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composable_kernel/profiler/include/profiler/profile_gemm_blockscale_wp_impl.hpp
Enrico Degregori ce99cab605 Wmma support for gemm_ab_scale (#3314) 
* Support gemm_ab_scale:

 - Add tests
 - Integrate scaling implementation in multiple D
 - Generalize existing b_scale for ab_scale
 - Add instances
 - Generalize implementation for ScaleBlockM, ScaleBlockN, ScaleBlockK
 - Add support for all layouts supported by xdl
 - Fix splitk xdl

* Fix copyright

* Wmma support for gemm_blockscale_wp (#3315)

* Support for  preshuffle with ab scale

 - add support for b preshuffle in GridwiseGemm_wmma_cshuffle_v3_ab_scale
 - add support for AScaleLayout amnd BScaleLayout (can be different
   from ALayout and BLayout, respectively)
 - add Run method in v1 pipeline to support preshuffle + scaling
 - add support for preshuffle gemms in common invoker
 - Add splitk support

* Fix copyright header
2025-12-11 09:06:20 +01:00

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// Copyright (c) Advanced Micro Devices, Inc., or its affiliates.
// SPDX-License-Identifier: MIT
#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_multiple_d_xdl_cshuffle_v3_blockscale_bpreshuffle.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/library/tensor_operation_instance/gpu/gemm_blockscale_wp.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 InOutDataType>
void preShuffleBuffer(const InOutDataType* src, InOutDataType* dst, int N, int K, int NXdl)
{
int KPack = 16;
int NLane = NXdl;
int KLane = ck::get_warp_size() / NLane;
int K0 = K / (KLane * KPack);
// K -> K0 KLane KPack
// N -> N0 NLane
// N, K -> N0 K0 KLane NLane KPack
int tempk;
for(int n = 0; n < N; ++n)
{
for(int k = 0; k < K; ++k)
{
int n0 = n / NLane;
int n1 = n % NLane;
int k0 = k / (KLane * KPack);
tempk = k % (KLane * KPack);
int k1 = tempk / KPack;
int k2 = tempk % KPack;
int outputIndex = n0 * KPack * NLane * KLane * K0 + k0 * KPack * NLane * KLane +
k1 * KPack * NLane + n1 * KPack + k2;
dst[outputIndex] = src[n * K + k];
}
}
}
template <typename A0DataType,
typename A1DataType,
typename B0DataType,
typename B1DataType,
typename ComputeDataType,
typename AccDataType,
typename EDataType,
index_t ScaleBlockM,
index_t ScaleBlockN,
index_t ScaleBlockK,
typename ALayout,
typename BLayout,
typename ELayout>
bool profile_gemm_blockscale_weightpreshuffle_impl(int do_verification,
int init_method,
bool do_log,
bool time_kernel,
int M,
int N,
int K,
int StrideA,
int StrideB,
int StrideE,
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, int& stride, auto layout) {
using namespace ck::literals;
if(is_same<decltype(layout), tensor_layout::gemm::RowMajor>::value)
{
auto desc = HostTensorDescriptor({row, col}, {static_cast<std::size_t>(stride), 1_uz});
if(stride <= 0)
stride = desc.GetStrides()[0];
return desc;
}
else
{
auto desc = HostTensorDescriptor({row, col}, {1_uz, static_cast<std::size_t>(stride)});
if(stride <= 0)
stride = desc.GetStrides()[1];
return desc;
}
};
ck::index_t Scale_Stride_AM = ((M + ScaleBlockM - 1) / ScaleBlockM);
ck::index_t Scale_Stride_BN = ck::is_same_v<BLayout, ck::tensor_layout::gemm::ColumnMajor>
? ((K + ScaleBlockK - 1) / ScaleBlockK)
: ((N + ScaleBlockN - 1) / ScaleBlockN);
Tensor<A0DataType> a0_m_k(f_host_tensor_descriptor(M, K, StrideA, ALayout{}));
Tensor<A1DataType> a1_m_k(f_host_tensor_descriptor((M + ScaleBlockM - 1) / ScaleBlockM,
(K + ScaleBlockK - 1) / ScaleBlockK,
Scale_Stride_AM,
ck::tensor_layout::gemm::ColumnMajor{}));
Tensor<B0DataType> b0_k_n(f_host_tensor_descriptor(K, N, StrideB, BLayout{}));
