/*
 * 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.
 */

#define LOG_TAG "Operations"

#include "SVDF.h"

#include <tensorflow/lite/kernels/internal/tensor_utils.h>

#include <algorithm>
#include <vector>

#include "CpuExecutor.h"
#include "CpuOperationUtils.h"
#include "Tracing.h"

namespace android {
namespace nn {

SVDF::SVDF(const Operation& operation, RunTimeOperandInfo* operands) {
    NNTRACE_TRANS("SVDF::SVDF");
    input_ = GetInput(operation, operands, kInputTensor);
    weights_feature_ = GetInput(operation, operands, kWeightsFeatureTensor);
    weights_time_ = GetInput(operation, operands, kWeightsTimeTensor);
    bias_ = GetInput(operation, operands, kBiasTensor);
    state_in_ = GetInput(operation, operands, kStateInTensor);

    const auto& rankOperand = *GetInput(operation, operands, kRankParam);
    params_.rank_ = getScalarDataWithDefault<int>(rankOperand, 0);
    const auto& activationOperand = *GetInput(operation, operands, kActivationParam);
    params_.activation_ = static_cast<ActivationFn>(getScalarDataWithDefault<int>(
            activationOperand, TfLiteFusedActivation::kTfLiteActNone));

    state_out_ = GetOutput(operation, operands, kStateOutTensor);
    output_ = GetOutput(operation, operands, kOutputTensor);
}

bool SVDF::Prepare(const Operation& operation, RunTimeOperandInfo* operands, Shape* stateShape,
                   Shape* outputShape) {
    NNTRACE_TRANS("SVDF::Prepare");
    // Check we have all the inputs and outputs we need.
    const int num_inputs = NumInputsWithValues(operation, operands);

    NN_CHECK(num_inputs == 6 || num_inputs == 7);
    constexpr int requiredInputs[] = {
            kInputTensor, kWeightsFeatureTensor, kWeightsTimeTensor, kStateInTensor,
            kRankParam,   kActivationParam,
    };
    for (const int requiredInput : requiredInputs) {
        NN_RET_CHECK(!IsNullInput(GetInput(operation, operands, requiredInput)))
                << "required input " << requiredInput << " is omitted";
    }
    NN_CHECK_EQ(NumOutputs(operation), 2);

    // Check that the scalar operands' buffers are large enough.
    const auto& rankOperand = *GetInput(operation, operands, kRankParam);
    NN_RET_CHECK(rankOperand.length >= sizeof(int));
    const auto& activationOperand = *GetInput(operation, operands, kActivationParam);
    NN_RET_CHECK(activationOperand.length >= sizeof(int));

    const RunTimeOperandInfo* input = GetInput(operation, operands, SVDF::kInputTensor);
    const RunTimeOperandInfo* weights_feature =
            GetInput(operation, operands, SVDF::kWeightsFeatureTensor);
    const RunTimeOperandInfo* weights_time =
            GetInput(operation, operands, SVDF::kWeightsTimeTensor);

    // Check all the parameters of tensor match within themselves and match the
    // input configuration.
    const int rank = getScalarData<int>(*GetInput(operation, operands, kRankParam));
    const uint32_t batch_size = SizeOfDimension(input, 0);
    const uint32_t num_filters = SizeOfDimension(weights_feature, 0);
    NN_CHECK_EQ(num_filters % rank, 0u);
    const uint32_t num_units = num_filters / rank;
    const uint32_t memory_size = SizeOfDimension(weights_time, 1);
    NN_CHECK_EQ(SizeOfDimension(input, 1), SizeOfDimension(weights_feature, 1));
    NN_CHECK_EQ(SizeOfDimension(weights_time, 0), num_filters);

    const RunTimeOperandInfo* bias = GetInput(operation, operands, kBiasTensor);
    if (!IsNullInput(bias)) {
        NN_CHECK_EQ(SizeOfDimension(bias, 0), num_units);
    }

    // Resize state.
    const Shape& inputShape = input->shape();
    stateShape->type = inputShape.type;
    stateShape->dimensions = {batch_size, memory_size * num_filters};
    stateShape->offset = inputShape.offset;
    stateShape->scale = inputShape.scale;

    // Resize output.
    outputShape->type = inputShape.type;
    outputShape->dimensions = {batch_size, num_units};
    outputShape->offset = inputShape.offset;
    outputShape->scale = inputShape.scale;

    return true;
}

bool SVDF::Eval() {
    NNTRACE_TRANS("SVDF::Eval");
    switch (input_->type) {
        case OperandType::TENSOR_FLOAT16: {
            std::vector<float> inputDataFloat32(getNumberOfElements(input_->shape()));
            convertFloat16ToFloat32(reinterpret_cast<_Float16*>(input_->buffer), &inputDataFloat32);
            std::vector<float> inputStateDataFloat32(getNumberOfElements(state_in_->shape()));
            convertFloat16ToFloat32(reinterpret_cast<_Float16*>(state_in_->buffer),
                                    &inputStateDataFloat32);
            std::vector<float> biasDataFloat32(getNumberOfElements(bias_->shape()));
            if (!IsNullInput(bias_)) {
                convertFloat16ToFloat32(reinterpret_cast<_Float16*>(bias_->buffer),
                                        &biasDataFloat32);
            }
            std::vector<float> weightsFeatureDataFloat32(
                    getNumberOfElements(weights_feature_->shape()));
            convertFloat16ToFloat32(reinterpret_cast<_Float16*>(weights_feature_->buffer),
                                    &weightsFeatureDataFloat32);
            std::vector<float> weightsTimeDataFloat32(getNumberOfElements(weights_time_->shape()));
            convertFloat16ToFloat32(reinterpret_cast<_Float16*>(weights_time_->buffer),
                                    &weightsTimeDataFloat32);
            std::vector<float> outputDataFloat32(getNumberOfElements(output_->shape()));
            std::vector<float> outputStateDataFloat32(getNumberOfElements(state_out_->shape()));

