Migrate elementwise_util callers to the variants with out_dtypes in template arguments

kernels/portable/cpu/op_add.cpp

@@ -52,17 +52,19 @@ Tensor& add_out(
 
   ET_SWITCH_REALB_TYPES(compute_type, ctx, op_name, CTYPE_COMPUTE, [&]() {
     const CTYPE_COMPUTE val_alpha = utils::scalar_to<CTYPE_COMPUTE>(alpha);
-    utils::apply_bitensor_elementwise_fn<CTYPE_COMPUTE, op_name>(
-        [val_alpha](const CTYPE_COMPUTE val_a, const CTYPE_COMPUTE val_b) {
+    utils::apply_bitensor_elementwise_fn<
+        CTYPE_COMPUTE,
+        op_name,
+        utils::SupportedTensorDtypes::REALHBBF16>(
+        [val_alpha](const auto val_a, const auto val_b) {
           return val_a + val_alpha * val_b;
         },
         ctx,
         a,
         utils::SupportedTensorDtypes::REALHBBF16,
         b,
         utils::SupportedTensorDtypes::REALHBBF16,
-        out,
-        utils::SupportedTensorDtypes::REALHBBF16);
+        out);
   });
 
   return out;
@@ -100,17 +102,19 @@ Tensor& add_scalar_out(
   static constexpr const char op_name[] = "add.Scalar_out";
 
   ET_SWITCH_REALB_TYPES(compute_type, ctx, op_name, CTYPE_COMPUTE, [&]() {
-    utils::apply_unitensor_elementwise_fn<CTYPE_COMPUTE, op_name>(
-        [b, alpha](const CTYPE_COMPUTE val_a) {
+    utils::apply_unitensor_elementwise_fn<
+        CTYPE_COMPUTE,
+        op_name,
+        utils::SupportedTensorDtypes::SAME_AS_COMMON>(
+        [b, alpha](const auto val_a) {
           CTYPE_COMPUTE val_b = utils::scalar_to<CTYPE_COMPUTE>(b);
           CTYPE_COMPUTE val_alpha = utils::scalar_to<CTYPE_COMPUTE>(alpha);
           return val_a + val_alpha * val_b;
         },
         ctx,
         a,
         utils::SupportedTensorDtypes::REALHBBF16,
-        out,
-        utils::SupportedTensorDtypes::SAME_AS_COMMON);
+        out);
   });
 
   return out;

kernels/portable/cpu/op_addmm.cpp

@@ -88,17 +88,19 @@ Tensor& addmm_out(
           n,
           p);
 
-      utils::apply_bitensor_elementwise_fn<CTYPE, op_name>(
-          [alpha_val, beta_val](const CTYPE val_a, const CTYPE val_b) {
+      utils::apply_bitensor_elementwise_fn<
+          CTYPE,
+          op_name,
+          utils::SupportedTensorDtypes::REALHBF16>(
+          [alpha_val, beta_val](const auto val_a, const auto val_b) {
             return val_a * alpha_val + val_b * beta_val;
           },
           ctx,
           out,
           utils::SupportedTensorDtypes::REALHBF16,
           in,
           utils::SupportedTensorDtypes::REALHBF16,
-          out,
-          utils::SupportedTensorDtypes::REALHBF16);
+          out);
     }
   });
 

kernels/portable/cpu/op_atan2.cpp

@@ -55,17 +55,19 @@ Tensor& atan2_out(
   static constexpr const char op_name[] = "atan2.out";
 
   ET_SWITCH_FLOAT_TYPES(compute_type, ctx, op_name, CTYPE_COMPUTE, [&]() {
-    utils::apply_bitensor_elementwise_fn<CTYPE_COMPUTE, op_name>(
-        [](const CTYPE_COMPUTE val_a, const CTYPE_COMPUTE val_b) {
+    utils::apply_bitensor_elementwise_fn<
+        CTYPE_COMPUTE,
+        op_name,
+        utils::SupportedTensorDtypes::FLOATHBF16>(
+        [](const auto val_a, const auto val_b) {
           return std::atan2(val_a, val_b);
         },
         ctx,
         a,
         utils::SupportedTensorDtypes::REALHBBF16,
         b,
         utils::SupportedTensorDtypes::REALHBBF16,
-        out,
-        utils::SupportedTensorDtypes::FLOATHBF16);
+        out);
   });
 
   return out;

kernels/portable/cpu/op_clamp.cpp

@@ -134,8 +134,12 @@ Tensor& clamp_out(
   static constexpr const char op_name[] = "clamp.out";
 
   ET_SWITCH_REALB_TYPES(compute_type, ctx, op_name, CTYPE_COMPUTE, [&]() {
-    utils::apply_unitensor_elementwise_fn<CTYPE_COMPUTE, op_name>(
+    utils::apply_unitensor_elementwise_fn<
+        CTYPE_COMPUTE,
+        op_name,
+        utils::SupportedTensorDtypes::SAME_AS_COMMON>(
         [has_min, min_opt, has_max, max_opt](const CTYPE_COMPUTE val_in) {
+          // TODO: rewrite this to be vectorization-capable.
           CTYPE_COMPUTE val_out = val_in;
           if (has_min) {
             val_out = utils::max_override(
@@ -150,8 +154,7 @@ Tensor& clamp_out(
         ctx,
         in,
         utils::SupportedTensorDtypes::REALHBBF16,
-        out,
-        utils::SupportedTensorDtypes::SAME_AS_COMMON);
+        out);
   });
 
   return out;
@@ -210,11 +213,15 @@ Tensor& clamp_tensor_out(
   static constexpr const char op_name[] = "clamp.Tensor_out";
 
