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Commit a01ca8e6 authored by Adam Osewski's avatar Adam Osewski
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Add gtests for gemm splitk using ckProfiler API.

parent e9fd26c8
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test/gemm_split_k/CMakeLists.txt
View file @ a01ca8e6
add_test_executable(test_gemm_split_k gemm_split_k.cpp) add_gtest_executable(test_gemm_splitk test_gemm_splitk.cpp)
target_link_libraries(test_gemm_split_k PRIVATE utility) target_link_libraries(test_gemm_splitk PRIVATE utility device_gemm_splitk_instance)
target_link_libraries(test_gemm_split_k PRIVATE device_gemm_splitk_instance)
test/gemm_split_k/gemm_split_k.cpp deleted 100644 → 0
View file @ e9fd26c8
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include <iostream>
#include <initializer_list>
#include <cstdlib>
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/library/tensor_operation_instance/gpu/gemm_splitk.hpp"
#include "ck/library/utility/check_err.hpp"
#include "ck/library/utility/device_memory.hpp"
#include "ck/library/utility/host_tensor.hpp"
#include "ck/library/utility/host_tensor_generator.hpp"
#include "ck/library/utility/literals.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_gemm.hpp"
#include "ck/library/utility/host_gemm.hpp"
enum struct GemmMatrixLayout
{
MK_KN_MN, // 0
MK_NK_MN, // 1
KM_KN_MN, // 2
KM_NK_MN, // 3
};
template <typename T>
static bool check_out(const Tensor<T>& ref, const Tensor<T>& result)
{
float max_diff = 1e-6;
for(std::size_t i = 0; i < ref.mData.size(); ++i)
{
float diff = std::abs(double(ref.mData[i]) - double(result.mData[i]));
if(max_diff < diff)
{
return false;
}
}
return true;
}
struct gemmArgs
{
GemmMatrixLayout layout;
int M;
int N;
int K;
int StrideA;
int StrideB;
int StrideC;
int KBatch;
};
int test_gemm(const gemmArgs& args)
{
using Row = ck::tensor_layout::gemm::RowMajor;
using Col = ck::tensor_layout::gemm::ColumnMajor;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
bool a_row_major, b_row_major, c_row_major;
switch(args.layout)
{
case GemmMatrixLayout::MK_KN_MN:
a_row_major = true;
b_row_major = true;
c_row_major = true;
break;
case GemmMatrixLayout::MK_NK_MN:
a_row_major = true;
b_row_major = false;
c_row_major = true;
break;
case GemmMatrixLayout::KM_KN_MN:
a_row_major = false;
b_row_major = true;
c_row_major = true;
break;
case GemmMatrixLayout::KM_NK_MN:
a_row_major = false;
b_row_major = false;
c_row_major = true;
break;
default: printf("not supported layout"); return 1;
}
auto f_host_tensor_descriptor =
[](std::size_t row, std::size_t col, std::size_t stride, bool row_major) {
using namespace ck::literals;
if(row_major)
{
return HostTensorDescriptor({row, col}, {stride, 1_uz});
}
else
{
return HostTensorDescriptor({row, col}, {1_uz, stride});
}
};
Tensor<float> a_m_k(f_host_tensor_descriptor(args.M, args.K, args.StrideA, a_row_major));
Tensor<float> b_k_n(f_host_tensor_descriptor(args.K, args.N, args.StrideB, b_row_major));
Tensor<float> c_m_n_host_result(
f_host_tensor_descriptor(args.M, args.N, args.StrideC, c_row_major));
Tensor<float> c_m_n_device_result(
f_host_tensor_descriptor(args.M, args.N, args.StrideC, c_row_major));
// init data
std::size_t num_thread = 1;
a_m_k.GenerateTensorValue(GeneratorTensor_2<float>{-5, 5}, num_thread);
b_k_n.GenerateTensorValue(GeneratorTensor_2<float>{-5, 5}, num_thread);
// set zero to c_device_buf
c_m_n_device_result.GenerateTensorValue(GeneratorTensor_0<float>{}, num_thread);
