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 "HalInterfaces.h"
20 #include "IndexedShapeWrapper.h"
21 #include "OperationResolver.h"
22 #include "OperationsUtils.h"
23
24 namespace android {
25 namespace nn {
26 namespace select_op {
27
28 constexpr uint32_t kNumInputs = 3;
29 constexpr uint32_t kInputCondition = 0;
30 constexpr uint32_t kInputTensor1 = 1;
31 constexpr uint32_t kInputTensor2 = 2;
32
33 constexpr uint32_t kNumOutputs = 1;
34 constexpr uint32_t kOutputTensor = 0;
35
36 namespace {
37
38 using namespace hal;
39
40 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)41 bool compute(const bool8* conditionData, const Shape& conditionShape, const T* aData,
42 const Shape& aShape, const T* bData, const Shape& bShape, T* outputData,
43 const Shape& outputShape) {
44 // The code assumes that condition has the same shape as all other tensors.
45 // This should be checked during preparation stage.
46 uint32_t size = getNumberOfElements(conditionShape);
47 for (uint32_t i = 0; i < size; ++i) {
48 T a = aData[i];
49 T b = bData[i];
50
51 if constexpr (std::is_same_v<T, uint8_t> || std::is_same_v<T, int8_t>) {
52 a = requantize<T>(a, aShape, outputShape);
53 b = requantize<T>(b, bShape, outputShape);
54 }
55 outputData[i] = conditionData[i] ? a : b;
56 }
57 return true;
58 }
59
60 template <typename T>
executeTyped(IOperationExecutionContext * context)61 bool executeTyped(IOperationExecutionContext* context) {
62 return compute<T>(
63 context->getInputBuffer<bool8>(kInputCondition),
64 context->getInputShape(kInputCondition), context->getInputBuffer<T>(kInputTensor1),
65 context->getInputShape(kInputTensor1), context->getInputBuffer<T>(kInputTensor2),
66 context->getInputShape(kInputTensor2), context->getOutputBuffer<T>(kOutputTensor),
67 context->getOutputShape(kOutputTensor));
68 }
69
70 } // namespace
71
validate(const IOperationValidationContext * context)72 bool validate(const IOperationValidationContext* context) {
73 NN_RET_CHECK_EQ(context->getNumInputs(), kNumInputs);
74 NN_RET_CHECK_EQ(context->getNumOutputs(), kNumOutputs);
75 OperandType inputType = context->getInputType(kInputTensor1);
76 NN_RET_CHECK(inputType == OperandType::TENSOR_FLOAT16 ||
77 inputType == OperandType::TENSOR_FLOAT32 ||
78 inputType == OperandType::TENSOR_INT32 ||
79 inputType == OperandType::TENSOR_QUANT8_ASYMM ||
80 inputType == OperandType::TENSOR_QUANT8_ASYMM_SIGNED)
81 << "Unsupported input operand type for select op: " << toString(inputType);
82 NN_RET_CHECK(validateInputTypes(context, {OperandType::TENSOR_BOOL8, inputType, inputType}));
83 NN_RET_CHECK(validateOutputTypes(context, {inputType}));
84 return validateHalVersion(context, HalVersion::V1_2);
85 }
86
prepare(IOperationExecutionContext * context)87 bool prepare(IOperationExecutionContext* context) {
88 Shape inputCondition = context->getInputShape(kInputCondition);
89 Shape input1 = context->getInputShape(kInputTensor1);
90 if (inputCondition.dimensions.size() != input1.dimensions.size()) {
91 LOG(ERROR) << "Condition and input tensor dimensions are not equal";
92 return false;
93 }
94 for (int i = 0; i < inputCondition.dimensions.size(); ++i) {
95 if (inputCondition.dimensions[i] != input1.dimensions[i]) {
96 LOG(ERROR) << "Condition and input tensor dimensions are not equal";
97 return false;
98 }
99 }
100
101 Shape input2 = context->getInputShape(kInputTensor2);
102 NN_RET_CHECK(SameShape(input1, input2));
103
104 Shape output = context->getOutputShape(kOutputTensor);
105 NN_RET_CHECK(SetShape(input1, &output));
106 return context->setOutputShape(kOutputTensor, output);
107 }
108
execute(IOperationExecutionContext * context)109 bool execute(IOperationExecutionContext* context) {
110 switch (context->getInputType(kInputTensor1)) {
111 case OperandType::TENSOR_FLOAT16:
112 return executeTyped<_Float16>(context);
113 case OperandType::TENSOR_FLOAT32:
114 return executeTyped<float>(context);
115 case OperandType::TENSOR_INT32:
116 return executeTyped<int32_t>(context);
117 case OperandType::TENSOR_QUANT8_ASYMM:
118 return executeTyped<uint8_t>(context);
119 case OperandType::TENSOR_QUANT8_ASYMM_SIGNED:
120 return executeTyped<int8_t>(context);
121 default:
122 NN_RET_CHECK_FAIL() << "Unsupported tensor type for SELECT op.";
123 }
124 }
125
126 } // namespace select_op
127
128 NN_REGISTER_OPERATION(SELECT, "SELECT", select_op::validate, select_op::prepare,
129 select_op::execute);
130
131 } // namespace nn
132 } // namespace android
133