mirror of
https://github.com/ROCm/composable_kernel.git
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* modify ckProfiler_gemm output
* fix syntax
* change ckProfiler output and return 0
* fix syntax
* output datatype
* fix syntax
* output datatype in another way
* fix syntax
* fix syntax
* test return values of ckProfiler
* add layout info and tests, make sure ckprofiler returns 0
* fix syntax
* change layout output
* fix syntax
* fix syntax again
* update script to process perf results
* rearrange jenkins stages
* fix typo
* add python packages to Docker file
* adding setuptools-rust package
* modify parsing for new test parameters
* test db credentials on jenkins
* fix syntax
* update python script to handle incomplete lines
* ungrade python to 3.8 and write the gemm_params table
* add sqlalchemy package to docker
* move perf data processing to master node
* move the master node inside a steps region
* add new stage for result processing
* move results processing to separate stage
* reduce number of tests to speedup debugging
* pass config to processPerfResults stage
* run script on master in a docker container
* replace show_node_info
* try loading docker on master node again
* use ansible node instead of master
* get rid of pymysql package
* try ssh connection using paramiko
* put back pymysql
* put the perf data processing back on the gpu node
* put back artifact definition
* archive the perf_log before parsing
* clean up jenkinsfile, fix parsing
* fix typo
* enable all perf tests
* put all stages in original order, finalize script
* fix gpu_arch version
* update parsing script
* remove obsolete file causing merge conflict
[ROCm/composable_kernel commit: 1085794df3]
158 lines
7.8 KiB
C++
158 lines
7.8 KiB
C++
#include <iostream>
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#include <numeric>
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#include <initializer_list>
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#include <cstdlib>
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#include <stdlib.h>
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#include <half.hpp>
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#include "profile_grouped_gemm_impl.hpp"
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enum struct GemmMatrixLayout
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{
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MK_KN_MN, // 0
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MK_NK_MN, // 1
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KM_KN_MN, // 2
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KM_NK_MN, // 3
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MK_KN_NM, // 4
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MK_NK_NM, // 5
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KM_KN_NM, // 6
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KM_NK_NM, // 7
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};
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enum struct GemmDataType
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{
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F32_F32_F32, // 0
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F16_F16_F16, // 1
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BF16_BF16_BF16, // 2
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INT8_INT8_INT8, // 3
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};
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std::vector<int> argToIntArray(char* input)
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{
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std::vector<int> out;
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std::istringstream in(input);
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std::string item;
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while(std::getline(in, item, ','))
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{
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out.push_back(std::stoi(item));
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}
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return out;
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}
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int profile_grouped_gemm(int argc, char* argv[])
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{
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if(!(argc == 14))
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{
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printf("arg1: tensor operation (grouped_gemm: Grouped GEMM)\n");
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printf("arg2: data type (0: fp32; 1: fp16; 2: bf16; 3: int8)\n");
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printf("arg3: matrix layout (0: A[m, k] * B[k, n] = C[m, n];\n");
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printf(" 1: A[m, k] * B[n, k] = C[m, n];\n");
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printf(" 2: A[k, m] * B[k, n] = C[m, n];\n");
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printf(" 3: A[k, m] * B[n, k] = C[m, n])\n");
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printf("arg4: verification (0: no; 1: yes)\n");
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printf("arg5: initialization (0: no init; 1: integer value; 2: decimal value)\n");
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printf("arg6: print tensor value (0: no; 1: yes)\n");
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printf("arg7: time kernel (0=n0, 1=yes)\n");
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printf("arg8 to 13: Ms, Ns, Ks, StrideAs, StrideBs, StrideCs (e.g., 256,256 128,128 64,64 "
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"64,64 64,64 128,128)\n");
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exit(1);
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}
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const auto data_type = static_cast<GemmDataType>(std::stoi(argv[2]));
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const auto layout = static_cast<GemmMatrixLayout>(std::stoi(argv[3]));
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const bool do_verification = std::stoi(argv[4]);
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const int init_method = std::stoi(argv[5]);
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const bool do_log = std::stoi(argv[6]);
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const bool time_kernel = std::stoi(argv[7]);
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const auto Ms = argToIntArray(argv[8]);
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const auto Ns = argToIntArray(argv[9]);
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const auto Ks = argToIntArray(argv[10]);
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const auto StrideAs = argToIntArray(argv[11]);
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const auto StrideBs = argToIntArray(argv[12]);
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const auto StrideCs = argToIntArray(argv[13]);
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if(data_type == GemmDataType::F16_F16_F16 && layout == GemmMatrixLayout::MK_KN_MN)
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{
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ck::profiler::profile_grouped_gemm_impl<ck::half_t,
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ck::half_t,
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ck::half_t,
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ck::tensor_layout::gemm::RowMajor,
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ck::tensor_layout::gemm::RowMajor,
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ck::tensor_layout::gemm::RowMajor>(do_verification,
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init_method,
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do_log,
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time_kernel,
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Ms,
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Ns,
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Ks,
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StrideAs,
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StrideBs,
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StrideCs);
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}
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else if(data_type == GemmDataType::F16_F16_F16 && layout == GemmMatrixLayout::MK_NK_MN)
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{
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ck::profiler::profile_grouped_gemm_impl<ck::half_t,
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ck::half_t,
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ck::half_t,
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ck::tensor_layout::gemm::RowMajor,
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ck::tensor_layout::gemm::ColumnMajor,
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ck::tensor_layout::gemm::RowMajor>(do_verification,
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init_method,
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do_log,
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time_kernel,
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Ms,
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Ns,
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Ks,
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StrideAs,
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StrideBs,
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StrideCs);
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}
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else if(data_type == GemmDataType::F16_F16_F16 && layout == GemmMatrixLayout::KM_KN_MN)
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{
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ck::profiler::profile_grouped_gemm_impl<ck::half_t,
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ck::half_t,
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ck::half_t,
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ck::tensor_layout::gemm::ColumnMajor,
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ck::tensor_layout::gemm::RowMajor,
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ck::tensor_layout::gemm::RowMajor>(do_verification,
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init_method,
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do_log,
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time_kernel,
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Ms,
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Ns,
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Ks,
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StrideAs,
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StrideBs,
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StrideCs);
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}
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else if(data_type == GemmDataType::F16_F16_F16 && layout == GemmMatrixLayout::KM_NK_MN)
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{
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ck::profiler::profile_grouped_gemm_impl<ck::half_t,
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ck::half_t,
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ck::half_t,
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ck::tensor_layout::gemm::ColumnMajor,
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ck::tensor_layout::gemm::ColumnMajor,
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ck::tensor_layout::gemm::RowMajor>(do_verification,
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init_method,
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do_log,
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time_kernel,
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Ms,
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Ns,
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Ks,
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StrideAs,
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StrideBs,
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StrideCs);
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}
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else
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{
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throw std::runtime_error("wrong! this GEMM data_type & layout is not implemented");
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}
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return 0;
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}
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