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      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 "OperationResolver.h"
     21 #include "OperationsUtils.h"
     22 #include "Tracing.h"
     23 
     24 #include <cmath>
     25 
     26 namespace android {
     27 namespace nn {
     28 namespace log_softmax {
     29 
     30 constexpr char kOperationName[] = "LOG_SOFTMAX";
     31 
     32 constexpr uint32_t kNumInputs = 3;
     33 constexpr uint32_t kInputTensor = 0;
     34 constexpr uint32_t kInputBeta = 1;
     35 constexpr uint32_t kInputAxis = 2;
     36 
     37 constexpr uint32_t kNumOutputs = 1;
     38 constexpr uint32_t kOutputTensor = 0;
     39 
     40 template <typename T>
     41 inline bool compute(const T* input, const Shape& shape, T beta, uint32_t axis, T* output) {
     42     const uint32_t outerSize = getNumberOfElements(shape, 0, axis);
     43     const uint32_t axisSize = getSizeOfDimension(shape, axis);
     44     const uint32_t innerSize = getNumberOfElements(shape, axis + 1, getNumberOfDimensions(shape));
     45     for (uint32_t outer = 0; outer < outerSize; ++outer) {
     46         for (uint32_t inner = 0; inner < innerSize; ++inner) {
     47             // We subtract the maximum value from each element to ensure
     48             // numerical stability, taking advantage of the following equality:
     49             // exp(x[i])/sum(exp(x[i])) == exp(x[i]+C)/sum(exp(x[i]+C))
     50             T maxValue = input[outer * axisSize * innerSize + inner];
     51             for (uint32_t i = 1; i < axisSize; ++i) {
     52                 maxValue = std::max(maxValue, input[(outer * axisSize + i) * innerSize + inner]);
     53             }
     54 
     55             T sum = 0;
     56             for (uint32_t i = 0; i < axisSize; ++i) {
     57                 sum += std::exp(static_cast<double>(
     58                         (input[(outer * axisSize + i) * innerSize + inner] - maxValue) * beta));
     59             }
     60 
     61             const T logSum = std::log(static_cast<double>(sum));
     62             for (uint32_t i = 0; i < axisSize; ++i) {
     63                 output[(outer * axisSize + i) * innerSize + inner] =
     64                         (input[(outer * axisSize + i) * innerSize + inner] - maxValue) * beta -
     65                         logSum;
     66             }
     67         }
     68     }
     69     return true;
     70 }
     71 
     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(kInputTensor);
     76     std::vector<OperandType> inExpectedTypes;
     77     std::vector<OperandType> outExpectedTypes;
     78     if (inputType == OperandType::TENSOR_FLOAT32) {
     79         inExpectedTypes = {OperandType::TENSOR_FLOAT32, OperandType::FLOAT32, OperandType::INT32};
     80         outExpectedTypes = {OperandType::TENSOR_FLOAT32};
     81     } else if (inputType == OperandType::TENSOR_FLOAT16) {
     82         inExpectedTypes = {OperandType::TENSOR_FLOAT16, OperandType::FLOAT16, OperandType::INT32};
     83         outExpectedTypes = {OperandType::TENSOR_FLOAT16};
     84     } else {
     85         LOG(ERROR) << "Unsupported input tensor type for operation " << kOperationName;
     86         return false;
     87     }
     88     NN_RET_CHECK(validateInputTypes(context, inExpectedTypes));
     89     NN_RET_CHECK(validateOutputTypes(context, outExpectedTypes));
     90     return validateHalVersion(context, HalVersion::V1_2);
     91 }
     92 
     93 bool prepare(IOperationExecutionContext* context) {
     94     return context->setOutputShape(kOutputTensor, context->getInputShape(kInputTensor));
     95 }
     96 
     97 bool execute(IOperationExecutionContext* context) {
     98     int32_t axis = context->getInputValue<int32_t>(kInputAxis);
     99     NN_RET_CHECK(handleNegativeAxis(context->getInputShape(kInputTensor), &axis));
    100     switch (context->getInputType(kInputTensor)) {
    101         case OperandType::TENSOR_FLOAT16:
    102             return compute(context->getInputBuffer<_Float16>(kInputTensor),
    103                            context->getInputShape(kInputTensor),
    104                            context->getInputValue<_Float16>(kInputBeta), axis,
    105                            context->getOutputBuffer<_Float16>(kOutputTensor));
    106         case OperandType::TENSOR_FLOAT32:
    107             return compute(context->getInputBuffer<float>(kInputTensor),
    108                            context->getInputShape(kInputTensor),
    109                            context->getInputValue<float>(kInputBeta), axis,
    110                            context->getOutputBuffer<float>(kOutputTensor));
    111         default:
    112             NN_RET_CHECK_FAIL() << "Unsupported tensor type for operation " << kOperationName;
    113     }
    114 }
    115 
    116 }  // namespace log_softmax
    117 
    118 NN_REGISTER_OPERATION(LOG_SOFTMAX, log_softmax::kOperationName, log_softmax::validate,
    119                       log_softmax::prepare, log_softmax::execute);
    120 
    121 }  // namespace nn
    122 }  // namespace android
    123