + res_imag * res_imag - - -def reform_residual_list(atom_numbers, start, end): - """Re-format residual list""" - idx = onp.arange(atom_numbers).reshape(1, -1) - start = start.reshape(-1, 1) - end = end.reshape(-1, 1) - mask = onp.logical_and(idx >= start, idx < end).astype('int32') - return mask - - -def reform_excluded_list(excluded_list, excluded_list_start, exlcuded_list_number): - """Re-format excluded list: (E,) -> (N, MAX_NEIGHBOUR)""" - atom_numbers = excluded_list_start.shape[0] - max_neighbour = exlcuded_list_number.max() - excluded_list_end = excluded_list_start + exlcuded_list_number - excluded_matrix = onp.full((atom_numbers, max_neighbour), -1, onp.int32) - for i in range(atom_numbers): - excluded_matrix[i, :exlcuded_list_number[i]] = excluded_list[excluded_list_start[i]:excluded_list_end[i]] - return excluded_matrix - - -def get_pme_bc(fftx, ffty, fftz, box, beta): - '''get pme_bc''' - z_range = fftz // 2 + 1 - - b1 = list(map(lambda i: getb(i, fftx, 4), range(fftx))) - b2 = list(map(lambda i: getb(i, ffty, 4), range(ffty))) - b3 = list(map(lambda i: getb(i, fftz, 4), range(z_range))) - - mprefactor = PI * PI / -beta / beta - volume = box[0] * box[1] * box[2] - - kxend = int(fftx / 2) - kxrp_l = onp.arange(kxend + 1) - kxrp_r = onp.arange(fftx - kxend - 1, 0, -1) - kxrp = onp.hstack((kxrp_l, kxrp_r)).reshape(-1, 1, 1) / box[0] - - kyend = int(ffty / 2) - kyrp_l = onp.arange(kyend + 1) - kyrp_r = onp.arange(ffty - kyend - 1, 0, -1) - kyrp = onp.hstack((kyrp_l, kyrp_r)).reshape(1, -1, 1) / box[1] - - kzrp = onp.arange(z_range).reshape(1, 1, -1) / box[2] - - msq = kxrp * kxrp + kyrp * kyrp + kzrp * kzrp - b1 = onp.broadcast_to( - onp.array(b1).reshape(-1, 1, 1), msq.shape).ravel() # pylint: disable=too-many-function-args - b2 = onp.broadcast_to( - onp.array(b2).reshape(1, -1, 1), msq.shape).ravel() # pylint: disable=too-many-function-args - b3 = onp.broadcast_to( - onp.array(b3).reshape(1, 1, -1), msq.shape).ravel() # pylint: disable=too-many-function-args - msq = msq.ravel() - - pme_bc = onp.zeros_like(msq) - msq = msq[1:] - pme_bc[1:] = 1.0 / PI / msq * onp.exp(mprefactor * msq) / volume - pme_bc = pme_bc * b1 * b2 * b3 - - return pme_bc diff --git a/tests/st/sponge/functions/constrain.py b/tests/st/sponge/functions/constrain.py deleted file mode 100644 index faf0f5369d4..00000000000 --- a/tests/st/sponge/functions/constrain.py +++ /dev/null @@ -1,112 +0,0 @@ -# Copyright 2021 Huawei Technologies Co., Ltd -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================ -'''constrain''' - -import mindspore.ops as ops -import mindspore.numpy as np -from mindsponge.md.functions.common import get_periodic_displacement - -int32_four = np.array(4, dtype=np.int32) - - -def refresh_uint_crd(half_exp_gamma_plus_half, crd, quarter_cof, frc, - mass_inverse): - '''refresh uint crd ''' - mass_inverse_mul = np.expand_dims(mass_inverse, -1) - crd_lin = (crd + half_exp_gamma_plus_half * frc * mass_inverse_mul).astype("float32") - tempi = (crd_lin * quarter_cof).astype("int32") - uint_crd = (tempi * int32_four).astype("uint32") - - return uint_crd - - -def constrain_force_cycle_with_virial(uint_crd, scaler, pair_dr, atom_i_serials, - atom_j_serials, constant_rs, constrain_ks, frc, virial): - '''constrain force cycle with virial''' - dr = get_periodic_displacement(uint_crd[atom_i_serials], uint_crd[atom_j_serials], scaler) - r_1 = 1. / np.norm(dr, axis=-1) - frc_abs = (1. - constant_rs * r_1) * constrain_ks - frc_lin = np.expand_dims(frc_abs, -1) * pair_dr - frc_sum = (frc_lin * pair_dr).astype("float32") - - virial -= np.sum(frc_sum, -1) - - frc = ops.tensor_scatter_add(frc, np.expand_dims(atom_j_serials, -1), frc_lin) - frc = ops.tensor_scatter_add(frc, np.expand_dims(atom_i_serials, -1), -frc_lin) - - return frc, virial - - -def constrain_force_cycle(uint_crd, scaler, pair_dr, atom_i_serials, - atom_j_serials, constant_rs, constrain_ks, frc): - '''constrain force cycle''' - dr = get_periodic_displacement(uint_crd[atom_i_serials], uint_crd[atom_j_serials], scaler) - r_1 = 1. / np.norm(dr, axis=-1) - frc_abs = (1. - constant_rs * r_1) * constrain_ks - frc_lin = np.expand_dims(frc_abs, -1) * pair_dr - - frc = ops.tensor_scatter_add(frc, np.expand_dims(atom_j_serials, -1), frc_lin) - frc = ops.tensor_scatter_add(frc, np.expand_dims(atom_i_serials, -1), -frc_lin) - - return frc - - -def constrain(atom_numbers, constrain_pair_numbers, iteration_numbers, half_exp_gamma_plus_half, - crd, quarter_cof, mass_inverse, scalar, pair_dr, atom_i_serials, atom_j_serials, constant_rs, - constrain_ks, - need_pressure): - """ - Calculate the constraint force and virial depends on pressure calculation. - - Args: - atom_numbers (int): the number of atoms N. - constrain_pair_numbers (int): the number of constrain pairs M. - iteration_numbers (int): the number of iteration numbers p. - half_exp_gamma_plus_half (float32): half exp_gamma plus half q. - crd(Tensor, float32): [N, 3], the coordinate of each atom. - quarter_cof (Tensor, float32): [3,], the coefficient mapping coordinates to uint coordinates. - mass_inverse(Tensor, float32): [N, ], the inverse value of mass of each atom. - scalar (Tensor, float32): [3,], the 3-D scale factor (x, y, z), - pair_dr(Tensor, float32): [M, 3], the displacement vector of each constrained atom pair. - atom_i_serials(Tensor, int32): [M, ], the first atom index of each constrained atom pair. - atom_j_serials(Tensor, int32): [M, ], the second atom index of each constrained atom pair. - constant_rs(Tensor, float32): [M, ], the constrained distance of each constrained atom pair. - constrain_ks(Tensor, float32): [M, ], the constrained distance of each constrained atom pair. - need_pressure(Tensor, int32), [1, ]or [], if need pressure, 1 else 0. - - Outputs: - uint_crd(Tensor, float32): [N, 3], the unsigned int coordinate value of each atom. - frc(Tensor, float32): [N, 3], the constraint force on each atom. - virial(Tensor, float32): [M, ], the constraint virial on each atom - - """ - frc = np.zeros(crd.shape, np.float32) - virial = np.zeros((constrain_pair_numbers,), np.float32) - uint_crd = np.zeros((atom_numbers, 3)).astype("uint32") - - while iteration_numbers > 0: - uint_crd = refresh_uint_crd(half_exp_gamma_plus_half, crd, quarter_cof, frc, - mass_inverse) - if need_pressure: - frc, virial = constrain_force_cycle_with_virial(uint_crd, scalar, - pair_dr, - atom_i_serials, - atom_j_serials, constant_rs, constrain_ks, frc, virial) - else: - frc = constrain_force_cycle(uint_crd, scalar, pair_dr, atom_i_serials, - atom_j_serials, constant_rs, constrain_ks, frc) - iteration_numbers -= 1 - - return uint_crd, frc, virial diff --git a/tests/st/sponge/functions/constrain_force_cycle.py b/tests/st/sponge/functions/constrain_force_cycle.py deleted file mode 100644 index 84837c4a058..00000000000 --- a/tests/st/sponge/functions/constrain_force_cycle.py +++ /dev/null @@ -1,51 +0,0 @@ -# Copyright 2021 Huawei Technologies Co., Ltd -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================ -'''constrain force cycle with virial''' - -import mindspore.numpy as np -import mindspore.ops as ops -from mindsponge.md.functions.common import get_periodic_displacement - - -def constrain_force_cycle(uint_crd, scaler, pair_dr, atom_i_serials, - atom_j_serials, constant_rs, constrain_ks): - """ - Calculate the constraint force and virial in each iteration. - Args: - uint_crd(Tensor, uint32): [N, 3], the unsigned int coordinate value of each atom. - scaler(Tensor, float32): [3,], the 3-D scale factor (x, y, z). - pair_dr(Tensor, float32): [M, 3], the displacement vector of each constrained atom pair. - atom_i_serials(Tensor, int32): [M, ], the first atom index of each constrained atom pair. - atom_j_serials(Tensor, int32): [M, ], the second atom index of each constrained atom pair. - constant_rs(Tensor, float32): [M, ], the constrained distance of each constrained atom pair. - constrain_ks(Tensor, float32): [M, ], the constrained distance of each constrained atom pair. - - Returns: - test_frc(Tensor, float32): [N, 3], the constraint force. - - Supported Platforms: - ``GPU`` - """ - test_frc = np.zeros(uint_crd.shape, np.float32) - - dr = get_periodic_displacement(uint_crd[atom_i_serials], uint_crd[atom_j_serials], scaler) - r_1 = 1. / np.norm(dr, axis=-1) - frc_abs = (1. - constant_rs * r_1) * constrain_ks - frc_lin = np.expand_dims(frc_abs, -1) * pair_dr - - test_frc = ops.tensor_scatter_add(test_frc, np.expand_dims(atom_j_serials, -1), frc_lin) - test_frc = ops.tensor_scatter_add(test_frc, np.expand_dims(atom_i_serials, -1), -frc_lin) - - return test_frc diff --git a/tests/st/sponge/functions/constrain_force_cycle_with_virial.py b/tests/st/sponge/functions/constrain_force_cycle_with_virial.py deleted file mode 100644 index e78ac1ee240..00000000000 --- a/tests/st/sponge/functions/constrain_force_cycle_with_virial.py +++ /dev/null @@ -1,58 +0,0 @@ -# Copyright 2021 Huawei Technologies Co., Ltd -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================ -'''constrain force cycle with virial''' - -import mindspore.numpy as np -import mindspore.ops as ops - -from .common import get_periodic_displacement - - -def constrain_force_cycle_with_virial(constrain_pair_numbers, uint_crd, scaler, pair_dr, atom_i_serials, - atom_j_serials, constant_rs, constrain_ks): - """ - Calculate the constraint force and virial in each iteration. - Args: - constrain_pair_numbers (int): the number of constrain pairs M. - uint_crd(Tensor, uint32): [N, 3], the unsigned int coordinate value of each atom. - scaler(Tensor, float32): [3,], the 3-D scale factor (x, y, z). - pair_dr(Tensor, float32): [M, 3], the displacement vector of each constrained atom pair. - atom_i_serials(Tensor, int32): [M, ], the first atom index of each constrained atom pair. - atom_j_serials(Tensor, int32): [M, ], the second atom index of each constrained atom pair. - constant_rs(Tensor, float32): [M, ], the constrained distance of each constrained atom pair. - constrain_ks(Tensor, float32): [M, ], the constrained distance of each constrained atom pair. - - Returns: - test_frc(Tensor, float32): [N, 3], the constraint force. - atom_virial(Tensor, float32): [M, ], the virial caused by constraint force of each atom. - - Supported Platforms: - ``GPU`` - """ - frc = np.zeros(uint_crd.shape, np.float32) - atom_virial = np.zeros((constrain_pair_numbers,), np.float32) - - dr = get_periodic_displacement(uint_crd[atom_i_serials], uint_crd[atom_j_serials], scaler) - r_1 = 1. / np.norm(dr, axis=-1) - frc_abs = (1. - constant_rs * r_1) * constrain_ks - frc_lin = np.expand_dims(frc_abs, -1) * pair_dr - frc_sum = frc_lin * pair_dr - - atom_virial -= np.sum(frc_sum, -1) - - frc = ops.tensor_scatter_add(frc, np.expand_dims(atom_j_serials, -1), frc_lin) - frc = ops.tensor_scatter_add(frc, np.expand_dims(atom_i_serials, -1), -frc_lin) - - return frc, atom_virial diff --git a/tests/st/sponge/functions/crd_to_uint_crd.py b/tests/st/sponge/functions/crd_to_uint_crd.py deleted file mode 100644 index 1d738437c90..00000000000 --- a/tests/st/sponge/functions/crd_to_uint_crd.py +++ /dev/null @@ -1,37 +0,0 @@ -# Copyright 2021 Huawei Technologies Co., Ltd -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================ -'''crd to uint crd''' -import mindspore.numpy as np - -uint32_two = np.array(2, dtype=np.uint32) - - -def crd_to_uint_crd(crd_to_uint_crd_cof, crd): - """ - Convert FP32 coordinate to Uint32 coordinate. - - Args: - crd_to_uint_crd_cof (Tensor, float32): [3,], the coefficient mapping coordinates to uint - coordinates. - crd (Tensor, float32): [N, 3], the coordinates of each atom. - - Outputs: - uint_crd (Tensor, uint32): [N,3], the uint coordinates of each atom. - - Supported Platforms: - ``GPU`` - """ - - return (crd * crd_to_uint_crd_cof).astype(np.uint32) * uint32_two diff --git a/tests/st/sponge/functions/crd_to_uint_crd_quarter.py b/tests/st/sponge/functions/crd_to_uint_crd_quarter.py deleted file mode 100644 index 0c0b741fce8..00000000000 --- a/tests/st/sponge/functions/crd_to_uint_crd_quarter.py +++ /dev/null @@ -1,37 +0,0 @@ -# Copyright 2021 Huawei Technologies Co., Ltd -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================ -'''crd to uint crd quarter''' -import mindspore.numpy as np - -int32_four = np.array(4, dtype=np.int32) - - -def crd_to_uint_crd_quarter(crd_to_uint_crd_cof, crd): - """ - Convert FP32 coordinate to Uint32 coordinate. - - Args: - crd_to_uint_crd_cof (Tensor, float32): [3,], the coefficient mapping coordinates to uint - coordinates. - crd (Tensor, float32): [N, 3], the coordinates of each atom. - - Outputs: - uint_crd (Tensor, uint32): [N,3], the uint coordinates of each atom. - - Supported Platforms: - ``GPU`` - """ - - return ((crd * crd_to_uint_crd_cof).astype(np.int32) * int32_four).astype(np.uint32) diff --git a/tests/st/sponge/functions/dihedral_14_cf_energy.py b/tests/st/sponge/functions/dihedral_14_cf_energy.py deleted file mode 100644 index 9f94df874dd..00000000000 --- a/tests/st/sponge/functions/dihedral_14_cf_energy.py +++ /dev/null @@ -1,60 +0,0 @@ -# Copyright 2021 Huawei Technologies Co., Ltd -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================ -'''dihedral 14 cf energy''' -import mindspore.numpy as mnp -from .common import get_periodic_displacement - - -def nb14_cf_energy(nb14_numbers, atom_numbers, uint_crd, atom_lj_type, charge, - boxlength_f, nb14_atom_a, nb14_atom_b, cf_scale_factor): - """ - Calculate the Coulumb part of 1,4 dihedral energy correction for - each necessary dihedral terms on the corresponding atoms. - - .. math:: - - dr = (x_a-x_b, y_a-y_b, z_a-z_b) - - .. math:: - E = k*q_a*q_b/|dr| - - Args: - nb14_numbers (int): the number of necessary dihedral 1,4 terms M. - atom_numbers (int): the number of atoms N. - uint_crd (Tensor, uint32): [N, 3], the unsigned int coordinate value of each atom. - atom_lj_type (Tensor, int32): [N,], the Lennard-Jones type of each atom. - charge (Tensor, float32): [N,], the charge of each atom. - boxlength_f (Tensor, float32): [3,], the length of molecular simulation box in 3 dimensions. - nb14_atom_a (Tensor, int32): [M,], the first atom index of each dihedral 1,4 term. - nb14_atom_b (Tensor, int32): [M,], the second atom index of each dihedral 1,4 term. - cf_scale_factor (Tensor, float) - [M,], the scale factor for the - Coulomb part of force correction for each dihedral 1,4 terms. - - Outputs: - ene (Tensor, float32): [M,], the accumulated potential energy of each atom. - - Supported Platforms: - ``GPU`` - """ - r1_xyz = uint_crd[nb14_atom_a] # [uint_x, uint_y, uint_z] (M,3) - r2_xyz = uint_crd[nb14_atom_b] # [uint_x, uint_y, uint_z] (M,3) - dr_xyz = get_periodic_displacement(r2_xyz, r1_xyz, boxlength_f) - r_1 = 1. / mnp.norm(dr_xyz, axis=-1) - - r1_charge = charge[nb14_atom_a] - r2_charge = charge[nb14_atom_b] - ene_lin = r1_charge * r2_charge * r_1 - - return ene_lin * cf_scale_factor diff --git a/tests/st/sponge/functions/dihedral_14_lj_energy.py b/tests/st/sponge/functions/dihedral_14_lj_energy.py deleted file mode 100644 index a03696b5a44..00000000000 --- a/tests/st/sponge/functions/dihedral_14_lj_energy.py +++ /dev/null @@ -1,77 +0,0 @@ -# Copyright 2021 Huawei Technologies Co., Ltd -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================ -'''dihedral 14 lj energy''' -import mindspore.numpy as mnp -from .common import get_periodic_displacement - - -def nb14_lj_energy(nb14_numbers, atom_numbers, uint_crd_f, lj_type, charge, - boxlength, a_14, b_14, lj_scale_factor, lj_type_a, lj_type_b): - """ - Calculate the Lennard-Jones part of 1,4 dihedral energy correction for - each necessary dihedral terms on the corresponding atoms. - - .. math:: - dr = (x_a-x_b, y_a-y_b, z_a-z-b) - .. math:: - E = k*(A/|dr|^{12} - B/|dr|^{6}) - - Args: - nb14_numbers (int): the number of necessary dihedral 1,4 terms M. - atom_numbers (int): the number of atoms N. - uint_crd_f (Tensor, uint32): [N, 3], the unsigned int coordinate value of each atom. - lj_type (Tensor, int32): [N,], the Lennard-Jones type of each atom. - charge (Tensor, float32): [N,], the charge of each atom. - boxlength (Tensor, float32): [3,], the length of molecular simulation box in 3 dimensions. - a_14 (Tensor, int32): [M,], the first atom index of each dihedral 1,4 term. - b_14 (Tensor, int32): [M,], the second atom index of each dihedral 1,4 term. - lj_scale_factor (Tensor, float32): [M,], the scale factor for the - Lennard-Jones part of force correction of each dihedral 1,4 term. - lj_type_a (Tensor, float32): [Q,], the A parameter in Lennard-Jones scheme of each atom pair type. - Q is the number of atom pair. - lj_type_b (Tensor, float32): [Q,], the B parameter in Lennard-Jones shceme of each atom pair type. - Q is the number of atom pair. - - Outputs: - ene (Tensor, float32): [M,], the Lennard-Jones potential - energy correction for each necessary dihedral 1,4 term. - - Supported Platforms: - ``GPU`` - """ - r1_xyz = uint_crd_f[a_14] # [uint_x, uint_y, uint_z] (M,3) - r2_xyz = uint_crd_f[b_14] # [uint_x, uint_y, uint_z] (M,3) - dr_xyz = get_periodic_displacement(r2_xyz, r1_xyz, boxlength) - - dr2 = dr_xyz * dr_xyz - dr_2 = 1. / mnp.sum(dr2, 1) - dr_4 = dr_2 * dr_2 - dr_6 = dr_4 * dr_2 - dr_12 = dr_6 * dr_6 # (M,3) - - r1_lj_type = lj_type[a_14] # (M,) - r2_lj_type = lj_type[b_14] # (M,) - - y = mnp.abs(r2_lj_type - r1_lj_type) # (M,) - x = r2_lj_type + r1_lj_type # (M,) - - r2_lj_type = mnp.divide(x + y, 2, dtype=mnp.int32) - x = mnp.divide(x - y, 2, dtype=mnp.int32) - atom_pair_lj_type = mnp.divide(r2_lj_type * (r2_lj_type + 1), 2, dtype=mnp.int32) + x # (M,) - - ene_lin = 0.08333333 * lj_type_a[atom_pair_lj_type] * dr_12 - \ - 0.1666666 * lj_type_b[atom_pair_lj_type] * dr_6 - - return ene_lin * lj_scale_factor diff --git a/tests/st/sponge/functions/dihedral_14_ljcf_force_with_atom_energy.py b/tests/st/sponge/functions/dihedral_14_ljcf_force_with_atom_energy.py deleted file mode 100644 index ec19e9e1f19..00000000000 --- a/tests/st/sponge/functions/dihedral_14_ljcf_force_with_atom_energy.py +++ /dev/null @@ -1,98 +0,0 @@ -# Copyright 2021 Huawei Technologies Co., Ltd -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================ -'''dihedral 14 ljcf force with atom energy''' -from mindspore import numpy as np -from mindspore import ops - -from .common import get_zero_tensor, get_periodic_displacement - - -def dihedral_14_ljcf_force_with_atom_energy(atom_numbers, uint_crd, lj_type, charge, boxlength, a_14, b_14, - lj_scale_factor, cf_scale_factor, lj_type_a, lj_type_b): - """ - Calculate the Lennard-Jones and Coulumb energy correction and force correction - for each necessary dihedral 1,4 terms together and add them to the total force - and potential energy for each atom. - - The calculation formula of force correction is the same as operator - :class:`Dihedral14LJForceWithDirectCF`, and the energy correction part is the same - as operator :class:`Dihedral14LJEnergy` and :class:`Dihedral14CFEnergy`. - - Args: - atom_numbers (int): the number of atoms N. - uint_crd (Tensor, uint32): [N, 3], the unsigned int coordinate value of each atom. - lj_type (Tensor, int32): [N,], the Lennard-Jones type of each atom. - charge (Tensor, float32): [N,], the charge of each atom. - boxlength (Tensor, float32): [3,], the length of molecular simulation box in 3 dimensions. - a_14 (Tensor, int32): [M,], the first atom index of each dihedral 1,4 term. - b_14 (Tensor, int32): [M,], the second atom index of each dihedral 1,4 term. - lj_scale_factor (Tensor, float32): [M,], the scale factor for the - Lennard-Jones part of force correction of each dihedral 1,4 term. - cf_scale_factor (Tensor,float) - [M,], the scale factor for the - Coulomb part of force correction for each dihedral 1,4 terms. - lj_type_a (Tensor, float32): [Q,], the A parameter in Lennard-Jones scheme of each atom pair type. - Q is the number of atom pair. - lj_type_b (Tensor, float32): [Q,], the B parameter in Lennard-Jones shceme of each atom pair type. - Q is the number of atom pair. - - Outputs: - frc (Tensor, float32): [N, 3], the force felt by each atom. - atom_energy (Tensor, float32): [N,], the accumulated potential energy for each atom. - - Supported Platforms: - ``GPU`` - """ - r1 = uint_crd[a_14] - r2 = uint_crd[b_14] - crd_scaled = get_periodic_displacement(r2, r1, boxlength) - crd_2 = crd_scaled ** 2 - - dr2 = np.sum(crd_2, 1) - dr_2 = 1.0 / dr2 - dr_4 = dr_2 * dr_2 - dr_8 = dr_4 * dr_4 - dr_14 = dr_8 * dr_4 * dr_2 - dr_1 = dr_2 ** 0.5 - frc_cf_abs = cf_scale_factor * dr_2 * dr_1 - charge_mul = charge[a_14] * charge[b_14] - frc_cf_abs = -charge_mul * frc_cf_abs - - r1_lj_type = lj_type[a_14] - r2_lj_type = lj_type[b_14] - x = r2_lj_type + r1_lj_type - y = np.absolute(r2_lj_type - r1_lj_type) - r2_lj_type = (x + y) // 2 - x = (x - y) // 2 - atom_pair_lj_type = (r2_lj_type * (r2_lj_type + 1) // 2) + x - frc_abs = -lj_type_a[atom_pair_lj_type] * dr_14 + lj_type_b[atom_pair_lj_type] * dr_8 - frc_abs = frc_abs * lj_scale_factor - frc_abs = frc_abs + frc_cf_abs - - frc_abs_3 = np.expand_dims(frc_abs, -1) * crd_scaled - frc = get_zero_tensor((atom_numbers, 3)) - a_14_expended = np.expand_dims(a_14, -1) - b_14_expended = np.expand_dims(b_14, -1) - frc = ops.tensor_scatter_add(frc, b_14_expended, -frc_abs_3) - frc = ops.tensor_scatter_add(frc, a_14_expended, frc_abs_3) - - ene_lin = charge_mul * dr_1 - ene_lin = ene_lin * cf_scale_factor - ene_lin2 = 0.08333333 * lj_type_a[atom_pair_lj_type] * dr_4 * dr_8 - \ - 0.1666666 * lj_type_b[atom_pair_lj_type] * dr_4 * dr_2 - ene_lin2 = ene_lin2 * lj_scale_factor - ene_lin_sum = ene_lin + ene_lin2 - atom_energy = get_zero_tensor((atom_numbers,)) - atom_energy = ops.tensor_scatter_add(atom_energy, a_14_expended, ene_lin_sum) - return frc, atom_energy diff --git a/tests/st/sponge/functions/dihedral_14_ljcf_force_with_atom_energy_with_virial.py b/tests/st/sponge/functions/dihedral_14_ljcf_force_with_atom_energy_with_virial.py deleted file mode 100644 index da096b0a6d2..00000000000 --- a/tests/st/sponge/functions/dihedral_14_ljcf_force_with_atom_energy_with_virial.py +++ /dev/null @@ -1,104 +0,0 @@ -# Copyright 2021 Huawei Technologies Co., Ltd -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================ -'''dihedral 14 ljcf force with atom energy with virial''' - -import mindspore.numpy as np -import mindspore.ops as ops -from .common import get_zero_tensor, get_periodic_displacement - - -def dihedral_14_ljcf_force_with_atom_energy_with_virial(atom_numbers, uint_crd, lj_type, charge, boxlength, a_14, b_14, - lj_scale_factor, cf_scale_factor, lj_type_a, lj_type_b): - """ - Calculate the Lennard-Jones and Coulumb energy correction and force correction - for each necessary dihedral 1,4 terms together and add them to the total force - and potential energy for each atom. - - The calculation formula of force correction is the same as operator - :class:`Dihedral14LJForceWithDirectCF`, and the energy correction part is the same - as operator :class:`Dihedral14LJEnergy` and :class:`Dihedral14CFEnergy`. - - Args: - atom_numbers (int): the number of atoms N. - uint_crd (Tensor, uint32): [N, 3], the unsigned int coordinate value of each atom. - lj_type (Tensor, int32): [N,], the Lennard-Jones type of each atom. - charge (Tensor, float32): [N,], the charge of each atom. - boxlength (Tensor, float32): [3,], the length of molecular simulation box in 3 dimensions. - a_14 (Tensor, int32): [M,], the first atom index of each dihedral 1,4 term. - b_14 (Tensor, int32): [M,], the second atom index of each dihedral 1,4 term. - lj_scale_factor (Tensor, float32): [M,], the scale factor for the - Lennard-Jones part of force correction of each dihedral 1,4 term. - cf_scale_factor (Tensor,float) - [M,], the scale factor for the - Coulomb part of force correction for each dihedral 1,4 terms. - lj_type_a (Tensor, float32): [Q,], the A parameter in Lennard-Jones scheme of each atom pair type. - Q is the number of atom pair. - lj_type_b (Tensor, float32): [Q,], the B parameter in Lennard-Jones shceme of each atom pair type. - Q is the number of atom pair. - - Outputs: - frc (Tensor, float32): [N, 3], the force felt by each atom. - atom_energy (Tensor, float32): [N,], the accumulated potential energy for each atom. - atom_virial (Tensor, float32): [N,], the accumulated potential virial for each atom. - - Supported Platforms: - ``GPU`` - """ - r1 = uint_crd[a_14] - r2 = uint_crd[b_14] - crd_scaled = get_periodic_displacement(r2, r1, boxlength) - crd_2 = crd_scaled ** 2 - - dr2 = np.sum(crd_2, 1) - dr_2 = 1.0 / dr2 - dr_4 = dr_2 * dr_2 - dr_8 = dr_4 * dr_4 - dr_14 = dr_8 * dr_4 * dr_2 - dr_1 = dr_2 ** 0.5 - frc_cf_abs = cf_scale_factor * dr_2 * dr_1 - charge_mul = charge[a_14] * charge[b_14] - frc_cf_abs = -charge_mul * frc_cf_abs - - r1_lj_type = lj_type[a_14] - r2_lj_type = lj_type[b_14] - x = r2_lj_type + r1_lj_type - y = np.absolute(r2_lj_type - r1_lj_type) - r2_lj_type = (x + y) // 2 - x = (x - y) // 2 - atom_pair_lj_type = (r2_lj_type * (r2_lj_type + 1) // 2) + x - frc_abs = -lj_type_a[atom_pair_lj_type] * dr_14 + lj_type_b[atom_pair_lj_type] * dr_8 - frc_abs = frc_abs * lj_scale_factor - frc_abs = frc_abs + frc_cf_abs - - frc_abs_3 = np.expand_dims(frc_abs, -1) * crd_scaled - frc = get_zero_tensor((atom_numbers, 3)) - a_14_expended = np.expand_dims(a_14, -1) - b_14_expended = np.expand_dims(b_14, -1) - frc = ops.tensor_scatter_add(frc, b_14_expended, -frc_abs_3) - frc = ops.tensor_scatter_add(frc, a_14_expended, frc_abs_3) - - ene_lin = charge_mul * dr_1 - ene_lin = ene_lin * cf_scale_factor - ene_lin2 = 0.08333333 * lj_type_a[atom_pair_lj_type] * dr_4 * dr_8 - \ - 0.1666666 * lj_type_b[atom_pair_lj_type] * dr_4 * dr_2 - ene_lin2 = ene_lin2 * lj_scale_factor - ene_lin_sum = ene_lin + ene_lin2 - atom_energy = get_zero_tensor((atom_numbers,)) - atom_virial = get_zero_tensor((atom_numbers,)) - - frc_mul = np.sum(-frc_abs_3 * crd_scaled, axis=1) - - atom_energy = ops.tensor_scatter_add(atom_energy, a_14_expended, ene_lin_sum) - atom_virial = ops.tensor_scatter_add(atom_virial, a_14_expended, frc_mul) - return frc, atom_energy, atom_virial diff --git a/tests/st/sponge/functions/dihedral_energy.py b/tests/st/sponge/functions/dihedral_energy.py deleted file mode 100644 index 5f97c93c268..00000000000 --- a/tests/st/sponge/functions/dihedral_energy.py +++ /dev/null @@ -1,78 +0,0 @@ -# Copyright 2021 Huawei Technologies Co., Ltd -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================ -'''dihedral energy''' -from mindspore import numpy as np - -from .common import get_periodic_displacement - - -def dihedral_energy(dihedral_numbers, uint_crd_f, scalar_f, atom_a, atom_b, atom_c, atom_d, ipn, pk, gamc, gams, pn): - """ - Calculate the potential energy caused by dihedral terms for each 4-atom pair. - Assume our system has N atoms and M dihedral terms. - - .. math:: - - E = k(1 + cos(n*phi - phi_0)) - - Args: - dihedral_numbers (int) - the number of dihedral terms M. - uint_crd_f (Tensor, uint32): [N, 3], the unsigned int coordinates - value of each atom. - scalar_f (Tensor, float32): [3,], the 3-D scale factor between - the real space float coordinates and the unsigned int coordinates. - atom_a (Tensor, int32): [M,], the 1st atom index of each dihedral. - atom_b (Tensor, int32): [M,], the 2nd atom index of each dihedral. - atom_c (Tensor, int32): [M,], the 3rd atom index of each dihedral. - atom_d (Tensor, int32): [M,], the 4th atom index of each dihedral. - 4 atoms are connected in the form a-b-c-d. - ipn (Tensor, int32): [M,], the period of dihedral angle of each dihedral. - pk (Tensor, float32): [M,], the force constant of each dihedral. - gamc (Tensor, float32): [M,], k*cos(phi_0) of each dihedral. - gams (Tensor, float32): [M,], k*sin(phi_0) of each dihedral. - pn (Tensor, float32): [M,], the floating point form of ipn. - - Outputs: - ene (Tensor, float32): [M,], the potential energy for each - dihedral term. - - Supported Platforms: - ``GPU`` - """ - drij = get_periodic_displacement(uint_crd_f[atom_a], uint_crd_f[atom_b], scalar_f) - drkj = get_periodic_displacement(uint_crd_f[atom_c], uint_crd_f[atom_b], scalar_f) - drkl = get_periodic_displacement(uint_crd_f[atom_c], uint_crd_f[atom_d], scalar_f) - - r1 = np.cross(drij, drkj) - r2 = np.cross(drkl, drkj) - - r1_1 = 1. / np.norm(r1, axis=-1) - r2_1 = 1. / np.norm(r2, axis=-1) - r1_1_r2_1 = r1_1 * r2_1 - - phi = np.sum(r1 * r2, -1) * r1_1_r2_1 - phi = np.clip(phi, -0.999999, 0.999999) - phi = np.arccos(phi) - - sign = np.sum(np.cross(r2, r1) * drkj, -1) - phi = np.copysign(phi, sign) - - phi = np.pi - phi - nphi = pn * phi - - cos_nphi = np.cos(nphi) - sin_nphi = np.sin(nphi) - - return pk + cos_nphi * gamc + sin_nphi * gams diff --git a/tests/st/sponge/functions/dihedral_force_with_atom_energy.py b/tests/st/sponge/functions/dihedral_force_with_atom_energy.py deleted file mode 100644 index acf17f2e42b..00000000000 --- a/tests/st/sponge/functions/dihedral_force_with_atom_energy.py +++ /dev/null @@ -1,127 +0,0 @@ -# Copyright 2021 Huawei Technologies Co., Ltd -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================ -'''dihedral force with atom energy''' -from mindspore import ops -import mindspore.numpy as mnp -from mindspore.ops import functional as F -from .common import PI, get_periodic_displacement - - -def dihedral_force_with_atom_energy(dihedral_numbers, uint_crd_f, scaler_f, - atom_a, atom_b, atom_c, atom_d, - ipn, pk, gamc, gams, pn): - """ - Calculate dihedral force and potential energy together. - - The calculation formula is the same as operator DihedralForce() and DihedralEnergy(). - - Args: - dihedral_numbers (int) - the number of dihedral terms M. - uint_crd_f (Tensor, uint32): [N, 3], the unsigned int coordinates - value of each atom. - scaler_f (Tensor, float32): [3,], the 3-D scale factor between - the real space float coordinates and the unsigned int coordinates. - atom_a (Tensor, int32): [M,], the 1st atom index of each dihedral. - atom_b (Tensor, int32): [M,], the 2nd atom index of each dihedral. - atom_c (Tensor, int32): [M,], the 3rd atom index of each dihedral. - atom_d (Tensor, int32): [M,], the 4th atom index of each dihedral. - 4 atoms are connected in the form a-b-c-d. - ipn (Tensor, int32): [M,], the period of dihedral angle of each dihedral. - pk (Tensor, float32): [M,], the force constant of each dihedral. - gamc (Tensor, float32): [M,], k*cos(phi_0) of each dihedral. - gams (Tensor, float32): [M,], k*sin(phi_0) of each dihedral. - pn (Tensor, float32): [M,], the floating point form of ipn. - - Outputs: - frc_f (Tensor, float32): [N, 3], same as operator DihedralForce(). - ene (Tensor, float32): [N,], same as operator DihedralAtomEnergy(). - - Supported Platforms: - ``GPU`` - """ - uint_crd_i = uint_crd_f[atom_a] # (M, 3) - uint_crd_j = uint_crd_f[atom_b] # (M, 3) - uint_crd_k = uint_crd_f[atom_c] # (M, 3) - uint_crd_l = uint_crd_f[atom_d] # (M, 3) - - drij = get_periodic_displacement(uint_crd_i, uint_crd_j, scaler_f) # (M, 3) float32 - drkj = get_periodic_displacement(uint_crd_k, uint_crd_j, scaler_f) # (M, 3) float32 - drkl = get_periodic_displacement(uint_crd_k, uint_crd_l, scaler_f) # (M, 3) float32 - - def vecmul(veca, vecb): - """ - veca - [M, 3] - vecb - [M, 3] - Returns: - Tensor - [M, 1] - """ - return mnp.sum(veca * vecb, 1, keepdims=True) - - r1 = mnp.cross(drij, drkj) # (M, 3) - r2 = mnp.cross(drkl, drkj) # (M, 3) - - r1_1 = 1. / mnp.norm(r1, axis=-1).reshape(-1, 1) - r2_1 = 1. / mnp.norm(r2, axis=-1).reshape(-1, 1) - r1_2 = r1_1 * r1_1 # (M, 1) - r2_2 = r2_1 * r2_1 # (M, 1) - r1_1_r2_1 = r1_1 * r2_1 # (M, 1) - - phi = vecmul(r1, r2) * r1_1_r2_1 # (M, 1) - phi = mnp.clip(phi, -0.999999, 0.999999) - phi = mnp.arccos(phi) # (M, 1) - - phi = PI - phi # (M, 1) - - ipn = ipn.reshape(-1, 1) - pk = pk.reshape(-1, 1) - gamc = gamc.reshape(-1, 1) - gams = gams.reshape(-1, 1) - pn = pn.reshape(-1, 1) - nphi = pn * phi # (M, 1) - - cos_phi = mnp.cos(phi) # (M, 1) - sin_phi = mnp.sin(phi) # (M, 1) - cos_nphi = mnp.cos(nphi) # (M, 1) - sin_nphi = mnp.sin(nphi) # (M, 1) - - ipn = F.select(ipn % 2 == 0, ipn * 0, ipn) - lower = gamc * (pn - ipn + ipn * cos_phi) - upper = pn * (gamc * sin_nphi - gams * cos_nphi) / sin_phi # (M, 1) - de_dphi = F.select(mnp.abs(sin_phi) < 1e-6, lower, upper) # (M, 1) - - dphi_dr1 = r1_1_r2_1 * r2 + cos_phi * r1_2 * r1 # (M, 3) - dphi_dr2 = r1_1_r2_1 * r1 + cos_phi * r2_2 * r2 # (M, 3) - - de_dri = de_dphi * mnp.cross(drkj, dphi_dr1) - de_drl = de_dphi * mnp.cross(dphi_dr2, drkj) - de_drj_part = de_dphi * (mnp.cross(drij, dphi_dr1) + (mnp.cross(drkl, dphi_dr2))) - - fi = de_dri - fj = de_drj_part - de_dri - fk = -de_drl - de_drj_part - fl = de_drl - - n = uint_crd_f.shape[0] - frc = mnp.zeros((n, 3)) - frc = ops.tensor_scatter_add(frc, mnp.expand_dims(atom_a, -1), fi) - frc = ops.tensor_scatter_add(frc, mnp.expand_dims(atom_b, -1), fj) - frc = ops.tensor_scatter_add(frc, mnp.expand_dims(atom_c, -1), fk) - frc = ops.tensor_scatter_add(frc, mnp.expand_dims(atom_d, -1), fl) - - temp = pk + cos_nphi * gamc + sin_nphi * gams - ene = mnp.zeros((n, 1)) - ene = ops.tensor_scatter_add(ene, mnp.expand_dims(atom_a, -1), temp) - - return frc, ene.ravel() diff --git a/tests/st/sponge/functions/last_crd_to_dr.py b/tests/st/sponge/functions/last_crd_to_dr.py deleted file mode 100644 index 1a826722ba6..00000000000 --- a/tests/st/sponge/functions/last_crd_to_dr.py +++ /dev/null @@ -1,48 +0,0 @@ -# Copyright 2021 Huawei Technologies Co., Ltd -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================ -'''last crd to dr''' -import mindspore.numpy as np -import mindspore.ops as ops - -int32_four = np.array(4, dtype=np.int32) - - -def last_crd_to_dr(crd, quarter_cof, uint_dr_to_dr, atom_i_serials, atom_j_serials): - """ - Calculate the diplacement vector of each constrained atom pair. - - Args: - crd(Tensor, float32): [N, 3], the coordinate of each atom. - quarter_cof(Tensor, float32): [3, ], the 3-D scale factor. - uint_dr_to_dr(Tensor, float32): [3, ], the 3-D scale factor (x, y, z). - atom_i_serials(Tensor, int32): [M, ], the first atom index of each constrained atom pair. - atom_j_serials(Tensor, int32): [M, ], the second atom index of each constrained atom pair. - - Outputs: - pair_dr(Tensor, float32): [M, 3], the displacement vector of each constrained atom pair. - - Supported Platforms: - ``GPU`` - """ - tempi = (crd[atom_i_serials] * quarter_cof).astype("int32") - tempj = (crd[atom_j_serials] * quarter_cof).astype("int32") - - uint_crd_i = (tempi * int32_four).astype("uint32") - uint_crd_j = (tempj * int32_four).astype("uint32") - - int_pair_dr = (uint_crd_i - uint_crd_j).astype("int32") - int_pair_dr = ops.depend(int_pair_dr, int_pair_dr) - pair_dr = int_pair_dr * uint_dr_to_dr - return pair_dr diff --git a/tests/st/sponge/functions/lj_energy.py b/tests/st/sponge/functions/lj_energy.py deleted file mode 100644 index a78e2254fce..00000000000 --- a/tests/st/sponge/functions/lj_energy.py +++ /dev/null @@ -1,81 +0,0 @@ -# Copyright 2021 Huawei Technologies Co., Ltd -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================ -'''lj energy''' -import mindspore.numpy as np -from mindspore import Tensor - -from .common import get_neighbour_index, get_periodic_displacement - -zero_tensor = Tensor(0).astype("float32") - - -def lj_energy(atom_numbers, cutoff_square, uint_crd, atom_lj_type, scaler, - nl_atom_numbers, nl_atom_serial, lj_a, lj_b): - """ - Calculate the Van der Waals interaction energy described by Lennard-Jones - potential for each atom. Assume the number of atoms is N, and the number - of Lennard-Jones types for all atoms is P, which means there will be - Q = P*(P+1)/2 types of possible Lennard-Jones interactions for all kinds - of atom pairs. - - - .. math:: - - dr = (x_a-x_b, y_a-y_b, z_a-z_b) - - .. math:: - E = A/|dr|^{12} - B/|dr|^{6} - - Agrs: - atom_numbers(int): the number of atoms, N. - cutoff_square(float): the square value of cutoff. - uint_crd (Tensor, uint32): [N, 3], the unsigned int coordinate value of each atom. - atom_lj_type (Tensor, int32): [N,], the Lennard-Jones type of each atom. - scaler (Tensor, float32): [3,], the scale factor between real - space coordinate and its unsigned int value. - nl_atom_numbers - (Tensor, int32): [N,], the each atom. - nl_atom_serial - (Tensor, int32): [N, 800], the neighbor list of each atom, the max number is 800. - lj_a (Tensor, float32): [Q,], the Lennard-Jones A coefficient of each kind of atom pair. - Q is the number of atom pair. - lj_b (Tensor, float32): [Q,], the Lennard-Jones B coefficient of each kind of atom pair. - Q is the number of atom pair. - - Outputs: - d_LJ_energy_atom (Tensor, float32): [N,], the Lennard-Jones potential energy of each atom. - d_LJ_energy_sum (float), the sum of Lennard-Jones potential energy of each atom. - - Supported Platforms: - ``GPU`` - """ - nl_atom_serial_crd = uint_crd[nl_atom_serial] - r2_lj_type = atom_lj_type[nl_atom_serial] - crd_expand = np.expand_dims(uint_crd, 1) - crd_d = get_periodic_displacement(nl_atom_serial_crd, crd_expand, scaler) - crd_2 = crd_d ** 2 - crd_2 = np.sum(crd_2, -1) - nl_atom_mask = get_neighbour_index(atom_numbers, nl_atom_serial.shape[1]) - mask = np.logical_and((crd_2 < cutoff_square), (nl_atom_mask < np.expand_dims(nl_atom_numbers, -1))) - dr_2 = 1. / crd_2 - dr_6 = np.power(dr_2, 3.) - r1_lj_type = np.expand_dims(atom_lj_type, -1) - x = r2_lj_type + r1_lj_type - y = np.absolute(r2_lj_type - r1_lj_type) - r2_lj_type = (x + y) // 2 - x = (x - y) // 2 - atom_pair_lj_type = (r2_lj_type * (r2_lj_type + 1) // 2) + x - dr_2 = (0.083333333 * lj_a[atom_pair_lj_type] * dr_6 - 0.166666666 * lj_b[atom_pair_lj_type]) * dr_6 - ene_lin = mask * dr_2 - ene_lin = np.sum(ene_lin, -1) - return ene_lin diff --git a/tests/st/sponge/functions/lj_force_pme_direct_force.py b/tests/st/sponge/functions/lj_force_pme_direct_force.py deleted file mode 100644 index 8856ec5ad72..00000000000 --- a/tests/st/sponge/functions/lj_force_pme_direct_force.py +++ /dev/null @@ -1,97 +0,0 @@ -# Copyright 2021 Huawei Technologies Co., Ltd -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================ -'''lj force pme direct force''' -from mindspore import numpy as np -from mindspore import ops - -from .common import get_neighbour_index, get_periodic_displacement, get_zero_tensor - -TWO_DIVIDED_BY_SQRT_PI = 1.1283791670218446 -MAX_NUMBER_OF_NEIGHBOR = 800 - - -def lj_force_pme_direct_force(atom_numbers, cutoff, pme_beta, uint_crd, lj_type, charge, - scalar, nl_numbers, nl_serial, d_lj_a, d_lj_b): - """ - Calculate the Lennard-Jones force and PME direct force together. - - The calculation formula of Lennard-Jones part is the same as operator - ljForce(), and the PME direct part is within PME method. - - Agrs: - atom_numbers(int): the number of atoms, N. - cutoff(float): the square value of cutoff. - pme_beta(float): PME beta parameter, same as operator PMEReciprocalForce(). - uint_crd (Tensor, uint32): [N, 3], the unsigned int coordinate value of each atom. - lj_type (Tensor, int32): [N,], the Lennard-Jones type of each atom. - charge (Tensor, float32): [N,], the charge carried by each atom. - scaler (Tensor, float32): [3,], the scale factor between real - space coordinate and its unsigned int value. - nl_numbers (Tensor, int32): [N,], the each atom. - nl_serial (Tensor, int32): [N, 800], the neighbor list of each atom, the max number is 800. - d_lj_a (Tensor, float32): [Q,], the Lennard-Jones A coefficient of each kind of atom pair. - Q is the number of atom pair. - d_lj_b (Tensor, float32): [Q,], the Lennard-Jones B coefficient of each kind of atom pair. - Q is the number of atom pair. - - Outputs: - frc (Tensor, float32), [N, 3], the force felt by each atom. - - Supported Platforms: - ``GPU`` - """ - n = uint_crd.shape[0] - frc = get_zero_tensor((n, 3), np.float32) - r1 = np.tile(np.expand_dims(uint_crd, 1), (1, MAX_NUMBER_OF_NEIGHBOR, 1)) - r2 = uint_crd[nl_serial] - - dr = get_periodic_displacement(r2, r1, scalar) - dr_abs = np.norm(dr, axis=-1) - nl_atom_mask = get_neighbour_index(atom_numbers, nl_serial.shape[1]) - mask = np.logical_and((dr_abs < cutoff), (nl_atom_mask < np.expand_dims(nl_numbers, -1))) - - dr_1 = 1. / dr_abs - dr_2 = dr_1 * dr_1 - dr_4 = dr_2 * dr_2 - dr_8 = dr_4 * dr_4 - dr_6 = dr_4 * dr_2 - - r1_lj_type = np.expand_dims(lj_type, -1) - r2_lj_type = lj_type[nl_serial] - x = r2_lj_type + r1_lj_type - y = np.absolute(r2_lj_type - r1_lj_type) - r2_lj_type = (x + y) // 2 - x = (x - y) // 2 - atom_pair_lj_type = (r2_lj_type * (r2_lj_type + 1) // 2) + x - - frc_abs = (-d_lj_a[atom_pair_lj_type] * dr_6 + d_lj_b[atom_pair_lj_type]) * dr_8 - beta_dr = pme_beta * dr_abs - frc_cf_abs = beta_dr * TWO_DIVIDED_BY_SQRT_PI * np.exp(-beta_dr * beta_dr) + ops.Erfc()(beta_dr) - frc_cf_abs *= dr_2 * dr_1 - - charge1 = np.tile(np.expand_dims(charge, -1), MAX_NUMBER_OF_NEIGHBOR) - charge2 = charge[nl_serial] - frc_cf_abs *= charge1 * charge2 - - frc_abs -= frc_cf_abs - frc_lin = np.expand_dims(frc_abs, -1) * dr - # apply cutoff mask - mask = np.expand_dims(mask, -1) - frc_lin = np.where(mask, frc_lin, 0) - frc_record = np.sum(frc_lin, -2) - nl_serial = np.where(nl_atom_mask >= np.expand_dims(nl_numbers, -1), -1, nl_serial) - frc = ops.tensor_scatter_add(frc, np.expand_dims(nl_serial, -1), -frc_lin) - frc += frc_record - return frc diff --git a/tests/st/sponge/functions/lj_force_with_virial_energy.py b/tests/st/sponge/functions/lj_force_with_virial_energy.py deleted file mode 100644 index 1aafbf65e8b..00000000000 --- a/tests/st/sponge/functions/lj_force_with_virial_energy.py +++ /dev/null @@ -1,101 +0,0 @@ -# Copyright 2021 Huawei Technologies Co., Ltd -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================ -'''lj force with virial energy''' -from mindspore import numpy as np -from mindspore import ops - -from .common import get_neighbour_index, get_periodic_displacement, get_zero_tensor - -TWO_DIVIDED_BY_SQRT_PI = 1.1283791670218446 -MAX_NUMBER_OF_NEIGHBOR = 800 - - -def lj_force_with_virial_energy(atom_numbers, cutoff, pme_beta, uint_crd, lj_type, charge, - scalar, nl_numbers, nl_serial, d_lj_a, d_lj_b): - """ - Calculate the Lennard-Jones force and PME direct force together. - - The calculation formula of Lennard-Jones part is the same as operator - ljForce(), and the PME direct part is within PME method. - - Agrs: - atom_numbers(int): the number of atoms, N. - cutoff(float): the square value of cutoff. - pme_beta(float): PME beta parameter, same as operator PMEReciprocalForce(). - uint_crd (Tensor, uint32): [N, 3], the unsigned int coordinate value of each atom. - lj_type (Tensor, int32): [N,], the Lennard-Jones type of each atom. - charge (Tensor, float32): [N,], the charge carried by each atom. - scaler (Tensor, float32): [3,], the scale factor between real - space coordinate and its unsigned int value. - nl_numbers (Tensor, int32): [N,], the each atom. - nl_serial (Tensor, int32): [N, 800], the neighbor list of each atom, the max number is 800. - d_lj_a (Tensor, float32): [Q,], the Lennard-Jones A coefficient of each kind of atom pair. - Q is the number of atom pair. - d_lj_b (Tensor, float32): [Q,], the Lennard-Jones B coefficient of each kind of atom pair. - Q is the number of atom pair. - - Outputs: - frc (Tensor, float32), [N, 3], the force felt by each atom. - - Supported Platforms: - ``GPU`` - """ - n = uint_crd.shape[0] - frc = get_zero_tensor((n, 3), np.float32) - r1 = np.tile(np.expand_dims(uint_crd, 1), (1, MAX_NUMBER_OF_NEIGHBOR, 1)) - nl_atom_mask = get_neighbour_index(atom_numbers, nl_serial.shape[1]) - nl_serial = np.where(nl_atom_mask >= np.expand_dims(nl_numbers, -1), -1, nl_serial) - r2 = uint_crd[nl_serial] - - dr = get_periodic_displacement(r2, r1, scalar) - dr_abs = np.norm(dr, axis=-1) - - mask = np.logical_and((dr_abs < cutoff), (nl_atom_mask < np.expand_dims(nl_numbers, -1))) - - dr_1 = 1. / dr_abs - dr_2 = dr_1 * dr_1 - dr_4 = dr_2 * dr_2 - dr_8 = dr_4 * dr_4 - dr_6 = dr_4 * dr_2 - - r1_lj_type = np.expand_dims(lj_type, -1) - r2_lj_type = lj_type[nl_serial] - x = r2_lj_type + r1_lj_type - y = np.absolute(r2_lj_type - r1_lj_type) - r2_lj_type = (x + y) // 2 - x = (x - y) // 2 - atom_pair_lj_type = (r2_lj_type * (r2_lj_type + 1) // 2) + x - - frc_abs = (-d_lj_a[atom_pair_lj_type] * dr_6 + d_lj_b[atom_pair_lj_type]) * dr_8 - beta_dr = pme_beta * dr_abs - frc_cf_abs = beta_dr * TWO_DIVIDED_BY_SQRT_PI * np.exp(-beta_dr * beta_dr) + ops.Erfc()(beta_dr) - frc_cf_abs *= dr_2 * dr_1 - - charge1 = np.tile(np.expand_dims(charge, -1), MAX_NUMBER_OF_NEIGHBOR) - charge2 = charge[nl_serial] - frc_cf_abs *= charge1 * charge2 - - energy_lin = mask * charge1 * charge2 * ops.Erfc()(beta_dr) * dr_1 - virial_lin = -1 * (mask * frc_abs * dr_abs * dr_abs) - - frc_abs -= frc_cf_abs - frc_lin = np.expand_dims(frc_abs, -1) * dr - frc_lin = np.expand_dims(mask, -1) * frc_lin - frc_record = np.sum(frc_lin, -2) - frc = ops.tensor_scatter_add(frc, np.expand_dims(nl_serial, -1), -frc_lin) - frc += frc_record - direct_cf_energy = np.sum(energy_lin, -1) - lj_virial = np.sum(virial_lin, -1) - return frc, lj_virial, direct_cf_energy diff --git