Tensor<B0DataType> b_preshuffled_mfma16(
f_host_tensor_descriptor(K, N, StrideB, BLayout{})); // use layout only for size
Tensor<B0DataType> b_preshuffled_mfma32(
f_host_tensor_descriptor(K, N, StrideB, BLayout{})); // use layout only for size
Tensor<B1DataType> b1_k_n(f_host_tensor_descriptor((K + ScaleBlockK - 1) / ScaleBlockK,
(N + ScaleBlockN - 1) / ScaleBlockN,
Scale_Stride_BN,
BLayout{}));
Tensor<EDataType> e_m_n_host_result(f_host_tensor_descriptor(M, N, StrideE, ELayout{}));
Tensor<EDataType> e_m_n_device_result(f_host_tensor_descriptor(M, N, StrideE, ELayout{}));
// Update strides based on tensor properties if they are <= 0
auto get_stride = [](auto& tensor, auto layout, ck::index_t current_stride) -> ck::index_t {
if(current_stride <= 0)
{
if constexpr(std::is_same_v<decltype(layout), tensor_layout::gemm::RowMajor>)
{
return tensor.GetStrides()[0];
}
else
{
return tensor.GetStrides()[1];
}
}
return current_stride;
};
StrideA = get_stride(a0_m_k, ALayout{}, StrideA);
StrideB = get_stride(b0_k_n, BLayout{}, StrideB);
StrideE = get_stride(e_m_n_host_result, ELayout{}, StrideE);
int total_gemm_needed =
a0_m_k.GetElementSpaceSizeInBytes() + b0_k_n.GetElementSpaceSizeInBytes() +
a1_m_k.GetElementSpaceSizeInBytes() + b1_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 << "a0_m_k: " << a0_m_k.mDesc << std::endl;
std::cout << "a1_m_k: " << a1_m_k.mDesc << std::endl;
std::cout << "b0_k_n: " << b0_k_n.mDesc << std::endl;
std::cout << "b1_k_n: " << b1_k_n.mDesc << std::endl;
std::cout << "e_m_n: " << e_m_n_device_result.mDesc << std::endl;
std::cout << "rotating count: " << rotating_count << std::endl;
switch(init_method)
{
case 0: break;
case 1:
a0_m_k.GenerateTensorValue(GeneratorTensor_2<A0DataType>{-2, 2});
b0_k_n.GenerateTensorValue(GeneratorTensor_2<B0DataType>{-2, 2});
a1_m_k.GenerateTensorValue(GeneratorTensor_3<A1DataType>{0, 1.0});
b1_k_n.GenerateTensorValue(GeneratorTensor_3<B1DataType>{0, 1.0});
break;
default:
a0_m_k.GenerateTensorValue(GeneratorTensor_3<A0DataType>{-0.5, 0.5});
b0_k_n.GenerateTensorValue(GeneratorTensor_3<B0DataType>{-0.5, 0.5});
a1_m_k.GenerateTensorValue(GeneratorTensor_3<A1DataType>{0, 1.0});
b1_k_n.GenerateTensorValue(GeneratorTensor_3<B1DataType>{0, 1.0});
}
preShuffleBuffer(b0_k_n.mData.data(), b_preshuffled_mfma16.mData.data(), N, K, 16);
preShuffleBuffer(b0_k_n.mData.data(), b_preshuffled_mfma32.mData.data(), N, K, 32);
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using AElementOp = PassThrough;
using BElementOp = PassThrough;
using CElementOp = PassThrough;
const auto a_element_op = AElementOp{};
const auto b_element_op = BElementOp{};
const auto c_element_op = CElementOp{};
DeviceMem a0_device_buf(sizeof(A0DataType) * a0_m_k.mDesc.GetElementSpaceSize());
DeviceMem b_device_buf_mfma16(sizeof(B0DataType) * b0_k_n.mDesc.GetElementSpaceSize());
DeviceMem b_device_buf_mfma32(sizeof(B0DataType) * b0_k_n.mDesc.GetElementSpaceSize());
DeviceMem a1_device_buf(sizeof(A1DataType) * a1_m_k.mDesc.GetElementSpaceSize());
DeviceMem b1_device_buf(sizeof(B1DataType) * b1_k_n.mDesc.GetElementSpaceSize());
DeviceMem c_device_buf(sizeof(EDataType) * e_m_n_device_result.mDesc.GetElementSpaceSize());
a0_device_buf.ToDevice(a0_m_k.mData.data());
b_device_buf_mfma16.ToDevice(b_preshuffled_mfma16.mData.data());
b_device_buf_mfma32.ToDevice(b_preshuffled_mfma32.mData.data());
a1_device_buf.ToDevice(a1_m_k.mData.data());
b1_device_buf.ToDevice(b1_k_n.mData.data());
using DeviceOp =
ck::tensor_operation::device::DeviceGemmMultipleD_BlockScale_BPreshuffle<ALayout,
BLayout,
