ROCm/composable_kernel
1
0
Fork 0
mirror of https://github.com/ROCm/composable_kernel.git synced 2026-07-17 00:58:44 +00:00
Code Issues Packages Projects Releases Wiki Activity
Files
ca3d00dcbeeaeef4b80703ec24de08c00a86ade3
composable_kernel/profiler/include/profiler/profile_gemm_universal_streamk_impl.hpp
emezh db2524be2d Verify HostTensorDescriptor when it is created (#2829) 
* add proper GEMM layout verification

* Handle "auto" strides.

CalculateStrides only called when tensor's strides are empty or all of them are <=0 (auto strides).
CalculateStrides now supports GEMM::ColumnsMajor order. The assumption is still that it applies only to the inner two dims.
ValidateStrides throws if any of the tensor's strides is <=0.
profile_gemm_multiply_add updated to support "auto" strides for tensors.

Manual tests for profile_gemm_multiply_add (matrix B in Row and Col modes)
auto-strides
	bin/ckProfiler gemm_multiply_add 0 0 1 1 0 1 128 128 128 0 0 0 0 0
	bin/ckProfiler gemm_multiply_add 0 1 1 1 0 1 128 128 128 0 0 0 0 0
	bin/ckProfiler gemm_multiply_add 0 0 1 1 0 1 128 128 128 -1 -1 -1 -1 -1
Note, -1 should be deprecated (use 0 instead)

explicit strides (same as auto)
	bin/ckProfiler gemm_multiply_add 0 0 1 1 0 1 128 128 128 128 128 128 128 128
	bin/ckProfiler gemm_multiply_add 0 1 1 1 0 1 128 128 128 128 128 128 128 128

explicit strides (not the same as auto)
	bin/ckProfiler gemm_multiply_add 0 0 1 1 0 1 128 128 128 130 132 134 136 138
	bin/ckProfiler gemm_multiply_add 0 1 1 1 0 1 128 128 128 130 132 134 136 138

mix of explicit and auto strides
	bin/ckProfiler gemm_multiply_add 0 0 1 1 0 1 128 128 128 128 128 128 128 0

invalid stride
	bin/ckProfiler gemm_multiply_add 0 0 1 1 0 1 128 128 128 0 0 0 0 64
	terminate called after throwing an instance of 'std::runtime_error'
	  what():  Invalid strides for RowMajor: mLens: 128 128 , mStrides: 64 1
	Aborted (core dumped)

* - add more names to ck::tensor_layout for easier namespace hierarchy checking
- updated convolutional layouts to use explicit ones or BaseConvolutionalLayout where it is not clear which layout to use (TBD) - see include/ck/library/utility/convolution_host_tensor_descriptor_helper.hpp

* added handling of partially initialized strides for GEMM. fixed more tests.

* clang-format and more fixes

* replace long dash by a simple hyphen - causes build failure in CK codegen.

* increase sizeof input, otherwise output size becomes zero or negative with large filter size

* select stride based on layout

* specify layout explicitly to avoid errors in HostTensorDescriptor creation

* add validation for higher GEMM tensor dimensions.; Add docstring to `HostTensorDescriptor`

* Not clear why permute test in test/permute_scale/test_permute_scale.cpp uses a lot of invalid strides. Setting layout to BypassLayoutVerification to avoid a lot of errors

* fix test (incl removing invalid config)

* fix moe examples:
- (in .cpp) add layout argument to non-2D tensors
- (in .hpp) fix asserts/failures that show up in Debug mode, specifically addressing 2D tensor by a single index (and 3D tensor by 2d index)

* fix moe_gemm2 example.