host_gemm_mk_kn_mn(a_m_k,
b_k_n,
c_m_n_host_result,
ck::tensor_operation::element_wise::PassThrough{},
ck::tensor_operation::element_wise::PassThrough{},
ck::tensor_operation::element_wise::PassThrough{});
DeviceMem a_device_buf(sizeof(float) * a_m_k.mDesc.GetElementSpaceSize());
DeviceMem b_device_buf(sizeof(float) * b_k_n.mDesc.GetElementSpaceSize());
DeviceMem c_device_buf(sizeof(float) * c_m_n_device_result.mDesc.GetElementSpaceSize());
a_device_buf.ToDevice(a_m_k.mData.data());
b_device_buf.ToDevice(b_k_n.mData.data());
c_device_buf.ToDevice(c_m_n_device_result.mData.data());
auto test = [&](auto a_layout, auto b_layout, auto c_layout) {
bool success = false;
using DeviceOp = ck::tensor_operation::device::DeviceGemmSplitK<decltype(a_layout),
decltype(b_layout),
decltype(c_layout),
float,
float,
float,
PassThrough,
PassThrough,
PassThrough>;
const auto gemm_ptrs =
ck::tensor_operation::device::instance::DeviceOperationInstanceFactory<
DeviceOp>::GetInstances();
for(auto& gemm_ptr : gemm_ptrs)
{
auto argument_ptr =
gemm_ptr->MakeArgumentPointer(static_cast<float*>(a_device_buf.GetDeviceBuffer()),
static_cast<float*>(b_device_buf.GetDeviceBuffer()),
static_cast<float*>(c_device_buf.GetDeviceBuffer()),
args.M,
args.N,
args.K,
args.StrideA,
args.StrideB,
args.StrideC,
ck::tensor_operation::element_wise::PassThrough{},
ck::tensor_operation::element_wise::PassThrough{},
ck::tensor_operation::element_wise::PassThrough{},
args.KBatch);
auto invoker_ptr = gemm_ptr->MakeInvokerPointer();
if(gemm_ptr->IsSupportedArgument(argument_ptr.get()))
{
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;
break;
}
success = true;
}
}
return success;
};
bool success = false;
if(args.layout == GemmMatrixLayout::MK_KN_MN)
{
success = test(Row{}, Row{}, Row{});
}
else if(args.layout == GemmMatrixLayout::MK_NK_MN)
{
success = test(Row{}, Col{}, Row{});
}
else if(args.layout == GemmMatrixLayout::KM_KN_MN)
{
success = test(Col{}, Row{}, Row{});
}
else
{
success = test(Col{}, Col{}, Row{});
}
auto error_code = 0;
if(success)
{
std::cout << "test split k : Pass" << std::endl;
}
else
{
std::cout << "test split k: Fail " << std::endl;
error_code = -1; // test needs to report failure
}
return error_code;
}
int main(int argc, char* argv[])
{
std::vector<gemmArgs> test_cases;
if(argc == 1)
{
test_cases = {{GemmMatrixLayout::MK_KN_MN, 1024, 1024, 1024, 1024, 1024, 1024, 2},
{GemmMatrixLayout::MK_KN_MN, 1024, 1024, 1024, 1024, 1024, 1024, 8}};
}
else if(argc == 9)
{
const auto layout = static_cast<GemmMatrixLayout>(std::stoi(argv[1]));
const int M = std::stoi(argv[2]);
const int N = std::stoi(argv[3]);
const int K = std::stoi(argv[4]);
const int StrideA = std::stoi(argv[5]);
const int StrideB = std::stoi(argv[6]);
const int StrideC = std::stoi(argv[7]);
const int KBatch = std::stoi(argv[8]);
test_cases = {{layout, M, N, K, StrideA, StrideB, StrideC, KBatch}};
}
else
{
printf("arg1: matrix layout (0: A[m, k] * B[k, n] = C[m, n];\n");
printf(" 1: A[m, k] * B[n, k] = C[m, n];\n");
printf(" 2: A[k, m] * B[k, n] = C[m, n];\n");
printf(" 3: A[k, m] * B[n, k] = C[m, n])\n");
printf("arg2 to 7: M, N, K, StrideA, StrideB, StrideC KBatch\n");
return -1;
}
bool error = false;
for(const auto& kinder : test_cases)
{
error |= test_gemm(kinder);
}
return error ? 1 : 0;
}
test/gemm_split_k/test_gemm_splitk.cpp 0 → 100644
View file @ a01ca8e6
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
// #include <algorithm>
// #include <stdexcept>
#include <vector>
