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      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 #include "CpuOperationUtils.h"
     18 #include "OperationResolver.h"
     19 
     20 #include "tensorflow/lite/kernels/internal/optimized/legacy_optimized_ops.h"
     21 #include "tensorflow/lite/kernels/internal/reference/reference_ops.h"
     22 
     23 #include "Tracing.h"
     24 
     25 namespace android {
     26 namespace nn {
     27 namespace transpose {
     28 
     29 constexpr char kOperationName[] = "TRANSPOSE";
     30 
     31 constexpr uint32_t kNumInputs = 2;
     32 constexpr uint32_t kInputTensor = 0;
     33 constexpr uint32_t kPermTensor = 1;
     34 
     35 constexpr uint32_t kNumOutputs = 1;
     36 constexpr uint32_t kOutputTensor = 0;
     37 
     38 namespace {
     39 
     40 template <typename T>
     41 bool transposeGeneric(const T* inputData, const Shape& inputShape, const int32_t* perm,
     42                       const Shape& permShape, T* outputData, const Shape& outputShape) {
     43     NNTRACE_TRANS("transposeGeneric");
     44     // Reverse the permuted axes and convert to 4D due to the way Dims are
     45     // constructed.
     46     const int32_t kOutputDimensionNum = 4;
     47 
     48     // permData can be NO_VALUE representing a regular 2D matrix transpose
     49     int32_t permSize = perm == nullptr ? 2 : static_cast<int32_t>(getSizeOfDimension(permShape, 0));
     50     int32_t perm_tmp[2] = {1, 0};
     51     if (perm == nullptr) {
     52         perm = perm_tmp;
     53     }
     54     int32_t reversed_perm[kOutputDimensionNum];
     55     for (int32_t output_k = 0, input_k = permSize - 1; output_k < permSize; ++output_k, --input_k) {
     56         reversed_perm[output_k] = permSize - perm[input_k] - 1;
     57     }
     58     for (int32_t k = permSize; k < kOutputDimensionNum; ++k) {
     59         reversed_perm[k] = k;
     60     }
     61     NNTRACE_COMP_SWITCH("reference_ops::Transpose");
     62     tflite::reference_ops::Transpose(inputData, convertShapeToDims(inputShape), outputData,
     63                                      convertShapeToDims(outputShape), reversed_perm);
     64     return true;
     65 }
     66 
     67 }  // namespace
     68 
     69 bool validate(const IOperationValidationContext* context) {
     70     NN_RET_CHECK_EQ(context->getNumInputs(), kNumInputs);
     71     NN_RET_CHECK_EQ(context->getNumOutputs(), kNumOutputs);
     72 
     73     const OperandType inputType = context->getInputType(kInputTensor);
     74     if (inputType == OperandType::TENSOR_FLOAT32 || inputType == OperandType::TENSOR_QUANT8_ASYMM) {
     75         NN_RET_CHECK(validateHalVersion(context, HalVersion::V1_1));
     76     } else if (inputType == OperandType::TENSOR_FLOAT16) {
     77         NN_RET_CHECK(validateHalVersion(context, HalVersion::V1_2));
     78     } else {
     79         NN_RET_CHECK_FAIL() << "Unsupported tensor type for operation " << kOperationName;
     80     }
     81     return validateInputTypes(context, {inputType, OperandType::TENSOR_INT32}) &&
     82            validateOutputTypes(context, {inputType});
     83 }
     84 
     85 bool prepare(IOperationExecutionContext* context) {
     86     // Only the permutation tensor can be omitted.
     87     NN_RET_CHECK(!context->isOmittedInput(kInputTensor));
     88     NN_RET_CHECK(!context->isOmittedOutput(kOutputTensor));
     89 
     90     const Shape& input = context->getInputShape(kInputTensor);
     91     uint32_t numInputDims = getNumberOfDimensions(input);
     92     Shape output = context->getOutputShape(kOutputTensor);
     93     output.type = input.type;
     94     output.offset = input.offset;
     95     output.scale = input.scale;
     96 
     97     // permData can be NO_VALUE representing a regular 2D matrix transpose
     98     if (context->isOmittedInput(kPermTensor)) {
     99         NN_RET_CHECK_EQ(numInputDims, 2);
    100         output.dimensions = {getSizeOfDimension(input, 1), getSizeOfDimension(input, 0)};
    101     } else {
    102         const Shape& permShape = context->getInputShape(kPermTensor);
    103         const int32_t* permData = context->getInputBuffer<int32_t>(kPermTensor);
    104 
    105         // Transpose op only supports 1D-4D input arrays.
    106         NN_RET_CHECK_LE(numInputDims, 4);
    107 
    108         // perm need to be provided as a 1-D int32 tensor.
    109         NN_RET_CHECK(permShape.type == OperandType::TENSOR_INT32);
    110         NN_RET_CHECK_EQ(getNumberOfDimensions(permShape), 1);
    111         NN_RET_CHECK_EQ(numInputDims, getSizeOfDimension(permShape, 0));
    112 
    113         std::vector<uint32_t> outDims(numInputDims);
    114         for (int32_t idx = 0; idx < static_cast<int32_t>(numInputDims); ++idx) {
    115             NN_RET_CHECK(permData[idx] >= 0 && permData[idx] < static_cast<int32_t>(numInputDims));
    116             outDims[idx] = getSizeOfDimension(input, permData[idx]);
    117         }
    118         output.dimensions = outDims;
    119     }
    120     return context->setOutputShape(kOutputTensor, output);
    121 }
    122 
    123 bool execute(IOperationExecutionContext* context) {
    124     // Bypass execution in the case of zero-sized input.
    125     if (getNumberOfElements(context->getOutputShape(kOutputTensor)) == 0) return true;
    126 
    127     switch (context->getInputType(kInputTensor)) {
    128         case OperandType::TENSOR_FLOAT32:
    129             return transposeGeneric(context->getInputBuffer<float>(kInputTensor),
    130                                     context->getInputShape(kInputTensor),
    131                                     context->getInputBuffer<int32_t>(kPermTensor),
    132                                     context->getInputShape(kPermTensor),
    133                                     context->getOutputBuffer<float>(kOutputTensor),
    134                                     context->getOutputShape(kOutputTensor));
    135         case OperandType::TENSOR_FLOAT16:
    136             return transposeGeneric(context->getInputBuffer<_Float16>(kInputTensor),
    137                                     context->getInputShape(kInputTensor),
    138                                     context->getInputBuffer<int32_t>(kPermTensor),
    139                                     context->getInputShape(kPermTensor),
    140                                     context->getOutputBuffer<_Float16>(kOutputTensor),
    141                                     context->getOutputShape(kOutputTensor));
    142         case OperandType::TENSOR_QUANT8_ASYMM:
    143             return transposeGeneric(context->getInputBuffer<uint8_t>(kInputTensor),
    144                                     context->getInputShape(kInputTensor),
    145                                     context->getInputBuffer<int32_t>(kPermTensor),
    146                                     context->getInputShape(kPermTensor),
    147                                     context->getOutputBuffer<uint8_t>(kOutputTensor),
    148                                     context->getOutputShape(kOutputTensor));
    149         default:
    150             NN_RET_CHECK_FAIL() << "Unsupported tensor type for operation " << kOperationName;
    151     }
    152 }
    153 
    154 }  // namespace transpose
    155 
    156 NN_REGISTER_OPERATION(TRANSPOSE, transpose::kOperationName, transpose::validate, transpose::prepare,
    157                       transpose::execute, .allowOmittedOperand = true, .allowZeroSizedInput = true);
    158 
    159 }  // namespace nn
    160 }  // namespace android
    161