megenginelite-sys 1.8.2

A safe megenginelite wrapper in Rust
Documentation 
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
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157

/**
 * \file dnn/src/armv7/conv_bias/int8/strategy.cpp
 * MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
 *
 * Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
 *
 * Unless required by applicable law or agreed to in writing,
 * software distributed under the License is distributed on an
 * "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 */

#include "src/armv7/conv_bias/int8/strategy.h"
#include "src/arm_common/simd_macro/marm_neon.h"
#include "src/armv7/matrix_mul/asm/common.h"
#include "src/common/utils.h"
#include "src/fallback/conv_bias/common.h"

#include "src/arm_common/conv_bias/matmul_postprocess.h"
#include "src/armv7/matrix_mul/int8/kernel_4x2x16.h"

using namespace megdnn;
using namespace armv7;
using namespace armv7::matmul;

namespace impl {
template <BiasMode bmode, typename Op, int block_m, int block_n>
struct KernCaller;

template <BiasMode bmode, typename Op>
struct KernCaller<bmode, Op, 4, 2> {
    static void run(
            const dt_int8* packA, const dt_int8* packB, size_t M, size_t N, size_t K,
            dt_int8* C, size_t LDC, bool is_first_k, Op op, const dt_int32* bias,
            dt_int32* workspace) {
        megdnn_assert(is_first_k);

        constexpr size_t A_INTERLEAVE = 4;
        constexpr size_t B_INTERLEAVE = 2;
        //! K is packed to times of 4
        K = round_up<size_t>(K, 16);
        const int K4 = K * 4;
        const int K2 = K * 2;

        size_t m = 0;
        for (; m + A_INTERLEAVE - 1 < M; m += A_INTERLEAVE) {
            int8_t* output = C + (m * LDC);

            size_t n = 0;
            const dt_int8* cur_packB = packB;
            for (; n + B_INTERLEAVE - 1 < N; n += B_INTERLEAVE) {
                matmul_4x2x16::kern_4x2(
                        packA, cur_packB, K, workspace, 2, is_first_k, 4, 2);
                arm_common::ConvBiasMatmul<bmode, Op, dt_int8, 4, 2, 4, 2>::postprocess(
                        bias, workspace, output, LDC, op);
                output += B_INTERLEAVE;
                cur_packB += K2;
            }

            for (; n < N; n += B_INTERLEAVE) {
                matmul_4x2x16::kern_4x2(
                        packA, cur_packB, K, workspace, 2, is_first_k, 4,
                        std::min<size_t>(N - n, 2));
#define cb(m, n)                                                             \
    arm_common::ConvBiasMatmul<bmode, Op, dt_int8, 4, 2, 4, n>::postprocess( \
            bias, workspace, output, LDC, op);
                DISPATCH_N(cb, 4, std::min<size_t>(N - n, 2));
#undef cb
                output += B_INTERLEAVE;
                cur_packB += K2;
            }

            packA += K4;
            if (bmode == BiasMode::BROADCAST_CHANNEL_BIAS) {
                bias += A_INTERLEAVE;
            }
        }

        for (; m < M; m += A_INTERLEAVE) {
            int8_t* output = C + (m * LDC);

            size_t n = 0;
            const dt_int8* cur_packB = packB;
            for (; n < N; n += B_INTERLEAVE) {
                matmul_4x2x16::kern_4x2(
                        packA, cur_packB, K, workspace, 2, is_first_k,
                        std::min<size_t>(M - m, 4), std::min<size_t>(N - n, 2));
#define cb(m, n)                                                             \
    arm_common::ConvBiasMatmul<bmode, Op, dt_int8, 4, 2, m, n>::postprocess( \
            bias, workspace, output, LDC, op);
                DISPATCH_M(cb, std::min<size_t>(M - m, 4), std::min<size_t>(N - n, 2));
#undef cb

                output += B_INTERLEAVE;
                cur_packB += K2;
            }
            packA += K4;
            if (bmode == BiasMode::BROADCAST_CHANNEL_BIAS) {
                bias += A_INTERLEAVE;
            }
        }
    }
};

}  // namespace impl

MEGDNN_REG_GEMM_STRATEGY_IMPL(gemm_s8_4x2_nobias_identity)

void gemm_s8_4x2_nobias_identity::pack_A(
        dt_int8* outptr, const dt_int8* inptr, int ldin, int y0, int ymax, int k0,
        int kmax, bool /*transpose*/) const {
    MEGDNN_MARK_USED_VAR(matmul_4x2x16::gemm_s8_4x2_pack_A_t);
    matmul_4x2x16::gemm_s8_4x2_pack_A_n(outptr, inptr, ldin, y0, ymax, k0, kmax);
}

void gemm_s8_4x2_nobias_identity::pack_B(
        dt_int8* out, const dt_int8* in, int ldin, int x0, int xmax, int k0, int kmax,
        bool /*transpose*/) const {
    MEGDNN_MARK_USED_VAR(matmul_4x2x16::gemm_s8_4x2_pack_B_t);
    matmul_4x2x16::gemm_s8_4x2_pack_B_n(out, in, ldin, x0, xmax, k0, kmax);
}

size_t gemm_s8_4x2_nobias_identity::get_workspace_size() const {
    return 4 * 2 * sizeof(dt_int32);
}

#define KERN(_bias, _BIAS, _nonline, _OP)                                             \
    void gemm_s8_4x2_##_bias##_##_nonline::kern(                                      \
            const dt_int8* packA, const dt_int8* packB, size_t M, size_t N, size_t K, \
            dt_int8* C, size_t LDC, bool is_first_k, const dt_int32* bias,            \
            dt_int32* workspace) const {                                              \
        float scale_A = A_dtype.param<dtype::QuantizedS8>().scale;                    \
        float scale_B = B_dtype.param<dtype::QuantizedS8>().scale;                    \
        float scale_C = C_dtype.param<dtype::QuantizedS8>().scale;                    \
        DEFINE_OP(_OP);                                                               \
        impl::KernCaller<_BIAS, decltype(op), 4, 2>::run(                             \
                packA, packB, M, N, K, C, LDC, is_first_k, op, bias, workspace);      \
    }

#define DEFINE_OP(_Op) \
    arm_common::_Op<dt_qint32, dt_qint8> op(scale_A* scale_B, scale_C);

KERN(nobias, BiasMode::NO_BIAS, identity, TypeCvtOp)
KERN(nobias, BiasMode::NO_BIAS, relu, ReluOp)
KERN(nobias, BiasMode::NO_BIAS, hswish, HSwishOp)
#undef DEFINE_OP

#define DEFINE_OP(_Op)                             \
    arm_common::_Op<dt_qint32, dt_qint8, true> op( \
            scale_A* scale_B, scale_A* scale_B, scale_C);
KERN(bias_channel, BiasMode::BROADCAST_CHANNEL_BIAS, identity, AddOp)
KERN(bias_channel, BiasMode::BROADCAST_CHANNEL_BIAS, relu, FuseAddReluOp)
KERN(bias_channel, BiasMode::BROADCAST_CHANNEL_BIAS, hswish, FuseAddHSwishOp)
#undef DEFINE_OP

#undef KERN

// vim: syntax=cpp.doxygen