clang-format line-limit to 120 (#552)

* clang-format

* line-limit 120

.clang-format

+---
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+AlwaysBreakAfterReturnType: None
+AlwaysBreakBeforeMultilineStrings: true
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+BinPackArguments: false
+BinPackParameters: false
+BraceWrapping:
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+  AfterControlStatement: false
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+  IndentBraces:    false
+BreakBeforeBinaryOperators: None
+BreakBeforeBraces: Attach
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+BreakAfterJavaFieldAnnotations: false
+BreakStringLiterals: false
+ColumnLimit:     100
+CommentPragmas:  '^ IWYU pragma:'
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+ConstructorInitializerAllOnOneLineOrOnePerLine: true
+ConstructorInitializerIndentWidth: 4
+ContinuationIndentWidth: 4
+Cpp11BracedListStyle: true
+DerivePointerAlignment: false
+DisableFormat:   false
+ForEachMacros:   [ FOR_EACH_RANGE, FOR_EACH, ]
+IncludeCategories:
+  - Regex:           '^<.*\.h(pp)?>'
+    Priority:        1
+  - Regex:           '^<.*'
+    Priority:        2
+  - Regex:           '.*'
+    Priority:        3
+IndentCaseLabels: true
+IndentWidth:     2
+IndentWrappedFunctionNames: false
+KeepEmptyLinesAtTheStartOfBlocks: false
+MacroBlockBegin: ''
+MacroBlockEnd:   ''
+MaxEmptyLinesToKeep: 1
+NamespaceIndentation: None
+ObjCBlockIndentWidth: 2
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+SpacesInContainerLiterals: true
+SpacesInCStyleCastParentheses: false
+SpacesInParentheses: false
+SpacesInSquareBrackets: false
+Standard:        Cpp11
+TabWidth:        8
+UseTab:          Never
+...

torchani/cuaev/aev.cu

-#include <cub/cub.cuh>
 #include <thrust/equal.h>
 #include <torch/extension.h>
+#include <cub/cub.cuh>
 
 #include <ATen/Context.h>
 #include <THC/THC.h>
-#include <THC/THCThrustAllocator.cuh>
 #include <c10/cuda/CUDACachingAllocator.h>
+#include <THC/THCThrustAllocator.cuh>
 
 #define PI 3.141592653589793
 
-template <typename DataT, typename IndexT = int> struct AEVScalarParams {
+template <typename DataT, typename IndexT = int>
+struct AEVScalarParams {
   DataT Rcr;
   DataT Rca;
 
@@ -23,7 +24,8 @@ template <typename DataT, typename IndexT = int> struct AEVScalarParams {
 #define MAX_NSPECIES 10
 __constant__ int csubaev_offsets[MAX_NSPECIES * MAX_NSPECIES];
 
-template <typename DataT> struct PairDist {
+template <typename DataT>
+struct PairDist {
   DataT Rij;
   int midx;
   short i;
@@ -32,8 +34,7 @@ template <typename DataT> struct PairDist {
 
 // used to group Rijs by atom id
 template <typename DataT>
-__host__ __device__ bool operator==(const PairDist<DataT> &lhs,
-                                    const PairDist<DataT> &rhs) {
+__host__ __device__ bool operator==(const PairDist<DataT>& lhs, const PairDist<DataT>& rhs) {
   return lhs.midx == rhs.midx && lhs.i == rhs.i;
 }
 
@@ -42,23 +43,21 @@ __host__ __device__ bool operator==(const PairDist<DataT> &lhs,
 /// \param value Input value that is to be aligned
 /// \return Value aligned to boundary
 template <int32_t boundary>
-__host__ __device__ __forceinline__ int align(const int &value) {
-  static_assert((boundary & (boundary - 1)) == 0,
-                "Boundary for align must be power of 2");
+__host__ __device__ __forceinline__ int align(const int& value) {
+  static_assert((boundary & (boundary - 1)) == 0, "Boundary for align must be power of 2");
   return (value + boundary) & ~(boundary - 1);
 }
 
