mirror of
https://github.com/ROCm/composable_kernel.git
synced 2026-05-12 01:10:17 +00:00
initial stream-k implementation with example (#699)
* initial stream-k implementation with example * fix unexpected change in err * improve a little bit performance by reorganize pipeline. * improve perf a little bit by swizzle block idx * add profiler * update example * fix spelling * shrink karg for streamk * support dynamic buffer using memory coherence glc_slc bit from template * control memory coherence while construct dynamic buffer * update reduction for streamk(not ready yet) * Add template parameter to make_dynamic_buffer to support amd_buffer coherence setting * fix build issue * fix several bug * now result is correct, everything works (but has scratch) * remove scratch by manually reset coordinate * update device code * fix a bug in final reduce * fix something in example * update async memset * fix enum as camel case * modify coherence enum name * clean code and use atomic streamk by default * remove unused var * throw exception if have empty pointer * fix format * fix CI warning * fix type in init * modify CI error * filter out on gfx10+ * restore changed example code --------- Co-authored-by: Qianfeng Zhang <Qianfeng.Zhang@amd.com>
This commit is contained in:
carlushuang
265
profiler/include/profiler/profile_gemm_streamk_impl.hpp
Normal file
265
profiler/include/profiler/profile_gemm_streamk_impl.hpp
Normal file
@@ -0,0 +1,265 @@
|
||||
// SPDX-License-Identifier: MIT
|
||||
// Copyright (c) 2018-2022, 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/device_gemm_streamk.hpp"
|
||||
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
|
||||
|
||||
#include "ck/library/tensor_operation_instance/gpu/gemm_streamk.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 AccDataType,
|
||||
typename CDataType,
|
||||
typename ALayout,
|
||||
typename BLayout,
|
||||
typename CLayout>
|
||||
bool profile_gemm_streamk_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,
|
||||
uint32_t NumSKBlocks = 0xffffffff)
|
||||
{
|
||||
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{}));
|
||||
|
||||
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;
|
||||
|
||||
switch(init_method)
|
||||
{
|
||||
case 0: break;
|
||||
case 1:
|
||||
a_m_k.GenerateTensorValue(GeneratorTensor_2<ADataType>{-5, 5});
|
||||
b_k_n.GenerateTensorValue(GeneratorTensor_2<BDataType>{-3, 3});
|
||||
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());
|
||||
c_device_buf.ToDevice(c_m_n_device_result.mData.data());
|
||||
|
||||
using DeviceOp = ck::tensor_operation::device::DeviceGemmStreamK<ALayout,
|
||||
BLayout,
|
||||
CLayout,
|
||||
ADataType,
|
||||
BDataType,
|
||||
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, "
|
||||
<< (do_verification ? "with verification" : "without verification") << 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;
|
||||
|
||||
// profile device GEMM instances
|
||||
for(auto& op_ptr : op_ptrs)
|
||||
{
|
||||
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,
|
||||
NumSKBlocks);
|
||||
DeviceMem workspace;
|
||||
std::size_t workspace_size = op_ptr->GetWorkSpaceSize(argument_ptr.get());
|
||||
if(workspace_size != 0)
|
||||
{
|
||||
workspace.Realloc(workspace_size);
|
||||
op_ptr->SetWorkSpacePointer(argument_ptr.get(), workspace.GetDeviceBuffer());
|
||||
}
|
||||
|
||||
auto invoker_ptr = op_ptr->MakeInvokerPointer();
|
||||
|
||||
if(op_ptr->IsSupportedArgument(argument_ptr.get()))
|
||||
{
|
||||
// re-init C to zero before profiling next kernel
|
||||
c_device_buf.SetZero();
|
||||
|
||||
std::string op_name = op_ptr->GetTypeString();
|
||||
|
||||
float ave_time =
|
||||
invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, time_kernel});
|
||||
|
||||
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 << std::endl;
|
||||
|
||||
if(tflops > best_tflops)
|
||||
{
|
||||
best_op_name = op_name;
|
||||
best_tflops = tflops;
|
||||
best_ave_time = ave_time;
|
||||
best_gb_per_sec = gb_per_sec;
|
||||
}
|
||||
|
||||
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;
|
||||
}
|
||||
}
|
||||
}
|
||||
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 << " : " << best_ave_time
|
||||
<< " ms, " << best_tflops << " TFlops, " << best_gb_per_sec << " GB/s, "
|
||||
<< best_op_name << std::endl;
|
||||
|
||||
return pass;
|
||||
}
|
||||
|
||||
} // namespace profiler
|
||||
} // namespace ck
|
||||
Reference in New Issue
Block a user