1 /*
2 * Copyright (C) 2019 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 <vector>
20
21 #include "CpuOperationUtils.h"
22 #include "HalInterfaces.h"
23 #include "OperationResolver.h"
24
25 #include <tensorflow/lite/kernels/internal/optimized/legacy_optimized_ops.h>
26 #include <tensorflow/lite/kernels/internal/reference/reference_ops.h>
27
28 #include "Tracing.h"
29
30 namespace android {
31 namespace nn {
32 namespace transpose {
33
34 constexpr char kOperationName[] = "TRANSPOSE";
35
36 constexpr uint32_t kNumInputs = 2;
37 constexpr uint32_t kInputTensor = 0;
38 constexpr uint32_t kPermTensor = 1;
39
40 constexpr uint32_t kNumOutputs = 1;
41 constexpr uint32_t kOutputTensor = 0;
42
43 namespace {
44
45 using namespace hal;
46
47 template <typename T>
transposeGeneric(const T * inputData,const Shape & inputShape,const int32_t * perm,const Shape & permShape,T * outputData,const Shape & outputShape)48 bool transposeGeneric(const T* inputData, const Shape& inputShape, const int32_t* perm,
49 const Shape& permShape, T* outputData, const Shape& outputShape) {
50 NNTRACE_TRANS("transposeGeneric");
51 // Reverse the permuted axes and convert to 4D due to the way Dims are
52 // constructed.
53 const int32_t kOutputDimensionNum = 4;
54
55 // permData can be NO_VALUE representing a regular 2D matrix transpose
56 int32_t permSize = perm == nullptr ? 2 : static_cast<int32_t>(getSizeOfDimension(permShape, 0));
57 int32_t perm_tmp[2] = {1, 0};
58 if (perm == nullptr) {
59 perm = perm_tmp;
60 }
61 int32_t reversed_perm[kOutputDimensionNum];
62 for (int32_t output_k = 0, input_k = permSize - 1; output_k < permSize; ++output_k, --input_k) {
63 reversed_perm[output_k] = permSize - perm[input_k] - 1;
64 }
65 for (int32_t k = permSize; k < kOutputDimensionNum; ++k) {
66 reversed_perm[k] = k;
67 }
68 NNTRACE_COMP_SWITCH("reference_ops::Transpose");
69 tflite::reference_ops::Transpose(inputData, convertShapeToDims(inputShape), outputData,
70 convertShapeToDims(outputShape), reversed_perm);
71 return true;
72 }
73
74 } // namespace
75
validate(const IOperationValidationContext * context)76 bool validate(const IOperationValidationContext* context) {
77 NN_RET_CHECK_EQ(context->getNumInputs(), kNumInputs);
78 NN_RET_CHECK_EQ(context->getNumOutputs(), kNumOutputs);
79
80 const OperandType inputType = context->getInputType(kInputTensor);
81 if (inputType == OperandType::TENSOR_FLOAT32 || inputType == OperandType::TENSOR_QUANT8_ASYMM) {
82 NN_RET_CHECK(validateHalVersion(context, HalVersion::V1_1));
83 } else if (inputType == OperandType::TENSOR_FLOAT16) {
84 NN_RET_CHECK(validateHalVersion(context, HalVersion::V1_2));
85 } else if (inputType == OperandType::TENSOR_QUANT8_ASYMM_SIGNED) {
86 NN_RET_CHECK(validateHalVersion(context, HalVersion::V1_3));
87 } else {
88 NN_RET_CHECK_FAIL() << "Unsupported tensor type for operation " << kOperationName;
89 }
90 const Shape& input = context->getInputShape(kInputTensor);
91 if (hasKnownRank(input)) {
92 NN_RET_CHECK_LE(getNumberOfDimensions(input), 4);
93 }
94 return validateInputTypes(context, {inputType, OperandType::TENSOR_INT32}) &&
95 validateOutputTypes(context, {inputType});
96 }
97
prepare(IOperationExecutionContext * context)98 bool prepare(IOperationExecutionContext* context) {
99 // Only the permutation tensor can be omitted.
