layer_norm.h

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 * to you under the Apache License, Version 2.0 (the
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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/reduction.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 ffi::Optional<Tensor>& beta,
                         const ffi::Array<int64_t>& axis, double epsilon,
                         std::string name = "T_layer_norm", std::string tag = kInjective) {
  using namespace tvm::prim;
  const auto& data_type = data->dtype;
  const auto& gamma_type = gamma->dtype;
  const auto& beta_type = beta.has_value() ? beta.value()->dtype : data_type;
  TVM_FFI_ICHECK(data_type == gamma_type && data_type == beta_type)
      << "layer_norm: data, gamma and beta must have the same type";
  TVM_FFI_ICHECK(data_type == PrimType::Float(32) || data_type == PrimType::Float(16))
      << "layer_norm: only support float32 and float16 for now";
  bool is_float16 = data_type == PrimType::Float(16);
  // Two-pass algorithm for improved numerical stability:
  //   pass1: mean = E[x]
  //   pass2: var = E[(x - mean)^2]
  auto ndim = data->shape.size();
  TVM_FFI_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=*/false);
  PrimType f32_ty = PrimType::Float(32);
 
  auto make_eval_range = [&real_axis, &reduce_axes,
                          ndim](const ffi::Array<PrimVar>& non_reduce_indices) {
    ffi::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(non_reduce_indices[arg_counter]);
        arg_counter++;
      }
    }
    return eval_range;
  };
 
  Tensor temp_sum = te::compute(
      target_shape,
      [is_float16, &data, &reduce_axes, &make_eval_range,
       f32_ty](const ffi::Array<PrimVar>& indices) {
        auto eval_range = make_eval_range(indices);
        PrimExpr x = data(eval_range);
        if (is_float16) {
          x = prim::Cast(f32_ty, x);
        }
        return prim::sum(x, reduce_axes);
      },
      data->op.value()->name + "_sum", kCommReduce);
 
  PrimType reduce_dtype = is_float16 ? PrimType::Float(32) : PrimType(data->dtype);
  PrimExpr reduce_extent = MakeConst(reduce_dtype, 1);
  for (int i : real_axis) {
    reduce_extent *= data->shape[i];
  }
  Tensor temp_mean = te::compute(
      target_shape,
      [&temp_sum, &reduce_extent](const ffi::Array<PrimVar>& indices) {
        return temp_sum(indices) / reduce_extent;
      },
      data->op.value()->name + "_mean", kInjective);
 
  Tensor temp_var_sum = te::compute(
      target_shape,
      [is_float16, &data, &reduce_axes, &make_eval_range, &temp_mean,
       f32_ty](const ffi::Array<PrimVar>& indices) {
        auto eval_range = make_eval_range(indices);
        PrimExpr x = data(eval_range);
        if (is_float16) {
          x = prim::Cast(f32_ty, x);
        }
        PrimExpr diff = x - temp_mean(indices);
        return prim::sum(diff * diff, reduce_axes);
      },
      data->op.value()->name + "_var_sum", kCommReduce);
 
  auto layer_norm_func = [&](const ffi::Array<PrimVar>& indices) {
    ffi::Array<PrimVar> 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_mean(non_reduce_indices);
    auto var = temp_var_sum(non_reduce_indices) / reduce_extent;
    auto layer_norm = (data(indices) - mean) * prim::rsqrt(var + MakeConst(var.ty(), epsilon));
    if (is_float16) {
      layer_norm = prim::Cast(PrimType::Float(16), layer_norm);
    }
    layer_norm = topi::multiply(layer_norm, gamma(reduce_indices));
    if (beta.has_value()) {
      layer_norm = topi::add(layer_norm, beta.value()(reduce_indices));
    }
    return layer_norm;
  };
  return te::compute(data->shape, layer_norm_func, name, tag);
}
 
}  // namespace nn
}  // namespace topi
}  // namespace tvm
 
#endif  // TVM_TOPI_NN_LAYER_NORM_H_