Adding minimum and maximum operators

backends/cadence/aot/functions_hifi.yaml

@@ -62,10 +62,20 @@
     - arg_meta: null
       kernel_name: torch::executor::full_out
 
+- op: maximum.out
+  kernels:
+    - arg_meta: null
+      kernel_name: impl::HiFi::maximum_out
+
 - op: mean.out
   kernels:
     - arg_meta: null
-      kernel_name: impl::HiFi::mean_dim_out   
+      kernel_name: impl::HiFi::mean_dim_out
+
+- op: minimum.out
+  kernels:
+    - arg_meta: null
+      kernel_name: impl::HiFi::minimum_out
 
 - op: mul.out
   kernels:

backends/cadence/hifi/kernels/CMakeLists.txt

@@ -15,8 +15,8 @@ add_library(
   ${EXECUTORCH_ROOT}/backends/cadence/hifi/third-party/nnlib/xa_nn_elm_mul_f32_broadcast.c
   ${EXECUTORCH_ROOT}/backends/cadence/hifi/third-party/nnlib/xa_nn_elm_where_f32xf32_f32.c
   ${EXECUTORCH_ROOT}/backends/cadence/hifi/third-party/nnlib/xa_nn_reduce_32_32.c
+  ${EXECUTORCH_ROOT}/backends/cadence/hifi/third-party/nnlib/xa_nn_elm_minimum_maximum_f32.c
 )
-
 # Let files say "include <executorch/path/to/header.h>".
 set(_common_include_directories ${EXECUTORCH_ROOT}/..)
 
@@ -28,7 +28,7 @@ target_include_directories(
     ${EXECUTORCH_ROOT}/backends/cadence/hifi/third-party/nnlib/nnlib-hifi4/xa_nnlib/include/nnlib
     ${EXECUTORCH_ROOT}/backends/cadence/hifi/third-party/nnlib/nnlib-hifi4/xa_nnlib/include
     ${EXECUTORCH_ROOT}/backends/cadence/hifi/third-party/nnlib/nnlib-hifi4/xa_nnlib/algo/ndsp/hifi4/include/
-	${_common_include_directories}
+       ${_common_include_directories}
 )
 
 target_link_libraries(cadence_kernels PRIVATE xa_nnlib)

backends/cadence/hifi/kernels/kernels.h

@@ -77,6 +77,30 @@ extern "C" WORD32 xa_nn_elm_where_broadcast_4D_f32xf32_f32(FLOAT32 * __restrict_
                                 const unsigned char *__restrict__ p_condition,
                                 const WORD32 *const p_condition_shape);
 
+extern "C" WORD32 xa_nn_elm_maximum_f32xf32_f32(FLOAT32 * __restrict__ p_out,
+                               const FLOAT32 * __restrict__ p_inp1,
+                               const FLOAT32 * __restrict__ p_inp2,
+                               WORD32 num_elm);
+
+extern "C" WORD32 xa_nn_elm_maximum_broadcast_4D_f32xf32_f32(FLOAT32 * __restrict__ p_out,
+                      const WORD32 *const p_out_shape,
+                      const FLOAT32 * __restrict__ p_inp1,
+                      const WORD32 *const p_inp1_shape,
+                      const FLOAT32 * __restrict__ p_inp2,
+                      const WORD32 *const p_inp2_shape);
+
+extern "C" WORD32 xa_nn_elm_minimum_f32xf32_f32(FLOAT32 * __restrict__ p_out,
+                               const FLOAT32 * __restrict__ p_inp1,
+                               const FLOAT32 * __restrict__ p_inp2,
+                               WORD32 num_elm);
+
+extern "C" WORD32 xa_nn_elm_minimum_broadcast_4D_f32xf32_f32(FLOAT32 * __restrict__ p_out,
+                      const WORD32 *const p_out_shape,
+                      const FLOAT32 * __restrict__ p_inp1,
+                      const WORD32 *const p_inp1_shape,
+                      const FLOAT32 * __restrict__ p_inp2,
+                      const WORD32 *const p_inp2_shape);
+
 namespace impl {
 namespace HiFi {
 namespace kernels {

