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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 <nncc/core/ADT/tensor/Shape.h>
#include <nncc/core/ADT/tensor/Buffer.h>
#include <nncc/core/ADT/tensor/Overlay.h>
#include <nncc/core/ADT/tensor/LexicalLayout.h>
#include "nncc/core/ADT/tensor/IndexEnumerator.h"
#include <gtest/gtest.h>
namespace
{
using nncc::core::ADT::tensor::Shape;
using nncc::core::ADT::tensor::LexicalLayout;
using nncc::core::ADT::tensor::make_buffer;
using nncc::core::ADT::tensor::make_overlay;
template <typename T>
void run_test(const T *lhs, const T *rhs, const T *expected_output, const Shape &lhs_shape,
const Shape &rhs_shape, const Shape &out_shape, loco::DataType expected_datatype)
{
auto g = loco::make_graph();
// Fill lhs MatrixEncode
auto lhs_enc = g->nodes()->create<loco::MatrixEncode>();
{
auto lhs_enc_buf = make_buffer<T, LexicalLayout>(lhs_shape);
auto lhs_overlay = make_overlay<T, LexicalLayout>(lhs_shape, const_cast<T *>(lhs));
for (nncc::core::ADT::tensor::IndexEnumerator e{lhs_shape}; e.valid(); e.advance())
{
const auto &ind = e.current();
lhs_enc_buf.at(ind) = lhs_overlay.at(ind);
}
auto enc_data = locomotiv::make_data(lhs_enc_buf);
locomotiv::annot_data(lhs_enc, std::move(enc_data));
locomotiv::annot_domain(lhs_enc, loco::Domain::Matrix);
}
// Fill rhs MatrixEncode
auto rhs_enc = g->nodes()->create<loco::MatrixEncode>();
{
auto rhs_enc_buf = make_buffer<T, LexicalLayout>(rhs_shape);
auto rhs_overlay = make_overlay<T, LexicalLayout>(rhs_shape, const_cast<T *>(rhs));
for (nncc::core::ADT::tensor::IndexEnumerator e{rhs_shape}; e.valid(); e.advance())
{
const auto &ind = e.current();
rhs_enc_buf.at(ind) = rhs_overlay.at(ind);
}
auto enc_data = locomotiv::make_data(rhs_enc_buf);
locomotiv::annot_data(rhs_enc, std::move(enc_data));
locomotiv::annot_domain(rhs_enc, loco::Domain::Matrix);
}
// build MatMul
auto mat_mul = g->nodes()->create<loco::MatMul>();
mat_mul->lhs(lhs_enc);
mat_mul->rhs(rhs_enc);
// run interpreter
locomotiv::NodeExecution::get().run(mat_mul);
// get result of calculation
auto mat_mul_result = locomotiv::annot_data(mat_mul);
// check the result
ASSERT_NE(mat_mul_result, nullptr);
ASSERT_TRUE(mat_mul_result->dtype() == expected_datatype);
ASSERT_TRUE(*(mat_mul_result->shape()) == out_shape);
auto out_overlay = make_overlay<T, LexicalLayout>(out_shape, const_cast<T *>(expected_output));
for (nncc::core::ADT::tensor::IndexEnumerator e{out_shape}; e.valid(); e.advance())
{
const auto &ind = e.current();
if (expected_datatype == loco::DataType::FLOAT32)
ASSERT_FLOAT_EQ(out_overlay.at(ind), mat_mul_result->as_f32_bufptr()->at(ind));
else if (expected_datatype == loco::DataType::S32)
ASSERT_EQ(out_overlay.at(ind), mat_mul_result->as_s32_bufptr()->at(ind));
else
throw std::runtime_error("NYI for these DataTypes");
}
ASSERT_EQ(loco::Domain::Matrix, locomotiv::annot_domain(mat_mul));
}
} // namespace
// clang-format off
/* from the code below:
import numpy as np
a = [[-0.48850584, 1.4292705, -1.3424522],
[1.7021934, -0.39246717, 0.6248314]]
b = [[-0.0830195, 0.21088193, -0.11781317],
[0.07755677, 1.6337638, 1.0792778],
[-1.6922939, -1.5437212, 0.96667504]]
print(np.array(a) @ np.array(b))
*/
TEST(NodeExecution_MatMul, f32_2x3_3x3)
{
using nncc::core::ADT::tensor::Shape;
const float lhs[] =
{
-0.48850584, 1.4292705, -1.3424522,
1.7021934, -0.39246717, 0.6248314
};
const float rhs[] =
{
-0.0830195, 0.21088193, -0.11781317,
0.07755677, 1.6337638, 1.0792778,
-1.6922939, -1.5437212, 0.96667504
};
const float out[] =
{
2.42322878, 4.30444527, 0.30241731,
-1.2291521, -1.2468023, -0.02011299
};
run_test<float>(lhs, rhs, out, Shape{2, 3}, Shape{3, 3}, Shape{2, 3}, loco::DataType::FLOAT32);
}
/* from the code below:
import numpy as np
a = np.random.randint(10000, size=(4, 2))
b = np.random.randint(10000, size=(2, 6))
print(a)
print(b)
print(np.array(a) @ np.array(b))
*/
TEST(NodeExecution_MatMul, s32_4x2_2x6)
{
using nncc::core::ADT::tensor::Shape;
const int32_t lhs[] =
{
6392, 4993,
54, 9037,
3947, 5820,
5800, 4181
};
const int32_t rhs[] =
{
2694, 8376, 8090, 1285, 7492, 1652,
5427, 8798, 7634, 2229, 5439, 6999
};
const int32_t out[] =
{
44317059, 97467806, 89827842, 19343117, 75045791, 45505591,
49189275, 79959830, 69425318, 20212863, 49556811, 63339171,
42218358, 84264432, 76361110, 18044675, 61225904, 47254624,
38315487, 85365238, 78839754, 16772449, 66194059, 38844419
};
run_test<int32_t>(lhs, rhs, out, Shape{4, 2}, Shape{2, 6}, Shape{4, 6}, loco::DataType::S32);
}
// clang-format on
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