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/*
* Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
* Copyright 2019 The TensorFlow Authors. 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 "Softmax.h"
#include "Common.h"
#include "QuantizationHelpers.h"
#include <mir/ShapeRange.h>
#include <mir/Tensor.h>
#include <cmath>
namespace mir_interpreter
{
static inline void PopulateSoftmaxLookupTable(float *table, float input_scale, float beta)
{
const float scale = -input_scale * beta;
const int32_t max_uint8 = std::numeric_limits<uint8_t>::max();
for (int32_t val = 0; val <= max_uint8; ++val)
table[max_uint8 - val] = expf(scale * val);
}
template <typename T> struct SoftmaxImpl
{
static void run(const mir::TensorVariant &arg, int axis, mir::TensorVariant &result);
};
template <typename T>
void SoftmaxImpl<T>::run(const mir::TensorVariant &arg, int axis, mir::TensorVariant &result)
{
mir::Tensor<T> arg_accessor(arg);
mir::Tensor<T> res_accessor(result);
mir::Shape expsum_shape = arg.getShape();
expsum_shape.dim(axis) = 1;
mir::TensorType expsum_type(arg.getElementType(), expsum_shape);
mir::TensorVariant expsum(expsum_type);
mir::Tensor<T> expsum_accessor(expsum);
for (const auto &expsum_index : mir::ShapeRange(expsum_shape))
{
T sum = 0;
mir::Index arg_index = expsum_index;
std::int32_t axis_size = arg.getShape().dim(axis);
for (std::int32_t i = 0; i < axis_size; ++i)
{
arg_index.at(axis) = i;
sum += std::exp(arg_accessor.at(arg_index));
}
expsum_accessor.at(expsum_index) = sum;
}
for (const auto &res_index : mir::ShapeRange(result.getShape()))
{
mir::Index expsum_index = res_index;
expsum_index.at(axis) = 0;
res_accessor.at(res_index) =
std::exp(arg_accessor.at(res_index)) / expsum_accessor.at(expsum_index);
}
}
template <> struct SoftmaxImpl<uint8_t>
{
static void run(const mir::TensorVariant &input, int axis, mir::TensorVariant &output);
};
void SoftmaxImpl<uint8_t>::run(const mir::TensorVariant &input, int axis,
mir::TensorVariant &output)
{
const auto &input_type = input.getType();
const auto &output_type = output.getType();
assert(input_type.isQuantized());
assert(output_type.isQuantized());
const auto input_shape = input_type.getShape();
assert(input_type.getElementType() == mir::DataType::UINT8);
assert(axis == input_shape.rank() - 1); // supported only last dim axis
(void)axis;
double input_scale = input_type.getQuantization().getScale();
double output_scale = output_type.getQuantization().getScale();
const int trailing_dim = input_shape.rank() - 1;
int excluding_last_dim = 1;
for (int32_t i = 0; i < input_shape.rank() - 1; i++)
{
excluding_last_dim *= input_shape.dim(i);
}
const int last_dim = input_shape.dim(trailing_dim);
const int32_t clamp_max = std::numeric_limits<uint8_t>::max();
const int32_t clamp_min = std::numeric_limits<uint8_t>::min();
uint8_t *input_data = reinterpret_cast<uint8_t *>(input.atOffset(0));
float table[256];
PopulateSoftmaxLookupTable(table, input_scale, 1.f);
uint8_t *output_data = reinterpret_cast<uint8_t *>(output.atOffset(0));
for (int i = 0; i < excluding_last_dim; ++i)
{
int32_t max_val = std::numeric_limits<uint8_t>::min();
// Find max quantized value.
for (int j = 0; j < last_dim; ++j)
{
max_val = std::max(max_val, static_cast<int32_t>(input_data[j]));
}
float sum_exp = 0.0f;
const int32_t max_uint8 = std::numeric_limits<uint8_t>::max();
const float *table_offset = &table[max_uint8 - max_val];
// Calculate normalizer sum(exp(x)).
for (int j = 0; j < last_dim; ++j)
{
sum_exp += table_offset[input_data[j]];
}
const float inv_sum_exp = 1.0f / (sum_exp * output_scale);
// Normalize and quantize probabilities.
for (int j = 0; j < last_dim; ++j)
{
const float prob_rescaled = table_offset[input_data[j]] * inv_sum_exp;
const int32_t prob_quantized = static_cast<int32_t>(prob_rescaled + 0.5);
output_data[j] =
static_cast<uint8_t>(std::max(std::min(clamp_max, prob_quantized), clamp_min));
}
input_data += last_dim;
output_data += last_dim;
}
}
void Softmax(const mir::TensorVariant &arg, int axis, mir::TensorVariant &result)
{
dispatch<SoftmaxImpl>(arg.getElementType(), arg, axis, result);
};
} // namespace mir_interpreter
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