   ET_SWITCH_REALB_TYPES(compute_type, ctx, op_name, CTYPE_COMPUTE, [&]() {
-    utils::apply_tritensor_elementwise_fn<CTYPE_COMPUTE, op_name>(
+    utils::apply_tritensor_elementwise_fn<
+        CTYPE_COMPUTE,
+        op_name,
+        utils::SupportedTensorDtypes::REALHBBF16>(
         [has_min, has_max](
             const CTYPE_COMPUTE val_in,
             const CTYPE_COMPUTE val_min,
             const CTYPE_COMPUTE val_max) {
+          // TODO: rewrite this to be vectorization-capable.
           CTYPE_COMPUTE val_out = val_in;
           if (has_min) {
             val_out = utils::max_override(val_out, val_min);
@@ -231,8 +238,7 @@ Tensor& clamp_tensor_out(
         utils::SupportedTensorDtypes::REALHBBF16,
         max,
         utils::SupportedTensorDtypes::REALHBBF16,
-        out,
-        utils::SupportedTensorDtypes::REALHBBF16);
+        out);
   });
 
   return out;

kernels/portable/cpu/op_copy.cpp

@@ -47,15 +47,17 @@ Tensor& copy_out(
   static constexpr const char op_name[] = "copy.out";
 
   ET_SWITCH_REALHBBF16_TYPES(in.scalar_type(), ctx, "copy.out", CTYPE, [&]() {
-    utils::apply_bitensor_elementwise_fn<CTYPE, op_name>(
-        [](ET_UNUSED const CTYPE _, const CTYPE val_src) { return val_src; },
+    utils::apply_bitensor_elementwise_fn<
+        CTYPE,
+        op_name,
+        utils::SupportedTensorDtypes::REALHBBF16>(
+        [](ET_UNUSED const auto _, const auto val_src) { return val_src; },
         ctx,
         in,
         utils::SupportedTensorDtypes::REALHBBF16,
         src,
         utils::SupportedTensorDtypes::REALHBBF16,
-        out,
-        utils::SupportedTensorDtypes::REALHBBF16);
+        out);
   });
 
   return out;
@@ -80,15 +82,17 @@ Tensor& copy_(
   static constexpr const char op_name[] = "copy_";
 
   ET_SWITCH_REALHBBF16_TYPES(in.scalar_type(), ctx, "copy_", CTYPE, [&]() {
-    utils::apply_bitensor_elementwise_fn<CTYPE, op_name>(
-        [](ET_UNUSED const CTYPE _, const CTYPE val_src) { return val_src; },
+    utils::apply_bitensor_elementwise_fn<
+        CTYPE,
+        op_name,
+        utils::SupportedTensorDtypes::REALHBBF16>(
+        [](ET_UNUSED const auto _, const auto val_src) { return val_src; },
         ctx,
         in,
         utils::SupportedTensorDtypes::REALHBBF16,
         src,
         utils::SupportedTensorDtypes::REALHBBF16,
-        in,
-        utils::SupportedTensorDtypes::REALHBBF16);
+        in);
   });
 
   return in;

kernels/portable/cpu/op_div.cpp

@@ -58,17 +58,17 @@ Tensor& div_out(
   static constexpr const char op_name[] = "div.out";
 
   ET_SWITCH_FLOAT_TYPES(compute_type, ctx, op_name, CTYPE_COMPUTE, [&]() {
-    utils::apply_bitensor_elementwise_fn<CTYPE_COMPUTE, op_name>(
-        [](const CTYPE_COMPUTE val_a, const CTYPE_COMPUTE val_b) {
-          return val_a / val_b;
-        },
+    utils::apply_bitensor_elementwise_fn<
+        CTYPE_COMPUTE,
+        op_name,
+        utils::SupportedTensorDtypes::FLOATHBF16>(
+        [](const auto val_a, const auto val_b) { return val_a / val_b; },
         ctx,
         a,
         utils::SupportedTensorDtypes::REALHBBF16,
         b,
         utils::SupportedTensorDtypes::REALHBBF16,
-        out,
-        utils::SupportedTensorDtypes::FLOATHBF16);
+        out);
   });
 
   return out;
@@ -122,9 +122,13 @@ Tensor& div_out_mode(
   bool div_by_zero_error = false;
 
   ET_SWITCH_REAL_TYPES(compute_type, ctx, op_name, CTYPE_COMPUTE, [&]() {
-    utils::apply_bitensor_elementwise_fn<CTYPE_COMPUTE, op_name>(
+    utils::apply_bitensor_elementwise_fn<
+        CTYPE_COMPUTE,
+        op_name,
+        utils::SupportedTensorDtypes::REALHBF16>(
         [mode_is_trunc, &div_by_zero_error](
             const CTYPE_COMPUTE val_a, const CTYPE_COMPUTE val_b) {
+          // TODO: rewrite this to be vectorization-capable.
           if (is_integral_type<CTYPE_COMPUTE, /*includeBool=*/true>::value) {
             if (val_b == 0) {
               div_by_zero_error = true;
@@ -146,8 +150,7 @@ Tensor& div_out_mode(
         utils::SupportedTensorDtypes::REALHBBF16,
         b,
         utils::SupportedTensorDtypes::REALHBBF16,
-        out,
-        utils::SupportedTensorDtypes::REALHBF16);
+        out);
   });
 
