mirror of
https://github.com/ROCm/composable_kernel.git
synced 2026-05-12 01:10:17 +00:00
c544eb4da0
* init for reduce_threadwise multi_d * add reduce_threadwise_multi_d * add reduce_multi_d * clean * start add an other splitk device op * add reduce template parameter to SplitKBatchOffset * add reduce c matrix * clean up code * change example data type to bf16 * add bf16Ai8B example * remove reduce template parameter * add splitk atomic status to v4 * example add multi d parameters * device op add multi-d parameters * add multi-d to reduce * fix kbach=1 bug * change B layout to col in bf16Ai8B example * remove float adding struct * change multi-d interface * change file and class name * remove multi-d of bf16Ai8B example * change IsReduce function to IsReduceAdd * change example layout to RRR from RCR * according layout to set ds stride * reset parameter layout * add gemm universal reduce instance * add reduce factory * add profile_gemm_universal_reduce * add reduce to profiler * fix reduce instance * fix profiler reduce compiling bug * format * format library instance code * add mem instance for reduce library * fix call instance names * add workspace for reduce in ckProfiler * format * add mnpading to reduce library instance * add fp16 instance to reduce of profiler * change copyright time * restore profiler cmake file * add reduce text to instances * add DsLayout and DsDataType to instances template parameter * fixed gemm_reduce_multi_d * add an example without multi_d * Update common.hpp * Update gtest.cmake * Update gemm_xdl_splitk_reduce_bf16.cpp * clean * Update gtest.cmake * format * fixe api * format * default parameter change to RRR * add vector_len for multi_d * format * Update gtest.cmake * fix bf16A iBB elementwiseop * add ReduceDataType * move ReduceDataType to end position * format * remove googletest git method address * fix copyright time * update init data --------- Co-authored-by: root <jizhan@amd.com> Co-authored-by: letaoqin <letaoqin@amd.com> Co-authored-by: Jing Zhang <jizhan@meta.com> Co-authored-by: zjing14 <zhangjing14@gmail.com>
324 lines
13 KiB
C++
324 lines
13 KiB
C++
// SPDX-License-Identifier: MIT
|
|
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
|
|
|
|
#pragma once
|
|
|
|
#include <iomanip>
|
|
#include <iostream>
|
|
#include <typeinfo>
|
|
|
|
#include "ck/ck.hpp"
|
|
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
|
|
#include "ck/tensor_operation/gpu/device/impl/device_gemm_xdl_cshuffle_v3r1.hpp"
|
|
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
|
|
|
|
#include "ck/library/tensor_operation_instance/gpu/gemm_universal_reduce.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"
|
|
|
|
namespace ck {
|
|
namespace profiler {
|
|
|
|
template <typename ADataType,
|
|
typename BDataType,
|
|
typename DsDataType,
|
|
typename AccDataType,
|
|
typename CDataType,
|
|
typename ALayout,
|
|
typename BLayout,
|
|
typename DsLayout,
|
|
typename CLayout>
|
|
bool profile_gemm_universal_reduce_impl(int do_verification,
|
|
int init_method,
|
|
bool do_log,
|
|
bool time_kernel,
|
|
int M,
|
|
int N,
|
|
int K,
|
|
int StrideA,
|
|
int StrideB,
|
|
int StrideC,
|
|
int KBatch,
|
|
int n_warmup,
|
|
int n_iter,
|
|
uint64_t rotating = 0)
|
|
{
|
|
bool pass = true;
|
|
|
|
auto f_host_tensor_descriptor =
|
|
[](std::size_t row, std::size_t col, std::size_t stride, auto layout) {