* fix profile and wmma examples

* clean-up early mods for ckprofile. verified with:
```
ckProfiler gemm_multiply_add 0 0 1 1 0 1 128 128 128 0 0 0 0 0
ckProfiler gemm_multiply_add 0 1 1 1 0 1 128 128 128 0 0 0 0 0
ckProfiler gemm_multiply_add 0 0 1 1 0 1 128 128 128 130 132 134 136 138
ckProfiler gemm_multiply_add 0 1 1 1 0 1 128 128 128 130 132 134 136 138
#
ckProfiler gemm_fastgelu 1 0 1 2 0 1 128 128 128 0 0 0
ckProfiler gemm_fastgelu 1 1 1 2 0 1 128 128 128 0 0 0
ckProfiler gemm_fastgelu 1 2 1 2 0 1 128 128 128 0 0 0
ckProfiler gemm_fastgelu 1 3 1 2 0 1 128 128 128 0 0 0
ckProfiler gemm_fastgelu 1 0 1 2 0 1 128 128 128 128 128 128
#
ckProfiler gemm_add_relu 0 0 1 1 0 1 128 128 128 0 0 0 0
# ckProfiler gemm_add_relu 0 1 1 1 0 1 128 128 128 0 0 0 0    # not implemented
# ckProfiler gemm_add_relu 0 2 1 1 0 1 128 128 128 0 0 0 0    # not implemented
# ckProfiler gemm_add_relu 0 3 1 1 0 1 128 128 128 0 0 0 0    # not implemented
ckProfiler gemm_add_relu 0 0 1 1 0 1 128 128 128 128 128 128 128
#
ckProfiler gemm_add_relu_add_layernorm 1 0 1 1 0 0 128 128 128 0 0 0 0 0
ckProfiler gemm_add_relu_add_layernorm 1 1 1 1 0 0 128 128 128 0 0 0 0 0
ckProfiler gemm_add_relu_add_layernorm 1 2 1 1 0 0 128 128 128 0 0 0 0 0
ckProfiler gemm_add_relu_add_layernorm 1 3 1 1 0 0 128 128 128 0 0 0 0 0
ckProfiler gemm_add_relu_add_layernorm 1 0 1 1 0 0 128 128 128 130 132 134 136 138
#
example_gemm_add_multiply_dl_fp16
example_gemm_add_multiply_xdl_fp16
#
ckProfiler gemm_blockscale_wp 7 1 1 1 1 0 1 128 128 128 0 0 0
ckProfiler gemm_blockscale_wp 7 1 1 1 1 0 1 128 128 128 128 128 128
```

* temporary skip first 8 test configs - they throw error

* temporary skip first 8 test configs in wmma too - they throw error

---------

Co-authored-by: Illia Silin <98187287+illsilin@users.noreply.github.com>
2025-09-25 18:22:13 -07:00