 template <typename SpeciesT, typename DataT, typename IndexT = int>
 __global__ void pairwiseDistance(
-    torch::PackedTensorAccessor32<SpeciesT, 2, torch::RestrictPtrTraits>
-        species_t,
+    torch::PackedTensorAccessor32<SpeciesT, 2, torch::RestrictPtrTraits> species_t,
     torch::PackedTensorAccessor32<DataT, 3, torch::RestrictPtrTraits> pos_t,
-    PairDist<DataT> *d_Rij, IndexT max_natoms_per_mol) {
-
+    PairDist<DataT>* d_Rij,
+    IndexT max_natoms_per_mol) {
   extern __shared__ DataT spos[];
-  DataT *sx = &spos[0];
-  DataT *sy = &spos[max_natoms_per_mol];
-  DataT *sz = &spos[2 * max_natoms_per_mol];
+  DataT* sx = &spos[0];
+  DataT* sy = &spos[max_natoms_per_mol];
+  DataT* sz = &spos[2 * max_natoms_per_mol];
 
   int mol_idx = blockIdx.x;
   int tidx = threadIdx.y * blockDim.x + threadIdx.x;
@@ -74,7 +73,6 @@ __global__ void pairwiseDistance(
   int natom_pairs = max_natoms_per_mol * max_natoms_per_mol;
 
   for (int i = threadIdx.y; i < max_natoms_per_mol; i += blockDim.y) {
-
     SpeciesT type_i = species_t[mol_idx][i];
 
     DataT xi = sx[i];
@@ -107,21 +105,23 @@ __global__ void pairwiseDistance(
   }
 }
 
-template <typename SpeciesT, typename DataT, typename IndexT = int,
-          int TILEX = 8, int TILEY = 4>
+template <typename SpeciesT, typename DataT, typename IndexT = int, int TILEX = 8, int TILEY = 4>
 __global__ void cuAngularAEVs(
-    torch::PackedTensorAccessor32<SpeciesT, 2, torch::RestrictPtrTraits>
-        species_t,
+    torch::PackedTensorAccessor32<SpeciesT, 2, torch::RestrictPtrTraits> species_t,
     torch::PackedTensorAccessor32<DataT, 3, torch::RestrictPtrTraits> pos_t,
     torch::PackedTensorAccessor32<DataT, 1, torch::RestrictPtrTraits> ShfA_t,
     torch::PackedTensorAccessor32<DataT, 1, torch::RestrictPtrTraits> ShfZ_t,
     torch::PackedTensorAccessor32<DataT, 1, torch::RestrictPtrTraits> EtaA_t,
     torch::PackedTensorAccessor32<DataT, 1, torch::RestrictPtrTraits> Zeta_t,
     torch::PackedTensorAccessor32<DataT, 3, torch::RestrictPtrTraits> aev_t,
-    PairDist<DataT> *d_Rij, PairDist<DataT> *d_centralAtom,
-    int *d_nPairsPerCenterAtom, int *d_centerAtomStartIdx,
-    AEVScalarParams<DataT, IndexT> aev_params, int maxnbrs_per_atom_aligned,
-    int angular_length_aligned, int ncentral_atoms) {
+    PairDist<DataT>* d_Rij,
+    PairDist<DataT>* d_centralAtom,
+    int* d_nPairsPerCenterAtom,
+    int* d_centerAtomStartIdx,
+    AEVScalarParams<DataT, IndexT> aev_params,
+    int maxnbrs_per_atom_aligned,
+    int angular_length_aligned,
+    int ncentral_atoms) {
   extern __shared__ DataT smem[];
 
   int threads_per_catom = TILEX * TILEY;
@@ -135,25 +135,25 @@ __global__ void cuAngularAEVs(
   int laneIdx = threadIdx.x % threads_per_catom;
   int ncatom_per_tpb = blockDim.x / threads_per_catom;
 
-  DataT *saev = &smem[groupIdx * angular_length_aligned];
+  DataT* saev = &smem[groupIdx * angular_length_aligned];
 
   int offset = ncatom_per_tpb * angular_length_aligned;
-  DataT *sdx = &smem[offset + groupIdx * maxnbrs_per_atom_aligned];
+  DataT* sdx = &smem[offset + groupIdx * maxnbrs_per_atom_aligned];
 
   offset += ncatom_per_tpb * maxnbrs_per_atom_aligned;
-  DataT *sdy = &smem[offset + groupIdx * maxnbrs_per_atom_aligned];
+  DataT* sdy = &smem[offset + groupIdx * maxnbrs_per_atom_aligned];
 