a/tests/st/sponge/functions/md_iteration_gradient_descent.py b/tests/st/sponge/functions/md_iteration_gradient_descent.py deleted file mode 100644 index 17c754c80a8..00000000000 --- a/tests/st/sponge/functions/md_iteration_gradient_descent.py +++ /dev/null @@ -1,38 +0,0 @@ -# Copyright 2021 Huawei Technologies Co., Ltd -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================ -'''md information gradient descent''' -import mindspore.numpy as np - - -def md_iteration_gradient_descent(atom_numbers, learning_rate, crd, frc): - """ - Update the coordinate of each atom in the direction of potential for energy minimization. - - Args: - atom_numbers (int): the number of atoms N. - learning_rate (float): the update step length. - crd (Tensor, float32): [N, 3], the coordinate of each atom. - frc (Tensor, float32): [N, 3], the force felt by each atom. - - Returns: - crd (Tensor, float32): [N, 3], the coordinate of each atom. - frc (Tensor, float32): [N, 3], the force felt by each atom. - - Supported Platforms: - ``GPU`` - """ - crd = crd + learning_rate * frc - frc = np.zeros([atom_numbers, 3]) - return crd, frc diff --git a/tests/st/sponge/functions/md_iteration_leap_frog_liujian.py b/tests/st/sponge/functions/md_iteration_leap_frog_liujian.py deleted file mode 100644 index f408a901161..00000000000 --- a/tests/st/sponge/functions/md_iteration_leap_frog_liujian.py +++ /dev/null @@ -1,66 +0,0 @@ -# Copyright 2021 Huawei Technologies Co., Ltd -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================ -'''md iteration leap frog liujian''' -import mindspore.numpy as np -import mindspore.ops as ops - -from .common import get_full_tensor - -standard_normal = ops.StandardNormal() -standard_normal.add_prim_attr("use_curand", True) - - -def md_iteration_leap_frog_liujian(atom_numbers, half_dt, dt, exp_gamma, inverse_mass, - sqrt_mass_inverse, velocity, crd, frc, acc): - """ - One step of classical leap frog algorithm to solve the finite difference - Hamiltonian equations of motion for certain system, using Langevin dynamics - with Liu's thermostat scheme. Assume the number of atoms is N and the target - control temperature is T. - - Detailed iteration formula can be found in this paper: A unified thermostat - scheme for efficient configurational sampling for classical/quantum canonical - ensembles via molecular dynamics. DOI: 10.1063/1.4991621. - - Args: - atom_numbers (int): the number of atoms N. - half_dt (float): half of time step for finite difference. - dt (float): time step for finite difference. - exp_gamma (float): parameter in Liu's dynamic, equals - exp (-gamma_ln * dt), where gamma_ln is the firction factor in Langvin - dynamics. - inverse_mass (Tensor, float32): [N,], the inverse value of - mass of each atom. - sqrt_mass_inverse (Tensor, float32): [N,], the inverse square root value - of effect mass in Liu's dynamics of each atom. - velocity (Tensor, float32): [N, 3], the velocity of each atom. - crd (Tensor, float32): [N, 3], the coordinate of each atom. - frc (Tensor, float32): [N, 3], the force felt by each atom. - acc (Tensor, float32): [N, 3], the acceleration of each atom. - - Outputs: - velocity (float) - updated_crd (float) - acc (float) - - Supported Platforms: - ``GPU`` - """ - acc = np.tile(np.expand_dims(inverse_mass, 1), (1, 3)) * frc - velocity = velocity + get_full_tensor((atom_numbers, 3), dt) * acc - updated_crd = crd + get_full_tensor((atom_numbers, 3), half_dt) * velocity - velocity = exp_gamma * velocity + np.expand_dims(sqrt_mass_inverse, 1) * standard_normal((atom_numbers, 3)) - updated_crd = updated_crd + half_dt * velocity - return velocity, updated_crd, acc diff --git a/tests/st/sponge/functions/md_iteration_leap_frog_liujian_with_max_vel.py b/tests/st/sponge/functions/md_iteration_leap_frog_liujian_with_max_vel.py deleted file mode 100644 index 9e87840e0c7..00000000000 --- a/tests/st/sponge/functions/md_iteration_leap_frog_liujian_with_max_vel.py +++ /dev/null @@ -1,72 +0,0 @@ -# Copyright 2021 Huawei Technologies Co., Ltd -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================ -'''md iteration leap frog liujian with max velocity limitation''' -import mindspore.numpy as np -import mindspore.ops as ops - -from .common import get_full_tensor - -standard_normal = ops.StandardNormal() -standard_normal.add_prim_attr("use_curand", True) - - -def md_iteration_leap_frog_liujian_with_max_vel(atom_numbers, half_dt, dt, exp_gamma, inverse_mass, - sqrt_mass_inverse, velocity, crd, frc, acc, max_vel): - """ - One step of classical leap frog algorithm to solve the finite difference - Hamiltonian equations of motion for certain system, using Langevin dynamics - with Liu's thermostat scheme. Assume the number of atoms is N and the target - control temperature is T. - - Detailed iteration formula can be found in this paper: A unified thermostat - scheme for efficient configurational sampling for classical/quantum canonical - ensembles via molecular dynamics. DOI: 10.1063/1.4991621. - - Args: - atom_numbers (int): the number of atoms N. - half_dt (float): half of time step for finite difference. - dt (float): time step for finite difference. - exp_gamma (float): parameter in Liu's dynamic, equals - exp (-gamma_ln * dt), where gamma_ln is the firction factor in Langvin - dynamics. - inverse_mass (Tensor, float32): [N,], the inverse value of - mass of each atom. - sqrt_mass_inverse (Tensor, float32): [N,], the inverse square root value - of effect mass in Liu's dynamics of each atom. - velocity (Tensor, float32): [N, 3], the velocity of each atom. - crd (Tensor, float32): [N, 3], the coordinate of each atom. - frc (Tensor, float32): [N, 3], the force felt by each atom. - acc (Tensor, float32): [N, 3], the acceleration of each atom. - max_vel (flaot32): the max velocity of each atom. - - Outputs: - velocity (float) - updated_crd (float) - acc (float) - frc (float) - - Supported Platforms: - ``GPU`` - """ - acc = np.tile(np.expand_dims(inverse_mass, 1), (1, 3)) * frc - velocity = velocity + get_full_tensor((atom_numbers, 3), dt) * acc - - cur_vel = np.sqrt(np.sum(velocity ** 2, -1, keepdims=True)) - velocity = np.where(cur_vel < max_vel, velocity, max_vel / cur_vel * velocity) - - updated_crd = crd + get_full_tensor((atom_numbers, 3), half_dt) * velocity - velocity = exp_gamma * velocity + np.expand_dims(sqrt_mass_inverse, 1) * standard_normal((atom_numbers, 3)) - updated_crd = updated_crd + half_dt * velocity - return velocity, updated_crd, acc diff --git a/tests/st/sponge/functions/md_temperature.py b/tests/st/sponge/functions/md_temperature.py deleted file mode 100644 index f9916328e07..00000000000 --- a/tests/st/sponge/functions/md_temperature.py +++ /dev/null @@ -1,70 +0,0 @@ -# Copyright 2021 Huawei Technologies Co., Ltd -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================ -'''md temperature''' -import mindspore.numpy as np -from mindspore.ops import functional as F - - -constant_kb = np.array(0.00198716, np.float64) - - -def md_temperature_dense(mask, atom_vel_f, atom_mass): - """ - Calculate the MD Temperature without sparse - """ - residue_numbers = mask.shape[0] - res = atom_vel_f * atom_mass.reshape(1, -1, 1) - res = np.tile(res, (residue_numbers, 1, 1)) - momentum = np.sum(np.expand_dims(mask, -1) * res, 1) - res_mass = np.sum(mask * atom_mass.reshape(1, -1), -1) - ek = 2. * np.sum(momentum * momentum, -1) / res_mass * 0.5 / 3. / constant_kb / residue_numbers - return ek.astype(np.float32) - - -def md_temperature_sparse(mask, atom_vel_f, atom_mass): - """ - Calculate the MD Temperature with sparse - """ - residue_numbers = mask.shape[0] - res = atom_vel_f * atom_mass.reshape(-1, 1) - res_x, res_y, res_z = np.split(res, 3, axis=1) - momentum_x = mask * res_x.reshape(1, -1) - momentum_y = mask * res_y.reshape(1, -1) - momentum_z = mask * res_z.reshape(1, -1) - momentum_x = F.csr_reduce_sum(momentum_x, 1) - momentum_y = F.csr_reduce_sum(momentum_y, 1) - momentum_z = F.csr_reduce_sum(momentum_z, 1) - momentum = momentum_x * momentum_x + momentum_y * momentum_y + momentum_z * momentum_z - - res_mass = mask * atom_mass.reshape(1, -1) - res_mass = F.csr_reduce_sum(res_mass, 1) - n = 3. * residue_numbers - ek = momentum / res_mass / n / constant_kb - - return ek.astype(np.float32) - - -def md_temperature(mask, atom_vel_f, atom_mass, sparse=True): - """ - Calculate the MD Temperature. - - Calculate the temperature. - - Supported Platforms: - ``GPU`` - """ - if sparse: - return md_temperature_sparse(mask, atom_vel_f, atom_mass) - return md_temperature_dense(mask, atom_vel_f, atom_mass) diff --git a/tests/st/sponge/functions/neighbor_list_update.py b/tests/st/sponge/functions/neighbor_list_update.py deleted file mode 100644 index 5dee186177c..00000000000 --- a/tests/st/sponge/functions/neighbor_list_update.py +++ /dev/null @@ -1,197 +0,0 @@ -# Copyright 2021 Huawei Technologies Co., Ltd -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================ -'''neighbor list update''' -from mindspore import numpy as np -from mindspore import ops, Tensor -from .common import get_periodic_displacement, get_range_tensor, get_neighbour_index -from .crd_to_uint_crd import crd_to_uint_crd - -step = Tensor(1, np.int32) - - -def not_excluded_mask(atom_numbers, excluded_list_start, excluded_list, excluded_numbers): - not_excluded = np.full((atom_numbers, atom_numbers), True, np.bool_) - for i, v in enumerate(excluded_list_start): - if excluded_numbers[i] > 0: - excluded_serial = excluded_list[ops.tensor_range(v, v + excluded_numbers[i], step)] - not_excluded[i, excluded_serial] = False - return not_excluded - - -def find_atom_neighbors(atom_numbers, uint_crd, uint_dr_to_dr_cof, cutoff_skin_square): - dr = get_periodic_displacement(uint_crd, np.expand_dims(uint_crd, -2), uint_dr_to_dr_cof) - dr2 = np.sum(dr ** 2, -1) - atom_idx = get_range_tensor(atom_numbers) - nl_mask = np.logical_and(atom_idx.reshape(-1, 1) < atom_idx, dr2 < cutoff_skin_square) - return nl_mask - - -def delete_excluded_atoms_serial_in_neighbor_list( - atom_numbers, max_neighbor_numbers, nl_mask, not_excluded): - mask = np.logical_and(nl_mask, not_excluded) - serial_idx = get_neighbour_index(atom_numbers, atom_numbers) - nl_serial = np.where(mask, serial_idx, atom_numbers) - nl_serial = np.sort(nl_serial, -1)[:, : max_neighbor_numbers] - nl_numbers = np.sum(mask, -1) - return nl_numbers, nl_serial - - -def crd_periodic_map(crd, box_length): - crd = np.where(crd < 0, crd + box_length, crd) - crd = np.where(crd > box_length, crd - box_length, crd) - return crd - - -def find_atom_in_grid_serial(grid_length_inverse, crd, grid_n, nxy, atom_in_grid_serial): - grid_idx = (crd * grid_length_inverse).astype(np.int32) - grid_idx = np.where(grid_idx < grid_n, grid_idx, 0) - atom_in_grid_serial = grid_idx[..., 2] * nxy + grid_idx[..., 1] * grid_n[0] + grid_idx[..., 0] - return atom_in_grid_serial - - -def neighbor_list_update( - grid_numbers, atom_numbers, not_first_time, nxy, excluded_atom_numbers, - cutoff_square, half_skin_square, cutoff_with_skin, half_cutoff_with_skin, cutoff_with_skin_square, - refresh_interval, cutoff, skin, max_atom_in_grid_numbers, max_neighbor_numbers, - atom_numbers_in_grid_bucket, bucket, crd, box_length, grid_n, grid_length_inverse, atom_in_grid_serial, - old_crd, crd_to_uint_crd_cof, uint_crd, gpointer, nl_atom_numbers, nl_atom_serial, uint_dr_to_dr_cof, - not_excluded, need_refresh_flag, refresh_count): - """ - Update (or construct if first time) the Verlet neighbor list for the - calculation of short-ranged force. Assume the number of atoms is n, - the number of grids divided is G, the maximum number of atoms in one - grid is m, the maximum number of atoms in single atom's neighbor list - is L, and the number of total atom in excluded list is E. - - Args: - grid_numbers (int32): the total number of grids divided. - not_first_time (int32): whether to construct the neighbor - list first time or not. - nxy (int32): the total number of grids divided in xy plane. - excluded_atom_numbers (int32): the total atom numbers in the excluded list. - cutoff (float32): the cutoff distance for short-range force calculation. Default: 10.0. - skin (float32): the overflow value of cutoff to maintain a neighbor list. Default: 2.0. - cutoff_square (float32): the suqare value of cutoff. - half_skin_square (float32): skin*skin/4, indicates the maximum - square value of the distance atom allowed to move between two updates. - cutoff_with_skin (float32): cutoff + skin, indicates the - radius of the neighbor list for each atom. - half_cutoff_with_skin (float32): cutoff_with_skin/2. - cutoff_with_skin_square (float32): the square value of cutoff_with_skin. - refresh_interval (int32): the number of iteration steps between two updates of neighbor - list. Default: 20. - max_atom_in_grid_numbers (int32): the maximum number of atoms in one grid. Default: 64. - max_neighbor_numbers (int32): The maximum number of neighbors. Default: 800. - atom_numbers_in_grid_bucket (Tensor, int32) - [G,], the number of atoms in each grid bucket. - bucket (Tensor, int32) - (Tensor,int32) - [G, m], the atom indices in each grid bucket. - crd (Tensor, float32) - [n,], the coordinates of each atom. - box_length (Tensor, float32) - [3,], the length of 3 dimensions of the simulation box. - grid_n (Tensor, int32) - [3,], the number of grids divided of 3 dimensions of the - simulation box. - grid_length_inverse (float32) - the inverse value of grid length. - atom_in_grid_serial (Tensor, int32) - [n,], the grid index for each atom. - old_crd (Tensor, float32) - [n, 3], the coordinates before update of each atom. - crd_to_uint_crd_cof (Tensor, float32) - [3,], the scale factor - between the unsigned int value and the real space coordinates. - uint_crd (Tensor, uint32) - [n, 3], the unsigned int coordinates value fo each atom. - gpointer (Tensor, int32) - [G, 125], the 125 nearest neighbor grids (including self) of each - grid. G is the number of nearest neighbor grids. - nl_atom_numbers (Tensor, int32) - [n,], the number of atoms in neighbor list of each atom. - nl_atom_serial (Tensor, int32) - [n, L], the indices of atoms in neighbor list of each atom. - uint_dr_to_dr_cof (Tensor, float32) - [3,], the scale factor between - the real space coordinates and the unsigned int value. - excluded_list_start (Tensor, int32) - [n,], the start excluded index in excluded list for - each atom. - excluded_numbers (Tensor, int32) - [n,], the number of atom excluded in excluded list for - each atom. - not_excluded (Tensor, bool) - [n, n], marking the excluded atoms for each atom, where each - element ij indicates whether atom j is not excluded for atom i. - need_refresh_flag (Tensor, int32) - [n,], whether the neighbor list of each atom need update - or not. - refresh_count (Tensor, int32) - [1,], count how many iteration steps have passed since last - update. - - Outputs: - nl_atom_numbers (Tensor, int32) - [n,], the number of atoms in neighbor list of each atom. - nl_atom_serial (Tensor, int32) - [n, L], the indices of atoms in neighbor list of each atom. - crd (Tensor, float32) - [n,], the coordinates of each atom. - old_crd (Tensor, float32) - [n, 3], the coordinates before update of each atom. - need_refresh_flag (Tensor, int32) - [n,], whether the neighbor list of each atom need update - or not. - refresh_count (Tensor, int32) - [1,], count how many iteration steps have passed since last - update. - - Supported Platforms: - ``GPU`` - """ - half_crd_to_uint_crd_cof = 0.5 * crd_to_uint_crd_cof - if not_first_time: - if refresh_interval > 0: - refresh_cond = (refresh_count % refresh_interval) == 0 - trans_vec = np.full(3, -skin, np.float32) - crd = np.where(refresh_cond, crd + trans_vec, crd) - crd = np.where(refresh_cond, crd_periodic_map(crd, box_length), crd) - crd = np.where(refresh_cond, crd - trans_vec, crd) - old_crd = np.where(refresh_cond, crd, old_crd) - - uint_crd = np.where(refresh_cond, - crd_to_uint_crd(half_crd_to_uint_crd_cof, crd).astype(np.int32), - uint_crd.astype(np.int32)).astype(np.uint32) - - nl_mask = find_atom_neighbors( - atom_numbers, uint_crd, uint_dr_to_dr_cof, cutoff_square) - - nl_atom_numbers_updated, nl_atom_serial_updated = delete_excluded_atoms_serial_in_neighbor_list( - atom_numbers, max_neighbor_numbers, nl_mask, not_excluded) - nl_atom_numbers = np.where(refresh_cond, nl_atom_numbers_updated, nl_atom_numbers) - nl_atom_serial = np.where(refresh_cond, nl_atom_serial_updated, nl_atom_serial) - - refresh_count += 1 - else: - r1 = crd - old_crd - r1_2 = np.sum(r1, -1) - if (r1_2 > half_skin_square).any(): - trans_vec = np.full(3, skin, np.float32) - crd += trans_vec - crd = crd_periodic_map(crd, box_length) - crd -= trans_vec - old_crd[...] = crd - - uint_crd = crd_to_uint_crd(half_crd_to_uint_crd_cof, crd) - - nl_mask = find_atom_neighbors( - atom_numbers, uint_crd, uint_dr_to_dr_cof, cutoff_with_skin_square) - - nl_atom_numbers, nl_atom_serial = delete_excluded_atoms_serial_in_neighbor_list( - atom_numbers, max_neighbor_numbers, nl_mask, not_excluded) - - need_refresh_flag[0] = 0 - else: - trans_vec = np.full(3, skin, np.float32) - crd = crd_periodic_map(crd, box_length) - crd += trans_vec - old_crd[...] = crd - - uint_crd = crd_to_uint_crd(half_crd_to_uint_crd_cof, crd) - - nl_mask = find_atom_neighbors( - atom_numbers, uint_crd, uint_dr_to_dr_cof, cutoff_with_skin_square) - - nl_atom_numbers, nl_atom_serial = delete_excluded_atoms_serial_in_neighbor_list( - atom_numbers, max_neighbor_numbers, nl_mask, not_excluded) - - res = (nl_atom_numbers, nl_atom_serial, crd, old_crd, need_refresh_flag, refresh_count) - - return res diff --git a/tests/st/sponge/functions/pme_common.py b/tests/st/sponge/functions/pme_common.py deleted file mode 100644 index 6a1a934acec..00000000000 --- a/tests/st/sponge/functions/pme_common.py +++ /dev/null @@ -1,171 +0,0 @@ -# Copyright 2021 Huawei Technologies Co., Ltd -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================ -'''pme common''' -import mindspore.numpy as mnp -from mindspore import ops -from mindspore.ops import functional as F -from mindspore.ops import constexpr - -from .common import get_neighbour_index, get_periodic_displacement - -PERIODIC_FACTOR_INVERSE = 2.32830643e-10 -pme_ma = mnp.array([1.0 / 6.0, -0.5, 0.5, -1.0 / 6.0]) -pme_mb = mnp.array([0, 0.5, -1, 0.5]) -pme_mc = mnp.array([0, 0.5, 0, -0.5]) -pme_md = mnp.array([0, 1.0 / 6.0, 4.0 / 6.0, 1.0 / 6.0]) -fft3d = ops.FFT3D() -ifft3d = ops.IFFT3D() -real = ops.Real() -conj = ops.Conj() - - -@constexpr -def to_tensor(args): - return mnp.array(args) - - -def scale_list(element_numbers, tensor, scaler): - """Scale values in `tensor`.""" - if tensor.ndim > 0 and len(tensor) > element_numbers: - tensor = tensor[:element_numbers] - return tensor * scaler - - -# pylint: disable=too-many-function-args -def pme_a_near(uint_crd, pme_atom_near, pme_nin, periodic_factor_inverse_x, - periodic_factor_inverse_y, periodic_factor_inverse_z, atom_numbers, - fftx, ffty, fftz, pme_kxyz, pme_uxyz): - '''pme atom near''' - periodic_factor_inverse_xyz = to_tensor( - (periodic_factor_inverse_x, periodic_factor_inverse_y, periodic_factor_inverse_z)) - tempf = uint_crd.astype('float32') * periodic_factor_inverse_xyz - tempu = tempf.astype('int32') - tempu = ops.depend(tempu, tempu) - pme_frxyz = tempf - tempu - - cond = mnp.not_equal(pme_uxyz.astype(mnp.int32), tempu).any(1, True) - pme_uxyz = mnp.where(cond, tempu, pme_uxyz) - - tempu = tempu.reshape(atom_numbers, 1, 3) - kxyz = tempu - pme_kxyz.astype(mnp.int32) - kxyz_plus = kxyz + mnp.array([fftx, ffty, fftz]) - kxyz = ops.select(kxyz < 0, kxyz_plus, kxyz) - - kxyz = kxyz * to_tensor((pme_nin, fftz, 1)).reshape(1, 1, 3) - temp_near = mnp.sum(kxyz.astype(mnp.float32), -1).astype(mnp.int32) - pme_atom_near = mnp.where(cond, temp_near, pme_atom_near) - - return pme_frxyz, pme_uxyz, pme_atom_near - - -def pme_q_spread(pme_atom_near, charge, pme_frxyz, pme_q, pme_kxyz, atom_numbers): - '''pme q spread''' - pme_kxyz = pme_kxyz.astype(mnp.int32) - pme_ma_new = pme_ma[pme_kxyz] - pme_mb_new = pme_mb[pme_kxyz] - pme_mc_new = pme_mc[pme_kxyz] - pme_md_new = pme_md[pme_kxyz] - - tempf = pme_frxyz.reshape(atom_numbers, 1, 3) # (N, 1, 3) - tempf2 = tempf * tempf # (N, 1, 3) - temp_charge = charge.reshape(atom_numbers, 1) # (N, 1) - - tempf = pme_ma_new * tempf * tempf2 + pme_mb_new * tempf2 + pme_mc_new * tempf + pme_md_new # (N, 64, 3) - - tempq = temp_charge * tempf[..., 0] * tempf[..., 1] * tempf[..., 2] # (N, 64) - index = pme_atom_near.ravel() # (N * 64,) - tempq = tempq.ravel() # (N * 64,) - pme_q = ops.tensor_scatter_add(pme_q, mnp.expand_dims(index, -1), tempq) - - return pme_q - - -# pylint: disable=too-many-arguments -def pme_direct_energy(atom_numbers, nl_numbers, nl_serial, uint_crd, boxlength, charge, beta, cutoff_square): - '''pme direct energy''' - r2 = uint_crd[nl_serial] - - dr_xyz = get_periodic_displacement(r2, mnp.expand_dims(uint_crd, 1), boxlength) - dr2 = mnp.sum(dr_xyz * dr_xyz, -1) - - dr_abs = mnp.sqrt(dr2) - charge_i = charge.reshape(-1, 1) - charge_j = charge[nl_serial] - - ene_temp = charge_i * charge_j * ops.erfc(beta * dr_abs) - where_zeros = dr_abs == 0. - dr_abs[where_zeros] = 1. - ene_temp = ene_temp / dr_abs - - idx = get_neighbour_index(atom_numbers, nl_serial.shape[1]) - mask = mnp.logical_and(dr2 < cutoff_square, idx < mnp.expand_dims(nl_numbers, -1)) - - ene_lin = mnp.sum(ene_temp * mask) - return ene_lin - - -@constexpr -def get_pme_kxyz(): - k = mnp.arange(4) - x = mnp.repeat(k, 16).reshape(64, 1) - y = F.tile(mnp.repeat(k, 4), (4, 1)).reshape(64, 1) - z = F.tile(k, (16, 1)).reshape(64, 1) - pme_kxyz = mnp.column_stack((x, y, z)).astype('uint32') - return pme_kxyz - - -def pme_energy_reciprocal(pme_fq, bc): - return mnp.sum(real(conj(pme_fq) * pme_fq) * bc) - - -def pme_energy_product(tensor1, tensor2): - return mnp.sum(tensor1 * tensor2) - - -def pme_excluded_energy_correction_dense(uint_crd, scaler, charge, pme_beta, excluded_matrix): - '''pme excluded energy correction''' - mask = (excluded_matrix > -1) - excluded_crd = uint_crd[excluded_matrix] - - dr_xyz = get_periodic_displacement(excluded_crd, mnp.expand_dims(uint_crd, 1), scaler) - dr2 = mnp.sum(dr_xyz * dr_xyz, -1) - dr_abs = mnp.sqrt(dr2) - beta_dr = pme_beta * dr_abs - - excluded_charge = charge[excluded_matrix] - charge_mul = mnp.expand_dims(charge, 1) * excluded_charge - - ene_lin = charge_mul * ops.erf(beta_dr) - where_zeros = dr_abs == 0. - dr_abs[where_zeros] = 1. - ene_lin = ene_lin / dr_abs * mask - - return 0. - mnp.sum(ene_lin * mask) - - -def