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
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
|
/*
* Copyright (c) Facebook, Inc. and its affiliates.
* All rights reserved.
* This source code is licensed under the BSD-style license found in the
* LICENSE file in the root directory of this source tree.
*/
#pragma once
#include <cstdint>
namespace fbgemm {
// KERNEL_PROD is the product of all kernels.
// For example, KERNEL_PROD = 9 for 3x3, and 27 for 3x3x3.
template <int KERNEL_PROD>
class PackedDepthWiseConvMatrix {
public:
// smat in RSG layout
PackedDepthWiseConvMatrix(int K, const std::int8_t* smat);
virtual ~PackedDepthWiseConvMatrix();
const std::int8_t* PackedMat() const {
return pmat_;
}
private:
int K_;
std::int8_t* pmat_;
}; // Packed3x3ConvMatrix
using Packed3x3ConvMatrix = PackedDepthWiseConvMatrix<3 * 3>;
using Packed3x3x3ConvMatrix = PackedDepthWiseConvMatrix<3 * 3 * 3>;
/**
* Depth-wise 3x3 convolution with pad=1 and stride=1 and K a multiple of 8
* @params A The input image in NHWK layout
* @params Bp The pre-packed filter
*/
void depthwise_3x3_pad_1(
int N,
int H,
int W,
int K,
int stride_h,
int stride_w,
std::int32_t A_zero_point,
const std::uint8_t* A,
const Packed3x3ConvMatrix& Bp,
std::int32_t* C,
int thread_id = 0,
int num_threads = 1);
/**
* Depth-wise 3x3 convolution with pad=1 and stride=1 and K a multiple of 8
* This version is fused with requantization.
*/
void depthwise_3x3_pad_1(
int N,
int H,
int W,
int K,
int stride_h,
int stride_w,
std::int32_t A_zero_point,
const std::uint8_t* A,
std::int32_t B_zero_point,
const Packed3x3ConvMatrix& Bp,
float C_multiplier,
std::int32_t C_zero_point,
std::uint8_t* C,
const std::int32_t* col_offsets,
const std::int32_t* bias,
int thread_id = 0,
int num_threads = 1,
bool fuse_relu = false);
/**
* Depth-wise 3x3 convolution with pad=1 and stride=1 and K a multiple of 8
* This version is fused with requantization and uses per-channel quantization.
*/
void depthwise_3x3_per_channel_quantization_pad_1(
int N,
int H,
int W,
int K,
int stride_h,
int stride_w,
std::int32_t A_zero_point,
const std::uint8_t* A,
const std::int32_t* B_zero_point,
const Packed3x3ConvMatrix& Bp,
const float* C_multiplier,
std::int32_t C_zero_point,
std::uint8_t* C,
const std::int32_t* col_offsets,
const std::int32_t* bias,
int thread_id = 0,
int num_threads = 1);
void depthwise_3x3x3_pad_1(
int N,
int T,
int H,
int W,
int K,
int stride_t,
int stride_h,
int stride_w,
std::int32_t A_zero_point,
const std::uint8_t* A,
const Packed3x3x3ConvMatrix& Bp,
std::int32_t* C,
int thread_id = 0,
int num_threads = 1);
void depthwise_3x3x3_pad_1(
int N,
int T,
int H,
int W,
int K,
int stride_t,
int stride_h,
int stride_w,
std::int32_t A_zero_point,
const std::uint8_t* A,
std::int32_t B_zero_point,
const Packed3x3x3ConvMatrix& Bp,
float C_multiplier,
std::int32_t C_zero_point,
std::uint8_t* C,
const std::int32_t* col_offsets,
const std::int32_t* bias,
bool fuse_relu = false,
int thread_id = 0,
int num_threads = 1);
} // namespace fbgemm
|