   offset += ncatom_per_tpb * maxnbrs_per_atom_aligned;
-  DataT *sdz = &smem[offset + groupIdx * maxnbrs_per_atom_aligned];
+  DataT* sdz = &smem[offset + groupIdx * maxnbrs_per_atom_aligned];
 
   offset += ncatom_per_tpb * maxnbrs_per_atom_aligned;
-  DataT *sdist = &smem[offset + groupIdx * maxnbrs_per_atom_aligned];
+  DataT* sdist = &smem[offset + groupIdx * maxnbrs_per_atom_aligned];
 
   offset += ncatom_per_tpb * maxnbrs_per_atom_aligned;
-  DataT *sfc = &smem[offset + groupIdx * maxnbrs_per_atom_aligned];
+  DataT* sfc = &smem[offset + groupIdx * maxnbrs_per_atom_aligned];
 
   offset += ncatom_per_tpb * maxnbrs_per_atom_aligned;
-  int *stype = (int *)&smem[offset + groupIdx * maxnbrs_per_atom_aligned];
+  int* stype = (int*)&smem[offset + groupIdx * maxnbrs_per_atom_aligned];
 
   DataT EtaA = EtaA_t[0];
   DataT Zeta = Zeta_t[0];
@@ -171,8 +171,7 @@ __global__ void cuAngularAEVs(
   int i = d.i;
   int mol_idx = d.midx;
 
-  for (int iaev = laneIdx; iaev < aev_params.angular_length;
-       iaev += threads_per_catom) {
+  for (int iaev = laneIdx; iaev < aev_params.angular_length; iaev += threads_per_catom) {
     saev[iaev] = 0;
   }
 
@@ -202,16 +201,13 @@ __global__ void cuAngularAEVs(
 
     DataT fc_ij = sfc[jj];
 
-    for (int kk_start = jj + 1; kk_start < jnum;
-         kk_start += threads_per_catom) {
-
+    for (int kk_start = jj + 1; kk_start < jnum; kk_start += threads_per_catom) {
       int kk = kk_start + laneIdx;
       DataT theta = 0;
       if (kk < jnum) {
         const DataT Rik = sdist[kk];
-        theta = acos(
-            0.95 * (sdx[jj] * sdx[kk] + sdy[jj] * sdy[kk] + sdz[jj] * sdz[kk]) /
-            (Rij * Rik));
+        theta =
+            acos(0.95 * (sdx[jj] * sdx[kk] + sdy[jj] * sdy[kk] + sdz[jj] * sdz[kk]) / (Rij * Rik));
       }
 
       for (int srcLane = 0; kk_start + srcLane < min(32, jnum); ++srcLane) {
@@ -247,22 +243,20 @@ __global__ void cuAngularAEVs(
     }
   }
 
-  for (int iaev = laneIdx; iaev < aev_params.angular_length;
-       iaev += threads_per_catom) {
+  for (int iaev = laneIdx; iaev < aev_params.angular_length; iaev += threads_per_catom) {
     aev_t[mol_idx][i][aev_params.radial_length + iaev] = saev[iaev];
   }
 }
 
 template <typename SpeciesT, typename DataT, int THREADS_PER_RIJ>
 __global__ void cuRadialAEVs(
-    torch::PackedTensorAccessor32<SpeciesT, 2, torch::RestrictPtrTraits>
-        species_t,
+    torch::PackedTensorAccessor32<SpeciesT, 2, torch::RestrictPtrTraits> species_t,
     torch::PackedTensorAccessor32<DataT, 1, torch::RestrictPtrTraits> ShfR_t,
     torch::PackedTensorAccessor32<DataT, 1, torch::RestrictPtrTraits> EtaR_t,
     torch::PackedTensorAccessor32<DataT, 3, torch::RestrictPtrTraits> aev_t,
-    PairDist<DataT> *d_Rij, AEVScalarParams<DataT, int> aev_params,
+    PairDist<DataT>* d_Rij,
+    AEVScalarParams<DataT, int> aev_params,
     int nRadialRij) {
-
   int gidx = blockIdx.x * blockDim.x + threadIdx.x;
   int idx = gidx / THREADS_PER_RIJ;
 
@@ -290,104 +284,123 @@ __global__ void cuRadialAEVs(
 
     DataT GmR = 0.25 * exp(-EtaR * (Rij - ShfR) * (Rij - ShfR)) * fc;
 