|
|
using namespace ck::literals;
|
|
|
|
if(is_same<decltype(layout), tensor_layout::gemm::RowMajor>::value)
|
|
{
|
|
return HostTensorDescriptor({row, col}, {stride, 1_uz});
|
|
}
|
|
else
|
|
{
|
|
return HostTensorDescriptor({row, col}, {1_uz, stride});
|
|
}
|
|
};
|
|
|
|
Tensor<ADataType> a_m_k(f_host_tensor_descriptor(M, K, StrideA, ALayout{}));
|
|
Tensor<BDataType> b_k_n(f_host_tensor_descriptor(K, N, StrideB, BLayout{}));
|
|
Tensor<CDataType> c_m_n_host_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
|
|
Tensor<CDataType> c_m_n_device_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
|
|
|
|
int total_gemm_needed = a_m_k.GetElementSpaceSizeInBytes() + b_k_n.GetElementSpaceSizeInBytes();
|
|
int rotating_count = std::max(
|
|
1,
|
|
std::min(n_iter,
|
|
static_cast<int>(std::ceil(static_cast<double>(rotating) / total_gemm_needed))));
|
|
|
|
std::cout << "a_m_k: " << a_m_k.mDesc << std::endl;
|
|
std::cout << "b_k_n: " << b_k_n.mDesc << std::endl;
|
|
std::cout << "c_m_n: " << c_m_n_device_result.mDesc << std::endl;
|
|
std::cout << "rotating count: " << rotating_count << std::endl;
|
|
|
|
switch(init_method)
|
|
{
|
|
case 0: break;
|
|
case 1:
|
|
a_m_k.GenerateTensorValue(GeneratorTensor_2<ADataType>{-1, 2});
|
|
b_k_n.GenerateTensorValue(GeneratorTensor_2<BDataType>{-1, 2});
|
|
break;
|
|
default:
|
|
a_m_k.GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0});
|
|
b_k_n.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
|
|
}
|
|
|
|
using AElementOp = ck::tensor_operation::element_wise::PassThrough;
|
|
using BElementOp = ck::tensor_operation::element_wise::PassThrough;
|
|
using CElementOp = ck::tensor_operation::element_wise::PassThrough;
|
|
|
|
const auto a_element_op = AElementOp{};
|
|
const auto b_element_op = BElementOp{};
|
|
const auto c_element_op = CElementOp{};
|
|
|
|
DeviceMem a_device_buf(sizeof(ADataType) * a_m_k.mDesc.GetElementSpaceSize());
|
|
DeviceMem b_device_buf(sizeof(BDataType) * b_k_n.mDesc.GetElementSpaceSize());
|
|
DeviceMem c_device_buf(sizeof(CDataType) * 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());
|
|
|
|
using DeviceOp = ck::tensor_operation::device::DeviceGemmV2R1<ALayout,
|
|
BLayout,
|
|
DsLayout,
|
|
CLayout,
|
|
ADataType,
|
|
BDataType,
|
|
DsDataType,
|
|
CDataType,
|
|
AElementOp,
|
|
BElementOp,
|
|
CElementOp>;
|
|
|
|
// get device op instances
|
|
const auto op_ptrs = ck::tensor_operation::device::instance::DeviceOperationInstanceFactory<
|
|
DeviceOp>::GetInstances();
|
|
|
|
std::cout << "found " << op_ptrs.size() << " instances" << std::endl;
|
|
|
|
// Run reference GEMM
|
|
if(do_verification)
|
|
{
|
|
using ReferenceGemmInstance = ck::tensor_operation::host::ReferenceGemm<ADataType,
|
|
BDataType,
|
|
CDataType,
|
|
AccDataType,
|
|
AElementOp,
|
|
BElementOp,
|
|
CElementOp>;
|
|
|
|
auto ref_gemm = ReferenceGemmInstance{};
|
|
auto ref_invoker = ref_gemm.MakeInvoker();
|
|
|
|
auto ref_argument = ref_gemm.MakeArgument(
|
|
a_m_k, b_k_n, c_m_n_host_result, a_element_op, b_element_op, c_element_op);
|
|
|
|
ref_invoker.Run(ref_argument);
|
|
}
|
|
|
|
std::string best_op_name;
|
|
float best_ave_time = 0;
|
|
float best_tflops = 0;
|
|
float best_gb_per_sec = 0;
|
|
float best_kbatch = 0;
|
|
|
|
// profile device GEMM instances
|
|
for(auto& op_ptr : op_ptrs)
|
|
{
|
|
