mirror of
https://github.com/ROCm/composable_kernel.git
synced 2026-04-20 06:49:15 +00:00
[CK Tile] contraction multi d - kernel & example (#2901)
* Initial commit. create batched_contraction_kernel file * initial problem definition * implement initial example to launch kernel * add universal gemm to contraction. initial phase * complete implementation for special case all Dims are 1 and no Ds * clean code * initial changes to support multi dimensional G * more progress in implementing multiple G * tmp commit * manage dynamic NumDimG in kernel * improving example for multi M,N,K,G handling. start generalizing kernel. it is a temporary commit * implement the example for general Multi dimension G M N K and test different reference calculation algorithms * 2 functions for reference using multi dimensional and flat indexing * clean the code for muti dimentional G, M, N, K contraction and add some logs * Add Make descriptor function in kernel for merging Ms, Ns, Ks for A, B, E * some cleaning on kernel * clean the code for calculating the offsets from flatten batch number * Start adding MultiD support to kernel and example * more changes to manage multi D in kernel and example * manage passing multi d to kernel and testing. * complete multi D support in kernel. modify example code to support it * Correct algorithm to calc the correct offset values for D tensor batches and some code cleaning * Minor fix * Generalize example code for variable NumD tensors and apply cleanup based on review feedback * Refactored code and addressed review feedback * refactoring, cleaning, add documents, in kernel side and example codes * Optimize batch offset calculation in kernel * Inline CalculateBatchOffset in batched contraction kernel, update CHANGELOG.md --------- Co-authored-by: Adam Osewski <19374865+aosewski@users.noreply.github.com>
This commit is contained in:
msaffari-amd
265
include/ck_tile/host/reference/reference_batched_contraction.hpp
Normal file
265
include/ck_tile/host/reference/reference_batched_contraction.hpp
Normal file
@@ -0,0 +1,265 @@
|
||||
// SPDX-License-Identifier: MIT
|
||||
// Copyright (c) 2018-2025, Advanced Micro Devices, Inc. All rights reserved.
|
||||
|
||||
#pragma once
|
||||
|
||||
#include <cstdlib>
|
||||
#include <thread>
|
||||
|
||||
#include "ck_tile/core.hpp"
|
||||
#include "ck_tile/host/host_tensor.hpp"
|
||||
|
||||
namespace ck_tile {
|
||||
|
||||
template <typename ADataType,
|
||||
typename BDataType,
|
||||
typename DDataType,
|
||||
typename EDataType,
|
||||
typename AccDataType,
|
||||
typename CDEElementWise>
|
||||
|
||||
void calculate_reference_flat_indexing(
|
||||
const ck_tile::HostTensor<ADataType>& a_full_dims,
|
||||
const ck_tile::HostTensor<BDataType>& b_full_dims,
|
||||
const std::vector<ck_tile::HostTensor<DDataType>>& ds_full_dims_host,
|
||||
ck_tile::HostTensor<EDataType>& e_full_dims_host_ref,
|
||||
ck_tile::index_t G_total,
|
||||
ck_tile::index_t M_total,
|
||||
ck_tile::index_t N_total,
|
||||
ck_tile::index_t K_total,
|
||||
const CDEElementWise& cde_elementwise)
|
||||
{
|
||||
std::cout << "Calculating reference using optimized flat indexing with parallel processing..."
|
||||
<< std::endl;
|
||||
|
||||
// Parallel computation over G and M dimensions using pattern from reference_batched_gemm.hpp
|
||||
auto f_gm = [&](auto g_flat, auto m_flat) {
|
||||
for(ck_tile::index_t n_flat = 0; n_flat < N_total; ++n_flat)
|
||||
{
|
||||
AccDataType sum = 0;
|
||||
|
||||
// Compute dot product over K dimension
|
||||
for(ck_tile::index_t k_flat = 0; k_flat < K_total; ++k_flat)
|
||||
{
|
||||
auto a_val =
|
||||
a_full_dims.mData[g_flat * M_total * K_total + m_flat * K_total + k_flat];
|
||||
auto b_val =
|
||||
b_full_dims.mData[g_flat * N_total * K_total + n_flat * K_total + k_flat];
|
||||
