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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 "Concat.h"
#include "Common.h"
#include <cmath>
#include <cstring>
namespace mir_interpreter
{
template <typename T> struct ConcatImpl
{
static void run(const std::vector<std::reference_wrapper<const mir::TensorVariant>> &inputs,
int axis, mir::TensorVariant &output);
};
template <typename T>
void ConcatImpl<T>::run(const std::vector<std::reference_wrapper<const mir::TensorVariant>> &inputs,
int axis, mir::TensorVariant &output)
{
const auto &output_shape = output.getShape();
const size_t inputs_count = inputs.size();
const int32_t concat_dims = output_shape.rank();
int64_t concat_size = 0;
for (size_t i = 0; i < inputs_count; i++)
{
const auto &input_shape = inputs[i].get().getShape();
assert(input_shape.rank() == concat_dims);
for (int32_t j = 0; j < concat_dims; j++)
{
if (j != axis)
{
assert(input_shape.dim(j) == output_shape.dim(j));
}
}
concat_size += input_shape.dim(axis);
}
assert(concat_size == output_shape.dim(axis));
// Outer size before axis
int32_t outer_size = 1;
for (int32_t i = 0; i < axis; i++)
outer_size *= output_shape.dim(i);
// Inner size after axis
int32_t base_inner_size = 1;
for (int32_t i = axis + 1; i < concat_dims; i++)
base_inner_size *= output_shape.dim(i);
// flatten = outer_size * dim(axis) * base_inner_size;
std::vector<int32_t> copy_sizes;
std::vector<char *> input_ptrs;
for (size_t i = 0; i < inputs_count; i++)
{
const auto input_shape = inputs[i].get().getShape();
copy_sizes.push_back(input_shape.dim(axis) * base_inner_size);
input_ptrs.push_back(inputs[i].get().atOffset(0));
}
char *output_ptr = output.atOffset(0);
const size_t elem_size = inputs[0].get().getElementSize();
for (int32_t i = 0; i < outer_size; i++)
{
for (size_t j = 0; j < inputs_count; j++)
{
std::memcpy(output_ptr, input_ptrs[j], copy_sizes[j] * elem_size);
output_ptr += copy_sizes[j] * elem_size;
input_ptrs[j] += copy_sizes[j] * elem_size;
}
}
}
template <> struct ConcatImpl<uint8_t>
{
static void run(const std::vector<std::reference_wrapper<const mir::TensorVariant>> &inputs,
int axis, mir::TensorVariant &output);
};
void ConcatImpl<uint8_t>::run(
const std::vector<std::reference_wrapper<const mir::TensorVariant>> &inputs, int axis,
mir::TensorVariant &output)
{
const size_t inputs_count = inputs.size();
std::vector<int32_t> input_zeropoints(inputs_count);
std::vector<float> input_scales(inputs_count);
const auto &output_shape = output.getShape();
const int32_t concat_dimensions = output_shape.rank();
int64_t concat_size = 0;
for (size_t i = 0; i < inputs_count; i++)
{
const auto &input_type = inputs[i].get().getType();
assert(input_type.isQuantized());
assert(input_type.getElementType() == mir::DataType::UINT8);
const auto &input_shape = input_type.getShape();
assert(input_shape.rank() == concat_dimensions);
for (int32_t j = 0; j < concat_dimensions; j++)
if (j != axis)
assert(input_shape.dim(j) == output_shape.dim(j));
concat_size += input_shape.dim(axis);
input_zeropoints[i] = input_type.getQuantization().getZeroPoint();
input_scales[i] = input_type.getQuantization().getScale();
}
assert(concat_size == output_shape.dim(axis));
const auto &output_type = output.getType();
assert(output_type.isQuantized());
int32_t output_zeropoint = output_type.getQuantization().getZeroPoint();
float output_scale = output_type.getQuantization().getScale();
// Outer size before axis
int32_t outer_size = 1;
for (int32_t i = 0; i < axis; i++)
outer_size *= output_shape.dim(i);
// Inner size after axis
int32_t base_inner_size = 1;
for (int32_t i = axis + 1; i < concat_dimensions; i++)
base_inner_size *= output_shape.dim(i);
// flatten = outer_size * dim(axis) * base_inner_size;
uint8_t *output_ptr = reinterpret_cast<uint8_t *>(output.atOffset(0));
const float inverse_output_scale = 1.f / output_scale;
for (int k = 0; k < outer_size; k++)
{
for (size_t i = 0; i < inputs_count; ++i)
{
const mir::TensorVariant &input = inputs[i];
const int copy_size = input.getShape().dim(axis) * base_inner_size;
const char *input_data = input.atOffset(0) + k * copy_size;
const uint8_t *input_ptr = reinterpret_cast<const uint8_t *>(input_data);
if (input_zeropoints[i] == output_zeropoint && input_scales[i] == output_scale)
{
std::memcpy(output_ptr, input_ptr, copy_size);
}
else
{
const float scale = input_scales[i] * inverse_output_scale;
const float bias = -input_zeropoints[i] * scale;
for (int j = 0; j < copy_size; ++j)
{
const int32_t value =
static_cast<int32_t>(std::round(input_ptr[j] * scale + bias)) + output_zeropoint;
output_ptr[j] = static_cast<uint8_t>(std::max(std::min(255, value), 0));
}
}
output_ptr += copy_size;
}
}
}
void Concat(const std::vector<std::reference_wrapper<const mir::TensorVariant>> &inputs, int axis,
mir::TensorVariant &output)
{
dispatch<ConcatImpl>(inputs[0].get().getElementType(), inputs, axis, output);
}
} // namespace mir_interpreter
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