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Commit c5f34e61 authored by Artur Wojcik's avatar Artur Wojcik
Browse files

Merge branch 'uif2-initial' into uif2-migraphx

parents 35804f12 d4261237
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Showing with 198 additions and 194 deletions
example/01_gemm/gemm_xdl_fp16.cpp
View file @ c5f34e61
......@@ -9,13 +9,13 @@
using ADataType = ck::half_t;
using BDataType = ck::half_t;
using AccDataType = float;
using CShuffleDataType = float;
using CShuffleDataType = ck::half_t;
using CDataType = ck::half_t;
using F16 = ck::half_t;
using ALayout = Row;
using BLayout = Col;
using BLayout = Row;
using CLayout = Row;
using AElementOp = PassThrough;
......@@ -39,7 +39,7 @@ using DeviceGemmInstance1 = ck::tensor_operation::device::DeviceGemm_Xdl_CShuffl
// ######| | | | Type| Type| Type| Type| DataType| Elementwise| Elementwise| Elementwise| Spacialization| Prefetch| Size| Block| Block| Block| | | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| _MBlock_MWaveMPerXdl| ScalarPerVector|
// ######| | | | | | | | | Operation| Operation| Operation| | Stage| | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NWaveNPerXdl| _NWaveNPerXdl|
// ######| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
< ALayout, BLayout, CLayout, ADataType, BDataType, CDataType, AccDataType, CShuffleDataType, AElementOp, BElementOp, CElementOp, GemmDefault, 1, 256, 256, 128, 32, 8, 8, 32, 32, 4, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>;
< ALayout, BLayout, CLayout, ADataType, BDataType, CDataType, AccDataType, CShuffleDataType, AElementOp, BElementOp, CElementOp, GemmDefault, 1, 256, 256, 128, 32, 8, 2, 32, 32, 4, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<8, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, 0, 1, 2, S<1, 16, 1, 16>, 8, ck::LoopScheduler::Interwave, ck::PipelineVersion::v1>;
// clang-format on
using DeviceGemmInstance = DeviceGemmInstance1;
......
include/ck/host_utility/kernel_launch.hpp
View file @ c5f34e61
......@@ -33,10 +33,13 @@ float launch_and_time_kernel(const StreamConfig& stream_config,
printf("Warm up 1 time\n");
#endif
// warm up
kernel<<<grid_dim, block_dim, lds_byte, stream_config.stream_id_>>>(args...);
hip_check_error(hipGetLastError());
for(int i = 0; i < stream_config.cold_niters_; ++i)
{
kernel<<<grid_dim, block_dim, lds_byte, stream_config.stream_id_>>>(args...);
hip_check_error(hipGetLastError());
}
const int nrepeat = 10;
const int nrepeat = stream_config.nrepeat_;
#if DEBUG_LOG
printf("Start running %d times...\n", nrepeat);
#endif
......
include/ck/stream_config.hpp
View file @ c5f34e61
......@@ -11,4 +11,6 @@ struct StreamConfig
hipStream_t stream_id_ = nullptr;
bool time_kernel_ = false;
int log_level_ = 0;
int cold_niters_ = 50;
int nrepeat_ = 200;
};
library/include/ck/library/tensor_operation_instance/gpu/gemm.hpp
View file @ c5f34e61
......@@ -377,7 +377,7 @@ struct DeviceOperationInstanceFactory<
if constexpr(is_same_v<ALayout, Row> && is_same_v<BLayout, Row> &&
is_same_v<CLayout, Row>)
{
