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 "OperationResolver.h"
20 #include "OperationsUtils.h"
21 #include "Tracing.h"
22
23 namespace android {
24 namespace nn {
25 namespace channel_shuffle {
26
27 constexpr char kOperationName[] = "CHANNEL_SHUFFLE";
28
29 constexpr uint32_t kNumInputs = 3;
30 constexpr uint32_t kInputTensor = 0;
31 constexpr uint32_t kNumGroups = 1;
32 constexpr uint32_t kInputAxis = 2;
33
34 constexpr uint32_t kNumOutputs = 1;
35 constexpr uint32_t kOutputTensor = 0;
36
37 template <typename T>
eval(const T * inputData,const Shape & inputShape,int32_t numGroups,int32_t axis,T * outputData)38 inline bool eval(const T* inputData, const Shape& inputShape, int32_t numGroups, int32_t axis,
39 T* outputData) {
40 const uint32_t outerSize = getNumberOfElements(inputShape, 0, axis);
41 const uint32_t axisSize = getSizeOfDimension(inputShape, axis);
42 const uint32_t innerSize =
43 getNumberOfElements(inputShape, axis + 1, getNumberOfDimensions(inputShape));
44 const uint32_t groupSize = axisSize / numGroups;
45 for (uint32_t outer = 0; outer < outerSize; ++outer) {
46 for (uint32_t inner = 0; inner < innerSize; ++inner) {
47 const T* inputBase = inputData + outer * axisSize * innerSize + inner;
48 T* outputBase = outputData + outer * axisSize * innerSize + inner;
49 for (uint32_t i = 0; i < groupSize; i++) {
50 for (uint32_t j = 0; j < static_cast<uint32_t>(numGroups);
51 j++, outputBase += innerSize) {
52 *outputBase = inputBase[innerSize * (i + j * groupSize)];
53 }
54 }
55 }
56 }
57 return true;
58 }
59
validate(const IOperationValidationContext * context)60 Result<Version> validate(const IOperationValidationContext* context) {
61 NN_RET_CHECK_EQ(context->getNumInputs(), kNumInputs);
62 NN_RET_CHECK_EQ(context->getNumOutputs(), kNumOutputs);
63 auto inputType = context->getInputType(kInputTensor);
64 NN_RET_CHECK(inputType == OperandType::TENSOR_FLOAT16 ||
65 inputType == OperandType::TENSOR_FLOAT32 ||
66 inputType == OperandType::TENSOR_QUANT8_ASYMM ||
67 inputType == OperandType::TENSOR_QUANT8_ASYMM_SIGNED)
68 << "Unsupported tensor type for operation " << kOperationName;
69 const Shape& inputShape = context->getInputShape(kInputTensor);
70 if (hasKnownRank(inputShape)) {
71 NN_RET_CHECK_LE(getNumberOfDimensions(inputShape), 4);
72 }
73 NN_RET_CHECK(validateInputTypes(context, {inputType, OperandType::INT32, OperandType::INT32}));
74 NN_RET_CHECK(validateOutputTypes(context, {inputType}));
75 if (inputType == OperandType::TENSOR_QUANT8_ASYMM_SIGNED) {
76 return Version::ANDROID_R;
77 } else {
78 return Version::ANDROID_Q;
79 }
80 }
81
prepare(IOperationExecutionContext * context)82 bool prepare(IOperationExecutionContext* context) {
83 Shape input = context->getInputShape(kInputTensor);
84 int32_t numGroups = context->getInputValue<int32_t>(kNumGroups);
85 int32_t axis = context->getInputValue<int32_t>(kInputAxis);
86 NN_RET_CHECK(handleNegativeAxis(input, &axis));
87 NN_RET_CHECK(numGroups > 0);
88 NN_RET_CHECK(getSizeOfDimension(input, axis) % numGroups == 0);
89 return context->setOutputShape(kOutputTensor, input);
90 }
91
execute(IOperationExecutionContext * context)92 bool execute(IOperationExecutionContext* context) {
93 int32_t numGroups = context->getInputValue<int32_t>(kNumGroups);
94 int32_t axis = context->getInputValue<int32_t>(kInputAxis);
95 NN_RET_CHECK(handleNegativeAxis(context->getInputShape(kInputTensor), &axis));
96 switch (context->getInputType(kInputTensor)) {
97 case OperandType::TENSOR_FLOAT16:
98 return eval(context->getInputBuffer<_Float16>(kInputTensor),
99 context->getInputShape(kInputTensor), numGroups, axis,
100 context->getOutputBuffer<_Float16>(kOutputTensor));
101 case OperandType::TENSOR_FLOAT32:
102 return eval(context->getInputBuffer<float>(kInputTensor),
103 context->getInputShape(kInputTensor), numGroups, axis,
104 context->getOutputBuffer<float>(kOutputTensor));
105 case OperandType::TENSOR_QUANT8_ASYMM:
106 return eval(context->getInputBuffer<uint8_t>(kInputTensor),
107 context->getInputShape(kInputTensor), numGroups, axis,
108 context->getOutputBuffer<uint8_t>(kOutputTensor));
109 case OperandType::TENSOR_QUANT8_ASYMM_SIGNED:
110 return eval(context->getInputBuffer<int8_t>(kInputTensor),
111 context->getInputShape(kInputTensor), numGroups, axis,
112 context->getOutputBuffer<int8_t>(kOutputTensor));
113 default:
114 NN_RET_CHECK_FAIL() << "Unsupported tensor type for operation " << kOperationName;
115 }
116 }
117
118 } // namespace channel_shuffle
119
120 NN_REGISTER_OPERATION(CHANNEL_SHUFFLE, channel_shuffle::kOperationName, channel_shuffle::validate,
121 channel_shuffle::prepare, channel_shuffle::execute);
122
123 } // namespace nn
124 } // namespace android
125