-    atomicAdd(&aev_t[mol_idx][i][type_j * aev_params.radial_sublength + ishfr],
-              GmR);
+    atomicAdd(&aev_t[mol_idx][i][type_j * aev_params.radial_sublength + ishfr], GmR);
   }
 }
 
 template <typename DataT>
-void cubScan(const DataT *d_in, DataT *d_out, int num_items,
-             cudaStream_t stream) {
-  auto &allocator = *c10::cuda::CUDACachingAllocator::get();
+void cubScan(const DataT* d_in, DataT* d_out, int num_items, cudaStream_t stream) {
+  auto& allocator = *c10::cuda::CUDACachingAllocator::get();
 
   // Determine temporary device storage requirements
-  void *d_temp_storage = NULL;
+  void* d_temp_storage = NULL;
   size_t temp_storage_bytes = 0;
-  cub::DeviceScan::ExclusiveSum(d_temp_storage, temp_storage_bytes, d_in, d_out,
-                                num_items, stream);
+  cub::DeviceScan::ExclusiveSum(d_temp_storage, temp_storage_bytes, d_in, d_out, num_items, stream);
 
   // Allocate temporary storage
   auto buffer_tmp = allocator.allocate(temp_storage_bytes);
   d_temp_storage = buffer_tmp.get();
 
   // Run exclusive prefix sum
-  cub::DeviceScan::ExclusiveSum(d_temp_storage, temp_storage_bytes, d_in, d_out,
-                                num_items, stream);
+  cub::DeviceScan::ExclusiveSum(d_temp_storage, temp_storage_bytes, d_in, d_out, num_items, stream);
 }
 
 template <typename DataT, typename IndexT>
-int cubEncode(const DataT *d_in, DataT *d_unique_out, IndexT *d_counts_out,
-              int num_items, int *d_num_runs_out, cudaStream_t stream) {
-  auto &allocator = *c10::cuda::CUDACachingAllocator::get();
+int cubEncode(
+    const DataT* d_in,
+    DataT* d_unique_out,
+    IndexT* d_counts_out,
+    int num_items,
+    int* d_num_runs_out,
+    cudaStream_t stream) {
+  auto& allocator = *c10::cuda::CUDACachingAllocator::get();
 
   // Determine temporary device storage requirements
-  void *d_temp_storage = NULL;
+  void* d_temp_storage = NULL;
   size_t temp_storage_bytes = 0;
-  cub::DeviceRunLengthEncode::Encode(d_temp_storage, temp_storage_bytes, d_in,
-                                     d_unique_out, d_counts_out, d_num_runs_out,
-                                     num_items, stream);
+  cub::DeviceRunLengthEncode::Encode(
+      d_temp_storage,
+      temp_storage_bytes,
+      d_in,
+      d_unique_out,
+      d_counts_out,
+      d_num_runs_out,
+      num_items,
+      stream);
 
   // Allocate temporary storage
   auto buffer_tmp = allocator.allocate(temp_storage_bytes);
   d_temp_storage = buffer_tmp.get();
 
   // Run encoding
-  cub::DeviceRunLengthEncode::Encode(d_temp_storage, temp_storage_bytes, d_in,
-                                     d_unique_out, d_counts_out, d_num_runs_out,
-                                     num_items, stream);
+  cub::DeviceRunLengthEncode::Encode(
+      d_temp_storage,
+      temp_storage_bytes,
+      d_in,
+      d_unique_out,
+      d_counts_out,
+      d_num_runs_out,
+      num_items,
+      stream);
 
   int num_selected = 0;
-  cudaMemcpyAsync(&num_selected, d_num_runs_out, sizeof(int), cudaMemcpyDefault,
-                  stream);
+  cudaMemcpyAsync(&num_selected, d_num_runs_out, sizeof(int), cudaMemcpyDefault, stream);
   cudaStreamSynchronize(stream);
   return num_selected;
 }
 
 template <typename DataT, typename LambdaOpT>
-int cubDeviceSelect(const DataT *d_in, DataT *d_out, int num_items,
-                    int *d_num_selected_out, LambdaOpT select_op,
+int cubDeviceSelect(
+    const DataT* d_in,
+    DataT* d_out,
+    int num_items,
+    int* d_num_selected_out,
+    LambdaOpT select_op,
     cudaStream_t stream) {
-  auto &allocator = *c10::cuda::CUDACachingAllocator::get();
+  auto& allocator = *c10::cuda::CUDACachingAllocator::get();
 
