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/*
* Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
* Copyright 2018 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 "NodeExecution.h"
#include "NodeDataImpl.h"
#include "NodeDomain.h"
#include "Validation.h"
#include <nncc/core/ADT/tensor/Shape.h>
#include <nncc/core/ADT/tensor/Buffer.h>
#include <nncc/core/ADT/tensor/Index.h>
#include <nncc/core/ADT/tensor/IndexEnumerator.h>
#include <nncc/core/ADT/tensor/LexicalLayout.h>
#include <cassert>
#include <stdexcept>
namespace
{
using nncc::core::ADT::tensor::Buffer;
using nncc::core::ADT::tensor::Shape;
using nncc::core::ADT::tensor::Index;
using nncc::core::ADT::tensor::LexicalLayout;
using nncc::core::ADT::tensor::make_buffer;
/**
* @brief Calculate Matrix Multiplication
*/
template <typename T> Buffer<T> calc_mat_mul(const Buffer<T> *lhs_buf, const Buffer<T> *rhs_buf)
{
const auto lhs_shape = lhs_buf->shape();
const auto rhs_shape = rhs_buf->shape();
assert(lhs_shape.rank() == 2 && "lhs rank must be 2");
assert(rhs_shape.rank() == 2 && "rhs rank must be 2");
// lhs width should be the same as rhs height
assert(lhs_shape.dim(1) == rhs_shape.dim(0) && "height/width mismatch");
const uint32_t lhs_height = lhs_shape.dim(0);
const uint32_t lhs_width = lhs_shape.dim(1);
const uint32_t rhs_width = rhs_shape.dim(1);
const uint32_t output_height = lhs_height;
const uint32_t output_width = rhs_width;
Shape output_shape{output_height, output_width};
auto output_buf = make_buffer<T, LexicalLayout>(output_shape);
for (uint32_t out_y = 0; out_y < output_height; ++out_y)
{
for (uint32_t out_x = 0; out_x < output_width; ++out_x)
{
T total = static_cast<T>(0); // accumulator
// Accumulate through axis
for (uint32_t axis = 0; axis < lhs_width; ++axis)
{
total += lhs_buf->at(Index({out_y, axis})) * rhs_buf->at(Index({axis, out_x}));
}
// Set output value
output_buf.at(Index({out_y, out_x})) = total;
}
}
return output_buf;
}
} // namespace
namespace
{
using namespace locomotiv;
void execute_node(loco::MatMul *mat_mul)
{
auto lhs_data = annot_data(mat_mul->lhs());
auto rhs_data = annot_data(mat_mul->rhs());
validate(lhs_data, "Can't find left matrix data of MatMul");
validate(lhs_data->shape()->rank() == 2, "lhs rank must be 2");
validate(rhs_data, "Can't find right matrix data of MatMul");
validate(rhs_data->shape()->rank() == 2, "rhs rank must be 2");
validate(annot_domain(mat_mul->lhs()) == loco::Domain::Matrix,
"Left matrix of MatMul is not a Matrix");
validate(annot_domain(mat_mul->rhs()) == loco::Domain::Matrix,
"Right matrix of MatMul is not a Matrix");
std::unique_ptr<NodeData> mat_mul_result = nullptr;
if (lhs_data->dtype() == loco::DataType::FLOAT32 && rhs_data->dtype() == loco::DataType::FLOAT32)
{
const auto lhs_buf = lhs_data->as_f32_bufptr();
const auto rhs_buf = rhs_data->as_f32_bufptr();
auto mat_mul_buf = calc_mat_mul<float>(lhs_buf, rhs_buf);
mat_mul_result = make_data(mat_mul_buf);
}
else if (lhs_data->dtype() == loco::DataType::S32 && rhs_data->dtype() == loco::DataType::S32)
{
const auto lhs_buf = lhs_data->as_s32_bufptr();
const auto rhs_buf = rhs_data->as_s32_bufptr();
auto mat_mul_buf = calc_mat_mul<int32_t>(lhs_buf, rhs_buf);
mat_mul_result = make_data(mat_mul_buf);
}
else
throw std::runtime_error("NYI for these DataTypes");
assert(mat_mul_result != nullptr);
annot_data(mat_mul, std::move(mat_mul_result));
annot_domain(mat_mul, loco::Domain::Matrix);
}
} // namespace
namespace locomotiv
{
void NodeExecution::execute(loco::MatMul *mat_mul) { execute_node(mat_mul); }
} // namespace locomotiv
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