backends/cadence/hifi/operators/CMakeLists.txt

@@ -22,7 +22,9 @@ endif()
 set(_aten_ops__srcs
     "${EXECUTORCH_ROOT}/backends/cadence/hifi/operators/op_add.cpp"
     "${EXECUTORCH_ROOT}/backends/cadence/hifi/operators/op_div.cpp"
+    "${EXECUTORCH_ROOT}/backends/cadence/hifi/operators/op_maximum.cpp"
     "${EXECUTORCH_ROOT}/backends/cadence/hifi/operators/op_mean.cpp"
+    "${EXECUTORCH_ROOT}/backends/cadence/hifi/operators/op_minimum.cpp"
     "${EXECUTORCH_ROOT}/backends/cadence/hifi/operators/op_mul.cpp"
     "${EXECUTORCH_ROOT}/backends/cadence/hifi/operators/op_sigmoid.cpp"
     "${EXECUTORCH_ROOT}/backends/cadence/hifi/operators/op_sub.cpp"

backends/cadence/hifi/operators/op_maximum.cpp

@@ -0,0 +1,172 @@
+/*
+ * Copyright (c) Meta Platforms, Inc. and affiliates.
+ * All rights reserved.
+ *
+ * This source code is licensed under the BSD-style license found in the
+ * LICENSE file in the root directory of this source tree.
+ */
+
+#include <executorch/kernels/portable/cpu/scalar_utils.h>
+#include <executorch/kernels/portable/cpu/util/broadcast_util.h>
+#include <executorch/kernels/portable/cpu/util/math_util.h>
+#include <executorch/runtime/kernel/kernel_includes.h>
+#include <executorch/backends/cadence/hifi/kernels/kernels.h>
+
+using exec_aten::ScalarType;
+using exec_aten::Tensor;
+using executorch::runtime::can_cast;
+using executorch::runtime::CppTypeToScalarType;
+using torch::executor::Error;
+using executorch::aten::RuntimeContext;
+
+namespace impl {
+namespace HiFi {
+namespace native {
+namespace {
+
+template <
+    bool can_cast,
+    typename CTYPE_A,
+    typename CTYPE_B,
+    typename CTYPE_IN,
+    typename CTYPE_OUT>
+struct MaximumInner;
+
+template <
+    typename CTYPE_A,
+    typename CTYPE_B,
+    typename CTYPE_IN,
+    typename CTYPE_OUT>
+struct MaximumInner<true, CTYPE_A, CTYPE_B, CTYPE_IN, CTYPE_OUT> {
+  static void run(const Tensor& a, const Tensor& b, Tensor& out) {
+    apply_binary_elementwise_fn<CTYPE_A, CTYPE_B, CTYPE_OUT>(
+        // NOLINTNEXTLINE(facebook-hte-ConstantArgumentPassByValue)
+        [](const CTYPE_A val_a, const CTYPE_B val_b) {
+          CTYPE_IN a_casted = static_cast<CTYPE_IN>(val_a);
+          CTYPE_IN b_casted = static_cast<CTYPE_IN>(val_b);
+          CTYPE_IN value = torch::executor::native::utils::max_override(a_casted, b_casted);
+
+          return static_cast<CTYPE_OUT>(value);
+        },
+        a,
+        b,
+        out);
+  }
+};
+
+struct ReportCanCastBug {
+  static void run(const Tensor&, const Tensor&, Tensor&) {
+    ET_DCHECK_MSG(false, "BUG: canCast should have been checked above");
+  }
+};
+
+template <
+    typename CTYPE_A,
+    typename CTYPE_B,
+    typename CTYPE_IN,
+    typename CTYPE_OUT>
+struct MaximumInner<false, CTYPE_A, CTYPE_B, CTYPE_IN, CTYPE_OUT>
+    : public ReportCanCastBug {};
+
+} // namespace
+
+Tensor& maximum_out(
+    RuntimeContext& ctx,
+    const Tensor& a,
