Revert "Add vectorization in elementwise_util (#9432)"

This reverts commit 4c35fe0.

Author: JacobSzwejbka

.lintrunner.toml

@@ -272,8 +272,6 @@ exclude_patterns = [
     'exir/verification/bindings.cpp',
     'extension/**',
     # Uses properly-gated (ET_USE_PYTORCH_HEADERS) ATen include.
-    'kernels/portable/cpu/util/elementwise_util.h',
-    'kernels/portable/cpu/util/math_util.h',
     'kernels/portable/cpu/util/vectorized_math.h',
     'kernels/optimized/**',
     'runtime/core/exec_aten/**',

kernels/portable/cpu/op_add.cpp

@@ -102,18 +102,14 @@ Tensor& add_scalar_out(
   static constexpr const char op_name[] = "add.Scalar_out";
 
   ET_SWITCH_REALB_TYPES(compute_type, ctx, op_name, CTYPE_COMPUTE, [&]() {
-    CTYPE_COMPUTE val_b = utils::scalar_to<CTYPE_COMPUTE>(b);
-    CTYPE_COMPUTE val_alpha = utils::scalar_to<CTYPE_COMPUTE>(alpha);
-    auto val_alpha_times_b = val_alpha * val_b;
     utils::apply_unitensor_elementwise_fn<
         CTYPE_COMPUTE,
         op_name,
         utils::SupportedTensorDtypes::SAME_AS_COMMON>(
-        [val_alpha_times_b](const auto val_a) {
-          // Cast here supports vectorization; either it does nothing
-          // or it casts from CTYPE_COMPUTE to
-          // Vectorized<CTYPE_COMPUTE>.
-          return val_a + decltype(val_a)(val_alpha_times_b);
+        [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,

kernels/portable/cpu/op_atan2.cpp

@@ -60,7 +60,7 @@ Tensor& atan2_out(
         op_name,
         utils::SupportedTensorDtypes::FLOATHBF16>(
         [](const auto val_a, const auto val_b) {
-          return executorch::math::atan2(val_a, val_b);
+          return std::atan2(val_a, val_b);
         },
         ctx,
         a,

kernels/portable/cpu/op_clamp.cpp

@@ -138,8 +138,9 @@ Tensor& clamp_out(
         CTYPE_COMPUTE,
         op_name,
         utils::SupportedTensorDtypes::SAME_AS_COMMON>(
-        [has_min, min_opt, has_max, max_opt](const auto val_in) {
-          auto val_out = val_in;
+        [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(
                 val_out, utils::scalar_to<CTYPE_COMPUTE>(min_opt.value()));

kernels/portable/cpu/op_elu.cpp

@@ -48,7 +48,8 @@ Tensor& elu_out(
         CTYPE,
         op_name,
         utils::SupportedTensorDtypes::SAME_AS_COMMON>(
-        [negcoef, math_scale, math_input_scale](const CTYPE x) {
+        [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;

kernels/portable/cpu/op_fmod.cpp

@@ -61,7 +61,7 @@ Tensor& fmod_Tensor_out(
         utils::SupportedTensorDtypes::REALHBF16>(
         [&div_by_zero_error](
             const CTYPE_COMPUTE val_a, const CTYPE_COMPUTE val_b) {
-          // TODO: rewrite this to be vectorization-capable?
+          // TODO: rewrite this to be vectorization-capable.
           CTYPE_COMPUTE value = 0;
           if (is_integral_type<CTYPE_COMPUTE, /*includeBool=*/true>::value) {
             if (val_b == 0) {
@@ -138,8 +138,10 @@ Tensor& fmod_Scalar_out(
         CTYPE_COMPUTE,
         op_name,
         utils::SupportedTensorDtypes::REALHBF16>(
-        [val_b](const auto val_a) {
-          return executorch::math::fmod(val_a, (decltype(val_a))val_b);
+        [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,

kernels/portable/cpu/op_maximum.cpp

@@ -49,7 +49,7 @@ Tensor& maximum_out(
         CTYPE_COMPUTE,
         op_name,
         utils::SupportedTensorDtypes::REALHBBF16>(
-        [](const auto val_a, const auto val_b) {
+        [](const CTYPE_COMPUTE val_a, const CTYPE_COMPUTE val_b) {
           return utils::max_override(val_a, val_b);
         },
         ctx,

kernels/portable/cpu/op_minimum.cpp

@@ -49,7 +49,8 @@ Tensor& minimum_out(
         CTYPE_COMPUTE,
         op_name,
         utils::SupportedTensorDtypes::REALHBBF16>(
-        [](const auto val_a, const auto val_b) {
+        [](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,

kernels/portable/cpu/op_mul.cpp

@@ -72,7 +72,9 @@ Tensor& mul_out(
           CTYPE_COMPUTE,
           op_name,
           utils::SupportedTensorDtypes::REALHBBF16>(
-          [](const auto val_a, const auto val_b) { return val_a * val_b; },
+          [](const CTYPE_COMPUTE val_a, const CTYPE_COMPUTE val_b) {
+            return val_a * val_b;
+          },
           ctx,
           a,
           utils::SupportedTensorDtypes::REALHBBF16,

kernels/portable/cpu/op_native_dropout.cpp

@@ -57,11 +57,8 @@ std::tuple<Tensor&, Tensor&> native_dropout_out(
     }
     ET_SWITCH_FLOATHBF16_TYPES(
         input.scalar_type(), ctx, op_name, CTYPE_COMPUTE, [&]() {
-          utils::apply_bitensor_elementwise_fn<
-              CTYPE_COMPUTE,
-              op_name,
-              utils::SupportedTensorDtypes::SAME_AS_COMMON>(
-              [](const CTYPE_COMPUTE val, const CTYPE_COMPUTE mask_val) {
+          utils::apply_bitensor_elementwise_fn<CTYPE_COMPUTE, op_name>(
+              [](const auto val, const auto mask_val) {
                 if (!mask_val) {
                   return static_cast<decltype(val)>(0);
                 }
@@ -73,7 +70,8 @@ std::tuple<Tensor&, Tensor&> native_dropout_out(
               mask,
               // TODO: should really be just BOOL
               utils::SupportedTensorDtypes::BOOL_OR_BYTE,
-              out);
+              out,
+              utils::SupportedTensorDtypes::SAME_AS_COMMON);
         });
   } else if (input.numel() > 0) {
     std::memcpy(out.mutable_data_ptr(), input.data_ptr(), input.nbytes());