local_response_norm.h

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 * distributed with this work for additional information
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 * to you under the Apache License, Version 2.0 (the
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 *
 *   http://www.apache.org/licenses/LICENSE-2.0
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#ifndef TVM_TOPI_NN_LOCAL_RESPONSE_NORM_H_
#define TVM_TOPI_NN_LOCAL_RESPONSE_NORM_H_
 
#include <tvm/te/operation.h>
#include <tvm/topi/tags.h>
 
#include <string>
 
namespace tvm {
namespace topi {
namespace nn {
 
using namespace tvm::te;
 
inline Tensor lrn(const Tensor& data, int size, int axis = 1, float alpha = 0.0001,
                  float beta = 0.75, float bias = 2, std::string name = "tensor",
                  std::string tag = kBroadcast) {
  TVM_FFI_ICHECK_EQ(data->shape.size(), 4) << "LRN requires 4-D input";
  TVM_FFI_ICHECK_EQ(size % 2, 1) << "size should be odd number";
  TVM_FFI_ICHECK(axis == 1 || axis == 3) << "axis should be 1 or 3 for NCHW and NHWC";
  // LRN only requires a floating-point element kind; lane encoding is irrelevant here.
  TVM_FFI_ICHECK_EQ(data->dtype.code(), DLDataTypeCode::kDLFloat) << "datatype should be float";
  auto input_shape = data->shape;
  ffi::Array<PrimExpr> pad_before{0, 0, 0, 0};
  ffi::Array<PrimExpr> pad_after{0, 0, 0, 0};
  pad_before.Set(axis, static_cast<PrimExpr>(size / 2));
  pad_after.Set(axis, static_cast<PrimExpr>(size / 2));
  auto pad_data = pad(data, pad_before, pad_after, 0, "pad_data");
  auto rxs = tvm::te::reduce_axis(Range(0, size), "rxs");
  Tensor sqr_sum;
  if (axis == 1) {
    sqr_sum = tvm::te::compute(
        input_shape,
        [&](PrimVar i, PrimVar l, PrimVar j, PrimVar k) {
          return tvm::sum(pad_data(i, l + rxs, j, k) * pad_data(i, l + rxs, j, k), {rxs});
        },
        "tensor", "sqr_sum");
  } else if (axis == 3) {
    sqr_sum = tvm::te::compute(
        input_shape,
        [&](PrimVar i, PrimVar l, PrimVar j, PrimVar k) {
          return tvm::sum(pad_data(i, l, j, k + rxs) * pad_data(i, l, j, k + rxs), {rxs});
        },
        "tensor", "sqr_sum");
  }
  PrimExpr alpha_imm = tvm::te::MakeConst(PrimType(data->dtype), alpha);
  PrimExpr beta_imm = tvm::te::MakeConst(PrimType(data->dtype), beta);
  PrimExpr bias_imm = tvm::te::MakeConst(PrimType(data->dtype), bias);
  auto sqrt_sum_up = tvm::te::compute(
      input_shape,
      [&](PrimVar i, PrimVar j, PrimVar k, PrimVar l) {
        return tvm::pow(bias_imm + (div(alpha_imm * sqr_sum(i, j, k, l), size)), beta_imm);
      },
      "tensor", kElementWise);
  return topi::divide(data, sqrt_sum_up);
}
}  // namespace nn
}  // namespace topi
}  // namespace tvm
#endif  // TVM_TOPI_NN_LOCAL_RESPONSE_NORM_H_