   ET_KERNEL_CHECK_MSG(
@@ -188,13 +191,15 @@ Tensor& div_scalar_out(
 
   ET_SWITCH_FLOAT_TYPES(compute_type, ctx, op_name, CTYPE_COMPUTE, [&]() {
     const CTYPE_COMPUTE val_b = utils::scalar_to<CTYPE_COMPUTE>(b);
-    utils::apply_unitensor_elementwise_fn<CTYPE_COMPUTE, op_name>(
-        [val_b](const CTYPE_COMPUTE val_a) { return val_a / val_b; },
+    utils::apply_unitensor_elementwise_fn<
+        CTYPE_COMPUTE,
+        op_name,
+        utils::SupportedTensorDtypes::SAME_AS_COMMON>(
+        [val_b](const auto val_a) { return val_a / val_b; },
         ctx,
         a,
         utils::SupportedTensorDtypes::REALHBBF16,
-        out,
-        utils::SupportedTensorDtypes::SAME_AS_COMMON);
+        out);
   });
 
   return out;

kernels/portable/cpu/op_elu.cpp

@@ -44,17 +44,20 @@ Tensor& elu_out(
     ET_EXTRACT_SCALAR(scale, math_scale);
     ET_EXTRACT_SCALAR(input_scale, math_input_scale);
     const auto negcoef = math_alpha * math_scale;
-    utils::apply_unitensor_elementwise_fn<CTYPE, op_name>(
-        [negcoef, math_scale, math_input_scale](auto x) {
+    utils::apply_unitensor_elementwise_fn<
+        CTYPE,
+        op_name,
+        utils::SupportedTensorDtypes::SAME_AS_COMMON>(
+        [negcoef, math_scale, math_input_scale](const auto x) {
+          // TODO: rewrite this to be vectorization-capable.
           return MathT(x) <= MathT(0)
               ? std::expm1(MathT(x) * math_input_scale) * negcoef
               : MathT(x) * math_scale;
         },
         ctx,
         in,
         utils::SupportedTensorDtypes::FLOATHBF16,
-        out,
-        utils::SupportedTensorDtypes::SAME_AS_COMMON);
+        out);
   });
   return out;
 }

kernels/portable/cpu/op_floor_divide.cpp

@@ -53,9 +53,13 @@ Tensor& floor_divide_out(
   bool div_by_zero_error = false;
 
   ET_SWITCH_REAL_TYPES(compute_type, ctx, op_name, CTYPE_COMPUTE, [&]() {
-    utils::apply_bitensor_elementwise_fn<CTYPE_COMPUTE, op_name>(
+    utils::apply_bitensor_elementwise_fn<
+        CTYPE_COMPUTE,
+        op_name,
+        utils::SupportedTensorDtypes::REALHBF16>(
         [&div_by_zero_error](
             const CTYPE_COMPUTE val_a, const CTYPE_COMPUTE val_b) {
+          // TODO: rewrite this to be vectorization-capable.
           if (is_integral_type<CTYPE_COMPUTE, /*includeBool=*/true>::value) {
             if (val_b == 0) {
               div_by_zero_error = true;
@@ -69,8 +73,7 @@ Tensor& floor_divide_out(
         utils::SupportedTensorDtypes::REALHBBF16,
         b,
         utils::SupportedTensorDtypes::REALHBBF16,
-        out,
-        utils::SupportedTensorDtypes::REALHBF16);
+        out);
   });
 
   ET_KERNEL_CHECK_MSG(

kernels/portable/cpu/op_fmod.cpp

@@ -55,9 +55,13 @@ Tensor& fmod_Tensor_out(
   bool div_by_zero_error = false;
 
   ET_SWITCH_FLOAT_TYPES(compute_type, ctx, op_name, CTYPE_COMPUTE, [&]() {
-    utils::apply_bitensor_elementwise_fn<CTYPE_COMPUTE, op_name>(
+    utils::apply_bitensor_elementwise_fn<
+        CTYPE_COMPUTE,
+        op_name,
+        utils::SupportedTensorDtypes::REALHBF16>(
         [&div_by_zero_error](
             const CTYPE_COMPUTE val_a, const CTYPE_COMPUTE val_b) {
+          // TODO: rewrite this to be vectorization-capable.
           CTYPE_COMPUTE value = 0;
           if (is_integral_type<CTYPE_COMPUTE, /*includeBool=*/true>::value) {
             if (val_b == 0) {
@@ -73,8 +77,7 @@ Tensor& fmod_Tensor_out(
         utils::SupportedTensorDtypes::REALHBBF16,
         b,
         utils::SupportedTensorDtypes::REALHBBF16,
-        out,
-        utils::SupportedTensorDtypes::REALHBF16);
+        out);
   });
 
   ET_KERNEL_CHECK_MSG(
@@ -131,16 +134,19 @@ Tensor& fmod_Scalar_out(
 
   ET_SWITCH_FLOAT_TYPES(compute_type, ctx, op_name, CTYPE_COMPUTE, [&]() {
     const CTYPE_COMPUTE val_b = utils::scalar_to<CTYPE_COMPUTE>(b);
-    utils::apply_unitensor_elementwise_fn<CTYPE_COMPUTE, op_name>(
+    utils::apply_unitensor_elementwise_fn<
+        CTYPE_COMPUTE,
+        op_name,
+        utils::SupportedTensorDtypes::REALHBF16>(
         [val_b](const CTYPE_COMPUTE val_a) {
+          // TODO: rewrite this to be vectorization-capable.
           CTYPE_COMPUTE value = std::fmod(val_a, val_b);
           return value;
         },
         ctx,
         a,
         utils::SupportedTensorDtypes::REALHBBF16,
-        out,
-        utils::SupportedTensorDtypes::REALHBF16);
+        out);
   });
 
   return out;

kernels/portable/cpu/op_maximum.cpp

@@ -45,7 +45,10 @@ Tensor& maximum_out(
   static constexpr const char op_name[] = "maximum.out";
 
   ET_SWITCH_REALB_TYPES(compute_type, ctx, op_name, CTYPE_COMPUTE, [&]() {
-    utils::apply_bitensor_elementwise_fn<CTYPE_COMPUTE, op_name>(
+    utils::apply_bitensor_elementwise_fn<
+        CTYPE_COMPUTE,
+        op_name,
+        utils::SupportedTensorDtypes::REALHBBF16>(
         [](const CTYPE_COMPUTE val_a, const CTYPE_COMPUTE val_b) {
           return utils::max_override(val_a, val_b);
         },
@@ -54,8 +57,7 @@ Tensor& maximum_out(
         utils::SupportedTensorDtypes::REALHBBF16,
         b,
         utils::SupportedTensorDtypes::REALHBBF16,
-        out,
-        utils::SupportedTensorDtypes::REALHBBF16);
+        out);
   });
 
   return out;

kernels/portable/cpu/op_minimum.cpp

@@ -45,17 +45,20 @@ Tensor& minimum_out(
   static constexpr const char op_name[] = "minimum.out";
 
