profile_gemm_universal_batched.cpp 9.03 KB
Newer Older
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
// SPDX-License-Identifier: MIT
// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.

#include <cstdint>
#include <iostream>
#include <numeric>
#include <initializer_list>
#include <cstdlib>

#include "profiler/profile_gemm_universal_batched_impl.hpp"
#include "profiler_operation_registry.hpp"

#include "ck/library/tensor_operation_instance/gpu/gemm_universal_batched.hpp"

enum struct GemmMatrixLayout
{
    MK_KN_MN, // 0
    MK_NK_MN, // 1
    KM_KN_MN, // 2
    KM_NK_MN, // 3
};

enum struct GemmDataType
{
    BF16_BF16_BF16, // 0
    F8_F8_BF16,     // 1
};

#define OP_NAME "gemm_universal_batched"
#define OP_DESC "Batched GEMM Universal"

int profile_batched_gemm_universal(int argc, char* argv[])
{
34
    if(argc != 19 && argc != 22)
35
36
37
38
39
40
41
42
43
44
45
46
    {
        // clang-format off
        printf("arg1: tensor operation (" OP_NAME ": " OP_DESC ")\n");
        printf("arg2: data type (0: bf16, 1: fp8->bf16)\n");
        printf("arg3: matrix layout (0: A[g, m, k] * B[g, k, n] = C[g, m, n];\n");
        printf("                     1: A[g, m, k] * B[g, n, k] = C[g, m, n];\n");
        printf("                     2: A[g, k, m] * B[g, k, n] = C[g, m, n];\n");
        printf("                     3: A[g, k, m] * B[g, n, k] = C[g, m, n])\n");
        printf("arg4: verification (0: no; 1: yes)\n");
        printf("arg5: initialization (0: no init; 1: integer value; 2: decimal value)\n");
        printf("arg6: print tensor value (0: no; 1: yes)\n");
        printf("arg7: time kernel (0=n0, 1=yes)\n");
47
        printf("arg8 to 18: M, N, K, StrideA, StrideB, StrideC, BatchStrideA, BatchStrideB, BatchStrideC, BatchCount, KBatch\n");
48
        printf("optional:\n");
49
50
51
        printf("arg19: number of warm-up cycles (default 1)\n");
        printf("arg20: number of iterations (default 10)\n");
        printf("arg21: memory for rotating buffer (default 0, size in MB)\n");
52
53
54
55
56
57
58
        // clang-format on
        exit(1);
    }

    int n_warmup      = 1;
    int n_iter        = 10;
    uint64_t rotating = 0;
59
    if(argc == 22)
60
    {
61
62
63
        n_warmup = std::stoi(argv[19]);
        n_iter   = std::stoi(argv[20]);
        rotating = std::stoull(argv[21]) * 1024 * 1024;
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
    }

    const auto data_type       = static_cast<GemmDataType>(std::stoi(argv[2]));
    const auto layout          = static_cast<GemmMatrixLayout>(std::stoi(argv[3]));
    const bool do_verification = std::stoi(argv[4]);
    const int init_method      = std::stoi(argv[5]);
    const bool do_log          = std::stoi(argv[6]);
    const bool time_kernel     = std::stoi(argv[7]);

    const int M = std::stoi(argv[8]);
    const int N = std::stoi(argv[9]);
    const int K = std::stoi(argv[10]);

    const int StrideA = std::stoi(argv[11]);
    const int StrideB = std::stoi(argv[12]);
    const int StrideC = std::stoi(argv[13]);

    const int BatchStrideA = std::stoi(argv[14]);
    const int BatchStrideB = std::stoi(argv[15]);
    const int BatchStrideC = std::stoi(argv[16]);

    const int BatchCount = std::stoi(argv[17]);
86
    const int KBatch     = std::stoi(argv[18]);
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
158
159
160
161
162