   // Determine temporary device storage requirements
-  void *d_temp_storage = NULL;
+  void* d_temp_storage = NULL;
   size_t temp_storage_bytes = 0;
-  cub::DeviceSelect::If(d_temp_storage, temp_storage_bytes, d_in, d_out,
-                        d_num_selected_out, num_items, select_op);
+  cub::DeviceSelect::If(
+      d_temp_storage, temp_storage_bytes, d_in, d_out, d_num_selected_out, num_items, select_op);
 
   // Allocate temporary storage
   auto buffer_tmp = allocator.allocate(temp_storage_bytes);
   d_temp_storage = buffer_tmp.get();
 
   // Run selection
-  cub::DeviceSelect::If(d_temp_storage, temp_storage_bytes, d_in, d_out,
-                        d_num_selected_out, num_items, select_op, stream);
+  cub::DeviceSelect::If(
+      d_temp_storage,
+      temp_storage_bytes,
+      d_in,
+      d_out,
+      d_num_selected_out,
+      num_items,
+      select_op,
+      stream);
 
   int num_selected = 0;
-  cudaMemcpyAsync(&num_selected, d_num_selected_out, sizeof(int),
-                  cudaMemcpyDefault, stream);
+  cudaMemcpyAsync(&num_selected, d_num_selected_out, sizeof(int), cudaMemcpyDefault, stream);
   cudaStreamSynchronize(stream);
 
   return num_selected;
 }
 
 template <typename DataT>
-DataT cubMax(const DataT *d_in, int num_items, DataT *d_out,
-             cudaStream_t stream) {
-  auto &allocator = *c10::cuda::CUDACachingAllocator::get();
+DataT cubMax(const DataT* d_in, int num_items, DataT* d_out, cudaStream_t stream) {
+  auto& allocator = *c10::cuda::CUDACachingAllocator::get();
   // Determine temporary device storage requirements
-  void *d_temp_storage = NULL;
+  void* d_temp_storage = NULL;
   size_t temp_storage_bytes = 0;
-  cub::DeviceReduce::Max(d_temp_storage, temp_storage_bytes, d_in, d_out,
-                         num_items, stream);
+  cub::DeviceReduce::Max(d_temp_storage, temp_storage_bytes, d_in, d_out, num_items, stream);
 
   // Allocate temporary storage
   auto buffer_tmp = allocator.allocate(temp_storage_bytes);
   d_temp_storage = buffer_tmp.get();
 
   // Run min-reduction
-  cub::DeviceReduce::Max(d_temp_storage, temp_storage_bytes, d_in, d_out,
-                         num_items, stream);
+  cub::DeviceReduce::Max(d_temp_storage, temp_storage_bytes, d_in, d_out, num_items, stream);
 
   int maxVal = 0;
   cudaMemcpyAsync(&maxVal, d_out, sizeof(DataT), cudaMemcpyDefault, stream);
@@ -396,40 +409,50 @@ DataT cubMax(const DataT *d_in, int num_items, DataT *d_out,
   return maxVal;
 }
 
-void initConsts(AEVScalarParams<float> &aev_params, cudaStream_t stream) {
+void initConsts(AEVScalarParams<float>& aev_params, cudaStream_t stream) {
   int num_species = aev_params.num_species;
   assert(num_species <= MAX_NSPECIES);
   // precompute the aev offsets and load to constand memory
-  int *subaev_offsets = new int[num_species * num_species];
+  int* subaev_offsets = new int[num_species * num_species];
   for (int t = 0; t < num_species; ++t) {
     int offset = 0;
     for (int s = 0; s < num_species; s++) {
       if (t < num_species - s) {
-        subaev_offsets[s * num_species + s + t] =
-            aev_params.angular_sublength * (offset + t);
-        subaev_offsets[(s + t) * num_species + s] =
-            aev_params.angular_sublength * (offset + t);
+        subaev_offsets[s * num_species + s + t] = aev_params.angular_sublength * (offset + t);
+        subaev_offsets[(s + t) * num_species + s] = aev_params.angular_sublength * (offset + t);
       }
       offset += num_species - s;
     }
   }
-  cudaMemcpyToSymbolAsync(csubaev_offsets, subaev_offsets,
-                          sizeof(int) * num_species * num_species, 0,
-                          cudaMemcpyDefault, stream);
+  cudaMemcpyToSymbolAsync(
+      csubaev_offsets,
+      subaev_offsets,
+      sizeof(int) * num_species * num_species,
+      0,
+      cudaMemcpyDefault,
+      stream);
   delete[] subaev_offsets;
 }
 
