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[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
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265
include/ck_tile/host/reference/reference_batched_contraction.hpp
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// SPDX-License-Identifier: MIT
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// Copyright (c) 2018-2025, Advanced Micro Devices, Inc. All rights reserved.
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#pragma once
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#include <cstdlib>
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#include <thread>
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#include "ck_tile/core.hpp"
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#include "ck_tile/host/host_tensor.hpp"
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namespace ck_tile {
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template <typename ADataType,
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typename BDataType,
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typename DDataType,
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typename EDataType,
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typename AccDataType,
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typename CDEElementWise>
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void calculate_reference_flat_indexing(
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const ck_tile::HostTensor<ADataType>& a_full_dims,
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const ck_tile::HostTensor<BDataType>& b_full_dims,
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const std::vector<ck_tile::HostTensor<DDataType>>& ds_full_dims_host,
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ck_tile::HostTensor<EDataType>& e_full_dims_host_ref,
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ck_tile::index_t G_total,
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ck_tile::index_t M_total,
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ck_tile::index_t N_total,
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ck_tile::index_t K_total,
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const CDEElementWise& cde_elementwise)
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{
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std::cout << "Calculating reference using optimized flat indexing with parallel processing..."
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<< std::endl;
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// Parallel computation over G and M dimensions using pattern from reference_batched_gemm.hpp
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auto f_gm = [&](auto g_flat, auto m_flat) {
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for(ck_tile::index_t n_flat = 0; n_flat < N_total; ++n_flat)
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{
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AccDataType sum = 0;
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// Compute dot product over K dimension
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for(ck_tile::index_t k_flat = 0; k_flat < K_total; ++k_flat)
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{
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auto a_val =
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a_full_dims.mData[g_flat * M_total * K_total + m_flat * K_total + k_flat];
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auto b_val =
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b_full_dims.mData[g_flat * N_total * K_total + n_flat * K_total + k_flat];
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sum += static_cast<AccDataType>(a_val) * static_cast<AccDataType>(b_val);
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}
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// Apply elementwise operation with D tensors
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EDataType result = static_cast<EDataType>(sum);
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if(ds_full_dims_host.size() == 0)
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{
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;
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}
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else if(ds_full_dims_host.size() == 1)
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{
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cde_elementwise(result,
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ck_tile::type_convert<float>(sum),
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ck_tile::type_convert<float>(
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ds_full_dims_host[0].mData[g_flat * M_total * N_total +
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m_flat * N_total + n_flat]));
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}
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else if(ds_full_dims_host.size() == 2)
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{
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cde_elementwise(
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result,
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ck_tile::type_convert<float>(sum),
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ck_tile::type_convert<float>(
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ds_full_dims_host[0]
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.mData[g_flat * M_total * N_total + m_flat * N_total + n_flat]),
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ck_tile::type_convert<float>(
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ds_full_dims_host[1]
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.mData[g_flat * M_total * N_total + m_flat * N_total + n_flat]));
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}
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else if(ds_full_dims_host.size() == 3)
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{
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cde_elementwise(
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result,
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ck_tile::type_convert<float>(sum),
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ck_tile::type_convert<float>(
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ds_full_dims_host[0]
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.mData[g_flat * M_total * N_total + m_flat * N_total + n_flat]),
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ck_tile::type_convert<float>(
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ds_full_dims_host[1]
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.mData[g_flat * M_total * N_total + m_flat * N_total + n_flat]),
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ck_tile::type_convert<float>(
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ds_full_dims_host[2]
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.mData[g_flat * M_total * N_total + m_flat * N_total + n_flat]));
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}
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else if(ds_full_dims_host.size() == 4)
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{
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cde_elementwise(
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result,
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ck_tile::type_convert<float>(sum),
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ck_tile::type_convert<float>(
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ds_full_dims_host[0]
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.mData[g_flat * M_total * N_total + m_flat * N_total + n_flat]),
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ck_tile::type_convert<float>(
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ds_full_dims_host[1]
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.mData[g_flat * M_total * N_total + m_flat * N_total + n_flat]),
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ck_tile::type_convert<float>(
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ds_full_dims_host[2]
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.mData[g_flat * M_total * N_total + m_flat * N_total + n_flat]),
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ck_tile::type_convert<float>(
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ds_full_dims_host[3]
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.mData[g_flat * M_total * N_total + m_flat * N_total + n_flat]));
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}
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else
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{
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throw std::runtime_error("Unsupported NumDTensor for reference calculation");
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}
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// Store result
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e_full_dims_host_ref.mData[g_flat * M_total * N_total + m_flat * N_total + n_flat] =
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static_cast<EDataType>(result);
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}
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};
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// Execute parallel computation using hardware concurrency
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// Parallelize over G_total and M_total dimensions for optimal CPU utilization
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make_ParallelTensorFunctor(f_gm, G_total, M_total)(std::thread::hardware_concurrency());
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}
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template <typename ADataType,
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typename BDataType,
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typename DDataType,
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typename EDataType,