sum += static_cast<AccDataType>(a_val) * static_cast<AccDataType>(b_val);
|
||||
}
|
||||
|
||||
// Apply elementwise operation with D tensors
|
||||
EDataType result = static_cast<EDataType>(sum);
|
||||
if(ds_full_dims_host.size() == 0)
|
||||
{
|
||||
;
|
||||
}
|
||||
else if(ds_full_dims_host.size() == 1)
|
||||
{
|
||||
cde_elementwise(result,
|
||||
ck_tile::type_convert<float>(sum),
|
||||
ck_tile::type_convert<float>(
|
||||
ds_full_dims_host[0].mData[g_flat * M_total * N_total +
|
||||
m_flat * N_total + n_flat]));
|
||||
}
|
||||
else if(ds_full_dims_host.size() == 2)
|
||||
{
|
||||
cde_elementwise(
|
||||
result,
|
||||
ck_tile::type_convert<float>(sum),
|
||||
ck_tile::type_convert<float>(
|
||||
ds_full_dims_host[0]
|
||||
.mData[g_flat * M_total * N_total + m_flat * N_total + n_flat]),
|
||||
ck_tile::type_convert<float>(
|
||||
ds_full_dims_host[1]
|
||||
.mData[g_flat * M_total * N_total + m_flat * N_total + n_flat]));
|
||||
}
|
||||
else if(ds_full_dims_host.size() == 3)
|
||||
{
|
||||
cde_elementwise(
|
||||
result,
|
||||
ck_tile::type_convert<float>(sum),
|
||||
ck_tile::type_convert<float>(
|
||||
ds_full_dims_host[0]
|
||||
.mData[g_flat * M_total * N_total + m_flat * N_total + n_flat]),
|
||||
ck_tile::type_convert<float>(
|
||||
ds_full_dims_host[1]
|
||||
.mData[g_flat * M_total * N_total + m_flat * N_total + n_flat]),
|
||||
ck_tile::type_convert<float>(
|
||||
ds_full_dims_host[2]
|
||||
.mData[g_flat * M_total * N_total + m_flat * N_total + n_flat]));
|
||||
}
|
||||
else if(ds_full_dims_host.size() == 4)
|
||||
{
|
||||
cde_elementwise(
|
||||
result,
|
||||
ck_tile::type_convert<float>(sum),
|
||||
ck_tile::type_convert<float>(
|
||||
ds_full_dims_host[0]
|
||||
.mData[g_flat * M_total * N_total + m_flat * N_total + n_flat]),
|
||||
ck_tile::type_convert<float>(
|
||||
ds_full_dims_host[1]
|
||||
.mData[g_flat * M_total * N_total + m_flat * N_total + n_flat]),
|
||||
ck_tile::type_convert<float>(
|
||||
ds_full_dims_host[2]
|
||||
.mData[g_flat * M_total * N_total + m_flat * N_total + n_flat]),
|
||||
ck_tile::type_convert<float>(
|
||||
ds_full_dims_host[3]
|
||||
.mData[g_flat * M_total * N_total + m_flat * N_total + n_flat]));
|
||||
}
|
||||
else
|
||||
{
|
||||
throw std::runtime_error("Unsupported NumDTensor for reference calculation");
|
||||
}
|
||||
|
||||
// Store result
|
||||
e_full_dims_host_ref.mData[g_flat * M_total * N_total + m_flat * N_total + n_flat] =
|
||||
static_cast<EDataType>(result);
|
||||
}
|
||||
};
|
||||
|
||||
// Execute parallel computation using hardware concurrency
|
||||
// Parallelize over G_total and M_total dimensions for optimal CPU utilization
|
||||
make_ParallelTensorFunctor(f_gm, G_total, M_total)(std::thread::hardware_concurrency());
|
||||
}
|
||||
|
||||
template <typename ADataType,
|
||||
typename BDataType,
|
||||
typename DDataType,
|
||||
typename EDataType,
|
||||
typename AccDataType,
|
||||
typename CDEElementWise>
|
||||
void calculate_reference_multi_dimensional(
|
||||
const HostTensor<ADataType>& a_full_dims,
|
||||
const HostTensor<BDataType>& b_full_dims,
|
||||
const std::vector<HostTensor<DDataType>>& ds_full_dims_host,
|
||||
HostTensor<EDataType>& e_full_dims_host_ref,
|
||||
const std::vector<index_t>& G_dims,
|
||||
const std::vector<index_t>& M_dims,
|
||||
const std::vector<index_t>& N_dims,
|
||||
const std::vector<index_t>& K_dims,
|
||||
const std::vector<index_t>& A_dims,
|
||||
const std::vector<index_t>& B_dims,
|
||||
const std::vector<index_t>& E_dims,
|
||||
const CDEElementWise& cde_elementwise)
|
||||
{
|
||||
std::cout << "Calculating reference using multi-dimensional indexing..." << std::endl;
|
||||
|
||||
std::vector<std::size_t> g_idx(G_dims.size());
|
||||
std::vector<std::size_t> m_idx(M_dims.size());
|
||||
std::vector<std::size_t> n_idx(N_dims.size());
