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Commit 6e3cf8b0 authored by Jing Zhang's avatar Jing Zhang
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merge develop

parents 4ad62d7f ba58a93f
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Showing with 531 additions and 123 deletions
profiler/src/profile_gemm_bias_2d.cpp
View file @ 6e3cf8b0
......@@ -36,8 +36,8 @@ int profile_gemm_bias_2d(int argc, char* argv[])
printf(" 3: A[k, m] * B[n, k] = C[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("arg8: print tensor value (0: no; 1: yes)\n");
printf("arg7: run kernel # of times (>1)\n");
printf("arg6: print tensor value (0: no; 1: yes)\n");
printf("arg7: time kernel (0=n0, 1=yes)\n");
printf("arg8 to 13: M, N, K, StrideA, StrideB, StrideC\n");
printf("arg14: alpha\n");
printf("arg15: beta\n");
......@@ -50,7 +50,7 @@ int profile_gemm_bias_2d(int argc, char* argv[])
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 int nrepeat = std::stoi(argv[7]);
const bool time_kernel = std::stoi(argv[7]);
const int M = std::stoi(argv[8]);
const int N = std::stoi(argv[9]);
......@@ -76,7 +76,7 @@ int profile_gemm_bias_2d(int argc, char* argv[])
do_verification,
init_method,
do_log,
nrepeat,
time_kernel,
M,
N,
K,
......@@ -99,7 +99,7 @@ int profile_gemm_bias_2d(int argc, char* argv[])
do_verification,
init_method,
do_log,
nrepeat,
time_kernel,
M,
N,
K,
......@@ -122,7 +122,7 @@ int profile_gemm_bias_2d(int argc, char* argv[])
do_verification,
init_method,
do_log,
nrepeat,
time_kernel,
M,
N,
K,
......@@ -145,7 +145,7 @@ int profile_gemm_bias_2d(int argc, char* argv[])
do_verification,
init_method,
do_log,
nrepeat,
time_kernel,
M,
N,
K,
......@@ -168,7 +168,7 @@ int profile_gemm_bias_2d(int argc, char* argv[])
do_verification,
init_method,
do_log,
nrepeat,
time_kernel,
M,
N,
K,
......@@ -191,7 +191,7 @@ int profile_gemm_bias_2d(int argc, char* argv[])
do_verification,
init_method,
do_log,
nrepeat,
time_kernel,
M,
N,
K,
......@@ -214,7 +214,7 @@ int profile_gemm_bias_2d(int argc, char* argv[])
do_verification,
init_method,
do_log,
nrepeat,
time_kernel,
M,
N,
K,
......@@ -237,7 +237,7 @@ int profile_gemm_bias_2d(int argc, char* argv[])
do_verification,
init_method,
do_log,
nrepeat,
time_kernel,
M,
N,
K,
......
profiler/src/profile_gemm_bias_relu.cpp
View file @ 6e3cf8b0
......@@ -36,8 +36,8 @@ int profile_gemm_bias_relu(int argc, char* argv[])
printf(" 3: A[k, m] * B[n, k] = C[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("arg8: print tensor value (0: no; 1: yes)\n");
printf("arg7: run kernel # of times (>1)\n");
printf("arg6: print tensor value (0: no; 1: yes)\n");
printf("arg7: time kernel (0=n0, 1=yes)\n");
printf("arg8 to 13: M, N, K, StrideA, StrideB, StrideC\n");
printf("arg14: split k into mulitiple batch\n");
exit(1);
......@@ -48,7 +48,7 @@ int profile_gemm_bias_relu(int argc, char* argv[])
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 int nrepeat = std::stoi(argv[7]);
const bool time_kernel = std::stoi(argv[7]);
const int M = std::stoi(argv[8]);
const int N = std::stoi(argv[9]);
......@@ -69,7 +69,7 @@ int profile_gemm_bias_relu(int argc, char* argv[])
do_verification,
init_method,
do_log,
nrepeat,
time_kernel,
M,
N,
K,
......@@ -88,7 +88,7 @@ int profile_gemm_bias_relu(int argc, char* argv[])
do_verification,
init_method,
do_log,
nrepeat,
time_kernel,
M,
N,
K,
......@@ -107,7 +107,7 @@ int profile_gemm_bias_relu(int argc, char* argv[])
do_verification,
init_method,
do_log,
nrepeat,
time_kernel,
M,
N,
K,
......@@ -126,7 +126,7 @@ int profile_gemm_bias_relu(int argc, char* argv[])
do_verification,
init_method,
do_log,
nrepeat,
time_kernel,
M,
N,
K,
......
