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
21 #include <algorithm>
22 #include <vector>
23
24 #include "IndexedShapeWrapper.h"
25 #include "OperationsExecutionUtils.h"
26 #include "Tracing.h"
27
28 namespace android {
29 namespace nn {
30 namespace maximum_minimum {
31
32 namespace {
33
34 template <typename T>
evalGeneric(const T * aData,const Shape & aShape,const T * bData,const Shape & bShape,bool isMinimum,T * outputData,const Shape & outputShape)35 bool evalGeneric(const T* aData, const Shape& aShape, const T* bData, const Shape& bShape,
36 bool isMinimum, T* outputData, const Shape& outputShape) {
37 IndexedShapeWrapper aShapeIndexed(aShape);
38 IndexedShapeWrapper bShapeIndexed(bShape);
39 IndexedShapeWrapper outputShapeIndexed(outputShape);
40
41 std::vector<uint32_t> curIndex(outputShape.dimensions.size(), 0);
42 bool lastIndex = false;
43 do {
44 uint32_t outputFlatIndex;
45 NN_CHECK(outputShapeIndexed.indexToFlatIndex(curIndex, &outputFlatIndex));
46 uint32_t aFlatIndex;
47 NN_CHECK(aShapeIndexed.broadcastedIndexToFlatIndex(curIndex, &aFlatIndex));
48 uint32_t bFlatIndex;
49 NN_CHECK(bShapeIndexed.broadcastedIndexToFlatIndex(curIndex, &bFlatIndex));
50
51 outputData[outputFlatIndex] = isMinimum ? std::min(aData[aFlatIndex], bData[bFlatIndex])
52 : std::max(aData[aFlatIndex], bData[bFlatIndex]);
53
54 NN_CHECK(outputShapeIndexed.nextIndexInplace(&curIndex, &lastIndex));
55 } while (!lastIndex);
56
57 return true;
58 }
59
60 template <typename T>
evalQuant8(const T * aData,const Shape & aShape,const T * bData,const Shape & bShape,bool isMinimum,T * outputData,const Shape & outputShape)61 bool evalQuant8(const T* aData, const Shape& aShape, const T* bData, const Shape& bShape,
62 bool isMinimum, T* outputData, const Shape& outputShape) {
63 IndexedShapeWrapper aShapeIndexed(aShape);
64 IndexedShapeWrapper bShapeIndexed(bShape);
65 IndexedShapeWrapper outputShapeIndexed(outputShape);
66
67 std::vector<uint32_t> curIndex(outputShape.dimensions.size(), 0);
68 bool lastIndex = false;
69 do {
70 uint32_t outputFlatIndex;
71 NN_CHECK(outputShapeIndexed.indexToFlatIndex(curIndex, &outputFlatIndex));
72 uint32_t aFlatIndex;
73 NN_CHECK(aShapeIndexed.broadcastedIndexToFlatIndex(curIndex, &aFlatIndex));
74 uint32_t bFlatIndex;
75 NN_CHECK(bShapeIndexed.broadcastedIndexToFlatIndex(curIndex, &bFlatIndex));
76
77 T aValue = requantize<T>(aData[aFlatIndex], aShape, outputShape);
78 T bValue = requantize<T>(bData[bFlatIndex], bShape, outputShape);
79 outputData[outputFlatIndex] =
80 isMinimum ? std::min(aValue, bValue) : std::max(aValue, bValue);
81
82 NN_CHECK(outputShapeIndexed.nextIndexInplace(&curIndex, &lastIndex));
83 } while (!lastIndex);
84
85 return true;
86 }
87
88 } // namespace
89
prepare(const Shape & in1,const Shape & in2,Shape * out)90 bool prepare(const Shape& in1, const Shape& in2, Shape* out) {
91 NN_CHECK(in1.type == in2.type);
92 return calculateBroadcastedShape(in1, in2, out);
93 }
94
eval(const void * in1,const Shape & shape1,const void * in2,const Shape & shape2,bool isMinimum,void * output,const Shape & outputShape)95 bool eval(const void* in1, const Shape& shape1, const void* in2, const Shape& shape2,
96 bool isMinimum, void* output, const Shape& outputShape) {
97 NNTRACE_COMP("maximum_minimum::eval");
98 switch (shape1.type) {
99 case OperandType::TENSOR_FLOAT16: {
100 return evalGeneric(reinterpret_cast<const _Float16*>(in1), shape1,
101 reinterpret_cast<const _Float16*>(in2), shape2, isMinimum,
102 reinterpret_cast<_Float16*>(output), outputShape);
103 }
104 case OperandType::TENSOR_FLOAT32: {
105 return evalGeneric(reinterpret_cast<const float*>(in1), shape1,
106 reinterpret_cast<const float*>(in2), shape2, isMinimum,
107 reinterpret_cast<float*>(output), outputShape);
108 }
109 case OperandType::TENSOR_INT32: {
110 return evalGeneric(reinterpret_cast<const int32_t*>(in1), shape1,
111 reinterpret_cast<const int32_t*>(in2), shape2, isMinimum,
112 reinterpret_cast<int32_t*>(output), outputShape);
113 }
114 case OperandType::TENSOR_QUANT8_ASYMM: {
115 return evalQuant8(reinterpret_cast<const uint8_t*>(in1), shape1,
116 reinterpret_cast<const uint8_t*>(in2), shape2, isMinimum,
117 reinterpret_cast<uint8_t*>(output), outputShape);
118 }
119 case OperandType::TENSOR_QUANT8_ASYMM_SIGNED: {
120 return evalQuant8(reinterpret_cast<const int8_t*>(in1), shape1,
121 reinterpret_cast<const int8_t*>(in2), shape2, isMinimum,
122 reinterpret_cast<int8_t*>(output), outputShape);
123 }
124 default: {
125 LOG(ERROR) << "Unsupported data type: " << shape1.type;
126 return false;
127 }
128 }
129 }
130
131 } // namespace maximum_minimum
132 } // namespace nn
133 } // namespace android
134