ck::Tuple<>,
ELayout,
A0DataType,
A1DataType,
B0DataType,
B1DataType,
ck::Tuple<>,
EDataType,
ScaleBlockM,
ScaleBlockN,
ScaleBlockK,
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)
{
Tensor<AccDataType> c_m_n({M, N});
Tensor<float> a_m_k({M, K});
Tensor<float> b_k_n({K, N});
for(int m = 0; m < M; m++)
{
for(int k = 0; k < K; k++)
{
a_m_k(m, k) = ck::type_convert<float>(a0_m_k(m, k)) *
a1_m_k(m / ScaleBlockM, k / ScaleBlockK);
}
}
for(int n = 0; n < N; n++)
{
for(int k = 0; k < K; k++)
{
b_k_n(k, n) = ck::type_convert<float>(b0_k_n(k, n)) *
b1_k_n(k / ScaleBlockK, n / ScaleBlockN);
}
}
using ReferenceGemmInstance = ck::tensor_operation::host::ReferenceGemm<float,
float,
AccDataType,
AccDataType,
AElementOp,
BElementOp,
PassThrough,
float>;
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, PassThrough{}, PassThrough{}, PassThrough{});
ref_invoker.Run(ref_argument);
for(int m = 0; m < M; ++m)
{
for(int n = 0; n < N; ++n)
{
e_m_n_host_result(m, n) = ck::type_convert<EDataType>(c_m_n(m, n));
}
}
}
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)
{
int NPerXdl = op_ptr->GetPreShuffleParameters();
auto argument_ptr = op_ptr->MakeArgumentPointer(
static_cast<A0DataType*>(a0_device_buf.GetDeviceBuffer()),
static_cast<B0DataType*>(NPerXdl == 16 ? b_device_buf_mfma16.GetDeviceBuffer()
: b_device_buf_mfma32.GetDeviceBuffer()),
std::array<const void*, 0>{},
static_cast<EDataType*>(c_device_buf.GetDeviceBuffer()),
M,
N,
K,
StrideA,
StrideB,
std::array<ck::index_t, 0>{},
StrideE,
a1_device_buf.GetDeviceBuffer(),
b1_device_buf.GetDeviceBuffer(),
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(e_m_n_device_result.mData.data());
#if defined CK_ENABLE_FP8
// set softer tolerances for fp8
if constexpr(is_same_v<A0DataType, f8_t> || is_same_v<B0DataType, f8_t> ||
is_same_v<EDataType, f8_t>)
{
std::string msg = "Error: Incorrect results!";
double rtol = 5e-2;
double atol = 5e-2;
bool current_pass = ck::utils::check_err(
e_m_n_device_result, e_m_n_host_result, msg, rtol, atol);
pass = pass & current_pass;
if(!current_pass)
{
std::cout << op_ptr->GetTypeString() << " failed" << std::endl;
}
}
else
{
#endif
pass = pass & ck::utils::check_err(e_m_n_device_result, e_m_n_host_result);
if(!pass)
{
std::cout << op_ptr->GetTypeString() << " failed" << std::endl;
}
#if defined CK_ENABLE_FP8
}
#endif
if(do_log)
{
LogRangeAsType<float>(std::cout << "a : ", a0_m_k.mData, ",") << std::endl;
LogRangeAsType<float>(std::cout << "b: ", b0_k_n.mData, ",") << std::endl;
LogRangeAsType<float>(std::cout << "c_host : ", e_m_n_host_result.mData, ",")
<< std::endl;
LogRangeAsType<float>(std::cout << "c_device: ", e_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(A0DataType) * M * K + sizeof(B0DataType) * K * N + sizeof(EDataType) * 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;
}
}
else
{
std::cout << op_ptr->GetTypeString() << " does not support this problem" << std::endl;
}
}
if constexpr(is_same<EDataType, float>::value)
{
std::cout << "Best Perf for datatype = f32";
}
else if constexpr(is_same<EDataType, half_t>::value)
{
std::cout << "Best Perf for datatype = f16";
}
else if constexpr(is_same<EDataType, bhalf_t>::value)
{
std::cout << "Best Perf for datatype = bf16";
}
else if constexpr(is_same<EDataType, 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 << " StrideE = " << StrideE << " : " << best_ave_time
<< " ms, " << best_tflops << " TFlops, " << best_gb_per_sec << " GB/s, "
<< best_op_name << std::endl;
return pass;
}
} // namespace profiler
} // namespace ck
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