+    const Tensor& b,
+    Tensor& out) {
+  (void)ctx;
+
+  ET_KERNEL_CHECK(
+      ctx,
+      resize_to_broadcast_target_size(a, b, out) == Error::Ok,
+      InvalidArgument,
+      out);
+
+  constexpr int kNnlibMaxDim = 4; /*fallback if broadcast and dim > 4 */
+
+  ScalarType a_type = a.scalar_type();
+  ScalarType b_type = b.scalar_type();
+  ScalarType common_type = promoteTypes(a_type, b_type, /*half_to_float*/ true);
+  ScalarType out_type = out.scalar_type();
+
+  ET_KERNEL_CHECK(ctx, canCast(common_type, out_type), InvalidArgument, out);
+
+  bool optimized = 1;
+  /*find broadcast*/
+  bool a_is_broadcasted = !out.sizes().equals(a.sizes());
+  bool b_is_broadcasted = !out.sizes().equals(b.sizes());
+  bool broadcast = (a_is_broadcasted || b_is_broadcasted);
+
+  int max_dim = a.dim() > b.dim() ? a.dim() : b.dim();
+  max_dim = out.dim() > max_dim ? out.dim() : max_dim;
+
+  if((a_type != ScalarType::Float) || (b_type != ScalarType::Float))
+      optimized = 0;
+  if((broadcast == 1) && (max_dim > kNnlibMaxDim))
+      optimized = 0;
+
+  if(optimized)
+  {
+    float* a_data = a.mutable_data_ptr<float>();
+    float* b_data = b.mutable_data_ptr<float>();
+    float* out_data = out.mutable_data_ptr<float>();
+
+    if(broadcast == 1)
+    {
+       int out_shape[kNnlibMaxDim];
+       int inp1_shape[kNnlibMaxDim];
+       int inp2_shape[kNnlibMaxDim];
+
+       for(int i = 0; i < kNnlibMaxDim; i++)
+       {
+          out_shape[i] = 1;
+          inp1_shape[i] = 1;
+          inp2_shape[i] = 1;
+       }
+
+       int off_o = kNnlibMaxDim - out.dim();
+       int off_a = kNnlibMaxDim - a.dim();
+       int off_b = kNnlibMaxDim - b.dim();
+
+       for(int i = 0; i < out.dim(); i++){
+            out_shape[i+off_o] = out.size(i);}
+
+       for(int i = 0; i < a.dim(); i++)
+            inp1_shape[i+off_a] = a.size(i);
+
+       for(int i = 0; i < b.dim(); i++)
+            inp2_shape[i+off_b] = b.size(i);
+
+       xa_nn_elm_maximum_broadcast_4D_f32xf32_f32(out_data, out_shape, a_data, inp1_shape, b_data, inp2_shape);
+    }
+    else
+    {
+        xa_nn_elm_maximum_f32xf32_f32(out_data, a_data, b_data, out.numel());
+    }
+  }
+  else
+  {
+    ET_SWITCH_REALHB_TYPES(a_type, ctx, "maximum.out", CTYPE_A, [&]() {
+      ET_SWITCH_REALHB_TYPES(b_type, ctx, "maximum.out", CTYPE_B, [&]() {
+        using CTYPE_IN = typename torch::executor::
+            promote_types<CTYPE_A, CTYPE_B, /*half_to_float*/ true>::type;
+        ET_DCHECK(CppTypeToScalarType<CTYPE_IN>::value == common_type);
+        ET_SWITCH_REALHB_TYPES(out_type, ctx, "maximum.out", CTYPE_OUT, [&]() {
+          MaximumInner<
+              can_cast<CTYPE_IN, CTYPE_OUT>::value,
+              CTYPE_A,
+              CTYPE_B,
+              CTYPE_IN,
+              CTYPE_OUT>::run(a, b, out);
+        });
+      });
+    });
+  }
+
+  return out;
+}
+
+} // namespace native
+} // namespace HiFi
+} // namespace impl