Migrate elementwise_util callers to the variants with out_dtypes in template arguments

CMakeLists.txt

@@ -643,13 +643,18 @@ target_link_options_shared_lib(executorch)
 # Real integrations should supply their own YAML file that only lists the
 # operators necessary for the models that will run.
 #
+if(EXECUTORCH_BUILD_KERNELS_OPTIMIZED)
+  # find pytorch lib here to make it available to all
+  # sub-directories. Find it before including portable so that
+  # optimized_portable_kernels can use it.
+  find_package_torch_headers()
+endif()
+
 if(BUILD_EXECUTORCH_PORTABLE_OPS)
   add_subdirectory(${CMAKE_CURRENT_SOURCE_DIR}/kernels/portable)
 endif()
 
 if(EXECUTORCH_BUILD_KERNELS_OPTIMIZED)
-  # find pytorch lib here to make it available to all sub-directories
-  find_package_torch_headers()
   add_subdirectory(${CMAKE_CURRENT_SOURCE_DIR}/kernels/optimized)
 endif()
 

kernels/optimized/CMakeLists.txt

@@ -62,6 +62,7 @@ message("Generated files ${gen_command_sources}")
 list(TRANSFORM _optimized_kernels__srcs PREPEND "${EXECUTORCH_ROOT}/")
 add_library(optimized_kernels ${_optimized_kernels__srcs})
 target_include_directories(optimized_kernels PRIVATE ${TORCH_INCLUDE_DIRS} "${EXECUTORCH_ROOT}/third-party/pocketfft")
+target_compile_definitions(optimized_kernels PRIVATE ET_USE_PYTORCH_HEADERS)
 target_link_libraries(
   optimized_kernels PUBLIC executorch_core cpublas extension_threadpool
 )

kernels/portable/CMakeLists.txt

@@ -66,13 +66,13 @@ gen_operators_lib(
 # Portable kernels support optional parallelization (and, in the
 # future, perhaps other performance features). If support is present,
 # produce an optimized version.
-set(BUILD_OPTIMIZED_PORTABLE_KERNELS EXECUTORCH_BUILD_PTHREADPOOL)
-
-if(BUILD_OPTIMIZED_PORTABLE_KERNELS)
+if(EXECUTORCH_BUILD_PTHREADPOOL AND EXECUTORCH_BUILD_KERNELS_OPTIMIZED)
   add_library(optimized_portable_kernels ${_portable_kernels__srcs})
   target_link_libraries(optimized_portable_kernels PRIVATE executorch)
   target_link_libraries(optimized_portable_kernels PUBLIC extension_threadpool)
   target_compile_options(optimized_portable_kernels PUBLIC ${_common_compile_options})
+  target_include_directories(optimized_portable_kernels PRIVATE ${TORCH_INCLUDE_DIRS})
+  target_compile_definitions(optimized_portable_kernels PRIVATE ET_USE_PYTORCH_HEADERS)
   install(
     TARGETS optimized_portable_kernels
     DESTINATION lib

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(