   ET_SWITCH_REALB_TYPES(compute_type, ctx, op_name, CTYPE_COMPUTE, [&]() {
-    utils::apply_bitensor_elementwise_fn<CTYPE_COMPUTE, op_name>(
+    utils::apply_bitensor_elementwise_fn<
+        CTYPE_COMPUTE,
+        op_name,
+        utils::SupportedTensorDtypes::REALHBBF16>(
         [](const CTYPE_COMPUTE val_a, const CTYPE_COMPUTE val_b) {
+          // TODO: rewrite this to be vectorization-capable.
           return utils::min_override(val_a, val_b);
         },
         ctx,
         a,
         utils::SupportedTensorDtypes::REALHBBF16,
         b,
         utils::SupportedTensorDtypes::REALHBBF16,
-        out,
-        utils::SupportedTensorDtypes::REALHBBF16);
+        out);
   });
 
   return out;

kernels/portable/cpu/op_mul.cpp

@@ -52,7 +52,10 @@ Tensor& mul_out(
       out);
 
   ET_SWITCH_REALB_TYPES(compute_type, ctx, op_name, CTYPE_COMPUTE, [&]() {
-    utils::apply_bitensor_elementwise_fn<CTYPE_COMPUTE, op_name>(
+    utils::apply_bitensor_elementwise_fn<
+        CTYPE_COMPUTE,
+        op_name,
+        utils::SupportedTensorDtypes::REALHBBF16>(
         [](const CTYPE_COMPUTE val_a, const CTYPE_COMPUTE val_b) {
           return val_a * val_b;
         },
@@ -61,8 +64,7 @@ Tensor& mul_out(
         utils::SupportedTensorDtypes::REALHBBF16,
         b,
         utils::SupportedTensorDtypes::REALHBBF16,
-        out,
-        utils::SupportedTensorDtypes::REALHBBF16);
+        out);
   });
 
   return out;
@@ -95,13 +97,15 @@ Tensor& mul_scalar_out(
 
   ET_SWITCH_REALB_TYPES(compute_type, ctx, op_name, CTYPE_COMPUTE, [&]() {
     const CTYPE_COMPUTE val_b = utils::scalar_to<CTYPE_COMPUTE>(b);
-    utils::apply_unitensor_elementwise_fn<CTYPE_COMPUTE, op_name>(
-        [val_b](const CTYPE_COMPUTE val_a) { return val_a * val_b; },
+    utils::apply_unitensor_elementwise_fn<
+        CTYPE_COMPUTE,
+        op_name,
+        utils::SupportedTensorDtypes::SAME_AS_COMMON>(
+        [val_b](const auto val_a) { return val_a * val_b; },
         ctx,
         a,
         utils::SupportedTensorDtypes::REALHBBF16,
-        out,
-        utils::SupportedTensorDtypes::SAME_AS_COMMON);
+        out);
   });
 
   return out;

kernels/portable/cpu/op_pow.cpp

@@ -53,17 +53,20 @@ Tensor& pow_Tensor_Tensor_out(
   static constexpr const char op_name[] = "pow.Tensor_Tensor_out";
 
   ET_SWITCH_FLOAT_TYPES(compute_type, ctx, op_name, CTYPE_COMPUTE, [&]() {
-    utils::apply_bitensor_elementwise_fn<CTYPE_COMPUTE, op_name>(
+    utils::apply_bitensor_elementwise_fn<
+        CTYPE_COMPUTE,
+        op_name,
+        utils::SupportedTensorDtypes::REALHBF16>(
         [](const CTYPE_COMPUTE val_a, const CTYPE_COMPUTE val_b) {
+          // TODO: rewrite this to be vectorization-capable.
           return std::pow(val_a, val_b);
         },
         ctx,
         a,
         utils::SupportedTensorDtypes::REALHBBF16,
         b,
         utils::SupportedTensorDtypes::REALHBBF16,
-        out,
-        utils::SupportedTensorDtypes::REALHBF16);
+        out);
   });
 
   return out;
@@ -104,13 +107,16 @@ Tensor& pow_Tensor_Scalar_out(
 
   ET_SWITCH_FLOAT_TYPES(compute_type, ctx, op_name, CTYPE_COMPUTE, [&]() {
     const CTYPE_COMPUTE val_b = utils::scalar_to<CTYPE_COMPUTE>(b);
-    utils::apply_unitensor_elementwise_fn<CTYPE_COMPUTE, op_name>(
+    utils::apply_unitensor_elementwise_fn<
+        CTYPE_COMPUTE,
+        op_name,
+        utils::SupportedTensorDtypes::REALHBF16>(
+        // TODO: rewrite this to be vectorization-capable.
         [val_b](const CTYPE_COMPUTE val_a) { return std::pow(val_a, val_b); },
         ctx,
         a,
         utils::SupportedTensorDtypes::REALHBBF16,
-        out,
-        utils::SupportedTensorDtypes::REALHBF16);
+        out);
   });
 
   return out;
@@ -151,13 +157,16 @@ Tensor& pow_Scalar_out(
 
   ET_SWITCH_FLOAT_TYPES(compute_type, ctx, op_name, CTYPE_COMPUTE, [&]() {
     const CTYPE_COMPUTE val_a = utils::scalar_to<CTYPE_COMPUTE>(a);
-    utils::apply_unitensor_elementwise_fn<CTYPE_COMPUTE, op_name>(
+    utils::apply_unitensor_elementwise_fn<
+        CTYPE_COMPUTE,
+        op_name,
+        utils::SupportedTensorDtypes::REALHBF16>(
+        // TODO: rewrite this to be vectorization-capable.
         [val_a](const CTYPE_COMPUTE val_b) { return std::pow(val_a, val_b); },
         ctx,
         b,
         utils::SupportedTensorDtypes::REALHBBF16,
-        out,
-        utils::SupportedTensorDtypes::REALHBF16);
+        out);
   });
 
   return out;

kernels/portable/cpu/op_remainder.cpp

@@ -53,9 +53,13 @@ Tensor& remainder_Tensor_out(
   bool div_by_zero_error = false;
 
