Reuse GELU implementation from PyTorch core

.ci/scripts/build_llama_android.sh

@@ -10,6 +10,12 @@ set -exu
 # shellcheck source=/dev/null
 source "$(dirname "${BASH_SOURCE[0]}")/utils.sh"
 
+if [[ -z "${PYTHON_EXECUTABLE:-}" ]]; then
+  PYTHON_EXECUTABLE=python3
+fi
+which "${PYTHON_EXECUTABLE}"
+CMAKE_PREFIX_PATH="$(python3 -c 'import torch as _; print(_.__path__[0])')"
+
 install_executorch_and_backend_lib() {
   echo "Installing executorch and xnnpack backend"
   clean_executorch_install_folders
@@ -22,6 +28,7 @@ install_executorch_and_backend_lib() {
     -DANDROID_ABI="${ANDROID_ABI}" \
     -DCMAKE_INSTALL_PREFIX=cmake-android-out \
     -DCMAKE_BUILD_TYPE=Release \
+    -DCMAKE_PREFIX_PATH="${CMAKE_PREFIX_PATH}" \
     -DEXECUTORCH_BUILD_EXTENSION_DATA_LOADER=ON \
     -DEXECUTORCH_BUILD_EXTENSION_MODULE=ON \
     -DEXECUTORCH_BUILD_EXTENSION_TENSOR=ON \
@@ -47,6 +54,7 @@ build_llama_runner() {
     -DEXECUTORCH_BUILD_KERNELS_OPTIMIZED=ON \
     -DEXECUTORCH_BUILD_KERNELS_QUANTIZED=ON \
     -DEXECUTORCH_BUILD_KERNELS_CUSTOM=ON \
+    -DCMAKE_PREFIX_PATH="${CMAKE_PREFIX_PATH}" \
     -Bcmake-android-out/examples/models/llama examples/models/llama
 
     cmake --build cmake-android-out/examples/models/llama -j4 --config Release

.ci/scripts/test_llama.sh

@@ -152,6 +152,7 @@ cmake_install_executorch_libraries() {
     rm -rf cmake-out
     retry cmake \
         -DCMAKE_INSTALL_PREFIX=cmake-out \
+        -DCMAKE_PREFIX_PATH="$(python3 -c 'import torch as _; print(_.__path__[0])')" \
         -DCMAKE_BUILD_TYPE="$CMAKE_BUILD_TYPE" \
         -DEXECUTORCH_BUILD_EXTENSION_DATA_LOADER=ON \
         -DEXECUTORCH_BUILD_EXTENSION_MODULE=ON \

.ci/scripts/test_model.sh

@@ -50,10 +50,12 @@ prepare_artifacts_upload() {
 
 build_cmake_executor_runner() {
   echo "Building executor_runner"
+  CMAKE_PREFIX_PATH="$(python3 -c 'import torch as _; print(_.__path__[0])')"
   rm -rf ${CMAKE_OUTPUT_DIR}
   cmake -DCMAKE_BUILD_TYPE=Debug \
       -DEXECUTORCH_BUILD_KERNELS_OPTIMIZED=ON \
       -DPYTHON_EXECUTABLE="$PYTHON_EXECUTABLE" \
+      -DCMAKE_PREFIX_PATH="$CMAKE_PREFIX_PATH" \
       -B${CMAKE_OUTPUT_DIR} .
 
   cmake --build ${CMAKE_OUTPUT_DIR} -j4 --config Debug
@@ -98,8 +100,7 @@ test_model() {
 
 build_cmake_xnn_executor_runner() {
   echo "Building xnn_executor_runner"
-  SITE_PACKAGES="$(${PYTHON_EXECUTABLE} -c 'from distutils.sysconfig import get_python_lib; print(get_python_lib())')"
-  CMAKE_PREFIX_PATH="${SITE_PACKAGES}/torch"
+  CMAKE_PREFIX_PATH="$(python3 -c 'import torch as _; print(_.__path__[0])')"
 