 // NOTE: assumes size of EtaA_t = Zeta_t = EtaR_t = 1
 template <typename ScalarRealT = float>
-torch::Tensor cuComputeAEV(torch::Tensor coordinates_t, torch::Tensor species_t,
-                           double Rcr_, double Rca_, torch::Tensor EtaR_t,
-                           torch::Tensor ShfR_t, torch::Tensor EtaA_t,
-                           torch::Tensor Zeta_t, torch::Tensor ShfA_t,
-                           torch::Tensor ShfZ_t, int64_t num_species_) {
-  TORCH_CHECK((species_t.dtype() == torch::kInt32) &&
-                  (coordinates_t.dtype() == torch::kFloat32),
+torch::Tensor cuComputeAEV(
+    torch::Tensor coordinates_t,
+    torch::Tensor species_t,
+    double Rcr_,
+    double Rca_,
+    torch::Tensor EtaR_t,
+    torch::Tensor ShfR_t,
+    torch::Tensor EtaA_t,
+    torch::Tensor Zeta_t,
+    torch::Tensor ShfA_t,
+    torch::Tensor ShfZ_t,
+    int64_t num_species_) {
+  TORCH_CHECK(
+      (species_t.dtype() == torch::kInt32) && (coordinates_t.dtype() == torch::kFloat32),
       "Unsupported input type");
-  TORCH_CHECK(EtaR_t.size(0) == 1 || EtaA_t.size(0) == 1 || Zeta_t.size(0) == 1,
+  TORCH_CHECK(
+      EtaR_t.size(0) == 1 || EtaA_t.size(0) == 1 || Zeta_t.size(0) == 1,
       "cuda extension is currently not supported for the specified "
       "configuration");
 
@@ -448,35 +471,29 @@ torch::Tensor cuComputeAEV(torch::Tensor coordinates_t, torch::Tensor species_t,
   aev_params.radial_sublength = EtaR_t.size(0) * ShfR_t.size(0);
   aev_params.radial_length = aev_params.radial_sublength * num_species;
 
-  aev_params.angular_sublength =
-      EtaA_t.size(0) * Zeta_t.size(0) * ShfA_t.size(0) * ShfZ_t.size(0);
-  aev_params.angular_length =
-      aev_params.angular_sublength * (num_species * (num_species + 1) / 2);
+  aev_params.angular_sublength = EtaA_t.size(0) * Zeta_t.size(0) * ShfA_t.size(0) * ShfZ_t.size(0);
+  aev_params.angular_length = aev_params.angular_sublength * (num_species * (num_species + 1) / 2);
 
   int aev_length = aev_params.radial_length + aev_params.angular_length;
 
-  auto aev_t = torch::zeros({n_molecules, max_natoms_per_mol, aev_length},
-                            coordinates_t.options());
+  auto aev_t = torch::zeros({n_molecules, max_natoms_per_mol, aev_length}, coordinates_t.options());
 
   if (species_t.numel() == 0) {
     return aev_t;
   }
 
   cudaStream_t stream = at::cuda::getCurrentCUDAStream();
-  auto thrust_allocator =
-      THCThrustAllocator(at::globalContext().lazyInitCUDA());
+  auto thrust_allocator = THCThrustAllocator(at::globalContext().lazyInitCUDA());
   auto policy = thrust::cuda::par(thrust_allocator).on(stream);
-  auto &allocator = *c10::cuda::CUDACachingAllocator::get();
+  auto& allocator = *c10::cuda::CUDACachingAllocator::get();
 
   // precompute the aev offsets and load to constand memory
   initConsts(aev_params, stream);
 
   // buffer to store all the pairwise distance (Rij)
-  auto total_natom_pairs =
-      n_molecules * max_natoms_per_mol * max_natoms_per_mol;
-  auto buffer_Rij =
-      allocator.allocate(sizeof(PairDist<float>) * total_natom_pairs);
-  PairDist<float> *d_Rij = (PairDist<float> *)buffer_Rij.get();
+  auto total_natom_pairs = n_molecules * max_natoms_per_mol * max_natoms_per_mol;
+  auto buffer_Rij = allocator.allocate(sizeof(PairDist<float>) * total_natom_pairs);
+  PairDist<float>* d_Rij = (PairDist<float>*)buffer_Rij.get();
 
