1 /*
2 * Copyright (C) 2018 The Android Open Source Project
3 *
4 * Licensed under the Apache License, Version 2.0 (the "License");
5 * you may not use this file except in compliance with the License.
6 * You may obtain a copy of the License at
7 *
8 * http://www.apache.org/licenses/LICENSE-2.0
9 *
10 * Unless required by applicable law or agreed to in writing, software
11 * distributed under the License is distributed on an "AS IS" BASIS,
12 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
13 * See the License for the specific language governing permissions and
14 * limitations under the License.
15 */
16
17 #define LOG_TAG "Operations"
18
19 #include "MaximumMinimum.h"
20 #include "IndexedShapeWrapper.h"
21 #include "OperationsUtils.h"
22 #include "Tracing.h"
23
24 namespace android {
25 namespace nn {
26 namespace maximum_minimum {
27
28 namespace {
29
30 template <typename T>
evalGeneric(const T * aData,const Shape & aShape,const T * bData,const Shape & bShape,bool isMinimum,T * outputData,const Shape & outputShape)31 bool evalGeneric(const T* aData, const Shape& aShape, const T* bData, const Shape& bShape,
32 bool isMinimum, T* outputData, const Shape& outputShape) {
33 IndexedShapeWrapper aShapeIndexed(aShape);
34 IndexedShapeWrapper bShapeIndexed(bShape);
35 IndexedShapeWrapper outputShapeIndexed(outputShape);
36
37 std::vector<uint32_t> curIndex(outputShape.dimensions.size(), 0);
38 bool lastIndex = false;
39 do {
40 uint32_t outputFlatIndex;
41 NN_CHECK(outputShapeIndexed.indexToFlatIndex(curIndex, &outputFlatIndex));
42 uint32_t aFlatIndex;
43 NN_CHECK(aShapeIndexed.broadcastedIndexToFlatIndex(curIndex, &aFlatIndex));
44 uint32_t bFlatIndex;
45 NN_CHECK(bShapeIndexed.broadcastedIndexToFlatIndex(curIndex, &bFlatIndex));
46
47 outputData[outputFlatIndex] = isMinimum ? std::min(aData[aFlatIndex], bData[bFlatIndex])
48 : std::max(aData[aFlatIndex], bData[bFlatIndex]);
49
50 NN_CHECK(outputShapeIndexed.nextIndexInplace(&curIndex, &lastIndex));
51 } while (!lastIndex);
52
53 return true;
54 }
55
evalQuant8(const uint8_t * aData,const Shape & aShape,const uint8_t * bData,const Shape & bShape,bool isMinimum,uint8_t * outputData,const Shape & outputShape)56 bool evalQuant8(const uint8_t* aData, const Shape& aShape, const uint8_t* bData,
57 const Shape& bShape, bool isMinimum, uint8_t* outputData,
58 const Shape& outputShape) {
59 IndexedShapeWrapper aShapeIndexed(aShape);
60 IndexedShapeWrapper bShapeIndexed(bShape);
61 IndexedShapeWrapper outputShapeIndexed(outputShape);
62
63 std::vector<uint32_t> curIndex(outputShape.dimensions.size(), 0);
64 bool lastIndex = false;
65 do {
66 uint32_t outputFlatIndex;
67 NN_CHECK(outputShapeIndexed.indexToFlatIndex(curIndex, &outputFlatIndex));
68 uint32_t aFlatIndex;
69 NN_CHECK(aShapeIndexed.broadcastedIndexToFlatIndex(curIndex, &aFlatIndex));
70 uint32_t bFlatIndex;
71 NN_CHECK(bShapeIndexed.broadcastedIndexToFlatIndex(curIndex, &bFlatIndex));
72
73 uint8_t aValue = requantize(aData[aFlatIndex], aShape, outputShape);
74 uint8_t bValue = requantize(bData[bFlatIndex], bShape, outputShape);
75 outputData[outputFlatIndex] =
76 isMinimum ? std::min(aValue, bValue) : std::max(aValue, bValue);
77
78 NN_CHECK(outputShapeIndexed.nextIndexInplace(&curIndex, &lastIndex));
79 } while (!lastIndex);
80
81 return true;
82 }
83
84 } // namespace
85
prepare(const Shape & in1,const Shape & in2,Shape * out)86 bool prepare(const Shape& in1, const Shape& in2, Shape* out) {
87 NN_CHECK(in1.type == in2.type);
88 return calculateBroadcastedShape(in1, in2, out);
89 }
90
eval(const void * in1,const Shape & shape1,const void * in2,const Shape & shape2,bool isMinimum,void * output,const Shape & outputShape)91 bool eval(const void* in1, const Shape& shape1, const void* in2, const Shape& shape2,
92 bool isMinimum, void* output, const Shape& outputShape) {
93 NNTRACE_COMP("maximum_minimum::eval");
94 switch (shape1.type) {
95 case OperandType::TENSOR_FLOAT16: {
96 return evalGeneric(reinterpret_cast<const _Float16*>(in1), shape1,
97 reinterpret_cast<const _Float16*>(in2), shape2, isMinimum,
98 reinterpret_cast<_Float16*>(output), outputShape);
99 }
100 case OperandType::TENSOR_FLOAT32: {
101 return evalGeneric(reinterpret_cast<const float*>(in1), shape1,
102 reinterpret_cast<const float*>(in2), shape2, isMinimum,
103 reinterpret_cast<float*>(output), outputShape);
104 }
105 case OperandType::TENSOR_INT32: {
106 return evalGeneric(reinterpret_cast<const int32_t*>(in1), shape1,
107 reinterpret_cast<const int32_t*>(in2), shape2, isMinimum,
108 reinterpret_cast<int32_t*>(output), outputShape);
109 }
110 case OperandType::TENSOR_QUANT8_ASYMM: {
111 return evalQuant8(reinterpret_cast<const uint8_t*>(in1), shape1,
112 reinterpret_cast<const uint8_t*>(in2), shape2, isMinimum,
113 reinterpret_cast<uint8_t*>(output), outputShape);
114 }
115 default: {
116 LOG(ERROR) << "Unsupported data type: " << toString(shape1.type);
117 return false;
118 }
119 }
120 }
121
122 } // namespace maximum_minimum
123 } // namespace nn
124 } // namespace android
125