profiler/src/profile_gemm_bias_relu_add.cpp
View file @ 6e3cf8b0
......@@ -36,8 +36,8 @@ int profile_gemm_bias_relu_add(int argc, char* argv[])
printf(" 3: A[k, m] * B[n, k] = C[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("arg8: print tensor value (0: no; 1: yes)\n");
printf("arg7: run kernel # of times (>1)\n");
printf("arg6: print tensor value (0: no; 1: yes)\n");
printf("arg7: time kernel (0=n0, 1=yes)\n");
printf("arg8 to 14: M, N, K, StrideA, StrideB, StrideC, StrideC1\n");
printf("arg15: split k into mulitiple batch\n");
exit(1);
......@@ -48,7 +48,7 @@ int profile_gemm_bias_relu_add(int argc, char* argv[])
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 int nrepeat = std::stoi(argv[7]);
const bool time_kernel = std::stoi(argv[7]);
const int M = std::stoi(argv[8]);
const int N = std::stoi(argv[9]);
......@@ -70,7 +70,7 @@ int profile_gemm_bias_relu_add(int argc, char* argv[])
do_verification,
init_method,
do_log,
nrepeat,
time_kernel,
M,
N,
K,
......@@ -90,7 +90,7 @@ int profile_gemm_bias_relu_add(int argc, char* argv[])
do_verification,
init_method,
do_log,
nrepeat,
time_kernel,
M,
N,
K,
......@@ -110,7 +110,7 @@ int profile_gemm_bias_relu_add(int argc, char* argv[])
do_verification,
init_method,
do_log,
nrepeat,
time_kernel,
M,
N,
K,
......@@ -130,7 +130,7 @@ int profile_gemm_bias_relu_add(int argc, char* argv[])
do_verification,
init_method,
do_log,
nrepeat,
time_kernel,
M,
N,
K,
......
profiler/src/profile_gemm_reduce.cpp
View file @ 6e3cf8b0
......@@ -32,8 +32,8 @@ int profile_gemm_reduce(int argc, char* argv[])
printf(" 3: A[k, m] * B[n, k] = C[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("arg8: print tensor value (0: no; 1: yes)\n");
printf("arg7: run kernel # of times (>1)\n");
printf("arg6: print tensor value (0: no; 1: yes)\n");
printf("arg7: time kernel (0=n0, 1=yes)\n");
printf("arg8 to 13: M, N, K, StrideA, StrideB, StrideC\n");
printf("arg14: split k into mulitiple batch\n");
exit(1);
......@@ -44,7 +44,7 @@ int profile_gemm_reduce(int argc, char* argv[])
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 int nrepeat = std::stoi(argv[7]);
const bool time_kernel = std::stoi(argv[7]);
const int M = std::stoi(argv[8]);
const int N = std::stoi(argv[9]);
......@@ -66,7 +66,7 @@ int profile_gemm_reduce(int argc, char* argv[])
do_verification,
init_method,
do_log,
nrepeat,
time_kernel,
M,
N,
K,
......@@ -87,7 +87,7 @@ int profile_gemm_reduce(int argc, char* argv[])
do_verification,
init_method,
do_log,
nrepeat,
time_kernel,
M,
N,
K,
......@@ -108,7 +108,7 @@ int profile_gemm_reduce(int argc, char* argv[])
do_verification,
init_method,
do_log,
nrepeat,
time_kernel,
M,
N,
K,
......@@ -129,7 +129,7 @@ int profile_gemm_reduce(int argc, char* argv[])
do_verification,
init_method,
do_log,
nrepeat,
time_kernel,
M,
N,
K,
......
profiler/src/profile_grouped_gemm.cpp
View file @ 6e3cf8b0
......@@ -54,8 +54,8 @@ int profile_grouped_gemm(int argc, char* argv[])
printf(" 3: A[k, m] * B[n, k] = C[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("arg8: print tensor value (0: no; 1: yes)\n");
printf("arg7: run kernel # of times (>1)\n");
printf("arg6: print tensor value (0: no; 1: yes)\n");
printf("arg7: time kernel (0=n0, 1=yes)\n");
printf("arg8 to 13: Ms, Ns, Ks, StrideAs, StrideBs, StrideCs (e.g., 256,256 128,128 64,64 "
"64,64 64,64 128,128)\n");
exit(1);
......@@ -66,7 +66,7 @@ int profile_grouped_gemm(int argc, char* argv[])
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 int nrepeat = std::stoi(argv[7]);
const bool time_kernel = std::stoi(argv[7]);
const auto Ms = argToIntArray(argv[8]);
const auto Ns = argToIntArray(argv[9]);
......@@ -86,7 +86,7 @@ int profile_grouped_gemm(int argc, char* argv[])
ck::tensor_layout::gemm::RowMajor>(do_verification,
init_method,
do_log,
nrepeat,
time_kernel,
Ms,
Ns,
Ks,
......@@ -104,7 +104,7 @@ int profile_grouped_gemm(int argc, char* argv[])
ck::tensor_layout::gemm::RowMajor>(do_verification,
init_method,
do_log,
nrepeat,
time_kernel,
Ms,
Ns,
Ks,
......@@ -122,7 +122,7 @@ int profile_grouped_gemm(int argc, char* argv[])
ck::tensor_layout::gemm::RowMajor>(do_verification,
init_method,
do_log,
nrepeat,
time_kernel,
Ms,
Ns,
Ks,
......@@ -140,7 +140,7 @@ int profile_grouped_gemm(int argc, char* argv[])
ck::tensor_layout::gemm::RowMajor>(do_verification,
init_method,
do_log,
nrepeat,
time_kernel,
Ms,
Ns,
Ks,
......
