link mean dim kernels

Differential Revision: D68845587

Pull Request resolved: #8053

Author: zonglinpeng

backends/cadence/fusion_g3/operators/op_mean.cpp

@@ -60,7 +60,7 @@ int prepare_data(
   return num_axis_dims;
 }
 
-Tensor& mean_dim_out(
+Tensor& mean_out(
     KernelRuntimeContext& ctx,
     const Tensor& in,
     optional<ArrayRef<int64_t>> dim_list,
@@ -169,29 +169,32 @@ Tensor& mean_dim_out(
         InvalidArgument,
         out);
 
-    ET_SWITCH_REALHB_TYPES(in.scalar_type(), ctx, "mean.out", CTYPE_IN, [&] {
-      ET_SWITCH_FLOATH_TYPES(
-          out.scalar_type(), ctx, "mean.out", CTYPE_OUT, [&] {
-            CTYPE_OUT* out_data = out.mutable_data_ptr<CTYPE_OUT>();
-            const size_t num =
-                torch::executor::get_reduced_dim_product(in, dim_list);
-            for (size_t out_ix = 0; out_ix < out.numel(); ++out_ix) {
-              CTYPE_OUT sum = 0;
-              if (in.numel() > 0) {
-                sum = torch::executor::
-                    map_reduce_over_dim_list<CTYPE_IN, CTYPE_OUT>(
-                        [](CTYPE_IN v) { return static_cast<CTYPE_OUT>(v); },
-                        [](CTYPE_OUT outv, CTYPE_OUT acc) {
-                          return acc + outv;
-                        },
-                        in,
-                        dim_list,
-                        out_ix);
-              }
-              out_data[out_ix] = sum / static_cast<float>(num);
-            }
-          });
-    });
+    ET_SWITCH_REALHBBF16_TYPES(
+        in.scalar_type(), ctx, "mean.out", CTYPE_IN, [&] {
+          ET_SWITCH_FLOATHBF16_TYPES(
+              out.scalar_type(), ctx, "mean.out", CTYPE_OUT, [&] {
+                CTYPE_OUT* out_data = out.mutable_data_ptr<CTYPE_OUT>();
+                const size_t num =
+                    torch::executor::get_reduced_dim_product(in, dim_list);
+                for (size_t out_ix = 0; out_ix < out.numel(); ++out_ix) {
+                  CTYPE_OUT sum = 0;
+                  if (in.numel() > 0) {
+                    sum = torch::executor::
+                        map_reduce_over_dim_list<CTYPE_IN, CTYPE_OUT>(
+                            [](CTYPE_IN v) {
+                              return static_cast<CTYPE_OUT>(v);
+                            },
+                            [](CTYPE_OUT outv, CTYPE_OUT acc) {
+                              return acc + outv;
+                            },
+                            in,
+                            dim_list,
+                            out_ix);
+                  }
+                  out_data[out_ix] = sum / static_cast<float>(num);
+                }
+              });
+        });
   }
 
   return out;