100 NN_RET_CHECK(!context->isOmittedInput(kInputTensor));
101 NN_RET_CHECK(!context->isOmittedOutput(kOutputTensor));
102
103 const Shape& input = context->getInputShape(kInputTensor);
104 uint32_t numInputDims = getNumberOfDimensions(input);
105 Shape output = context->getOutputShape(kOutputTensor);
106 output.type = input.type;
107 output.offset = input.offset;
108 output.scale = input.scale;
109
110 // permData can be NO_VALUE representing a regular 2D matrix transpose
111 if (context->isOmittedInput(kPermTensor)) {
112 NN_RET_CHECK_EQ(numInputDims, 2);
113 output.dimensions = {getSizeOfDimension(input, 1), getSizeOfDimension(input, 0)};
114 } else {
115 const Shape& permShape = context->getInputShape(kPermTensor);
116 const int32_t* permData = context->getInputBuffer<int32_t>(kPermTensor);
117
118 // Transpose op only supports 1D-4D input arrays.
119 NN_RET_CHECK_LE(numInputDims, 4);
120
121 // perm need to be provided as a 1-D int32 tensor.
122 NN_RET_CHECK(permShape.type == OperandType::TENSOR_INT32);
123 NN_RET_CHECK_EQ(getNumberOfDimensions(permShape), 1);
124 NN_RET_CHECK_EQ(numInputDims, getSizeOfDimension(permShape, 0));
125
126 std::vector<uint32_t> outDims(numInputDims);
127 for (int32_t idx = 0; idx < static_cast<int32_t>(numInputDims); ++idx) {
128 NN_RET_CHECK(permData[idx] >= 0 && permData[idx] < static_cast<int32_t>(numInputDims));
129 outDims[idx] = getSizeOfDimension(input, permData[idx]);
130 }
131 output.dimensions = outDims;
132 }
133 return context->setOutputShape(kOutputTensor, output);
134 }
135
execute(IOperationExecutionContext * context)136 bool execute(IOperationExecutionContext* context) {
137 // Bypass execution in the case of zero-sized input.
138 if (getNumberOfElements(context->getOutputShape(kOutputTensor)) == 0) return true;
139
140 switch (context->getInputType(kInputTensor)) {
141 case OperandType::TENSOR_FLOAT32:
142 return transposeGeneric(context->getInputBuffer<float>(kInputTensor),
143 context->getInputShape(kInputTensor),
144 context->getInputBuffer<int32_t>(kPermTensor),
145 context->getInputShape(kPermTensor),
146 context->getOutputBuffer<float>(kOutputTensor),
147 context->getOutputShape(kOutputTensor));
148 case OperandType::TENSOR_FLOAT16:
149 return transposeGeneric(context->getInputBuffer<_Float16>(kInputTensor),
150 context->getInputShape(kInputTensor),
151 context->getInputBuffer<int32_t>(kPermTensor),
152 context->getInputShape(kPermTensor),
153 context->getOutputBuffer<_Float16>(kOutputTensor),
154 context->getOutputShape(kOutputTensor));
155 case OperandType::TENSOR_QUANT8_ASYMM:
156 return transposeGeneric(context->getInputBuffer<uint8_t>(kInputTensor),
157 context->getInputShape(kInputTensor),
158 context->getInputBuffer<int32_t>(kPermTensor),
159 context->getInputShape(kPermTensor),
160 context->getOutputBuffer<uint8_t>(kOutputTensor),
161 context->getOutputShape(kOutputTensor));
162 case OperandType::TENSOR_QUANT8_ASYMM_SIGNED:
163 return transposeGeneric(context->getInputBuffer<int8_t>(kInputTensor),
164 context->getInputShape(kInputTensor),
165 context->getInputBuffer<int32_t>(kPermTensor),
166 context->getInputShape(kPermTensor),
167 context->getOutputBuffer<int8_t>(kOutputTensor),
168 context->getOutputShape(kOutputTensor));
169 default:
170 NN_RET_CHECK_FAIL() << "Unsupported tensor type for operation " << kOperationName;
171 }
172 }
173
174 } // namespace transpose
175
176 NN_REGISTER_OPERATION(TRANSPOSE, transpose::kOperationName, transpose::validate, transpose::prepare,
177 transpose::execute, .allowOmittedOperand = true, .allowZeroSizedInput = true);
178
179 } // namespace nn
180 } // namespace android
181