|
||||
std::vector<std::size_t> k_idx(K_dims.size());
|
||||
std::vector<std::size_t> a_idx, b_idx, e_idx;
|
||||
|
||||
a_idx.reserve(A_dims.size());
|
||||
b_idx.reserve(B_dims.size());
|
||||
e_idx.reserve(E_dims.size());
|
||||
|
||||
for(ck_tile::index_t g_flat = 0; g_flat < calculate_total_elements(G_dims); ++g_flat)
|
||||
{
|
||||
ck_tile::index_t temp = g_flat;
|
||||
for(int i = G_dims.size() - 1; i >= 0; --i)
|
||||
{
|
||||
g_idx[i] = temp % G_dims[i];
|
||||
temp /= G_dims[i];
|
||||
}
|
||||
|
||||
for(ck_tile::index_t m_flat = 0; m_flat < calculate_total_elements(M_dims); ++m_flat)
|
||||
{
|
||||
temp = m_flat;
|
||||
for(int i = M_dims.size() - 1; i >= 0; --i)
|
||||
{
|
||||
m_idx[i] = temp % M_dims[i];
|
||||
temp /= M_dims[i];
|
||||
}
|
||||
|
||||
for(ck_tile::index_t n_flat = 0; n_flat < calculate_total_elements(N_dims); ++n_flat)
|
||||
{
|
||||
temp = n_flat;
|
||||
for(int i = N_dims.size() - 1; i >= 0; --i)
|
||||
{
|
||||
n_idx[i] = temp % N_dims[i];
|
||||
temp /= N_dims[i];
|
||||
}
|
||||
|
||||
AccDataType sum = 0;
|
||||
|
||||
for(ck_tile::index_t k_flat = 0; k_flat < calculate_total_elements(K_dims);
|
||||
++k_flat)
|
||||
{
|
||||
temp = k_flat;
|
||||
for(int i = K_dims.size() - 1; i >= 0; --i)
|
||||
{
|
||||
k_idx[i] = temp % K_dims[i];
|
||||
temp /= K_dims[i];
|
||||
}
|
||||
|
||||
a_idx.clear();
|
||||
b_idx.clear();
|
||||
|
||||
a_idx.insert(a_idx.end(), g_idx.begin(), g_idx.end());
|
||||
a_idx.insert(a_idx.end(), m_idx.begin(), m_idx.end());
|
||||
a_idx.insert(a_idx.end(), k_idx.begin(), k_idx.end());
|
||||
|
||||
b_idx.insert(b_idx.end(), g_idx.begin(), g_idx.end());
|
||||
b_idx.insert(b_idx.end(), n_idx.begin(), n_idx.end());
|
||||
b_idx.insert(b_idx.end(), k_idx.begin(), k_idx.end());
|
||||
|
||||
auto a_val = a_full_dims(a_idx);
|
||||
auto b_val = b_full_dims(b_idx);
|
||||
|
||||
sum += static_cast<AccDataType>(a_val) * static_cast<AccDataType>(b_val);
|
||||
}
|
||||
|
||||
e_idx.clear();
|
||||
e_idx.insert(e_idx.end(), g_idx.begin(), g_idx.end());
|
||||
e_idx.insert(e_idx.end(), m_idx.begin(), m_idx.end());
|
||||
e_idx.insert(e_idx.end(), n_idx.begin(), n_idx.end());
|
||||
|
||||
EDataType result = static_cast<EDataType>(sum);
|
||||
if(ds_full_dims_host.size() == 0)
|
||||
{
|
||||
;
|
||||
}
|
||||
else if(ds_full_dims_host.size() == 1)
|
||||
{
|
||||
cde_elementwise(result,
|
||||
ck_tile::type_convert<float>(sum),
|
||||
ck_tile::type_convert<float>(ds_full_dims_host[0](e_idx)));
|
||||
}
|
||||
else if(ds_full_dims_host.size() == 2)
|
||||
{
|
||||
cde_elementwise(result,
|
||||
ck_tile::type_convert<float>(sum),
|
||||
ck_tile::type_convert<float>(ds_full_dims_host[0](e_idx)),
|
||||
ck_tile::type_convert<float>(ds_full_dims_host[1](e_idx)));
|
||||
}
|
||||
else if(ds_full_dims_host.size() == 3)
|
||||
{
|
||||
cde_elementwise(result,
|
||||
ck_tile::type_convert<float>(sum),
|
||||
ck_tile::type_convert<float>(ds_full_dims_host[0](e_idx)),
|
||||
ck_tile::type_convert<float>(ds_full_dims_host[1](e_idx)),
|
||||
ck_tile::type_convert<float>(ds_full_dims_host[2](e_idx)));
|
||||
}
|
||||
else if(ds_full_dims_host.size() == 4)
|
||||
{
|
||||
cde_elementwise(result,
|
||||
ck_tile::type_convert<float>(sum),
|
||||
ck_tile::type_convert<float>(ds_full_dims_host[0](e_idx)),
|
||||
ck_tile::type_convert<float>(ds_full_dims_host[1](e_idx)),
|
||||
ck_tile::type_convert<float>(ds_full_dims_host[2](e_idx)),
|
||||
ck_tile::type_convert<float>(ds_full_dims_host[3](e_idx)));
|
||||
}
|
||||
else
|
||||
{
|
||||
throw std::runtime_error("Unsupported NumDTensor for reference calculation");
|
||||
}
|
||||
|
||||
e_full_dims_host_ref(e_idx) = static_cast<EDataType>(result);
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
} // namespace ck_tile
|
||||
Reference in New Issue
Block a user