fmha_fprop_fp16_kernel.sm80.cu 10.7 KB
Newer Older
Tri Dao's avatar
Tri Dao committed
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
/******************************************************************************
 * Copyright (c) 2011-2021, NVIDIA CORPORATION.  All rights reserved.
 * 
 * Redistribution and use in source and binary forms, with or without
 * modification, are permitted provided that the following conditions are met:
 *     * Redistributions of source code must retain the above copyright
 *       notice, this list of conditions and the following disclaimer.
 *     * Redistributions in binary form must reproduce the above copyright
 *       notice, this list of conditions and the following disclaimer in the
 *       documentation and/or other materials provided with the distribution.
 *     * Neither the name of the NVIDIA CORPORATION nor the
 *       names of its contributors may be used to endorse or promote products
 *       derived from this software without specific prior written permission.
 * 
 * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
 * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
 * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
 * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
 * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
 * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
 * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
 * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
 * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
 * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 *
 ******************************************************************************/

#include "fmha.h"
#include "fmha_fprop_kernel_1xN.h"

template<typename Kernel_traits, bool Is_dropout, bool Is_causal, bool Return_softmax>
__global__ void fmha_fprop_fp16_sm80_loop_kernel(Fused_multihead_attention_fprop_params params) {
    fmha::device_1xN_loop<Kernel_traits, Is_dropout, Is_causal, Return_softmax>(params);
}

template<typename Kernel_traits>
void run_fmha_fp16_sm80_loop_(Launch_params<Fused_multihead_attention_fprop_params> &launch_params,
                            const bool configure) {
    bool is_causal = launch_params.params.is_causal;
    // TD [2022-04-27]: This case work is pretty ugly, maybe there's a better way?
    auto kernel = launch_params.is_dropout
        ? (is_causal
           ? (launch_params.return_softmax ? &fmha_fprop_fp16_sm80_loop_kernel<Kernel_traits, true, true, true> : &fmha_fprop_fp16_sm80_loop_kernel<Kernel_traits, true, true, false>)
           : (launch_params.return_softmax ? &fmha_fprop_fp16_sm80_loop_kernel<Kernel_traits, true, false, true> : &fmha_fprop_fp16_sm80_loop_kernel<Kernel_traits, true, false, false>))
        : (is_causal
           ? (launch_params.return_softmax ? &fmha_fprop_fp16_sm80_loop_kernel<Kernel_traits, false, true, true> : &fmha_fprop_fp16_sm80_loop_kernel<Kernel_traits, false, true, false>)
           : (launch_params.return_softmax ? &fmha_fprop_fp16_sm80_loop_kernel<Kernel_traits, false, false, true> : &fmha_fprop_fp16_sm80_loop_kernel<Kernel_traits, false, false, false>));

    constexpr int N = Kernel_traits::Cta_tile_p::N;
    const int loop_steps = (launch_params.params.s + N - 1) / N;
    constexpr int smem_size_softmax_lse = Kernel_traits::Smem_dp_sum::BYTES_PER_TILE;
    // Don't need smem_size_softmax_lse if we're not looping
    const int smem_size = fmha::get_dynamic_smem_size<Kernel_traits>()
        + (loop_steps > 1 ? smem_size_softmax_lse : 0);

    if( smem_size >= 48 * 1024 ) {
        FMHA_CHECK_CUDA(cudaFuncSetAttribute(kernel, cudaFuncAttributeMaxDynamicSharedMemorySize, smem_size));
    }

    if (configure) {
        using Mma_tile_p = fmha::Hmma_tile<typename Kernel_traits::Cta_tile_p>;
        constexpr int M = Kernel_traits::Cta_tile_p::M;
        size_t STEPS = (launch_params.params.s + M - 1) / M;
        constexpr size_t MMAS_M = Mma_tile_p::MMAS_M;
        constexpr size_t MMAS_N = Mma_tile_p::MMAS_N;
        size_t elts_per_head = STEPS * MMAS_M * MMAS_N * 8 * loop_steps;
        launch_params.elts_per_thread = elts_per_head;
        return;
    }

71
    dim3 grid(launch_params.params.b, launch_params.params.h);
Tri Dao's avatar
Tri Dao committed
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
    kernel<<<grid, Kernel_traits::THREADS, smem_size, launch_params.stream>>>(
        launch_params.params);

