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/*
* Copyright (c) 2019 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 "NodeExecution.h"
#include "locomotiv/NodeData.h"
#include "NodeDataImpl.h"
#include "NodeDomain.h"
#include <loco/IR/PermutingCodec.h>
#include <nncc/core/ADT/tensor/Shape.h>
#include <nncc/core/ADT/tensor/Buffer.h>
#include <nncc/core/ADT/tensor/LexicalLayout.h>
#include <nncc/core/ADT/tensor/IndexEnumerator.h>
#include <gtest/gtest.h>
using nncc::core::ADT::tensor::Index;
using nncc::core::ADT::tensor::Shape;
using nncc::core::ADT::tensor::LexicalLayout;
using nncc::core::ADT::tensor::make_buffer;
using nncc::core::ADT::tensor::IndexEnumerator;
using nncc::core::ADT::tensor::Buffer;
// This file is intended to test MatrixEncode and MatrixDecode at once
namespace
{
class NodeExecution_MatrixCodec : public ::testing::Test
{
private:
loco::Graph g;
protected:
/// @brief Make Pull node and set data by given buffer and data type
template <typename DT> loco::Pull *pull_layer(Buffer<DT> &pull_buf, loco::DataType dtype)
{
auto pull = g.nodes()->create<loco::Pull>();
pull->dtype(dtype);
auto pull_data = locomotiv::make_data(pull_buf);
locomotiv::annot_data(pull, std::move(pull_data));
locomotiv::annot_domain(pull, loco::Domain::Tensor);
return pull;
}
/// @brief Make MatrixEncode node with given input and encoding permutation
loco::MatrixEncode *matrix_encode_layer(loco::Node *input,
const loco::Permutation<loco::Domain::Matrix> &perm)
{
auto encoder = std::unique_ptr<loco::PermutingEncoder<loco::Domain::Matrix>>(
new loco::PermutingEncoder<loco::Domain::Matrix>);
encoder->perm(perm);
auto enc = g.nodes()->create<loco::MatrixEncode>();
enc->input(input);
enc->encoder(std::move(encoder));
return enc;
}
/// @brief Make MatrixDecode node with given input and decoding permutation
loco::MatrixDecode *matrix_decode_layer(loco::Node *input,
const loco::Permutation<loco::Domain::Matrix> &perm)
{
auto decoder = std::unique_ptr<loco::PermutingDecoder<loco::Domain::Matrix>>(
new loco::PermutingDecoder<loco::Domain::Matrix>);
decoder->perm(perm);
auto dec = g.nodes()->create<loco::MatrixDecode>();
dec->input(input);
dec->decoder(std::move(decoder));
return dec;
}
};
} // namespace
TEST_F(NodeExecution_MatrixCodec, HW_s32)
{
const uint32_t H = 3;
const uint32_t W = 4;
// Make HW data for pull node
auto pull_buf = make_buffer<int32_t, LexicalLayout>(Shape{H, W});
int32_t i = 0;
for (IndexEnumerator e{pull_buf.shape()}; e.valid(); e.advance())
{
pull_buf.at(e.current()) = i;
++i; // Doesn't matter what it is
}
// Make HW permutation for encoder and decoder
loco::Permutation<loco::Domain::Matrix> HW;
HW.axis(loco::MatrixAxis::Height) = 0;
HW.axis(loco::MatrixAxis::Width) = 1;
// Pull
auto pull = pull_layer(pull_buf, loco::DataType::S32);
// MatrixEncode
auto enc = matrix_encode_layer(pull, HW);
locomotiv::NodeExecution::get().run(enc);
// Test MatrixEncode
auto enc_data = locomotiv::annot_data(enc);
ASSERT_NE(enc_data, nullptr);
ASSERT_EQ(loco::DataType::S32, enc_data->dtype());
ASSERT_EQ((Shape{H, W}), *(enc_data->shape())); // locomotiv matrix is HW
auto enc_buf = enc_data->as_s32_bufptr();
for (uint32_t h = 0; h < H; ++h)
for (uint32_t w = 0; w < W; ++w)
ASSERT_EQ(enc_buf->at(Index{h, w}), pull_buf.at(Index{h, w}));
ASSERT_EQ(loco::Domain::Matrix, locomotiv::annot_domain(enc));
// MatrixDecode
auto dec = matrix_decode_layer(enc, HW);
locomotiv::NodeExecution::get().run(dec);
// Test MatrixDecode: Encode -> Decode == identity
auto dec_data = locomotiv::annot_data(dec);
ASSERT_NE(dec_data, nullptr);
ASSERT_EQ(loco::DataType::S32, dec_data->dtype());
ASSERT_EQ((Shape{H, W}), *(dec_data->shape()));
auto dec_buf = dec_data->as_s32_bufptr();
for (uint32_t h = 0; h < H; ++h)
for (uint32_t w = 0; w < W; ++w)
ASSERT_EQ(dec_buf->at(Index{h, w}), pull_buf.at(Index{h, w}));
ASSERT_EQ(loco::Domain::Tensor, locomotiv::annot_domain(dec));
}
TEST_F(NodeExecution_MatrixCodec, WH_f32)
{
const uint32_t W = 6;
const uint32_t H = 5;
// Make crazy WH data for pull node
auto pull_buf = make_buffer<float, LexicalLayout>(Shape{W, H});
float f = 0.0f;
for (IndexEnumerator e{pull_buf.shape()}; e.valid(); e.advance())
{
pull_buf.at(e.current()) = f;
f += 0.1f; // Doesn't matter what it is
}
// Make WH permutation for encoder and decoder
loco::Permutation<loco::Domain::Matrix> WH;
WH.axis(loco::MatrixAxis::Width) = 0;
WH.axis(loco::MatrixAxis::Height) = 1;
// Pull
auto pull = pull_layer(pull_buf, loco::DataType::FLOAT32);
// MatrixEncode
auto enc = matrix_encode_layer(pull, WH);
locomotiv::NodeExecution::get().run(enc);
// Test MatrixEncode
auto enc_data = locomotiv::annot_data(enc);
ASSERT_NE(enc_data, nullptr);
ASSERT_EQ(loco::DataType::FLOAT32, enc_data->dtype());
ASSERT_EQ((Shape{H, W}), *(enc_data->shape())); // locomotiv matrix is HW
auto enc_buf = enc_data->as_f32_bufptr();
for (uint32_t h = 0; h < H; ++h)
for (uint32_t w = 0; w < W; ++w)
ASSERT_FLOAT_EQ(enc_buf->at(Index{h, w}), pull_buf.at(Index{w, h}));
ASSERT_EQ(loco::Domain::Matrix, locomotiv::annot_domain(enc));
// MatrixDecode
auto dec = matrix_decode_layer(enc, WH);
locomotiv::NodeExecution::get().run(dec);
// Test MatrixDecode: Encode -> Decode == identity
auto dec_data = locomotiv::annot_data(dec);
ASSERT_NE(dec_data, nullptr);
ASSERT_EQ(loco::DataType::FLOAT32, dec_data->dtype());
ASSERT_EQ((Shape{W, H}), *(dec_data->shape()));
auto dec_buf = dec_data->as_f32_bufptr();
for (uint32_t h = 0; h < H; ++h)
for (uint32_t w = 0; w < W; ++w)
ASSERT_FLOAT_EQ(dec_buf->at(Index{w, h}), pull_buf.at(Index{w, h}));
ASSERT_EQ(loco::Domain::Tensor, locomotiv::annot_domain(dec));
}
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