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 "IndexedShapeWrapper.h"
20 #include "OperationResolver.h"
21 #include "OperationsUtils.h"
22
23 namespace android {
24 namespace nn {
25 namespace select_op {
26
27 constexpr uint32_t kNumInputs = 3;
28 constexpr uint32_t kInputCondition = 0;
29 constexpr uint32_t kInputTensor1 = 1;
30 constexpr uint32_t kInputTensor2 = 2;
31
32 constexpr uint32_t kNumOutputs = 1;
33 constexpr uint32_t kOutputTensor = 0;
34
35 namespace {
36
37 template <typename T>
compute(const bool8 * conditionData,const Shape & conditionShape,const T * aData,const Shape & aShape,const T * bData,const Shape & bShape,T * outputData,const Shape & outputShape)38 bool compute(const bool8* conditionData, const Shape& conditionShape, const T* aData,
39 const Shape& aShape, const T* bData, const Shape& bShape, T* outputData,
40 const Shape& outputShape) {
41 // The code assumes that condition has the same shape as all other tensors.
42 // This should be checked during preparation stage.
43 uint32_t size = getNumberOfElements(conditionShape);
44 for (uint32_t i = 0; i < size; ++i) {
45 T a = aData[i];
46 T b = bData[i];
47
48 if constexpr (std::is_same_v<T, uint8_t> || std::is_same_v<T, int8_t>) {
49 a = requantize<T>(a, aShape, outputShape);
50 b = requantize<T>(b, bShape, outputShape);
51 }
52 outputData[i] = conditionData[i] ? a : b;
53 }
54 return true;
55 }
56
57 template <typename T>
executeTyped(IOperationExecutionContext * context)58 bool executeTyped(IOperationExecutionContext* context) {
59 return compute<T>(
60 context->getInputBuffer<bool8>(kInputCondition),
61 context->getInputShape(kInputCondition), context->getInputBuffer<T>(kInputTensor1),
62 context->getInputShape(kInputTensor1), context->getInputBuffer<T>(kInputTensor2),
63 context->getInputShape(kInputTensor2), context->getOutputBuffer<T>(kOutputTensor),
64 context->getOutputShape(kOutputTensor));
65 }
66
67 } // namespace
68
validate(const IOperationValidationContext * context)69 Result<Version> validate(const IOperationValidationContext* context) {
70 NN_RET_CHECK_EQ(context->getNumInputs(), kNumInputs);
71 NN_RET_CHECK_EQ(context->getNumOutputs(), kNumOutputs);
72 OperandType inputType = context->getInputType(kInputTensor1);
73 NN_RET_CHECK(inputType == OperandType::TENSOR_FLOAT16 ||
74 inputType == OperandType::TENSOR_FLOAT32 ||
75 inputType == OperandType::TENSOR_INT32 ||
76 inputType == OperandType::TENSOR_QUANT8_ASYMM ||
77 inputType == OperandType::TENSOR_QUANT8_ASYMM_SIGNED)
78 << "Unsupported input operand type for select op: " << inputType;
79 NN_RET_CHECK(validateInputTypes(context, {OperandType::TENSOR_BOOL8, inputType, inputType}));
80 NN_RET_CHECK(validateOutputTypes(context, {inputType}));
81 return Version::ANDROID_Q;
82 }
83
prepare(IOperationExecutionContext * context)84 bool prepare(IOperationExecutionContext* context) {
85 Shape inputCondition = context->getInputShape(kInputCondition);
86 Shape input1 = context->getInputShape(kInputTensor1);
87 if (inputCondition.dimensions.size() != input1.dimensions.size()) {
88 LOG(ERROR) << "Condition and input tensor dimensions are not equal";
89 return false;
90 }
91 for (int i = 0; i < inputCondition.dimensions.size(); ++i) {
92 if (inputCondition.dimensions[i] != input1.dimensions[i]) {
93 LOG(ERROR) << "Condition and input tensor dimensions are not equal";
94 return false;
95 }
96 }
97
98 Shape input2 = context->getInputShape(kInputTensor2);
99 NN_RET_CHECK(SameShape(input1, input2));
100
101 Shape output = context->getOutputShape(kOutputTensor);
102 NN_RET_CHECK(SetShape(input1, &output));
103 return context->setOutputShape(kOutputTensor, output);
104 }
105
execute(IOperationExecutionContext * context)106 bool execute(IOperationExecutionContext* context) {
107 switch (context->getInputType(kInputTensor1)) {
108 case OperandType::TENSOR_FLOAT16:
109 return executeTyped<_Float16>(context);
110 case OperandType::TENSOR_FLOAT32:
111 return executeTyped<float>(context);
112 case OperandType::TENSOR_INT32:
113 return executeTyped<int32_t>(context);
114 case OperandType::TENSOR_QUANT8_ASYMM:
115 return executeTyped<uint8_t>(context);
116 case OperandType::TENSOR_QUANT8_ASYMM_SIGNED:
117 return executeTyped<int8_t>(context);
118 default:
119 NN_RET_CHECK_FAIL() << "Unsupported tensor type for SELECT op.";
120 }
121 }
122
123 } // namespace select_op
124
125 NN_REGISTER_OPERATION(SELECT, "SELECT", select_op::validate, select_op::prepare,
126 select_op::execute);
127
128 } // namespace nn
129 } // namespace android
130