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 <algorithm>
20 #include <vector>
21
22 #include "MaximumMinimum.h"
23 #include "HalInterfaces.h"
24 #include "IndexedShapeWrapper.h"
25 #include "OperationsUtils.h"
26 #include "Tracing.h"
27
28 namespace android {
29 namespace nn {
30 namespace maximum_minimum {
31
32 namespace {
33
34 using namespace hal;
35
36 template <typename T>
evalGeneric(const T * aData,const Shape & aShape,const T * bData,const Shape & bShape,bool isMinimum,T * outputData,const Shape & outputShape)37 bool evalGeneric(const T* aData, const Shape& aShape, const T* bData, const Shape& bShape,
38 bool isMinimum, T* outputData, const Shape& outputShape) {
39 IndexedShapeWrapper aShapeIndexed(aShape);
40 IndexedShapeWrapper bShapeIndexed(bShape);
41 IndexedShapeWrapper outputShapeIndexed(outputShape);
42
43 std::vector<uint32_t> curIndex(outputShape.dimensions.size(), 0);
44 bool lastIndex = false;
45 do {
46 uint32_t outputFlatIndex;
47 NN_CHECK(outputShapeIndexed.indexToFlatIndex(curIndex, &outputFlatIndex));
48 uint32_t aFlatIndex;
49 NN_CHECK(aShapeIndexed.broadcastedIndexToFlatIndex(curIndex, &aFlatIndex));
50 uint32_t bFlatIndex;
51 NN_CHECK(bShapeIndexed.broadcastedIndexToFlatIndex(curIndex, &bFlatIndex));
52
53 outputData[outputFlatIndex] = isMinimum ? std::min(aData[aFlatIndex], bData[bFlatIndex])
54 : std::max(aData[aFlatIndex], bData[bFlatIndex]);
55
56 NN_CHECK(outputShapeIndexed.nextIndexInplace(&curIndex, &lastIndex));
57 } while (!lastIndex);
58
59 return true;
60 }
61
62 template <typename T>
evalQuant8(const T * aData,const Shape & aShape,const T * bData,const Shape & bShape,bool isMinimum,T * outputData,const Shape & outputShape)63 bool evalQuant8(const T* aData, const Shape& aShape, const T* bData, const Shape& bShape,
64 bool isMinimum, T* outputData, const Shape& outputShape) {
65 IndexedShapeWrapper aShapeIndexed(aShape);
66 IndexedShapeWrapper bShapeIndexed(bShape);
67 IndexedShapeWrapper outputShapeIndexed(outputShape);
68
69 std::vector<uint32_t> curIndex(outputShape.dimensions.size(), 0);
70 bool lastIndex = false;
71 do {
72 uint32_t outputFlatIndex;
73 NN_CHECK(outputShapeIndexed.indexToFlatIndex(curIndex, &outputFlatIndex));
74 uint32_t aFlatIndex;
75 NN_CHECK(aShapeIndexed.broadcastedIndexToFlatIndex(curIndex, &aFlatIndex));
76 uint32_t bFlatIndex;
77 NN_CHECK(bShapeIndexed.broadcastedIndexToFlatIndex(curIndex, &bFlatIndex));
78
79 T aValue = requantize<T>(aData[aFlatIndex], aShape, outputShape);
80 T bValue = requantize<T>(bData[bFlatIndex], bShape, outputShape);
81 outputData[outputFlatIndex] =
82 isMinimum ? std::min(aValue, bValue) : std::max(aValue, bValue);
83
84 NN_CHECK(outputShapeIndexed.nextIndexInplace(&curIndex, &lastIndex));
85 } while (!lastIndex);
86
87 return true;
88 }
89
90 } // namespace
91
prepare(const Shape & in1,const Shape & in2,Shape * out)92 bool prepare(const Shape& in1, const Shape& in2, Shape* out) {
93 NN_CHECK(in1.type == in2.type);
94 return calculateBroadcastedShape(in1, in2, out);
95 }
96
eval(const void * in1,const Shape & shape1,const void * in2,const Shape & shape2,bool isMinimum,void * output,const Shape & outputShape)97 bool eval(const void* in1, const Shape& shape1, const void* in2, const Shape& shape2,
98 bool isMinimum, void* output, const Shape& outputShape) {
99 NNTRACE_COMP("maximum_minimum::eval");
100 switch (shape1.type) {
101 case OperandType::TENSOR_FLOAT16: {
102 return evalGeneric(reinterpret_cast<const _Float16*>(in1), shape1,
103 reinterpret_cast<const _Float16*>(in2), shape2, isMinimum,
104 reinterpret_cast<_Float16*>(output), outputShape);
105 }
106 case OperandType::TENSOR_FLOAT32: {
107 return evalGeneric(reinterpret_cast<const float*>(in1), shape1,
108 reinterpret_cast<const float*>(in2), shape2, isMinimum,
109 reinterpret_cast<float*>(output), outputShape);
110 }
111 case OperandType::TENSOR_INT32: {
112 return evalGeneric(reinterpret_cast<const int32_t*>(in1), shape1,
113 reinterpret_cast<const int32_t*>(in2), shape2, isMinimum,
114 reinterpret_cast<int32_t*>(output), outputShape);
115 }
116 case OperandType::TENSOR_QUANT8_ASYMM: {
117 return evalQuant8(reinterpret_cast<const uint8_t*>(in1), shape1,
118 reinterpret_cast<const uint8_t*>(in2), shape2, isMinimum,
119 reinterpret_cast<uint8_t*>(output), outputShape);
120 }
121 case OperandType::TENSOR_QUANT8_ASYMM_SIGNED: {
122 return evalQuant8(reinterpret_cast<const int8_t*>(in1), shape1,
123 reinterpret_cast<const int8_t*>(in2), shape2, isMinimum,
124 reinterpret_cast<int8_t*>(output), outputShape);
125 }
126 default: {
127 LOG(ERROR) << "Unsupported data type: " << toString(shape1.type);
128 return false;
129 }
130 }
131 }
132
133 } // namespace maximum_minimum
134 } // namespace nn
135 } // namespace android
136