Add bf16 support for knn_cpu, radius_cpu and graclus_cpu (#144)

csrc/cpu/graclus_cpu.cpp

@@ -47,7 +47,7 @@ torch::Tensor graclus_cpu(torch::Tensor rowptr, torch::Tensor col,
   } else {
     auto weight = optional_weight.value();
     auto scalar_type = weight.scalar_type();
-    AT_DISPATCH_ALL_TYPES_AND(at::ScalarType::Half, scalar_type, "_", [&] {
+    AT_DISPATCH_ALL_TYPES_AND2(at::ScalarType::Half, at::ScalarType::BFloat16, scalar_type, "graclus_cpu", [&] {
       auto weight_data = weight.data_ptr<scalar_t>();
 
       for (auto n = 0; n < num_nodes; n++) {

csrc/cpu/knn_cpu.cpp

@@ -25,7 +25,7 @@ torch::Tensor knn_cpu(torch::Tensor x, torch::Tensor y,
 
   std::vector<size_t> out_vec = std::vector<size_t>();
 
-  AT_DISPATCH_ALL_TYPES_AND(at::ScalarType::Half, x.scalar_type(), "_", [&] {
+  AT_DISPATCH_ALL_TYPES_AND2(at::ScalarType::Half, at::ScalarType::BFloat16, x.scalar_type(), "knn_cpu", [&] {
     // See: nanoflann/examples/vector_of_vectors_example.cpp
 
     auto x_data = x.data_ptr<scalar_t>();

csrc/cpu/radius_cpu.cpp

@@ -25,7 +25,7 @@ torch::Tensor radius_cpu(torch::Tensor x, torch::Tensor y,
 
   std::vector<size_t> out_vec = std::vector<size_t>();
 
-  AT_DISPATCH_ALL_TYPES_AND(at::ScalarType::Half, x.scalar_type(), "_", [&] {
+  AT_DISPATCH_ALL_TYPES_AND2(at::ScalarType::Half, at::ScalarType::BFloat16, x.scalar_type(), "radius_cpu", [&] {
     // See: nanoflann/examples/vector_of_vectors_example.cpp
 
     auto x_data = x.data_ptr<scalar_t>();

test/utils.py

 import torch
 
-dtypes = [torch.half, torch.float, torch.double, torch.int, torch.long]
+dtypes = [torch.half, torch.bfloat16, torch.float, torch.double,
+          torch.int, torch.long]
 grad_dtypes = [torch.half, torch.float, torch.double]
 
 devices = [torch.device('cpu')]