pme_excluded_energy_correction_sparse(uint_crd, scaler, charge, pme_beta, excluded_list, atom_excluded_index): - '''pme excluded energy correction''' - charge_mul = charge[atom_excluded_index] * charge[excluded_list] - - dr = get_periodic_displacement(uint_crd[excluded_list], uint_crd[atom_excluded_index], scaler) - dr_2 = mnp.sum(mnp.square(dr), -1) - - dr_abs = mnp.sqrt(dr_2) - beta_dr = pme_beta * dr_abs - - ene_lin = charge_mul * ops.erf(beta_dr) / dr_abs - ene = mnp.sum(ene_lin) - - return 0. - ene diff --git a/tests/st/sponge/functions/pme_energy.py b/tests/st/sponge/functions/pme_energy.py deleted file mode 100644 index 13b09da9a88..00000000000 --- a/tests/st/sponge/functions/pme_energy.py +++ /dev/null @@ -1,98 +0,0 @@ -# Copyright 2021 Huawei Technologies Co., Ltd -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================ -'''pme energy''' -import mindspore.numpy as mnp -from .common import PI, get_zero_tensor, get_full_tensor -from .pme_common import scale_list, pme_a_near, pme_q_spread, pme_direct_energy, pme_energy_reciprocal, \ - pme_excluded_energy_correction_sparse, pme_excluded_energy_correction_dense, pme_energy_product,\ - get_pme_kxyz, PERIODIC_FACTOR_INVERSE, fft3d - -CUT_OFF = 10.0 - - -def pme_energy(atom_numbers, beta, fftx, ffty, fftz, pme_bc, uint_crd, charge, - nl_numbers, nl_serial, scaler, **kargs): - """ - Calculate the Coulumb energy of the system using PME method. - - .. math:: - - E = sum_{ij} q_iq_j/r_{ij} - - Args: - atom_numbers (int): the number of atoms, N. - beta (float): the PME beta parameter, determined by the - non-bond cutoff value and simulation precision tolerance. - fftx (int): the number of points for Fourier transform in dimension X. - ffty (int): the number of points for Fourier transform in dimension Y. - fftz (int): the number of points for Fourier transform in dimension Z. - uint_crd (Tensor, uint32): [N, 3], the unsigned int coordinates value of each atom. - charge (Tensor, float32): [N,], the charge carried by each atom. - nl_numbers - (Tensor, int32): [N,], the each atom. - nl_serial - (Tensor, int32): [N, 800], the neighbor list of each atom, the max number is 800. - scaler (Tensor, float32): [3,], the scale factor between real space - coordinates and its unsigned int value. - Keyword Arguments: - excluded_matrix (Tensor, int32): [N, k] containing the excluded atoms for each atom, where k - is the maximum number of excluded atoms for all atoms. - excluded_list (Tensor, int32):[E,] containing the CSR format column indices of excluded atom. - E is the number of excluded atoms. - atom_excluded_index (Tensor, int32):[E,] containing the row indices corresponding excluded_list, - i.e. each CSR format row index is replicated to map to one CSR format column index. - - Outputs: - reciprocal_ene (float) - the reciprocal term of PME energy. - self_ene (float) - the self term of PME energy. - direct_ene (float) - the direct term of PME energy. - correction_ene (float) - the correction term of PME energy. - - Supported Platforms: - ``GPU`` - """ - pme_nin = ffty * fftz - pme_nall = fftx * ffty * fftz - - pme_kxyz = get_pme_kxyz() # (64, 3) - - pme_uxyz = get_full_tensor((atom_numbers, 3), 2 ** 30, mnp.uint32) - pme_atom_near = get_zero_tensor((atom_numbers, 64), mnp.int32) - pme_frxyz, pme_uxyz, pme_atom_near = pme_a_near(uint_crd, pme_atom_near, pme_nin, - PERIODIC_FACTOR_INVERSE * fftx, - PERIODIC_FACTOR_INVERSE * ffty, - PERIODIC_FACTOR_INVERSE * fftz, atom_numbers, - fftx, ffty, fftz, pme_kxyz, pme_uxyz) - - pme_q = get_full_tensor(pme_nall, 0, mnp.float32) - pme_q = pme_q_spread(pme_atom_near, charge, pme_frxyz, pme_q, pme_kxyz, atom_numbers) - - pme_q = pme_q.reshape(fftx, ffty, fftz).astype('float32') - pme_fq = fft3d(pme_q) - - reciprocal_ene = pme_energy_reciprocal(pme_fq, pme_bc.reshape((fftx, ffty, fftz // 2 + 1))) - - self_ene = pme_energy_product(charge, charge) - self_ene = scale_list(1, self_ene, -beta / mnp.sqrt(PI)) - - direct_ene = pme_direct_energy(atom_numbers, nl_numbers, nl_serial, uint_crd, scaler, charge, beta, - CUT_OFF * CUT_OFF) - - if "excluded_matrix" in kargs.keys(): - correction_ene = pme_excluded_energy_correction_dense(uint_crd, scaler, charge, beta, kargs["excluded_matrix"]) - else: - correction_ene = pme_excluded_energy_correction_sparse(uint_crd, scaler, charge, beta, - kargs["excluded_list"], kargs["atom_excluded_index"]) - res = (mnp.atleast_1d(reciprocal_ene), mnp.atleast_1d(self_ene), \ - mnp.atleast_1d(direct_ene), mnp.atleast_1d(correction_ene)) - return res diff --git a/tests/st/sponge/functions/pme_energy_update.py b/tests/st/sponge/functions/pme_energy_update.py deleted file mode 100644 index 240c62ed0db..00000000000 --- a/tests/st/sponge/functions/pme_energy_update.py +++ /dev/null @@ -1,66 +0,0 @@ -# Copyright 2021 Huawei Technologies Co., Ltd -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================ -'''pme energy update''' -import mindspore.numpy as mnp -from .pme_energy import pme_energy - - -def pme_energy_update(atom_numbers, beta, fftx, ffty, fftz, pme_bc, uint_crd, charge, - nl_numbers, nl_serial, scaler, excluded_list, atom_excluded_index, neutralizing_factor): - """ - Calculate the Coulumb energy of the system using PME method. - - .. math:: - - E = sum_{ij} q_iq_j/r_{ij} - - Args: - atom_numbers (int): the number of atoms, N. - beta (float): the PME beta parameter, determined by the - non-bond cutoff value and simulation precision tolerance. - fftx (int): the number of points for Fourier transform in dimension X. - ffty (int): the number of points for Fourier transform in dimension Y. - fftz (int): the number of points for Fourier transform in dimension Z. - uint_crd (Tensor, uint32): [N, 3], the unsigned int coordinates value of each atom. - charge (Tensor, float32): [N,], the charge carried by each atom. - nl_numbers - (Tensor, int32): [N,], the each atom. - nl_serial - (Tensor, int32): [N, 800], the neighbor list of each atom, the max number is 800. - scaler (Tensor, float32): [3,], the scale factor between real space - coordinates and its unsigned int value. - excluded_list (Tensor, int32):[E,] containing the CSR format column indices of excluded atom. - E is the number of excluded atoms. - atom_excluded_index (Tensor, int32):[E,] containing the row indices corresponding excluded_list, - i.e. each CSR format row index is replicated to map to one CSR format column index. - neutralizing_factor -(Tensor, float32): [1, ] the factor parameter to be updated in pressure calculation. - - Outputs: - reciprocal_ene (float) - the reciprocal term of PME energy. - self_ene (float) - the self term of PME energy. - direct_ene (float) - the direct term of PME energy. - correction_ene (float) - the correction term of PME energy. - - Supported Platforms: - ``GPU`` - """ - reciprocal_ene, self_ene, direct_ene, correction_ene = pme_energy(atom_numbers, beta, fftx, ffty, fftz, - pme_bc, uint_crd, charge, nl_numbers, - nl_serial, scaler, excluded_list=excluded_list, - atom_excluded_index=atom_excluded_index) - - d_beta = mnp.sum(charge, -1) - self_ene += neutralizing_factor * d_beta * d_beta - res = (mnp.atleast_1d(reciprocal_ene), mnp.atleast_1d(self_ene), \ - mnp.atleast_1d(direct_ene), mnp.atleast_1d(correction_ene)) - return res diff --git a/tests/st/sponge/functions/pme_excluded_force.py b/tests/st/sponge/functions/pme_excluded_force.py deleted file mode 100644 index 4a9a842f326..00000000000 --- a/tests/st/sponge/functions/pme_excluded_force.py +++ /dev/null @@ -1,116 +0,0 @@ -# Copyright 2021 Huawei Technologies Co., Ltd -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================ -'''pme excluded force''' -import math -import mindspore.numpy as np -import mindspore.ops as ops -from mindspore.common import CSRTensor -from mindspore.ops import functional as F - -from .common import get_periodic_displacement - -sqrt_pi = 2/math.sqrt(3.141592654) -zero_tensor = np.array(0, dtype=np.float32) - - -def pme_excluded_force_sparse(atom_numbers, beta, uint_crd, scaler, charge, excluded_csr, excluded_row): - """ - Calculate the excluded part of long-range Coulumb force using - PME(Particle Meshed Ewald) method - """ - excluded_crd = uint_crd[excluded_csr.values] - excluded_crd_row = uint_crd[excluded_row] - crd_d = get_periodic_displacement(excluded_crd, excluded_crd_row, scaler) - crd_2 = crd_d ** 2 - crd_sum = np.sum(crd_2, -1) - crd_abs = np.sqrt(crd_sum) - crd_beta = crd_abs * beta - frc_abs = crd_beta * sqrt_pi * np.exp(-crd_beta ** 2) + ops.erfc(crd_beta) - frc_abs = (frc_abs - 1.) / crd_sum / crd_abs - excluded_charge = charge[excluded_csr.values] - excluded_charge_row = charge[excluded_row] - charge_mul = excluded_charge * excluded_charge_row - frc_abs = -charge_mul * frc_abs - frc_lin = np.expand_dims(frc_abs, -1) * crd_d - indptr = excluded_csr.indptr.astype(np.int32) - indices = excluded_csr.indices.astype(np.int32) - shape = (atom_numbers, excluded_row.shape[0]) - x, y, z = np.split(frc_lin, 3, -1) - frc_lin_x = F.csr_reduce_sum(CSRTensor(indptr, indices, x.ravel(), shape), 1) - frc_lin_y = F.csr_reduce_sum(CSRTensor(indptr, indices, y.ravel(), shape), 1) - frc_lin_z = F.csr_reduce_sum(CSRTensor(indptr, indices, z.ravel(), shape), 1) - frc_outer = np.concatenate((frc_lin_x, frc_lin_y, frc_lin_z), -1) - res = ops.tensor_scatter_add(frc_outer, excluded_csr.values.reshape(-1, 1), -frc_lin) - return res - - -def pme_excluded_force_dense(beta, uint_crd, scaler, charge, excluded_matrix): - """ - Calculate the excluded part of long-range Coulumb force using - PME(Particle Meshed Ewald) method - """ - mask = (excluded_matrix > -1) - excluded_crd = uint_crd[excluded_matrix] - crd_d = get_periodic_displacement(excluded_crd, np.expand_dims(uint_crd, 1), scaler) - crd_2 = crd_d ** 2 - crd_sum = np.sum(crd_2, -1) - crd_abs = np.sqrt(crd_sum) - crd_beta = crd_abs * beta - frc_abs = crd_beta * sqrt_pi * np.exp(-crd_beta ** 2) + ops.erfc(crd_beta) - frc_abs = (frc_abs - 1.) / crd_sum / crd_abs - frc_abs = np.where(mask, frc_abs, zero_tensor) - excluded_charge = charge[excluded_matrix] - charge_mul = np.expand_dims(charge, 1) * excluded_charge - frc_abs = -charge_mul * frc_abs - frc_lin = np.expand_dims(frc_abs, 2) * crd_d - frc_outer = np.sum(frc_lin, axis=1) - frc_inner = -frc_lin.reshape(-1, 3) - excluded_list = excluded_matrix.reshape(-1, 1) - res = ops.tensor_scatter_add(frc_outer, excluded_list, frc_inner) - return res - - -def pme_excluded_force(atom_numbers, beta, uint_crd, scaler, charge, **kargs): - """ - Calculate the excluded part of long-range Coulumb force using - PME(Particle Meshed Ewald) method. Assume the number of atoms is - N, and the length of excluded list is E. - - Args: - atom_numbers (int): the number of atoms, N. - beta (float): the PME beta parameter, determined by the - non-bond cutoff value and simulation precision tolerance. - uint_crd (Tensor, uint32): [N, 3], the unsigned int coordinates value of each atom. - scaler (Tensor, float32): [3,], the scale factor between real space - coordinates and its unsigned int value. - charge (Tensor, float32): [N,], the charge carried by each atom. - Keyword Arguments: - excluded_matrix (Tensor, int32): [N, k] containing the excluded atoms for each atom, where k - is the maximum number of excluded atoms for all atoms. - excluded_csr: (CSRTensor, int32): [N, E] containing the excluded atoms for each atom, has the - same meaning as excluded_matrix, but in sparse csr format. - excluded_row: (Tensor, int32): [E,] contains the row indices for each element in the excluded - atoms list (i.e., excluded_csr.values). - - Outputs: - force (Tensor, float32): [N, 3], the force felt by each atom. - - Supported Platforms: - ``GPU`` - """ - if "excluded_matrix" in kargs.keys(): - return pme_excluded_force_dense(beta, uint_crd, scaler, charge, kargs["excluded_matrix"]) - return pme_excluded_force_sparse(atom_numbers, beta, uint_crd, scaler,\ - charge, kargs["excluded_csr"], kargs["excluded_row"]) diff --git a/tests/st/sponge/functions/pme_reciprocal_force.py b/tests/st/sponge/functions/pme_reciprocal_force.py deleted file mode 100644 index 4191c78ba2a..00000000000 --- a/tests/st/sponge/functions/pme_reciprocal_force.py +++ /dev/null @@ -1,102 +0,0 @@ -# Copyright 2021 Huawei Technologies Co., Ltd -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================ -'''pme reciprocal force''' -from mindspore import numpy as np -from mindspore import dtype as mstype -from .common import get_full_tensor -from .pme_common import get_pme_kxyz, pme_a_near, pme_q_spread, to_tensor, \ -PERIODIC_FACTOR_INVERSE, pme_ma, pme_mb, pme_mc, pme_md, fft3d, ifft3d - - -MAXINT = 1073741824 -pme_dma = np.array([0.5, -1.5, 1.5, -0.5], np.float32) -pme_dmb = np.array([0, 1, -2, 1], np.float32) -pme_dmc = np.array([0, 0.5, 0, -0.5], np.float32) - - -def pme_final(pme_atom_near, charge, pme_q, pme_frxyz, pme_kxyz, pme_inverse_box_vector): - '''pme final''' - dqf = -pme_q[pme_atom_near] * np.expand_dims(charge, -1) # N * 64 - - fxyz = np.expand_dims(pme_frxyz, -2) - fxyz_2 = fxyz ** 2 - - # N * 64 * 3 - pme_kxyz = pme_kxyz.astype(np.int32) - xyz = (pme_ma[pme_kxyz] * fxyz * fxyz_2 + pme_mb[pme_kxyz] * fxyz_2 + - pme_mc[pme_kxyz] * fxyz + pme_md[pme_kxyz]) - dxyz = pme_dma[pme_kxyz] * fxyz_2 + pme_dmb[pme_kxyz] * fxyz + pme_dmc[pme_kxyz] - qxyz = dxyz * pme_inverse_box_vector - - x, y, z = xyz[..., 0], xyz[..., 1], xyz[..., 2] - qx, qy, qz = qxyz[..., 0], qxyz[..., 1], qxyz[..., 2] - - qx *= y * z * dqf - qy *= x * z * dqf - qz *= x * y * dqf - - force = np.stack((qx, qy, qz), axis=-1) - return np.sum(force, axis=1) - - -def pme_reciprocal_force(atom_numbers, fftx, ffty, fftz, box_length_0, box_length_1, - box_length_2, pme_bc, uint_crd, charge): - """ - Calculate the reciprocal part of long-range Coulumb force using - PME(Particle Meshed Ewald) method. Assume the number of atoms is N. - - The detailed calculation formula of PME(Particle Meshed Ewald) method - can be found in this paper: A Smooth Particle Mesh Ewald Method. DOI: - 10.1063/1.470117. - - Args: - atom_numbers (int): the number of atoms, N. - fftx (int): the number of points for Fourier transform in dimension X. - ffty (int): the number of points for Fourier transform in dimension Y. - fftz (int): the number of points for Fourier transform in dimension Z. - box_length_0 (float): the value of boxlength idx 0 - box_length_1 (float): the value of boxlength idx 1 - box_length_2 (float): the value of boxlength idx 2 - uint_crd (Tensor, uint32): [N, 3], the unsigned int coordinates value of each atom. - charge (Tensor, float32): [N,], the charge carried by each atom. - - Outputs: - force (Tensor, float32): [N, 3], the force felt by each atom. - - Supported Platforms: - ``GPU`` - """ - pme_nall = fftx * ffty * fftz - pme_nin = ffty * fftz - pme_atom_near = get_full_tensor((atom_numbers, 64), 0, np.int32) - pme_uxyz = get_full_tensor((atom_numbers, 3), MAXINT, np.uint32) - pme_kxyz = get_pme_kxyz() - pme_inverse_box_vector = to_tensor((fftx / box_length_0, ffty / box_length_1, fftz / box_length_2)) - - pme_frxyz, pme_uxyz, pme_atom_near = pme_a_near( - uint_crd, pme_atom_near, pme_nin, PERIODIC_FACTOR_INVERSE * fftx, PERIODIC_FACTOR_INVERSE * ffty, - PERIODIC_FACTOR_INVERSE * fftz, atom_numbers, fftx, ffty, fftz, pme_kxyz, pme_uxyz) - - pme_q = get_full_tensor(pme_nall, 0, np.float32) - pme_q = pme_q_spread(pme_atom_near, charge, pme_frxyz, pme_q, pme_kxyz, atom_numbers) - - pme_q = pme_q.reshape(fftx, ffty, fftz) - pme_bc = pme_bc.reshape(fftx, ffty, fftz // 2 + 1) - pme_fq = fft3d(pme_q) - pme_fq *= pme_bc.astype(mstype.complex64) - pme_q = ifft3d(pme_fq) - pme_q = pme_q.ravel() - - return pme_final(pme_atom_near, charge, pme_q, pme_frxyz, pme_kxyz, pme_inverse_box_vector) diff --git a/tests/st/sponge/functions/refresh_boxmap_times.py b/tests/st/sponge/functions/refresh_boxmap_times.py deleted file mode 100644 index eebb38ae48b..00000000000 --- a/tests/st/sponge/functions/refresh_boxmap_times.py +++ /dev/null @@ -1,40 +0,0 @@ -# Copyright 2021 Huawei Technologies Co., Ltd -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================ -'''refresh boxmap times''' - -import mindspore.numpy as np - - -def refresh_boxmap_times(crd, old_crd, box_length_inverse, box_map_times): - """ - Refresh the box-crossing times of each atom. - Args: - atom_numbers (int): the number of atoms N. - crd (Tensor, float32): [N, 3], the coordinate of each atom. - old_crd (Tensor, float32): [N, 3], the coordinate of each atom at last update. - box_length_inverse (Tensor, float32): [3,], the inverse value of box length in 3 dimensions. - box_map_times (Tensor, int32): [N, 3], the number of times each atom has crossed the box - - Outputs: - box_map_times(Tensor, int32): [N, 3], the number of times each atom has crossed the box after updating. - old_crd (Tensor, float32): [N, 3], the coordinate of each atom at last update. - - Supported Platforms: - ``GPU`` - """ - box_map_times += np.floor((old_crd - crd) * box_length_inverse + 0.5).astype("int32") - old_crd = crd - - return box_map_times, old_crd diff --git a/tests/st/sponge/functions/refresh_crd_vel.py b/tests/st/sponge/functions/refresh_crd_vel.py deleted file mode 100644 index 6f4331432ef..00000000000 --- a/tests/st/sponge/functions/refresh_crd_vel.py +++ /dev/null @@ -1,45 +0,0 @@ -# Copyright 2021 Huawei Technologies Co., Ltd -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================ -'''refresh crd vel''' - -import mindspore.numpy as np - - -def refresh_crd_vel(dt_inverse, exp_gamma, half_exp_gamma_plus_half, crd, vel, test_frc, - mass_inverse): - """ - Refresh the coordinate and velocity of each constrained atom after all iterations have ended. - - Args: - dt_inverse (float32): the inverse value of simulation time step. - exp_gamma (float32): constant value exp(gamma * dt). - half_exp_gamma_plus_half (float32): constant value 1.0 + exp(gamma)/2 - crd (Tensor, float32): [N, 3], the coordinate of each atom. - vel (Tensor, float32): [N, 3], the velocity of each atom. - test_frc(Tensor, float32): [N, 3], the constraint force. - mass_inverse(Tensor, float32): [N, ], the inverse value of mass of each atom. - - Returns: - crd_lin (Tensor, float32): [N, 3], the coordinate of each atom after updating. - vel_lin (Tensor, float32): [N, 3], the velocity of each atom after updating. - - Supported Platforms: - ``GPU`` - """ - frc_lin = test_frc * np.expand_dims(mass_inverse, -1) - crd_lin = crd + half_exp_gamma_plus_half * frc_lin - vel_lin = vel + exp_gamma * frc_lin * dt_inverse - - return crd_lin, vel_lin diff --git a/tests/st/sponge/functions/refresh_uint_crd.py b/tests/st/sponge/functions/refresh_uint_crd.py deleted file mode 100644 index 418a55cd906..00000000000 --- a/tests/st/sponge/functions/refresh_uint_crd.py +++ /dev/null @@ -1,47 +0,0 @@ -# Copyright 2021 Huawei Technologies Co., Ltd -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================ -'''refresh uint crd''' - -import mindspore.numpy as np - -int32_four = np.array(4, dtype=np.int32) - - -def refresh_uint_crd(half_exp_gamma_plus_half, crd, quarter_cof, test_frc, - mass_inverse): - """ - Refresh the unsigned coordinate of each constrained atom in each constrain iteration. - - Args: - half_exp_gamma_plus_half (float32): constant value (1.0 + exp(gamma * dt)) if Langvin-Liu thermostat is used, - where gamma is friction coefficient and dt is the simulation time step, 1.0 otherwise. - crd(Tensor, float32): [N, 3], the coordinate of each atom. - quarter_cof (Tensor, float32): [3,], the coefficient mapping coordinates to uint coordinates. - test_frc(Tensor, float32): [N, 3], the constraint force. - mass_inverse(Tensor, float32): [N, ], the inverse value of mass of each atom. - - Outputs: - uint_crd(Tensor, uint32): [N, 3], the unsigned int coordinate value of each atom. - - Supported Platforms: - ``GPU`` - """ - - mass_inverse_mul = np.expand_dims(mass_inverse, -1) - crd_lin = (crd + half_exp_gamma_plus_half * test_frc * mass_inverse_mul).astype("float32") - tempi = (crd_lin * quarter_cof).astype("int32") - uint_crd = (tempi * int32_four).astype("uint32") - - return uint_crd diff --git a/tests/st/sponge/simulation_np.py b/tests/st/sponge/simulation_np.py deleted file mode 100644 index 0547b12e65b..00000000000 --- a/tests/st/sponge/simulation_np.py +++ /dev/null @@ -1,404 +0,0 @@ -# Copyright 2021 Huawei Technologies Co., Ltd -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================ -'''Simulation''' -import numpy as np - -import mindspore.common.dtype as mstype -from mindspore import Tensor -from mindspore import nn -from mindspore.common.parameter import Parameter -from mindspore.ops import functional as F -from mindspore.ops import operations as P -from tests.st.sponge.sponge_cuda.angle import Angle -from tests.st.sponge.sponge_cuda.bond import Bond -from tests.st.sponge.sponge_cuda.dihedral import Dihedral -from tests.st.sponge.sponge_cuda.langevin_liujian_md import LangevinLiujian -from tests.st.sponge.sponge_cuda.lennard_jones import LennardJonesInformation -from tests.st.sponge.sponge_cuda.md_information import MdInformation -from tests.st.sponge.sponge_cuda.nb14 import NonBond14 -from tests.st.sponge.sponge_cuda.neighbor_list import NeighborList -from tests.st.sponge.sponge_cuda.particle_mesh_ewald import ParticleMeshEwald -from tests.st.sponge.functions.dihedral_14_lj_energy import nb14_lj_energy -from tests.st.sponge.functions.dihedral_14_cf_energy import nb14_cf_energy -from tests.st.sponge.functions.common import reform_excluded_list, reform_residual_list, get_pme_bc -from tests.st.sponge.functions.angle_energy import angle_energy -from tests.st.sponge.functions.angle_force_with_atom_energy import angle_force_with_atom_energy -from tests.st.sponge.functions.bond_force_with_atom_energy import bond_force_with_atom_energy -from tests.st.sponge.functions.crd_to_uint_crd import crd_to_uint_crd -from tests.st.sponge.functions.dihedral_14_ljcf_force_with_atom_energy import dihedral_14_ljcf_force_with_atom_energy -from tests.st.sponge.functions.lj_energy import lj_energy -from tests.st.sponge.functions.md_iteration_leap_frog_liujian import md_iteration_leap_frog_liujian -from tests.st.sponge.functions.pme_excluded_force import pme_excluded_force -from tests.st.sponge.functions.lj_force_pme_direct_force import lj_force_pme_direct_force -from tests.st.sponge.functions.dihedral_force_with_atom_energy import dihedral_force_with_atom_energy -from tests.st.sponge.functions.bond_energy import bond_energy -from tests.st.sponge.functions.md_temperature import md_temperature -from tests.st.sponge.functions.dihedral_energy import dihedral_energy -from tests.st.sponge.functions.pme_energy import pme_energy -from tests.st.sponge.functions.pme_reciprocal_force import pme_reciprocal_force - - -class Controller: - '''controller''' - - def __init__(self): - self.input_file = "./data/NVT_290_10ns.in" - self.amber_parm = "./data/WATER_ALA.parm7" - self.initial_coordinates_file = "./data/WATER_ALA_350_cool_290.rst7" - self.command_set = {} - self.md_task = None - self.commands_from_in_file() - - def commands_from_in_file(self): - '''command from in file''' - file = open(self.input_file, 'r') - context = file.readlines() - file.close() - self.md_task = context[0].strip() - for val in context: - if "=" in val: - assert len(val.strip().split("=")) == 2 - flag, value = val.strip().split("=") - value = value.replace(",", '') - flag = flag.replace(" ", "") - if flag not in self.command_set: - self.command_set[flag] = value - else: - print("ERROR COMMAND FILE") - - -class Simulation(nn.Cell): - '''simulation''' - - def __init__(self): - super(Simulation, self).