   ET_SWITCH_REAL_TYPES(compute_type, ctx, op_name, CTYPE_COMPUTE, [&]() {
-    utils::apply_bitensor_elementwise_fn<CTYPE_COMPUTE, op_name>(
+    utils::apply_bitensor_elementwise_fn<
+        CTYPE_COMPUTE,
+        op_name,
+        utils::SupportedTensorDtypes::REALHBF16>(
         [&div_by_zero_error](
             const CTYPE_COMPUTE val_a, const CTYPE_COMPUTE val_b) {
+          // TODO: rewrite this to be vectorization-capable.
           CTYPE_COMPUTE value = 0;
           if (is_integral_type<CTYPE_COMPUTE, /*includeBool=*/true>::value) {
             if (val_b == 0) {
@@ -71,8 +75,7 @@ Tensor& remainder_Tensor_out(
         utils::SupportedTensorDtypes::REALHBBF16,
         b,
         utils::SupportedTensorDtypes::REALHBBF16,
-        out,
-        utils::SupportedTensorDtypes::REALHBF16);
+        out);
   });
 
   ET_KERNEL_CHECK_MSG(
@@ -126,15 +129,18 @@ Tensor& remainder_Scalar_out(
 
   ET_SWITCH_REAL_TYPES(compute_type, ctx, op_name, CTYPE_COMPUTE, [&]() {
     const CTYPE_COMPUTE val_b = utils::scalar_to<CTYPE_COMPUTE>(b);
-    utils::apply_unitensor_elementwise_fn<CTYPE_COMPUTE, op_name>(
+    utils::apply_unitensor_elementwise_fn<
+        CTYPE_COMPUTE,
+        op_name,
+        utils::SupportedTensorDtypes::REALHBF16>(
         [val_b](const CTYPE_COMPUTE val_a) {
+          // TODO: rewrite this to be vectorization-capable.
           return utils::remainder_override(val_a, val_b);
         },
         ctx,
         a,
         utils::SupportedTensorDtypes::REALHBBF16,
-        out,
-        utils::SupportedTensorDtypes::REALHBF16);
+        out);
   });
 
   return out;

kernels/portable/cpu/op_rsub.cpp

@@ -52,15 +52,17 @@ Tensor& rsub_scalar_out(
   ET_SWITCH_REAL_TYPES(compute_type, ctx, op_name, CTYPE_COMPUTE, [&]() {
     const CTYPE_COMPUTE val_b = utils::scalar_to<CTYPE_COMPUTE>(b);
     const CTYPE_COMPUTE val_alpha = utils::scalar_to<CTYPE_COMPUTE>(alpha);
-    utils::apply_unitensor_elementwise_fn<CTYPE_COMPUTE, op_name>(
-        [val_b, val_alpha](const CTYPE_COMPUTE val_a) {
+    utils::apply_unitensor_elementwise_fn<
+        CTYPE_COMPUTE,
+        op_name,
+        utils::SupportedTensorDtypes::SAME_AS_COMMON>(
+        [val_b, val_alpha](const auto val_a) {
           return val_b - val_alpha * val_a;
         },
         ctx,
         a,
         utils::SupportedTensorDtypes::REALHBF16,
-        out,
-        utils::SupportedTensorDtypes::SAME_AS_COMMON);
+        out);
   });
 
   return out;

kernels/portable/cpu/op_sigmoid.cpp

@@ -45,17 +45,20 @@ Tensor& sigmoid_out(KernelRuntimeContext& ctx, const Tensor& in, Tensor& out) {
   static constexpr const char op_name[] = "sigmoid.out";
 
   ET_SWITCH_FLOAT_TYPES(compute_type, ctx, op_name, CTYPE_COMPUTE, [&]() {
-    utils::apply_unitensor_elementwise_fn<CTYPE_COMPUTE, op_name>(
-        [](const CTYPE_COMPUTE val_in) {
+    utils::apply_unitensor_elementwise_fn<
+        CTYPE_COMPUTE,
+        op_name,
+        utils::SupportedTensorDtypes::FLOATHBF16>(
+        [](const auto val_in) -> CTYPE_COMPUTE {
+          // TODO: rewrite this to be vectorization-capable
           CTYPE_COMPUTE out_val = static_cast<CTYPE_COMPUTE>(1.0) /
               (static_cast<CTYPE_COMPUTE>(1.0) + exp(-val_in));
           return out_val;
         },
         ctx,
         in,
         utils::SupportedTensorDtypes::REALHBBF16,
-        out,
-        utils::SupportedTensorDtypes::FLOATHBF16);
+        out);
   });
 
   return out;

kernels/portable/cpu/op_sub.cpp

@@ -56,17 +56,19 @@ Tensor& sub_out(
 
   ET_SWITCH_REAL_TYPES(compute_type, ctx, op_name, CTYPE_COMPUTE, [&]() {
     const CTYPE_COMPUTE val_alpha = utils::scalar_to<CTYPE_COMPUTE>(alpha);
-    utils::apply_bitensor_elementwise_fn<CTYPE_COMPUTE, op_name>(
-        [val_alpha](const CTYPE_COMPUTE val_a, const CTYPE_COMPUTE val_b) {
+    utils::apply_bitensor_elementwise_fn<
+        CTYPE_COMPUTE,
+        op_name,
+        utils::SupportedTensorDtypes::REALHBF16>(
+        [val_alpha](const auto val_a, const auto val_b) {
           return val_a - val_alpha * val_b;
         },
         ctx,
         a,
         utils::SupportedTensorDtypes::REALHBF16,
         b,
         utils::SupportedTensorDtypes::REALHBF16,
-        out,
-        utils::SupportedTensorDtypes::REALHBF16);
+        out);
   });
 
