Fix tensor creation (#222)

* adjust tensor creation

* update

csrc/cpu/diag_cpu.cpp

@@ -13,7 +13,7 @@ torch::Tensor non_diag_mask_cpu(torch::Tensor row, torch::Tensor col, int64_t M,
   auto row_data = row.data_ptr<int64_t>();
   auto col_data = col.data_ptr<int64_t>();
 
-  auto mask = torch::zeros(E + num_diag, row.options().dtype(torch::kBool));
+  auto mask = torch::zeros({E + num_diag}, row.options().dtype(torch::kBool));
   auto mask_data = mask.data_ptr<bool>();
 
   int64_t r, c;

csrc/cpu/metis_cpu.cpp

@@ -44,7 +44,7 @@ torch::Tensor partition_cpu(torch::Tensor rowptr, torch::Tensor col,
     vwgt = optional_node_weight.value().data_ptr<int64_t>();
 
   int64_t objval = -1;
-  auto part = torch::empty(nvtxs, rowptr.options());
+  auto part = torch::empty({nvtxs}, rowptr.options());
   auto part_data = part.data_ptr<int64_t>();
 
   if (recursive) {
@@ -99,7 +99,7 @@ mt_partition_cpu(torch::Tensor rowptr, torch::Tensor col,
 
   mtmetis_pid_type nparts = num_parts;
   mtmetis_wgt_type objval = -1;
-  auto part = torch::empty(nvtxs, rowptr.options());
+  auto part = torch::empty({nvtxs}, rowptr.options());
   mtmetis_pid_type *part_data = (mtmetis_pid_type *)part.data_ptr<int64_t>();
 
   double *opts = mtmetis_init_options();

csrc/cpu/relabel_cpu.cpp

@@ -64,7 +64,7 @@ relabel_one_hop_cpu(torch::Tensor rowptr, torch::Tensor col,
   std::unordered_map<int64_t, int64_t> n_id_map;
   std::unordered_map<int64_t, int64_t>::iterator it;
 
-  auto out_rowptr = torch::empty(idx.numel() + 1, rowptr.options());
+  auto out_rowptr = torch::empty({idx.numel() + 1}, rowptr.options());
   auto out_rowptr_data = out_rowptr.data_ptr<int64_t>();
 
   out_rowptr_data[0] = 0;
@@ -76,12 +76,12 @@ relabel_one_hop_cpu(torch::Tensor rowptr, torch::Tensor col,
     out_rowptr_data[i + 1] = offset;
   }
 
-  auto out_col = torch::empty(offset, col.options());
+  auto out_col = torch::empty({offset}, col.options());
   auto out_col_data = out_col.data_ptr<int64_t>();
 
   torch::optional<torch::Tensor> out_value = torch::nullopt;
   if (optional_value.has_value()) {
-    out_value = torch::empty(offset, optional_value.value().options());
+    out_value = torch::empty({offset}, optional_value.value().options());
 
     AT_DISPATCH_ALL_TYPES(optional_value.value().scalar_type(), "relabel", [&] {
       auto value_data = optional_value.value().data_ptr<scalar_t>();

csrc/cpu/sample_cpu.cpp

@@ -19,7 +19,7 @@ sample_adj_cpu(torch::Tensor rowptr, torch::Tensor col, torch::Tensor idx,
   auto col_data = col.data_ptr<int64_t>();
   auto idx_data = idx.data_ptr<int64_t>();
 
-  auto out_rowptr = torch::empty(idx.numel() + 1, rowptr.options());
+  auto out_rowptr = torch::empty({idx.numel() + 1}, rowptr.options());
   auto out_rowptr_data = out_rowptr.data_ptr<int64_t>();
   out_rowptr_data[0] = 0;
 
@@ -117,9 +117,9 @@ sample_adj_cpu(torch::Tensor rowptr, torch::Tensor col, torch::Tensor idx,
   auto out_n_id = torch::from_blob(n_ids.data(), {N}, col.options()).clone();
 
   int64_t E = out_rowptr_data[idx.numel()];
-  auto out_col = torch::empty(E, col.options());
+  auto out_col = torch::empty({E}, col.options());
   auto out_col_data = out_col.data_ptr<int64_t>();
-  auto out_e_id = torch::empty(E, col.options());
+  auto out_e_id = torch::empty({E}, col.options());
   auto out_e_id_data = out_e_id.data_ptr<int64_t>();
 
   i = 0;

csrc/cpu/spmm_cpu.cpp

@@ -118,7 +118,7 @@ torch::Tensor spmm_value_bw_cpu(torch::Tensor row, torch::Tensor rowptr,
   auto K = mat.size(-1);
   auto B = mat.numel() / (N * K);
 