profiler/src/profile_reduce.cpp
View file @ 6e3cf8b0
......@@ -144,7 +144,7 @@ class AppArgs
bool do_dumpout = false;
int init_method;
int nrepeat;
bool time_kernel;
bool need_indices = false;
......@@ -295,7 +295,7 @@ class AppArgs
throw std::runtime_error("Invalid cmd-line arguments, more argumetns are needed!");
init_method = std::atoi(argv[optind++]);
nrepeat = std::atoi(argv[optind]);
time_kernel = std::atoi(argv[optind]);
if(scales.empty())
{
......@@ -354,7 +354,7 @@ int profile_reduce(int argc, char* argv[])
args.init_method,
args.do_log,
args.do_dumpout,
args.nrepeat,
args.time_kernel,
args.inLengths,
args.reduceDims,
args.reduceOp,
......@@ -369,7 +369,7 @@ int profile_reduce(int argc, char* argv[])
args.init_method,
args.do_log,
args.do_dumpout,
args.nrepeat,
args.time_kernel,
args.inLengths,
args.reduceDims,
args.reduceOp,
......@@ -387,7 +387,7 @@ int profile_reduce(int argc, char* argv[])
args.init_method,
args.do_log,
args.do_dumpout,
args.nrepeat,
args.time_kernel,
args.inLengths,
args.reduceDims,
args.reduceOp,
......@@ -414,7 +414,7 @@ int profile_reduce(int argc, char* argv[])
args.init_method,
args.do_log,
args.do_dumpout,
args.nrepeat,
args.time_kernel,
args.inLengths,
args.reduceDims,
args.reduceOp,
......@@ -429,7 +429,7 @@ int profile_reduce(int argc, char* argv[])
args.init_method,
args.do_log,
args.do_dumpout,
args.nrepeat,
args.time_kernel,
args.inLengths,
args.reduceDims,
args.reduceOp,
......@@ -454,7 +454,7 @@ int profile_reduce(int argc, char* argv[])
args.init_method,
args.do_log,
args.do_dumpout,
args.nrepeat,
args.time_kernel,
args.inLengths,
args.reduceDims,
args.reduceOp,
......@@ -471,7 +471,7 @@ int profile_reduce(int argc, char* argv[])
args.init_method,
args.do_log,
args.do_dumpout,
args.nrepeat,
args.time_kernel,
args.inLengths,
args.reduceDims,
args.reduceOp,
......@@ -486,7 +486,7 @@ int profile_reduce(int argc, char* argv[])
args.init_method,
args.do_log,
args.do_dumpout,
args.nrepeat,
args.time_kernel,
args.inLengths,
args.reduceDims,
args.reduceOp,
......
test/CMakeLists.txt
View file @ 6e3cf8b0
......@@ -22,7 +22,8 @@ include_directories(BEFORE
${PROJECT_SOURCE_DIR}/external/include/half
)
add_custom_target(check COMMAND ${CMAKE_CTEST_COMMAND} --output-on-failure -C ${CMAKE_CFG_INTDIR})
include(googletest)
add_custom_target(tests)
......@@ -61,3 +62,6 @@ add_subdirectory(grouped_gemm)
add_subdirectory(convnd_fwd)
add_subdirectory(reduce)
add_subdirectory(conv2d_bwd_weight)
add_subdirectory(convnd_bwd_data)
add_subdirectory(block_to_ctile_map)
# DONOT add client_app, that is tested via CI independently
test/batched_gemm_reduce/batched_gemm_reduce_fp16.cpp
View file @ 6e3cf8b0
......@@ -22,7 +22,7 @@ int main()
Row,
Row,
Row>(
true, 1, false, 1, M, N, K, K, N, N, BatchCount);
true, 1, false, false, M, N, K, K, N, N, BatchCount);
pass = pass && ck::profiler::profile_batched_gemm_reduce_impl<ck::half_t,
ck::half_t,
......@@ -31,7 +31,7 @@ int main()
Row,
Col,
Row>(
true, 1, false, 1, M, N, K, K, K, N, BatchCount);
true, 1, false, false, M, N, K, K, K, N, BatchCount);
pass = pass && ck::profiler::profile_batched_gemm_reduce_impl<ck::half_t,
ck::half_t,
......@@ -40,7 +40,7 @@ int main()
Col,
Row,
Row>(
true, 1, false, 1, M, N, K, M, N, N, BatchCount);
true, 1, false, false, M, N, K, M, N, N, BatchCount);
pass = pass && ck::profiler::profile_batched_gemm_reduce_impl<ck::half_t,
ck::half_t,
......@@ -49,7 +49,7 @@ int main()
Col,
Col,
Row>(
true, 1, false, 1, M, N, K, M, K, N, BatchCount);
true, 1, false, false, M, N, K, M, K, N, BatchCount);
if(pass)
{
......