   return out;
@@ -110,15 +112,17 @@ Tensor& sub_scalar_out(
   ET_SWITCH_REAL_TYPES(compute_type, ctx, op_name, CTYPE_COMPUTE, [&]() {
     const CTYPE_COMPUTE val_b = utils::scalar_to<CTYPE_COMPUTE>(b);
     const CTYPE_COMPUTE val_alpha = utils::scalar_to<CTYPE_COMPUTE>(alpha);
-    utils::apply_unitensor_elementwise_fn<CTYPE_COMPUTE, op_name>(
-        [val_b, val_alpha](const CTYPE_COMPUTE val_a) {
+    utils::apply_unitensor_elementwise_fn<
+        CTYPE_COMPUTE,
+        op_name,
+        utils::SupportedTensorDtypes::SAME_AS_COMMON>(
+        [val_b, val_alpha](const auto val_a) {
           return val_a - val_alpha * val_b;
         },
         ctx,
         a,
         utils::SupportedTensorDtypes::REALHBF16,
-        out,
-        utils::SupportedTensorDtypes::SAME_AS_COMMON);
+        out);
   });
 
   return out;

kernels/portable/cpu/op_where.cpp

@@ -43,19 +43,21 @@ Tensor& where_out(
   static constexpr const char op_name[] = "where.self_out";
 
   ET_SWITCH_REALB_TYPES(compute_type, ctx, op_name, CTYPE_COMPUTE, [&]() {
-    utils::apply_tritensor_elementwise_fn<CTYPE_COMPUTE, op_name>(
-        [](const CTYPE_COMPUTE val_a,
-           const CTYPE_COMPUTE val_b,
-           const CTYPE_COMPUTE val_c) { return val_c ? val_a : val_b; },
+    utils::apply_tritensor_elementwise_fn<
+        CTYPE_COMPUTE,
+        op_name,
+        utils::SupportedTensorDtypes::SAME_AS_COMMON>(
+        [](const auto val_a, const auto val_b, const auto val_c) {
+          return val_c ? val_a : val_b;
+        },
         ctx,
         a,
         utils::SupportedTensorDtypes::REALHBBF16,
         b,
         utils::SupportedTensorDtypes::REALHBBF16,
         cond,
         utils::SupportedTensorDtypes::BOOL_OR_BYTE,
-        out,
-        utils::SupportedTensorDtypes::SAME_AS_COMMON);
+        out);
   });
 
   return out;

kernels/portable/cpu/pattern/bitwise_op.h

@@ -80,15 +80,18 @@ Tensor& bitwise_tensor_out(
 
   ET_SWITCH_INT_TYPES_AND(
       Bool, compute_type, ctx, op_name, CTYPE_COMPUTE, [&]() {
-        utils::apply_bitensor_elementwise_fn<CTYPE_COMPUTE, op_name>(
+        utils::apply_bitensor_elementwise_fn<
+            CTYPE_COMPUTE,
+            op_name,
+            utils::SupportedTensorDtypes::REALHBBF16>(
+            // TODO: rewrite this to be vectorization-capable.
             BitwiseFnForOp<CTYPE_COMPUTE, op_name>::value,
             ctx,
             a,
             utils::SupportedTensorDtypes::INTB,
             b,
             utils::SupportedTensorDtypes::INTB,
-            out,
-            utils::SupportedTensorDtypes::REALHBBF16);
+            out);
       });
 
   return out;
@@ -121,16 +124,19 @@ Tensor& bitwise_scalar_out(
   ET_SWITCH_INT_TYPES_AND(
       Bool, compute_type, ctx, op_name, CTYPE_COMPUTE, [&]() {
         const CTYPE_COMPUTE val_b = utils::scalar_to<CTYPE_COMPUTE>(b);
-        utils::apply_unitensor_elementwise_fn<CTYPE_COMPUTE, op_name>(
+        utils::apply_unitensor_elementwise_fn<
+            CTYPE_COMPUTE,
+            op_name,
+            utils::SupportedTensorDtypes::REALHBBF16>(
             [val_b](const CTYPE_COMPUTE val_a) {
+              // TODO: rewrite this to be vectorization-capable.
               return BitwiseFnForOp<CTYPE_COMPUTE, op_name>::value(
                   val_a, val_b);
             },
             ctx,
             a,
             utils::SupportedTensorDtypes::INTB,
-            out,
-            utils::SupportedTensorDtypes::REALHBBF16);
+            out);
       });
 
   return out;

kernels/portable/cpu/pattern/comparison_op.h

@@ -91,15 +91,18 @@ Tensor& comparison_tensor_out(
   ScalarType compute_type = utils::get_compute_type(common_type);
 