            EvalFloat32(inputDataFloat32.data(), inputStateDataFloat32.data(),
                        biasDataFloat32.data(), weightsFeatureDataFloat32.data(),
                        weightsTimeDataFloat32.data(), outputDataFloat32.data(),
                        outputStateDataFloat32.data());
            convertFloat32ToFloat16(outputDataFloat32,
                                    reinterpret_cast<_Float16*>(output_->buffer));
            convertFloat32ToFloat16(outputStateDataFloat32,
                                    reinterpret_cast<_Float16*>(state_out_->buffer));
            break;
        }
        case OperandType::TENSOR_FLOAT32: {
            EvalFloat32(reinterpret_cast<float*>(input_->buffer),
                        reinterpret_cast<float*>(state_in_->buffer),
                        reinterpret_cast<float*>(bias_->buffer),
                        reinterpret_cast<float*>(weights_feature_->buffer),
                        reinterpret_cast<float*>(weights_time_->buffer),
                        reinterpret_cast<float*>(output_->buffer),
                        reinterpret_cast<float*>(state_out_->buffer));
            break;
        }
        default: {
            LOG(ERROR) << "Unsupported data type: " << static_cast<int>(input_->type);
            return false;
        }
    }
    return true;
}

void SVDF::EvalFloat32(const float* inputData, const float* inputStateData, const float* biasData,
                       const float* weightsFeatureData, const float* weightsTimeData,
                       float* outputData, float* outputStateData) {
    NNTRACE_COMP("SVDF::EvalFloat32");

    const int rank = params_.rank_;
    const int batch_size = SizeOfDimension(input_, 0);
    const int input_size = SizeOfDimension(input_, 1);
    const int num_filters = SizeOfDimension(weights_feature_, 0);
    const int num_units = num_filters / rank;
    const int memory_size = SizeOfDimension(weights_time_, 1);

    memcpy(outputStateData, inputStateData, sizeof(float) * batch_size * memory_size * num_filters);
    // Compute conv1d(inputs, weights_feature).
    for (int b = 0; b < batch_size; b++) {
        float* state_ptr_batch = outputStateData + b * memory_size * num_filters;
        for (int c = 0; c < num_filters; c++) {
            float* state_ptr = state_ptr_batch + c * memory_size;
            state_ptr[memory_size - 1] = 0.0;
        }
    }

    // Clear scratch (the matmul is accumulative).
    float scratch[batch_size * num_filters];
    std::fill_n(scratch, batch_size * num_filters, 0.0f);
    tflite::tensor_utils::MatrixBatchVectorMultiplyAccumulate(
            weightsFeatureData, num_filters, input_size, inputData, batch_size, scratch);

    // Copy the latest activation from scratch into activation_state:
    // The last, i.e. (memory_size-1)th entry for each batch, and filter.
    for (int i = 0; i < batch_size * num_filters; ++i) {
        outputStateData[i * memory_size + memory_size - 1] = scratch[i];
    }

    // Begin ApplyTimeWeightsBiasAndActivation
    // Compute matmul(state, weights_time).
    for (int b = 0; b < batch_size; b++) {
        float* state_out_ptr_batch = outputStateData + b * memory_size * num_filters;
        float* scratch_ptr_batch = scratch + b * num_filters;
        tflite::tensor_utils::BatchVectorBatchVectorDotProduct(
                weightsTimeData, state_out_ptr_batch, memory_size, num_filters, scratch_ptr_batch);
    }

    // Reduction sum
    tflite::tensor_utils::ReductionSumVector(scratch, outputData, batch_size * num_units, rank);

    // Add bias if provided.
    if (!IsNullInput(bias_)) {
        tflite::tensor_utils::VectorBatchVectorAdd(biasData, num_units, batch_size, outputData);
    }

    // Apply activation.
    tflite::tensor_utils::ApplyActivationToVector(
            outputData, batch_size * num_units,
            static_cast<TfLiteFusedActivation>(params_.activation_), outputData);
    // Finished ApplyTimeWeightsBiasAndActivation

    // Right shift the state.
    for (int b = 0; b < batch_size; b++) {
        float* state_out_ptr_batch = outputStateData + b * memory_size * num_filters;
        for (int f = 0; f < num_filters; f++) {
            std::copy(state_out_ptr_batch + 1, state_out_ptr_batch + memory_size,
                      state_out_ptr_batch);
            state_out_ptr_batch[memory_size - 1] = 0.0;
            state_out_ptr_batch += memory_size;
        }
    }
}

}  // namespace nn
}  // namespace android