test/block_to_ctile_map/CMakeLists.txt 0 → 100644
View file @ 6e3cf8b0
add_gtest_executable(test_block_to_ctile_map test_block_to_ctile_map.cpp)
\ No newline at end of file
test/block_to_ctile_map/test_block_to_ctile_map.cpp 0 → 100644
View file @ 6e3cf8b0
#include <ck/config.hpp>
#include "ck/tensor_operation/gpu/grid/block_to_ctile_map.hpp"
#include "gtest/gtest.h"
#include <iostream>
#include <vector>
using namespace ck;
static auto I0 = Number<0>{};
static auto I1 = Number<1>{};
TEST(BlockToCTileMap, TestBlockToCTileMap_M00_N00_M01_N01_DeviceCTileIndexCheck1)
{
const index_t M = 384;
const index_t N = 384;
const index_t MPerBlock = 128;
const index_t NPerBlock = 128;
const index_t MBlock = M / MPerBlock;
const index_t NBlock = N / NPerBlock;
const index_t M01 = 4;
const index_t N01 = 4;
auto c_grid_desc_m_n = make_naive_tensor_descriptor(make_tuple(M, N), make_tuple(I1, I1));
printf("(M, N, MPerBlock, NPerBlock, M01, N01) = (%d, %d, %d, %d, %d, %d)\n",
M,
N,
MPerBlock,
NPerBlock,
M01,
N01);
BlockToCTileMap_M00_N00_M01_N01<MPerBlock, NPerBlock, decltype(c_grid_desc_m_n), true> tile_map(
c_grid_desc_m_n, M01, N01);
EXPECT_TRUE(tile_map.CheckValidity(c_grid_desc_m_n) == true);
EXPECT_TRUE(tile_map.CalculateGridSize(c_grid_desc_m_n) == 16);
// clang-format off
std::vector<std::vector<int>> expected = {
{0, 0, 1},
{0, 1, 1},
{0, 2, 1},
{0, 3, 0},
{1, 0, 1},
{1, 1, 1},
{1, 2, 1},
{1, 3, 0},
{2, 0, 1},
{2, 1, 1},
{2, 2, 1},
{2, 3, 0},
{3, 0, 0},
{3, 1, 0},
{3, 2, 0},
{3, 3, 0}
};
// clang-format on
for(index_t i = 0; i < tile_map.CalculateGridSize(c_grid_desc_m_n); i++)
{
auto m0n0_idx = tile_map.CalculateBottomIndex(make_multi_index(i));
std::cout << "block_1d_id = " << i << ", m0, n0 = " << m0n0_idx[I0] << ", " << m0n0_idx[I1];
std::cout << ", valid = " << tile_map.ValidCTileIndex(m0n0_idx, make_tuple(MBlock, NBlock))
<< std::endl;
bool equal =
expected[i] ==
std::vector<int>{m0n0_idx[I0],
m0n0_idx[I1],
tile_map.ValidCTileIndex(m0n0_idx, make_tuple(MBlock, NBlock))};
EXPECT_TRUE(equal);
}
}
TEST(BlockToCTileMap, TestBlockToCTileMap_M00_N00_M01_N01_DeviceCTileIndexCheck0)
{
const index_t M = 384;
const index_t N = 384;
const index_t MPerBlock = 128;
const index_t NPerBlock = 128;
// const index_t MBlock = M / MPerBlock;
// const index_t NBlock = N / NPerBlock;
const index_t M01 = 4;
const index_t N01 = 4;
auto c_grid_desc_m_n = make_naive_tensor_descriptor(make_tuple(M, N), make_tuple(I1, I1));
printf("(M, N, MPerBlock, NPerBlock, M01, N01) = (%d, %d, %d, %d, %d, %d)\n",
M,
N,
MPerBlock,
NPerBlock,
M01,
N01);
BlockToCTileMap_M00_N00_M01_N01<MPerBlock, NPerBlock, decltype(c_grid_desc_m_n), false>
tile_map(c_grid_desc_m_n, M01, N01);
EXPECT_TRUE(tile_map.CheckValidity(c_grid_desc_m_n) == false);
}
test/client_app/CMakeLists.txt 0 → 100644
View file @ 6e3cf8b0
cmake_minimum_required(VERSION 3.15)
project(ck_app)
add_compile_options(-std=c++14)
find_package(composable_kernel 1.0.0 COMPONENTS device_operations host_tensor)
find_package(hip REQUIRED PATHS /opt/rocm)
message(STATUS "Build with HIP ${hip_VERSION}")
add_executable(test_client_app client_app.cpp)
target_link_libraries(test_client_app PRIVATE composable_kernel::device_operations composable_kernel::host_tensor hip::host)
test/client_app/client_app.cpp 0 → 100644
View file @ 6e3cf8b0
#include <iostream>
#include <numeric>
#include <initializer_list>
#include <cstdlib>
#include <stdlib.h>
#include <half.hpp>
#include <vector>
#include "client_app_impl.hpp"
int main(int argc, char* argv[])
{
if(argc != 25)
{
printf("arg1: tensor operation (conv_fwd: ForwardConvolution)\n");
printf("arg2: data type (0: fp32; 1: fp16)\n");
printf("arg3: input tensor layout (0: NCHW; 1: NHWC)\n");
printf("arg4: weight tensor layout (0: KCYX; 1: KYXC)\n");
printf("arg5: output tensor layout (0: NKHW; 1: NHWK)\n");
printf("arg6: verification (0: no; 1: yes)\n");
printf("arg7: initialization (0: no init; 1: integer value; 2: decimal value)\n");
printf("arg8: print tensor value (0: no; 1: yes)\n");
printf("arg9: time kernel (0=n0, 1=yes)\n");
printf("arg10 to 24: N, K, C, Y, X, Hi, Wi, Sy, Sx, Dy, Dx, LeftPy, LeftPx, RightPy, "
"RightPx\n");
exit(1);
}
const ConvDataType data_type = static_cast<ConvDataType>(std::stoi(argv[2]));
const int in_layout = static_cast<ConvInputLayout>(std::stoi(argv[3]));
const int wei_layout = static_cast<ConvWeightLayout>(std::stoi(argv[4]));
const int out_layout = static_cast<ConvOutputLayout>(std::stoi(argv[5]));
const bool do_verification = std::stoi(argv[6]);
const int init_method = std::stoi(argv[7]);
const bool do_log = std::stoi(argv[8]);
const bool time_kernel = std::stoi(argv[9]);
const ck::index_t N = std::stoi(argv[10]);
const ck::index_t K = std::stoi(argv[11]);
const ck::index_t C = std::stoi(argv[12]);
const ck::index_t Y = std::stoi(argv[13]);
const ck::index_t X = std::stoi(argv[14]);
const ck::index_t Hi = std::stoi(argv[15]);
const ck::index_t Wi = std::stoi(argv[16]);
const ck::index_t conv_stride_h = std::stoi(argv[17]);
const ck::index_t conv_stride_w = std::stoi(argv[18]);
const ck::index_t conv_dilation_h = std::stoi(argv[19]);
const ck::index_t conv_dilation_w = std::stoi(argv[20]);
const ck::index_t in_left_pad_h = std::stoi(argv[21]);
const ck::index_t in_left_pad_w = std::stoi(argv[22]);
const ck::index_t in_right_pad_h = std::stoi(argv[23]);
const ck::index_t in_right_pad_w = std::stoi(argv[24]);
const ck::index_t YEff = (Y - 1) * conv_dilation_h + 1;
const ck::index_t XEff = (X - 1) * conv_dilation_w + 1;
const ck::index_t Ho = (Hi + in_left_pad_h + in_right_pad_h - YEff) / conv_stride_h + 1;
const ck::index_t Wo = (Wi + in_left_pad_w + in_right_pad_w - XEff) / conv_stride_w + 1;
ck::app::profile_conv_fwd_impl(do_verification,
init_method,
do_log,
time_kernel,
data_type,
N,
K,
C,
std::vector<ck::index_t>{Hi, Wi},
std::vector<ck::index_t>{Y, X},
std::vector<ck::index_t>{Ho, Wo},
std::vector<ck::index_t>{conv_stride_h, conv_stride_w},
std::vector<ck::index_t>{conv_dilation_h, conv_dilation_w},
std::vector<ck::index_t>{in_left_pad_h, in_left_pad_w},
std::vector<ck::index_t>{in_right_pad_h, in_right_pad_w});
return 1;
}
test/client_app/client_app_impl.hpp 0 → 100644
View file @ 6e3cf8b0
#pragma once
#include "host_interface.hpp"
enum ConvDataType
{
F32_F32_F32, // 0
F16_F16_F16, // 1
BF16_BF16_BF16, // 2
INT8_INT8_INT8, // 3
};
enum ConvInputLayout
{
NCHW, // 0
NHWC, // 1
};
enum ConvWeightLayout
{
KCYX, // 0
KYXC, // 1
};
enum ConvOutputLayout
{
NKHW, // 0
NHWK, // 1
};
void check_hip_error(void)
{
hipError_t err = hipGetLastError();
if(err != hipSuccess)
{
std::cerr << "Error: " << hipGetErrorString(err) << std::endl;
exit(err);
}
}
std::string getDeviceName(int device)
{
struct hipDeviceProp_t prop;
hipGetDeviceProperties(&prop, device);
check_hip_error();
return std::string(prop.name);
}
int getDriver(void)
{
int driver;
hipDriverGetVersion(&driver);
check_hip_error();
return driver;
}
namespace ck {
namespace app {
struct DeviceMem
{
DeviceMem() = delete;
DeviceMem(std::size_t mem_size);
void* GetDeviceBuffer();
void ToDevice(const void* p);
void FromDevice(void* p);
~DeviceMem();
void* mpDeviceBuf;
std::size_t mMemSize;
};
DeviceMem::DeviceMem(std::size_t mem_size) : mMemSize(mem_size)
{
hipGetErrorString(hipMalloc(static_cast<void**>(&mpDeviceBuf), mMemSize));
}
void* DeviceMem::GetDeviceBuffer() { return mpDeviceBuf; }
void DeviceMem::ToDevice(const void* p)
{
hipGetErrorString(
hipMemcpy(mpDeviceBuf, const_cast<void*>(p), mMemSize, hipMemcpyHostToDevice));
}
void DeviceMem::FromDevice(void* p)
{
hipGetErrorString(hipMemcpy(p, mpDeviceBuf, mMemSize, hipMemcpyDeviceToHost));
}
DeviceMem::~DeviceMem() { hipGetErrorString(hipFree(mpDeviceBuf)); }
void profile_conv_fwd_impl(int do_verification,
int init_method,
bool do_log,
bool time_kernel,
ConvDataType data_type,
ck::index_t N,
ck::index_t K,
ck::index_t C,
std::vector<ck::index_t> input_spatial_lengths,
std::vector<ck::index_t> filter_spatial_lengths,
std::vector<ck::index_t> output_spatial_lengths,
std::vector<ck::index_t> conv_filter_strides,
std::vector<ck::index_t> conv_filter_dilations,
std::vector<ck::index_t> input_left_pads,
std::vector<ck::index_t> input_right_pads)
{
const ck::index_t Y = filter_spatial_lengths[0];
const ck::index_t X = filter_spatial_lengths[1];
const ck::index_t Hi = input_spatial_lengths[0];
const ck::index_t Wi = input_spatial_lengths[1];
const ck::index_t Ho = output_spatial_lengths[0];
const ck::index_t Wo = output_spatial_lengths[1];
const auto in_sz = N * C * Hi * Wi;
const auto wei_sz = K * C * Y * X;
const auto out_sz = N * K * Ho * Wo;
using WeiDataType = float;
using InDataType = float;
using OutDataType = float;
app::DeviceMem in_device_buf(sizeof(InDataType) * in_sz);
app::DeviceMem wei_device_buf(sizeof(WeiDataType) * wei_sz);
app::DeviceMem out_device_buf(sizeof(OutDataType) * out_sz);
// data is already on device!
// add device Conv instances
std::vector<DeviceConvFwdPtr_t> conv_ptrs;
if(data_type == F16_F16_F16)
{
add_device_conv2d_fwd_xdl_c_shuffle_nhwc_kyxc_nhwk_f16_instances_t(conv_ptrs);
add_device_conv2d_fwd_xdl_nhwc_kyxc_nhwk_f16_instances_t(conv_ptrs);
}
else if(data_type == BF16_BF16_BF16)
add_device_conv2d_fwd_xdl_nhwc_kyxc_nhwk_bf16_instances_t(conv_ptrs);
else if(data_type == F32_F32_F32)
add_device_conv2d_fwd_xdl_nhwc_kyxc_nhwk_f32_instances_t(conv_ptrs);
else if(data_type == INT8_INT8_INT8)
add_device_conv2d_fwd_xdl_nhwc_kyxc_nhwk_int8_instances_t(conv_ptrs);
else
throw std::runtime_error("wrong! Invalid data type");
if(conv_ptrs.empty())
{
throw std::runtime_error("wrong! no device Conv instance found");
}
std::string best_conv_name;
float best_ave_time = 0;
float best_tflops = 0;
float best_gb_per_sec = 0;
int deviceIndex = 0;
hipSetDevice(deviceIndex);
check_hip_error();
StreamConfig stream_config{nullptr, time_kernel};
hipStreamCreate(&stream_config.stream_id_);
check_hip_error();
// profile device Conv instances
for(auto& conv_ptr : conv_ptrs)
{
auto argument_ptr =
conv_ptr.MakeArgumentPointer(static_cast<void*>(in_device_buf.GetDeviceBuffer()),
static_cast<void*>(wei_device_buf.GetDeviceBuffer()),
static_cast<void*>(out_device_buf.GetDeviceBuffer()),
N,
K,
C,
input_spatial_lengths,
filter_spatial_lengths,
output_spatial_lengths,
conv_filter_strides,
conv_filter_dilations,
input_left_pads,
input_right_pads);
auto invoker_ptr = conv_ptr.MakeInvokerPointer();
if(conv_ptr.IsSupportedArgument(argument_ptr.get()))
{
std::string conv_name = conv_ptr.GetTypeString();
float ave_time = invoker_ptr->Run(argument_ptr.get(), stream_config);
std::size_t flop = std::size_t(2) * N * K * Ho * Wo * C * Y * X;
std::size_t num_btype = sizeof(InDataType) * (N * C * Hi * Wi) +
sizeof(WeiDataType) * (K * C * Y * X) +
sizeof(OutDataType) * (N * K * Ho * Wo);
float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
float gb_per_sec = num_btype / 1.E6 / ave_time;
std::cout << "Perf: " << ave_time << " ms, " << tflops << " TFlops, " << gb_per_sec
<< " GB/s, " << conv_name << std::endl;
if(tflops > best_tflops)
{
best_conv_name = conv_name;
best_tflops = tflops;
best_ave_time = ave_time;
best_gb_per_sec = gb_per_sec;
}
}
}
std::cout << "Best Perf: " << best_ave_time << " ms, " << best_tflops << " TFlops, "
<< best_gb_per_sec << " GB/s, " << best_conv_name << std::endl;
}
} // namespace app
} // namespace ck
test/conv2d_bwd_weight/conv2d_bwd_weight.cpp
View file @ 6e3cf8b0
......@@ -28,10 +28,10 @@ int test_self()
ck::tensor_layout::convolution::NHWC,
ck::tensor_layout::convolution::KYXC,
ck::tensor_layout::convolution::NHWK>(
1, // do_verification,
1, // init_method,
0, // do_log,
1, // nrepeat,
true, // do_verification
1, // init_method
false, // do_log
false, // time_kernel
param.N_,
param.K_,
param.C_,
......@@ -52,10 +52,10 @@ int test_self()
ck::tensor_layout::convolution::NHWC,
ck::tensor_layout::convolution::KYXC,
ck::tensor_layout::convolution::NHWK>(
1, // do_verification,
1, // init_method,
0, // do_log,
1, // nrepeat,
true, // do_verification
1, // init_method
false, // do_log
false, // time_kernel
param.N_,
param.K_,
param.C_,
......@@ -72,8 +72,8 @@ int test_self()
}
int main(int argc, char* argv[])
{
int data_type = 0;
int init_method = 0;
int data_type = 1;
int init_method = 1;
// Conv shape
ck::index_t N = 128;
......@@ -155,10 +155,10 @@ int main(int argc, char* argv[])
ck::tensor_layout::convolution::NHWC,
ck::tensor_layout::convolution::KYXC,
ck::tensor_layout::convolution::NHWK>(
1,
true, // do_verification
init_method,
0,
1,
false, // do_log
false, // time_kernel
param.N_,
param.K_,
param.C_,
......@@ -180,10 +180,10 @@ int main(int argc, char* argv[])
ck::tensor_layout::convolution::NHWC,
ck::tensor_layout::convolution::KYXC,
ck::tensor_layout::convolution::NHWK>(
1,
true, // do_verification
init_method,
0,
1,
false, // do_log
false, // time_kernel
param.N_,
param.K_,
param.C_,
......
test/convnd_bwd_data/convnd_bwd_data.cpp
View file @ 6e3cf8b0
......@@ -27,10 +27,10 @@ int main()
ck::tensor_layout::convolution::NWC,
ck::tensor_layout::convolution::KXC,
ck::tensor_layout::convolution::NWK>(
1, // do_verification,
1, // init_method,
0, // do_log,
1, // nrepeat,
true, // do_verification
1, // init_method
false, // do_log
false, // time_kernel
param.N_,
param.K_,
param.C_,
......@@ -50,10 +50,10 @@ int main()
ck::tensor_layout::convolution::NWC,
ck::tensor_layout::convolution::KXC,
ck::tensor_layout::convolution::NWK>(
1, // do_verification,
1, // init_method,
0, // do_log,
1, // nrepeat,
true, // do_verification
1, // init_method
false, // do_log
false, // time_kernel
param.N_,
param.K_,
param.C_,
......@@ -73,10 +73,10 @@ int main()
ck::tensor_layout::convolution::NWC,
ck::tensor_layout::convolution::KXC,
ck::tensor_layout::convolution::NWK>(
1, // do_verification,
1, // init_method,
0, // do_log,
1, // nrepeat,
true, // do_verification
1, // init_method
false, // do_log
false, // time_kernel
param.N_,
param.K_,
param.C_,
......@@ -96,10 +96,10 @@ int main()
ck::tensor_layout::convolution::NWC,
ck::tensor_layout::convolution::KXC,
ck::tensor_layout::convolution::NWK>(
1, // do_verification,
1, // init_method,
0, // do_log,
1, // nrepeat,
true, // do_verification
1, // init_method
false, // do_log
false, // time_kernel
param.N_,
param.K_,
param.C_,
......@@ -128,10 +128,10 @@ int main()
ck::tensor_layout::convolution::NHWC,
ck::tensor_layout::convolution::KYXC,
ck::tensor_layout::convolution::NHWK>(
1, // do_verification,
1, // init_method,
0, // do_log,
1, // nrepeat,
true, // do_verification
1, // init_method
false, // do_log
false, // time_kernel
param.N_,
param.K_,
param.C_,
......@@ -151,10 +151,10 @@ int main()
ck::tensor_layout::convolution::NHWC,
