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//
// Copyright © 2021 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
#include "Reduce.hpp"
#include <armnn/utility/NumericCast.hpp>
#include <backendsCommon/WorkloadData.hpp>
#include <cmath>
#include <cstddef>
#include <functional>
#include <limits>
namespace armnn
{
bool NextIndex(const unsigned int numDims, const armnn::TensorShape& dims, std::vector<unsigned int>& current)
{
unsigned int carry = 1;
for (unsigned int idx = numDims; idx-- > 0; )
{
unsigned int current_val = current[idx] + carry;
if (dims[idx] == current_val)
{
current[idx] = 0;
}
else
{
current[idx] = current_val;
carry = 0;
break;
}
}
return (carry == 0);
}
unsigned int ReducedOutputOffset(const unsigned int numDims,
const armnn::TensorShape& dims,
std::vector<unsigned int>& index,
const unsigned int numAxis,
const std::vector<unsigned int>& axis)
{
unsigned int offset = 0;
for (unsigned int idx = 0; idx < numDims; ++idx)
{
bool isAxis = false;
if (!axis.empty())
{
for (unsigned int axisIdx = 0; axisIdx < numAxis; ++axisIdx)
{
if (idx == axis[axisIdx])
{
isAxis = true;
break;
}
}
}
if (!isAxis)
{
offset = offset * dims[idx] + index[idx];
}
}
return offset;
}
void Reduce(const TensorInfo& inputInfo,
const TensorInfo& outputInfo,
Decoder<float>& input,
Encoder<float>& output,
const std::vector<uint32_t> axis,
const ReduceOperation reduceOperation)
{
armnn::TensorShape inputDims = inputInfo.GetShape();
unsigned int inputNumDims = inputInfo.GetNumDimensions();
unsigned int numOutputs = outputInfo.GetNumElements();
// Initialise temp output
std::vector<float> tempOut(numOutputs);
switch(reduceOperation)
{
case ReduceOperation::Mean:
case ReduceOperation::Sum:
std::fill(tempOut.begin(), tempOut.end(), 0.0f);
break;
case ReduceOperation::Max:
std::fill(tempOut.begin(), tempOut.end(), -1 * std::numeric_limits<float>::max());
break;
case ReduceOperation::Min:
std::fill(tempOut.begin(), tempOut.end(), std::numeric_limits<float>::max());
break;
default:
throw armnn::InvalidArgumentException("Unknown reduce method: " +
std::to_string(static_cast<int>(reduceOperation)));
}
// Initialise temp index
std::vector<unsigned int> tempIndex(inputNumDims, 0);
std::vector<unsigned int> resolvedAxis = axis;
if (resolvedAxis.empty())
{
for (unsigned int idx = 0; idx < inputNumDims; ++idx)
{
resolvedAxis.push_back(idx);
}
}
auto numResolvedAxis = armnn::numeric_cast<unsigned int>(resolvedAxis.size());
// Iterates through input_data and operates over the reduced axis
for (bool hasNext = true; hasNext; hasNext = NextIndex(inputNumDims, inputDims, tempIndex))
{
unsigned int inputOffset = ReducedOutputOffset(inputNumDims, inputDims, tempIndex, 0, {});
unsigned int outputOffset = ReducedOutputOffset(inputNumDims, inputDims, tempIndex,
numResolvedAxis, resolvedAxis);
input[inputOffset];
auto inputValue = input.Get();
if (reduceOperation == ReduceOperation::Max)
{
if (inputValue > tempOut[outputOffset])
{
tempOut[outputOffset] = inputValue;
}
}
else if (reduceOperation == ReduceOperation::Min)
{
if (inputValue < tempOut[outputOffset])
{
tempOut[outputOffset] = inputValue;
}
}
else
{
tempOut[outputOffset] += inputValue;
}
}
// Takes average by num of elements added to get MEAN
size_t numElementsInAxis = 1;
for (unsigned int idx = 0; idx < numResolvedAxis; ++idx)
{
unsigned int current = inputDims[resolvedAxis[idx]];
ARMNN_ASSERT(armnn::numeric_cast<float>(current) <
(std::numeric_limits<float>::max() / armnn::numeric_cast<float>(numElementsInAxis)));
numElementsInAxis *= current;
}
for (unsigned int idx = 0; idx < numOutputs; ++idx)
{
output[idx];
if (reduceOperation == ReduceOperation::Mean)
{
if (numElementsInAxis > 0)
{
output.Set(tempOut[idx] / armnn::numeric_cast<float>(numElementsInAxis));
}
}
else
{
output.Set(tempOut[idx]);
}
}
}
} //namespace armnn
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