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/*
* Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
*
* 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 "kernel/cpu/OperationUtils.h"
#include <cmath>
#include <algorithm>
#include <cassert>
namespace neurun
{
namespace kernel
{
namespace cpu
{
uint32_t getNumberOfDimensions(const Shape &shape) { return shape.dimensions.size(); }
uint32_t getNumberOfElements(const Shape &shape)
{
uint32_t count = 1;
for (size_t i = 0; i < shape.dimensions.size(); i++)
{
count *= shape.dimensions[i];
}
return count;
}
uint32_t getSizeOfDimension(const Shape &shape, uint32_t dimensionIdx)
{
if (dimensionIdx >= shape.dimensions.size())
{
// TODO, log the error
return 0;
}
return shape.dimensions[dimensionIdx];
}
bool QuantizeMultiplierSmallerThanOne(double double_multiplier, int32_t *quantized_multiplier,
int32_t *right_shift)
{
assert(double_multiplier >= 0.);
assert(double_multiplier < 1.);
if (double_multiplier == 0.)
{
*quantized_multiplier = 0;
*right_shift = 0;
return true;
}
assert(double_multiplier > 0.);
const double q = std::frexp(double_multiplier, right_shift);
*right_shift *= -1;
int64_t q_fixed = static_cast<int64_t>(std::round(q * (1ll << 31)));
assert(q_fixed <= (1ll << 31));
if (q_fixed == (1ll << 31))
{
q_fixed /= 2;
--*right_shift;
}
assert(*right_shift >= 0);
assert(q_fixed <= std::numeric_limits<int32_t>::max());
*quantized_multiplier = static_cast<int32_t>(q_fixed);
return true;
}
bool GetQuantizedConvolutionMultipler(const Shape &inputShape, const Shape &filterShape,
const Shape &biasShape, const Shape &outputShape,
float *multiplier)
{
const float input_product_scale = inputShape.scale * filterShape.scale;
const float bias_scale = biasShape.scale;
const float output_scale = outputShape.scale;
// The following conditions must be guaranteed by the training pipeline.
assert(std::abs(input_product_scale - bias_scale) <=
1e-6 * std::min(input_product_scale, bias_scale));
assert(input_product_scale >= 0);
assert(input_product_scale < output_scale);
*multiplier = input_product_scale / output_scale;
return true;
}
bool QuantizeMultiplierGreaterThanOne(double double_multiplier, int32_t *quantized_multiplier,
int *left_shift)
{
assert(double_multiplier > 1.);
const double q = std::frexp(double_multiplier, left_shift);
int64_t q_fixed = static_cast<int64_t>(std::round(q * (1ll << 31)));
assert(q_fixed <= (1ll << 31));
if (q_fixed == (1ll << 31))
{
q_fixed /= 2;
++*left_shift;
}
assert(*left_shift >= 0);
assert(q_fixed <= std::numeric_limits<int32_t>::max());
*quantized_multiplier = static_cast<int32_t>(q_fixed);
return true;
}
void CalculateActivationRangeFloat(int32_t activation, float *activation_min, float *activation_max)
{
if (activation == ANEURALNETWORKS_FUSED_RELU)
{
*activation_min = 0.f;
*activation_max = std::numeric_limits<float>::max();
}
else if (activation == ANEURALNETWORKS_FUSED_RELU6)
{
*activation_min = 0.f;
*activation_max = 6.f;
}
else if (activation == ANEURALNETWORKS_FUSED_RELU1)
{
*activation_min = -1.f;
*activation_max = 1.f;
}
else if (activation == ANEURALNETWORKS_FUSED_NONE)
{
*activation_min = std::numeric_limits<float>::lowest();
*activation_max = std::numeric_limits<float>::max();
}
else
{
std::cout << "Unsupported fused activation function." << std::endl;
}
}
void CalculateActivationRangeUint8(int32_t activation, const Shape &outputShape, int32_t *act_min,
int32_t *act_max)
{
const int32_t qmin = std::numeric_limits<uint8_t>::min();
const int32_t qmax = std::numeric_limits<uint8_t>::max();
const auto scale = outputShape.scale;
const auto zero_point = outputShape.offset;
auto quantize = [scale, zero_point](float f) {
return zero_point + static_cast<int32_t>(std::round(f / scale));
};
if (activation == ANEURALNETWORKS_FUSED_RELU)
{
*act_min = std::max(qmin, quantize(0.0));
*act_max = qmax;
}
else if (activation == ANEURALNETWORKS_FUSED_RELU6)
{
*act_min = std::max(qmin, quantize(0.0));
*act_max = std::min(qmax, quantize(6.0));
}
else if (activation == ANEURALNETWORKS_FUSED_RELU1)
{
*act_min = std::max(qmin, quantize(-1.0));
*act_max = std::min(qmax, quantize(1.0));
}
else if (activation == ANEURALNETWORKS_FUSED_NONE)
{
*act_min = qmin;
*act_max = qmax;
}
else
{
std::cout << "Unsupported fused activation function." << std::endl;
}
}
int32_t CalculateInputRadius(int input_integer_bits, int input_left_shift)
{
const double max_input_rescaled = 1.0 * ((1 << input_integer_bits) - 1) *
(1ll << (31 - input_integer_bits)) / (1ll << input_left_shift);
// Tighten bound using floor. Suppose that we could use the exact value.
// After scaling the difference, the result would be at the maximum. Thus we
// must ensure that our value has lower magnitude.
return static_cast<int32_t>(std::floor(max_input_rescaled));
}
Shape getShape(const ::neurun::model::operand::Object &o)
{
Shape shape;
shape.type = static_cast<OperandType>(static_cast<int32_t>(o.typeInfo().type()));
shape.dimensions = std::vector<uint32_t>(o.shape().dims().begin(), o.shape().dims().end());
shape.scale = o.typeInfo().scale();
// shape.offset = _offset;
return shape;
}
size_t sizeOfData(OperandType type, const std::vector<uint32_t> &dimensions)
{
size_t size = 4;
switch (type)
{
case OperandType::SCALAR_FLOAT32:
case OperandType::SCALAR_INT32:
case OperandType::SCALAR_UINT32:
case OperandType::TENSOR_FLOAT32:
case OperandType::TENSOR_INT32:
size = 4;
break;
case OperandType::TENSOR_QUANT8_ASYMM:
size = 1;
break;
default:
throw std::runtime_error("Not supported operand type.");
break;
}
for (auto d : dimensions)
{
size *= d;
}
return size;
}
} // namespace cpu
} // namespace kernel
} // namespace neurun
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