add_device_gemm_xdl_f32_f32_f32_mk_kn_mn_instances(op_ptrs);
/// add_device_gemm_xdl_f32_f32_f32_mk_kn_mn_instances(op_ptrs);
#ifdef DL_KERNELS
add_device_gemm_dl_f32_f32_f32_mk_kn_mn_instances(op_ptrs);
#endif
......@@ -386,7 +386,7 @@ struct DeviceOperationInstanceFactory<
else if constexpr(is_same_v<ALayout, Row> && is_same_v<BLayout, Col> &&
is_same_v<CLayout, Row>)
{
add_device_gemm_xdl_f32_f32_f32_mk_nk_mn_instances(op_ptrs);
/// add_device_gemm_xdl_f32_f32_f32_mk_nk_mn_instances(op_ptrs);
#ifdef DL_KERNELS
add_device_gemm_dl_f32_f32_f32_mk_nk_mn_instances(op_ptrs);
#endif
......@@ -395,7 +395,7 @@ struct DeviceOperationInstanceFactory<
else if constexpr(is_same_v<ALayout, Col> && is_same_v<BLayout, Row> &&
is_same_v<CLayout, Row>)
{
add_device_gemm_xdl_f32_f32_f32_km_kn_mn_instances(op_ptrs);
/// add_device_gemm_xdl_f32_f32_f32_km_kn_mn_instances(op_ptrs);
#ifdef DL_KERNELS
add_device_gemm_dl_f32_f32_f32_km_kn_mn_instances(op_ptrs);
#endif
......@@ -404,7 +404,7 @@ struct DeviceOperationInstanceFactory<
else if constexpr(is_same_v<ALayout, Col> && is_same_v<BLayout, Col> &&
is_same_v<CLayout, Row>)
{
add_device_gemm_xdl_f32_f32_f32_km_nk_mn_instances(op_ptrs);
/// add_device_gemm_xdl_f32_f32_f32_km_nk_mn_instances(op_ptrs);
#ifdef DL_KERNELS
add_device_gemm_dl_f32_f32_f32_km_nk_mn_instances(op_ptrs);
#endif
......@@ -418,7 +418,7 @@ struct DeviceOperationInstanceFactory<
if constexpr(is_same_v<ALayout, Row> && is_same_v<BLayout, Row> &&
is_same_v<CLayout, Row>)
{
add_device_gemm_xdl_f16_f16_f16_mk_kn_mn_instances(op_ptrs);
/// add_device_gemm_xdl_f16_f16_f16_mk_kn_mn_instances(op_ptrs);
#ifdef DL_KERNELS
add_device_gemm_dl_f16_f16_f16_mk_kn_mn_instances(op_ptrs);
add_device_gemm_dl_f16_f16_f16_mk_kn_mn_irregular_instances(op_ptrs);
......@@ -430,7 +430,7 @@ struct DeviceOperationInstanceFactory<
else if constexpr(is_same_v<ALayout, Row> && is_same_v<BLayout, Col> &&
is_same_v<CLayout, Row>)
{
add_device_gemm_xdl_f16_f16_f16_mk_nk_mn_instances(op_ptrs);
/// add_device_gemm_xdl_f16_f16_f16_mk_nk_mn_instances(op_ptrs);
#ifdef DL_KERNELS
add_device_gemm_dl_f16_f16_f16_mk_nk_mn_instances(op_ptrs);
add_device_gemm_dl_f16_f16_f16_mk_nk_mn_irregular_instances(op_ptrs);
......@@ -443,7 +443,7 @@ struct DeviceOperationInstanceFactory<
else if constexpr(is_same_v<ALayout, Col> && is_same_v<BLayout, Row> &&
is_same_v<CLayout, Row>)
{
add_device_gemm_xdl_f16_f16_f16_km_kn_mn_instances(op_ptrs);
/// add_device_gemm_xdl_f16_f16_f16_km_kn_mn_instances(op_ptrs);
#ifdef DL_KERNELS
add_device_gemm_dl_f16_f16_f16_km_kn_mn_instances(op_ptrs);
add_device_gemm_dl_f16_f16_f16_km_kn_mn_irregular_instances(op_ptrs);
......@@ -455,7 +455,7 @@ struct DeviceOperationInstanceFactory<
else if constexpr(is_same_v<ALayout, Col> && is_same_v<BLayout, Col> &&
is_same_v<CLayout, Row>)
{
add_device_gemm_xdl_f16_f16_f16_km_nk_mn_instances(op_ptrs);
/// add_device_gemm_xdl_f16_f16_f16_km_nk_mn_instances(op_ptrs);
#ifdef DL_KERNELS
add_device_gemm_dl_f16_f16_f16_km_nk_mn_instances(op_ptrs);
add_device_gemm_dl_f16_f16_f16_km_nk_mn_irregular_instances(op_ptrs);
......