   // init all Rij to inf
   PairDist<float> init;
@@ -485,28 +502,31 @@ torch::Tensor cuComputeAEV(torch::Tensor coordinates_t, torch::Tensor species_t,
 
   // buffer to store all the pairwise distance that is needed for Radial AEV
   // computation
-  auto buffer_radialRij =
-      allocator.allocate(sizeof(PairDist<float>) * total_natom_pairs);
-  PairDist<float> *d_radialRij = (PairDist<float> *)buffer_radialRij.get();
+  auto buffer_radialRij = allocator.allocate(sizeof(PairDist<float>) * total_natom_pairs);
+  PairDist<float>* d_radialRij = (PairDist<float>*)buffer_radialRij.get();
 
   auto buffer_count = allocator.allocate(sizeof(int));
-  int *d_count_out = (int *)buffer_count.get();
+  int* d_count_out = (int*)buffer_count.get();
 
   const int block_size = 64;
 
   dim3 block(8, 8, 1);
   // Compute pairwise distance (Rij) for all atom pairs in a molecule
-  pairwiseDistance<<<n_molecules, block, sizeof(float) * max_natoms_per_mol * 3,
-                     stream>>>(
+  pairwiseDistance<<<n_molecules, block, sizeof(float) * max_natoms_per_mol * 3, stream>>>(
       species_t.packed_accessor32<int, 2, torch::RestrictPtrTraits>(),
       coordinates_t.packed_accessor32<float, 3, torch::RestrictPtrTraits>(),
-      d_Rij, max_natoms_per_mol);
+      d_Rij,
+      max_natoms_per_mol);
 
   // Extract Rijs that is needed for RadialAEV comptuation i.e. all the Rij <=
   // Rcr
   int nRadialRij = cubDeviceSelect(
-      d_Rij, d_radialRij, total_natom_pairs, d_count_out,
-      [=] __device__(const PairDist<float> d) { return d.Rij <= Rcr; }, stream);
+      d_Rij,
+      d_radialRij,
+      total_natom_pairs,
+      d_count_out,
+      [=] __device__(const PairDist<float> d) { return d.Rij <= Rcr; },
+      stream);
 
   int nblocks = (nRadialRij * 8 + block_size - 1) / block_size;
   cuRadialAEVs<int, float, 8><<<nblocks, block_size, 0, stream>>>(
@@ -514,40 +534,41 @@ torch::Tensor cuComputeAEV(torch::Tensor coordinates_t, torch::Tensor species_t,
       ShfR_t.packed_accessor32<float, 1, torch::RestrictPtrTraits>(),
       EtaR_t.packed_accessor32<float, 1, torch::RestrictPtrTraits>(),
       aev_t.packed_accessor32<float, 3, torch::RestrictPtrTraits>(),
-      d_radialRij, aev_params, nRadialRij);
+      d_radialRij,
+      aev_params,
+      nRadialRij);
 
   // reuse buffer allocated for all Rij
   // d_angularRij will store all the Rij required in Angular AEV computation
-  PairDist<float> *d_angularRij = d_Rij;
+  PairDist<float>* d_angularRij = d_Rij;
 
   // Extract Rijs that is needed for AngularAEV comptuation i.e. all the Rij
   // <= Rca
   int nAngularRij = cubDeviceSelect(
-      d_radialRij, d_angularRij, nRadialRij, d_count_out,
-      [=] __device__(const PairDist<float> d) { return d.Rij <= Rca; }, stream);
+      d_radialRij,
+      d_angularRij,
+      nRadialRij,
+      d_count_out,
+      [=] __device__(const PairDist<float> d) { return d.Rij <= Rca; },
+      stream);
 
-  auto buffer_centralAtom =
-      allocator.allocate(sizeof(PairDist<float>) * nAngularRij);
-  PairDist<float> *d_centralAtom = (PairDist<float> *)buffer_centralAtom.get();
+  auto buffer_centralAtom = allocator.allocate(sizeof(PairDist<float>) * nAngularRij);
+  PairDist<float>* d_centralAtom = (PairDist<float>*)buffer_centralAtom.get();
 