    FMHA_CHECK_CUDA(cudaPeekAtLastError());
}

void run_fmha_fp16_sm80(Launch_params<Fused_multihead_attention_fprop_params> &launch_params,
                        const bool configure) {
    if (launch_params.params.d == 16) {
        if( launch_params.params.s == 128 ) {
            using Kernel_traits = FMHA_kernel_traits<128, 16, 16, 1, 4, 0x08u>;
            run_fmha_fp16_sm80_loop_<Kernel_traits>(launch_params, configure);
        } else if( launch_params.params.s == 256 ) {
            using Kernel_traits = FMHA_kernel_traits<256, 16, 16, 1, 4, 0x08u>;
            run_fmha_fp16_sm80_loop_<Kernel_traits>(launch_params, configure);
        } else {
            // TD [2022-05-15] 512 gives wrong results rn
            // using Kernel_traits = FMHA_kernel_traits<512, 16, 16, 1, 4, 0x08u>;
            using Kernel_traits = FMHA_kernel_traits<256, 16, 16, 1, 4, 0x08u>;
            run_fmha_fp16_sm80_loop_<Kernel_traits>(launch_params, configure);
        }
    } else if (launch_params.params.d == 32) {
        if( launch_params.params.s == 128 ) {
            using Kernel_traits = FMHA_kernel_traits<128, 32, 16, 1, 4, 0x08u>;
            run_fmha_fp16_sm80_loop_<Kernel_traits>(launch_params, configure);
        } else if( launch_params.params.s == 256 ) {
            using Kernel_traits = FMHA_kernel_traits<256, 32, 16, 1, 4, 0x08u>;
            run_fmha_fp16_sm80_loop_<Kernel_traits>(launch_params, configure);
        } else {
            using Kernel_traits = FMHA_kernel_traits<256, 32, 16, 1, 4, 0x08u>;
            run_fmha_fp16_sm80_loop_<Kernel_traits>(launch_params, configure);
        }
    } else if (launch_params.params.d == 64) {
        if( launch_params.params.s == 128 ) {
            using Kernel_traits = FMHA_kernel_traits<128, 64, 16, 1, 4, 0x08u>;
            run_fmha_fp16_sm80_loop_<Kernel_traits>(launch_params, configure);
Tri Dao's avatar
Tri Dao committed
108
109
110
111
112
113
        } else if( launch_params.params.s >= 256 ) {
            auto dprops = at::cuda::getCurrentDeviceProperties();
            if (dprops->major == 8 && dprops->minor >= 0) {
                using Kernel_traits = FMHA_kernel_traits<256, 64, 16, 1, 4, 0x08u>;
                run_fmha_fp16_sm80_loop_<Kernel_traits>(launch_params, configure);
            } else if (dprops->major == 7 && dprops->minor == 5) {
114
115
116
117
118
119
120
                if (launch_params.is_dropout) { // Need to use the same block size as backward
                    using Kernel_traits = FMHA_kernel_traits<128, 64, 16, 1, 4, 0x08u>;
                    run_fmha_fp16_sm80_loop_<Kernel_traits>(launch_params, configure);
                } else {
                    using Kernel_traits = FMHA_kernel_traits<256, 64, 16, 1, 4, 0x08u>;
                    run_fmha_fp16_sm80_loop_<Kernel_traits>(launch_params, configure);
                }
Tri Dao's avatar
Tri Dao committed
121
            }
Tri Dao's avatar
Tri Dao committed
122
123
        }
    } else if (launch_params.params.d == 128) {
Tri Dao's avatar
Tri Dao committed
124
125
126
127
128
        if( launch_params.params.s == 128 ) {
            using Kernel_traits = FMHA_kernel_traits<128, 128, 16, 1, 4, 0x08u>;
            run_fmha_fp16_sm80_loop_<Kernel_traits>(launch_params, configure);
        } else {
            auto dprops = at::cuda::getCurrentDeviceProperties();
Tri Dao's avatar
Tri Dao committed
129
            if (dprops->major == 8 && dprops->minor >= 0 && !launch_params.is_dropout) {
Tri Dao's avatar
Tri Dao committed
130
131
132
133
134
135
136
137
138
                // TD [2022-06-05] Keep K in registers to reduce register spilling
