1/*
2 * Licensed to the Apache Software Foundation (ASF) under one
3 * or more contributor license agreements. See the NOTICE file
4 * distributed with this work for additional information
5 * regarding copyright ownership. The ASF licenses this file
6 * to you under the Apache License, Version 2.0 (the
7 * "License"); you may not use this file except in compliance
8 * with the License. You may obtain a copy of the License at
9 *
10 * http://www.apache.org/licenses/LICENSE-2.0
11 *
12 * Unless required by applicable law or agreed to in writing,
13 * software distributed under the License is distributed on an
14 * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
15 * KIND, either express or implied. See the License for the
16 * specific language governing permissions and limitations
17 * under the License.
18 */
19
20/*!
21 * \brief layer normalization op constructions
22 * \file nn/layer_norm.h
23 */
24#ifndef TVM_TOPI_NN_LAYER_NORM_H_
25#define TVM_TOPI_NN_LAYER_NORM_H_
26
27#include <tvm/te/operation.h>
28#include <tvm/topi/tags.h>
29
30#include <string>
31
32namespace tvm {
33namespace topi {
34namespace nn {
35
36using namespace tvm::te;
37
38/*!
39 * \brief Layer normalization.
40 * \param data N-D tensor with shape [d_0, d_1, ..., d_{N-1}]
41 * \param gamma K-D tensor with shape [r_0, r_1, ..., r_{K-1}] where K == len(axis) and
42 * d_{axis_k} == r_k
43 * \param beta Optional, K-D tensor with shape [r_0, r_1, ..., r_{K-1}] where
44 * d_{axis_k} == r_k
45 * \param axis The axis to normalize over.
46 * \param epsilon The epsilon value to avoid division by zero.
47 * \param name The name of the operation.
48 * \param tag The tag to mark the operation.
49 * \return The normalized tensor, with the same shape as data.
50 */
51inline Tensor layer_norm(const Tensor& data, const Tensor& gamma, const Tensor& beta,
52 const Array<Integer>& axis, double epsilon,
53 std::string name = "T_layer_norm", std::string tag = kInjective) {
54 // sum x and x^2
55 auto ndim = data->shape.size();
56 ICHECK_NE(ndim, 0) << "Cannot reduce a 0 dim Tensor";
57 auto real_axis = GetRealAxis(static_cast<int>(ndim), axis);
58 auto reduce_axes = MakeReduceAxes(real_axis, data);
59 auto target_shape =
60 MakeReduceTargetShape(real_axis, data, /*keepdims=*/false, /*atleast1d=*/true);
61 auto func = MakeTupleSumReducer();
62
63 auto compute = [ndim, &real_axis, &reduce_axes, &func, &data](const Array<Var>& indices) {
64 Array<PrimExpr> eval_range;
65 int arg_counter = 0;
66 int red_counter = 0;
67
68 for (size_t i = 0; i < ndim; ++i) {
69 if (std::find(real_axis.begin(), real_axis.end(), i) != real_axis.end()) {
70 // real_axis contains i
71 eval_range.push_back(reduce_axes[red_counter]);
72 red_counter++;
73 } else {
74 eval_range.push_back(indices[arg_counter]);
75 arg_counter++;
76 }
77 }
78 auto square = [](const PrimExpr& x) { return x * x; };
79 return func({data(eval_range), square(data(eval_range))}, reduce_axes, nullptr);
80 };
81
82 auto temp_x_x2 =
83 tvm::te::compute(target_shape, compute, data->op->name + "_red_temp", kCommReduce);
84
85 auto temp_x = temp_x_x2[0];
86 auto temp_x2 = temp_x_x2[1];
87
88 auto reduce_extent = make_const(data->dtype, 1);
89 for (int i : real_axis) {
90 reduce_extent *= data->shape[i];
91 }
92 auto layer_norm_func = [&](const Array<Var>& indices) {
93 Array<Var> reduce_indices, non_reduce_indices;
94 for (int i = 0, n = static_cast<int>(indices.size()); i < n; ++i) {
95 if (std::find(real_axis.begin(), real_axis.end(), i) != real_axis.end()) {
96 reduce_indices.push_back(indices[i]);
97 } else {
98 non_reduce_indices.push_back(indices[i]);
99 }
100 }
101 auto mean = temp_x(non_reduce_indices) / reduce_extent;
102 auto var = temp_x2(non_reduce_indices) / reduce_extent - mean * mean;
103 auto layer_norm = (data(indices) - mean) * tvm::rsqrt(var + make_const(var->dtype, epsilon));
104 layer_norm = topi::multiply(layer_norm, gamma(reduce_indices));
105 if (beta.defined()) {
106 layer_norm = topi::add(layer_norm, beta(reduce_indices));
107 }
108 return layer_norm;
109 };
110 return tvm::te::compute(data->shape, layer_norm_func, name, tag);
111}
112
113} // namespace nn
114} // namespace topi
115} // namespace tvm
116
117#endif // TVM_TOPI_NN_LAYER_NORM_H_
118