#include "gtest/gtest.h"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "test_gemm_splitk_util.hpp"
using F16 = ck::half_t;
using F32 = float;
using Row = ck::tensor_layout::gemm::RowMajor;
using Col = ck::tensor_layout::gemm::ColumnMajor;
namespace {
template <typename X, typename Y>
struct tuple_concat;
template <typename... Xs, typename... Ys>
struct tuple_concat<std::tuple<Xs...>, std::tuple<Ys...>>
{
using type = std::tuple<Xs..., Ys...>;
};
} // namespace
template <typename Tuple>
class TestGemmSplitK_MK_KN
: public ck::test::TestGemmSplitK<typename tuple_concat<std::tuple<Row, Row>, Tuple>::type>
{
};
// template <typename Tuple>
// class TestGemmSplitK_MK_NK : public ck::test::TestGemmSplitK<tuple_concat<std::tuple<Row, Col>,
// Tuple>::type> {};
// template <typename Tuple>
// class TestGemmSplitK_KM_KN : public ck::test::TestGemmSplitK<tuple_concat<std::tuple<Col, Row>,
// Tuple>::type> {};
// template <typename Tuple>
// class TestGemmSplitK_KM_NK : public ck::test::TestGemmSplitK<tuple_concat<std::tuple<Col, Col>,
// Tuple>::type> {};
// clang-format off
using KernelTypes = ::testing::Types<
// ADataType, BDataType, CDataType
std::tuple< F16, F16, F16>,
std::tuple< F32, F32, F32>
>;
// clang-format on
TYPED_TEST_SUITE(TestGemmSplitK_MK_KN, KernelTypes);
// TYPED_TEST_SUITE(TestGemmSplitK_MK_NK, KernelTypes);
// TYPED_TEST_SUITE(TestGemmSplitK_KM_KN, KernelTypes);
// TYPED_TEST_SUITE(TestGemmSplitK_KM_NK, KernelTypes);
#include "test_gemm_splitk_ut_cases.inc"
test/gemm_split_k/test_gemm_splitk_ut_cases.inc 0 → 100644
View file @ a01ca8e6
#pragma once
TYPED_TEST(TestGemmSplitK_MK_KN, SmallM)
{
std::vector<int> Ms{0, 1, 2, 3, 4, 5, 6};
int N = 512;
int K = 320;
int StrideA = K;
int StrideB = N;
int StrideC = N;
for(int M : Ms)
this->Run(M, N, K, StrideA, StrideB, StrideC);
}
test/gemm_split_k/test_gemm_splitk_util.hpp 0 → 100644
View file @ a01ca8e6
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include <string>
#include <sstream>
#include <tuple>
#include <vector>
#include <gtest/gtest.h>
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "include/ck/utility/data_type.hpp"
#include "profiler/profile_gemm_splitk_impl.hpp"
namespace ck {
namespace test {
template <typename Tuple>
class TestGemmSplitK : public testing::Test
{
using Row = ck::tensor_layout::gemm::RowMajor;
using F32 = float;
protected:
using ALayout = std::tuple_element_t<0, Tuple>;
using BLayout = std::tuple_element_t<1, Tuple>;
using CLayout = Row;
using ADataType = std::tuple_element_t<2, Tuple>;
using BDataType = std::tuple_element_t<3, Tuple>;
using CDataType = std::tuple_element_t<4, Tuple>;
public:
bool verify_ = true;
int init_method_ = 1; // decimal value initialization
bool log_ = false;
bool bench_ = false; // measure kernel performance
std::vector<int> k_batches_;
void SetUp() override { k_batches_ = {1, 2, 3, 5, 8}; }
void Run(const int M,
const int N,
const int K,
const int StrideA,
const int StrideB,
const int StrideC)
{
for(auto kb : k_batches_)
{
RunSingle(M, N, K, StrideA, StrideB, StrideC, kb);
}
}
void RunSingle(const int M,
const int N,
const int K,
const int StrideA,
const int StrideB,
const int StrideC,
int kbatch = 1)
{
bool pass = ck::profiler::profile_gemm_splitk_impl<ADataType,
BDataType,
F32,
CDataType,
ALayout,
BLayout,
CLayout>(
verify_, init_method_, log_, bench_, M, N, K, StrideA, StrideB, StrideC, kbatch);
EXPECT_TRUE(pass);
}
};
} // namespace test
} // namespace ck
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