#if defined(CK_USE_FP8_ON_UNSUPPORTED_ARCH) || defined(CK_USE_GFX94)
    using F8 = ck::f8_t;
#endif
    using BF16 = ck::bhalf_t;

    using Row = ck::tensor_layout::gemm::RowMajor;
    using Col = ck::tensor_layout::gemm::ColumnMajor;

    auto profile =
        [&](auto a_type, auto b_type, auto c_type, auto a_layout, auto b_layout, auto c_layout) {
            using ADataType  = decltype(a_type);
            using BDataType  = decltype(b_type);
            using DsDataType = ck::Tuple<>;
            using CDataType  = decltype(c_type);

            using ALayout  = decltype(a_layout);
            using BLayout  = decltype(b_layout);
            using DsLayout = ck::Tuple<>;
            using CLayout  = decltype(c_layout);

            const int DefaultStrideA = ck::is_same_v<ALayout, Row> ? K : M;
            const int DefaultStrideB = ck::is_same_v<BLayout, Row> ? N : K;
            const int DefaultStrideC = ck::is_same_v<CLayout, Row> ? N : M;

            const int StrideA_ = (StrideA < 0) ? DefaultStrideA : StrideA;
            const int StrideB_ = (StrideB < 0) ? DefaultStrideB : StrideB;
            const int StrideC_ = (StrideC < 0) ? DefaultStrideC : StrideC;

            const int DefaultBatchStrideA = (ck::is_same_v<ALayout, Row> ? M : K) * StrideA_;
            const int DefaultBatchStrideB = (ck::is_same_v<BLayout, Row> ? K : N) * StrideB_;
            const int DefaultBatchStrideC = (ck::is_same_v<CLayout, Row> ? M : N) * StrideC_;

            const int BatchStrideA_ = (BatchStrideA < 0) ? DefaultBatchStrideA : BatchStrideA;
            const int BatchStrideB_ = (BatchStrideB < 0) ? DefaultBatchStrideB : BatchStrideB;
            const int BatchStrideC_ = (BatchStrideC < 0) ? DefaultBatchStrideC : BatchStrideC;

            using AElementOp = ck::tensor_operation::element_wise::PassThrough;
            using BElementOp = ck::tensor_operation::element_wise::PassThrough;
            using CElementOp = ck::tensor_operation::element_wise::PassThrough;

            using DeviceOp = ck::tensor_operation::device::DeviceBatchedGemmV2MultiD<ALayout,
                                                                                     BLayout,
                                                                                     DsLayout,
                                                                                     CLayout,
                                                                                     ADataType,
                                                                                     BDataType,
                                                                                     DsDataType,
                                                                                     CDataType,
                                                                                     AElementOp,
                                                                                     BElementOp,
                                                                                     CElementOp>;

            bool pass = ck::profiler::profile_gemm_universal_batched_impl<ADataType,
                                                                          BDataType,
                                                                          CDataType,
                                                                          ALayout,
                                                                          BLayout,
                                                                          CLayout,
                                                                          AElementOp,
                                                                          BElementOp,
                                                                          CElementOp,
                                                                          DeviceOp>(do_verification,
                                                                                    init_method,
                                                                                    do_log,
                                                                                    time_kernel,
                                                                                    M,
                                                                                    N,
                                                                                    K,
                                                                                    BatchStrideA_,
                                                                                    BatchStrideB_,
                                                                                    BatchStrideC_,
                                                                                    StrideA_,
                                                                                    StrideB_,
                                                                                    StrideC_,
                                                                                    BatchCount,
163
                                                                                    KBatch,
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
                                                                                    n_warmup,
                                                                                    n_iter,
                                                                                    rotating);

            return pass ? 0 : 1;
        };

    if(data_type == GemmDataType::BF16_BF16_BF16 && layout == GemmMatrixLayout::MK_NK_MN)
    {
        return profile(BF16{}, BF16{}, BF16{}, Row{}, Col{}, Row{});
    }
#if defined(CK_USE_FP8_ON_UNSUPPORTED_ARCH) || defined(CK_USE_GFX94)
    else if(data_type == GemmDataType::F8_F8_BF16 && layout == GemmMatrixLayout::MK_NK_MN)
    {
        return profile(F8{}, F8{}, BF16{}, Row{}, Col{}, Row{});
    }
#endif
    else
    {
        std::cout << "this data_type & layout is not implemented" << std::endl;

        return 1;
    }
}

REGISTER_PROFILER_OPERATION(OP_NAME, OP_DESC, profile_batched_gemm_universal);