Reuse GELU implementation from PyTorch core

Pull Request resolved: #7041

kernels/optimized doesn't need to support embedded systems, so it can just take a header-only dep on PyTorch.

Note that, because we will pick up Sleef internally and ignore it
externally thanks to ATen vec, this PR gets to enable optimized GELU in OSS.

Testing: CI to make sure this doesn't break mobile build modes; happy to take advice on anything not currently covered that might break.
ghstack-source-id: 259204954
@exported-using-ghexport

Differential Revision: [D66335522](https://our.internmc.facebook.com/intern/diff/D66335522/)

kernels/optimized/CMakeLists.txt

@@ -60,6 +60,8 @@ message("Generated files ${gen_command_sources}")
 
 list(TRANSFORM _optimized_kernels__srcs PREPEND "${EXECUTORCH_ROOT}/")
 add_library(optimized_kernels ${_optimized_kernels__srcs})
+find_package(Torch CONFIG REQUIRED)
+target_include_directories(optimized_kernels PRIVATE ${TORCH_INCLUDE_DIRS})
 target_link_libraries(
   optimized_kernels PRIVATE executorch_core cpublas extension_threadpool
 )

kernels/optimized/cpu/op_gelu.cpp

@@ -13,6 +13,7 @@
 
 #include <cmath>
 
+#include <ATen/native/cpu/Gelu.h>
 #include <executorch/runtime/kernel/kernel_includes.h>
 #include <executorch/runtime/platform/assert.h>
 
@@ -46,48 +47,26 @@ void gelu(
   CTYPE* out_data = output.mutable_data_ptr<CTYPE>();
   size_t lim = input.numel();
 
-  // TODO: Add fast path for tanh using sleef's tanh
   if (approximate == "tanh") {
-    // 0.5 * x * (1 + Tanh(sqrt(2 / pi) * (x + 0.044715 * x^3))
-    for (size_t i = 0; i < lim; ++i) {
-      const CTYPE x = in_data[i];
-      const CTYPE kBeta = M_SQRT2 * M_2_SQRTPI * 0.5;
-      const CTYPE kKappa = 0.044715;
-      auto x_cube = x * x * x;
-      auto inner = kBeta * (x + kKappa * x_cube);
-      out_data[i] = CTYPE(0.5) * x * (CTYPE(1) + std::tanh(inner));
+    using Vec = at::vec::Vectorized<CTYPE>;
+    int i = 0;
+    for (; i < lim - (lim % Vec::size()); i += Vec::size()) {
+      Vec x = Vec::loadu(in_data + i);
+      at::native::vectorized_gelu_approximated_with_tanh(x).store(out_data + i);
     }
-  } else if (approximate == "none") { // dont appx
-    // GELU(x) = x * Φ(x) where Φ(x) is the is the Cumulative Distribution
-    // Function for Gaussian Distribution.
-
-#ifndef __aarch64__
-    for (size_t i = 0; i < lim; ++i) {
-      const CTYPE x = in_data[i];
-      out_data[i] = CTYPE(0.5) * x * (CTYPE(1) + std::erf(x * M_SQRT1_2));
+    for (; i < lim; ++i) {
+      out_data[i] = at::native::scalar_gelu_approximated_with_tanh(in_data[i]);
     }
-#else
-    size_t i = 0;
-    if (std::is_same<CTYPE, float>::value) {
-      for (; i + 4 < lim; i += 4) {
-        const float32x4_t in =
-            vld1q_f32(static_cast<const float*>(&in_data[i]));
-        const float32x4_t m_sqrt1_2x4 = {
-            M_SQRT1_2, M_SQRT1_2, M_SQRT1_2, M_SQRT1_2};
-        const float32x4_t ones = vmovq_n_f32(1.0);
-        const float32x4_t halves = vmovq_n_f32(0.5);
-        float32x4_t out = Sleef_erff4_u10(vmulq_f32(in, m_sqrt1_2x4));
-        vst1q_f32(
-            static_cast<float*>(&out_data[i]),
-            vmulq_f32(vmulq_f32(vaddq_f32(out, ones), in), halves));
-      }
+  } else if (approximate == "none") {
+    using Vec = at::vec::Vectorized<CTYPE>;
+    int i = 0;
+    for (; i < lim - (lim % Vec::size()); i += Vec::size()) {
+      Vec x = Vec::loadu(in_data + i);
+      at::native::vectorized_gelu(x).store(out_data + i);
     }
     for (; i < lim; ++i) {
-      const CTYPE x = in_data[i];
-      out_data[i] = CTYPE(0.5) * x * (CTYPE(1) + std::erf(x * M_SQRT1_2));
+      out_data[i] = at::native::scalar_gelu(in_data[i]);
     }
-#endif // __aarch64__
-
   } else {
     ET_KERNEL_CHECK_MSG(
         context,

kernels/optimized/cpu/targets.bzl

@@ -28,12 +28,9 @@ _OPTIMIZED_ATEN_OPS = (
     op_target(name = "op_sigmoid"),
     op_target(
         name = "op_gelu",
-        deps = select({
-            "DEFAULT": [],
-            "ovr_config//cpu:arm64": [
-                "fbsource//third-party/sleef:sleef_arm",
-            ],
-        }),
+        deps = [
+            ":aten_headers_for_executorch",
+        ],
     ),
     op_target(
         name = "op_le",
@@ -94,6 +91,13 @@ _OPTIMIZED_ATEN_OPS = (
     ),
 )
 
+
+def get_sleef_preprocessor_flags():
+    if runtime.is_oss:
+        return []
+    return ["-DAT_BUILD_ARM_VEC256_WITH_SLEEF"]
+
+
 def define_common_targets():
     """Defines targets that should be shared between fbcode and xplat.
 

kernels/optimized/optimized-oss.yaml

@@ -40,6 +40,11 @@
     - arg_meta: null
       kernel_name: torch::executor::opt_sigmoid_out
 
+- op: gelu.out
+  kernels:
+    - arg_meta: null
+      kernel_name: torch::executor::opt_gelu_out
+
 - op: le.Scalar_out
   kernels:
     - arg_meta: null