   ET_SWITCH_REALB_TYPES(compute_type, ctx, op_name, CTYPE_COMPUTE, [&]() {
-    utils::apply_bitensor_elementwise_fn<CTYPE_COMPUTE, op_name>(
+    utils::apply_bitensor_elementwise_fn<
+        CTYPE_COMPUTE,
+        op_name,
+        utils::SupportedTensorDtypes::REALHBBF16>(
+        // TODO: rewrite this to be vectorization-capable.
         ComparisonFnForOp<CTYPE_COMPUTE, op_name>::value,
         ctx,
         a,
         utils::SupportedTensorDtypes::REALHBBF16,
         b,
         utils::SupportedTensorDtypes::REALHBBF16,
-        out,
-        utils::SupportedTensorDtypes::REALHBBF16);
+        out);
   });
 
   return out;
@@ -127,15 +130,18 @@ Tensor& comparison_scalar_out(
 
   ET_SWITCH_REALB_TYPES(compute_type, ctx, op_name, CTYPE_COMPUTE, [&]() {
     const CTYPE_COMPUTE val_b = utils::scalar_to<CTYPE_COMPUTE>(b);
-    utils::apply_unitensor_elementwise_fn<CTYPE_COMPUTE, op_name>(
+    utils::apply_unitensor_elementwise_fn<
+        CTYPE_COMPUTE,
+        op_name,
+        utils::SupportedTensorDtypes::REALHBBF16>(
         [val_b](const CTYPE_COMPUTE val_a) {
+          // TODO: rewrite this to be vectorization-capable.
           return ComparisonFnForOp<CTYPE_COMPUTE, op_name>::value(val_a, val_b);
         },
         ctx,
         a,
         utils::SupportedTensorDtypes::REALHBBF16,
-        out,
-        utils::SupportedTensorDtypes::REALHBBF16);
+        out);
   });
 
   return out;

kernels/portable/cpu/pattern/logical_op.h

@@ -34,15 +34,18 @@ Tensor& logical_tensor_out(
       InvalidArgument,
       out);
 
-  utils::apply_bitensor_elementwise_fn<bool, op_name>(
+  utils::apply_bitensor_elementwise_fn<
+      bool,
+      op_name,
+      utils::SupportedTensorDtypes::REALHBBF16>(
+      // TODO: rewrite this to be vectorization-capable.
       fn,
       ctx,
       a,
       utils::SupportedTensorDtypes::REALHBBF16,
       b,
       utils::SupportedTensorDtypes::REALHBBF16,
-      out,
-      utils::SupportedTensorDtypes::REALHBBF16);
+      out);
 
   return out;
 }

kernels/portable/cpu/util/dtype_util.h

@@ -290,6 +290,25 @@ bool check_tensor_dtype(
     SupportedTensorDtypes dtypes,
     const ScalarType compute_type);
 
+/// Return the one output type we are willing to emit specialized code
+/// to handle, given a compute type of CTYPE_COMMON and supported
+/// output types of out_dtypes.
+template <typename CTYPE_COMPUTE>
+inline constexpr ScalarType specialized_output_scalar_type(
+    SupportedTensorDtypes out_dtypes) {
+  switch (out_dtypes) {
+    case SupportedTensorDtypes::BOOL_OR_BYTE:
+      return ScalarType::Bool;
+    case SupportedTensorDtypes::REALHBBF16:
+    case SupportedTensorDtypes::REALHBF16:
+    case SupportedTensorDtypes::FLOATHBF16:
+    case SupportedTensorDtypes::INTB:
+    case SupportedTensorDtypes::SAME_AS_COMPUTE:
+    case SupportedTensorDtypes::SAME_AS_COMMON:
+      return CppTypeToScalarType<CTYPE_COMPUTE>::value;
+  }
+}
+
 } // namespace internal
 } // namespace utils
 } // namespace native

kernels/portable/cpu/util/elementwise_util.h

@@ -51,6 +51,44 @@ inline int64_t scalar_to<int64_t>(const Scalar& s) {
 }
 
 namespace internal {
+template <
+    typename CTYPE_COMPUTE,
+    typename CTYPE_OUT,
+    typename Op,
+    typename... Args>
+inline void dtype_specialized_elementwise_fn_impl(
+    const Op& compute_fun,
+    KernelRuntimeContext& ctx,
+    const Tensor& out,
+    Args... inputs) {
+  constexpr auto kNumInputs = sizeof...(inputs);
+  ET_DCHECK(((inputs.first->element_size() == sizeof(CTYPE_COMPUTE)) && ...));
+
+  ::executorch::extension::parallel_for(
+      0,
+      out.numel(),
+      ::executorch::extension::internal::GRAIN_SIZE,
+      [&](const auto begin, const auto end) {
+        std::array<const CTYPE_COMPUTE*, kNumInputs> inputs_data_ptrs = {
+            inputs.first->template const_data_ptr<CTYPE_COMPUTE>()...};
+
+        CTYPE_OUT* const data_out = out.mutable_data_ptr<CTYPE_OUT>();
+
+        const auto range =
+            BroadcastIndexesRange<kNumInputs>(out, (*inputs.first)...);
+        auto begin_it = range.begin();
+        begin_it += begin;
+        for (; (*begin_it)[0] < end; ++begin_it) {
+          const auto& indexes = *begin_it;
+          std::array<CTYPE_COMPUTE, kNumInputs> loaded_inputs;
+          for (const auto idx : c10::irange(kNumInputs)) {
+            loaded_inputs[idx] = inputs_data_ptrs[idx][indexes[idx + 1]];
+          }
+          data_out[indexes[0]] = std::apply(compute_fun, loaded_inputs);
+        }
+      });
+}
+
 template <typename CTYPE_COMPUTE, typename Op, typename... Args>
 inline bool validate_elementwise_fn_inputs(
     const Op& compute_fun,
@@ -81,18 +119,12 @@ template <
     const char* op_name,
     typename Op,
     typename... Args>
-inline void apply_elementwise_fn(
+inline void apply_elementwise_fn_generic_impl(
     const Op& compute_fun,
     KernelRuntimeContext& ctx,
     const Tensor& out,
     SupportedTensorDtypes out_dtypes,
     Args... inputs) {
-  const bool inputs_valid = validate_elementwise_fn_inputs<CTYPE_COMPUTE>(
-      compute_fun, ctx, out, out_dtypes, inputs...);
-  if (!inputs_valid) {
-    return;
-  }
-
   constexpr auto kNumInputs = sizeof...(inputs);
 
   struct InputInfo {
@@ -138,6 +170,63 @@ inline void apply_elementwise_fn(
       });
 }
 
