fix small bug

example/libspconv/main.cu

@@ -229,9 +229,10 @@ int main(int argc, char **argv) {
               weights, pair, indices_kernel_num, arch, out_features_real.dim(0),
               inverse, is_subm,
               static_cast<int>(tv::gemm::SparseConvAlgo::kNative),
-              reinterpret_cast<std::uintptr_t>(stream), bias, 1.0,
-              /*bias alpha, only used for leaky relu*/, 0.0,
-              tv::gemm::Activation::kReLU);
+              reinterpret_cast<std::uintptr_t>(stream), bias,
+              1.0
+              /*bias alpha, only used for leaky relu*/,
+              0.0, tv::gemm::Activation::kReLU);
         } else {
           // regular conv use numbers in indices_kernel_num to perform gemm
           // so we don't need to slice.
@@ -265,10 +266,10 @@ int main(int argc, char **argv) {
         // only regular conv need direct table.
         bool use_direct_table = direct_table && !is_subm;
         auto max_act_out_theory = SpconvOps::get_handcrafted_max_act_out(
-            real_num_act_in, ksize, stride, padding, dilation);
+            static_num_act_in, ksize, stride, padding, dilation);
         // query workspace size.
         int workspace_size = SpconvOps::get_indice_gen_workspace_size(
-            KV, real_num_act_in, out_inds_num_limit, max_act_out_theory,
+            KV, static_num_act_in, out_inds_num_limit, max_act_out_theory,
             is_subm, use_int64_hash_k, use_direct_table);
         // you should return workspace size in tensorrt plugin method.
         tv::Tensor workspace = tv::empty({workspace_size}, tv::uint8, 0);
@@ -278,9 +279,9 @@ int main(int argc, char **argv) {
         // dynamic allocator, in c++ (inference engine) we need to use
         // fixed-size workspace and create a static allocator.
         auto ws_tensors = SpconvOps::get_indice_gen_tensors_from_workspace(
-            workspace.raw_data(), KV, real_num_act_in,
-            is_subm ? real_num_act_in : out_inds_num_limit, max_act_out_theory,
-            is_subm, use_int64_hash_k, use_direct_table);
+            workspace.raw_data(), KV, static_num_act_in,
+            is_subm ? static_num_act_in : out_inds_num_limit,
+            max_act_out_theory, is_subm, use_int64_hash_k, use_direct_table);
         // pair can also have a upper bound.
         // !!!!!IMPORTANT!!!!!!! if you provide a static (padded) pair_fwd and
         // other indice data, the output layout is tight pair_fwd_correct =
@@ -288,7 +289,7 @@ int main(int argc, char **argv) {
         // real_pair_size) this valid for pair_fwd, pair_bwd, pair_mask_fwd,
         // pair_mask_bwd, mask_argsort_fwd, mask_argsort_bwd.
         int pair_fwd_size_padded =
-            is_subm ? real_num_act_in : out_inds_num_limit;
+            is_subm ? static_num_act_in : out_inds_num_limit;
         tv::Tensor pair_fwd_padded =
             tv::empty({KV, pair_fwd_size_padded}, tv::int32, 0);
         // you can find equivalent python code of following code in python
@@ -373,7 +374,7 @@ int main(int argc, char **argv) {
         int num_act_out_real = std::get<1>(pair_res);
 
         tv::Tensor out_features =
-            tv::empty({is_subm ? real_num_act_in : out_inds_num_limit, 64},
+            tv::empty({is_subm ? static_num_act_in : out_inds_num_limit, 64},
                       tv::float16, 0);
         auto out_features_real =
             out_features.slice_first_axis(0, num_act_out_real);