Files
composable_kernel/profiler/src/profile_batched_gemm_gemm.cpp
ltqin 370efa6c08 batched_gemm + multiple_d + gemm + multiple_d (#394)
* refactor

* start

* add device gemm file

* add BatchStrideD0

* add stridd0

* add gridwise file

* add d0 parameters to gridwise gemm

* add c layout transformer

* add d0 threadwise copy

* init kernel

* init kernel

* regular code

* nm desc put to out

* kernel parameter can not use reference

* host add bias+gelu

* run right for bias+gelu

* change AddFastGelu into another file

* interface add d1 bias parameters

* add d1 parameter to argument

* add d1 parameter to gridwise

* first all code,not verify

* gelu change to relu and GetElementSpaceSize bug

* add instance

* start add to ckprofiler

* ckprofiler finish code

* change input parameter for ckProfiler

* fix host bias+gelu bug

* show help for ckProfiler

* fix bug for lunch kernel ignore parametes

* add pad and fix about bug

* mutiple d0

* add dynamic d0_element_op

* change profiler and  instance to mutiple d0

* example have 2 d0

* remove some comments not using

* change 2 d0 have self  parameters

* change d element_op name

* change class name(multiple_d)

* fix bug

* fix bug that don't find file

* update profiler

* refactor

* update profiler

* clean

* revert example change

* add gon layout

* optimize parameter for gno

* add gon to gemm+gemm

* change helping input parameters

* change to GemmPadder_v2

* using ForEach

* fix gb_per_sec

Co-authored-by: Chao Liu <lc.roy86@gmail.com>
Co-authored-by: ltqin <letaoqin@amd.com>
2022-09-14 17:54:18 -05:00

182 lines
6.3 KiB
C++

// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include <iostream>
#include <numeric>
#include <initializer_list>
#include <cstdlib>
#include "profiler/include/profile_batched_gemm_gemm_impl.hpp"
using F16 = ck::half_t;
using F32 = float;
using Row = ck::tensor_layout::gemm::RowMajor;
using Col = ck::tensor_layout::gemm::ColumnMajor;
int profile_batched_gemm_gemm(int argc, char* argv[])
{
enum struct GemmMatrixLayout
{
MK_NK_NO_MO, // 0
MK_NK_ON_MO, // 0
};
enum struct GemmDataType
{
F32_F32_F32_F32, // 0
F16_F16_F16_F16, // 1
};
GemmDataType data_type = GemmDataType::F16_F16_F16_F16;
GemmMatrixLayout layout = GemmMatrixLayout::MK_NK_NO_MO;
bool do_verification = true;
int init_method = 1;
bool do_log = 0;
bool time_kernel = false;
// GEMM shape
ck::index_t M = 1024;
ck::index_t N = 1024;
ck::index_t K = 64;
ck::index_t O = 128;
ck::index_t BatchCount = 4;
ck::index_t StrideA0 = -1;
ck::index_t StrideB0 = -1;
ck::index_t StrideB1 = -1;
ck::index_t StrideE1 = -1;
ck::index_t BatchStrideA0 = -1;
ck::index_t BatchStrideB0 = -1;
ck::index_t BatchStrideB1 = -1;
ck::index_t BatchStrideE1 = -1;
if(argc == 8)
{
data_type = static_cast<GemmDataType>(std::stoi(argv[2]));
layout = static_cast<GemmMatrixLayout>(std::stoi(argv[3]));
do_verification = std::stoi(argv[4]);
init_method = std::stoi(argv[5]);
do_log = std::stoi(argv[6]);
time_kernel = std::stoi(argv[7]);
}
else if(argc == 13)
{
data_type = static_cast<GemmDataType>(std::stoi(argv[2]));
layout = static_cast<GemmMatrixLayout>(std::stoi(argv[3]));
do_verification = std::stoi(argv[4]);
init_method = std::stoi(argv[5]);
do_log = std::stoi(argv[6]);
time_kernel = std::stoi(argv[7]);
M = std::stoi(argv[8]);
N = std::stoi(argv[9]);
K = std::stoi(argv[10]);
O = std::stoi(argv[11]);
BatchCount = std::stoi(argv[12]);
}
else if(argc == 21)
{
data_type = static_cast<GemmDataType>(std::stoi(argv[2]));
layout = static_cast<GemmMatrixLayout>(std::stoi(argv[3]));
do_verification = std::stoi(argv[4]);
init_method = std::stoi(argv[5]);
do_log = std::stoi(argv[6]);
time_kernel = std::stoi(argv[7]);
M = std::stoi(argv[8]);
N = std::stoi(argv[9]);
K = std::stoi(argv[10]);
O = std::stoi(argv[11]);
BatchCount = std::stoi(argv[12]);
StrideA0 = std::stoi(argv[13]);
StrideB0 = std::stoi(argv[14]);
StrideB1 = std::stoi(argv[15]);
StrideE1 = std::stoi(argv[16]);
BatchStrideA0 = std::stoi(argv[17]);
BatchStrideB0 = std::stoi(argv[18]);
BatchStrideB1 = std::stoi(argv[19]);
BatchStrideE1 = std::stoi(argv[20]);
}
else
{
printf("arg1: tensor operation (batched_gemm_gemm: Batched_GEMM+Gemm)\n");
printf("arg2: data type (1: fp16)\n");
printf("arg3: matrix layout (0: Relu(A0[m, k] * B0[n, k] + D0[m, n]) * B1[n, o] + D1[m, o] "
"= E1[m, o]; 1: Relu(A0[m, k] * B0[n, k] + D0[m, n]) * B1[o, n] + D1[m, o] = E1[m, "
"o];)\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=no, 1=yes)\n");
printf("arg8 to 12: M, N, K, O, Batch\n");
printf("arg13 to 16: StrideA0, StrideB0, StrideB1, StrideE1\n");
printf("arg17 to 20: BatchStrideA0, BatchStrideB0, BatchStrideB1, BatchStrideE1 \n");
exit(1);
}
if(data_type == GemmDataType::F16_F16_F16_F16 && layout == GemmMatrixLayout::MK_NK_NO_MO)
{
ck::profiler::profile_batched_gemm_gemm_impl<F16, // A0DataType,
F16, // B0DataType,
F16, // B1DataType,
F16, // E1DataType,
Row, // A0Layout,
Col, // B0Layout,
Row, // B1Layout,
Row> // E1Layout,
(do_verification,
init_method,
do_log,
time_kernel,
M,
N,
K,
O,
BatchCount,
StrideA0,
StrideB0,
StrideB1,
StrideE1,
BatchStrideA0,
BatchStrideB0,
BatchStrideB1,
BatchStrideE1);
}
else if(data_type == GemmDataType::F16_F16_F16_F16 && layout == GemmMatrixLayout::MK_NK_ON_MO)
{
ck::profiler::profile_batched_gemm_gemm_impl<F16, // A0DataType,
F16, // B0DataType,
F16, // B1DataType,
F16, // E1DataType,
Row, // A0Layout,
Col, // B0Layout,
Col, // B1Layout,
Row> // E1Layout,
(do_verification,
init_method,
do_log,
time_kernel,
M,
N,
K,
O,
BatchCount,
StrideA0,
StrideB0,
StrideB1,
StrideE1,
BatchStrideA0,
BatchStrideB0,
BatchStrideB1,
BatchStrideE1);
}
else
{
throw std::runtime_error("wrong! this data_type & layout is not implemented");
}
return 0;
}