+template <
+    typename CTYPE_COMPUTE,
+    const char* op_name,
+    typename Op,
+    typename... Args>
+inline void apply_elementwise_fn_runtime_out_dtypes(
+    const Op& compute_fun,
+    KernelRuntimeContext& ctx,
+    const Tensor& out,
+    SupportedTensorDtypes out_dtypes,
+    Args... inputs) {
+  const bool inputs_valid = validate_elementwise_fn_inputs<CTYPE_COMPUTE>(
+      compute_fun, ctx, out, out_dtypes, inputs...);
+  if (!inputs_valid) {
+    return;
+  }
+
+  apply_elementwise_fn_generic_impl<CTYPE_COMPUTE, op_name>(
+      compute_fun, ctx, out, out_dtypes, inputs...);
+}
+
+template <
+    typename CTYPE_COMPUTE,
+    const char* op_name,
+    SupportedTensorDtypes out_dtypes,
+    typename Op,
+    typename... Args>
+inline void apply_elementwise_fn(
+    const Op& compute_fun,
+    KernelRuntimeContext& ctx,
+    const Tensor& out,
+    Args... inputs) {
+  const bool inputs_valid = validate_elementwise_fn_inputs<CTYPE_COMPUTE>(
+      compute_fun, ctx, out, out_dtypes, inputs...);
+  if (!inputs_valid) {
+    return;
+  }
+
+  constexpr auto compute_type = CppTypeToScalarType<CTYPE_COMPUTE>::value;
+  const bool all_inputs_compute_dtype =
+      ((inputs.first->scalar_type() == compute_type) && ...);
+
+  constexpr ScalarType out_specialized_scalar_type =
+      specialized_output_scalar_type<CTYPE_COMPUTE>(out_dtypes);
+  if (all_inputs_compute_dtype &&
+      out.scalar_type() == out_specialized_scalar_type) {
+    using CTYPE_OUT =
+        typename ScalarTypeToCppType<out_specialized_scalar_type>::type;
+    dtype_specialized_elementwise_fn_impl<CTYPE_COMPUTE, CTYPE_OUT>(
+        compute_fun, ctx, out, inputs...);
+    return;
+  }
+
+  apply_elementwise_fn_generic_impl<CTYPE_COMPUTE, op_name>(
+      compute_fun, ctx, out, out_dtypes, inputs...);
+}
+
 /// DEPRECATED: prefer the variant with out_dtypes in the template argument.
 template <typename CTYPE_COMPUTE, const char* op_name, typename Op>
 inline void apply_unitensor_elementwise_fn(
@@ -147,7 +236,7 @@ inline void apply_unitensor_elementwise_fn(
     SupportedTensorDtypes a_dtypes,
     const Tensor& out,
     SupportedTensorDtypes out_dtypes) {
-  internal::apply_elementwise_fn<CTYPE_COMPUTE, op_name>(
+  internal::apply_elementwise_fn_runtime_out_dtypes<CTYPE_COMPUTE, op_name>(
       compute_fun, ctx, out, out_dtypes, std::make_pair(&a, a_dtypes));
 }
 
@@ -162,8 +251,8 @@ inline void apply_unitensor_elementwise_fn(
     const Tensor& a,
     SupportedTensorDtypes a_dtypes,
     const Tensor& out) {
-  internal::apply_elementwise_fn<CTYPE_COMPUTE, op_name>(
-      compute_fun, ctx, out, out_dtypes, std::make_pair(&a, a_dtypes));
+  internal::apply_elementwise_fn<CTYPE_COMPUTE, op_name, out_dtypes>(
+      compute_fun, ctx, out, std::make_pair(&a, a_dtypes));
 }
 
 /**
@@ -179,7 +268,7 @@ inline void apply_bitensor_elementwise_fn(
     SupportedTensorDtypes b_dtypes,
     const Tensor& out,
     SupportedTensorDtypes out_dtypes) {
-  internal::apply_elementwise_fn<CTYPE_COMPUTE, op_name>(
+  internal::apply_elementwise_fn_runtime_out_dtypes<CTYPE_COMPUTE, op_name>(
       compute_fun,
       ctx,
       out,
@@ -206,11 +295,10 @@ inline void apply_bitensor_elementwise_fn(
     const Tensor& b,
     SupportedTensorDtypes b_dtypes,
     const Tensor& out) {
-  internal::apply_elementwise_fn<CTYPE_COMPUTE, op_name>(
+  internal::apply_elementwise_fn<CTYPE_COMPUTE, op_name, out_dtypes>(
       compute_fun,
       ctx,
       out,
-      out_dtypes,
       std::make_pair(&a, a_dtypes),
       std::make_pair(&b, b_dtypes));
 }
@@ -230,7 +318,7 @@ inline void apply_tritensor_elementwise_fn(
     SupportedTensorDtypes c_dtypes,
     const Tensor& out,
     SupportedTensorDtypes out_dtypes) {
-  internal::apply_elementwise_fn<CTYPE_COMPUTE, op_name>(
+  internal::apply_elementwise_fn_runtime_out_dtypes<CTYPE_COMPUTE, op_name>(
       compute_fun,
       ctx,
       out,
@@ -275,11 +363,10 @@ inline void apply_tritensor_elementwise_fn(
     const Tensor& c,
     SupportedTensorDtypes c_dtypes,
     const Tensor& out) {
-  internal::apply_elementwise_fn<CTYPE_COMPUTE, op_name>(
+  internal::apply_elementwise_fn<CTYPE_COMPUTE, op_name, out_dtypes>(
       compute_fun,
       ctx,
       out,
-      out_dtypes,
       std::make_pair(&a, a_dtypes),
       std::make_pair(&b, b_dtypes),
       std::make_pair(&c, c_dtypes));