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typename AccDataType,
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typename CDEElementWise>
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void calculate_reference_multi_dimensional(
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const HostTensor<ADataType>& a_full_dims,
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const HostTensor<BDataType>& b_full_dims,
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const std::vector<HostTensor<DDataType>>& ds_full_dims_host,
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HostTensor<EDataType>& e_full_dims_host_ref,
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const std::vector<index_t>& G_dims,
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const std::vector<index_t>& M_dims,
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const std::vector<index_t>& N_dims,
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const std::vector<index_t>& K_dims,
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const std::vector<index_t>& A_dims,
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const std::vector<index_t>& B_dims,
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const std::vector<index_t>& E_dims,
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const CDEElementWise& cde_elementwise)
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{
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std::cout << "Calculating reference using multi-dimensional indexing..." << std::endl;
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std::vector<std::size_t> g_idx(G_dims.size());
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std::vector<std::size_t> m_idx(M_dims.size());
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std::vector<std::size_t> n_idx(N_dims.size());
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std::vector<std::size_t> k_idx(K_dims.size());
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std::vector<std::size_t> a_idx, b_idx, e_idx;
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a_idx.reserve(A_dims.size());
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b_idx.reserve(B_dims.size());
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e_idx.reserve(E_dims.size());
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for(ck_tile::index_t g_flat = 0; g_flat < calculate_total_elements(G_dims); ++g_flat)
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{
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ck_tile::index_t temp = g_flat;
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for(int i = G_dims.size() - 1; i >= 0; --i)
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{
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g_idx[i] = temp % G_dims[i];
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temp /= G_dims[i];
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}
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for(ck_tile::index_t m_flat = 0; m_flat < calculate_total_elements(M_dims); ++m_flat)
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{
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temp = m_flat;
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for(int i = M_dims.size() - 1; i >= 0; --i)
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{
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m_idx[i] = temp % M_dims[i];
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temp /= M_dims[i];
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}
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for(ck_tile::index_t n_flat = 0; n_flat < calculate_total_elements(N_dims); ++n_flat)
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{
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temp = n_flat;
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for(int i = N_dims.size() - 1; i >= 0; --i)
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{
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n_idx[i] = temp % N_dims[i];
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temp /= N_dims[i];
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}
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AccDataType sum = 0;
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for(ck_tile::index_t k_flat = 0; k_flat < calculate_total_elements(K_dims);
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++k_flat)
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{
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temp = k_flat;
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for(int i = K_dims.size() - 1; i >= 0; --i)
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{
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k_idx[i] = temp % K_dims[i];
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temp /= K_dims[i];
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}
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a_idx.clear();
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b_idx.clear();
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a_idx.insert(a_idx.end(), g_idx.begin(), g_idx.end());
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a_idx.insert(a_idx.end(), m_idx.begin(), m_idx.end());
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a_idx.insert(a_idx.end(), k_idx.begin(), k_idx.end());
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b_idx.insert(b_idx.end(), g_idx.begin(), g_idx.end());
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b_idx.insert(b_idx.end(), n_idx.begin(), n_idx.end());
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b_idx.insert(b_idx.end(), k_idx.begin(), k_idx.end());
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auto a_val = a_full_dims(a_idx);
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auto b_val = b_full_dims(b_idx);
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sum += static_cast<AccDataType>(a_val) * static_cast<AccDataType>(b_val);
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}
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e_idx.clear();
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e_idx.insert(e_idx.end(), g_idx.begin(), g_idx.end());
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e_idx.insert(e_idx.end(), m_idx.begin(), m_idx.end());
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e_idx.insert(e_idx.end(), n_idx.begin(), n_idx.end());
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EDataType result = static_cast<EDataType>(sum);
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if(ds_full_dims_host.size() == 0)
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{
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;
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}
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else if(ds_full_dims_host.size() == 1)
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{
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cde_elementwise(result,
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ck_tile::type_convert<float>(sum),
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ck_tile::type_convert<float>(ds_full_dims_host[0](e_idx)));
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}
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else if(ds_full_dims_host.size() == 2)
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{
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cde_elementwise(result,
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ck_tile::type_convert<float>(sum),
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ck_tile::type_convert<float>(ds_full_dims_host[0](e_idx)),
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ck_tile::type_convert<float>(ds_full_dims_host[1](e_idx)));
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}
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else if(ds_full_dims_host.size() == 3)
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{
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cde_elementwise(result,
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ck_tile::type_convert<float>(sum),
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ck_tile::type_convert<float>(ds_full_dims_host[0](e_idx)),
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ck_tile::type_convert<float>(ds_full_dims_host[1](e_idx)),
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ck_tile::type_convert<float>(ds_full_dims_host[2](e_idx)));
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}
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else if(ds_full_dims_host.size() == 4)
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{
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cde_elementwise(result,
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ck_tile::type_convert<float>(sum),
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ck_tile::type_convert<float>(ds_full_dims_host[0](e_idx)),
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ck_tile::type_convert<float>(ds_full_dims_host[1](e_idx)),
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ck_tile::type_convert<float>(ds_full_dims_host[2](e_idx)),
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ck_tile::type_convert<float>(ds_full_dims_host[3](e_idx)));
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}
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else
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{
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throw std::runtime_error("Unsupported NumDTensor for reference calculation");
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}
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e_full_dims_host_ref(e_idx) = static_cast<EDataType>(result);
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}
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}
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}
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}
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} // namespace ck_tile
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