layer_norm.h

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#ifndef TVM_TOPI_NN_LAYER_NORM_H_
#define TVM_TOPI_NN_LAYER_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 layer_norm(const Tensor& data, const Tensor& gamma, const Tensor& beta,
                         const Array<Integer>& axis, double epsilon,
                         std::string name = "T_layer_norm", std::string tag = kInjective) {
  const auto& data_type = data->dtype;
  const auto& gamma_type = gamma.defined() ? gamma->dtype : data_type;
  const auto& beta_type = beta.defined() ? beta->dtype : data_type;
  ICHECK(data_type == gamma_type && data_type == beta_type)
      << "layer_norm: data, gamma and beta must have the same type";
  ICHECK(data_type == DataType::Float(32) || data_type == DataType::Float(16))
      << "layer_norm: only support float32 and float16 for now";
  bool is_float16 = data_type == DataType::Float(16);
  // sum x and x^2
  auto ndim = data->shape.size();
  ICHECK_NE(ndim, 0) << "Cannot reduce a 0 dim Tensor";
  auto real_axis = GetRealAxis(static_cast<int>(ndim), axis);
  auto reduce_axes = MakeReduceAxes(real_axis, data);
  auto target_shape =
      MakeReduceTargetShape(real_axis, data, /*keepdims=*/false, /*atleast1d=*/true);
  auto func = MakeTupleSumReducer();
 
  auto compute = [ndim, is_float16, &real_axis, &reduce_axes, &func,
                  &data](const Array<Var>& indices) {
    Array<PrimExpr> eval_range;
    int arg_counter = 0;
    int red_counter = 0;
 
    for (size_t i = 0; i < ndim; ++i) {
      if (std::find(real_axis.begin(), real_axis.end(), i) != real_axis.end()) {
        // real_axis contains i
        eval_range.push_back(reduce_axes[red_counter]);
        red_counter++;
      } else {
        eval_range.push_back(indices[arg_counter]);
        arg_counter++;
      }
    }
    auto square = [is_float16](const PrimExpr& x) {
      if (is_float16) {
        return Cast(DataType::Float(32), x) * Cast(DataType::Float(32), x);
      }
      return x * x;
    };
    if (is_float16) {
      return func({Cast(DataType::Float(32), data(eval_range)), square(data(eval_range))},
                  reduce_axes, nullptr);
    } else {
      return func({data(eval_range), square(data(eval_range))}, reduce_axes, nullptr);
    }
  };
 
  auto temp_x_x2 =
      tvm::te::compute(target_shape, compute, data->op->name + "_red_temp", kCommReduce);
 
  auto temp_x = temp_x_x2[0];
  auto temp_x2 = temp_x_x2[1];
 
  auto reduce_extent = make_const(data->dtype, 1);
  for (int i : real_axis) {
    reduce_extent *= data->shape[i];
  }
  auto layer_norm_func = [&](const Array<Var>& indices) {
    Array<Var> reduce_indices, non_reduce_indices;
    for (int i = 0, n = static_cast<int>(indices.size()); i < n; ++i) {
      if (std::find(real_axis.begin(), real_axis.end(), i) != real_axis.end()) {
        reduce_indices.push_back(indices[i]);
      } else {
        non_reduce_indices.push_back(indices[i]);
      }
    }
    auto mean = temp_x(non_reduce_indices) / reduce_extent;
    auto var = temp_x2(non_reduce_indices) / reduce_extent - mean * mean;
    auto layer_norm = (data(indices) - mean) * tvm::rsqrt(var + make_const(var->dtype, epsilon));
    if (is_float16) {
      layer_norm = Cast(DataType::Float(16), layer_norm);
    }
    layer_norm = topi::multiply(layer_norm, gamma(reduce_indices));
    if (beta.defined()) {
      layer_norm = topi::add(layer_norm, beta(reduce_indices));
    }
    return layer_norm;
  };
  return tvm::te::compute(data->shape, layer_norm_func, name, tag);
}
 
}  // namespace nn
}  // namespace topi
}  // namespace tvm
 
#endif  // TVM_TOPI_NN_LAYER_NORM_H_