profiler/include/profiler/profile_gemm_impl.hpp
View file @ c5f34e61
......@@ -6,6 +6,7 @@
#include <iomanip>
#include <iostream>
#include <typeinfo>
#include <unistd.h>
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
......@@ -20,6 +21,7 @@
#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/fill.hpp"
namespace ck {
namespace profiler {
......@@ -69,14 +71,17 @@ int profile_gemm_impl(int do_verification,
switch(init_method)
{
case 0: break;
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);
break;
case 1:
a_m_k.GenerateTensorValue(GeneratorTensor_2<ADataType>{-5, 5});
b_k_n.GenerateTensorValue(GeneratorTensor_2<BDataType>{-5, 5});
ck::utils::FillUniformDistributionIntegerValue<ADataType>{-5.f, 5.f}(a_m_k);
ck::utils::FillUniformDistributionIntegerValue<BDataType>{-5.f, 5.f}(b_k_n);
break;
default:
a_m_k.GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 0.1});
b_k_n.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.01, 0.01});
ck::utils::FillUniformDistribution<ADataType>{-1.f, 1.f}(a_m_k);
ck::utils::FillUniformDistribution<BDataType>{-1.f, 1.f}(b_k_n);
}
using AElementOp = ck::tensor_operation::element_wise::PassThrough;
......@@ -130,11 +135,10 @@ int profile_gemm_impl(int do_verification,
ref_invoker.Run(ref_argument);
}
std::string best_op_name;
float best_avg_time = 0;
float best_tflops = 0;
float best_gb_per_sec = 0;
float best_tflops = 0;
int best_instance_id = 0;
int instance_id = 0;
// profile device op instances
for(auto& op_ptr : op_ptrs)
{
......@@ -178,10 +182,8 @@ int profile_gemm_impl(int do_verification,
if(tflops > best_tflops)
{
best_op_name = op_name;
best_tflops = tflops;
best_avg_time = avg_time;
best_gb_per_sec = gb_per_sec;
best_instance_id = instance_id;
best_tflops = tflops;
}
if(do_verification)
......@@ -205,53 +207,94 @@ int profile_gemm_impl(int do_verification,
{
std::cout << op_ptr->GetTypeString() << " does not support this problem" << std::endl;
}
}
if constexpr(is_same<CDataType, float>::value)
{
std::cout << "Best Perf for datatype = f32";
}
else if constexpr(is_same<CDataType, half_t>::value)
{
std::cout << "Best Perf for datatype = f16";
instance_id++;
}
else if constexpr(is_same<CDataType, bhalf_t>::value)
{
std::cout << "Best Perf for datatype = bf16";
}
else if constexpr(is_same<CDataType, int8_t>::value)
sleep(2);
// Run the best instance again
{
std::cout << "Best Perf for datatype = int8";
}
auto& op_ptr = op_ptrs[best_instance_id];
auto argument_ptr =
op_ptr->MakeArgumentPointer(static_cast<ADataType*>(a_device_buf.GetDeviceBuffer()),
static_cast<BDataType*>(b_device_buf.GetDeviceBuffer()),
static_cast<CDataType*>(c_device_buf.GetDeviceBuffer()),
M,
N,
K,
StrideA,
StrideB,
StrideC,
a_element_op,
b_element_op,
c_element_op);
auto invoker_ptr = op_ptr->MakeInvokerPointer();
if(op_ptr->IsSupportedArgument(argument_ptr.get()))
{
std::string op_name = op_ptr->GetTypeString();
float avg_time = invoker_ptr->Run(argument_ptr.get(),
StreamConfig{nullptr, time_kernel, 0, 50, 200});
std::size_t flop = std::size_t(2) * M * N * K;
std::size_t num_btype =
sizeof(ADataType) * M * K + sizeof(BDataType) * K * N + sizeof(CDataType) * M * N;
float tflops = static_cast<float>(flop) / 1.E9 / avg_time;
float gb_per_sec = num_btype / 1.E6 / avg_time;
if constexpr(is_same<CDataType, float>::value)
{
std::cout << "Best Perf for datatype = f32";
}
else if constexpr(is_same<CDataType, half_t>::value)
{
std::cout << "Best Perf for datatype = f16";
}
else if constexpr(is_same<CDataType, bhalf_t>::value)
{
std::cout << "Best Perf for datatype = bf16";
}
else if constexpr(is_same<CDataType, int8_t>::value)
{
std::cout << "Best Perf for datatype = int8";
}
#if defined CK_ENABLE_FP8
else if constexpr(is_same<CDataType, f8_t>::value)
{
std::cout << "Best Perf for datatype = fp8";
}
