Support Half/BFloat16 in avg_pool_2d

kernels/portable/cpu/op_avg_pool2d.cpp

@@ -67,50 +67,53 @@ Tensor& avg_pool2d_out(
       out);
 
   ScalarType in_type = in.scalar_type();
-  ET_SWITCH_FLOAT_TYPES_AND(Long, in_type, ctx, "avg_pool2d.out", CTYPE, [&]() {
-    if (divisor_override.has_value()) {
-      int64_t divisor = divisor_override.value();
-      // If divisor_override is specified, then we don't need to use `count` in
-      // the calculation. Simply sum x / divisor to get the output.
-      apply_kernel_2d_reduce_then_map_fn<CTYPE>(
-          [](const CTYPE in_val,
-             int64_t in_idx,
-             CTYPE accum,
-             int64_t accum_idx) {
-            // Average pooling does not track indexes, so return 0 for accum_idx
-            return std::tuple<CTYPE, int64_t>(in_val + accum, 0);
-          },
-          [divisor](const int64_t count, const CTYPE accum) {
-            return accum / static_cast<CTYPE>(divisor);
-          },
-          count_include_pad,
-          in,
-          kernel_size,
-          stride,
-          padding,
-          {},
-          out);
-    } else {
-      apply_kernel_2d_reduce_then_map_fn<CTYPE>(
-          [](const CTYPE in_val,
-             int64_t in_idx,
-             CTYPE accum,
-             int64_t accum_idx) {
-            // Average pooling does not track indexes, so return 0 for accum_idx
-            return std::tuple<CTYPE, int64_t>(in_val + accum, 0);
-          },
-          [](const int64_t count, const CTYPE accum) {
-            return accum / static_cast<CTYPE>(count);
-          },
-          count_include_pad,
-          in,
-          kernel_size,
-          stride,
-          padding,
-          {},
-          out);
-    }
-  });
+  ET_SWITCH_FLOATHBF16_TYPES_AND(
+      Long, in_type, ctx, "avg_pool2d.out", CTYPE, [&]() {
+        if (divisor_override.has_value()) {
+          int64_t divisor = divisor_override.value();
+          // If divisor_override is specified, then we don't need to use `count`
+          // in the calculation. Simply sum x / divisor to get the output.
+          apply_kernel_2d_reduce_then_map_fn<CTYPE>(
+              [](const CTYPE in_val,
+                 int64_t in_idx,
+                 CTYPE accum,
+                 int64_t accum_idx) {
+                // Average pooling does not track indexes, so return 0 for
+                // accum_idx
+                return std::tuple<CTYPE, int64_t>(in_val + accum, 0);
+              },
+              [divisor](const int64_t count, const CTYPE accum) {
+                return accum / static_cast<CTYPE>(divisor);
+              },
+              count_include_pad,
+              in,
+              kernel_size,
+              stride,
+              padding,
+              {},
+              out);
+        } else {
+          apply_kernel_2d_reduce_then_map_fn<CTYPE>(
+              [](const CTYPE in_val,
+                 int64_t in_idx,
+                 CTYPE accum,
+                 int64_t accum_idx) {
+                // Average pooling does not track indexes, so return 0 for
+                // accum_idx
+                return std::tuple<CTYPE, int64_t>(in_val + accum, 0);
+              },
+              [](const int64_t count, const CTYPE accum) {
+                return accum / static_cast<CTYPE>(count);
+              },
+              count_include_pad,
+              in,
+              kernel_size,
+              stride,
+              padding,
+              {},
+              out);
+        }
+      });
 
   return out;
 }