-  auto out = torch::zeros(row.numel(), grad.options());
+  auto out = torch::zeros({row.numel()}, grad.options());
 
   auto row_data = row.data_ptr<int64_t>();
   auto rowptr_data = rowptr.data_ptr<int64_t>();

csrc/cpu/spspmm_cpu.cpp

@@ -33,11 +33,11 @@ spspmm_cpu(torch::Tensor rowptrA, torch::Tensor colA,
 
   if (!optional_valueA.has_value() && optional_valueB.has_value())
     optional_valueA =
-        torch::ones(colA.numel(), optional_valueB.value().options());
+        torch::ones({colA.numel()}, optional_valueB.value().options());
 
   if (!optional_valueB.has_value() && optional_valueA.has_value())
     optional_valueB =
-        torch::ones(colB.numel(), optional_valueA.value().options());
+        torch::ones({colB.numel()}, optional_valueA.value().options());
 
   auto scalar_type = torch::ScalarType::Float;
   if (optional_valueA.has_value())

csrc/cpu/utils.h

@@ -61,7 +61,7 @@ choice(int64_t population, int64_t num_samples, bool replace = false,
     return torch::multinomial(weight.value(), num_samples, replace);
 
   if (replace) {
-    const auto out = torch::empty(num_samples, at::kLong);
+    const auto out = torch::empty({num_samples}, at::kLong);
     auto *out_data = out.data_ptr<int64_t>();
     for (int64_t i = 0; i < num_samples; i++) {
       out_data[i] = uniform_randint(population);
@@ -72,7 +72,7 @@ choice(int64_t population, int64_t num_samples, bool replace = false,
     // Sample without replacement via Robert Floyd algorithm:
     // https://www.nowherenearithaca.com/2013/05/
     // robert-floyds-tiny-and-beautiful.html
-    const auto out = torch::empty(num_samples, at::kLong);
+    const auto out = torch::empty({num_samples}, at::kLong);
     auto *out_data = out.data_ptr<int64_t>();
     std::unordered_set<int64_t> samples;
     for (int64_t i = population - num_samples; i < population; i++) {

csrc/cuda/convert_cuda.cu

@@ -27,7 +27,7 @@ torch::Tensor ind2ptr_cuda(torch::Tensor ind, int64_t M) {
   CHECK_CUDA(ind);
   cudaSetDevice(ind.get_device());
 
-  auto out = torch::empty(M + 1, ind.options());
+  auto out = torch::empty({M + 1}, ind.options());
 
   if (ind.numel() == 0)
     return out.zero_();
@@ -57,7 +57,7 @@ torch::Tensor ptr2ind_cuda(torch::Tensor ptr, int64_t E) {
   CHECK_CUDA(ptr);
   cudaSetDevice(ptr.get_device());
 
-  auto out = torch::empty(E, ptr.options());
+  auto out = torch::empty({E}, ptr.options());
   auto ptr_data = ptr.data_ptr<int64_t>();
   auto out_data = out.data_ptr<int64_t>();
   auto stream = at::cuda::getCurrentCUDAStream();

csrc/cuda/diag_cuda.cu

@@ -51,7 +51,7 @@ torch::Tensor non_diag_mask_cuda(torch::Tensor row, torch::Tensor col,
   auto row_data = row.data_ptr<int64_t>();
   auto col_data = col.data_ptr<int64_t>();
 
-  auto mask = torch::zeros(E + num_diag, row.options().dtype(torch::kBool));
+  auto mask = torch::zeros({E + num_diag}, row.options().dtype(torch::kBool));
   auto mask_data = mask.data_ptr<bool>();
 
   if (E == 0)

csrc/cuda/spmm_cuda.cu

@@ -213,7 +213,7 @@ torch::Tensor spmm_value_bw_cuda(torch::Tensor row, torch::Tensor rowptr,
   auto B = mat.numel() / (N * K);
   auto BLOCKS = dim3((E * 32 + THREADS - 1) / THREADS);
 
-  auto out = torch::zeros(row.numel(), grad.options());
+  auto out = torch::zeros({row.numel()}, grad.options());
 
   auto row_data = row.data_ptr<int64_t>();
   auto rowptr_data = rowptr.data_ptr<int64_t>();

csrc/cuda/spspmm_cuda.cu

@@ -59,11 +59,11 @@ spspmm_cuda(torch::Tensor rowptrA, torch::Tensor colA,
 
   if (!optional_valueA.has_value() && optional_valueB.has_value())
     optional_valueA =
-        torch::ones(colA.numel(), optional_valueB.value().options());
+        torch::ones({colA.numel()}, optional_valueB.value().options());
 
   if (!optional_valueB.has_value() && optional_valueA.has_value())
     optional_valueB =
-        torch::ones(colB.numel(), optional_valueA.value().options());
+        torch::ones({colB.numel()}, optional_valueA.value().options());
 
   auto scalar_type = torch::ScalarType::Float;
   if (optional_valueA.has_value())
@@ -108,7 +108,7 @@ spspmm_cuda(torch::Tensor rowptrA, torch::Tensor colA,
     cudaMalloc(&buffer, bufferSize);
 
     // Step 3: Compute CSR row pointer.
-    rowptrC = torch::empty(M + 1, rowptrA.options());
+    rowptrC = torch::empty({M + 1}, rowptrA.options());
     auto rowptrC_data = rowptrC.data_ptr<int>();
     cusparseXcsrgemm2Nnz(handle, M, N, K, descr, colA.numel(), rowptrA_data,
                          colA_data, descr, colB.numel(), rowptrB_data,
@@ -116,11 +116,11 @@ spspmm_cuda(torch::Tensor rowptrA, torch::Tensor colA,
                          nnzTotalDevHostPtr, info, buffer);
 
     // Step 4: Compute CSR entries.
-    colC = torch::empty(nnzC, rowptrC.options());
+    colC = torch::empty({nnzC}, rowptrC.options());
     auto colC_data = colC.data_ptr<int>();
 
     if (optional_valueA.has_value())
-      optional_valueC = torch::empty(nnzC, optional_valueA.value().options());
+      optional_valueC = torch::empty({nnzC}, optional_valueA.value().options());
 
     scalar_t *valA_data = NULL, *valB_data = NULL, *valC_data = NULL;
     if (optional_valueA.has_value()) {