kernels/portable/cpu/op_le.cpp

/*
 * 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/functional_util.h>
#include <executorch/runtime/kernel/kernel_includes.h>
#include <executorch/runtime/platform/assert.h>

namespace torch {
namespace executor {
namespace native {

using Tensor = exec_aten::Tensor;
using ScalarType = exec_aten::ScalarType;

Tensor& le_tensor_out(
    RuntimeContext& ctx,
    const Tensor& a,
    const Tensor& b,
    Tensor& out) {
  // Determine output size and resize for dynamic shapes
  ET_KERNEL_CHECK(
      ctx,
      resize_to_broadcast_target_size(a, b, out) == Error::Ok,
      InvalidArgument,
      out);

  ScalarType a_type = a.scalar_type();
  ScalarType b_type = b.scalar_type();
  ScalarType common_type = promoteTypes(a_type, b_type);
  ScalarType out_type = out.scalar_type();

  ET_SWITCH_REAL_TYPES_AND(Bool, a_type, ctx, "le.Tensor_out", CTYPE_A, [&]() {
    ET_SWITCH_REAL_TYPES_AND(
        Bool, b_type, ctx, "le.Tensor_out", CTYPE_B, [&]() {
          ET_SWITCH_REAL_TYPES_AND(
              Bool, common_type, ctx, "le.Tensor_out", CTYPE_IN, [&]() {
                ET_SWITCH_REAL_TYPES_AND(
                    Bool, out_type, ctx, "le.Tensor_out", CTYPE_OUT, [&]() {
                      apply_binary_elementwise_fn<CTYPE_A, CTYPE_B, CTYPE_OUT>(
                          [](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);
                            bool value = a_casted <= b_casted;
                            return static_cast<CTYPE_OUT>(value);
                          },
                          a,
                          b,
                          out);
                    });
              });
        });
  });

  return out;
}

Tensor& le_scalar_out(
    RuntimeContext& ctx,
    const Tensor& a,
    const Scalar& b,
    Tensor& out) {
  (void)ctx;

  // Resize for dynamic shape
  ET_KERNEL_CHECK_MSG(
      ctx,
      resize_tensor(out, a.sizes()) == Error::Ok,
      InvalidArgument,
      out,
      "Failed to resize output tensor.");

  ScalarType a_type = a.scalar_type();
  ScalarType b_type = utils::get_scalar_dtype(b);
  ScalarType common_type = utils::promote_type_with_scalar(a_type, b);
  ScalarType out_type = out.scalar_type();

  ET_SWITCH_REAL_TYPES_AND(Bool, a_type, ctx, "le.Scalar_out", CTYPE_A, [&]() {
    ET_SWITCH_SCALAR_OBJ_TYPES(b_type, ctx, "le.Scalar_out", CTYPE_B, [&]() {
      ET_SWITCH_REAL_TYPES_AND(
          Bool, common_type, ctx, "le.Scalar_out", CTYPE_IN, [&]() {
            ET_SWITCH_REAL_TYPES_AND(
                Bool, out_type, ctx, "le.Scalar_out", CTYPE_OUT, [&]() {
                  CTYPE_B val_b = 0;
                  utils::extract_scalar(b, &val_b);
                  apply_unary_map_fn(
                      [val_b](const CTYPE_A val_a) {
                        CTYPE_IN a_casted = static_cast<CTYPE_IN>(val_a);
                        CTYPE_IN b_casted = static_cast<CTYPE_IN>(val_b);
                        bool value = a_casted <= b_casted;
                        return static_cast<CTYPE_OUT>(value);
                      },
                      a.const_data_ptr<CTYPE_A>(),
                      out.mutable_data_ptr<CTYPE_OUT>(),
                      out.numel());
                });
          });
    });
  });

  return out;
}

} // namespace native
} // namespace executor
} // namespace torch