__init__() - self.control = Controller() - self.md_info = MdInformation(self.control) - self.bond = Bond(self.control) - self.angle = Angle(self.control) - self.dihedral = Dihedral(self.control) - self.nb14 = NonBond14(self.control, self.dihedral, self.md_info.atom_numbers) - self.nb_info = NeighborList(self.control, self.md_info.atom_numbers, self.md_info.box_length) - self.lj_info = LennardJonesInformation(self.control, self.md_info.nb.cutoff, self.md_info.sys.box_length) - self.liujian_info = LangevinLiujian(self.control, self.md_info.atom_numbers) - self.pme_method = ParticleMeshEwald(self.control, self.md_info) - self.bond_energy_sum = Tensor(0, mstype.int32) - self.angle_energy_sum = Tensor(0, mstype.int32) - self.dihedral_energy_sum = Tensor(0, mstype.int32) - self.nb14_lj_energy_sum = Tensor(0, mstype.int32) - self.nb14_cf_energy_sum = Tensor(0, mstype.int32) - self.lj_energy_sum = Tensor(0, mstype.int32) - self.ee_ene = Tensor(0, mstype.int32) - self.total_energy = Tensor(0, mstype.int32) - # Init scalar - self.ntwx = self.md_info.ntwx - self.atom_numbers = self.md_info.atom_numbers - self.residue_numbers = self.md_info.residue_numbers - self.bond_numbers = self.bond.bond_numbers - self.angle_numbers = self.angle.angle_numbers - self.dihedral_numbers = self.dihedral.dihedral_numbers - self.nb14_numbers = self.nb14.nb14_numbers - self.nxy = self.nb_info.nxy - self.grid_numbers = self.nb_info.grid_numbers - self.max_atom_in_grid_numbers = self.nb_info.max_atom_in_grid_numbers - self.max_neighbor_numbers = self.nb_info.max_neighbor_numbers - self.excluded_atom_numbers = self.md_info.nb.excluded_atom_numbers - self.refresh_count = Parameter(Tensor(self.nb_info.refresh_count, mstype.int32), requires_grad=False) - self.refresh_interval = self.nb_info.refresh_interval - self.skin = self.nb_info.skin - self.cutoff = self.nb_info.cutoff - self.cutoff_square = self.nb_info.cutoff_square - self.cutoff_with_skin = self.nb_info.cutoff_with_skin - self.half_cutoff_with_skin = self.nb_info.half_cutoff_with_skin - self.cutoff_with_skin_square = self.nb_info.cutoff_with_skin_square - self.half_skin_square = self.nb_info.half_skin_square - self.beta = self.pme_method.beta - self.fftx = self.pme_method.fftx - self.ffty = self.pme_method.ffty - self.fftz = self.pme_method.fftz - self.box_length_0 = self.md_info.box_length[0] - self.box_length_1 = self.md_info.box_length[1] - self.box_length_2 = self.md_info.box_length[2] - self.random_seed = self.liujian_info.random_seed - self.dt = self.liujian_info.dt - self.half_dt = self.liujian_info.half_dt - self.exp_gamma = self.liujian_info.exp_gamma - self.init_tensor() - self.op_define() - self.update = False - - def init_tensor(self): - '''init tensor''' - self.crd = Parameter( - Tensor(np.float32(np.asarray(self.md_info.coordinate).reshape([self.atom_numbers, 3])), mstype.float32), - requires_grad=False) - self.crd_to_uint_crd_cof = Tensor(np.asarray(self.md_info.pbc.crd_to_uint_crd_cof, np.float32), mstype.float32) - self.uint_dr_to_dr_cof = Parameter( - Tensor(np.asarray(self.md_info.pbc.uint_dr_to_dr_cof, np.float32), mstype.float32), requires_grad=False) - self.box_length = Tensor(self.md_info.box_length, mstype.float32) - self.charge = Parameter(Tensor(np.asarray(self.md_info.h_charge, dtype=np.float32), mstype.float32), - requires_grad=False) - self.old_crd = Parameter(Tensor(np.zeros([self.atom_numbers, 3], dtype=np.float32), mstype.float32), - requires_grad=False) - self.last_crd = Parameter(Tensor(np.zeros([self.atom_numbers, 3], dtype=np.float32), mstype.float32), - requires_grad=False) - self.uint_crd = Parameter(Tensor(np.zeros([self.atom_numbers, 3], dtype=np.uint32), mstype.uint32), - requires_grad=False) - self.mass_inverse = Tensor(self.md_info.h_mass_inverse, mstype.float32) - res_start = np.asarray(self.md_info.h_res_start, np.int32) - res_end = np.asarray(self.md_info.h_res_end, np.int32) - self.res_start = Tensor(res_start) - self.res_end = Tensor(res_end) - self.mass = Tensor(self.md_info.h_mass, mstype.float32) - self.velocity = Parameter(Tensor(self.md_info.velocity, mstype.float32), requires_grad=False) - self.acc = Parameter(Tensor(np.zeros([self.atom_numbers, 3], np.float32), mstype.float32), requires_grad=False) - self.bond_atom_a = Tensor(np.asarray(self.bond.h_atom_a, np.int32), mstype.int32) - self.bond_atom_b = Tensor(np.asarray(self.bond.h_atom_b, np.int32), mstype.int32) - self.bond_k = Tensor(np.asarray(self.bond.h_k, np.float32), mstype.float32) - self.bond_r0 = Tensor(np.asarray(self.bond.h_r0, np.float32), mstype.float32) - self.angle_atom_a = Tensor(np.asarray(self.angle.h_atom_a, np.int32), mstype.int32) - self.angle_atom_b = Tensor(np.asarray(self.angle.h_atom_b, np.int32), mstype.int32) - self.angle_atom_c = Tensor(np.asarray(self.angle.h_atom_c, np.int32), mstype.int32) - self.angle_k = Tensor(np.asarray(self.angle.h_angle_k, np.float32), mstype.float32) - self.angle_theta0 = Tensor(np.asarray(self.angle.h_angle_theta0, np.float32), mstype.float32) - self.dihedral_atom_a = Tensor(np.asarray(self.dihedral.h_atom_a, np.int32), mstype.int32) - self.dihedral_atom_b = Tensor(np.asarray(self.dihedral.h_atom_b, np.int32), mstype.int32) - self.dihedral_atom_c = Tensor(np.asarray(self.dihedral.h_atom_c, np.int32), mstype.int32) - self.dihedral_atom_d = Tensor(np.asarray(self.dihedral.h_atom_d, np.int32), mstype.int32) - self.pk = Tensor(np.asarray(self.dihedral.h_pk, np.float32), mstype.float32) - self.gamc = Tensor(np.asarray(self.dihedral.h_gamc, np.float32), mstype.float32) - self.gams = Tensor(np.asarray(self.dihedral.h_gams, np.float32), mstype.float32) - self.pn = Tensor(np.asarray(self.dihedral.h_pn, np.float32), mstype.float32) - self.ipn = Tensor(np.asarray(self.dihedral.h_ipn, np.int32), mstype.int32) - self.nb14_atom_a = Tensor(np.asarray(self.nb14.h_atom_a, np.int32), mstype.int32) - self.nb14_atom_b = Tensor(np.asarray(self.nb14.h_atom_b, np.int32), mstype.int32) - self.lj_scale_factor = Tensor(np.asarray(self.nb14.h_lj_scale_factor, np.float32), mstype.float32) - self.cf_scale_factor = Tensor(np.asarray(self.nb14.h_cf_scale_factor, np.float32), mstype.float32) - self.grid_n = Tensor(self.nb_info.grid_n, mstype.int32) - self.grid_length_inverse = Tensor(self.nb_info.grid_length_inverse, mstype.float32) - self.bucket = Parameter(Tensor( - np.asarray(self.nb_info.bucket, np.int32).reshape([self.grid_numbers, self.max_atom_in_grid_numbers]), - mstype.int32), requires_grad=False) - self.atom_numbers_in_grid_bucket = Parameter(Tensor(self.nb_info.atom_numbers_in_grid_bucket, mstype.int32), - requires_grad=False) - self.atom_in_grid_serial = Parameter(Tensor(np.zeros([self.nb_info.atom_numbers], np.int32), mstype.int32), - requires_grad=False) - self.pointer = Parameter( - Tensor(np.asarray(self.nb_info.pointer, np.int32).reshape([self.grid_numbers, 125]), mstype.int32), - requires_grad=False) - self.nl_atom_numbers = Parameter(Tensor(np.zeros([self.atom_numbers], np.int32), mstype.int32), - requires_grad=False) - self.nl_atom_serial = Parameter( - Tensor(np.zeros([self.atom_numbers, self.max_neighbor_numbers], np.int32), mstype.int32), - requires_grad=False) - excluded_list_start = np.asarray(self.md_info.nb.h_excluded_list_start, np.int32) - excluded_list = np.asarray(self.md_info.nb.h_excluded_list, np.int32) - excluded_numbers = np.asarray(self.md_info.nb.h_excluded_numbers, np.int32) - self.excluded_list_start = Tensor(excluded_list_start) - self.excluded_list = Tensor(excluded_list) - self.excluded_numbers = Tensor(excluded_numbers) - self.excluded_matrix = Tensor(reform_excluded_list(excluded_list, excluded_list_start, excluded_numbers)) - self.residual_matrix = Tensor(reform_residual_list(self.atom_numbers, res_start, res_end)) - box = (self.box_length_0, self.box_length_1, self.box_length_2) - self.pme_bc = Tensor(get_pme_bc(self.fftx, self.ffty, self.fftz, box, self.beta), mstype.float32) - self.need_refresh_flag = Tensor(np.asarray([0], np.int32), mstype.int32) - self.atom_lj_type = Tensor(self.lj_info.atom_lj_type, mstype.int32) - self.lj_a = Tensor(self.lj_info.h_lj_a, mstype.float32) - self.lj_b = Tensor(self.lj_info.h_lj_b, mstype.float32) - self.sqrt_mass = Tensor(self.liujian_info.h_sqrt_mass, mstype.float32) - self.rand_state = Parameter(Tensor(self.liujian_info.rand_state, mstype.float32)) - self.zero_fp_tensor = Tensor(np.asarray([0], np.float32)) - - def op_define(self): - '''op define''' - self.neighbor_list_update_init = P.NeighborListUpdate(grid_numbers=self.grid_numbers, - atom_numbers=self.atom_numbers, not_first_time=0, - nxy=self.nxy, - excluded_atom_numbers=self.excluded_atom_numbers, - cutoff_square=self.cutoff_square, - half_skin_square=self.half_skin_square, - cutoff_with_skin=self.cutoff_with_skin, - half_cutoff_with_skin=self.half_cutoff_with_skin, - cutoff_with_skin_square=self.cutoff_with_skin_square, - refresh_interval=self.refresh_interval, - cutoff=self.cutoff, skin=self.skin, - max_atom_in_grid_numbers=self.max_atom_in_grid_numbers, - max_neighbor_numbers=self.max_neighbor_numbers) - self.neighbor_list_update = P.NeighborListUpdate(grid_numbers=self.grid_numbers, atom_numbers=self.atom_numbers, - not_first_time=1, nxy=self.nxy, - excluded_atom_numbers=self.excluded_atom_numbers, - cutoff_square=self.cutoff_square, - half_skin_square=self.half_skin_square, - cutoff_with_skin=self.cutoff_with_skin, - half_cutoff_with_skin=self.half_cutoff_with_skin, - cutoff_with_skin_square=self.cutoff_with_skin_square, - refresh_interval=self.refresh_interval, cutoff=self.cutoff, - skin=self.skin, - max_atom_in_grid_numbers=self.max_atom_in_grid_numbers, - max_neighbor_numbers=self.max_neighbor_numbers) - - def simulation_beforce_caculate_force(self): - '''simulation before calculate force''' - crd_to_uint_crd_cof = 0.5 * self.crd_to_uint_crd_cof - uint_crd = crd_to_uint_crd(crd_to_uint_crd_cof, self.crd) - return uint_crd - - def simulation_caculate_force(self, uint_crd, scaler, nl_atom_numbers, nl_atom_serial): - '''simulation calculate force''' - bond_f, _ = bond_force_with_atom_energy(self.atom_numbers, self.bond_numbers, - uint_crd, scaler, self.bond_atom_a, - self.bond_atom_b, self.bond_k, - self.bond_r0) - - angle_f, _ = angle_force_with_atom_energy(self.angle_numbers, uint_crd, - scaler, self.angle_atom_a, - self.angle_atom_b, self.angle_atom_c, - self.angle_k, self.angle_theta0) - - dihedral_f, _ = dihedral_force_with_atom_energy(self.dihedral_numbers, - uint_crd, scaler, - self.dihedral_atom_a, - self.dihedral_atom_b, - self.dihedral_atom_c, - self.dihedral_atom_d, self.ipn, - self.pk, self.gamc, self.gams, - self.pn) - - nb14_f, _ = dihedral_14_ljcf_force_with_atom_energy(self.atom_numbers, uint_crd, - self.atom_lj_type, self.charge, - scaler, self.nb14_atom_a, self.nb14_atom_b, - self.lj_scale_factor, self.cf_scale_factor, - self.lj_a, self.lj_b) - - lj_f = lj_force_pme_direct_force(self.atom_numbers, self.cutoff_square, self.beta, - uint_crd, self.atom_lj_type, self.charge, scaler, - nl_atom_numbers, nl_atom_serial, self.lj_a, self.lj_b) - - pme_excluded_f = pme_excluded_force(self.atom_numbers, self.beta, uint_crd, scaler, - self.charge, excluded_matrix=self.excluded_matrix) - - pme_reciprocal_f = pme_reciprocal_force(self.atom_numbers, self.fftx, self.ffty, - self.fftz, self.box_length_0, self.box_length_1, - self.box_length_2, self.pme_bc, uint_crd, self.charge) - force = bond_f + angle_f + dihedral_f + nb14_f + lj_f + pme_excluded_f + pme_reciprocal_f - return force - - def simulation_caculate_energy(self, uint_crd, uint_dr_to_dr_cof): - '''simulation calculate energy''' - bond_e = bond_energy(self.atom_numbers, self.bond_numbers, uint_crd, uint_dr_to_dr_cof, - self.bond_atom_a, self.bond_atom_b, self.bond_k, self.bond_r0) - bond_energy_sum = bond_e.sum(keepdims=True) - - angle_e = angle_energy(self.angle_numbers, uint_crd, uint_dr_to_dr_cof, - self.angle_atom_a, self.angle_atom_b, self.angle_atom_c, - self.angle_k, self.angle_theta0) - angle_energy_sum = angle_e.sum(keepdims=True) - - dihedral_e = dihedral_energy(self.dihedral_numbers, uint_crd, uint_dr_to_dr_cof, - self.dihedral_atom_a, self.dihedral_atom_b, - self.dihedral_atom_c, self.dihedral_atom_d, - self.ipn, self.pk, self.gamc, self.gams, - self.pn) - dihedral_energy_sum = dihedral_e.sum(keepdims=True) - - nb14_lj_e = nb14_lj_energy(self.nb14_numbers, self.atom_numbers, uint_crd, - self.atom_lj_type, self.charge, uint_dr_to_dr_cof, - self.nb14_atom_a, self.nb14_atom_b, self.lj_scale_factor, self.lj_a, - self.lj_b) - nb14_cf_e = nb14_cf_energy(self.nb14_numbers, self.atom_numbers, uint_crd, - self.atom_lj_type, self.charge, uint_dr_to_dr_cof, - self.nb14_atom_a, self.nb14_atom_b, self.cf_scale_factor) - nb14_lj_energy_sum = nb14_lj_e.sum(keepdims=True) - nb14_cf_energy_sum = nb14_cf_e.sum(keepdims=True) - - lj_e = lj_energy(self.atom_numbers, self.cutoff_square, uint_crd, - self.atom_lj_type, uint_dr_to_dr_cof, - self.nl_atom_numbers, self.nl_atom_serial, self.lj_a, - self.lj_b) - lj_energy_sum = lj_e.sum(keepdims=True) - reciprocal_e, self_e, direct_e, correction_e = pme_energy(self.atom_numbers, - self.beta, self.fftx, - self.ffty, self.fftz, - self.pme_bc, - uint_crd, self.charge, - self.nl_atom_numbers, - self.nl_atom_serial, - uint_dr_to_dr_cof, - excluded_matrix=self.excluded_matrix) - - ee_ene = reciprocal_e + self_e + direct_e + correction_e - total_energy = bond_energy_sum + angle_energy_sum + dihedral_energy_sum + \ - nb14_lj_energy_sum + nb14_cf_energy_sum + lj_energy_sum + ee_ene - res_cpt_ene = (bond_energy_sum, angle_energy_sum, dihedral_energy_sum, nb14_lj_energy_sum, nb14_cf_energy_sum, \ - lj_energy_sum, ee_ene, total_energy) - return res_cpt_ene - - def simulation_temperature(self): - '''caculate temperature''' - res_ek_energy = md_temperature(self.residual_matrix, self.velocity, self.mass, sparse=False) - temperature = res_ek_energy.sum() - return temperature - - def simulation_md_iteration_leap_frog_liujian(self, inverse_mass, sqrt_mass_inverse, crd, frc): - '''simulation leap frog iteration liujian''' - return md_iteration_leap_frog_liujian(self.atom_numbers, self.half_dt, - self.dt, self.exp_gamma, inverse_mass, - sqrt_mass_inverse, self.velocity, - crd, frc, self.acc) - - def construct(self): - '''construct''' - self.last_crd = self.crd - res = self.neighbor_list_update(self.atom_numbers_in_grid_bucket, - self.bucket, - self.crd, - self.box_length, - self.grid_n, - self.grid_length_inverse, - self.atom_in_grid_serial, - self.old_crd, - self.crd_to_uint_crd_cof, - self.uint_crd, - self.pointer, - self.nl_atom_numbers, - self.nl_atom_serial, - self.uint_dr_to_dr_cof, - self.excluded_list_start, - self.excluded_list, - self.excluded_numbers, - self.need_refresh_flag, - self.refresh_count) - self.nl_atom_numbers = F.depend(self.nl_atom_numbers, res) - self.nl_atom_serial = F.depend(self.nl_atom_serial, res) - self.uint_dr_to_dr_cof = F.depend(self.uint_dr_to_dr_cof, res) - self.old_crd = F.depend(self.old_crd, res) - self.atom_numbers_in_grid_bucket = F.depend(self.atom_numbers_in_grid_bucket, res) - self.bucket = F.depend(self.bucket, res) - self.atom_in_grid_serial = F.depend(self.atom_in_grid_serial, res) - self.pointer = F.depend(self.pointer, res) - uint_crd = self.simulation_beforce_caculate_force() - force = self.simulation_caculate_force(uint_crd, self.uint_dr_to_dr_cof, self.nl_atom_numbers, - self.nl_atom_serial) - - bond_energy_sum, angle_energy_sum, dihedral_energy_sum, nb14_lj_energy_sum, nb14_cf_energy_sum, \ - lj_energy_sum, ee_ene, total_energy = self.simulation_caculate_energy(uint_crd, self.uint_dr_to_dr_cof) - - temperature = self.simulation_temperature() - self.velocity = F.depend(self.velocity, temperature) - self.velocity, self.crd, _ = self.simulation_md_iteration_leap_frog_liujian(self.mass_inverse, - self.sqrt_mass, self.crd, force) - res_main = (temperature, total_energy, bond_energy_sum, angle_energy_sum, dihedral_energy_sum, \ - nb14_lj_energy_sum, nb14_cf_energy_sum, lj_energy_sum, ee_ene, res) - return res_main diff --git a/tests/st/sponge/sponge_cuda/__init__.py b/tests/st/sponge/sponge_cuda/__init__.py deleted file mode 100644 index 1a9a6172bd1..00000000000 --- a/tests/st/sponge/sponge_cuda/__init__.py +++ /dev/null @@ -1,14 +0,0 @@ -# Copyright 2020-2021 Huawei Technologies Co., Ltd -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================ diff --git a/tests/st/sponge/sponge_cuda/angle.py b/tests/st/sponge/sponge_cuda/angle.py deleted file mode 100644 index 51f0f3f3046..00000000000 --- a/tests/st/sponge/sponge_cuda/angle.py +++ /dev/null @@ -1,175 +0,0 @@ -# Copyright 2021 Huawei Technologies Co., Ltd -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================ -'''Angle''' - - -class Angle: - '''Angle''' - - def __init__(self, controller): - self.module_name = "angle" - self.h_atom_a = [] - self.h_atom_b = [] - self.h_atom_c = [] - self.h_angle_k = [] - self.h_angle_theta0 = [] - self.type_theta0 = [] - self.type_k = [] - self.angle_numbers = 0 - if controller.amber_parm is not None: - file_path = controller.amber_parm - self.read_information_from_amberfile(file_path) - self.is_initialized = 1 - else: - self.read_in_file(controller) - - def read_in_file(self, controller): - """read_in_file""" - print("START INITIALIZING ANGLE:") - name = self.module_name + "_in_file" - if name in controller.command_set: - path = controller.command_set[name] - file = open(path, 'r') - context = file.readlines() - self.angle_numbers = int(context[0].strip()) - print(" angle_numbers is ", self.angle_numbers) - for i in range(self.angle_numbers): - val = list(map(float, context[i + 1].strip().split())) - self.h_atom_a.append(int(val[0])) - self.h_atom_b.append(int(val[1])) - self.h_atom_c.append(int(val[2])) - self.h_angle_k.append(val[3]) - self.h_angle_theta0.append(val[4]) - self.is_initialized = 1 - file.close() - print("END INITIALIZING ANGLE") - - def read_information_from_amberfile(self, file_path): - '''read amber file''' - file = open(file_path, 'r') - context = file.readlines() - file.close() - for idx, val in enumerate(context): - if idx < len(context) - 1: - if "%FLAG POINTERS" in val + context[idx + 1] and "%FORMAT(10I8)" in val + context[idx + 1]: - start_idx = idx + 2 - count = 0 - value = list(map(int, context[start_idx].strip().split())) - self.angle_with_h_numbers = value[4] - self.angle_without_h_numbers = value[5] - self.angle_numbers = self.angle_with_h_numbers + self.angle_without_h_numbers - information = [] - information.extend(value) - while count < 15: - start_idx += 1 - value = list(map(int, context[start_idx].strip().split())) - information.extend(value) - count += len(value) - self.angle_type_numbers = information[16] - print("angle type numbers ", self.angle_type_numbers) - break - - self.h_atom_a = [0] * self.angle_numbers - self.h_atom_b = [0] * self.angle_numbers - self.h_atom_c = [0] * self.angle_numbers - self.h_type = [0] * self.angle_numbers - angle_count = 0 - for idx, val in enumerate(context): - if "%FLAG ANGLES_INC_HYDROGEN" in val: - count = 0 - start_idx = idx - information = [] - while count < 4 * self.angle_with_h_numbers: - start_idx += 1 - if "%FORMAT" in context[start_idx]: - continue - else: - value = list(map(int, context[start_idx].strip().split())) - information.extend(value) - count += len(value) - for _ in range(self.angle_with_h_numbers): - self.h_atom_a[angle_count] = int(information[angle_count * 4 + 0] / 3) - self.h_atom_b[angle_count] = int(information[angle_count * 4 + 1] / 3) - self.h_atom_c[angle_count] = int(information[angle_count * 4 + 2] / 3) - self.h_type[angle_count] = information[angle_count * 4 + 3] - 1 - angle_count += 1 - - break - - for idx, val in enumerate(context): - if "%FLAG ANGLES_WITHOUT_HYDROGEN" in val: - count = 0 - start_idx = idx - information = [] - while count < 4 * self.angle_without_h_numbers: - start_idx += 1 - if "%FORMAT" in context[start_idx]: - continue - else: - value = list(map(int, context[start_idx].strip().split())) - information.extend(value) - count += len(value) - for _ in range(self.angle_without_h_numbers): - self.h_atom_a[angle_count] = int(information[(angle_count - self.angle_with_h_numbers) * 4 + 0] / 3) - self.h_atom_b[angle_count] = int(information[(angle_count - self.angle_with_h_numbers) * 4 + 1] / 3) - self.h_atom_c[angle_count] = int(information[(angle_count - self.angle_with_h_numbers) * 4 + 2] / 3) - self.h_type[angle_count] = information[(angle_count - self.angle_with_h_numbers) * 4 + 3] - 1 - angle_count += 1 - break - self.processor(context, angle_count) - - def processor(self, context, angle_count): - ''' processor ''' - self.type_k = [0] * self.angle_type_numbers - for idx, val in enumerate(context): - if "%FLAG ANGLE_FORCE_CONSTANT" in val: - count = 0 - start_idx = idx - information = [] - while count < self.angle_type_numbers: - start_idx += 1 - if "%FORMAT" in context[start_idx]: - continue - else: - value = list(map(float, context[start_idx].strip().split())) - information.extend(value) - count += len(value) - self.type_k = information[:self.angle_type_numbers] - break - - self.type_theta0 = [0] * self.angle_type_numbers - for idx, val in enumerate(context): - if "%FLAG ANGLE_EQUIL_VALUE" in val: - count = 0 - start_idx = idx - information = [] - while count < self.angle_type_numbers: - start_idx += 1 - if "%FORMAT" in context[start_idx]: - continue - else: - value = list(map(float, context[start_idx].strip().split())) - information.extend(value) - count += len(value) - self.type_theta0 = information[:self.angle_type_numbers] - break - if self.angle_numbers != angle_count: - print("angle count %d != angle_number %d ", angle_count, self.angle_numbers) - - self.h_angle_k = [] - self.h_angle_theta0 = [] - for i in range(self.angle_numbers): - self.h_angle_k.append(self.type_k[self.h_type[i]]) - self.h_angle_theta0.append(self.type_theta0[self.h_type[i]]) diff --git a/tests/st/sponge/sponge_cuda/bond.py b/tests/st/sponge/sponge_cuda/bond.py deleted file mode 100644 index 143c6918a01..00000000000 --- a/tests/st/sponge/sponge_cuda/bond.py +++ /dev/null @@ -1,164 +0,0 @@ -# Copyright 2021 Huawei Technologies Co., Ltd -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================ -'''Bond''' - - -class Bond: - '''Bond''' - - def __init__(self, controller): - self.module_name = "bond" - self.h_atom_a = [] - self.h_atom_b = [] - self.h_k = [] - self.h_r0 = [] - self.bond_numbers = 0 - self.is_initialized = 0 - if controller.amber_parm is not None: - file_path = controller.amber_parm - self.read_information_from_amberfile(file_path) - self.is_initialized = 1 - else: - self.read_in_file(controller) - - def read_in_file(self, controller): - """read_in_file""" - print("START INITIALIZING BOND:") - name = self.module_name + "_in_file" - if name in controller.command_set: - path = controller.command_set[name] - file = open(path, 'r') - context = file.readlines() - self.bond_numbers = int(context[0].strip()) - print(" bond_numbers is ", self.bond_numbers) - for i in range(self.bond_numbers): - val = list(map(float, context[i + 1].strip().split())) - self.h_atom_a.append(int(val[0])) - self.h_atom_b.append(int(val[1])) - self.h_k.append(val[2]) - self.h_r0.append(val[3]) - self.is_initialized = 1 - file.close() - print("END INITIALIZING BOND") - - def read_information_from_amberfile(self, file_path): - '''read amber file''' - file = open(file_path, 'r') - context = file.readlines() - file.close() - for idx, val in enumerate(context): - if idx < len(context) - 1: - if "%FLAG POINTERS" in val + context[idx + 1] and "%FORMAT(10I8)" in val + context[idx + 1]: - start_idx = idx + 2 - count = 0 - value = list(map(int, context[start_idx].strip().split())) - self.bond_with_hydrogen = value[2] - self.bond_numbers = value[3] - self.bond_numbers += self.bond_with_hydrogen - information = [] - information.extend(value) - while count < 16: - start_idx += 1 - value = list(map(int, context[start_idx].strip().split())) - information.extend(value) - count += len(value) - self.bond_type_numbers = information[15] - print("bond type numbers ", self.bond_type_numbers) - break - - for idx, val in enumerate(context): - if "%FLAG BOND_FORCE_CONSTANT" in val: - count = 0 - start_idx = idx - information = [] - while count < self.bond_type_numbers: - start_idx += 1 - if "%FORMAT" in context[start_idx]: - continue - else: - value = list(map(float, context[start_idx].strip().split())) - information.extend(value) - count += len(value) - self.bond_type_k = information[:self.bond_type_numbers] - break - - for idx, val in enumerate(context): - if "%FLAG BOND_EQUIL_VALUE" in val: - count = 0 - start_idx = idx - information = [] - while count < self.bond_type_numbers: - start_idx += 1 - if "%FORMAT" in context[start_idx]: - continue - else: - value = list(map(float, context[start_idx].strip().split())) - information.extend(value) - count += len(value) - self.bond_type_r = information[:self.bond_type_numbers] - break - self.processor(context) - - def processor(self, context): - '''processor''' - for idx, val in enumerate(context): - if "%FLAG BONDS_INC_HYDROGEN" in val: - self.h_atom_a = [0] * self.bond_numbers - self.h_atom_b = [0] * self.bond_numbers - self.h_k = [0] * self.bond_numbers - self.h_r0 = [0] * self.bond_numbers - - count = 0 - start_idx = idx - information = [] - while count < 3 * self.bond_with_hydrogen: - start_idx += 1 - if "%FORMAT" in context[start_idx]: - continue - else: - value = list(map(int, context[start_idx].strip().split())) - information.extend(value) - count += len(value) - - for i in range(self.bond_with_hydrogen): - self.h_atom_a[i] = int(information[3 * i + 0] / 3) - self.h_atom_b[i] = int(information[3 * i + 1] / 3) - tmpi = information[3 * i + 2] - 1 - self.h_k[i] = self.bond_type_k[tmpi] - self.h_r0[i] = self.bond_type_r[tmpi] - break - - for idx, val in enumerate(context): - if "%FLAG BONDS_WITHOUT_HYDROGEN" in val: - - count = 0 - start_idx = idx - information = [] - while count < 3 * (self.bond_numbers - self.bond_with_hydrogen): - start_idx += 1 - if "%FORMAT" in context[start_idx]: - continue - else: - value = list(map(int, context[start_idx].strip().split())) - information.extend(value) - count += len(value) - - for i in range(self.bond_with_hydrogen, self.bond_numbers): - self.h_atom_a[i] = int(information[3 * (i - self.bond_with_hydrogen) + 0] / 3) - self.h_atom_b[i] = int(information[3 * (i - self.bond_with_hydrogen) + 1] / 3) - tmpi = information[3 * (i - self.bond_with_hydrogen) + 2] - 1 - self.h_k[i] = self.bond_type_k[tmpi] - self.h_r0[i] = self.bond_type_r[tmpi] - break diff --git a/tests/st/sponge/sponge_cuda/dihedral.py b/tests/st/sponge/sponge_cuda/dihedral.py deleted file mode 100644 index 11b360e47f9..00000000000 --- a/tests/st/sponge/sponge_cuda/dihedral.py +++ /dev/null @@ -1,227 +0,0 @@ -# Copyright 2021 Huawei Technologies Co., Ltd -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================ -'''Dihedral''' -import math - - -class Dihedral: - '''Dihedral''' - - def __init__(self, controller): - self.constant_pi = 3.1415926535897932 - self.module_name = "dihedral" - self.h_atom_a = [] - self.h_atom_b = [] - self.h_atom_c = [] - self.h_atom_d = [] - self.h_ipn = [] - self.h_pn = [] - self.h_pk = [] - self.h_gamc = [] - self.h_gams = [] - self.dihedral_numbers = 0 - if controller.amber_parm is not None: - file_path = controller.amber_parm - self.read_information_from_amberfile(file_path) - self.is_initialized = 1 - else: - self.read_in_file(controller) - - def read_in_file(self, controller): - """read_in_file""" - print("START INITIALIZING DIHEDRAL:") - name = self.module_name + "_in_file" - if name in controller.command_set: - path = controller.command_set[name] - file = open(path, 'r') - context = file.readlines() - self.dihedral_numbers = int(context[0].strip()) - print(" dihedral_numbers is ", self.dihedral_numbers) - for i in range(self.dihedral_numbers): - val = list(map(float, context[i + 1].strip().split())) - self.h_atom_a.append(int(val[0])) - self.h_atom_b.append(int(val[1])) - self.h_atom_c.append(int(val[2])) - self.h_atom_d.append(int(val[3])) - self.h_ipn.append(val[4]) - self.h_pn.append(val[4]) - self.h_pk.append(val[5]) - self.h_gamc.append(math.cos(val[6]) * val[5]) - self.h_gams.append(math.sin(val[6]) * val[5]) - - self.is_initialized = 1 - file.close() - print("END INITIALIZING DIHEDRAL") - - def read_information_from_amberfile(self, file_path): - '''read amber file''' - file = open(file_path, 'r') - context = file.readlines() - file.close() - for idx, val in enumerate(context): - if idx < len(context) - 1: - if "%FLAG POINTERS" in val + context[idx + 1] and "%FORMAT(10I8)" in val + context[idx + 1]: - start_idx = idx + 2 - count = 0 - value = list(map(int, context[start_idx].strip().split())) - self.dihedral_with_hydrogen = value[6] - self.dihedral_numbers = value[7] - self.dihedral_numbers += self.dihedral_with_hydrogen - information = [] - information.extend(value) - while count < 15: - start_idx += 1 - value = list(map(int, context[start_idx].strip().split())) - information.extend(value) - count += len(value) - self.dihedral_type_numbers = information[17] - print("dihedral type numbers ", self.dihedral_type_numbers) - break - - self.phase_type = [0] * self.dihedral_type_numbers - self.pk_type = [0] * self.dihedral_type_numbers - self.pn_type = [0] * self.dihedral_type_numbers - - for idx, val in enumerate(context): - if "%FLAG DIHEDRAL_FORCE_CONSTANT" in val: - count = 0 - start_idx = idx - information = [] - while count < self.dihedral_type_numbers: - start_idx += 1 - if "%FORMAT" in context[start_idx]: - continue - else: - value = list(map(float, context[start_idx].strip().split())) - information.extend(value) - count += len(value) - self.pk_type = information[:self.dihedral_type_numbers] - break - - for idx, val in enumerate(context): - if "%FLAG DIHEDRAL_PHASE" in val: - count = 0 - start_idx = idx - information = [] - while count < self.dihedral_type_numbers: - start_idx += 1 - if "%FORMAT" in context[start_idx]: - continue - else: - value = list(map(float, context[start_idx].strip().split())) - information.extend(value) - count += len(value) - self.phase_type = information[:self.dihedral_type_numbers] - break - - for idx, val in enumerate(context): - if "%FLAG DIHEDRAL_PERIODICITY" in val: - count = 0 - start_idx = idx - information = [] - while count < self.dihedral_type_numbers: - start_idx += 1 - if "%FORMAT" in context[start_idx]: - continue - else: - value = list(map(float, context[start_idx].strip().split())) - information.extend(value) - count += len(value) - self.pn_type = information[:self.dihedral_type_numbers] - break - self.processor(context) - - def processor(self, context): - '''processor''' - self.h_atom_a = [0] * self.dihedral_numbers - self.h_atom_b = [0] * self.dihedral_numbers - self.h_atom_c = [0] * self.dihedral_numbers - self.h_atom_d = [0] * self.dihedral_numbers - self.h_pk = [] - self.h_gamc = [] - self.h_gams = [] - self.h_pn = [] - self.h_ipn = [] - for idx, val in enumerate(context): - if "%FLAG DIHEDRALS_INC_HYDROGEN" in val: - count = 0 - start_idx = idx - information = [] - while count < 5 * self.dihedral_with_hydrogen: - start_idx += 1 - if "%FORMAT" in context[start_idx]: - continue - else: - value = list(map(int, context[start_idx].strip().split())) - information.extend(value) - count += len(value) - for i in range(self.dihedral_with_hydrogen): - self.h_atom_a[i] = information[i * 5 + 0] / 3 - self.h_atom_b[i] = information[i * 5 + 1] / 3 - self.h_atom_c[i] = information[i * 5 + 2] / 3 - self.h_atom_d[i] = abs(information[i * 5 + 3] / 3) - tmpi = information[i * 5 + 4] - 1 - self.h_pk.append(self.pk_type[tmpi]) - tmpf = self.phase_type[tmpi] - if abs(tmpf - self.constant_pi) <= 0.001: - tmpf = self.constant_pi - tmpf2 = math.cos(tmpf) - if abs(tmpf2) < 1e-6: - tmpf2 = 0 - self.h_gamc.append(tmpf2 * self.h_pk[i]) - tmpf2 = math.sin(tmpf) - if abs(tmpf2) < 1e-6: - tmpf2 = 0 - self.h_gams.append(tmpf2 * self.h_pk[i]) - self.h_pn.append(abs(self.pn_type[tmpi])) - self.h_ipn.append(int(self.h_pn[i] + 0.001)) - break - for idx, val in enumerate(context): - if "%FLAG DIHEDRALS_WITHOUT_HYDROGEN" in val: - count = 0 - start_idx = idx - information = [] - while count < 5 * (self.dihedral_numbers - self.dihedral_with_hydrogen): - start_idx += 1 - if "%FORMAT" in context[start_idx]: - continue - else: - value = list(map(int, context[start_idx].strip().split())) - information.extend(value) - count += len(value) - for i in range(self.dihedral_with_hydrogen, self.dihedral_numbers): - self.h_atom_a[i] = information[(i - self.dihedral_with_hydrogen) * 5 + 0] / 3 - self.h_atom_b[i] = information[(i - self.dihedral_with_hydrogen) * 5 + 1] / 3 - self.h_atom_c[i] = information[(i - self.dihedral_with_hydrogen) * 5 + 2] / 3 - self.h_atom_d[i] = abs(information[(i - self.dihedral_with_hydrogen) * 5 + 3] / 3) - tmpi = information[(i - self.dihedral_with_hydrogen) * 5 + 4] - 1 - self.h_pk.append(self.pk_type[tmpi]) - tmpf = self.phase_type[tmpi] - if abs(tmpf - self.constant_pi) <= 0.001: - tmpf = self.constant_pi - tmpf2 = math.cos(tmpf) - if abs(tmpf2) < 1e-6: - tmpf2 = 0 - self.h_gamc.append(tmpf2 * self.h_pk[i]) - tmpf2 = math.sin(tmpf) - if abs(tmpf2) < 1e-6: - tmpf2 = 0 - self.h_gams.append(tmpf2 * self.h_pk[i]) - self.h_pn.append(abs(self.pn_type[tmpi])) - self.h_ipn.append(int(self.h_pn[i] + 0.001)) - break - for i in range(self.dihedral_numbers): - if self.h_atom_c[i] < 0: - self.h_atom_c[i] *= -1 diff --git a/tests/st/sponge/sponge_cuda/langevin_liujian_md.py b/tests/st/sponge/sponge_cuda/langevin_liujian_md.py deleted file mode 100644 index df45543a800..00000000000 --- a/tests/st/sponge/sponge_cuda/langevin_liujian_md.py +++ /dev/null @@ -1,112 +0,0 @@ -# Copyright 2021 Huawei Technologies Co., Ltd -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================ -'''LagevinLiuJian''' -import math - -import numpy as np - - -class LangevinLiujian: - '''LagevinLiuJian''' - - def __init__(self, controller, atom_numbers): - self.module_name = "langevin_liu" - self.atom_numbers = atom_numbers - self.h_mass = [] - print("START INITIALIZING LANGEVIN_LIU DYNAMICS:") - if controller.amber_parm is not None: - file_path = controller.amber_parm - self.read_information_from_amberfile(file_path) - else: - self.read_mass_file(controller) - self.constant_time_convertion = 20.455 - self.constant_kb = 0.00198716 - - self.target_temperature = 300.0 if "target_temperature" not in controller.command_set else float( - controller.command_set["target_temperature"]) - self.gamma_ln = 1.0 - if "gamma" in controller.command_set: - self.gamma_ln = float(controller.command_set["gamma"]) - if "langevin_liu_gamma" in controller.command_set: - self.gamma_ln = float(controller.command_set["langevin_liu_gamma"]) - print(" langevin_liu_gamma is ", self.gamma_ln) - - self.random_seed = 1 if "seed" not in controller.command_set else int( - controller.command_set["seed"]) - - print(" target temperature is {} K".format(self.target_temperature)) - print(" friction coefficient is {} ps^-1".format(self.gamma_ln)) - print(" random seed is ", self.random_seed) - self.dt = 0.001 if "dt" not in controller.command_set else float( - controller.command_set["dt"]) * self.constant_time_convertion - self.half_dt = 0.5 * self.dt - - self.float4_numbers = math.ceil(3 * self.atom_numbers / 4.0) - self.rand_state = np.float32(np.zeros([self.float4_numbers * 16])) - self.gamma_ln = self.gamma_ln / self.constant_time_convertion - self.exp_gamma = math.exp(-1 * self.gamma_ln * self.dt) - self.sqrt_gamma = math.sqrt((1. - self.exp_gamma * self.exp_gamma) * self.target_temperature * self.constant_kb) - self.h_sqrt_mass = [0] * self.atom_numbers - for i in range(self.atom_numbers): - self.h_sqrt_mass[i] = self.sqrt_gamma * math.sqrt(1. / self.h_mass[i]) if self.h_mass[i] != 0 else 0 - - self.max_velocity = 0.0 - if "velocity_max" in controller.command_set: - self.max_velocity = float(controller.command_set["velocity_max"]) - if "langevin_liu_velocity_max" in controller.command_set: - self.max_velocity = float(controller.command_set["langevin_liu_velocity_max"]) - print(" max velocity is ", self.max_velocity) - - self.h_mass_inverse = [0] * self.atom_numbers - for i in range(self.atom_numbers): - self.h_mass_inverse[i] = 1. / self.h_mass[i] if self.h_mass[i] != 0 else 0 - - self.is_initialized = 1 - - print("END INITIALIZING LANGEVIN_LIU DYNAMICS") - - def read_mass_file(self, controller): - if "mass_in_file" in controller.command_set: - path = controller.command_set["mass_in_file"] - file = open(path, 'r') - context = file.readlines() - for idx, val in enumerate(context): - if idx > 0: - self.h_mass.append(float(val.strip())) - file.close() - - def read_information_from_amberfile(self, file_path): - '''read amber file''' - file = open(file_path, 'r') - context = file.readlines() - file.close() - self.h_mass = [0] * self.atom_numbers - for idx, val in enumerate(context): - if "%FLAG MASS" in val: - count = 0 - start_idx = idx - information = [] - while count < self.atom_numbers: - start_idx += 1 - if "%FORMAT" in context[start_idx]: - continue - else: - value = list(map(float, context[start_idx].strip().split())) - information.extend(value) - count += len(value) - - for i in range(self.atom_numbers): - self.h_mass[i] = information[i] - break diff --git a/tests/st/sponge/sponge_cuda/lennard_jones.py b/tests/st/sponge/sponge_cuda/lennard_jones.py deleted file mode 100644 index b47da4b731a..00000000000 --- a/tests/st/sponge/sponge_cuda/lennard_jones.py +++ /dev/null @@ -1,170 +0,0 @@ -# Copyright 2021 Huawei Technologies Co., Ltd -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================ -'''Lennard Jones''' -import mindspore.common.dtype as mstype -from mindspore import Tensor -from mindspore.ops import operations as P - - -class LennardJonesInformation: - '''Lennard Jones''' - - def __init__(self, controller, cutoff, box_length): - self.module_name = "LJ" - self.is_initialized = 0 - self.constant_unit_max_float = 4294967296.0 - self.constant_pi = 3.1415926535897932 - self.cutoff = cutoff - self.box_length = box_length - - if controller.amber_parm is not None: - file_path = controller.amber_parm - self.read_information_from_amberfile(file_path) - self.is_initialized = 1 - else: - self.read_in_file(controller) - - if self.is_initialized: - self.totalc6get = P.Totalc6get(self.atom_numbers) - self.read_information() - - def read_in_file(self, controller): - """read_in_file""" - print("START INITIALIZING LENNADR JONES INFORMATION:") - name = self.module_name + "_in_file" - if name in controller.command_set: - path = controller.command_set[name] - file = open(path, 'r') - context = file.readlines() - self.atom_numbers, self.atom_type_numbers = map(int, context[0].strip().split()) - print(" atom_numbers is ", self.atom_numbers) - print(" atom_LJ_type_number is ", self.atom_type_numbers) - self.pair_type_numbers = self.atom_type_numbers * (self.atom_type_numbers + 1) / 2 - self.h_lj_a = [] - self.h_lj_b = [] - self.h_atom_lj_type = [] - startidx = 1 - count = 0 - print(startidx) - while count < self.atom_type_numbers: - if context[startidx].strip(): - val = list(map(float, context[startidx].strip().split())) - count += 1 - self.h_lj_a.extend(val) - startidx += 1 - assert len(self.h_lj_a) == self.pair_type_numbers - self.h_lj_a = [x * 12.0 for x in self.h_lj_a] - - count = 0 - print(startidx) - while count < self.atom_type_numbers: - if context[startidx].strip(): - val = list(map(float, context[startidx].strip().split())) - count += 1 - self.h_lj_b.extend(val) - startidx += 1 - assert len(self.h_lj_b) == self.pair_type_numbers - self.h_lj_b = [x * 6.0 for x in self.h_lj_b] - for idx, val in enumerate(context): - if idx > startidx: - self.h_atom_lj_type.append(int(val.strip())) - file.close() - self.is_initialized = 1 - print("END INITIALIZING LENNADR JONES INFORMATION") - - def read_information(self): - """read_information""" - self.uint_dr_to_dr_cof = [1.0 / self.constant_unit_max_float * self.box_length[0], - 1.0 / self.constant_unit_max_float * self.box_length[1], - 1.0 / self.constant_unit_max_float * self.box_length[2]] - print("copy lj type to new crd") - self.atom_lj_type = Tensor(self.h_atom_lj_type, mstype.int32) - self.lj_b = Tensor(self.h_lj_b, mstype.float32) - self.factor = self.totalc6get(self.atom_lj_type, self.lj_b) - print(" factor is: ", self.factor) - self.long_range_factor = float(self.factor.asnumpy()) - self.long_range_factor *= -2.0 / 3.0 * self.constant_pi / self.cutoff / self.cutoff / self.cutoff / 6.0 - self.volume = self.box_length[0] * self.box_length[1] * self.box_length[2] - print(" long range correction factor is: ", self.long_range_factor) - print(" End initializing long range LJ correction") - - def read_information_from_amberfile(self, file_path): - '''read amber file''' - file = open(file_path, 'r') - context = file.readlines() - file.close() - - for idx, val in enumerate(context): - if idx < len(context) - 1: - if "%FLAG POINTERS" in val + context[idx + 1] and "%FORMAT(10I8)" in val + context[idx + 1]: - start_idx = idx + 2 - count = 0 - value = list(map(int, context[start_idx].strip().split())) - self.atom_numbers = value[0] - self.atom_type_numbers = value[1] - self.pair_type_numbers = int( - self.atom_type_numbers * (self.atom_type_numbers + 1) / 2) - break - self.h_atom_lj_type = [0] * self.atom_numbers - for idx, val in enumerate(context): - if "%FLAG ATOM_TYPE_INDEX" in val: - count = 0 - start_idx = idx - information = [] - while count < self.atom_numbers: - start_idx += 1 - if "%FORMAT" in context[start_idx]: - continue - else: - value = list(map(int, context[start_idx].strip().split())) - information.extend(value) - count += len(value) - for i in range(self.atom_numbers): - self.h_atom_lj_type[i] = information[i] - 1 - break - self.h_lj_a = [0] * self.pair_type_numbers - for idx, val in enumerate(context): - if "%FLAG LENNARD_JONES_ACOEF" in val: - count = 0 - start_idx = idx - information = [] - while count < self.pair_type_numbers: - start_idx += 1 - if "%FORMAT" in context[start_idx]: - continue - else: - value = list(map(float, context[start_idx].strip().split())) - information.extend(value) - count += len(value) - for i in range(self.pair_type_numbers): - self.h_lj_a[i] = 12.0 * information[i] - break - self.h_lj_b = [0] * self.pair_type_numbers - for idx, val in enumerate(context): - if "%FLAG LENNARD_JONES_BCOEF" in val: - count = 0 - start_idx = idx - information = [] - while count < self.pair_type_numbers: - start_idx += 1 - if "%FORMAT" in context[start_idx]: - continue - else: - value = list(map(float, context[start_idx].strip().split())) - information.extend(value) - count += len(value) - for i in range(self.pair_type_numbers): - self.h_lj_b[i] = 6.0 * information[i] - break diff --git a/tests/st/sponge/sponge_cuda/md_information.py b/tests/st/sponge/sponge_cuda/md_information.py deleted file mode 100644 index e83755f2fee..00000000000 --- a/tests/st/sponge/sponge_cuda/md_information.py +++ /dev/null @@ -1,330 +0,0 @@ -# Copyright 2021 Huawei Technologies Co., Ltd -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================ -'''MD Information''' -import numpy as np -from .system_information import (PeriodicBoxConditionInformation, SystemInformation, - NonBondInformation, NveIteration, ResidueInformation, TrajectoryOutput) - - -class MdInformation: - '''MD Information''' - - def __init__(self, controller): - constant_time_convertion = 20.455 - - self.md_task = controller.md_task - - self.netfrc = 0 if "net_force" not in controller.command_set else int(controller.command_set["net_force"]) - self.ntwx = 1000 if "write_information_interval" not in controller.command_set else int( - controller.command_set["write_information_interval"]) - self.atom_numbers = 0 - self.residue_numbers = 0 - self.density = 0.0 - self.lin_serial = [] - self.h_res_start = [] - self.h_res_end = [] - - self.h_charge = [] - self.h_mass = [] - self.h_mass_inverse = [] - self.h_charge = [] - self.coordinate = [] - self.box_length = [] - self.vel = [] - self.crd = [] - self.velocity = [] - - self.mode = self.read_mode(controller) - # read dt - self.dt = 0.001 * constant_time_convertion if "dt" not in controller.command_set else float( - controller.command_set["dt"]) * constant_time_convertion - self.dt_in_ps = 0.001 if "dt" not in controller.command_set else float(controller.command_set["dt"]) - - if controller.amber_parm is not None: - self.read_basic_system_information_from_amber_file(controller.amber_parm) - if controller.initial_coordinates_file is not None: - self.read_basic_system_information_from_rst7(controller.initial_coordinates_file) - else: - self.read_coordinate_and_velocity(controller) - self.read_mass(controller) - self.read_charge(controller) - self.crd = self.coordinate - - self.sys = SystemInformation(controller, self) - self.nb = NonBondInformation(controller, self) - self.output = TrajectoryOutput(controller) - self.nve = NveIteration(controller) - self.res = ResidueInformation(controller, self) - self.pbc = PeriodicBoxConditionInformation(self.box_length) - - if not self.h_res_start: - self.h_res_start = self.res.h_res_start - self.h_res_end = self.res.h_res_end - self.residue_numbers = self.res.residue_numbers - - # Atom_Information_Initial - self.acc = np.zeros([self.atom_numbers, 3]) - self.frc = np.zeros([self.atom_numbers, 3]) - self.sys.freedom = 3 * self.atom_numbers - self.is_initialized = 1 - - self.velocity = np.reshape(np.asarray(self.velocity, np.float32), [self.atom_numbers, 3]) - self.step_limit = self.sys.step_limit - - @staticmethod - def read_mode(controller): - """read_mode""" - if "mode" in controller.command_set: - if controller.command_set["mode"] in ["NVT", "nvt", "1"]: - print(" Mode set to NVT\n") - mode = 1 - elif controller.command_set["mode"] in ["NPT", "npt", "2"]: - print(" Mode set to NPT\n") - mode = 2 - elif controller.command_set["mode"] in ["Minimization", "minimization", "-1"]: - print(" Mode set to Energy Minimization\n") - mode = -1 - elif controller.command_set["mode"] in ["NVE", "nve", "0"]: - print(" Mode set to NVE\n") - mode = 0 - else: - print( - " Warning: Mode {} is not match. Set to NVE as default\n".format(controller.command_set["mode"])) - mode = 0 - else: - print(" Mode set to NVE as default\n") - mode = 0 - return mode - - def read_coordinate_in_file(self, path): - '''read coordinates file''' - file = open(path, 'r') - print(" Start reading coordinate_in_file:\n") - context = file.readlines() - atom_numbers = int(context[0].strip()) - if self.atom_numbers != 0: - if self.atom_numbers is not atom_numbers: - print(" Error: atom_numbers is not equal: ", atom_numbers, self.atom_numbers) - exit(1) - else: - self.atom_numbers = atom_numbers - print(" atom_numbers is ", self.atom_numbers) - - for idx in range(self.atom_numbers): - coord = list(map(float, context[idx + 1].strip().split())) - self.coordinate.append(coord) - - self.box_length = list(map(float, context[-1].strip().split()))[:3] - print(" box_length is: x: {}, y: {}, z: {}".format( - self.box_length[0], self.box_length[1], self.box_length[2])) - self.crd = self.coordinate - file.close() - - def read_velocity_in_file(self, path): - '''read velocity file''' - file = open(path, 'r') - print(" Start reading velocity_in_file:\n") - context = file.readlines() - for idx, val in enumerate(context): - if idx == 0: - atom_numbers = int(val.strip()) - if self.atom_numbers > 0 and atom_numbers != self.atom_numbers: - print(" Error: atom_numbers is not equal: %d %d\n", idx, self.atom_numbers) - exit(1) - else: - self.atom_numbers = atom_numbers - else: - vel = list(map(float, val.strip().split())) - self.velocity.append(vel) - self.vel = self.velocity - file.close() - - def read_coordinate_and_velocity(self, controller): - """read_coordinate_and_velocity""" - if "coordinate_in_file" in controller.command_set: - self.read_coordinate_in_file(controller.command_set["coordinate_in_file"]) - if "velocity_in_file" in controller.command_set: - self.read_velocity_in_file(controller.command_set["velocity_in_file"]) - else: - print(" Velocity is set to zero as default\n") - self.velocity = [0] * 3 * self.atom_numbers - - def read_mass(self, controller): - """read_mass""" - print(" Start reading mass:") - if "mass_in_file" in controller.command_set: - path = controller.command_set["mass_in_file"] - file = open(path, 'r') - self.total_mass = 0 - context = file.readlines() - for idx, val in enumerate(context): - if idx == 0: - atom_numbers = int(val.strip()) - if self.atom_numbers > 0 and (atom_numbers != self.atom_numbers): - print(" Error: atom_numbers is not equal: ", atom_numbers, self.atom_numbers) - exit(1) - else: - self.atom_numbers = atom_numbers - else: - mass = float(val.strip()) - self.h_mass.append(mass) - self.total_mass += mass - if mass == 0: - self.h_mass_inverse.append(0.0) - else: - self.h_mass_inverse.append(1 / mass) - file.close() - else: - print(" mass is set to 20 as default") - self.total_mass = 20 * self.atom_numbers - self.h_mass = [20] * self.atom_numbers - self.h_mass_inverse = [1 / 20] * self.atom_numbers - - print(" End reading mass") - - def read_charge(self, controller): - """read_charge""" - if "charge_in_file" in controller.command_set: - print(" Start reading charge:") - path = controller.command_set["charge_in_file"] - file = open(path, 'r') - context = file.readlines() - for idx, val in enumerate(context): - if idx == 0: - atom_numbers = int(val.strip()) - if self.atom_numbers > 0 and (atom_numbers != self.atom_numbers): - print(" Error: atom_numbers is not equal: %d %d\n", idx, self.atom_numbers) - exit(1) - else: - self.atom_numbers = atom_numbers - else: - self.h_charge.append(float(val.strip())) - file.close() - else: - self.h_charge = [0.0] * self.atom_numbers - print(" End reading charge") - - def read_basic_system_information_from_amber_file(self, path): - '''read amber file''' - file = open(path, 'r') - context = file.readlines() - for idx, val in enumerate(context): - if idx < len(context) - 1: - if "%FLAG POINTERS" in val + context[idx + 1] and "%FORMAT(10I8)" in val + context[idx + 1]: - start_idx = idx + 2 - value = list(map(int, context[start_idx].strip().split())) - self.atom_numbers = value[0] - count = len(value) - 1 - while count < 10: - start_idx += 1 - value = list(map(int, context[start_idx].strip().split())) - count += len(value) - self.residue_numbers = list(map(int, context[start_idx].strip().split()))[ - 10 - (count - 10)] # may exist bug - break - - if self.residue_numbers != 0 and self.atom_numbers != 0: - for idx, val in enumerate(context): - if "%FLAG RESIDUE_POINTER" in val: - count = 0 - start_idx = idx - while count != self.residue_numbers: - start_idx += 1 - if "%FORMAT" in context[start_idx]: - continue - else: - value = list(map(int, context[start_idx].strip().split())) - self.lin_serial.extend(value) - count += len(value) - for i in range(self.residue_numbers - 1): - self.h_res_start.append(self.lin_serial[i] - 1) - self.h_res_end.append(self.lin_serial[i + 1] - 1) - self.h_res_start.append(self.lin_serial[-1] - 1) - self.h_res_end.append(self.atom_numbers + 1 - 1) - break - self.processor(context) - - def processor(self, context): - '''processor''' - for idx, val in enumerate(context): - if "%FLAG MASS" in val: - count = 0 - start_idx = idx - while count != self.atom_numbers: - start_idx += 1 - if "%FORMAT" in context[start_idx]: - continue - else: - value = list(map(float, context[start_idx].strip().split())) - self.h_mass.extend(value) - count += len(value) - for i in range(self.atom_numbers): - if self.h_mass[i] == 0: - self.h_mass_inverse.append(0.0) - else: - self.h_mass_inverse.append(1.0 / self.h_mass[i]) - self.density += self.h_mass[i] - break - for idx, val in enumerate(context): - if "%FLAG CHARGE" in val: - count = 0 - start_idx = idx - while count != self.atom_numbers: - start_idx += 1 - if "%FORMAT" in context[start_idx]: - continue - else: - value = list(map(float, context[start_idx].strip().split())) - self.h_charge.extend(value) - count += len(value) - break - - def read_basic_system_information_from_rst7(self, path): - '''read rst7 file''' - file = open(path, 'r') - context = file.readlines() - file.close() - x = context[1].strip().split() - irest = 1 if len(x) > 1 else 0 - atom_numbers = int(context[1].strip().split()[0]) - if atom_numbers != self.atom_numbers: - print("ERROR") - else: - print("check atom_numbers") - information = [] - count = 0 - start_idx = 1 - if irest == 1: - self.simulation_start_time = float(x[1]) - while count <= 6 * self.atom_numbers + 3: - start_idx += 1 - value = list(map(float, context[start_idx].strip().split())) - information.extend(value) - count += len(value) - self.coordinate = information[: 3 * self.atom_numbers] - self.velocity = information[3 * self.atom_numbers: 6 * self.atom_numbers] - self.box_length = information[6 * self.atom_numbers:6 * self.atom_numbers + 3] - else: - while count <= 3 * self.atom_numbers + 3: - start_idx += 1 - value = list(map(float, context[start_idx].strip().split())) - information.extend(value) - count += len(value) - self.coordinate = information[: 3 * self.atom_numbers] - self.velocity = [0.0] * (3 * self.atom_numbers) - self.box_length = information[3 * self.atom_numbers:3 * self.atom_numbers + 3] - self.coordinate = np.array(self.coordinate).reshape([-1, 3]) - self.velocity = np.array(self.velocity).reshape([-1, 3]) - print("system size is ", self.box_length[0], self.box_length[1], self.box_length[2]) diff --git a/tests/st/sponge/sponge_cuda/nb14.py b/tests/st/sponge/sponge_cuda/nb14.py deleted file mode 100644 index a538765ac07..00000000000 --- a/tests/st/sponge/sponge_cuda/nb14.py +++ /dev/null @@ -1,176 +0,0 @@ -# Copyright 2021 Huawei Technologies Co., Ltd -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================ -'''NON BOND''' - - -class NonBond14: - '''NON BOND''' - - def __init__(self, controller, dihedral, atom_numbers): - self.module_name = "nb14" - self.atom_numbers = atom_numbers - self.h_atom_a = [] - self.h_atom_b = [] - self.h_lj_scale_factor = [] - self.h_cf_scale_factor = [] - self.nb14_numbers = 0 - self.is_initialized = 0 - if controller.amber_parm is not None: - self.dihedral_with_hydrogen = dihedral.dihedral_with_hydrogen - self.dihedral_numbers = dihedral.dihedral_numbers - self.dihedral_type_numbers = dihedral.dihedral_type_numbers - file_path = controller.amber_parm - self.read_information_from_amberfile(file_path) - self.h_atom_a = self.h_atom_a[:self.nb14_numbers] - self.h_atom_b = self.h_atom_b[:self.nb14_numbers] - self.h_lj_scale_factor = self.h_lj_scale_factor[:self.nb14_numbers] - self.h_cf_scale_factor = self.h_cf_scale_factor[:self.nb14_numbers] - self.is_initialized = 1 - else: - self.read_in_file(controller) - - def read_in_file(self, controller): - """read_in_file""" - name = self.module_name + "_in_file" - if name in controller.command_set: - path = controller.command_set[name] - file = open(path, 'r') - context = file.readlines() - self.nb14_numbers = int(context[0].strip()) - print(" non-bond 14 numbers is", self.nb14_numbers) - for i in range(self.nb14_numbers): - val = list(map(float, context[i + 1].strip().split())) - self.h_atom_a.append(int(val[0])) - self.h_atom_b.append(int(val[1])) - self.h_lj_scale_factor.append(val[2]) - self.h_cf_scale_factor.append(val[3]) - self.is_initialized = 1 - file.close() - - def read_information_from_amberfile(self, file_path): - '''read amber file''' - file = open(file_path, 'r') - context = file.readlines() - file.close() - - self.cf_scale_type = [0] * self.dihedral_type_numbers - self.lj_scale_type = [0] * self.dihedral_type_numbers - - for idx, val in enumerate(context): - if "%FLAG SCEE_SCALE_FACTOR" in val: - count = 0 - start_idx = idx - information = [] - while count < self.dihedral_type_numbers: - start_idx += 1 - if "%FORMAT" in context[start_idx]: - continue - else: - value = list(map(float, context[start_idx].strip().split())) - information.extend(value) - count += len(value) - self.cf_scale_type = information[:self.dihedral_type_numbers] - break - - for idx, val in enumerate(context): - if "%FLAG SCNB_SCALE_FACTOR" in val: - count = 0 - start_idx = idx - information = [] - while count < self.dihedral_type_numbers: - start_idx += 1 - if "%FORMAT" in context[start_idx]: - continue - else: - value = list(map(float, context[start_idx].strip().split())) - information.extend(value) - count += len(value) - self.lj_scale_type = information[:self.dihedral_type_numbers] - break - self.processor(context) - - def processor(self, context): - '''processor''' - self.h_atom_a = [0] * self.dihedral_numbers - self.h_atom_b = [0] * self.dihedral_numbers - self.h_lj_scale_factor = [0] * self.dihedral_numbers - self.h_cf_scale_factor = [0] * self.dihedral_numbers - nb14_numbers = 0 - for idx, val in enumerate(context): - if "%FLAG DIHEDRALS_INC_HYDROGEN" in val: - count = 0 - start_idx = idx - information = [] - while count < 5 * self.dihedral_with_hydrogen: - start_idx += 1 - if "%FORMAT" in context[start_idx]: - continue - else: - value = list(map(int, context[start_idx].strip().split())) - information.extend(value) - count += len(value) - for i in range(self.dihedral_with_hydrogen): - tempa = information[i * 5 + 0] - tempi = information[i * 5 + 1] - tempi2 = information[i * 5 + 2] - tempb = information[i * 5 + 3] - tempi = information[i * 5 + 4] - - tempi -= 1 - if tempi2 > 0: - self.h_atom_a[nb14_numbers] = tempa / 3 - self.h_atom_b[nb14_numbers] = abs(tempb / 3) - self.h_lj_scale_factor[nb14_numbers] = self.lj_scale_type[tempi] - if self.h_lj_scale_factor[nb14_numbers] != 0: - self.h_lj_scale_factor[nb14_numbers] = 1.0 / self.h_lj_scale_factor[nb14_numbers] - self.h_cf_scale_factor[nb14_numbers] = self.cf_scale_type[tempi] - if self.h_cf_scale_factor[nb14_numbers] != 0: - self.h_cf_scale_factor[nb14_numbers] = 1.0 / self.h_cf_scale_factor[nb14_numbers] - nb14_numbers += 1 - break - for idx, val in enumerate(context): - if "%FLAG DIHEDRALS_WITHOUT_HYDROGEN" in val: - count = 0 - start_idx = idx - information = [] - while count < 5 * (self.dihedral_numbers - self.dihedral_with_hydrogen): - start_idx += 1 - if "%FORMAT" in context[start_idx]: - continue - else: - value = list(map(int, context[start_idx].strip().split())) - information.extend(value) - count += len(value) - for i in range(self.dihedral_with_hydrogen, self.dihedral_numbers): - tempa = information[(i - self.dihedral_with_hydrogen) * 5 + 0] - tempi = information[(i - self.dihedral_with_hydrogen) * 5 + 1] - tempi2 = information[(i - self.dihedral_with_hydrogen) * 5 + 2] - tempb = information[(i - self.dihedral_with_hydrogen) * 5 + 3] - tempi = information[(i - self.dihedral_with_hydrogen) * 5 + 4] - - tempi -= 1 - if tempi2 > 0: - self.h_atom_a[nb14_numbers] = tempa / 3 - self.h_atom_b[nb14_numbers] = abs(tempb / 3) - self.h_lj_scale_factor[nb14_numbers] = self.lj_scale_type[tempi] - if self.h_lj_scale_factor[nb14_numbers] != 0: - self.h_lj_scale_factor[nb14_numbers] = 1.0 / self.h_lj_scale_factor[nb14_numbers] - self.h_cf_scale_factor[nb14_numbers] = self.cf_scale_type[tempi] - if self.h_cf_scale_factor[nb14_numbers] != 0: - self.h_cf_scale_factor[nb14_numbers] = 1.0 / self.h_cf_scale_factor[nb14_numbers] - nb14_numbers += 1 - break - - self.nb14_numbers = nb14_numbers diff --git a/tests/st/sponge/sponge_cuda/neighbor_list.py b/tests/st/sponge/sponge_cuda/neighbor_list.py deleted file mode 100644 index 9a0f3312e05..00000000000 --- a/tests/st/sponge/sponge_cuda/neighbor_list.py +++ /dev/null @@ -1,182 +0,0 @@ -# Copyright 2021 Huawei Technologies Co., Ltd -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================ -'''Neighbor List''' - - -class NeighborList: - '''Neighbor List''' - - def __init__(self, controller, atom_numbers, box_length): - self.constant_unit_max_float = 4294967296.0 - print("START INITIALIZING NEIGHBOR LIST:") - self.module_name = "NeighborList" - self.refresh_interval = 20 if "refresh_interval" not in controller.command_set else int( - controller.command_set["refresh_interval"]) - self.max_atom_in_grid_numbers = 64 if "max_atom_in_grid_numbers" not in controller.command_set else int( - controller.command_set["max_atom_in_grid_numbers"]) - self.max_neighbor_numbers = 800 if "max_neighbor_numbers" not in controller.command_set else int( - controller.command_set["max_neighbor_numbers"]) - - self.skin = 2.0 if "skin" not in controller.command_set else float(controller.command_set["skin"]) - self.cutoff = 10.0 if "cutoff" not in controller.command_set else float(controller.command_set["cutoff"]) - self.cutoff_square = self.cutoff * self.cutoff - self.cutoff_with_skin = self.cutoff + self.skin - self.half_cutoff_with_skin = 0.5 * self.cutoff_with_skin - self.cutoff_with_skin_square = self.cutoff_with_skin * self.cutoff_with_skin - self.half_skin_square = 0.25 * self.skin * self.skin - self.atom_numbers = atom_numbers - self.box_length = box_length - self.update_volume() - - self.initial_neighbor_grid() - self.not_first_time = 0 - self.is_initialized = 1 - self.refresh_count = [0] - - if controller.amber_parm is not None: - file_path = controller.amber_parm - self.read_information_from_amberfile(file_path) - - def read_information_from_amberfile(self, file_path): - '''read amber file''' - file = open(file_path, 'r') - context = file.readlines() - file.close() - self.excluded_list_start = [0] * self.atom_numbers - self.excluded_numbers = [0] * self.atom_numbers - - for idx, val in enumerate(context): - if idx < len(context) - 1: - if "%FLAG POINTERS" in val + context[idx + 1] and "%FORMAT(10I8)" in val + context[idx + 1]: - start_idx = idx + 2 - count = 0 - value = list(map(int, context[start_idx].strip().split())) - information = [] - information.extend(value) - while count < 11: - start_idx += 1 - value = list(map(int, context[start_idx].strip().split())) - information.extend(value) - count += len(value) - self.excluded_atom_numbers = information[10] - print("excluded atom numbers ", self.excluded_atom_numbers) - break - for idx, val in enumerate(context): - if "%FLAG NUMBER_EXCLUDED_ATOMS" in val: - count = 0 - start_idx = idx - information = [] - while count < self.atom_numbers: - start_idx += 1 - if "%FORMAT" in context[start_idx]: - continue - else: - value = list(map(int, context[start_idx].strip().split())) - information.extend(value) - count += len(value) - count = 0 - for i in range(self.atom_numbers): - self.excluded_numbers[i] = information[i] - self.excluded_list_start[i] = count - count += information[i] - break - - total_count = sum(self.excluded_numbers) - self.excluded_list = [] - for idx, val in enumerate(context): - if "%FLAG EXCLUDED_ATOMS_LIST" in val: - count = 0 - start_idx = idx - information = [] - while count < total_count: - start_idx += 1 - if "%FORMAT" in context[start_idx]: - continue - else: - value = list(map(int, context[start_idx].strip().split())) - information.extend(value) - count += len(value) - - count = 0 - for i in range(self.atom_numbers): - tmp_list = [] - if self.excluded_numbers[i] == 1: - tmp_list.append(information[count] - 1) - if information[count] == 0: - self.excluded_numbers[i] = 0 - count += 1 - else: - for _ in range(self.excluded_numbers[i]): - tmp_list.append(information[count] - 1) - - count += 1 - tmp_list = sorted(tmp_list) - self.excluded_list.extend(tmp_list) - break - - def initial_neighbor_grid(self): - '''init neighbor grid''' - half_cutoff = self.half_cutoff_with_skin - self.nx = int(self.box_length[0] / half_cutoff) - self.ny = int(self.box_length[1] / half_cutoff) - self.nz = int(self.box_length[2] / half_cutoff) - self.grid_n = [self.nx, self.ny, self.nz] - self.grid_length = [self.box_length[0] / self.nx, - self.box_length[1] / self.ny, - self.box_length[2] / self.nz] - self.grid_length_inverse = [1.0 / self.grid_length[0], 1.0 / self.grid_length[1], 1.0 / self.grid_length[2]] - - self.nxy = self.nx * self.ny - self.grid_numbers = self.nz * self.nxy - self.atom_numbers_in_grid_bucket = [0] * self.grid_numbers - self.bucket = [-1] * (self.grid_numbers * self.max_atom_in_grid_numbers) - - self.pointer = [] - temp_grid_serial = [0] * 125 - for i in range(self.grid_numbers): - nz = int(i / self.nxy) - ny = int((i - self.nxy * nz) / self.nx) - nx = i - self.nxy * nz - self.nx * ny - count = 0 - for l in range(-2, 3): - for m in range(-2, 3): - for n in range(-2, 3): - xx = nx + l - if xx < 0: - xx = xx + self.nx - elif xx >= self.nx: - xx = xx - self.nx - yy = ny + m - if yy < 0: - yy = yy + self.ny - elif yy >= self.ny: - yy = yy - self.ny - zz = nz + n - if zz < 0: - zz = zz + self.nz - elif zz >= self.nz: - zz = zz - self.nz - temp_grid_serial[count] = zz * self.nxy + yy * self.nx + xx - count += 1 - temp_grid_serial = sorted(temp_grid_serial) - self.pointer.extend(temp_grid_serial) - - def update_volume(self): - self.quarter_crd_to_uint_crd_cof = [0.25 * self.constant_unit_max_float / self.box_length[0], - 0.25 * self.constant_unit_max_float / self.box_length[1], - 0.25 * self.constant_unit_max_float / self.box_length[2]] - self.uint_dr_to_dr_cof = [1.0 / self.constant_unit_max_float * self.box_length[0], - 1.0 / self.constant_unit_max_float * self.box_length[1], - 1.0 / self.constant_unit_max_float * self.box_length[2]] diff --git a/tests/st/sponge/sponge_cuda/particle_mesh_ewald.py b/tests/st/sponge/sponge_cuda/particle_mesh_ewald.py deleted file mode 100644 index 2b29db16455..00000000000 --- a/tests/st/sponge/sponge_cuda/particle_mesh_ewald.py +++ /dev/null @@ -1,128 +0,0 @@ -# Copyright 2021 Huawei Technologies Co., Ltd -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================ -'''PME''' -import math - - -class ParticleMeshEwald(): - '''PME''' - def __init__(self, controller, md_info): - self.module_name = "PME" - self.constant_pi = 3.1415926535897932 - self.cutoff = 10.0 if "cutoff" not in controller.command_set else float(controller.command_set["cutoff"]) - self.tolerance = 0.00001 if "Direct_Tolerance" not in controller.command_set else float( - controller.command_set["Direct_Tolerance"]) - self.fftx = -1 if "fftx" not in controller.command_set else int(controller.command_set["fftx"]) - self.ffty = -1 if "ffty" not in controller.command_set else int(controller.command_set["ffty"]) - self.fftz = -1 if "fftz" not in controller.command_set else int(controller.command_set["fftz"]) - self.atom_numbers = md_info.atom_numbers - self.box_length = md_info.box_length - - self.volume = self.box_length[0] * self.box_length[1] * self.box_length[2] - - if self.fftx < 0: - self.fftx = self.get_fft_patameter(self.box_length[0]) - if self.ffty < 0: - self.ffty = self.get_fft_patameter(self.box_length[1]) - if self.fftz < 0: - self.fftz = self.get_fft_patameter(self.box_length[2]) - print(" fftx: ", self.fftx) - print(" ffty: ", self.ffty) - print(" fftz: ", self.fftz) - print("pme cutoff", self.cutoff) - print("pme tolerance", self.tolerance) - self.pme_nall = self.fftx * self.ffty * self.fftz - self.pme_nin = self.ffty * self.fftz - self.pme_nfft = self.fftx * self.ffty * (int(self.fftz / 2) + 1) - self.pme_inverse_box_vector = [self.fftx / self.box_length[0], - self.ffty / self.box_length[1], - self.fftz / self.box_length[2]] - - self.beta = self.get_beta(self.cutoff, self.tolerance) - self.neutralizing_factor = -0.5 * self.constant_pi / (self.beta * self.beta * self.volume) - self.is_initialized = 1 - - @staticmethod - def check_2357_factor(number): - '''CHECK FACTOR''' - while number > 0: - if number == 1: - return 1 - tempn = number // 2 - if tempn * 2 != number: - break - number = tempn - while number > 0: - if number == 1: - return 1 - tempn = number / 3 - if tempn * 3 != number: - break - number = tempn - while number > 0: - if number == 1: - return 1 - tempn = number // 5 - if tempn * 5 != number: - break - number = tempn - while number > 0: - if number == 1: - return 1 - tempn = number // 7 - if tempn * 7 != number: - break - number = tempn - return 0 - - @staticmethod - def get_beta(cutoff, tolerance): - '''GET BETA''' - high = 1.0 - ihigh = 1 - while 1: - tempf = math.erfc(high * cutoff) / cutoff - if tempf <= tolerance: - break - high *= 2 - ihigh += 1 - ihigh += 50 - low = 0.0 - for _ in range(1, ihigh): - beta = (low + high) / 2 - tempf = math.erfc(beta * cutoff) / cutoff - if tempf >= tolerance: - low = beta - else: - high = beta - return beta - - def get_fft_patameter(self, length): - '''GET FFT PARAMETER''' - tempi = math.ceil(length + 3) >> 2 << 2 - if 60 <= tempi <= 68: - tempi = 64 - elif 120 <= tempi <= 136: - tempi = 128 - elif 240 <= tempi <= 272: - tempi = 256 - elif 480 <= tempi <= 544: - tempi = 512 - elif 960 <= tempi <= 1088: - tempi = 1024 - while 1: - if self.check_2357_factor(tempi): - return tempi - tempi += 4 diff --git a/tests/st/sponge/sponge_cuda/restrain.py b/tests/st/sponge/sponge_cuda/restrain.py deleted file mode 100644 index dd500e2ee7a..00000000000 --- a/tests/st/sponge/sponge_cuda/restrain.py +++ /dev/null @@ -1,66 +0,0 @@ -# Copyright 2021 Huawei Technologies Co., Ltd -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================ -'''restrain_information''' - - -class RestrainInformation: - '''restrain_information''' - - def __init__(self, controller, atom_numbers, crd): - self.module_name = "restrain" - self.atom_numbers = atom_numbers - self.crd = crd - self.controller = controller - self.weight = 100.0 if "weight" not in controller.command_set else float( - controller.command_set["weight"]) - print(" %s_weight is %.0f\n" % (self.module_name, self.weight)) - self.is_initialized = 0 - name = self.module_name + "_in_file" - if name in controller.command_set: - print("START INITIALIZING RESTRAIN\n") - path = controller.command_set[name] - self.read_in_file(path) - self.read_crd_ref() - self.is_initialized = 1 - print("END INITIALIZING RESTRAIN\n") - - def read_in_file(self, path): - """read_in_file""" - file = open(path, 'r') - context = file.readlines() - self.restrain_numbers = 0 - h_lists = [] - for _, val in enumerate(context): - h_lists.append(float(val.strip())) - self.restrain_numbers += 1 - print(" restrain_numbers is %d\n", self.restrain_numbers) - file.close() - self.restrain_ene = [0] * self.restrain_numbers - self.sum_of_restrain_ene = [0] - - def read_crd_ref(self): - """read_crd_ref""" - self.crd_ref = [] - if "coordinate" not in self.controller.command_set: - print(" restrain reference coordinate copy from input coordinate") - self.crd_ref = self.crd - else: - print(" reading restrain reference coordinate file") - file = open(self.controller.command_set["coordinate"], 'r') - context = file.readlines() - atom_numbers = int(context[0].strip()) - print(" atom_numbers is %d", atom_numbers) - for i in range(atom_numbers): - self.crd_ref.append(list(map(float, context[i + 1].strip().split()))) diff --git a/tests/st/sponge/sponge_cuda/simple_constrain.py b/tests/st/sponge/sponge_cuda/simple_constrain.py deleted file mode 100644 index dfe00e9515a..00000000000 --- a/tests/st/sponge/sponge_cuda/simple_constrain.py +++ /dev/null @@ -1,206 +0,0 @@ -# Copyright 2021 Huawei Technologies Co., Ltd -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================ -"""SimpleConstarin""" - - -class BondInformation: - def __init__(self): - self.bond_numbers = 0 - self.atom_a = [] - self.atom_b = [] - self.bond_r = [] - - -class Information: - """Information""" - - def __init__(self): - self.atom_numbers = 0 - self.dt = 0.001 - self.uint_dr_to_dr_cof = [] - self.quarter_crd_to_uint_crd_cof = [] - self.volume = 0.0 - self.exp_gamma = 1.0 - self.step_length = 1.0 - self.iteration_numbers = 25 - self.constrain_mass = 3 - - -class SimpleConstarin: - """SimpleConstrain""" - - def __init__(self, controller, md_info, bond, angle, liujian_info): - self.module_name = "simple_constrain" - self.constant_unit_max_float = 4294967296.0 - self.controller = controller - self.md_info = md_info - self.bond = bond - self.angle = angle - self.liujian_info = liujian_info - self.bond_info = BondInformation() - self.info = Information() - - self.atom_numbers = md_info.atom_numbers - self.bond_numbers = bond.bond_numbers - self.angle_numbers = angle.angle_numbers - - self.is_initialized = 0 - self.constrain_frc = [] - self.pair_virial = [] - self.virial = [] - self.test_uint_crd = [] - self.last_pair_dr = [] - self.dt_inverse = 0 - self.half_exp_gamma_plus_half = 0 - self.bond_constrain_pair_numbers = 0 - self.angle_constrain_pair_numbers = 0 - # in the real calculation, use constrain pair - self.constrain_pair_numbers = 1 - self.system_freedom = 0 - self.h_bond_pair = [] - self.h_constrain_pair = [] - if "constrain_mode" in self.controller.command_set and \ - self.controller.command_set["constrain_mode"] == "simple_constrain": - print("START INITIALIZING SIMPLE CONSTRAIN:") - self.constrain_pair_numbers = 0 - self.add_hbond_to_constrain_pair(self.bond_numbers, bond.h_atom_a, bond.h_atom_b, bond.h_r0, md_info.h_mass) - self.add_hangle_to_constrain_pair() - if liujian_info.is_initialized: - self.initial_simple_constrain(md_info.atom_numbers, md_info.dt, md_info.sys.box_length, - liujian_info.exp_gamma, 0, md_info.h_mass, md_info.sys.freedom) - else: - self.initial_simple_constrain(md_info.atom_numbers, md_info.dt, md_info.sys.box_length, - 1.0, md_info.mode == -1, md_info.h_mass, md_info.sys.freedom) - self.is_initialized = 1 - print("END INITIALIZING SIMPLE CONSTRAIN\n") - - def add_hbond_to_constrain_pair(self, bond_numbers, atom_a, atom_b, bond_r, atom_mass): - """ add hbond to constrain pair - :param bond_numbers: - :param atom_a: - :param atom_b: - :param bond_r: - :param atom_mass: - :return: update bond info - """ - - self.info.constrain_mass = 3.0 - name = self.module_name + "_in_file" - if name in self.controller.command_set: - self.info.constrain_mass = 0 - if "mass" in self.controller.command_set: - self.info.constrain_mass = float(self.controller.command_set["mass"]) - self.h_bond_pair = [] - s = 0 - for i in range(bond_numbers): - mass_a = atom_mass[atom_a[i]] - mass_b = atom_mass[atom_b[i]] - if (float(mass_a) < self.info.constrain_mass and mass_a > 0) or \ - (float(mass_b) < self.info.constrain_mass and mass_b > 0): - constrain_k = \ - atom_mass[atom_a[i]] * atom_mass[atom_b[i]] / (atom_mass[atom_a[i]] + atom_mass[atom_b[i]]) - self.h_bond_pair.append([atom_a[i], atom_b[i], bond_r[i], constrain_k]) - s += 1 - self.bond_constrain_pair_numbers = s - self.bond_info.bond_numbers = bond_numbers - self.bond_info.atom_a = atom_a - self.bond_info.atom_b = atom_b - self.bond_info.bond_r = bond_r - - def add_hangle_to_constrain_pair(self): - """add hangle to constrain_pair""" - - self.h_angle_pair = [] - self.angle_constrain_pair_numbers = 0 - - def initial_simple_constrain(self, atom_numbers, dt, box_length, exp_gamma, is_minimization, atom_mass, - system_freedom): - """initial simple constrain - :param atom_numbers: - :param dt: - :param box_length: - :param exp_gamma: - :param is_minimization: - :param atom_mass: - :param system_freedom: - :return: - """ - - self.system_freedom = system_freedom - self.atom_mass = atom_mass - self.info.atom_numbers = atom_numbers - self.info.dt = dt - self.info.quarter_crd_to_uint_crd_cof = [0.25 * self.constant_unit_max_float / box_length[0], - 0.25 * self.constant_unit_max_float / box_length[1], - 0.25 * self.constant_unit_max_float / box_length[2]] - self.info.uint_dr_to_dr_cof = [1 / self.constant_unit_max_float * box_length[0], - 1 / self.constant_unit_max_float * box_length[1], - 1 / self.constant_unit_max_float * box_length[2]] - self.info.volume = box_length[0] * box_length[1] * box_length[2] - self.info.exp_gamma = exp_gamma - self.half_exp_gamma_plus_half = 0.5 * (1. + self.info.exp_gamma) - if is_minimization: - self.info.exp_gamma = 0.0 - self.info.iteration_numbers = 25 if "iteration_numbers" not in self.controller.command_set else int( - self.controller.command_set["iteration_numbers"]) - print(" constrain iteration step is ", self.info.iteration_numbers) - self.info.step_length = 1 if "step_length" not in self.controller.command_set else float( - self.controller.command_set["step_length"]) - print(" constrain step_length is ", self.info.step_length) - self.extra_numbers = 0 - name = self.module_name + "_in_file" - if name in self.controller.command_set: - path = self.controller.command_set[name] - with open(path, 'r') as pf: - context = pf.readlines() - self.extra_numbers = int(context[0].strip()) - - self.dt_inverse = 1 / self.info.dt - self.constrain_pair_numbers = \ - self.bond_constrain_pair_numbers + self.angle_constrain_pair_numbers + self.extra_numbers - self.system_freedom -= self.constrain_pair_numbers - - print(" constrain pair number is ", self.constrain_pair_numbers) - - for i in range(self.bond_constrain_pair_numbers): - self.h_constrain_pair.append(self.h_bond_pair[i]) - self.h_constrain_pair[i][-1] = self.info.step_length / self.half_exp_gamma_plus_half \ - * self.h_constrain_pair[i][-1] - for i in range(self.angle_constrain_pair_numbers): - self.h_constrain_pair.append(self.h_bond_pair[i]) - self.h_constrain_pair[i + self.bond_constrain_pair_numbers][-1] =\ - self.info.step_length / self.half_exp_gamma_plus_half * \ - self.h_constrain_pair[i + self.bond_constrain_pair_numbers][-1] - if name in self.controller.command_set: - path = self.controller.command_set[name] - self.read_in_file(path) - - def read_in_file(self, path): - """ read in_file - - :param path: - :return: in_file context - """ - - with open(path, 'r') as pf: - context = pf.readlines() - count = self.bond_constrain_pair_numbers + self.angle_constrain_pair_numbers - for i in range(self.extra_numbers): - val = list(map(float, context[i + 1].strip().split())) - atom_i, atom_j, constant_r = int(val[0]), int(val[1]), float(val[2]) - constrain_k = self.info.step_length / self.half_exp_gamma_plus_half * self.atom_mass[atom_i] * \ - self.atom_mass[atom_j] / (self.atom_mass[atom_i] + self.atom_mass[atom_j]) - self.h_constrain_pair.append([atom_i, atom_j, constant_r, constrain_k]) - count += 1 diff --git a/tests/st/sponge/sponge_cuda/system_information.py b/tests/st/sponge/sponge_cuda/system_information.py deleted file mode 100644 index 174e668a57e..00000000000 --- a/tests/st/sponge/sponge_cuda/system_information.py +++ /dev/null @@ -1,291 +0,0 @@ -# Copyright 2021 Huawei Technologies Co., Ltd -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================ -'''System information''' -import numpy as np - - -class PeriodicBoxConditionInformation: - """PeriodicBoxConditionInformation""" - - def __init__(self, box_length): - constant_unit_max_float = 4294967296.0 - self.crd_to_uint_crd_cof = np.array([constant_unit_max_float / box_length[0], - constant_unit_max_float / box_length[1], - constant_unit_max_float / box_length[2]]) - self.quarter_crd_to_uint_crd_cof = 0.25 * self.crd_to_uint_crd_cof - self.uint_dr_to_dr_cof = 1.0 / self.crd_to_uint_crd_cof - - -class SystemInformation: - """SystemInformation""" - - def __init__(self, controller, md_info): - constant_pres_convertion_inverse = 0.00001439506089041446 - self.md_info = md_info - self.box_length = self.md_info.box_length - self.steps = 0 - self.step_limit = 1000 if "step_limit" not in controller.command_set else int( - controller.command_set["step_limit"]) - self.target_temperature = 300.0 if "target_temperature" not in controller.command_set else float( - controller.command_set["target_temperature"]) - if md_info.mode == 2 and "target_pressure" in controller.command_set: - self.target_pressure = float(controller.command_set["target_pressure"]) - else: - self.target_pressure = 1 - self.target_pressure *= constant_pres_convertion_inverse - self.d_virial = 0 - self.d_pressure = 0 - self.d_temperature = 0 - self.d_potential = 0 - self.d_sum_of_atom_ek = 0 - self.freedom = 3 * md_info.atom_numbers - - -class NonBondInformation: - """NonBondInformation""" - - def __init__(self, controller, md_info): - self.md_info = md_info - self.skin = 2.0 if "skin" not in controller.command_set else float(controller.command_set["skin"]) - print(" skin set to %.2f Angstram" % (self.skin)) - self.cutoff = 10.0 if "cutoff" not in controller.command_set else float(controller.command_set["cutoff"]) - self.atom_numbers = self.md_info.atom_numbers - self.excluded_atom_numbers = 0 - self.h_excluded_list_start = [] - self.h_excluded_numbers = [] - self.h_excluded_list = [] - if controller.amber_parm is not None: - file_path = controller.amber_parm - self.read_information_from_amberfile(file_path) - else: - self.read_exclude_file(controller) - - def read_exclude_file(self, controller): - """read_exclude_file""" - if "exclude_in_file" in controller.command_set: - print(" Start reading excluded list:") - path = controller.command_set["exclude_in_file"] - file = open(path, 'r') - context = file.readlines() - atom_numbers, self.excluded_atom_numbers = list(map(int, context[0].strip().split())) - if self.md_info.atom_numbers > 0 and (atom_numbers != self.md_info.atom_numbers): - print(" Error: atom_numbers is not equal: ", atom_numbers, self.md_info.atom_numbers) - exit(1) - else: - self.md_info.atom_numbers = atom_numbers - count = 0 - for idx, val in enumerate(context): - if idx > 0: - el = list(map(int, val.strip().split())) - if el[0] == 1 and -1 in el: - self.h_excluded_numbers.append(0) - else: - self.h_excluded_numbers.append(el[0]) - self.h_excluded_list_start.append(count) - if el: - self.h_excluded_list.extend(el[1:]) - count += el[0] - print(" End reading excluded list") - file.close() - else: - print(" Set all atom exclude no atoms as default") - count = 0 - for i in range(self.md_info.atom_numbers): - self.h_excluded_numbers[i] = 0 - self.h_excluded_list_start[i] = count - for _ in range(self.h_excluded_numbers[i]): - self.h_excluded_list[count] = 0 - count += 1 - print(" End reading charge") - - def read_information_from_amberfile(self, file_path): - '''read amber file''' - file = open(file_path, 'r') - context = file.readlines() - file.close() - self.h_excluded_list_start = [0] * self.atom_numbers - self.h_excluded_numbers = [0] * self.atom_numbers - - for idx, val in enumerate(context): - if idx < len(context) - 1: - if "%FLAG POINTERS" in val + context[idx + 1] and "%FORMAT(10I8)" in val + context[idx + 1]: - start_idx = idx + 2 - count = 0 - value = list(map(int, context[start_idx].strip().split())) - information = [] - information.extend(value) - while count < 11: - start_idx += 1 - value = list(map(int, context[start_idx].strip().split())) - information.extend(value) - count += len(value) - self.excluded_atom_numbers = information[10] - print("excluded atom numbers ", self.excluded_atom_numbers) - break - for idx, val in enumerate(context): - if "%FLAG NUMBER_EXCLUDED_ATOMS" in val: - count = 0 - start_idx = idx - information = [] - while count < self.atom_numbers: - start_idx += 1 - if "%FORMAT" in context[start_idx]: - continue - else: - value = list(map(int, context[start_idx].strip().split())) - information.extend(value) - count += len(value) - count = 0 - for i in range(self.atom_numbers): - self.h_excluded_numbers[i] = information[i] - self.h_excluded_list_start[i] = count - count += information[i] - break - - total_count = sum(self.h_excluded_numbers) - self.h_excluded_list = [] - for idx, val in enumerate(context): - if "%FLAG EXCLUDED_ATOMS_LIST" in val: - count = 0 - start_idx = idx - information = [] - while count < total_count: - start_idx += 1 - if "%FORMAT" in context[start_idx]: - continue - else: - value = list(map(int, context[start_idx].strip().split())) - information.extend(value) - count += len(value) - - count = 0 - for i in range(self.atom_numbers): - tmp_list = [] - if self.h_excluded_numbers[i] == 1: - tmp_list.append(information[count] - 1) - if information[count] == 0: - self.h_excluded_numbers[i] = 0 - count += 1 - else: - for _ in range(self.h_excluded_numbers[i]): - tmp_list.append(information[count] - 1) - - count += 1 - tmp_list = sorted(tmp_list) - self.h_excluded_list.extend(tmp_list) - break - - -class NveIteration: - """NveIteration""" - - def __init__(self, controller): - self.max_velocity = -1 if "nve_velocity_max" not in controller.command_set else float( - controller.command_set["nve_velocity_max"]) - - -class ResidueInformation: - """ResidueInformation""" - - def __init__(self, controller, md_info): - self.md_info = md_info - self.residue_numbers = 0 - self.h_mass = [] - self.h_mass_inverse = [] - self.h_res_start = [] - self.h_res_end = [] - self.momentum = [] - self.center_of_mass = [] - self.sigma_of_res_ek = 0 - self.res_ek_energy = 0 - self.sigma_of_res_ek = 0 - self.is_initialized = 0 - print(" Start reading residue list:") - if "residue_in_file" in controller.command_set: - self.read_residule_file(controller) - if controller.amber_parm is not None: - print("amber_parm7 in residue_information") - self.residue_numbers = self.md_info.residue_numbers - self.h_res_start = md_info.h_res_start - self.h_res_end = md_info.h_res_end - self.is_initialized = 1 - self.read_res_mass() - else: - self.residue_numbers = md_info.atom_numbers - self.h_res_start = list(range(self.residue_numbers)) - self.h_res_end = list(range(1, self.residue_numbers + 1)) - self.read_res_mass() - self.is_initialized = 1 - print(" End reading residue list") - - def read_res_mass(self): - """ Read_AMBER_Parm7 """ - if self.md_info.h_mass: - for i in range(self.residue_numbers): - temp_mass = 0 - for j in range(self.h_res_start[i], self.h_res_end[i]): - temp_mass += self.md_info.h_mass[j] - self.h_mass.append(temp_mass) - if temp_mass == 0: - self.h_mass_inverse.append(0) - else: - self.h_mass_inverse.append(1.0 / temp_mass) - else: - print(" Error: atom mass should be initialized before residue mass") - exit(1) - - def read_residule_file(self, controller): - """read_residule_file""" - if "residue_in_file" in controller.command_set: - path = controller.command_set["residue_in_file"] - file = open(path, 'r') - context = file.readlines() - atom_numbers, self.residue_numbers = list(map(int, context[0].strip().split())) - print(" residue_numbers is ", self.residue_numbers) - if self.md_info.atom_numbers > 0 and (atom_numbers != self.md_info.atom_numbers): - print(" Error: atom_numbers is not equal: ", atom_numbers, self.md_info.atom_numbers) - exit(1) - else: - self.md_info.atom_numbers = atom_numbers - print(" residue_numbers is ", self.residue_numbers) - - count = 0 - for idx, val in enumerate(context): - if idx > 0: - self.h_res_start.append(count) - temp = int(val.strip()) - count += temp - self.h_res_end.append(count) - print(" End reading excluded list") - file.close() - self.is_initialized = 1 - if self.is_initialized: - self.read_res_mass() - - -class TrajectoryOutput: - """TrajectoryOutput""" - - def __init__(self, controller): - self.current_crd_synchronized_step = 0 - self.is_molecule_map_output = 0 - if "molecule_map_output" in controller.command_set: - self.is_molecule_map_output = int(controller.command_set["molecule_map_output"]) - self.amber_irest = -1 - self.write_trajectory_interval = 1000 if "write_information_interval" not in controller.command_set else int( - controller.command_set["write_information_interval"]) - self.write_restart_file_interval = self.write_trajectory_interval if "write_restart_file_interval" not in \ - controller.command_set else \ - int(controller.command_set["write_restart_file_interval"]) diff --git a/tests/st/sponge/test_polypeptide.py b/tests/st/sponge/test_polypeptide.py deleted file mode 100644 index 719ac31ada8..00000000000 --- a/tests/st/sponge/test_polypeptide.py +++ /dev/null @@ -1,34 +0,0 @@ -from tests.st.sponge.simulation_np import Simulation -import pytest -import mindspore.context as context - - -@pytest.mark.level1 -@pytest.mark.platform_x86_gpu_training -@pytest.mark.env_onecard -def test_sponge_polypeptide(): - """ - Feature: Test the polypeptide case from sponge. - Description: Test polypeptide net - Expectation: Success. - """ - - context.set_context(mode=context.GRAPH_MODE, device_target="GPU", save_graphs=False, enable_graph_kernel=True) - context.set_context(graph_kernel_flags="--enable_expand_ops=Gather \ - --enable_cluster_ops=TensorScatterAdd,UnSortedSegmentSum,GatherNd \ - --enable_recompute_fusion=false --enable_parallel_fusion=true") - simulation = Simulation() - for steps in range(1000): - temperature, total_potential_energy, sigma_of_bond_ene, sigma_of_angle_ene, sigma_of_dihedral_ene, \ - nb14_lj_energy_sum, nb14_cf_energy_sum, lj_energy_sum, ee_ene, _ = simulation() - if steps == 1000: - print(temperature, total_potential_energy, sigma_of_bond_ene, sigma_of_angle_ene, \ - sigma_of_dihedral_ene, nb14_lj_energy_sum, nb14_cf_energy_sum, lj_energy_sum, ee_ene) - assert 250 < temperature < 350 - assert -7500 < total_potential_energy < -6500 - assert 800 < sigma_of_bond_ene < 1300 - assert 10 < sigma_of_dihedral_ene < 25 - assert 3 < nb14_lj_energy_sum < 9 - assert 130 < nb14_cf_energy_sum < 220 - assert 1200 < lj_energy_sum < 1800 - assert -12000 < ee_ene < -7000