                // Gives about 6% speedup compared to using block size 128.
                using Kernel_traits = FMHA_kernel_traits<256, 128, 16, 1, 4, 0x18u>;
                run_fmha_fp16_sm80_loop_<Kernel_traits>(launch_params, configure);
            } else {  // Need to use the same block size as backward
                using Kernel_traits = FMHA_kernel_traits<128, 128, 16, 1, 4, 0x08u>;
                run_fmha_fp16_sm80_loop_<Kernel_traits>(launch_params, configure);
            }
        }
Tri Dao's avatar
Tri Dao committed
139
140
    }
    // if (launch_params.params.d == 64) {
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
    //     // using Kernel_traits = FMHA_kernel_traits<128, 64, 16, 1, 4, 0x08u>;
    //     // using Kernel_traits = FMHA_kernel_traits<64, 64, 16, 1, 4, 0x08u>;
    //     // using Kernel_traits = FMHA_kernel_traits<512, 64, 16, 1, 8, 0x08u>;
    //     using Kernel_traits = FMHA_kernel_traits<128, 64, 16, 1, 4, 0x08u>;
    //     run_fmha_fp16_sm80_loop_<Kernel_traits>(launch_params, configure);
    // }
    // if (launch_params.params.d == 64) {
    //     if( launch_params.params.s == 128 ) {
    //         using Kernel_traits = FMHA_kernel_traits<128, 64, 16, 1, 4, 0x08u>;
    //         run_fmha_fp16_sm80_loop_<Kernel_traits>(launch_params, configure);
    //     } else if( launch_params.params.s >= 256 ) {
    //         auto dprops = at::cuda::getCurrentDeviceProperties();
    //         if (dprops->major == 8 && dprops->minor >= 0) {
    //             using Kernel_traits = FMHA_kernel_traits<256, 64, 16, 1, 4, 0x08u>;
    //             run_fmha_fp16_sm80_loop_<Kernel_traits>(launch_params, configure);
    //         } else if (dprops->major == 7 && dprops->minor == 5) {
157
158
159
160
161
162
163
    //             if (launch_params.is_dropout) { // Need to use the same block size as backward
    //                 using Kernel_traits = FMHA_kernel_traits<128, 64, 16, 1, 4, 0x08u>;
    //                 run_fmha_fp16_sm80_loop_<Kernel_traits>(launch_params, configure);
    //             } else {
    //                 using Kernel_traits = FMHA_kernel_traits<256, 64, 16, 1, 4, 0x08u>;
    //                 run_fmha_fp16_sm80_loop_<Kernel_traits>(launch_params, configure);
    //             }
164
165
    //         }
    //     }
Tri Dao's avatar
Tri Dao committed
166
    // }
Tri Dao's avatar
Tri Dao committed
167
168
169
170
171
172
    // if (launch_params.params.d == 128) {
    //     if( launch_params.params.s == 128 ) {
    //         using Kernel_traits = FMHA_kernel_traits<128, 128, 16, 1, 4, 0x08u>;
    //         run_fmha_fp16_sm80_loop_<Kernel_traits>(launch_params, configure);
    //     } else {
    //         auto dprops = at::cuda::getCurrentDeviceProperties();
Tri Dao's avatar
Tri Dao committed
173
    //         if (dprops->major == 8 && dprops->minor >= 0 && !launch_params.is_dropout) {
Tri Dao's avatar
Tri Dao committed
174
175
176
177
178
179
180
181
182
183
    //             // TD [2022-06-05] Keep K in registers to reduce register spilling
    //             // Gives about 6% speedup compared to using block size 128.
    //             using Kernel_traits = FMHA_kernel_traits<256, 128, 16, 1, 4, 0x18u>;
    //             run_fmha_fp16_sm80_loop_<Kernel_traits>(launch_params, configure);
    //         } else {  // Need to use the same block size as backward
    //             using Kernel_traits = FMHA_kernel_traits<128, 128, 16, 1, 4, 0x08u>;
    //             run_fmha_fp16_sm80_loop_<Kernel_traits>(launch_params, configure);
    //         }
    //     }
    // }
Tri Dao's avatar
Tri Dao committed
184
}