   (rm -rf ${CMAKE_OUTPUT_DIR} \
     && mkdir ${CMAKE_OUTPUT_DIR} \

.ci/scripts/test_phi_3_mini.sh

@@ -22,8 +22,10 @@ NPROC=8
 if hash nproc &> /dev/null; then NPROC=$(nproc); fi
 
 cmake_install_executorch_libraries() {
+  CMAKE_PREFIX_PATH="$(python3 -c 'import torch as _; print(_.__path__[0])')"
   cmake -DPYTHON_EXECUTABLE=python \
       -DCMAKE_INSTALL_PREFIX=${BUILD_DIR} \
+      -DCMAKE_PREFIX_PATH="${CMAKE_PREFIX_PATH}" \
       -DEXECUTORCH_ENABLE_LOGGING=1 \
       -DCMAKE_BUILD_TYPE=${BUILD_TYPE} \
       -DEXECUTORCH_BUILD_EXTENSION_DATA_LOADER=ON \
@@ -39,8 +41,10 @@ cmake_install_executorch_libraries() {
 }
 
 cmake_build_phi_3_mini() {
+  CMAKE_PREFIX_PATH="$(python3 -c 'import torch as _; print(_.__path__[0])')"
   cmake -DPYTHON_EXECUTABLE=$PYTHON_EXECUTABLE \
       -DCMAKE_INSTALL_PREFIX=${BUILD_DIR} \
+      -DCMAKE_PREFIX_PATH="${CMAKE_PREFIX_PATH}" \
       -DCMAKE_BUILD_TYPE=${BUILD_TYPE} \
       -DEXECUTORCH_BUILD_KERNELS_CUSTOM=ON \
       -DEXECUTORCH_BUILD_KERNELS_OPTIMIZED=ON \

.ci/scripts/utils.sh

@@ -136,6 +136,7 @@ cmake_install_executorch_lib() {
   clean_executorch_install_folders
   retry cmake -DBUCK2="$BUCK" \
           -DCMAKE_INSTALL_PREFIX=cmake-out \
+          -DCMAKE_PREFIX_PATH="$($PYTHON_EXECUTABLE -c 'import torch as _; print(_.__path__[0])')" \
           -DCMAKE_BUILD_TYPE=Release \
           -DPYTHON_EXECUTABLE="$PYTHON_EXECUTABLE" \
           -Bcmake-out .

.github/workflows/pull.yml

@@ -135,6 +135,8 @@ jobs:
         CONDA_ENV=$(conda env list --json | jq -r ".envs | .[-1]")
         conda activate "${CONDA_ENV}"
 
+        source .ci/scripts/utils.sh
+        install_executorch "use-pt-pinned-commit"
         BUILD_TOOL="cmake"
         PYTHON_EXECUTABLE=python \
         bash .ci/scripts/build_llama_android.sh  "${BUILD_TOOL}"

.github/workflows/trunk.yml

@@ -385,6 +385,7 @@ jobs:
         rm -rf cmake-out
         cmake \
             -DCMAKE_INSTALL_PREFIX=cmake-out \
+            -DCMAKE_PREFIX_PATH="$(python -c 'import torch as _; print(_.__path__[0])')" \
             -DCMAKE_BUILD_TYPE=Release \
             -DEXECUTORCH_BUILD_EXTENSION_DATA_LOADER=ON \
             -DEXECUTORCH_BUILD_EXTENSION_MODULE=ON \
@@ -402,6 +403,7 @@ jobs:
         cmake \
             -DCMAKE_INSTALL_PREFIX=cmake-out \
             -DCMAKE_BUILD_TYPE=Release \
+            -DCMAKE_PREFIX_PATH="$(python -c 'import torch as _; print(_.__path__[0])')" \
             -DEXECUTORCH_BUILD_KERNELS_CUSTOM=ON \
             -DEXECUTORCH_BUILD_KERNELS_OPTIMIZED=ON \
             -DEXECUTORCH_BUILD_KERNELS_QUANTIZED=ON \

build/Utils.cmake

@@ -321,3 +321,20 @@ function(resolve_python_executable)
     )
   endif()
 endfunction()
+
+# find_package(Torch CONFIG REQUIRED) replacement for targets that
+# have a header-only Torch dependency. Because find_package sets
+# variables in the parent scope, we use a macro to preserve this
+# rather than maintaining our own list of those variables.
+macro(find_package_torch_headers)
+  # We cannot simply use CMAKE_FIND_ROOT_PATH_BOTH, because that does
+  # not propagate into TorchConfig.cmake.
+  foreach(mode_kind IN ITEMS PACKAGE LIBRARY INCLUDE)
+    set(OLD_CMAKE_FIND_ROOT_PATH_MODE_${mode_kind} ${CMAKE_FIND_ROOT_PATH_MODE_${mode_kind}})
+    set(CMAKE_FIND_ROOT_PATH_MODE_${mode_kind} BOTH)
+  endforeach()
+  find_package(Torch CONFIG REQUIRED)
+  foreach(mode_kind IN ITEMS PACKAGE LIBRARY INCLUDE)
+    set(CMAKE_FIND_ROOT_PATH_MODE_${mode_kind} ${OLD_CMAKE_FIND_ROOT_PATH_MODE_${mode_kind}})
+  endforeach()
+endmacro()

build/build_android_llm_demo.sh

@@ -7,6 +7,12 @@
 
 set -ex
 
+if [[ -z "${PYTHON_EXECUTABLE:-}" ]]; then
+  PYTHON_EXECUTABLE=python3
+fi
+which "${PYTHON_EXECUTABLE}"
+CMAKE_PREFIX_PATH="$(python3 -c 'import torch as _; print(_.__path__[0])')"
+
 build_jar() {
   pushd extension/android
   ./gradlew build
@@ -36,6 +42,7 @@ build_android_native_library() {
   fi
 
   cmake . -DCMAKE_INSTALL_PREFIX="${CMAKE_OUT}" \
+    -DCMAKE_PREFIX_PATH="${CMAKE_PREFIX_PATH}" \
     -DCMAKE_TOOLCHAIN_FILE="${ANDROID_NDK}/build/cmake/android.toolchain.cmake" \
     -DANDROID_ABI="${ANDROID_ABI}" \
     -DANDROID_PLATFORM=android-26 \
@@ -69,6 +76,7 @@ build_android_native_library() {
     -DANDROID_ABI="${ANDROID_ABI}" \
     -DANDROID_PLATFORM=android-26 \
     -DCMAKE_INSTALL_PREFIX="${CMAKE_OUT}" \
+    -DCMAKE_PREFIX_PATH="${CMAKE_PREFIX_PATH}" \
     -DEXECUTORCH_ENABLE_LOGGING=ON \
     -DEXECUTORCH_LOG_LEVEL=Info \
     -DCMAKE_FIND_ROOT_PATH_MODE_PACKAGE=BOTH \

kernels/optimized/CMakeLists.txt

@@ -63,6 +63,8 @@ message("Generated files ${gen_command_sources}")
 
 list(TRANSFORM _optimized_kernels__srcs PREPEND "${EXECUTORCH_ROOT}/")
 add_library(optimized_kernels ${_optimized_kernels__srcs})
+find_package_torch_headers()
+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 = [
+            "//executorch/runtime/core/portable_type/c10: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

@@ -1,8 +1,8 @@
 # Copyright (c) Meta Platforms, Inc. and affiliates.
 #
 # This yaml file contains operators that have optimized kernels available.
-# Note that this is a copy of optimized.yaml that does not include gelu and
-# log_softmax, due to the OSS build not currently including sleef.
+# Note that this is a copy of optimized.yaml that does not include log_softmax,
+# due to the OSS build not currently including sleef.
 # TODO (T183193812)
 
 - op: add.out
@@ -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

shim/xplat/executorch/kernels/optimized/op_registration_util.bzl

@@ -134,5 +134,5 @@ def define_op_target(name, deps):
 
 def is_op_disabled(name):
     # TODO (gjcomer) Enable ops with sleef dependency in OSS
-    disabled_ops = ["op_gelu", "op_log_softmax"]
+    disabled_ops = ["op_log_softmax"]
     return name in disabled_ops

test/run_oss_cpp_tests.sh

@@ -22,13 +22,20 @@ elif [[ $(uname) == "Linux" ]]; then
   export LLVM_COV="${LLVM_COV:-llvm-cov}"
 fi
 
+if [[ -z "${PYTHON_EXECUTABLE:-}" ]]; then
+  PYTHON_EXECUTABLE=python3
+fi
+which "${PYTHON_EXECUTABLE}"
+
 build_executorch() {
   BUILD_VULKAN="OFF"
   if [ -x "$(command -v glslc)" ]; then
     BUILD_VULKAN="ON"
   fi
+  CMAKE_PREFIX_PATH="$(python3 -c 'import torch as _; print(_.__path__[0])')"
   cmake . \
     -DCMAKE_INSTALL_PREFIX=cmake-out \
+    -DCMAKE_PREFIX_PATH="${CMAKE_PREFIX_PATH}" \
     -DEXECUTORCH_USE_CPP_CODE_COVERAGE=ON \
     -DEXECUTORCH_BUILD_KERNELS_OPTIMIZED=ON \
     -DEXECUTORCH_BUILD_KERNELS_QUANTIZED=ON \