std::vector<int> kbatch_list = {1, 2, 4, 8, 12, 16, 19, 20, 32, 38};
|
|
|
|
if(KBatch > 0)
|
|
{
|
|
kbatch_list = {KBatch};
|
|
}
|
|
|
|
for(std::size_t i = 0; i < kbatch_list.size(); i++)
|
|
{
|
|
auto kbatch_curr = kbatch_list[i];
|
|
|
|
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,
|
|
kbatch_curr,
|
|
a_element_op,
|
|
b_element_op,
|
|
c_element_op);
|
|
|
|
auto invoker_ptr = op_ptr->MakeInvokerPointer();
|
|
|
|
if(op_ptr->IsSupportedArgument(argument_ptr.get()))
|
|
{
|
|
|
|
DeviceMem gemm_workspace_dev(op_ptr->GetWorkSpaceSize(argument_ptr.get()));
|
|
op_ptr->SetWorkSpacePointer(
|
|
argument_ptr.get(), gemm_workspace_dev.GetDeviceBuffer(), StreamConfig{});
|
|
|
|
// re-init C to zero before profiling next kernel
|
|
c_device_buf.SetZero();
|
|
|
|
invoker_ptr->Run(argument_ptr.get(),
|
|
StreamConfig{nullptr, false, 0, n_warmup, n_iter});
|
|
|
|
if(do_verification)
|
|
{
|
|
c_device_buf.FromDevice(c_m_n_device_result.mData.data());
|
|
|
|
pass = pass & ck::utils::check_err(c_m_n_device_result, c_m_n_host_result);
|
|
|
|
if(do_log)
|
|
{
|
|
LogRangeAsType<float>(std::cout << "a : ", a_m_k.mData, ",") << std::endl;
|
|
LogRangeAsType<float>(std::cout << "b: ", b_k_n.mData, ",") << std::endl;
|
|
LogRangeAsType<float>(
|
|
std::cout << "c_host : ", c_m_n_host_result.mData, ",")
|
|
<< std::endl;
|
|
LogRangeAsType<float>(
|
|
std::cout << "c_device: ", c_m_n_device_result.mData, ",")
|
|
<< std::endl;
|
|
}
|
|
}
|
|
|
|
std::string op_name = op_ptr->GetTypeString();
|
|
|
|
float ave_time = invoker_ptr->Run(argument_ptr.get(),
|
|
StreamConfig{nullptr,
|
|
time_kernel,
|
|
0,
|
|
n_warmup,
|
|
n_iter,
|
|
rotating_count > 1,
|
|
rotating_count});
|
|
|
|
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 / ave_time;
|
|
|
|
float gb_per_sec = num_btype / 1.E6 / ave_time;
|
|
|
|
std::cout << "Perf: " << std::setw(10) << ave_time << " ms, " << tflops
|
|
<< " TFlops, " << gb_per_sec << " GB/s, " << op_name << ", KBatch "
|
|
<< kbatch_curr << std::endl;
|
|
|
|
#if defined CK_ENABLE_FP8
|
|
// set softer tolerances for fp8
|
|
if constexpr(is_same_v<ADataType, f8_t> || is_same_v<BDataType, f8_t> ||
|
|
is_same_v<CDataType, f8_t>)
|
|
{
|
|
std::string msg = "Error: Incorrect results!";
|
|
double rtol = 1e-1;
|
|
double atol = 1e-1;
|
|
pass = pass & ck::utils::check_err(
|
|
c_m_n_device_result, c_m_n_host_result, msg, rtol, atol);
|
|
}
|
|
else
|
|
{
|
|
#endif
|
|
pass = pass & ck::utils::check_err(c_m_n_device_result, c_m_n_host_result);
|
|
#if defined CK_ENABLE_FP8
|
|
}
|
|
#endif
|
|
|
|
if(tflops > best_tflops)
|
|
{
|
|
best_op_name = op_name;
|
|
best_tflops = tflops;
|
|
best_ave_time = ave_time;
|
|
best_gb_per_sec = gb_per_sec;
|
|
best_kbatch = kbatch_curr;
|
|
}
|
|
}
|
|
else
|
|
{
|
|
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";
|
|
}
|
|
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 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";
|
|
}
|
|
|
|
std::cout << " M = " << M << " N = " << N << " K = " << K << " StrideA = " << StrideA
|
|
<< " StrideB = " << StrideB << " StrideC = " << StrideC << " KBatch = " << best_kbatch
|
|
<< " : " << best_ave_time << " ms, " << best_tflops << " TFlops, " << best_gb_per_sec
|
|
<< " GB/s, " << best_op_name << std::endl;
|
|
|
|
return pass;
|
|
}
|
|
|
|
} // namespace profiler
|
|
} // namespace ck
|