common/operations/FullyConnected.cpp

/*
 * Copyright (C) 2017 The Android Open Source Project
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 *      http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */

#include "Operations.h"
#include "CpuOperationUtils.h"

#include "tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h"

namespace android {
namespace nn {

// executionMutex is used to protect concurrent access of non-threadsafe resources
// like gemmlowp::GemmContext.
// std::mutex is safe for pthreads on Android.
static std::mutex executionMutex;

bool fullyConnectedFloat32(const float* inputData, const Shape& inputShape,
                           const float* weightsData, const Shape& weightsShape,
                           const float* biasData, const Shape& biasShape,
                           int32_t activation,
                           float* outputData, const Shape& outputShape) {
    float output_activation_min, output_activation_max;
    CalculateActivationRangeFloat(activation, &output_activation_min,
                                  &output_activation_max);

    tflite::optimized_ops::FullyConnected(
            inputData, convertShapeToDims(inputShape),
            weightsData, convertShapeToDims(weightsShape),
            biasData, convertShapeToDims(biasShape),
            output_activation_min, output_activation_max,
            outputData, convertShapeToDims(outputShape));

    return true;
}

bool fullyConnectedQuant8(const uint8_t* inputData, const Shape& inputShape,
                          const uint8_t* weightsData, const Shape& weightsShape,
                          const int32_t* biasData, const Shape& biasShape,
                          int32_t activation,
                          uint8_t* outputData, const Shape& outputShape) {
    int32_t inputOffset = -inputShape.offset;
    int32_t weightsOffset = -weightsShape.offset;
    int32_t outputOffset = outputShape.offset;

    float real_multiplier = 0.0;
    int32_t output_multiplier = 0;
    int32_t output_shift = 0;
    int32_t output_activation_min = 0;
    int32_t output_activation_max = 0;

    if (!GetQuantizedConvolutionMultipler(inputShape, weightsShape, biasShape,
                                          outputShape, &real_multiplier) ||
            !QuantizeMultiplierSmallerThanOne(real_multiplier, &output_multiplier,
                                              &output_shift)) {
        return false;
    }
    CalculateActivationRangeUint8(activation, outputShape,
                                  &output_activation_min,
                                  &output_activation_max);

    static gemmlowp::GemmContext gemm_context;

    // Prevent concurrent executions that access gemm_context.
    std::unique_lock<std::mutex> lock(executionMutex);
    // Alow gemmlowp automatically decide how many threads to use.
    gemm_context.set_max_num_threads(0);

    tflite::optimized_ops::FullyConnected(
            inputData, convertShapeToDims(inputShape), inputOffset,
            weightsData, convertShapeToDims(weightsShape), weightsOffset,
            biasData, convertShapeToDims(biasShape),
            outputOffset, output_multiplier, output_shift,
            output_activation_min, output_activation_max,
            outputData, convertShapeToDims(outputShape), &gemm_context);

    return true;
}
}  // namespace nn
}  // namespace android