1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
|
/*
* 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 "NodeDataImpl.h"
#include "NodeDomain.h"
#include "Validation.h"
#include <nncc/core/ADT/tensor/LexicalLayout.h>
#include <nncc/core/ADT/tensor/IndexEnumerator.h>
#include <stdexcept>
#include <cassert>
namespace
{
using nncc::core::ADT::tensor::Buffer;
using nncc::core::ADT::tensor::make_buffer;
using nncc::core::ADT::tensor::LexicalLayout;
using nncc::core::ADT::tensor::Shape;
using nncc::core::ADT::tensor::IndexEnumerator;
using nncc::core::ADT::tensor::Index;
template <typename T>
std::unique_ptr<locomotiv::NodeData> matrix_decode(const loco::MatrixDecode *node,
const Buffer<T> *input_buf)
{
auto decoder = node->decoder();
// Make MatrixShape from input. Note that matrix in locomotiv represented as HW
loco::MatrixShape input_shape;
assert(input_buf->shape().rank() == 2);
input_shape.height() = input_buf->shape().dim(0);
input_shape.width() = input_buf->shape().dim(1);
loco::TensorShape node_shape = decoder->shape(input_shape);
// Make tensor buffer from TensorShape
Buffer<T> node_buf =
make_buffer<T, LexicalLayout>(Shape{node_shape.dim(0).value(), node_shape.dim(1).value()});
// Copy buffer in an order arranged by decoder
for (IndexEnumerator e{node_buf.shape()}; e.valid(); e.advance())
{
loco::MatrixIndex matrix_index = decoder->value(e.current());
Index buf_index({matrix_index.row(), matrix_index.column()});
node_buf.at(e.current()) = input_buf->at(buf_index);
}
return locomotiv::make_data(node_buf);
}
} // namespace
namespace locomotiv
{
void NodeExecution::execute(loco::MatrixDecode *matrix_dec)
{
auto input_data = annot_data(matrix_dec->input());
validate(input_data, "Input not ready");
validate(annot_domain(matrix_dec->input()) == loco::Domain::Matrix,
"Input domain should be Matrix");
validate(input_data->shape()->rank() == 2, "Input data rank must be 2");
std::unique_ptr<NodeData> matrix_dec_data = nullptr;
switch (input_data->dtype())
{
case loco::DataType::S32:
{
auto input_buf = input_data->as_s32_bufptr();
matrix_dec_data = matrix_decode<int32_t>(matrix_dec, input_buf);
break;
}
case loco::DataType::FLOAT32:
{
auto input_buf = input_data->as_f32_bufptr();
matrix_dec_data = matrix_decode<float>(matrix_dec, input_buf);
break;
}
default:
throw std::runtime_error("NYI for this DataType");
}
assert(matrix_dec_data != nullptr);
annot_data(matrix_dec, std::move(matrix_dec_data));
annot_domain(matrix_dec, loco::Domain::Tensor);
}
} // namespace locomotiv
|