422 lines
17 KiB
C++
Raw Blame History

// SPDX-License-Identifier: MIT
// Copyright (c) 2024-2025, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include <iomanip>
#include <iostream>
#include <typeinfo>
#include <hip/hip_runtime.h>
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_gemm_xdl_cshuffle_streamk_v3.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_gemm_wmma.hpp"
#include "ck/library/tensor_operation_instance/gpu/gemm_universal_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"
#include "ck/library/reference_tensor_operation/gpu/reference_gemm.hpp"
namespace ck {
namespace profiler {
template <typename ADataType,
typename BDataType,
typename ComputeDataType,
typename AccDataType,
typename CDataType,
typename ALayout,
typename BLayout,
typename CLayout>
bool profile_gemm_universal_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,
int Streamk_sel,
int Grid_size,
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}, layout);
}
else
{
return HostTensorDescriptor({row, col}, {1_uz, stride}, layout);
}
};
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{}));
Tensor<CDataType> c_m_n_device_ref_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());
DeviceMem c_m_n_device_ref_buf(sizeof(CDataType) *
c_m_n_device_ref_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::DeviceGemm_Streamk_V2<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" << std::endl;
// Run reference GEMM
if(do_verification)
{
// Use GPU validation
using ReferenceGemmInstanceGPU =
ck::tensor_operation::device::ReferenceGemm<ALayout,
BLayout,
CLayout,
ADataType,
BDataType,
CDataType,
AccDataType,
AElementOp,
BElementOp,
CElementOp,
ComputeDataType,
ComputeDataType>;
auto ref_gemm_gpu = ReferenceGemmInstanceGPU{};
auto ref_invoker_gpu = ref_gemm_gpu.MakeInvoker();
auto ref_argument_gpu = ref_gemm_gpu.MakeArgument(
static_cast<ADataType*>(a_device_buf.GetDeviceBuffer()),
static_cast<BDataType*>(b_device_buf.GetDeviceBuffer()),
static_cast<CDataType*>(c_m_n_device_ref_buf.GetDeviceBuffer()),
M,
N,
K,
a_element_op,
b_element_op,
c_element_op);
if(ref_gemm_gpu.IsSupportedArgument(&ref_argument_gpu))
{
ref_invoker_gpu.Run(ref_argument_gpu, StreamConfig{nullptr, true});
c_m_n_device_ref_buf.FromDevice(c_m_n_host_result.mData.data());
}
else
{
std::cerr << "GPU reference GEMM does not support this problem configuration so does "
"CPU validation."
<< std::endl;
// Use CPU validation
using ReferenceGemmInstanceCPU =
ck::tensor_operation::host::ReferenceGemm<ADataType,
BDataType,
CDataType,
AccDataType,
AElementOp,
BElementOp,
CElementOp,
ComputeDataType>;
auto ref_gemm_cpu = ReferenceGemmInstanceCPU{};
auto ref_invoker_cpu = ref_gemm_cpu.MakeInvoker();
auto ref_argument_cpu = ref_gemm_cpu.MakeArgument(
a_m_k, b_k_n, c_m_n_host_result, a_element_op, b_element_op, c_element_op);
ref_invoker_cpu.Run(ref_argument_cpu);
}
}
std::string best_op_name;
float best_ave_time = 0;
float best_tflops = 0;
float best_gb_per_sec = 0;
float best_grid_size = 0;
float best_streamk_sel = 0;
// Get number of SMs on the current GPU
int device_id;
hipError_t err = hipGetDevice(&device_id);
if(err != hipSuccess)
{
std::cerr << "hipGetDevice failed: " << hipGetErrorString(err) << std::endl;
return false;
}
hipDeviceProp_t props;
err = hipGetDeviceProperties(&props, device_id);
if(err != hipSuccess)
{
std::cerr << "hipGetDeviceProperties failed: " << hipGetErrorString(err) << std::endl;
return false;
}
int num_sms = props.multiProcessorCount;
// Generate grid sizes based on SM count with multipliers
std::vector<float> multipliers = {0.2f, 0.4f, 0.6f, 0.8f, 1.0f, 1.2f, 1.4f, 1.6f, 2.0f};
std::vector<int> grid_size_list;
for(float mult : multipliers)
{
int grid_size = static_cast<int>(num_sms * mult);
if(grid_size > 0)
{
grid_size_list.push_back(grid_size);
}
}
std::cout << "Number of SMs: " << num_sms << std::endl;
std::cout << "Grid sizes to test: ";
for(auto gs : grid_size_list)
{
std::cout << gs << " ";
}
std::cout << std::endl;
// profile device GEMM instances
for(auto& op_ptr : op_ptrs)
{
std::vector<int> streamk_sel_list = {
0, 1, 2, 3, 4}; // 0: Data Parallel (DP) mode (Stream-K OFF), 1: 1-tile Stream-K+ DP,
// 2:2-tile Stream-K + DP
if(Grid_size == -1)
{
grid_size_list = {Grid_size};
}
if(Streamk_sel != -1)
{
streamk_sel_list = {Streamk_sel};
}
for(std::size_t j = 0; j < streamk_sel_list.size(); j++)
{
for(std::size_t i = 0; i < grid_size_list.size(); i++)
{
auto grid_size_curr = grid_size_list[i];
index_t streamk_sel_curr = streamk_sel_list[j];
printf("streamk_sel_curr=%0d\n", streamk_sel_curr);
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,
streamk_sel_curr,
grid_size_curr,
a_element_op,
b_element_op,
c_element_op);
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();
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());
// Always compare against CPU reference results computed earlier
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 << ", Grid_size "
<< grid_size_curr << ", streamk selection strategy"
<< streamk_sel_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_grid_size = grid_size_curr;
best_streamk_sel = streamk_sel_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
<< " Grid_size = " << best_grid_size
<< " Stream-K selection strategy = " << best_streamk_sel << " : " << 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 View Git Blame Copy Permalink