-  auto buffer_numPairsPerCenterAtom =
-      allocator.allocate(sizeof(int) * nAngularRij);
-  int *d_numPairsPerCenterAtom = (int *)buffer_numPairsPerCenterAtom.get();
+  auto buffer_numPairsPerCenterAtom = allocator.allocate(sizeof(int) * nAngularRij);
+  int* d_numPairsPerCenterAtom = (int*)buffer_numPairsPerCenterAtom.get();
 
   // group by center atom
-  int ncenter_atoms =
-      cubEncode(d_angularRij, d_centralAtom, d_numPairsPerCenterAtom,
-                nAngularRij, d_count_out, stream);
+  int ncenter_atoms = cubEncode(
+      d_angularRij, d_centralAtom, d_numPairsPerCenterAtom, nAngularRij, d_count_out, stream);
 
-  auto buffer_centerAtomStartIdx =
-      allocator.allocate(sizeof(int) * ncenter_atoms);
-  int *d_centerAtomStartIdx = (int *)buffer_centerAtomStartIdx.get();
+  auto buffer_centerAtomStartIdx = allocator.allocate(sizeof(int) * ncenter_atoms);
+  int* d_centerAtomStartIdx = (int*)buffer_centerAtomStartIdx.get();
 
   cubScan(d_numPairsPerCenterAtom, d_centerAtomStartIdx, ncenter_atoms, stream);
   {
     const int nthreads_per_catom = 32;
-    const int nblocks_angAEV =
-        (ncenter_atoms * nthreads_per_catom + block_size - 1) / block_size;
+    const int nblocks_angAEV = (ncenter_atoms * nthreads_per_catom + block_size - 1) / block_size;
     auto smem_size = [&aev_params](int max_nbrs, int ncatom_per_tpb) {
       int sm_aev = sizeof(float) * align<4>(aev_params.angular_length);
       int sxyz = sizeof(float) * max_nbrs * 3;
@@ -558,14 +579,14 @@ torch::Tensor cuComputeAEV(torch::Tensor coordinates_t, torch::Tensor species_t,
       return (sm_aev + sxyz + sRij + sfc + sj) * ncatom_per_tpb;
     };
 
-    int maxNbrsPerCenterAtom =
-        cubMax(d_numPairsPerCenterAtom, ncenter_atoms, d_count_out, stream);
+    int maxNbrsPerCenterAtom = cubMax(d_numPairsPerCenterAtom, ncenter_atoms, d_count_out, stream);
 
     int maxnbrs_per_atom_aligned = align<4>(maxNbrsPerCenterAtom);
 
-    cuAngularAEVs<<<nblocks_angAEV, block_size,
-                    smem_size(maxnbrs_per_atom_aligned,
-                              block_size / nthreads_per_catom),
+    cuAngularAEVs<<<
+        nblocks_angAEV,
+        block_size,
+        smem_size(maxnbrs_per_atom_aligned, block_size / nthreads_per_catom),
         stream>>>(
         species_t.packed_accessor32<int, 2, torch::RestrictPtrTraits>(),
         coordinates_t.packed_accessor32<float, 3, torch::RestrictPtrTraits>(),
@@ -574,13 +595,20 @@ torch::Tensor cuComputeAEV(torch::Tensor coordinates_t, torch::Tensor species_t,
         EtaA_t.packed_accessor32<float, 1, torch::RestrictPtrTraits>(),
         Zeta_t.packed_accessor32<float, 1, torch::RestrictPtrTraits>(),
         aev_t.packed_accessor32<float, 3, torch::RestrictPtrTraits>(),
-        d_angularRij, d_centralAtom, d_numPairsPerCenterAtom,
-        d_centerAtomStartIdx, aev_params, maxnbrs_per_atom_aligned,
-        align<4>(aev_params.angular_length), ncenter_atoms);
+        d_angularRij,
+        d_centralAtom,
+        d_numPairsPerCenterAtom,
+        d_centerAtomStartIdx,
+        aev_params,
+        maxnbrs_per_atom_aligned,
+        align<4>(aev_params.angular_length),
+        ncenter_atoms);
   }
   return aev_t;
 }
 
-TORCH_LIBRARY(cuaev, m) { m.def("cuComputeAEV", &cuComputeAEV<float>); }
+TORCH_LIBRARY(cuaev, m) {
+  m.def("cuComputeAEV", &cuComputeAEV<float>);
+}
 
 PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {}