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Commit 5dee1b64 authored by Ville Pietilä's avatar Ville Pietilä
Browse files

Merge remote-tracking branch 'origin/develop' into vpietila/ggemm-profiling

parents 870c3a76 355893cd
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Showing with 840 additions and 171 deletions
profiler/include/profiler/profile_batched_gemm_softmax_gemm_impl.hpp
View file @ 5dee1b64
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
......@@ -174,7 +174,7 @@ bool profile_batched_gemm_softmax_gemm_impl(bool do_verification,
break;
default:
a_g_m_k.GenerateTensorValue(GeneratorTensor_1<ADataType>{1});
b0_g_k_n.GenerateTensorValue(GeneratorTensor_Sequential<1>{});
b0_g_k_n.GenerateTensorValue(GeneratorTensor_Sequential<B0DataType, 1>{});
b1_g_n_o.GenerateTensorValue(GeneratorTensor_Diagonal<B1DataType>{});
}
......
profiler/include/profiler/profile_batched_gemm_softmax_gemm_permute_impl.hpp
View file @ 5dee1b64
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
......@@ -140,7 +140,7 @@ bool profile_batched_gemm_softmax_gemm_permute_impl(bool do_verification,
break;
default:
a_gs_ms_ks.GenerateTensorValue(GeneratorTensor_1<ADataType>{1});
b0_gs_ns_ks.GenerateTensorValue(GeneratorTensor_Sequential<1>{});
b0_gs_ns_ks.GenerateTensorValue(GeneratorTensor_Sequential<B0DataType, 1>{});
b1_gs_os_ns.GenerateTensorValue(GeneratorTensor_Diagonal<B1DataType>{});
}
......
profiler/include/profiler/profile_gemm_impl.hpp
View file @ 5dee1b64
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
......@@ -74,8 +74,8 @@ int profile_gemm_impl(int do_verification,
switch(init_method)
{
case 0:
ck::utils::FillConstant<ADataType>{static_cast<ADataType>(1.f)}(a_m_k);
ck::utils::FillConstant<BDataType>{static_cast<BDataType>(1.f)}(b_k_n);
ck::utils::FillConstant<ADataType>{type_convert<ADataType>(1.f)}(a_m_k);
ck::utils::FillConstant<BDataType>{type_convert<BDataType>(1.f)}(b_k_n);
break;
case 1:
ck::utils::FillUniformDistributionIntegerValue<ADataType>{-5.f, 5.f}(a_m_k);
......
script/process_perf_data.py
View file @ 5dee1b64
......@@ -82,7 +82,7 @@ def parse_logfile(logfile):
StrideA=[]
StrideB=[]
StrideC=[]
if 'perf_gemm.log' in logfile:
if 'perf_gemm' in logfile and 'gemm_bilinear' not in logfile:
for line in open(logfile):
if 'Best Perf' in line:
lst=line.split()
......@@ -260,7 +260,7 @@ def main():
conn = sqlEngine.connect()
#save gemm performance tests:
if 'perf_gemm.log' in filename:
if 'perf_gemm' in filename and 'gemm_bilinear' not in filename:
#write the ck_gemm_test_params table only needed once the test set changes
#post_test_params(test_list,conn)
for i in range(1,len(results)+1):
......
script/process_perf_data.sh
View file @ 5dee1b64
......@@ -11,9 +11,22 @@
#process results
python3 process_perf_data.py perf_gemm.log
python3 process_perf_data.py perf_onnx_gemm.log
python3 process_perf_data.py perf_resnet50_N256.log
python3 process_perf_data.py perf_resnet50_N4.log
file=./perf_onnx_gemm_gfx10.log
if [ -e "$file" ]; then
python3 process_perf_data.py perf_onnx_gemm_gfx10.log
fi
file=./perf_onnx_gemm_gfx11.log
if [ -e "$file" ]; then
python3 process_perf_data.py perf_onnx_gemm_gfx11.log
fi
file=./perf_onnx_gemm_gfx12.log
if [ -e "$file" ]; then
python3 process_perf_data.py perf_onnx_gemm_gfx12.log
fi
file=./perf_fmha_fwd_gfx942.log
if [ -e "$file" ]; then
python3 process_perf_data.py perf_fmha_fwd_gfx942.log
......
script/process_qa_data.sh
View file @ 5dee1b64
......@@ -24,6 +24,18 @@ python3 process_perf_data.py perf_splitK_gemm.log
python3 process_perf_data.py perf_onnx_gemm.log
python3 process_perf_data.py perf_mixed_gemm.log
file=./perf_onnx_gemm_gfx10.log
if [ -e "$file" ]; then
python3 process_perf_data.py perf_onnx_gemm_gfx10.log
fi
file=./perf_onnx_gemm_gfx11.log
if [ -e "$file" ]; then
python3 process_perf_data.py perf_onnx_gemm_gfx11.log
fi
file=./perf_onnx_gemm_gfx12.log
if [ -e "$file" ]; then
python3 process_perf_data.py perf_onnx_gemm_gfx12.log
fi
file=./perf_fmha_fwd_gfx942.log
if [ -e "$file" ]; then
python3 process_perf_data.py perf_fmha_fwd_gfx942.log
......
script/run_full_performance_tests.sh
View file @ 5dee1b64
......@@ -5,7 +5,7 @@
# post your new test results to the database and compare them to the baseline
# please contact Illia.Silin@amd.com for more details
#
# run the script as "./run_full_performance_tests.sh <verification> <tag for your test environment> <branch name> < node name>
# run the script as "./run_full_performance_tests.sh <verification> <tag for your test environment> <branch name> <node name>
# input arguments:
# verification = 0 : do not verify result correctness on CPU
# = 1 : verifuy correctness on CPU (may take a long time)
......
script/run_gemm_performance_tests.sh 0 → 100755
View file @ 5dee1b64
#!/bin/bash
#
# in order to run this script you'd first need to build the ckProfiler executable in ../build/bin/
# run the script as "./run_gemm_performance_tests.sh <verification> <tag for your test environment> <branch name> <node name> <arch>
# input arguments:
# verification = 0 : do not verify result correctness on CPU
# = 1 : verify correctness on CPU (may take a long time)
# environment tag : a string describing the specifics of your test environment
# branch name : name of the branch in git repo (git status | grep -e 'On branch')
# node name : $hostname
# arch : GPU architecture, e.g. "gfx9" or "gfx1100"
#get the command line arguments:
export verify=$1
echo 'Verification: ' $verify
export env_type=$2
echo 'Environment type: ' $env_type
export branch=$3
echo 'Branch name: ' $branch
export host_name=$4
echo 'Host name: ' $host_name
export arch=$5
echo 'GPU architecture: ' $arch
function print_log_header(){
rm -f $1;
echo 'On branch ' $3 &> $1;
echo 'Node name: ' $4 >> $1;
#get GPU_arch and number of compute units from rocminfo
echo -n "GPU_arch: " >> $1; rocminfo | grep "Name:" | grep "gfx" >> $1;
rocminfo | grep "Compute Unit:" >> $1;
hipcc --version | grep -e 'HIP version' >> $1;
echo 'Environment type: ' $2 >> $1;
/opt/rocm/bin/amdclang++ --version | grep -e 'InstalledDir' >> $1;
}
#run ONNX gemm tests
export onnx_log="perf_onnx_gemm_$arch.log"
print_log_header $onnx_log $env_type $branch $host_name
./profile_onnx_gemm.sh gemm 0 0 $verify 1 0 1 2>&1 | tee -a $onnx_log
./profile_onnx_gemm.sh gemm 1 0 $verify 1 0 1 2>&1 | tee -a $onnx_log
script/run_performance_tests.sh
View file @ 5dee1b64
#!/bin/bash
#
# in order to run this script you'd first need to build the ckProfiler executable in ../build/bin/
# run the script as "./run_performance_tests.sh <verification> <tag for your test environment> <branch name> < node name>
# run the script as "./run_performance_tests.sh <verification> <tag for your test environment> <branch name> <node name>
# input arguments:
# verification = 0 : do not verify result correctness on CPU
# = 1 : verify correctness on CPU (may take a long time)
......@@ -51,20 +51,11 @@ print_log_header $gemm_log $env_type $branch $host_name
./profile_gemm.sh gemm 2 3 $verify 1 0 1 | tee -a $gemm_log
./profile_gemm.sh gemm 3 3 $verify 1 0 1 | tee -a $gemm_log
#run grouped_fwd fp16 tests
export grouped_conv_fwd_log="perf_grouped_conv_fwd_fp16.log"
print_log_header $conv_fwd_log $env_type $branch $host_name
./profile_grouped_conv_fwd.sh grouped_conv_fwd 1 1 0 $verify 1 0 1 256 2>&1 | tee -a $grouped_conv_fwd_log
#run grouped_bwd_data fp16 tests
export grouped_conv_bwd_data_log="perf_grouped_conv_bwd_data_fp16.log"
print_log_header $grouped_conv_bwd_data_log $env_type $branch $host_name
./profile_grouped_conv_bwd_data.sh grouped_conv_bwd_data 1 1 $verify 1 0 1 256 2>&1 | tee -a $grouped_conv_bwd_data_log
#run grouped_bwd_weight fp16 tests
export grouped_conv_bwd_weight_log="perf_grouped_conv_bwd_weight_fp16.log"
print_log_header $grouped_conv_bwd_weight_log $env_type $branch $host_name
./profile_grouped_conv_bwd_weight.sh grouped_conv_bwd_weight 1 1 $verify 1 0 1 256 1 2>&1 | tee -a $grouped_conv_bwd_weight_log
#run ONNX gemm tests
export onnx_log="perf_onnx_gemm.log"
print_log_header $onnx_log $env_type $branch $host_name
./profile_onnx_gemm.sh gemm 0 0 $verify 1 0 1 2>&1 | tee -a $onnx_log
./profile_onnx_gemm.sh gemm 1 0 $verify 1 0 1 2>&1 | tee -a $onnx_log
#run resnet50 tests
export resnet256_log="perf_resnet50_N256.log"
......
test/ck_tile/CMakeLists.txt
View file @ 5dee1b64
add_subdirectory(image_to_column)
add_subdirectory(gemm)
add_subdirectory(batched_gemm)
add_subdirectory(grouped_gemm)
test/ck_tile/gemm/test_gemm_mem_pipeline.cpp
View file @ 5dee1b64
......@@ -8,35 +8,29 @@
#include "ck_tile/host.hpp"
#include "test_gemm_mem_pipeline_util.hpp"
using F16 = ck_tile::half_t;
using F32 = float;
using Row = ck_tile::tensor_layout::gemm::RowMajor;
using Col = ck_tile::tensor_layout::gemm::ColumnMajor;
static constexpr auto Intrawave = ck_tile::GemmPipelineScheduler::Intrawave;
static constexpr auto Interwave = ck_tile::GemmPipelineScheduler::Interwave;
template <typename Tuple>
class TestCkTileGemmMemPipelineIntrawave : public TestCkTileGemmMemPipeline<Tuple, Intrawave>
{
};
template <typename Tuple>
class TestCkTileGemmMemPipelineInterwave : public TestCkTileGemmMemPipeline<Tuple, Interwave>
{
};
using F16 = ck_tile::half_t;
using F32 = float;
using Row = ck_tile::tensor_layout::gemm::RowMajor;
using Col = ck_tile::tensor_layout::gemm::ColumnMajor;
using Intrawave = ck_tile::integral_constant<ck_tile::GemmPipelineScheduler,
ck_tile::GemmPipelineScheduler::Intrawave>;
using Interwave = ck_tile::integral_constant<ck_tile::GemmPipelineScheduler,
ck_tile::GemmPipelineScheduler::Interwave>;
// clang-format off
using KernelTypes = ::testing::Types<
// ALayout, BLayout, CLayout, ADataType, BDataType, AccDataType, CDataType
std::tuple< Row, Col, Row, F16, F16, F32, F16>,
std::tuple< Col, Row, Row, F16, F16, F32, F16>,
std::tuple< Row, Row, Row, F16, F16, F32, F16>,
std::tuple< Col, Col, Row, F16, F16, F32, F16>
// ALayout, BLayout, CLayout, ADataType, BDataType, AccDataType, CDataType, GemmPipelineScheduler
std::tuple< Row, Row, Row, F16, F16, F32, F16, Intrawave>,
std::tuple< Row, Row, Row, F16, F16, F32, F16, Interwave>,
std::tuple< Row, Col, Row, F16, F16, F32, F16, Intrawave>,
std::tuple< Row, Col, Row, F16, F16, F32, F16, Interwave>,
std::tuple< Col, Row, Row, F16, F16, F32, F16, Intrawave>,
std::tuple< Col, Row, Row, F16, F16, F32, F16, Interwave>,
std::tuple< Col, Col, Row, F16, F16, F32, F16, Intrawave>,
std::tuple< Col, Col, Row, F16, F16, F32, F16, Interwave>
>;
// clang-format on
TYPED_TEST_SUITE(TestCkTileGemmMemPipelineIntrawave, KernelTypes);
TYPED_TEST_SUITE(TestCkTileGemmMemPipelineInterwave, KernelTypes);
TYPED_TEST_SUITE(TestCkTileGemmMemPipeline, KernelTypes);
#include "test_gemm_mem_pipeline_ut_cases.inc"
test/ck_tile/gemm/test_gemm_mem_pipeline_ut_cases.inc
View file @ 5dee1b64
......@@ -3,11 +3,7 @@
#pragma once
//------------------------------------------------------------------------------------------------
// INTERWAVE SCHEDULER
//------------------------------------------------------------------------------------------------
TYPED_TEST(TestCkTileGemmMemPipelineInterwave, SmallM)
TYPED_TEST(TestCkTileGemmMemPipeline, SmallM)
{
std::vector<int> Ms{1, 2, 3, 4, 5, 6};
constexpr int N = 1024;
......@@ -17,7 +13,7 @@ TYPED_TEST(TestCkTileGemmMemPipelineInterwave, SmallM)
this->Run(M, N, K);
}
TYPED_TEST(TestCkTileGemmMemPipelineInterwave, MidLargeM)
TYPED_TEST(TestCkTileGemmMemPipeline, MidLargeM)
{
std::vector<int> Ms{127, 255, 312, 799, 1573};
constexpr int N = 1024;
......@@ -27,7 +23,7 @@ TYPED_TEST(TestCkTileGemmMemPipelineInterwave, MidLargeM)
this->Run(M, N, K);
}
TYPED_TEST(TestCkTileGemmMemPipelineInterwave, PaddK)
TYPED_TEST(TestCkTileGemmMemPipeline, PaddK)
{
std::vector<int> Ms{127};
constexpr int N = 1024;
......@@ -37,7 +33,7 @@ TYPED_TEST(TestCkTileGemmMemPipelineInterwave, PaddK)
this->Run(M, N, K);
}
TYPED_TEST(TestCkTileGemmMemPipelineInterwave, Regular)
TYPED_TEST(TestCkTileGemmMemPipeline, Regular)
{
std::vector<int> Ms{512};
constexpr int N = 1024;
......@@ -47,46 +43,15 @@ TYPED_TEST(TestCkTileGemmMemPipelineInterwave, Regular)
this->Run(M, N, K);
}
//------------------------------------------------------------------------------------------------
// INTRAWAVE SCHEDULER
//------------------------------------------------------------------------------------------------
TYPED_TEST(TestCkTileGemmMemPipelineIntrawave, SmallM)
TYPED_TEST(TestCkTileGemmMemPipeline, NotSupportedArgument)
{
std::vector<int> Ms{1, 2, 3, 4, 5, 6};
constexpr int N = 1024;
constexpr int K = 320;
for(int M : Ms)
this->Run(M, N, K);
}
constexpr int M = 512;
constexpr int N = 1025;
constexpr int K = 513;
TYPED_TEST(TestCkTileGemmMemPipelineIntrawave, MidLargeM)
{
std::vector<int> Ms{127, 255, 312, 799, 1573};
constexpr int N = 1024;
constexpr int K = 320;
for(int M : Ms)
this->Run(M, N, K);
}
constexpr bool PadM = false;
constexpr bool PadN = false;
constexpr bool PadK = false;
TYPED_TEST(TestCkTileGemmMemPipelineIntrawave, PaddK)
{
std::vector<int> Ms{127};
constexpr int N = 1024;
constexpr int K = 432;
for(int M : Ms)
this->Run(M, N, K);
}
TYPED_TEST(TestCkTileGemmMemPipelineIntrawave, Regular)
{
std::vector<int> Ms{512};
constexpr int N = 1024;
constexpr int K = 512;
for(int M : Ms)
this->Run(M, N, K);
EXPECT_THROW((this->template Run<PadM, PadN, PadK>(M, N, K)), std::runtime_error);
}
test/ck_tile/gemm/test_gemm_mem_pipeline_util.hpp
View file @ 5dee1b64
......@@ -11,7 +11,7 @@
#include "ck_tile/ops/epilogue.hpp"
#include "ck_tile/ops/gemm.hpp"
template <typename Tuple, ck_tile::GemmPipelineScheduler Scheduler_>
template <typename Tuple>
class TestCkTileGemmMemPipeline : public ::testing::Test
{
protected:
......@@ -22,7 +22,7 @@ class TestCkTileGemmMemPipeline : public ::testing::Test
using BDataType = std::tuple_element_t<4, Tuple>;
using AccDataType = std::tuple_element_t<5, Tuple>;
using CDataType = std::tuple_element_t<6, Tuple>;
static constexpr auto Scheduler = Scheduler_;
static constexpr auto Scheduler = std::tuple_element_t<7, Tuple>::value;
// TODO: expose tile size through test t-param ?
struct gemm_args
......@@ -39,6 +39,7 @@ class TestCkTileGemmMemPipeline : public ::testing::Test
ck_tile::index_t stride_C;
};
template <bool PadM, bool PadN, bool PadK>
void invoke_gemm(const gemm_args& args, const ck_tile::stream_config& s)
{
// TODO: This should be parameterized in tests
......@@ -54,9 +55,9 @@ class TestCkTileGemmMemPipeline : public ::testing::Test
constexpr ck_tile::index_t N_Warp_Tile = 32;
constexpr ck_tile::index_t K_Warp_Tile = 8;
constexpr bool kPadM = true;
constexpr bool kPadN = true;
constexpr bool kPadK = true;
constexpr bool kPadM = PadM;
constexpr bool kPadN = PadN;
constexpr bool kPadK = PadK;
constexpr int kBlockPerCu = 1;
......@@ -107,6 +108,11 @@ class TestCkTileGemmMemPipeline : public ::testing::Test
const dim3 grids = Kernel::GridSize(args.M, args.N, args.kbatch);
constexpr dim3 blocks = Kernel::BlockSize();
if(!Kernel::IsSupportedArgument(kargs))
{
throw std::runtime_error("Wrong! Arguments not supported! Skipping gemm!\n");
}
if(s.log_level_ > 0)
{
std::cout << "Launching kernel with args:"
......@@ -212,6 +218,7 @@ class TestCkTileGemmMemPipeline : public ::testing::Test
void SetUp() override { k_batches_ = {1}; }
template <bool PadM = true, bool PadN = true, bool PadK = true>
void Run(const int M,
const int N,
const int K,
......@@ -221,10 +228,11 @@ class TestCkTileGemmMemPipeline : public ::testing::Test
{
for(auto kb : k_batches_)
{
RunSingle(M, N, K, StrideA, StrideB, StrideC, kb);
RunSingle<PadM, PadN, PadK>(M, N, K, StrideA, StrideB, StrideC, kb);
}
}
template <bool PadM, bool PadN, bool PadK>
void RunSingle(const int M,
const int N,
const int K,
......@@ -301,7 +309,7 @@ class TestCkTileGemmMemPipeline : public ::testing::Test
args.stride_B = stride_B;
args.stride_C = stride_C;
invoke_gemm(args, ck_tile::stream_config{nullptr, false});
invoke_gemm<PadM, PadN, PadK>(args, ck_tile::stream_config{nullptr, false});
c_m_n_dev_buf.FromDevice(c_m_n_dev_result.data());
bool pass = true;
......
test/ck_tile/grouped_gemm/CMakeLists.txt 0 → 100644
View file @ 5dee1b64
# Currently ck_tile is only built on gfx9
if(GPU_TARGETS MATCHES "gfx9")
add_gtest_executable(test_ck_tile_grouped_gemm test_grouped_gemm.cpp)
endif()
test/ck_tile/grouped_gemm/test_grouped_gemm.cpp 0 → 100644
View file @ 5dee1b64
// SPDX-License-Identifier: MIT
// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
#include <tuple>
#include "gtest/gtest.h"
#include "ck_tile/host.hpp"
#include "test_grouped_gemm_util.hpp"
using F16 = ck_tile::half_t;
using F32 = float;
using Row = ck_tile::tensor_layout::gemm::RowMajor;
using Col = ck_tile::tensor_layout::gemm::ColumnMajor;
// clang-format off
using KernelTypes = ::testing::Types<
// ALayout, BLayout, CLayout, ADataType, BDataType, AccDataType, CDataType
std::tuple< Row, Row, Row, F16, F16, F32, F16>,
//std::tuple< Col, Row, Row, F16, F16, F32, F16>,
std::tuple< Row, Col, Row, F16, F16, F32, F16>//,
//std::tuple< Col, Col, Row, F16, F16, F32, F16>
>;
// clang-format on
TYPED_TEST_SUITE(TestCkTileGroupedGemm, KernelTypes);
#include "test_grouped_gemm_ut_cases.inc"
test/ck_tile/grouped_gemm/test_grouped_gemm_ut_cases.inc 0 → 100644
View file @ 5dee1b64
#pragma once
TYPED_TEST(TestCkTileGroupedGemm, Basic)
{
const int group_count = 16;
std::vector<int> Ms;
std::vector<int> Ns;
std::vector<int> Ks;
std::vector<int> stride_As;
std::vector<int> stride_Bs;
std::vector<int> stride_Cs;
for(int i = 0; i < group_count; i++)
{
Ms.push_back(256 + 256 * i);
Ns.push_back(128 + 128 * i);
Ks.push_back(128 + 64 * i);
stride_As.push_back(Ks[i]);
stride_Bs.push_back(Ks[i]);
stride_Cs.push_back(Ns[i]);
}
this->Run(Ms, Ns, Ks, stride_As, stride_Bs, stride_Cs, group_count);
}
test/ck_tile/grouped_gemm/test_grouped_gemm_util.hpp 0 → 100644
View file @ 5dee1b64
// SPDX-License-Identifier: MIT
// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include <sstream>
#include <gtest/gtest.h>
#include "ck_tile/core.hpp"
#include "ck_tile/host.hpp"
#include "ck_tile/host/kernel_launch.hpp"
#include "ck_tile/ops/epilogue.hpp"
#include "ck_tile/ops/gemm.hpp"
#include "ck_tile/ops/gemm/kernel/grouped_gemm_kernel.hpp"
template <typename Tuple>
class TestCkTileGroupedGemm : public ::testing::Test
{
protected:
using ALayout = std::tuple_element_t<0, Tuple>;
using BLayout = std::tuple_element_t<1, Tuple>;
using CLayout = std::tuple_element_t<2, Tuple>;
using ADataType = std::tuple_element_t<3, Tuple>;
using BDataType = std::tuple_element_t<4, Tuple>;
using AccDataType = std::tuple_element_t<5, Tuple>;
using CDataType = std::tuple_element_t<6, Tuple>;
struct GroupedGemKernelParam
{
static const bool kPadM = false;
static const bool kPadN = false;
static const bool kPadK = false;
static const bool kTilePermute = false;
static const ck_tile::index_t kOutputRank = 2;
static const int kBlockPerCu = 1;
static const ck_tile::index_t M_Tile = 128;
static const ck_tile::index_t N_Tile = 128;
static const ck_tile::index_t K_Tile = 32;
static const ck_tile::index_t M_Warp = 2;
static const ck_tile::index_t N_Warp = 2;
static const ck_tile::index_t K_Warp = 1;
static const ck_tile::index_t M_Warp_Tile = 32;
static const ck_tile::index_t N_Warp_Tile = 32;
static const ck_tile::index_t K_Warp_Tile = 8;
};
using CodegenGemmShape =
ck_tile::TileGemmShape<ck_tile::sequence<GroupedGemKernelParam::M_Tile,
GroupedGemKernelParam::N_Tile,
GroupedGemKernelParam::K_Tile>,
ck_tile::sequence<GroupedGemKernelParam::M_Warp,
GroupedGemKernelParam::N_Warp,
GroupedGemKernelParam::K_Warp>,
ck_tile::sequence<GroupedGemKernelParam::M_Warp_Tile,
GroupedGemKernelParam::N_Warp_Tile,
GroupedGemKernelParam::K_Warp_Tile>>;
using TilePartitioner = ck_tile::GemmTile1DPartitioner<CodegenGemmShape>;
template <typename CLayout>
using GemmEpilogue =
std::conditional_t<std::is_same_v<CLayout, ck_tile::tensor_layout::gemm::ColumnMajor>,
ck_tile::CShuffleEpilogue<
ck_tile::CShuffleEpilogueProblem<AccDataType,
CDataType,
GroupedGemKernelParam::kPadM,
GroupedGemKernelParam::kPadN,
GroupedGemKernelParam::kTilePermute,
GroupedGemKernelParam::kOutputRank,
1,
0,
TilePartitioner::MPerBlock,
TilePartitioner::NPerBlock>>,
ck_tile::Default2DEpilogue<
ck_tile::Default2DEpilogueProblem<AccDataType,
CDataType,
GroupedGemKernelParam::kPadM,
GroupedGemKernelParam::kPadN>>>;
template <typename ALayout, typename BLayout, typename CLayout>
using CodegenGemmTraits = ck_tile::TileGemmTraits<GroupedGemKernelParam::kPadM,
GroupedGemKernelParam::kPadN,
GroupedGemKernelParam::kPadK,
ALayout,
BLayout,
CLayout>;
template <typename ALayout, typename BLayout, typename CLayout>
using CodegenPipelineProblem =
ck_tile::GemmPipelineProblem<ADataType,
BDataType,
AccDataType,
CodegenGemmShape,
CodegenGemmTraits<ALayout, BLayout, CLayout>>;
using CodegenGemmPolicy = ck_tile::UniversalGemmPipelineAgBgCrPolicy;
template <typename ALayout, typename BLayout, typename CLayout>
using CodegenGemmPipeline =
ck_tile::GemmPipelineAGmemBGmemCRegV1<CodegenPipelineProblem<ALayout, BLayout, CLayout>,
CodegenGemmPolicy>;
template <typename ALayout, typename BLayout, typename CLayout>
using Kernel = ck_tile::GroupedGemmKernel<TilePartitioner,
CodegenGemmPipeline<ALayout, BLayout, CLayout>,
GemmEpilogue<CLayout>>;
using grouped_gemm_kargs = ck_tile::GroupedGemmHostArgs;
std::size_t GetWorkspaceSize(const std::vector<grouped_gemm_kargs>& gemm_descs)
{
return Kernel<std::nullptr_t, std::nullptr_t, std::nullptr_t>::GetWorkSpaceSize(gemm_descs);
}
template <typename ALayout, typename BLayout, typename CLayout>
void invoke_grouped_gemm(const std::vector<grouped_gemm_kargs>& gemm_descs,
const ck_tile::stream_config& s,
void* p_workspace_)
{
using GroupedGemmKernel = Kernel<ALayout, BLayout, CLayout>;
auto arguments = GroupedGemmKernel::MakeKargs(gemm_descs);
const dim3 grids = GroupedGemmKernel::GridSize(gemm_descs);
constexpr dim3 blocks = GroupedGemmKernel::BlockSize();
ck_tile::hip_check_error(hipMemcpyWithStream(
p_workspace_,
arguments.data(),
arguments.size() * sizeof(typename GroupedGemmKernel::GemmTransKernelArg),
hipMemcpyHostToDevice,
s.stream_id_));
if(s.log_level_ > 0)
{
std::cout << "Launching kernel with args:"
<< " grid: {" << grids.x << ", " << grids.y << ", " << grids.z << "}"
<< ", blocks: {" << blocks.x << ", " << blocks.y << ", " << blocks.z << "}"
<< std::endl;
}
ck_tile::launch_kernel(s,
ck_tile::make_kernel<blocks.x, GroupedGemKernelParam::kBlockPerCu>(
GroupedGemmKernel{},
grids,
blocks,
0,
ck_tile::cast_pointer_to_constant_address_space(p_workspace_),
gemm_descs.size()));
}
public:
void Run(const std::vector<int>& Ms,
const std::vector<int>& Ns,
const std::vector<int>& Ks,
std::vector<int>& stride_As,
std::vector<int>& stride_Bs,
std::vector<int>& stride_Cs,
const int group_count = 16)
{
using namespace ck_tile::literals;
auto f_host_tensor_descriptor = [](std::size_t row,
std::size_t col,
std::size_t stride,
auto layout) {
if constexpr(std::is_same_v<decltype(layout), ck_tile::tensor_layout::gemm::RowMajor>)
{
return ck_tile::HostTensorDescriptor({row, col}, {stride, 1_uz});
}
else
{
return ck_tile::HostTensorDescriptor({row, col}, {1_uz, stride});
}
};
auto f_get_default_stride =
[](std::size_t row, std::size_t col, std::size_t stride, auto layout) {
if(stride == 0)
{
if constexpr(std::is_same_v<decltype(layout),
ck_tile::tensor_layout::gemm::RowMajor>)
{
return col;
}
else
{
return row;
}
}
else
return stride;
};
std::vector<ck_tile::HostTensor<ADataType>> a_m_k_tensors;
std::vector<ck_tile::HostTensor<BDataType>> b_k_n_tensors;
std::vector<ck_tile::HostTensor<CDataType>> c_m_n_tensors;
a_m_k_tensors.reserve(group_count);
b_k_n_tensors.reserve(group_count);
c_m_n_tensors.reserve(group_count);
std::vector<std::unique_ptr<ck_tile::DeviceMem>> a_m_k_dev_buf;
std::vector<std::unique_ptr<ck_tile::DeviceMem>> b_k_n_dev_buf;
std::vector<std::unique_ptr<ck_tile::DeviceMem>> c_m_n_dev_buf;
a_m_k_dev_buf.reserve(group_count);
b_k_n_dev_buf.reserve(group_count);
c_m_n_dev_buf.reserve(group_count);
std::vector<grouped_gemm_kargs> gemm_descs;
gemm_descs.reserve(group_count);
for(int i = 0; i < group_count; ++i)
{
const ck_tile::index_t M = Ms[i];
const ck_tile::index_t N = Ns[i];
const ck_tile::index_t K = Ks[i];
stride_As[i] = f_get_default_stride(M, N, stride_As[i], ALayout{});
stride_Bs[i] = f_get_default_stride(K, N, stride_Bs[i], BLayout{});
stride_Cs[i] = f_get_default_stride(M, N, stride_Cs[i], CLayout{});
a_m_k_tensors.push_back(ck_tile::HostTensor<ADataType>(
f_host_tensor_descriptor(M, K, stride_As[i], ALayout{})));
b_k_n_tensors.push_back(ck_tile::HostTensor<BDataType>(
f_host_tensor_descriptor(K, N, stride_Bs[i], BLayout{})));
c_m_n_tensors.push_back(ck_tile::HostTensor<CDataType>(
f_host_tensor_descriptor(M, N, stride_Cs[i], CLayout{})));
std::cout << "gemm[" << i << "]"
<< " a_m_k: " << a_m_k_tensors[i].mDesc
<< " b_k_n: " << b_k_n_tensors[i].mDesc
<< " c_m_n: " << c_m_n_tensors[i].mDesc << std::endl;
ck_tile::FillUniformDistribution<ADataType>{-5.f, 5.f}(a_m_k_tensors[i]);
ck_tile::FillUniformDistribution<BDataType>{-5.f, 5.f}(b_k_n_tensors[i]);
a_m_k_dev_buf.push_back(std::make_unique<ck_tile::DeviceMem>(
a_m_k_tensors[i].get_element_space_size_in_bytes()));
b_k_n_dev_buf.push_back(std::make_unique<ck_tile::DeviceMem>(
b_k_n_tensors[i].get_element_space_size_in_bytes()));
c_m_n_dev_buf.push_back(std::make_unique<ck_tile::DeviceMem>(
c_m_n_tensors[i].get_element_space_size_in_bytes()));
a_m_k_dev_buf[i]->ToDevice(a_m_k_tensors[i].data());
b_k_n_dev_buf[i]->ToDevice(b_k_n_tensors[i].data());
c_m_n_dev_buf[i]->SetZero();
c_m_n_tensors[i].SetZero();
const void* p_a = a_m_k_dev_buf[i]->GetDeviceBuffer();
const void* p_b = b_k_n_dev_buf[i]->GetDeviceBuffer();
void* p_c = c_m_n_dev_buf[i]->GetDeviceBuffer();
gemm_descs.push_back(
{p_a, p_b, p_c, M, N, K, stride_As[i], stride_Bs[i], stride_Cs[i]});
}
ck_tile::DeviceMem gemm_workspace;
gemm_workspace.Realloc(GetWorkspaceSize(gemm_descs));
invoke_grouped_gemm<ALayout, BLayout, CLayout>(
gemm_descs, ck_tile::stream_config{nullptr, false}, gemm_workspace.GetDeviceBuffer());
for(int i = 0; i < group_count; i++)
{
c_m_n_dev_buf[i]->FromDevice(c_m_n_tensors[i].data());
}
bool pass{true};
for(int i = 0; i < group_count; ++i)
{
ck_tile::HostTensor<CDataType> c_m_n_host_ref(
f_host_tensor_descriptor(Ms[i], Ns[i], stride_Cs[i], CLayout{}));
c_m_n_host_ref.SetZero();
ck_tile::reference_gemm<ADataType, BDataType, AccDataType, CDataType>(
a_m_k_tensors[i], b_k_n_tensors[i], c_m_n_host_ref);
pass &= ck_tile::check_err(c_m_n_tensors[i], c_m_n_host_ref);
}
EXPECT_TRUE(pass);
}
};
test/data_type/CMakeLists.txt
View file @ 5dee1b64
......@@ -9,13 +9,38 @@ if (USE_BITINT_EXTENSION_INT4)
endif()
endif()
add_gtest_executable(test_fp8 test_fp8.cpp)
if(result EQUAL 0)
target_link_libraries(test_fp8 PRIVATE utility)
add_custom_target(test_fp8)
if (CK_USE_OCP_FP8)
add_gtest_executable(test_fp8_ocp test_fp8_ocp.cpp)
if(result EQUAL 0)
target_link_libraries(test_fp8_ocp PRIVATE utility)
endif()
add_gtest_executable(test_bf8_ocp test_bf8_ocp.cpp)
if(result EQUAL 0)
target_link_libraries(test_bf8_ocp PRIVATE utility)
endif()
add_dependencies(test_fp8 test_fp8_ocp)
add_dependencies(test_fp8 test_bf8_ocp)
endif()
add_gtest_executable(test_bf8 test_bf8.cpp)
if(result EQUAL 0)
target_link_libraries(test_bf8 PRIVATE utility)
if (CK_USE_FNUZ_FP8)
add_gtest_executable(test_fp8_fnuz test_fp8_fnuz.cpp)
if(result EQUAL 0)
target_link_libraries(test_fp8_fnuz PRIVATE utility)
endif()
add_gtest_executable(test_bf8_fnuz test_bf8_fnuz.cpp)
if(result EQUAL 0)
target_link_libraries(test_bf8_fnuz PRIVATE utility)
endif()
add_dependencies(test_fp8 test_fp8_fnuz)
add_dependencies(test_fp8 test_bf8_fnuz)
endif()
add_gtest_executable(test_custom_type test_custom_type.cpp)
......
test/data_type/test_bf8.cpp → test/data_type/test_bf8_fnuz.cpp
View file @ 5dee1b64
......@@ -5,158 +5,169 @@
#include "ck/utility/data_type.hpp"
#include "ck/utility/type_convert.hpp"
using ck::bf8_t;
using ck::bf8_fnuz_t;
using ck::f8_convert_rne;
using ck::f8_convert_sr;
using ck::half_t;
using ck::type_convert;
TEST(BF8, NumericLimits)
TEST(BF8FNUZ, NumericLimits)
{
// constants given for negative zero nan mode
EXPECT_EQ(ck::NumericLimits<bf8_t>::Min(), type_convert<bf8_t>(0x04));
EXPECT_EQ(ck::NumericLimits<bf8_t>::Max(), type_convert<bf8_t>(0x7F));
EXPECT_EQ(ck::NumericLimits<bf8_t>::Lowest(), type_convert<bf8_t>(0xFF));
EXPECT_EQ(ck::NumericLimits<bf8_t>::QuietNaN(), type_convert<bf8_t>(0x80));
EXPECT_EQ(ck::NumericLimits<bf8_fnuz_t>::Min(), type_convert<bf8_fnuz_t>(0x04));
EXPECT_EQ(ck::NumericLimits<bf8_fnuz_t>::Max(), type_convert<bf8_fnuz_t>(0x7F));
EXPECT_EQ(ck::NumericLimits<bf8_fnuz_t>::Lowest(), type_convert<bf8_fnuz_t>(0xFF));
EXPECT_EQ(ck::NumericLimits<bf8_fnuz_t>::QuietNaN(), type_convert<bf8_fnuz_t>(0x80));
}
TEST(BF8, ConvertFP32Nearest)
TEST(BF8FNUZ, ConvertFP32Nearest)
{
// fix the tolerance value
float abs_tol = 1e-6;
// convert 0 float to bf8 and back, check if holds
ASSERT_NEAR(0.0f, type_convert<float>(f8_convert_rne<bf8_t>(0.0f)), abs_tol);
ASSERT_NEAR(0.0f, type_convert<float>(f8_convert_rne<bf8_fnuz_t>(0.0f)), abs_tol);
// don't run the next test on gfx11 devices
#ifndef CK_SKIP_FLAKY_F8_TEST
// convert minimal float to bf8 and back, check if holds
ASSERT_NEAR(std::numeric_limits<float>::min(),
type_convert<float>(f8_convert_rne<bf8_t>(std::numeric_limits<float>::min())),
type_convert<float>(f8_convert_rne<bf8_fnuz_t>(std::numeric_limits<float>::min())),
abs_tol);
#endif
// convert maximal bf8_t to float and check if equal to 57344.0
ASSERT_NEAR(57344.0f, type_convert<float>(f8_convert_rne<bf8_t>(57344.0f)), abs_tol);
const auto max_bf8_t_float = type_convert<float>(ck::NumericLimits<bf8_fnuz_t>::Max());
// convert maximal bf8_fnuz_t to float and check if equal to 57344.0
ASSERT_NEAR(
max_bf8_t_float, type_convert<float>(f8_convert_rne<bf8_fnuz_t>(max_bf8_t_float)), abs_tol);
// convert maximal float to bf8 and back, check if clipped to 57344.0
ASSERT_NEAR(57344.0f,
type_convert<float>(f8_convert_rne<bf8_t>(std::numeric_limits<float>::max())),
ASSERT_NEAR(max_bf8_t_float,
type_convert<float>(f8_convert_rne<bf8_fnuz_t>(std::numeric_limits<float>::max())),
abs_tol);
// convert inf float to bf8_t and check if it is qNan
ASSERT_NEAR(type_convert<bf8_t>(0x80),
f8_convert_rne<bf8_t>(std::numeric_limits<float>::infinity()),
// convert inf float to bf8_fnuz_t and check if it is qNan
ASSERT_NEAR(ck::NumericLimits<bf8_fnuz_t>::QuietNaN(),
f8_convert_rne<bf8_fnuz_t>(std::numeric_limits<float>::infinity()),
abs_tol);
// positive norm float value to bf8 and back, check if holds
float pos_float = 0.0000762939f;
ASSERT_NEAR(pos_float, type_convert<float>(f8_convert_rne<bf8_t>(pos_float)), abs_tol);
ASSERT_NEAR(pos_float, type_convert<float>(f8_convert_rne<bf8_fnuz_t>(pos_float)), abs_tol);
// negative norm float value to bf8 and back, check if holds
float neg_float = -0.0000610351f;
ASSERT_NEAR(neg_float, type_convert<float>(f8_convert_rne<bf8_t>(neg_float)), abs_tol);
ASSERT_NEAR(neg_float, type_convert<float>(f8_convert_rne<bf8_fnuz_t>(neg_float)), abs_tol);
// positive subnorm float value to bf8 and back, check if holds
pos_float = 0.0000305175f;
ASSERT_NEAR(pos_float, type_convert<float>(f8_convert_rne<bf8_t>(pos_float)), abs_tol);
ASSERT_NEAR(pos_float, type_convert<float>(f8_convert_rne<bf8_fnuz_t>(pos_float)), abs_tol);
// negative subnorm float value to bf8 and back, check if holds
neg_float = -0.0000152587f;
ASSERT_NEAR(neg_float, type_convert<float>(f8_convert_rne<bf8_t>(neg_float)), abs_tol);
ASSERT_NEAR(neg_float, type_convert<float>(f8_convert_rne<bf8_fnuz_t>(neg_float)), abs_tol);
}
TEST(BF8, ConvertFP32Stochastic)
TEST(BF8FNUZ, ConvertFP32Stochastic)
{
// fix the tolerance value
float abs_tol = 1e-6;
// convert 0 float to bf8 and back, check if holds
ASSERT_NEAR(0.0f, type_convert<float>(f8_convert_sr<bf8_t>(0.0f)), abs_tol);
ASSERT_NEAR(0.0f, type_convert<float>(f8_convert_sr<bf8_fnuz_t>(0.0f)), abs_tol);
// convert minimal float to bf8 and back, check if holds
ASSERT_NEAR(std::numeric_limits<float>::min(),
type_convert<float>(f8_convert_sr<bf8_t>(std::numeric_limits<float>::min())),
type_convert<float>(f8_convert_sr<bf8_fnuz_t>(std::numeric_limits<float>::min())),
abs_tol);
// convert maximal bf8_t to float and check if equal to 57344.0
ASSERT_NEAR(57344.0f, type_convert<float>(f8_convert_sr<bf8_t>(57344.0f)), abs_tol);
const auto max_bf8_t_float = type_convert<float>(ck::NumericLimits<bf8_fnuz_t>::Max());
// convert maximal bf8_fnuz_t to float and check if equal to 57344.0
ASSERT_NEAR(
max_bf8_t_float, type_convert<float>(f8_convert_sr<bf8_fnuz_t>(max_bf8_t_float)), abs_tol);
// convert maximal float to bf8 and back, check if clipped to 57344.0
ASSERT_NEAR(57344.0f,
type_convert<float>(f8_convert_sr<bf8_t>(std::numeric_limits<float>::max())),
ASSERT_NEAR(max_bf8_t_float,
type_convert<float>(f8_convert_sr<bf8_fnuz_t>(std::numeric_limits<float>::max())),
abs_tol);
// convert inf float to bf8_t and check if it is qNan
ASSERT_NEAR(type_convert<bf8_t>(0x80),
f8_convert_sr<bf8_t>(std::numeric_limits<float>::infinity()),
// convert inf float to bf8_fnuz_t and check if it is qNan
ASSERT_NEAR(ck::NumericLimits<bf8_fnuz_t>::QuietNaN(),
f8_convert_sr<bf8_fnuz_t>(std::numeric_limits<float>::infinity()),
abs_tol);
// positive norm float value to bf8 and back, check if holds
float pos_float = 0.0000762939f;
ASSERT_NEAR(pos_float, type_convert<float>(f8_convert_sr<bf8_t>(pos_float)), abs_tol);
ASSERT_NEAR(pos_float, type_convert<float>(f8_convert_sr<bf8_fnuz_t>(pos_float)), abs_tol);
// negative norm float value to bf8 and back, check if holds
float neg_float = -0.0000610351f;
ASSERT_NEAR(neg_float, type_convert<float>(f8_convert_sr<bf8_t>(neg_float)), abs_tol);
ASSERT_NEAR(neg_float, type_convert<float>(f8_convert_sr<bf8_fnuz_t>(neg_float)), abs_tol);
// positive subnorm float value to bf8 and back, check if holds
pos_float = 0.0000305175f;
ASSERT_NEAR(pos_float, type_convert<float>(f8_convert_sr<bf8_t>(pos_float)), abs_tol);
ASSERT_NEAR(pos_float, type_convert<float>(f8_convert_sr<bf8_fnuz_t>(pos_float)), abs_tol);
// negative subnorm float value to bf8 and back, check if holds
neg_float = -0.0000152587f;
ASSERT_NEAR(neg_float, type_convert<float>(f8_convert_sr<bf8_t>(neg_float)), abs_tol);
ASSERT_NEAR(neg_float, type_convert<float>(f8_convert_sr<bf8_fnuz_t>(neg_float)), abs_tol);
}
TEST(BF8, ConvertFP16Nearest)
TEST(BF8FNUZ, ConvertFP16Nearest)
{
// fix the tolerance value
float abs_tol = 1e-3;
// convert 0 fp16 to bf8 and back, check if holds
ASSERT_NEAR(half_t{0.0}, type_convert<half_t>(f8_convert_rne<bf8_t>(half_t{0.0})), abs_tol);
ASSERT_NEAR(
half_t{0.0}, type_convert<half_t>(f8_convert_rne<bf8_fnuz_t>(half_t{0.0})), abs_tol);
// convert minimal fp16 to bf8 and back, check if holds
ASSERT_NEAR(ck::NumericLimits<half_t>::Min(),
type_convert<half_t>(f8_convert_rne<bf8_t>(ck::NumericLimits<half_t>::Min())),
type_convert<half_t>(f8_convert_rne<bf8_fnuz_t>(ck::NumericLimits<half_t>::Min())),
abs_tol);
// convert maximal bf8_t to fp16 and check if equal to 57344.0
const auto max_bf8_t_half = type_convert<half_t>(ck::NumericLimits<bf8_fnuz_t>::Max());
// convert maximal bf8_fnuz_t to fp16 and check if equal to 57344.0
ASSERT_NEAR(
half_t{57344.0}, type_convert<half_t>(f8_convert_rne<bf8_t>(half_t{57344.0})), abs_tol);
max_bf8_t_half, type_convert<half_t>(f8_convert_rne<bf8_fnuz_t>(max_bf8_t_half)), abs_tol);
// convert maximal fp16 to bf8 and back, check if clipped to 57344.0
ASSERT_NEAR(half_t{57344.0},
type_convert<half_t>(f8_convert_rne<bf8_t>(ck::NumericLimits<half_t>::Max())),
ASSERT_NEAR(max_bf8_t_half,
type_convert<half_t>(f8_convert_rne<bf8_fnuz_t>(ck::NumericLimits<half_t>::Max())),
abs_tol);
// convert QuietNaN fp16 to bf8_t and check if it is QuietNaN
ASSERT_NEAR(type_convert<bf8_t>(0x80),
f8_convert_rne<bf8_t>(ck::NumericLimits<half_t>::QuietNaN()),
// convert QuietNaN fp16 to bf8_fnuz_t and check if it is QuietNaN
ASSERT_NEAR(ck::NumericLimits<bf8_fnuz_t>::QuietNaN(),
f8_convert_rne<bf8_fnuz_t>(ck::NumericLimits<half_t>::QuietNaN()),
abs_tol);
// positive norm fp16 value to bf8 and back, check if holds
half_t pos_half = half_t{0.0000762939};
ASSERT_NEAR(pos_half, type_convert<half_t>(f8_convert_rne<bf8_t>(pos_half)), abs_tol);
ASSERT_NEAR(pos_half, type_convert<half_t>(f8_convert_rne<bf8_fnuz_t>(pos_half)), abs_tol);
// negative norm fp16 value to bf8 and back, check if holds
half_t neg_half = half_t{-0.0000610351};
ASSERT_NEAR(neg_half, type_convert<half_t>(f8_convert_rne<bf8_t>(neg_half)), abs_tol);
ASSERT_NEAR(neg_half, type_convert<half_t>(f8_convert_rne<bf8_fnuz_t>(neg_half)), abs_tol);
// positive subnorm fp16 value to bf8 and back, check if holds
pos_half = half_t{0.0000305175};
ASSERT_NEAR(pos_half, type_convert<half_t>(f8_convert_rne<bf8_t>(pos_half)), abs_tol);
ASSERT_NEAR(pos_half, type_convert<half_t>(f8_convert_rne<bf8_fnuz_t>(pos_half)), abs_tol);
// negative subnorm fp16 value to bf8 and back, check if holds
neg_half = half_t{-0.0000152587};
ASSERT_NEAR(neg_half, type_convert<half_t>(f8_convert_rne<bf8_t>(neg_half)), abs_tol);
ASSERT_NEAR(neg_half, type_convert<half_t>(f8_convert_rne<bf8_fnuz_t>(neg_half)), abs_tol);
}
TEST(BF8, ConvertFP16Stochastic)
TEST(BF8FNUZ, ConvertFP16Stochastic)
{
// fix the tolerance value
float abs_tol = 1e-3;
// convert 0 fp16 to bf8 and back, check if holds
ASSERT_NEAR(half_t{0.0}, type_convert<half_t>(f8_convert_sr<bf8_t>(half_t{0.0})), abs_tol);
ASSERT_NEAR(half_t{0.0}, type_convert<half_t>(f8_convert_sr<bf8_fnuz_t>(half_t{0.0})), abs_tol);
// convert minimal fp16 to bf8 and back, check if holds
ASSERT_NEAR(ck::NumericLimits<half_t>::Min(),
type_convert<half_t>(f8_convert_sr<bf8_t>(ck::NumericLimits<half_t>::Min())),
type_convert<half_t>(f8_convert_sr<bf8_fnuz_t>(ck::NumericLimits<half_t>::Min())),
abs_tol);
// convert maximal bf8_t to fp16 and check if equal to 57344.0
const auto max_bf8_t_half = type_convert<half_t>(ck::NumericLimits<bf8_fnuz_t>::Max());
// convert maximal bf8_fnuz_t to fp16 and check if equal to 57344.0
ASSERT_NEAR(
half_t{57344.0}, type_convert<half_t>(f8_convert_sr<bf8_t>(half_t{57344.0})), abs_tol);
max_bf8_t_half, type_convert<half_t>(f8_convert_sr<bf8_fnuz_t>(max_bf8_t_half)), abs_tol);
// convert maximal fp16 to bf8 and back, check if clipped to 57344.0
ASSERT_NEAR(half_t{57344.0},
type_convert<half_t>(f8_convert_sr<bf8_t>(ck::NumericLimits<half_t>::Max())),
ASSERT_NEAR(max_bf8_t_half,
type_convert<half_t>(f8_convert_sr<bf8_fnuz_t>(ck::NumericLimits<half_t>::Max())),
abs_tol);
// convert QuietNaN fp16 to bf8_t and check if it is QuietNaN
ASSERT_NEAR(type_convert<bf8_t>(0x80),
f8_convert_sr<bf8_t>(ck::NumericLimits<half_t>::QuietNaN()),
// convert QuietNaN fp16 to bf8_fnuz_t and check if it is QuietNaN
ASSERT_NEAR(ck::NumericLimits<bf8_fnuz_t>::QuietNaN(),
f8_convert_sr<bf8_fnuz_t>(ck::NumericLimits<half_t>::QuietNaN()),
abs_tol);
// positive norm fp16 value to bf8 and back, check if holds
half_t pos_half = half_t{0.0000762939};
ASSERT_NEAR(pos_half, type_convert<half_t>(f8_convert_sr<bf8_t>(pos_half)), abs_tol);
ASSERT_NEAR(pos_half, type_convert<half_t>(f8_convert_sr<bf8_fnuz_t>(pos_half)), abs_tol);
// negative norm fp16 value to bf8 and back, check if holds
half_t neg_half = half_t{-0.0000610351};
ASSERT_NEAR(neg_half, type_convert<half_t>(f8_convert_sr<bf8_t>(neg_half)), abs_tol);
ASSERT_NEAR(neg_half, type_convert<half_t>(f8_convert_sr<bf8_fnuz_t>(neg_half)), abs_tol);
// positive subnorm fp16 value to bf8 and back, check if holds
pos_half = half_t{0.0000305175};
ASSERT_NEAR(pos_half, type_convert<half_t>(f8_convert_sr<bf8_t>(pos_half)), abs_tol);
ASSERT_NEAR(pos_half, type_convert<half_t>(f8_convert_sr<bf8_fnuz_t>(pos_half)), abs_tol);
// negative subnorm fp16 value to bf8 and back, check if holds
neg_half = half_t{-0.0000152587};
ASSERT_NEAR(neg_half, type_convert<half_t>(f8_convert_sr<bf8_t>(neg_half)), abs_tol);
ASSERT_NEAR(neg_half, type_convert<half_t>(f8_convert_sr<bf8_fnuz_t>(neg_half)), abs_tol);
}
test/data_type/test_bf8_ocp.cpp 0 → 100644
View file @ 5dee1b64
// SPDX-License-Identifier: MIT
// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
#include "gtest/gtest.h"
#include "ck/utility/data_type.hpp"
#include "ck/utility/type_convert.hpp"
using ck::bf8_ocp_t;
using ck::f8_convert_rne;
using ck::f8_convert_sr;
using ck::half_t;
using ck::type_convert;
TEST(BF8OCP, NumericLimits)
{ // constants given for OCP FP8
EXPECT_EQ(ck::NumericLimits<bf8_ocp_t>::Min(),
type_convert<bf8_ocp_t>(0x04)); // 0b00000100 = 2^-14
EXPECT_EQ(ck::NumericLimits<bf8_ocp_t>::Max(),
type_convert<bf8_ocp_t>(0x7B)); // 0b01111011 = 57344
EXPECT_EQ(ck::NumericLimits<bf8_ocp_t>::Lowest(),
type_convert<bf8_ocp_t>(0xFB)); // 0b11111011 = -57344
EXPECT_EQ(ck::NumericLimits<bf8_ocp_t>::QuietNaN().data,
type_convert<bf8_ocp_t>(0x7D).data); // 0b01111101
EXPECT_FALSE(ck::NumericLimits<bf8_ocp_t>::QuietNaN() ==
ck::NumericLimits<bf8_ocp_t>::QuietNaN());
EXPECT_TRUE(ck::fp8_is_inf(type_convert<bf8_ocp_t>(0xFC)) &&
ck::fp8_is_inf(type_convert<bf8_ocp_t>(0x7C)));
}
TEST(BF8OCP, ConvertFP32Nearest)
{
// fix the tolerance value
float abs_tol = 1e-6;
// convert 0 float to bfp8 and back, check if holds
ASSERT_NEAR(0.0f, type_convert<float>(f8_convert_rne<bf8_ocp_t>(0.0f)), 0.0f);
// convert minimal float to bf8 and back, check if holds
ASSERT_NEAR(std::numeric_limits<float>::min(),
type_convert<float>(f8_convert_rne<bf8_ocp_t>(std::numeric_limits<float>::min())),
abs_tol);
const auto max_bf8_t_float = type_convert<float>(ck::NumericLimits<bf8_ocp_t>::Max());
// convert maximal bf8_ocp_t to float and check if equal to bf8 max
ASSERT_NEAR(
max_bf8_t_float, type_convert<float>(f8_convert_rne<bf8_ocp_t>(max_bf8_t_float)), 0.0f);
// convert maximal float to bf8 and back, check if clipped to bf8 max (saturation to finite)
ASSERT_NEAR(max_bf8_t_float,
type_convert<float>(f8_convert_rne<bf8_ocp_t>(std::numeric_limits<float>::max())),
0.0f);
// convert float infinity to bf8_ocp_t and check if it is max value (saturation to finite)
ASSERT_EQ(ck::NumericLimits<bf8_ocp_t>::Max(),
f8_convert_rne<bf8_ocp_t>(std::numeric_limits<float>::infinity()));
// positive normal float value to bf8 and back, check if holds
float pos_float = 0.0000762939f; // 10*2^-17
ASSERT_NEAR(pos_float, type_convert<float>(f8_convert_rne<bf8_ocp_t>(pos_float)), abs_tol);
// negative smallest normal bf8 value to bf8 and back, check if holds
constexpr auto neg_min_bf8 = -0.00006103515625f; //-2^-14
ASSERT_NEAR(neg_min_bf8, type_convert<float>(f8_convert_rne<bf8_ocp_t>(neg_min_bf8)), 0.0f);
// positive subnorm float value to bf8 and back, check if holds
constexpr auto pos_subnorm_bf8 = 0.000030517578125f; // 2^-15
ASSERT_NEAR(
pos_subnorm_bf8, type_convert<float>(f8_convert_rne<bf8_ocp_t>(pos_subnorm_bf8)), 0.0f);
// min subnorm bf8 value to bf8 and back, check if holds
constexpr auto min_subnorm_bf8 = -0.0000152587890625f; //-2^-16
ASSERT_NEAR(
min_subnorm_bf8, type_convert<float>(f8_convert_rne<bf8_ocp_t>(min_subnorm_bf8)), 0.0f);
// smaller than min subnorm bf8 value to bf8 must be zero
constexpr auto less_than_min_subnorm = 0.00000762939453125f; // 2^-17
ASSERT_EQ(0.0f, type_convert<float>(f8_convert_rne<bf8_ocp_t>(less_than_min_subnorm)));
// convert quiet NaN to bf8_ocp_t and check if it is quiet NaN
const auto bf8_nan = f8_convert_rne<bf8_ocp_t>(std::numeric_limits<float>::quiet_NaN());
ASSERT_TRUE(ck::fp8_impl::ocp_bf8_is_nan(bf8_nan.data));
}
TEST(BF8OCP, ConvertFP32Stochastic)
{
// fix the tolerance value
float abs_tol = 1e-6;
// convert 0 float to bfp8 and back, check if holds
ASSERT_NEAR(0.0f, type_convert<float>(f8_convert_sr<bf8_ocp_t>(0.0f)), 0.0f);
// convert minimal float to bf8 and back, check if holds
ASSERT_NEAR(std::numeric_limits<float>::min(),
type_convert<float>(f8_convert_sr<bf8_ocp_t>(std::numeric_limits<float>::min())),
abs_tol);
const auto max_bf8_t_float = type_convert<float>(ck::NumericLimits<bf8_ocp_t>::Max());
// convert maximal bf8_ocp_t to float and check if equal to bf8 max
ASSERT_NEAR(
max_bf8_t_float, type_convert<float>(f8_convert_sr<bf8_ocp_t>(max_bf8_t_float)), 0.0f);
// convert maximal float to bf8 and back, check if clipped to bf8 max (saturation to finite)
ASSERT_NEAR(max_bf8_t_float,
type_convert<float>(f8_convert_sr<bf8_ocp_t>(std::numeric_limits<float>::max())),
0.0f);
// convert float infinity to bf8_ocp_t and check if it is max value (saturation to finite)
ASSERT_EQ(ck::NumericLimits<bf8_ocp_t>::Max(),
f8_convert_sr<bf8_ocp_t>(std::numeric_limits<float>::infinity()));
// positive normal float value to bf8 and back, check if holds
float pos_float = 0.0000762939f; // 10*2^-17
ASSERT_NEAR(pos_float, type_convert<float>(f8_convert_sr<bf8_ocp_t>(pos_float)), abs_tol);
// negative smallest normal bf8 value to bf8 and back, check if holds
constexpr auto neg_min_bf8 = -0.00006103515625f; //-2^-14
ASSERT_NEAR(neg_min_bf8, type_convert<float>(f8_convert_sr<bf8_ocp_t>(neg_min_bf8)), 0.0f);
// positive subnorm float value to bf8 and back, check if holds
constexpr auto pos_subnorm_bf8 = 0.000030517578125f; // 2^-15
ASSERT_NEAR(
pos_subnorm_bf8, type_convert<float>(f8_convert_sr<bf8_ocp_t>(pos_subnorm_bf8)), 0.0f);
// min subnorm bf8 value to bf8 and back, check if holds