else if constexpr(is_same<CDataType, f8_t>::value)
{
std::cout << "Best Perf for datatype = fp8";
}
#endif
if constexpr(is_same<ALayout, tensor_layout::gemm::RowMajor>::value)
{
std::cout << " ALayout = RowMajor";
}
else if constexpr(is_same<ALayout, tensor_layout::gemm::ColumnMajor>::value)
{
std::cout << " ALayout = ColumnMajor";
}
if constexpr(is_same<ALayout, tensor_layout::gemm::RowMajor>::value)
{
std::cout << " ALayout = RowMajor";
}
else if constexpr(is_same<ALayout, tensor_layout::gemm::ColumnMajor>::value)
{
std::cout << " ALayout = ColumnMajor";
}
if constexpr(is_same<BLayout, tensor_layout::gemm::RowMajor>::value)
{
std::cout << " BLayout = RowMajor";
}
else if constexpr(is_same<BLayout, tensor_layout::gemm::ColumnMajor>::value)
{
std::cout << " BLayout = ColumnMajor";
}
if constexpr(is_same<BLayout, tensor_layout::gemm::RowMajor>::value)
{
std::cout << " BLayout = RowMajor";
}
else if constexpr(is_same<BLayout, tensor_layout::gemm::ColumnMajor>::value)
{
std::cout << " BLayout = ColumnMajor";
}
std::cout << " M = " << M << " N = " << N << " K = " << K << " StrideA = " << StrideA
<< " StrideB = " << StrideB << " StrideC = " << StrideC << " : " << best_avg_time
<< " ms, " << best_tflops << " TFlops, " << best_gb_per_sec << " GB/s, "
<< best_op_name << std::endl;
std::cout << " M = " << M << " N = " << N << " K = " << K << " StrideA = " << StrideA
<< " StrideB = " << StrideB << " StrideC = " << StrideC << " : " << avg_time
<< " ms, " << tflops << " TFlops, " << gb_per_sec << " GB/s, " << op_name
<< std::endl;
}
}
return pass ? 0 : 1;
}
......
profiler/src/profile_transpose.cpp deleted 100644 → 0
View file @ 35804f12
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
#include <iostream>
#include <numeric>
#include <initializer_list>
#include <cstdlib>
#include "profiler/profile_transpose_impl.hpp"
#include "profiler_operation_registry.hpp"
enum struct MatrixLayout
{
NCDHW, // 0
NCHWD, // 1
};
enum struct DataType
{
F32_F32_F32_F32_F32, // 0
F16_F16_F16_F16_F16, // 1
};
#define OP_NAME "transpose"
#define OP_DESC "Transpose"
int profile_transpose(int argc, char* argv[])
{
if(argc != 15)
{
printf("arg1: tensor operation (" OP_NAME ": " OP_DESC ")\n");
printf("arg2: data type (0: fp32; 1: fp16)\n");
// printf("arg3: matrix layout (NCDHW -> NDCHW);\n");
printf("arg4: verification (0: no; 1: yes)\n");
printf("arg5: initialization (0: no init; 1: integer value; 2: decimal value)\n");
printf("arg6: print tensor value (0: no; 1: yes)\n");
printf("arg7: time kernel (0=no, 1=yes)\n");
printf("arg8 to 13: N, C, D, H, W\n");
exit(1);
}
const auto data_type = static_cast<DataType>(std::stoi(argv[2]));
// const auto layout = static_cast<MatrixLayout>(std::stoi(argv[3]));
const bool do_verification = std::stoi(argv[3]);
const int init_method = std::stoi(argv[4]);
const bool do_log = std::stoi(argv[5]);
const bool time_kernel = std::stoi(argv[6]);
std::vector<index_t> lengths = std::stoi(argv[7]);
/**const int N = std::stoi(argv[7]);
const int C = std::stoi(argv[8]);
const int D = std::stoi(argv[9]);
const int H = std::stoi(argv[10]);
const int W = std::stoi(argv[11]);**/
using F32 = float;
using F16 = ck::half_t;
auto profile = [&](auto a_type, auto b_type) {
using ADataType = decltype(a_type);
using BDataType = decltype(b_type);
bool pass = ck::profiler::profile_transpose_impl<ADataType, BDataType>(
do_verification, init_method, do_log, time_kernel, lengths);
return pass ? 0 : 1;
};
if(data_type == GemmDataType::F32_F32_F32_F32_F32)
{
return profile(F32{}, F32{});
}
else if(data_type == GemmDataType::F16_F16_F16_F16_F16)
{
return profile(F16{}, F16{});
}
else
{
std::cout << "this data_type & layout is not implemented" << std::endl;
return 1;
}
}
REGISTER_PROFILER_OPERATION(OP_NAME, OP_DESC, profile_gemm_transpose);
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