ck::tensor_layout::convolution::KYXC,
ck::tensor_layout::convolution::NHWK>(
1, // do_verification,
1, // init_method,
0, // do_log,
1, // nrepeat,
true, // do_verification
1, // init_method
false, // do_log
false, // time_kernel
param.N_,
param.K_,
param.C_,
......@@ -174,10 +174,10 @@ int main()
ck::tensor_layout::convolution::NHWC,
ck::tensor_layout::convolution::KYXC,
ck::tensor_layout::convolution::NHWK>(
1, // do_verification,
1, // init_method,
0, // do_log,
1, // nrepeat,
true, // do_verification
1, // init_method
false, // do_log
false, // time_kernel
param.N_,
param.K_,
param.C_,
......@@ -197,10 +197,10 @@ int main()
ck::tensor_layout::convolution::NHWC,
ck::tensor_layout::convolution::KYXC,
ck::tensor_layout::convolution::NHWK>(
1, // do_verification,
1, // init_method,
0, // do_log,
1, // nrepeat,
true, // do_verification
1, // init_method
false, // do_log
false, // time_kernel
param.N_,
param.K_,
param.C_,
......@@ -232,10 +232,10 @@ int main()
ck::tensor_layout::convolution::NDHWC,
ck::tensor_layout::convolution::KZYXC,
ck::tensor_layout::convolution::NDHWK>(
1, // do_verification,
1, // init_method,
0, // do_log,
1, // nrepeat,
true, // do_verification
1, // init_method
false, // do_log
false, // time_kernel
param.N_,
param.K_,
param.C_,
......@@ -255,10 +255,10 @@ int main()
ck::tensor_layout::convolution::NDHWC,
ck::tensor_layout::convolution::KZYXC,
ck::tensor_layout::convolution::NDHWK>(
1, // do_verification,
1, // init_method,
0, // do_log,
1, // nrepeat,
true, // do_verification
1, // init_method
false, // do_log
false, // time_kernel
param.N_,
param.K_,
param.C_,
......@@ -278,10 +278,10 @@ int main()
ck::tensor_layout::convolution::NDHWC,
ck::tensor_layout::convolution::KZYXC,
ck::tensor_layout::convolution::NDHWK>(
1, // do_verification,
1, // init_method,
0, // do_log,
1, // nrepeat,
true, // do_verification
1, // init_method
false, // do_log
false, // time_kernel
param.N_,
param.K_,
param.C_,
......@@ -301,10 +301,10 @@ int main()
ck::tensor_layout::convolution::NDHWC,
ck::tensor_layout::convolution::KZYXC,
ck::tensor_layout::convolution::NDHWK>(
1, // do_verification,
1, // init_method,
0, // do_log,
1, // nrepeat,
true, // do_verification
1, // init_method
false, // do_log
false, // time_kernel
param.N_,
param.K_,
param.C_,
......
test/gemm_reduce/gemm_reduce_fp16.cpp
View file @ 6e3cf8b0
......@@ -16,22 +16,22 @@ int main()
pass = pass &&
ck::profiler::
profile_gemm_reduce_impl<ck::half_t, ck::half_t, ck::half_t, float, Row, Row, Row>(
true, 1, false, 1, M, N, K, K, N, N);
true, 1, false, false, M, N, K, K, N, N);
pass = pass &&
ck::profiler::
profile_gemm_reduce_impl<ck::half_t, ck::half_t, ck::half_t, float, Row, Col, Row>(
true, 1, false, 1, M, N, K, K, K, N);
true, 1, false, false, M, N, K, K, K, N);
pass = pass &&
ck::profiler::
profile_gemm_reduce_impl<ck::half_t, ck::half_t, ck::half_t, float, Col, Row, Row>(
true, 1, false, 1, M, N, K, M, N, N);
true, 1, false, false, M, N, K, M, N, N);
pass = pass &&
ck::profiler::
profile_gemm_reduce_impl<ck::half_t, ck::half_t, ck::half_t, float, Col, Col, Row>(
true, 1, false, 1, M, N, K, M, K, N);
true, 1, false, false, M, N, K, M, K, N);
if(pass)
{
......
test/gemm_split_k/gemm_split_k.cpp
View file @ 6e3cf8b0
......@@ -187,9 +187,10 @@ int test_gemm(const gemmArgs& args)
if(gemm_ptr->IsSupportedArgument(argument_ptr.get()))
{
invoker_ptr->Run(argument_ptr.get(), 0);
invoker_ptr->Run(argument_ptr.get());
c_device_buf.FromDevice(c_m_n_device_result.mData.data());
if(!check_out(c_m_n_host_result, c_m_n_device_result))
{
success = false;
......
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