constexpr auto min_subnorm_bf8 = -0.0000152587890625f; //-2^-16
ASSERT_NEAR(
min_subnorm_bf8, type_convert<float>(f8_convert_sr<bf8_ocp_t>(min_subnorm_bf8)), 0.0f);
// smaller than min subnorm bf8 value to bf8 alternates between 0 and 2^-16
constexpr auto less_than_min_subnorm = 0.00000762939453125f; // 2^-17
ASSERT_NEAR(0.0f,
type_convert<float>(f8_convert_sr<bf8_ocp_t>(less_than_min_subnorm)),
0.0000152587890625f);
// convert quiet NaN to bf8_ocp_t and check if it is quiet NaN
const auto bf8_nan = f8_convert_sr<bf8_ocp_t>(std::numeric_limits<float>::quiet_NaN());
ASSERT_TRUE(ck::fp8_impl::ocp_bf8_is_nan(bf8_nan.data));
}
TEST(BF8OCP, ConvertFP16Nearest)
{
// fix the tolerance value
constexpr half_t half_t_tol = 1e-3;
constexpr half_t half_t_zero = 0.0;
// convert 0 half_t to bfp8 and back, check if holds
ASSERT_NEAR(
half_t_zero, type_convert<half_t>(f8_convert_rne<bf8_ocp_t>(half_t_zero)), half_t_zero);
// convert minimal half_t to bf8 and back, check if holds
ASSERT_NEAR(ck::NumericLimits<half_t>::Min(),
type_convert<half_t>(f8_convert_rne<bf8_ocp_t>(ck::NumericLimits<half_t>::Min())),
half_t_tol);
const auto max_bf8_t_half_t = type_convert<half_t>(ck::NumericLimits<bf8_ocp_t>::Max());
// convert maximal bf8_ocp_t to half_t and check if equal to bf8 max
ASSERT_NEAR(max_bf8_t_half_t,
type_convert<half_t>(f8_convert_rne<bf8_ocp_t>(max_bf8_t_half_t)),
half_t_zero);
// convert maximal half_t to bf8 and back, check if clipped to bf8 max (saturation to finite)
ASSERT_NEAR(max_bf8_t_half_t,
type_convert<half_t>(f8_convert_rne<bf8_ocp_t>(ck::NumericLimits<half_t>::Max())),
half_t_zero);
// convert half_t infinity to bf8_ocp_t and check if it is max value (saturation to finite)
ASSERT_EQ(
ck::NumericLimits<bf8_ocp_t>::Max(),
f8_convert_rne<bf8_ocp_t>(type_convert<half_t>(std::numeric_limits<float>::infinity())));
// positive normal bf8 value to bf8 and back, check if holds
constexpr half_t pos_norm_bf8{0.0000762939f}; // 10*2^-17
ASSERT_NEAR(
pos_norm_bf8, type_convert<half_t>(f8_convert_rne<bf8_ocp_t>(pos_norm_bf8)), half_t_tol);
// negative smallest normal bf8 value to bf8 and back, check if holds
constexpr half_t neg_min_bf8{-0.00006103515625f}; //-2^-14
ASSERT_NEAR(
neg_min_bf8, type_convert<half_t>(f8_convert_rne<bf8_ocp_t>(neg_min_bf8)), half_t_zero);
// positive subnorm bf8 value to bf8 and back, check if holds
constexpr half_t pos_subnorm_bf8{0.000030517578125f}; // 2^-15
ASSERT_NEAR(pos_subnorm_bf8,
type_convert<half_t>(f8_convert_rne<bf8_ocp_t>(pos_subnorm_bf8)),
half_t_zero);
// min subnorm bf8 value to bf8 and back, check if holds
constexpr half_t min_subnorm_bf8{-0.0000152587890625f}; //-2^-16
ASSERT_NEAR(min_subnorm_bf8,
type_convert<half_t>(f8_convert_rne<bf8_ocp_t>(min_subnorm_bf8)),
half_t_zero);
// smaller than min subnorm bf8 value to bf8 must be zero
constexpr half_t less_than_min_subnorm{0.00000762939453125f}; // 2^-17
ASSERT_EQ(half_t_zero, type_convert<half_t>(f8_convert_rne<bf8_ocp_t>(less_than_min_subnorm)));
// convert quiet NaN to bf8_ocp_t and check if it is quiet NaN
const auto bf8_nan = f8_convert_rne<bf8_ocp_t>(ck::NumericLimits<half_t>::QuietNaN());
ASSERT_TRUE(ck::fp8_impl::ocp_bf8_is_nan(bf8_nan.data));
}
TEST(BF8OCP, ConvertFP16Stochastic)
{
// fix the tolerance value
constexpr half_t half_t_tol = 1e-3;
constexpr half_t half_t_zero = 0.0;
constexpr auto min_subnorm_bf8 = 0.0000152587890625f; // 2^-16
// convert 0 half_t to bfp8 and back, check if holds
ASSERT_NEAR(
half_t_zero, type_convert<half_t>(f8_convert_sr<bf8_ocp_t>(half_t_zero)), half_t_zero);
// convert minimal half_t (6.103515625e-05) to fp8 and back
ASSERT_NEAR(ck::NumericLimits<half_t>::Min(),
type_convert<half_t>(f8_convert_sr<bf8_ocp_t>(ck::NumericLimits<half_t>::Min())),
half_t_zero);
const auto max_bf8_t_half_t = type_convert<half_t>(ck::NumericLimits<bf8_ocp_t>::Max());
// convert maximal bf8_ocp_t to half_t and check if equal to bf8 max
ASSERT_NEAR(max_bf8_t_half_t,
type_convert<half_t>(f8_convert_sr<bf8_ocp_t>(max_bf8_t_half_t)),
half_t_zero);
// convert maximal half_t to bf8 and back, check if clipped to bf8 max (saturation to finite)
ASSERT_NEAR(max_bf8_t_half_t,
type_convert<half_t>(f8_convert_sr<bf8_ocp_t>(ck::NumericLimits<half_t>::Max())),
half_t_zero);
// convert half_t infinity to bf8_ocp_t and check if it is max value (saturation to finite)
ASSERT_EQ(
ck::NumericLimits<bf8_ocp_t>::Max(),
f8_convert_sr<bf8_ocp_t>(type_convert<half_t>(std::numeric_limits<float>::infinity())));
// positive normal bf8 value to bf8 and back, check if holds
constexpr half_t pos_norm_bf8{0.0000762939f}; // 10*2^-17
ASSERT_NEAR(
pos_norm_bf8, type_convert<half_t>(f8_convert_sr<bf8_ocp_t>(pos_norm_bf8)), half_t_tol);
// negative smallest normal bf8 value to bf8 and back, check if holds
constexpr half_t neg_min_bf8{-0.00006103515625f}; //-2^-14
ASSERT_NEAR(
neg_min_bf8, type_convert<half_t>(f8_convert_sr<bf8_ocp_t>(neg_min_bf8)), half_t_zero);
// positive subnorm bf8 value to bf8 and back, check if holds
constexpr half_t pos_subnorm_bf8{0.000030517578125f}; // 2^-15
ASSERT_NEAR(pos_subnorm_bf8,
type_convert<half_t>(f8_convert_sr<bf8_ocp_t>(pos_subnorm_bf8)),
half_t_zero);
// min subnorm bf8 value to bf8 and back, check if holds
ASSERT_NEAR(half_t{-min_subnorm_bf8},
type_convert<half_t>(f8_convert_sr<bf8_ocp_t>(half_t{-min_subnorm_bf8})),
half_t_zero);
// smaller than min subnorm bf8 value to bf8 alternates between 0 and 2^-16
constexpr half_t less_than_min_subnorm{0.00000762939453125f}; // 2^-17
ASSERT_NEAR(half_t_zero,
type_convert<half_t>(f8_convert_sr<bf8_ocp_t>(less_than_min_subnorm)),
half_t{min_subnorm_bf8});
// convert quiet NaN to bf8_ocp_t and check if it is quiet NaN
const auto bf8_nan = f8_convert_sr<bf8_ocp_t>(ck::NumericLimits<half_t>::QuietNaN());
ASSERT_TRUE(ck::fp8_impl::ocp_bf8_is_nan(bf8_nan.data));
}
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